Introduction: Cultivated land quality degradation is a critical challenge to food security, requiring effective nature-based restoration strategies based on comprehensive assessments of land quality. However, existing methods are often costly, limited in scope, and fail to capture the multidimensional complexity of the degradation processes.Methods: This study integrated vegetation indices, topographic data, and soil physical and chemical properties to construct a model for identifying cultivated land degradation. Remote sensing indices were calculated using Google Earth Engine, enabling large-scale spatial analysis. Machine learning, combined with SHapley Additive exPlanations (SHAP), was employed to explore the driving factors of degradation.Results: The results indicate that 11.86% of cultivated land in Yugan County is degraded, primarily in the central plain and riparian zones, driven by both natural factors (precipitation, temperature) and anthropogenic factors (straw incorporation, fertilization management). Soil erosion was concentrated in southern hills and near rivers, fertility decline occurred in the central plain, and soil acidification was evenly distributed with generally low degradation levels.Discussion: Based on these findings, vegetation-based restoration solutions, including deep-rooted crops, crop rotation and intercropping, and straw incorporation, are proposed to address different types of cultivated land quality degradation and support sustainable land management.

Cultivated land is an important material carrier providing multiple supplies for production, living, and ecological benefits. As an area of cultivated land subject to high levels of human activity during urbanization, the functional level and structure of suburban cultivated land have been strongly impacted. In this study, we took the suburban cultivated land in Nanchang City as the research subject and constructed a multi-functional indicator system for cultivated land in the context of production, ecology, and landscape culture. Using the improved CRITIC-entropy weight method and the optimal parameter geographical detector model, we examined the multi-functional evolution of suburban cultivated land from 2012 to 2022 and its response mechanisms to urbanization. The results showed that (1) the multifunctionality of cultivated land in the peri-urban area of Nanchang City shows a general decreasing trend, with the decline gradually decreasing with an increase in distance from the central urban area. (2) In contrast, the multifunctionality level of a few cultivated land areas away from the central area of the city showed a small to medium increase. The function of cultivated land in the peri-urban area showed a decreasing trend in 2012–2022 due to the urbanization level. However, the production and ecological functions increased slightly, whereas the cultural function decreased significantly. (3) The impact of urbanization on the multifunctional transformation of peri-urban cultivated land is a complex process that is largely shaped by economic urbanization and is influenced by a multitude of factors. Therefore, cultivated land management policies must fully mobilize the socioeconomic resources of each region.

Soil texture, defined by the proportions of sand, silt, and clay particles in the soil, is one of the most essential physical properties of soil. High-resolution soil texture data can provide critical parameter support for soil hydrological modeling, agricultural production management, and ecosystem assessment. In digital soil mapping, previous studies often predicted the sand, silt, and clay contents in soil and then indirectly calculated soil texture. Currently, approaches that directly map soil texture by classification modeling are gaining increasing attention due to the decreased error from data conversion, but few studies have systematically compared these two methods yet. In this study, we comprehensively assessed the performance of direct and indirect predicting soil texture using four machine learning algorithms (e.g., extreme gradient boosting, random forest, gradient boosting decision tree, and extremely randomized tree) with 190 covariates from the Digital Elevation Model, Sentinel-1/2 satellite images, and classification maps and generated a 10 m resolution soil texture map based on 405 topsoil (0–20 cm) sample data collected in Suichuan County, China. The results showed that compared with indirect predictions, direct predictions improved overall accuracy (OA) by 20.57–44.19% and the Kappa coefficient (Kappa) by 0.220–0.402. Among the models used, the XGB model achieved the highest accuracy (OA: 0.948; Kappa: 0.931) and the lowest uncertainty (confusion index: 0.052). The direct prediction map (nine classes recorded) exhibited more detailed and diverse spatial distribution patterns than the indirect prediction map (six classes recorded), aligning better with the actual environment. Based on accuracy validation and spatial distribution, the performance of the XGB model was best during direct prediction. The Shapley additive explanation from the XGB model revealed that the normalized height and stream power indices were the most significant factors driving the soil texture in the study area. Our results provide a reference for future studies on soil texture mapping using machine learning models.

Mapping soil properties in sub-watersheds is critical for agricultural productivity, land management, and ecological security. Machine learning has been widely applied to digital soil mapping due to a rapidly increasing number of environmental covariates. However, the inclusion of many environmental covariates in machine learning models leads to the problem of multicollinearity, with poorly understood consequences for prediction performance. Here, we explored the effects of variable selection on the prediction performance of two machine learning models for multiple soil properties in the Haihun River sub-watershed, Jiangxi Province, China. Surface soils (0–20 cm) were collected from a total of 180 sample points in 2022. The optimal covariates were selected from 40 environmental covariates using a recursive feature elimination algorithm. Compared to all-variable models, the random forest (RF) and extreme gradient boosting (XGBoost) models with variable selection improved in prediction accuracy. The R² values of the RF and XGBoost models increased by 0.34 and 0.47 for the soil organic carbon, by 0.67 and 0.62 for the total phosphorus, and by 0.43 and 0.62 for the available phosphorus, respectively. The models with variable selection presented reduced global uncertainty, and the overall uncertainty of the RF model was lower than that of the XGBoost model. The soil properties showed high spatial heterogeneity based on the models with variable selection. Remote sensing covariates (particularly principal component 2) were the major factors controlling the distribution of the soil organic carbon. Human activity covariates (mainly land use) and organism covariates (mainly potential evapotranspiration) played a predominant role in driving the distribution of the soil total and soil available phosphorus, respectively. This study indicates the importance of variable selection for predicting multiple soil properties and mapping their spatial distribution in sub-watersheds.

Soil total phosphorus plays an important role in soil fertility, plant growth, and bioge-ochemical cycles. This study aims to determine the spatial distribution characteristics of soil total phosphorus and identify its main influencing factors in the study area, thereby providing a basis for the scientific management of soil total phosphorus. Here, we conducted a comprehensive analysis by combining classical statistical analysis, ge-ostatistics methods, Pearson correlation analysis, one-way analysis of variance (ANOVA), and structural equation modeling (SEM) to explore the spatial distribution patterns of soil total phosphorus and its influencing factors. The results showed that soil total phosphorus in the study area ranged from 161.00 to 991.00 mg/kg, with an average of 495.71 mg/kg. Spatially, soil total phosphorus exhibited a patchy distribu-tion pattern, with high values primarily concentrated in cultivated areas along rivers and low values mainly located in forested areas in the southeastern and central re-gions. Additionally, the nugget effect of soil total phosphorus was 71.5%, indicating a moderate level of spatial variability. The Pearson correlation analysis revealed that soil total phosphorus content was significantly correlated with multiple factors, including land use types, soil parent material, distance from settlements, slope, and soil pH. Based on these findings, we employed ANOVA to analyze the impacts of various fac-tors. The results indicated that soil total phosphorus content showed significant differences under the influence of different factors. Subsequently, we further explored in depth the action paths through which these factors affect soil total phosphorus us-ing SEM. The SEM results showed that the absolute values of the total effects of the influencing factors on soil total phosphorus, ranked from highest to lowest, were as follows: land use types (0.499) > soil parent material (0.240) > distance from settle-ments (0.178) > slope (0.161) > elevation (0.127) > soil pH (0.114) > normalized differ-ence vegetation index (0.103). These findings provide a scientific foundation for the effective management of soil total phosphorus in similar study areas.