Delineating soil fertility management zones using geostatistics and fuzzy clustering in semi-arid maize systems in India

This study quantified spatial variability in soil fertility attributes to delineate management zones (MZs) for site-specific nutrient management (SSNM) in a 4-ha maize field in northern Telangana, India. A total of 200 geo-referenced surface (0-15 cm) soil samples were analyzed for pH, electrical conductivity, organic carbon, and available nutrients (e.g., P, K, S, Fe, Mn, Zn, and Cu). Geostatistical analysis using ordinary kriging revealed that spherical models best were the best fit for describing the spatial structure of most parameters, with strong spatial dependence (nugget/sill < 0.25). Principal Component Analysis (PCA) reduced dimensionality, and fuzzy C-means clustering of the principal components delineated three distinct MZs, which were validated by ANOVA. Integration of MZs with targeted yield-based fertilizer recommendation equations enabled differential NPK application, resulting nutrient use efficiency gain equivalent to savings of up to 36 kg N, 39 kg P₂O₅ and 31 kg K₂O ha⁻¹ in MZ -3. The maize yield increased from 7.27 t ha^-1 under conventional farmer practices to 7.79 t ha^-1 in MZ -1, 7.93 t ha^-1 in MZ-2 and 8.02 t ha^-1 in MZ -3 with corresponding benefit-cost ratio of 2.54, 2.60 and 2.65. MZ-3 consistently outperformed other zones in yield and economic return, demonstrating the agronomic and economic efficiency of site-specific nutrient management. This work demonstrates the potential of combining geostatistics and fuzzy clustering for optimal nutrient use efficiency and profitability in smallholder maize-based agroecosystems.