BENEFIT-REALISE Arba Minch University Cluster Baseline Study · BENEFIT-REALISE Arba Minch...

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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


% % % % % % % % %

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)

BENEFIT-REALISE Arba Minch University Cluster Baseline Study · BENEFIT-REALISE Arba Minch...

Documents

Transcript of BENEFIT-REALISE Arba Minch University Cluster Baseline Study · BENEFIT-REALISE Arba Minch...