Results and experiences using value chain analysis, FEAST  and Techfit tools in the Ethiopian Livestock Feed Project     B...
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 Bir...
Objectives To analyze sheep and feed value chain and assess the determinants of sheep and feed market supply in the study ...
MethodologyStudy area: Angolela Tera districts 107 km away fromAddis.For PRA study 2 Kebeles and within each kebele 12repr...
Sheep VC actors and major channels                                        Identified                                      ...
Sheep market routes at North Shewa connected to Addis AbabaProducers     Primary Mkt    Secondary Mkt     Tertiary Mkt    ...
Costs and margins of actors in a market channel selling sheep to export abattoirs, butchers and supermarkets              ...
Feed VC actors and major channels                               Identified channels for feed                              ...
Costs and margins of actors in a market channel         selling crop residue and concentrate to users                     ...
Constraints and opportunities for sheep and feed value chain          Constraints                    OpportunitiesProblems...
Ways forwardIntervention measures needs to correspond to the household flock holdings, bestbred but small flock size.Resea...
Lesson learned on VCA toolStrengths  Connects demand and supply  It is a quick problem identification and quick fix approa...
FEAST  presented byLiyusew Ayalew (EIAR Holetta)                  14
Using FEAST to Characterize Livestock         Production Systems                 in    Wolemera Districts, Ethiopia       ...
The FEAST (Feed Assessment Tool)• Is a rapid tool designed to assess livestock  production systems;  – To identify constra...
Our Objective To test the application of FEAST tool for rapid  assessment of the livestock production systems and  the av...
MethodologySelection Criteria:    Type of dairy production system       One village dominated by local cattle, no milk m...
Major findings
Land holding -Wolmera                                              Berffeta Tokkoffa (local cows)                         ...
Average area (ha) per hh of dominant arable crops                                                                         ...
Forage crops grown in the area                                                   The dominant fodder crops grown in the ar...
Major sources of income for livelihoods -               WolmeraBerffetta Tokkoffa (local cows)   Robe-Gebya (cross-bred)
Average livestock holding (TLU) – WolmeraBerffetaa Tokkoffa (local cows)                                                  ...
Feed resources contribution to the diet - WolmeraBerffetaa Tokkoffa (local cows)   Robe Gebya (cross-bred)
Important problems identified by       farmers using pair wise ranking -                  WolmeraBerffeta Tokkofaa (local ...
Lessons learned using the FEAST toolStrength                          Weaknesses• first such tool                 • indivi...
Potential solutions suggested by                   farmersBerffetaa Tokkoffaa (localcows)                                R...
Techfit  presented by  Adissu Aberra(EIAR Debre Zeit)                    29
Application of TechFit Tool forPrioritization of Feed Technologies for         Smallholder Fattening                      ...
IntroductionTechFit• It is a tool developed for systematic ranking  and prioritization of potential feed  technologies for...
METHODOLOGY Adama District       Kechema       Wonji Kuriftu Arsi Negele  District       Ali Wayo       Kersa IlalaS...
Methodology of The TechFit Tool               • Land               • LabourPRA               • CreditExercise/FGD         ...
Match farmers’ context to technologyScore for                             Score for contexttechnology                     ...
Excel template for scoring and ranking of technologies                                                  Score the pre-sele...
Technologies Filtered using TechFit tool                        Eg: Kechema kebele                                        ...
Lessons learned from application                of TechFitStrengths• Lists most feed technologies• filters technologies ac...
Lessons learned from         application of TechFitWeaknesses• does not consider water availability• the scoring may mask ...
Opportunities for further use• wide context range = wider application• wide technology range = wide application  across AEZ
More Information:http://elfproject.wikispaces.com                                   40
Upcoming SlideShare
Loading in …5
×

Results and experiences using value chain analysis, FEAST and Techfit tools in the Ethiopian Livestock Feed Project

2,875 views

Published on

Presented by Beneberu Tefera, Liyusew Ayalew and Adissu Aberra at the Ethiopian Livestock Feed Project Synthesis workshop, Addis Ababa, 28-29 May 2012

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
2,875
On SlideShare
0
From Embeds
0
Number of Embeds
739
Actions
Shares
0
Downloads
59
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Results and experiences using value chain analysis, FEAST and Techfit tools in the Ethiopian Livestock Feed Project

