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Augmented Intelligence (AI) for

Climate Smart Agriculture and Forestry

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Acknowledgements: The AI-CLIMATE an National AI Research Institutes (NAIRIs). These Institutes aim to catalyze collaborative efforts across institutions of higher education, federal agencies, industry, and others to pursue transformative AI advances that are ethical, trustworthy, responsible, and serve the public good. Also, they bolster AI R&D infrastructure and support the development of a diverse AI workforce. They will drive breakthroughs in critical areas, including climate, agriculture, energy, etc.

Shashi Shekhar

Director, AI-CLIMATE

Panel on AI in Ag, Digital Ag and Health, AI-VO Summit on AI Leadership Expo., Oct. 26th, 2023

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Agriculture and Forestry: Societal Importance

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  • Agriculture nourishes us with
    • Food, Feed, Fiber, Fuel

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  • Economic Opportunities
    • 10% of U.S. Jobs
    • Early adopter of technology, e.g., GPS, UAV, …

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  • Steward of natural resources
    • Conservation, Water quality, Soil health, …

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  • Curb climate change
    • Traditional: Reduce soil emissions (10% of US emissions) + Biofuels …
    • Carbon removal: Soil, forest, nature can remove 21% of US (33% global) Emissions

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Agriculture and Forestry: Challenges

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  • Environmental
    • Climate Change
    • Soil degradation, Bee colony collapse

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  • Cyber
    • Sensors, Data, Algorithms, Models, Tools, Automation
    • Crop yield/stress, soil health, …

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  • Social
    • Population growth
    • Aging workforce, Labor shortage

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How can AI help?

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  • Situation
    • Carbon Markets need more accurate estimates of carbon removal
    • Farmers and Foresters know climate smart practice, e.g., no-tilling, cover-crops, …
    • Q? Which climate smart practice should be used where? When? How long?
    • …

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  • How can AI help?
    • Better Decision Support Tools
    • More accurate Prediction Models of soil GHG emissions
    • Improve Data, e.g., finer resolution maps of soil moisture, organic carbon, …
    • Scalable Algorithms, e.g., multi-objective optimization

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AI for Better Decision Tools: Collaborative GeoDesign

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  • Q? Which climate smart practice should be used where?

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Details: Y. Xie, B. Runck, S. Shekhar, L. Kne, D. Mulla, N. Jordan, and P. Wringa, Collaborative Geodesign and Spatial Optimization for Fragment-Free Land Allocation, ISPRS Int. J. Geo-Inf. 2017, 6(7), 226; https://doi.org/10.3390/ijgi6070226.

Conservation tillage with stover removal

Low phosphorous application

Prairie grass

Switchgrass

Conventional tillage

Conservation tillage

7-mile Creek Watershed, MN

23,552 acres, 36.8 sq. miles

Sediment: 2585 ton/year

Unchangeable landscape

Public water

Watershed outlet

Watershed boundary

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AI for Better Prediction Models: Greenhouse Gas Emission

Sources: Prof. Vipin Kumar, Prof. Zhenong Jin, University of Minnesota

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Need: Predict soil greenhouse gas emissions (to curb climate change)

Challenges: Data scarcity, variation across places, …

Approach: Knowledge Guided Machine Learning (KGML) = AI + Laws of Nature + Sensor Data

Impact: Improved prediction accuracy

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Details: L. Liu, et al. , Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems . Nature Communication, 15, 357 (2024). 

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AI for Better Decision Tools

Tool

Stakeholder

Scale

Example Decisions

AI-COMET Farm

Land Stewards

Farm, Forest Stand

Compare climate smart management practices

AI-GeoDesign

Groups

Watershed

Social learning of spatial interactions

and tradeoffs

All

Companies

All

Cost and payment for each practice

AI-Earth-Economy, Soils-Revealed

Policy Makers

Country, State, Watershed

Compare policy interventions

and ecosystem service tradeoffs

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AI for Better Data: Make Sharper Maps

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  • Need: Soil moisture map
  • Challenge: Satellite map too blurry
  • ?unwater dry-spots, overwater wet-spots
  • AI can make sharper maps
  • Map Soil moisture (at 5cm depth of top soil near roots)

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NASA SMAP Satellite (9 Km Resolution, July 16-17 )

AI + Ground Sensor (30 m Resolution, July 16)

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Details: P. Khandelwal, et al., DeepSoil: A Science-guided Framework for Generating High Precision Soil Moisture Maps by Reconciling Measurement Profiles Across In-situ and Remote Sensing Data, ACM SIGSPATIAL 2024.

Source: Prof. Shrideep Pallickara

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Soil Emissions: Greenhouse Study Perennial Crops Soil Emissions: Field Measurement

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Summary

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  • Agriculture and Forestry are important
  • These are facing challenge
  • Farmers and Foresters know Climate Smart practices
    • Q? Which climate smart practice should be used where? When? How long?

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  • How can AI help?
    • Better Decision Support Tools
    • More accurate Prediction Models of soil GHG emissions
    • Improve Data, e.g., finer resolution maps of soil moisture, organic carbon, …
    • Scalable Algorithms, e.g., multi-objective optimization

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Institution Approach

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