... ..
«w _
ﬂax;
wmﬁmm,

v... .,.

c
I...

4
L ”J...
E .
3:55
a.

.33.... .5

£3.4qu

 

 

 

 

.rxhh‘..&maﬂ.cnv v n ‘ v
.ﬂﬂv‘mwﬁﬁt .‘mnr
.I: rufwwnwr
ruﬁa is.“
1:! vs. «-

fly":
.4

a. 1A...
u. I\ t.“

t: .
.t,,

5.. Eur? RE? A . am:
5 u. 71.23.

 

 

 

 

2/ LIBRARY
2 or); MiChlgcm State
f University

 

This is to certify that the
dissertation entitled

Soil Resource Heterogeneity and Ecosystem Processes:
Effects of Litter Aggregation on Soil Microbial Processes and

Plant Root Foraging
presented by
Terrance David Loecke

has been accepted towards fulﬁllment
of the requirements for the

Ph.D. degree in Crop and Soil Sciences

of? \ZZaQA

Major Professor’s Signature
/ / " F ' 0?-

Date

 

 

MSU is an affirmative-action, equal-opportunity employer

 

 

PLACE IN RETURN BOX to remove this checkout from your record.
TO AVOID FINES return on or before date due.
MAY BE RECALLED with earlier due date if requested.

 

DAIEDUE

DAIEDUE

DATEDUE

 

APR 2 3 2009

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

6/07 p:/ClRC/DateDue.indd-p.1

 

SOIL RESOURCE HETEROGENEITY AND ECOSYSTEM PROCESSES:
EFFECTS OF LITTER AGGREGATION ON SOIL MICROBIAL PROCESSES
AND PLANT ROOT FORAGING

By

Terrance David Loecke

A DISSERTATION
Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY

Department of Crop and Soil Sciences

2007

ABSTRACT

SOIL RESOURCE HETEROGENEITY AND ECOSYSTEM PROCESSES:
EFFECTS OF LITTER AGGREGATION ON SOIL MICROBIAL PROCESSES
AND PLANT ROOT FORAGING
By
Terrance David Loecke

Resource spatial distribution alone can alter ecosystem process rates.
Soil resource aggregation within the scale of individual plants can potentially
affect primary productivity, plant C allocation, plant N acquisition, decomposition,
net N mineralization, N20 emissions, and ecosystem N retention. To understand
how plant litter aggregation affects these processes I distributed Trifolium
pratensis litter in soil across an aggregation gradient from uniformly distributed to
highly aggregated. I examined the effects of this aggregation gradient on
decomposition rates and N20 emissions with two laboratory studies and plant
growth and N cycling with two ﬁeld experiments.

Results show that litter aggregation in soil delayed decomposition for 5 to
7 days and that this delay was likely caused by insufficient 02 supply to the
interior of the litter aggregates. In contrast, aggregated litter stimulated
emissions of the greenhouse gas N20 7-fold compared to uniformly distributed
litter. Elevated N20 emissions in response to litter aggregation were found
regardless if the litter was ﬁnely ground or chopped into 5 mm pieces.

Plant root systems can respond to litter aggregation by foraging into

resource-rich microsites; however, the degree to which plants beneﬁt from root

foraging into microsites of varying quality is largely unknown. I examined
whether root foraging into microsites of varying quality depended on plant
growth. I found that Avena sativa L. root foraging was positively correlated with
growth in response to pairwise choices of contrasting microsite qualities. In
contrast, root foraging by Bromus inermis L. was not related to plant growth
response. In addition, I found that plant N status plays an important role in
regulating Zea mays L. root foraging under ﬁeld conditions. These two results
suggest that root foraging is only an important mechanism for plant N acquisition
under heterogeneous conditions where N is limiting plant productivity.

To better understand the effects of litter aggregation on plant growth and
N cycling I distributed 15N-Iabeled litter across an aggregation gradient and
followed the fate of the litter-N into plant and soil N pools. Under N-limited
conditions maize was 14% more productive in response to aggregated than
unifome distributed T. pratensis litter. In contrast, Secale cereale litter
aggregation did not affect maize growth. Litter distribution did not affect root to
shoot ratio; however, total belowground C allocation appeared to be greater in
response to uniformly distributed than to aggregated T. pratensis litter. Plant N
acquisition was greater in response to aggregated than uniformly distributed
litter. Litter aggregation also increased litter-derived N mineralization by 20%,
shoot N by 18%, and root N by 33% relative to unifome distribUted litter. I
suggest that the spatial coupling of roots and litter aggregates is an important
factor regulating C and N cycling in agricultural system with heterogeneous

resource distributions and where N is limiting plant productivity.

ACKNOWLEDGEMENTS

I would like to thank my advisor, Dr. G. Philip Robertson, for his advice and
guidance throughout my dissertation. I would also like to thank my committee
members, Dr. Katherine Gross, Dr. Michael Klug, and Dr. Sieglinde Snapp for
their time, feedback, patience, and assistance with many different stages of this
process. Many other faculty members at K88 and MSU have helped me achieve
my of goal of attending my Ph.D. as well including Drs. Steve Hamilton, Dale

Mutch, Alvin Smucker, Jeff Conner, and Jay Lennon.

Many people contributed to the ﬁeld and lab work required for this research. For
this I would like to thank Sarah Seehaver, Chn's Smart, Mathew Beckwith, Matt
Gorentz, Claire McSwiney, Todd Robinson, Brook Wiley, Jarad Mellard, Stacey
VanderWulp, Joe Simmons, Greg Parker, Bob Adams, Jim Stonebumer, Nina
Consolatti, Jim Bronson, Larry Langshaw, Stu Bassett, Justin Rensch, Todd
Martin, Suzanna Sippel, Andrea Twolerston, Liberty Asbury, Besty Muellen, Dave

Weed, Sara .Kelly, Brian Rensch, Brian Demming, Sven Bohm, and Carol Baker

I consider my time at KBS time well spent because I was intellectually challenged
and many friendships were made. I would especially like to acknowledge these
KBS’ers: Stuart Grandy, Sara Parr, Sven Bohm, Kurt Smemo, Wendy Mahaney,

Rich Smith, Sarah Emery, Brook Wilke, Emily Wilke, Jarad Mellard, Emily

Grrnan, Zach Aanderud, Chad Brasil, Tara Darcy—Hall, Spencer Hall and Claire

McSwiney for being such great friends and colleagues.

