DIGITAL SOIL MAPPING APPROACH TO ESTIMATE SOIL PLASTICITY USING GEOREFERENCED TECHNICAL DATA OF TRACTORS

Abstract

Soil and terrain properties are the most important environmental elements for characterization of within-field variability supporting the management zone definition. This study aims to develop a digital soil mapping approach to map texture related soil property, namely soil plasticity (Ka) using georeferenced machinery data derived from log file created along soil-tillage activities. The results indicated that speed and the distance values have the highest correlation with Ka, having values around 0,4-0,5. The development of the regression model resulted in an adjusted R² value of 0.71, but the p-value was still around 0.2. These results indicate that the approach has a good potential, but statistics needs to be improved with data preprocessing.