Results and experiences using value chain analysis, FEAST
and Techfit tools in the Ethiopian Livestock Feed Project
Beneberu Tefera, Liyusew Ayalew and Adissu Aberra
Ethiopian Livestock Feed Project Synthesis workshop, Addis Ababa, 28-
29 May 2012

Value Chain Analysis
presented by
Beneberu Tefera
(ARARI Debre Birhan)
2

Sheep and feed value chain analysis in
North Shewa, Amhara Region
Debre Birhan Agricultural Research Center, Debre Birhan, Ethiopia
3

Objectives
To analyze sheep and feed value chain and assess the
determinants of sheep and feed market supply in the study
area
To identify major constraints and opportunities for sheep
and feed value chain in the study area
To test tools prepared for analysis of sheep and feed value
chain and provide feedback for further improvement
4

Methodology
Study area: Angolela Tera districts 107 km away from
Addis.
For PRA study 2 Kebeles and within each kebele 12
representative producers were selected with the help of
district agrl’ office experts
Age, sex, wealth and educational level were considered
Feed and sheep traders of the districts were interviewed
representing secondary/intermediate markets.
Export abattoir were also interviewed representing
terminal market.
Data was analyzed using descriptive and cost margin
analysis 5

Sheep VC actors and major channels
Identified
channels for
sheep marketing
CH 1- Sheep purchased for
breeding/ fattening purpose by
farmers
CH 2- Sheep purchased by
hotels and individual
consumers in the study areas
CH 3- Sheep transported to
Addis Ababa butchers ,
supermarkets and consumer
markets
CH 4- Sheep slaughtered at
Modjo export abattoirs (Luna)
6

Sheep market routes at North Shewa connected to Addis Ababa
Producers Primary Mkt Secondary Mkt Tertiary Mkt
7

Costs and margins of actors in a market channel selling
sheep to export abattoirs, butchers and supermarkets
Export abattoirs Butchers Super markets
Producers selling price (Birr/head) 750 1400 1300
Selling price (Birr/head) 1283 2120 1915
Marketing cost (Birr/head) 87 61 96
Marketing margin (Birr/head) 373 535 515
Net margin (Birr/head) 286 475 419
Producer's share of final price (%) 58 66 68
8

Feed VC actors and major channels
Identified channels for feed
marketing
CH 1. Crop residue purchased
for nearby town dairy
production
CH 2. Concentrate purchased
by traders and cooperatives for
distribution to farmers
(rearing/fattening/dairy)
9

Costs and margins of actors in a market channel
selling crop residue and concentrate to users
Crop residues Concentrate
Small
Producers Traders
traders
Selling price (Birr/sack) 35 55 Selling price (Birr/Qt) 325
Marketing cost Purchase from Addis
- 8 280
(Birr/sack) (Birr/Qt)
Marketing M.(Birr/sack) 20 Gross margin 45
Net margin (Birr/sack) 12 Marketing cost (Birr/Qt) 18
Producer's share of final
34.29 Net margin(Birr/Qt) 27
price (%)
Concentrate include wheat bran and/or nug cake
10

Constraints and opportunities for sheep and feed value chain
Constraints Opportunities
Problems in input supply
- Shortage of: Improved rams, forage seed, drug An increasingly high demand for sheep
supply
- Credit - high interest, group collateral
meat and animal feed in local markets
Production constraints Government's commitment and support
– Feed shortage
– Inadequate livestock health services to increase export of meat
– Traditional housing and feeding practices
The establishment of Livestock
Transportation constraints
– High cost of transportation Development and Health Agency
Marketing constraints
– Lack of reliable source of mkt information Individuals engaged in fattening
– Lack of market place for feed practice
– Poor livestock marketing infrastructure
– Seasonality in SS and DD for sheep and feed Farmers Awareness increasing
Institutional and organizational constraints
Transport access to the main market
• Double taxation
– There is double taxation –at d/t checkpoints Increase in number of export abattoirs
• Lack of sheep and feed trader cooperatives
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• In adequate training (Skills and knowledge)

Ways forward
Intervention measures needs to correspond to the household flock holdings, best
bred but small flock size.
Research needs to provide information on efficient and economic utilization of
the available resources to improve the traditional fattening practice.
There is a need to provide timely and reliable market information to enhance
informed decision making by farmers
Support the private sector actors willing to invest in sheep and feed production by
availing appropriate information including the costs and benefits production.
Farmers have to be equipped with the skills of innovative knowledge that can
make them improve the management and storages of crop residues and proper
supplementations.
12

Lesson learned on VCA tool
Strengths
Connects demand and supply
It is a quick problem identification and quick fix approach
It has holistic approach and is inclusive
It can be done with less expertise and interdisciplinary
Flexible
Weaknesses
The tool was not specific to commodities
Has difficult to remember trend questions
13

FEAST
presented by
Liyusew Ayalew
(EIAR Holetta)
14

Using FEAST to Characterize Livestock
Production Systems
in
Wolemera Districts, Ethiopia
Dairy team
Holeta Agricultural Research Centre
15

The FEAST (Feed Assessment Tool)
• Is a rapid tool designed to assess livestock
production systems;
– To identify constraints and opportunities
– To identify potential intervention strategies
• The tool was tested in two selected Woredas
(Wolmera and Wuchale) in the central
highlands of Ethiopia

Our Objective
 To test the application of FEAST tool for rapid
assessment of the livestock production systems and
the available feed resource base in the two Woredas.