The support staff at KBS makes life and research there a joy. For this I would
like to thank: Barb Fox, Nina Consollati, Sally Shaw, Alice Gillespie, John

Gorentz, and Melissa Yost for their assistance.

I reserve my deepest appreciation for my .wife and colleague, Amy Burgin, for
without her to share my life and work I cannot imagine. My family Dave, Janice,
Don, Debbie, Steve, Jodi, Matt, and Jessica have always supported my curiosity

and for this I thank you.

This research was funded by the K38 Long-Tenn Ecological Research (National
Science Foundation, DEB) grant, a Doctorial Dissertation Improvement Grant
(NSF), a Michigan State Sustainable Agriculture grant (USDA), a Sustainable
Agricultural Graduate Student Fellowship (North-central SARE-USDA), Whiteside
Fellowship, a travel grant from the Biogeochemistry Program (MSU) and a

Michigan State University recruitment fellowship.

TABLE OF CONTENTS

LIST OF TABLES ................................................................................ VIII

LIST OF FIGURES .............................................................................. XII

CHAPTER 1: SOIL RESOURCE HETEROGENEITY AND ECOSYSTEM

PROCESSES ....................................................................................... 1
INTRODUCTION .......................................................................... 1
QUESTIONS ADDRESSED ......................................................... 6
REFERENCES ............................................................................. 9

CHAPTER 2: ROOT FORAGING IS RESPONSIVE TO SOIL MICROSITE

RESOURCE QUALITY AND AVAILABILITY ............................................. 13
INTRODUCTION ........................................................................ 13
MATERIALS AND METHODS ........................................................ 15
RESULTS ................................................................................. 21

. DISCUSSION ............................................................................. 24
CONCLUSIONS ......................................................................... 28
REFERENCES ........................................................................... 29

CHAPTER 3: LITTER AGGREGATION HAS A DISPROPORTIONATE

INFLUENCE ON NITROUS OXIDE FLUX ................................................. 42
INTRODUCTION ........................................................................ 42
MATERIALS AND METHODS ........................................................ 45
RESULTS ................................................................................. 52
DISCUSSION ............................................................................. 56
CONCLUSIONS ......................................................................... 60
REFERENCES ........................................................................... 62

CHAPTER 4: AGGREGATED SOIL RESOURCES ENHANCE PRIMARY

PRODUCTIVITY .................................................................................. 86
INTRODUCTION ........................................................................ 86
MATERIALS AND METHODS ........................................................ 87
RESULTS ................................................................................. 97
DISCUSSION .......................... , ................................................. 100

vi

CONCLUSIONS ........................................................................ 106

REFERENCES ......................................................................... 107
CHAPTER 5: LITTER AGGREGATION ALTERS TERRESTRIAL NITROGEN
CYCLING ......................................................................................... 124

INTRODUCTION ....................................................................... 124

MATERIALS AND METHODS ....................................................... 126

RESULTS ................................................................................ 133

DISCUSSION ........................................................................... 136

CONCLUSIONS ........................................................................ 142

REFERENCES ......................................................................... 143
CHAPTER 6: SUMMARY OF RESOURCE AGGREGATION EFFECTS AND
IMPLICATIONS ................................................................................. 168

SUMMARY .............................................................................. 168

IMPLICATIONS173

vii

LIST OF TABLES

CHAPTER 2

Table 2.1: SAS code for ﬁtting a quadratic plateau model to the difference
in root mass between the amended and the control cores ............................ 33

CHAPTER 3

Table 3.1: Analysis of variance of CO2 ﬂux in response to litter aggregation and
soil moisture as water-ﬁlled pore space (WF PS) across a 39 d incubation at
22°C ..................................................................................................................... 65

Table 3.2: Interaction slice of CO2 ﬂux for sampling date by soil moisture
treatment .............................................................................................................. 66

Table 3.3: Analysis of variance of CO2 ﬂux response to litter aggregation at
50%WFPS across a 39 d incubation at 22°C ...................................................... 67

Table 3.4: Analysis of variance of C02 ﬂux rate from 50% WFPS treatment by
sampling date and aggregation treatment means with multiple comparisons
adjusted by Tukey—Kramer procedure .............................................................. 68

Table 3.5: Analysis of variance of N20 ﬂux in response to litter aggregation and
soil moisture across a 39 d incubation at 22°C .................................................... 69

Table 3.6: Analysis of variance of N20 ﬂux in response to litter aggregation with
50% WFPS soil across repeated sampling of a 39 d incubation at 22°C ............ 70

Table 3.7: Analysis of variance of N20 ﬂux in response to litter aggregation with
80% WFPS soil across repeated sampling of a 39 d incubation at 22°C ........... 71

Table 3.8: Analysis of variance of N20 ﬂux rate from the 55% WFPS treatment
by sampling date and aggregation treatment means with multiple comparisons
adjusted by Tukey-Kramer procedure72

Table 3.9: Analysis of variance of CO2 ﬂux in response to litter aggregation and
litter particle size across a 18 d incubation at 23°C73

viii

Table 3.10: Analysis of variance of N20 ﬂux rate from the 80% WFPS treatment
by sampling date and aggregation treatment means with multiple comparisons
adjusted by Tukey-Kramer procedure .................................................................. 74

Table 3.1 1: Analysis of variance of CO2 ﬂux rate in response to litter particle size
and aggregation treatment by sampling date and means with multiple
comparisons adjusted by Tukey—Kramer procedure. .......................................... 75

Table 3.12: Analysis of variance of N20 ﬂux in response to litter aggregation and
litter particle size across a 18 d incubation at 23°C.. ........................................... 76

Table 3.13: Analysis of variance of N20 ﬂux rate in response to litter particle size
and aggregation treatment by sampling date and means with multiple
comparisons adjusted by Tukey—Kramer procedure. .......................................... 77

Table 3.14: Analysis of variance of N20 ﬂux in response to litter aggregation with
a growing maize plant across repeated sampling ................................................ 78

Table 3.15: Analysis of variance of N20 ﬂux in response to litter aggregation and
growing plants by sampling date and means with multiple comparisons adjusted
by Tukey-Kramer procedure. .............................................................................. 79

CHAPTER 4

Table 4.1: Repeated measures analysis of variance to determine effects of
clustered soil amendments on aboveground maize biomass during Experiment
#1 .. ................................................................................................................... 1 1 1

