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BENEFIT-REALISE
Arba Minch University
Cluster Baseline Study
September 2019
Lidya Samuel1, Mebratu Alemu1, Amanuel Lulie2, Tewodros Tefera2, Mulugeta Diro2
1 Haramaya University Cluster, BENEFIT-REALISE project
2 BENEFIT-REALISE project, Project Management Unit
With the support of WUR
i
Acknowledgments
We would like to acknowledge the PMU staff for their facilitation and follow up during the Baseline
survey. We give special thanks to Mr Amanuel Lulie and Ms Lavinia Plataroti for their support in data
management, analysis and sending the reporting template in collaboration with their colleagues. The
WoA staff, food security experts and DAs have also contributed much in providing the list of PSNP and
non-PSNP farmers, as well as unreserved support during the field survey; without their support, the data
collection process would have been impossible. Also, special thanks are extended to the farmers in Kola
Barena and Yayke kebles of Merab Abaya woreda.
ii
Contents
Acknowledgments ....................................................................................................................i
List of Tables ......................................................................................................................... iii
List of figures ......................................................................................................................... iv
List of abbreviations and acronyms ......................................................................................... v
Executive Summary............................................................................................................... vii
Introduction ...........................................................................................................................1
Objective of the Baseline survey .............................................................................................2
Methodology ...........................................................................................................................2
Design of questionnaire ............................................................................................................ 2
Sample methodology ............................................................................................................... 2
Data quality assurance ............................................................................................................. 3
Training of enumerators ........................................................................................................... 3
Data collection methods and tools .............................................................................................. 3
Data entry and analysis ............................................................................................................ 3
Outline of this report ...............................................................................................................3
Results ....................................................................................................................................4
Goal level Key Performance Indicators ....................................................................................4
Household characteristics ......................................................................................................... 5
Food Security ......................................................................................................................... 7
Socio-economic status ............................................................................................................. 9
Agricultural resilience ............................................................................................................. 12
Best Fit Practices Pathway .................................................................................................... 14
Seasonal crops and perennial crops.......................................................................................... 15
Farming practices and agricultural extension services ................................................................. 16
Seed Pathway ....................................................................................................................... 21
Seed and varieties in seasonal and perennial crops .................................................................... 21
Gender .................................................................................................................................. 23
Conclusion ............................................................................................................................ 26
The Way forward ................................................................................................................... 28
References ............................................................................................................................ 29
iii
List of Tables Table 1. Household sample .......................................................................................................... 2 Table 2. Number of households in each category by PSNP status and gender of household head ............ 5 Table 3 Household characteristics ................................................................................................. 5 Table 4. Average plot sizes of households ...................................................................................... 6 Table 5. Land shared in/out to and from others .............................................................................. 6 Table 6. Land rented in/out to and from others ............................................................................... 6 Table 7. Baseline and target food gap months per tercile ................................................................. 7 Table 8. Percentage of Households consuming the food item in the previous 24 hours .......................... 9 Table 9. Mean Dietary Diversity Score (DDS) ................................................................................. 9 Table 10. Household Asset index ................................................................................................ 11 Table 11. Percentage of migration and wage labour by PSNP households .......................................... 12 Table 12. Impacts of natural hazards .......................................................................................... 13 Table 13. Percentages of households using particular coping strategies in response to natural hazards . 14 Table 14. Percentage of households cultivating the top three most important seasonal and perennial
crops ..................................................................................................................................... 15 Table 15. Belg and meher season productivity (Qt/ha) of households ............................................... 15 Table 16. Major Crop utilization of the households ........................................................................ 16 Table 17. Meher season chemical fertilizer application ................................................................... 16 Table 18. NPS application during meher season ............................................................................ 17 Table 19. Belg season NPS application ........................................................................................ 17 Table 20. Belg season urea application ........................................................................................ 17 Table 21. Application rates of FYM (compost/farm yard manure) for major crops (kg/ha) .................... 18 Table 22. Pesticide usage of households ...................................................................................... 18 Table 23. Intercropping practice ................................................................................................. 19 Table 24. Frequency of visits by extension agents and farmer’s perceptions of the value of visits ......... 20 Table 25. Cluster level access to market information ..................................................................... 21 Table 26. Types of seed varieties used ........................................................................................ 21 Table 27. Number of crop varieties used by households for specific crops ......................................... 22 Table 28. Reasons for choosing improved seed varieties ................................................................ 22 Table 29 seed source and acquiring mechanisms .......................................................................... 23 Table 30. Decision making about access of female household members to farm equipment ................. 23 Table 31. Access of female household members to extension services .............................................. 24 Table 32. Participation of women in different types of training ........................................................ 24 Table 33 Frequency of visits by Extension agents (DAs) ................................................................. 24 Table 34 Percentage of women reporting a heavy workload in each month ....................................... 25 Table 35. Participation of women in decision making about chicken keeping ...................................... 26
iv
List of figures
Figure 1. Map of the Baseline study area of Merab Abaya of the two kebeles (Qola Barena and Yayke) .. vi Figure 2. Mean food gap months by PSNP status ............................................................................. 7 Figure 3. Mean food gap months by gender of household head ......................................................... 8 Figure 4. Percentage of Respondents reporting food shortage in a particuar month .............................. 8 Figure 6 Top three natural hazards occurring in the past three years ............................................... 12 Figure 7. Percentage of households practicing irrigation ................................................................. 19 Figure 9. Decision making about participation of women in field work .............................................. 25
v
List of abbreviations and acronyms
AMU Arba Minch University
BENEFIT Bilateral Ethiopia-Netherlands Effort for Food Security; Income and Trade
CSA Central Statistics Agency
CSPro Census and Survey Processing System
DA Development Agent
DDS Dietary Diversity Score
FHH Female Headed Household
FYM Farmyard Manure
GoE Government of Ethiopia
HDDS Household Dietary Diversity Score
KPI Key Performance Indicator
MHH Male Headed Household
PMU Project management Unit
PRA Participatory Rural Appraisal
PSNP Productive Safety Net Program
REALISE Realising Sustainable Agricultural Livelihood Security in Ethiopia
SNNPR Southern Nations Nationalities and Peoples Region
TLU Tropical Livestock Unit
TOT Training of trainers
vi
Figure 1. Map of the Baseline study area of Merab Abaya of the two kebeles (Qola Barena and
Yayke)
Source; Woreda Agricultural Office
vii
Executive Summary
The Realising Sustainable Agricultural Livelihood Security in Ethiopia (REALISE) project, a three-year
programme aligned with the GoE flagship PSNP programme, aims to take lessons learned from the
Wageningen University and Research Capacity building for Scaling up of evidence-based best Practices in
Agricultural Production in Ethiopia (CASCAPE) and Integrated Seed Sector Development (ISSD)
programmes to PSNP target woredas. REALISE conducted a Baseline survey in two kebeles of Meab
Abadya woreda, namely Kola Barena and Yayke. The findings were based on a randomly selected sample
of 150 farm households based on Probability Proportional to Size (PPS) of 90 (60%) PSNP and 60 (40%)
non-PSNP beneficiaries. The survey revealed that the average food gap months of the first tercile was
2.44 months, the second 5.02 months and the third 6.58 months. The average duration of food gap
months was 5 months, and food shortages mainly occurred between January and July. During this
period, FHHs suffered more from food shortage than MHHs, both in PSNP and non-PSNP households.
Moreover, the diet diversity score was similar among PSNP and non-PSNP households. Cereals were the
most consumed food group, followed by vegetables, and protein-rich foods such as fish, eggs and meat
were rarely consumed. Therefore, the actual DDS for the cluster was 4.53 (rounded to 5) which means
that in 24 hours, households consumed an average of 5 food groups out of the 12 groups available,
which was greater than the SNNPR mean DDS of 3.98 (rounded to 4). The average ownership of tropical
livestock units (TLU) was 1.68, and the mean productive assets were similar among PSNP and non-PSNP
households (less than 1). Mains electricity was the major source of light (50%) followed by solar
(31.3%). The mean asset indexes for non-PSNP households was 2.35 and for PSNP 2.06, while the asset
indices for MHHs and FHHs were 2.29 and 1.91, respectively. There was no participation in migration or
wage labour for farm income in the previous 12 months from the data collection period.
