The humified organic matter in soil, operationally separated in humic acid (HA), fulvic acid (FA), and humin (HU), is considered the most microbially stable reservoir of SOC, and the most important component for the maintenance of the soil physical-chemical and biological quality (Piccolo 1996). While the operationally non-extractable HU is the most passive fraction of humified matter (Hayes et al. 1989; Simpson et al. 2007), the extractable humic and fulvic fractions exert the essential role of intermediary compartments between the more labile organic compounds and the stable SOM pool. Humic matter results from the progressive accumulation in soil of hydrophobic organic molecules (Deport et al. 2006; Spaccini and Piccolo 2009) surviving the microbial degradation of plant residues. During humus formation, biolabile compounds present in the soil solution are progressively incorporated in the humic hydrophobic superstructures (Spaccini et al. 2000; Piccolo 2002). Moreover, humic molecules are also stabilized by the formation of complexes with different metals and adsorption on surfaces of soil minerals (Nebbioso and Piccolo 2009; Cornejo and Hermosin 1996).
The operational extraction of HS from soil consists in the alkaline separation of organic matter from soil mineral components and isolation of fulvic and humic acids based on their solubility at different pH values (Swift 1996). However, it is yet to be proven that the humic superstructures observed in extracts maintain the same intermolecular arrangement as in soil before extraction (Piccolo 1996; Spaccini et al. 2000; Kelleher and Simpson 2006; Nebbioso and Piccolo 2011). Nevertheless, due to the HS essential role in conferring soil quality and controlling all SOM transformation processes, their characterization is regarded as a valuable tool to appraise the effects of different management practices on long-term sequestration of organic carbon in cultivated soils (Swift 2001). Moreover, extractable humic matter (HA and FA) was found to be in short-time dynamic interactions with other soil components (Spaccini et al. 2000). Thus, in this work, HS (jointly HA and FA) were alkaline extracted from soils subjected to the MESCOSAGR project treatments. The extracts were neutralized and purified as customary for humic acids (Spaccini et al. 2009), before being characterized by solid-state NMR spectroscopy.
The non-destructive solid-state 13C cross-polarization-magic-angle-spinning (CPMAS) NMR technique provides the molecular distribution of organic carbons in solid matrices without extensive sample pre-treatment. This solid-sate NMR technique is widely used to characterize the composition of litter, SOM, and humic substances, as well as the transformation of plant tissues in soil (Kogel-Knabner 2000; Hatcher et al. 2001; Conte et al. 2004; Zhou et al. 2010). Although CPMAS spectra are not strictly quantitative, a reproducible quantitation of molecular distribution may be achieved from spectra of soil humic fractions when acquisition parameters are correctly adopted (Kinchesh et al. 1995; Piccolo et al. 2005b; Spaccini et al. 2006).
A detailed comparison among NMR spectra may become excessively tedious and time consuming for the large number of CPMAS spectra obtained for HS extracts within the MESCOSAGR project. This burden can be reduced by applying chemometric methods or multivariate analyses. Among these, principal component analysis (PCA) is widely used to simplify interpretation of chemical and spectro-scopic data for complex systems (Einax et al. 1997). The main purpose of PCA is to reduce the original data set, represented by an 'n' dimensional space (where n is the number of variables or experimental results), into a few principal components (PC), which concomitantly retain the maximum percentage of original information contained in the data set. The principal components are derived as a linear combination of the original variables, such as NMR spectral areas. The variables are multiplied by loadings, which are vectors of constants generated during PCA. The numerical values of loadings reflect the importance of original variables in the direction of each PC. The resulting PC can be used to project the originally multidimensional data into only a two- or three-dimensional space, which is called a score plot (Brereton 2003). Even without a specific knowledge of statistical implications, these plots enable a rapid and direct evaluation of similarities, differences, and groupings among the original samples, which were extracted by PC analysis. Principal component analysis performed on 13C NMR data has been successfully applied for statistical differentiation of humic substances and organic matter in agricultural soils (Novotny et al. 2007; Smejkalova et al. 2008). Previous works have proved that PCA of 13C-CPMAS NMR spectra of humic matter provide the required discrimination among heterogeneous samples and may be useful to evaluate SOM quality.
In this study, HS were extracted for each experimental site from the initial undisturbed soil and from soils at the end of each cropping cycle after harvesting of either maize or wheat. The four soil replicates from field treatments were mixed in order to obtain about 1 kg of composite soil sample. The free-lipid components were first removed from soil with two consecutive extractions with a 1:10 w/v of dichloromethane-methanol (2:1 v/v) solution. The, humic substances were then extracted by shaking the soil (20 g) overnight with 100 ml of 0.1 M NaOH-Na4P2O7 (1:1 v/v) solution under N2 atmosphere. After centrifugation, the solid residue was washed with distilled water until pH 7. The supernatant and washings were combined, filtered on a quartz filter (Whatman GF/C), neutralized to pH 6 with 1 M HCl, dialyzed against deionized water, and freeze dried. The extractions were conducted in duplicate.
13C-CPMAS-NMR spectra of the compost materials as well as of HS extracted from soils were acquired on a Bruker AV-300, equipped with a 4 mm wide-bore MAS probe. Spectra were obtained with the following parameters: 13,000 Hz of rotor spin rate; 1 s of recycle time; 1 ms of contact time; 20 ms of acquisition time; 4,000 scans. Samples were packed in 4 mm zirconia rotors with Kel-F caps. The pulse sequence was applied with a 1H ramp to account for non-homogeneity of the Hartmann-Hahn condition at high spin rotor rates. For the interpretation of 13C-CPMAS-NMR spectra, the overall chemical shift range was divided into the following main resonance regions (Spaccini et al. 2009): alkyl-C (0-45 ppm); methoxyl-C and N-alkyl-C (45-60 ppm); O-alkyl-C (60-110 ppm); unsubstituted and alkyl-substituted aromatic-C (110-145 ppm); oxygen substituted aromatic-C (145-160 ppm); carboxyl- and carbonyl-C (160-200 ppm). The area of each spectral region (R;abs) was divided by the sum of all spectral areas, in order to obtain a relative percentage (R%):
The values were used as variables for the multivariate statistical analysis.
In order to summarize the modifications brought about by different soil treatments on the molecular composition of humic extra cts, the following structural indexes were calculated from the relative amount of C distribution over the NMR spectra:
HB _ S(0 - 45ppm) + (45 - 60ppm) + (110 - 160ppm) _ S(45 - 60 ppm) + (60 - 110 ppm) + (160 - 190 ppm)
The hydrophobic index (HB) is the ratio of signal intensity in chemical shift intervals for hydrophobic C components over that in intervals for hydrophilic C components. The Lignin Ratio is the ratio of signal intensity in the 60-45 ppm interval over that in the 160-145 ppm interval. The aromaticity index, Ar, is the relative percentage of aromatic components in the samples. The larger the HB and Ar values of a OM sample, the greater is its hydrophobic character and its content of aromatic molecules, respectively (Zhou et al. 2010). Likewise, the smaller the value of the Lignin Ratio, the larger is the content of lignin-derived material in the humic extract or the lesser is the amount of biolabile hydrophilic carbon, such as in peptides (C-N in the 60-45 ppm interval) (Spaccini et al. 2009).
Was this article helpful?