Influence of agricultural soil management on humus composition and dynamics: Classical and modern analytical techniques

Plant and Soil 142: 259-271, 1992. © 1992 Kluwer Academic Publishers. Printed in the Netherlands. PLSO 9297

Influence of agricultural soil management on humus composition and dynamics: Classical and modern analytical techniques

HANS-ROLF SCHULTEN and REINHOLD HEMPFLING l Fachhochschule Fresenius, Department of Trace Analysis, Dambachtal 20, DW-6200 Wiesbaden, Germany. ~ Present address: Fresenius Consult GmbH, Research and Development Division, hn Maisel 14, DW-6204 Taunusstein-Neuhof, Germany

Received 26 July 1991. Revised December 1991

Key words: chemometric evaluation, crop rotation, pyrolysis-mass spectrometry, soil management, soil organic matter

Abstract

In-source pyrolysis-field ionization mass spectrometry (Py-FIMS), in combination with complementary elemental, wet-chemical, biochemical, and microbiological data, has been used to characterize humus composition and dynamics in soil samples from several field plots that have been cultivated in long-term experiments under different management conditions.

Thermograms and Py-FI mass spectra of whole-soil samples from field plots that under very different management show significant differences in humus composition, which may be due to varying stages of decomposition of plant residues and humus genesis. The intensity of soil management significantly affects high-molecular-weight subunits such as dimeric lignin-, arylalkyl-, and aliphatic constituents, even though humus quantity is similar for plots under more practically oriented management, such as crop rotation. The differences in molecular humus subunits of soil samples from different plots, in combination with complementary data, demonstrated that less parent (i.e. primary) material is incorporated in the humus matrix under intense soil management conditions. Samples from different field plots can thus be objectively differentiated on the basis of humus properties using multivariate statistical techniques such as principal component and cluster analyses. This statistical discrimination, using Py-FI mass spectra of the samples, corresponds well with microbial biomasses but is somewhat inconsistent with elemental data and results of chemical degradation procedures. The microflora populations in soils under intense management are limited by low availability and/or quality of carbon substrates. The resulting restricted internal nitrogen cycle causes those soils to have a reduced capacity to immobilize N, leading to relative enrichment of heterocyclic nitrogen compounds that are resistant to mineralization.

Introduction

Soil organic matter (SOM) of agricultural land is in a dynamic state, as it is affected by amendment, degradation and immobilization of both residual constituents and metabolic products from primary and secondary resources. These processes are influenced not only by climatic and pedogenetic factors but also by soil management.

Although the influences of different types of soil management on quantitative elemental parameters, such as total organic carbon (Corg) and nitrogen (Nt) contents, are well known (e.g. Dalai and Mayer, 1986; Follett and Schimel, 1989; Schimel, 1986; Skjemstad et al., 1986; Strebel et al., 1988), the corresponding effects on qualitative properties and structure of humus and on the chemodynamics of specific humus

260 Schulten and Hempfling

constituents during carbon and nitrogen turnover in soils are not yet well defined (Andreux et al., 1990). Chemical degradative procedures involv- ing hydrolysis, oxidation, and reduction processes combined with derivatization, separation, and identification have been employed in studies of the building blocks of SOM. However, re- coveries of identified products from digests of chemical degradative procedures of the heteropolymeric, polydisperse organic material in soils are only around 20-30% of Corg (An- dreux et al., 1990; Hayes et al., 1989).

Nondegradative spectroscopic methods, such as FTIR and 13C NMR spectroscopy, have been applied on whole-soil samples, humic substances fractions, particle and density fractions, and sol- vent extracts (e.g. Oades et al., 1988; Skjemstad et al., 1986). These techniques do not produce well-differentiated spectra for bulk samples of arable soils because of the low carbon content of such soils. In particular, FTIR spectroscopy is strongly influenced by the inorganic matrix and gives no useful spectra for typical agricultural soils with 1-5% SOM. Furthermore, nondegradative spectroscopic methods do not yield sufficient chemical information for direct identification of individual, molecular subunits, and they are consequently better suited to provide a chemical overview. Pyrolysis-field ionization mass spectrometry (Py-FIMS) gives data that can be used to objectively discriminate between agricultural soils that have been managed different- ly even if those samples yield similar ~3C NMR spectra (Schulten et al., 1990).