Pest infestation, drought and erratic rainfall were the major natural hazards faced by households. The
major impacts to the households caused by these hazards were crop loss and a decrease in food
diversity, which affected virtually all respondents. Coping mechanisms to deal with these hazards
included reactive strategies such as selling livestock and looking for loans, and proactive strategies such
as crop diversification and natural resource management. The study indicated that remittances from
relatives working abroad were received by 5.8% of farmers in the cluster, and resource transfers
(including food, cash, seed, livestock or other resources) were received by 5.4%, which implies that the
resilience was very low in the study area.
The survey revealed that maize, cotton and haricot bean ranked as the top three most important
seasonal crops, and moringa, mango and banana as the top three perennial crops. The production of
these crops was low in the meher season, but relatively better in the belg season. Belg was the preferred
planting season for the cluster. Households consumed 100% of the maize and haricot bean crops, and
sold 100% of the onion, tomato and cotton crops. Fertilizer application in the meher and belg seasons
was high for these crops because it was expected that higher application rates would increase their
yields. 100% of the households used pesticide for insect and disease control. Irrigation and intercropping
were very rarely practiced. Improved seed varieties were highly preferred by households because they
produce higher yields and because of promotion by the government. 53.97% of the seed source was
from cooperatives and 41.27% was purchased from the local market.
Men and women in MHHs had unequal decision making power, with men dominating in many households.
Access to assets without asking permission was higher for women in FHHs than in MHHs. Regarding
decisions on female participation in field work during the planting season, husbands and wives often
decided together, and female household heads frequently consulted their elder brothers. April, May, June
and July were months of high workloads for women, and these months were also often times of food
shortage. These months of higher workloads coincided with the belg and meher planting seasons.
Women were unable to make decisions by themselves about the profits they earned from selling chickens
or eggs. Generally, the voices of women were not loud, and were not heard in different aspects of their
lives. Women were often marginalized from the benefits they were supposed to gain, although some
received training in agronomy, finance, nutrition and health.
1
Introduction
REALISE: The project Realising Sustainable Agricultural Livelihood Security in Ethiopia is a three - year
(2018-2020) programme that aligns with the Government of Ethiopia’s Productive Safety Net Programme
(PSNP).
The aim of the programme is improved sustainable food security, income and trade among rural
households in Ethiopia. This will be achieved through four primary pathway related outcomes
Developed best fit practices that meet expressed needs and have the potential to contribute to
increased productivity and resilience are available for scaling in selected PSNP woredas;
Increased availability, timely delivery and use of quality seed of new, improved, and/or farmer
preferred varieties through diverse channels;
Enhanced human, organizational and institutional capacities for matching, adapting, validating
and scaling best fit practices; and
A conducive environment exists for the institutionalisation of evidence-based system
innovations.
Arba Minch University is one of the cluster offices of the BENEFIT REALISE programme operating in
SNNPR as a satellite cluster to Hawassa University. Since the REALISE programme was approved in May
2018, the REALISE programme of Arba Minch University has performed different preparatory activities,
including conducting the Baseline survey. The AMU-REALISE programme is one of the partner
Universities managing four programme intervention woredas in different zones of SNNPR, namely: Merab
Abaya (where the Baseline study was conducted), Kucha, Zalla and Derashe woredas. These preparatory
activities are initial tasks that led to the collection of data and designing a context based proposal for the
cluster.
For each primary outcome/path way, the project has also developed the following activities for the Arba
Minch cluster that will contribute to the results for each pathway:
KPI’s for best fit practices
60 best fit practices validated
12,000 farmers adopt best fit practices through extension systems in selected 4 woredas
30 % productivity increase of/by best fit practice. KPI’s for seed pathway
8,000 farmers regularly use quality seed made available by seed producers in informal,
intermediary and formal seed systems
Four linkages between seed producers and inputs, services and markets established
The number of crops and varieties for which quality seed is available diversified by 50% by the
end of 2019.
KPI’s for capacity building pathway
Four research and extension systems staff are given the capacity to match, and/or adapt, and/or
validate and/or scale best fit practices
One presentation of evidence-based programme results well received by relevant stakeholders
and discussed at national and regional stakeholder platforms
Four woreda plans informed by REALISE
Emergence of new seed related agricultural service providers in target woredas as a result of
capacity strengthening
Regional and national policy makers are informed about evidence-based and context-specific
programme findings and recommendations, and
Target communities have demonstrated an increase in resilience.
2
Objective of the Baseline survey
The primary objective of the Baseline study is to provide an information base against which to monitor
and assess completed activities.
Within the planning process, it is of prime importance to collect Baseline data in order to determine the
requirements for appropriate interventions in the implementing woredas. The collected background
information will provide the basis for planned interventions. Furthermore, the Baseline data allows the
team to compare conditions before and after planned interventions, to determine whether the
interventions are working.
Methodology
The Baseline survey data were collected from 150 HHs in Merab Abaya woredas of Gamo Zone , 90 from
PSNP and 60 from non-PSNP households. This woreda was selected from among the four target project
woredas, for the following reasons: the distance from Arba Minch was less than the other woredas, and
because Arba Minch is a satellite cluster, after 2020 the research woreda for the cluster is planned to be
Merab Abaya. Furthermore, before Baseline data collection, PRA data were collected from two kebeles of
the woreda, so that the Baseline data were collected in the woreda for triangulation.
Design of questionnaire The household questionnaire was 38 pages long and was designed to capture information on indicators
identified in the REALISE framework. It consisted of different modules designed to address food security,
nutrition, assets and resilience of both PSNP and non-PSNP households.
Sample methodology One woreda was selected to conduct the Baseline survey for the Arba Minch cluster as per the agreement
reached between the national management unit in Addis Ababa and Arba Minch Baseline team, including
the programme coordinator. Accordingly, two kebeles, Yayke and Kola Barana, were selected, based on
relevant criteria made for the Baseline study through discussion with the woreda Disaster and Risk
Preparedness and Prevention Office, and the PSNP facilitation team of the woreda in particular.
The Baseline team were provided with a list of targeted households in the two selected kebeles,
representing both the PSNP and non-PSNP list. The total number of all the targeted households identified
from the lists of both groups was 150 randomly selected households.
The numbers of households of each category included in the AMU cluster sample are shown in Table 1.
Table 1. Household sample
Sample
Kebeles
MHH
Sub-Total
FHH
Sub-Total
Total PSNP Non-PSNP PSNP Non-PSNP
Kola
Barena
28 23 51 17 7 24 75
Yayke 32 23 55 13 7 20 75
Total 106 44 150
3
Data quality assurance Data were cross-checked with kebele and woreda level stakeholders when deemed necessary to ensure
the quality of the data.
Training of enumerators Before conducting the Baseline data collection, two enumerators and one socio-economist were trained in
the use of CSPro and the tablet computers in Hawassa, SNNPR, on October 15-18, 2018. The training
was conducted for three consecutive days by both the Wageningen University Research team and the
PMU. During this workshop, the Baseline team obtained key inputs for the Baseline survey that helped to
facilitate the data collection.
Data collection methods and tools The sampling method used during the Baseline study was random interval sampling. In the Baseline
study woreda, two kebeles were selected, and two enumerators collected data, one from Yayke kebele
and the other from Kola-Barena kebele. The tablet computers used for data collection were programmed
to collect the necessary information from the respondents.
Data entry and analysis Android tablet computers equipped with the CSPro programme were used to collect the data. The data
were cleaned and further processed, and descriptive analysis was used to summarize and describe the
Baseline study, and tabulated at the PMU to translate into actionable information. Prior to the report
write up, a workshop was organized to present the preliminary results.
Outline of this report
This report provides Baseline results. The household characteristics section gives a general description on
gender of household headships, as well as their education level, age and land tenure. The food security
section describes the food gap months of households, the DDS and differences in these measures
between different household categories. The socio-economics status section gives information on asset
holdings of households, including TLU, and off farm income. The resilience section of the report gives
details of of the top three natural hazards that had occurred during the previous three months, the
impact of these hazards, household coping strategies and community level coping strategies. The best fit
practice section gives information about the top three seasonal and perennial crops in the study area,
productivity in the two seasons, and crop utilization. Under the farming practices and agricultural
extension services section, data and information are provided about fertilizer application rates in both
the meher and belg seasons, pesticide usage, intercropping practices, farmers’ participation in extension
activities, frequency of visits of DAs, and farmers’ perceptions of the usefulness of extension visits and
their access to information. In the seed pathway section of the report, distinct seed portfolios, types of
seed varieties, reasons for using improved seeds, seed sources and means of acquiring the seeds are
discussed. Under the gender section, data and information are provided on women’s access to farm
equipment, women’s periods of heavy workload, decisions on revenue from selling chickens and eggs,
and training received by female farmers.
