Spatial pattern analysis of agricultural soil properties using GIS

Agricultural soil properties exhibit variation over field plot scales that can ultimately effect the yield. This study performs multiple spatial pattern analyses in order to design spatially dependent regression models to better understand the interaction between these soil properties. The Cation Exchange Capacity (CEC) and Calcium-Magnesium Ratio (CaMgR) are analyzed with respect to Calcium, Magnesium, and soil moisture values. The CEC and CaMgR are then used to determine impact on the yield values present for the field. Results of this study show a significant measure of model parsimony (0.979) for the Geographically Weighted Regression (GWR) model of the CEC with free Ca, Mg, and soil moisture as explanatory variables. The model for CaMgR using the same explanatory variables has a much lower measure of model fit. The yield model using the CEC and CaMgR as explanatory variables is also low, which is representative of the underlying processes also impacting yield.