Regional Variety Trials: Reducing Information Asymmetries in the Western Canadian CWRS Wheat Industry
Bibliographic record
Abstract
In Western Canada, publically funded Regional Variety Trials (RVTs) enable informed comparisons of Canadian Western Red Spring (CWRS) wheat varieties. RVTs allow for the publication of an annual provincial Crop Variety Guide, producing information on yield, days to maturity, and quality enhancing factors. The annual guide plays an important role in reducing information asymmetries between CWRS wheat breeding institutions and the producers who adopt the varieties. Since RVTs are government funded, and the value of RVTs information is unknown, governments often face pressure to reduce funding into the performance trials. In order to estimate the value of RVT information, a benefit/cost analysis will serve to quantify the economic impact of the public investment in order to better inform the process of resource allocation. A neo-classical profit maximizing framework is used to identify factors that drive farmer adoption of varieties. The framework utilizes real options to incorporate the sunk cost associated with adoption. Variety yield expectations are developed within a Bayesian learning framework. Theoretical relationships are used to develop an econometric model of variety adoption, which is then estimated using CWRS wheat variety data from 1972 to 2011. The variety adoption model, which included a period of disadoption, fits the data well. A number of factors including expected yield, days to maturity, time since varietal release, number of varieties available per year, breeding institutions, and quality resistance factors are assessed and found to be statistically significant. The parameter estimates from the econometric model are used to estimate the economic benefits of RVT testing using counterfactual scenarios. The counterfactual scenarios simulate variety adoption for cases where variety tests provide less reliable information. Expectations are revised using Bayesian decision theory based on the accuracy of information being provided to producers. The benefits are estimated by comparing revenue functions of historical data to counterfactual scenarios. Benefit/cost ratios are calculated, comparing the benefits to the cost of implementing RVTs in Western Canada. The results of the benefit/cost analysis indicate the benefits of accurate CWRS wheat yield expectations far outweigh the cost of producing the information. If the reliability of yield information was reduced by 50 percent in the absence of RVTs, Western Canadian farmers would forgo $70.7 million in revenue each year by growing lower performance CWRS wheat varieties. In this case, each $1 invested in RVT returns $63 to producers, or has a benefit cost ratio of 63 to 1. With this large benefit/cost ratio a strong case can be made to government or producers to maintain RVT funding.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".