Contract No: 7200AA21C00047
Deliverable: Mid-term Evaluation Report
Submitted To:
DDI/ITR/I/EPIC
United States Agency for International Development
 
October 2022
This publication was produced at the request of the United States Agency for International 
Development. It was prepared independently by David Hemson (team leader), Sarah Mangones, Elise 
Pinners, Adelaide Bryan, Laurah Hester, Dexis Consulting Group.
Water and Energy for Food: A Grand Challenge for 
Development (WE4F) Mid-term Evaluation Report 
i 
EXECUTIVE SUMMARY
Much of the world’s attention is directed at food production to meet needs in the aftermath of 
international crises, marked by food shortages, inflation, and famines. The Water and Energy 
for Food Program (WE4F), is highly relevant in this current context given its focus on 
providing financial, organizational, and strategic resources to support innovations to upscale, 
increase sustainable food production, raise income among the marginalized, and promote 
environment and climate change. This Midterm Review (MTR) presents findings on program 
design, analyzing the portfolio, the promise of targets, and initial performance. 
WE4F Program design 
The WE4F design is based on the Securing Water for Food (SWFF) and Powering 
Agriculture Energy Grand Challenge (PAEGC) programs. Using these tested models, WE4F 
has taken a portfolio approach to achieve impact though regional units providing Technical 
Assistance (TA). With multi-donor support, three Regional Innovation Hubs (RIH) support a 
diverse set of 81 innovations to pursue their own goals (with some modification) more 
effectively. Additionally, through the hubs, innovations’ financial resources are augmented, 
and their organizational capacity is strengthened through TA (for example, training, peer 
learning, direct advice, and (global) networking). Through the portfolio approach WE4F 
anticipates that 40% of the innovations will reach their targets for most indicators and 10% 
will achieve wide scale adoption. 
The innovations range from small to medium size companies, and the grant resources 
allocated are comparatively modest; despite this, the expectations of WE4F are quietly 
ambitious. WE4F, with support from the Funding Partners, seeks build innovators’ expertise 
to make a significant contribution to increasing food production sustainably by improving 
and economizing on the use of water and energy. WE4F also works to help innovations and 
end-users control “externalities” by promoting climate change adaptation and avoiding 
negative effects on biodiversity. In a broad assessment this MTR finds the design of WE4F 
innovative and ambitious on its main goals. 
Method and approach: The evaluation pursued mixed methods with data collection through 
key informant interviews (KII), surveys completed by the innovations themselves, and end 
user surveys. In this MTR questions on Relevance and Coherence are fully answered, on 
Effectiveness mostly; Efficiency, Impact and Sustainability are answered with acknowledged 
limits due to the stage of the intervention and the available data.
WE4F Relevance 
WE4F Relevance, finding: WE4F is seen as highly relevant to the needs of the countries in 
which it operates; it builds on existing innovations. There is a high degree of confidence in 
the significance of WE4F in accelerating food production in sustainable conditions. The 
sharply rising cost of fertilizers particularly highlights WE4F’s relevance in improving yields 
in sustainable agriculture.
Relevance is broadly assessed both by the ownership of innovations embedded in a country 
and growing numbers of end users. It was found most innovation companies are incorporated 
in participating countries and also operate in adjoining countries in the region. With three 
quarters or more of the end users in the category of poorer farmers WE4F is reaching the base 
of the pyramid (BoP). 
ii
Recommendations: 
• Highlight the innovations (in terms of costs, sizes, quantities) which have proven to 
be most successful in meeting the needs of marginalized groups (including women). 
WE4F Coherence
Finding: WE4F is contributing to achieving the Sustainable Development Goals (SDG) 
relating to famine and poverty, gender equality, emission reduction and climate change 
adaptation, and provides avenues to reach SDGs, with a focus on the common cause of 
sustainable agriculture. There remains concern to get a full country alignment between the 
WE4F and other donor programs. 
In the short time of its operation there is little evidence yet of the achievement of coherence
between the innovations and national government interventions. There is keen interest in 
interaction between WE4F and donor activities, and between innovations and it is projected 
that coherence will advance over time. 
Recommendations: 
• RIH interventions could, at a national and regional level, present sets of innovations 
which help advance a particular SDG, to promote innovations as part of government 
efforts to attain SDGs. 
• To advance coherence between the WE4F and national plans, the Annual Work Plans 
(AWP) could be aligned with the SDG for zero hunger and poverty, sustainable land 
use, gender equality, reduced inequality, climate action and innovation (SDG 2, 1, 15, 
5, 10, 13, and 9).
WE4F Effectiveness
Finding: WE4F is succeeding in key measures of effectiveness by improving the capacities 
of innovations (on business, fund raising, etc.) to make innovation accessible to end users 
including those at the bottom of the pyramid. 
The effect of training and support by the RIH, at innovation level, is shown in the growth of 
end user numbers and gross sales. The Quality of Service Survey (QoSS) provides further 
evidence of high levels of approval of RIH training and support in the following:
• Pre-award: support contributed to innovations’ administrative and financial systems. 
• Technical assistance, (especially about business subjects), received very good scores. 
Knowledge on marketing, BoP, and gender, on average, increased; more than 90% 
(all 3 hubs) which indicate a gender knowledge increase. 
• Investment facilitation received very good scores. For the few reporting, it helped 
increase external investment; 19 of the innovations mobilized 8.6m USD in external 
investment; 91 partnerships were created raising the potential for additional co￾funding. 
• Support for an enabling environment received good scores. All comments underline 
the importance of removing barriers. 
• Communication from the RIH goes through many channels: annual meetings, 
bootcamps, personal communication, and the newsletter all play an effective role 
iii
• Monitoring & Evaluation: overall readiness for data collection is good but data use 
varies by region; mostly for decision making, sales and fundraising. Respondents call 
for more standardized, simplified and/or automated (and yet adaptable) tools. 
The evaluation’s own interviews confirm this approval. For example, aspects most 
appreciated include the program’s responsiveness, ad-hoc support, help with the company’s 
organization and paperwork for the grant, other applications and permits. There is also scope 
for improving the Enabling Environment work to better adapt it to the local context. 
The resulting effect of these services on the growth of innovations is anticipated: at least 40% 
of innovations should meet their own end user target; and 50% did. 
Recommendations: 
• Pre-award: improve documentation and tailor or simplify for smaller innovations. 
• Ask that innovators more explicitly relate the intended impact of innovations to 
specific SDGs. 
• Technical assistance: strengthen the promotion strategy to increase women 
participation at the end user level. WE4F needs to emphasize technologies (for food 
security) to draw in more women end users. 
• Showcase marginalized group participation: innovations successful in meeting the 
needs of these groups should be highlighted in WE4F communication and outreach 
materials, and on the WE4F website (e.g., success stories). 
• Ensure sufficient access to finance for innovative solutions that address the causes of 
the gender yield gap. 
• Enabling environment: optimize the learning from participants’ own expertise, or 
local experts, and business member organizations, to find context-appropriate ways 
and unite forces to address barriers. 
• Extend end user support: link new finance to encouraging innovations develop 
innovations (design) and training materials accessible to more marginalized end users
• Examine training methodologies to see if women’s expertise could be used in dealing 
with barriers, work to focus training to produce action plans to advocate and address 
bureaucratic obstacles. 
• Consider gender mainstreaming business plans (e.g. on job and pay equality) and 
tailoring innovations to the needs of more marginalized groups.
WE4F and Innovations Efficiency
Finding: WE4F is efficient on the basis of the following measures; firstly, as a program in 
achieving the greatest impact in the deployment of relatively limited resources available and 
secondly in terms of the value arising from fund management in comparison with other 
Challenge Funds. 
Findings on the portfolio approach: WE4F overall efficiency can be assessed on the basis 
of the use of relatively limited resources to achieve impact. This strategy is constructed on 
the design proven to be efficient in the previous SWFF and PAEGC programs with rigorous 
monitoring and continuous interaction between the RIH and innovations. The WE4F model 
has evolved from these prior programs with intensive risk and performance management to 
achieve greater efficiency. 
iv
Innovation progress is measured by standard and program-specific indicators which are 
assessed against their output milestones. The proportionally increased costs associated with 
more intense fund management, TA and capacity building inputs are compensated by the 
active capture of learning and time-targeted goals.
WE4F constrains costs through established networks of local consultants. The portfolio 
approach allows diversity in method and subject and encourages out-performance which 
compensates for modest or failing results in innovations which may have value in other 
dimensions. WE4F anticipates that innovations can raise external funds many times larger 
than the initial grant; this is confirmed by initial findings. This compares favorably with other 
programs. 
The overall efficiency of WE4F depends on the quality of its management of time, resources, 
skills, and budgets necessary to accomplish all these interrelated tasks. At its core, WE4F 
anticipates high efficiency with modest targeted funding and advanced practices in learning, 
reporting, and planning leading to elevated (but mutually agreed and carefully planned) 
results. This devolves more responsibility to local actors which involves scheduling teams to 
identify the fastest, cheapest, or most suitable approach.
A large number of diverse innovations is a key feature of WE4F which makes it distinct from 
“traditional” interventions. This “managed portfolio” approach is more than numbers alone, it 
is based on diversification which involves investing in dissimilar innovations which have 
different kinds of financial and capability assets which are combined in anticipation of 
maximizing return for many complex risks. Operations are also spread through 
decentralization at various levels: from the centralized Secretariat, to Hubs, and ultimately to 
the innovations. Overall, this could prove to be more efficient in maximizing returns from 
relatively modest resources. There are also RIH reports of early synergies in-country 
between WE4F goals and donor country initiatives and other openings; learning from results 
will increase efficiency over time. 
Recommendations: 
• Continue data exchanges to accumulate comparative quantitative measures of 
efficiency across Grand Challenge Funds. 
• The Monitoring, Evaluation, and Learning (MEL) system is complex and could be 
leaner, more efficient if possible. The Performance Monitoring & Evaluation Plan 
should add navigation/headings and an explanation on how targets are set at different 
levels (innovation, hub, program levels. There should also be explanation of how 
targets are set and may be changed, and indicator standards and methodology refined. 
• Share summaries of key MEL information and reflect on the summaries in bootcamps 
to yield valuable feedback. 
• Adopt leaner and less administrative MEL system and reporting formats for SMEs. 
WE4F Impact 
Finding: Large increases in food production are reported by the limited number of 
innovations reporting. There is increased use of renewable energy particularly with solar 
water pumps. The transition to renewable energy is gaining strength. In relation to energy 
savings, most innovations met their year 1 targets. At the crucial level of end users, of those 
surveyed most reported an increase in income, more so among women. 
v 
There is considerable evidence of increased water accessibility, end users report increased 
availability and access to water, but water savings are not yet reported on any scale. Some 
concern is expressed about the overuse of water resources, which could be leading to falling 
water tables. 
WE4F’s goal is to increase food production and productivity, achieving higher yields while 
making water- and energy-savings. As the Evaluation has data on production but not on 
yield, it is not yet possible to quantify aspects of productivity increase to know whether 
increased production is due more intensive farming or from an increase in area farmed. 
End users, who are keenly interested in increasing yields, are confident that the innovation is 
assisting as, a high proportion are committed to recommend the innovation to their peers. 
Further detailed surveys are needed to confirm the proportion of smallholders rising out of 
poverty through the intervention. While the data does not show a widened range of crops 
cultivated, there is evidence of greater yields and of new combinations of animal husbandry 
with crop production. Data collection is needed to assess the relative contributions of various 
elements to which change can be attributed: water, energy, the innovation, or external factors. 
In relation to end users, the first year results, in two of three regions addressed in this MTR 
show a growth of 254,354 end users which is 11% of the life of project (LOP) target of 
2,500,000. In the long run, this rate of growth across the combined numbers of all 81 
innovations, will bring the LOP target within range and beyond. The projection is for this 
target to be exceeded. 
End users report an improving level of women’s participation (approaching 30% of all end 
users) and some increase in income. Positive impact on marginalized groups in income, 
water/environmental, energy/environmental is also reported, but not in job creation in 
innovations. 
Set against the global struggle to improve food security, the current achievements are modest. 
The key question in effectiveness and efficiency, however, is whether the model of WE4F 
(which could be described as a “managed portfolio”) can be used more widely to effect 
greater impact with greater resources. 
Recommendations: 
• Give closer and systematic attention to the issue of water savings and the challenge of 
water overuse and falling water tables by following procedures introduced by IWMI 
for groundwater responsive solar irrigation. 
• Promote innovations' success stories in achieving significant savings in energy and 
water. 
• Further end user surveys to confirm income increases for smallholder farmers, and the 
reasons for changes.
Recommendations for present phase and extension: 
• RIHs and innovations should continue to focus on calculating, capturing, and 
reporting crop yields; changes in crop yields need consistent examination over time to 
identify factors in patterns and change. 
• The elements in increased food production need to be researched on the basis of 
establishing past and present yields. This should confirm for selections of farmers - 
vi 
that yields are increasing and conclude that these are based on sustainable intensive 
farming rather than greater use of natural resources. 
• The RIHs are already recording yields of various crops and areas; this needs to 
continue as a focus in recording and drawing a selection of farmers into monitoring as 
active participants. The resulting insights could be shared with other farmers using the 
same crops and innovations and with other RIH and innovations. 
• Discuss the reasons for low levels of food processing to understand the challenges 
involved. As WE4F grows the food production data needs to be rigorously examined. 
Sustainability of WE4F 
Finding: Sustainability is being approached and, in the course of time, will be achieved by a 
proportion of innovations and their associated end users. Sustainability in the context of 
WE4F is increased food production undertaken by large and small farmers drawing on less 
energy and water and making sustainable use of land. Sustainability also includes the use of 
other natural resources and maintaining and improving biodiversity. 
The achievement of sustainability of WE4F depends on the viability of innovations 
(expanding end users, making a profit, creating jobs) as well as the improving farming 
practices leading to sustainable food production. There are environmental considerations in 
farming activities including the effects on land use, natural resources, and biodiversity as well 
as carbon emissions and socioeconomic considerations.
In relation to financial sustainability currently 33% of innovations make a profit (making 
profit is defined in WE4F as passing the threshold of 8% EBITDA); that leads to the finding 
that more than 8% of innovations are meeting WE4F’s target of making a profit at this 
measured standard. During interviews the respondents reported themselves confident of 
continued financial sustainability. 
Environmental sustainability in the context of WE4F primarily relates to water and energy
efficiency. WE4F has to achieve impact by simultaneously reducing the use of non￾renewables (and emission) and the use of water. WE4F has an energetic system to assess 
sustainability issues and build in mitigation strategies, using the Initial Environmental 
Examination (IEE) and Environmental Mitigation and Monitoring Plan (EMMP) tools, as 
part of compliance.
The IEE and EMMP tend to focus on on-site and immediate effects without consistently 
accounting for or addressing impact beyond the immediate intervention. Such factors along 
the lifecycle include effects on natural resources more removed from the farm, at source of 
materials or end of use etc. In the WE4F approach these are mostly ‘externalities’. There 
were no reports of negative feedback from marginalized groups participating in an 
innovation. 
Recommendations: 
• Since KPI1 (profit) reporting is uneven, specialized TA support should be provided as 
needed. 
• Water pump promotion (solar-powered or otherwise) should be conditional, i.e., tied 
to a water-saving package. Solar panel grants should be on condition that these are 
sourced from countries not using forced labor and take back old panels at end of use 
• As WE4F progresses, more greenhouse gasses (GHG) data is needed; more
innovations need to share how they measure GHG emissions; the methods chosen 
vii 
could include the effects of changes in land use. Analysis of the data, innovation by 
innovation, could reveal more trends and patterns in reducing GHG emissions. 
• Provide assistance to weaker performing innovations, with careful target setting. 
• Raise government awareness on innovations serving public goals on climate change, 
e.g. in the Nationally Determined Contribution, to further government efforts to 
improve the Enabling Environment and secure funding for WE4F type innovations. 
• A simple tool (based on lifecycle assessment tools) is suggested below to assist 
farmers and innovators (together) to identify environmental and socioeconomic 
effects of an innovation; such a tool serves learning purposes and helps find win-win 
solutions (or) to manage trade-offs.
Recommendations for the extension: 
• Choose more efficient yet wider-scoped ways to identify environmental and 
socioeconomic trade-offs along the innovation lifecycle. A simple tool is suggested 
below (using the lifecycle analyses as theoretic frame, but tailoring it and using it 
selectively, and only for a selection of farmers) can help innovators and end users 
together, to identify effects along the lifecycle, and especially at farm level, e.g., to 
capture effects of increased land use, and waste, on natural resources and biodiversity, 
find trade-offs and win-win solutions. This tool is for learning purpose, and can be 
tried out in this phase, as an alternative to the more compliance-oriented IEE
• A comparative discussion of USAID and EU policy on environmental sustainability 
would be fruitful in developing a holistic approach and providing common tools and 
monitoring.
WE4F is ambitious but is constrained in its resources to meet a global challenge. It must 
spread its resources across a wide range of innovations particularly to the programmatic 
level. Despite this, the portfolio of innovations may prove the value of diversity in spreading 
risk and averaging returns to achieve defined targets as initial projections indicate. Since the 
portfolio model is likely to lead to the achievement of most key targets, additional 
funding is warranted.
The preliminary conclusions are: 
• Three key measures demonstrate WE4F’s viability: increasing food production, a 
rising number of end users who find the innovations improving their practices, raising 
income, and improving food security. This is matched by the financial resilience of 
innovations which make such impacts possible. Early results show increased food 
production, and end users targets being achieved. 
• The forward projection of existing end user trends shows that WE4F will meet the 
LOP target. The projections are positive and accumulatively these overall targets will 
be met. The financial resilience of innovations needs further examination over time.
viii
TABLE OF CONTENT
Executive Summary i 
Table of Content viii 
Lists of tables and figures ix 
Acronyms xii 
Acronyms for data classification xiv 
Definitions xv 
1 Introduction 1 
COVID-19 threats, constraints, and mitigation 2 
Structure of the Program 4 
2 Methodology and Approach 7 
3 Findings 9 
3.1 Relevance 9 
3.1.1 Countries participating in the program 9 
3.1.2 Local ownership 10 
3.1.3 End user feedback to innovations 10 
3.2 Coherence at program level 11 
3.3 Effectiveness: innovations’ capacities 13 
3.3.1 Quality of Service and effect of TA, IF and EE 14 
3.3.2 Innovations’ assessment of learning events on TA, IF and EE (all 3 OC) 15 
3.3.3 WE4F disbursement and Innovations’ capacities to mobilise funding (OC2) 17 
3.3.4 Enabling environment (OC3) 20 
3.3.5 Innovations’ business results: end user numbers, gross sales 22 
3.4 Efficiency at program and innovation level 25 
3.5 Impact: baseline and aspects on progress end user level 31 
3.5.1 Impact on end-user numbers (IM1) 32 
3.5.2 End users using the innovation (IM2) 33 
3.5.3 Impact on food production, and water & energy saving (IM3) 33 
3.5.4 Impact on income (IM4) 39 
3.6 Sustainability 41 
3.6.1 Innovations’ profit, and job creation 42 
3.6.2 Innovations monitoring protection of water, biodiversity 44 
3.6.3 GHG emission saved by region 44 
3.6.4 Area improved 45 
3.6.5 Socioeconomic and environmental effects along the innovation lifecycle 46 
3.7 Overarching hypotheses 49 
3.7.1 Hypothesis I: … 50
ix
3.7.2 Hypothesis II: … 50 
3.7.3 Hypothesis III: … 50 
4 Main Findings, Conclusions and Recommendations 51 
Annex 1: The evaluation SOW 61 
Annex 2: WE4F Evaluation Questions and Indicators 72 
Annex 3: Statements of difference 76 
Annex 4: Data collection and analysis tools 77 
Annex 5: Statements on conflicts of interest 78 
Annex 6: WE4F Theory of Change 80 
Annex 7: Evaluation matrix 82 
Annex 8: List of country innovations and other tables 91 
Annex 9: Comments on indicators 105 
Annex 10: The Evaluation Team 109 
Annex 11: Limitations, Risks, and Evaluation Mitigation Strategies 112 
Annex 12: Report: Data Quality Assessment of M&E data 117 
Purpose and Summary 117 
Key procedures in assessing quality of M&E data 119 
Summary of limitations 120 
Classification of Data 121 
Classification of data issues 123 
Recommendations 125 
Annex 13: Environmental analysis 126 
Annex 14: The scope of the MTR 133 
Annex 15: Methodology 136 
Annex 16: Comparative Information Across Global Challenge Funds 151 
Lists of tables and figures
Table 1: Innovations by WE4F support, by region .................................................................... 5 
Table 2: Innovations by WE4F engagement history.................................................................. 5 
Table 3: Innovations’ key words describing the innovation (n=see note below) .................... 12 
Table 4: Proportion of participants scoring high Quality of Service (new indicator) ............. 15 
Table 5: CFI1 innovations attending learning events: their participant score (%*) ................ 16 
Table 6: Funds committed and disbursed (USD) per cohort ................................................... 18 
Table 7: External investment mobilized in year 1 (USD), by source (KPI9).......................... 18 
Table 8: WE4F CFI1 grants and innovations’ co-funding (Gin33) ......................................... 19 
Table 9: Innovations’ annual targets numbers of end user household members, all innovations 
(n=81) ....................................................................................................................................... 22 
Table 10: Innovations’ year 1 end user targets and results, by region (CFI1, n=27) ............... 24
x 
Table 11: Innovation gross sales (USD), by region (Gin4), compared to innovations’ own 
year 1 targets ............................................................................................................................ 24 
Table 12: Number of end user household members (CFI1), by region, wealth and gender 
(KPI2) ...................................................................................................................................... 32 
Table 13: End users’ length of time with the innovation ......................................................... 33 
Table 14: Food produced (t, year 1) by region, by producer types (KPI3) .............................. 33 
Table 15: Water saved (l, year 1), by end user type, by region (KPI6) ................................... 35 
Table 16: Quantity of water used changed .............................................................................. 35 
Table 17: Innovations' energy saving targets and results, year 1 (kWh) ................................. 37 
Table 18: Energy saved in year 1 (kWh), by region, by end user type (KPI5) ........................ 37 
Table 19: Ways the innovation has improved yields (survey in SSEA) .................................. 38 
Table 20: Number of end user household members (‘end users’) that saw household income 
increased (KPI7) ...................................................................................................................... 39 
Table 21: End users reporting whether their income increased as result of the innovation .... 40 
Table 22: End users reporting their income (USD), by region ................................................ 40 
Table 23: EBITDA margins of CFI1 innovations in year 1(KPI1) ......................................... 42 
Table 24: Jobs created through Program support, by region (Gin10) ...................................... 43 
Table 25: GHG emission saved (tCO2e year 1) by innovation focus (Gin6) ........................... 44 
Table 26: Area improved (ha) as a result of the CFI1 innovations (from innovation records)45 
Table 27: Farm sizes in in the program ................................................................................... 45 
Table 28: CFI1 innovations with IEEs and EMMPs ............................................................... 47 
Table 29: Main findings, conclusions and recommendations .................................................. 52 
Table 30: Evaluation questions as presented in the SOW ....................................................... 74 
Table 31: Complete list of country-innovations by country, CFI and innovation focus ......... 91 
Table 32: Innovations’ gross sales (USD), by innovation, by country as of July 2022 (Gin4) 
.................................................................................................................................................. 94 
Table 33: Innovations’ end user targets and results, by innovation, by country as of July 2022 
.................................................................................................................................................. 95 
Table 34: Money not disbursed per innovation SSEA CFI1 ................................................... 95 
Table 35: WE4F grant funds committed, targeted and disbursed in year 1 (USD), from 
inception until August 2022 (received October 2022) ............................................................. 96 
Table 36: CFI1 grants disbursed in year 1 (USD), by region, by innovation type, against end 
user numbers ............................................................................................................................ 97 
Table 37: WE4F committed funding and overall funding (USD), for MENA CFI1............... 97 
Table 38: Innovation numbers: total, grants only, CFI1 only, by region ................................. 98 
Table 39: Country-innovations numbers: country-grants and -funding, and country￾innovation focus ....................................................................................................................... 98 
Table 40: Representation of innovation type for region .......................................................... 98 
Table 41: Investment Facilitation, how it helped and funding amounts (USD), by region 
(EQ1h)...................................................................................................................................... 99 
Table 42: Numbers of end users using financing mechanisms for the innovation, by region 
(Gin28) ..................................................................................................................................... 99 
Table 43: No. of countries participating, by region ............................................................... 100 
Table 44: Innovations’ annual targets numbers of end user household members, all 
innovations (n=81), by region ................................................................................................ 100 
Table 45: Innovation companies and where they are incorporated ....................................... 100 
Table 46: Number of end users that participated in the survey ............................................. 101 
Table 47: Participating countries end user respondents ......................................................... 101 
Table 48: Participating innovations end user respondents: by innovation ............................ 101 
Table 49: Food processed (t) by country (KPI4) ................................................................... 101
xi 
Table 50: Impacts reported (SSEA)....................................................................................... 101 
Table 51: Food produced (t, year 1) by innovation focus, by producer types ....................... 102 
Table 52: Water saved (l, year 1), by end user type, by innovation focus (KPI6) ................. 102 
Table 53: Breakdown of end user targets and results, per innovation ................................... 102 
Table 54: Changes in farming practice, as result of the innovation ....................................... 103 
Table 55: Water capture/storage, reuse/treatment, consumption/irrigation (EQ1e) .............. 103 
Table 56: Numbers of end users using financing mechanisms for the innovation, by region 
(Gin28) ................................................................................................................................... 103 
Table 57: End users reporting whether their income has increased as result of the innovation, 
by gender ................................................................................................................................ 103 
Table 58: Jobs created through Program support, by innovation focus (Gin10) ................... 104 
Table 59: GHG emission saved (tCO2e year 1) by region (Gin6) ........................................ 104 
Table 60: Range of performance for end users (KPI2) and gross sales (Gin4) ..................... 104 
Table 61: Comments on indicators........................................................................................ 105 
Table 62: Indicator updated targets (source: USAID RIH Targets_Oct2022.gsheet) ........... 108 
Table 63: Risks and the evaluation mitigation strategies....................................................... 112 
Table 64: Types of challenges in data quality (issues and limitation) ................................... 120 
Table 65: Summary of Data Limitations ............................................................................... 121 
Table 66: Schedule of detail in registering and resolving data issues ................................... 123 
Table 67: Summary and review of illustrative IEEs and EMMPs......................................... 127 
Table 68: Defining coverage and limits: Evaluation Questions, KPIs and General Indicators 
................................................................................................................................................ 133 
Table 69: Number of innovations and sites per RIH, January 2022 ...................................... 136 
Table 70: Names of Innovations in the sample by RIH, country, and innovation focus ....... 137 
Table 71: Women Led/Women Owned innovation in Sample .............................................. 138 
Table 72: Distribution of sample by innovation focus, compared to CFI1 and all innovations 
................................................................................................................................................ 138 
Table 73: KII innovation sample, representation women-led innovations compared to CFI1 
and all innovations................................................................................................................. 138 
Table 74: Number and focus of Evaluation Questions and subsidiary questions .................. 139 
Table 75: Types of challenges in data quality analysis (issues and limitations) ................... 145 
Table 76: Overview of key DQA issues ................................................................................ 146 
Table 77: Overview of key data limitations........................................................................... 146 
Table 78: Key risks identified and their mitigation ............................................................... 147 
Table 79: KII schedule for innovations ................................................................................. 149 
Table 80: End user surveys: innovations and countries ......................................................... 150 
Table 81: Administrative costs of challenge funds (source: SWFF evaluation report)......... 151 
Figure 1: WE4F intervention areas and countries ...................................................................... 2 
Figure 2: End user targets for all innovations.......................................................................... 23 
Figure 3: Cumulative Growth based on yearly targets ............................................................ 23 
Figure 4: Amount awarded vs disbursed in year 1, for CFI1 (tbd) .......................................... 96 
Figure 5: From extraction to use: 5 Steps in Data Quality Assessment................................. 119 
Figure 6: Sustainable intensification in land systems: trade-offs, scales, and contexts (source: 
Thomson et.al., Current Opinion in Environmental Sustainability, Vol. 38, June 2019 (p. 37-
43) https://images.app.goo.gl/BaXmPi5A9nMBuvcq6 ......................................................... 131 
Figure 7: Life-cycle assessment phases, cradle-to-gate and cradle-to-grave (NB: this model 
ignores the input of land/biodiversity and water) .................................................................. 132 
Figure 8: On-farm lifecycle components and flow between the environment and the 
production system (NB: this model ignores the input of land/biodiversity) ......................... 132
xii 
ACRONYMS
AAR After Action Review
ACTED Agency for Technical Cooperation and Development 
ADS USAID Automated Directives System
AWP Annual Work Plan
BFS Bureau for Food Security 
BMO Business Member Organizations
BMZ German Federal Ministry for Economic Cooperation and Development 
BoP Base of the Pyramid
CEO Company Executive Officer 
CFI Call for Innovations (these are sequenced: CFI1, CFI2, etc.) 
CKM Communications and Knowledge Management 
CLA Collaboration, Learning, and Adapting 
CLEER Clean Energy Emission Reduction (Tool, see: www.cleertool.org) 
CO/COR Contracting Officer / Representative
CPAR Contract Performance Assessment Report
DEC Development Experience Clearinghouse 
DELC Dexis Evaluators & Local Consultants
DEXIS Dexis Interactive, Inc. dba Dexis Consulting Group 
DFI Development Finance Institution
Ef Energy-focus
EA East Africa
EBITDA Earnings Before Interest, Taxes, Depreciation and Amortization
EBRD European Bank for Reconstruction and Development 
EDGE Enhancing Development and Growth through Energy
EE Enabling Environment 
EEA European Economic Area 
EGB Employee Gender Breakdown 
EIA Environmental Impact Assessment
EIB European Investment Bank 
EMMP Environmental Mitigation and Monitoring Plan 
ESG Environmental, Social and Governance (guidelines, criteria) 
EQ Evaluation Question
ES, WES External Surveyor, WE4F External Surveyor 
EU European Union 
FAO Food and Agricultural Organization 
FAQ Frequently Asked Questions
FGD Focus Group Discussion 
FMO Nederlandse Financierings-Maatschappij voor Ontwikkelingslanden (FMO)
FP Founding Partners 
FTF Feed the Future 
GA Grant Agreement 
GCD Global Capacity Development 
GCF Grand Challenge Fund 
GD Global Development Alliance
GDP Gross Domestic Product
GDPR General Data Protection Regulations (EU)
GH-Pro Global Health Program Cycle Improvement Project
GIN General Indicator
xiii
GIZ Gesellschaft für Internationale Zusammenarbeit GmbH 
iBEAM Improving Business Environments for Agile Markets 
ICT Information and Communication Technology 
IEE Initial Environmental Examination
IF Investment Facilitation
IFI International Finance Institution
IIAC Innovation Investment Advisory Committee 
IoT Internet of Things
IM Impact numbered in relation to the ToC 
IP Implementation Partner
IRC International Rescue Committee
IWMI International Water Management Institute
KII Key Informant Interview
KM Knowledge Management
KPI Key Performance Indicator
LC Lead Consultant 
LCA Life Cycle Assessment
LE Local Evaluators
LOE Level of Effort 
LOP Life of Project
M&E Monitoring & Evaluation
MCC Millennium Challenge Corporation
MEL Monitoring, Evaluation and Learning 
MENA Middle East and North Africa
MFA-NL Ministry of Foreign Affairs of the Kingdom of the Netherlands 
MTR Mid Term Report 
M&E Monitoring and Evaluation
NGO Non-Governmental Organization 
Norad Norwegian Agency for Development Cooperation 
NpM Platform for Inclusive Finance (Dutch)
OC Outcome numbered in relation to the ToC
OECD DAC Organization of Economic Co-operation and Development, Development 
Assistance Committee
PAD Project Activity Document 
PAEGC Powering Agriculture Energy Grand Challenge 
PII Personal Identifiable Information
PIRS Performance Indicator Reference Sheet
PMEP Performance Monitoring & Evaluation Plan
PMU Project Management Unit
POC Point of Contact 
PPP Public-Private Partnership
Q&A Questions & Answers
QCA Qualitative Comparative Analysis
QoS(S) Quality of Service (Survey)
RAC Regional Advisory Committee 
RAS Recirculating Aquaculture System
RFP Request for Proposal
RIH Regional Innovation Hub 
SSEA South & South-East Asia
SCA South & Central Africa
xiv 
SC Senior Consultant 
SDG Sustainable Development Goals 
SEWOH One World No Hunger (GIZ program) 
SGE Small and Growing Enterprises
Sida Swedish International Development Cooperation Agency 
SME Small & Medium Enterprise
SOW Statement of Work
SPIS Toolbox on Solar-Powered Irrigation Systems 
STTA Short Term Technical Assistance
SWFF Securing Water for Food
SWP Strategic Work Plan
TA, TAF Technical Assistance, Facility
TBD To Be Decided
ToC Theory of Change 
UNDP United Nations Development Program 
USAID United States Agency for International Development 
USG United States Government 
WA West Africa
WE4F Water and Energy for Food: A Grand Challenge for Development 
WEf Water-Energy focus 
WES, ES WE4F External Surveyors 
Wf Water-focus
WFP World Food Program
WO-WL Women-Owned/Women-Led 
WWW World Water Week
Acronyms for data classification
The various tables in this report have data, i.e., text or amounts with a code is used to 
indicate:
ND No Data – Used when there is a target for an indicator and there should be results, 
but no information was provided or possibly no documentation was provided, so 
the results were recorded as 0; another possible issue was that this information was 
to be shared by the external surveyor but is not yet available. 
NA Non-Applicable – Used mostly for innovators that have been terminated from the 
program, and there is no longer information coming from these innovators 
NAWP Not part of the AWP – Used when the indicator is not part of the AWP, so there is 
no target for this indicator for the innovator, and the innovator is not tracking this 
information. 
OT One Target – Used when there is only one target provided for multiple countries 
that are part of the innovator’s project; targets or results were not disaggregated by 
country. 
NT No Target – Used when there is no target provided for that Year or LOP in the 
AWP, but there were other targets for other milestones. 
NYM Not Yet Measured – Typically used when projects or innovators started late; this is 
often the case for CFI2, CFI Iraq and SCA. 
xv 
DEFINITIONS
Stakeholders – A result (in a ToC) is a change in stakeholder behaviour e.g. end users 
producing more effectively/efficiently/sustainably. Thus, it is important to define them and 
categorise (food, energy and/or water indicators are proxy).
End users – firms, individuals or organized groups of individuals who use WE4F 
innovations; they are the ‘customers in the market’ (related to IM2) and can also be identified 
as ultimate beneficiaries (related to IM4). In this group there are subdivisions: 
• Smallholder farmer households – categorised by household income (quintiles 1, 2)1 
• Larger farmer households – categorised by household income (quintiles 3, 4, 5) 
• End user household members (f,m); number of innovator clients/households multiplied 
by average household size in a country (or something like it)
• Processing entities (companies, cooperatives, or farmer households) – 
• Providers of inputs or services for farming (this includes middlemen and transporters, 
as providers of marketing services)
• Marginalized groups: end users that typically face more barriers to access innovations;
to further define at national level (weighing gender, caste, ethnicity, distance to markets 
& services and other factors); in this report, indicators for marginalization are 
disaggregating for gender and income
• Rural communities. 
Innovations: Companies that are supported by WE4F so that they can upscale the 
innovations WE4F is interested in. In this report, this group can be subdivided according to: 
• WE4F support: the companies are recipients of grants (“grantee”), or only TA
• WE4F engagement history: ‘legacy’, CFI1 and CFI2 
• Innovation focus: Water only (Wf), Energy only (Ef) or both (Wef)
• Innovation type (subcategories of the innovation focus): see Annex 8 
• Women-led/-owned innovator enterprises: enterprises where a) >50% ownership/stake is 
held by a woman/woman; or b) at least 1 woman is in a senior management position or 
>30% of the board of directors are women (where a board exists); or c) women make up 
>50% of management and are a part of daily operations and decision-making in cases 
where a board does not exist.
Country-innovations: This is where innovations are counted for each country where they 
operate (with WE4F support); so then, innovation names are repeated.
Productivity: The term is often used but not defined in the project documentation; it is in the 
overall goal and the focus areas of the project, together with ‘economic productivity’ and 
‘agricultural sector productivity’, in annual reports and in the SOW. In EQ1g2
 productivity is 
used alongside climate change adaptation, however, some EQ1g questions seem to refer to 
production (-targets; there are no productivity targets).
USAID in WE4F3
 points out that a discussion of productivity is ‘out of scope’, or in a grey 
area, where use of natural resources (land and -quality) is concerned. Here, productivity is 
defined in restricted terms: agricultural production minus and water and energy inputs 
(per ha, so it excludes labour and other natural resources). The main impact question is using 
1 The income criteria used here as it is more universally applicable. WE4F also categorizes smallholders by farm-size, e.g., 
in the PMEP July 22, smallholder farms are less than 5 ha, in our surveys the majority of smallholders are found to have 
farms of less than 1 ha. Another term used by WE4F and not defined: “customers in the market”
2 EQ1g: Did WE4F-supported projects lead to more agricultural productivity and adaptation to climate change? Did WE4F 
projects meet their agricultural productivity targets? Overall, across all innovators, did the program meet agricultural 
productivity targets? What were the unintended effects of WE4F supported projects on agricultural productivity?
3 Personal communication USAID, L. McMahan, on January 20th 2023) 
xvi 
this definition: are we producing more food with more efficient use of water and energy? 
When farmers may take decisions that negatively affect the environmental, that is an 
‘externality’; it is given attention, but only for secondary measures. “We’re happier if poor 
people had some part in production, and benefits, and if there is no net environmental harm”. 
Lifecycle Assessment (LCA) vs Environmental Impact Assessment (EIA): EIA are 
commonly used in the world and also by USAID, in WE4F, to assess an innovations’ 
environmental impact at grant approval stage. It originates as an instrument for political 
decision-making and is embedded in national legislation in many countries, subject to rules, 
and compromises. EIA are rather ‘rough’ analyses with a focus on on-site effects of a 
process; the scope is limited in space and time, it often neglects a large amount of the actual 
environmental impact (indirect effects may be higher) – a big point of criticism against EIA.
LCA can be more detailed and in-depth, taking into account up- and down-stream activities 
throughout the entire life cycle of a project.4
Justification for introducing the word lifecycle here is that it serves as a proxy for ‘along the 
value chain’ which is mentioned in several indicators and EQ; EQl, EQm and EQn require to 
take a look beyond the economics of crop production and consider (unintended) effects or 
trade-offs beyond just production: emission, natural resources (including but not exclusively 
ground water), biodiversity, and socioeconomic effects on marginalised groups or rural 
communities. The term value chain is often used in more restrictive ways, limited to factors 
that can be monetarised.
4 Adapted from: https://ecochain.com/knowledge/environmental-impact-assessment-eia-how-is-it-different-from-lca/ 
1 
1 INTRODUCTION
The WE4F program 
The Water and Energy for Food (WE4F) Grand Challenge for Development program launched after 
an agreement between the German Federal Ministry for Economic Cooperation and Development 
(BMZ) through GIZ, the European Union (EU), the Ministry of Foreign Affairs of the Government 
of the Netherlands (MFA-NL), the Norwegian Agency for Development Cooperation (Norad), the 
Swedish International Development Cooperation Agency (Sida), and the US Agency for 
International Development (USAID). WE4F was announced at the World Water Week 2018 and 
launched at the Social Capital Markets 2019 signature event in San Francisco, California, the $85 
million (€80.5 million) program capitalizes on the lessons learned from its predecessor programs, 
Powering Agriculture: An Energy Grand Challenge for Development (PAEGC) and the Securing 
Water for Food (SWFF) Grand Challenge. These stated that grants alone will not help innovations 
scale and that technical assistance (TA), and investment facilitation are necessary to help innovations 
sustainably grow. 
The WE4F mission is to upscale food-production related innovations and strategies that focus on the 
food nexus to water, energy, or both. WE4F’s objective is to increase sustainability of agri-food 
value chains by producing more food with more efficient water and energy usage and address 
environmental sustainability and climate change adaptation in developing countries and emerging 
markets, with a particular focus on the poor and women. 
To achieve its mission, WE4F, through its Regional Innovation Hubs (RIHs), works with multiple 
stakeholders, including partners from the private sector, non-governmental organizations (NGOs), 
government offices, research institutions, and other donors who share the common goal of increasing 
food production through sustainable and more efficient water and renewable energy usage while 
reducing pressure on natural resources.
WE4F provides funding to innovations to achieve greater water and energy efficiency in farming and 
processing, to attain higher agricultural yields and better processing outcomes. Each innovation is 
unique in its own rationale and intervention. 
2 
Figure 1: WE4F intervention areas and countries
WE4F is implemented in five regions: South and Southeast Asia (SSEA), Middle East and North 
Africa (MENA), East Africa (EA), West Africa (WA) and South-Central Africa (SCA). In 2020 
activities started in SSEA, MENA, EA, and WA. This MTR looks at the MENA and SSEA and SCA 
regions. 
MENA Region WA Region SCA Region SSEA Region EA Region
Algeria Benin Angola Afghanistan Ethiopia
Egypt Burkina Faso Botswana Bangladesh Kenya
Jordan Cote d’Ivoire Central African 
Republic
Bhutan Malawi
Iraq Ghana Chad Cambodia Rwanda
Lebanon Senegal Democratic Republic 
of the Congo
India Somalia
Morocco Mali Eswatini Indonesia Tanzania
The Palestine 
Territories
Niger Lesotho Laos
Tunisia Nigeria Mozambique Malaysia
Sudan Togo Namibia Myanmar
Yemen South Africa Nepal
Zambia Philippines
Zimbabwe Singapore
Sri Lanka
Timor Leste
Thailand
Vietnam
WE4F aims to:
1. Increase food production along the value chain through a more sustainable and efficient 
usage of water and/or energy 
2. Increase income for base of the pyramid women and men in both rural and urban areas. 
3. Sustainably scale innovations’ solutions to meet the challenges in the WE4F nexus 
4. Promote climate change adaptation, environmental resilience, and biodiversity through 
the sustainable, holistic management of natural resources and ecosystems.
These aims are leading the WE4F Theory of Change (ToC), at the impact level: see Annex 6: WE4F 
Theory of Change. Linked to the results, WE4F developed 10 Key Performance Indicators (KPI) and 
44 General indicators (Gin); the KPI are listed in Annex 2. To help put the performance indicators 
into context it is important to note that using the SWFF model, WE4F expects that 40% of the 
innovations will reach their targets (or be within 80% of the target). Also, with the portfolio approach 
WE4F understands that not every innovation will contribute to every KPI. For example, some 
innovations will focus more on energy savings, and others on water savings. 
COVID-19 threats, constraints, and mitigation 
During the inception and early phases, the COVID-19 pandemic weighed heavily on the 
development of WE4F. The Secretariat took on a multi-level set of acts in mitigation to lessen the 
impact on innovations, investors, farmers, and other key ecosystem actors. Activities implemented to 
ease the COVID-19 risks included adding mitigation measures into RIH work plans, organizing 
virtual events and expediting CFIs so financial and nonfinancial help to quickly reach innovations 
and also added dedicated COVID-19 support. 
There were inevitable delays on the launch of the CFIs and a negative impact on funding and Tas.
3 
Equally there was geopolitical instability during the early stages of implementation. The MENA 
RIH, for example, faced a devastating explosion in the port area of Beirut, Lebanon, on August 4, 
2020 which destroyed its center of operations. The planning and kickoff of MENA was delayed by 
several weeks, but the RIH team adapted quickly and became fully operational before the end of 
2020. Then from late February 2022 there was the invasion of Ukraine leading to leaping prices in 
oil, gas, and grains. There were also national factors: increasing power cuts hampered the SCA 
operating in South Africa. 
Deteriorating environment 
The international crises led to the deterioration in WE4F’s environment. In SSEA and SCA there was 
immediate hunger and water insecurity with the possibility of an increase in extreme poverty by 148 
million people (20% of the population internationally), increased emergency food assistance and 
water insecurity. This crisis, however, also made the goals of WE4F increasingly relevant. The 
agriculture sector showed tremendous resilience and helped growing numbers rebuild their lives and 
the need for related small and medium enterprises (SMEs) increasingly relevant. 
COVID-19: Pivoting to reinforce strategy 
WE4F’s leadership was acutely aware of the negative impact of COVID-19 on innovations, 
investors, farmers, and other key ecosystem actors. Responses to the short- and long-term risks 
included integrating mitigation of impacts into RIH proposals and work plans, organizing virtual 
events, expediting CFIs so financial and nonfinancial help could quickly reach innovations, working 
on a guarantee mechanism for future use by eligible innovations, sharing information such as impact￾investing trends with innovations, and forging partnerships with different external partners. 
Specifically, for example, the SCA undertook an Investment Landscape Mapping Report to develop 
a responsive approach. Questions relating to COVID-19 impact were integrated into interviews with 
innovations, investors, and service providers. The increased cautiousness in deploying capital due to 
uncertainties and the inability to conduct due diligence as a result of travel restrictions have been 
taken into account in this MTR. 
Overall, WE4F works to build resilience and confidence through such measures as guarantees for 
future use by eligible innovations, sharing information such as impact-investing trends with 
innovations, and forging partnerships with different external partners. 
Impact on RIHs  
The hubs faced pandemic-related challenges with lockdowns and logistical challenges. Each hub 
faced travel restrictions, both local and regional, which led on to difficulties in team coordination, 
penetration of local markets for recruiting, and engagement with innovations, investors, and partners. 
Budgets were stressed by cost increases in travel and fuel which affected whole regions. 
COVID-19 constraints and impacts included: 
• Delays in launches and in financial support
• Logistic challenges and rising travel costs
• Reallocation of anticipated funding to fight the pandemic 
• Canceled or reduced orders faced by innovations 
• Delay in provision of Technical Assistance to innovations 
• Challenges in exercising due diligence in selection and follow-up of innovations 
• Data reporting delayed due to the effect of COVID-19 as innovations struggled to report 
results on all indicators particularly relating to end users as external surveyors could only 
enter the field in late 2021
• Postponement of innovation peer-to-peer discussions learning from challenges. 
4 
All hubs faced challenges in assembling local staff and commencing operations with lockdowns, 
travel restrictions, and faced shocks in supply chains with, potentially, lasting impact on women and 
the poor. With such a sharp decline a long and slow recovery is likely to impact many businesses and 
investment opportunities. These factors have to be considered in relation to gauging the effectiveness 
and impact at a Program level and, indeed, at all levels. 
Adaptation and Mitigation measures included: 
• Much greater use of virtual communications 
• Shifting hubs to virtual working mode to overcome logistical logjams
• Pivoting planning to meet new challenges in delivering on Y1 milestones by adjusting Y2 
and Y3 targets 
• Shifting Y1 to Y2 targets as potential staff candidates did not want to leave existing positions
• Shifting TAs to Y2
• Conducting site visits and training courses virtually. 
Each RIH’s local roots, vast networks, and thorough understanding of the corporate landscape and 
markets helped ensure outreach to and engagement with the right partners and enterprises. In the case 
of MENA, pivoting and adapting quickly enabled the team to successfully deliver on key Year 1 
milestones. 
It is hard to separate impacts on the hubs and innovations. MENA mentions delays in relation to: 
Abu Erdan, Alva Tech, Platfarms, SuWaCo. In SSEA innovations with delays included aQysta, 
ATEC (Bangladesh), ZooFresh Foods, Gham Power, Husk Power Systems, Oorja Development, 
Punam Energy(Onergy Solar), Sumba Sustainable Solutions, Tun Yat. 
Impacts on end users  
Some regions experienced high levels of infection in rural communities as well as urban areas. In 
India, some farming communities reported COVID-19 illness and death, and cut back in buying 
innovations. While there were fewer lockdowns in villages than in cities, there was a state of trauma 
and disturbance. In the examination of EMMPs in Section 3.6.3 there are more details on COVID-19 
impacts on end users. 
Structure of the Program 
Central to WE4F’s operation are the hubs which operate in the three regions (SSEA, MENA, and 
SCA). Each hub works to the same key performance measures but came into operation in different 
sequences. 
The SSEA RIH is based in Bangkok, Thailand, and supports enterprises in 15 countries: Bangladesh, 
Bhutan, Cambodia, India, Indonesia, Laos, Malaysia, Myanmar, Nepal, Philippines, Singapore, Sri 
Lanka, Thailand, Timor-Leste, and Vietnam. The MENA RIH is based in Beirut, Lebanon, and 
includes innovations in the following countries: Algeria, Egypt, Iraq, Jordan, Lebanon, Morocco, the 
Palestinian Territories, Sudan, Syria, Tunisia, and Yemen. The SCA RIH is based in Pretoria, South 
Africa, is administered by Tetra Tech and includes innovations in South Africa, Zimbabwe, Zambia, 
Botswana, Mozambique, Namibia, Angola, Chad, and the DRC.
With multi-donor backing, the hubs support a diverse portfolio of 81 innovations to pursue their own 
goals (with some modification) more effectively. For example, via the hubs, the innovations’ 
financial resources are augmented, organizational capacity strengthened through TA in the form of 
training, direct support, and innovation networks are opened globally. For more details on the 
portfolio refer to section 3.4. 
WE4F Portfolio overview: by region and by engagement history (legacy, CFI) 
At the time of reporting (August/September 2022) WE4F was active in SSEA, MENA, and then 
subsequently in SCA, which brought the total innovations to 81. An overview of all innovations are 
5 
provided in Table 1, organized by WE4F support and by region, and Table 2, by WE4F engagement 
history and by region.
Table 1: Innovations by WE4F support, by region
WE4F support SSEA MENA SCA Total
TA only 8 6 3 17 
Grantee 22 30 8 60 
Terminated* (no data for these) 2 2 0 4 
Total 32 38 11 81
* Because they did not reach their milestones 
Source: WE4F Innovator List_As of July22.gsheet
As is made clear in the definition of terms, the Grantee is the primary form of financial support and 
is the focus of this evaluation. The TA Only innovations have, as the term implies, only TA support 
and have a lower-level responsibility in reporting results. 
Table 2: Innovations by WE4F engagement history
WE4F engagement history SSEA MENA SCA Total
Legacy* 5 0 2 7 
CFI1 14 17 9 40
CFI1 Iraq** 0 8 0 8 
CFI2 (no data yet) 13 13 0 26
Total 32 38 11 81
*: legacy is from a previous program
**: Iraq is listed differently in the M&E data: although labeled as CFI1, there is currently no data for these innovations
Source: WE4F Innovator List_As of July22.gsheet
Further description of the innovation portfolio is provided in Table 2. Grantees are defined by 
cohorts, i.e., by the two Calls for Innovations; the CFI1 innovations have had support now for more 
than 12 months with support starting in spring of 2021 (Awards announced in March 2021 and 
signed in May 2021). In addition, there were bootcamps for MENA and SSEA in May 2022 for CFI1 
and CFI2. The CFI2 innovations just started to receive support and have yet to report on results. The 
CFII2 grants were signed in mid-June 2022. In addition, there is the group of CFI1 Iraq which the 
grants were signed in Feb 2022, those who have been terminated and Legacy innovations which have 
had USAID support for a few years. When discussing year 1 for CFI1 that is May 2021 to April 
2022, for CFI2 year one is mid-June 2022 to mid-June 2023 and for the CFI1 Iraq year 1 is May 
2022 to April 2023. 
More information on the portfolio is presented in 3.8.1 Portfolio analysis. In short, the innovations 
are spread across the regions in the following order: MENA 38, SSEA 32 and SCA 11; making a 
total of 81 innovations in July 2022. As is discussed below, most of the end users are in SSEA.
Purpose of the evaluation 
The purpose of the Evaluation is primarily to inform WE4F management (at the level of RIH and the 
Secretariat), and the founding partners. This midterm report covers WE4F’s work in the SSEA and 
MENA regions; Final Evaluation will also cover the SCA region. 
In this evaluation stage the focus is on program design issues (strategy/ToC, structure, the M&E 
system), presenting and analyzing the portfolio, the promise of targets, and initial performance. 
The Evaluation Team collected data to answer the evaluation questions through documentary review 
and directly at the levels of actors or stakeholders5
 in the program: 
1. Program and meta level: founding partners, WE4F executive, policy makers and regional 
organizations supporting the innovation 
2. Administrative level: WE4F Secretariat, Regional Innovation Hubs (RIH), experts
3. Innovation level: Innovations and their service providers (incl. finance institutions) 
5 A more detailed brief on the understanding of who are the targeted stakeholders is provided in chapter 7.
6 
4. End user level: all customers using the innovation, incl. farmers, food processors. 
This MTR, together with the final performance/ impact evaluation, provides an overall assessment of 
WE4F that not only makes transparent the challenges met and results achieved, but also contributes 
to content-related and systematic learning by answering questions of “how” and “why” the 
interventions of WE4F were successful or were met with challenges. 
The evaluation questions 
This section summarizes the main evaluation questions; a complete list of evaluation questions is 
presented in the SOW in Annex 1, and again in Annex 2 where the questions are linked to the 
indicators. For convenience, the relevant EQ and indicators are also presented at the beginning of 
each (sub)section in Chapter 3. As is demonstrated here, the findings are structured along the OECD￾DAC evaluation criteria.
At end user level:
• Relevance and coherence at innovation level (sections 3.1 and 3.2) – The focus of this 
baseline and mid-term report is on the relevance and coherence of the program. The main 
questions EQa,b are therefore situated in the context, and explore end user demand, local 
ownership and addressing the needs of more vulnerable groups. In relation to Coherence 
the contribution to SDGs is explored (EQ1c). 
• Baseline for effectiveness (outcomes) at innovation level (section 3.3) – The baseline 
for innovations’ effectiveness is explored with the main question EQ1h, and indicators on 
innovations’ progress on mobilizing investment and partnerships (KPI9, Gin20). 
• Baseline for impact at end user level (section 3.4) – To establish the baseline for 
measuring impact at end user level, the main questions (EQ1e, 1f, 1g, 1i, 1l) on water￾and energy saving, food production and related income increase, and EQ2: the attribution 
question. 
• Sustainability at various levels (section 3.5) – On the subject of sustainability (EQ1l, 
1m, 1n, 1o, 1p), the questions focus on:
◦ sustainability of innovations’ business: profit and job creation (KPI1, Gin10) 
◦ environmental sustainability at innovation level: monitoring protection of water, 
biodiversity (KPI8)
◦ environmental sustainability at end user level, related to GHG emission (Gin6), and 
area improved (Gin45). 
And at program level: 
• Relevance at program level (section 3.6) – This is explored in interactions at the 
international and national level. 
• Effectiveness (outputs and outcomes) at program/RIH level (section 3.6) – To 
establish the baseline for measuring outcomes at this level, the main questions (EQa,b,c 
and EQ1d,h) explore outputs of what the RIH delivered in terms of Technical Assistance 
(TA, incl. training on various subjects) and Investment Facilitation (IF). 
• Effectiveness (outcome) at program/meta and level (section 3.7) – There are questions 
the questions about learning (EQa, EQm, EQn), external financing and other enabling 
environment work (Gin28). 
• Efficiency at program level (section 3.8) – The question EQb is the most important 
here, at this MTR phase; it requires that the Evaluation Team carry out a portfolio 
analysis. 
Collecting data for the evaluation questions will contribute to analyses that help to answer the 
overarching hypotheses I, II and III. These hypotheses, as questions, are presented as ‘evaluation 
questions’ and included in Annex 2.
7 
2 METHODOLOGY AND APPROACH
The Evaluation Team used a mixed method approach which included the analysis of the programs 
quantitative data, surveys and qualitative data collected through KIIs conducted by the Evaluation 
Team and observations. A full description of the processes of data collection (conducting a data 
quality assessment of administrative data, the survey of innovations, qualitative data collection and 
surveys of end users) is set out in Annex 15 for reasons of economy. 
Here the methodology is comprehensively set out with the following headings: the method of data 
collection, sampling, data analysis plan, administrative data validation process, data security, 
qualitative analysis, quantitative analysis, data quality assurance of M&E data, limitations of data 
from innovations’ Annual Working Plan (AWP), limitations of the evaluation and reports on 
progress in data collection.
The evaluation matrix sets out the evaluation questions, the relevant indicators and the various 
sources of data and is presented in Annex 7. The scope of the MTR is also presented here in the 
detail of the questions which will be fully addressed, partially addressed either by lack of data or not 
fully addressed because of the stage of WE4F or not addressed at all as they are inappropriate to the 
MTR and apply to the final evaluation. 
The full set of Evaluation Questions which apply to the entire evaluation are listed in Annex 2. In the 
MTR the continued relevance of WE4F and progress towards achieving its planned objectives is 
undertaken. 
The evaluation presents a challenge with 66 questions to which are added the KPI and General 
Indicators which are also assessed and present a range of concrete targets. The evaluation questions 
are gathered under the OECD development criteria for which at the MTR stage; there are, however, 
limits in reporting in two dimensions. Firstly, the MTR covers the early stage of WE4F when not all 
activities matured and effectively measured and secondly, there are data gaps in administrative data, 
innovation surveys and, particularly, end user surveys which can only be closed over time. 
Rather than excluding whole sets of questions as the WE4F has not yet matured (such as those 
gathered under Efficiency, Impact and Sustainability), it was decided to assess the evaluation 
questions and data to see what could and could not be answered. A full set of questions is presented 
in Annex14 and presented according to the following criteria: 
a) Full: address fully and conclusively 
b) Limit stage: can only answer to a limited extent in the MTR as a longer period of the program 
is needed
c) Limit data: we partially answer because there is limited data and 
d) Not Addressed: these questions are not addressed in the MTR but will be in the 
Final Evaluation. 
This somewhat broader scope for the MTR including each of the OECD criteria is used because of 
the need to assess the preliminary results as the basis for the final evaluation. For instance, the 
preparation and foundations for sustainability need to be established in the early results, etc. 
In the MTR all the OECD criteria are addressed at various levels. Not all Evaluation Questions or 
GINs are fully and finally assessed. Particular attention is given to Relevance, Coherence, 
Effectiveness (specific aspects), Efficiency (to some extent), Impact (to a limited extent, relating to 
end user responses) and Sustainability (with a focus on the short term i.e. viability of the program 
and the innovations) as well as foundational aspects such as end users reporting long term 
engagement in an innovation. 
The assessment of the Evaluation Questions leads to the following: Relevance and Cohesion are 
reported in full. Effectiveness with 3 questions answered in full, 5 with data limitations, and 4 with 
limitations due to the stage); Efficiency with one question Not Answered, 4 with limitations due to 
8 
stage and 2 with limitations due to data); Impact with one question Not Answered, 4 with limitations 
due to data; and finally, Sustainability with one question answered in full, 8 with limitations due to 
the data, and 1 with limitations due to the stage. 
Presented in this way it may appear that that the stage of development is not the deciding factor in 
answering or not answering a question; aspects of a question may be answered partially. 
The instances of limits to data may appear like the system is lacking at this time, but a full and 
comprehensive data collection in administrative and in independent evaluation research is somewhat 
linked to the stage of development. Data systems improve and repeated surveys and interviews are 
needed to confirm or reject patterns of change, this has been an ongoing process for the project 
during the midterm evaluation. 
Furthermore, because of the interrelationship between the many questions, it was found hard to 
disentangle the related paths leading to the intended result. For instance, in many fields Effectiveness 
is a step to Impact in a longer loop towards Sustainability. 
This approach allows most Evaluation Questions to be addressed at this stage, for related 
observations and data analysed in full or partial answer, and to establish early findings and 
recommendations to allow revision and refocusing as an extension of WE4F is considered. 
There are, challenges in assessing change over time. In the early stages of a program rigorous data 
collection often establishes the baseline for subsequent development over years rather than a measure 
of change. The baseline of the number of end users, for instance, was found to be set at zero; in this 
case change and growth is assessed against Year 1 Results by progress towards or failure to reach 
targets. 
Overall, this approach leads to critical issues being identified to assess if WE4F is fit for purpose and 
for extension. Specifically, the MTR identifies achieved food production, expansion of end users and 
proven financial viability of innovations as three planks for progress and extension. 
9 
3 FINDINGS 
This chapter presents findings in sections that follow the OECD criteria and presents the results for 
SSEA and MENA CFI1 grantees as the results for SCA, CFI1 Iraq and CFI2 are not yet available. 
The format of this section is to first discuss the data, both qualitative and quantitative, and then 
provide a summary of the findings and conclusions are the end. The recommendations follow in the 
corresponding section. 
Section 3.1 addresses relevance evaluation questions with a focus on how the innovations respond 
to end users’ needs, measured by the uptake (growing numbers of end users) and qualitative data on 
local ownership and end user feedback. 
Section 3.2 discusses coherence at program level.
Section 3.3 presents the effectiveness focusing on program outcome level: the effectiveness of the 
hubs’ delivery of TA (OC1), the effectiveness of delivery of WE4F funding, compared to 
innovations’ co-funding and mobilization of external funding (OC2), and promoting an enabling 
environment (OC3).
Section 3.4 considers the program design and (in relation) a number of management issues related to 
efficiency, including the MEL system.
Section 3.5 looks at impact-level results (baseline and progress) in terms of the growth of end user 
numbers (IM1), the use of the innovations (IM2), food production while saving water and/or energy 
(IM3) and income (IM4).
Section 3.6 assesses the sustainability (EQ1l, EQ1m, EQ1n, EQ1o and EQ1p) at the program, 
innovation, and end user levels, presenting data from indicators KPI1 (profit), Gin10 (jobs), KPI8 
(monitoring water & biodiversity), Gin6 (GHG emission), Gin45 (surface area of land better 
managed) and a discussion of the social, economic, and environmental effects along the innovation 
lifecycle.
Section 3.7 addresses the three overarching hypotheses (EQHI, HII, HIII). 
The main data sources are the WE4F Administration’s M&E system, the Dexis Innovation Survey, 
Dexis Key Informant Interviews (KII) with innovations, and the Dexis End user Survey. The Final 
Evaluation will include interviews with the Founding Partners and the WE4F Secretariat.
