soil organic nitrogen - virginia tech€¦ · soil organic nitrogen ... excellent guidance, endless...
TRANSCRIPT
Soil Organic Nitrogen
— Investigation of Soil Amino Acids and Proteinaceous Compounds
Li Ma
Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State
University in partial fulfillment of the requirements for the degree of
Doctor of Philosophy
In
Crop and Soil Environmental Sciences
Kang Xia, Chair
Matthew J. Eick
Mark A. Williams
Chao Shang
Jian Wang
April 8, 2015
Blacksburg, VA
Keywords: Free amino acids, hydrolysable amino acids, proteins/peptides,
transects, soil organic carbon, mineral surface, orientation, synchrotron near edge
X-ray fine structure spectroscopy
Copyright © 2015, Li Ma
Soil Organic Nitrogen
— Investigation of Soil Amino Acids and Proteinaceous Compounds
Li Ma
Abstract
Soil carbon (C) and nitrogen (N) are predominantly in organic form. Proteins/ peptides,
as an important organic form of N, constitute a substantial part of soil organic matter. On one
hand, proteins/peptides are an important N source for plants and microorganisms, particularly in
soils where inorganic N is limited. On the other hand, their stabilization in soils by forming
organo-mineral associates or macromolecule complex reduces the C loss as CO2 into the
atmosphere. Therefore, studies on the turnover, abundance, composition, and stability of
proteins/peptides are of crucial importance to agricultural productivity and environmental
sustainability. In the first part of this study, the bioavailability and distribution of amino acids,
(building block of proteins/peptides), were investigated, in soils across the North-South and
West-East transects of continental United States. The second part of this study aimed to
understand the variations of organic C speciation in soils of continental United States. Previous
investigations of the interactions between soil minerals and proteins/peptides were mostly
limited to batch sorption experiments in labs, seldom of which gave the details at the molecular
scales. Therefore, in the third part of this study, the molecular orientation of self-assembled
oligopeptides on mineral surfaces was investigated by employing synchrotron based
polarization-dependent Near Edge X-ray Adsorption Fine Structure Spectroscopy (NEXAFS)
techniques. Specific aims of this study were: 1) to assess potentially bioavailable pool of
iii
proteinaceous compounds and the immediately bioavailable pool of free amino acids in surface
and subsurface soils of various ecosystems; 2) to evaluate the relationship between
environmental factors and levels/composition of the two pools; 3) to investigate the organic C
speciation in soils of various land use; and 4) to understand molecular level surface organization
of small peptides on mineral surfaces.
The levels of free amino acids and hydrolysable amino acids which represent the
potentially bioavailable pool of proteinaceous compounds in A-horizon soils were significantly
high than in C-horizon soils due to the accumulation of organic matter in surface. On average,
free amino acids accounted for less than 4 % of hydrolysable amino acids which represent the
total proteinaceous compounds in soils. The composition of free amino acids was significantly
different between surface soil and subsurface soil and was significantly influenced by mean
annual temperature and precipitation. A relatively uniform composition of hydrolysable amino
acids was observed irrespective of a wide range of land use. Significant variations were observed
for the levels of free and hydrolysable amino acids along mean annual temperature and
precipitation gradients, as well as among vegetation types of continental USA, suggesting levels
of free and hydrolysable amino acids were associated with the above-ground biomass and root
distribution. Organic C speciation investigation revealed the presence of carboxylic-C (38%),
aliphatic-C (~ 22%), aromatic-C (~ 18%), O/N-alkyl-C (~ 16%), and phenolic-C (< 6%). Factors
such as temperature and vegetation cover were revealed in this study to account for the
fluctuations of the proportions of aromatic-C and phenolic-C, in particular. Phenolic-C may
serve as a good indicator for the effect of temperature or vegetation on the composition of SOC.
The average composition of soil organic C, over the continental scale, was relatively uniform
over various soil ecosystems and between two soil horizons irrespective of surface organic C
iv
content. Polarization dependent NEXAFS analysis showed the oligopeptides tend to orient on
mineral surface with an average tilt angle of 40 ° between the molecular chain and the mineral
surface.
v
Acknowledgements
First of all, I would like to sincerely thank my advisor Dr. Kang Xia for her
excellent guidance, endless support, valuable advice and consistent patience during my
research. She gave me the opportunity to be involved in the synchrotron work and access
the wonderful analytical instrument. I gained a lot through the research. I would also like
to express my great gratitude to committee members Dr. Matthew Eick, Dr. Mark
Williams, Dr. Shang Chao and Dr. Jian Wang for their guidance and support during my
PhD studies. Dr. Matthew Eick gave good advice in the methodologies and lots of
guidance in Soil Chemistry class. Dr. Williams taught me how to use the statistical
software to process the data and shared experiences with me in PhD pursuing. Dr. Shang
provided me with lots of information in solving the problems encountered during my
research. Dr. Wang taught me how to operate the synchrotron beamline. I really
appreciate his patience in explaining to me the basic theories and the procedures to deal
with the original data though Skype.
I would also like to thank the technical staff in Canadian Light Source and
Synchrotron Radiation Center in WI for their hard work. I want to thank Dr. David B.
Smith from the United States Geological Services (USGS), who provided the samples,
allowing me to gain meaningful data to fulfill the thesis.
I am grateful to the faculty and staff members in Crop and Soil Environmental
Sciences for their encouraging, concern and support. Dr. Lee Daniels patiently helped me
solve the problems during my academic process. Dr. Xunzhong Zhang shared with me
lots of information in paper writing and career pursuing skills. I would also like to thank
the staff and graduate students, Jude Moon, Rosana Pineda, Richard Rodrigues, Madhavi
vi
Kakumanu and Kerri Mills in Rhizosphere-Soil Microbial Ecology and Biogeochemistry
lab of Horticulture Department, for collecting samples and providing the technical
support during my stay in their lab. They shared everything in the lab with me and took
me as one member of the lab, where I finished one third of my experimental work. I
would also like to thank Dr. Alan Esker in Chemistry Department who provided the
equipment. I am also thankful to our lab manager Hubert Walker, my colleagues Chao
Qin, Theresa Sosienski, Julia Cushman, Lucas Waller and Fatmaalzhraa Awad, and my
roommate Shan Sun and Ying Ni for their warm help and encouragement during my PhD
study.
Finally, I would like to extent my deepest gratitude to my family members for
their endless love and unrequited support. I would like to thank my husband Wei Lu’s
meticulous care and long live understanding. I cannot thank enough my parents’
confidence and comfort. Their love and support are spiritual pillars of my life.
Funding by the USDA-AFRI (award #2012-67019-30227) and NSF (award #
EAR 0949653 10010064) is gratefully appreciated. Research based on synchrotron
techniques was performed at the Canadian Light Source, which is supported by Natural
Sciences and Engineering Research Council of Canada (NSERC), National Research
Council (NRC), Canadian Institutes of Health Research (CIHR), and the University of
Saskatchewan.
vii
Attributions
Chapter 3: Immediately Bioavailable Free Amino Acids in Soils of North-South and
West-East Transects of Continental United States
Kang Xia, PhD (Crop and Soil Environmental Sciences, Virginia Tech): Dr. Xia was the
co-principle investigator for the grant (USDA-AFRI, award #2012-67019-30227)
supporting the research.
Mark A. Williams, PhD (Horticulture Department, Virginia Tech): Dr. Williams was the
principle investigator for the grant (USDA-AFRI, award #2012-67019-30227) supporting
the research.
David. B. Smith, PhD (United State Geological Services): Dr. Smith provided samples
for the research.
Chapter 4: Hydrolysable Amino Acids in Soils of North-South and West-East
Transects of Continental United States
Kang Xia, PhD (Crop and Soil Environmental Sciences, Virginia Tech): Dr. Xia was the
co-principle investigator for the grant (USDA-AFRI, award #2012-67019-30227)
supporting the research.
Mark A. Williams, PhD (Horticulture Department, Virginia Tech): Dr. Williams was the
principle investigator for the grant (USDA-AFRI, award #2012-67019-30227) supporting
the research.
David. B. Smith, PhD (United State Geological Services): Dr. Smith provided samples
for the research.
Chapter 5: Carbon K-edge Near Edge X-ray Fine Structure Spectroscopic
Investigation of Organic Carbon Speciation in Soils of North-South and West-East
Transects of Continental United States
Kang Xia, PhD (Crop and Soil Environmental Sciences, Virginia Tech): Dr. Xia was the
co-principle investigator for the grant (USDA-AFRI, award #2012-67019-30227)
supporting the research.
Jinyoung Moon (Horticulture Department, Virginia Tech): Ms. Moon is a PhD candidate
who helped me collect data in synchrotron radiation center, in WI.
viii
Mark A. Williams, PhD (Horticulture Department, Virginia Tech): Dr. Williams was the
principle investigator for the grant (USDA-AFRI, award #2012-67019-30227) supporting
the research.
David B. Smith, PhD (United State Geological Services): Dr. Smith provided samples for
the research.
Chapter 6: Polarization dependent X-ray Photoemission Electron Microscopic and
Near Edge X-ray Fine Structure Spectroscopic Investigation of Hexa-glycine
Surface Orientation Sorbed on Montmorillonite
Kang Xia, PhD (Crop and Soil Environmental Sciences, Virginia Tech): Dr. Xia was the
principle investigator for the grant (NSF, award # EAR 0949653 10010064) supporting
the research.
Jian Wang, PhD (Canadian Light Source): Dr. Wang is a research scientist in Canadian
Light Source who helped us collect and analyze the data.
Mark A. Williams, PhD (Horticulture Department, Virginia Tech): Dr. Williams was the
co-principle investigator for the grant (NSF, award # EAR 0949653 10010064)
supporting the research.
ix
Table of Contents Abstract ........................................................................................................................................... ii
Acknowledgements ......................................................................................................................... v
Attributions .................................................................................................................................... vii
List of Figures .............................................................................................................................. xiii
List of Tables ............................................................................................................................... xvii
List of Abbreviations .................................................................................................................. xviii
1. Introduction ............................................................................................................................... 1
1.1. Background ................................................................................................................1
1.2. Objectives ...................................................................................................................4
References .........................................................................................................................6
2. Literature Review .................................................................................................................... 10
2.1. Biogeochemistry of amino acids in soils .................................................................... 10
2.1.1 Distribution and occurrence ...................................................................................... 14
2.1.2. Transformation of peptides/proteins into amino acids .......................................... 18
2.1.3. Fate of amino acids .................................................................................................... 21
2.2. Mineral-associated organic N ................................................................................... 22
2.2.1. Mechanisms involved in the mineral organic interactions .................................... 22
2.2.2. Factors influencing mineral-organic N interactions ............................................... 24
2.2.3. Evaluation of molecular orientation on mineral surfaces ...................................... 26
2.2.4. Synchrotron based spectroscopic method to study organic C and N speciation . 28
2.3. Summary .................................................................................................................. 31
References ....................................................................................................................... 33
3. Immediately Bioavailable Free Amino Acids in Soils of North-South and West-East
Transects of Continental United States ..................................................................................... 45
3.1. Abstract .................................................................................................................... 46
3.2. Introduction ............................................................................................................. 48
3.3. Materials and methods ............................................................................................. 50
3.3.1. Study sites and soil sampling .................................................................................... 50
3.3.2. Chemicals ................................................................................................................... 51
3.3.3. Free amino acid extraction from soil ....................................................................... 52
3.3.4. Amino acids derivatization ....................................................................................... 52
3.3.5. HPLC/FLD analysis of derivatized amino acids ..................................................... 54
x
3.3.6. Statistical analysis ...................................................................................................... 55
3.4. Results and discussion .............................................................................................. 56
3.4.1. Composition and concentrations of extractable soil amino acids ......................... 56
3.4.2. Extractable amino acids in A and C horizon soils .................................................. 60
3.4.3. Variations of extractable soil amino acids along MAT and MAP gradients of
continental United States .................................................................................................... 65
3.4.4. Variations of extractable soil amino acids among different vegetation covers .... 72
3.5. Conclusions .............................................................................................................. 75
3.6. Acknowledgements ................................................................................................... 76
References ....................................................................................................................... 77
4. Hydrolysable Amino Acids in Soils of North-South and West-East Transects of
Continental United States ........................................................................................................... 85
4.1. Abstract .................................................................................................................... 86
4.2. Introduction ............................................................................................................. 88
4.3. Materials and methods ............................................................................................. 89
4.3.1. Study sites and sampling ........................................................................................... 89
4.3.2. Chemicals ................................................................................................................... 90
4.3.3. Hydrolysis and purification ...................................................................................... 91
4.3.4. Amino acid derivatization ......................................................................................... 92
4.3.5. Analysis of derivatized amino acids on HPLC/FLD ............................................... 93
4.3.6. Statistical analysis ...................................................................................................... 94
4.4. Results and discussion .............................................................................................. 95
4.4.1. The composition and concentrations of HAAs ........................................................ 95
4.4.2. HAAs in A and C horizons ...................................................................................... 97
4.4.3. Variations of HAAs along MAT and MAP gradients of continental United States
............................................................................................................................................. 101
4.4.4. Variations of HAAs among different vegetation covers ....................................... 104
4.4.5. Comparisons of THAAs with TFAAs and implications ....................................... 109
4.5. Conclusions ............................................................................................................ 114
4.6. Acknowledgements ................................................................................................. 115
References ..................................................................................................................... 116
5. Carbon K-edge Near Edge X-ray Fine Structure Spectroscopic Investigation of Organic
Carbon Speciation in Soils of North-South and West-East Transects of Continental United
States ........................................................................................................................................... 124
xi
5.1. Abstract .................................................................................................................. 125
5.2. Introduction ........................................................................................................... 126
5.3. Materials and Methods ........................................................................................... 128
5.3.1. Study sites and sampling ......................................................................................... 128
5.3.2. Sample preparation ................................................................................................. 129
5.3.3. Date collecting .......................................................................................................... 129
5.3.4. Data processing ........................................................................................................ 130
5.3.5. Statistics .................................................................................................................... 131
5.4. Results and Discussion ............................................................................................ 131
5.4.1. Soil organic C speciation and relative composition characterization ................. 131
5.4.2. Soil organic C speciation and relative composition in A and C horizons ........... 135
5.4.3. Soil organic C speciation and relative composition variations along temperature
and precipitation gradients of continental United States ............................................... 137
5.4.4. Soil organic C speciation and relative composition variations among different
vegetation covers ................................................................................................................ 141
5.5. Conclusions ............................................................................................................ 144
5.6. Acknowledgements ................................................................................................. 144
References ..................................................................................................................... 145
6. Polarization Dependent X-ray Photoemission Electron Microscopic and Near Edge X-
ray Fine Structure Spectroscopic Investigation of Hexa-glycine Surface Orientation
Sorbed on Montmorillonite ........................................................................................... 155
6.1. Abstract .................................................................................................................. 156
6.2. Introduction ........................................................................................................... 157
6.3. Materials and methods ........................................................................................... 160
6.3.1 Chemicals and materials .......................................................................................... 160
6.3.2. Preparation of monolayer montmorillonite .......................................................... 161
6.3.3. Polarization-dependent N K-edge NEXAFS ......................................................... 164
6.3.4. Data processing ........................................................................................................ 164
6.4. Results and Discussion ............................................................................................ 165
6.5. Conclusions ............................................................................................................ 173
6.6. Acknowledgements ................................................................................................. 174
References ..................................................................................................................... 175
7. Conclusions ............................................................................................................................ 182
xii
Appendix .................................................................................................................................... 186
Appendix A. Geochemical data for samples of surface soils (A horizon) and subsoil (C
horizon) collected in the conterminous United States .................................................... 186
Appendix B. Mineralogical data for samples from the soil C and A horizons in the
conterminous United States ........................................................................................... 192
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil) 203
Appendix D. Concentrations of hydrolysable amino acids detected in soils (µmol kg-1 dry
soil) ............................................................................................................................... 212
xiii
List of Figures
Chapter 1: Introduction Figure 1.1.Simulated terrestrial N cycle modified based on Xu and Prentice (2008); a: sorption; b:
desorption; c: decomposition; d: humification; e: cell uptake (immobilization); f: exudation; g:
autolysis; h: root uptake; i: mineralization; j: tropic interaction; k: degradation & utilization; l:
fixation............................................................................................................................................. 1
Chapter 2: Literature Review Figure 2. 1.The basic structure of amino acid ............................................................................... 10
Figure 2. 2. Combined paradigms of the soil N cycle modified according to (Schimel and Bennett,
2004). Solid line section are common steps for both traditional and new paradims, while dash line
section is exclusive to the new paradim. Red line indicates the rate-limiting step of N cycle in
each paradim. a: depolymerization; b: root uptake; c: mineralization; d: immobilization; e: cell
uptake (immobilization); f: degradation; g: nitrification; h: leaching. .......................................... 22
Figure 2. 3. Geometry of peptide bond (modified based on Liu et al. (2006)). The p-orbital (large
arrow) is oriented perpendicular to the plane of peptide bond. The oligopeptide molecules are
self-assembled on the surface of wafer (lower). ............................................................................ 27
Figure 2. 4. N (1s) K-edge NEXAFS spectra of a 16-unit peptides bound to gold surface recorded
at two incident x-ray angles (Iucci et al., 2008). ........................................................................... 28
Figure 2. 5. Typical C (above) and N (below) K-edge NEXAFS spectrum (TFY) deconvolution
from the mineral-organic fraction of a soil sample. Typical C (above) and N (below) K-edge
NEXAFS spectrum (TFY) deconvolution from the mineral-organic fraction of a soil sample. ... 30
Chapter 3: Immediately Bioavailable Free Amino Acids in Soils of North-South and
West-East Transects of Continental United States Figure 3. 1. Location of soil sampling sites from west to east and north to south transects on
gradients of (above) MAP, and (below) MAT. Sites were grouped into sub-continental areas as
shown in circles. The legends at the right of each sub-figure apply to the circles. ....................... 50
Figure 3. 2. Chromatograms of derivatized amino acids in (above) 10µM standard and (below) a
A-horizon soil sample from a grassland site in Minnesota. 1=Asp; 2=Glu; 3= 6-aminoquinoline;
4=Ser+Asn; 5=Gly; 6=Gln; 7=His; 8=NH4+; 9=Arg; 10=Tau; 11=Cit; 12=Thr; 13=Ala;
14=GABA; 15=Pro; 16=AABA (internal standard); 17=Tyr; 18=Cys-Cys; 19=Val; 20=Met;
21=Orn; 22=Ile; 23=Lys; 24=Leu; 25=Phe; 26=Trp. .................................................................... 59
Figure 3. 3. NMS ordination of 298 samples from 149 sampling sites. Sites were grouped into A
horizon and C horizon. High correlation of variables (cut off r2 = 0.2) with ordination was
indicated in biplot vector, where length and direction represent the magnitude and direction of the
correlation, respectively. Ordination of sites captured two dimensions with a final stress of 14
where Axis 1 explained 58 % and Axis 2 explained 34 % of total variance respectively. ............ 62
Figure 3. 4. Average composition of individual and sum of eight dominant FAAs in A and C
horizons from different transects. Scale on right side applies to sum of the eight major FAA.
Different lower case letters indicate statistical significance by pairwise comparison (α = 0.05).
Values are expressed as mean ± Standard Error of Mean (SEM). ................................................ 63
xiv
Figure 3. 5. Average concentration of eight major FAAs and TFAAs in two horizon soils from
different transects. Different lower case letters indicate statistical significance by pairwise
comparison (α=0.05). Scale on right side applies to TFAAs. Values are expressed as mean ± SEM.
....................................................................................................................................................... 65
Figure 3. 6. Correlations of NMS axes with MAT and MAP. Black dots represents sampling sites.
The percentages in Y-axis are the variability explained by each NMS ordination axis. ............... 67
Figure 3. 7. NMS ordinations of 298 samples from in four groups. Correlations of variables with
ordination with r2 > 0.2 were indicated in biplot vector, where length and direction represent the
magnitude and direction of the correlation, respectively. Ev, evergreen; Gr, grassland; Sh, shrub;
De, deciduous forest; Cr, cropland; Pa, pasture; Fa, fallow; Re, residential ................................. 68
Figure 3. 8. Average concentrations of eight major FAAs and TFAAs in soils of A and C
horizons along the MAT and MAP gradients of continental US. Scales on right side applies to
TFAAs. MAT and MAP gradients shown in the legends differentiated by color are from the
circled areas specified in Figure 3.1. Values were expressed as mean ± SEM. # mean annual
temperature; * mean annual precipitation; § soils from west coast. .............................................. 71
Figure 3. 9. Average composition of individual and sum of eight dominant FAAs in A and C
horizon soils with different vegetation cover. Scales on right side apply to sum of eight major
FAA proportions. Different lower case letters indicate statistical significance among groups.
Values are expressed as mean ± SEM. .......................................................................................... 74
Figure 3. 10. Concentration of TFAAs (A) and total soil organic C content (B) among four
vegetation covers in two horizons. A = A horizon; C = C horizon; A/C Ratio = the ratio of
average TFAA level or soil total organic C content in the A horizon to that in the C horizon.
Values are expressed as mean ± SEM. .......................................................................................... 75
Chapter 4: Hydrolysable Amino Acids in Soils of North-South and West-East
Transects of Continental United States Figure 4. 1. Chromatograms of (A) amino acid derivatives with amino acids standard (10µM) and
(B) the amino acids in a surface soil sampled from a pasture area in Minnesota. Peaks: 1= 6-
aminoquinoline; 2=Asp; 3=Ser; 4=Glu; 5=Gly; 6=His; 7=NH4+; 8=Arg; 9=Thr; 10=Ala; 11=Pro;
12=Tyr; 13=Cys-Cys; 14=Val; 15=Met; 16=L-norvaline; 17=Lys; 18=Ile; 19=Leu; 20=Phe. The
peak between 16 and 17 in (A) is ornithine. .................................................................................. 97
Figure 4. 2. Average composition of individual and sum of eight major HAAs in samples of two
soil horizons from different transects. Scale on right side applies to sum of eight major FAA
proportions. Different lower case letters indicate statistical significance based on pairwise
comparison (α = 0.05). Values are expressed as mean ± Standard Error of Mean (SEM). ........... 98
Figure 4. 3. Average concentration of eight major HAAs and HAAs in two horizon soils from
different transects. Scale on right side applies to THAAs. Different lower case letters indicate
statistical significance among groups (α=0.05). Values are expressed as mean ± SEM. ............ 100
Figure 4. 4. Linear relationship between the concentrations of THAAs and major HAAs with total
soil organic C content (wt%) from two horizons. ....................................................................... 101
Figure 4. 5. Average concentrations of eight major HAAs and THAAs in A- and C-horizon soils
along the MAT and MAP gradients. Scales on right side apply to THAAs. The above two and the
bottom two sub-figures indicate the trend of amino acid level with MAT and MAP, respectively.
Temperature or precipitation gradients shown in the legends differentiated by color are from the
xv
circle areas specified in Figure 3.1. Values were expressed as mean ± SEM. # mean annual
temperature; * mean annual precipitation; § soils from west coast. ............................................ 102
Figure 4. 6. Average composition of individual and sum of eight dominant HAAs in A and C
horizon soils with different vegetation cover. Scales on right side applies to sum of eight major
HAA proportions. Different lower case letters indicate statistical significance among groups
(α=0.05). Values are expressed as mean ± SEM. ........................................................................ 106
Figure 4. 7. Concentration of THAAs (A) and total soil organic C content (B) among four
vegetation covers in two soil horizons. A = A horizon; C = C horizon; A/C Ratio = the ratio of
average THAA level or soil total organic C content in the A horizon to that in the C horizon.
Values are expressed as mean ± SEM. ........................................................................................ 108
Figure 4. 8. The average proportions of each amino acid in the HAA or FAA form. ................. 113
Figure 4. 9. Relationship between TFAAs and THAAs in soils investigated from the 93 sites. Red
and green represent samples from C and A horizon, respectively. ............................................. 114
Chapter 5: Carbon K-edge Near Edge X-ray Fine Structure Spectroscopic
Investigation of Organic Carbon Speciation in Soils of North-South and West-East
Transects of Continental United States Figure 5. 1. C K-edge NEXAFS spectrum deconvolution showing the six main 1s-π* transition
and two σ* transitions and the arctangent step function (290 eV) from a deciduous forest soil
from Missouri. ............................................................................................................................. 133
Figure 5. 2. Carbon K-edge NEXAFS of A-horizon (A) and C-horizon (C) soil samples from a
mixed forest site (California), a shrubland site (New Mexico), and a grassland/herbaceous site
(Oklahoma). ................................................................................................................................. 134
Figure 5. 3. The relative contents (in % of total organic C) of soil organic C species along an A-
horizon soil organic C (wt. %) gradient. ..................................................................................... 137
Figure 5. 4. Relative contents (in % of total organic C) of soil organic C species from A- and C-
horizon soils along the W-E mean annual precipitation transect. The box plots show the median
(the line in the box), 5th/95th percentile (lower and upper bars), and outliers (black dots). ....... 138
Figure 5. 5. Relative contents (in % of total organic C) of soil organic C species from A- and C-
horizon soils along the N-S mean annual temperature transect. The box plots show the median
(the line in the box) and 5th/95th percentile (lower and upper bars). .......................................... 138
Figure 5. 6. The relative contents (in % of total organic C) of soil organic C species in A- and C
horizon soils with different vegetation cover. ............................................................................. 142
Figure 5. 7. Weight ratios of (left) (in wt %) of soil organic C species in A- horizon to that in C-
horizon soils with different vegetation cover and (right) ratios of total soil organic C content
(wt %) in A-horizon soil to that in C-horizon soil. ...................................................................... 142
Chapter 6: Polarization dependent X-ray Photoemission Electron Microscopic and
Near Edge X-ray Fine Structure Spectroscopic Investigation of Hexa-glycine
Surface Orientation Sorbed on Montmorillonite Figure 6. 1. Schematic diagram of (a) monolayer montmorillonite preparation using the LB
trough technique and (b) procedure for preparation of monolayer hexa-glycine on
montmorillonite surface............................................................................................................... 163
xvi
Figure 6. 2. AFM image (5µm × 5µm)(left) of montmorillonite-coated Si water. The cross-section
profile (lower right) was determined along the line in the zoomed image (upper right).The height
differences (1.43 nm) indicates the thickness of the montmorilonite sheet. ............................... 163
Figure 6. 3. (a) PEEM image recorded at the Al K-edge at a photon energy of 1579 eV before
(left) and after montmorillonite region (bright area) were selected (middle) and the Al 1s
NEXAFS spectrum of selected area (right); (b) PEEM image recorded at the N K-edge at a
photon energy of 411.2 eV before (left) and after bright area were selected (middle) and the N 1s
NEXAFS spectrum of selected area (right) at grazing incidence; (c) PEEM image recorded at the
N K-edge at the photon energy of 400.1 eV before (left) and after bright area were selected
(middle) and the N 1s NEXAFS spectrum of selected area (right) at normal incidence. ............ 167
Figure 6. 4. N K-edge NEXAFS spectra of peptides adsorped onto monolayer montmorillonite on
Si substrate recorded at normal and grazing incidence. .............................................................. 169
Figure 6. 5. (a)Structural formula, (b) ß-sheet strand and (c) sideview of the sheet of self-
assembled Hexa-glycine; (d) Simplified scheme of assembled peptides on montmorillonite and
definitions of angles used to characterize the molecular orientations. All the angles were
calculated with respect to surface normal. Angle θ1 and θ2 represent the incidence angle at normal
and grazing incidence, and θ1E and θ2E are angles of electric vector with respect to surface normal.
While α is the angle between peptide p-orbital with surface normal, equal to the tilt angle of
peptide backbone with surface. ................................................................................................... 170
Figure 6. 6. Distribution different dissociation states of dissolved Hexa-glycine as a function of
pH, determined based on the published dissociation constants (pKa) of carboxylic acid (3.13) and
the ammonium ion acid (7.69) (Glasstone and Hammel, 1941). ................................................. 172
Figure 6. 7. The proposed schematic diagrams of the orientation of the adsorbed hexa-glycine on
montmorillonite. .......................................................................................................................... 172
xvii
List of Tables Chapter 2: Literature Review
Table 2. 1. Properties of naturally occurred amino acids used in this study ................................. 11
Table 2. 2. C/N 1s NEXAFS approximate fit energy position of primary peaks .......................... 31
Chapter 3: Immediately Bioavailable Free Amino Acids in Soils of North-South and
West-East Transects of Continental United States Table 3. 1. Detection limits, recovery and the precision of the determination of amino acid
derivatives. .................................................................................................................................... 55
Table 3. 2. Concentrations (mg kg-1
dry soil) of free amino acids ................................................ 63
Chapter 4: Hydrolysable Amino Acids in Soils of North-South and West-East
Transects of Continental United States Table 4. 1. Detection limits, recovery and the precision of the determination of amino acid
derivatives. .................................................................................................................................... 94
Table 4. 2. Concentrations (mg kg-1
dry soil) of major HAAs and THAAs. ................................. 98
xviii
List of Abbreviations
AFM Atomic force microscopy
AQC 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate
FAAs Free amino acids
FLD Fluorescence detector
FTIR Fourier transform infrared spectroscopy
FWHM Full-width at high maximum
HAAs Hydrolysable amino acids
HPLC High performance liquid chromatography
ISON Insoluble organic nitrogen
MAP Mean annual precipitation
MAT Mean annual temperature
MRPP Multiple response permutation procedure
NEXAFS Near edge X-ray fine structure spectroscopy
NMR Nuclear magnetic resonance
NMS Nonmetric multidimensional scaling
N-S North-South
Py-FIMS Pyrolysis-field ionization mass spectrometry
SIMS Secondary ion mass spectrometry
SOC Soil organic carbon
SOM Soil organic matter
SON Soluble organic nitrogen
STXM Scanning transmission X-ray microscope
TEA Triethylamine
TEM Transmission electron microscopy
TEY Total electron yield
TFAAs Total free amino acids
TFY Fluorescence yield
THAAs Total hydrolysable amino acids
W-E West-east
X-PEEM X-ray photoemission electron microscope
XPS X-ray photoelectron spectroscopy
1
1. Introduction
1.1. Background
Soil organic matter (SOM) consists of living soil organisms, plant residues, soil fauna,
and the remains of previous living organisms in their various degrees of decomposition (Paul,
2006). As shown in Figure 1.1, it is estimated that more than 85% of terrestrial N is in organic
form. There is a dynamic flux between the soil soluble organic nitrogen (SON) pool and the soil
insoluble organic nitrogen pool (ISON) (Figure 1.1) The N cycle starts from the degradation of
SOM and proceeds, via the depolymerization of proteins/peptides into free amino acids (FAAs),
which, if not taken up by plant root or microorganisms, are further broken down to ammonium,
followed by the process of nitrification and denitrification.
Figure 1.1.Simulated terrestrial N cycle modified based on Xu and Prentice (2008); a: sorption;
b: desorption; c: decomposition; d: humification; e: cell uptake (immobilization); f: exudation; g:
autolysis; h: root uptake; i: mineralization; j: tropic interaction; k: degradation & utilization; l:
fixation.
Vegetation(5.3 Pg N)
Litter(4.6 Pg N)
Soil(68 Pg N)
Organic N(67 Pg N)
Inorganic N(0.9 Pg N)
SON
Proteins
Peptides
Amino acids
ISON
minerals
organic matter
microorganisms
plant roots
Terrestrial N
ab
cd
ef, g
f, c
h
hi
f j
e
k
c
l
Leaching i
a bd
2
Soil SON is operationally defined as organic form of N dissolved in water or extracted by
0.01M to 2M salt solutions (e.g. CaCl2, KCl, K2SO4)(Chen and Xu, 2006). Unlike inorganic
forms of N which are simple compounds, SON is usually a complex mixture of compounds, such
as phenols, amino acids, amino-sugars, proteins peptides or tannins. Most of tannins and
proteins/peptides are of hydrophobic fraction (Smolander and Kitunen, 2002), while phenols,
amino acids, and amino-sugars are of hydrophilic fraction. The pool size of SON varies with
different soil systems. The SON pool can be as large as the inorganic N pool, both of which can
take up ~ 50% of the soluble N pool (Murphy et al., 2000). The SON consists of two pools: the
SON that is labile and readily available to plants and microorganisms, and the SON that is
recalcitrant and not readily metabolizable (Neff et al., 2003; Ge et al., 2010). It is reported that
nearly 40% of the total soil N is present in form of protein and peptides (Leinweber and Schulten,
1998). Study has shown that approximate 60% of N extracted by 0.01M CaCl2 (i.e SON) from an
agricultural soil was amino acids and peptides (Mengel et al., 1999). These biomolecules, which
are thought to have biologically labile chemical structures, are expected to be quickly
mineralized during early stages of organic matter stabilization (Knicker, 2004).
The classic study on SON assumes microbial mineralization a critical step in terrestrial N
cycling and plants only use inorganic N and are poor competitors for available N relative to
microbes (Fisk and Schmidt, 1995; Schimel and Bennett, 2004). Contrary to the mineralization-
focused paradigm, recent findings suggest that plants can “short circuit” the N cycling via uptake
of soluble organic N such as amino acids (Jones and Darrah, 1994; Kielland, 1994; Neff et al.,
2003), amino sugars (Roberts and Jones, 2012) or peptides and proteins (Abuzinadah and Read,
1989; Paungfoo-Lonhienne et al., 2008). Depolymerization of peptides and proteins was
proposed in the recent paradigm as the rate-limiting step in the transformation from polymetric N
3
to bioavailable N (Schimel and Bennett, 2004; Xu and Prentice, 2008; Rennenberg et al., 2009).
Despite the important roles of peptides and proteins in the terrestrial N cycling, little attention
has been given on their biogeochemistry compared to soil organic C (SOC).
Soluble organic N can be produced from microbial turnover (Seely and Lajtha, 1997) and
through microbial generation of extracellular enzymes (Leirós et al., 2000). In spite of its
importance, there is still uncertainty on its dynamic transformation between the SON and the
ISON pools. It is of great importance to investigate the mechanisms involved in the mobility,
reactivity and stability of soil organic N. There are several mechanisms to explain the stability of
soil organic N in soil systems, for example, encapsulation into hydrophobic macromolecules
(Knicker and Hatcher, 1997), sequestration in nanopores that are too small for organisms or
enzymes to enter, or formation of mineral-organic associates (Baldock and Skjemstad, 2000), the
latter of which was affected by soil mineralogy, mineral surface reactivity, or organic matter
content, etc (Moore et al., 1992; Kaiser and Zech, 2000). The degradability of some organic
compounds will decrease after their sequestration. The interactions between minerals and organic
compounds were widely recognized as of crucial importance in SOM stability (Kleber et al.,
2007). Nevertheless, the early investigations on mineral-organic associates were mostly limited
to observations at the macroscopic scale based on batch equilibrium experiments (Dashman and
Stotzky, 1982, 1984). Little is known about the nature of those interactions at the molecular scale,
especially for proteinaceous compounds regarding the molecular surface organization which, in
turn, will provide information on their bioavailability and stability.
Soluble organic N, accounting for 0.3-1% of the total N in arable soils (Mengel, 1985)
and 0.3-2% in temperate forest soils (Zhong and Makeschin, 2003), serves as both source and
sink for soil inorganic N (Zhong and Makeschin, 2003). It plays an important role on the rapid
4
inorganic N cycling. Despite its importance in the N cycle, there is a gap in our understanding
regarding the composition, sinks, sources, bioavailability, and stability of SON. Better
understanding of N flux between the SON and ISON pools and factors affecting the stabilization
of protein- and peptide-like compounds on mineral surfaces is essential for understanding
terrestrial N dynamics. This knowledge will help maximize the N utilization and minimize SON
loss in agricultural production. Since all forms of organic N are connected to a “C-backbone”,
this implies that SON has not only an indirect but also a direct impact on SOC cycling. The
major forms of SOC in soils are aliphatic-C, carboxylic-C, aromatic-C, phenolic-C and N/O-
alkyl-C as reveled by C K-edge Near Edge X-ray Fine structure Spectroscopic (NEXAFS)
Investigation (Solomon et al., 2005). The information of SOC speciation helps reveal the
changes of SOM composition with cultivation, decipher mechanisms involved in SOC
accumulations from biologically contrasting organic residues, and then provide us insights into
the regulation of SOC stabilization and C sequestration. So far, such investigations are really
lacking. Therefore, the information on stability and the bioavailability of SON as well as the
speciation of SOC not only benefits the agricultural production and the ecosystem management,
but helps better understand soil C sequestration.
1.2. Objectives
The main objective of this research is to investigate the status of the immediately
bioavailable pool of free amino acids and the potentially bioavailable pool of total proteinaceous
compounds across a wide range of soil ecosystems and how the small peptides interact with soil
minerals at molecular scale.
Specific objectives are: 1) to assess the immediately bioavailable pool of free amino acids
s and the potentially bioavailable pool of total proteinaceous compounds in soils of North-South
5
and West-East transects of continental United States and the relationship between their
composition and levels with environmental factors; 2) to evaluate the organic C speciation in
bulk soils of various ecosystems; and 3) to understand molecular level surface organization of
small peptides on mineral surfaces.
To accomplish the above objectives, Objective 1 was covered in Chapter 3 and 4;
Objective 2 was investigated in Chapter 5; Objective 3 was revealed in Chapter 6 respectively.
6
1.3. References
Abuzinadah, R.A., Read, D.J., 1989. The role of proteins in the nitrogen nutrition of
ectomycorrhizal plants .4. The utilization of peptides by birch (betula-pendula l) infected
with different mycorrhizal fungi. New Phytologist 112, 55-60.
Baldock, J.A., Skjemstad, J.O., 2000. Role of the soil matrix and minerals in protecting natural
organic materials against biological attack. Organic Geochemistry 31, 697-710.
Chen, C.R., Xu, Z.H., 2006. On the nature and ecological functions of soil soluble organic
nitrogen (son) in forest ecosystems. Journal of Soils and Sediments 6, 63-66.
Dashman, T., Stotzky, G., 1982. Adsorption and binding of amino-acids on homoionic
montmorillonite and kaolinite. Soil Biology & Biochemistry 14, 447-456.
Dashman, T., Stotzky, G., 1984. Adsorption and binding of peptides on homoionic
montmorillonite and kaolinite. Soil Biology & Biochemistry 16, 51-55.
Fisk, M.C., Schmidt, S.K., 1995. Nitrogen mineralization and microbial biomass nitrogen
dynamics in 3 alpine tundra communities. Soil Science Society of America journal 59,
1036-1043.
Ge, T.D., Nie, S., Huang, D.F., Xiao, H.A., Jones, D.L., Iwasaki, K., 2010. Assessing soluble
organic nitrogen pools in horticultural soils: A case study in the suburbs of shanghai
(china). Acta Agriculturae Scandinavica Section B-Soil and Plant Science 60, 529-538.
Jones, D.L., Darrah, P.R., 1994. Amino-acid influx at the soil-root interface of zea-mays l and its
implications in the rhizosphere. Plant and Soil 163, 1-12.
Kaiser, K., Zech, W., 2000. Sorption of dissolved organic nitrogen by acid subsoil horizons and
individual mineral phases. European journal of soil science 51, 403-411.
7
Kielland, K., 1994. Amino-acid-absorption by arctic plants - implications for plant nutrition and
nitrogen cycling. Ecology 75, 2373-2383.
Kleber, M., Sollins, P., Sutton, R., 2007. A conceptual model of organo-mineral interactions in
soils: Self-assembly of organic molecular fragments into zonal structures on mineral
surfaces. Biogeochemistry 85, 9-24.
Knicker, H., 2004. Stabilization of n-compounds in soil and organic-matter-rich sediments - what
is the difference? Marine Chemistry 92, 167-195.
Knicker, H., Hatcher, P.G., 1997. Survival of protein in an organic-rich sediment: Possible
protection by encapsulation in organic matter. Naturwissenschaften 84, 231-234.
Leinweber, P., Schulten, H.R., 1998. Nonhydrolyzable organic nitrogen in soil size separates
from long-term agricultural experiments. Soil Science Society of America journal 62,
383-393.
Leirós, M.C., Trasar-Cepeda, C., Seoane, S., Gil-Sotres, F., 2000. Biochemical properties of acid
soils under climax vegetation (atlantic oakwood) in an area of the european temperate–
humid zone (galicia, nw spain): General parameters. Soil Biology and Biochemistry 32,
733-745.
Mengel, K., 1985. Dynamics and availability of major nutrients in soils, In: Stewart, B.A. (Ed.),
Advances in soil science. Springer New York, pp. 65-131.
Mengel, K., Schneider, B., Kosegarten, H., 1999. Nitrogen compounds extracted by
electroultrafiltration (euf) or cacl2 solution and their relationships to nitrogen
mineralization in soils. Journal of Plant Nutrition and Soil Science 162, 139-148.
Moore, T.R., Desouza, W., Koprivnjak, J.F., 1992. Controls on the sorption of dissolved organic-
carbon by soils. Soil Science 154, 120-129.
8
Murphy, D.V., Macdonald, A.J., Stockdale, E.A., Goulding, K.W.T., Fortune, S., Gaunt, J.L.,
Poulton, P.R., Wakefield, J.A., Webster, C.P., Wilmer, W.S., 2000. Soluble organic
nitrogen in agricultural soils. Biology and Fertility of Soils 30, 374-387.
Neff, J.C., Chapin, F.S., Vitousek, P.M., 2003. Breaks in the cycle: Dissolved organic nitrogen in
terrestrial ecosystems. Frontiers in Ecology and the Environment 1, 205-211.
Paul, E.A., 2006. Soil microbiology, ecology and biochemistry. Academic press.
Paungfoo-Lonhienne, C., Lonhienne, T.G.A., Rentsch, D., Robinson, N., Christie, M., Webb,
R.I., Gamage, H.K., Carroll, B.J., Schenk, P.M., Schmidt, S., 2008. Plants can use protein
as a nitrogen source without assistance from other organisms. Proceedings of the
National Academy of Sciences of the United States of America 105, 4524-4529.
Rennenberg, H., Dannenmann, M., Gessler, A., Kreuzwieser, J., Simon, J., Papen, H., 2009.
Nitrogen balance in forest soils: Nutritional limitation of plants under climate change
stresses. Plant Biology 11, 4-23.
Roberts, P., Jones, D.L., 2012. Microbial and plant uptake of free amino sugars in grassland soils.
Soil Biology & Biochemistry 49, 139-149.
Schimel, J.P., Bennett, J., 2004. Nitrogen mineralization: Challenges of a changing paradigm.
Ecology 85, 591-602.
Seely, B., Lajtha, K., 1997. Application of a 15n tracer to simulate and track the fate of
atmospherically deposited n in the coastal forests of the waquoit bay watershed, cape cod,
massachusetts. Oecologia 112, 393-402.
Smolander, A., Kitunen, V., 2002. Soil microbial activities and characteristics of dissolved
organic c and n in relation to tree species. Soil Biology and Biochemistry 34, 651-660.
9
Solomon, D., Lehmann, J., Kinyangi, J., Liang, B.Q., Schafer, T., 2005. Carbon k-edge nexafs
and ftir-atr spectroscopic investigation of organic carbon speciation in soils. Soil Science
Society of America journal 69, 107-119.
Xu, R.I., Prentice, I.C., 2008. Terrestrial nitrogen cycle simulation with a dynamic global
vegetation model. Global Change Biology 14, 1745-1764.
Zhong, Z., Makeschin, F., 2003. Soluble organic nitrogen in temperate forest soils. Soil Biology
and Biochemistry 35, 333-338.
10
2. Literature Review
2.1. Biogeochemistry of amino acids in soils
Amino acids, which are constitutes of proteins or peptides, are usually α-amino acids
with common structure that consists of hydrogen atoms, a R-group, an amino and a carboxyl
functional group attached to the same tetrahedral carbon atom, α-carbon (Figure 2.1). Each
amino acid has its own distinctive R-group (Table 2.1). The presence of amino acids in soils and
organic matter is due to the breakdown of native proteins derived from plants, microbes and
animal tissue (Senwo and Tabatabai, 1998). Amino acids in soils are classified into three pools
(Yu et al., 2002; Jämtgård, 2010): 1) free amino acids (FAAs), which are those dissolved in soil
solution and are directly available to organisms; 2) exchangeable amino acids, which are bound
to charged surfaces on clay particles and soil organic matter (SOM), but potentially
exchangeable into soil solution; and 3) hydrolysable amino acids (HAAs), mostly proteinaceous
compunds (e.g., proteins and peptides), that are tightly bound to mineral surfaces and difficult to
be exchanged into soil solutions.
Figure 2. 1.The basic structure of amino acid
11
Table 2. 1. Properties of naturally occurred amino acids used in this study
Amino acid Abbreviation Structure MW Chemical Families Polarity Side-chain
Chemistry
Glycine Gly
75.1 Aliphatic Nonpolar Neutral
Alanine Ala
89.1 Aliphatic Nonpolar Neutral
Valine Val
117.1 Aliphatic Nonpolar Neutral
Leucine Leu
131.2 Aliphatic Nonpolar Neutral
Isoleucine Ile
131.2 Aliphatic Nonpolar Neutral
Serine Ser
105.1 Non-Aromatic
Hydroxyl Polar Neutral
Threonine Thr
119.1 Non-Aromatic
Hydroxyl Polar Neutral
Methionine Met
149.2 Sulfur-Containing Nonpolar Neutral
12
Table 2. 1. Properties of naturally occurred amino acids used in this study
Amino acid Abbreviation Structure MW Chemical Families Polarity Side-chain
Chemistry
Cystine Cys-Cys
240.3 Sulfur-Containing Nonpolar Neutral
Aspartic Acid Asp
133.1 Acidic & Amides Polar Acidic
Asparagine Asn
132.1 Acidic & Amides Polar Neutral
Glutamic
Acid Glu
147.1 Acidic & Amides Polar Acidic
Glutamine Gln
146.1 Acidic & Amides Polar Neutral
Arginine Arg
174.2 Basic Polar Basic
Lysine Lys
146.2 Basic Polar Basic
Histidine His
155.1 Basic Polar Basic
(Continued)
13
Table 2. 1. Properties of naturally occurred amino acids used in this study
Amino acid Abbreviation Structure MW Chemical Families Polarity Side-chain
Chemistry
Phenylalanine Phe
165.2 Aromatic Nonpolar Neutral
Tyrosine Tyr
181.2 Aromatic (Hydroxyl ) Nonpolar Neutral
Tryptophan Trp
204.2 Aromatic Nonpolar Neutral
Proline Pro
115.1 Cyclic Nonpolar Neutral
(Continued)
14
2.1.1 Distribution and occurrence
Free amino acids in soils make up only a small fraction of the soil organic N, normally
less than 5% of the total soil organic N (Jones et al., 2004; Ge et al., 2010). In spite of their low
fraction in the soluble organic N (SON) pool, they are widely distributed in different soil systems.
Acidic amino acids including aspartic acid and glutamic acid; neutral amino acids including
glycine, alanine, leucine, isoleucine, valine, serine, and threonine; basic amino acids including
arginine, lysine, and histidine; Imino amino acids including proline and hydroxyproline;
aromatic amino acids including phenylalanine, tyrosine and tryptophane, and non-protein amino
acids such as ornithine and gama-aminobutyric acid have been detected in soils (Gotoh et al.,
1986b; Stevenson, 1994).
Concentration of FAAs can vary many folds among different soil systems (Rothstein,
2009b).The total free amino acids (TFAAs) calculated as the sum of each FAA detected was
reported to be in the range of 20 to 350 nmol per gram dry soil in boreal forest (Werdin-Pfisterer
et al., 2009); 0.1 to 12.7 µM in the agricultural soil solutions (Jämtgård, 2010), from 1.6 to 29.9
µM in forest soil solutions (Yu et al., 2002), and 2 to 10 μM in Arctic and Antarctic soil
solutions (Jones et al., 2005). The concentrations of FAAs in soils or soil solution were found to
exhibit seasonal variations in arctic tundra (Weintraub and Schimel, 2005b) and a young sub-
boreal forest in Wisconsin (Johnson and Pregitzer, 2007) and to be site dependent (Jämtgård,
2010). An increased TFAA concentration in soils across a boreal successional soil sequence was
observed (Werdin-Pfisterer et al., 2009). The major FAAs detected in this study included
glutamic acid, glutamine, aspartic acid, asparagine, alanine, and histidine in all successional
stage (Werdin-Pfisterer et al., 2009). The possible reasons for the increasing FAA concentrations
with soil succession were due to the accumulation of the SOM and subsequent higher biomass
15
across succession, which are sources of amino acids, increased proteolytic activity across
succession, and the greater fine root inputs in later successional stages, which decompose and
turnover faster than the above ground litter inputs (Werdin-Pfisterer et al., 2009). Rothstein
(2009b) found that the pool of FAAs diminished rapidly as site fertility increased along a
temperate forest fertility gradient of northern Lower Michigan, USA, ranging from low mineral
N availability, oak-dominated forests to high mineral N availability, maple-basswood forests.
The concentrations of FAAs in the low fertility site were higher and positively correlated to the
concentration of soluble peptides but universally lower at high fertility sites. These studies
support the hypothesis that peptides or proteins maybe a replenishing source for FAAs. Besides
these, other factors also influence the FAA concentrations in soils, for example plant uptake
(Theodore K. Raab, 1999). The net pool of FAAs is likely a balanced result between productive
processes (i.e. proteolysis, exudation by roots and microbes) and consumptive processes (i.e.
assimilation, mineralization) (Rothstein, 2009b).
In order to quantify peptides/proteins, the largest contributions to the soil organic N pool,
effective methods are needed to cleave all proteins to single amino acids which can be
quantitatively analyzed using modern analytical instrument. The strong hot acid hydrolysis
(normally 6N HCl) has been commonly used to break the peptide bond to liberate FAAs. The
acid released amino acids were called hydrolysable amino acids (HAAs) in this study. This
method accounted for more than half of the total soil N (Olk, 2007). Compared to FAAs in the
soil solution, the total amount of amino acids released by 6 N HCl hydrolysis of SON is usually
dozens of folds higher than TFAAs (Yu et al., 2002; Paul and Williams, 2005; Jämtgård, 2010).
For example, in the soil solutions of agricultural soils, the TFAA concentrations ranged from 0.1
to 12.7 µM while the concentrations of total amount of amino acids released by hydrolysis of
16
SON were 50 times higher than the TFAA concentrations (Jämtgård, 2010). The content of
amino acids released by hydrolyzing the whole soil changed from site to site and was influenced
by crop types or cultivation methods. The concentrations of total hydrolysable amino acids
(THAAs) in 8 soils under arable, grassland and forest use ranged from 34 to 855µmol N g-1
soil
(Friedel and Scheller, 2002). Legumes were suggested to enrich soil HAAs but pearl millet
depleted them in soil, while the application of residues of manure reversed this effect of pearl
millet (Praveen et al., 2002b). This study suggests that soil amendment of organic materials will
increase soil organic N that can be hydrolyzed to amino acids (Jansson et al., 1982; Campbell,
1991; Senwo and Tabatabai, 1998). Research has also suggested that the concentrations of
soluble or total soil HAAs were different based on its origin. The total HAAs ranged from 566 to
1509 mg amino acids kg -1
soil (equivalent to about several to a dozen µmol per gram soil)
(Senwo and Tabatabai, 1998) in the organic matter of surface soils in Iowa with two cropping
systems, around dozens of µM (Jämtgård, 2010) in soil solution, and from 0.70 to 6.1µmol
amino acid-N g -1
dry soil in micro-organisms, from 656 to 855µmol amino acid-N g -1
in litter
layer soils (Friedel and Scheller, 2002).
Despite of substantial concentration differences in soils with different cultivation and
rotation, the composition of the HAAs shows minor differences (Stevenson, 1956; Campbell,
1991; Senwo and Tabatabai, 1998). The composition of individual amino acid expressed as
molar percentage of total amino acids was rather uniform among agricultural soils (Gotoh et al.,
1986b) and soils of different climatic conditions (Sowden et al., 1977). Studies conducted by
Gotoh et al. (1986b) on the rice field soil showed uniform amino acid composition for both
FAAs and HAAs in whole soils regardless of organic amendment. This result suggests that the
distribution pattern of amino acids is not greatly influenced by the organic matter forms. And
17
among the individual amino acid, aspartic acid, glutamic acid, glycine, and alanine were also
found to be dominant amino acids in various soils (Gotoh et al., 1986b; Campbell, 1991; Friedel
and Scheller, 2002). This indicates the relative abundance of dominant amino acids may also be
uniform. The relative molar distribution of HAAs in the whole soil investigated by Friedel and
Scheller (2002) was rather uniform despite a wide range of site properties and different land use.
It was also observed that the pattern of HAAs of the bulk soil was inconsistent to that derived
from microorganisms of the same soil. This study suggested that microbial cell walls were not
the major direct contributor to the bulk of the amino acids in the soil. This observation was
partially inconsistent with the statement by Sowden et al. (1977) and Leinweber and Schulten
(1998) that microorganisms play a major role in the formation of major soil amino acids. Plant
residues, however, were suggested to be the largest contributor to soil amino acids (Friedel and
Scheller, 2002).
Acid hydrolysis of a soil is a commonly used method to extract the soil bound amino
acids, allowing us to determine the fraction of amino acids bound in biomass and those
associated with non-living SOM (i.e. peptides/proteins). The hydrolysis with 6 N HCl normally
releases only 64 - 80% of Kjeldahl-N and bout 20% to 35% of Kjeldahl-N cannot be hydrolyzed
by 6N HCl (Schnitzer and Ivarson, 1982). Mechanism involved in the protection of proteins from
strong acid hydrolysis is by interaction with mineral phase, sorption to clay particles, forming
protein-tannin complexes encapsulation into hydrophobic structures (Knicker, 2011) and other
physical protection such as forming soil aggregation (Rillig et al., 2007). This non-hydrolysable
part of N needs to be qualified and quantified by some other methods such as Pyrolysis-Field
Ionization Mass Spectrometry (Py-FIMS) or X-ray photoelectron spectroscopy (XPS), or
18
synchrotron-based spectroscopy to reveal its relationship with the soil properties and
environmental factors.
2.1.2. Transformation of peptides/proteins into amino acids
It is generally recognized that depolymerization or proteolysis of protein and peptides by
extracellular enzymes is the most important process by which FAAs are produced in soils
(Lipson and Nasholm, 2001; Jones and Kielland, 2002; Schimel and Bennett, 2004). The
proteinaceous or peptidic compounds, once broken down into amino acids, are subject to plant
uptake, leaching, or immobilized and mineralized by soil organisms and microbes. Therefore, to
study the transformation of peptides/proteins is of great significance to better understand the N
flux between SON and ISON pools.
Since extracellular enzymes play a crucial role in peptides/proteins transformation
process, the activity of the enzymes may be used as a measurement of FAA replenishment
capacity (Lipson and Monson, 1998; Weintraub and Schimel, 2005b). The soil N-enzyme
activities can even be used as potential indicators of soil SON pools in the temperate forest
ecosystem because concentrations of soil SON were positively correlated with the soil N-
degrading enzymes independent of sampling time (Yang et al., 2012). Such correlation has also
been observed in the plantation forests in subtropical China (Xing et al., 2010).These results
indicated the production of soil SON was strongly influenced by N-degrading enzymes. The
contribution of microbial turnover to the SON pool, the presence of microbial origin extracellular
enzymes as well as the high microbial biomass in SON (Leirós et al., 2000; Neff et al., 2003;
Xing et al., 2010; Vranova et al., 2013) suggested the major role of microbes involved in the
transformation process. The factors affecting the enzyme activity, such as temperature, substrate,
and pH, will influence the transformation of peptides/proteins into FAAs in soils. Related studies
19
have been reported over various soil systems. Research performed by Berthrong and Finzi (2006)
showed that proteolysis was substrate limited in cold-temperate forests. The protease activity
exponentially increased in response to soil temperature range of -2 to 21 (Fraser et al., 2013).
Both soil protease activities and amino acid flux were inversely correlated with soil pH in Taiga
forest (r2>0.90) (Kielland et al., 2007). Contrary to results by Fraser et al. (2013), the amino acid
transformation was not responsive to 10 temperature differences among the successional
coniferous ecosystems (Kielland et al., 2007). This discrepancy suggested that the soil pH may
exert more influence than the temperature. Other factors such as certain polyphenolic compounds,
aromatic moieties and tannins in particular may retard the transformation process by
sequestrating the substrate or deactivating enzymes (Ladd and Brisbane, 1967; Fierer et al., 2001;
Kraus et al., 2004).
The results by Weintraub and Schimel (2005b) on Alaskan arctic tundra soils were
inconsistent with the findings of Kielland et al. (2007) and Rothstein (2009b). Firstly, Weintraub
and Schimel (2005b) observed that the protein degradation was generally limited by enzyme
activity and only became substrate limited at the times when activity was the highest. This
indicates the protein degradation wasn’t substrate limited when soils were amended with protein
at the time enzyme activities were not the highest. Secondly, Weintraub and Schimel (2005b)
suggested that the recalcitrant SON pool fueled the increase in protease activity, rather than the
labile SON pool in the tundra soils. But in the study of Kielland et al. (2007), it was suggested
that the labile SON from decomposition of fine roots facilitates the protease activity in the
Alaska black spruce stands. Lastly, Weintraub and Schimel (2005b) also observed that
concentrations of TFAAs did not track soil soluble protein levels, and the two often had
opposing patterns, with increasing soluble proteins and decreasing in TFAA levels. Based on the
20
fact that concentrations of TFAAs were low when protease activity was high, the author
suggested that the decline in TFAAs was caused by increased plant and microbial uptake of
amino acids rather than a decline in their supplies. The conclusion was that N limitation induced
protease synthesis was the most likely explanation for increases in protease potential observed in
these tundra soils because an increase in protease was driven by increased N demand but not the
protein availability. While in the study performed by Kielland et al. (2007), the soil protease
activity was closely related to the TFAAs (r2=0.97) and exhibited a curvilinear relationship to
soil soluble protein (extracted with 0.1 M NaHCO3) levels, indicating that the increased
proteolysis maybe protein supply driven based on the natural substrate pool.
It is assumed the microbial biomass usually undergoes large seasonal fluctuations and
contributes to labile soil organic N (Lipson and Nasholm, 2001). Lipson et al. (1999b) observed
that microorganisms declined immediately after snowmelt, releasing proteins to the soil along
with the saturated proteases but later became substrate limited. Lipson and Monson (1998)’s
study, however, showed that the microorganisms in alpine soils were resistant to freezing and
drying stress, and insignificant amount of biomass was reduced during the adverse environment;
the water-extractable amino acids decreased by freezing, but increased by drying. These results
contrast to the claims by others that large microbial population was killed during the dry-
rewetting or freeze-thaw situation (Kieft et al., 1987; Skogland et al., 1988). Thus, the variation
of microbial biomass at challenging environment and their contribution to the amino acid
turnover need to be re-evaluated. In summary, as discussed by Weintraub and Schimel (2005b),
two processes are limiting the proteolysis, one by substrate availability controlled by fine roots
or microbial turnover, and the other by increased N demand controlled by solubilization of
recalcitrant protein complexes. It seems microorganisms play an important role in the process by
21
excreting extracellular enzymes and contributing to increased substrate availability or by
absorbing the amino acids resulting in decreased substrate availability.
2.1.3. Fate of amino acids
Amino acids are subject to plant uptake, microorganism immobilization, leaching loss,
humification into organic matter, and decomposition into inorganic N (Figure 1.1 and 2.1). The
fact that plant can uptake amino acids has been demostrated in different ecosystems, such as
agricultural fields (Lipson and Nasholm, 2001), arctic coastal salt marsh (Henry and Jefferies,
2003), and tundra (Schimel and Chapin, 1996), and by different plant species (Persson and
Näsholm, 2001). Amino acid transporters are suggested to be facilitators for amino acid
assimilation by roots (Rentsch et al., 2007). Plants assimilate amino acids in direct competition
with soil microbes which immolilize amino acid for biomass acquisition (Berthrong and Finzi,
2006). Figure 2.2 showed a comparison of the traditional and new pagradigms which recognized
different steps that regulate overal N cycling. Micorbial mineralization of amino acids is an
essential step for transformation of organic N to inorganic N, which is emphasized in the
traditional N cyclying theory (Figure 2.2). Amino acids may regulate the rate of ammonification
and nitrification in soil by providing the substrate for these conversion. At the same time, the
inorganic forms of N (e.g. NH4+) can be immobilized by microbes to synthesize amino acids,
which will be liberated after microbial death and lysis of micorbial cells. Therefore, the amino
acids may serve as both sink and source for inorganic N (Chen and Xu, 2006). The amino acid
loss by leaching was evidenced in many soil systems (Perakis and Hedin, 2002; Neff et al., 2003).
