Biosecurity of agroecosystems under technogenic and environmental risks

Anar Zhumadilova, Saule Zhigitova, Maira Turalina, Alina Kudriavytska
Received: 30.11.2024 Revised: 29.05.2025 Accepted: 25.06.2025
Retrieved from Vol. 28, No. 7, 2025 Pages: 62-78
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Abstract

The study aimed to establish the relationship between technogenic and environmental factors affecting the biosecurity of agroecosystems in Ukraine, with the development of adaptive monitoring and risk mitigation strategies through the integration of digital technologies. The research methodology was based on an interdisciplinary approach combining ecotoxicological analysis, biogeochemical modelling, and spatiotemporal assessment of anthropogenic impacts using geographic information systems, satellite observation, and algorithmic risk prediction based on Artificial Intelligence and big data analytics. The application of machine learning methods, spectral pollution analysis, and multi-level agroecosystem mapping revealed hidden patterns of agri-landscape degradation, assessed their ecological resilience, and formulated adaptive approaches to environmental management to reduce biological risks. The findings indicated an elevated chemical load on Ukrainian agroecosystems, manifested in exceedances of maximum permissible concentrations for ammonia (20- 28 μg/m³ in air), nitrogen oxides (over 35 μg/m³), nitrates (over 50 mg/L in water), and pesticides (up to 0.05  mg/L). Humus content in chernozems decreased to 1.2- 1.5%, accompanied by soil degradation. A correlational link was established betweenincreased technogenic pressure and higher prevalence of oncological diseases, cardiovascular and respiratory pathologies, as well as reduced life expectancy (by 7-10 years) in highly polluted regions. Negative demographic trends were recorded, including rising child mortality, declining fertility, and increased environmentally driven migration. The results confirm the efficacy of digital technologies in enhancing the quality of monitoring, diagnostics, and risk management in agroecosystems undergoing transformational anthropogenic pressures

Keywords

artificial intelligence; machine learning; risk management; agri-landscape degradation; biosphere

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