Tillage-residues affect mineral-associated organic matter on Vertisols in northern Mexico
Bibliographic record
Abstract
Managing croplands for increased storage of soil organic matter (SOM) contributes to the development of resilient farming systems in a changing climate. We examined SOM dynamics in a wheat (Triticum durum L.) – maize (Zea mays L.) irrigated bed planting system established near Ciudad Obregón, Sonora, Mexico. Soil samples (0–15 cm) were collected from conventionally tilled raised beds (CTB) with all crop residues incorporated (CTB-I) and permanent raised beds (PB) with crop residues burned (PB-B), removed (PB-R), partly retained (PB-P) or fully retained (PB-K) receiving 0, 150 or 300 kg N ha−1, and analyzed for organic C (OC), total N (TN) and δ13C in whole-soil, light fraction (LF) and coarse- (sand) and fine- (silt and clay) mineral-associated organic matter (MAOM). Results indicated that PB-K and PB-B increased soil OC (p <0.05) in whole-soil relative to CTB-I, mainly through increases in sand- and silt-size MAOM, respectively. Similarly, N-fertilization increased soil OC and TN contents in whole-soil, coarse-MAOM and fine-MAOM, but not in the LF pool. Soil δ13C was higher (p <0.05) in PB-K (−20.18‰) relative to PB-B (−20.67‰), possibly due to the stabilization of partly decomposed maize-C in silt- and clay-size MAOM. Composition of SOM surveyed by CPMAS 13C NMR was not affected by tillage-residue management, and roughly consisted of 35% O-alkyl-C, 31% alkyl-C, 24% aromatic-C and 10% carboxyl-C. Our results indicate that long-term PB-K and PB-B adoption increased surface soil OC contents relative to CTB-I, even though pathways of SOM stabilization differed between systems. Under PB-K, accumulation of fine-MAOM was mostly related to straw-C inputs, whereas in PB-B it was closely associated to black-C precursors. Fine-MAOM appeared responsive to crop residue management, and should be therefore considered when analyzing mechanisms of SOM stabilization in irrigated croplands.
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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".