Predicting Loblolly Pine Site Index from Soil Properties Using Partial Least-Squares Regression

Data from 15 sites with loblolly pine (Pinus taeda L.) plantations located in the southeastern United States were used to predict site index ( SI) from soil physical and chemical properties from the top 15 cm of the mineral soil. Two modeling approaches were used to predict SI from soil properties. First, the ordinary least-squares method of multiple linear regression was used, which selected calcium, potassium, and sand percentage as the significant predictor variables. Second, partial least-squares regression was used, which selected total nitrogen, carbon, calcium, magnesium, and sand percentage as the significant predictor variables. The partial least-squares regression approach addressed multicollinearity in the data and produced a better model to predict SI. The partial least-squares regression model explained 77% of the variation in SI.