Techfit development

1. Techfit development Group 1

3. Sorna village • Reasonably wealthy farmers • Good market access • Strong farmer capacity • Good gender equity • Cash not a major constraint • Mobile phone use high • Equipment available • Small herd size but mainly x-bred dairy cows • Low yields – 3-6 litres per cow per day but some high outliers (15 litres per cow per day) • Intensive cropping • Irrigation available • Feeding: purchased bhusa, maize/sorghum residue, napier, berseem, oats, concentrate (from coop) – quite sophisticated feeding

4. Our framework • Diagnosis using FEAST for baseline, context • Inventory of all possible technologies • Screening by quantity, quality, seasonality • Technology long list: scores for technology attributes e.g. Land, labour, cash, complexity, communality, environmental impact, seed systems, novelty, water, risk, multi-functionality • Score for household potential benefit e.g. Cultivated grass = 5, urea straw = 1 • Technology long list: scores for context – same categories to weight by context. • Multiplied tech score by context score to give overall score to give ranked list of potential technologies

5. Lessons learnt • Diagnosis using FEAST for baseline, context – useful but perhaps too much detail? Also some things missing – might need to modify slightly to target Techfit better. • Inventory of all possible technologies – modified from what we started with – difficult to get right balance between detail and generality in deciding on what constitutes “a technology”. Should be possible though. • Screening by quantity, quality, seasonality – hard to decide on what the issue is and hard to decide which technology deals with each issue. Requires tacit expert knowledge. • Technology filter 1: scores for technology attributes e.g. Land, labour, cash, complexity, communality, environmental impact, seed systems, novelty, water, risk, multi-functionality - too complex. Many were subjective and context specific, some redundancy, diluted the screening process. Reduced the number to the key context attributes: land, labour, cash, communality, complexity

6. More lessons learnt • Score for household potential benefit e.g. Cultivated grass = 5, urea straw = 1 – rather subjective and related to experience of experts. But is a crucial step. • Technology filter 2: scores for context – same categories to weight by context. – initial attribute list was too complex, hence cut it down to 5 easy to score attributes. • Multiplied tech score by context score to give overall score to give ranked list of potential technologies – take top 10. Could bring “potential benefit” score to the last stage?

7. Gaps and next steps • Technology list needs quite a lot of development – could use wiki approach but needs a strong template and some good examples. • Quantity, quality, seasonality: Could form one category in technology description on wiki but often not completely clear. • Might need a step to fit technology to stage of development/degree of intensification. • Might need further stage following development of short list involving assessing “scope for change” with key actors. • “Potential benefit” is important but needs some further thought. • Change scoring from 1-5 to 1-3? • Sensitivity analysis • Try this out in various contexts – ELKS, imGoats, MilkIT, CRP3.7, EADD.