Multi-objective optimization of allocating sustainable mechanization for spraying and harvesting systems in paddy fields

Applying agricultural operations under different mechanization systems has different economic, social, and environmental consequences. Conflicts among these dimensions complicate the selection and allocation of sustainable mechanization systems. In this study, a method was used to allocate optimal patterns of spraying and harvesting systems in the Ramhormoz region to achieve agricultural sustainability. Indices included satisfaction, ease of work, health and safety, employment in the machine sector, labor force, diesel fuel consumption, pesticide consumption, farm load intensity, and operating costs. Three spraying systems namely backpack, tractor, and unmanned aerial vehicle (UAV), and three harvesting systems namely two-stage (harvesting and manual feeding to grain combine), direct harvesting with a grain combine harvester, and harvesting with rice combine harvester were included in the model. Combining AHP and TOPSIS methods, the similarity index for social and environmental dimensions was calculated and this value along with the cost of each system was used as coefficients of objective functions. The multi-objective optimization model to achieve sustainable agricultural mechanization was analyzed using a genetic algorithm. Pareto optimal results showed that in the absence of existing machine constraints, the development of the operational capacity of modern systems like spraying with UAV up to 2000 hectares and direct harvesting with rice harvesters up to 1000 hectares will be optimal scenarios for agricultural sustainability. Using the proposed method, not only can sustainable goals be achieved in identifying the best patterns of mechanization systems, but it is also possible to examine the effect of different scenarios under different constraints.