Adaptability and variety adoption: Implications for plant breeding policy in a changing climate
Bibliographic record
Abstract
Abstract Adaptability of a seed variety to a wide range of environmental conditions is important in farmers' variety adoption decisions, especially with the increased environmental volatility induced by climate change. Despite the apparent need for information, variety trial reports generally report average relative yields, but they do not provide farmers with measures of variety adaptability. Our theoretical model postulates that the adaptability of seed varieties matters in farmers' variety adoption choices. To test this conjecture, and to measure the magnitude of the effect, we develop a new measure of variety adaptability and estimate an empirical model of adoption in Western Canada. We find that a 1% increase in the adaptability of a variety will increase its adoption by 0.45%. This effect is statistically and economically significant. Our results imply that adding a measure of variety adaptability to crop variety guides could enhance the adoption of superior crop varieties, benefiting both farmers and breeders.
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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 | 0.001 | 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 | 0.001 | 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".