PREDICTION OF CROP YIELDS ACROSS FOUR CLIMATE ZONES IN GERMANY: AN ARTIFICIAL NEURAL NETWORK APPROACH

Thomas Heinzow and Richard Tol

No FNU-34, Working Papers from Research unit Sustainability and Global Change, Hamburg University

Abstract: This paper shows the ability of artificial neural network technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data. The method may later be used to construct regional time series of agricultural output under climate change, based on the highly resolved output of the global circulation models and regional models. Three 30-year combined historical data sets of rural district yields (oats, spring barley and silage maize), daily temperatures (mean, maximum, dewpoint) and precipitation were constructed. They were used with artificial neural network technology to investigate, simulate and predict historical time series of crop yields in four climate zones of Germany. Final neural networks, trained with data sets of three climate zones and tested against an independent northern zone, have high predictive power (0.83

Keywords: global change; agriculture; artificial neural networks; yield prediction (search for similar items in EconPapers)
JEL-codes: Q54 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2003-09, Revised 2003-09
References: View complete reference list from CitEc
Citations: View citations in EconPapers (11)

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