Cost effective tools for soil organic carbon monitoring · point sample to landscape scales (Hobley...
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Cost effective tools for soil organic carbon monitoring Keith Shepherd & Ermias Betemariam 9 April 2013 EGU General Assembly, Vienna
Transcript of Cost effective tools for soil organic carbon monitoring · point sample to landscape scales (Hobley...
Cost effective tools for
soil organic carbon
monitoring
Keith Shepherd & Ermias
Betemariam
9 April 2013
EGU General Assembly,
Vienna
Outline • Context - soils as basis for ecosystem
functioning
• Decisions before measurement
• Emerging technologies (Applications in Africa)
• Measurement and uncertainties
Context
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Soils the largest carbon reservoir of the terrestrial carbon -
small change could cause large effects on climate system
Soil comes to the global agenda: Sustainable intensification;
Global Environmental Benefits (GEF/UNCCD)
SOC as useful indicator of soil health. Taxonomic soil
classification systems provide little information on soil
functionality in particular the productivity function (Mueller et al
2010)
But lack of coherent and rigorous sampling and assessment
frameworks
High spatial variability in soil properties - large data sets
required
Soil monitoring is expensive to maintain
Digital wall-to-wall soil mapping of soil functional properties
desired
Decisions before
Measurement
• Little evidence for impact of monitoring initiatives on
real-world decision making/management
• Information has no value unless it has the potential
to change a decision
• The measurement inversion − most measurement
effort in business cases is spent on variables that have
the least information value
Review of the Evidence on Indicators,
Metrics and Monitoring Systems
DFID-commissioned review: Shepherd et al. 2013
http://r4d.dfid.gov.uk/output/192446/default.aspx
• Decisions before measurement
• Value of information analysis – model the decisions
with uncertainties on all variables – tells you which
variables are important to measure and how much you
should spend measuring them (Hubbard, 2010)
Review Recommendations
• Do we need to ameliorate soil organic carbon?
• Do we know what is a good or bad level?
• What is the value of accurately monitoring soil
carbon levels if we don’t know how to interpret?
• Is critical limit the high-information-value variable?
Soil carbon decisions – need for
specificity
• How much soil carbon credit to pay out?
• Soil carbon has increased by x t/ha with 90%
certainty
• Which variable has largest uncertainty and highest
risk of being wrong: Bulk density? Sampling error
due to spatial variation? Lab measurement?
Emerging technologies
Applications in Africa
SPECTROSCOPY HISTORY
Shepherd KD and Walsh MG. (2002) Development
of reflectance spectral libraries for characterization
of soil properties. Soil Science Society of America
Journal 66:988-998.
Instrumentation
Dispersive VNIR FT-NIR FT-MIR Handheld NIR/MIR
• Portable
• Repeatability?
• External service
• No validation
• Benchtop
• Repeatability ***
• Self serviceable
• Validation in-built
• ISO compliant
• Industry proven
• Multipurpose
• Benchtop
• Repeatability***
• No gas purging
• Some servicing
• Robotic
• Validation in-built
• ISO compliant
• Outperforms NIR
• Handheld
• Sample
homogeneity?
• Variable
moisture?
• Repeatability?
• Still expensive
• Rapidly
developing
• Need to prepare
by developing
soil reference
libraries
Soil mid-infrared Spectroscopy for rapid soil
characterization
Rapid Low cost Reproducible Predicts many soil functional properties
• Hi resolution monitoring • Decision/policy support
Soil-Plant Spectral Diagnostics Lab
• 500 visitors in 2012
• Outreach spectral labs
growing & demand
support
• Capacity building
Spectral Diagnostics – Capacity
Building
•IAMM, Mozambique
•AfSIS, Sotuba, Mali
•AfSIS, Salien, Tanzania
•AfSIS, Chitedze, Malawi
•ICRAF, Nairobi, Kenya
•CNRA, Abidjan, Cote
D’Ivoire
•KARI, Nairobi, Kenya
•ICRAF, Yaounde,
Cameroon
•Obafemi Awolowo
University, Ibadan, Nigeria
•IAR, Zaria, Nigeria
•ICRAF, Nairobi, Kenya
Planned
•Eggerton University,
Kenya
•ATA, Ethiopia (6)
•IITA (3)
•Liberia
Personal request for
info
AfSIS
✓60 primary sentinel sites
➡ 9,600 sampling plots
➡ 19,200 “standard” soil samples
➡ ~ 38,000 soil spectra
EthioSIS
97 Sentinel sites
Applications
Land Health
out-scaling projects
Tibetan Plateau/ Mekong
Vital signs
Cocoa - CDI Parklands Malawi
National surveillance
systems
Regional Information Systems
Project baselines
Ethiopia
Rangelands E/W Africa SLM Cameroon MICCA EAfrica
Global-Continental Monitoring Systems
Evergreen Ag / Horn of Africa
CRP5 pan-tropical basins
AfSIS
Living Standards Measurement Study
Integrated Surveys on Agriculture (LSMS-IMS)
Improve measurements of agricultural
productivity through methodological
validation and research
Responding to policy needs to provide data
to understand the determinants of social
sector outcomes.
