Spatial Variability in Soil Properties, Delineation Site-specific Management Division Based on Soil Fertility Using Fuzzy Clustering in Gwalior, Madhya Pradesh, India
T. C. Yadav *
Faculty of Agriculture, VGU, Jaipur 302012, Rajasthan, India and Department of Soil Science and Agricultural Chemistry, RVSKVV, Gwalior, 474002, Madhya Pradesh, India.
Y. P. Singh
Department of Soil Science and Agricultural Chemistry, RVSKVV, Gwalior, 474002, Madhya Pradesh, India.
Shashi S. Yadav
Department of Soil Science and Agricultural Chemistry, RVSKVV, Gwalior, 474002, Madhya Pradesh, India.
Akhilesh Singh
Department of Soil Science and Agricultural Chemistry, RVSKVV, Gwalior, 474002, Madhya Pradesh, India.
Subhash
ICAR-Indian Institute of Soil Science, Bhopal Madhya Pradesh, 462038, India.
Tirunima Patle
Department of Soil Science and Agricultural Chemistry, RVSKVV, Gwalior, 474002, Madhya Pradesh, India.
*Author to whom correspondence should be addressed.
Abstract
Farmers who want to improve nitrogen usage efficiency (NUE) and crop yield must have access to information regarding the geographical variability and distribution of soil parameters. Fertilizer application relying on soil characteristic maps and fertilizers recommendations may help reduce fertilizer input without sacrificing crop production. The current research focused heavily on evaluating the variability of soil fertility status in the Madhya Pradesh Gwalior region using geostatistical techniques. To do this, 150 GPS-based surfaces (0–15 cm) soil samples were obtained from the Gwalior region's five districts (Gwalior, Shivpuri, Datia, Guna, & Ashok Nagar) during crop harvest during the rabi season of 2019–20. Statistics and geo-statistics were used to analyze the results of the laboratory analysis. The analysis revealed that the soil samples' pH, EC, SOC, and CaCO3 values, respectively, varied from 4.40 to 8.30, 0.09 to 1.03 dSm-1, 2.0 to 10.60 gkg-1, and 3.0 to 24.0 gkg-1. In contrast, the amounts of N, P, K, and S that are present in soil vary from 102.0 to 356.0 kg ha-1, 6.0 to 61.0 kg ha-1, 114.0 to 896.0 kg ha-1, and 5.90 to 49.20 mg kg-1, respectively.
Through building semi-variograms and mapping the data utilizing standard kriging techniques, the information was studied using both traditional statistics and geostatistics. For soil properties, semi-variograms were created, and their geographical distributions were delineated. The Nugget/Sill (Co/Co+ C) ratio for the modeled variables revealed moderate to high spatial dependences. The best-fit models for the reported soil characteristics were exponential, spherical, and circular. The findings of this research demonstrated that soil fertility quality varied significantly across the Gwalior area. This knowledge may aid in making choices about crop succession and the usage of plant nutrients to increase farmers' financial returns.
Keywords: Geostatistical approach, NUE, soil quality, productivity