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Volume 10 - No: 2

Modelling Insect Dispersal in Agricultural Landscapes Using Agent-Based Models (ABM)

  • Hayder Muhamed Abas Department 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.com
    https://orcid.org/0009-0009-0963-1267
  • Gulsara Ruzieva Department of Natural Sciences, Termez University of Economics and Service, Termez, Surxondaryo, Uzbekistan.
    gulsara_ruziyeva@tues.uz
    https://orcid.org/0009-0008-7464-4518
  • Deepa Rajesh Department of AMET Business School, AMET University, Kanathur, Tamil Nadu, India.
    deeparajesh@ametuniv.ac.in
    https://orcid.org/0009-0008-9743-4791
  • Maqsad Matyakubov PhD Researcher (Agriculture), Department of Fruits and Vegetable Growing, Urgench State University, Khorezm, Uzbekistan.
    maksadbek995@gmail.com
    https://orcid.org/0009-0002-5892-6458
  • Dr.P. Sundara BalaMurugan Associate Professor, Department of Management Studies, St. Josephs Institute of Technology, OMR, Chennai, Tamil Nadu, India.
    sundarabalamurugan@gmail.com
    https://orcid.org/0009-0002-7440-0181
  • Prachi Gurudiwan Assistant Professor, Kalinga University, Raipur, India.
    ku.prachigurudiwan@kalingauniversity.ac.in
    https://orcid.org/0009-0008-0150-5250
DOI: 10.28978/nesciences.1763848
Keywords: Agent-based models (ABM), animal dispersal, agricultural landforms, biomodelling integration periphery data, pest issues.

Abstract

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.

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Date

August 2025

Page Number

305-314