Cultivated land quality is an essential agricultural resource and production element. To increase cultivated land quality in a targeted way, it is crucial to understand the features of regional elements that constrain its quality. In order to diagnose the restriction factors affecting the improvement of cultivated land quality grades and conduct a quantitative analysis of the regional cultivated land quality improvement restriction factors, the restriction index model was used in this study to analyze the distribution characteristics of cultivated land quality grades in East Anhui, China. The results show that there are six main restriction factors of cultivated land quality in the study area, which are effective soil layer thickness, soil organic matter, soil acidity and alkalinity, plough layer texture, irrigation capacity, drainage capacity, and obstacle factors, among which the main restriction factors of the first to third grades are soil organic matter, soil acidity and alkalinity, and the main restriction factors of the fourth to seventh grades are soil organic matter, and soil acidity and alkalinity. The main restriction factors of the soil organic matter, effective soil layer thickness, and obstacle factors are the key restriction factors in the ninth to tenth classes.

Anhui Province is located in the eastern China, in the middle and lower reaches of the Yangtze River and Huaihe River, and contains three major basins, i.e., the Yangtze River, Huaihe River, and Xin’an River basins. Based on the statistical data of livestock and poultry quantity and farmland area in Anhui Province in 2019, ArcGIS was used to analyze the spatial distribution characteristics of the livestock and poultry excreta (LPE) pollutants in Anhui Province in order to explore the potential pollution risk posed by livestock and poultry farming to the farmland and the water environment in Anhui Province. The equivalent pollution load method was adopted to compare and assess the release of LPE pollutants in various cities and to analyze the causes. Through the calculation of the farmland carrying capacity load and early warning value of LPE and the water load of livestock and poultry pollutant release based on the equivalent pollution index method, a comprehensive assessment of the potential pollution risk posed by livestock and poultry farming on farmland and the water environment in Anhui Province was carried out. In this study, the spatial distribution of the livestock and poultry pollution in Anhui Province was analyzed, the effect of the pollution load of the livestock and poultry on the cultivated land and water environment was evaluated, and suggestions for environmental protection measures are provided. The results of this study revealed that the total pig equivalent of the livestock and poultry farming in Anhui Province was 55,068,400 and the LPE output was 47,778,600 t in 2019. The LPE pollutant output was 1,707,700 t, and the total release was 510,400 t. The release of pollutant chemical oxygen demand (COD) accounted for 71.67% of the total release. The average farmland load of the pig manure equivalent was 8.09 t/hm² in the province. The average pollutant diffusion concentration of in the water was 31.63 mg/L. The average equivalent pollution index of LPE was 5.23, indicating a mild pollution impact on the water environment. Overall, the spatial distribution of the LPE pollutant output and pig manure equivalent farmland load in Anhui Province increased from south to north. Fuyang and Suzhou cities had a high risk of water environment pollution and should be the key regions for livestock and poultry pollution prevention and control measures. The optimization of the layout of livestock and poultry farming areas, smoothing the cycles of crop and livestock farming, and the vigorous promotion of the resource utilization of the LPE are proposed.

Marine redox conditions and their dynamic changes were a major factor that controlled the formation of black shale and caused the late Ordovician marine extinction in the Upper Yangtze Basin (South China). However, the spatiotemporal variation and potential controlling factors of marine redox conditions in this area remain unclear. We analyzed whole-rock geochemistry and pyrite sulfur isotopes (δ³⁴S_py) of 47 shale samples from the Late Katian to Rhuddanian in a shelf-to-slope (Qianjiang Shaba section and Wc-1 well) region of northeastern Upper Yangtze Basin, and reconstructed water column redox conditions during the Late Ordovician–Early Silurian Transition. The geochemical characteristics of shale, including the ratio of elements, discriminant function and ternary diagram location in the study area suggest a passive continental margin sedimentary environment, wherein the terrigenous detritus is mainly derived from felsic igneous rocks in the upper crust, showing characteristics of near-source deposition. The redox indices (Fe speciation, C_org/P, U_EF, and Mo_EF) showed that the development of anoxic water, especially euxinia, has obvious spatiotemporal heterogeneity. Under conditions of high availability of active organic carbon and limited sulfate supply, high active Fe input and strong biological irrigation in the shallow water area may effectively remove H₂S produced by microbial sulfate reduction, conducive to the prevalence of ferruginous water columns. However, for this deep water area, the rapid accumulation rate of organic matter, decrease in dissolved Fe (caused by upwelling in the open sea), and seawater stratification (caused by the rising of sea level) promoted the development of a euxinic water column. This inference is supported by the covariant relationship between organic carbon accumulation rate, chemical index of alteration, Co × Mn, and δ³⁴S_py. Our study highlights the potential control effects of sea level change, continental weathering and upwelling on the development of euxinic water columns.

Cultivated land quality is an essential measure of cultivated land production capability. Establishing a cultivated land quality inversion model based on high-resolution remote sensing data provides a scientific basis for regional cultivated land resource management and sustainable utilization. Utilizing field survey data, cultivated land quality evaluation data, and high-resolution remote sensing data, a spectral index-cultivated land quality model was constructed and optimized with the machine learning method, and cultivated land quality inversion and verification in Chuzhou City in 2021 were carried out. The results showed that the distribution of cultivated land quality in the study area depicted with the remote sensing inversion model based on random forest was consistent with the actual cultivated land quality. Although the accuracy of the SVT-CLQ inversion model established using four spectral indices is slightly lower than that of the MSVT-CLQ group established using 15 indices, it can still accurately reflect the distribution of cultivated land quality in the study area. Compared with the two models of the MSVT-CLQ and SVT-CLQ groups, the field survey data of sampling points is reduced, the time and energy of field sampling and analysis are correspondingly saved, the efficiency of cultivated land quality evaluation is improved, and the dynamic monitoring and rapid evaluation of cultivated land quality are realized.

Traditional soil organic matter mapping is mostly polygonal drawing, which is even more difficult to accurately depict in complex terrain areas. The spatial distribution of soil organic matter is closely related to agricultural production, natural resources, environmental governance, and socio-economic development. Efficiently, economically, and accurately obtaining information on changes in soil organic matter in areas with diverse topography is an urgent problem to be solved. Mingguang City has a high research value because of its unique topography and natural landscape. To solve the problem of soil organic matter mapping in this area, this study will construct an excellent organic matter prediction model. Using 173 soil survey samples (123 for training and 50 for testing), the optimal feature variable subsets selected from 31 environmental variables through Pearson correlation, stepwise regression-variance inflation factor, and recursive feature elimination models based on different algorithms were employed. Each selected feature subset was then used to construct organic matter prediction models using multiple advanced machine learning algorithms. By comparing accuracy validation and model performance, the organic matter prediction model suitable for Mingguang City (RFE-RF_SVM) was obtained, that is, the prediction model of organic matter based on support vector machines with the feature variables screened by the feature recursive elimination algorithm of random forest with RMSE = 3.504, VSI = 0.036, and R-squared = 0.730. Furthermore, the analysis focused on assessing the significance of the predictive factors. The mapping results of this study show that the soil organic matter content in the central and northwestern parts of the study area is low, and the reasons for this situation are different. The central part is mainly caused by the change of land use and topography, while the northwestern part is caused by the loose soil structure caused by the parent material. The government can take targeted measures to improve the soil in the areas with poor organic matter.