Amino acid leaching limits the accumulation and stock of organic N in terrestrial ecosystems but
enhance N availability in aquatic ecosystems.
22
Figure 2. 2. Combined paradigms of the soil N cycle modified according to (Schimel and
Bennett, 2004). Solid line section are common steps for both traditional and new paradims, while
dash line section is exclusive to the new paradim. Red line indicates the rate-limiting step of N
cycle in each paradim. a: depolymerization; b: root uptake; c: mineralization; d: immobilization;
e: cell uptake (immobilization); f: degradation; g: nitrification; h: leaching.
2.2. Mineral-associated organic N
2.2.1. Mechanisms involved in the mineral organic interactions
In contrast to FAAs which have short turnover time in the range of hours (Jones and
Kielland, 2002; Kielland et al., 2007), peptides and proteins tend to be adsorbed to clay or
immobilized by minerals and thus have longer residence time up to centuries (Amelung et al.,
2006). Chirality measurement of some amino acids showed that certain proteins may persist
hundreds of years (Amelung et al., 2006). The different residence time of FAAs and peptides
suggest that they have different turnover kinetics depending on their molecular structure and
chemical environment (Miltner et al., 2009). The adsorption of proteins or peptides to mineral
surfaces protects them from extracellular enzyme attack because of their multifunctional
structure. The hydrophilic and hydrophobic functional groups of proteins or peptides made them
easily sorbed to any surface area over a wide range of pH and the electrostatic attraction can be
reinforced by conformational changes to gain entropy (Kleber et al., 2007).
23
There are several models explaining the mineral associated organic N. The “onion-
layering model” specified by Sollins et al. (2006) stated that the peptidic compounds form a
stable inner organic layer onto which hydrophobic and less polar organics could sorb more
readily than onto the highly charged mineral surface. The “bilayer model” of organo-mineral
interactions showed that the hydrophilic functional groups were firstly bonded to the minerals
surface, while the hydrophobic groups of the bonded molecule were shielded away from polar
water phase by a second layer of amphiphiles, forming a bilayer (Wershaw et al., 1996). The
most recently developed “zone model” (Kleber et al., 2007) suggested three zones of mineral-
associated molecules: contact zone, hydrophobic zone, and kinetic zone. In the contact zone, the
amphiphilic portion of the compounds was attached to the mineral surface by electrostatic
interactions, with the hydrophobic portions pointing to the polar aqueous solution. In the
hydrophobic zone, the hydrophobic portion of the adsorbed molecules was shielded from polar
aqueous phase through association with hydrophobic moieties of other amphiphilic molecules to
form a bilayer. In the kinetic zone, proteins were adsorbed to the hydrophilic exterior of the
hemimicellar coatings loosely via cation bridging, by hydrogen bonding and other interactions,
allowing frequent exchange with surrounding soil solution. In the three models, it is the
amphiphiles that show the key roles. The proteins and peptides are amphiphiles, therefore, it may
be the reason that proteins and peptides form strong interactions with minerals. This can be at
least partially corroborated by the decreased C/N ratio after sorption of peptidic compounds to
minerals (Sollins et al., 2006; Kleber et al., 2007). Evidences that proteins are preferentially
adsorbed to mineral surfaces were given by other studies (Omoike and Chorover, 2006). The
adsorption phenomena can be observed by using isotope amino acid tracking technique and
confirmed by solid state 14
C, 13
C or 15
N NMR spectroscopy. For instance, by using solid state 13
C
24
NMR, Wershaw et al. (1996) found the compost leachate dissolved organic C adsorbed on
alumina by forming a bilayer. Similarly, Solomon et al. (2012b) by using Scanning Transmission
X-ray Microscope coupled with Near Edge X-ray Absorption Fine Structure Spectroscopy
(STXM–NEXAFS), suggested the protonated and deprotonated termini of the proteinaceous
compounds could serve as a binding link bridging mineral and hydrophobic matter (black-C
substances) in a “three way” association.
2.2.2. Factors influencing mineral-organic N interactions
Factors such as mineral types, pH, and temperature have been reported to influence the
adsorption of peptides/proteins on mineral surface. Early studies by Greenland et al. (1962)
showed increased adsorption of glycine peptides with concentration and molecular weight, while
Dashman and Stotzky (1984) contended the molecular weight and basicity of amino acids were
not as important in their adsorption and binding as its amino or carboxyl functional groups.
Studies conducted by Jones and Hodge (1999) indicated the amount of amino acids sorbed to the
clay loam soil was concentration dependent and followed the series lysine > glycine > glutamate
for all concentrations. Research conducted by Kalra et al. (2003) showed that the maximum
adsorption of simple peptides of glycine and peptides of glycine-alanine on montmorillonite with
or without metal ion substitution at neutral pH (7.02) and a temperature of 23 . Adding cations
increased sorption of peptides on clay surfaces. For example, Ca2+
-montmorillonite exhibited
better adsorption of small peptides of glycine and peptides of glycine-alanine as compared to
montmorillonite without Ca2+
or Mg2+
(Kalra et al., 2003).The charge property of the amino
acids backbone of a peptide/protein seemed to affect the adsorption behavior of the minerals.
Basic amino acids tended to bind strongly to negatively charged aluminosilicate minerals
(Aufdenkampe et al., 2001) while the acid amino acids inclined to attach to metal oxides such as
25
ferrihydrite with positive charges (Matrajt and Blanot, 2004). Most of these studies are limited to
observations at the macroscopic scale based on macroscopic batch equilibrium experiments
(Greenland et al., 1962; Dashman and Stotzky, 1982, 1984; Murphy et al., 1990).
Leinweber and Schulten (2000) found the non-hydrolysable organic N in soils resulted
from its binding to reactive surfaces (i.e. silicates, pedogenic oxides) and the proportions of
amino-N were underestimated by approximately 25% of the non-hydrolysable N if the mineral-
bound peptides were not solubilized. This result is consistent with their previous study
(Leinweber and Schulten, 1998). Mikutta et al. (2006) confirmed that stabilization of organic
matter by interaction with poorly crystalline minerals and polymeric metal species was the most
important mechanisms for organic matter preservation in the acidic subsoil horizons. Mikutta et
al. (2010) studied the mineralogical impact on organic N across a long-term soil chronosequence
(0.3 - 4100 kyr). Mineral associated organic N was characterized by XPS and synchrotron-based
NEXAFS spectroscopy. The results showed the youngest site contained the largest proportion of
hydrolysable amino sugars and amino acids, the intermediate weathering stage contained more
minerals associated organic N but a smaller proportion of hydrolysable amino sugars and amino
acids, while in the final weathering stage, less mineral associated organic N was held. Poorly
crystalline minerals in intermediate sites retained more organic N than the youngest and oldest
sites with primary minerals (olivine, pyroxene, feldspar) and secondary Fe/Al oxides and kaolin
minerals respectively. This study provided strong evidence that the soil mineral composition
affects the N cycling by controlling the amount and chemical composition of soil organic N. This
implied that soil mineralogy played an important role in the stabilization of proteinaceous
compounds. While whether this phenomenon is universal in a wider range of soils systems needs
to be evaluated.
26
2.2.3. Evaluation of molecular orientation on mineral surfaces
Molecular surface organization of compounds sorbed on mineral surfaces may strongly
influence their reactivity, stability, and bioavailability, as well as modify the surface property of
organo-minerals. Little is known about the nature of interactions between peptides/proteins and
minerals at molecular scale, especially their surface organization such as molecular orientation,
spatial distribution, packing density, etc. Recent molecular level investigation by spectroscopy
showed that amino acid molecules tend to self-organize themselves on the mineral surface in
response to different environment. According to Sverjensky et al. (2008), at pH of 3, glutamine
sorbed on titanium dioxide surface “lying down” at low concentration, and “standing up” at high
concentration. It was assumed that the surface coverage accounted for the change since at higher
concentration, the standing up organization maximized the coverage of the molecules sorbed.
Polarization-dependent NEXAFS has been used to reveal the molecular orientation of the
sorbed molecules on surfaces (Peters et al., 2002; Liu et al., 2006; Samuel et al., 2006; Cao et al.,
2011). The angular dependence of the π*-transition intensities of the NEXAFS spectra can be
used to determine the average molecule tilt angle of the attached molecules with respect to the
surface (Seifert et al., 2007). Typical elements in biomolecules, such as C and N, exhibit simple
s-to-p transitions, which are dipole-allowed if the electric field vector E of the incident X-rays is
parallel to the transition dipole moment. The intensity of the s-to-p transition peaks in a
NEXAFS spectrum follows a cos2θE pattern around that axis. Rotating the sample changes the
polar angle of incidence and therefore the angle θE of the electric field vector E with respect to
the surface normal (for p-polarized light). For a peptide, amino acids are linked by peptides bond.
As summarized by Liu et al. (2006), the peptide bond electrons are delocalized across the entire
peptide bond, so the double-bound character are extended to both the carbon-oxygen and the
27
carbon-nitrogen bonds. The shared π* orbital limits the free rotation of peptide C-N, so the six
atoms surrounding a peptide group lie in a single plane. The π* orbital (p orbital) is oriented
perpendicular to the peptide plane (Figure 2.3).
Figure 2. 3. Geometry of peptide bond (modified based on Liu et al. (2006)). The p-orbital (large
arrow) is oriented perpendicular to the plane of peptide bond. The oligopeptide molecules are
self-assembled on the surface of wafer (lower).
The N K-edge NEXAFS spectra in Figure 2.4 exhibit a strong θ dependence for the peak
of π* peptide bond (normally around 402.2eV). By collecting two NEXAFS spectra, for example
one at 90° incident X-ray angle and the other at 16°, the tilt angle of a molecule bound to the
surface can be calculated using equation (Stöhr, 1992):
𝐈(𝛉𝟐)
𝐈(𝛉𝟏) = 1 + p[
𝟐
𝐬𝐢𝐧𝟐 𝛂− 𝟑] [𝐬𝐢𝐧𝟐𝛉𝟐 − 𝐬𝐢𝐧𝟐𝛉𝟏]
where θ is the angle of incident X-ray from sample surface (θ=90°-𝜃𝐸), α is the tilt angle of p-
orbital of peptide bond from surface normal, and P is the degree of polarization of the X-rays.
Polarization dependent NEXAFS can be used to investigate the surface orientation of
peptides/proteins sorbed on mineral surface. It is of great importance to shed light on the
28
molecular level surface organization which appears to strongly affect the reactivity, stability and
bioavailability of the sorbed molecules, as well as modify the surface property of organo-
minerals.
Figure 2. 4. N (1s) K-edge NEXAFS spectra of a 16-unit peptides bound to gold surface
recorded at two incident x-ray angles (Iucci et al., 2008).
2.2.4. Synchrotron based spectroscopic method to study organic C and N speciation
The synchrotron based NEXAFS is a powerful technique to analyze organic C and N
speciation in soils (Vairavamurthy and Wang, 2002; Leinweber et al., 2007). Compared to other
spectroscopic techniques such as NMR, synchrotron based NEXAFS requires minimum sample
manipulation, is elemental specific, and has higher spatial resolution, lower detection limit (Dhez
et al., 2003). An NEXAFS spectrum is usually characterized by intense resonance features,
arising from 1s to π* transitions and from multiple scattering of the emitted photoelectrons, then
total fluorescence yield (TFY) and total electron yield (TEY) are collected (Leinweber et al.,
29
2007). Different N or C species have different binding energy, and generate unique spectral
features for ammonia N, nitro N, amino acids, peptides or aromatic-heterogeneities and aromatic
C, phenolic-C, aliphatic-C, carboxylic-C, and O-alkyl-C (Solomon et al., 2005; Gillespie et al.,
2009). By deconvoluting a C or N K-edge NEXAFS spectrum using a series of Gaussian curves
(G) at energy positions of known transitions, along with a step function at the edge, qualification
and semi-quantification of different organic N and C speciation in a sample can be achieved. An
example of typical C/N K-edge NEXAFS spectra showing the main 1s-π* transitions and two σ*
transitions for bulk soils are shown in Figure 2.5 (TFY mode). The spectra showed multiple
peaks of K-edge NEXAFS region for C (284 – 290 eV) and N (400 – 410 eV). By referring to
standard substances or published data, the peak resonances were correlated to different
functional groups. Table 2.2 shows the general approximate transition energy ranges and peak
assignments for primary adsorption peaks (Vairavamurthy and Wang, 2002; Jokic et al., 2004;
Lehmann et al., 2005; Kinyangi et al., 2006b; Leinweber et al., 2007; Wan et al., 2007; Gillespie
et al., 2009; Gillespie et al., 2011; Heymann et al., 2011; Kleber et al., 2011; Kiersch et al., 2012).
So far, NEXAFS technique has been increasingly used in soil samples, such as extracted humic
substances (Scheinost et al., 2001; Solomon et al., 2005), black C (Liang et al., 2006; Liang et al.,
2008; Heymann et al., 2011), soil colloids (Rothe et al., 2000; Schumacher et al., 2005), soil
organo-mineral microaggregates (Kinyangi et al., 2006a; Wan et al., 2007), microbial residues
(Liang et al., 2006; Keiluweit et al., 2012), and other environmental samples (Brandes et al.,
2004; Braun, 2005; Schumacher et al., 2006), to investigate the elemental distribution, speciation
or spatial heterogeneities.
30
Figure 2. 5. Typical C (above) and N (below) K-edge NEXAFS spectrum (TFY) deconvolution
from the mineral-organic fraction of a soil sample. Typical C (above) and N (below) K-edge
NEXAFS spectrum (TFY) deconvolution from the mineral-organic fraction of a soil sample.
31
Table 2. 2. C/N 1s NEXAFS approximate fit energy position of primary peaks
Functional groups Transition Fit position Deconvolution curve
C Form
Quinone type-C 1s-π* 283-284.5 G1
Aromatic-C 1s-π* 284.9-285.5 G2
Phenolic-C 1s-π* 286.0-287.2 G3
Aliphatic-C 1s-3p/σ* 287.3-287.6 G4
Carboxylic-C
1s-π* 288.5-288.8 G5
O-alkyl-C 1s-π* 289.2-290.0 G6
N Form
Pyridines/pyrazines/imines
1s- π* 398.7 G1
Pyrazoles/nitriles 1s- π* 399.9 G2
Amide (protein)
1s- π* 401.3 G3
Pyrrollic
1s- π* 402.5 G4
Nitroaromatic
1s- π* 403.7 G5
Nitrate 1s- π* 405.8 G6
Alkyl N 1s-σ* 406.2 G7
Unsaturated/heterocycles 1s-σ* 407.4 G8
2.3. Summary
As reviewed, although there have been attempts of characterizing soil amino acids and
proteins and peptides, those studies have focused on a limited number of ecosystems.
Information on the dynamics of amino acids and proteins/peptides, an important form of soluble
organic N, across a wider range of ecosystems is still lacking. In addition, little is known about
the factors affecting the transformation of proteins/peptides into FAAs and the molecular level
interactions between organic N and mineral surfaces. In our research, FAAs and HAAs in soils
of North-South and West-East transects of continental United States and the relationship between
their composition with environmental factors were investigated (Objective 1). For Objective 2,
i.e. to evaluate the organic C speciation in bulk soils of various ecosystems, different soil organic
C moieties were studied by NEXAFS spectroscopy to test the hypothesis that organic forms of C
have an overall uniform composition among the wide range of ecosystems investigated. To
understand molecular level surface organization of small peptides on mineral surfaces (Objective
32
3), hexa-glycine was organized on montmorillonite to test the hypothesis that oligopeptides
sorbed to mineral surfaces tend to form a well-extended structure with an angle with respect to
the mineral surfaces.
Synchrotron-based techniques, characterized by their high sensitivity, in situ
investigation at the molecular level, and spatial resolution, have revolutionized our way we
approach the investigation of soil organic matter (Lombi and Susini, 2009). Besides to record the
element speciation, the techniques will be employed to reveal nano-scale complexity and
conduct multi-elemental mapping of soil organic matter.
33
2.4. References
Amelung, W., Zhang, X., Flach, K.W., 2006. Amino acids in grassland soils: Climatic effects on
concentrations and chirality. Geoderma 130, 207-217.
Aufdenkampe, A.K., Hedges, J.I., Richey, J.E., Krusche, A.V., Llerena, C.A., 2001. Sorptive
fractionation of dissolved organic nitrogen and amino acids onto fine sediments within
the amazon basin. Limnology and Oceanography 46, 1921-1935.
Berthrong, S.T., Finzi, A.C., 2006. Amino acid cycling in three cold-temperate forests of the
northeastern USA. Soil Biology & Biochemistry 38, 861-869.
Brandes, J.A., Lee, C., Wakeham, S., Peterson, M., Jacobsen, C., Wirick, S., Cody, G., 2004.
Examining marine particulate organic matter at sub-micron scales using scanning
transmission x-ray microscopy and carbon x-ray absorption near edge structure
spectroscopy. Marine Chemistry 92, 107-121.
Braun, A., 2005. Carbon speciation in airborne particulate matter with c (1s) nexafs spectroscopy.
Journal of Environmental Monitoring 7, 1059-1065.
Campbell, C.A., 1991. Thirty-year crop rotations and management practices effects on soil and
amino nitrogen. Soil Science Society of America journal 55, 739.
Cao, L., Zhang, W., Han, Y., Chen, T., Zheng, Z., Wan, L., Xu, F., Ibrahim, K., Qian, H., Wang,
J., 2011. Angular dependent nexafs study of the molecular orientation of ptcda
multilayers on au (111) surface. Chinese Science Bulletin 56, 3575-3577.
Chen, C.R., Xu, Z.H., 2006. On the nature and ecological functions of soil soluble organic
nitrogen (son) in forest ecosystems. Journal of Soils and Sediments 6, 63-66.
Dashman, T., Stotzky, G., 1982. Adsorption and binding of amino-acids on homoionic
montmorillonite and kaolinite. Soil Biology & Biochemistry 14, 447-456.
34
Dashman, T., Stotzky, G., 1984. Adsorption and binding of peptides on homoionic
montmorillonite and kaolinite. Soil Biology & Biochemistry 16, 51-55.
Dhez, O., Ade, H., Urquhart, S.G., 2003. Calibrated nexafs spectra of some common polymers.
Journal of Electron Spectroscopy and Related Phenomena 128, 85-96.
Fierer, N., Schimel, J.P., Cates, R.G., Zou, J.P., 2001. Influence of balsam poplar tannin fractions
on carbon and nitrogen dynamics in alaskan taiga floodplain soils. Soil Biology &
Biochemistry 33, 1827-1839.
Fraser, F.C., Hallett, P.D., Wookey, P.A., Hartley, I.P., Hopkins, D.W., 2013. How do enzymes
catalysing soil nitrogen transformations respond to changing temperatures? Biology and
Fertility of Soils 49, 99-103.
Friedel, J.K., Scheller, E., 2002. Composition of hydrolysable amino acids in soil organic matter
and soil microbial biomass. Soil Biology and Biochemistry 34, 315-325.
Ge, T.D., Nie, S., Huang, D.F., Xiao, H.A., Jones, D.L., Iwasaki, K., 2010. Assessing soluble
organic nitrogen pools in horticultural soils: A case study in the suburbs of shanghai
(china). Acta Agriculturae Scandinavica Section B-Soil and Plant Science 60, 529-538.
Gillespie, A., Walley, F., Farrell, R., Leinweber, P., Eckhardt, K.-U., Regier, T., Blyth, R., 2011.
Xanes and pyrolysis-fims evidence of organic matter composition in a hummocky
landscape. Soil Science Society of America journal 75, 1741-1755.
Gillespie, A.W., Walley, F.L., Farrell, R.E., Leinweber, P., Schlichting, A., Eckhardt, K.-U.,
Regier, T.Z., Blyth, R.I.R., 2009. Profiling rhizosphere chemistry: Evidence from carbon
and nitrogen k-edge xanes and pyrolysis-fims. Soil Sci. Soc. Am. J. 73, 2002-2012.
35
Gotoh, S., Araragi, M., Koga, H., Ono, S.I., 1986b. Hydrolyzable organic forms of nitrogen in
some rice soil profiles as affected by organic-matter application. Soil Science and Plant
Nutrition 32, 535-550.
Greenland, D.J., Laby, R.H., Quirk, J.P., 1962. Adsorption of glycine and its di-, tri-, and tetra-
peptides by montmorillonite. Transactions of the Faraday Society 58, 829-841.
Henry, H.A.L., Jefferies, R.L., 2003. Plant amino acid uptake, soluble n turnover and microbial n
capture in soils of a grazed arctic salt marsh. Journal of Ecology 91, 627-636.
Heymann, K., Lehmann, J., Solomon, D., Schmidt, M.W.I., Regier, T., 2011. C 1s k-edge near
edge x-ray absorption fine structure (nexafs) spectroscopy for characterizing functional
group chemistry of black carbon. Organic Geochemistry 42, 1055-1064.
Iucci, G., Battocchio, C., Dettin, M., Gambaretto, R., Polzonetti, G., 2008. A nexafs and xps
study of the adsorption of self‐assembling peptides on tio2: The influence of the side
chains. Surface and Interface Analysis 40, 210-214.
Jämtgård, S., 2010. <the occurrence of amino acids in agricultural soil and their uptake by
plants.Pdf>. Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta
Universitatis agriculturae Sueciae,1652-6880.
Jansson, S., Persson, J., Stevenson, F., Bremmer, J., Hauck, R., Keeney, D., 1982. Nitrogen in
agricultural soils. Nitrogen in agricultural soils 22.
Johnson, R.M., Pregitzer, K.S., 2007. Concentration of sugars, phenolic acids, and amino acids
in forest soils exposed to elevated atmospheric co2 and o-3. Soil Biology & Biochemistry
39, 3159-3166.
Jokic, A., Cutler, J., Anderson, D., Walley, F., 2004. Detection of heterocyclic n compounds in
whole soils using n-xanes spectroscopy. Canadian Journal of Soil Science 84, 291-293.
36
Jones, D., Farrar, J., Newsham, K., 2005. Rapid amino acid cycling in arctic and antarctic soils.
Water, Air, & Soil Pollution: Focus 4, 169-175.
Jones, D.L., Hodge, A., 1999. Biodegradation kinetics and sorption reactions of three differently
charged amino acids in soil and their effects on plant organic nitrogen availability. Soil
Biology & Biochemistry 31, 1331-1342.
Jones, D.L., Kielland, K., 2002. Soil amino acid turnover dominates the nitrogen flux in
permafrost-dominated taiga forest soils. Soil Biology & Biochemistry 34, 209-219.
Jones, D.L., Shannon, D., V. Murphy, D., Farrar, J., 2004. Role of dissolved organic nitrogen
(don) in soil n cycling in grassland soils. Soil Biology and Biochemistry 36, 749-756.
Kalra, S., Pant, C.K., Pathak, H.D., Mehata, M.S., 2003. Studies on the adsorption of peptides of
glycine/alanine on montmorillonite clay with or without co-ordinated divalent cations.
Colloids and Surfaces A: Physicochemical and Engineering Aspects 212, 43-50.
Keiluweit, M., Bougoure, J.J., Zeglin, L.H., Myrold, D.D., Weber, P.K., Pett-Ridge, J., Kleber,
M., Nico, P.S., 2012. Nano-scale investigation of the association of microbial nitrogen
residues with iron (hydr)oxides in a forest soil o-horizon. Geochimica Et Cosmochimica
Acta 95, 213-226.
Kieft, T.L., soroker, E., firestone, M.K., 1987. Microbial biomass response to a rapid increase in
water potential when dry soil is wetted. Soil Biology and Biochemistry 19, 119-126.
Kielland, K., McFarland, J.W., Ruess, R.W., Olson, K., 2007. Rapid cycling of organic nitrogen
in taiga forest ecosystems. Ecosystems. 10, 360-368.
Kiersch, K., Kruse, J., Regier, T.Z., Leinweber, P., 2012. Temperature resolved alteration of soil
organic matter composition during laboratory heating as revealed by c and n xanes
spectroscopy and py-fims. Thermochimica Acta 537, 36-43.
37
Kinyangi, J., Solomon, D., Liang, B., Lerotic, M., Wirick, S., Lehmann, J., 2006a. Nanoscale
biogeocomplexity of the organomineral assemblage in soil. Soil Sci. Soc. Am. J. 70,
1708-1718.
Kinyangi, J., Solomon, D., Liang, B.I., Lerotic, M., Wirick, S., Lehmann, J., 2006b. Nanoscale
biogeocomplexity of the organomineral assemblage in soil: Application of stxm
microscopy and c 1s-nexafs spectroscopy. Soil Science Society of America journal 70,
1708-1718.
Kleber, M., Nico, P.S., Plante, A., Filley, T., Kramer, M., Swanston, C., Sollins, P., 2011. Old
and stable soil organic matter is not necessarily chemically recalcitrant: Implications for
modeling concepts and temperature sensitivity. Global Change Biology 17, 1097-1107.
Kleber, M., Sollins, P., Sutton, R., 2007. A conceptual model of organo-mineral interactions in
soils: Self-assembly of organic molecular fragments into zonal structures on mineral
surfaces. Biogeochemistry 85, 9-24.
Knicker, H., 2011. Soil organic n - an under-rated player for c sequestration in soils? Soil
Biology & Biochemistry 43, 1118-1129.
Kraus, T.E.C., Zasoski, R.J., Dahlgren, R.A., Horwath, W.R., Preston, C.M., 2004. Carbon and
nitrogen dynamics in a forest soil amended with purified tannins from different plant
species. Soil Biology & Biochemistry 36, 309-321.
Ladd, J.N., Brisbane, P.G., 1967. Release of amino acids from soil humic acids by proteolytic
enzymes. Australian Journal of Soil Research 5, 161-171.
Lehmann, J., Liang, B., Solomon, D., Lerotic, M., Luizão, F., Kinyangi, J., Schäfer, T., Wirick,
S., Jacobsen, C., 2005. Near‐edge x‐ray absorption fine structure (nexafs)
38
spectroscopy for mapping nano‐scale distribution of organic carbon forms in soil:
Application to black carbon particles. Global Biogeochemical Cycles 19.
Leinweber, P., Kruse, J., Walley, F.L., Gillespie, A., Eckhardt, K.-U., Blyth, R.I.R., Regier, T.,
2007. Nitrogen k-edge xanes - an overview of reference compounds used to identify
'unknown' organic nitrogen in environmental samples. Journal of Synchrotron Radiation
14, 500-511.
Leinweber, P., Schulten, H.R., 1998. Nonhydrolyzable organic nitrogen in soil size separates
from long-term agricultural experiments. Soil Science Society of America journal 62,
383-393.
Leinweber, P., Schulten, H.R., 2000. Nonhydrolyzable forms of soil organic nitrogen:
Extractability and composition. Journal of Plant Nutrition and Soil Science-Zeitschrift
Fur Pflanzenernahrung Und Bodenkunde 163, 433-439.
Leirós, M.C., Trasar-Cepeda, C., Seoane, S., Gil-Sotres, F., 2000. Biochemical properties of acid
soils under climax vegetation (atlantic oakwood) in an area of the european temperate–
humid zone (galicia, nw spain): General parameters. Soil Biology and Biochemistry 32,
733-745.
Liang, B., Lehmann, J., Solomon, D., Kinyangi, J., Grossman, J., O'Neill, B., Skjemstad, J.O.,
Thies, J., Luizao, F.J., Petersen, J., Neves, E.G., 2006. Black carbon increases cation
exchange capacity in soils. Soil Science Society of America journal 70, 1719-1730.
Liang, B., Lehmann, J., Solomon, D., Sohi, S., Thies, J.E., Skjemstad, J.O., Luizao, F.J.,
Engelhard, M.H., Neves, E.G., Wirick, S., 2008. Stability of biomass-derived black
carbon in soils. Geochimica Et Cosmochimica Acta 72, 6069-6078.
39
Lipson, D., Nasholm, T., 2001. The unexpected versatility of plants: Organic nitrogen use and
availability in terrestrial ecosystems. Oecologia 128, 305-316.
Lipson, D.A., Monson, R.K., 1998. Plant-microbe competition for soil amino acids in the alpine
tundra: Effects of freeze-thaw and dry-rewet events. Oecologia 113, 406-414.
Lipson, D.A., Schmidt, S.K., Monson, R.K., 1999b. Links between microbial population
dynamics and nitrogen availability in an alpine ecosystem. Ecology 80, 1623-1631.
Liu, X., Jang, C.-H., Zheng, F., Jurgensen, A., Denlinger, J.D., Dickson, K.A., Raines, R.T.,
Abbott, N.L., Himpsel, F.J., 2006. Characterization of protein immobilization at silver
surfaces by near edge x-ray absorption fine structure spectroscopy. Langmuir 22, 7719-
7725.
Lombi, E., Susini, J., 2009. Synchrotron-based techniques for plant and soil science:
Opportunities, challenges and future perspectives. Plant and Soil 320, 1-35.
Matrajt, G., Blanot, D., 2004. Properties of synthetic ferrihydrite as an amino acid adsorbent and
a promoter of peptide bond formation. Amino Acids 26, 153-158.
Mikutta, R., Kaiser, K., Doerr, N., Vollmer, A., Chadwick, O.A., Chorover, J., Kramer, M.G.,
Guggenberger, G., 2010. Mineralogical impact on organic nitrogen across a long-term
soil chronosequence (0.3-4100 kyr). Geochimica Et Cosmochimica Acta 74, 2142-2164.
Mikutta, R., Kleber, M., Torn, M.S., Jahn, R., 2006. Stabilization of soil organic matter:
Association with minerals or chemical recalcitrance? Biogeochemistry 77, 25-56.
Miltner, A., Kindler, R., Knicker, H., Richnow, H.-H., Kästner, M., 2009. Fate of microbial
biomass-derived amino acids in soil and their contribution to soil organic matter. Organic
Geochemistry 40, 978-985.
40
Murphy, E.M., Zachara, J.M., Smith, S.C., 1990. Influence of mineral-bound humic substances
on the sorption of hydrophobic organic-compounds. Environmental Science &
Technology 24, 1507-1516.
Neff, J.C., Chapin, F.S., Vitousek, P.M., 2003. Breaks in the cycle: Dissolved organic nitrogen in
terrestrial ecosystems. Frontiers in Ecology and the Environment 1, 205-211.
Olk, D., 2007. Organic forms of nitrogen. Soil sampling and method of analysis, ed. MR Carter
and EG Gregorich, 667-674.
Omoike, A., Chorover, J., 2006. Adsorption to goethite of extracellular polymeric substances
from bacillus subtilis. Geochimica Et Cosmochimica Acta 70, 827-838.
Paul, J.P., Williams, B.L., 2005. Contribution of alpha-amino n to extractable organic nitrogen
(don) in three soil types from the scottish uplands. Soil Biology & Biochemistry 37, 801-
803.
Perakis, S.S., Hedin, L.O., 2002. Nitrogen loss from unpolluted south american forests mainly
via dissolved organic compounds. Nature 415, 416-419.
Persson, J., Näsholm, T., 2001. Amino acid uptake: A widespread ability among boreal forest
plants. Ecology Letters 4, 434-438.
Peters, R.D., Nealey, P.F., Crain, J.N., Himpsel, F.J., 2002. A near edge x-ray absorption fine
structure spectroscopy investigation of the structure of self-assembled films of
octadecyltrichlorosilane. Langmuir 18, 1250-1256.
Praveen, K., Tripathi, K.P., Aggarwal, R.K., 2002b. Influence of crops, crop residues and
manure on amino acid and amino sugar fractions of organic nitrogen in soil. Biology and
Fertility of Soils 35, 210-213.
41
Rentsch, D., Schmidt, S., Tegeder, M., 2007. Transporters for uptake and allocation of organic
nitrogen compounds in plants. FEBS Letters 581, 2281-2289.
Rillig, M., Caldwell, B., Wösten, H.B., Sollins, P., 2007. Role of proteins in soil carbon and
nitrogen storage: Controls on persistence. Biogeochemistry 85, 25-44.
Rothe, J., Denecke, M.A., Dardenne, K., 2000. Soft x-ray spectromicroscopy investigation of the
interaction of aquatic humic acid and clay colloids. Journal of Colloid and Interface
Science 231, 91-97.
Rothstein, D.E., 2009b. Soil amino-acid availability across a temperate-forest fertility gradient.
Biogeochemistry 92, 201-215.
Samuel, N.T., Lee, C.-Y., Gamble, L.J., Fischer, D.A., Castner, D.G., 2006. Nexafs
characterization of DNA components and molecular-orientation of surface-bound DNA
oligomers. Journal of Electron Spectroscopy and Related Phenomena 152, 134-142.
Scheinost, A.C., Kretzschmar, R., Christl, I., Jacobsen, C., 2001. Carbon group chemistry of
humic and fulvic acid: A comparison of c-1s nexafs and c-13-nmr spectroscopies. Humic
Substances: Structures, Models and Functions, 39-47.
Schimel, J.P., Bennett, J., 2004. Nitrogen mineralization: Challenges of a changing paradigm.
Ecology 85, 591-602.
Schimel, J.P., Chapin, F.S., 1996. Tundra plant uptake of amino acid and nh4+ nitrogen in situ:
Plants compete well for amino acid n. Ecology 77, 2142-2147.
Schnitzer, M., Ivarson, K.C., 1982. Different forms of nitrogen in particle size fractions
separated from two soils. Plant and Soil 69, 383-389.
Schumacher, M., Christl, I., Scheinost, A.C., Jacobsen, C., Kretzschmar, R., 2005. Chemical
heterogeneity of organic soil colloids investigated by scanning transmission x-ray
42
microscopy and c-1s nexafs microspectroscopy. Environmental Science & Technology
39, 9094-9100.
Schumacher, M., Christl, I., Vogt, R.D., Barmettler, K., Jacobsen, C., Kretzschmar, R., 2006.
Chemical composition of aquatic dissolved organic matter in five boreal forest
catchments sampled in spring and fall seasons. Biogeochemistry 80, 263-275.
Seifert, S., Gavrila, G.N., Zahn, D.R.T., Braun, W., 2007. The molecular orientation of DNA
bases on h-passivated si(1 1 1) surfaces investigated by means of near edge
x-ray absorption fine structure spectroscopy. Surface Science 601, 2291-2296.
Senwo, Z.N., Tabatabai, M.A., 1998. Amino acid composition of soil organic matter. Biology
and Fertility of Soils 26, 235-242.
Skogland, T., Lomeland, S., Goksøyr, J., 1988. Respiratory burst after freezing and thawing of
soil: Experiments with soil bacteria. Soil Biology and Biochemistry 20, 851-856.
Sollins, P., Swanston, C., Kleber, M., Filley, T., Kramer, M., Crow, S., Caldwell, B.A., Lajtha,
K., Bowden, R., 2006. Organic c and n stabilization in a forest soil: Evidence from
sequential density fractionation. Soil Biology and Biochemistry 38, 3313-3324.
Solomon, D., Lehmann, J., Kinyangi, J., Liang, B.Q., Schafer, T., 2005. Carbon k-edge nexafs
and ftir-atr spectroscopic investigation of organic carbon speciation in soils. Soil Science
Society of America journal 69, 107-119.
Solomon, D., Lehmann, J., Wang, J., Kinyangi, J., Heymann, K., Lu, Y., Wirick, S., Jacobsen, C.,
2012b. Micro-and nano-environments of c sequestration in soil: A multi-elemental stxm–
nexafs assessment of black c and organomineral associations. Science of The Total
Environment 438, 372-388.
43
Sowden, F.J., Chen, Y., Schnitzer, M., 1977. Nitrogen distribution in soils formed under widely
differing climatic conditions. Geochimica Et Cosmochimica Acta 41, 1524-1526.
Stevenson, F.J., 1956. Effect of some long-time rotations on the amino acid composition of the
soil1. Soil Science Society of America journal 20, 204.
Stevenson, F.J., 1994. Humus chemistry. 2nd. Edn, j wiley, new york 496.
Stöhr, J., 1992. Nexafs spectroscopy, vol. 25 of springer series in surface sciences. Springer,
Heidelberg.
Sverjensky, D.A., Jonsson, C.M., Jonsson, C.L., Cleaves, H.J., Hazen, R.M., 2008. Glutamate
surface speciation on amorphous titanium dioxide and hydrous ferric oxide.
Environmental Science & Technology 42, 6034-6039.
Theodore K. Raab, D.A.L.a.R.K.M., 1999. Soil amino acid utilization among species of the
cyperaceae: Plant and soil processes. Ecology, Ecological Society of America 80, 2408-
2419.
Vairavamurthy, A., Wang, S., 2002. Organic nitrogen in geomacromolecules: Insights on
speciation and transformation with k-edge xanes spectroscopy. Environmental Science &
Technology 36, 3050-3056.
Vranova, V., Rejsek, K., Formanek, P., 2013. Proteolytic activity in soil: A review. Applied Soil
Ecology 70, 23-32.
Wan, J., Tyliszczak, T., Tokunaga, T.K., 2007. Organic carbon distribution, speciation, and
elemental correlations within soil microaggregates: Applications of stxm and nexafs
spectroscopy. Geochimica Et Cosmochimica Acta 71, 5439-5449.
Weintraub, M.N., Schimel, J.P., 2005b. Seasonal protein dynamics in alaskan arctic tundra soils.
Soil Biology & Biochemistry 37, 1469-1475.
44
Werdin-Pfisterer, N.R., Kielland, K., Boone, R.D., 2009. Soil amino acid composition across a
boreal forest successional sequence. Soil Biology & Biochemistry 41, 1210-1220.
Wershaw, R.L., Llaguno, E.C., Leenheer, J.A., 1996. Mechanism of formation of humus
coatings on mineral surfaces 3. Composition of adsorbed organic acids from compost
leachate on alumina by solid-state 13c nmr. Colloids and Surfaces A: Physicochemical
and Engineering Aspects 108, 213-223.
Xing, S.H., Chen, C.R., Zhou, B.Q., Zhang, H., Nang, Z.M., Xu, Z.H., 2010. Soil soluble organic
nitrogen and active microbial characteristics under adjacent coniferous and broadleaf
plantation forests. Journal of Soils and Sediments 10, 748-757.
Yang, K., Zhu, J., Yan, Q., Zhang, J., 2012. Soil enzyme activities as potential indicators of
soluble organic nitrogen pools in forest ecosystems of northeast china. Annals of Forest
Science 69, 795-803.
Yu, Z., Zhang, Q., Kraus, T.E.C., Dahlgren, R.A., Anastasio, C., Zasoski, R.J., 2002.
Contribution of amino compounds to dissolved organic nitrogen in forest soils.
Biogeochemistry 61, 173-198.
45
3. Immediately Bioavailable Free Amino Acids in Soils of North-
South and West-East Transects of Continental United States
L. Maa, K. Xia
a*, M. A. Williams
b, and D. B. Smith
c
aDepartment of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State
University, Blacksburg, VA 24061, USA
bRhizosphere and Soil Microbial Ecology Laboratory, Department of Horticulture & Molecular
Plant Sciences, Virginia Tech, VA 24061, USA
cUS Geological Survey, MS 973, Denver, CO 80225, USA
*Corresponding author. Tel.: 540-231-9323; Email address: [email protected]
46
3.1. Abstract
Free amino acids are those dissolved in soil solution or weakly bound to charged soil
surfaces and are extractable using 0.01M KCl which mimic the ionic strength of a typical soil
solution. The extractable free amino acids are considered to be immediately bioavailable to plant
and organisms. In spite of their low fraction (less than 5%) in the soil soluble organic nitrogen
pool, free amino acids play a vital role in plant nutrition and nitrogen fluxes in terrestrial
ecosystems. Research has been conducted to characterize free amino acids in a limited number of
surface soils, it is unknown if those findings apply to a wide range of ecosystems. In addition,
little is understood about the status of subsurface amino acids. The primary objective of this
study was to assess the levels and composition of free amino acids in A and C horizons of a large
number of soils from 149 sites along north-south temperature and west-east precipitation
transects of continental United States. The soil samples were a subset of the samples collected
from 2007 to 2010 for the USGS Geochemical Landscapes Project which assessed the
abundance and spatial distribution of chemical elements and minerals in soils of 4,857 sites (1
site/1,600 km2) of the conterminous United State. Free amino acids in soil samples were
extracted using 0.01M KCl, derivatized, and analyzed on a high performance liquid
chromatography equipped with a fluorescence detector. A total of 24 amino acids were extracted
and quantified. The results showed significant variations for the levels of total free amino acids
among soils from different sites. The concentrations of total free amino acids in the A-horizon
were several to dozens times higher than in the C-horizon soils, ranging from 0.74 to 273 mg kg-
1 soil (dry weight basis) in the A horizon and from 0.12 to 22 mg kg
-1 soil (dry weight basis) in
the C horizon. The concentrations of individual free amino acid were also significantly higher in
the A-horizon than in the C-horizon soils (p<0.0001). Although the major free amino acids in
47
both soil horizons were glutamic acid, glutamine, aspartic acid, leucine, alanine, threonine,
glycine and valine, the mole percent composition of free amino acids was significantly different
between the two horizons, suggesting different physicochemical processes affecting the
dynamics of amino acids along soil depth. For both soil horizons, significant variations were
observed for the levels or composition of soil free amino acids along the mean annual
temperature, mean annual precipitation, and vegetation gradients of continental United States,
suggesting that environmental factors might play an important role in affecting organic nitrogen
dynamics.
48
3.2. Introduction
Amino acids (in the form of peptide/protein) play a critical role in the terrestrial nitrogen
(N) cycling due to their considerable proportion amongst the organic nitrogen pool to serve as
potential N source to organisms and plants. Free amino acids (FAAs) are those dissolved in soil
solution or weakly bound to charged soil surfaces, which are easily consumed by plants and
organisms. In soluble organic N (SON) pool, FAAs take the smallest fraction, normally less than
5% (Jones et al., 2005), but they are immediately bioavailable to plants particularly when the soil
inorganic N is insufficient for plant uptake (Jones et al., 2005). Numerous studies confirmed the
fact that plants can consume amino acids through roots via transporters or even without any
assistance from other organisms such as mycorrhizas (Kielland, 1994; Lipson and Nasholm,
2001; Rentsch et al., 2007; Jämtgård et al., 2008; Paungfoo-Lonhienne et al., 2008). The
dynamics and cycling of FAAs have been extensively studied in a limited number of agricultural
soils (Brzostek et al., 2012), tundra soils (Weintraub and Schimel, 2005a), forest soils
(McFarland et al., 2002; Berthrong and Finzi, 2006), and grassland soils (Warren and Taranto,
2010). The levels of FAAs were different in different soil systems (Rothstein, 2009a). They are
subjective to seasonal changes (Weintraub and Schimel, 2005a; Johnson and Pregitzer, 2007;
Warren and Taranto, 2010), successional sequence (Werdin-Pfisterer et al., 2009) and fertility
gradient (Rothstein, 2009a), and variations of soil properties (Rothstein, 2010). The
depolymerization of proteinaceous compounds by extracellular enzymes is the key process that
affecting levels of FAAs in soils (Jones and Kielland, 2002; Schimel and Bennett, 2004). The
levels of soil protein/peptides thus influence the concentrations and composition of soil FAAs.
Fine root turnover (Kielland et al., 2007), root secretion (Dakora and Phillips, 2002), and
bacterial cell lysis under adverse environment (Lipson and Monson, 1998) can enhance the
49
concentrations of soluble proteins, which can be used as substrates for microbial utilization and
in turn influence the production rate of FAAs in soils, resulting in their significant variation
during seasonal change, cultivation or sequence succession (Lipson et al., 1999b; Hertenberger et
al., 2002; Weintraub and Schimel, 2005a; Werdin-Pfisterer et al., 2009). Amino acid uptake by
plants and sorption to soil minerals could decrease their concentrations in soil solution (Lipson
and Nasholm, 2001; Weintraub and Schimel, 2005a; Rothstein, 2010). Therefore, soil properties
and vegetation may regulate the amino acid availability to plants and microorganisms to some
extent. Free amino acids can be produced by the proteolytic enzymes via peptide bond cleavage
of proteins and peptides, while microbial consumption/mineralization or plant uptake can deplete
them from the bioavailable pool. The levels and relative composition of soil FAAs are therefore
a constant balance between the productive and consumptive processes in soils (Rothstein, 2009a).
Information of soil FAAs is able to provide a snapshot of this balance.
Compared to studies on levels of FAAs (Yu et al., 2002; Weintraub and Schimel, 2005a;
Amelung et al., 2006; Berthrong and Finzi, 2006; Formanek et al., 2008), there have been fewer
investigations on the composition of FAAs. In spite of the large variations in levels of FAAs, a
relatively constant composition of FAAs was reported across a boreal forest successional
sequence(Werdin-Pfisterer et al., 2009). It is unknown, however, whether this situation is
applicable to the FAAs across various ecosystems. It is also unclear whether there is any trend of
FAA abundance and composition with soil depth and along a temperature and a precipitation
gradient. So far, no such information of FAAs has been reported in soils of a wide land use.
In this part of research, the levels and composition of FAAs in the soils of A horizon and
C horizons along the north-south (N-S) and west-east (W-E) transects of continental United
States were investigated. The following hypotheses were tested: 1) the A-horizon soil FAA
50
levels and profiles are significantly different from those of C horizon; 2) soil FAAs vary
significantly among different vegetation systems; 3) soil FAAs are affected by the mean annual
temperature (MAT) and precipitation (MAP) gradients at the continental scale. To the best of our
knowledge, this work reveals, for the first time, the status of FAAs in soils of a wide range of
ecosystems in the United States.
3.3. Materials and methods
3.3.1. Study sites and soil sampling
Soil samples of A and C horizons from 149 sites along N-S and W-E transects of
continental United States (Figure 3.1) were a subset of samples collected from a total of 4871
sites (1 site/1600 km2) by the USGS from 2007 to 2010 for the USGS Geochemical Landscapes
Project. Detailed sampling protocols were described elsewhere (Smith et al., 2013). Briefly,
visible plant materials were removed from each collected soil, sieved through 2-mm sieve, air
dried, and stored in glass jars at 4 oC until further analysis. Mineralogical and chemical data on
all the soils were published by the USGS (Smith et al., 2013).
Figure 3. 1. Location of soil sampling sites from west to east and north to south transects on
gradients of (above) MAP, and (below) MAT. Sites were grouped into sub-continental areas as
shown in circles. The legends at the right of each sub-figure apply to the circles.
40-60 in20-40 in4-20 in40-60 in
East-West Transect
Precipitation:
35-50oF50-55oF55-60oF
North-South Transect
Temperature:
51
3.3.2. Chemicals
Waters AccQ·FluorTM
Reagent Kit was purchased from Waters (Milford, MA, USA).
The Kit included Waters AccQ·Fluor derivative powder, 6-aminoquinolyl-N-
hydroxysuccinimidyl carbamate (AQC), Waters AccQ·Fluor dilution solution, and 0.2 M borate
buffer (pH 8.8). The AccQ·Fluor (powder) was sealed with paraffin wax film (Pechiney,
Menasha, WI, USA) and kept in a desiccator. Unopened AccQ·Fluor Reagent kit may be stored
at room temperature for one year. Once opened, the kit is better to be used within six months.
Ultrapure water (18MΩ) was produced with Millipore Q water systems from Millipore Corp
(Bedford, MA, U.S.A.). The ACS reagent grade sodium acetate (CH3COONa), sodium EDTA
(EDTANa2·2H2O), sodium azide (NaN3), hydrochloric acid (HCl, 37%), phosphoric acid (H3PO4,
85%) were purchased from Sigma (St. Louis, MO, U.S.A). The HPLC grade acetonitrile (ACN),
triethylamine (TEA) were purchased from Fisher Scientific (New Jersey, USA). Individual
amino acid standard including alanine (Ala), arginine (Arg), aspartic acid (Asp), glutamic acid
(Glu), glycine (Gly), histidine (His), isoleucine (Ile), leucine (Leu), lysine (Lys), methionine
(Met), phenylalanine (Phe), proline (Pro), serine (Ser), threonine (Thr), tyrosine (Tyr), and valine
(Val), cystine (Cys–Cys), asparagine (Asn), glutamine (Gln), tryptophan (Trp), ϒ-aminobytyric
acid (GABA), taurine (Tau), citrulline (Cit), ornithine HCl (Orn) with purity >97% were
purchased from Sigma. Alpha-amino butyric acid (AABA) purchased from Sigma was used as
internal standard for the amino acids analysis.
Each amino acid stock solution and internal standard was prepared at 25mM or 2.5mM
by dissolving the target amino acid in 0.1M HCl. The 2.5mM and 25mM stock solutions were
stored at -20 and can be used for up to 3 and 6 months, respectively. An intermediate
composite standard was prepared by combining appropriate amount of individual amino acid
52
stock solution to achieve a final concentration of 0.25mM for each amino acid. The 0.25mM
intermediate composite standard was then mixed with appropriate amount of AABA in various
amounts of ultrapure water to yield mixed amino acid calibration standards ranging from 0.005
to 0.1mM for each of the 24 amino acids and 0.1mM for AABA. The calibration standards can
be stored at -20 and reused within one month.
3.3.3. Free amino acid extraction from soil
The extractant of FAAs should be mild enough without lysing the soil microbial cells and
should prevent protein hydrolysis or enrichment of any other form of amino acid during the soil
extraction (Lojkova et al., 2006). High-concentration salt solution, such as 1M KCl or 0.5M
ammonia acetate, can extract too much ammonia, resulting in chromatographic interferences
with FAAs. Therefore, 0.01M KCl was used in this study as extractant to mimic the soil solution.
Two grams of air dried soil was weighed into 50 mL sterile polypropylene centrifuge tube
(Fisher Scientific, Mexico). Ten mL 0.01M KCl containing 10mM NaN3 was added into the
centrifuge tube. An aliquot of 8 µL 2.5 mM internal standard was spiked to the mixture to
achieve a final concentration the same as that in the calibration solution, assuming 100%
recovery during the extraction. The mixture was shaken gently on a reciprocal shaker at room
temperature for 15min, followed by centrifugation at 3500 rpm for 15min at room temperature.
After centrifugation, the supernatant was collected and filtered through a 0.22µm polyvinylidene
fluoride (PVDF) membrane syringe filter into a new polypropylene centrifuge tube. An aliquot
of exactly 500 µL of the filtrate was pipetted into a glass vial for freeze drying. The dried residue
shall be immediately stored at -20 oC if not immediately derivatized.
3.3.4. Amino acids derivatization
53
The amino acids in the free dried soil extracts were derivatized using the Waters
AccQ·Fluor TM
Reagent Kit (AccQ, 1993). To prepare the AccQ·Fluor reagent for amino acids
derivatization, 1 mL Waters AccQ·Fluor dilution solution was transferred into the AccQ·Fluor
reagent vial containing Waters AccQ·Fluor reagent powder, tightly capped, mixed on a vortex
for 15 s, and then incubated at 55 in an oven (Model 40GC Lab Oven; Quincy Lab Inc.,
Chicago, IL, USA) for 10 min until the powder was completely dissolved. The final reconstituted
AccQ·Fluor solution was colorless and transparent and contained AccQ·Fluor reagent at about
3mg/mL (ca.10 mM). Appearance of color or precipitation indicates deactivation of the reagent
or contamination and needs to be discarded. The tightly sealed reconstituted AccQ·Fluor solution
can be stored in a desiccator at room temperature or at 4 and reused within one or two weeks,
respectively.
The freeze-dried soil extract residue was reconstituted in 10 µL 0.05M HCl, followed by
addition of 70 µL borate buffer to adjust the pH to 8 - 10 for subsequent optimum derivatization
using the AccQ·Fluor reagent. The mixture was then briefly vortexed for several seconds,
followed by addition of 20 µL reconstituted AccQ·Fluor solution. The mixture was immediately
capped with a silicon-lined septum, mixed on a vortex for 15 s to prevent 6-aminoquinolyl-N-
hydroxysuccinimidyl carbamate hydrolysis to 6-aminoquinoline, and incubated for 1 min at
room temperature. The mixture was then incubated at 55 in an oven for 10 min to complete
the amino acids derivatization before analysis on a high performance liquid chromatography
equipped with a fluorescence detector (HPLC/FLD). Ten µL of amino acids mixture standard at
each concentration was derivatized following the same procedure as that for soil extracts
described above. The derivatized samples are stable at room temperature for one week before the
HPLC analysis.
54
3.3.5. HPLC/FLD analysis of derivatized amino acids
Derivatized amino acids were analyzed using an HPLC 1260 Infinity system (Agilent
Technologies, USA) coupled with a fluorescence detector (FLD). Separation of derivatized
amino acids was carried out on a Waters X-Terra MS C18 column (2.1 mm × 150 mm, 3.5 µm
particle size, Waters Corporation, USA). The mobile phase consisted of A: a solution containing
140 mM sodium acetate, 17 mM TEA with 0.1% (g/L, w/v) EDTA-2Na (titrated to pH 5.8 with
phosphoric acid) and B: ACN/water (60:40, v/v). The mobile phase flow rate was 0.35ml/min
with gradient conditions at: 0 - 17 min, 100 - 93 % A, 17 - 21 min 93 - 90 % A, 21 - 30 min 90 -
70 % A, 30 -35 min 70% A, 35 - 36 min 70 - 0 % A, and 36-40 min 0 % A. The column was then
further re-equilibrated for 9 min at the initial gradient of 100 % A. Before beginning the gradient,
the column was equilibrated in 100% A for 30 min. After every sequence was done, the column
was washed with Eluent B at 0.2 mL/min for 1 hour. The column temperature was maintained at
45 . The injection volume was 5 µL. The fluorescence excitation and emission wavelengths
were set at 250 nm and 395 nm respectively.
Each derivatized amino acid in a sample was identified by comparing its retention time
with that of derivatized individual amino acid standard and quantified using the internal standard
method (Hou et al., 2009; Fiechter and Mayer, 2011). The detection limits, precision (relative
standard deviation %), and recoveries of this method were summarized in Table 3.1.
55
Table 3. 1. Detection limits, recovery and the precision of the determination of amino acid
derivatives.
Amino acid Detection limit
(nmol kg-1
dry soil) a
Recovery (%) b
Precision (RSD %) c
Asp 22 86.7 1.4
Glu 15 98.6 2.8
Ser+Asn 6 78.2 6.2
Gly 11 88.1 2.2
Gln 10 79.7 2.7
His 39 15.1 1.6
Arg 24 23 1.5
Tau 6 88.8 0.5
Cit 7 88.8 0.5
Thr 8 87.9 5.3
Ala 6 89.7 5.5
GABA 5 74.2 3.6
Pro 9 94.8 2.4
Tyr 9 80.9 6.6
Cys-Cys 22 70.8 9.7
Val 3 90.4 3.4
Met 6 85.1 5.8
Orn 21 20.8 2.9
Ile 3 93 6.6
Lys 36 17.7 1.9
Leu 4 89.9 6.3
Phe 4 85.6 5.9
Trp 269 62 13 a The detection limits in soil samples were estimated from the detection limit of a 0.5 µM amino
acid standard on column based on a S/N ratio of 3:1 and the recovery of amino acids in spiked
samples. b
the recovery was evaluated by spiking amino acid standard at concentration of 0.5µmol kg-1
dry
soil and then the amino acids in the spiked sample and the same soil samples without spiking
were also determined at the same time. The recover was calculated as the percentage of the
concentration differences relative to the spiked concentration. c n = 3
3.3.6. Statistical analysis
The composition was calculated as the molar percentages of each amino acid out of total.
The concentrations were measured at dry soil basis. The composition and concentration
variations of the FAAs between A and C horizons were evaluated by matched pair’s t test
56
analysis. The composition and concentration variations of FAAs along MAT and MAP gradients,
and among different vegetation covers were analyzed using multiple comparisons based on
Turkey’s honest significant difference (HSD) test, provided by the statistical software JMP 9.0
(SAS Software, Cary, NC), and nonmetric multidimensional scaling (NMS), provided in
statistical software PC-ORD ver.6 (MjM Software, OR). The Sorenson (Bray-Curtis) distance
was used to represent compositional dissimilarity. For the NMS analysis, a maximum of 250
iterations were used for 50 runs with real data, with a stability criterion of 0.00001. The
recommended dimensions with proper stress in each case were used. Amino acid concentration
(µmol kg-1
soil air dry soil basis) at 149 sites were saved as the main matrix which, after
relativization become a matrix of amino acid percent composition. The amino acids as response
variable were always quantitative in the main matrix. Amino acids Cit, Tau, Cys-Cys, Met and
Trp were eliminated from the tested samples before transformation to reduce noise due to the
fact that their levels in most of the samples were below their detection limits. Explanatory
variables, depth, vegetation, MAT and MAP, were imported into PC-ORD as the second matrix.
Pearson correlation coefficients between MAT, MAP and the ordination axes were calculated
and the significance of the correlation was tested using JMP 9. In the multidimensional cases,
only two axes that accounted for largest variance were displayed. The relationship of amino acid
composition and explanatory variables was explored using biplot, which indicated the direction
and the strength of the highly correlated explanatory variables. Multiple Response Permutation
Procedure (MRPP) were performed to examine the differences in amino acid composition
between A and C horizons.
3.4. Results and discussion
3.4.1. Composition and concentrations of extractable soil amino acids
57
Twenty-two derivatized amino acids in a sample were separated on a chromatogram
except for Ser and Asn which were co-eluted (Figure 3.2). The molar percentages of each FAA
varied site from site. Despite the site variations, the FAA pool was dominated by the following
amino acids: Asp, Glu, Gly, Gln, Thr, Ala, Val and Leu. These eight major FAAs accounted for
44 - 85% of the TFAA pool. The average molar percentages of the eight major FAAs were 5 %,
20 %, 8 %, 8 %, 16 %, 6 %, 5 % and 4 % for Asp, Glu, Gly, Thr, Ala, Leu, Val, and Gln,
respectively. Similar trend has also been observed in most of other studies (Kielland, 1995;
Nordin et al., 2001; Yu et al., 2002). These major amino acids are mostly the major constitutes of
soil proteinaceous compounds (Senwo and Tabatabai, 1998; Moura et al., 2013). In addition, the
prevalent abundance of polar amino acids compared to nonpolar ones among the eight major
amino acids coincides with the fact that polar amino acids are generally located on the surfaces
of protein structure (Nelson et al., 2008), allowing a preferential initial decomposition. Amino
acids, His and Arg were observed as minor FAA components in this study, however, they have
been reported as major FAAs in the boreal forest soils of Alaska (Werdin-Pfisterer et al., 2009,
2012), boreal forest soils of Sweden (Nordin et al., 2001), and heathland soils (Abuarghub and
Read, 1988). The vegetation such as alder transports more precursor of Arg, thus possibly
facilitates the synthesis and storage of Arg in such soils (Werdin-Pfisterer et al., 2009). The
slower diffusion of basic amino acids through soil might constrain the uptake rates by plants or
organisms relative to other amino acids (Nasholm and Persson, 2001), extractant with high ionic
strength, therefore, can extract more basic amino acids from the adsorption sites. The highly
abundant His, in the heathland soil for instance, could be released by 0.5 M NH4OAC from soil
exchangeable sites where amino acids from microbial or plant tissues not yet readily utilized
were sorbed (Abuarghub and Read, 1988). In our study, Lys, Met and traces of Trp and Cys-Cys
58
were also detected as minor FAAs, which are similar to other studies (Werdin-Pfisterer et al.,
2009). The trace levels of non-protein amino acids including GABA, Tau, Cit and Orn, which
may be from bacterial osmolytes (Lipson and Nasholm, 2001) were detected at < 3% of TFAA in
the soils investigated. The small contribution of non-protein amino acids to the TFAA pool could
corroborate the assumption that soil protenaceous substances may mainly originate from plant
materials.
Among the naturally occurring amino acids detected in the studied soils, the average
relative molar percentages followed the order of neutral > acidic > basic > aromatic amino acids,
which averaged 60 %, 24 %, 6 % and 5 % of the TFAA pool, respectively. The amino acids
interact with soil components via ligand exchange, bridges of polyvalent cations, Van Der Waals
forces or hydrophobic interactions, and ligand exchange occurs as the strongest interaction
(Sollins et al., 1996). Considering the large number of neutral amino acids and the high
proportions of acidic amino acids in this study, our results more or less supported the statement
presented by Jones and Hodge (1999) that the sorption to colloidal fraction of the soil was
greatest for positively charged amino acids, intermediate for neutral amino acids and least for
negatively charged amino acids. The low recovery of spiked basic amino acids extracted with
water and salt solution demonstrated the strong sorption of these positively charged amino acids
(Paul and Schmidt, 1960; Gilbert and Altman, 1966). Strong base such as Ba(OH)2 was shown to
be a good basic solution to increase the recovery of those basic amino acids due to its ability to
replace sorbed basic amino acids and flocculate the soils (Paul and Schmidt, 1960). Rothstein
(2010), however, found that the negatively-charged Glu was sorbed as strongly as the positively-
charged Arg. This discrepancy with abovementioned statement could be explained by the
59
considerate anion exchange capacity or the irreversible chemical adsorption of anion amino acids
to humic acids in those soils (Rosenfeld, 1979).
