Hands-on Soil Infrared Spectroscopy Training

Hands-on Soil Infrared
Spectroscopy Training Course
Getting the best out of light
11 – 14 November 2013
Applications of soil spectroscopy on
Land Health Surveillance
Ermias Betemariam
Erick Towett

Context (i)
• Soil comes to the global agenda:
– Sustainable intensification took soil as a x-cutting
– Global Environmental Benefits - land degradation and soils are among
the priority global benefits (GEF/UNCCD)
• SOC as useful indicator of soil health
• Importance of soil carbon in global carbon cycle and climate
mitigation
• carbon trading purposes requires high levels of measurement
precision
• Increasing demand for soil data at fine spatial resolution
1Ermias Betemariam, Erick Towett | Hands-on soil infrared spectroscopy training course | Nairobi | May 2014 |

Context
 There is a lack of coherent and rigorous sampling and assessment
frameworks that enable comparison of data (i.e. meta-studies) across a
wide range of environmental conditions and scales
 Soil monitoring is expensive to maintain
 Soil degradation and loss is a challenge
 High spatial variability in soil properties- large data sets reduce
uncertainty
Context (iI)
High spatial variability of SOC can rise sevenfold when scaling up from point
sample to landscape scales, resulting in high uncertainties in calculations of
SOC stocks. This hinders the ability to accurately measure changes in stocks at
scales relevant to emissions trading schemes (Hobley and Willgoose, 2010)
Soil spectroscopy key for Land Health Surveillance
Ermias Betemariam, Erick Towett | Hands-on soil infrared spectroscopy training course | Nairobi | May 2014 | 2

Land Health (SD4)
Land Health - the capacity of land to sustain delivery of
essential ecosystem services
Land health surveillance aims to provide statistically
valid estimates of land health problems, quantify key
risk factors associated with land degradation, and
target cost-effective interventions to reduce or reverse
these risks.

Land Health Projects
No. Name of project
1Africa Soil Information Service (AfSIS)/Africa Soils (SSA)
2Strengthening capacity for diagnosis and management of soil micronutrient deficiencies (SSA)
3Soil monitoring protocol for the World Bank Living Standards Measurement Study (Ethiopia & ..)
4Carbon sequestration options in pastoral & agro-pastoral systems in Africa (Burkina Faso &
Ethiopia)
5Land health surveillance for high value biocarbon development (Kenya, Burkina Faso & Sierra
Leone)
6Land health surveillance system for smallholder cocoa in Ivory Coast
7Trees for food security in Eastern Africa (Rwanda, Ethiopia, Burundi & Uganda)
8Land health surveillance for mitigation of climate change in agriculture (Kenya & Tanzania)
9Land health surveillance system in support of Malawi food security project (Malawi)
10Land health surveillance system for targeting agroforestry based interventions for sustainable
land productivity in the western highlands of Cameroon
11A Protocol for Measurement and Monitoring Soil Carbon Stocks in Agricultural Landscapes
Land Health Projects (i)

5
Land Health
Projects (ii)
Ermias Betemariam, Erick Towett | Hands-on soil infrared spectroscopy training course | Nairobi | May 2014 |

Land Health
out-scaling projects (iii)
Tibetan Plateau/ Mekong
Parklands Malawi
National surveillance systems
Regional Information Systems
Project baselines
Rangelands E/W AfricaSLM Cameroon MICCA E. Africa
Global-Continental Monitoring Systems
Evergreen Ag / Horn of Africa
CRP5 pan-tropical basins AfSIS
EthioSIS- Ethiopia
6
Cocoa - CDI

AfSIS
✓60 primary sentinel sites
➡ 9,600 sampling plots
➡ 19,200 “standard” soil samples
➡ ~ 38,000 soil spectra
AfSIS: Soil functional properties (1)
7
EthioSIS
97 Sentinel sites

AfSIS: Soil functional properties
Spectral diagnostics tools can be used to
produce soil maps
Prediction map for soil organic carbon for
sub-Saharan Africa. (Source: Africa Soil Information
Service)

AfSIS: Soil functional properties
From polygon-based to probabilistic mapping
+
Probability of observing
cultivation
Current lime requirement ? ~ min
[prob(pH < 5.5), prob(cult)]
Probability topsoil pH < 5.5
... very acid soils
Grid-based probabilistic maps increases the reliability of the map and its
power to be combined with other data sources (remote sensing & terrain data)
(Walsh, 2013)
=
Taxonomic soil classification systems provide little information on soil
functionality in particular the productivity function (Mueller et al 2010)

Living Standards Measurement Study-LSMS-IMS (3)
Improve measurements of agricultural productivity
through methodological validation and research
Mobile phones for quick soil screening- being tested
1
0
Low cost MIR soil testing for smallholder farmers

Carbon sequestration in pastoral & agro-pastoral systems (4)
Effects of range management on soil organic carbon stocks in savanna
ecosystems of Burkina Faso & Ethiopia
Fire (controlled burning -
19 years) – Burkina Faso
Grazing (Exclosures 12-
36 years) – Ethiopia
Fire influence:
• Carbon allocation - SOC gain
• Decrease input - SOC loss
1
1

