Applying multi-objective genetic algorithm (MOGA) to optimize the energy inputs and greenhouse gas emissions (GHG) in wetland rice production. (November 2020) - Elsoragaby, Suha; Yahya, Azmi; Mahadi, Muhammad Razif; Nawi, Nazmi Mat; Mairghany, Modather; M Elhassan, Sami Mustafa; Kheiralla, A.F.
Abstract: Efficient use of energy in crops production will minimize greenhouse gas emission (GHG), prevent destruction of natural resources, and promote sustainable agriculture as an economical crop production system. The aim of this study is applying the multi-objective genetic algorithm MOGA to optimize the energy inputs and reduce the greenhouse gas emissions (GHG) for wetland rice production in Malaysia. The developed multi-objective genetic algorithm (MOGA) model, showed an excess of energy inputs used by the farmers more than the required energy by 37.8% and 40% for the transplanting and broadcast seeding methods. The potential of GHG emissions reduction by MOGA was computed as 95.89 and 236.13 kg CO2eq /ha. Nitrogen represents the highest contributor to the reduction of both, total energy input and total GHG emissions in the two cultivation methods transplanting and broadcast seeding methods. Despite lower consumption of inputs by MOGA, crop yield is estimated at 9.4 ton/ha in transplanting and 9.2 ton/ha in broadcast seeding, which is close to the region's maximum under current condition. The main finding that MOGA model showed an excess of energy inputs used and the potential of GHG emissions reduction was 19.6% and 46.37%.for the transplanting and broadcast seeding methods. Graphical abstract: Highlights: 37.8% and 40% of the total energy input can be reduced by using MOGA. 19.6% and 46.37% of the total GHG emissions can be reduced by using MOGA. Nitrogen held the first rank with a reduction of 41.29% and 51.31% of GHG emissions. Rice yield by using MOGA was close to the region's maximum under current condition.