Grazing management effects on cover crop responses and cotton lint yield
Bibliographic record
Abstract
Abstract Grazing cover crops can improve land‐use efficiency and diversification, making agricultural enterprises more resilient to market fluctuations. We investigated how grazing intensity affects cover crop forage responses and cotton ( Gossypium hirsutum L.) lint yield. Cover crops were a rye ( Secale cereale L.)–oat ( Avena sativa L.) mixture managed as follows: no grazing + 34 kg N ha –1 (NG34), no grazing + 90 kg N ha –1 (NG90), heavy grazing (HG), moderate grazing (MG), and light grazing (LG), compared with a no cover crop control. All grazed treatments received 90 kg N ha –1 . Average postgrazing herbage mass (HM) for HG, MG, and LG was 520, 1,350, and 2,120 kg dry matter ha –1 , respectively. Herbage accumulation (HA) rate was greater for LG than HG, with MG being intermediate. Forage crude protein (CP) and in vitro digestible organic matter (IVDOM) concentrations decreased as the season progressed and were usually greater for HG than MG and LG. Stubble residue before cover crop termination was greatest for NG34 and NG90 in 2018 and 2020, however, in 2019 NG90 had greater stubble residue before termination than NG34 (7540 vs. 6650 kg dry matter ha –1 ). Heavy grazing resulted in greater weed proportion (17 vs. 6.5%) and lesser soil cover (49 vs. 70%) than nongrazed cover crops. Cotton lint yield was low and unaffected by treatment, reaching a maximum of 520 kg ha –1 in 2019. Although lint yield was not affected by cover crop fertilization or grazing during 3 yr, HG reduced soil cover and increased weed presence.
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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".