SPATIAL PREDICTION OF SOIL ORGANIC CARBON IN PANAMARAM BLOCK, KERALA - A COMPARITIVE STUDY OF VARIOUS TECHNIQUES

Soil Organic Carbon (SOC) is the carbon that is contained within soil organic matter and it plays a main role to soil health, fertility and ecosystem services, which includes food production – making its preservation and restoration essential for sustainable agriculture development. SOC plays an important role in the global carbon cycle and, consequently, in climate change mitigation and adaptation. Sustainable management of SOC in the soil demands the scientific knowledge of its spatial distribution. In this study, we used five spatial interpolation algorithms to spatially predict the SOC in the Panamaram Block of Wayanad District in Kerala. Five spatial interpolation algorithms, including two variants of Kriging, were applied in the soil nutrient data. The performance of the prediction algorithms was compared using prediction accuracy parameters such as R2, RMSE and MAE. Experimental results revealed that predictions generated by Inverse Distance Weighting (IDW) exhibit the highest accuracies with an R2 value of 99.25%, followed by the Radial Basis Polynomial Interpolation Method (67.03%), Ordinary Kriging (26.79%), Local Polynomial Interpolation (18.96%), and Simple Kriging Method (13.07%), respectively. This research contributes valuable insights into understanding the spatial distribution of Soil Organic Carbon which is pivotal for informed land management and environmental conservation strategies.