Determination and modelling of energy consumption in wheat production using neural networks: “A case study in Canterbury province, New Zealand”

This study was conducted on irrigated and dryland wheat fields in Canterbury in the 2007–2008 harvest year based on an extensive process of data collection involving a questionnaire and interviews. Total energy consumption in wheat production was estimated at 22,566 MJ/ha. On average, fertilizer and electricity were used more than other energy sources, at around 10,654 (47%) and 4870 (22%) MJ/ha, respectively. The energy consumption for wheat production in irrigated and dryland farming systems was estimated at 25,600 and 17,458 MJ/ha, respectively.In this study, several direct and indirect factors have been identified to create an artificial neural networks (ANN) model to predict energy use in wheat production. The final model can predict energy consumption based on farm conditions (size of crop area), farmers’ social considerations (level of education), and energy inputs (N and P use and irrigation frequency), and it predicts energy use in Canterbury arable farms with an error margin of ±12% (±2900 MJ/ha). Furthermore, comparison between the ANN model and a Multiple Linear Regression (MLR) model showed that the ANN model can predict energy consumption relatively better than the MLR multiple model on the selected training set and validation set.

► Total energy consumption in wheat production was estimated at 22,566 MJ/ha.
► On average, fertilizer and electricity were used more than other energy sources.
► The final ANN model can predict energy consumption based on farm conditions, farmers’ social considerations, and energy inputs.
► It predicts energy use in Canterbury arable farms with an error margin of ±2900 MJ/ha.
► The ANN model can predict energy consumption relatively better than the Multiple Linear Regression model.