4
Results
Goal level Key Performance Indicators The table 2, a brief description of goal level indicators is provided.
KPIS are:
Key
performance
Indicator
Indicator
definition
Baseline indicator Target by the end of the
project (2020)
KPI 1: Food gap
months reduced
by REALISE PSNP
households over
project period
compared to
regional average
Reduction in food gap
months
Tercile Food gap months by REALISE
PSNP households close to zero
for the first tercile or drop to
2.44 and 5.02 months for the
second and third terciles
respectively over project
period
1st Tercile 2.44
2nd Tercile 5.02
3rd Tercile 6.58
KPI 2: Increase in
Dietary Diversity
Score of REALISE
PSNP households
over project
period compared
to regional
average;
Increase in Dietary
Diversity Score
Dietary Diversity
score is an indicator
to measure food
security. It is a proxy
measure for HH food
access.
The rounded diet diversity
score for the cluster is
4.53
The Regional (SNNPR) diet
diversity score is 4
rounded to the nearest
whole number
Increase in the number of
food groups consumed from a
cluster mean dietary score of
4 to 5 over the project period
KPI 3: Significant
increase in asset
building or
prevention of
asset depletion of
REALISE target
households over
project period
compared to the
Baseline
Contribute to asset
building
The asset index is a
composite measure of
a household's
resilience to various
shocks and a proxy
measure of
cumulative living
standard.
The mean asset index for
the cluster is 2.18
Increase the mean asset
index value of the cluster
close to 1
KPI 4: Target
communities report
and demonstrate
significant increase
in Resilience
Increase in
resilience
Resilience is the
capacity to remain or
bounce easily
back/forward to a
situation of food
security when being
faced with shocks and
stressors.
Existing farming
capacities in terms of
fertilizer application,
use of improved
varieties, intercropping
and pesticide use.
33.3% reactive and
31.6% proactive
strategies use against
shocks and stressors
1. 50% of the PSNP
respondents have improved
food production by at least
1 new capacity
2. Improved capacities to deal
with shocks and stressors to
secure food production
(shifting from reactive to
proactive coping strategies)
5
Household characteristics Household headship was a major variable which affected food security. Table 2 shows that in the AMU
cluster, MHHs predominated in both PSNP and non-PSNP households.
Table 2. Number of households in each category by PSNP status and gender of household head
PSNP NonPSNP Total
MHH FHH Total MHH FHH Total
59 32 91 46 13 59 150
Source; cluster own survey,2018
Table 3 presents the average age, education level (years spent in school), and size of household for each
household category. The number of years spent in school was very low in the study area. The average
for both PSNP and non-PSNP household heads lies between 4-5 years of schooling. There is high level of
illiteracy which may contribute negatively to food insecurity. This may perhaps be a limitation for the
farmers to adapting new agricultural technologies that would improve productivity and ease food
insecurity.
The average age of household heads was 50 years (Table 3), and since the capacity to perform farm
activities decreases with age, this could be an issue affecting food security. The survey revealed that
there was a difference between PSNP and Non-PSNP groups in household size, with PSNP recipients
having more household members.
Table 3 Household characteristics
Source; cluster own survey, 2018.
Land is perhaps the most important resource, as it is the basis for all economic activities in the rural and
agricultural sector. The survey indicated that the average land holding was 1.4 Ha (Table 4). The
availability of sufficient land per household is essential for food self-sufficiency.
Table 4 shows the average plot size of different categories of households. The plot sizes of the PSNP and
non-PSNP households disaggregated by gender showed that male headed non-PSNP households had an
average land holding of 1.84 ha, followed by MHHs in the PSNP programme with 1.29 ha. FHHs under the
PSNP programme had 1.14 ha of plots and FHHs under non-PSNP programme had 1.096 ha.
PSNP Non PSNP Total
Male Female Total Male Female Total
Mean SD Mean SD Mean SD Mean SD
Age (avg of
household)
49.94 13.08 48.15 11.06 49 49.21 11.97 53 14.23 51.1 50
Education
level
household
head
4.88 5.49 3.843 7.22 4.3 4.71 5.68 1.53 2.53 3.12 3.71
Size of
household
5.41 0.91 4.50 1.44 4.95 5.78 0.79 3.08 1.61 4.43 4.69
6
Table 4. Average plot sizes of households
Mean Plot
size
PSNP Non-PSNP Total
MHH FHH Total MHH FHH Total MHH FHH Total
1.290 1.137 1.236 1.838 1.096 1.674 1.530 1.125 1.409
Land tenure; Percentage of households sharing in/out land to and from others
Table 5 indicates that >70 % of both PSNP and non-PSNP households did not share out land from others,
or share in their land to others. This table also indicates that more FHHs shared out their land compared
to MHHs, maybe indicating that they lacked the physical capacity to fully work their land.
Table 5. Land shared in/out to and from others
PSNP or
Non-PSNP
Gender
Share out land to others? Share in land from others?
No Yes Total No Yes Total
N % N % N % N % N % N %
PSNP
MHH 54 91.5 5 8.5 59 100 44 74.6 15 25.4 59 100
FHH 24 75.0 8 25.0 32 100 29 90.6 3 9.4 32 100
Total 78 85.7 13 14.3 91 100 73 80.2 18 19.8 91 100
Non-PSNP MHH 41 89.1 5 10.9 46 100 37 80.4 9 19.6 46 100
FHH 11 84.6 2 15.4 13 100 13 100.0 0 0.0 13 100
Total 52 88.1 7 11.9 59 100 50 84.7 9 15.3 59 100
Total MHH 95 90.5 10 9.5 105 100 81 77.1 24 22.9 105 100
FHH 35 77.8 10 22.2 45 100 42 93.3 3 6.7 45 100
Total 130 86.7 20 13.3 150 100 123 82.0 27 18.0 150 100
Land tenure –percentage renting in/out land to and from others
Table 6 illustrates that a majority (> 90%) of both PSNP and non-PSNP households did not either rent
land out to others or rent in from others.
Table 6. Land rented in/out to and from others
PSNP or Non-PSNP Gender
Rent out land to others? Rent in land from others?
No Yes No Yes
N Row N % N Row N % N Row N
% N Row N %
PSNP MHH 54 91.5 5 8.5 58 98.3 1 1.7
FHH 31 96.9 1 3.1 31 96.9 1 3.1
Total 85 93.4 6 6.6 89 97.8 2 2.2
Non-PSNP MHH 45 97.8 1 2.2 41 89.1 5 10.9
FHH 13 100.0 0 0.0 13 100.0 0 0.0
Total 58 98.3 1 1.7 54 91.5 5 8.5
Total MHH 99 94.3 6 5.7 99 94.3 6 5.7
FHH 44 97.8 1 2.2 44 97.8 1 2.2
Total 143 95.3 7 4.7 143 95.3 7 4.7
7
Food Security According to the Mid line report (2018), the mean number of food gap months for the south region for
PSNP beneficiaries was 2.96 months, and 2.64 months for female headed and male headed households,
respectively, and for non-PSNP households, 3.01 months for female headed and 2.29 months for male
headed households.
The study indicates that the average number of food gap months in the study area was about 5 months.
Coping with such long periods of food shortage distracts farmers from participating in other activities
between January and July. The number of food gap months for the first tercile1 was 2.44, and the cluster
target was to reduce this to 0. The food gap months for the second tercile were 5.02 and the target was
to reduce this to 2.44, while the third tercile was 6.58 months and the cluster’s target was to reduce it to
5.02.
Table 7. Baseline and target food gap months per tercile
Tercile Baseline Indicator Target
1st 2.44 0.00
2nd 5.02 2.44
3rd 6.58 5.02
KPI 1: Food gap months – Months of Adequate Food provisioning indicator
The average duration of food gap months differed between PSNP and non-PSNP households. As shown in
Figure 2., the average food gap months for PSNP households was 4.8 months (rounded to 5) and for
non-PSNP households it was is 4.4 months (rounded to 4) in the Baseline reporting period of the year
2018.
