1. Infrared Spectroscopy Keith D Shepherd Optimizing Fertilizer Recommendations for Africa (OFRA) Project Inception 25-27 November 2013

2. 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. Increase sample density in landscapes Direct prediction of soil- plant responses to management

3. 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

4. Infrared spectroscopy Dispersive VNIRFT-NIRFT-MIR RoboticFT-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

5. Calibration Soil 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.spec Soil spectral file conversion, data exploration and regression functions

6. Spectral prediction performance

7. Spectral libraries

8. 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

9. Spectral fingerprinting Total X-ray fluorescence spectroscopy X-ray diffraction spectroscopy Mineral Semi- quant (%) Quartz Albite Microcline Kaolinite Hematite Muscovite Diopside 69.2 5.0 4.3 9.9 2.8 4.3 4.6 Infrared spectroscopy

10. 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)

11. 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.

12. Calibrating plant response to IR http://afsis-dt.ciat.cgiar.org

13. MTT-Finland FoodAfrica Soil Micronutrients Healthy soils Healthy crops Healthy livestock Healthy people Evidence-based micronutrient management

14. Land Health Surveillance Out-scaling Tibetan Plateau/ Mekong Vital signs Cocoa - CDIParklands Malawi National surveillance systems Regional Information Systems Project baselines Ethiosis Rangelands E/W AfricaSLM CameroonMICCA EAfrica Global-Continental Monitoring Systems Evergreen Ag / Horn of Africa CRP pan-tropical sites AfSIS

15. 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