At present time, sustainable crop production is of prime importance due to the expansion of human population and diminishing cultivable land. Insects attack the plants’ roots, blooms and leaves and lessen the agricultural production across the globe. In this research work, we propose a nonlinear mathematical model to manage the spray of insecticides to control insect population and increase crop production. In the model formulation, we consider that the spraying of insecticides is attributed to both the density of insects and loss in crop production. This study identifies the range of spraying rate of insecticides at which the model system shows bistability behavior and its threshold value after which system stabilizes to the equilibrium with higher crop production. Further, we have also demonstrated that the model undergoes transcritical, saddle-node, Hopf, and Bogdanov–Takens bifurcations. The extensive numerical simulation is performed to validate the analytical findings.
In this research work, a nonlinear mathematical model is proposed and analyzed to study the adverse effects of insects on agricultural productivity by controlling the insect population using insecticides. In the model formulation, it is assumed that agricultural crops grow logistically and the growth rate of insects wholly depends on agricultural crops with Holling type-II functional response. It is further assumed that insects uptake insecticides; thus, the amount of insecticides decreases at a rate proportional to its amount and the density of insect population, and the growth rate of insect population decrease in the same proportion. The feasibility of all non-negative equilibria and their stability properties are discussed. Stability analysis specifies that agricultural crop consumption rate destabilizes the system; however, the spraying rate of insecticides stabilizes the system. The conditions for the existence of pitchfork and Hopf-bifurcation are derived. Considering the spraying rate of insecticides as time-dependent, we have also discussed the optimal control strategy to minimize both insect density and the associated cost. The numerical simulation validates the analytical findings.
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