3.1 Relevance
Section 3.1 addresses relevance evaluation questions. At program level the questions on how well the program 
addresses needs of marginalized groups and women, and the need to assure environmental and socioeconomic 
sustainability (EQc and EQd). At innovation level, the questions on how well the innovations adapt their outputs to 
the requirements of SDG, and to the needs of more marginalized households (EQ1a and EQ1b). 
3.1.1 Countries participating in the program
• In total, there are 81 innovations across 26 countries. 
The number of countries participating in WE4F, and the number of country-innovations is an 
indicator of its relevance. Please refer to Annex 8 Table 31 for a complete list of country-innovations 
as well as a table detailing the number of innovations per country and the innovation focus. 
As made evident in the summary below, WE4F is relevant throughout a broad reach of participating 
countries across the SSEA, MENA, and SCA regions. For example, there are 32 innovations across 9 
SSEA countries, 38 innovations across 10 MENA countries, and 11 innovations across 7 SCA 
countries. 
In terms of the innovation types, the data shows that the number of water, energy, and water and 
energy focused innovations is well spread over the different country-innovations (the latter is the 
10 
focus for half of the country-innovations). For example, of the innovations, approximately 27% are 
water, 23% are energy, and 50% are water and energy focused innovations. 
EQc – Compatibility of interventions to impact on water, energy, and food security6
EQc finding: In this first stage finding of the evaluation, the participation of the different countries 
as well as the well spread balance of the innovation focus areas shows broad relevance. To date, the 
broad relevance of the WE4F Program is evidenced through its country participation which includes 
9 countries in the SSEA region, 10 in the MENA region and 7 in the SCA region. In interviews with 
RIHs and innovations, the focus areas are regarded as highly relevant to these regions. 
3.1.2 Local ownership 
• Most innovation companies are incorporated in participating countries. 
Local ownership is an important indicator of relevance to agencies involved in agriculture 
production. Local ownership in EQ1a is understood as being an innovation that is indigenous to a 
locality/country (not a foreign implant) and is a measure of relationship/relevance to country and 
government. To express this, the Evaluation Team investigated the relationship of the innovation 
company with the countries in which they work. Ownership is shown; whether the innovation works, 
or not, in the country in which it is incorporated. 
In terms of where the innovations are incorporated, approximately 75 % of innovations work in the 
same country as incorporated, 11 percent do not work in the country they are incorporated in, and 
17% there was on data available at the time of data collection and analysis. Additional details 
broken down by region in Table 45 in Annex 8. 
A large majority of innovations work in the country in which they are incorporated; since these are 
participating countries in WE4F this indicates an immediate level of relevance to local institutions 
and end users. Those innovations which are not incorporated in a local country may also be in 
another participating country, examples being innovations incorporated in Egypt but functioning in 
Lebanon and elsewhere. During the Annual Convening innovations shared how their experience and 
knowledge on the ground created a demand for their innovations and allowed them to adapt to the 
needs of the end users, this was particular true for increasing numbers of women end users for some
innovations in Egypt and India. 
EQ1a – Demand and local ownership for the innovations7
EQ1a finding: Local ownership is evident in that most innovation companies are incorporated in 
participating countries; they may also operate in other adjoining countries in the region. This finding 
should also be cross-referenced to the impact findings. 
EQ1a recommendation: The Program needs to strengthen technical assistance support in the legal 
and regulatory areas to address the barriers that women face both as innovations and as end users. 
This is being achieved with the innovations and financial services, but could also drive other 
stakeholders, e.g., government, to demonstrate that engaging more women and make them benefit, it 
requires them to be given equal freedoms and equal wages (on wages, this can be addressed in 
innovation companies). 
3.1.3 End user feedback to innovations 
• There is evidence of 67% of innovations make products that are relevant to their end 
users and reliant on client feedback. 
Relevance is also indicated by innovations adapting products for the user of end users the innovation 
survey (conducted in two – SSEA & MENA – out of the three regions) asked 12 CFI1 innovations 
6 EQc – To what extent were the interventions implemented at each WE4F RIH compatible in achieving impacts related to water 
efficiency, energy efficiency and use of renewable energies, or food security across the program?
7 EQ1a – Is there demand and local ownership for the WE4F innovations (individually and across all innovations)?
11 
whether they provided different products/ innovations for farmers of different sizes; 67% responded 
yes, and all of them confirmed to have a mechanism for collecting client feedback. Examples of 
feedback that innovations collected from their clients, summary include: 
• a need to make their interface with clients more use-friendly
• satisfaction with the seed selection and fertilizers available 
• a need to receive additional support post-harvest in using post-processing units to add value 
• coconut char-briquettes for chick-brooding are good and simple to use
• pay-per-use services are much more affordable and hassle-free.
Given the cross-sectional analysis, further details are included in the section on impact and 
effectiveness in this report. 
EQ1b – Strategy to address marginalized groups’ needs related to water, energy, food security8 
EQ1b findings: In terms of key needs of end users, there is evidence of 67% of innovations make 
products that are relevant to their end users and reliant on client feedback. Overall, the increasing 
numbers of end users are evidence that that the innovations respond to key needs; with an increasing 
demand for most of the innovations, the innovations are found to be relevant to end users including 
marginalized groups (more on that in 3.5.1). 
EQ1b recommendation: WE4F needs a promotion strategy to show how more women end users 
can succeed. These efforts may draw more women into production. Consideration should also be 
given to technology of which ergonomics are adapted to women (smaller in size), and technology 
benefits for women, e.g., biodigesters (a heavy investment that is often paid for by men) benefit 
women in terms of health and cooking-fuel time saving, but considering the time spent on charging 
the biodigester (needs a lot of water) women could also benefit from bio-slurry, for food crops. 
Additional research into successes in raising participation of marginalized groups should be 
undertaken to identify innovations (in terms of costs, sizes, quantities) which have proven to be most 
successful in meeting the needs of these groups. Additional focus should be given to marginalized 
groups during future surveys. 
3.2 Coherence at program level
Coherence in this MTR is related to whether the intervention is coherent with its own and broader 
strategic objectives. More specifically the MTR assesses both the internal workings of WE4F, such 
as the allocation of funds to sectors which reach towards strategic objectives and externally, such as 
the relationship between WE4F and other significant development initiatives nationally and 
internationally. In addition, of particular interest is the relationship between Hubs and innovations 
with other initiatives undertaken by donors, such as programs on microfinancing. 
Internal coherence
• There is internal cohesion between the key focus areas of innovations and the strategic 
objectives of WE4F.
Firstly, internal coherence is assessed as the alignment of all parts of the program with its strategic 
objectives. This is assessed by the self-identification of innovations of their focus to assess their self￾identification with the strategic objectives. 
The Evaluation Team examined the internal coherence of the innovations with the strategic 
objectives of WE4F by way of using innovations’ keywords they used to capture the essential nature 
of their innovations. These words were then organised in three clusters (natural resources / 
8 EQ1b – To what extent does WE4F’s innovation support strategy address key needs related to water efficiency, energy efficiency 
and use of renewable energies, or food security of marginalized groups (the poor, women, youth, ethnic minorities, rural 
communities)?
12 
technology / socioeconomic), as shown in Table 3, where the comments were counted and presented 
by innovation focus. 
Table 3: Innovations’ key words describing the innovation (n=see note below) 
Innovation focus water energy water & energy total
Natural resource/climate aspects: resource efficiency, solar, renewable, 
organic waste, biogas, bioslurry, saves water, organic fertilizer, climate smart, 
cleaner, weather, endemic seed
6 12 10 28
Technology aspects: innovation, linking technology, best practices, adaptable, 
easy to use, reliable, livestock breeding, AI, poultry brooding, hybrid seed, 
endemic seed, irrigation, pump
2 4 9 15
Socio-economic aspects: production increase, (labor, land) intensification, zero 
hunger, SDG market facilitation, pay-per-use, scale up, business model, value 
chain, competition, supply chain, finance, carbon-credit, fast, savings, farm 
management, digitalization, data driven, monitoring, effective, service delivery, 
decentralized, affordable, savings, smallholders, inclusive, empowerment, 
gender, LGBTQI, community-managed, cultural
14 18 19 51
Source: Dexis innovator survey (Clean version August 10 Survey Monkey Innovator Survey.xlsx) 
Note: The numbers represent “mentions’ in response to the question and not the number of innovations responding.
The most common key words (51) are found in the socioeconomic cluster followed by natural 
resource/climate aspects (28) and finally technology aspects (15). Technology key words are 
mentioned especially in the innovations with the combined water and energy focus. In relation to the 
other clusters there is fairly even spread of key words in natural resources and socio-economic 
aspects.
Another positive internal cohesion aspect of the Program is found in the frequent mention of the 
productive interrelationship between innovations learning from each other. This occurred during the 
bootcamps that the hubs hosted and during the annual convening. There was camaraderie among the 
innovations from each hub, and this extended to all the regions during the convening. The 
innovations are able to discuss common problems, and solutions and also network so materials and 
equipment can be sourced from other countries if a supply chain issue develop. 
External coherence
• There is still not concrete cohesion between the Hubs and innovations and other in￾country donor programs.
Secondly, the content of the self-description provided by the innovations can be related to aspects 
that are also covered in the SDG for food security, inclusion (smallholders, gender, LGTBQI, 
marginalized communities), sustainable land & natural resource management (saves water, resource 
efficiency, endemic seed) and climate mitigation and adaptation (solar, renewable). In addition, in 
KIIs both the RIH and innovations have mentioned their alignment to a particular SDG and to 
government programs to implement these international goals.
The SDG operate at a national and international level to direct resources to meet major challenges in 
agriculture, environment, climate change and income. 
Despite the overall alignment with the SDG mention was also made of friction points or trade-offs 
between goals directly related to the innovation (and to WE4F) and broader SDG. The RIH 
respondents provided examples of these, naming various (negative) socioeconomic, environmental, 
biodiversity and climate change effects of the innovations: 
1. RIH concerns about (solar) water pumps causing depletion of water resources, and “water 
may not be managed so well,” and “unintended water-related consequences are yet to be 
identified”. There was evidence that some innovation strategies do not address this, as it is 
assumed that “other schemes encourage micro irrigation.” 
13 
2. Mention was also made that goat breeding (in a semi-arid area, apparently beneficial for 
marginalised women farmers who increase the goat herd) depletes the community’s water 
resources. 
3. The control of negative environmental effects can be costly, e.g., alternatives to chemical 
pesticides can a burden, especially for smallholder farmers, small organizations. 
Finally, there may not be cohesion between the WE4F RIHs and other in-country donor programs. In 
interviews with Hubs this was mentioned positively in one instance, but this aspect has not yet been 
extensively examined. 
EQ1c – Contributing towards SDG9
EQ1c finding: Overall there is internal cohesion between the key focus areas of innovations and the 
strategic objectives of WE4F. There is real interest expressed in interaction between innovations 
themselves within WE4F. These relationships started during bootcamps and the annual convening 
and will continue. 
WE4F provides avenues to reach particular SDGs in relation to socioeconomic, environmental, 
biodiversity and lowering of emissions in agriculture and broadly towards eliminating poverty and 
hunger. There are, however, some contradictory aspects of SDG which may be ‘off-site’ i.e., 
elsewhere in the innovation lifecycle / food commodity value chain, affecting others than the end 
users including marginalised groups. This is discussed in more detail 3.6.5. 
EQ1c recommendation: Since SDGs are integrated into country plans the RIHs could initiate 
discussions with policy makers and civil society to achieve mutual coordination and support for 
innovations. Attention should be given to SDG (SDG2: Zero Hunger, SDG1: No Poverty, SDG15: 
Life on Land, SDG5: Gender Equality, SDG10: Reduced Inequality, SDG13: Climate Action, SDG9: 
Industry, Innovation, and Infrastructure). 
At a national and regional level, the Program should present sets of innovations which help advance 
particular SDG, to promote innovations to support government efforts to attain SDGs. 
WE4F is giving particular attention to and developing tools to measure progress in relation to 
SDG15: Protect, restore, and promote sustainable use of terrestrial ecosystems, sustainably manage 
forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. 
Interventions by the RIH could improve the enabling environment for innovations by showcasing the 
prioritization given in improving sustainable agriculture in all these dimensions. 
3.3 Effectiveness: innovations’ capacities
The effectiveness is related to the outcomes of the services provided by WE4F: 
OC1: Capacities of innovators are improved
OC2: Mobilization of external funding for innovators is increased
OC3: Enabling environment for innovators and relevant stakeholders in targeted regions is improved
This section covers two new indicators (2021 Annual Report): 
• The score(s) for the Quality of Service Survey (QoSS) in 3.3.1. 
• The proportion of innovations reaching their own end user targets in 3.3.510. 
Subsection 3.3.1 is about the services provided, covering the QoSS results and -indicator which covers all 
services. Subsections 3.3.2, 3.3.3 and 3.3.4 provide specific information on Technical Assistance (TA), 
Investment Facilitation (IF) and Enabling Environment (EE, including financial support for end users, 
removing hurdles, sharing lessons). Lastly, in 3.3.5 the effect of these services on innovators’ businesses: end 
user numbers and gross sales. 
9 EQ1c – To what extent do WE4F innovations contribute towards or contradict SDG including those related to poverty, hunger, 
gender equality, affordable and clean energy, preservation of water resources or biodiversity, and climate change adaptation? 10 The WE4F impact indicator on end users (KPI2) is discussed in the impact chapter (3.5.1).
14 
3.3.1 Quality of Service and effect of TA, IF and EE
• The services are generally very good, close to, or more often above the 80% mark. 
• Improving the Enabling Environment services is most important: there is a need for it 
and needs adequate knowledge of the local context; it is recommended to mobilize and 
build capacity of apex organizations that could influence the enabling environment with 
the benefit of knowing the local context. 
• Pre-award services could be streamlined and tailored, e.g., different entry systems for 
smaller and larger business. 
• The disbursement of WE4F funds is generally on track. 
• Innovations have been able to raise funds by co-funding and external investors.
The WE4F QoS Survey (QoSS) assessed services in 7 areas11. The QoSS gives a large quantity of 
interesting qualitative and quantitative information. Key findings from the November 2022 QoSS: 
• Generally, the various services provided by WE4F are much appreciated and found to be 
(very) relevant and useful; the WE4F hub staff is commended for their responsiveness and 
helpfulness, “professional”, “knowledgeable”, “communicative”, “they give their best” and 
“quick and ready”. Negative responses are mostly constructive and helpful. 
• Generally, of all different services, TA and ‘networking’ were considered the most useful, in 
all regions; on usefulness of the services related to Investment Facilitation, Enabling 
Environment, gender, environment and M&E, there were relatively fewer mentions
• Pre-award support: all respondents found that the process positively impacted their 
organization’s administrative and financial systems; suggestions include improve 
documentation, e.g., tailoring/simplifying requirements for smaller innovations 
• Technical Assistance: received very good (above 8) scores in all three regions (in SSEA, 
MENA and SCA resp. 8.1, 8.2 and 8.6); TA is mostly used for business planning, -financing 
and marketing; “TA helps with policies [..] used as long as the company operates”; for 
improvement it is suggested to (further) tailor TA to needs, and to (further) outsource TA
• Investment Facilitation: support is also receiving very good scores (resp. 8.6, 8.8 and 8.5) 
from innovations who received it (not all as yet); suggestions include, seek financing more 
tailored to innovations’ situations, and explore blended financing and fast-track investment 
• Enabling Environment: support receives rather good scores (7.6, 7.6 and 7.4 respectively); 
many comments suggest a critical need, in all three regions, to address many barriers 
regarding registration and environmental compliance (“bureaucracy”), import and export, 
lack of finance, and political instability. Suggestions include, provide more specific
references especially on biomass challenges: “local government lacking independence on 
waste management”, “regulations on compost (quality, trade) are challenging, or lacking”, 
“import of portable digesters is highly taxed”, and “WE4F keeps mis-classifying our project 
as a renewable energy (biogas) project yet we trade bio-slurry”. 
• Knowledge on marketing, BoP and gender, on average, increased; more than 90% (all 3 
hubs) indicate gender knowledge increase; most effective are 1-1 sessions, peer learning and 
expert sessions. “Grant funds allowed us to reach most of the remote farmers who are at the 
BOP”, and “the hub's persistence for our ESG policies to go beyond hiring ratios to better 
understand the role that women can have in supply of our feedstock” 
• Communication: Most respondents received communications through the newsletter and
participated in learning events on the subjects of gender, and environmental sustainability 
(incl. climate change adaptation, biodiversity). The newsletter is appreciated (more in SSEA 
than in MENA or SCA) although nearly a third of the respondents scores its helpfulness as 
neutral or less; many respondents like to see more success stories and information about 
funding opportunities. More personal communication is most valued: emails, 1-1 calls and 
11 QoSS analysis to answer EQn.xlsx
15 
conferences (with peers). Webinars, workshops, annual meetings and bootcamps work well 
for learning on gender and environmental sustainability. Suggestion, include providing 
direct feedback on environmental sustainability issues, including climate change adaptation. 
• Monitoring & Evaluation: readiness for data collection is good, but to an extent; it is “not 
easy”. Data use varies by region; mostly for decision making, sales and fundraising. The 
recommendations vary: tools to be improved and standardized and with the possibility to 
adapt or simplify tools (e.g. for BoP end users); data collection should be more efficient (or 
given more time), and automated (suggested several times).
For the new indicator counting the proportion of participants scoring high Quality of Service 
(assuming this is a score of 8/10 or higher), the results are as presented in Table 4. 
Table 4: Proportion of participants scoring high Quality of Service (new indicator)
Region TA (Technical 
Assistance) 
Investment 
Facilitation (IF) 
Enabling 
Environment (EE) 
average score
(not weighted)
n 
(total n = 166)
SSEA
n
19 (79%) 8 (89%) 15 (65%) 75%
24 9 23 56
MENA
n
25 (76%) 9 (75%) 18 (55%) 67%
33 12 33 78
SCA
n 
11 (100%) 8 (80%) 7 (64%) 81%
11 10 11 32
Average: (w = weighted) 85% 81% 60% 
Total n 68 31 67 72% (w) 166
Source: QoSS Nov. 2022
New indicator on QoSS: Quality of service score; target: 80% in the categories “very” a 
“extremely’’ positive/effective (a score of 8 or higher). 
New indicator on QoSS finding: The average scoring follows a fairly similar pattern in all three 
hubs: near or above the 80% mark for TA and IF, and below 70% on the subject of EE. 
EQb: EE, TA and IF differences in meeting innovations needs and scaling innovations12
EQb finding: Participant’s scores in the QoSS show generally high satisfaction with the TA and IF 
services understanding and responding to needs. There is a difference in EE services; this is no 
dissatisfaction but the marks for understanding needs are lower. This is in line with the interview
data.
EQe: TA leading to immediate delivery as awardee expected, and long-term success (advice 
adopted, valued)13
Limited stage
3.3.2 Innovations’ assessment of learning events on TA, IF and EE (all 3 OC) 
In 2021 WE4F delivered official 33 training events in the SSEA and MENA regions, which 
benefited 30 innovations. Most training is about business development, investment, marketing, and 
the Base of the Pyramid (BoP). The WE4F data do not indicate the number of participants14. 
Table 5 presents survey data: 11 innovation respondents scored learning events organised by WE4F.
12 EQb: How do WE4F interventions, including enabling environment work, TA and IF, differ in meeting the needs of WE4F￾supported partners and the scaling of their innovations?
13 EQe: To what extent did each RIH provide WE4F innovators with TA that led to an immediate success (a support engagement is 
defined as an immediate success if deliverables formally agreed to by the awardee in the work plan were delivered as the awardee 
expected) and long term success (a support engagement is defined as a long-term success if the product or advice delivered is actually 
adopted by the innovator and results in recognized value, such as a shift in strategy, an effective partnership, additional funding, new 
financial forecasting capabilities, or an improved manufacturing approach or product design)?
14 Source: WE4F MENA Hub TA Update_220220821.xlsx
16 
Table 5: CFI1 innovations attending learning events: their participant score (%*)
Region market 
development
business 
investment 
facilitation (IF)
financing for 
women
technology input 
access
remove 
bureaucratic 
hurdles (EE)
av. score
SSEA (n=5) 3 5 2 2 0 1 13
80% 62% 75% 50% - 10% 62%
MENA (n=6) 6 4 3 4 3 3 23
80% 83% 63% 55% 53% 37% 65%
Total (n=11) 9 9 5 6 3 4 36
80% 71% 68% 53% 53% 30% 64%
*: calculation of score: very useful = 90%; fairly = 60%, little useful = 40%, not useful = 10%.
Source: Dexis innovation survey (Innovation survey 7 11 22.xlsx)
The table shows that most innovations score a high rate for the training quality; the best scores are 
for the business-related subjects: market development, investment facilitation, financing for women, 
the lowest for removing bureaucratic hurdles; this mirrors the results from the QoSS. 
The preference for business-related subjects is also arising from an interview with a MENA RIH 
respondent, indicating that “those that produce fast results (among other things)”, are much valued,
referring to training on business optimization, marketing strategy and business planning15. 
Participants note that much of the training is provided to middle management, whereas top 
management is facing the biggest barriers. Business-oriented training scores high because it directly 
“enables companies to be more structured and process oriented, optimizing operations.” 
One respondent explains why training on removing bureaucratic hurdles is less effective: “we 
understand the local officers, bureaucracy, politics better and are capable of handling at our own 
level. Someone external may not help much in this.”16 
One to one support to the innovations (e.g., organizational support, pre-award support, and help with 
permits and networking) is considered very useful. 
Learning between innovations - In all three RIH respondents indicate that bootcamps in particular 
allow learning between innovations, helpful to discuss common problems, and network, e.g., 
connections to help with supply chain issues, and to identify future growth opportunities. 
TA and gender (EQn) – RIH respondents report efforts to improve women’s access to innovations, 
through innovations, e.g., by i) selecting women-managed innovation companies; ii) selecting 
companies that know “how to integrate [..] marginalized groups”, iii) seeking alternative banking 
schemes; iv) (asking innovations to) address women’s education gaps in specific training, or v) find 
more gender sensitive partners for innovations. However, efforts to increase benefits for women are 
hampered by considered “cultural barriers”: women’s education being less than that of men, 
women’s opportunities to travel more limited, whereas the RIH feel they should focus on market-, 
capital- and technology barriers that women face17.These barriers were discussed in working 
sessions during the annual convening allowing innovations to share different approaches that have 
worked in India, Egypt, and Nepal to target women end users. One of approaches includes looking at 
the entire process of accessing the innovation from a women's perspective and trying to help and 
reduce barriers along the way.18
EQ1d – RIH help to overcome organizational capacity, EE barriers19
EQa – TA usefulness, effectiveness20
15 Source: Workspace RIH KII we4f.docx EQg 16 Source: Workspace RIH KII we4f.docx EQ1d 17 Source: Workspace RIH KII we4f.docx (EQc, EQd, EQ1l, EQ1i) 18 Annual Convening Notes 2022 19 EQ1d – Does each RIH help its relevant innovators overcome organizational capacity? Were there additional barriers that were not 
addressed? By the RIH?
20 EQa – To what extent did each WE4F RIH provide WE4F innovators with TA that innovators deemed useful? Did WE4F enable 
certain new expertise to be deployed that would otherwise likely not have been deployed/used by the individual partners?
17 
EQ1d & EQa findings: The TA provided by RIH generally appears to help improve innovations’ 
capacity on organizational/operational matters and innovations find it useful. Training on 
technology, input access and especially removal of bureaucratic hurdles is found less useful. Ad hoc 
(or 1-1 direct, tailored) support provided by the RIH is also very impactful and viewed as very 
valuable by the innovations.
EQn – Support to target BoP end users, gender21
EQn findings: Both SSEA and MENA RIH provided training on ‘Base of the Pyramid.’
Recommendations on all services: 
Pre-award: there is likely a trade-off between the choice of (often more established and larger) 
innovations that are capable to grow and contribute relatively much to WE4F targets, and companies 
that are smaller and – even as they grow – contribute little to WE4F targets; consider two different 
entry systems: tailored for smaller and larger companies
EE is important and needs to be improved. The Evaluation would like to know more about 
innovations’ own activity in policy advocacy, e.g., through Business Member Organization, doing
policy advocacy; for such organization, there is the advantage of understanding the local context; 
there may be scope to organize and lobby e.g., for better biomass-related regulation.
General (TA, IF, EE): To ensure adequate expertise on the local context, use more local TA 
especially for the work on removing hurdles (enabling environment), e.g., through Business Member 
Organizations (BMO), but also for more technical subjects
Gender: The Evaluation Team will look into training materials in the next phase, with a focus on 
how gender is mainstreamed in RIH country strategies/plans at all stages: conception (understanding 
the gender context), pre-award and service provision (TA, IF, EE), training methodology (adult 
learning, using women’s expertise e.g. on dealing with barriers), and gender mainstreaming business 
plan, e.g. on job and pay equality, and tailoring innovations to needs of more marginalised groups 
including women (and the business case to do this). 
Data from relevant (country-based) indexes could be used to highlight gender gaps, e.g., in business 
investment and financing, technology (design), input access (check the gender yield gap), 
bureaucracy, and “cultural barriers”, to be used to update training materials. 
EQn recommendations: The Evaluation Team will look into training materials in the next phase, 
focussing on how gender is mainstreamed in RIH country plans, in work on TA, IF and EE:
• introducing and understanding the gender context (e.g., by using data from relevant indexes 
for a country), highlighting gender gaps, e.g. in business investment, finance, technology, 
input access (check the gender yield gap), bureaucracy, and also “cultural barriers”, to find 
solutions that address these, e.g., innovations can to some extent adapt training materials, 
location, timing and content to women 
• training methodology (adult learning, using women’s expertise e.g., on dealing with barriers). 
3.3.3 WE4F disbursement and Innovations’ capacities to mobilise funding (OC2)
A key strategy of the WE4F program funding is to generate (more) external funding, to perpetuate 
the scaling up of the innovations. 
The main questions explored in this subsection are about WE4F grants to innovations, in relation to 
co-funding from innovations themselves, and the external funds that innovations have been able to 
21 EQn – To what extent did each RIH provide WE4F innovators with support that improved their ability to more effectively target 
end users such as women and the poor by improving access to or the usefulness of innovations for these groups? To what extent did 
each WE4F RIH provide WE4F innovators with support to strengthen their organizational capacities with respect to gender (e.g., 
expanding the female workforce, female leadership within innovator companies)? What were the unintended effects of innovator 
support provided by RIHs on cross-cutting issues of gender and poverty?
18 
mobilise. The most important indicator is KPI9 about external investment that is mobilized and in 
this section these amounts will be compared to WE4F funding, and innovations’ own funding. 
Disbursement - Table 6 presents the funds committed and disbursed for the different cohorts; it 
represents data that are yet to be completed.
Table 6: Funds committed and disbursed (USD) per cohort
Cohort WE4F committed amount for all 3 years) ($) disbursed (as of 8/30/22) ($) % disbursed
CFI1* (n=23) 4,344,200 2,271,700 52
CFI2** (n=12) 1,855,000 516,000 28
Iraq CFI1*** (n=8) 860,000 344,000 40
COVID-19 grant 25,000 12,093 48
Total 7,084,200 3,143,793 
Source : budget tracker (received October 2022)
*: GA were signed early May 2021, thus the 1st year for CFI1 GA ranges from May-21 to Apr-22
**: GA were signed mid-Jun 2022, thus the 1st year for CFI2 GA ranges from mid-Jun 22 to mid-Jun 23
***: CFI Iraq was signed end Feb 2022 (with one early Apr 22) and the GA ranges from Mar 2022 to April 2023
The disbursements are more or less on track, and in so far as there are delays (in SSEA) these can be 
explained as mostly the result of the COVID-19 pandemic. Please note that more details are provided 
in Annex 8, including a list of the funds awarded and disbursed for the CFI1 in Table 33, as well as 
more detailed data on disbursement in Table 37. 
External funding - A critical factor in the program effectiveness is the additional funding that can 
be attracted, which will allow greater effectiveness of the resources put in by WE4F. Table 7
presents the value of external investment mobilized by innovations themselves, by region.
Table 7: External investment mobilized in year 1 (USD), by source (KPI9) 
Region external investment
cash/public cash/private in-kind/public in-kind/private unknown total external
SSEA (n=8) 0 3,076,485 0 0 755,552 3,832,037
MENA (n=11) 71,217 4,650,924 23,950 0 0 4,746,091
Total (n=19) 71,217 7,727,409 23,950 0 755,552 8,578,128
% 0.8% 90.1% 0.3% 0 8.8% 100%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
In about a year, 19 innovations of the 81 CFI1 innovations had mobilised 8.6 million USD external 
investment. The data are too limited to draw reliable further findings. A tentative prognosis with 
some assumptions could be: 
1. the 19 innovations raise another 0.9 million until LOP, thus 9.5 million USD in total, or 0.5 
million USD/innovation. 
2. the remaining 62 innovations (given some reporting bias22) raise half as much, thus 250,000 
USD/innovation (LOP). Innovations’ own targets for external investment - In SSEA 
innovations reported on the external investments for the innovation: 4 of 11 of those reporting 
had met their own year 1 target. In MENA, 4 of these 7 innovations reportedly met their year 
1 target.
EQp – Cross-cutting issues impact increasing the interest, and finance from external 
investors23
Not yet assessed (limited stage). 
22 About bias: taking Y1 results (vs Y1 targets), on food production 10/18 pass their target, on end users 18/34 pass their end user 
target. So just over half of the innovations pass their y1 target. Which ones provide data on funds raised? Comparing with the food 
results: all 10 passing food targets provide data on external funds raised; 8 not passing also do.
Comparing with end user results: 8 passing end user targets provide data on external funds raised; 4 not passing also do.
This would suggest some bias. This calculation is just a tentative check and cannot be used further. 23 EQp: To what extent did the strengthening of innovators’ impact on cross-cutting issues including gender, poverty, and 
environmental sustainability/biodiversity contribute to increased access to external financing or interest from external investors?
19 
EQ1p – Public/social and -/private engagement balance? Private funds help WE4F goals?24
EQ1p finding: The public sector is engaging very little when it comes to (external) funding: less 
than 1 %. As all innovations are private companies, the public sector is not itself among innovations. 
KPI9 – Investment from external sources25
Limited data (data gaps are too many to conclude about reaching the 34 million USD LOP target)
KPI9 finding: 19 out of 81 innovations reported on having mobilized external investment, the 19 
innovations raised 8.6 million USD.
Table 8 shows the WE4F grants compared to innovations’ co-funding (their own funds). 
Table 8: WE4F CFI1 grants and innovations’ co-funding (Gin33) 
Region
WE4F grant 
disbursed ($)
% WE4F 
committed funds
innovation co￾funding ($)
% innovations’ own 
co funding
SSEA (n=9) 911,700 40% 1,229,715 35%
MENA (n=14) 1,360,000 60% 2,240,028 65%
Total 2,271,700 100% 3,469,743 100%
Source: AWPs for MENA CFI1, Budget Tracker (received October 2020) WE4F Admin data (MENA, SCA SSEA 
compiled data.xlsx) and the information from the Annual Report
For the 2.3 million USD disbursed by WE4F, an additional 3.5 million USD was co-funded by 
innovations themselves, that amounts to 153% of WE4F’s support. These figures are from a limited 
number of innovations, but this proportion (above 100%) suggests that innovations do match the 
funds that WE4F provides. 
Gin33 – Share of co-funding (%) provided by innovations themselves to match WE4F grant. 
Gin33 finding: Innovations provide 153% of the funds disbursed by WE4F so far (program 
disbursement is about halfway of the funds committed). 
As data in the above two tables are not from the same cohort, there cannot be further findings at this 
stage on the relation between the three: WE4F funding, innovations’ co-funding, and external 
investment. 
Grant funds disbursed by innovation type – In terms of funding, the three most important 
innovation types are Energy – production & infrastructure, Energy – farm inputs, and Digital 
solutions; Water – irrigation is a good fourth, and Energy – agricultural processing is fifth.
One may try to find differences between innovation types in terms of numbers of end user household 
members they reach with each dollar of investment: in SSEA, the average Energy- farm input type
uses 27.9 USD/end user household member, in MENA it is just 5.8 USD/household member. The 
inverse applies for the Water – irrigation type: in SSEA the cost is 2.2 USD/household member and – 
unsurprisingly in a region that is mostly semiarid, and with larger farm areas per household member 
– in MENA it is 87.4 USD/household member. Given the very different context and different 
innovations, not too much can be found on this (details on these data are found in Annex 8, Table 37
on CFI grants disbursed). 
Gin40 – Number of contacts of potential investors provided to innovations by WE4F 
Limited stage
Gin41 – Share of innovations that made use of the financial guarantee instruments
Limited data
Gin40 & Gin41 findings: From the innovation survey it is clear that many contacts were made with 
investors, and a beginning is made with data collection to link this to funding mobilized. Only a few 
24 EQ1p – What is the balance between public/social engagement and private/public engagement? To what extent have private funds 
been generated that contribute to WE4F developmental objectives during and following WE4F awards. 25 KPI9 – Value of investment (USD) by investment type* that WE4F innovators have mobilized from external sources; baseline: 0 
USD, WE4F LOP target: $34,000,000. 
*: types: i) public/cash, ii) public/in-kind; iii) private/cash, iv) private/in-kind; v) unknown
20 
innovations used a financial guarantee mechanism, but most innovations appreciate WE4F 
investment facilitation activities, and some already note a positive effect.
Using the WE4F M&E information it was determined that 91 partnerships have been created (57 in 
SSEA and 34 in MENA). This is an early stage in partnering. Most interview respondents discussed 
how introductions have been made but these had not yet resulted in partnerships, e.g.: “They have 
connected us with some organization, and we have submitted proposals but no success so far.” “They 
are open to help but no successful connection so far.” That notwithstanding, all 8 respondents found 
the help on partnerships useful: “knowledge increased”, “an opportunity to understand and develop 
linkages with these partners” and “helped to develop financial and procurement policy guidelines, 
and bottom-up pyramid analysis for market access”.
Gin20 – Number of external partnerships formed by innovations 
Limited data 
Gin 20 finding: Partnering appears at an early stage, but progress is being made, and 91 partnerships 
have been created.
EQ1h: PPP meeting their energy or water efficiency targets26
Limited data
EQ1h finding: From the KIIs and the innovation survey there is evidence that innovations know 
about the PPP approach, but they have yet to build this into their partnering and financial strategy.
EQf: IF leading to additional funding, new IF and partnerships27
Limited stage
3.3.4 Enabling environment (OC3) 
• More work is needed to help remove barriers for access for women and to increase 
access to financial support to end users.
Improving the enabling environment (EE) includes inclusion of marginalised groups and end user 
financing (EQd, Gin28, EQl), and sharing of lessons (KPI10). 
The enabling environment at the inception of WE4F, the establishment of the RIHs and for some 
period beyond has been strongly affected by COVID-19 related disruptions. This was described in 
the introduction, but this disruption was not explored by innovations and end users, possibly to avoid 
a return to the trauma of the pandemic. Despite this, reports on India and other countries of SSEA 
report severe illness and many deaths in rural communities, all of which would impact 
disproportionally on the more marginalised groups; it was noted that people from these groups would 
have fewer financial resources to buy innovations. 
EQd: EE work impact on marginalized groups’ water & energy efficiency, renewables, food 
security28
Limit data
EQd findings: Both RIHs focus the enabling environment work on financial services. On the subject 
of financial services, some ideas they have (when applied) will or are likely enable women to benefit 
more from the innovations. However, the results in the MENA region show that much more needs to 
be done to remove barriers for women. 
EQd recommendations: Several recommendations are already made in section 3.3.1 with regard to 
understanding the gender context and adapt any training to this. The same would apply to other 
26 EQ1h: To what extent have the established partnerships with companies (in the form of PPPs) been able to meet their energy or 
water efficiency targets? Can the PPP approach be deemed successful overall in contributing to WE4F program-level goals? 27 EQf: To what extent did each RIH provide WE4F innovators with IF that led to additional funding, new financial forecasting 
capabilities? To what extent did each WE4F RIH provide WE4F innovators with IF that innovator deemed useful? Did WE4F enable 
certain new IF and partnerships to be deployed that would otherwise likely not have been deployed/used by the individual partners?
28 EQd: To what extent was the enabling environment work carried out by WE4F compatible or not with the intended positive impacts 
on marginalized groups related to water efficiency, energy efficiency and use of renewable energies, or food security?
21 
activities (beyond training); understanding the gender context should be integrated at the conception 
of all activities.
Other enabling environment results - SSEA and MENA RIH respondents list different challenges 
in the enabling environment. In SSEA the first challenge is to hire middle-management people and 
then having to train them. In MENA it is “tricky business climate, limited rule of law, limited trust in 
the market [..] risky.”29
Innovations themselves raised similar and more issues of access to finance or hard currency, 
currency fluctuations, dependency on imported components, high prices of raw materials, public 
regulations and legislation and unstable policy environment, fast changes in taxing, and finding and 
retaining talented staff.30 
EQm: EE support, useful, effect on EE around innovations? New networks?31
Limited stage, limited data
EQm finding: Innovation survey feedback suggests that some initiatives in the environment were 
found helpful e.g., where donors step in to raise the issue of bureaucratic hurdles. Also see 3.3.4.
Financial support for end users - In the 3 SSEA innovations where end user funding was received, 
2,910 end users (only poorer end users, in income quintiles 1 and 2), had access to end user 
financing, a small fraction (1.4%) of end users in these income quintiles. Details are presented in 
Annex 8, Table 43).
No respondents in the end user survey in SSEA (n=143) could confirm they benefit from end user 
financing for the innovation (93% said no, 10 did not respond, nobody said yes). Interestingly, 
whereas some respondents would have liked the innovation to be cheaper, many more respondents 
suggested that the design be improved32. This was not discussed in detail in the survey but during the 
annual convening innovations shared how they have adapted designs to different clients. And in 
some cases, the farmers have adapted the innovations to their need. Such as the sensor to measure the 
moisture content of the soil and help the farmer determine if watering/irrigation is needed. The 
small-scale farmers use the innovation as is and the larger scale farmers can adapt the innovation 
with additional equipment to their needs. This is an example of how the end user has as adapted the 
design for their needs and could be something other innovations could help facilitate. 
Gin28 – Number of end user farmers* (f,m) by -household (hh) income quintiles** using financing 
mechanisms33 
Limited data 
Gin28 finding: The number of poorer end users accessing end user financing is at this stage still 
very low. Financial support may be built into pricing or credit, but the low-level direct end user 
financing is a weakness in WE4F.
EQl: End user financing improved innovation access, uptake? Contributed to BoP income?34
Limited stage, limited data
So far, few end users have benefited from end user finance, therefore their recommendations may 
turn to what is visible, thus the comments on design quality come first. 
29 Source: Workspace RIH KII we4f.docx 30 Source: Workspace INNOVATION KII we4f.docx 31 EQm: To what extent did each RIH provide innovators with support that improved the enabling environment around WE4F 
innovations? To what extent did each WE4F RIH provide innovators enabling environment to support that innovators deemed useful? 
Did WE4F enable certain new networks to be created that would otherwise likely not have been deployed/used by the individual 
partners? 
32 Source: Tables from SSEA end user data. Docs (generated from Monkey Survey for end users) 33 the financing mechanisms adapted or created through WE4F engagement; * NB: the no. of end user farmers is multiplied by av. hh 
size/country; baseline: 0; target: t.b.d.; **: quintiles 1 and 2
34 EQl: To what extent did each RIH provide WE4F innovation end users with end user financing that i) improved access to and 
uptake of WE4F-support innovations? ii) contributed to local poverty alleviation and/or increases in income, especially for women and 
the rural poor?
22 
Sharing lessons 
KPI10 – Number of strategies, standards/regulations, guidelines, or projects of international, 
regional, or local organizations adopting and disseminating lessons learned from WE4F publications, 
events, or presentations
Limited stage35
3.3.5 Innovations’ business results: end user numbers, gross sales
• WE4F expects that 40% of the innovations will meet their end user targets. Over 50% 
of the innovations have met their year 1 target. 
• Whereas 37% of innovations reach their gross sales target, the WE4F LOP target is 
unlikely to be reached if current gross sales are the basis for it (and assuming sales 
double in years 2 and 3). But due to COVID-19 the year 1 results may be tempered, and 
higher sales may be possible.
The effect of all WE4F services together is to improve innovations’ capacities, which in turn should 
translate into business growth. This section covers the innovations’ own targets on end users and 
gross sales.
Innovations’ end user numbers (new indicator on innovations’ end user numbers)
Aside from impact on end users captured in 3.5.1 (with KPI2 on end users), this section looks into 
the new indicator on innovations’ own end user targets. 
Table 9 shows the sums of innovations’ end user targets from the AWPs for the LOP, broken down 
per project year.
Table 9: Innovations’ annual targets numbers of end user household members, all innovations (n=81)
 LOP* year 1 targets* year 2 targets* year 3 targets*
Total 4,714,263 1,557,168 2,486,424 1,336,681 
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) AWPs from innovators
*: LOP target is the sum of each year. In this table it is lower than the sum of three year-targets, because year 2 and year 3 targets were 
adjusted (upwards or downwards) by the Innovations themselves. Based on year 1 performance. These sums are from innovations’ 
Annual Work Plans and presume that these are additional end users (so the baseline is 0).
The aggregated total of innovations’ end user targets at LOP, over 4.7 million, is much higher than 
WE4F’s target at LOP, which is 2.5 million end users (that is KPI2, in 3.5.1) 
Growth models - Innovation interviews show that the innovations grasp of the potential for growth 
of the end user numbers. For example: “4 billion people have no access to clean cooking”; “large 
numbers of poor, women, landless climate-refugees reclaim barren land in riverbeds”; “a lot of room 
for improvement that would propel to more growth.”; some work we do is adopted by state 
governments, impacting farmers across the country (‘millions rather than thousands’)”. These 
statements confirm innovations are aware of the immense challenges of water supply and food 
production in developing regions. 
We start by examining the yearly end user targets as obtained from the innovations AWP; Figure 2 
shows the underlying models of growth targets by region, and Figure 3 shows the cumulative end 
user target growth, from year 1 to year 3, by region. 
35 Source: Workspace RIH KII we4f.docx
23 
Figure 2: End user targets for all innovations 
SSEA innovations plan an initial rapid increase in end users towards year 2, followed by a sharply 
reduction, MENA innovations plan a steady rise, and SCA project a flat rate: the for the next 3 years. 
Figure 3: Cumulative Growth based on yearly targets
SSEA has uneven levels of targets over the years and then MENA and SCA have flat or moderate 
percentage increases over each year: lower numbers, but nonetheless sustained. There can be various 
reasons for these differences. SSEA innovations may see fewer barriers to initial growth picking 
low-hanging fruit, and more challenges to consolidate growth (companies’ capacities are limited, and 
having many end users would require customer service, evaluation, all usurping capacity). And 
MENA and SCA innovations may consider slow growth a safer option, e.g., considering (more) 
challenges in the business environment. This is up for debate and could be answered with additional 
information from the innovations and hubs. 
From innovations’ end user data (in the Annex, Table 35) it is found that the WE4F program well 
exceeded this target of 40% of innovations: in SSEA 10 out of 14 innovations, and in MENA 10 out 
of 16 innovations reached within 80% of their own year 1 end user targets, that makes 67%. 
This will contribute to WE4F overall end user targets (KPI2, in 3.5.1), however, the overall numbers, 
with some outliers, can give a distorted view. Therefore Table 10 compares CFI1 innovations in 
ranges related to the approaching or passing of their year 1 target. The innovations are grouped 
0
200
400
600
800
1000
1200
1400
1600
1800
Year 1 targets Year 2 targets Year 3 targets
Thousands
End User Targets for All Innovators
SSEA MENA SCA
 -
 500
 1,000
 1,500
 2,000
 2,500
 3,000
Y 1 targets Cumulative Y1 and Y2
growth
Cumulative Y1, Y2 and Y3
Growth
Thousands
Cumulative Growth based on yearly targets
SSEA MENA SCA
24 
according to performance in four segments (Missed Target by 51-100%, Missed Target by 0-50%, 
Surpassed Target by 0-100% and Surpassed Target by over 100%). 
Table 10: Innovations’ year 1 end user targets and results, by region (CFI1, n=27)
Segment SSEA MENA average result 
vs target (%) 
SSEA
average result vs 
target (%) 
MENA
% of innovations 
in this segment 
SSEA
% of innovations 
in this segment 
MENA
Missed target by 51-100% 3 4 -68% -76% 25% 27%
Missed target by >0-50% 2 5 -21% -17% 17% 33%
Surpassed target by 0-100% 6 1 +41% +4% 50% 7%
Surpassed target by >100% 1 5 +604% +169%* 8% 33%
TOTAL 12 15 100% 100%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) and Innovators AWP
Note: * An outlier of achievement of tens of thousands (in SSEA) was removed in calculating the average.
The table shows a wide range of performance reaching end user targets. On the low end, in both 
regions there are about a quarter of the innovations that are really far below their targets. About half 
(14/27) are approaching (above 50%) or passing their targets (up to +100%). On the high end the 
outliers: these 6 innovations provide the lion’s share of end users. 
New indicator: innovations’ end user targets: proportion of innovations reaching their end user 
targets36
New indicator finding: Overall (including the outlier not in the table), half of the innovations meet 
or surpass their target and that is more than the expected 40% (new indicator).
New indicator recommendation: Reflection with innovations on end user growth projection and 
targets.
Innovations’ gross sales
Table 11 shows innovations’ gross sales targets and results. For more details see Annex 8 (Table 32).
Table 11: Innovation gross sales (USD), by region (Gin4), compared to innovations’ own year 1 targets
Region innovations’ 
LOP target ($)
innovations’ 
year 1 target ($)
year 1 result 
($)
result % 
of target
no. of innovations 
reaching year 1 target
% of innovations 
reaching the target
SSEA (n=12) 24,490,893 8,559,139 5,670,840 66% 3 25%
MENA (n=15) 38,723,522 7,505,755 9,830,742 131% 7 47%
Total (n=27) 62,214,415 16,064,894 15,501,582 96% 10 37%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
These data show that 96% of the year 1 target for gross sales is reached; this is due to some 
innovations being highly successful, exceeding their targets by a large margin, thus considerably 
lifting the average. Only 37% of the innovations reached or exceeded their target; this could be due 
to COVID-19 and other external factors; in KII, innovations noted floods, inflation, and other 
factors. 
Gin4 – Innovations’ gross sales from marketing WE4F supported innovations37
Limited data
Gin4 finding: The gross sales year 1 result is as yet incomplete; also, sales may have been reduced 
due to COVID-19 and not provide a basis for reliable projection. Based on available data, it is 
possible that the LOP target can be met.
Gin4 recommendations, current phase: More reflection on end user growth models could be 
useful, especially for new innovations, to opt for a relevant growth model and realistic targets.
36 From WE4F annual report 2022. Targets: i) 40% reach within 80% of their own targets; ii) 10% of innovations reach >100,000 
customers/end users*. * NB: this second part of the indicator will be assessed for the final report. 37 Baseline $0, RIH LOP targets: SSEA: -; MEA: -; SCA: $2,250,000. Program LOP target: $75,000,000. 
NB: RIH data are insufficient to revise the LOP targets (source: L. McMahan, USAID). 
25 
Gin4 recommendations for extension phase: More reflection on end user growth models could be 
useful, especially for new innovations, to opt for a relevant growth model and realistic targets.
EQ1o – Financial and social sustainability of the organizations supporting the innovation?38
Limited data
EQ1o finding: Most of the innovations appear to be for-profit companies dependent on sales of 
innovations, sharing of infrastructure, etc., to ensure profitability. There seem to be few alternative 
sources of support or finance at this stage, apart from the (successful) mobilization of financial 
support with the help of WE4F. 
3.4 Efficiency at program and innovation level
• WE4F is efficient as a program in achieving the greatest impact in the deployment of 
relatively limited resources available and in terms of the costs of fund management in 
comparison with other Challenge Funds. 
• Funding partners have added efficiency by assisting on specific issues of enabling 
environment. 
• WE4F has shown particularly efficiency in assisting innovations to raise external funds 
multiple times that of the WE4F grant; on this it compares favorably with other 
programs. 
• The RIH are reported by innovations to be efficient in streamlining services and 
tailoring support. 
• The MEL system produces an impressive amount of quantitative and qualitative data
which provides an overview of the whole leading to greater efficiencies; it can improve 
indicator definitions (incl. targets) and streamlining data collection. 
WE4F program design 
EQj: To what extent was each RIH efficiently set up, organized, and managed?
Limited data
Notwithstanding recommendations on streamlining the pre-award process for innovators, and the 
feedback on indicators (definitions) and data quality, the overall impression of the team is that the 
RIH are set up and organized rather efficiently, given the volume of outputs they produce managing 
the award process and providing services. 
EQ1j: Innovations using WE4F resources efficiently, to max. impact on marginalized groups39
Limited data 
It cannot be assumed that the innovations have an intention to maximize impact on marginalized 
groups. Where innovations see possibilities to include such groups among their clients, they are 
interested to take measures to do so, as shown in their feedback on BoP and gender learning events. 
But USAID and WE4F work from the assumption that the innovations follow their business 
approach and prioritize increasing end user numbers (targeting BoP or not) as well as profit; not all 
innovations disaggregate client needs or client feed-back. However, most end users hail from smaller 
income quintiles, where a disproportionate number of more marginalized end users is found. 
EQh: WE4F results balance with efforts and resources (at all levels)40
Limited stage
38 EQ1o – Were WE4F-supported projects likely to be (become) financially and socially sustainable by the organizations supporting 
the innovation?
39 EQ1j: To what extent were resources and support provided by WE4F used efficiently by innovators and other WE4F-supported 
partners to maximize positive impacts on marginalized groups (through income, employment, water/environmental, 
energy/environmental impacts)?
40 EQh: To what extent were the WE4F results to date in balance with the level of effort and resources (funds, human resources 
including by the FPs, RIHs, and Secretariat)?
26 
EQi: Innovations’ effort and resources against the added value WE4F brings41
Limited stage
In relation to the internal measures of efficiency, innovation progress is measured by standard and 
program-specific indicators which are assessed against their output milestones. The proportionally 
increased costs associated with more intense fund management, TA and capacity building inputs are 
compensated by the active capture of learning. 
WE4F is also found to constrain costs through established networks of local consultants. The 
portfolio approach allows diversity in method and subject and encourages out-performance which 
compensates for modest or failing results in innovations which may have other values.WE4F 
anticipates that innovations can raise external funds many times larger than the initial grant; this is 
confirmed by initial findings; from limited data available (presented in 3.3.2, re. Gin33), it appears 
that innovations’ co-funding is in good proportion to (153% of) what WE4F has disbursed so far.
EQi finding: From limited data available, it appears that innovations’ co-funding is in good 
proportion to what WE4F has disbursed so far. On internal Efficiency, WE4F may compare 
favorably with other programs, because of the co-funding raised.
EQk: Comparing administrative costs with similar challenge funds42
Limited data (see also data in Annex 16) 
This question is assessed by an external comparative approach. Two studies provide evidence on the 
comparative costs of challenge funds: the analysis undertaken in the evaluation of SWFF and the 
evaluation of Sida’s Challenge Funds (from both, the relevant tables are presented in Annex 16)43. 
In Table 81 (SWFF) the comparative fund management costs show a significant spread, from 5% to 
57% of the total grant. SWFF was found to sit in the middle with 26%. Fund management costs 
were, however, made up of several elements including M&E and technical assistance of various 
kinds. SWFF provided an intensive work program and high levels of support to innovations. 
The IPE Triple Line study of Sida’s challenge funds takes the comparative costs of fund 
management further; challenge funds may work through achieving economies of scale such as by 
providing similar support to a wide range of groups. The study makes an extensive comparative 
qualitative analysis of fund management and concludes from an analysis of Sida’s 10 Challenge 
Funds that the more intensively managed funds, with a more “hands-on approach” had a greater 
degree of success in ensuring sustainable development outcomes than the “lighter touch funds”. In 
addition, those Challenge Funds which enlisted wider stakeholder support were more likely to 
deliver sustainable impact.
Such comparative analysis leads to the finding that the proportion of fund management costs needs 
to be related to the activities of management and to outcomes. The WE4F model has an active 
management style with high quality monitoring and evaluation, careful planning and setting of 
targets, and on-going communications and interaction including technical assistance. This is a high￾intensity management model which can lead to mutual learning between all participants and a high 
level of efficiency. WE4F Technical assistance tends to put the emphasis on a tailored assistance
which is focused on concrete needs.
To return to the question of costs: analysis of Sida’s Grand Challenge Funds (Table 82) finds the 
costs of fund management range between 22 and 50% of total budget, so with that, the SWFF 26% is 
41 EQi: To what extent is the level of effort and resources spent by applicants/innovators in balance with the added value WE4F 
brings? 
42 EQk: To what extent are the administrative costs for managing WE4F above, below, or on par with the cost of similar Challenge 
funds? (Special Consideration should be made for funds that provide TA to their innovators.)
43 Evaluation of Sida’s Global Challenge Funds . https://cdn.sida.se/publications/files/sida62181en-evaluation-of-sidas-global￾challenge-funds.pdfAnnex K: Analysis of fund management costs
27 
in the lower range. At this stage, expenditure figures from WE4F are not yet compiled to position 
WE4F along this spectrum but it is likely to be in a similar position, Anecdotal evidence on co￾funding, an important measure of efficiency, in which WE4F is compared with an NGO program 
finds that WE4F asks 5:1 external funds vs grant, whereas the NGO only asks 1:1.The expectation is 
that the various thrusts towards efficiency from a more intensively managed fund will have a greater 
degree of success in ensuring targeted development outcomes. 
EQk finding: SWFF is found to have a highly intensive mode of management. Its administration and 
fund management costs are generally higher as a function of the total value of support to awardees 
than other challenge funds, but such comparisons need to be considered with care. The significant 
level of support provided to awardees and the impacts need to be considered. Support activities are 
well regarded by innovators as providing valuable assistance. 
EQk recommendation: Sida, USAID and other funders should consider the use of a framework for 
cross-comparison, to enable a more rigorous assessment of the efficiency and effectiveness of 
different models of intervention. The Evaluation will make a proposal to that extent. 
EQq: Investment risk management44
Limited data
EQr: WE4F ensuring that partners can sustain medium to long-term impact45
Limited stage, limited data, but assessed in EQ1g. 
Innovations’ feedback - Responding to a question on hampering factors in the enabling 
environment, some innovations (n=2) used the question to provide feedback on the WE4F program: 
“low level of flexibility”, “reporting demand”, lacking “tools and expertise” and “grant funding 
period too short”46. 
EQ3: Differences between project plans and delivery47
Limited stage
EQ3 finding: Considering that the planning and delivery may be expressed in end user numbers and 
gross sales, this question is largely addressed in 3.2. 
Findings on program design: WE4F is efficient on the basis of the following measures; firstly, as a 
program it is efficient in achieving the greatest impact in the deployment of relatively limited 
resources available and secondly in terms of the costs of fund management in comparison with other 
Challenge Funds. 
The design and strategy are based on the design proven to be efficient in the previous SWFF and 
PAEGC programs, accompanied by rigorous monitoring and on the achievement of results with 
economical means by continuous interaction between the RIH and innovations. The WE4F model 
has evolved from these prior programs with intensive risk and performance management to achieve 
greater efficiency. 
The overall efficiency of WE4F depends on the quality of its management of time, resources, skills, 
and budgets necessary to accomplish all these interrelated tasks. At its core, WE4F anticipates high 
efficiency with modest targeted funding and advanced practices in learning, reporting, and planning 
leading to elevated (but mutually agreed and carefully planned) results. This devolves more 
44 EQq: How effectively have investment risks been managed by the program? (Number of failed projects, timeliness of reaction on 
problems observed etc.)
45 EQr: To what extent has the WE4F program ensured that impacts can be sustained in the medium to long-term by the partners 
themselves?
46 Source: Workspace INNOVATION KII we4f.docx 47 EQ3: To what extent are there differences between the planned WE4F-supported projects and what was actually delivered in year 1 
and then years 2-4 of the projects?
28 
responsibility to local actors which involves scheduling teams to identify the fastest, cheapest, or 
most suitable approach. 
The portfolio approach: The portfolio of a large number of diverse innovations is a key feature of 
WE4F which makes it distinct from conventional or “traditional” programs. WE4F is not unique in 
funding a large number of implementing partners, it does, however, fund considerably more 
innovations than others and manage communications and support more closely. 
This “managed portfolio” approach is based on diversification which involves investing in dissimilar 
innovations which have different kinds of financial and capability assets in water, energy and 
agriculture which are combined in anticipation of maximizing return for many complex risks. 
Operations are also spread through decentralization at various levels: from the centralized 
Secretariat, to Hubs, and ultimately to the innovations. Overall, this could prove to be more efficient 
in maximizing returns from relatively modest resources.
Conclusions on portfolio approach: At this stage the overall efficiency of WE4F can be assessed 
on the basis of proven design, rigorous monitoring and on the achievement of results with 
economical means. The WE4F model has evolved from prior programs with intensive risk and 
performance management to achieve greater efficiency. Innovation progress is measured by standard 
and program-specific indicators which are assessed against their output milestones. The 
proportionally increased costs associated with more intense fund management, technical assistance 
and capacity building inputs are compensated by the active capture of learning. Such learning is 
absorbed by the RIH and passed on to all innovations. 
WE4F constrains costs through established networks of local consultants. The portfolio approach 
allows diversity in method and subject and encourages out-performance which compensates for 
modest or failing results in innovations which are not performing well but may have other values.
WE4F anticipates that innovations can raise external funds many times larger than the initial grant; 
this is confirmed by initial findings. This may compare favorably with most other programs.
The overall efficiency of WE4F depends on the quality of its management of time, resources, skills, 
and budgets necessary to accomplish all these interrelated tasks. At its core, WE4F anticipates high 
efficiency with modest targeted funding and advanced practices in learning, reporting, and planning 
leading to elevated (but mutually agreed and carefully planned) results. This devolves more 
responsibility to local actors which involves scheduling teams to identify the fastest, cheapest, or 
most suitable approach. 
There are reports from RIH of synergies in-country between WE4F goals and donor country 
initiatives and other openings; learning from results will increase efficiencies over time. 
WE4F partners’ involvement
EQg: Founding partners interaction within WE4F, alignment with RIH for EE, lessons48
Limited stage
EQg finding: There are reports from RIH of synergies between Program goals and donor country 
initiatives and other openings; there is opportunity to expand on such experience.
Comparative cost of WE4F – A MENA RIH respondent is confident that WE4F is doing well on 
IF, comparing the amount it invests with (e.g.) the amount of external funding that innovations raise, 
and, that this can be a proxy for impact on emission saving. 
48 EQg: How well did founding partners interact within WE4F and what lessons to take from their interactions? To what extent did 
alignment between FPs and with RIHs contribute towards an improved enabling environment for innovators?
29 
An NGO working with younger companies in a shorter program anticipates its innovations will raise 
as funds in amounts equal to the grant (1:1). The RIH respondent expects WE4F to achieve a ratio of 
5:1 (five times more funds raised than the amount of the grant). Quantitative data on this are 
presented in section 3.3.3. 
The SCA RIH respondent notes that the WE4F approach of combining grants with TA, IF and 
support for an enabling (policy) environment is unique.49
Findings on founding partners involvement: The directly engaged partners of USAID and SIDA 
in MENA are regarded as providing “outstanding” support in scaling up results; they have connected 
with local missions and partners and leveraged grants (IF). The USAID secretariat is regarded as 
having strong expertise and mentoring which is well appreciated50. 
Each donor has its own policy emphasis and coordination of this is improving and now the RIH can 
work in harmony with the donors, to achieve objectives: “the bigger picture”. 
Founding partners have a direct effect on enabling environment. A MENA respondent gives 
examples:
• in Jordan, MFA-NL helped an innovation working on composting, to get through bureaucracy 
and get the right permits.
• support in IF: SIDA and USAID arranged for an investment fund financing innovations for early￾stage, smaller enterprises; the respondent notes that embassies are not involved in a systematic 
manner and that this something to improve on.51
From the bootcamps it was mentioned that donors could support the project by introducing the Hubs 
to local missions and connect the hubs with private sector companies that are working on sustainable 
agriculture. Donors are reported in MENA supporting major labs in each country and collaborating 
with governmental institutions to find ways to efficiency and standardization of testing with 
collaboration with universities. These bootcamps also discussed challenges in the hubs that affected 
the enabling environment. The bootcamps allowed the innovations to share challenges which were 
prioritized, and an action plan developed.52
WE4F MEL system
EQ1k: Innovations using WE4F resources efficiently, to max. impact on environmental 
sustainability, biodiversity53
Limited stage, also see EQo.
EQ1k findings: The MEL system, where it applies to innovations’ impact on environment, is still in 
development. Training has been provided on monitoring environmental impact (e.g., on water, 
energy, emission) and environmental monitoring tools are introduced and used (more on that in 3.6). 
Indicators and -targets are often presented in separate documents, and units are not always clear; see 
detailed feedback on the indicators in Annex 9.54 The PMEP is a rather inaccessible document, in the 
sense that it does not allow any navigation in its 41 pages: not in the sidebar (no headings defined), 
and not from the table of content. This discourages those searching for the instructions (including 
this Evaluation, to some extent). The document does not explain the overall purpose of the MEL 
system; a learning purpose is clear from the section on learning (p.18), the purpose of informing 
program management is also obvious. Learning events mention engagement of the WE4F Steering 
49 Source: Workspace RIH KII we4f.docx EQk 50 Source: Workspace RIH KII we4f.docx EQg 51 Source: Workspace RIH KII we4f.docx EQg 52 Source: MENA-EE-deck-donor meeting and SSEA-EE deck Donor Meeting 53 EQ1k: To what extent were resources and support provided by WE4F used efficiently by innovators and other WE4F-supported 
partners to maximize positive impacts on environmental sustainability or biodiversity?
54 Source. indicators as presented in WE4F PMEP_January22.docx, WE4F KPI PIRS_Jan22.docx, WE4F Full 
PIRS_November21.docx). 
30 
Committee, the Secretariat and the RIHs, but innovations are not mentioned in these activities.55 For 
the first 6 months of the program, Microsoft Excel spreadsheets were used to collect and M&E data, 
in fall 2021 the use of Sales Force started, and this will help with M&E data quality. 
Updating of targets - In May and June 2022, the MENA and SSEA innovations revised their CFI1 
targets listed in the AWP. The Year 1 targets did not change. For some indicators, the Year 2 and 
some LOP targets are being updated based on year 1 performance by innovations (especially since 
there was no baseline). 
One reason to do so is that the total of RIH targets do not add up to the USAID target which is often 
