biological indicators of soil quality and soil organic matter characteristics in an agricultural...

Biological indicators of soil quality and soil organic mattercharacteristics in an agricultural management continuum

Kristen S. Veum • Keith W. Goyne •

Robert J. Kremer • Randall J. Miles •

Kenneth A. Sudduth

Received: 15 October 2012 / Accepted: 15 May 2013 / Published online: 1 June 2013

� US Government 2013

Abstract Relationships among biological indicators

of soil quality and organic matter characteristics were

evaluated across a continuum of long-term agricultural

practices in Missouri, USA. In addition to chemical and

physical soil quality indicators, dehydrogenase and

phenol oxidase activity were measured, 13C nuclear

magnetic resonance (13C NMR) and diffuse reflectance

Fourier transform (DRIFT) spectra of soil organic

matter were collected, and visible, near-infrared reflec-

tance (VNIR) spectra of whole soil were collected.

Enzyme activities were positively correlated with

several soil quality indicators and labile fractions of

soil organic matter (r = 0.58–0.92), and were nega-

tively correlated with DRIFT indices of decomposition

stage and recalcitrance (r = -0.62 to -0.76). A

comparison of vegetative and land management prac-

tices was scored using the soil management assessment

framework (SMAF)—a soil quality index. Perennial

vegetation (i.e., native prairie, restored prairie, and

timothy) plots exhibited the greatest soil quality (SMAF

scores 93.6–98.6 out of 100), followed by no-till and

conventionally cultivated plots, with wheat outranking

corn. Among fertilization practices, soil quality fol-

lowed the order: manure [ inorganic fertilizer [una-

mended soil. Finally, in the estimation of soil properties,

VNIR spectra generally outperformed DRIFT spectra

using partial least squares regression (PLSR) and

multiple, linear regression (MLR). The strongest esti-

mates of dehydrogenase and phenol oxidase activity

were found using MLR models of VNIR spectra

(R2 [ 0.78, RPD [ 2.20). Overall, this study demon-

strates the potential utility and versatility of enzymes in

modeling and assessing changes in soil organic carbon

fractions and soil quality, and emphasizes the benefits of

maintaining long-term agricultural experiments.

Keywords Dehydrogenase � Diffuse reflectance

infrared Fourier transform (DRIFT) � Phenol oxidase �Nuclear magnetic resonance (NMR) � Soil quality �Soil management assessment framework (SMAF) �Visible near-infrared reflectance (VNIR)

Introduction

A wide range of environmental factors interact to

determine the composition of soil organic matter,

including soil type, climate, topography, vegetation,

and land management practices. In addition, bacteria

Responsible Editor: Colin Bell

K. S. Veum � K. W. Goyne � R. J. Miles

Department of Soil, Environmental and Atmospheric

Sciences, University of Missouri, 302 ABNR Building,

Columbia, MO 65211-7250, USA

e-mail: [email protected]

K. S. Veum � R. J. Kremer (&) � K. A. Sudduth

U.S. Department of Agriculture—Agricultural Research

Service, Cropping Systems and Water Quality Unit,

302 ABNR Building, Columbia, MO 65211-7250, USA

e-mail: [email protected]

123

Biogeochemistry (2014) 117:81–99

DOI 10.1007/s10533-013-9868-7

and fungi are key drivers of biogeochemical processes

in soil, and microbial enzymes are widely recognized

as proximate drivers of organic matter transformation

and decomposition. In turn, shifts in organic matter

composition affect microbial function (Adl 2003;

Sinsabaugh et al. 2002). Microbial enzymes, however,

are rarely incorporated into biogeochemical models

(Treseder et al. 2012). Therefore, more research is

needed to develop models that incorporate this com-

plex feedback mechanism in order to effectively

understand and predict the dynamics of soil organic

matter (Lawrence et al. 2009).

Soil quality is broadly defined as the capacity of a

soil to function (Karlen et al. 1997) and can be

assessed using a wide variety of biological, physical

and chemical indicators (Doran and Parkin 1994).

Biological indicators typically include microbial bio-

mass carbon (Jordan et al. 1995; Karlen et al. 1997)

and microbial enzyme activities (Bandick and Dick

1999; Eivazi et al. 2003). Microbial enzyme activities

reflect metabolic factors and may serve as early

indicators of soil quality improvement or degradation

in agroecosystems (Dick 1994). Chemical indicators

of soil quality include soil pH (Smith and Doran 1996),

soil nutrients, and soil organic matter (Karlen et al.

2008). The quantity and quality of soil organic matter

can be measured as total soil organic C, or by

estimating more biologically available fractions, such

as particulate organic C (Cambardella and Elliott

1992), active C (Blair et al. 1995), or water-extractable

organic C (Chantigny 2003). Common physical indi-

cators of soil quality include bulk density (Logsdon

and Karlen 2004) and aggregate stability (Angers et al.

1992).

Integrating multiple indicators into an index of soil

quality provides a way to comprehensively monitor

changes in soil quality as a result of land management

over time. The soil management assessment frame-

work (SMAF) is a soil quality index that was developed

to assess the impact of soil management practices on

soil function using non-linear scoring curves based on

soil biological, physical, chemical, and nutrient data

(Andrews et al. 2004). The SMAF soil quality index

has been used in large-scale environmental assess-

ments and conservation planning, and to evaluate field-

scale effects of agricultural management practices on

soil quality (Andrews et al. 2004; Cambardella et al.

2004; Karlen et al. 2008). Currently, the SMAF index

incorporates b-glucosidase and microbial biomass-C

scoring curves (Stott et al. 2010), and there are plans to

expand the biologic component of the index to include

several microbial enzymes under a metabolic activity

scoring curve (Diane Stott 2012, personal communi-

cation). Such an approach is also likely to enhance the

comprehensiveness of biogeochemical models, due to

the relationships among soil enzymes, soil organic

carbon fractions, and soil chemistry as highlighted in

this work.

While no single enzyme captures the entire meta-

bolic activity of the soil, select enzymes represent

important degradation pathways that are useful in

modeling. The SMAF index incorporates b-glucosi-

dase, which catalyzes the degradation of cellulose into

simple sugars and reflects the decomposition of plant

residues (Stott et al. 2010). In contrast, dehydrogen-

ases are an important endocellular component of the

enzyme system of all living microorganisms, reflect-

ing a broad range of microbial oxidative activities and

the decomposition of a diverse array of organic

materials (Casida 1977; Tabatabai and Dick 2002).

Phenol oxidases, on the other hand, are endo- and

extracellular enzymes produced by fungi and bacteria

(i.e., Phanerochaete and Actinomyces). They are

keystone enzymes involved in humification and the

depolymerization of lignin (Ander and Eriksson

1976). Phenol oxidases accumulate in the soil,

reflecting the long-term, cumulative effects of micro-

bial activity in addition to the activity of living

microorganisms. Phenol oxidase activity is expected

to increase as the lignin content of the soil increases,

and increase as organic matter declines in degraded

soils (Sinsabaugh 2010).

In addition to traditional soil quality indicators,

spectroscopic techniques provide insight into the

decomposition and transformation of soil organic

matter. Solid state 13C nuclear magnetic resonance

(13C NMR) spectra provide bulk structural and organic

C composition data (Mao et al. 2000) that are useful in

decomposition models (Baldock et al. 1997; Xing

et al. 1999), carbon stabilization models (Baldock and

Skemstad 2000), and for evaluating land management

effects (Ding et al. 2002). Diffuse reflectance Fourier

transform (DRIFT) spectra from the mid-infrared

region (400–4,000 cm-1) are commonly used to

evaluate changes in the relative abundance of organic

functional groups during the decomposition of organic

composts (Chefetz et al. 1998; Inbar et al. 1989), and

to evaluate changes in soil organic matter resulting

82 Biogeochemistry (2014) 117:81–99

123

from tillage (Ding et al. 2002) and fertilization

(Ellerbrock et al. 1999) practices.

Furthermore, visible, near-infrared reflectance

(VNIR) spectra (350–2,500 nm) represent harmonics

and overtones of fundamental molecular vibrations

occurring in the mid-infrared region (i.e., within DRIFT

spectra) that are associated with organic and inorganic

functional groups. These signals are much weaker than

fundamental bands in the mid-infrared region, and

therefore, interpretation of the VNIR spectra require

more advanced statistical techniques (Viscarra Rossel

and Behrens 2010). Multivariate techniques, such as

partial least squares regression (PLSR) and multiple

linear regression (MLR), have been successfully applied

to VNIR spectra to estimate soil chemical properties

such as soil organic C, total N, and active C (Chaudhary

et al. 2012; Sudduth et al. 2012), as well as biological

properties such as b-glucosidase activity (Sudduth et al.

2012), microbial biomass-C, and soil respiration (Chang

et al. 2001). The PLSR technique is especially useful

when handling highly correlated prediction variables

(i.e., multicollinearity) and is commonly used to

evaluate spectral data where the number of prediction

variables is greater than the number of observations

(Haaland and Thomas 1988). Multivariate techniques

have also been applied to the estimation of soil

properties using DRIFT spectra (e.g., Janik and Skj-

emstad 1995; Leifield 2006).

In agroecosystems, soil organic matter contributes

to vital soil functions such as sustained biological

activity, nutrient cycling, and crop productivity, and it

is a key determinant of soil quality (Doran and Parkin

1994; Karlen et al. 1997). Agricultural practices affect

soil organic matter pools as well as microbial function

(Doran et al. 1998), yet little is known about the

complex feedback mechanisms between microbial

function and organic matter composition. Ultimately,

more long-term, field-scale research is needed to

effectively incorporate microbial enzyme activities

and other soil quality indicators into larger-scale

biogeochemical models.

Using soil samples representing native prairie and a

continuum of agricultural practices from a long-term

agroecosystem, the objectives of this study were to: (1)

examine relationships among microbial enzyme activ-

ity (dehydrogenase and phenol oxidase), chemical and

physical indicators of soil quality, and the composition

of soil organic matter using 13C NMR, DRIFT, and

VNIR spectroscopy; (2) evaluate the effects of

differing vegetation, soil disturbance, fertilization

practices, and periodic burning on microbial enzyme

activity and other soil quality indicators; and (3)

evaluate soil quality indicators, including microbial

enzyme activity, in the context of the SMAF index.

Across the range of soils, we expected greater soil

quality in plots under native, perennial vegetation

relative to all cultivated plots. Among cultivated soils,

we expected greater soil quality to correspond with the

least amount of soil disturbance, and increased

aromatic-C content to correspond with more frequent

burning. Overall, we expected soil enzyme activity to

reflect the trends in other soil quality indicators. In

particular, we hypothesized that dehydrogenase activ-

ity would show positive correlations with total soil

organic C, labile fractions of organic C, and SMAF

soil quality scores. In contrast, we hypothesized that

phenol oxidase activity would exhibit a positive

correlation with the aromatic-C content of the soil

and the DRIFT indices of decomposition, and nega-

tively correlate with overall soil quality.

