Cost effective tools for monitoring Soil Organic Carbon (SOC)
•Descargar como PPTX, PDF•
1 recomendación•456 vistas
This slide introduces new and cost effective techniques used for monitoring Soil Organic Carbon. Such techniques include, mid-infrared Spectroscopy.
Recomendados
Recomendados
Más contenido relacionado
La actualidad más candente
La actualidad más candente (20)
Destacado
Similar a Cost effective tools for monitoring Soil Organic Carbon (SOC)
Similar a Cost effective tools for monitoring Soil Organic Carbon (SOC) (20)
Más de Land Health Decisions (SD4) ICRAF
Más de Land Health Decisions (SD4) ICRAF (7)
Último
Último (20)
Cost effective tools for monitoring Soil Organic Carbon (SOC)
- 1. Cost effective tools for soil organic carbon monitoring Keith Shepherd & Ermias Betemariam 9 April 2013 EGU General Assembly, Vienna
- 2. Outline • Context - soils as basis for ecosystem functioning • Decisions before measurement • Emerging technologies (Applications in Africa) • Measurement and uncertainties
- 3. Context 3 Soils the largest carbon reservoir of the terrestrial carbon - small change could cause large effects on climate system Soil comes to the global agenda: Sustainable intensification; Global Environmental Benefits (GEF/UNCCD) SOC as useful indicator of soil health. Taxonomic soil classification systems provide little information on soil functionality in particular the productivity function (Mueller et al 2010) But lack of coherent and rigorous sampling and assessment frameworks High spatial variability in soil properties - large data sets required Soil monitoring is expensive to maintain
- 5. • Little evidence for impact of monitoring initiatives on real-world decision making/management • Information has no value unless it has the potential to change a decision • The measurement inversion − most measurement effort in business cases is spent on variables that have the least information value Review of the Evidence on Indicators, Metrics and Monitoring Systems DFID-commissioned review: Shepherd et al. 2013 http://r4d.dfid.gov.uk/output/192446/default.aspx
- 6. • Decisions before measurement • Value of information analysis – model the decisions with uncertainties on all variables – tells you which variables are important to measure and how much you should spend measuring them (Hubbard, 2010) Review Recommendations
- 7. • Do we need to ameliorate soil organic carbon? • Do we know what is a good or bad level? • What is the value of accurately monitoring soil carbon levels if we don’t know how to interpret? • Is critical limit the high-information-value variable? Soil carbon decisions – need for specificity • How much soil carbon credit to pay out? • Soil carbon has increased by x t/ha with 90% certainty • Which variable has largest uncertainty and highest risk of being wrong: Bulk density? Sampling error due to spatial variation? Lab measurement?
- 8. Emerging technologies Applications in Africa
- 9. SPECTROSCOPY HISTORY 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.
- 10. Instrumentation Dispersive VNIR FT-NIR FT-MIR Handheld NIR/MIR •Portable •Repeatability? •External service •No validation •Benchtop •Repeatability *** •Self serviceable •Validation in-built •ISO compliant •Industry proven •Multipurpose •Benchtop •Repeatability*** •No gas purging •Some servicing •Robotic •Validation in-built •ISO compliant •Outperforms NIR •Handheld •Sample homogeneity? •Variable moisture? •Repeatability? •Still expensive •Rapidly developing •Need to prepare by developing soil reference libraries
- 11. Soil mid-infrared Spectroscopy for rapid soil characterization Rapid Low cost Reproducible Predicts many soil functional properties • Hi resolution monitoring • Decision/policy support
- 12. Soil-Plant Spectral Diagnostics Lab • 500 visitors in 2012 • Outreach spectral labs growing & demand support • Capacity building
- 13. Spectral Diagnostics – Capacity Building •IAMM, Mozambique •AfSIS, Sotuba, Mali •AfSIS, Salien, Tanzania •AfSIS, Chitedze, Malawi •ICRAF, Nairobi, Kenya •CNRA, Abidjan, Cote D’Ivoire •KARI, Nairobi, Kenya •ICRAF, Yaounde, Cameroon •Obafemi Awolowo University, Ibadan, Nigeria •IAR, Zaria, Nigeria •ICRAF, Nairobi, Kenya Planned •Eggerton University, Kenya •ATA, Ethiopia (6) •IITA (3) •Liberia Personal request for
- 14. AfSIS ✓ 60 primary sentinel sites ➡ 9,600 sampling plots ➡ 19,200 “standard” soil samples ➡ ~ 38,000 soil spectra EthioSIS 97 Sentinel sites Applications
- 15. Land Health out-scaling projects Tibetan Plateau/ Mekong Vital signs Cocoa - CDIParklands Malawi National surveillance systems Regional Information Systems Project baselines Ethiopia Rangelands E/W AfricaSLM Cameroon MICCA EAfrica Global-Continental Monitoring Systems Evergreen Ag / Horn of Africa CRP5 pan-tropical basins AfSIS
- 16. 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
