Soil Monitoring for Africa Soil Information Service
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1. Soil spectroscopy is being used in the Africa Soil Information Service (AfSIS) to monitor soils across Africa and identify soil properties and issues. 2. Infrared spectroscopy allows identification of mineral composition, organic matter, and other properties in soils to help with agricultural and environmental management. 3. AfSIS has established a network of soil spectral labs across Africa and provides online tools and services to analyze soil spectra and properties.
![Soil Spectroscopy in the Africa Soil
Information Service
Getting the best out of light
[WG1] Soil Monitoring for Mankind and Environment Safety
20th World Congress of Soil Science, 8 – 13 June 2014, Jeju, Korea
Keith D Shepherd, Land Health Decisions
World Agroforestry Centre (ICRAF), Nairobi, Kenya
Earth Institute, Columbia University (adjunct)](https://image.slidesharecdn.com/soilspectroscopyintheafricasoilinformationservice-150505032918-conversion-gate02/85/Soil-spectroscopy-in-the-africa-soil-information-service-Getting-the-best-out-of-light-1-320.jpg)
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Soil Monitoring for Africa Soil Information Service
- 1. Soil Spectroscopy in the Africa Soil Information Service Getting the best out of light [WG1] Soil Monitoring for Mankind and Environment Safety 20th World Congress of Soil Science, 8 – 13 June 2014, Jeju, Korea Keith D Shepherd, Land Health Decisions World Agroforestry Centre (ICRAF), Nairobi, Kenya Earth Institute, Columbia University (adjunct)
- 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_Rep ort_lowres.pdf Identify problem Develop case defintition Develop screening test(s) Measure prevalence (no. cases/area) Measure incidence (no. cases/area/time) Confirm risk factors Measure environmental correlates Differentiate risk factors Infrared spectroscopy 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.
- 3. Africa Soil Information Service Consistent field protocol Soil spectroscopy Coupling with remote sensingPrevalence, Risk factors, Digital Sentinel sites Randomized sampling schemes
- 4. Data & soil library management Barcoding Soil archiving system 1.2 km shelving holds over 40 t of soil
- 5. 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
- 6. Field spectroscopy Shepherd KD and Walsh MG. (2002) Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66:988-998.
- 7. Infrared spectroscopy Dispersive VNIR FT-NIR FT-MIR Robotic FT-MIR Portable Handheld MIR Mobile phone devices 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
- 8. 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
- 9. Laser diffraction particle size analysis
- 10. Main AfSIS workflow, products & services overview Markus Walsh, AfSIS
- 11. Markus Walsh, AfSIS Africa Soil Information Service www.africasoils.net
- 12. On-line Spectral Prediction Engine Bayesian Additive Regression Trees Jiehua Chen & William Wu Columbia University
- 13. On-line Spectral Prediction Engine Bayesian Additive Regression Trees
- 14. Africa Spectral Lab Network •IAMM, Mozambique •AfSIS, Sotuba, Mali •AfSIS, Salien, Tanzania •AfSIS, Chitedze, Malawi •CNLS, 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 (6) •IITA, Ibadan, Nigeria •IITA, Yaounde, Cameroon •IER, Arusha, Tanzania •FMARD, Nigeria •CNLS, Nairobi, Kenya •BLGG, Kenya (mobile)
- 15. 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 Cameroon MICCA EAfrica Global-Continental Monitoring Systems Evergreen Ag / Horn of Africa CRP pan-tropical sites AfSIS
- 16. Futures • Capacity building • Spatial-spectral prediction of soil properties • Direct prediction of management response • Low cost mobile devices
Editor's Notes
- A quick reminder of our conceptual framework and tools, we work by a set of surveillance science principles, which are similar to those used in public health surveillance – which emphasize quantifying health problems and associated risk factors in populations. We implement those science principles through a set of tools, which encompass use of randomized, landscape level sampling schemes. The use of consistent field sampling protocols so we collect data on land health indicators in the same way everywhere. The use of soil spectroscopy methods to provide high throughput low cost analysis of key soil health metrics, centred on soil functional properties. Coupling of the field and lab observations with remote sensing data, to provide consistent data on the population distributions and prevalence of land health problems, associated risk factors and digital mapping of indicators.