Short-Term Soil Organic Matter and Carbon Responses to Contrasting Grazing Intensities in Integrated Crop-Livestock Systems
Bibliographic record
Abstract
Combining integrated crop-livestock systems under no-till management may improve soil organic matter (SOM) build up and improve soil C sequestration. Grazing cover crops appears as a possibility to combine crops and livestock in a farm system. Further SOM and soil C increase can be achieved by adding perennial grasses into crop rotations. However, the effect of grazing intensity in such systems are not fully understood. This 2-yr study investigated short-term effects of cropping system [winter cover crops-summer cotton (Gossypium hirsutum L.) and winter cover crops-summer bahiagrass (Paspalum notatum Flüggé) rotations], grazing intensity (no grazing, heavy, moderate, and light grazing), and N fertilization (34 and 90 kg N ha-1 ), on OM and soil C of the soil-surface (0-15 cm) and deep-soil (0-90 cm) under no-till. Preliminary results indicate that treatments containing bahiagrass improved SOM in 1.5 g kg-1 compared to winter grazing on cover crops-cotton systems (P = 0.017). There were no differences among treatments for soil total C stock (15.4 Mg ha-1) and particulate OM-C (4.8 Mg ha-1) at the 15-cm depth (P > 0.1). Carbon concentration increased from 8.0 to 12.6 g kg-1 as aggregate fraction decreased from 250 – 2000 to < 53 µm (P < 0.001). Nonetheless, C stock was not affected by aggregate fraction, with each fraction containing 3.8 Mg C ha-1, on average. Carbon stocks from 0-15, 15-30, 30-60, and 60-90-cm depths did not differ among treatments (P = 0.743), totalizing 30.4 Mg C ha-1 in the soil profile. Long-term studies are necessary to better understand the role of cropping system and grazing intensities on soil OM and C responses on surface and deep soil.
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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".