Selection for High Yield and Stability among Early Maturing Greengram Genotypes
Bibliographic record
Abstract
Greengram, Vigna radiata (L.) Wilczek is an important source of proteins and micronutrients to small holder farmers in eastern and northern regions of Uganda. Most of the landraces currently grown by small scale farmers are characterized by low yields and late maturity. In order to identify high yielding and stable varieties, an experiment was conducted to evaluate the yield performance of introduced early maturing genotypes from the World Vegetable Centre (AVDRC) in seven locations of Uganda. Yield performance data for the genotypes was subjected to analysis of variance (ANOVA) to test the significance of genotype × environmental interactions as well as stability analysis using the yield – stability statistic (YSi) as an aid for simultaneous selection for high yield and stability. Analyses of variance showed that genotype × environmental interactions (G×E) were significant and therefore could not be ignored. Following the detection of significant genotype × environmental interactions, yield – stability statistics (YSi) were generated and used for simultaneous selection for high yield and stability among the introduced genotypes. Yield-stability statistics (YSi), indicated that three introduced genotypes (Filsan, Sunshine, and Blackgram) in addition to the local check were stable and high yielding. These genotypes need to be assessed for farmer preferences/tastes and other quality traits in on-farm participatory trials before they can be recommended for release.
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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 | 0.000 |
| 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".