Optimisation Of Crop Planting Strategies Based on Matrix Genetic Algorithm
K.Y. Wu, Liangshun Wang · Highlights in Business Economics and Management · 2025
With the increasing demand for sustainable agricultural development, it has become crucial to develop crop planting optimisation strategies, which are important for enhancing production efficiency, reducing planting risks, and using resources efficiently. This study focuses on the optimisation strategy of rural crop cultivation in the mountainous areas of North China, and adopts matrix genetic algorithm to take into account the actual influencing factors such as sales volume, yield per unit area, cultivation cost and sales price, and establishes a multi-period dynamic optimisation model to explore the optimal cultivation strategy for the next seven years under two scenarios, namely, the excess yield stagnation and the price reduction, and the optimal cultivation strategy for the next seven years under two scenarios, namely, the excess yield stagnation and the price reduction. In this way, the optimal planting strategies for the next seven years were investigated under the two scenarios of excess production and sales. The average annual profits for the next seven years under these two scenarios reached RMB 2,527,837 and RMB 2,684,160, respectively. Based on the results, it can be concluded that the optimisation of planting strategies carried out by this model is very considerable and has practical applicability, which solves the current situation that the optimisation of crop planting strategies fails to integrate the actual situation.