Plant clinics act to bolster extension delivery in smallholder farming in China
Bibliographic record
Abstract
The Chinese government has increasingly opened up its agricultural extension sector over the last three decades in response to growing service demand, bringing many actors into the scene.One outcome has been the diversification of information sources for farmers, which now include agricultural input suppliers, farmers' associations, the mass media, nongovernmental organisations etc. Information is an essential input in farming decision-making and the lack of it, the wrongful use of it or the use of an inaccurate form of it could produce catastrophic consequences for farmers.For information to generate the greatest benefit for farmers it must be credible, timely, specific to farmers' concerns, packaged based on their circumstances and delivered by an authority they regard as credible.In a pluralistic extension delivery system farmers face the difficulty of determining which source of information is dependable.And since farmers' information-seeking behaviour is dependent on their intrinsic and socioeconomic qualities, ensuring access by all farmers to reliable information is difficult to guarantee.The Chinese Academy of Agricultural Sciences and Plantwise undertook a study October 2013 to April 2014 in Peng Shan county, Si Chuan province of China among 144 households to learn about how farmers interacted with the providers of agricultural advice, including the newly introduced plant clinics, and if lessons existed that could help in refining and enhancing the extension services.Plant clinics act to bolster extension delivery in smallholder farming in China Key highlights• Farmers use a blend of information providers, but the government's agricultural extension is the most trusted and only about 4% of its information is not adopted compared with 20% for information from other farmers.• The government extension system does not extend to the village level or have enough workers to reach all farmers when they need services.Private sector players are filling this gap as well as providing more diversified services.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".