Drivers and Barriers to Producer Adoption of Climate Resilient Pea Varieties in the Western Canadian Prairies
Bibliographic record
Abstract
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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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 | 0.002 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".