1 
Mozambique 
Integrated Recovery 
and Resilience 
Project
Baseline Assessment Report
Submitted: August 30, 2019
2 
Table of Contents
Table of Contents.................................................................................................................................... 2
1 Introduction ......................................................................................................................................... 3
Background ..................................................................................................................................... 3
Purpose of the Baseline .................................................................................................................. 3
2 Baseline Methodology and Implementation ...................................................................................... 3
Household Targeting Approach ...................................................................................................... 3
Baseline Methodology .................................................................................................................... 4
Enumerator Training and Pre-test .................................................................................................. 5
Baseline Implementation................................................................................................................ 5
Data Validation, Cleaning, and Analysis.......................................................................................... 6
Challenges and Limitations............................................................................................................. 6
3 Results and Findings............................................................................................................................. 7
Demographic information............................................................................................................... 7
Crop-related Activities.................................................................................................................... 8
4 Conclusion......................................................................................................................................... 10
Annex I: Data collection Instruments.................................................................................................... 11
Annex II: List of Key informants............................................................................................................ 16
Annex III: Baseline Households Interviewed......................................................................................... 17
Annex IV: Annual Performance Data Table (APDT) .............................................................................. 19
3 
1 Introduction
This baseline report provides performance baseline data for the Mozambique Integrated Recovery 
and Resilience (MIRAR) project implemented by Land O’Lakes Venture37. It is prepared and 
submitted according to the requirement in grant agreement section 1.5(b)(3)(B)(ii). 
Background
The mainly agrarian population in the Beira corridor is grappling with widespread damage to their 
productive assets, raising concerns over food security and the sustainability of agriculture-based 
livelihoods. Over 100,000 homes of largely resource-poor households were destroyed, 711,000 
hectares of crops ruined, and the livestock that survived Cyclone Idai lack access to grazing and risk 
disease as they move through the region and congregate in dry areas. In Chimoio District alone, 
local authorities report that more than 451,300 hectares of agricultural land have been destroyed, 
comprising 65 per cent of the total crops in the Province (OCHA 1 April 2019 Update). 
In partnership with both USAID and USDA, Land O´Lakes Venture37 (Venture37) operates 
throughout the Beira corridor, implementing crops and livestock activities and has documented 
significant damage to crops and death, disease, and malnutrition among livestock. Much of the 
market infrastructure has been damaged at a time when both local supply of resources and demand 
for agriculture products is already low. 
MIRAR is a USAID/OFDA-funded project running from June 1, 2019 to April 30, 2020 with a budget of 
USD 799,897. The goal of MIRAR is to assist agrarian households in Sofala and Manica Provinces to 
stabilize their household food security during the main agricultural season following Cyclone Idai by 
starting to recover their mixed seeds and cropping assets. To do this, Venture37 will distribute seven 
kilograms of maize and legume seeds to 15,000 households in Nhamatanda District of Sofala 
Province and Sussundenga District of Manica Province. The distribution of seeds will allow 
households affected by Cyclone Idai floods to plant most of their normal planting land during the 
main agricultural season so they can improve food security for their family through a normal harvest. 
Using existing seed systems to distribute these seeds will familiarize these households with the 
means of getting seeds in the future if they cannot set aside enough of their own. 
Purpose of the Baseline
The purpose of this MIRAR internal baseline assessment is to: 
1. Provide contextual information about crop farmers in the targeted area; and 
2. Establish baseline levels of key performance outcome-level indicators. 
2 Baseline Methodology and Implementation
This section provides an overview for the household targeting, baseline methodology, and 
implementation. All tools used can be found in Annex I. 
Household Targeting Approach
The MIRAR team consulted with district government authorities in Nhamatanda and Sussundenga
Provinces to obtain a list of the most affected villages which have not received seeds from other 
NGO efforts. To ensure that MIRAR does not duplicate efforts, the Program Manager coordinated 
with the Seed Distribution Cluster in Beira (a group of NGOs and government) who discussed and 
presented distributions expected from their respective organizations. To further coordinate the 
location selection, the MIRAR project contacted the Provincial Secretariat, National Institute for 
Disaster Management (INGC), Provincial Directorate of Agriculture and Food Security (DPASA), 
District Governments, District Economic Service (SDAE), Administrative Posts and Localities.