Materials and methods

Study sites and soil sampling

Sanborn Field (38�560N, 92�190W) is a long-term,

agricultural experiment located in Boone County,

Missouri, USA that was established in 1888. Tucker

Prairie (38�560N, 91�590W) is a 50 ha, remnant native

prairie ecosystem dominated by bluestem grasses

located about 30 km east of Sanborn Field in Callaway

County, Missouri, USA. The claypan soil at Sanborn

Field and Tucker Prairie is a Mexico silt loam (fine,

smectitic, mesic Vertic Epiaqualfs) and is character-

ized by an argillic subhorizon (Balesdent et al. 1988)

that impedes drainage and induces lateral, subsurface

water flow (Arnold et al. 2005; Blanco-Canqui et al.

2002). The parent materials are loess and loamy

sediments derived from pre-Illinoian glacial till (Un-

klesbay and Vineyard 1992). Soils with restrictive

sub-horizons, including claypan soils, encompass

approximately 2.9 million km2 globally (USDA-

NRCS 2006) and are used extensively in agricultural

production.

In May, 2008, three subsamples were collected from

the surface (0–10 cm) and subsurface (10–20 cm) of

ten Sanborn Field plots (Table 1). The Sanborn Field

Biogeochemistry (2014) 117:81–99 83

123

plots selected for this study represent a continuum of

agricultural practices including three cultivated crops

[continuous corn (Zea mays L.), continuous wheat

(Triticum aestivum L.), and continuous, perennial

timothy (Phleum pratense L.)], three fertilization

practices (manure, inorganic fertilizer, and no amend-

ment), three tillage practices (conventional tillage,

inter-row tillage, and no-till), and an annually burned,

restored prairie plot planted to warm season grasses.

Surface residues and litter were removed prior to soil

sample collection, and undisturbed soil cores were

collected from each sampling location at each depth

using 7.62 cm diameter aluminum rings following the

soil core method of Grossman and Reinsch (2002). For

general characterization of soil properties, bulk soil

from each depth at each location was collected using a

hand trowel. It is important to note that crop residues

were removed from all Sanborn Field plots prior to

1950; since then, crop residues have been returned.

Each Sanborn Field plot is 31 m by 10 m with a 1.5 m

border. The management practices for each plot,

including dates of establishment, can be found in

Table 1, and a detailed history of Sanborn Field can be

found in Miles and Brown (2011).

Tucker Prairie, a 50 ha, native prairie (i.e., never

cultivated) site served as a reference soil for this study.

The native vegetation at Tucker Prairie consists

predominantly of little bluestem [Schizachyrium

scoparius (Michx.) Nash] and big bluestem (Andro-

pogon gerardi Vitman) (Kucera 1958). Tucker Prairie

developed on the same soil series as Sanborn Field,

and the vegetation is assumed to represent the

vegetation that existed at Sanborn Field prior to

cultivation in 1888 (Balesdent et al. 1988). In May,

2008, three subsamples were collected from the

surface (0–10 cm) and subsurface (10–20 cm) of

two Tucker Prairie plots following the same field

protocol used to sample Sanborn Field. The Tucker

Prairie plots included a recently burned plot with

visible surface char, and a plot of unknown, periodic

burn history. A summary of all sample locations can

be found in Table 1.

General soil properties

The following laboratory analyses were performed in

duplicate or triplicate on all subsamples (3 subsamples

per plot 9 2 depths 9 12 plots; n = 72) unless

Table 1 Agricultural practices (crop, tillage method, fertiliza-

tion practice, and year treatment established) and general soil

properties in Sanborn Field plots (numbered) and Tucker

Prairie (unknown, periodic burn history, TPU; recently burned,

TPB), including cation exchange capacity (CEC, cmolc kg-1),

base saturation (BS, %), water pH (pHw), bulk density

(g cm-3), soil organic C (SOC, g kg-1), and total nitrogen

(TN, g kg-1). All crop residues were removed from all

Sanborn Field plots prior to 1950. Data represents composited

soil samples except where the standard error is stated in

parentheses (n = 3)

Plot Crop Tillage

method

Fert.

practice

Year treat.

estab.

% Clay CEC BS pHw Bulk density SOC TN

6 Corn MP Fulla 1950 24.7 19.8 77 6.2 1.00 (0.053) 13.3 (0.75) 1.2 (0.09)

7 Corn No-till Full 1950b 18.8 16.9 54 5.1 1.07 (0.076) 22.3 (4.15) 2.0 (0.31)

17 Corn MP None 1888 27.8 20.9 64 5.3 1.23 (0.015) 9.4 (0.47) 0.8 (0.04)

18 Corn MP Manurec 1888 29.4 22.9 92 7.1 1.16 (0.018) 17.0 (2.75) 1.4 (0.12)

2 Wheat MP Full 1888 23.8 20.0 77 6.4 1.21 (0.024) 15.1 (0.29) 1.3 (0.10)

9 Wheat MP None 1888 22.1 16.7 56 5.2 1.21 (0.054) 10.3 (0.31) 1.0 (0.05)

10 Wheat MP Manure 1888 23.9 21.1 74 6.3 1.14 (0.016) 19.9 (1.57) 1.8 (0.04)

22 Timothy – Manure 1888 17.1 18.1 82 6.6 0.92 (0.043) 27.4 (0.52) 2.6 (0.06)

23 Timothy – None 1888 15.8 15.6 68 6.0 1.01 (0.029) 22.2 (0.17) 2.0 (0.04)

45 Native grass – None 1991 17.4 16.2 81 6.6 1.21 (0.018) 20.8 (0.73) 1.8 (0.08)

TPU Native grass – None – 19.6 18.6 39 5.3 0.73 (0.039) 29.9 (2.09) 2.6 (0.14)

TPB Native grass – None – 20.5 21.9 59 5.7 0.74 (0.041) 37.1 (3.25) 3.1 (0.28)

a Full, inorganic N, P & K additions based on annual soil testsb Plot converted to no-till in 1971c Manure, 13.5 Mg manure ha-1


123

otherwise specified. Gravimetric water content and

soil bulk density were determined by oven drying soil

cores at 105 �C following the method of Grossman

and Reinsch (2002). Bulk soil samples were air-dried

and sieved (\2 mm); visible roots were removed prior

to air-drying and during the sieving process. Air-dried

soils were ground with a mortar and pestle for

subsequent analysis, except where noted. Particle size,

cation exchange capacity, base saturation, and water

pH were determined on air-dried, ground soil samples

following USDA-NRCS procedures (Burt 2004).

Total SOC and total nitrogen were determined on a

LECOTM combustion C and N analyzer (LECO Corp.,

St. Joseph, MI). General soil characteristics for each

plot can be found in Table 1.

Air-dried water stable aggregates were measured

on undisturbed core samples following an adapted

method of Kemper and Rosenau (1986) as described in

Veum et al. (2012).

The active C fraction, measured as potassium

permanganate-oxidizable organic C, was determined

as in Weil et al. (2003) with the following modifica-

tions: samples were reacted on an end-to-end shaker

for 15 min and centrifuged at 3,600 rpm for 5 min

prior to diluting the supernatant and reading the

absorbance at 550 nm in a 1 cm quartz cell on a

Genesys-8 spectrophotometer (Thermo-Fisher Sci.,

Madison, WI). Results were expressed as mg active

C kg-1 dry soil. Water-extractable organic C and total

N were measured following the general extraction

method of Burford and Bremner (1975) as described in

Veum et al. (2012). All samples were extracted at the

same ratio of soil to solution volume; therefore, results

for the extracts reflect abundance in an equivalent soil

mass.

The SMAF soil quality index

The SMAF soil quality index uses a set of indicators

grouped according to critical soil functions. Each

indicator represents a quantitative laboratory mea-

surement that is transformed using a nonlinear scoring

curve into a unitless value ranging from 0 to 1, where 1

represents the greatest potential functioning under the

given site characteristics. The individual indicator

values are then combined to generate a single score

that can be used to quantitatively compare soil quality

among sites. Currently, the SMAF includes 13 indi-

cators with fully developed, nonlinear scoring curves

that represent chemical, biological, physical and

nutrient categories of soil function. More details

regarding the development and implementation of

the SMAF can be found in Andrews et al. (2004), Stott

et al. (2010), and the references therein. All twelve

plots from Sanborn Field and Tucker Prairie were

evaluated and ranked in the SMAF soil quality index

using soil organic C, bulk density, aggregate stability,

and water pH.

Enzyme activities

Dehydrogenase activity was measured on field-moist

soil following an adapted method of Casida (1977). In

brief, 3 ml of 2 % CaCO3 and 2 ml of 3 % 2,3,

5-triphenyltetrazolium chloride (TTC) were added to

6 g samples of field-moist soil. Samples were incu-

bated at 37 �C in the dark for 16 h. The incubation was

terminated with methanol, and the soil suspension was

filtered through a 7 cm Whatman 42TM filter paper

using methanol to extract the colored 2,3,5-triphenyl

formazan (TPF) product. Sample absorbance was

immediately read at 485 nm using a Genesys-8

spectrophotometer (Thermo-Fisher Sci., Madison,

WI). Blanks (reagents without soil) and controls (soil

with all reagents except substrate) were analyzed

concurrently. Water content was determined at the

time of the assay. Results were expressed as lg TTC

converted to TPF per gram of equivalent oven-dried

soil per hour based on a 5-point standard curve.

Phenol oxidase activity was determined on field-

moist soil following an adapted method of Pind et al.

(1994) and Sinsabaugh et al. (2008). In summary, 2 g

samples of field-moist soil were homogenized with

20 ml of 50 mM sodium carbonate (pH 8.0). Three ml

of soil suspension was transferred to digestion tubes to

provide two assay replicates and a soil control (soil

with reagents except substrate). Then 3 ml of 5 mM L-

3,4-dihydroxyphenylalanine (L-DOPA) in 50 mM

sodium carbonate (pH 8.0) was added to the assay

replicates. The tubes were incubated in the dark at

37 �C on an end-to-end shaker for 1 h, and filtered

using prewashed 0.45 and 0.2 lm Whatman Pura-

discTM syringe filters in series to remove particulates

and colloidal material. Absorbance was read immedi-

ately at 460 nm using a Genesys-8 spectrophotometer

(Thermo-Fisher Sci., Madison, WI). Blanks (reagents

without soil) and controls (soil with all reagents except

substrate) were analyzed concurrently. Water content


123

was determined at the time of the assay. Results were

expressed as lmol L-DOPA converted to 3-dihydro-

indole-5,6-quinone-2-carboxylate (DIQC) per gram of

equivalent oven-dried soil per hour based on a 5-point

standard curve. Given the range of soil pH (5.1–7.1),

an acidic buffer such as sodium acetate (pH 5.0) may

have better optimized the assay for the study soils

(German et al. 2011; Sinsabaugh et al. 2008).

