USING ARTIFICIAL NEURAL NETWORKS AND REGRESSION TO PREDICT PERCENTAGEOF APPLIED NITROGEN LEACHED UNDER TURFGRASS

The objective of this study was to develop an Artificial Neural Networ k (ANN) model that accurately predicts the percentage of applied nitro gen (N) that leaches through the upper 50 cm of soil under a variety o f conditions. The statistical regression models were used for comparis on with the ANN model. The Sum of the Squared Error (SSE) between the anticipated values (from research data) and the predicted values (prod uced by the model) was calculated to be 0.3 for the ANN model and 0.1 for the third order regression. In this particular project, the first and second order regression equations are not useful; however, the thi rd order equation could be used by turf managers along side the ANN mo del to accurately predict leachate under given field conditions. These models enable the turfgrass manager to determine the effects of manag ement practices on N leaching.