DISCLAIMER: This publication is made possible by the support of the American people through the United States Agency for International Development (USAID). The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. BASELINE SURVEY REPORT FOR THE KNOWLEDGE-BASED INTEGRATED SUSTAINABLE AGRICULTURE IN NEPAL II (KISAN II) PROJECT January 2019 This publication was produced at the request of the United States Agency for International Development. It was prepared independently by CAMRIS International, Inc. i ABSTRACT The survey collected quantitative information in KISAN II project’s catchment areas to capture the baseline value of performance indicators. It also included a Resilience Module to track indicators related to market access, household exposure to shocks and stressors, social capital, and coping strategies. The baseline survey team used a two-stage cluster sampling design with a systematic selection of participants. It collected data from 1,860 households across 21 districts using tablets with a structured questionnaire. The survey took place from September 3-27, 2018. The surveyed households had an average of 0.55 hectares each (owned or leased), and no households had more than 5 hectares. Average yield (10.7 MT/Ha) and average annual sales ($335) per household was highest for vegetables. Yield for vegetables in ZOI 2 (12.7 MT/Ha) was higher compared to ZOI 1 (9.4 MT/Ha). HHs in ZOI 2 had highest average sales value for vegetables whereas in ZOI 1 highest average sales value was for rice. Less than 5 percent of households that produced rice, maize, lentils, and goats had received support from the government, a project or the private sector in the previous 12 months about achieving increased yields or sales. Support was highest for vegetables (17%) and support received by HHs for vegetables in ZOI 1 (19%) was higher compared to ZOI 2 (15%). Only 12.7 percent of the households accessed formal agriculture finance. Lack of access to irrigation and limited knowledge of “how to achieve higher yields and sales” were found to be the major constraints in increasing yield and sales. Less than 10 percent of households accessed information on crop MPTs, market prices, and animal health and less than 20 percent of households accessed information on financial services, fertilizer, and seeds. The sampled households applied an average of 11.5 improved management practices and technologies (MPTs) from among 64 potential ones. There was no difference in use of average number of MPT across two ZOIs. Almost all the households (97–100 percent) owned agricultural land, buildings (barns or storage) and hand tools (hoe, spade/shovel, rake, sickle, pickaxe, axe, pruning shears, etc.). Seventy four percent of households participated in at least one local group, with mothers’ groups, savings and credit groups, and cooperatives being the most common ones (26–34 percent). Nearly two-thirds of households (63 percent) had been exposed to at least one shock or stressor (such as landslide, drought, illness, and flood) in the past 12 months. Severe impact on income (72 percent) and food consumption (51 percent) was reported by those exposed. The most commonly reported coping strategies (eight of the top ten) were either neutral or positive. Only 24 percent of households had used a negative coping strategy with the most common being selling livestock. Social capital was lower than expected, based on the relatively small percentage of households receiving or providing support during shocks or stressors. Such support was mostly from relatives living inside and outside of their community. ii Conclusively, the survey delivered the baseline values of 20 KISAN II performance indicators including the findings that portray the current resilience capacities of the selected households. iii Baseline Survey Report for the Knowledge-Based Integrated Sustainable Agriculture in Nepal project II (KISAN II) May 2019 Contract Number AID-367-C-15-00001 Cover photograph: An enumerator administering the baseline survey questionnaire to a local farmer in the Palpa district. Photo credit: Khem Raj Neupane iv CONTENTS Abstract ................................................................................................................................................................................. i Contents ..............................................................................................................................................................................iv List of tables.......................................................................................................................................................................vi List of figures .................................................................................................................................................................. viii Abbreviations ....................................................................................................................................................................ix Team Members and Acknowledgements .............................................................................................................. xi Executive Summary......................................................................................................................................................... 1 Purpose of Baseline Survey .......................................................................................................................................... 5 1 Project Background ............................................................................................................................................... 5 2 Survey Methodology .............................................................................................................................................. 8 2.1 Methodology............................................................................................................................................................................ 8 2.2 Limitations..............................................................................................................................................................................13 3 Baseline Findings ...................................................................................................................................................15 3.1 General Findings on the Sample Population...................................................................................................................15 3.2 Findings Related to KISAN II Performance Indicators..................................................................................................20 3.3 Baseline Resilience Findings................................................................................................................................................30 4 Conclusions..............................................................................................................................................................50 4.1 Agriculture Module...............................................................................................................................................................50 4.2 Resilience Module................................................................................................................................................................. 51 4.3 Gender ....................................................................................................................................................................................54 Appendix I: Baseline Survey Scope of Work.......................................................................................................56 Appendix II: Lessons for Future Survey ................................................................................................................69 Appendix III: Detailed Methodology.......................................................................................................................73 Appendix IV: Additional Data Tables With Disaggregates Agriculture and Resilience Modules 86 Appendix V: KISAN or GFSS Indicators and Contribution Questions .................................................122 Appendix VI: KISAN II Baseline Questionnaire..............................................................................................128 Appendix VII: Study and Data Collection Team ............................................................................................175 v Appendix VIII: Disclosure of Any Conflict of Interest ..................................................................................176 vi LIST OF TABLES Table 1: Disaggregates Featured in the KISAN I1 Baseline Data Analysis.................................................. 10 Table 2: KISAN II Baseline Data and Reports Submitted to USAID/Nepal and KISAN II...................... 10 Table 3: Data Limitations........................................................................................................................................ 13 Table 4: Sample Breakdown of KISAN II Baseline Survey Population ......................................................... 15 Table 5: Education Level of Commodity Decision Makers............................................................................. 16 Table 6: Vulnerable Households........................................................................................................................... 16 Table 7: Number of Sellers of KISAN II Commodities................................................................................... 17 Table 8: Percentage of Survey Households with Year-round Access to Irrigation .................................. 20 Table 9: Percentage of Households with Year-Round Irrigation by Source of Water............................ 20 Table 10: KISAN II Outcome Indicators Related to the Application of Improved MPTs....................... 21 Table 11: Average Number of Individual MPTs Applied Per Household (Farm) and Number of Hectares with MPTs, Disaggregated by Technology Type ............................................................................. 22 Table 12: Percentage of Households Using Hybrid and Improved Seeds for Staple Crops................... 23 Table 13: Average Number of Individual MPTs and Hectares Per Household, Disaggregated by Commodity ................................................................................................................................................................ 24 Table 14: Percentage of Households Accessing Information on MPTs Through Improved ICT Channels or Content............................................................................................................................................... 24 Table 15: KISAN II Outcome Indicators Related to Access to Financial Services.................................... 25 Table 16: Participation in Informal Savings and Credit Groups..................................................................... 26 Table 17: Average Formal Loan Value, Disaggregated by Type of Financial Institution and Gender... 26 Table 18: Value of Household Savings Deposits and Outstanding Balance on Agricultural Loans of USG-assisted Smallholders (Individual)................................................................................................................ 27 Table 19: KISAN II Outcome Indicators Related to Production, Yields, and Sales.................................. 27 Table 20: Production and Consumption of Nutrient-rich Vegetables and Goats..................................... 30 Table 21: Share of Annual Household Income by Sources............................................................................. 32 Table 22: Number of Sources of Household Income Disaggregated by Income Level (Above and Below the Poverty Line of $1.90 Day/Person).................................................................................................. 33 Table 23: Proximity to Market Infrastructure and Services – Percentage of Households Within Two Hours’ Walking or Five KM to Markets.............................................................................................................. 33 Table 24: Most Significant Sources of Information, Training, and/or Services........................................... 36 Table 25 Percentage of Households Unable to Access a Local Agricultural Advisor, Inputs, or a Buyer at Least Once in the Past 12 Months....................................................................................................... 36 Table 26: Farmers’ Interactions with Agrovets................................................................................................. 37 Table 27: Farmers’ Interactions with Commercial Buyers, Traders, and Wholesalers........................... 38 Table 28: Percentage of Households Exposed to a Shock or Stressor and Average Number of Shocks or Stressors, by Province and Ecological Region .............................................................................................. 42 Table 29: Percentage of Households Exposed to a Shock or Stressor That Used At Least One Coping Strategy, Disaggregated by Households Above and Below the Poverty Line ($1.90/Person/Day) per da ..................................................................................................................................... 44 Table 30: Percentage of Households Who Received Support, Disaggregated by Source and Type.... 45 Table 31: Percentage of Households That Gave Support to Others Exposed to Shocks & Stressors 46 Table 32: Percentage of Respondents Who Experienced Hunger in the Past 30 Days (HFIES) Disaggregated by Poverty Status (<$1.90 per day) .......................................................................................... 47 Table 33: Post-Shock Perceptions About Food Security and Future Local Government Responsiveness.......................................................................................................................................................... 48 Table 34: Expectations About Social Capital ..................................................................................................... 49 vii Table A1.1: List of KISAN II Indicators to Be Collected Under this SOW................................................ 57 Table A1.2: Sampling Methods for Each Stage of Sampling............................................................................. 60 Table A1.3: Parameters Used to Calculate the Initial Sample Size............................................................... 62 Table A1.4: Adjustments from the Initial Sample Size to the Final Sample Size........................................ 64 Table A1.5: Timeline of KISAN II Baseline Survey ........................................................................................... 67 Table A2.1: Issues and Lessons Learned from the KISAN II Baseline Survey............................................ 69 Table A3.1: Survey Team Composition and Responsibilities......................................................................... 73 Table A3.2: Household Listing and Screening Form ........................................................................................ 79 Table A3.3: KISAN II Household-level Outcome Indicators......................................................................... 80 Table A3.4: FTF ZOI-level Indicators Related to Resilience and Women’s Empowerment.................. 82 Table A3.5: Baseline Survey Report Outline...................................................................................................... 85 viii LIST OF FIGURES Figure 1: USAID Nepal’s GFSS Zone of Influence (ZOI).................................................................................. 6 Figure 2: Map of KISAN II Catchment Areas and Baseline Survey Sample Wards..................................... 9 Figure 3: Resilience Data Analysis Framework.................................................................................................. 12 Figure 4: Percentage of Households Producing Targeted Commodities and Average Number of Hectares/HH of Crops or KG Off-take/HH of Goats..................................................................................... 17 Figure 5: Average Prices (USD/KG) Received for Commodities Disaggregated by Gender................. 18 Figure 6: Sellers of Commodities, Disaggregated by Gender........................................................................ 18 Figure 7: Percentage of Households Who Owned or Leased Agricultural Land, and Average ha/Household, by DAG and non-DAG groups................................................................................................. 19 Figure 8: Percentage of Households that had Received Agricultural Support in Previous 12 Months, by Commodity........................................................................................................................................................... 19 Figure 9: Average Volume of Production of Targeted Commodities (KG/HH)........................................ 28 Figure 10: Average Yield of Targeted Commodities (MT/ha)........................................................................ 29 Figure 11: Average Annual Sales by Commodity and Gender (Of Households That Sold the Commodity)............................................................................................................................................................... 29 Figure 12: Percentage of Households Owning Productive Agricultural and Other Assets.................... 31 Figure 13: Average Number of Assets Owned, by Gender........................................................................... 31 Figure 14: Proximity to Market Infrastructure and Services – Percentage of Households Under 0.5 and 1 Hour to Markets (Using Their Typical Mode of Transport) .............................................................. 34 Figure 15: Percentage of Households That Received Information, Training, and/or Services............... 35 Figure 16: Percentage of Households Participating in Local Groups........................................................... 39 Figure 18: Percentage of Households Exposed to Various Shocks and Stressors.................................... 40 Figure 19: Percentage of Households Exposed to Various Shocks and Stressors, Disaggregated by Income (Above and Below the Poverty Line of $1.90 Day/Person)............................................................ 41 Figure 20: Percentage of Households Exposed to a Shock or Stressor That Reported Severe or Extremely Severe Impacts on Income and Food Consumption.................................................................... 42 Figure 21: Most Common Coping Strategies Used by Households Exposed to Shocks and Stressors (n=1,173) .................................................................................................................................................................... 43 Figure 22: Less Common Coping Strategies of Households Exposed to Shocks or Stressors............. 44 Figure 23: Percentage of Households Exposed to a Shock or Stressor Who Used Various Coping Strategies, Disaggregated by Household Income Above or Below the Poverty Line (<$1.90/Person/Day) ................................................................................................................................................ 45 Figure 24: Percentage of Respondents Who Experienced Hunger in the Past 30 Days (HFIES).......... 47 Figure A3.1: Example of Sketch Map for Listing Households......................................................................... 78 ix ABBREVIATIONS ADS Agriculture Development Strategy AMS Annual Monitoring Survey BBS Beneficiary-Based Survey BFS Bureau for Food Security, USAID CAPI Computer Aided Personal Interviews CBS Central Bureau of Statistics CLA Collaborating Learning and Adapting CSV Comma-Separated Values CEAPRED Centre for Environmental and Agricultural Policy, Research, Extension and Development COR Contracting Officer’s Representative DAG Disadvantaged Group DCOP Deputy Chief of Party DEPROSC Development Project Service Centre DM Decision Maker FGD Focus Group Discussion FINGO Financial Intermediary Non-Governmental Organization FTF Feed the Future FTFMS Feed the Future Monitoring System FY Fiscal Year GESI Gender and Social Inclusion GFSS Global Food Security Strategy GIS Geographical Information System GoN Government of Nepal HA Hectare HFIES Household Food Insecurity Experience Scale HH Household ICT Information and Communication Technology IM Implementing Mechanism IPM Integrated Pest Management KG Kilogram KISAN Knowledge-Based Integrated Sustainable Agriculture in Nepal M&E Monitoring and Evaluation MFI Micro Finance Institution MOALD Ministry of Agriculture and Livestock Development MOE Margin of Error MPT Management Practices and Technologies MUS Multi-Use Water System MT Metric Ton N Population Size (statistical notation) n Sample Size (statistical notation) N/A Not Applicable NRM Natural Resources Management NPR Nepali Rupees x NSAF Nepal Seed and Fertilizer (activity) OSC Overseas Consulting Ltd. PAHAL Promoting Agriculture Health and Alternative Livelihoods (activity) PBS Population-Based Survey PIF Partnerships and Innovation Fund PII Personally Identifiable Information PIRS Performance Indicator Reference Sheet PMP Performance Management Plan PPR Performance Plan and Report REAL Resilience Analysis, Evaluation and Learning Award SABAL Sustainable Action for Resilience and Food Security (activity) SACCO Savings and Credit Cooperative SAMS Selected Agriculture Market Systems SEED Social, Environment and Economic Development, USAID Nepal SOW Scope of Work SPSS Statistical Package for the Social Sciences SPPM Office of Strategic Planning and Performance Management, USAID BFS TBD To Be Determined TOPS Technical and Operational Performance Support Program USAID United States Agency for International Development USD United States Dollar USG United States Government ZOI Zone of Influence xi TEAM MEMBERS AND ACKNOWLEDGEMENTS BASELINE SURVEY TEAM MEMBERS Lorene Flaming, Team Leader and Writer, an independent consultant contracted by CAMRIS Kshitiz Shrestha, Evaluation Specialist (Senior Level), the Nepal MEL Activity, CAMRIS Ganesh Sharma, Statistician and Data Analyst Specialist, the Nepal MEL Activity, CAMRIS Swadesh Gurung, M&E Specialist, the Nepal MEL Activity, CAMRIS Ram Khoju Shrestha, Data Management Officer, the Nepal MEL Activity, CAMRIS ACKNOWLEDGEMENTS The baseline survey team would like to acknowledge the support of USAID Nepal, particularly Murari Adhikari, Contracting Officers’ Representative (COR) for the MEL Activity; Carolyn O’Donnell, M&E Fellow and Knowledge Management Specialist; Navin Hada, COR for the KISAN II project; and Chip Bury, Resilience and Food Security Specialist. In addition, the team would like to thank the KISAN II team, Sunil Regmi, MEL Director; Praveen Baidya, Deputy Chief of Party (DCOP); Zarin Pradhan, Senior M&E Manager; and Harish Devkota, Agriculture Director. These individuals helped refine the baseline survey scope of work, map catchment areas and define representative households, adapt resilience questions from existing USAID resilience measurement tools, identify market linkage indicators, and review preliminary data and findings. We appreciate the thoughtful input and feedback we received and the spirit of collaboration. 1 EXECUTIVE SUMMARY The Nepal Monitoring, Evaluation, and Learning (MEL) Activity, which is implemented by CAMRIS International, Inc., conducted a household-level, baseline survey for the Knowledge-Based Integrated Sustainable Agriculture in Nepal (KISAN II) activity in 2018. The Full Bright Consultancy Pvt. Ltd. helped implement the survey’s field activities. BASELINE SURVEY PURPOSE The survey includes two distinct modules. The Agriculture Module collected quantitative information on smallholder agriculture production to provide baseline values for 20 project outcome indicators that will be tracked in KISAN II’s annual monitoring surveys (AMSs) and three Feed the Future (FTF) Zone of Influence-level resilience indicators. The Resilience Module comprised data related to market access (information, inputs, services, and buyers), household exposure to shocks and stressors, livelihoods diversification, social capital, coping strategies, and perceptions about current capacities to cope with future shocks and stressors. PROJECT BACKGROUND KISAN II is one of USAID/Nepal’s flagship activities under the Feed the Future (FTF) initiative. The Mission awarded the five-year, $32.7 million project implementation contract to Winrock International in July 2017. KISAN II facilitates market development by building private sector capacities to help smallholder farmers graduate to commercial agriculture by improving on-farm production, market linkages, and market infrastructure. The project focuses on the value chains of the targeted commodities that are important for food security (rice, maize, and lentils), are high-value (off-season vegetables and goats) and are nutrient-rich. The project works primarily through private sector partners who apply for grants to implement market development activities. Consistent with the project’s market-driven approach, partners identify the specific interventions, target areas, and farmers they will focus on within the parameters of the project’s grant program. The project’s initial 48 partners were the primary sources of information on catchment areas and for the expected number of project participants for this survey. METHODOLOGY In each sample household, the survey team interviewed the member who was most knowledgeable about agriculture. Only households that had produced at least one of the project’s targeted commodities within the previous 12 months were included in the sample frame. Households were selected randomly after preparing the complete list of potential beneficiary households in KISAN II catchment areas by performing the household listing operation. The baseline survey team used a two-stage cluster sampling design with a systematic selection of participants. The data was collected from 1,860 households across 21 districts using tablets with a structured questionnaire that was customized in 2 “SurveyToGo” data collection software. The survey took place from September 3-27, 2018. The survey team captured the data, taking the reference period of the 12 months from September 1, 2017 to August 31, 2018. Box 0 lists the main data analysis questions. The questionnaire appears in Appendix V. FINDINGS AND CONCLUSIONS Agriculture The survey team collected data for the 20 FTF outcome indicators. The interviews with household respondents took about 2.5 hours each. Seventeen percent of vegetable-producing households had received support for achieving increased yields or sales from the government, a project or the private sector in the previous 12 months- compared to less than 5 percent of households that produced other targeted commodities (rice, maize, lentils and goats). Support received by HHs for vegetables in ZOI 1 (19%) was higher compared to ZOI 2 (15%). Year round irrigation was found to be similar across ZOI 1 and 2. Spring or river (other than those managed by community) was the major source of irrigation water for both ZOIs, but higher proportion of HHs from ZOI 2 (89 percent) used it as the main source compared to ZOI 1 (68 percent). Yield for vegetables (10.7 MT/Ha) was highest among all households. ZOI 2 had higher yields for rice (3.6 MT/Ha), maize (2.3 MT/ha) and vegetables (12.7 MT/Ha) compared to ZOI 1 whereas ZOI 1 had higher yield for lentil (0.6 MT/Ha). Average volumes produced Box 0: Baseline Data Analysis Questions Agriculture Module • What are the baseline data for the project’s 20 Feed the Future (FTF) outcome indicators? Resilience Module • What have been the primary recent shocks and stressors in the survey area? • How have households, communities, institutions, and systems responded to the shocks and stresses? • What are the characteristics of asset ownership, proximity to markets, access to information and services, and group participation among the activity's target population? • What are the high-level findings and conclusions about the current state of resilience capacities in the survey area? • Which findings appear most important for informing potential modifications to the activity’s strategy, implementation approaches, and theory of change? • Are there any important information gaps that should be considered in activity or Mission level learning agendas? 3 per farm were highest for rice and vegetables and lowest for lentils. Average yields per farm were highest for vegetables. Average annual sales per household were highest for vegetables ($335), rice ($322), and goats ($181). There was difference in mean value of annual sales between ZOI 1 and 2 with ZOI 2 having higher average annual sales value ($438 vs $289). HHs in ZOI 2 had highest mean sales value for vegetables whereas in ZOI 1 highest mean sales value was for rice. Only 253 individuals in 236 households accessed formal agriculture finance1 , representing 12.7 percent of all households. The top three constraints to increasing yields and sales of targeted commodities were reported to be lack of access to irrigation water, limited knowledge of how to achieve higher yields and sales, and lack of access to inputs. Almost all the households (99 percent) had applied at least one out of 63 the improved management practices and technologies (MPT) in the last 12 months. The sampled households applied an average of 11.5 improved MPTs from among 63 unique potential ones. Vegetables and rice had the highest use of MPTs (10 and 6.7 MPTs/HH, respectively). Among ZOIs, average MPT use was similar; however, noticeably higher proportion of HHs in ZOI 1 had used irrigation (83 percent vs. 66 percent) and agriculture water management (60 percent vs. 45 percent) technology compared to HHs in ZOI 2. Out of 7,624 individuals aged 15 or above in surveyed households, 2,848 individuals applied at least one MPT. There was very little use (11 percent of total households) of information and communications technologies (ICT) for accessing information on agriculture despite 55% owning a smartphone. Resilience Almost all the households (97–100%) owned agricultural land, buildings (barns or storage) and hand tools (hoe, spade/shovel, rake, sickle, pickaxe, axe, pruning shears, etc.). More than 80 percent of households are within walking distance (5 kilometers or two hours) to communications signals, schools, health centers, electricity, agricultural inputs, extension services, and buyers. Out of all 1860 households surveyed, only 1 percent went without eating for a whole day within the prior 30 days; 6 percent ate less than they thought they should, and 29 percent worried about not having enough food. Almost two-thirds of households (63 percent) had been exposed to at least one shock or stressor (out of 18) in the past 12 months. The most common were livestock or crop diseases, pests and not enough rain. Of the 1,173 households that had been exposed to a shock or stressor in the past 12 months, up to 72 percent reported that the shock or stressor had severely impacted household income while up to 51 percent reported that it had severely impacted household food consumption. 1 Finance accessed from Bank, Finance companies and Cooperatives. 4 The most commonly reported coping strategies (eight of the top ten) were either neutral or positive. Only 24 percent of households had used a negative coping strategy with the most common being selling livestock, and one could argue that this should be considered a “neutral to potentially negative” coping strategy, like using household savings. Social capital was lower than expected, based on the relatively small percentage of households that had received (26 percent) or given (32 percent) support in response to a shock or stressor. The ability to recover from a shock or stressor is relatively high, based on household perceptions – 93 percent of households reported that their ability to meet current household food needs was better or the same as before exposure. 5 PURPOSE OF BASELINE SURVEY The main purpose of this baseline survey was to collect quantitative information on smallholder agriculture production in the activity’s catchment areas. The data provide baseline values for 20 indicators for USAID’s Knowledge-Based Integrated Sustainable Agriculture in Nepal (KISAN II) activity. The survey’s objectives appear in Box 1. In addition, the survey included a resilience module to track indicators related to market access (information, inputs, services, and buyers), household exposure to shocks and stressors, livelihoods diversification, social capital, coping strategies, and perceptions about current capacities to cope with future shocks and stressors. Farmers selected for the baseline survey reported information about practices and production for USAID fiscal year 2018 (1 October 2017–30 September 2018). The primary users of the baseline data will be the KISAN II activity team, their private sector partners, other FTF activities, and USAID/Nepal. Other stakeholders may use baseline data and findings, including the Government of Nepal (GON) and other non-USAID agencies. 1 PROJECT BACKGROUND KISAN II is one of USAID/Nepal’s flagship activities under the Feed the Future (FTF) Initiative. The Mission awarded the contract to Winrock International in July 2017. KISAN II will contribute to the GON’s Agricultural Development Strategy (ADS) and the USG Global Food Security Strategy (GFSS). This five-year $32.7 million project will facilitate systemic changes in the agricultural sector including: • Greater intensification of staple crops, diversification into higher value commodities, and wider application of climate-smart management practices and technologies. • Strengthening local market systems to support more competitive and resilient value chains and agricultural related businesses. • Improving the enabling environment for agricultural market systems development. The activity is working in close coordination with the private sector and GON’s Ministry of Agriculture and Livestock Development (MOALD). In addition, Winrock International implements the activity in collaboration with three Nepali organizations as subcontractors: The Center for Environmental and Agricultural Policy, Research, Extension and Development (CEAPRED); the Development Project Service Center (DEPROSC), and Siddhartha Consulting Inc. Pvt. Ltd. (Siddhartha Inc.); as well as Box 1: Baseline Survey Objectives • Collect baseline data for KISAN II’s 20 outcome indicators to support the measurement of results. • Characterize the activity’s target population to inform target setting, project design, and the implementation strategy. • Collect baseline resilience data to support USAID Nepal’s and KISAN II’s resilience research, learning, and storytelling. 6 two international subcontractors: Digital Green Foundation and Overseas Strategic Consulting, Ltd. (OSC). The project targets 200,000 households across 25 districts in the following four provinces: • Province 3 (four districts): Kavrepalanchok, Makwanpur, Nuwakot, and Sindhupalchowk. • Province 5 (seven districts): Arghakhanchi, Gulmi, Kapilvastu, Palpa, Banke, Bardiya, and East Rukum. • Province 6 (eight districts): Salyan, Surkhet, Dailekh, Jajarkot, Dang, Rolpa, Pyuthan and West Rukum. • Province 7 (six districts): Achham, Baitadi, Dadeldhura, Doti, Kailali, and Kanchanpur. These 25 districts are regarded as USAID Nepal’s Global Food Security Strategy Zone of Influence (ZOI) (Figure 1). Figure 1: USAID Nepal’s GFSS Zone of Influence (ZOI) The project has the following five components: • Component 1: Improved productivity of selected agricultural market systems. • Component 2: Strengthened competitiveness, resilience, and inclusiveness of selected agricultural market systems. • Component 3: Strengthened enabling environment for selected agricultural market systems. • Component 4: Increased ability of vulnerable communities to act on business opportunities within selected market systems. 7 • Component 5: Collaborating, learning and adapting (CLA) applied to market systems. KISAN II facilitates market development by building private sector capacities to help smallholder farmers graduate to commercial agriculture by improving on-farm production, market linkages, and market infrastructure. The project focuses on value chains of targeted commodities that are rice, maize, lentils high-value vegetables and goats, and the project works primarily through private sector partners, who apply for grants to implement market development activities. KISAN II will tailor its approach to empower and graduate farming households into more productive, reliable, and lucrative agricultural enterprises evolving from vulnerable to developing, to commercially-minded, and finally to competitive household agricultural enterprises. The project’s targeting strategy provided the basis for identifying representative farm households for the baseline survey. The project’s initial 48 partners are the primary source of information on the catchment areas. The current strategy is to break down KISAN II’s working area to “commercial” and “less commercial” areas, permit partners to select farmers within targeted wards, and then at the end of Year 2 and periodically thereafter analyze the population for the following: a) overlap with other KII partner farmers; b) overlaps with KISAN I population; c) vulnerable (based on commercial vs. non-commercial location); d) gender; e) inclusion. Based on the analysis and grantee performance, KII will make the targeting changes for Year 3. 