13C NMR analysis of enriched soil organic matter

To reduce interference from inorganic and paramag-

netic materials in 13C NMR analysis, soil organic matter

was enriched from composite whole soil samples using

5 % HF (v/v) following the procedure of Skjemstad

et al. (1994). Due to the high cost of instrument time, 13C

NMR spectra of enriched soil organic matter were

collected from composite surface samples representing

each plot (1 surface composite sample 9 12 plots;

n = 12). The spectra were obtained on a Bruker Avance

DRX300 widebore NMR spectrometer (Bruker, Bille-

rica, MA) equipped with a 7 mm cross-polarization,

magic angle spinning (CPMAS) probe. The operating

frequency was 300.13 MHz for proton and 75.48 MHz

for carbon, respectively. Cross-polarization with

magic angle spinning and total sideband suppression

(CPMAS-TOSS1) was acquired with 1 ms contact time

and 1.03 s repetition delay. Spectra were collected at a

spin rate of 5 kHz, a recycle delay (relaxation time) of

1 s and a contact time of 1 ms. The 13C chemical shift

was externally referenced to the carbonyl carbon signal

of glycine at 176.03 ppm. A minimum of 5,000 scans

were collected for each sample. Line broadening of

150 Hz was applied to the data before Fourier transfor-

mation. Bruker XWIN-NMR version 3.6 software was

used for data collection and initial processing. Spectral

areas were calculated by integration (Xing et al. 1999)

and divided into the following regions: 0–110 ppm

(total aliphatic-C), 0–45 ppm (unsubstituted alkyl-C),

45–110 ppm (O and N substituted alkyl-C), 45–60 ppm

(methoxyl-C), 60–94 ppm (carbohydrate-C), 94–110

ppm (di-O-alkyl-C), 110–160 ppm (total aromatic-C),

110–142 ppm (aryl-C), 142–160 ppm (phenolic-C),

160–215 ppm (total carbonyl-C), 160–190 ppm (car-

boxyl-C), and 190–215 ppm (carbonyl-C) as in Wilson

et al. (1981) and Mathers et al. (2002). The proportion of

each functional group was converted to soil concentra-

tions (g kg-1 soil) by multiplying the functional group

proportion by the total SOC concentration (Cheesman

et al. 2012; Sjogersten et al. 2003).

DRIFT analysis of enriched soil organic matter

The DRIFT spectra were also collected on enriched

soil organic matter from composited surface and

composited subsurface samples from each plot

(1 composite sample 9 2 depths 9 12 plots; n = 24).

The enrichment of soil organic matter with hydrofluoric

acid improves the quality of DRIFT spectra, in particular

the resolution of organic peaks that overlap with the Si–

O region such as carbohydrate C–O bonds (Rumpel et al.

2006). The spectra of 8 % sample in pre-ground KBr

were collected using a NicoletTM 4700 infrared spec-

trophotometer (Thermo-Fisher, Madison, WI) equipped

with a DRIFT accessory. The DRIFT cell was purged

with CO2 and H2O-free air for 15 min prior to analysis.

A background spectrum of KBr was collected at least

every 4 h to account for potential changes in ambient

environmental conditions, and 400 scans per sample

were collected at a resolution of 2 cm-1 from 400 to

4,000 cm-1. Spectra were converted to Kubelka–Munk

units and baseline corrected using Grams/32 v.8.0

software (Galactic Corp., Salem, NH). General peak

assignments were based on Baes and Bloom (1989),

Inbar et al. (1989), and Stevenson (1994). The primary

peak assignments used in this study included CH

deformation of CH3, and CH bending of CH2 at

1,423 cm-1, aromatic C=C, COO- and H-bonded

C=O at 1,648 cm-1, C=O stretching of COOH at

1,724 cm-1, and asymmetric and symmetric stretching

of C–H in CH2 at 2,930 and 2,850 cm-1, respectively.

Five DRIFT peak height ratios were calculated to

evaluate relative decomposition, transformation, and

recalcitrance (i.e., reduced biological reactivity). Index 1

(1,648 cm-1/2,924 cm-1), index 2 (1,648 cm-1/

2,850 cm-1), and index 3 (1,648 cm-1/1,423 cm-1)

determine the relative aromatic to aliphatic content, and

have been shown to increase with increasing decompo-

sition and maturity (Chefetz et al. 1998; Inbar et al.

1989). Index 4 (1,648 cm-1/1,724 cm-1) and index 5

(2,924 cm-1/1,724 cm-1) represent relative recalci-

trance by determining the relative C functionality

(aromatic and aliphatic, respectively) to O-functionality.

Oxygen-containing groups have been shown to decrease

relative to overall C content as recalcitrance increases

and biological reactivity decreases (Chefetz et al. 1998;

Ding et al. 2002; Wander and Traina 1996).


123

VNIR analysis of whole soil

The VNIR spectra were collected on all subsamples

(n = 72) of air-dried, sieved (\2 mm), and ground

whole soil from the surface and subsurface of the 12

plots using an ASD FieldSpec Pro FR spectrometer

(Analytical Spectral Devices, Boulder, CO), scanning

from 350 to 2,500 nm in 1 nm intervals. Each

spectrum, the average of 30 scans, was adjusted using

dark current scans, and a Spectralon white reference

standard (Labsphere Inc., North Sutton, N.H.) was

scanned after every 10 samples to convert the raw

spectral data to decimal reflectance. Spectra were

obtained in triplicate by rotating the sample cup ca. 60

degrees between sets of scans, and averaged for each

sample. The spectra from the subsamples of each plot

at each soil sampling depth were averaged for a total of

24 VNIR spectra (2 depths 9 12 plots; n = 24).

Wavelengths in the visible region (400–700 nm) are

associated with iron-containing minerals, such as

hematite and goethite (Sherman and Waite 1985),

whereas components of soil organic matter, such as

humic acid, are primarily associated with the NIR

range (700–2,500 nm) due to harmonics and over-

tones of O–H, C–H and N–H absorptions (Clark 1999;

Clark et al. 1990). A detailed list of VNIR wavelengths

and associated soil constituents can be found in

Viscarra Rossel and Behrens (2010).

Statistical analyses

Relationships among soil properties in the surface

layer across all plots (1 depth 9 12 plots; n = 12)

were explored with stepwise, linear regression and

correlation analysis using SAS Enterprise Guide, v.

4.3 (SAS Institute, Cary, NC) software. Regression

models were selected based on Akaike’s Information

Criterion and the coefficient of determination (R2).

Due to the fact that the Sanborn Field experiment

commenced in 1888, prior to the inception of modern

biometrics, a modified two-factor ANOVA was used

to account for the lack of plot-level replication and

evaluate the main effects of crop and fertilization

practice. Eight plots from Sanborn Field representing

three crops (continuous corn, continuous wheat, and

continuous timothy) and three fertilization practices

(manure, inorganic fertilizer, and no amendment)

were compared using SAS Proc Mixed with Tukey’s

HSD procedure as the multiple comparison test. All

analyses were conducted at the a = 0.05 significance

level. Specifically, each crop by fertilization treatment

combination has no ‘‘true’’ replication. Instead, each

experimental unit (i.e., plot) has one replicate with

three subsamples. Consequently, using a standard two-

factor ANOVA with subsamples results in an error

term with zero degrees of freedom. In this modified

ANOVA, the interaction term serves as the replica-

tion, or error, term in formulating tests for main

effects, and an additional random effect due to

subsampling is incorporated. Further details of this

modified ANOVA and a full justification for this

model specification can be found in Veum et al.

(2011).

The VNIR and DRIFT (2 depths 9 12 plots;

n = 24) spectral estimation of soil properties using

PLSR and MLR analysis were implemented in

Unscrambler v. 10.1 (CAMO Inc., Oslo, Norway).

Similar analyses were attempted using 13C NMR

spectra, but the sample size (1 depth 9 12 plots;

n = 12) was insufficient. First, VNIR spectra were

restricted to the 400–2,500 nm range, and DRIFT

spectra were restricted to the 1,025–3,000 cm-1 range

to eliminate regions with a low signal to noise ratio.

The VNIR spectra were log-transformed to absor-

bance units [log (1/reflectance)] prior to analysis. All

spectra were mean-normalized and centered. Calibra-

tion and self-validation of PLSR and MLR models

were conducted using the leave-one-out, cross-vali-

dation method. Models were compared based on the

coefficient of determination (R2), root mean square

error (RMSE), and the ratio of the standard deviation

to the RMSE (RPD). The RPD facilitates comparison

of results from data sets with different degrees of

variability, with a greater RPD signifying greater

accuracy and reliability of model prediction. In this

study, an RPD less than 1.6 was considered poor,

1.6–2.0 was acceptable, and greater than 2.0 was

excellent (Dunn et al. 2002; Pirie et al. 2005).

Results

Relationships among soil quality indicators

In the surface layer (1 depth 9 12 plots; n = 12),

many soil quality indicators measured in this study

were significantly correlated with each other


123

(Table 2). Dehydrogenase activity was highly and

positively correlated (r C 0.82) with soil organic C,

total N, active C, water-extractable organic C, aggre-

gate stability, and 13C NMR organic C functional

groups including total aliphatic-C, alkyl-C, O-alkyl-C,

and carbohydrate-C. Less significant, yet similar

relationships were found with phenol oxidase activity.

Using stepwise, MLR, the enzyme activities explained

89 % of the variation in soil organic C (Eq. 1) and

77 % of the variation in carbohydrate-C content

(Eq. 2). Together, dehydrogenase activity and pH

explained 81 % of the variation in active C content

(Eq. 3), although pH was not highly correlated with

active C and the other measured variables in this study

(data not shown). It is known that regression and

correlation relationships do not necessarily reflect

causality among the variables of interest, thus we

acknowledge that these regression models and the

correlations in Table 2 do not suggest that soil enzyme

activities are controlling soil organic matter content.

Other MLR relationships using enzyme activity were

evaluated; however, the models were poor or did not

present an improvement over the simple linear

regression and correlation relationships.

