Nitrogen Fixation and Resource Partitioning in Alfalfa (Medicago sativa L.), Cicer Milkvetch (Astragalus cicer L.) and Sainfoin (Onobrychis viciifolia Scop.) Using 15N Enrichment under Controlled Environment Conditions
Bibliographic record
Abstract
Availability of nitrogen (N) limits pasture production. Inclusion of legumes into grass pastures can provide an alternative N source through biological N2 fixation (BNF), and enhance retention and cycling of soil C and N. Despite the use of alfalfa (Medicago sativa L.), cicer milkvetch (Astragalus cicer L.) and sainfoin (Onobrychis viciifolia Scop.) in grass-legume pastures to improve forage quality, relative BNF potentials and resource partitioning are unknown. We quantified BNF using 15N isotope dilution and estimated resource partitioning in alfalfa, two cultivars of cicer milkvetch and two cultivars of sainfoin under controlled conditions. Percentage of nitrogen derived from atmosphere followed the order alfalfa (92%) > cicer milkvetch (87%) > sainfoin (81%); corresponding to estimated N contributions of 200, 128 and 65 kg N ha−1 yr−1, respectively, based on total herbage. Root dry matter was 24% to 36% greater than shoot dry matter in all of the legumes, providing substantial below-ground C and N. Cultivars of the same species did not differ in any measured parameter (p > 0.05). Despite the lower BNF in cicer milkvetch and sainfoin compared to alfalfa, their use may not negatively affect stand productivity and C storage.
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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 | 0.000 | 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".