Farmers in Rwanda reap benefits with advice from plant clinics
Bibliographic record
Abstract
Plant clinics have been operating in Rwanda since 2011 when 8 were launched in the four zones of the country by the Rwanda Agriculture Board (RAB) in partnership with Plantwise.The plant clinics are intended to help smallholder farmers to manage the sometimes devastating crop diseases and pests by acting as an easily accessible source of current, timely, practical, trustworthy and cost-effective advice.In addition, as a community-based early-warning mechanism for crop problems, they are expected to generate data that give an insight into the spread and occurrence of important crop health concerns.Early notification of a devastating crop disease can save a whole harvest from destruction and can help the government to set up prevention and control measures.That was what happened with maize lethal necrosis when a plant health clinic in Byangabo in the north first recognized it in 2013.RAB and Plantwise undertook a study in 2015 involving 116 farmers, among whom 39 were regular plant clinic users, 37 were first-time users and 40 had never used the clinics, to learn how the clinics were playing their role.The study also sought to determine the nature of the interaction of the plant clinics with other services supporting farmers and the opportunities for enhancing the relationship. Key highlights• All the farmers who visit the plant clinics use or plan to use the advice from the plant doctors.• Approximately 50% of the farmers who used the advice from the plant clinics saw increases of 47-127% in the yield of maize, beans, banana, eggplant and pineapple.• More men than women and more wealthy than poor farmers use plant clinics.• Amongst the plant clinic users the preferred source of plant health information is plant doctors, while those who do not use the clinics prefer obtaining the information from neighbours.• Almost all the plant clinic users share the advice from the plant doctors with others.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".