Hands-on Soil Infrared Spectroscopy Training Course Getting the best out of light 11 – 15 November 2013 Why Soil Spectroscopy? Keith D Shepherd

Surveillance Science Measure frequency of problems and associated risk factors in populations using statistical sampling designs & standardized measurement protocols UNEP 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. 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.

Simplicity of light Wavelength unit converter.xls

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

Soil mineralogy nutrient quantity (stock) and intensity (strength of retention by soil) pH and buffering, variable charge anion and cation exchange capacity carbon saturation; protection aggregate stability, dispersion/flocculation resistance to erosion Soil organic matter soil structure aggregate stability, resistance to erosion; water holding capacity carbon storage and turnover cation exchange capacity nitrogen, organic P, sulphur supply Soil function largely determined by soil mineralogy and soil organic matter

Origin of infrared spectral absorption features Water vibrations movie Carbon dioxide-vibrations movie SpectraSchool - Royal Society of Chemistry

Soil IR fundamentals 1 = Fingerprint region e.g Si-O-Si stretching/bending 2 = Double-bond region (e.g. C=O, C=C, C=N) 3 = Triple bond (e.g. C≡C, C≡N) 4 = X–H stretching (e.g. O–H stretching) NIR = Overtones; key features clay lattice and water OH; SOM affects overall shape

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:

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: Terhoeven-Urselmans T, Vagen T-G, Spaargaren O, Shepherd KD Prediction of soil fertility properties from a globally distributed soil mid- infrared spectral library. Soil Sci. Soc. Am. J. 74:1792–1799

Sample preparation/presentation

Instrument protocols Fourier Transform Spectrometer Dispersive spectrometer

Reference analyses

Data & soil library management Barcoding Soil archiving system 1.2 km shelving to hold over 40 t of soil

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

Spectral libraries

Inter-instrument calibration transfer Robotoic high throughput MIR

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

Soil-Plant Spectral Diagnostics Lab 500 visitors/yr again 338 instruction 13 PhD, 4 MSc training

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)

Spectral fingerprinting Total X-ray fluorescence spectroscopy X-ray diffraction spectroscopy Mineral Semi- quant (%) Quartz Albite Microcline Kaolinite Hematite Muscovite Diopside Infrared spectroscopy

Land Health Surveillance Consistent field protocol Soil spectroscopy Coupling with remote sensing Prevalence, Risk factors, Digital mapping Sentinel sites Randomized sampling schemes

✓ 60 primary sentinel sites ➡ 9,600 sampling plots ➡ 19,200 “standard” soil samples ➡ ~ 38,000 soil spectra ➡ 3,000 infiltration tests ➡ ~ 1,000 Landsat scenes ➡ ~ 16 TB of remote sensing data to date AfSIS

Spectral prediction performance

Main AfSIS workflow, products & services overview Markus Walsh, August 2013

Ethiopia: current spatial coverage of new ground observations and measurements Africa Soil Information Service Markus Walsh, August 2013

Probability topsoil pH < very acid soils prob(pH < 5.5) Africa Soil Information Service Markus Walsh

[N] ppm [P] ppm [K] ppm [S] ppm[Ca] ppm[Mg] ppm “Best” current topsoil macro-nutrient (N,P,K,S,Ca & Mg) concentration predictions Africa Soil Information Service Markus Walsh

Living Standards Measurement Study Integrated Surveys on Agriculture LSMS-IMS Improve measurements of agricultural productivity through methodological validation and research Responding to policy needs to provide data to understand the determinants of social sector outcomes. Soil fertility monitoring component Two pilot countries

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

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

Future directions Centralized calibration service on-line Direct calibration of MIR to plant/soil response data Rural MIR labs providing low cost soil testing for smallholder farmers Complementarity of IR, TXRF, XRD, Handheld XRF Decision cases