The on-line coupling of pyrolysis with gas chromatography/mass spectrometry (Py-GC/ MS) or with mass spectrometry (Py-MS) has proven to be adequate for the chemical characterization of soil organic matter fractions and bulk soil samples (Bracewell, 1971; Bracewell and Robertson, 1987; Haider and Schulten, 1985; Saiz-Jimenez et al., 1979; Saiz-Jimenez and de Leeuw, 1986; Schulten, 1987; Schulten and Schnitzer, 1992). Coupling of in-source pyrolysis with soft ionization techniques such as field ionization yields results that are especially suitable for chemometrical data evaluation and for infer- ences on authentic organic subunits in soils (Hempfling and Schulten, 1988, 1990, 1991; Schnitzer and Schulten, 1992; Schulten et al.,

1988, 1991; Schulten and Schnitzer, 1991). Re- cently, Schulten et al. (1990) used Py-FIMS to demonstrate that the relative contribution of carbohydrates, lignin, and proteinaceous material to SOM decreases with increasing intensity of soil management. In addition, the influence of long- term fertilization with farmyard manure on soil organic matter has been studied (Schulten and Leinweber, 1991).

The objective of the present study was to investigate the influence of different soil management practices on selected properties of humus using both Py-FIMS and elemental, wet-chemical, biochemical and microbiological investigations. This integrated approach provides the capability: 1. to discriminate between arable soils on the

basis of those properties of humus that are influenced by cultivation under different long- term management conditions;

2. to compare mass spectrometric data with elemental, wet-chemical, biochemical, and microbiological data to evaluate the suitability and specificity of individual results;

3. to evaluate the influence of agricultural soil management on carbon and nitrogen turnover.

Materials and methods

Soil samples

The soil samples were obtained from sites in Puch (Luvisol) and Neuhof (Gleysol), both in Southern Bavaria, Germany. The first site has been under continuous management since 1953 and represents extreme differences in management in addition to crop rotation, whereas on the second site farming started in 1977 and is characterized in general by practically oriented, graded intensity of crop rotation systems (Table 1). For Neuhof, Gr6blinghoff et al. (1989) have demonstrated that the annual carbon inputs from plant residues and manure are quantitatively similar (around 3 tons per ha per year) but differ significantly in composition and degradability.

Representative soil samples were taken ran- domly from each agricultural monoculture and rotation experimental plot in the spring of 1987

Agricultural soil management 261

Table 1. Soil management at the two sites examined

Site Abbreviation Management

Puch BF Bare fallow MP(- ) Potatoes only, no manure MP(+) Potatoes only, manure P/CR~ Crop rotation ~, manure P/CR 2 Crop rotation ~, manure P/CR~ Crop rotatioff', manure GL Grassland

Neuhof N/CR 0 Crop rotation b, N/CRj Crop rotation b, N/CR_, Crop rotation c, N/CR~ Crop rotation c,

18 x 109 kgha ~ manure 12 × 103 kgha 1 manure, 3 7 k g N h a straw, intercrop, 124 kg N ha straw, intercrop, 182 kg N ha

Crop rotation with 2/3 cereals, 1/6 red clover, 1/6 potatoes. b Crop rotation with summer barley, potatoes, winter wheat, oats, clover. c Crop rotation with summer barley, potatoes, winter wheat, oats, sugar beet.

Actual crops cultivated in 1987: Site Puch: P/CR~ winter wheat; P/CR 2 summer barley; P/CR 3 oats. Site Neuhof: winter wheat.

and 1988 from the Ap horizons (0-10 cm) in 100 soil cores with a diameter of 2.4 cm. The original, sieved and mixed bulk material (2.5 to 3.0kg) was freeze-dried and finely ground (<0.5mm) for classical analyses. For Py-MS, about 1 g of this material was pulverized to an average particle size of approx. 50/xm in an agate mortar. A chemical description (elemental analysis, polysaccharide contents, a-amino nitrogen, 13C NMR spectroscopy) and details of the management methods have been reported by Capriel et al. (1990) and Schulten et al. (1990). Biochemical and microbiological properties of these soils have been described by Beck (1989), Gr6blinghoff et al. (1989) and Zelles et al. (1992).

