Measuring and monitoring soil carbon stocks from point to continental scale in Australia

AGRICULTURE AND FOOD

Jeff Baldock, Mike Grundy, Raphael Viscarra RosselCSIRO

Outline

• Quantifying soil organic carbon stocks and changes over time.

• Current approaches in the Australian Emission Reduction Fund

• Composition of soil organic carbon and why it is important

• A proposed measurement/modelling/prediction framework

Approaches to measuring/predicting soil carbon stocks

Remote sensingDirect measurement Proximal sensing

Accuracy of values derived for a defined location

Spatial representativeness

Computer model

1. Derive the true uncertainty associated with each measurement type2. At what level of spatial variability do we sacrifice analytical certainty for

better spatial coverage?

Quantifying soil carbon stocks

The manner in which soil samples are collected and processed is important

97.6Soil carbon stock (Mg C/ha) 92.9 101.6

Bulkdensity

(Mg/m3)=

Soil carbonstocks

(Mg C/ha)

Soil layer

thickness(cm)

x x xCarboncontent

(g C/kg AD soil)(1 + m)x 1 -

Proportionof gravel(>2mm)

x 0.10

Source Soil property Actual

Soil Analysis

OC (g OC/kg soil) 25.4

m (g water/g soil) 0.12

Gravel (g gravel/g soil) 0.12

Soil Sampling

Bulk density (Mg soil/m3 soil) 1.25

Depth (cm) 30.0

Measured

25.4

0.12

0.12

1.30

30.2

Measured

25.4

0.14

0.10

1.30

30.2

Temporal changes in 0-30 cm soil organic carbon stock at Armidale (grazed tall fescue pasture)

Effect p-valueTime 0.276

Potential sources of variation

• Spatial• Temporal• Sampling• Preparation• Analytical

Carbon estimation

area

Rep 1

Rep 4

Rep 2

Rep 3

t0 samplingt1 samplingt2 sampling

t24 sampling

Reference state

Referencesurface0

10

20

30Soil

dept

h (c

m)

>30 cm

<30 cm

Expressing variations in soil carbon stocks on the basis of an equivalent soil mass

Increase

x

x

Decrease

y

y

Temporal change in bulk density

Variation in samplingdepth

Too deep

Too shallow

Temporal changes in Equivalent Soil Mass organic carbon stock at Armidale (grazed tall fescue pasture)

Effect p-valueTime0.778

Using ESM has removed• Spatial and temporal

variations in bulk density• Sampling issues (depth,

compaction)

Residual variance• Spatial and temporal OC• Preparation• Analytical

Carbon estimation

area

Rep 1

Rep 4

Rep 2

Rep 3

t0 samplingt1 samplingt2 sampling

t24 sampling

Baseline sampling round (t0)

Direct measurement soil carbon ERF methodology

Method prerequisites: • based on direct measurement• no prior knowledge of SOC spatial variability• allow two depth layers, and • conservative in its estimate of stock change

CEA – Carbon Estimation Area

• Stratified random sampling within equal area strata

• Create composite samples by acquiring a soil core from each stratum

• Each composite sample encompasses spatial variability

• Variability between samples sent for analysis is reduced

• Improved ability to detect temporal change

t1 sampling roundt2 sampling round

2010 2015 2020 2025 203040

50

60

70

80

90

100

Measured SOC stockLinear (Measured SOC stock)

Measurement year

Equi

vale

nt so

il m

ass o

rgan

ic c

arbo

n st

ock

(Mg

C/ha

)

Regression statisticsy = 2.26x - 4497.8

R² = 0.7897StdErr Slope =0.583

df = (n-2) = 4

Monitoring change in soil carbon stocks – calculating both the magnitude and certainty of stock change

80% probability of exceedance

0 1 2 3 4 5 60.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Cumulative probability distribution50% probability of exceedance

Rate of SOC stock change(Mg C/ha/y)

Cum

ulati

ve p

roba

bilit

y (o

ne-ta

iled

t-dist

ributi

on)

2.26

80% probability of exceedance

1.71

Soi

l org

anic

carb

onComposition of soil organic carbon

Crop residues on the soil surface (SPR)

Buried crop residues (>2 mm) (BPR)

Particulate organic carbon (2 mm – 0.05 mm) (POC)

Humus organic carbon (<0.05 mm) (HOC)

High CH2O (energy rich)

Recalcitrance increases

Decreasing C/N/P (nutrient rich)

Resistant organic carbon (ROC): dominated by charcoal

Composition of soil organic carbon: impact on vulnerability

NSW000073 NSW000045 NSW000066 NSW000077 NSW000101 NSW0000790

10

20

30

40

50

60

70

80

90

POCHOCROC

Location and soil type

Carb

on co

nten

t (m

g C/

g so

il)

4832

31

11

9

20

36 48

49

60

59

59

1720

29332120

Vulnerability to change

POC

HOC + ROC=

Baldock et al. 2013 Soil Research 51 561-576

Role of soil organic carbon fractions in national inventory

DPM

RPM

PlantInputs

BIO

HUM

CO2

Variant of RothC

IOMFire

Substitute conceptual

pools with

measured fractions

RPM = POC, HUM = HOC,IOM = ROC

National accounts – CO2-e emissions

Calibration of model with measured stocks

A complete measurement/modelling/prediction system

(c) SOC stock change modelModelling within a spatial framework

that accounts for uncertainty

• Georeferenced soil C stocks • Continuous covariates (predictors)

Spatial layers of current state and certainty

(a) Definition of current soil carbon state (b) Carbon inputs to soil from plant production

• Measurement• Computer simulation • Remote sensing

(e) Bayesian hierarchical modelling for improved model parameterisation

0.02 0.06 0.08

K1

0.5 1.0 1.5 2.0

µp

(d) Predicted future statesSoil carbon stocks Risk of outcomes Certainty of trajectory

(f) Impacts on soil• Nutrient provision• Available water• Infiltration

Thank youJeff BaldockPMB 2, Glen Osmond, SA 5064Email: jeff.baldock@csiro.auPhone: (08) 8303 8537

CSIRO LAND AND WATER/ SUSTAINABLE AGRICULTURE FLAGSHIP

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