No abstract

Development of more efficient (rapid) and cost-effective methodologies are needed in soil survey to meet the demand for quantitative data in digital soil mapping and updates. The objective of this study was to pilot the application of mid-infrared (MIR)-diffuse reflectance spectroscopy (DRS) coupled with partial least squares regression (PLSR) in a soil survey field office where soil samples are processed, MIR spectra are acquired, and predictions are obtained using calibration models developed and validated from the Kellogg Soil Survey Laboratory spectral library. Mid-infrared models were built for total C, organic C, CaCO 3 equivalent, total clay, cation exchange capacity, 1500 kPa water, and pH in water and CaCl 2 for Mollisols of the central United States. Validation results (from the MIR library) using Lin's concordance correlation (r c) of measured versus predicted values showed that most properties predicted very well (r c = 0.967-0.996), whereas models for total clay in B horizons and 1500 kPa water in B horizons predicted fairly well (r c = 0.844-0.955). Models for pH predicted the least well (r c = 0.750-0.921). The MIR-DRS coupled with PLSR was successful in predicting soil properties for completely independent samples that were collected, processed, and MIR scanned in a soil survey field office. Predicted results using r c ranged from 0.697 to 0.992, with pH in water having the lowest r c and CaCO 3 having the highest r c. All properties except pH had an acceptable level of accuracy for use in soil survey and a marginal level of acceptable accuracy for total clay. Direct calibration transfer was feasible.

A Partial Least Squares (PLS) carbonate (CO 3 ) prediction model was developed for soils throughout the contiguous United States using mid-infrared (MIR) spectroscopy. Excellent performance was achieved over an extensive geographic and chemical diversity of soils. A single model for all soil types performed very well with a root mean square error of prediction (RMSEP) of 12.6 g kg -1 and was further improved if Histosols were excluded (RMSEP 11.1 g kg -1 ). Exclusion of Histosols was particularly beneficial for accurate prediction of CO 3 values when the national model was applied to an independent regional dataset. Little advantage was found in further narrowing the taxonomic breadth of the calibration dataset, but higher precision was obtained by running models for a restricted range of CO 3 . A model calibrated using only on the independent regional dataset, was unable to accurately predict CO 3 content for the more chemically diverse national dataset. Ten absorbance peaks enabling CO 3 prediction by mid-infrared (MIR) spectroscopy were identified and evaluated for individual and combined predictive power. A single-band model derived from an absorbance peak centered at 1796 cm - yielded the lowest RMSEP of 13.5 g kg -1 for carbonate prediction compared to other single-band models. This predictive power is attributed to the strength and sharpness of the peak, and an apparent minimal overlap with confounding co-occurring spectral features of other soil components. Drawing from the 10 identified bands, multiple combinations of 3 or 4 peaks were able to predict CO 3 content as well as the full-spectrum national models. Soil CO 3 is an excellent example of a soil parameter that can be predicted with great effectiveness and generality, and MIR models could replace direct laboratory measurement as a lower cost, high quality alternative.

No abstract

Diffuse reflectance spectroscopy in the near-infrared (NIR: 350-2500 nm) region offers a relatively rapid, non-destructive, and high throughput alternative to wet chemistry measurements of soil health. Infrared absorbance frequencies of soil constituents such as organic matter and clay minerals form the basis for developing reliable calibrations for predicting soil health indicators (SHI). To demonstrate suggested practices, and potential challenges to the use of NIR for soil health measurements, the chapter describes use of a NIR spectral dataset of diverse United States soils (n=709) from the USDA NRCS National Soil Survey Center to develop chemometric prediction models of representative SHI: total organic C (TOC), aggregate stability, clay content, and β-glucosidase activity. Future directions for NIR prediction of SHI and thus infrared spectroscopy-based soil health assessment and monitoring are also discussed.

No abstract