8 2 SURVEY METHODOLOGY 2.1 METHODOLOGY The documents used to guide the design and conduction of the survey are listed in Appendix II. 2.1.1 SAMPLING APPROACH The sampling approach for the KISAN II baseline survey followed the “catchment area” approach, as mentioned in sampling guide for beneficiary based survey2 , which prescribes identifying the geographic area that defines the population to be reached by the market being strengthened and conducting a survey among that population of producers who are participating in the market and, thus, would be considered project participants. For this, KISAN II provided a list of target districts and wards (which it identified in consultation with its private sector partners), along with estimates of the expected number of future project participants in each catchment area. The baseline survey used a two-stage cluster sampling design with a systematic random selection of representative farm households, for which two separate clusters were required – wards and households. In the first stage, 93 wards (primary sampling units or enumeration areas) were selected from KISAN II’s initial catchment areas. In the second stage, 20 representative farm households were selected per ward, resulting in a sample population of 1,860 households. To identify the household sampling frame, the enumerators first conducted a household listing of potential project participants, defined as households that met the project’s targeting criteria of: • Being currently engaged in farming. • Having produced at least one of KISAN II’s target crops or livestock in the past 12 months. The sample population calculations were based on guidance in the 2016 FTF Sampling Guide for beneficiary-based surveys and were provided in the scope of work (SOW) for the survey (see Appendix I). They reflect adjustments related to finite population correction, design effect, nonresponse rates, and minimum responses for each targeted commodity. This sample size is sufficient to measure a meaningful change for three key FTF indicators (the standard specified in FTF guidance) related to: • The value of annual sales of targeted commodities. • The number of hectares under improved management practices or technologies. • The number of individuals who have applied improved management practices or technologies. 2 https://www.agrilinks.org/sites/default/files/resource/files/Sampling-Guide-Beneficiary-Based-Surveys￾Feb2016.pdf 9 Figure 2: Map of KISAN II Catchment Areas and Baseline Survey Sample Wards Section 4.3a of the baseline survey’s scope of work indicates that the sample should be stratified by province. The calculation of the sample size provided in the scope of work did not reflect stratification, although it did allow for the disaggregation of data by commodity and province, which will likely be statistically significant. While preparing the sample frame, stratification was done by province, as all wards were systematically listed by province, district, rural or urban municipality, and ward in order of the Central Bureau of Statistics’ codes. 2.1.2 DATA COLLECTION The survey team collected data between September 3 and 27, 2018 and the survey took the reference period of last 12 months from September 1, 2017 to August 31, 2018 to capture production, sales of targeted commodities etc. Full Bright, a local research firm with experience conducting large-scale agriculture surveys, collected data using Samsung Galaxy tablets and “SurveyToGo”, a data collection software designed for computer-aided personal interviews (CAPI) on Android devices. Fifteen supervisors, 3 quality control staff, and 45 enumerators were trained in conducting interviews, data entry, and data quality checks. Full Bright was primarily responsible for the training with extensive oversight and support provided by the Nepal MEL Activity and additional support from KISAN II. 2.1.3 DATA ANALYSIS The Nepal MEL Activity’s statisticians cleaned, processed, and analyzed the data. They exported the data to the Statistical Package for the Social Sciences (SPSS) and Stata for 10 analysis. Also, they performed mean, median, and range checks for numeric variables, and descriptive analysis for categorical variables as part of exploratory data analysis. The KISAN II indicator values were calculated in accordance with the FTF Indicator Handbook Performance Indicator Reference (PIRS) (March 2018) sheets for FTF indicators and the KISAN II MEL Plan (April 2018) for custom indicators. The survey team calculated FTF indicator disaggregates in accordance with the FTF performance indicator reference sheet (PIRS) (except as noted in the data limitations table) and presented in the FTF Monitoring System (FTFMS) spreadsheet. Table 1 lists key disaggregates analyzed for this baseline survey report. Table 1: Disaggregates Featured in the KISAN I1 Baseline Data Analysis DISAGGREGATES DATA Primary Decision Maker • Commodities produced. • Application of improved management practices and technologies (MPTs). • Access to financial services (saving deposits and taking loans). Commodity • Number of households producing. • Number of hectares planted. • Number of MPTs used. • Volumes produced. • Yields. • Sales volumes and value. Gender • Primary decision makers. • Application of improved MPTs. • Prices received for commodities. • Top three production and marketing constraints. • Ownerships of assets. Poverty Line • Top three production and marketing constraints. • Household Food Insecurity Experience Scale (HFIES). Geography • Exposure to shocks and stressors. The survey team submitted data in an iterative process that prioritized deliverables based on USAID Nepal and KISAN II deadlines for setting performance targets, updating the FTFMS, and reporting to Washington. Table 2 describes the deliverables and gives the dates they were submitted. Table 2: KISAN II Baseline Data and Reports Submitted to USAID/Nepal and KISAN II DATE DELIVERABLES 15 Oct 2018 An unanalyzed raw and clean dataset in different file formats (SPSS or Stata and CSV) and the questionnaire codebook. 15 Oct 2018 Analyzed data presented in indicator tables, including disaggregates. 2 Nov 2018 Relevant data for USAID Nepal’s Performance Plan and Report (PPR). 7 Nov 2018 KISAN II FTF indicators and disaggregates in a FTFMS spreadsheet. 16 Nov 2018 KISAN II custom indicators and disaggregates. 7 Dec 2018 Preliminary findings presented to KISAN II team focused on characterizing representative households rather than population estimates. See Appendix IX for a summary of the related discussion. 11 10 Dec 2018 Preliminary findings and recommendations again presented to USAID Nepal focused on characterizing representative households rather than population estimates. The survey team conducted substantial additional data analysis after the presentation. Refer to Appendix IX for a summary of the discussion and the list of additional data calculations. 18 Jan 2019 Draft baseline survey report. 28 Mar 2019 Final baseline survey report. The survey team prepared a resilience data analysis framework to show how elements of the survey’s agriculture and resilience modules would contribute to assessing resilience in the baseline sample population. The framework (see Figure 3) can be adapted for future resilience measurement efforts. USAID Nepal indicated that they were most interested in assessing findings and inter-relationships between the following categories of information: • Access to markets, groups, and government services. • Assets, livelihoods, and food security. • Exposure to shocks and stressors. • Coping strategies (post-shock responses). • Well-being outcomes (ability to recover). The Mission indicated it was less interested in categorizing absorptive, adaptive and transformative capacities, which were initial concepts in USAID’s evolving resilience measurement framework. The bulleted topics in Figure 3 are color-coded to show where these resilience capacities are reflected in the framework (refer to key). 12 Figure 3: Resilience Data Analysis Framework 13 2.2 LIMITATIONS Three data limitations are presented in Table 3. Two resulted from not fully capturing FTF disaggregation requirements in the survey questionnaire – these do not affect aggregate data for any of KISAN II’s FTF indicators. Table 3: Data Limitations LIMITATIONS RESPONSES Firm size disaggregate calculations for farms/households – The FTF Indicator Handbook does not provide a definition for “employee” or specify using “full-time equivalents” for the following disaggregates: • “Type of producer/firm” for the value of annual sales of farms and firms (EG.3.2-26, Handbook page 101). • “Size of the recipient” for formal agricultural loans (EG.3.2-27, Handbook page 106). The survey team calculated the number of employees based on the number of unique individuals hired by smallholders on a seasonal or daily wage basis. The employee numbers, therefore, overstate the size of producers and loan recipients. Evidence-based interpretation – When interpreting disaggregated results, it was more accurate to disregard the number of employees and assume the following: • All sellers were “producers,” not “firms” (defined as “non-farm enterprises”). Producers will be divided into “smallholders” and “non-smallholders” using the methodology described in the row below. The corrected disaggregate figures will be submitted to FTFMS. • All borrowers were “individuals/microenterprises” (employed less than 10 people in the previous 12 months), not “small,” “medium,” or “large” enterprises (employed 10-49, 50-249, or more than 240 people, respectively). Smallholder goat producers – The FTF Indicator Handbook defines a smallholder engaged in goat production as one who holds a combined total of no more than five adult ewes and does (page 101). The questionnaire did not include a question about goat age and gender. This affects the calculation of smallholder disaggregates for FTF indicators related to the following data: • Use of improved technologies and management practices (e.g., .3.2-24,-25,-28, pages 92, 99, and 109). • Yields (e.g., 3-10,-11,-12, page 54). • Value of annual sales (e.g., .3.2-26, page 103). The main purpose of this baseline survey was to capture values of 20 KISAN performance indicators and their desegregates which did not require calculating number of smallholder goat producers as an indicator or as a desegregate. However, this study recommends capturing this information to calculate FTF indicators in similar future survey. Gender of producers, proprietors, and other decision makers – The FTF Indicator Handbook states, “If the enterprise has more than one proprietor, classify the firm as Male if all the proprietors are male, as Female if all the proprietors are female, and as Mixed if the proprietors are male and female” (page102). At KISAN II’s request, the questionnaire did not include an option for “Mixed” and instead asked respondents to identify the primary decision maker. KISAN II noted that approximately 90 percent of decisions were reported as “Joint” in KISAN I surveys, which obscured the role of gender in decision making. Neither approach enables the calculation of the total number of unique decision-makers nor adequately captures the role of gender in decision making. The decision to omit “Mixed” affected the calculation of gender disaggregates for indicators related to the Supplemental focus group discussions – Given the inevitable trade-offs between identifying a single primary decision maker or mixed decision makers, the survey team recommends that household decision making be explored more fully through focus group discussions. Although such findings will not affect the calculation of FTF disaggregates, they would inform the project team’s gender and social inclusion (GESI) strategy. 14 LIMITATIONS RESPONSES value of annual sales (EG.3.2-26, page 103) and formal agriculture loans (EG.3.2-27, page 106). Refer to Appendix III for a more detailed description of the survey methodology. 15 3 BASELINE FINDINGS 3.1 GENERAL FINDINGS ON THE SAMPLE POPULATION This section characterizes the sample population with respect to size, socioeconomic characteristics, vulnerability; the percentage of households that produce and sell KISAN II’s targeted commodities, hold arable land, received prior support for agriculture, have access to year-round irrigation, and report post-harvest losses; and the average prices received for commodities. These data are derived from questions under the Agriculture Module. It is not reported in FTFMS or progress reports but is useful for characterizing representative households in KISAN II catchment areas. Unless otherwise noted all the data in Chapter 4 reflect conditions over the 12 months from 1 September 2017 to 31 August 2018. 3.1.1 SUB-POPULATIONS As noted earlier, the sample included 1,860 randomly selected households from KISAN II’s catchment areas. Out of these selected households, less than 1 percent of the households had received support from KISAN I. Table 4 shows important sub-populations that are relevant to understanding who is counted in subsequent tables. These populations reflect the number of households, female-headed households, number of household members, and the number of individuals who were identified as the primary decision maker for each of the four types of decisions listed below. Table 4: Sample Breakdown of KISAN II Baseline Survey Population POPULATIONS POPULATION Total Households 1,860 Households in ZOI 13 1,360 Households in ZOI 24 500 Female-headed households 413 (22%) Household members 10,447 HH members 15 years or older 7,624 Average household size (members/household) 5.6 Unique agriculture decision makers5 who: • Applied improved management practices and technologies 2,848 • Produced commodities 2,560 • Participated in informal savings schemes 2,436 • Accessed informal and formal credit 823 3 20 districts from West, Mid-West and Far-West Region 4 4 districts from Central Development Region 5 We expect that agriculture decision makers tend to make decisions across multiple issues. It is not possible to calculate the total number of unique decision makers due to the number of decision-making variables in the sample. However, the number is estimated to be between 2,848 and 3,000 individuals. 16 3.1.2 SOCIOECONOMIC CHARACTERISTICS An estimated 34 percent of the 2,560 commodity decision makers in the sample were illiterate (Table 5). These may or may not have been the heads of households. Not much difference was observed between two ZOI by education. Table 5: Education Level of Commodity Decision Makers EDUCATION LEVEL PERCENTAGE OF COMMODITY DECISION MAKERS (TOTAL) ZOI 1 ZOI 2 Illiterate and never attended school 34% 34% 36% Literate but had never attended school 16% 16% 15% Primary (Grades 1–5) 21% 21% 21% Secondary (Grades 6–10) 22% 22% 22% Higher secondary (Grades 11–12) 5% 5% 5% Graduate or above 2% 2% 1% A high percentage of households (78 percent) met at least one of USAID Nepal’s three vulnerability criteria (Table 6). Note that whether a member of a disadvantaged ethnic group is disadvantaged varies due to several factors (such as location), so 55 percent may be an overstatement of the incidence of vulnerability associated with ethnicity. In contrast, exposure to natural disasters may understate the actual vulnerability as it does not capture exposure to other significant shocks and stressors listed in Section 4.3. Table 6: Vulnerable Households USAID NEPAL VULNERABILITY CRITERIA PERCENTAGE OF HHS MALE HEADED HH FEMALE HEADED HH ZOI 1 ZOI 2 Income: <$1.90/day/person 26% 26% 28% 26% 28% Disadvantaged group: Dalits, Janajatis & Muslims 55% 55% 54% 59% 43% Affected by a natural disaster in the past 12 months 32% 32% 30% 34% 25% Unique vulnerable households 78% 79% 77% 81% 72% 3.1.3 FARM CHARACTERISTICS Most surveyed households were producing several of the targeted commodities (Figure 4 below). The number of hectares devoted to rice production was more than double the area devoted to each of the other commodities. Note that goat production was based on off￾take, and not all households that owned goats in the past 12 months consumed or sold goats. 17 Figure 4: Percentage of Households Producing Targeted Commodities and Average Number of Hectares/HH of Crops or KG Off-take/HH of Goats Across the 1,860 households, 1,412 households had sold at least one of the targeted commodities in the previous 12 months (Table 7). The households sold an average of 1.45 commodities each with vegetables and goats having the most sellers – roughly two-thirds or more of all sellers each. Average annual sales per household were highest for vegetables ($334.92) followed by rice ($321.94). In ZOI 1, average annual sales per household was highest for rice ($346) whereas in ZOI 2, it was highest for vegetables ($525). Overall, average sales per household was higher for ZOI 2 ($438) compared to HHs in ZOI 1 ($289). Table 7: Number of Sellers of KISAN II Commodities COMMODITIES NUMBER OF HH PRODUC ERS NUMBE R OF HH SELLERS PERCENTA GE OF TOTAL SELLERS AVERAGE ANNUAL SALES PER HH ZOI 1- AVERAGE ANNUAL SALES ZOI 2- AVERAGE ANNUAL SALES Rice 1,383 457 33% $321.94 $346.4 $206.5 Maize 1,311 246 19% $67.79 $53.7 $96.5 Lentils 602 175 29% $73.18 $73.2 - Vegetables 1,334 770 58% $334.92 $192.9 $524.6 Goats 1,056 788 75% $180.89 $172.5 $194.6 Number of Unique Sellers 1,860 1,412 76% $408.67 $289.1 $437.8 Goats were by far the highest value commodity. Lentils were a higher value than vegetables. Female sellers received slightly higher average prices for lentils, vegetables, and goats than male sellers, although men received a slightly higher average price for rice (Figure 5 below). 74% 70% 32% 61% 57% 0% 20% 40% 60% 80% 100% Rice Maize Lentils Veg Goats 0.48 0.20 0.19 0.09 51 18 Figure 5: Average Prices (USD/KG) Received for Commodities Disaggregated by Gender Figure 6 shows the percentage of sellers who were male or female for each commodity. Most sellers for rice, maize, lentils, and vegetables were men, and most goat sellers were women. Figure 6: Sellers of Commodities, Disaggregated by Gender As expected, almost all the households had access to agricultural land (owned or leased) since only households producing a targeted commodity were included in the sample (Figure 7). The less than one percent of households that did not have access to agricultural land were only producing goats. A similar proportion of DAG and non-DAG groups owned land whereas a higher percentage of DAG group leased (36 percent) compared to non-DAG 74% 63% 77% 57% 41% 26% 37% 23% 43% 59% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% Rice Maize Lentils Vegetables Goat Male Female 0.25 0.22 0.49 0.29 0.22 0.22 0.55 0.30 0.24 0.22 0.51 0.29 0 0.1 0.2 0.3 0.4 0.5 0.6 Rice Maize Lentils Vegetables USD/KG Male Female Average 3.86 4.2 4.04 3.6 3.7 3.8 3.9 4 4.1 4.2 4.3 Goat 19 group (28 percent). The households had an average of 0.55 hectares each, and no households had more the 5 hectares, the FTF definition of a smallholder. Figure 7: Percentage of Households Who Owned or Leased Agricultural Land, and Average ha/Household, by DAG and non-DAG groups6 Only 1–5 percent of households that produced rice, maize, lentils, or goats had received support from the government, a project or the private sector in the past 12 months for producing or marketing each commodity. Support was highest for households that produced vegetables (Figure 8). There wasn’t much difference between two ZOIs, except for support for vegetables. Figure 8: Percentage of Households that had Received Agricultural Support in Previous 12 Months, by Commodity 6 DAG and non-DAG groups are based on caste/ethnicity only. DAG group includes Dalit, Janajati and Muslim population whereas caste/ethnicities such as Brahmin/Chhetri, Madhesi, and Newars are kept in non-DAG group. 96% 36% 97% 28% 97% 32% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% % Owned % Leased Disadvantaged group (n=1012) Non-disadvantaged group (n=838) Total (n=1850) 0.40 ha 0.55 ha 0.30 ha 5% 4% 1% 17% 3% 6% 5% 1% 19% 3% 4% 4% 1% 15% 4% 0% 5% 10% 15% 20% Rice Maize Lentils Vegetables Goat Total ZOI 1 ZOI 2 0.45 ha 20 Of the 1,849 households who had harvested at least one targeted crop in the last 12 months, 44 percent had year-round access to irrigation water (Table 8). These households had irrigated an area of between 0.01 and 3.33 hectares each, with an average of 0.42 hectares each. Table 8: Percentage of Survey Households with Year-round Access to Irrigation ACCESS TO YEAR-ROUND IRRIGATION NUMBER OF HHS PERCENTAGE OF HHS ZOI 1 ZOI 2 Yes 812 44% 44% 42% No 1,037 56% 56% 58% Total 1,849 100% 1,349 500 Most households that irrigated (68%) got their irrigation water from a spring or river that was not managed by a community. There was substantial difference in source of irrigation water between ZOI 1 and 2, with 61 percent of HHs in ZOI 1 using spring or river water for irrigation compared to 89 percent HHs in ZOI 2. Similarly, 27 percent HHs from ZOI 1 used groundwater or well and only 8 percent HHs from ZOI 2 used the same source to irrigate their land. Ten and one percent HHs from ZOI 1 and 2 used river managed by a community to irrigate their land, respectively. Table 9: Percentage of Households with Year-Round Irrigation by Source of Water SOURCE OF IRRIGATION WATER NUMBER OF HHS WITH YEAR￾ROUND IRRIGATION PERCENTAGE OF HHS ZOI 1 ZOI 2 • River managed by a community 59 7% 10% 1% • Other spring or river 555 68% 61% 89% • Reservoir, lake, or pond 18 2% 2% 2% • Groundwater or well 180 22% 27% 8% 3.2 FINDINGS RELATED TO KISAN II PERFORMANCE INDICATORS This section focuses on KISAN II’s indicators. The sample-weighted population totals for each indicator required for the FTFMS have been provided in an Excel spreadsheet, including disaggregates. The corresponding aggregate-level data for each indicator appear in the KISAN II outcome indicator table at the beginning of each section (Tables 11, 16, and 20). All other figures and tables display alternative calculations of the same data including average per household or decision maker and percentage of households and decision makers to provide more meaningful data for understanding the behavior of representative households and decision makers. 21 3.2.1 IMPROVED MANAGEMENT PRACTICES AND TECHNOLOGIES Table 10 lists performance indicators and baseline data related to the application of improved management practices and technologies (MPTs). Two FTF indicators track 13 FTF-specified ‘technology type categories’ and focus on the number of individuals (EG.3.2- 24) and hectares (EG.3.2-25). Four related indicators use disaggregated data to focus on a single technology type category; these are cross-referenced as EG.3.2-24D. Table 10: KISAN II Outcome Indicators Related to the Application of Improved MPTs KISAN II AND GFSS INDICATOR NUMBERS HOUSEHOLD-LEVEL OUTCOME INDICATORS RESULTS K4 Nepal 2.1.1-2 EG.3.2-24 Number of individuals in the agriculture system who have applied improved management practices or technologies with USG assistance7 Male: 1,484 (52%) Female: 1,364 (48%) Total: 2,848 K5 Custom 1 Average number of improved management practices or technologies applied per household with USG assistance. 11 MPTs/HH K8 Nepal 2.1.1-1 EG.3.2-25 Number of hectares under improved management practices or technologies with USG assistance 1,139 ha K12 EG.3.2-28 Number of hectares under improved management practices or technologies that promote improved climate risk reduction and/or natural resources management with USG assistance 334 ha K13 Custom 1 Nepal 2.1.1-2 EG.3.2-24D Average number of climate-smart technologies or practices applied per farmer with USG assistance. Mitigation: 1.0/HH Adaptation: 1.8/HH K19 Nepal 2.1.1-2 EG.3.2-24D Number of individuals in the agriculture system who have applied improved management practices or technologies with USG assistance – related to marketing and distribution 744 Individuals K20 Custom 2 Percentage of USG-assisted farmers accessing information on improved technologies and practices through improved ICT channels or content 11% K31 Nepal 2.1.1-2 EG.3.2-24D Number of farmers and others who applied improved management practices or technologies– on food grading or safety 1,946 K46 EG.11-6 Nepal 2.1.1-2 EG.3.2-24D Number of people using climate information or implementing risk-reducing actions to improve resilience to climate change as supported by USG assistance Male: 678 Female: 386 Total: 1,064 The KISAN II project team anticipates that its private sector partners will promote 63 individual MPTs (within the 14 technology type categories). Almost all the households had applied at least one MPT (99%), and almost all hectares under production had at least one MPT applied. This is like the aggregate-level MPT indicator findings from KISAN I, which were not particularly meaningful and prompted the project to conduct additional data analysis to better understand the use of MPTs (for example, changes in the average number of MPTs per household). For this reason, the following Tables 12 to 15 present data 7 As the project has not reached the beneficiaries yet, it should be noted that they have been applying improved management practices or technologies without USG assistance 22 disaggregated by technology type category or by individual MPT. Some tables also disaggregate by commodity. The application of improved MPTs varied substantially across commodities with respect to the percentage of households applying at least one of them and the number of individual MPTs applied (Table 11): • Post-harvest handling, crop genetics, and irrigation were the most common (78-91 percent of households), specifically sorting, improved seeds, and low-tech irrigation methods such as hand-watering, drip, sprinkler, rainwater harvesting, etc. However, use of irrigation was noticeably higher in ZOI 1 (83 percent) compared to ZOI 2 (66%) (Annex Table 8). Similarly, use of agriculture water management (non-irrigation based) was higher in ZOI 1 (60 percent) compared to ZOI 2 (45 percent). • Only half of the households had applied climate adaptation MPTs. • ICT and value-added processing were the least common MPTs (11 and 3 percent of households). Among ZOIs, marginally higher proportion of HHs in ZOI 2 (13 percent) had used ICT compared to ZOI 1 (10 percent) (Annex Table 8). • Most ICT users were aged 30–59 (80%) and were men (72%). Note that irrigation MPT data is not comparable to access to irrigation data as the latter is related to the availability of irrigation water. Table 11: Average Number of Individual MPTs Applied Per Household (Farm) and Number of Hectares with MPTs, Disaggregated by Technology Type TECHNOLOGY TYPES % OF HHS WHO APPLIED AT LEAST ONE MPT POTENTIAL NO. OF MPTS AVG. NO. OF MPTS PER HH NUMBER OF HECTARES WITH AN IMPROVED MPT AVERAGE NUMBER OF HECTARES WITH AN IMPROVED MPT Post-harvest handling and storage 91% 6 1.7 n/a n/a Crop genetics 79% 2 1.4 623.1 0.42 Irrigation (drip, surface, sprinkler) 78% 4 1.3 715 0.49 Livestock management 62% 6 2.5 n/a n/a Soil fertility and conservation 62% 7 1.6 713.9 0.62 Cultural practices 59% 4 2 121.4 0.11 Agriculture water management 56% 3 1.1 434.1 0.42 Pest and disease management 50% 4 2 487.2 0.53 Climate adaptation 50% 7 1.8 495.6 0.53 Marketing and distribution 37% 6 1.5 n/a n/a Other 37% 5 1.7 n/a n/a 23 TECHNOLOGY TYPES % OF HHS WHO APPLIED AT LEAST ONE MPT POTENTIAL NO. OF MPTS AVG. NO. OF MPTS PER HH NUMBER OF HECTARES WITH AN IMPROVED MPT AVERAGE NUMBER OF HECTARES WITH AN IMPROVED MPT Climate mitigation 12% 2 1 70.1 0.32 Improved ICT channels or content8 11% 6 1.4 n/a n/a Value-added processing 3% 1 1.0 n/a n/a UNIQUE UNITS POPULATION UNIQUE HA Unique individuals 2,848 n/a n/a n/a Unique households 1,849 n/a n/a n/a n/a Unique hectares n/a n/a n/a 1,139 n/a The percentage of households using improved seeds was highest for rice (44 percent) and lowest for lentils (6 percent). Use of hybrid seeds was lower compared to improved seeds with 32 percent HHs using it for rice, 21 percent using it for Maize whereas only 1 percent used it for lentils (Table 12). Men accounted for 80 percent of decision makers for this improved MPT. Table 12: Percentage of Households Using Hybrid and Improved Seeds for Staple Crops COMMODITY NUMBER OF PRODUCER HH IMPROVED SEEDS HYBRID SEEDS N % N % Rice 1,383 602 44% 441 32% Maize 1,311 300 23% 278 21% Lentils 602 37 6% 9 1% Almost all the households (99%) had applied at least one improved MPT in the previous 12 months (Table 13). The application of improved MPTs varied substantially by commodity with respect to the percentage of households that had applied at least one MPT and the number of individual MPTs applied (among those that had produced the commodity). Vegetables had the highest use of MPTs. It could be linked with higher support received by households for vegetables (17 percent of the households reported receiving support for vegetables compared to only 4-5 percent for other commodities). Note that all hectares with improved MPTs were classified as cropland. 8 Information and communications technologies (ICT) is a custom technology type category that includes Digital Green’s ICT content and the use of text messages, extension videos and radio. 24 Table 13: Average Number of Individual MPTs and Hectares Per Household, Disaggregated by Commodity COMMODITY NO. OF HHs THAT PRODUCED EACH COMMODITY % OF HHs THAT APPLIED AT LEAST ONE MPT POTENTIAL NO. OF MPTs AVERAGE NO. OF MPTs/HH NO. OF HA WITH AN MPT AVERAGE NO. OF HA/HH Rice 1,383 74% 49 6.7 756 0.47 Maize 1,311 68% 49 3.8 220 0.15 Lentils 602 31% 48 3.3 53 0.09 Vegetables 1,140 61% 57 10.0 109 0.08 Goats 1,368 54% 27 3.7 n/a n/a Unique HHs, MPTs, Ha n/a 99% 63 11.5 1,139 n/a Access to information on improved MPTs through ICT channels was extremely low (10.7 percent of total households) and was mostly by men who were 30 years or older (non￾youth) (Table 14). Brahmins, Chhetris, and Janajatis, had much higher use of ICT than members of other groups. The most common channels are radio and television. Less than 1 percent use text messages or mobile applications to access information on MPTs. Although slightly more than half of 2,848 decision makers are females, only 28% have access to improved ICT channels. Table 14: Percentage of Households Accessing Information on MPTs Through Improved ICT Channels or Content INDIVIDUAL ICTs NO. OF HHs % OF HHs Radio 130 7.0% Websites and television 104 5.3% Extension videos 23 1.0% Mobile applications 9 0.5% SMS and text messaging 7 0.4% Unique households 199 10.7% NUMBER OF INDIVIDUALS DISAGGREGATE COMPOSITION (SUM=100%) Gender Male 150 72% Female 57 28% Age 15-29 years 22 11% 30+ years 185 89% Caste and Ethnicity Dalits 20 10% Brahmins & Chhetris 106 51% Newars 8 4% 25 Janajatis 64 31% *Other 9 4% * Note: Other includes Terai, Madhesi and Muslim groups. Appendix III gives a more detailed breakdown showing the percentage of households that applied each of the 63 MPTs, disaggregated by gender. This data will be useful for the KISAN II project team and its private sector partners. 3.2.2 ACCESS TO FINANCIAL SERVICES The number of individuals and households with access to financial services was small, as was the total value of loans (Table 15). Note that the GDNR-2 and YOUTH-3 indicators are disaggregates of EG.4.2-7. Though the indicator titles are phrased inconsistently, they measure the percentage of participants in group-based savings, micro-finance, or lending programs who are women or youth. Table 15: KISAN II Outcome Indicators Related to Access to Financial Services KISAN II AND GFSS INDICATOR NUMBERS HOUSEHOLD-LEVEL OUTCOME INDICATORS RESULTS K23 EG.3.2-27 Value of agriculture-related financing (USD) accessed as a result of USG assistance. Male: $77,729 Female: $52,615 Total: $130,345 K24 EG.4.2-7 Number of individuals participating in group-based savings, microfinance or lending programs with USG assistance. Savings: 2,436 Credit: 823 Individuals: 1,437 K6 GNDR-2 Percentage of female participants in USG-assisted programs designed to increase access to productive economic resources. 73.2% of participants K7 YOUTH-3 Percentage of participants who are youth in USG-assisted programs designed to increase access to productive economic resources. 22.4% of the participants K25 Reporting 4 Value of household savings deposits of USG-assisted smallholders. $ 285,821 K26 Reporting 5 Value of the outstanding balance on agriculture loans of USG￾assisted households. $ 94,924 Of the 2,848 individuals who made decisions about applying improved MPTs and were, therefore, most likely to use savings or seek credit to purchase inputs, 50 percent had participated in informal savings and credit groups in the last 12 months, including farmer groups, savings and credit groups, or other groups (Table 16). Participation in saving was much higher than access to credit. Note that individuals have been double-counted if they participated in more than one group. 26 Table 16: Participation in Informal Savings and Credit Groups DISAGGREGATES NO. OF PARTICIPANTS % OF INDIVIDUALS ACCESSING FINANCIAL SERVICES AMONG MPT DECISION MAKERS Number of unique individuals 1,437 50% Saving9 2,436 n/a Credit10 823 n/a Disaggregates Number Percent Gender Male 388 27% Female 1,049 73% Age 15-29 years 316 22% 30+ years 1,121 78% Only 253 individuals in 236 households accessed formal agriculture finance, representing 12.7 percent of all households (Table 17). Almost twice as many women farmers (163) had borrowed money than men farmers (90), but the women’s average loan values across all types of financial institutions ($323) was approximately one third that of the men’s loans ($864) and was consistently lower than the men’s loans across all types of financial institutions. Table 17: Average Formal Loan Value, Disaggregated by Type of Financial Institution and Gender TYPE OF FINANCIAL INSTITUTION ALL BORROWERS MALE FEMALE N TOTAL VALUE (USD) AVG. VALUE (USD) N TOTAL VALUE (USD) AVG. VALUE (USD) N TOTAL VALUE (USD) AVG. VALUE (USD) Cooperatives 112 $55,929 $499 52 $30,862 $594 60 $25,067 $418 Micro finance institutions 66 $22,219 $337 8 $8,860 $1,108 58 $13,359 $230 Banks 58 $45,599 $786 26 $34,516 $1,328 32 $11,082 $346 Finance companies 13 $3,744 $288 4 $1,724 $431 9 $2,020 $224 Other institutions 12 $2,854 $238 5 $1,767 $353 7 $1,087 $155 Total 253 $130,345 NA 90 $77,729 $864 163 $52,615 $323 The average value of savings per male ($232) was considerably higher than for females ($91) (Table 18). The average outstanding loan value per male ($751) was more than three times that of the females ($232). 9,10 The same individual is counted twice if the individual is saving and taking loans 27 Table 18: Value of Household Savings Deposits and Outstanding Balance on Agricultural Loans of USG-assisted Smallholders (Individual) 3.2.3 PRODUCTION, YIELDS, AND SALES The following data presents population estimates for FTF indicators related to production, sales, and consumption (Table 19). Table 19: KISAN II Outcome Indicators Related to Production, Yields, and Sales KISAN II AND GFSS INDICATOR NUMBERS HOUSEHOLD-LEVEL OUTCOME INDICATORS RESULTS K9 Reporting 1 Total farm-level volumes (MT) produced of targeted agricultural commodities with USG assistance (rice, maize, lentils, vegetables, goats). Rice: 2,305.1 MT Maize: 517.7 MT Lentils: 62.3 MT Vegetables: 1,096.0 MT Goats: 54.5 MT K10 EG.3- 10/11/12 Yield of targeted agricultural commodities among program participants with USG assistance (rice, maize, lentils, vegetables, and goats) (MT/ha). Rice: 3.5 MT/ha Maize: 2.0 MT/ha Lentils: 0.6 MT/ha Vegetables: 10.7 MT/ha Goats: 6.3 kg/goat K17 EG.3.2-26 Value of annual sales of farms (and firms) receiving USG assistance (rice, maize, lentils, vegetables, and goats). Rice: $147,128 Maize: $16,676 DISAGGREGATES SAVING DEPOSITS OUTSTANDING BALANCE ON AGRICULTURE LOANS VALUE IN $ AVG SAVINGS DEPOSIT PER INDIVIDUAL INDIVIDUAL (N) VALUE IN $ AVG OUTSTANDIN G BALANCE PER INDIVIDUAL ($) INDIVIDUAL (N) Gender Male 142,006 232 610 62,371 751 83 Female 143,815 91 1,572 32,552 232 140 Age 15-29 years 46,738 99 474 12,398 335 37 30+ years 239,083 140 1,708 82,525 444 186 Caste & Ethnicity Brahmins & Chettris 134,649 164 823 51,367 597 86 Janajatis 102,805 105 976 20,134 216 93 Newars 24,374 283 86 4,895 350 14 Dalits 15,875 75 211 3,691 217 17 Terai & Madhesi groups 7,832 109 72 4,597 383 12 Muslims 286 20 14 239 239 1 Total/average 285,821 131 2,182 94,923 426 223 28 KISAN II AND GFSS INDICATOR NUMBERS HOUSEHOLD-LEVEL OUTCOME INDICATORS RESULTS Lentils: $12,806 Vegetables: $257,892 Goats: $142,540 K18 Reporting 2 Volume of annual sales (MT) of farms receiving USG assistance (rice, maize, lentils, vegetables, and goats). Rice: 607.9 MT Maize: 75.6 MT Lentils: 25.3 MT Vegetables: 890.0 MT Goats: 35.3 MT K41 Custom 5 Quantity of nutrient-rich value chain commodities produced by direct beneficiaries with USG assistance that is set aside for home consumption (MT). 