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.

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.

Accurate soil management has long been the focus of research in agroecology. Crop productivity can be enhanced while reducing environmental threats from excessive fertilization by fully comprehending the spatial variability of soil properties and delineating management zones (MZs). A field investigation was carried out at experimental sites outside Hefei City’s administrative districts in China to study the spatial variability of soil properties and the delineation of MZs. A total of 9601 soil samples were collected in the study area. A variety of soil properties were analyzed, including the pH, organic matter, total nitrogen, alkali-hydrolyzable nitrogen, available phosphorus, available potassium, slowly released potassium, available sulfur, available boron, available copper, available zinc, available iron, and available manganese. The coefficient of variation for various soil properties exhibited a wide range, spanning from 12.2% to 100.5%. The geostatistical results show that most soil properties have moderate to strong spatial autocorrelation, and the ordinary kriging method is used to map the distribution of soil properties. The principal component analysis method was used to reduce the dimension of 13 soil properties to 4 principal components, and the fuzzy c-means clustering method was used to delineate MZs. The calculation results of the fuzzy performance index and normalized classification entropy show that the optimum number of MZs is five. In the study area, the western part exhibits the highest soil fertility, primarily attributed to its elevated organic matter content. Additionally, organic matter emerges as a key factor influencing sustainable agricultural production in this region. These results form the basis for soil managing areas outside the administrative districts of Hefei City.