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Aggregate Organic Carbon in North Florida Slash and Loblolly Pine Ecosystems

Permanent Link: http://ufdc.ufl.edu/UFE0042220/00001

Material Information

Title: Aggregate Organic Carbon in North Florida Slash and Loblolly Pine Ecosystems
Physical Description: 1 online resource (83 p.)
Language: english
Creator: Azuaje, Elena
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: aggregate, analysis, carbon, discriminant, florida, incubation, loblolly, mineralization, pine, plantation, sandy, slash, soil, sonication, spectroscopy, spodosol
Soil and Water Science -- Dissertations, Academic -- UF
Genre: Soil and Water Science thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: It's been found that of the estimated 4,110 GT of carbon (C) found in the world?s C cycle approximately 38% can be found in the soil. There has been a growing interest in C sequestration through improved soil management practices. Aggregation is a process by which C can be physically protected by encrustation. Sandy surface horizons, which have been thought of as weak structure, have been previously found to have small aggregates with upwards of 50% soil organic C (SOC) contained in those aggregates. This study targets pine ecosystems under the two dominant species in the southeast US, which are loblolly pine (Pinus taeda) and slash pine (Pinus elliottii). The objectives were to a) validate ultrasonic methodology, b) to investigate soil aggregate energy at which SOC is held and determine if this is influenced by pine ecosystems of loblolly vs. slash; c) determine if aggregate dispersive energy is a controlling factor in aggregate organic C (AOC) turnover; and d) determine the chemical fingerprint of AOC as aggregate dispersive energy increases within species ecosystem. The data show that soil under slash pine was slightly better aggregated with 4.1% more AOC as a percentage of total SOC. However, loblolly had 33% more AOC (g g-1soil) than did slash. Incubation and mid-infrared (mid-IR) results showed that there was no discernable difference in AOC held at different energy levels. On the other hand, SOC was influenced by pine species. Slash pine soils were higher in aliphatic C and had higher specific mineralization rate; while loblolly was higher in aromatic C. These results are the first contrast of SOC under these two species. It suggests that loblolly management, as normally practiced by landowners, will store more soil C; yet that soil C will not be protected from mineralization by soil aggregation.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Elena Azuaje.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Comerford, Nicholas B.
Local: Co-adviser: Harris, Willie G.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042220:00001

Permanent Link: http://ufdc.ufl.edu/UFE0042220/00001

Material Information

Title: Aggregate Organic Carbon in North Florida Slash and Loblolly Pine Ecosystems
Physical Description: 1 online resource (83 p.)
Language: english
Creator: Azuaje, Elena
Publisher: University of Florida
Place of Publication: Gainesville, Fla.
Publication Date: 2010

Subjects

Subjects / Keywords: aggregate, analysis, carbon, discriminant, florida, incubation, loblolly, mineralization, pine, plantation, sandy, slash, soil, sonication, spectroscopy, spodosol
Soil and Water Science -- Dissertations, Academic -- UF
Genre: Soil and Water Science thesis, M.S.
bibliography   ( marcgt )
theses   ( marcgt )
government publication (state, provincial, terriorial, dependent)   ( marcgt )
born-digital   ( sobekcm )
Electronic Thesis or Dissertation

Notes

Abstract: It's been found that of the estimated 4,110 GT of carbon (C) found in the world?s C cycle approximately 38% can be found in the soil. There has been a growing interest in C sequestration through improved soil management practices. Aggregation is a process by which C can be physically protected by encrustation. Sandy surface horizons, which have been thought of as weak structure, have been previously found to have small aggregates with upwards of 50% soil organic C (SOC) contained in those aggregates. This study targets pine ecosystems under the two dominant species in the southeast US, which are loblolly pine (Pinus taeda) and slash pine (Pinus elliottii). The objectives were to a) validate ultrasonic methodology, b) to investigate soil aggregate energy at which SOC is held and determine if this is influenced by pine ecosystems of loblolly vs. slash; c) determine if aggregate dispersive energy is a controlling factor in aggregate organic C (AOC) turnover; and d) determine the chemical fingerprint of AOC as aggregate dispersive energy increases within species ecosystem. The data show that soil under slash pine was slightly better aggregated with 4.1% more AOC as a percentage of total SOC. However, loblolly had 33% more AOC (g g-1soil) than did slash. Incubation and mid-infrared (mid-IR) results showed that there was no discernable difference in AOC held at different energy levels. On the other hand, SOC was influenced by pine species. Slash pine soils were higher in aliphatic C and had higher specific mineralization rate; while loblolly was higher in aromatic C. These results are the first contrast of SOC under these two species. It suggests that loblolly management, as normally practiced by landowners, will store more soil C; yet that soil C will not be protected from mineralization by soil aggregation.
General Note: In the series University of Florida Digital Collections.
General Note: Includes vita.
Bibliography: Includes bibliographical references.
Source of Description: Description based on online resource; title from PDF title page.
Source of Description: This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Statement of Responsibility: by Elena Azuaje.
Thesis: Thesis (M.S.)--University of Florida, 2010.
Local: Adviser: Comerford, Nicholas B.
Local: Co-adviser: Harris, Willie G.

Record Information

Source Institution: UFRGP
Rights Management: Applicable rights reserved.
Classification: lcc - LD1780 2010
System ID: UFE0042220:00001


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AGGREGATE ORGANIC CARBON IN NORTH FLORIDA SLASH AND LOBLOLLY
PINE ECOSYSTEMS




















By

ELENA AZUAJE


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER IN SCIENCE

UNIVERSITY OF FLORIDA

2010

































2010 Elena Azuaje
































To my parents who inspired, allowed and encouraged me to follow my goals since
childhood.









ACKNOWLEDGMENTS

I thank my parents for always encouraging me to pursue my lifelong goals. I thank

my mother Meber Dasilva for being strong and allowing me to fly away from home at a

young age to pursue a better future; also my mommy Deborah Azuaje for taking me

under her wings unconditionally. I especially thank my father, Carlos Azuaje for being

my inspiration and an example of an individual with high quality of intellect, morals and

humbleness that only a few posses and that many wish to have. I thank Julio Madriz for

his love and company that has smoothed my ride through the rollercoaster of the past

few years. This work is a product of many minds that influenced and imparted my

understanding and knowledge. I sincerely thank my master's committee: Nicholas

Comerford for his guidance, consistency and everlasting patience while teaching me the

science of research; Willie Harris for being an example can only hope to be, a humble

and incredibly knowledgeable individual; Jim Reeves for his extensive knowledge and

willingness to teach and enthusiasm to live life.

Finally, I thank the project through which my research was funded: Rapid

Assessment and Trajectory Modeling of Changes in Soil Carbon across a Southeastern

Landscape (Florida); funded by the U.S. Department of Agriculture (USDA), National

Research Initiative (NRI), CSREES; now National Institute of Food and Agriculture

(NIFA), USDA-CSREES-NRI-AFRI grant award 2007.









TABLE OF CONTENTS

page

ACKNOW LEDG M ENTS......... .... .. ........ .... .......... ................... ... .. 4

LIS T O F T A B LE S ....................... ............................. ......... ....... ............... 7

L IS T O F F IG U R E S .......................................................... .. ................................ 8

LIST OF ABBREVIATIONS .............. ...... .... ...... .... ... ........................ 9

ABSTRACT .................. ....... .................................. ....... ......... 10

CHAPTER

1 INTRODUCTION TO AGGREGATE ORGANIC CARBON ................. ......... 12

2 SOIL AGGREGATE STABILITY AND ITS INFLUENCE ON CARBON IN PINE
ECOSYSTEMS IN NORTH FLORIDA.................. ............. ................17

M methodology ........... ....... ... ................... .............. ....... ......... .....................19
Experimental Site for Validation of the Sonication Method .............................. 19
Experimental Sites for Stability Levels, Mineralization and Chemical
A n a ly s is ................ ............................................ ............ ....... 1 9
L a b orato ry M eth o d s ................ ....................................................... 2 1
S on icatio n m ethod ........... .......... .......... .......... .............................. 2 1
ADE curves for pine ecosystems ................. .......................................... 22
S statistic a l a n a lys is ........................................................................... 2 3
R es u lts ........................................ .............................................. 2 4
Objective 1. Sonication M ethod Evaluation ..................................................... 24
Objective 2. Contrasting Aggregation in Loblolly and Slash Pine
E cosyste m s ....................................................... 2 5
D discussion .................................................................................... 2 5
Objective 1. Sonication Method Evaluation ....................................................... 25
Objective 2. Contrasting Aggregation in Loblolly and Slash Pine
E c o s ys te m s ............................................................................... 2 6

3 DOES AGGREGATION PROTECT C IN VERY SANDY SURFACE SOILS?.........32

M ethodology .............. ......... ............. ..................... 35
Experimental Sites and Field Sampling ............... ............... ... ............. 35
Laboratory M methods .......... .......... ............. ......... .............. .. 36
M in e ra liz a tio n .............................. ....... ............................... .. ................3 7
Dissolved organic carbon (DOC) ......................................... ......................... 38
Fourier transformed infra-red reflectance (FTIR) spectroscopy ................. 38
Statistical Analysis .................................................. 39
R e s u lts .......................................... .......................................................... 4 0









Objective 1. Determine if Aggregate Stability Levels and Ecosystem Cover
Types were a Controlling Factor in SOC and AOC Accumulation and
M ineralization.. ............................... ..................................... 40
Objective 2. Identify SOC Chemical Characteristics that could explain
Differences at AOC Stability Levels and Mineralization between
E cosystem s C over T ypes ..................................................................... .... .... 4 1
Discussion ....................................................................... .......... 42

4 SY N T H E S IS ....................................................................... ... ...... 53

APPENDIX

A POPULATION CHARACTERIZATION ................................................................... 57

B AGGREGATE DISPERSION ENERGY CURVES..................................................60

C MID-INFRARED REFLECTANCE (MID-IR) ............................... ................73

LIS T O F R E F E R E N C E S ................ .......................................................... .... 76

B IO G R A P H IC A L S K E T C H ........................................................................ ................. 83









LIST OF TABLES


Table page

2-1 Sample characterization of pine sites located in north central Florida ...............30

2-2 Characterization of population and sample means and standard error .............. 30

2-3 Summary of aggregate organic carbon (AOC) as influenced by tree species.....31

3-1 D description of sam ple sites ......................................................... .... ....... 51

3-2 Population and sample means with corresponding standard errors for total
carbon (TC), soil organic carbon (SO C)................................... ...................... 51

3-3 Total dissolved organic carbon mg g-1 soil with corresponding standard errors
and species com prisons. ....................................................... .................. .... 51

3-4 Discriminant analysis for the separation of species from spectral analysis.........51

3-5 Discriminant analysis for spectral separation ash-subtracted of energy level.....52

3-6 Discriminant analysis for mineralization study of SOC by species....................... 52

A-1 Complete dataset of 56 pine sites located in north central Florida ................. 57

A-2 Slash (S) and loblolly (L) pine ecosystem population descriptive data ...............58









LIST OF FIGURES


Figure page


2-1 Number of aggregates per gram of soil as affected by level of dispersion
energy applied and by the soil size fraction. ......................... ........................ 28

2-2 Aggregate organic carbon (AOC) as a function of dispersive energy................. 29

3-1 Comparison of the carbon (C) mg g-1 soil by dispersion energy levels (J mL-1)
b y w e e k ................ .................................................................. 4 6

3-2 Total dissolved organic carbon (DOC). .................................. ................. 47

3-3 Diffuse reflectance infrared Fourier-transform spectra fingerprint of the
average ash subtracted spectra. ........... ... ............................. ................ 47

3-5 Diffuse reflectance Fourier-transform spectra of the difference between
spectra of ash-subtracted loblolly minus slash pine influenced soils................... 48

3-6 Spectral subtraction between weeks 29 and 1 for loblolly pine influenced
s o ils ............... .................................. ..................... ................. 4 9

3-7 Spectral subtraction between weeks 29 and 1 for slash pine influenced soils.... 50

C-1 Mid-infrared non-ashed spectrum of the average of loblolly pine influenced
s o il....................... .. .. ......... .. .. ............................................. 7 3

C-2 Mid-infrared non-ashed spectrum of the average of slash pine influenced soil... 73

C-3 Mid-infrared spectrum of ash-subtracted loblolly pine influenced soil at
beginning of incubation study. ..................................... ............................ ......... 74









LIST OF ABBREVIATIONS

ADEC Aggregate Dispersion Energy

AOC Aggregate Organic Carbon

C Carbon

DOC Dissolved Organic Carbon

DRIFTS Diffuse Reflectance Infrared Fourier Transform Spectroscopy

FTIR Fourier Transform Infrared Spectroscopy

Mid-IR Mid-Infrared

SOM Soil Organic Matter

SOC Soil Organic Carbon

TOC Total Organic Carbon









Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master in Science

AGGREGATE ORGANIC CARBON IN NORTH FLORIDA SLASH AND LOBLOLLY
PINE ECOSYSTEMS

By

Elena Azuaje

August 2010

Chair: Nicholas Comerford
Cochair: Willie Harris
Major: Soil and Water Science

It's been found that of the estimated 4,110 GT of carbon (C) found in the world's

C cycle approximately 38% can be found in the soil. There has been a growing interest

in C sequestration through improved soil management practices. Aggregation is a

process by which C can be physically protected by encrustation. Sandy surface

horizons, which have been thought of as weak structure, have been previously found to

have small aggregates with upwards of 50% soil organic C (SOC) contained in those

aggregates. This study targets pine ecosystems under the two dominant species in the

southeast US, which are loblolly pine (Pinus taeda) and slash pine (Pinus elliottii). The

objectives were to a) validate ultrasonic methodology, b) to investigate soil aggregate

energy at which SOC is held and determine if this is influenced by pine ecosystems of

loblolly vs. slash; c) determine if aggregate dispersive energy is a controlling factor in

aggregate organic C (AOC) turnover; and d) determine the chemical fingerprint of AOC

as aggregate dispersive energy increases within species ecosystem. The data show

that soil under slash pine was slightly better aggregated with 4.1% more AOC as a

percentage of TOC. However, loblolly had 33% more AOC (g g-soil) than did slash.









Incubation and mid-infrared (mid-IR) results showed that there was no discernable

difference in AOC held at different energy levels. On the other hand, SOC was

influenced by pine species. Slash pine soils were higher in aliphatic C and had higher

specific mineralization rate; while loblolly was higher in aromatic C. These results are

the first contrast of SOC under these two species. It suggests that loblolly management,

as normally practiced by landowners, will store more soil C; yet that soil C will not be

protected from mineralization by soil aggregation.









CHAPTER 1
INTRODUCTION TO AGGREGATE ORGANIC CARBON

Of the estimated 4,110 GT of C found in the world's C cycle approximately 38%

can be found in the soil (Leu, 2007). Moreover, soil organic C (SOC) is critical to soil

processes as it affects water retention, soil structure, and nutrient cycling (Sanchez and

Ruark, 1995). Soil C is protected by a variety of mechanisms including: physiochemical

by sorption to clay; biochemical through formation of recalcitrant C compounds; physical

via soil aggregation (Stone et al., 1993; Six et al., 2002; Blanco-Canqui and Lal, 2004);

chemical by combining with metals such as aluminum; translocational by C moving to

deeper soil horizons where decomposition is limited by either microbial populations or

oxygen; and depositional under anaerobic/anoxic environmental conditions.

The architectural organization of sand, silt, and clay by organic compounds and

inorganic cementing agents creates aggregates (Blanco-Canqui and Lal, 2004). An

aggregate hierarchy in soils has been suggested where aggregates of different stability,

often controlled by organic materials, can be classified (Oades and Waters, 1991).

Three models have been used to explain soil aggregation and SOC (Blanco-Canqui and

Lal 2004). The first was proposed by Tisdall and Oades (1982) and based on aggregate

hierarchy. In this model, stage 1 is the binding of primary particles into microaggregates

(53-250pm) where soil organic matter (SOM) is often the principal binding agent. Stage

2 is characterized by the formation of medium-sized microaggregates; while stage 3

encompasses the formation of large microaggregates and macroaggregates.

The second model was proposed by Oades (1984) which was a revision of the

above mentioned model where macroaggregates form first and microaggregates are

created afterward. The third model, developed by Six et al. (2000), begins with the









formation of macroaggregates created by the binding of organic residues. This is

followed by intra-aggregate particulate OM stabilizing these macroaggregates. The next

stage is the formation of microaggregates encrusted by fine organic particles. Finally,

stable microaggregates are formed by the degradation of macroaggregates.

Aggregate formation depends on factors that develop aggregate stability. Soil

aggregation depends on changes in water status, freezing or thawing, tillage, movement

of large biota (plant roots, earthworms and macro fauna) and clay content (Oades and

Waters, 1991). Roots, fungal hyphae, and polysaccharides that are intimately

associated with the mineral fraction appear to be important stabilizing agents (Kay,

1997). A study on macroaggregate stability showed that hyphae are linked to

aggregation in sandy soils (Degens et al. 1996). It is often assumed that SOC within

aggregates is not available to microbial decay, and as a consequence the greater the

stability of the aggregate against dispersion leads to greater protection of the SOC.

Studies have shown mixed results when investigating the relationship between

aggregate tensile strength and SOC (Blanco-Canqui and Lal, 2004). A strong

correlation was found by Golchin et al. (1995) between particulate organic matter and

stability of 1-2 mm macroaggregates (increasing SOC increases aggregate stability);

while Chaney and Swift (1984) found a high correlation between aggregate stability,

SOM, total carbohydrate, and humic material. Conversely, Jastrow et al. (1998) found a

poor correlation between SOC and aggregate sizes.

The purpose of this thesis was to investigate soil C and soil aggregation in very

sandy soils supporting southern pine plantations. In Florida, pine lands made up almost

exclusively of slash pine (Pinus elliottii) and loblolly pine (Pinus taeda), cover 1,252,569









ha of which 30% (371,481 ha) are in conservation areas or managed areas; 56%

(702,182 ha) are in private ownership. The remaining 14% encompasses private lands

in conservation programs (FFWCC, 2005). Spodosols cover 27% of Florida's area

(Stone et al., 1993). Florida Spodosols contain 0.05% of the global C pool only on

approximately 0.028% of the total land area (Stone et al., 1993). Within our sampling

study area, pinelands represent 25% of the land cover. Florida's sandy surface soils are

not known for their ability to store C due to their weak structure and low clay content

(Carlisle et al., 1981, 1988, 1989) resulting in a low ability to physiochemically protect C

from microbial decomposition.

The few studies that have investigated aggregation in Southeastern Coastal Plain

sandy soils have shown that surface soil aggregation is dominated by microaggregates

over macroaggregates. The 'weak soil structure' of these soils implies poor aggregation,

yet Sarkhot et al. (2007a) have shown aggregate hierarchy with at least 50% of the total

C found in aggregates (Sarkhot et al., 2007a). However, the degree of protection

provided by soil C in Lower Coastal Plain sandy soils is unknown.

The overall purpose of this study was to investigate soil aggregate C as to its

importance in forest land use and its role in SOC protection; it was addressed by

investigating three principle objectives. The first objective of this research was to

investigate the range in stability of soil aggregates in these sandy surface horizons and

determine if ecosystem vegetation dominated by P. taeda vs P. elliottii influence the

amount of SOC in aggregates and the strength of aggregates in which it is held. This is

the focus of Chapter 2. Aggregate stability is the ability of the soil to retain its

arrangement of solid and void space when exposed to stress (Kay, 1997). Due to the









low content of clay (<2%) and the sandy nature of North Florida Spodosols, aggregation

it thought to have low capabilities to physically protect SOC from microbial

decomposition. However, aggregate stability has been found to occur in Florida's

surface soils (Sarkhot et al., 2007a) and is found to be proportional to the energy

required to disperse the aggregates via sonication. While this Chapter addresses the

amount and type of aggregation, it does not investigate whether more stable aggregates

provide superior protection to SOC from microbial use.

This thesis's second objective was to determine if aggregate dispersive energy (a

measure of stability) is a controlling factor in aggregate organic C (AOC) turnover in

sandy soils (Chapter 3). By dispersing aggregates at different energies and then

mineralizing the SOC, this objective tested the hypothesis that SOC held at higher

dispersive energies is better protected against decomposition. When all soil aggregates

are dispersed, a higher rate of mineralization would be expected if aggregates do

protect SOC.

