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 LaboratoryColorado State University

This research is supported by NASA New Investigator Program grant NAG5-10593 to Conant.

Soil

carb

on

C sequestration in grasslands

Amount?Characteristics?

Duration?

Rate?

Recovery?Shape?Slope?

Amount?Influences?

Native/Nominal management

Improved management

Degraded grassland(1O overgrazing)

Improved managementwith high inputs

Time

C sequestration potential = of some or all

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).

Grassland areaOvergrazed area

Overgrazing severityLightModerateStrongExtreme

Activity data – grasslands degraded by overgrazing

Potential C sequestration (tC/ha/yr)-0.50 - -0.25-0.25 - 0.00 0.00 - 0.25 0.25 - 0.55

C sequestration potentialOvergrazed grassland area

C sequestration in grasslands degraded by overgrazing

Potential regional to global C sequestration in grasslandsT

g C

yr-1

0

100

200

300

400

500

CRP

Cons. tillage (US)SE pastures (US) Grazing (global)Grasslands (US) Grasslands (global)

2.5 8.045.5

69.8 71.5

460

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.

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

An alternative approach – Production efficiency models

Incoming PAR Reflected PAR

TransmittedPAR

fAPAR = [(PARAC – PARAC) -(PARBC – PARBC)]

PARAC

NPP = fAPAR ε

C fixation α to fAPAR

Rf2

Rf1

Rc3

Rc2

C

fAPAR

Sulmet Sulstr

L1

L2

L3 S2

S1

SolmetSolstrRc1

Grass PEM

Century soil organic matter model

Three pool decomposition model• Century-like• Tillage impacts Slow C turnover• Soil texture influences transfers to passive pool

Sulmet

Sulstr

Solmet

Solstr Slow C

Active C

Passive C

CO2

CO2

CO2

CO2

CO2

CO2

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

Rf Rc CL S

C sink strength within the model

> > > >

Rf Rc CL S> > > >

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

Mixed prairie

Short-grasssteppe

Field application – western Great Plains (US)

Field application – western Great Plains (US)

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.

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?

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?

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.

MODIS NDVI (7/3/01) MODIS NDVI (6/29/02)

Heavy

Moderate

Light

CRP

Research sites – treatments; ground data

Time

Sta

nd

ing

bio

mas

s

Season

NP

P/L

UE

Cu

mu

lati

ve N

PP

Time

High intensity grazingModerate intensity grazingExclosure

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