European Agricultural Fund for Rural Development (EAFRD) and Common Agricultural Policy Strategic Plan 2023—2027 agreement No. 25-00-C0LA1601-000019
Project partners are Latvian Rural Consultation and Education Centre (leading partner), LSFRI "Silava", the association "Preiļi Farmers' Union", the forest owners' cooperative "Mežsaimnieks", Institute of Agricultural Resources and Economics, and the farm "Lielvaicēni".
Total funding is EUR 299,859, incl. EAFRD funding EUR 269,873.
The project aims to develop and test in practice a multi-objective decision support tool based on remote sensing and machine learning methods for identifying agricultural lands with increased risk of economic activity and evaluating the most optimal management scenarios (agriculture, forestry, agroforestry, renaturalization). The tool will model farm-level benefits/costs and environmental performance (biodiversity, carbon sequestration, soil health) and provide recommendations on areas where afforestation or wetland renaturalization is economically and ecologically superior to continued land use for agriculture, as well as on alternative agrotechnical practices for risk reduction in LIZ. The tool developed in the project will help Latvian farmers adapt to the increase in hydrological and meteorological extremes caused by climate change and reduce their impact on yields and economic stability. Increasing risks: flooding, drought, frost, veld, erosion and deterioration of soil bearing capacity; creates a need for an integrated, data-driven solution that analyzes spatial, climatic, soil, terrain and economic indicators in a unified system to identify high-risk agricultural lands and evaluate the most suitable management scenarios.
A multi-purpose digital tool will be developed and field-tested that models both financial outcomes and environmental benefits – biodiversity, carbon sequestration and soil health – at farm and field level. The solution will also take into account landowner objectives and site location, providing individualized scenarios.
The project will create a unified spatial database, develop a risk indicator system, forecasting models with uncertainty assessment and a scenario optimization module that compares alternative land management strategies according to economic and environmental criteria. The final result will be a user-friendly web prototype with maps, analytical reports and recommendations. Such an integrated solution does not exist at the national level so far, therefore the project simultaneously creates both a methodological innovation in data integration and a practical digital tool with high implementation potential in the agricultural sector.