Pyrolysis-field ionization mass spectrometry

For temperature-resolved Py-FIMS, about 200 ~g of a whole-soil sample was thermally degraded in the ion source of a Finnigan MAT 731 mass spectrometer (Schulten et al., 1987). While heating at a rate of about 1.2 K s-l, 30 to 35 magnetic scans per sample were recorded in the temperature range 50 ° to 750°C for the mass range m/z 50-500. The Spectro-System SS 200 can integrate these single spectra to obtain summed spectra. Four replicates per sample were analyzed and averaged to one survey spectrum,

from which diagrams of ion intensities of individual signals, or series, or the whole sum of FI-mass signals versus the pyrolysis temperature can be constructed. Weight averaged (Mw) and number averaged (lVln) molecular weights of the survey spectra were calculated according to Lat- timer et al. (1989).

Chemometrical evaluation of the data sets

Each nominal mass is regarded as one variable in the A R T H U R programme package for chemometrical evaluation of the Py-FI mass spectra (Harper et al., 1977). The mass range was reduced to m/z 50-200 in order to compare only signals with intensities greater than 1% relative abundance. Preselection of the variables by Fisher weighting (Fisher, 1936), thus yielding the relevant variables for discrimination, is the first step in the pattern recognition procedure (Schulten et al., 1988). This Fisher weight is calculated with normalized and standardized data after transformation of the Py-FI mass signals by ARTHURSSX in an ARTHUR-compat- ible form. Variables with high Fisher weight values show low intra-sample and high inter- sample variation. Principal component (p.c.) analysis (Malinowski and Howery, 1980) and cluster analysis (Bratchell, 1989) of mass spectrometric (=internal) and elemental, wet-chemi-


cal, biochemical, microbiological (=external) data were carried out by subroutines of the A R T H U R package. Cluster analysis was performed with calculated p.c. scores for internal and external data. Correlations with qualitative and quantitative humus properties, as determined by the above mentioned techniques, were calculated for the 43 (~ n - 1) mass signals with highest Fisher weight. Correlation analysis was also performed with the scores of the dominating p.c.'s (Eigenvalues >1) from internal (4p.c.'s) and external (3 p.c.'s) data.

Results and discussion

Concentrating on differences in humus composition and dynamics due to the intensity of soil management, the evaluation and interpretation of results from Py-FIMS studies is performed in three steps: 1. visual inspection, 2. chemometrical evaluation, and 3. integrated approach: comparison with complementary data.

Visual inspection of thermograms and pyrolysis- field ionization mass spectra

Thermograms Thermograms, which are plots of ion intensities of the whole sum of FI-mass signals (total ion intensity =TII) versus the pyrolysis temperature, indicate that the thermal evolution of SOM from four representative soil samples obtained by in- source Py-FIMS occurred mainly between 200 ° and 700°C (Fig. 1). Total ion intensities for samples of equal size increase from bare fallow (BF; a) to monoculture potato (MP; b) and crop rotation (CR; c) and then decrease for grassland (GL; d). Increased intensities from BF to MP to CR can be explained by the increasing carbon content of those samples; however, the decrease observed from CR to GL occurs despite a further increase in carbon content. Concurrently, the weight average molecular weights (Mw) of the pyrolysis products increases slightly from BF to MP to CR_ and then significantly from CR to GL (BF: Mw=94.3, MP: Mw=122.4, CR: IVlw= 133.4, GL: IVlw=216.4). This indicates that the positive effect of increasing carbon content on ion intensities is overshadowed by the

reducing effect of an increasing fraction of high molecular weight pyrolysis products. The prod- uct of the integrated intensities (summed TII) with /~1 w of these samples increases from BF to GL similar to the carbon content (BF: 26.7 × 106, MP: 68.2 x 106, CR: 92.7 x 10 6, GL: 137.6 x 106). Maxima of the registered ion intensities are observed around 400 ° to 450°C for all samples from arable soils. The thermogram of the grassland sample (d) is characterized by a higher portion of volatiles in the temperature range 250 ° to 350°C.

Mass spectra The Puch samples whose mass spectra are given in Fig. 1 (BF (a), MP (b), CR (c), and GL (d)) represent extreme differences in soil management. Major differences in the mass range m/z 50 to 200 indicate that intense management of arable soil causes a decrease in (non-)humified constituents of plant residues such as carbohydrates, monomeric lignin subunits and polyphenols, lipids and proteinaceous materials (Schulten et al., 1990). In addition, signal intensities in the higher mass range (m/z 200-400) decrease with increasing intensity of management. Only minor differences are seen in the Py-FI mass spectra of MP and CR of Puch. However, the results demonstrate that Py-FIMS detects changes in humus composition due to extreme differences in soil management such as BF, MP, CR, and GL when applied to arable soil samples even with carbon contents of around 1%.

