Modelling Insect Dispersal in Agricultural Landscapes Using Agent-Based Models (ABM)
Hayder Muhamed AbasDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq; Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq. iu.tech.eng.iu.comp.haideralabdeli@gmail.comhttps://orcid.org/0009-0009-0963-1267
Gulsara RuzievaDepartment of Natural Sciences, Termez University of Economics and Service, Termez, Surxondaryo, Uzbekistan. gulsara_ruziyeva@tues.uzhttps://orcid.org/0009-0008-7464-4518
Deepa RajeshDepartment of AMET Business School, AMET University, Kanathur, Tamil Nadu, India. deeparajesh@ametuniv.ac.inhttps://orcid.org/0009-0008-9743-4791
Maqsad MatyakubovPhD Researcher (Agriculture), Department of Fruits and Vegetable Growing, Urgench State University, Khorezm, Uzbekistan. maksadbek995@gmail.comhttps://orcid.org/0009-0002-5892-6458
Dr.P. Sundara BalaMuruganAssociate Professor, Department of Management Studies, St. Josephs Institute of Technology, OMR, Chennai, Tamil Nadu, India. sundarabalamurugan@gmail.comhttps://orcid.org/0009-0002-7440-0181
Prachi GurudiwanAssistant Professor, Kalinga University, Raipur, India. ku.prachigurudiwan@kalingauniversity.ac.inhttps://orcid.org/0009-0008-0150-5250
This research focuses on insect dispersal within farming landscapes using agent-based models (ABMs). ABM allows individual insect actions and their environmental responses to be simulated in detail. The model integrates landscape components including crop type, hedgerows, and natural barriers. The results demonstrate these features' substantial impact on the movement pathways and distance traveled. Simulations validated through fieldwork showed spatial dispersal consistency relative to changing conditions. High concentration risk areas for pest accumulation were discovered with scenario evaluation. These results can enhance the precision of pest control approaches and reveal new, sophisticated methods of dealing with pest issues. The research illustrates the potential of ABM in ecosystem analysis and agricultural resource management. The ABM framework is readily adjustable to other species of insects and landscapes owing to its scalability. The spatial behavior decomposition also reveals a strong dependency of different behavioral settings on distances covered. Furthermore, it allows for combining GIS databases for better-defined regional precision coordinates. The system described assists in creating forecasting instruments for ecological agriculture.