Table 2 Pearson linear correlation coefficients across all plots

(n = 12) for values from the 0–10 cm sampling depth

including the soil management assessment framework score

(SMAF), soil organic C (SOC), total nitrogen (TN), active C

(POXC), dehydrogenase activity (DHA), phenol oxidase

activity (POA), water stable aggregates (WSA), water-extract-

able organic C (WEOC), the concentration of organic C

functional groups determined by 13C NMR (g kg-1), and

indices of decomposition and recalcitrance as determined by

diffuse reflectance Fourier infrared transform (DRIFT)

SMAF SOC TN POXC DHA POA WSA WEOC

TN 0.89*** 0.99***

POXC 0.94*** 0.94*** 0.95***

DHA 0.72** 0.92*** 0.92*** 0.85**

POA 0.76** 0.79* 0.78** 0.68* 0.70*

WSA 0.88*** 0.93*** 0.94*** 0.91*** 0.92*** 0.84***

WEOC 0.79** 0.93*** 0.92*** 0.87*** 0.90*** 0.66* 0.83***

Total aliphatic-C 0.77** 0.95*** 0.94*** 0.86*** 0.94*** 0.72** 0.89*** 0.85***

Alkyl-C 0.63* 0.83*** 0.82** 0.72** 0.89*** 0.60* 0.81** 0.72**

O-Alkyl-C 0.87*** 0.95*** 0.95*** 0.92*** 0.82** 0.77** 0.85*** 0.90***

Methoxyl-C 0.68* 0.82** 0.82** 0.79** 0.72** NS 0.63* 0.90***

Carbohydrate-C 0.87*** 0.96*** 0.95*** 0.91*** 0.82** 0.80** 0.85*** 0.89***

Di-O-alkyl-C 0.75** 0.75** 0.78** 0.76** NS 0.73** 0.72** 0.61*

Total aromatic-C NS NS NS NS NS NS NS NS

Aryl-C NS NS NS NS NS NS NS NS

Phenolic-C 0.82*** 0.71* 0.72* 0.78** NS NS 0.66* 0.74**

Total carbonyl-C 0.68* 0.66* 0.63* 0.59* NS 0.58* NS 0.77**

Index 1a -0.93*** -0.90*** -0.91*** -0.95*** -0.75** -0.66* -0.83*** -0.81**

Index 2b -0.93*** -0.91*** -0.92*** -0.96*** -0.76** -0.67* -0.84*** -0.82**

Index 3c -0.89*** -0.78** -0.77* -0.90*** -0.62* NS -0.66* -0.72**

Index 4d -0.85*** -0.83*** -0.83** -0.93*** -0.69* NS -0.74** -0.77**

Index 5e -0.75** -0.71** -0.70* -0.83*** -0.63* NS -0.64* -0.66*

NS not significant

*** Significant at p \ 0.001; ** significant at p \ 0.01; * significant at p \ 0.05a DRIFT peak height ratio of 1,648/2,924 cm-1

b DRIFT peak height ratio of 1,648/2,850 cm-1

c DRIFT peak height ratio of 1,648/1,724 cm-1

d DRIFT peak height ratio of 1,648/1,423 cm-1

e DRIFT peak height ratio of 2,924/1,724 cm-1


123

SOC ¼ 10:4þ 0:75 DHAþ 0:02 POA; R2 ¼ 0:89

ð1Þ

CARB-C ¼ 2:04þ 0:104 DHAþ 0:006 POA;

R2 ¼ 0:77 ð2Þ

POXC ¼�0:28þ0:05 DHAþ 0:17 pH; R2 ¼ 0:81

ð3Þ

Where SOC is soil organic C (g kg-1), DHA is

dehydrogenase activity (lg g-1 h-1), POA is phenol

oxidase activity (lmol h-1 g-1), CARB-C is the

carbohydrate-C content (g kg-1), and POXC is active

C (g kg-1).

The DRIFT indices of organic matter maturity and

decomposition stage (indices 1–3) were negatively

correlated with aggregate stability (r = –0.64 to -0.83),

active C (r = -0.83 to -0.96), and other soil organic

C fractions. Additionally, dehydrogenase activity was

negatively correlated (r = -0.62 to -0.76) with the

indices of decomposition stage (indices 1–3), while

phenol oxidase activity was moderately, negatively

correlated with indices 1 and 2 (r = -0.66 and -0.67,

respectively). The indices of organic matter recalci-

trance (indices 4 and 5) were highly negatively

correlated with the organic C fractions, but only

moderately negatively correlated with dehydrogenase

activity and aggregate stability (Table 2). Relation-

ships among these variables in the subsurface layer

were similar, but less significant (data not shown).

Effects of vegetation and land management

Trends in the effects of vegetation and land manage-

ment on indicators of soil quality were evaluated, and

a modified two-factor ANOVA (a = 0.05) was used

specifically to evaluate the effects of crop and

fertilization practice (Table 3). Timothy demonstrated

significantly greater soil organic C, total N, active C,

dehydrogenase activity, phenol oxidase activity, and

aggregate stability, reflecting an approximate 2-fold or

greater increase in these soil properties relative to

wheat and corn. Between wheat and corn, soil quality

indicators tended to be greater under wheat, although

the differences were not significant except for dehy-

drogenase activity. Among fertilization practices,

plots amended with manure demonstrated signifi-

cantly greater soil organic C, total nitrogen, and water

stable aggregates relative to plots amended with

inorganic fertilizer or no amendments. Phenol oxidase

activity was nearly 6-fold greater in plots amended

with manure relative to inorganic fertilizer, and

although not significantly greater, mean dehydroge-

nase activity was nearly double in plots amended with

manure relative to plots with inorganic fertilizer or no

amendments. Across all 12 plots, a 10-fold range in

dehydrogenase activity and a 100-fold range in phenol

oxidase activity were observed, with the greatest

activities found in the native prairie plots relative to all

cultivated plots.

Across treatments, the 13C NMR spectra of

enriched soil organic matter were dominated by total

aliphatic-C, representing 60–75 % of total organic C,

and aromatic-C, representing 15–28 % of total organic

C. Representative spectra can be found in Fig. 1. The

proportions of each organic C functional group

relative to the total organic C content was determined

by direct integration of the 13C NMR spectra and

compared across treatments using the modified

ANOVA (data not shown). The proportion of aro-

matic-C was significantly greater under corn (28 %)

relative to timothy (25 %) and wheat (15 %), while

wheat contained more than double the proportion of

alkyl-C relative to corn (46 and 22 %, respectively).

Across all 12 plots, the proportion of aromatic-C was

smallest in the native prairie plots (9–12 %), followed

by the wheat plots (13–18 %), the timothy and corn

plots (23–31 %), and the annually burned, restored

prairie plot (32 %). Among fertilization practices,

there were no significant differences and no obser-

vable trends in the proportions of 13C NMR organic C

functional groups.

In contrast, the soil concentrations of 13C NMR

functional groups varied among crop and fertilization

practices (Table 4). Soils under timothy contained

significantly greater concentrations of most functional

groups relative to wheat and corn, corresponding to a

nearly 2-fold or greater increase. Wheat contained a

significantly greater concentration of alkyl-C relative

to corn, and a comparison of fertilization practices

found that soil amended with manure contained the

greatest concentrations of all functional groups,

although these differences were only significant for

total aliphatic-C and O-alkyl-C.

Across all plots (1 depth 9 12 plots; n = 12),

DRIFT spectra of enriched soil organic matter were


123

Ta

ble

3M

ean

val

ues

for

soil

pro

per

ties

and

dif

fuse

refl

ecta

nce

Fo

uri

erin

frar

edtr

ansf

orm

(DR

IFT

)in

dic

eso

fd

eco

mp

osi

tio

n(1

–3

)an

dre

calc

itra

nce

(4–

5)

by

cro

pan

d

fert

iliz

atio

np

ract

ice

Cro

pF

erti

liza

tio

np

ract

ice

Co

rnW

hea

tT

imo

thy

Fu

llM

anu

reN

on

e

Bu

lkd

ensi

ty(g

cm-

3)

1.1

3a

(0.0

70

)1

.19

a(0

.02

2)

0.9

7a

(0.0

43

)1

.10

a(0

.10

5)

1.0

8a

(0.0

77

)1

.15

a(0

.07

1)

pH

w6

.2a

(0.5

2)

6.0

a(0

.38

)6

.3a

(0.3

0)

6.3

a(0

.10

)6

.7a

(0.2

3)

5.5

a(0

.25

)

Wat

erst

able

agg

reg

ates

(%)

21

.0b

(2.6

5)

24

.9b

(4.9

7)

43

.8a

(7.0

0)

20

.6b

(0.3

3)

37

.1a

(7.3

6)

24

.2b

(6.3

4)

So

ilo

rgan

icC

(gk

g-

1)

13

.2b

(2.2

1)

15

.1b

(2.7

6)

24

.8a

(2.6

3)

14

.2b

(0.8

8)

21

.4a

(3.1

1)

13

.9b

(4.1

2)

To

tal

N(g

kg

-1)

1.1

7b

(0.1

78

)1

.32

b(0

.21

7)

2.2

9a

(0.2

65

)1

.25

c(0

.03

1)

1.9

1a

(0.3

31

)1

.28

b(0

.37

4)

C:N

rati

o1

1.2

a(0

.17

1)

11

.4a

(0.3

77

)1

0.9

a(0

.10

7)

11

.4a

(0.4

7)

11

.3a

(0.2

6)

10

.9a

(0.1

4)

Act

ive

C(g

kg

-1)

0.6

5b

(0.1

48

)0

.81

b(0

.18

6)

1.4

3a

(0.2

20

)0

.89

a(0

.07

0)

1.1

5a

(0.2

59

)0

.67

b(0

.27

2)

Deh

yd

rog

enas

eac

tiv

ity

(lg

g-

1h

-1)

1.6

8c

(0.8

18

)5

.25

b(0

.99

8)

13

.19

a(2

.77

3)

4.4

2a

(0.5

24

)8

.36

a(2

.68

5)

4.4

1a

(1.6

66

)

Ph

eno

lo

xid

ase

acti

vit

y(l

mo

lh

-1

g-

1)

12

7.5

b(5

8.8

6)

98

.0b

(44

.66

)2

64

.4a

(55

.64

)3

5.6

b(5

.75

)2

10

.3a

(16

.76

)1

67

.7a

(76

.35

)

Wat

er-e

xtr

acta

ble

org

anic

C(m

gk

g-

1)

10

2b

(17

.7)

13

4b

(24

.7)

22

0a

(22

.5)

13

2ab

(6.6

)1

76

a(3

7.5

)1

18

b(4

0.4

)

Wat

er-e

xtr

acta

ble

N(m

gk

g-

1)

11

.9b

(2.0

8)

18

.0a

(3.5

0)

22

.7a

(6.3

1)

16

.2ab

(3.0

0)

22

.4a

(4.1

6)

11

.9b

(2.4

8)

DR

IFT

ind

ex1

a2

.60

a(0

.11

7)

2.3

8a

(0.1

79

)1

.64

b(0

.06

6)

2.2

8a

(0.0

15

)2

.00

a(0

.22

3)

2.5

6a

(0.2

52

)

DR

IFT

ind

ex2

b3

.48

a(0

.30

3)

3.1

8a

(0.2

36

)2

.17

b(0

.10

5)

