Enhancing Crop Strategies with Linear Programming: Leveraging Historical Constraint for Optimal Decision-Making

Farmers face significant challenges in implementing effective crop management and allocation strategies designed to maximize the economic yield of their agricultural products. Linear programming offers a powerful solution to these challenges—a mathematical optimization method that maximizes or minimizes specific outcomes to improve production efficiency. This study employed linear programming in the Spercheios River basin to enhance crop efficiency and increase economic returns by maximizing total crop yield. The model incorporated multiple constraints, including fertilization requirements, irrigation availability, labor resources, and land utilization. The primary objective was to evaluate optimal crop distribution within the study area while introducing a novel variable called the historical constraint, which had not been previously tested. A comparative analysis was conducted between scenarios with and without this historical constraint, alongside other established limitations. Linear programming was implemented using Excel software to identify the optimal solution for maximizing crop yield under these specific constraints. Currently, 26 different crops are cultivated in the Spercheios River basin. The study results revealed that without the historical constraint, the model recommended concentrating production on only six crops. However, when the historical constraint was incorporated, the framework suggested maintaining cultivation of all 26 crops. Furthermore, the research demonstrated that resource requirements—including irrigation, fertilization, labor, and land—can vary significantly depending on the combination of constraints applied.