Are integrated crop-livestock clinics an option for Kenyan smallholder farmers to address One Health issues?
Bibliographic record
Abstract
The majority of smallholder farmers in sub-Saharan Africa rely on both crops and animals for their livelihoods.However, the scarcity of farmer advisory services leaves them with insufficient knowledge about crop and animal health, as well as the wider health issues emanating from farming.There is a need for adapted, innovative services to meet the needs of smallholders.CABI's long-standing work with plant clinics sparked new ideas on how to better serve smallholder farmers by including animal advice in the clinics.This, in turn, revealed a potential to explore 'One Health benefits' of such an integrated service.Uganda began piloting crop-livestock clinics in 2021, and Kenya followed in 2022.Before launching the Kenyan pilot, a study was conducted to ascertain farmers': (i) awareness of 'One Health issues' related to crops and animals, (ii) information needs on crop and livestock farming, and (iii) perceptions on joint crop-livestock clinics as a new type of integrated farmer service.About 70% of the farmers, mainly from Trans-Nzoia and Machakos counties, were aware of some 'One Health issues', including mycotoxins such as aflatoxin as well as pesticide residues on food and fodder.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".