Figure 2. Mean food gap months by PSNP status
Fig 3 shows that the mean food gap months for PSNP FHHs was 5.08 and for MHHs 4.33 months, which
means that female headed households suffered nearly an additional month of food shortage. Non-PSNP
FHHs suffered an average of 4.88 months of food shortage, and MHHs a little less. Generally the survey
results show that the food gap months for the survey area were high.
1 A tercile is a mean value calculated for each group by dividing the sample into three equal parts after sorting from highest to lowest
4.80
4.49
4.68
4.30
4.35
4.40
4.45
4.50
4.55
4.60
4.65
4.70
4.75
4.80
4.85
PSNP NON PSNP Total
Mean Food Gap Months
8
Figure 3. Mean food gap months by gender of household head
As shown in Figure 4, May(94.4%) , June(91.0%), April (88.9), March (76.4%) and Feburary (52.1%)
were the months when a majority of households suffered from food gaps.
Figure 4. Percentage of Respondents reporting food shortage in a particuar month
KPI 2: Dietary Diversity Score
Table 8 shows the consumption of different food categories within 24 hours of a normal day for sample
households. There were notable similarities between categories of households in the consumption
behaviour of food items. The survey result also indicated that cereal was consumed in by virtually all
households (99%) for both PSNP and non-PSNP groups, followed by vegetables (94%). Non-PSNP groups
tended to consume a relatively higher variety of food. The least consumed food types reported were fish,
eggs and meat, and sugar or honey.
4.76
4.88
4.33
5.08
3.80 4.00 4.20 4.40 4.60 4.80 5.00 5.20
PSNP Male headed
PSNP Female headed
Non PSNP Male headed
Non PSNP Female headed
Mean food gap months by Gender of HH head
2.1 2.1 2.8 4.2
31.9
52.1
76.4
88.9 94.4 91.0
38.2
11.1
0102030405060708090
100
Sep
tem
ber
Oct
ob
er
No
vem
ber
Dec
em
ber
Jan
uar
y
Feb
ruar
y
Mar
ch
Ap
ril
May
Jun
e
July
Au
gust
Months of food Gap %
9
Table 8. Percentage of Households consuming the food item in the previous 24 hours
No
Food Item
PSNP
(n=91)
Non PSNP
(n= 59)
Total Sample
(n= 150)
N % n % N %
1 Cereals (Enjera, Bread, Rice,
Pasta, Biscuits)
91 100.00 58 98.31 149 99.33
2 Root and Tuber Crops 11 12.09 10 16.95 21 14.00
3 Vegetables 84 92.31 58 98.31 142 94.67
4 Fruits 18 19.78 12 20.34 30 20.00
5 Meat 3 3.30 2 3.39 5 3.33
6 Eggs 1 1.10 2 3.39 3 2.00
7 Fish (Fresh, Dried or Fried) 0 0.00 0 0.00 0 0.00
8 Pulses 13 14.29 13 22.03 26 17.33
9 Dairy Products 16 17.58 25 42.37 41 27.33
10 Food of Oil and Fats 61 67.03 48 81.36 109 72.67
11 Sugar or Honey 1 1.10 5 8.47 6 4.00
12 Condiments, Spices 89 97.80 59 100.00 148 98.67
Source; cluster own survey, 2018
Intensity of Household Dietary Diversity Score
Following FAO (2011), in order to further assess dietary diversity, three categories were formulated,
namely: low dietary diversity category (<=3 food groups); medium diversity category (4 to 6 food
groups) and high diversity category (>=7 food groups) for households (Table 9). Results show that a
considerable proportion of sample households fall under the medium (72%) household dietary diversity
score followed by low dietary diversity score (20%). Only 8% of households in Arba Minch cluster were
found to have a high HDDS.
As indicated in Table 9. the mean dietary diversity score for AMU cluster result was higher (4.53) than
the Regional value (3.98)
Table 9. Mean Dietary Diversity Score (DDS)
Cluster Actual
mean DDS
Rounded
DDS
Low HDDS Medium HDDS High HDDS
Arba Minch University 4.53 5 30(20%) 108(72%) 12(8%)
SNNPR 3.98 4 120(34%) 218(62%) 13(4%)
Source; cluster own survey, 2018.
Socio-economic status KPI 3: Asset building/ asset depletion
The Principal Component Analysis (PCA) method was employed to construct household asset indices. The
household asset index is a composite measure that uses multiple items which have local importance in
defining assets at household level. A similar approach was employed by Filmer and Pritchett (2001).
They incorporated asset ownership and household characteristics in creating an asset index as a proxy to
long-term household welfare. They used the statistical procedure of PCA to determine the weights for an
index of the asset variables. In REALISE, we considered the number of rooms the household residents
occupy, farm tools owned, animal holdings measured in Tropical Livestock Units (TLU) and sources of
lighting to construct a household asset index.
10
Figure 5. Household Asset Indices calculated using PCA
The descriptive statistic showed that the sample households owned on average 1.68 TLU, 2.76 rooms,
0.89 hoes, 0.75 ox ploughs, and 0.77 sickles. The majority of them used mains electricty (50%) for
lighting followed by solar (31.3%) kerosene (9.3%) and battery (9.3%). The household asset index was
constructed using the assets and lighting sources. The details of the variables used for asset construction
are detailed in Table 10, along with sources of lighting and the index values themselves.
Number of
farm tools
owned
Number of
rooms the
household
occupy
TLU
Source of
lightening
Household Asset Index
The household asset index
value is ranked in
descending order from large
to small and mean value
calculated for each tercile.
The KPI target is set to
increase the asset holding of
each tercile by 25% during
the project period.
11
Table 10. Household Asset index
Asset holdings Mean Percent
TLU 1.68
Number of rooms 2.76
Hoe/Machete 0.89
Ox plough 0.75
Broad Bed Maker (BBM) 0.00
Sickle 0.77
N
Source of lighting 4.35
Candle 0 0
Kerosene 14 9.3
Battery 14 9.3
Electricity 75 50
Generator 0 0
Solar 4 31.3
Mean
Asset index 2.18
Asset index MHH 2.29
Asset index FHH 1.91
Asset index PSNP 2.06
Asset index non-PSNP 2.35
The average TLU varied considerably between PSNP and Non-PSNP farmers. On average, PSNP
households owned 2.58 TLU, while the value for non-PSNP households was 4 according to the Baseline
data. The implication is that interventions that improve the asset ownership of the households need to be
targeted at a larger scale to fill the gap between the two groups.
According to the PSNP midline report (2018), the mean value of productive assets increased among
PSNP households in SNNPR and one of the key goals of the PSNP 4 is to "prevent asset depletion so that
food insecure households do not have to lose their assets in order to provide food for themselves" (Mid
line report, 2018). The average TLU in SNNPR is 2.07 .
The off-farm income in the past 12 months was computed as off-farm income from migration and wage
labour. Altogether, 19% of household members migrated from their living kebele for more than 6
months, and 43% of household members were involved in labouring activities, as shown in Table 11.
These are quite high numbers compared to other clusters.
12
Table 11. Percentage of migration and wage labour by PSNP households
In the past 12
months, did any
member of your
household migrate
for work for more
than 6 months
Is the household beneficiary of PSNP
Yes No Total
Count % Count % Count %
No 71 78.0 50 84.7 121 80.7
Yes 20 22.0 9 15.3 29 19.3
In the past 12
months did any
member of your
household work as
wage labourer
Is the household beneficiary of PSNP
Yes No Total
Count % Count % Count %
No 46 50.5 40 67.8 86 57.3
Yes 45 49.5 19 32.2 64 42.7
Source; cluster own survey,2018
Agricultural resilience
KPI 4: Target communities report and demonstrate significant increase in Resilience
Sampled households from both PSNP and non-PSNP households were asked if they had encountered
natural hazards or shocks in the previous three months. The top three natural hazards mentioned were
pest infestation, drought, and erratic rainfall (Figure 6). The direct impacts from the hazards encountered
were mainly a decrease in food diversity and lost crops or reduction of yield.
