Adoption of beneficial management practices to improve soil health
Bibliographic record
Abstract
Healthy soils are fundamental to building prosperous and resilient farms and to efforts to reduce greenhouse gas emissions and enhance overall environmental impacts from agriculture. Understanding the adoption of beneficial management practices (BMPs) that promote soil health is necessary for these benefits to be obtained. Drawing from a survey of Ontario farmers ( n = 247) with 60% being crop producers and 22% livestock farmers, we explore the variation in adoption for six soil health BMPs: cover crops, crop rotations, no-till, soil testing, conservation buffers, and organic amendments. Soil testing had the highest rate of adoption, while conservation buffers had the least. The majority of farmers (73%) implemented four or more BMPs as the use of practices such as a rotation with winter wheat, cover cropping, and no-till tend to be positively correlated. Adopters of the BMPs tend to operate larger farms both in the area operated and farm cash receipts than non-adopters. Improving soil health was the most widely selected motivation for adoption across all six BMPs. The most effective interventions to enhance adoption among non-adopters include financial incentives, easily accessible information and advice, and farmer-to-farmer learning. Our results suggest that farmers that adopt BMPs do so primarily to enhance soil health rather than solely for economic considerations. Encouraging use among non-adopters may require monitoring and promoting the benefits of soil health. The results should aid in the development of strategic frameworks that facilitate innovations in policy to enhance soil health.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".