Xiaodong Song

The status of soil available potassium (AK) and their spatiotemporal changes are essential for nutrient management and food security. However, high-resolution spatial mapping of soil AK at the regional scale in combination with multiple environmental variables remains limited. Using data from 3,318 sites in the 1980s and 2010s and a variety of machine learning methods, this study systematically assessed the spatial distribution and stock changes of available potassium density (AKD) in central-western China. The results showed that the random forest model achieved moderate predictive performance in both periods (RMSE: 0.55–0.59 g m⁻², R²: 0.36–0.43). From the 1980s to the 2010s, the total soil AK stock in central-western China decreased by 12.65%. AK stock in natural ecosystems declined by 16.01%, likely reflecting insufficient sustained replenishment, increasing water limitation, ecological degradation, and grazing disturbance, whereas AK stock in agroecosystems increased by 12.71% under enhanced potassium fertilization and management practices such as straw incorporation. Spatially, AKD generally exhibited a pattern of higher values in the west and lower values in the central region. AK stock on the Tibetan Plateau declined markedly, whereas regions such as the Chengdu Plain and the Loess Plateau showed evident AK accumulation. SHAP analysis indicated that topographic and climatic factors were the dominant factors affecting the spatial differentiation of AKD. Slope, SAGA topographic wetness index (TWI), annual evaporation (Evap), sunshine duration (Solar) and elevation (DEM) showed relatively high contributions, while land use and vegetation played important regulatory roles. This study highlights the necessity of high-resolution assessment of soil potassium dynamics and provides a scientific basis for precision nutrient management, cultivated land quality improvement, and regional agricultural development and ecological restoration.

Soil inorganic carbon (SIC) pools are comparable in size to soil organic carbon pools and are vulnerable to climate change; however, SIC responses to climate change remain uncertain because of the lack of process-based simulations. Here, we developed a new process-based model integrating daily water balance dynamics with carbonate chemical equilibria at a 10 cm vertical resolution to predict the effects of climate change on the SIC pool down to a soil depth of 2 m in China until 2100. We found that across the four shared socioeconomic pathways, SIC stock in China’s topsoil (0–10 cm) decreased by 314 ± 8 Tg C, accompanied by a loss of 217 ± 9 Tg C from the 2 m soils. These findings challenge the traditional view of SIC stability in terrestrial carbon cycles, reveal substantial losses of SIC in both topsoils and deep soils, and highlight the projection of future climate and global inorganic carbon cycle feedback.

Quantifying changes in soil properties greatly benefits our understanding of soil management and sustainable land use, especially in the context of strong anthropogenic activities and climate change. This study investigated the effects of long-term reclamation on soil properties in an artificial oasis region with a cultivation history of more than 50 years. Critical soil properties were measured at 77 sites, and a total of 462 soil samples were collected down to a depth of 1 m, which captures both surface and subsurface processes that are critical for long-term cultivation effects. Thirteen critical soil properties were analyzed, among which four properties—soil organic carbon (SOC), total phosphorus (TP), pH, and ammonium nitrogen (NH₄⁺)—were selected for detailed analysis due to their ecological significance and low intercorrelation. By comparing cultivated soils with nearby desert soils, this study found that semicentennial cultivation led to significant improvements in soil properties, including increased concentrations of SOC, NH₄⁺, and TP, as well as reduced pH throughout the soil profile, indicating improved fertility and reduced alkalinity. Further analysis suggested that environmental factors—including temperature, clay content, evaporation differences between surface and subsurface layers, sparse vegetation cover, cotton root distribution, as well as prolonged irrigation and fertilization—collectively contributed to the enhancement of SOC decomposition and the reduction of soil alkalinity. Furthermore, three-dimensional digital soil mapping was performed to investigate the effects of long-term cultivation on the distributions of soil properties at unvisited sites. The soil depth functions were separately fitted to model the vertical variation in the soil properties, including the exponential function, power function, logarithmic function, and cubic polynomial function, and the parameters were extrapolated to unvisited sites via the quantile regression forest (QRF), boosted regression tree, and multiple linear regression techniques. The QRF technique yielded the best performance for SOC (R² = 0.78 and RMSE = 0.62), TP (R² = 0.79 and RMSE = 0.12), pH (R² = 0.78 and RMSE = 0.10), and NH₄⁺ (R² = 0.71 and RMSE = 0.38). The results showed that depth function coupled with machine learning methods can predict the spatial distribution of soil properties in arid areas efficiently and accurately. These research conclusions will lead to more effective targeted measures and guarantees for local agricultural development and food security.

Bulk Density (BD) represents a critical soil property essential for understanding soil compaction, element stocks, and ecological processes. However, obtaining accurate BD data in large and diverse regions, especially in complex terrain such as the Qinghai-Tibetan Plateau (QTP), remains challenging owing to the labor-intensive measurement procedures. In this study, we integrated soil pedotransfer functions and machine learning techniques to predict and generate a 90 m resolution BD grid dataset for the QTP. This dataset effectively captures the spatial heterogeneity of BD across the region. We rigorously validated the accuracy of the maps and assessed the reliability of uncertainty quantification, achieving RMSE values ranging from 0.144 to 0.190. The high spatial and depth resolution renders the data suitable for various practical applications, including element storage estimation and soil water transport modeling. These maps provide a robust scientific foundation for informing regional ecological restoration efforts, environmental protection initiatives, and the formulation of sustainable development strategies.

