(untitled) (Modern American Journal of Linguistics, Education, and Pedagogy, 2026)
The advanced use of land resources has become a decisive scientific and practical direction for countries where agricultural productivity, rural employment, food security and ecological stability depend directly on the condition of irrigated soils. In Uzbekistan, the problem is especially significant because crop production is strongly linked with irrigation, drainage, soil salinity control, land reclamation, crop rotation and the rational distribution of limited water resources under increasing climatic pressure. The aim of this article is to develop a scientifically grounded conceptual model for advanced land-resource use based on integrated land-water management, digital monitoring, soil-reclamation assessment and agroecological planning. The study applies an IMRAD-based analytical design and synthesizes land-use theory, sustainable soil management principles, irrigation-sector modernization priorities and Uzbekistan’s strategic policy context. The article argues that advanced use of land resources should not be reduced to increasing crop output per hectare; rather, it must be understood as a system of long-term productivity, soil fertility preservation, water-use efficiency, salinity prevention, digital cadastral accuracy, ecological risk reduction and farmer-level decision support. The proposed model combines five interrelated blocks: cadastral and geospatial inventory, agroecological zoning,irrigation and drainage optimization, soil-health restoration, and institutional-economic incentives. The results indicate that sustainable intensification is achievable only when land-use decisions are based on measurable indicators such as soil salinity, groundwater depth, irrigation efficiency, organic matter balance, erosion risk, crop suitability and economic return per cubic metre of water. The study concludes that the future of land-resource management in Uzbekistan depends on the transition from extensive land exploitation to precision-based, data-driven and ecology-sensitive land governance.