Role of Crop Rotations in the Dynamic of Soil Organic Matter Pools
Bibliographic record
Abstract
Soil organic matter is considered a key attribute for a sustainable agricultural production and is influenced by the quantity and quality of the crop residue deposited on the soil surface. Therefore, different crop rotations could change the soil organic matter pools. The objectives of this study were to evaluate the soil carbon pools obtained by chemical and physical fractionation methods and the humification index under different crop rotations in a no-till system. We test the following hypothesis: a) the distribution of C and N among the soil organic matter fractions depends on plant species rotation schemes and; b) labile fractions are more sensitive to the input of crop residues and therefore, more suitable for evaluating the impact of different crop rotations in the soil organic matter quality. We evaluated four crop sequences (corn/corn/corn; corn/wheat/corn; soybean/wheat/corn and soybean/corn/corn) in a no-till system. A five-year reforested area was used as reference. We determined the total C and N contents, the mineral-associated C and N, the light fraction of C and N, the labile carbon extracted with KMnO4 and the soil organic matter humification index. We found narrow differences between the crop rotation systems in the total C and N levels, the mineral-associated C and N fractions and the labile C extracted with KMnO4. The diversification of the agricultural system with soybean in crop rotation favored the accumulation of light fraction C and N in the soil that were more efficient to provide information about the changes in the soil organic matter 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 | 0.001 | 0.001 |
| 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".