4 
The MIRAR team conducted thirteen key informant interviews (KIIs) with six community leaders in 
Sussundenga and Macate districts in the Manica province, and seven community leaders in 
Nhamatanda district in the Sofala province. The purpose of the KIIs were to validate the data from 
the district authorities to ensure they meet the MIRAR targeting criteria. The criteria and validation 
efforts are described below. 
• Household vulnerability. This is defined as low-income status and affected by the cyclone, 
with particular emphasis on households headed by women. All households in the villages in 
the project area are low-income. MIRAR used the list from the district officials and the KIIs to 
validate and refine our targeting and make the final target list of villages.
• Household head has prior experience in the selected crops. Validated by asking the district 
official in the key informant interviews whether the local residents have experience with 
maize, cowpea, pigeon pea, or lablab bean.
• Household has not received seed from other recovery efforts. The district governments 
provided lists of villages that they believe have not received seeds from any other NGO. 
MIRAR validated this through the Seed Distribution Cluster group and also by asking the local 
village officials whether the village is receiving seeds from any other source
The key informant interviews (see Annex II for a full list of interviews conducted) focused on the 
following: 
 Number of households in each community, 
 How the cyclone affected the community, 
 Which crops are usually planted in that area, and 
 Any seed distribution plans in the community (government, INGC, NGO). 
The analysis of the key informant interview data showed that 91% of the villages in Nhamatanda 
district and 63% of villages in Sussundenga district were affected by rains and severe flooding. The 
villages in the Macate district were affected by wind and rains only. Given this result and MIRAR’s 
objective to focus on villages affected by flooding who were most likely to have lost their seed 
sources, the baseline sampling frame included the affected villages from Sussundenga and 
Nhamatanda districts. 
Baseline Methodology
The understand the current state of farmers in the target villages and set baseline values for project 
indicators, the MIRAR team developed a household survey (see survey in Annex II) to administer to a 
randomly selected sample of households in the target villages. The survey asked questions about 
household demographics, crops grown and harvested in the previous season, source of seeds in the 
previous season, and expectations for planting area in the next season. 
The number of households affected by floods in Nhamatanda and Sussundenga Districts, 19,610 was 
used as the sample frame. The sample size was calculated using the formula below at the 95% 
significance and 5% margin of error at the District level. 
5 
The final minimum sample size was determined to be 378 for Nhamatanda and 294 for Sussundenga. 
However, the team decided to oversample areas in Nhamatanda to adjust for level of vulnerability 
by allocating the proportion based on the extent of flooding, ensuring that the sampled number of 
households remained above the minimum sample size for each district. Table 1 below shows the 
minimum sample size, the planned sample and the total number of surveys conducted. Note that 
after cleaning, 994 surveys were deemed viable. See Annex III for a full list of the Administrative 
Posts and Villages sampled.
Table 1: Baseline household survey sample and actual
District Sample 
size 
Oversample 
adjusted for most 
affected (planned)
# of surveys 
conducted (actual)
# survey viable 
after cleaning
Sussundenga 294 300 303 303
Nhamatanda 378 700 699 691
Totals 672 1,000 1,002 994
Households were randomly selected within each village. In more populated areas, every 5th
household was selected. In less populated areas, every 2nd household was selected. This was done 
until the 1,000 surveys were reached. 
Enumerator Training and Pre-test
The quantitative survey tool was finalized in English and translated to Portuguese. The survey tool 
was coded into an electronic database for data entry purposes. 
Two full days of enumerator training took place at the Venture37 office in Chimoio. The training was 
led by the Global Monitoring Evaluation and Learning (MEL) Manager with sessions conducted by 
the MIRAR Project Manager, MEL Advisor and a Gender Advisor. A total of twenty-four participants 
(18 enumerators and 6 MIRAR field technicians) participated in the training. Topics included the 
baseline objectives, overview of the MIRAR project, gender concerns, the household questionnaire, 
guidelines for administering surveys, practical interviews, observations and feedback on the baseline 
household survey. On the second day of training, the team performed a pilot test. The MIRAR MEL 
Specialist conducted an after-action review with the enumerators and incorporated the
recommendations into the survey. Although not being an intervention area of the MIRAR project, 
the pre-test was conducted in Chimoio District due to its proximity to the MIRAR office and focused 
on the questionnaire and the data collection process.
