The environmental and economic efficacy of on-farm beneficial management practices for mitigating soil-related greenhouse gas emissions in Ontario, Canada
Bibliographic record
Abstract
Abstract Agriculture is a large source of greenhouse gas (GHG) emissions but changing management practices to those more beneficial to the environment could help mitigate climate change as long as they are economically and environmentally viable. This study examines the environmental (public) and economic (private) effects of adopting ten different beneficial management practices on a representative corn farm in Ontario, Canada. The study integrates changes in GHG emissions in carbon equivalents (CO 2e ) and changes in profit from changes in costs and revenues in two dimensions to reveal the scope and scale of different kinds of practices. 4R nitrogen management practices are smaller in scale compared to cropping practices and, therefore, have smaller potential costs and benefits. Land use changes, from practices including biomass, afforestation, crop rotation and cover cropping, have larger impacts on soil sequestration and carbon-equivalent GHG reduction, but with significantly greater costs. Seven practices were found to, at least partially, be economically and environmentally beneficial. The adoption of no-till and N-rate reduction is firmly positive, whereas the production of biomass has the largest potential economic and environmental gains. Crop rotation and diversification and cover cropping can be mutually beneficial, as can changing N-application practices. The use of inhibitors may be economically beneficial if yield gains outweigh purchase costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".