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ROLES OF ARTIFICIAL INTELLIGENCE IN PREDICTIVE AGROECOLOGICAL MODELING AND SUSTAINABLE LAND MANAGEMENT IN RESPONSE TO CLIMATE VARIABILITY IN THE MOSCOW REGION
Abstract
Agroecology, which merges ecological principles with agricultural practices, offers a pathway to food security while ensuring environmental sustainability. However, its implementation faces challenges stemming from ineffective ecological resource monitoring, predetermined climate conditions, and insufficient real-time data for precision farming and well-informed decision-making. The aimed first, to develop models that monitor soil health and predict crop yields under different agroecological conditions powered by AI algorithms. Second, optimize land use efficiency and minimize the negative environmental impact from unsustainable agriculture. The methodology includes three (3) AI supported algorithms for processing spatial and temporal data for real-time decision support, data collection validation through pilot studies in varied agroecological regions. We examined the potential of data-driven modern tools to enhance predictive models in agroecology and facilitate sustainable land management, especially during climate change and resource constraints. The result proved there is a significant contribution of AI to the enhancement of agroecological processes, land use efficiency and reduction in environmental degradation from farming activity. The study also addresses significant environmental challenges, such as soil erosion and nutrient depletion, by promoting eco-friendly diversified farming practices, including polycultures and organic soil management, while ushering a new era of sustainable agriculture that secures livelihoods and protects the environment. Our results will not only evaluate existing practices but also simulate scenarios that predict the long-term environmental and economic effects of various response strategies. Hence, it will contribute to adaptive measures that can adjust to changing climate conditions, preserve natural resources, and increase flexibility in both smallholder and large-scale farming.
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