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

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)