Dairy Team Holetta Research Center 28-29 May, 2012, A.A

1. Application of “Techfit” tool for prioritization of feed technologies for smallholder dairy production systems Dairy Team Holetta Research Center 28-29 May, 2012, A.A

2. Presentation Outline • Introduction (TechFit background) • Methodology (The testing process) • Findings • Challenges and lessons learned • Suggestions for improvement of the tool

3. Introduction • Smallholder dairy production – Dominant (>95%) • Feed among the major constraints • The need for interventions • Research efforts (NARS, ILRI) • Basket of alternative feed technologies generated and recommended • Limited applications by farmers

4. Introduction • Systematic approach for prioritizing feed technologies for targeting interventions lacking. • TechFit – a tool developed as remedy • Used to filter and prioritize best bet technologies from a basket of technologies • 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

5. Match farmers’ context to technology If technology demands land => low score for land If farmers do not have or very small land holding => Low score for land

6. Objective • To test the application of the tool to rank and prioritize suitable feed technologies for smallholder dairy production in Wolmera and Wuchale woredas in the central highlands of Ethiopia

7. Methodology • Parties involved • ILRI • EIAR – Holetta (a team of 4 experts) • Woreda offices of agri – Livestock dev`t experts • Village DAs • Village chairmen • Selected farmers

8. Methodology • Two villages with contrasting dairy production in each woreda • One village – market oriented dairy production using crossbred cows • The other village – no market oriented dairying (local cows) • 12 – 14 farmers (2-5 women) selected in each kebele (varying land ownership, gender, age groups and affiliation to market oriented dairy production)

9. Methodology • FGD held using a ckecklist to assess context of the farmers (availabilities of land, labour, credit, inputs and knowledge) that would enable possible interventions • The farmers were encouraged to debate on each of the points and reach consensus for scoring with their justifications

10. Technology Filter Total lists of possible technologies = 36 Technologies subjected to ranking for the areas = 22 Area context

11. The selected technologies were then subjected to pre-filter based on the context attributes • Context relevance X Impact potential score = Total context score • Technologies with high total context scores were subjected to the main filter • The context attribute scores given by farmers were entered into the TechFit Excel template corresponding to each of the pre-filtered technologies and the technologies were ranked based on their total score (sum of context attribute X technology attribute scores for the five attributes)

12. Findings • 5 to 8 top ranking technologies (based on total score) were identified for each of the villages under the two woredas • Land and labour requirements – major determinants for ranking the technologies • Technologies with high context relevance, but which require more land and labour ranked lower – those w/c do not require land and labour highly favored by the ranking process Rob-Gebya, Berfata, Jate, Mechela-Wertu

13. Observations and Lessons • Some farmers` indegionous practices that may need to be included in the lists of technologies – e.g. Re-threshing and mixing of different crop residues, use of oats grain and hull as an alternative to wheat bran • Farmers` contexts highly varied within a woreda and even a village – need for screening technologies accordingly • Some results contrary to the ground reality emerged from the ranking process e.g. Local brewery waste Vs native hay at Wuchale

14. Challenges/Limitations • Methodology to generate the data required to do cost-benefit analysis on the short-listed technologies

15. Suggestions for improvement • Special attention should be given for the technology pre-filter process – also inclusion of available indigenous practices • Technologies may be clustered and ranked within their clusters than holistic ranking • The high ranking technologies from each cluster may be subjected to further per-wise ranking by farmers for targeting interventions

16. THANK YOU