The third and last objective (Chapter 3) was to determine if the chemical

characteristics of SOC and AOC are different between slash and loblolly ecosystems;

and as aggregate dispersive energy increases. Qualitative analysis of SOC functional

groups was analyzed by a form of Fourier-transformed infrared reflectance

spectroscopy (FTIRS) called DRIFTS (Diffuse Reflectance Infrared Fourier Transformed

Spectroscopy). Fourier transform infrared spectroscopy is a cost effective, time saving,

non-destructive and environmentally sound technique of soil analysis (Dunn et al.,

2002); while Boehm titrations provide qualitative and quantitative information on soil

functional groups (Radovic et al, 2003).









Chapter 4 is a summary of this thesis and attempts to encapsulate the salient

points presented in the preceding chapters as well as identify the continuing gaps in

knowledge and suggest future research directions.









CHAPTER 2
SOIL AGGREGATE STABILITY AND ITS INFLUENCE ON CARBON IN PINE
ECOSYSTEMS IN NORTH FLORIDA

A soil aggregate is a group of soil particles that cohere more strongly to each

other than to other adjoining particles (Sylvia et al., 2005). In Florida's sandy soils,

organic matter (OM) is the dominant input that leads to the formation and stabilization of

soil aggregates, which in turn may protect soil organic C (SOC) from microbial

decomposition (Lal et al., 1997). Soil organic matter influences the formation,

stabilization, and degradation of soil aggregates and is the major aggregate binding

agent that generates aggregate hierarchy (Tisdall and Oades, 1982; Oades and Waters,

1991). Aggregates assist in the reduction of erosion, in the improvement of infiltration

and in the movement of water. They can also affect plant growth.

Different theories have been suggested about aggregates and their capability to

protect C. Tisdall and Oades (1982) determined that the age, size and stability of an

aggregate is a function of organic agents: transient agents decompose rapidly and are

associated with aggregates larger than 250 um; temporary binding agents are

associated with OM that comes from vesicular-arbuscular (VA) mycorrhizal hyphae

(Tisdall and Oades, 1979) and persist for months or years; and persistent agents are

resistant aromatic components. Soil microorganisms can promote soil aggregation

through the production of polysaccharides, glomalin and hyphae (Sylvia et al., 2005). It

has been shown that more labile OM is tied up in macroaggregates (2000-250 pm) and

more decomposed OM is tied up in microaggregates (<250 pm) (Six J et al. 2001).

Puget et al. (1995) also suggested that the organic C concentration increased with

aggregate size, and that organic C is more labile in micro than in macroaggregates.

This was supported by a study where the microbial biomass and activity in soil









aggregates was higher in macroaggregates than in microaggregates for native prairie

soils (Gupta and Germida, 1988). However, other theories support the idea of

"encrustation of SOM" inside microaggregates providing the protection that result in

SOC sequestration (Tisdall and Oades, 1982; Golchin et al., 1994; Jastrow and Miller,

1996).

The capacity of OM incorporation into aggregates has most often been estimated

from the soil clay and silt content (Hassink et al., 1997); however, using sound waves to

disrupt the aggregates has also been employed by others (North 1976; Christensen

1992; Cambardella and Elliot, 1993; Six et al., 2001; Swanston et al., 2005; Sarkhot et

al. 2007a). Aggregate dispersion energy curves (ADEC) have been used to examine

aggregate strength and determine the quantity of C in aggregates held at different

dispersion energies. This method quantifies the physically protected C (Sarkhot et al.

2007a; North, 1976; Christensen, 1992; Cambardella and Elliot, 1993; Six et al., 2001;

Swanston et al., 2005). Through this methodology Sarkhot et al. (2007a) found

evidence that suggested aggregate hierarchy in Florida's sandy soils.

The overall purpose was to investigate the role that aggregation plays in C

incorporation and sequestration in the surface sandy soils of the lower coastal plain

supporting southern pine. This was addressed by focusing on two objectives. The first

objective was to validate a sonication method that has been previously used to look at

aggregate dispersive energy (Sarkhot et al., 2007a). While the method has been used

as noted above, a detailed look at aggregate size and disappearance had not been

made.









The second objective was to investigate the amount of SOC incorporated into

aggregates in surface sandy soils; and determine if there was a difference due to

growing loblolly or slash pine ecosystems.

Methodology

Experimental Site for Validation of the Sonication Method

The study site was located 10 km north of Gainesville, FL on a flat topography

(<2% slope) within a forest plantation growing on a Pomona fine sand (sandy siliceous

hyperthermic Ultic Alaquod). The climate of the region is a hot and humid with an

average yearly precipitation 113.3 cm, and yearly average minimum and maximum

temperatures of 14 and 270 c respectively (southeast regional climate center, 2010).

The study design has previously been describe in detail since it has been a

subject of multiple soil and above-ground investigations (Colbert et al. 1990; Dalla-tea

and Jokela, 1991; Jokela and Martin, 2000; Martin and Jokela, 2004); but is briefly

described here. In 1983, P. elliottii and P. taeda seedlings were planted at a1.8 x 3.6 m

spacing in a 2x2x2 factorial design employed in three blocks. Only one pineland block

was sampled. The method was evaluated on soil from two depths: 0-5cm and 5-10cm.

(The first letters of principal words must be capitalized).


Experimental Sites for Stability Levels, Mineralization and Chemical Analysis

The experimental sites were primarily chosen from a previous stratified random

sampling plan for a large scale research project with the objective of developing a soil C

inventory of the state of Florida (Myers unpublished data), designed to proportionally

sample sites relative to the area's land use pattern. The study area is the USDA

Conservation Area 2 in North Florida. From this sampling plan 13 sites were selected.









Seven sites supported a loblolly pine plantation and 6 sites supported slash pine

plantations. The sites were chosen so that they represented the mean and the range of

soil C reported in the larger data set. "The pinelands category includes north and south

Florida pine flatwoods, south Florida Pine rocklands, and commercial pine plantations

[...]"(FFWCC, 2004).

Soil samples were collected at the sampling locations with 4 cylindrical metal

cores of 20 x 5.8 cm on the surface soil. Samples were measured for bulk density and

moisture content then air-dried and dry-sieved to soil samples less than 2 mm (Myers et

al., unpublished data). All study sites are Spodosols (Aquods). Samples were located in

the north Florida counties of Alachua, Citrus, Clay, Duval, Flagler, Lafayette, St. Johns,

Putnam, Taylor, and Volusia (Table 2-1).

The sampling sites were located on flatwood landscapes; which are predominantly

somewhat-poorly to poorly-drained soils with a seasonally high water table. Loblolly

and slash ecosystems had a common understory of saw palmetto (S. serenoa repens

Small), wax myrtle (myrica cerifera L.), gallberry (ilex glabra L.), brakenfern (pteridium

aquilinum L.), blackberry (rubus sp.), fetterbush (Lyonia lucida Lam.); various grasses

such as bluestem (Andropogon virginicus L.) and wiregrass (Aristida beyrichiana); and

young oaks (quercus sp.) like water oak (Quercus nigra L.); also sweetgum (liquidambar

styraciflua L.), bays (persea sp.), and Florida maple (acer barbatum). Both loblolly and

slash ages range from 10-20 years with most of them in a plantation setting. However,

one loblolly and one slash pine site were natural pine ecosystems.









Laboratory Methods

Sonication method

Soil samples were air-dried in a greenhouse and then passed through a 2 mm

sieve. They were dry-sieved through a horizontal mechanical shaker for 5 min at 75 rpm

using 53-, 150-, and 250-[tm sieves with 100 grams of dry soil at a time (Sarkhot et al.,

2007a).

An ADEC was developed for one block, three curves by size fraction with three

replications of each. Each ADEC was built by placing 10 five-gram subsamples into

beakers with 100 mL of deionized water. Using a sonic dismembrator (Fisher Scientific,

Model 500, Hampton, NH), immersing the sonic probe 10 mm below the surface of the

water, and applying an energy level. Energy levels from 0 to 200 j mL-1 were applied by

using a range of amplitude (20-69%) and time (1-7 min) combinations. A correction

factor, as described by Sarkhot et al. (2007a), was applied. Temperature rise was

controlled by a pulse method (60 s on and 30 s off) (Sarkhot et al. 2007a). In this

manner aggregates in each size fraction were incrementally disrupted. After disruption,

each sample was passed through the same-sized sieve used to obtain the size fraction.

The SOC remaining on the sieve after each sonication represented particulate SOC

(POC) and aggregate OC (AOC) that resisted dispersion. The SOC passing through the

sieve was considered the AOC that was dispersed by sonication. Sieve retentive were

dried in a forced-air oven to 65 to 700C.

Three 0.1 g subsamples of the retentive oven dried material were collected on

the sieve, in turn, placed under a microscope and the numbers of aggregates were

counted with the help of a 1cm x 1cm grid system.









ADE curves for pine ecosystems

Previous studies have investigated soil C by size fraction, so initial analysis for

sonication validation of ADEC was completed by size fractions to be consistent with

those studies (Oades and Waters, 1991; Roscoe et al., 2000; Six et al., 2001; Sarkhot

et al., 2007a). After these preliminary studies it was decided to work on whole soils

having the range of energy that was necessary to disperse aggregates and create the

ADECs. Samples were dry-sieved through a horizontal mechanical shaker for 5 min at

75 rpm using a 53 |tm sieve size with a 100 grams of dry soil (soil size < 2mm) at a time

(Sarkhot et al., 2007a). Samples used were all larger than 53 |tm and smaller than

2000 tm.

Each energy curve was made up of ten points at increasing dispersion energy

levels until all aggregates had been broken down. The curve ranged from 0-153 j mL-1'

this last level was selected based on the sonication validation study described above,

where up to 153 j mL-1 were required to break down most aggregates. In order to set up

a sonic dismembrator it is necessary to supply amplitude (20-90%) and time (2-14 min).

The pulse method (60 s on and 30 s off) utilized was to avoid high temperatures that

could interfere in the measurement (Sarkhot et al., 2007a). The energy output applied

by the sonic dismembrator was given in joules and was internally calculated by the

software. Since the energy output of the machine was calculated for the electrical

energy (using an internal voltmeter) the conversion of electrical to mechanical energy at

the probe tip is not 100% efficient so the energy absorbed by the water was calculated.

The energy dissipated into the suspension was calculated with a correction factor. This









actual energy was calibrated calorimetrically with a Dewar vessel as shown by Schmidt

et al. (1999).

Each of the 13 sites had three ADEC replicates. Five grams were weighed in a

250 mL beaker for each subsample and then the sample was placed on a 250 mL

thermal container and the probe was maintained at a constant depth of 12 mm. 100 mL

of DI water was added slowly avoiding formation of bubbles that could provide a

disruption in the energy applied to soil. The thermal container was then placed in a

sound box, with the probe always immersed at the same depth and avoiding touching

any of the walls of the container. After each sub-sample had been sonicated, to

separate the material and quantify the C released by the broken down aggregates and

the C still in aggregate, the next procedure applied was wet sieving. The content in the

Dewar vessel was poured onto a 53 micron sieve. The AOC and SOC remaining on the

sieve and the SOC that passed through the sieve were measured using loss-on-ignition

(LOI). It was assumed that the energy applied did not disrupt particulate plant material

debris which could result in a transfer of C that does not originate from aggregate

disruption. The great advantage of using an ultrasonic vibration method is that it

provides the opportunity to quantify the amount of energy applied to the soil in

suspension and then it is possible to quantify the C that comes from that specific

aggregate stability level.

Statistical analysis

An SOC analysis was ran to determine the distribution of population and sample.

Variables SOC%, pH and forest floor were log normal. Outlier analysis was run and

then an ANOVA was utilized. In order to compare means between energy level and

species (categorical variables) for AOC g g-1 and AOC as percent of SOC (dependent









variables) an analysis of covariance (ANCOVA; STATISTICA 9.0, Stat soft, Inc.; Tulsa

OK) procedure was used for each dependent variable. The covariate, TC was utilized to

account for the variability that occurred between the sampling sites. Sampling

distributions were log normal both for AOC (g g-1 soil) and AOC as a percent of SOC

(AOC %), but for the purposes of the figures and tables units were back transformed

from log(10). Differences were considered significant if p < 0.05. If significant main effects

or interactions were found, post hoc multiple mean comparisons were evaluated using

Tukey's procedure.

Results

Species differences were found in the SOC percent of the population and

sample. Loblolly pine ecosystems were found to have a significant higher SOC content

than slash pine ecosystems for population and sample (44%,130%) respectively (Table

2). However, pH and litter were similar for both population and sample.

Objective 1. Sonication Method Evaluation

Aggregate count was completed under a dissecting microscope (Fig. 2-1). The

number of aggregates per gram of soil increased with decreasing aggregate size. The

2000 to 250 pm soil size fraction was dispersed by 100 j mL-1; while the two smaller size

fractions required more than 200 j mL-1 for complete dispersion; however, at about 150 j

mL-1 the curve began to asymptote revealing stabilization. This study shows that

aggregates were dispersed by ultrasonic energy. Observations of aggregates aided by

microscopy were consistent with mycorrhizal hypha and fine roots being incorporated

into aggregates. Other materials among the aggregates were fecal pellets, root

epidermis and insect carcasses.









Objective 2. Contrasting Aggregation in Loblolly and Slash Pine Ecosystems

ANCOVA for the variable, AOC (g g-1 soil), revealed that the covariate explained

a significant amount of the variability (p<0.001), while the main effect, pine species

(categorical variable), was significant (p<0.001). The dispersion energy was significant

when used either as a continuous or categorical variable (p<0.001) (Fig. 2-2a, Table2-

2). On average over all energy levels, loblolly pine AOC was 50% higher than that under

slash pine. At the highest dispersion energy, loblolly, on average had 41% higher AOC

(Fig. 2-2a, Table 2-3).

ANCOVA for the variable %AOC as % of SOC suggests that the covariate explained a

significant amount of the variability (p <0.001); while the main effect, species

(categorical variable), was significantly different (p < 0.001), and the dispersion energy

was also significant when used either as a continuous or categorical variable (p<0.001).

In this case slash ecosystems revealed a significant 4.1% AOC increase, with an

average AOC of 20.5 % (1.6 std. error) for slash and 16.4% (1.2 std. error) for loblolly.

Initial water stable aggregates were equal for both ecosystems (Fig. 2-2b, Table 2-3).

Discussion

Objective 1. Sonication Method Evaluation

Aggregate dispersion curves have only recently been used to describe

qualitatively and quantitatively the amount of AOC in a soil. Microscopic evidence

verified that sonication was dispersing soil aggregates, therefore the method was

deemed useful for the purpose for which it was employed. The reason for higher

aggregate stability in the 150-250pm, and the higher dispersion energy required for total

aggregate dispersion, needs to be explored both for mechanism of stability and

potential for C sequestration.









Objective 2. Contrasting Aggregation in Loblolly and Slash Pine Ecosystems

Our findings suggest that AOC in g g-1 soil was clearly greater in sandy surface

horizons under loblolly than under slash pine ecosystems. This result could be related

to the fact that there was approximately 30% more SOC reported in loblolly pine

ecosystems when compared to the SOC in slash pine ecosystems (Table 2-2). One

plausible explanation for this difference in SOC is that there is greater organic C

addition from fine roots and leaf litter under loblolly.

Previous studies support a possible explanation about difference in SOC added

to loblolly vs. slash ecosystems. A number of studies have indicated that loblolly pine

produce more leaf area (Colbert et al., 1990; Jokela and Martin, 2000; Will et al., 2001;

Xiao et al., 2002; Burkes et al., 2003), more fine roots (Burkes et al., 2003; Nowak and

Friend, 2006) and a larger forest floor (Polglase et al., 1992) than does slash pine,

particularly in plantation environments. Since SOC is a function of rate of input versus

rate of mineralization, loblolly clearly inputs more organic C into the soil. Even under

equal rates of mineralization, this suggests that loblolly should have higher SOC levels

as long a mineralization rate under loblolly are not less than slash pine.

Our observations of aggregation on surface sandy horizons of Spodosols in

North Florida revealed that soil aggregates were bound by mycorrhizal hyphae, fine

roots, and microbial and fungal debris. These observations are consistent with those of

Kay (1997) and Degens et al. (1996) in sandy soils. These observations combined with

higher fine-root biomass and total SOC under loblolly pine plantations are sufficient to

explain the increased AOC under loblolly. This knowledge enables managers to use the

species that fit their needs whether it is to have more SOC added to the soil, or more

crown cover for habitat management.









The slight increase of the %AOC under slash pine plantations compared to

loblolly (4.1% increase on average) indicated that slash pine systems may have a slight

advantage over loblolly systems in aggregating soil C. We were unable to infer the

reasons for this difference; however, more importantly, data indicated that greater than

40% of total SOC was incorporated into soil aggregates under both slash and loblolly

pine ecosystems. Sarkhot et al. (2007a) also found that approximately 45% of total SOC

was incorporated in aggregates. These results suggest that if AOC is physically

protected in sandy soils of north Florida, then promoting loblolly pine and soil

aggregation could be potentially important management objectives for increasing soil C

sequestration in these soils.

Due to their dominance in Florida ecosystems, pine ecosystems play an

important role in the conversion of atmospheric CO2 into the SOM pool to sequester

SOC. Loblolly pine ecosystems in north central Florida have 30% more SOC than slash

pine ecosystems. This species benefit translates into a C increase that would be equal

to an increase of about three million dollars of C02 in C credits for north central Florida;

based a price of twelve dollars per ton of C02 (Clear Sky Solutions, 2008). Clearly the

selection of loblolly over slash pine on flatwood soils affects more than just forest

productivity.













3000


2500
0
U,
S2000


0 1500
o

c 1000
0)
m 500


0


0 50 100 150 200


80000

70000
(D
60000 -
(D
0
50000 c

40000
(n
30000 I

20000 g

10000 c
3


250


Dispersion Energy (J/mL)


*2000mm-250Jpm


I250pc-150Jpm


A150Jpc-53pm


Figure 2-1. Number of aggregates per gram of soil as affected by level of dispersion
energy applied and by the soil size fraction.














0.012

0.010-

0.008

0.006 -

0.004-

0.002

0.000 -


Effect
Species
energy level
species energy level


p value
0.0000
0.0000


0 20 40 60 80 100 120 140 160 180
Dispersion Energy (jrmL)
-- Loblolly FrCI g gg1 soil
S- Slash C g -1 s
- Slash,<'.Cgg soil


Effect p value
Species 0.0000
energy level 0.0000
species* energy level 0. 3-1.


0 20 40 60 80 100 120 140 160 180
DispersionEnergy (mrrL)

-- Loblolly ACi:C as % of : CiC
-- Slash f OC as % of: LCJ



Figure 2-2. Aggregate organic carbon (AOC) as a function of dispersive energy applied
to the soil when expressed as a) %AOC (% of Soil Organic Carbon) by
species and b) AOC (g g-1) soil by species.











Table 2-1. Sample characterization of pine sites located in North Central Florida.


Dominant
County Species
Alachua L
Duval L
Lafayette L
St. Johns L
Taylor L
Taylor L
Flagler L
Clay S
Clay S
Lafayette S
St. Johns S
Clay S
Volusia S


Latitude
29.711580
30.465195
30.017988
29.858920
29.866656
29.836545
29.486148
30.159932
29.891132
30.017128
30.088603
29.849814
29.206476


Longitude
-82.343671
-81.530360
-83.302321
-81.361572
-83.418372
-83.428403
-81.317456
-81.945865
-81.853681
-83.154864
-81.534517
-81.663151
-81.512600


Taxonomic class
hyperthermic Ultic Alaquods
thermic typic Alaquods
thermic Aeric Alaquods
hyperthermic Ultic Haplaquods
thermic Alfic Alaquods
thermic Spodic Psammaquents
hyperthermic Aeric Alaquods
thermic Ultic Alaquods
thermic Aeric Alaquods
thermic Ultic Alaquods
hyperthermic Typic Alaquods
thermic Aeric Alaquods
hyperthermic Aeric Alaquods


Table 2-2. Characterization of population and sample, significant differences (p<0.05)
by species.
Species Soil Organic Carbon % pH Forest Floor (Kg m2)
Population Means (Std. Error)
Loblolly 1.72 (0.2) ** 3.53 (0.09) 1.52 (0.49)
Slash 1.20 (0.09) 3.68 (0.06) 1.92 (0.45)


c


Loblolly 2.36 (0.26)**
Slash 1.02 (0.15)*
Significantly lower means
** Significantly higher means


ample Means (Std. Error)
3.48(0.17)
3.44(0.10)


4.07 (1.55)
2.23 (0.35)









Table 2-3. Summary of aggregate organic carbon (AOC) as influenced by tree species.
Statistical differences are outlined for lower and ** for higher means.
Species summary

Slash Loblolly
Mean std. error Mean std. error
AOC (g g-1 soil) 0.0065 0.0011 0.011** 0.0008
AOC (% of Soil
Organic Carbon) 60 ** 3 53 1


AOC (g g-1 soil)
AOC (% of Soil
Organic Carbon)


Highest dispersion level
0.0022 0.0002

20.5097** 1.6502


0.0033 ** 0.0003

16.4116* 1.2633









CHAPTER 3

DOES AGGREGATION PROTECT C IN VERY SANDY SURFACE SOILS?