lower (adjusting because innovators often overestimate their ability to reach targets, for some RIH 
expected to miss targets, or simply because it is hard to set a target when innovations’ own 
assessment of what their targets could be is still not available).
Target setting is generally a 3-step process:
1. In the theory of change phase, high-level program targets, based on work of Syspons in 
facilitation sessions are established. In the multi-donor setting, some targets were lowered
based on the requests of specific donors 
2. Some KPI targets are entered into the RIH contracts; for example, KPI2 (end users) is a hard 
target. On this indicator, a LOP in SCA is 1,000,000 and in MENA 750,000; these are both 
hard targets, related to contractual responsibilities. Other targets are technical targets, which 
are adjusted based on innovator’s performance
3. Finally, there are mutually agreed targets set between USAID and the WE4F secretariat, on 
various indicators.
Adjustments may be made as data from innovations’ results become available to RIH, allowing them 
to set reasonable targets. Innovations may be over-ambitions (‘at the edge of realism’) when they set 
their targets and after the first results may need to reset targets.
Details on how targets are set are provided in Annex 9. 
Some additional targets were added in the AWP that were not present in the original AWP, such as 
Gin45 (area under improved management practices). 
The reporting for Year 1 Semi-annual reporting was undertaken in Excel, but as WE4F has, 
Salesforce will be used the upcoming years. 
For the food, water and energy indicators, the methodology and standards to collect data were fully 
developed and trainings were provided in October 2022. For other indicators, targets are being 
updated, or units further explained; this means that a constantly updating PMEP document should be 
accessible to many.
Recommendations for the MEL system: 
1. Improve the PMEP document, including the addition of navigation/headings, and an explanation 
on how targets are set at different levels (innovation level, hub level, program level) and how 
targets may be changed, or indicator standards and methodology refined.
2. Update and clarify indicators (see suggestions in Annex 9)
3. If not already intended (the Evaluation Team is yet to verify), it is recommended that (summaries 
of) relevant MEL information continue to be shared and reflected on in bootcamps; this has 
already yielded valuable feedback for WE4F; this can include a reflection on the next point:
4. Monitoring by innovations for WE4F could be improved, in terms of making indicators clearer 
(do all innovations understand the indicators, e.g., how to calculate profit with the EBITDA, how 
to measure carbon emission), and use them in the same way, making monitoring relevant for 
innovations themselves, making monitoring leaner, more efficient if possible. 
5. Reconsider how EIA is undertaken (efficiency, scope); see suggestions in 3.6.5. 
55 Source: WE4F PMEP_July22.docx 
31 
Food production data collection – In all three RIH the calculation is done “based on experience;” 
innovations are helped to create a method to calculate production. One RIH respondent notes “we are 
developing a manual for each of the indicators”56. The USAID Secretariat noted that the WE4F 
manual and trainings for all KPIs and Gins that most innovators report to were updated and finalized 
in October 2022 and were used in the 2023 semi-annual reporting cycle (which concluded between 
October and December for all WE4F innovators in SSEA, MENA, and SCA). 
Water data collection – The SSEA RIH respondent trusts innovations’ data on water saving. All 
RIHs are gaining expertise in assessing water saving57. 
Energy data collection – The SSEA RIH respondent explains that most data on energy consumption 
come directly from meters; there are standards to estimate energy generated by solar panels and 
windmills and the benchmark for diesel is 0.2 kWh/liter. The methodology does not differ much 
between innovations. 
To report carbon emissions, there are also “standard emissions calculations, same as IBCC standard, 
except not as detailed.” The CLEER tool58 is, however, an international standard and its use in 
WE4F is becoming well established.
Both SSEA and MENA RIH respondents are confident about accuracy of energy use data (MENA: 
“100%”)59. 
3.5 Impact: baseline and aspects on progress end user level
• End user numbers are robust, and growth is projected to reach targets. 
• A range of farming practices are being improved, according to end-user surveys, and 
further insight on innovations impact on the farming systems and beyond (along the 
lifecycle socioeconomic and environmental impact) will be gained over time. 
• Food production targets are met, mostly from medium and larger scale farms. 
• Data on income increase are limited; preliminary findings show that most end users 
report an income increase. 
• Women end-users are reporting higher income from agricultural activities. 
• There is evidence of energy savings (for 13 innovations); most innovations are yet to 
report on energy savings. 
• Greater access to water is reported but water savings (and targets) are not yet reported 
for most innovations (as of June 2022) and were not being achieved on any scale. 
IM1: Innovations have scaled sustainable new solutions to challenges in the WE4F Nexus
IM2: Customers in the market are using the newly developed products or services of the innovations 
IM3: WE4F contributes to increased food production along the food value chain through a more 
sustainable and efficient usage of water and/or energy
IM4: WE4F contributes to increased income for women and men including the poor, in rural and 
urban areas
This section looks into impact-level results, in terms of end user growth (IM1, in 3.5.1), the overall 
impact at smallholder level (IM2, in 3.5.2), food production and processing while saving water 
and/or energy (IM3, in 3.5.3) and income (IM4, in 3.5.4). 
56 Source: Workspace RIH KII we4f.docx /SSEA re. KPI3 57 Source: Workspace RIH KII we4f.docx re. EQ1e 58 USAID Clean Energy Emission Reduction (CLEER) Tool: a user-friendly calculator based on internationally accepted 
methodologies, enabling users to calculate emissions reduced or avoided from clean energy activities. The tool helps to:
• estimate, track, and report greenhouse gases (GHGs) reduced or avoided from clean energy,
• estimate and project the amount of energy generated or saved from clean energy activities,
• identify high impact activities with cost-effective GHG reductions,
• evaluate the emissions reduction potential of planned activities and possible alternatives, and
• estimate projected GHG emissions reduced or avoided to 2050. 59 Source: Workspace RIH KII we4f.docx re. EQ1f
32 
Since WE4F has been in operation for just over a year all impacts will not yet be realized. Despite 
this, some direct measures of impact on end users will be assessed on the basis of recall as 
respondents report changes in recent memory and the administrative data also carries report on key 
factors such as food production measured against targets. 
Given the inability to compare current results to baseline information, the Evaluation Team has 
contrasted results compared to the targets. Comparisons made from the M&E data can also be made 
with data received from the end user survey and the interviews with innovations. 
The contribution of WE4F (EQ2) will be considered in this phase but not finally judged. Since this is 
an MTR we will see the beginning of some early impacts, but the focus is more at establishing a 
baseline from which to measure progress. 
3.5.1 Impact on end-user numbers (IM1)
A key indication of the progress of WE4F is the ability of those innovations receiving support in 
funding and TA from WE4F to significantly increase their numbers of end users: scaling up. This 
aspect is reported on in section 3.3.1, as it relates to effects in terms of innovations’ capacities (to 
reach end users). In this section, the focus is on KPI2: the total number of end users and looking at 
these figures it needs to be kept in mind that some (few) innovations with high end user numbers can 
contribute disproportionally to these totals. 
Table 12 shows the numbers of end user household members in CFI1: year 1 result compared to the 
MENA RIH and SSEA RIH year 1 targets; the results are disaggregated for wealth (income 
quintiles) and gender. 
Table 12: Number of end user household members (CFI1), by region, wealth and gender (KPI2)
Region Year 1 
target at RIH 
level
Year 1 
result
% end users 
being poor
# end users Q1 & Q2 
(poorer)
# end users Q3, Q4 & 
Q5 (better off)
total % female* total % female*
SSEA (n=12) 70,000 157,966 94% 149,221 36% 8,817 32% 
MENA (n=15) 37,500 96,388 72% 69,343 13% 27,045 10% 
Total 107,500 254,354 86% 218,564 29% 35,862 16% 
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Overall, WE4F reached the year 1 target and the two RIH’s year 1 target was surpassed by 2.3 times. 
The percentage of the poorest end users indicated by Quintile 1 and Quintile 2 was 86%. The 
aggregated results show that innovations in both RIHs have largely exceeded their respective end 
user targets: SSEA 226% and MENA 257% of the target. The proportion of end users in lower 
income quintiles is high: 94% in SSEA and 72% in MENA 72% of end users is found in this 
category of poorer end users. Only 29% of the end users in Quintiles 1 and 2 were female. Showing 
that while WE4F is meeting the BOP with 86% of end users being in the Quintile 1 and 2 only a little 
more than a quarter is female. 
Data from the end user survey show a similar picture when looking at farm size. In SSEA 74% of 
end user households have smallholder farms of less than 1 ha (the median is 0.4 ha, n=125); most of 
these farms (80%) are focused on irrigated crops. This confirms that WE4F in SSEA is reaching the 
“base of the pyramid”. In MENA only 10% of the end users farm on less than 1 ha. Here the context 
is very different, and the drier conditions may present a different calibration of the relative size of a 
smallholder farm.
60
60 Source: Tables from SSEA end user data. docs (generated from Monkey Survey for end users)
33 
KPI2 - Number of end user farmers (f,m)61
Limited data: not consistently disaggregated. 
KPI2 findings: The end user targets set at the RIH level are being met and exceeded by large 
margins. There is considerable variance between innovations, as discussed in 3.3.5: there is some 
over-achievement (and for some, extremely so), and some under-performance. On average, however, 
the end user results remain robust. As for gender disaggregation: 29% of the end users in Quintiles 1 
and 2 are female (in the other quintiles it is 16%).
KPI2 recommendations, current phase: More reflection on the setting of targets could be 
considered to ensure targets set by the innovations are realistic (neither too high nor too low) 
especially when new innovations join WE4F. 
KPI2 recommendations for extension: There should be further reflection and discussion with 
innovations in setting targets (end user and all targets). 
3.5.2 End users using the innovation (IM2) 
This section focuses on Impact 2 (IM2), on end users applying the innovation. 
Time with the innovation – To assess improved agricultural productivity, end users must have spent 
some time with the innovation and, as a result, have adopted changed farming practices. The 163 
survey respondents (143 respondents in SSEA incl. 36 females; 20 respondents in MENA, none were 
female) indicated the average length of time they used the innovation (Table 13).
Table 13: End users’ length of time with the innovation
Length of time SSEA (years) (n=43) MENA (years) (n=20)
Average 2.25 1.83
Minimum 0.33 0.25
Max 10 8 
Median 1.42 1.5
Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022 
The average time reported by end users employing the innovation is unexpected. WE4F support is 
less than 2 years, and the average length of time is greater.
3.5.3 Impact on food production, and water & energy saving (IM3) 
3.5.3.1 Food produced 
Here the report will look at KPI3, which is food produced with support from the innovation. Table 
14 shows the food produced (KPI3), by producer type and by region. 
Table 14: Food produced (t, year 1) by region, by producer types (KPI3) 
Region smallholder farms larger farms Companies total program y1 
production (t) % production (t) % production (t) % target
SSEA (n=9 innovations) 21,361 99% 138 1% 156 1% 21,655 170,000
MENA (n=12 innovations) 321,272 27% 831,016 70% 35,906 3% 1,188,194 101,250
Total 342,633 28% 831,154 69% 36,062 3% 1,209,849 271,250
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
From Table 14 we see that 1.2 million tons of food has been reported for the WE4F program. With 
the 17 innovations reporting, WE4F has produced considerably more food than the year 1 targets,
69% of the food produced came from larger farms and 28% from smaller farms. The larger farms are 
concentrated in MENA and in the water and energy innovation focus. The table with the breakdown 
of this information is in Annex 8.
61 by -household (hh) income quintiles* using energy or water-efficient WE4F innovations multiplied by av. hh size/country; gender 
target: 30% f; baseline: 0; RIH LOP targets: SSEA: 1,000,000; MENA: 750,000; SCA: 1,000,000; year 1 targets: SSEA: 70,000; 
MENA: 37,500.
NB: program LOP target (originally 2,400,000, now 2,500,000) is lower than total of RIH targets.
*: 5 quintiles: i) …; ii) …; iii) …; iv) …; v) … (define) (Q1, 2: poorer farmers; Q3, 4, & 5: the ones better off)
34 
Given the BoP approach, the production at the base of the pyramid seems small in comparison to 
what is produced by larger farms; or this raises questions on how the categories ‘smallholder farms’ 
and ‘larger farms’ are defined (without taking into account the context).
Innovations’ own food production targets and results – For WE4F 10 innovations met their year 
1 target and 9 did not meet the target, the other 5 either did not report. There were 4 innovations that 
did not have data and 1 that had not yet measured. Taken as a whole this establishes only about 50% 
of the innovations met their year 1 target.62 
While food production is the focus of WE4F the end user survey found that the innovations have had 
some effect on the quality of the food produced: 27% of respondents confirmed this, while 34% of 
respondents found there were no such effect (n=143).63
KPI3 - Food produced (t) as result of WE4F innovations64*:types: i) smallholder farmers; ii) larger 
farmers; iii) small companies; vi) other companies; v) cooperatives 
Limited data
KPI3 findings: Data are insufficient at this stage to draw in-depth conclusions. 
Production targets are being met but from larger scale farmers in the water and energy sector. From 
the results of 21 innovations the target was passed by more than tenfold. Medium-large scale farms 
are responsible for most (69%) of the food produced. 
KPI3 recommendation for the current phase: Recalibrate the KPI3 indicator on user types 
(smallholder vs larger farmers) in a regional, national, or local context. This could be done in the 
surveys, besides indicating the farm size, farmers can themselves declare in which category they 
belong. Then the averages of each category can be calculated so that the range is clear for each farm 
type/size. As WE4F continues, attention should be paid to smaller farmers (which are the 
overwhelming majority in SSEA). 
KPI3 recommendations for extension: Particular attention must be made to strengthening the 
potential for food production among small holder farmers at the base of the pyramid. Further studies 
are needed to ensure that this strategic objective is being met.
3.5.3.2 Food processed
KPI4 tracks the food processed by the innovations with support from WE4F. Only 3 innovations 
provided data on food processing technology in their AWP and had year 1 results. 
The three innovations processed 2000 tons of food during year 1 of the program. Promethean from
SSEA manages a hub serving multiple clients, collecting milk; this can explain why the amount of 
food processed is so high and accounts for 97% of the food processed. 
During the interviews with the innovations only one respondent (1 of 7) shared that the innovation 
has helped increase food processing due to cleaner water. Four other innovations shared that the 
innovations have not helped yet, but there are possibilities they expand and improve services in the 
future for this to be the case. From these discussions it looks like in the coming years of WE4F the 
amount of food produced will increase. 
KPI4 – Food processed (t) as a result of WE4F innovations, by end user type65
62 Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 63 Source: Tables from SSEA end user data.docs (generated from Monkey Survey for end users) 64 by end user type*; baseline: 0 t; 
RIH LOP targets: SSEA: 3,000,000 t; MENA: 2,025,000 t; SCA: 1,300,000 t
Year 1 targets: SSEA 170,000 t; MENA: 101,250 t
NB: program LOP target (originally 5,300,000 t now 5,500,000 t) is lower than total of RIH targets.
65 baseline: 0 t; RIH LOP targets: SSEA: 100,000 t; MENA: 810,000 t; SSEA: 475,200 t
Year 1 target: SSEA: 1,500; MENA: 40,500 t
NB: program LOP target (originally 2,340,000 t, now 2,125,000 t) is higher than total of RIH targets, it is revised downward because 
food processing innovations are fewer than foreseen, probably the target will be further adjusted downward (source: L. McMahan, 
USAID). 
*: types: i) smallholder farmers; ii) other farmers; iii) small companies; vi) other companies; v) cooperatives
35 
Limited data 
KPI5 finding: At the time of the MTR there has been more than 2000-ton of food processed by only 
3 innovations. Currently it is not clear if this is an issue regarding reporting or if this is not a focus of 
the innovations that are part of the project. 
Despite the enormous growth of food processing in the food supply chain, this is a relatively 
undeveloped aspect of food production and distribution among the innovations. 
KPI4 recommendations for the extension: Since the marketing of food is associated with food 
processing, further discussion is needed on lag in food processing. In relation to overall food 
production, the 21 innovations for which data is available, exceeded the year 1 targets more than 
tenfold in year 1. This is being achieved in conditions in which overall there is a decline in the use of 
energy although some increase in use of water. 
KPI4 recommendations: To effectively measure the relationship between improved food 
production, effects on land use (area), and improved practices and more efficient use of water and 
energy, additional information needs to be gathered regarding crop yields, and to identify patterns or 
causes of the changes. Particular attention must be made to strengthen food production from 
smallholder farmers at the base of the pyramid. 
3.5.3.3 Water saved
KPI6 tracks water saved by the innovations. Water saved is one of the key goals of WE4F, but it is 
one of the most difficult indicators to calculate except where closely monitored irrigation is a feature 
of farming. Most of the data in this section comes from the end user survey, as there is limited data 
regarding water saved at the time of writing.
Table 15 presents the water saved, by end user type, by region, for 9 innovations. 
Table 15: Water saved (l, year 1), by end user type, by region (KPI6) 
Region smallholder farmers larger farmers companies total (l)
volume (l) % volume (l) % volume (l) % 
SSEA (n=1) 10,492,000 100% 0 0 0 0 10,492,000
MENA (n=8) 254,329,838 74% 39,917,000 23% 3,844,000 2% 258,213,755
Total (n=9) 138,170,000 76% 39,917,000 22% 3,844,000 2% 181,930,000
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
*Rounded to the nearest thousand
From the first year of WE4F 9 innovations reported 181,930,000 liters of water was saved. From 
Table 15, analysis is made from a low number of innovations reporting. Most water in year 1 was 
saved in the MENA region and three quarters of the water saved is on smallholder farms. 
Surprisingly, more water is saved in energy focussing innovations than with innovations with a water 
and energy focus. It is to be kept in mind that the data is not complete. The total amount of water 
saved for year 1 is 34% of the LOP target. This is a good start in reaching the overall LOP goal. 
The end user survey had several questions regarding water usage. Table 16 shows results from the 
end user survey, on the question on changing water use. 
Table 16: Quantity of water used changed
Response SSEA MENA
Increased water use 26 2 
Decreased water use 1 1 
No change in amount of water used 104 17
Total responses (n=151) 131 20
No Response 12 0 
Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022
36 
Analysis of the 151 respondents establishes that most smallholders (80%) the amount of water use 
did not change (104, or 73%), or no response (12, or 8%)66. Among the remaining 28 reported the 
water use had increased (20% of farms in SSEA, 10% of farms in MENA). This could be due to 
access to additional water pumps (possibly solar water pumps) leading to more irrigation which 
increases yields. A clear conclusion cannot be made at this time and will be further investigated. 
When looking at the approach of the innovations regarding water capture and storage there are 9 
innovations that focus on irrigation and 6 that work to reduce water use. Remarkably, there is no 
innovation for reuse or treatment of water.
Water targets – Of the 10 innovations with targets and data only 2 had met their year 1 targets. 
There are 12 innovations that do not have targets for water in their AWP. 
Water unintended consequences – At the hub level there are some reports of concern that solar 
pumps could lead to increase in water use. “There are schemes to encourage micro irrigation, 
government assistance in critical areas.” There are also reports on innovations focused on selling 
water- and/or energy-efficient innovations, not necessarily on making farming more climate resilient.
Water saved is one of the key goals of WE4F, but it is one of the most difficult data to capture except 
where closely monitored irrigation is a feature of farming. There are opportunities for greater rigor in 
this field. In the interview with the SCA RIH it was mentioned that discussions were being pursued 
with the International Water Management Institute (IWMI) in Southern Africa to improve water
monitoring. Progress is happening on calculating water savings with the use of solar power.67
During the annual convening there were a few innovations from the SSEA region that discussed the 
importance of tracking and monitoring water usage. That increasing access to water pumps and water 
is important but so is understanding the entire water balance of the area. This information can be 
used by local authorities to help with permitting water usage especially for high use industries such 
as tourism.68
EQ1e – Water efficiency/accessibility? Meeting targets? Unintended effects?69
Limited data
EQ1e findings: There is considerable evidence of increased water availability and accessibility and 
through use of solar water pumps for groundwater. In innovation KIIs there is also concern reported 
about the potential overuse of water resources. Water savings are not yet reported being achieved on 
any scale. From end-user surveys most respondents reported no change, or increased volumes of 
water used, and a small minority mentioned savings being achieved. 
EQ1e recommendation: WE4F needs to give closer and systematic attention to the issue of water 
savings and the challenge of water overuse and falling water tables. The program should share 
approaches that some innovations are using to understand water balance in areas where access to 
water increases.
KPI6 – Total water consumption reduction* (l) by end user type**, as result of the use of WE4F 
innovations70
66 Source: Tables from SSEA end user data.docs (generated from Monkey Survey for end users) 67 Source: Nancy Shalaby, Managing Director IRSC Power for Generations, email to DH, 14 May 2022 68 Annual Convening 2022 notes 69 EQ1e – Did WE4F-supported projects increase water efficiency/make water more accessible? Did WE4F projects meet their 
water efficiency/availability targets? Overall, across all innovators, did the program meet the water efficiency/availability targets? 
What were the unintended effects of WE4F-supported projects on local water efficiency and water resources? 70 baseline: 0 l; RIH LOP targets: SSEA: 1,000,000,000 l; MENA: 750,000; SCA: 5,000,000 l; Year 1 targets; SSEA 6,000,000 l; 
MENA: 37,968,750 l60 
NB: program LOP target (2,000,000,000 l, unchanged) is much higher than total of RIH targets, but given experience from SWFF it is 
expected that the target will be exceeded (source: L. McMahan, USAID). 
*: saved in irrigation: compared to alternative technology, or practice before the innovation was used; saved in water storage: storage 
capacity x times filled x % used in agriculture; saved in water re-use/treatment: water re-used or treated, used in agriculture; saved in 
aquaculture/water re-use: water input - water released + water used for on farm irrigation 
**: types: i) smallholder farmers; ii) other farmers; iii) small companies; vi) other companies; v) cooperatives
37 
Limited data
KPI6 finding: The data are insufficient, to fully understand the water savings of the innovations in 
terms of innovation reporting and clarification on how water is saved by the innovation. 
3.5.3.4 Energy saved 
This section of the report will look at KPI5, or energy saved. 
Table 17 shows the innovations’ energy targets and results for year 1 at the regional level. 
Table 17: Innovations' energy saving targets and results, year 1 (kWh)
Innovation LOP target Year 1 target Year 1 result Year 1 target met
SSEA (n=6) 7,247,990 1,588,304 10,179,541 4 yes, 2 no
MENA (n=7) 150,231,444 34,173,862 13,728,834 4 yes, 3 no
Total (n=13) 157,479,434 35,762,166 23,908,376 8 yes, 5 no
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
There are 13 innovations that contributed to the saved energy in year 1 of 24 MkWh. Since only 13 
for CFI1 have reported, it is clear that most of the innovations have yet to report for this KPI at this 
time. As WE4F continues and more data is available it will be easier to determine the how the 
innovations contribute to energy saved. 
There is evidence of energy savings for all innovations as 8 out of 13 met their first-year target. The 
amount of savings is greater in the MENA region. And taking all innovations’ targets together, in 
SSEA the total result is 40% of the sum of targets, and in MENA the total result is 67% of the sum of
targets. The targets are set by each innovation with consultation with the Hubs, and it seems that 
during the first year it has been difficult for the innovations to reach the targets they have set. This 
could be due to delays in implementation and COVID-19. 
To investigate the energy savings further Table 18 shows the energy saved in year 1 of WE4F, by 
region and by end user type. 
Table 18: Energy saved in year 1 (kWh), by region, by end user type (KPI5) 
Region Smallholder farmers Medium & larger scale farmers Companies & coops Total
Year 1 % Year 1 % Year 1 % 
SSEA (n=6) 2,267,540 100% 0 0 0 0 2,267,540
MENA (n=7) 4,391,191 32% 8,803,812 64% 533,832 4% 13,728,835
Total (n=13) 6,658,731 42% 8,803,812 55 533,832 3% 15,996,375
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Of the nearly 24 M kWh energy saved, 42% is from smallholder farms, 55% from medium and 
larger scale farms, and the remaining 3% in companies and cooperatives. In SSEA all energy saving 
data are on smallholder farms. The data is rather incomplete (n=13), the M&E data does not share 
how the energy is saved, additional investigation would be needed to determine this. The end user 
survey gives an indication of the energy transition: in SSEA the use of renewables was reported by 
end users: 24 respondents name hydropower, 23 name solar. In SSEA 69% (n=81) end users changed 
their energy source. From a small sample of MENA innovations, no energy source changes away 
from fossil fuel were reported. 
Progress is being made on calculating energy savings with the use of solar power. One innovation 
estimates that in irrigation, replacing diesel by solar (Nano System Grid) cuts fuel costs by half and 
then more; the investment pays itself back in 18 months, and will then provide “23 years electricity 
for free”71. 
The end user surveys had multiple questions dealing with energy and whether the innovation has 
helped access to energy or the type of energy used: 37 of the 152 (24%) respondents reported that the 
innovation led to less energy use. A larger proportion (46.5% of all respondents) reported improved
71 Source: Nancy Shalaby, Managing Director IRSC Power for Generations, email to DH, 14 May 2022; with file: Comparison Solar 
& Diesel Calculator.xlsx
38 
access to energy. In SSEA, 37% of respondents (n=96) reported that adopting the innovation led to 
using less energy; in MENA this is the case for 14% of respondents (n=14)72. 
While solar water pumps are welcomed there is reporting of “over-pumping” and declining water 
tables.
EQ1f – Energy efficiency/increase renewable energy? Meeting targets? Unintended effects?73
Limited data
EQ1f finding: Most innovations met and passed their year 1 energy efficiency targets as all indicate 
evidence of energy savings. 
In relation to the energy transition: from end user surveys 69% of SSEA respondents reported 
changing their energy source; in MENA no energy source changes were reported as transition away 
from fossil fuels. 
There is unevenness among innovations and there is greater energy savings in SSEA by smallholder 
farmers and in MENA by medium-large farmers. 
At this stage there are no reported unintended consequences. 
KPI5 – Total energy saved* (kWh) by end user type** as result of the use of WE4F innovations74
Limited data 
KPI5 finding: From a limited number of innovations a considerable amount of energy is saved at the 
small holder farms despite this total energy saved is 68% of the LOP target. 
Progress is being made in energy saving but targets are not being met in the case of a number of 
innovations for which data is available. 
KPI5 recommendations for the current phase: This is a major achievement, and the success of 
this intervention should be written down. 
Success stories from innovations with significant savings should be written and circulated.
KPI5 recommendations for extension: There should be more tracking of energy transition from 
fossil fuels to renewable sources. 
3.5.3.5 Productivity 
Productivity (as explained in the definition section at the beginning of the report) is defined here as 
the output (production) against input in terms of area (that is: yield), and energy and water. 
End users reporting changes in the farming system that have an effect on yield – Survey 
respondents in SSEA innovations were asked how the innovation helped improve yields (to indicate 
more than one); unfortunately, in MENA there is insufficient data. Table 19 shows the responses. 
Table 19: Ways the innovation has improved yields (survey in SSEA) 
Response (n=58 respondents out of 163 end users surveyed) instances
Reducing reliance on rain 20
Better water management/irrigation area 19
Improved production practices 15
Other 12
Total instances 66
72 Source: Tables from SSEA end user data.docs (generated from Monkey Survey for end users) 73 EQ1f – Did WE4F-supported projects increase agricultural energy efficiency/increase the use of renewable energy? Did WE4F 
projects meet their energy efficiency targets? Overall, across all innovators, did the program meet the energy efficiency/availability 
targets? What were the unintended effects of WE4F-supported projects on local agricultural energy efficiency, access, and energy 
resources? 
74 baseline: 0 kWh; RIH LOP targets: SSEA: 20,000,000 kWh; MENA: 400,000,000 kWh63
NB: program LOP target (originally 674,000,000 kWh, now 675,000,000 kWh) is probably going to be revised downward as not every 
RIH can make its (over-ambitious) targets (source: L. McMahan, USAID). 
*: saved compared to alternative technology, or practice before the innovation was used 
**: types: i) smallholder farmers; ii) other farmers; iii) small companies; vi) other companies; v) cooperatives
39 
Source: cleaned tables end user data for SSEA September 19 2022
A full set of responses to key questions is available from surveys of SSEA innovations. Of the 163 
end users surveyed, 58 (35.5%) responded that yields had improved in one way or another. The 
largest instances mentioned were due to changed production practices largely including water 
management, as a result of the innovation. 
There is another indication, on end users’ confidence: nearly all say they will recommend the 
innovation to others. 
EQ1g – Agricultural productivity and cc adaptation? Unintended effects?75 
Limited stage, limited data
EQ1g finding: A definition of productivity is provided in definitions (beginning of this report). With 
that definition, the findings here concentrate on productivity76. 
A proportion of end users reported some impact on productivity and identified what change in 
production methods led to it. From the end user surveys improved yields were reported due to 
‘reducing reliance on rain’, ‘better water management/irrigation’ and better production practices. 
In relation to end users adapting to climate change, within the definition of productivity this would 
be about (reducing) the use of water, and that is discussed in 3.5.3.3.
Without quantitative yield data, productivity cannot be quantified. Data on food production, when 
not linked to data on area for that same production, cannot be used to calculate yield. 
EQ1g recommendation to initiate in this phase, and fully implement in the next phase: 
Apart from collecting production data (useful, and necessary for KPI3), there is a need to focus at 
farm level, and from a small selection of end users (farms) to collect specific data on crop yields; 
changes in crop yields need consistent examination over time to identify patterns and causes of 
change. Doing this, a distinction can be made between farms from end users at the base of the 
pyramid and larger farms; this could then be compared with available gender yield gap data 
(synergy). 
3.5.4 Impact on income (IM4) 
To look at income increase the Evaluation Team looks at KPI7 and responses from the end user 
surveys. There is uncertainty in what the data on income increase represents. End users surveyed by 
Dexis provide a more positive picture than the M&E data from WE4F – and the latter may be 
incomplete due to the requirement that innovators be able to validate an increase in income among 
end-users and some WE4F innovators don’t have a direct relationship with the end-users of their 
innovation. Table 20 shows the number and fraction of end users that experienced an income 
increase related to the innovation. 
Table 20: Number of end user household members (‘end users’) that saw household income increased (KPI7) 
Region Quintile 1 & quintile 2 end users Q3, Q4, & Q5 end users 
Total no. of end 
users (KPI2) 
End users whose
income increased
% female % with income 
increased
Whose income increased
SSEA (n=2) 157,966 17,255 54% 11% 0 
MENA (n=2) 96,388 1,605 2% 2% 0 
Total 254,354 18,860 49% 7% 0 
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
75 EQ1g – Did WE4F-supported projects lead to more agricultural productivity and adaptation to climate change? Did WE4F projects 
meet their agricultural productivity targets? Overall, across all innovators, did the program meet the agricultural productivity targets? 
What were the unintended effects of WE4F projects on agricultural productivity?
76 The ‘unintended effects' part of EQ1g is duplicated in other questions (EQ1l, EQ1m, and EQ1n, with the same terminology or using 
the word ‘trade-off’), which are discussed in 3.6 Sustainability (in 3.6.5).
40 
These are early results, based on limited data, (only 4 innovations reported), 7% of the poorer end 
users seems to have experienced an increase in income. WE4F saw a total of 18,860 end users that 
experienced an income increase with 49% of that being female end users. This only gives a total of 
7% of the total end users that have experienced an increase in income.
With the data available a large majority of end users in the administrative data did not report an 
income increase due to an inability to validate their income claims with end-users. Data collected by 
the Dexis Evaluation Team from end users directly provides a more positive picture.
Just three CFI1 innovations responded to the question on what proportion of their smallholder clients 
would have increased their income as a result of the innovation. They provided figures between 50% 
and 90%77. 
The following tables report further on the findings form the end user survey regarding income. 
Table 21 shows end user responses on the question whether the innovation contributed to an income 
increase.
Table 21: End users reporting whether their income increased as result of the innovation 
Response (n=163) SSEA MENA
not at all 16 1 
a little 87 10
quite a bit 30 9 
respondent total 133 20
no response 10 0 
Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022
Response (n=163) SSEA MENA
No 3 0 
Yes 111 19
% Yes 97% 100%
Respondent total 114 19
No response 29 1 
Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022
Overall, 88.8% of the respondents shared their income increased as a result of the innovation, in 
SSEA, 117 of 133 (88%) of the respondents indicate that the innovation led to increase in income, in 
most cases ‘a little’. From the small sample in MENA 95% reported an increase. This high 
percentage was also true for if the innovation increased women’s income, 97.7% % (130 of 133) of 
the respondents shared that the innovation increased women’s income78. 
The incomes of end users are presented in Table 22 that were self-reported in the end user survey. 
Table 22: End users reporting their income (USD), by region
Income per year (USD) SSEA ($) (n=141) MENA ($) (n=19)
avg 3,979 3,927
Min 260 2,200
Max 250,000 6,200
Median 1,800 3,688
Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022
The overall average income for all respondents (n=160) was 3,953 USD. The table shows that 
income averages are similar between SSEA and MENA, but in SSEA the range is much wider, and 
the median much lower than the average; in MENA the median and average are nearly the same. 
This would be the baseline data as there is no data of the income of the end users before they started 
using the innovation. From the end user survey 97% from SSEA and 100% from MENA of the end 
user surveyed reported that their income increased due to the innovation. From the data available 
77 Source: Dexis innovation survey (Innovation survey 7 11 22.xlsx) 78 Source: Tables from SSEA end user data.docs (generated from Monkey Survey for end users)
41 
from the end user survey, there is evidence that incomes for the end users have increased for women 
in the household as well. 
There is no concrete evidence regarding income increase, there is qualitative data from the survey, 
but the M&E data did not have this detailed data. The average income would be a baseline now and 
can be compared to the information collected for the final evaluation. 
EQ1i – BoP, smallholder farmers, women incomes79
Limited data
EQ1i finding: The end users in the survey nearly all report an income increase, this proportion is as 
high or higher for women. This proportion is not found in administrative data.
KPI7 – Number of end user farmers (f,m), by -household (hh) income quintiles* that experience an 
increase in income, multiplied by av. hh size/country; baseline: 0; gender target: 30% f80
Limited data
KPI7 finding: There is uncertainty in what the data on income increase represent. End users 
surveyed by the Dexis Evaluation Team provide a more positive picture than the data from WE4F as 
WE4F data reported from WE4F innovators was incomplete at the time of this report. 
In the end user survey from the Dexis Team, 88.8% of the respondents reported their income 
increased as a result of the innovation a high level of end users reported women’s income also 
increased. 
None of the end users surveyed reported receiving financing to buy the innovation, such financing is, 
however, found in the M&E data. 
KPI7 recommendations: Further end user surveys are needed to confirm increases in income and 
the reasons for such changes.
EQ2 – Outcome, impact attribution to WE4F81
Limited stage 
EQ2 finding: Since WE4F provides only a proportion of the funds employed by innovations WE4F
inputs are regarded as contributions towards results. Further data collection is needed to assess the 
relative contributions of various donors and partnerships. 
3.6 Sustainability
• Across the SSEA and MENA regions, 29% of innovations make a sufficient profit (already). 
• The number of jobs created is unlikely to reach its target; most jobs created are for men (only 
18% for women). 
• Innovations (with the IEE) and end users do not identify all effects on environment and 
climate, including biodiversity and (community) natural resources (incl. water); where 
innovations increase the use of land, water, pasture and forest resources, the effects are not 
always considered. 
Section 3.6 assesses the sustainability (EQ1l, EQ1m, EQ1n, EQ1o and EQ1p) at the program, 
innovation, and end user levels, presenting data from indicators KPI1 (profit), Gin10 (jobs), KPI8 
(monitoring water & biodiversity), Gin6 (GHG emission), Gin45 (surface area of land better 
managed) and a discussion of the social, economic, and environmental effects along the innovation 
lifecycle. 
79 EQ1i – Did WE4F-supported projects contribute to increased incomes of smallholder farmers, especially women farmers and 
those in the Base of Pyramid?
80 Year 1 targets: SSEA 50,000; MENA 15,000. Program LOP target (was 687,500 is 557,500) is balanced; based on aggregation of 
RIH targets, the latter are lower but based on SWFF the program is expected to reach the LOP target (source: L. McMahan, USAID). 81 EQ2 – How much of the measured change reflected in outcome and impact-level WE4F indicators can, in fact, be attributed to the 
WE4F-supported projects? That is, what portion of the results reported is not explained by the projects examined by the evaluation? 
(WE4F recognizes that this is a difficult question to answer but wants the evaluator to make the best effort to answer this question). 
42 
3.6.1 Innovations’ profit, and job creation 
At the level of end users, 87% of the end user survey respondents (n=143) think the innovation 
results (improvements) are sustainable, and 92% of respondents (n=143) indicate they would 
recommend the innovation to others (1% would not). Such data confirms that end user support will 
be contributing to sustainability as end users are pleased with the innovation and such attitudes will 
contribute to growth.82
Profit results against innovations’ own targets
The reporting on financial numbers by CFI1 innovations with year 1 results is uneven; 21 
innovations report while others do not. On examination there are various reasons: for some there is 
ND (no data), others, NYM (not yet measured), yet others, NAWP (target not in the AWP). 
Profit results against the WE4F target of >8% EBITDA margin (KPI1) 
Table 23 shows CFI1 innovations’ EBITDA margins in year 1, ranked within each region. EBITDA 
is a widely used measure of core corporate profitability. An accounting term and acronym that
represents earnings before interest, taxes, depreciation, and amortization. In WE4F this figure should 
be calculated and reported at the innovation level.
83 
Table 23: EBITDA margins of CFI1 innovations in year 1(KPI1) 
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Table 23 presents the results from 21 innovations in WE4F that report profits (KPI1); 7 in SSEA 
report and 14 in MENA. The program target is that more than 8% of innovations will pass beyond 
the threshold (the EBITDA margin of >8%); this 8% target is based on the expectation that only 10% 
of innovations reach widescale adoption. The table indicates that of the 21 innovations, 7 innovations 
(33%) reached the threshold of the KPI1 indicator (>8% EBITDA margin). 
82 Source: Tables from SSEA end user data.docs (generated from Monkey Survey for end users) 83 For an explanation of how to calculate the EBITDA, see the resource linked here using the Operating Income “Method”: 
(investopedia). EBITDA Margin is equal to the EBITDA divided by gross sales or gross revenue. 
Region Innovations with Results year 1 result met EBITDA threshold
SSEA Gham Power 11% Yes
Oorja Solutions 8% Yes
Onergy Solar (Punam Energy) 7% No 
Promethean 4% No 
Sumba Solutions 0% No 
Goat Trust (Nandinandan Breeds and Seeds) -4% No 
aQysta -22% No 
SSEA (n=7) 29%
MENA Abu Erdan 46% Yes
Biomass SAL 38% Yes
Chitosan 23% Yes
Schaduf 8% Yes
Green Essence 8% Yes
Robinson Agri 7% No
IRSC 5% No
Platform, The 4% No
Go Baladi / Hajjar Foods 3% No
Baramoda -1% No
Compost Baladi -4% No
SOWIT Maroc -13% No
SuWaCo (Benaa Foundation) -20% No
Alva Tech -332% No
MENA (n=14) 29%
All (n=21) 29%
43 
In addition, of those 7 successful innovations, 3 are female led (which passes the target of 25%). 
From the innovation interviews all 5 respondents on the subject indicate that profitability is expected 
in 3-5 years, and most respondents report to be (very) confident on financial sustainability84. 
KPI1 – Fraction of innovations (f,m) making a profit* from marketing WE4F supported 
innovations85 
Limited data 
KPI1 finding: The KPI1 target (8% of innovations reaching or passing beyond the threshold of the 
EBITDA margin of >8%) is reached and passed: 33% of innovations have passed the KPI1 
threshold, with an EBITDA margin of >8%. 
During the interviews, a larger fraction of the innovations reported they were (very) confident of 
achieving financial sustainability. 
KPI1 recommendation: KPI1 reporting needs to be more comprehensively reported with 
specialized TA support as needed.
Jobs (Gin10) - With financial support and expansion of the innovations, jobs should be added; 
Gin10 tracks the jobs created by WE4F. Due to the lack of baseline data, job creation is assessed in 
relation to the targets set by the innovations and the program targets. Table 24 presents the numbers 
of jobs created, by region, and the proportion of those jobs held by women. 
Table 24: Jobs created through Program support, by region (Gin10) 
Region total jobs female jobs % of jobs for women
SSEA (n=13) 111 13 12%
MENA (n=15) 101 26 26%
Total (n=28) 212 39 18%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
There are 28 innovations that have reported the creation of jobs, a total of 212 jobs are reported. 
Gin10 sets a target of 100 jobs being created through participation in WE4F. Table 24 shows the 
numbers of jobs created by region. From the 28 innovations reporting there is a program target of 
100 jobs created; 212 jobs are reported. 
In both regions nearly equal amounts of jobs are created, only 39 (18%) of the 212 jobs are held by 
women. Nearly all jobs are created by innovations with a water and energy focus (86%). It is worth 
noting that those innovations with the sole energy focus have a much higher proportion of women; in 
this focus 46% of jobs are held by women. 
From the Dexis innovation survey a total of 221 management jobs and 99 other/part-time jobs were 
created; 45% of the management jobs are filled by women, and 22% of the other/part-time jobs are
filled by women86. 
Gin10 – Number of new jobs created in WE4F innovation companies87 
Limited data
Gin10 findings: The job creation target is unlikely to be reached: so far, an average of less than 10 
jobs per innovation. The innovations in energy seem to be giving the greatest opportunity to women. 
Gin10 recommendation for current phase: Success stories in job creation should be recorded and 
distributed. Post MTR there should be further discussion of progress towards this target.
84 Source: Workspace RIH KII we4f.docx 85 baseline 0%, target: 8% of innovations, of which ¼ women-led) 
*: measured as >8% EBITDA margin= EBITDA (earnings before interest, tax, depreciation, amortization) / Total Revenue. Earnings = 
revenue minus expenses (see H6 Data Collection Phase 1 and 2/1. Phase Mid Term Data Collection/5. Administrative M&E 
Review/WE4F MEL Training August 2022.pptx)
86 Source: Dexis innovation survey (Innovation survey 7 11 22.xlsx) 87: baseline: 0 jobs, target: 100 jobs/country-innovation For clarity of concept and use, the indicators are reviewed in Annex 9 which 
presents the indicators as used in this evaluation. In the case of jobs, Gin are found in: source: WE4F Full PIRS_July22.docx and it 
reads that the target is: ‘100 jobs’, it is assumed this would be 100 across the Program.
44 
3.6.2 Innovations monitoring protection of water, biodiversity 
In all SSEA CFI1, only one innovation has a target on water protection in their AWP. Elsewhere 
there are few targets, some for CFI1 in Iraq and some for CFI2 (source: 81 innovation AWP). This is 
due to the fact that this indicator is reported at the RIH level with a summary for the region. There is 
some information that shows that some innovations sell monitoring applications and one self￾reported client satisfaction of being enabled to monitor environmental factors (High Atlas 
Foundation, Morocco)88. 
KPI8 – Share of innovations that use tools, methods, or processes to monitor the protection of water 
or biodiversity; Target 80% (this is a RIH summary indicator to be found in RIH reporting)
Limited data
KPI8 finding: The Evaluation has not explored the RIH reports to find about any such tools, other 
than the EEI which is discussed in 3.6.6. 
KPI8 recommendation for current phase: Additional surveys and tracking will be needed to 
follow up with the KPI8, and the measures protection of water or biodiversity. 
3.6.3 GHG emission saved by region 
The USAID secretariat provided training to the innovations regarding the use of CLEER to calculate 
the GHG emissions. (The CLEER Tool is a user-friendly calculator based on internationally accepted 
methodologies, enabling users to calculate emissions reduced or avoided from clean energy 
activities.) In feedback from the RIHs they reported the tool is i) relevant; ii) practical/as tool; iii) 
adaptable to local conditions; iv) reliable in output (perception).
The calculations include the emission savings from change of fuel; it does not include emission￾change effects in other parts of the process or value chain. Emissions could rise from the expansion 
of farm area and fertilizer use, both adding to emissions. Such emissions could be significant while a 
reduction in transport including fuel transport, could lead to lower emissions. 
Table 25 presents the GHG emissions by focus of innovation. 
Table 25: GHG emission saved (tCO2e year 1) by innovation focus (Gin6)
Innovation focus Total tCO2e % From women % From smallholders
Water - - - 
Energy 14,056 77% 100%
Water and energy 57,005,454 20% 16%
Total (n=16; 9 SSEA, 7 MENA) 57,019,519 20% 16%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
The data from the 16 innovations reporting indicates that nearly all emission saving is concentrated 
in the innovations with a water and energy focus as opposed to water focus and energy focus 
separately. From the small sample 20% of the energy saved can be attributed to women and only 
16% to small holder farmers. These preliminary results show considerable disproportions as MENA 
accounts for 99.88% of the energy saved. 
From this data, the larger farmers in SSEA (49% of the end users, section 3.1.2) did not contribute 
anything to emission reduction; women (representing 44% of smallholders) here contribute 55% of 
these savings. 
In the MENA region 76% of the end users are poor (quintiles 1 & 2, in section 3.1.2) and 84% of the 
emission reduction is from the larger farmers (or companies).
At this stage an explanation of these data is difficult; more information is needed on the numbers of 
innovations that calculated emission savings, their innovation focus, the tool used, and exactly how 
the calculations were done in each case. In relation to data collection WE4F provided training on 
88 Source: Dexis innovation survey (Innovation survey 7 11 22.xlsx).
45 
calculating emission saving and referred to the CLEER tool; it would be worthwhile getting feedback 
from the innovation-users of this tool. 
Gin6 – Total GHG emission (tCO2) saved* by year by end user type** and -gender, through use of 
WE4F innovations89 
Limited data
Gin6 findings: Innovations with focus on water & energy are contributing more than those with only 
energy focus. Additional analysis of the data, innovation by innovation, could reveal trends and 
patterns in reducing GHG emissions. 
Almost all the energy saved is from the MENA region. 
Recommendations for current phase: As WE4F moves forward more data over time is needed and 
should be share on how the GHG savings are achieved by different innovations. Changes in land use 
need to be assessed, with tools such as the Agriculture, Forestry, Land Use (AFOLU) and the Ex￾Ante Carbon-balance Tool (EX-ACT); the latter is developed by FAO to estimate the impact of 
agriculture and forestry development projects.
3.6.4 Area improved
This Gin45 indicator was added late in the program, therefore just few innovations produced year 1 
targets. Only 12 innovations have information reported for year 1. Table 26 shows the area 
improved, for a limited number of innovations. 
Table 26: Area improved (ha) as a result of the CFI1 innovations (from innovation records)
Region # of respondents by innovation focus LOP (ha) Result year 
1 (ha) 
% of LOP 
water energy water & energy reached
SSEA (n=6) 1 1 4 84,271 20,064 24.0% 
MENA (n=6) 6 1 5 417,693 52,226 12.5%
Total (n=12) 7 2 9 803,724 72,290 8.9%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
An extrapolation from the results from 12 innovations could indicate that about 488,000 ha would be 
improved in the first year90 . Such an extrapolation assumes that 12 innovations are representative of 
the 81 innovations. In SSEA, comparing the result with innovations’ own year target shows a fair 
result: 24,0% in year 1 gives an indication that the target can be reached, assuming some 
acceleration. In MENA, interestingly, the LOP is much higher than for SSEA and the current result 
of 12.5% of the LOP is rather off the mark. Judgement needs to be reserved until there is further 
examination of the collection of data. 
Area of land improved depends on the available land and farm size. Survey data on farm size ranges 
are used to estimate the average farm size is presented in Table 27. 
Table 27: Farm sizes in in the program
Range of farm sizes in the program calculation area (‘average’) (ha) SSEA MENA
Smallholder I (less than 1 ha) 0.5 89 2 
Smallholder II (between 1 and 3 ha) 2 31 5 
Smallholder III (between 3 and 5 ha) 4 0 4 
Medium (5-100 ha) 25 0 8 
Large scale farmer (more than 100 ha) 120 0 1 
Average farm size (ha) 0.60 17.25
Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022 
89 baseline: 0 tCO2; RIH LOP targets: SSEA: 200,000 tCO2, MENA 2,530 tCO2; SCA: 3,444 tCO2. The RIH targets will be reviewed 
in January (source: L. McMahan, USAID). The program LOP target is 500,000,000 tCO2
*: standard tool: CLEER (for other tools permission must be given); it provides annual energy and GHG savings for one unit. The 
semi-annual results are “(result x # of innovation units) % 2” which is understood as: (semi-annual results = x # of innovation units) / 2 
(source: WE4F MEL training_August 2022.pptx)
**: types: i) smallholder farmers; ii) other farmers; iii) small companies; vi) other companies; v) cooperatives
90 ((72,290/12)*81) 
46 
Marked differences in the farm size based on the geographic area are observed. There appears a 
challenge in relation to achieving the aggregated sum of all innovation targets ( LOP) due to 
available land. From calculations based on average farm size, the concern is the land used now is 
smaller than improved land LOP; this would indicate a considerable addition of farm land. There is 
not enough data at this stage to draw any firm findings91. 
Gin45 – Area (ha) under improved management practices as a result of WE4F innovations92 
Limited data
Gin45 findings: This is a new indicator, and early data are not conclusive. In some regions more 
progress is made. In SSEA the area improved (about 20,000 ha or 24% of innovations’ target) is 
reasonable progress, given the small average farm sizes and work in irrigation. In MENA however, 
the innovations only reach 12.5% of innovations’ target area, however, the target area (LOP) seems 
overly optimistic. This will be investigated further in the final evaluation. 
3.6.5 Socioeconomic and environmental effects along the innovation lifecycle
In the SSEA end user survey (Annex 8, Table 50), end users mention a variety of effects, some 
positive, some more negative: 
• Agro-biodiversity: there were indications that crop varieties increased (22 end users)
• Use of ground water increased (19 end users) 
From interviews with innovations there are references to how changing farm practices affected the 
use of inputs, natural resources and labor; for all three one can find references to increase or 
decrease, positive or negative effects, and some effects within an innovation can partly cancel each 
other out (e.g. with biodigesters, women’s labor for collecting fuel and cooking reduces, whereas 
labor for collection of biodigester feedstock, and production and transport of bio-slurry/compost 
increases. 
Agrobiodiversity and soil health – End user survey respondents say the innovations do not have a 
negative effect on agrobiodiversity. In SSEA 22 of 129 respondents indicate that the variety of crops 
on the farm increased, and 2 of 123 indicate that the livestock variety increased. There are similar 
figures for MENA. There are no reports on negative effects on soil health, while some indicate 
positive effects (especially in MENA). Based on the survey, the local, off-farm biodiversity is mostly 
unaffected or positive; water pollution is not identified as an issue93. 
There are negatives too. Noteworthy in SSEA, for instance, 19 of 129 respondents indicate that there 
is an increase in the use of ground- or surface water rather than savings in water and in MENA 1 of 
18 respondents also indicates one positive effect while all others report ‘no effect’94. 
When asked if the cultivated area had increased since using the innovation, 7.8% of the end user 
respondents confirmed such an increase (n=128). But no respondent (n=143) reported any negative 
effects on community resources (can be a result of increasing farm areas).
91 From the survey, we see that in SSEA (n=96) the average area on which the innovation is applied is 0.6 ha (the median is 0.33 ha). 
Taking the number of end users in SSEA from Table 23. (662,759 end user household members / 6.95 person/hh = 100,570 
households), times 0.6 ha/household, makes a total of 60,342 ha on which the innovation could, in theory (as a maximum) be used, 
assuming that no new end users are added. In MENA the same calculation (90,297 end user household members / 5.6 person/hh = 
16,124 households (excluding one with 300 hh members, which must be the large-scale farmer) times 17.25 ha/hh, that makes a total 
of 278,147 ha on which the innovation could, in theory (as maximum) be used, if no new end users are added. This area is much 
smaller than the total LOP of 6 innovations only.
92baseline: 0 ha; RIH LOP targets: SSEA: -; MEA: -; SCA 500,000 ha. Program LOP target is 500,000 ha. 
NB: the RIH data are insufficient to revise the LOP targets (source: L. McMahan, USAID). 93 Source: Tables from SSEA end user data.docs (generated from Monkey Survey for end users) 94 Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, 
47 
Environmental compliance – WE4F provides training on environmental aspects, and (presumably 
in that training) based on USAID’s environmental impact assessment (EIA) tool95, WE4F uses Initial 
Environmental Examination (IEE), and subsequent Environmental Mitigation and Monitoring Plan 
(EMMP); innovations are required to apply these instruments: a compliance exercise.
Table 28 shows how many innovations have done an IEE and EMMP. 
Table 28: CFI1 innovations with IEEs and EMMPs
Region total innovations active innovations* innovations with IEEs innovations with EMMPs
SSEA 14 12 11 10
MENA 17 15 13 10
Total 31 27 24 20
*: inactive are those whose grant was terminated as they did not reach their targets
Source: Google Drive WE4F CFI1 Environmental Documentation
The table shows that nearly all innovations have an IEE , and 20 innovations have an EMMP
confirming first level environmental monitoring and mitigation with a possible 20 having both IEE 
and EMMPs.
IEE and EMMP – The documentation produced with these instruments is comprehensive; it is
explored in Annex 13, which provides a detailed table of analysis and an overview of findings from 
such reporting. These findings are summarized here.
The EEI helps to identify environmental and socioeconomic issues related to their interventions, and
the EMMP address some of the issues identified in the IEE. 
For example, farm inputs. In several cases some of the effects (e.g., increased use of agrochemicals, 
or increase waste, or water use) are not managed by the innovation and left as ‘external’. This 
includes depending on ‘another project’ (to recycle waste) or in the case of agrochemicals, the 
assumption that toxic effects will be dealt with by a training (by another party) on agrochemicals. It 
suggests a rather limited responsibility for the provider/innovation to address ‘externalities’.
1. The IEE (EIA) instruments, from innovations’ survey, serve for compliance processes with other 
funding opportunities96
2. The instruments however (feedback from the QoSS) take time and are not always clear; more on 
this is said in the KII with innovations: “monitoring poverty reduction is difficult,” “not much 
access to end users, lack of insight into what is happening beyond the point of sale”; “not much 
monitoring poverty impact, got some framework and used it”. 
3. WE4F staff comments in the IEE submitted by innovations show good number of additional 
questions; some of it suggests that some environmental or socioeconomic effects remain 
unexplored or underexplored, especially where such effects can be considered ‘externalities’ or 
are not directly visible or related to the innovation activities
4. The Evaluation also finds that some apparently relevant effects could have and were not 
addressed in the EMMP.
The IEE data are varied, insufficient to reliably respond to evaluation questions on intended or 
unintended, positive, or negative effects on environment, climate, water resources, biodiversity, rural 
communities, and poverty numbers in marginalized groups or rural communities. 
The IEE tend to cover only the immediate direct effects of the activity, e.g., innovations may assess 
the activity of biodigesters, briquette making, goat rearing, pumpkin farming, or bamboo furniture 
making, but not further explore more indirect positive or negative effects on, respectively, use of 
chemical fertilizer, forest management, community water resources (where donkeys compete), water 
pollution (agrochemical use on riverbed farms) and forest governance, and other land, water and 
biodiversity related issues. Just some questions that arise.
95 Source: WE3F Evaluation shared folder / Data files / Enabling Environment Work / Environmental impact training materials / 
Monitoring EIA_Tool_Revised_4Dec2017_FINAL.DOCX 
96 Annual Convening 2022 notes
48 
EQ1l – Innovations impact on marginalized groups? Specifically, numbers in poverty, women. 
Unintended 97
Limited stage, limited data; insufficient information on impact on marginalized groups, rural 
communities (beyond the end user households), apart from jobs created. The unintended effects are 
not sufficiently identified or monitored with the EMMP.
EQ1l finding: The end user surveys report that the results include some positive impact on 
marginalized groups (the poor, women, youth, ethnic minorities, rural communities) in terms of
income (not in terms of jobs, or availability of community natural resources on which such groups 
disproportionally depend). The IEE also includes a negative effect (using forest resources governed 
by an indigenous community). 
EQ1m – Environmental effects (water resources, biodiversity)? Intended or unintended98
Limited data (same argument as for EQ1l) 
EQ1m findings: The data from interviews with innovations and end users, and the IEE 
documentation, all give a mixed picture of positive and negative (unintended) effects on a variety of 
environmental issues.
EQ1n – Negative trade-offs of socioeconomic (marginalized groups) and environment 
impacts99
Limited data (same argument as for EQ1l) 
EQ1n findings: There was no report of negative feedback from marginalized groups from 
participating in an innovation but there are not data on effects (also some reported and potentially 
negatives ones) on marginalized groups not participating. There is no way to assess the importance 
of any effects. 
EQo – TA impacts, via innovators, on environment and climate, management of water 
resources, biodiversity100
Limited data
EQo finding: WE4F provided support to monitor water or energy use – that is part of the 
environmental impact (chapter 5). Aside from that, the IEE is a fair instrument to identify some 
environmental effects, with a focus on on-site, more immediate effects; negative effects are not
necessarily addressed in the plan. 
Conclusion on IEE and EEMP - The IEE and EMMP instruments are quite demanding, producing
a considerable amount of reporting to assess. In spite of this, IEE and EMMPs completed by 
innovations and responses from RIH indicate that most mitigations are focused on direct (local) 
effects, rather than lifecycle effects of innovations; effects further afield are not identified or left 
aside as ‘externalities’. The EIA compliance approach invites innovations to get compliance done but 
is not sufficiently compelling innovators to look beyond the direct effects of interventions and 
technologies and integrate sustainability more holistically in the company’s core business (social 
value). In addition, IEE duplicate EIA requirements that governments (may or should) have. 
EQ1l, EQ1m, EQ1n recommendations for this phase: Further research will be undertaken in this 
regard in the future as part of the final evaluation. For this, the Evaluation will use the Lifecycle 
97 EQ1l: Have marginalized groups (the poor, women, youth, ethnic minorities, rural communities) been positively and/or negatively 
impacted (through income, employment, water/environmental, energy/environmental) from WE4F supported innovations? 
Specifically, has WE4F reduced the number of people in poverty as a result of supporting WE4F-supported innovations and has 
WE4F increased the number of women benefiting from WE4F-supported innovations. What were the unintended effects of 
WE4F innovations on marginalized groups? 98 EQ1m – Were WE4F-supported projects environmentally sustainable (i.e., did they provide positive environmental benefit, or did 
they do more environmental harm than good)? Did WE4F- supported projects have negative effects on water resources or 
biodiversity? What intended or unintended impacts did WE4F-supported projects have on other natural resources (e.g., land use 
changes, battery waste, etc.)?
99 EQ1n: To what extent are there negative trade-offs observed between WE4F innovations’ impacts on marginalized groups in terms 
of economic or social results and impacts on environmental sustainability or biodiversity?
100 EQo: To what extent did each RIH provide WE4F innovators with support that improved their positive impacts on environment 
and climate, including sustainable management of water resources and biodiversity? What were the unintended effects of innovator 
support provided by RIHs on cross-cutting issues of environmental sustainability/biodiversity?
49 
Assessment (LCA), produce a simplified analysis tool, and assess the effects for a selection of 
innovations, including simple case studies and a small selection (1-2) of the innovations’ end users. 
EQo recommendation to initiate in this phase, and fully implement in the extension phase: 
Beyond knowing about water and energy efficiency, there is also a need to better assess the other,
positive and negative environmental and socioeconomic effects: to understand what changes end user 
level e.g. in the farming system (the economics, labor, gender issues, soil health), but also the effects 
further up- and down the innovations’ lifecycle: the sourcing of materials from nearby community 
resources (effects on marginalized groups) or further afield, the effects of by-products or waste on 
local and ocean waters, for example. Without such understanding one cannot assess what is traded￾off. This requires recognition of complexity, and a wider scope (context), to get it right. 
However, that need has to be balanced with considerations of efficiency. It should not require more 
resources, preferably less, than wat is currently needed for the IEE and EMMP. 
USAID and EU have in recent years developed instruments like the Lifecycle Assessment (LCA)101, 
and the agroecology M&E tool102, taking a more comprehensive view on environmental effects. 
This, when adapted and used in a more creative process of innovation and business vision 
development than in a process of compliance, could be yielding more insights, more useful for the 
innovations too; it should be geared to identify trade-offs and find win-win solutions that work for 
the innovation business. 
An argument against LCA is that it requires more resources, which must be avoided. The 
recommendation is to use the framework to produce a much simpler tool. Used for learning 
purposes, not for compliance, it can be less demanding and more productive. 
Suggestion for the current phase: Based on the LCA and/or agroecology tool, it is suggested to 
develop a simple checklist with some qualitative questions and some scores (quantitative); it must be
short and tailored for an innovation and its context103, then tested in a small group: 1 innovation, 2 
end users and 1 WE4F expert or consultant in a role of advocate for other (especially marginalized) 
stakeholders or affected. The group will use and adapt the list, and work out the effects, producing no 
more than a flip-chart summary for the project. This can be repeated for 3 different types of 
innovations. Then the exercise can be evaluated: what is the value of such case studies: i) for the 
innovation? ii) for project monitoring environmental and socioeconomic effects? Can this replace the 
IEE and EMPP? Will this increase innovators taking responsibility for where and how the source￾materials are obtained, how the innovation affects end users and consumers’ health, and what will 
happen with by-products and end of use? It could help develop more comprehensive service 
packages, e.g., water pumps with micro-irrigation and pest management. Win-win solutions that 
inspire governments and donors: innovations to serve end users and public goals. 
3.7 Overarching hypotheses
Section 3.7 addresses the three overarching hypotheses (EQHI, HII, HIII). 
101 A practical tool from ATTRA: Live-Cycle Assessment in Africultural Systems; other sources:
https://images.app.goo.gl/BaXmPi5A9nMBuvcq6; https://images.app.goo.gl/DfiSGkef1Di2ULTt9; 
https://images.app.goo.gl/qMVyvY6PvmgQVsnz9
102 FAO tools to evaluate agroecology performance. In addition, GIZ is also adapting these tools to monitor progress on agroecology
Participatory monitoring and evaluation to enable social learning, adoption, and out-scaling of regenerative agriculture 103 The ATTRA tool can be used as theoretic framework. The tool could: i) let farmers score the effect on climate change adaptation, 
using their own criteria (be it in savings in a woodlot, saved time, water security or other); ii) let the group decide what the innovation 
means for sustainable use of land, ground water; iii) at least ask questions on effects on biodiversity (on- and off-farm, about soils and 
soil cover, crop diversity, and natural vegetation and fauna), to reflect on; iv) score effects on labor quality and quantity disaggregated 
for gender; v) ecosystem services (dealing with waste/pollution from agrochemicals including emission, solar panel waste, etc.).
50 
EQc: To what extent were the interventions implemented at each WE4F RIH compatible in 
achieving impacts related to water efficiency, energy efficiency and use of renewable energies, or 
food security across the program?
This section was placed to remind us of what will appear in the Final Evaluation and as part of the 
subject of discussion of the potential longer-term results following the MTR.
3.7.1 Hypothesis I: …
EQHI: By investing in innovations at the water, energy and agricultural nexus, the pace of 
development in all three sectors will be substantially faster than if we relied on “traditional” (more 
sectoral) development programming alone. 
3.7.2 Hypothesis II: …
EQHII: By sourcing technologies and business model innovations that have already achieved early
adoption (1,000-10,000 end users), WE4F innovations are much more likely to reach wide scale 
adoption, transition to scale (10,000-1,000,000 end users) or scale (greater than 1,000,000 end users). 
3.7.3 Hypothesis III: …
EQHIII: By investing in acceleration-oriented TA and IF (including for end users to purchase WE4F 
innovations), we will substantially increase the likelihood that innovations will have the knowledge, 
tools, and resources to bring their innovations to scale. 
51 
4 MAIN FINDINGS, CONCLUSIONS AND RECOMMENDATIONS
The purpose of the WE4F Evaluation is to provide the key stakeholders with key conclusions in 
relation to WE4F’s contribution to increased sustainable food production along value chains, i.e., 
with reduced negative impact on natural resources (soil, water), biodiversity and reduced GHG 
emission (IM3)104 as well as increased income for poor men and women in rural and urban areas 
(IM4). In Table 29 lessons learned from the MTR.
104 The Evaluation Team's understanding of more sustainable agricultural production is inclusive of all effects, as this is also 
emphasized by the Founding Partners (re. comments on biodiversity and inclusion of poor / marginalized groups).
52 
Table 29: Main findings, conclusions and recommendations
EQ key words Findings and conclusions Recommendations for current phase For the extension
Relevance (section 3.1) 
EQ1a demand and 
ownership
 