Table 4.2: Analysis of variance to determine effects of soil amendments on
aboveground maize biomass for each sampling date during Experiment #1.
Contrasts compare the mean effect of aggregated litter versus uniform litter
distribution between (interaction) and among the two litter species. . . . . . . . .....1 12

Table 4.3: Repeated measures analysis of variance to determine effects of
random litter distribution on aboveground maize biomass during Experiment
#2 ....................................................................................................................... 1 1 3

Table 4.4: Partition of the variance attributable to litter distribution by sampling
date (DOY) interaction effect of a repeated measures analysis of variance to
determine effects of random litter distribution on aboveground maize biomass
during Experiment #2114

Table 4.5: Repeated measures analysis of variance to determine effects of soil
amendments of randomly distributed litter aggregates and +N control treatments
on soil surface CO2 ﬂux from containers with maize plants during Experiment #2.
........................................................................................................................... 1 15

CHAPTER 5

Table 5.1: Repeated measures analysis of variance to determine effects of litter
distribution on soil inorganic N (N03' and NH4")65

Table 5.2: Analysis of variance to determine effects of litter distribution on soil
inorganic N (N0;;' + NH4“) for each date sampled during the growing season.
The contrasts compare the mean effect of aggregated litter versus uniform litter
distribution. . . .. ..................................................................................................... 66

Table 5.3: Repeated measures analysis of variance to determine effects of litter
distribution on litter-derived soil inorganic N (N0;;' + NH?) ................................. 67

Table 5.4: Analysis of variance to determine effects of litter distribution on litter-
derived soil inorganic N (N03' and NHX) for each sampling date during the
growing season. The contrasts compare the mean effect of aggregated litter
versus uniform litter distribution ........................................................................ 68

Table 5.5: Repeated measures analysis of variance to determine effects of litter
distribution on maize shoot N content. ............................................................. 69

Table 5.6: Analysis of variance to determine effects of litter distribution on maize
shoot N content for each sampling date during the growing season. The
contrasts compare the mean effect of aggregated litter versus uniform litter
distribution ............................................................................................................ 70

Table 5.7: Repeated measures analysis of variance to determine effects of litter
distribution on the proportion of maize shoot N derived from red clover litter. . ...71

Table 5.8: Repeated measures analysis of variance to determine effects of litter
distribution on maize root N content ................................................................ 72

Table 5.9: Analysis of variance to determine effects of litter distribution on maize
root N content for each sampling date during the growing season. The contrasts
compare the mean effect of aggregated litter versus uniform litter distribution...73

Table 5.10: Repeated measures analysis of variance to determine effects of litter
distribution on the proportion of maize root N derived from red clover litter ........ 74

Table 5.1 1: Repeated measures analysis of variance to determine effects of litter
distribution on nitrate leached from the containers .............................................. 75

Table 5.12: Repeated measures analysis of variance to determine effects of litter
distribution on litter-derived N03' leached from the containers ............................ 76

Table 5.13: Repeated measures analysis of variance to determine effects of litter
distribution on net litter-N mineralization ..................... . ........................................ 77

Table 5.14: Analysis of variance to determine effects of litter distribution on net
litter-N mineralized for each sampling date during the growing season. The
contrasts compare the mean effect of aggregated litter versus uniform litter
distribution ............................................................................................................ 78

Table 5.15: Percent litter-”N recovery into maize shoots and roots, soil organic

and inorganic N, leached N, and unexplained N loss in response to litter
distribution :t one standard error at the end of the growing season ..................... 79

xi

LIST OF FIGURES

(”I—ABBA

Figure 1.1 Effect of variation .in % water ﬁlled pore space on mean rates of
microbial respiration in homogeneous and heterogeneous environments. The
exponential curve represents the microbial respiration functional response to
relative soil moisture. The dotted lines represent the respiration rate of
heterogeneous system with half of its soil pores are 40% water ﬁlled and the
other half are 50% water ﬁlled. The middle solid line denotes the mean microbial
respiration rate in a homogeneous system and the middle dashed line represents
the mean respiration rate in a heterogeneous system. This ﬁgure is redrawn
from Stark, 199412

CHAPTER 2

Figure 2.1 Mean aboveground biomass of B. inermis (top) and A. sativa (bottom)
in response to litter choice of treatments: AB = clover shoots/clover roots; A0 =
clover shoots/oat leaves; AD = clover shoots/oat straw; BC = clover roots/oat
leaves; SD = clover roots/oat straw; CD = oat leaves/oat straw. Different litter
above the bars indicate signiﬁcant differences (P = 0.05) ............................... 34

Figure 2.2. Total root length in the surface 70 mm of sand in response to litter
quality choice treatments (same as Figure 1). Vertical bars represent treatment
means +/- one standard error. Different letters above each bar indicate
signiﬁcant differences (alpha = 0.05) ................................................................... 35

Figure 2.3. Proportion of root length growth of B. inermis (top) and A. sativa
(bottom) into patches of higher quality litter. Uniform line at 0.5 represents an
even distribution of roots into quadrants of each litter type. The 95% Cl. line
represents the upper conﬁdence interval around the uniform distribution line.
Treatment means falling on or below the 95% 0|. are statistically
indistinguishable from the uniform root length distribution, i.e. no selective
foraging. Different letters above the vertical bars indicate signiﬁcant differences .
(Tukey-Kramer adjustment for multiple comparisons) .............................. _ ..... 36

Figure 2.4. Corn grain yield across an N fertilization gradient. The top and
bottom dotted lines represent the 95% conﬁdence intervals around the ﬁtted
quadratic plateau model (middle solid line). The grain plateau was reached with
11.69 N m'2 at a maximum of 1321 g rn'2 and a minimum of 986 g rn'2 ............. 37

xii

Figure 2.5. Mean corn root length in in-growth cores across an N fertilization
gradient .............................................................................................. 38

Figure 2.6. Speciﬁc root length of roots in amended and control in-growth cores
across our N fertilization gradient. The gray circles indicate the litter-amended
cores and the black circles indicate the unamended control cores ................... 39

Figure 2.7. Proportion of total in-growth core root length found in amended cores
as a function of the maize grain yield ................................................................... 40

