The role of adaptability in Western Canadian wheat variety adoption decisions
Bibliographic record
Abstract
With over 300 new varieties of wheat registered in the past 15 years (Canadian Food Inspection Agency, n.d.), Canadian wheat producers have plenty of options to choose from when deciding which variety(ies) to grow. However, each year, wheat acreage in Western Canada is concentrated in a select few of these available varieties. While no single factor appears to impel this result, increasing volatility in growing conditions due to climate change is expected to significantly impact the importance of the adaptability of varieties (i.e., the ability to yield consistently under a range of conditions) in these decisions moving forward. Filling a gap in the agricultural economics literature, this research aims to empirically identify which factors drive adoption of new wheat varieties in the Canadian Prairies, focusing primarily on the role that adaptability plays in these decisions. To do this, I first develop a conceptual framework rooted in the theory of the firm that explains the predicted relationship between varietal adaptability, measured using Torshizi’s (2015) degree of specificity, and adoption. Then, using risk area level data spanning from 2009 to 2018, I empirically examine which variety attributes factor most heavily into the adoption of new wheat varieties at the prairie-wide, provincial, and wheat class levels. I also compare these results to those acquired when yield variance is alternatively used to measure varietal adaptability. In each case, I employ Pesaran and Zhou’s (2018) fixed effects filter empirical approach to obtain these estimates. Prairie-wide results reveal that more widely adopted varieties are those that have higher adaptability. These estimates also indicate that the overall success of a variety is linked to its height, protein content, fusarium head blight tolerance, and yield potential, with slight variations in results for provincial and wheat class level modeling. Further, comparison of the explanatory iii power of different models points to the degree of specificity as the better measure of adaptability, relative to yield variance. These insights into variety adoption indicate that prioritizing breeding for varietal attributes such as wider adaptability and improved yield potential may be beneficial, although balancing this with the continued need for some varieties tailored to perform under specific conditions remains important. Additionally, reporting an intuitive measure of varietal adaptability along side the other variety attribute information currently available in provincial publications would allow farmers to easily compare different varieties and ensure they are selecting the one(s) that best meet their needs. Finally, some of the challenges associated with obtaining data for this research suggest a need for more easily accessible, consistent, and representative data across Alberta, Manitoba, and Saskatchewan, to the benefit of Canadian wheat producers and industry researchers.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".