 
EQ1b addressing 
key needs of 
marginalized 
groups
 
 
EQc interventions 
impact on 
environment a.o.
 
EQd enabling env. 
for marginalized 
groups
 
 
EQ1a: Local ownership indicates one degree of relevance and is 
evident in that most innovation companies are incorporated in 
participating countries; they may also operate in other adjoining 
countries in the region. However, half of the innovations do not 
reach their own year 1 target for end user numbers. Continued 
relevance will be demonstrated with rising numbers of end users 
into the future. Since in SSEA 94% of end users are in the lower 
income quintiles (Q1 and Q2), and in MENA three quarter of the 
end users are found in this category of poorer end users this is 
confirmation that WE4F, through the innovations, are reaching the 
base of the pyramid.
While in SSEA there is evidence of a relatively high level of 
women’s participation; this is not found in the MENA region and 
needs to be addressed.
EQ1b: In terms of key needs of end users, there is evidence of 67% 
of innovations make products that are relevant to their end users 
and reliant on client feedback. Overall, the increasing numbers of 
end users are evidence that that the innovations respond to key 
needs; with an increasing demand for most of the innovations, the 
innovations are found to be relevant to end users including 
marginalized groups. 
EQc: In this first stage finding of the evaluation, the participation 
of the different countries as well as the well spread balance of the 
innovation focus areas shows broad relevance. To date, the broad 
relevance of the WE4F Program is evidenced through its country 
participation which includes 9 countries in the SSEA region, 10 in 
the MENA region and 7 in the SCA region. In interviews with 
RIHs and innovations, the focus areas are regarded as highly 
relevant to these regions and the impacts are assessed in sections 
below
EQd: Both RIHs focus on the extension of financial services and 
some strategies could enable more women to benefit more from the 
innovations. The results in the MENA region show that much more 
needs to be done to remove barriers to women participation. 
EQ1a: WE4F needs to strengthen the enabling 
environment to address the barriers that women face 
both as innovations and as end users. This is being 
achieved with the innovations and financial 
services, but could also drive other stakeholders, 
e.g., government, to demonstrate that engaging 
more women and make them benefit, it requires 
them to be given equal freedoms and equal wages 
(on wages, this can be addressed in innovation 
companies).
 
EQ1b: WE4F needs a promotion strategy to show 
more women end users can succeed. Currently 26% 
of end users are women, rising to 29% among the 
poorer. The prioritization of food security may draw 
more women into production. Consideration should 
also be given to technology of which ergonomics 
are adapted to women (smaller in size), and 
technology benefits for women, e.g., biodigesters (a 
heavy investment that is often paid for by men) 
benefit women in terms of health and cooking-fuel 
time saving, but considering the time spent on 
charging the biodigester (needs a lot of water) 
women could also benefit from bio-slurry, for food 
crops.
Research into successes in raising participation of 
marginalized groups (including women) should be 
undertaken to identify innovations (in terms of 
costs, sizes, quantities) which have proven to be 
most successful in meeting the needs of these 
groups. 
 
53 
EQ key words Findings and conclusions Recommendations for current phase For the extension
(Program level) Structured support to innovations on the inclusion of women could 
help address this challenge.
Conclusion: WE4F is seen as highly relevant to the needs of the 
regions and countries in which it operates; it builds on existing 
institutions and the rising number of end users shows that the
innovations are meeting smallholders’ interests. The challenges 
relate to scaling and greater participation by women.
Coherence (section 3.2) 
EQ1c contributing 
towards or 
contradict SDG
 
 
 
 
EQ1c: Overall there is internal cohesion between the key focus 
areas of innovations and the strategic objectives of WE4F. There is 
real interest expressed in interaction between innovations 
themselves within WE4F. 
WE4F provides avenues to reach particular SDGs in relation to 
socioeconomic, environmental, biodiversity and lowering of 
emissions in agriculture and broadly towards eliminating poverty 
and hunger. There are, however, some contradictory aspects of 
SDG which may be ‘off-site’ i.e., elsewhere in the innovation 
lifecycle / food commodity value chain, affecting others than the 
end users including marginalized groups. 
Conclusion: WE4F is broadly aligned in coherence with a number 
of SDG and focuses in on sustainable agriculture, a key factor in 
developing regions. Such broad alignment, however, may not lead 
to effective coordination with national policies to achieve these 
SDG and coordination may need to be explicit.
EQ1c: Since SDGs are integrated into country plans 
the RIHs could initiate discussions with policy 
makers and civil society to achieve mutual 
coordination and support for innovations.
Attention should be given to SDG (SDG2: Zero 
Hunger, SDG1: No Poverty, SDG15: Life on Land, 
SDG5: Gender Equality, SDG10: Reduced 
Inequality, SDG13: Climate Action, SDG9: 
Industry, Innovation, and Infrastructure). 
RIH interventions could, at a national and regional 
level, make the case to governments, by showing 
how innovations help advance particular SDG, and 
how governments can create a more enabling 
environment for such innovations as these help to 
attain SDGs. 
 
Effectiveness (section 3.3) 
OC1, OC2, OC3
innovations’ 
capacity
PART 1: QoS, and 
all 3: TA, IF, EE
Indicator QoS: 
score
The QoS score average follows a fairly similar pattern in all three 
hubs: near or above the 80% mark for TA and IF, and below 70% 
on the subject of EE.
Pre-award support: all respondents found that the process 
positively impacted their organization’s administrative and 
financial systems.
Technical Assistance (TA) is receiving very good scores in all 
three regions (in SSEA, MENA and SCA resp. 8.1, 8.2 and 8.6); it 
is mostly used for business planning, -financing and marketing.
Enabling Environment (EE) there is a critical need for improving 
the enabling environment, considering the large number of 
comments, from all three regions, that point to a wide variety of 
barriers, including many references to registration and 
EQ1d: 
• Use trainers that have experience in adult 
learning so that sessions make more use of 
participants’ own expertise, especially on the 
subject of removing bureaucratic hurdles; such 
a training could help innovations to produce 
their action plan to advocate, to address 
bureaucracy.
• Collect data from trainees, more systematically, 
to evaluate training: participants, facilitation 
quality, content relevance and quality, 
usefulness.
54 
EQ key words Findings and conclusions Recommendations for current phase For the extension
EQb TA, IF, EE 
differences
EQ1d overcoming 
organizational 
capacity, barriers
EQa TA deemed 
useful by 
innovations 
EQn
support improved 
ability to more 
effectively target 
end users
environmental compliance (“bureaucracy”), import and export 
barriers, lack of finance, and political instability
Monitoring & Evaluation: readiness for data collection is good, 
but to an extent; it is not that easy.
EQ1d and EQa: The TA appears to help improve innovations’ 
capacity on organizational/operational matters and innovations find 
it useful. Training on technology and input access is found less 
useful. The same for EE: removal of bureaucratic hurdles1
. 
EQn: Both SSEA and MENA RIH provided training on the ‘Base 
of the Pyramid.’
QoSS shows generally high satisfaction with the TA and IF 
services; for EE services there is no dissatisfaction but the marks 
for understanding needs are lower
• Continue the ad hoc support to the innovations, 
this support could also be organized if many 
innovations need the same support a TA could 
be developed to provide it.
EE is important and needs to be improved. 
Innovations’ own activity in policy advocacy (, 
through BMO) could be supported as such 
organization better understanding the local context,
e.g., lobby together for biomass-related regulations. 
Services in general: use more local TA especially 
for EE work 
EQn: The Evaluation Team will look into training 
materials, in the next phase, with a focus on how 
gender is mainstreamed in RIH country 
strategies/plans, in work on Enabling Environment, 
Investment Facilitation and Technical Assistance: 
• introducing and understanding of the gender 
context (e.g., by using data from relevant 
indexes for the region or country), highlighting 
gender gaps, e.g. in business investment and 
financing, technology (design), input access 
(check the gender yield gap), and in 
bureaucracy, but also including “cultural 
barriers” in the understanding of the context, to 
find solutions that address these, e.g., 
innovations can adapt training materials, 
location, timing and content to women
• training methodology (adult learning, using 
women’s expertise e.g., on dealing with 
barriers)
• gender mainstreaming business plan, on job and 
pay equality, and tailoring innovations to needs 
of more marginalised groups including women 
(and the business case to do this).
Pre-award service: there is 
likely a trade-off between 
the choice of (often more 
established and larger) 
innovations that are capable 
to grow and contribute 
relatively much to WE4F 
targets, and companies that 
are smaller and – even as 
they grow – contribute little 
to WE4F targets; consider 
two different entry systems: 
tailored for smaller and 
larger companies
55 
EQ key words Findings and conclusions Recommendations for current phase For the extension
PART II: funding
KPI9 external 
investment
Gin33 co-funding
EQ1p balance 
between public 
engagement and 
private support
Gin41 using fin. 
guarantee 
instruments
Gin20 # external 
partnerships
EQ1h
PPP
KPI9: With 19 out of 81 innovations having mobilized 8.6 million 
USD external investment.
EQp: TA and access to finance link not yet assessed.
Gin33: Innovations provide 153% of the funds disbursed by WE4F
so far (program disbursement is about halfway of the funds 
committed).
EQ1p: While the innovations are mainly companies, they have a 
public orientation, but one tied to market opportunities to ensure 
their financial sustainability.
Gin40 & Gin41: From the survey it is clear that many contacts 
were made with investors, and a beginning is made with data 
collection to link this to funding mobilized. Only a few innovations 
used a financial guarantee mechanism, but most innovations 
appreciate WE4F investment facilitation activities, and some 
already note a positive effect. 
Gin20: Partnering appears at an early stage, but progress is being 
made, and 91 partnerships have been created.
EQ1h: From the KIIs and the innovation survey there is evidence 
that innovations know what a PPP approach is, but they have yet to 
build this into their partnering and financial strategy.
 
PART III: EE
EQd
enabling 
environment work 
compatible with 
impact on 
marginalized 
groups
EQm enabling 
environment work 
deemed useful
Gin28 end user 
financing
EQd: Both RIHs focus the enabling environment work on financial 
services. On the subject of financial services, some ideas they have 
(when applied) will or are likely enable women to benefit more 
from the innovations. However, the results in the MENA region 
show that much more needs to be done to remove barriers for 
women.
EQm: Some survey feedback suggests that some initiatives in the 
environment were found helpful e.g., where donors step in to raise 
the issue of bureaucratic hurdles.
Gin28: The number of poorer end users accessing end user 
financing is at this stage still very low. Financial support may be 
built into pricing or credit, but the low-level direct end user 
financing is a weakness in WE4F. 
EQd: Several recommendations are already made in 
section 3.3.1 with regard to understanding the 
gender context and adapt any training to this. The 
same would apply to other activities (beyond 
training); understanding the gender context should 
be integrated at the conception of all activities.
 
PART IV: effect 
on the innovations 
business 
New indicator:
innovations’ end 
user growth
New indicator: Overall, half of the innovations meet or surpass 
their target; that is more than the expected 40% (new indicator).
Continue with reflection on end user growth models 
could be useful, especially for new innovations, to 
opt for a relevant growth model and realistic targets
and document and share discussions held on these 
topics during the monthly calls with innovators
56 
EQ key words Findings and conclusions Recommendations for current phase For the extension
Gin4 gross sales
EQ1o – Financial 
and social 
sustainability of the 
organizations 
supporting the 
innovation?
Gin4: The gross sales year 1 result is as yet incomplete; also, sales 
may have been reduced due to COVID-19 and not provide a basis 
for reliable projection. Based on available data, it is possible that 
the LOP target can be met.
EQ1o: There seem to be few alternative sources of support or 
finance at this stage, apart from the (successful) mobilization of 
financial support with the help of WE4F. 
Efficiency (section 3.4) 
PART I: Program 
design
EQi effort & 
resources balance
PART II: 
Partners’ 
involvement
EQg founding 
partners – RIH 
lessons, alignment 
PART III: MEL 
system
EQ1k resources 
used to max. impact 
on env. & 
biodiversity
EQi: From limited data available (presented in 3.3.2, re. Gin33), it 
appears that innovations’ co-funding is in good proportion to 
(153% of) what WE4F has disbursed so far.
Conclusion: WE4F has shown particularly effective in assisting 
innovations to raise external funds multiple times larger than the 
WE4F grant; on this it compares favorably with other programs.
The RIH are reported by innovations to be efficient (save 
recommendations on streamlining pre-award, MEL)
WE4F is efficient as a program in achieving the greatest impact in 
the deployment of relatively limited resources available and 
secondly in terms of the costs of fund management in comparison 
with other Challenge Funds. 
EQg: There are reports from RIH of synergies between Program 
goals and donor country initiatives and other openings; there is 
opportunity to expand on such experience.
Conclusion: Founding partners have assisted efficiency by 
assisting on specific issues of enabling environment
EQ1k: The MEL system, where it applies to innovations’ impact 
on environment, is still in development. Training has been provided 
on monitoring environmental impact on water, energy, emission, 
and environmental monitoring tools are introduced and used
Conclusion: The MEL system produces an impressive amount of 
quantitative and qualitative data which provides an overview of the 
whole leading to greater efficiencies; it can improve indicator 
definitions (incl. targets) and streamlining data collection.
EQg: In making a full assessment of the various 
efficiencies of WE4F there should be continuing 
data exchange between M&E and the evaluation.
MEL system:
1.improve the PMEP document, including the 
addition of navigation/headings, and an 
explanation on how targets are set at different 
levels (innovation level, hub level, program level) 
and how targets may be changed, or indicator 
standards and methodology refined. 
2.update and clarify indicators. 
3.if not already intended (the Evaluation Team is 
yet to verify), it is recommended that (summaries 
of) relevant MEL information be shared and 
reflected on in bootcamps, to reflect on; this can 
yield valuable feedback for WE4Fme; this can 
include a reflection on the next point:
4.Monitoring by innovations for WE4F could be 
improved, in terms of making indicators clearer 
(do all innovations understand the indicators, e.g.,
EBITDA and carbon emission measuring), and 
use them in the same way, making monitoring 
relevant for innovations themselves, making 
monitoring leaner, more efficient if possible 
 
MEL system: 
• follow-up on feedback 
from bootcamps on how 
M&E is done
• more engagement when 
developing and setting 
targets. As WE4F is 
extended there should be 
guidelines developed to 
help the innovations set 
targets (not just the end 
user targets discussed in 
this section) 
• assistance for weaker 
performing innovations, 
with possibly more 
careful target setting
57 
EQ key words Findings and conclusions Recommendations for current phase For the extension
Impact baseline (section 3.5) 
IM1
KPI2 # end users
KPI2: The end user targets set at the RIH level are being met and 
passed by large margins. There is however considerable variance 
between innovations. 
Continued reflection on target setting to be 
considered to ensure targets set by the 
innovations are realistic (neither too high nor too 
low) especially when new innovations join 
WE4F.
Further reflection and 
discussion with 
innovations in setting 
targets (end user and all 
targets).
IM2
Using the 
innovation
IM3
KPI3 food 
produced 
 
KPI4
food processed
KPI3: Production targets are being met but from larger scale 
farmers in the water and energy sector. The target was passed by 
more than tenfold.
Medium-large scale farms are responsible for 69% of the food 
produced.
KPI4: At the time of the MTR there has been more than 2000-ton 
food processed by only 3 innovations. 
Currently is it not clear if this in issue regarding reporting or it not 
being a focus of the innovations that are part of the project.
Despite the enormous growth of food processing in the food supply 
chain, this is a relatively undeveloped aspect of food production 
and distribution among the innovations.
KPI3: WE4F should continue to focus smaller 
farmers in SSEA
The reasons for low levels of food processing 
should be discussed to understand the challenges 
involved.
Particular attention must be made to strengthening 
food production at the base of the pyramid, with 
smallholder farmers.
As WE4F moves forward 
the data on food production 
needs to be rigorously 
examined.
Further discussion is needed 
regarding food processed 
due to the marketing of 
food.
EQ1e water 
efficiency, -access, 
-targets & other 
effects
KPI6 water 
efficiency, -access
EQ1e: There is considerable evidence of increased water 
availability and accessibility and through use of solar water pumps. 
Associated is concern of overuse of water resources and falling 
water tables. Water savings are not yet being achieved on any scale. 
From end-user surveys most respondents reported no change, or 
increased volumes of water used while a small minority mentioned 
savings being achieved. 
KPI6: The data are insufficient, to fully understand the water 
savings of the innovations in two in terms innovation reporting and 
clarification on how water is saved by the innovation.
EQ1e – WE4F needs to give closer and systematic 
attention to the issue of water savings and the 
challenge of water overuse and falling water tables. 
The International Water Management Institute 
(IWMI) is setting procedures for groundwater 
responsive solar irrigation.2
58 
EQ key words Findings and conclusions Recommendations for current phase For the extension
EQ1f energy 
efficiency, -use, -
targets & other 
effects
KPI5 energy saving
KPI5 energy saved
EQ1f: Most innovations passed their year 1 targets even as for all 
innovations there is evidence of energy savings. 
Energy Transition: In SSEA 69% end users changed their energy 
source; in MENA no energy source changes were report (which 
was a move away from fossil fuels)
There is unevenness among innovations and there is greater savings 
in SSEA for smallholder farmers and in MENA for medium-large 
farmers. 
At this stage there are no reported unintended consequences. 
KPI5: From a limited number of innovations a considerable 
amount of energy is saved at the small holder farms despite this 
total energy saved is 68% of the LOP target. 
Progress is being made in energy savings, but targets are not being 
met in the case 5 of 13 innovations for which data is available.
EQ1f: This is a major achievement, and the success 
of this intervention should be written up.
 
 
Success stories from innovations with significant 
savings should be written and circulated.
There should be more 
tracking of energy transition 
from fossil fuels to 
renewable sources
EQ1g agricultural 
productivity
EQ1g: A proportion of end users reported some impact on 
productivity and identified what change in production methods led 
to it. From the end user surveys improved yields were reported due 
to ‘reducing reliance on rain’, ‘better water management/irrigation’ 
and better production practices. 
Without quantitative yield data, productivity cannot be quantified.
EQ1g: to initiate in this phase, and fully implement in the next phase: 
Apart from collecting production data (useful, and necessary for KPI3), there is a 
need to focus at farm level, and from a small selection of end users (farms) to 
collect specific data on crop yields; changes in crop yields need consistent 
examination over time to identify patterns and causes of change. Doing this, a 
distinction can be made between farms from end users at the base of the pyramid 
and larger farms; this could then also be compared with available gender yield gap 
data (synergy).
IM4 
EQ1i incomes of 
smallholder & 
women farmers
KPI7 income
 
EQ2 attribution to 
WE4F, how much
EQ1i: If only looking at the Dexis end user survey data, it can be 
found that nearly all end users report an income increase, and this 
proportion is as high or higher for women.
KPI7: There is some contradiction and uncertainty in what the data 
on income increase represent. End users surveyed by Dexis provide 
a more positive picture than the data from WE4F – and the latter 
may be incomplete.
From the end user survey, 88.8% of the respondents shared their 
income increased as a result of the innovation a high level of end 
users reported women’s income also increased.
From the M&E data 7% of the total end users that have experienced 
an increase in income.
But at this time None of the end users surveyed were able to secure 
or receive financing to buy the innovation even though there were 
some beneficiaries to financing in the M&E data
EQ2: Since WE4F provides only a proportion of the funds 
employed by innovations WE4F inputs are regarded as 
contributions towards results. Further data collection is needed to 
assess the relative contributions of various donors and partnerships.
Further end user surveys are needed to confirm 
increases in income and the reasons for such 
changes.
 