Figure 2.8. Selective maize root proliferation into amended and unamended root
in-growth cores across our N fertilization gradient as indicated by the difference
in root length between amended and unamended cores (root length in amended
core minus length in unamended core). The top dotted line depicts the upper
95% conﬁdence interval, middle line solid depicts the ﬁtted quadratic plateau
model and the lower dotted line indicates the lower 95% conﬁdence interval. The
maximum difference occurred at 32,500 m rn'2 +/- 1765 SE. and the plateau of
226 m m'2 was reached at 15.8 g N m'2.. ............................................................ 41

CHAPTER 3

Figure 3.1 Spatial treatment layout of the litter aggregation gradient. The top
row shows a vertical proﬁle of the litter distribution placement in the 2.6-L
containers. The bottom row of depicts a top-down view of the horizontal
distribution of the litter ........................................................................... 80

Figure 3.2 Schematic depiction of the spatial layout of litter distribution
treatments conducted in 50-L plastic containers used in the ﬁeld experiment.
Containers were set into holes in a ﬁeld and surrounded by maize plants. The
left panel contains a side-cut view of the container components and vertical
layout. The right panel is a top-down view illustrating the spatial distribution of
the litter treatments ............................................................................... 81

Figure 3.3 002 and N20 ﬂux response to litter aggregation at 50% WFPS during
a 39d incubation. The results of the ANOVA contrast comparing the treatment
means of all the aggregated litter to the uniformly distributed litter are presented
across the top of each graph. *, **, and *** indicate statistical signiﬁcance at P=

_ 0.05, 0.01, and 0.001 levels, respectively for the contrast ............................. 82

Figure 3.4 002 and N20 ﬂux response to litter aggregation and at 80% WFPS
during a 38d incubation. The results of the ANOVA contrast comparing the
treatment means of all of the aggregated litters to the uniformly distributed litter
are presented across the top of the N20 ﬂux graph. *, **, and *** indicate
statistical signiﬁcance at the 0.05, 0.01, and 0.001 levels, respectively for the
contrast. The repeated measures ANOVA indicated that the CO2 ﬂux was

xiii

greater (P<0.05) in the uniform distribution than the mean of the aggregated litter
across the entire incubation .................................................................... 83

Figure 3.5 C02 and N20 ﬂux response to litter aggregation and litter particle size
during an 18d incubation. The results of the ANOVA are presented across the
top of each graph. *, **, and *** indicate statistical signiﬁcance at the 0.05, 0.01,
and 0.001 levels, respectively for the two main effects, Distribution (Dist) and
Particle Size (Size) and their interaction term (X) ........................................ 84

Figure 3.6 Nitrous oxide ﬂux response to litter aggregation with growing maize
plants in 50-L containers. The results of the ANOVA are presented across the
top of the graph. * and ** indicate statistical signiﬁcance at the 0.05 and 0.01
levels, respectively, for the contrast between aggregated and uniformly
distributed litter .................................................................................... 85

CHAPTER 4

Figure 4.1 Schematic depiction of the spatial layout of litter distribution treatment
for the clustered (Experiment #1) and random (Experiment #2) distribution
experiments conducted in 50-L plastic containers set into pits in the soil and
surrounded by other maize plants. In the top panels are side-cut views of the
container components and vertical layout. At the bottom top-down views
illustrating the spatial distribution of the litter treatments .............................. 116

Figure 4.2 Aboveground biomass response to the distribution of rye and red
clover litter and +N control soil amendments during Experiment #1. Symbols
above data from each sampling date represents statistical signiﬁcance, ns =
P>0.05 and * = P<0.05. The arrow on the x-axis indicates the date of litter
application to soil in the ﬁeld contarners117

Figure 4.3 Final harvest aboveground biomass response to red clover and rye
litter distributed into 8, or 32 patches or unifome and 8 patches or uniformly,
respectively, during Experiment #1. Vertical bars represent treatment means :I: 1
standard error of the mean. The two horizontal lines represent aboveground
biomass in response to containers fertilized with 1.6 g N container“ as KN03
(control + N) or not fertilized (control — N) ................................................. 118

Figure 4.4 Aboveground biomass response to red clover litter distributed into 8,
24, or 72 patches or uniformly and +N control soil amendments during
Experiment #2. Symbols above data from each sampling date represents
statistical signiﬁcance, ns = P>0.05 and * = P<0.05. The arrow on the x-axis .
indicates the date of litter application to soil in the ﬁeld centainers ................ 119

Figure 4.5 Final harvest aboveground biomass response to red clover litter
distributed into 8, 24, or 72 patches or uniformly from Experiment #2. Vertical

xiv

bars represent treatment means :I: 1 standard error of the mean. The horizontal
line representSI ﬁnal aboveground biomass' In response to containers fertilized
with 1.6 g N container as NaN03 (control + N) ......................................... 120

Figure 4.6 Root biomass and root to shoot ratio response to control +N
treatment during Experiment #1 ............................................................. 121

Figure 4.7 Root biomass response to red clover litter distributed into 8, 24, or 72
patches or uniformly and +N control soil amendments during Experiment #2.
Symbols above data from each sampling date represents statistical signiﬁcance,
ns= P>0. 05 and *- -.P<0 05 .................................................................. 122

Figure 4.8 Soil surface 002 ﬂux with and without maize plants and with no litter,
uniform litter, or aggregated litter (8, 24, and 72 patches) during Experiment #2.
Error bars denote one standard error of the mean CO2 ﬂux. The repeated
measures ANOVA of the containers with growing plants indicated no significant
litter distribution x sampling date interaction and a signiﬁcantly greater CO2 ﬂux
from uniform vs. aggregated litter. The arrow on the x-axis denotes the time of
container deployment to the ﬁeld ............................................................ 123

CHAPTER 5

Figure 5.1 Schematic of the spatial layout of litter distribution treatments
conducted in 50—L plastic containers set into holes in a ﬁeld and surrounded by
maize plants. The left panel contains a side-cut view of the container
components and vertical layout. The right panel is a top-down view illustrating
the spatial distribution of the litter treatments ............................................. 162

Figure 5.2 Total soil inorganic N (N03' and NH4+) in the surface 190 mm of soil
and proportion of soil inorganic N derived from soil amended red clover litter
(bottom panel) in response to litter distribution. Error bars denote :I: one standard
error ................................................................... . ............................................ 163

Figure 5. 3 Maize shoot N content (top panel) and proportion of shoot N derived
from soil amended red clover litter (bottom panel)‘ In response to litter distribution.
Error bars denote :I: one standard error .................................................... 164

