Nitrogen use efficiency of irrigated dry bean (Phaseolus vulgaris L.) genotypes in southern Alberta
Bibliographic record
Abstract
Although dry bean (Phaseolus vulgaris L.) is a legume capable of fixing nitrogen, fertilizer N is usually recommended for its production in Canada because it is believed to be an inefficient N fixer. Using genotypes with high N use efficiency (NUE) would reduce the amount of applied N, which usually has deleterious environmental effects. We evaluated 22 bean genotypes for N uptake efficiency (NUpE) in a greenhouse trial and 16 genotypes for N use efficiency (NUE) and its components: NUpE and N utilization efficiency (NUtE) in two seasons (2012 and 2013) of a field trial at 30 kg N ha−1 (30N) and 100 kg N ha−1 (100N) soil levels. Root biomass and surface area were highly correlated with NUpE in the greenhouse, where NUpE at 100N was 48% lower than that at 30N. In the field trial, grain yield was highly correlated with NUpE and NUtE at both 30N and 100N. The NUpE at 100N was 65% lower than that at 30N, and NUE at 100N was 68% lower than that at 30N. The low NUE at 100N in the field trial was mainly due to the difference in NUpE. This study also identified nine genotypes, including five germplasm lines (PI 136692, GH-196, UNS-117, UI-239, and LEF2RB) and four cultivars (Othello, Viva, AC Redbond, and Island), that were both N-efficient (at 30N) and N-responsive (to 100N).
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".