59 
EQ key words Findings and conclusions Recommendations for current phase For the extension
Sustainability (section 3.6) 
KPI1 profit (>8% 
EBITDA) for >8% 
of innovations
Gin10 number of 
new jobs
KPI8 tools to 
monitor environ 
protection
Gin6 total GHG 
emission saved per 
year
Gin45 % 
innovations use 
methods to monitor 
water, biodiversity
EQ1l positive/
negative impacts 
and poverty
 
EQ1m negative 
feedback 
marginalized 
KPI1: The KPI1 target is reached: 33% of innovations (more than 
the target of 8% of innovations) have passed the KPI1 threshold, 
with an EBITDA margin of >8%.
In interviews, a larger fraction of respondents reported to be (very) 
confident of financial sustainability.
Gin10: The job creation target is unlikely to be reached: so far, an 
average of less than 10 jobs per innovation. The innovations in 
energy seem to be giving the greatest opportunity to women.
KPI8: not explored (other than IEE, see below).
Gin6: It appears that smallholder farmers and women are 
disproportionally contributing to emission saving. Innovations with 
focus on water & energy are contributing more than those with only 
energy focus. Additional analysis of the data, innovation by 
innovation, could reveal trends and patterns in reducing GHG
emissions.
Almost all the energy saved is from the MENA region.
Gin45: In SSEA the area improved (about 20,000 ha or 24% of 
innovations’ target) is reasonable progress, given the small average 
farm sizes and work in irrigation. In MENA however, the 
innovations only reach 12.5% of innovations’ target area; the target 
area (LOP) seems overly optimistic.
EQ1l: The end user surveys report that the results include some 
positive impact on marginalized groups (the poor, women, youth, 
ethnic minorities, rural communities) in terms of income (not in 
terms of jobs, or availability of community natural resources on 
which such groups disproportionally depend). The IEE also 
includes a negative effect (using forest resources governed by an 
indigenous community).
EQm: The data from interviews with innovations and end users, 
and the IEE documentation, all give a mixed picture of positive and 
negative (unintended) effects on a variety of environmental issues.
KPI1 reporting needs to be more comprehensively 
reported with specialized TA support as needed. 
Gin10: Success stories in job creation should be 
recorded and distributed. 
KPI8: Additional surveys and tracking will be 
needed to follow up with KPI8 (measures of 
protection of water or biodiversity)
As WE4F moves forward more data over time is 
needed and should be share on how the GHG 
savings are achieved by different innovations. 
Changes in land use need to be assessed. 
Gin6: Further analysis of the data, innovation by 
innovation, could reveal trends and patterns in 
reducing GHG emissions.
EQ1l, EQ1m, EQ1n: Further research will be 
undertaken in this regard in the future as part of the 
final evaluation. For this, the Evaluation will use the 
Lifecycle Assessment (LCA), produce a simplified 
analysis tool, and assess the effects for a selection of 
innovations, including simple case studies and a 
small selection (1-2) of the innovations’ end users.
Gin10: Further discussion of 
progress towards this target
 
60 
EQ key words Findings and conclusions Recommendations for current phase For the extension
EQ1n negative 
trade-offs socio￾economic+
 
EQo TA impacts,
via innovators, on 
environment and 
climate, WRM, 
biodiversity
EQ1n: There was no report of negative feedback from 
marginalized groups from participating in an innovation but there 
are not data on effects (also some reported and potentially negatives 
ones) on marginalized groups not participating. There is no way to 
assess the importance of any effects. 
EQo: WE4F provided support to monitor water or energy use – that 
is part of the environmental impact (chapter 5). Aside from that, the 
IEE is a fair instrument to identify some environmental effects, 
with a focus on on-site, more immediate effects; negative effects 
are not necessarily addressed in the plan. 
Conclusion on IEE and EEMP – The IEE and EMMP instruments 
are quite demanding, producing a considerable amount of reporting 
to assess. In spite of this, IEE and EMMPs completed by 
innovations and responses from RIH indicate that most mitigations 
are focused on direct (local) effects, rather than lifecycle effects of 
innovations; effects further afield are not identified or left aside as 
‘externalities’. The EIA compliance approach invites innovations to 
get compliance done but is not sufficiently compelling innovators 
to look beyond the direct effects of interventions and technologies 
and integrate sustainability more holistically in the company’s core 
business (social value). In addition, IEE duplicate EIA 
requirements that governments (may or should) have.
EQo: Based on the LCA and/or agroecology tool, it 
is suggested to develop a simple checklist with 
some qualitative questions and some scores 
(quantitative); it must be short and tailored for an 
innovation and its context, then tested in a small 
group: 1 innovation, 2 end users and 1 WE4F expert 
or consultant in a role of advocate for other 
(especially marginalized) stakeholders or affected. 
The group will use and adapt the list, and work out 
the effects, producing no more than a flip-chart 
summary for the project. This can be repeated for 3 
different types of innovations. Then the exercise can 
be evaluated: what is the value of such case studies: 
i) for the innovation? ii) for project monitoring 
environmental and socioeconomic effects? Can this 
replace the IEE and EMPP? It could help develop 
more comprehensive service packages, e.g., water 
pumps with micro-irrigation and pest management. 
Win-win solutions that inspire governments and 
donors: innovations to serve end users and public 
goals.
Overarching hypotheses (section 3.7) 
 To be examined in the Final Evaluation Report. 
61 
ANNEX 1: THE EVALUATION SOW
SECTION C – DESCRIPTION/SPECIFICATION/STATEMENT OF WORK
C.1 Introduction
Water and Energy for Food: A Grand Challenge for Development (WE4F) is a multi-donor funding 
program that aims to increase the sustainability of agricultural food value chains and address 
environmental and climate adaptation in developing countries and emerging markets by expanding 
the sustainable scale of small and growing enterprises (SGEs) that impact the sectors of food and 
water, food and energy, or all three sectors at the nexus (food, water, energy) – with a particular 
focus on the poor and women. As stated in the partner program approval document of WE4F, a mid￾term review and final evaluation is required of the Water and Energy for Food: Grand Challenge 
Development. The mid-term and final performance/pseudo-impact evaluation should provide an 
overall assessment of WE4F that not only makes transparent the challenges met and results achieved, 
but also contributes to content-related and systematic learning by answering questions of “how” and 
“why” the interventions of WE4F were successful or were met with challenges. While pursuing this 
objective, both the mid-term and final performance/pseudo impact evaluations should be designed, 
structured, and implemented to assess the WE4F program and its interventions in alignment with 
OECD-DAC evaluation criteria including relevance, coherence, effectiveness, efficiency, impact, 
and sustainability. 
C.2 Background 
Four founding partners (FPs) have embarked on the Water and Energy for Food: A Grand Challenge 
for Development (WE4F) program as a joint international initiative including: the Deutsche 
Gesellschaft für Internationale Zusammenarbeit GmbH (GIZ) on behalf of the German Federal 
Ministry for Economic Cooperation and Development (BMZ), Sweden through the Swedish 
International Development Cooperation Agency (Sida), the Ministry of Foreign Affairs of the 
Kingdom of the Netherlands (MFA-NL), and the United States Agency for International 
Development (USAID). WE4F is an innovation and acceleration initiative that seeks to build on the 
efforts and achievements of the Securing Water for Food: Grand Challenge Development (WE4F) 
and the Powering Agriculture: Energy Grand Challenge for Development (PAEGC). WE4F and 
PAEGC aimed to source, select, and accelerate innovations that would enable the production of more 
food with less water or less energy. 
Launched in 2012, PAEGC and WE4F aimed to address existing trends and challenges in the water￾energy-food nexus through the scaling of scientific and technological innovations which aim to 
improve energy and water efficiency in the agricultural sector, while simultaneously enhancing food 
production and increasing employment and income opportunities for women and men living in 
poverty. PAEGC and WE4F pursued this goal by focusing on the provision of traditional grants, 
technical assistance, and financed guarantees which supported the development and scaling of these 
innovations. 
PAEGC was a partnership launched by USAID, the Government of Sweden (Sida), the Government 
of Germany (BMZ), Duke Energy Corporation and the United States OPIC with the overall goal “to 
support new and sustainable approaches to accelerate the development and deployment of clean 
energy solutions for increasing agriculture productivity and/or value in developing countries”. Its 
objective was to scale up innovations and accelerate the pace at which renewable energy is supplied 
to the agriculture sector in developing countries. Through two calls for innovations, 24 innovators1 
and their clean energy solutions were selected and supported through the PAEGC East Africa 
regional innovation hub in Nairobi, Kenya, providing the basis for the use of additional regional 
innovation hubs in the WE4F. 
62 
Since 2013, USAID, Sweden through Sida, the Ministry of Foreign Affairs of the Kingdom of the 
Netherlands supported innovators through WE4F which identified and accelerated water related 
science and technology innovations and market-driven approaches to help agricultural producers. 
Since then, WE4F’s 80 innovators have helped save or reallocate more than 19 billion liters of water 
to the food value chain. One key success factor has been a careful innovator selection process, 
supported by tailored technical assistance to innovators by local consultants (local vendor system). 
The Water and Energy for Food: A Grand Challenge for Development seeks to build on the lessons 
learned from PAEGC and WE4F by adapting their approaches and creating a new model comprising: 
1) adaptations to PAEGC and WE4F’s organizational structure and 2) an expansion of innovation 
support and services beyond that of PAEGC and WE4F. 
This new model reflects adaptations to the organizational approaches of PAEGC and WE4F by 
implementing WE4F interventions through regional innovation hubs. The WE4F Founding Partners 
recognize the constraints that exist and prevent the high volume of investments which are made in 
the private sector from reaching the many innovations in the market seeking wide-scale adoption. 
WE4F aims to have at least 8% of funded innovators, of which at least one-quarter are led by 
women, successfully market their WE4F supported innovations to reach wide-scale adoption (greater 
than 100,000 customers). In order to do so, USAID and BMZ (the lead program implementers) have, 
together with the other WE4F Founding Partners, developed Regional Innovation Hubs (RIHs) to 
provide a number of services to help innovators accelerate their progress in order to reach wider￾scale adoption. This includes three RIHs which will be contracted to third parties and overseen by 
USAID and two RIHs managed directly by BMZ. 
Additionally, this new model reflects an expansion of program interventions and engagement with 
innovations which includes new areas of innovation support such as: financial brokering including 
end-user financing, enabling environment support, and capacity development. All activities which 
are implemented at RIHs fall within 6 pillars of hub activity including: grants management, 
investment facilitation, technical assistance, enabling environment work, capacity development, and 
end-user financing. Additionally, the program remains committed to incorporating cross-cutting 
issues of gender, poverty, local ownership, and environment/climate/biodiversity into the 
programming and implementation of these activities. 
At each RIH, the Regional Advisory Committee (RAC) provides advice on selection of innovators, 
assessment of progress and determination of benchmarks to make sure that the Founding Partners 
support those innovations that most likely will achieve success. WE4F’s cross-cutting issues are also 
prioritized during these processes by assessing applicants’ proposed impact on gender, local poverty, 
and environmental sustainability or biodiversity. Additionally, strategies aligned with these cross￾cutting issues are incorporated into innovators’ scaling strategies and assessed throughout the life of 
the program. 
The goal of WE4F is to expand the sustainable scale of small and growing enterprises (SGEs) that 
impact the sectors of food and water, food and energy, or all three sectors at the nexus (food, water, 
energy) to increase the sustainability of agricultural food value chains and address environmental 
issues and climate change adaptation in developing countries and emerging markets. WE4F 
innovators continue to scale innovations which are designed to support the program’s overall goal. 
The focus areas of innovations supported by WE4F: 
• water and food: improving food productivity, food value chain sustainability, and climate 
change adaptation through the efficient use of water resources. 
• energy and food: improving food productivity, food value chain sustainability, and climate 
change adaptation through the efficient use of energy resources and renewable energy. 
• water, energy and food: improving both water and energy efficiency including the use of 
renewable energy along the food value chain to improve food productivity, food value chain 
sustainability, and climate change adaptation 
63 
The innovations in these areas may include improved energy or water-efficient technologies for: 
aggregation, storage, farm inputs, farm production and mechanization, residential and commercial 
energy production, value-added processing, water capture/storage, water reuse/efficiency, or water 
salinity. WE4F also seeks to support business and financial innovations that enable the increased 
adoption and dissemination of associated technology solutions. 
The WE4F Founding Partners (USAID, Sida, BMZ and MFA-NL) share the common goal of 
advancing international development through improved access to sustainable water and energy 
sources for agricultural applications. Values that are common to the Founding Partners include: 
sustainability, efficiency, sourcing of market-based solutions, gender inclusion, climate adaptation 
and mitigation, benefit to poor people, and a commitment to not only avoid negative effects on water 
resources, biodiversity, and other environmental harms but also strive to have positive impacts on 
these central areas of the water-energy-food nexus. 
A. Description of the Problem, Development Hypothesis(es), and Theory of 
Change
Through WE4F, the Founding Partners hope to source and accelerate high potential solutions that 
will have multiplier effects at various levels of a country’s economy and food value chain. The 
following three hypotheses are both meaningful and practical measurements of WE4F’s development 
impact potential:
1.By investing in innovations at the water, energy and agricultural nexus, the pace of development 
in all three sectors will be substantially faster than if we relied on “traditional” development 
programming alone. The basis for this hypothesis is that innovation plays key roles in creating 
economic growth opportunities through entrepreneurism, investment, research and development, 
partnership, technology commercialization, and widespread technological adoption – including 
adoption by the poor. Adoption of WE4F innovations will either have direct economic benefit on 
end users by increasing efficiency and/or profitability, or indirect economic benefit by improving 
food security writ large. To explore this, WE4F intends to utilize some key metrics (e.g., USD 
spent per beneficiary), comparing WE4F to “traditional” development programming.
2.By sourcing technologies and business model innovators that have already achieved early 
adoption (1,000-10,000 end users), WE4F innovations are much more likely to reach wide scale 
adoption {transition to scale (10,000-1,000,000 end users) or scale (greater than 1,000,000 end 
users). The basis for this hypothesis is that a sufficiently large number of proven technologies 
already exist, and many are already on the market. These technologies require adaptation and/or 
validation for local markets and/or in-depth support for wide scale growth and distribution. Early￾stage innovations require a higher level of support, testing, and – most importantly – time. Based 
on our analysis, WE4F investments are best placed at least at the early adoption stage. In 
addition, a lesson learned from previous Grand Challenges, only 10-20% of innovations 
supported from previous Grand Challenges have strong potential for wide-scale adoption, even 
after having been taken through a rigorous evaluation process. Using this lesson learned, WE4F 
has designed a milestone-based tiered grant structure that will be used in select RIH and will only 
continue to fund the most promising innovations over time. These milestones are related to 
business scaling and financial achievements as well as objectives related to WE4F’s cross-cutting 
issues including gender, poverty, and the environment/climate/biodiversity. Other RIHs will not 
follow the milestone-funding structure and the evaluation can compare similarities and 
differences in both innovator growth and end-user impacts. We expect that the selected external 
evaluator will administer a survey with 5 groups (WE4F innovators that received more than 18 
months of milestone-based funding; WE4F innovators that received between 1 year to 18 months 
of milestone-based funding; WE4F innovators that received more than 18 months of non￾milestone based funding; WE4F innovators that received between 1 year to 18 months of non￾milestone based funding; WE4F non-innovators2) to help determine whether or not there is 
evidence to support this hypothesis.
64 
3.By investing in acceleration-oriented technical assistance and investment facilitation (including 
for end users to purchase WE4F innovations), we will substantially increase the likelihood that 
innovators will have the knowledge, tools, and resources to bring their innovations to scale. The 
basis for this hypothesis is that grant financing alone will not be enough to bring any innovation 
to scale. Partnerships with the private sector, government, NGOs (for distribution), and others are 
necessary to (among other things) accelerate business-to-business linkages, catalyze investment, 
improve distribution, and ultimately stimulate adoption. We expect that the selected external 
evaluator with administer a survey with 3 groups (WE4F innovators at USAID RIHs that 
received 2+ years of TA; WE4F innovators that received 1 year or 18 months of TA; non-WE4F 
innovators that received TA from other non-WE4F actors) to help determine whether or not there 
is evidence to support this hypothesis. 
B. Summary Strategy/Project/Activity/Intervention to be evaluated
The mid-term and final evaluations focus on evaluating WE4F at two levels: the WE4F program 
level and the WE4F-support innovation level. Overall, the WE4F program will in the end have 
supported at least 100 innovations in 40 eligible OECD/DAC 1-4 countries. Expected outcomes of 
WE4F: 
1) At least 8 proposals/innovations that improve water and/or energy efficiency including the use of 
renewable energy in the food chain have been adopted, brought to scale and/or commercialized by 
businesses in least 8 developing and low-to-middle income countries. 
2) Demand for and availability of these innovations have increased. 
3) More food has been produced with less water or more water has been made available for food 
production in the eligible countries (25% resource efficiency improvement by the program 
innovations as compared to standard practice in the implementation countries). 
4) More food has been produced and/or processed with more efficient or renewable energy (12.5% 
resource efficiency improvement by the program innovations as compared to standard practice in the 
implementation countries). 
5) This program will also contribute to increased water-related and/or energy-related adaptation to 
climate change.
6) Impact indicators include (see WE4F PMEP for specific program indicators and indicator targets): 
a) Share of supported innovators that successfully marketed their climate friendly, energy and/or 
water efficient innovations with profit. 
b) Number of smallholder farmers and other end users using energy or water efficient WE4F 
innovations in their activities
c) Total mass of food produced as a result of WE4F innovations 
d) Total mass of food processed as a result of WE4F innovations 
e) Total amount of energy saved in the food value chain as a result of the use of WE4F 
innovations 
f) Total volume of water consumption reduction in the food value chain as a result of the use of 
WE4F innovations 
g) Number of smallholder farmers and other end users that experience an increase in income
h) Share of innovators that use tools, methods or processes to monitor the protection of water 
and/or biodiversity 
i) Value of investment in US Dollars that WE4F innovators have mobilized from external sources 
65 
j) Number of strategies, guidelines, or projects of international, regional or local organizations 
adopting and disseminating lessons learned from WE4F publications, events, or presentations 
WE4F will provide the external evaluator with all awardees, RIH, and Secretariat data as well as 
overall program data that were collected on a semi-annual and annual basis. The WE4F program 
directly feeds into the overarching Development, Democracy, and Innovation (DDI) Monitoring, 
Evaluation, and Learning (MEL) Framework as well as the BMZ MEL Framework. WE4F’s MEL 
processes enable timely and consistent collection of comparable performance data in order to make 
informed program management decisions. In line with the GCD MEL Operational Plan, the WE4F 
PMEP fully incorporates the DDI Results Framework and tracks and reports on indicators at three 
levels: 1) the meta-level (enabling analysis across GCDs); 2) the program-level (WE4F level); and 3) 
awardee-level.
To facilitate awardee reporting, each awardee works with the relevant RIH on overall compliance, 
data collection, quality control, and to facilitate the collection of results data from innovators and 
entry of validated results into the Salesforce online platform. In some cases, the RIH conducts field 
visits to verify awardee achievements and assist with addressing any shortcomings. A milestone is an 
intermediary result on the way to achieving the overall objective. In the case of WE4F, milestones 
are used to determine – among other things – the awardee’s ability to achieve wide scale with their 
innovation. Each awardee works with the Regional Innovation Hub to establish quantifiable 
milestones. Importantly, the achievement of milestones determines future WE4F funding. 
As mentioned in the introduction, WE4F innovators are not required to develop individual MEL 
Plans. However, (for USAID RIHs) each awardee’s Acceleration Work Plan has a MEL section and 
describes the quantifiable milestones by which the Founding Partners can evaluate performance. 
These milestones have been tracked against timelines and resources. However, as with any 
innovation programming, WE4F knows that some innovations fail. Therefore, if at any annual 
review an innovation is no longer meeting agreed upon milestones and matching fund requirements, 
Water and Energy for Food may not continue to fund it. WE4F will also provide the external 
evaluator with all awardee Acceleration Work Plans.
C.3 Evaluation questions
Based on the performance of the innovations that WE4F funded and/or supported, this mid-term 
review and final program evaluation should determine whether or not the WE4F program as a whole 
led to improved food production, food value chain sustainability, and climate adaptation through the 
use of water and/or energy efficient innovations as the well the impact of these changes on 
dimensions of gender, poverty, and the environment. In addition to these questions, the final 
evaluation will also answer how both the intended and unintended impacts of the program have 
changed since the time of the mid-term review as well as how the innovation support model adopted 
by WE4F has played a role in these intended and unintended impacts. During both of these 
evaluations, the evaluator will examine evidence provided to WE4F by RIHs and their associated 
WE4F innovators as well as independent impact data garnered from the program’s External 
Surveyors, in order to determine the intended as well as unintended impact of the program around 
the following questions. All of the questions listed below are viewed as important and are expected 
to be answered through the mid-term and final evaluation:
A. WE4F Innovation Level 
1.To what extent has WE4F contributed to the outcomes and results outlined by the indicators and 
the WE4F results Framework in the WE4F PMEP during project implementation and post project 
implementation? In answering this question, the evaluator should take into consideration the 
following:
Relevance 
66 
a. Is there demand and local ownership for the WE4F innovations (individually and across all 
innovations)? 
b. To what extent does WE4F’s innovation support strategy address key needs related to water 
efficiency, energy efficiency and use of renewable energies, or food security for marginalized 
groups (the poor, women, youth, ethnic minorities, rural communities)? 
Coherence 
c. To what extent do WE4F innovations contribute towards or contradict the United Nations’ 
Sustainable Development Goals including those related to poverty, hunger, gender equality, 
affordable and clean energy, preservation of water resources or biodiversity, and climate 
change adaptation? 
Effectiveness 
d. Does each RIH help its relevant innovators overcome organizational capacity? Were there 
additional barriers that were not addressed? 
e. Did WE4F-supported projects increase water efficiency/make water more accessible? Did 
WE4F projects meet their water efficiency/availability targets? Overall, across all innovators, 
did the program meet the water efficiency/availability targets? What were the unintended 
effects of WE4F-supported projects on local water efficiency and water resources? 
f. Did WE4F-supported projects increase agricultural energy efficiency/increase the use of 
renewable energy? Did WE4F projects meet their energy efficiency targets? Overall, across 
all innovators, did the program meet the energy efficiency/availability targets? What were the 
unintended effects of WE4F-supported projects on local agricultural energy efficiency, 
access, and energy resources? 
g. Did WE4F-supported projects lead to more agricultural productivity and adaptation to 
climate change? Did WE4F projects meet their agricultural productivity targets? Overall, 
across all innovators, did the program meet the agricultural productivity targets? What were 
the unintended effects of WE4F supported projects on agricultural productivity? 
h. To what extent have the established partnerships with companies (in the form of PPPs) been 
able to meet their energy or water efficiency targets? Can the PPP approach be deemed 
successful overall in contributing to WE4F program-level goals?
i. Did WE4F-supported projects contribute to increased incomes of smallholder farmers, 
especially women farmers and those in the Base of Pyramid?
Efficiency
j. To what extent were resources and support provided by WE4F used efficiently by innovators 
and other WE4F-supported partners to maximize positive impacts on marginalized groups 
(through income, employment, water/environmental, energy/environmental impacts)? 
k. To what extent were resources and support provided by WE4F used efficiently by innovators 
and other WE4F-supported partners to maximize positive impacts on environmental 
sustainability or biodiversity?
Impact
l. Have marginalized groups (the poor, women, youth, ethnic minorities, rural communities) 
been positively and/or negatively impacted (through income, employment, 
water/environmental, energy/environmental) from WE4F supported innovations? 
Specifically, has WE4F reduced the number of people in poverty as a result of supporting 
WE4F-supported innovations and has WE4F increased the number of women benefiting from 
WE4F-supported innovations. What were the unintended effects of WE4F innovations on 
marginalized groups? 
m. Were WE4F-supported projects environmentally sustainable (i.e., did they provide positive 
environmental benefit, or did they do more environmental harm than good)? Did WE4F￾supported projects have negative effects on water resources or biodiversity? What intended or 
67 
unintended impacts did WE4F-supported projects have on other natural resources (e.g., land 
use changes, battery waste, etc.)?
n. To what extent are there negative trade-offs observed between WE4F innovations’ impacts 
on marginalized groups in terms of economic or social results and impacts on environmental 
sustainability or biodiversity? 
Sustainability
o. Were WE4F-supported projects likely to be financially and socially sustainable by the 
organizations supporting the innovation? 
p. What is the balance between public/social engagement and private/public engagement? To 
what extent have private funds been generated that contribute to the developmental objectives 
of the program both during and following WE4F awards. 
2. How much of the measured change reflected in outcome and impact-level WE4F indicators can, in 
fact, be attributed to the WE4F-supported projects? That is, what portion of the results reported is not 
explained by the projects examined by the evaluation? (WE4F recognizes that this is a difficult 
question to answer but wants the evaluator to make the best effort to answer this question). 
3. To what extent are there differences between the planned WE4F-supported projects and what was 
actually delivered in Year 1 and then Years 2-4 of the projects? 
For the final evaluation only: 
4. To what extent has WE4F-supported projects’ contribution to the outcomes and results outlined by 
the indicators and the WE4F results Framework changed since the time of the mid-term review?
5. To what extent has WE4F’s new model (i.e., locally led Regional Innovation Hubs providing 
technical assistance, enabling environment support and investment facilitation as compared to a 
centralized Washington DC incubator/accelerator; & focus on the enabling environment activities) 
support contributed to the outcomes and results reported by the WE4F indicators? What have been 
the positive and negative impacts of the WE4F model on innovator-level results? What unintended 
impacts has the model used by WE4F had on innovator-level results?
B. WE4F Program Level 
Relevance
a) To what extent did each WE4F RIH provide WE4F innovators with technical assistance that 
innovators deemed useful? Did WE4F enable certain new expertise to be deployed that would 
otherwise likely not have been deployed/used by the individual partners? 
b) How do WE4F interventions including enabling environment work, technical assistance, and 
investment facilitation differ in meeting the needs of WE4F-supported partners and the 
scaling of their innovations? 
Coherence 
c) To what extent were the interventions implemented at each WE4F Regional Innovation Hub 
compatible in achieving impacts related to water efficiency, energy efficiency and use of 
renewable energies, or food security across the WE4F program? 
d) To what extent was the enabling environment work carried out by WE4F compatible or not 
with the intended positive impacts on marginalized groups related to water efficiency, energy 
efficiency and use of renewable energies, or food security?
Effectiveness
e) To what extent did each RIH provide WE4F innovators with technical assistance that led to 
an immediate success (a support engagement is defined as an immediate success if 
deliverables formally agreed to by the awardee in the work plan were delivered as the 
awardee expected) and long term success (a support engagement is defined as a long-term 
68 
success if the product or advice delivered is actually adopted by the innovator and results in 
recognized value, such as a shift in strategy, an effective partnership, additional funding, new 
financial forecasting capabilities, or an improved manufacturing approach or product 
design)? 
f) To what extent did each RIH provide WE4F innovators with investment facilitation that led 
to additional funding, new financial forecasting capabilities? To what extent did each WE4F 
RIH provide WE4F innovators with investment facilitation that innovators deemed useful? 
Did WE4F enable certain new investment facilitation and partnerships to be deployed that 
would otherwise likely not have been deployed/used by the individual partners? 
g) How well did founding partners interact within WE4F and what lessons should be taken from 
their interactions? To what extent did alignment between FPs and with RIHs contribute 
towards an improved enabling environment for innovators? 
Efficiency
h) To what extent were the WE4F results to date in balance with the level of effort and 
resources (funds, human resources including by the FPs, RIHs, and Secretariat? 
i) To what extent is the level of effort and resources spent by applicants/innovators in balance 
with the added value WE4F brings? 
j) To what extent was each RIH efficiently set up, organized and managed? 
k) To what extent are the administrative costs for managing WE4F above, below, or on par with 
the cost of similar Challenge funds? (Special Consideration should be made for funds that 
provide technical assistance to their innovators.) 
Impact
l) To what extent did each RIH provide WE4F innovation end users with end-user financing 
that improved access to and uptake of WE4F-support innovations? To what extent did each 
RIH provide WE4F innovation end users with end-user financing that contributed to local 
poverty alleviation and/or increases in income. Especially for women and the rural poor? 
m) To what extent did each RIH provide WE4F innovators with support that improved the 
enabling environment around WE4F innovations? To what extent did each WE4F RIH 
provide WE4F innovators with enabling environment to support those innovators deemed 
useful? Did WE4F enable certain new networks to be created that would otherwise likely not 
have been deployed/used by the individual partners? 
n) To what extent did each RIH provide WE4F innovators with support that improved their 
ability to more effectively target end users such as women and the poor by improving access 
to or the usefulness of innovations for these groups? To what extent did each WE4F RIH 
provide WE4F innovators with support to strengthen their organizational capacities with 
respect to gender (e.g., expanding the female workforce, female leadership within innovator 
companies)? What were the unintended effects of innovator support provided by RIHs on 
cross-cutting issues of gender and poverty? 
o) To what extent did each RIH provide WE4F innovators with support that improved their 
positive impacts on environment and climate, including sustainable management of water 
resources and biodiversity? What were the unintended effects of innovator support provided 
by RIHs on cross-cutting issues of environmental sustainability/biodiversity?
p) To what extent did the strengthening of innovators’ impact on cross-cutting issues including 
gender, poverty, and environmental sustainability/biodiversity contribute to increased access 
to external financing or interest from external investors? 
Sustainability 
q) How effectively have investment risks been managed by the program? (Number of failed 
projects, timeliness of reaction on problems observed etc.) 
r) To what extent has the WE4F program ensured that impacts can be sustained in the medium 
to long-term by the partners themselves? 
69 
For the final evaluation only:
s) To what extent have answers to the above program-level questions changed since the time of 
the mid-term review?
t) What positive or negative impact has WE4F’s new model had on each RIH’s organizational 
capacity regarding set-up, management, and implementation of innovator support? What 
unintended impacts has WE4F’s new model had on each RIH’s organizational capacity in 
this manner? 
For all above questions related to A. WE4F Innovation Level and B. WE4F Program Level, the final 
evaluation will also answer how the results for each question have changed since the time of the mid￾term review.
C.4 Evaluation design and methodology
WE4F expects that the evaluator will conduct the following in the mid-term review and final 
evaluation: 
It is expected that the mid-term review will review WE4F program operations and supported 
innovations from Jan. 2021 – October 2021 to validate/invalidate evidence presented by WE4F 
innovators/the WE4F program. The final evaluation will review awards provided November 2021-
June 2023 to validate/invalidate evidence presented by WE4F innovators/WE4F program as well as 
how this validated evidence has changed since the conclusion of the mid-term review (To be 
determined in workplan).
Both evaluations should include a description of what types of innovations the WE4F program 
supports including the following information:
a. Technically, what types of innovations have been supported in these projects? How many 
innovations have been awarded funding? What are the basic demographics/descriptors of 
innovators? 
b. What types of enterprises are participating and with what kind of (financial) interests?
Additionally, the final evaluation should include descriptions cited in questions (a.) and (b.) above 
for innovators and other WE4F-supported projects which joined WE4F after the mid-term evaluation 
as well as the following information:
A. What innovators and types of innovations included in the mid-term evaluation have remained 
sustainable and further scaled since the mid-term evaluation?
The mid-term review and final evaluation will draw on WE4F awardee site visits, data reported by 
awardees, RIHs, and the Secretariat, as well as External Surveyor data examining program 
implementation, organizational strength, as well as the contributions to cross-cutting issues of gender 
and poverty, water-efficiency, energy-efficiency, and agriculture productivity gains by the awarded 
innovations. These and other findings will be matched with secondary data from research on similar 
innovations. Data used for the mid-term review and final evaluation may include information from 
rejected proposals and their applicants. All data collected and managed by external evaluators must 
be compliant with the data security guidelines of the European Union’s General Data Protection 
Regulations (GDPR). In addition, as noted above, the selected Evaluation Team should conduct a 
survey to gather data from WE4F applicants that did not receive award funding with comparable 
results from WE4F innovators so that we can see if there is a difference in outcomes between the 
groups. The evaluation must also conform to the OECD DAC Quality Standards for Development 
Evaluation (2019) in its planning, design, implementation, and reporting while evaluating the WE4F 
program in terms of the following criteria: relevance, coherence, effectiveness, efficiency, impact, 
and sustainability. 
C.5 Data collection and analysis
USAID requests that the evaluator complete the following table as part of its detailed design and evaluation plan. 
70 
C.6 Key personnel / Evaluation Team composition
WE4F leaves it up to the subcontractor to determine the Evaluation Team. A typical team should include one 
team leader who will serve as the primary coordinator with USAID/WE4F Secretariat. At least one team 
member should be an evaluation specialist. At least 2 members of the team should be agriculture specialists 
(not mutually exclusive with the other requirements below). The recruitment of local evaluators is highly 
encouraged. Requested qualifications and/or skills may relate to: (1) evaluation design, methods, 
management, and implementation; (2) specific relevant technical subject matter expertise, (c) experience with 
gender equality /women’s economic empowerment, poverty reduction, and business approaches which target 
the Base of the Pyramid (d) regional or country experience; (e) local language skills. All team members will 
be required to provide a signed statement attesting to a lack of conflict of interest or describing any existing 
conflict of interest.
The Evaluation Team shall demonstrate familiarity with USAID’s evaluation policies and guidance included 
in the USAID Automated Directive System (ADS) in Chapter 200 and the OECD/DAC Quality Standards for 
Development Evaluation (2019). The Evaluation Team must also have at least one member with demonstrated 
experience conducting evaluations within the German public sector on at least one occasion. Additionally, 
demonstrated experience conducting at least one evaluation with USAID is also required. Applicants must 
also have at least one team member with demonstrated experience conducting at least one evaluation of an 
international cooperation project according to the OECD/DAC criterion. 
WE4F requires:
Lead consultant (1): Will be the contract holder and responsible for deliverables. Minimum 12 years of 
experience in MEL, as well as: 
1. a focus on either institutional arrangements or water/ag and/or energy/ag sector marketing in 
developing countries within regions where WE4F is operational. 
2. a focus on gender and Base of the Pyramid inclusion; knowledge regarding sustainable natural 
resource management including water and biodiversity. 
3. and a good understanding of the policy environment of the Founding Partners; prior experience 
evaluating Grand Challenge innovation programs.
Senior Consultant (2): Supports lead consultant. Minimum7 years of experience in: 
1. water/ag and/or energy/ag sector marketing in developing countries. 
2. gender and Base of the Pyramid inclusion. 
3. sustainable natural resource management including water and biodiversity. 
4. and a demonstrated experience and results working in major evaluations. 
5. One of the two senior consultants must have experience evaluating Grand Challenge innovation 
programs. 
Local consultants from each of the 3 regions listed below to provide more regional/country specific context to 
the related innovations for the following regions: S./S.E. Asia, S. and Central Africa, Middle East/North 
Africa and (3): Supports lead consultant. Minimum 5 years of experience including: 
71 
1. marketing of water/ag and/or energy/ag related products and other interventions in developing 
countries 
2. and demonstrated experience and results working in major evaluations. 
3. At least 2 local consultants must have energy/ag experience.
Time input
Lead Consultant: 175 days, within total timeframe of 36 months
Consultant 2 and 3: 120 days each, within total timeframe of 36 months
Local Consultants 5: 30 days each, within a total timeframe of 6 months (2 months for Mid-Term Evaluation; 
2 months for Final Evaluation)
The time allocations above are illustrative, and we expect the awarded contractor to have their proposal in the 
final evaluation plan. The contract may have more than 3 local consultants if necessary. The WE4F Secretariat 
MEL Managers will participate on the Evaluation Team in an advisory role. The WE4F Team Lead of the 
Evaluation may observe all of the data collection efforts.
[END OF SECTION C]
72 
ANNEX 2: WE4F EVALUATION QUESTIONS AND INDICATORS 
Below a list of the indicators, and further down the evaluation questions. The EQ and indicator codes are presented in 
the headings of the relevant (sub)sections. EQ and indicators are written in full in orange boxes in the text, just before 
the summary findings and recommendations. 
KPI
KPI1: Share of supported innovators that successfully marketed their climate friendly, energy and/ or water efficient 
innovations with profit
KPI2: Number of smallholder farmers and other end users using energy-efficient, water-efficient, or climate 
adaptation-related WE4F innovations in their activities
KPI3: Total mass of food produced as a result of WE4F innovations
KPI4: Total mass of food processed as a result of WE4F innovations 
KPI5: Total energy saved in the food value chain as a result of the use of WE4F innovations
KPI6: Total volume of water consumption reduction in the food value chain as a result of WE4F innovations
KPI7: Number of smallholder farmers and other end users that experience an increase in income
KPI8: Share of innovators that use tools, methods or processes to monitor the protection of water or biodiversity
KPI9: Value of investment in US Dollars that WE4F innovators have mobilized from external sources
KPI10: Number of strategies, guidelines, or projects of international, regional or local organizations adopting and 
disseminating lessons learned from WE4F publications, events, or presentations.
Gin
Gin description nr
Production/delivery of WE4F Innovation 1 
Share of innovators that have introduced their product/ service to at least 1 new geographical market 2 
Innovator’s gross sales in the field of WE4F 4 
Total greenhouse gas emissions saved per year by end users through use of products/services of 
WE4F innovators
6 
Share of food producers using products/services of WE4F innovators that have increased food 
production by at least 20% through a more sustainable and efficient usage of water
7 
Share of food producers using products/services of WE4F innovators that have increased food 
production by at least 20% through a more sustainable and efficient usage of energy
8 
Number of new jobs created in WE4F innovator companies 10
Share of innovators that use new tools, methods or processes: a. to enhance business processes b. to 
address or monitor impact dimensions (e.g., gender, poverty reduction, or environmental gains)
13
Share of innovators that report an improved enabling environment in a field of WE4F engagement in 
terms of a. (potential) clients having increased access to the product/services; b. access to inputs 
having improved; c. bureaucratic hurdles having lowered
15
Share of innovators that have developed a viable business model for their product/service 16
Number of innovators that have increased knowledge of aspects of: 
a. gender equity; 
b. poverty reduction;
c. environmental gains
17 
 
 
 
Share of innovators that report increased knowledge of investment opportunities 18
Number of external partnerships formed by innovators 20
Share of innovators that are assessed as “investment ready” by potential investors or loan providers 
from the WE4F network
21
Share of innovators that have increased knowledge on specifics of their market due to learnings from 
or facilitated by WE4F
22
Number of innovators that have gained new insights from exchange activities with other innovators 
within the region or within other RIHs with regards to a. technological aspects b. market specifics c. 
their business model
23
Number of local or regional policy dialogues organized without WE4F involvement that discuss 
solutions provided by innovators
24
Number of finance institutions that have participated in instances of technical assistance regarding 
business opportunities in WE4F nexus
25
73 
Gin description nr
Number of external partnerships formed by Regional innovation Hubs 27
Number of end users using financing mechanisms adapted or created through WE4F engagement 28
Number of events or presentations by WE4F staff at external events that specifically deal with the 
sharing of lessons learned within the WE4F nexus on the following levels: a. global level b. regional 
innovation level
29
Number of publications that specifically deal with the sharing of lessons learned within the WE4F 
nexus with regards to:
a. knowledge gaps on markets and technologies
b. learnings within and between regional innovation RIHs including innovators
c. interactions with policymakers
d. sensitizing finance institutions on business opportunities within the WE4F nexus
30
Share of external stakeholders on: 
a . global level; 
b. regional innovation level;
that are aware of relevant lessons learned made by WE4F
31
Grant volume provided per innovator 32
Share of co-funding provided by innovators themselves to match grant for developing/advancing 
innovation
33
Number of innovators being selected in the each WE4F Nexus Innovation Type 34
Ratio of selected innovators to applicants per call 35
Number of innovators that are selected outside the call structure 36
Number of instances of technical assistance provided 37
Quality of instances of technical assistance assessed by participants 38
Number of contacts of potential investors provided to innovators by WE4F 40
Share of innovators that made use of the financial guarantee instruments 41
Number of events organized to facilitate knowledge exchange horizontally within the WE4F structure 
(e.g., regional or interregional conference)
42
Number of events organized, or publication produced by WE4F to advocate for a more favorable 
enabling environment: a. on the global level; b. on the regional level
43
Number of hectares under improved management practices as a result of WE4F innovations 45
74 
Table 32 shows the evaluation questions. The headings of this report arrange some of the questions in different places. 
Table 30: Evaluation questions as presented in the SOW
105 See definitions at the beginning of this report, for definition of productivity 
Evaluation Questions
Relevance – Innovation level
1. To what extent has WE4F contributed to the outcomes and results outlined by the indicators and the WE4F results 
framework in the WE4F PMEP during program implementation and post program implementation?
1a. Is there demand and local ownership for the WE4F innovations (individually and across all innovations)?
1b. To what extent does WE4F’s innovation support strategy address key needs related to water efficiency, energy efficiency and 
use of renewable energies, or food security of marginalized groups (the poor, women, youth, ethnic minorities, rural 
communities)? 
Coherence – Innovation level
1c. To what extent do WE4F innovations contribute towards or contradict SDG including those related to poverty, hunger, 
gender equality, affordable and clean energy, preservation of water resources or biodiversity, and climate change adaptation?
Effectiveness – Innovation level
Impact – Innovation level
1d. Does each RIH help its relevant innovators overcome organizational capacity? Were there additional barriers that were 
not addressed?
1e. Did WE4F-supported projects increase water efficiency/make water more accessible? Did WE4F projects meet their water 
efficiency/availability targets? Overall, across all innovators, did the program meet the water efficiency/availability targets? What 
were the unintended effects of WE4F-supported projects on local water efficiency and water resources?
1f. Did WE4F-supported projects increase agricultural energy efficiency/increase the use of renewable energy? Did WE4F 
projects meet their energy efficiency targets? Overall, across all innovators, did the program meet the energy 
efficiency/availability targets? What were the unintended effects of WE4F-supported projects on local agricultural energy 
efficiency, access, and energy resources?
1g. Did WE4F-supported projects lead to more agricultural productivity and adaptation to climate change? Did WE4F 
projects meet their agricultural productivity targets? Overall, across all innovators, did the program meet agricultural productivity 
targets? What were the unintended effects of WE4F supported projects on agricultural productivity?105
1h. To what extent have the established partnerships with companies (in the form of PPPs) been able to meet their energy or 
water efficiency targets? Can the PPP approach be deemed successful overall in contributing to WE4F program-level goals?
1i. Did WE4F-supported projects contribute to increased incomes of smallholder farmers, especially women farmers and those 
in the Base of Pyramid?
1l. Have marginalized groups (the poor, women, youth, ethnic minorities, rural communities) been positively and/or negatively 
impacted (through income, employment, water/environmental, energy/environmental) from WE4F supported innovations? 
Specifically, has WE4F reduced the number of people in poverty as a result of supporting WE4F-supported innovations and has 
WE4F increased the number of women benefiting from WE4F-supported innovations. What were the unintended effects of WE4F 
innovations on marginalized groups?
2. How much of the measured change reflected in outcome and impact-level WE4F indicators can, in fact, be attributed to the 
WE4F-supported projects? That is, what portion of the results reported is not explained by the projects examined by the 
evaluation? (WE4F recognizes that this is a difficult question to answer but wants the evaluator to make the best effort to answer 
this question).
Sustainability – Innovation level
1h. To what extent have the established partnerships with companies (in the form of PPPs) been able to meet their energy or 
water efficiency targets? Can the PPP approach be deemed successful overall in contributing to WE4F program-level goals?
1m. Were WE4F-supported projects environmentally sustainable (i.e., did they provide positive environmental benefit, or did 
they do more environmental harm than good)? Did WE4F- supported projects have negative effects on water resources or 
biodiversity? What intended or unintended impacts did WE4F-supported projects have on other natural resources (e.g., land use 
changes, battery waste, etc.)?
1n. To what extent are there negative trade-offs observed between WE4F innovations’ impacts on marginalized groups in 
terms of economic or social results and impacts on environmental sustainability or biodiversity?
1o. Were WE4F-supported projects likely to be (become) financially and socially sustainable by the organizations supporting 
the innovation?
1p. What is the balance between public/social engagement and private/public engagement? To what extent have private funds 
been generated that contribute to the developmental objectives of the program both during and following WE4F awards?
Efficiency of project management – project level
1d. Does each RIH help its relevant innovators overcome organizational capacity? Were there additional barriers that were 
not addressed?
75 
Evaluation Questions
1j. To what extent were resources and support provided by WE4F used efficiently by innovators and other WE4F-supported 
partners to maximize positive impacts on marginalized groups (through income, employment, water/environmental, 
energy/environmental impacts)?
1k. To what extent were resources and support provided by WE4F used efficiently by innovators and other WE4F-supported 
partners to maximize positive impacts on environmental sustainability or biodiversity?
3. To what extent are there differences between the planned WE4F-supported projects and what was actually delivered in year 1 
and then years 2-4 of the projects?
a) To what extent did each WE4F RIH provide WE4F innovators with TA that innovators deemed useful? Did WE4F enable 
certain new expertise to be deployed that would otherwise likely not have been deployed/used by the individual partners?
b) How do WE4F interventions, including enabling environment work, TA and IF, differ between RIH in meeting the needs of 
WE4F-supported partners and the scaling of their innovations? 
c) To what extent were the interventions implemented at each WE4F RIH compatible in achieving impacts related to water 
efficiency, energy efficiency and use of renewable energies, or food security across the WE4F program?
d) To what extent was the enabling environment work carried out by WE4F compatible or not with the intended positive
impacts on marginalized groups related to water efficiency, energy efficiency and use of renewable energies, or food security?
e) To what extent did each RIH provide WE4F innovators with TA that led to an immediate success (a support engagement is 
defined as an immediate success if deliverables formally agreed to by the awardee in the work plan were delivered as the 
awardee expected) and long term success (a support engagement is defined as a long-term success if the product or advice 
delivered is actually adopted by the innovator and results in recognized value, such as a shift in strategy, an effective partnership, 
additional funding, new financial forecasting capabilities, or an improved manufacturing approach or product design)?
f) To what extent did each RIH provide WE4F innovators with IF that led to additional funding, new financial forecasting 
capabilities? To what extent did each WE4F RIH provide WE4F innovators with IF that innovators deemed useful? Did WE4F 
enable certain new IF and partnerships to be deployed that would otherwise likely not have been deployed/used by the individual 
partners?
g) How well did founding partners interact within WE4F and what lessons should be taken from their interactions? To what 
extent did alignment between FPs and with RIHs contribute towards an improved enabling environment for innovators?
h) To what extent were the WE4F results to date in balance with the level of effort and resources (funds, human resources 
including by the FPs, RIHs, and Secretariat)?
i) To what extent is the level of effort and resources spent by applicants/innovators in balance with the added value WE4F brings?
j) To what extent was each RIH efficiently set up, organized and managed?
k) To what extent are the administrative costs for managing WE4F above, below, or on par with the cost of similar Challenge 
funds? (Special Consideration to be made for funds that provide TA to their innovators)
l) To what extent did each RIH provide WE4F innovation end users with end user financing that improved access to and uptake of 
WE4F-support innovations? To what extent did each RIH provide WE4F innovation end users with end user financing that 
contributed to local poverty alleviation and/or increases in income. Especially for women and the rural poor?
m) To what extent did each RIH provide innovators with support that improved the enabling environment around WE4F 
innovations? To what extent did each WE4F RIH provide innovators enabling environment to support that innovators deemed 
useful? Did WE4F enable certain new networks to be created that would otherwise likely not have been deployed/used by the 
individual partners?
n) To what extent did each RIH provide WE4F innovators with support that improved their ability to more effectively target end 
users such as women and the poor by improving access to or the usefulness of innovations for these groups? To what extent did
each WE4F RIH provide WE4F innovators with support to strengthen their organizational capacities with respect to gender (e.g., 
expanding the female workforce, female leadership within innovator companies)? What were the unintended effects of innovator 
support provided by RIHs on cross-cutting issues of gender and poverty?
o) To what extent did each RIH provide WE4F innovators with support that improved their positive impacts on environment and 
climate, including sustainable management of water resources and biodiversity? What were the unintended effects of innovator 
support provided by RIHs on cross-cutting issues of environmental sustainability/biodiversity?
p) To what extent did the strengthening of innovators’ impact on cross-cutting issues including gender, poverty, and 
environmental sustainability/biodiversity contribute to increased access to external financing or interest from external investors?
q) How effectively have investment risks been managed by the program? (Number of failed projects, timeliness of reaction on 
problems observed etc.)
r) To what extent has the WE4F program ensured that impacts can be sustained in the medium to long-term by the partners 
themselves?
Hypothesis I – By investing in innovations at the water-energy-agricultural nexus, the pace of development in all 3 sectors will be 
substantially faster than if we relied on ‘traditional’ (more sectoral) development programming alone.
Hypothesis II – By sourcing technologies and business model innovators that have already achieved early adoption (1,000-
10,000 end users), WE4F innovations are much more likely to transition to scale (10,000-1,000,000 end users) or reach wide￾scale adoption (greater than 1,000,000 end users).
Hypothesis III – By investing in acceleration-oriented TA and IF (incl. for end users to purchase WE4F innovations), we will 
substantially increase the likelihood that innovators will have the knowledge, tools and resources to scale their innovations. 
76 
ANNEX 3: STATEMENTS OF DIFFERENCE 
Any statements of difference regarding significant unresolved differences of opinion by funders, 
implementers, and/or members of the Evaluation Team
77 
ANNEX 4: DATA COLLECTION AND ANALYSIS TOOLS
All data collection and analysis tools used in conducting the evaluation, such as questionnaires, 
checklists, survey instruments, discussion guides. 
All sources of information, properly identified and listed
• KII questionnaire for Innovators
• KII questionnaire for Regional Innovation Hubs
• End User survey (Survey Monkey) 
• Innovation survey (Survey Monkey) 
78 
ANNEX 5: STATEMENTS ON CONFLICTS OF INTEREST
Forms for all Evaluation Team members, either attesting to a lack of conflicts of interest or 
describing existing conflicts of interest
 
79 
80 
ANNEX 6: WE4F THEORY OF CHANGE
81 
Inputs 
IN1: International 
and regional 
expertise 
IN2: Financial 
resources 
IN3: Material 
resources 
Activities 
A1: Provision of grants to new 
innovators 
A2: Identification of new 
innovators with high impact 
potential in the WE4F nexu 
A3: Provision of technical 
assistance to existing and new 
innovators 
A4: Training on investment 
readiness of innovators 
A5: Match making between 
innovators a well as provision 
of financing instruments 
A6: Knowledge generation 
and facilitation of 
knowledge exchange 
horizontally and vertically 
within the WE4F structure 
A7: Advocacy for an enabling 
environment on the global, 
regional, and local levels 
A8: Capacity development of 
innovators, multipliers 
financing institutions, and 
other stakeholder 
Outputs 
OP1: Newly selected 
innovators have developed a 
viable business model on 
company level 
OP2: Newly selected and 
existing innovators have 
strengthened their 
development impacts – 
gender, poverty, environment, 
etc. 
OP3: Newly selected and 
existing innovators have 
enhanced processes and 
structures for achieving 
development impact in 
place 
OP4: Innovators have 
gained knowledge on 
and contacts for 
attracting investments or 
finance 
OP5: Innovators’ 
business models are able 
to scale solutions in 
WE4F nexus 
OP6: Selected 
knowledge gaps on 
markets and 
technologies have been 
addressed 
OP7: Regional hubs and 
innovators have learned 
from field experiences of 
other hubs and innovators 
OP8: Policy-makers & other 
stakeholders are sensitized 
for challenges & solutions 
in WE4F nexus 
OP9: Finance institutions are 
sensitized and trained in 
business opportunities in the 
WE4F nexus (end-user 
finance) 
OP10: Learnings, 
knowledge, and experiences 
are complied and shared 
with stakeholders in the 
WE4F nexus on local, 
regional, and global level 
Outcomes 
OC1: Capacities of innovators are 
improved 
OC2: Mobilization of external 
funding for innovators is increased 
OC3: Enabling environment for 
innovators and relevant 
stakeholders in the targeted regions 
is improved 
Impacts 
IM1: Innovators have 
scaled sustainable new 
solutions to challenges 
in the WE4F nexus 
IM2: Customers in the 
market are using the 
newly developed products 
or services of the 
innovators 
IM3: WE4F contributes 
to increased food 
production along the 
food value chain 
through a mere 
sustainable and efficient 
usage of water and/or 
energy 
IM4: WE4F contributes 
to increased income for 
women and men 
including the poor in 
both rural and urban 
areas 
Source: Syspons 2019 
82 
ANNEX 7: EVALUATION MATRIX OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen -
tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
Innovation level 
Relevance
1. WE4F contributing to results, 
Consider: 1a. Local ownership for 
the WE4F innovations 
IM-2 
2 
X X 
1b. Strategy addressing 
marginalized groups’ key needs 
related to water & energy 
efficiency, renewables, food 
security
IM-1,4 
1, 7 
X X 
Coherence 
1c. Contributing towards or 
contradict SDG
OP-2,3 
IM-1,3,4 
2 to 
7 
X X X 
SSEffectiveness
1d. RIH helping innovations 
overcome organizational capacity 
barriers
OP-1-7, 
OP-10, 
OC-1,3 
A3,6,7,8 1, 
3 to 
10 
X X 
1e. Projects increase water 
efficiency, -accessibility? Meet 
efficiency/availability targets? 
Unintended effects on water 
efficiency, -resources?
OP-6? 
IM1,2,3
X X X 
83 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
1f. Projects increase agricultural 
energy efficiency, use of renewable 
energy? 
Meet energy efficiency/ 
availability targets? Unintended 
effects on ag. energy efficiency, -
access, -resources?
OP-6? 
IM3 
X 
1g. More ag. productivity and 
adaptation to climate change? 
Unintended effects on agricultural 
productivity? 
OP-2,3 
IM-4 
X X X 
1h. PPP meet their energy or water 
efficiency targets? PPP approach 
contributing to WE4F program￾level goals?
OP￾8,9,10 
OC-3 
X X X 
1i. Did WE4F-supported projects 
contribute to increased incomes of 
smallholders, especially women 
farmers and those in the Base of 
Pyramid?
IM-4 X X X X 
Efficiency
1j. Resources and support used 
efficiently by innovations, a.o. 
partners to maximize positive 
impacts on marginalized groups? 
OP-1,2, 
4-7,10, 
OC-1, 
IM-3,4 
2 to
7 
X X 
84 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
1k. Resources and support used 
efficiently by innovations, a.o. 
partners to maximize 
environmental sustainability or 
biodiversity impact? 
OP-1,2, 
4-7,10 
OC-1, 
IM-1,2,3
6 
X X 
Impact
1l. Marginalized positively and/or 
negatively impacted by 
innovations? # people in poverty ?
# women benefiting ? Unintended 
effects on marginalized groups?
IM-3,4 
1 to 
7 
X X X X 
1m. Projects environmentally 
sustainable? Negative effects on 
water resources, biodiversity? 
(Un)intended impacts on other 
NR? 
IM-3 X X X X 
1n. Negative trade-offs between 
innovations’ impact on 
marginalized groups: socio￾economic results and impacts on 
environmental sustainability or 
biodiversity? 
 X X X 
85 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
Sustainability
1o. Projects likely to be (become) 
financially and socially 
sustainable?
 X X 
1p. Balance between public/social 
engagement and private/public 
engagement? Private funds 
contribute to developmental 
objectives during, following 
awards? 
OC2
OP4,9?
9 
X X X 
Impact
2. How much outcome, impact can
be attributed to the supported 
projects? What portion of results 
not explained by the evaluation? 
OP3,6 
all
X X X 
Efficiency
5 Differences between planned 
and delivered projects in year 1 
and then years 2-4 of the 
projects?
 X X 
Program Level 
Relevance
a) RIH provided innovations with
useful TA? Enable new expertise 
to be deployed that would 
otherwise not have been deployed 
by partners?
OP-1,6 X X 
b) Interventions (enabling 
environment work, TA, IF), differ 
from other grant challenges in 
meeting partners’ needs and 
scaling their innovations? 
OP-1,6 X X 
86 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
Coherence 
c) RIH interventions compatible in 
achieving water efficiency, energy 
efficiency use of renewables, food 
security across the program?
OP-2 X 
d) Enabling environment work 
compatible with the intended 
positive impacts on marginalized 
groups related to water efficiency, 
energy efficiency, use of 
renewables, or food security?
A-7 
OP-8,9, 
10 10
X X X 
Effectiveness
e) RIH provided innovations with 
TA that led to immediate success 
(i.e., delivered as awardee 
expected) and long term success 
(actually adopted by the 
innovation, results value 
recognized, such as a shift in 
strategy, an effective partnership, 
additional funding, new financial 
forecasting capabilities, or 
improved manufacturing approach, 
product design)? 
OC-1 
A3
1, 2, 
9 
X X X 
87 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
6 RIH provided innovations with 
IF that led to additional 
funding, new financial 
forecasting capabilities? 
RIH provided innovations with 
useful IF? WE4F enabled new IF 
and partnerships that would 
otherwise likely not have been 
deployed by the individual 
partners?
OC-2,9 
A5
9 
X X X 
g) How well did FP interact within 
WE4F and what lessons should be 
taken from their interactions? FP 
and RIH alignment contributed 
towards improved enabling 
environment? 
A-7 X X 
Efficiency
h) To what extent were the WE4F 
results to date in balance with the 
level of effort and resources (funds, 
human resources including by the 
FPs, RIHs, and Secretariat)?
 X X X 
i) To what extent is the level of 
effort and resources spent by 
applicants/innovations in balance 
with the added value WE4F 
brings? 
All IM X X X 
88 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
j) To what extent was each RIH 
efficiently set up, organized and 
managed? 
 X X X 
k) How do administrative costs for 
managing compare with cost of 
similar Challenge funds? 
(consideration should be made for 
funds that provide TA to their 
innovations)
 X X 
Impact
l) RIH provide end users with 
financing that improved access to 
and uptake of WE4F innovations? 
RIH provided end users with 
financing to contribute to local 
poverty alleviation, income
increases, especially for women, 
poor? 
 
7 
X X X X 
89 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
m) RIH provided innovations with 
support that improved enabling 
environment around innovations? 
RIH provided innovations with 
useful enabling environment 
support? Enable new networks that 
would otherwise likely not have 
been deployed/used by the 
individual partners?
 
1 
X X X 
n) RIH provided innovations with 
support that improved ability to 
more effectively target end users 
(women, poor), by improving 
access to or the usefulness of 
innovations for these groups? RIH 
provided support to strengthen 
innovations’ organizational 
capacities, gender (e.g., expanding 
female workforce, female 
leadership)? Unintended effects of 
innovation support on cross-cutting 
issues? (gender, poverty) 
 
7 
X X X 
90 
OECD
Evaluation Questions 
(shorter formulation; for original 
formulation see Annex 3)
Relevant 
Result
KPI
#
Data source / method
Documen￾tation
USAID 
/COR
Service 
providers / 
survey, KII 
RIH team / 
survey, KII
Innovations / 
survey, KII
End users / 
survey (Dexis)
o) RIH provided innovations 
support that improved positive 
impacts on environment and 
climate, incl. sustainable WRM,
biodiversity? Unintended effects of 
innovation support on cross-cutting 
issues of 
environment/sustainability/
biodiversity? 
 
