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

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

Introduction Smallholder dairy production – Dominant (>95%)dairy production – 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

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

Match farmers’ context to technology Score for technology attribute (req`t) Score for context attribute (availability)Total LandX = LaborX = CreditX = InputX = KnowledgeX = If technology demands land => low score for land If farmers do not have or very small land holding => Low score for land

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

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

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)

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

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

The selected technologies were then subjected to pre-filter based on the context attributes – Context relevance X Impact potential score = Total context score Context relevance X Impact potential 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)

Findings 5 to 8 top ranking technologies (based on total score) were identified for each of the villages under the two woredas 5 to 8 top ranking technologies 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 processcontext relevance Rob-GebyaRob-Gebya, Berfata, Jate, Mechela-WertuBerfataJateMechela-Wertu

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 Wuchalee.g. Local brewery waste Vs native hay at Wuchale

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

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

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