Abstract:
The agriculture sector plays a crucial role in the Ethiopian economy, providing income, employment, and revenue so mechanized farming is essential for increasing efficiency and productivity. Agriculture mechanization is not only to increase the number of farm machinery; it needs enhanced power and land input. This study was conducted at Lole State Farm and the main problem faced by the Farm was the mismatch of farm power or machinery and the total cultivated land, which led to operational delays, high costs, and low efficiency. The study aimed to optimize farm machinery selection and cropland allocation through a linear objective function and constraints. The two objective functions designed for Lole State Farm were to maximize farm operation efficiency per hour and crop profit per area by satisfying various constraints. The parameters of the objective function were machine field capacity (hectare per hour) and crop profit (Birr per hectare) for farm machinery selection and cropland allocation, respectively. In line with this, the constraints that were considered during the optimization of farm machinery selection were total machinery cost, area of cultivated land, working days, and hours. On the other hand, the constraints considered during cropland allocation were total production costs, (fertilizers, herbicides, chemicals such as, pesticides, fungicides, insecticides, etc.! seed, labor, and machine hour cost, crop rotation, and total land area). By subjecting objective function to farm constraints, and using linear programming, optimization of farm machinery and land use was achieved via mathematical modeling. The result of the optimization indicated that, thirteen Messy Ferguson465 (150hp.each) and four John Deere 7230 tractors (230 hp. each), in total seventeen tractors, were proved adequate for primary tillage operation. These tractors can cover all farm operations after primary tillage. Regarding the combine harvesters, five Tucano5060s (150hp.) were selected as optimal. Regarding cropland allocation, the optimization results indicated that wheat and potato are the first and second profitable crops, respectively for Lole State Farm, followed by fava bean, food barley, and rapeseed as economic options. In conclusion, the LP model optimization process has improved decision-making on farm machinery selection and cropland allocation taking into account farm constraints
