NATURAL RESOURCE MANAGEMENT INVOLVING TECHNOLOGICAL APPROACH (ARTIFICIAL INTELLIGENCE) TO SEQUESTER CARBON, LIMIT GLOBAL WARMING TO WELL BELOW 2°C AND ACHIEVING LAND DEGRADATION NEUTRALITY IN REPUBLIC OF MOLDOVA

• DOI: 10.47068/ctns.2024.v13i26.024
• Publication Date: 2025-03-27T17:12:45Z
• Authors: Leonid Popov; Tatiana David; Elena Hristev; Cora Gheorghe

The Republic of Moldova is part of the region with a major risk of the ecological state, characterized by the super intensive use of agricultural landscapes. In the last decades, many eco-pedological problems have worsened considerably in connection with the degradation of the potential of soil resources, the decrease in the efficiency of ecological control and, as a result, the reduction of people's standard of living. It is possible to prevent the intensification of negative processes through a detailed assessment of determining factors. The use of artificial intelligence allows us to quickly and effectively identify and assess the main forms of soil degradation, evaluate the degree of their severity, develop measures to stop and combat them in order to improve the ecosystem situation. Given the results of the research, the analysis reveals that the "Land Use/Land Cover Data Collection Methodology", an innovative approach using geostatistical modeling combined with satellite imagery, has been developed. The concept for "Methodology of data collection for the potential indicator of land productivity and soil carbon stocks" was also developed. The scientific approach based on geospatial analysis and Artificial Intelligence (AI) enables the automation of information in an efficient, fast, and cost-effective way for documenting and systematizing baseline data for land degradation neutrality that will enable advanced and evidence-based operational decisions, including the development of sustainable land use strategies. Methodologies provide time-efficient means with minimal expense to identify the best areas for a given crop based on multifactor analysis and they have an impact on increasing the operational capacities of decision-making, planning, evaluation, monitoring and control for central and local public authorities and agricultural producers with a reduction of expenses of up to 50%.