Baseline Implementation
Based on the results of the pilot test, data was collected using paper-based surveys and then 
entered into the IMPACTS database. The data collection took place within a period of 12 days, from 
July 31st to August 12th, 2019. There was no data collection on August 5th. 
The MIRAR Project Manager met and received authorization from the SDAEs, three Heads of 
Administrative Posts in Chirassicua, Lamego and Metochira Pita, the Councilor of Urbanization of the 
Nhamatanda Municipal Council and community leaders prior to initiating the baseline data 
collection within the targeted communities. He also received a list of the number of households 
affected by the Cyclone, number of existing resettlement centers, and a list of Guides from the
community leaders. 
To ensure efficiency in the data collection process, MIRAR Field Technicians oversaw teams of 
enumerators and were responsible for assessing the daily quality and reliability of the data collected 
by the enumerators. Enumerators were divided into work teams according to the number of 
6 
households in each community and tasked with collecting data for the baseline and participant 
registration. Each enumerator had a daily goal of conducting 5 baseline surveys. During this process, 
the Field Technicians and the MEL Specialist conducted a daily review of the number of interviews 
conducted and the difficulties encountered and incorporated the learning into the next day’s
scheduling activities. 
Actual baseline interviews and household registrations during the timeframe averaged 6 baseline 
interviews conducted per enumerator per day. The government officials recommended that MIRAR 
also focus on the affected populations who had to be resettled in the highlands (out of danger)
following Cyclone Idai. 
The table below indicates the number and types of staff that were utilized in the baseline exercise. 
Table 2: Staff allocated to the Baseline
Function #
Project Manager 1
MEL Specialist 1
Field Technicians 6
Enumerators 18
Drivers 2
Local Guides 45
Data entry staff 3
Total # of staff 30
Data Validation, Cleaning, and Analysis 
The MEL Specialist conducted data cleaning in the SPSS statistical package. Data cleaning focused on 
identifying the following: 
 Duplicate observations, 
 Inconsistent data,
 Unrealistic data,
 Blank or empty observations and outliers, 
 Validation of responses for similar questions, and
 Incorrect totals where applicable. 
The Global Monitoring, Evaluation and Learning team with support from the MEL Specialist 
conducted data analysis using the STATA statistical package. 
Challenges and Limitations
The team encountered the following challenges: 
• House-to-house registration took place simultaneously with the baseline. However, houses in 
some villages were far apart from each other as far as one km apart which hindered registration 
in these districts1
• Roads and bridges that were destroyed during Cyclone Idai remained flooded or impassable 
during the key informant interviews. Field Technicians used canoes and rented motorbikes to 
reach the affected villages. 
 
1 Macorococho, Lamego, Joaquim Marra, Maguimba A, B, C and Micoroa, kobola, Muda Mufo in Nhamatanda 
District and Zinguena, Muoco, in Sussundenga District
7 
• It was difficult to anticipate the number of households in each surveyed area, which it necessary 
to constantly adapt the data collection logistics. Although the MIRAR team received lists of 
households from community leaders prior to starting the survey, the lists were not accurate or 
comprehensive. 
• In some cases, the language was a challenge as the interviewers had no skills in certain mother 
languages and depended on the local guides to assist with translation. 
3 Results and Findings
This section describes the results of the baseline household survey, including demographic profiles 
of the households and a description of their crop related activities. A total of 1,002 surveys were 
administered and after the data cleaning and validation 994 household surveys were analyzed. 
Demographic information 
The typical respondent interviewed was a head of household (79.4%), married (63.1%) and was a 
farmer by profession (98.2%). Ages ranged considerably, with a large minority between 26 and 44 
(41.2%) years old. Only 6.0% indicated having another profession in addition to farming. Very few 
respondents had a disability (1.9% or 19 respondents). Respondents in Nhamatanda and 
Sussendenga Districts were fairly similar. However, fewer heads of households and more spouses 
were interviewed in Sussendenga than Nhamatanda District (71.0% versus 83.1%). Polygamy was 
also much more prevalent in Sussendenga than Nhamatanda District (14.5% versus 3.5%), but the 
total proportion married stayed consistent. Table 3 – 6 below provide more information about the 
demographics of the respondent. 