58.5 MT The following charts feature household averages. Average volumes produced per household were highest for rice and vegetables and lowest for lentils and goats (Figure 9). Figure 9: Average Volume of Production of Targeted Commodities (KG/HH) Average yields per farm were highest for vegetables and goats (Figure 10). 1,667 395 103 961 76 0 200 400 600 800 1,000 1,200 1,400 1,600 1,800 Rice Maize Lentils Vegetables Goat 29 Figure 10: Average Yield of Targeted Commodities (MT/ha) Average annual sales per household were highest for vegetables ($335), rice ($322), and goats ($181). Males had higher sales than females, although women received higher prices, due to differences in the volumes sold by men and women. Figure 11: Average Annual Sales by Commodity and Gender (Of Households That Sold the Commodity) About 71 percent of the households produced nutrient-rich vegetables (okra, cabbages, cauliflowers, spinach, bitter gourds, carrots, broccoli, long beans, french beans, capsicums, pumpkins) and/or goats, and 57 percent of the households set aside some for home consumption (Table 20). On average they set aside only 12.5 percent of the total volume produced – preferring to sell because they are high value. 3.5 2 0.6 10.7 6.3 Kg/Goat 0 2 4 6 8 10 12 Rice Maize Lentils Vegetables Goats $322 $68 $73 $335 $181 $409 $199 $422 $0 $50 $100 $150 $200 $250 $300 $350 $400 $450 $500 Rice Maize Lentil Vegetables Goat Total Female Male 30 Table 20: Production and Consumption of Nutrient-rich Vegetables and Goats BEHAVIORS PERCENTAGE OF HOUSEHOLDS KG/HH Producing 71% 352 Consuming 57% 56 Top 4 Set Aside for consumption Cabbages 23% 35 Cauliflowers 23% 31 Pumpkins 12% 36 Goats 8% 14 3.3 BASELINE RESILIENCE FINDINGS Consistent with the resilience data analysis framework presented earlier (Figure 3), this section organizes resilience findings by the following questions and sub-sections (Box 2). Box 2: Resilience Analysis Questions • Context: What are the characteristics of asset ownership and income diversification (3.3.1) and access to markets and services (3.3.2) in the sample population? • Exposure: What are the primary shocks and stressors in the survey area (3.3.3)? • Coping strategies: How do households, communities, institutions, and systems respond to shocks and stressors (3.3.4)? • Well-being outcomes: Are households able to recover from shocks and stressors (3.3.6)? Cross-Cutting • What are the high-level findings and conclusions about the current state of resilience capacities in the survey area? • Which findings appear most important for informing most potential modifications to the project strategy, implementation approaches and theory of change? • Are there any important information gaps that should be considered in project or Mission level learning agendas? Note that questions throughout the Resilience Module do not ask about project interventions such as targeted commodities or improved technologies and management practices. Rather, they focus on characterizing the sample population according to the resilience analysis framework. 3.3.1 OWNERSHIP OF ASSETS AND INCOME DIVERSIFICATION Almost all the households (97–100 percent) owned agricultural land, buildings (barns or storage) and hand tools (hoe, spade/shovel, rake, sickle, pickaxe, axe, pruning shears, etc.) (Figure 12). Far fewer (18 percent) owned mechanized farm equipment (mechanical or 31 motorized water pumps, tillers, tractors, corn shellers, motorized grain mills, tractor-drawn plows), which is expected as the size of smallholder farms does not support ownership of large equipment and farmers tend to rent them if they use them at all. Ownership rates were high for large livestock (73 percent of households owned cattle or oxen), but less for small livestock (16 percent of households owned pigs and/or sheep excluding goats). Note that the non-mechanized farm equipment mainly included halo and juwa (plows and bullock yokes), carts and wheelbarrows. Figure 12: Percentage of Households Owning Productive Agricultural and Other Assets On average, male farmers owned more assets than female farmers (Figure 13). Disaggregation of asset ownership by type of asset (as done in Figure 12) would be needed to draw meaningful conclusions from this data. Figure 13: Average Number of Assets Owned, by Gender Table 21 ranks sources of household income by the number of surveyed households that earn income from that source. The sources are color-coded to indicate agricultural income (blue) and other income (gray). Except for the sales of KISAN II targeted 24 crops and 97% 59% 18% 100% 73% 16% 54% 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Agricultural land Non mechanized Mechanized Hand tools Large livestock Small livestock Poultry 15.2 11.9 14.5 0 2 4 6 8 10 12 14 16 Male Female Total 32 vegetables, 746 households (41.8 percent) reported earning income from selling other crops with an average annual income of $277. Foreign remittances ($2,098/HH), salaried work ($2,010/HH), non-agricultural business ($1,240/HH) and non-agricultural wage labor ($766/HH) generated the highest average annual incomes per household. Crop sales ($277/HH), livestock sales ($303/HH), agricultural wage labor ($162/HH), and fish sales ($209/HH) generated the lowest average annual incomes per household. Table 21: Share of Annual Household Income by Sources SOURCES OF HOUSEHOLD INCOME % OF HHs NO. OF HHs AVERAGE ANNUAL INCOME PER HH (NPR) AVERAGE ANNUAL INCOME PER HH (USD) Own crop sales 41.8 746 28,872 $277 Non-agricultural wage labor 37.3 665 79,995 $766 Own livestock sales (planned, not under stress) 25.2 449 31,583 $303 Cash from HH members working in another country (foreign remittances) 24.3 433 219,003 $2,098 Government allowances (senior citizen allowance, maternity allowance, etc.) 23.2 414 54,869 $526 Salaried work 23.0 410 209,878 $2,010 Agricultural wage labor 22.1 395 16,944 $162 Non-agricultural business, trade, or self￾employment 22.0 392 129,508 $1,240 Rental of house or rooms 5.2 93 58,916 $564 Agricultural business, trade, or self-employment 4.0 72 75,892 $727 Rental of land 4.0 72 27,740 $266 Sale of land and or assets (not under stress) 2.5 45 928,156 $8,890 Fish sales 1.7 30 21,854 $209 Households below the poverty line tended to have fewer sources of income – 64 percent of poor households had only one to two sources of income, compared to 26 percent of non￾poor households (Table 22). Only 13 percent of poor households had four or more sources of income, compared to 41 percent of non-poor households. Income data is not available for 12 households probably due to enumerator data input errors. 33 Table 22: Number of Sources of Household Income Disaggregated by Income Level (Above and Below the Poverty Line of $1.90 Day/Person) NUMBER OF INCOME SOURCES PERCENTAGE OF HOUSES BELOW THE POVERTY LINE ABOVE THE POVERTY LINE TOTAL Single source 26% 5% 201 Two sources 38% 21% 475 Three sources 23% 32% 552 Four sources 10% 24% 374 Five or more sources 3% 17% 246 Total 493 1,355 1,848 3.3.2 ACCESS TO MARKETS The following table ranks proximity to market infrastructure and services based on the percentage of households within two hours walking distance or 5 kilometers. This is a relatively “low bar.” Consequently, a high percentage of households are considered within walking distance to most of the infrastructure and services listed below by this definition. Drawing meaningful conclusions from these findings is difficult. Collection centers and warehouse facilities rank among the least accessible. Given that other forms of transport would generally be required to transport goods to these facilities, access would be better assessed using the method presented in Figure 14. Table 23: Proximity to Market Infrastructure and Services – Percentage of Households Within Two Hours’ Walking or Five KM to Markets PERCENTAGE OF HOUSEHOLDS MARKET INFRASTRUCTURE AND SERVICES >90% • Communications: mobile phone signal, national and local TV signals, national and local FM radio signals. • A health facility. • A primary and secondary school. • Electricity. 80–89% • Agriculture: fertilizer, private veterinary or agriculture extension services and places for selling agricultural commodities and buying seeds. • Finance: depositing savings and borrowing money. 70–79% • Agriculture: transportation for agricultural commodities and government veterinary services. 60–69% • Agriculture: collection center and government extension agent • Communications: public telephone. <50% • Warehouse or storage facility (42%). Figure 14 assesses proximity to market infrastructure and services based on travel time (under a half and under one hour) using the respondents’ typical mode of transport. Both approaches to measuring proximity produce similar rankings. Proximity to warehouse facilities and government extension agents is less common (41 percent) and 58 percent respectively. Proximity to private sector agrovets (78 percent) is more common. Proximity 34 is most common for school-secondary (88 percent) and other services that included electricity (91 percent), local TV signal (92 percent), national TV signal (93 percent) and local FM radio signal each, national radio signal and mobile phone signal 99 percent each. Figure 14: Proximity to Market Infrastructure and Services – Percentage of Households Under 0.5 and 1 Hour to Markets (Using Their Typical Mode of Transport) The survey team asked, “Did you or anyone in your household receive any information, training and/or services in the past 12 months?” and provided a list of topics and services to consider. • The percentage of households that reported they had access to various agriculture information (indicated by gray bars in Figure 15 below) was 19 percent or lower – despite 92-99 percent reporting they had access to radio, TV, and mobile phone signals (Figure 14). This data is consistent with respondents citing “lack of knowledge of how to achieve higher yields and sales” as one of their top three agriculture-related constraints in a separate open-ended question. • Households also had very low rates of access to the following: 6-7 percent of households had access to information about crop health, irrigation, natural resources management and early warning systems; and 5 percent or less of households had access to information about buyers, agriculture transport, market information via ICT, weather, post-harvest handling, and climate change (omitted from Figure 15 to simplify the figure). 90% 66% 64% 64% 60% 58% 58% 57% 57% 56% 55% 52% 45% 40% 36% 27% 9% 22% 17% 17% 20% 25% 18% 21% 21% 20% 22% 17% 19% 23% 22% 14% 0% 20% 40% 60% 80% 100% Others Secondary Schools Buying Fertilizer Deposit Savings Borrow Money Health Facility Selling Ag. Commodities Ag. Extension (Agrovet) Vet.Service (Agrovet) Transport for Ag. Products Buying Seeds Collection Center Public Telephone Vet. Service (GoN) Ag. Extension (GoN) Warehouse/Storage facility 0.5 h 1 h 35 • More than half of respondents said they had access to information about where to buy quality seeds and fertilizer (55 percent and 64 percent, respectively); however, respondents ranked the “availability of inputs” as the third most common constraint for increasing yields and sales in a separate open-ended question about their top three constraints to achieving increased yields and sales. Measurement Issues. This question in the survey was complex, resulting in a data limitation. The respondents were not able to distinguish between whether they had received information, services, or both for any given topic in the prompt list. Research on the effectiveness of information channels or project information campaigns would be better conducted as a stand-alone M&E activity that uses best practice communications measurement techniques, rather than integrated into a comprehensive project baseline or Annual Monitoring Survey. Figure 15: Percentage of Households That Received Information, Training, and/or Services Half of the households that reported receiving information, training, or services related to at least one of the above topics said that neighbors, friends, and relatives were their most common source of information, followed by newspapers, the radio, and TV (Table 24). The internet, SMSs, and mobile apps were the least common sources of information. 31% 30% 25% 19% 16% 15% 12% 10% 9% 9% 9% 0% 10% 20% 30% 40% 50% Equal Rights (Gender) Nutrition & Health Gender-Based Violence Seeds Emergency Support Fertilizer Financial Services Livestock MPTs Animal Health Market Prices Crop MPTs Key Agriculture Other 36 Table 24: Most Significant Sources of Information, Training, and/or Services SOURCE NUMBER OF RECIPIENT HHs WHO CITED THE SOURCE PERCENTAGE OF RECIPIENT HHs WHO CITED THE SOURCE Neighbors, friends, relatives 629 54% Newspapers, radio, TV 481 41% Government 361 31% Community groups 327 28% NGOs or projects 302 26% Agrovets 200 17% Cooperatives 148 13% Other private sector firms 116 10% Internet, SMS, Mobile Apps 62 5% Miller, processor, feed industries 13 1% Unique households 1,173 n/a (multiple responses) Less than half of the households reported being unable to access agricultural information (49 percent), inputs (33 percent) or a buyer (18 percent) at least once during the past 12 months (Table 25). Table 25 Percentage of Households Unable to Access a Local Agricultural Advisor, Inputs, or a Buyer at Least Once in the Past 12 Months MARKET LINKAGE CONSTRAINTS (CONSISTENCY OF ACCESS) % OF HHs UNABLE TO ACCESS AT LEAST ONCE NO. OF HHs Unable to obtain agricultural information or advice because no local advisor was available. 49% 911 Unable to obtain agricultural inputs because no local supplier and/or inventory was available. 33% 614 Unable to sell produce because no buyer/trader was available. 18% 335 Two-thirds of the households had obtained quality inputs from an agrovet in the previous 12 months (Table 26). These households had interacted with an average of two agrovets each. Most households (72 percent) reported that input prices had increased over the previous year. Only 16 percent of households were not satisfied against 51 percent of households who were satisfied with inputs and/or services received from Agrovets. 37 Table 26: Farmers’ Interactions with Agrovets INTERACTIONS PERCENTAG E OF HHs Interacted with agrovet (advice or inputs) 67% Average number of agrovets interacted with 1.9 agrovets Obtained high-quality seeds or other inputs from an agrovet 65% Change in prices paid for inputs compared to the previous year Increased 72% Remained the same 21% Decreased 2% Did not know 5% Satisfaction with inputs and/or services Not satisfied 16% Somewhat satisfied 29% Satisfied 51% Very satisfied 3% Table 27 below presents findings on farmers’ interactions with all types of buyers. Fewer households had interacted with buyers (42 percent) than agrovets (67 percent). Approximately one-third of households had sold to a buyer and one fourth obtained quality seeds from a buyer. Households interacted with an average of 2.8 buyers each. Among those households who sold agricultural commodities, annual sales increased for 41 percent, remained the same for 24 percent, and decreased for 33 percent of them compared to the previous year. The reasons for these mixed findings are unknown. Most respondents reported that the prices they paid for inputs had increased over the previous year. It is unknown if farmers paid higher prices for the same inputs (indicating price inflation) or higher quality inputs (a desirable finding). Only 16 percent of respondents indicated that they were not satisfied whereas 35 percent were satisfied with the inputs or services received from commercial buyers, traders and wholesalers. 38 Table 27: Farmers’ Interactions with Commercial Buyers, Traders, and Wholesalers INTERACTIONS PERCENTAGE OF HHS Interacted with a buyer 42% Sold commodities 37% Obtained quality seed from a buyer 26% Average number of buyers interacted with 2.8 buyers Change in total value of sales compared to the previous year (n = 696) Increased 41% Remained the same 24% Decreased 33% Did not know 2% Change in prices paid for inputs by respondents compared to the previous year Increased 75% Remained the same 20% Decreased 2% Did not know 3% Respondents’ satisfaction with prices received (n = 696, 37%) Not satisfied 22% Somewhat satisfied 31% Satisfied 43% Very satisfied 3% Satisfaction with inputs and/or services (n=181, 26%) Not satisfied 16% Somewhat satisfied 48% Satisfied 35% Very satisfied 1% Participation in farmer and forest user groups was low relative to cooperatives because the project’s catchment areas are in urban and semi-urban areas (Figure 16 below). Participation in business literacy programs was very low. Overall, 74 percent of the total households participated in at least one of the local groups. 39 Figure 16: Percentage of Households Participating in Local Groups Respondents were asked the following open-ended question (and were not prompted on potential responses), “What are the top three issues that prevent you from achieving higher yields or sales, starting with the most important?” Figure 17 ranks constraints according to the percentage of households that listed them among their top three. Lack of access to irrigation water, limited knowledge of how to achieve higher yields and sales, and lack of availability of inputs were cited by households as top three constraints in both ZOIs. Out of the top three, lack of availability of inputs was cited by more households (a difference of 10%) as a constraint in ZOI 2 than in ZOI 1. Similarly, 29 percent households cited access to finance in ZOI 2 compared to 21 percent in ZO I. Figure 17: Percentage of Households That Reported a Factor as a “Top 3” Constraint for Achieving Higher Yields or Sales by ZOI 1 and ZOI 2 34% 31% 26% 23% 9% 9% 5% 2% 0.3% 74% 0% 10% 20% 30% 40% 50% 60% 70% 80% Savings & Credit Cooperative Mothers Other Farmer Forest User Irrigation Water Users Bus. Literacy Unique HHs 8.0% 9.6% 14.2% 14.4% 18.0% 18.6% 21.4% 22.8% 52.0% 56.4% 59.0% 5.0% 5.4% 12.4% 12.2% 12.9% 14.6% 29.3% 18.0% 62.5% 59.2% 63.8% 0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% Wild animals/Disease Other (Specify) Price Received Access to Buyers Natural Disaster Input Prices Finance Labour Availability of Inputs Knowledge of Yield or… Irrigation Water ZOI 2 ZOI 1 Key Agriculture Other Total 40 3.3.3 EXPOSURE TO SHOCKS AND STRESSORS The following figure ranks exposure to various shocks and stressors. Almost two-thirds of the households (63 percent) had been exposed to at least one agriculture-related shock or stressor in the past 12 months. The most common were livestock and crop diseases, pests and inadequate rain (Figure 18). The least common were death in the family and lack of access to livestock inputs (5 percent each); loss of land and erosion or landslide (4% each); and various forms of theft and earthquake (1 percent each)-omitted from Figure 18 to simplify the figure. Only 5-7 percent of households identified lack of access to crop or livestock inputs as a significant stressor. Gender disaggregation of exposure to shocks and stressors is given in Annex (Table 29). Figure 18: Percentage of Households Exposed to Various Shocks and Stressors Figure 19 presents the data from Figure 18, disaggregated by households above and below the poverty line. Income levels do not appear to affect the ranking of exposure to shocks or stressors, although there are differences in the percentage of households exposed to some shocks and stressors. Notably, households with higher incomes report higher rates of exposure to the stressors, “unable to sell at a fair price” (25 percent vs. 14 percent), “insects affecting crops” (38 percent vs. 32 percent), and “too much rain” (19 percent vs. 14 percent), and slightly higher rates of exposure to “freezing temperatures,” “sharp increases in food prices,” and “lack of access to crop inputs”. The reasons for this are unknown. 5% 7% 9% 11% 13% 14% 16% 20% 21% 23% 23% 0% 5% 10% 15% 20% 25% Others Lack of Access-Crop Inputs High Food Prices Too Much Rain Freezing Temp Unable to Sell at Fair Price Too little rain Crop Disease Severe illness Pests Lifestock Disease 41 Figure 19: Percentage of Households Exposed to Various Shocks and Stressors, Disaggregated by Income (Above and Below the Poverty Line of $1.90 Day/Person) Of the 1,173 households that had been exposed to a shock or stressor in the past 12 months, up to 72 percent reported that it had severely impacted their household incomes and up to 51 percent reported that it had severely impacted food consumption (Figure 20). Across all types of shocks and stressors, more households reported severe impacts on income than food consumption. Crop theft and death or illness in the family produced the most severe impacts. However, a significant percentage of households reported severe impacts across all other shocks and stressors (20-60 percent). 8% 10% 13% 14% 14% 17% 26% 31% 32% 37% 40% 8% 11% 15% 19% 25% 21% 26% 32% 38% 31% 36% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Others Lack of Access-Crop Inputs Sharp Increase in Food Price Too Much Rain Unable to Sell at Fair Price Freezing Temp Too Little Rain Disease Affecting Crops Insects Affecting Crops Severe Illness in the Family Livestock Disease > 1.9 $ per day < 1.9 $ per day 42 Figure 20: Percentage of Households Exposed to a Shock or Stressor That Reported Severe or Extremely Severe Impacts on Income and Food Consumption The percentage of households exposed to a shock or stressor in the past 12 months varied by province (55–79 percent), but less so between ecological regions (60–65 percent) (Table 28). Among exposed households, the average number of shocks and stressors had ranged between 2.6 and 3.4 per household. Table 28: Percentage of Households Exposed to a Shock or Stressor and Average Number of Shocks or Stressors, by Province and Ecological Region Percentage of HHs Exposed Average Number of Shocks & Stressors Per HH Ecological Region Terai 60% 2.8 Hills 65% 2.8 Province Province 3 65% 2.6 Province 5 55% 2.7 Province 6 79% 3.4 Province 7 68% 2.9 72% 68% 62% 60% 53% 50% 47% 45% 44% 42% 40% 40% 38% 38% 33% 31% 25% 20% 51% 36% 48% 24% 39% 23% 26% 35% 21% 26% 30% 27% 32% 31% 19% 23% 17% 20% 0% 50% 100% 150% Death Severe illness Crop Theft Theft Loss of land Livestock Disease Lack of Access- Livestock Inputs Too Much Rain Unable to Sell at Fair Price Crop Disease Too Little Rain Pests Erosion or Landslide Food Prices (sharp increase) Freezing Temp Lack of Access- Crop Inputs Livestock Theft Earthquake % Impact on Income % Impact on Food Consumption 43 3.3.4 COPING STRATEGIES AND SUPPORT The households that reported experiencing a shock or stressor in the past year were asked if they had used any coping strategies from a list of 17. The most common coping strategies reported were neutral and positive strategies, making up eight of the top ten strategies (Figure 21). Twenty-four percent of exposed households reported using a negative coping strategy, the most common of which was to sell their livestock. A negative coping strategy is defined as one that can undermine the ability to recover from future shocks or stressors. However, selling livestock may more accurately be defined as a neutral to potentially negative coping strategy, like using household savings, as livestock is widely viewed as a form of saving in Nepal. Figure 21: Most Common Coping Strategies Used by Households Exposed to Shocks and Stressors (n=1,173) The most negative coping strategies were the least commonly used, making up six of the bottom nine strategies (Figure 22). Fourteen percent of households reported using no coping strategy – an unexpected negative finding. 55% 47% 41% 32% 27% 24% 17% 17% 0% 10% 20% 30% 40% 50% 60% Used HH Savings Increased Wage Labor Borrowed Cash (other HH) Bought on Credit Borrowed Cash (informal group) Sold Livestock Remittances Neighbor Shared Food Key Positive Neutral to Potentially Neg. Negative 44 Figure 22: Less Common Coping Strategies of Households Exposed to Shocks or Stressors Of the 1,173 households exposed to at least one shock or stressor in the past year, 28 percent lived below the poverty line and 72 percent above it (Table 29). A high percentage of households within each group had applied at least one coping strategy (85–88 percent). Table 29: Percentage of Households Exposed to a Shock or Stressor That Used At Least One Coping Strategy, Disaggregated by Households Above and Below the Poverty Line ($1.90/Person/Day) per da APPLIED A COPING STRATEGY HHs BELOW THE POVERTY LINE HHs ABOVE THE POVERTY LINE NO. OF HHs % OF HHs NO. OF HHs % OF HHs Yes: Applied at least one 286 88% 720 85% No: Applied none 38 12% 129 15% No. of HHs exposed to a shock or stressor 324 28% 849 72% The types of coping strategies used varied between households living above and below the poverty line. Households above the poverty line were more likely to have used household savings or sold their livestock. Households below the poverty line were more likely to have sought additional wage labor work, borrowed cash from another household, bought food or other items on credit, received food from another household in their community, eaten less or lower-cost food, had a household member emigrate overseas to earn money, harvested crops prematurely, or had a household member return from overseas to help. Note that in Figure 23 “others” includes received food aid from a relief organization, local group, or project (7 percent for both above and below the poverty line households), pulled one or more children out of school (5 percent versus 3 percent), sold land under stress (5 percent for both types), sold another asset under stress (5 percent versus 4 percent), and had a household member return from overseas to help (4 percent versus 6 percent). 15% 10% 7% 6% 5% 5% 4% 4% 14% 0% 10% 20% 30% 40% 50% 60% Ate Less or Cheaper Migrated Food Aid Harvested Early Returned from Overseas Sold Land Sold Other Asset Took Child out of School No Strategy Key Positive Neutral to Potentially Neg. Negative 45 Figure 23: Percentage of Households Exposed to a Shock or Stressor Who Used Various Coping Strategies, Disaggregated by Household Income Above or Below the Poverty Line (<$1.90/Person/Day) Only 26 percent of the exposed households reported that they had received support following exposure to a shock or stressor, indicating relatively low levels of social capital (Table 30). Most support came from relatives (95 percent) in the form of a cash loan (77 percent) or farm labor (21 percent). Sharing food was less common than expected (10%). Tables 30 and 31 complement the FTF RESIL-b Social Capital indicator, which focuses on expectations about receiving or giving future support rather than experience receiving or giving support. Table 30: Percentage of Households Who Received Support, Disaggregated by Source and Type DISAGGREGATES PERCENT OF HHS WHO RECEIVED SUPPORT (BY SOURCE AND TYPE): Support received from (source) (multiple responses) Relatives in community 66.6% Relatives outside community 28.5% Non-relatives in community 22.8% Non-relatives outside community 2.3% Type of support received (multiple responses) Cash loan 76.8% Farm labor 20.5% Food 9.9% Other 6.0% Housing 3.0% Unique households 25.7% 7% 10% 13% 14% 21% 23% 23% 27% 41% 45% 47% 53% 7% 5% 10% 18% 25% 12% 15% 27% 29% 39% 59% 44% 0% 20% 40% 60% 80% 100% 120% Others Harvested Early for Food A HH Member Migrated Remittance Sold Livestock Under Stress Ate Less Food or Ate Lower-cost Food Another HH in the Community Shared their Food Borrowed Cash from an Informal Group Bought Food or other HH Items on Credit Borrowed Cash from Another HH Used HH savings Increased Wage labour <1.9 $ per day >1.9 $ per day 46 Table 31 below shows the support given by households to other households that had suffered from a shock or stressor in the past 12 months. More households gave than received support (33% vs. 26%). Like the findings in Table 30, most support went to relatives (81%) as cash loans (71%). Table 31: Percentage of Households That Gave Support to Others Exposed to Shocks & Stressors DISAGGREGATES PERCENT OF HHs WHO GAVE SUPPORT (BY SOURCE AND TYPE): Support given to (multiple responses): Relatives in community 59% Relatives outside 22% Non-relatives in community 30% Non-relatives outside 4% Type of support given (multiple responses) Cash loan 71% Farm labor 27% Food 9% Other 7% Housing 2% Unique households 32.7% 3.3.5 EXPERIENCE OF HUNGER Figure 25 lists the households’ experiences of hunger during the previous 30 days, in descending order of severity, using the Household Food Insecurity Experience Scale (HFIES). Red bars indicate eating less food. Blue bars indicate eating lower quality food or simply worrying about not having enough food. In accordance with the FTF PIRS, all households were included in the sample and not just those exposed to a shock or stressor. Note that this will understate seasonal variations in hunger, particularly since data was collected during a better than average harvest season and approaching the main annual festival season. 47 Figure 24: Percentage of Respondents Who Experienced Hunger in the Past 30 Days (HFIES) Table 32 presents the data from Figure 24, disaggregated by income and grouped in descending order of severity: worried, mild hunger, and moderate to severe hunger. The households below the poverty line had experienced substantially more hunger in the past 30 days across all categories than households above the poverty line. Table 32: Percentage of Respondents Who Experienced Hunger in the Past 30 Days (HFIES) Disaggregated by Poverty Status (<$1.90 per day) EXPERIENCE OF HUNGER % OF HHs BELOW THE $1.90 POVERTY LINE % OF HHs ABOVE THE $1.90 POVERTY LINE Perception Worried about not having enough food to eat 42% 23% Mild Hunger Unable to eat healthy and nutritious food 29% 16% Ate only a few kinds of food 31% 16% Had to skip a meal 8% 3% Ate less than they thought they should 12% 4% Moderate to Severe Hunger Did not have food 9% 3% Were hungry but did not eat 10% 3% Went without eating for a whole day 2% 1% Unique Households 493 1,355 3.3.6 POST-SHOCK PERCEPTIONS ABOUT THE ABILITY TO RECOVER Post-shock perceptions about current and future food security are the primary indicator of households’ ability to recover from a shock or stressor in the resilience data analysis framework (Figure 3). 29% 20% 19% 6% 5% 5% 4% 1% 0% 50% Worried about not having enough food Ate only a few kinds of foods Unable to eat nutritious food Ate less than you thought you should Were hungry but did not eat Did not have food Had to skip a meal Went without eating for a whole day 48 A very high percentage of households said that they were either better off than before they had been exposed to a shock or stressor (45 percent) or the same as before (49 percent) (Table 33). This unexpected response likely reflects that interviews occurred just after a better than normal harvest. In addition, a high percentage of households (73 percent) reported that they believed that the government would respond adequately to future shocks and stressors; however, past experience conducting surveys in Nepal indicates that answers to questions related to satisfaction with services are often biased because many Nepalis are reluctant to speak negatively of others, especially to a stranger. Table 33: Post-Shock Perceptions About Food Security and Future Local Government Responsiveness PERCEPTIONS % OF HHs Ability to Meet Current HH Food Needs Better than before 45% Same as before 49% Worse than before 7% Ability to Meet HH Food Needs in the Future Better than before 63% Same as before 31% Worse than before 6% Responsiveness of Local Government During the Next Shock or Stressor Yes, local government will respond effectively 73% No, local government will not respond effectively 26% It is unlikely that I will need support <1% Survey participants were also asked the following questions: • Will your household be able to lean on others for financial or food support during difficult times (shocks or stressors)? • Will the same people that you will be able to lean on during difficult times also be able to lean on you for financial or food support during their difficult times (shocks or stressors)? Similar to the patterns related to actually receiving and giving support (Tables 29 and 30), more households expected to give than receive support in response to future shocks or stressors; more households expected that support would be provided by relatives (rather than non-relatives), and relatively fewer households expected support from non-relatives from outside their communities (Table 34). Note that the table title and source descriptions reflect the FTF RESIL-b indicator title and terminology. 49 Table 34: Expectations About Social Capital EXPECTED SOURCES OF SUPPORT TYPE OF SOCIAL CAPITAL HHS THAT EXPECT THEY WILL RECEIVE SUPPORT TO: HHS THAT EXPECT THEY WILL GIVE SUPPORT TO: N % N % Relatives living in your community Bonding 1,582 85% 1,764 95% Relatives living outside your community Bridging 1,417 77% 1,625 88% Non-relatives living in your community Bonding 1,339 73% 1,598 86% Non-relatives living outside your community Bridging 716 40% 1,166 64% 50 4 CONCLUSIONS The conclusions section summarizes key findings related to agriculture, resilience, and gender, as well as learning and recommendations for future FTF and resilience surveys. 4.1 AGRICULTURE MODULE As noted earlier, the agricultural baseline data provides a basis for the Implementing Partner’s target-setting exercise and future efforts to measure change. Findings that are unexpected and/or critical to KISAN II’s theory of change are flagged for additional follow￾up action. Access to land: The households had an average of 0.55 hectares each, and no households had more the 5 hectares, the FTF definition of a smallholder. Volumes and sales: Average volumes produced per farm were highest for rice and vegetables and lowest for lentils. HHs in ZOI 1 produced higher mean volume of rice and lentil compared to HHs in ZOI 2 whereas higher mean volume of vegetables, maize and goats was produced in ZOI 2 compared to ZOI 1. Average annual sales per household were highest for vegetables ($335), rice ($322), and goats ($181). There was difference in mean value of annual sales between ZOI 1 and 2 with ZOI 2 having higher average annual sales value ($438 vs $289). HHs in ZOI 2 had highest mean sales value for vegetables whereas in ZOI 1 highest mean sales value was for rice. Support: Less than five percent of households that produced rice, maize, lentils, and goats had received support from the government, a project or the private sector in the previous 12 months related to achieving increased yields or sales. Seventeen percent of vegetable￾producing households had received such support. Higher proportion of HHs in ZOI 1 (19 percent) compared to ZOI 2 (15 percent) received support for vegetables. Marginally higher proportion of HHs in ZOI 1 had received support for rice (6 percent), maize (5 percent) and lentil (1 percent) compared to HHs in ZOI 2. Nutrient-rich commodities: About 71 percent of the households produced nutrient-rich vegetables and/or goats and 57 percent of these households set aside some for home consumption. However, on average they set aside only 12.5 percent of the total volume produced, presumably selling because they are high value. Constraints: The top three constraints to increasing yields and sales of targeted commodities were lack of access to irrigation water, limited knowledge of how to achieve higher yields and sales, and lack of availability of inputs. Out of the top three, lack of availability of inputs was cited by more households (a difference of 10%) as a constraint in ZOI 2 than in ZOI 1. Similarly, 29 percent households cited access to finance in ZOI 2 compared to 21 percent in ZO I . The data will be useful to share with the activity’s potential private sector partners, as it points to unmet demand for agricultural inputs and services. Analysis of costs and benefits should be conducted to assess the conditions under which investments make economic sense and to identify potential financing requirements. Perceptions about the importance of finance are expected to change as farmers shift to higher value production, requiring higher levels of investment. 