An artificial neural network (ANN) model was applied for predicting primary
productivity (PP) from a 12 year time series (1985-1996) of monthly observ
ations on a set of environmental and climatic variables from the Gullmar Fo
rd (south-western Sweden). Results indicate a good fit between observed and
predicted PP values. ANN can be regarded as a novel tool for primary produ
ction modelling and more generally when the numbers of environmental and cl
imatic co-variates are large. ANN models fitted the data with a lower root
mean square error of prediction (RMSEP) than more conventional and classic
methods, such as multiple regression. Predictions of future changes in prim
ary, production from the same set of input variables using network set-ups
with PP leading the input variables by 1, 2 and 3 month lags indicated that
RMSEP was about the same as for the case with no lag These results show th
e possibility, of generating patterns of future fluctuations in primary pro
ductivity using ANN.