Joint of Modeling Techniques and Life Cycle Assessment for Prediction of Yield, Economic Profit, and Global Warming Potential for Wheat Farms

Abstract

Forecasting crop yield and its environmental effects can help increase agricultural energy efficiency and reduce environmental impacts. This study provides mathematical, adaptive network-based fuzzy inference system (ANFIS) and neural networks (ANNs) techniques for forecast yield, economic profit, and global warming of wheat production. For this purpose, 75 wheat farms located in the central area of Hamadan province were selected randomly and data were gathered through oral interview. Then, computed the input and output energy, life cycle assessment (LCA) was utilized to specify the environmental effects of wheat cultivation. The calculations displayed that the averages of inputs and outputs energy were about 43055 MJ ha−1 and 117407 MJ ha−1, respectively. The LCA results demonstrated that wheat cultivation cause to the emissions of 624.29 kg CO2 eq. ton−1. ANN structures for predicting yield, economic profit and global warming in wheat production with two hidden layers were the best topologies. ANFIS model results indicated that in the 3-level ANFIS model, the highest R2 is found for net return (0.962). The results comparison showed that ANN and ANFIS models outperform linear models to predict yield, economic profit, and global warming of wheat production.