Agricultural Analysis & Modeling

RTI helps government entities, commercial companies, foundations, and international development partners understand the costs, potential, and impacts of regenerative agriculture and forestry practices. Our tools estimate how investments translate into practice adoption, economic benefits, and environmental outcomes, helping decision-makers compare tradeoffs and identify the most cost-effective pathways for achieving desired goals.

Traditionally, the best way to gather data on land use was via surveyors in the field, assessing crops by hand in a slow, expensive process. New technologies can provide a faster, more comprehensive, and more consistent picture of agricultural status. RTI uses drone or other high-resolution imagery to calibrate satellite-based AI analytics models that monitor crop planting, classification, planted area, growing season progress, crop health, and potential yields. These insights help organizations identify emerging risks, allocate resources, and evaluate the impact of agricultural investments and development projects.

Many of the key economic models used to project changes in agricultural, forest, and other land covers provide aggregated outputs at a state or national level. However, there are many important outcomes that vary depending on which specific land areas are converted. RTI EcoShift™ uses machine learning to predict future land use changes at a disaggregated level under alternative scenarios. The high-resolution projections help decision-makers better understand how varying land conversion scenarios will affect carbon stocks, water resources, biodiversity, endangered species habitat, and other important ecosystem services so that they can plan more effectively.