Samples from the Neuhof site are better suited for the comparison of more practically managed plots. The recorded FI mass spectra of these samples reveal the effects of different intensities of crop rotation on humus properties. In addition, at two plots of Neuhof ( N / C R 1 = a and N /CR 2 = b), the different management systems (mixed organic/inorganic fertilization and exten- sive integrated inorganic fertilization) result in almost identical carbon contents. The Py-FI mass spectra of both soil plots (Fig. 2) can be compared solely in terms of the influence of agricultural management on humus composition. Any effects of the inorganic matrix can be excluded because quantitative humus parameters such as C t and N t a r e almost identical. The Py-FI mass


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spectra of the soil samples from both plots show quite similar signal patterns in the mass range m/z 50-200 but can be differentiated distinctly by the signal patterns of the higher weight molecular pyrolysis products (Fig. 2). Specifical- ly, intensities of signals related to alkylbenzenes (m/z 204, 218, 232, 246, 260), C15 -C18-alkenes (m/z 210, 224, 238, 252), C l s - C21 -alkadienes (m/z 208, 222, 236, 250, 264, 278, 292), and partly biodegraded dimeric lignin subunits (m/z 244, 258,272, 284, 286, 302,320, 358) are higher in the spectrum of the less intensely managed sample (a). Thus, in addition to lignin, polymeric aliphatic and arylalkyl subunits in the original soil sample are distinctly decreased by increased intensity of agricultural management.

Weight-averaged molecular weights (lVlw) of the pyrolysis products are also different for both samples. This is evident for the mass range m/z 50 to 400 (N/CRI:IVl w = 140; N/CR2:M w = 127) and also for the higher molecular-weight pyrolysis products in the mass range m/z 200 to 400 (N/CRa:M w=251; N/CR2"M w=239).

Thus, for soils of similar carbon content signal intensities in the higher mass range are more intense for samples under less intense soil management. This demonstrates that the intensity of soil management significantly influences humus composition and quality, despite similar humus quantities. The discussed differences in SOM composition due to agricultural management are even more apparent for the two plots at Neuhof representing the extremes for crop rotation systems (N/CR0, N/CR3). However, 13C-NMR spectra of whole-soil samples and extracted humic substances from the four plots of Neuhof were very similar (Schulten et al., 1990).

The results of Gr6blinghoff et al. (1989) on investigations of biochemical transformations of samples from plots under different agricultural management may explain differences in humus composition. The authors reported a rapid biodegradation of straw and an enhanced decomposition of lignin for the more intensely managed systems. Less parent material from primary resources is incorporated in microbial biomass and

amorphous SOM (humus matrix), thus leading to a reduced portion of subsequently mineralizable SOM in intensely managed soils. These results correspond with the reduced signal pattern recorded in the Py-FI mass spectra of soils from plots under intense management. The less heterogeneous and therefore possibly less active humus in these soils may have significant effects on sorption and desorption phenomena as well as on physical/mechanical soil stability (Hemp- fling et al., 1990).

Differences in practically oriented soil management are clearly reflected by Py-FIMS, especially in the mass range m/z 200 to 400, which indicates differences for lignin biodegradation and contents of alkylbenzenes and paraffinic subunits. Extreme differences in soil management systems recorded are also in the mass range m/z 50 to 200 of Py-FIMS, indicating further effects on the contents of major plant litter constituents such as carbohydrates, lipids, and proteinaceous materials.

Chemometrical evaluation of mass spectrometric data

In order to obtain an objective differentiation and classification of the various plots on the basis of humus composition, the recorded Py-FI mass spectra of the samples from Puch and Neuhof were evaluated by principal component (p.c.) analysis. For this purpose the calculation of the total ion intensities was carried out excluding the dominating signals at m/z 58 and 96 to avoid intense variations (Meuzelaar et al., 1982). This data set consisted of 44 summed spectra that were divided into 11 categories. The 43 mass signals with a Fisher weight >12.4 were selected for the calculation of the p.c. The first 2 p.c.'s calculated describe 78.5% of the total variance of the whole data set (Table 2). The high-loading mass signals (loadings >10.17]) of p.c. 1 represent pyrolysis products of carbohydrates (m/z 82, 85, 98, 110, 112, 126, 162), lignin and polyphenols (m/z 110, 136, 138, 150, 152, 164, 200), and alkylbenzenes (m/z 162, 176), whereas mass signals related to aliphatic structures at m/z 56, 84 and N-containing subunits at m/z 79, 87, 107, 109, 183 dominate p.c. 2 (loadings >10.17[).

