Artificial Neural Network Methodology for Modelling and Forecasting Maize Crop Yield

Rama Krishna Singh and Prajneshu

Agricultural Economics Research Review, 2008, vol. 21, issue 01

Abstract: A particular type of “Artificial neural network (ANN)”, viz. Multilayered feedforward artificial neural network (MLFANN) has been described. To train such a network, two types of learning algorithms, namely Gradient descent algorithm (GDA) and Conjugate gradient descent algorithm (CGDA), have been discussed. The methodology has been illustrated by considering maize crop yield data as response variable and total human labour, farm power, fertilizer consumption, and pesticide consumption as predictors. The data have been taken from a recently concluded National Agricultural Technology Project of Division of Agricultural Economics, I.A.R.I., New Delhi. To train the neural network, relevant computer programs have been written in MATLAB software package using Neural network toolbox. It has been found that a three-layered MLFANN with (11,16) units in the two hidden layers performs best in terms of having minimum mean square errors (MSE) for training, validation, and test sets. Superiority of this MLFANN over multiple linear regression (MLR) analysis has also been demonstrated for the maize data considered in the study. It is hoped that, in future, research workers would start applying not only MLFANN but also some of the other more advanced ANN models, like ‘Radial basis function neural network’, and ‘Generalized regression neural network’ in their studies.

Keywords: Crop; Production/Industries (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:ags:aerrae:47354

DOI: 10.22004/ag.econ.47354

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