The two major biological fluxes of C dioxide in nature are photosynthetic fixation

and global respiration. They cycle about 7% of atmospheric C annually. Moreover, 15

years of photosynthetic fixation, without renewal by respiration, would cause the

exhaustion of the C dioxide in the atmosphere (Sylvia et al, 2005). The large pool of

global soil C is susceptible to anthropological changes that occur with land use that can

result in positive or negative changes. Further understanding of how C is stored in soils

is necessary in order to determine the possible management strategies that maintain

soils as a major C sink. These strategies should be a function of soil characteristics.

Soil is a C sink when it can protect the SOC from decomposition and when C

input occurs at a rate faster than C release. It is thought that SOC is protected within

aggregates physically, chemically and physiochemically (Golchin et al., 1994; Blanco-

Canqui and Lal, 2004). Soil organic C turnover rates have been found to decrease from

macro- to microaggregates, therefore suggesting an increasing protection of SOC by

microaggregates (Besnard et al., 1996); an idea supported by Franzluebbers and

Arshad (1997) as well as by Sainju et al. (2003). It has been revealed that C storage

provided by macroaggregates is greater in quantity but transient in terms of physical

protection (Tisdall and Oades, 1982). Wild (1988) documented that the quality of the

SOC in microaggregates is biochemically recalcitrant with low turnover rates.

Physical protection by aggregates is the incorporation of SOM within macro and

microaggregates (Tisdall and Oades, 1982; Golchin et al., 1994; Jastrow, 1996); yet the

ability of soils to protect C through aggregation is also dependent on soil texture and

time. SOC residence times in macro- and microaggregates differ depending on the









physiochemical attraction between mineral and organic particles, and the location of the

organic material within the aggregate (Emerson, 1959). Buyanovsky et al. (1994)

worked with agricultural soils in a 4 yr decomposition study and found that turnover

rates were 1-3 yrs for macroaggregates and about 7 yrs for microaggregates. On the

other hand, Skjemstad et al (1990) found no remarkable difference between the macro

and micro-aggregates of an Australian sandy soil. Christensen (1987), working with

loamy sand and sandy loam soils, found no physical protection by aggregation.

Sandy soils make up a soil subgroup that, while having been studied for micro

and macroagregation, (Sarkhot, 2007a,b; Tisdall and Oades, 1982; North, 1976;

Christensen, 1992; Cambardella and Elliot, 1993; Six et al., 2001; Swanston et al.,

2005), have not been well evaluated as to its ability to protect C through aggregation

(Sarkhot et al., 2007a).

As with aggregation in general, the amount of DOC contained in aggregates and

the mineralizability of the DOC in aggregates has been poorly understood, particularly

for sandy soils. Dissolved organic matter is a controlling factor in soil formation. DOC in

forest floors have been hypothesized to be generated from leaching and microbial

decay of humus (McDowell and Likens, 1988). Fluxes of DOC from the forest floor that

leaches into the mineral soil were estimated to represent 35% of the annual litterfall

(Guggenberger and Zech, 1993; Currie et al., 1996; Michalzik and Matzner, 1999;

Solinger et al., 2001). Previous incubations showed 5-93% of DOM in soil solutions to

be potentially microbially degradable (Kalbitz and Kaiser, 2008; Jandl, 1999; Kalbitz et

al., 2000; Sachse et al., 2001; Kalbitz et al., 2003; Don and Kalbitz, 2005; Kiikkil et al.,

2006). Dissolved organic matter with large amounts of C was found to be rich in









aromatic functional groups and poor in carbohydrates (Quails and Haines, 1992; Jandl

and Sollins, 1997; Jandl and Sletten, 1999; Volk et al. 1997; Kalbitz et al., 2003; Kiikkila

et al., 2006). As a result, a large amount of dissolved organic matter that percolates to

the mineral soil appears to be stable (Froberg et al., 2006); supporting the DOC

mineralizability numbers on the lower end of the range. However, if DOC is a stable

source of C then if it is within aggregates it could be another pool of protected C.

The strength with which aggregates are held together describes their ability to

withstand disturbance. Aggregate dispersive energy (ADE) has been used to

characterize aggregate strength in a variety of soils (North, 1976; Christensen, 1992;

Cambardella and Elliot, 1993; Six et al., 2001; Swanston et al., 2005). One would

assume that ADE would play a role in protection of soil aggregate C; however, the

relationship between ADE and C mineralization within aggregates has not been studied.

The overall objective of this study was to evaluate the role that aggregation plays

in SOC protection in the very sandy soils of the Lower Coastal Plain found under loblolly

and slash pine ecosystems. This was achieved by addressing 2 aims. The first aim was

to determine if ADE and ecosystem cover type (slash vs. loblolly) were controlling

factors in SOC and AOC accumulation and mineralization. The central hypothesis was

that ADE expressed more control than species because the strength of aggregates

would inhibit the release of AOC, while minimizing the entry of decomposing

microorganisms. This conclusion was drawn from aggregation theories that proposed

that encrustation of SOM in microaggregates is the principal pathway of C

sequestration.









The second aim was to Identify SOC chemical characteristics that could explain

differences at AOC stability levels and SOC chemical characteristics that could explain

differences in SOC mineralization between ecosystems cover types. We hypothesized

above that in North Florida's sandy soils; the mineralizability of AOC would be a function

of the ADE but not of ecosystem cover type. We hypothesized here that the chemical

fingerprint of SOM via DRIFTS would back up those suppositions by showing

differences in the SOC fingerprints where mineralization differences were identified.

loblolly or slash pine ecosystems.

Methodology

Experimental Sites and Field Sampling

The experimental sites were chosen from a previous stratified random sampling

plan for a large scale research project with the objective of developing a soil C inventory

of the state of Florida (Myers, unpublished data), designed to proportionally sample

sites relative to the land use pattern. The study area used for this research was NRCS

Conservation Area 2 located in North Florida. From this set of 55 sampling locations, 13

sites were selected. Seven sites supported a loblolly pine (Pinus taeda L.) ecosystem

and 6 sites supported slash pine (Pinus elliottii Engelm.) ecosystems. Originally 7 slash

pine sites were chosen but during the study it was discovered that one site had been

misclassified and was discarded. The sites were chosen so that they represented the

mean and the range of soil C reported in the larger data set. Soil samples in the surface

20 cm were collected at the sampling locations with 4 cylindrical cores of 30 x 5.8 cm.

The forest floor was sampled at the same sites with a 23 cm2 litter sampler. Sampling

sites were located in the north Florida counties of Alachua, Citrus, Clay, Duval, Flagler,

Lafayette, St. Johns, Putnam, Taylor, and Volusia (Table 1). Most study sites were









Spodosols (Aquods) (Table 3-1). The climate of the region was hot and humid with an

average yearly precipitation of 113.3 cm, and yearly average minimum and maximum

temperatures of 140C and 27C, respectively (Southeast Regional Climate Center,

2010).

The sampling sites were located on Flatwood landscapes; which are

predominantly somewhat poorly to poorly-drained soils with a seasonally high water

table. Loblolly and slash ecosystems had a common understory of saw palmetto (S.

serenoa repens Small), wax myrtle (myrica cerifera L), gallberry (ilex glabra L.),

brakenfern (pteridium aquilinum L.), blackberry (rubus sp.), and fetterbush (Lyonia

lucida Lam.). Various grasses were common, such as bluestem (Andropogon virginicus

L.) and wiregrass (Aristida beyrichiana). Perennial woody species included young oaks

(Quercus sp.) like water oak (Quercus nigra L.), sweetgum (Liquidambar styraciflua L.),

bays (Persea sp.), and Florida maple (acerbarbatum). Both loblolly and slash in ages

ranged from 10-20 years. All but one of each species was in a plantation setting.

However, one loblolly and one slash pine site were natural pinelands.

Laboratory Methods

Soil Samples obtained from the field were measured for moisture content, and

then air-dried in a greenhouse before passing through a 2mm sieve. Bulk density was

calculated by a core method (Mclntyre, 1974). Soil pH was measured by the protocol of

Thomas (1996). A comparison of the 13 sites used in this study to the total sites

available for sampling from the larger study show that these study sites are

representative (Table 3-2).

One-hundred grams of air-dried and sieved samples were dry sieved through a

53 microns sieve for 5 min at 75 rpm using a horizontal mechanical shaker (Sarkhot et









al., 2007a). Samples used in analyses were all larger than 53 pm and smaller than

2000 pm (2mm), this would include macro and microaggregates.

Mineralization

An incubation study was utilized to determine if the ADEC was a controlling

factor in AOC mineralization/protection. To that end microcosm for each soil sample

were constructed, where each microcosm contained 20 grams of soil, 0.05 grams of soil

inoculum and a liquid base trap of 20 mL of 0.25 M NaOH. The soil inoculum was added

to provide a microbial population that could have been eliminated due to the sonic

energy application. Each site used in this study was represented by nine microcosms

that reflected three replications of three treatments. The treatments were no energy

applied, energy applied at mid-range of the ADE curves (ADEC), and energy applied at

the higher level of the ADEC (0, 140, and 260 j mL-1 respectively).

Since the sonication process required soil saturation, subsequent to the

application the ADE each sample was filtered through a vacuum system at a pressure

of 0.33 bars using a 22pm membrane (Schwesig et al., 2003).Each soil sample (20 g)

were placed in a microcosm to begin the incubation study. The 0.22 pm membrane was

used to minimize the amount of DOC removed from the soil. The DOC was measured

with a Shimadzu TOC-VCPH Analyzer (Shimadzu Scientific, Columbia, MD.).

The microcosms were placed in an incubator at 35C. The water content was

maintained by weight, and the base traps were changed periodically at weeks 4, 7, 12,

15, 19, 21, 25, and 29. When base traps were changed each sample was opened and

its atmosphere was replaced by ambient air. The soil respiration, or alkaline trap

method, was utilized to determine the rate of C dioxide (CO2) evolution (Anderson,









1982). In this method the trap functioned as a sink for evolved C02 from the soil. The

0.25 M NaOH base solution was titrated with 0.1 M hydrochloric acid (HCI).

The following formula was utilized to measure C (mg) from respired C02

measured through titration from the SOC and DOC (see below) incubation studies:

C (mg) respired = [(B-T) x M x E]/ DF

Where B is the mL needed to titrate blank aliquot. T is the volume in mL needed

to titrate the sample aliquot. M is the normality of the acid, in this case HCI. E is the

equivalent weight, in this case the equivalent weight of C (E=6) was utilized. DF is the

dilution factor of the base trap (Anderson, 1982).

Dissolved organic carbon (DOC)

In order to determine the mineralization rates of the DOC solutions, a

mineralization study was initiated where 60 mL of soil solution were poured into a 250

mL Erlenmeyer flask which had 0.05 g of soil inoculum. The average of C in 60 mL was

0.54 mg with a standard error of 0.26. The C02 evolved was measured as detailed

above. All microcosms were sealed and placed in a dark incubator at 350 C for 84 days.

Samples were periodically shaken manually. Due to the small portion that DOC

represents of the TOC, and due to the lack of differences between species and

dispersion level, the mean DOC mineralization rate was used to calculate DOC

mineralized over time and this was added to the mineralization of SOC to provide the

total C mineralized from the TOC.

Fourier transformed infra-red reflectance (FTIR) spectroscopy

Fourier transform infrared spectroscopy's utility is based on its sensitivity,

spectral precision, reproducibility and fast spectral acquisition time (Johnston et al.,

1996). This method was utilized to describe organic C chemistry present by: species,









incubation periods and dispersion energy levels. The chemical composition of species,

dispersion levels, and mineralization progression was investigated using DRIFTS in the

mid-IR (4000-400cm-1 or 2500-25000 nm). Spectra were run on a DigiLab FTS-7000

FTIR instrument (Varian, Inc., Walnut Creek, CA). Samples were placed in a Pike

AutoDiff 60-cup auto sampler (PIKE Technologies, Madison, WI) (Reeves, personal

communication). Ground samples were placed in the auto sampler cups and scanned

using a KBr beam splitter and deuterated triglycine sulfate (DTGS) detector. Spectral

subtraction of ashed samples from un-ashed samples was utilized to emphasize the

organic composition; this subtraction was completed utilizing GRAMS/AI software,

Version 7.02 (Thermo Galactic, Salem, NH) (Sarkhot et al., 2007a). Spectrum by

species were averaged from the ash-subtracted spectra from each site, and a

discriminant analysis was performed to quantify how different was the separation

between ash-subtracted species, mineralization days, and dispersion energy levels.

Discriminant analysis was completed using a modified SAS program adapted to

examine spectra (Reeves and Delwiche, 2008, Sarkhot et al., 2007a).

Statistical Analysis

Two separate analyses were run, one for the SOC incubation study and another

for the combination of the SOC and DOC called TOC. The results of the statistical

analyses for both differ only in the last three time periods of the incubation study. These

results suggest that DOC can be an influential fraction of the TOC turnover especially

towards the end of the incubation study, since the addition of the DOC rate influenced

the results from having species that were significantly different (without DOC addition)

to statistically insignificant species. The statistical results presented here were for the

analysis of the TOC mineralization. The soil and DOC mineralization rates were









combined to define TOC mineralization and a GLM repeated measures analysis was

ran in STATISTICA (Statsoft, Inc. 9.0; Tulsa OK). The main effects were Species and

Energy Level. Differences were considered significant if p < 0.05. If significant main

effects or interactions were found, Tukey's post hoc multiple mean comparisons

procedure was utilized.

An ANCOVA was applied to the total DOC data and the DOC released at the

three sonication energy levels. The covariate in these analyses was TOC (mg g-1 soil),

while the main effects were Energy Level and Species (Table 3-3). Differences were

considered significant if p < 0.05. If significant main effects or interactions were found,

Tukey's post hoc multiple mean comparisons procedure was utilized. For the purposes

of figures and tables all log(1o) means resulting from statistical analyses were back

transformed.

Results

Objective 1. Determine if Aggregate Stability Levels and Ecosystem Cover Types
were a Controlling Factor in SOC and AOC Accumulation and Mineralization.

Our results revealed that the ADE level of soil aggregates was not a factor in

protecting AOC (Fig. 3-2). Even though periodic specific mineralization rates of TOC

under slash pine was not statistically different from loblolly pine, the cumulative effect

over time was significant resulting in higher specific C mineralization rates in slash pine

influenced soils; approximately 8% higher than found in soils influenced by loblolly pine

(Fig. 3-1 a).

Loblolly pine soils released more DOC from the aggregates than did slash pine

soils as ADE increased (Table 3, Fig. 3-3). At the highest ADE, loblolly pine soil

released an average of 36% more DOC in mg g-1 soil than did slash pine soils. This is









attributed to the fact that loblolly pine soils had higher amount of TOC. Dissolved

Organic C incubation results revealed that by day 84 there was no significant effect of

the dispersion energy or species on DOC mineralization rate (Table 3-3).

Objective 2. Identify SOC Chemical Characteristics that could explain Differences
at AOC Stability Levels and Mineralization between Ecosystems Cover Types.

Discriminate analysis produced a poor separation of ash-subtracted dispersion

energy levels as evidenced by R2s of 0.07, 0.00, and 0.05, respectively (Table 3-5).

However, DRIFTS analysis identified distinctly different spectra for slash pine and

loblolly pine influenced soils. Spectral bands between 2000 and 1200 cm-1 in the non-

ashed samples (Fig. C-1, C-2) are related to silica, revealing soils with high mineral

matter (Reeves III and Smith, 2009). Yet, comparison of the average of ash-subtracted

spectra for both species supported the higher organic matter in loblolly soils (Figure 4),

and indicated that the functional group assemblages were also influenced by

ecosystems. Figure 5 represents the difference of ash-subtracted loblolly minus slash

averaged spectra. Slash appeared to have more aliphatic C-H indicated by the bands

between 2900-3000 cm-1 (Reeves III and Smith, 2009; Madari et al., 2005). Loblolly

appeared to have more aromatic carboxylic acids as indicated by the bands around

1600-1700 cm-1 (Reeves III et al., 2006; Celi et al., 1997).

Discriminant analysis for ash subtracted (R2 = 0.875, SE = 0.176) and for non-

ash subtracted spectra (R2 =0.977, SE = 0.176) showed that ash-subtracted spectra

provided a stronger contrast; indicating that organic compounds were responsible for

the differences seen between species, rather than mineralogy (Table 3-4). Therefore,

ash-subtracted spectra were used for subsequent analyses.









Ash-subtracted spectra for week 29 and week 1 from the incubation study provided a

distinct difference. Discriminant analysis produced a high R2 (Table 3-6) and indicated

that mineralization had affected each species differently. Figure 3-6 shows loblolly pine

ecosystems experienced a large decrease in reflectance in the wave number range of

3000 to 3600 cm-1. This is a general region for OH and typical for humic acids. The

triplet band at 1800 to 2000 cm-1 is due to an increase in silica over time as OM

decreases. Finally, an accumulation of aromatics occurs with the degradation of the

humic substances. Alternatively, the subtraction spectra between week 29 and week 1

for slash pine ecosystems also appeared to have an increase in C-H aliphatic carbon,

indicated by the bands around 2900-3000 cm-1. Figure 3-7 reveals large peaks at 3000-

3500 cm-1 (peaks 3531, 3460, 3170, and 3054) and a specific peak at 1295 cm-1 which

are attributed to aliphatic materials. The triplet band at 1800 to 2000 cm-1 is due to an

increase in silica over time as OM decreases. Overall, there is an accumulation of

aliphatic materials in slash pine over time; on the other hand, loblolly shows an

accumulation of humic materials and aromatic material over time as other OC degrades.

Discussion

Our initial hypothesis was that ADE would be a controlling factor in the

accumulation and protection/sequestration of AOC; while ecosystem cover type would

not be a factor. Our results from the TOC incubation study suggest no effects of

aggregate dispersive energy on turnover rates, and an effect between ecosystem cover

types; rejecting our hypothesis (Fig. 3-2).

Total organic C mineralization rates were a product of the combination the DOC

and SOC separate incubations. In sandy soils under pine ecosystems of slash and

loblolly pine there was no significant difference in the TOC mineralization rates of









aggregates at different stability levels. Findings support the contention held by some

that SOC mineralization is more related to the stability/liability of the SOC than to the

physical protection of aggregation (Christensen, 1985, 1987; Buyanovsky et al.1994).

Our results conflict with the concept that the formation of macroaggregates facilitates

the accumulation of organic matter, and given favorable conditions physical protection is

promoted (Jastrow, 1996). The reason why our results conflict with this concept may be

that favorable conditions are not promoted in Florida's soils with characteristics like: <10

cmolc kg-1 cation exchange capacity, and less than 5% silt plus clay (Carlisle et al.,

1981,1988, 1989).

While we did not expect species to have a control on mineralization; the SOC

under the two different ecosystem cover types did mineralize differently (Fig. 3-1). The

soil under slash pine had higher specific mineralization rates, even though the total

mineralization under loblolly pine was greater due to a significantly larger SOC content

under that species (Table 3-2). This is the first time that SOC mineralization from soils

influenced by these two ecosystems has been contrasted; both in terms of

mineralizability and DRIFTS spectra for chemical differences. The lower mineralization

rates, combined with the higher organic matter inputs under loblolly pine explain the

higher SOC levels found in the previous chapter.

Mid-infrared spectroscopy has been found capable to predict soil properties like

organic and total C (Minasny et al., 2009). Mid-infrared spectra were utilized to analyze

relationships throughout the incubation study. This method identified chemical

differences in the samples from the incubation study between: dispersion energy levels,

pine ecosystems and incubation periods. Results revealed no irrefutable difference









between dispersion energy levels or stability levels measured in the incubation study.

