The price impact of increased protein demand and enhanced protein peas (EPP)
Bibliographic record
Abstract
Over the last decade, consumers have become increasingly concerned about the health risks and\nthe environmental impacts of the food products they buy and have increased their preference for\nplant-based protein. This increase in the demand for plant-based protein has created great\ninvestment opportunities for the pulse industry in Canada especially Saskatchewan, being the\nhighest producer of peas in Canada. The P-POD project anticipates to introduce an enhanced\nyellow pea with protein content of about 26% by 2024. The objective of this study is to quantify\nthe price and economic effects of this enhanced protein pea under several growth scenarios, with\nthe goal of identifying the best strategies for capturing Canadian value from the enhanced peas.\nWe develop a partial equilibrium simulation model that is parameterized to reflect the current\nindustry structure, the introduction of EPP varieties, and future demand growth for 20 years\n(2020 to 2040). Our results show that the premium on EPP is largely influenced by the protein\nisolate growth faced by the industry, the higher the isolate demand growth, the larger the\npremium on EPP and the faster the occurrence of a drastic innovation. Even though with the\nanticipated growth in pea protein and pea protein isolate, the a priori expectation was that this\ninnovation will increase farm income and benefit pea producers, our results show otherwise. We\nfind that if EPP varieties are agronomically equal to RPP varieties and have the same royalty\nstructure, both peas will trade at the same price for many years with large benefits accruing to\npea processors and pea protein consumers and very little or negative short-run benefits for pea\nproducers. Given the limited gains for producers who have typically funded pea breeding,\nalternative royalty models may be required to fund the needed investment in breeding.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".