Inclusion of the social costs of N2O emissions increases the financial benefits from N inhibitor use on corn production in Ontario, Canada
Bibliographic record
Abstract
Context Crop agriculture contributes to climate change from N application, including direct nitrous oxide (N 2 O) emissions and indirect N 2 O emissions from volatilized ammonia (NH 3 ) and leached nitrate (NO 3 − ). To achieve future climate goals, the government of Canada seeks to reduce greenhouse gas emissions from agriculture N use by 30 % by 2030, including the use of N inhibitors. Objective This study examines the effects of N inhibitor use, with UAN and urea, at different N rates, on the financial and environmental performance of corn production in Elora, Ontario. Methods The study employs a bioeconomic model that incorporates yield, and environmental effects generated by the De-nitrification De-composition (DNDC) model with an economic maximization model that chooses optimal private (from a producer standpoint), and social (from a social planner standpoint) inhibitor use and N rate for two N sources, based on 30 weather-years. Results and conclusions The results indicate that, from an average private financial perspective, combined nitrification and urease inhibitors on corn production in Elora, Ontario, may not maximize profit with UAN or urea under the baseline price conditions ($302/t corn; $1.79/kg N). However, a variety of factors could make inhibitor use profitable: 1) specific weather conditions; 2) lower N rates; 3) consideration of social profits; 4) increasing social costs of N 2 O emissions; and 5) enhanced yield effects around 2 %. Therefore, the results show that inhibitor application can be recommended when there are favourable yield and weather conditions, when social costs are considered, and when applying N rates below the private economic optimum. N inhibitors may also reduce direct and indirect emissions from UAN application by between 33 % and 19.8 %, and Urea by between 17.7 % and 11.8 %, becoming less effective as the N application rate increases from 100 kg N/ha to 225 kg N/ha. Significance Inhibitors could play an important role in both on-farm GHG mitigation efforts and enhanced financial performance.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".