Drivers and Barriers to Producer Adoption of Climate Resilient Pea Varieties in the Western Canadian Prairies
Notice bibliographique
Résumé
Currently, challenges associated with root rots and drought affect the relative advantage of field pea production, and the severity of these challenges is exacerbated by a changing climate. Advances in crop breeding offer potential solutions in the form of new varieties that address these biotic and environmental stressors, but producer adoption is key. In this research, I examine the opportunities and constraints to adoption of field pea in producers’ rotation decisions, with a focus on the impacts of producer perceptions, uncertainty preferences and technology acceptance. Data was collected through an online survey of 461 Western Canadian field crop producers that incorporated a discrete choice experiment (DCE), Tanaka et al.’s (2010) prospect theory game, and Ellsberg’s (1961) two-urns paradox. These methods allowed us to explore how risk, loss, and ambiguity aversion affected demand for varietal attributes—such as climate resilience (root rot resistance and drought tolerance), royalty models (e.g., Variety Use Agreements), and new technologies (e.g., gene editing). Analyzed through a mixed multinomial logit model, the results suggest the inclusion of field pea in the crop rotation is motivated by the benefits of diversification, but the benefits must outweigh the loss of financial certainty or incentives to justify their place in the crop rotation. This motivation is supported with a significant demand found for root rot resistance among risk and ambiguity averse producers within the full sample. Further, loss aversion was found to have significant positive impacts on demand for drought tolerance. The results point towards root rot challenging the financial certainty of pea, whereas in the case of drought tolerance, demand appears to be driven from a search for protection from overall losses, rather than a guarantee of gains. However, uncertainty behaviours are found to have mixed explanatory power over adoption decisions, likely driven by a heterogenous population. The influence of uncertainty is found to be variable between technologies, growing zones, and producer experience with pea. Sub-sample analysis by soil zone and grower type yielded significant but variable results regarding the impact of uncertainty behaviours. Risk, loss, and ambiguity aversion influenced the perceived utility of traits such as root rot resistance and drought tolerance, with the direction and magnitude of these effects differing across sub-samples and traits. A similar pattern is observed among producers exhibiting ambiguity aversion in relation to the adoption of gene-edited varieties, as adoption was found to be limited or increased by a gene-edited designation when ambiguity aversion was present. The presence of Varietal Use Agreements (VUAs) in new varieties is found to significantly limit adoption, with ambiguity aversion magnifying this effect. Overall, there is a demand for climate-resilient traits among sub-samples, however the usage of gene editing and VUA are found to limit adoption within the sample. The variable results by sub- analysis highlight the potential importance of localized and heterogenous factors on the demand for agricultural technologies and practices.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».