Upload
jonas-burcham
View
215
Download
2
Embed Size (px)
Citation preview
Infrared Spectroscopy
Keith D Shepherd
Optimizing Fertilizer Recommendations for Africa
(OFRA) Project Inception
25-27 November 2013
Surveillance Science• Measure frequency of problems and associated risk factors in populations
using statistical sampling designs & standardized measurement protocolsUNEP. 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.
• Increase sample density in landscapes
• Direct prediction of soil-plant responses to management
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
Infrared spectroscopy Dispersive VNIR FT-NIR FT-MIR Robotic FT-MIR Portable
Handheld MIR ?Mobile phone cameras ?
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
CalibrationSoil organic carbon 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
Training Out-of-bag validation
Soil pH
R package soil.specSoil spectral file conversion, data exploration and regression functions
Spectral prediction performance
Spectral libraries
• 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
• All subscribers benefit
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
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
•ICRAF, Nairobi, Kenya
Planned
•Eggerton University, Kenya
•MoA, Liberia
•IER, Arusha, Tanzania
•FMARD, Nigeria
•NIFOR, Nigeria
•CNLS, Nairobi
•BLGG, Kenya (mobile labs)
Plant, compost, fertilizer analysis
• IR for plant N/protein, organic resource quality/decomposition
• Handheld XRF for plant P, K, Ca, Mg, micronutrients (in progress)
• Handheld XRF for fertilizer quality control (in progress)
Shepherd KD, Palm CA, Gachengo CN and Vanlauwe B (2003) Rapid characterization of organic resource quality for soil and livestock management in tropical agroecosystems using near infrared spectroscopy. Agronomy Journal 95:1314-1322.
Shepherd, KD, Vanlauwe B, Gachengo CN Palm CA (2005) Decomposition and mineralization of organic residues predicted using near infrared spectroscopy. Plant and Soil 277:315-333.
Calibrating plant response to IR
http://afsis-dt.ciat.cgiar.org
MTT-Finland FoodAfrica
Soil Micronutrients
Healthy soils
Healthy crops
Healthy livestock
Healthy people
Evidence-based micronutrient management
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
Critical success factors• Consistent field sampling protocol
• Soil-Plant sample labeling, drying, preparation, sub-sampling, shipping, back-up storage
• Data management, linking
• Judicious selection of samples for reference analysis
• Consistency of reference analyses
• Use MIR as a soil covariate
• Direct calibration of MIR to plant/soil response
http://worldagroforestry.org/research/land-health/spectral-diagnostics-laboratory