A comparative of modeling techniques and life cycle assessment for prediction of output energy, economic profit, and global warming potential for wheat farms
Hassan Ghasemi Mobtaker, Ali Kaab, Shahin Rafiee, Ashkan Nabavi‐Pelesaraei
Abstract
Uncertainty about the energy use efficiency, lack of knowledge about economic outcomes, and its environmental consequences have always take risks in changing cultivation patterns and moving towards the optimal path. Accordingly, this study provided mathematical, artificial neural networks (ANNs), adaptive neuro-fuzzy inference system (ANFIS) methods to predict output energy, economic profit, and global warming potential (GWP) of wheat production. For this purpose, 75 wheat farms located in the central area of Hamadan province, Iran, were selected randomly, and data were gathered through oral interviews. After collecting input and output energies data, the averages of inputs and outputs energies were obtained about 43055 MJ ha−1 and 117407 MJ ha−1, respectively. Economic analysis has performed in the next step. Its results revealed that the benefit-to-cost ratio and net return were computed about 2.33 and 488.29 $ per ha for wheat production. Then, life cycle assessment (LCA) was utilized to specify the environmental effects of wheat cultivation, and its results demonstrated that GWP is the most important environmental impact which caused 624.29 kg CO2 eq. during 1 ton of wheat production. Modeling results illustrated R2 was varied between 0.264 and 0.978 in the linear regression, 0.313 and 954 in the best structure of ANN with two hidden layers, and 0.520 and 0.962 in the ANFIS with three-level structure. Modeling comparison indicated that generally, ANFIS model with considering all uncertainty items can be offered better prediction models among all and after that ANN with considering non-linear parameters is in the next rank.