Figure 5.4 Root N content (top panel) and proportion of root N content derived
from soil amended red clover litter (bottom panel) in response to litter distribution.
Error bars denote :I: one standard error .................................................... 165

Figure 5.5 Nitrate leached and proportion of nitrate leached derived from litter-N

in response to red clover litter distribution. Error bars denote one standard error.
....................................................................................................... 166

XV

Figure 5.6 Estimated net mineralized litter-N in response to litter distribution.
Error bars denote 1 one standard error .................................................. 167

xvi

Chapter 1: Soil Resource Heterogeneity and Ecosystem

Processes

INTRODUCTION

Jensen’s Inequality (Jensen 1906) predicts that rates of ecosystem
processes such as decomposition will differ between systems with uniform
versus heterogeneous distributions of factors that control ecosystem processes if
the functional response to those factors is nonlinear. This means that in
terrestrial ecosystems where nonlinear functional responses to resources are
common and soil resource heterogeneity is the rule (Stark 1994), that the
average resource availability in soils will not accurately predict rates of
ecosystem processes.

For example, in most soils microbial respiration rate is a nonlinear function
of % water-ﬁlled pore space (WFPS) (Linn and Doran 1984) such that between
10 and 60% WFPS the relative microbial respiration rate exponentially
accelerates with increasing moisture (Figure 1.1). Jensen’s Inequality can be
illustrated by examining the effects of soil-water distribution on soil microbial
respiration in two soils each with the same mean WFPS but different distributions
of WFPS microsites. If for example, in a homogeneous soil system all of the soil
pores are 45% water ﬁlled, the soil microbial respiration rate will be 34% of the
maximum rate (Figure 1.1). In contrast, in a heterogeneous soil in which half of
the soil pores are 40% water-ﬁlled and the other half are 50% water-ﬁlled, half of

the microsites will have a respiration rate of 20% and the other half will have a

respiration rate of 56% of the maximum (Figure 1.1). This results in an average
respiration rate of 38% of the maximum for the heterogeneous soil, which is more
than 10% higher than in the homogeneous system despite the same mean
WFPS of 45% in each system.

In contrast, if the soil microbial respiration rate were a linear function of
%WF PS then these homogeneous and heterogeneous systems would be
respiring at the same rate. Exponentially decelerating functional responses to
resource availability are also common in soils. For example, denitriﬁcation rates
display Michaelis-Menten kinetics as a function of soil N03‘ concentratibn in the
absence of 02 (Tiedje 1988). In this case spatial or temporal heterogeneity in
soil N03' concentration will decrease the mean denitriﬁcation rate relative to a
uniform N03' concentration. In general, the direction and magnitude of the
differences between hetero,- and homogeneous systems will depend on the
shape of the functional response to the most limiting resource or environmental
control and the spatial or temporal variability of that controlling factor.

Field observations, manipulations, and modeling have all contributed to
our understanding of the causes and consequences of heterogeneity at multiple
spatial scales. Research linking species distributions and process rates to
landscape and larger-scale spatial heterogeneity has been a major theme in
ecology since the discipline’s inception (Wrens 2000). Recent statistical
advances (e.g., the introduction of geostatistical analysis to soil science and
ecology) (Trangmar et al. 1985, Robertson 1987) as well as advances in

microbial ecology and instrumentation have allowed similar questions to be

asked at much smaller scales (Ettema and Wardle 2002). For example,
Grundmann et al. (2001) were able to link the spatial distributions of ammonium
oxidizing bacteria at < 1 mm scale to the spatial heterogeneity of the oxidation of
NH.+ to N02“ and N02' to N03‘. They were further able to model the system to
determine that the cause of the community heterogeneity was associated with
soil mircoporosity. Several researchers have used micro-electrodes to examine
the spatial extent to which hotspots of labile organic matter (e.g., animal manure
and decomposing leaves) inﬂuence N-mineralization, nitriﬁcation, and
denitriﬁcation (e.g., Nielsen and Revsbech 1998, Meyer et al. 2002).
Manipulations of resource heterogeneity have shown that the spatial distribution
of plant litter can increase and decrease decomposition, N mineralization, N20
emissions, and denitriﬁcation (Breland 1994, Ambus and Jensen 1997, Magid et
al. 2006).

Within the scale of individual plants resource heterogeneity can potentially
alter the efﬁciency of plant nutrient acquisition (see Caldwell 1994).
Improvements in nutrient uptake efﬁciencies under heterogeneous resource
conditions relative to uniform conditions appear to be related to both the plant
root response to the resource distribution and the mobility and quantity of
resources acquired from the microsite. As roots encounter different
concentrations of nutrients in soil, ion speciﬁc uptake systems are up and down
regulated according to the plant’s genetics, soil nutrient status, and the plant

demand for that speciﬁc nutrient (Clarkson 1985). Following the induction of

speciﬁc uptake enzyme systems in individual roots, a proliferation of lateral roots
is typically observed (Robinson 1996).

For the proliferation response to be beneﬁcial to the plant the resources
acquired from a microsite must be of sufﬁcient quantity relative to the speciﬁc
resource demand of the plant. This root proliferation into heterogeneously
distributed nutrient rich microsites is clearly advantageous for the acquisition of
relatively immobile nutrients (e.g., phosphorous) that are limited by diffusive ﬂow
to the root surface (Kovar and Barber 1989, Caldwell et al. 1991 ). The nutrient
concentration itself appears to be the signal that is inducing the plant root
proliferation response (Zhang et al. 2007). If the signal is associated with a large
pool of nutrients then the root proliferation response may be a cost effective
allocation of root C; however, if the resource is quickly depleted then the
proliferation response may be an extravagant expenditure of plant C (Wiesler
and Horst 1994, vanVuuren et al. 1996). Hodge et al (1999) found that pasture
grasses grown together acquired more N from heterogeneously distributed
resources than from unifonnly distributed resources only when the source of N
was in a complex organic form (e.g., plant litter) and not when N was in the form
of amino acids or inorganic N sources. This result supports the idea that under
N-limited conditions plants may more effectively acquire N from heterogeneous
distributions of the organic N compounds than from a uniform distribution.

