The Adoption of Semidwarf Spring Wheat and the Associated Nitrous Oxide Effects in Saskatchewan
Bibliographic record
Abstract
After the Green Revolution, semidwarf varieties of wheat increased in popularity worldwide. With an increase in lodging resistance and higher responsiveness to nitrogen, farmers have the ability to apply more nitrogen to achieve higher yields. However, while semidwarf varieties are favorable to farmers seeking to increase productivity, the net change in greenhouse gas emissions resulting from the increased use of nitrogen fertilizer remains underexplored. This thesis studies the joint determination of semidwarf variety selection and nitrogen use in Saskatchewan, Canada—one of the leading provinces in wheat production. We develop a Control Function (CF) model to estimate the joint choices of semidwarf wheat varieties and nitrogen application rates using field-level data of Saskatchewan farms between 2011 and 2019. After that, we employ emission factors from the literature to estimate\nchanges in direct nitrous oxide (N2O) emissions when farmers adopt\nsemidwarf wheat and subsequently change nitrogen rates. Our regres-\nsion model suggests a 5.9% expected increase in nitrogen application rate when a farmer switches from conventional to semidwarf wheat. The subsequent analysis suggests that although semidwarf wheat generally has higher nitrogen application rates than conventional wheat, their fertilizer-induced direct N2O emissions per tonne of grain production are fairly similar. Based on the adoption status of semidwarf wheat and conventional wheat in 2019, if all conventional wheat acres in Saskatchewan switch to semidwarf wheat, the value of environmental damage associated with the direct N2O emissions induced by nitrogen fertilizer applied to Saskatchewan spring wheat would increase by at least $0.29 millions of CAD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".