3.0

7a

(0.0

62

)2

.66

a(0

.31

8)

3.3

8a

(0.4

90

)

DR

IFT

ind

ex3

c4

.86

a(0

.81

5)

4.2

0a

(0.9

77

)2

.58

b(0

.14

9)

3.5

7a

(0.5

08

)3

.04

a(0

.47

8)

5.1

8a

(0.9

95

)

DR

IFT

ind

ex4

d3

.11

a(0

.48

1)

2.5

9a

(0.3

42

)1

.49

b(0

.04

6)

2.2

2a

(0.1

59

)2

.12

a(0

.40

6)

3.0

6a

(0.5

99

)

DR

IFT

ind

ex5

e1

.16

a(0

.06

8)

1.0

5a

(0.1

11

)0

.91

b(0

.01

0)

1.0

7a

(0.0

88

)0

.92

a(0

.08

1)

1.1

2a

(0.1

10

)

Val

ues

foll

ow

edb

ya

dif

fere

nt

low

erca

sele

tter

wit

hin

row

san

dfo

ra

giv

enfa

cto

r(c

rop

or

fert

iliz

atio

np

ract

ice)

wer

esi

gn

ifica

ntl

yd

iffe

ren

tam

ong

trea

tmen

ts(u

sin

gT

uk

ey’s

HS

D)

ata

=0

.05

.S

tan

dar

der

rors

are

stat

edin

par

enth

eses

aP

eak

hei

gh

tra

tio

of

1,6

48

/2,9

24

cm-

1

bP

eak

hei

gh

tra

tio

of

1,6

48

/2,8

50

cm-

1

cP

eak

hei

gh

tra

tio

of

1,6

48

/1,4

23

cm-

1

dP

eak

hei

gh

tra

tio

of

1,6

48

/1,7

24

cm-

1

eP

eak

hei

gh

tra

tio

of

2,9

24

/1,7

24

cm-

1


123

dominated by a sharp peak at 1,648 cm-1, (aromatic

C=C, COO– and H-bonded C=O), small, distinct

peaks at 2,850 and 2,924 cm-1 (stretching of CH2 and

CH3 groups), and a distinct shoulder at 1,724 cm-1

(carbonyl groups) (Fig. 2). The indices reflecting

maturity and decomposition stage (indices 1–3), and

the indices reflecting recalcitrance (indices 4 and 5),

were all significantly greater under wheat and corn

relative to timothy (Table 3). Between wheat and

corn, the indices (1–5) were greater under corn,

although the values were not significantly different.

Among fertilization practices, no significant differ-

ences were found in the decomposition stage and

recalcitrance indices; however, the trend among all

indices was in the order: no amendment [ inorganic

fertilizer [ manure.

A direct, qualitative comparison of the recently

burned native prairie plot and the native prairie plot of

unknown burn history suggested that recent burning

increased dehydrogenase activity by 48 %, phenol

oxidase activity by 19 %, water-extractable organic C

by 96 %, and aromatic-C content by 70 % (data not

shown). Additionally, relative to the native prairie plots,

the restored prairie plot exhibited 25 % less dehydroge-

nase activity, 30 % less soil organic C, and greater

DRIFT indices of decomposition stage and recalcitrance.

Fig. 1 Representative 13C nuclear magnetic resonance (13C

NMR) spectra from Sanborn Field: continuous, perennial

timothy (a plot 10), continuous, conventionally tilled corn

(b plot 17), prairie restoration (c plot 45), and Tucker Prairie

(d unknown burn history)

Table 4 Mean concentration of 13C NMR functional groups (g kg-1 soil) by crop or fertilization practice as determined by

multiplying the proportion of each functional group by the total soil organic C content

Assignment Crop Fertilization practice

Corn Wheat Timothy Full Manure None

Total aliphatic-C 7.7c (1.15) 11.2b (1.78) 16.0a (2.94) 9.6ab (2.56) 14.2a (2.62) 9.0b (2.11)

Alkyl-C 2.5b (0.07) 6.4a (1.03) 6.8a (2.25) 4.4a (2.04) 6.6a (2.02) 3.9a (0.71)

O-alkyl-C 5.2b (1.10) 4.8b (0.82) 9.2a (0.78) 5.2ab (0.52) 7.6a (1.27) 5.1b (1.69)

Methoxyl-C 1.0b (0.12) 1.5ab (0.29) 2.0a (0.37) 1.4a (0.46) 1.8a (0.32) 1.2a (0.25)

Carbohydrate-C 2.9b (0.73) 2.7b (0.49) 5.1a (0.23) 2.9a (0.27) 4.3a (0.63) 2.8a (0.56)

Di-O-alkyl-C 1.2b (0.26) 0.6c (0.05) 2.1a (0.14) 0.9a (0.22) 1.6a (0.44) 1.1a (0.41)

Total aromatic-C 3.8ab (0.83) 2.2b (0.20) 6.2a (0.12) 3.1a (1.02) 4.6a (1.10) 3.4a (1.33)

Aryl-C 2.7ab (0.52) 1.1b (0.09) 4.1a (0.02) 2.0a (0.76) 2.8a (0.98) 2.4a (0.90)

Phenolic-C 1.1ab (0.32) 1.1b (0.30) 2.1a (0.16) 1.1a (0.26) 1.8a (0.22) 1.0a (0.44)

Total carbonyl-C 1.8a (0.37) 1.8a (0.95) 2.6a (0.46) 1.6a (0.65) 2.6a (0.53) 1.6a (0.73)

Carboxyl-C 1.4a (0.33) 1.2a (0.56) 2.0a (0.40) 1.2a (0.53) 1.9a (0.21) 1.2a (0.60)

Carbonyl-C 0.3a (0.05) 0.6a (0.39) 0.4a (0.10) 0.3a (0.11) 0.7a (0.33) 0.2a (0.02)

Values followed by a different lowercase letter within rows and for a given factor (crop or fertilization practice) were significantly

different among treatments (using Tukey’s HSD) at a = 0.05. Standard error is stated in parentheses


123

Across all plots, soil quality indicators such as soil

organic C, total N, active C, and aliphatic-C, declined

in the order: native prairie [ timothy = prairie resto-

ration [ wheat = corn (for a given fertilization prac-

tice). The DRIFT indices of decomposition and

recalcitrance followed the opposite trend. Relative to

conventionally tilled corn, the no-till corn plot exhib-

ited reduced DRIFT indices of decomposition, 38 %

more active-C, 18 % more dehydrogenase activity,

and an approximate doubling in soil organic C, total

nitrogen, aliphatic-C, carbohydrate-C, and phenol

oxidase activity. Relative to the prairie restoration

plot, aggregate stability was reduced by 14 % in the

no-till corn plot.

SMAF soil quality rankings

Four variables (soil organic C, aggregate stability,

bulk density, and pH) were used with the SMAF soil

quality index to score and rank all the 12 plots for

overall soil quality. The SMAF scores were positively

correlated with many soil quality indicators, including

dehydrogenase activity, phenol oxidase activity,

active C, and water-extractable organic C (r = 0.72–

0.94), and negatively correlated with all five DRIFT

indices of decomposition stage and recalcitrance

(r = -0.75 to -0.93) (Table 2). Across all 12 plots,

the SMAF rankings (Fig. 3) suggest a continuum of

soil quality starting with the perennial vegetation plots

(native prairie, restored prairie, and timothy), followed

by the wheat and corn plots. Among the amended

plots, soil amended with manure exhibited greater soil

quality scores than the plots amended with inorganic

fertilizer, followed by plots receiving no amendments.

In addition, wheat plots consistently ranked higher

than corn plots for a given fertilization practice.

VNIR and DRIFT estimation of soil quality

indicators using PLSR and MLR

Summary statistics for the estimation of soil organic

C, total nitrogen, active C, dehydrogenase activity,

phenol oxidase activity, aggregate stability, and soil

pH are presented in Table 5. Calibration and valida-

tion models for other soil quality indicators, such as

the C:N ratio, were very poor and the results are not

shown. Based on R2 and RPD values, VNIR spectra of

whole soil outperformed DRIFT spectra of enriched

soil organic matter in the estimation of soil organic C,

active C, dehydrogenase activity, soil pH, and aggre-

gate stability using PLSR, and active C, dehydroge-

nase activity, and phenol oxidase activity using MLR.

In contrast, the DRIFT spectra outperformed VNIR

spectra in the estimation of soil organic C and

aggregate stability using MLR. The strongest models

were VNIR estimates of active C using PLSR

(R2 = 0.94, RPD = 3.15) and MLR (R2 = 0.91,

RPD = 3.19). Both VNIR and DRIFT spectra per-

formed well in the estimation of soil organic C and

total N using PLSR (R2 [ 0.76, RPD [ 1.99) and

MLR (R2 [ 0.83, RPD [ 2.46). Dehydrogenase and

phenol oxidase activity were estimated best using

MLR models of VNIR spectra (R2 [ 0.78, RPD [2.20) and only moderately to poorly estimated by the

other methods.

The wavelengths selected by the MLR models and

the most relevant wavelengths in the PLSR models

[based on variable importance in the projection (VIP)

Fig. 2 Representative diffuse reflectance infrared Fourier

transform (DRIFT) spectra from Sanborn Field: continuous,

perennial timothy (a plot 10), continuous, conventionally tilled

corn (b plot 17), prairie restoration (c plot 45), and Tucker

Prairie (d unknown burn history)


123

scores] varied for each of the seven soil quality

indicators estimated. In general, DRIFT models pri-

marily utilized wavenumbers in the regions represent-

ing aliphatic OH (1,150–1,190 cm-1), phenolic OH

(1,260–1,280 and 3,400–3,600 cm-1), COO- and

aliphatic CH (1,320–1,350 cm-1), aromatic C=C

bonds and H-bonded C=O (1,640–1,650 cm-1), C=O

stretching of COOH (1,740–1,700 cm-1), and CH

stretching of CH2 (2,930–2,850 cm-1). Estimation of

these soil quality indicators using VNIR spectra

primarily relied on wavelengths at the low end of the

visible region (400–550 nm), representing iron oxide

minerals such as hematite and goethite (Sherman and

Waite 1985), and the upper end of the near-infrared

region (1,400–2,200 nm), representing clay minerals,

methyl groups, phenolics and carboxylic acids (Clark

1999; Clark et al. 1990).