Figure 3. 2. Chromatograms of derivatized amino acids in (above) 10µM standard and (below) a
A-horizon soil sample from a grassland site in Minnesota. 1=Asp; 2=Glu; 3= 6-aminoquinoline;
4=Ser+Asn; 5=Gly; 6=Gln; 7=His; 8=NH4+; 9=Arg; 10=Tau; 11=Cit; 12=Thr; 13=Ala;
14=GABA; 15=Pro; 16=AABA (internal standard); 17=Tyr; 18=Cys-Cys; 19=Val; 20=Met;
21=Orn; 22=Ile; 23=Lys; 24=Leu; 25=Phe; 26=Trp.
m in15 20 25 30 35
LU
0
10
20
30
40
50
60
70
80
12
3
4
5 6
7
8
910
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
m in15 20 25 30 35
LU
0
10
20
30
40
50
1
2
3
4 5
6
89
11
12
13
14
15
16
17
19
20
21
22
23
24
25
60
The concentrations of TFAAs ranged from 0.12 to 273 mg kg-1
in soils of the 149 sites,
respectively. The wide concentration range of TFAA could be due to variations in soil type,
vegetation change, management practices and environmental conditions among sites. If an
average ratio of 1.4 mole N per mole of amino acids was used to convert concentrations of FAAs
to that of amino acid-N (Rothstein, 2009a), concentrations of total free amino acid N ranged
from 0.017 to 36.48 mg N kg-1
in all the soil investigated, a range that is comparable to the
reported concentrations from 0.438 to 4.867 mg N kg-1
dry soil for total amino acid-N extracted
using water from the top 20 cm soils across a boreal forest successional sequence (Werdin-
Pfisterer et al., 2009). The average concentrations of the eight major amino acids were 0.5 mg
kg-1
for Asp, 2 mg kg-1
for Glu, 0.3 mg kg-1
for Gly, 0.8 mg kg-1
for Gln, 0.6 mg kg-1
for Thr, 0.9
mg kg-1
for Ala, 0.4 mg kg-1
for Val and 0.5 mg kg-1
for Leu. These FAAs are mostly common
among the major amino acids mentioned (Lipson and Nasholm, 2001). The concentration of the
major amino acids in this study fall into the range reported by others (Werdin-Pfisterer et al.,
2009, 2012).
3.4.2. Extractable amino acids in A and C horizon soils
The MRPP analysis illustrated in Figure 3.3 suggested significant differences in amino
acid composition between A and C horizons (agreement statistic [A] = 0.052; P<0.0001). The
amino acids shown in the biplot vector have a high correlation (r2 > 0.2) with the ordinate axes,
indicating the depth distribution of them was more pronounced than others. Glu and Ala have the
strongest preferential distribution in A and C horizon, respectively. In addition, Gln is more
abundant in A horizon, Thr, Gly, and Ser/Asn in C horizon, while Val, Leu, Phe, and Ile in both
horizons. Figure 3.4 shows that ~ 75% of the FAAs in soils of both A and C horizons consist of
eight amino acids, out of a total of 24 amino acids measured. Although the types of major amino
61
acids as well as their total mole percent relative to the FAAs in both A and C horizon soils are
near the same, their relative distribution is different between the two soil horizons (Figure 3.4).
The Glu, Asp and Gln were relatively more abundant in A-horizon than in C horizon, while the
opposite was observed for Gly, Thr and Ala (Figure 3.4) similar to the observations of NMS. The
molar percentages relative to total FAA for Val and Leu in the A horizon soils are similar to that
in the C horizon soils. These shifts of amino acid composition with soil depth are primarily
related to the changes in the molar abundance of the acidic amino acids Asp, Glu and neutral
amino acids Gly, Gln and Ala (Figure 3.3 and 3.4). The preferential decomposition of acidic
amino acids by enzymatic decarboxylation at α-C to form nonprotein amino acids ß-alanine and
γ-aminobutyric acid in deep soil possibly results in the decreasing molar abundance of Asp and
Glu with soil depth (Rosenfeld, 1979; Andersson et al., 2000). The amide Gln, which is the most
abundant FAA in boreal forest soil and tree leaves (Edfast et al., 1990; Werdin-Pfisterer et al.,
2012), originates most likely from aboveground xylem and phloem. Amino acid Gly and Ala,
which are more refractory from the bacterial utilization than others, are more abundant in
refractory proteinaceous compounds and tend to accumulate in deep soil profile as the degree of
degradation progressed (Yamashita and Tanoue, 2003). More extensive investigations at
different depth intervals are needed to test whether this pattern of amino acid distribution is a
common phenomenon. The results from this study support the findings of Abuarghub and Read
(1988) that the molar percentages of Asp and Glu in heathland soils decreased with soil depth
while the neutral components were generally higher in deep soil horizons. Similar trend of FAAs
were also reported in sediments (Rosenfeld, 1979).
62
Figure 3. 3. NMS ordination of 298 samples from 149 sampling sites. Sites were grouped into A
horizon and C horizon. High correlation of variables (cut off r2 = 0.2) with ordination was
indicated in biplot vector, where length and direction represent the magnitude and direction of
the correlation, respectively. Ordination of sites captured two dimensions with a final stress of 14
where Axis 1 explained 58 % and Axis 2 explained 34 % of total variance respectively.
Glu
Ser+Asn
Gly Gln
Arg
ThrAla
ValIle
LeuPhe
-3 -1 1 3
-3
-1
1
3
Axis 1 (58 %)
Axis
2 (
34 %
)
Depth
C horizonA horizon
63
Figure 3. 4. Average composition of individual and sum of eight dominant FAAs in A and C
horizons from different transects. Scale on right side applies to sum of the eight major FAA.
Different lower case letters indicate statistical significance by pairwise comparison (α = 0.05).
Values are expressed as mean ± Standard Error of Mean (SEM).
Table 3. 2. Concentrations (mg kg-1
dry soil) of free amino acids
A horizon C horizon
Range Average Range Average
Asp 0.03 – 9.8 0.95 < 1.2 0.09
Glu 0.2 – 29.4 3.9 < 4.9 0.41
Gly 0.024 – 3.6 0.44 0.005 - 1 0.1
Gln 0.005 – 1.3 1.6 < 0.5 0.07
Thr 0.05 – 9.3 0.96 0.006 - 2.3 0.17
Ala 0.069 – 15.3 1.5 0.013 – 2.8 0.25
Val 0.023 - 5 0.59 < 1.5 0.12
Leu 0.031 – 5.9 0.82 < 1.9 0.13
TFAAs 0.74 - 273 15.8 0.12 - 22 2.1
Asp Glu Gly Gln Thr Ala Val Leu N.a.N. SUMAm
ino
ac
id c
om
po
sit
ion
re
lati
ve
to
to
tal (m
ol %
)
0
5
10
15
20
25
30
0
20
40
60
80
A horizon
C horizon
a
b
a
b
a
b
a
b
ab
a
b
ab
64
Concentrations of TFAAs ranged from 0.12 to 22 and 0.74 to 273 mg kg-1
in soils of C
and A horizon, respectively, corresponding to total extracted amino acid-N ranging from 0.017 to
3.36 mg kg-1
and from 0.11 to 36.48 mg kg-1
in C and A horizons (Table 3.2), similar to the
summarized range: 0.1-8 µg N g-1
soil in the surface organic horizon and 0.5-21 µg N g-1
soil in
the surface mineral soil horizon (Berthrong and Finzi, 2006). As shown in Figure 3.5, there is
significantly higher amount of FAAs in the A-horizon soils comparing to C-horizon soils due to
the accumulation of soil organic matter and soil microorganisms in surface soils (Goh, 1972).
Similar results were also observed in soils of the temperate forest (Berthrong and Finzi, 2006;
Song et al., 2008) and boreal forest (Werdin-Pfisterer et al., 2012). Although the proteolytic
activities in each horizon were not tested, the enzyme activities of organic matter rich surface
soils were assumed to be higher than in subsoil (Berthrong and Finzi, 2006). Larger proteolytic
activities in substrate rich surface soils thereby replenish more FAAs. The opposite trend was
also noticed in several sites which showed greater amount of TFAAs in the C-horizon than in the
A-horizon soils, possibly because of the patchy distribution of buried organic horizons or to the
hotspots of amino acids like dead animals and visible fine roots in subsoil, as well as to manual
turbulence such as deep tillage (Brzostek et al., 2012). The detected range and average
concentrations of amino acid in A and C horizon were summarized in Table 3.2. The
concentrations of each major amino acid are variable and are in the same order of magnitude
with the reported results of major FAAs from soil surface to 35 cm depth profile in a
stagnohumic gley soil (Abuarghub and Read, 1988).
65
Figure 3. 5. Average concentration of eight major FAAs and TFAAs in two horizon soils from
different transects. Different lower case letters indicate statistical significance by pairwise
comparison (α=0.05). Scale on right side applies to TFAAs. Values are expressed as mean ±
SEM.
3.4.3. Variations of extractable soil amino acids along MAT and MAP gradients of
continental United States
Soil samples were classified into four groups based on transects and horizons: north-
south transect A horizon, north-south transect C horizon, west-east transect A horizon, and west-
east transect C horizon. The N-S transect crosses significant gradients in MAT from extreme
cold in the north to extreme heat in the south. The W-E transect has significant differences in
precipitation, along humid west coast, to arid and semiarid interior western half of the USA, and
all the way to the humid eastern half of the country (Woodruff et al., 2009). The N-S transect has
less dramatic precipitation changes and the W-E transect has minor temperature differences.
Results of the four groups were projected by NMS onto a two dimensional ordination based on
amino acid molar percentages. Scores of ordination axes were correlated with values of MAT
and MAP (Figure 3.6). Mean annual precipitation significantly correlated with the composition
Asp Glu Gly Gln Thr Ala Val Leu N.a.N.TFAAs
Co
ncen
trati
on
s (
mg
kg
-1 d
ry s
oil
)
0
1
2
3
4
5
0
2
4
6
8
10
12
14
16
18
20
A horizon
C horizon
a
b
a
a
a
a
a
aa
b
a
bbbb
bb
b
66
of the extracted FAAs in soils of both A and C horizons of W-E transects and significant
correlations between MAT and composition of extracted FAAs were observed for A- and C-
horizon soils from the N-S transect. Figure 3.7 shows how the environmental factors affect the
composition. Along the N-S temperature gradient, the composition of FAA in A horizon tends to
be different among vegetation. Along the W-E precipitation gradient, the vegetation tends to be
distributed in a more scattered manner. It is postulated the temperature and precipitation
influenced the composition of FAA by their effect on vegetation cover, microbial activity and
mineralogy. Great precipitation increases soil moisture, and then enhances plant coverage, which
causes changes in microbial biomass by way of changing organic C inputs (Zak et al., 2003;
Zhao et al., 2011). High temperature, however, reduces soil moisture, plant coverage and soil
organic C content (Jobbagy and Jackson, 2000; Zhang et al., 2013). Precipitation and
temperature enhance weathering of parent minerals and mineral loss through leaching. The
combination of mineral inheritance from parent materials and development of secondary
minerals through weathering and leaching determined the mineralogy of a specific site
(Woodruff et al., 2009). Typical mineralogical characteristics in response to climatic changes
usually regulate the stabilization of amino acids (Vieublé Gonod et al., 2006; Mikutta et al.,
2010).
67
Figure 3. 6. Correlations of NMS axes with MAT and MAP. Black dots represents sampling
sites. The percentages in Y-axis are the variability explained by each NMS ordination axis.
MAT (oC)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
Ax
is 2
(2
9.3
)
2
4
6
8
10
12
14
16
18
20
MAT (oC)
2 4 6 8 10 12 14 16 18 20
Ax
is 1
(4
7.9
%)
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
MAP (cm)
0 20 40 60 80 100 120 140
Ax
is 2
(1
7.9
%)
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
MAP (cm)
0 20 40 60 80 100 120 140
Ax
is 1
(4
7.4
%)
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
y=-0.0764x + 0.7114
r2=0.2331
P<0.001
y=-0.0879x + 0.8133
r2=0.2044
p<0.01
y=-0.0062x + 0.4502
r2=0.2399
P<0.001
y=-0.0089x + 0.6473
r2=0.2582
P<0.001
N-S A horizon N-S C horizon
W-E A horizon W-E C horizon
68
Figure 3. 7. NMS ordinations of 298 samples from in four groups. Correlations of variables with
ordination with r2 > 0.2 were indicated in biplot vector, where length and direction represent the
magnitude and direction of the correlation, respectively. Ev, evergreen; Gr, grassland; Sh, shrub;
De, deciduous forest; Cr, cropland; Pa, pasture; Fa, fallow; Re, residential
As an aid to illustrate large-scale patterns of FAAs along MAT and MAP gradients,
sample sites were grouped based on MAT and MAP range (Figure 3.1). As shown in Figure 3.8,
the concentrations of each of eight major FAAs and TFAAs in soils of A horizon decreased with
increasing MAT, while an opposite trend was observed for soils of C horizon. No consistent
patterns of amino acid concentrations with the MAP gradient in the A-horizon soils were found,
but there was an initial increasing followed by a decreasing trend along the gradient. In C
horizon, however, an overall decreasing trend of average concentrations of each major FAAs and
TFAAs with increasing MAP was observed. Though the effects of climate on soil organic matter
MAPAsp
GluSer+Asn
Gly
Gln
Thr
Ala
Pro
Val
Ile
Lys
Leu
Phe
-2.0 -1.0 0.0 1.0 2.0
-2.0
-1.0
0.0
1.0
2.0
C-EW
Axis 1 (47.4%)
Axi
s 2
(3
4.5
%)
Vegetation
EvGrShDeCrPaFaRe
MAT
MAP
Asp
Glu
Gly
Gln
Arg
Thr
Ala
GABA
Tyr
Val
Met
Orn
Ile
Lys
LeuPhe
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
A-NS
Axis 1 (62.2%)
Axi
s 2
(2
9.3
%)
Vegetation
GrShDeCrPaFa MAT
MAP
Asp
Glu
Ser+Asn
Gly
Gln
His
Arg
Thr
Ala
GABA
Val
Orn
Ile
Lys
Leu
Phe
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
C-NS
Axis 1 (47.9%)
Axi
s 2
(4
5.3
%)
Vegetation
GrShDeCrPaFa
MAP
Glu
Gln
Arg
Thr
Ala
GABA
Tyr
Val
Met
Orn
Ile
Lys
Leu
Phe
-2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
A-EW
Axis 1 (76.3%)
Axis
2 (
17
.9%
)
Vegetation
EvGrShDeCrPaFaRe
MAPAsp
GluSer+Asn
Gly
Gln
Thr
Ala
Pro
Val
Ile
Lys
Leu
Phe
-2.0 -1.0 0.0 1.0 2.0
-2.0
-1.0
0.0
1.0
2.0
C-EW
Axis 1 (47.4%)
Axis
2 (
34.5
%)
Vegetation
EvGrShDeCrPaFaRe
N-S A horizon N-S C horizon
W-E A horizon W-E C horizon
69
formation were complicated, the most important drivers are temperature and precipitation (Jenny,
1941). Higher precipitation enhances plant productivity resulting in above-ground C additions,
while higher temperature increase organic C decomposition rates, leading to less above-ground C
accumulations (Dixon and Weed, 1989; Smith et al., 2013). Higher concentration of organic C
provides larger amount of proteinaceous substrates for FAA enrichment. The pattern of FAA
level in A horizon along N-S transect matches the organic matter pattern of decreasing organic C
concentration with increasing MAT from north to south. The pattern of FAA level in A horizon
along W-E transect didn’t track exactly the reported pattern of high organic C in west coast, low
organic C in neighboring interior region and increasingly higher organic C from west to east
(Woodruff et al., 2009). It is possible that some combinations of local temperature or topography
explain this inconsistence.
The vertical distribution of soil organic C had a stronger relationship with vegetation than
with climate (Jobbagy and Jackson, 2000). The vegetation across N-S transect is covered by
shallow-rooted cultivated crops in the north, and deeply rooted shrubs in the south. So
underground soil organic inputs should increase from the north to the south. The pattern of
increasing FAA levels in the C horizon from north to south generally matches the pattern of
underground organic inputs which provide substrates for extracellular enzymes. The W-E
transect crosses notable vegetation transition from mainly evergreen forest in the west coast, to
shrubs in the arid and semiarid regions, and to grassland/pasture and cultivated crops in the
central and eastern region of the United States. Generally, forests have a deepest root system and
the highest root biomass, followed by shrubs, grasslands or pastures, and cultivated crops
(Canadell et al., 1996; Jackson et al., 1996). From the low precipitation west to the high
precipitation east, the FAA levels generally follow the pattern of underground organic input
70
contributed by root biomass. However, along the west coast covered majorly by deep-rooted
forests, levels of FAAs were the lowest among the sub-continental areas. This suggested the
substrate abundance may be not the only factors affecting FAA enrichment in the C horizon,
other component such as protease activities could influence the depolymerization rates. The high
lignin or tannin content of the hard-woody forest in the west coast could slow the rates of
proteolysis (Northup et al., 1995; Yu et al., 2003; Berthrong and Finzi, 2006). The amino acid
pattern in C horizon generally tracks vegetation root distributions. Root exudates and root
residues could also be contributing to the deep soil SOM deposition (Dick et al., 2005). Root
turnover rates increased with MAP for fine roots of grassland, forests and shrubland (Gill and
Jackson, 2000).
71
Figure 3. 8. Average concentrations of eight major FAAs and TFAAs in soils of A and C
horizons along the MAT and MAP gradients of continental US. Scales on right side applies to
TFAAs. MAT and MAP gradients shown in the legends differentiated by color are from the
circled areas specified in Figure 3.1. Values were expressed as mean ± SEM. # mean annual
temperature; * mean annual precipitation; § soils from west coast.
Asp Glu Gly Gln Thr Ala Val Leu N.a.N. TFAAs
0
1
2
3
4
5
6
7
0
5
10
15
20
25
30
Asp Glu Gly Gln Thr Ala Val Leu N.a.N. TFAAs
0
2
4
6
8
10
0
10
20
30
40
50Asp Glu Gly Gln Thr Ala Val Leu N.a.N. TFAAs
Co
ncen
trati
on
s (
mo
l kg
-1 d
ry s
oil
)
0
10
20
30
40
50
0
20
40
60
80
100
120
140
160
35-50
50-55
55-60
A horizon
C horizon
Asp Glu Gly Gln Thr Ala Val Leu N.a.N. TFAAs
0
10
20
30
40
50
60
0
50
100
150
200
250
300
4-20
20-40
40-60
40-60
A horizon
C horizon
#MAT (
oF)
*MAP (in)
§
72
3.4.4. Variations of extractable soil amino acids among different vegetation covers
The relative abundance of all eight FAAs in both A and C horizons was relatively
uniform among the four vegetation covers, although significant differences were observed for
certain major amino acids (Figure 3.9). Glu had the largest variation among the eight dominant
amino acids. Gly and Ala were more abundant in C-horizon while Glu was much richer in A
horizon. The results were consistent with what was revealed by NMS (Figure 3.3). The relative
molar percent composition of FAAs observed in this study was similar to the observations by
Werdin-Pfisterer et al. (2009) in soils of five successional stages (willow, alder, balsam poplar,
white spruce and black spruce), possibly because, on a global scale, the overstory and understory
plant litter, the major sources of FAAs, contain similar chemical structure and share the similar
biochemistry in the production and re-synthesis of amino acids during SOM formation, and in
the ecosystem processes linking them, regardless of vegetation type, soil environment, or climate.
The concentrations of TFAAs in soils of the four vegetation sites of either horizon didn’t
track the content of total soil organic C (Figure 3.10). The organic C content in A-horizon soils
follows the order as evergreen > pasture > grassland > shrub, consistent with the sequence of
above-ground biomass of the vegetation types (Mendoza-Ponce and Galicia, 2010). The levels of
TFAAs in A horizon followed in the order: evergreen ≈ pasture ≈ grassland > shrub (Figure
3.10). The divergence was probably due to the low C quality (highest C-to-N and lignin-to-N
ratios) in the hard woody forests and shrubs, resulting in low rates of decomposition in forest
sites (Aitkenhead and McDowell, 2000; Lovett et al., 2004; Grunzweig et al., 2007). In addition,
the pH of high precipitation forest soils is usually lower than soils in arid or semi-arid region, not
favoring microbial mediated proteolytic enzymatic activities. The contribution of the higher
above-ground biomass to proteinaceous substrate was eclipsed by the higher C to N ratio in
73
woody forest sites compared to grassland and pasture sites. Therefore, the similar levels of FAAs
in evergreen forest, grassland and pasture sites could be a compromise of organic inputs, organic
matter qualities and microbial activities. The total organic C content in C horizon follows the
order as: evergreen > shrub > pasture ≈ grassland, but evergreen sites have the lowest
concentration of TFAAs. The lowest TFAA level in evergreen site is consistent with the lowest
FAA concentrations in the west coast as illustrated in Figure 3.8. One explanation for this was
the semi-arid grass/pasture and shrubland have higher protease activity than the high
precipitation forest sites because, on average, semi-arid grassland and shrub sites have the
highest pH, favoring both bacterial biomass and proteolytic enzyme activity (Hofmockel et al.,
2010). In grassland and pasture sites, the higher ratios of TFAA concentration than that of the
total organic C in the A-horizon soil to that in C-horizon soil indicated the amino acids or
organic matter tend to be depleted during the vertical transport from surface soil to subsoil.
74
Figure 3. 9. Average composition of individual and sum of eight dominant FAAs in A and C
horizon soils with different vegetation cover. Scales on right side apply to sum of eight major
FAA proportions. Different lower case letters indicate statistical significance among groups.
Values are expressed as mean ± SEM.
Asp Glu Gly Gln Thr Ala Val Leu N.a.N. SUM
Am
ino
acid
co
mp
osit
ion
rela
tive t
o t
ota
l (m
ol %
)
0
5
10
15
20
25
0
20
40
60
80
Asp Glu Gly Gln Thr Ala Val Leu N.a.N. SUM
0
5
10
15
20
25
30
0
20
40
60
80
Evergreen
Grassland
Pasture
Shrub
abab
a
b
abab
a
b
abab
a
b
a
aba
b
ab
a a
b
a
ab ab
b
abb
b
a
ababa
b
C horizon
A horizon
75
Figure 3. 10. Concentration of TFAAs (A) and total soil organic C content (B) among four
vegetation covers in two horizons. A = A horizon; C = C horizon; A/C Ratio = the ratio of
average TFAA level or soil total organic C content in the A horizon to that in the C horizon.
Values are expressed as mean ± SEM.
3.5. Conclusions
Evergreen Grassland Pasture ShrubConcentr
ation o
f T
FA
As (
mol kg-1
soil)
0
50
100
150
200
250
Concentr
ation r
atio (
A/C
)
0
2
4
6
8
10
12
14
A
C
A/C Ratio
Land cover
Evergreen Grassland Pasture Shrub
Concentr
ation o
f soil
org
anic
C c
onte
nt (w
t)
0
2
4
6
8
10
12
Conte
nt
ratio (
A/C
)
0
2
4
6
8
10
12
A
C
A/C Ratio
B.
A.
76
This research is the first attempt to assess the levels and composition of FAAs from soils
of such variable ecosystems at continental scale. Findings in this research generally support the
original hypotheses. The concentrations of FAAs of the A-horizon soils were significantly higher
than that of the C-horizon soils and the compositional pattern was separated by depth. The major
FAAs were Asp, Glu, Gly, Gln, Thr, Ala, Val and Leu. The concentrations of TFAAs varied
significantly among the four major vegetation covers, but the composition of individual FAAs
relative to total was relatively uniform though some noticeable variations of certain amino acids
were observed. Concentrations of FAAs decreased with increasing temperature in A horizon and
an opposite trend was observed in C horizon. Though no consistent trend of FAA concentrations
with MAP was found in A horizon, a decreasing trend of FAA concentrations with increasing
precipitation was found in C horizon. The climatic factors control A-horizon FAA levels
generally by their effect on the proteinaceous substrate inputs while the root distributions of a
specific vegetation cover regulate the subsoil FAA levels along the MAT and MAP gradients.
Significant associations between amino acid distribution and the climatic factors were found.
Temperature and precipitation could influence the amino acid composition indirectly via their
effect on organic matter decomposition rates, vegetation cover, microbial activities and soil
mineralogy.
3.6. Acknowledgements
We would like to thank USGS for proving the soil samples. We thank the financial
support of USDA-AFRI (award #2012-67019-30227).
77
3.7. References
Abuarghub, S.M., Read, D.J., 1988. The biology of mycorrhiza in the ericaceae. Xii.
Quantitative analysis of individual 'free' amino acids in relation to time and depth in the
soil profile. New Phytologist 108, 433-441.
AccQ, W., 1993. Tag chemistry package instruction manual. Millipore Waters Chromatography.
Aitkenhead, J.A., McDowell, W.H., 2000. Soil c : N ratio as a predictor of annual riverine doc
flux at local and global scales. Global Biogeochemical Cycles 14, 127-138.
Amelung, W., Zhang, X., Flach, K.W., 2006. Amino acids in grassland soils: Climatic effects on
concentrations and chirality. Geoderma 130, 207-217.
Andersson, E., Simoneit, B.R.T., Holm, N.G., 2000. Amino acid abundances and
stereochemistry in hydrothermally altered sediments from the juan de fuca ridge,
northeastern pacific ocean. Applied Geochemistry 15, 1169-1190.
Berthrong, S.T., Finzi, A.C., 2006. Amino acid cycling in three cold-temperate forests of the
northeastern USA. Soil Biology & Biochemistry 38, 861-869.
Brzostek, E.R., Blair, J.M., Dukes, J.S., Frey, S.D., Hobbie, S.E., Melillo, J.M., Mitchell, R.J.,
Pendall, E., Reich, P.B., Shaver, G.R., Stefanski, A., Tjoelker, M.G., Finzi, A.C., 2012.
The effect of experimental warming and precipitation change on proteolytic enzyme
activity: Positive feedbacks to nitrogen availability are not universal. Global Change
Biology 18, 2617-2625.
Canadell, J., Jackson, R.B., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996.
Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583-595.
Dakora, F., Phillips, D., 2002. Root exudates as mediators of mineral acquisition in low-nutrient
environments. Plant and Soil 245, 35-47.
78
Dick, D.P., Goncalves, C.N., Dalmolin, R.S.D., Knicker, H., Klamt, E., Kogel-Knaber, I.,
Simoes, M.L., Martin-Neto, L., 2005. Characteristics of soil organic matter of different
brazilian ferralsols under native vegetation as a function of soil depth. Geoderma 124,
319-333.
Dixon, J., Weed, S., 1989. An introduction to organic matter in mineral soils. Atmosphere 3, 5.
Edfast, A.B., Nasholm, T., Ericsson, A., 1990. Free amino-acid-concentrations in needles of
norway spruce and scots pine trees on different sites in areas with 2 levels of nitrogen
deposition. Canadian Journal of Forest Research-Revue Canadienne De Recherche
Forestiere 20, 1132-1136.
Fiechter, G., Mayer, H.K., 2011. Uplc analysis of free amino acids in wines: Profiling of on-lees
aged wines. Journal of Chromatography B-Analytical Technologies in the Biomedical
and Life Sciences 879, 1361-1366.
Formanek, P., Rejsek, K., Vranova, V., Marek, M.V., 2008. Bio-available amino acids and
mineral nitrogen forms in soil of moderately mown and abandoned mountain meadows.
Amino Acids 34, 301-306.
Gilbert, R., Altman, J., 1966. Ethanol extraction of free amino acids from soil. Plant and Soil 24,
229-238.
Gill, R.A., Jackson, R.B., 2000. Global patterns of root turnover for terrestrial ecosystems. New
Phytologist 147, 13-31.
Goh, K.M., 1972. Amino acid levels as indicators of paleosols in new zealand soil profiles.
Geoderma 7, 33-47.
79
Grunzweig, J.M., Gelfand, I., Fried, Y., Yakir, D., 2007. Biogeochemical factors contributing to
enhanced carbon storage following afforestation of a semi-arid shrubland.
Biogeosciences 4, 891-904.
Hertenberger, G., Zampach, P., Bachmann, G., 2002. Plant species affect the concentration of
free sugars and free amino acids in different types of soil. Journal of Plant Nutrition and
Soil Science-Zeitschrift Fur Pflanzenernahrung Und Bodenkunde 165, 557-565.
Hofmockel, K.S., Fierer, N., Colman, B.P., Jackson, R.B., 2010. Amino acid abundance and
proteolytic potential in north american soils. Oecologia 163, 1069-1078.
Hou, S., He, H., Zhang, W., Xie, H., Zhang, X., 2009. Determination of soil amino acids by high
performance liquid chromatography-electro spray ionization-mass spectrometry
derivatized with 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate. Talanta 80, 440-447.
Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996. A
global analysis of root distributions for terrestrial biomes. Oecologia 108, 389-411.
Jämtgård, S., Näsholm, T., Huss-Danell, K., 2008. Characteristics of amino acid uptake in barley.
Plant and Soil 302, 221-231.
Jenny, H., 1941. Factors of soil formation.
Jobbagy, E.G., Jackson, R.B., 2000. The vertical distribution of soil organic carbon and its
relation to climate and vegetation. Ecological applications 10, 423-436.
Johnson, R.M., Pregitzer, K.S., 2007. Concentration of sugars, phenolic acids, and amino acids
in forest soils exposed to elevated atmospheric co2 and o-3. Soil Biology & Biochemistry
39, 3159-3166.
Jones, D., Farrar, J., Newsham, K., 2005. Rapid amino acid cycling in arctic and antarctic soils.
Water, Air, & Soil Pollution: Focus 4, 169-175.
80
Jones, D.L., Hodge, A., 1999. Biodegradation kinetics and sorption reactions of three differently
charged amino acids in soil and their effects on plant organic nitrogen availability. Soil
Biology & Biochemistry 31, 1331-1342.
Jones, D.L., Kielland, K., 2002. Soil amino acid turnover dominates the nitrogen flux in
permafrost-dominated taiga forest soils. Soil Biology & Biochemistry 34, 209-219.
Kielland, K., 1994. Amino-acid-absorption by arctic plants - implications for plant nutrition and
nitrogen cycling. Ecology 75, 2373-2383.
Kielland, K., 1995. Landscape patterns of free amino acids in arctic tundra soils.
Biogeochemistry 31, 85-98.
Kielland, K., McFarland, J.W., Ruess, R.W., Olson, K., 2007. Rapid cycling of organic nitrogen
in taiga forest ecosystems. Ecosystems. 10, 360-368.
Lipson, D., Nasholm, T., 2001. The unexpected versatility of plants: Organic nitrogen use and
availability in terrestrial ecosystems. Oecologia 128, 305-316.
Lipson, D.A., Monson, R.K., 1998. Plant-microbe competition for soil amino acids in the alpine
tundra: Effects of freeze-thaw and dry-rewet events. Oecologia 113, 406-414.
Lipson, D.A., Schmidt, S.K., Monson, R.K., 1999b. Links between microbial population
dynamics and nitrogen availability in an alpine ecosystem. Ecology 80, 1623-1631.
Lojkova, L., Klejdus, B., Formanek, P., Kuban, V., 2006. Supercritical fluid extraction of
bioavailable amino acids from soils and their liquid chromatographic determination with
fluorometric detection. Journal of Agricultural and Food Chemistry 54, 6130-6138.
Lovett, G.M., Weathers, K.C., Arthur, M.A., Schultz, J.C., 2004. Nitrogen cycling in a northern
hardwood forest: Do species matter? Biogeochemistry 67, 289-308.
81
McFarland, J.W., Ruess, R.W., Kielland, K., Doyle, A.P., 2002. Cycling dynamics of nh4 + and
amino acid nitrogen in soils of a deciduous boreal forest ecosystem. Ecosystems 5, 0775-
0788.
Mendoza-Ponce, A., Galicia, L., 2010. Aboveground and belowground biomass and carbon
pools in highland temperate forest landscape in central mexico. Forestry 83, 497-506.
Mikutta, R., Kaiser, K., Doerr, N., Vollmer, A., Chadwick, O.A., Chorover, J., Kramer, M.G.,
Guggenberger, G., 2010. Mineralogical impact on organic nitrogen across a long-term
soil chronosequence (0.3-4100 kyr). Geochimica Et Cosmochimica Acta 74, 2142-2164.
Moura, A., Savageau, M.A., Alves, R., 2013. Relative amino acid composition signatures of
organisms and environments. PLoS ONE 8, e77319.
Nasholm, T., Persson, J., 2001. Plant acquisition of organic nitrogen in boreal forests.
Physiologia Plantarum 111, 419-426.
Nelson, D.L., Lehninger, A.L., Cox, M.M., 2008. Lehninger principles of biochemistry.
Macmillan.
Nordin, A., Hogberg, P., Nasholm, T., 2001. Soil nitrogen form and plant nitrogen uptake along
a boreal forest productivity gradient. Oecologia 129, 125-132.
Northup, R.R., Yu, Z.S., Dahlgren, R.A., Vogt, K.A., 1995. Polyphenol control of nitrogen
release from pine litter. Nature 377, 227-229.
Paul, E.A., Schmidt, E.L., 1960. Extraction of free amino acids from soil1. Soil Sci. Soc. Am. J.
24, 195-198.
Paungfoo-Lonhienne, C., Lonhienne, T.G.A., Rentsch, D., Robinson, N., Christie, M., Webb,
R.I., Gamage, H.K., Carroll, B.J., Schenk, P.M., Schmidt, S., 2008. Plants can use protein
82
as a nitrogen source without assistance from other organisms. Proceedings of the
National Academy of Sciences of the United States of America 105, 4524-4529.
Rentsch, D., Schmidt, S., Tegeder, M., 2007. Transporters for uptake and allocation of organic
nitrogen compounds in plants. FEBS Letters 581, 2281-2289.
Rosenfeld, J.K., 1979. Amino-acid diagenesis and adsorption in nearshore anoxic sediments.
Limnology and Oceanography 24, 1014-1021.
Rothstein, D., 2009a. Soil amino-acid availability across a temperate-forest fertility gradient.
Biogeochemistry 92, 201-215.
Rothstein, D.E., 2010. Effects of amino-acid chemistry and soil properties on the behavior of
free amino acids in acidic forest soils. Soil Biology and Biochemistry 42, 1743-1750.
Schimel, J.P., Bennett, J., 2004. Nitrogen mineralization: Challenges of a changing paradigm.
Ecology 85, 591-602.
Senwo, Z.N., Tabatabai, M.A., 1998. Amino acid composition of soil organic matter. Biology
and Fertility of Soils 26, 235-242.
Smith, D.B., Cannon, W.F., Woodruff, L.G., Solano, F., Kilburn, J.E., Fey, D.L., 2013.
Geochemical and mineralogical data for soils of the conterminous united states. Data
series 801.
Sollins, P., Homann, P., Caldwell, B.A., 1996. Stabilization and destabilization of soil organic
matter: Mechanisms and controls. Geoderma 74, 65-105.
Song, L.-c., Hao, J.-m., Cui, X.-y., 2008. Soluble organic nitrogen in forest soils of northeast
china. Journal of Forestry Research 19, 53-57.
Vieublé Gonod, L., Jones, D.L., Chenu, C., 2006. Sorption regulates the fate of the amino acids
lysine and leucine in soil aggregates
83
devenir de deux acides aminés aux propriétés d'adsorption contrastées, la lysine et la leucine,
dans le sol. European journal of soil science 57, 320-329.
Warren, C.R., Taranto, M.T., 2010. Temporal variation in pools of amino acids, inorganic and
microbial n in a temperate grassland soil. Soil Biology and Biochemistry 42, 353-359.
Weintraub, M., Schimel, J., 2005a. The seasonal dynamics of amino acids and other nutrients in
alaskan arctic tundra soils. Biogeochemistry 73, 359-380.
Werdin-Pfisterer, N.R., Kielland, K., Boone, R.D., 2009. Soil amino acid composition across a
boreal forest successional sequence. Soil Biology & Biochemistry 41, 1210-1220.
Werdin-Pfisterer, N.R., Kielland, K., Boone, R.D., 2012. Buried organic horizons represent
amino acid reservoirs in boreal forest soils. Soil Biology & Biochemistry 55, 122-131.
Woodruff, L.G., Cannon, W.F., Eberl, D.D., Smith, D.B., Kilburn, J.E., Horton, J.D., Garrett,
R.G., Klassen, R.A., 2009. Continental-scale patterns in soil geochemistry and
mineralogy: Results from two transects across the united states and canada. Applied
Geochemistry 24, 1369-1381.
Yamashita, Y., Tanoue, E., 2003. Distribution and alteration of amino acids in bulk dom along a
transect from bay to oceanic waters. Marine Chemistry 82, 145-160.
Yu, Z., Zhang, Q., Kraus, T.E.C., Dahlgren, R.A., Anastasio, C., Zasoski, R.J., 2002.
Contribution of amino compounds to dissolved organic nitrogen in forest soils.
Biogeochemistry 61, 173-198.
Yu, Z.S., Kraus, T.E.C., Dahlgren, R.A., Horwath, W.R., Zasoski, R.J., 2003. Mineral and
dissolved organic nitrogen dynamics along a soil acidity-fertility gradient. Soil Science
Society of America journal 67, 878-888.
84
Zak, D.R., Holmes, W.E., White, D.C., Peacock, A.D., Tilman, D., 2003. Plant diversity, soil
microbial communities, and ecosystem function: Are there any links? Ecology 84, 2042-
2050.
Zhang, N., Liu, W., Yang, H., Yu, X., Gutknecht, J.M., Zhang, Z., Wan, S., Ma, K., 2013. Soil
microbial responses to warming and increased precipitation and their implications for
ecosystem c cycling. Oecologia 173, 1125-1142.
Zhao, J., Wang, X., Shao, Y., Xu, G., Fu, S., 2011. Effects of vegetation removal on soil
properties and decomposer organisms. Soil Biology and Biochemistry 43, 954-960.
85
4. Hydrolysable Amino Acids in Soils of North-South and West-East
Transects of Continental United States
L. Maa, K. Xia
a*, M. A. Williams
b, and D. B. Smith
c
aDepartment of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State
University, Blacksburg, VA 24061, USA
bRhizosphere and Soil Microbial Ecology Laboratory, Department of Horticulture & Molecular
Plant Sciences, Virginia Tech, VA 24061, USA
cUS Geological Survey, MS 973, Denver, CO 80225, USA
*Corresponding author. Tel.: 540-231-9323; Email address: [email protected]
86
4.1. Abstract
Proteins or peptides are significant contributors to soil organic matter pool. Their
stabilization and bioavailability are considered to be critical points in terrestrial nitrogen cycle.
However, in depth studies on their abundance, composition, and turnover are lacking especially
in soils across a wide range of ecosystems. Our aims were to quantify contents of hydrolysable
amino acid in A-and C-horizon soils from north-south temperature and west-east precipitation
transects of continental United States and to investigate changes of hydrolysable amino acids
along vegetation, temperature and precipitation gradients. Hydrolysable amino acid is a measure
of acid cleaved amino acids. It indicates levels of soil proteins and peptides either in free form or
associated with minerals. Soil samples were hydrolyzed using 6 N HCl at 115 for 24 h. Amino
acids in the hydrolysates were derivatized with 6-aminoquinolyl-N-hydroxysuccinimidyl
carbamate, followed by analysis on a High Performance Liquid Chromatography equipped with
a fluorescence detector. Seventeen amino acids were characterized. The concentrations of total
and individual hydrolysable amino acids were significantly higher in A- than in C-horizon soils,
ranging from 211 mg kg-1
dry soil to 15 g kg-1
dry soil in A horizon and from 18 mg kg-1
dry soil
to 6.4 g kg-1
dry soil in C horizon soils, respectively. The levels of hydrolysable amino acids
were highly correlated with soil organic carbon content and levels of total free amino acids were
highly correlated with that of total hydrolysable amino acids. The concentration ratios of total
hydrolysable amino acid level in the A-horizon soils to that in the C-horizon soils were higher
for the grassland and pasture sites, than the evergreen forests and shrubs sites. Levels of major
and total hydrolysable amino acids decreased in A horizon and increased in C horizon with
increasing mean annual temperature, while increased in A horizon and decreased in C horizon
with increasing mean annual precipitation. These results suggested levels of hydrolysable amino
87
acids were associated with the above-ground biomass and root vertical distribution. The most
abundant hydrolysable amino acids were neutral, followed by acidic, basic and aromatic amino
acids in all soils. The composition of hydrolysable amino acids in the whole soils was rather
uniform regardless of variable concentrations. Major amino acids aspartic acid, serine, glutamic
acid, glycine, threonine, alanine, proline and valine took up 58 to 88% of the total hydrolysable
amino acids while the major free amino acids were glutamic acid, glutamine, aspartic acid,
leucine, alanine, threonine, glycine and valine. The overall composition of hydrolysable amino
acids differs from that of the free amino acids in A and C horizons possibly because microbial
turnover, root exudate and fine root turnover contribute to the free amino acid pool in addition to
the microbial mediated depolymerization of proteinaceous compounds.
88
4.2. Introduction
Soil is the biggest reservoir of terrestrial nitrogen (N) and ~90% of total soil N is in the
organic form (Senwo and Tabatabai, 1998; Butterbach-Bahl et al., 2011). Proteins and peptides,
in particular, play a crucial role in terrestrial N cycling, constituting ~ 40% of total soil N and as
a regulator of overall N availability (Schulten and Schnitzer, 1997; Chen and Xu, 2006;
Paungfoo-Lonhienne et al., 2008). Proteins and peptides mainly originate from plant, animal or
microbial residues at different stages of decomposition (Miltner et al., 2009). Proteins and
peptides are thus an important form of both labile and stabilized organic N. Studies on the levels
and composition of the proteinaceous compounds, therefore, are crucial to understand their
stabilization and bioavailability.
Proteins and peptides were combined amino acids with peptide linkages. Their levels
were usually measured indirectly by quantifying the acid cleaved amino acids. A small amount
of proteinaceous compounds may escape from the chemical hydrolysis because of the strong
adsorption to soil minerals, entrapment in mesopores or microaggregates, or complexing with
some organic macromolecules for their amphipathic nature (Sollins et al., 1996; Kleber et al.,
2007; Rillig et al., 2007; Knicker, 2011). By hydrolyzing proteins/peptides into free amino acids
(FAAs), we were able to quantify most of the proteinaceous substances associated to minerals,
immobilized by microorganism, as well as those in the free form. The previous studies on
hydrolysable amino acids (HAAs) were mostly limited to isolated sites. So far, no information
about the levels and composition of HAAs in soils of a wide ecosystems and different depth was
available. The concentrations of HAAs were very variable site to site and subject to the influence
of soil management, cultivation method or environmental changes (Senwo and Tabatabai, 1998;
Praveen et al., 2002a; Brodowski et al., 2005). Despite of the substantial differences in
89
concentrations, the molar percent composition of the HAAs was shown by most of previous
investigations to exhibit minor variations with various land use, climatic conditions and of soil
depth (Gotoh et al., 1986a; Campbell, 1991; Senwo and Tabatabai, 1998). Research conducted
by Sowden et al. (1977) suggested the composition of HAAs in agriculture soils was rather
uniform regardless of organic amendment. The relative distribution of HAAs in the whole soil
investigated by Friedel and Scheller (2002) was also relatively constant irrespective of a wide
land use and site conditions. In addition, Asp, Glu, Gly and Ala were found by most of others to
be the dominant amino acids in a variety of soils (Sowden et al., 1977; Gotoh et al., 1986a;
Friedel and Scheller, 2002). However, noticeable changes in HAA distribution were reported in
the composting cotton wastes (Baca et al., 1994), humic substances (Ding et al., 2001), and
decomposing plant materials (Rovira et al., 2005). The main objective of the current is therefore
to investigate the levels and overall composition of HAAs in soils across wide ecosystems. The
data collected from this investigation might help us interpret the disparities of results by others.
In this research, HAAs in A- and C-horizon soils of north-south (N-S) temperature and
west-east (W-E) precipitation transects of continental United States were investigated with the
aim to examine levels and composition of HAAs in A-and C- horizon soils of various
ecosystems and the effect of climatic factors on the concentrations and distribution of HAAs at
the continental scale. To the best of our knowledge, this was the first attempt to assess the status
of HAAs in soils over a wide range of ecosystems in United States.
4.3. Materials and methods
4.3.1. Study sites and sampling
A subset of soil samples of A and C horizons from 93 sites along N-S and W-E transects
of continental United States (Figure 3.1) were selected from a total of 4871 sites (1site/1600km2)
90
by the USGS from 2007 to 2010 for the USGS Geochemical Landscapes Project (Smith et al.,
2013). Details on sampling protocols and sample treatment were provided elsewhere (Smith et
al., 2013). Briefly, visible plant materials were removed from each collected soil, air dried,
sieved through 2-mm sieve, and stored in glass jars at 4 oC until analysis. Mineralogical and
chemical analysis on all the soils was carried out by the USGS (Smith et al., 2013).
4.3.2. Chemicals
Waters AccQ·FluorTM
Reagent Kit was purchased from Waters (Milford, MA, USA).
The kit included Waters AccQ·Fluor derivative powder, Waters AccQ·Fluor dilution solution
and 0.2 M borate buffer (pH 8.8)(Liu et al., 1998). The ultra-pure water was produced by a Milli-
Q water purification system (Millipore, Milford, MA, USA). The ACS grade sodium acetate
(CH3COONa), anhydrous oxalic acid (H2C2O4, 98%), sodium EDTA (EDTA-Na2·2H2O),
sodium azide (NaN3), hydrochloric acid (HCl, 37%), phosphoric acid (H3PO4, 85%) were
purchased from Sigma (St. Louis, MO, U.S.A). Ammonium solution (NH4OH, 5M) was
obtained from Ricca Chemicals (Arlington, Texas, USA). The HPLC grade acetonitrile (ACN),
triethylamine (TEA) and sodium hydroxide (NaOH) pellets were purchased from Fisher
Scientific (New Jersey, USA). Individual amino acid standards including alanine (Ala), arginine
(Arg), aspartic acid (Asp), glutamic acid (Glu), glycine (Gly), histidine (His), isoleucine (Ile),
leucine (Leu), lysine (Lys), methionine (Met), phenylalanine (Phe), proline (Pro), serine (Ser),
threonine (Thr), tyrosine (Tyr), valine (Val), and cystine (Cys–Cys), as well as the internal
standard L-norvaline were purchased from Sigma. Dowex 50WX8 50-100 (H) cation exchange
resin was purchased from Fisher.
Each amino acid stock solution was prepared at the concentration of 25 mM by
dissolving precalculated amino acids standard in 0.1 M HCl solution. The stock solutions were
91
stored at -20 and can be used for 6 months. An intermediate composite standard was prepared
by combining appropriate amounts of the 17 amino acid stock solution to achieve a final
concentration of 0.25 mM for each amino acid. The intermediate composite standard was then
mixed with 2.5 mM L-norvaline in appropriate amounts of ultrapure water to yield mixed amino
acid calibration standards ranging from 0.005 to 0.2 mM for 17 amino acid and 0.1 mM for
internal standard. The calibration standards can be stored at -20 and reused within one month.
4.3.3. Hydrolysis and purification
The proteins/peptides in soils were acid liberated by 6N HCl. The hydrolytes were
purified according to the modified procedure of Amelung and Zhang (2001). One gram of air
dried soil was weighed into the 12 mL glass vials. Ten mL of 6N HCl was added into the vials.
An aliquot of 50 µl internal standard solution containing 1.25 µmol L-norvaline was spiked to
the mixture to achieve a final concentration the same as that in the calibration solution. The vials
were then incubated in the ~115 bead bath for ~ 24h. After the hydrolysis, the vials were
allowed to stand still at room temperature for cooling. About 1.5 mL of the supernatant from
each vial was transferred to the 2mL polypropylene centrifuge tubes and centrifuged at 10,000×g
for 10min. After the centrifugation, an aliquot of 400 µl of hydrolyte was pipetted into a 50 mL
centrifuge tube, which was brought to 50 mL graduation with ultra-pure water. The diluent was
loaded to a polypropylene sample preparation cartridge, pre-filled with 3 g Dowex 50 W X8
cation exchange resin which was prepared and conditioned as described by Boas (1953)and Küry
and Keller (1991). Briefly, the resin was conditioned with 25 mL of 2M NaOH, neutralized by
25 mL 2 M HCl and then rinsed with 40 ml ultrapure water to get an approximate neutral pH
(checked with pH test strip). After all the hydrolytes were transferred onto the cartridge, the resin
was rinsed with 25 mL 0.1 M oxalic acid (pH 1.7 ± 0.1, adjusted with NH4OH), 5mL 0.01 M
92
HCl and 8 mL ultrapure water in turn. Finally, the retained amino acids were eluted with 5 × 5
ml of 3 M NH4OH. After filtering through 0.22 µm PVDF syringe filters, 500 µL of the
ammonium filtrate was dried in SpeedVac at 60 for 2 h. The dried residue was reconstituted
with 10 µL 0.05 mM HCl, waiting for derivatization.
4.3.4. Amino acid derivatization
The detailed procedure for amino acid derivatization procedure using the Waters
AccQ·FluorTM
Reagent Kit can be found elsewhere (AccQ, 1993). To reconstitute the
AccQ·Fluor Reagent, 1mL of AccQ·Fluor Reagent diluent was transferred to the vial containing
the AccQ·Fluor Reagent power. The vial was tightly capped, mixed on a vortex for 10 seconds,
and then incubated at 55 in an oven for 10 min until the powder was completely dissolved.
The final reconstituted AccQ·Fluor solution was colorless and transparent and contained
AccQ·Fluor reagent at about 3mg/mL (ca.10 mM). The tightly sealed reconstituted AccQ·Fluor
solution can be stored in a desiccator at room temperature or at 4 and reused within two to
four weeks.
The dried residue was reconstituted in 10 µL of 0.05 M HCl and buffered by 70 µL
borate buffer. The mixture was vortexed for 10 seconds, followed by addition of 20 µL
reconstituted AccQ·Fluor solution. The mixture was mixed immediately on a vertex for 10
seconds and incubated for 1 min at room temperature. The mixture was then transferred to the
bottom of an autosampler vial limited volume insert, capped with a silicone-lined septum and
incubated at 55 in an oven for 10 min to complete the derivatization of amino acids in the
sample before being analyzed on the High Performance Liquid Chromatography equipped with a
fluorescence detector (HPLC/FLD). An aliquot of 10 µL of each calibration standard was
93
pipetted into the HPLC sample vial with 200 µL insert followed the described steps to initiate the
derivatization.
4.3.5. Analysis of derivatized amino acids on HPLC/FLD
Derivatized amino acids were analyzed using a HPLC 1260 Infinity system (Agilent
Technologies, USA) coupled with a fluorescence detector. Separation of the 17 amino acids was
carried out on a Waters X-Terra MS C18 column (2.1 mm × 150 mm, 3.5 µm particle size,
Waters Corporation, USA). The mobile phase consisted of A: a solution containing 140 mM
sodium acetate, 17 mM TEA, and 0.1% (g/L, w/v) EDTA-2Na (pH 5.05, adjusted with
phosphoric acid solution) and B: ACN/water (60:40, v/v). The gradient conditions were 0 - 17
min 100 - 93% A, 17 - 21 min 93 - 90% A, 21 - 30 min 90 - 70% A, 30 - 35 min 70% A, 35 - 36
min 70 - 0% A, and 0 % A for 4 min. The column was thermostated at 50 and operated at a
flow rate of 0.35 ml/min. The injection volume was set at 5 µL. The detection was accomplished
by fluorescence with excitation and emission wavelengths set at 250 nm and 395 nm separately.
Each derivatized amino acid in a sample was identified by comparing its retention time with that
of a derivatized individual amino acid standard and quantified using the internal standard method.
The detection limits, precision (relative standard deviation %), and recoveries of this method
were shown in Table 4.1.
94
Table 4. 1. Detection limits, recovery and the precision of the determination of amino acid
derivatives.
Amino acid
Detection limit
(µmol kg-1
dry
soil) a
Recovery (%) b Precision (RSD %)
c
Asp 8.9 100.4 0.5
Ser 9.6 108.9 2.9
Glu 18.5 101.5 0.1
Gly 9.9 111.5 4.3
His 8.2 99.6 1.7
Arg 8.4 82.9 3.6
Thr 6.6 98.3 2.2
Ala 5.5 103.9 3.2
Pro 7.4 105.2 3.3
Tyr 3.7 96.5 2.8
Cys-Cys 9.3 83.8 2.0
Val 1.9 102.5 0.2
Met 3.4 76.0 1.1
Lys 4.0 94.7 1.0
Ile 1.8 103.0 2.0
Leu 2.0 103.3 2.0
Phe 2.0 95.2 2.8 a The detection limits in soil samples were estimated from the detection limit of a 0.5 µM amino
acid standard on column based on a S/N ratio of 3:1 and the recovery of amino acids in spiked
samples. b
the recovery was evaluated by spiking amino acid standard at concentration of 125 µmol kg-
1dry soil and then the amino acids in the spiked sample and the same soil samples without
spiking were also determined at the same time. The recover was calculated as the percentage of
the concentration differences relative to the spiked concentration. c n = 3
4.3.6. Statistical analysis
The composition was calculated as the molar percentages of each amino acid out of total.
The concentrations were measured at dry soil basis. Concentration of total hydrolysable amino
acids (THAAs) was calculated as the sum of each HAA. The composition and concentration
variations of the FAAs between A and C horizons was evaluated by matched pair’s t test analysis.
The composition and concentration of HAAs between A and C horizons, along mean annual
temperature (MAT) and precipitation (MAP) gradients, and among different vegetation covers
were analyzed with multiple comparisons of means using a Tukey’s HSD (honest significant
95
difference), provided by JMP Pro 11 (SAS Software, Cary, NC), and nonmetric
multidimensional scaling (NMS), provided by PC-ORD ver.6 (MjM Software, OR). Linear
regression was performed by JMP. Amino acid Cys-Cys and Met was discarded while
performing NMS to reduce noise due to the fact that their levels in most of the samples were
below their detection limit. The significance level α was set at 0.05.
4.4. Results and discussion
4.4.1. The composition and concentrations of HAAs
Totally 20 peaks at the chromatogram were baseline separated (Figure 4.1), including the
derivatives of 17 amino acids, ammonium, L-norvaline (internal standard) and 6-aminoquinoline
(by product of derivatization). The chromatogram was clean and with no noticeable interferences,
allowing a good quantification of amino acid derivatives. Among the 17 HAAs identified, Asn
and Gln were transformed to Asp and Glu respectively during the hydrolysis. The concentrations
of Asp thus were reported as the sum of Asp and Asn, and Glu as the sum of Glu and Gln. A
trace levels of Met and Cys-Cys were recovered in our results due to oxidation loss during
hydrolysis (Davidson, 1997), similar to the reports by Rovira et al. (2008).
The THAA pool was dominated by eight amino acids: Asp, Ser, Glu, Gly, Thr, Ala, Pro
and Val, which were summed to 58 to 88 % of the THAA pool. The average mole percent
relative to total were 15 % for Asp, 7 % for Ser, 11 % for Glu, 16 % for Gly, 7 % for Thr, 6 %
for Pro, 12 % for Ala, and 5 % for Val in all the soil samples. Amino acids Arg, Lys, Ile, Leu,
and Phe each composts of < 5% of the THAAs. Neutral amino acids took the largest part of
THAA pool, followed by acidic, basic and aromatic amino acids, which were, on average, 61 %,
26 %, 9 %, and 4 % in molar percentages relative to THAAs, respectively. These results are
congruent with most of the previous studies (Gotoh et al., 1986a; Friedel and Scheller, 2002).
96
The average molar composition of the 17 HAAs in our research are generally consistent with the
relative abundance of amino acids (mostly hydrolysable) from more than 100 different
environmental conditions (eg., Aquatic, terrestrial and host-associated) (Moura et al., 2013). It is
suggested the HAAs were majorly of plant origin rather than microbial origin (Friedel and
Scheller, 2002).
The overall concentration of THAAs ranged from 18 mg kg-1
to 15 g kg-1
(corresponding
to ~ 3 mg to 2.5 g hydrolysable amino acid-N per kilogram of dry soil). The average
concentrations of the eight dominant amino acids were for 330 mg kg-1
for Asp (35 mg amino
acid-N kg-1
), 145 mg kg-1
for Ser (20 mg amino acid-N kg-1
), 274 mg kg-1
for Glu (27 mg amino
acid-N kg-1
), 209 mg kg-1
for Gly (39 mg amino acid-N kg-1
), 152 mg kg-1
for Thr (18 mg amino
acid-N kg-1
), and 194 mg kg-1
for Ala (31 mg amino acid-N kg-1
). These concentrations are
consistent with the previous reported results (Senwo and Tabatabai, 1998; Hou et al., 2009;
Creamer et al., 2013).
97
Figure 4. 1. Chromatograms of (A) amino acid derivatives with amino acids standard (10µM)
and (B) the amino acids in a surface soil sampled from a pasture area in Minnesota. Peaks: 1= 6-
aminoquinoline; 2=Asp; 3=Ser; 4=Glu; 5=Gly; 6=His; 7=NH4+; 8=Arg; 9=Thr; 10=Ala; 11=Pro;
12=Tyr; 13=Cys-Cys; 14=Val; 15=Met; 16=L-norvaline; 17=Lys; 18=Ile; 19=Leu; 20=Phe. The
peak between 16 and 17 in (A) is ornithine.
4.4.2. HAAs in A and C horizons
The composition of the HAAs was fairly uniform between A and C horizons, though
fluctuations of the proportions of certain major amino acids were noted (Figure 4.2). The overall
m in10 15 20 25 30 35
LU
0
10
20
30
40
50
60
1
23
4
5
6
7
8
9
10
11
12
14
15
16 17
18
19
20
m in10 15 20 25 30 35
LU
0
5
10
15
20
25
30
35
40
45
1
2
3
45
6
7
8 9 1011
12
13
14
15
16
17
18
1920A.
B.
98
variations of the major amino acids in A and C horizon were less pronounced than that of FAAs.
The variation of Asp was more pronounced than that of other HAAs.
Figure 4. 2. Average composition of individual and sum of eight major HAAs in samples of two
soil horizons from different transects. Scale on right side applies to sum of eight major FAA
proportions. Different lower case letters indicate statistical significance based on pairwise
comparison (α = 0.05). Values are expressed as mean ± Standard Error of Mean (SEM).
Table 4. 2. Concentrations (mg kg-1
dry soil) of major HAAs and THAAs.