Figure 5 Top three natural hazards occurring in the past three years
These natural hazards and pest outbreaks affected the households in a variety of ways. Table 12 shows
the level of impacts these hazards caused in the past three years, principally, a crop loss experienced by
100% of households, and a decrease in food diversity experienced by between 73% and 82% of
households. These major impacts cause malnutrition and other diseases. 43.8% of the FHH PSNP
Drought Pest infestation Erratic rainfall
NPSNP(MHH) 51.1 100 97.8
NPSNP(FHH) 50 83.3 100
PSNP(MHH) 62.7 88.1 91.5
PSNP(FHH) 68.8 90.6 100
0
20
40
60
80
100
120
Top 3 hazards occurring in last 3 months
NPSNP(MHH) NPSNP(FHH) PSNP(MHH) PSNP(FHH)
13
households were able to bridge the food gap for one month if a hazard occurred today. 50.0% and
33.3% respectively of non-PSNP MHHs and FHHs were able to bridge the food gap months if a hazard
occurred today. This implies that MHHs are more capable of bridging food gap months than FHHs.
Table 12. Impacts of natural hazards
Direct impact of the hazard
on the households
PSNP non-PSNP
MHH FHH MHH FHH
N % N % N % N %
Lost crops / reduction of
yields
59 100 32 100 43 95.6 12 100
Lost livestock 23 39 7 21.9 18 40 4 33.3
Food shortage for less than 3
months
9 15.3 7 21.9 13 28.9 4 33.3
Food shortage for 3-6
months
29 49.2 16 50 14 31.1 4 33.3
School dropout 19 32.2 9 28.1 5 11.1 0 0
Health impact, diseases
outbreak
5 8.5 0 0 1 2.2 0 0
Damage to assets 4 6.8 0 0 0 0 0 0
Decreasing food diversity 43 72.9 25 78.1 68 74.7 37 82.2
How long are you able to bridge the food gap
2 weeks 6 10.3 4 12.5 3 6.8 4 33.3
1 month 26 44.8 14 43.8 22 50.0 4 33.3
2 months 12 20.7 7 21.9 8 18.2 2 16.7
3 months and
more
14 24.1 7 21.9 11 25 2 16.7
Source; survey result, 2018
When natural hazards occurred, farmers used either reactive or proactive coping mechanisms to deal
with them (Table 13). The results show that selling livestock and looking for loans were commonly used
reactive coping strategies. Proactive coping strategies were much more rarely used, and included crop
diversification, management of natural resources and water conservation mechanisms, collecting
firewood and making charcoal for money.
14
Table 13. Percentages of households using particular coping strategies in response to natural
hazards
Coping strategies of
the households
PSNP non-PSNP
MHH % FHH % MHH % FHH %
Sold my livestock 19 33.3 16 50 31 68.9 7 87.5
Sold my seed reserve 0 0 0 0 0 0 0 0
Sold land 0 0 0 0 0 0 0 0
Looked for loan,
borrowing
18 31.6 15 16.9 13 28.9 2 25
HH member went for
begging
0 0 0 0 0 0 0 0
Send children to
relatives
1 1.8 0 0 1 2.2 0 0
Crop diversification,
intercropping and
adapt management
practice
1 1.8 0 0 1 2.2 0 0
Better crop varieties,
seeds
0 0 0 0 0 0 0 0
Managed natural
resource/
environmental
conservation
11 19.3 4 12.5 16 35.6 3 37.5
Water conservation
mechanisms
11 19.3 4 12.5 16 35.6 3 12.5
Cultivation of crops
that Yield twice a
year
0 0 0 0 0 0 0 0
Collecting fire wood
and making charcoal
for money
12 21.1 9 28.1 7 15.6 0 0
Others 33 57.9 17 53.3 16 35.6 3 12.5
Community level resilience
The community pursued different type of strategies to build resilience. These included receipt of
remittances from outside the community to improve resilience during the time of shock, and transfers of
resources within the community. The Baseline study showed that only 5.8% of community members had
received remittances in the previous 12 months. This proportion was relatively low and its implication is
that the community’s buffer against shocks and stressors is inadequate. Resource transfer occurred
among community members to cope with unexpected or chronic shocks and stressors, and community
members engaged in the transfer of food, cash, seed, livestock and other resources. The Baseline study
showed that this was also inadequate in scale, and only about 5.4% of the respondent households
reported that they had received transfers in the previous 12 months.
Best Fit Practices Pathway This section covers results on the farm level practices collected based on the three important seasonal
and perennial crops in the 2017 production period.
15
Seasonal crops and perennial crops Table 14 indicates that maize, cotton and haricot bean were reported as being the three most important
seasonal crops cultivated in the study area.
Table 14. Percentage of households cultivating the top three most important seasonal and perennial crops
Seasonal crop PSNP Non-PSNP
MHH FHH MHH FHH
Maize 86.8 61.5 73.7 80
Cotton 41.2 66.7 41.7 66.7
Haricot bean 42.9 - - -
Perennial crop
Moringa 87.5 50.2 70 75
Mango 50 20.0 50 42
Banana 80 13 88 20
Source; own survey, 2018
There was a slight difference between male headed and female headed PSNP households in their
preference for maize or cotton among the most important crops. The preference for haricot bean as the
third most important crop was high for male headed PSNP households.
The most important perennial crop for all categories of household was Moringa.
In Table 15, The project Baseline result shows the productivity of maize was 2.14 qt/ha for PSNP
households and 3.95 qt/ha for non-PSNP households in the meher season, and in the belg season,
productivity was 4.86 qt/ha for PSNP households and 6.51 qt/ha for non-PSNP households. The
productivity of other crops is also presented on the table for both seasons. The CSA report revealed that
at national level, the productivity of these crops was much higher at 31.52 qt/ha for maize and 18.85 qt/
ha for Haricot bean. The possible reasons for yield reduction in the study area included erratic rainfall
and American fall worm infestation. The KPI target of a 30% increase in the difference between the
Baseline and CSA yields amounts to 29.38 qt/ha for Maize, 6qt/ ha for cotton and 14.85 qt/ha for Haricot
bean.
According to informal discussion in parallel with the households survey, the cluster learned that the main
cropping season preferred by farmers under the study (both among respondents and non-respondents)
was the belg season between mid-March and April or May, based on the start of belg rain. Cultivation
also takes place in the meher season, but the belg season is preferred. The bimodal rain pattern, with
rain usually falling at the start of the meher and belg cropping seasons, has become so erratic and brief
as to disrupt cultivation of the annual crops. This makes the households highly suspicious and inflexible
when receiving advice from development agents on how and what to cultivate. Farmers have suffered
massive crop failures for seven seasons in a row (2008/9-2016/17), when a majority of the farm lands
(>80%) failed to provide harvests to their respective households. Most cultivation in the indicated years
either failed completely, or ended up as biomass used in animal feed. It was evident that the two kebeles
under study had already been selected as highly food insecure and vulnerable by the local and national
disaster prevention and emergency preparedness offices.
Table 15. Belg and meher season productivity (Qt/ha) of households
Crop type belg season meher season
PSNP non-PSNP PSNP non-PSNP
Maize 4.86 6.51 2.14 3.95
Sorghum 3.22 5.90 1.52 0.80
Teff 2.00 - 2.00 4.20
Haricot bean 1.20 2.00 4.00 -
16
Pigeon pea 0.83 0.70 2.40 -
Cotton 2.93 2.48 6.00 6.00
Source; survey result, 2018
Table 16 shows that 100% of the production of maize and haricot bean was consumed, in both PSNP and
non-PSNP households. On the other hand, 100% of crops such as onion, cabbage, tomato and cotton
were sold.
Table 16. Major Crop utilization of the households
Is the household PSNP beneficary
Crop type Yes No
Produced
Qt
Sold
%
Consumed
%
Save
for
seed%
Produced
Qt
Sold
%
Consumed% Saved
for
seed%
Maize 33.64 0 99.58 0.42 18.75 0 100 0
Sorghum 4.57 0 100 0 0.6 0 100 0
Teff 8.25 42.42 57.58 0 2.10 47.62 47.62 4.76
Haricot
bean
1 0 100 0 0 0 0 0
Pigeon pea .30 0 100 0 0 0 0 0
Green
pepper
0 3 100 0 0
Head
cabbage
0 67 95.52 4.48 0
Onion 10 100 0 0 165 97.82 2.18 0
Tomato 2.50 100 0 0 30.5 98.36 1.64 0
Cotton 7.50 100 0 0 6 100 0 0
Source; survey result, 2018.