Discussion

The effects of changing vegetation or land manage-

ment practices on soil quality and organic matter

dynamics often requires long-term studies (Ellert and

Bettany 1995). The long-term agricultural experiment

at Sanborn Field, USA, represents a continuum of

management practices that have been influencing soil

properties for nearly 125 years. Woodruff (1990)

summarized the changes in soils from long-term

management at Sanborn Field as ‘‘slow changes,

minute as they are from one year to another, are not

measurable except over a period of years’’. However,

most of the measurements at that time were physical

and chemical. In contrast, more recent assessments at

Sanborn Field using active carbon (Miles and Brown

2011) and soil enzymes (Eivazi et al. 2003) have

highlighted the utility of biological soil quality

indicators. In this study, dehydrogenase activity and

phenol oxidase activity demonstrated sensitivity to

changes in soil organic matter composition, and

versatility in a range of soil quality modeling

approaches. Strong correlations suggest that dehydro-

genase activity may serve as a good predictor of

organic matter composition, decomposition stage, and

overall soil quality. In particular, dehydrogenase

activity was negatively correlated with DRIFT indices

Fig. 3 Ranking of all 12 plots using soil quality scores from the

soil management assessment framework. Tucker Prairie plots

are represented by the recently burned plot (native peren. burn)

and the plot of unknown burn history (native peren. unburn).

Sanborn Field plots represent a range of agricultural practices

including restoration with native grasses (native peren. restor.),

continuous timothy (tim.), continuous wheat (wheat), continu-

ous corn (corn), no-till (no-till), conventional, moldboard plow

(MP) tillage, manure additions of 13.5 Mg ha-1 (man.),

inorganic fertilizer additions based on soil tests (full), or no

amendments (none)


123

4 and 5, which represent the relative recalcitrant

groups to O-functionality of the organic matter. As

organic matter decomposes, O-containing compo-

nents such as carbohydrates and carbonyl groups are

utilized by microbes first, while recalcitrant C,

presumably derived from lignin structures, accumu-

lates (Baldock et al. 1997; Kalbitz et al. 2003).

Additionally, active C content decreased as DRIFT

indices 4 and 5 increased, reflecting the loss of fresh,

labile organic components as organic matter matures.

Phenol oxidase activity represents lignolytic deg-

radation; thus, it was expected that soils with increased

aromatic-C and reduced soil quality indicators would

exhibit increased phenol oxidase activity (Sinsabaugh

2010). Contrary to this hypothesis, phenol oxidase

activity followed the same trends as dehydrogenase

activity, only the relationships were not as strong. As

expected, the proportion of aromatic-C was the

smallest in the native prairie plots; however, in this

study, aromatic-C concentration was not significantly

correlated with any soil quality indicators. The

greatest concentration of aromatic-C was found in

the annually burned restored prairie plot, yet it

exhibited increased soil quality indicators and a

greater SMAF score relative to the cultivated wheat

and corn plots. Furthermore, among cultivated plots,

the manure amended plots generally demonstrated

increased soil quality relative to the other cultivated

plots even though manure is known to be enriched in

recalcitrant organic compounds relative to plant

residues (Paustian et al. 1997). Thus, increased

aromatic-C concentrations were not correlated with

reduced soil quality indicators in the plots studied. The

recalcitrant components stemming from periodic

burning and manure amendments are likely stimulat-

ing phenol oxidase activity, while increased inputs of

Table 5 Validation statistics for partial least squares regres-

sion (PLSR) and multiple linear regression (MLR) for visible-

near-infrared (VNIR) and diffuse reflectance infrared Fourier

transform (DRIFT) estimation of soil organic C (SOC, g kg-1),

total nitrogen (TN, g kg-1), active C (POXC, g kg-1),

dehydrogenase activity (DHA, lg h-1 g-1), phenol oxidase

activity (POA, lmol h-1 g-1), water pH (pHw), and water

aggregate stability (WSA, %)

Soil property PLSR

VNIR (n = 24) DRIFT (n = 24)

No. factors Validation R2 RMSEa RPDb No. factors Validation R2 RMSE RPD

SOC 8 0.85 2.77 2.44 4 0.76 3.38 1.99

TN 6 0.78 0.29 2.02 1 0.78 0.28 2.08

POXC 8 0.94 0.12 3.15 1 0.69 0.22 1.02

DHA 7 0.57 4.11 1.51 2 0.41 4.97 1.25

POA 3 0.67 70.7 1.72 3 0.62 78.1 1.56

pHw 10 0.66 0.38 1.69 2 0.46 0.47 1.35

WSA 4 0.68 10.6 1.40 1 0.42 11.6 1.27

Soil property MLR

VNIR (n = 24) DRIFT (n = 24)

No. bands Validation R2 RMSE RPD No. bands Validation R2 RMSE RPD

SOC 6 0.83 2.74 2.46 4 0.89 2.19 3.08

TN 6 0.83 0.23 2.51 4 0.87 0.21 2.78

POXC 7 0.91 0.12 3.19 4 0.83 0.15 2.55

DHA 4 0.78 2.83 2.20 4 0.58 3.93 1.58

POA 5 0.89 39.4 3.09 5 0.60 75.4 1.62

pHw 5 0.80 0.28 2.26 6 0.77 0.30 2.14

WSA 6 0.60 9.17 1.61 6 0.70 7.96 1.85

a Root mean square error of validationb Ratio of the standard deviation to the root mean square error


123

labile organic C components are simultaneously

stimulating dehydrogenase activity. In addition, lign-

olytic microorganisms require a readily metabolizable

co-substrate (e.g., cellulose or glucose) to effectively

degrade lignin (Haider 1992; Zeikus 1981). More

recently, Talbot and Treseder (2011) also noted the

‘priming effect’ of carbohydrates on lignin decompo-

sition. Thus, overall organic matter decomposition

(dehydrogenase activity) and lignin depolymerization

(phenol oxidase activity) were both positively corre-

lated with indicators of soil quality, although dehy-

drogenase demonstrated stronger, more significant

relationships. In this study, the broad range of

oxidative microbial activity represented by dehydro-

genase was a better indicator of soil quality, organic

matter composition, and decomposition stage than

phenol oxidase, which targets a specific niche of the

transformation and decomposition process. Therefore,

dehydrogenase activity may serve as a sensitive

indicator in soil quality indices (i.e., the SMAF), and

serve as an effective predictor of soil organic matter

composition in biogeochemical models.

Across the vegetative treatments evaluated in this

study, plots with perennial vegetation (i.e., native

prairie, restored prairie and timothy) demonstrated

increased microbial enzyme activity, increased active

C, greater SMAF scores, and reduced DRIFT ratios of

decomposition stage and recalcitrance, suggesting

increased inputs of labile organic C and more rapid

turnover of soil organic matter. However, the concom-

itant increase in overall concentrations of aromatic-C

and alkyl-C groups suggest enhanced retention of

recalcitrant organic matter may also have occurred

under the perennial vegetation (Baldock et al. 1997).

Even though the proportion of aromatic-C was the

smallest in the native prairie plots, greater inputs of

labile organic matter under perennial vegetation from

residues and roots (both root turnover and exudates)

stimulate and sustain microbial activity, while recalci-

trant fractions accumulate in the soil over time.

The frequency, intensity, and duration of soil

disturbance are also important factors in organic

matter transformation and decomposition. Relative to

perennial vegetation and conservation tillage, conven-

tional tillage is known to destroy soil structure (Beare

et al. 1994) and lead to organic matter loss (Buyanov-

sky and Wagner 1998). Previous studies of Sanborn

Field found that the no-till corn plot exhibited greater

microbial activity (Eivazi et al. 2003) and greater

microbial biomass carbon (Jordan et al. 1995) relative

to the conventionally tilled corn plot. The results of this

study noted reduced DRIFT indices of decomposition

stage and recalcitrance, a 38 % increase in active-C, an

18 % increase in dehydrogenase activity, and an

approximate doubling in soil organic C, total nitrogen,

aliphatic-C, carbohydrate-C, and phenol oxidase activ-

ity, demonstrating improved soil quality 37 years after

conversion to no-till. Relative to the prairie restoration

plot, however, the no-till corn plot still exhibited 14 %

less aggregate stability, reflecting the lack of a

continuous, living, root-biomass on soil structure,

even under conservation tillage. Additionally, relative

to the native prairie plots, the annually burned, restored

prairie plot exhibited 25 % less dehydrogenase activ-

ity, 30 % less soil organic C, 46 % more aromatic-C

content, and greater DRIFT indices of decomposition

stage and recalcitrance. These differences reflect the

influence of annual burning, as well as the continuing

influence of historical cultivation, even 17 years post-

prairie reestablishment.

Between the conventionally tilled wheat and corn

plots, trends in the data suggest that organic matter was

more degraded under corn than wheat for all amendment

types. In particular, corn plots had greater proportions

and concentrations of aromatic-C, lower proportions

and concentrations of aliphatic-C, increased DRIFT

indices of decomposition stage, and reduced SMAF

scores. Previous studies at Sanborn Field estimated the

total C input (roots and residues) to soil from corn was

more than double the C input from wheat (Buyanovsky

and Wagner 1986), and found the lignin content of

wheat residues (14 %) was more than double that of

corn (5.6 %) (Broder and Wagner 1988). Thus, one

might expect greater total soil organic C content and a

reduced proportion of aromatic-C under corn relative to

wheat. However, this study found that soil organic C

content was similar between the crops, and that corn had

a greater proportion of aromatic-C (28 %) relative to

wheat (15 %). This may be due to several factors,

including increased fungal populations and a greater

long-term decomposition rate for corn residues relative

to wheat residues (Broder and Wagner 1988), in

conjunction with added soil disturbance under corn

from inter-row cultivation. At Sanborn Field, corn plots

receive inter-row cultivation for weed control in addi-

tion to cultivation at planting, whereas the wheat plots

are only cultivated at planting. Moreover, corn has a

greater nutrient demand than wheat (Buchholz 2004) in


123

part due to greater production of plant biomass and

grain. Together, these factors may contribute to

enhanced decomposition of organic matter and reduced

SMAF soil quality scores under corn relative to wheat.

Furthermore, trends in microbial activity, organic

matter characteristics, and the SMAF soil quality

ranking were observed among fertilization practices.

Overall, soil amended with manure outranked soil

amended with inorganic fertilizer, followed by una-

mended soil. These results confirm previous studies of

Sanborn Field that noted increased soil organic C and

active C in plots amended with manure (Miles and

Brown 2011). The application of inorganic fertilizers

and manure often leads to increased soil organic matter

relative to unamended soil, which in turn leads to

enhanced biological activity (Dick 1992) including

dehydrogenase activity (Fließbach et al. 2007) and other

soil enzymes (Eivazi et al. 2003). In addition to

supplying nutrients, manure also supplies an exogenous

source of organic C. Therefore, the increase in soil

organic matter is typically greater with manure additions

than with inorganic fertilizers (Dick 1992; Paustian et al.

