Avoiding soil degradation and improving crop productivity could be achieved by performing sustainable soil nutrient management with an appropriate understanding of soil properties’ spatial variability. The present fertilizer recommendations for the region where the study area is located are typically symmetric for large regions. This leads to the under-application of fertilizers in zones with low nutrient contents and over-application in zones with high nutrient contents. Therefore, this study was conducted to assess soil management zones (MZs) in the study area for effective soil nutrient management and to evaluate soil properties’ spatial variability. A total of 100 geo-referenced soil samples were collected at a depth of 0–20 cm, processed and analyzed for pH, available nitrogen (AN), available phosphorus (AP), available potassium (AK), soil organic carbon (SOC), total nitrogen (TN) and total phosphorous (TP), while C:N, C:P and N:P ratios were calculated. Soil properties’ coefficients of variation (CVs) widely varied from low (1.132%) to moderate (45.748%). Ordinary kriging and semi-variogram analysis showed differed spatial variability patterns for the studied soil properties with spatial dependence ranged from weak to strong. MZs were delineated by performing principal component analysis (PCA) and fuzzy K-means clustering. Four PCs with eigen values more than 1 dominated 84.44% of the total variance, so they were retained for clustering analysis. Three MZs were delineated based on the two criteria modified partition entropy (MPE) and fuzzy performance index (FPI). The studied soil properties differed significantly among MZs. Thus, the methodology used for MZ delineation could be used effectively for soil site-specific nutrient management for avoiding soil degradation concurrently with maximizing crop production in the study area.

Both contemporary and historical factors are documented to be crucial in regulating species diversity and distribution. Soil fauna contribute substantially to global biodiversity and ecosystem functioning, while it is unclear whether and to what extent historical factors shape their diversity patterns. Here, we used soil nematodes as a model organism to test historical effects on soil fauna and to investigate the relative importance of climatic, soil, and historical factors. Based on nematode distribution data in 16 natural sites at a large scale (ranging from 22 to 40°N) in mainland China, we conducted elastic net regression model to test the effects of climatic (e.g., mean and seasonality of temperature/precipitation), soil (e.g., soil carbon, nitrogen, and pH), and historical (e.g., temperature/precipitation anomaly and the velocity of the change since the Last Glacial Maximum) variables on nematode genus richness and Shannon's diversity. Additionally, variation partitioning was used to determine the contribution of the three predictor sets to the explanation of both Jaccard and Bray–Curtis community dissimilarity. We found that climate generally explained more variations in both diversity and composition than soil and historical predictors in our samples. We also showed that although historical factors (e.g., temperature change velocity) were correlated with nematode diversity and composition, the pure effects of these historical factors were negligible. In other words, the historical effects were commonly represented by their interactions with current climatic and soil factors within our selected sites. Our results indicated that contemporary factors, especially climate, may outperform historical factors in regulating soil nematode diversity patterns at large scales.