Effects of Nitrogen Application on Nitrogen Fixation in Common Bean Production
Bibliographic record
Abstract
L.) in association with rhizobia is often characterized as poor compared to other legumes, and nitrogen fertilizers are commonly used in bean production to achieve high yields, which in general inhibits nitrogen fixation. In addition, plants cannot take up all the nitrogen applied to the soil as a fertilizer leading to runoff and groundwater contamination. The overall objective of this work is to reduce use of nitrogen fertilizer in common bean production. This would be a major advance in profitability for the common bean industry in Canada and would significantly improve the ecological footprint of the crop. In the current work, 22 bean genotypes [including recombinant inbred lines (RILs) from the Mist × Sanilac population and a non-nodulating mutant (R99)] were screened for their capacity to fix atmospheric nitrogen under four nitrogen regimes. The genotypes were evaluated in replicated field trials on N-poor soils over three years for the percent nitrogen derived from atmosphere (%Ndfa), yield, and a number of yield-related traits. Bean genotypes differed for all analyzed traits, and the level of nitrogen significantly affected most of the traits, including %Ndfa and yield in all three years. In contrast, application of rhizobia significantly affected only few traits, and the effect was inconsistent among the years. Nitrogen application reduced symbiotic nitrogen fixation (SNF) to various degrees in different bean genotypes. This variation suggests that SNF in common bean can be improved through breeding and selection for the ability of bean genotypes to fix nitrogen in the presence of reduced fertilizer levels. Moreover, genotypes like RIL_38, RIL_119, and RIL_131, being both high yielding and good nitrogen fixers, have potential for simultaneous improvement of both traits. However, breeding advancement might be slow due to an inconsistent correlation between these traits.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".