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 Analysispresented 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

MethodologyStudy 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

6

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)

Sheep market routes at North Shewa connected to Addis Ababa

7

Producers Primary Mkt Secondary Mkt Tertiary Mkt

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

9

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)

Costs and margins of actors in a market channel selling crop residue and concentrate to users

Crop residues Concentrate

ProducersSmall traders Traders

Selling price (Birr/sack) 35 55 Selling price (Birr/Qt) 325

Marketing cost (Birr/sack) - 8 Purchase from Addis

(Birr/Qt) 280

Marketing M.(Birr/sack) 20 Gross margin 45

Net margin (Birr/sack) 12 Marketing cost (Birr/Qt) 18

Producer's share of final price (%) 34.29 Net margin(Birr/Qt) 27

10

Concentrate include wheat bran and/or nug cake

Constraints and opportunities for sheep and feed value chain

ConstraintsProblems in input supply

- Shortage of: Improved rams, forage seed, drug supply

- Credit - high interest, group collateral

Production constraints– Feed shortage– Inadequate livestock health services– Traditional housing and feeding practices

Transportation constraints– High cost of transportation

Marketing constraints– Lack of reliable source of mkt information– Lack of market place for feed– Poor livestock marketing infrastructure– Seasonality in SS and DD for sheep and feed

Institutional and organizational constraints• Double taxation

– There is double taxation –at d/t checkpoints• Lack of sheep and feed trader cooperatives• In adequate training (Skills and knowledge)

Opportunities

An increasingly high demand for sheep

meat and animal feed in local markets

Government's commitment and support

to increase export of meat

The establishment of Livestock

Development and Health Agency

Individuals engaged in fattening

practice

Farmers Awareness increasing

Transport access to the main market

Increase in number of export abattoirs

11

Ways forward

12

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.

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 questions13

FEAST presented by

Liyusew Ayalew(EIAR Holetta)

14

Using FEAST to Characterize Livestock Production Systems

in Wolemera Districts, Ethiopia

Dairy teamHoleta 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

Berffeta Tokkoffa (local cows)Robe-Gebya (cross-bred)

Land holding -Wolmera

0

5

10

15

20

25

30

35

40

45

Landless Small farmer Medium farmer Large farmer

0 Up to 1 1 to 2 More than 2

% o

f hou

seho

lds t

hat f

all i

nto

the

cate

gory

Range of land size in hectar

Group Information

Total

0

10

20

30

40

50

60

Landless Small farmer Medium farmer Large farmer

0 Up to 1 1 to 2 More than 2

% o

f hou

seho

lds t

hat f

all i

nto

the

cate

gory

Range of land size in hectar

Group Information

Total

Major crops grown

Berffetta tokkofaa (local cows)

Robe gebya(cross-bred)

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Tef (Eragrostis tef) Wheat (Triticum aestivum)

Chickpeas (Cicer arietinum)

Grass pea (Lathyrus sativus)

Potato (Solanum tuberosum)

Aver

age

area

per

hou

seho

ld (h

ecta

res

Average area (ha) per hh of dominant arable crops

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Barley (Hordeum vulgare)

Tef (Eragrostis tef) Wheat (Triticum aestivum)

Common Beans (Phaseolus vulgaris)

Potato (Solanum tuberosum)

Aver

age

area

per

hou

seho

ld (h

ecta

res

Average area (ha) per hh of dominant arable crops

Forage crops grown in the area

0

0.005

0.01

0.015

0.02

0.025

Oat (Avena sativa) Naturally occuring pasture - tropical

Sesbania (Sesbania sesban)

Napier grass (Pennisetum purpureum)

Fodder Beat (Beta vulgaris)

Aver

age

area

of c

rop

grow

n pe

r hou

seho

ld

(hec

tare

s)

The dominant fodder crops grown in the area

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

Napier grass (Pennisetum purpureum)

Oat (Avena sativa) Sesbania (Sesbania sesban)

Aver

age

area

of c

rop

grow

n pe

r hou

seho

ld

(hec

tare

s)The dominant fodder crops grown in the area

• Berffetta tokkofaa (local cows)

Robe Gabya (cross-bred)

Berffetta Tokkoffa (local cows)Robe-Gebya (cross-bred)

Major sources of income for livelihoods - Wolmera

Berffetaa Tokkoffa (local cows) Robe-Gebya (cross-bred)

Average livestock holding (TLU) – Wolmera

0.00

0.50

1.00

1.50

2.00

2.50

3.00

Fattening and draught cattle

Local Dairy Cattle Donkeys Horse Improved Dairy cattle

Average livestock holdings per household -dominant species (TLU)

0.00

0.50

1.00

1.50

2.00

2.50

3.00

3.50

4.00

4.50

5.00

Improved Dairy cattle Local Dairy Cattle Fattening and draught cattle

Horse Sheep

Average livestock holdings per household -dominant species (TLU)

