Integrating forage legumes reduces dependence on N fertilizer and increases the stability of grazing systems
Bibliographic record
Abstract
Midst increasing global demand for livestock products, grassland-livestock systems face challenges including pasture degradation and climate change. The introduction of nitrogen (N)-fixing legumes into grass monocultures addresses these challenges and may sustain or increase livestock production with fewer off-farm inputs. This 10-yr study assessed N fertilization level and legume integration effects on cool- and warm- season herbage responses, animal performance, and system stability of bahiagrass ( Paspalum notatum Flügge) pastures. Including diverse legume species added a total of 139 kg N ha −1 yr −1 , 66 kg ha −1 during the cool season and 73 kg ha −1 during the warm season, via biological N fixation. The inclusion of rhizoma peanut (RP; Arachis glabrata Benth.) and clovers ( Trifolium spp.) resulted in similar animal performance to N-fertilized, grass-only systems. Cool + warm-season liveweight gain on Grass+N and Grass+RP systems averaged 635 and 626 kg ha −1 , respectively, with the legume integration reducing N fertilizer inputs by 85 % (224 vs. 34 kg N ha −1 yr −1 ). The proportion of RP in feces was 49.5 % compared with ∼35 % in pasture herbage mass, indicating the preference of grazing animals for RP. Cattle average daily gain was successfully predicted from fecal δ 13 C (‰) ( P < 0.001). Over a decade, the grass-legume mixture was more stable than the other grazing systems ( P = 0.07), and increasing the system biodiversity improved overall system performance. In conclusion, integrating forage legumes into bahiagrass pastures reduced dependence on N fertilizers without sacrificing cattle performance, potentially improving the economic return and stability of the system.
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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) | 0.001 | 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".