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Infrared Spectroscopy Keith D Shepherd. Optimizing Fertilizer Recommendations for Africa (OFRA) Project Inception 25-27 November 2013. Surveillance Science.
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Infrared Spectroscopy Keith D Shepherd Optimizing Fertilizer Recommendations for Africa (OFRA) Project Inception 25-27 November 2013
Surveillance Science 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 • Increase sample density in landscapes • Direct prediction of soil-plant responses to management • Measure frequency of problems and associated risk factors in populations using statistical sampling designs & standardized measurement protocols 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.
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 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 Handheld MIR ? Mobile phone cameras ?
Calibration Soil organic carbon • Spectralpretreatments • Derivatives, smoothing • Data miningalgorithms: • PLS + • SupportVectorMachines • Neural networks • MultivariateAdaptiveRegressionSplines • BoostedRegressionTrees • RandomForests • BayesianAdditiveRegressionTrees Training Out-of-bag validation Soil pH R package soil.spec Soil spectral file conversion, data exploration and regression functions
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 fingerprinting X-ray diffraction spectroscopy Total X-ray fluorescence spectroscopy Infrared spectroscopy
Spectral Lab Network • Planned • Eggerton University, Kenya • MoA, Liberia • IER, Arusha, Tanzania • FMARD, Nigeria • NIFOR, Nigeria • CNLS, Nairobi • BLGG, Kenya (mobile labs) • 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 • ObafemiAwolowo University, Ibadan, Nigeria • IAR, Zaria, Nigeria • ATA, Addis Ababa, Ethiopia (+ 5 on order) • IITA, Ibadan, Nigeria • IITA, Yaounde, Cameroon • ICRAF, Nairobi, Kenya
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 FoodAfricaSoil Micronutrients Evidence-based micronutrient management Healthy crops Healthy livestock Healthy soils Healthy people
Land HealthSurveillance Out-scaling Global-Continental Monitoring Systems Vital signs CRP pan-tropical sites AfSIS Regional Information Systems National surveillance systems Tibetan Plateau/ Mekong Evergreen Ag / Horn of Africa Ethiosis Project baselines SLM Cameroon Parklands Malawi Rangelands E/W Africa Cocoa - CDI MICCA EAfrica
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