1. Remote sensing and ecological modeling for assessing C sequestration in semiarid grassland soils Richard T. Conant, Randall B. Boone, and Moffatt K. Ngugi Natural Resource Ecology Laboratory Colorado State University This research is supported by NASA New Investigator Program grant NAG5-10593 to Conant.

2. Soil carbon C sequestration in grasslands Amount? Characteristics? Duration? Rate? Recovery? Shape? Slope? Amount? Influences? Native/ Nominal management Improved management Degraded grassland (1 O overgrazing) Improved management with high inputs Time C sequestration potential =  of some or all

3. Tier 3: Use a combination of dynamic models along with detailed soil C emission/stock change inventory methods. Tier 2: Stock change factor values can be estimated from long- term experiments or other field measurements. Tier 1: Net soil C changes for mineral soils are estimated on the basis of relative stock change factors, applied over a 20 year inventory period IPCC, most other modeling methods All three tiers combine activity data with some estimate of changes in C stocks (average from broader literature, most pertinent literature, or modeled).

4. Grassland area Overgrazed area Activity data – grasslands degraded by overgrazing

5. C sequestration potential Overgrazed grassland area C sequestration in grasslands degraded by overgrazing

6. Potential regional to global C sequestration in grasslands 2.5 8.0 45.5 69.871.5 460

7. Methodological limitations Model applicability is limited by activity data (land use, land management, land use history, etc.), data on soil C stocks, or both Uncertainty about soil C stocks contributes more to overall uncertainty than activity data for the US National Agricultural Inventory (though not much more) US activity data are among the best available. Activity data for US grasslands and for agriculture and grasslands in other countries is much less detailed. Activity data are often non-spatial; makes correlation with other factors that impact soil C stocks (climate, soil texture, topography, etc.) impossible.

8. An alternative approach Provision 1: Constraint using independent, spatial data Provision 2: Incorporation of management practices Provision 3: Utility across a variety of grassland systems Provision 4: Applicability at multiple scales Provision 5: Capacity to generate uncertainty estimates

9. An alternative approach – Production efficiency models Incoming PAR Reflected PAR Transmitted PAR fAPAR = [(PAR  AC – PAR  AC ) -(PAR  BC – PAR  BC )] PAR  AC NPP = fAPAR  ε C fixation α to fAPAR

10. R f2 R f1 R c3 R c2 C fAPAR Sul met Sul str L1L1 L2L2 L3L3 S2S2 S1S1 Sol met Sol str R c1 Grass PEM

11. Century soil organic matter model Three pool decomposition model Century-like Tillage impacts Slow C turnover Soil texture influences transfers to passive pool Sul met Sul str Sol met Sol str Slow C Active C Passive C CO 2

12. Short-term physiological ε responses: Increased allocation to rapidly growing tissues Allocation shifts favoring tissues that accumulate more efficiently (i.e., leaf tissue rather than seeds) Alternatively, shifts may favor inefficient accumulation (i.e., secondary compounds rather than leaf tissue) Above:belowground allocation Grazing and LUE Long-term sp. composition-driven impacts on ε : Changes in root C allocation Shifts in nutrient and water uptake Differential responses to physioclimatic stress (i.e., shifts to species poorly adapted to local climate regimes We hope to be able to model this without characterizing spp. composition

13. RfRf RcRc C LS C sink strength within the model >> > > RfRf RcRc C LS >> > > No water stress: Water stress: All tissues have respiration requirements If respiration requirement>C supply, tissue senesces C allocation to roots increases with water stress Grazing impacts standing biomass, but not ability of plant tissue to fix C Grazing could lead to increased C if (a) increases belowground allocation or (b) increases belowground turnover

14. Mixed prairie Short-grass steppe Field application – western Great Plains (US)

15. 1) Characterize grazing management impacts on light use efficiency. 2) Assess C supply/sink relations for different tissues. 3) Select reflectance models for determining canopy structure. 4) Test performance of water model**. 5) Assess impact of omitting N and plant reproduction. 6) Evaluate soil C stock predictions.

16. Unknowns/uncertainties How important is omission of plant reproduction? When is this omission most important? Can the model work well consistently without accounting for N limitation? Is it possible to resolve canopy constituents or should we rely upon allometric data? Will the model, derived from AVHRR data, run as well with MODIS data? How does this more constrained plant production model interact with the Century model? Does it accurately predict soil C stocks?

17. Some thoughts on C sequestration in Mali Challenges: Soils -heavily weathered -coarse texture Climate -flooded + arid every year Vegetation -Low C inputs: stover removed from many fields Time -Cultivated soils have not been heavily tilled over time -Rangelands degraded beyond the point of simple interventions Opportunities: Increased yield? Use of manure? Cover crops? Better grazing management?

18. Conclusions Grass PEM is an alternative approach to C modeling. Data are broadly available, frequently repeatable, and uniform for entire study area. PEM NPP estimates in grasslands must account for biomass removal; temporal resolution is important. Accurate estimates of C fixation can be made without accounting for responses to grazing other than altering LAI/APAR.

19. MODIS NDVI (7/3/01)MODIS NDVI (6/29/02) Heavy Moderate Light CRP Research sites – treatments; ground data

20. Time Standing biomass Season NPP/LUE Cumulative NPP Time High intensity grazing Moderate intensity grazing Exclosure Challenges due to grazing Optimal allocation theory (Thornley 1972, Field 1995) Assumption of PEMs: grazing impacts standing biomass, but not ability of plant tissue to fix C