Above and belowground litter decomposition of cover crops grazed at different intensities
Bibliographic record
Abstract
Abstract Grazing cover crops may increase land‐use efficiency while promoting sustainability. We investigated how grazing intensity affects cover crop litter quantity, quality, decomposition, and cotton (Gossypium hirsutum L.) N uptake. Cover crops were a mixture of rye (Secale cereale L.) and oat (Avena sativa L.) 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). Grazed treatments received 90 kg N ha−1. After cover crop termination, above‐ and belowground litter was collected and incubated in situ for 0, 4, 8, 16, 32, 64, and 128 days, with cotton plants sampled on the same days to estimate N recovery and synchrony between N release from litter and uptake by cotton. By Day 128, only 13% of initial NG34 aboveground biomass had disappeared, whereas 42% of HG disappeared. Nitrogen retained in aboveground litter of HG was less than NG90 (27 vs. 60 kg N ha−1), and aboveground final N stock (at Day 128) of HG was less than NG90 and LG (16, 47, and 41 kg N ha−1, respectively). Belowground litter contributed 98 kg N ha−1 versus 46 for aboveground. Belowground N disappearance from litter bags was greater from NG90 than NG34 (39 vs. 21 kg N ha−1). Cotton N uptake by Day 128 was similar across treatments (191 kg N ha−1). Grazing cover crops impact aboveground litter quantity, quality, and decomposition rates, and belowground litter plays an important role on the N cycling.
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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".