Techfit development

Group 1

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

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

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

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?

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.