Mechanization Index and Machinery Energy Ratio Assessment by means of an Artificial Neural Network: a Mexican Case Study

A single hidden layer artificial neural network (ANN) model was developed to estimate simultaneously two mechanization indicators, Mechanization Index (MI) and Machinery Energy Ratio (MER), used to characterize a group of farms in a target farming region. Values of the two mechanization indicators could be obtained without direct calculation of their equations by using the ANN model. To develop the model, data representative of a developing farming system in Mexico were obtained from farmers, local makers of agricultural machinery, researchers and government officials, as well as from relevant databases. A wide range of variables of farming activities were examined, and from these, 11 were used as input variables for the model. The values of the model’s outputs correlated well (Pearson’s= 0.963 and 0.947 for MI and MER respectively) with actual, calculated values, indicating that the model is valid. Sensitivity analyses were also conducted to investigate the effects of each input item on the output values. Since the ANN model can predict two mechanization indicators for a target farming system, it could be a good tool for appraising mechanization of regional farms. Also it overcomes some of the limitations of using as inputs simple data available from local databases that may contain errors.