Methodology
Selection Criteria:
 Type of dairy production system
 One village dominated by local cattle, no milk market
 The other village dominated by crossbred cattle with
milk markets
 A total of 12 – 14 farmers (2-5 women) selected from each village
based on wealth status, gender, age groups.
 Qualitative data collected through key informant interviews

 Quantitative data process by Microsoft Excel template

Major findings

Land holding -Wolmera
Berffeta Tokkoffa (local cows) Robe-Gebya (cross-bred)
Group Information Group Information
45 60
% of households that fall into the category
% of households that fall into the category
40
50
35
30 40
25
30
20
15 20
Total Total
10
10
5
0 0
Landless Small farmer Medium farmer Large farmer Landless Small farmer Medium farmer Large farmer
0 Up to 1 1 to 2 More than 2 0 Up to 1 1 to 2 More than 2
Range of land size in hectar Range of land size in hectar

Average area (ha) per hh of dominant arable crops
0.60
Major crops 0.50
Average area per household (hectares
0.40
grown 0.30
0.20
Berffetta tokkofaa 0.10
(local cows) 0.00
Barley (Hordeum Tef (Eragrostis tef) Wheat (Triticum Common Beans Potato (Solanum
vulgare) aestivum) (Phaseolus vulgaris) tuberosum)
Average area (ha) per hh of dominant arable crops
1.00 Robe gebya
0.90
(cross-bred)
Average area per household (hectares
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
Tef (Eragrostis tef) Wheat (Triticum Chickpeas (Cicer Grass pea (Lathyrus Potato (Solanum
aestivum) arietinum) sativus) tuberosum)

Forage crops grown in the area
The dominant fodder crops grown in the area
0.025
Average area of crop grown per household
0.02
0.015 • Berffetta tokkofaa
(hectares)
0.01 (local cows)
0.005
0
Oat (Avena sativa) Naturally occuring Sesbania (Sesbania Napier grass Fodder Beat (Beta
pasture - tropical sesban) (Pennisetum vulgaris)
purpureum)
The dominant fodder crops grown in the area
0.14
Average area of crop grown per household
0.12
0.1
Robe Gabya
(hectares)
0.08
0.06
(cross-bred) 0.04
0.02
0
Napier grass (Pennisetum Oat (Avena sativa) Sesbania (Sesbania sesban)
purpureum)

Major sources of income for livelihoods -
Wolmera
Berffetta Tokkoffa (local cows) Robe-Gebya (cross-bred)

Average livestock holding (TLU) – Wolmera
Berffetaa Tokkoffa (local cows) Robe-Gebya (cross-bred)
Average livestock holdings per household - Average livestock holdings per household -
dominant species (TLU) dominant species (TLU)
3.00 5.00
4.50
2.50
4.00
3.50
2.00
3.00
1.50 2.50
2.00
1.00
1.50
1.00
0.50
0.50
0.00 0.00
Fattening and Local Dairy Cattle Donkeys Horse Improved Dairy cattle Improved Dairy cattle Local Dairy Cattle Fattening and Horse Sheep
draught cattle draught cattle

Feed resources contribution to the diet - Wolmera
Berffetaa Tokkoffa (local cows) Robe Gebya (cross-bred)

Important problems identified by
farmers using pair wise ranking -
Wolmera
Berffeta Tokkofaa (local Robe Gebya (cross-bred)
cows) 1st Low milk prices Vs. high cost
1st Feed shortage (quality and of milk production
quantity)
2nd Poor AI services
2nd Lack of knowledge about
livestock management 3rd Feed shortage (quality and
quantity)
3th Lack of improved breeds
4th Lack of availability of
4th Lack of management about improved breed
natural resources
5th Trekking of long distance to
5th Lack of access to animal fetch water
health services
•

Lessons learned using the FEAST tool
Strength Weaknesses
• first such tool • individual sample size is too
• 'farmer problems; farmer small/farmer
solutions'
• it is knowledge intensive
• good to facilitate
discussion/participation (needs experts)
• helps identify problems and • productivity parameters
farmer solutions limited to milk?
• captures livelihood issues • lack of clarity on spatial
• it's rapid (less farmer time) scale
• offers an opportunity to
educate farmers