51 Access to information: Access to agricultural information is low, with less than 10 percent of households accessing information on crop MPTs, market prices, and animal health and less than 20 percent of households accessing information on financial services, fertilizer, and seeds. Neighbors, friends, and relatives were their most common source of information, followed by newspapers, the radio and TV (54% and 41% of households, respectively). The internet, SMSs, and mobile apps were the least common sources of information (5%). Improved MPTs: Almost all households (99%) had applied at least one improved management practice or technology in the previous 12 months, with an average of 11.5 improved MPTs per household (out of 64 potential ones). The application of improved MPTs varied substantially with respect to the percentage of households applying them and the commodities to which they were applied. Vegetables and rice had the highest use of MPTs (10 and 6.7 MPTs/HH, respectively). Maize, lentils, and goats had only 3.3-3.8 MPTs/HH. It could reflect the higher level of support received for vegetables (17 percent), compared to only 4-5 percent for other commodities. Post-harvest handling, crop genetics, and irrigation were the most common MPT categories (78-91 percent of households), specifically sorting, improved seeds, and low-tech irrigation methods such as hand-watering, drip, sprinkler, rainwater harvesting, etc. Use of irrigation was higher for HHs in ZOI 1 (83 percent) compared to ZOI 2 (66 percent). Similarly, use of water management technology was higher for HHs in ZOI 1 (60 percent) compared to ZOI 2 (45 percent). Only half of the households had applied climate adaptation MPTs. Few households used information and communications technologies (ICT) to access information on agriculture (11%), despite 55% owning a smartphone. Higher proportion of HHs in ZOI 2 (13 percent) had used ICT compared to ZOI 1 (10 percent). Value-added processing was the least common (3%). KISAN II has already identified improving ICT channels and content and access to climate smart MPTs as priorities. In addition, it would be helpful to track individual MPTs (not categories of MPTs) to provide detailed feedback to the project team and the project’s private sector partners, since the application of MPTs is the primary driver for achieving increased yields and sales in the project’s theory of change. This level of detail is required to understand which MPTs are being used and to focus follow-up research on potential barriers to adoption. 4.2 RESILIENCE MODULE Almost two thirds of households were exposed to a shock or stressor. Of households exposed, up to 72 percent reported that the shock or stressor had severely impacted household income and up to 51 percent reported that it had severely impacted household food consumption. Household perceptions of the ability to recover from a shock or stressor were unexpectedly positive – over 90 percent of households reported that their ability to meet current and future household food needs was better or the same as before the exposure. This can be attributed to using coping strategies that were largely positive or neutral. Giving and receiving support in response to a shock or stressor (“social capital”) 52 was less prevalent than expected. KISAN II interventions are expected to enhance resilience by improving agricultural incomes and use of climate-smart technologies and management practices through improved access to inputs, services, information, and buyers. Refer to the end of this section for recommendations related to resilience measurement. 4.2.1 CONTEXT Literacy: An estimated 34 percent of commodity decision makers in the sample were illiterate, which limits their ability to access information, keep farm records, and transact. KISAN II will offer business literacy training. Vulnerability: A high percentage of households (78%) met at least one of USAID Nepal’s three vulnerability criteria: household income less than $1.90/day/person (26%), member of disadvantaged group (55%), and affected by a natural disaster in the past 12 months (32%). Vulnerability associated with being disadvantaged may be overstated since it varies by location, while vulnerability associated with exposure to shocks and stressors is understated because it only considered natural disasters. The Mission may consider revising the definition of “vulnerable” accordingly. Household Food Insecurity Experience Scale (HFIES): Only 1 percent of households went without eating for a whole day within the prior 30 days; 6 percent ate less than they thought they should, and 29 percent worried about not having enough food. Note that the HFIES scale does not capture seasonal variations in hunger, particularly since data was collected during a better than average harvest season and approaching the main annual festival season. As expected, the gap between households above and below the poverty line is significant. Two percent of poor households went without eating for a whole day, compared to 1% of non-poor households. Forty-two percent of poor households worried about having enough food to eat, compared to 23 percent of non-poor households. Asset ownership: Almost all the households (97–100%) owned agricultural land, buildings (barns or storage) and hand tools (hoe, spade/shovel, rake, sickle, pickaxe, axe, pruning shears, etc.). Livelihood sources and diversification: Non-agricultural income from foreign remittances, salaried work, non-agricultural business, and non-agricultural wage labor (approximately $750-2,000/HH/year each) far exceeded agricultural income from crop, livestock, and fish sales and agricultural wage labor (approximately $160-$300/HH/year each). Households below the poverty line tended to have fewer sources of income – 64 percent of poor households had only one to two sources of income, compared to 26 percent of non-poor households. Access to markets: Over 80 percent of households are within walking distance (5 kilometers or two hours) to communications signals, schools, health centers, electricity, agricultural inputs, extension services, and buyers. Less than 80 percent of households have access to agricultural transport services, government veterinary services and extension agents, or a collection center. Only 42 percent of households have access to a storage facility. 53 Group participation: Overall, 74 percent of households participated in at least one local group. The most common were mothers groups, savings and credit groups, and cooperatives (26–34%). Participation in farmer and forest user groups was low (9% each) because the project’s catchment areas are in urban and semi-urban areas. Participation in business literacy programs was very low (0.3%). 4.2.2 EXPOSURE Exposure to shocks and stressors: Almost two-thirds of households (63%) had been exposed to at least one of 18 ‘shocks or stressors’ in the past 12 months. The most common were livestock disease, pests, severe illness in the family, crop disease, inadequate rain, and unable to sell at a fair price (14–23% of households). Agriculture-related shocks and stressors will be directly addressed by KISAN II. Income levels: Household income levels (above or below the poverty line) do not appear to affect the ranking of exposure to shocks or stressors; however, they do affect the percentage of households exposed by a few percentage points. Households below the poverty line had greater exposure to the most common shocks or stressors listed above. Households above the poverty line reported greater exposure to lack of access to inputs, sharp increases in food prices, too much rain, unable to sell at a fair price, and freezing temperatures. The reasons for this are unknown, particularly since the focus is on exposure (not impact), and exposure to market and weather- related shocks and stressors is not expected to vary by income level. Differences between provinces and agro-ecological zones: The percentage of households exposed to a shock or stressor in the past 12 months varied by province (55% in Province 5 to 79% in Province 6), but less so between agro-ecological zones (60% in the Terai and 65% in the hills). Among exposed households, the average number of shocks and stressors ranged between 2.6 and 3.4 per household across provinces, and was 2.8 per household in both the Terai and hills. Impacts: Of households exposed, up to 72 percent reported that the shock or stressor had severely impacted household income and up to 51 percent reported that it had severely impacted household food consumption. Crop theft, death, and severe illness in the family produced the most severe impacts. As noted earlier, KISAN II will mitigate cumulative impacts by directly addressing the other agriculture-related to shocks and stressors. 4.2.3 COPING STRATEGIES Coping strategies: The most commonly reported coping strategies (seven of the top eight) were either neutral or positive: using household savings (55%), increasing wage labor (47%), borrowing cash from another household (41%), buying on credit (32%), borrowing cash from an informal group (27%), remittances (17%), and a neighbor sharing food (17%). Twenty-four percent of households had sold livestock, deemed a potentially negative coping 54 strategy by some resilience advisors because it is selling a productive asset, though it is similar to using household savings, a neutral coping strategy. The most negative coping strategies were the least commonly used, making up six of the bottom nine strategies: ate less or cheaper (15%), no coping strategy (14%), harvested early (6%), sold land or another asset (4-5%), and took a child out of school (4%). Social capital: Social capital was lower than expected, based on the relatively small percentage of households that had received (26%) or given (32%) support in response to a shock or stressor. Respondents may have been confused about what constitutes support, since 41 percent reported being able to borrow cash from another household. Sixty-seven percent of exposed households received support from relatives within their community, the most common source of support. The most common forms of support were cash loan (77%) or farm labor (21%). Sharing food was less common than expected (10%). 4.2.4 WELL-BEING OUTCOMES Post-shock perceptions: Household perceptions of the ability to recover from a shock or stressor were unexpectedly positive – 93 percent of households reported that their ability to meet current household food needs was better or the same as before exposure (45% and 49%, respectively). Similarly, 94 percent of households reported that their ability to meet future household food needs was better or the same as before exposure (63% and 31%, respectively). The reasons for why their ability to meet household food needs was better are unknown. Expectations about social capital: Similar to the patterns related to actually receiving and giving support reported under coping strategies, more households expected to give than receive support in response to future shocks or stressors; more households expected that support would be provided by relatives rather than non-relatives, and relatively fewer households expected support from non-relatives from outside their communities. 4.3 GENDER The survey data indicate that significant differences exist between men and women with respect to who makes decisions, ownership of assets, use of improved MPTs, and access to finance. As noted earlier, the decision to omit “mixed” decision makers as an option likely under-reports the role of women in decision making. Further research is warranted to better understand how to improve women’s access to information, services, productive assets, and buyers. Focus group research methods are recommended. Following is a brief summary of gender-related findings. Household heads: Household heads are predominantly male (78%). Decision-making authority: Only one percent of respondents cited lack of decision￾making authority as a priority constraint; however, additional questions would be required to confirm that this is not an issue. 55 Ownership of assets: On average, male farmers owned more assets (15.2) than female farmers (11.2). Disaggregation of asset ownership by type of asset would be needed to draw meaningful conclusions from this data. Improved MPTs: The average number of improved MPTs applied varied slightly between female-headed households (10.7) and male-headed households (11.7). ICT use: Only 8 percent of farmers accessed information on improved MPTs through ICT. Most ICT users were aged 30–59 (80%) and were men (72.5%). Participation in group-based savings, micro-finance, or lending programs: More women saved (27%) than men (10%). Of those who participated in a group-based savings program, 73 percent were women and 27 percent were men. The average value of savings deposits for females ($91) was significantly lower than per male ($232). Access to agriculture-related finance: Only 12.7 percent of households had accessed formal agricultural finance. Almost twice as many women had borrowed money than men, but the women’s average loan values ($323) was approximately one third that of the men’s ($864), and was consistently lower than the men’s across all types of financial institutions. Prices: Female sellers received slightly higher average prices than men for lentils ($0.55/kg versus $0.49/kg), vegetables ($0.30/kg versus $0.29/kg), and goats ($4.20/kg versus $3.86/kg), although men received a slightly higher average price for rice ($0.25/kg versus $0.22/kg). The reasons for this are unknown. Value of agricultural sales: Males reported higher average annual sales ($442) than females ($199), despite women receiving higher prices, because men sold larger volumes (80% of total volume). 56 APPENDIX I: BASELINE SURVEY SCOPE OF WORK Note that Sections 1 and 2 of this scope of work (SOW) (purpose and background) are omitted here as they appear in the main body of the report. 3. Scope of the Baseline Survey USAID Nepal is seeking services to undertake a baseline survey for the KISAN II project. The recommended time frame includes six weeks of preparatory work and twelve weeks of data collection, analysis, and reporting work, and can be modified with the contractor after the award. The Contractor is responsible for the following tasks: • Finalizing tools, protocols, and guidance for household level baseline data collection. • Collecting quantitative baseline data related to the effectiveness and reach of KISAN II indicators. • Analyzing collected baseline data and consolidating data into a report for use by KISAN II, other FTF implementing partners, Government of Nepal (GON), and US Government (USG) agencies. • Submitting baseline values for the list of indicators included in this SOW. The Contractor must apply instruments that ensure rigor and are representative of the target beneficiary population in KISAN II’s operating area. Tools must remain consistent in content and implementation methodology throughout the period of work so that the data will be standardized. The Contractor is expected to use surveys and other interview tools for gathering quantitative information. The Contractor will be expected to follow USAID guidelines for sampling, questionnaire development and analysis according to the Feed the Future M&E guidance (https://www.agrilinks.org/sites/default/files/resource/files/Sampling￾Guide-Beneficiary-Based-Surveys-Feb122016.pdf, https://www.agrilinks.org/post/feed-future￾indicator-handbook). 4. Methodological and Technical Considerations 4.1 Indicators to be Reported Feed the Future activity-level indicators There are 20 KISAN II performance indicators to be measured through the KISAN II Baseline Survey. These are listed in Table A1.1. Detailed definitions for each indicator will be provided and are available for all standard FTF indicators in the Indicator Handbook, linked below.11 Detail list of indicators with footnotes are included in Annex 2. This descriptive baseline survey will collect data from KISAN II participant households. All indicators collected by this survey will be collected and reported at the household level. 11 The revised Feed the Future Indicator Handbook can be found at: https://feedthefuture.gov/sites/default/files/resource/files/FTF-Indicator-Handbook-March-2018.pdf. 57 Table A1.1: List of KISAN II Indicators to Be Collected Under this SOW KISAN OR GFSS NO. PROJECT COMPONENTS AND INDICATORS TYPE LOP TARGETS Cross-cutting indicators K4 Nepal 2.1.1-2 EG.3.2-24 Number of individuals in the agriculture system who have applied improved management practices or technologies with USG assistance (IM-Level) Outcome 190,000 K5 Custom 1 Average number of improved management practices or technologies applied per household with USG assistance. Outcome KPI TBD K6 GNDR-2 Percentage of female participants in USG assisted programs designed to increase access to productive resources Outcome TBD K7 New YOUTH-3 Percentage of participants in USG-assisted programs designed to increase access to productive economic resources who are youth (15-29) (IM-Level) Outcome TBD Component 1: Improve the productivity of SAMS 1.1 Facilitate intensification and diversification of farmers into higher-value commodities K8 Nepal 2.1.1-1 EG.3.2-25 Number of hectares under improved management practices or technologies with USG assistance (IM￾Level) Outcome KPI 114,000 ha K9 Reporting 1 Total farm-level volumes (MT) produced of targeted agricultural commodities with USG assistance (rice, maize, lentils, vegetables, goats) Outcome Reporting n/a K10 New EG.3-10, -11, - 12 Yield of targeted agricultural commodities among program participants with USG assistance (rice, maize, lentils, vegetables, goats) (IM-Level) (MT/ha) Outcome KPI TBD 1.2 Strengthen the capacity of input supply systems to deliver timely and affordable productivity-enhancing technologies 1.3 Increase the adoption of profitable, productivity￾enhancing, and climate-smart technologies K12 New EG.3.2-28 Number of hectares under improved management practices or technologies that promote improved climate risk reduction and/or natural resources management with USG assistance (IM-Level) Outcome TBD K13 Custom 1 Nepal 2.1.1-2 EG.3.2-24 Average number of climate-smart technologies or practices applied per farmer with USG assistance. Outcome TBD 58 KISAN OR GFSS NO. PROJECT COMPONENTS AND INDICATORS TYPE LOP TARGETS Disaggregate Component 2: Strengthen competitiveness (C), resilience (R) and inclusiveness (I) of SAMS 2.0 Cross-cutting K17 New EG.3.2-26 Value of annual sales of farms (and firms) receiving USG assistance (rice, maize, lentils, vegetables, goats) Outcome KPI TBD K18 New Disaggregate Reporting 2 Volume of annual sales (MT) of farms receiving USG assistance (rice, maize, lentils, vegetables, goats) Outcome Reporting n/a 2.1 Strengthen the organization and coordination of SAMS K19 Nepal 2.1.1-2 EG.3.2-24 Disaggregate Number of Individuals in the agriculture system who have applied improved management practices or technologies with USG assistance – related to marketing and distribution Outcome TBD 2.2 Strengthen lead firms and other SMEs to support SAMS K20 Custom 2 Percentage of USG-assisted farmers accessing information on improved technologies and practices through improved ICT channels or content Outcome 90% 2.3 Enhance financial services markets that serve SAMS K23 New EG.3.2-27 Value of agriculture-related financing (USD) accessed as a result of USG assistance (IM-Level) Outcome TBD K24 New EG.4.2-7 Number of individuals participating in group-based savings, microfinance or lending programs with USG assistance (IM-Level) Outcome KPI TBD K25 Reporting 4 Value of household savings deposits of USG-assisted smallholders Outcome Reporting n/a K26 Reporting 5 Value of the outstanding balance on agriculture loans of USG-assisted households Outcome TBD Component 3: Strengthen the enabling environment of SAMS K31 Nepal 2.1.1-2 EG.3.2-17 Disaggregate Number of farmers and others who applied improved management practices or technologies– on food grading or safety Outcome 100,000 Component 4: Increase ability of vulnerable communities to act on business opportunities 59 KISAN OR GFSS NO. PROJECT COMPONENTS AND INDICATORS TYPE LOP TARGETS 4.1 Literacy and business skills K41 Custom 5 Quantity of nutrient-rich value chain commodities produced by direct beneficiaries with USG assistance that is set aside for home consumption (MT) Outcome TBD Component 5: Apply CLA to market systems development 5.1, 5.2, 5.3 Advance competitiveness, inclusiveness, and resilience K46 EG.11-6 Nepal 2.1.1-2 EG.3.2-17 Disaggregate Number of people using climate information or implementing risk-reducing actions to improve resilience to climate change as supported by USG assistance Outcome 57,000 Resilience Module In addition to the list of indicators, the baseline survey will include a brief resilience module to collect baseline data on shock exposure, resilience coping strategies and perceived ability to cope with a shock. These will measure, more specifically: • Number of types of shock exposure (covariate and idiosyncratic). • Shock response behaviors, categorized by specific coping strategies (e.g., use of savings, use of formal loans, borrowing, use of assets, changes in food intake, livelihood diversity, etc.). • Perceived ability to respond to and recover from a shock (see FTF indicator handbook: RESIL-a, RESIL-b, RESIL-c). 4.2 Geographical Focus of the Survey The geographic focus area for this survey is the Feed the Future ZOI in Nepal, which covers 25 districts across four Provinces. The ZOI is the geographic area where Feed the Future programs are expected to have an impact on hunger, poverty, and nutrition. These districts fall within Provinces 3, 5, 6, and 7: • Province 3: Kavrepalanchok, Nuwakot, Makwanpur, and Sindhupalchok. • Province 5: Kapilbastu, Palpa, Arghakhanchi, Gulmi, Banke, Bardiya, and Rukum (East). • Province 6: Surkhet, Dailekh, Jajarkot, Salyan, Rukum (West). Dang, Rolpa and Pyuthan • Province 7: Baitadi, Kailali, Kanchanpur, Doti, Achham, and Dadeldhura. KISAN II is operating through private sector partners across these 25 districts and will use the farmers who are already buying from their private sector partners as the sampling frame within these districts. The Contractor will need to coordinate with KISAN II to understand their approach and the catchment areas of the private sector partners to more closely define the wards and villages where intended farmer beneficiaries are. 60 4.3 Sampling 4.3a Sampling Design The sampling design described here follows the Feed the Future Sampling Guide for Beneficiary Based Surveys, 2016. The KISAN II Baseline Survey will use a random sample representative of the beneficiary households in KISAN II operational areas. The baseline survey will use a two-stage cluster sampling design with a systematic selection of participants12, for which two separate sampling frames (Cluster ward level frame and Frame of participants) are required. In the first stage, the Contractor shall randomly select Wards, which will serve as the cluster, from a sampling frame composed of all Wards served by KISAN II (Refer pages 53- 55 of the sampling guide, 2016). The second stage participant frame consists of the list of participants served by KISAN II in the selected wards. Per the sampling guide, direct participants are those who come in direct contact with KISAN II interventions; for KISAN II, participants include any farmer who receives direct training by KISAN II or its sub￾grantees or partners; and/or individuals who purchased and used targeted improved inputs or services from KISAN I-affiliated sub-grantee/partner. Indirect beneficiaries will not be included in the sampling frame. KISAN II will provide a detailed definition of “participants.” Among those identified project Wards, households are randomly selected. The sample should be stratified by the four provinces and by crops: rice, maize, lentils, tomatoes, cauliflower, cucumber, bitter gourd, and goats. The Contractor shall prepare the project participants list by strata and allocate the sample proportionally based on the population of each stratum (provinces 7, 6, 5, and 3) by systematic random sampling. The contractor shall then divide the sub-samples by the number of households to be interviewed per Ward to compute the number of Wards to be visited per stratum. Table A1.2: Sampling Methods for Each Stage of Sampling STAGE 1: SELECTION OF WARDS STAGE 2: SELECTION OF HOUSEHOLDS SELECTION OF INDIVIDUALS Method of sampling Systematic Random Sampling Simple random sampling Household head or member who is a projected participant from a set of project implementation clusters or primary sampling unit 12 Diana Maria Stukel and Gregg Friedman. 2016. Sampling Guide for Beneficiary-Based Surveys for Select Feed the Future Agricultural Annual Monitoring Indicators. Washington, DC: Food and Nutrition Technical Assistance Project, FHI 360, pages 32-35. 61 4.3b Sample Size The Baseline Survey sample size has been calculated following guidelines developed by the Bureau for Food Security. The final sample size for the baseline survey should be 1,850 households after adjustments of finite population correction, design effect, nonresponse and minimum response required for each targeted commodity. This sample size should be sufficient to capture a meaningful change for the three Feed the Future indicators for farm level changes among participating households in: • Value of annual sales. • Number of hectares under improved technologies or management practices. • Number of individuals who have applied improved technologies or management practices. The general recommendation is that the sample size for all key indicators from among the indicators being collected in the survey be calculated and that the largest sample size resulting from all candidate sample sizes computed be chosen. See Table A1.3 OR A1.4 for sample size calculations according to the three indicators listed above. 62 Table A1.3: Parameters Used to Calculate the Initial Sample Size INDICATORS N MAX MIN S P TOTAL VALUE OF INDICATOR 2018 MARGIN OF ERROR Z N SAMPLE SIZE FINITE POP CORRECTION FINITE CORRECTION NEEDED, IF MORE THAN 5 % DESIGN EFFECT FINAL SAMPLE SIZE ADJUSTI NG NON￾RESPON SE FINAL SAMPLE SIZE ROUN DED POP. OF BENEFICIA RIES ESTIMATE OF THE MAXIMUM ESTIMATE FOR THE MINIMUM STANDARD DEVIATION ACCEPTABLE % OF ERROR CRITICAL VALUE (N) (FOR S) (FOR S) (S) (FOR MOE) (FOR MOE) (MOE) 1.96 2 5% Number of farmers who have adopted improved technologies or practices 30,000 1.00 0 0.50 10 28,500 2,850 1.96 107 1.3% Not required 214 226 230 Number of ha under improved technologies or practices 30,000 4.06 0.002 0.68 10 14,250 1,425 1.96 780 2.6% Not required 1,560 1,643 1,643 Reporting year sale (USD) 30,000 1960 0.74 326.54 10 13,000,000 1,300,000 1.96 219 1.3% Not required 438 462 470 1. The Initial sample size was calculated as follows: n= N2 * Z2 * S2/MOE2, where N is the total number of beneficiaries, Z=Critical value for normal distribution, S=Standard deviation of the distribution and MOE is the margin of error. The standard deviation of the distribution of beneficiary is taken from last year’s survey. Adjustment to the sample will be made for a finite population (> 5%), design effect (2) and nonresponse (5%) as suggested by FTF guide. The sample size is also adjusted for analysis by crop and geographical zones (Hills and Terai). 2. The recommended practice is to calculate the final sample size from among at least three agriculture indicators and then choose the largest sample size from among the computed. 3. As the sample size of 1,643 is the largest sample size among the three FTF indicators computed above, it exceeds the requirement. 4. It exceeds the minimum sample size recommended by FTF sampling guidelines, 2016. It ensures precision for the FTF disaggregates, compensates possible diminished sample size and ensures precision for different strata (e.g., hills and Terai) (Source: FTF Sampling guide, 2016). 63 5. Calculation for value for incremental sales (VIS) is based on reporting year total sales as recommended by the FTF sampling guide and email response of 27 May 2016 by Diana Stukel on some questions raised during webinar of 24 May 2016. 64 The starting values and expected meaningful changes (targets) for the three selected indicators were obtained from the KISAN MEL Plan, 2018 as mentioned below in Table A1.4. A 95 percent confidence level and 10 percent margin of error were used across the board. The anticipated non-response rate (5%) used here to adjust the sample size mirrors the non￾response rate obtained in the 2017 KISAN survey. The initial sample size is recommended to adjust by finite population correction, design effect, nonresponse, and the minimum number of respondents required for each targeted commodity. The three adjustments are made in Table A1.4. The fourth adjustment is proposed to have a minimum number of households per commodity. For this, the estimated values for percent of households involved in each commodity are obtained from the KISAN annual survey, 2017 results. Adjustment to the number of households engaged in cultivating targeted crops or raising goat is made with the assumptions that at least 150 samples are available for the interview, which is statistically sufficient for crops outcome analysis (Table A1.4). Table A1.4: Adjustments from the Initial Sample Size to the Final Sample Size COMMODITY PERCENT IN THE BENEFICIARY HH AS PER 2017 KISAN SURVEY NUMBER OF ESTIMATED HH ENGAGED BY COMMODITIES IN NEW SAMPLE SIZE NUMBER OF HH PER STRATA (S=7) NUMBER OF HH REQUIRED TO MAKE AT LEAST 150 PER STRATA BY CROPS PERCENT REQUIRED TO ADJUST SAMPLE SIZE (+) Rice 46 756 108 42 5.6 Maize 43 706 101 49 6.9 Lentils 34 559 80 70 12.6 Vegetable 53 871 124 26 2.9 Goats NA NA NA NA NA As the percentage required to adjust the sample size of lentils is highest (12.6%) among others, the total sample size has to be increased by 12.6% (i.e., it should be 1,850). FTF suggests clusters include 15-35 households; given geographic variation, USAID Nepal suggests clusters of fewer households, but the Contractor may determine the most appropriate design for cluster size. For example, the sample may be divided by approximately 20 households per cluster, resulting in a selection of a total of at least 93 clusters, or Wards. 3. Proposal for Baseline Design To be considered for this baseline survey, the Offeror must submit a proposal with a comprehensive overview of the proposed baseline survey design. The technical proposal is limited to 20 pages and shall be written in English and typed on standard A4 or 8 ½” x 11” paper (216 mm by 297 mm paper), single-spaced, 12 pt. Times New Roman font with no smaller than 1-inch margins with each page numbered consecutively. Pages that exceed 65 the page limitation will not be evaluated. Proposal content should focus on the baseline survey and not extensive background discussions of the socio-political environment in Nepal. This proposal overview must describe data collection and analysis and project management. The overview must include: A. Detailed plan of the processes and systems for data gathering, data cleaning, and analysis, including the type of design; clustering and sampling methodology/criteria/sizes; the mix of data collection methods and sources; and plans for data storage, cleaning, and analysis. B. Summary and description of the instruments and tools that will be used in data collection and the plan for their testing. USAID Nepal requires that data collection be conducted using tablet computers or Personal Data Assistants (PDAs). If the Offeror does not have these items available, tablet computers and accessories will be procured and provided by USAID Nepal. The Offeror’s proposal must describe the number of devices and the technical specifications of tablet computers, survey software, information platforms, and other accessories required for baseline field data collection and transmission. USAID Nepal will have final approval on the devices and software selected. This equipment must be returned to USAID Nepal after completion of the baseline survey. C. Sampling methodologies for identifying and selecting households to be surveyed – For quantitative sampling size, the number of households will be 1, 850. In the 25 FTF districts, the Contractor must cover a sampling of KISAN II’s target Wards based on information provided by the KISAN II Team. In the FTF focus districts, the Contractor must conduct a random sampling of Wards. Please refer to Section 4.2 above for the ZOI FTF Nepal focus districts. D. Detailed description of the quantitative and qualitative approaches to data collection. E. Tools used for data analysis, which may include, but are not limited to, Statistical Package for the Social Sciences (SPSS), other statistical packages, and Geographic Information System (GIS). F. Plan and process to be used for data quality analysis. G. Plans for training data enumerators and processors on tablets and surveys. H. Team composition with positions and responsibilities. Suggested structure is recommended in Annex 1. 4. Deliverables The Contractor must complete the following key deliverables: 4.1. Work Plan The Contractor must complete a detailed work plan which will be approved by USAID Nepal before beginning the baseline survey. The work plan must include sufficient time for enumerator training, piloting, and revising tools before the data collection. 66 4.2. Data Collection Design Protocol The Contractor shall submit a Survey Study, Protocol (including the questionnaire and all translated versions), which will be approved by USAID Nepal. The protocol will include a description and justification of the selection of the tools, sampling strategies, survey questionnaires, and sampling sizes to be used for gathering quantitative and qualitative information from key informants and focus groups. These tools must remain consistent in content and implementation methodology throughout the baseline survey so that the data will be standardized. 4.3. Data Set The contractor should submit the datasets along with the final report. The dataset should include both raw and cleaned data set in different file formats (SPSS or STATA and CSV). The contractor should also submit edit rules for cleaning data; data dictionary/codebook; syntax for all data analysis and variable transformations; sampling weights at each stage, final sampling weights used to tabulate the aggregate-level estimates for the indicators, if any, and appropriate metadata for open data purposes (DDL). 4.4. Baseline Survey Reports (in multiple drafts) The Contractor must collect, prepare, and analyze the baseline data. The Contractor must submit a draft report containing the findings and conclusions to USAID Nepal. The baseline report must be in a font size no smaller than 12 pt. and must be written or comprehensively edited by a fluent, experienced English writer. The report must include signed disclosures of conflict of interest from each member who worked on any part of the reporting process. When applicable, the report must include statements regarding any significant unresolved differences of opinion on the part of the funders, implementers and/or members of the evaluation team. And the format for the baseline survey report must be as follows: Table of Contents: • Executive summary. • Table of contents, including abbreviations. • Background information. • Survey approach and methodology. • Summary of indicator results. • Survey findings. • Conclusions. Annexes: • Demographic and other tables. • Indicator values table, with disaggregates as required in PIRS. • Survey tools and instruments. • Bibliography and interview lists. 67 • Questionnaires. After review and discussion during the presentation of findings, the contractor will submit a second draft that incorporates written and verbal feedback from USAID and CAMRIS. The contractor will be responsible for revising the report until it meets USAID requirements for quality. 4.5. Presentation on Baseline Survey Findings The Contractor must conduct a PowerPoint (or similar) presentation on the important findings and conclusions of the baseline survey to an audience of USAID Nepal staff and implementing partners. This presentation must be conducted after the submission of the draft baseline survey report and before submission of the final version of the baseline survey report. 5. Coordination The Contractor must survey households in a sampling of the wards under rural and urban municipalities in 25 districts where KISAN II is operating. The Contractor must coordinate with KISAN II implementing partner on the identification of municipalities and palikas to be surveyed. Final selection of municipalities and palikas will be determined by the Contractor and is subject to approval by USAID Nepal. 6. Timeline The timeline for this SOW is four months. This includes six weeks of preparatory work, enumerator training, and survey testing followed by 12 weeks of data collection, data cleaning and analysis, and reporting work. Table A1.5: Timeline of KISAN II Baseline Survey ESTIMATED TIME ACTIVITIES 2 weeks Work plan preparation, documentation review and planning, baseline data collection methodology (survey design, sampling plan) and tools. 4 weeks Enumerator recruitment and training, testing of survey questionnaires and equipment. 4 weeks Field data collection, data entry system design. 4 weeks Data cleaning and analysis. 1 week Debriefing presentation on major findings, recommendations to USAID Nepal and comment incorporation. 2 weeks Draft report and data set submission to USAID Nepal. 1 week Submission of the final report and raw data to USAID Nepal. USAID Nepal will provide comments within 10 working days of the submission of the draft report. A revised final draft will be submitted within 10 working days after receipt of comments from USAID Nepal. The baseline survey report will be final after it is approved in writing by USAID Nepal. 