A horizon C horizon
Range Average Range Average
Asp 33 - 1899 549 3 - 966 111
Ser 12 - 1012 250 2 - 581 39
Glu 31 - 1869 466 1 - 802 83
Gly 24 - 1315 360 2 - 615 58
Thr 10 - 1034 259 1 - 464 44
Ala 22 - 1378 332 1 - 568 55
Pro 8 - 924 219 < 361 33
Val 4 - 991 197 0.1 - 301 31
THAAs 211 - 15110 3646 18 - 6442 618
Asp Ser Glu Gly Thr Ala Pro Val N.a.N. SUMAm
ino
ac
id c
om
pso
itio
n r
ela
tive
to
to
tal (m
ol %
)
0
2
4
6
8
10
12
14
16
18
0
20
40
60
80
100
A horizon
C-horizon a
b
ab
ab
ab
ab
99
The concentration ranges of each major HAAs and THAAs were summarized in Table
4.2 and plotted in Figure 4.3.The sum of the 17 amino acids, defined as THAAs, ranged from
211 mg kg-1
to 15 g kg-1
in A horizon soils and 18 mg kg-1
to 6.4 g kg-1
in C horizon soils. The
average concentration of the major HAAs and THAAs in this study are congruent with the
previously reported value in the Hawaiian soils (about dozens g kg-1
soil in total)(Mikutta et al.,
2010), the corn tillage field soils (about several g kg-1
soil in total)(Martens and Loeffelmann,
2003), four kinds of surface soils from China (about several g kg-1
soil in total, Ultisol, Alfisol,
Inceptisol and Mollisol), and the organic matter from the cropping surface soils (about 566 to
1509 mg kg-1
soil in total)(Senwo and Tabatabai, 1998). If an average ratio of 1.4 mole N per
mole of amino acids was used to convert concentrations of THAAs to that of total hydrolysable
amino acid-N (Rothstein, 2009a), the total amino acid-N was ranged from 35 to 2488 mg kg-1
dry soil and from 2 to 1065 mg kg-1
dry soil in A and C horizon, respectively. The reported
concentrations of total hydrolysable amino acid-N in New Zealand soil profiles fell well in this
range (from 566 to 2084 mg kg-1
soil in A horizon and 28 to 156 mg kg-1
soil in C horizon) (Goh,
1972). The concentrations of HAAs were significantly higher in A horizon than that in C horizon
(Figure 4.3). The similar trend was also found for other HAAs. The higher amount of HAAs in
the A- than in the C-horizon soils was mostly ascribed to the high organic matter content in the
surface soil (Gotoh et al., 1986a; Praveen et al., 2002a; Frunze, 2011). The concentrations of
THAAs and each major HAA were positively correlated with soil organic C content (P<0.001)
(Figure 4.4), because the organic N is always connected to a C backbone. The THAAs were also
found by Senwo and Tabatabai (1998) to be highly correlated with the soil organic C from the
ten surface soils of two cropping systems. The high positive correlations also suggest the
proteinaceous compounds are associated with soil organic matter by forming complexes with
100
recalcitrant substances such as lignins, tannins and polyphenols (Warman and Isnor, 1991; Rillig
et al., 2007). At sites with low C content (low C:N ratio, high organic C quality), soil amino acid
concentrations were less scattered than that in sites with high C content (high C:N ratio, low
organic C quality) (Figure 4.4), possibly driven by the quality of soil organic C.
Figure 4. 3. Average concentration of eight major HAAs and HAAs in two horizon soils from
different transects. Scale on right side applies to THAAs. Different lower case letters indicate
statistical significance among groups (α=0.05). Values are expressed as mean ± SEM.
Asp Ser Glu Gly Thr Ala Pro Val N.a.N.THAAsAm
ino
ac
id c
on
ce
ntr
ati
on
s (
mg
kg
-1 d
ry s
oil)
0
100
200
300
400
500
600
700
0
1000
2000
3000
4000
5000
A horizon
C horizon a
b
a
a
a
a
a
aa
a
b
bb
b bb b
b
101
Figure 4. 4. Linear relationship between the concentrations of THAAs and major HAAs with
total soil organic C content (wt%) from two horizons.
4.4.3. Variations of HAAs along MAT and MAP gradients of continental United States
In order to illustrate large-scale patterns of amino acid distribution with the climatic
gradients, the sampling sites were grouped into coherent, sub-continental areas (Figure 3.1).
Consistent trends of HAA variations with environmental controls were found (Figure 4.5).
Concentrations of eight major HAAs and THAAs decreased with increasing MAT in the A-
horizon soils, but an opposite trend was found in the C-horizon soils. Similarly, the levels of
eight major HAAs and THAAs exhibited an overall increasing trend with increasing MAP in the
A-horizon soils. In C horizon, however, as the precipitation went up from the west to the east,
0 2 4 6 8 10
0
20
40
60
80
100
Y=2.089+13.029X
R2=0.667P<0.001
THAAs
0 2 4 6 8 10
0
2
4
6
8
10
12
14
Y=0.694+1.336X
R2=0.553P<0.0001
Asp
0 2 4 6 8 10
0
2
4
6
8
10
Y=0.212+0.889X
R2=0.605P<0.0001
Ser
0 2 4 6 8 10
Am
ino
ac
id c
on
ce
ntr
ati
on
s (
mm
ol kg
-1 d
ry s
oil)
0
2
4
6
8
10
12
Y=0.450+1.007X
R2=0.576P<0.0001
Glu
0 2 4 6 8 10
0
2
4
6
8
10
12
14
16
Y=0.602+1.634X
R2=0.616P<0.0001
Gly
0 2 4 6 8 10
0
2
4
6
8
Y=0.280+0.759X
R2=0.562P<0.0001
Thr
0 2 4 6 8 10
0
2
4
6
8
10
12
14
Y=0.449+1.314X
R2=612P<0.0001
Ala
0 2 4 6 8 10
0
1
2
3
4
5
6
7
Y=0.220+0.667X
R2=0.556P<0.0001
Pro
Total soil organic C content (wt. %)
0 1 2 3 4 5 6
0
2
4
6
8
C horizon
A horizon
Y=-0.044+0.929X
R2=845P<0.0001
Val
102
the concentrations decreased. But at the west coast, the concentrations rose and become the
highest among the four sub-continental areas.
Figure 4. 5. Average concentrations of eight major HAAs and THAAs in A- and C-horizon soils
along the MAT and MAP gradients. Scales on right side apply to THAAs. The above two and
the bottom two sub-figures indicate the trend of amino acid level with MAT and MAP,
respectively. Temperature or precipitation gradients shown in the legends differentiated by color
are from the circle areas specified in Figure 3.1. Values were expressed as mean ± SEM. # mean
annual temperature; * mean annual precipitation; § soils from west coast.
C horizon
Asp Ser Glu Gly Thr Ala Pro Val N.a.N. THAAs
0.0
0.5
1.0
1.5
2.0
2.5
0
2
4
6
8
10
12
Asp Ser Glu Gly Thr Ala Pro Val N.a.N. THAAs
Am
ino
acid
co
nc
en
trati
on
s (
mm
ol
kg
-1 d
ry s
oil
)
0
1
2
3
4
5
6
7
0
10
20
30
40
50
35-50
50-55
55-60
A horizon
Asp Ser Glu Gly Thr Ala Pro Val N.a.N. THAAs
0
2
4
6
8
10
0
10
20
30
40
50
60
70
Asp Ser Glu Gly Thr Ala Pro Val N.a.N. THAAs
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
0
5
10
15
20
25
4-20
20-40
40-60
40-60
¡
§
A horizon
C horizon
#MAT (
oF)
*MAP (in)
103
The N-S temperature gradient has less dramatic precipitation changes but has significant
differences in MAT, and the W-E precipitation gradient has less distinct temperature variations
but has important differences in MAP. Temperature and precipitation, therefore, are the major
controlling factors along the N-S transect and W-E transect, respectively. Cold whether inhibits
the decomposition rates of organic matter, thus, resulting in the large accumulations of organic C
at the surface soil. The organic C content in the A horizon soils, therefore, decreased with
increasing MAT from the north to the south along the N-S MAT transects. High precipitation
increases the organic C accumulations by increasing above-ground vegetation productivity. The
organic C content of the A-horizon soils thus should be high at the west coast, low in the
intermediate area, and gradually increase to the east along the W-E precipitation gradient. The
pattern of organic C distribution along the two transects were illustrated further by Woodruff et
al. (2009). Since the levels of HAAs were positively correlated with soil organic C content, the
trends of decreasing levels of HAA with increasing MAT and decreasing MAP match the
patterns of soil organic C distribution in the A-horizon soils of the two transects. It is concluded
that the climatic factors affect the HAA abundance in the same way as they influence levels of
soil organic C. The importance of these environmental controls, however, switched with depth,
with root distributions the dominant affecting factor in subsoil (Jobbagy and Jackson, 2000). The
major vegetation along the N-S transect changed from crops in the north to shrubs in the south.
The vegetation along the west coast was dominated with evergreen forests. From the sub-
continental area next to the west coast to the east areas, the major vegetation changed from
shrubs/evergreen forests to pastures/grasslands/crops. Among these vegetation covers, forests
have the deepest root system and highest root biomass, followed by shrubs, grasslands/pastures,
and crops/grains (Canadell et al., 1996; Jackson et al., 1996). The subsoil organic C content
104
should be the highest for the forest sites, intermediate for pasture/grassland sites, and least for
cultivation sites. The trends of subsoil HAAs thus generally track the underground root profiles
and biomass.
4.4.4. Variations of HAAs among different vegetation covers
In general, the composition of HAAs was fairly uniform among vegetation covers (Figure
4.6). In addition, the mole abundance of each major amino acid tended to be relatively constant
with either MAT or MAP along the two transects (data not shown), although a consistent change
of the concentrations with MAT and MAP was observed. These results are in line with the
previous findings of a uniform HAA distribution in soils of various cropping and cultivating
systems or from different climate zones (Sowden et al., 1977; Gotoh et al., 1986a; Senwo and
Tabatabai, 1998; Friedel and Scheller, 2002). Soil protein and peptides originate from plant
residues and microbial biomass. The amino acid composition from fresh plants or living
microorganisms, however, was supposed to differ from that in the bulk soils (Stouthamer, 1973;
Scheller et al., 2000; Friedel and Scheller, 2002; Creamer et al., 2013).The reasons why the bulk
HAA composition remains no substantial change despite of the dissimilar composition in
original plant residues and litter are unclear. Possibly, there is an N transformation process after
the plant residues and microbial biomass are stabilized to the SOM, which, once aged, doesn’t
show significant chemical alternation. Study by Miltner et al. (2009) suggested the microbial
amino acids were quickly turned over in the microbial food web or were stabilized into non-
living SOM. And the redistribution of microbial biomass into non-living SOM doesn’t alter the
chemical composition of SOM significantly and the composition of amino acids gradually tended
to approach a stable level similar to that of bulk SOM. Likewise, as suggested by Friedel and
Scheller (2002), the amino acid transformation already takes place at the first stage of
105
degradation of plant residues. In view of this, it is postulated although the amino acid
composition from plant residue or living microorganism was distinguished from that of the bulk
soil, in the processes of forming SOM, there is a conversion process to allow the amino acid
composition to reach a steady state. After the bulk SOM formation, there is a tendency for the
chemical composition of the organic N pool to be maintained in soils (Gotoh et al., 1986a). And
the minor fluctuations may be caused by the limited amount of amino acids in living
microorganisms or particulate organic matter.
By summarizing results of the previous work on the relative distribution of HAAs, we
found that the statements on uniformness of HAA composition were subjective to the statistic
methodologies. For example, only small differences were found in the patterns of HAA
distribution related to treatments when the data were plotted as curves, but measurable
distinction in the patterns of HAA distribution was seen between soils with and without manure
application by employing the multivariate analysis (Beavis and Mott, 1996, 1999; Rovira et al.,
2008; Moura et al., 2013). This suggested the multivariate methods may be a good statistical tool
to fingerprint the amino acids. Multivariate analysis by NMS showed the composition of HAA in
this research were relatively uniform along the two transects and in two soil horizons, suggesting
the minimum climatic effects on composition alteration of HAAs.
106
Figure 4. 6. Average composition of individual and sum of eight dominant HAAs in A and C
horizon soils with different vegetation cover. Scales on right side applies to sum of eight major
HAA proportions. Different lower case letters indicate statistical significance among groups
(α=0.05). Values are expressed as mean ± SEM.
In A horizon, the pattern of the total organic C content among the four vegetation covers
generally matches that of above-ground biomass inputs (Mendoza-Ponce and Galicia, 2010)
(Figure 4.7). The levels of THAAs among the four vegetation covers, however, didn’t follow that
of total organic C content, with higher THAA concentrations in grassland and pasture soils than
Asp Ser Glu Gly Thr Ala Pro Val N.a.N. SUM
Co
mp
osit
ion
of
am
ino
acid
s r
ela
tive t
o t
ota
l (m
ol
%)
0
2
4
6
8
10
12
14
16
18
20
0
20
40
60
80
100
Evergreen
Grassland
Pasture
Shrub
Asp Ser Glu Gly Thr Ala Pro Val N.a.N. SUM
0
2
4
6
8
10
12
14
16
18
20
0
20
40
60
80
100
A horizon
C horizon
a a
bab
ab
aab
b
ab aba
b
a
babab
a
a
b
ab
ab abab
aba
bab
107
expected (Figure 4.7). This is likely because the low quality organic inputs (high C-to-N ratio
and lignin to N ratio) in the woody forest and shrub sites inhibited the initial microbial
breakdown of plant residues than grassland and pasture sites, thereby decreasing the protein-
substrate availability (Aitkenhead and McDowell, 2000; Lovett et al., 2004; Grunzweig et al.,
2007). The lowest THAA content in shrub sites was ascribed not only to the woody structure but
also the lowest aboveground biomass. The lower THAA content in C horizon and higher THAA
ratios in A-horizon to that in C-horizon soils of grassland and pasture sites suggested the organic
matter inputs were depleted in the subsoil compared to that in the surface soil (Figure 4.7). The
lower THAA levels in grassland and pasture soils compared to shrub and evergreen forest soils
were in contrast to that for the total free amino acids (TFAAs) which were found to be contained
in higher concentrations in grassland and pasture soils than other sites (Figure 3.10). This
indicates the FAA production is not only affected by protein-substrates availability but also other
factors which influence the proteolysis rates. Though hydrolysis can liberate most of the
proteinaceous substances from the mineral associates or protein-polyphenol complexes, the
proteolysis rates were reduced by those complexes (Berthrong and Finzi, 2006).
108
Figure 4. 7. Concentration of THAAs (A) and total soil organic C content (B) among four
vegetation covers in two soil horizons. A = A horizon; C = C horizon; A/C Ratio = the ratio of
average THAA level or soil total organic C content in the A horizon to that in the C horizon.
Values are expressed as mean ± SEM.
Evergreen Grassland Pasture Shrub
Co
nc
en
tra
tio
ns
of
TH
AA
s (
mm
ol k
g-1
dry
so
il)
0
10
20
30
40
50
Co
nc
en
tra
tio
n r
ati
o (
A/C
)
-14
-12
-10
-8
-6
-4
-2
0
2
4
6
8
10
12
14A
C
A/C Ratio
Vegetation
Everygreen Grassland Pasture Shrub
To
tal o
rga
nic
C c
on
ten
t (w
t.%
)
0
1
2
3
4
Co
nte
nt
rati
o (
A/C
)
-4
-2
0
2
4
6A
C
A/C Ratio
109
4.4.5. Comparisons of THAAs with TFAAs and implications
In order to compare the composition of HAAs with that of FAAs, non-protein amino
acids and Trp in the FAA pool were discarded because these amino acids were either destroyed
by deamination during hydrolysis or below detection limit. Amino acid Gln in the TFAA pool
was included in the calculation of Glu because Gln was transformed to its acid form as Glu
during hydrolysis. Asn cannot be separated from Ser, so Ser of FAAs was a sum of Ser and Asn.
The composition of THAAs was mostly different from that of TFAAs, especially for Asp, Glu,
Gly and Ala (Figure 4.8).
There are several possibilities responsible for the differences of FAAs and HAAs in
composition. As we discussed before, the FAAs were produced majorly by enzymatic hydrolysis
of soil proteinaceous compounds (Schimel and Bennett, 2004), the preferential hydrolysis could
influence the composition of FAA released. Polar amino acids, which are generally located on
the surface of protein structures (Nelson et al., 2008), may be more vulnerable to enzymes while
Gly, which are more refractory from the bacterial attack than other amino acids, tend to be less
abundant as hydrolysis proceeded (Yamashita and Tanoue, 2003). A relatively high Glu and low
Gly percentages in FAAs than in HAA thus seem feasible (Figure 4.8). In addition, plant roots
FAAs and microbial cell walls contain higher Glu and Ala than Asp and Gly than in bulk soils
(Chiang and Nip, 1973; Friedel and Scheller, 2002) suggested plant root exudate, along with fine
root and microbial turnover are an important sources of soil extracted FAA.
The concentrations and composition of FAAs tend to be more diverse among sites than
that of HAAs. The concentrations of both FAAs and HAAs share generally similar trend with
environmental factors both in A and C horizons, but the composition of FAAs was more
subjective to climatic changes while a rather uniform composition of HAAs was found. In
addition, the variability of FAA composition was more pronounced between the two soil
110
horizons while a relatively uniform composition was observed for HAAs. These results
suggested FAA production is more subjective to environmental or seasonal changes while HAAs
are more stabilized in soils. Large variations of composition and concentrations of FAAs were
also observed in temperature-forest soils across the landscape fertility gradient (Rothstein, 2009b)
and in Alaskan Arctic tundra soils and a temperate grassland soil to exhibit seasonal dynamics
due to plant-driven or microbial fueled process (Weintraub and Schimel, 2005a; Warren and
Taranto, 2010). The review on literature data also shows a variable composition of FAA in
different ecosystems (Werdin-Pfisterer et al., 2009).
Hydrolysable amino acids represent soil total amino acids with and without peptide
linkages, scarcely in free form and largely in insoluble form sequestrated by organo-mineral
associates or microorganisms, entrapped in micropores or stabilized by macromolecules
(Knicker, 2011). There is a flux between of N between the soluble and insoluble N pools
(Schimel and Bennett, 2004; Chen and Xu, 2006). Regardless of whether HAAs are in soluble or
insoluble form, the majority of them is not directly bioavailable. Depolymerization is thus
needed to transform the polymeric N to bioavailable dissolved organic N such as FAAs, in
particular. The high correlations of the concentration of TFAAs with that of THAAs further
confirmed the fact that soil FAAs are majorly produced via depolymerization of HAAs (Figure
4.10). Any factors such as pH, temperature and substrate availability and quality that influence
enzymatic activities could influence the production of FAAs (Fierer et al., 2001; Weintraub and
Schimel, 2005b; Kielland et al., 2007). On average, less than 4 % of THAAs was in the form of
TFAAs (Figure 4.10). Such small fraction of organic N, however, serves as an alternative N
source for plants because plants can compete with microbes to “short circuit” amino acids (Neff
et al., 2003).
111
The capacity of plant uptake FAAs seems to be ubiquitous and has been recorded from
various plant species (Persson and Näsholm, 2001; Weigelt et al., 2005; Kielland et al., 2006;
Jämtgård et al., 2008).The uptake of amino acid by microbes was majorly used for microbial
biomass production and partially for respiration (Vinolas et al., 2001). Uptake of FAAs by plant
or microorganisms is due to active transport, mediated by specific membrane transporters
(Jämtgård, 2010). Generally, the uptake rates are concentration dependent, increasing with
increased concentrations of amino acids (Jämtgård et al., 2008). The concentration dependencies
were described by Michaelis-Menten kinetics (Vinolas et al., 2001; Jämtgård et al., 2008).
Experiment on isotopically labeled amino acids, on the other hand, showed preferential uptake of
certain FAAs in a solution with some amino acids at the same initial concentration. Weigelt et al.
(2005) found a grass species was more effective in using Gly than complex amino acid Phe while
another species preferred Ser. Such species-specific preferential uptake of amino acids by plants
has also been demonstrated by others (Miller and Bowman, 2003). Complementary preferences
for amino acids between plants and microbes were also found by Lipson et al. (1999a) and
Endres and Mercier (2003). In their studies, bromeliad and alpine sedge preferentially adsorb
Gly over other amino acid such as Gln and Glu due to decreased microbial demand for this
compound. On the contrary Jämtgård et al. (2008) found differences in amino acid (incubation
solution of Ser, Glu, Gly, Arg and Ala at four concentration levels, 2, 5, 10 and 25 µM) uptake
by barley root were small, suggesting no preferential uptake of amino acids in barley. We thus
suggested, in soils with certain vegetation cover, the preferences for FAA types are a function of
the substrate concentration, i.e. differences in uptake of different FAAs in soil solution reflect the
edaphic variations in availability of the FAAs rather than any preferential uptake of amino acids.
Among soils with different vegetation cover, on the other hand, preferential uptake of amino
112
acids could happen based on specific requirement of different vegetation for C or N. Actually,
the rapid uptake of added, isotopically labeled amino acids suggested the released FAAs in soils
were very short-lived and will be used up by microbes and plants once they are available even in
low concentrations (Jones and Kielland, 2002; Hobbie and Hobbie, 2012). It is thus suggested
these amounts of detected FAAs in soils are those protected via trapping in microaggregates,
pores or chemically sorbed, or released from fine root turnover and microbial lysis during
extraction (Hobbie and Hobbie, 2012).
113
Figure 4. 8. The average proportions of each amino acid in the HAA or FAA form.
Asp SerG
lu Gly
His
Arg ThrAla
Pro Tyr
Cys
-Cys Val
Met
Lys Ile Leu phe
Co
mp
os
itio
n o
f a
min
o a
cid
re
lati
ve t
o t
ota
l (m
ol %
)
0
5
10
15
20
25
30
35
HAAs
FAAs
A horizon
Asp SerG
lu Gly
His
Arg ThrAla
Pro Tyr
Cys
-Cys Val
Met
Lys Ile Leu phe0
5
10
15
20
25C horizon
114
Figure 4. 9. Relationship between TFAAs and THAAs in soils investigated from the 93 sites.
Red and green represent samples from C and A horizon, respectively.
4.5. Conclusions
Our result confirmed that the overall composition of HAAs was uniform irrespective of
site properties and land use. Up to eighty percent of the soil THAAs was composed of Asp, Ser,
Glu, Gly, Thr, Ala, Pro, and Val. The THAA levels were found to be related to soil organic C
content. They were significantly higher in the surface soil than in the subsoil due to more organic
substance in the surface soil. Concentrations of major HAAs and THAAs were found to decrease
with increasing MAT and decreasing MAP in the A-horizon soils along the N-S and W-E
transect, respectively. The trends were opposite in the C-horizon soils. The climatic factors
mainly affected the variation of surface HAAs via their influences on soil organic C inputs. The
levels of subsoil HAAs were majorly related to the vegetation types. Soils in the evergreen and
shrub sites contained less THAAs than grassland and pasture sites in the A horizon. This
suggested the quality of above-ground biomass plays an important role in HAA production. Soils
of evergreen forest followed by shrub sites with deep root system and large root biomass had
Concentration of THAAs (mmol kg-1 dry soil)
0 20 40 60 80 100
Co
nc
en
tra
tio
n o
f T
FA
As
(
mo
l k
g-1
so
il)
0
100
200
300
400
500
600
Y= -1.141+3.376x
r2=0.570
P<0.0001
115
more THAA content in the subsoil due to larger underground organic C biomass than soils of the
shallow-rooted grassland, pasture and cropland sites. The composition of FAAs varied more
among sites and between depth than that of HAAs, suggesting that FAAs are more voluntary to
environmental factors and epidemic changes. On average, less than 4 % of HAAs was in the
form of FAAs. HAAs served as substrate for FAA enrichment. The differences of relative molar
percentages of major amino acids between FAAs and HAAs were possibly due to preferential
hydrolysis of proteinaceous compounds, FAAs enrichment by microbial or fine root turnover and
root exudate, as well as the sorption of FAAs to soil.
4.6. Acknowledgements
We thank USGS for proving the soil samples. We thank the financial support of USDA-
AFRI (award #2012-67019-30227).
116
4.7. References
AccQ, W., 1993. Tag chemistry package instruction manual. Millipore Waters Chromatography.
Aitkenhead, J.A., McDowell, W.H., 2000. Soil c : N ratio as a predictor of annual riverine doc
flux at local and global scales. Global Biogeochemical Cycles 14, 127-138.
Amelung, W., Zhang, X., 2001. Determination of amino acid enantiomers in soils. Soil Biology
and Biochemistry 33, 553-562.
Baca, M.T., Fernandez-Fígares, I., De Nobili, M., 1994. Amino acid composition of composting
cotton waste. Science of The Total Environment 153, 51-56.
Beavis, J., Mott, C.J.B., 1996. Effects of land use on the amino acid composition of soils: 1.
Manured and unmanured soils from the broadbalk continuous wheat experiment,
rothamsted, england. Geoderma 72, 259-270.
Beavis, J., Mott, C.J.B., 1999. Effects of land use on the amino acid composition of soils:: 2.
Soils from the park grass experiment and broadbalk wilderness, rothamsted, england.
Geoderma 91, 173-190.
Berthrong, S.T., Finzi, A.C., 2006. Amino acid cycling in three cold-temperate forests of the
northeastern USA. Soil Biology & Biochemistry 38, 861-869.
Boas, N.F., 1953. Method for the determination of hexosamines in tissues. Journal of Biological
Chemistry 204, 553-563.
Brodowski, S., Amelung, W., Lobe, I., Preez, C., 2005. Losses and biogeochemical cycling of
soil organic nitrogen with prolonged arable cropping in the south african highveld —
evidence from d- and l-amino acids. Biogeochemistry 71, 17-42.
Butterbach-Bahl, K., Gundersen, P., Ambus, P., Augustin, J., Beier, C., Boeckx, P., Dannenmann,
M., Sanchez Gimeno, B., Ibrom, A., Kiese, R., 2011. Nitrogen processes in terrestrial
117
ecosystems. The European nitrogen assessment: sources, effects and policy perspectives,
99-125.
Campbell, C.A., 1991. Thirty-year crop rotations and management practices effects on soil and
amino nitrogen. Soil Science Society of America journal 55, 739.
Canadell, J., Jackson, R.B., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996.
Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583-595.
Chen, C.R., Xu, Z.H., 2006. On the nature and ecological functions of soil soluble organic
nitrogen (son) in forest ecosystems. Journal of Soils and Sediments 6, 63-66.
Chiang, M.S., Nip, W.K., 1973. Free amino acid content in leaf and root tissues of clubroot-
resistant and clubroot-susceptible cabbages. Euphytica 22, 393-398.
Creamer, C., Filley, T., Olk, D., Stott, D., Dooling, V., Boutton, T., 2013. Changes to soil
organic n dynamics with leguminous woody plant encroachment into grasslands.
Biogeochemistry 113, 307-321.
Davidson, I., 1997. Hydrolysis of samples for amino acid analysis, In: Smith, B. (Ed.), Protein
sequencing protocols. Humana Press, pp. 119-129.
Ding, G., Mao, J., Xing, B., 2001. Characteristics of amino acids in soil humic substances.
Communications in Soil Science and Plant Analysis 32, 1991-2005.
Endres, L., Mercier, H., 2003. Amino acid uptake and profile in bromeliads with different habits
cultivated in vitro. Plant Physiology and Biochemistry 41, 181-187.
Fierer, N., Schimel, J.P., Cates, R.G., Zou, J.P., 2001. Influence of balsam poplar tannin fractions
on carbon and nitrogen dynamics in alaskan taiga floodplain soils. Soil Biology &
Biochemistry 33, 1827-1839.
118
Friedel, J.K., Scheller, E., 2002. Composition of hydrolysable amino acids in soil organic matter
and soil microbial biomass. Soil Biology and Biochemistry 34, 315-325.
Frunze, N.I., 2011. Amino acid pool of a typical chernozem of moldova. Eurasian Soil Science
44, 1139-1143.
Goh, K.M., 1972. Amino acid levels as indicators of paleosols in new zealand soil profiles.
Geoderma 7, 33-47.
Gotoh, S., Araragi, M., Koga, H., Ono, S.-i., 1986a. Hydrolyzable organic forms of nitrogen in
some rice soil profiles as affected by organic matter application. Soil Science and Plant
Nutrition 32, 535-550.
Grunzweig, J.M., Gelfand, I., Fried, Y., Yakir, D., 2007. Biogeochemical factors contributing to
enhanced carbon storage following afforestation of a semi-arid shrubland.
Biogeosciences 4, 891-904.
Hobbie, J., Hobbie, E., 2012. Amino acid cycling in plankton and soil microbes studied with
radioisotopes: Measured amino acids in soil do not reflect bioavailability.
Biogeochemistry 107, 339-360.
Hou, S., He, H., Zhang, W., Xie, H., Zhang, X., 2009. Determination of soil amino acids by high
performance liquid chromatography-electro spray ionization-mass spectrometry
derivatized with 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate. Talanta 80, 440-447.
Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996. A
global analysis of root distributions for terrestrial biomes. Oecologia 108, 389-411.
Jämtgård, S., 2010. <the occurrence of amino acids in agricultural soil and their uptake by
plants.Pdf>. Diss. (sammanfattning/summary) Umeå : Sveriges lantbruksuniv., Acta
Universitatis agriculturae Sueciae,1652-6880.
119
Jämtgård, S., Näsholm, T., Huss-Danell, K., 2008. Characteristics of amino acid uptake in barley.
Plant and Soil 302, 221-231.
Jobbagy, E.G., Jackson, R.B., 2000. The vertical distribution of soil organic carbon and its
relation to climate and vegetation. Ecological applications 10, 423-436.
Jones, D.L., Kielland, K., 2002. Soil amino acid turnover dominates the nitrogen flux in
permafrost-dominated taiga forest soils. Soil Biology & Biochemistry 34, 209-219.
Kielland, K., McFarland, J., Olson, K., 2006. Amino acid uptake in deciduous and coniferous
taiga ecosystems. Plant and Soil 288, 297-307.
Kielland, K., McFarland, J.W., Ruess, R.W., Olson, K., 2007. Rapid cycling of organic nitrogen
in taiga forest ecosystems. Ecosystems. 10, 360-368.
Kleber, M., Sollins, P., Sutton, R., 2007. A conceptual model of organo-mineral interactions in
soils: Self-assembly of organic molecular fragments into zonal structures on mineral
surfaces. Biogeochemistry 85, 9-24.
Knicker, H., 2011. Soil organic n - an under-rated player for c sequestration in soils? Soil
Biology & Biochemistry 43, 1118-1129.
Küry, D., Keller, U., 1991. Trimethylsilyl-o-methyloxime derivatives for the measurement of
[6,6-2h2]-d-glucose-enriched plasma samples by gas chromatography-mass spectrometry.
Journal of Chromatography B: Biomedical Sciences and Applications 572, 302-306.
Lipson, D.A., Raab, T.K., Schmidt, S.K., Monson, R.K., 1999a. Variation in competitive
abilities of plants and microbes for specific amino acids. Biology and Fertility of Soils 29,
257-261.
Liu, H.J., Sanuda-Pena, M.C., Harvey-White, J.D., Kalra, S., Cohen, S.A., 1998. Determination
of submicromolar concentrations of neurotransmitter amino acids by fluorescence
120
detection using a modification of the 6-aminoquinolyl-n-hydroxysuccinimidyl carbamate
method for amino acid analysis. Journal of Chromatography A 828, 383-395.
Lovett, G.M., Weathers, K.C., Arthur, M.A., Schultz, J.C., 2004. Nitrogen cycling in a northern
hardwood forest: Do species matter? Biogeochemistry 67, 289-308.
Martens, D.A., Loeffelmann, K.L., 2003. Soil amino acid composition quantified by acid
hydrolysis and anion chromatography−pulsed amperometry. Journal of Agricultural and
Food Chemistry 51, 6521-6529.
Mendoza-Ponce, A., Galicia, L., 2010. Aboveground and belowground biomass and carbon
pools in highland temperate forest landscape in central mexico. Forestry 83, 497-506.
Mikutta, R., Kaiser, K., Doerr, N., Vollmer, A., Chadwick, O.A., Chorover, J., Kramer, M.G.,
Guggenberger, G., 2010. Mineralogical impact on organic nitrogen across a long-term
soil chronosequence (0.3-4100 kyr). Geochimica Et Cosmochimica Acta 74, 2142-2164.
Miller, A., Bowman, W., 2003. Alpine plants show species-level differences in the uptake of
organic and inorganic nitrogen. Plant and Soil 250, 283-292.
Miltner, A., Kindler, R., Knicker, H., Richnow, H.-H., Kästner, M., 2009. Fate of microbial
biomass-derived amino acids in soil and their contribution to soil organic matter. Organic
Geochemistry 40, 978-985.
Moura, A., Savageau, M.A., Alves, R., 2013. Relative amino acid composition signatures of
organisms and environments. PLoS ONE 8, e77319.
Neff, J.C., Chapin, F.S., Vitousek, P.M., 2003. Breaks in the cycle: Dissolved organic nitrogen in
terrestrial ecosystems. Frontiers in Ecology and the Environment 1, 205-211.
Nelson, D.L., Lehninger, A.L., Cox, M.M., 2008. Lehninger principles of biochemistry.
Macmillan.
121
Paungfoo-Lonhienne, C., Lonhienne, T.G.A., Rentsch, D., Robinson, N., Christie, M., Webb,
R.I., Gamage, H.K., Carroll, B.J., Schenk, P.M., Schmidt, S., 2008. Plants can use protein
as a nitrogen source without assistance from other organisms. Proceedings of the
National Academy of Sciences of the United States of America 105, 4524-4529.
Persson, J., Näsholm, T., 2001. Amino acid uptake: A widespread ability among boreal forest
plants. Ecology Letters 4, 434-438.
Praveen, K., Tripathi, K., Aggarwal, R., 2002a. Influence of crops, crop residues and manure on
amino acid and amino sugar fractions of organic nitrogen in soil. Biology and Fertility of
Soils 35, 210-213.
Rillig, M., Caldwell, B., Wösten, H.B., Sollins, P., 2007. Role of proteins in soil carbon and
nitrogen storage: Controls on persistence. Biogeochemistry 85, 25-44.
Rothstein, D., 2009a. Soil amino-acid availability across a temperate-forest fertility gradient.
Biogeochemistry 92, 201-215.
Rothstein, D.E., 2009b. Soil amino-acid availability across a temperate-forest fertility gradient.
Biogeochemistry 92, 201-215.
Rovira, P., Fernàndez, P., Coûteaux, M.M., Ramón Vallejo, V., 2005. Changes in the amino acid
composition of decomposing plant materials in soil: Species and depth effects.
Communications in Soil Science and Plant Analysis 36, 2933-2950.
Rovira, P., Kurz-Besson, C., Hernàndez, P., Coûteaux, M.-M., Vallejo, V.R., 2008. Searching for
an indicator of n evolution during organic matter decomposition based on amino acids
behaviour: A study on litter layers of pine forests. Plant and Soil 307, 149-166.
Scheller, E., Friedel, J., Alföldi, T., Lockeretz, W., Niggli, U., 2000. Amino acids in soils, humic
substances and soil microbial biomass, IFOAM 2000: the world grows organic.
122
Proceedings 13th International IFOAM Scientific Conference, Basel, Switzerland, 28 to
31 August, 2000. vdf Hochschulverlag AG an der ETH Zurich, pp. 6-9.
Schimel, J.P., Bennett, J., 2004. Nitrogen mineralization: Challenges of a changing paradigm.
Ecology 85, 591-602.
Schulten, H.R., Schnitzer, M., 1997. The chemistry of soil organic nitrogen: A review. Biology
and Fertility of Soils 26, 1-15.
Senwo, Z.N., Tabatabai, M.A., 1998. Amino acid composition of soil organic matter. Biology
and Fertility of Soils 26, 235-242.
Smith, D.B., Cannon, W.F., Woodruff, L.G., Solano, F., Kilburn, J.E., Fey, D.L., 2013.
Geochemical and mineralogical data for soils of the conterminous united states. Data
series 801.
Sollins, P., Homann, P., Caldwell, B.A., 1996. Stabilization and destabilization of soil organic
matter: Mechanisms and controls. Geoderma 74, 65-105.
Sowden, F.J., Chen, Y., Schnitzer, M., 1977. Nitrogen distribution in soils formed under widely
differing climatic conditions. Geochimica Et Cosmochimica Acta 41, 1524-1526.
Stouthamer, A.H., 1973. A theoretical study on the amount of atp required for synthesis of
microbial cell material. Antonie van Leeuwenhoek 39, 545-565.
Vinolas, L.C., Healey, J.R., Jones, D.L., 2001. Kinetics of soil microbial uptake of free amino
acids. Biology and Fertility of Soils 33, 67-74.
Warman, P.R., Isnor, R.A., 1991. Amino-acid-composition of peptides present in organic-matter
fractions of sandy loam soils. Soil Science 152, 7-13.
Warren, C.R., Taranto, M.T., 2010. Temporal variation in pools of amino acids, inorganic and
microbial n in a temperate grassland soil. Soil Biology and Biochemistry 42, 353-359.
123
Weigelt, A., Bol, R., Bardgett, R.D., 2005. Preferential uptake of soil nitrogen forms by
grassland plant species. Oecologia 142, 627-635.
Weintraub, M., Schimel, J., 2005a. The seasonal dynamics of amino acids and other nutrients in
alaskan arctic tundra soils. Biogeochemistry 73, 359-380.
Weintraub, M.N., Schimel, J.P., 2005b. Seasonal protein dynamics in alaskan arctic tundra soils.
Soil Biology & Biochemistry 37, 1469-1475.
Werdin-Pfisterer, N.R., Kielland, K., Boone, R.D., 2009. Soil amino acid composition across a
boreal forest successional sequence. Soil Biology & Biochemistry 41, 1210-1220.
Woodruff, L.G., Cannon, W.F., Eberl, D.D., Smith, D.B., Kilburn, J.E., Horton, J.D., Garrett,
R.G., Klassen, R.A., 2009. Continental-scale patterns in soil geochemistry and
mineralogy: Results from two transects across the united states and canada. Applied
Geochemistry 24, 1369-1381.
Yamashita, Y., Tanoue, E., 2003. Distribution and alteration of amino acids in bulk dom along a
transect from bay to oceanic waters. Marine Chemistry 82, 145-160.
124
5. Carbon K-edge Near Edge X-ray Fine Structure Spectroscopic
Investigation of Organic Carbon Speciation in Soils of North-South
and West-East Transects of Continental United States
L. Maa, Jinyoung Moon, K. Xia
a*, M. A. Williams
b, and D. B. Smith
c
aDepartment of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State
University, Blacksburg, VA 24061, USA
b Rhizosphere and Soil Microbial Ecology Laboratory, Department of Horticulture, Virginia
Polytechnic Institute and State University, Blacksburg, VA 24061, USA
cUS Geological Survey, MS 973, Denver, CO 80225, USA
*Corresponding author. Email address: [email protected]
125
5.1. Abstract
Soil is the largest reservoir of terrestrial C. The studies of the distribution of organic C
speciation are of significance to better understanding of soil C sequestration. In this study, the
organic C speciation in surface and subsurface soils from north-south and west-east transects of
continental United States was investigated using C K-edge Near Edge X-ray Fine Structure
Spectroscopy. The aim is to explore the effect of climatic factors on soil organic C speciation at
different soil depth. Carbon K-edge spectra showed the presence of carboxylic-C (38%),
aliphatic-C (~ 22%), aromatic-C (~ 18%) and O/N-alkyl-C (~ 16%) and phenolic-C (< 6%)
moieties. Although certain fluctuations of the proportions of aromatic-C and phenolic-C species
were observed, the overall composition of soil organic C was relatively uniform among sites and
between two horizons irrespective of the variations in organic C content. Factors such as
temperature and vegetation cover were revealed in this study to account for the changes of pool
size of aromatic-C and phenolic-C species. Phenolic-C may be a good indicator for the effect of
temperature or vegetation on the composition of soil organic C. The study showed synchrotron
based Near Edge X-ray Fine Structure Spectroscopy was a powerful technique to reveal
chemical structure of soil organic matter.
126
5.2. Introduction
On average, 70% of the terrestrial C is in soils (Eswaran et al., 1993). The enrichment of
soil organic C (SOC) enhances the nutrient supply to crops, thus improves soil quality and crop
productivity (Cooper et al., 2004). Degradation of SOC, ultimately released as CO2 to
atmosphere, contributes to the greenhouse effect (Hansen et al., 1981). The decomposition,
stability and sequestration of SOC play a paramount role in climate change and agricultural
management (Dai et al., 2011).
The major forms of SOC in soils are aliphatic-C, carboxylic-C, aromatic-C, phenolic-C
and polysaccharides (Kunlanit et al., 2014). The general composition of SOC, revealed by 13
C
nuclear magnetic resonance (NMR), was reported to be relatively uniform despite a wide range
of land use and climatic conditions (Mahieu et al., 1999) and great chemical heterogeneity of
SOC was also observed at nano-scale soil aggregates as revealed by STXM (Schumacher et al.,
2005; Kinyangi et al., 2006b). Some studies indicated the chemical composition of SOC can be
altered by organic residues with contrasting C quality (Kunlanit et al., 2014), vegetation types
(Quideau et al., 2001; Hannam et al., 2004), pedogenic environment (Rumpel et al., 2002) and
anthropogenic perturbations (Solomon et al., 2002; Solomon et al., 2007). The major mechanism
of the effect of these factors on chemical alteration of SOC was possibly associated with
different rates of decomposition (Rovira and Vallejo, 2002). Cellulose, hemicelluloses, and
lignin, the major components of plant materials, degrade into phenols and aromatics, etc (Kögel-
Knabner, 2002; Jokic et al., 2003). The decomposition or anthropogenic perturbations foster the
repletion of labile proteinaceous and carbohydrate fractions and the accumulation of refractory
compounds (Solomon et al., 2002; Keiluweit et al., 2010). A clear knowledge of SOC
composition helps decipher mechanisms involved in SOC accumulations from biologically
127
contrasting organic residues and betters our understanding of vegetation and climate driven
alteration of SOC composition in soils, which provide us insights into the regulation of SOC
stabilization and C sequestration (Alvarez-Arteaga et al., 2012; Kunlanit et al., 2014). So far,
most of the previous investigations of the composition of SOC were limited to ecosystems in a
small scale. Collecting such soils information over a wide range of land use remains largely
unknown.
Many techniques such as NMR, 13
C CPMAS NMR in particular (Mahieu et al., 1999),
pyrolysis field-ionization mass-spectrometry (Py-FIMS), secondary ion mass spectrometry
(SIMS), X-ray photoelectron spectroscopy (XPS), stable isotopic composition and radiocarbon
dating, and transmission electron microscopy (TEM)(Beyer et al., 1992; Victoria et al., 1995;
Bird and Pousai, 1997; Martin et al., 2002; Gerin et al., 2003; Gonzalez Perez et al., 2004; Jones
and Singh, 2014; Poch and Virto, 2014) have been used for soil organic matter characterization.
NMR spectroscopy can provide most of the information about the functional groups of SOC, but
suffers from interferences by paramagnetic minerals (Li et al., 2013). Py-FIMS yields
information of the decomposition products of the volatile macromolecules but cannot resolve
elemental functionality (Gillespie et al., 2009). Although laboratory spectroscopy and
microscopy techniques such as TEM, SIMS and XPS can provide atomic information or
information on the organic C species, they lack the sensitivity to differentiate different forms of
C (Myneni, 2002).
Synchrotron-based Near Edge X-ray Fine Structure (NEXAFS) spectroscopy provides
detailed information on SOC speciation and enable semi-quantitative estimation of relative
composition of each SOC species (Lehmann et al., 2009). The technique uses intense, highly
polarized X-ray provided by a synchrotron radiation light source to probe element speciation in
128
samples. When the energy of incident electrons from synchrotron radiation matches the binding
energy of the electron at C atom orbitals, the electron is suddenly removed from the atom in an
ionization event (Lehmann et al., 2009). If the energy is just below the absorption edge, the
energy can lift an electron to unoccupied, not fully occupied, or higher orbitals; these pre-edge
resonances between 284 to 290eV are typical C K-edge NEXAFS features. The vacancy of
removed inner electron can be filled by electrons from higher orbitals, and the energy differences
is emitted in the form of fluorescent photon or Auger electron emission (Stöhr, 1992). The
NEXAFS spectra can be collected both in total fluorescent yield (TFY) and total electron yield
(TEY) mode, providing information of soil characteristics at a depth of ~ 100nm and at surface
(~ 10nm), respectively. The lower pre-edge resonances of C K-edge NEXAFS is usually
associated with the excitation to lowest π* anti-bonding orbitals and mixed Rydberg/valence
states for molecules with double or triple covalent bonds, and the higher energy features (near
290eV) are typically σ* transitions from saturated covalent bonds (Kinyangi et al., 2006a).
Compared to NMR, NEXAFS spectroscopy is nondestructive, sensitive and element selective
(Creamer et al., 2013). The approach has been extensively applied to environmental samples
(Vairavamurthy and Wang, 2002; Lehmann et al., 2009). The objective of the current study was
to employ NEXAFS spectroscopy to identify and quantify the speciation of SOC associated with
different functional groups in surface and subsurface soils from north-south (N-S) and west-east
(W-E) transects of continental United States.
5.3. Materials and Methods
5.3.1. Study sites and sampling
Surface (A horizon) and subsurface (C horizon) soil samples collected from 93 sites
across United States were investigated. This set of soil samples is a subset of samples collected
129
from a total of 4871 sites (nominal density of 1 site per 1600 km2 in conterminous USA) by the
USGS from 2007 to 2010 for the USGS Geochemical Landscapes Project (Smith et al., 2013).
After collection, visible plant materials and stones were picked out from soils, which were air
dried, sieved through 2-mm sieve, and then stored in glass jars at 4 oC temperature in a
warehouse at the USGS Denver Federal Center. The 93 sites selected for the current study
represent locations along N-S temperature gradient and W-E precipitation gradient transects of
continental United States. The samples were stored in plastic vials at 4oC until analysis.
Mineralogical and chemical analysis on all the soils was conducted by the USGS (Smith et al.,
2013). Mean annual precipitation (MAP) and temperature (MAT) of the sampling sites were
obtained from a database at USA.COM.
5.3.2. Sample preparation
The air dried bulk soil samples were grinded and pulverized to fine powder. About 100
mg of the powder was pressed by a small metal cylinder onto a pre-prepared 0.5 × 1 cm soft
indium foil taped on a steel sample holder to form a flat thin layer. Loosely attached soil particles
were removed by slightly tapping the side of the sample holder on a hard surface. The indium
foils were taped to a steel sample holder using double-sided conductive carbon tape without
exposing the C tape. The sample holders were screwed onto a mechanical arm to be transferred
into the chamber. Each arm held 6 samples each time, including one empty indium foil as blank
and one with crystallized glycine deposited for energy calibration.
5.3.3. Date collecting
Carbon K-edge NEXAFS spectra were acquired on the high energy resolution
monochromator (HERMON) beamline at the Synchrotron Radiation Center (SRC), University of
Wisconsin-Madison. This is a bending magnet beamline covering the energy range from 62-1400
130
eV to achieve a resolving power in excess of 10,000 in the soft X-ray range. The sample was
introduced into the measurement chamber by a load lock, and the pressure of the chamber was
maintained at less than 10-10
Torr (Luk et al., 2004). Medium energy grating (MEG) was chosen
to cover the energy range for C detecting. At the beginning, the entrance and the exit slits were
set at 250 and 500µm separately to focus the visible beam spot on the sample. Then decrease
them to 20 and 40 µm to ensure the resolution. The spectra were recorded in TEY mode with a
fine step of 0.1 eV at the energy range from 255 to 310 eV where the major resonance peaks
appear (270 - 296 eV), and at larger steps elsewhere (0.5 eV). At the same time, spectra of the in-
line gold mesh were collected to compensate for the “optics” effect due to the non-uniform
absorption by the beamline optics. After each collection, the in-line gold mesh was refreshed by
shifting to a new target position. Each samples were scanned for once, otherwise three times by
moving the sample if the intensity was low.
The monochromator energy scale was calibrated by setting the C (1s) to π* transition in
Glycine to 288.5 eV. Thin film of calibration substance was prepared by drying 100 µL of
glycine standard dissolved in ultra-pure water (1mg/mL) onto a clean indium foil under a fume
hood. Spectra of the glycine reference and indium foil blank were acquired after every 12
samples.
5.3.4. Data processing
All the data were saved as TXT files which can be read by Excel. Regions with signal
glitches in the spectra were manually removed and substituted with a straight line. Each
spectrum was normalized to that of the in-line gold mesh collected simultaneously during sample
data collection. The normalized spectra were subject to linear background subtraction at the pre-
edge region which was then normalized to a unity absorption jump height of C1s absorption edge
131
from 280 to 300eV. All the above mentioned steps were conducted suing IDL6.1 Virtual
Machine Version of aXis2000 (Hitchcock Group, Canada). The spectrum was deconvoluted
using eight Gaussians at energy position of known transitions with an arctangent function (AT)
for the ionization step at 290 eV. A full-width at high maximum (FWHM) was restricted to
around 0.4 eV for the first six deconvoluted peaks, which fingerprint the 1s- π* resonances
around 284.3, 285.0, 286.5, 287.3, 288.4, and 289.3 eV Kinyangi et al. (2006a). The two broad
resonances (around 290 and 291eV) were simulated with FWHM of 0.8 ~ 1.2 and 1 ~ 2eV
respectively. Each spectrum was curve fitted using the Microsoft Excel Solver platform. The
deconvolution procedure was applied to the extracted spectra through the energy range from 280
to 294eV.
5.3.5. Statistics
The area of six fitted peaks representing different functional groups was summarized.
The relative composition of each SOC species was calculated using the ratio of the peak area of
each species to sum of the six peak areas. Since no replicas were taken in each sampling site, we
combined all the results of organic-C composition and divided them into different groups
according to transects, vegetation, or horizon for comparison. One-way ANOVA followed by a
multiple comparisons of means using a Tukey’s HSD was implemented to compare the content
and proportion of functional groups in each group.
5.4. Results and Discussion
5.4.1. Soil organic C speciation and relative composition characterization
The deconvoluted C K-edge NEXAFS spectra indicating the six main 1s-π* and two σ*
transitions were shown in Figure 5.1. The peak at 284.3eV represents quinone-C and protonated
and alkylated aromatic-C (Brandes et al., 2004; Braun et al., 2007; Solomon et al., 2012a). The
132
resonance near 285.2 eV is associated with aromatic-C including the ring structures of polycylic
hydrocarbons, unsaturated hydrocarbons and olefins (Solomon et al., 2005; Gillespie et al., 2011;
Solomon et al., 2012a). The sum of the two resonances was regarded as aromatic-C in the
present study. Peak near 286.5eV corresponds to phenolic-C including a variety of compounds
ranging from simple phenol derivatives such as hydroquinone to substances with complex
structures such as tannins, lignins, and flavonoids (Kögel-Knabner, 2002; Brandes et al., 2004;
Braun et al., 2007; Lehmann et al., 2009). Phenols can be oxidized to quinones. Phenolic-C and
quinone-C groups in soils mostly originate from plant derived lignin degradation (Kinyangi et al.,
2006a; Solomon et al., 2012a) and represent the recalcitrant part of SOC. The feature near 287.3
eV is related to aliphatic-C, caused by C1s-3p/σ* Rydberg-like excitations from CH, CH2, and
CH3 groups of functionalities of amino acids and phospholipid fatty acid, etc (Kaznacheyev et al.,
2002; Zubavichus et al., 2005). The absorption band near 288.4 eV is ascribed to carboxylic-C,
while the fingerprint near 289.3eV features the characteristics of O/N-alkyl-C (Myneni, 2002;
Kinyangi et al., 2006a; Zhou et al., 2008). Carboxylic-C can serve as both labile and recalcitrant
form of C in soils due to its zwitterion nature. The labile portion of carboxylic-C such as water
soluble proteins, peptides and FAAs are rapidly cycled with a short turnover time; the
amphoteric part, however, tend to form highly recalcitrant complex such as polyphenol-protein,
lignin-protein complex, allowing a long residence time in soils. O/N-alkyl-C was a labile form of
soil organic C, represented by polysaccharides which constitute the cell walls of microbes and
higher plants and serves as energy substances (Lehmann et al., 2009). Since the peaks of two σ*
transitions are very broad and overlap, only the six 1s-π* resonances were involved in
subsequent data analysis.
133
Figure 5. 1. C K-edge NEXAFS spectrum deconvolution showing the six main 1s-π* transition
and two σ* transitions and the arctangent step function (290 eV) from a deciduous forest soil
from Missouri.
A series of normalized C K-edge NEXAFS spectra, exampled by three sites, revealed
changes of contents of typical organic C species with horizon and land cover (Figure 5.2). In the
grassland/herbaceous site, larger carboxylic (at 288.4 eV) content was found in C horizon than in
A horizon, possibly due to an strong oxidation of plant materials (lignin,, tannins or flavoids) in
C horizon (Jokic et al., 2003). The other functionalities bear similar content in both horizons. In
the shrub site, aromatic-C and O/N-alkyl-C functionalities (at 284.4, 285.2 and 289.3 eV) were
more pronounced in subsoil than in surface soil. No substantial differences in the content of each
functional species were observed in the mixed forest site. The surface soil spectra for the mixed
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
280 282 284 286 288 290 292 294
No
rmalz
ied Inte
nsity
Photon Energy (eV)
284.4
285.2
286.6
287.6
288.5
289.3
290.4291.6
134
forest site indicated an increase in the aromatic-C moiety compared with other two sites, and the
spectra for shrub site featured the most prominent carboxylic-C form than others. In subsoil, on
the other hand, shrub site contained the largest amount of aromatic-C group, followed by mixed
forest site and then grassland/herbaceous site. The content of carboxylic-C functionality was
largest in shrub soil, moderate in grassland/herbaceous site, and least in mixed forest site. The
O/N-alkyl-C group was prominent in the shrub subsoil and remained no substantial change in
other soils. The phenolic-C resonance was less developed in all the spectra. The aliphatic-C
feature appeared majorly as shoulder in most of the spectra and was more or less similar in
abundance except in shrub subsoil which showed the highest amount among the three sites.
Figure 5. 2. Carbon K-edge NEXAFS of A-horizon (A) and C-horizon (C) soil samples from a
mixed forest site (California), a shrubland site (New Mexico), and a grassland/herbaceous site
(Oklahoma).
Photon Energy (eV)
280 285 290 295 300 305 310
Norm
aliz
ed inte
nsity
0
1
2
3
4
5
6
7
Grassland/herbaceous C
Grassland/herbaceous A
Shrubland C
Shrubland A
Mixed forest C
Mixed forest A
135
The normalized spectra indicated the subsoil SOC storage is not ignorable. The deep
SOC majorly originates from root litter (Rumpel et al., 2002). Root litter inputs and quality could
impact the content and composition of subsoil organic C (Rumpel et al., 2002). Deep SOC was
dominated with low quality C (e.g., high lignin concentration, high C-to-N ratio) (Bosatta and
Agren, 1999) and are majorly mineral associated (Diochon and Kellman, 2008). The clay content,
shoot/root allocations and root biomass could possibly influence the subsoil organic C storage
(Burke et al., 1989; Bird et al., 2003; Rasse et al., 2005). So far, studies documenting on deep
soil organic C dynamics were scant. Studies on soil SOC dynamics at low soil profile therefore
need more attention.
5.4.2. Soil organic C speciation and relative composition in A and C horizons
The curve fitting results revealed the SOC was dominated by carboxylic-C, representing
on average 38% of the total SOC identified by C K-edge NEXAFS spectroscopy. Moderate
proportions of aliphatic-C (~ 22%), aromatic-C (~ 18%) and O/N-alkyl-C (~ 16%) and least
proportion of phenolic-C (< 6%) moieties were characterized. These results were partially in line
with the C K-edge NEXAFS results presented by Solomon et al. (2005), where on average
values of 36.5%, 23.8%,14.6%, 14.1%, and 11% were reported for carboxylic-C, O-alkyl-C,
phenolic-C, aromatic-C, and aliphatic-C groups, respectively, in humic substances from the clay
fractions of natural forest, tea plantation and cultivation soils.
As an aid to illustrate the variations of SOC species in both horizons revealed by surface
SOC condition, samples were divided into four groups according to the A-horizon soil total SOC
content (wt. %) (Figure 5.3). In both A and C horizons, no consistent changes of proportions of
each functional group were observed with total SOC gradient. It is found that the proportions of
aliphatic-C, carboxylic-C and O/N-alkyl-C groups were similar in A- and C-horizon soils. The
136
overall uniform composition of SOC in surface soil and subsoil irrespective of surface soil
surface organic C input was in accordance with the results presented by Mahieu et al. (1999),
who, by summarizing the 13
C NMR data from the published literature and their own results on
311 soil samples, found a remarkable similarity in the structure make-up of SOC despite a wide
range of land use, climate or cropping practices. The composition of SOC didn’t change
markedly with depth, in agreement with the previous findings (Dick et al., 2005; Fontaine et al.,
2007). The composition of SOC was determined by the composition of the plant remains and the
decomposition rate of these resources (Swift et al., 1979). The relative uniform composition of
SOC could be due to the fact that the composition of plant residues is approximately similar in
all systems worldwide (Mahieu et al., 1999) and the decomposition pattern could be similar with
rates being altered by temperature and moisture (Jenkinson and Ayanaba, 1977). Soil mineralogy,
physical or chemical properties could have small modifications on the chemical structure of SOC.
It is possibly that SOM maintains the abundance of the major chemical elements at homeostatic
values. Compared to the fairly uniform composition of bulk SOC, a large variability in the
composition of SOC were evidenced between soil size fractions (Mahieu et al., 1999; Solomon et
al., 2005). In addition, anthropogenic perturbations (Solomon et al., 2002; Solomon et al., 2007),
local environmental conditions (Hannam et al., 2004), and temperature (Fissore et al., 2008)
could more or less alter the chemical composition of SOC. The results investigated in this study
showed, on average, an overall uniform chemical composition. Some fluctuations of certain
functional groups, however, were noticed with SOC content.
In A horizon, the proportions of phenolic-C was low (~ 3 %) in response to moderate
amount of surface SOC and high (~ 7.5 %) corresponding to the lowest and highest SOC content.
In C horizon, a different trend was observed with the highest proportion (~7.5 %) appearing in
137
the presence of moderate surface organic C content (1-2 %) and constant proportions (~ 5 %)
elsewhere. Similarly, the aromatic-C moiety tended to accumulate in A horizon but decreased in
C horizon with increasing surface SOC content (from 1% to > 3). The phenolic-C content may
have positive relationship with the surface SOC content (Martens et al., 2004). An increasing
proportion of phenolic-C and aromatic-C with soil depth had been observed (Dick et al., 2005) in
Ferralsols soils with changing mineralogical characteristics along depth. These results suggested
surface SOC has no major effect on the overall chemical structure of SOC while local pedogenic
environment or climatic conditions, to name a few, could make some patchy modifications.
Figure 5. 3. The relative contents (in % of total organic C) of soil organic C species along an A-
horizon soil organic C (wt. %) gradient.
5.4.3. Soil organic C speciation and relative composition variations along temperature and
precipitation gradients of continental United States
Organic C species
Rela
tive t
o t
ota
l o
rgan
ic C
(%
)
0
10
20
30
40
50
60
Aro
matic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
Aro
matic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
<1% OC1-2% OC2-3% OC>3% OC
A-horizon C-horizon
<1% OC1-2% OC2-3% OC>3% OC
138
Figure 5. 4. Relative contents (in % of total organic C) of soil organic C species from A- and C-
horizon soils along the W-E mean annual precipitation transect. The box plots show the median
(the line in the box), 5th/95th percentile (lower and upper bars), and outliers (black dots).
Figure 5. 5. Relative contents (in % of total organic C) of soil organic C species from A- and C-
horizon soils along the N-S mean annual temperature transect. The box plots show the median
(the line in the box) and 5th/95th percentile (lower and upper bars).
R
ela
tive t
o t
ota
l o
rgan
ic C
(%
)
0
10
20
30
40
50
60
70
40-60
20-40
4 - 20
40-60
* MAP (in)
§
Aro
matic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
Aro
matic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
Organic C species
A-horizon C-horizon
Rela
tive t
o t
ota
l o
rgan
ic C
(%
)
0
10
20
30
40
50
60
70A-horizon
35-50
50-55
55-60
C-horizon* MAT (o
F)
Aro
matic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
Aro
matic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
Organic C species
139
The effects of environmental factors on soil organic C speciation were illustrated in
Figure 5.4 and 5.5. In both the A- and C-horizon soils along the W-E precipitation transect, there
was no consistent change of the relative percentages of each organic C species with precipitation,
suggesting the minimum effect of precipitation on chemical alteration of SOC. Precipitation
induced vegetation change could affect the composition (Quideau et al., 2001). The high rainfall
(40 - 60 in) west coast, on average, contained the highest phenolic-C, and the sites with moderate
precipitation (20 - 40 in) the least of all in both A and C horizons. The high phenolic-C in west
coast was possibly associated with the high lignin content in woody forest which was the major
vegetation cover in the moist west coast.
In A-horizon soils along the N-S temperature transect, there was a generally decreasing
relative proportion of aromatic-C with increasing MAT and an opposite trend for phenolic-C
moiety. The aliphatic-C, carboxylic-C and O/N-alkyl-C moieties exhibited no obvious changes
with temperature change. In C horizon, however, no consistent trend was observed. The
composition of each functional group was relatively uniform in the C-horizon soil. These results
suggested temperature has more impact than precipitation on dynamics of surface soil organic C
speciation. It was also noted the variability of the composition of SOC along the precipitation
gradient was more pronounced than that along the temperature gradient possibly because of the
larger sample size along the precipitation transect.