Soil tillage in agricultural systems often results in a heterogeneous
distribution and layering of crop plant litter in the soil (Staricka et al. 1991). This

heterogeneous distribution of crop litter then stimulates maize root growth to

match the litter distribution, although it is unknown if this spatial coupling between
roots and litter affects maize productivity (Van Noordwijk et al. 1993). In a cold
arid grassland, Jackson and Caldwell (1993) characterized the distributions of
many soil properties and found that P was spatially aggregated at scales likely to
be inﬂuenced by root proliferation of the three main perennial plants in the
system. When they added P-enriched microsites to soils between these three
perennial plants they found that root proliferation into the microsites was
dependent on the plant species present and not necessarily related to the P
acquired from the patches (Jackson and Caldwell 1989).

These investigations into soil microbial processes and plant responses to
resource heterogeneity have contributed signiﬁcantly to our understanding of the
role of small scale heterogeneity for regulating ecosystem level processes;
however, research on soil processes and plant growth responses to
heterogeneity have mostly been considered separately. This is despite many
longstanding observations that soil and plant processes interact to affect
ecosystem level 0 and N cycling (e.g., Birch 1959). Additionally, many of the
factors that cause soil resource heterogeneity (e.g., tillage, burrowing animals, or
root distributions) also alter several other aspects of the soil environment, and
thus it can be difﬁcult to conclude that resource heterogeneity per se is
inﬂuencing C and N cycling. This leads to my interest in examining questions

regarding the role of soil resource distribution for regulating C and N cycling.

QUESTIONS ADDRESSED

The primary objective of my dissertation is to address the general question
of how the spatial distribution of soil microbial and plant resources inﬂuences
rates of C and N cycling. My general approach for addressing this question is to
manipulate the spatial distribution of plant litter across an aggregation gradient in
soil and then to follow speciﬁc microbial and plant responses separately and then
together. In addition, I vary other aspects of the litter or soil environment to
mechanistically link the microbial and plant responses to the patterns induced by
the litter aggregation gradient.

Plant species vary in their root proliferation response to microsites of
inorganic N; however, it is unclear if species vary in their root proliferation
response to microsites of N in the form of complex organic matter. Furthermore,
it is unknown if plant N demand has the same control over root proliferation into
microsites of organic N as into microsites of inorganic N. In the following, I
address three questions with the overall objective to examine the soil conditions
in which plant root proliferation may play an important role in regulating
ecosystem level C and N cycling: 1) does root proliferation vary with microsite
quality; 2) does the proliferation response correspond to increased productivity;
and 3) how does the plant demand for N inﬂuence root proliferation in patches of
N-rich organic matter? I address these questions with two experiments: one in
the greenhouse and one in the ﬁeld.

In Chapter 3 I examine the inﬂuence of resource heterogeneity on soil

C02 and N20 emissions. The emissions of these gases from litter in soils are

controlled by several nonlinear functional microbial responses and thus are likely
to differ between unifome and heterogeneously distributed litter. Additionally,
any alteration of C and N cycling in response to litter aggregation will potential
affect plant acquisition of litter-N. I examine the inﬂuence of resource
heterogeneity soil processes by manipulating plant litter across an aggregation
gradient. This gradient allows me to address four questions: 1) does the intensity
of plant litter aggregation affect litter decomposition and N20 emissions; 2) does
the aggregation effect on decomposition and N20 ﬂuxes vary with soil moisture
and hence diffusional constraints; 3) does plant litter particle size affect CO2 and
N20 emissions similarly when litter is uniformly distributed and aggregated; and
4) does the presence of growing plants alter the N20 emissions in response to
litter aggregation? I address these questions in two laboratory incubation and
one ﬁeld experiment.

In Chapter 4, I employ two litter aggregation gradients to address the
questions: 1) does the aggregation of plant litter inﬂuence the growth of individual
maize plants in an N-limited system; 2) is the root to shoot ratio and belowground
C allocation altered by resource aggregation, and 3) does the distribution of
aggregated resources inﬂuence maize productivity? I address these questions
with two ﬁeld experiments. I

Chapter 5 is a companion to Chapter 4 where I ask how N cycling is
inﬂuenced by resource aggregation. Speciﬁcally, I address three questions: 1)
does resource aggregation inﬂuence above and belowground plant N acquisition;

2) does resource aggregation inﬂuence litter-derived net N mineralization; and 3)

does resource aggregation inﬂuence whole system N retention. I address these
questions using 15N-labeled plant litter in a ﬁeld experiment.

Finally in Chapter 6, l summarize the results of the previous four chapters
and make linkages between the microbial and plant responses to litter
aggregation. Further I make predictions as to under what conditions litter
aggregation and soil resource heterogeneity may have signiﬁcant inﬂuences on

C and N cycling.

REFERENCES

Ambus, P., and E. S. Jensen. 1997. Nitrogen mineralization and denitriﬁcation as
inﬂuenced by crop residue particle size. Plant Soil 197:261-270.

Birch, H. F. 1959. Simultaneous decomposition processes in soils. Nature
183:1415-141 5.

Breland, T. A. 1994. Enhanced mineralization and denitriﬁcation as a result of
heterogeneous distribution of clover residues in soil. Plant Soil 16621-12.

Caldwell, M. M. 1994. Exploiting nutrients in fertile soil microsites. Pages 325-
348 in M. M. Caldwell and R. W. Pearcy, editors. Exploitation of
Environmental Heterogeneity by Plants. Academic Press, San Diego, CA,
USA.

Caldwell, M. M., J. H. Manwaring, and R. B. Jackson. 1991. Exploitation of
Phosphate from Fertile Soil Microsites by 3 Great-Basin Perennials When
in Competition. Funct. Ecol. 5:757-764.

Clarkson, D. T. 1985. Factors affecting mineral nutrient acquisition by plants.
Annu. Rev. Plant Physio. 36:77-115.

Ettema, C. H., and D. A. Wardle. 2002. Spatial soil ecology. Trends Ecol. Evol.
17:177-183.

Grundmann, G. L., A. Dechesne, F. Bartoli, J. P. Flandrois, J. L. Chasse, and R.
Kizungu. 2001. Spatial modeling of nitriﬁer microhabitats in soil. Soil Sci.
Soc. Am. J. 65:1709—1716.

Hodge, A., D. Robinson, B. S. Grifﬁths, and A. H. Fitter. 1999. Why plants bother:
root proliferation results in increased nitrogen capture from an organic
patch when two grasses compete. Plant Cell Environ. 22:811-820.

