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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 857865, 7 pages
http://dx.doi.org/10.1155/2014/857865
Research Article

Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models

1School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4702, Australia
2College of Computer & Information Engineering, Inner Mongolia Agricultural University, Hohhot, Inner Mongolia 010018, China

Received 31 October 2013; Accepted 27 November 2013; Published 23 January 2014

Academic Editor: Chih-Cheng Hung

Copyright © 2014 William W. Guo and Heru Xue. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.