Spatial distribution and drivers of soil organic carbon content in croplands of Morocco
Soil organic carbon (SOC), a critical component of soil organic matter, plays a vital role in enhancing soil productivity, ensuring soil stability, and mitigating CO2 emissions. Climate, mineralogy, and vegetation-derived factors may each contribute to the cycling of SOC, but whether its distribution varies predictably in Mediterranean arid croplands remains ambiguous. In a spatiotemporal machine learning framework, a multi-year dataset of topsoil organic carbon concentrations from over 31,000 cropland sites in Morocco were matched with corresponding environmental covariates including climate, vegetation, topography, and soil properties. The spatiotemporal dataset was used for model training and cross-validation, while model extrapolations estimated SOC spatiotemporal changes between 2000 and 2020 at a 250 m ground resolution. The aim was to assess the environmental drivers influencing spatiotemporal changes of SOC concentrations in these croplands. Measured topsoil SOC concentrations showed low median 11.71 g C kg-1 with high variability across the studied soils (Q1 = 8.46 and Q3 = 16.24 g C kg-1). Fifty-seven percent of the variance in SOC content was explained by a suite of bioclimatic proxies related to temperature, vegetation, and precipitation, with temperature seasonality and annual mean temperature having the highest impact on carbon concentrations. Collectively, the national carbon dataset supports a new basis for understanding the local drivers of SOC gains and losses in arid croplands of Morocco. This will partly address controversy concerning carbon cycling in arid soils and responses to climate change.
| Item Type | Article |
|---|---|
| Open Access | Not Open Access |
| Keywords | Bioclimatic factors , Carbon sequestration , Machine learning , Precipitation , Remote sensing , Temperature , Vegetation |
| Project | Image analysis for plant phenotyping - machine learning based methods for analysis of multi-model and multi-dimensional remote sensing data from high-throughput plant phenotyping |
| Date Deposited | 05 Dec 2025 10:45 |
| Last Modified | 19 Dec 2025 14:58 |
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