Spatiotemporal Changes and Driving Factors of Cultivated Soil Organic Carbon in Northern China’s Typical Agro-Pastoral Ecotone in the Last 30 Years

Abstract

In order to explore the spatiotemporal changes and driving factors of soil organic carbon (SOC) in the agro-pastoral ecotone of northern China, we took Aohan banner, Chifeng City, Inner Mongolia Autonomous Region as the study area, used the random forest (RF) method to predict the SOC from 1989 to 2018, and the geographic detector method (GDM) was applied to analyze quantitatively the natural and anthropogenic factors that are affecting Aohan banner. The results indicated that: (1) After adding the terrain factors, the R2 and residual predictive deviation (RPD) of the RF model increased by 1.178 and 0.39%, with root mean square errors (RMSEs) of 1.42 g/kg and 1.05 g/kg, respectively; (2) The spatial distribution of SOC was higher in the south and lower in the north; the negative growth of SOC accounted for 55.923% of the total area, showing a trend of degradation; (3) Precipitation was the main driving factor of SOC spatial variation in the typical agro-pastoral ecotone of northern China, which was also affected by temperature, elevation, soil type and soil texture (p < 0.01). (4). Anthropogenic factors (carbon input and gross domestic product (GDP)) had a greater impact on SOC than did climate factors (temperature and precipitation), making anthropogenic factors the dominant factors affecting SOC temporal variation (p < 0.01). The results of this work constitute a basis for a regional assessment of the temporal evolution of organic carbon in the soil surface, which is a key tool for monitoring the sustainable development of agropastoral ecotones.

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

“…We found that the XGBoost models allowed spatially explicit predictions of soil EC and SOC stock to be made, with the R 2 value reaching 0.85 and 0.81, and RMSE reaching 1.41 dS m −1 and 0.46 kg m −2 , respectively, based on the 10-fold cross-validation procedures. Our results were close to that of previous studies conducted at a similar spatial scale, in which the machine learning models with dozens of covariates as model inputs were applied to mapping soil EC or SOC stock [4,7,26,33,62]. Considering the high efficiency and precision, the XGBoost models were recommended to predict the spatio-temporal patterns of soil salinity and SOC stock, especially at a higher resolution over a broad scale, using both the time-varying and static environmental covariates after variables' selection.…”

“…Setia et al [12] simulated the historic loss of SOC due to soil salinization using the RothC model and estimated that soils have experienced an average loss of 3.47 t ha −1 due to salinization across the globe. The data-driven models based on machine learning models were also used for modeling SOC stock changes over space and time from regional to continental scales [7,11,[31][32][33][34]. For instance, Li et al [4] quantified the effects of climate warming and precipitation variation on SOC stock and projected its future trends based on a random forest model, and SOC stocks in the top 100 cm were projected to decrease significantly due to more carbon release as a result of the stimulated decomposition rates as affected by climate warming and deepened active layers in the permafrost.…”

Effects of cropland reclamation on soil organic carbon in China's black soil region over the past 35 years

“…The conversion of dry land to a paddy field and afforestation (The Shelter Forest Program in 1970s in China) were also proved to lead to an increase in SOC content in existing studies in China (Wang et al, 2021; Xie et al, 2021). The microbial decay constrained the rate of SOC decomposition because of the anaerobic conditions for paddy fields (Xie et al, 2021), and afforestation prevented soil erosion and conserves water in soils (Wang et al, 2021). We need to know how much SOC content and density have been lost over the past few decades in Northeast China, because Northeast China has a large agricultural production potential.…”

“…On the contrary, the implementation of agricultural policy and management practice led to the increase of crop productivity and crop yield, and crop yield data represented the C input by roots, leading to an increase in SOC content (Han et al, 2017; Zhao et al, 2018). The conversion of dry land to a paddy field and afforestation (The Shelter Forest Program in 1970s in China) were also proved to lead to an increase in SOC content in existing studies in China (Wang et al, 2021; Xie et al, 2021). The microbial decay constrained the rate of SOC decomposition because of the anaerobic conditions for paddy fields (Xie et al, 2021), and afforestation prevented soil erosion and conserves water in soils (Wang et al, 2021).…”

Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests

“…SVM is a powerful calibration method based on the kernel learning method that offers the possibility of training nonlinear classifiers in high-dimensional spaces using small training sets [40]. RF is a machine regression model that combines decision trees with bagging algorithms, and this model calculation strategy can both improve prediction accuracy and avoid overfitting [41]. At the same time, the training process of using machine learning methods to build models requires the configuration of a large number of hyperparameters, and the selection of these hyperparameters greatly depends on experience [42], which is computationally intensive and subjective.…”