Berffetaa Tokkoffa (local cows)Robe Gebya (cross-bred)

Feed resources contribution to the diet - Wolmera

Important problems identified by farmers using pair wise ranking -Wolmera

Berffeta Tokkofaa (local cows)

1st Feed shortage (quality and quantity)

2nd Lack of knowledge about livestock management

3th Lack of improved breeds

4th Lack of management about natural resources

5th Lack of access to animal health services

Robe Gebya (cross-bred)

1st Low milk prices Vs. high cost of milk production

2nd Poor AI services

3rd Feed shortage (quality and quantity)

4th Lack of availability of improved breed

5th Trekking of long distance to fetch water

•

Lessons learned using the FEAST tool

Strength Weaknesses• first such tool• 'farmer problems; farmer

solutions'• good to facilitate

discussion/participation• helps identify problems and

farmer solutions• captures livelihood issues• it's rapid (less farmer time)• offers an opportunity to

educate farmers

• individual sample size is too small/farmer

• it is knowledge intensive (needs experts)

• productivity parameters limited to milk?

• lack of clarity on spatial scale

Potential solutions suggested by farmers

Berffetaa Tokkoffaa (local cows) Robe-Gebya (cross-bred)

• crops at backyard, around fence, farm side

• Reducing the herd size• Improving the utilization of

straws of different food crops • Providing farmers with continues

training

• Organizing farmers to transport their milk to terminal market (Addis Ababa),

• Providing farmers with a greater understanding of common diseases in the area will improve the health of their animals

• Strengthen the capacity of 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 byAdissu Aberra

(EIAR Debre Zeit)

29

Application of TechFit Tool for Prioritization of Feed Technologies for Smallholder Fattening

ByDr. Solomon MengestuAddisu AberaSolomon AbeyiFantahun Dereje

May, 2012EIAR

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

• Land • Labour• Credit• Inputs• Knowledge

PRAExercise/FGD

• Farmers participatory scoring of the 5 attributes

Assessment of the 5 attributes

• Based on context vis-à-vis technology attribute scores

Filtering of Technologies

Methodology of The TechFit Tool

33

Match farmers’ context to technology

Score for technology attribute

Score for context attribute

Land X Land =

Labor X Labor =

Credit X Credit =

Input X Input =

Knowledge X Knowledge =

If technology demands land => low score for landIf farmers do not have or very small land holding => Low score for land

III.

TECHNOLOGY FILTER

(Technology options to address quantity,

quality, seasonality issues) .

Utilise better-Produce more-Import

Pre-select the obvious (5-6) based

on context relevance and impact potential

Score the pre-selected technologies based on the requirement, availability and scope for improvement of five technology attributes

Attribute 1:

Land

Attribute 2:

Labour

Attribute 3: Cash /credit

Attribute 4:

Input delivery

Attribute 5: Knowledge /skill

Scope for improvement of

attributes

Total Score

Context relevan

ce (score

1-6; low-

high))

Impact potential

(score

1-6; low-high)

Total score

(context X

impact)

Requirement

Score 1-3 (1

for more;

3 for less)

Availability Score 1-3 (1 for less; 3

for more)

Requirement

Score 1-3 (1

for more; 3 for

less)

Availability Score 1-3 (1 for less; 3

for more)

Requirement

Score 1-3 (1

for high; 3 for low)

Availability Score 1-3 (1 for less; 3

for more)

Requirement

Score 1-3 (1

for high; 3 for low)

Availability Score

1-3 (1 for less;

3 for

more)

Requirement Score 1-3

(1 for high;

3 for low)

Availability Score

1-3 (1

for less; 3 for

more)

Score 1-5 (1 for less

and 5 for more)

Improvements of crop residues

Machine chopping of residues

4 4 16 3 2 3 2 1 1 2 2 3 2 326

Hand chopping of residues 4 3 12 3 1 3 3 3

Generous feeding of CRs

4 5 20 2 2 2 2 3 1 3 2 3 2 4 27

Treatment of crop residues (e.g. urea treatment)

2 4 8 3 1 1 1 1 2 2

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

Excel template for scoring and ranking of technologies

No. Selected TechnologyTotal score Rank

1 Generous feeding of CRs 27 12 Machine chopping of residues 26 23 Supplement with agro-industrial by-products 25 34 Smart feeding 22 45 Use of improved annual grass-legume mixture 20 5

6Fodder trees (Sesbania, Leucaena, Tagasaste, Gliricidia) 20 6

Technologies Filtered using TechFit toolEg: Kechema kebele

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

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

Lessons learned from application of TechFit

Opportunities for further use

• wide context range = wider application

• wide technology range = wide application across AEZ

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

40