Digital Agriculture to estimate the net climate benefits of agricultural management practices at scale | 9am Tues, May 27, 2024

[Grigory Bronevetsky]

Modeling Talks

Digital Agriculture to estimate the net climate benefits of agricultural management practices at scale Bruno Basso, Michigan State University Tues, May 27, 2025 | 9am PT Meet | Youtube Stream Hi all, The presentation will be via Meet and all questions will be addressed there. If you cannot attend live, the event will be recorded and can be found afterward at https://sites.google.com/modelingtalks.org/entry/digital-agriculture-to-estimate-the-net-climate-benefits-of-agricultural More information on previous and future talks: https://sites.google.com/modelingtalks.org/entry/home Abstract: Agriculture accounts for over 10% of direct greenhouse gas emissions, with synthetic fertilizers in the US being a significant contributor. Digital agriculture offers promising solutions to enhance resource efficiency and mitigate the environmental impacts of production agriculture. This presentation will explore how the Digital Twin technology and Multi-Model Ensemble are being applied to estimate the net climate benefits of agricultural management practices at scale.   Bio: Dr. Bruno Basso is a Hannah University Distinguished Professor at Michigan State University. His research focuses on the long-term sustainability of agricultural systems, modeling of water, carbon, and nutrients fluxes across agricultural landscapes under current and future climates.   He is a Fellow of the American Association for the Advancement of Science (AAAS), a Fellow of the American Society of Agronomy, and a Fellow of the Soil Science Society of America. He received the 2021 Morgan Stanley Sustainable Solutions Prize and the 2025 Tech Transfer Achievement Award by MSU. He is the co-founder of CIBO Technologies. He is a member of the Board of Agriculture and Natural Resources of the US National Academies of Sciences, and Office of Science of the US Department of Energy.   He has published more than 250 scientific papers.  He received his PhD from Michigan State University.

[Grigory Bronevetsky]

Video Recording: https://youtube.com/live/Ot3E-TODi2A

Slides: https://drive.google.com/file/d/1l4Wk-c60WGz1tI6TwR_jlK-Wae7f31vB/view?usp=sharing

Summary:

• Challenges:

• Grow nutritious food with fewer resources in changing climate

• Protect environment: soil, water, air quality, water quality, biodiversity

• Reach negative GHG emissions

• Reliable revenue stream for farmers to incentivize positive changes

• Threats: urbanization, deforestation/desertification, fires/land degradation, soil erosion

• Breakthrough in Agriculture: mechanization, fertilizers/agrochemicals, breeding/biotech, data science&modeling

• Present: data science, circularity and sustainability

• Focus: digital twins for farms for scaling solutions

• Sensing, yield stability, multi-model ensembles, carbon intensity

• Yield stability maps

• Map yield of different pixels within the same field

• Some subsets of fields are consistently more/less productive, others are unstable

• Water availability

• Opportunity for targeted treatment: more efficient use of inputs

• Can relate other covariates (e.g. temperature) to yield, per pixel

• Used on-ground data to train satellite model of yield stability; applied to 80M acres in US Midwest

• 48% stable high productivity, 25% stable low, 27% unstable

• Locations of stable low regions is consistent across years, even when fertilizer is applied

• Collected data on fertilizer application at fine resolution, related this to yield

• Data shows that application of more fertilizer in stable low zones does not help yield

• Precision Conservation

• Profitability map from corn 

• -> Variable fertilizer application rate based on yield

• -> Changes in nature impacts (carbon, biodiversity, etc)

• Together->Profitability from both yield and carbon/biodiversity payments

• Scope 3 GHG emissions

• Climate impacts are being increasingly disclosed by companies

• Challenge: measurement and modeling of Soil Organic Carbon (SOC)

• Measurements: lab procedures, spatial variability, bulk density, remote sensing, spectroscopy

• Critical to account for soil bulk density when measuring SOC

• Deeper soils are more dense

• Models: Process-based, ML+Process-based

• Measured SOC across Midwest

• Multi-model ensemble to model Midwest growing practices

• Relating tillage, crop rotation, fertilizer, cover cropping

• Critical to use dynamic baselines when analyzing interventions

• Compare impact of climate smart in 10 years to conventional in 10 years, not conventional in today’s state

• E.g. continuation of conventional may keep losing SOC

• Models show that no-till and cover crops emit a little more GHGs but also sequester a lot more carbon in the soil, so the net impact is net-negative CO2