Development of a regionally validated mid-infrared soil spectral library and predictive models to enable efficient, accurate and low-cost characterization of soil carbon content and sequestration potential in Nova Scotia agricultural and forest soils

Drawing upon existing and new agriculture and forestry soil collections acquired from diverse project partners (NSFA, NRCan, NSDA, OMAFRA, IRDA), this project will characterize soil organic carbon (SOC) content and storage potential, as influenced by inherent soil properties (e.g., soil texture) and management practices, including practices designed to increase SOC content. Mid-infrared (MIR) spectroscopy is effective for characterizing the organic and inorganic components of soils, avoids the use of chemical reagents, and allows efficient processing of large sample volumes at reduced cost. Hence, we will develop predictive SOC models based on MIR spectroscopy. As high sampling densities are required to document small temporal changes in SOC compared to background levels, reducing analytical costs is

critical towards tracking changes resulting from improved practices (i.e., nature-based solutions). The project objective is to develop the regions first spectral library and validated models to enable accurate and cost-effective prediction of SOC content and storage potential in Nova Scotia’s soils.

We are leveraging team projects providing a large and diverse database of existing soil samples but request resources for technical support and analytical costs for sample processing and analysis required to develop the spectral library and validate the predictive models. The lead project team, based at Dalhousie University, has the expertise, including two fully funded post-doctoral fellows, and analytical infrastructure to carry out this work.

This project will:

1. Reduce costs associated with monitoring SOC and thus improve adoption of BMPs that improve SOC and soil health in agriculture and forestry;
2. Enhance performance of high-resolution digital soil mapping (DSM) models tracking SOC dynamics and sequestration potential;
3. Support innovation and cost efficiencies in delivering of services linked to the provision of SOC credits and soil resource mapping; and
4. Improve efficiency of soil sample processing and analysis by provincial and private laboratories.