1997). Although manure is enriched in refractory

organic compounds relative to plant residues (Paustian

et al. 1997), no differences in the proportions of organic

functional groups were found among fertilization prac-

tices. This may be partly due to the low manure

application rate (13.4 Mg ha-1) used at Sanborn Field,

whereas application rates up to 45 Mg ha-1 are not

uncommon (Paustian et al. 1997). On the other hand,

trends in the concentrations of all organic functional

groups indicated greater pools of labile and recalcitrant

organic fractions in plots amended with manure relative

to inorganic fertilizer and unamended plots, although

these differences were not statistically greater. These

enhanced pools of organic fractions may be supporting

increased dehydrogenase and phenol oxidase activity in

manure amended plots.

As demonstrated previously in other studies (Chau-

dhary et al. 2012; Janik et al. 2009; Sudduth et al.

2012), DRIFT and VNIR spectra performed well in the

estimation of soil organic C, total N, and active C

using PLSR and MLR. Although VNIR spectra of

whole soil outperformed DRIFT spectra of enriched

soil organic matter in the estimation of several soil

properties, DRIFT models generally included fewer

factors (PLSR) or spectral bands (MLR). The reduc-

tion of the mineral signature in the DRIFT spectra via

HF treatment of the samples may have influenced the

performance of the spectra in these analyses. In

addition, the relative power of each spectral range is

probably affected by several aspects of soil sample

handling and sample pretreatment, and most likely

varies from study to study. In this case, VNIR spectra

also performed moderately well in the estimation of

dehydrogenase and phenol oxidase activity using

MLR. Additionally, Sudduth et al. (2012) used VNIR

to estimate b-glucosidase activity with moderate

success. These results illustrate the complexity of

information that enzymes represent, confirm that soil

enzyme activities reflect many soil properties, includ-

ing VNIR and DRIFT spectra, and highlight the

potential utility of soil enzymes in biogeochemical

models.

Conclusions

Microbial enzymes are the drivers of organic matter

transformation and decomposition in the soil. This

study highlights the link between microbial function,

traditional measures of soil quality, and soil organic

matter composition across a continuum of vegetative

and land management practices. Soil quality was

greatest under native, perennial vegetation, and

declined with increasing levels of soil disturbance

resulting from cultivation. As soil quality scores (i.e.,

SMAF rankings) decreased, total organic C decreased,

active C decreased, the decomposition stage of organic

matter increased, and microbial function declined. The

results of this study suggest that microbial enzyme

activities may potentially serve as effective predictors

of soil organic matter dynamics using field-scale data

and a range of modeling techniques. Overall, this

study confirms the need for continued maintenance of

long-term experimental sites such as Sanborn Field

and native reference sites such as Tucker Prairie, to

provide a foundation for the development of complex,

large-scale biogeochemical models.

Acknowledgments This work was partially funded through

the University of Missouri Center for Agroforestry under

cooperative agreements 58-6227-1-004, 58-6227-2-008 and

58-6227-5-029 with the USDA-ARS. Any opinions, findings,

conclusions or recommendations expressed in this publication

are those of the author(s) and do not necessarily reflect the view

of the U.S. Department of Agriculture. Mention of trade names

or commercial products is solely for the purpose of providing

specific information and does not imply recommendation or

endorsement by the U.S. Department of Agriculture or the


123

University of Missouri. We wish to thank Steve Troesser for

field assistance, Dr. Russell Dresbach of the University of

Missouri Soil Characterization Lab for analytical assistance,

Dr. Wei Wycoff of the University of Missouri NMR Facility for

assistance with 13C NMR analyses, Scott Drummond of the

USDA-ARS for assistance with VNIR data collection and

analysis, and Dr. Diane Stott of the USDA-ARS for helpful

comments on the manuscript.

References

Adl SM (2003) The ecology of Soil decomposition. CABI

Publishing, Cambridge

Ander P, Eriksson K-E (1976) The importance of phenol oxi-

dase activity in lignin degradation by the white-rot fungus

Sporotrichum pulverulentum. Arch Microbiol 109(1):1–8

Andrews SS, Karlen DL, Cambardella CA (2004) The soil man-

agement assessment framework: a quantitative soil quality

evaluation method. Soil Sci Soc Am J 68:1945–1962

Angers DA, Pesant A, Vigneux J (1992) Early cropping-induced

changes in soil aggregation, organic matter, and microbial

biomass. Soil Sci Soc Am J 56(1):115–119

Arnold JG, Potter KN, King KW, Allen PM (2005) Estimation

of soil cracking and the effect on surface runoff in a Texas

Blackland Prairie watershed. Hydrol Process 19:589–603

Baes AU, Bloom PR (1989) Diffuse reflectance and transmis-

sion Fourier transform infrared (DRIFT) spectroscopy of

humic and fulvic acids. Soil Sci Soc Am J 53:695–700

Baldock JA, Skemstad JO (2000) Role of the soil matrix and

minerals in protecting natural organic materials against

biological attack. Org Geochem 31:697–710

Baldock JA, Oades JM, Nelson PN, Skene TM, Golchin A,

Clarke P (1997) Assessing the extent of decomposition of

natural organic materials using solid-state 13C NMR

spectroscopy. Aust J Soil Res 35:1061–1083

Balesdent J, Wagner GH, Mariotti A (1988) Soil organic matter

turnover in long-term field experiments as revealed by

carbon-13 natural abundance. Soil Sci Soc Am J 52:

118–124

Bandick AK, Dick RP (1999) Field management effects on soil

enzyme activities. Soil Biol Biochem 31(11):1471–1479

Beare MH, Hendrix PF, Cabrera ML, Coleman DC (1994)

Aggregate-protected and unprotected organic matter pools

in conventional- and no-tillage soils. Soil Sci Soc Am J

58(3):787–795

Blair GJ, Lefroy RDB, Lisle L (1995) Soil carbon fractions

based on their degree of oxidation, and the development of

a carbon management index for agricultural systems. Aust

J Agric Res 46:1459–1466

Blanco-Canqui H, Gantzer CJ, Anderson SH, Alberts EE, Ghi-

dey F (2002) Saturated hydraulic conductivity and its

impact on simulated runoff for claypan soils. Soil Sci Soc

Am J 66(5):1596–1602

Broder MW, Wagner GH (1988) Microbial colonization and

decomposition of corn, wheat, and soybean residue. Soil

Sci Soc Am J 52(1):112–117

Buchholz DD (2004) Soil test interpretations and recommen-

dations handbook. University of Missouri College of

Agriculture, p 35

Burford JR, Bremner JM (1975) Relationships between the

denitrification capacities of soils and total, water-soluble

and readily decomposable soil organic matter. Soil Biol

Biochem 7(6):389–394

Burt R (2004) Soil survey laboratory methods manual. Soil

survey investigations report no. 42, Version 4.0. In: Vol

soil survey laboratory investigations report no. 42. USDA-

NRCS, Lincoln, NE

Buyanovsky GA, Wagner GH (1986) Post-harvest residue input

to cropland. Plant Soil 93(1):57–65

Buyanovsky GA, Wagner GH (1998) Carbon cycling in culti-

vated land and its global significance. Glob Change Biol

4:131–141

Cambardella CA, Elliott ET (1992) Particulate soil organic-

matter changes across a grassland cultivation sequence.

Soil Sci Soc Am J 56(3):777–783

Cambardella C, Moorman TB, Andrews SS, Karlen DL (2004)

Watershed-scale assessment of soil quality in the loess hills

of southwest Iowa. Soil Till Res 78:237–247

Casida LE Jr (1977) Microbial metabolic activity in soil as

measured by dehydrogenase determinations. Appl Environ

Microbiol 34(6):630–636

Chang CW, Laird DA, Mausbach MJ, Hurburgh CR (2001)

Near-infrared reflectance spectroscopy-principal compo-

nents regression analysis of soil properties. Soil Sci Soc

Am J 65:480–490

Chantigny MH (2003) Dissolved and water-extractable organic

matter in soils: a review on the influence of land use and

management practices. Geoderma 113:357–380

Chaudhary VP, Sudduth KA, Kitchen NR, Kremer RJ (2012)

Reflectance spectroscopy detects management and land-

scape differences in soil carbon and nitrogen. Soil Sci Soc

Am J 76(2):597–606

Cheesman AW, Turner BL, Ramesh Reddy K (2012) Soil

phosphorus forms along a strong nutrient gradient in a

tropical ombrotrophic wetland. Soil Sci Soc Am J 76(4):

1496–1506

Chefetz B, Adani F, Genevini P, Tambone F, Hadar Y,

Chen Y (1998) Humic-acid transformation during

composting of municipal solid waste. J Environ Qual

27:794–800

Clark RN (1999) Spectroscopy of rocks and minerals, and

principles of spectroscopy. In: Rencz N (ed) Remote

sensing for the earth sciences: manual of remote sensing.

Wiley, New York, pp 3–52

Clark RN, King TVV, Klejwa M, Swayze GA, Vergo N (1990)

High spectral resolution reflectance spectroscopy of min-

erals. J Geophys Res 95(B8): 12653–12680

Dick RP (1992) A review: long-term effects of agricultural

systems on soil biochemical and microbial parameters.

Agric Ecosyst Environ 40(1–4): 25–36

Dick RP (1994) Soil enzyme activity as an indicator of soil

quality. In: Doran JW, Coleman DC, Stewart BA, Bezdicek

DF (eds) Defining soil quality for a sustainable environ-

ment. SSSA Spec. Publ. No. 35. Soil Science Society of

America Inc, Madison, pp 107–124

Ding G, Novak JM, Amarasiriwardena D, Hunt PG, Xing B

(2002) Soil organic matter characteristics as affected by

tillage management. Soil Sci Soc Am J 66:421–429

Doran JW, Parkin TB (1994) Defining and assessing soil qualit.

In: Doran JW, Coleman DC, Bezdicek DF, Stewart BA


123

(eds) Defining soil quality for a sustainable environment.

