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Soil Spectroscopy in the Africa Soil Information Service
Getting the best out of light
[WG1] Soil Monitoring for Mankind and Environment Safety
20th World Congress of Soil Science, 8 – 13 June 2014, Jeju, Korea
Keith D Shepherd, Land Health DecisionsWorld Agroforestry Centre (ICRAF), Nairobi, Kenya
Earth Institute, Columbia University (adjunct)
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
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.
Africa Soil Information Service
Consistent field protocol
Soil spectroscopyCoupling with
remote sensingPrevalence, Risk factors, Digital mapping
Sentinel sites Randomized sampling
schemes
Data & soil library management
Barcoding
Soil archiving system
1.2 km shelving holds over 40 t of soil
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
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 FT-NIR FT-MIR Robotic FT-MIR Portable
Handheld MIR Mobile phone devices
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 mid-infrared spectral library. Soil Sci. Soc. Am. J. 74:1792–1799
Spectral fingerprintingTotal X-ray fluorescence spectroscopy
X-ray diffraction spectroscopy
Mineral Semi-quant (%)
Quartz
Albite
Microclin
e
Kaolinite
Hematite
Muscovit
e
Diopside
69.2
5.0
4.3
9.9
2.8
4.3
4.6
Infrared spectroscopy
Laser diffraction particle size analysis
Main AfSIS workflow, products & services overview
Markus Walsh, AfSIS
On-line Spectral Prediction EngineBayesian Additive Regression Trees
Jiehua Chen & William Wu Columbia University
On-line Spectral Prediction EngineBayesian Additive Regression Trees
Africa Spectral Lab Network
•IAMM, Mozambique
•AfSIS, Sotuba, Mali
•AfSIS, Salien, Tanzania
•AfSIS, Chitedze, Malawi
•CNLS, 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 (6)
•IITA, Ibadan, Nigeria
•IITA, Yaounde, Cameroon
•IER, Arusha, Tanzania
•FMARD, Nigeria
•CNLS, Nairobi, Kenya
•BLGG, Kenya (mobile)
Land HealthSurveillance Out-scaling
Tibetan Plateau/ Mekong
Vital signs
Cocoa - CDIParklands Malawi
National surveillance systems
Regional Information Systems
Project baselines
EthioSis
Rangelands E/W AfricaSLM Cameroon MICCA EAfrica
Global-Continental Monitoring Systems
Evergreen Ag / Horn of Africa
CRP pan-tropical sites
AfSIS
Futures
• Capacity building• Spatial-spectral prediction of soil properties• Direct prediction of management response• Low cost mobile devices