Table 3: Respondent relationship to head of household
Relation Sussundenga Nhamatanda Total
Self 71.0% 83.1% 79.4%
Spouse 26.4% 15.2% 18.6%
Other 2.6% 1,7% 2.0%
N = 303 691 994
Table 4: Age of respondent
Age Range Sussundenga Nhamatanda Total
65 and over 11.6% 14.1% 13.3%
55-64 15.5% 15.7% 15.6%
45-54 20.8% 16.8% 18.0%
35-44 20.8% 19.3% 19.7%
25-34 19.8% 22.2% 21.5%
18-24 11.6% 12.0% 11.9%
N = 303 690 993
Table 5: Marital status of respondent
Marital Status Sussundenga Nhamatanda Total
Married 53.8% 67.0% 63.0%
Married (Polygamous) 14.5% 3.5% 6.8%
Cohabitating 4.3% 1.2% 2.1%
Single 3.6% 5.1% 4.6%
Widow 20.5% 17.1% 18.1%
Divorced 3.3% 6.2% 5.3%
N = 303 691 994
8 
Table 6: Profession of Respondent 
Profession Sussundenga Nhamatanda Total
Farmer 96.0% 99.1% 98.2%
Self employed 5.0% 2.7% 3.4%
Employee 3.9% 2.0% 2.6%
Retired 0.3% 0% 0.1%
Student 1.0% 0.1% 0.4%
Other 2.3% 1.0% 1.4%
N = 303 691 994
*Note that multiple responses were allowed. N is the unique number of individuals, but the % adds up to more than 100. 
The households sampled were generally headed by males (70.8%) with a fairly large minority headed 
by females (29.2%). This was consistent between districts. Households had 5.7 members on average, 
slightly larger in Susundenga than Nhamatanda district (6.3 versus 5.4). Male and female household 
members were about equally prevalent in the household. This information on household size and 
gender composition is important in the calculation of the MIRAR/OFDA indicator: Number of people 
directly benefiting from improving agricultural production and/or food security activities
disaggregated by sex. MIRAR will use the household size to understand how many individuals were 
affected by the seed distribution at the household level. Table 7 and 8, below, provide more 
information about the household. 
Table 7: Head of household by sex
Sussundenga Nhamatanda Total
Males 68.3 71.9 70.8
Females 31.7 28.1 29.2
N= 303 691 994
Table 8: Household Family members by sex
Sussundenga Nhamatanda Total
Average # of all household members 6.3 (N=303) 5.4 (N=686) 5.7 (N=989)
Average # of males per household 3.1 (N=285) 2.7 (N=665) 2.9 (N=950)
Average # of females per household 3.4 (N=296) 2.8 (N=664) 3.0 (N=960)
Crop-related Activities 
The majority of households planted maize in the last crop season (95.4%), while a minority planted 
the other target crops: cow peas (34.1%), pigeon peas (18.6%), common beans (12.6%), lablab 
(4.3%), and soy (1.3%). A large number planted other types of crops (60.0%), including rice, sesame, 
sorghum, peanut and tomatoes. The average total number of hectares planted was 4.6, with a 
median of 2. A few outlier households had a total hectarage above 50, with one household that 
planted on 400 hectares. The project approach is to distribute to all households in a selected village, 
so these outliers were kept in the sample to reflect the population. The amount of land planted was 
slightly higher in Nhamatanda than Sussundenga district (5 versus 3.7 Ha). Maize was grown on the 
most land (1.9 Ha), while the other crops were each grown on about half as much land as maize (0.9-