On the other hand, definite differences were found in respect to species chemical

composition. Spectra comparisons between the two soils under different ecosystems

reflect the greatest distinction between ecosystems (Fig. 3-3, 3-4). More aliphatic C-H

present in slash could be related to waxes or methyl groups which are more

bioavailable than the larger amount of aromatic carboxylics present in loblolly pine

ecosystems. Differences found comparing incubation times were also evident only for

week 1 and week 29 (last week of the incubation). The last week of the experiment

revealed a change in the patterns of the chemical composition: a decrease in humic

acids and increase in aromatics of loblolly, and an increase in the aliphatic C-H of slash

over time (Fig. 3-5, 3-6).

Another significant finding was related to the DOC incubation study, in that it was

identified as an important component of TOC turnover. Our findings are congruent with

Zhao and Kalbitz (2008); who did a study in China in forested Typ-lshumisol soils

sampled to a depth of 20 cm and revealed an average mineralization of 0.06 g kg-1 day-

of DOC, which was similar to our findings of 0.05 g kg-'day-1 of DOC mineralization rate.

Schwesig et al (2003) studied DOC mineralization released by water extractable C

coming from the surface 20 cm of spodosols dominated by Norway spruce (Picea

abies); however, they found a total mineralization rate over 97 days of 0.0004g kg-1. Our

results are similar to Zhao's measurements and our findings suggest that DOC is an

important component of aggregation in surface spodosols; the evidence is shown in

Figure 2 where as increasing ultrasonic energy is applied there is an increasing release

of DOC.









In the end, our findings warranted rejection of our hypothesis. No differences

were revealed between energy dispersion levels, a finding supported by both incubation

and mid-IR analyses. However, findings suggest a significant difference in the C quality

between species. The quality of needles and roots of loblolly vs. slash has not been

widely studied and these data imply that more detailed work on the quality of organic

matter inputs (roots, leaves, branches) will be necessary in order to quantify C cycling

between these species. Comparisons between cover types are important in order to

understand how SOC is related to the forest floor material and, ultimately, its influence

in soils as a sink of C.








80
a)
70

60 week29
Sweek25
50
o week 21
40 weekl9

30- week 15
Sweek12
20 Mweek7

10- week

0



b) 90
80
70 _.week 29

60 week25
e 50 -- '.eek 21
40 week 19
E week 15
Sdweek 12
20
U week 7
10 E week 4
0
0 58 153
Dispersion energy J ml-1


Figure 3-1. Comparison of the carbon (C) mg g-1 soil by dispersion energy levels (J mL
1) by week for: a) loblolly and b) slash pine ecosystems. There were no
significant differences found between the ADE levels for each period.











0.45 -

0.40 -


0 0.35-

S0.30 -
E
0 0.25-

P- 0.20 -


Dispersion energy j/mL
S Loblolly pine
-- Salsh pine


Figure 3-2. Total dissolved organic carbon (DOC) dispersed from the soil when three
dispersion energy levels were applied via sonication. Significant differences
are illustrated at dispersion energy of 153 j mL1.


-0.24
-0.4...


1mON


2000


3000


40001


- Salsh
.......... Loblolly


Wave number (cm1)


Figure 3-3.Diffuse reflectance infrared Fourier-transform spectra fingerprint of the
average ash subtracted spectra.


0.15 -

0.0 --------------------


50o0










0.12-


0.10




1U.06


0.04


0.02 -


0.00
0 1 O 200 3000 4000 5000
Wave number cmn"1)


Figure 3-4. Diffuse reflectance Fourier-transform spectra of the difference between
spectra of ash-subtracted loblolly minus slash pine influenced soils. In this
graph it is possible to see slash pine ecosystems appear to have more
aliphatic C-H indicated by the region around 2900-3000 cm-1 than loblolly
pine; and loblolly pine ecosystems reveal more aromatic carboxylic acids
present at wavelengths 1600-1700 cm-1.











0.04 -


0.02

.00

-0.02-










f1 20 &30 4000 504G@
Wave nu m ber em -1)


Figure 3-5. Spectral subtraction between weeks 29 and 1 for loblolly pine influenced
soils. Absorbance located above 0.00 indicates an increase of week 29 over
week 1 and absorbance below 0.00 indicate the opposite. This figure reveals
-0.12 ----------------------








that in the wave number range of 3000 to 3600 cm-1 in week 1 there was
more OH and humic acids. The triplet band at 1800 to 2000 is an increase in
silica over time as organic matter decreases










0.12 -


0.10 -


8 o.0


8 0.104
k


S1.02



-0.02 -

-0.04 .....
0 10 2M 3001 4000 500
Wave number Ccmn1)


Figure 3-6. Spectral subtraction between weeks 29 and 1 for slash pine influenced soils.
Absorbance located above 0.00 indicates an increase of week 29 over week
1 and absorbance below 0.00 indicate the opposite. Wave numbers between
1600 and 400 reveal over time increase in the CH, OH and aliphatic
materials, and the large peaks at 3000-3500 and a specific peak at 1295 are
also an increase over time attributed to aliphatic materials.









Table 3-1. Description of sample sites


Dominant Species
L
L
L
L
L
L
L
S
S
S
S
S
S


Latitude
29.71158008
30.46519541
30.01798816
29.85892000
29.86665683
29.83654543
29.48614818
30.15993245
29.89113266
30.01712800
30.08860306
29.84981430
29.20647699


Longitude
-82.34367117
-81.53036099
-83.30232129
-81.36157200
-83.41837283
-83.42840383
-81.31745613
-81.94586597
-81.85368181
-83.15486400
-81.53451760
-81.66315102
-81.51260040


County
Alachua
Duval
Lafayette
St. Johns
Taylor
Taylor
Flagler
Clay
Clay
Lafeyette
St. Johns
Clay
Volusia


Table 3-2.Population and Sample means with corresponding standard errors for total
carbon (TC), soil organic carbon (SOC)
Species TC % SOC % pH Forest Floor (Kg m^2)
Population Means
loblolly 2.08 (1.05) 2.00 (1.04) 3.70 (0.74) 3.34 (3.62)
Slash 1.57 (1.06) 1.57 (1.06) 3.74 (0.42) 2.72 (2.79)
Sample Means


loblolly
Slash


2.45 (0.68)
1.68 (1.51)


2.45 (0.68)
1.67 (1.52)


3.5 (0.45)
3.66 (0.57)


3.83 (2.56)
3.74 (3.80)


Table 3-3. Total Dissolved Organic Carbon mg g-1 soil with corresponding standard
errors and species comparisons. Significant differences (p<0.05) by species
are indicated by a for lower and ** for higher means.
J mL-1 Slash Loblolly
0 0.17748 (0.002) 0.19561 (0.021)*
58 0.22793 (0.024) 0.26372 (0.028)**
260 0.25140 (0.028) 0.39023 (0.045) **

Table 3-4. Discriminant analysis for the separation of species from spectral analysis
displayed by R2 and Residual Mean Squared Deviation (RMSD).
R2 RMSD
ash-subtracted 0.875 0.176
non ash-subtracted 0.977 0.075


Taxonom ic class
hyperthermic Ultic Alaquods
thermic Typic Alaquods
thermic Aeric Alaquods
hyperthermic Ultic Alaquods
thermic Alfic Alaquods
thermic Spodic Psammaquents
hypertherm ic Aeric Alaquods
thermic Ultic Alaquods
thermic Aeric Alaquods
thermic Ultic Alaquods
hyperthermic Typic Alaquods
thermic Aeric Alaquods
hypertherm ic Aeric Alaquods









Table 3-5. Discriminant analysis for spectral separation ash-subtracted of energy level
is displayed by R2 for dispersion energy level by species
Dispersion energy loblolly slash
Oj mL-1 0.57 0.2
58 j mL-1 0.05 0.47
153 j mL-1 0.05 0.48

Table 3-6. Discriminant analysis for mineralization study of SOC by species with R2 and
Residual Mean Squared Deviation (RMSD).
loblolly slash
week R2 RMSD R2 RMSD
1 0.8 0.09 0.85 0.08
15 0.77 0.23 0.78 0.23
21 0.69 0.23 0.7 0.22
29 0.87 0.18 0.85 0.19









CHAPTER 4
SYNTHESIS

Florida's pine lands are in decline; nevertheless they are a landuse of key

importance. Although in decline, pine flatwoods comprise the most extensive Florida

ecosystem covering approximately 50% of the land area (Florida Forest Stewardship,

2006). Total organic C is known as a key factor to the quality of Florida's sandy soils.

Knowledge about aggregate distribution and strength are crucial to the dynamics of

water and air movement through soil, which in turn affects soil quality and its

productivity (Blanco-Canqui and Lal, 2004). A current issue is the capability of soil

aggregates to protect C from transformation by mineralization, due to the potential of

soils to protect C from evolving into CO2 which affects directly our climate change

problems.

The first gap of knowledge filled by our study was the sonication method

evaluation. Our results revealed microscopic evidence that sonication successfully

disperses soil aggregates. The sonication method was deemed adequate to disrupt

aggregates without affecting chemical composition of the organic matter. However, in

order to fully understand this method, further studies on its ability to break young

particulate organic matter fractions are still necessary.

Our study outlines main differences between loblolly and slash pine ecosystems

in terms of total SOC, as percent and per gram of soil. SOC% was in both cases higher

in loblolly than slash pine ecosystems. Although our sample was small it still was

representative of the differences seen in the population (Table 2-2). The values are not

necessarily reflecting equivalent management regimes, but they represent how slash

and loblolly are managed in the real-life landscape. Further understanding of the relative









distribution of SOC among these two ecosystems is key, in order to understand

ecosystems functions and the contributions of each species on its soil as potential C

sink.

It was found that loblolly had a 40% higher AOC (g g-1 soil) than slash

ecosystems. One plausible explanation for this difference is that there is greater organic

C addition from fine roots and leaf litter under loblolly (Nowak and Friend, 2006; Burkes

et al. 2003; Xiao et al., 2002; Will et al. 2001; Polglase et al., 1992; Colbert et al., 1990).

We suggest further research where a fractionation of soil may increase sensitivity and

find which aggregate sizes differ by ecosystem. Previous preliminary research has

shown that size fraction 150-250 pm (Azuaje, unpublished data) contain aggregates

with the highest stability levels. Knowledge about AOC size fractions within 2000 and 53

pm and their relative ability to protect C has yet to be studied.

The slight increase of the AOC, as a % of SOC, under slash pine plantations

compared to loblolly (4.1% increase on average) indicates that slash pine systems have

a slight advantage over loblolly systems in aggregating soil C. We were unable to infer

the reasons for this difference. However, more importantly, data indicated that greater

than 40% of SOC was incorporated into soil aggregates under both slash and loblolly

pine ecosystems, a result supported by Sarkhot et al. (2007b).

Our findings clearly show that C in Florida's sandy soils under loblolly and slash

pine ecosystem is not physically protected from mineralization. Whether the results

would be different with a more specific size fraction needs to be investigated. Luckily,

forest management maintains a soil that is relatively undisturbed for close to two

decades. During that time C will accumulate as seen by this study. However, harvesting









and subsequent reforestation will put all soil C in jeopardy of being lost given this lack of

physical protection.

The chemical fingerprint provided by mid-IR analysis was crucial in

understanding C quality differences for stability levels and species. There were no

spectral differences found between stability levels of aggregates, supporting the lack of

physical protection suggested by the incubation study. As for species, mid-IR spectra

revealed significant differences in the C quality in the SOC of loblolly vs. slash pine

ecosystems; also supporting the previous differences in mineralization rates found

between species. Further research into the chemical differences between species

should yield information that will make modeling SOC mineralization more accurate.

These studies should concentration on the original components of the SOM (i.e. roots,

needles, and woodstem). Their comparison with SOC may reveal information about the

origin of compounds present in the soil C matrix.

DOC was identified as an important component of TOC turnover based on the

evidence that as increasing ultrasonic energy is applied there is an increasing release of

DOC. This component should be investigated as to whether it is a significant agent for

keeping microaggregates together.

Results from previous studies suggest the importance for innovative approaches

for studying aggregation in sandy soils, in order to assess the long-term effects of

management practices on the soil C (Sarkhot, 2007a). Due to their dominance in Florida

ecosystems, pine lands play an important role in the conversion of atmospheric C02

into the SOM pool that sequesters SOC. Loblolly pine ecosystems in north central

Florida contain approximately 13.44 metric tons of C per ha-20cm depth more than









slash, which translates to four hundred million dollars in C02 C credits increase if slash

had been loblolly pine. Agricultural activities, including forestry, are net sinks that offsets

over 4% of all US GHG (green house gas) emissions. Forests can mitigate GHG by

adjusting the type and intensity of agricultural production (Follett, 2010). In the case of

soil science a key to the reduction of GHG is in the combination of improved soil and

landuse management techniques. Information about the quality and quantity of AOC at

different stability levels and size fractions can aid in the generation of C footprint

baselines from which we can measure improvement of C sequestration in soils.









APPENDIX A
POPULATION CHARACTERIZATION

Table A-1. Complete dataset of 56 pine sites located in north central Florida under


spodosols.
County Species
Alachua S
Alachua S
Alachua S
Alachua S
Baker S
Baker S
Baker S
Baker S
Baker S
Clay S
Clay S
Clay S
Duval S
Flagler S
Hamilton S
Hamilton S
Lafayette S
Lafayette S
Lafayette S
Lafayette S
Nassau S
Nassau S
Nassau S
Putnam S
Putnam S
Putnam S
St. Johns S
St. Johns S
St. Johns S
Union S
Volusia S
Volusia S
Alachua L
Alachua L
Baker L
Baker L
Baker L


Latitude
29.712375
29.704885
29.7535
29.708777
30.297969
30.302311
30.298978
30.294036
30.235531
30.159932
29.891132
29.890983
30.342972
29.481865
30.527465
30.530920
30.017128
29.88705
30.062431
30.018853
30.65586
30.725651
30.699379
29.84981
29.645555
29.590073
30.005943
30.088603
29.717134
30.077723
29.206476
29.113007
29.711580
29.715106
30.304482
30.333257
30.256181


Longitude
-82.154249
-82.311552
-82.198581
-82.327257
-82.088372
-82.081807
-82.081279
-82.066642
-82.310725
-81.945865
-81.853681
-81.858647
-81.869493
-81.273662
-82.979794
-82.736469
-83.154864
-83.313558
-83.311331
-83.279406
-81.554301
-81.997664
-81.596138
-81.663151
-81.824897
-81.932761
-81.539609
-81.534517
-81.301320
-82.40143
-81.51260
-80.993003
-82.343671
-82.150826
-82.067178
-82.477562
-82.088706


TC %
0.719
1.056
0.824
5.045
2.317
0.692
1.476
1.171
0.794
0.688
1.185
0.984
2.323
0.830
0.776
1.509
1.580
3.373
1.207
1.755
1.055
1.116
2.552
0.950
1.480
1.097
1.155
0.921
1.990
1.499
4.708
1.495
1.272
3.099
0.700
0.889
0.797


SOC %
0.719
1.056
0.824
5.045
2.317
0.692
1.476
1.171
0.794
0.688
1.185
0.984
2.323
0.830
0.776
1.509
1.580
3.373
1.207
1.755
1.055
1.116
2.552
0.950
1.480
1.097
1.155
0.921
1.990
1.499
4.708
1.495
1.272
3.099
0.700
0.889
0.797


PH
4.15
4.04
3.42
3.46
3.58
3.9
3.71
4.13
3.85
3.79
3.54
3.24
3.94
3.73
4.94
3.64
3.33
4.17
3.6
4.2
3.94
3.86
3.15
3.4
4.01
3.39
3.57
4.74
3.46
3.48
3.16
3.21
3.73
3.09
3.46
3.6
3.69


Litter (Kg mA2)
NA
4.012
1.518
0.660
0.867
0.613
0.418
0.576
0.939
0.464
0.990
1.623
6.571
6.083
2.447
0.397
2.514
1.273
NA
2.032
2.218
0.331
0.996
9.542
1.752
6.642
3.223
1.481
1.722
1.936
7.473
10.237
4.145
3.628
2.016
0.811
1.339









Table A-1. Continued
County Species Latitude
Dixie L 29.542032
Duval L 30.465195
Duval L 30.134640
Flagler L 29.486148
Hamilton L 30.401772
Hamilton L 30.533555
Lafayette L 30.017988
Marion L 29.233783
Nassau L 30.596419
Putnam L 29.689604
St. Johns L 29.85892
St. Johns L 29.952492
Taylor L 29.866656
Taylor L 29.836545
Alachua 29.712529
Baker 30.241833
Hamilton 30.493726
Taylor 30.195384


Longitude
-83.2539598
-81.530360
-81.635989
-81.3174561
-82.681447
-82.735459
-83.302321
-81.917593
-81.907466
-81.745887
-81.361572
-81.454457
-83.418372
-83.428403
-82.1519448
-82.301556
-82.873999
-83.8684674


TC %
0.786
3.468
2.977
2.360
1.699
2.265
2.182
0.621
1.513
4.145
2.493
2.958
2.973
2.401
5.376
1.212
1.291
1.845


SOC %
0.786
3.468
2.977
2.360
1.699
2.265
2.182
0.621
1.513
4.145
2.493
1.404
2.973
2.401
5.376
1.212
1.291
1.845


PH
4.1
3.56
3
3.07
3.97
3.49
3.2
3.76
4.18
3.16
3.23
6.29
3.33
4.4
2.98
3.57
3.53
5.55


Litter (Kg mA2)
0.848
8.590
15.775
3.673
2.388
0.202
5.322
0.769
2.048
0.820
4.276
2.225
3.221
1.449
3.044
0.251
2.697
1.424


Table A-2. Slash (S) and Loblolly (L) pine ecosystem population descriptive data of Soil
Organic Carbon % (SOC %), pH, and forest floor (Kg m2).
Species SOC % pH Forest Floor (Kg m2)
S 1.495 3.21 10.237
S 4.708 3.16 7.473
S 0.830 3.73 6.083
S 1.097 3.39 6.642
S 1.480 4.01 1.752
S 1.056 4.04 4.012
S 5.045 3.46 0.660
S 0.719 4.15 NA
S 1.990 3.46 1.722
S 0.824 3.42 1.518
S 0.950 3.4 9.542
S 3.373 4.17 1.273
S 0.984 3.24 1.623
S 1.185 3.54 0.990
S 1.155 3.57 3.223
S 1.580 3.33 2.514
S 1.755 4.2 2.032
S 1.207 3.6 NA









Table A-2.
Species
S
S
S
S
S
S
S
S
S
S
S
S
S
S
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L
L


Continued
SOC %
1.499
0.921
0.688
0.794
1.171
2.317
1.476
0.692
2.323
0.776
1.509
1.055
2.552
1.116
0.621
2.360
0.786
4.145
1.272
3.099
2.401
2.493
2.973
1.404
2.182
2.977
0.797
0.700
0.889
1.699
3.468
2.265
1.513
5.376
1.845
1.212
1.291


pH
3.48
4.74
3.79
3.85
4.13
3.58
3.71
3.9
3.94
4.94
3.64
3.94
3.15
3.86
3.76
3.07
4.1
3.16
3.73
3.09
4.4
3.23
3.33
6.29
3.2
3
3.69
3.46
3.6
3.97
3.56
3.49
4.18
2.98
5.55
3.57
3.53


Forest Floor (Kg m2)
1.936
1.481
0.464
0.939
0.576
0.867
0.418
0.613
6.571
2.447
0.397
2.218
0.996
0.331
0.769
3.673
0.848
0.820
4.145
3.628
1.449
4.276
3.221
2.225
5.322
15.775
1.339
2.016
0.811
2.388
8.590
0.202
2.048
3.044
1.424
0.251
2.697










APPENDIX B
AGGREGATE DISPERSION ENERGY CURVES


0 20 40 60 80 100 120 140 160 180
Energy j/mL


0 20 40 60 80 100 120 140 160


Energy j/mL
-- Dispersion energy (JlmL) vs AOC as % of total
B

Figure B-1 Aggregate dispersion energy curve samples for loblolly pine ecosystem in
aggregate organic carbon (AOC) as % of total soil organic carbon (SOC) by
energy in JmL1.


























