Cultivar, Planting Date, and Row Spacing Effects on Mungbean Performance in Virginia
Bibliographic record
Abstract
Mungbean [ Vigna radiata (L.) R. Wilczek, Fabaceae] is one of the most important food legume crops in Asia. It is also gaining importance in other parts of the world such as Australia and Canada. The United States imported mungbean worth ≈22 million dollars during 2014. To establish domestic production and to determine if mungbean can be produced in rotation with winter wheat ( Triticum aestivum L.), replicated experiments were conducted during 2012 and 2013 using two cultivars (Berken and TexSprout), two planting dates (early and late July), and two row spacings (37.5 and 75 cm). Cultivar and planting date effects on seed yield were not significant, however, narrow row spacing resulted in significant higher seed yield and concentration of protein over the wider row spacing (1.76 vs. 0.86 Mg⋅ha −1 yield and 24.9% vs. 23.7% protein). Early planting resulted in lower sugar and oil concentrations over late planting (4.4% vs. 5.5% sugar and 1.24% vs. 1.99% oil). Average mungbean values for seed yield, seed size, and concentrations of protein, sugars, and oil were 1.31 Mg⋅ha −1 , 7.08 g/seed 100 , 24.3%, 4.91%, and 1.59%, respectively. Low harvest index values (17% to 25%) indicated that potential exists for improvement in mungbean seed yield. The results indicated that mungbean can be easily produced in rotation with winter wheat in the mid-Atlantic region of the United States.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".