Modelling the Effect of Land Management Changes on Soil Organic Carbon Stocks in a Mediterranean Cultivated Field
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Bibliographic record
Abstract
Abstract Land management in agricultural lands has important effects on soil organic carbon (SOC) dynamics. These effects are particularly relevant in the Mediterranean region, where soils are fragile and prone to erosion. Increasing interest of modelling to simulate SOC dynamics and the significance of soil erosion on SOC redistribution have been linked to the development of some recent models. In this study, the SPEROS‐C model was implemented in a 1.6‐ha cereal field for a 150‐year period covering 100 years of minimum tillage by animal traction, 35 years of conventional tillage followed by 15 years of reduced tillage by chisel to evaluate the effects of changes in land management on SOC stocks and lateral carbon fluxes in a Mediterranean agroecosystem. The spatial patterns of measured and simulated SOC stocks were in good agreement, and their spatial variability appeared to be closely linked to soil redistribution. Changes in the magnitude of lateral SOC fluxes differed between land management showing that during the conventional tillage period the carbon losses is slightly higher (0.06 g C m −2 yr −1 ) compared to the period of reduced till using chisel (0.04 g C m −2 yr −1 ). Although the results showed that the SPEROS‐C model is a potential tool to evaluate erosion induced carbon fluxes and assess the relative contribution of different land management on SOC stocks in Mediterranean agroecosystems, the model was not able to fully represent the observed SOC stocks. Further research (e.g. input parameters) and model development will be needed to achieve more accurate results. Copyright © 2016 John Wiley & Sons, Ltd.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it