Changes during winter in water‐stable aggregation due to crop residue quality
Bibliographic record
Abstract
Abstract There is a need to develop practices that contribute to increased water‐stable aggregation (WSA) during winter in a humid temperate climate when soil is particularly prone to water erosion. Our objectives were to determine the effects of crop residue quality on WSA during winter and to relate these effects to biochemical indicators of fungal and bacterial biomass. Three graminae crop residues were selected for their different C/N ratios and biochemical characteristics (green oat residues, C/N = 18.8; wheat straw, C/N = 125.6; and mature miscanthus residues, C/N = 311.3). In October 2009, crop residues were added with an equivalent amount of C in the 0–10 cm layer to a Luvisol in north‐west France. WSA, expressed as mean weight diameter (MWD), amino sugar, soil mineral N and water contents were measured at regular intervals during 5 months. Aggregate MWD of the control soil decreased rapidly and remained low until the last sampling date in March which illustrates the structural vulnerability of bare soils in winter in this pedo‐climatic area. The incorporation of all three crop residues had significant positive impacts on aggregate MWD. Despite widely different C/N ratios, the maximum MWD under each treatment was similar (three times greater than the control soil). Maximum MWD occurred at times that clearly depended on residue quality. Maximum values occurred early for green oat (29 day), but were delayed to 50 day for wheat straw and to 154 day for miscanthus. Results from correlation analysis suggest that variations in WSA were partly mediated by microbial agents with a dominant effect of bacteria for green oat and a combined role of fungal and bacterial biomass for wheat straw. We suggest that the maximum MWD associated with the miscanthus late in the experiment is related to changes in the composition of the fungal community. Overall, our study shows that autumn application of crop residues increases WSA during winter with its effect being microbially mediated and determined by residue quality.
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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 | 0.001 | 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".