Prediction of soil properties using fuzzy membership values

Detailed information on the spatial variation of soils is desirable for many agricultural and environmental applications. This research explores three approaches that use soil fuzzy membership values to predict detailed spatial variation of soil properties. The first two are weighted average models with which the soil property value at a location is the average of the typical soil property values of the soil types weighted by fuzzy membership values. We compared two options to determine the typical property values: one that uses the representative values from existing soil survey and the other that uses the property value of a field observation typical of a soil type. The third approach is a multiple linear regression in which the soil property value at a location is predicted using a regression between the soil property and fuzzy membership values. We compared this to multiple linear regression with environmental variables. In a case study in the Driftless Area of Wisconsin, the models were also compared with a predictive model based on existing soil survey. The results showed that regression with environmental variables works well for areas where the soil–terrain relationship is relatively simple but regression with fuzzy membership values is an improvement for areas where soil–terrain relationships are more complicated. From the perspectives of data requirement and model simplicity as well as accuracy of prediction the weighted average with maximum fuzzy membership option has obvious advantages.