It is assumed that MAT influences SOC dynamics via its controls on the litter
decomposition rate, litter production, and stabilization of the decomposed products (Dalias et al.,
2001; Thornley and Cannell, 2001; Raich et al., 2006; Conant et al., 2011). The faster rates of
litter decomposition than production result in a reduced C accumulation at soil surface (Raich et
al., 2006). During this process, increasing temperature stimulates microbial activity, resulting in
140
the rapid utilization of liable SOC and ensuing higher accumulation of stable compounds such as
the macromolecules formed by polymerization and condensation (Fissore et al., 2008). The
increasing recalcitrant phenolic-C form with MAT in our study partially agrees with the results
by Fissore et al. (2008) who found a decreased SOC quality (high decay-resistant compounds)
with MAT by conducting a laboratory incubation study on forest soils from a temperature
gradient. The relatively constant composition of the labile fractions along the temperature
gradient in the current study, on the other hand, was different from results by Dalias et al. (2001)
who found a rapid exhaustion of the liable fractions of the decomposition materials at higher
incubation temperatures. This can be explained by the fact that these dissolved organic matter
was sorbed directly to mineral surfaces during their translocation (Kaiser and Guggenberger,
2000; Kaiser and Zech, 2000) or they could be resynthesized into more recalcitrant polymers by
polymerization reactions (Bremner, 1967). The discrepancy suggested the need of long term and
in situ experiments to further illustrate the environmental effect on SOC quality. Another
explanation for the temperature effect could be associated with the enhanced transfers of organic
C from unprotected to stabilized pools at warmer sites (Dalias et al., 2001; Thornley and Cannell,
2001). Lignin-derived compounds can incorporate N to form condensation products. Physical
protection of such molecules from enzymes was also important for stabilization. In this study, it
is assumed the physical-chemical reactions which facilitate phenolic-C stabilization may be
enhanced by warming. A relatively inert response of chemical composition of SOC to
temperature in C horizon than A horizon suggested that temperature facilitated decomposition
rate of SOC could diminish with soil depth (VanDam et al., 1997; Jobbagy and Jackson, 2000)
possibly because the stabilized SOC in subsoil was unable to provide enough energy to sustain
microbial activity (Fontaine et al., 2007).
141
5.4.4. Soil organic C speciation and relative composition variations among different
vegetation covers
Figure 5.6 revealed the relatively larger variability of aromatic-C and phenolic-C, similar
to what was observed in Figure 5.3.The proportions of aromatic-C in A-horizon and C-horizon
soil were lower in shrub and evergreen forest soils than that in grass and pasture soils. The
proportion of phenolic-C moiety generally followed opposite order with that of aromatic-C group.
The ratios of the content of phenolic-C in A horizon over C horizon were greater for grass and
pasture sites than for shrub and evergreen sites which were consistent with horizontal ratios of
total SOC content among these sites. The aromatic-C ratio of A-horizon over C-horizon soils
also varied among the four vegetation sites.
The depth distribution of other three species was rather similar among four vegetation
sites (Figure 5.7). The results suggested a potential effect of vegetation types in the larger
variability of aromatic-C and phenolic-C depth distribution. Grass and pasture sites had higher
ratios of total SOC in A- to that in C-horizon soils compared with shrub and evergreen site
(Figure 5.7), in accordance with the low root biomass and shallow root depth of grass and
pasture as compared with forest and shrub (Jackson et al., 1996). These results suggested that
vegetation types serve as potential modifiers in the chemical alteration of SOC, along with litter
quality, shoot/root ratio and root distribution (Jackson et al., 1996; Jobbagy and Jackson, 2000).
Vegetation was reported as a major factor controlling SOM composition in California mountain
soils (Quideau et al., 2001).
142
Figure 5. 6. The relative contents (in % of total organic C) of soil organic C species in A- and C
horizon soils with different vegetation cover.
Figure 5. 7. Weight ratios of (left) (in wt %) of soil organic C species in A- horizon to that in C-
horizon soils with different vegetation cover and (right) ratios of total soil organic C content
(wt %) in A-horizon soil to that in C-horizon soil.
Organic C species
Rela
tive t
o t
ota
l o
rgan
ic C
(%
)
0
10
20
30
40
50
60A
rom
atic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
Aro
matic-C
Phenolic-C
Aliphatic-C
Carb
oxyl
ic-C
O/N
-alk
yl-C
ShurbEvergreenGrassPasture
A-horizon C-horizon
Organic C species
A-h
ori
zo
n/C
-ho
rizo
n
0
5
10
15
Aro
mat
ic-C
Phen
olic-C
Alip
hatic
-C
Car
boxylic
-C
O/N
-alk
yl-C
Land cover
Shru
b
Eve
rgre
en
Gra
ss
Pas
ture
CA
-ho
rizo
n/C
C-h
ori
zo
n
0
2
4
6
8ShurbEvergreenGrassPasture
143
Plants residues are the primary source of SOC (Kögel-Knabner, 2002).The surface SOM
in these sites was thus mainly from the responding plant residues related to the chemical
structure of plant cell walls (Martens et al., 2004). Lignin and tannins are abundant constitutes of
plant and are important source of refractory materials in soils (Kögel-Knabner, 2002). Soil
phenolic-C and quinone-C could be mostly of lignin derived (Lorenz and Lal, 2005; Solomon et
al., 2005). The source of SOM at different decomposition status could influence the phenolic-C
and aromatic-C content. The relatively higher proportions of phenolic-C in both A and C
horizons in shrub and evergreen sites could be associated with the high lignin content in woody
plant remains. The higher composition of aromatic-C in grass and pasture sites suggested a
higher degree of decomposition (Kögel-Knabner et al., 1988; Kleber and Johnson, 2010) or the
contribution of black C (Rodionov et al., 2010) or due to the lower proportions of phenolic-C. In
general, forest litter contains higher lignin due to woody tissues than herbaceous materials, and
roots higher than leaves (Wang et al., 2004). High refractory contents such as lignin and tannins
of plant roots compared to shoots, woody structure to herbaceous materials, probably lead to the
lower decomposition rate of woody materials and roots. Plant root/shoot allocations are probably
the major determinants of the relative distribution of SOC with depth (Jobbagy and Jackson,
2000). Vegetation types differing in their vertical root distribution therefore would imprint the
depth distribution of SOC. The average rooting depth follows the order as shrub > forest > grass
(Canadell et al., 1996).The depth to which 95% root biomass occurs is the lowest for grasses,
highest for shrubs, and intermediate for forest (Jackson et al., 1996; Jackson et al., 1997). Root
litter and rhizodeposition account for a substantial deep SOC input (Fernandes et al., 1997). The
large SOC storage can transfer C into subsoil horizons of on average several meters (Canadell et
al., 1996). Shrublands and forests, for example, have more SOC storage in the second to third
144
meters than grassland until 1-m depth (Lorenz and Lal, 2005).The higher ratios of SOC in A
horizon than that in C horizon for grass and pasture sites as compared with shrub and forest sites
were expected in this results (Figure 5.7). The data suggested the relative abundance of phenolic-
C may be the best indicator of the effect of vegetation on the composition of SOC.
5.5. Conclusions
The C K-edge NEXAFS results indicated carboxylic-C (38 %) moiety was the dominant
form of SOC followed by moderate proportions of aliphatic-C (~ 22 %), aromatic-C (~ 18 %)
and O/N-alkyl-C (~ 16 %) and minor proportion of phenolic-C (< 6 %) moieties. Of all the C
species, aromatic-C and phenolic-C were greatly affected by climatic factors. Temperature was
shown to have more effect than precipitation in chemical alteration of surface SOC, possibly by
way of affecting decomposition rates and adsorption of organic C species. Vegetation can be
another factor controlling SOM composition due to their differences in litter quality, shoot/root
allocations and root depth distribution. Phenolic-C may be a good indicator of the effect of
temperature or vegetation on the composition of SOC.
5.6. Acknowledgements
We would like to thank the technical staff at the UW-Madison Synchrotron Radiation
Center for their technical support. We thank the financial support of USDA-AFRI (award #2012-
67019-30227).
145
5.7. References
Alvarez-Arteaga, G., Krasilnikov, P., Garcia-Calderon, N.E., 2012. Vertical distribution and soil
organic matter composition in a montane cloud forest, oaxaca, mexico. European Journal
of Forest Research 131, 1643-1651.
Beyer, L., Schulten, H.-R., Fründ, R., 1992. Properties and composition of soil organic matter in
forest and arable soils of schleswig-holstein: 1. Comparison of morphology and results of
wet chemistry, cpmas-13c-nmr spectroscopy and pyrolysis-field ionization mass
spectrometry. Zeitschrift für Pflanzenernährung und Bodenkunde 155, 345-354.
Bird, M., Kracht, O., Derrien, D., Zhou, Y., 2003. The effect of soil texture and roots on the
stable carbon isotope composition of soil organic carbon. Australian Journal of Soil
Research 41, 77-94.
Bird, M.I., Pousai, P., 1997. Variations of δ13c in the surface soil organic carbon pool. Global
Biogeochemical Cycles 11, 313-322.
Bosatta, E., Agren, G.I., 1999. Soil organic matter quality interpreted thermodynamically. Soil
Biology & Biochemistry 31, 1889-1891.
Brandes, J.A., Lee, C., Wakeham, S., Peterson, M., Jacobsen, C., Wirick, S., Cody, G., 2004.
Examining marine particulate organic matter at sub-micron scales using scanning
transmission x-ray microscopy and carbon x-ray absorption near edge structure
spectroscopy. Marine Chemistry 92, 107-121.
Braun, A., Mun, B.S., Huggins, F.E., Huffman, G.P., 2007. Carbon speciation of diesel exhaust
and urban particulate matter nist standard reference materials with c(1s) nexafs
spectroscopy. Environmental Science & Technology 41, 173-178.
Bremner, J., 1967. Nitrogenous compounds. Soil biochemistry 1, 19-66.
146
Burke, I.C., Yonker, C.M., Parton, W.J., Cole, C.V., Flach, K., Schimel, D.S., 1989. Texture,
climate, and cultivation effects on soil organic-matter content in us grassland soils. Soil
Science Society of America journal 53, 800-805.
Canadell, J., Jackson, R.B., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996.
Maximum rooting depth of vegetation types at the global scale. Oecologia 108, 583-595.
Conant, R.T., Ryan, M.G., Agren, G.I., Birge, H.E., Davidson, E.A., Eliasson, P.E., Evans, S.E.,
Frey, S.D., Giardina, C.P., Hopkins, F.M., Hyvonen, R., Kirschbaum, M.U.F., Lavallee,
J.M., Leifeld, J., Parton, W.J., Steinweg, J.M., Wallenstein, M.D., Wetterstedt, J.A.M.,
Bradford, M.A., 2011. Temperature and soil organic matter decomposition rates -
synthesis of current knowledge and a way forward. Global Change Biology 17, 3392-
3404.
Cooper, G., Gordon, M., Tulumello, D., Turci, C., Kaznatcheev, K., Hitchcock, A.P., 2004. Inner
shell excitation of glycine, glycyl-glycine, alanine and phenylalanine. Journal of Electron
Spectroscopy and Related Phenomena 137–140, 795-799.
Creamer, C., Filley, T., Olk, D., Stott, D., Dooling, V., Boutton, T., 2013. Changes to soil
organic n dynamics with leguminous woody plant encroachment into grasslands.
Biogeochemistry 113, 307-321.
Dai, F.Q., Su, Z.A., Liu, S.Z., Liu, G.C., 2011. Temporal variation of soil organic matter content
and potential determinants in tibet, china. Catena 85, 288-294.
Dalias, P., Anderson, J.M., Bottner, P., Couteaux, M.M., 2001. Temperature responses of carbon
mineralization in conifer forest soils from different regional climates incubated under
standard laboratory conditions. Global Change Biology 7, 181-192.
147
Dick, D.P., Goncalves, C.N., Dalmolin, R.S.D., Knicker, H., Klamt, E., Kogel-Knaber, I.,
Simoes, M.L., Martin-Neto, L., 2005. Characteristics of soil organic matter of different
brazilian ferralsols under native vegetation as a function of soil depth. Geoderma 124,
319-333.
Diochon, A., Kellman, L., 2008. Natural abundance measurements of (13)c indicate increased
deep soil carbon mineralization after forest disturbance. Geophysical Research Letters 35.
Eswaran, H., Vandenberg, E., Reich, P., 1993. Organic-carbon in soils of the world. Soil Science
Society of America journal 57, 192-194.
Fernandes, E.C.M., Motavalli, P.P., Castilla, C., Mukurumbira, L., 1997. Management control of
soil organic matter dynamics in tropical land-use systems. Geoderma 79, 49-67.
Fissore, C., Giardina, C.P., Kolka, R.K., Trettin, C.C., King, G.M., Jurgensen, M.F., Barton,
C.D., Mcdowell, S.D., 2008. Temperature and vegetation effects on soil organic carbon
quality along a forested mean annual temperature gradient in north america. Global
Change Biology 14, 193-205.
Fontaine, S., Barot, S., Barre, P., Bdioui, N., Mary, B., Rumpel, C., 2007. Stability of organic
carbon in deep soil layers controlled by fresh carbon supply. Nature 450, 277-U210.
Gerin, P.A., Genet, M.J., Herbillon, A.J., Delvaux, B., 2003. Surface analysis of soil material by
x-ray photoelectron spectroscopy. European journal of soil science 54, 589-604.
Gillespie, A., Walley, F., Farrell, R., Leinweber, P., Eckhardt, K.-U., Regier, T., Blyth, R., 2011.
Xanes and pyrolysis-fims evidence of organic matter composition in a hummocky
landscape. Soil Science Society of America journal 75, 1741-1755.
148
Gillespie, A.W., Walley, F.L., Farrell, R.E., Leinweber, P., Schlichting, A., Eckhardt, K.-U.,
Regier, T.Z., Blyth, R.I.R., 2009. Profiling rhizosphere chemistry: Evidence from carbon
and nitrogen k-edge xanes and pyrolysis-fims. Soil Sci. Soc. Am. J. 73, 2002-2012.
Gonzalez Perez, M., Martin-Neto, L., Saab, S.C., Novotny, E.H., Milori, D.M.B.P., Bagnato,
V.S., Colnago, L.A., Melo, W.J., Knicker, H., 2004. Characterization of humic acids
from a brazilian oxisol under different tillage systems by epr, c-13 nmr, ftir and
fluorescence spectroscopy. Geoderma 118, 181-190.
Hannam, K., Quideau, S., Oh, S.-W., Kishchuk, B., Wasylishen, R., 2004. Forest floor
composition in aspen-and spruce-dominated stands of the boreal mixedwood forest. Soil
Science Society of America journal 68, 1735-1743.
Hansen, J., Johnson, D., Lacis, A., Lebedeff, S., Lee, P., Rind, D., Russell, G., 1981. Climate
impact of increasing atmospheric carbon-dioxide. Science 213, 957-966.
Jackson, R.B., Canadell, J., Ehleringer, J.R., Mooney, H.A., Sala, O.E., Schulze, E.D., 1996. A
global analysis of root distributions for terrestrial biomes. Oecologia 108, 389-411.
Jackson, R.B., Mooney, H.A., Schulze, E.D., 1997. A global budget for fine root biomass,
surface area, and nutrient contents. Proceedings of the National Academy of Sciences of
the United States of America 94, 7362-7366.
Jenkinson, D.S., Ayanaba, A., 1977. Decomposition of c-14-labeled plant material under tropical
conditions. Soil Science Society of America journal 41, 912-915.
Jobbagy, E.G., Jackson, R.B., 2000. The vertical distribution of soil organic carbon and its
relation to climate and vegetation. Ecological applications 10, 423-436.
Jokic, A., Cutler, J.N., Ponomarenko, E., van der Kamp, G., Anderson, D.W., 2003. Organic
carbon and sulphur compounds in wetland soils: Insights on structure and transformation
149
processes using k-edge xanes and nmr spectroscopy. Geochimica Et Cosmochimica Acta
67, 2585-2597.
Jones, E., Singh, B., 2014. Organo-mineral interactions in contrasting soils under natural
vegetation. Frontiers in Environmental Science 2.
Kaiser, K., Guggenberger, G., 2000. The role of dom sorption to mineral surfaces in the
preservation of organic matter in soils. Organic Geochemistry 31, 711-725.
Kaiser, K., Zech, W., 2000. Sorption of dissolved organic nitrogen by acid subsoil horizons and
individual mineral phases. European journal of soil science 51, 403-411.
Kaznacheyev, K., Osanna, A., Jacobsen, C., Plashkevych, O., Vahtras, O., Ågren, Carravetta, V.,
Hitchcock, A.P., 2002. Innershell absorption spectroscopy of amino acids. The Journal of
Physical Chemistry A 106, 3153-3168.
Keiluweit, M., Nico, P.S., Johnson, M.G., Kleber, M., 2010. Dynamic molecular structure of
plant biomass-derived black carbon (biochar). Environmental Science & Technology 44,
1247-1253.
Kinyangi, J., Solomon, D., Liang, B., Lerotic, M., Wirick, S., Lehmann, J., 2006a. Nanoscale
biogeocomplexity of the organomineral assemblage in soil. Soil Sci. Soc. Am. J. 70,
1708-1718.
Kinyangi, J., Solomon, D., Liang, B.I., Lerotic, M., Wirick, S., Lehmann, J., 2006b. Nanoscale
biogeocomplexity of the organomineral assemblage in soil: Application of stxm
microscopy and c 1s-nexafs spectroscopy. Soil Science Society of America journal 70,
1708-1718.
150
Kleber, M., Johnson, M.G., 2010. Advances in understanding the molecular structure of soil
organic matter: Implications for interactions in the environment. Advances in Agronomy,
Vol 106 106, 77-142.
Kögel-Knabner, I., 2002. The macromolecular organic composition of plant and microbial
residues as inputs to soil organic matter. Soil Biology and Biochemistry 34, 139-162.
Kögel-Knabner, I., Zech, W., Hatcher, P.G., 1988. Chemical composition of the organic matter
in forest soils: The humus layer. Zeitschrift für Pflanzenernährung und Bodenkunde 151,
331-340.
Kunlanit, B., Vityakon, P., Puttaso, A., Cadisch, G., Rasche, F., 2014. Mechanisms controlling
soil organic carbon composition pertaining to microbial decomposition of biochemically
contrasting organic residues: Evidence from middrifts peak area analysis. Soil Biology
and Biochemistry 76, 100-108.
Lehmann, J., Solomon, D., Brandes, J., Fleckenstein, H., Jacobson, C., Thieme, J., 2009.
Synchrotron-based near-edge x-ray spectroscopy of natural organic matter in soils and
sediments, Biophysico-chemical processes involving natural nonliving organic matter in
environmental systems. John Wiley & Sons, Inc., pp. 729-781.
Li, J.L., Evanylo, G.K., Xia, K., Mao, J.D., 2013. Soil carbon characterization 10 to 15 years
after organic residual application: Carbon (1s) k-edge near-edge x-ray absorption fine-
structure spectroscopy study. Soil Science 178, 453-464.
Lorenz, K., Lal, R., 2005. The depth distribution of soil organic carbon in relation to land use
and management and the potential of carbon sequestration in subsoil horizons. Advances
in Agronomy, Vol 88 88, 35-66.
151
Luk, Y.-Y., Abbott, N.L., Crain, J.N., Himpsel, F.J., 2004. Dipole-induced structure in aromatic-
terminated self-assembled monolayers—a study by near edge x-ray absorption fine
structure spectroscopy. The Journal of Chemical Physics 120, 10792-10798.
Mahieu, N., Randall, E.W., Powlson, D.S., 1999. Statistical analysis of published carbon-13
cpmas nmr spectra of soil organic matter. Soil Sci. Soc. Am. J. 63, 307-319.
Martens, D.A., Reedy, T.E., Lewis, D.T., 2004. Soil organic carbon content and composition of
130-year crop, pasture and forest land-use managements. Global Change Biology 10, 65-
78.
Martin, P.D., Malley, D.F., Manning, G., Fuller, L., 2002. Determination of soil organic carbon
and nitrogen at the field level using near-infrared spectroscopy. Canadian Journal of Soil
Science 82, 413-422.
Myneni, S.C.B., 2002. Soft x-ray spectroscopy and spectromicroscopy studies of organic
molecules in the environment. Applications of Synchrotron Radiation in Low-
Temperature Geochemistry and Environmental Sciences 49, 485-579.
Poch, R., Virto, I., 2014. Micromorphology techniques for soil organic carbon studies, In:
Hartemink, A.E., McSweeney, K. (Eds.), Soil carbon. Springer International Publishing,
pp. 17-26.
Quideau, S.A., Chadwick, O.A., Benesi, A., Graham, R.C., Anderson, M.A., 2001. A direct link
between forest vegetation type and soil organic matter composition. Geoderma 104, 41-
60.
Raich, J.W., Russell, A.E., Kitayama, K., Parton, W.J., Vitousek, P.M., 2006. Temperature
influences carbon accumulation in moist tropical forests. Ecology 87, 76-87.
152
Rasse, D.P., Rumpel, C., Dignac, M.F., 2005. Is soil carbon mostly root carbon? Mechanisms for
a specific stabilisation. Plant and Soil 269, 341-356.
Rodionov, A., Amelung, W., Peinemann, N., Haumaier, L., Zhang, X.D., Kleber, M., Glaser, B.,
Urusevskaya, I., Zech, W., 2010. Black carbon in grassland ecosystems of the world.
Global Biogeochemical Cycles 24.
Rovira, P., Vallejo, V.R., 2002. Labile and recalcitrant pools of carbon and nitrogen in organic
matter decomposing at different depths in soil: An acid hydrolysis approach. Geoderma
107, 109-141.
Rumpel, C., Kogel-Knabner, I., Bruhn, F., 2002. Vertical distribution, age, and chemical
composition of organic, carbon in two forest soils of different pedogenesis. Organic
Geochemistry 33, 1131-1142.
Schumacher, M., Christl, I., Scheinost, A.C., Jacobsen, C., Kretzschmar, R., 2005. Chemical
heterogeneity of organic soil colloids investigated by scanning transmission x-ray
microscopy and c-1s nexafs microspectroscopy. Environmental Science & Technology
39, 9094-9100.
Smith, D.B., Cannon, W.F., Woodruff, L.G., Solano, F., Kilburn, J.E., Fey, D.L., 2013.
Geochemical and mineralogical data for soils of the conterminous united states. Data
series 801.
Solomon, D., Fritzsche, F., Tekalign, M., Lehmann, J., Zech, W., 2002. Soil organic matter
composition in the subhumid ethiopian highlands as influenced by deforestation and
agricultural management d. Solomon, current address: College of agriculture and life
sciences, dep. Of crop and soil sciences, cornell univ., bradfield and emerson halls, ithaca,
ny 14853. Soil Sci. Soc. Am. J. 66, 68-82.
153
Solomon, D., Lehmann, J., Harden, J., Wang, J., Kinyangi, J., Heymann, K., Karunakaran, C.,
Lu, Y., Wirick, S., Jacobsen, C., 2012a. Micro- and nano-environments of carbon
sequestration: Multi-element stxm–nexafs spectromicroscopy assessment of microbial
carbon and mineral associations. Chemical Geology 329, 53-73.
Solomon, D., Lehmann, J., Kinyangi, J., Amelung, W., Lobe, I., Pell, A., Riha, S., Ngoze, S.,
Verchot, L.O.U., Mbugua, D., Skjemstad, J.A.N., SchÄFer, T., 2007. Long-term impacts
of anthropogenic perturbations on dynamics and speciation of organic carbon in tropical
forest and subtropical grassland ecosystems. Global Change Biology 13, 511-530.
Solomon, D., Lehmann, J., Kinyangi, J., Liang, B.Q., Schafer, T., 2005. Carbon k-edge nexafs
and ftir-atr spectroscopic investigation of organic carbon speciation in soils. Soil Science
Society of America journal 69, 107-119.
Stöhr, J., 1992. Nexafs spectroscopy, vol. 25 of springer series in surface sciences. Springer,
Heidelberg.
Swift, M.J., Heal, O.W., Anderson, J.M., 1979. Decomposition in terrestrial ecosystems. Univ of
California Press.
Thornley, J.H.M., Cannell, M.G.R., 2001. Soil carbon storage response to temperature: An
hypothesis. Annals of Botany 87, 591-598.
Vairavamurthy, A., Wang, S., 2002. Organic nitrogen in geomacromolecules: Insights on
speciation and transformation with k-edge xanes spectroscopy. Environmental Science &
Technology 36, 3050-3056.
VanDam, D., Veldkamp, E., VanBreemen, N., 1997. Soil organic carbon dynamics: Variability
with depth in forested and deforested soils under pasture in costa rica. Biogeochemistry
39, 343-375.
154
Victoria, R.L., Fernandes, F., Martinelli, L.A., De CÁSsia Piccolo, M., De Camargo, P.B.,
Trumbore, S., 1995. Past vegetation changes in the brazilian pantanal arboreal–grassy
savanna ecotone by using carbon isotopes in the soil organic matter. Global Change
Biology 1, 165-171.
Wang, W.J., Baldocka, J.A., Dalala, R.C., Moody, P.W., 2004. Decomposition dynamics of plant
materials in relation to nitrogen availability and biochemistry determined by nmr and
wet-chemical analysis. Soil Biology & Biochemistry 36, 2045-2058.
Zhou, D., Metzler, R.A., Tyliszczak, T., Guo, J.H., Abrecht, M., Coppersmith, S.N., Gilbert,
P.U.P.A., 2008. Assignment of polarization-dependent peaks in carbon k-edge spectra
from biogenic and geologic aragonite. Journal of Physical Chemistry B 112, 13128-
13135.
Zubavichus, Y., Shaporenko, A., Grunze, M., Zharnikov, M., 2005. Innershell absorption
spectroscopy of amino acids at all relevant absorption edges. The Journal of Physical
Chemistry A 109, 6998-7000.
155
6. Polarization Dependent X-ray Photoemission Electron
Microscopic and Near Edge X-ray Fine Structure
Spectroscopic Investigation of Hexa-glycine Surface
Orientation Sorbed on Montmorillonite
K. Xiaa*, L. Ma
a, J. Wang
b, and M. A. Williams
c
aDepartment of Crop and Soil Environmental Sciences, Virginia Polytechnic Institute and State
University, Blacksburg, VA 24061, USA
bCanadian Light Source Inc., Saskatoon, Canada SK S7N 2V3
cDepartment of Horticulture, Virginia Polytechnic Institute and State University, Blacksburg, VA
24061, USA
*Corresponding author. Email address: [email protected]
156
6.1. Abstract
Proteins and peptides constitute a substantial part of soil organic N pools. The
interactions of proteins and peptides with minerals have strong effect on their reactivity,
availability and stability. Previous investigations on protein/peptide-mineral interactions,
however, have been largely limited to macroscopic scale batch equilibrium experiments. There is
limited understanding of their interactions on mineral surfaces at molecular levels. The
molecular level surface orientation of hexa-glycine, selected as a model peptide compound,
sorbed on montmorillonite was investigated using X-ray Photo Emission Electron Microscopy
and polarization dependent Near Edge X-ray Fine Structure Spectroscopy. The results suggested
that hexa-glycine sorbed on montmorillonite surfaces at an average angle of about 40° relative to
surface. The techniques enable us gain further insights into the interpretation of adsorption
behaviors of peptides/proteins.
157
6.2. Introduction
Proteins and peptides, constituting up to half of the soil organic N pool, play a vital role
in the terrestrial N cycling (Sowden et al., 1977; Senwo and Tabatabai, 1998; Friedel and
Scheller, 2002). Their stabilization and turnover have become an important topic of research. It
has been reported that sorption of proteins and peptides on mineral surface affect its stabilization
against biodegradation (Sollins et al., 1996; Mikutta et al., 2006). Studies on the interactions
between proteinaceous compounds and minerals thus benefit our understanding of N cycle in
soils.
Amino acids, especially the basic amino acids, were reported to be adsorbed strongly to
clay minerals due to their positive charge (Lipson and Nasholm, 2001; Vieublé Gonod et al.,
2006; Kitadai et al., 2009). The amphipathic proteins and peptides tend to bind to mineral
surfaces by conformation change to maximize entropy due to the polar and apolar side chains
and their ability to develop positive and negative charge (Kleber et al., 2007; Rillig et al., 2007).
The mechanisms of the protein/peptides and mineral interactions were illustrated by the “onion”
zonal model and “bilayer” layering model, which both emphasize that the amphiphiles serve as a
reactive chemical coatings on mineral surface for coupling and cross linking of other C-rich
organic matter (Wershaw et al., 1996; Kleber et al., 2007). However, there have been limited
molecular level spectroscopic observations of behaviors of these proteinaceous substances
sorbed on soil mineral surface.
Although plenty of studies have been conducted on the sorption characteristics of
proteins or peptides on minerals, most of them were limited to observations at the macroscopic
scale based on batch equilibrium experiments (Greenland et al., 1962; Dashman and Stotzky,
1982, 1984; Kalra et al., 2003). There is paucity of information on molecular-level adsorption
158
behaviors, especially regarding the surface organization (molecular orientation, spatial
distribution, packing density, etc.) which may affect the reactivity, stability, and bioavailability
of the adsorbed molecules. Recent spectroscopic investigation of amino acids surface speciation
suggested that amino acids of different speciation states attach to the material surface with a
variety of spatial arrangements. For example, a study by Sverjensky et al. (2008) suggested at pH
3-5 and low (1× 10-5
M ) and intermediate (1× 10-4
M) concentrations, glutamate adsorbed “lying
down” on oxide surface as a divalent anion. While at high concentrations, glutamate adsorbed
“standing up” by the γ-carboxylate end only with the α-carboxylate and amine groups pointing
away from the surface. Similarly, Kitadai et al. (2009) suggested that lysine sorbed vertically on
the montmorillonite surface through the protonated side-chain amino group. The adsorbed lysine
was present mainly as cationic state over the whole pH range 4.9 - 9.7. However, it is not known
whether this surface organization applied to proteins or peptides sorbed on soil minerals.
So far, investigations of the interactions between proteins/peptides and the minerals
suggested that factors such as pH, composition of inorganic cations and anions, mineral surface
properties, and chemical structure and molecular weight of the adsorbate which affect
protein/peptide adsorption capacity may influence their surface organization (Greenland et al.,
1962; Dashman and Stotzky, 1984; Jones and Hodge, 1999; Kalra et al., 2003; Pradier et al.,
2007; Rocha et al., 2007). The recently developed powerful synchrotron based techniques enable
us to investigate the sorption behavior at molecular scale. Synchrotron based polarization
dependent near edge X-ray adsorption fine structure (NEXAFS) spectroscopy has been used to
assess the orientation of amino acids, peptides, proteins or other organic substance sorbed on
gold, silver, TiO2, silicon substrate (Peters et al., 2002; Petoral and Uvdal, 2003; Liu et al., 2006;
159
Iucci et al., 2008; Battocchio et al., 2010). Little NEXAFS work has been performed on surface
orientation investigation of peptides/proteins assembled on soil clay minerals.
Numerous studies on interactions between peptides and surfaces of biomedical materials
have shown that peptides can be self-assembled on the surfaces of metal oxides and semi-
conduct substrates (Au, Si, TiO2, etc.) to form ordered extended monolayers with the peptide
chains in ß-sheet conformation (Polzonetti et al., 2006). Non-covalent interactions such as
hydrogen bonding, hydrophilic/hydrophobic forces or electrostatic forces are the driving forces
for peptides self-assemblance on the surfaces of biomedical related materials (Filiberto et al.,
2011). The tilt angle between the peptide backbone and the surface normal can be investigated
by polarization dependent NEXAFS, which has been successfully used to investigate the
molecular orientation of 16-unit peptides EAK16 on TiO2 (Polzonetti et al., 2006; Iucci et al.,
2007).
Synchrotron based X-ray is highly polarized. Rotating the sample changes the incidence
angle of synchrotron X-ray and the ensuing angle (θE) of the electric field vector E with respect
to the surface normal (Figure 6.5). The C and N atoms in peptides exhibit simple s-to-p
transitions, which are dipole-allowed if the electric field vector E of the incident X-rays and the
transition dipole moment are parallel with each other. The transition intensity maximizes when
the electric field vector E lies along the direction of the final state molecular orbital and vanishes
when E is normal to it (Hahner, 2006). Therefore, the intensities of the transition depend on the
orientation of the electric field vector E relative to the orientation of the molecule (Hahner, 2006).
By changing the angle of the incidence light, the tilt angle of the molecules can be determined.
For a peptide, amino acids are linked by peptides bond. The peptide bond electrons are
delocalized across the entire peptide bond, so the double-bound character is extended to both the
160
carbon-oxygen and the carbon-nitrogen bonds (Liu et al., 2006). As described in Figure 2.3 the
shared π* orbital limits the free rotation of peptide C-N, so the six atoms surrounding a peptide
group are coplanar. The π* orbital (p orbital) is oriented perpendicular to the peptide plane.
The N (1s) NEXAFS spectra exhibit a strong θ dependence for the peak of π* peptide
bond (401.5 eV). By collecting two NEXAFS spectra, one at normal incident X-ray angle and
the other at a different incident X-ray angle, the tilt angle of a molecule bound to the surface can
be calculated by using equation 2.1 below (Stöhr, 1992; Liu et al., 2007),
𝐈(𝛉𝟐)
𝐈(𝛉𝟏) = 1 + p[
𝟐
𝐬𝐢𝐧𝟐 𝛂− 𝟑] [𝐬𝐢𝐧𝟐𝛉𝟐 − 𝐬𝐢𝐧𝟐𝛉𝟏]
where θ2 and θ1 are the angles of incidence from the surface normal taken at grazing and normal
incidence respectively, α is the tilt angle of p-orbital of peptide bond from surface normal, and P
is the degree of polarization of the X-rays. A value 0.95 was assigned to P for threefold or higher
symmetry substrates. The tilt angle between the π* vector orbital of the C=O bond and the
normal to the surface which equal the angle between the molecular main chain with mineral
surface can be calculated from the intensity (area) ratio of Iθ2/Iθ1, determined for the selected
resonance by peak fitting. In the present study, peptide, hexa-glycine, was selected as a model
peptide compound to assess the feasibility of using polarized NEXAFS to investigate surface
orientation of peptides sorbed on montmorillonite, a common 2:1 clay minerals in soils.
6.3. Materials and methods
6.3.1 Chemicals and materials
Hexa-glycine (Gly-Gly-Gly-Gly-Gly-Gly) (MW: 360.3232 g/mol) was supplied by
Sigma. The 2-D structure of Hexa-glycine was shown in Figure 6.5 a. Methanol (HPLC grade)
and chloroform (>99.9%) was purchased from Fisher and Fluka (New Jersey, USA) respectively.
Na-montmorillonite (SWy-2, Crook County, Wyoming) was obtained from the Source Clays
161
Repository of the Clay Minerals Society (Purdue University, West Lafayette, IN). The cation
exchange capacity and theoretical external surface area of SWy-2 provided by the Clay Minerals
Society were 76.4 cmolc/kg and 31.82 ± 0.22 m2/g, respectively. The octadecylammonium
chloride (ODAH+Cl
-) was purchased from Alfa Aesar (Ward Hill, MA, USA). Silicon (100)
wafer was purchased from SPI Supplies (West Chester, PA).
6.3.2. Preparation of monolayer montmorillonite
Monolayer montmorillonite was prepared on pretreated 2 cm × 2 cm Si wafers using the
Langmuir-Blodgett (LB) trough technique (Takahashi et al., 2003). The Si wafers were
pretreated by immersion in a mixed solution of 30% hydrogen peroxide and 25% ammonium
hydroxide (1:1 v/v) at 80 °C for 2 h. After the pretreatment, the Si wafers were thoroughly rinsed
with ultra-pure water according to Takahashi et al. (2003). The hydrophilicity is associated with
the formation of OH groups on the surface(Grundner and Jacob, 1986). To prepare the
monolayer montmorillonite sorbed on the Si wafer, 3 to 4 mg montmorillonite was dispersed in 2
L ultrapure water and then poured into a LB trough reservoir (KSV Instrument; Connecticut,
USA). Surfactant (ODAH+Cl
-) dissolved in chloroform and methanol mixture (10:1, v/v) at a
concentration of 1.68 ×10-4
mol L-1
, used as spreading solution, was slowly drop-spread onto the
surface of the aqueous phase containing dispersed montmorillonite until a monolayer film was
formed on the surface when the surface tension reached to ~7mN/m. The system was left
undisturbed for about 90 minutes to allow chloroform to evaporate. After chloroform
evaporation, Si wafers vertically clamped side by side on a plate were mechanically dipped
slowly into the aqueous solution in the LB trough reservoir. The monolayer film on the surface
of the aqueous phase was then compressed from both ends of the aqueous surface at a rate of
10cm2/min until the surface tension reached to 15mN/m. The Si wafers were mechanically lifted
162
out of the aqueous phase in the LB trough reservoir at a rate of 1mm/min. At this point,
monolayer montmorillonite was sandwiched between the Si wafer and the monolayer surfactant
(Fig 6.1). The monolayer surfactant was then removed by soaking the montmorillonite coated Si
wafers in MeOH for 20 h followed by washing with ultrapure water, and then air drying. Figure
6.2 showed the surface topography and height of monolayer montmorillonite determined by a
Nanoscope III Atomic Force Microscopy (AFM) apparatus by using tapping mode in the
scanning range of 5 × 5µm.
Appropriate amount of hexa-glycine to achieve monolayer surface coverage on
montmorillonite surface was dissolved in aqueous solution at pH 6, incubated for 12 h with the
prepared monolayer montmorillonite sorbed on the Si wafer, then rinsed with ultra-pure water to
remove excess hexa-glycine (Figure 6.1). The freshly prepared hexa-glycine-montmorillonite
was air dried and immediately analyzed using the polarization-dependent NEXAFS spectroscopy.
163
Figure 6. 1. Schematic diagram of (a) monolayer montmorillonite preparation using the LB
trough technique and (b) procedure for preparation of monolayer hexa-glycine on
montmorillonite surface.
Figure 6. 2. AFM image (5µm × 5µm)(left) of montmorillonite-coated Si water. The cross-
section profile (lower right) was determined along the line in the zoomed image (upper
right).The height differences (1.43 nm) indicates the thickness of the montmorilonite sheet.
aqueous
phase
air
Si wafer
surfactant
aqueous
phase
air
montmorillonite
sheet
monolayer
surfactant
aqueous
phase
air
montmorillonite
sheet
Cl-surfactant: octadecylammonium chloride
LB-trough preparation of monolayer montmorillonite on Si wafer
Immerse in MeOH (20h)
React with hexa-glycine at pH=6 (12h)
mono-layer hexa-glycine
Sorption of monolyer hexa-glycine on montmorillonite surface
a
b
1.43nm
164
6.3.3. Polarization-dependent N K-edge NEXAFS
All samples were previewed under the optical microscope in order to choose
representative areas before loading in the X-ray Photo Emission Electron Microscope (X-PEEM)
microscope. The Polarization-dependent NEXAFS spectra were collected in total electron yield
mode at the beamline 10ID-1 (SM) with the X-PEEM endstation and an elliptically polarized
undulator (EPU) at the Canadian Light Source (CLS) located at the University of Saskatchewan,
Saskatoon, Canada. X-ray photon energy at N K-edges was calibrated using the gas phase
NEXAFS of N2 (1s→ π* at 400.87eV). The N K-edge stacks and Al K-edge stacks were scanned
from 395 to 420eV and 1570 to1610 eV, respectively, with an energy step of 0.1 to 0.2 eV
around the NEXAFS peaks and of 0.4 to 1.0 eV in the pre-edge and continuum. The Al K-edge
was scanned first to locate its position and make a stack in order to locate the position of
montmorillonite sorbed on the Si Wafer. The stack scan size was defined to include region of
interest (I) and blank region (I0) where there was no sorbed montmorillonite. The silicon wafer
pretreated with HF under vacuum was prepared as blank. The N (1s) spectrum were collected at
normal (0° of X-rays with surface normal, E-vector in surface plane) and grazing (74° of X-rays
with surface normal, E-vector near surface normal) incidence angles of the polarized photon
beam. All the samples were cooled with liquid nitrogen during spectra collection in order to
alleviate radiation damage to the N-containing hexa-glycine.
6.3.4. Data processing
All the NEXAFS data and images are manipulated with IDL6.1 Virtual Version of
aXis2000 (Hitchcock Group, Canada). Aluminum is firstly located in the image and make a stack,
based on which, the spectra of N was retrieved. The spectra of N was firstly normalized though
division by the intensity of background (I0). Before curve fitting, the pre-edge was linear
165
background subtracted from the spectrum which was then normalized to a unity absorption jump
height of the N 1s absorption edge (from the pre-edge at 395eV to the post-edge at 420eV). The
normalized N 1s spectra at grazing and normal incidence were plotted in overlay format. The
angle of hexa-glycine relative to the surface normal of montmorillonite was calculated using
Equation 2.1.
6.4. Results and Discussion
The Al K-edge NEXAFS spectrum was recorded in order to locate the montmorillonite
sheet adsorbed on the Si wafer. Montmorillonite is a naturally occurring cation exchangeable
material that consists of two Si tetrahedral sheets sandwiching a Al octahedral sheet with the
total thickness of ca. 0.95 nm (Brown and Brindley, 1980). The negative charge occurs as a
result of isomorphous substitution of Al(Ⅲ) with Mg(Ⅱ) in the octahedral sheet or Si (Ⅳ)with
Al(Ⅲ) in the tetrahedral sheet. ODAH+ was immobilized on montmorillonite surface by
electrostatic forces (Moraru et al., 1981). Dispersed montmorillonite was strongly fixed on a
hydrophilic Si substrate possibly due to electrostatic forces between the negatively charged clay
layer and the protonated OH termini on Si substrate surface (Grundner and Jacob, 1986;
Takahashi et al., 2003). The AFM image showed a relatively uniform structure and parallel
orientation of the monolayer montmorillonite deposited on the Si substrate as demonstrated by
in-plane X-ray diffraction measurements of similar studies (Takahashi et al., 2002)(Figure 6.2).
The calculated thickness of monolayer montmorillonite was about 1.43 nm by AFM, larger than
the reported 0.95 nm, probably due to the solvated Na+ ions between clay layer and a Si wafer
(Takahashi et al., 2003). The PEEM image acquired at Al K-edge was shown in Fig 6.3(a). The
bright region in the image which indicated the presence of Al formed a contrast with Al free dark
area. The sharply bright dots could be the area with uneven surface. The Al covered region as
166
shown in the middle image of Figure 6.3(a) was selected. The Al spectral signatures were
resolved and revealed a distinctive adsorption band near 1575-1590 eV. Ildefonse et al. (1994)
ascribed these resonances to the results of the transitions of core electrons to the first empty
electronic bond state orbitals (1s-3p) and multiple scattering effect. The spectrum revealed two
main sharp resonances at 1579.4 and 1581.8 eV and a minor feature near 1586.8 eV. These
spectral features and energy positions were similar to the spectra collected from six-fold
coordinated Al in montmorillonite (Doyle et al., 1999).
The PEEM image recorded at N K-edge at grazing and normal incidence was shown the
same way as that of the Al K-edge (Figure 6.3.b and 6.3.c). The average N K-edge NEXAFS
spectra of montmorillonite region, defined by the Al K-edge image, was processed because the
focus of this study was those hexa-glycine self-assembled on the montmorillonite not on the Si
wafer. The N spectra after normalization, recorded at normal and grazing incidence, were
reported in Figure 6.4 to evidence the angular dependence behavior. The sharp peak around
401.6 eV was ascribed to transition of N 1s to π* of peptide C=O bond, and the broad bands near
~ 407 and ~ 412eV could be assigned to N 1s to σ* resonances. These peaks were in good
agreement with the reported glycine-based oligopeptides (Gordon et al., 2003; Cooper et al.,
2004; Zubavichus et al., 2004).
167
Figure 6. 3. (a) PEEM image recorded at the Al K-edge at a photon energy of 1579 eV before
(left) and after montmorillonite region (bright area) were selected (middle) and the Al 1s
NEXAFS spectrum of selected area (right); (b) PEEM image recorded at the N K-edge at a
photon energy of 411.2 eV before (left) and after bright area were selected (middle) and the N 1s
NEXAFS spectrum of selected area (right) at grazing incidence; (c) PEEM image recorded at the
N K-edge at the photon energy of 400.1 eV before (left) and after bright area were selected
(middle) and the N 1s NEXAFS spectrum of selected area (right) at normal incidence.
168
A strong polarization dependence effect was detectable, as evidenced by the differences
of the peptide peak height near 401.6 eV (Figure 6.4). The ratio of the peak intensities near 401.6
eV at normal and grazing incidence, acquired by peak curve fitting, was employed to calculate
the tilt angle between the p orbital of C=O bond and the surface normal according to formula 2.1.
Hexa-glycine was assumed to exhibit ß-sheet conformation after self-assembling from
aqueous solution according to the Fourier transform infrared spectroscopy (FTIR) investigation
on the backbone conformation of peptides EAK 16 assembled on TiO2 (Iucci et al., 2008). The
six atoms connect peptide bond are coplanar as shown in the shadow plane (Figure 6.5b). Two
adjacent planes are connected by α-C atoms. The peptide bonds of adjacent residues point in
opposite directions. The delocalization of the peptide bond electrons restricts the free movement
of the peptide bond. Two planes can be rotated freely around α-atoms. It was assumed the
assembled molecules form a well extended conformation with “head” attached to the mineral
surface and the “tail” extended to outmost surface, forming a zig-zag sideview (Figure 6.5.c).
Molecules interact with each other by H-bonding to form ß-sheet. The intensity of the NEXAFS
spectra is a combination of each individual peptide bond. Since p-orbital of the peptide bond is
normal to the shaded plane, the average vector of p-orbital should be perpendicular to the peptide
axis. The average peptide bond direction defines a molecular axis along the chain and the
effective peptide p-orbital was, on average, perpendicular to the backbone. So based on this
assumption, a simplified scheme of assembled peptides on montmorillonite and definitions of
angles used to characterize the molecular orientations were illustrated in Figure 6.5.d.
169
Figure 6. 4. N K-edge NEXAFS spectra of peptides adsorped onto monolayer montmorillonite
on Si substrate recorded at normal and grazing incidence.
The intensity difference of the peptide feature at grazing and normal incidence implied
the peptide molecules were averagely well-organized in the montmorillonite surface. The
intensity maximized when the electric vector is lined with the direction of the final orbital but
vanishes when the electric vector is orthogonal to the direction of the final orbital (Petoral and
Uvdal, 2003, 2005). The intensity of the peptide feature in our experiment was higher at grazing
than normal incidence (Figure 6.4). The average tilt angle (α) of the π* vector orbital with
respect to the surface normal was about 40° acquired by using Equation 2.1. That is the
molecules have a tilt angle of about 40° with mineral surface, similar to some of the reported
angles on biomedical materials (Battocchio et al., 2010).
N K-edge
Photon Energy (eV)
400 405 410 415
No
rma
lize
d In
ten
sit
y
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
Grazing incidence
Normal incidence
170
Figure 6. 5. (a)Structural formula, (b) ß-sheet strand and (c) sideview of the sheet of self-
assembled Hexa-glycine; (d) Simplified scheme of assembled peptides on montmorillonite and
definitions of angles used to characterize the molecular orientations. All the angles were
calculated with respect to surface normal. Angle θ1 and θ2 represent the incidence angle at
normal and grazing incidence, and θ1E and θ2E are angles of electric vector with respect to
surface normal. While α is the angle between peptide p-orbital with surface normal, equal to the
tilt angle of peptide backbone with surface.
π*
α
αθ2
θ1
θ2E
θ1E
Surface normal
171
At the studied pH values (pH = 6), the most abundant species of hexa-glycine is the
zwitterionic form (Figure 6.6) and the surface of montmorillonite is negatively charged because
the point of zero charge of the montmorillonite ranged from 1 to 2.5 (Juang et al., 2002).
Previous studies on the adsorption of self-assembling peptides on biocompatible materials or
minerals suggested an electrostatic interaction between the carboxylic oxygen atoms of the
peptides and the positive charge site of the oxide substrate (Tzvetkov et al., 2004; Polzonetti et
al., 2008). Studies on the adsorption of glycine or lysine on montmorillonite indicated the
hydrogen bonding between the NH3+ group and the basal oxygen of the montmorillonite
interlayer surface (Kitadai et al., 2009; Ramos and Huertas, 2013). Hexa-glycine in this study
was most likely to interact via protonated amino-terminal end (NH3+) with monolayer
montmorillonite surface driven by electrostatic forces or hydrogen boding. In addition, at the
studied pH, the edge sites of montmorillonite should have a positive charge by protonation of Al
sites below pH ~7 (Rozalen et al., 2009). Hexa-glycine is thus in contact with the positively
charged edges through the –COO- group of the zwitterion form and the >AlOH2
1/2+ group of
montmorillonite. Taking account of all the above analysis, an adsorption model of the hexa-
glycine species was shown in Figure 6.7.
172
Figure 6. 6. Distribution different dissociation states of dissolved Hexa-glycine as a function of
pH, determined based on the published dissociation constants (pKa) of carboxylic acid (3.13)
and the ammonium ion acid (7.69) (Glasstone and Hammel, 1941).
Figure 6. 7. The proposed schematic diagrams of the orientation of the adsorbed hexa-glycine on
montmorillonite.
pH
2 4 6 8 10
Mole
Fra
ction
0.0
0.2
0.4
0.6
0.8
1.0Cationic
Zwitterionic Anionic
-Si – OHAl – OH2
1/2+
Si – OH
173
The polarization dependent NEXAFS results in this study indicate the monolayer
peptides adsorbed to montmorillonite are oriented in order on the surface. The investigation of
the tilting angle of self-assembled peptides with respect to different mineral surface, with
differences in pH, ionic strength, molecular weight, and side chain chemistry, would help fill the
gaps and resolve inconsistencies from the previous batch equilibrium experimental results. For
example, Greenland et al. (1962) found an increasing sorption of small peptides on layer silicates
with increasing molecular weight of the peptides; Dashman and Stotzky (1982, 1984), however,
found the chain length may not necessarily enhance the adsorption. Later studies on the
oligopeptides of glycine by Kalra et al. (2003) found similar results as Greenland et al. (1962).
Polarization dependent NEXAFS results suggest it was likely that smaller molecules tend to
form a “lying down” orientation with title angle close to zero, while large molecules or those
with longer chains or more side chains take a “standing up” orientation, with higher degree of
title angles, in order to maximize the surface coverage. The long chain molecules, with alternate
hydrophobic and hydrophilic groups and different kinds of side chains in particular, are more
likely to adopt a more upright orientation via forming well extended conformation. Molecules
with more ordered organization tend to be adsorbed more than those with disordered
arrangement. A better interpretation on the results of previous batch equilibrium experiments
will be drawn after our further investigation on these factors in the near future.
6.5. Conclusions
In summary, AFM, PEEM and NEXAFS were used in our study to investigate the
molecular orientations of Hexa-glycine self-assembled on monolayer montmorillonite sheets.
The AFM image proved that the LB trough technique is capable of preparing a single layer
hybrid film. Hexa-glycine interacts with montmorillonite surface via NH3+ group to form an
174
extended structure. Minor amount of hexa-glycine was adsorbed to the Al edge site at the
carboxylic end. Previous studies suggested the interaction was driven by electrostatic forces
between the positively charged NH3+ group and the negatively charged montmorillonite surface
or by hydrogen bonding between NH3+ group and the basal oxygen of montmorillonite.
Polarization dependent NEXAFS analysis revealed hexa-peptides tend to form an average tilt
angle of 40° between the molecular axis and the montmorillonite surface. The results
demonstrated polarized NEXAFS can be used to evaluate the surface orientation of oligopeptides
adsorbed on montmorillonite. However, more factors such as pH, mineral type, peptide chain
length need to be tested at the molecular scale to investigate their effect on the adsorption
behavior.
6.6. Acknowledgements
We would like to thank Dr. Alan Esker and his graduate students in Chemistry
Department of Virginia Tech for the technical assistance. We thank the financial support from
NSF (award # EAR 0949653 10010064).
175
6.7. References
Battocchio, C., Iucci, G., Dettin, M., Carravetta, V., Monti, S., Polzonetti, G., 2010. Self-
assembling behaviour of self-complementary oligopeptides on biocompatible substrates.
Materials Science and Engineering: B 169, 36-42.
Brown, G., Brindley, G., 1980. Crystal structures of clay minerals and their x-ray identification.
Mineralogical Society, London, 361-410.
Cooper, G., Gordon, M., Tulumello, D., Turci, C., Kaznatcheev, K., Hitchcock, A.P., 2004. Inner
shell excitation of glycine, glycyl-glycine, alanine and phenylalanine. Journal of Electron
Spectroscopy and Related Phenomena 137–140, 795-799.
Dashman, T., Stotzky, G., 1982. Adsorption and binding of amino-acids on homoionic
montmorillonite and kaolinite. Soil Biology & Biochemistry 14, 447-456.
Dashman, T., Stotzky, G., 1984. Adsorption and binding of peptides on homoionic
montmorillonite and kaolinite. Soil Biology & Biochemistry 16, 51-55.
Doyle, C.S., Traina, S., Ruppert, H., Kendelewicz, T., Rehr, J., Brown, G., 1999. Xanes studies
at the al k-edge of aluminium-rich surface phases in the soil environment. Journal of
Synchrotron Radiation 6, 621-623.
Filiberto, M., Giulia, F., Raimondo, Q., Raffaele, V., Enrico, G., 2011. Self-assembled
monolayers (sams): Which perspectives in implant dentistry? Journal of Biomaterials and
Nanobiotechnology 02, 533-533.
Friedel, J.K., Scheller, E., 2002. Composition of hydrolysable amino acids in soil organic matter
and soil microbial biomass. Soil Biology and Biochemistry 34, 315-325.
176
Glasstone, S., Hammel, E.F., 1941. Physico-chemical studies of the simpler polypeptides. I. The
dissociation constants of mono-, di-, tri-, tetra-, penta-, hexa- and hepta-glycine and their
esters1. Journal of the American Chemical Society 63, 243-248.
Gordon, M.L., Cooper, G., Morin, C., Araki, T., Turci, C.C., Kaznatcheev, K., Hitchcock, A.P.,
2003. Inner-shell excitation spectroscopy of the peptide bond: Comparison of the c 1s, n
1s, and o 1s spectra of glycine, glycyl-glycine, and glycyl-glycyl-glycine. The Journal of
Physical Chemistry A 107, 6144-6159.
Greenland, D.J., Laby, R.H., Quirk, J.P., 1962. Adsorption of glycine and its di-, tri-, and tetra-
peptides by montmorillonite. Transactions of the Faraday Society 58, 829-841.
Grundner, M., Jacob, H., 1986. Investigations on hydrophilic and hydrophobic silicon (100)
wafer surfaces by x-ray photoelectron and high-resolution electron energy loss-
spectroscopy. Applied Physics A 39, 73-82.
Hahner, G., 2006. Near edge x-ray absorption fine structure spectroscopy as a tool to probe
electronic and structural properties of thin organic films and liquids. Chemical Society
Reviews 35, 1244-1255.
Ildefonse, P., Kirkpatrick, R., Flank, A., Lagarde, P., 1994. 27a1 mas nmr and aluminum x-ray
absorption near edge structure study of imogolite and allophanes. Clays and Clay
Minerals 42, 276-287.
Iucci, G., Battocchio, C., Dettin, M., Gambaretto, R., Di Bello, C., Borgatti, F., Carravetta, V.,
Monti, S., Polzonetti, G., 2007. Peptides adsorption on tio2 and au: Molecular
organization investigated by nexafs, xps and ir. Surface Science 601, 3843-3849.
177
Iucci, G., Battocchio, C., Dettin, M., Gambaretto, R., Polzonetti, G., 2008. A nexafs and xps
study of the adsorption of self‐assembling peptides on tio2: The influence of the side
chains. Surface and Interface Analysis 40, 210-214.
Jones, D.L., Hodge, A., 1999. Biodegradation kinetics and sorption reactions of three differently
charged amino acids in soil and their effects on plant organic nitrogen availability. Soil
Biology & Biochemistry 31, 1331-1342.
Juang, R.S., Lin, S.H., Tsao, K.H., 2002. Mechanism of sorption of phenols from aqueous
solutions onto surfactant-modified montmorillonite. Journal of Colloid and Interface
Science 254, 234-241.
Kalra, S., Pant, C.K., Pathak, H.D., Mehata, M.S., 2003. Studies on the adsorption of peptides of
glycine/alanine on montmorillonite clay with or without co-ordinated divalent cations.
Colloids and Surfaces A: Physicochemical and Engineering Aspects 212, 43-50.
Kitadai, N., Yokoyama, T., Nakashima, S., 2009. In situ atr-ir investigation of l-lysine adsorption
on montmorillonite. Journal of Colloid and Interface Science 338, 395-401.
Kleber, M., Sollins, P., Sutton, R., 2007. A conceptual model of organo-mineral interactions in
soils: Self-assembly of organic molecular fragments into zonal structures on mineral
surfaces. Biogeochemistry 85, 9-24.
Lipson, D., Nasholm, T., 2001. The unexpected versatility of plants: Organic nitrogen use and
availability in terrestrial ecosystems. Oecologia 128, 305-316.
Liu, X., Jang, C.-H., Zheng, F., Jurgensen, A., Denlinger, J.D., Dickson, K.A., Raines, R.T.,
Abbott, N.L., Himpsel, F.J., 2006. Characterization of protein immobilization at silver
surfaces by near edge x-ray absorption fine structure spectroscopy. Langmuir 22, 7719-
7725.
178
Liu, X., Zheng, F., Jürgensen, A., Perez-Dieste, V., Petrovykh, D.Y., Abbott, N.L., Himpsel, F.J.,
2007. Self-assembly of biomolecules at surfaces characterized by nexafs. Canadian
Journal of Chemistry 85, 793-800.
Mikutta, R., Kleber, M., Torn, M.S., Jahn, R., 2006. Stabilization of soil organic matter:
Association with minerals or chemical recalcitrance? Biogeochemistry 77, 25-56.
Moraru, V.N., Markova, S.A., Ovcharenko, F.D., 1981. Adsorption of cationic surfactants on
montmorillonite from aqueous-solutions. Ukrainskii Khimicheskii Zhurnal 47, 1058-
1064.
Peters, R.D., Nealey, P.F., Crain, J.N., Himpsel, F.J., 2002. A near edge x-ray absorption fine
structure spectroscopy investigation of the structure of self-assembled films of
octadecyltrichlorosilane. Langmuir 18, 1250-1256.
Petoral, R.M., Uvdal, K., 2003. Structural investigation of 3,4-dihydroxyphenylalanine-
terminated propanethiol assembled on gold. Journal of Physical Chemistry B 107, 13396-
13402.
Petoral, R.M., Uvdal, K., 2005. Nexafs study of amino acid analogues assembled on gold.
Physica Scripta T115, 851-854.
Polzonetti, G., Battocchio, C., Dettin, M., Gambaretto, R., Di Bello, C., Carravetta, V., Monti, S.,
Iucci, G., 2008. Self-assembling peptides: A combined xps and nexafs investigation on
the structure of two dipeptides ala-glu, ala-lys. Materials Science & Engineering C-
Biomimetic and Supramolecular Systems 28, 309-315.
Polzonetti, G., Battocchio, C., Iucci, G., Dettin, M., Gambaretto, R., Di Bello, C., Carravetta, V.,
2006. Thin films of a self-assembling peptide on tio2 and au studied by nexafs, xps and ir
spectroscopies. Materials Science and Engineering: C 26, 929-934.
179
Pradier, C.M., Humblot, V., Stievano, L., Methivier, C., Lambert, J.F., 2007. Salt concentration
and ph-dependent adsorption of two polypeptides on planar and divided alumina surfaces.
In situ ir investigations. Langmuir 23, 2463-2471.
Ramos, M.E., Huertas, F.J., 2013. Adsorption of glycine on montmorillonite in aqueous
solutions. Applied Clay Science 80-81, 10-17.
Rillig, M., Caldwell, B., Wösten, H.B., Sollins, P., 2007. Role of proteins in soil carbon and
nitrogen storage: Controls on persistence. Biogeochemistry 85, 25-44.
Rocha, S., Pereira, M.C., Coelho, M.A.N., Mohwald, H., Brezesinski, G., 2007. Adsorption of
the fusogenic peptide b18 onto solid surfaces: Insights into the mechanism of peptide
assembly. Langmuir 23, 5022-5028.
