Ecological Mysteries: The Impact of Various Factors on Ecological Balance

Abstract

"The earth does not belong to us, we belong to the earth" --Mary Matlin. In recent years, there has been a growing emphasis on the imperative of ecological conservation among humanity. This study seeks to model the ecological stability challenges associated with the shift from forested areas to agricultural lands. By employing mathematical modeling, it tracks species dynamics within the ecosystem and evaluates the effects of organic farming practices on ecological balance. In Task One, we have summarized the Dynamic Food Web (DFW) differential equation model by analyzing the impact relationships between species occupying different ecological niches. Initially, we simulate community changes over a one-year period to emphasize the seasonal effects on population variations within each community. Employing the fourth-order Runge-Kutta method, we derive the population curves for various species in the ecosystem throughout the year, thereby assessing the ecosystem's stability. Furthermore, to simulate the agricultural ecosystem over a longer duration, we extend the simulation time to 100 years and refine the model by excluding seasonal factors, enabling a more comprehensive evaluation of the ecosystem's stability. In Task Two, we reintroduce the trees and deer from the original forest ecosystem, eliminate the impacts of herbicides, accounted for more intricate factors related to bat influence, and introduce a new butterfly species. By considering the effects of various species' ecological niches on ecosystem stability, we derive the population curves for species over a 100-year time frame within the ecosystem.In Task Three, we establish seven sub-indicators across three evaluation criteria: economic benefits, environmental impacts, and technical feasibility. We assess four reasonable and reliable organic agricultural management methods and develop an organic agriculture evaluation model. Utilizing the AHP-EWM combined weighting method, we assigned weights to the seven indicators, which are then computed using the arithmetic mean, geometric mean, and eigenvalue methods, followed by normalization. Consequently, we obtain comprehensive scores for the four organic agricultural management schemes, with the highest score reaching 4.70.Finally, we conduct a sensitivity analysis on Model One. The results demonstrate that our model is robust and can offer a reliable reference for practical issues. Additionally, we systematically summarize and analyze the model's strengths and weaknesses. We also craft a letter to farmers engaged in organic agriculture, exploring strategies to augment economic benefits within the organic agricultural framework.