Determine of Energy indicators and Modeling of output energy for alfalfa production using artificial neural network in Lorestan province of Iran
Journal Title: International Journal of Farming and Allied Sciences - Year 2016, Vol 5, Issue 3
Abstract
The aim of this study was to examine energy use pattern and predict the output yield for alfalfa production in Lorestan province of Iran. The data used in this study were collected from growers by using a face to face survey. The results revealed that diesel fuel (43.36%), electricity (24.25%) and N fertilizer (12.42%) consumed the bulk of energy. Energy use efficiency, energy productivity and net energy were found to be 4.83, 0.27 and 190383.09 MJ ha-1respectively. In this study, several direct and indirect factors have been identified to create an artificial neural networks (ANN) model to predict alfalfa production. The final model can predict output yield based on human power, machinery, diesel fuel, chemical fertilizer, water for irrigation, seed and chemical poisons. The results of ANNs analyze showed that the (7‐12‐12‐1)‐MLP, namely, a network having 12 neurons in the first and second hidden layer was the best‐suited model estimating the alfalfa production. For this topology, R2 and RMSE were 0.0457, and 96%, respectively.
Authors and Affiliations
Mohadeseh Ahmadvand
Keywords
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