Rozalen, M., Brady, P.V., Huertas, F.J., 2009. Surface chemistry of k-montmorillonite: Ionic
strength, temperature dependence and dissolution kinetics. Journal of Colloid and
Interface Science 333, 474-484.
Senwo, Z.N., Tabatabai, M.A., 1998. Amino acid composition of soil organic matter. Biology
and Fertility of Soils 26, 235-242.
Sollins, P., Homann, P., Caldwell, B.A., 1996. Stabilization and destabilization of soil organic
matter: Mechanisms and controls. Geoderma 74, 65-105.
Sowden, F.J., Chen, Y., Schnitzer, M., 1977. Nitrogen distribution in soils formed under widely
differing climatic conditions. Geochimica Et Cosmochimica Acta 41, 1524-1526.
Stöhr, J., 1992. Nexafs spectroscopy, vol. 25 of springer series in surface sciences. Springer,
Heidelberg.
180
Sverjensky, D.A., Jonsson, C.M., Jonsson, C.L., Cleaves, H.J., Hazen, R.M., 2008. Glutamate
surface speciation on amorphous titanium dioxide and hydrous ferric oxide.
Environmental Science & Technology 42, 6034-6039.
Takahashi, S., Tanaka, R., Wakabayashi, N., Taniguchi, M., Yamagishi, A., 2003. Design of a
chiral surface by modifying an anionically charged single-layered inorganic compound
with metal complexes. Langmuir 19, 6122-6125.
Takahashi, S., Taniguchi, M., Omote, K., Wakabayashi, N., Tanaka, R., Yamagishi, A., 2002.
First observation of in-plane x-ray diffraction arising from a single layered inorganic
compound film by a grazing incidence x-ray diffraction system with a conventional
laboratory x-ray source. Chemical physics letters 352, 213-219.
Tzvetkov, G., Koller, G., Zubavichus, Y., Fuchs, O., Casu, M.B., Heske, C., Umbach, E., Grunze,
M., Ramsey, M.G., Netzer, F.P., 2004. Bonding and structure of glycine on ordered al2o3
film surfaces. Langmuir 20, 10551-10559.
Vieublé Gonod, L., Jones, D.L., Chenu, C., 2006. Sorption regulates the fate of the amino acids
lysine and leucine in soil aggregates
devenir de deux acides aminés aux propriétés d'adsorption contrastées, la lysine et la leucine,
dans le sol. European journal of soil science 57, 320-329.
Wershaw, R.L., Llaguno, E.C., Leenheer, J.A., 1996. Mechanism of formation of humus
coatings on mineral surfaces 3. Composition of adsorbed organic acids from compost
leachate on alumina by solid-state 13c nmr. Colloids and Surfaces A: Physicochemical
and Engineering Aspects 108, 213-223.
181
Zubavichus, Y., Zharnikov, M., Schaporenko, A., Grunze, M., 2004. Nexafs study of glycine and
glycine-based oligopeptides. Journal of Electron Spectroscopy and Related Phenomena
134, 25-33.
182
7. Conclusions
Proteins and peptides, as a regulator of overall N availability, play a central role in
terrestrial N cycling. Proteins and peptides, by interacting actively with minerals and
macromolecules in soil solution due to their amphoteric nature, are thus a critical pool for soil
organic matter (SOM) and an important form of stabilized organic C and N. Proteins and
peptides, in the soluble organic N (SON) pool, can be stabilized on mineral surface, immobilized
by microorganisms or consumed by plants, making contributions to the insoluble organic N
(ISON) pool and vice versa. There is a rapid flux of organic N between the two pools and its
availability to both plants and microorganisms. The conversion of organic N from stabilized
form to soluble form was regarded as the rate limiting step in terrestrial N cycling. This
dissertation, therefore, focused on the dynamics of organic N in soils especially extractable free
amino acids (FAAs) and hydrolysable amino acids (HAAs). To accomplish this, a series of
experiments were designed and various techniques were applied. High Performance Liquid
Chromatography (HPLC) was used to quantify both FAAs and HAAs. C and N K-edge near
edge X-ray absorption fine structure (NEXAFS) spectroscopy was employed to investigate the
oligopeptides orientation on mineral surface and SOC speciation.
In order to evaluate the amino acid level and composition variability, we investigated
hundreds of soil samples from North-South (N-S) and West-East (W-E) transects of continental
United States. These soils were a subset of samples collected from a total of 4871 sites (1
site/1600 km2) by the USGS from 2007 to 2010 for the USGS Geochemical Landscapes Project.
The soils feature different vegetation cover, C content, depth (surface and subsurface) and most
noticeably mean annual temperature (MAT) or mean annual precipitation (MAP) gradients.
183
We firstly assessed the FAA level in 298 soil samples (half from surface, half from
subsurface counterpart). The levels of FAAs in surface soil were turn out to be extremely higher
than in subsurface soils. Major FAAs identified were Glu, Gln, Asp, Leu, Ala, Thr, Gly and Val.
Though no overall big differences in composition of FAAs among vegetation cover as observed
by multiple comparisons, the composition in surface soil was distinguished from subsurface as
witnessed by NMS. The depth effect in FAA composition could be due to the fine root exudate
and turnover and the selective sorption between. It was also revealed the FAA composition was
highly correlated with MAT and MAP. Significant variations were observed for the levels of soil
FAAs along the MAT and MAP gradient, and among vegetation types, suggesting that
environmental factors might play an important role in affecting organic N dynamic and, therefore,
extractable amino acids.
FAAs, immediately bioavailable to plants and microorganisms, constitute a very small
fraction of soluble organic N pool. The HAAs, calculated as sum of acid cleaved amino acid
from proteins/peptides, are potentially bioavailable. The concentrations of HAAs are about 50
times higher than FAAs. When whole soil was hydrolyzed in hot acid, both water soluble
proteins/peptides and those bound to organo-mineral interface were released. Similar to FAAs,
HAAs existed in extreme high concentration in surface than in subsurface soils due to higher
organic matter accumulations on soil surface. The composition of HAAs was relatively more
uniform than FAAs and no high correlation were found between the HAA amino acid pattern
with environmental factors (MAT and MAP). Major HAAs were Asp, Ser, Glu, Gly, Thr, Ala,
Pro, Val, most of which are also abundant in FAA pool. The uniform composition of HAAs was
similar with the claimed Redfield ratios (references), i.e. the ratios of certain elements were
constant and similar in the organisms and their living environment. It was suggested the uniform
184
composition of amino acids may probably be due to the homeostatic effect of SOM which
maintain the environmental abundance of major elements during the transformation of plant and
microbial residue to degradation products to form SOM. The high correlations of MAT and
MAP with composition of FAAs instead of hydrolysable suggested amino acid FAA were more
subjective to environmental changes. The differences of composition between FAAs and HAAs
are possibly due to the contribution of microbial turnover, fine root exudate and turnover to the
FAA pool.
Although proteins and peptides constitute a substantial part of SOM, other forms of
organic N/C such as aromatics, phenols, polysaccharides, aliphatic and carboxylic compounds
have also been identified in large quantity dependent on soil types. Our C K-edge NEXAFS
spectra revealed the presence of largest proportion of carboxylic-C (38 %), moderate proportions
of aliphatic-C (~ 22 %), aromatic-C (~ 18 %) and O/N-alkyl-C (~ 16 %) and least proportion of
phenolic-C (< 6 %) moieties. The composition of SOC was relatively uniform among sites and
between two horizons irrespective of surface organic C content. Factors such as temperature and
vegetation cover were revealed in this study to account for the fluctuations of the proportions of
aromatic-C and phenolic-C species, in particular. This is somehow in accordance with the
relative even distribution of amino acids. The C K-edge NEXAFS spectra were acquired with
total electron yield (TEY) mode which only probes the surface soil up to 10nm, total
fluorescence yield (TFY) spectra which reflect deep soil (~100nm) features as well as N K-edge
NEXAFS spectra should be collected in future once we got more access to the more powerful
synchrotron tools.
As is stated in the introduction part, there is a flux of proteinaceous compounds between
SON and ISON pool by way of sorption and desorption. Previous studies on adsorption were
185
mostly based on batch equilibrium experiment in labs, seldom of them used spectroscopic
methods to investigate the adsorption behavior in molecular scales. The polarization dependent
NEXAFS combined with photoemission electron microscopy (PEEM) enabled us to explore the
interactions of small peptides with mineral surfaces at microscopic level, especially regarding the
molecular surface organization (molecular orientation, spatial distribution, packing density, etc).
Surface organization regulated the availability, stability and reactivity of the adsorbed molecules.
Using hexa glycine as model peptide compound, our research demonstrated that it tends to form
a “lying down” orientation with an average tilt angle of 40 ° with mineral surface. The “lying
down” organization enhances the interaction between the molecules and the mineral surface,
fosters the entropy gain, thus facilitates the molecular stability. The “lying down” orientation
may be attributed to the lower concentration of the peptides. As the increase of the concentration,
molecules could be “standing up” to maximize the coverage of the molecules sorbed, contrary to
“lying down” configuration. As observed by the batch equilibrium experiment, others factors all
could influence the adsorption behavior, such as the peptide side chain length, mineral types,
temperature, or peptide chain chemistry, which need to be interpreted at molecular scales. Since
the depolymeriztion of proteinaceous substances by extracellular enzymes into FAAs are the rate
limiting step in new N cycling paradigm, environmental factors which influence FAA
enrichment need to be evaluated in our future study.
186
Appendix
Appendix A. Geochemical data for samples of surface soils (A horizon) and subsoil (C horizon) collected in the conterminous United
States
Sampling
date Land Cover1 Land Cover2
Surface ("A") Subsurface ("C")
ID Stat
e
Transec
t
MAT
(°C)
MAP
(cm) Latitude Longitude Depth
(cm)
C
(wt. %)
Depth
(cm)
C
(wt. %)
15-Jul-10 Forested Upland Mixed Forest 0-26 2.6 70-80 0.5 9823 CA WE 13 119 39.073 -123.471
15-Jul-10 Forested Upland Mixed Forest 0-22 3.4 67-80 0.7 6751 CA WE 14 115 38.90431 -123.338
13-Jul-10 Planted/Cultivated Fallow 0-10 0.9 81-87 0.2 10591 CA WE 17 61 38.35028 -121.231
10-Jul-10 Herbaceous Upland Grassland/Herbaceous 0-20 3.3 80-100 0.4 5007 CA WE 8 125 38.182 -120.385
09-Jul-10 Forested Upland Evergreen Forest 0-26 5.7 80-103 1.1 1935 CA WE 15 107 38.278 -120.311
01-Jul-10 Forested Upland Mixed Forest 0-28 2.5 38-48 1.4 6031 CA WE 15 99 37.836 -120.053
01-Jul-10 Forested Upland Mixed Forest 0-34 1.2 93-110 0.4 2959 CA WE 14 97 37.7864 -119.891
09-Jul-10 Forested Upland Evergreen Forest 0-18 0.8 84-102 0.4 9103 CA WE 9 27 38.178 -119.322
01-Jul-10 Forested Upland Evergreen Forest 0-18 4.2 18-24 3.8 9423 CA WE 9 35 37.936 -119.251
01-Jul-10 Shrubland Shrubland 0-21 0.9 38-43 0.7 207 CA WE 9 35 38.006 -119.154
27-Jul-08 Shrubland Shrubland 0-3 0.4 70-80 0.1 8079 NV WE 11 15 38.20693 -118.391
14-Jul-08 Shrubland Shrubland 0-11 0.3 40-50 0.3 12495 NV WE 12 15 37.82428 -118.231
28-Jul-08 Shrubland Shrubland 0-4 0.4 80-90 0.2 10607 NV WE 10 18 38.18129 -117.587
29-Jul-08 Shrubland Shrubland 0-4 0.2 65-75 0.3 7535 NV WE 10 18 38.09232 -117.014
01-Aug-08 Shrubland Shrubland 0-5 1.3 60-65 0.4 10351 NV WE 10 18 38.4116 -116.44
03-Aug-08 Shrubland Shrubland 0-5 0.5 80-90 0.3 3183 NV WE 13 19 38.09385 -116.136
15-Jul-08 Shrubland Shrubland 0-5 0.3 100-110 0.3 10863 NV WE 13 19 37.78021 -115.445
15-Jul-08 Shrubland Shrubland 0-10 0.2 60-70 0.5 3695 NV WE 12 20 37.86564 -115.019
17-Jul-08 Shrubland Shrubland 0-5 0 80-90 0 11231 NV WE 9 29 38.18024 -114.949
15-Jul-08 Shrubland Shrubland 0-7 0.5 60-70 0.2 1759 NV WE 9 29 37.86475 -114.722
15-Jul-08 Shrubland Shrubland 0-18 0.5 90-100 0.5 9951 NV WE 10 34 37.75686 -114.211
24-Jun-08 Shrubland Shrubland 0-5 0.5 70-80 0.4 10975 UT WE 9 30 37.91777 -113.781
24-Jun-08 Shrubland Shrubland 0-5 0.7 100-116 0 303 UT WE 10 27 38.15714 -113.244
26-Jun-08 Shrubland Shrubland 0-10 5 60-70 0.7 10287 UT WE 9 29 37.84254 -112.539
187
Appendix A. Geochemical data for samples of surface soils (A horizon) and subsoil (C horizon) collected in the conterminous United
States
Sampling
date Land Cover1 Land Cover2
Surface ("A") Subsurface ("C")
ID Stat
e
Transec
t
MAT
(°C)
MAP
(cm) Latitude Longitude Depth
(cm)
C
(wt. %)
Depth
(cm)
C
(wt. %)
26-Jun-08 Shrubland Shrubland 0-5 3 45-55 0.6 2095 UT WE 9 27 38.1999 -112.346
26-Jun-08 Shrubland Shrubland 0-5 1.7 47-57 1.6 6191 UT WE 10 31 37.82539 -112.11
26-Jun-08 Shrubland Shrubland 0-5 2.4 80-90 0.3 3931 UT WE 8 27 38.05361 -111.827
28-Jun-08 Forested Upland Evergreen Forest 0-5 0.2 100-120 0.4 8347 UT WE 9 22 37.92292 -111.234
28-Jun-08 Shrubland Shrubland 0-3 0.5 100-120 0.3 9371 UT WE 9 40 37.88751 -111.105
28-Jun-08 Shrubland Shrubland 0-4 0.3 105-118 0.2 1179 UT WE 13 21 37.80933 -110.629
02-Jul-08 Forested Upland Evergreen Forest 0-5 0.4 100-115 0.5 4507 UT WE 13 21 38.03381 -110.185
02-Jul-08 Forested Upland Mixed forest 0-16 4.8 110-130 0.4 3227 UT WE 10 30 37.76386 -109.771
30-Jun-08 Shrubland Shrubland 0-5 0.6 102-124 0.5 2203 UT WE 12 24 38.27357 -109.568
01-Jul-08 Shrubland Shrubland 0-8 1.4 100-115 0.2 411 UT WE 12 24 37.78493 -109.308
01-Oct-08 Forested Upland Evergreen Forest 0-6 2.2 20-30 1.4 5675 CO WE 9 35 37.82026 -108.726
01-Oct-08 Shrubland Shrubland 0-5 1.4 5-13 1.5 9883 CO WE 7 48 37.65978 -108.4
01-Oct-08 Forested Upland Deciduous forest 0-10 8.8 28-36 4.5 10907 CO WE 7 48 37.74476 -108.148
10-Jun-08 Forested Upland Evergreen Forest 0-8 1.9 70-80 0.1 3291 CO WE 3 58 37.84253 -107.24
02-Oct-08 Forested Upland Mixed Forest 0-5 0.9 10-18 0.6 4571 CO WE 5 29 38.2135 -106.572
05-Jun-08 Shrubland Shrubland 0-12 0.4 45-65 0.2 475 CO WE 6 31 38.11813 -105.995
09-Jun-08 Forested Upland Evergreen Forest 0-6 3 30-35 0.7 5355 CO WE 6 33 38.05137 -105.537
09-Jun-08 Forested Upland Evergreen Forest 0-5 6 50-65 0.5 9451 CO WE 9 40 38.06527 -105.125
09-Jun-08 Herbaceous Upland Grassland/Herbaceous 0-7 0.9 100-115 0.5 6379 CO WE 10 36 38.10829 -104.665
17-Jun-08 Herbaceous Upland Grassland/Herbaceous 0-4 0.6 90-115 0.5 4155 CO WE 11 30 37.87116 -103.988
17-Jun-08 Herbaceous Upland Grassland/Herbaceous 0-5 1.8 90-105 0.6 2539 CO WE 12 29 37.92374 -103.731
17-Jun-08 Herbaceous Upland Grassland/Herbaceous 0-3 0.9 90-105 0.6 683 CO WE 12 36 38.09524 -103.024
21-Jun-08 Herbaceous Upland Grassland/Herbaceous 0-3 3 100-115 0.5 4779 CO WE 12 39 37.95591 -102.467
20-Jun-08 Herbaceous Upland Grassland/Herbaceous 0-4 1.6 100-115 0.6 12971 CO WE 11 41 38.18673 -102.201
28-Apr-08 Planted/Cultivated Small Grains 0-12 1.2 90-100 0.3 5803 KS WE 12 43 38.00617 -101.754
28-Apr-08 Herbaceous Upland Grassland/Herbaceous 0-10 1.7 40-50 0.7 4523 KS WE 12 47 37.92534 -101.158
27-Apr-08 Planted/Cultivated Small Grains 0-10 1.9 90-100 0.6 10859 KS WE 12 51 37.93475 -100.573
(Continued)
188
Appendix A. Geochemical data for samples of surface soils (A horizon) and subsoil (C horizon) collected in the conterminous United
States
Sampling
date Land Cover1 Land Cover2
Surface ("A") Subsurface ("C")
ID Stat
e
Transec
t
MAT
(°C)
MAP
(cm) Latitude Longitude Depth
(cm)
C
(wt. %)
Depth
(cm)
C
(wt. %)
27-Apr-08 Herbaceous Upland Grassland/Herbaceous 0-10 1.3 70-80 0.4 8619 KS WE 12 51 38.06555 -100.557
27-Apr-08 Herbaceous Upland Grassland/Herbaceous 0-12 1.6 70-80 0.4 12715 KS WE 12 51 38.17113 -100.335
27-Apr-08 Planted/Cultivated Pasture/Hay 0-11 1 70-80 0.6 12584 KS WE 12 53 37.88446 -100.044
26-Apr-08 Planted/Cultivated Row Crops 0-10 3.2 80-90 0.8 6872 KS WE 12 57 38.20009 -99.5269
26-Apr-08 Planted/Cultivated Small Grains 0-8 1 80-90 0.5 5848 KS WE 13 66 38.16625 -98.8945
26-Apr-08 Planted/Cultivated Row Crops 0-12 1 90-100 0.6 10280 KS WE 13 69 37.91554 -98.6917
25-Apr-08 Developed Low Intensity Residential 0-10 2.2 45-55 0.7 7384 KS WE 13 85 37.99772 -97.3322
24-Apr-08 Planted/Cultivated Pasture/Hay not recorded 2.9 90-100 1.7 12952 KS WE 13 85 38.04795 -96.8593
24-Apr-08 Planted/Cultivated Pasture/Hay 0-8 2.4 40-50 1.2 4760 KS WE 13 94 37.98804 -96.1589
22-Apr-08 Planted/Cultivated Pasture/Hay 0-6 3.5 55-65 0.6 664 KS WE 13 101 37.95192 -95.7368
22-Apr-08 Herbaceous Upland Grassland/Herbaceous 0-12 3.2 50-60 1 3736 KS WE 13 101 37.93123 -95.189
19-Jun-08 Herbaceous Upland Grassland/Herbaceous 0-30 1.1 60-70 0.8 1944 MO WE 13 106 38.11101 -94.5866
19-Jun-08 Planted/Cultivated Pasture/Hay 0-20 1.9 75-90 0.5 11656 MO WE 13 112 37.8354 -94.0932
23-Jun-08 Planted/Cultivated Pasture/Hay 0-30 0.7 55-70 1.2 12936 MO WE 13 111 37.93072 -93.396
20-Jun-08 Forested Upland Deciduous forest 0-5 3.3 30-40 0.7 648 MO WE 13 113 38.11709 -92.9008
20-Jun-08 Planted/Cultivated Pasture/Hay 0-8 2.5 40-50 0.9 1160 MO WE 13 111 38.06131 -92.4946
20-Jun-08 Developed Low Intensity Residential 0-5 3.1 20-35 2.1 5256 MO WE 13 111 37.9503 -92.2735
17-Jun-08 Planted/Cultivated Pasture/Hay 0-20 1.2 55-70 0.5 10376 MO WE 13 111 38.22512 -91.7151
17-Jun-08 Planted/Cultivated Pasture/Hay 0-20 1.3 65-80 0.1 2184 MO WE 13 112 38.05478 -91.4892
28-Jun-08 Planted/Cultivated Pasture/Hay 0-5 2.4 75-100 0.1 1032 MO WE 13 110 37.93733 -90.4681
11-Aug-10 Planted/Cultivated Row Crops 0-20 1.6 120-160 0.1 8200 IL WE 13.4 108 38.062 -90.0358
11-Aug-10 Planted/Cultivated Urban/Recreational Grasses 0-19 1.3 124-151 0.1 2804 IL WE 13.3 108 37.90189 -89.8154
12-Aug-10 Herbaceous Upland Grassland/Herbaceous 0-25 1.3 116-155 0.3 3828 IL WE 13 108 38.15436 -89.0082
13-Aug-10 Planted/Cultivated Pasture/Hay 0-18 2.4 122-158 0.2 7924 IL WE 13 111 38.11849 -88.4933
13-Aug-10 Planted/Cultivated Row Crops 0-20 1.9 119-150 0 12020 IL WE 13 113 38.18385 -88.3259
13-Aug-10 Planted/Cultivated Row Crops 0-18 1.4 117-150 0.2 5356 IL WE 13 115 38.21364 -88.1738
14-Jul-10 Planted/Cultivated Row Crops 0-17 1.2 85-109 0.1 9452 IN WE 13 118 38.06446 -87.8145
(Continued)
189
Appendix A. Geochemical data for samples of surface soils (A horizon) and subsoil (C horizon) collected in the conterminous United
States
Sampling
date Land Cover1 Land Cover2
Surface ("A") Subsurface ("C")
ID Stat
e
Transec
t
MAT
(°C)
MAP
(cm) Latitude Longitude Depth
(cm)
C
(wt. %)
Depth
(cm)
C
(wt. %)
14-Jul-10 Herbaceous Upland Grassland/Herbaceous 0-19 1.3 19-29 1 12524 IN WE 13 120 38.06611 -87.4459
15-Jun-10 Planted/Cultivated Row Crops 0-5 2.4 105-120 0.3 4332 KY WE 13 122 37.88713 -86.8794
15-Jun-10 Planted/Cultivated Row Crops 0-25 1 100-120 0.1 6060 KY WE 13 120 37.98769 -86.3855
16-Jun-10 Forested Upland Deciduous forest 0-1 16.8 95-120 0.4 11180 KY WE 13 115 37.85822 -85.7503
16-Jun-10 Herbaceous Upland Grassland/Herbaceous 0-5 5.2 58-65 0.4 2988 KY WE 13 116 37.95921 -85.4513
15-Jun-10 Planted/Cultivated Pasture/Hay 0-9 3.1 85-95 0.3 5292 KY WE 13 116 38.02005 -85.1837
16-Jun-10 Planted/Cultivated Pasture/Hay 0-20 1.4 95-110 0.1 9644 KY WE 13 114 38.12804 -84.9519
08-Jun-10 Planted/Cultivated Pasture/Hay 0-3 4.8 100-120 0.2 12460 KY WE 13 115 37.95727 -84.6275
05-Jun-10 Forested Upland Mixed forest 0-12 1.1 50-60 0.3 1708 KY WE 12 119 37.87397 -83.6725
05-Jun-10 Forested Upland Deciduous forest 0-8 4.1 60-75 0.3 5804 KY WE 12 117 38.17695 -83.6147
06-Jun-10 Forested Upland Deciduous forest 0-13 4 110-130 4.5 4780 KY WE 12 113 37.93259 -82.6477
11-Sep-08 Forested Upland Deciduous forest 0-20 1.5 36-56 0.4 12972 WV WE 12 115 37.89719 -82.4245
11-Sep-08 Forested Upland Mixed forest 0-15 3.7 15-28 3.6 5596 WV WE 13 117 37.80871 -81.9172
12-Sep-08 Forested Upland Deciduous forest 5-36 1 99-119 0.2 12764 WV WE 13 115 38.08966 -81.6225
16-Sep-08 Forested Upland Deciduous forest 0-30 3.2 81-102 4.3 5852 WV WE 11 128 38.06657 -80.6321
27-May-10 Forested Upland Mixed forest 2-3 38.6 85-100 0.1 6876 VA WE 9 110 38.06033 -79.5412
27-May-10 Herbaceous Upland Grassland/Herbaceous 0-12 1.6 90-110 0.5 2780 VA WE 10 109 37.97994 -79.4946
28-May-10 Forested Upland Mixed forest 1-2 16.7 80-95 0.2 7324 VA WE 11 109 37.86616 -79.1468
31-May-10 Planted/Cultivated Pasture/Hay 0-18 1.6 80-100 0.1 3228 VA WE 10 104 38.18265 -79.0993
15-Nov-10 Forested Upland Deciduous forest 0-10 2.3 114-142 0.2 9372 VA WE 13 112 37.97075 -77.9308
05-Nov-10 Forested Upland Mixed forest 0-15 24.4 127-157 0.1 9916 VA WE 13 106 38.09006 -77.4293
04-Nov-10 Herbaceous Upland Grassland/Herbaceous 0-25 0.6 114-132 0 2748 VA WE 14 106 38.0045 -76.9432
04-Nov-10 Forested Upland Deciduous forest 0-5 1.7 132-152 0.1 1724 VA WE 15 112 37.79612 -76.4342
10-Jul-08 Planted/Cultivated Pasture/Hay 0-25 1.8 100-115 0 4852 MD WE 14 113 38.13279 -75.7937
04-Nov-10 Forested Upland Evergreen Forest 0-15 1.6 91-102 0.1 8948 VA WE 14 113 37.92581 -75.6325
19-Jun-10 Planted/Cultivated Row Crops 0-20 1.4 87-109 0.6 9556 TX NS 18 25 31.59578 -106.239
17-Apr-09 Herbaceous Upland Grassland/Herbaceous 0-40 0.9 40-70 0.4 11327 NM NS 12 46 33.51821 -105.512
(Continued)
190
Appendix A. Geochemical data for samples of surface soils (A horizon) and subsoil (C horizon) collected in the conterminous United
States
Sampling
date Land Cover1 Land Cover2
Surface ("A") Subsurface ("C")
ID Stat
e
Transec
t
MAT
(°C)
MAP
(cm) Latitude Longitude Depth
(cm)
C
(wt. %)
Depth
(cm)
C
(wt. %)
17-Apr-09 Shrubland Shrubland 0-30 0.9 0-30 0.8 8687 NM NS 13 47.1 32.06299 -105.447
16-Apr-09 Shrubland Shrubland 0-10 2.9 0-10 2.9 4591 NM NS 10 57 32.88267 -105.152
11-Apr-09 Shrubland Shrubland 0-25 1.2 55-100 0.5 5439 NM NS 12 38 33.86169 -105.121
17-Apr-09 Shrubland Shrubland 0-50 1.1 50-100 0.5 5615 NM NS 13 47 32.14141 -105.084
17-Apr-09 Shrubland Shrubland 0-20 0.8 0-20 1.1 12783 NM NS 13 47 32.24229 -104.929
16-Apr-09 Shrubland Shrubland 0-5 1.4 0-5 2.1 7231 NM NS 15 38 33.36948 -104.905
13-Apr-08 Shrubland Shrubland 0-30 0.9 0-30 0.9 7919 NM NS 14 38.1 34.52685 -104.619
13-Apr-09 Herbaceous Upland Grassland/Herbaceous 0-60 0.4 60-100 0.2 9535 NM NS 14 38 34.39302 -104.495
13-Apr-09 Shrubland Shrubland 0-70 0.5 70-100 1.2 2111 NM NS 14 38.2 34.57883 -104.246
04-Apr-09 Shrubland Shrubland 0-10 1 10-50 0.6 3135 NM NS 14 40 35.1692 -104.244
04-Apr-09 Shrubland Shrubland 0-30 0.4 90-120 0 9195 NM NS 12 42 35.61959 -103.679
03-Apr-09 Shrubland Shrubland 0-50 1.6 50-100 0.5 6891 NM NS 11 44 36.09216 -103.223
01-Apr-09 Herbaceous Upland Grassland/Herbaceous 0-20 0.6 40-100 0.6 1643 OK NS 12 43 36.56712 -102.715
20-Jun-08 Shrubland Shrubland 0-4 1.2 100-115 0.3 12075 CO NS 13 45 37.35293 -102.162
28-Apr-08 Planted/Cultivated Small Grains 0-18 0.4 90-100 0.2 9323 KS NS 13 45 37.73737 -101.769
28-Apr-08 Planted/Cultivated Small Grains 0-12 1.2 90-100 0.3 5803 KS NS 12 43 38.00617 -101.754
29-Apr-08 Planted/Cultivated Row Crops 0-10 1.2 90-100 0.8 11435 KS NS 11 49 38.74379 -101.104
15-Apr-08 Planted/Cultivated Row Crops 0-10 1.4 60-70 0.5 3947 KS NS 11 55 39.63387 -100.425
15-Apr-08 Herbaceous Upland Grassland/Herbaceous 0-15 1.1 80-90 0.5 8088 KS NS 11 58 39.78564 -99.9978
16-Aug-07 Planted/Cultivated Row Crops 0-15 2 58-100 0.5 12888 NE NS 10 60 40.61944 -99.4847
25-Jul-07 Planted/Cultivated Row Crops 0-18 1.5 81-100 0.3 600 NE NS 9 66 41.09167 -98.8833
27-Jul-07 Herbaceous Upland Grassland/Herbaceous 0-23 1.7 64-100 0.4 4952 NE NS 9 67 41.44944 -98.5008
27-Jul-07 Planted/Cultivated Pasture/Hay 0-13 0.8 30-100 0.5 4248 NE NS 9 68 41.625 -98.4456
26-Aug-09 Planted/Cultivated Pasture/Hay 0-18 4.5 89-114 1.3 153 ND NS 4 50 48.21375 -98.1167
24-Jun-09 Planted/Cultivated Pasture/Hay 0-13 3.1 38-43 0.5 5977 ND NS 4 49 48.87624 -97.9825
26-Aug-09 Planted/Cultivated Row Crops 0-36 1.9 86-109 0.4 4249 ND NS 4 50 48.37001 -97.7849
04-Aug-07 Planted/Cultivated Small Grains 0-15 0.8 46-97 0.5 2248 NE NS 9 68 42.68194 -97.7761
(Continued)
191
Appendix A. Geochemical data for samples of surface soils (A horizon) and subsoil (C horizon) collected in the conterminous United
States
Sampling
date Land Cover1 Land Cover2
Surface ("A") Subsurface ("C")
ID Stat
e
Transec
t
MAT
(°C)
MAP
(cm) Latitude Longitude Depth
(cm)
C
(wt. %)
Depth
(cm)
C
(wt. %)
12-Aug-09 Planted/Cultivated Small Grains 0-23 1.2 75-90 0.5 12488 SD NS 9 64 42.96555 -97.7024
02-Aug-07 Planted/Cultivated Row Crops 0-20 0.6 74-102 0.2 4440 NE NS 9 69 42.29806 -97.6411
24-Jun-09 Planted/Cultivated Row Crops 0-20 2.4 86-114 0.1 1881 ND NS 4 49 48.72058 -97.5585
23-Nov-09 Planted/Cultivated Fallow 0-23 1.5 74-89 0.6 10440 SD NS 9 65 42.992 -97.5547
14-Nov-08 Herbaceous Upland Grassland/Herbaceous 0-20 3.8 100-115 0.8 7368 SD NS 8 66 43.60178 -97.095
30-Sep-09 Planted/Cultivated Row Crops 0-22 3.4 100-120 0.7 9049 MN NS 4 50 48.79665 -96.9522
30-Sep-09 Planted/Cultivated Fallow 0-23 1.7 100-120 0.9 4953 MN NS 4 50
-96.9098
18-Aug-09 Planted/Cultivated Row Crops 0-35 3.1 90-140 0.1 11208 MN NS 7 69 44.15255 -95.8536
29-Sep-09 Forested Upland Deciduous forest 0-9 4.4 80-100 0 6617 MN NS 4 59 47.62273 -95.5756
19-Sep-09 Planted/Cultivated Row Crops 0-30 3.7 100-140 0.2 12232 MN NS 7 70 44.7382 -95.3656
29-Aug-09 Forested Upland Deciduous forest 0-15 4.2 40-50 0.4 9433 MN NS 5 67 46.22335 -95.3268
19-Sep-09 Planted/Cultivated Row Crops 0-30 3.2 95-125 0.2 6041 MN NS 6.5 70 45.0163 -95.0542
28-Aug-09 Forested Upland Deciduous forest 0-5 2.7 80-110 0.1 12505 MN NS 5 67 46.84748 -95.0427
28-Sep-09 Planted/Cultivated Pasture/Hay 0-5 1 115-130 0.1 2521 MN NS 4 65 47.47011 -95.0292
29-Aug-09 Planted/Cultivated Pasture/Hay 0-25 0.6 95-125 0.1 2713 MN NS 5 67 46.50399 -94.8848
29-Aug-09 Herbaceous Upland Grassland/Herbaceous 0-25 1.9 95-105 0 7065 MN NS 6 69 45.91189 -94.87
04-Sep-09 Herbaceous Upland Grassland/Herbaceous 0-25 1.9 60-100 0.2 10137 MN NS 6 71 45.39033 -94.8242
Sampling date, date sample was collected in MM/DD/YY; Land Cover 1, primary classification from National Land Cover Database 1992 Classification System; Land Cover 2,
secondary classification from National Land Cover Database 1992 Classification System; “A” and “C” in the column heading indicate A and C horizon; C (wt. %), organic carbon
content; “ID” in the column heading signifies unique identifier assigned by generalized random tessellation stratified design software. State, abbreviation for state name as follows:
CA, California; NV, Nevada; UT, Utah; CO, Colorado; KS, Kansas; MO, Missouri; IL, Illinois; KY, Kentucky; IN, Indiana; WV, West Virginia; VA, Virginia; MD, Maryland;
TX, Texas; NM, New Mexico; OK, Oklahoma; NE, Nebraska; ND, North Dakota; SD, South Dakota; MN, Minnesota; “WE” and “NS” in the transect column mean West-East
and North-South transect, respectively; “MAT” and “MAP” signify mean annual temperature and mean annual precipitation, respectively.
(Continued)
192
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
982
3 C 48.2 7 13.5 20.4 3.2 6.5 3.6 13.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.1
675
1 C 33 4.2 15.9 20.1 4.2 17 N.D. 21.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.7
105
91 C 26.9 2.1 10.8 12.9 11.5 7.6 N.D. 19.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.5 N.D. N.D. N.D. N.D. N.D. N.D. 40.7
500
7 C 9.7 N.D. N.D. N.D. N.D. 4.3 79.9 84.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.5 N.D. N.D. N.D. N.D. 5.6
193
5 C 4.2 N.D. N.D. N.D. N.D. 13.3 56.8 70.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.5 2.3 N.D. N.D. N.D. N.D. 19.9
603
1 C 35.8 11.7 12.8 24.5 3.5 9 N.D. 12.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 6.1 N.D. N.D. N.D. N.D. N.D. N.D. 21.2
295
9 C 34 3.2 30.9 34.1 1.1 20.4 N.D. 21.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 9.4 N.D. N.D. N.D. N.D. N.D. N.D. 1
910
3 C 37.7 21.2 34.9 56.2 N.D. 2.8 N.D. 2.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.4
942
3 C 25.6 4.2 6.7 11 3.2 4.7 N.D. 7.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 55.6
207 C
27.3 13.3 19.3 32.7 1 6.5 N.D. 7.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.7 N.D. N.D. N.D. N.D. N.D. N.D. 30.9
807
9 C 39.7 22.7 35.9 58.7 N.D. 1.7 N.D. 1.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
124
95 C 10 20.2 43.1 63.3 N.D. 5.3 N.D. 5.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.2 N.D. 0.6 N.D. N.D. N.D. N.D. 20.6
106
07 C 11.9 12.2 34.4 46.6 N.D. 1.4 3.1 4.6 N.D. 2.7 N.D. N.D. 2.7 N.D. 4.9 4.9 0.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 29
753
5 C 26.5 16.5 25.5 42 N.D. 7.6 N.D. 7.6 N.D. 1.9 N.D. N.D. 1.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 22
103
51 C 30.4 15.8 25.4 41.1 N.D. 7.7 N.D. 7.7 N.D. 0.3 N.D. N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 20.5
131
83 C 50.4 3.2 3.8 7 N.D. 14.4 3.3 17.7 N.D. 0.8 N.D. N.D. 0.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24.1
108
63 C 18.7 23 37.4 60.3 N.D. 2.1 N.D. 2.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.9
369
5 C 11.1 11.2 32 43.2 4.3 1.8 N.D. 6.1 N.D. 7.4 N.D. N.D. 7.4 N.D. N.D. N.D. N.D. N.D. 0.9 N.D. N.D. N.D. N.D. N.D. N.D. 31.3
175
9 C 13.7 8.3 17 25.3 N.D. 5.1 0.8 5.9 N.D. 32 9 N.D. 41 N.D. N.D. N.D. N.D. N.D. 0.7 N.D. N.D. N.D. N.D. N.D. N.D. 13.4
112
31 C 7.4 3.3 3.4 6.7 N.D. 2.4 N.D. 2.4 N.D. 25.9 49.9 N.D. 75.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.7
109
75 C 20.6 7.3 17.5 24.8 3.7 4.8 N.D. 8.5 N.D. 20.7 N.D. N.D. 20.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.4
303 C
19.4 2.4 2.7 5.2 N.D. 8.4 5.7 14.1 N.D. 19.9 10 N.D. 29.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 31.5
102
87 C 6.1 4.6 20.5 25.1 1.4 14 N.D. 15.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 53.4
120
95 C 20.5 0.7 1.7 2.5 N.D. 9.6 N.D. 9.6 N.D. 43.1 N.D. N.D. 43.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24.4
619
1 C 19.7 N.D. 19.9 19.9 N.D. 4 N.D. 4 N.D. 25 N.D. N.D. 25 0.3 N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. 7.2 N.D. N.D. 23.7
393
1 C 33 6.6 10.9 17.5 6.1 9.9 N.D. 16 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.4 N.D. 10.6 N.D. N.D. 21.5
834
7 C 21.8 5.9 N.D. 5.9 N.D. 32.2 4.2 36.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 8.3 27.6
117
9 C 69.5 5.4 3.9 9.3 3.1 5.8 N.D. 8.9 N.D. 5.2 N.D. N.D. 5.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.1
193
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
937
1 C 63.9 4.4 3.7 8.1 N.D. 10.1 4 14.1 N.D. 1 N.D. N.D. 1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 13
450
7 C 59.1 4.6 3.5 8.1 N.D. 5.9 1.3 7.2 N.D. 3.9 11.7 N.D. 15.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 10.1
322
7 C 56.4 2.3 2.5 4.8 2.6 10.5 3.4 16.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.4 N.D. N.D. N.D. N.D. 22
220
3 C 79.9 7.1 N.D. 7.1 N.D. 6.9 N.D. 6.9 N.D. 1.8 N.D. N.D. 1.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 4.3
411 C
59.9 3 4.5 7.6 N.D. 14.2 2.4 16.5 N.D. 0.6 N.D. N.D. 0.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 15.5
567
5 C 58.4 5.7 6.1 11.8 2 7.2 2.5 11.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.1
988
3 C 53.2 4.8 5.5 10.3 N.D. 9.4 3.1 12.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.9
109
07 C 56.1 2.1 2.9 5 N.D. 10.9 2.5 13.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.6
329
1 C 6.6 22.7 44.7 67.3 N.D. 0.5 8.9 9.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 16.7
457
1 C 3.5 15.2 53.5 68.6 N.D. 7.7 N.D. 7.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 20.2
475 C
22 9.3 45 54.2 N.D. 16.9 N.D. 16.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.6 N.D. 0.9 N.D. N.D. N.D. N.D. 2.4
535
5 C 33.8 10.8 23.9 34.7 0.7 7.6 0.7 9.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.4 N.D. N.D. N.D. N.D. 21.1
945
1 C 25.1 15.6 40 55.5 1.3 2.9 N.D. 4.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 2.3 N.D. 1.2 N.D. N.D. N.D. N.D. 11.8
637
9 C 24.2 5.5 17.6 23.1 4.4 8.6 N.D. 13 N.D. 17.3 N.D. N.D. 17.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 22.4
415
5 C 29.9 12 11.1 23 5.9 7.7 N.D. 13.7 N.D. 9.5 N.D. N.D. 9.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24
253
9 C 24.1 4.3 6.6 10.9 3.7 5.5 1.6 10.8 N.D. 28.8 N.D. N.D. 28.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.5
683 C
54.6 15 6.2 21.2 N.D. 3.9 N.D. 3.9 N.D. 12.9 N.D. N.D. 12.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.5
477
9 C 35 7.1 7.4 14.5 3.9 6.7 1.2 11.8 N.D. 8.5 N.D. N.D. 8.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 30.3
129
71 C 46 8 11.3 19.3 3.8 6.7 N.D. 10.5 N.D. 6.9 N.D. N.D. 6.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 17.3
580
3 C 39.4 6.4 14.2 20.6 3 9.4 N.D. 12.4 N.D. 4.6 N.D. N.D. 4.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.1
452
3 C 44.3 8.6 13.8 22.3 N.D. 9.1 N.D. 9.1 N.D. 4.1 N.D. N.D. 4.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 20.3
108
59 C 29.9 3.1 10.4 13.4 2.3 11.1 N.D. 13.4 N.D. 8 N.D. N.D. 8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 35.3
861
9 C 39.5 3.8 10.8 14.6 N.D. 11.7 N.D. 11.7 N.D. 5.7 N.D. N.D. 5.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.4
127
15 C 48.3 3.7 9.8 13.5 N.D. 14.1 N.D. 14.1 N.D. 2.7 N.D. N.D. 2.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.4
125
84 C 27.7 4.6 3.3 7.8 N.D. 6.8 N.D. 6.8 N.D. 39.6 N.D. N.D. 39.6 N.D. N.D. N.D. 0.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 17.7
584
8 C 55.7 5.6 13.6 19.2 1.3 8.7 N.D. 10 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 15.2
687
2 C 40.9 4.2 12.2 16.4 2 9.7 N.D. 11.8 N.D. 2.6 N.D. N.D. 2.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.5
102
80 C 61.4 5.3 9.8 15.1 1.5 7.8 N.D. 9.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 14.3
(Continued)
194
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
738
4 C 46.8 2.8 7.9 10.7 5 9.9 N.D. 14.9 N.D. 3.3 N.D. N.D. 3.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24.3
129
52 C 45.3 3 6.7 9.6 2.3 11.3 N.D. 13.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 31.5
476
0 C 51.6 2.3 8.1 10.4 N.D. 11.9 4.2 16.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.8
664 C
63.9 N.D. 5.9 5.9 N.D. 7.8 4.7 12.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.3 N.D. N.D. N.D. 16.4
373
6 C 47.6 1.8 5.2 7 7.1 13.4 N.D. 20.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24.8
194
4 C 63.6 1.1 3.6 4.7 N.D. 20.7 N.D. 20.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.4 N.D. N.D. N.D. 10.6
116
56 C 70.7 0.4 1 1.3 N.D. 11.1 11.5 22.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 5.4
129
36 C 46.4 N.D. 2.3 2.3 14.5 30.7 N.D. 45.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.4 N.D. N.D. N.D. 5.8
648 C
75.2 1.6 3.3 4.9 N.D. 10.1 4 14.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 5.8
116
0 C 52.8 2.1 2.8 4.9 N.D. 21.1 N.D. 21.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.5 N.D. N.D. N.D. 20.7
525
6 C 66.3 2.3 2.9 5.2 N.D. 10.6 N.D. 10.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 17.9
103
76 C 69.9 2.3 4.8 7 N.D. 23.1 N.D. 23.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
218
4 C 89.8 1.9 2.4 4.3 N.D. 5.9 N.D. 5.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
103
2 C 68.8 3 6 9 N.D. 11.8 N.D. 11.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 10.4
820
0 C 48.8 5.5 11.1 16.6 4 5.6 N.D. 9.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.1
280
4 C 52.6 7.3 15 22.3 4.7 5.9 N.D. 10.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 14.6
382
8 C 59.8 4 8.8 12.7 N.D. 9.1 3.4 12.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 15
792
4 C 58.9 4.4 10.7 15.1 1.8 10.1 2.2 14 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12
120
20 C 40.6 4.7 8.7 13.5 2.5 10.4 N.D. 12.9 N.D. 3.6 15.5 N.D. 19.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 13.9
535
6 C 66.9 8.1 5.8 13.8 1.7 9 N.D. 10.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 8.6
945
2 C 74.2 1.6 3.2 4.7 2.5 4.4 2.8 9.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 11.4
125
24 C 34.8 N.D. 6.2 6.2 6.3 20.8 6.7 33.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24.8
433
2 C 78 3.1 6.4 9.5 N.D. 6.6 1.3 7.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 4.6
606
0 C 88.4 0.7 1.8 2.4 N.D. 2.9 4.8 7.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.4
111
80 C 29 0.7 1.9 2.6 5.8 17.3 5 28.1 N.D. 6.3 10.1 N.D. 16.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.9
298
8 C 21.5 4 2.7 6.7 2.3 16.9 2 21.2 N.D. 19.6 N.D. N.D. 19.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 31
529
2 C 20.3 5.4 2.6 7.9 5 16 2.3 23.2 N.D. 20.5 N.D. N.D. 20.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.1
964
4 C 86.2 0.9 N.D. 0.9 N.D. 7.2 4.3 11.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.4
(Continued)
195
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
124
60 C 56.6 N.D. N.D. N.D. N.D. 26 N.D. 26 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 9.3 N.D. N.D. N.D. 8.1
170
8 C 88.3 1 1.5 2.5 N.D. 2.9 1.4 4.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 4.8
580
4 C 76.9 1.1 3.5 4.6 1.4 6.6 3.1 11.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.4
478
0 C 44.2 1.4 5.1 6.4 4.4 16.8 6.7 27.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.6
129
72 C 63.4 1.8 7.6 9.4 1.1 10.7 7.5 19.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.9
559
6 C 50.3 1.6 N.D. 1.6 4.7 18.6 8.6 31.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 16.2
127
64 C 82.5 N.D. N.D. N.D. N.D. 8.2 9.1 17.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.3
585
2 C 48.7 1.1 N.D. 1.1 N.D. 25.2 12.4 37.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12.6
687
6 C 88.6 N.D. N.D. N.D. N.D. 7.2 N.D. 7.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 4.2
278
0 C 71.9 N.D. 1.4 1.5 1.8 12.3 1.5 15.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 11.1
732
4 C 30.7 19.7 N.D. 19.7 3.7 11 23.1 37.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 11.9
322
8 C 47.9 3.2 N.D. 3.2 N.D. 13.8 11.7 25.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.5 N.D. N.D. N.D. N.D. 22.8
937
2 C 11.1 N.D. N.D. N.D. N.D. N.D. 57.7 57.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 10.5 N.D. N.D. N.D. 20.8
991
6 C 91.2 2 N.D. 2 N.D. 6.7 N.D. 6.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
274
8 C 83.1 10.7 3.6 14.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 2.6
172
4 C 84.4 1 N.D. 1 N.D. 8.9 2 10.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.7
485
2 C 92.7 1.7 3.7 5.4 N.D. 1.9 N.D. 1.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
894
8 C 88.2 4.8 6.3 11.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.6
955
6 C 21.5 2.8 8.1 10.8 6.4 7.1 3.4 16.9 N.D. 7.6 N.D. N.D. 7.6 N.D. N.D. N.D. 1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 42.2
113
27 C 67.4 3.6 1.2 4.8 N.D. 6.6 3.7 10.3 N.D. 8.5 N.D. N.D. 8.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 8.9
868
7 C 44.8 10.5 10.5 21 N.D. 7.2 N.D. 7.2 N.D. 9.8 N.D. N.D. 9.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.4 N.D. N.D. N.D. N.D. 16.8
459
1 C 36.4 7.4 4.1 11.5 N.D. 10.8 N.D. 10.8 N.D. 8.7 3.7 N.D. 12.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.9
543
9 C 35.9 7.8 11.3 19.1 N.D. 6.3 N.D. 6.3 N.D. 16.4 N.D. N.D. 16.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.4 N.D. 1.9 N.D. N.D. 20.1
561
5 C 8.7 3.1 2.7 5.8 N.D. 7.1 N.D. 7.1 N.D. 20.2 3.4 N.D. 23.6 N.D. N.D. N.D. 33.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.7
127
83 C 52 10.6 8.8 19.4 N.D. 5.2 N.D. 5.2 N.D. 4.4 4.6 N.D. 8.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 14.5
723
1 C 30.8 5.4 5.6 11 3 6.8 N.D. 9.8 N.D. 24.9 N.D. N.D. 24.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.3 N.D. N.D. N.D. N.D. 23.2
791
9 C 81.4 5.4 4.8 10.2 N.D. 3.6 N.D. 3.6 N.D. 0.9 N.D. N.D. 0.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.8
953
5 C 71.7 3.9 3.5 7.3 N.D. 7.3 N.D. 7.3 N.D. 4.6 N.D. N.D. 4.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 9.1
(Continued)
196
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
211
1 C 46.9 3.7 2.9 6.6 N.D. 9.6 N.D. 9.6 N.D. 19.3 N.D. N.D. 19.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 17.6
313
5 C 54.4 2.8 6.4 9.2 N.D. 13.8 N.D. 13.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 22.6
919
5 C 82.7 5.6 4.5 10.1 N.D. N.D. 0.5 0.5 N.D. 0.8 N.D. N.D. 0.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 6
689
1 C 42.6 2.7 2.9 5.6 N.D. 5.4 N.D. 5.4 N.D. 34.4 N.D. N.D. 34.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12
164
3 C 64.9 8.7 5.1 13.8 N.D. 5.3 N.D. 5.3 N.D. 11.4 N.D. N.D. 11.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 4.6
120
75 C 57.9 13.8 8.6 22.4 N.D. 7 N.D. 7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12.8
932
3 C 60.2 5.9 13.3 19.2 N.D. 6.1 N.D. 6.1 N.D. 2.4 N.D. N.D. 2.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12.2
114
35 C 29.5 2.4 10.3 12.7 5 10.4 N.D. 15.4 N.D. 8.5 N.D. N.D. 8.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 33.9
394
7 C 34.1 3.3 11 14.3 1.8 15.5 N.D. 17.3 N.D. 6.4 N.D. N.D. 6.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28
808
8 C 42.8 4 18.6 22.6 1.4 9.2 N.D. 10.6 N.D. 0.6 N.D. N.D. 0.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.4
128
88 C 45.6 5.7 15.3 21.1 6.6 13.5 1.2 21.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12.1
600 C
35.4 4 13.4 17.4 3.5 12.6 N.D. 16.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 31.1
495
2 C 36.3 3.2 12.4 15.6 2.3 10.1 N.D. 12.4 N.D. 1.8 1.4 N.D. 3.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 32.5
424
8 C 40.7 7.5 10.6 18.1 0.7 15.2 1.4 17.3 N.D. 3.1 1.9 N.D. 4.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.9
153 C
31.1 3.5 8.7 12.2 6.6 3.5 N.D. 10.1 N.D. 6.9 10.3 N.D. 17.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 29.5
597
7 C 39.6 N.D. 11.9 11.9 N.D. 6.5 0.9 7.3 N.D. 0.6 6.7 N.D. 7.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.7 33.2
424
9 C 57.3 5.8 19.5 25.2 0.5 3.6 N.D. 4.1 N.D. 5.7 3.9 N.D. 9.6 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. 3.5
224
8 C 37.8 4.3 7.1 11.4 4.4 11.9 N.D. 16.4 N.D. 3.1 2.7 N.D. 5.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.6
124
88 C 45.1 4.7 14.1 18.8 3 4 N.D. 7 N.D. 3.9 7.2 N.D. 11.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18
444
0 C 69.6 8.8 13.9 22.7 N.D. 4.3 N.D. 4.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.5
188
1 C 33.2 4.8 7.3 12.1 5.7 6.6 N.D. 12.3 N.D. 3.6 16.3 N.D. 19.9 N.D. N.D. N.D. 1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.6
104
40 C 32.8 5.1 9.2 14.3 5.3 5.1 N.D. 10.5 N.D. 11 9.8 N.D. 20.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.6
736
8 C 35.4 5.4 8.7 14.1 8.1 6.1 N.D. 14.2 N.D. 4 5.1 N.D. 9.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 27.2
904
9 C 20.8 4 5.5 9.5 11.5 7 N.D. 18.5 N.D. 5.6 9.7 N.D. 15.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 36
495
3 C 18.8 4 5.9 9.9 12.4 7.2 1.6 21.2 N.D. 1.2 7.2 N.D. 8.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 41.7
112
08 C 37.7 3.7 10.5 14.2 3.1 5.2 N.D. 8.3 N.D. 12.2 8.6 N.D. 20.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 19
661
7 C 32.2 5.4 14.7 20.1 0.8 3.2 N.D. 4 N.D. 9.6 20.6 N.D. 30.2 N.D. N.D. N.D. N.D. N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. 13.2
122
32 C 50.7 5 16.9 21.9 0.7 3 N.D. 3.7 N.D. 3 8.2 N.D. 11.2 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. 12.3
(Continued)
197
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
943
3 C 47.3 8.3 27.9 36.2 1.7 4.7 N.D. 6.4 N.D. 0.2 N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. 1.3 N.D. N.D. N.D. N.D. N.D. N.D. 8.6
604
1 C 38.3 4.7 14.8 19.5 0.8 6 N.D. 6.8 N.D. 4.3 12.3 N.D. 16.6 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. 18.6
125
05 C 61.5 8.9 26.3 35.1 N.D. 2.2 N.D. 2.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D.
252
1 C 61.9 9.1 21.7 30.9 N.D. 2.6 N.D. 2.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1 N.D. N.D. N.D. N.D. N.D. N.D. 3.8
271
3 C 65.2 7.2 22.6 29.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.8 N.D. N.D. N.D. N.D. N.D. N.D. 4.2
706
5 C 60.3 8.1 26.6 34.7 N.D. 2.8 1 3.9 N.D. N.D. 0.4 N.D. 0.4 N.D. N.D. N.D. N.D. N.D. 0.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D.
