Prediction of Cultivar Performance Based on Single‐ versus Multiple‐Year Tests in Soybean
Bibliographic record
Abstract
Because of the omnipresent genotype × year or genotype × location × year interactions in crop performance trials, it is commonly believed that multiple‐year data should be used in selecting cultivars for the next year. An implicated but rarely tested hypothesis is that multiple‐year data are more predictive than single‐year data of cultivar performance in the next year. Yield data of the 1991 to 2000 Ontario Soybean Variety Trials in the 2800 Crop Heat Unit (CHU) area were used to study the power of single‐year, multiple‐location trials in predicting cultivar performances in the following year, and to see if data from multiple‐year trials are more predictive. Mixed models were used to estimate best linear unbiased predictions (BLUP) of tested genotypes on the basis of single‐ or multiple‐year trials, and the t‐statistic of BLUP (tBLUP) was used as a measure of cultivar performance. Results indicated that a single‐year, multiple‐location trial had sufficient power for identifying genotypes that would perform well or poorly in the next year. Two to four years' data gave only slightly better predictions of next‐year performances than single‐year data but allowed more genotypes to be evaluated conclusively. The tBLUP of genotype effects based on 2 yr of multiple‐location trials should be used as a basis for soybean cultivar selection and recommendation in the 2800 CHU area of Ontario.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".