Classification of the samples by the score plot


Table 2. Py-FIMS signals from soil samples of Puch and Neuhof (1987) that load the first two principal components (p.c.) with loadings >]0.171

p.c. 1 2

Variance (%) 65.6 12.9

m/z 58,82,85,98,110,112 56", 79, 84", 87 126,136, 138,150, 152 107,109,183 162,164,176,200

" Negative Ioadings.

of the first versus the second p.c. (Fig. 3) allows distinct discrimination of all samples. A detailed discussion of the discrimination of the plots from Puch on the basis of Py-FI mass spectra, includ- ing the discrimination of samples from 1987 and 1988 and a comparison of the discrimination regarding whole-soil samples and extracted humic substances, has been reported recently by Schulten et al. (1990). A subjective subdivision of the whole data set into seven categories (dashed lines) is performed on the basis of the obtained score plot (Fig. 3). These seven categories that correspond well with the results of hierarchical clustering of a subset of the largest p.c.'s calculated from these data (see Fig. 4a) are distinguished on the basis of differences in humus composition as represented by the scores of p.c. 1 and 2. They comprise the plots

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Fig. 4. Dendrograms resulting from hierarchical cluster analyses (complete link) of selected p.c. scores (Eigenvalues >1) of Py-FIMS data (a) and elemental, wet-chemical, biochemical and microbiological data (h) from 1987.

BF (A1), MP ( - ) and MP (+) (B1), N/CR2, N/CR3, and P/CR 1 (B2), N /CR 0 and N/CR 1 (B3), P/CR 3 (C1), P/CR 2 (C2), and GL (D2). For both samples representing the extremes bare fallow and grassland, the scores of p.c.1 show lowest and highest values, respectively. The sample P/CR~ is closely related to N/CR2, 3 although different sites are compared. This may be due to a similar agrotechnological management and to crop effects as the actual crop for these plots is winter wheat. All plots with winter wheat are grouped into category B, but this category also comprises the potato group (MP). This can be explained with the existing crop rotation systems, since on the plots with winter wheat potatoes had been grown the year before.

Integrated approach: Comparison with elemental wet-chemical, biochemical, and microbiological data

Humus composition The data in Table 3 are given in columns whose sequence of clusters is simply 1A; 1B, 2B, 3B; 1C, 2C; and 2D using only those seven categories from Figure 3 that contain p.c. data. When viewed in this sequence, the external data in Table 3 exhibit some noteworthy trends. At the level of resolution of the first p.c., in Figure 3, i.e., A, B, C, D, most of the parameters in Table 3 gradually increase from A to D. Accord- ingly, these parameters are sensitive to extreme differences in soil management practices. Fine resolution between samples, e.g., on the basis of crop rotation, was found using the second p.c. of the Py-FIMS data in Figure 3. In Table 3, however, these distinctions are much less evident. In fact, only microbial biomass appears to be distinctly different within a group of clusters with the same first p.c. (e.g., 1B, 2B, 3B). It is tentatively concluded that both Py-FIMS data and microbiol biomass are indicators of not only the overall abundance of soil humus but also its composition and 'quality'.

Comparison of soil microbial biomass for samples of the plots at Neuhof with and without manure (N/CR0,1 and N/CR2.3, respectively, see Table 3) indicates that manure amendments positively affect microbial activities in soils and thus humus quality. These facts offer a more detailed insight into the effects of practically oriented agricultural soil management on humus properties. Therefore, Py-FIMS is a promising tool not only for fingerprinting and structural work on SOM but also for the description of the dynamics of the individual molecular humus subunits.