8 
X X X 
p) Strengthening innovations’ 
impact on cross-cutting issues, 
does it contribute to increased 
access to external financing, 
investors’ interest?
 
9 
X X X 
Sustaina 
bility
q) Investment risks management? X X X X 
r) Impact sustained in medium to 
long-term by the partners?
 X X 
91 
ANNEX 8: LIST OF COUNTRY INNOVATIONS AND OTHER TABLES
Below presents the complete list of country-innovations, by country. In bold yellow, the 
companies that operate in more than one country. Grey italicized lines indicate CFI2 
innovations. 
Table 31: Complete list of country-innovations by country, CFI and innovation focus
Region Country-innovation WE4F 
history
c-focus 
*, **
women￾led
WE4F support **Dexis 
survey
Dexis 
KII
SSEA 9 countries (and 2 innovations from 
unknown country/countries). 
32 innovations;
37 country-innovations
7 lega 
cy,
14 CFI1,
13 CFI2
innova -
tions
8 Wf
15 Ef
16 Wef 
1 ND
8 women 
led, 6 no,
18 ND
2 terminated
8 TA only
22 grants (9 
CFI1, 13 
CFI2)
(27 c-grants) 
17 survey
comple -
ted
15 KII
Bangladesh ATEC Australia Int. Ltd. 1 Ef No grant No V 
Pumpkin Plus Agro Innovation 1 Wf Yes grant Yes Yes
Cambodia ATEC Australia Int. Ltd. 1 Ef No grant No Yes
Covestro 2 Ef ND grant No
Entrepreneurs du Monde 2 Wf ND grant Yes
Khmer Green Charcoal 1 Ef Yes grant Yes
India Adaptive Symbiotic Technologies 
(AST) 
legacy Wf ND TA only No
aQysta 1 Wef Yes grant Yes Yes
Center for Aquatic Livelihood 
Jaljeevika 
2 Wf ND grant Yes
Claro Energy 1 Wef No TA only No
Husk power systems legacy Ef ND TA only No
Goat Trust (Nandinandan Breeds 
and Seeds)
1 Wef No grant Yes Yes
New Leaf Dynamic Technologies 2 Ef ND grant No
Onergy Solar (Punam Energy) 1 Wef Yes grant Yes Yes
Oorja Development Solutions 1 Wef Yes grant Yes Yes
Promethean Spenta Technologies 1 Ef No TA only Yes
Rural Development Organization 1 Wef Yes TA only No
S4S Technology 2 Ef ND grant Yes
ZooFresh Foods 1 Wef Yes TA only No
Indonesia aQysta Nepal 1 Wef Yes grant Yes v 
Komodo water 2 Wef ND grant No
Sumba Sustainable Solutions 1 Wef Yes grant No Yes
Yayasan Rumah Energi 2 Ef ND grant Yes
Laos Covestro 2 Ef ND grant No
Myanmar Agrosolar (Agros. Pte Ltd.) 2 Wef ND grant No
Techno Hill 2 Wef ND grant No
Tun Yat 1 Ef No TA only No
Village Link (MyVAS4Agri) 2 Ef ND grant Yes
Nepal aQysta 1 Wef Yes grant Yes v 
Gham power 1 Wef No grant Yes
Mandala Agrifresh 2 Wef ND grant Yes
Thailand Recyclo 2 Wef ND grant No
Vietnam Covestro 2 Ef ND grant No
Egreen Technology Joint Stock 
Company
2 Ef ND grant Yes
Mimosa Technology legacy Wf ND Legacy No
ND Sistema-Bio legacy Ef ND terminated No
VIA legacy ND ND terminated No
92 
Region Country-innovation WE4F 
history
c-focus 
*, **
women￾led
WE4F support **Dexis 
survey
Dexis 
KII
MENA 11 countries (incl. 1 unknown); 
38 innovations;
59 country-innovations
0 legacy,
25 CFI1,
15 CFI2
18 Wf
8 Ef
33 Wef 
7 women 
led, 8 no,
23 ND
2 terminated
6 TA only
30 grants (18 
CFI1, 12 
CFI2)
(46 c-grants) 
25 survey
comple -
ted
Algeria Green Eagle Tech 2 Wef ND grant No
Egypt Abu Erdan DMCC 1 Wef Yes TA only No
Agrisolar 2 Ef ND grant No
Baramoda 1 Wef No TA only No
Chitosan Egypt 1 Wf Yes grant No Yes
EGYMAG Biotechnology 2 Wef ND grant Yes
Green Eagle Tech 2 Wef ND grant No
Integrated Renewable and 
Sustainable Communities (IRSC)
1 Wef Yes grant No Yes
Irma & Co 2 Wf ND grant Yes
Natagri 2 Wf ND TA only Yes
Raptor Engineering Supplies and 
Contracting
2 Wef ND grant Yes
Schaduf for Agriculture & Trade 1 Wef No TA only No Yes
SuWaCo (Benaa Foundation) 1 Wef Yes TA only No Yes
The Platform for Smart Solutions 1 Wef No grant Yes Yes
Iraq Albu Saif 1 Wf ND grant No
Ainda Agricultural Center 1 Wef ND grant Yes
Bahir Al Kamal 1 Ef ND grant No
Burj Al Iraq 1 Wf ND grant No
Dhiya Al Alamiyah 1 Wf ND grant No
EGYMAG Biotechnology 2 Wef ND grant Yes
Green scape 1 Wf ND grant No
Green shovel (green scape) 1 Wf ND grant No
Irma & Co 2 Wf ND grant No
Nakhla company 1 Wef ND grant Yes
Natagri 2 Wf ND TA only Yes
Jordan Abu Erdan DMCC 1 Wef Yes TA only No
Agritopia 2 Wf ND grant No
EGYMAG Biotechnology 2 Wef ND grant Yes
Maan Society (Shabab Ma’an ..) 2 Wef ND grant Yes
Lebanon Agrisolar 2 Ef ND grant No
Biomass 1 Wef No grant No Yes
Compost Baladi 1 Ef No grant Yes Yes
EGYMAG Biotechnology 2 Wef ND grant Yes
Green Essence Lebanon 1 Wef No grant No
Go Baladi / Hajjar Foods 1 Wef Yes grant No
Irma & Co 2 Wf ND grant Yes
Natagri 2 Wf ND TA only Yes
Robinson Agri 1 Wef Yes grant Yes
Morocco Agrisolar 2 Ef ND grant No
Alvatech 1 Wef No TA only No
Biodôme 2 Ef ND grant No
Green Eagle Tech 2 Wef ND grant No
Green Watech 2 Wf ND grant No
High Atlas Foundation 1 Wef Yes grant Yes
Mozare3 Agri-fintech 2 Wef ND grant Yes
Sowit Maroc 1 Wef No grant Yes Yes
Palestine Agritopia 2 Wf ND grant No
Sudan** Agrisolar 2 Ef ND grant No
93 
Region Country-innovation WE4F 
history
c-focus 
*, **
women￾led
WE4F support **Dexis 
survey
Dexis 
KII
Green Eagle Tech 2 Wef ND grant No
Hydroponics Africa 2 Wf ND grant Yes
Mozare3 Agri-fintech 2 Wef ND grant Yes
Schaduf for Agriculture and 
Trade
1 Wef No TA only No v 
Sudagardlic 2 Wf ND grant Yes
Syria Schaduf for Agriculture and 
Trade
1 Wef No TA only No v 
Tunisia Irma & Co 2 Wf ND grant Yes
Mozare3 Agri-fintech 2 Wef ND grant Yes
Sowit Maroc 1 Wef No grant Yes v 
ND Spark Renewables 1 Ef ND terminated No
SWME 1 Wef ND terminated No
SCA 6 countries
11 innovations (+1 unique in SCA 
but already present in Sudan/
MENA); 
12 country-innovations
2 legacy,
9 CFI1,
1 CFI2
6 Wf
2 Ef
4 Wef 
0 women 
led, 0 no, 
11 ND
0 terminated
3 TA only
10 grants (9 
CFI1, 1 CFI2)
(10 c-grants)
3 survey
comple -
ted
DRC Kivu Green 1 Wf ND grant No
Kenya Hydroponics Africa (also present in 
MENA/Sudan)**
2 Wf ND grant Yes
Mozambi -
que 
Rural Integrated Engineering 1 Wf ND grant No
S. Africa COMACO 1 Wf ND grant No
Meat Naturally legacy Wf ND TA only Yes
Reel Gardening legacy Wf ND TA only Yes
Zambia Solar Village 1 Wef ND grant No
Sylva Food Solutions 1 Wef ND grant No
Zimbabwe Farmhut Africa 1 Ef ND grant No
Lanforce Energy 1 Ef ND grant No
Powerlive 1 Wef ND grant No
Zonful Energy 1 Wef ND grant No
TOTAL 26 countries (+ 4 country￾innovations with country ND); 
81 innovations
108 country-innovations
7 legacy,
55 CFI1,
46 CFI2
29 Wf
25 Ef
53 Wef 
1 ND
15 women 
led, 14 no,
52 ND
9 terminated
17 TA only
60 grants
(83 c-grants)
45 survey
comple -
ted****
*: Wf = Water focus; Ef = Energy focus; Wef = Water & Energy focus by country
**: where innovations operate in more than one country, they are counted for each country they operate in: 
country-innovations
**: One MENA innovation also operates in Sudan.
****: Each country-innovation is counted as survey.
Analysing the portfolio of the SSEA and MENA RIH (Annex 8, Table 32), it is found that the 
main differences between the RIH are: 
• compared to SSEA, in MENA the number of CFI1 grants is doubled; MENA has a few 
more countries, and many more country-innovations 
• compared to SSEA, MENA has more innovations focussed on water, much less on energy 
(water and energy innovation focus is not much different); this difference is also reflected 
in the innovation types; in SSEA the most mentioned type is energy production & 
infrastructure, in MENA water-irrigation
• compared to SSEA, MENA has on average a lower funding amount for innovations; e.g., 
MENA has twice as many innovations for water-irrigation, but year 1 funding for water￾irrigation innovation types is relatively low; and for year 1 energy – farm input 
innovation types, SSEA the funding is $68,333/innovation (in fewer countries?) and for 
94 
MENA $48,333, to conclude: SSEA generally commits larger grants than MENA, 
however: delayed disbursement of CFI1 grants is an issue in SSEA 
Table 32: Innovations’ gross sales (USD), by innovation, by country as of July 2022 (Gin4)
Country /RIH Innovation, name LOP target year 1 target year 1 result year 1 target 
reached
Bangladesh 
/SSEA
ATEC Australia Int. Ltd.* 4,201,965 587,340 244,757 N 
Pumpkin Plus Agro Innovation 39,608 27,108 2,177 N 
Cambodia 
/SSEA
ATEC Australia Int. Ltd. * * * 
Khmer Green Charcoal 14,700 1,838 21,048 Y 
India /SSEA aQysta 65,000 150,000 0 N 
Goat Trust (Nandinandan Breeds & 
Seeds)
90,000 30,000 324,824 Y 
Onergy Solar (Punam Energy) 9,360,000 1,560,000 1,265,817 N 
Oorja Development Solutions 238,390 88,081 75,612 N 
Promethean Spenta Technologies 2,400,000 900,000 842,545 N 
Rural Development Organization NAWP 0 ND
ZooFresh Foods 7,540,000 5,040,000 2,271,336 N 
Indonesia /SSEA Sumba Sustainable Solutions 600,000 250,000 26,888 N 
Myanmar /SSEA Tun Yat 1,075,230 389,220 460,418 Y 
Nepal /SSEA aQysta ****
Gham power 866,000 300,000 135,418 N 
SSEA 26,490,893 8,559,139 5,670,840 3/12 
Egypt /MENA Abu Erdan DMCC** 981,600 309,000 136,122 N 
Baramoda 2,478,591 529,791 947,394 Y 
Chitosan Egypt 3,309,200 875,000 755,793 N 
IRSC 1,431,966 315,837 252,321 N 
Schaduf for Agriculture and Trade 1,373,718 573,718 536,403 N 
SuWaCo (Benaa Foundation) 3,073,716 56,777 59,968 Y 
The Platform for Smart Solutions 1,456,003 204,935 73,009 N 
Jordan /MENA Abu Erdan DMCC** ** **
Lebanon /MENA Biomass 1,838,720 218,720 1,200,182 Y 
Compost Baladi 1,864,429 36,429 232,219 Y 
Go Baladi (Hajjar foods) 1,233,000 243,000 128,471 N 
Green Essence Lebanon 2,345,550 95,550 744,000 Y 
Robinson Agri 6,203,841 1,759,600 4,081,011 Y 
Morocco /MENA Alvatech 6,000,000 1,500,000 44,375 N 
High Atlas Foundation 3,861,900 620,100 257,342 N 
Sowit Maroc 1,271,288 167,298 382,132 Y 
Tunisia /MENA Sowit Maroc*** *** ***
MENA 38,723,522 7,505,755 9,830,742 7/15 
Total 65,214,415 16,064,894 15,501,582 11/30
*: result combined Bangladesh & Cambodia
**: result combined Egypt & Jordan
***: result combined Jordan & Tunisia
****: result combined with India
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
The CFI2 funds disbursement is 28% but this is when just 2.5 months have passed since 
signing; it shows swift action once a Grant Authorization (GA) is signed. The same applies to 
the Iraq cohort: just 6 months underway since signing, and 40% of the funds are disbursed.
95 
Table 33: Innovations’ end user targets and results, by innovation, by country as of July 2022
Country /RIH Innovation, name LOP target year 1 
target
year 1 
result
year 1 target 
reached
Year 1 within 
80% of target
Bangladesh 
/SSEA
ATEC Australia Int. Ltd.* 9,135 1,260 2,192 Y Y 
Pumpkin Plus Agro Innovation 7,350 4,200 3,108 N N 
Cambodia 
/SSEA
ATEC Australia Int. Ltd. * * *
Khmer Green Charcoal 63,000 7,875 15,750 Y Y 
India /SSEA
aQysta 10,000 2,500 967 N N 
Claro Energy 60,000 20,000 21,100 Y Y 
Goat Trust (Nandinandan Breeds & 
Seeds) 42,000 21,000 21,000 Y Y 
Onergy Solar (Punam Energy) 144,000 24,000 37,558 Y Y 
Oorja Development Solutions 32,940 5,400 4,992 N Y 
Promethean Spenta Technologies 19,800 6,600 8,166 Y Y 
Rural Development Organization 11,167 20,000 5,167 N N 
ZooFresh Foods 5,500 1,500 470 N N 
Indonesia 
/SSEA Sumba Sustainable Solutions 7,500 2,500 2,080 N Y 
Myanmar 
/SSEA Tun Yat 9,000 3,500 24,625 Y Y 
Nepal /SSEA aQysta **** 2,500
Gham power 50,000 10,000 10,791 Y Y 
SSEA 471,392 132,835 157,966 8/14 10/14
Egypt /MENA
Abu Erdan DMCC** 13,045 1,545 732 N N 
Baramoda 13,090 2,460 995 N N 
Chitosan Egypt 19,819 8,365 23,424 Y Y 
IRSC 14,058 3,995 2,565 N N 
Schaduf for Agriculture and Trade 20,150 4,150 32 N N 
SuWaCo (Benaa Foundation) 33,000 1,500 1,250 N Y 
Spark Renewables 90,000 21,000 ND
The Platform for Smart Solutions 183,303 1,089 3,676 Y Y 
Jordan/MENA Abu Erdan DMCC** ** **
Lebanon
/MENA
Biomass 11,991 7,690 6,496 N Y 
Compost Baladi 100,089 5,369 19,244 Y Y 
Go Baladi (Hajjar foods) 8,715 215 1,578 Y Y 
Green Essence Lebanon 7,811 11 5,948 Y Y 
Robinson Agri 10,485 2,500 2,200 N Y 
Morocco 
/MENA
Alvatech 9,000 2,000 110 N N 
High Atlas Foundation 132,000 12,000 12,514 Y Y 
Sowit Maroc 71,204 19,500 15,624 N Y 
Tunisia/MENA Sowit Maroc*** *** ***
MENA 737,760 93,389 96,388 6/16 10/16
Total 1,065,152 225,135 254,354 14/30 20/30
*: result combined Bangladesh & Cambodia; **: result combined Egypt & Jordan; ***: result combined Jordan & Tunisia; ****: result combined 
with India Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Table 34: Money not disbursed per innovation SSEA CFI1
Innovation Organization Unspent Year 1 (USD)
aQysta Nepal Pvt. Ltd 20,000
ATEC Australia International Limited 30,000
Gham Power 25,000
Khmer Green Charcoal 15,000
Oorja Development Solutions 60,000
Promethean Spenta Technologies Pvt Ltd 122,000
Punam Energy Pvt. Ltd. (Onergy Solar) 25,000
96 
Innovation Organization Unspent Year 1 (USD)
Rural Development Organization 105,000
Sumba Sustainable Solutions 22,000
Total 302,000
Year 1 1,335,700
Disbursed 911,700 
Table 35: WE4F grant funds committed, targeted and disbursed in year 1 (USD), from inception until 
August 2022 (received October 2022)
Region/cohort no. 
grantees
no. country 
grantees
total funds 
committed ($)
fund targets year 
1 ($)
funds disbursed 
year 1 ($)
% disbursed vs 
target for year 1
SSEA CFI1 9 12 1,964,200 1,335,700 911,700 68%
MENA CFI1 14 31 2,380,000 1,160,000 1,360,000 117%*
MENA CFI2 12 1,720,000 946,000 516,000 55%
Iraq CFI1 8 8 860,000 817,000 344,000 42%
Total 48 6,924,200 4,258,700 3,131,700 74%
Source: Annual report data (Final_WE4F Annual Report Data_02_14_2022.xlsx, Budget Tracker received 
October 2022) * Updated based on the email. This was new information and not included in the document sent 
by Lanakila McMahan. Note: The total amount paid to date is $1,360,000. $200,000 of that amount is Y2 milestones.
In SSEA the committed funding amount per CFI1 grant (innovation) is 217,911 USD, 
whereas in MENA (CFI1 including Iraq) it is a much smaller amount of 154,286 USD. 
Likewise, in SSEA the amount per country-innovation is 163,683 USD/country, in MENA 
(CFI1 including Iraq) nearly half of this, at 86,077 USD/country. At the time of record 
(October 2022) there is evidence of gaps between the planned and disbursed funds 
particularly in MENA CFI2 (just starting), in MENA/Iraq CFI1 (understandably, as it started 
later), and in SSEA CFI. Especially in SSEA this gap is of concern: of the funds committed 
for year 1 (in the table: ‘fund targets year 1), 32% (424,000 USD) had not been disbursed. In 
the CFI1 cohort there are 9 innovations that are awaiting undisbursed funds ranging from 
15,000 to 122,000 USD; the full breakdown is shown in Annex 8 (Table 32).
Figure 4 shows the money committed and the funds that were actually disbursed for CFI1 
innovations in SSEA and MENA. 
 Figure 4: Amount awarded vs disbursed in year 1, for CFI1 (tbd)
Source: Annual report data (Final_WE4F Annual Report Data_02_14_2022.xlsx) and fully executed grant 
agreements
In SSEA 68% of the CFI1 planned funds for year 1 were disbursed; in MENA it was 117%. 
This 117% is due the fact that the actual disbursement includes 200,000 USD of year 2. 
$1,335,700 
$1,160,000 
$911,700 
$1,360,000 
 $-
 $200,000
 $400,000
 $600,000
 $800,000
 $1,000,000
 $1,200,000
 $1,400,000
 $1,600,000
 SSEA CFI 1 MENA CFI 1 
Money Planned for Year 1 vs Disbursed CFI1
 Money Planned (year 1) Money disbursed (year 1)
97 
Table 36: CFI1 grants disbursed in year 1 (USD), by region, by innovation type, against end user 
numbers
Innovation type SSEA end 
user hh 
members 
(n=12)
SSEA funds 
disbursed ($) 
(n= 9)
SSEA $/
end user 
hh 
member
MENA end 
user hh 
members 
(n=14)
MENA funds 
disbursed ($)*
(n=14)
MENA $/
end user hh 
member
Energy – Production & infrastructure 22,822 252,500 11.1 8,513 205,000 24.1
Energy – Farm Input 7,359 205,000 27.9 50,159 290,000 5.8
Digital solutions 56,886 187,000 3.3 20,032 270,000 13.5
Water – Irrigation 59,625 130,000 2.2 2,232 195,000 87.4
Water – Reuse in agriculture 0 1,250 30,000 24.0
Energy – Agricultural processing 8,166 110,000 1,578 140,000 88.7
Other 3,108 27,200 8.8 12,514 230,000 18.4
Water – Quality/salinity 110 0 
Water – Reuse in agriculture 0 1,250
Total results year 1 157,966 911,700 96,388 1,360,000
Innovation targets year 1 132,835 93,389
Proportion of year 1 end user target 113% 105%
Funding target year 1 1,335,700  1,160,000 
Proportion of year 1 funding disbursed 68% 117% 
Source: Annual report data (Final_WE4F Annual Report Data_02_14_2022.xlsx)
*: does not include disbursements for Iraq CFI1 and MENA CFI2 as we do not have KPI2 results for those
The first-year data show that the numbers of end users exceed the aggregated target by 19% 
in SSEA and by 3% in MENA. There is further discussion of end user targets and the 
prospect of the Program reaching the LOP target of 2.5 million in the section on 
sustainability.
Table 37: WE4F committed funding and overall funding (USD), for MENA CFI1 
Innovation name WE4F committed funding ($) proposed funding from other sources ($) % from WE4F
Abu Erdan 100,000 1,600,000 6%
Alva Tech - - - 
Baramoda 125,000 2,480 5%
Biomass SAL 200,000 0 100%
Chitosan 140,000 1,065,366 12%
Compost Baladi 240,000 578,500 29%
Go Baladi 200,000 888,000 18%
Green Essence 200,000 2685,000 7%
High Atlas Fntn. 160,000 220,000 42%
IRSC 120,000 1,124,075 10%
The Platform 180,000 1,758,673 9%
Robinson Agri 240,000 5,511,381 4%
Schaduf 240,000 1,060,000 18%
SOWIT 200,000 408,000 33%
SuWaCo 125,571 129,429 49%
Total 2,370,571 17,030,904 15%
Source: Annual report data From AWPs for all CFI1 MENA (Final_WE4F Annual Report 
Data_02_14_2022.xlsx) 
The information on funding shows that WE4F’s part in the innovations’ funding is very 
variable, and certainly not the largest part; one exception aside: the funding is between 4% 
and 49% of what is proposed funding from other sources (over the total, WE4F funding is 
15%). 
98 
Numbers of countries: country-innovations and innovation focus
In this section the support for innovations in the developing regions identified, the numbers 
of countries involved, the range of focus areas and other characteristics.
At this stage it is important to make a distinction between an innovation (with in most cases a 
WE4F grant agreement) and a country-innovation (this is also explained in the Definitions 
before the Introduction of this report).
Table 40 presents innovation numbers, by region, and Table 41 shows the innovation types 
for each region. This is to demonstrate how the innovations are relevant for each country. See 
Annex 8 (Table 31) For a complete list of country-innovations. 
Table 38: Innovation numbers: total, grants only, CFI1 only, by region 
Region Countries Total no. of innovations Grants CFI1 grants
SSEA 9 * 32 22 9 
MENA 10* 38 30 18
SCA 6 11 10 9 
Total >25* 81 61 36
*: in SSEA as well as in MENA, each has 2 country-innovations with the country known
Source: WE4F Innovator List_As of July22.gsheet
Table 39: Country-innovations numbers: country-grants and -funding, and country-innovation focus
Region countries total no. of country￾innovations
country￾grants*
CFI1 country￾grants
country-innovation focus
water energy water & energy
SSEA 9 ** 37 27 11 5 15 16
MENA 10** 59 46 18 18 8 33
SCA 6 12 10*** 9 6 2 4 
Total >25** 108 83 38 29 25 53
*: as country-innovations, country-grants multiply grants numbers by the number of countries
**: in SSEA as well as in MENA, each has 2 country-innovations with country 
*** ND
Source: WE4F Innovator List_As of July22.gsheet
Both SSEA and MENA have somewhat similar numbers of countries (9 vs 10). The water 
and energy innovation focus are the most prevalent in SSEA and MENA; in MENA this is 
56% of the country-innovations, in SSEA less, as energy focus innovations are almost as 
prevalent. In SCA the water innovation focus is the most prevalent with water & energy as a 
good second. Considering the context, it is unsurprising that in MENA (with many semi-arid 
areas) the water-related innovations are more prevalent. This is also clear in the next table. 
Table 40: Representation of innovation type for region
Innovation Type SSEA MENA SCA
Water – Reuse in agriculture 1 1 0 
Digital Solutions 6 5 3 
Water – Irrigation 6 12 3 
Energy – Energy production and infrastructure in agriculture 8 4 0 
Other 2 3 1 
Energy – Agricultural processing 2 2 1 
Water – Quality/salinity 0 2 0 
Energy – Farm input 3 6 0 
Energy – Aggregation and storage in agriculture 3 0 1 
Water – Capture/storage in agriculture 0 1 2 
Energy – Farm production 0 1 0 
Water treatment 0 0 0 
ND 1 1 0 
Total 32 38 11
99 
In SSEA, the greatest number of innovations are in energy production and infrastructure in 
agriculture, water – irrigation, and digital solutions. In MENA, water – irrigation stands out 
above all others. 
Investment facilitation and other enabling environment results (EQ1h)
Gin20 (ext. partnerships), Gin21 (investment ready), Gin1 (innovation delivery), Gin2 (new 
market/country), Gin15 (accessibility of the innovation and inputs, reduced bureaucratic 
hurdles). 
Gin40 – Number of contacts of potential investors provided to innovations by WE4F 
Gin41 – Share of innovations that made use of the financial guarantee instruments
To look at investment facilitation it is important to see how many contacts were made due to 
the innovation, for SSEA it was 51, and for MENA it was 29 (Gin40). This information was 
collected from 12 innovations in MENA and 14 in SSEA. 
Only 3 innovations in SSEA used the financial guarantee mechanism (Gin41) There were no 
innovations in MENA that reported use of financial guaranteed mechanism.
Below table shows what innovations had to say about investment facilitation, and about 
funding sources and amounts.
Table 41: Investment Facilitation, how it helped and funding amounts (USD), by region (EQ1h)
Region Investment facilitation Funding amounts /source ($)
private 
loans
private 
equity
private 
grant
public 
grant
self￾funding
SSEA 
(n=5)
10 respondents described the help they got: 
suggestions / new ideas / advice, emails, 
connections.
On the effect 4 responses: 1 with effect, 2 not, 1 pending
50-100k
50-100k 0 25-50k <25k <25k
<25k
>200k <25k
>200k 0 0 0 >200k
MENA
(n=6)
7 respondents described the help they got: emails, 
networking, investment readiness, explaining different 
potential partners, help to make pitches to potential, 
“investment readiness teaser”, introductions also to 
other regions. On the effect 2 responses: “it reshaped the 
company”, and “it secured major funding”
0 0 0 0 0 
100-200k
>200k >200k
0 0 0 0 0 
>200k 100-200k
0 0 >200k <25k 0 
Source: Dexis innovation survey (Innovation survey 7 11 22.xlsx)
The innovations’ responses are mostly appreciated about WE4F’s investment facilitation 
activities, and some note a positive effect, some not; it is however too early to say, and too 
few data.
Table 42: Numbers of end users using financing mechanisms for the innovation, by region (Gin28)
Region total number of end 
users in Q1 and Q2
end users that 
received financing
% Of Q1 
and Q2
% Of 
females
% Of end 
users in Q1
SSEA (n=3) 149,221 2,910 100% 41% 47%
MENA (n=0) 69,343 ND - - - 
SCA NYM NYM - - - 
Total 218,535 2,910 1.42%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx); the SSEA data is from 3 innovations (Onergy Solar, ATEC, aQysta).
100 
Table 43: No. of countries participating, by region
Region Countries no. of country-innovations* country-innovation focus
water energy water & energy total
SSEA
Bangladesh 2 1 1 0 2 
Cambodia 4 1 3 0 4 
India 7 2 4 7 13
Indonesia 4 0 1 3 4 
Laos 1 0 1 0 1 
Myanmar 3 0 2 2 4 
Nepal 3 0 0 3 3 
Thailand 1 0 0 1 1 
Vietnam 2 1 2 0 3 
ND** 0 0 1 ND 2 
Total 9 countries 27 5 15 16 37
MENA
Algeria 1 0 0 1 1 
Egypt 8 3 1 9 13
Iraq 10 7 1 3 11
Jordan 3 1 0 3 4 
Lebanon 8 2 2 5 9 
Morocco 5 0 1 5 6 
Palestine 1 1 0 0 1 
Sudan 5 2 1 3 6 
Syria 0 0 0 1 1 
Tunisia 3 1 0 2 3 
ND 2 1 2 1 4 
Total 11 countries 46 18 8 33 59
SCA
DRC 1 1 0 0 1 
Kenya 1 1 0 0 1 
Mozambique 1 1 0 0 1 
S. Africa 1 3 0 0 3 
Zambia 2 0 0 2 2 
Zimbabwe 3 0 2 2 4 
Total 7 countries 9 6 2 5 12
Total 26 countries 82 29 25 54 108
*: country-innovations multiply when operating (re. the innovation) in more than one country 
**: both legacies are terminated; for only one the focus was indicated 
Source: WE4F Innovator List as of July22.gshee 
Table 44: Innovations’ annual targets numbers of end user household members, all innovations 
(n=81), by region
Region LOP* year 1 targets* year 2 targets* year 3 targets*
SSEA* 2,215,009 933,809 1,545,092 158,207 
MENA** 1,665,824 222,999 528,572 762,814 
SCA 833,430 400,360 412,760 415,660 
Total 4,714,263 1,557,168 2,486,424 1,336,681 
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) AWPs from innovators 
*: LOP target is the sum of each year. In this table it is lower than the sum of three year-targets, because year 2 and year 3 targets were adjusted 
(upwards or downwards) by the Innovations themselves. Based on year 1 performance 
**: LOP target is higher than the sum of three year-targets, because year 1 target (here) was adjusted downwards. 
Table 45: Innovation companies and where they are incorporated
Region
Yes: works in same country as 
incorporated
No: does not work in the country it is in 
incorporated ND Total
SSEA 23 6 3 32
MENA 27 2 9 38
SCA 8 1 2 11
Source: https://we4f.org/innovators
101 
Table 46: Number of end users that participated in the survey
End User Survey Responses SSEA MENA
Total number of responses 143 20
Number of female respondents 36 0 
Number of male respondents 107 20
Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022
Table 47: Participating countries end user respondents
Countries SSEA MENA
Bangladesh 27 0 
Egypt 0 19
India 72 0 
Nepal 33 0 
No Response 11 1 
Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022 
For now, the end users have been surveyed from Bangladesh, Nepal, India and Egypt. 
Table 48: Participating innovations end user respondents: by innovation
Countries SSEA MENA
Chitosan 0 17
IRSC 0 1 
Platfarm 0 1 
Pumpkin Plus 26 0 
Barsha pump 30 0 
Oorja Development, 34 0 
Goat Trust 38 0 
No response 15 1 
Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022 
Table 49: Food processed (t) by country (KPI4)
Region Innovation name Food 
processed (t) % Type of food/service
India 
/SSEA
Promethean Spenta 
Technologies 1,942 97%
Small-capacity milk chillers, digital milk testing 
equipment, links to dairy processor/buyers (data￾driven decision making, transparency
Indonesia 
/SSEA
Sumba Sustainable 
Solutions 26 1.3% Solar power for mills/grinders for rice, corn, 
coconut, coffee
Lebanon 
/MENA
Go Baladi / Hajjar 
Foods 39 1.9% Goat milk and cheese
Total 2,007 100%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Table 50 presents the information from the end user survey regarding impacts of the 
innovation. 
Table 50: Impacts reported (SSEA)
Impact Increase Decrease No effect
Variety of different crops grown on your farm 22 0 107
Variety of animal species used in husbandry activities 2 0 121
Quality of soil, soil health on your farm 1 0 124
The number or variety of birds and insects in your local area 9 120
Use of ground- or surface water 19 0 110
Pollution of ground- or surface water 0 1 123
Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022
102 
Table 51 presents production data from the same producer types, now broken down by 
innovation focus. 
Table 51: Food produced (t, year 1) by innovation focus, by producer types
Innovation focus Smallholder farmers Medium & larger scale farmers Companies Total 
Production (t) % Production (t) % Production (t) % production (t)
Water 328 100% 0 0% 0 0% 328
Energy 2,073 88% 138 6% 156 7% 2,367
Water & energy 320,944 27% 831,016 70% 35,906 3% 1,187,866
Total 342,633 27% 831,154 70% 36,062 3% 1,209,849
*Information from 21 innovations (9 SSEA, 12 MENA) Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Table 51 shows that water & energy focussed innovations work more with medium & larger 
scale farmers (in MENA especially), and this produce 69% of the total production. 
Unfortunately, this data may be incomplete. 
Table 52 presents the same water saved, by end user type, but now by innovation focus. 
Table 52: Water saved (l, year 1), by end user type, by innovation focus (KPI6)
Innovation focus Smallholder farmers Medium & larger scale farmers Companies
Volume (l)* % Volume (l)* % Volume (l)* % 
Water 116,162,838 0% NAWP 0 NAWP 0 
Energy 40,528,000 29% 0 0 
Water & energy 9,764,200 71% 39,917,000 100% 3,844,000 100%
Total 138,167,000 100% 39,917,000 100% 3,844,000 100%
Proportion 76% 22% 2%
*Information from 9 innovations (1 SSEA, 8 MENA)
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
*Rounded to the nearest thousand
Table 53: Breakdown of end user targets and results, per innovation
Innovation LOP target Year 1 target Year 1 result Year 1 target met
SSEA (n=6) 7,247,990 1,588,304 10,179,541 4 yes, 2 no
aQysta 37,500 10,000 10,240 yes 
Goat Trust (Nandinandan Breeds and 
Seeds) 50,000 150,000 143,418 no 
Onergy Solar (Punam Energy) 5,760,000 960,000 8,791,152 yes
Oorja Solutions 608,178 92,148 64,182 no 
Promethean 100,000 30,000 194,560 yes
Rural Development Organization 692,312 346,156 975,989 yes
MENA (n=7) 150,231,444 34,173,862 13,728,834 4 yes, 3 no
Abu Erdan 490,556 40,000 114,566 yes
Go Baladi / Hajjar Foods 150,400 - 29,283 yes
Green Essence 3,644,299 682,500 1,327,680 yes
IRSC 9,545,825 2,197,250 4,633,600 yes
Platform, The 37,375,875 10,241,152 1,693,143 no 
SOWIT Maroc 98,505,143 21,000,000 5,924,179 no 
SuWaCo (Benaa Foundation) 519,346 12,960 6,384 no 
Total (n=13) 157,479,434 35,762,166 23,908,376 8 yes, 5 no
103 
Table 54: Changes in farming practice, as result of the innovation
Response SSEA
Added or changed irrigation system: … 55
Other (mostly improved production -18) 31
Changed use of fertilizer / type of fertilizer (that includes biofertilizer, also bio￾slurry, compost enriched with bio-slurry, etc.): by type: 25
Changed the volume of water used: … 24
Changed the number of crops and/or varieties: … (increased / reduced) 18
Changed the size the crop area: … (increased / reduced) 15
Changed the variety of agricultural activities: … (increased / reduced) 11
Get advice to decide when to plant / changed sowing dates or seasons: … 4 
Crop management & weed control: … 3 
Added or changed storage or refrigeration to reduce crop losses:… 0 
Other power source for land preparation: … 0 
Shifted to organic and regenerative farming 0 
Table 55: Water capture/storage, reuse/treatment, consumption/irrigation (EQ1e) 
Country/ 
region country-innovation capture/ 
storage water
reuse/ 
treatment
reduce 
water use irrigation other none
Cambodia 
/SSEA Khmer Green Charcoal v 
India
/SSEA
Goat Trust (Nandinandan Br.&Seeds) v 
Onergy Solar (Punam Energy) v v 
Oorja Development Solutions v 
Promethean Spenta Technologies v 
Indonesia 
/SSEA
aQysta Nepal v 
Gham power v v v 
Egypt 
/MENA The Platform for Smart Solutions v v v 
Lebanon 
/MENA
Compost Baladi v v 
Robinson Agri v v 
Morocco 
/MENA
High Atlas Foundation v v v 
Sowit Maroc v 
Source: Dexis innovation survey (Innovation survey 7 11 22.xlsx)
Table 56: Numbers of end users using financing mechanisms for the innovation, by region (Gin28)
Region total number of end 
users in Q1 and Q2
end users that 
received financing
% Of Q1 
and Q2
% Of 
females
% Of end 
users in Q1
SSEA (n=3) 149,221 2,910 100% 41% 47%
MENA (n=0) 69,343 ND - - - 
SCA NYM NYM - - - 
Total 218,535 2,910 1.42%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx); the SSEA data is from 3 innovations 
(Onergy Solar, ATEC, aQysta).
As part of the end user survey, it was discovered that in SSEA (n=143) no end user secured 
financing for the innovation (93% said no, 10 did not respond, nobody said yes).
Table 57: End users reporting whether their income has increased as result of the innovation, by 
gender
Response (n=163) SSEA MENA
No 3 0 
Yes 111 19
% Yes 97% 100%
Respondent total 114 19
No response 29 1 
*the yes for MENA was attributed to polluted water 
Source: Cleaned September 20 2022 MENA End User WE4F Evaluation, cleaned tables end user data for SSEA September 19 2022
104 
Table 58: Jobs created through Program support, by innovation focus (Gin10) 
Innovation focus Total jobs Female jobs % Of jobs for women
Water 4 0 0%
Energy 24 10 42%
Water & energy 184 29 16%
Total 212 39 18%
*information from 28 innovations (13 SSEA, 15 MENA)
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Table 59: GHG emission saved (tCO2e year 1) by region (Gin6)
Region Total tCO2e % From women % From smallholders
SSEA (n=9) 66,232 55% 100%
MENA (n=7) 56,953,277 20% 16%
Total (n=16) 57,019,519 20% 16%
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) 
Table 60: Range of performance for end users (KPI2) and gross sales (Gin4)
Comparison to Year 1 Target KPI2 Gin 4 SSEA 
(KPI2)
SSEA 
(Gin4)
MENA 
(KPI2)
MENA 
(Gin4)
Surpassed Target by over 100% 6 8 1 2 5 6 
Surpassed Target by 0-100% 7 3 6 1 1 2 
Missed Target by 0-50% 7 7 2 3 5 4 
Missed Target by 51-100% 7 9 3 6 4 3 
Total 27 27 12 12 15 15
Source: WE4F Admin data (MENA, SCA SSEA compiled data.xlsx) and Innovations AWP
 
