Good Harvest Regen Ag Carbon Offset Program

Good Harvest is the first grower and rancher-centric carbon offset project of its kind, and the most transparent and data-driven carbon project in the market. Our goal is to generate new revenue for growers and ranchers for more profitable and sustainable agriculture production in the Permian Basin. To learn how you can benefit from the growing demand for carbon offsets contact us today.

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Learn the carbon value of what’s in your fields, and what opportunities you have to maximize carbon offset potential. We’ll also be upfront in disclosing bid prices for carbon credits and associated fees


Monetize up to five years of historical practices and reserve the option to sell your credits today or bank them credits to sell in the future.

Eliminate the Middle Man

Match your credits to local energy company buyers already working in your communities, without having to pay a third-party.

Data-Driven Insights

Receive a water table and soil properties map of your farm to maximize agronomic practices.

Security and Integrity

We back up your carbon claims with the most comprehensive data stack in the carbon market for the highest quality credits.

Contact Us

Enter your email address and we will contact you with more information and answer any questions you may have. We know harvest is right around the corner, and we guarantee we won’t get in your way.

Matt Rohlik 

Managing Director of Farm Data & Strategic Partnerships

(320) 894 - 3838

Contributions to Climate Science

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.

Carbon & N2O Quantification

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.