1. TechFit : A Tool for Prioritizing Feed Technologies Adugna Tolera

2. Objectives To have a common understanding, interpretation and application of the tool To learn how to score and match technology attributes and context attributes of farmers To customize the application of the tool to the local context

3. Background Reality No. 1 (Reality of farmers) – Livestock production is important – Feed is a major constraints (FEAST & Other reports) – Farmers are looking for a remedy to the problem Reality No. 2 (Reality of research & development efforts) – Various feed technologies generated by the research system – Lack of systematic approach for prioritizing available feed technologies – Poor adoption rate of available technologies – Wastage of substantial efforts and resources

4. Failure to place feed in broader livelihood context Lack of farmer design and ownership Neglect of how interventions fit the context: land, labour, cash, knowledge etc FEAST Techfit Feed interventions often do not work – why?

5. What is TechFit? A discussion tool for prioritizing feed technologies Helps to identify suitable technologies for evaluation and screening Designed to filter best bet technologies from a basket of technologies available to farmers Provides better understanding of why and why not technologies work or do not work

6. Technology options to address feed problem (list of potentially available technologies) Technologies are filtered at different levels Only technologies with high total scores carried forward to the main filter How does it work?

7. How does it work? (Cont …) Main filter – involves combining scores of technology and context attributes to arrive at total score Technology attributes – requirement of a given technology for land, labor, cash/credit, inputs and knowledge – High score => low likelihood of adoption Context attributes – availability of land, labor, cash/credit, inputs and knowledge – High score => high likelihood of adoption

8. Match farmers’ context to technology Score (1-5) for technology attribute Score (1-5) for context attribute Land (1-5)X = Labor (1-5)X = Credit (1-5)X = Input (1-5)X = Knowledge (1-5)X = Total score= Rank? If technology demands land => low score for land If farmers do not have or have very small land holding => Low score for land

10. How to do scoring and ranking? List of potential technologies obtained from the research system Context relevance and impact potential – by experts at each specific location Technology attributes (requirement of the technology for land, labor, etc. ) – by experts Context of farmers (availability of land, labor etc.) – by farmers (interview a group of representative farmers and ask them to score)

11. Cost benefit analysis Short list the best 3-4 technologies for cost- benefit analysis What does the technology cost? (type of feed, amount used, % of total feed, cost, % of total feed cost) What does the technology deliver? (animal performance measure, % contribution to the performance change, % contribution to income gain) Is it worthwhile?

12. Cost-benefit analysis Method not yet well developed and refined Mostly based on a number of assumptions using partial budget analysis – Compare additional costs and additional benefits i.e. marginal benefits

13. Intervention name Clear description focusing processes and actions with pictures and glossary for specific terms Technical Information Key technology attributes Land area required Labour, including gender Skills/Knowledge Cash/Credit External inputs Capital / infrastructure Applicability Purpose / Addresses constraints – opportunities Which animal? Agroecological, farming system suitability including socio-cultural issues (e.g., taboos) if applicable Scale History of use Potential to integrate with … Benefits Primary (including time dimension, etc.) Secondary … Adoptability characteristics (=conclusion: simplicity, observability, use, etc. …

14. Adoptability Protocol - Process Past experiences regarding introduction of technologies, including uptake, community feeling, etc. Ranking of livelihood ambitions/aspirations in general and for agriculture and livestock in particular After becoming more and more reductionist and analytical, bring it back into the broader perspective Objective  Subjective  FGD on options Give info on options Ask community to rank Discuss ranking, ‘why’, etc. (guiding points/questions)  Link to CBA data  Select trial farmers for AR (model or pioneer farmers)

15. FactorGuiding points/questions to keep in mind in FGD Relative advantage superiority CBA analysis, but subjective points may be raised in group Quality of labour (drudgery), etc. Compatibility Riskiness - technology, risk aversion Social acceptability &/or taboos Effect on gender aspects or child labour Possibility of adapting to or in local situation Complexity Relatable to something simple, familiar, routine, etc. Trialability Resources present for implementation Observability (Should perhaps be made as Techfit filtre) Delivery process Competence, capacity & buy-in of local extension staff Enabling environment

16. Main constraint – Seasonality – Quantity – Quality Dominant commodity – Beef – Dairy – Sheep/Goats – Pigs/poultry Farming system – Pastoral – Agro-pastoral/mixed – Intensive/mixed (crop-livestock) – Landless Core context attributes – Requirement for land – Requirement for labour – Requirement for cash credit – Requirement for inputs – Requirement for knowledge/skills Data we need to derive from FEAST to feed into Techfit

17. Consult seasonal calendar – estimate proportion of minimum availability to maximum availability – 1.0 = 0 – >0.75 = 1 – >0.5 = 2 – >0.25 = 3 – >0.0 = 4 Is minimum in the dry/winter season? – Winter season scarcity Is minimum in the growing season? – Growing season scarcity Seasonality

18. If you place more basal feed in front of your animals would they consume it? – With extreme enthusiasm = 4 – With considerable interest = 3 – With some interest = 2 – Yes but not immediately = 1 – No = 0 Something also about interest in supplemental/high quality feed? Quantity

19. Quality If you placed more basal feed in front of your animals would they consume it? – With extreme enthusiasm = 0 – With considerable interest = 1 – With some interest = 2 – Yes but not immediately = 3 – No = 4

20. Commodity focus On a scale from 1 to 10 how important are the following enterprises to cash income: – Beef Fattening Breeding stock – Dairy – Sheep/Goats Fattening Breeding stock – Pigs/poultry 0-2 = 0 2-4 = 1 4-6 = 2 6-8 = 3 8-10 = 4

21. Which of the following best describes the target group: – Pastoral – Agro-pastoral/mixed – Intensive/mixed (crop-livestock) – Landless Farming system

23. Experiences in testing and application of the tool Tested to prioritize feed technologies for 3 different commodities (dairy, beef, sheep) in different parts of Ethiopia Preceded by assessment of livestock production and feeding systems using Feed Assessment Tool (FEAST) Enabled rapid prioritization and short listing of potential feed technologies The pre-filter (context relevance score) helped a great deal to focus attention on those technologies that are relevant in the area.

24. Strengths of the tool Enables rapid location specific prioritization and short listing of feed technologies in different agro- ecologies and production systems Puts feed in a broader context and filters technologies for specific contexts (agro-ecology, production system, farmers’ contexts etc.) It is robust in screening out technologies that are not relevant in a given context Gives good indication why some technologies are not easily adopted

25. Limitations of the tool All scores are based on subjective judgments. Thus one has to be well versed with the subject matter and the local conditions to give a realistic score. Cost benefit analysis is based on a number of assumptions and the validity depends on the soundness of each assumption. Most feed technologies make only partial contribution to the total diet  a challenge of partitioning the contribution of the feed in question to the performance of the animal