Comparison of Variety Performance Trials and Farm Level Yield Gains of Wheat Varieties Adopted in Saskatchewan
Bibliographic record
Abstract
Wheat producers in Saskatchewan rely on information from variety performance trials to make informed decisions about new variety adoption, considering factors such as yield potential, weed, pest, and disease resistance, and climatic adaptability. These variety performance trials, however, are conducted under controlled conditions that may not adequately reflect real-world farming or heterogeneity in yield across risk zones. Limited research has compared relative yield gains observed in variety performance trials to on-farm experiences. In this study, we use fixed-effects panel data analysis to compare on-farm relative yield gains to variety performance trials’ relative yield gains. In addition, we examine if any heterogeneity exists in terms of producer relative yield gains across risk zones in the province. Our findings indicate significant differences in relative yield gains between variety performance trials and on-farm results using the F-test. Finally, using the Log-likelihood Ratio test we find that the performance of different wheat varieties varies significantly across risk zones in Saskatchewan. Producers must consider the unique characteristics of their region, market conditions, and risk factors when selecting and adopting varieties. Thus, they should consider multiple factors beyond yield potential when selecting varieties, be encouraged to continue conducting their own on-farm trials. Policymakers should consider using both on-farm and field trial data in the variety performance trial. They can also develop a mechanism to allow producers to upload the results of their on-farm experiments, include producers as partners in variety evaluation and if possible, extend the coverage of performance trials across risk zones and multiple diverse agro-climatic areas.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".