105 
ANNEX 9: COMMENTS ON INDICATORS
Table 63 discusses the indicators and the description provided. This table uses the WE4F 
MEL training and the WE4F Full indicator list. The training goes in-depth on sampling, data 
collection and -management, and on the indicators: how to use them to calculate impact. The 
Evaluation discusses the indicator information and offers suggestions, to make the finding, 
reading, and understanding of indicators more accessible.
Table 61: Comments on indicators
# Keywords WE4F description Recommended alternative (targets from Oct. 2022)
General Indicator descriptions lack details, e.g., units and (baseline, target) values (details kept in different 
places). The terminology to stakeholder-related units is confusing. Looking at the data, some 
indicators have considerably changed (e.g., Gin33).
KPI1 Profit Innovator successfully 
marketed their climate 
friendly, energy and/ or 
water efficient innovations 
with profit
KPI1 - Fraction of innovations (f,m) (in %) that makes a profit* from 
marketing WE4F supported innovations; baseline 0%, target: 8% of 
innovations, of which ¼ women-led) *: counting as ‘making profit’ is 
when the profit passes >8% EBITDA margin = EBITDA (earnings before 
interest, tax, depreciation & amortization) / Total Revenue. Earnings = revenue 
minus expenses
KPI2 End users No. of smallholder farmers 
and other end users using 
energy or water-efficient 
WE4F innovations in their 
activities
KPI2 – No. of end user household members* (f,m), by -household (hh) 
income quintiles** using energy or water-efficient WE4F innovations. 
*: this is a number obtained by multiplying end users with an average 
household size (national, regional, or rural household size?).
**: 5 quintiles: i) …; ii) …; iii) …; iv) …; v) … (define); (Q1, 2 are the poorer 
farmers, Q3, 4, & 5 better off)
RIH LOP targets: SSEA: 1,000,000; MENA: 750,000; SCA: 1,000,000
Year 1 targets: SSEA 70,000; MENA 37,500
The gender disaggregation here is linked to household members’ 
gender but linked to the innovation’s gender (then multiplied)
NB: program LOP target (originally 2,400,000, now 2,500,000) is 
lower than total of RIH targets (source: L. McMahan)
KPI3 Food 
produced
Total mass of food produced 
as a result of WE4F 
innovations
KPI3 - Food produced (t) as a result of WE4F innovations, by end user 
type*; baseline: 0 t; *: types: i) smallholder farmers; ii) larger farmers; 
iii) small companies; vi) other companies; v) coops
RIH LOP targets: SSEA: 3,000,000 t; MENA: 2,025,000 t; SCA: 
1,300,000 t 
Year 1 targets: SSEA 170,000 t; MENA: 101,250 t
Recommendation: Recalibrate farmer end user types (smallholder vs 
larger farmers), taking into account national or local agroclimatic
contexts in the countries/regions. 
KPI4 Food 
processed
Total food processed as a 
result of WE4F innovations, 
by end user type
KPI4 - Food processed (t) as a result of WE4F innovations, by end user 
type*; baseline: 0 t
RIH LOP targets: SSEA: 100,000 t; MENA: 810,000 t; SSEA: 475,200
t; Year 1 target: SSEA: 1,500; MENA: 40,500 t 
*: types: i) smallholder farmers; ii) other farmers; iii) small companies; 
vi) other companies; v) cooperatives
KPI5 Energy 
saved
Total energy saved in the 
food value chain as a result 
of the use of WE4F 
innovations
KPI5 - Total energy saved* (kWh) by end user type** as result of the 
use of WE4F innovations; baseline: 0 kWh
RIH LOP targets: SSEA: 400,000,000 kWh; MENA: 20,000,000 kWh
*: saved compared to alternative technology, or practice before the 
innovation was used; **: types: i) smallholder farmers; ii) other 
farmers; iii) small companies; vi) other companies; v) cooperatives
106 
# Keywords WE4F description Recommended alternative (targets from Oct. 2022)
KPI6 Water 
saved 
Total volume of water 
consumption reduction in the 
food value chain as a result 
of WE4F innovations
KPI6 - Total water consumption reduction* (l) by end user type** as 
result of the use of WE4F innovations; baseline: 0 l
RIH LOP targets: SSEA: 1,000,000,000 l; MENA: 750,000; SCA: 
5,000,000 l
Year 1 targets; SSEA 6,000,000 l; MENA: 37,968,750 l
*: saved in irrigation: compared to alternative technology, or practice 
before innovation was used; saved in water storage: storage capacity x 
times filled x % used in agriculture; saved in water re-use/ treatment: 
water re-used or treated, used in agriculture; saved in aquaculture/water 
re-use: water input into - water released + water used for irrigation
**: types: i) smallholder farmers; ii) other farmers; iii) small 
companies; vi) other companies; v) cooperatives
KPI7 Income No. of smallholder farmers 
and other end users that 
experience an increase in 
income
KPI7 – No. of end user farmers (f,m), by -household (hh) income 
quintiles* that experience an increase in income, multiplied by av. hh
size/country; baseline: 0; 30% female
RIH LOP targets: SSEA: 400,000; MENA: 57,500; SCA: 100,000
Year 1 targets: SSEA 50,000; MENA 15,000
KPI8 Monitoring 
tools, 
methods
Share of innovators a.o. 
stakeholders using tools, 
methods, or processes to 
monitor the protection of 
water or biodiversity
KPI8 – Share of innovations using tools, methods, or processes to 
monitor the protection of water or biodiversity. Target: 80% NB: other 
stakeholders is not very specific.
(this is a RIH summary indicator to be found in RIH reporting)
KPI9 Investment
(outcome 
2)
Value of investment in US 
Dollars that WE4F 
innovators have mobilized 
from external sources
KPI9 – Value of investment ($) by investment type* that WE4F 
innovations have mobilized from external sources; baseline: $0 *: 
types: i) public/cash, ii) public/in-kind; iii) private/cash, iv) private/in-kind; v) 
unknown
RIH LOP targets: SSEA: $20,000,000; MENA: $7,500,000; SCA: 
$7,500,000. Year 1 targets: SSEA: $7,500,000; MENA: $300,000
KPI
10
Lessons 
adoption
No. of strategies, guidelines, 
projects of international, 
regional, local organizations 
adopting, disseminating 
lessons from WE4F 
publications, events, 
presentations
KPI10 – No. of strategies, standards/regulations, guidelines, or 
projects of international, regional, local organizations adopting, 
disseminating lessons from WE4F publications, events, or 
presentations
Gin4 Gross sales Innovators’ gross sales in the 
field of WE4F
Gin4 – Innovations’ gross sales from marketing WE4F supported 
innovations; baseline: $0; 
RIH LOP targets: SSEA: -; MEA: -; SCA $2,250,000
Gin6 GHG 
emission 
reduction
Total GHG emission saved 
per year by end users 
through use of 
products/services of WE4F 
innovators
Gin6 – Total GHG emission (tCO2) saved* by year by end user type**
and -gender, through use of WE4F innovations; 
*: standard tool: CLEER; **: types: i) smallholder farmers; ii) other 
farmers; iii) small companies; vi) other companies; v) cooperatives
baseline: 0 tCO2, RIH LOP targets: SSEA: 200,000 tCO2, MENA 2,530 
tCO2; SCA: 3,444 tCO2 
Gin10 Jobs No. of new jobs created in 
WE4F innovator companies
Gin10 – No. of new jobs created in WE4F innovation companies; 
baseline: 0 jobs, target: 100 jobs/country-innovation
Gin20 Innovation
partnership
No. of external partnerships 
formed by innovators
Gin20 - No of external partnerships formed by innovations (no change)
Gin27 RIH 
partnership
No. of external partnerships 
formed by RIH
Gin27 – No. of external partnerships formed by RIH (no change)
Gin28 End user
financing
No. of end users using 
financing mechanisms 
adapted or created through 
WE4F engagement
Gin28 – No. of end user farmers (f,m) by -household (hh) income 
quintiles* using financing mechanisms adapted or created through 
WE4F engagementt, multiplied by av. hh size/country; baseline: 0; 
target: t.b.d. *: quintiles 1 and 2 
Gin33 Innovation
co-funding
Share of co-funding 
provided by innovators to 
match grant for 
developing/advancing 
innovation 
Gin33 - Share of innovations’ own co-funding (%)
to match WE4F grant
107 
# Keywords WE4F description Recommended alternative (targets from Oct. 2022)
Gin40 Investor 
contacts
No. of contacts of potential 
investors provided to 
innovators 
Gin40 – No. of contacts of potential investors provided to innovations 
(not changed)
Gin41 Financial 
guarantee 
instruments
Share of innovators that 
made use of the financial 
guarantee instruments
Gin41 - Share of innovations that made use of the financial guarantee 
instruments
(not changed)
Gin45 Area 
improved
No. of hectares under 
improved management 
practices 
Gin45 - Area (ha) under improved management practices as a result of 
WE4F innovations; baseline: 0 ha
RIH LOP targets: SSEA: -; MEA: -; SCA 500,000 ha
Sources: WE4F MEL Training August 2022.pptx; WE4F Full PIRS_July22.docx; WE4F KPI 
PIRS_July22.docx WE4F PMEP_July22.doc; USAID RIH Targets_Oct2022 
New indicators from 2021 Annual Report 
Innovations set their own targets as part of indicators that look like some of the WE4F 
indicators. For example, WE4F measures the total number of end users in KPI2, whereas 
innovations measure their results against their own targets.
In its annual report 2021106 the WE4F program suggests additional indicators, which are now 
added: 
1. Quality of service score; target: 80% in the categories “very” a “extremely’’ 
positive/effective (assuming score 8/10 or higher) – in section 3.3 on Effectiveness 
2. Proportion of innovations reaching their end user targets; targets: i) 40% reach within 
80% of their own targets, and ii) 10% of innovations reach >100,000 customers/end 
users. 
106 WE4F annual report 2021, p7 for the first indicator, p47 for the second indicator. https://we4f.org/wp￾content/uploads/2022/05/WE4F-2021-Annual-Report.pdf 
108 
Table 62: Indicator updated targets (source: USAID RIH Targets_Oct2022.gsheet)
SC Africa RIH MENA RIH SSE Asia RIH USAID Secretariat 
CY21 CY21 + CY22 LOP CY21 CY21 + CY22 LOP CY21 CY21 + CY22 LOP LOP target revised
KPI1 0% 8% 8% 8% 8% 8% 0% - 8% 8% (1/4 women)
KPI2 0 200,000 1,000,000 37,500 225,000 750,000 70,000 X 1,000,000 2,500,000
KPI3 0 264,000 1,300,000 101,250 607,500 2,025,000 170,000 X 3,000,000 5,500,000
KPI4 0 95,040 475,200 40,500 243,000 810,000 1,500 100,000 2,125,000
KPI5 - - - - - 400,000,000 - - 20,000,000 675,000,000
KPI6 0 1,000,000 5,000,000 37,968,750 227,812,500 750,000,000 6,000,000 - 1,000,000,000 2,000,000,000
KPI7 0 20,000 100,000 15,000 90,000 57,500 50,000 - 400,000 557,500
KPI8 0% 80% 80% 0% 80% 80% 80% 80% 80% 80%
KPI9 - 250,000 $7,500,000 $300,000 $1,500,000 $7,500,000 $7,500,000 - $20,000,000 $34,000,000
Gin4 $250,000 $1,000,000 $2,250,000 - - - - - - $75,000,000
Gin6 - 500 3,444 320 900 2,530 690 2,071 200,000 500,000,000
Gin45 200,000 500,000 - - - - - - 500,000
The total of RIH targets does not add up to the USAID target, in most cases the RIH targets total is higher because some are expected to miss their targets; sometimes lower 
because they haven’t set their targets yet (re. innovations’ targets). In general, 3-step process: 
Step 1: ToC phase, high-level program targets, based on work of Syspons in facilitation sessions to make the ToC.
In a political combination, some targets lowered (GIZ was uncomfortable w high numbers). USAID secretary would be responsible for 75-80% of the target, e.g. of 
3,000,000 because we know from SWFF how we collect data.
Step 2: Some KPI targets, USAID wrote them into the RIH contracts, KPI2 are hard targets. LOP of SCA 1,000,000 and MENA 750,000 is a hard target, related to 
contractual responsibilities. Other targets are technical targets.
Step 3 (‘third level’): mutually agreed targets between McK and L. McMahan, re. KPI and Gin.
Some had not set their targets yet as data not available from innovations. Innovations are over-ambitions (‘at the edge of realism’).
KPI2 & 3: KPI2: 2.5m lower than the RIH total, because GIZ political accommodation. KPI3 idem.
KPI4 the opposite: RIH target is much lower, they thought there would be more done in energy , food processing than we now see. They’ll probably revise these. 
KPI5: not every RIH made it
KPI6: RIH combined will exceed that significantly (given experience w SWFF).
KPI7: target is balanced; aggregation of RIH targets. 34m for WE4F, but RIH is lower but based on previous program we think we’ll meet the overall target. 
KPI9: add up RIH targets, are higher and they already exceeded these targets.
Gin4 and Gin45: aren’t there yet, not enough data.
Gin6: RIH target for SSEA is higher than MENA, SCA. Those are going to be revised in January.
RIH target are ambitious but realistic. Program targets rise, as GIZ set too-low targets (GIZ has more money than we have, we 34m$, they 34-35m$).
109 
ANNEX 10: THE EVALUATION TEAM
David Hemson, Team Leader 
Dr. Hemson is a senior researcher and evaluator with 24 years of experience in water services 
and resource management, water policy, climate change, clean energy policy, social policy, 
and gender analysis. His research includes public private partnerships, performance and 
impact evaluation, gender analysis, rural development, infrastructure, labor market analysis, 
and disaster risk reduction. Dr. Hemson’s extensive evaluation experience includes serving as 
the Team Lead for the SWFF Evaluation, for which he designed surveys and qualitative 
research instruments, supervised data collection in seven countries, and led the production of 
evaluation reports. He was the Director of the Social Policy Program at the University of 
Durban-Westville and the Research Director with the Human Sciences Research Council, a 
statutory research institution in South Africa. He wrote numerous research reports, published 
peer-reviewed articles and book chapters, reviewed manuscripts on water, sanitation, and 
hygiene issues for publication and edited a book on poverty and water. He has a Ph.D. in 
Sociology from the University of Warwick, and a BA in African Studies from the University 
of Natal. His country experience includes Ethiopia, Bangladesh, India, South Africa, and 
Indonesia. 
As the Lead Consultant, Dr. Hemson will be the primary interlocutor with the Contracting 
Officer’s Representative (COR) and the Founding Partners. He will develop the workplan, 
evaluation design and data collection templates, and be responsible for the quality and timely 
execution of evaluation reports. He will ensure all deliverables adhere to Organization for 
Economic Co-operation and Development's Development Assistance Committee Quality 
Standards for Development Evaluation, European Union’s General Data Protection 
Regulations, and the USAID Evaluation Policy. 
Elise Pinners, Senior Consultant 
Ms. Pinners has over 15 years of evaluation experience in agriculture, farming systems, 
agroforestry, land tenure, and gender. She has conducted evaluations for numerous clients 
and brings extensive German public sector experience. Ms. Pinners is currently leading an 
evaluation Low Emission Development Strategies in eight countries across Africa for the 
United Nations Environment Program. Ms. Pinners holds an MSc in Tropical Agriculture.
She is fluent in English, French, Dutch, and Kiswahili and highly proficient in German. 
As one of the two WE4F Evaluation Senior Consultants, she will work closely with the lead 
consultant in the development of the evaluation design and data collection instruments. She 
will also work closely with Ms. Sarah Mangones during the data collection to provide quality 
checks on the incoming data from the Local Evaluators, to train the evaluators in each region, 
and to contribute to the data analysis. 
Sarah Mangones, Senior Consultant
Ms. Mangones brings twelve years of experience with water/environmental management, 
monitoring and evaluation, data collection, and report analysis and writing. In addition, Ms. 
Mangones brings experience within the agricultural sector, water, and sanitation sector, as 
well as the housing and urban development sector. She designed and wrote proposals 
(regarding solar systems, gravity fed spring systems etc.) for current and future water 
projects. Additionally, she collected geophysics data, information on water sources 
supply/water quality, well tests for feasibility studies, and developed a sanitation program to 
be implemented in rural communities throughout Haiti. Ms. Mangones has considerable 
110 
experience with the process of assuring compliance with USAID environmental standards.
She holds a MA in Environmental Engineering from Michigan Technological University, a 
Graduate Certificate in Monitoring and Evaluation from American University and received 
her BA in Chemical Engineering from the Illinois Institute of Technology. 
As one of the two WE4F Evaluation Senior Consultants, she works closely with the lead 
consultant in the development of the evaluation design and data collection instruments. She 
also works closely with Ms. Elisabeth Pinners during the data collection to provide quality 
checks on the incoming data from the Local Evaluators, to train the evaluators in each region, 
and to contribute to the data analysis.
Virendar Khatana, SSEA Region Local Evaluator 
Dr. Khatana brings regional Southeast Asia experience and over 25 years' experience in 
designing and managing development sector projects in the field of resilient agriculture, rural 
livelihoods, education, poverty and vulnerability, and climate change related issues, including 
in remote rural and conflict affected areas. Dr. Khatana holds an MSc in Management as 
well as an MA in Geography (specialization in Agricultural & Rural studies), and a PhD from 
Meerut University. He is well versed in data collection methods, how to conduct key 
informant interviews, focus group discussions, and has done data collection for projects in 
several Asian, African, European, and American countries. 
As one of the Local Evaluators, Dr. Khatana is responsible for data collection taking place in 
the Southeast Asia Region. In this role he is responsible for submitting data collection 
updates, data, and feedback on the surveys to his point of contact throughout the evaluation. 
Additionally, he carries out site visits and builds relationships with the regional hubs, 
Innovations, and end users. 
Samir ElGwely, MENA Region Local Evaluator 
Dr. ElGwely brings MENA regional experience and over 17 years’ experience in designing 
and managing development sector projects in the field of rural development, water 
management and sustainability, agricultural management and climate change adaptation, 
poverty, and gender related issues. Dr. ElGwely holds a PhD and MSc in Agricultural 
Extension, as well as a BSc in Agricultural Sciences. He has a strong background in the 
design and implementation of evaluation studies, capacity building, needs assessment studies, 
and Farmer Field Schools. His strengths in data collection and monitoring are centered 
around his experience primarily working in the MENA region (Egypt Jordan, Lebanon, Iraq). 
As one of the Local Evaluators, Dr. ElGwely is responsible for data collection taking place 
in the Middle East North Africa Region. In this role he is responsible for submitting data 
collection updates, data, and feedback on the surveys to his point of contact throughout the 
evaluation. Additionally, he carries out site visits and builds relationships with the regional 
hubs, Innovations, and end users. 
Adelaide Bryan, Project Management Unit, Evaluation Specialist 
Ms. Bryan has over 8 years of experience with Monitoring, Evaluation, and Learning projects 
operating in conflict, transitional, or post conflict environments throughout the Middle East 
and North Africa region. Ms. Bryan holds an MA in Arab Studies from Georgetown 
University’s School of Foreign Service. She also holds a BA in Anthropology and Near 
Eastern Languages and Cultures, with a Minor in Linguistics from Indiana University. She
has worked in Egypt India, Iraq, and Turkey, as well as worked remotely based in 
Washington DC on projects in Jordan, South Sudan, and Syria. 
111 
Ms. Bryan provides in-house subject matter expertise and review technical deliverables. She 
participates in project management and supports the Project Management Unit (PMU). Ms. 
Bryan also establishes a continuous feedback loop between the WE4F Evaluation Team and 
Client to assess performance standards and overall satisfaction. Lastly, she supports with the 
creation of infographics, internal and public-facing webinars, as well as public presentations 
of key results and recommendations.
Laurah Hester, Project Management Unit, Project Associate
Ms. Hester has over 3 years of professional experience supporting global development and 
security programs with a specialization in quantitative/qualitative research and analysis. Ms. 
Hester holds an MA in International Affairs from The Hertie School, as well as a BA in 
International Relations: Politics & History from Jacobs University. Her country experience 
includes Angola, Brazil, China, France, and Germany. 
Ms. Hester provides administrative support including procurement, international travel, and 
logistics support. She supports mobilization and management of the consultant team. As the 
Project Associate, she reviews monthly field expenditures and processes expense reports. She 
also maintains consistent communication with all personnel to respond to needs and support 
accurate timekeeping. Ms. Hester organizes project records and files and reviews and edits 
routine progress reports for quality control. She supports project procurements in accordance 
with client and Dexis policies as well as provides surge support for the recruitment of 
consultants. 
112 
ANNEX 11: LIMITATIONS, RISKS, AND EVALUATION MITIGATION STRATEGIES 
Table 63: Risks and the evaluation mitigation strategies
Risks Mitigation Measures Taken
COVID-19 Pandemic and Environmental Risks
Difficulties in getting face-to-face time with 
innovations to capture their views and observe 
the conditions of innovations on the ground 
(due to Covid)
All protocols and instructions include that COVID-19 
rules should be followed, venues chosen wisely, 
distances kept, masks worn, and whatever rules that 
apply be followed. When absolutely necessary, face to 
face interviews will be replaced by online interviews; in 
that case, site visits will also be managed virtually. RIH 
will propose a list of key resource persons and propose 
which ones will be interviewed how (face to face where 
possible, or online, or by phone). Attention will also be 
paid to avoid scheduling data collection activities over 
major religious and public holidays. 
Covid-related delays may affect the evaluation, 
just as it also slowed the launch of RIHs and 
prevented program activities from being 
implemented
COVID-19 related disruption particularly on RIHs have 
made intense demands on staff and constrained 
availability of staff for KII. Risks related to COVID-19 
have been discussed above, in kick-off interviews with 
RIH, who can give their assessment on COVID-19 
situations in the different countries, and possible effects 
on the evaluation.
Environment-related delays, particularly 
synchronizing field surveys after the harvest 
season. Timing needs to be coordinated with 
planting/harvest as well as Evaluation Team
availability. Travel may also be affected by 
dry/rainy seasons and determine anticipated 
timelines.
Risks related to environmental factors will be discussed 
early on in the planning and scheduling of RIH data 
collection efforts. To the extent possible, the Evaluation 
Team will sequence data collection site selection in 
correspondence to the agricultural seasons and analyze 
the administrative data during the agricultural off 
seasons. 
Undertaking a Data Quality Assessment (DQA) 
of the Administrative Data, closing gaps in 
availability and developing a dataset with high 
levels of verifications
The Evaluation Team to the extent possible will 
sequence the data collection and review of the 
administrative data in relation to when the RIH are 
launched and implementing.
Data Collection and Analysis Risks
Insufficient access to customers and non￾customers to understand surveys of sufficient 
numbers of end users to ensure confidence in 
the success factors for the innovations. 
Efforts will be made to plan as far in advance as possible 
with the innovations to impress upon them the need to 
meet with as many customers and non-customers as 
feasible within the allotted time.
In addition, access is needed to Customer Response 
Strategies which the Innovations have undertaken.
113 
Risks Mitigation Measures Taken
Insufficient budget and time to undertake a 
representative sample
On sampling, at this stage, the team does not have a full 
list of estimated end users per innovation, and their 
locations. Site selection and cluster sampling at the site
is critically important in sampling. Questions to help 
establish 'representativeness' will be raised in the survey 
and in KII’s.
For efficiency, support from the innovation will be 
requested in terms of orientation and travel to the site 
and advice is also requested on translation as needed. If 
possible, local maps will be accessed in advance of the 
consultant proceeding to the field to undertake surveys.
The high number of Evaluation Questions and 
the availability and willingness of interviewees 
to respond with detailed or complete answers.
Establishing questionnaires for KIIs and surveys, to elicit 
meaningful and accurate responses, is difficult; and 
responding requires lengthy time commitments from 
interviewees. The team is working on minimizing the 
interview length while maximizing the data gathered 
through the questions. Questionnaires will be tested. 
The team will also use the Administrative Data to 
supplement field data. 
Possible uneven quality of data gathered by 
external surveyors lessens confidence in 
indicators of results.
Confidence in the data is being ensured by careful 
construction of questionnaires on a selection of 
indicators and preparation of local consultants. The 
interviews include some questions which overlap to 
some or other extent with each other, allowing for 
triangulation. This is also done for similar questions 
asked to both end users and innovations, or both 
innovations and RIH.
As part of the Dexis quality assurance, every deliverable 
will pass from LC Hemson to the PMU of WE4F, for a 
final quality check before submission to the client.
Timeline for the recruitment of the 
local/regional consultants for the data collection 
The PMU will work closely with the Evaluation Team to 
recruit in accordance with the sequence of data 
collection sites to mitigate potential delays to data 
collection. 
Data assembled by M&E and Dexis may not 
reflect responses to the same questions or use 
precisely the same terms and definitions
The Evaluation design provides details on the common 
instrument for use by the HIB and Dexis in surveys of 
end users. This will relate to the alignment of questions 
and data. During the data assembly process there has 
been an intensive DQA which included the alignment of 
questions and data in columns and rows. In the first stage 
of analysis any irregularities will be noted and resolved.
In the KII, qualitative data will be coded and entered into 
Nvivo, which will be adapted to each region/country to 
take into account any different terms and definitions, 
which will be specified in a manual for the purpose.
114 
Risks Mitigation Measures Taken
Samples of innovations may not be sufficiently 
representative of the portfolio. The sample of 
end users may also not be sufficiently 
representative of all types and layers.
Budgetary and other considerations lead to relatively 
small samples being achieved among End user groups. 
We need to confirm with the RIH that the information 
being collected during visits is sufficiently diverse to 
cover as many different kinds of interventions across as 
many geographies within the portfolio as possible within 
the time and funding constraints. Given the diversity of 
projects and the purposeful element in sampling, 
findings from the sample may not infer findings in the 
universe of the WE4F program.
The methods of hierarchical modeling could assist in 
dealing with the challenge of nested data (i.e., the close 
association among respondents in the same cluster).
The sampling strategy for all research should be 
synchronized between M&E and Dexis and, if possible, 
a panel established.
Disaggregation of household income to that of 
women and men’s income is difficult to achieve
Since most women farmers participate in agricultural 
activities as part of a household together with their 
husbands it is difficult to measure their income 
separately (IM4.1). To mitigate this, the household 
income indicator will have an additional proxy indicator 
where women self-report: in a survey question on the 
estimated female/male share of the additional income 
and other benefits, and in qualitative interviews on their 
contribution and compensation in material terms. 
Each innovation is relatively unique and there 
is a challenge in ensuring comparability of data 
across this diverse group of innovations 
The WE4F Evaluation Team will need to be mindful of 
this challenge at all stages of the analysis and to assess 
and ensure comparability in undertaking analysis. 
The Evaluation Team does not know what data 
will look like from the Salesforce ICT platform 
and the timeline for data transfers. 
The WE4F Evaluation Team has worked closely with the 
WE4F Program through the DQA exercise to align Dexis 
and the Salesforce data and update the work schedule as 
needed. 
Information for certain KPI and Gin for some 
innovations is not available, due to lack in 
documentation, delay by external surveys
Through the DQA the evaluation has been confident to 
undertake the data analysis with the data provided as part 
of the final set shared by July 29th 2022 for MENA and 
SSEA.
The full set of SCA RIH information is not 
currently available 
The team will sequence MENA and SSEA RIHs first 
and will update the schedule and sampling with SCA 
Innovations once that data is available. 
Achieving knowledge and understanding of the 
relative contributions of various agencies 
within and without the Program.
The team will work to triangulate from multiple sources 
in assessing contributions and proportional contributions 
from many points of support.
In addition, process tracing will be planned and 
undertaken to trace the movement from input to output 
and finally to impact on end users.
115 
Risks Mitigation Measures Taken
The comparability across different GCF in 
terms of costings is proving challenging as they 
each operate in different ways and offer 
different support. Finding the right comparators 
may not be straightforward.
The Evaluation Team recognizes that this may be a 
challenge and is working to reduce this risk particularly 
by liaising with Syspons to help clarify approaches to 
this issue and seeking advice from policy makers who 
have experience in a number of GCF.
Incomplete compliance with the European 
Union’s GDPR
The Team Leader is working closely with the PMU to 
ensure that all data be handled according to the EU's 
General Data Protection Regulations (GDPR 
2016/679)107 as well as USAID's Evaluation Policy. 
Difficulty in assembling responses from control 
group without having an incentive to report to 
this WE4F Evaluation Team. 
The evaluation has attempted to construct controls but 
there have been many challenges. In the interview 
introduction (protocol) will explain that the interview 
questions are not just to gather data but also useful to 
reflect on one's own business.
Further, a lottery will determine which control group 
company is invited to one (or several) innovating 
companies to (volunteer to) host a peer learning 
gathering on the subject of agri-water-energy innovation.
Potential volunteer company (or companies) are 
approached informally through WE4F's network 
(alternatively, interviews with WE4F innovations could 
end with a question to ask whether they would be 
interested to host a meeting between companies). 
Defining smallholders & farm size In the definition of terms, the Evaluation Team has 
defined smallholders and farm size. This will be 
reassessed after the conclusion of end-user surveys. 
Impact 3, Illustrative Indicator (II-IM3): on 
GHG emission: "Total GHG emission/year 
saved by end users through use of products/ 
services of WE4F innovations." The impact 
notes 'along the food value chain' and the II￾IM3 secondary data source is 'lifecycle 
assessment'. This may suggest a wider scope: 
the emission effect along the lifecycle of the 
innovation.
1. Estimating emissions is a herculean task; the Team 
will explore the CLEER Tool as a resource as a way 
of assessing pre/post changes in emissions. The 
following need to be undertaken:
2. The Evaluation will quantify the fuel effect directly 
at farm/end user level, comparing pre- and post￾innovation fuel use (efficiency, fuel type change); 
this corresponds to KPI5 (total energy saved). 
3. The Evaluation will identify, by comparing pre-and 
post-innovation, non-fuel emission factors along 
the lifecycle of the innovation, on-farm (e.g. 
changing farm practices) and off-farm (through input 
use, by-products, transport, etc.).
But given the complexity, the emission effect cannot be 
quantified for a lifecycle of innovation-related products, 
and a comprehensive carbon balance not be produced.
107 The General Data Protection Regulation (EU) 2016/679 (GDPR) is a regulation in EU law on data protection and privacy 
in the European Union (EU) and the European Economic Area (EEA). It also addresses the transfer of personal data outside 
the EU and EEA areas. The GDPR's primary aim is to enhance individuals' control and rights over their personal data and to 
simplify the regulatory environment for international business.
https://www.oecd.org/development/evaluation/qualitystandards.pdf 
116 
Risks Mitigation Measures Taken
Definitions of indicators for the same term may 
differ. Defining environmental sustainability:
Emission is one element of sustainability and 
also biodiversity is to be integrated - often 
singled out - in the Evaluation's approach to 
assessing environmental sustainability. These 
indicators refer to environmental sustainability:
• IM1/KPI10: climate friendly, energy 
and other water efficient innovations 
• IM2/KPI2: energy and/or water 
efficient innovations 
• IM3/II-IM3a: GHG emission, and 
KPI5&6 (water- & fuel use efficiency) 
• IM3/II-IM3b: 'improved' (*sustainable) 
farming practice 
• OC1/II-OC1d/KPI8: monitor 
environmental gains 
• OP2/II-OP2d/KPI8: environment 
knowledge 
• The EQ offer yet more environment￾related questions. 
The Evaluation will use elements from the Agroecology 
framework108 to assess – beyond merely energy and 
water – the on-farm environmental effect of changes in 
farm practice (resulting from the innovation), with a 
focus on 1. Recycling (water, biomass); 2. Input 
reduction (water, fuel); 3. Soil health (fertilizer); 4. 
Animal health (manure); 5. Biodiversity (agro￾biodiversity and NRM); 6. Synergy; 7. Economic 
diversification.
See Figure 4: The AgroEcology framework.
Area under improved management practice - as 
impact-level indicator - lacks meaning without 
context (II-IM3b on): pre-innovation the area 
may be larger, or unrelated to post-innovation 
(e.g., when more harvest/year is possible, or the 
innovation leads to change of crops), some 
innovations are not area-specific, or require off￾farm resources (e.g., biomass from grazing 
areas), some innovations may help farmers to 
increase the (irrigated) farm area, and for 
biogas or processing-related innovations there 
is no clear link to area. Similar indicators: OC1 
and OP2 are similar, and II-OC1b,c,d and II￾OP2a,b,c are very similar, and OP2 is rather 
impact
The DQA involved the evaluation in assembling 
innovation-relevant data on production, crop areas, area 
under irrigation, to be used for various indicators related 
to production, yield, and effect on income. For some 
innovations this will produce a meaningful figure as 'area 
under improved management practice'.
The evaluation will combine the different-level 
indicators (while keeping the topics of gender, poverty 
and environment separate).
Uncertainty of terms; smallholder farmer. Base 
of the Pyramid: comparing KPI2 with KPI7 and 
assuming that use of the innovation improves 
income in nearly all cases, it appears that less 
than 6% of end users are smallholder farmers? 
The other 94% are larger farmers and some 
processing companies?
In the definition of terms, the Evaluation clarified what 
is meant by 'smallholder farmer'; is this an end user who 
is fully engaged in farming and having a field large 
enough to subsist on farming? Does this exclude the 
category “larger farmer”? 
The distinctions within the category smallholder farmer 
are important as we are often required to distinguish 
between farmers and marginalized farmers.
108 2020_08_21_Agroecology_ProSoil_print_version.pdf proposed by the HLPE in 2019
Level 1, 2, &3: 
Elements Principles 
Efficiency 2. Input reduction 
Recycling 1.Recycling 
Regulation 4. Animal health 
Diversity 
5. Biodiversity 
7. Economic diversification 
Resilience 7. Economic diversification 
Synergies 
3. Soil health 
5. Biodiversity 
6. Synergy 
117 
ANNEX 12: REPORT: DATA QUALITY ASSESSMENT OF M&E DATA
Purpose and Summary
This evaluation depends to a large extent on administrative data for many KPIs and GINs. 
This applies particularly to the number, location, and characteristics of the innovations as 
well as their performance indicators. 
Gathering the evidence to answer the evaluation questions relies on accessing this data, 
appraising its quality, and then compiling datasets for analysis. This covering note and the 
tables below serve to identify challenges in using the administrative data available and ways 
in which these could be resolved. 
Broadly speaking we identified challenges in two main areas: 
1. Aligning USAID and WE4F indicators 
2. Errors identified the data including missing characteristics
2. Accessing the full set of data for all indicators across all innovations. 
The use of Monitoring and Evaluation data is central to the datasets needed to be used in this 
evaluation. The access of such data and its integration into the datasets available to the 
evaluator and the assessment of data quality is a major issue in the evaluation. It has 
dominated interactions between evaluator and client. 
For practical reasons, the Evaluation Team has accepted that it should independently seek 
key baseline and subsequent data from the Innovations. Instead, it should a) accept the M&E 
data from the Program, assess its validity and then use this data and b) it should focus its data 
collection on surveys at the base of the pyramid and from surveys of Innovations and 
qualitative interviews with Innovation and Program officials.
Given the centrality of administrative data, the Evaluation Team has had to give close 
attention to the Data integrity of this data through a rigorous examination in the form of Data 
Quality Assessment. Data integrity has been the focus, to examine the design of the Programs 
database and to authenticate the metadata, procedures for data collection, internal verification 
of this data. We have worked to the establish the Programs’ methodology in data collection, 
procedures, error checking and validation routines and to also conduct an independent 
authentication of these procedures and the data itself. 
The processes and methodology of DQA involved firstly accessing the M&E data and then 
(broadly) validating and then verifying the data. Validation involved when a record is 
initially created or updated to establish, check, and assess the consistency and accuracy of the 
data; verification then followed as a recurring data quality process to establish the truth, 
accuracy, or reality. 
We have given close attention to validation of the M&E data as it was being migrated and the 
Dexis dataset assembled from this outside data source. Part of this migration process required 
validation that records fitted the full WE4F evaluation criteria and were copied accurately 
from the source system. Small errors in data arising from migration could result in continuing 
mistakes; it was also important to migrate only that data which was unambiguously accurate. 
Cross checks were needed to verify the information in the Dexis dataset system and ensure it 
matched that of the source.
118 
These processes were not separated in time. The challenges were those of returning to fully 
access the data (and to become familiar with the many data sources available from WE4F 
M&E), to build a Dexis master dataset which conforms exactly to the criteria of the KPIs and 
GINs (such as including disaggregation and other details), to verify the data when 
inconsistencies or inaccuracies appeared, and finally to locate and verify the reasons for 
empty cells.
Verification can involve sampling data from both the source and destination systems to 
manually verify accuracy, or it can involve automated processes that perform full verification 
of the imported data, matching all of the records and flagging exceptions. 
The team has worked within the overarching USAID's five quality standards—validity, 
integrity, precision, reliability, and timeliness in assessing the quality of data. These are 
defined as follows:
VALIDITY – Data should clearly and adequately represent the intended result.
INTEGRITY – Data collected should have safeguards to minimize the risk of transcription 
error or data manipulation.
PRECISION – Data have a sufficient level of detail to permit management decision making; 
e.g. the margin of error is less than the anticipated change.
RELIABILITY – Data should reflect stable and consistent data collection processes and 
analysis methods over time. 
TIMELINESS – Data should be available at a useful frequency, should be current, and 
should be timely enough to influence management decision-making. 
These are demanding criteria. Overall the quality assessment involves scientific ways to 
determine if data is usable as objective data for the purpose of evaluation. This involves 
establishing the metadata (such as in standardizing units), the serviceability (as in the 
measure of features that support the ease and speed of which corrective maintenance and 
preventive maintenance can be conducted on a system), reliability (to ensure data is complete 
and accurate as the foundation for building data trust) and the structuring of data (to ensure 
that data is a standardized format).
There are also “common sense” criteria such as logical sequences in data: increased funding 
cannot be represented by declining numbers. Data cleaning involves the identification of 
open cells which could represent None (0) i.e. nothing, such as no cattle, a missing value 
(ND), or a skipped value (N/A). The Team has examined the strengths and limitations of 
data, the checks by USAID M&E team and the overall usability of the data.
An essential aspect is that the data is standardized to make it usable within the Program as 
also in the evaluation itself. Data standardization is the critical process of bringing data into a 
common format that allows for collaborative research, large-scale analytics, and sharing of 
sophisticated tools and methodologies. 
The Evaluation Team is undertaking the following procedures in data quality assessment: 
extract, prepare, explore, and check, validate and, finally, use and analysis. 
119 
Figure 5: From extraction to use: 5 Steps in Data Quality Assessment
Source of DQA criteria: Conducting Data Quality Assessments
Key procedures in assessing quality of M&E data
The full access and use of M&E data involves processes advancing from validation of 
accessed data migrated from M&E tables to the verification involving the checking of this 
data to ensure that it is accurate, consistent, and reflects its intended purpose.
From a study of the procedures of DQA set out in USAID documents, the focus in data 
quality procedures, will be on the following generic issues: 
1. EXTRACT: Definitions of indicators in PIRS and on types of disaggregation 
2. PREPARE: Methodology in data collection used by M&E identified 
3. EXPLORE AND CHECK: Use of data collection procedures by Innovations 
identified and use confirmed 
4. VERIFY: Use of data quality procedures by USAID confirmed by evaluator 
5. USE AND ANALYZE: Data verification by USAID undertaken and confirmed 
The identified issues are shared in Table 67 and classifies the issue according to the USAID 
DQA criteria: Validity, Integrity, Precision, Reliability and Timeliness. Some of the key 
deficiencies included: 
1. Clarification (14) 
2. Missing data (8) 
3. Contradictory data (4) 
4. Incorrect total (2) 
5. Unexplained change (2) 
6. Missing characteristic (1), ...etc 
It is important to note that the final data for the evaluation was provided in late August 2022, 
but some data was shared beforehand so work initially started in the design phase. This 
proved difficult as information was being updated and needed to be checked again as new 
information became available. 
1 Extract 
1. Access M&E data source 
2. Establish definitions of PIRS, clarity on 
disaggregation 
3. Build full set of innovations 
4. Identify irregularities in data 
2 Prepare 
1. Data cleaning: identifying missing values + 
2. Methodology identified in data collection 
3. Establish metadata incl duration/time span 
4. Data size-volume, duration, sampling 
3 Explore and check 
1. Check USAID uses prescribed 
methodology 
2. Confirm USAID verifies data 
3. Update definitions as needed 
4. Procedures for standardization 
4 Verify 
1. Check M&E method and procedures practiced at source 
2. Add innovations and update data over time 
3. Check use of algorithms to establish values 
4. Assess validity: units, procedures at source and USAID 
5. Convert/format data in a common software dataset 
5 Use and analyze 
1. Perform Initial Data Analysis 
(IDA) as possible 
2. Create tables 
3. Check against other data sources
120 
Table 64: Types of challenges in data quality (issues and limitation)
USAID Data Quality Assurance 
criteria
Identified issues Resolution
1 VALIDITY – Data should clearly 
and adequately represent the 
intended result. 
Missing characteristic
Unexplained change 
Clarification needed
A table was shared with specific 
questions regarding numbers and 
units missing or not making sense 
2 INTEGRITY – Data collected 
should have safeguards to 
minimize the risk of transcription 
error or data manipulation. 
Missing characteristic 
Missing data, Incorrect total 
A table was shared with specific 
questions regarding numbers and 
units missing or not making sense 
3 PRECISION – Data have a 
sufficient level of detail to permit 
management decision making; 
e.g. the margin of error is less 
than the anticipated change. 
Data Clarification and Missing 
Data 
Some data not disaggregated by 
country or gender (Clarification 
14?)
The PIRS for Gin6 Carbon 
emissions
When possible, this is done, if it 
was not done then the data is not 
there, will have to proceed as is 
The PIRS was updated for ease of 
use and now it is tCO2 rather than 
tCO2 per kg of food) 
4 RELIABILITY – Data should 
reflect stable and consistent data 
collection processes and analysis 
methods over time. 
Procedure/formula not available or 
incorrectly applied
Unexplained changes (2 
instances)?
Innovations did not provide proper 
documents so either reported as 
No Data or at 80% of results 
reported (see table 2)
Continuous training has been 
undertaken by the innovations and
has been shared with the ET
5 TIMELINESS – Data should be 
available at a useful frequency, 
should be current, and should be 
timely enough to influence 
management decision-making. 
Contradictory data eg target lower 
than baseline, some older data still 
in the table 
The AWPs were updated while the 
ET was working on the data 
analysis this information had to be 
updated to continue the analysis
The baselines were set at “0” and 
“No” 
Questions regarding older data 
and/comments were addressed
New AWP information was used
Table 64 shows the criteria for DQA that is used by USAID and aligns the issues that were 
identified in the data for each criteria and shares the resolution. 
The numbers indicate the frequency with which these data quality issues occur. 
Some of the data quality issues arise at the Innovation level. 
Many of these issues are noted by M&E and corrected. 
The detail for these issues is presented below in Table 69. In Table 69 the issues are 
organized by Region and for issues that affect all the data. These issues were identified 
during the analysis and resolved with the support from the USAID secretariat on phone calls, 
emails, and comments on the shared document. 
Summary of limitations
Table 78 lists out the limitations found in the M&E data and the 81 AWPs. For many of these 
there no current resolution, as this is the data that is currently available for the Evaluation 
Team. These limitations remain after the verification process in the data analysis. It is 
possible that improvements can be made as the project moves forward that allows for easier 
data analysis for the final evaluation. Some issues that could be addressed or improved upon 
include: 
a) Not all AWP include such objectives with associated KPIs
b) The RIH have different criteria for reporting.
121 
Table 65: Summary of Data Limitations
No. Characterization Issue/Question Source
1 Validity
Results reported as zero since no documentation is provided and/or 
the information is not available as it will be collected by an external 
surveyor, or innovation miscalculated the results
SSEA and MENA 
Gin28, KPI7
2 Integrity Some of the target data is missing for indicators SSEA CFI1 AWP
3 Precision
There are formatting differences for the RIH AWPs, some 
innovations do not have the same format for the numbers (for 
example KPI1 there are some numbers instead of the EBITDA %)
MENA, SSEA and 
SCA
4 Precision The targets and results provided in the AWP and the M&E data are 
not disaggregated by gender, country, income quintile 
AWPs M&E data, 
KPI5, PKI6, Gin6, 
etc.
5 Precision Uncertainty about how many countries an innovation may have or is 
currently working in 
M&E data and 
AWPs
6 Precision For some innovations’ results the number is reduced to 80% the value 
due to lack of details on end users KPI2, etc
7 Precision Baseline is assumed to be zero and “No” KPI1, Gin13, 
Gin23, Gin41 
8 Precision
Due to limited information the gender was disaggregated based on 
country demographics of end users not on information from the 
innovation
SSEA and MENA 
Gin6, KPI7, others
9 Reliability The SSEA CFI1 AWPs do not contain a budget sheet which would 
show the contributions from other funders SSEA CFI1 AWP
10 Reliability Do not have country, focus area, and innovation type, sector focus 
and women led for all innovations 
M&E data
11 Reliability
Uneven data availability: currently only information for CFI1 SSEA 
and MENA are available. (As of August 15th, no data has been 
received for SCA)
M&E data
12 Reliability
There are different reporting requirements for TA only, Legacy and 
CFI1 and CFI2, also some KPIs and Gins do not apply based on the 
type of innovation
M&E data, AWP
13 Reliability
Regarding innovation targets, unfortunately there's no single 
document which compiles innovations' targets. The targets are listed 
separately in each innovation's AWP/TWP (81 total)
AWP
14 Timeliness
Data being updated: The LOP and yearly targets were updated for 
SSEA and MENA in August 2022, new data had to be incorporated 
into the MTR. 
AWPs
Classification of Data 
As part of the data analysis and to ensure that it is clear that all cells in the M&E are filled in
the following codes were used. In undertaking the verification of the data, the ET did not 
want any empty cells as this could be confusing regarding what information was missing or 
not reported. Results are reported as follows:
• ND - no data. This is used when there is a target for an indicator and there should be 
results, but no information was provided or possibly no documentation was provided so 
the results were recorded as 0 another possible issue was that this information was to be 
shared by the external surveyor, but this is not yet available. 
• NA - non applicable. This is used mostly for innovations that have been terminated from 
the project and so there is no longer information coming from these innovations, so the 
results are non-applicable at this point. 
• NAWP - Not part of the AWP. This is used when the indicator is not part of the AWP, so 
there is no target for this indicator for the innovation and the innovation is not tracking 
this information. 
122 
• OT - One Target. This is used when there is only one target provided for multiple 
countries that are part of the innovations project but there are not targets or results 
disaggregated by country 
• NT - No Target. This is used when there is no target provided for that Year or LOP in 
AWP. But there were other targets for other milestones. 
• NYM - Not yet measured. This is used when results are not measured as these projects or 
innovations started late, this is usually the case for CFI2, CFI Iraq and SCA.
123 
Classification of data issues 
Table shows the issues that have been identified when reviewing the M&E data and the AWPs from the innovations. This data was first 
reviewed by the USAID secretariat and then shared with the ET. During the review it was noted that the format and details from the hubs were 
different. There seemed to be more incorrect addition and typos in the data coming from the SSEA hub. 
Table 66: Schedule of detail in registering and resolving data issues
No. Type of Issue Issue/Question Resolution
SSE Asia 
1 Incorrect total In KPI2 for Pumpkin Plus the total did not add up Total is 3108
2 Incorrect total Gin6 Claro Energy the inputs for male and female did not add up Small holder farmer is 17000
3 Contradictory Data Gin28 aQysta had contradictory information from old comments in the M&E file Numbers remain as is
4 Missing 
characteristics KPI9 Oorja the type of investment was not included in the file Missing data
5 Missing data Gin1 Zoo Fresh, Sumba Solutions, Promethean Units for Foods not provided
Units added
ZOO Fresh # of fish orders
Sumba 13 active mills
Promethean –milk chillers
6 Data Clarification Do not have Gin2 numbers for SSEA innovations Not part of AWP, but will double check
7 Data Clarification How should part time jobs be counted for Gin10 Overall total - 
8 Data Clarification KPI6 a unit of kL but appears in litres was used for KPI6 for Onergy 
9 Contradictory Data 
Updated targets comment states that the LOP and Year did not change but the numbers do not 
match the original AWP (for KPI2)
Go with the new updated numbers as what to report 
on
10 Data clarification Claro RDO (TA only)
Missing KPI1 data for TA
KPI1 and others, there is not hard and fast rule for TA 
only indicators, case by case basis
MENA
1 Missing Data What are the units for IRSC Egypt for Gin1 # Of solar pumping systems
2 Missing Data What are the end users for Alva Tech
KPI6 Disaggregated results for end user type not provided by innovation for KPI6 No data provided, reported as ND
3 Missing Data
Data clarification KPI8 is not included in the file
Not included because documented on the secretariat 
level, all grantees automatically receive a result of 
yes, since they have been required to have an EMMP 
created, TA only always receives a No Result
4 Data clarification What does SA1 and SA2 refer to for Gin20, the total 
GIN20 is a semiannual indicator, the results shown 
are the annual total. In the comments it shows the 
results for partnerships and reported in the first and 
semi-annual period (SA1) and second semiannual
period (SA2)
124 
No. Type of Issue Issue/Question Resolution
5 Contradictory Data Legacy or CF2, On the list there are innovations that are listed as CF2 but have legacy targets 
(agrosolar, recyglo) Are part of the CFI2 cohort
6 Missing Data There are no LOP targets for KPI1 for Platform and Missing data
7 Clarification needed LOP and all numbers changing in TWP but little explanation for some innovations Use the information in the updated AWPs
8 Unexplained 
Change Year 1 target for Gin20 was changed for Compost Baladi Use new numbers
9 Unexplained 
Change KPI1 for SuWaCo did not update the format Use the information in the updated AWPs
SSC
1 Missing Data Are there other innovations that we have not received information for (only have some 
information for Meat Naturally and Reel Gardening) No other SCA innovation data collected at this time
Other / Overall 
1 Missing data The innovation list of July 2022 is missing names of innovations that are no longer with the 
program List updated
2 Data Clarification Baseline for the data with Yes or No answers, is this “ No” Baseline is No
3 Data Clarification Number of farmer or END user (before multiplied by HH) is given in the comments for most of 
the innovations for SSE but not for MENA
This was due to old comments left by Tetra Tech, 
find the individual count by dividing result by HH
4 Missing Data Not all the innovations have the updated AWP for example Tun Yat Use information in the updated AWPs
5 Data Clarification KPI1 Sales with profit – There are different ways in which data are entered, some mention the 
EBITDA margin, others not. 
Information updated when possible use EBITDA 
data, it can be positive or negative
7 Data Clarification
KPI5 Energy saved – This energy does not relate to whether it is saved for either water or energy 
or both (innovation type: see comment below*). The period is not specified, it would help that the 
data confirm the unit (kWh/y). 
This is for year 1 of the project
8 Data Clarification
Contradictory Data
KPI6 Water saved – This water does not relate to whether it is saved for either water or energy or 
both (innovation type: see comment below*). The period is not specified. 
A review of SSEA and MENA data finds some challenges in interpretation. For example, in 
MENA and SSEA LOP targets are higher than the Y1 targets, which is right (expected for water￾related innovations); however, in SCA there are baseline values that are higher than LOP targets. 
This is for year 1 of the project and is now just liters 
of water, baselines are set to zero
9 Data Clarification KPI7 No. of end users’ income increase – For evaluation purposes we need to know in how far 
this applies to the more vulnerable categories of users (incl. poorer, women).
10 Data Clarification Gin6 GHG emission saved/kg food – We note gender disaggregation, would like to explore this a 
little bit further. A critical factor is identifying the BAU and the improved practice, leading to 
reduced emission. Will this be a question in the external surveyors’ questionnaire? 
This is not just GHG saved, and innovations use 
CLEER tool to calculate this
125 
Data issues with KII
Had data questions, but USAID did not want this included, so then the ET became fully 
dependent on the M&E data 
Data issues with Innovation Survey Data
The survey link was shared with all the innovations and a total of 31 innovations responded, 
of which the 12 innovations from CF1 were analyzed. 
Recommendations 
The following recommendations are made to help address the issues identified during the 
analysis of the M&E data:
• The same format by RIH can be used for reporting for each of the innovations so that it is 
easier to quality check 
• Continued trainings to keep procedures and methodologies are kept up to date and current 
for the innovations. 
• Improve upon the coordination that was taken to review, clean up the data and organize it 
together into one from the SSEA and MENA year 1 results, annual results and the 81 
AWPS. We note that the CFI1 SCA data will soon be available 
126 
ANNEX 13: ENVIRONMENTAL ANALYSIS
USAID has environmental requirements (source: https://www.usaid.gov/environmental-procedures/environmental-compliance-esdm-program￾cycle/22-cfr-216-usaid-eia-process) but these are last updated in 2013 and quite lengthy. 
The instrument used in the WE4F program is the EIA tool and was last revised in 2017109. This is done with two main instruments110: 
1. The IEEs identify potential environmental effects (source: https://www.usaid.gov/documents/1860/usaid-environmental-impact￾assessment-tool-word) 
2. The EMMP translates IEE into specific mitigation measures (source: https://www.usaid.gov/environmental-procedures/environmental￾compliance-esdm-program-cycle/mitigation-monitoring-reporting)
Irrespective of the presence and quality of more accessible materials for innovations to assess their possible impact on the environment, we can 
also use common sense, with roots in the lifecycle approach (some documentation on this approach is offered at the end of this annex). And this 
while keeping in mind the relevant evaluation questions:
• EQ1c: contribute towards or contradict SDG 
• EQ1e: unintended effects on ‘local water efficiency and water resources’
• EQ1f: unintended effects on ‘local agricultural energy efficiency, access, and energy resources’
• EQ1l: effects on marginalized groups (intended, unintended) 
• EQ1m: effects on water resources or biodiversity
• EQ1n: trade-offs between impact on marginalized groups and environmental sustainability, biodiversity. 
The last question EQ1n provides an excellent concentration of the questions: the trade-offs between different water- or energy-related effects, 
natural resources (water) and biodiversity, and socioeconomic effects.
Table 67 presents, for the sample of CFI1 innovations interviewed, a summary and analysis of illustrative IEE: Initial Environmental Evaluation 
(IEEs) and Environmental Mitigation and Monitoring Plans (EMMPs).
109 Source: WE3F Evaluation shared folder / Data files / Enabling Environment Work / Environmental impact training materials / Monitoring EIA_Tool_Revised_4Dec2017_FINAL.DOCX 110 WE4F processed the requirements into a training provided to innovators (source: L. McMahan, meeting dd 6 Oct. 2022). That is presumably about the USAID environmental impact 
assessment tool. 
127 
Table 67: Summary and review of illustrative IEEs and EMMPs
Region Innovation* Country IEE EMMP comments / questions for next interview
SSEA
(n=7)
aQysta Nepal
Water-powered 
pumps for crop 
irrigation
Nepal, India Negative determination, all risks low. 
Potential impact (Table 3A):
a. Withdrawal surface water, water quality
b. Riparian vegetation, erosion, hydrological 
function disruption
c. Aquatic spp.
d. Construction materials, -erosion
e. Increase in irrigable land use, agrochemical use
Table 5A mitigation measures:
a. Pumps not running rivers dry (inherent)
b. Minimize water diversion, on banks no 
construction, erosion control
c. Educate farmers on water/pump management, 
waste minimalization (recovery, reuse)
d. Train farmers on water management and farming 
practices
For marginal farmers, innovative 
EASI-Pay model (Enhancing Access 
to Sustainable Irrigation with pay￾after harvest model): pumps in combi 
w ag. extension service, critical agri￾inputs, and market access for harvest. 
Farmers pay through a proportion of 
the harvest.
IEE: is a rather comprehensive assessment of 
‘low-level’ risks.
The summary does not recall the farming 
practices education, it is limited to the 
innovation itself.
EMMP does no explain what critical agri￾inputs / agrochemicals. Does the innovation 
also introduce innovative farming i.e. 
reducing agro-chemicals by introducing a 
different approach for crop health?
ATEC 
(portable) 
biodigester 
production
Cambodia, 
Bangladesh
Negative determination, all risks low. 
Potential impact (Table 3A):
a. Land, water, air pollution, solid waste, 
b. Energy use
Table 5A mitigation measures:
a. Reuse, recycling, product shipping boxes
b. Use low-emission power (gas), want to move to 
solar/wind when feasible 
The IEE mentions manure as a first feedstock input, 
and identifies that 4 million rural households in 
Bangladesh own livestock (>3 cattle).
End user level effects considered no 
risk
Pretty comprehensive assessment, however, 
the feedstock source (apparently mostly 
manure) has an important environmental 
footprint and this is not discussed: livestock 
takes up considerable land to be fed, and this 
competes with food production. Many more 
millions of rural households do not qualify 
(and do not have the means to own enough 
livestock to run a biodigester) but have to 
compete for land.
Goat Trust
Goat breeding 
services
India Negative determination, all risks low. 
Potential impact (Table 3A): need to collect fodder 
from forest, and produce fodder on ‘wasteland’
Strategic grazing’ in the forest, to 
prevent forest fires
Strategic grazing not explained, and 
education on forest management not clear. 
Also not discussed whose forest this is?
128 
Region Innovation* Country IEE EMMP comments / questions for next interview
Onergy
Solar pumps for 
irrigation
(Tetra Tech 
consortium)
India Negative determination, moderate and high climate 
risks. Potential impact (Table 3A): improper use can 
lead to clearing more land (deforestation), and over￾drawing of water
Electronic waste.
‘Connected action (not funded by 
WE4F). Recommended mitigation 
measures will be incorporated, as 
appropriate. Waste management plan.
Promote water efficient farming, drip 
irrigation
IEE: is a rather comprehensive.
Oorja
Off-grid solar 
projects
India Negative determination, all risks low. 
Potential impact (Table 3A): recycling, disposal of 
waste all by Oorja (they operate the projects
Plan: ethical sourcing of products Interesting, to see that ethical sourcing of 
solar panels is noted.
Also noted: solar panels waste disposal.
Pumpkin Plus
Sandbar 
cropping (in 
riverine areas,
’barren 
transitional lands’)
Bangladesh Negative determination, low and moderate risks.
Potential impact (Table 3A):
a. Chemical inputs ☐ water quality
b. Land clearing
c. Erosion
d. PV end of use
e. Water use altering river flow
f. Vegetation removal
Training on pesticides (prior: develop 
a PERSUAP = ?)
Erosion measures present (ok)
Pretty comprehensive assessment, and plan. 
Pumpkin farming – in this document - seems 
to need pesticides; the plan does not suggest 
an alternative approach to crop health, yet 
farms in riverine areas.
Sumba Solutions
Bamboo 
workshop w solar 
panels
Indonesia Negative determination, all risks low. 
Potential impact (Table 3A):
a. Construction bamboo workshop: land clearing, 
loss of biodiversity, habitat
b. Operation toxic substances: potentially 
contaminate water
c. Solar panel end-of-life and batteries: toxic 
substances
Table 5A mitigation measures:
a. For construction 9 measures
b. For operation: circular system, monitoring 
groundwater, fire risk
c. Solar panels: upcycle lithium ion batteries, 
collect e-waste, recycle
Additional risks: extraction of NTFP 
adverse effects, travel effects, etc.’ 
(NB: for this, no mitigation is 
mentioned)
Pretty comprehensive assessment.
Bamboo is not related to food production.
Negative determination, in spite of:
• not indicating the source of bamboo (in 
a forest with indigenous people)
• the size of the area to be cleared (for 
construction) and biodiversity
• etc. (many comments in the margin yet 
to be dealt with) 
129 
Region Innovation* Country IEE EMMP comments / questions for next interview
Tetra Tech
Briquettes
Source:
Char_briquettes 
EMMP 2022 
revised (1).docx
Cambodia n.d. Environmental issues to monitor: 
1. sourcing of raw material 
competing with other uses
2. facility construction land rights, -
use issues
3. power for production
Monitoring: # of incidents of 
negative impacts on environment 
No mitigation plan.
Issues 1 & 2 are discussed as only 
socioeconomic issues.
Issue 3: no mitigation suggested.
No environmental issues planned to be 
addressed (making the environmental 
indicators obsolete).
Unfortunately, the source of raw materials 
for the briquettes is not identified.
MENA
(n=8)
Biomass 
white label 
organic products
Lebanon All negative determination, 2 moderate risks. Sub activities: 1. Greenhouses 
(plastic); 2. Farming equipment incl. 
drip irrigation stuff; 3. Solar power. 
Recycling of equipment, batteries - 
not greenhouse plastic.
Many comments from RIH on the IEE show 
that the document leaves unanswered 
questions.
Chitosan
organic pesticides 
& fungicides fr 
shrimp shell
Egypt All negative determination, some low, some 
moderate, some high risks.
Water use excess, water contamination, solid waste, 
medical waste, but currently no hazardous waste (no 
waste tracking). Strong acids or bases for extraction.
No EMMP available. EMMP?
Compost Baladi
Compost, -
equipment, 
organic, urban 
waste collection
Lebanon Negative determination, all risks low. 
Excavation, decentralized treatment, leachate 
drainage system. Mostly in urban plots.
Long list of mitigation measures for 
moderate risks.
IRSC Egypt
Platform Egypt
Schaduf
mini-greenhouse 
(box) hydroponic 
farming systems
(rooftop, urban)
Egypt, 
Sudan, Syria
Negative & deferred per 22 CFR 216.3(a)(7)(iv), low 
and moderate risks
Table 3A: waste (plastic)
No EMMP available EMMP?
SOWIT software 
for plot-specific 
irrigation 
recommendations
Morocco, 
Tunisia
Negative determination, all risks low. ‘ground truthing’
regular farm visits, for local proof of 
value experiments
ok
130 
Region Innovation* Country IEE EMMP comments / questions for next interview
SUWACO
Wastewater 
treatment / re-use 
in rural ag. 
communities
Egypt Negative determination, all risks low. 
Equipment, energy use, solid waste
No EMMP available EMMP?
Why not a less energy-using method, 
artificial wetlands?
What is done w the solid waste?
*: the innovation sin the grey areas are not part of the sample 
Source: WE3F Evaluation shared folder / Data files / Environmental Monitoring / CFI1 / [region] / IEEs [or EMMPs]
131 
What was good in this documentation is that it shows rather strong efforts made towards the 
identification of environmental effects directly related to the actions of the innovation. Multiple, 
apparently relevant potential effects are identified. And for most (not all), the EMMPs suggest 
some ways of mitigating.
What is also seen (taking into account many RIH comments asking for more details, more 
clarify) is that some potential effects are not clearly described in the IEE, or if they are, not re￾appearing in the EMMPs, or not in the same way, or not in a clear way. 
It appears a lot more time should be spent on these instruments, to make them complete and clear 
to outsiders. And that raises questions of efficiency: how much time could or should be spent on 
this, to what effect?
The choice of the instruments can also be questioned: the limitations of EIA are that they focus 
on the more direct effects of the action, and that can be a narrow framework, leaving out 
(responsibilities for) effects along the lifecycle of the innovation and-in-connection the farming: 
from sourcing of materials (effects of mining, forced labor, deforestation) to consumer health 
and end-of use disposal (and all along there are marginalized groups – not only considering end 
users; the WE4F ToC mentions ‘along the food value chain’ which could imply many different 
stakeholder groups).
Among several innovations that include solar panels, there is one that refers to ethical sourcing 
of solar panels (a USAID requirement).
Below some references to lifecycle assessments:
ATTRA, 2013. Life-Cycle Assessment in Agricultural Systems
https://www.e-education.psu.edu/geog3/sites/www.e-education.psu.edu.geog3/files/Mod10/life_cycle_assessment.pdf 
https://images.app.goo.gl/DfiSGkef1Di2ULTt9 
https://images.app.goo.gl/qMVyvY6PvmgQVsnz9 
Figure 6: Sustainable intensification in land systems: trade-offs, scales, and contexts (source: Thomson 
et.al., Current Opinion in Environmental Sustainability, Vol. 38, June 2019 (p. 37-43) 
https://images.app.goo.gl/BaXmPi5A9nMBuvcq6
Plants to Fields
• Increase input efficiency and 
productivity 
• Minimize nutrient loss 
Farms to Landscapes • Maintain ecosystem services and 
biodiversity 
• Minimize habitat loss and land 
degradation 
National to Global 
• Maximize environmental value 
• Minimize trade-offs 
• Accelerate the rate of change
132 
Figure 7: Life-cycle assessment phases, cradle-to-gate and cradle-to-grave (NB: this model ignores the 
input of land/biodiversity and water)
Source: https://www.e-education.psu.edu/geog3/sites/www.e￾education.psu.edu.geog3/files/Mod10/life_cycle_assessment.pdf
Figure 8: On-farm lifecycle components and flow between the environment and the production system 
(NB: this model ignores the input of land/biodiversity)
Source: https://www.e-education.psu.edu/geog3/sites/www.e￾education.psu.edu.geog3/files/Mod10/life_cycle_assessment.pdf
Natural Resource 
Extraction 
Production of Farm Inputs 
Pesticides 
Fertilizer 
Fuel 
On Farm 
Production 
Phase 
Post Harvest Activities 
Packaging 
Value added production
Consumer Retail Store 
Disposal 
Cradle to Gate Cradle to Gate 
Cradle to Gate 
Cradle to Grave Cradle to Grave 
Cradle to Grave 
Cradle to 
Grave 
Cradle to 
Grave 
Cradle to Grave 
Transport: truck, 
Ship, Train 
Transport, 
truck, ship, train 
Transport: 
Truck 
Transport: 
Transport: truck, train 
Car 
Transport: 
Truck 
133 
ANNEX 14: THE SCOPE OF THE MTR 
Table 68: Defining coverage and limits: Evaluation Questions, KPIs and General Indicators
Report section Code: EQ, KPI or GIN Code* Explanation 
3.1.1 Relevance EQc Interventions relevance to water, 
energy, food 
Full Data available from self-reported focus of 
innovations.
3.1.2 Relevance EQ1a Demand, local ownership for 
innovations 
Full Available data is reviewed. Expressed also in 
end users 
3.1.4 Relevance EQ1b support addressing key WE4F 
needs 
Full Undertaken. 
3.2 
Cohesion 
EQ1c Interventions contribute to SDG (or 
not)
Full Innovation self-description matched to 
objectives. 
3.3.1 Effectiveness EQ1d help to overcome organizational 
capacity 
Full Scores of TA events are assessed.
3.3.1 Effectiveness EQa innovations found TA useful Data limit Available admin data and KIIs analyzed. 
3.3.1 Effectiveness EQn help that improved end user targeting 
(marg. groups) 
Data limit More data to collect on TA content/quality 
3.3.2 Effectiveness EQp strengthening innovations’ impact on 
cross-cutting issues helped to access ext. 
finance 
Limit stage Early data analyzed; PPPs not yet fully 
examined. 
3.3.2 Effectiveness KPI9 Ext. Funding mobilized by 
innovations 
Full Extensive data available. 
3.3.2 Effectiveness Gin33 co-funding for innovations Full Extensive data available. 
3.3.2 Effectiveness Gin40 # of contacts of potential investors Limit stage Early stage in reporting. 
3.3.2 Effectiveness Gin41 innovations’ share using fin. 
guarantee instrument 
Limit data Further surveys needed. 
3.3.2 Effectiveness Gin20 # of ext. partnerships formed by 
innovations 
Limit data Further surveys needed. 
3.3.2 Effectiveness EQ1h to what extent PPP meet energy, 
water targets 
Limit data PPP understood but limited data available. 
3.3.2 Effectiveness EQf to what extent IF led to additional 
funding 
Limit stage Limited early data analyzed. 
3.3.3 Effectiveness EQd enabling env. work compatible w 
marg. groups 
Limit data RIH KIIs undertaken, more data collection 
needed. 
3.3.3 Effectiveness EQm extent to which innovations find 
enabling env. support useful 
Limit stage 
Limit data 
Innovation survey explored some of this. 
3.3.3 Effectiveness EQb interventions (TA, IF, enabling env.) 
differ in meeting innovation needs 
Limit stage 
Limit data 
Analyzed data available, no finding made on 
comparative value of different interventions. 
3.3.3 Effectiveness Gin28 # end users using -financing Limit data End user surveys, limited responses 
3.3.3 Effectiveness EQl end user finance improved uptake Limit stage 
Limit data 
Further data collection in surveys needed. 
3.3.3 Effectiveness KPI10 # strategies, guidelines adopted Limit stage Early stage and limited reporting as yet. 
3.3.3 Effectiveness Gin4 innovations’ gross sales Limit data Available data analyzed, many data gaps 
identified.
3.4 
Efficiency 
EQg founding partners’ interaction, 
lessons 
Limit stage 
 