- 17. Predicting SOC stocks using soil infrared spectroscopy 17 A case study from Western Kenya
- 18. Uncertainties in SOC stock monitoring
- 19. • Measuring soil carbon stock changes for carbon trading purposes requires high levels of measurement precision • But there is still large uncertainty on whether the costs of measurement exceed the benefits • Sample size • Bulk density and measuring based on equal-depth Measurement uncertainties High spatial variability of SOC can rise sevenfold when scaling up from point sample to landscape scales (Hobley and Willgoose, 2010) • High uncertainties in calculations of SOC stocks. • This hinders the ability to accurately measure changes in stocks at scales relevant to emissions trading schemes
- 20. 20 • Tillage increases the thickness per unit area • A management that leads to a DECREASE in bulk density will UNDER ESTIMATES SOC stocks & vice versa (Ellert and Bettany, 1995) C conc.( %) Depth(cm ) Bulk density(g/cm) SOC stock (Mg/ha) Error 1.5 150 1.2 270 1.5 150 1 225 -16.67% Monitoring SOC stock change Bulk density as confounding variable in comparing SOC stocks Think mass not depth
- 21. 21 Comparing SOC stocks between treatments or monitoring over time on equivalent soil mass basis No need to dig pits for deep bulk density Monitoring SOC change What is the minimum detectable change? What time interval for monitoring? Determining auger-hole volume using sand filling method Cumulative soil mass sampling plate: to recover soil samples for measuring soil mass Cumulative soil mass sampling
- 22. 0 2000 4000 6000 10 50 100 150 200 250 Carbonmeasurmentcost(USD) Number of samples NIR spectroscopy Thermal oxidation Sample preparation Soil sampling 0 3 6 9 12 15 Carbonmeasurementcostper sample(USD) NIR spectroscopy Thermal oxidation Sample preparation Soil sampling Cost –error analysis 0 2000 4000 6000 8000 0 500 1000 1500 Carbonmeasurementcost(USD) Number of samples Thermal oxidation NIR spectroscopy Comparisons of costs of measuring SOC using a commercial lab and NIR Cost IR is cheaper (<~ 56%) than combustion method for large number of samples Throughput Combustion ~ 30-60 samples/day NIR ~ 350 samples/day MIR ~ 1000/day
- 23. Cost –error analysis 0.00 2.00 4.00 6.00 8.00 10.00 0 200 400 600 800 1000 Half95%confidenceinterval(tCha-1) Number of samples 0.00 2.00 4.00 6.00 8.00 10.00 0 5000 10000 15000 20000 Half95%confidenceinterval(tCha-1) Cost of carbon measurement (USD)
- 24. Final remarks • Need to be clear on the decisions that we are trying to make before designing measurements • Uncertainty analysis and decision modelling can guide where to focus measurement effort and how much to spend on measurements (new CGIAR research area) • Soil spectroscopy methods provide low cost alternative for rapid and reproducible measurement of soil carbon and other functional properties. • Need for large investments in establishing soil spectral libraries. • Supplementary spectral measurements may aid interpretation (x-ray, laser)
- 25. References • Hobley E & Willgoose G. 2010. Measuring soil organic carbon stocks – issues and considerations. Symposium 1.5.1. Quantitative monitoring of soil change. 62-65. 19th WCSS, Brisbane, Australia, Published on DVD • Hubbard D. 2010. How to Measure Anything: Finding the Value of Intangibles in Business, 2nd ed., John Wiley & Sons • Shepherd KD & Walsh MG. 2002. Development of reflectance spectral libraries for characterization of soil properties. Soil Science Society of America Journal 66:988-998 • Shepherd KD, Farrow A, Ringler C, Gassner A, Jarvis A. 2013. Review of the Evidence on Indicators, Metrics and Monitoring Systems. Commissioned by the UK Department for International Development (DFID). Nairobi: World Agroforestry Centre http://r4d.dfid.gov.uk/output/192446/default.aspx
- 26. 26 Contact us: Email: k.shepherd@cgiar.org e.betemariam@cgiar.org Web: www.worldagroforestrycentre.org
Notas del editor
- A recent global review of monitoring systems related to agro-ecosystems and livelihoods, commissioned by DFID, and led by my colleague Keith Shepherd, came with striking conclusions. The review also looked at relevant experience in other fields including Public health surveillance, Systems thinking in industry and public services and Decision sciences. The most striking finding was that there was little evidence for the impact of monitoring initiatives on real world decision making or management. In fact we can consider that information has no value unless it has the potential to change a decision. A second important revelation has been termed the measurement inversion – and that is that it has been found across numerous business cases that most measurement effort is spent on variables that have the least information value - in terms of power to change a decision.
