REGRESSION-MODELS FOR ESTIMATING SOIL PROPERTIES BY LANDSCAPE POSITION

Slope geometry and the associated variation in soil properties influen ce runoff, drainage, soil temperature, the extent of soil erosion and deposition, and crop yields. With the current emphasis on prescription farming, approaches are needed to more effectively match inputs to pr oduction system needs while accounting for variation in soil and water resources within a field. The objective of the study was to develop s implified regression models to predict soil properties on different la ndscape positions from observed values on the nearly level upper inter fluve. Soil samples were taken from the upper and lower interfluve, sh oulder, upper and lower linear, and footslope at each of four sites in eastern Nebraska. Predictive equations were developed for 20 soil pro perties using multiple linear regression. Independent variables includ ed were observed values of the property being modeled from the upper i nterfluve, sampling depth, and an irrigation code. Of the 100 models d eveloped, only eight included significant contributions from all three independent variables. Models for pH, organic matter, electrical cond uctivity, exchangeable K, base saturation percentage, and available P and K consistently had R(2) values greater than 0.50. The upper interf luve contributed significantly to the prediction of each of these prop erties except electrical conductivity. A comparison between average ob served and predicted values for each soil property at each sampling de pth revealed that the observed values generally fell within a 95% conf idence interval about the predicted values. The confidence interval ha lf-width was generally <10% of the mean for the observed values, Furth er evaluation with independent data sets could be used to help strengt hen and refine such generalized or geographically based models.