| Modeling and investigation of energy and emissions of barley production using machine learning in Nazarabad city |
| Paper ID : 1356-NICAME1402 |
| Authors: |
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seyed omid davodalmosavi *1, Shahin Rafiee2, ali jafary2 1Department of Agricultural Machinery, University of Tehran 2Department of Agricultural Machinery, Faculty of Agriculture and Natural Resources, University of Tehran |
| Abstract: |
| choosing the correct and suitable methods of agricultural operations reduces energy consumption and reduces the production of greenhouse gases in the production of agricultural products. In this study, the amount of energy input, output and emission of greenhouse gases of barley production in Nazarabad city of the Alborz province was investigated. Various amounts of input use and comprehensive information were collected at each stage from planting to harvesting through interviews and filling specialized questionnaires. Energy consumption and emission production were equated using energy conversion coefficients and greenhouse gas emissions extracted from sources. According to the obtained results, the average total energy consumption was 14443.16 megajoules per hectare. The total global warming potential (GWP) due to different activities in the farm was 10.641 kg equivalent to CO2 per hectare. The first emission of greenhouse gases was related to nitrogen fertilizer and diesel fuel. The indices of energy ratio, energy efficiency, energy intensity and net energy are 5.03, 0.34(kg/Mj), 2.91(Mj/Kg) and 58348(Mj) respectively. Energy modeling was done with three methods DTR, RFRuGBR and 0.76, 0.79 and 0.76 respectively and RRMSE was 0.04, 0.05 and 0.06 respectively. The calculation of the results showed that the DTR method can perform more accurately. will be predict the energy production. Forecasting analysis was done with SHAP and the most effective input on energy nose was nitrogen fertilizer. The results of this research showed that energy consumption and greenhouse gas emissions can be reduced through reducing fuel consumption and reducing the use of chemical fertilizers. |
| Keywords: |
| Sensitivity analysis with SHAP, Nazarabad city, energy efficiency, barley, machine learning |
| Status : Paper Accepted (Poster Presentation) |