Farming practices and agricultural extension services Tables 17 and 18 show the application rates of chemical fertilizers applied to the major crops. The
application rates of urea and NPS were high for maize, sorghum, tomatoes and onion for both PSNP and
non-PSNP households. The rate of urea and NPS application was higher in non-PSNP households,
especially for head cabbage and teff.
Table 17. Meher season chemical fertilizer application
crops
PSNP Non-PSNP
Area
Amount of Urea used Kg/Ha Area
Amount of Urea used Kg/Ha
Ha Kg Kg/ha Ha Kg Kg/ha
Maize 19.75 1425.00 72.15 5.75 450.00 78.26
Sorghum 2.25 150.00 66.67
Teff 1.00 50.00 50.00 .50 50.00 100.00
Head cabbage (Tikil
gomen) .75 150.00 200.00
Onion .75 50.00 66.67 3.25 425.00 130.77
Tomatoes .06 10.00 166.67 .50 75.00 150.00
Source; survey result, 2018
17
Table 18. NPS application during meher season
Crop type
PSNP
Kg/ha Non-PSNP
72.15 Kg/ha
Maize 66.67 78.26
Sorghum 50.00
Teff 100.00
Head cabbage (Tikil gomen) 66.67 200.00
Onion 166.67 130.77
Tomatoes
Source; survey result, 2018
Tables 19 and 20 show that the application rates of urea and NPS were higher for crops like maize,
sorghum and teff in the belg season, and the rates for head cabbage and tomato were even higher. The
high application rate of these fertilizers for commercial crops like tomato, onion and head cabbage was
because of the assumption that higher application of fertilizer would led to higher yields, which would
directly enhance their profit.
Table 19. Belg season NPS application
PSNP
Fertilizer area Non-PSNP
Crop type Sum
Total area
cultivated
% of fertilized
area
Amount of NPS used Kg/ Ha
Fertilized Area
Total cultivated
area
% of fertilized area
Amount of NPS used Kg/Ha
30.73 Sum Kg/Ha Sum Sum Kg/Ha
Maize 1.00 70.89 43.34 1675.00 52.70 52.93 56.10 94.35 1442.86 29.53
Sorghum .12 3.43 29.15 100.00 100.00 1.62 2.12 76.42 200.00 123.46
Teff .75 16.00 15.00 125.00
Onion .75 150.00 200.00
Tomato
Source; survey result, 2018
Table 20. Belg season urea application
PSNP Non-PSNP
Crop type Area Amount of Urea used Kg/Ha Area
Amount of Urea used Kg/Ha
Sum Sum Kg/Ha Sum Sum Kg/Ha
Maize 30.73 1675.00 52.70 52.93 1442.86 29.53
Sorghum 1.00 100.00 100.00 1.62 200.00 123.46
Teff .12 15.00 125.00
Onion .75 100.00 133.33
Tomato .50 50.00 100.00
Source; survey result, 2018
18
The use of farm yard manure for soil fertility showed that head cabbage required a high level of
application (Table 21). The application rate of FYM by non-PSNP farmers for head cabbage was 1,160
kg/ha, for onion, 550 kg/ ha and for maize 542.86 kg/ha.
Table 21. Application rates of FYM (compost/farm yard manure) for major crops (kg/ha)
Crop type
PSNP Non-PSNP
Area Amount of FYM used Kg/Ha Area
Amount of FYM used Kg/Ha
Ha Kg Kg/ha Ha Kg Kg/ha
Food barley
Maize 4.75 1110.00 233.68 1.75 950.00 542.86
Sorghum .50 30.00 60.00
Chickpea .25 15.00 60.00
Head cabbage (Tikil gomen)
.50 580.00 1160.00
Onion 1.00 550.00 550.00
Source; survey result, 2018
Table 22 shows the pesticide usage of households. Pesticides were not much used, but for those PSNP
and non-PSNP households that did use them, the usage was almost entirely for insect and disease
control.
Table 22. Pesticide usage of households
Purpose
of
pesticide
use
PSNP Non-PSNP
MHH FHH Total MHH FHH Total
Count % Count % Count % Count % Count % count %
Insect
control
2 100 0 0 2 100 8 88.9 3 100 11 91.7
Disease
control
2 100 0 0 2 100 4 44.4 3 100 7 58.3
Weed
control
0 0 0 0 0 0 0 0 0 0 0 0
Other 0 0 0 0 0 0 0 0 0 0 0 0
Source; survey result, 2018
The use by households of intercropping is presented in Table 23. Intercropping was practiced by an
average of 41% of households, and at similar rates by male and female headed households. On the other
hand, PSNP beneficiaries exhibited a relatively lower intercropping uptake than Non-PSNP groups (37.8%
of households versus 46.5%).
19
Table 23. Intercropping practice
Intercropped PSNP Non-PSNP Total
MHH FHH Total MHH FHH Total MHH FHH Total
N % N % N % N % N % N % N % N % N %
No 3
8
62.
3
13 61.
9
5
1
62.
2
1
8
52.
9
5 55.6 2
3
53.
5
5
6
58.
9
1
8
60.
0
74 59.2
Yes 2
3
37.
7
8 38.
1
3
1
37.
8
1
6
47.
1
4 44.4 2
0
46.
5
3
9
41.
1
1
2
40.
0
51 40.8
Total 6
1 100 21 100
8
2 100
3
4 100 9 100
4
3 100
9
5 100
3
0 100
12
5 100
Source; own survey result, 2018
Figure 7 indicates that the irrigation rate was very low in the study area in both PSNP and non-PSNP
households. Only 35 % of households practiced irrigation.
Figure 6. Percentage of households practicing irrigation
Source; own survey, 2018
Figure 8 shows that a majority of farmers did not participate in farmers’ field days, training and
demonstrations. The survey result shows participation was higher in demonstrations and farmers’ field
days. There was higher participation by PSNP HHs in all activities, and 58 PSNP households were involved
compared with only 12 non-PSNP households.
65.33%
34.60%
Irrigation
No Yes
20
Figure 8. Number of farmers participating in extension service field days, training and
demonstrations
Source; survey result, 2018
Contact with extension agents is a proxy for access to extension services. The Baseline results showed
that the frequency of the sampled households being visited by the extension service was very low (Table
24). For example, 42.35% and 46.15% of PSNP and non-PSNP households were never visited by
extension agents, or in a further 16.5% and 14.6% of households they were visited just once per year,
which is very limited, as indicated in Table 24. Although the frequency of visits was low, a majority of the
respondents replied that the information provided by the extension agents was useful, as indicated Table
24.
Table 24. Frequency of visits by extension agents and farmer’s perceptions of the value of visits
Frequency of visit PSNP non-PSNP Total
N % N % N %
Never 36 42.35 24 46.15 60 43.80
Once a year 14 16.47 6 11.54 20 14.6
Every month 35 41.58 22 42.31 57 41.61
Perception of farmers
about the visit
N % N % N %
Very useful 8 9.41 5 9.62 13 9.49
Useful 43 50.59 30 57.69 73 53.28
Neutral 34 40 17 32.69 51 37.23
Source; survey result, 2018
Table 25 shows that nearly all farmers reported having access to market information in a timely manner.
PSNP (NO) PSNP (YES) NPSNP (NO) NPSNP(YES)
On field day 78 9 52 1
On training 49 36 41 11
On demonstration 72 13 52 0
0
10
20
30
40
50
60
70
80
90
FARMERS' PARTCIPATION
On field day On training On demonstration
21
Table 25. Cluster level access to market information
Source; survey result, 2018
Seed Pathway
Under the seed pathway, the second programme primary outcome, the project has designed three Key
Performance Indicators. First, 120,000 farming households use quality seed as a result of REALISE
interventions; second, the number of crops an varieties for which quality seed is currently available are
diversified by 50% by the end of 2020; third, 30 linkages between seed producers and inputs, services
and markets are established. The baseline study focuses on the second KPI only. By 2020, the REALISE
Arba Minch Cluster will achieve the following indicator:
Increase the existing crop varieties by 50 % or increase the varieties from 5 to 9 distinct count crop
varieties
Seed and varieties in seasonal and perennial crops Table 26 shows that a majority of households used improved varieties of seed. This was the case for both
seasonal and perennial crops.