Soil fertility monitoring component
Two pilot countries
Predicting SOC stocks using soil infrared
spectroscopy
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A case study from Western Kenya
Uncertainties in SOC
stock monitoring
• Measuring soil carbon stock changes for carbon trading purposes
requires high levels of measurement precision
• But there is still large uncertainty on whether the costs of
measurement exceed the benefits
• Sample size
• Bulk density and measuring based on equal-depth
Measurement uncertainties
High spatial variability of SOC can rise sevenfold when scaling up from
point sample to landscape scales (Hobley and Willgoose, 2010)
• High uncertainties in calculations of SOC stocks.
• This hinders the ability to accurately measure changes in stocks at
scales relevant to emissions trading schemes
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• Tillage increases the thickness per unit area
• A management that leads to a DECREASE in bulk density will UNDER
ESTIMATES SOC stocks & vice versa (Ellert and Bettany, 1995)
C conc.(%)
Depth(cm)
Bulk density(g/cm)
SOC stock (Mg/ha) Error
1.5 150 1.2 270
1.5 150 1 225 -16.67%
Monitoring SOC stock change
Bulk density as
confounding variable in
comparing SOC stocks
Think mass not depth
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Comparing SOC stocks between treatments or monitoring over time on
equivalent soil mass basis
No need to dig pits for deep bulk density
Monitoring SOC change
What is the minimum detectable
change?
What time interval for monitoring?
Determining auger-hole volume
using sand filling method
Cumulative soil mass sampling
plate: to recover soil samples for
measuring soil mass
Cumulative soil mass sampling
0
2000
4000
6000
10 50 100 150 200 250
Ca
rbo
n m
ea
su
rme
nt co
st (U
SD
)
Number of samples
NIR spectroscopy
Thermal oxidation
Sample preparation
Soil sampling
0
3
6
9
12
15
Ca
rbo
n m
ea
su
rem
en
t co
st p
er
sa
mp
le (
US
D)
NIR spectroscopy
Thermal oxidation
Sample preparation
Soil sampling
Cost –error analysis
0
2000
4000
6000
8000
0 500 1000 1500
Car
bo
n m
easu
rem
ent
cost
(U
SD)
Number of samples
Thermal oxidationNIR spectroscopy
Comparisons of costs of measuring SOC using a commercial lab
and NIR
Cost
IR is cheaper (<~ 56%) than combustion
method for large number of samples
Throughput
Combustion ~ 30-60 samples/day
NIR ~ 350 samples/day
MIR ~ 1000/day
Cost –error analysis
0.00
2.00
4.00
6.00
8.00
10.00
0 200 400 600 800 1000
Hal
f 9
5%
co
nfi
den
ce in
terv
al (
t C
ha-1
)
Number of samples
0.00
2.00
4.00
6.00
8.00
10.00
0 5000 10000 15000 20000
Hal
f 9
5%
co
nfi
den
ce in
terv
al (
t C
ha-1
)
Cost of carbon measurement (USD)
Final remarks • Need to be clear on the decisions that we are trying to
make before designing measurements
• Uncertainty analysis and decision modelling can guide
where to focus measurement effort and how much to
spend on measurements (new CGIAR research area)
• Soil spectroscopy methods provide low cost alternative for
rapid and reproducible measurement of soil carbon and
other functional properties.
• Need for large investments in establishing soil spectral
libraries.
• Supplementary spectral measurements may aid
interpretation (x-ray, laser)
References • Hobley E & Willgoose G. 2010. Measuring soil organic carbon stocks – issues
and considerations. Symposium 1.5.1. Quantitative monitoring of soil change.
62-65. 19th WCSS, Brisbane, Australia, Published on DVD
• Hubbard D. 2010. How to Measure Anything: Finding the Value of Intangibles
in Business, 2nd ed., John Wiley & Sons
• Shepherd KD & Walsh MG. 2002. Development of reflectance spectral libraries
for characterization of soil properties. Soil Science Society of America Journal
66:988-998
• Shepherd KD, Farrow A, Ringler C, Gassner A, Jarvis A. 2013. Review of the
Evidence on Indicators, Metrics and Monitoring Systems. Commissioned by
the UK Department for International Development (DFID). Nairobi: World
Agroforestry Centre http://r4d.dfid.gov.uk/output/192446/default.aspx