10






50 --
60


50


0 20 40 60 80 100 120 140 160 180


Figure B-1. Continued


0 20 40 60 80 100 120 140 160 180






























0 20 40 60 80 100 120 140 160 180

Energy j/mL
-*-- DErsoiLrin e-,i]y (JmL)vs ADC as % of total


Figure B-1. Continued


60-


50 -


40


30


20


0 20 40 60 80 100 120 140 160 180











70 -

60-

50 -

o 40 -

W 30

0
20 /


10 -



0 20 40 60 80 100 120 140 160 180
Energy j/mL
-*- Dispesion energy (J/mL) vs AOC as % of total
G

Figure B-1. Continued



























0 20 40 60 80 10 120 140 160 180
E energy JimL


0.018 *

0.016

0.014

0.012

09.010








fTM.1 -
0.006


0 20 40 G 81 1 100 120 140 1& 10W
EneargyjImL
-- Dispersion energy CJImL}vsAOC gWg soil


Figure B-2. Aggregate dispersion energy curves samples for slash pine ecosystem in
aggregate organic carbon (AOC) as % of total soil organic carbon (SOC) by
energy in JmL-1(Fig. A-F)












100 -


| 60-


40-


20


-
0-


0 20 40 60 89 100 120 140 160 181


80 -


U I
0 20 40 60 W0 100 120 140 160 1 0



Figure B-2. Continued

















100 -



80-



60 -



40 -



20



0-


0 20 40 60 8 100 120 140 160 180

EnergyjimL
-- Dispersion energy (J/mL)vs ACC as% of total


0
0
O

30-

- 2
m 20 -
0
0
10-



0-


0 20 40 60 80 100 120 140 160 180

Energy m L
-- Dispersion energy (JimL)vs AOC as% of total


Figure B-2. Continued











0.018

0.016

0.014

0.012

0.010

0.008

0.006

0.004

0.002

0.000


0 20 40 60 S 100 120 140 160 1S0
A


0.018

0.016



0.012

0.010




0 .00


0 20 40 60 BO 100 120 140 160 160

EnergyjlmL
-- Dispersion energy (J mL) vs A0C g g soil


Figure B-3. Aggregate dispersion energy curve samples for loblolly pine ecosystem in
aggregate organic carbon (AOC) g/g of soil by energy in J/mL (Fig. A-F)










0.012


0.010





0.006


0 0.4

0.002

11.011


0 20 40 60 & 8G 100 120 140 160 181


0.006 -


0.005 -


0.004 -


1.003 -


0.002


0.001


0.000


0 20 40 60 18 100 120 140 160 181

Energyj/mL
-*- Dispersion energy J/mL) vsAOC ~g soil


Figure B-3. Continued











0.018

0.016

0.014

0.012

0.010

0.008

0.006

0.004

0.002

0.000




0.014 -

0.012-

0.010 -
OA98O


0.004 -


0.000


0 20 40 60 8~ 100 120 140 160 180 E


0 20 40 60 80 100 120 140 160 180

Energy j/mL


Figure B-3. Continued










0.005 -
0.004






S0.0 03






0.002 I
0.001


0 .0 00 ..--- i -- -- i -- -- i -
0 20 40 60 80 100 121 140 160 180A


0.005


0.004 -









0.002 -... .

01.001 i i i i i i i -----


0 20 40 60 &0 100 120 140 160 1W
Energyj/mL
-- Dispersion energy [J/mL)vsAOC gg soil
B

Figure B-4. Aggregate Dispersion Energy Curves samples for slash pine ecosystem in
Aggregate Organic Carbon (AOC) g/g of soil by energy in J/MI (Fig. A-F)











0.007 *

0.006 4

0.005




0.003






0.000


0 20 40 60 an 100 120 140 160 180C
C


0.018 -

0.016 -

0.014 -

0.012





0.100
0.010
-


0.006 *

0.004

0.002

0.000


fG 20 40 60 86 1T0 120 140 16f0 1B0

Energyj/mL


Figure B-4. Continued










0.0118


O.O4B
0.006


0.004 -













0.008







0.002 -







-1.002 .........
0 20 40 60 81 101 120 140 160 181

Energy rmL
isrsin e nergy nJ L)vs AOC gg snil


Figure B-4. Continued
Figure B-4. Continued











APPENDIX C
MID-INFRARED REFLECTANCE (MID-IR)


0.0 -


-0.2
0




Figure C-1. Mid-infrared


OJ5 -






02-


OD -


-02


-0.4
0




Figure C-2. Mid-infrared


I I I I I
1000 2000 3000 4000 5000

Wave number {cm(n


non-ashed spectrum average of loblolly pine influenced soil.


I I I I I
1Cwo 2000 3000 4000 &M0

Wave number (crn1)


non-ashed spectrum average of slash pine influenced soil.































-U.Z I I I I I
0 1000 203 3000 4000 aBm0

'av number (cmn-)


Figure C-3. Mid-infrared average spectrum of ash-subtracted loblolly pine influenced
soil.


'- I I I I II


rave number (crnm)


Figure C-4. Mid-infrared average spectrum of ash-subtracted slash pine influenced soil.


























-0.2 -


-0.4


1000


2000O


Wave n number {cm')


Figure C-5. Mid-infrared average spectrum of ash-subtracted loblolly pine influenced
soils at week 29 of incubation study.


-0.2 -


-0.4


1000


2000


3000 4000


50


-1
Wav rnumrba {c )


Figure C-6. Mid-infrared average spectrum of ash-subtracted slash pine influenced
soils, at week 29 of incubation study.


3000


4000


5OO









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BIOGRAPHICAL SKETCH

The author was born in San Felipe, Venezuela. While raised between different

family members (mom, dad, grandmother, and aunt), she had the opportunity to live in

many places throughout childhood. As soon as high school ended she knew that an

environmental career was her passion, however, in Puerto Ordaz, Venezuela the only

colleges available had no environmental majors. As she started a major in sociology

she became an entrepreneur by creating the first environmental group called MAWIDA

at the Andres Bello Catholic University. She became recognized through this group and

as a result the university granted her the opportunity to create and administrate a

business (MAWIDA's cafe) that would provide a half scholarship for a student and also

funds for the group. MAWIDA's cafe was a place from which she would impart

environmental values through healthy meals, recycling and an eco-friendly environment.

Although she was satisfied with her accomplishments, Elena knew she had to expand

her knowledge in order to be able to convey environmental awareness. She then made

the decision to transfer to Valencia community college in the United States as she

prepared herself to begin at the University of Florida completing a bachelor in forest

resources and conservation. In her senior year while working as a laboratory assistant

for the forest soils laboratory, she started a combined degree to acquire a master's

degree in soil and water science.





PAGE 1

1 AGGREGATE ORGANIC CARBON IN NORTH FLO RIDA SLASH AND LOBLOLLY PINE ECOSYSTEMS By ELENA AZUAJE A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DE GRE E OF MASTER IN SCIENCE UNIVERSITY OF FLORIDA 2010

PAGE 2

2 2010 Elena Azuaje

PAGE 3

3 To my parents who inspired, allowed and encouraged me to follow my goals since childhood.

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4 ACKNOWLEDGMENTS I thank my parents for always encouraging me to pursue my lif elong goals. I thank m y mother Meber Dasilva for being strong and allowing me to fly away from home at a young age to pursue a better future; also my mommy Deborah Azuaje for taking me under her wings unconditionally. I e specially thank my father Carlos A zuaje for being my inspiration and an example of an individual with high quality of intellect, morals and humbleness that only a few posses and that many wish to have I thank Julio Madriz for his love and company that has smoothed my ride through the roll ercoaster of the past few years. This work is a product of many minds that influenced and imparted my understanding and knowledge. I sincerely thank my master s committee: Nicholas Comerford for his guidance consistency and everlasting patience while teac hing me the science of research; Willie Harris for being an example can only hope to be, a humble and incredibly knowledgeable individual; Jim Reeves fo r his extensive knowledge and willingness to teach and enthusiasm to live life Finally, I thank the pr oject through whi ch my research was funded: Rapid Assessment and Trajectory Modeling of Changes in Soil Carbon across a Southeastern Landscape (Florida) ; funded by the U.S. De partment of Agriculture (USDA), National Research Initiative (NRI), CSREES; now N ational Institute of Food and Agriculture (NIFA), USDA -CSREESNRI -AFRI grant award 2007.

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5 TABLE OF CONTENTS ACKNOWLEDGMENTS ...................................................................................................... 4 page LIST OF TABLES ................................................................................................................ 7 LIST OF FIGURES .............................................................................................................. 8 LIST OF ABBREVIATIONS ................................................................................................ 9 ABSTRACT ........................................................................................................................ 10 CHAPTER 1 INTRODUCTION TO AGGREGATE ORGANIC CARBON ...................................... 12 2 SOIL AGGREGATE STABILITY AND ITS INFLUENCE ON CARBON IN PINE ECOSYSTEMS IN NORTH FLORIDA ....................................................................... 17 Methodology ............................................................................................................... 19 Experimental Site for Validation of the Sonication Method ................................ 19 Experimental Sites for Stability Levels, Mineralization and Chemical Analysis ............................................................................................................. 19 Laboratory Methods ............................................................................................. 21 Sonication method ......................................................................................... 21 ADE curves for pine ecosystems .................................................................. 22 Statistical analysis ................................................................................................ 23 Results ........................................................................................................................ 24 Objective 1. Sonication Method Evaluation ........................................................ 24 Objective 2. Contrasting Aggregation in Loblolly and Slash Pine Ecos ystems ....................................................................................................... 25 Discussion ................................................................................................................... 25 Objective 1. Sonication Method Evaluation ........................................................ 25 Objective 2. Contrasting Aggregation in Loblolly and Slash Pine Ecosystems ....................................................................................................... 26 3 DOES AGGREGATION PROTECT C IN VERY SANDY SURFACE SOILS? ......... 32 Methodology ............................................................................................................... 35 Experimental Sites and Field Sampling .............................................................. 35 Laboratory Methods ............................................................................................. 36 Mineralization ................................................................................................ 37 Dissolved organic carbon (DOC) .................................................................. 38 Fourier transformed infra -red reflectance (FTI R) spectroscopy .................. 38 Statistical Analysis ............................................................................................... 39 Results ........................................................................................................................ 40

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6 Objective 1. Determine if Aggregate Stability Levels and Ecosystem Cover Types were a Controlling Factor in SOC and AOC Accumulation and Mineralization. ................................................................................................... 40 Objective 2. Identify SOC Chemical Characteris tics that could explain Differences at AOC Stability Levels and Mineralization between Ecosystems Cover Types. ................................................................................ 41 Discussion ................................................................................................................... 42 4 SYNTHESIS ................................................................................................................ 53 APPENDIX A POPULATION CHARACTERIZATION ...................................................................... 57 B AGGREGATE DISPERSION ENERGY CURVES .................................................... 60 C MID -INFRARED REFLECTANCE (MID-IR) .............................................................. 73 LIST OF REFERENCES ................................................................................................... 76 BIOGRAPHICAL SKETCH ................................................................................................ 83

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7 LIST OF TABLES Table page 2 -1 Sample characterization of pine sites located in north c entral Florida. ................ 30 2 -2 Characterization of population and sample means and standard error. .............. 30 2 -3 Summary of aggregate organic carbon (AOC) as influenced by tree species. .... 31 3 -1 Description of sample sites .................................................................................... 51 3 -2 Population and s ample means with corresponding standard errors for total carbon (TC), soil organic carbon (SOC) ................................................................ 51 3 -3 Total dissolved organic c arbon mg g1 soil with corresponding standard errors and species comparisons ..................................................................................... 51 3 -4 Discriminant an alysis for the separation of species from spectral analysis ........ 51 3 -5 Discriminant analysis for spectral separation ash-subtracted of energy level ..... 52 3 -6 Discriminant analysis for mineralization study of SOC by species. ...................... 52 A-1 Complete dataset of 56 pine sites located in north central Florida ...................... 57 A-2 Slash (S) and l oblolly (L) pine ecosystem population descriptive data. ............... 58

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8 LIST OF FIGURES Figure page 2 -1 Number of aggregates per gram of soil as affected by level of dispersion energy applied and by the soil size fraction. ......................................................... 28 2 -2 Aggregate organic carbon (AOC) as a function of dispers ive energy .................. 29 3 -1 Comparison of the carbon (C) mg g1 soil by dispersion energy levels (J mL1) by week ................................................................................................................... 46 3 -2 Total dissolved o rganic carbon (DOC) ................................................................. 47 3 -3 Diffuse reflectance infrared Fourier -transform spectra fingerprint of the average ash subtracted spectra. ........................................................................... 47 3 -5 Diffuse reflectance Fourier -transform spectra of the difference between spectra of ash -subtracted loblolly minus slash pine influenced soils. .................. 48 3 -6 Spectral subtraction between w eeks 29 an d 1 for loblolly pine influenced soils ........................................................................................................................ 4 9 3 -7 Spectral subtraction between weeks 29 and 1 for slash pine influenced soils ... 50 C -1 Mid -infrared nonashed spectrum of the average of loblolly pine influenced soil. .......................................................................................................................... 73 C -2 Mid -infrared nonashed spectrum of the average of slash pine influenced soil. .. 73 C -3 Mid -infrared spectrum of ash-subtracted loblolly pine influenced soil at beginning of incubation study. ............................................................................... 74

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9 LIST OF ABBREVIATION S ADEC Aggregate Dispersion Energy AOC Aggregate Organic Carbon C Carbon DOC Dissolved Organic Carbon DRIFTS Diffuse Reflectance Infrared Fourier Transform Spectroscopy FTIR Fourier Transform Infrared Spectroscopy Mid -IR Mid -Infrared SOM Soil Organic Matter SOC Soil Organic Carbon TO C Total Organic Carbon

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10 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master in Science AGGREGATE ORGANIC CARBON IN NORTH FLORIDA SLAS H AND LOBLOLLY PINE ECOSYSTEMS By Elena Azuaje August 2010 Chair: Nicholas Comerford Cochair: Willie Harris Major: Soil and Water Science Its been found that o f the estimated 4,110 GT of carbon (C) found in the worlds C cycle approximately 38% can be found in the soil. There has been a growing interest in C sequestration through improved soil management practices. Aggregation is a process by which C can be physically protected by encrustation. S andy surface horizons which have been thought of as weak structure, have been previously found to have small aggregates with upwards of 50% soil organic C ( SOC ) contained in those aggregate s. This study targets pine ecosystems under the two dominant species in the southeast US which are loblolly pine ( Pinus ta eda) and slash pine ( Pinus elliottii). The objectives were to a) validate ultrasonic methodology, b) to investigate soil aggregate energy at which SOC is held and determine if this is influenced by pine ecosystems of loblolly vs. slash; c) determine if aggregate dispersive energy is a controlling factor in aggregate organic C (AOC) turnover; and d) determine the chemical fingerprint of AOC as aggregate dispersive energy increases within species ecosystem. The data show that soil under slash pine was slightly better aggregated with 4.1% more AOC as a percentage of TOC. However, loblolly had 33% more AOC (g g1soil) than did slash.

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11 Incubation and mid -infrared (mid-IR) results showed that there was no discernable difference in AOC held at different energy level s. On the other hand, SOC was influenced by pine species. Slash pine soils were higher in aliphatic C and had higher specific mineralization rate; while loblolly was higher in aromatic C. These results are the first contrast of SOC under these two species It suggests that loblolly management, as normally practiced by landowners, will store more soil C ; yet that soil C will not be protected from mineralization by soil aggregation.

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12 CHAPTER 1 INTRODUCTION TO AGGREGATE ORGANIC CARBON Of th e estimated 4,110 GT of C found in the world s C cycle approximately 38% can be found in the soil (Leu, 2007) Moreover, soil organic C (SOC) is critical to soil processes as it affects water retention, soil structure, and nutrient cycling (Sanchez and Ruark, 1995 ). Soil C is protected by a variety of mechanisms including: physiochemical by sorption to clay; biochemical through formation of recalcitrant C compounds; physical via soil aggregation (Stone et al., 1993; Six et al., 2002; Blanco -Canqui and Lal, 2004); chemical b y combining with metals such as aluminum; translocational by C moving to deeper soil horizons where decomposition is limited by either microbial populations or oxygen; and depositional under anaerobic/anoxic environmental conditions. The architectural org anization of sand, silt, and clay by organic compounds and inorganic cementing agents creates aggregates ( Blanco-Canqui and Lal 2004). An aggregate hierarchy in soils has been suggested where aggregates of different stability, often controlled by organic materials, can be classified ( Oades and Waters 1991). Three models have been used to explain soil aggregation and SOC ( Blanco -Canqui and Lal 2004). The first was proposed by Tisdall and Oades (1982) and based on aggregate hierarchy. In this model sta ge 1 is the binding of primary particles into microaggregates (53where soil organic matter ( SOM ) is often the principal binding agent. Stage 2 is characterized by the formation of medium -sized microaggregates; while stage 3 encompasses the formation of large microaggregates and macroaggregates. The second m odel was proposed by Oades (1984) which was a revision of the above mentioned model where macroaggregates form first and microaggregates are created afterward. The third model, developed by Six et al. (2000), begins with the

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13 formation of macroaggregates cr eated by the binding of organic residues. This is followed by intra aggregate particulate OM stabilizing these macroaggregates. The next stage is the formation of microaggregates encrusted by fine organic particles. Finally, stable microaggregates are form ed by the degradation of macroaggregates. Aggregate formation depends on factors that develop aggregate stability. Soil aggregation depends on changes in water status, freezing or thawing, tillage, movement of large biota (plant roots, earthworms and macro fauna) and clay content (Oades and Waters, 1991) Roots, fungal hyphae, and polysaccharides that are intimately associated with the mineral fraction appear to be important stabilizing agents (Kay, 1997). A study on macroaggregate stability showed that hyphae are linked to aggregation in sandy soils (Degens et al. 199 6) It is often assumed that SOC within aggregates is not available to microbial decay, and as a consequence the greater the stability of the aggregate against dispersion leads to greater protection of the SOC. Studies have shown mixed results when investigating the relationship between aggregate tensile strength and SOC ( Blanco -Canqui and Lal, 2004). A strong correlation was found by Golchin et al (1995) between particulate organic m atter and stability of 12 mm m a croaggregates (increasing SOC increases aggregate stability) ; while Chaney and Swift (1984) found a high correlation between aggregate stability, SOM, total carbohydrate, and humic material. Conversely, Jastrow et al. (1998) found a poor correlation between SOC and aggregate sizes. The purpose of this thesis was to investigate soil C and soil aggregation in very sandy soils supporting southern pine plantations. In Florida, pine lands made up almost exclusively of s lash pine (Pinus elliottii) and l oblolly pine ( Pinus taeda) cover 1,252,569

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14 ha of which 30% (371,481 ha) are in conservation areas or managed areas ; 56% (702,182 ha) are in private ownership. The remaining 14% encompasses private lands in conservation programs (FF WCC, 2005). Spodosols cover 27% of Floridas area (Stone et al., 1993). Florida S podosols contain 0.05% of the global C pool only on approximately 0.028% of the total land area (Stone et al., 1993) Within our sampling study area, pinelands represent 25% of the land cover. Floridas sandy surface soils are not known for their ability to store C due to their weak structure and low clay content (Carlisle et al., 1981, 1988, 1989) resulting in a low ability to physiochemically protect C from microbial decompo sition. The few studies that have investigated aggregation in Southeastern Coastal Plain sandy soils have shown that surface soil aggregation is dominated by microaggregates over macroaggregates. The weak soil structure of these soils implies poor aggr egation, yet Sarkhot et al. (2007a) have shown aggregate hierarchy with at least 50% of the total C found in aggregates (Sarkhot et al., 2007a). However, the degree of protection provided by soil C in Lower Coastal Plain sandy soils is unknown. The overall purpose of this study was to investigate soil aggregate C as to its importance in forest land use and its role in SOC protection; it was addressed by investigating three principle objectives. The first objective of this research was to investigate the r ange in stability of soil aggregate s in these sandy surface horizons and determine if ecosystem vegetation dominated by P. taeda vs P. elliottii influence the amount of SOC in aggregates and the strength of aggregates in which it is held. This is the focus of Chapter 2. Aggregate stability is the ability of the soil to retain its arrangement of solid and void space when exposed to stress (Kay, 1997). Due to the

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15 low content of clay (<2%) and the sandy nature of North Florida Spodosols, aggregation it thoug ht to have low capabilities to physically protect SOC from microbial decomposition. However, aggregate stability has been found to occur in Floridas surface soils (Sarkhot et al., 2007a) and is found to be proportional to the energy required to disperse t he aggregates via sonication. While this Chapter addresses the amount and type of aggregation, it does not investigate whether more stable aggregates provide superior protection to SOC from microbial use. This thesiss second objective was to determine if aggregate dispersive energy (a measure of stability) is a controlling factor in aggregate organic C (AOC) turnover in sandy soils (Chapter 3). By dispersing aggregates at different energies and then mineralizing the SOC, this objective test ed the hypothes is that SOC held at higher dispersive energies is better protected against decomposition. When all soil aggregates are dispersed, a higher rate of mineralization would be expected if aggregates do protect SOC. The third and last objective (Chapter 3 ) wa s to determine if the chemical characteristics of SOC and AOC are different between slash and loblolly ecosystems; and as aggregate dispersive energy increases. Qualitative analysis of SOC functional groups was analyzed by a form of Fourier transformed i n frared r eflectance s pectroscopy (F T IRS) called DRIFTS ( Diffuse Reflectance Infrared Fourier Transformed Spectroscopy). Fourier transform infrared spectroscopy is a cost effective, time saving, non-destructive and environmentally sound technique of soil an alysis (Dunn et al., 2002); while Boehm titrations provide qualitative and quantitative information on soil functional groups (Radovic et al, 2003).