SSSA Spec. Publ. No. 35, Madison, WI. p 410

Doran JW, Elliott ET, Paustian K (1998) Soil microbial activity,

nitrogen cycling, and long-term changes in organic carbon

pools as related to fallow tillage management. Soil Till Res

49:3–18

Dunn BW, Beecher HG, Batten ED, Ciavarella S (2002) The

potential of near infrared reflectance spectroscopy for soil

analysis: a case study from the Riverine Plain of south-

eastern Australia. Aust J Exp Agric 42:607–614

Eivazi F, Bayan M, Schmidt K (2003) Select soil enzyme

activities in the historic Sanborn Field as affected by long-

term cropping systems. Commun Soil Sci Plant Anal

34(15/16):2259–2275

Ellerbrock RH, Hohn A, Rogasik J (1999) Functional analysis of

soil organic matter as affected by long-term manurial

treatment. Eur J Soil Sci 50(1):65–71

Ellert BH, Bettany JR (1995) Calculation of organic matter and

nutrients stored in soils under contrasting management

regimes. Can J Soil Sci 75:529–538

Fließbach A, Oberholzer H-R, Gunst L, Mader P (2007) Soil

organic matter and biological soil quality indicators after

21 years of organic and conventional farming. Agric

Ecosyst Environ 118(1–4):273–284

German DP, Weintraub MN, Grandy AS, Lauber CL, Rinkes

ZL, Allison SD (2011) Optimization of hydrolytic and

oxidative enzyme methods for ecosystem studies. Soil Biol

Biochem 43(7):1387–1397

Grossman RB, Reinsch TG (2002) Bulk density and linear

extensibility. In: Dane JH, Topp GC (eds) Methods of soil

analysis. Part 4. Physical methods. Soil Science Society of

America, Madison, pp 201–254

Haaland DM, Thomas EV (1988) Partial least-squares methods

for spectral analyses. 1. Relation of other quantitative

calibration methods and the extraction of qualitative

information. Anal Chem 60(11):1193–1202

Haider K (1992) Problems related to the humification processes

in soils of temperate climates. In: Stotzky G, Bollag JM

(eds) Soil biochemistry, vol 7. Marcel Dekker, Inc., New

York, pp 55–94

Inbar Y, Chen Y, Hadar Y (1989) Solid-state carbon 13 nuclear

magnetic resonance and infrared spectroscopy of com-

posted organic matter. Soil Sci Soc Am J 53:1695–1701

Janik LJ, Skjemstad JO (1995) Characterization and analysis of

soils using mid-infrared partial least-squares. II. Correla-

tions with some laboratory data. Aust J Soil Res 33:

637–650

Janik LJ, Forrester ST, Rawson A (2009) The prediction of soil

chemical and physical properties from mid-infrared spec-

troscopy and combined partial least-squares regression and

neural networks (PLS-NN) analysis. Chemom Intell Lab

Syst 97(2):179–188

Jordan D, Kremer RJ, Bergfield WA, Kim KY, Cacnio VN

(1995) Evaluation of microbial methods as potential indi-

cators of soil quality in historical agricultural fields. Biol

Fertil Soils 19(4):297–302

Kalbitz K, Schwesig D, Schmerwitz J, Kaiser K, Haumaier L,

Glaser B, Ellerbrock R, Leinweber P (2003) Changes in

properties of soil-derived dissolved organic matter induced

by biodegradation. Soil Biol Biochem 35(8):1129–1142

Karlen DL, Mausbach MJ, Doran JW, Cline RG, Harris RF,

Schuman GE (1997) Soil quality: a concept, definition, and

framework for evaluation. Soil Sci Soc Am J 61:4–10

Karlen DL, Andrews SS, Wienhold BJ, Zobeck TM (2008) Soil

quality assessment: past, present and future. J Integr Biosci

6(1):3–14

Kemper WD, Rosenau RC (1986) Aggregate stability and size

distribution. In: Klute A (ed) Methods of soil analysis, part

I. Physical and mineralogical methods—agronomy mono-

graph no. 9. American Society of Agronomy—Soil Science

Society of America, Madison, pp 425–442

Kucera CL (1958) Some changes in the soil environment of a

grazed-prairie community in central Missouri. Ecology

39(3):538–540

Lawrence CR, Neff JC, Schimel JP (2009) Does adding

microbial mechanisms of decomposition improve soil

organic matter models? A comparison of four models using

data from a pulsed rewetting experiment. Soil Biol Bio-

chem 41(9):1923–1934

Leifield J (2006) Application of diffuse reflectance FT-IR

spectroscopy and partial least-squares regression to predict

NMR properties of soil organic matter. Eur J Soil Sci

57:846–857

Logsdon SD, Karlen DL (2004) Bulk density as a soil quality

indicator during conversion to no-tillage. Soil Till Res

78(2):143–149

Mao J-D, Hu W-G, Schmidt-Rohr K, Davies G, Ghabbour EA,

Xing B (2000) Quantitative characterization of humic

substances by solid-state carbon-13 nuclear magnetic res-

onance. Soil Sci Soc Am J 64:873–884

Mathers NJ, Xu Z, Berners-Price SJ, Perera MCS, Saffigna PG

(2002) Hydrofluoric acid pre-treatment for improving 13C

CPMAS NMR spectral quality of forest soils in south-east

Queensland, Australia. Aust J Soil Res 40:655–674

Miles RJ, Brown JR (2011) The Sanborn Field experiment:

implications for long-term soil organic carbon levels.

Agron J 103(1):268–278

Paustian K, Collins HP, Paul EA (1997) Management controls

on soil carbon. In: Paul EA, Paustian K, Elliot ET, Cole CV

(eds) Soil organic matter in temperate agroecosystems.

CRC Press, Boca Raton, pp 15–49

Pind A, Freeman C, Lock M (1994) Enzymic degradation of

phenolic materials in peatlands—measurement of phenol

oxidase activity. Plant Soil 159(2):227–231

Pirie A, Singh B, Islam K (2005) Ultra-violet, visible, near-

infrared, and mid-infrared diffuse reflectance spectro-

scopic techniques to predict several soil properties. Soil

Res 43(6):713–721

Rumpel C, Rabia N, Derenne S, Quenea K, Eusterhues K, Ko-

gel-Knabner I, Mariotti A (2006) Alteration of soil organic

matter following treatment with hydrofluoric acid (HF).

Org Geochem 37:1437–1451

Sherman DM, Waite TD (1985) Electronic spectra of Fe3?

oxides and oxide hydroxides in the near IR to near UV. Am

Mineral 70:1262–1269

Sinsabaugh RL (2010) Phenol oxidase, peroxidase and organic

matter dynamics of soil. Soil Biol Biochem 42(3):391–404

Sinsabaugh RL, Carreiro MM, Alvarez S (2002) Enzyme and

microbial dynamics of litter decomposition. In: Burns RG,

Dick RP (eds) Enzymes in the environment: activity,


123

ecology, and applications. Marcel Dekker, Inc., New York,

pp 249–265

Sinsabaugh RL, Lauber CL, Weintraub MN, Ahmed B, Allison

SD, Crenshaw C, Contosta AR, Cusack D, Frey S, Gallo

ME, Gartner TB, Hobbie SE, Holland K, Keeler BL,

Powers JS, Stursova M, Takacs-Vesbach C, Waldrop MP,

Wallenstein MD, Zak DR, Zeglin LH (2008) Stoichiometry

of soil enzyme activity at global scale. Ecol Lett

11(11):1252–1264

Sjogersten S, Turner BL, Mahieu N, Condron LM, Wookey PA

(2003) Soil organic matter biochemistry and potential

susceptibility to climatic change across the forest-tundra

ecotone in the Fennoscandian mountains. Glob Change

Biol 9(5):759–772

Skjemstad JO, Clarke P, Taylor JA, Oades JM, Newman RH

(1994) The removal of magnetic materials from surface

soils. A solid state 13C CP/MAS n.m.r. study. Aust J Soil

Res 32:1215–1229

Smith JL, Doran JW (1996) Measurement and use of pH and

electrical conductivity for soil quality analysis. In: Doran

JW, Jones AJ (eds) Methods for assessing soil quality. Soil

Science Society of America Inc., Madison, pp 169–185

Stevenson FJ (1994) Humus chemistry: genesis, composition,

reactions. Wiley, New York

Stott DE, Andrews SS, Leibig MA, Wienhold BJ, Karlen DL

(2010) Evaluation of B-glucosidase activity as a soil

quality indicator for the soil management assessment

framework. Soil Sci Soc Am J 74(1):107–119

Sudduth KA, Kremer RJ, Kitchen NR, Myers DB (2012) Esti-

mating soil quality indicators with diffuse reflectance

spectroscopy. In: Proceedings of the 10th international

conference on precision agriculture. Indianapolis, IN.

CDROM

Tabatabai MA, Dick WA (2002) Enzymes in soil: research and

developments in measuring activities. In: Burns RG, Dick RP

(eds) Enzymes in the environment: activity, ecology, and

applications. Marcel Dekker, Inc., New York, pp 567–596

Talbot JM, Treseder KK (2011) Interactions among lignin,

cellulose, and nitrogen drive litter chemistry—decay rela-

tionships. Ecology 93(2):345–354

Treseder KK, Balser TC, Bradford MA, Brodie EL, Dubinsky

EA, Eviner VT, Hofmockel KS, Lennon JT, Levine UY,

MacGregor BJ, Pett-Ridge J, Waldrop MP (2012)

Integrating microbial ecology into ecosystem models:

challenges and priorities. Biogeochemistry 109:7–18

Unklesbay AG, Vineyard JD (1992) Missouri geology. Uni-

versity of Missouri Press, Columbia

USDA-NRCS (2006) Land resource regions and major land

resource areas of the United States, the Caribbean and the

Pacific Basin. Agriculture Handbook 296. In: USDA-

NRCS, Government Printing Office, Washington

Veum KS, Goyne KW, Holan SH, Motavalli PP (2011)

Assessment of soil organic carbon and total nitrogen under

conservation management practices in the Central Claypan

Region, Missouri, USA. Geoderma 167(168):188–196

Veum KS, Goyne KW, Kremer RJ, Motavalli PP (2012) Rela-

tionships among water stable aggregates and organic

matter fractions under conservation management. Soil Sci

Soc Am J 76(6):2143–2153

Viscarra Rossel RA, Behrens T (2010) Using data mining to

model and interpret soil diffuse reflectance spectra. Geo-

derma 158:46–54

Wander MM, Traina SJ (1996) Organic matter fractions from

organically and conventionally managed soils: II. Char-

acterization of composition. Soil Sci Soc Am J

60:1087–1094

Weil RR, Islam KR, Stine MA, Gruver JB, Samson-Liebig SE

(2003) Estimating active carbon for soil quality assess-

ment: a simplified method for laboratory and field use. Am

J Altern Agric 18(1):3–17

Wilson MA, Barron PF, Goh KM (1981) Differences in struc-

ture of organic matter in two soils as demonstrated by 13C

cross polarisation nuclear magnetic resonance spectros-

copy with magic angle spinning. Geoderma 26(4):323–327

Woodruff CM (1990) A history of the department of soils and

soil science at the University of Missouri. Special report

413. College of Agriculture, University of Missouri

Xing B, Mao JD, Hu W-G, Schmidt-Rohr K, Davies G, Ghab-

bour EA (1999) Evaluation of different solid-state 13C

NMR techniques for characterizing humic acids. In: Davies

G, Ghabbour EA (eds) Understanding humic substances:

advanced methods, properties and applications. The Royal

Society of Chemistry, Cambridge, pp 49–61

Zeikus JG (1981) Lignin metabolism and the carbon cycle. In:

Alexander M (ed) Advances in microbial ecology, vol 5.

Plenum Press, New York, pp 211–243


123

biological indicators of soil quality and soil organic matter characteristics in an agricultural...

Documents