SBIR-STTR Award

Healthy soils are essential to human well-being and to overall environmental quality. Key to soil health is maintenance of the soil's organic carbon content. Recently carbon credit markets have emerged a means of incentivizing practices that increase the organic carbon content of soils. The resulting carbon sequestration in soils will also contribute to stabilization of atmospheric CO2levels. However such markets are not feasible without cost-effective and efficient means for verifying the soil organic carbon content. The outcome of this proposed project is a prototype unit the Subterra Green Model P that enables land managers to rapidly and accurately map the organic carbon content of their soils in three dimensions to depth of one meter. The unit employs a visible/near-infrared spectroscopic probe that is pushed into the soil at intervals and is small and maneuverable enough to be operated by one person in many different vegetative conditions. In the proposed work plan we will significantly modify the design of our existing prototype hardware by incorporating new spectrometers and a GPS unit and new chemometric analysis and mapping algorithms. To assure the required accuracy and precision the modified prototype will be tested at four Ohio sites with varying soil texture organic matter content and land cover conditions and then it will be iteratively improved based on the test results.

Key to soil health is maintenance of the soil's organic carbon content.Carbon credit marketsincentivize practices that increase the soil organic carbon content so that the resulting carbonsequestration will contribute to stabilization of atmospheric carbon dioxide levels. Howeversuch markets are not feasible without accurate and cost-effective means for verifying the soilorganic carbon stock.The ultimate goal of this effort is a prototype unit the Subterra Green that can rapidly andaccurately map soil organic carbon in three dimensions to depth of 90 cm.The unit employs avisible/near-infrared spectroscopic probe that is pushed into the soil at intervals and is smalland maneuverable enough to be operated by one person.Building on the Phase I results thespecific objectives of the Phase II project are (1) to extend the generalizability of the Subterramethod to a large agriculturally important region of the U.S.; (2) to define the the sitecharacteristics for which a given model is applicable; (3) to improve and document theminimum change in per-site soil organic carbon stock detectable using the Subterra method;and (4) to continue to improve the accuracy and precision of per-sample soil organic carbonmodels.Our approach is to extend generalizability while improving accuracy and precision bysampling a broad range of sites in the target area in order to increase the quality and volume ofdata input into machine learning models.Work is organized around four field campaigns inwhich a total of eight sites will be intensively sampled and mapped. Lower-intensity samplingwill be also be conducted at an additional 36 sites.To ensure optimal distribution of the inputdata site selection and data collection will be evenly spread over a rubric of relevantenvironmental co-variates in the target area.Global regional and local models of soil organiccarbon content will be developed and model performance will be continuously tracked on theenvironmental co-variates rubric.We expect to consistently achieve a precision of better than 0.3 Mg C per hectare for soilorganic carbon stock determination sufficient for verification in the carbon credit market. Therecord of model performance with respect to the co-variates rubric will demonstrate that thesehigh accuracy levels are achievable in commercial application throughout the target areawithout the need for large numbers of additional calibration samples so that the SubterraGreen can be included among accepted protocols for measurement recording and verificationin the carbon credit market.Beyond the scope of this Phase II project but still important forcommercial application in large agricultural settings we plan to automate the collection ofSubterra Green data by mounting it on a robotic unit.