Change Drivers and Spatial Distribution of Soil Organic Carbon Concentrations in Croplands of Morocco
Bibliographic record
Abstract
Soil organic carbon (SOC), a vital component of soil organic matter, plays a critical role in soil productivity, stability, and mitigating CO2 emissions. Factors such as climate, mineralogy, and vegetation influence SOC cycling, but its distribution patterns in Mediterranean arid croplands remain unclear. Using a spatiotemporal modeling approach, researchers analyzed a multi-year dataset of topsoil organic carbon concentrations from over 31,000 cropland sites in Morocco. These data were linked with environmental variables, including climate, vegetation, topography, and soil characteristics, to identify the drivers of spatiotemporal SOC changes.The analysis revealed a low median SOC concentration of 11.71 g C kg⁻¹, with significant variability (Q1 = 8.46, Q3 = 16.24 g C kg⁻¹). Bioclimatic factors, particularly temperature seasonality and annual mean temperature, accounted for 57% of the variation in SOC content, along with contributions from vegetation and precipitation. This national dataset provides new insights into the environmental drivers of SOC variability in Morocco's arid croplands, shedding light on the mechanisms of SOC gain and loss and informing discussions about carbon cycling in arid soils and their response to climate change.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".