1.5 Ha). See Table 9 below for a breakdown of the amount of land planted by crop. 
9 
Table 9: Crops planted (ha)
Sussundenga Nhamatanda Total
Average Ha N Average Ha N Average Ha N
Total 3.7 298 5.0 669 4.6 991
Maize 2.7 297 1.5 648 1.9 945
Pigeon peas 1.1 76 1.3 108 1.2 184
Cow peas 1.2 126 1.6 212 1.5 338
Lablab 0.7 28 1.3 15 0.9 43
Soybeans 0.9 10 2.4 3 1.2 13
Common beans 1.2 70 1.3 55 1.2 125
Other 1.6 169 1.4 425 1.5 594
Data reflects number of hectares planted for those that planted any of that crop
Over half (51.8%: 49.9% in Nhamatanda; 56.1% in Sussundenga) of households interviewed reported 
that they were not able to harvest any crops last season. This may demonstrate the devastating 
effects of the cyclone. This was a problem across all crops, but a higher proportion of soy (92.3%), 
pigeon peas (73.9%) and lablab (95.3%) growers had issues than beans (66.4%), cowpeas (62.9%), 
and maize (54.6%). The yield of each crop depicted in Table 10 below includes the numerous 
households that harvested no crop. Only counting those that harvested some crop would result in 
much higher yield measures: maize (152.0 Kg/Ha), pigeon peas (37.6 Kg/Ha), cow peas (41.6 Kg/Ha), 
lablab (6 Kg/Ha), soy (320 Kg/Ha), and beans (17.6 Kg/Ha). 
MIRAR used the kilograms harvested or predicted to be harvested of each crop at baseline and final 
evaluation to estimate the OFDA indicator: Number of months of household food self-sufficiency as a 
result of seed system security programming. Using the assumption that the average household (5.7 
members) needs 50kg of maize per month to sustain their household, we will calculate how much 
maize they could buy for the price they could receive of the crops they harvested. Then we can 
calculate how many months that would last them. Since many households did not harvest any crop 
in the last season, many have 0 months of food self-sufficiency. The average month of food self 
sufficiency amongst respondents is 2.6 months. Food self-sufficiency is slightly higher in 
Sussundenga district (3.0 months) than Nhamatanda district (2.4 months). 
Table 10: Crop Yield (KG/Ha)
Crop Sussundenga Nhamatanda Total
Yield (Kg/Ha) N Yield (Kg/Ha) N Yield (Kg/Ha) N
Maize 84.6 297 61.5 646 68.8 943
Pidgeon peas 9.6 76 9.9 108 9.8 184
Cow peas 11.5 125 11.2 210 11.3 335
Lablab 0.4 28 0.07 15 0.3 43
Soybeans 0 10 106.7 3 24.6 13
Common beans 23.6 68 9.5 54 17.3 122
This table depicts yield for those that grew the crop, including those that did not harvest. 
The majority of respondents used their own stock (56.7%) or purchased (41.0%) seed to plant in the 
last season. A larger proportion of farmers used their own seed in Sussundenga than Nhamatanda 
district (68.5% versus 51.4%), and the opposite was true for buying seed (26.7% versus 47.5%). 
Other sources of seeds included family and neighbors. A very small number (.6%) of farmers received 
seeds from aid workers. This is used to calculate the baseline value for the custom indicator: 
Percentage of households with awareness of how to access seeds. Nearly all (99.5%) households
were able to access seed in the last planting season. This result is higher than the MIRAR target of
75%, as the households did not have a natural disaster before the last season. 
10
Table 11: Sources of Seeds
Source Sussundenga Nhamatanda Total
Bought from source 26.7% 47.5% 41.0%
Received seeds from AID worker 0.7% 0.6% 0.6%
Received seeds from other source 6.6% 5.9% 6.1%
Used own stock 68.5% 51.4% 56.7%
Other source 1.0% 0.9% 1.0%
N= 289 648 937
This question allowed for multiple responses, so the total adds up to more than 100%
Respondents indicated that they wanted to grow an average of 2.6 hectares next planting season, 
which ranged from 0 to 205 Ha. The amount of land households want to cultivate next season is 
slightly higher in Sussundenga (3.6 Ha) than Nhamatanda (2.2 Ha). This is interesting, as we reported 
earlier that respondents in Nhamatanda District actually planted significantly more last in the last 
season than Susundenga. Perhaps this is an effect of Nhamatanda being hit harder by flooding in the 
cyclone. This data was used to provide a baseline for the OFDA indicator: Percentage of households 
with access to sufficient seed to plant. Only 57.3% of households grew on as much land in the last
cropping season as they want to grow on in the next cropping season. Assuming that the amount of
land to grow stays constant between the years, this would indicate that slightly over half had enough
seed to grow on all their land.