Table 26. Types of seed varieties used
Types of
varieties
PSNP non-PSNP Total
MHH FHH MHH FHH N %
N % N % N % N %
Improved 40 75.5 10 71.4 50 74.6 22 73.3 122 74.3
Local 13 24.5 4 28.6 17 25.4 8 26.7 42 25.6
Source; survey result, 2018
The overall distinct count of improved crop varieties was 5 for both PSNP and non-PSNP households.
Table 27 shows the distinct count of varieties for onion was 2 and 1 for non-PSNP and PSNP programme
households respectively. One improved variety of maize and one of tomato was used by both PSNP and
non-PSNP HHs. There was also one improved variety of teff in use by PSNP households of the study.
Did you access market
Information in a timely manner?
PSNP Non-PSNP Total
No 4 3 7
4.71 5.77 5.11
Yes 81 49 130
95.29 94.23 94.89
Total 85 52 137
100.00 100.00 100.00
22
Table 27. Number of crop varieties used by households for specific crops
PSNP_PROGRAM Crop Distinct Count of Variety
Non-PSNP Maize 1
Onion 2
Tomatoes 1
Non-PSNP Total
4
PSNP Maize 1
Onion 1
Teff 1
Tomatoes 1
PSNP Total
4
Over all total 5
Table 28 shows that the main reasons for households using improved seed varieties were expectations of
high yields, and promotion of the varieties by the government. Altogether, 75.9% of the total
respondents used improved varieties expecting higher yields.
Table 28. Reasons for choosing improved seed varieties
What were the main
reasons for making the decision to use the
improved variety?
Total
Higher yields N 60
% 75.95
Market considerations N 2
% 2.53
No other varieties available N 1
% 1.27
Promoted by neighbours N 1
% 1.27
Promoted by government N 12
% 15.19
Variety is suitable N 3
% 3.80
Total N 79
% 100.00
Source; survey results, 2018
Table 29 shows that the major sources of seed for the households were purchase from the local market
and cooperatives. The local seed businesses (LSBs) and seed saved from the farmers’ own harvests were
not important sources of seed in the study area.
23
Table 29 seed source and acquiring mechanisms
Seed source of last meher planted PSNP Non-PSNP Total
Farm-saved harvest/own harvest 3.61 2.33 3.17
Friends 1.20 0 0.79
Local market 36.14 51.16 41.27
Community 1.20 0 0.79
Cooperative 57.83 46.51 53.97
How they acquired
Saved from my own stocks 3.61 2.3 3.2
Gift (friend/relatives/neighbors) 3.6 0 2.4
Purchase/buy 92.8 97.7 94.4
Source; survey result, 2018
Gender
Table 30 shows the decision making power of women over their access to farm equipment in male (MHH)
and female-headed households (FHH). 13.3% of women in MHHs and 82% of women in FHHs confirmed
that they can access farm equipment without asking permission, indicating that a high proportion of
husbands in MHHs have sole decision making power on this issue.
Table 30. Decision making about access of female household members to farm equipment
Access to
farm
equipment
PSNP Non-PSNP Total
MHH FHH Total MHH FHH Total MHH FHH Total
N % N % N % N % N % N % N % N % N %
Access
without
asking
permission
9 15.3 26 81.3 35 38.5 5 10.9 11 84.6 16 27.1 14 13.3 37 82.2 51 34.0
Access but
I have to
ask
permission
first
49 83.1 4 12.5 53 58.2 41 89.1 2 15.4 43 72.9 90 85.7 6 13.3 96 64.0
No access 1 1.7 1 3.1 2 2.2 0 0.0 0 0.0 0 0.0 1 1.0 1 2.2 2 1.3
My
household
does not
own any
farm
equipment
0 0.0 1 3.1 1 1.1 0 0.0 0 0.0 0 0.0 0 0.0 1 2.2 1 .7
Access to Extension Services
Smallholder farmer’s participation in extension services in general plays a crucial role in disseminating
valuable agronomic advice. Table 31 shows women’s access to extension services including field days
and training. With regard to training, 32% of the women responded that they had participated in training
in the previous year. Women in FHHs had relatively better access to training (40%) compared to women
in the MHHs (28.6%). The result also revealed that Women PSNP beneficiaries had better access to
training (39.6%) than women in Non-PSNP household (20%).
24
Table 31. Access of female household members to extension services
Event
type
PSNP Non-PSNP Total
MHH FHH Total MHH FHH Total MHH FHH Total
N % N % N % N % N % N % N % N % N %
Field
day
2 3.4 7 21.9 9 9.9 1 2.2 0 0 1 1.7 3 2.9 7 15.6 10 6.7
Training 20 33.9 16 50 36 39.6 10 21.7 2 15.4 12 20.3 30 28.6 18 40 48 32
Training type
In relation to the training type, the Baseline survey results highlighted that women mainly received
training in agronomy, nutrition, health and finance (Table 32). A similar pattern of training was observed
in women from both MHHs and FHHs. However, a notable difference was seen among PSNP and non-
PSNP beneficiary women, with 88.9% of women in PSNP households having trained in agronomy
compared with 41.7% in Non-PSNP households, and 91.7% of Non-PSNP beneficiary women, compared
with 52.8% for PSNP beneficiaries having received training in Nutrition and Health.
Table 32. Participation of women in different types of training
Trainig
type
PSNP Non-PSNP Total
MHH FHH Total MHH FHH Total MHH FHH Total
N % N % N % N % N % N % N % N % N %
Agronomy 17 85.0 15 93.8 32 88.9 4 40.0 1 50.0 5 41.7 21 70.0 16 88.9 37 77.1
Animal
husbandry
4 20.0 7 43.8 11 30.6 0 0.0 0 0.0 0 0.0 4 13.3 7 38.9 11 22.9
Natural
resources
4 20.0 9 56.3 13 36.1 0 0.0 0 0.0 0 0.0 4 13.3 9 50.0 13 27.1
Finance 8 40.0 12 75.0 20 55.6 7 70.0 2 100.0 9 75.0 15 50.0 14 77.8 29 60.4
Nutrition 8 40.0 11 68.8 19 52.8 9 90.0 2 100.0 11 91.7 17 56.7 13 72.2 30 62.5
Health 8 40.0 11 68.8 19 52.8 9 90.0 2 100.0 11 91.7 17 56.7 13 72.2 30 62.5
Total 20 100 16 100 36 100 10 100 2 100 12 100 30 100 18 100 48 100
Table 33 shows that out of the 150 households included in the survey, 39% (38.1% MHHs and 42.2%
FHHs) of households reported that DAs had visited their farms every month, while 15% (16.2% MHHs
and 13.3% FHHs) reported that DAs had only visited their farms once a year. The survey findings also
indicated that there were no weekly DA visits. 45% - nearly half of the women (46% in MHHs and 44%
in FHHs) responded that they had never been visited by a DA. Patterns of response were similar between
PSNP and Non PSNP beneficiaries.
Table 33 Frequency of visits by Extension agents (DAs)
Frequncy
DA visit
PSNP Non-PSNP Total
MHH FHH Total MHH FHH Total MHH FHH Total
N % N % N % N % N % N % N % N % N %
Never 28 47.5 11 34.4 39 42.9 20 43.5 9 69.2 29 49.2 48 45.7 20 44.4 68 45.3
Once a
year
12 20.3 4 12.5 16 17.6 5 10.9 2 15.4 7 11.9 17 16.2 6 13.3 23 15.3
Every
month
19 32.2 17 53.1 36 39.6 21 45.7 2 15.4 23 39.0 40 38.1 19 42.2 59 39.3
Every two
weeks
0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0
Weekly 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0
Total 59 100 32 100 91 100 46 100 13 100 59 100 105 100 45 100 150 100
source; cluster own survey,2018
25
Figure 9 summarizes the decision making power of men and women over the issue of the participation of
women in work in the fields. In MHHs, a big majority of husbands and wives made these decisions
together. In FHHs, over half of women consulted their elder brother on this issue.
