This presentation was presented during the 2 Parallel session on Theme 1, Monitoring, mapping, measuring, reporting and verification (MRV) of SOC, of the Global Symposium on Soil Organic Carbon that took place in Rome 21-23 March 2017. The presentation was made by Mr. Jeff Baldock, from CSIRO - Australia, in FAO Hq, Rome

1. Measuring and monitoring soil carbon stocks from
point to continental scale in Australia
AGRICULTURE AND FOOD
Jeff Baldock, Mike Grundy, Raphael Viscarra Rossel
CSIRO

2. 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

3. 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 type
2. At what level of spatial variability do we sacrifice analytical certainty for
better spatial coverage?

4. 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
Bulk
density
(Mg/m3)
=
Soil carbon
stocks
(Mg C/ha)
Soil
layer
thickness
(cm)
x x x
Carbon
content
(g C/kg AD soil)
(1 + m)x 1 -
Proportion
of 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

5. Temporal changes in 0-30 cm soil organic carbon stock at
Armidale (grazed tall fescue pasture)
Effect p-value
Time 0.276
Potential sources
of variation
• Spatial
• Temporal
• Sampling
• Preparation
• Analytical
Carbon
estimation
area
Rep 1
Rep 4
Rep 2
Rep 3
t0 sampling
t1 sampling
t2 sampling
t24 sampling

6. Reference
state
Reference
surface0
10
20
30
Soildepth(cm)
>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 sampling
depth
Too
deep
Too
shallow

7. Temporal changes in Equivalent Soil Mass organic carbon
stock at Armidale (grazed tall fescue pasture)
Effect p-value
Time 0.778
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 sampling
t1 sampling
t2 sampling
t24 sampling

8. 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 round
t2 sampling round

9. 40
50
60
70
80
90
100
2010 2015 2020 2025 2030
Equivalentsoilmassorganiccarbonstock
(MgC/ha)
Measurement year
Measured SOC stock
Linear (Measured SOC stock)
Regression statistics
y = 2.26x - 4497.8
R² = 0.7897
StdErr 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
0.0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
0 1 2 3 4 5 6Cumulativeprobability
(one-tailedt-distribution) Rate of SOC stock change
(Mg C/ha/y)
Cumulative probability distribution
50% probability of exceedance
2.26
80% probability of exceedance
1.71

10. Soilorganic
carbon
Composition 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

11. Composition of soil organic carbon: impact on vulnerability
0
10
20
30
40
50
60
70
80
90
NSW000073 NSW000045 NSW000066 NSW000077 NSW000101 NSW000079
Carboncontent(mgC/gsoil)
Location and soil type
POC
HOC
ROC
48
32
31
11
9
20
36 48
49
60
59
59
17
20
29
3321
20
Vulnerability
to change
POC
HOC + ROC
=
Baldock et al. 2013 Soil Research 51 561-576

12. Role of soil organic carbon fractions in national inventory
DPM
RPM
Plant
Inputs
BIO
HUM
CO2
Variant of RothC
IOM
Fire
Substitute
conceptual
pools
with
measured
fractions
RPM = POC, HUM = HOC,
IOM = ROC
National accounts – CO2-e
emissions
Calibration of model
with measured stocks

13. A complete measurement/modelling/prediction system
(c) SOC stock change model
Modelling 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 states
Soil carbon stocks Risk of outcomes Certainty of trajectory
(f) Impacts on soil
• Nutrient provision
• Available water
• Infiltration

14. Thank you
Jeff Baldock
PMB 2, Glen Osmond, SA 5064
Email: jeff.baldock@csiro.au
Phone: (08) 8303 8537
CSIRO LAND AND WATER/ SUSTAINABLE AGRICULTURE FLAGSHIP
Presentation title | Presenter name | Page 14