Traits affecting early season nitrogen uptake in nine legume species
Bibliographic record
Abstract
Legume crops are known to have low soil N uptake early in their life cycle, which can weaken their ability to compete with other species, such as weeds or other crops in intercropping systems. However, there is limited knowledge on the main traits involved in soil N uptake during early growth and for a range of species. The objective of this research was to identify the main traits explaining the variability among legume species in soil N uptake and to study the effect of the soil mineral N supply on the legume strategy for the use of available N sources during early growth. Nine legume species were grown in rhizotrons with or without N supply. Root expansion, shoot and root biomass, nodule establishment, N 2 fixation and mineral soil N uptake were measured. A large interspecific variability was observed for all traits affecting soil N uptake. Root lateral expansion and early biomass in relation to seed mass were the major traits influencing soil N uptake regardless of the level of soil N availability. Fenugreek, lentil, alfalfa, and common vetch could be considered weak competitors for soil N due to their low plant biomass and low lateral root expansion. Conversely, peanut, pea, chickpea and soybean had a greater soil N uptake. Faba bean was separated from other species having a higher nodule biomass, a higher N 2 fixation and a lower seed reserve depletion. Faba bean was able to simultaneously fix N 2 and take up soil N. This work has identified traits of seed mass, shoot and root biomass, root lateral expansion, N 2 fixation and seed reserve depletion that allowing classification of legume species regarding their soil N uptake ability during early growth.
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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) | 0.000 | 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".