Table 12: Intended Planting area (ha)
Sussundenga Nhamatanda Total
Hectares 3.6 2.2 2.6
N= 301 665 966
4 Conclusion
The baseline data confirmed that the households targeted were likely greatly impacted by the 
cyclone. Over half of households did not harvest any crop, despite planting seed last season. This 
was slightly more prevalent in Sussundenga than Nhamatanda district. The baseline also was able to 
provide baseline values for key indicators. Annex III lists the baseline value for the indicators and 
their appropriate disaggregates. 
11
Annex I: Data collection Instruments
Key informant Interview Guide: Household Targeting – Village Leaders
RF1 – Date of Interview
RF2 – Survey Number
RF3 – Village
RF4 – District
RF5 – Province
RF6 – Community Name
RF7 – GPS Coordinates Longitude: Latitude: 
Good Morning/ Good Afternoon: My name is _______. I am working as a researcher for Land O’ Lakes International 
Development for a new project funded by USAID. We are conducting interviews to understand the current situation after 
the Cyclone in order to inform agriculture activities in your community.
CONFIDENTIALITY AND CONSENT: Your answers will be kept confidential, and your individual responses cannot be 
traced back to you. You will not receive any direct benefit by virtue of participating in this survey, neither any harm will be 
done to you as a result of participating. Your honest answers to these questions will be appreciated.
The survey will take approximately 45 minutes. 
Do I have your consent to conduct this interview? 
1. Yes  = Thank the participant for accepting and proceed to next question
2. No  = Stop the interview and thank the participant
A – Basic Data 
Ask to speak with the leader of the village. 
A1 – What is your full name? A1a
First name: 
a1b
Middle Name: (Skip if do not have)
A1c: Surname
A2 – What is your position in the community? _____________ 01
A6 - What is your age?
(if respondent does not know, ask in what year he/she 
was born)
Less than 18 01
18-24 02
25-35 03
45-55 04
55-65 05
65 or more 06
Year born ___________ 07
A7 - What is your marital status? Married 01
Married (Polygamous) 02
Not married but living with someone 03
Single 04
Widow 05
Divorced 06
A8 - Person with disability (Fill in; do not ask 
respondent)
Yes 01
no 02
A9 - What is your profession or full time job?
Select as many as apply
Farmer 01
Self Employed 02
Employee 03
Retired 04
Other (Specify) ______________ 06
A10 - Do you have a mobile phone? Yes (Ask A4.1) 01
12
No (Ask A7) 02
A10.1 – What is your mobile number?
A11 - How many inhabitants do you have in your 
community?
_________
Needs assessment questions
Now we will ask some questions about how the cyclone and farming activities
SC1. How has the cyclone affected your community? Flood 01
Wind 02
None 03
Other (Specify) _____________ 04
If the answer is 03, interrupt the interview and thank the leader for participating in the surrvey. Skip to SC3.
SC2. What percentage (%) estimate of your 
community was affected by the cyclone?
________ 01
SC3. During the last season, what crops has your 
community planted?
(select as many as apply) 
Maize 01
Legumes: Peas 
 Pidgeon Peas 02
 Cowpeas 03
Legumes: Beans 04
 Lablab 05
 Soybeans 06
 Common Beans (sugar beans) 07
They do not plant these crops 08
If this is the answer in 08, interrupt the interview and thank the leader for participating in the survey.
SC4. Does the Government, INGC or an NGO, others 
have any seed distribution plans here in your 
community?
Yes (which one) _____ 01
No 02
Now I will ask you a question about intercropping
SC5. Does your commuity know about intercropping? Yes (with which crops) _____ 01
No 02
End Survey
Thanks very much for your participation. The information you provided was very useful. 
Village Leader Technician 
_____________________________ _____________________________
Date:___/_____/______ Date:____/______/______
13
Baseline & Needs Assessment Survey 
Enumerator: Fill in this information before you talk to the farmer. Do not ask the farmer)
RF1 – Date of Interview
RF2 – Survey Number
RF3 – Village
RF4 – District
RF5 – Province
RF6 – Community Name
RF7 – Enumerator ID
RF8 – GPS Coordinates Longitude: Latitude: 
Good Morning/ Good Afternoon: My name is _______. I am working as a researcher for Land O’ Lakes International 
Development for a new project funded by USAID. We are conducting a survey to understand the current situation after 
the Cyclone in order to inform agriculture activities in your community. 
