The purpose of this project is to validate DayCent and Integrated Biosphere Simulator (IBIS) soil organic matter models for grasslands and shrublands, respectively, on Department of Defense (DoD) lands through direct, cost-effective measurement using Mobile Inelastic Neutron Scattering (MINS) across large areas of diverse DoD rangelands. A comparison of predicted model outputs with actual observed soil measurements will be conducted.

DayCent is a daily time step biogeochemical model designed to analyze the flow of organic matter and nitrogen between the atmosphere, plants, and soil, primarily in grasslands and agroecosystems. It is best suited for understanding and predicting changes in grasslands and agroecosystems. The IBIS model is a variable time step biogeochemical model that simulates the Earth's terrestrial biosphere, considering the interactions between energy, water, and nutrient exchange among plants, the atmosphere, and the soil. Its focus on vegetation management practices are well suited for understanding and predicting changes in shrub agroecosystems. MINS technology delivers an accurate, cost-effective soil organic matter measurement and mapping capability to a depth of 30cm.

DoD currently does not account for soil organic matter outside a static Soil Survey Geographic Database data layer at 250m resolution. MINS technology is a scale order more efficient and cost-effective than current soil measurement standards and will provide the means to effectively measure soil health at landscape scales on DoD lands. Knowledge of soil health on DoD training lands is important for resilience to ensure training readiness: increased soil organic matter increases water infiltration and retention, reducing flood and wildfire risks during extreme precipitation events; binds residues and other undesirable materials from fire training exercises that can pose operational risks; and keeps soils intact to ensure availability for usage. Understanding the spatial distribution and concentration of soil organic matter across diverse DoD training lands, validating models that can predict these variations, and identifying areas of improvement will all aid in improving training land resilience to ensure availability and realism. (Anticipated Project Completion - 2026)