Potential solutions suggested by
farmers
Berffetaa Tokkoffaa (local
cows) Robe-Gebya (cross-bred)
• crops at backyard, around fence, • Organizing farmers to transport
farm side their milk to terminal market
(Addis Ababa),
• Reducing the herd size • Providing farmers with a greater
• Improving the utilization of understanding of common
diseases in the area will improve
straws of different food crops the health of their animals
• Providing farmers with continues • Strengthen the capacity of
training farmers to use underground
water
• Use of AI service to selected best
body condition local dairy cows
and increasing awareness in
improved livestock management

Techfit
presented by
Adissu Aberra
(EIAR Debre Zeit)
29

Application of TechFit Tool for
Prioritization of Feed Technologies for
Smallholder Fattening
By
Dr. Solomon Mengestu
Addisu Abera
Solomon Abeyi
Fantahun Dereje
May, 2012
EIAR

Introduction
TechFit
• It is a tool developed for systematic ranking
and prioritization of potential feed
technologies for intervention
• Involves combining scores of technology and
context attributes to arrive at an overall score
for how a technology is likely to fit a particular
context

METHODOLOGY
 Adama District
 Kechema
 Wonji Kuriftu
 Arsi Negele
District
 Ali Wayo
 Kersa Ilala
Selection criteria
• Presence of smallholder
beef fattening activities
• Accessibility

Methodology of The TechFit Tool
• Land
• Labour
PRA
• Credit
Exercise/FGD
• Inputs
• Knowledge
Assessment of
the 5 • Farmers participatory scoring of
attributes the 5 attributes
Filtering of • Based on context vis-à-vis
Technologies technology attribute scores
33

Match farmers’ context to technology
Score for Score for context
technology attribute
attribute
Land X Land =
Labor X Labor =
Credit X Credit =
Input X Input =
Knowledge X Knowledge =
If technology demands land => low score for land
If farmers do not have or very small land holding => Low score for land

Excel template for scoring and ranking of technologies
Score the pre-selected technologies based on the requirement, availability and scope for improvement of
III. TECHNOLOGY five technology attributes
FILTER Pre-select the obvious
(Technology options (5-6) based Scope for
to address quantity, on context relevance improve
Attribute 1: Attribute 2: Attribute 3: Attribute 4: Attribute 5:
quality, seasonality and impact potential ment of
Land Labour Cash /credit Input delivery Knowledge /skill
issues) attribute
. s
Utilise better-Produce Context Impact Total Require Availabil Require Availabil Require Availabil Require AvailabiliRequirem Availabili Score 1-5 Total
more-Import relevan potenti score ment ity Score ment ity Score ment ity Score ment ty Score ent Score ty Score (1 for less
Score
ce al (context Score 1- 1-3 Score 1- 1-3 Score 1- 1-3 Score 1- 1-3 1-3 1-3 and 5 for
(score (score X 3 (1 for 3 (1 for 3 (1 for 3 (1 for (1 for (1 for more)
1-6; 1-6; impact) (1 for less; (1 for less; (1 for less; (1 for less; high; less;
low- low- more; 3 for more; 3 for high; 3 for high; 3 for 3 for low) 3 for
high)) high) 3 for more) 3 for more) 3 for more) 3 for more) more)
less) less) low) low)
Improvements of crop
residues
Machine chopping of 4 4 16 3 2 3 2 1 1 2 2 3 2 3
26
residues
Hand chopping of
4 3 12 3 1 3 3 3
residues
Generous feeding of 4 5 20 2 2 2 2 3 1 3 2 3 2 4 27
CRs
Treatment of crop
residues (e.g. urea 2 4 8 3 1 1 1 1 2 2
treatment)
Feeding of home
grown legume residues 3 4 12 3 2 3 1 3 3 3 6
Feeding of bought in
legume residues 1 4 4 3 3 1 3 3 2 2
Supplementation

Technologies Filtered using TechFit tool
Eg: Kechema kebele
Total
No. Selected Technology score Rank
1 Generous feeding of CRs 27 1
2 Machine chopping of residues 26 2
3 Supplement with agro-industrial by-products 25 3
4 Smart feeding 22 4
5 Use of improved annual grass-legume mixture 20 5
Fodder trees (Sesbania, Leucaena, Tagasaste,
6 Gliricidia) 20 6
After short listing the first 3-4
technologies, go for cost benefit
analysis

Lessons learned from application
of TechFit
Strengths
• Lists most feed technologies
• filters technologies according to contexts of the
farmer
• considers most limiting factors, e.g. land
• a rapid tool
• quick and comprehensive
• puts feed in a broader context
• helps to systematize short listing of technology
options

Lessons learned from
application of TechFit
Weaknesses
• does not consider water availability
• the scoring may mask some potential
technologies
• narrow scoring range for attributes and
contexts (1-3 only)
• gives equal weights to all attributes
• not yet complete, also the cost benefit
tool

Opportunities for further use
• wide context range = wider application
• wide technology range = wide application
across AEZ

More Information:
http://elfproject.wikispaces.com
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