Jackson, R. B., and M. M. Caldwell. 1989. The timing and degree of root
proliferation in fertile-soil microsites for 3 cold-desert perennials.
Oecologia 81:149-153.

Jackson, R. B., and M. M. Caldwell. 1993. Geostatistical patterns of soil
heterogeneity around individual perennial plants. J. Ecol. 81:683-692.

Jensen, J. L. W. V. 1906. Sur les fonctions convexes et les inegalites entre les
valeurs moyennes. Acta Mathematica 30:175-193.

Kovar, J. L., and S. A. Barber. 1989. Reasons for differences among soils in
placement of phosphorus for maximum predicted uptake. Soil Sci. Soc.
Am. J. 53:1733-1736.

Linn, D. M., and J. W. Doran. 1984. Effect of water-ﬁlled pore-space on carbon-
dioxide and nitrous-oxide production in tilled and nontilled soils. Soil Sci.
Soc. Am. J. 48:1267-1272.

Magid, J., A. De Neergaard, and M. Brandt. 2006. Heterogeneous distribution
may substantially decrease initial decomposition, long-tenn microbial
growth and N-immobilization from high C-to-N ratio resources. Euro. J.
Soil Sci. 57 :51 7-529.

Meyer, R. L., T. Kjaer, and N. P. Revsbech. 2002. Nitriﬁcation and denitriﬁcation
near a soil-manure interface studied with a nitrate-nitrite biosensor. Soil
Sci. Soc. Am. J. 66:498-506.

Nielsen, T. H., and N. P. Revsbech. 1998. Nitriﬁcation, denitriﬁcation, and N-
liberation associated with two types of organic hot-spots in soil. Soil Biol.
Biochem. 30:611-619.

Robertson, G. P. 1987. Geostatistics in Ecology - Interpolating with Known
Variance. Ecology 68:744-748.

Robinson, D. 1996. Resource capture by localized root proliferation: why do
plants bother? Ann. Bot. 77:179-186.

Staricka, J. A., R. R. Allmaras, and W. W. Nelson. 1991. Spatial variation of crop
residue incorporated by tillage. Soil Sci. Soc. Am. J. 55: 1 668-1 674.

Stark, J. M. 1994. Causes of soil nutrient heterogeneity at different scales. Pages
255-284 in M. M. Cladwell and R. W. Pearcy, editors. Exploitation of

10

environmental heterogeneity by plants. Academic Press Inc., San Diego,
CA.

Tiedje, J. M. 1988. Ecology of denitriﬁcation and dissimilatory nitrate reduction to
ammonium. Pages 179-244 in A. J. B. Zehnder, editor. Biology of
Anaerobic Microorganisms. Wiley, New York.

Trangmar, B. B., R. S. Yost, and G. Uehara. 1985. Application of geostatistics to
spatial studies of soil properties. Adv. Agron. 38:45-94.

Van Noordwijk, M., P. C. Deruiter, K. B. Zwart, J. Bloem, J. C. Moore, H. G.
Vanfaassen, and 8. Burgers. 1993. Synlocation of biological-activity,
roots, cracks and recent organic inputs in a sugar-beet ﬁeld. Geodenna
56:265-276.

vanVuuren, M. M. l., D. Robinson, and B. S. Grifﬁths. 1996. Nutrient inﬂow and
root proliferation during the exploitation of a temporally and spatially
discrete source of nitrogen in soil. Plant Soil 178:185-192.

Wiens, J. A. 2000. Ecological heterogeneity: an ontogeny of concepts and
approaches. Pages 9-33 in M. J. Hutchings, E. A. John, and A. J. A.
Stewart, editors. The Ecological Consequences of Environmental
Heterogeneity. Blackwell Science Ltd., Oxford, UK.

Wresler, F ., and W. J. Horst. 1994. Root growth and nitrate utilization of maize
cultivars under ﬁeld conditions. Plant Soil 163:267-277.

Zhang, H. M., H. L. Rong, and D. Pillbeam. 2007. Signalling mechanisms
underlying the morphological responses of the root system to nitrogen in
Arabidopsis thaliana. J. Exp. Bot. 58:2329-2338.

11

 

.3
o
o

 

 

 

 

 

 

 

 

E‘

3

.E

E

u- 80 “

0

if;

c

5 60 -

c I
0

'ﬁ .
'3. Mean respiration rate in a
8 40 - "unlisterogeneous system
g \ Respiration rate in a
9 homogeneous system i
.2 20 - . =
E 2 E
o' : :
> = .
z: ' =
E 5 E
g 0 ' I 1 I

10 20 30 40 50 60

% Water Filled Pore Space

Figure 1.1 Effect of variation in % water ﬁlled pore space on mean rates of
microbial respiration in homogeneous and heterogeneous environments. The
exponential curve represents the microbial respiration functional response to
relative soil moisture. The dotted lines represent the respiration rate of
heterogeneous system with half of its soil pores are 40% water ﬁlled and the
other half are 50% water ﬁlled. The middle solid line denotes the mean
microbial respiration rate in a homogeneous system and the middle dashed
line represents the mean respiration rate in a heterogeneous system. This
ﬁgure is redrawn from Stark. 1994.

12

Chapter 2. Root Foraging is Responsive to Soil Microsite
Resource Quality and Availability

INTRODUCTION

Soil nitrogen (N) availability is the primary constraint on plant productivity
_ in many terrestrial ecosystems, and plant litter-N is often the most important
source of soluble N compounds (e.g., NHi, N03', and amino acids) available for
plant uptake. Plant litter and other soil properties are not uniformly distributed. in
soil and thus plant available N is commonly heterogeneously distributed (Stark
1994). In response, plants have evolved many physiological and morphological
adaptations to optimally acquire soil nutrients, including N, from heterogeneous
soils (Caldwell 1994). As roots encounter different concentrations of nutrients in
soil, ion-peciﬁc uptake enzyme systems are up- and down-regulated according to
the plant genetics, soil nutrient status, and the plant demand for that speciﬁc
nutrient (Clarkson 1985).

