We are devoted to making any product developed by Quick Carbon transparent, open-source, and accessible to researchers and practitioners alike. We see the opportunity to fill the void of rapid carbon measurement as something much greater than our own initiative — hence, we're sharing what we're doing and welcoming your feedback. If you are equally passionate about the need for this technology, we’d love to work with you to accomplish our objectives faster and more effectively.

Research Objectives

In 2 years, have a tool that allows users to make spatially explicit estimates of soil C concentrations and stocks fully in-field with a degree of accuracy that enables quantification of management interventions or landscape variation. This tool will remain transparent, open, affordable, user-friendly, and can be applied by land managers and scientists. For now, estimates will be on both mass and depth dependent basis to 30 cm in US rangelands and croplands.


By December 2019, we will have a functional prototype of the Quick Carbon app, that produces in-field estimates anywhere in the continental US, with regional modules (at higher accuracies) for the southern Great Plains, northern High Plains, and central CA. We will also build the infrastructure for scientists and practitioners to submit data that expands the database.


By December 2020, we will have a fully functional Quick Carbon app, that produces recommendations on sampling intensity based on understanding of uncertainty and accuracy. Comprehensive answers to issues of sampling power/accuracy, and the ability to estimate landscape-scale stocks and concentrations.


How do we meet this objective over the next two years:

  1. Assemble dataset including SOC and spectra with broad variation in SOC, soil taxa, soil color, climate, use, cover, and geography. This will include collecting new data from soil archives around the continental US, and building harmonization techniques to crosswalk datasets.

  2. Modeling and data science to make soil C concentration estimates using national-scale data, with built-in uncertainty estimates.

  3. Determining best practices for conversion of concentration (% carbon) estimates to landscape-level stocks (tons/acre) or patterns of concentration, including map-making and interpolation techniques.

  4. Integration of steps one, two, and three into software that allows users to see concentration estimates in the field, leveraging cloud-based processing to run Quick Carbon algorithms over a cellular or wifi signal

  5. Refining in-field tools for soil extraction, bulk density, soil drying, and soil reflectance

Aspirations beyond the next two years

  1. Applicable to other ecosystems (e.g. forests and other high SOC systems)

  2. Increased capability at depth (e.g. 1 meter)

  3. Also measure in-field other soil variables important to understanding SOC (e.g. pH, texture, SIC)

  4. Provide additional info related to SOC (e.g. WHC, aggregation, etc)

  5. Integration w/ process-based models

  6. Broad adoption beyond just us