1. Effect of the Conservation Reserve Program (CRP) on Soil Carbon By Jay D. Atwood Steven R. Potter Jimmy R. Williams M. Lee Norfleet 22 March 2005 Atwood and Norfleet are with USDA, NRCS, Resource Inventory and Assessment Division. Potter and Williams are with The Texas A&M University System, Texas Agricultural Experiment Station, Blackland Research and Extension Center, Temple TX Presented at the “Third USDA Symposium on Greenhouse Gases and Carbon Sequestration in Agriculture and Forestry”, March 21-24, 2005, in Baltimore, Maryland

2. * 18,446 total NRI CRP points

3. Figure 2. Regions defined for analysis of 1997 NRI CRP. (only the 8 – digit Hydrologic Units with analyzed CRP points are shaded) West Northern Great Plains Southern Great Plains Upper Midwest South Central Northeast Southeast

4. Figure 3. Domain of the 1997 NRI CRP analysis. * Acres of practices exceed enrollment since each enrolled acre may have multiple practices.

5. Figure 4. Acreage of crops modeled.

6. Figure 5. CRP cover by type and region

7. Figure 6. Definition of Model Representative Land Units and Crop and CRP simulations for the 1997 NRI CRP points. NRI CRP Points Define Prior Crop simulations, with acreage weights, based on NRI crop history If not corn, wheat or pasture If corn, wheat or pasture Determine acreage shares for corn, pasture, and wheat types Conduct Crop scenario model simulations for 3 tillage types for each crop Crop type 1 Crop type n Calculate weighted total/average results for Crop Scenario with acreage weights and shares Define CRP simulations for 4 cover types with acreage weights based on county level CRP enrollment data, e.g., 75% Introduced Grasses 15% Native Grasses 7% Trees 3% Wildlife habitat Conduct CRP scenario model simulations for each CRP cover type Calculate weighted total/average results for CRP Scenario using acreage weights and shares Compare Scenarios Cluster points by region, state, climate, soil, and type of structural conservation practices

8. Figure 7. Counts and average acreage representation of model land units and simulations by region. Representative Model Land Units Simulations per model land unit

9. Figure 8. Key analytical assumptions 1.If land had not been enrolled in CRP, the former crop mix would have continued, but tillage, conservation practice, and nutrient management would have evolved like non-enrolled land. 2.Procedures were applied to set initial soil carbon levels at levels consistent with having a history of cultivation 3.Simulations made with the EPIC model (version with Century model type carbon accounting) 4.Five sets of simulations, each with different stochastically generated weather, were averaged 5.Continuous mono-cropping except for wheat – fallow rotations. 6.Wind erosion calibrated to 1997 NRI levels via lower limit on soil surface moisture 7.Net field level soil loss estimates with Modified Theoretical Small Watershed (MUST) version of USLE were on average 50% the magnitude of the NRI USLE soil losses

10. Figure 9. Overview of soil C and N processes in the EPIC model. C and N dynamics interact directly with soil moisture, temperature, erosion, tillage, soil density, leaching, and translocation functions of EPIC; Equations describe the role of soil texture in stabilization of soil organic matter; C and N compounds are allocated into five compartments in terms of turnover time: –Metabolic litter, with a time span of months; –Structural litter, with a time span of months to years; –Biomass (active), with a time span of months; –Slow humus, with a time span of 20 to 50 years; –Passive humus, with a time span of 400 to 2000 years; C and N can be lost through leaching or in gaseous form to the atmosphere; There are four key differences between the equations of EPIC and Century (Izaurralde et al., 2001) : –EPIC’s leaching equations move organic matter from surface litter to subsurface layers; –Temperature and water controls affecting transformation rates are calculated with equations already in EPIC; –The surface litter fraction in EPIC has a slow, but not passive compartment; and –Lignin concentration in EPIC is modeled as a sigmoidal function of plant age.

11. Figure 10. Initialization of Soil Carbon (%) Starting Point Issue – Many of the pedon samples in the soil survey database appear to be from non-cultivated conditions; soil carbon is high relative to cultivated conditions. Soils in the soil survey data base were screened for “AP” layer, indicating history of cultivation. Those with an “AP” layer and having less than 10% organic matter (5.7% organic carbon i.e., mineral soils only), were used to fit the following equation: Y = aX -bX where Y = soil organic carbon (%) X = depth (in cm) The equation was initially fit at the hydrologic group level within each of the 10 USDA Farm Production regions. The regions were subsequently combined into four groups. The equation was used to set the soil organic carbon by layer in all soils for the study except for the “Organic” and “other” texture groups which were left at soil survey levels.

12. Figure 11. Hydrologic Group Effect of Formula to Set Initial Soil Carbon (%) A – low runoff potential, high infiltration rate B – moderate infiltration C – slow infiltration D – high runoff potential, very slow infiltration rate

13. Figure 12. Regional Effect of Formula to set Initial Soil Carbon (%)

14. Figure 13. Water Induced Soil Loss Estimation USLE = C*P*R*K*(LS) where C is the crop management factor (range of 0 to 1) P is the conservation practice supporting factor (range of 0 to 1) R is the rainfall factor K is the soil erodibility factor (LS) is the factor based on slope length (L) and slope (S) *EPIC calculates USLE daily, with daily C and R The NRI uses long run average annual C and R, prediction of long term annual average soil erosion MUST replaces R with R = 2.5*(Q*qp) 0.5 whereQ is runoff volume in mm is a function of daily rainfall, a retention parameter, and soil water content - retention parameter depends on soil, land use, management, and slope qp is the peak runoff rate in mm per hour is a function of infiltration characteristics, rainfall intensity, and watershed area * MUST was theoretically derived from sediment concentration data bases MUST is predicted for every storm event; individual storm events are summed for the year **Except for a few comparison tables and charts, all results based on MUST.

15. Figure 14. Crop water erosion rates by region, period, and scenario.

16. Figure 15. Crop wind erosion rates by region, period, and scenario.

17. Figure 16. Regional soil carbon storage benefit due to CRP.

18. Figure 17. Year end soil carbon by region for non-forage crops and CRP.

19. Figure 18. Difference in annual soil C change ((CRP t -CRP t-1 )-(crop t -crop t-1 )) *. Years * Only non-forage crops included.

20. Figure 19. CRP affect on soil C storage by region, period, and tillage type (non-forage crops only).

21. Figure 20. Percent of area losing and gaining > 5% soil C by region and period. Years 1-10 Years 11-20 Years 21-30

22. Figure 21. Average (non-acreage weighted) CRP C sequestration rates by soil texture group and period. Truncated from 6.5, 5.5, and 1.9

23. Figure 22. Estimated CRP Soil C Benefits

24. Figure 23. Effect of alternative soil erosion and initial soil C on CRP C sequestration rate estimate, national level. Reported Alternative: CRP erosion benefit of 10.5 t/a/y Soil survey initial C No smoothing for wheat-fallow

25. Figure 24. Effect of alternative soil erosion and initial soil C on selected regional C accumulation (tons/acre). Upper Midwest alternative CRP and Crop CRP erosion benefit of 10.5 t/a/y Soil survey initial C No smoothing for wheat-fallow NE alternative CRP and Crop Upper Midwest report CRP and Crop NE report CRP and Crop

26. Figure 25. Effect of erosion and initial C on difference in annual C change ((CRPt-CRPt-1)-(cropt-cropt-1))*. Alternative: CRP erosion benefit of 10.5 t/a/y Soil survey initial C No smoothing for wheat-fallow