Plant health rallies as an extension tool in small-scale farming in Kenya
Bibliographic record
Abstract
Kenya has consistently reported new and serious disease and pest problems on key crops over the years, often associated with substantial crop yield losses.In just the last six years, for example, maize lethal necrosisa disease responsible for crop losses valued at about US$ 4.1 million in 2014 aloneand tomato leaf miner entered and got established in the country.For small-scale farmers, who make up 80% of the farming community and contribute 25% of the GDP, an attack by such diseases could spell doom for their income and food supply.Decisive action is needed to prevent new pests and diseases from spreading and becoming established.Also, other well-established crop pests and diseases regularly cause major crop losses.Farmers need help to take preventative measures and avoid costly and often less effective treatments after the problem has entered the crop.Extension campaigns can play a critical role in controlling crops pests and diseases by acting as a source of timely information.One such approach, plant health rallies, has been embraced in Kenya, though so far on a limited scale.In 2015 the University of Nairobi and Plantwise undertook a study in parts of Kenya among 150 farmers and 27 extension staff in five counties to get a picture of extension campaigns in crop health and to understand how the role of plant health rallies could be enhanced in delivering a comprehensive service to farmers.The study focused on maize lethal necrosis, mango fruit fly, Napier grass stunt, tomato leaf miner and wheat stem rust, all which have the potential for high economic impact. Key highlights• Plant health rallies supported by Plantwise are already a feature in Kenya's extension programme and the only clearly visible extension campaign method used.The rallies are perhaps the single most significant extension initiative for maize lethal necrosis.• About three-quarters of the extension staff interviewed knew of the plant health rallies and about two-thirds had taken part in them.• More than 95% of all farmers interviewed were aware of the target problemsincluding those who did not grow the target cropsand most likely obtained this information through indirect sources such as other farmers and agrochemical dealers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".