Excitation‐Emission‐Matrix Fluorescence Spectroscopy of Soil Water Extracts to Predict Nitrogen Mineralization Rates. Issue 1 (12th January 2018)

• Record Type: Journal Article
• Authors: Rinot, Oshri; Osterholz, William R.; Castellano, Michael J.; Linker, Raphael; Liebman, Matt; Shaviv, Avi
• Is Part Of: Soil Science Society of America Journal. Volume 82:Issue 1(2018)
• Page Start: 126
• Page End: 135
• Publication Date: 2018-01-12
• Subjects: Soils -- United States -- Periodicals; Soil science -- Periodicals; Periodicals; 631.4973
• DOI: 10.2136/sssaj2017.06.0188
• Languages: English
• ISSNs: 0361-5995

Abstract

Abstract : Core Ideas: EEM spectroscopy was used to characterize soluble OM pools in agricultural soils. PARAFAC was used to quantify OM meaningful EEM spectral components. PARAFAC components were correlated to N mineralization rates in specific sites. NPLS modeling of EEM data can estimate N mineralization rates in diverse soils. Rapid, easy, and frequent prediction of gross or potential nitrogen mineralization rates (GNMR and PNMR, respectively) is desirable for improved understanding and quantification of soil N dynamics and for enabling advanced sustainable N management. Our goal was to extend the use of excitation–emission matrix (EEM) fluorescence spectroscopy to characterize constituents of soluble organic matter (OM) pools in agricultural soils and to couple these characterizations with advanced chemometric techniques to improve prediction of PNMR and GNMR. To achieve this, EEM‐based predictions must be valid across diverse soils, climates, and management systems. Accordingly, we analyzed soil water extracts spanning a broad range of OM contents from midwest United States (MUS) and Israeli (ISL) agroecosystems under organic‐ and mineral‐based N management strategies. Parallel factors analysis, a multiway data analysis method, was used to quantify meaningful EEM spectral components, which were used to detect changes in labile soil OM pools and to predict N mineralization rates. N‐way partial least squares regression (NPLS) was also applied to EEM data to obtain spectral factors correlated to total organic carbon concentration potential and gross N mineralization rates. This NPLS analysis led to reliable estimation of all three tested properties for ISL and MUS soils.