Delineation of nutrient management zones for precise fertilizer management in wheat crop using geo-statistical techniques

Understanding the variability in crop yield in relation to the spatial variations in soil properties can help to more efficiently apply agricultural inputs on a site-specific basis. This study was designed to characterize and quantify the spatial variation in soil properties and wheat yield and to delineate prescription maps for site-specific fertilization. A wheat field was selected at the research farm of the University of Agriculture, Faisalabad, Pakistan, and a grid pattern (30m * 30m) was established at the experimental sites to collect soil and crop yield samples at two sampling depths (0-15 & 15-30 cm). The soil samples were analysed for soil nitrogen (N), phosphorus (P), potassium (K), pH, electrical conductivity (EC), soil organic matter (SOM) and saturation (S) at both sampling depths. The locations of the sampling points and field boundary were marked with a GPS receiver. Results of classical, geo-statistical and interpolated maps revealed significant spatial variability in soil properties and crop yield, demanding for site-specific nutrient management. The cluster analysis was performed to group the soil and yield data into five productivity zones termed as 'very low', 'low', 'medium', 'high' and `very high' without prior knowledge of productivity potential with the internal homogeneity and external heterogeneity at a similarity level of greater than 70%. Results of correlation matrix suggested significant relationships among the crop yield and the soil properties. Results of means comparison in different productivity zones indicated that the crop yield and soil properties were significantly different in developed management zones except low' and 'very low' zones. Significant variation of soil nutrients in very low-to-very high productivity zones at both sampling depths suggested that the soil nutrients in combination with soil pH and organic matter can be used to develop prescription maps for site-specific fertilization. Furthermore, the results of this study emphasize the need to predict soil and crop variables using sensors, in future studies, to delineate prescription maps, as soil sampling and analysis is expansive and time consuming. Site-specific fertilization based on soil and crop needs can improve economic and environmental efficiency.