  1. 1. Results and experiences using value chain analysis, FEAST and Techfit tools in the Ethiopian Livestock Feed Project Beneberu Tefera, Liyusew Ayalew and Adissu AberraEthiopian Livestock Feed Project Synthesis workshop, Addis Ababa, 28- 29 May 2012
  2. 2. Value Chain Analysis presented by Beneberu Tefera(ARARI Debre Birhan) 2
  3. 3. Sheep and feed value chain analysis in North Shewa, Amhara Region Debre Birhan Agricultural Research Center, Debre Birhan, Ethiopia 3
  4. 4. 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
  5. 5. MethodologyStudy area: Angolela Tera districts 107 km away fromAddis.For PRA study 2 Kebeles and within each kebele 12representative producers were selected with the help ofdistrict agrl’ office expertsAge, sex, wealth and educational level were consideredFeed and sheep traders of the districts were interviewedrepresenting secondary/intermediate markets.Export abattoir were also interviewed representingterminal market.Data was analyzed using descriptive and cost marginanalysis 5
  6. 6. 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
  7. 7. Sheep market routes at North Shewa connected to Addis AbabaProducers Primary Mkt Secondary Mkt Tertiary Mkt 7
  8. 8. Costs and margins of actors in a market channel selling sheep to export abattoirs, butchers and supermarkets Export abattoirs Butchers Super marketsProducers selling price (Birr/head) 750 1400 1300Selling price (Birr/head) 1283 2120 1915Marketing cost (Birr/head) 87 61 96Marketing margin (Birr/head) 373 535 515Net margin (Birr/head) 286 475 419Producers share of final price (%) 58 66 68 8
  9. 9. 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
  10. 10. Costs and margins of actors in a market channel selling crop residue and concentrate to users Crop residues Concentrate Small Producers Traders tradersSelling price (Birr/sack) 35 55 Selling price (Birr/Qt) 325Marketing cost Purchase from Addis - 8 280(Birr/sack) (Birr/Qt)Marketing M.(Birr/sack) 20 Gross margin 45Net margin (Birr/sack) 12 Marketing cost (Birr/Qt) 18Producers share of final 34.29 Net margin(Birr/Qt) 27price (%) Concentrate include wheat bran and/or nug cake 10
  11. 11. Constraints and opportunities for sheep and feed value chain Constraints OpportunitiesProblems 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 marketsProduction constraints Governments commitment and support – Feed shortage – Inadequate livestock health services to increase export of meat – Traditional housing and feeding practices The establishment of LivestockTransportation constraints – High cost of transportation Development and Health AgencyMarketing 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 increasingInstitutional 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 11• In adequate training (Skills and knowledge)
  12. 12. Ways forwardIntervention measures needs to correspond to the household flock holdings, bestbred but small flock size.Research needs to provide information on efficient and economic utilization ofthe available resources to improve the traditional fattening practice.There is a need to provide timely and reliable market information to enhanceinformed decision making by farmersSupport the private sector actors willing to invest in sheep and feed production byavailing appropriate information including the costs and benefits production.Farmers have to be equipped with the skills of innovative knowledge that canmake them improve the management and storages of crop residues and propersupplementations. 12
  13. 13. Lesson learned on VCA toolStrengths 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 FlexibleWeaknesses The tool was not specific to commodities Has difficult to remember trend questions 13
  14. 14. FEAST presented byLiyusew Ayalew (EIAR Holetta) 14
  15. 15. Using FEAST to Characterize Livestock Production Systems in Wolemera Districts, Ethiopia Dairy team Holeta Agricultural Research Centre 15
  16. 16. 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
  17. 17. 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.
  18. 18. MethodologySelection 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
  19. 19. Major findings
  20. 20. 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
  21. 21. 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)
  22. 22. Forage crops grown in the area The dominant fodder crops grown in the area 0.025Average 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)
  23. 23. Major sources of income for livelihoods - WolmeraBerffetta Tokkoffa (local cows) Robe-Gebya (cross-bred)
  24. 24. Average livestock holding (TLU) – WolmeraBerffetaa 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 4.502.50 4.00 3.502.00 3.001.50 2.50 2.001.00 1.50 1.000.50 0.500.00 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
  25. 25. Feed resources contribution to the diet - WolmeraBerffetaa Tokkoffa (local cows) Robe Gebya (cross-bred)
  26. 26. Important problems identified by farmers using pair wise ranking - WolmeraBerffeta Tokkofaa (local Robe Gebya (cross-bred) cows) 1st Low milk prices Vs. high cost1st Feed shortage (quality and of milk production quantity) 2nd Poor AI services2nd Lack of knowledge about livestock management 3rd Feed shortage (quality and quantity)3th Lack of improved breeds 4th Lack of availability of4th Lack of management about improved breed natural resources 5th Trekking of long distance to5th Lack of access to animal fetch water health services •
  27. 27. Lessons learned using the FEAST toolStrength 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• its rapid (less farmer time) scale• offers an opportunity to educate farmers
  28. 28. Potential solutions suggested by farmersBerffetaa Tokkoffaa (localcows) 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
  29. 29. Techfit presented by Adissu Aberra(EIAR Debre Zeit) 29
  30. 30. Application of TechFit Tool forPrioritization of Feed Technologies for Smallholder Fattening By Dr. Solomon Mengestu Addisu Abera Solomon Abeyi Fantahun Dereje May, 2012 EIAR
  31. 31. IntroductionTechFit• 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
  32. 32. METHODOLOGY Adama District  Kechema  Wonji Kuriftu Arsi Negele District  Ali Wayo  Kersa IlalaSelection criteria • Presence of smallholder beef fattening activities • Accessibility
  33. 33. Methodology of The TechFit Tool • Land • LabourPRA • CreditExercise/FGD • Inputs • KnowledgeAssessment ofthe 5 • Farmers participatory scoring ofattributes the 5 attributesFiltering of • Based on context vis-à-visTechnologies technology attribute scores 33
  34. 34. Match farmers’ context to technologyScore for Score for contexttechnology attributeattributeLand 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
  35. 35. 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 cropresiduesMachine chopping of 4 4 16 3 2 3 2 1 1 2 2 3 2 3 26residuesHand chopping of 4 3 12 3 1 3 3 3residuesGenerous feeding of 4 5 20 2 2 2 2 3 1 3 2 3 2 4 27CRsTreatment of cropresidues (e.g. urea 2 4 8 3 1 1 1 1 2 2treatment)Feeding of homegrown legume residues 3 4 12 3 2 3 1 3 3 3 6Feeding of bought inlegume residues 1 4 4 3 3 1 3 3 2 2Supplementation
  36. 36. Technologies Filtered using TechFit tool Eg: Kechema kebele TotalNo. Selected Technology score Rank1 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 Fodder trees (Sesbania, Leucaena, Tagasaste,6 Gliricidia) 20 6 After short listing the first 3-4 technologies, go for cost benefit analysis
  37. 37. Lessons learned from application of TechFitStrengths• 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
  38. 38. Lessons learned from application of TechFitWeaknesses• 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
  39. 39. Opportunities for further use• wide context range = wider application• wide technology range = wide application across AEZ
  40. 40. More Information:http://elfproject.wikispaces.com 40

×