Uncertainty in national-scale soil C inventory estimates Keith Paustian 1,2, Stephen Ogle 2, Jay Breidt 3 1 Dept of Soil and Crop Sciences, Colorado State Univ. 2 Natural Resource Ecology Lab, Colorado State Univ. 3 Dept. of Statistics, Colorado State Univ. Inventory reporting increasingly focusing on uncertainty Policy all about setting priorities, e.g. GHG mitigation strategies Large potential, low uncertainty policy actions, investment $$$ Large potential, high uncertainty R&D $$$ (maybe) Small potential good luck! Uncertainty the policy context Special challenges for soil C estimation Management hugely important in determining rates and trajectories of SOC change good data on spatial/temporal management trends is often lacking Soil stocks have large inertia dependencies on long-term previous LU & mgmt history Soil C measurement data relatively sparse essentially no designed remeasurement inventory systems at national scale Uncertainty components addressed in US agricultural soil C estimates Management inputs (levels of fertilization, tillage, manuring) Model structure/parameter uncertainty Initial conditions and land use history Upscaling of inventory point data to national- scale Database Management Run Control Simulation Model: Century Structural Uncertainty Estimator Management Activity Environmental Conditions Point Scale Data (NRI Survey) PDF Model Inputs Database Results Database Bottom-up modeling framework Data sources National Resource Inventory (NRI) Statistically-based sample of ca. 800,000 points since 1979 LU, soils, crop rotations/vegetation Most land management practices were NOT collected in NRI (but new data since 2003) County-, state- and regional survey data of management practices E.g. tillage, fertilization, manuring, irrigation Regional-level LU practices (pre-1980) US National Resources Inventory (NRI): Point-Based Survey Data Johnson County, IA 563 points Note: spatial references shown are approximate Source: US Dept. of Agriculture Integrating survey with point data Tillage Practices (CTIC) PDF Mineral N Fertilization (USDA-ERS) PDF Manure Amendments (USDA and EPA) PDF Johnson County, IA Monte Carlo Analysis Model Structural Uncertainty Model algorithms, parameterization and measurement error Empirically-Based Approached Simulate management impacts on SOC storage for experimental sites ca. 50 sites with over 800 management treatment observations Linear mixed effect models Model Structural Uncertainty Fixed effects (X) include both categorical (e.g. type of system, tillage practice) and continuous (e.g. temperature, precipitation, clay content) variables Random effects () account for spatiotemporal dependencies in experimental data (thus incorporates scale-dependencies in uncertainties) Adjusts for biases and provides estimates of uncertainty Totals for US Croplands (1990s) : 22% Tg CO 2 eq. yr : 16% Tg CO 2 eq. yr -1 USDA Major Land Resource Areas (MLRAs) X X 525% tonnes CO 2 eq. ha -1 yr 49% Tg CO 2 eq. yr 644% tonnes CO 2 eq. ha -1 yr 45% Tg CO 2 eq. yr -1 Reducing Uncertainties Using Enhanced Vegetation Index to Improve Estimation of Crop Production (NASA- CASA model) Developing a national measurement network to refine uncertainty analysis ( sites)