Spatial variability of soil variables using geostatistical approaches in the hot arid region of India

• Published in: Environmental earth sciences 2024-07, Vol.83 (14), p.432, Article 432
• Main Authors: Nogiya, Mahaveer, Moharana, Pravash Chandra, Meena, RoshanLal, Yadav, Brijesh, Jangir, Abhishek, Malav, Lal Chand, Sharma, Ram Prasad, Kumar, Sunil, Meena, Ram Swaroop, Sharma, Gulshan Kumar, Jena, Roomesh Kumar, Mina, Bansi Lal, Patil, Nitin Gorakh
• Format: Article
• Language: English
• Subjects: Agricultural production; Arid regions; Arid zones; Biogeosciences; Calcium; Calcium carbonate; Calcium carbonates; Carbon; Carbonates; Copper; Earth and Environmental Science; Earth science; Earth Sciences; Electrical conductivity; Electrical resistivity; Environmental Science and Engineering; Geochemistry; Geology; Geostatistics; Hydrology/Water Resources; Iron; Land use planning; Manganese; Mathematical models; Nitrogen; Nutrients; Organic carbon; Organic phosphorus; Organic soils; Original Article; Phosphorus; Potassium; Soil; Soil chemistry; Soil investigations; Soil pH; Soil sciences; Soil surfaces; Soils; Spatial variability; Spatial variations; Terrestrial Pollution; Variability; Variables; Water shortages; Zinc
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-024-11737-5

Geostatistics tools like ordinary kriging was utilized to investigate the spatial variation in soil reaction (pH), electrical conductivity (EC), soil organic carbon (OC), calcium carbonate (CaCO 3 ), available nitrogen (N), available phosphorus (P), available potassium (K), DTPA-extractable copper (Cu), zinc (Zn), manganese (Mn), and iron (Fe) in hot arid region of Western India. For this, total 132 surface soil samples (0–20 cm depth) were obtained with GPS coordinates from the study area. The study’s finding showed that EC showed the highest variability (390%) whereas soil pH showed the least variability (3.72%). The best variogram fit model in ordinary kriging was selected on the basis of largest R 2 values. For pH, EC CaCO 3 , N, K, Zn, and Fe, an exponential model imparted the best variogram fit, while a Gaussian model imparted the best variogram fit for OC, P, and Mn. Semi-variogram analysis (nugget/sill ratio) revealed that EC (0.25), CaCO 3 (0.072) and N (0.027) were weakly spatial dependent, whereas pH (0.37), OC (0.456), P (0.598), K (0.70), Mn (0.57) and Fe (0.59) were moderately spatial dependent. However, Cu (1.0) and Zn (0.78) were strongly spatial dependent. The largest goodness-of-prediction criterion (G) values were found for the exponential model for soil pH, EC, CaCO 3 , N, K, Zn, and Fe while the largest G values were found for the Gaussian model for OC, P, and Mn. The exponential and Gaussian models of ordinary kriging, were able to map the spatial variations in investigated soil-variables.