68 7. Logistics and USAID Nepal Participation The Contractor is responsible for managing all logistics required for completing the baseline survey. This includes, but is not limited to, arranging for transportation, meeting venues, and appointments. USAID Nepal will provide key documents and background materials. 69 APPENDIX II: LESSONS FOR FUTURE SURVEY While designing and implementing the survey, the team encountered certain issues and learned valuable issues that provided good directions for similar future surveys and which appear below in Table A2.1. Table A2.1: Issues and Lessons Learned from the KISAN II Baseline Survey ISSUES LESSONS LEARNED Measurement Approaches Questionnaire length – The interviews took about 2.5 hours each. Long interview times diminish the accuracy of responses. The resilience questions were most likely affected as they were asked later in the interviews than the agriculture questions. Participants’ lack of a stake in the survey may have also influenced the quality of reflection and answers, particularly as the interview length extended. Data reliability – Reduce the time needed to administer questionnaires to enhance data reliability and cost-effectiveness and minimize the burden on respondents and enumerators. Relevance and other question selection criteria – Agree on and adhere to clear criteria on what to include, starting with identifying a specific, actionable use of the data generated by each question. Refer to Appendix VII for an example of an analytical framework developed during the post-test phase of the survey instrument design process to reach agreement on the final set of questions. Mixed methods. Explore topics using “best fit” methods to reduce the burden on annual monitoring survey interviews and produce more reliable, in-depth evidence for project learning. See specific recommendations below. Reliance on quantitative data – In accordance with the SOW, the questionnaire relied on quantitative closed-ended questions. This approach is well suited for capturing results data, which was the primary focus of the Agriculture Module. However, it does not capture complex, in￾depth information that is helpful for understanding constraints, decision making (“why” or “why not”), behavior change, and resilience – which are important for understanding both agriculture and resilience outcomes and informing project interventions. This conclusion is noted for learning purposes. Use mixed methods for data collection: • Use focus group discussions to better understand gender, resilience, and behavior change, rather than closed survey questions. • Use GIS mapping to provide a better decision support tool for market actors and project teams than asking about proximity to markets. • Assess communications-related results in a separate measurement activity (outside of the annual monitoring survey), using techniques designed specifically for this. Firm-level data – The survey did not include data from firms because its scope of work (SOW) focused exclusively on households. Two of the KISAN II FTF indicators listed in the SOW require data from firms: • value of annual sales of farms and firms (EG.3.2- 26, page 101) • formal agricultural loans (EG.3.2-27, page 106). Survey of firms – As indicated in the KISAN II MEL Plan, data from firms will be collected in a separate firm￾level survey for relevant FTF indicators. Market system development indicators and mixed methods – KISAN II would benefit from identifying a small set of relevant, custom market system development indicators and updating their 2018 MEL Plan accordingly. The market linkage questions considered to date are provided in Appendix VI for reference. Additional discussion is warranted, as the fast pace of the baseline survey design process did not allow for adequate reflection and consensus on this important issue. 70 ISSUES LESSONS LEARNED FTF complexity. FTF indicator definitions are complex and require repeated, iterative reviews of the FTF Indicator Handbook to ensure compliance. Some definitions may also require consultations with the Bureau for Food Security (BFS) to ensure correct interpretation and/or address gaps in guidance. Even though the survey team reviewed the handbook on an almost daily basis to check and recheck definitions and requirements, a few errors were made at the survey instrument design phase, reflecting the difficulty of achieving full compliance. Survey oversight – As done for the KISAN II Baseline Survey: • Ensure all FTF survey teams include an individual with in-depth knowledge and experience of FTF indicators. • Engage the implementing partner’s technical and NMEL staff in survey design, enumerator training, and data interpretation phases to ensure that survey questions accurately reflect the targeting strategy, project interventions, and theory of change. Data quality knowledge management: • Document data quality measures for FTF survey teams and implementing partners in a format that can be readily replicated or adapted across the FTF portfolio in Nepal. Refer to Appendix VIII of this report and Annex B of the KISAN II MEL Plan for examples. • Refer to the next row for recommendations on capturing learning related to using tablets for data collection and quality control. Analyzing who makes decisions – At the project’s request, the survey team identified the primary decision maker for a large set of decisions (for example, each improved MPT applied by the household rather than the set of MPTs). This significantly complicated data analysis without significantly enhancing understanding of gender aspects of household agricultural decision making. • Simplify – In calculations designed to characterize households (not designed to provide results for FTF indicators), identify no more than two key agricultural decision makers at the household level. • Understand the data limitations of identifying a single decision maker. • Alternative method – Use focus group discussions with male and female farmers to better understand gender in agricultural decision making. Storytelling constraints – During the presentation of preliminary results, USAID Nepal asked: “How can we better tell the story about resilience?” Although outside the scope of this survey, the survey team offers a few observations and recommendations. • Existing USAID reporting templates are not designed for telling stories. • References to results frameworks and performance indicators are useful for reporting results up to the chain of command to Washington, DC. But they are not sufficient for fostering compelling stories. Storytelling tips • Audience needs – Projects have multiple audiences, including internal audiences (project staff) and external audiences (project partners and participants, etc.). Most writing is oriented toward USAID, Congress, and/or other US stakeholders. Understand your audiences and frame stories accordingly. • Participant-centered research – Whether the focus is on households, communities, firms or market systems; there is no substitute for interviewing participants in ways that allow for discussion and engage them in identifying what matters most from their perspective. • Mixed methods –“Stories are data with a soul” (quote from Brene Brown’s TED Talk about her research on vulnerability). Use both quantitative and qualitative research methods. Appreciative inquiry research methods can more fully capture individual stories and a deeper understanding of behavior change and participants’ perspectives of what interventions and outcomes could make a significant contribution to their well-being and resilience. • Photographs – Images make stories and individuals more relatable. They convey large amounts of information about participants and context in a way that is easier to take in than data and narratives. 71 ISSUES LESSONS LEARNED • Focus on participant and project learning over successes – Learning from setbacks is central to developing resilience. “Success stories” often focus on end results without describing the twists and turns it took to get there. The description of obstacles and setbacks make for much better storytelling and learning than truncated, sanitized accounts. • Knowledge management – Encourage implementing partners, CORs and others to share examples of compelling stories. Examples help build storytelling capacities and signal that stories are valued. Agriculture Module Actionable Findings for KISAN II – The most significant agricultural findings for the implementer are 1) those related to the application of improved management practices and technologies (MPTs), for which detailed information is provided in Appendix III, and 2) respondents’ perceptions of their top three constraints to achieving increased yields and sales. The use of ICT by farmers for accessing information was extremely low and lack of knowledge related to achieving increased yields and sales ranked as a top three constraint. KISAN II has already identified improving ICT channels and content as a priority intervention. The findings support this strategy. Track individual MPTs – Analyze information on the use of improved MPTs at the level of individual MPTs (not types or categories of MTPs) to provide detailed feedback to the project team and the project’s private sector partners. The application of MPTs is the primary driver for achieving increased yields and sales in the project’s theory of change. This level of detail is required to understand which MPTs are being used and to focus follow-up research on potential barriers to adoption. Actionable Findings? • A large share of the resilience findings are not actionable for KISAN II’s private sector partners. They may help tell a general resilience story, but not a story about resilience outcomes that are directly attributable to project interventions. Focus of resilience measurement • Focus resilience measurement activities on aspects that the project can influence via its private sector partners (such as market linkages) and that are most likely to strengthen the firms’ business models. For example, firms will focus on addressing the shocks and stressors that generate revenues for the firm, such as the sale of improved MPTs. A possible next step is to identify a smaller set of resilience questions of potential interest to the project’s private sector partners and to vet these questions with partners to solicit their input. Refer to additional recommendations below. • Instead of household panel surveys, use more suitable mixed methods to measure resilience. For example, most of the shocks and stressors that have implications for market systems can be tracked through standard news channels such as newspapers and radio, and therefore do not warrant collection of household survey data. 72 ISSUES LESSONS LEARNED Resilience information gaps? • The Resilience Module generated more data than it is feasible to analyze. There is a greater need to simplify and focus resilience data collection than to identify ways to expand it. • The primary data gaps are indicators of the resilience of market systems, which are the primary focus of KISAN II’s interventions. A practical approach to resilience measurement • Applying a resilience lens is feasible and strategic for all USAID Nepal projects. The Mission should continue to encourage implementing partners to focus on this by 1) considering how project interventions affect the resilience of households, communities, and market systems, and 2) monitoring shocks and stressors and responding as appropriate to mitigate potential impacts on project outcomes. • See recommendations in the row above. 73 APPENDIX III: DETAILED METHODOLOGY 1. Baseline Survey Design Process 1.1 Survey Team Members and Responsibilities Table A3.1: Survey Team Composition and Responsibilities POSITIONS NO. OF STAFF RESPONSIBILITIES NMEL Team Kshitiz Shrestha, Evaluation Specialist (Senior Level) 1 • Oversee the entire survey effort. • Plan and manage all survey stages. • Provide overall guidance on survey quality, activities and timeline. Ganesh Sharma, Statistician and Data Analyst 1 • Conduct sample selection. • Prepare a data analysis plan. • Conduct data analysis and quality control. Swadesh Gurung, M&E Specialist and Data Analyst 1 • Conduct data analysis and quality control. Ram Khoju, Application Programmer 1 • Develop the data entry program in SurveyToGo. • Conduct spot checks for quality assurance. • Support training and fieldwork activities on tablets as needed • Quality control of data. External Consultant Lorene Flaming, Team Leader 1 • Provide technical guidance and strategic support to the baseline survey team. • Review and finalize the draft baseline survey methodology (including sampling methodology) and work plan. • Review and revise the draft questionnaire in consultation with the NMEL Team, USAID Nepal, and KISAN II, comprising both an agriculture module and resilience module. • Prepare a table for the initial enumerator training and instrument test that identifies interview questions that require feedback on clarity, content, and/or interview time involved • Contribute to programming instructions related to skipping functions and data quality. • Review the field manual prepared by the research firm and help prepare data collection quality control checklists. • Advise on data analysis and the resilience analysis framework • Present a PowerPoint (or similar) presentation on important survey findings, conclusions, and recommendations to USAID/staff and implementing partners. • Write the draft and final baseline survey reports. Research Firm Survey Manager/Agriculture Specialist 1 • Serve as the primary contact point for the survey firm • Survey quality assurance. • Lead data collection activities and overall logistical management for successful completion of fieldwork. Research Officer/Assistant 1 • Monitor survey progress on a day-to-day basis • Checking data • Ensuring that all aspects of survey operations are implemented according to protocol. • Helping lead the coordination and management of field operations. 74 POSITIONS NO. OF STAFF RESPONSIBILITIES Data Manager 1 • Help the Data Programmer develop the data entry program in SurveyToGo. • Training field teams on tablets. • Version control of the data entry program at all stages of the survey. • Checking data. • Editing, cleaning, and submitting raw and clean datasets to the NMEL team. Data Assistants 3 • Train field teams on using tablets. • Conduct data checks. • Handle tablet related logistics during training and fieldwork as required. Quality Controllers 3 • Participate in pre-test and trainings. • Accompany the teams in the field to ensure that all survey protocols are properly followed and that the teams are doing their best to ensure that the quality of survey data is maintained at the highest level possible. Supervisors 15 • Participate in pre-test and training sessions. • Lead the data collection teams in the field. • Overall logistics management (including lodging, transportation, and security of team members). • Supervise fieldwork. • Conduct daily review meetings to share issues and learning. Enumerators 45 • Complete fieldwork training and field practice on the survey instrument. • Conduct field interviews and record data using tablets. • Maintain data quality. • Participate in daily review meetings with team members to share issues and learning. 1.2 Methodology 13 The baseline survey methods and tools will comply with the following USAID guidelines for sampling, indicator measurement (“who” and “what” counts”), survey questions, and data analysis to ensure rigor: • FTF Sampling Guide for Beneficiary Based Surveys (February 2016)14. • FTF Survey Implementation Document: Sampling Guide for Population-Based Surveys (April 2018). • FTF Survey Implementation Document: Household Listing Manual for ZOI Surveys (February 2018). 13 Note that this writeup was written before the survey was carried out and thus had the appropriate tense for then. 14 The baseline survey SOW cites the FTF Sampling Guide for Beneficiary Based Surveys (2016) because at the time it was written USAID Nepal and KISAN II believed information would be available to identify a beneficiary sampling frame. The survey design has since been modified to reflect a population based survey for reasons explained under “Sample Size and Approach”. 75 • FTF Indicator Handbook (April 2018). • TOPS Household Resilience Measurement Options (October 2017). Also, the survey team considered the performance indicator reference sheets in KISAN II’s MEL Plan (April 2018). Most of the survey questions in the Resilience Module are not linked to a project performance indicator and therefore do not have a PIRS or indicator title (except for the four FTF resilience indicators). The survey team will not specify indicator titles or PIRS for these indicators; however, it will list data analysis requirements for all survey questions in a separate Data Analysis Plan. The methods and tools will remain consistent throughout the project implementation period so that data will be comparable across multiple surveys. Sample Size and Approach To minimize bias, the baseline sampling approach seeks to identify a sample that is representative of KISAN II’s life-of-project (LOP) household participant population. The Baseline Survey SOW indicated that KISAN II would provide a list of project participants (or the specific households its private sector partners had already identified and planned to enroll in the project), in time to create a sampling frame for a beneficiary-based survey (BBS). As of late July 2018, the partners had not progressed to a point in their implementation process to produce this list. Instead, KISAN II has provided a list of the target districts and wards (aka “catchment areas”) and proposed number of project participants its partners plan to focus on within the ZOI. Hence, the baseline survey enumerators will need to do a household listing of “potential project participants”. The enumerators, accompanied by the KISAN II field team, will identify the eligible households by administering the screening questions before randomly selecting households in each ward. This sampling approach lies somewhere between a BBS and a population-based survey (PBS), and is best considered a modified PBS. The 2016 FTF Sampling Guide for BBS confirms that conducting a PBS within catchment areas is a valid sampling approach for projects that work through the private sector to support farmers, as KISAN II does.15 It states that in such cases, all farmers within the catchment area can be considered direct beneficiaries16 (aka project participants). To ensure a representative sample, the sampling frame will include only potential project participants that meet the project’s targeting criteria, hereafter referred to as “representative farm households.” 15 “Alternatively, the project could define the catchment area served by the value chain actors that they are facilitating, consider all the farmers within the catchment area as direct beneficiaries, and conduct a PBS within that catchment area (page 15, footnote 18). 16 For KISAN II, a direct beneficiary (aka project participant) is any farmer who receives direct training or market linkages support by the KISAN II project team or its sub-grantees or partners; and/or individuals who purchase and use targeted improved inputs or services from a KISAN II-affiliated sub-grantee/partner. In the context of the baseline survey, conducted before participants have been identified, farmers that meet the project’s targeting criteria can be considered potential project participants. 76 The baseline survey will use a two-stage cluster17 sampling design with a systematic random selection of representative farm households, for which two separate clusters are required: wards and households18. In the first stage, 93 wards (primary sampling units or enumeration areas) from KISAN II’s initial catchment areas will be selected19. In the second stage, 20 representative farm households per ward will be selected, resulting in a sample population of 1,860 households. The calculations for these numbers are based on guidance provided in the 2016 FTF Sampling Guide for BBS. They were provided in the Scope of Work (SOW) for this baseline survey and are presented in Annex A. They reflect adjustments of finite population correction, design effect, nonresponse, and minimum response required for each targeted commodity. This sample size is sufficient to measure a meaningful change for three key FTF indicators (the standard specified in FTF guidance) related to the value of annual sales, the number of hectares under improved technologies or management practices, and number of individuals who have applied improved technologies or management practices. Based on KISAN I project data about production patterns in the ZOI, we anticipate that the sample size is also sufficient to capture at least 15020 farm households per commodity, the threshold required for statistical significance at the commodity disaggregate level (as noted in the sample calculation in the SOW). Goats are a possible exception, as goat production is not as prevalent as the project’s other target commodities. The narrative in the SOW (Section 4.3a) indicates that the sample should be stratified by province.21 The calculation of the sample size in the SOW does not reflect stratification; however, it does allow for disaggregation of data by commodity and province that will likely be statistically significant. While preparing the sample frame, stratification was done by province, as all wards were systematically listed in order of the Central Bureau of Statistics (CBS) codes by province, district, rural or urban municipality, and ward. First Stage: Ward Selection The first stage frame consists of 588 target wards that comprise the catchment areas identified by KISAN II’s initial set of 58 private sector partners. These are production pockets with high market potential for the project’s targeted commodities. Wards will be identified using the location codes and ward numbers established by the CBS. A total of 93 wards were selected to serve as clusters. The Probability Proportionate to Size (PPS) 17 2018 FTF HH Listing Manual (section 1.4): The cluster is the smallest area unit selected for a survey. Clusters can take many forms. If the sampling frame is the latest population census conducted in the country, then a cluster could be an enumeration area as defined by the census. If the sampling frame is the roster of villages, in the case of rural areas, then the cluster could be an entire village, a segment of the village, or a group of villages. 18 Diana Maria Stukel. Feed the Future Survey Implementation Document, Feed the Future Population-Based Survey Sampling Guide. Washington, DC: Food and Nutrition Technical Assistance Project, FHI 360. 19 Refer pages 53-55 of the 2016 FTF Sampling Guide for BBS. 20 This number was specified in the Baseline Survey SoW prepared for the NMEL project. 21 Stratification is warranted by province if results are expected to vary significant between provinces. 77 method was used for ward selection to ensure that the sample is allocated proportionally to the eligible households of the sampling frame (based on the initial targets set by project partners for the number of household project participants). Refer to Annex B for a list of randomly selected wards and additional details on ward selection. Second Stage: Household Listing, Screening, and Selection The second stage frame consists of representative farm households in the wards selected in the first stage. As noted earlier, the survey team will need to conduct a household listing in consultation with the KISAN II field team and administer a screening exercise to identify farming households. The household listing approach reflects the 2018 FTF Household Listing Manual for ZOI Surveys and seeks to make the exercise as logistically feasible as possible given the tight survey timeline, added work associated with the listing, and geographical variations that are unique to Nepal. Since most wards will have many households, a listing of all households would require more time than is feasible within the survey implementation timeline. In such cases, the wards will be divided into smaller segments, only one of which will be randomly selected for household listing. To identify the sampling frame and expedite the listing process, KISAN II will provide the following information: • Before fieldwork, the project will provide a spreadsheet that identifies the catchment area-based ward frame (588 wards) and the expected number of household participants for each ward.22 Based on this, the survey team will randomly select the wards for the survey and begin making travel arrangements for fieldwork. • Ideally, the project will generate ward-level GIS maps for segmentation purposes, showing ward boundaries, landmarks, geographic features, infrastructure, and settlement areas. These maps would draw from the data layers KISAN II used for its production pocket mapping activities. Upon arrival in the field, the survey teams will coordinate with KISAN II field staff present in each ward. Using the GIS ward map, the team, local ward representative, and/or KISAN II focal person will jointly decide where segmentation boundaries should be drawn to produce roughly equivalent segment populations of farming households. The purpose is to minimize bias associated with unequal distribution of farming households in segments, if any. The team will draw sketch maps that show major landmarks (public buildings, markets, main roads 22 On July 25th, KISAN II provided an Excel spreadsheet with the following information: List of wards in catchment areas, ward numbers, palika type (municipality or rural municipality), ward household population based on 2011 Central Bureau of Statistics census data, estimates of the number of farm households in each ward engaged in producing at least one of the project’s target commodities (column N), sum of the farm household target numbers identified by project partners in each ward - with possible double-counting of some households across multiple partners (column P, a subset of N), and the number of targeted households disaggregated by commodity -- rice, maize, lentil, vegetables, and goats (columns Q-U). 78 leading to the cluster) and easily identifiable segment boundaries (such as rivers, roads, pathways, etc.). Refer to Figure A2.1 for an example of a sketch map. For efficiency and accuracy considerations, NMEL proposes segmentation if the ward has 120 or more households. This results in a segment size of 60 or more households. The rationale is that the sampling frame size should be at least three times the required sample size of 20 representative farm households per ward. Once the segments have been identified, the survey team will randomly select one segment for household listing. Typically, a listing and mapping team would subsequently produce a “sketch map” in selected segments showing HH locations, to be used by enumerators during a later interview phase. However, the baseline survey will conduct interviews immediately following the listing exercise, using the same team of enumerators. A sketch map is not needed because the enumerators will be able to rely on information in their listing exercise forms to locate the HHs selected for an interview. The field teams will use a screening form to capture the following information on all households in selected segments: 1) identifiers like name and address and 2), whether any member of the household is currently engaged in farming; and 3) whether the household has produced at least one of KISAN II’s target crops or livestock in the past 12 months. This information is sufficient to determine if the household meets the targeting criteria for at least one of the four categories of potential project participants shown in Table A3.2.23 23 Baseline survey data on household characteristics and agricultural production can be analyzed to assess how well the baseline survey sample represents KISAN II’s target population (for the Baseline Survey Report), and to subsequently assess how well the baseline survey sample represents the project’s actual participant population (for KISAN II’s Annual Monitoring Survey reports). 79 The field teams will list the qualifying farm household’s number in a separate column (column B), which will serve as the frame for household selection (refer to Table A3.2). Once all the households are listed in a segment, the survey team will determine if the sample size requirement of 20 qualifying households has been met. The teams will calculate the sampling interval by dividing the total number of qualifying households by the required number of samples in the ward (20). The first index household will be selected randomly, the second household will be selected by adding the interval to the first household, the third household will be selected by adding the interval to the second household, and so on. If an interview for a household is not successful for any reason, the field team will identify a replacement household using the following process. For example, if the selected household number is 15, the team will move first to household number 14. If that is not successful, the team will move to household number 13. If that is not successful, the team will be to household number 16, followed by number 17 until the interview is successful. Table A3.2: Household Listing and Screening Form 80 1.3 Survey Instruments Survey instruments will comprise two modules: An Agriculture Module that focuses on KISAN II’s outcome indicators (listed in Table A3.3)24 and a Resilience Module that captures additional data for resilience monitoring and learning purposes. Table A3.3: KISAN II Household-level Outcome Indicators KISAN II AND GFSS INDICATOR NUMBERS (D=DISAGGREGATE) HOUSEHOLD-LEVEL OUTCOME INDICATORS K4 Nepal 2.1.1-2 EG.3.2-24 Number of individuals in the agriculture system who have applied improved management practices or technologies with USG assistance. K5 Custom 1 Average number of improved management practices or technologies applied per household with USG assistance. K8 Nepal 2.1.1-1 EG.3.2-25 Number of hectares under improved management practices or technologies with USG assistance. K9 Reporting 1 Total farm-level volumes (MT) produced of targeted agricultural commodities with USG assistance (rice, maize, lentils, vegetables, and goats). K10 EG.3-10/11/12 Yield of targeted agricultural commodities among program participants with USG assistance (rice, maize, lentils, vegetables, and goats) (MT/ha). K12 EG.3.2-28 Number of hectares under improved management practices or technologies that promote improved climate risk reduction and/or natural resources management with USG assistance K13 Custom 1 Nepal 2.1.1-2 EG.3.2-24D Average number of climate-smart technologies or practices applied per farmer with USG assistance. K17 EG.3.2-26 Value of annual sales of farms (and firms) receiving USG assistance (rice, maize, lentils, vegetables, and goats) K18 Reporting 2 Volume of annual sales (MT) of farms receiving USG assistance (rice, maize, lentils, vegetables, and goats). K19 Nepal 2.1.1-2 EG.3.2-24D Number of Individuals in the agriculture system who have applied improved management practices or technologies with USG assistance – related to marketing and distribution. K20 Custom 2 Percentage of USG-assisted farmers accessing information on improved technologies and practices through improved ICT channels or content. K23 EG.3.2-27 Value of agriculture-related financing (USD) accessed as a result of USG assistance. K24 EG.4.2-7 Number of individuals participating in group-based savings, microfinance or lending programs with USG assistance. K25 Reporting 4 Value of household savings deposits of USG-assisted smallholders. K26 Reporting 5 Value of the outstanding balance on agriculture loans of USG-assisted households. K31 Nepal 2.1.1-2 EG.3.2-17D Number of farmers and others who applied improved management practices or technologies– on food grading or safety. 24 The SOW listed two output indicators that have been omitted from this table because they do not require baseline data: GNDR-2 Percentage of female participants in USG assisted programs designed to increase access to productive resources; and YOUTH-3 Percentage of participants in USG-assisted programs designed to increase access to productive economic resources who are youth (15-29) (IM-Level). 81 KISAN II AND GFSS INDICATOR NUMBERS (D=DISAGGREGATE) HOUSEHOLD-LEVEL OUTCOME INDICATORS K41 Custom 5 Quantity of nutrient-rich value chain commodities produced by direct beneficiaries with USG assistance that is set aside for home consumption (MT). K46 EG.11-6 Nepal 2.1.1-2 EG.3.2-17D Number of people using climate information or implementing risk-reducing actions to improve resilience to climate change as supported by USG assistance. For the Agriculture Module, the Nepal MEL Activity will adapt existing KISAN I questionnaires as needed to: • Comply with the new 2018 FTF Indicator Handbook’s Performance Indicator Reference Sheets (PIRS) for relevant Implementing Mechanism-level outcome indicators (indicator numbers beginning with “EG” in Table A3.3). • Incorporate non-GFSS indicators that are new for KISAN II (“reporting” or “custom” indicators in Table A3.3). Most of the Resilience Module comprises household resilience questions from the USAID TOPS Resilience Measurement Household Questionnaire (October 2017) or FTF ZOI Survey Core Questionnaire (June 2018) that have been adapted to reflect KISAN II’s project design and conditions in Nepal. Resilience-related questions will be grouped by theme and sequenced to follow a logical flow: • Market access and systems development (information, inputs, services, buyers). • Exposure to shocks and stressors and coping strategies. • Women’s decision making and economic empowerment. It incorporates within these sections three FTF ZOI-level indicators that use data from households that experienced a shock or stressor within the past 12 months. Refer to Table A3.4. 82 Table A3.4: FTF ZOI-level Indicators Related to Resilience and Women’s Empowerment KISAN II AND GFSS INDICATOR NUMBERS ZOI-LEVEL INDICATOR TITLES DESCRIPTION TBD 25 RESIL-a Ability to recover from shocks and stressors index A “shock exposure corrected” index the shocks or stressors to which a household is exposed, the perceived severity of the shock on household income and food consumption, and perceptions of their ability to meet current and future food needs. TBD RESIL-b Index of social capital at the household level Expectations about receiving or giving support in response to a future shock or stressor TBD RESIL-c The proportion of households that believe the local government will respond effectively to future shocks and stresses Self-explanatory. Local government responsiveness can refer to either local leaders and/or institutions. Typically, ZOI-level indicators are not collected at the Implementing Mechanism (IM aka project) level. However, collecting it at the project level for KISAN II will allow comparisons between changes among household project participants and the general population of households. All four indicators use household data the project had already planned to capture, so they do not increase the data collection requirements. At the request of the Suaahara Project and USAID Nepal, the draft KISAN II baseline survey questionnaire incorporates several questions from Suaahara’s survey instrument related to their nutrition and health interventions. Note that the household sample for the KISAN II baseline survey is not representative of Suaahara’s target population. Before finalizing the survey instrument, the survey team will seek to reach consensus with stakeholders on which health and nutrition data is most relevant and prioritize and streamline survey questions accordingly. Refer to Annex C for the draft survey questionnaire (submitted separately). All survey questions will be tested for clarity and the interview time required before fieldwork. Questions will be modified or omitted as needed to ensure that the total interview time is feasible, given the survey schedule and resources. The length of time respondents can be expected to participate in an interview is also an important consideration, particularly since the survey sample is of representative households, not actual project participants (who would have a clearer stake in the project). Based on past experience conducting surveys, the survey team recommends that interviews require no more than two hours to complete per household. 