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United States Department of Agriculture

Agricultural Research Service


Location: Forage and Livestock Production Research

Title: Near infrared reflectance-based tools for predicting soil chemical properties of Oklahoma grazinglands)

item Northup, Brian
item Daniel, John

Submitted to: Agronomy Journal
Publication Type: Peer reviewed journal
Publication Acceptance Date: 4/13/2012
Publication Date: 6/6/2012
Citation: Northup, B.K., Daniel, J.A. 2012. Near infrared reflectance-based tools for predicting soil chemical properties of Oklahoma grazinglands. Agronomy Journal. 104(4):1122-1129.

Interpretive Summary: Soil carbon (C), nitrogen (N) and organic matter (OM) are important measures of soil condition of agricultural land in central Oklahoma. They are indicators of soil fertility and health, and change in condition in response to management. The laboratory techniques used to measure these soil properties generally require large amounts of time, labor, and expensive equipment, and points to the need for more-timely and less costly estimates. We undertook a study to describe the capacity of near infrared reflectance spectroscopy (NIRS), to provide estimates of soil properties in central Oklahoma. Near infrared reflectance spectroscopy measures which infrared light wavelengths are reflected by samples, and relates the reflected wavelengths to chemical bonds in the sample. Soils included in the experiment were collected from three pastures of native prairie managed under different grazing regimes, and one pasture of winter wheat used for grazing, during 1978 to 2004. We collected samples from the top 10 inches of soil along an elevation gradient common to all pastures. The samples were processed, scanned by reflectance spectrophotometer, and analyzed by laboratory techniques to develop the data required to develop the NIRS equation used in estimations. We then tested the capacity of the developed equation developed by NIRS to describe amounts of C, N, and OM based on reflected wavelengths recorded by NIRS. The study showed equations, based on laboratory measurements and wavelengths, defined 85 to 92% of the information related to C, N and OM in soils. These results suggest NIRS could provide useful estimates of soil properties. More accurate equations that can be applied to a wider area will require samples from a larger range of landscape positions, soil types, and management regimes.

Technical Abstract: Near infrared reflectance spectroscopy (NIRS) has potential to provide timely, and lower cost estimates of soil properties than current laboratory techniques. This study defined the capacity of NIRS to predict soil organic matter (SOM), total carbon (C) and nitrogen (N) in native prairie (n=3) and conventionally tilled wheat (n=1) experimental paddocks (1.6 ha) in central Oklahoma, under different forms of long-term (1978 to 2004) management. Samples were collected from paddocks along 150 m transects situated between a ridge and toe slope. The A horizon was divided into sections (0-5, 5-10, and 10-25 cm), reflectance (R) measurements (log 1/R) collected, and absorption spectra (750-2500 nm) developed for random samples collected from all paddocks (total n=124 for C and N; n=214 for SOM). Calibration equations between wavelengths and laboratory-measured properties were developed by multivariate partial least squares regression, and tested with an independent validation set of observations. Relationship between laboratory values and NIRS estimates (n=62 for C and N; n=75 for SOM) generated significant calibration equations (0.91<R^2< 0.98; P<0.01; 2.5< RPD ratios<3.7). Application of calibration equations to validation datasets (n=62 for C and N; n=139 for SOM) generated different slopes that did not differ from the calibration equations (0.70<p<0.76), with significant relationships (0.85<R^2<0.92; P<0.01). Results suggest the developed equations could provide useful predictions of soil properties for routine determination of responses to management. More accurate and broadly applicable equations for central Oklahoma will require samples from a wider range of landscape positions, soil types, and management regimes.

Last Modified: 8/24/2016
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