Results
No Sig difference in SOC between burned and unburned plots
1
2

Results
No Sig difference in SOC between burned and unburned plots
1
3

Results
No sig. difference in SOC between closed and open plots for all age categories
1
4

Challenges in cocoa productionBiocarbon development in East and West Africa (5)
• Develop effective and cost efficient carbon monitoring, reporting and
verification systems that can enable smallholders to access carbon markets
• Soil spectroscopy will be key component
Estimating biocarbon using LiDAR data- Taita, Kenya
(a) indigenous forest, (b) mixed stand of local and exotic species (Eucalyptus sp.) and (c)
cropland with scattered trees
Janne et al., 2013
1
5

Smallholder cocoa in Ivory Coast-V4C (6)
Disease + pest?
Soil fertility?
Major challenges
LDSF and soil spectroscopy to
identify constraints & target
interventions in cocoa production
1
6

Trees for food security –ACIAR
Rwanda
Ethiopia
Characterize land health constraints and assessing Agroforestry intervention outcomes
1
7

Mitigating Climate Change in Agriculture-MICCA (8)
East African Dairy Development
(EADD- Kenya)
Conservation agriculture
(CARE- Tanzania)
Characterize (baseline) and assess impacts of climate smart agriculture practices
1
8

Measurement and Monitoring Soil Carbon Stock (11)
Can we measure soil carbon cost effectively?
1
9

Land Health Surveillance
Consistent field
protocol
Soil spectroscopy
Coupling with
remote sensingPrevalence, Risk factors, Digital
mapping
Sentinel sites
Randomized sampling schemes
2
0

Why measure
carbon?
1
What will the
protocol deliver?
2
3
How much will it
cost?
4 Sampling
5 Field work
6 Lab work
7 Data analysis
8 Presenting results
2
1

Sample size
determination
Sample allocation Moisture content
Soil Carbon stock
Error
Web and excel based tool
…. and reporting
DATA  INFORMATION  KNOWLEDGE  WISDOM
2
2

A management that leads to a DECREASE in bulk density will UNDER
ESTIMATES SOC stocks & vice versa
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 stocks
(Ellert and Bettany, 1995)
Bulk density as confounding
variable in comparing SOC stocks
Think mass not depth
Why cumulative soil mass?
2
3

0
2000
4000
6000
10 50 100 150 200 250
Cost(USD)
Number of samples
NIR spectroscopy
Thermal oxidation
Sample preparation
Soil sampling
0
3
6
9
12
15
Costpersample(USD)
NIR spectroscopy Thermal oxidation
Sample preparation Soil sampling
Cost –error analysis
0
2000
4000
6000
8000
0 500 1000 1500
Cost(USD)
Number of samples
Thermal oxidation
NIR spectroscopy
Comparisons of costs of measuring SOC using a commercial lab and NIR
Cost
IR is cheaper (~ 56%) than dry combustion
method for large number of samples
Throughput
Combustion ~ 30-60 samples/day
NIR ~ 350 samples/day
MIR ~ 1000/day
2
4

0.00
2.00
4.00
6.00
8.00
10.00
0 200 400 600 800 1000
Half95%confidenceinterval(tCha-1)
Number of samples
0.00
2.00
4.00
6.00
8.00
10.00
0 5000 10000 15000 20000
Half95%confidenceinterval(tCha-1)
Cost of carbon measurement (USD)
Costs of measurement often exceed the benefits – soil spectroscopy address
this challenge
2
5

Activity Sources of uncertainty
Sampling
Sampling design (random, stratified random)
Sample size
SOC measurement
Natural variability (spatial)
Sample preparation (e.g. contamination, subsampling)
Lab method used (instrument resolution)
Human error
Field data collection (e.g. soil mass, volume)
SOC prediction
using IR
Imported uncertainties (from reference data)
Model (assumption)
Instrument and human errors
Mapping SOC
Covariates used
Image pre -processing (geometric and radiometric corrections)
Scale/resolution (e.g. farm vs landscape)
Model (assumption, strength)
Sources of uncertainty
2
6

Common causes of measurement uncertainty
• the instruments used,
• the item being measured,
• the environment,
• the operator,
• other sources
2
7
CASE 1
 High precision (repeatable)
 High accuracy
 Random error (less biased)
CASE 2
 High precision (repeatable)
 Low accuracy
 Systematic error (biased)
CASE 3
 Low precision (not repeatable)
 High accuracy
 Random error (less biased)
CASE 4
 Low precision (not repeatable)
 Low accuracy
 Systematic error (biased)
Accuracy versus precision

Things to be careful!
Proper labelingAvoid contamination
Lets do it right
2
8

Data archiving/publishing
Datasaving – dataverse: http://thedata.harvard.edu/dvn/
2
9

• More research on cost-effective measurement tools
• Web services are needed that allow optimised soil information to be
automatically exchanged via the internet
• Proximal soil sensing
• Reduce uncertainties in measurements- error propagates
• Develop national capacities, networking and partnership
• Baselines are established for important soil properties across Africa
• Soil spectroscopy filling the data gaps- at National, Regional & Global
levels
• Enable decision makers have clear understanding of soil status and trends
• Spectroscopy is proved good- adoption and application
• Cross sentinel/regional sites analysis
Finally…
3
0

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