Publicly funded agricultural research at land-grant universities generates substantial economic and societal benefits (Fuglie and Heisey 2007). Yet research teams often lack accessible and standardized analytical tools to estimate and communicate the potential impacts of emerging innovations at the project level. As interdisciplinary research and team science become increasingly central to agri-food R&D, the need for transparent, data-driven methods to quantify anticipated outcomes has grown. Without such tools, the future value of scientific discoveries, including new technologies, practices, and management strategies, can remain difficult to articulate to stakeholders, funders, and policymakers.

This poster introduces Michigan State University’s AgImpact Calculator, a collaborative analytics platform designed to support research teams in estimating and communicating the realizable economic impacts of agri-food innovations. The tool translates agronomic, economic, and adoption-related data into transparent, stakeholder-ready metrics, enabling researchers to generate forward-looking estimates of farm-level and statewide economic benefits.

The calculator integrates economic modeling, scientometric indicator development, and AI-assisted analytics to support evidence-based impact estimation. The analytical framework integrates two input categories:

(1) Market and production data, including area harvested, herd size, yields, production volumes, and prices from authoritative sources such as the USDA National Agricultural Statistics Service (NASS); and (2) Research output–specific parameters provided by research teams, including expected yield gains, avoided losses, cost savings, adoption rates, and adoption timelines. Using a partial budget framework, the calculator compares profitability under “with technology” and “without technology” scenarios to estimate per-acre (or per-head) net benefits and scale impacts on statewide projections over a multi-year horizon. AI-assisted data processing and structured input templates support interdisciplinary collaboration while maintaining transparency and reproducibility.

The AgImpact Calculator is supported by a modular data-processing and analytics architecture that integrates structured agricultural datasets with model-driven and AI-assisted computation pipelines. The system ingests heterogeneous inputs from authoritative statistical sources and researcher-provided parameters, normalizing them into a unified schema for downstream analysis. A deterministic economic modeling layer implements partial budget calculations and scenario-based projections, while an AI-assisted inference layer supports data validation, parameter harmonization, and natural-language synthesis of model outputs. Time-series processing and interpolation techniques are used to extend historical datasets into short-term projections, enabling sensitivity-aware forecasting under varying adoption and performance assumptions. The architecture emphasizes reproducibility and transparency through explicit transformation steps, schema-constrained inputs, and traceable intermediate outputs: allowing interdisciplinary users to interact with complex analytical workflows without sacrificing methodological rigor.

Through the generation of ex ante, project-level estimates of economic impact, AgImpact Calculator enables research teams to communicate the potential value of innovations before widespread adoption. Its standardized analytics framework improves stakeholder communication, supports research prioritization, enables cross-project comparison of impact indicators, and facilitates collaboration among Principal Investigators, economists, agronomists, data scientists, and research administrators. By providing a transparent and replicable approach for estimating future benefits, the tool provides a model for how team science and modern data analytics can strengthen research impact assessment and support more data-informed management of public agricultural research investments.