Dynamic Modeling of Agroecosystems and Natural Restoration Mechanisms: Seasonal Evolution Analysis Based on Four-Dimensional Food Chain

Abstract

This study presents a computational framework for modeling the dynamics of agricultural ecosystems transformed from forest areas, with a focus on quantifying natural recovery processes under the influence of invasive species and seasonal fluctuations. Addressing the limitations of existing models that overlook the synergistic effects of invasive competition and agricultural cycles, we introduce a four-dimensional system of ordinary differential equations (ODEs) that integrates crop biomass, primary consumers (native pests), secondary consumers (natural predators), and pesticide concentrations. The model employs logistic growth functions and Monod kinetics to capture ecological interactions, while incorporating seasonal parameters and invasive competitors represented by forestry pests. Through extensive numerical simulations spanning 20 years, utilizing the odeint solver, and validated via R² goodness-of-fit (0.87-0.93) and polar coordinate visualization, our results indicate that invasive forestry pests diminish crop yields by approximately 18.7±2.3% due to resource competition. Furthermore, we establish a strong correlation between species return rates and ecosystem recovery efficiency (r=0.82, p<0.01), and identify three distinct stability regimes through phase space analysis. These findings underscore the pivotal roles of seasonal variability and invasive species dynamics in ecosystem resilience, offering actionable insights for restoration management. The proposed model serves as a versatile tool for assessing ecological stability in anthropogenically altered landscapes, facilitating predictive analytics for sustainable land use practices.