1. In-Situ NIR Spectroscopy to Measure Carbon and Other Soil Attributes Colin Christy, Veris Technologies David Laird, USDA-ARS Kenneth Sudduth, USDA-ARS

2. PROBLEM: Laboratory soil testing is expensive. Some attributes are highly spatially variable. Dense sampling would be very expensive. THEREFORE WE NEED: Inexpensive, rapid, and dense measurement of soil attributes.

3. GOAL: Use on-the-go NIR reflectance to predict soil attributes Use NIR with three auxiliary sensors to predict attributes

4. Shank-Based Near Infrared Spectroscopy (NIRS) Absorption of light due to overtones of fundamental molecular vibrations Important functional groups are C-H, N-H, O-H Produces an absorption spectrum

5. Calibration/Validation Objectives Calibrate NIR spectra to 9 or 10 different attributes Compare four different regression algorithms Compare performance of spectra with and without auxiliary sensor data (EC, temperature, pH)

6. Calibration experiments were performed on two different data sets. Data Set I: 1-16 hectare Iowa field (144 samples) 1-16 hectare Kansas field (144 samples) Data Set II: 5 fields from Central Kansas Field NumberLocationSize (Hectares)Number of Reference Samples 1Saline County1641 2Saline County29 3Saline County1716 4Dickenson County3242 5Rice County1622

7. Data Set I calibrated as a single set of 288 samples.

8. Data Set I calibrated on a field-by-field basis.

9. A similar analysis was performed for Data Set II. (whole-set analysis shown here)

10. Analysis of Results – Improvement with usage of Auxiliary sensors (by set coverage)

11. Summary of Predictive Capability Using Shank-Based System at 900 – 1700 nm.

12. Constructing Maps

13. The sensor derived map provides extra detail.

14. NIR Probe

15. Sample Pattern for Field Experiment 8 locations in Kansas 13 locations in Missouri 13 locations in Iowa

16. Normalized Spectra (N=200) 1150 to 2200 nm

17. Single Calibration for all Three states. Leave 1 location (six samples) out

18. Leave One Location (6 samples) Out: Calibration by State, RMSE = 0.47, R ^ 2=0.86

19. Carbon versus Depth in an Iowa Field

20. Change from 5 cm to 2 cm. Depth resolution.

21. Conclusions Field-mobilized NIRS can accurately predict calcium, carbon, CEC, and moisture. Field-mobilized NIRS was inconsistent in predicting pH and magnesium and will require further investigation. Augmentation of NIR spectra with EC, temperature, and pH further enhanced prediction accuracy, especially when the calibrations spanned multiple fields. The NIR probe will enable investigation of vertical variability. Future research will be done to establish sample needs for calibration and validation. Thanks for support from USDA and DOE.