This study was conducted over 35,300 hectares of irrigated and dry land wheat fields in Canterbury, New Zealand in the 2007-2008 harvest year. The Artificial Neural Network models (ANNs), after examining more than 140 several direct and indirect parameters, can predict energy use and fuel consumption based on farm conditions, farmers' social considerations, farm operation, machinery condition and farm inputs, arable farms in Canterbury with an error margin of ±12% (± 2900 MJ/ha) and ±8% (± 5.6 l/ha), respectively. © 2012 Springer-Verlag.
CITATION STYLE
Safa, M., & Samarasinghe, S. (2012). Modelling energy use and fuel consumption in wheat production using indirect factors and artificial neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7664 LNCS, pp. 25–32). https://doi.org/10.1007/978-3-642-34481-7_4
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