An artificial neural networks (ANN) model was developed for the prediction of annual nitrate-N (NO3-N)
losses into the drain flow at Eugene F. Whelan Experimental Farm (Agriculture Canada, Woodslee, Ontario, Canada).
Data consisted of daily measurements of nitrate-N taken from eight different soil conservation treatments during 1992-
1994. The experiment consisted of four crop/tillage and two water table management systems. Due to the moderate size of
the data set, a tenfold cross validation method was used for model validation. A sensitivity analysis was also performed to
assess the effect of the input variables on the performance of the networks. The results of this study indicated that the
performance of network predictions of nitrate-N was highly satisfactory for 6 of the treatments and acceptable for the
remaining two. The sensitivity analysis demonstrated that network predictions of nitrate-N were not affected when either
drain flow or evapotranspiration data were excluded from the network training files. Overall, this study reveals that, from
adequate input information, Artificial Neural Networks could effectively predict loss of nitrate-N in drain outflows. While
the ANN model itself is not transportable to any other site, it does provide another method of estimating nitrate-N losses
from agricultural fields with fewer input parameters. In addition, they could also be used to identify the unnecessary
parameters for ANN modeling and thus save valuable time and resources in data collection.