The role of adaptability in Western Canadian wheat variety adoption decisions
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».