摘要：Methodologies for determining soil chemical properties have evolved dramatically during the past century. Early geochemical analyses were conducted exclusively through the use of wet chemistry techniques that were relatively reliable but painstaking and subject to errors at various stages of analysis. Near infrared reflectance spectroscopy (NIRS) has emerged as a new approach for rapidly analyzing a variety of materials including soils. In this study soil samples were taken from eight study areas across the Ozark Highlands of Arkansas, and NIRS calibration models were developed to determine the accuracy of using NIRS to analyze soils compared with standard soil chemical analysis protocols. Multivariate regression models were highly effective for analyzing several important elements. C and N models explained 92% and 88% of their variation, respectively, and Ca, Mg, P, and Mn models explained 72-88% of the variability in these elements. Models for C:N and pH explained 82% and 86% of their variability, respectively. Models for micronutrients Cu and Zn did not fit as well with 22% and 40% of their variability explained, respectively. Our findings suggest that additional NIRS calibration and modeling is promising for rapidly analyzing the chemical composition of soils, and it is desirable to develop model libraries that are calibrated for the soils of a given region.