Figure 7. Decision making about participation of women in field work
Women in general face heavy workloads in their homes and in farming activities. The Baseline survey
revealed that most women reported heavy workloads in April, May, June and July, which were also
months of food shortage (Table 34).
Table 34 Percentage of women reporting a heavy workload in each month
Stressful Months PSNP Non-PSNP Total
MHH FHH Total MHH FHH Total MHH FHH Total
% % % % % % % % %
August 30.4 15.8 23.8 31.6 25.0 30.4 31.0 17.4 26.2
July 34.8 15.8 26.2 31.6 25.0 30.4 33.3 17.4 27.7
June 39.1 68.4 52.4 63.2 100.0 69.6 50.0 73.9 58.5
May 69.6 89.5 78.6 100.0 75.0 95.7 83.3 87.0 84.6
April 87.0 84.2 85.7 94.7 75.0 91.3 90.5 82.6 87.7
March 39.1 36.8 38.1 21.1 25.0 21.7 31.0 34.8 32.3
February 21.7 31.6 26.2 5.3 25.0 8.7 14.3 30.4 20.0
January 13.0 10.5 11.9 0.0 0.0 0.0 7.1 8.7 7.7
December 4.3 0.0 2.4 0.0 0.0 0.0 2.4 0.0 1.5
November 4.3 5.3 4.8 5.3 0.0 4.3 4.8 4.3 4.6
October 34.8 31.6 33.3 68.4 25.0 60.9 50.0 30.4 43.1
September 56.5 47.4 52.4 84.2 50.0 78.3 69.0 47.8 61.5
Source; cluster own survey
The Baseline study showed that only 9.5% of PSNP FHHs and 33.3% of non-PSNP FHHs owned chickens.
And only 6.8% of PSNP and 15.6% of non-PSNP MHHs owned chickens. Women in FHHs thus owned
more chickens than women in MHHs. Table 35 shows that in most MHHs, the husband and wife made
joint decisions about chicken management.
3.4
1.7
6.8
88
.1
52
.4
47
.6
0.0
0.0
0.0
10
0.0
33
.3
66
.7
1.9
1.0
3.8
93
.3
48
.1
51
.9
0
20
40
60
80
100
120
Husband only I am informedabout thedecision
I am consultedabout thedecision
We decidetogether
I am consultedabout thedecision
I consult myelder brother
MHH FHH
% of women decisions on field work
PSNP Non-PSNP Total
26
Table 35. Participation of women in decision making about chicken keeping
MHH FHH
Decision with regards to revenue from sale of
chickens /eggs
We decide together 7(63.6%) 2(50%)
I consult my husband 2(18.2%)
I inform my husband
Wife only 2(18.2%)
Me only _ 2(50%)
Decision with regard to selling of chickens We decide together 7(63.6%) 2(50%)
I consult my husband
Me only _ 2(50%)
I consult my husband 2(18.2%)
wife only 2(18.2%)
Decision regarding purchasing new chickens We decide together 8(72.7%) 2(50%)
I consult my husband 2(18.2)
Wife only 1(9.1)
Me only _ 2(50%)
Conclusion
This section summarizes the Baseline survey results under each chapter.
Household characteristics
The average age of household heads in both MHHs and FHHs was similar for PSNP and non-
PSNP respondents.
The number of years spent in school was on average 4 years. The rate of illiteracy was very
high, and was similar across the households.
The mean household size for PSNP HHs was 5 for PSNP and 4 for non-PSNP.
The agricultural plot size was larger for non-PSNP respondents. The PSNP groups owned
very small plot sizes for agriculture.
Food security
The mean Food gap months were 4.49months for non-PSNP and 4.80 months for PSNP
households.
January, February, March, April and May were months when households faced food
shortages in the study area.
The households consumed an average of 4 food groups (out of a total of 12) within a 24
hour period.
Socioeconomic status
The mean TLU of the cluster was 1.68.
The average productive asset index was less than 1 for the cluster.
Mains electricity was the major source of lighting, followed by solar power.
› 50% of the respondents reported that they had not migrated for work or performed any
wage labour activities in the past 12 months.
Agricultural and Resilience
During the previous 3 months ≥50% of the households had experienced drought, › 80%
experienced pest infestation and › 90% erratic rainfall.
27
These natural hazards impacted the lives of the household members catastrophically, by
causing massive crop failure and over 70% reduction in diet diversity of the households.
Selling livestock, looking for loans, and borrowing were the main coping strategies of the
community, and a few households engaged in management of natural resources/
environmental conservation and water conservation mechanisms.
Regarding HHs food gap, 94.8% of MHHs reported that they were are able to bridge a gap
of one month, and 77.1% of FHHs reported that they were also able to bridge this gap.
Receipt of remittances and resource transfers were inadequate is the study area.
Best fit practice
Maize, cotton and haricot bean were the top three seasonal crops preferred by all the
households.
Moringa, mango and banana were the top three perennial crops of the households.
The productivity of the above listed important crops was very low in both the belg and
meher planting seasons.
The utilization of crops varied, with some such as maize and haricot bean being consumed
immediately after harvesting, and others such as onion, tomato and cotton being sold after
they were harvested.
Farming practices and Agricultural extension services
Chemical fertilizer application was higher for maize, sorghum, tomato and onion in both
planting seasons, because of expectations of higher yields.
The application rates of farmyard manure were highest for head cabbage, maize and onion.
Pesticides were widely used for disease and insect control.
Seed and varieties in seasonal and perennial crops
More than 70% of the respondents reported that they were using improved varieties of both
seasonal and perennial crops. Less than 30% of households used local seed varieties
because of high yields and government promotion of the improved seed varieties.
The sources of the seed were mainly from cooperatives and the local market, and these
seeds were acquired through purchase.
The overall number of improved seed varieties used was five.
Gender
More women in female headed households had access to farm equipment without asking
permission than women living with their husbands.
Decision making about women’s work in the fields was predominantly made by their
husbands or their elder brothers.
April, May, June and July were the months of highest workload for women. These were also
months of food shortage.
Access to extension services such as field days and training was higher for women in FHHs.
There was a notable difference between women in FHHs and MHHs in decision making in
every area studied. Men have the main decision making power over most issues when they
are household heads.
Visits by extension agents are often not sufficiently frequent, and many households only
receive visits once per year.
Chickens were kept by a small minority of households and decisions regarding the selling of
chickens/eggs and purchasing of new chickens were made together with other household
members in FHHs and with husbands in MHHs.
28
The Way forward
The REALISE Arba Minch Cluster is aiming to put in place a number of different activities to achieve
project goals and targets. In order to improve households’ adoption of agricultural technology, in situ
training will be provided. In addition, the cluster will conduct ToT training for scaling up best fit practices,
which will enhance farmers’ knowledge to apply recommended rates of inputs to enhance the
productivity of their plots.
Arba Minch cluster should contribute to the increase food security by targeting interventions that
introduce drought resistant crop varieties, nutritious crop varieties, and home gardening of vegetables.
Moreover, the cluster intends to work on different off-farm activities that have the potential to generate
income such as bamboo production. Trainings based on needs assessments will be part of the strategy.
In terms of agriculture production, the cluster aims to increase the adoption of intercropping as well as to
promote and strengthen linkages between agro- dealers to access agro- chemicals that will be used to
control pests.
With regard to best fit practices pathway, Arba Minch University cluster will adopt valid and high yielding
crops. In addition, developing standard composition/mix of farmyard manure components for home
gardening crops, will contribute to improve adoption of best fit practices.
On seed pathway, the cluster will encourage farmers to adopt validated certified and quality crop
varieties that will increase productivity. At the same time, local seed businesses and cooperatives will be
supported with the aim to boost supply of farmers’ quality seed demanded.
Activities related to empowering women through training on how to efficiently use the resources they
have at hand, will be strengthened by the project to ensure that the benefits of the interventions reach
out to women.
29
References
CSA Report; The Federal Democratic Republic of Ethiopia Central Statistical Agency Agricultural Sample
Survey2017/18 (2010 E.C.)
FAO 2011; Food and Agricultural Organization of the United Nations Report, 2011
Filmer and Pritchett 2001; Measuring Relative Wealth using Households Asset Indicators, 2001
PSNP Mid-line report 2018; The Productive Safety Net Programme 4 Midline Survey, 2018
SNNPR data source; (Merab Abaya Bureau of Agriculture)
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