Integrating forage legumes reduces dependence on N fertilizer and increases the stability of grazing systems
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 it