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Use of Near-infrared Spectroscopy for Monitoring and Analysis of Carbon Sequestration in Soil

Use of Near-infrared Spectroscopy for Monitoring and Analysis of Carbon Sequestration in Soil. by P.D. Martin, and D.F. Malley PDK Projects, Inc. Winnipeg, Manitoba, Canada. Vision. Soil and plant analyses are available when and where they are needed

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Use of Near-infrared Spectroscopy for Monitoring and Analysis of Carbon Sequestration in Soil

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  1. Use of Near-infrared Spectroscopy for Monitoring and Analysis of Carbon Sequestration in Soil by P.D. Martin, and D.F. Malley PDK Projects, Inc. Winnipeg, Manitoba, Canada

  2. Vision • Soil and plant analyses are available when and where they are needed • Need for information, rather than analytical cost, to dictate the number and kinds of analyses • Analyses promote sound, sustainable environmental and agricultural management

  3. Purpose • Introduce Near-infrared Spectroscopy (NIRS) • Describe: • Benefits to use of NIRS • How NIR can be used for soil carbon assessment • Services available from PDK

  4. NIR Facts • NIRS provides rapid, chemical-free, flexible analysis • NIRS is used globally for food and feed analysis • NIRS has enormous potential for agro-environmental applications, including soil carbon assessment

  5. Near-infrared Spectroscopy • Utilizes the absorbance of NIR light (780 - 2500 nm) by vibrating bonds between atoms in molecules • O-H, C-H, C-N, C-O, P-O, S-O • Molecular spectroscopy - analyzes intact samples • NIR absorbances obey the Beer/Lambert law

  6. The Work of Doing NIR Analysis • Compositional information on samples (n ~>100) is correlated with the spectral information to develop statistical calibration models • The calibrations “train” the instrument to analyze future unknown samples

  7. Features • does not destroy the sample • is rapid, < 2 min/test • analyzes many constituents simultaneously • analyzes compositional and functional properties • field portable

  8. Lab and Field Instrument: Zeiss Corona

  9. Organic Matter Compositional Parameters • Organic matter/organic C • % OM, % OC • Total C (LECO) • %C HUMUS • Humic acid fractions • Humic and Fulvic • Fulvic acid fractions • Lignin content • Cellulose content r2 0.81-0.97 0.93-0.96 0.94 0.95 0.91 0.63 0.77-0.83 0.81 Performance good – exc. v.good - exc. v.good v.good v.good poor good good

  10. Compositional Parameters cont’d r2 performance • % Clay 0.81-0.87 good • Total N 0.86-0.96 good - v.good • % moisture 0.93-0.98 v.good – exc. • CEC 0.9 v.good

  11. Miniota area Newdale Soil Assoc. Dried, ground samples (2mm) N = 267 1100 - 2500 nm r2 = 0.78 SEP = 0.33 % Organic Carbon

  12. “Field-moist” applications • Moisture corrected calibration • 0.033 and 1.5 MPa moisture tension • r2 = 0.89 • SEP = 0.23 % • Range = 0.45 – 3.16 % OC Sudduth, K.A. and J.W. Hummel (1993). Soil organic matter, CEC and moisture sensing with a portable NIR spectrophotometer. Trans of the ASAE 36:1571-1582

  13. Example of On-site Soil Testing Method • Soil cores - grid or stratified sampling • Cores sliced on-site • Presentation of static, “as is”, field moist samples • Multiple constituents simultaneously

  14. NIRS Benefits • COST ! • LECO OC = $27/sample • NIR OC = $6/sample • Minimal sample preparation • Dried and ground (2mm mesh) • Potential for “as is” or “field moist” determinations • Timeliness • Potential for immediate analysis

  15. NIRS Benefits, cont. • Precision • Precision of NIR equal or better than reference • Does not destroy the sample • The same sample can be analyzed many times over • Positive implications for long term and/or incubative studies

  16. NIRS Limitations • Site to Site Bias • Potential for bias in predictions of samples from one site using calibrations derived from samples from another site. • Affects absolute accuracy • Does not affect precision This can be corrected by “incorporating” a small number of samples from the “new” site into the calibration. • At present, this means that NIRS is not practical for small sample groups

  17. How can NIRS work for you? • Objective sample selection1 • NIRS can be used to select sample sets from a large group of samples which: • Retain a maximum representation of overall sample population variability • Samples selected better than random because: • Greater recovery of range • Higher variance • Better Kurtosis (more even distribution) 1Stenberg, B. et al. (1995) Use of near infrared reflectance spectra of soils for objective selection of samples. Soil Science. 159:109-114.

  18. Objective Sample Selection, cont. • Using NIR for selecting analytical samples reduces cost directly by lowering the number of samples that need to be analyzed to encompass soil variability. • Stenberg, et al. estimated a 70% reduction in cost for their study using this method • For their study, the overall n = 144 samples, selected n = 20 samples

  19. Calibration and Prediction • Calibrations are developed on a selected set of samples (ie. using the NIR selection method) • These calibrations can be used to predict the remaining samples. • Requires large sample sets • ncalibration :100 samples recommended

  20. Calibration and Prediction, cont. • Extra cost of calibration and accompanying wet chemistry is offset by a large economy of scale • Once a calibration is developed, it only requires updating with a much smaller number of QA/QC samples • Calibrations will eventually exist for various soils, bringing initial costs down

  21. Monitoring and Long-term Soil Quality Assessment • NIR spectra contain information for both carbon quantity, and carbon quality in soil • High precision plus lower cost of NIR results make large scale assessments of soil carbon flux much more feasible, both: • Over time • Under varying management practices.

  22. Monitoring and Long-term Soil Quality Assessment, cont. • Non destructive nature of NIR, coupled with “as-is” and/or “on-site” assessment potential mean that: • The same sample could be analyzed indefinitely over time. • Could reduce potential subsampling error • Could increase relevance of results

  23. Sensing Soil QualityLarge Area Surveillance of Soil Condition and Trend http://www.worldagroforestrycentre.org/sites/program1/specweb/home.htm

  24. Services Available from PDKIntroductory Pricing • Objective Sample Selection Samples submitted dried and ground (2mm) • $6.00 per sample

  25. Services Available from PDK, cont. • Compositional Analysis • Calibration Samples (100+ samples, 5 g/sample min) submitted dried and ground in borosilicate vials or bags Reference values submitted for constituents of interest, including QA/QC data from the analytical laboratory. (Reference chemistry can be arranged at a Lab of your choice, at commercial rates -extra) • First calibration: $6.00/sample plus $150 • Each additional calibration: $250

  26. Compositional Analysis, cont. 2. Prediction of future samples Prediction of future unknown samples of the same type as in the calibrations, submitted dried and ground • First constituent: $6.00/sample • Each additional constituent: $1.00/sample

  27. Services available from PDK, cont. • Consulting • Custom Quote for: • Setup of personal NIR program • Setup of field portable instrument • Contract Research • Instrument selection/evaluation

  28. Conclusions • NIR is the only practical method for analyzing large numbers of samples for measurement of C stores • NIR has potential to determine quality/persistence of organic C in soil

  29. Acknowledgments • Foss NIRSystems Inc., USA • Carl Zeiss, Germany • Agriculture and Agri-Food Canada • Manitoba Rural Adaptation Council (MRAC) • Industrial Research Assistance Program (IRAP)

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