Integrated Crop–Livestock–Forest Systems With No‐Till Can Restore Soil Organic Carbon Stocks in a Brazilian Ferralsol
Bibliographic record
Abstract
Crop–livestock–forest integration (CLFI) systems offer a promising approach to enhancing soil organic carbon (SOC) content within various aggregate size classes, thereby improving soil productivity and its capacity for atmospheric carbon (C) sequestration. This study aimed to assess SOC content across different water‐stable aggregate size classes and its influence on aggregate formation in a Ferralsol under various long‐term farming systems, including CLFI with pasture rotation (CLFI‐PA), CLFI with crop rotation (CLFI‐CR), conventional continuous cropping (CCC), permanent pasture without fertilization (PP‐WoF), permanent pasture with fertilization (PP‐WF), and the native Cerrado as a reference. All cropping systems evaluated in this study are managed under a no‐till system, except for the CCC system. Undisturbed soil samples were collected from the 0.0‐ to 0.10‐m layer to evaluate aggregate stability in water, SOC stocks, and SOC distribution across different soil aggregate classes. The geometric mean diameter (GMD) and mean weight diameter (MWD) were calculated. After 23 years of CCC, there was a 53% (12.94 Mg ha −1 ) reduction in SOC compared to the Cerrado (27.26 Mg ha −1 ), characterized by a predominance of microaggregates and significantly lower GMD and MWD values ( p < 0.05). The MWD and GMD values ranked as follows: Cerrado > PP‐WoF > CLFI‐PA > CLFI‐CR > PP‐WF > CCC. In the no‐till systems, macroaggregates were predominant, with higher GMD and MWD values. The PP‐WF, PP‐WoF, CLFI‐PA, and CLFI‐CR systems showed SOC stocks of 25.70, 21.53, 21.40, and 20.38 Mg ha −1 , respectively, with a positive correlation between SOC stocks and macroaggregates ( p < 0.05). The findings highlight the potential of CLFI systems to store carbon in the soil and promote macroaggregate formation, comparable to pastures established for 25 years and the native Cerrado.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".