Modeling The Dynamics Of Schistocerca gregaria Swarms In Sindh, Pakistan With A Spatial Forecasting Method

Abstract

This study examines the dynamics of locust swarms through various modeling frameworks, including Cellular Automata, Agent-Based, and Grid-Based models. Utilizing a 100x100 cell grid, the research simulates locust movements in a structured environment, categorizing initial locust densities as low (10-50), moderate (50-100), and high (100-200), revealing that higher densities significantly enhance collective movement and interaction. The Agent-Based model incorporates 10,000 locust agents, capturing diverse interactions influenced by demographic factors and environmental conditions. Key variables such as temperature, humidity, vegetation cover, and wind speed are integrated to enhance understanding of locust behavior and predict agricultural impacts, with simulations indicating potential yield losses of up to 50% in high-density scenarios.

Behavioral observations detail feeding patterns, migration triggers, reproductive strategies, and social interactions, providing insights into swarm dynamics. The study assesses the impacts of locust infestations on crops like wheat, rice, vegetables, and fruit trees, proposing mitigation strategies such as early warning systems and integrated pest management to reduce damage. The analysis of historical data from 2018 to 2021 reveals significant correlations between swarm density, crop damage, and weather anomalies, underscoring the need for adaptive pest management strategies.

Finally, stakeholder perspectives highlight the importance of collaborative approaches in locust management. By integrating ecological and agricultural considerations, this research aims to improve predictive models and inform effective management practices for mitigating the adverse effects of locust swarms on agriculture.