CONFIDENTIALITY AND CONSENT: Your answers will be kept confidential, and your individual responses cannot be 
traced back to you. You will not receive any direct benefit by virtue of participating in this survey, neither any harm will be 
done to you as a result of participating. Your honest answers to these questions will be appreciated.
The survey will take approximately 15 minutes. With your permission, I would like to ask you questions about your 
household and farming activities. 
Do I have your consent to conduct this interview? 
3. Yes  = Thank the participant for accepting and proceed to next question
4. No  = Stop the interview and thank the participant
Section A biodata questions will take care of the selection criteria: single, windowed and divorced, female headed 
households, & age range for the elderly.
A – Basic Data 
Ask to speak with the head of household.
A1 – What is your full name? A1a 
First Name: 
A1b 
Middle Name: (skip if do not have)
A1c Surname: 
A2 – Are you the head of the household? Yes (ask A4) 01
No (ask A2) 02
If yes, ask A5 and continue, if no ask A2 and continue
A3 – What is your relationship to the head of 
household?
Mother 01
Father 02
Brother 03
Sister 04
Grandmother 05
Grandfather 06
Son 07
Daughter 08
Husband 09
Wife 10
Other ________ 11
A4 – What is the sex of the head of household? Male 01
Female 02
A5 – Sex (Fill in; do not ask respondent) Male 01
Female 02
A6 - What is your age?
(if respondent does not know, ask in what year he/she 
was born)
Under 18 years 01
18-24 02
25-34 03
14
35-44 04
45-54 05
55 - 64 06
65 and over 07
Do not know _________ 08
A7 - What is your marital status? Married 01
Married (Polygamous) 02
Not married but living with someone 03
Single 04
Widow 05
Divorced 06
A8 – Person with disability (Fill in; do not ask 
respondent)
Yes 01
No 02
A9 – What is your profession or full-time job? 
Select all that apply.
Farmer 01
Self employed 02
Employee 03
Retired 04
Student 05
Other (Specify) ______________ 06
A10 – Do you have a mobile phone? Yes (ask A4.1) 01
No (Ask A7) 02
A10.1 – What is your phone number? 
A11 – How many family members live in your 
household? (including self)
_________
A12 – What is the sex of your family members who 
live in your household? 
A12a Number of males
A12 b Number of females
A13 – What are the ages of your family members who 
live in your household? 
Age ranges #
A13a 0-15
A13b 16-35
A13c 36-60
A13d 61+
Now we will ask a few questions about your crop related activities before the cyclone.
SC1.During the last panting season, which crops did 
you plant? 
(select all that apply)
Maize 01
Legumes: Peas 
 Pidgeon Peas 02
 Cowpeas 03
Legumes: Beans 
 Lablab 04
 Soybeans 05
 Common Beans (sugar beans) 06
Other: _____________ 07
I did not plant any crops/Farmer did not pant any 
legumes or maize. 
08
SC2. Last season, how many hectares did you plant of 
the following crops ____? Cultivated area Hectares
SC2a Maize
SC2b Pidgeon Peas
SC2c Cow Peas
SC2d Lablab
SC2e Soybeans
SC2f Common Beans 
(sugar beans)
SC2g Other 
_____________
SC2h Total Hectares___________
SC3 - How much of each crop did you harvest in the CROP KG 
15
last season (Kg)? SC3a Maize
SC3b Pidgeon Peas
SC3c Cow Peas
SC3d Lablab
SC3e Soybeans
SC3f Common beans 
(sugar beans)
SC3g Other (specify) 
____________
SC3h Total KG_________
SC4. Where did you get the seeds you planted last 
season? 
(select all that apply)
Bought from _____ 01
Received seeds from AID worker 02
Received seeds from Other _________ 03
Used own stock 04
Other source: _________ 05
Now I will ask you a question about the upcoming planting season for your crops.
SC5. For the upcoming planting season, what is the 
size of the area where you intend to plant your crop? 
_____ hectares
End Survey
Thanks very much for your participation. The information you provided was very useful. 
