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