25 The four indicators in Table A3.4 will be added to KISAN II’s MEL Plan in their next annual update and assigned project indicator numbers at that time. 83 2. Data Collection Full Bright, a local research firm with past experience conducting large-scale agriculture surveys, will perform data collection using Computer Aided Personal Interviews (CAPI) on tablets. They will use a Samsung Galaxy tablet A and SurveyToGo, a data collection software designed for CAPI methods on Android devices, with which the MEL Activity has experience. This requires more design time up front but less time entering and checking data. The composition of Field Teams: The MEL Activity plans to deploy approximately 15 teams in the field for data collection, comprised of three enumerators and one supervisor each. Also, approximately three quality control staff from Full Bright will accompany the teams in the field to ensure that all survey protocols are properly followed and that the teams are doing their best to ensure that the quality of survey data is maintained at the highest level possible. Selection and Recruitment of Field Staff: Enumerators, supervisors and quality control staff will be recruited based on the following criteria: 1) past experience with large-scale agriculture surveys and tablet-based data collection, 2) references verifying successful past performance on survey assignments and no evidence of poor performance, and 3) gender balance in the field team. Also, qualified individuals with fluency in a relevant local language to help mitigate any gender and language issues that may arise. Training of Supervisors and Quality Controllers and Pre-testing of Survey Instruments: Full Bright will train 15 supervisors and 3 quality control staff on the questionnaires and tablets. The MEL Activity will oversee the training, and KISAN II staff may attend to be available to answer questions about farming practices or survey areas. The training is expected to take about a week. Immediately after, the initial trainees will pre-test the survey instruments and tablet programs in a nearby non-sample area for finalization. The MEL Activity will identify a farming community with some developing and commercial farmers (as described in the project’s farmer evolution model) so that the test location is as similar as possible to the sample wards. The MEL Activity will facilitate post-test feedback sessions with the instrument test team to identify any changes that are required in the interview questions or tablet programs to enhance clarity, correct program bugs, and/or streamline. The MEL Activity will subsequently finalize the instrument, and Full Bright will modify the Field Guide and training materials as needed. Training of Enumerators: Full Bright will prepare and deliver a two-week training course for 45 enumerators, with the MEL Activity providing oversight. Enumerators will first be trained on paper on the interview questions, and then on data entry in the tablets. Full Bright will maintain daily attendance sign-in sheets to ensure that all enumerators sent to the field have not missed more than two consecutive training days. The MEL Activity will conduct periodic exams to assess how well participants have understood the content of the training. Before mobilizing for fieldwork, the enumerators will engage in two days of field practice to make sure they are comfortable and confident interviewing farm households and collecting data in tablets. Trainees that do not demonstrate proficiency with survey instruments and 84 processes will not be mobilized for fieldwork. Extra enumerators will be trained to allow for some attrition. 3. Quality Assurance The MEL Activity will ensure that data quality is maintained at the highest level feasible. Specifically: • The tablet-based questionnaire will provide detailed instructions, prevent enumerators from progressing through the data collection fields if data is missing, catch values that are outside expected range or conflict with other responses, limit responses to a unique set of codes, and other measures designed to catch data entry errors in real time so that they can be addressed before completion of the interview. • Datasets will be thoroughly checked every day, and logs/records of errors and inconsistencies will be maintained and updated regularly. • The survey team leader (consultant) and the MEL Activity will prepare a list of data quality queries, and the data quality team at the research firm will check data every day based on the list. • The research firm will be responsible for ensuring and verifying that there are no backlogs in the server and the data is thoroughly checked every day for consistency. However, the MEL Activity will oversee this process and instantly communicate with the research firm in case of any inconsistencies or backlogs. • The MEL Activity will conduct periodic monitoring visits in the field to ensure that quality controllers are thoroughly checking the work of enumerators and supervisors every day, identifying issues and problems, suggesting corrective actions, and sharing learning across all enumerator teams. • If too many mistakes or inconsistencies are found in the dataset, the MEL Activity will have the right to ask enumerators to redo interviews without additional cost. Any field staff who does not maintain data quality will be replaced by one of the back-up enumerators trained to allow for attrition. 4. Data Submission After completing data collection, the MEL Activity will submit datasets at three different times: • Approximately the fourth week of October (immediately after Dashain holidays), the unanalyzed dataset will be submitted to USAID Nepal and KISAN II, including both raw and clean data in different file formats (SPSS or STATA and CSV) and the questionnaire codebook. • Approximately the first week of November (two weeks after submitting the clean dataset), the analyzed data will be submitted to USAID Nepal and presented in indicator tables, including disaggregation. • Within 90 days of submitting the final report, edit rules for cleaning data, final data dictionary (codebook), syntax for all data analysis and variable transformations, sampling 85 weights at each stage, final sampling weights used to tabulate the aggregate-level estimates for the indicators, if any, and appropriate metadata will be submitted to Development Data Library (DDL) to comply with the U.S. Government’s Open Data Policy. 5. Data Analysis and Reporting Data cleaning will begin as soon as they start uploading on the server. The MEL Activity’s statistician will clean, process, and analyze the data. Data will be exported to SPSS/Stata for analysis. Mean, median, range checks for numeric variables, and descriptive analysis for categorical variables will be performed as part of exploratory data analysis. Values for 20 key indicators will be calculated based on the indicator definition and method of calculation specified in relevant FTF PIRSs and the KISAN II MEL Plan (April 2018), and presented in the main body of the survey report. Indicators related to yields and sales will be disaggregated by commodity. Other disaggregated values will be presented in an annex (age, gender, caste/ethnicity), etc. Refer to Table A3.5 for the baseline survey report outline. Table A3.5: Baseline Survey Report Outline MAIN BODY ANNEXES • Executive summary • Acronyms • Table of contents • Background • Survey approach and methodology • Summary of indicator results • Survey findings • Conclusions • Table showing links between interview questions and indicators • Demographic tables • Indicator values tables with disaggregates as required in PIRS • Survey tools and instruments • Bibliography and interview lists • Questionnaires • Conflict of interest statements 6. Presentation on Baseline Survey Findings The team leader will provide a PowerPoint (or similar) presentation on the important findings and conclusions of the baseline survey approximately 1-2 weeks before the submission of the draft baseline survey report. Presentations may be iterative, starting with the KISAN II team followed by USAID Nepal or a joint presentation. The MEL Activity envisions the presentation to be conducted by the team leader and the MEL Activity survey team members. 86 APPENDIX IV: ADDITIONAL DATA TABLES WITH DISAGGREGATES AGRICULTURE AND RESILIENCE MODULES 5.1 GUIDE TO INTERPRETING THE DATA TABLES This appendix contains the complete data tables for all agriculture outcome indicators, including all disaggregates. Tables are organized to present the following information:  Table title. Clearly describes the data. The wording may differ from the indicator title for clarity and/or to reflect differences between the baseline sample population and USG-assisted project participants.  Performance indicator number and title. As shown in the KISAN II MEL Plan.  The number of individuals or households in the sample, disaggregated. In all cases, these are smallholder households.  Sample composition (sum=100 percent). The percentage share of each individual disaggregates within each disaggregate category (for example, 22.2 percent female and 77.8 percent male for the sex category). However, a few tables and graphs might include multiple responses that do not add up to 100%.  Indicator baseline estimates for the sample population. Baselines are calculated in accordance with the FTF Indicator Handbook and KISAN II MEL Plan. The corresponding sample-weighted population estimates are not provided in this report but are available in a separate Excel spreadsheet. Table notes. Provided as needed below the table to clarify calculation rules related to “who” and “what” counts and/or cases where double-counting is allowed. 5.2 DEMOGRAPHIC CHARACTERISTICS OF SAMPLE POPULATION The sample included 1,860 households comprised of 10,447 individuals. The following tables present demographic data on the sample population. This data is used for calculating the cross-cutting performance indicator disaggregates presented in the following sections -- related to the province, sex, education, caste/ethnicity, age, and commodities produced. Within the sample, 14 percent are youth (age 15-20), 51 percent are female, 60 percent are from a disadvantaged caste or ethnic group (Dalit, Janajati, and Muslim), 26% percent live on less than $1.90 per person per day, and 100 percent are smallholders, defined as owning or leasing 5 hectares or less of arable land or five adult female goats (does) or less (source: 2018 FTF Indicator Handbook, p. 50). The average household size is 5.6 people. As the sample focused on smallholder households (in accordance with the SOW), “firm size” disaggregates that capture the number of employees are based on the number of unique individuals hired by smallholders on a seasonal or daily wage basis (the FTF Indicator Handbook does not provide a definition for “employee” or reference “full-time equivalent”). The employee numbers, therefore, overstate the “firm size.” Data on firms will be collected in a separate firm-level baseline survey. Refer to the Handbook p. 101 for the “firm size” definition. References to “participants” or “UGS assistance” in the indicator titles apply to future Annual Results Surveys only. The table titles have been adjusted to reflect that this is a population-based survey (PBS) conducted before enrollment of project participants. In this context, what is being measured is the number of individuals or households that produce KISAN II targeted commodities who are applying technologies, producing, selling, or accessing finance, etc. 87 Household Characteristics Number of Households (1,860 HHs) Sample Composition (Sum=100%) Province Province 3 500 26.9% Province 5 840 45.2% Province 6 280 15.1% Province 7 240 12.9% Household Land Ownership Status Owned only 1,250 67.2% Leased only 52 2.8% Both 548 29.5% None 10 0.5% Average Arable Land Size 0.56 Hectares Size of Firm (Farm) None employed 939 50.5% Micro-enterprise (1-10 employees) 357 19.2% Small (11-49 employees) 450 24.2% Medium (50-249 employees) 109 5.9% Large (>250 employees) 5 0.3% Household Head Characteristics Sex Female 413 22.2% Male 1,447 77.8% Education Level Illiterate and never attended school 551 29.6% Literate but never attended school 239 12.9% Primary (1-5) 479 25.8% Secondary (6-10) 476 25.6% Higher secondary (11-12) 81 4.4% Graduate and above 34 1.8% Education level (four categories) Illiterate and never attended school 551 29.6% Literate but never attended school 239 12.8% Primary and secondary 955 51.3% Higher secondary and above 115 6.2% Caste/Ethnicity Brahmin/Chhetri 666 35.8% Dalit 194 10.4% TABLE 1. CHARACTERISTICS OF FARMS AND HOUSEHOLD HEADS 88 Household Characteristics Number of Households (1,860 HHs) Sample Composition (Sum=100%) Janajati 786 42.3% Muslim 37 2.0% Newar 74 4.0% Tarai/Madhesi 103 5.5% Demographic Characteristics Number of Individuals (10,447) Sample Composition (Sum=100%) Province Province 3 2,536 24.3% Province 5 4,878 46.7% Province 6 1,466 14.0% Province 7 1,567 15.0% Sex Female 5,336 51.1% Male 5,111 48.9% Caste Brahmin/Chhetri 3,469 33.2% Dalit 1,084 10.4% Janajati 4,542 43.5% Muslim 298 2.9% Newar 368 3.5% Tarai/Madhesi 686 6.6% Age Less than 5 822 7.9% 5 to 14 2,001 19.2% 15-24 2,418 23.1% 25 to 34 1,630 15.6% 35 to 44 1,196 11.4% 45 and above 2,380 22.8% TABLE 2. CHARACTERISTICS OF HOUSEHOLD MEMBERS 89 Demographic Characteristics Number of Individuals (10,447) Sample Composition (Sum=100%) Education level for members more than 5 years of age 9,444 Illiterate and never attended school 1,900 20.1% Literate but never attended school 708 7.5% Primary (1-5) 2,295 24.3% Secondary (6-10) 3,227 34.2% Higher secondary (11-12) 994 10.5% Graduate and above 320 3.4% Migration** Number Percent HHs with at least one migrant 421 23% Male migrants (individuals) 494 - Female migrants (individuals) 63 - HH with male migrants 400 22% HH with female migrants 56 3% **Migration includes those HH who has at least one member currently living outside Nepal and/or at least one member who has returned from outside in last 12 months The sample population varies across indicators, depending on the unit of measurement for each indicator. Subsets of the household sample are listed in descending order. Unit of Measurement Definition Number in Sample Households Family members that live together in the same dwelling. As noted earlier, the household sample was randomly selected from KISAN II’s catchment areas, based on a listing of households that produced KISAN II’s targeted commodities. 1,860 Smallholders Households with 5 ha or less arable land and 5 goats does or less – owned or leased (source: FTF Indicator Handbook p. 101) 1,860 Household Members All household members within the HH sample 10,447 Household Members >15 years Only household members age 15 years and higher 7,624 Agriculture Decision Makers (aka Farmers) Decision makers within households for at least one KISAN II targeted commodity (aka “individuals in the agriculture system”). 2,848 TABLE 3. SAMPLE POPULATIONS 90 Criteria for Vulnerable or Disadvantaged Number of HHs in Sample (of 1,860) Percentage of HHs in Sample Living on less than $1.90/day/person 488 26.2% Disadvantaged caste, ethnic, or religious group (Dalit, Janajati, or Muslim) 1,017 54.7% Affected by a natural disaster within the past 12 months 587 31.6% Total Number of Unique Households 1,458 78.4% Within the sample population of smallholder households, 74 percent produce rice, 71 percent produce maize, 33 percent produce lentil, 61 percent produce vegetables, and 74 percent produce goats. This indicates that farmers are producing multiple crops plus goats. Variations in the prevalence of individual crops across provinces reflect differences in agro-ecological conditions. TABLE 4.1 INCOME AND AVERAGE SALES BY YEAR-ROUND IRRIGATION Income and Average value of sales Year-round irrigation Yes No Income Less than $1.9 per day 198 (41%) 290 (59%) More than $1.9 per day 607 (45%) 743 (55%) Average value of sales $ 429.3 $ 393.1 TABLE 4. VULNERABLE POPULATIONS 91 Commodity and Province Number of Households (1,860) Sample Composition (Sum=100%) Percentage of HHs That Produced Each Commodity Number of Farmers (2,560) Rice Province 3 315 22.8% 63.2% Province 5 653 47.2% 77.7% Province 6 185 13.4% 66.1% Province 7 230 16.6% 95.8% Rice Total 1,383 100% 74.4% 1,383 Maize Province 3 459 35.0% 91.8% Province 5 483 36.8% 57.5% Province 6 252 19.2% 90.0% Province 7 117 8.9% 48.8% Maize Total 1,311 100% 70.5% 1,311 Lentil Province 3 8 1.3% 1.6% Province 5 401 66.6% 47.7% Province 6 82 13.6% 29.3% Province 7 111 18.4% 46.3% Lentil Total 602 100% 32.4% 602 Vegetables Province 3 270 23.7% 54.0% Province 5 494 43.3% 58.8% Province 6 184 16.1% 65.7% Province 7 192 16.8% 80.0% Vegetables Total 1,140 100% 61.3% 1,334 Goat Province 3 314 29.7% 62.8% Province 5 454 43.0% 54.0% Province 6 171 16.2% 61.1% Province 7 117 11.1% 48.8% Goat Total 1,056 100% 56.8% 1,056 Disaggregates Number of Hectares Sample Composition (Sum=100%) Province Province 3 231.4 20.3% Province 5 623.1 54.7% Province 6 117.2 10.3% TABLE 5. NUMBER AND DISTRIBUTION OF HOUSEHOLDS AND FARMERS BY COMMODITY AND PROVINCE TABLE 6. NUMBER AND DISTRIBUTION OF HECTARES BY COMMODITY AND PROVINCE 92 Disaggregates Number of Hectares Sample Composition (Sum=100%) Province 7 168.4 14.8% Commodity Rice 660.0 57.9% Maize 264.4 23.2% Lentil 113.1 9.9% Vegetables 102.6 9.0% Total 1140.1 100.0% 5.3 TECHNOLOGY ADOPTION PERFORMANCE INDICATORS The following tables present information on the number of households or individuals applying improved management practices or technologies (MPTs) (Tables 5-11) and the number of hectares on which MPTs have been applied (Tables 12-13). Total of 2848 individuals applied at least one MPT. Note that “double counting” of individuals occurs across disaggregates because individuals can be counted under more than one commodity and under more than one MPT type. Also, individuals can be counted more than once for the same individual MPT if it falls under more than one type. Double counting does not occur within demographic disaggregates. Indicator K4/Nepal 2.1.1-2/EG.3.2-24: Number of individuals in the agriculture system who have applied improved management practices or technologies with USG assistance (IM￾Level) Indicator K19/EG.3.2-24 subset: Marketing and distribution Aggregate Results Number of Farmers Who Applied an MPT (of 2,560) Total number of unique farmers applying at least one MPT 2,848 Commodity Disaggregates Rice 1,913 Maize 1,598 Lentil 723 Vegetables 1,551 Goats 1,544 MPT Type Categories Crop genetics 2,519 Cultural practices 1,294 Livestock management 1,157 Pest and disease management 1,166 Soil-related fertility and conservation 2,136 Irrigation 2,358 Agriculture water management (non-irrigation based) 1,574 Climate mitigation 311 TABLE 7. NUMBER OF INDIVIDUALS WHO HAVE APPLIED IMPROVED MANAGEMENT PRACTICES OR TECHNOLOGIES (MPT), COUNTED AT THE TYPE CATEGORY LEVEL 93 Indicator K4/Nepal 2.1.1-2/EG.3.2-24: Number of individuals in the agriculture system who have applied improved management practices or technologies with USG assistance (IM￾Level) Indicator K19/EG.3.2-24 subset: Marketing and distribution Climate adaptation/climate risk management 1,486 Information and communications technologies (ICT) 1,361 Marketing and distribution 4,771 Post-harvest handling and storage 88 Value-added processing 1,663 Other 1,166 Demographic Disaggregates Number of Farmers Who Applied an MPT (2,848) Percentage of Total Who Applied (Sum=100%) Value Chain Actor Type Smallholder Producers 2,848 100% Non-Smallholder Producers 0 0% People in Government 0 0% People in Private Sector Firms 0 0% People in Civil Society 0 0% Others 0 0% Age 15 to 29 (Youth) 363 12.7% 30 to 45 1,189 41.7% 46 to 59 905 31.8% Above 59 391 13.7% Sex Female 1,484 52.1% Male 1,364 47.9% Caste-ethnicity Brahmin/Chettri 1,027 36.1% Dalit 292 10.3% Janajati 1,216 42.7% Muslim 51 1.8% Newar 109 3.8% Others (Terai/Madhesi) 153 5.4% Single-Counting Rules (refer above table for double-counting rules):  Count everyone who applies an MPT.  Count individuals no more than once per year for gender, age, and commodity disaggregates.  Individuals applying an MPT Type more than once in the same year can only be counted once for the MPT Type Category.  Count an individual once per Type Category regardless of how many specific MPTs within it they applied. Types of Individuals  Farmers, producers.  Private sector: entrepreneurs, input suppliers, traders, processors, manufacturers, distributors, service providers, wholesalers, and retailers.  Government: policy makers, extension workers, and natural resource managers.  Civil society: researchers, academics, NGO, and CBO staff. 94 Almost all households apply at least one MPT (99.4 percent). Table 8 shows the average number of individual (unique) MPTs applied per household to better assess behavior change related to the increased application of improved MPTs. Indicator K5/Custom I: Average number of improved management practices or technologies applied per household with USG assistance. Commodity and Type Disaggregates Number of Households Who Applied an MPT (of 1,860) Potential Number of MPTs Avg. Number (±SD) of Individual MPTs Applied Per HH Aggregate Results Total Number of Unique Households and MPTs 1,849 63 11.5 (±6.5) Disaggregates Commodity Rice 1,383 49 6.7 (±3.3) Maize 1,260 49 3.8 (±2.5) Lentil 575 48 3.3 (±2.3) Vegetables 1,133 57 10 (±6) Goats 1,012 27 3.7 (±2.1) MPT Type Category Crop genetics 1477 2 1.4 Cultural practices 1101 4 2 Livestock management 1160 6 2.5 Pest and disease management 928 4 2 Soil related fertility and conservation 1154 7 1.6 Irrigation 1459 4 1.3 Agriculture water management (non-irrigation based) 1036 3 1.1 Climate mitigation 222 2 1 Climate adaptation/climate risk management 929 7 1.8 Information and communications technologies (ICT) 199 6 1.4 Marketing and distribution 691 6 1.5 Post-harvest handling and storage 1684 6 1.7 Value-added processing 62 1 1 Other 692 5 1.7 Demographic Disaggregates Province Disaggregates Province 3 499 11.6 (±6.7) Province 5 832 10.4 (±5.9) Province 6 278 11.6 (±7.3) Province 7 240 14.4 (±6.1) Sex (HH Head) Female 409 10.7 (±6.1) TABLE 8. AVERAGE NUMBER OF IMPROVED MPTS APPLIED PER HOUSEHOLD, COUNTED AT THE INDIVIDUAL MPT LEVEL 95 Indicator K5/Custom I: Average number of improved management practices or technologies applied per household with USG assistance. Commodity and Type Disaggregates Number of Households Who Applied an MPT (of 1,860) Potential Number of MPTs Avg. Number (±SD) of Individual MPTs Applied Per HH Male 1,440 11.7 (±6.6) Caste (HH Head) Brahmin/Chhetri 664 12.9 (±6.9) Dalit 193 11.4 (±7.1) Janajati 779 10.7 (±5.9) Muslim 37 6.2 (±3.3) Newar 74 12.2 (±6.8) Tarai/Madhesi 102 9.4 (±5.2) Age (HH Head) 15-24 31 10.1 (±6.8) 25 to 34 270 11.1 (±6.6) 35 to 44 434 11.7 (±6.3) 45 and above 1,114 11.5 (±6.6) Education Level (HH Head) Illiterate and never attended school 545 9.9 (±5.5) Literate but never attended school 238 11.8 (±6.7) Primary (1-5) 478 11.5 (±6.6) Secondary (6-10) 474 12.5 (±6.8) Higher secondary (11-12) 81 14.9 (±7.3) Graduate and above 33 12.4 (±7.5) Use of MPT by ZOI ZOI 1 1,350 11.4 (±6.7) ZOI 2 499 11.6 (±6.7) Use of ICT by ZOI ZOI 1 137 10% ZOI 2 63 13% Use of Irrigation by ZOI ZOI 1 1,125 83% ZOI 2 329 66% Use of Agriculture water management (non￾irrigation based) ZOI 1 809 60% ZOI 2 224 45% 96 TABLE 8.1: AVERAGE NUMBER OF INDIVIDUAL MPTS APPLIED PER HOUSEHOLD DISAGGREGATED BY COMMODITY AND TECHNOLOGY TYPE Technology type Rice (n=1383) Maize (n=1311) Lentil (n=602) Vegetables (n=1334) Goat (n=1056) % HH applying tech Average sub-tech applied % HH applying tech Average sub-tech applied % HH applying tech Average sub-tech applied % HH applying tech Average sub-tech applied % HH applying tech Average sub-tech applied Crop Genetics 66% 1.1 45% 1.0 15% 1.0 69% 1.2 - - Cultural Practices 26% 1.0 0% - 0% - 70% 2.2 - - Pest and disease management 29% 1.7 5% 1.6 3% 1.4 51% 2.0 - - Soil fertility and conservation 51% 1.3 45% 1.3 22% 1.2 53% 1.6 - - Irrigation (drip, surface, sprinkler) 89% 1.0 13% 1.0 16% 1.0 65% 1.2 - - Agriculture water management 51% 1.0 10% 1.0 9% 1.1 52% 1.1 - - Climate mitigation 9% 1.0 3% 1.0 4% 1.0 9% 1.0 - - Climate adaptation 43% 1.4 27% 1.4 18% 1.2 32% 1.7 - - Improved ICT channels or content 7% 1.3 6% 1.3 4% 1.1 10% 1.4 7% 1.3 Marketing and distribution 31% 1.4 20% 1.2 28% 1.3 29% 1.6 11% 1.3 Post-harvest handling and storage 88% 1.4 81% 1.4 88% 1.4 71% 1.6 97% 1.1 Value-added processing 1% 1.0 1% 1.0 2% 1.0 3% 1.0 0% - Other 34% 1.5 26% 1.4 30% 1.5 31% 1.8 25% 1.5 Livestock management - - - - - - - - 110% 2.5 97 Tables 9 and 10 provide additional disaggregate data for the two climate-smart technology type categories in EG.3.2-24 (Table 7). Just over half of households in the sample (52.3 percent) applied a climate-smart technology or practice. Of these, they applied an average of 1.9 climate-smart technologies or practices per household. The average number did not vary substantially across commodities (1.3 to 1.8). TABLE 9. AVERAGE NUMBER OF CLIMATE-SMART TECHNOLOGIES OR PRACTICES (CSTPS) APPLIED PER HOUSEHOLD, COUNTED AT THE INDIVIDUAL MPT LEVEL The following indicator was dropped by FTF in April 2018 and replaced with EG.3.2-28 to focus on the number of hectares rather than the number of individuals (refer to Table 15). It is included here because it remains in the KISAN II MEL Plan. K46 (EG.11-6, Nepal 2.1.1-2, EG.3.2-17): Number of people using climate information or implementing risk￾reducing actions to improve resilience to climate change as supported by USG assistance Aggregate Results Number of Farmers in Sample Number of Farmers Addressing Climate Risk Percentage of Farmers Addressing Climate Risk Households 1,860 972 52.3% Unique Producers 2,560 1,064 41.6% Commodity Disaggregates Rice 1,383 638 46% Maize 1,311 373 28% Lentil 602 128 21% Vegetables 1,334 474 36% K13/Custom 1: Average number of climate-smart technologies or practices applied per household with USG assistance. Number of HHs that Applied a CSTP Number of HHs Percentage of HHs that Applied a CSTP Average No. of CSTPs per HH (of 1,860) Aggregate Results Total Number of Unique HHs and CSTPs 972 1,860 52.3% 1.9 (±1.2) Commodity Disaggregates Rice (out of 6) 627 1,383 45.3% 1.6 (±0.8) Maize (out of 6) 369 1,311 28.1% 1.5 (±0.7) Lentil (out of 6) 125 602 20.8% 1.3 (±0.5) Vegetables (out of 9) 466 1,140 40.9% 1.8 (±1.1) TABLE 10. NUMBER OF FARMERS USING CLIMATE INFORMATION OR IMPLEMENTING RISK-REDUCING ACTIONS TO IMPROVE RESILIENCE TO CLIMATE CHANGE 98 Demographic Disaggregates of Decision Maker Number of Farmers Sample Composition (Sum=100%) Percentage of Farmers Addressing Climate Risk Age 15 to 29 (Youth) 100 9.4% 3.0% 30 to 45 463 43.5% 21.5% 46 to 59 359 33.7% 28.0% Above 59 142 13.3% 16.5% Sex Female 386 36.3% 9.9% Male 678 63.7% 18.2% Table 11 provides additional data for the marketing and distribution technology type category in EG.3.2-24 (Table 5). 99 K19/Nepal 2.1.1-2/EG.3.2-24 Subset: Number of Individuals in the agriculture system who have applied improved management practices or technologies with USG assistance – related to marketing and distribution Aggregate Result Number of Farmers in Sample Number of Farmers Who Applied Percentage of Farmers Who Applied Total Number of Unique Households and MPTs 2,560 744 29.1% Commodity Rice 1,383 431 31.2% Maize 1,311 257 19.6% Lentil 602 166 27.6% Vegetables 1,334 386 28.9% Goats 1,056 121 11.5% Table 12 provides additional data for the ICT technology type category in EG.3.2-24 (Table 7). Only 8.1 percent of farmers access extension information through ICT channels. Note that this is much lower than the number of farmers using ICT reported in Table 7 because Table 7 captures all uses of ICT (such as checking market prices), not only accessing information on improved MPTs. K20/Custom 2: Percentage of USG-assisted farmers accessing information on improved technologies and practices through improved ICT channels or content Aggregate Results Number of Farmers in Sample Number of Farmers Accessing Info via ICT Percentage of Farmers Accessing Info via ICT Number of Unique Farmers 2,560 207 8.1% Commodity Disaggregates Rice 1,383 94 6.8% Maize 1,311 74 5.6% Lentil 602 25 4.2% Vegetables 1,334 137 10.3% Goats 1,056 76 7.2% Demographic Disaggregates of Decision Maker Number of Farmers Accessing Info via ICT (207) Composition of Farmers Accessing Info (Sum=100%) Percentage of Farmers Accessing Info via ICT Age 15 to 29 (Youth) 22 10.6% 7.6% 30 to 45 87 42.0% 8.2% 46 to 59 79 38.2% 9.6% Above 59 19 9.2% 4.9% Sex TABLE 11. NUMBER OF FARMERS WHO HAVE APPLIED IMPROVED MPTS RELATED TO MARKETING AND DISTRIBUTION TABLE 12. PERCENTAGE OF FARMERS ACCESSING INFORMATION ON IMPROVED MPTS THROUGH IMPROVED ICT CHANNELS OR CONTENT 100 Female 57 27.5% 4.7% Male 150 72.5% 11.1% Table 13 provides additional information on food grading and safety -- a subset of the post-harvest handling and storage MPT type category in EG.3.2-24 (Table 7). Note that the indicator title in the table has been edited (from how it is presented in the KISAN II MEL Plan) to match the wording for EG.3.2-24, which was modified in the 2018 FTF Indicator Handbook. TABLE 13. NUMBER OF FARMERS WHO APPLIED IMPROVED MPTS RELATED TO FOOD GRADING AND SAFETY K 31/Nepal 2.1.1-2/EG.3.2-24 Subset: Number of Individuals in the agriculture system who have applied improved management practices or technologies with USG assistance – related to food grading or safety Aggregate Results Number of Farmers in Sample Number of Farmers Who Applied Percentage of Farmers Who Applied Number of Unique Farmers 2,560 1,946 76.0% Commodity Disaggregates Rice 1,383 1,222 88.4% Maize 1,311 1,058 80.7% Lentil 602 528 87.7% Vegetables 1,334 944 70.8% Goats 1,056 1,019 96.5% Demographic Disaggregates of Decision Maker Number of Farmers Who Applied (1,946) Composition of Farmers Who Applied (Sum=100%) Percentage of Farmers Who Applied Age 15 to 29 (Youth) 227 11.7% 6.8% 30 to 45 851 43.7% 39.5% 46 to 59 630 32.4% 49.2% Above 59 238 12.2% 27.6% Sex Female 1,079 55.4% 27.6% Male 867 44.6% 23.3% Caste/Ethnicity Brahmin/Chettri 710 36.5% 27.6% Dalit 205 10.5% 27.9% Janajati 810 41.6% 24.1% Muslim 39 2.0% 21.3% Newar 72 3.7% 23.5% Others (Terai/Madhesi) 110 5.7% 23.7% Size of Producers None employed 985 50.6% 25.2% Micro-enterprise (1-10 employees) 377 19.4% 27.8% Small (11-49 employees) 464 23.8% 25.3% Medium (50-249 employees) 113 5.8% 22.3% Large (>250 employees) 7 0.4% 31.8% 101 Tables 14 and 15 focus on the number of hectares under improved MPT. No data was collected for the “association-applied” disaggregates, as this is outside the SOW for the household baseline survey. Indicator K8/Nepal 2.1.1-1/EG.3.2-25: Number of hectares under improved management practices or technologies with USG assistance (IM-Level) Aggregate Results No. of Hectares Cultivated No. of Ha with an Improved MPT Percentage of Ha With an Improved MPT Number of unique hectares with at least one MPT applied 1,140 1,138.6 99.8% Type of Hectare Disaggregates Crop land 1,140 1,138.6 100% Cultivated pasture 0 0 0% Rangeland 0 0 0% Conservation and mixed-used landscapes 0 0 0% Freshwater or marine ecosystems, aquaculture 0 0 0% Other 0 0 0% Commodity Disaggregates Rice 1,369 648.5 57.0% Maize 1,004 201.7 17.7% Lentil 317 52.0 4.6% Vegetables 1,118 92.1 8.1% MPT Type Categories (Land-Based Only) Crop genetics 1,477 623.1 54.7% Cultural practices 1,101 121.4 10.7% Pest and disease management 928 487.2 42.8% Soil related fertility and conservation 1,154 713.9 62.7% Irrigation 1,459 715.0 62.8% Agriculture water management (not irrigation) 1,036 434.1 38.1% Climate mitigation 222 70.1 6.2% Climate adaptation/climate risk management 929 495.6 43.5% Demographic Disaggregates of Decision Maker No. of Individuals Who Applied an MPT (2,359) No. of Ha with an Improved MPT Composition of Individuals Who Applied an MPT (Sum=100%) Age 15 to 29 (Youth) 270 80.1 7.0% 30 to 45 987 448.5 39.4% 46 to 59 777 445.7 39.1% Above 59 325 164.3 14.4% Association-Applied Not measured Not measured Not measured TABLE 14. NUMBER OF HECTARES UNDER IMPROVED MPT 102 Indicator K8/Nepal 2.1.1-1/EG.3.2-25: Number of hectares under improved management practices or technologies with USG assistance (IM-Level) Aggregate Results No. of Hectares Cultivated No. of Ha with an Improved MPT Percentage of Ha With an Improved MPT Sex Female 984 310.2 27.2% Male 1,375 828.4 72.8% Association-Applied Not measured Not measured Not measured Caste/Ethnicity Brahmin/Chettri 862 331.6 29.1% Dalit 246 78.0 6.9% Janajati 999 564.0 49.5% Muslim 38 34.0 3.0% Newar 88 32.3 2.8% Others (Terai/Madhesi) 126 98.8 8.7% Notes to implementer: Yearly totals should not be summed to count application by unique individuals over the life of the project. Also, this indicator captures results where they were achieved, regardless of whether interventions were carried out, and results achieved, in the ZOI. Table 15 provides additional disaggregate information for the two climate-smart technology type categories in EG.3.2-25 (Table 12). Indicator K12/EG.3.2-28: Number of hectares under improved management practices or technologies that promote improved climate risk reduction and/or natural resources management with USG assistance (IM-Level) Aggregate Results No. of Hectares Planted (1,064) No. of Hectares Planted with an MPT Percentage of Hectares Planted with an MPT Number of unique hectares with at least one MPT applied related to climate or NRM 1,140 333.9 29.3% Type of Hectare Disaggregates Crop land 1,140 332.6 100% Cultivated pasture 0 0 0% Rangeland 0 0 0% Conservation and mixed-used landscapes 0 0 0% Freshwater or marine ecosystems, aquaculture 0 0 0% Other 0 0 0% Commodity Disaggregates Rice 638 258.6 40.5% Maize 373 69.4 18.6% TABLE 15. NUMBER OF HECTARES UNDER IMPROVED MPT THAT PROMOTE IMPROVED CLIMATE RISK REDUCTION AND/OR NATURAL RESOURCES MANAGEMENT 103 Lentil 128 21.8 17.0% Vegetables 474 43.2 9.1% MPT Type Disaggregates Climate Adaptation/Climate Risk Management 1,140 310.6 27.2% Climate Mitigation 1,140 62.8 5.5% Demographic Disaggregates of Decision Maker No. of Farmers Who Applied an MPT (1,064) No. of Hectares With an MPT Composition of Hectares (Sum=100%) Age 15 to 29 (Youth) 100 21.1 6.3% 30 to 45 463 136.0 40.7% 46 to 59 359 132.2 39.6% Above 59 142 44.6 13.4% Sex Female 385 80.3 24.0% Male 679 253.6 76.0% Caste/Ethnicity Brahmin/Chettri 398 95.7 28.7% Dalit 104 21.3 6.4% Janajati 431 164.3 49.2% Muslim 16 10.0 3.0% Newar 49 10.6 3.2% Others (Terai/Madhesi) 66 31.9 9.6% 5.4 Production, Yield, and Sales Indicators Farmers produced 4,036 MT of the five commodities targeted by KISAN II. Rice accounts for most of this volume (57 percent). Maize and vegetables are also significant (13 and 27 percent of total volume, respectively). Lentil and goats comprised less than 2 percent of total volume each. Indicator K9/Reporting 1: Total farm-level volumes (MT) produced of targeted agricultural commodities with USG assistance Aggregate Results Number of HHs Volume (MT) Percentage of Total Volume Average Kg Per HH Total produced 1,860 4,035.6 2,169.7 Commodity Disaggregates All Commodities Rice 1,383 2,305.1 57.1% 1666.7 Maize 1,311 517.7 12.8% 394.9 Lentil 602 62.3 1.5% 103.5 Vegetables 1,140 1,096 27.2% 961.4 Goats 1,056 54.5 1.4% 76.5 Vegetable Disaggregates Vegetables Only Bitter Gourd 180 41.0 3.7% 227.8 Bottle Gourd 165 20.5 1.9% 124.2 Broccoli 10 1.9 0.2% 190.0 TABLE 16. FARM-LEVEL VOLUMES (MT) PRODUCED OF TARGETED AGRICULTURAL COMMODITIES 104 Cabbage 399 175.1 16.0% 438.8 Capsicum 60 71.0 6.5% 1,183.3 Carrot 3 0.1 0.0% 33.3 Cauliflower 447 245.1 22.4% 548.3 Chilli 153 40.4 3.7% 264.1 Cucumber 227 90.7 8.3% 399.6 Eggplant 70 14.2 1.3% 202.9 French bean 129 19.6 1.8% 151.9 Long bean 152 14.3 1.3% 94.1 Okra 130 11.5 1.0% 88.5 Onion 232 19.4 1.8% 83.6 Peas 48 5.1 0.5% 106.3 Pumpkin 202 25.5 2.3% 126.2 Radish 217 42.4 3.9% 195.4 Spinach 160 20.3 1.9% 126.9 Sponge gourd 106 14.3 1.3% 134.9 Tomato 368 214.0 19.5% 581.5 Other vegetables 71 9.6 0.9% 135.2 Demographic Disaggregates of Decision Maker Number of Farmers Volume (MT) Farmers’ Contributio n to Total Volume (Sum=100%) Age 15 to 29 (Youth) 289 277.9 6.9% 30 to 45 1,059 1,579.9 39.1% 46 to 59 827 1,628.4 40.4% Above 59 385 549.4 13.6% Sex Female 1,210 1,016.1 25.2% Male 1,350 3,019.5 74.8% Caste/Ethnicity Brahmin/Chettri 943 1,230.7 30.5% Dalit 257 232.2 5.8% Janajati 1,077 1,968.1 48.8% Muslim 47 96.5 2.4% Newar 105 154.4 3.8% Others (Terai/Madhesi) 131 353.7 8.8% Indicator K10/EG.3-10 (crops) and -12 (livestock): Yield of targeted agricultural commodities among program participants with USG assistance (IM-Level) Commodities Number of Hectares Average Yield Rice 1,383 3.5 MT/Ha Maize 1,311 2.0 MT/Ha TABLE 17. YIELD OF TARGETED AGRICULTURAL COMMODITIESULTURAL COODITIES 105 Indicator K10/EG.3-10 (crops) and -12 (livestock): Yield of targeted agricultural commodities among program participants with USG assistance (IM-Level) Lentil 602 0.6 MT/Ha Vegetables 1,334 10.7 MT/Ha Goats 1,056 6.3 Kg/Goat Vegetable Disaggregates MT/Ha Bitter Gourd 180 12.9 Bottle Gourd 165 15.3 Broccoli 10 6.1 Cabbage 399 14.4 Capsicum 60 16.3 Carrot 3 6.0 Cauliflower 447 11.6 Chilli 153 8.1 Cucumber 227 11.7 Eggplant 70 15.0 French bean 129 5.3 Long bean 152 2.3 Okra 130 6.0 Onion 232 5.4 Peas 48 2.1 Pumpkin 202 9.9 Radish 217 8.1 Spinach 160 8.2 Sponge Gourd 106 13.9 Tomato 368 14.4 Other vegetables 71 4.1 Farm Size Disaggregates Number of Producers Average Total Yield for All Crops (MT/ha) Smallholder 2,560 3.5 Non-smallholder 0 n/a Goat Production Systems Number of Producers Average Total Yield For Goats (Kg/Goat) Agro-pastoral/extensive grassland 0 n/a Small holder mixed livestock-crop 1,056 6.3 Kg/Goat Urban/peri-urban 0 n/a Intensive industrial 0 n/a Demographic Disaggregates of Decision Maker Number of Producers (2,560) Average Total Yield for All Crops (MT/ha) Age 15 to 29 (Youth) 289 4.0 30 to 45 1,059 3.6 46 to 59 827 3.5 Above 59 385 3.1 Sex Female 1,210 3.1 106 Indicator K10/EG.3-10 (crops) and -12 (livestock): Yield of targeted agricultural commodities among program participants with USG assistance (IM-Level) Male 1,350 3.6 Caste/Ethnicity Brahmin/Chettri 943 3.7 Dalit 257 3.2 Janajati 1,077 3.3 Muslim 47 2.8 Newar 105 4.9 Others (Terai/Madhesi) 131 4.1 Table 18 presents the value of annual sales for farms only, as firms are outside the SOW for the household baseline survey. Of the 2,560 individuals who produced commodities targeted by KISAN II, 1,750 individuals sold (66 percent). Average annual sales for women ($199) were less than half of men’s ($442). Indicator K17/EG.3.2-26: Value of annual sales of farms and firms receiving USG assistance (conversion rate: NPR 104.4/USD) Aggregate Result Number of HHs That Sold Annual Sales (USD) Contribution to Total Sales (Sum=100%) Average Annual Sales per HHs That Sold Total Farm Sales 1,412 $577,043 $408.67 Commodity Disaggregates All Commodities Rice 457 $147,128 25.5% $321.94 Maize 246 $16,676 2.9% $67.79 Lentil 175 $12,806 2.2% $73.18 Vegetables 770 $257,892 44.7% $334.92 Goat 788 $142,540 24.7% $180.89 Vegetable Disaggregates Veg Sales Only Bitter Gourd 122 $9,045 4.9% Bottle Gourd 81 $2,247 1.2% Broccoli 5 $602 0.3% Cabbage 264 $26,361 14.4% Capsicum 59 $36,011 19.6% Carrot 0 n/a Cauliflower 309 $64,104 34.9% Chilli 134 $15,546 8.5% Cucumber 152 $18,717 10.2% Eggplant 43 $2,651 1.4% French bean 86 $6,209 3.4% Long bean 88 $2,994 1.6% Okra 64 $2,027 1.1% Onion 74 $1,110 0.6% Peas 25 $963 0.5% TABLE 18. VALUE OF ANNUAL SALES (FARMS ONLY) 107 Pumpkin 56 $1,915 1.0% Radish 90 $5,182 2.8% Spinach 66 $4,049 2.2% Sponge gourd 30 $2,095 1.1% Tomato 314 $52,266 28.5% Other vegetables 43 $3,798 2.1% Demographic Disaggregates of Decision Maker Number of Farmers Annual Sales (USD) Farmers’ Contribution to Total Sales (Sum=100%) Average Annual Sales per Farmer That Sold Age 15 to 29 (Youth) 200 $47,654 8.3% $238.27 30 to 45 747 $225,743 39.1% $302.20 46 to 59 573 $236,041 40.9% $411.94 Above 59 230 $67,605 11.7% $293.93 Sex Female 807 $160,522 27.8% $198.91 Male 943 $416,521 72.2% $441.70 Caste-ethnicity Brahmin/Chettri 675 $242,374 38.6% $359.07 Dalit 154 $44,599 8.8% $289.60 Janajati 718 $209,477 41.0% $291.75 Muslim 23 $7,686 1.3% $334.17 Newar 85 $27,847 4.9% $327.61 Others (Terai/Madhesi) 95 $45,061 5.4% $474.33 Size of Firm (Farm) None employed 828 $188,042 32.6% $227.1 Micro-enterprise (1-10 employees) 317 $72,879 12.6% $229.9 Small (11-49 employees) 466 $173,880 30.1% $373.1 Medium (50-249 employees) 132 $128,949 22.3% $976.9 Large (>250 employees) 7 $13,292 2.3% $1,898.9 Households sold 1,634 MT of commodities targeted by KISAN II. 108 Indicator K18: Volume of annual sales (MT) of farms receiving USG assistance Aggregate Results Number of HHs That Sold Annual Sales (MT) Contributio n to Sales (Sum=100%) Average Annual Sales Volume per HH That Sold (Kg) Totals 1,412 1,634.1 - 1157.3 Commodity Disaggregates Rice 457 607.9 37.2% 1330.2 Maize 246 75.6 4.6% 307.3 Lentil 175 25.3 1.5% 144.6 Vegetables 770 890.0 54.5% 1155.8 Goat 788 35.3 2.2% 44.8 Vegetables Veg Only Bitter Gourd 122 33.1 3.7% 271.3 Bottle Gourd 81 10.6 1.2% 130.9 Broccoli 5 1.4 0.2% 280.0 Cabbage 264 142.9 16.1% 541.3 Capsicum 59 68.1 7.6% 1154.2 Carrot 0 0.0 0.0% - Cauliflower 309 221.9 24.9% 718.1 Chilli 134 35.1 3.9% 261.9 Cucumber 152 74.4 8.4% 489.5 Eggplant 43 11.6 1.3% 269.8 French bean 86 15.3 1.7% 177.9 Long bean 88 8.9 1.0% 101.1 Okra 64 6.4 0.7% 100.0 Onion 74 3.6 0.4% 48.6 Peas 25 1.9 0.2% 76.0 Pumpkin 56 9.6 1.1% 171.4 Radish 90 28.2 3.2% 313.3 Spinach 66 13.4 1.5% 203.0 Sponge Gourd 30 9.7 1.1% 323.3 Tomato 314 187.1 21.0% 595.9 Other vegetables 43 7.0 0.8% 162.8 Demographic Disaggregates of Decision Marker Number of Households That Sold (1,750) Annual Sales (MT) Contri￾bution to Sales Volume (Sum=100% ) Average Annual Sales Volume per HH That Sold (Kg) Age 15 to 29 (Youth) 200 128.7 7.9% 643.5 30 to 45 747 643.8 39.4% 861.8 TABLE 19. VOLUME OF ANNUAL SALES 109 Indicator K18: Volume of annual sales (MT) of farms receiving USG assistance Aggregate Results Number of HHs That Sold Annual Sales (MT) Contributio n to Sales (Sum=100%) Average Annual Sales Volume per HH That Sold (Kg) 46 to 59 573 686.4 42.0% 1197.9 Above 59 230 175.2 10.7% 761.7 Sex Female 807 326.6 20.0% 404.7 Male 943 1,307.5 80.0% 1,386.5 Caste-ethnicity Brahmin/Chettri 675 543.8 38.5% 805.6 Dalit 154 100.0 8.8% 649.4 Janajati 718 685.3 41.0% 954.5 Muslim 23 27.3 1.3% 1,187.0 Newar 85 101.1 4.9% 1,189.4 Others (Terai/Madhesi) 95 176.7 5.4% 1,860.0 Size of Firm None employed 828 541.1 33.1% 653.5 Micro-enterprise (1-10 employees) 317 202.6 12.4% 639.1 Small (11-49 employees) 466 425.6 26.0% 913.3 Medium (50-249 employees) 132 414.9 25.4% 3,143.2 Large (>250 employees) 7 50.0 3.1% 7,142.9 Over 70 percent of households in the sample produced 466 MT of nutrient-rich commodities targeted by KISAN II. Over half of households set aside some for home consumption, but only 12.5 percent of the total volume produced. 