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Arva Carbon Programs

Market Carbon the Right Way

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Rooted in Research

 

Arva Intelligence’s roots in greenhouse gas emission modeling and carbon sequestration studies began in 2016 with the AR1K research collaborative. Glennoe Farms, the University of Arkansas, along with Lawrence Berkeley and Oak Ridge National Laboratories, took the latest in environmental science and remote sensing technology from the lab to the field, compiling dense data and real-world application on over 1000 acres in Arkansas. The results of AR1K led to the artificial intelligence models that became Arva Intelligence. Today, Arva is still deeply involved in research and development, collaborative projects, and environmental research.

What is a Carbon Program?

Learn more about Arva's Carbon Programs

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What is the science behind Carbon markets?

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Arva Intelligence’s roots in greenhouse gas emission modeling and carbon sequestration studies began in 2016 with the AR1K research collaborative. Glennoe Farms, the University of Arkansas, along with Lawrence Berkeley and Oak Ridge National Laboratories, took the latest in environmental science and remote sensing technology from the lab to the field, compiling dense data and real-world application on over 1000 acres in Arkansas. The results of AR1K led to the artificial intelligence models that became Arva Intelligence. Today, Arva is still deeply involved in research and development, collaborative projects, and environmental research.

Contributions to Climate Science

Learn more about Arva's Carbon Programs

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GHG to Carbon Sink in Precision Ag

From GHG Emissions to Carbon Sink with Precision Ag: Arva is leading an ARPA-E SMARTFARM research in data science from agricultural GHG footprints to validate and enable regenerative land management practices that capture ecosystem service values for farmers.

Meet the team behind your carbon sequestration

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Carbon and N2O Sequestration

As part of a Phase II ARPA-E team, Arva is collaborating with Dagan and Veris Technologies to build a scientifically sound, scalable N2O quantification platform that will solve the challenge of quantifying hot spots and hot moments of N2O fluxes by integrating advanced sensing technologies with the DNDC biogeochemical model for understanding the drivers of N2O fluxes.

XAI Models for Terrestrial Carbon Cycles

In concert with Lawrence Berkeley Labs and the University of Arkansas, we are developing deep learning XAI models to extend our predictive understanding to the terrestrial carbon cycle for three commodity crops, aiding farmers to profitably mine the atmosphere for carbon, improving soil health and to reduce the need for non-renewable inputs.