- One of the key recommendations of the review is to put decisions before measurements. It is only by modelling our current knowledge of the uncertainties on all variables affecting a decision that we can identify which variables deserve more measurement effort and how much we should spend measuring them. Experience shows that we are poor at predicting what are high information variables.
- What decisions are we trying to inform when we design soil carbon measurements? We need to be specific on the decisions first if we are to design sensible measurement schemes. For example perhaps are we trying to decide whether to recommend organic matter amelioration at a site based on soil organic carbon levels? But do we know what is a good or bad level? What is the value of accurately monitoring soil carbon levels if we do not know how to interpret them? Perhaps the knowing the critical soil carbon limit is the variable with the highest information value? On the other hand if the decision is how much soil carbon credit to pay out, there may be a requirement in terms of measurement certainty. But which variables contribute most to the uncertainty? Is it bulk density, spatial variation, lab measurement? Where should we be investing most measurement effort to optimise the decision? At the World Agroforestry Centre under the CGIAR Program on Water Land and Ecosystems, we have initiated a new research project on decision and value of information analysis to better link our measurement systems to real world decisions.
- Next we want to show how we are applying emerging technologies to increase the reproducibility and reduce costs of soil carbon measurements in Africa.
- We started with visible-near-infrared field spectroscopy and demonstrated its successful application in predicting soil functional properties, including soil organic carbon, as well as profiling out different major soil types even in sediments – here in a sediment plume in Lake Victoria, which is dominated by alluvial soils from the plains (shown in green).
- Global libraries are needed to be able to quickly bring new instruments into calibration There is a widening range of instrument types available.
- Our Soil-Plant Spectral Diagnostics lab continues to attract much interest – we received over 500 visitors every year, over half of which received some training. Requests for capacity building are growing exponentially and we need to increase our staff to deal with it. We are now using a wide range of spectroscopic techniques, including x-ray diffraction, x-ray fluorescence, and laser diffraction particle size analysis. Soil mineralogy and soil particle size both influence soil carbon protection and critical limits in soils.
- The capacity building demand is also fueled by the growing network of spectral labs we are supporting. These include national labs in Mozambique, Mali, Tanzania, Malawi, Cote D’Iviore, Kenya, Cameroon, and two recently in Nigeria. Forthcoming are another 10 at least. Our technical staff have conducted training courses in most of these countries. Bill Gates, on a recent visit to Ethiopia, became very interested in this technology and made a special request for an information package, which we provided. We are also constantly collaborating with various leading instrument manufacturers to evaluate handheld and miniature instruments.
- Our land health work is rapidly taking off at every level of scale. At the global to continental level, we are moving from the Africa Soil Information Service into an Africa Agricultural Monitoring System, and there are new opportunities in CRP5 on Water, Land and Ecosystems to expand into a set of 9 major river basins across the tropics. At the regional scale, Jianchu has been looking at the Tibetian Plateaux-Mekong transect, and we have proposed a surveillance system for the Great Green Wall Project. AfSIS is now moving to support national soil health surveillance systems. At the project level, the land health surveillance methods are supporting intervention targeting and impact assessment in an increasing number of projects, including SLM in Cameroon, food security in Malawi, rangeland carbon in East and West Africa, the smallholder cocoa project in Cote D’Ivoire, and as you saw on the field trip, climate change adaptation and mitigation projects in Kenya and Tanzania. The framework is becoming a standard inclusion in new ICRAF land management projects.
- We won a grant to incorporate a field-scale soil monitoring component in the World Bank’s Living Standards Measurement Study, which has been helping a number of Governments establish household panel surveys and agricultural monitoring over several decades. We will be piloting a soil fertility monitoring component in two African countries.
- Conversion of grazing land to forest land may may increase C, but decrease in bulk density. This situation will under assesses SOC stocks if compared on equal depth basis. This method is employed in the vast majority of publications comparing OC stocks between treatments or over time periods. Figure 1 also shows that OC content can be quantified consistently when one uses the same (or equivalent) soil mass as the basis for comparison Soil mass layers (e.g., 0-1000, 1000- 2000, 2000-3000 Mg ha-1) are similar to soil depth layers (0-10, 10-20, 20-30 cm), but the mass of soil in a soil mass layer, by definition, is fixed, whereas the mass of soil in a given depth layer will vary as bulk density varies as a result of tillage, change in land use systems, or inherent soil variability. For this reason, equivalent soil mass layers are the only consistent way to quantify and compare anything in the soil, such as OC. By fitting the data to a curve function, in this case a cubic spline, one can obtain very good estimates of cumulative OC contents for any chosen reference soil mass in the profile. Interpolation error can also be reduced by taking more and smaller core sections, which provides more points on the cumulative soil mass/soil OC mass profile, and thus a better fit. Increased core sections are most important for improving the cubic spline fit where gradients in soil C concentration are the greatest, usually in the upper 20-30 cm of soil.