When using cluster analysis, the use of a group of highly correlated variables in the original data which provide the same information on some aspects of the data may give greater weight to that aspect than to other equally important aspects (Bratchell, 1989). In order to reduce problems arising from highly correlated variables, cluster analysis is applied to a subset of the largest principal components (Eigenvalues >1)


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of internal and external data. Cluster analysis clearly separates the various sites into five groups at similarity values >0.59 on the basis of Py- FIMS data (Fig. 4a). These groups represent: A: MP(+) , MP(- ) , P/CR3; B: N/CR0_2; C: P/ CRI,/, N/CR3; D: GL; E: BF (Fig. 4a). In contrast, at similarity values >0.64, the discrimination of the soil samples by cluster analysis on the basis of external data (Fig. 4b) yields the six groups A: N/CR2, 3, B: GL; C: N/CR0,1; D: MP(+) , MP(- ) ; E: P/CRI_3; F: BF. At similarity values >0.67, the discrimination by cluster analysis of internal data corresponds- with ex- ception of N/CR 2 - to the subjective subdivision of the score plot (dashed lines) calculated from p.c. analysis of internal data (see Fig. 3). The different classification of N/CR 2 may be due to the use of 4 p.c.'s for cluster analysis and only 2p.c. 's for the score plot values. However, in contrast to p.c. analysis, cluster analysis can not provide information on the importance of individual humus constituents for discrimination. Thus values for p.c. are used with equal signifi- cance. Nonetheless, this technique supports the subjective subdivision performed on the basis of p.c. analysis.

At similarity values >0.14 cluster analysis of p.c.'s calculated from external data (Fig. 4b) separates the arable soils of Puch from those of Neuhof, indicating that external data reflect more the effects of site conditions than of intensity of management on humus properties. The different discriminations obtained from external and internal data demonstrate that the type of information compared is somewhat different. As discussed before, elemental, wet-chemical, and biochemical parameters only partly reflect the classification calculated on the basis of Py-FIMS data. However, the correlation analysis between p.c. scores for internal and external data yields correlation coefficients of r = 0.90 for the first p.c.'s and r = 0.67 for the second p.c.'s of both data sets. This means that the high-loading mass signals of the first p.c., as calculated for internal data relating to carbohydrates, lignin, polyphenols, and alkylbenzenes, correspond well with the high-loading parameters of the first p.c. from the external data (loadings >10.311) that relate to Corg, Nt, and the net amounts of a- amino nitrogen, /3-glucosidase, polysaccharides,

biomass, and potentially mineralizable nitrogen. Correlation between quantitative soil parameters and the relative contribution of carbohydrates, lignin, polyphenols, and alkylbenzenes in humus indicates the existence of a general trend for the influence of agricultural soil management on humus properties. This is evident in both data sets and emphasizes the relation existing between humus quantity, microbial activity, and humus composition. This relation exists for samples representing extreme differences in management systems, such as BF, MP, CR, and GL because these are discriminated by the first p.c. (see Fig. 3).

The correlation between the second p.c. of both data sets represents a correspondence between the relative portion of aliphatic and nitrogen-containing compounds determined by Py-FIMS with specific external data such as the relative content of carbohydrates and a-amino nitrogen, /3-glucosidase activity, and the Cmic/ Corg ratio. This correspondence of external and internal humus parameters reflects the impact of differences in soil management on humus properties of plots under practically oriented management such as different crop rotation systems (see Fig. 3), and it demonstrates the importance of qualitative humus parameters for evaluating the effects of soil management on humus properties. Therefore, the exact description of internal carbon and nitrogen cycles in soils under different agricultural management is essential.

Carbon and nitrogen turnover In the following step, correlation analysis was carried out between internal and external data in order to compare total carbon and nitrogen turnover under different soil management with dynamics of individual humus constituents, and their relation to biochemical and microbiological processes in soils. For these purposes, the intensities of the 43 mass signals used for the calculation of p.c.'s (see Table 2 and Fig. 3) were correlated with elemental (Corg, Nt), wet-chemical (polysaccharides, a-amino nitrogen), biochemical (potentially mineralizable nitrogen, /3- glucosidase), and microbiological (biomass) parameters. Contents were calculated as net amounts and partly as relative amounts (see Table 3). As expected from Figure 3 and Table

3, most of the signals correlate with Corg and N t. Further positive correlations were found for the net amounts of polysaccharides, soil microbial biomass (mg C/100g), Cmic/Corg, and /3- glucosidase (% biomass) with the intensity of signals related to carbohydrates. In addition, the amounts of a-amino nitrogen (mg N/100g) and potentially mineralizable nitrogen (mgN/100g) are positively correlated with the intensity of signals related to nitrogen-containing subunits. These correlations are useful for confirmation of peak assignments, but do not give new insight into carbon and nitrogen turnover.