101
37 C 54.8 7.4 24 31.4 N.D. 1.6 N.D. 1.6 N.D. 5.2 6.1 N.D. 11.3 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. 0.7
9823 A 48.3 6 15.3 21.3 N.D. 8.7 4.8 13.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 17
6751 A 30.9 4.9 17.2 22.1 2.3 17.8 5.5 25.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.4
10591 A 53.5 3.2 12.8 15.9 N.D. 7.4 N.D. 7.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.9 N.D. 0.3 N.D. N.D. N.D. N.D. 22
5007 A 44.9 N.D. N.D. N.D. N.D. 16.2 23.4 39.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 15.5
1935 A 3.8 N.D. 1.3 1.3 N.D. 25.2 43.7 68.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.6 1.5 N.D. N.D. N.D. N.D. 23.9
6031 A 33.2 11.4 12.9 24.3 4.2 11.3 N.D. 15.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.6 N.D. N.D. N.D. N.D. N.D. N.D. 23.4
2959 A 30.8 5.9 26.7 32.6 N.D. 10.2 N.D. 10.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 4.7 N.D. N.D. N.D. N.D. N.D. N.D. 21.7
9103 A 37.3 22.2 38.2 60.4 N.D. 2.3 N.D. 2.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
9423 A 16.5 10 8.7 18.6 1.8 3.2 1.9 6.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 58
207 A 26.4 11.6 19.5 31.1 N.D. 5 N.D. 5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.7 N.D. N.D. N.D. N.D. N.D. N.D. 36.8
8079 A 32 13.6 50.5 64.1 0.9 2.3 N.D. 3.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D.
12495 A 12.9 16.4 36.6 53 N.D. 4.3 N.D. 4.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.6 N.D. N.D. N.D. N.D. N.D. N.D. 29.2
10607 A 11.9 11.7 32 43.7 N.D. 2.5 1.7 4.1 N.D. N.D. N.D. N.D. N.D. N.D. 4 4 N.D. N.D. 0.4 N.D. N.D. N.D. N.D. N.D. N.D. 35.8
7535 A 25.3 17 29.6 46.6 N.D. 4.8 N.D. 4.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.7 N.D. N.D. N.D. N.D. N.D. N.D. 22.6
10351 A 37 7.6 15.3 22.9 N.D. 14.2 N.D. 14.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.6 N.D. N.D. N.D. 25.4
13183 A 54.4 4.4 5.4 9.8 N.D. 13.9 N.D. 13.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.9
10863 A 14.9 19.4 35.2 54.6 N.D. 2 N.D. 2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.4
3695 A 11.2 17.4 45 62.4 N.D. 1.6 N.D. 1.6 N.D. 1.1 N.D. N.D. 1.1 N.D. N.D. N.D. N.D. N.D. 1.7 N.D. N.D. N.D. N.D. N.D. N.D. 22.1
1759 A 19.3 9.2 21.1 30.3 0.3 4.7 N.D. 5 N.D. 20.8 8.7 N.D. 29.5 N.D. N.D. N.D. N.D. N.D. 0.6 N.D. N.D. N.D. N.D. N.D. N.D. 15.4
11231 A 11.9 4.9 8.5 13.4 N.D. 4.3 N.D. 4.3 N.D. 15.2 43.4 N.D. 58.6 N.D. N.D. N.D. N.D. N.D. 0.5 N.D. N.D. N.D. N.D. N.D. N.D. 11.4
10975 A 31.4 20.5 28.9 49.4 N.D. 3 N.D. 3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 16.2
(Continued)
198
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
303 A 66.9 6 12.3 18.3 N.D. N.D. N.D. N.D. N.D. 5.1 2.1 N.D. 7.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.6
10287 A 19.4 11.1 24.1 35.2 N.D. 12.1 N.D. 12.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 33.3
12095 A 26.9 2.6 1.9 4.5 3.1 7.2 N.D. 10.3 N.D. 31.5 N.D. N.D. 31.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 26.9
6191 A 29.3 N.D. 23.2 23.2 2.4 7.4 N.D. 9.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.1 N.D. N.D. 30.7
3931 A 37.7 6.8 10 16.8 N.D. 10.7 N.D. 10.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.1 N.D. 7.5 N.D. N.D. 26.1
8347 A 36.1 4.3 N.D. 4.3 N.D. 14.6 4.4 19 N.D. 3.5 9.2 N.D. 12.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 27.9
1179 A 68.7 4.9 3.6 8.5 1.3 6.1 N.D. 7.4 N.D. 2.8 N.D. N.D. 2.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12.5
9371 A 67.2 9.3 6.7 16 N.D. 6.1 2.3 8.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 8.4
4507 A 76.8 5.6 N.D. 5.6 4 N.D. 0.4 4.4 N.D. 0.5 N.D. N.D. 0.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.6 N.D. N.D. 11.1
3227 A 56.9 5.8 6.1 11.8 3.9 0.9 1.6 6.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24.9
2203 A 84.4 8.9 N.D. 8.9 N.D. 3.7 N.D. 3.7 N.D. 0.9 N.D. N.D. 0.9 2.1 N.D. 2.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
411 A 69.4 5.1 6.2 11.3 N.D. 6.4 0.9 7.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12.1
5675 A 55.7 6.7 7.9 14.6 1.2 5.1 1.6 7.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.9
9883 A 57.9 N.D. 6.1 6.1 N.D. 9.2 6.2 15.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 20.6
10907 A 46.6 1 2 3 N.D. 9.2 N.D. 9.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 41.2
3291 A 22.5 17.9 27 44.9 N.D. 2.2 10.5 12.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 20
4571 A 4.7 24.6 43.6 68.1 N.D. 6.1 N.D. 6.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.1
475 A 31.6 17.8 46.1 63.9 N.D. 4.5 N.D. 4.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
5355 A 34.5 11.1 21.2 32.2 N.D. 7.5 N.D. 7.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.2 N.D. N.D. N.D. N.D. 24.6
9451 A 25.5 13.2 29.7 43 N.D. 3.2 N.D. 3.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.4
6379 A 26.2 7.8 21.3 29.1 N.D. 9.5 1.4 10.8 N.D. 10.6 N.D. N.D. 10.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.4
4155 A 55.4 16.6 14.9 31.5 1.7 4.3 N.D. 6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.1
2539 A 34.7 11.3 13 24.3 N.D. 9.2 1.8 11 N.D. 5.2 N.D. N.D. 5.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 24.9
683 A 50.2 15.5 9.1 24.5 N.D. 6.1 0.9 7.1 N.D. 1.8 N.D. N.D. 1.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 16.4
4779 A 38.2 8.8 9.3 18.2 1 10 N.D. 10.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 32.7
12971 A 38.3 8.4 12.4 20.8 N.D. 10.7 1.9 12.6 N.D. 1.5 N.D. N.D. 1.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 26.8
5803 A 40.2 7.4 11.3 18.7 2.6 9 N.D. 11.5 N.D. 4.2 N.D. N.D. 4.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.4
4523 A 35 5.4 8.3 13.7 N.D. 14.7 1.2 15.9 N.D. 0.3 N.D. N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 35.1
(Continued)
199
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
10859 A 43.3 7 14.1 21.1 N.D. 11.3 2.7 14 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.6
8619 A 37.3 4.2 12 16.2 1 7 N.D. 8 N.D. 4.4 N.D. N.D. 4.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 34.2
12715 A 42.9 5.5 10.6 16.1 N.D. 13.3 N.D. 13.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 27.7
12584 A 36.6 3.7 9.7 13.3 1.7 12.1 N.D. 13.7 N.D. 6.8 N.D. N.D. 6.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 29.5
5848 A 65 5.5 14.1 19.6 N.D. 7.1 N.D. 7.1 N.D. 0.4 N.D. N.D. 0.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 8
6872 A 39.7 5.5 10.1 15.5 N.D. 10.9 2.1 13 N.D. 1.9 N.D. N.D. 1.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 29.9
10280 A 64.2 6.8 8.7 15.5 N.D. 7.8 N.D. 7.8 N.D. 0.2 N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 12.3
7384 A 46.4 4.1 6.9 11 N.D. 12 N.D. 12 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 30.7
12952 A 63.1 7.4 8.2 15.6 N.D. 12.8 N.D. 12.8 N.D. 1.7 N.D. N.D. 1.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 6.8
4760 A 44.6 3 7.7 10.7 1.8 15.7 3.5 20.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.8
664 A 55.4 2.1 3.4 5.5 N.D. 9.5 N.D. 9.5 N.D. 2.7 N.D. N.D. 2.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 27
3736 A 56.1 2 6.4 8.4 N.D. 13.1 N.D. 13.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 22.5
1944 A 72.4 2.1 5.6 7.7 N.D. 9.5 N.D. 9.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 10.4
11656 A 78.1 1.7 1.9 3.7 N.D. 4.2 2.6 6.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 11.4
12936 A 72.6 3.4 5.8 9.2 2.7 3.6 N.D. 6.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 11.9
648 A 72.5 3.6 5.5 9.1 N.D. 12.1 N.D. 12.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 6.4
1160 A 67.7 2.8 3 5.8 N.D. 12.7 N.D. 12.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 13.8
5256 A 64 4 3.2 7.2 N.D. 10.7 N.D. 10.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.1
10376 A 68.1 6.3 6.5 12.8 N.D. 9 N.D. 9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 10
2184 A 54.8 2.6 4.9 7.4 N.D. 15 N.D. 15 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 22.7
1032 A 53.4 3.3 5.5 8.7 5.1 5.5 N.D. 10.6 N.D. N.D. 11.5 N.D. 11.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 15.8
8200 A 58.3 8.5 10.2 18.7 N.D. 4.5 N.D. 4.5 N.D. 0.5 N.D. N.D. 0.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18
2804 A 70.2 11.2 13.1 24.3 1.8 1.1 N.D. 2.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 2.6
3828 A 62.7 7.3 8.1 15.4 N.D. 7.5 N.D. 7.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 14.4
7924 A 60.4 7.1 6.1 13.3 N.D. 9.8 N.D. 9.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 16.6
12020 A 62.6 7.1 8 15 N.D. 4.6 N.D. 4.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 17.7
5356 A 54.6 8.5 6.7 15.2 0.3 13.1 N.D. 13.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 16.9
9452 A 61.2 9.3 12.9 22.2 N.D. 6.8 N.D. 6.8 N.D. 1.5 0.5 N.D. 2 N.D. N.D. N.D. N.D. N.D. 0.4 N.D. N.D. N.D. N.D. N.D. N.D. 7.4
(Continued)
200
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
12524 A 41.3 4.4 7.4 11.8 7.8 15.4 N.D. 23.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.7
4332 A 59.5 3 8.8 11.7 2.4 5.2 N.D. 7.6 N.D. 2 N.D. N.D. 2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 19.3
6060 A 73.2 3.4 4.3 7.7 N.D. 9.4 N.D. 9.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 9.8
11180 A 36.5 6.4 N.D. 6.4 1.7 13.9 N.D. 15.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 41.5
2988 A 46.4 2.7 2.1 4.8 N.D. 17.2 N.D. 17.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1 N.D. N.D. N.D. 30.7
5292 A 55.8 4.9 4.3 9.1 2.4 14.6 N.D. 17 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.1
9644 A 76.8 3.5 4.3 7.7 2.2 6.8 N.D. 9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 6.4
12460 A 63 1.2 3.7 4.9 N.D. 8.9 N.D. 8.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 23.2
1708 A 84.6 1.2 2.3 3.5 2.4 3.7 N.D. 6.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.3 N.D. N.D. N.D. 5.5
5804 A 45.7 1.7 2.6 4.3 3 18.5 9.5 31 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 19.1
4780 A 47.2 5.1 4.7 9.8 6.7 13.5 N.D. 20.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 22.8
12972 A 66.3 3.1 8.7 11.7 1.9 8.2 4.3 14.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.6
5596 A 49.8 0.4 2.6 3 5.1 16.7 7.1 28.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.3
12764 A 89.9 1.9 N.D. 1.9 N.D. N.D. 6.3 6.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 2
5852 A 63.4 N.D. N.D. N.D. N.D. 13.2 11.5 24.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 9.2 N.D. N.D. 2.8
6876 A 37 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 63
2780 A 79.9 0.9 2.1 3 3.1 10.3 N.D. 13.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.6
7324 A 27.7 10.7 N.D. 10.7 N.D. 8.8 14 22.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 38.9
3228 A 71.1 1.1 N.D. 1.1 N.D. 6.4 3.9 10.4 N.D. N.D. N.D. 1.1 1.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 16.3
9372 A 65.3 1.9 1.9 3.8 1.3 0.4 15.4 17.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.2 N.D. N.D. N.D. N.D. 10.7
9916 A 54.8 3 N.D. 3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 42.2
2748 A 86.3 5 3 7.9 N.D. 3 0.9 3.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.8
1724 A 90.9 0.4 2.4 2.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 6.3
4852 A 73.7 2 4 6 N.D. N.D. N.D. N.D. N.D. 4.5 N.D. N.D. 4.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 15.8
8948 A 84 1.7 4.1 5.8 N.D. 5.3 1.8 7.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3
9556 A 28.3 3.7 8.4 12.1 0.7 13.1 3 16.8 N.D. 5.6 N.D. N.D. 5.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 37.2
11327 A 64.7 1.7 4.3 6 N.D. 9.8 2.9 12.6 N.D. 2.8 N.D. N.D. 2.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 13.9
8687 A 48.6 11.8 8.7 20.5 N.D. 6.4 N.D. 6.4 N.D. 7.3 N.D. N.D. 7.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.7 N.D. N.D. N.D. N.D. 16.6
(Continued)
201
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
4591 A 39 7.7 6.4 14.1 N.D. 9 N.D. 9 N.D. 7 3.6 N.D. 10.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.5 N.D. N.D. N.D. N.D. 26.9
5439 A 40.6 10.2 16.3 26.5 N.D. 8.8 N.D. 8.8 N.D. 1.9 N.D. N.D. 1.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.7 N.D. N.D. N.D. N.D. 21.5
5615 A 27.4 4.2 12.3 16.6 N.D. 11.1 N.D. 11.1 N.D. 15.8 4.1 N.D. 20 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25
12783 A 49.6 8.4 12.4 20.7 N.D. 2.8 N.D. 2.8 N.D. 5.8 7.2 N.D. 13 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 14
7231 A 26.6 3.4 6.2 9.5 N.D. 6.9 N.D. 6.9 N.D. 29.3 1.1 N.D. 30.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 26.6
7919 A 84.2 6.1 5.3 11.4 N.D. 4.1 N.D. 4.1 N.D. 0.3 N.D. N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D.
9535 A 89.1 3.8 3.5 7.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.6
2111 A 78 5 4.4 9.4 N.D. 5.4 N.D. 5.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 7.2
3135 A 62 2.7 9.2 11.9 N.D. 11.6 N.D. 11.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 14.5
9195 A 81.4 6.2 6.6 12.8 N.D. 4.3 N.D. 4.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.5
6891 A 46.8 5.4 4.6 10 N.D. 6.7 N.D. 6.7 N.D. 17.3 N.D. N.D. 17.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 19.2
1643 A 69.3 6.7 9.3 16.1 N.D. 5 N.D. 5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. 9.5
12075 A 74.1 19.3 6.1 25.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.5
9323 A 64 17 11.4 28.4 N.D. 4.7 N.D. 4.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 2.9
11435 A 40.2 5.7 13 18.7 N.D. 11.5 N.D. 11.5 N.D. 0.6 N.D. N.D. 0.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 29.1
3947 A 45.1 8.3 16.5 24.8 N.D. 13 N.D. 13 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 17.1
8088 A 41.7 6.6 16.3 22.8 N.D. 10.4 N.D. 10.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.1
12888 A 39.4 5.1 13.7 18.8 N.D. 12.6 N.D. 12.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 29.3
600 A 42 4.7 16.7 21.4 N.D. 11.2 N.D. 11.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.4
4952 A 35.9 4.3 10.3 14.6 6.1 8.5 N.D. 14.5 N.D. 0.8 N.D. N.D. 0.8 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 34.2
4248 A 36.3 5 11.6 16.6 3.5 8 N.D. 11.4 N.D. 2.8 1.9 N.D. 4.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 31
153 A 39.2 3.3 12.9 16.2 N.D. 6.8 N.D. 6.8 N.D. 0.4 0.7 N.D. 1.1 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. 36.6
5977 A 50.2 3.5 8.9 12.4 1.9 9.8 N.D. 11.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.7
4249 A 52.1 4.9 16.1 21.1 N.D. 4.6 N.D. 4.6 N.D. 3.6 N.D. N.D. 3.6 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. 18.5
2248 A 36.8 3.5 6.7 10.3 2 12.3 N.D. 14.3 N.D. 3.1 3.1 N.D. 6.2 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 32.5
12488 A 50.9 5.1 14.6 19.7 4 6.6 N.D. 10.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 18.8
4440 A 73.4 8.6 12.1 20.7 N.D. 1.7 N.D. 1.7 N.D. 0.7 N.D. N.D. 0.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 3.6
1881 A 49.4 5.1 14.9 19.9 1.7 6.8 N.D. 8.5 N.D. 1.1 N.D. N.D. 1.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 21.1
(Continued)
202
Appendix B. Mineralogical data for samples from the soil C and A horizons in the conterminous United States
ID H Qz
Tot_K
_fs
Tot_Pl
ag
Tot_Fl
ds
Tot_1
4A
Tot_1
0A Kao
Tot_Cl
ay Gs Cc Dm An
Tot_C
arb Ac Hd
Tot_Z
eol Gyp Talc Hor Ser Her Goe Pyr Pt
Oth
er Aph
wt.
% wt. % wt. % wt. % wt. % wt. %
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
% wt. %
wt.
%
wt.
% wt. %
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
wt.
%
10440 A 48.1 5.6 13 18.6 0.5 9.9 N.D. 10.4 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 22.9
7368 A 39.7 7.3 11.5 18.8 1.8 11.7 N.D. 13.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.1
9049 A 23.7 15.3 5.5 20.8 7 10.1 N.D. 17.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 38.5
4953 A 17.1 3.2 4.9 8.1 15.4 6.7 N.D. 22.2 N.D. 0.2 7.3 N.D. 7.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 45.2
11208 A 41.2 5.8 11.7 17.4 N.D. 10 4.3 14.3 N.D. N.D. N.D. N.D. N.D. 0.3 N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 26.8
6617 A 51.9 8.8 19.4 28.2 N.D. 4.6 N.D. 4.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.8 N.D. N.D. N.D. N.D. N.D. N.D. 14.6
12232 A 35.7 5.1 12 17.1 6.1 6.8 N.D. 13 N.D. 4.8 0.9 N.D. 5.7 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 28.6
9433 A 45.5 8.3 23.3 31.6 0.8 0.8 N.D. 1.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1 N.D. N.D. N.D. N.D. N.D. N.D. 20.2
6041 A 35.8 12.3 12.1 24.4 6.3 6.3 N.D. 12.6 N.D. 0.9 0.7 N.D. 1.6 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 25.6
12505 A 63.1 9.1 24.8 33.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. 2.7
2521 A 52.8 9.3 24 33.3 N.D. 2.1 N.D. 2.1 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 1.2 N.D. N.D. N.D. N.D. N.D. N.D. 10.6
2713 A 67.9 9.5 20.1 29.6 N.D. 1.8 N.D. 1.8 N.D. 0.2 N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. 0.5 N.D. N.D. N.D. N.D. N.D. N.D. N.D.
7065 A 54.4 9.6 18.1 27.7 1.8 0.3 N.D. 2 N.D. N.D. N.D. 0.5 0.5 N.D. N.D. N.D. N.D. N.D. 0.2 N.D. N.D. N.D. N.D. N.D. N.D. 15.2
10137 A 51.1 7.1 22.5 29.6 0.6 3.3 N.D. 3.9 N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. N.D. 0.3 N.D. N.D. N.D. N.D. N.D. N.D. 15.1
“ID” in the column heading signifies unique identifier assigned by generalized random tessellation stratified design software. H, horizon; Qz, quartz; Tot, total; Tot_K_fs, total
potassium feldspar; Tot_Plag, total plagioclase feldspar; Tot_Flds, total feldspar; Tot_14A, total 14-angstrom clay minerals; Tot_10A, total 10-angstrom clay minerals; Kao,
kaolinite; Gs, gibbsite; Cc, calcite; Dm, Dolomite; An, aragonite; Tot_Carb, total carbonate minerals; Ac: analcime; Hd, heulandite; Tot_Zeol, total zeolite minerals; Gyp, Gypsum;
Hor, hornblende; Ser, serpentine; Her, heratite; Geo, goethite; Pyr, pyroxene; Pt, pyrite Aph, amorphous; N.D. , note detected.
(Continued)
203
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
9823 C 0.04 0.92 0.38 0.16 0.16 0.04 0.21 0 0.047 0.31 0.9 0.23 0.12 0.17 0.1 0.2 0.11 0.017 0.065 0.098 0.4 0.1 0
6751 C 0.43 1.74 0.74 0.98 0.013 0.04 1.66 0 0.092 0.77 2.07 0.51 0.28 0.1 0.4 0.33 0.16 0.12 0.04 0.17 0.42 0.14 0.17
10591 C 0.16 1.57 0.22 0.36 0 0 0.058 0 0.16 0.47 0.77 0.38 0.11 0.058 0.18 0.26 0.062 0.02 0.098 0.031 0.28 0.12 0
5007 C 0.1 0.2 0.68 0.97 0.063 0.03 0.47 0 0.16 1.01 2.32 1.03 0.64 0.15 0.11 0.57 0.078 0.11 0.24 0.11 0.66 0.42 0.13
1935 C 0 0.21 0.42 0.52 0 0.005 0.67 0 0.012 0.41 1.34 0.27 0.57 0.03 0 0.15 0.07 0.12 0.04 0.03 0.19 0.047 0
6031 C 0.46 2.61 1.52 1.25 0.4 0.17 5.12 0 0.21 1.01 3.1 1.25 1.29 0.26 0.21 0.48 0.29 0.27 0.12 0.6 0.67 0.25 0
2959 C 0.09 0.54 0.42 0.53 0.049 0.047 1.53 0 0.06 0.36 1.2 0.36 0.32 0.074 0 0.17 0.098 0.13 0.057 0.22 0.22 0.22 0
9103 C 0.28 2.53 0.54 0.66 0.31 0.082 0.65 0 0.12 0.28 0.73 0.18 0.28 0.09 0 0.086 0.082 0.077 0.012 0.17 0.14 0.062 0
9423 C 2.09 7.46 6.74 4.71 2.15 0.48 10.1 0 0.81 2.94 9.85 10.18 3.14 0.95 0 1.27 0.93 0.89 0.23 2.66 1.74 0.65 0.74
207 C 0.9 3.49 1.38 0.94 0.64 0.06 1.28 0.1 0.39 1.25 2.61 0.77 0.78 0.46 0 0.74 0.35 0.15 0.31 0.57 1.3 0.57 0.13
8079 C 0.1 0.4 0.2 0.22 0.06 0.018 0.1 0 0.08 0.14 0.32 0.066 0.077 0.039 0 0.06 0.037 0.057 0.001 0.077 0.12 0.04 0
12495 C 0.52 3.86 1.04 0.96 0.41 0.05 0.14 0 0.14 1 1.98 0.44 0.83 0.17 0 0.71 0.19 0 0.25 0.03 0.79 0.34 0
10607 C 0.31 0.87 0.42 0.6 0.068 0.035 0.036 0.04 0.06 0.32 0.84 0.2 0.14 0.06 0.027 0.23 0.05 0.098 0.087 0.063 0.21 0.079 0
7535 C 0.5 2.73 0.72 0.88 0.42 0.094 0.16 0 0.49 1.02 1.93 0.094 0.26 0.26 0 0.77 0.22 0.056 0.34 0.12 0.96 0.41 0.1
10351 C 0.42 1.16 0.72 0.75 0.31 0.1 0.18 0 0.037 1.02 2.02 0.07 0.24 0.28 0.32 0.78 0.21 0.05 0.42 0.08 1.04 0.41 0
3183 C 0.6 2.87 0.94 1.17 0.67 0.16 0.44 0 0.15 1.39 2.73 0.14 0.19 0.28 0 1 0.28 0.06 0.46 0.13 1.21 0.51 0
10863 C 0.09 0.37 0.192 0.2 0.04 0.01 0.026 0 0.076 0.16 0.33 0.055 0 0.038 0 0.076 0.033 0.043 0 0.037 0.093 0.029 0
3695 C 0.56 1.59 1.62 1.29 0.37 0.22 0.16 0 0.08 1.54 2.75 0 0.26 0.37 0.08 1.08 0.27 0.31 0.48 0.17 1.22 0.53 0
11231 C 0.75 3.62 1.38 1.44 0.79 0.2 0.52 0 0.16 1.6 3.2 0.11 0.28 0.36 0 1.15 0.31 0.067 0.47 0.16 1.32 0.59 0
1759 C 0.56 3.22 1.04 1.05 0.64 0.13 0.37 0 0.29 1.16 2.15 0.16 0.3 0.29 0 0.81 0.23 0.082 0.4 0.18 1.06 0.43 0.12
9951 C 0.91 1.27 0.6 1.84 0.01 0.07 0.23 0 0.039 1.25 3.1 1.08 0 0.04 0.06 1.04 0.045 0.09 0.28 0.068 0.41 0.28 0
10975 C 0.13 0.77 0.44 0.33 0.16 0.04 0.026 0 0.16 0.25 0.48 0.13 0.088 0.07 0 0.12 0.04 0.17 0.024 0.065 0.14 0.043 0
303 C 0.036 0.84 0.172 0.07 0.14 0.02 0.0006 0 0.03 0.07 0.15 0.09 0 0 0.05 0 0.006 0 0 0 0 0 0
10287 C 0.25 1.51 0.62 0.4 0.19 0.08 0.17 0 0.28 0.55 1.08 0.078 0.13 0.27 0.018 0.45 0.17 0.058 0.12 0.089 0.66 0.29 0
2095 C 1.74 1.53 0.98 1.64 0 0.2 0.53 0 0.16 1.61 1.68 1.24 0.36 0.21 0 0.46 0.17 0.48 0.13 0.62 0.63 0.21 0.1
6191 C 1.5 4.05 2.88 3.13 0.63 0.44 0.33 0 0.45 4.12 6.71 0.29 1.15 0.95 0.026 3.08 0.58 0.16 1.4 0.27 3.08 1.33 0.14
3931 C 0.16 0.69 0.42 0.32 0.12 0.07 0.1 0 0.17 0.43 1.03 0.12 0.16 0.18 0 0.46 0.12 0.067 0.13 0.059 0.64 0.26 0
8347 C 0.13 0.46 0.28 0.27 0.13 0.02 0.034 0 0.03 0.3 0.64 0.12 0.07 0.08 0 0.2 0.07 0.03 0.065 0.045 0.29 0.093 0
9371 C 0.39 1.54 0.38 1.01 0.2 0.02 0.079 0 0.11 0.74 1.41 0.14 0.15 0.1 0 0.6 0.08 0.04 0.29 0.06 0.46 0.2 0
1179 C 0.27 0.65 0.62 0.48 0.2 0.13 0.22 0 0.086 0.47 1.04 0.069 0.13 0.17 0 0.37 0.13 0.064 0.067 0.11 0.5 0.22 0
4507 C 0.23 0.73 1.1 0.69 0.015 0.14 0.015 0 0.05 0.46 0.76 0.11 0.08 0.08 0.04 0.22 0.028 0.36 0.037 0.08 0.19 0.08 0
3227 C 0.23 0.77 0.62 0.55 0.085 0.11 0.12 0 0.12 0.72 1.93 0.049 0.23 0.22 0.13 0.67 0.13 0.022 0.34 0.031 0.91 0.32 0.13
2203 C 0.55 2.25 0.88 0.97 0.41 0.21 0.44 0 0.32 1.09 1.96 0.11 0.24 0.26 0 0.79 0.25 0.11 0.24 0.24 0.92 0.47 0
411 C 0.47 0.65 0.24 0.16 0.026 0.023 0.025 0 0.14 0.21 0.6 0.08 0 0.04 0 0.23 0.05 0 0.04 0 0.26 0.07 0
5675 C 0.25 1.11 0.54 0.44 0.23 0.035 0.21 0.13 0.043 0.78 1.84 0.29 0.12 0.45 0 0.57 0.49 0 0.35 0.28 1.38 0.45 0.1
9883 C 2.08 18.13 3.28 2.84 2.62 0.13 1.91 0 0.74 3.64 6.73 1.08 0.64 1.19 0.16 2.43 1.11 0.12 1.19 0.34 3.9 1.6 0
204
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
10907 C 9.44 17.94 11.7 9.84 1.71 0.17 0.64 0.5 0.6 19.64 25.79 0.42 1.16 3.97 0 11.12 1.59 0.77 6.18 0.89 12.66 5.67 0.23
3291 C 0.23 0.53 0.82 0.79 0 0.017 0.14 0 0.024 0.88 1.63 0.3 0.25 0.015 0 0.39 0.04 0 0.09 0.096 0.21 0.16 0
4571 C 1.87 6.02 2.34 1.87 0.58 0.11 1.58 0 0.12 2.07 5.26 3.63 0.41 0.36 0 1.12 0.28 0.67 0.48 0.67 1.33 0.51 0.12
475 C 0.31 2.05 0.6 0.61 0.34 0.06 0.2 0 0.18 0.57 1.15 0.08 0.24 0.16 0.2 0.38 0.13 0.09 0.21 0.11 0.53 0.2 0
5355 C 0.44 1.95 0.4 0.34 0.6 0.03 0.08 0.04 0.13 0.49 0.87 0.12 0 0.18 0.1 0.29 0.13 0 0.07 0.17 0.44 0.16 0
9451 C 0.46 0.4 1.32 0.83 0.06 0.05 0.13 0.16 0 2.23 2.94 0.21 0 1.05 0.37 1.15 0.72 0.036 0.53 0.28 1.33 0.4 0
6379 C 0.41 2.79 0.8 1.13 0.5 0.05 0.27 0 0.41 1.07 2.15 0.28 0.24 0.18 0 0.72 0.16 0.03 0.27 0.03 0.74 0.36 0
4155 C 0.29 0.94 0.62 0.57 0.25 0.05 0.11 0.15 0.054 0.65 1.29 0.19 0.21 0.18 0 0.45 0.12 0.038 0.17 0.058 0.57 0.22 0.12
2539 C 0.56 1.26 1.34 0.99 0.38 0.18 0.29 0 0.23 1.21 2.29 0.18 0.32 0.39 0.022 0.88 0.23 0.13 0.32 0.13 1.14 0.5 0
683 C 0.46 1.13 0.52 0.99 0.097 0.1 0.27 0.02 0.011 0.69 1.42 0.13 0.16 0.15 0 0.51 0.11 0.043 0.16 0.082 0.6 0.24 0
4779 C 0.88 2.17 0.8 1.87 0.29 0.14 0.19 0.03 0.15 1.44 3.11 0.46 0.4 0.28 0 1.08 0.16 0.032 0.38 0.024 0.94 0.44 0.11
12971 C 0.44 1.61 0.62 0.94 0.28 0.035 0.13 0.03 0.09 0.74 1.51 0.18 0.21 0.15 0 0.5 0.11 0.05 0.23 0.07 0.53 0.22 0
5803 C 0.29 2.16 0.46 0.77 0.35 0.039 0.14 0 0.3 0.71 1.34 0.16 0.21 0.16 0 0.46 0.12 0.048 0.19 0.077 0.54 0.24 0.11
4523 C 1.85 6.77 3.66 3.63 1.57 0.3 1.74 0 0.89 4.07 6.72 0.51 1.18 1.05 0 3.43 0.84 0.28 1.66 0.39 3.75 1.67 0
10859 C 0.23 1.75 0.5 0.41 0.23 0.046 0.046 0 0.12 0.57 1.12 0.26 0.19 0.17 0 0.45 0.09 0.026 0.14 0.055 0.52 0.18 0
8619 C 0.47 2.25 0.82 1.15 0.32 0 0.079 0 0.24 1.36 2.11 0.2 0.13 0.11 0 0.77 0.07 0.036 0.37 0.044 0.46 0.24 0
12715 C 0.61 4.09 0.86 1.15 0.64 0 0.28 0 0.3 0.97 2 0.41 0.27 0.2 0 0.76 0.15 0.06 0.34 0.1 0.7 0.32 0
12584 C 0.57 3.2 1.2 1.32 0.3 0.056 0.3 0 0.45 1.13 2.08 0.24 0.24 0.18 0 0.74 0.12 0.1 0.34 0.096 0.61 0.3 0
6872 C 0.62 3.27 0.9 1.69 0.43 0 0.47 0 0.29 1.74 3.47 0.4 0.33 0.19 0 1.51 0.15 0.069 0.64 0.064 1.07 0.53 0
5848 C 0.79 3.19 1.94 2.31 0.11 0.03 0.35 0.05 0.13 2.5 3.9 0.68 0.34 0.19 0 1.29 0.1 0.21 0.59 0.14 0.94 0.65 0
10280 C 1.01 6.11 1.78 2 0.48 0.1 0.59 0 0.33 2.23 4.07 0.5 0.56 0.4 0 1.39 0.3 0.14 0.56 0.17 1.35 0.67 0
7384 C 0.56 5.03 1.26 1.31 0.66 0.03 0.75 0 0.35 1.13 2.72 0.43 0.44 0.26 0 0.88 0.19 0.074 0.42 0.074 0.97 0.42 0.13
12952 C 1.74 12.01 2.3 3.58 0.82 0.055 0.35 0.13 0.36 3.67 8.31 0.86 0.53 0.6 0.04 3.52 0.47 0.2 1.68 0.21 3.3 1.33 0.16
4760 C 1.4 7.26 2.22 2.95 0.76 0.07 1.5 0 1.04 3.03 7.12 1.07 0.45 0.4 0 2.74 0.41 0.08 1.3 0 2.42 1.11 0
664 C 0.22 0.74 0.24 0.26 0 0.05 0.033 0.05 0 0.34 1.04 0.19 0.14 0.2 0.015 0.35 0.16 0.053 0.14 0.075 0.7 0.21 0
3736 C 0.52 3.6 2.12 2.83 0.18 0.012 1 0.11 0.36 2.9 5.31 0.84 0.77 0.32 0.22 1.64 0.21 0.23 0.74 0.19 1.34 0.9 0.1
1944 C 0.3 0.99 2.9 2.56 0.25 0 2.56 0 0.44 2.01 4.71 0.61 0.36 0.32 0 1.07 0.27 0.15 0.4 0.17 1.27 0.69 0
11656 C 0 0.81 1.4 2.09 0.63 0 0.94 0 0.12 0.72 2.61 0.32 0.42 0.19 0.18 0.19 0.14 0.12 0.1 0.12 0.35 0.17 0
12936 C 0.96 1.83 9.98 8.57 0.08 0.1 1.06 0 0.66 13.67 19.46 0.65 4.47 1.38 0 5.63 0.24 0.03 2.48 0.18 4.27 4.29 0
648 C 0.53 3.28 2.52 3.11 1.3 0 1.96 0.18 0.25 1.61 3.61 0.62 0.79 0.39 0.85 0.41 0.36 0.24 0.094 0.24 0.83 0.34 0.22
1160 C 0.77 3.96 3.18 2.87 0.26 0.04 2.65 0 0.33 3.03 7.9 1.27 0.45 0.49 0 2.61 0.35 0.21 1.22 0.2 2.21 1.22 0
5256 C 5.17 18.14 5.14 4.48 2.3 0.16 5.7 0 1.29 4.89 13.43 3.73 3.07 1.44 0.42 3.3 1.7 0.88 1.55 1.26 4.65 1.88 0.49
10376 C 0.45 1.23 4.3 4.05 0.68 0.098 0.8 0 0.66 4.88 8.08 1.07 1.54 0.67 0.14 2.23 0.21 0.26 1.04 0.2 2.1 1.59 0
2184 C 0 0.18 0.24 0.24 0.09 0 0.048 0 0.03 0.15 0.4 0.12 0 0.05 0 0.023 0.034 0.03 0 0.04 0.05 0.02 0
1032 C 0.09 0.17 0.26 0.28 0 0.005 0.07 0.73 0.005 0.2 0.41 0.065 0.11 0 0.24 0.045 0.003 0.016 0.006 0.038 0.04 0 0
8200 C 0 0.29 0.09 0.31 0 0.08 0.078 0 0.076 0.11 0.28 0.05 0.1 0.032 0 0.012 0.013 0 0 0.019 0.031 0.023 0
(Continued)
205
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
2804 C 0.15 0.93 0.44 0.39 0.16 0 0.11 0 0.17 0.17 0.44 0.1 0.087 0.063 0 0.1 0.048 0.075 0 0.059 0.1 0.067 0
3828 C 0.055 0.45 0.48 1.01 0.047 0 0.23 0.03 0.11 0.54 1.2 0.14 0.15 0.06 0 0.26 0.054 0.011 0.083 0.059 0.28 0.2 0
7924 C 2.17 9.65 3.9 4.2 0.88 0.18 1.73 0 1.51 4.29 12.86 0.82 1.05 0.98 0 3.67 0.69 0.13 1.83 0.17 3.74 1.69 0.12
12020 C 0.2 1.43 0.3 0.58 0.056 0.098 0.14 0 0.27 0.51 1.11 0.036 0.13 0.097 0 0.39 0.06 0 0.15 0.014 0.41 0.21 0
5356 C 0.017 0.12 0.28 0.38 0 0.031 0.14 0 0.026 0.23 0.52 0.15 0.1 0.019 0.22 0.08 0.039 0.031 0 0.051 0.13 0.065 0
9452 C 0.05 0.35 0.12 0.2 0.05 0.0035 0.018 0 0.09 0.12 0.24 0.07 0 0.045 0 0.024 0.023 0.035 0 0.029 0.046 0.012 0
12524 C 0.43 8.85 1.64 0.58 1.96 0 0.99 0 0.81 0.92 2.03 0.2 0.38 0.24 0 0.59 0.17 0.023 0.31 0.15 0.73 0.3 0
4332 C 0.14 0.45 0.52 0.82 0 0.02 0.14 0 0.077 0.56 1.09 0.087 0.07 0.03 0.11 0.25 0.03 0.034 0.04 0.04 0.19 0.15 0
6060 C 0.03 0.15 0.58 0.54 0.016 0.004 0.034 0 0.05 0.61 0.89 0.14 0.24 0.08 0 0.28 0.07 0.087 0.12 0.048 0.297 0.17 0
11180 C 0.29 3.37 0.8 0.93 0.57 0.037 0.39 0 0.35 0.97 2.23 9.96 0.16 0.18 0 0.76 0.14 0.044 0.34 0.06 0.89 0.42 0.11
2988 C 0.96 8.94 3.62 2.25 1.75 0.16 0.94 0 0.48 2.6 7 0.36 0.36 0.54 0 2.03 0.46 0.07 0.97 0 2.16 1.08 0
5292 C 0.14 1.33 0.4 0.51 0.16 0.018 0.075 0 0.19 0.44 0.91 0.069 0.062 0.019 0 0.26 0.034 0.016 0.1 0 0.28 0.12 0
9644 C 0 0.19 0.22 0.45 0.14 0.006 0.03 0 0.037 0.13 0.33 0.11 0 0.05 0 0 0.04 0.035 0 0.05 0.05 0 0
12460 C 0 0.14 0.18 0.23 0 0 0.035 0 0.03 0.11 0.33 0.09 0.09 0 0 0 0.025 0.012 0 0 0 0 0
1708 C 0.51 2.04 0.52 0.69 0.34 0.03 1.4 0.02 0.15 0.57 1.6 0.27 0.1 0.15 0 0.3 0.24 0.03 0.12 0.24 0.58 0.2 0
5804 C 0 0.43 0.58 1.44 0.13 0.02 0.37 0.02 0.085 0.48 1.66 0.3 0.3 0.03 0 0.14 0.09 0.11 0.007 0.14 0.13 0.05 0
4780 C 0.3 1.48 0.64 1.12 0.44 0 0.56 0 0.21 0.62 1.74 0.23 0.34 0.15 0 0.26 0.15 0.11 0.077 0.19 0.35 0.13 0
12972 C 3.63 6.08 3.3 7.21 0.84 0.23 1.47 0.44 0.27 2.86 6.91 4.88 1.02 0.24 0.21 1.29 0.12 9.02 0.27 7.58 0.6 0.4 0.1
5596 C 1.85 3.6 3.42 2.25 0.73 0.04 0.45 0.32 0.19 4.46 7.17 0.94 0.88 1.64 0.32 2.49 0.6 0.39 1.26 0.62 2.85 1.24 0.49
12764 C 0.26 0.52 0.62 0.86 0.059 0.14 0.6 0 0.08 0.96 0.98 0.11 0.57 0.047 0 0.32 0.048 0.11 0.14 0.27 0.26 0.13 0
5852 C 0 0.16 0.38 0.26 0.066 0.013 0.04 0.03 0.05 0.34 0.65 0.087 0.075 0.13 0.1 0.17 0.09 0.058 0.09 0.076 0.27 0.09 0
6876 C 0 0.12 0.28 0.25 0.21 0.02 0.23 0 0.008 0.2 0.49 0.11 0.11 0 0 0.024 0.03 0 0.012 0.013 0.022 0 0
2780 C 0.084 0.36 0.5 0.92 0.11 0.017 0.36 0 0.06 0.34 1.07 0.11 0.075 0.01 0 0.11 0.048 0.035 0 0.056 0.09 0.05 0
7324 C 0 0 0.3 0.37 0.015 0 0.13 0 0.03 0.3 0.7 0.08 0.12 0 0.088 0.069 0.002 0.04 0.003 0 0.1 0.04 0
3228 C 0 0.14 0.44 0.46 0.13 0.013 0.15 0.02 0.05 0.26 0.67 0.11 0.25 0.06 0 0.097 0.09 0.14 0.056 0.063 0.13 0.077 0
9372 C 0 0.06 0.22 0.29 0 0 0.046 0 0.024 0.45 0.71 0.092 0.14 0.016 0 0.21 0.004 0 0.08 0 0.22 0.13 0
9916 C 0 0.21 0.36 0.21 0.65 0 0.06 0 0.015 0.098 0.4 0.12 0.054 0 0.17 0.006 0.024 0 0 0 0.006 0 0
2748 C 0.047 0.15 0.056 0.17 0 0.006 0.01 0 0.002 0.077 0.18 0.025 0.003 0 0.15 0 0 0.01 0 0.002 0 0 0
1724 C 0 0.2 0.54 0.45 1.02 0 0.08 0 0.03 0.32 0.56 0.12 0.11 0.068 0 0.06 0.08 0.045 0.068 0.053 0.11 0.065 0
4852 C 0.069 0.14 0.26 0.19 0.03 0.07 0.097 0 0.082 0.11 0.22 0.058 0 0.056 0.058 0.019 0.04 0.039 0.008 0.058 0.027 0.022 0
8948 C 0.09 0.39 0.24 0.31 0.17 0.009 0.12 0.02 0.06 0.15 0.37 0.08 0 0.058 0.12 0.013 0.045 0.07 0.02 0.12 0.067 0.011 0
9556 C 0.65 2.78 0.64 1.08 0.35 0.01 0.058 0 0.11 1.22 2.27 0.1 0.31 0.21 0 0.97 0.16 0 0.47 0.034 0.97 0.48 0
11327 C 1.14 2.45 1.24 2.01 0.33 0.27 0.36 0 0.2 1.71 3.58 0.22 0.38 0.36 0 1.48 0.27 0.085 0.61 0.085 1.55 0.65 0.14
8687 C 1.47 6.9 2.44 2.66 1.65 0.32 0.51 0 0.26 3.7 6.09 0.08 0.68 0.94 0 2.65 0.7 0.2 1.34 0.3 3.23 1.33 0.32
4591 C 8.76 33.5 12.88 13.4 2.83 1.44 1.27 0.3 0.43 17.75 31.5 0.31 2.57 4.42 0.25 12.5 3.18 0.88 7.14 0.92 14.25 6.66 0.71
5439 C 0.34 1.33 0.72 0.84 0.34 0.085 0.15 0 0.035 1.12 2.25 0.12 0.33 0.3 0.05 9.2 0.19 0.03 0.34 0.057 1.16 0.44 0
(Continued)
206
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
5615 C 0.33 2.02 0.62 0.53 0.35 0.11 0.1 0 0.2 0.4 0.93 0.24 0.16 0.12 0 0.26 0.08 0.13 0.056 0.1 0.2 0.11 0
12783 C 2.29 6.47 4.54 2.95 1.64 0.47 0.92 0.26 0.61 3.39 3.4 0.47 1.86 0.82 0.26 1.78 0.37 0.13 0.84 0.31 1.8 1.07 0.36
7231 C 2.43 14.44 4.06 4.84 3.43 0.46 0.92 0 0.67 5.61 7.19 0.27 0.91 1.15 0 3.78 0.92 0.084 1.68 0.21 3.75 1.62 0.16
7919 C 1.72 2.72 2.54 3.09 0.21 0.33 0.28 0.13 0.14 3.97 6.89 0.09 0.65 0.92 0 2.99 0.65 0.13 1.38 0.24 3.75 1.54 0.11
9535 C 0.74 2.81 1.04 1.72 0.5 0.18 0.3 0 0.19 1.39 3 0.22 0.24 0.3 0 1.11 0.23 0.07 0.39 0.1 1.09 0.5 0
2111 C 0.21 0.87 0.36 0.48 0.11 0.055 0.12 0 0.075 0.6 1.25 0.086 0.22 0.17 0.073 0.51 0.11 0.029 0.13 0.045 0.64 0.26 0
3135 C 0.9 4.3 1.1 1.8 0.83 0.15 0.38 0 0.18 1.71 3.32 0.36 0.51 0.44 0 1.57 0.32 0.09 0.78 0.091 1.71 0.71 0
9195 C 0.16 0.6 0.22 0.32 0.023 0.051 0.083 0 0.02 0.38 0.9 0.21 0.15 0.13 0.14 0.32 0.092 0.023 0.049 0.038 0.45 0.14 0
6891 C 0.25 1.04 0.6 0.57 0.095 0.11 0.17 0 0.094 0.67 1.3 0.098 0.22 0.19 0.08 0.49 0.097 0.074 0.13 0.082 0.65 0.27 0
1643 C 1.15 4.68 1.3 2.17 0.64 0.2 0.54 0 0.25 1.84 3.84 0.46 0.48 0.41 0 1.45 0.34 0.09 0.54 0.12 1.49 0.65 0
12075 C 0.53 2.75 1.36 1.33 0.43 0.05 1.69 0 0.18 1.14 2.22 0.31 0.25 0.2 0 0.73 0.15 0.13 0.37 0.12 0.72 0.37 0
9323 C 0.26 1.94 0.34 0.62 0.31 0.045 0.17 0 0.11 0.5 1 0.1 0.15 0.11 0 0.3 0.087 0.034 0.15 0.08 0.37 0.16 0
5803 C 0.29 2.16 0.46 0.77 0.35 0.039 0.14 0 0.3 0.71 1.34 0.16 0.21 0.16 0 0.46 0.12 0.048 0.19 0.077 0.54 0.24 0.11
11435 C 0.43 3.64 0.74 0.93 0.51 0.04 0.24 0 0.24 0.97 1.79 0.18 0.25 0.22 0 0.63 0.16 0.04 0.3 0.07 0.75 0.32 0.1
3947 C 0.57 4.11 0.9 1.53 0.62 0.12 0.81 0 0.32 1.41 3.19 0.29 0.26 0.31 0.18 1.18 0.22 0 0.51 0.075 1.22 0.57 0
8088 C 0.51 4.15 0.94 1 0.7 0.025 0.37 0 0.25 1.14 2.28 0.19 0.33 0.23 0 0.83 0.17 0.067 0.43 0.12 0.91 0.39 0.1
12888 C 0.5 1.8 0.72 1.51 0.12 0.018 0.12 0 0.14 1.25 2.27 0.2 0.12 0.14 0 0.9 0.11 0 0.32 0 0.68 0.39 0
600 C 0.32 2.5 0.52 0.82 0.26 0.014 0.25 0 0.14 0.72 2.7 0.2 0.13 0.08 0 0.47 0.06 0.01 0.21 0.01 0.48 0.21 0
4952 C 0.75 4.62 1.68 1.04 1.55 0 0.5 0 0.3 0.94 2.12 0.53 0.46 0.22 0.3 0.54 0.12 0.09 0.24 0.09 0.61 0.28 0
4248 C 1.75 9.06 2.7 1.46 2.06 0.08 1.09 0 1.86 1.71 3.51 0.77 0.77 0.44 0 1.09 0.23 0.05 0.57 0.14 1.25 0.56 0
153 C 0.66 3.58 1.12 1.04 0.57 0 0.18 0 0.38 0.9 2.08 0.25 2.04 0.18 0 0.67 0.12 0.004 0.3 0.066 0.71 0.33 0
5977 C 0.6 1.88 0.88 1.16 0.43 0.14 0.34 0 0.25 1.19 2.25 0.7 0.14 0.29 0 1.12 0.19 0.068 0.35 0.24 1.02 0.53 0
4249 C 0.26 0.41 0.42 0.44 0.082 0.11 0.18 0 0.1 0.43 0.69 0.11 0.11 0.096 0.02 0.28 0.069 0.087 0.012 0.12 0.23 0.13 0
2248 C 0.54 2.53 1.28 0.58 0.67 0.02 0.29 0 0.2 0.54 1.07 0.59 0.57 0.22 0 0.41 0.09 0.04 0.19 0.079 0.47 0.22 0
12488 C 0.49 1.55 0.68 0.95 0.16 0.13 0.52 0 0.087 0.84 1.78 0.18 0.11 0.19 0.04 0.72 0.13 0 0.27 0.064 0.71 0.32 0
4440 C 0.31 1.32 0.66 1.04 0.11 0.036 0.12 0.03 0.11 0.6 1.01 0.099 0.24 0.13 0 0.26 0.089 0.089 0.13 0.12 0.28 0.17 0
1881 C 0.45 0.86 0.4 1.02 0 0.11 0.11 0 0.25 0.9 1.77 0.1 0.15 0.09 0 0.79 0.063 0.009 0.32 0.05 0.51 0.3 0
10440 C 0.72 4.07 1.36 1.28 1.2 0.13 0.73 0 0.48 1.35 3.18 0.067 0.36 0.37 0 1.04 0.23 0 0.46 0.088 1.21 0.54 0.15
7368 C 0.51 4.07 0.98 1.23 0.86 0 0.44 0 0.94 1.07 2.62 0.18 0.19 0.25 0 1.01 0.17 0.04 0.4 0.087 1.06 0.46 0
9049 C 0.23 1.34 0.52 0.73 0 0.08 0.088 0 0.17 0.76 1.55 0.018 0.18 0.092 0 0.58 0.057 0 0.24 0 0.46 0.23 0
4953 C 0.57 2.29 1.48 1.59 0.32 0 0.27 0.58 0.25 1.77 3.44 0.11 0.43 0.31 0 1.44 0.11 0.07 0.76 0.065 1.14 0.6 0
11208 C 0.32 1.8 0.42 0.8 0.15 0.09 0.25 0 0.21 0.77 1.61 0.055 0.18 0.16 0 0.6 0.1 0 0.23 0.015 0.61 0.3 0
6617 C 0.29 1.36 0.56 0.59 0.43 0.084 0.21 0 0.25 0.5 1.19 0.13 0.15 0.11 0 0.34 0.078 0.044 0.12 0.073 0.31 0.16 0
12232 C 0.08 0.29 0.1 0.23 0 0.08 0.068 0 0.077 0.19 0.33 0 0.068 0.031 0.15 0.099 0.014 0 0.006 0.004 0.085 0.058 0
9433 C 1.5 1.59 1.34 1.9 0.23 0.11 1.87 0 0.12 1.22 2.43 0.57 0.78 0.28 0 0.55 0.17 0.24 0.07 0.46 0.5 0.26 0
6041 C 0.11 0.24 0.22 0.23 0.046 0.067 0.084 0 0.12 0.25 0.36 0.072 0.088 0.058 0 0.12 0.039 0.036 0.016 0.053 0.081 0.054 0
(Continued)
207
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
12505 C 0.2 0.71 0.38 0.41 0.053 0 0.28 0 0.076 0.44 0.89 0.056 0.13 0.069 0 0.16 0.037 0 0.033 0.092 0.15 0.092 0
2521 C 0.18 0.47 0.4 0.45 0.076 0.068 0.45 0 0.11 0.27 0.55 0.12 0.2 0.073 0 0.093 0.052 0.056 0.013 0.097 0.07 0.065 0
2713 C 0.13 0.42 0.24 0.48 0 0.035 0.22 0 0.02 0.21 0.45 0.12 0.2 0.042 0.083 0.027 0.014 0.053 0.015 0.14 0.069 0.017 0
7065 C 0.12 0.23 0.32 0.29 0.03 0.072 0.13 0 0.09 0.087 0.28 0.077 0.087 0.067 0 0.053 0.047 0.068 0 0.08 0.044 0.039 0
10137 C 0.072 0.27 0.26 0.14 0.052 0.068 0.093 0 0.088 0.055 0.25 0.056 0 0.062 0.026 0.035 0.044 0.056 0 0.059 0.051 0.034 0
9823 A 3.43 11.73 4.82 4.86 1.95 0.2 0.93 1.46 1.11 8.11 18.05 1.39 1.07 2.75 0.54 5.65 2.75 0.74 2.62 2.11 7.67 2.28 0
6751 A 6.77 11.87 6.18 3.51 1.47 0.22 1.11 0.4 0.41 7.85 18.96 2.23 0.88 2.97 0 6.6 3.43 0 3.58 2.75 11.99 4.31 0
10591 A 17.84 81.05 15.86 18.6 28.09 0 5.79 1.82 15.45 13.88 39.23 15.58 9.66 5.4 0 11.78 4.32 2.53 5.71 3.31 13.31 7.32 0.59
5007 A 6.32 12.98 16.42 2.67 34.15 0 8.86 0 1.38 3.33 10.33 21.5 8.89 1.19 0 1.77 0.33 0.67 0.69 2.03 1.65 0.93 0
1935 A 1.92 20.11 3.26 2.85 4.44 0.22 5.29 0 0.97 2.74 6.02 4.86 3.68 1.01 0 1.1 0.83 0.42 0.49 1.05 2.07 0.64 0.3
6031 A 7.87 20.24 9.5 3.6 8.09 0.3 13.59 0 1.38 4.02 10.83 12.69 3.58 1.43 0 1.84 0.99 0.56 0.71 2.45 2.51 1.04 0
2959 A 0.78 2.4 1.94 1.74 0.51 0.17 3.46 0 0.35 2.01 5.69 1.31 2.62 0.76 0.16 1.34 0.85 0.55 0.75 1.62 2.45 0.93 0.22
9103 A 0.9 4.24 1.28 1.3 0.86 0.04 0.92 0.08 0.26 0.66 1.6 1.77 1.08 0.11 0 0.2 0.17 0.16 0.048 0.48 0.33 0.1 0.24
9423 A 0.99 3.08 2.12 2.55 1.15 0.2 1.64 0.2 0.24 1.43 3 1.33 0 0.55 0 0.47 0.28 0.33 0.11 1.17 0.81 0.3 0.21
207 A 3.69 13.05 4.28 2.1 4.99 0.23 1.54 0 0.99 2.25 5.88 3.99 1.61 0.89 0.05 1.18 0.53 0.37 0.48 1.04 1.92 0.9 0
8079 A 0.78 3.45 0.8 0.48 1.24 0.16 0.6 0 0.46 0.66 0.92 0.18 0.3 0.5 0 0.27 0.09 0.075 0.13 0.26 0.31 0.19 0.12
12495 A 0.28 1.38 0.48 0.49 0.23 0.014 0.08 0.03 0.16 0.78 1.05 0.08 0.12 0.23 0 0.47 0.088 0 0.19 0.11 0.69 0.39 0
10607 A 1.26 4.97 1.56 1.83 1.47 0.22 0.57 0 0.36 1.9 2.97 0.19 1.41 0.42 0 1.09 0.25 0 0.43 0.19 1.11 0.59 0
7535 A 0.45 3.21 0.48 0.54 0.72 0.05 0.38 0 0.24 0.49 0.81 0.11 0.17 0.14 0.2 0.22 0.09 0.05 0.1 0.12 0.29 0.12 0
10351 A 1.37 8.95 2.28 1.89 1.37 0.14 0.88 0 0.54 2.71 3.45 0.33 0.97 0.55 0 1.55 0.38 0.069 0.73 0.25 1.59 0.75 0.2
3183 A 0.68 5.07 0.98 1.19 1.07 0.13 0.41 0 0.38 1.32 1.73 0.16 0.29 0.29 0 0.74 0.19 0.053 0.33 0.21 0.79 0.4 0.13
10863 A 1.39 4.66 1.46 1.7 1.52 0.17 0.52 0 0.36 1.94 2.75 0.074 0.29 0.38 0 1.07 0.23 0.023 0.45 0.15 1.1 0.55 0.18
3695 A 2.49 14.7 4.46 4.23 2.04 0.47 1.07 0 0.72 5.85 7.98 0.28 0.64 1.22 0 3.69 1.11 0.25 1.85 0.51 4.39 1.98 0.3
11231 A 1.86 9.59 2.38 2.65 2.24 0.33 0.88 0 0.61 3.25 3.38 0.35 0.45 0.66 0 1.64 0.42 0.086 0.62 0.24 1.42 0.77 0.27
1759 A 2.31 7.26 2.26 2.1 1.97 0.27 1.64 0 0.95 2.13 2.62 0.27 0.45 0.59 0 1 0.23 0.11 0.4 0.3 1.03 0.7 0.22
9951 A 1.26 3.36 2.1 2.07 0.14 0.06 0.27 0.15 0.37 3.05 4.58 0.19 0.39 0.99 0.11 1.9 0.36 0.09 0.82 0.26 2.02 1.05 0
10975 A 7.91 22.96 12.34 20.2 2.65 0.29 6.54 14.1 1.79 12.08 36.48 1.68 4.91 4.14 0 12.97 1.27 0.22 7.55 5.3 16.56 5.43 0
303 A 1.74 3.91 0.96 1.26 1 0.13 0.72 0 0.38 1.01 1.37 0.11 0.25 0.27 0 0.54 0.15 0.057 0.2 0.21 0.57 0.32 0.12
10287 A 6.93 68.86 10.4 5.52 29.84 0.29 5.42 0.78 2.84 8.82 15.85 6.75 8.65 2.01 0.18 6.92 1.79 0.53 3.26 0.98 6.66 2.48 0.34
2095 A 4.14 32.25 4.5 3.46 5.97 0.19 2.47 0.6 1.56 3.5 7.72 3.72 2.66 0.97 0.09 1.56 0.91 0.35 0.64 0.58 2.51 1.12 0.42
6191 A 1.45 12.69 2.76 2.87 2.68 0.21 0.61 0 0.63 3.57 3.24 0.11 0.58 0.63 0 1.74 0.44 0.07 0.71 0.14 1.6 0.85 0.24
3931 A 7.56 49.65 8.6 4.53 9.42 0.27 4.38 0.59 1.46 6.41 13.64 5.17 9.78 1.99 0 3.78 1.88 0.36 2.21 1 6.22 2.54 0.44
8347 A 0.59 3.36 0.9 0.99 0.83 0.12 0.36 0 0.25 1.03 1.4 0.14 0.31 0.23 0 0.54 0.15 0.052 0.21 0.11 0.55 0.29 0.18
9371 A 1.68 9.2 3.1 0.87 1.8 0.05 0.57 0.06 0.41 1.1 1.88 0.51 1.22 0.23 0 0.49 0.21 0 0.15 0.19 0.61 0.28 0
1179 A 0.78 3.42 1.2 0.97 1.49 0.16 0.53 0 0.36 1.06 1.33 0.32 0.18 0.21 0 0.48 0.09 0.06 0.15 0.12 0.46 0.25 0
4507 A 0.26 1.6 0.34 0.39 0.45 0.05 0.28 0 0.12 0.44 0.77 0.13 0.098 0.067 0 0.25 0.07 0 0.046 0 0.24 0.11 0
