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DIVISION S-8-NUTRIENT MANAGEMENT & SOIL & PLANT ANALYSIS

Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties

^a Dep. of Agronomy, Iowa State Univ., Ames, IA 50011
^b USDA-ARS, National Soil Tilth Laboratory, 2150 Pammel Drive, Ames, IA 50011
^c USDA-NRCS, Soil Survey and Resource Assessment, P.O. Box 2890, 14th Independence Ave. S.W., Washington, DC 20250
^d Jr., Dep. of Agricultural & Biosystems Engineering, Iowa State Univ., Ames, IA 50011

Corresponding author (laird{at}nstl.gov)

A fast and convenient soil analytical technique is needed for soil quality assessment and precision soil management. The main objective of this study was to evaluate the ability of near-infrared reflectance spectroscopy (NIRS) to predict diverse soil properties. Near-infrared reflectance spectra, obtained from a Perstrop NIR Systems 6500 scanning monochromator (Foss NIRSystems, Silver Spring, MD), and 33 chemical, physical, and biochemical properties were studied for 802 soil samples collected from four Major Land Resource Areas (MLRAs). Calibrations were based on principal component regression (PCR) using the first derivatives of optical density [log(1/R)] for the 1300- to 2500-nm spectral range. Total C, total N, moisture, cation-exchange capacity (CEC), 1.5 MPa water, basal respiration rate, sand, silt, and Mehlich III extractable Ca were successfully predicted by NIRS (r² > 0.80). Some Mehlich III extractable metals (Fe, K, Mg, Mn) and exchangeable cations (Ca, Mg, and K), sum of exchangeable bases, exchangeable acidity, clay, potentially mineralizable N, total respiration rate, biomass C, and pH were also estimated by NIRS but with less accuracy (r² = 0.800.50). The predicted results for aggregation (wt% > 2, 1, 0.5, 0.25 mm, and macroaggregation) were not reliable (r² = 0.460.60). Mehlich III extractable Cu, P, and Zn, and exchangeable Na could not be predicted using the NIRS–PCR technique (r² < 0.50). The results indicate that NIRS can be used as a rapid analytical technique to simultaneously estimate several soil properties with acceptable accuracy in a very short time.

Abbreviations: CEC, cation-exchange capacity • ICP-AES, inductively coupled plasma-atomic emission spectroscopy • LWR, locally weighted regression • MLRA, major land resource area • NIRS, near-infrared reflectance spectroscopy • PC, principal component • PCR, principal components regression • PLSR, partial least squares regression • RMSECV, root mean standard error of cross validation • RPD, the ratio of standard deviation to RMSECV • SMLR, stepwise multiple linear regression • SQ-NRI, soil quality-natural resource inventory

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