Some negative correlations also exist between external data and signal intensities in the Py-FI mass spectra. The most important for carbon and nitrogen turnover in soils are the negative correlations between m/z 79 (pyridine) and N t (r-- -0.87"**), Corg (r = -0.89***), microbial biomass (r = -0.84"*), polysaccharides (r = - 0.91 * * * ), a - amino nitrogen (r = - 0.79" * ), and mineralizable nitrogen ( r= -0 .70* ) , and between m/z 67 (pyrrole) and N t (r = -0.71"), Corg ( r = - 0 . 7 6 " * ) , polysaccharides ( r= -0 .68* ) , a- amino nitrogen (r = - 0.79"*), and mineralizable nitrogen (r = -0.64*), respectively. The negative correlations between the intensities of pyrrole and pyridine and N t of the samples indicates the existence of nitrogen-containing compounds in soil that are relatively enriched, even though N t

decreases. Pyrrole and pyridine may be gener- ated by thermal degradation of amino acids, peptides, and proteins that are present in soil either free or bound to the inorganic/organic soil matrix. However, no further pyrolysis products of proteinaceous material (e.g. aniline, benzonit- rile, indole, benzylcyanide, and methylindole) show a similar correlation. Thus, the pyrolysis products probably originate from pre-existing pyrrole and pyridine moieties in the soil humus.

The negative correlation between N t and the summed ion intensities of m/z 67 and 79 (pyrrole, pyridine) for the samples taken 1987 and 1988 is shown in Figure 5. The data indicate that intense soil management causes a relative enrichment of heterocyclic nitrogen-containing soil constituents. Mineralization and immobilization processes in soils are controlled by the metabo- lism of decomposable organic matter. The reduced incorporation of parent material from pri-


T ~g

Z

-'6

2

"1-

",\ • o

\ ~ o

\ \ • o

"\ o ~' ,~

0 •

\

o

• o " - .

",,\

, . . . . .

0.10 0.15 0.20

Total Nitrogen [%] -- :>

Fig. 5. Correlation between total nitrogen (%) and summed intensities (% TII) of the FI signals m/z 67 (pyrrole) and m/z 79 (pyridine) for soil samples of the plots from Puch and Neuhof 1987 (©) and 1988 (O).

mary resources in the humus matrix of intensely managed soils decreases the ability of these soils to immobilize and conserve mineral N, probably due to reduced availability of C substrate for microbial growth (Follett and Schimel, 1989). This assumption is corroborated by the negative correlation between signal intensities of m/z 67 and 79 and wet-chemically determined contents of polysaccharides and a-amino nitrogen of the samples. A reduced immobilization capacity for mineral N of soils under intense management is a reflection of the relative enrichment of heterocyclic, biologically refractory nitrogen compounds. This fact is of importance for soil fertility, utiliza- tion of nitrogen fertilizers, and accumulation of nitrate in groundwaters. However, our results indicate that this reduced immobilization capacity may be overcome by manure amendment, which improves humus quality by incorporation of parent material from primary resources in the humus matrix.

C o n c l u s i o n s

The results of this research demonstrate that: 1. Py-FIMS is a fast, sensitive, and specific tool


for the analysis of humus composition and quality in whole agricultural soils;

2. parameters describing molecular-level humus composition and/or microbiological data allow soils under different practically-oriented agricultural management to be distinguished and classified on the basis of humus properties;

3. the reduced incorporation of plant residues into the humus matrix of intensively managed soils decreases the ability of these soils to immobilize and conserve mineral N;

4. humus quality and composition in arable soils are of considerable importance for the internal nitrogen cycle and thus for the storage and leaching of mineral nitrogen fertilizers.

Acknowledgements

This work was supported financially by the Deutsche Forschungsgemeinschaft and the Bun- desministerium f/Jr Forschung und Technologie, Bonn-Bad Godesberg, Germany. The authors are grateful to Dr Haider, Bundesforschungsan- stalt f~r Landwirschaft, Braunschweig and Prof. E M Perdue, Georgia Institute of Technology, Atlanta, for discussions and critical comments, and to Dr. Beck, Dr Capriel and Dr Pfrogner, Bayerische Landesanstalt fiir Bodenkultur und Pflanzenbau, M/inchen, for the supply of soil samples as well as for agricultural, elemental, wet-chemical, and microbiological data.

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Influence of agricultural soil management on humus composition and dynamics: Classical and modern analytical techniques