Yield Stability of Dry Bean Genotypes across Nitrogen‐Fixation‐Dependent and Fertilizer‐Dependent Management Systems
Bibliographic record
Abstract
ABSTRACT Despite the inherent N2–fixing ability of legumes, the actual symbiotic N2 fixation (SNF) of dry bean (Phaseolus vulgaris L.) compared with other legumes is relatively low. Accordingly, application of inorganic nitrogen in bean fields has often been recommended to maximize economical yield. The genetic diversity for SNF in common bean may provide the opportunity to develop bean genotypes with stable yield across N‐fertilizer‐dependent and SNF‐dependent production practices. A population of 140 recombinant inbred lines (RILs) of a cross between high and low N‐fixing genotypes, ‘Mist’ and ‘Sanilac’, respectively, was evaluated under two different N management conditions, SNF and N fertilizer dependent, across multiple environments. While N management did not significantly affect the overall yield, genotypes responded differentially to SNF‐dependent and N‐fertilizer‐dependent environments. Among the RILs with higher than average yield, the stability analysis identified 6% as generally adapted to all environments, regardless of N management. The study highlights the opportunity to select bean genotypes that maintain their yielding ability under SNF‐dependent management systems.
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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 | 0.001 | 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) | 0.001 | 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".