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16 Chapter 4 is a summary of this thesis and attempts to encapsulate the salient points presented in the preceding chapters as well as identify the continuing gaps in knowledge and suggest future research directions

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17 CHAPTER 2 SOIL AGGREGATE STABI LITY AND ITS INFLUEN CE ON CARBON IN PINE ECOSYSTEMS IN NORTH FLORIDA A soil aggregate is a group of soil particles th at cohere more strongly to each other than to other adjoining particles (Sylvia et al., 2005). In Floridas sandy soils, organic matter (OM) is the dominant input that leads to the formation and stabilization of soil aggregates, which in turn may protect s oil organic C (SOC) fro m microbial decomposition (Lal et al., 1997). S oil organic matter influences the formation, stabilization, and degradation of soil aggregates and is the major aggregate binding agent that generates aggregate hierarchy (Tisdall and Oades, 1982; Oades and Waters, 1991). Aggregates assist in the reduction of erosion, in the improvement of infiltration and in the movement of water The y can also affect plant growth. D ifferent theories have been suggested about aggregates and their capabil ity to protect C. Tisdall and Oades (1982) determined that the age, size and stability of an aggregate is a function of organic agents: transient agents decompose rapidly and are associated with aggregates larger than 250 um; temporary binding agents are a ssociated with OM that comes from vesicular arbuscular (VA) mycorrhizal hyphae (Tisdall and Oades, 1979) and persist for months or years; and persistent agents are resistant aromatic components. Soil microorganisms can promote soil aggregation through the production of polysaccharides, glomalin and hyphae (Sylvia et al., 2005). It has been shown that more labile OM is tied up in macroaggregates (2000250 m) and more decomposed OM is tied up in microaggregates (<250 m) (Six J et al. 2001). Puget et al. (1995) also suggested that the organic C concentration increased with aggregate size, and that organic C is more labile in micro than in macroaggregates This was supported by a study where the microbial biomass and ac tivity in soil

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18 aggregates was higher in macroaggregates than in microaggregates for native prairie soils ( Gupta and Germida, 1988 ). However, other theories support the idea of encrustation of SOM inside microaggregates providing the protection that resu lt in SOC sequestration (Tisdall and Oades, 1982; Golchin et al., 1994; J astrow and Miller 1996). The capacity of OM incorporation into aggregates has most often been estimated from the soil clay and silt content (Hassink et al., 1997) ; however, using sound waves to disrupt the aggregates has also been employed by others (North 1976; Christensen 1992; Cambardella and Elliot, 1993; Six et al., 2001; Swanston et al., 2005; Sarkhot et al. 2007a). Aggregate d ispersion e nergy c urves (ADEC) have been used to ex amine aggregate strength and determine the quantity of C in aggregates held at different dispersion energies. This method quantifies the physically protected C (Sarkhot et al. 2007a; North, 1976; Christensen, 1992; Cambardella and Elliot, 1993; Six et al., 2001; Swanston et al., 2005). Through this methodology Sarkhot et al. (2007a) found evidence that suggest ed aggregate hierarchy in Floridas sandy soils. The overall purpose was to investigate the role that aggregation plays in C incorporation and seques tration in the surface sandy soils of the lower coastal plain supporting southern pine. This was addressed by focusing on two objectives. The first objective was to validate a sonication method that has been previously used to look at aggregate dispersive energy (Sarkhot et al., 2007a). While the method has been used as noted above, a detailed look at aggregate size and disappearance had not been made.

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19 The second objective was to investigate the amount of SOC incorporated into aggregates in surface sandy soils; and determine if there was a difference due to growing loblolly or slash pine ecosystems Methodology Experimental Site for Validation of the S onication M ethod The study site was located 10 km north of Gainesville, FL on a flat topography (<2% slope) within a forest plantation growing on a Pomona fine sand (sandy siliceous hyperthermic Ultic Alaquod). The climate of the region is a hot and humid with an average yearly precipitation 113.3 cm and yearly average minimum and maximum temperatures of 14 and 27 c respectively (southeast regional climate center 2010 ). The study design has previously been describe in detail since it has been a subject of multiple soil and aboveground investigations (Colbert et al. 1990; Dalla -tea and Jokela, 1991; Jokel a and Martin, 2000; Martin and Jokela, 2004); but is briefly described here. In 1983, P. elliottii and P. taeda seedlings were planted at a1.8 x 3.6 m spacing in a 2x2x2 factorial design employed in three blocks. O nly one pineland block was sampled. The method was evaluated on soil from two depths : 0 5cm and 5-10cm. (The first letters of principal words must be capitalized). Experimental Sites fo r Stability Levels, Mineralization and Chemical Analysis The experimental sites were primarily chosen from a previous stratified random sampling plan for a large scale research project with the objective of developing a soil C inventory of the stat e of Florida (Myers unpublished data), designed to proportionally sample sites relative to the area s land use pattern. The study area is the USDA Conservation Area 2 in North Florida. From this sampling plan 13 sites were selected.

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20 Seven sites supported a loblolly pine plantation and 6 sites supported slash pine plantations. The sites were chosen so that they represented the mean and the range of soil C reported in the larger data set. The pinelands category includes north and south Florida pine flatwoods, south Florida Pine rocklands, and commercial pine plantations [](FFWCC, 2004) Soil samples were collected at the sampling locations with 4 cylindrical metal cores of 20 x 5.8 cm on the surface soil. Samples were measured for bulk density and moisture content then air -dried and dry -sieved to soil samples less than 2 mm (Myers et al., unpublished data). All study s ites are Spodosols (Aquods) Samples were located in the north Florida counties of Alachua, Citrus, Clay, Duval, Flagler, Lafayette, St. Johns, Putnam, Taylor, and Volusia ( Table 2 1 ). The sampling sites were located on flatwood landscapes; which are pred ominantly somewhat poorly to poorly -drained soil s with a seasonal ly high water table. Loblolly and slash ecosystems had a common understory of saw palmetto ( S. s erenoa repens Small ), wax myrtle ( myrica cerifera L ), gallberry ( ilex glabra L ), brakenfern (pteridium aquilinum L ), blackberry ( rubus sp .), fetterbush ( Lyonia lucida Lam ); various grasses such as bluestem ( Andropogon virginicus L ) and wiregrass ( Aristida beyrichiana); and young oaks ( quercus sp .) like water oak (Quercus nigra L.) ; also sweet gum ( liquidambar styraciflua L ), bays ( persea sp.), and F lorida maple ( acer barbatum ). Both l oblolly and s lash ages range from 1020 years with most of them in a plantation setting. However, one loblolly and one slash pine site were natural pine ecosystem s

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21 Laboratory M ethods Sonication m ethod Soil samples were air -dried in a greenhouse and then passed through a 2 mm sieve. They were dry -sieved through a horizontal mechanical shaker for 5 min at 75 rpm using 53-, 150, and 250m sieve s with 100 grams o f dry soil at a time ( Sarkhot et al., 2007a). An ADEC was developed for one block, three curves by size fraction with three replications of each. Each ADEC was built by placing 10 fivegram subsamples into beakers with 100 mL of deionized water. Using a s onic dismembrator (Fisher Scientific, Model 500, Hampton, NH) immersing the sonic probe 10 mm below the surface of the water, and applying an energy level. Ene rgy levels from 0 to 200 j m L1 were applied by using a range of amplitude (2069%) and time (1 7 min) combinations. A correction factor, as described by Sarkhot et al. (2007a), was applied. Temperature rise was controlled by a pulse method (60 s on and 30 s off) (Sarkhot et al. 2007a). In this manner aggregates in each size fraction were increment ally disrupted. After disruption, each sample was passed through the same-sized sieve used to obtain the size fraction. The SOC remaining on the sieve after each sonication represented particulate SOC (POC) and aggregate OC (AOC) that resisted dispersion. The SOC passing through the sieve was considered the AOC that was dispersed by sonication. Sieve retentive were dried in a forcedair oven to 65 to 700C. Three 0.1 g subsamples of the retentive oven dried material were collected on the sieve, in turn, pl aced under a microscope and the numbers of aggregates were counted with the help of a 1cm x 1cm grid system.

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22 ADE curves for pine ecosystems Previous studies have investigated soil C by size fraction, so initial analysis for sonication validation of ADEC wa s completed by size fractions to be consistent with those studies ( Oades and Waters, 1991 ; Roscoe et al., 2000; Six et al., 2001 ; Sarkhot et al., 2007a). After these preliminary studies it was decide d to work on whole soils having the range of energy that was necessary to disperse aggregates and create the ADECs. Samples were dry -sieved through a horizontal mechanical shaker for 5 min at 75 rpm using a 53 m sieve size with a 100 grams of dry soil (soil size < 2mm) at a time (Sarkhot et al., 2007a). Sample s used were all larger than 53 m and smaller than 2000 m Each energy curve was made up of ten points at increasing dispersion energy levels until all aggregates had been broken down. The curve ranged from 0153 j m L1 this last level was selected base d on the sonication validation study described above, where up to 153 j m L1 were required to break down most aggregates. In order to set up a s onic d ismembrator it is necessary to supply amplitude (20-90%) and time (2 14 min). The pulse method (60 s on and 30 s off) utilized was to avoid high temperatures that could interfere in the measurement ( Sarkhot et al., 2007a). The energy output applied by the s onic dismembrator was given in joules and was internally calculated by the software. Since the energy output of the machine was calculated for the electrical energy (using an internal voltmeter) the conversion of electrical to mechanical energy at the probe tip is not 100% efficient so the energy absorbed by the water was calculated. The energy dissipated into the suspension was calculated with a correction factor. This

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23 actual energy was calibrated calorimetrically with a Dewar vessel as shown by Schmidt et al. (1999). Each of the 13 sites had three ADEC replicates. Five grams were weigh ed in a 250 mL beaker for each subsample and then the sample was placed on a 250 mL thermal container and the probe was maintained at a constant depth of 12 mm. 100 mL of DI water was added slowly avoiding formation of bubbles that could provide a disruption in the energy a pplied to soil The thermal container was then placed in a sound box w ith the probe always immerse d at the same depth and avoiding touching any of the walls of the container. After each sub-sample had been sonicated, to separate the material and quantify the C released by the broken down aggregates and the C s till in aggregate, the next procedure applied was wet sieving. The content in the Dewar vessel was poured onto a 53 micron sieve. The AOC and SOC remaining on the sieve and the SOC that passed throug h the sieve were measured using loss on-ignition (LOI). It was assumed that the energy applied did not disrupt particulate plant material debris which could result in a transfer of C that does not originate from aggregate disruption The great advantage of using an ultrasonic vibration method is that it provides the opportunity to quantify the amount of energy applied to the soil in suspension and then it is possible to quantify the C that comes from that specific aggregate stability level. Statistical analysis An SOC analysis was ran to determine the distribution of population and sample. Variables SOC%, pH and forest floor were log normal. Outlier analysis was run and then an ANOVA was utilized. In order to compare means between energy level and species ( categorical variables) f or AOC g g1 and AOC as percent of SOC (dependent

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24 variable s ) an analysis of covariance ( ANCOVA; STATISTICA 9.0, Stat soft Inc.; Tulsa OK ) procedure was used for each dependent variable. The covariate, TC was utilized to account for the variability that occurred between the sampling sites. Sampling distributions were log normal both for AOC (g g1 soil) and AOC as a percent of SOC (AOC % ), but for the purposes of the figures and tables units were back transformed from log(10). Diff erences were considered significant if p < 0.05. If significant main effects or interactions were found, post hoc multiple mean comparisons were evaluated using Tukeys procedure. Results Species differences were found in the SOC percent of the population and sample. Loblolly pine ecosystems were found to have a significant higher SOC content than slash pin e ecosystems for population and sample (44%,130%) respectively (Table 2). However, pH and litter were similar for both population and sample. Objective 1. Sonication Method Evaluation Aggregate count was completed under a dissecting microscope ( Fig. 2 -1 ). The number of aggregates per gram of soil increased with decreasing aggregate size. The 2000 to 250 m soil size fraction was dispersed by 100 j m L1; w hile the two smaller size fractions required more than 20 0 j m L1 for complete dispersion ; however, at about 150 j m L1 the curve began to asymptote revealing stabilization. This study shows that aggregates were dispersed by ultrasonic energy. Observations of aggregates aided by microscopy were consistent with mycorrhizal hypha and fine roots being incorporated into aggregates. O ther materials among the aggregate s were fecal pellets, root epidermis and insect carcasses

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25 Objective 2. Contrasting Aggregation in Loblolly and Slash Pine Ecosystems ANCOVA for the variable, AOC (g g1 soil), revealed that the covariate explained a significant amount of the variability (p < 0.00 1 ), while the main effect, pine species (categorical variable), was significant (p< 0.00 1 ) T he dispersion energy was significant when used either as a continuous or categorical variable (p< 0.00 1 ) ( Fig. 2 2a, T able 2 2 ). On average over all energy levels loblolly pine AOC was 50 % higher tha n that under slash pine. At the highest dispersion energy, loblolly, on average had 41% higher AOC (Fig. 2 -2a T able 2 3). ANCOVA for the variable % AOC as % of S OC suggests that the covariate explained a significant amount of the variability (p < 0.001 ); while the main effect, species (categorical variable ) was significantly different (p < 0.00 1 ), and the dispersion energy was also significant when used either as a continuous or categorical variable (p< 0.001 ). In this case slash ecosystems revealed a significant 4.1% AOC increase, with an average AOC of 2 0.5 % (1.6 std. error) for slash and 16.4% (1.2 std. error) for loblolly. I nitial water stable aggregates were equal for both ecosystems (F ig. 2 -2b, T able 2 3). Discussion Objective 1. Sonication Method Evaluation Aggregate dispersion curves have only re cently been used to describe qualitatively and quantitatively the amount of AOC in a soil. Microscopic evidence verified that sonication was dispersing soil aggregates, therefore the method was deemed useful for the purpose for which it was employed. The reason for higher aggregate stability in the 150250 m, and the higher dispersion energy required for total aggregate dispersion, needs to be explored both for mechanism of stability and potential for C sequestration.

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26 Objective 2. Contrasting Aggregation in Loblolly and Slash Pine Ecosystems Our findings s uggest that AOC in g g1 soil was clearly greater in sandy surface horizons under loblolly than under slash pine ecosystems This result could be related to the fact that there was approximately 30% more SOC reported in lobloll y pine ecosystems when compar ed to the SOC in slash pine ecosystems (Table 2 -2 ). One plausible explanation for this difference in SOC is that there is greater organic C addition from fine roots and leaf litter under loblolly. Previous studies support a possible explanation about dif ference in SOC added to loblolly vs. slash ecosystems A number of studies have indicated that loblolly pine produce more leaf area ( Colbert et al., 1990; Jokela and Mart in, 2000; Will et al., 2001 ; Xiao et al., 2002; Burkes et al., 2003), more fine roots (Burkes et al., 2003; Nowak and Friend, 2006) and a larger forest floor (Polglase et al., 1992) than does slash pine, particularly in plantation environments. Since SOC is a function of rate of input versus rate of mineralization, loblolly clearly inputs more organic C into the soil. Even under equal rates of mineralization, this suggests that loblolly should have higher SOC levels as long a mineralization rate under loblolly are not less than slash pine. Our observations of aggregation on surface sandy horizons of Spodosols in North Florida reveal ed that soil aggregates were bo u nd by mycorrhizal hyphae, fine roots, and microbial and fungal debris. These observations are consistent with those of Kay (1997) and Degens et al. (1996) in sandy soils. These o bservations combined with higher fine -root biomass and total SOC under loblolly pine plantations are sufficient to explain the increased AOC under loblolly. This knowledge enables managers to use the species that fit their needs whether it is to have more SOC added to the soil, or more crown cover for habitat management.

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27 The slight increase of the % AOC under slash pine plantations compared to loblolly ( 4.1% increase on average) indicated that slash pine systems may have a slight advantage over loblolly syst ems in aggregating soil C. We were unable to infer the reasons for this difference; however more importantly, data indicated that great er than 4 0% of total SOC was incorporated into soil aggregates under both slash an d loblolly pine ecosystems. Sarkhot et al. (2007a ) also found that approximately 45% of total SOC was incorporated in aggregates. These results suggest that if AOC is physically protected in sandy soils of north Florida, then promoting loblolly pine and soil aggregation could be potentially im portant management objectives for increasing soil C sequestration in these soils. Due to their dominance in Florida ecosystems pine ecosystems play an important role in t he conversion of atmospheric CO2 into the SOM pool to sequester SOC. Loblolly pine ec osystems in north central Florida have 30% more SOC than slash pine ecosystems. This species benefit translates into a C increase that would be equal to an increase of about three million dollars of CO2 in C credits for north central Florida; based a price of twelve dollars per ton of CO2 (Clear Sky Solutions, 2008). Clearly the selection of loblolly over slash pine on flatwood soils affects more than just forest productivity.

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28 Figure 21 Number of aggregates per gram of soil as affected by level of disp ersion energy applied and by the soil size fraction.