110 K4/Custom 5: Quantity of nutrient-rich value chain commodities produced by direct beneficiaries with USG assistance that is set aside for home consumption (MT) Aggregate Results Number of Households Quantity (MT) Percentage of Sample (1,860 HHs) Number of households producing nutrient￾rich commodities 1,326 466.3 71.3% Number of households that set aside nutrient rich commodities for home consumption 1,050 58.5 56.5% Nutrient-Rich Commodities Number of Households Consuming Quantity Set Aside (MT) Percentage of Production (58.5 MT) Bitter Gourd 177 4.3 7.4% Broccoli 10 0.3 0.5% Cabbage 389 13.5 23.1% Capsicum 57 0.8 1.4% Carrot 3 0.1 0.2% Cauliflower 441 13.7 23.4% French bean 127 2.6 4.4% Long bean 148 3.7 6.3% Okra 129 3.5 6.0% Pumpkin 200 7.1 12.1% Spinach 158 4.5 7.7% Goats 317 4.5 7.7% TABLE 20. QUANTITY OF NUTRIENT-RICH COMMODITIES SET ASIDE FOR HOME CONSUMPTION 111 5.5 Access to Finance Indicators Table 21 features output indicators, for which the baselines are zero by definition. The data is provided for reference only. Of 2,848 farmers (agriculture decision makers) in the sample, 50.5 percent participate in group-based savings, microfinance, or lending programs (the FTF definition for having “access to productive resources”). Of these, 73.2 percent are women and 22.4 percent are youth. Indicator K24/EG.4.2-7: Number of individuals participating in group-based savings, microfinance or lending programs with USG assistance (IM-level output) Total Number of Farmers Who Participate Percentage of Farmers Who Participate (N=2,848) Number of unique individual farmers 1,437 50.5% Financial Product Disaggregates Saving 2,436 - Credit 823 - Indicator K6/GNDR-2: Percentage of female participants in USG assisted programs designed to increase access to productive resources (IM-level output) Indicator K7/YOUTH-3: Percentage of participants in USG-assisted programs designed to increase access to productive economic resources who are youth (15-29) (IM-Level output) Demographic Disaggregates Number of Farmers Saving Composition of Savers (Sum=100%) Percentage of Farmers Who Save Age 15 to 29 (YOUTH-3) 322 22.4% 9.7% 30+ 1,115 77.6% 26.0% Sex Female (GNDR-2) 1,052 73.2% 26.9% Male 385 26.8% 10.4% Note that annual results data for indicator K24 will include duration disaggregates: “new” and “continuing.” They are omitted from the above table because they are not relevant for baseline data. Approximately 80 percent of smallholder households in the sample have savings deposits, averaging USD 132 per household. TABLE 21. NUMBER OF FARMERS PARTICIPATING IN GROUP-BASED SAVINGS, MICROFINANCE OR LENDING PROGRAMS 112 Of 2,858 farmers in the sample, 253 (9.9 percent) have access to agriculture-related financing. Indicator K23/EG.3.2-27: Value of agriculture-related financing (USD) accessed as a result of USG assistance (IM-Level) Total Number with Access Value of Financing (USD) Number of Ag Decision Makers in Sample Percentage of Ag DMs with Access Unique individuals (borrowers) 253 $130,345 2,848 11.3% Unique households 236 $130,345 1,860 12.7% Finance Disaggregates Number of Borrowers Value (USD) Contribution (Sum=100%) Type of Financing Debt 252 $130,259 99.9% Non-debt 1 $86 0.1% Type of Debt TABLE 22. VALUE OF HOUSEHOLD SAVINGS DEPOSITS OF SMALLHOLDERS Indicator K25/Reporting 4: Value of household savings deposits of USG-assisted smallholders Total Number of Households With Savings Value (USD) of Savings Percentage of HH Sample That Saves Average Savings per Househol d Households with saving deposits 1,498 $285,821 80.5% $132 Demographic Disaggregates of Decision Makers Number of Individuals Saving (2,182) Value (USD) of Savings Contributio ns to Total Savings (Sum=100%) % of Ag Decision Makers in Sample (2,848) Age 15 to 29 (Youth) 474 $46,738 16.4% 14.2% 30 to 45 996 $158,610 55.5% 46.3% 46 to 59 546 $58,017 20.3% 42.6% Above 59 166 $22,457 7.9% 19.2% Sex Female 1,572 $143,815 50.3% 40.2% Male 610 $142,006 49.7% 16.4% Caste/Ethnicity Brahmin/Chettri 823 $134,649 47.1% 32.0% Dalit 211 $15,875 5.6% 28.7% Janajati 976 $102,805 36.0% 29.0% Muslim 14 $286 0.1% 7.7% Newar 86 $24,374 8.5% 28.0% Others (Terai/Madhesi) 72 $7,832 2.7% 15.5% TABLE 23. VALUE OF AGRICULTURE-RELATED FINANCING 113 Cash 253 $130,345 100.0% In-kind 0 0 0 Demographic Disaggregates Number of Borrowers Value (USD) Contribution (Sum=100%) Percentage of Ag DMs with Access Size of Firm (Farm) None employed 95 $25,393 19.5% $267 Micro-enterprise (1-10 employees) 52 $30,493 23.4% $586 Small (11-49 employees) 78 $49,593 38.0% $636 Medium (50-249 employees) 26 $18,640 14.3% $717 Large (>250 employees) 2 $6,226 4.8% $3,113 Age 15 to 29 (Youth) 42 $20,168 15.5% $480 30 to 45 121 $62,033 47.6% $513 46 to 59 77 $43,356 33.3% $563 Above 59 13 $4,789 3.7% $368 Sex Female 163 $52,615 40.4% $323 Male 90 $77,730 59.6% $864 Of the 253 individuals with access to agriculture-related financing, 223 (88 percent) have an outstanding balance. The average balance varies substantially between men (USD 751) and women (USD 233). K26/Reporting 5: Value of the outstanding balance on agriculture loans of USG-assisted households by type of debt and financing type Aggregate Results Number with an Outstanding Balance Value (USD) Average Outstanding Balance Percentage of Sample Households (of 1,860) 208 $94,924 $456.37 11.2% Borrowers (of 2,848 Ag Decision Makers) 223 $94,924 $425.67 8.7% Type of Debt Number with an Outstanding Balance Value (USD) Composition of Those With a Balance (Sum=100%) Cash 223 $94,924 100.0% In-kind 0 0 0% Type of Financing Debt 222 $94,838 99.9% Non-debt 1 $86 0.1% Demographic Disaggregates Number with an Outstanding Balance Value (USD) Composition of Those with a Balance (Sum=100%) Average Balance per Borrower (USD) Size of Firm TABLE 24. VALUE OF THE OUTSTANDING BALANCE ON AGRICULTURE LOANS 114 K26/Reporting 5: Value of the outstanding balance on agriculture loans of USG-assisted households by type of debt and financing type None employed 82 $18,931 19.9% $231 Micro-enterprise (1-10 employees) 46 $22,393 23.6% $487 Small (11-49 employees) 69 $34,708 36.6% $503 Medium (50-249 employees) 24 $14,027 14.8% $584 Large (>250 employees) 2 $4,866 5.1% $2,433 Age 15 to 29 (Youth) 37 $12,398 13.1% $335 30 to 45 103 $43,705 46.0% $424 46 to 59 70 $34,640 36.5% $495 Above 59 13 $4,180 4.4% $322 Sex Female 140 $32,552 34.3% $233 Male 83 $62,371 65.7% $751 Total 223 $94,924 5.6 Additional Tables TABLE 25: OWNERSHIP OF ASSESTS BY HH HEAD AND DAG AND NON-DAG GROUPS Assets Male headed HH (n=1447) Female headed HH (n=413) Non￾Disadvantaged (n=843) Disadvantaged (n=1017) Total Hand tools 100% 100% 100% 100% 100% Agri Land 97% 97% 98% 97% 97% Livestock 93% 88% 91% 92% 92% Non mechanized 62% 49% 52% 65% 59% Poultry 54% 56% 38% 67% 54% Mechanized 20% 11% 20% 16% 18% Building 99% 99% 99% 99% 99% Mobile 96% 94% 96% 95% 96% Smartphone 54% 58% 57% 53% 55% 115 TABLE 26: SHARE OF INCOME SOURCE BY DAG AND NON-DAG GROUP Income source Non-DAG group DAG group HHs % HH Total Income (USD) Average income (USD) HHs % HH Total Income (USD) Average income (USD) Own crop sales 345 41% $ 122,335.5 $ 354.6 401 39% $ 83,974.7 $ 209.4 Own livestock sales (planned, not under stress) 215 26% $ 78,537.9 $ 365.3 234 23% $ 57,292.6 $ 244.8 Agricultural wage labor 145 17% $ 22,137.5 $ 152.7 250 25% $ 41,970.3 $ 167.9 Non-agricultural wage labor 195 23% $ 132,680.5 $ 680.4 470 46% $ 376,863.9 $ 801.8 Fish sales 15 2% $ 3,304.8 $ 220.3 15 1% $ 2,975.2 $ 198.3 Agricultural business, trade, or self￾employment 33 4% $ 22,983.7 $ 696.5 39 4% $ 29,355.4 $ 752.7 Non-agricultural business, trade, or self-employment 168 20% $ 204,928.2 $ 1,219.8 224 22% $ 281,346.4 $ 1,256.0 Rental of land 49 6% $ 12,988.5 $ 265.1 23 2% $ 6,142.7 $ 267.1 Rental of house or rooms 58 7% $ 40,971.3 $ 706.4 35 3% $ 11,511.5 $ 328.9 Cash from HH members working in another country (foreign remittances) 195 23% $ 437,442.7 $ 2,243.3 238 23% $ 470,873.6 $ 1,978.5 Government allowances (senior citizen allowance, maternity allowance, etc.) 207 25% $ 121,661.5 $ 587.7 207 20% $ 95,922.7 $ 463.4 Salaried work 217 26% $ 465,808.6 $ 2,146.6 193 19% $ 358,425.7 $ 1,857.1 Sale of land and or assets (not under stress) 27 3% $ 353,687.7 $ 13,099.5 18 2% $ 46,379.3 $ 2,576.6 TABLE 27: NUMBER OF SOURCES OF HOUSEHOLD INCOME BY DAG AND NON- DAG Sources Non-DAG DAG Total Number Percent Number Percent Single source 102 12% 111 11% 213 Two sources 209 25% 266 26% 475 Three sources 250 30% 302 30% 552 Four sources 170 20% 204 20% 374 Five or more sources 112 13% 134 13% 246 Total 843 100% 1,017 100% 1,860 116 TABLE 28: SUAAHARA-II RELATED RESULTS Heard of Suaahara before? Number Percent Yes 569 31.6% No 1227 66.0% Not able to interview primary female decision maker 64 3.4% Total 1,860 100% Suaahara staff member ever visited home? Number Percent Yes 202 35.5% No 360 63.3% Don't know 7 1.2% Total 569 100% FCHVs visiting home Number Percent Yes 314 56.1% No 233 41.6% Don't know 3 0.5% Respondent is FCHV herself 19 3.4% Total 569 100% Heard of “Bhanchin Ama”? Number Percent Yes 373 65.6% No 193 33.9% Don't know 3 0.5% Total 569 100% Listened to “Bhanchin Ama”? Number Percent Yes 306 82.0% No 65 17.0% Don't know 2 1.0% Total 373 100% Owned mobile phone Number Percent Yes 468 82.3% No 101 17.8% Total 569 100% Received nutrition related messages in their phones Number Percent 117 Yes 22 4.7% No 373 79.7% Don't know 73 15.6% Total 468 100% TABLE 29: EXPOSURE TO SHOCKS/STRESSORS BY HH CHARACTERISTICS HH characteristics Experiencing shocks/stressor in last 12 months n % Province Province 3 324 27.6 Province 5 465 39.6 Province 6 221 18.8 Province 7 163 13.9 Eco Region Hills 727 62.0 Terai 446 38.0 Sex of HH head Male 919 78.3 Female 254 21.7 Caste/Ethnicity Brahmin/Chhetri 448 38.2 Dalit 138 11.8 Janajati 455 38.8 Muslim 27 2.3 Newar 48 4.1 Tarai/Madhesi 57 4.9 Total 1,173 100.0 118 TABLE 30: FIRST AND SECOND MOST AFFECTED HH MEMBERS BY SHOCKS/STRESSORS IN LAST 12 MONTHS DISAGGREGATED BY AGE AND SEX Background characteristics of individuals First most affected Second most affected % n % n Age Less than 15 1.1 19 2.1 39 15-29 10.4 183 18.8 355 30-44 40.2 708 41.2 777 45-59 32.7 577 27.4 517 60+ 15.7 276 10.5 197 Sex Male 63.1 1,113 41.4 780 Female 36.9 650 58.6 1,105 TABLE 31: SEVERE OR EXTREMELY SEVERE IMPACT ON INCOME AND FOOD DUE TO EXPOSURE TO SHOCKS AND STRESSORS IN LAST 12 MONTHS DISAGGREGATED BY INCOME LEVEL Type of Shocks/Stressor Income level: Less than $1.9 per day Income level: More than $1.9 per day HHs with severe or extremely severe impact on income Total exposed HH HHs with severe or extremely severe impact on income (%) HHs with severe or extremely severe impact on income Total exposed HH HHs with severe or extremely severe impact on income (%) Livestock Disease 69 129 53% 150 307 49% Severe illness 89 121 74% 173 267 65% Insects affecting crops 45 105 43% 125 321 39% Disease affecting crops 45 99 45% 109 271 40% Too little rain 31 84 37% 90 219 41% Freezing temperatures 22 54 41% 56 179 31% Unable to sell at fair price 20 46 43% 92 209 44% Too much rain 20 44 45% 71 159 45% Food prices 17 42 40% 47 128 37% Lack of Access-Crop Inputs 7 33 21% 33 95 35% Land erosion/ Landslide 8 27 30% 20 47 43% Lack of Access-Livestock Inputs 10 26 38% 32 63 51% Death 21 25 84% 44 65 68% Loss of land 9 20 45% 32 57 56% Theft 3 5 60% 12 20 60% Livestock Theft 1 4 25% 2 8 25% Crop Theft 1 3 33% 12 18 67% Earthquake 0 2 0% 1 3 33% 119 Type of Shocks/Stressor Income level: Less than $1.9 per day Income level: More than $1.9 per day HHs with severe or extremely severe impact on food Total exposed HH HHs with severe or extremely severe impact on food (%) HHs with severe or extremely severe impact on food Total exposed HH HHs with severe or extremely severe impact on food (%) Livestock Disease 32 129 25% 67 307 22% Severe illness 46 121 38% 92 267 34% Insects affecting crops 42 105 40% 74 321 23% Disease affecting crops 34 99 34% 63 271 23% Too little rain 23 84 27% 69 219 32% Freezing temperatures 9 54 17% 35 179 20% Unable to sell at fair price 15 46 33% 39 209 19% Too much rain 14 44 32% 58 159 36% Food prices 15 42 36% 38 128 30% Lack of Access-Crop Inputs 7 33 21% 23 95 24% Land erosion/ Landslide 6 27 22% 18 47 38% Lack of Access-Livestock Inputs 6 26 23% 17 63 27% Death 18 25 72% 28 65 43% Loss of land 8 20 40% 22 57 39% Theft 1 5 20% 5 20 25% Livestock Theft 1 4 25% 1 8 13% Crop Theft 2 3 67% 8 18 44% Earthquake 0 2 0% 1 3 33% TABLE 32: COPING STRATEGIES APPLIED BY HHS EXPERIENCING SHOCKS AND STRESSORS, DISAGGREGATED BY INCOME LEVEL Type of coping strategy Coping strategies <$1.9 per day (n=324) >$1.9 per day (n=849) Number Percent Number Percent Positive strategies Increased Wage Labor 173 53% 376 44% Remittances 45 14% 157 18% Returned from Overseas 14 4% 48 6% Neighbor Shared Food 75 23% 125 15% Food Aid 23 7% 58 7% Neutral or Potentially Negative Strategies Borrowed Cash (other HH) 145 45% 334 39% Borrowed Cash (informal group) 87 27% 228 27% Bought on Credit 132 41% 247 29% Migrated 41 13% 82 10% Used HH Savings 153 47% 498 59% Negative strategies Sold Livestock 68 21% 210 25% Sold Land 15 5% 45 5% 120 Sold Other Asset 15 5% 34 4% Harvested Early 32 10% 43 5% Ate Less or Cheaper 73 23% 102 12% Took Child out of School 17 5% 25 3% None applied 38 12% 129 15% TABLE 33: EXPECTATIONS ABOUT SOCIAL CAPITAL BY DAG AND NON-DAG GROUPS Expected Sources of Support Type of Social Capital Non-DAG (n=843) DAG (n=1017) Number Percent Number Percent HHs That Expect They Will Receive Support to: Relatives living in your community Bonding 722 86% 860 85% Relatives living outside your community Bridging 660 78% 757 74% Non-relatives living in your community Bonding 636 75% 703 69% Non-relatives living outside your community Bridging 346 41% 370 36% HHs That Expect They Will Give Support to: Relatives living in your community Bonding 799 95% 965 95% Relatives living outside your community Bridging 746 88% 879 86% Non-relatives living in your community Bonding 744 88% 854 84% Non-relatives living outside your community Bridging 565 67% 601 59% TABLE 34: OWNED AND LEASED LAND AREA BY ZOI (IN HECTARES) ZOI group Type of land HHs Total land (Ha) Average land (Ha) ZOI 1 Owned 1311 570.52 0.44 Leased 443 230.73 0.52 Total 1350 801.26 0.59 ZOI 2 Owned 487 176.88 0.36 Leased 157 40.31 0.26 Total 500 217.19 0.43 Total Land Area Owned 1798 747.40 0.42 Leased 600 271.05 0.45 Total 1850 1018.45 0.55 121 TABLE 35: WATER AVAILABILITY AND ITS VARIATION FOR IRRIGATION Water availability and variation Number of HHs Percent Access to irrigation (year-round) (out of total HH=1,860) 812 44% Amount of water available for irrigation changed over the last 5 years Yes 387 48% No 407 50% Don't know 18 2% Total 812 100% Change in the amount of water availability over the last 5 years Big increase 33 9% Moderate increase 79 20% Moderate decrease 239 62% Big decrease 36 9% Total 387 100% Variation in the water supply (highs and lows) for irrigation from year to year changed Yes 271 70% No 111 29% Don't know 5 1% Total 387 100% Amount of variation in the water supply (highs and lows) for irrigation from year to year changed Much more variable 40 15% Somewhat more variable 73 27% Somewhat less variable 151 56% Much less variable 7 3% Total 271 100% 122 APPENDIX V: KISAN OR GFSS INDICATORS AND CONTRIBUTION QUESTIONS 123 KISAN or GFSS Number Indicators Project Components and Indicators Contributing Question Numbers and Desegregation Levels Captured Disaggregation Required as Shared by KISAN II Team with the MEL Activity K4 Nepal 2.1.1- 2 EG.3.2-24 Number of individuals in the agriculture system who have applied improved management practices or technologies with USG assistance (IM-Level) Land based technologies: Questions 602A, 603A, 604A, 605A, 606A Non-land-based technologies: Questions 602B, 603B, 604B, 605B, 606B Disaggregation: -Every next column of technology applied (205, 204), 105, 601A & 601B, 707 Disaggregation: - Age, Sex Caste/Ethnicity, Management practice or technology type, , Size of producers, Type of commodities K5 Custom 1 Average number of improved management practices or technologies applied per household with USG assistance. Land based technologies: Questions 602A, 603A, 604A, 605A, 606A Non-land-based technologies: Questions 602B, 603B, 604B , 605B, 606B Disaggregation: - 501A, 502B, 502C 601A & 601B Disaggregation: - Type of commodities K6 GNDR-2 Percentage of female participants in USG assisted programs designed to increase access to productive resources Indicator EG.4.2-7: (1307, 1310, 1404, 1407) Note: females will be identified from 13081405 and 1409 Disaggregation possible. But KISAN M&E plan says no disaggregation required. K7 New YOUTH-3 Percentage of participants in USG-assisted programs designed to increase access to productive economic resources who are youth (15-29) (IM-Level) Indicator EG.4.2-7: (1307, 1310, 1404, 1407) Note: youths (15-29) will be identified from 1308, 1405 and 1409 Disaggregation possible. But KISAN M&E plan says no disaggregation required. 124 KISAN or GFSS Number Indicators Project Components and Indicators Contributing Question Numbers and Desegregation Levels Captured Disaggregation Required as Shared by KISAN II Team with the MEL Activity K8 Nepal 2.1.1-1 EG.3.2-25 Number of hectares under improved management practices or technologies with USG assistance (IM￾Level) Land based technologies: Questions 602A, 603A, 604A, 605A, 606A Disaggregation: - 607A (105, 204, 205), 601A , 501A, 502B, 502C, 701-705 Disaggregation: - Age, Caste/Ethnicity, Management practice or technology type, Sex, Type of commodities, Type of hectare K9 Reporting 1 Total farm-level volumes (MT) produced of targeted agricultural commodities with USG assistance (rice, maize, lentil, vegetables, goats) 505A, 506B, 505C Disaggregation: - 514A,515B,514C (105,204,205), 501A,502B, 502C Disaggregation: - Age, Caste/Ethnicity, Sex, Type of commodities, Type of Vegetables K10 New EG.3-10, -11, -12 Yield of targeted agricultural commodities among program participants with USG assistance (rice, maize, lentil, vegetables, goats) (IM-Level) (MT/ha) 505A /504A 506B/505B 507C/505C Disaggregation: - 514A,515B,514C (105,204,205), 501A, 502B, 502C 707, 701-705, 515C Disaggregation: - Age, Caste/Ethnicity, Sex, Type of commodities, Type of Vegetables, Farm size, Production system K12 New EG.3.2-28 Number of hectares under improved management practices or technologies that promote improved climate risk reduction and/or natural resources management with USG assistance (IM-Level) 602A, 603A, 604A, 605A, 606A 6A. VIII, 6A. IX Disaggregation: - 607A & 607B (105,204,205), 601A, 701-705 Disaggregation: - Age, Caste/Ethnicity, Management practice or technology type, Sex, Type of commodities, Type of hectare 125 KISAN or GFSS Number Indicators Project Components and Indicators Contributing Question Numbers and Desegregation Levels Captured Disaggregation Required as Shared by KISAN II Team with the MEL Activity K13 Custom 1 Nepal 2.1.1- 2 EG.3.2-24 Disaggregate Average number of climate-smart technologies or practices applied per farmer with USG assistance. 602A, 603A, 604A, 605A, 606A 6A. VIII, 6A. IX Disaggregation: - 601A Disaggregation: -Climate adaptation technology type K17 New EG.3.2-26 Value of annual sales of farms (and firms) receiving USG assistance (rice, maize, lentil, vegetables, goats) 512A, 513B, 512C Disaggregation: - 514A,515B,514C (105,204,205) 501A, 502B, 501C Disaggregation: Age, Size of Firm, Type of commodities, Type of product, Type of Vegetables K18 New Disaggregate Reporting 2 Volume of annual sales (MT) of farms receiving USG assistance (rice, maize, lentil, vegetables, goats) 511A, 512B, 511C Disaggregation: - 514A,515B,514C (205),707 501A ,502B,501C Disaggregation: - Age, Size of Firm, Type of commodities, Type of product, Type of Vegetables K19 Nepal 2.1.1- 2 EG.3.2-24 Disaggregate Number of Individuals in the agriculture system who have applied improved management practices or technologies with USG assistance – related to marketing and distribution 602B, 603B, 604B, 605B, 606B 6B. II Disaggregation: -607B (105,204, 205), 601B, 707 Disaggregation: - Age, Caste/Ethnicity, Management practice or technology type, Sex, Size of producers, Type of commodities, Type of Individuals, Value chain actor type K20 Custom 2 Percentage of USG-assisted farmers accessing information on improved technologies and practices through improved ICT channels or content 602B, 603B, 604B, 605B, 606B 6B. I Disaggregation: -601B, 607B (204) Disaggregation: -ICT Technology type, Sex 126 KISAN or GFSS Number Indicators Project Components and Indicators Contributing Question Numbers and Desegregation Levels Captured Disaggregation Required as Shared by KISAN II Team with the MEL Activity K23 New EG.3.2-27 Value of agriculture-related financing (USD) accessed as a result of USG assistance (IM-Level) 1307, 1310 906,909 Disaggregation: -1308(204,205), 707, 1302, 1303, 1304 Disaggregation: -Age, Sex, Size of Firm, Type of debt, Type of Financing accessed K24 New EG.4.2-7 Number of individuals participating in group-based savings, microfinance or lending programs with USG assistance (IM-Level) 1401& 1402 Disaggregation: -1405 & 1410(204, 205) 1409,1403, 1406 Disaggregation: - Age, Sex, Type of financial product K25 Reporting 4 Value of household savings deposits of USG-assisted smallholders 1404 Disaggregation: - 1405(204, 205), 1403 Disaggregation: - Age, Sex, Type of financial product K26 Reporting 5 Value of the outstanding balance on agriculture loans of USG-assisted households 1308 Disaggregation: - 1308(204, 205), 707, 1303, 1304 Disaggregation: - Age, Sex, Size of Firm, Type of debt, Type of Financing accessed K31 Nepal 2.1.1-2 EG.3.2-17 Disaggregate Number of farmers and others who applied improved management practices or technologies– on food grading or safety 6B.102-6B.106 6B.III Disaggregation: - 6B.107, 6B.101, 402, Disaggregation: - Age, Caste/Ethnicity, Management practice or technology type, Sex, Size of producers, Type of commodities, Type of Individuals, Value chain actor type 127 KISAN or GFSS Number Indicators Project Components and Indicators Contributing Question Numbers and Desegregation Levels Captured Disaggregation Required as Shared by KISAN II Team with the MEL Activity K41 Custom 5 Quantity of nutrient-rich value chain commodities produced by direct beneficiaries with USG assistance that is set aside for home consumption (MT) 508B Disaggregation: - 502B (okra, cabbage, cauliflower, spinach, bitter gourd, carrot, Broccoli, beans, capsicum, pumpkin and goats) Disaggregation: -Type of vegetables (nutritious) K46 EG.11-6 Nepal 2.1.1- 2 EG.3.2-17 Disaggregate Number of people using climate information or implementing risk-reducing actions to improve resilience to climate change as supported by USG assistance 602A, 603A, 604A, 605A, 606A 6A. VIII, 6A. IX Disaggregation: - 601A Disaggregation: -Climate adaptation technology type, 128 APPENDIX VI: KISAN II BASELINE QUESTIONNAIRE 129 FARMER INTERVIEW FORM KISAN FY2018 Survey 2 September 2018 Baseline information for 12 months period: Bhadra 01, 2074 to Shrawan 31, 2075 INFORMED CONSENT Namaste! My name is [enumerator name] from Full Bright Consultancy. We are conducting a survey for the KISAN II project covering the 12-month period from Bhadra 01, 2074 to Shrawan 31, 2075. You have been selected to participate in this interview, along with 1,860 other households in 22 districts. These questions will take approximately 2.5 hours to complete and your participation is entirely voluntary. If you agree to participate, you can choose to stop at any time or skip any questions you do not want to answer. Your answers will be completely confidential; we will not share information outside of the research team. After entering your answers into a database, we will remove information that links your answers to your name. Do you consent to this interview? (1) Yes, (2) No. Thank you for the opportunity to speak with you. If you have any questions about this study, please contact: Manjul K. Manandhar Full Bright Consultancy (Pvt.) Ltd. 316 Baburam Acharya Sadak, Sinamangal, Kathmandu Tel: +977 1 4468749, 4468118 Fax: +977 1 4465604 Email: fbc@mos.com.np URL : http:www.fbc.com.np 130 SURVEY TEAM AND INTERVIEW DATE & TIME Session Status of interview Note in case of unsuccessful interview Interview date and time Visits by Interviewer (day/month/year and outcome): Note: Second and third row applicable if interview was not successful in first visit/attempt. 1st: _ _ /_ _/_ _ Interview Date _ _ /_ _ /_ _ 2nd: _ _ /_ _/_ _ Interview Start Time _ _ : _ _ 3rd: _ _ /_ _/_ _ Interview End Time _ _ : _ _ Status of interview codes: 01= Refused interview 03= No household member at home 02= Fixed time for next visit 05= Postponed/Unavailable 04= Entire household absent for extended period of time 07= Too ill to respond/cognitively impaired 06= Dwelling vacant/address not a dwelling 96= Other (specify): 08= Partially complete 1. BASIC INFORMATION 101. Respondent Name (First Name) (Last Name) 102. Mobile _ _ _ _ _ _ _ _ _ _ 103. Name of HH Head (ask if respondent is not the HH Head) (First Name) (Last Name) 104. Mobile _ _ _ _ _ _ _ _ _ _ 105. Caste/ Ethnicity _ __ __ __ __ __ __ _ Code Options (Caste/ethnicity) 01=brahmin/chhetri, 02=dalits, 03=janajatis, 04=muslims, 05=newars, 06=tarai/madhesi, 07=others 106. Name and code of province _ __ __ __ __ __ __ _ 107. Name and code of district _ __ __ __ __ __ __ 131 108. Name and code of Municipality/Gaunpalika _ __ __ __ __ __ __ _ 109. Ward no. 110. Village/Tole name __ __ __ ___ __ ___ _ 2. HOUSEHOLD ROSTER 201 202 203 204 205 206 207 208 209 210 211 212 213 Line No List names of people who are currently living in this household (always start with HH head) Relation￾ship with HH head? (see codes below) Gender 01 = male 02 = female 03 = third gender What is this person’s completed age in years? (If age is less than 1 year then enter 0) What is the highest level of education this person has completed? (only ask for above 5 years, see codes below) What is this person’s primary occupation? (only ask for 5 to 59 age, see codes below) Marital Status (only for more than 10 years of age) Is this member currently living in this household? Did this person partici￾pate in Business Literacy Program 2015-16? (only ask for 15 to 59 age) 01=yes, 02 = no Did this person return from abroad (including India) in the last 12 months? (only ask for 15 to 59 age) 01=yes, 02=no Which country did he/she return from? 1=India, 2=Other How many months total did this person spend in that country in the last 12 months? 132 Relationship with HH Head Education Codes Marital Status Codes Currently living in this HH codes Primary Occupation Codes 01= Head 02 = Wife or husband 03 = Son or daughter 04 = Son-in-law or daughter-in-law 05 = Grandchild 06 = Parent 07 = parent-in-law 08 = Brother or sister 09 = Brother-in-law or sister-in-law 10 = Niece/Nephew 11 = Other relatives 12 = Not related 01 = Illiterate and never attended school 02 = Literate and never attended school 03 = primary (class 1-5) 04 = secondary (class 6-10) 05 = higher secondary (class11-12) 06 = graduate and above 07 = NA 01= Married 02= Single 03= Separated 04= Divorced 05= Widow/ Widower 06= NA 01= Yes 02= No, living outside Nepal 03= No, living outside this district 01= Student 02 = Housewife 03 = Government employee Private service 04 =Private employee 05 = Wage labour 06 = Self-employed (business) 07 = Agribusiness (self￾employed) 08 = Unemployed 09 = Teacher 10 = Other (specify) 11 = Not Applicable (<5 years) 3. PRIOR SUPPORT FOR AGRICULTURE SN QUESTIONS RESPONSE SKIP 304 Did you receive support on rice production or sales in the last 12 months (Bhadra 01, 2074 to Shrawan 31, 2075)? 1=Yes 2=No ->306 305 From which organization/project did you receive support? (open ended, record only one main organization) ______________________ 306 Did you receive support on maize production or sales in the last 12 months? 1=Yes 2=No ->308 133 307 From which organization/project did you receive support? (open ended, record only one main organization) ______________________ 308 Did you receive support on lentil production or sales in the last 12 months? 1=Yes 2=No ->310 309 From which organization/project did you receive support? (open ended, record only one main organization) ______________________ 310 Did you receive support on vegetable production or sales in the last 12 months? 1=Yes 2=No ->312 311 From which organization/project did you receive? (open ended, record only one main organization) ______________________ 312 Did you receive support on goat production or sales in the last 12 months? 1=Yes 2=No ->Next section 313 From which organization/project did you receive support? (open ended, record only one main organization) ______________________ 4. AGRICULTURE LAND UNITS, OWNERSHIP, AND IRRIGATION QN QUESTION RESPONSE SKIP LOGIC (GO TO) 401 What is the land unit in this area? 1=Ropani 2=Kattha 402 In the last 12 months (Bhadra 01, 2074 to Shrawan 31, 2075), of total cultivated land, what portions are: i) owned versus leased and (Kat/Rop) Owned Leased Upland Lowland 134 ii) upland versus lowland? (Kat/Rop) 403 Do you currently have access to an irrigation system that delivers water to your farm year-round? 1=Yes 2=No ->If no, skip to next section 404 How many Kat/Rop can you irrigate year round? 405 Where does the water for irrigation come from (what is the water source)? 1=Spring or river managed by a Community Forest Group 2=Other spring or river 3=Reservoir, lake, or pond 4 =Groundwater (well) 5=Other (Specify) 406 Has the amount of water available for irrigation changed over the last 5 years? 1=Yes, 2=No, 3=Don’t Know 1=Big increase 2=Moderate increase 3=Moderate decrease 4=Big decrease 407 Has the amount of variation in the water supply (highs and lows) from year to year changed? 1=Yes , 2=No, 3=Don’t Know 1=Much more variable 2=Somewhat more variable 3=Somewhat less variable 4=Much less variable 408 How is water for irrigation transported from the source to your farm? (Only one answer possible) 1= Water source on my land 2=Large irrigation canal/pipe constructed by government 3=Group-managed irrigation canal/Nahar 97=Other (specify) 135 5A. PRODUCTION, CONSUMPTION, LOSSES AND SALES (CEREALS) Let us review your cereals production from Bhadra 01, 2074 to Shrawan 31, 2075. 501A 502A 503A 504A 505A 506A 507A 508A 509A 510A 511A 512A 513A 514A 515A Cereal/ Lentil/ Crop Did you harvest any? Yes=1, No=2 (go to next) Seed Type What type of seed did you use? Seed Variety Name (open￾ended or don’t know) Production Consumption (Kg) Sales Decision￾making Income Use Area Planted (K/R) Total Production (Kg) Used for seed (kg) Set aside for home consump￾tion (kg) Post harvest losses (kg) Other uses (Puja, gift, animal feed) (kg) In store (kg) (exclude storage of more than 1 year ago) Quantity Sold (Kg) Value of Sales (Rs) Avg. Unit Price (Rs/Kg) Who in your HH made the decision to plant this crop? (select from HH roster) How did you use the income from selling [cereal/lentil/ crop]? (multiple answers possible, see codes) Rice 1=Improved 2=Hybrid 3=Local Maize 1=Improved 2=Hybrid 3=Local Lentil 1=Improved 2=Hybrid 3=Local Codes for 515A: 01 = Buy vegetable/fruits for consumption 136 02 = Buy meat for consumption 03 = Buy eggs for consumption 04 = Buy dairy products for consumption 05 = Buy other food items for consumption 06 = Buy clothes 07 = Buy school supplies/pay fees 08 = Buy medicine 09 = Buy family planning products 10 = Buy WASH-related supplies (pan, soap, etc.) 11= Seek health care 12 = Savings 96 = Other (specify)___________ 137 5B. PRODUCTION, CONSUMPTION, LOSSES AND SALES (VEGETABLES) Let us review your vegetable production from Bhadra 01, 2074 to Shrawan 31, 2075. 501B Did you harvest 20 Kgs or more of any single type of vegetable from Bhadra 01, 2074 to Shrawan 31, 2075? 