Integrated crop‐livestock systems result in less nitrate leaching than ungrazed crop systems in North Florida
Bibliographic record
Abstract
Abstract Integrated crop‐livestock systems provide an array of benefits to agricultural systems, including a reduction in nitrogen (N) leaching. A farm approach to integrate crops and livestock is the adoption of grazed cover crops. Moreover, the addition of perennial grasses into crop rotations may improve soil organic matter and decrease N leaching. However, the effect of grazing intensity in such systems is not fully understood. This 3‐year study investigated short‐term effects of cover crop planting (cover and no cover), cropping system (no grazing, integrated crop‐livestock [ICL], and sod‐based rotation [SBR]), grazing intensity (heavy, moderate, and light grazing), and cool‐season N fertilization (0, 34, and 90 kg N ha −1 ) on NO 3 ‐N and NH 4 ‐N concentration in leachate, and cumulative N leaching by using 1.5‐m deep drain gauges. The ICL was a cool‐season cover crop‐cotton ( Gossypium hirsutum L.) rotation, whereas SBR was a cool‐season cover crop‐bahiagrass ( Paspalum notatum Flüggé) rotation. There was a treatment × year × season for cumulative N leaching ( p = 0.035). Further contrast analysis indicated that cover crops decreased cumulative N leaching compared to no cover (18 vs. 32 kg N ha −1 season −1 ). Nitrogen leaching was lesser for grazed compared to nongrazed systems (14 vs. 30 kg N ha −1 season −1 ). Treatments containing bahiagrass had lesser NO 3 ‐N concentration in leachate (7 vs. 11 mg L −1 ) and cumulative N leaching (8 vs. 20 kg N ha −1 season −1 ) compared to ICL systems. Adding cover crops can reduce cumulative N leaching in crop‐livestock systems; moreover, warm‐season perennial forages can further enhance this benefit.
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How this classification was reachedexpand
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".