Plant clinics in Bangladesh: are farmers losing less and feeding more?
Bibliographic record
Abstract
Agriculture accounts for around one-third of Bangladesh's gross domestic product and more than 30% of money earned from exporting to other countries.Nearly two-thirds of the country's population works in agriculture, and around 80% depend on it for their livelihoods.The country's major crop is rice, planted on 75% of farmland, with the remainder including high-value vegetables, fruits and spices.Pests destroy between 10% and 25% of harvests, despite the estimated 49,000 tonnes of pesticides used by farmers every year.The Plantwise programme, led by CABI, aims to contribute to increased food security, alleviated poverty and improved livelihoods by enabling men and women farmers around the world to lose less, produce more and improve the quality of their crops.In Bangladesh, a 2017 study has revealed that farmers who regularly attend plant clinics are more likely than non-users to be able to identify and manage crop problems, as well as increase crop yields and profitability.With increased knowledge of improved farm practices, plant clinic users are also having to rely less on chemical fertilisers to manage pests and diseases. Key highlights• 4,046 queries were recorded across 30 plant clinics between January 2015 and April 2017.• 100% of plant clinic users felt their ability to quickly identify crop problems had increased, compared to 16% of non-users.• Plant clinic users were 4.6 times more likely to indicate that their farm management knowledge had increased compared to non-users.• 93% of plant clinic users fully implemented the advice they received from plant doctors; 100% of these users stated that the advice worked well in tackling crop health problems. How to cite this paper
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.030 | 0.004 |
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".