Sustainable Pest Management through Improved Advice in Agricultural Extension
Bibliographic record
Abstract
This 5-year study addresses how improved quality of agricultural extension may lead to more sustainable pest management. We studied 112 agricultural extension workers trained as plant doctors under the Plantwise program in China. They run 70 plant clinics in Beijing, Guangxi, and Sichuan provinces. We analysed 47,156 recommendations issued by these plant doctors to 13,051 different growers between 2012 and 2017, and this for 250 different plant health problems on 91 crops. We also interviewed growers who had taken queries to plant clinics. On average, 86% of plant doctors provided comprehensive integrated pest management recommendations to the growers, with a 16% improvement in comprehensiveness over years. This most often included advice of synthetic pesticides (66%) with its frequency not much changing with time. In contrast, as a likely result of Plantwise interventions and China’s pesticide reduction policies, recommendations for biological control increased from 2% to 42%, pest monitoring by 8%, and cultural control by 11%. Recommendations of problematic plant protection agents as listed in the Montreal Protocol, Stockholm or Rotterdam convention, or as highly toxic under WHO’s toxicity classification were already rare in 2013 (1.9%) and nearly phased out by 2017 (0.2%). About 92% of growers implemented the advice, suggesting that agricultural extension services may contribute to changes in agricultural practices at scale. Further investment in such agricultural extension services may be warranted instead of phasing them out.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".