Why Soil Spectroscopy?
Keith D Shepherd
Hands-on Soil Infrared
Spectroscopy Training Course
Getting the best out of light
11 – 15 November 2013
Surveillance Science
•
Measure frequency of problems and associated risk factors in populations
using statistical sampling designs & standardized measurement protocols
UNEP. 2012. Land Health Surveillance: An Evidence-Based Approach to Land Ecosystem Management. Illustrated
with a Case Study in the West Africa Sahel. United Nations Environment Programme, Nairobi.
http://www.unep.org/dewa/Portals/67/pdf/LHS_Report_lowres.pdf
Identify problem
Develop case
defintition
Develop
screening test(s)
Measure prevalence
(no. cases/area)
Measure
environmental
correlates
Measure incidence
(no. cases/area/time)
Differentiate risk
factors
Infrared
spectroscopy
Confirm risk factors
Shepherd KD and Walsh MG (2007) Infrared
spectroscopy—enabling an evidence-based diagnostic
surveillance approach to agricultural and environmental
management in developing countries. Journal of Near
Infrared Spectroscopy 15: 1-19.
Simplicity of light
Wavelength unit converter.xls
Spectral shape relates to basic soil
properties
•
•
•
•
Mineral composition
Iron oxides
Organic matter
Water (hydration,
hygroscopic, free)
• Carbonates
• Soluble salts
• Particle size distribution
 Functional properties
Soil function largely determined by soil mineralogy
and soil organic matter
• Soil mineralogy
• nutrient quantity (stock) and intensity (strength of retention by
soil)
• pH and buffering, variable charge
• anion and cation exchange capacity
• carbon saturation; protection
• aggregate stability, dispersion/flocculation
• resistance to erosion
• Soil organic matter
• soil structure
• aggregate stability, resistance to erosion; water holding
capacity
• carbon storage and turnover
• cation exchange capacity
• nitrogen, organic P, sulphur supply
Origin of infrared spectral absorption
features
Water vibrations movie
Carbon dioxide-vibrations movie
SpectraSchool - Royal Society of Chemistry
http://www.rsc.org/
Soil IR fundamentals
1 = Fingerprint region e.g Si-O-Si stretching/bending
2 = Double-bond region (e.g. C=O, C=C, C=N)
3 = Triple bond (e.g. C≡C, C≡N)
4 = X–H stretching (e.g. O–H stretching)
NIR = Overtones; key features clay lattice and water OH; SOM affects overall
shape
Field spectroscopy
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.
Infrared spectroscopy
Dispersive VNIR
Handheld MIR ?
FT-NIR
FT-MIR Robotic
Mobile phone cameras
?
FT-MIR Portable
Brown D, Shepherd KD, Walsh MG (2006). Global
soil characterization using a VNIR diffuse reflectance
library and boosted regression trees. Geoderma
132:273–290.
Shepherd KD and Walsh MG (2007) Infrared
spectroscopy—enabling an evidence-based
diagnostic surveillance approach to agricultural and
environmental management in developing countries.
Journal of Near Infrared Spectroscopy 15: 1-19.
Terhoeven-Urselmans T, Vagen T-G, Spaargaren O,
Shepherd KD. 2010. Prediction of soil fertility
properties from a globally distributed soil midinfrared spectral library. Soil Sci. Soc. Am. J.
Sample
preparation/presentation
Instrument protocols
Dispersive spectrometer
Fourier Transform Spectrometer
Reference analyses
Data & soil library management
Soil archiving system
1.2 km shelving to hold over 40 t of soil
Barcoding
Calibration
Soil organic carbon
Training
Out-of-bag
validation
Soil pH
Spectral pretreatments
• Derivatives, smoothing
Data mining algorithms:
• PLS +
• Support Vector Machines
• Neural networks
• Multivariate Adaptive
Regression Splines
• Boosted Regression
Trees
• Random Forests
• Bayesian Additive
Regression Trees
R package soil.spec
Soil spectral file
conversion, data
exploration and
regression functions
Spectral libraries
Inter-instrument calibration transfer
Robotoic high throughput
MIR
• Submit batch of spectra
online
• Uncertainties estimated for
each sample
• Samples with large error
submitted for reference
analysis
• Calibration models improve
as more samples submitted
Soil-Plant Spectral Diagnostics Lab
•
•
•
500 visitors/yr again
338 instruction
13 PhD, 4 MSc training
Spectral Lab Network
• IAMM, Mozambique
• AfSIS, Sotuba, Mali
• AfSIS, Salien, Tanzania
• AfSIS, Chitedze, Malawi
• CNLS, Nairobi, Kenya
• ICRAF, Nairobi, Kenya
•CNRA,
Abidjan, Cote
D’Ivoire
•KARI, Nairobi, Kenya
•ICRAF, Yaounde,
Cameroon
•Obafemi Awolowo
University, Ibadan, Nigeria
•IAR, Zaria, Nigeria
•ATA, Addis Ababa, Ethiopia
(+ 5 on order)
•IITA, Ibadan, Nigeria
•IITA, Yaounde, Cameroon
Planned
•Eggerton University,
Kenya
•MoA, Liberia
•IER, Arusha, Tanzania
•FMARD, Nigeria
•NIFOR, Nigeria
•CNLS, Nairobi
•BLGG, Kenya (mobile
labs)
Spectral fingerprinting
Infrared spectroscopy
Total X-ray
fluorescence
spectroscopy
X-ray diffraction
spectroscopy
Mineral
Semiquant
(%)
Quartz
69.2
Albite
5.0
Microcline
4.3
Kaolinite
9.9
Hematite
2.8
Muscovite
4.3
Diopside
4.6
Land Health Surveillance
Sentinel sites
Randomized sampling schemes
Consistent field
protocol
Prevalence, Risk factors, Digital
Coupling with
remote sensing
Soil spectroscopy
AfSIS
✓60 primary sentinel sites
➡ 9,600 sampling plots
➡ 19,200 “standard” soil samples
➡ ~ 38,000 soil spectra
➡ 3,000 infiltration tests
➡ ~ 1,000 Landsat scenes
➡ ~ 16 TB of remote sensing data to
Spectral prediction performance
Main AfSIS workflow, products & services overview
Markus Walsh, August
Ethiopia: current spatial coverage of new ground observations and measurements
Africa Soil
Information Service
www.africasoils.net
Markus Walsh, August
Probability topsoil pH < 5.5 ... very acid soils
prob(pH < 5.5)
Africa Soil
Information Service
www.africasoils.net
Markus Walsh
“Best” current topsoil macro-nutrient (N,P,K,S,Ca & Mg) concentration predictions
[N] ppm
[P] ppm
[K] ppm
[S] ppm
[Ca] ppm
[Mg] ppm
Africa Soil
Information Service
www.africasoils.net
Markus
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
MTT-Finland
FoodAfrica
Soil Micronutrients
Evidence-based micronutrient
management
Healthy
soils
Healthy
crops
Healthy
livestock
Healthy
people
Land Health
Surveillance
Out-scaling
Global-Continental Monitoring Systems
CRP pan-tropical sites
Vital signs
AfSIS
Regional Information Systems
Tibetan Plateau/ Mekong
Evergreen Ag / Horn of Africa
National surveillance
systems
Ethiosis
Project baselines
SLM Cameroon
Parklands Malawi
Rangelands E/W Africa
Cocoa - CDI
MICCA EAfrica
Future directions
• Centralized calibration service on-line
• Direct calibration of MIR to plant/soil response data
• Rural MIR labs providing low cost soil testing for smallholder
farmers
• Complementarity of IR, TXRF, XRD, Handheld XRF
• Decision cases