Impact of ICT Based Extension Services on Dairy Production and Household Welfare: The Case of iCow Service in Kenya
Bibliographic record
Abstract
Smallholder dairy farmers have the challenges of accessing timely and reliable agricultural information, and this limits them from realizing maximum farm output. The use of information and communication technologies (ICT) as a farming extension tool by smallholder farmers has the potential to reverse the scenario and improve farmers’ outputs and incomes leading to increased welfare. This study employed the Propensity score Matching approach to evaluate the impacts of ICT-based extension services, in this case, iCow services on milk production, milk income, and household income using cross-sectional data from a survey of dairy farmers in Uasin Gishu, Nyandarua and Bomet counties of Kenya. The use of ICT-based iCow services is shown to increase Annual milk production per cow, milk income, and household income by 13%, 29%, and 22%, respectively. Partnerships between network providers and research institutes should be encouraged as part of bridging the extension gap occasioned by reduced public expenditure on extension services.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".