Use of gross income as a measure of productivity in rice breeding
Bibliographic record
Abstract
Rice breeders consider grain yield and milled rice percentages in developing cultivars, but usually do not consider gross income. This study’s objectives were to identify rice genotypes that produced high and stable expected gross incomes using genotype plus genotype × environment (GGE) biplot analysis. Uniform Regional Rice Nursery data on 47 long-grain genotypes grown at five locations (AR, LA, MO, MS, and TX) during 2001 to 2003 were analyzed. Gross income of each genotype was estimated based on grain yield, milled rice percentages, market prices, and direct and counter-cyclical payments. Based on GGE biplot analysis, the genotypes with the highest yield and highest gross income for the main crop were different in 12 out of 13 environments. RU0103184, Francis, and RU0003178 were the genotypes with the highest gross income in six, four, and three environments, respectively. Rice breeders should consider gross income as a selection criterion in the release of new cultivars. Key words: Rice, GGE, GE, breeding, gross income
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".