|Hurburgh jr, Charles r|
Submitted to: Soil Science
Publication Type: Peer reviewed journal
Publication Acceptance Date: 11/16/2004
Publication Date: 4/4/2005
Citation: Chang, C., Laird, D.A., Hurburgh Jr, C. 2005. Influence of soil moisture on near-infrared reflectance spectroscopic measurement of soil properties. Soil Science. 170(4):244-255. Interpretive Summary: Soil analysis is an important component of modern farming systems. However the use of soil analysis is limited by labor and laboratory costs. Near infrared reflectance spectroscopy (NIRS) is an analytical technique that is widely used in industry for quality control. Our research shows that NIRS analyses of dry soils are only slightly more accurate than NIRS analyses of moist soils. Much development work is still needed, but this study demonstrates that NIRS is not limited by soil moisture and therefore NIRS can potentially be used in agricultural fields for routine soil analysis. This research will benefit farmers, agricultural extension agents, agricultural consultants, the NRCS, and environmentalists by advancing the development of an inexpensive, rapid method for soil analysis.
Technical Abstract: Near-infrared reflectance spectroscopy (NIRS) may someday be used to rapidly and simultaneously quantify several soil properties. The objectives of this study were to examine the influence of moisture content on both NIR spectra of soils and on the accuracy of NIRS analysis of soil properties. Four hundred agricultural soil samples (less than 2mm) from Iowa and Minnesota were studied at two moisture levels: moist and air-dried. The soil properties tested included total C, organic C, inorganic C, total N, CEC, pH, texture, and potentially mineralizable N. Generally, when soil moisture content increased, the 1400nm and 1900nm absorption peaks shifted to longer wavelengths, and the height and width index of these peaks increased. Around 70 per cent of the Iowa samples were selected for the Calibration Set, and the rest of the Iowa samples and all of the Minnesota samples were assigned to Validation Set I and Validation Set II, respectively. Calibrations were based on partial least squares regression (PLSR) using the first differentials of log (1/R) for the 1100-2500nm spectral range. The results of cross-validation indicated that NIRS-PLSR was able to predict most soil properties with reasonable accuracy for both the air-dried (r**2greater than 0.76) and moist (r**2greater than 0.74) soils. The results for Validation Set II showed that NIRS-PLSR was able to predict some properties of soils (total C, organic C, total N, CEC, and moisture content) from a different geographic region. Although NIRS-PLSR predictions are slightly more accurate for air-dried soils than for moist soils, the results indicate that NIRS-PLSR can be used for analysis of field moist samples with acceptable accuracy.