1=Yes, 2=No (Skip to 5C) 502B 503B 504B 505B 506B 507B 508B 509B 510B 511B 512B 513B 514B 515B 516B Vegetables Did you harvest 20 kgs or more of [vegetable]? Seed Type What type of seed did you use? Seed Variety Name (open ended or don’t know) Production Consumption (KG) Sales Decision￾making Income Use Area Planted (K/R) Total Produc￾tion (Kg) Used for seed Set aside for home consump￾tion Other uses (puja, gift, animal feed) Post harvest losses In store (Kg) (exclude storage of more than 1 year ago) Quantity Sold (Kg) Value of Sales (Rs) Avg. Unit Price (Rs/Kg) Who in your household made the decision to plant this crop? (select from HH roster) How did you use the income from selling vegetables? (multiple answers possible, see codes) Capsicum 1=Improved Ask once for all vegetables 2=Hybrid 3=Local Peas 1=Improved 2=Hybrid 3=Local Radish 1=Improved 2=Hybrid 3=Local Broccoli 1=Improved 2=Hybrid 3=Local 138 Carrot 1=Improved 2=Hybrid 3=Local Cauliflower 1=Improved 2=Hybrid 3=Local Cabbage 1=Improved 2=Hybrid 3=Local French Beans 1=Improved 2=Hybrid 3=Local Tomatoes 1=Improved 2=Hybrid 3=Local Onions 1=Improved 2=Hybrid 3=Local Cucumber 1=Improved 2=Hybrid 3=Local Chilli 1=Improved 139 2=Hybrid 3=Local Bottle Gourd 1=Improved 2=Hybrid 3=Local Long Beans 1=Improved 2=Hybrid 3=Local Eggplant 1=Improved 2=Hybrid 3=Local Sponge Gourd 1=Improved 2=Hybrid 3=Local Okra 1=Improved 2=Hybrid 3=Local Bitter Gourd 1=Improved 2=Hybrid 3=Local Pumpkin, Squash, and Zucchini 1=Improved 2=Hybrid 140 3=Local Spinach and Green Vegetables 1=Improved 2=Hybrid 3=Local Other vegetables 1=Improved 2=Hybrid 3=Local Codes for 516B: 01 = Buy grain for consumption 02 = Buy fruits for consumption 03 = Buy meat for consumption 04 = Buy eggs for consumption 05 = Buy dairy products for consumption 06 = Buy other food items for consumption 07 = Buy clothes 08 = Buy school supplies/pay fees 09 = Buy medicine 10 = Buy family planning products 11 = Buy WASH-related supplies (pan, soap, etc.) 12 = Seek health care 13 = Saving 96 = Other (specify)___________ 141 5C. PRODUCTION, CONSUMPTION, LOSSES AND SALES (GOATS) Let us review your goat rearing. 501C Did you rear goats from Bhadra 01, 2074 to Shrawan 31, 2075? Yes = 1, No = 2 (if no, go to section 6) 502C 503C 504C 505C 506C 507C 508C 509C 510C 511C 512C 513C 514C Production Consumption Sales DM Which production system did you use? (single response) Did you rear improved or local goats? What breed of goat did you rear? How many [type] goats did you rear from Bhadra 2074 to Shrawan 2075? Number of goats currently available Live weight of entire [type] goat herd offtake (Kg)* Live weight of [type] goats consumed at home (Kg) Live weight of [type] goats used for other purposes (gifted, puja, exchanged (Kg) Live weight of theft or dead [type] goats (Kg) Live weight of sold [type] goats (Kg) Value of Sales of [type] goats (Rs) Avg. Unit Price (Rs/Kg) of [type] goats Who in your HH made the decision to rear [type] goats? (select from HH roster) See codes below Improved Local 515C How did you use the income from selling goats? (multiple answers possible, see codes) Production System Codes (refer to photos): 1=Agro-pastoral, extensive grassland 2=Small holder mixed livestock-crop 3=Urban/peri-urban 4=Intensive industrial *Display the total here for the enumerator to tally, should be equal to the sum of 508C, 509C, 510C, and 511C 142 Codes for 515C: 01 = Buy grain for consumption 02 = Buy fruits for consumption 03 = Buy meat for consumption 04 = Buy eggs for consumption 05 = Buy dairy products for consumption 06 = Buy other food items for consumption 07 = Buy clothes 08 = Buy school supplies/pay fees 09 = Buy medicine 10 = Buy family planning products 11 = Buy WASH-related supplies (pan, soap, etc.) 12 = Seek health care 13 = Saving 96 = Other (specify)___________ QN QUESTIONS ANSWERS SKIP LOGIC 515 Did you rear breeding bucks from Bhadra 01, 2074 to Shrawan 31, 2075? Yes……………………………………..1 No………………………………………2  Go to next section 516 How many breeding bucks did you rear? 517 How much did you get paid total for providing buck breeding services from Bhadra 01, 2074 to Shrawan 31, 2075?? 143 6. Improved Technologies and Management Practices What agriculture technologies and management practices have you used from Bhadra 01, 2074 to Shrawan 31, 2075? We will go through a list. (Complete columns 6A.102 – 7A.107 for each technology and management practice used. For each row, count land units each time the technology or practice is applied to a crop during the assessment period. The area planted from Tables 5A and 5B is the maximum area technologies can be applied to per crop cycle. If applied to two vegetable cycles, count twice under vegetables). 6A. LAND BASED TECHNOLOGIES: SN 601A 602A 603A 604A 605A 606A 607A No. Rice Maize Lentil Vegetables Goats N/A N/A No. Improved technologies and management practices (TMP) How many K/R has [this TMP] been applied to for rice? (Area) Who in your HH made the decision to use the [TMP]? (choose from HH roster) How many K/R has [this TMP] been applied to for Maize? (Area) Who in your HH made the decision to use the [TMP]? (choose from HH roster) How many K/R has [this TMP] been applied to for Lentil? (Area) Who in your HH made the decision to use the [TMP]? (choose from HH roster) How many K/R has [this TMP] been applied to for Vegetables? (Area) Who in your HH made the decision to use the [TMP]? (choose from HH roster) How many goats has [this TMP] been applied to? (Number) Who in your HH made the decision to use the [TMP]? (choose from HH roster) 6A.I Crop Genetics Improved seed Certified seed Hybrid seed 6A.II Cultural Practices Raise bed farming Improved nursery management 144 and Seedling production Improve stacking practices Mulching 6A.III Livestock Management Improved livestock (goat) breeds and livestock health management Improve goat shed management Waste management (Jutto) Fodder cultivation Improve stall feeding management 6A.IV Pest and Disease Management Integrated pest management Judicious use of pesticides 145 Safe use of pesticides Use of Bio pesticides 6A.V Soil-related Fertility and Conservation Application of Lime and micronutrients Improved fertilizer application practices Fertigation Use of liquid fertilizers Soil PH assessment Use of soil organic matter Green manuring 6A.VI Irrigation Drip irrigation Surface irrigation Sprinkler irrigation Irrigation schemes (other systems) 146 6A.VII Agriculture Water Management - Non-irrigation-based Water harvesting Sustainable water use practices Practices that improve water quality 6A.VIII Climate Mitigation Drip irrigation Upgrades of agriculture infrastructure and supply chains (use of pollution reducing technologies such as SRI, zero tillage, afforestation and so on) 6A.IX Climate Adaptation/ Climate Risk Management Drought and flood resistant varieties Short-duration varieties Adjustment of sowing time 147 Crop / livestock Insurance Diversification Protective agriculture (Use of tunnels and plastic house) Use of climate information in farming 148 What other agriculture technologies and management practices have you used from Bhadra 01, 2074 to Shrawan 31, 2075? We will go through a list. 6B. NON-LAND BASED TECHNOLOGIES (yes/no questions) 601B 602B 603B 604B 605B 606B 607B NO. Improved technologies and management practices Did you apply in rice Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in maize Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in lentil Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in vegetabl es Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in goats Who in your household made the decision to use [TMP]? (choose from HH roster) 6B.I ICT Technology Type Extension Videos SMS and Text Messaging Mobile Application Radio Website Others (Specify…………) 6B.II Marketing and Distribution Contract farming/ livestock technologies and practices Improved input purchase 149 601B 602B 603B 604B 605B 606B 607B NO. Improved technologies and management practices Did you apply in rice Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in maize Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in lentil Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in vegetabl es Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in goats Who in your household made the decision to use [TMP]? (choose from HH roster) technologies and practices Improved commodity sale technologies and practices Improved market information system technologies and practices Buyer’s/seller’s forum Business promotion practices 6B.III Post-harvest Handling and Storage Sorting, grading and packaging Sanitary and safety handling practices Improved quality control 150 601B 602B 603B 604B 605B 606B 607B NO. Improved technologies and management practices Did you apply in rice Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in maize Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in lentil Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in vegetabl es Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in goats Who in your household made the decision to use [TMP]? (choose from HH roster) technologies and practices Improved vegetable cooling practices Use of grain pro bags Use of plastic crates 6B.IV Value-added Processing Improved packaging practices and materials 6B.V Other Farm record keeping (such as log books) Crop/ Livestock budgeting 151 601B 602B 603B 604B 605B 606B 607B NO. Improved technologies and management practices Did you apply in rice Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in maize Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in lentil Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in vegetabl es Who in your household made the decision to use [TMP]? (choose from HH roster) Did you apply in goats Who in your household made the decision to use [TMP]? (choose from HH roster) Calculated gross margins or return on investment Improved capacity to repair agricultural/livesto ck equipment Improved quality of agricultural/ livestock products or technology 152 7. LAND TYPE AND LABOR USED FOR PRODUCTION QN QUESTION: What type of land did you use for [crop] harvested from Bhadra 01, 2074 to Shrawan 31, 2075 ANSWERS SKIP LOGIC 701 Rice 1=Crop land 2=Cultivated pasture 3=Rangeland 4=Conservation/protected area 5=Freshwater/aquaculture 6=N/A, rice not harvested 7=Others (specify__________) ->702 702 Maize 1=Crop land 2=Cultivated pasture 3=Rangeland 4=Conservation/protected area 5=Freshwater/aquaculture 6=N/A, maize not harvested 7=Others (specify___________) ->703 703 Lentil 1=Crop land 2=Cultivated pasture 3=Rangeland 4=Conservation/protected area 5=Freshwater/aquaculture 6=N/A, lentil not harvested 7=Others (specify___________) ->704 704 Vegetables 1=Crop land 2=Cultivated pasture 3=Rangeland 4=Conservation/protected area 5=Freshwater/aquaculture 6=N/A, vegetables not harvested 7=Others (specify___________) ->705 705 Goat 1=Crop land 2=Cultivated pasture 3=Rangeland 4=Conservation/protected area 5=Freshwater/aquaculture 6=N/A, goat not produced 7=Others (specify___________) ->706 153 706 Did you hire someone who is not a household member for seasonal agriculture labor from Bhadra 01, 2074 to Shrawan 31, 2075? 1=Yes 2=No ->next section 707 If yes, how many people did you hire? 154 RESILIENCE MODULE 8. Household Ownership of Assets QN 801 802 Major Household Assets Do you own any of the [asset], either by yourself or jointly with someone else? How many [asset] does your HH own? Productive Assets Circle all that apply: 1=self, 2=spouse/partner, 3=other HH member, 4=joint, 5=No, doesn’t own any Area/Number a. Agricultural land Ropani/Kattha b. Building: barn, storage c. Hand tools: hoe, spade/shovel, rake, sickle, pick axe, axe, pruning shears, etc. d. Non-mechanized farm equipment: halo and juwa (bullock and plow), cart, wheelbarrow, etc. e. Mechanized farm equipment: water pump (mechanical or motorized), tiller, tractor, corn sheller, motorized grain mill, tractor-drawn plow. f. Large livestock (like: cattle, oxen, etc.) g. Small livestock (like goats, pigs, sheep, etc.) h. Poultry (chicken, duck, pigeon) Other Assets i. Mobile phone If no, skip to k j. A smart phone with access to the Internet k. Jewelry (gold, gems) 9. Proximity to Markets, Infrastructure, and Services 901 902 No . Markets, Infrastructure, and Services How much time does it usually take you to travel to [place] from your home, using your typical mode of transportation? 1=less than 30 minutes 2=30-60 minutes 3=1-2 hours 4=more than 2 hours 5=Not Applicable 99=DNK Are services available within 2 hours of walking distance or 5 km from your village/tole? 1=Yes 2=No 99=Do not know a. Formal or informal institutions where you can borrow money 155 b. Institutions where you can make savings deposits c. Markets/stores for buying seeds d. Markets/stores/locations for buying fertilizer e. Markets for selling agricultural products f. Collection center (if applicable) g. Agriculture extension services (GoN) h. Agriculture extension services (Agrovet or other private sector) i. Veterinary services (GoN) j. Veterinary services (Agrovet or other private sector) k. Pick-up point for transport of agricultural produce to market l. Warehouse or storage facility m. Schools - primary n. Schools – secondary o. Health facility p. Electricity q. Public telephone r. Mobile phone signal s. Local TV signal (local language) t. National TV signal u. Local FM radio signal (local language) v. National radio signal 156 10. Access to Agricultural Information and Services (by topic, source, and gender) 1001 1002 1003 1004 1005 Topics Access SMS Sources Gender Influence In the last 12 months… Did you or anyone in your HH receive any information, training or/and services related to [topic]? 1=Yes 2=No 99=Do not know (If 2 or 99, skip to next topic) Did you receive any information related to [topic] by text message on your mobile phone? 1=Yes 2=No 99=Do not know What are your most important sources of information or services related to [topic]? (multiple responses possible) 1=Agrovet 2=Miller/processor/feed industry 3=Other private sector firms 4=Government 5=NGO or project 6=Community group 7=Cooperative 8=Neighbor, friend, relative 9=Newspaper, radio, TV 10=Internet, SMS, mobile apps Who in your HH usually accesses information or services about [topic]? 1=Female 2=Male Did the information or service received influence any HH decisions? 1=Yes 2=No 99=Do not know a. Early warning for natural hazards (flooding, hail, landslide) b. Long-term changes in weather patterns c. Rainfall/weather prospects for coming growing season d. Irrigation (infrastructure or water) e. Animal health (e.g., disease, epidemic, prevention) f. Crop health (e.g., insect, disease outbreaks, prevention) g. Crop production practices/technologies 157 1001 1002 1003 1004 1005 Topics Access SMS Sources Gender Influence In the last 12 months… Did you or anyone in your HH receive any information, training or/and services related to [topic]? 1=Yes 2=No 99=Do not know (If 2 or 99, skip to next topic) Did you receive any information related to [topic] by text message on your mobile phone? 1=Yes 2=No 99=Do not know What are your most important sources of information or services related to [topic]? (multiple responses possible) 1=Agrovet 2=Miller/processor/feed industry 3=Other private sector firms 4=Government 5=NGO or project 6=Community group 7=Cooperative 8=Neighbor, friend, relative 9=Newspaper, radio, TV 10=Internet, SMS, mobile apps Who in your HH usually accesses information or services about [topic]? 1=Female 2=Male Did the information or service received influence any HH decisions? 1=Yes 2=No 99=Do not know h. Livestock production practices (fodder, husbandry) i Seeds j. Fertilizer k. Post-harvest handling or food safety (including storage) l. Linking to buyers or marketing m. Transportation of produce to markets n. Current market prices for agricultural products (crops or animals) o. Financial services 158 1001 1002 1003 1004 1005 Topics Access SMS Sources Gender Influence In the last 12 months… Did you or anyone in your HH receive any information, training or/and services related to [topic]? 1=Yes 2=No 99=Do not know (If 2 or 99, skip to next topic) Did you receive any information related to [topic] by text message on your mobile phone? 1=Yes 2=No 99=Do not know What are your most important sources of information or services related to [topic]? (multiple responses possible) 1=Agrovet 2=Miller/processor/feed industry 3=Other private sector firms 4=Government 5=NGO or project 6=Community group 7=Cooperative 8=Neighbor, friend, relative 9=Newspaper, radio, TV 10=Internet, SMS, mobile apps Who in your HH usually accesses information or services about [topic]? 1=Female 2=Male Did the information or service received influence any HH decisions? 1=Yes 2=No 99=Do not know p. Using mobile phones to access market information (SMS or app) q. Nutrition and health information r. Equal rights for women and men s. Gender-based violence t. Natural resource management u. Sources of emergency support 159 Following are examples of sources for Question 1003:  Government: MOAD, palika, research farm, other offices.  Community: groups, other farmer, cooperative, demo farm, teacher, relatives, friends, neighbors. 11. Number and Consistency of Agriculture Market Linkages Consistency of Access QN Question Response 1101 During the past 12 months, were you ever unable to obtain agricultural advice or information because no local advisor was available? 1=Yes, 2=No, 99=Do not know 1102 During the past 12 months, were you ever unable to obtain agricultural inputs because no local supplier and/or inventory was available? 1=Yes, 2=No, 99=Do not know 1103 During the past 12 months, were you ever unable to sell your produce because no buyer/trader was available? 1=Yes, 2=No, 99=Do not know 1104 During the past 12 months, did your HH have interaction with Agrovet? 1=Yes 2=No (skip to 1106) 1105 How many agrovets did your HH interact with during that period? 1106 During the past 12 months, did you obtain/ buy high quality seeds or other inputs from agrovets? 1=Yes 2=No (skip to 1109) 99=Do not know (skip to 1109) 1107 Did the prices you pay for quality seeds or other inputs offered by the agrovets change compared to the previous year? Note: If respondent says “No”, Circle 2, if respondent says “Yes” probe if increased or decreased 1= prices increased 2= remained the same 3= prices decreased 98=do not know 97=not applicable 1108 How satisfied were you with the inputs and/or services provided by your agrovet in the past 12 months? 1=not satisfied 2=somewhat satisfied 3=satisfied 4=very satisfied 98 97 1109 During the past 12 months, did your HH have interaction with commercial buyers, traders, aggregators or wholesalers? 1=Yes 2=No (skip to 1111) 1110 How many commercial buyers, traders, aggregators or wholesalers did your HH interact with during the past 12 months? 1111 During the past 12 months, did your HH sell agricultural products to any commercial buyers, traders, aggregators or wholesalers? 1=Yes 2=No (skip to 1114) 99=Do not know (skip to 1114) 160 QN Question Response 1112 During the past 12 months, did your total sales (in Rs) of agricultural products to commercial buyers, traders, wholesalers or aggregators change compared to the previous year? Note: If respondent says “No”, Circle 2, if respondent says “Yes” probe if increased or decreased 1=increased 2=no, remained the same 3=decreased 98=do not know 97=not applicable 1113 How satisfied are you with the sales/price you received from buyers/traders/aggregators or wholesalers in the past 12 months? 1=not satisfied 2=somewhat satisfied 3=satisfied 4=very satisfied 98=do not know 97=not applicable 1114 During the past 12 months, did you obtain high quality seeds or other inputs from the buyers, traders, wholesalers or aggregators? 1=Yes 2=No (skip to next section) 99=Do not know (skip to next section) 1115 During the past 12 months, did the prices you pay for quality seeds or other inputs from the buyers, traders, wholesalers or aggregators change compared to the previous year? Note: If respondent says “No”, Circle 2, if respondent says “Yes” probe if increased or decreased 1= prices increased 2= remained the same 3= decreased 98=do not know 97=not applicable 1116 How satisfied are you with the prices you pay for quality seeds and other inputs from buyers/traders/aggregators or wholesalers in the past 12 months? 1=not satisfied 2=somewhat satisfied 3=satisfied 4=very satisfied 98=do not know 97=not applicable 12. Group Participation QN Question Response 1201 During the past 12 months, have you and other members of your household participated in any groups as group member/s? 1=Yes 2=No (Go to section 13) 1202 During the past 12 months, which groups have you and other members of your household participated in as group members? (multiple answers possible) 1=Farmers group 2=Drinking water users group 3=Irrigation water users group 4=Mothers group 5=Savings and credit group 6=Cooperative group 7=Business literacy group 8=Community forest user group 9=Other groups (specify) 161 QN Question Response 1203 If “yes” to any groups under b, who facilitated you or your household member to become a member of that/those group/s? 1=KISAN project 2=Female Community Health Volunteer (FCHV) 3=Suaahara 4=Other 5=Do Not Know 1204 During the past 12 months, which of these groups have you and other members of your household participated in more than half of all group meetings? (multiple answers possible) Only those groups identified in question b, plus: 8=None 1205 During the past 12 months, have you or any members of your HH had a leadership position in a group; for example, Chair, Vice Chair, Treasurer or Secretary? 1=Yes, male 2=Yes, female 3=No 162 Now I am going to ask questions about loans received from Bhadra 01, 2074 to Shrawan 31, 2075. 13. ACCESS TO FINANCE FROM FORMAL FINANCIAL INSTITUTIONS 1301 Has any HH member received a loan (cash or kind) from a financial institution that is not a group? (Note: if a single member of the household has taken loans from multiple institutions or multiple members of a household have taken loans from a single institution, sum all of them. In short, it is the total number of loans received during the period by the household.) 1=Yes (list all loans below) 2=No 1302 1303 1304 1305 1306 1307 1308 1308 1310 Type of Financial Institution 1=Bank 2=Finance Co. 3=MFI 4=Cooperative 5=Other Type of debt 1=Payable debt, 2=Non-payable debt support (equity, convert￾ible debt, or leasing), 3=Both Type of loan: 1=Cash, 2=In-kind, 3=Both Date loan received (MM/DD/YY) Name of Institution Value of Loan received (NPR) Outstanding loan amount (NPR) Name of borrower (select from HH roster, single answers possible) How much of the loan amount was used for agriculture? (NPR) Now I’m going to ask about your household access to group-based savings and credit from Bhadra 01, 2074 to Shrawan 31, 2075. 163 14. ACCESS TO INFORMAL SOURCES OF SAVINGS AND CREDIT (GROUP BASED FINANCIAL SERVICES) 1401 Did your household deposit savings with a group within the past 12 months? Yes=1, No=2 (If no, skip 1403-1405) 1402 Did you borrow money from a group within the past 12 months? Yes=1, No=2 (If no, skip 1406-1408) 1403 1404 1405 1406 1407 1408 1409 1410 Group Name Savings Loans Documents Join Date What type of group did your household deposit savings with? 1=Farmer group, 2= savings and credit group How much did you deposit with this group? (NPR) Who in the household made the savings deposits to this group? (choose from household roster, multiple answers possible) What type of group(s) did your household borrow money from? How much did you borrow from this group? (NPR) Who in the HH borrowed money? (choose from HH roster, multiple answers possible) Does the household member have a checkbook or passbook? Yes=1, No=2 Did this household member join the [group] before Bhadra 1, 2074? Yes=1, No=2 164 15. PERCEPTION OF CONSTRAINTS What are the top three issues that prevent you from achieving higher yields or sales, starting with the most important? (This is an open-ended question; do not prompt them by listing options. Listen to their response and circle one option for each row, based on their ranking. The farmer may use different words, select the option that most closely matches.) 1501 #1 Constraint (most important): (a) labor, (b) money/access to finance (c) irrigation water, (d) decision-making authority, (e) knowledge, (f) availability of quality inputs, (g) access to buyers, (h) natural disasters/weather, (i) prices for quality inputs, (j) prices received from buyers, traders, wholesalers, aggregators, (k) other (specify): 1502 #2 Constraint (next most important): (a) labor, (b) money/access to finance (c) irrigation water, (d) decision-making authority, (e) knowledge, (f) availability of quality inputs, (g) access to buyers, (h) natural disasters/weather, (i) prices for quality inputs, (j) prices received from buyers, traders, wholesalers, aggregators, (k) other (specify): 1503 #3 Constraint (next most important): (a) labor, (b) money/access to finance (c) irrigation water, (d) decision-making authority, (e) knowledge, (f) availability of quality inputs, (g) access to buyers, (h) natural disasters/weather, (i) prices for quality inputs, (j) prices received from buyers, traders, wholesalers, aggregators, (k) other (specify): 165 RM 2: EXPOSURE TO SHOCKS AND STRESSORS AND COPING STRATEGIES For all tables in Section 2, interview the primary female decision-maker or whoever is most knowledgeable about the food consumption of household members. Enter into this section with caution (language from PBS questionnaire): This module contains questions that are sensitive. Ensure complete privacy before continuing with this module. 16. HOUSEHOLD FOOD INSECURITY EXPERIENCE SCALE (FIES) No. Questions: During the past 30 days, was there a time when you or others in your household… Incidence (1=Yes, 2=No, 99=DNK) 1601 …were worried you would not have enough food to eat because of a lack of money or other resources? 1602 … were unable to eat healthy and nutritious food because of a lack of money or other resources? 1603 … ate only a few kinds of foods because of a lack of money or other resources? 1604 … had to skip a meal because of a lack of money or other resources to get food? 1605 … ate less than you thought you should because of a lack of money or other resources? 1606 … did not have food because of a lack of money or other resources? 1607 … were hungry but did not eat because there was not enough money or other resources for food? 1608 … went without eating for a whole day because of a lack of money or other resources? 166 17. Exposure to Shocks and Stressors and Severity SN 1701 1702 1703 1704 1705 1706 1707 Shocks and Stressors Did your HH experience any shocks Answer only for shocks experienced during the last 3 years? during the last 12 months? How severe was the impact on your (HH) income? How severe was the impact on your HH food consumption? Who in your HH was most affected? (rank top 3) 1=Yes, 2=No, 3=DNK 1=Not affected, 2=Not severe, 3=Somewhat severe, 4=severe, 5=extremely severe, 99=DNK If “1” or “99” in 1703 and 1704, go to next topic First most affected Second most affected Third most affected Extreme Weather and Natural Disasters a. Too much rain b. Too little rain c. Land erosion/ Landslide d. Freezing temperatures* e. Earthquake* Markets f. Sharp increase in the price of food you buy g. Not being able to access inputs or services for crops h. Not being able to access inputs or services for livestock 167 i. Not being able to sell crops, livestock, or other products at a fair price Disease and Death j. Disease affecting crops k. Insects affecting crops l. Disease affecting livestock m. Severe illness in the family n. Death in the household Loss and Theft o. Loss of land p. Someone stealing or destroying belongings q. Theft of crops r. Someone stealing animals 168 Questions in Table 18 relate to the set of shocks and stressors experienced, and are not designed to be asked for each shock or stressor. Skip if the HH did not experience any shocks and stressors. 18. Coping Strategies in Response to a Shock or Stressor SN 1801 Coping Strategies During the Past 12 months, which of these sources did you use in response to a shock or stressor? 1=Yes, 2=No, 3=DNK Positive Strategies a. Took up new wage labor or extended work hours b. Cash from HH members working in another country (foreign remittances) c. A HH member returned from overseas to help d. Another HH in the community shared their food e. Relief organization, local group, or project provided food aid Neutral or Potentially Negative Strategies f. Borrowed cash from another household g. Borrowed cash from an informal group (farmer group or savings and credit group) h. Bought food or other HH items on credit i. A HH member migrated for foreign employment j. Used HH savings Negative Strategies k. Sold livestock under stress l. Sold land under stress m. Sold another asset under stress n. Harvested immature crops early for food o. Ate less food or ate lower-cost food p. Pulled one or more children out of school q. Other (specify): 169 19. Household income from various sources in past 12 months: 20. HH Expectations About Social Capital Table 20 focuses on expectations about potential future help, not actual experience. SN 2001 2002 2003 2004 Type of Social Capital a) Bonding b) Bridging c) Bonding d) Bridging Questions 1=Yes, 2=No, 3=DNK Relatives living in your community? Relatives living outside your community? Non-relatives living in your community? Non-relatives living outside your comm.? a. Will your HH be able to lean on others for financial or food support during difficult times (shocks or stressors)? b. Will the same people that you will be able to lean on SN 1901 Income sources What was your total household income from following sources in the past 12 months? (Amount NRS) A Own crop sales (except mentioned above) B Own livestock sales (except goats) C Agricultural wage labor D Non-agricultural wage labor E Fish sales F Agricultural business, trade, or self-employment G Non-agricultural business, trade, or self-employment H Rental of land I Rental of house or rooms J Cash from HH members working in another country (foreign remittances) K Government allowances (senior citizen allowance, maternity allowance, pension, etc.) L Salaried work M Sale of land and or assets 170 during your difficult times also be able to lean on you for financial or food support during their difficult times (shocks or stressors)? Table 21 focuses on actual experience. 21. HH Experience Using Social Capital SN 2101 2102 2103 Questions and Codes If no, skip 2102 and 2103 1=Relatives living in your community 2=Relatives living outside your community 3=Non-relatives living in your community 4=Non-relatives living outside your community 1=farm labor 2=cash loan 3=food 4=housing 5=other a. During difficult times (shocks or stressors) experienced in the last 12 months, did your HH receive any assistance from relatives or community members? 1=Yes 2=No 3=Support not needed From whom did you receive support? What kind of support did you receive? b. In the last 12 months, did your HH give any assistance to relatives or community members experiencing difficult times (shocks or stressors)? 1=Yes 2=No 3=Support not needed To whom did you provide support? What kind of support did you provide? 171 Skip Table 22 if the HH did not experience any shocks and stressors. 22. Post-shock perceptions about future food security SN Questions Response Codes 2201 Would you say that right now, your household's ability to meet your food needs is: 1. Better than before the shocks and stressors experienced in the past 12 months? 2. The same as before the shocks and stressors experienced in the past 12 months? 3. Worse than before the shocks and stressors experienced in the past 12 months? 2201 Looking ahead over the next year, do you believe your household's ability to meet your food needs will be: 1. Better than before the shocks and stressors experienced in the past 12 months? 2. The same as before the shocks and stressors experienced in the past 12 months? 3. Worse than before the shocks and stressors experienced in the past 12 months? 23. Perceptions of Government Effectiveness(Resil-c) Questions Response Codes 2301 Do you believe your local government (the palika) will respond effectively during the next shock or stressor? 1=Yes 2=No, I do not expect them to be responsive 3=It is unlikely that I will need support 172 RM 4: SUAAHARA EXPOSURE Questions in Table 24 should be asked of the primary female decision maker. Introductory statement: Now we have some questions about your knowledge of Suaahara and your interactions with individuals working with Suaahara. 24. EXPOSURE TO SUAAHARA PROJECT K2 SN SH SN Question Response Skip 2401 901 Before this interview, had you ever heard of Suaahara? 1=Yes 2=No 3=Don’t know >End 2402 914 Has a Suaahara staff member (e.g. field supervisor, CNF, WASH Triggerer) ever visited your home? 1=Yes 2=No 3=Don’t know >2407 2403 915 How many times has a Suaahara staff member (e.g. field supervisor, CNF, WASH Triggerer) visited your home in the past 6 months? [Number of visits] 2404 918 What did the Suaahara staff member (e.g. field supervisor, CNF, WASH Triggerer) do at your home? Multiple answers possible. Probe, but don’t read possible answers. a=checked vegetable garden b=checked chicken husbandry c=advised on improving garden d=advised on improving chicken husbandry e=demonstrated gardening skills f=demonstrated chicken husbandry skills g=advised on making child food or child feeding h=demonstrated making child food or child feeding i=discussed maternal health or illnesses j=discussed child health or illnesses j= discussed maternal nutrition or diet k=discussed child nutrition or diet l=checked toilet and its use m=discussed water purification n=discussed hand washing with soap and water o=discussed disposal of child feces p=vitamin A distribution q=support on childbirth (deliveries) r=discussed gender norms, values, or cultural practices s=discussed division of household and homestead food production work by family members t=discussed or advised on family planning u=other v=don’t know 2605 919 Did the Suaahara staff member 1=Yes 173 K2 SN SH SN Question Response Skip (e.g. field supervisor, CNF, WASH Triggerer) speak with you during this visit? 2=No 3=Don’t know 2606 920 Which other family members did the Suaahara staff member (e.g. field supervisor, CNF, WASH Triggerer) speak with? Multiple answers possible. Probe, but don’t read possible answers. a=spouse b=mother or mother-in-law c=father or father-in-law d=other adult household member e=other child household member f=adolescent g=no one or n/a h=don’t know 2407 925 Has a Female Community Health Volunteer ever visited your home? 1=Yes 2=No 3=Don’t know >2409 2408 926 How many times has a Female Community Health Volunteer visited your home in the past 6 months. [number] 2409 205 How many currently pregnant women are there in this household? [number] 2410 017 How many children 0-23 months of age live in this household and are your sons and daughters? [number total] [number sons] [number daughters] 2411 939 Have you ever heard of a radio program called “Bhanchhin Aama” (Amma Kahat Batin; Bhannicchin Ama)? 1=Yes 2=No 3=Don’t know >2414 2412 940 Have you ever listened to “Bhanchhin Aama” (Amma Kahat Batin; Bhannicchin Ama)? 1=Yes 2=No 3=Don’t know >2414 2413 941 How often do you listen to “Bhanchhin Aama” (Amma Kahat Batin; Bhannicchin Ama)? 1=Every week 2=2-3 times per month 3=Once a month 4=Less than once a month 5=Only listened once or twice 2414 944 Do you own a mobile phone? 1=Yes 2=No >2417 2415 945 Did you receive any health or nutrition related text messages on your mobile phone in the last 1 month? 1=Yes 2=No 3=Don’t know >2417 174 K2 SN SH SN Question Response Skip 2416 946 How many messages did you receive in the last 1 month Number ______________ Do not know 2417 904 In your household, do men participate in [activity] Multiple answers possible. A=Discussions with Suaahara (Field Supervisor, CNF, etc.) during home visits B=Community Health Volunteer-led groups (HMGs, savings) C=Suaahara II Homestead Food Production group D=Community events (such as “key life”) E= Listening to Bhanchhin Aama F =Being a Village Model Farm G=Being a gender equality champion H=Other Suaahara II platform/activity (specify): ______ Thank you 175 APPENDIX VII: STUDY AND DATA COLLECTION TEAM TECHNICAL AND ADMINISTRATIVE STAFF OF FULLBRIGHT PVT. LTD. CORE TEAM MEMBER: Manjul Manandhar QUALITY CONTROL STAFFS: Rishi Ram Koirala Ramesh Poudel Shankar Karki DATA QUALITY CHECKERS: Bijaya Pandey Chandrika Rai Krishtal Chaudhary Shushil KC Sita Maharjan Pravakar Jaisawal Shamjhana Shrestha FIELD DATA COLLECTION STAFFS: Supervisors/Enumerators Supervisors Enumerators Enumerators Bikim Shrestha Bishnu Pandey Braj Kishor Shah Hari Bhakta Saud Krishna Khanal Mahesh Dev Prakash Giri Premdip Adhikari Rajendra Shrestha Ram Chandra Paneru Ram Dutta Panta Suman Ghimire Umesh Shrestha Arjun Giri Arjun Magar Asmi Paudel Bishnu Datta Pandey Bishnu Khatiwada Bramhadev Chaudhary Deepak Singh Karki Dipila Pant Dirga Thapa Ganesh Khadka Ganga Pd. Paudel Geeta Chaudhari Ghanasyam Gaire Ishu Gurung Jamuna Acharya Keshab Datta Bhatta Kul Bahadur Khatri Kumar Sanjel Madhav Gyawali Mahesh Mahato Manisha Bhatta Manisha Hamal Manoj Shah Min Koirala Mukti Nath Adhikari Nagendra Shah Navraj Awasthi Nirmal Banjara Niroj Dhodari Pradip Basnet Pratima Bhatta Paudel Purushottam Dahal Rajendra Acharya Ramesh Thapa Ranjana Bista Roshan Karki Rupa Barma Samiksha Aryal Sanjay Sharma Shivji Budhathoki Sudesh Ghimire Tara Devi Khatiwada 176 APPENDIX VIII: DISCLOSURE OF ANY CONFLICT OF INTEREST 177 U.S. AGENCY FOR INTERNATIONAL DEVELOPMENT 1300 PENNSYLVANIA AVENUE, NW WASHINGTON, DC 20523