Explored in KIIs and will be further explored. 
3.4 Efficiency EQk admin cost compared to similar 
funds
Limit data Data being assembled; more KIIs with donors 
and innovations needed. 
3.4 Efficiency EQj RIH set up, management efficiency Limit data Available data analyzed and findings made. 
3.4 Efficiency EQ1j innovations used resources 
efficiently for max. impact on marg. 
groups, environment 
Limit data More time needed, depends on completing data 
on support provided, and impact 
134 
Report section Code: EQ, KPI or GIN Code* Explanation 
3.4 Efficiency EQe TA immediate, and long term 
success 
Limit stage On-going reporting and interviewing needed. 
3.4 Efficiency EQh WE4F results balance with efforts 
and resources 
Limit stage Early stage more data collection and analysis 
needed. 
3.4 Efficiency EQi WE4F added value balance with 
efforts resources 
Limit stage Good early data available; continuous data 
collection needed. 
3.4 Efficiency EQq investment risk management NA, 
Limit data 
Early stage, more time needed beyond Year 1 
for data collection. 
3.4 Efficiency EQr WE4F ensure partner can sustain 
impact 
Limit stage, 
Limit data 
Assessed in EQ1g. Continuous data collection 
needed, will be assessed in Final Evaluation. 
3.4 Efficiency EQ1k innovations used resources 
efficiently for max. impact on 
environment 
Limit stage Early stage; focus currently on Effectiveness. 
Economical use will be assessed over time. 
3.4 Efficiency EQ3 difference planned/delivered in Y1, 
and Y2-4 
Limit stage We do compare innovations plans w y1 results, 
e.g. 3.3.3: gross sales 
3.5.1 
Impact 
KPI2 # end users Full Some limits, gaps in data as not all innovations 
are reporting. 
3.5.2 
Impact 
EQ1g projects lead to Ag. Productivity, cc 
adaptation 
Limit stage, 
Limit data 
Critical question: data on all rounded factors 
e.g. land use, resource use, and particularly 
yields etc., urgently needed. 
3.5.3.1 Impact KPI3 food produced Limit stage, 
Limit data 
Critical question; measures of volume and area 
not fully reported. 
3.5.3.2 Impact KPI4 food processed Limit data Critical question: poorly reported. 
3.5.3.3 Impact EQ1e water efficiency Limit data Complex calculations needed; volume of water 
unevenly reported. 
3.5.3.3 Impact KPI6 water use reduction Limit data Complex calculations needed; volume of water 
unevenly reported. 
3.5.3.4 Impact KPI5 energy saved Limit data Complex calculations needed; volume of water 
unevenly reported. 
3.5.3.4 Impact EQ1f energy efficiency Limit data Rigorous calculations needed; volume of 
water unevenly reported. 
3.5.4 
Impact 
KPI7 income increase Limit data Unevenly reported: contradictory signals from 
surveys and admin data. 
EQ1i income increase of women, BoP Limit data 
 
Unevenly reported: more data and analysis 
needed. 
3.5.4 Impact EQ2 attribution of impact to WE4F NA: Limit 
stage Limit 
data 
Decisive question: contribution of WE4F 
examined more data collection and analysis 
needed. 
3.6.1 Sustainability KPI1 innovations making profit (fraction) Limit data Unevenly reported, significance of the 
indicator needs assessment. 
3.6.1 Sustainability Gin10 new jobs Limit data Unevenly reported. 
3.6.2 Sustainability KPI8 tools, monitoring water, 
biodiversity 
Limit data Unevenly reported, further data collection on 
TAs needed. 
3.6.3 Sustainability Gin6 GHG emission Limit data Unevenly reported; possibly another tool 
needed to have comprehensive range in 
capturing data. 
3.6.4 Sustainability Gin45 land under improved management 
practice 
Limit data A late indicator, unevenly reported. 
3.6.5 Sustainability EQo WE4F support improved 
innovations’ positive impact on all 
environment, climate, unintended effects 
Limit stage All data available (surveys and KIIs) analyzed. 
135 
Report section Code: EQ, KPI or GIN Code* Explanation 
EQ1l impact on marg. groups, reduced # 
in poverty 
Limit stage 
Limit data 
More end user surveys needed. Limited data on 
direct impact (hh level, on-site); EIA scope too 
limited 
3.6.5 Sustainability EQ1m environmentally sustainable 
projects 
Limit data Cross reference to EQ1g; currently limited 
data. 
3.6.5 Sustainability EQ1n negative trade-offs Limit data Further data collection from innovation and 
end user surveys. 
3.6.5 Sustainability EQ1o innovations financially, socially 
sustainable 
Limit data A full set of social and environmental aspects 
need to be drawn together and assessed in end 
user surveys. 
3.6.5 Sustainability EQ1p public-private engagement balance Full Full analysis made from available data. 
3.7.1 Hypotheses EQHI WE4F investing is faster NA Comparative data on Grand Challenge Funds 
being assembled. 
3.7.2 Hypotheses EQHII with more progressed innovations 
achieving wider adoption 
NA Comparative data on Grand Challenge Funds 
being assembled. 
3.7.3 Hypotheses EQHIII acceleration oriented TA, IF 
increases change innovation scales 
NA Comparative data on Grand Challenge Funds 
being assembled. 
* legend: Full: addressed fully and conclusively 
 Limit stage: limited stage of the program; a longer program period 
needed to answer the question fully 
 Limit data: 
 - limited data available 
- more time needed for data collection and analysis 
 NA: Not addressed; will be addressed in the Final Evaluation 
 
136 
ANNEX 15: METHODOLOGY 
For reasons of space and to fix attention to the Findings, Conclusions and Recommendations in 
an otherwise long report, the details of the methodology are presented here as an Annex. 
Data collection methodology 
Data collection for the midterm report included data from the WE4F program’s own M&E 
system, as well as data collected independently, through: 
• Review and use of the WE4F Administrative Data 
• Survey of innovations (grantees as well as those only receiving TA) 
• Qualitative data collection through Key Informant Interviews (KII) with a sample of 
innovations and Regional Innovation Hubs 
• Surveys of end users. 
The main methods used for data analysis are:
• Data Quality Assessment (DQA) which has involved an intensive long process of 
downloading and assessing administrative data and has satisfactory results: the data gaps 
were mostly filled, while some data issues are unresolved. 
• Triangulation of data, e.g., by use of surveys and qualitative data in comparing 
innovations’ perception of the water- and or energy-saving qualities, or time- and/or cost￾saving qualities, with data obtained from end users 
• Extrapolation of trends to anticipate likely developments. 
Sampling 
The Evaluation Team developed a sampling frame from all WE4F program innovations in Excel. 
Sources for the Master File or sampling universe came from WE4F documents, Administrative 
Data, and from meetings with the WE4F Program. The Master File has been updated when 
additional data becomes available. The entire innovation portfolio is detailed in Annex 8. 
Sampling of Innovations
The sampling universe includes a total of 37 innovations and 50 country sites. Since several 
innovations implement in different countries, a site refers to an innovation as implemented in a 
single country. Several innovations such as Alva Tech, RecyGlo and aQysta are implemented in 
several countries; this leads to the total number of sites being greater than the total number of 
innovations. Data was collected at each level: from the founding partners, RIH, innovations and 
this information provided norms and measures for the field surveys. 
In February 2022. The team conducted this sampling exercise. The initial sample contained all 
38 innovations available at the time: 18 from SSEA, 14 from MENA and 7 from SCA. The SCA 
CFI1 innovations were then removed as the Hub and its Innovations were not fully established, 
thus in SCA only 2 legacy innovations remained. From the remaining 32 (presented in Table 70), 
only grantee innovations (excluding 2 legacy and 1 TA only) were chosen. That created the 
sample of 29 innovations for the survey: the innovations with colored cells in the CFI1 or CFI2 
columns. From this list, two innovations were removed as they were terminated (not having 
reached their targets).
Table 69: Number of innovations and sites per RIH, January 2022
RIH Number of innovations Number of country-innovations or sites
MENA 14 15
SSEA 11 14
Grand Total 25 29
137 
The team generated a sample using multi-level sorting by criteria in order of importance, such as 
the RIH or geographic region (the most critical data cut), followed by country, innovation focus, 
innovation type, size of targeted number of end users, and female/non-female ownership. 
Innovations were disaggregated by country and tables generated as below. 
Multi-stage sorting was undertaken from the sample frame made up of the MENA and SSEA 
innovations to establish a representative sample using these criteria: 
1. Grantees 
2. Innovation/Country 
3. RIH: MENA or SSEA 
4. Focus and Type: Water-Food, Energy-Food, Water-Energy-Food, etc. 
5. Women-led/Women-owned. 
6. KPI2: Y1 targeted end users 
The team checked the results against the full sample frame, including the following: type, target 
Y1 end users, gender, RIH and made a table for the proposed Phase 1 Sample (Annex 8). 
Using these procedures, the Evaluation Team arrived at a selection of 12 innovations (48% of all 
25 grantee innovations) and 15 country-innovations (52% of all 29 sites in the Sample Frame) in 
the attempt to secure a representative sample (in terms of key criteria, but with a purposeful 
element, that of the practicality of undertaking the highest number of field surveys within the 
available time and budget). The Evaluation Team is working with WE4F Program to confirm the 
innovation sites, visit status and site localities of innovations. In addition, there is assistance 
being provided in relation to coordination with the activities of the WE4F External Surveyors.
The Sample Frame is systematically updated as additional data becomes available to quickly 
inform the universe of innovations and sites. Table 71 shows the sample made from the total 
number of innovations in all RIHs, and the number of sampled and non-sampled innovations by 
RIH.
Table 70: Names of Innovations in the sample by RIH, country, and innovation focus
Region No. Innovation* Country Innovation focus Innovation type
SSEA
(n=7)
1 aQysta Nepal Nepal, India water & energy Water - Irrigation
2 ATEC Cambodia, Bangladesh energy Energy - Farm input
3 Goat Trust India water & energy Digital solutions
4 Onergy India water & energy Water - Irrigation
5 Oorja India water & energy Energy – Energy production and 
infrastructure in agriculture
6 Pumpkin Plus Bangladesh water Other
7 Sumba Solutions Indonesia water & energy Energy – Energy production and 
Infrastructure in agriculture
MENA
(n=8)
8 Biomass Lebanon water & energy Energy - Farm input
9 Chitosan Egypt water Energy - Farm input
10 Compost Baladi Lebanon energy Energy - Farm input
11 IRSC Egypt water & energy Energy – Energy production and 
infrastructure in agriculture
12 Platform Egypt water & energy Digital solutions
13 Schaduf Egypt, Sudan, Syria water & energy Water - Irrigation
14 SOWIT Morocco, Tunisia water & energy Digital solutions
15 SUWACO Egypt water & energy Water - Reuse in agriculture
Given the risks to field research posed by travel during the pandemic, access to end users, and 
the need to include one innovation from each country. This sample represents some over￾sampling to allow for review and adjustment; the final sample will also include grantees in the 
138 
SCA RIH. The final effective sample will be governed by the conditions of the pandemic, 
availability of innovation leadership and budgetary considerations. 
As presently, an analysis of the sampled Innovations and Countries shows that:
• There are 8 countries, 12 innovations and 15 sites (8 in MENA and 7 in SSEA). 
• By focus, the 15 sampled sites are divided as follows: 2 Energy-Food, 3 Water-Food, and 
10 Water-Energy-Food. 
• Of the 15 in the sample, 8 are Women led/Women Owned. 
In total, 12 innovations and 15 sites were selected (the “Sample”), 8 in MENA (53% of the total 
number of sites) and 7 in SSEA (50% of the total number of sites in SSEA), for a total of 52% of 
all sites in the Sample Frame. The team over sampled the Sample Frame through a process of 
selecting an innovation from each country, made proportional to the sample frame to the metrics 
of targets in end users (an indication of anticipated upscaling) and female owned. 
By the focus area, the 15 sampled sites are divided as follows: 2 Energy-Food (33% of the 
Sample Frame), 3 Water-Food (60% of the Sample Frame), and 10 Water-Energy-Food (56% of 
the Sample Frame); as illustrated in Table 72: a breakdown of the number of Women 
Led/Women Owned by RIH as well as by the innovation in the sample. The full classification of 
all 15 sites selected for the sample is presented in Annex 8.3
Table 71: Women Led/Women Owned innovation in Sample
Not women led Women led Grand total
RIH NS S NS S 
MENA 3 4 4 4 15
SSE Asia 6 3 1 4 14
Grand Total 9 7 5 8 29
Of the 15 innovations sampled, 8 are Women Led/Women Owned; the sample is broadly 
comparable to the Sample Frame with the exception of SSEA where there appears something of 
an oversampling of those innovations which are women led and owned. The team worked 
closely with WE4F and the Secretariat to obtain missing information on the RIHs. Much of this 
data is vital to understanding the nature of the innovations and planning data collection. 
Table 72 compares the innovation focus proportions of the sample with those of all CFI1 
innovations, and then against all innovations. 
Table 72: Distribution of sample by innovation focus, compared to CFI1 and all innovations
Innovation focus Sample (n=15) CFI1 innovations (n=31) All innovations (n=81)
Numbers Proportion Numbers Proportion Numbers Proportion
Water 2 13% 2 6% 22 27%
Energy 2 13% 6 19% 19 23%
Water & energy 11 73% 23 74% 39 28%
ND 0 0 1 1%
Total 15 100% 31 100% 81 100%
Table 72 shows the number of innovations that are women led, in the sample, in the CFI1 
innovations, and in all the innovations. The table shows that the sample innovation focus is fairly 
similar to that of all CFI1 innovations.
Table 73: KII innovation sample, representation women-led innovations compared to CFI1 and all 
innovations
Women-led  Sample CFI1 SSEA and MENA All innovations
Yes  8 15 (8 + 7 respectively) 15
No  7 14 14
ND   0 17 52
Total 15 46 81
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The table shows that in the sample the proportion of women-led innovations is close to that in all 
CFI1 innovations and all innovations. 
Data analysis plan 
In summary, the team prioritized the administration of an innovation survey and interviewing 
members of the RIHs, before interviewing innovations and end users; the innovation survey and 
RIH KIIs come first as these provide the basic data on the program and its implementation. Data 
collection was planned geographically and hierarchically. The Evaluation Team started with 
well-established hubs and moved down the organizational hierarchy towards the country sites. 
The surveys and KIIs serve to provide the information and norms and standards relating to the 
innovations, the end users and size of farms, crop yields, income, poverty, and other key aspects 
of agriculture, such as the agricultural calendar, and the working of the innovation. This also 
ensured that the Evaluation Team and the local consultant were well informed of specific norms 
and, importantly, on approaches to establishing sensitive information such as the current incomes 
of end users. All this information is critical to the accurate collection of data and the successful 
timing of surveys to ensure that harvest data is collected after the most recent harvest.
This section sets out the procedures to assemble and clean the data in a dataset to channel 
carefully identified incoming data and prepare for rigorous analysis as set out below. The 
evaluation involves a considerable range of evaluation questions (and many sub-questions which 
also require appropriate data collection). In Table 74 the numbers of Evaluation Questions are 
presented requiring data as evidence to make findings.
Table 74: Number and focus of Evaluation Questions and subsidiary questions
OECD Criteria Innovation Program Total
Relevance 2 3 5 
Coherence 1 2 3 
Effectiveness 14 6 20
Efficiency 2 4 6 
Impact 6 11 17
Sustainability 6 2 8 
Final evaluation only 4 3 7 
35 31 66
Please note that these numbers of questions are not directly correlated with the evaluation 
questions presented in Annex 2 for this reason: the Evaluation Questions are often bundled i.e., 
made up of a primary question with several subsidiary questions. Each primary and secondary 
requires its own data to be collected as evidence to findings.
The data collection to answer this extensive set of primary and subsidiary evaluation questions 
requires further questions to gather the necessary data as evidence. These “questionnaire 
questions” are set out in Annex 2 which assembles the three questionnaires against the 
evaluation questions. 
These are not yet separated into the final instruments in KIIs and surveys for this reason: the 
subsidiary questions can still be read, from left to right, running across the end users, innovations 
and RIH. This is a distinct advantage as every question is tightly related to the primary EQ and 
the appropriate coding provide the necessary perspective to see the integration of each of the 
lesser questions with the greater EQ. This sets the frame for the final dataset which has columns 
headed with questions and codes allowing the data to be readily utilized in cross-sectional 
analysis.
The subsidiary questions are necessary to capture the precise data to answer the broader 
questions: 
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• Data collection procedures for contracting Dexis local consultants, scheduling 
RIH/country data collection, training Dexis local consultants, logistical arrangements. 
• Data management plan: this will include a coding system to apply to data collection 
instruments, sources, document reviews, databases, data quality control. 
A strategic plan was prepared based on the preliminary sample of innovations as suggested 
above. This occurred for the SSEA and MENA hubs but not, yet, for the Southern and Central 
African hub which is still assembling innovations. 
Administrative data validation process 
The validation processes for the Team surveys are set out here; following data assembly and 
cleaning, Initial Data Analysis (IDA) followed. This led on from the handling of missing values 
and making transformations of variables (from acres to hectare, etc., hopefully minimal) as 
needed. Frequency tables were run to provide a collective examination of the data and to spot 
obvious errors. The validation process continues, as time permits, with the circulation of 
descriptive tables and selected cross-tabulations for feedback and validation, extensive 
discussion of initial analysis and finally the circulation of preliminary findings for feedback. 
Data security 
The protection of personal identities and confidential data, particularly of those interviewed in 
field surveys and data security for this comprehensive data set, is of high concern. The rigorous 
procedures set out in the General Data Protection Regulation (GDPR) of the European Union 
(EU) are followed. The team followed the protocols as summarized in Eurostat procedures to 
ensure confidentiality in 'Personal data' (any information relating to an identified or identifiable 
natural person or "Data Subject") and “Confidential data” (relating to data in which statistical 
units could be identified, either directly or indirectly, thereby disclosing individual information).4
This ensures the protocols relating to the EU’s were followed. There is the need for due 
sensitivity in relation to confidential commercial data and protocols were developed together 
with WE4F to ensure such confidentiality as appropriate. In addition, the team followed all 
relevant USAID data security requirements as outlined in ADS, chapter 545 and through 
consultation with the designated Information System Security Officer, McKenzie Horwitz. 
Qualitative Analysis 
Qualitative data was gathered to provide interpretation and meaning to results. This was 
collected as direct responses from participants in the various levels of WE4F and presented as 
carrying value and in giving interpretation to the results of quantitative data analysis.
A full set of the EQ as well as the subsidiary questions to both survey and KII instruments is 
carried in Annex 2. The subsidiary questions can be read, from left to right, running across the 
end users, innovations and RIH which provides the necessary perspective to see the integration 
of the lesser questions with the greater EQ.
The team prepared question guides to carry interview questions flowing from the evaluation 
questions in the KII. Any questions which have a quantitative character are incorporated into the 
online survey for innovations and end users. 
To code qualitative data into Nvivo, adaptations were done for each RIH/country, to consider 
any different terms and definitions; this is specified in a mini manual.
These KII are designed to be exploratory and to encourage a broad view of the whole Program; 
providing particularly the data which provides evidence in answering the final evaluation 
questions and hypotheses. 
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These question guides may be of considerable length because of the range of questions and the 
KII may need more than one session with an interviewee to complete. 
Each of the question guides are a NVivo aligned instrument, tested in the first KII reviewed and 
improved. The NVivo software is initially more time-consuming but, after initial engagement, 
provides rapid data capture and auto-coding which will enable deeper and more rigorous analysis 
and quicker reporting. 
The KII was conducted by the team at the higher levels of the Program AS online research. The 
KII question guide was designed to engage the diverse perspectives of many participants 
particularly on the broader questions and cross-sectional issues that can be simultaneously 
captured. This should provide an advantage in exploring such issues as unanticipated impacts 
and the instrument of the question guide will lead on to rigorous analysis and quicker reporting. 
The team prepared an instrument utilizing many of the generic questions in the KII question 
guide which are reticulated to Evaluation Questions. 
NVivo is the software of choice. It provides for deep analysis of text to lead to coding and search 
queries to combine multiple words and phrases using Boolean operators. This can be a valid 
method of establishing evidence for the truth or falsity of statements and questions through the 
combination of associated terms. The illustration below depicts searches for items containing 
either or both of two terms (OR), searching for items containing both terms (AND), searches for 
items containing the first term but not the second term (NOT), and finally searches for items 
containing the required term and optionally the second term. Such analytical procedures are 
explored and assessed.
Quantitative Analysis 
Quantitative data is crucial to the evaluation; all involved and who read the evaluation report will 
want to see the numbers on critical questions and then the qualitative data to provide meaning 
and explanation. 
The questionnaires cover all 66 evaluation questions and subsidiary questions. With the wide￾ranging data generated, the team has recognized the need to guide the data carefully to the right 
place in a comprehensive dataset.
Each question was coded by the evaluation question (EQ), the related KPI, the Theory of 
Change, and the Illustrative Indicators. This enables each series of data to be identified and 
captured in a separate column in a final dataset. Since there is data from several sources (from 
WE4F, Dexis or directly from the RIH) the Team established a clear protocol for the integration 
of such data prior to analysis.
Quantitative data. The data came from several sources: comprehensive administrative data from 
WE4F M&E, survey data from the surveys completed by the Innovations and RIHs; and from 
the field surveys. 
Data cleaning and assembly. The first task is to source and collect data, to process, 
systematically label and clean the data, to note the irregularities and incidence of missing values. 
To the primary Evaluation Questions will be added the data arising from the specific questions in 
the questionnaire. This will lead to an extensive set of columns running across the dataset, 
possibly 180 or so, requiring careful assembly, management, and appropriate software. 
An Initial Data Analysis (IDA) followed; this focuses on the handling of missing values and 
making transformations of variables (from acres to hectare, etc., hopefully minimal) as needed. 
Frequency tables was run.
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Simple descriptive tables and cross-tabulations were undertaken and distributed for discussion. 
Proxy indicators were identified as needed and composite indicators compiled to assemble single 
indicators into a single index based on an underlying model of the multi-dimensional concept 
that is being measured.
The data was assembled and compiled initially in Excel. Each new addition of data had regular 
analysis to establish the frequencies, etc., and to systematically enter and upgrade the metadata 
(such as the source of data, number of responses, a glossary of codes in EQ, KPI, etc. as needed 
to establish the identity and necessary location of data, etc.). Any new attributes, such as the type 
of Technical Assistance or Financial Advice, were entered.
Data quality is central to the evaluation before, during and after data collection. Accuracy and 
good quality data achieved at the crucial base of the pyramid, with the end users, through the 
following procedures: 
Pre-survey preparation is undertaken through the surveys sand KII with the relevant RIH, and 
Innovation help establish norms on such issues as ranges in the size of plot with local and 
universal units, yield per hectare and advice about appropriate sites to ensure rigorous cluster 
sampling of all end users. 
During the survey the local consultants used this information to establish ranges and then 
categories in flash cards to capture sensitive information such as size of farm, yield, and income 
as necessary. From experience this can help elicit the appropriate sensitive data without the 
respondent having to make a statement. 
In relation to much of the data at the higher levels, the sample of innovations, RIH, TA and other 
experts will be comprehensive and, if possible, built on an all-inclusive set of interviews. This 
ensures a highly representative sample at these levels.
The Team reviewed the draft KII and other instruments to ensure that no gaps remain in 
agricultural aspects. Particular attention went to measures of agricultural production and to the 
types and forms of interventions. The team also examined the types of crops and their 
significance to households (for instance are the crops subsistence or cash crops and is the 
intended intervention for a partial or greater aspect of agricultural production).
After establishing the systematic reliability of the data underlying indicators, procedures in 
data analysis were undertaken to construct tables of evidence to answer the evaluation questions. 
This occurred in Excel; the resulting tables were carefully cross-checked, and the syntax and 
tables saved in Share Point. 
Making findings: Integration of Qualitative and Quantitative Analysis
One of the challenges in analysis and reporting is giving equal weight to the quantitative and 
qualitative data to ensure that qualitative data is not simply an aside to the quantitative analysis. 
A concurrent triangulation strategy was employed in which the qualitative and quantitative 
phases of analysis and presentation are conducted at the same time. An appropriate weight is 
needed for each phase, with the results of both interpreted concurrently to determine agreement, 
supporting evidence, in the data collected by the mixed methods. Qualitative methods and data 
can be a useful additional insight of the results of quantitative surveys. 
As appropriate statistical results may be presented first followed by rankings, quotes from the 
qualitative phase used to provide background and meaning. Qualitative data will, however, also 
be presented independently in the form of case studies, profiles of the greatest achievement or 
failure, ranked responses by men and women and in other ways. 
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Concurrent analysis of relationships was undertaken by various mechanisms (quantitative or 
qualitative) to examine interrelationships such as sets of indicators underlying for a particular 
Strategic Outcome or Intermediate Result. Alternating between quantitative and qualitative data, 
the analysis progresses to establish findings.
Testing and validating the three overarching WE4F hypotheses 
The three overarching hypotheses provide the most encompassing perspective over the whole 
program and its parts and will be used to draw conclusions to be of lasting value. 
In general, through the WE4F, the Founding Partners hope to source and accelerate high 
potential solutions that have multiplier effects at various levels of a country’s economy and food 
value chain. 
The three hypotheses, which cut across the many specific evaluation questions, require 
meaningful and practical measures of WE4F’s development impact potential. Definitive 
confirmation or rejection of these hypotheses will be of considerable value in policy decisions 
relating to agricultural, water and energy interventions.
Discussion of the hypotheses
All three hypotheses establish WE4F as distinct from other Grand Challenge for Development 
(GCD), the latter being more 'traditional' or more sectoral (Hypothesis I), or less selective, less 
focused on more progressed innovations that already achieved early adoption i.e., 1,000 to 
10,000 end users (Hypothesis II), and less providing in terms of TA and investment facilitation 
(Hypothesis III).
The evidence bases for each of the questions identified below substantiates an aspect of the 
hypothesis and provides evidence for the validation or rejection of the hypothesis. 
Hypothesis I - By investing in innovations at the water-energy-agricultural nexus, the pace of 
development in all three sectors will be substantially faster than if we relied on “traditional” 
(more sectoral) development programming alone. 
The hypotheses and final evaluation questions will only be thoroughly examined in the final 
evaluation itself. Prior to that point, evidence to validate or nullify this hypothesis shall be 
gathered in the course of answering the following questions: 
1. (How) is a nexus-based (multisectoral) innovation approach more enabling for innovations, 
where a more sectoral development program is not (or would exclude innovations)? (EQHI, 
compare with other programs) 
2. Considering annual client (innovation end-user) numbers buying the innovation (y0-baseline, 
y1, y2, y3), what is the annual client number growth rate? A growth curve will be drawn for 
each innovation (EQ1b/1&2). While the data will be prepared, no findings can be made until 
the end-stage of the evaluation. 
3. Did WE4F projects meet their agricultural productivity targets? What were the unintended 
effects of WE4F supported projects on agricultural productivity? (EQ1g/1; EQ1g/1a) 
Hypothesis II - By sourcing technologies and business model innovations that have already 
achieved early adoption (1,000-10,000 end users), WE4F innovations are much more likely to 
transition to scale (10,000-1,000,000 end users) or reach wide-scale adoption (greater than 
1,000,000 end users).
This is about the timing of support in the trajectory of client-number growth. WE4F seeks to 
provide support at a later stage, where innovations have already reached at least 1,000 users. For 
this hypothesis a comparison is made between early- and later-stage innovations (all innovations 
or within specific categories/types of innovations or the investment cost of an innovation), 
144 
looking at client-growth-curves. To validate or nullify this hypothesis requires answering the 
following questions: 
1. Considering annual client (innovation end-user) numbers buying the innovation (y0-baseline, 
y1, y2, y3), what is the annual client number growth rate? (Make a curve for each 
innovation) (EQ1a/4, EQ1b/1&2) 
2. Where in the client-growth curve is the WE4F support effect the strongest? 
Hypothesis III - By investing in acceleration-oriented TA and IF (including for end users to 
purchase WE4F innovations), we will substantially increase the likelihood that innovations will 
have the knowledge, tools, and resources to scale their innovations.
To validate or nullify this hypothesis requires answering the following questions: 
1. Comparing WE4F with other programs, what is the share of innovations that reach their sales 
targets? (EQ1a/3) 
2. Of all TA and IF provided, which elements are the most effective in getting innovations to 
reach more clients? (EQa/1&3; EQ1b/1&2; EQl/1; EQl/1; EQm/2; EQn/1; EQp/2) 
3. As for Hypothesis I: (How) is a nexus-based (multisectoral) innovation approach more 
enabling for innovations, where a more sectoral development program is not (or would 
exclude innovations)? (RIH KII, EQb/1; compare with other programs). 
It is difficult to clearly state how an innovation would have fared in terms of pace of adoption 
without the support from WE4F. Data points to be collected include adoption, speed of adoption, 
types of support provided, satisfaction of the innovation with WE4F support, and innovation’s 
own assessments of pace of adoption. Analyzing these aspects will allow the team to make a 
judgement on the hypothesis with a medium level of confidence. For clarity of concept and use, 
the indicators are reviewed in Annex 9 which presents the indicators as used in this evaluation. 
This also considers data available for these indicators.
Data Quality Assurance of M&E Data 
This evaluation relies strongly on Monitoring and Evaluation otherwise termed administrative 
data. Such data goes beyond the basic information on the universe of Innovations and their status 
to include key indicators (both KPI and GINs). This was necessitated by the need to protect the 
innovations from constant and repetitive requests for the same data and to avoid “research 
fatigue” on their part and possible resistance to respond. 
This condition is highly unusual in an evaluation which needs to maintain independence of 
research, data gathering and analysis of its own surveys. This was, however, accepted by the 
evaluator for three reasons:
a. this available data would be subjected to a vigorous Data Quality Assurance set of exercises 
to ensure it meets USAID's five quality standards—validity, integrity, precision, reliability,
and timeliness 
b. to avoid the possible unnecessary duplication of data requests which could lead to accessing 
data which may have differed from that of the Secretariat but for reasons of irregular 
compilation and making insignificant differences and 
c. access to the M&E data through the Secretariat would enable the checking and correction of 
identified irregularities. 
The full DQA report is included in Annex 12. 
Before the data analysis could begin the data needed to be compiled, organized, cleaned and in 
some cases corrected. From a study of the procedures of DQA set out in USAID documents, the 
focus in data quality procedures, will be on the following generic issues: 
145 
1. EXTRACT: Definitions of indicators in PIRS and on types of disaggregation  
2. PREPARE: Methodology in data collection used by M&E identified   
3. EXPLORE AND CHECK: Use of data collection procedures by innovations identified and 
use confirmed   
4. VERIFY: Use of data quality procedures by USAID confirmed by evaluator  
5. USE AND ANALYZE: Data verification by USAID undertaken and confirmed  
Table 75 provides a summary of the issues and limitations that were identified during the 
verification process of the data. 
Table 75: Types of challenges in data quality analysis (issues and limitations)
USAID Data Quality 
Assurance criteria
Identified issues Resolution
1 VALIDITY – Data should 
clearly and adequately 
represent the intended result 
Missing characteristic
Unexplained change 
Clarification needed
A table was shared with specific 
questions regarding numbers and 
units missing or not making sense 
2 INTEGRITY – Data collected 
should have safeguards to 
minimize the risk of 
transcription error or data 
manipulation
Missing characteristic 
Missing data, Incorrect total 
A table was shared with specific 
questions regarding numbers and 
units missing or not making sense 
3 PRECISION – Data have a 
sufficient level of detail to 
permit management decision 
making, e.g., the margin of 
error is less than the 
anticipated change
Data Clarification and Missing Data 
Some data not disaggregated by 
country or gender (Clarification 14?)
The PIRS for Gin6 Carbon emissions
When possible, this is done, if it was 
not done then the data is not there, 
will have to proceed as is. 
The PIRS was updated for ease of use 
and now it is tCO2 rather than tCO2
per kg of food) 
4 RELIABILITY – Data should 
reflect stable and consistent 
data collection processes and 
analysis methods over time
Innovations did not provide proper 
documents so either reported as No 
Data or at 80% of results reported 
(M&E data)
Continuous training has been 
undertaken by the innovations and 
has been shared with the ET
5 TIMELINESS – Data should 
be available at a useful 
frequency, should be current, 
and should be timely enough 
to influence management 
decision-making
Contradictory data, e.g., target lower 
than baseline, some older data still in 
the table. 
The AWPs were updated while the 
ET was working on the data analysis. 
This information had to be updated to 
continue the analysis.
The baselines were set at “0” and 
“No” 
Questions regarding older data 
and/comments were addressed
New AWP information was used
The year 1 data for MENA and SSEA for the CFI1 innovations is currently available and there 
were some issues with typos and missing data that were corrected with the help from USAID, a 
summary list of these issues is shared in Table 10. The complete list of issues is listed in Annex 
12 – DQA report. There were also issues where a zero was recorded due to lack of 
documentation or since the data will be collected by the external surveyor but is not yet 
available, this is now recorded as “ND” no data to reduce confusion. The table in Annex 12 also 
lists the data clarification that was asked of USAID to help the Evaluation Team fully understand 
the data available
The data limitations/gaps summary is included in Table 77 with a complete detailed list in Annex 
7. Some of the limitations include not having targets that were disaggregated by gender and/or 
country, not having budget projections for the SSEA CFI1 innovations in the AWP, having 
information reported a zero due to lack of documentation and other reasons and having some 
target information missing for indicators. 
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Table 76: Overview of key DQA issues
Issue/Question Location Resolution
Some data not disaggregated by country 
or gender
SSEA and MENA When possible, this was done, if it was 
not done then the data is not there and 
will have to proceed as is
Uncertainty about Unit measurements 
for KPIs
Gin6, GIN6 liters of water PIRS were updated and have become 
clearer and more user friendly.
Inputs do not add up These were typos and were corrected
Missing data (such as types of end users, 
and units for Gin1)
Gin1, GIN9, GIN6 This data was added when available
How should part time jobs be counted Gin10 Counted as full-time job equivalents
Clarification of the baseline response for 
Yes/No indicators
KPI1, Gin13, Gin23, Gin41 The baseline is No
The Innovation list of July 2022 is 
missing innovations that are no longer in 
the program
The 4 terminated innovations were 
added
The DQA proceeded with the close cooperation of the Secretariat’s M&E which was also 
engaged in improving data quality with training of innovations. Following the DQA exercise all 
available efforts had been made to validate and verify all the data available; there were, however, 
limits to the universality of the data (for instance, some KPIs were not recorded in one Hub, etc.) 
and there were other qualifications necessary.
The summary of key data limitations/gaps which are limitations or gaps not possible to resolve is 
included in Table 77; a complete detailed list is provided Annex 7. 
Table 77: Overview of key data limitations
Issue/Question Location
Reported as zero since no documentation is provided and/or the information is 
not available as it will be collected by an external surveyor, or innovation
miscalculated the results
SSEA and MENA Gin28, 
GIN7
For some innovations’ results the number is reduced to 80% due to lack of 
details on end users
GIN2
Due to limited information the gender was disaggregated based on country 
demographics of end users not on information from the innovation
SSEA and MENA Gin6, GIN7, 
others
The SSEA CFI1 AWPs do not contain a budget sheet SSEA CFI1 AWP
Some of the target data is missing for indicators SSEA CFI1 AWP
Results and targets are not disaggregated by country GIN5, PKI 6, Gin6, etc.
Assumption that all baseline is zero KPI1, Gin13, Gin23, Gin41
As of August 15th, no data has been received for SCA M&E data
Formatting for the AWPs is not consistent MENA, SSEA and SCA
The targets provided in the AWP are not disaggregated by gender, country, 
income quintile 
AWPs
Do not have country, innovation focus, and innovation type, sector focus and 
women led for all innovations 
M&E data
Analysis of unresolved data limitations finds that these arise from the 81 AWPs were read, 
investigated, and found to be of different formats: 
• Each RIH has a different format and AWP are presented in spreadsheets 
• The SSEA format includes a workplan, followed by a narrative (summary, major 
milestones). 
• In SSEA there are multiple tabs in the spreadsheets with limited data and no financial 
information. 
• In MENA the AWP are formatted differently with KPI, milestones, and LOP targets. 
• MENA also presents a narrative per year, includes TA support and a budget. 
147 
Some of the data limitations include not having targets that were disaggregated by gender and/or 
country, not having budget projections for the SSEA CFI1 innovations in the AWP, having 
information reported a zero due to lack of documentation and other reasons and having some 
target information missing for indicators. 
Limitations of data from innovations’ AWP 
Data reliability: WE4F funding tranches disbursed is tied to achieving targets for technical and 
financial indicators (In the milestone plan). Annually, milestones are reviewed against market 
conditions to ensure that the innovation is still viable and with potential to achieve wide-scale 
adoption. Thus, if milestone targets are not met, the award can be terminated (in some 
circumstances minor adjustments of milestone targets can happen). 
Provisional Finding – This system appears to be fit for purpose, however, the innovations that 
struggle to reach their milestone targets are under pressure (risking grant termination) and this 
could potentially create a reporting ‘bias’ or unreliability. After completing the data analysis and 
verification process, the Evaluation Team has the following recommendations to address issues 
identified during the analysis of the M&E data: 
• The same format by RIH can be used for reporting for each of the innovations so that it is 
easier to quality check 
• Continued trainings to keep procedures and methodologies are kept up to date and current for 
the innovations. 
• Improve upon the coordination that was taken to review, clean up the data and organize it 
together into one from the SSEA and MENA year 1 results, annual results and the 81 AWPS. 
We note that the CFI1 SCA data will soon be available. 
Limitations of the evaluation
Particular attention to the limitations associated with the evaluation methodology (e.g., selection 
bias, recall bias, unobservable differences between comparator groups, etc.) Considering the risk 
analysis (in the design, the risk table), there are no new risks. The Evaluation Team is conscious 
of the risks and limitations associated with delivering an evaluation of the quality expected by 
the Founding Partners and the wider WE4F community. To overcome these risks and limitations, 
the team drew on the considerable strengths of the team members with relevant expertise on the 
nexus of water, energy and agriculture, and significant experience in a few WE4F portfolio 
regions and countries. All have had solid experience with multi-country and multi-level 
evaluations. 
Table 78: Key risks identified and their mitigation
Risks Mitigation Measures Taken 
COVID-19 Pandemic and Environmental Risks 
Undertaking an Administrative Data DQA, 
closing gaps in availability and developing 
a dataset with high verification levels 
The Evaluation Team sequenced the data collection and review of the administrative data to 
the extent possible in relation to when the RIH are launched and implemented. 
Data Collection and Analysis Risks 
Insufficient access to customers and non￾customers to understand surveys of 
sufficient numbers of end users to ensure 
confidence in the innovation’s success 
factors. 
Efforts will be made to plan as far in advance as possible with the innovations to impress upon 
them the need to meet with as many customers and non-customers as feasible within the 
allotted time. In addition, access is needed to Customer Response Strategies which the 
Innovations have undertaken. 
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Risks Mitigation Measures Taken 
Insufficient budget and time to undertake a 
representative sample 
On sampling, at this stage, the team does not have a full list of estimated end users per 
innovation, and their locations. Site selection and cluster sampling at the site is critically 
important in sampling. Questions to help establish 'representativeness' will be raised in the 
survey and in KII’s. For efficiency, support from the innovation will be requested in terms of 
orientation and travel to the site and advice is also requested on translation as needed. If 
possible, local maps will be accessed in advance of the consultant proceeding to the field to 
undertake surveys. 
The high number of Evaluation Questions 
and the availability and willingness of 
interviewees to respond with detailed or 
complete answers. 
 
Establishing questionnaires for KIIs and surveys, to elicit meaningful and accurate responses, 
is difficult; and responding requires lengthy time commitments from interviewees. The team is 
working on minimizing the interview length while maximizing the data gathered through the 
questions. Questionnaires will be tested. The team will also use the Administrative Data to 
supplement field data. 
Possible uneven quality of data gathered by 
external surveyors lessens confidence in 
indicators of results. 
Confidence in the data is being ensured by careful construction of questionnaires on a selection 
of indicators and preparation of local consultants. The interviews include some questions 
which overlap to some or other extent with each other, allowing for triangulation. This is also 
done for similar questions asked to both end users and innovations, or both innovations and 
RIH. As part of the Dexis quality assurance, every deliverable will pass from LC Hemson to 
the PMU of WE4F, for a final quality check before submission to the client. 
Timeline for the recruitment of the 
local/regional consultants for the data 
collection 
The PMU will work closely with the Evaluation Team to recruit in accordance with the 
sequence of data collection sites to mitigate potential delays to data collection. 
Samples of innovations may not be 
sufficiently representative of the portfolio. 
The sample of end users may also not be 
sufficiently representative of all types and 
layers. 
Budgetary and other considerations lead to relatively small samples being achieved among End 
user groups. We need to confirm with the RIH that the information being collected during 
visits is sufficiently diverse to cover as many different kinds of interventions across as many 
geographies within the portfolio as possible within the time and funding constraints. Given the 
diversity of projects and the purposeful element in sampling, findings from the sample may not 
infer findings in the universe of the WE4F program. 
The methods of hierarchical modeling could assist in dealing with the challenge of nested data 
(i.e., the close association among respondents in the same cluster). The sampling strategy for 
all research should be synchronized between M&E and Dexis and, if possible, a panel 
established. 
Disaggregation of household income to 
that of women and men’s income is 
difficult to achieve 
Since most women farmers participate in agricultural activities as part of a household together 
with their husbands it is difficult to measure their income separately (IM4.1). To mitigate this, 
the household income indicator will have an additional proxy indicator where women self￾report: in a survey question on the estimated female/male share of the additional income and 
other benefits, and in qualitative interviews on their contribution and compensation in material 
terms. 
Each innovation is unique and there is a 
challenge in ensuring comparability of data 
across this diverse group of innovations 
The WE4F Evaluation Team will need to be mindful of this challenge at all stages of the 
analysis and to assess and ensure comparability in undertaking analysis. 
The Evaluation Team does not know what 
data will look like from the Salesforce ICT 
platform and the data transfers timeline. 
The WE4F Evaluation Team has worked closely with the WE4F Program through the DQA 
exercise to align Dexis and the Salesforce data and update the work schedule as needed. 
The full set of SCA RIH information was 
not available 
The team will sequence MENA and SSEA RIHs first and will update the schedule and 
sampling with SCA Innovations once that data is available. 
Achieving knowledge and understanding 
of the relative contributions of various 
agencies within and without the Program. 
The team will work to triangulate from multiple sources in assessing contributions and 
proportional contributions from many points of support. 
In addition, process tracing will be planned and undertaken to trace the movement from input 
to output and finally to impact on end users. 
The comparability across different GCF in 
terms of costings is challenging as each 
operates in different ways and offers 
different support. Finding the right 
comparators may not be straightforward. 
The Evaluation Team recognizes that this may be a challenge and will work to reduce this risk 
particularly by liaising with Syspons to help clarify approaches to this issue and seeking advice 
from policy makers who have experience in several GCF. 
149 
Risks Mitigation Measures Taken 
Difficulty in assembling responses from 
control group without having an incentive 
to report to this WE4F Evaluation Team. 
The Evaluation Team has attempted to construct controls but there have been many challenges. 
In the interview introduction (protocol) will explain that the interview questions are not just to 
gather data but also useful to reflect on one's own business. Further, a lottery will determine 
which control group company is invited to one (or several) innovating companies to (volunteer 
to) host a peer learning gathering about agri-water-energy innovation. Potential volunteer 
company (or companies) are approached informally through WE4F's network (alternatively, 
interviews with WE4F innovations could end with a question to ask whether they would be 
interested in hosting a meeting between companies). 
Impact 3, Illustrative Indicator (II-IM3): on 
GHG emission: "Total GHG emission/year 
saved by end users through use of 
products/ services of WE4F innovations." 
The impact notes 'along the food value 
chain' and the II-IM3 secondary data 
source is 'lifecycle assessment'. This may 
suggest a wider scope: the emission effect 
along the lifecycle of the innovation. 
Estimating emissions is a herculean task; the Team will explore the CLEER Tool (The CLEER 
Tool is a user-friendly calculator based on internationally-accepted methodologies, enabling 
users to calculate emissions reduced or avoided from clean energy activities, www.cleeer.org)
as a resource as a way of assessing pre/post changes in emissions. The following needs to be 
undertaken: 
The Evaluation Team will quantify the fuel effect directly at farm/end user level, comparing 
pre- and post-innovation fuel use (efficiency, fuel type change); this corresponds to KPI5 (total 
energy saved). 
The Evaluation Team will identify, by comparing pre-and post-innovation, non-fuel emission 
factors along the lifecycle of the innovation, on-farm (e.g., changing farm practices) and off￾farm (through input use, by-products, transport, etc.). 
But given the complexity, the emission effect cannot be quantified for a lifecycle of 
innovation-related products, and a comprehensive carbon balance cannot be produced. 
Gaps in the availability of Administrative 
Data 
One limitation was the data quality, initially. These limitations were extensively discussed in 
the previous section 2.4. Raising data quality issues was necessary, and there were gaps in the 
WE4F administrative data, however, close collaboration with the WE4F secretariat worked out 
well, and many issues were clarified and/or resolved. This collaboration helped to produce 
WE4F data to an acceptable, useable level. 
Data collection progress
Table 79: KII schedule for innovations 
Regional 
Hub
Date completed No. Innovation KII Country Date completed
Part 1 of Phase 1 Part 2 of Phase 1
SSEA Part 1: May 
5,2022
Part 2: May 17, 
2022
Part 3: July 20, 
2022
1 aQysta Nepal Nepal, India NA NA
2 ATEC Cambodia, Bangladesh May 18, 2022 August 15, 2022
3 Goat Trust India May 18, 2022 June 4, 2022
4 Onergy India May 18, 2022 May 18, 2022
5 Oorja India May 18, 2022 May 18, 2022
6 Pumpkin Plus Bangladesh May 18, 2022 June 3, 2022
7 Sumba 
Solutions
Indonesia NA NA
MENA Part 1: May 10, 
2022
Part 2: August 
8,2022
8 Biomass Lebanon May 12, 2022 May 12, 2022
9 Chitosan Egypt May 12, 2022 August 8, 2022
10 Compost Lebanon May 12, 2022 May 12, 2022
11 IRSC Egypt May 12, 2022 August 8, 2022
12 Platform Egypt May 12, 2022 May 12, 2022
13 Schaduf Egypt, Sudan, Syria May 12, 2022 May 12, 2022
14 SOWIT Morocco, Tunisia August 9. 2022 August 9, 2022
15 SUWACO Egypt May 12, 2022 May 12, 2022
Innovation Survey: 12 Innovations from CF1 participated in the survey administered through 
Survey Monkey. 
SSEA End User Data Collection: Data collection began on July 31, 2022, with the Goat Trust 
(Nandinandan Breeds and Seeds) innovation, as well as the Oorja Development Solutions 
innovation, where the local evaluator travelled to Lucknow, India, and Nanpara, India to conduct 
150 
end user data collection until July 30th, 2022. On August 31st, 2022, the local evaluator travelled 
to Kathmandu, Nepal and Lalitpur, Nepal to conduct data collection with the AQysta Innovation 
until September 3, 2022. Additionally, the Local Evaluator conducted Virtual data collection 
with the Pumpkin Plus Innovation. We anticipate sampling from a universe of 3,155 potential 
End Users. 
MENA End User Data Collection: Data collection began on September 5, 2022, with the 
Chitosan Egypt, Integrated Renewable and Sustainable Communities (IRSC), and the Platform 
for Smart Solutions Innovations and is scheduled to go until September 24th, 2022. This will be 
supplemented by virtual data collection visits to the Sowit Maroc Innovation from September 5, 
2022, to September 5, 2022. Due to current local situations in Lebanon on the ground, and time 
constraints for the Mid Term Evaluation, the team decided to primarily conduct data collection in 
Egypt rather than traveling to Lebanon or Jordan. We anticipate a universe of 925 potential End 
Users. 
SCA: Due to where the South-Central Africa regional Hub is in its development, the team 
decided in conjunction with WE4F to conduct data collection later in the evaluation’s period of 
performance when the innovations and end users are ready. 
Table 80 includes the innovations, countries and no. of end users that responded to the local 
evaluators. 
Table 80: End user surveys: innovations and countries
Regional hub Innovation country date completed
start date end date
SSEA aQysta Nepal Nepal, India Aug 31st, 22 Sept 3, 22 
Goat Trust India July 31, 22 July 30th, 22 
Oorja India July 31, 22 July 30th, 22 
Pumpkin Plus Bangladesh Virtual Sept 30, 22 
No. End Users 143
MENA Chitosan Egypt Sept 5, 22
IRSC Egypt Sept 5, 22
Platform Egypt TBD, Sept 24th, 22
SOWIT Morocco, Tunisia Virtual, Sept 5, 22 Virtual, Sept 5, 22
No. End Users 20
There have been challenges in conducting the field surveys in MENA countries and discussions 
with the Secretariat occurred to gain access and build a database of end users from which to 
sample and conduct the surveys. 
151 
ANNEX 16: COMPARATIVE INFORMATION ACROSS GLOBAL CHALLENGE FUNDS
In the SWFF evaluation, the evaluation team assembled some information to compare (efficiency 
of) programs, as shown in Table 81. Fund Management (FM) costs and are presented beside 
beneficiary numbers, awardee numbers, and funds leveraged. 
Table 81: Administrative costs of challenge funds (source: SWFF evaluation report)
Challenge 
Fund 
Beneficiaries Awardees FM as % of 
total fund 
budget 
FM as % of 
fund grant 
value 
Leveraged 
funds 
Source(s) 
AECF REACT 
SSA
16m (by 
2017)
268 
companies
30% US$658m IPE Triple Line Sida 
Report + Website: 
//aecfafrica.org/about￾us/who-we-are(accessed 
22 Jan 2020) (Awaiting 
further info) 
Amplify 
Change (2014)
N/A 757 N/A N/A N/A Amplify Change website 
(Accessed 14 April 2020)
Demo 
Environment
 22.3 28.7 IPE Triple Line Sida 
Report 
Global 
Innovation 
Fund (GIF)
87m 38 24.2 31.9 US$69m 
concurrent
US$165m 
follow-on
GIF Annual Review 
(Dated May 2019)
Innovations 
Against 
Poverty 
(IAP1) 
19k new jobs 
+ 567k 
access to 
goods & 
services
25 N/A N/A US$12m+ IAP Website: Accessed 14 
April 2020
Making All 
Voices Count 
(MAVC) 
(2012-17)
3.15m 178 57.1 133.0
 
US$2.5m+ MAVC Final Evaluation 
and correspondence with 
IMC Worldwide
Powering Ag 
(PAEGC)
234k 24 15.1 17.8 US$68.5 PAEGC Financial 
Information (FY19) and 
correspondence with 
USAID
Securing 
Water for Food 
(SWFF)
7.25m 24 (active 
& 
graduate)
26.2 40.9 US$25m+ SWFF Correspondence 
and SWFF Results 
Database
Sustainability 
& Resilience 
(2016)
N/A N/A 4.9 5.2 N/A IPE Triple Line Evaluation 
of Sida's GCF. As from 
S&R Activity Report 2017 
- No 
152 
In addition, Table 82 is from Sida’s evaluation; it compares Sida’s global challenge funds.
Table 82: Fund management costs of Sida's global challenge funds (source: table 6.16)
Source: Evaluation of Sida’s Global Challenge Funds, 2018:1 https://cdn.sida.se/publications/files/sida62181en￾evaluation-of-sidas-global-challenge-funds.pdf 
Currency Unit 
Challenge Fund Total Value of Donor 
Commitments 
Total 
Value 
of 
Grants 
Total Cost 
of Admin/ 
FM 
Admin/FM 
as % of 
total budget 
Admin/ 
FM as % 
of grant 
value 
Source 
SWIFF m USD 34.91 19.51 15.39 44.1% 78.9% SWIFF Budget Narrative Y4 
Uptate 
PA m USD 49.09 24.47 18.78 38.3% 76.7% 
PA Annual Report 
Financial Y2017 
Demo Environment '000s 
SEK 58,800 45,651 13,149 22.4% 28.8% 
Demo Environment III-AR 
2015-16 
AECF REACT SSA 
'000s SEK 
400,000
-
-
30.0%
-
Sida Appraisal for 
REACT SSA+AECF 
Interview 
IAP 1 
'000s SEK 
50, 990 25,500 25,490
50.0% 100.0% 
211-004499 
Annex 4, Fees 
and cost schedule 
IAP2 m EUR 7.40 4.12 3.28 44.3% 79.6% 
IAP-1st Progress 
Report 
GIF m USD 178.00 - -
24.1% -
Progress Report 
2016 
MAVC m GBP 24.86 11.67 7.17 28.8% 61.4% 
MAVC-EMU Final 
Evaluation Report 
Amplify Change 
m EUR 94.42 81.33 12.59 13.3% 15.4% 
AC AR 2016, 
Section A: Technical 
Sustainability and 
Resilience 
'000s 
SEK 
557,000 54,000 2,811 4.9% 5.2% Activity Report 2017