Quick Carbon is led by a core team of faculty, staff, and students at Yale’s School of Forestry & Environmental Studies (F&ES). The work of the F&ES team would not be possible without support and collaboration from critical academic and applied research partnerships, including Skidmore College, the Noble Research Institute, The Nature Conservancy, Our Sci, and many more.


Yale F&ES Science Team

Mark Bradford, PhD | Faculty Lead

Mark Bradford, PhD | Faculty Lead

Emily Oldfield, MS & PhD | Science Advisor

Emily Oldfield, MS & PhD | Science Advisor

Dan Kane, MS | Lead Researcher

Dan Kane, MS | Lead Researcher


Quick Carbon Collaborators

 

These collaborators from the worlds of academia, non-profit, business, and agricultural practice are key to facilitating research and application on our four primary work-streams

 

1. Spectrometer Optimization. The reduced-size VNIR sensors used by Quick Carbon employ only a handful of the wavelengths that benchtop VNIR spectrometers do, but our data reveal their capacity to estimate SOC with reliable accuracy. In this first work-stream we will work with (a) the publicly-available dataset of VNIR spectra for the US and Europe to select wavelength combinations that have the most power to estimate SOC concentrations and potential predictive variables (e.g. clay content); we will also work with (b) soils sampled at much higher spatial densities, within-landscapes, to test how spatial variation (or scale) affects the choice of wavelengths, given the potential that among-site analyses can fail to capture predictors of soil carbon dynamics that dominate within-sites.

Collaborators: Greg Austic (Our Sci) | Dan TerAvest (Our Sci) | Jon Sanderman (Woods Hole Research Center)

2. Sample Design. We will continue to build and enhance our ability to intelligently sample large (and small) landscapes for soil carbon. We will accomplish this through the design and testing of web-based mapping tools like Stratifi.

Collaborators: Charlie Bettigole (Skidmore College)

3. Dynamic Analysis and Visualization. Under this work-stream we will develop analytical and visualization applications that return readily-interpretable information about SOC – in real time – to the scientist and land manager in the field. These applications will apply machine-learning approaches to integrate data from field application of the spectrometers dynamically with GIS data sources and soil spectral libraries to produce spatially-explicit estimates of SOC concentrations with measurement uncertainty. The collective aim for Work-streams 1 and 2 is to develop a sensor system that enables production of fine-scale landscape maps of SOC in real-time without the laborious process of returning soils to the lab for elemental analysis.

Collaborators: Greg Austic (Our Sci) | Dan TerAvest (Our Sci)

4. Implementation. This fourth workstream is focused on the application of technology and methods from workstreams 1-3. However, we believe that structures and tools for application must proceed concurrently with the fundamental research necessary to optimize sensors, visualize data, and design sampling schemes. New data collected in the field through pilot projects, or through collaborations with organizations that hold existing soil archives, will be critical in providing training information for each of the first three workstreams. Workstream 4 will include designing ‘kits’ for dispersed research projects, overseeing data collection and testing of field equipment, and working with partners to seek out soil archives across the country.

Collaborators: To scale, we only succeed through work with our partner organizations from around the country.