Comparison Quality of Interpolation Methods to Estimate Spatial Distribution of Soil Moisture Content

Abstract

ABSTRACT Determining and predicting both the spatial and temporal variability of soil moisture content is a critical factor in crop production in semi-arid agricultural systems. The objective of this research was to predict the spatial distribution of soil moisture content using ordinary kriging (OK), regression kriging (RK) and cokriging (CK) models. For that purpose, the soil moisture was monitored in the wet period (March, April and May) and dry period (August, September and October) in Kadınhanı district of Konya Province, Turkey. In each monthly sampling period of the study, a total of 278 samples were collected from locations within an area of 29,608.6 ha. The coefficients of variation (CV) of the soil moisture in dry and wet soil conditions were 0.39 and 0.20, respectively. Soil texture, bulk density (BD) and land use were the most important factors influencing soil moisture. In addition, dry soil generally had a smaller nugget effect than wet soil. Overall, CK was able to better estimate the soil moisture content than OK and RK, as evidenced by the smaller mean absolute error (MAE) and mean square error (MSE). RK estimation is more vulnerable to error than CK and OK estimations because the low coefficients of determination in the regression equation lead to increased error and lower confidence in the estimates. The insights generated by the modeling utilized in this study can assist land use planners, land managers and farmers to more efficiently predict, manage and utilize soil moisture content and hence produce larger crops more sustainably.