[Spatial Variability of Soil Nutrients and Precision-management Zoning: A Case Study in the Chaohu Lake Region]

Abstract

This study investigated the spatial variability of soil nutrients and delineated precision management zones (MZs) in the Chaohu Lake Region, China, to optimize agricultural resource allocation and support sustainable production. A high-density sampling network comprising 7 624 soil samples was established across four counties (Chaohu City, Feidong, Feixi, and Lujiang), covering 8 266.8 km² of farmland. Nine soil nutrient indicators-pH, organic matter (OM), total nitrogen (TN), alkali-hydrolyzable nitrogen (AN), available phosphorus (AP), available potassium (AK), slowly available potassium (SK), available sulfur (AS), and available boron (AB)-were analyzed. Geostatistical methods, including semivariogram modeling and ordinary kriging interpolation, were applied to characterize spatial distribution patterns. Principal component analysis (PCA) reduced data dimensionality, and fuzzy C-means clustering (FCM) integrated with spatial coordinates was employed for PMZ delineation. The optimal number of clusters was determined using the fuzzy performance index (FPI) and normalized classification entropy (NCE), while analysis of variance (ANOVA) and coefficient of variation (CV) validated zoning effectiveness. Key findings include: ① The mean values of soil nutrients were 6.02 (pH), 21.06 g·kg^-1 (organic matter), 1.21 g·kg^-1 (total nitrogen), 122.75 mg·kg^-1 (alkali-hydrolyzable nitrogen), 16.81 mg·kg^-1 (available phosphorus), 127.71 mg·kg^-1 (available potassium), 272.69 mg·kg^-1 (slowly available potassium), 24.67 mg·kg^-1 (available sulfur), and 0.40 mg·kg^-1 (available boron). Soil nutrients exhibited moderate variability (CV: 11.70%-64.36%), with AP showing the highest variability (64.36%) and pH the lowest (11.70%). ② Spatial heterogeneity of pH (nugget-to-sill ratio: 21.34%) and AP (11.42%) was predominantly governed by structural factors (e.g., soil parent material and topography), whereas AK (57.44%), SK (71.82%), and AS (63.48%) were influenced by both structural and stochastic factors (e.g., fertilization practices). OM, TN, AN, and AB exhibited high random variability (nugget-to-sill ratio > 75%). ③ PCA extracted three principal components (cumulative variance: 70.15%), distinguishing nitrogen-related metrics (PC1), potassium dynamics (PC2), and phosphorus-boron characteristics (PC3). Biplots revealed distinct clustering patterns among nutrients. ④ FCM identified two optimal PMZs with significant inter-zone differences in nitrogen, phosphorus, and potassium levels (P < 0.001). Intra-zone CVs for key nutrients (e.g., OM, TN, and AP) decreased by 5%-15%, confirming reduced heterogeneity within zones. The results establish a scalable framework for precision soil management, directly guiding differentiated fertilization strategies in the Chaohu Lake Region. The integration of high-density sampling, multidimensional modeling, and spatial clustering enhances the practical applicability of PMZs for reducing fertilizer overuse and mitigating non-point source pollution. Future studies should incorporate crop-specific requirements, machine learning algorithms, and real-time monitoring to advance data-driven agricultural practices and dynamic soil quality governance. This work provides a technical paradigm for balancing agricultural productivity with ecological sustainability in rapidly developing regions.

Keywords: fuzzy C-means clustering (FCM); geographic information system(GIS); management zones; principal component analysis (PCA); soil nutrients; spatial variation.