16
Annex II: List of Key informants
Province District Administrative 
Post Locality ( Village) Community/Povoados Name of key 
informants
Sussendenga Dombe
Muoco Muoco Filimone Jaene 
Nhamissisua
Materara Materera
Manuel 
Joaquim 
Macanidje
Mabaia Mabaia
Francisco 
Sintura 
Cumbana
Muchambanha Muchambanha/Jasrela Benjamim 
Jemusse Kudza
Darue Darue
Armando 
Fernando 
Sanguene
Darue Zingwena
Inoque
Augusto 
Zinguena
Sofala Nhamatanda Tica
Vinho (Bebedo) Vinho Americo Faera 
Tabua
Matenga Matenga Bio batista 
Alfandenga
Macorococho Macorococho Paulo Filipe 
João
Chirassicua Maponese (Chirassicua)
Benjamim 
Filipe 
Nhamachoco
Siluvo Siluvo
Lourenço 
Mavungire 
Chungane
Nhampoca Nhampoca
Izaquiel 
Mambasse 
Colocho
Chiadeia Chiadeia
Antonio 
Massora 
Manuel
17
Annex III: Baseline Households Interviewed
District Administrative Post Village Community
HHs Resettlement 
Centers (RC)
Sussundenga Dombe Darue Muwawa 38 RC
Zinguena 62
Chihin 1
Matarara Thussene-Choma 0
Metchisso 0
Matarara 53 RC
Muriro 0
Mabaia Bairro Unidade 0 RC
Mabaia-25 de Setembro 1 RC
Macocoe 33 RC
Mucombe 31 RC
Matende 0
Javela Manhama 1 0 RC
Nahanhemba 1 0
Nahanhemba 2 0
Phambanissa 0
Manhama 2 0 RC
Muchai 0 RC
Gudza Sede 0 RC
Magueba 0 RC
Bunga 0
Muoco Nhamussissua 48 RC
Magaro 36 RC
Dombe 0 RC
TOTAL 
303
18
District
Administrative 
Post
Village Community
Households
Resettlement 
Centers (RC)
Nhamatanda
Tica
Chirassicua
Macorococho 30
Chirassicua 17
Maponesse 0
Pombamite 6
25 de Setembro 17
Macuzi1 9
Joaquim Marra 34
Madangua 14
Nguenhi 0
Mussanga Matope 4
Lamego
Emilio Guebuza 12
Hulumwa 0
Muda Mufo 122
Ndeja-John 
Segredo 157 RC
Nhamatanda Sede
Sede Kura 21 RC
Metochira Pita
Nhanssalaze A 50
Maguimba A 9
Maguimba B 33
Maguimba C 57
Nhangõna 26
Nhassalazi B 6
Kobola 21
Madangua 14
Mabaia 7
Mongomo1 17
Magoel 6
Micoroa 10
TOTAL 699
19
Annex IV: Annual Performance Data Table (APDT)
Project Mozambique Integrated Recovery and Resilience Project (MIRAR)
Start Date June 1, 2019
End Date April 30, 2020
# Indicator Name
(Output or Outcome)
Unit Disaggregation Baseline YR 1 Life of Project
Year Value Target 2
Actual Target Actual
Program Goal: Assist agrarian households in Sofala and Manaci Provinces stabilize their food security
OFDA Sector: Food Security and Agriculture
1 Percentage of households 
with awareness of how to 
access seeds
Custom outcome
Percentage Total
2019
99.4%
75% 75%
Male household 
head
99.2%
Female household 
head
99.6%
OFDA Sub-sector: Improving Agricultural Production / Food Security
2 Number of people directly 
benefiting from improving 
agricultural production and/or 
food security activities
OFDA output
Number of 
individuals
Total
2019
0
71,250 71,250
Male 0
Female 0
3 Number of months of 
household food self￾sufficiency as a result of 
improved agricultural 
production programming
OFDA outcome
Number of 
months 
Total
2019
2.6
+0.5 +0.5
Male household 
head
2.7
Female household 
head
2.2
4 Percentage of households 
with access to sufficient seeds 
to plant
OFDA outcome
Percentage Total
2019
57.3%
75% 75%
Male household 
head
50.8%
Female household 
head
67.6%
 
2 Targets were not disaggregated. The actual values will be disaggregated.