Net Greenhouse Gas Emissions and the Economics of Annual Crop Management Systems
Bibliographic record
Abstract
The aim of this study was to evaluate the relative economic profitability and net greenhouse gas (GHG) abatement potential of alternative tillage and cropping systems. A simulation model was parameterized using biophysical and economic data representing different crop rotations under conventional, minimum, and zero tillage cropping systems, in the Black soil zone of Saskatchewan. This model was used to estimate the relative potential of each of the management practices to sequester carbon and to emit GHG including, nitrous oxide and carbon dioxide. The model also provided estimates of the relative profitability of each of the management practices over time horizons of 30 years. These simulation results were used to develop trade‐off functions reflecting net income and net GHG abatement for each cropping system. An income risk measure was incorporated to facilitate an analysis of the relative economic attractiveness of the simulated cropping systems. Furthermore, sensitivity analysis was performed on nitrous oxide emission coefficients (an area of significant uncertainty in the literature) and on weather patterns to reflect uncertain future climate change impacts. Results indicated that net GHG emissions were relatively lower for reduced tillage management, while conventional tillage may be relatively more attractive from an economic perspective. However, results also indicated that such economic factors as risk and economies of size may have a significant influence on this latter result.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".