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29 F igure 22 Aggregate organic carbon (AOC) as a function of dispersive energy applied to the soil when expressed as a) % AOC ( % of S oil O rganic C arbon) by species and b) AOC (g g1) soil by speci es. 2b 2a

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30 Table 2 1. Sample characterization of pine sites located in North Central Florida County Dominant Species Latitude Longitude Taxonomic class Alachua L 29.711580 82.343671 hyperthermic Ultic A laquods Duval L 30.465195 81.530360 thermic typic Al aquods Lafayette L 30.017988 83.302321 thermic Aeric Alaquods St. Johns L 29.858920 81.361572 hyperthermic Ultic Haplaquods Taylor L 29.866656 83.418372 thermic Alfic Alaquods Taylor L 29.836545 83.428403 thermic Spodic Psammaquents Flagler L 29.486148 81.317456 hyperthermic Aeric A laquods Clay S 30.159932 81.945865 thermic Ultic A laquods Clay S 29.891132 81.853681 thermic Aeric A laquods Laf a yette S 30.017128 83.154864 thermic Ultic Alaquods St. Johns S 30.088603 81.534517 hyperth ermic Typic A laquods Clay S 29.849814 81.663151 thermic Aeric A laquods Volusia S 29.206476 81.512600 hyperthermic Aeric A laquods Table 2 2 Characterization of population and sample, significant differences (p<0.05) by species Species S oil O rgan ic C arbon % pH Forest Floor (Kg m 2 ) Population Means (Std. Error) L oblolly 1.72 (0.2) ** 3.53 (0.09) 1.52 (0.49) S lash 1.20 (0.09) 3.68 (0.06) 1.92 (0.45) Sample Means (Std. Error) Loblolly 2.36 (0.26)** 3.48 (0. 17) 4.07 (1.55) S lash 1.02 (0.15)* 3.44 (0.10) 2.23 (0.35) Significantly lower means ** Significantly higher means

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31 Table 2 3 Summary of aggregate organic carbon (AOC) as influenced by tree species. Statistical differences are outlined for lower and ** for higher means. Species summary Slash Loblolly Mean std. error Mean std. error AOC (g g 1 soil) 0.0065 0.0011 0.011** 0.0008 AOC (% of S oil O rganic C arbon ) 60 ** 3 53 1 Highest dis persion level AOC (g g 1 soil) 0.0022 0.0002 0.0033 ** 0.0003 AOC (% of S oil O rganic C arbon ) 20.5097 1.6502 16.4116 1.2633

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32 CHAPTER 3 DOES AGGREGATION PRO TECT C IN VERY SANDY SURFACE SOILS? The two major biological fluxes of C dioxide in n ature are photosynthetic fixation and global respiration. T hey cycle about 7% of atmospheric C annually. Moreover, 15 years of photosynthetic fixation, without renewal by respiration, would cause the exhaustion of the C dioxide in the atmosphere (Sylvia et al, 2005). The large pool of global soil C is susceptible to anthropological changes that occur with land use that can result in positive or negative changes Further understanding of how C is stored in soils is necessary in order to determine the possible management strategies that maintain soils as a major C sink These strategies should be a function of soil characteristics. Soil is a C sink when it can protect the SOC from decomposition and when C input occurs at a rate faster than C release. It is th ought that SOC is protected within aggregates physically chemically and physiochemically (Golchin et al., 1994 ; BlancoCanqui and Lal, 2004). S oil organic C turnover rates have been found to decrease from macro to microaggregates, therefore suggesting an increasing protection of SOC by microaggregates ( Besnard et al., 1996); an idea supported by Franzluebbers and Arshad (1997) as well as by Sainju et al. (2003). It has been revealed that C storage provided by macroaggregates is greater in quantity but transient in terms of physical protection (Tisdall and Oades, 1982). Wild (1988) documented that the quality of the SOC in microaggregates is biochemically recalcitrant with low turnover rates. Physical protection by aggregates is the incorporation of SOM w ithin macro and microaggregates (Tisdall and Oades, 1982; Golchin et al ., 1994; Jastrow, 1996); yet t he ability of soils to protect C through aggregation is also dependent on soil texture and time. SOC residence times in macroand microaggregates differ depending on the

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33 physiochemical attraction between mineral and organic particles, and the location of the organic material within the aggregate (Emerson, 1959). Buyanovsky et al. (1994) worked with agricultural soils in a 4 yr decomposition study and found that turnover rates were 1 3 yrs for macroaggregates and about 7 yrs for microaggregates. On the other hand, Skjemstad et al (1990) found no remarkable difference between the macro and micro aggregates of an Australian sandy soil. Christensen (1987) work ing with loamy sand and sandy loam soils found no physical protection by aggregation. Sandy soils make up a soil subgroup that, while having been studied for micro and macroagregation, (Sarkhot, 2007a,b; Tisdall and Oades, 1982 ; North, 1976; Christensen, 1992; Cambardella and Elliot, 1993; Six et al., 2001; Swanston et al., 2005) have not been well evaluated as to its ability to protect C through aggregation (Sarkhot et al., 2007a). As with aggregation in general, the amount of DO C contained in aggr egates and the mineralizability of the DOC in aggregates has been poorly understood, particularly for sandy soils. Dissolved organic matter is a contr olling factor in soil formation D O C in forest floors have been hypothesized to be generated from leaching and microbial decay of humus (McDowell and Likens, 1988). Fluxes of DOC from the forest floor that leaches into the mineral soil were estimated to represent 35% of the annual litterfall (Guggenberger and Zech, 1993; Currie et al., 1996; Michalzik and Matz ner, 1999; Solinger et al., 2001). Previous incubations showed 5 -93% of DOM in soil solutions to be potentially microbially degradable (Kalbitz and Kaiser, 2008; Jandl, 1999; Kalbitz et al., 2000; Sachse et al., 2001; Kalbitz et al., 2003; Don and Kalbitz, 2005; Kiikkil et al., 2006). Dissolved organic matter with large amounts of C was found to be rich in

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34 aromatic functional groups and poor in carbohydrates (Qualls and Haines, 1992; Jandl and Sollins, 1997; Jandl and Sletten, 1999; Volk et al. 1997; Kalbit z et al., 2003; Kiikkil et al., 2006). As a result, a large amount of dissolved organic matter that percolates to the mineral soil appears to be stable (Fr berg et al., 2006) ; supporting the DOC mineralizability numbers on the lower end of the range. However, if DOC is a stable source of C then if it is within aggregates it could be another pool of protected C. The strength with which aggregates are held together describes their ability to withstand disturbance. Aggregate dispersive energy (ADE) has been used to characterize a ggregate strength in a variety of soils (North, 1976; Christensen, 1992; Cambardella and Elliot, 1993; Six et al., 2001; Swanston et al., 2005). One would assume that ADE would play a role in protection of soil aggregate C; however, the relationship between ADE and C mineralization within aggregates has not been studied. The overall objective of this study was to evaluate the role that aggregation plays in SOC protection in the very sandy soils of the Lower Coastal Plain found under loblolly and slash pine ecosystems. This was achieved by addressing 2 aims. The first aim was to determine if ADE and ecosystem cover type (slash vs. loblolly) were controlling factor s in SOC and AOC accumulation and mineralization. The central hypothesis was that ADE expressed more control than species because the strength of aggregates would inhibit the release of AOC, while minimizing the entry of decomposing microorganisms. This conclusion was drawn from aggregation theories that proposed that encrustation of SOM in microaggregates is the principal pathway of C sequestration.

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35 The second aim was to Identify SOC chemical characteristics that could explain differences at AOC stability levels and SOC chemical characteristics that could explain differences in SOC mineralization between ecosystems cover types We hypothesized above that in North Floridas sandy soils; the mineralizability of AOC would be a function of the ADE but not of ecosystem cover type. We hypothesized here that the chemical fingerprint of SOM via DRIFTS woul d back up those suppositions by showing differences in the SOC fingerprints where mineralization differences were identified. loblolly or slash pine ecosystems Methodology Experimental Sites and Field Sampling The experimental sites were chosen from a pr evious stratified random sampling plan for a large scale research project with the objective of developing a soil C inventory of the state of Florida (Myers unpublished data), designed to proportionally sample sites relative to the land use pattern. The s tudy area used for this research was NRCS Cons ervation Area 2 located in North Florida. From this set of 55 sampling locations, 13 sites were selected. Seven sites supported a loblolly pine ( Pinus taeda L .) ecosystem and 6 sites supported slash pine ( Pinus elliottii Engelm.) ecosystems Originally 7 slash pine sites were chosen but during the study it was discovered that one site had been misclassified and was discarded. The sites were chosen so that they represented the mean and the range of soil C reported in the larger data set. Soil samples in the surface 20 cm were collected at the sampling locations with 4 cylindrical cores of 3 0 x 5.8 cm. The forest floor w as sampled at the same sites with a 23 cm2 litter sampler. Sampling sites were located in the north Florida counties of Alachua, Citrus, Clay, Duval, Flagler, Lafayette, St. Johns, Putnam, Tay lor, and Volusia (Table 1). Most study sites were

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36 Spodosols ( A quods) (Table 3 -1 ) The climate of the region was hot and humid with an average yearly precipi tation of 113.3 cm and yearly average minimu m and maximum temperatures of 14oC and 27C, respectively ( Southeast Regional Climate Center, 2010). The sampling sites were located on Flatwood landscapes; which are predominantly somewhat poorly to poorly -drai ned soil s with a seasonal ly high water table. Loblolly and slash ecosystems had a common understory of saw palmetto ( S. s erenoa repens Small ), wax myrtle ( myrica cerifera L ), gallberry ( ilex glabra L ), brakenfern ( pteridium aquilinum L ), blackberry ( rub us sp .), and fetterbush (Lyonia lucida Lam ). V arious grasses were common, such as bluestem ( Andropogon virginicus L ) and wiregrass ( Aristida beyrichiana). Perennial woody species included young oaks (Q uercus sp.) like water oak (Quercus nigra L.), sweet gum ( L iquidambar styraciflua L ), bays (P ersea sp.), and Florida maple ( acer barbatum ). Both l oblolly and s lash in ages range d from 10 20 years All but one of each species was in a plantation setting However, one loblolly and one slash pine site were nat ural pinelands. Laboratory Methods Soil Samples obtained from the field were measured for moisture content, and then air dried in a greenhouse before passing through a 2mm sieve. Bulk density was calculated by a core method (McIntyre, 1974). Soil pH wa s measured by the protocol of Thomas (1996) A comparison of the 13 sites used in this study to the total sites available for sampling from the larger study show that these study sites are representative (Table 3 -2). One hundred grams of air dri ed and s ieved samples were dry sieved through a 53 microns sieve for 5 min at 75 rpm using a horizontal mechanical shaker ( Sarkhot et

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37 al., 2007a ). Samples used in analyses and smaller than (2mm) this would include macro and microaggregates Mineralization An incubation study was utilized to determine if the ADEC was a controlling factor in AOC mineralization/protection. To that end micr ocosm for each soil sample were constructed, where each microcosm contained 20 grams of soil, 0.05 grams of soil inoculum and a liquid base trap of 20 mL of 0.25 M NaOH. The soil inoculum was added to provide a microbial population that could have been eli minated due to the sonic energy application. Each site used in this study was represented by nine microcosms that reflected three replications of three treatments. The treatments were no energy applied energy applied at mid -range of the ADE curves (ADE C ), and energy applied at the higher level of the ADEC (0, 140, and 260 j m L1 respectively). Since the sonication process required soil saturation, subsequent to the application the ADE each sample was filtered through a vacuum system at a pressure of 0.33 bars using a 22m membrane (Schwesig et al., 2003).Each soil sample (20 g) were placed in a microcosm to begin the incubation study. membrane was used to minimize the amount of DOC removed from the soil. The DOC was measured with a Shimadzu TOC -VCPH Analyzer (Shimadzu Scientific, Columbia, MD.). The microcosms were placed in an incubator at 35C. The water content was maintained by weight, and the base traps were changed periodically at weeks 4, 7, 12, 15, 19, 21, 25, and 29. When base traps were changed each sample was opened and its atmosphere was replaced by ambient air. The soil respiration, or alkaline trap method, was u tilized to determine the rate of C dioxide (CO2) evolution (Anderson,

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38 1982). In this method the trap function ed as a sink for evolved CO2 from the soil. The 0.25 M NaOH base solution was titrated with 0.1 M hydrochloric acid (HCl). The following formula w as utilized to measure C ( mg ) from respired C02 measured through titration from the SOC and DOC (see below) incubation studies: C (mg) respired = [(B -T) x M x E]/ DF Where B is the mL needed to titrate blank aliquot. T is the volume in mL needed to titrate the sample aliquot. M is the normality of the acid, in this case HCl. E is the equivalent weight, in this case the equivalent weight of C (E=6) was utilized. DF is the dilution factor of t he base trap (Anderson, 1982). Dissolved organic carbon (DOC) In order to determine the mineralization rates of the DOC solutions, a mineralization study was initiated where 60 mL of soil solution were poured into a 250 mL Erlenmeyer flask which had 0.05 g of soil inoculum The average of C in 60 mL was 0.54 mg with a s tandard error of 0.26. The CO2 evolved was measured as detailed above. All microcosms were sealed and placed in a dark incubator at 35 C for 84 days S amples were periodically shaken manually. Due to the small portion that DOC represents of the TOC, and due to the lack of differences between species and dispersion level, the mean DOC mineralization rate was used to calculate DOC mineralized over time and this was added to the mineralization of SOC to provide the total C mineralized from the TOC Fourier t ransformed infra -red reflectance (FTIR) spectroscopy Fourier t ransform i nfrared s pectroscopy s utility is based on its sensitivity, spectral precision, reproducibility and fast spectral acquisition time (Johnston et al., 1996). This method was utilized to describe organic C chemistry present by: species,

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39 incubation periods and dispersion energy levels. T he chemical composition of species, dispersion levels, and mineralization progression was investigated using DRIFTS in the mid -IR (4000 -400cm1 or 2500 -25000 nm ). Spectra were ru n on a DigiLab FTS -7000 FTIR instrument (Varian, Inc., Walnut Creek, CA) S amples were placed in a Pike AutoDiff 60-cup auto sampler (PIKE Technologies, Madison, WI) (Reeves, personal communication). Ground samples were placed in th e auto sampler cups and scanned using a KBr beam splitter and deuterated triglycine sulfate (DTGS) detector. Spectral subtraction of ashed samples from unashed samples was utilized to emphasize the organic composition; t his subtraction was completed util izing GRAMS/AI software, Version 7.02 (Thermo Galactic, Salem, NH) ( Sarkhot et al., 2007a). Spectr um by species w ere averaged from the ash-subtracted spectra from each site, and a discrimina nt analysis was performed to quantify how different was the separation between ash -subtracted species, mineralization days, and dispersion energy levels. Discriminant analysis was completed using a modified SAS program adapted to examine spectra (Reeves and Delwiche, 2008, Sarkhot et al., 2007a). Statistical Analysis T wo s eparate analyses were r u n one for the SOC incubation study and another for the combination of the SO C and DOC called TOC. The results of the statistical analyses for both differ only in the last three time periods of the incubation study. These result s suggest that DOC can be an influential fraction of the TOC turnover especially towards the end of the incubation study, since the addition of the DOC rate influenced the results from having species that were significantly different (without DOC addition) to statistically insignificant species. The statistical results presented here were for the analysis of the TOC mineralization. The soil and DOC mineralization rates were

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40 combined to define TOC mineralization and a GLM repeated measures analysis was ran i n STATISTICA (Statsoft, Inc. 9.0; Tulsa OK ). T he main effects were Species and Energy Level. Differences were considered significant if p < 0.05 If significant main effects or interactions were found, Tukeys post hoc multiple mean comparisons procedure w as utilized An ANCOVA was applied to the total DOC data and the DOC released at the three sonication energy levels The covariate in these analyses was T O C (mg g1 soil) while the main effects were Energy Level and Species (Table 3 3). Differences were considered significant if p < 0.05. If significant main effects or interactions were found, Tukeys post hoc multiple mean comparisons procedure was utilized. For the purposes of figures and tables all log(10) means resulting from statistical analyses wer e back transformed. Results Objective 1. Determine if Aggregate Stability Levels and Ecosystem Cover Types were a C ontrolling Factor in SOC and AOC Accumulation and Mineralization. Our results revealed that the ADE level of soil aggregates was not a fact or in protecting AOC (Fig. 3 -2). Even though periodic specific mineralization rates of TOC under slash pine was not statistically different from loblolly pine, the cumulative effect over time was significant resulting in higher specific C mineralization r ates in slash pine influenced soils; approximately 8% higher than found in soils influenced by loblolly pine (Fig. 3 -1a) Loblolly pine soils released more DOC from the aggregates than did slash pine soils as ADE increased (T able 3, Fig. 3 3). At the highest ADE, loblolly pine soil released an average of 36% more DOC in mg g1 soil th an did slash pine soils. This is

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41 attributed to the fact that loblolly pine soils had higher amount of TOC Dissolved Organic C incubation results revealed that by day 84 there was no significant effect of the dispersion energy or species on DOC mineralization rat e (Table 3 -3). Objective 2. Identify SOC Chemical Characteristics that could explain Differences at AOC Stability Levels and Mineralization between Ecosystems Cover T ypes. Dis criminate analysis produced a poor separation of ash-subtracted dispersion energy levels as evidenced by R2s of 0.07, 0.00, and 0.05, respectively (Table 3 -5 ) However, DRIFTS analysis identified distinctly different spectra for slash pine and l oblolly pine influenced soils. Spectral bands between 2000 and 1200 cm1 in the non ashe d samples (Fig. C -1, C -2 ) are related to silica, revealing soils with high mineral matter (Reeves III and Smith, 2009). Yet, c omparison of the average of ash -subtracte d spectra for both species supported the higher organic matter in loblolly soils (Figure 4), and indicated that the functional group assemblages were also influenced by ecosystems. Figure 5 represents the difference of ash-subtracted loblolly minus slash averaged spectra. S lash appear ed to have more aliphatic C -H indicated by the bands between 2900 -3000 cm1 (Reeves III and Smith, 2009; Madari et al., 2005). L oblolly appeared to have more aromatic carboxylic acids as indicated by the bands around 16001700 cm1 (Reeves III et al., 2006; Celi et al., 1997) Discriminant analysis for ash subtracted (R2 = 0.875, SE = 0.176) and for non ash subtracted spectra (R2 =0.977, SE = 0.176) showed that ash -subtracted spectra provided a stronger contrast; indicating that organic compounds were responsible for the differences seen between species, rather than mineralogy (Table 3 -4 ) Therefore, ash -subtracted spectra were used for subsequent analyses.

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42 Ash -subtracted spectra for week 29 and week 1 from the incubation study provided a distinct difference. Discriminant analysis produced a high R2 (Table 3 -6) and indicated that mineralization had affected each species differently. Figure 3 -6 shows loblolly pine ecosystems experienced a large decrease in reflectance in the wave number range of 3000 to 3600 cm1. This i s a general region for OH and typical for humic acids. The triplet band at 1800 to 2000 cm1 is due to an increase in silica over time as OM decreases. Finally, an accumulation of aromatics occurs with the deg radation of the humic substances. Alternatively, the subtraction spectra between week 29 and week 1 for slash pine ecosystems also appeared to have an i ncrease in C -H aliphatic carbon indicated by the bands around 2900 3000 cm1. Figure 3 -7 reveals large peaks at 30003500 cm1 (peaks 3531, 3460, 3170, and 3054) and a specific peak at 1295 cm1 which are attributed to aliphatic materials. The triplet band at 1800 to 2000 cm1 is due to an increase in silica over time as OM decreases. Overall, there is an accumulation of aliphatic materials in slash pine over time; on the other hand, loblolly shows an accumulation of humic materials and aromatic material over time as other OC degrades. Discussion Our initial hypothesis was that ADE would be a controlling fa ctor in the accumulation and protection/sequestration of AOC; while ecosystem cover type would not be a factor Our results from the TOC incubation study suggest no ef fects of aggregate dispersive energy on turnover rates and an effect between ecosystem cover types; rejecting our hypothesis (Fig. 3 2) Total o rganic C mineralization r ates were a product of the combination the DOC and SO C separate incubations In sandy soils under pine ecosystems of slash and loblolly pine there was no significant differe nce in the TOC miner alization rates of

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43 aggregates at different stability levels. Findings support the contention held by some that SOC mineralization is more related to the stability/liability of the SOC than to the physical protection of aggregation (Chri stensen, 1985, 1987 ; Buyanovsky et al. 1994). Our results conflict with the concept that the formation of macroaggregates facilitates the accumulation of organic matter and given favorable conditions physical protection is promoted ( Jastrow, 1996). The reason why our results conflict with this concept may be that favorable conditions are not promoted in F loridas soils with characteristics like: <10 cmolc kg1 cation exchange capacity, and less than 5% silt plus clay (Ca rlisle et al., 1981,1988, 1989) Whil e we did not expect species to have a control on mineralization; the SOC under the two different ecosystem cover types did mineralize differently (Fig. 3 1). The soil under slash pine had higher specific mineralization rates, even though the total mineralization under loblolly pine was greater due to a significantly larger SOC content under that species (Table 3 2). This is the first time that SOC mineralization from soils influenced by these two ecosystems has been contrasted; both in terms of mineraliz ability and DRIFTS spectra for chemical differences. The lower mineralization rates, combined with the higher organic matter inputs under loblolly pine explain the higher SOC levels found in the previous chapter. Mid -infrared spectroscopy has been found ca pable to predict soil properties like organic and total C (Minasny et al., 2009). Mid-infrared spectra were utilized to analyze relationships throughout the incubation study This method id entified chemical differences in the samples from the incubation st udy between : dispersion energy level s pine ecosystems and incubation periods Results revealed no irrefutable difference

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44 between dispersion energy levels or stability levels measured in the incubation study. On the other hand, d efinite differences were fo und in respect to species chemical composition. Spectra comparisons between the two soils under different ecosystems reflect the greatest distinction between ecosystems (Fig. 3 -3, 3 4 ). More aliphatic C -H present in slash could be related to waxes or methy l groups which are more bioavailable than the larger amount of aromatic carboxylics present in loblolly pine ecosystems. Differences found comparing incubation times were also evident only for week 1 and week 29 (last week of the incubation). The last week of the experiment revealed a change in the patterns of the chemical composition: a decrease in humic acids and increase in aromatics of loblolly, and an increase in the aliphati c C -H of slash over time (Fig. 3 -5, 3 -6 ). Another significant finding was re lated to the DOC incubation study, in that it was identified as an important component of TOC turnover. Our findings are congruent with Zhao and Kalbitz (2008) ; who did a study in China in forested Typ -Ishumisol soils sampled to a depth of 20 cm and reveal ed an average mineralization of 0.06 g kg1 day1 of DOC which was similar to our findings of 0.05 g kg1day1 of DOC mineralization rate. Schwesig et al (2003) studied DOC mineralization released by water extractable C coming from the surface 20 cm of s p odosols dominated by Norway spruce ( Picea abies ); however, t hey found a total mineralization rate over 97 days of 0.0004g kg1. Our results are similar to Zhaos measurements and our findings suggest that DOC is an important component of aggregation in sur face spodosols; the evidence is shown in Figure 2 where as increasing ultrasonic energy is applied there is an increasing release of DOC

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45 In the end, our findings warranted rejection of our hypothesis No differences were revealed between energy dispersion levels, a finding supported by both incubation and mid-IR analyses. H owever, findings suggest a significant difference in the C quality between species. The quality of needles and roots of loblolly vs. slash has not been widely studied and these data im ply that more detailed work on the quality of organic matter inputs (roots, leaves, branches) will be necessary in order to quantify C cycling between these species. Comparisons between cover types are important in order to understand how SOC is related to the forest floor material and, ultimately, its influence in soils as a sink of C.

