1. 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.

2.  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

3. 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

4. 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

5. 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

6. 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)

7. 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

8. 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

10. 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

11. Model Structural Uncertainty  Fixed effects (βX) include both categorical (e.g. type of system, tillage practice) and continuous (e.g. temperature, precipitation, clay content) variables  Random effects (γ) account for spatiotemporal dependencies in experimental data (thus incorporates scale-dependencies in uncertainties)  Adjusts for biases and provides estimates of uncertainty

12. Totals for US Croplands (1990s) 1990-1994: -62.0 ± 22% Tg CO 2 eq. yr -1 1995-2000: -64.0 ± 16% Tg CO 2 eq. yr -1

13. USDA Major Land Resource Areas (MLRAs) X X -0.61 ± 525% tonnes CO 2 eq. ha -1 yr -1 -1.12 ± 49% Tg CO 2 eq. yr -1 -1.17 ± 644% tonnes CO 2 eq. ha -1 yr -1 -0.59 ± 45% Tg CO 2 eq. yr -1

14. Reducing Uncertainties Using Enhanced Vegetation Index to Improve Estimation of Crop Production (NASA- CASA model) Developing a national measurement network to refine uncertainty analysis (3000-5000 sites)