%0 Journal Article
%T Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models
%A William W. Guo
%A Heru Xue
%J Mathematical Problems in Engineering
%D 2014
%I Hindawi Publishing Corporation
%R 10.1155/2014/857865
%X 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. 1. Introduction Crop yield forecasting plays an important role in farming planning and management, domestic food supply, international food trade, ecosystem sustainability, and so on [1¨C3]. For instance, China has the largest population in the world but with limited agricultural land so accurate crop forecasting helps the government provide sufficient food supply to the people. Australia has a small population with vast agricultural land so its concern on crop production is how to optimize revenue from international crop export to countries like China. There are many factors that have an influence on crop yield, such as plantation area, efficiency of irrigation systems, variations in rainfall and temperature, quality of crop seeds, topographic attributes, soil quality and fertilisation, and disease occurrences [4¨C8]. Crop growing follows seasonal cycles but many of the factors above are largely irrelevant to the temporal factor. For example, plantation area, rainfall, fertilising, and disease occurrence vary yearly; efficiency of irrigation systems, quality of crop seeds, and soil quality may be improved or degraded from year to year; and topographic attributes may largely remain the same for a long period of time. Effort has been made in using either statistics to identify relationships or neural networks to establish mappings between crop yield and some of these factors [4¨C10]. Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature in Queensland, Australia, has shown that
%U http://www.hindawi.com/journals/mpe/2014/857865/