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46 Figure 31 Comparison of the carbon ( C ) mg g1 soil by dispersion energy levels (J mL1) by week for: a) loblolly and b) slash pine ecosystems. There were no significant differences found between the ADE levels for each period. b) a)

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47 Figure 32 Total dissolved organic carbon (DOC) dispersed from the soil when three dispersion energy levels were applied via sonication. Significant differences are illustrated at dispersion energy of 153 j m L1. Figure 33. Diffuse reflectance infrared Fourier transform spectra fingerprint of the average ash subtracted spectra.

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48 Figure 34 Diffuse reflectance Fourier transform spectra of the difference between spe ctra of ash -subtracted loblolly minus slash pine influenced soils. In this graph it is possible to see slash pine ecosystems appear to have more aliphatic C -H indicated by the region around 2900 -3000 cm -1 than loblolly pine; and loblolly pine ecosystems reveal more aromatic carboxylic acids present at wavelengths 16001700 cm 1.

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49 Figure 35 Spectral subtraction between weeks 29 and 1 for loblolly pine influenced soils Absorbance located above 0.00 indicates an increase of week 29 over week 1 and absorbance below 0.00 indicate the opposite. This figure reveals that in the wave number range of 3000 to 3600 cm -1 in week 1 there was more OH and humic acids. The triplet band at 1800 to 2000 is an increase in silica over time as organic matter decreases

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50 Fig ure 36 Spectral subtraction between weeks 29 and 1 for slash pine influenced soils Absorbance located above 0.00 indicates a n increase of week 29 over week 1 and absorbance below 0.00 indicate the opposite. Wave numbers between 1600 and 400 reveal over time increase in the CH, OH and aliphatic materials, and the large peaks at 30003500 and a specific peak at 1295 are also an increase over time attributed to aliphatic materials.

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51 Table 3 1. Description of sample sites County Dominant Species Latitude Longitude Taxonomic class Alachua L 29.71158008 82.34367117 hyperthermic Ultic A laquods Duval L 30.46519541 81.53036099 thermic T ypic Ala quods Lafayette L 30.01798816 83.30232129 thermic Aeric Alaquods St. Johns L 29.85892000 81.36157200 hypert hermic Ultic A laquods Taylor L 29.86665683 83.41837283 thermic Alfic Alaquods Taylor L 29.83654543 83.42840383 thermic Spodic Psammaquents Flagler L 29.48614818 81.31745613 hyperthermic Aeric A laquods Clay S 30.15993245 81.94586597 thermic Ulti c A laquods Clay S 29.89113266 81.85368181 thermic Aeric A laquods Lafeyette S 30.01712800 83.15486400 thermic Ultic Alaquods St. Johns S 30.08860306 81.53451760 hyperthermic Typic A laquods Clay S 29.84981430 81.66315102 thermic Aeric A laquods Volusia S 29.20647699 81.51260040 hyperthermic Aeric A laquods Table 3 2 Population and Sample means with corresponding standard errors for total carbon (TC), soil organic carbon (SOC) Species TC % SOC % pH Forest Floor (Kg m^2) Population Means l oblolly 2.08 (1.05) 2.00 (1.04) 3.70 (0.74) 3.34 (3.62) S lash 1.57 (1.06) 1.57 (1.06) 3.74 (0.42) 2.72 (2.79) Sample Means l oblolly 2.45 (0.68) 2.45 (0.68) 3.5 (0.45) 3.83 (2.56) Slash 1.68 (1.51) 1.67 (1.52) 3.66 (0.57) 3.74 (3.80) Table 3 3 Total D issolved O rganic C arbon mg g1 soil with corresponding standard errors and species comparisons. S ignificant differences (p<0.05) by species are indicated by a for lower and ** for higher means. J m L 1 Slash Loblolly 0 0.17748 (0. 002) 0.19561 (0.021) 58 0.22793 (0.024) 0.26372 (0.028) ** 260 0.25140 (0.028) 0.39023 (0.045) ** Table 3 4 Discrimina nt analysis for the separation of species from spectral analysis displayed by R2 and Resi dual Mean Squared Deviation (RMSD ). R 2 RMSD ash subtracted 0.875 0.176 non ash subtracted 0.977 0.075

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52 Table 3 5 Discrimina nt analysis for spectral separation ash -subtracted of energy level is displayed by R2 for dispersion energy level by species Dispersion energy l oblolly s lash 0 j mL 1 0.57 0.2 58 j mL 1 0.05 0.47 153 j m L 1 0.05 0.48 Table 3 6 Discriminant analysis for mineralization study of SOC by species with R2 and Residual Mean Squared Deviation (RMSD). l oblolly s lash week R 2 RM SD R 2 RMSD 1 0.8 0.09 0.85 0.08 1 5 0.77 0.23 0.78 0.23 21 0.69 0.23 0.7 0.22 2 9 0.87 0.18 0.85 0.19

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53 CHAPTER 4 SYNTHESIS Floridas pine lands are in decline; nevertheless they are a landuse of key importance. Although in decline, pine flatwoods comprise the most extensive Florida ecosystem covering approximately 50% of the land area (Florida Forest Stewardship, 2006). Total organic C is known as a key factor to the quality of Floridas sandy soils. Knowledge about aggregate distribution and strength are crucial to the dynamics of water and air movement through soil, which in turn affects soil quality and its productivity ( Blanco -Canqui and Lal, 200 4). A current issue is the capability of soil aggregates to protect C from transformation by mineralizati on, due to the potential of soils to protect C from evolving into CO2 which affects directly our climate change problems. The first gap of knowledge filled by our study was the sonication method evaluation. Our results revealed m icroscopic evidence that so nication successfully disperses soil aggregates. The sonication method was deemed adequate to disrupt aggregates without affecting chemical composition of the organic matter. However, in order to fully understand this method, further studies on its abili ty to br eak young particulate organi c matter fractions are still necessary. Our study outlines main differences between loblolly and slash pine ecosystems in terms of total SOC, as percent and per gram of soil. SOC% was in both cases higher in loblolly tha n slash pine ecosystems Although our sample was small it still was representativ e of the differences seen in the population (Table 2 2 ). The values are not necessarily reflecting equivalent management regimes, but they represent how slash and loblolly are managed in the real life landscape. Further understanding of the relative

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54 distribution of SOC among these two ecosystems is key in order to understand ecosystems functions and the contributions of each species on its soil as potential C sink. It was fou nd that l oblolly ha d a 40% higher AOC ( g g -1 soil ) than slash ecosystems. One plausible expla nation for this difference is that there is greater organic C addition from fine roots and leaf litter under loblolly (Nowak and Friend, 2006; Burkes et al. 2003; Xiao et al., 2002; Will et al. 2001; Polglase et al., 1992; Colbert et al., 1990). We suggest further research where a fractionation of soil may increase sensitivity and find which aggregate sizes differ by ecosystem. Previous preliminary research has show n that size fraction 150m (Azuaje, unpublished data) contain aggregates with the high est stability levels. Knowledge about AOC size fractions within 2000 and 53 m a nd their relative ability to protect C has yet to be studied. The slight increase of the AOC as a % of S OC under slash pine plantations compared to loblolly ( 4.1 % increase on average) indicates that slash pine systems have a slight advantage over loblolly systems in aggregating soil C. We were unable to infer the reasons for this difference H owever more importantly, data indicated that great er than 40% of SOC was incorporated into soil aggregates under both slash and loblolly pine ecosystems a result supported by Sarkhot et al. ( 2007b). Our findings clearly show that C in Floridas sandy s oils under loblolly and slash pine ecosystem is not physically protected from mineralization. Whether the results would be different with a more specific size fraction needs to be investigated. Luckily, forest management maintains a soil that is relatively undisturbed for close to two decades. During that time C will accumulate as seen by this study. However, harvesting

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55 and subsequent reforestation will put all soil C in jeopardy of being lost given this lack of physical protection. The chemical fingerprint provided by mid IR analysis was crucial in understanding C quality differences for stability levels and species. There were no spectral differences found between stability levels of aggregates, supporting the lack of physical protection suggested by the i ncubation study. As for species, mid IR spectra revealed significant differences in the C quality in the SOC of loblolly vs. slash pine ecosystems; also supporting the previous differences in mineralization rates found between species. F urther research int o the chemical differences between species should yield information that will make modeling SOC mineralization more accurate. These s tudies should concentration on the original components of the SOM (i.e. roots, needles, and woodstem). Their comparison wit h SOC may reveal information about the origin of compounds present in the soil C matrix. DOC was identified as an important component of TOC turnover based on the evidence that as increasing ultrasonic energy is applied there is an increasing release of DOC. This component should be investigated as to whether it is a significant agent for keeping microaggregates together. Results from previous studies suggest the importance for innovative approaches for studying aggregation in sandy soils, in order to ass ess the long term effects of management practices on the soil C ( Sarkhot, 2007 a). Due to their dominance in Florida ecosystems pine lands play an important role in the conversion of atmospheric CO2 into the SOM pool that sequester s SOC. Loblolly pine ecos ystems in north central Florida contain approximately 13.44 metric tons of C per ha 20cm depth more than

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56 slash, which translates to four hundred million dollars in CO2 C credits increase if slash had been loblolly pine. A gricultural activities includ ing f orestry are net sink s that offsets over 4% of all US GHG (green house gas) emissions. F orests can mitigate GHG by adjusting the type and intensity of agricultural pr oduction (Follett, 2010). In the case of soil science a key to the reduction of GHG is in the combination of improved soil and landuse management techniques. Information about the quality and quantity of AOC at different stability level s and size fractions can aid in the generation of C footprint baselines from which we can measure improvem ent of C sequestration in soils.

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57 APPENDIX A POPULATION CHARACTERIZATION Table A-1 Complete dataset of 56 pine sites located in north central Florida under spodosols County Species Latitude Longitude TC % SOC % PH Litter (Kg m^2) Alachua S 29.712375 82. 154249 0.719 0.719 4.15 NA Alachua S 29.704885 82.311552 1.056 1.056 4.04 4.012 Alachua S 29.7535 82.198581 0.824 0.824 3.42 1.518 Alachua S 29.708777 82.327257 5.045 5.045 3.46 0.660 Baker S 30.297969 82.088372 2.317 2.317 3.58 0.867 Baker S 30.3 02311 82.081807 0.692 0.692 3.9 0.613 Baker S 30.298978 82.081279 1.476 1.476 3.71 0.418 Baker S 30.294036 82.066642 1.171 1.171 4.13 0.576 Baker S 30.235531 82.310725 0.794 0.794 3.85 0.939 Clay S 30.159932 81.945865 0.688 0.688 3.79 0.464 Clay S 29.891132 81.853681 1.185 1.185 3.54 0.990 Clay S 29.890983 81.858647 0.984 0.984 3.24 1.623 Duval S 30.342972 81.869493 2.323 2.323 3.94 6.571 Flagler S 29.481865 81.273662 0.830 0.830 3.73 6.083 Hamilton S 30.527465 82.979794 0.776 0.776 4.94 2.447 Hamilton S 30.530920 82.736469 1.509 1.509 3.64 0.397 Lafayette S 30.017128 83.154864 1.580 1.580 3.33 2.514 Lafayette S 29.88705 83.313558 3.373 3.373 4.17 1.273 Lafayette S 30.062431 83.311331 1.207 1.207 3.6 NA Lafayette S 30.018853 83.2 79406 1.755 1.755 4.2 2.032 Nassau S 30.65586 81.554301 1.055 1.055 3.94 2.218 Nassau S 30.725651 81.997664 1.116 1.116 3.86 0.331 Nassau S 30.699379 81.596138 2.552 2.552 3.15 0.996 Putnam S 29.84981 81.663151 0.950 0.950 3.4 9.542 Putnam S 29.64 5555 81.824897 1.480 1.480 4.01 1.752 Putnam S 29.590073 81.932761 1.097 1.097 3.39 6.642 St. Johns S 30.005943 81.539609 1.155 1.155 3.57 3.223 St. Johns S 30.088603 81.534517 0.921 0.921 4.74 1.481 St. Johns S 29.717134 81.301320 1.990 1.990 3.4 6 1.722 Union S 30.077723 82.40143 1.499 1.499 3.48 1.936 Volusia S 29.206476 81.51260 4.708 4.708 3.16 7.473 Volusia S 29.113007 80.993003 1.495 1.495 3.21 10.237 Alachua L 29.711580 82.343671 1.272 1.272 3.73 4.145 Alachua L 29.715106 82.150826 3.099 3.099 3.09 3.628 Baker L 30.304482 82.067178 0.700 0.700 3.46 2.016 Baker L 30.333257 82.477562 0.889 0.889 3.6 0.811 Baker L 30.256181 82.088706 0.797 0.797 3.69 1.339

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58 Table A -1. Continued County Species Latitude Longitude TC % SOC % PH Lit ter (Kg m^2) Dixie L 29.542032 83.2539598 0.786 0.786 4.1 0.848 Duval L 30.465195 81.530360 3.468 3.468 3.56 8.590 Duval L 30.134640 81.635989 2.977 2.977 3 15.775 Flagler L 29.486148 81.3174561 2.360 2.360 3.07 3.673 Hamilton L 30.401772 82.681 447 1.699 1.699 3.97 2.388 Hamilton L 30.5 33555 82.735459 2.265 2.265 3.49 0.202 Lafayette L 30.017988 83.302321 2.182 2.182 3.2 5.322 Marion L 29.233783 81.917593 0.621 0.621 3.76 0.769 Nassau L 30.596419 81.907466 1.513 1.513 4.18 2.048 Putnam L 29.689604 81.745887 4.145 4.145 3.16 0.820 St. Johns L 29.85892 81.361572 2.493 2.493 3.23 4.276 St. Johns L 29.952492 81.454457 2.958 1.404 6.29 2.225 Taylor L 29.866656 83.418372 2.973 2.973 3.33 3.221 Taylor L 29.836545 83.428403 2.401 2.401 4 .4 1.449 Alachua 29.712529 82.1519448 5.376 5.376 2.98 3.044 Baker 30.241833 82.301556 1.212 1.212 3.57 0.251 Hamilton 30.493726 82.873999 1.291 1.291 3.53 2.697 Taylor 30.195384 83.8684674 1.845 1.845 5.55 1.424 Table A-2 Slash (S) and Loblolly (L) pine ecosystem population descriptive data of Soil Organic Carbon % (SOC %), pH, and forest floor (Kg m2). Species SOC % pH Forest Floor (Kg m 2 ) S 1.495 3.21 10.237 S 4.708 3.16 7.473 S 0.830 3.73 6.083 S 1.097 3.39 6.642 S 1.480 4.01 1.7 52 S 1.056 4.04 4.012 S 5.045 3.46 0.660 S 0.719 4.15 NA S 1.990 3.46 1.722 S 0.824 3.42 1.518 S 0.950 3.4 9.542 S 3.373 4.17 1.273 S 0.984 3.24 1.623 S 1.185 3.54 0.990 S 1.155 3.57 3.223 S 1.580 3.33 2.514 S 1.755 4.2 2.032 S 1.207 3.6 NA

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59 T able A -2. Continued Species SOC % pH Forest Floor (Kg m 2 ) S 1.499 3.48 1.936 S 0.921 4.74 1.481 S 0.688 3.79 0.464 S 0.794 3.85 0.939 S 1.171 4.13 0.576 S 2.317 3.58 0.867 S 1.476 3.71 0.418 S 0.692 3.9 0.613 S 2.323 3.94 6.571 S 0.776 4.94 2.44 7 S 1.509 3.64 0.397 S 1.055 3.94 2.218 S 2.552 3.15 0.996 S 1.116 3.86 0.331 L 0.621 3.76 0.769 L 2.360 3.07 3.673 L 0.786 4.1 0.848 L 4.145 3.16 0.820 L 1.272 3.73 4.145 L 3.099 3.09 3.628 L 2.401 4.4 1.449 L 2.493 3.23 4.276 L 2.973 3.33 3. 221 L 1.404 6.29 2.225 L 2.182 3.2 5.322 L 2.977 3 15.775 L 0.797 3.69 1.339 L 0.700 3.46 2.016 L 0.889 3.6 0.811 L 1.699 3.97 2.388 L 3.468 3.56 8.590 L 2.265 3.49 0.202 L 1.513 4.18 2.048 5.376 2.98 3.044 1.845 5.55 1.424 1.212 3.57 0. 251 1.291 3.53 2.697

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60 APPENDIX B AGGREGATE DISPERSION ENERGY CURVES A B Figure B 1 Aggregate dispersion energy curve samples for loblolly pine ecosystem in aggregate organic carbon (AOC) as % of total soil organic carbon (SOC) by energy in JmL1.

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61 C D Figure B 1. Continued

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62 E F Figure B 1. Continued

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63 G Figure B 1. Continued

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64 A B Figure B 2. Aggregate dispersion energy curves samples for slash pine ecosystem in aggregate organic carbon (AOC) as % of total soil organic carbon (SOC) by energ y in JmL 1 (Fig. A F)

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65 C D Figure B 2. Continued

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66 E F Figure B 2. Continued

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67 A B Figure B3. Aggregate d ispersion e nergy curve samples for loblolly pine ecosystem in aggregate organic c arbon (AOC) g/g of soil by energy in J/mL (Fig. A -F)

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68 C D Fig ure B 3. Continued

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69 E F Figure B 3. Continued

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70 A B Figure B 4. Aggregate Dispersion Energy Curves samples for slash pine ecosystem in Aggregate Organic Carbon (AOC) g/g of soil by energy in J/Ml (Fig. A -F)

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71 C D Figure B 4. Continued

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72 E F Figure B 4. Continued

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73 APPENDIX C MID -INFRARED REFLECTANCE (MID -IR) Figure C -1. Mid -infrared nonashed spectrum average o f loblolly pine influenced soil Figure C -2. Mid -infrared nonashed spectrum average of slash pine influenced soil

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74 Figure C -3. Mid -i nfrared average spectrum of ash -subtracted loblolly pine influenced soil Figure C -4. Mid -infrared average spectrum of ash -subtra cted slash pine influenced soil

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75 Figure C -5. Mid -infrared average spectrum of ash -subtracted loblolly pine influenced soil s at week 29 of incubation study. Figure C -6. Mid -infrared average spectrum of ash -subtracted slash pine influenced soils, at week 29 of incubation study.

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83 BIOGRA PHICAL SKETCH The author was born in San Felipe, Venezuela. While r aised between different family members (mom, dad, grandmother, and aunt), she had the opportunity to live in many places throughout childhood. As soon as high school ended she knew that an environmental career was her passion, however, in Puerto Ordaz, Venezuela the only colleges available had no environmental majors. As she started a major in sociology she became an entrepreneur by creating the first environmental group called MAWIDA at the Andres Bello Catholic University S he became recognized through this group and as a result the university granted her the opportunity to create and administrate a business (MAWIDAs caf) that would provide a half scholarship for a student and also funds for the group. MAWIDAs caf was a place from which she would impart environmental values through healthy meals, recycling and an eco-friendly environment Although she was satisfied with her accomplishments, Elena knew she had to expand her knowledge in order to be able to convey environmental awareness. She then made the decision to transfer to Valencia community college in the United States as she prepared herself to begin at the Univ ersity of Florida completing a b a chelor in forest resources and c onserv ation. In her senior year while working as a laboratory assistant for the forest soils laboratory, she started a combined degree to acquire a m a sters degree in soil and water s cience.