Modelling and mapping Soil Organic Carbon in annual cropland under different farm management systems in the Apulia region of Southern Italy

Abstract

Soil Organic Carbon (SOC) plays a crucial role in many soil functions and ecosystem services. Monitoring its spatial and temporal changes is essential for planning strategies to minimize soil degradation and loss and maintain its quality. Conservation Agriculture (CA) can make a significant contribution to increasing SOC. This article reports on the spatially modeled SOC concentration in the topsoil (0–0.3 m) of the Annual Cropland (ACL) under Conventional Management (CM) and CA in the Apulia region in Italy. To assess the spatial and temporal dynamics of SOC at the regional scale, the “Scorpan-SSPFe” (soil spatial prediction function with spatially autocorrelated errors) approach to predictive modeling and mapping of soil, based on the Geographically Weighted Regression (GWR) model was performed. The method was implemented using a Geographic Information System (GIS) and Google Earth Engine (GEE) environment to calculate the percentage distribution for each SOC level, altitude, and slope class and their combination. 80 environmental variables and 250 soil samples were analyzed to map the SOC in ACL. The SOC values showed an average of 16.68 and 17.73 g/kg for CM and CA respectively. Adequate map accuracy was obtained by GWR, which showed an R2 of 0.71 for CA and R2 of 0.52 for CM The Root Mean Squared Error (RMSE) predictions obtained were better in CA (3.96 g/kg) than CM (5.65 g/kg) with a percentage RMSE difference of 30 %. Predicted SOC obtained by GWR ranged from 4.06 to 35.60 g/kg for CA and from 5.00 to 29.99 g/kg for CM. The proposed method was shown to be promising in predicting SOC in a region of the Mediterranean area and can be used to assess the effect of land use changes, such as the application of CA, on SOC in the whole basin.