Source: UNIVERSITY OF MINNESOTA submitted to
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
Funding Source
Reporting Frequency
Accession No.
Grant No.
Project No.
Proposal No.
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Program Code
Project Start Date
Jun 1, 2023
Project End Date
May 31, 2024
Grant Year
Project Director
Shekhar, S.
Recipient Organization
Performing Department
Non Technical Summary
OVERVIEW: Agriculture and forestry provide food, feed, fiber, fuel, lumber products, and environmental services while sustaining rural and urban economies. But US global competitiveness and nutrition security are at risk due to rising greenhouse gas (GHG) concentrations, resulting in climate change, degrading ag-forest system health, and an aging and skill-deficit workforce. To address these challenges, we propose to create a climate-focused Agriculture-Forestry-AI (AgFoAI) discipline, a community of practice, and functioning GHG markets by improving understanding of trade-offs and feedback loops between climate change mitigation and adaptation and between biomass productivity and GHG fluxes, developing AI-enhanced GHG and biomass estimation methods and spatially-explicit multiscale (field-to-market) decision support tools for equitable adaptation and mitigation. AI advances will include reliable, accurate out-of-sample prediction [55] from sparse ground-truth measurements with consideration of hard constraints, uncertainty, and spatiotemporal variability. We propose a virtuous cycle of discovery and inquiry in foundational AI (FAI) and use-inspired research (UIR) that considers decision-making at different scales. FAI research includes combining learning and AI reasoning, AI-aided multi-objective decision-making, and generalization theory, along with UIR areas of GHG flux estimation, land-use and cropping system change, biomass productivity, GHG markets, multi-scale decision support tools, knowledge-guided machine learning (KGML), computer-vision guided perception and analysis, and AI-guided digital twins.INTELLECTUAL MERIT: Our proposed research will advance Climate-Smart Agriculture and Forestry (CSAF) knowledge and understanding to create CSAF decision support systems using KGML for reliable out-of-sample prediction [55] (AI) in un- or under-sampled fields and parcels, and AI-aided multiscale and multicriteria decision support tools for evaluating tradeoffs between alternative CSAF practices for GHG mitigation and adaptation under current and future climate scenarios. It has the potential to transform machine learning from a soft-constraint (e.g., regularizers) and mono-objective (e.g., prediction accuracy) paradigm to confront hard constraints (e.g., mass and energy balance) and multiple objectives (e.g., decision making, prediction accuracy and domain interpretability, equity, economic return, and ecosystem services). Like ImageNet [174], it has the transformative potential to advance computer vision from a human-visible spectrum and point-cloud-based approach to a sensor-rich (e.g., optical, thermal, microwave) approach by publishing new CSAF_ImageNet benchmark data and use cases (estimate GHG fluxes, soil organic carbon, biomass productivity). Our core team has a history of synergistic research and the required skills, expertise, and access to data and sensor resources. To foster strong interactions across proposed research areas, workforce development, and collaboration nexus activities, a dedicated AI Institute is needed to integrate the expertise of investigators from diverse disciplines and institutes in close collaboration with stakeholders to cultivate a new AgFoAI discipline and community of practice.BROADER IMPACT: The proposed Institute will benefit society by catalyzing an AgFoAI discipline, a community of practice, and better functioning GHG markets. It will enhance the national research and educational infrastructure by sharing curated datasets and easy-to-use multi-scale decision support tools, including AI advances (e.g., KGML, AI-guided multi-objective optimization). It will grow the American AI workforce via the integration of AgFoAI research with education; mentoring of professional, post-doctoral, graduate, and undergraduate students; engagement of secondary school teachers and students; and co-development and training of farmers and foresters in the use of AI-inspired tools; with careful consideration of broadening participation via recruitment, retention, and placement of all program participants. The team includes minority-serving institutions as active participants in all activities. Community-building activities include shared data and tools, integration of partners, and knowledge transfer via co-creation, industry consortia, and the IP framework.
Animal Health Component
Research Effort Categories

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
Goals / Objectives
The Institute aspires to catalyze a new field of study and community of practice intersecting AI and CSAF and to become a credible source of scientific information since addressing the societal grand challenge of CSAF and developing the AI workforce requires nothing less. The institute will foster a multi-disciplinary and multi-institutional community of research and practice by serving as a national collaboration nexus that co-creates innovations. Specific goals include the following:The research goals include advancing GHG, soil organic carbon, and biomass estimation methods; understanding trade-offs and feedback loops between climate, productivity, and GHG fluxes; investigating macroeconomic ecosystem service payment markets; and creating multi-scale (field-to-market) decision support tools to study tradeoffs between adaptation and mitigation, along with AI advances such as knowledge-guided machine learning, combining learning and AI reasoning, computer vision guided perception and analysis, AI-aided multi-objective optimization and AI-guided Digital Twins.Education and workforce development (EWF) goals include producing educational materials and training programs to facilitate knowledge transfer and the adoption of best practices in AI in CSAF.The broadening participation (BP) objective is to foster a culture of inclusion across all areas, including research, collaboration nexus, and education and workforce development in a nondiscriminatory and inclusive manner by partnering with minority institutions and minority-serving organizations and using evidence-based strategies.Collaboration and Knowledge Transfer goals include intellectual exchange, co-creation, intellectual property framework and industry consortium development, and community building via annual meetings, panels, tools, and datasets.
Project Methods
This project will use a combination of sensing, modeling, and AI methods. Sensing is a powerful tool for monitoring spatial and temporal variability in crop and animal stress, but it cannot penetrate soil, clouds, or animal hides. Process-based crop and animal simulation models are powerful tools for understanding how biophysical processes affect temporal growth patterns. A lack of voluminous fine-scale CSAF (Climate-Smart Agriculture and Forestry) data limits scaling up models from plots to fields to watersheds and coarser scales. AI is a powerful tool for describing and predicting complex agricultural and forestry data relationships but is limited to modeling physical processes that may evolve and interact at multiple spatial and temporal scales. Thus, the project will integrate the unique strengths of sensing, modeling, and AI methods, creating vast synergies that drive innovation in CSAF and AI, as applied to the following integrative vignettes:Vignette 1: Advances in GHG flux estimation and verification.Vignette 2: Climate risks, adaptation, and geographic shifts in cropland-forest transitions.Vignette 3: Multi-criteria optimization of mitigation practices and productivity.Vignette 4: AI-guided emulators of Earth-Economy ecosystem service payment markets.Vignette 5: Multi-scale multi-criteria GHG decision support tools.