Comparison of Near Infrared Reflectance Spectroscopy with Combustion and Chemical Methods for Soil Carbon Measurements in Agricultural Soils. Issue 6 (25th March 2016)

• Record Type: Journal Article
• Authors: Wight, Jason P.; Allen, Fred L.; Ashworth, Amanda J.; Tyler, Donald D.; Labbé, Nicole; Rials, Timothy G.
• Is Part Of: Communications in soil science and plant analysis. Volume 47:Issue 6(2016)
• Page Start: 731
• Page End: 742
• Publication Date: 2016-03-25
• Subjects: NIR -- near infrared reflectance -- SOC -- soil organic carbon -- SOM -- soil organic matter; Soil science -- Periodicals; Plants -- Chemical analysis -- Periodicals; Agricultural chemistry -- Periodicals; 631.405
• DOI: 10.1080/00103624.2016.1146750
• Languages: English
• ISSNs: 0010-3624

Abstract

ABSTRACT: As interest in soil organic carbon (SOC) dynamics increases, so do needs for rapid, accurate, and inexpensive methods for quantifying SOC. Objectives were to i) evaluate near infrared reflectance (NIR) spectroscopy potential to determine SOC and soil organic matter (SOM) in soils from across Tennessee, USA; and ii) evaluate potential upper limits of SOC from forest, pasture, no-tillage, and conventional tilled sites. Samples were analyzed via dry-combustion (SOC), Walkley–Black chemical SOM, and NIR. In addition, the sample particle size was classified to give five surface roughness levels to determine effects of particle size on NIR. Partial least squares regression was used to develop a model for predicting SOC as measured by NIR by comparing against SOM and SOC. Both NIR and SOM correlated well ( R 2 > 0.9) with SOC (combustion). NIR is therefore considered a sufficiently accurate method for quantifying SOC in soils of Tennessee, with pasture and forested systems having the greatest accumulations.Abbreviations SOC, soil organic carbon; NIR, Near Infrared Reflectance Spectroscopy; MTREC, Middle Tennessee Research and Education Center; RECM, Research and Education Center at Milan; PREC, Plateau Research and Education Center; PLS, Partial least squares.