Neural network approach for modelling ammonia emission after manure application on the field

This paper presents a neural network approach, which enables one to simulate ammonia emission after manure application on the field. Based on the data from 227 experiments out of previously published research, it can be illustrated that the time course of accumulated ammonia emission follows a non-linear Michaelis–Menten-like function. This function is determined by the two parameters Emax and KM, which are dependent on manure-specific driving forces, application parameters and climate. 102 data sets of the 227 experiments showed sufficient data for training and validating neural networks for estimating Emax and KM. The neural networks could be trained to R2 values of 0.926 and 0.832 for the training set and the validation set of Emax, and to R2 values of 0.988 for the training set and 0.527 for the validation set of the KM-value, respectively.