Optimization of Crop Land Allocation under Large Scale Mechanized Farm Using Linear Programming: The Case of Lole State Farm

Abstract

Agriculture sector plays a crucial role in the Ethiopian economy, providing income, employment, and revenue generating, but the production and productivity are poor. Hence, resource optimization is essential for increasing efficiency and productivity. Good farm management is not only about increasing the amount of farm inputs; it also needs enhanced resource inputs managements. This study was conducted at Lole State Farm of Ethiopia which was faced by manual cropland allocation that wasn’t optimized relative to the degree of the crops' profitability. The study aimed to optimize cropland allocation through a linear objective function and constraints. The objective functions designed for Lole State Farm was to maximize crop profit per hectare by optimizing crop land allocation through satisfying various farm constraints. The parameters of the objective function were crop profit (Ethiopia Birr per hectare). In line with this, the constraints that were considered during the optimization of 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 the objective function to farm constraints and using linear programming, optimization of land use was achieved via mathematical modeling of linear programming. The result of the optimization 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 and suitable for this optimization. In conclusion, the LP model optimization process has improved decision-making on cropland allocation by taking into account farm constraints.

Keywords

• Agriculture
• Constraint
• farm
• objective function
• Profit
• Cost

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