Partial least squares regression for predicting economic loss of vegetables caused by acid rain

To predict the economic loss of crops caused by acid rain, we used partial least squares (PLS) regression to build a model of single dependent variable-the economic loss calculated with the decrease in yield related to the pH value and levels of Ca2+, NH4+, Na+, K+, Mg2+, SO42-, NO3-, and Cl- in acid rain. We selected vegetables which were sensitive to acid rain as the sample crops, and collected 12 groups of data, of which 8 groups were used for modeling and 4 groups for testing. Using the cross validation method to evaluate the performace of this prediction model indicates that the optimum number of principal components was 3, determined by the minimum of prediction residual error sum of squares, and the prediction error of the regression equation ranges from-2.25% to 4.32%. The model predicted that the economic loss of vegetables from acid rain is negatively corrrelated to pH and the concentrations of NH4+, SO42-, NO3-, and Cl- in the rain, and positively correlated to the concentrations of Ca2+, Na+, K+ and Mg2+. The precision of the model may be improved if the non-linearity of original data is addressed.