Evaluation of environmental impacts and energy use of sugar beet production and predicting the Yield using ANN and ANFIS in Chaharmahal and Bakhtiari province of Iran

Agricultural production systems seriously changed in the world because of more dependent on chemical fertilizers and pesticides and hybrid seeds. As a result, Significant changes in the energy consumption in agriculture will be more focused on fossil fuels. Today, in addition to energy evaluation in agriculture, increasing greenhouse gas emission lead to growing considerable interest to climate change and global warming phenomena. The aim of this study was to determine the energy indices and greenhouse gas emissions from sugar beet production units in Chaharmahal and Bakhtiari province. In addition, in this study crop yield was predicted and modeled using artificial intelligence methods, such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Required data was collected through questionnaires and face-to-face interviews with farm owners and agricultural authorities. Simapro software was utilized to evaluate the environmental impacts. The Energy Ratio (ER), Energy Productivity (EP), Specific Energy (SE) and Net Energy Gain (NEG) were 20.99, 1.24 kg/MJ, 0.8 MJ/kg and 75244.93 MJ/ha, respectively. The total energy consumption and output energy (yield) for the production of sugar beet was obtained 37640.464 and 790090 MJ/ha respectively. The total amount of greenhouse gases emissions was estimated 1556.859 kg CO2 equivalent per hectare, that the largest share of total GHG emissions was owned to nitrogen fertilizers (40.22%), fuel (31.66%) and electricity (21.76%), respectively. Both ANN and ANFIS models significantly predict sugar beet product performance, but ANN have better and more accurate results. The correlation coefficient for prediction of sugar beet yield using ANN was 1, while the value of ANFIS was 0.9991. However, the ANN model had a lower error coefficient of 0.00003 compared to the ANNIS model. It is understood that by substituting optimal amounts of inputs for agricultural production, the negative effects on the environment (GHG emissions) will be reduced.