Quantifying the rates of soil evolution greatly benefits our understanding of soil formation and management, especially in the context of strong anthropogenic activities and climate change. This study investigated soil evolution in an artificial oasis region with a reclamation history of more than 50 years, and critical soil properties were measured at 77 sites. A total of 462 soil samples were collected down to a depth of 1 m. A total of seven critical soil properties were analysed, and four (i.e., soil organic carbon (SOC), total phosphorus (TP), pH, and ammonium nitrogen (NH₄⁺)), which were not closely correlated with each other, were selected for further investigation. Through comparison with desert soils, this investigation found that semicentennial cultivation resulted in significant changes in soil properties, with strong vertical variations, including increases in the C, N and P contents and decreases in pH throughout the whole profile. The temperature, clay content, evaporation rate between the topsoil and subsoil, low vegetation cover, cotton lateral roots, irrigation and fertilization played crucial roles in promoting SOC decomposition and reducing soil alkalinity, thereby contributing to rapid soil evolution. Thus, reclaimed desert soil was scientifically confirmed to be suitable for agricultural use, which will ease the food production crisis, protect the environment, and promote soil evolution. Furthermore, three-dimensional digital soil mapping was performed to investigate the effects of long-term cultivation on the distributions of soil properties at unvisited sites. The soil depth functions were separately fitted to model the vertical variation in the soil properties, including the exponential function, power function, logarithmic function and cubic polynomial function, and the parameters were extrapolated to unvisited sites via the quantile regression forest (QRF), boosted regression tree and multiple linear regression techniques. The QRF technique yielded the best performance for SOC (R²= 0.78 and RMSE = 0.62), TP (R² = 0.79 and RMSE = 0.12), pH (R² = 0.78 and RMSE = 0.10) and NH₄⁺ (R² = 0.71 and RMSE = 0.38). The results showed that depth function coupled with machine learning methods can predict the spatial distribution of soil properties in arid areas efficiently and accurately. These research conclusions will lead to more effective targeted measures and guarantees for local agricultural development and food security.

Accurately identifying pollution risks and sources is crucial for regional land resource management. This study takes a certain coastal county in eastern China as the object to explore the spatial distribution, pollution risk, and source apportionment of heavy metals in topsoil. A total of 633 samples were collected from the topsoil with a depth ranging from 0 to 20 cm, which came from different topographical and land use types (e.g., farmland, industrial areas, and mining areas), and the concentrations of HMs and As were measured by using atomic fluorescence spectrometry and inductively coupled plasma mass spectrometry. Firstly, the spatial distribution of soil HMs (Cd, Cr, Hg, Ni, and Pb) and arsenic (As) was predicted by incorporating environmental variables strongly affecting soil formation into geostatistical methods and machine learning approaches. Then, various pollution indicators were employed to conduct pollution evaluations, and potential ecological risk assessments were implemented based on the generated soil map. Finally, source apportionment was conducted using random forest (RF), absolute principal component score–multiple linear regression (APCS-MLR), correlation analysis, and spatial distribution of soil HMs and As. Findings in this research reveal that the RF approach yielded the best spatial prediction performance (0.59 ≤ R² ≤ 0.73). The Nemerow and geoaccumulation indices suggest that various pollution levels exist in this area. The average concentrations of As, Hg, and Ni are 7.233 mg/kg, 0.051 mg/kg, and 27.43 mg/kg respectively, being 1.14 times, 1.27 times, and 1.15 times higher than the background levels, respectively. The central–northern region presented a slight potential ecological risk, with Hg and Cd being identified as the primary risk factors. Natural, agricultural, transportation, and industrial and mining activities were identified as the main HMs and As sources. These findings will assist in the design of targeted policies to reduce the risks of HMs and As in urban soil and offer useful guidelines for soil pollution research in similar regions.

Exogenous organic carbon (C) inputs and their subsequent microbial and mineral transformation affect the accumulation process of soil organic C (SOC) pool. Nevertheless, knowledge gaps exist on how different long-term forms of crop straw incorporation (direct straw return or pyrolyzed to biochar) modifies SOC composition and stabilization. This study investigated, in a 13-year long-term field experiment, the functional fractions and composition of SOC and the protection of organic C by iron (Fe) oxide minerals in soils amended with straw or biochar. Under the equal C input, SOC accumulation was enhanced with both direct straw return (by 43%) and biochar incorporation (by 85%) compared to non-amended conventional fertilization, but by different pathways. Biochar had greater efficiency in increasing SOC through stable exogenous C inputs and inhibition of soil respiration. Moreover, biochar-amended soils contained 5.0-fold greater SOCs in particulate organic matter (POM) and 1.2-fold more in mineral-associated organic matter (MAOM) relative to conventionally fertilized soils. Comparatively, although the magnitude of the effect was smaller, straw-derived OC was preserved preferentially the most in the MAOM. Straw incorporation increased the soil nutrient content and stimulated the microbial activity, resulting in greater increases in microbial necromass C accumulation in POM and MAOM (by 117% and 43%, respectively) compared to biochar (by 72% and 18%). Moreover, straw incorporation promoted poorly crystalline (Feo) and organically complexed (Fep) Fe oxides accumulation, and both were significantly and positively correlated with MAOM and SOC. The results address the decadal-scale effects of biochar and straw application on the formation of the stable organic C pool in soil, and understanding the causal mechanisms can allow field practices to maximize SOC content. These results are of great implications for better predicting and accurately controlling the response of SOC pools in agroecosystems to future changes and disturbances and for maintaining regional C balance.

A new geological epoch—the Anthropocene—was voted by the 34-member Anthropocene Working Group (AWG) to mark the profound ways in which humans have altered our planet