(Continued)
208
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
3227 A 9.63 51.77 14.46 8.76 8.61 0.63 6.94 0 1 13.77 30.26 3.67 2.03 3.63 0.3 9.56 3.74 1.41 5.17 2.11 14.76 5.41 0.5
2203 A 0.49 3.29 0.68 0.69 0.87 0.2 0.34 0.11 0.44 0.83 0.93 0.048 0.14 0.22 0 0.36 0.095 0 0.12 0.053 0.35 0.25 0.1
411 A 1.9 13.06 2.16 2.44 2.66 0.24 0.82 0.2 1.05 2.69 3.03 0.69 0.31 0.65 0 1.36 0.39 0.045 0.51 0.23 1.29 0.77 0.11
5675 A 1.42 14.86 2.8 1.83 3.56 0.22 1.14 0 0.8 1.82 3.21 0.9 0.61 0.39 0 0.61 0.33 0.14 0.23 0.22 0.79 0.36 0
9883 A 2.79 25.37 3.94 3.27 3.43 0.23 1.18 0.39 1.13 4.02 6.39 1.61 6.39 0.98 0 2.17 0.88 0.18 0.97 0.32 2.81 1.3 0
10907 A 39.14 200 22.04 13.5 5.83 0.8 9.78 0 10.11 28.49 65.5 7.08 4.77 6.62 0.28 42.9 5.36 1.93 7.32 1.07 26.5 9.05 0
3291 A 2.76 3.24 2.9 3.23 0.13 0.097 0.2 0.33 0.19 4.26 6.64 0.87 1.52 0.63 0 2.96 0.2 0.49 0.94 0.38 0.96 0.33 0
4571 A 2.12 26.33 3.9 1.94 5.29 0.17 1.67 0.33 1.26 2.93 4.92 1.49 0.43 0.68 0.034 1.47 0.46 0.09 0.65 0.35 1.85 0.84 0.19
475 A 1.52 7.82 1.38 2.01 0.79 0.29 1.03 0.31 0.93 2.11 4.19 0.98 0.26 0.61 0.07 1.24 0.74 0.18 0.46 0.71 2.22 0.85 0
5355 A 5.4 7.86 7.36 4.84 1.13 0.05 0.39 0.92 0.12 10.62 15.58 2.25 3.88 1.97 0.016 6.32 0.93 0.44 2.96 0.89 3.75 1.66 0
9451 A 7.22 46.73 15.66 7.07 8.06 0.67 2.68 1.13 3.74 18.93 42.92 2.19 2.61 7.63 0 20.3 7.11 0.8 8.34 4.82 45.1 11.45 1.7
6379 A 1.4 8.27 2.7 2.68 2.03 0.23 0.79 0 0.75 3.53 6.46 0.79 0.96 0.96 0 2.89 0.81 0.25 1.31 0.41 3.18 1.53 0.06
4155 A 1.19 7.56 0.9 1.07 1.04 0.13 0.48 0.17 0.4 0.91 1.8 0.71 0.42 0.33 0 0.48 0.26 0.061 0.11 0.24 0.7 0.29 0.1
2539 A 2.26 10.51 3.72 4.14 3.48 0.54 0.79 0 0.68 5.5 5.68 0.31 0.64 1.16 0 3.48 0.93 0.15 1.44 0.34 3.35 1.56 0.13
683 A 1.12 9.96 1.56 1.88 2.42 0.16 0.67 0 0.58 2.04 2.75 0.21 0.52 0.45 0 1.22 0.36 0.06 0.43 0.17 1.11 0.55 0
4779 A 2.1 4.6 3.5 3.33 0.31 0.34 0.58 0 0.27 4.93 10.67 0.25 0.86 1.4 0 3.76 1.24 0.08 1.81 0.35 5.51 2.19 0.25
12971 A 1.64 8.54 1.68 1.52 0.62 0.11 0.1 0 0.5 2.34 3.51 0.14 0.27 0.47 0.005 1.57 0.26 0.1 0.75 0.13 2.25 1.04 0
5803 A 2.34 18.15 3.24 3.38 4.36 0.66 1.88 0.46 1.68 3.81 4.57 0.72 0.59 0.82 0 1.95 0.51 0.21 0.92 0.53 1.95 1.13 0.36
4523 A 9.51 58.24 9.88 10.9 7.3 1.21 3.38 0.93 4.79 12.12 18.25 4.84 2.15 3.58 0 8.92 2.59 0.69 4.51 1.68 10.43 5.14 0.25
10859 A 2.6 16.93 4.6 4.64 2.87 0.17 0.9 0.3 2.5 3.58 6.75 3.35 3.48 1.03 0 2.36 0.74 0.63 0.96 0.54 2.37 1.23 0
8619 A 21.37 65.92 28.46 9.33 12.22 1.9 20.75 0 4.71 14.35 24.21 5.96 5.66 5.42 0 8.94 3.13 1.54 5.32 2.71 13.33 6.53 1.56
12715 A 11.15 53.15 12.68 10.1 5.97 0 10.14 0 2.97 14.59 33.05 11.61 3.18 3.65 0.22 9.88 3.17 3.71 5.03 2.09 12.6 5.85 0.84
12584 A 7.66 42.36 11 9.2 6.51 1.42 10.59 0 4.45 10.53 20.11 4.91 2.54 3.81 0 9.1 2.11 1.32 3.67 1.26 9.98 5.2 0.89
6872 A 13.2 69.7 11.9 15.3 6.14 1.14 2.82 0.91 2.54 15.62 27.42 2.3 3.55 4.41 0 12.33 3.19 0.92 6.47 1.66 14.36 6.9 0.48
5848 A 2.44 8.86 2.94 3.13 1.35 0.19 2.24 0.23 1.57 2.45 5.56 4.43 1.84 0.98 0 1.4 0.55 0.72 0.56 1.71 2.04 0.88 0.14
10280 A 3.03 19.98 4.16 3.04 3.55 0.21 3.05 0 1.7 4.03 8.48 3.11 2.48 0.89 0 2.17 0.75 0.46 1 0.86 2.78 1.23 0.19
7384 A 2.55 10.31 2.08 2.58 1.17 0.08 0.37 1.11 0.39 3.83 8.82 0.49 2.05 1.18 0.32 3.64 0.97 0.18 2.41 0.47 4.86 1.56 0.36
12952 A 1.54 9.43 2.08 2.82 0.59 0.17 0.48 0.23 0.77 2.56 8.57 1.45 1.13 0.71 0 2.59 0.6 0.29 1.17 0.2 2.9 1.01 0
4760 A 12.63 52.68 12.26 11.7 2.8 0.57 3.47 0 7.58 21.47 58.8 9.54 4.14 5.03 0 21.9 3.48 1.11 8.84 1.51 26.5 8.11 0.96
664 A 22.06 74.33 21.9 22.8 5.45 0.93 5.57 0 7.7 32.6 76.5 11.28 5.13 8.22 0.13 27.8 4.95 1.98 13.7 2.93 33 13.36 0.37
3736 A 38.65 83.25 33.7 33 4.81 1.335 5 2.5 9.5 42.4 125.6 35.1 18.5 9.55 0 37.35 7.1 4.5 17.5 7.2 43.5 12.35 1.23
1944 A 6.9 7.51 3.56 4.59 0.56 0.18 1.02 0.33 0.34 5.66 10.37 3.14 0.73 0.98 0.16 3.87 0.81 0.85 1.66 0.92 4.24 1.83 0
11656 A 1.79 5.85 1.86 1.79 0.32 0.19 0.27 0 0.61 2.73 6.08 0.5 0.95 1.21 0.29 1.96 0.84 0.26 1.35 0.59 2.88 1.13 0
12936 A 2.33 6.94 3.3 3.44 0.44 0.12 0.29 0 0.64 5.69 8.06 0.29 0.77 1.09 0 3.69 0.65 0.21 1.42 0.32 3.22 1.52 0
648 A 5.22 5.98 3.06 3.22 1.17 0.18 1.66 0.2 0.2 3.69 10.04 2.21 0.97 1.61 1.45 2.71 2.21 0.8 1.48 2.61 6.18 1.63 0.31
1160 A 2.27 18.11 4.72 3.34 2.41 0.17 0.74 0 2.46 7.63 15.08 0.44 1.05 1.8 0 6.1 1.54 0.47 3.26 0.71 10.9 4.65 0.57
(Continued)
209
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
5256 A 8.15 6.69 3.7 4.14 0.9 0.28 1 0 0.44 5.24 12.62 4.98 0.51 2 0.17 3.8 2.33 0.62 2.17 1.39 7.84 2.67 0
10376 A 5.09 6.61 4.78 4.63 0.35 0.37 1.04 0 1.3 6.31 19.42 1.82 4.19 2 0.53 4.93 1.9 0.89 2.48 1.01 7.74 2.78 0.13
2184 A 9.59 24.49 6.68 5.59 1.45 0.22 11.17 0 2.96 7.33 18.24 6.98 1.11 1.77 0.42 4.1 1.64 1.55 1.86 1.81 6.37 2.56 0.22
1032 A 10.61 21.99 9.94 10.9 1.82 0.39 2.93 1.16 0.57 17.05 34.87 5.08 1.75 4.08 0 12.46 3.82 0.58 6.41 1.4 16.95 7.75 1.7
8200 A 9.7 59.92 8.24 9.28 4.06 0.31 3.33 0 4.4 12.76 24.66 2.59 5 3.76 0 7.86 2.38 1.2 4.12 2.08 10.37 4.67 0.15
2804 A 8.58 31.27 9.74 5.48 14.88 0.44 10.4 0 1.62 6.3 13.63 7.22 2.44 2.01 0.2 2.42 1.87 0.99 1.28 3.66 5.18 1.79 0
3828 A 6.5 24.65 5.08 6.05 3.52 0.34 7.54 0 2.24 5.22 17.76 3.83 3.04 1.9 0.43 3.26 1.41 0.71 1.55 1.84 4.84 1.91 0
7924 A 12.79 64.51 12.16 10.4 6.37 0.62 5.99 1.09 2.9 15.42 34.9 9.22 4.34 4.88 0.76 10.88 3.33 1.71 5.59 3.16 15.92 6.46 0.43
12020 A 8.97 45.91 9.64 7.11 5.78 0.48 6.09 0 4.54 11.13 21.87 1.94 2.5 3.26 0.13 7.81 2.11 1.06 4.05 2.4 10.28 4.52 0.18
5356 A 4.45 16.29 6.88 6.47 4.34 0.33 3.05 0.81 2.26 7.25 14.58 2.65 2.33 3.51 0 4.15 1.83 0.74 2.23 2.72 7.44 3.16 0.71
9452 A 5.05 20.23 6.1 6.31 2.96 0.39 2.15 0 2.23 7.46 20.83 4.19 10.04 1.45 0 7.02 1.7 0.58 4.24 1.56 8.54 3.84 0.37
12524 A 2.8 14.7 3.4 3.67 2.09 0.21 2.83 0 0.72 5.16 9.13 2.06 0.89 1.2 0.053 4.39 1.14 0.57 2.13 0.58 4.61 2.05 0.25
4332 A 5.02 19.76 3.82 4.08 1.94 0.28 3.5 0 1 4.25 9.11 1.6 1.38 1.16 0 2.64 0.77 0.3 1.25 0.68 3.15 1.5 0.35
6060 A 4.43 15.23 6.52 5.81 8.57 0.31 5.17 0.7 3.49 5.51 12.99 4.73 2.77 1.66 0.2 3.36 0.89 1.44 1.52 2.49 3.31 1.67 0.46
11180 A 6.25 13.8 4.88 5.72 1.42 0.14 2.21 0.45 1.4 6.25 15.52 7.18 3.22 1.76 0 4.38 1.25 1.76 2.21 2.14 4.74 1.65 0.24
2988 A 17.91 83.67 11.9 14 9.43 0.87 19.97 1.43 4.89 12.47 36.69 14.57 10.43 4.55 1.19 7.91 3.58 1.5 3.87 3.45 11.64 4.88 0.71
5292 A 5.7 31.22 6.12 7.94 8.51 0 6.29 0.89 3.55 6.55 15.09 5.02 2.49 1.76 0.37 4.04 1.46 0.92 1.82 1.44 4.29 1.77 0.56
9644 A 4.29 16.84 5.28 5.37 7.02 0.37 11.98 0 1.69 4.23 12.48 4.95 3.21 1.24 0.61 2.04 1.41 1.09 0.96 2.63 3.26 1.2 0.41
12460 A 5.3 34.3 5.66 6.84 3.5 0.23 4.4 0 6.93 6.79 14.46 5.84 2.85 2.26 0 3.53 1.54 1.28 1.64 2.76 5.56 2.09 0.56
1708 A 5.57 21.65 3.88 2.93 1.75 0.27 7 0 1.53 4.47 9.85 1.69 0.89 1.1 0.2 2.34 0.95 0.61 1.14 1.22 3.68 1.49 0.36
5804 A 3.99 20.78 4.36 7.12 2.91 0.23 5.64 0.39 2.7 4.69 14.26 3.19 1.78 1.63 0.63 3.07 2.07 1.22 1.62 2.16 4.92 1.62 0.5
4780 A 2.83 13.88 2.82 2.94 1.02 0.09 2.74 0 2.11 4.26 9.64 1.46 0.94 1.05 0.12 2.64 0.9 0.2 1.26 0.69 3.4 1.36 0
12972 A 15.06 18.25 16.08 18.9 1.24 0.52 1.55 0.73 1.03 28.52 43.38 3 5.97 4.71 0 13.25 2.41 1.75 5.72 2.95 9.88 4.93 0.53
5596 A 9.9 11.3 18.8 13 0.46 0.07 0.67 1.26 0.64 26.25 40.5 1.31 1.17 1.92 0.79 11.82 2.43 1.57 5.17 1.71 7.52 3.9 0.26
12764 A 1.67 6.13 7.14 1.66 1.64 0.16 2.5 0.21 1.41 5.84 9.44 6.15 0.99 1.71 0.39 2.55 1.39 0 1.17 0.79 3.63 1.29 0.5
5852 A 3.13 3.7 5.5 3.14 1.61 0.13 0.41 0 1.66 5.84 9.61 1.49 3.03 3.17 1.61 3.42 2.24 0.79 1.5 1.41 3.9 1.93 2.88
6876 A 46.4 140.5 65.8 19 131.5 3.65 51 2 3.5 52 97.5 39.7 19.75 10.5 0.67 6.5 7.5 0.9 1.4 13 15.5 1.95 0.29
2780 A 7.59 29.48 5.58 4.84 2.2 0.51 12.78 0 4.82 6.05 15.49 6.03 2.1 1.54 0.47 2.93 1.56 0.9 1.43 2.89 4.8 1.88 0.47
7324 A 3.58 14.42 5.32 8.73 9.07 0.24 10.36 0 2.2 5.44 13.56 4.64 3.53 1.58 0 2.66 1.83 1.54 1.08 2.22 3.66 1.53 0.6
3228 A 10.72 35.08 13.26 6.32 10.36 0.84 18.31 0.97 8.04 7.89 24.54 9.17 2.9 2.62 0.49 4.55 2.23 1.91 2.34 6.95 8.05 3.68 0.91
9372 A 10.73 10.8 9.6 9.42 0.38 0.26 4.07 0 3.36 9.64 33.67 24.59 11.07 1.42 0.15 8.79 1.22 5.6 3.53 5.59 4.39 2.04 0.39
9916 A 65.5 189 363 48 886.5 0 108 0 10 78 171.5 53.5 18 4.65 1 10.5 4.65 6.5 2.15 11.3 2 0.55 8.4
2748 A 7.91 46.28 8.22 6.03 8.96 0.62 11.09 0.62 2.39 7.88 18.52 7.19 3.1 2.26 0.46 4.42 1.71 1.1 2.31 3.03 6.8 3.04 0.48
1724 A 18.59 78.52 16.62 9.15 67.44 0 48.36 0 2.13 13.09 25.4 27.31 3.17 2.38 0.36 2.17 1.96 10.4 1.15 10.4 4.52 0.94 0.96
4852 A 11.71 7.98 10.8 13 0.37 0.44 0.75 0.69 0.42 22.67 28.3 0.46 4 1.97 0 11.56 0.83 0.66 3.64 1.04 5.2 3.35 0.47
8948 A 8.83 57.66 18.28 6.91 43.38 0 20.68 0 2.83 7.91 15.49 9.18 2.77 1.72 0 2.17 1.38 1.05 0.91 2.22 3.74 1.85 0.8
(Continued)
210
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
9556 A 1.36 11.89 1.52 2.61 1.91 0.04 0.15 0.09 0.47 1.71 5.13 0.47 0.74 0.4 0 1.78 0.29 0.013 0.85 0 1.78 0.63 0
11327 A 1.23 6.35 1.54 1.6 1.08 0.24 0.65 0 0.36 1.92 3.64 0.27 0.35 0.61 0 1.63 0.44 0.2 0.89 0.23 2.07 0.9 0
8687 A 3.6 19.19 4.86 5.79 3.88 0.72 0.88 0 0.8 6.79 9 0.99 1.58 1.64 0 4.18 0.98 0.18 1.85 0.42 4.4 1.9 0.54
4591 A 6.12 12.92 10.58 10.1 0.85 1.22 0.57 0.43 0.23 14.44 24.85 0.41 2.16 3.85 0 10.65 2.88 0.7 5.93 0.74 13.74 5.79 0.73
5439 A 2.99 15.83 4.48 5.51 2.62 0.64 0.41 0.19 0.82 6.69 8.06 0.7 0.6 1.44 0 4.42 1.19 0.15 1.93 0.38 4.57 2.03 0.25
5615 A 3.12 9.48 2.66 2.7 1.75 0.33 0.3 0 0.39 3.85 5.99 0.28 0.44 0.97 0 2.92 0.85 0.1 1.22 0.22 3.4 1.36 0.24
12783 A 2.15 8.36 4.18 2.91 1.85 0.51 1.13 0.15 0.69 3.53 3.96 0.9 1.3 0.99 0.24 1.97 0.48 0.13 0.86 0.39 2.1 1.2 0
7231 A 5.51 22.81 7.88 8.07 5.85 0.85 1.32 1 1.04 9.33 12.52 0.79 2.8 2.27 0 6.43 1.39 0.25 3.16 0.44 6.73 2.91 0.28
7919 A 1.67 7.66 2.44 2.72 1.27 0.3 0.56 0 0.47 3.21 5.7 0.33 0.43 0.78 0 2.45 0.64 0.16 1.18 0.37 2.81 1.24 0
9535 A 1.01 7.5 1.3 1.32 0.84 0.14 1.01 0 0.46 1.22 2.47 1.02 0.55 0.3 0 0.66 0.24 0.16 0.21 0.32 0.85 0.41 0
2111 A 0.46 1.45 0.96 0.78 0.08 0.069 0.24 0 0.1 1.26 3.1 0.19 0.29 0.55 0.06 1.14 0.35 0.09 0.54 0.13 1.83 0.68 0
3135 A 0.37 1.4 0.36 0.32 0.038 0.044 0.15 0.05 0 0.56 1.36 0.19 0.15 0.39 0.03 0.42 0.37 0.093 0.27 0.22 1.24 0.38 0
9195 A 1.21 7.6 1.48 1.72 0.87 0.11 0.74 0.37 0.24 1.56 3.35 1.37 0.48 0.44 0 0.89 0.48 0.18 0.29 0.4 1.38 0.61 0
6891 A 1.54 3.74 3 2.97 0.61 0.41 0.41 0.23 0.14 4.21 7.83 0.16 0.48 1.27 0 3.31 1.02 0.17 1.56 0.35 4.41 1.82 0.27
1643 A 0.95 5.35 1.2 1.84 0.44 0.18 0.27 0 0.34 1.83 3.06 0.46 0.31 0.35 0 1.35 0.24 0.055 0.59 0.096 1.37 0.64 0
12075 A 1.14 6.6 1.8 1.84 0.63 0.2 0.56 0.18 0.48 2.3 4.22 0.36 0.71 0.7 0 1.58 0.54 0.1 0.7 0.37 2.29 1.06 0.21
9323 A 2.25 16.34 5.08 3.38 2.63 0.33 3.17 0 3.74 3.89 7.02 5.42 2.5 1.31 0 1.99 0.72 0.58 0.92 1.6 3.09 1.5 0.22
5803 A 2.34 18.15 3.24 3.38 4.36 0.66 1.88 0.46 1.68 3.81 4.57 0.72 0.59 0.82 0 1.95 0.51 0.21 0.92 0.53 1.95 1.13 0.36
11435 A 12.08 27.66 12.3 12.1 3.51 1.02 2.18 2.39 2.77 15.39 29.67 4.46 5 5.61 0.44 10.89 2.99 0.74 6.3 3.63 14.71 6.89 0.83
3947 A 4.05 21.06 6.28 5.26 2.61 0.29 2.92 0 2.56 5.38 14.14 5.47 3.74 1.72 0.22 3.76 1.41 0.99 1.53 1.81 5.5 2.26 0
8088 A 4.87 37.28 6.42 4.86 4.67 0.52 4 0 3.4 6.56 11.83 3.47 1.83 1.67 0 4.65 1.4 0.79 2.07 1.57 5.55 2.55 0.11
12888 A 10.92 53.08 11.68 7.93 5.77 0.62 5.86 1.65 4.12 13.29 22.51 6.91 2.64 3.33 0 7.49 1.56 1.02 3.86 2.04 9.58 4.28 0.4
600 A 3.01 13.68 3.76 5.39 1.93 0.11 1.43 0.23 0.76 3.32 9.26 2.73 2.51 0.92 0 1.74 0.61 0.64 0.65 0.75 2.12 0.9 0.18
4952 A 37.07 62.31 30.06 9.65 13.76 1.26 15.46 0 4.51 13.14 35.41 11.06 10.55 4.02 0.6 8.93 1.79 2.09 4.88 2.38 10.98 5.68 0.8
4248 A 13.69 35.94 9.22 6.36 9.53 0.53 4.95 0 2.86 7.69 12.53 2.06 0.95 1.95 0 4.46 1.48 0.49 2.3 1.26 4.98 2.61 0.52
153 A 14.19 30.47 13.52 14.4 1.12 1.16 1.76 0 1.19 20.14 36.77 1.46 2.51 4.9 0 13.65 4.21 1.2 8.03 2.31 19.8 7.78 0
5977 A 5.86 5.57 2.54 1.86 0.76 0.25 1.61 1.16 1.16 2.84 4.98 2.33 0 1.84 0 1.86 1.55 1.66 1.62 3.96 4.6 1.67 0.22
4249 A 4.42 19.28 5.42 5.64 2.16 0.44 1.8 0 1.63 6.45 12.42 0.64 0.83 1.68 0 5.73 1.36 0.59 2.72 1.25 6.73 2.8 0.22
2248 A 18.31 41.56 20.48 7.73 15.99 0.91 5.62 0 1.3 11.62 19.55 4.88 10.97 3.36 0 7.61 2 0.82 4.36 1.38 8.65 4.31 0.91
12488 A 4.31 31.09 6.1 4.86 4.81 0.28 3.46 0.56 1.75 5.17 11.93 4.23 1.94 1.73 0.35 2.57 1.11 0.48 1.25 1.15 4.08 1.61 0
4440 A 1.81 11.42 2.76 1.61 2.53 0.25 2.3 0.23 0.93 1.84 4.56 2.36 0.96 0.65 0 0.97 0.38 0.38 0.36 1.12 1.41 0.62 0
1881 A 3.06 10.49 4 4.6 0.48 0.48 1.04 0.34 0.46 5.36 10.94 0.29 0.73 1.27 0 4.36 1.23 0.4 2.18 0.71 5.85 2.26 0.17
10440 A 4.83 37.02 5.36 5.76 4.1 0.19 3.15 0 3 6.16 16.41 4.76 1.54 1.94 0.08 4.42 1.46 0.58 2.41 0.92 6.68 2.72 0.48
7368 A 31.84 118.5 16.88 13 8.88 0.75 4.8 0 5.31 17.92 40.5 6.41 12.68 4.47 0.23 12.62 4.41 1.02 6.69 1.44 17.15 7.04 0.57
9049 A 10.08 46.99 15.64 11.1 5.87 0.85 3.37 0 2.48 16.83 40.1 11.89 9.51 4.07 0.28 11.37 3.65 0.89 6.42 1.95 13.8 5.78 0.29
4953 A 0.88 4.75 1.44 1.89 0.77 0.1 0.17 0.25 0.27 1.64 3.52 0.41 0.69 0.37 0 1.39 0.32 0.11 0.59 0.093 1.33 0.58 0
(Continued)
211
Appendix C. Concentrations of free amino acids detected in soils (mmol kg-1 dry soil)
ID Horizon Asp Glu Ser+Asn Gly Gln His Arg Tau Cit Thr Ala GABA Pro Tyr Cys-Cys Val Met Orn Ile Lys Leu Phe Trp
11208 A 4.47 37.47 4.88 6.15 4.07 0.22 1.52 0 1.62 7.24 12.36 1.64 1.3 1.86 0.14 4.73 1.6 0.39 2.21 0.69 6.22 2.62 0
6617 A 74 138.5 28.28 15.3 8.07 4.7 28.64 0 9.12 46.5 96 9.21 7.14 7.08 0.58 19.5 4.65 3.61 7.3 6.75 21.65 8.85 0.35
12232 A 5.6 35.75 6.08 7.73 3.83 0.48 1.43 0.51 1.43 8.7 14.39 1.22 1.21 1.69 0.33 6.4 1.7 0.37 3.15 0.73 7.23 3.03 0
9433 A 7.26 38.57 5.64 4.7 1.89 0.39 7.24 0 4.65 7.38 18.37 3.7 1.56 2.46 0.56 4.94 2.38 0.82 2.59 3.04 9.71 3.15 0
6041 A 3.85 20.77 5.34 4.85 3.01 0.24 0.9 0 1.78 5.96 11.55 1.47 0.82 1.73 0 5.55 1.19 0.46 2.78 0.88 6.89 3 0
12505 A 2.3 4.45 1.68 1.42 0.74 0.26 1.74 0.32 0 2.31 5.27 0.96 0.74 2.02 0 1.22 1.64 0.43 0.47 1.98 3.38 1.36 0.46
2521 A 2.78 9.87 3.74 2.32 2.03 0.44 6.88 0 4.04 2.93 6.97 2.92 1.18 1.26 0.14 1.86 1.11 0.67 0.83 2.65 3.71 1.39 0
2713 A 4.54 6.98 3.62 3.06 0.9 0.28 7 0 0.73 2.31 9.29 2.64 1.11 1.02 0.11 1.75 1.22 1.17 0.81 2.16 3.42 1.12 0
7065 A 7 27 5.2 5.06 2.26 0.26 3.64 0.32 0.98 6.03 15.68 3.64 1.31 2.55 0.4 5.12 2.73 0.78 1.98 1.52 8.58 3.45 0
10137 A 5.79 21.31 6.12 4.1 2.36 0.21 8.53 0 1.68 5.28 11.77 2.02 1.1 1.78 0.19 3.65 1.49 1 1.93 1.4 6.02 2.44 0
“ID” in the column heading signifies unique identifier assigned by generalized random tessellation stratified design software; Asp, aspartic acid; Glu, glutamic acid; Ser, serine;
Asn, asparagine; Gly, glycine; Gln, glutamine; His, histidine; Arg, arginine; Tau, taurine; Cit, citrulline; Thr, threonine; Ala, alanine; GABA, ϒ-aminobytyric acid; Pro, proline;
Tyr, tyrosine; Cys-Cys, cystine; Val, valine; Met, methionine; Orn, ornithine HCl; Ile, isoleucine; Lys, lysine; Leu, leucine; Phe, phenylalanine; Trp, tryptophan
(Continued)
212
Appendix D. Concentrations of hydrolysable amino acids detected in soils (µmol kg-1 dry soil)
ID Horizon Asp Ser Glu Gly His Arg Thr Ala Pro Tyr Cys-Cys Val Met Lys Ile Leu Phe
10137 C 0.0225 0.0225 0.00625 0.0275 0 0 0.0075 0.0175 0 0.02625 0 0.0063 0 0.01125 0 0.00075 0
1032 C 0.0975 0.0425 0.1875 0.11625 0.03875 0.0225 0.0388 0.06875 0.03 0.06125 0.0025 0.065 0.02125 0.04875 0.01375 0.02 0.00125
10351 C 1.3388 0.3581 0.68125 0.74 0.12875 0.1138 0.405 0.71188 0.4269 0.065 0 0.4375 0 0.405 0.23063 0.34438 0.13688
10440 C 0.625 0.1288 0.42 0.59625 0.07625 0.0725 0.1913 0.445 0.1775 0.04 0 0.1688 0.00875 0.1575 0.07625 0.14375 0.04875
10591 C 0.1575 0.105 0.13 0.1 0 0.0463 0.0875 0.15438 0.0694 0.07938 0 0.055 0.00563 0.06938 0.045 0.06875 0.03188
10607 C 0.2013 0.0944 0.1975 0.19688 0.05813 0.0444 0.0881 0.23313 0.0856 0.01375 0 0.1119 0 0.08188 0.0575 0.09625 0.0375
10859 C 1.2369 0.3606 0.77938 0.80313 0.12438 0.13 0.3525 0.84563 0.4406 0.0875 0 0.4525 0.0075 0.40563 0.2125 0.33438 0.11625
10863 C 0.1288 0.0725 0.0675 0.17375 0.0225 0.0075 0.045 0.09375 0.03 0.005 0 0.0038 0.01125 0.01125 0.00375 0.01875 0.00375
10907 C 4.955 2.735 3.18125 5.505 0.50875 0.53 2.2113 4.0125 1.815 0.69625 0 1.4 0.22875 1.37125 0.775 1.38625 0.6575
10975 C 0.6888 0.2488 0.4575 0.53 0.10125 0.1013 0.2575 0.505 0.205 0.08 0 0.1838 0.01375 0.21 0.13375 0.1875 0.1
11208 C 0.1738 0.095 0.095 0.2925 0.05 0.0113 0.0688 0.1425 0.0188 0.06125 0 0.0063 0 0.03 0.01 0.025 0.00625
11231 C 1.185 0.2925 0.56 0.8475 0.06625 0.06 0.4263 0.62125 0.3163 0.03375 0 0.295 0 0.2425 0.14875 0.24625 0.0675
11327 C 0.2113 0.0838 0.14625 0.33 0.045 0.03 0.0838 0.16625 0.0238 0.00875 0 0.0075 0.0075 0.05375 0.015 0.045 0.01875
11435 C 1.0813 0.1138 0.46375 0.62 0.07 0.0625 0.1538 0.40875 0.2288 0.115 0 0.145 0.01125 0.22875 0.06625 0.1325 0.05
11656 C 0.7925 0.265 0.68 0.815 0.08875 0.0788 0.245 0.5425 0.1813 0.0475 0 0.2263 0.0225 0.28875 0.06125 0.11 0.03875
12020 C 0.2631 0.135 0.18875 0.255 0.06375 0.0425 0.1294 0.22125 0.1238 0.02125 0 0.1181 0 0.08563 0.05438 0.08438 0.03375
12075 C 1.0231 0.4119 0.65563 0.86375 0.09313 0.0994 0.6725 0.74938 0.395 0.055 0 0.3675 0 0.29813 0.17813 0.2425 0.09938
12232 C 0.0825 0.0413 0.03 0.09875 0.01 0.0038 0.0113 0.0475 0.0038 0.02625 0 0.005 0 0.00125 0.01125 0.0125 0.00125
12488 C 0.7225 0.25 0.34625 0.4925 0.03875 0.0688 0.1913 0.41375 0.2425 0.09375 0 0.1538 0.0125 0.16 0.07125 0.1575 0.06875
12495 C 0.5363 0.225 0.3825 0.565 0.0675 0.0575 0.1475 0.4125 0.1663 0.065 0 0.1013 0.00875 0.2275 0.05375 0.14375 0.03625
12505 C 0.1725 0.145 0.1425 0.2275 0.05375 0.0275 0.1038 0.14625 0.0913 0.0075 0 0.0613 0 0.05 0.03375 0.02875 0.045
12584 C 0.825 0.21 0.3625 0.525 0.075 0.0325 0.28 0.29 0.14 0.0625 0 0.1325 0.02 0.2025 0.0675 0.1425 0.05
12888 C 0.6506 0.2625 0.49125 0.47938 0.06125 0.0694 0.4288 0.55813 0.3381 0.0625 0 0.3 0 0.24 0.13563 0.17875 0.06625
1643 C 0.8481 0.2013 0.37813 0.48563 0.04625 0.0513 0.2606 0.37375 0.2294 0.05438 0 0.2113 0.0025 0.21125 0.10125 0.16938 0.06063
1708 C 0.6581 0.4131 0.46375 0.66875 0.10875 0.1331 0.32 0.545 0.3006 0.09125 0 0.2281 0.01813 0.27125 0.13688 0.23375 0.12125
1724 C 0.0925 0.0463 0.05375 0.07375 0.02125 0.0038 0.02 0.0525 0.0238 0.0025 0 0.01 0.01125 0.005 0.005 0.01 0.0025
1759 C 0.505 0.15 0.265 0.45375 0.03 0.0313 0.2225 0.345 0.125 0.02 0 0.1075 0.00625 0.1 0.0425 0.10125 0.01875
1881 C 0.5288 0.1313 0.345 0.51375 0.0575 0.0488 0.1238 0.36875 0.1275 0.045 0 0.1713 0.00625 0.10625 0.05125 0.0975 0.0425
1935 C 0.5369 0.335 0.5025 0.56813 0.08688 0.0675 0.2538 0.58313 0.1963 0.06313 0 0.1244 0.02938 0.21063 0.06625 0.11813 0.04938
2095 C 1.6413 0.4813 1.0975 1.25625 0.195 0.2038 0.7913 1.085 0.705 0.0625 0 0.835 0 0.62625 0.41375 0.6025 0.2325
2111 C 0.53 0.2975 0.27625 0.5125 0.09625 0.08 0.1813 0.32375 0.155 0.0775 0 0.0675 0.00875 0.16375 0.08625 0.115 0.0625
2184 C 0.0313 0.0225 0.03375 0.04375 0.025 0.0018 0.02 0.025 0.0238 0.00375 0 0.0088 0 0.01625 0.00375 0.0125 0.00125
2248 C 0.4463 0.165 0.275 0.50625 0.065 0.0788 0.2638 0.38375 0.1863 0.0125 0 0.2475 0 0.22125 0.10875 0.18125 0.0625
2521 C 0.24 0.1325 0.17875 0.22 0.05875 0.0413 0.135 0.1675 0.0675 0.05125 0 0.0788 0 0.0975 0.0325 0.06375 0.02375
2539 C 0.5113 0.1775 0.3375 0.4325 0.07375 0.0725 0.1588 0.355 0.1863 0.08875 0 0.16 0.01375 0.16625 0.0675 0.135 0.06125
2713 C 0.1838 0.1038 0.16375 0.23375 0.06 0.0188 0.1125 0.16125 0.0713 0.08625 0 0.1225 0 0.10875 0.04625 0.06625 0.025
213
Appendix D. Concentrations of hydrolysable amino acids detected in soils (µmol kg-1 dry soil)
ID Horizon Asp Ser Glu Gly His Arg Thr Ala Pro Tyr Cys-Cys Val Met Lys Ile Leu Phe
2748 C 0.0438 0.0263 0.0525 0.075 0.03 0 0.0113 0.03125 0.0288 0.05 0 0.0838 0 0.01375 0 0.005 0
2780 C 0.3638 0.2113 0.4525 0.67125 0.06375 0.06 0.1525 0.45625 0.0788 0.085 0 0.115 0.02 0.16125 0.0275 0.075 0.035
2959 C 0.805 0.2613 0.60125 0.68 0.08375 0.1125 0.2713 0.5875 0.1963 0.065 0 0.2775 0.00875 0.21375 0.1025 0.19 0.08625
2988 C 1.26 0.48 0.795 0.705 0.14 0.17 0.525 0.7425 0.415 0.105 0 0.4025 0.0375 0.3075 0.1675 0.3625 0.1675
3135 C 1.7625 0.5363 0.96 1.15688 0.11063 0.1369 1.0144 0.99563 0.6175 0.17063 0 0.415 0.01 0.53375 0.2275 0.41563 0.16063
3228 C 0.17 0.1019 0.17188 0.18938 0.04 0.03 0.0963 0.2 0.0888 0.02188 0 0.0775 0.00625 0.06688 0.0325 0.05813 0.02813
3291 C 0.2038 0.1075 0.1975 0.2475 0.0825 0.0325 0.1063 0.21125 0.0675 0.07875 0 0.14 0 0.14875 0.03625 0.06125 0.02375
3736 C 0.41 0.2463 0.42 0.67125 0.0925 0.0813 0.2363 0.52625 0.145 0.0425 0 0.0238 0.02125 0.2375 0.05625 0.085 0.03375
3828 C 0.2594 0.1319 0.29 0.14563 0.02938 0.0325 0.2094 0.24188 0.1575 0.03938 0.005 0.1413 0 0.17625 0.05563 0.08625 0.00875
3931 C 0.4513 0.13 0.37 0.455 0.0775 0.0488 0.285 0.3625 0.1175 0.0425 0 0.0638 0.03125 0.19125 0.03625 0.07 0.01
3947 C 1.1956 0.265 0.51063 0.44625 0.085 0.0888 0.32 0.48063 0.3481 0.125 0 0.2025 0.00438 0.31063 0.12625 0.20563 0.08188
4248 C 0.5781 0.285 0.48688 0.50125 0.12625 0.1419 0.3113 0.52063 0.3656 0.07188 0 0.3419 0.00625 0.32125 0.19438 0.32188 0.1325
4249 C 0.2856 0.1125 0.15688 0.235 0.05938 0.0394 0.1338 0.1825 0.0988 0.01313 0 0.11 0 0.0975 0.04563 0.06813 0.0225
4440 C 0.5925 0.5725 0.44 0.7125 0.125 0.1413 0.355 0.515 0.245 0.09375 0 0.1913 0.02375 0.21875 0.08125 0.13875 0.0625
4507 C 0.1138 0.0588 0.07375 0.1325 0 0 0.0313 0.1225 0.0225 0.045 0 0.0213 0.00375 0.02375 0.01125 0.02375 0.00625
4571 C 0.4725 0.2988 0.3925 0.7925 0.07625 0.1225 0.23 0.5475 0.1925 0.07125 0 0.0775 0.0275 0.21 0.0525 0.15625 0.065
4760 C 1.8213 0.8831 1.35625 1.17375 0.175 0.2825 0.9456 1.46063 0.7463 0.22063 0 0.6581 0.06938 0.63875 0.34813 0.5875 0.2575
4779 C 0.5063 0.1331 0.28563 0.28688 0.02063 0.0331 0.3056 0.28563 0.1906 0.00813 0 0.1531 0.00313 0.16313 0.06875 0.13375 0.03938
4780 C 0.4213 0.38 0.53625 0.88375 0.11125 0.1163 0.3513 0.63125 0.2313 0.05375 0 0.155 0.02125 0.27625 0.11 0.1825 0.08625
4852 C 0.0838 0.0413 0.065 0.09625 0.03875 0.0188 0.0488 0.0475 0.0175 0.04375 0 0.0163 0 0.04 0.01375 0.02375 0.0075
4952 C 0.4538 0.1838 0.31 0.4375 0.0625 0.0663 0.1438 0.33375 0.1413 0.0775 0.005 0.1163 0.0075 0.1525 0.06125 0.12 0.0525
4953 C 0.9088 0.3338 0.56 0.57375 0.06 0.1025 0.3713 0.76 0.2138 0.07625 0 0.35 0.04125 0.2125 0.18625 0.285 0.1225
5256 C 2.8213 1.9175 1.9825 4.12625 0.37125 0.4775 1.11 2.68125 1.0288 0.5 0.0375 0.5813 0.17875 1.2175 0.3575 0.72625 0.34625
5355 C 1.02 0.3963 0.845 1.0775 0.175 0.1613 0.6188 0.90125 0.5163 0.05125 0 0.7263 0 0.4625 0.38875 0.53875 0.2275
5596 C 2.5525 1.6263 1.67875 2.845 0.3125 0.3088 1.2863 2.06 0.9288 0.4275 0 0.7913 0.10625 0.86625 0.445 0.7775 0.36125
5615 C 1.5975 1.06 1.0525 1.6375 0.1925 0.2225 0.75 1.625 0.7075 0.2 0 0.84 0.01 0.4675 0.4425 0.755 0.29
5675 C 2.1609 1.0173 1.74202 2.2141 0.22606 0.3657 1.4295 1.76197 0.871 0.1 0 1.1436 0 0.78457 0.64495 1.07713 0.43883
5848 C 1.0588 0.515 1.03875 1.43125 0.17 0.1638 0.5938 1.0575 0.5963 0.27625 0 0.3913 0.05625 0.46875 0.19125 0.33875 0.15875
5852 C 0.3438 0.245 0.3275 0.36125 0.11125 0.0575 0.2288 0.35375 0.1113 0.07125 0 0.2263 0 0.25625 0.08 0.11125 0.05
5977 C 0.91 0.4175 0.55625 0.9025 0.0875 0.135 0.3075 0.72125 0.3438 0.08375 0 0.2413 0.0375 0.325 0.1075 0.22125 0.015
600 C 0.5388 0.1075 0.4225 0.38 0.075 0.0613 0.1388 0.32375 0.19 0.085 0 0.1213 0 0.1875 0.05625 0.10875 0.04
6041 C 0.1288 0.0513 0.065 0.11875 0.04625 0.0125 0.0463 0.08625 0.0338 0.08125 0 0.0375 0 0.01625 0.02 0.03875 0.0125
6060 C 0.075 0.0413 0.07125 0.065 0.0625 0.0125 0.0425 0.0525 0 0.08875 0 0.0088 0.00875 0.04625 0.015 0.01875 0.0075
6379 C 0.7325 0.2138 0.385 0.36875 0.08875 0.1013 0.2738 0.37875 0.2338 0.07875 0 0.14 0.01375 0.23125 0.11125 0.17125 0.0825
648 C 0.9138 0.6763 1.0125 1.0625 0.17125 0.2238 0.6388 1.04 0.5375 0.15 0 0.4425 0.0325 0.5325 0.28125 0.48 0.21625
6617 C 0.2669 0.1088 0.145 0.21938 0.02063 0.0256 0.1144 0.22 0.1394 0.00875 0 0.0869 0.00438 0.06438 0.02938 0.0675 0.02313
(Continued)
214
Appendix D. Concentrations of hydrolysable amino acids detected in soils (µmol kg-1 dry soil)
ID Horizon Asp Ser Glu Gly His Arg Thr Ala Pro Tyr Cys-Cys Val Met Lys Ile Leu Phe
6891 C 0.2875 0.0838 0.1225 0.19 0.0175 0.0038 0.065 0.13125 0.045 0.015 0 0.0275 0.00375 0.045 0.02125 0.04 0.025
7065 C 0.0625 0.0613 0.05 0.115 0.015 0.0075 0.0213 0.0725 0.0113 0.0275 0 0.0013 0 0.0125 0.01125 0.0175 0.00125
7231 C 4.0313 0.98 2.16125 2.61 0.2975 0.3975 1.505 2.03125 1 0.29875 0 1.1313 0.06125 1.1075 0.60875 1.03375 0.44625
7324 C 0.3738 0.1288 0.3175 0.31625 0.0625 0.045 0.1813 0.32125 0.1063 0.0825 0 0.2225 0 0.15875 0.0775 0.10375 0.0375
7368 C 1.1338 0.335 0.72375 1.16875 0.08625 0.11 0.2738 0.73375 0.2613 0.105 0 0.2113 0.0275 0.2875 0.09 0.175 0.08
7384 C 0.835 0.5113 0.64875 0.9525 0.1175 0.1413 0.3575 0.755 0.365 0.1825 0 0.255 0.025 0.33875 0.16125 0.295 0.13125
7919 C 1.3381 0.5381 0.83313 1.57063 0.09125 0.1688 0.4888 1.02313 0.4 0.21125 0 0.1569 0.07 0.33938 0.18188 0.31875 0.14875
8088 C 1.0688 0.2925 0.4775 0.67125 0.0775 0.0763 0.3888 0.4775 0.2425 0.1425 0 0.1825 0.03125 0.275 0.09875 0.2025 0.0725
8200 C 0.0575 0.0294 0.0675 0.055 0.01875 0.0044 0.0694 0.06813 0.0231 0.05063 0 0.0363 0.0025 0.02688 0.00875 0.01438 0
8687 C 4.1375 0.7975 1.7175 2.31 0.2175 0.2825 1.1625 1.57 0.7625 0.415 0 0.545 0.105 0.93 0.3425 0.7025 0.2975
8948 C 0.065 0.06 0.135 0.13125 0.035 0.0188 0.03 0.06375 0.02 0.08875 0.01 0.0738 0 0.0325 0.00038 0.00875 0.00125
9049 C 0.5438 0.245 0.34375 0.5375 0.06125 0.0413 0.2388 0.4425 0.1363 0.11 0 0.2 0.0075 0.09 0.0575 0.105 0.02625
9103 C 0.6663 0.33 0.58125 0.72 0.07563 0.0713 0.3369 0.5975 0.2094 0.0125 0 0.2363 0 0.28313 0.11313 0.18125 0.06375
9195 C 0.4538 0.0463 0.36875 0.4075 0.06125 0.0588 0.1238 0.26125 0.13 0.07625 0 0.1363 0.03 0.1775 0.05625 0.12 0.04875
9323 C 0.2975 0.0875 0.13625 0.29625 0.03375 0.0138 0.065 0.1475 0.0563 0.02 0 0.0263 0.01625 0.07 0.025 0.04125 0.01375
9423 C 7.265 5.5325 5.46 8.20375 1.03 1.2538 3.9013 6.38625 3.1388 0.95375 0 2.5713 0.20625 2.73125 1.58 2.68875 1.47375
9535 C 1.1488 0.3075 0.57125 0.94875 0.105 0.1213 0.5638 0.565 0.355 0.04375 0 0.4188 0 0.385 0.2225 0.3325 0.1375
9556 C 0.9938 0.2413 0.6325 0.93625 0.08625 0.1038 0.2963 0.67875 0.245 0.04875 0 0.3988 0 0.2325 0.1775 0.2925 0.13625
9644 C 0.1113 0.0769 0.11938 0.1125 0.04875 0.0281 0.0631 0.10313 0.0675 0.02563 0.005 0.065 0 0.10688 0.02563 0.03625 0.01563
9823 C 0.6288 0.3638 0.65125 0.8275 0.08875 0.13 0.4675 0.63875 0.2675 0.02 0 0.3663 0 0.4025 0.17875 0.31375 0.115
9938 C 0.3243 0.1934 0.35526 0.30921 0.075 0.0612 0.1875 0.32434 0.1566 0.01776 0 0.1849 0 0.23816 0.07368 0.09671 0.04013
10137 A 3.6013 2.0638 2.60375 4.1275 0.285 0.45 1.6775 2.96125 1.34 0.71125 0 0.785 0.23375 1.14625 0.57625 0.98625 0.4675
1032 A 4.2863 2.8175 3.25625 6.18125 0.47875 0.5988 2.325 4.35875 1.965 0.84 0 1.1588 0.335 1.4725 0.69 1.34625 0.585
10351 A 2.0925 1.0288 1.6325 2.2025 0.2575 0.3413 1.0988 1.88 0.9263 0.2425 0 0.6713 0.0775 0.71375 0.44625 0.8525 0.3475
10440 A 3.47 2.1863 2.885 4.41125 0.39625 0.5088 2.26 3.5225 1.76 0.32 0 1.4888 0.11375 1.43625 0.83375 1.37375 0.53875
10591 A 13.863 9.6375 12.7125 17.5375 1.65 3.15 8.6875 15.4875 8.0375 1.875 0 8.475 0.5375 5.6 5.375 9.7375 4.375
10607 A 0.245 0.1425 0.29375 0.42625 0.04125 0.0575 0.0863 0.33625 0.0863 0.00625 0 0.0725 0.0125 0.095 0.0125 0.07125 0.00625
10859 A 4.965 3.13 4.6925 5.81 0.44875 0.7688 3.22 4.80125 2.8638 0.445 0 2.5138 0.16625 1.82 1.65375 2.76125 1.12875
10863 A 0.6175 0.215 0.69125 0.89375 0.07125 0.1675 0.255 0.735 0.2563 0.05625 0 0.215 0.01875 0.22375 0.13625 0.28625 0.10875
10907 A 8.8188 5.5813 6.55625 10.1875 1.00625 1.2625 4.4938 8.06875 3.95 1.325 0 2.8375 0.5 3.18125 1.81875 3.4375 1.375
10975 A 0.5575 0.395 0.53875 1.07375 0.0525 0.1275 0.2638 0.6775 0.18 0.12 0 0.0988 0.075 0.18375 0.05125 0.20125 0.065
11208 A 4.7538 2.8063 3.46875 5.48 0.385 0.5988 2.4413 4.37875 2.1363 0.595 0 1.4688 0.25625 1.5525 0.83 1.49375 0.64
11231 A 4.1725 1.49 3.1625 4.43 0.34 0.6675 1.19 3.2575 1.245 0.4375 0 0.935 0.11 1.115 0.5375 1.26 0.555
11327 A 2.7875 0.91 1.5975 1.95625 0.25 0.3188 1.0375 1.4625 0.8163 0.3175 0 0.6113 0.05 0.815 0.395 0.6825 0.3225
11435 A 5.1388 1.9025 3.905 5.28875 0.48 0.9188 2.7875 3.96375 2.05 0.35625 0 2.8713 0 2.01875 1.69625 2.64125 1.2625
11656 A 5.5125 3.2625 3.6025 5.47 0.51375 0.7688 2.7313 4.195 2.3688 1.14 0 1.6275 0.16375 1.845 0.9975 1.835 0.93625
(Continued)
215
Appendix D. Concentrations of hydrolysable amino acids detected in soils (µmol kg-1 dry soil)
ID Horizon Asp Ser Glu Gly His Arg Thr Ala Pro Tyr Cys-Cys Val Met Lys Ile Leu Phe
12020 A 5.5945 3.5747 4.32927 7.35518 0.42683 0.8841 2.8963 5.05335 3.0183 0.43445 0 2.4238 0.15244 1.39482 1.46341 2.78963 1.1814
12075 A 1.0288 0.5575 0.79875 1.185 0.07625 0.17 0.4763 0.9675 0.4538 0.155 0 0.1913 0.05125 0.3875 0.1625 0.3675 0.13625
12232 A 14.279 2.658 8.24176 10.0069 0.75549 1.2706 4.2995 7.87088 3.6607 0.40522 0 6.0508 0 4.3544 3.35852 4.19643 2.16346
12488 A 3.3575 1.905 2.86125 3.13375 0.40875 0.5525 1.7213 3.085 1.7513 0.42875 0 1.3538 0.0725 1.40625 0.74625 1.2825 0.53125
12495 A 0.57 0.2538 0.425 0.61625 0.06 0.1013 0.445 0.52 0.1275 0.0175 0 0.1475 0.0175 0.17375 0.07375 0.17375 0.055
12505 A 3.47 3.6738 2.5125 4.9625 0.66875 0.7313 2.5 3.88625 2.18 0.6375 0 1.5938 0.08625 1.3525 1.07875 1.91 0.895
12584 A 2.2938 1.1538 1.8325 2.8025 0.2375 0.39 0.8163 2.05125 0.905 0.405 0 0.5388 0.1125 0.7725 0.305 0.7525 0.31875
12888 A 3.6225 2.8138 3.16875 4.805 0.455 0.7263 2.5013 3.8775 2.0488 0.65 0 1.5425 0.08375 1.4525 0.95125 1.7475 0.7675
1643 A 2.515 1.0863 1.825 2.40125 0.24625 0.38 1.5688 1.90625 1.1075 0.13 0 1.3275 0.0075 0.93375 0.7325 1.18375 0.45125
1708 A 1.8888 1.4888 1.50625 2.91 0.27375 0.3388 1.0525 2.085 0.9038 0.3775 0 0.59 0.175 0.75875 0.37625 0.7425 0.34125
1724 A 6.1063 3.6375 4.30625 6.79375 1.25 1.2563 3.35 4.875 2.7938 0.64375 0 3.4875 0.09375 2.14375 2.0875 3.0125 1.50625
1759 A 1.865 0.8275 1.855 2.77 0.2075 0.4625 0.785 2.15 0.9175 0.2625 0 0.8175 0.03 0.6075 0.4475 1.025 0.38
1881 A 6.36 2.5063 3.63 5.53125 0.28375 0.6288 2.4275 3.9825 1.7875 0.61625 0 1.63 0.17625 1.56375 1.065 1.635 0.82625
1935 A 8.8088 6.2925 6.29375 11.1025 1.06 1.1563 3.9038 8.0325 3.6738 1.07625 0 2.3225 0.49875 2.74625 1.37875 2.7625 1.02625
2095 A 3.7925 3.5525 3.0825 7.17 0.53125 0.8088 2.055 4.65 2.0488 0.84625 0 1.3025 0.29625 1.43 0.81 1.8925 0.84875
2111 A 1.185 0.5588 0.82875 1.11375 0.11375 0.175 0.64 0.92 0.505 0.1375 0 0.3713 0.02125 0.40625 0.2025 0.4225 0.17375
2184 A 2.5625 1.8375 2.1075 4.0625 0.3175 0.4038 1.5225 2.81 1.3063 0.365 0 0.7738 0.12375 1.02875 0.5325 0.92875 0.36875
2248 A 2.23 1.2188 1.74375 2.26125 0.315 0.43 1.1913 2.1975 1.3988 0.3275 0 0.8425 0.08875 0.985 0.52125 0.99375 0.42875
2521 A 3.6588 2.4938 2.4925 4.19 0.5075 0.6713 1.9213 3.08375 1.7263 0.51125 0 1.3338 0.11 1.53625 0.76625 1.39375 0.6825
2539 A 3.1438 1.1725 2.08125 2.76375 0.25125 0.3388 1.46 2.30625 1.2388 0.275 0 1.0575 0.05125 0.84375 0.59125 1.04125 0.4075
2713 A 3.4125 2.2975 2.54125 3.92625 0.5275 0.5663 1.9075 2.95625 1.4563 0.4475 0 1.1575 0.105 1.4075 0.67625 1.2 0.5325
2748 A 3.4575 2.615 2.91375 4.5475 0.52625 0.6725 2.23 3.3925 1.8025 0.08 0 1.5 0.0675 1.2775 0.85875 1.525 0.64625
2780 A 2.7238 2.1688 2.2525 4.42625 0.41375 0.5 1.5013 3.17125 1.4113 0.56875 0 0.8363 0.15375 1.2575 0.51625 1.02625 0.43875
2959 A 1.2013 0.6938 1.06125 1.2125 0.1025 0.2025 0.5438 1.14625 0.4288 0.33875 0 0.205 0.06125 0.44875 0.21125 0.4025 0.15
2988 A 10.544 6.5688 8.2125 11.1688 1.325 1.4438 5.9563 9.75 5.2313 1.275 0 4.0438 0.5375 4.10625 2.38125 4.2125 1.7125
3135 A 2.8825 1.0213 2.55375 3.39375 0.365 0.52 2.2863 2.79875 1.4775 0.13625 0 2.22 0.0125 1.34125 1.27375 1.94875 0.77
3228 A 5.5813 4.4363 4.24875 7.98 0.68625 0.8313 2.7125 5.5975 2.5675 0.6575 0 1.8163 0.24375 2.145 1.1675 2.08625 0.97375
3291 A 3.0088 2.0238 2.535 3.55125 0.3475 0.445 1.8113 2.90125 1.3363 0.37 0 1.0538 0.1375 1.1875 0.74875 1.28375 0.5125
3736 A 9.0975 6.8025 7.1725 14.6638 0.99875 1.3613 5.0338 10.0388 4.7025 1.49375 0 2.555 0.91 3.59875 1.74625 3.89375 1.52625
3828 A 6.175 2.5375 4.96875 5.725 0.925 1.1125 3.175 4.975 2.9688 0.3625 0 4.0313 0.11875 2.9125 2.39375 3.46875 1.75
3931 A 3.0975 3.3163 2.105 6.57125 0.4425 0.7425 1.6688 3.9325 1.7138 0.55375 0 1.0638 0.23125 1.315 0.765 1.57875 0.725
3947 A 2.0025 1.3763 1.535 2.61 0.1675 0.2875 1.0788 2.1225 1.0925 0.415 0 0.4475 0.1075 0.84125 0.36625 0.7675 0.3
4248 A 3.8238 2.1788 3.0675 4.535 0.4425 0.7075 2.0488 3.51 2.0675 0.65125 0 1.3213 0.21125 1.40125 0.84875 1.62625 0.6625
4249 A 5.4053 1.8447 3.56061 5.76894 0.54167 0.947 2.6439 4.04924 2.072 0.33712 0 2.9129 0.03409 2.01515 1.58712 2.2197 1.16288
4440 A 1.8488 0.84 1.5425 2.47375 0.2875 0.435 1.0363 1.78875 1.08 0.23125 0 1.3588 0.03625 0.88875 0.7875 1.17375 0.5075
4507 A 0.2838 0.1138 0.21375 0.325 0.03875 0.0388 0.0988 0.24375 0.0725 0.0275 0 0.0388 0.03125 0.09875 0.045 0.085 0.03125
(Continued)
216
Appendix D. Concentrations of hydrolysable amino acids detected in soils (µmol kg-1 dry soil)
ID Horizon Asp Ser Glu Gly His Arg Thr Ala Pro Tyr Cys-Cys Val Met Lys Ile Leu Phe
4571 A 2.2763 1.6363 2.15875 2.33125 0.2725 0.58 1.32 2.605 1.1963 0.4325 0 0.965 0.16125 0.8675 0.665 1.315 0.55125
4760 A 4.8563 3.2788 3.76 6.275 0.35625 0.655 2.4588 5.0025 2.38 0.77 0 1.18 0.38875 1.57125 0.915 1.77375 0.73625
4779 A 4.0472 1.3137 2.76672 3.61598 0.44453 0.6635 2.289 2.93259 1.5393 0.3052 0 2.2293 0 1.6189 1.26725 2.02362 0.92224
4780 A 3.8588 2.8388 3.10375 4.58 0.46875 0.6525 2.2138 3.63875 2.1588 0.38 0 1.7613 0.17125 1.36 1.08625 1.89 0.79
4852 A 6.1458 1.7188 3.45238 5.625 0.42411 0.692 2.1429 3.31101 1.8452 0.40179 0.0125 2.3438 0.0425 1.79315 1.36905 1.94196 0.95982
4952 A 8.2563 3.2875 5.46875 6.35625 0.9125 1.0938 4.4 5.7 3.6375 0.43125 0.025 4.55 0.10625 2.99375 2.59375 3.75 1.6125
4953 A 3.5875 1.65 2.50875 3.73875 0.2525 0.4588 1.5025 3.02 1.2675 0.33125 0 0.9975 0.125 1.065 0.6075 1.0525 0.42375
5256 A 9.1938 3.8688 6.4375 9.0875 1.06875 1.2563 4.9688 6.86875 3.9063 0.5375 0 5.1875 0 3.4125 3.00625 4.15 2
5355 A 4.8313 3.6875 3.8125 5.92875 0.5275 0.755 2.8225 4.7975 2.5713 0.64125 0 2.03 0.17375 1.39 1.33625 3.1 0.98875
5596 A 3.435 2.3913 2.24125 4.74875 0.40125 0.4638 1.7513 3.05375 1.28 0.49125 0 0.87 0.17875 1.22375 0.5525 1.0575 0.53125
5615 A 1.8775 0.7125 1.42 2.1975 0.155 0.3 1.1825 1.75 0.71 0.1375 0 0.5725 0.08 0.63 0.3225 0.68 0.235
5675 A 2.1713 1.5225 2.0825 2.2 0.21 0.4288 1.48 2.41625 1.1163 0.2825 0 0.8863 0.10625 0.83875 0.5775 1.11625 0.425
5848 A 3.1225 1.825 2.62125 4.135 0.44375 0.71 2.0488 3.1325 1.6763 0.21625 0 2.1588 0.01375 1.49125 1.34375 2.07875 0.95375
5852 A 7.0438 5.0825 4.90375 6.95625 0.99375 0.7763 4.3725 5.89375 3.2163 0.825 0 3.1588 0.29875 2.2825 1.74625 2.62125 1.40125
5977 A 3.6925 2.7188 3.08875 5.58625 0.51875 0.6638 2.4438 4.515 1.68 0.83875 0 1.1638 0.15375 1.79375 0.72375 1.32625 0.45625
600 A 2.245 1.5538 1.99875 4.085 0.325 0.3925 0.9063 2.33625 1.0088 0.46875 0 0.5875 0.16375 1.00875 0.32375 0.69125 0.305
6041 A 5.945 1.79 4.5125 6.2125 0.56 1.0888 2.8113 5.16125 2.875 0.38375 0 3.8388 0.13375 2.37125 2.3475 3.10625 1.44625
6060 A 5.2125 3.59 3.98625 5.3825 0.62 0.8038 2.9325 4.2975 2.7013 0.6475 0 2.06 0.0975 2.02875 1.265 2.14625 1.0525
6379 A 3.7788 1.5225 2.4525 3.57125 0.295 0.555 1.3013 2.715 1.2725 0.2825 0 1.2763 0.03125 1.03375 0.72875 1.3725 0.5825
648 A 7.535 5.4388 6.175 9.1225 1.075 1.2625 4.4513 7.6575 4.2288 0.805 0 3.345 0.2875 2.9675 2.0975 4.54375 1.4875
6617 A 6.355 5.7088 7.645 10.4413 1.4 2.065 5.9813 8 5.285 0.8625 0.0375 5.5 0.27625 3.52375 3.5 5.25 2.37375
6891 A 4.0663 1.2775 2.4075 3.42 0.2975 0.3475 1.825 2.5825 1.25 0.10625 0 1.0013 0.09 1.055 0.56 1.0225 0.41
7065 A 6.8638 3.9775 4.45125 6.94375 0.71625 0.78 3.4925 5.28625 2.36 0.76375 0 2.1 0.1975 2.2125 1.1625 1.9725 0.9825
7231 A 4.6675 1.4425 2.71 3.84 0.3225 0.4225 1.9725 2.7525 1.26 0.3825 0 1.385 0.025 1.165 0.6675 1.0325 0.4375
7324 A 4.4763 3.6063 3.86 6.75375 0.62 0.5263 2.3063 4.9825 2.0463 0.79375 0 1.21 0.335 1.7025 0.8 1.44 0.55875
7368 A 9.2735 3.4513 7.73637 10.9919 1.29785 1.4936 5.757 9.60702 5.0537 0.6018 0 6.6995 0.07251 4.57512 3.64704 4.9449 1.97216
7384 A 3.64 2.545 3.0475 5.34875 0.47 0.6163 1.6813 3.75875 1.8863 0.88375 0 0.98 0.245 1.44375 0.6 1.405 0.705
7919 A 1.4075 0.6063 0.9025 1.57875 0.105 0.16 0.575 1.10125 0.4925 0.28625 0 0.2013 0.03125 0.3725 0.17625 0.37375 0.14875
8088 A 2.9838 1.9763 2.3525 3.88875 0.3825 0.5663 1.855 2.9675 1.51 0.54875 0 1.06 0.1375 1.1525 0.64375 1.2125 0.52625
8200 A 5.9713 4.2225 5.44125 7.93125 0.75375 1.03 3.7325 6.2575 4.0638 0.63375 0.145 2.75 0.1725 2.26875 1.53375 2.8075 1.19875
8687 A 4.6625 1.1038 2.37625 2.96125 0.3625 0.4638 1.755 2.2225 1.1988 0.3375 0 1.23 0.0425 1.3525 0.66875 1.165 0.5025
8948 A 3.745 2.7863 3.795 4.46625 0.65125 0.9175 2.0088 3.76125 2.0688 0.94875 0 1.3213 0.2175 1.87625 0.98625 1.8625 0.89625
9049 A 6.0563 3.6738 4.8275 6.00125 0.52125 0.8075 2.7013 6.14625 2.8475 0.74875 0 1.8963 0.30625 1.955 1.09 2.21625 0.90875
9103 A 1.6038 1.1213 1.445 1.72 0.18625 0.3513 0.8813 1.62875 0.735 0.22875 0 0.7563 0.03625 0.44375 0.5075 1.005 0.41375
9195 A 0.4888 0.2663 0.39625 0.54875 0.06 0.0863 0.2488 0.45875 0.2 0.0575 0 0.06 0.025 0.22 0.05625 0.14375 0.04
9323 A 1.6775 1.0888 1.08 2.19625 0.1825 0.2788 0.605 1.49875 0.69 0.19625 0 0.4413 0.0625 0.6425 0.225 0.5975 0.26
(Continued)
217
Appendix D. Concentrations of hydrolysable amino acids detected in soils (µmol kg-1 dry soil)
ID Horizon Asp Ser Glu Gly His Arg Thr Ala Pro Tyr Cys-Cys Val Met Lys Ile Leu Phe
9423 A 3.7438 2.72 3.79 5.10625 0.585 0.7463 1.85 3.95875 1.83 0.7275 0 1.2575 0.18875 1.66375 0.9525 1.7075 0.82125
9535 A 1.0575 0.4963 0.87375 1.355 0.165 0.27 0.7425 0.9625 0.4475 0.075 0 0.6963 0 0.47875 0.37375 0.58125 0.255
9556 A 3.1725 1.66 2.8325 3.5625 0.3 0.575 1.1975 2.8 1.4 0.3975 0.05 1.175 0.0725 0.965 0.855 1.635 0.7625
9644 A 3.7188 2.5575 2.9225 4.7425 0.47625 0.435 2.0238 3.60125 1.4375 0.6175 0 0.9738 0.21625 1.35625 0.58625 1.09 0.475
9823 A 2.64 2.1225 2.38625 4.665 0.41625 0.4813 1.5913 3.2025 1.2725 0.4425 0 0.7663 0.1975 1.23125 0.5225 1.03375 0.4575
9938 A 0.5599 0.2904 0.41763 0.40813 0.0675 0.0801 0.247 0.4335 0.1904 0.01938 0 0.2294 0.00438 0.20488 0.11513 0.16075 0.07438
“ID” in the column heading signifies unique identifier assigned by generalized random tessellation stratified design software; Asp, aspartic acid; Glu, glutamic acid; Ser, serine;
Gly, glycine; His, histidine; Arg, arginine; Thr, threonine; Ala, alanine; Pro, proline; Tyr, tyrosine; Cys-Cys, cystine; Val, valine; Met, methionine; Lys, lysine; Ile, isoleucine; Leu,
leucine; Phe, phenylalanine
(Continued)