Following the induction of speciﬁc uptake enzyme systems in individual
roots in initially high nutrient microsites, a proliferation of lateral roots is typically
observed (Robinson 1996). This selective root proliferation into heterogeneously
distributed nutrient rich microsites, also known as root foraging, is clearly
advantageous for the acquisition of relative immobile nutrients (e.g.,
phosphorous) that are limited to diffusive ﬂow to the root surface. In contrast, N
uptake efﬁciency (N acquired per unit of root C expended) can be adversely
affected by root proliferation into microsites of N03‘ if the N03' pool is of

insufﬁcient quantity to compensate the plant for the C expended on the

13

proliferated root biomass. However, in several studies the root proliferation
response has been observed after the N03’ pool has been depleted (Wiesler and
Horst 1994, vanVuuren et al. 1996). Hodge et al (1999) found that pasture
grasses grown together acquired more N from heterogeneously distributed
resources than uniformly distributed resources only when the source of N was in
a complex organic form (e.g., plant litter) and not from amino acids or inorganic N
sources. This observation along with the identiﬁed genetic basis for N03'
induced root proliferation (Zhang et al. 2007) supports the hypothesis that N03‘
acts as a signal for root proliferation. As a result, if the N03' is associated with a
larger pool of plant available N derived from a microsite of N mineralizing organic
matter then the proliferation may increase N uptake efﬁciency; however, if
proliferation occurs into a microsite of N03' that is not associated with a source of
further plant available N then the proliferation response may be
disadvantageous. Thus, examining the inﬂuence of the distribution and N
mineralizing potential of labile organic matter on root proliferation and plant
growth may be necessary to understand the costs and beneﬁts of root
proliferation under N-limited conditions.

Wang and Bakken (1997) found that increasing the spatial separation
between N-rich and N-poor microsites of plant litter resulted in greater plant N
uptake from the N-rich site. By following the soil microbial C and N contents in
both the N-rich and N-poor microsites with and without growing plants they were
further able to conclude that the roots did not stimulate N mineralization from the

N-rich microsite, and rather that the roots intercepted inorganic N from ﬂowing to

14

the N-poor microsite. This further supports the idea that root proliferation in and
around microsites of varying quality has important implications for plant
productivity in heterogeneous soil that would not be evident from studying plant-
soil interaction under uniform conditions or in microsites of inorganic N alone. It
is known that plant species vary in their root proliferation response to microsites
of inorganic N (Einsmann et al. 1999, Rajaniemi and Reynolds 2004, Kembel and
Cahill 2005); however, it is unclear if species vary in response to microsites of
complex organic matter.

Here we present the results of two experiments to address three
questions: 1) does root proliferation vary with microsite quality; 2) does the
proliferation response correspond to increased productivity; and 3) does the plant
demand for N inﬂuence root proliferation in patches of N-rich organic matter?
MATERIALS AND METHODS
Experiment #1: Root Proliferation Response to Microsite Quality

We conducted this experiment in a greenhouse at the WK. Kellogg
Biological Station (KBS) in Michigan, USA. Plastic containers (3-L) were laid out
in a complete 2x6 Randomized Complete Block Design with 6 replicate
containers per block. Factors were 6 microsite quality choices and two plant
species (Avena sativa L. and Bromus inermis L.). The growing media was
coarse quartz sand with a bulk density of 1.6 Mg rn'3 and a 24 h water holding
capacity (WHC) of 0.28 9 H20 9 sand". The sand was air-dried prior to initiating
the experiment. The microsite qualities consisted of pair-wise combinations of

dried and ﬁnely ground (A) red clover shoots, (B) red clover roots, (C) green cat

15

leaves, or (D) oat straw. All possible combinations were used for a total of 6
microsite quality choice factors: red clover shoots vs. red clover roots (AB); red
clover shoots vs. oat leaves (AC); red clover shoots vs. cat straw (AD); red clover
roots vs. cat leaves (BC); red clover roots vs. cat straw (BD); and cat leaves vs.
cat straw (CD).

Each microsite was an intact mass of 0.5 g litter distributed into 12
separate patches per container. To ensure consistent patch distribution we
placed a 140 mm diameter acrylic template on top of 1.6 L of sand in each
container. The template was 10 mm thick and each quadrant had three 13 mm
diameter holes centered 65 mm out from the center of the template. Into each of
these 3 holes we placed 0.5 g of plant litter. The same litter type was placed in
the 3 holes of the apposite quadrant. In the other six holes we placed one of the
other litter types. After ﬁlling all 12 holes with litter, the template was carefully
removed and an additional 0.5 L of sand was poured into the container to ensure
that the microsites stayed in place. Additional sand was added to each container
to obtain a uniform mass of 2150 g.

The litter types used in this experiment were chosen because cat and red
clover are commonly grown together in low input agricultural systems of the corn
belt and the tissues of these two species have a wide C:N range, and well
deﬁned chemical characteristics and decomposition dynamics (Berg et al. 1987,
Malpassi et al. 2000, Hesselsoe et al. 2001). The C:N for the litters used in this
experiment were 12.8, 18.0, 20.2, and 57.3 for the red clover shoots, red clover

roots, oat leaves, and cat straw, respectively. Thus the mass of organic N added

16

to each container varied with the treatment combination: 3 g of red clover shoots
contained 97 mg N; of red clover roots contained 59 mg N; of oat leaves
contained 61 mg N; and of oat straw contained 23 mg N. The total organic N
added to AB, AC, AD, BC, BD, and CD was 156, 157, 119, 119, 82, and 83 mg N
container", respectively.

A modiﬁed Hoagland’s nutrient solution less N was used to maintain soil
moisture and fertility (Hewitt, 1966). Half strength nutrient solution (800 mL) was
added to each container to initiate microbial activity in microsites four days prior
to transplanting seedlings. After this initial pulse of solution, the moisture content
was adjusted by container every 5 to 7 days to maintain 80% WHC with quarter
strength Hoagland’s solution.

We chose Avena sativa (variety “lda”) as a test plant because of its well
known biology as well as its widespread use as a grain crop. Bromus inermis
was chosen because it has been used in previous studies of root foraging
(Rajaniemi and Reynolds 2004) and is a common forage crop. We transplanted
one four day old seedling into the center of each container we transplanted to a
depth of 30 to 40 mm. The mean initial dry mass of the cat and B. inermis seeds

were 42.8 mg and 2.7 mg, respectively.
’ Thirty-nine days after the seedlings were transplanted the plants were
harvested. Aboveground shoots were cut at the sand surface and dried at 60°C
for 4 d and weighed. We cut the root systems into ﬁve sections: the bottom 70
mm of each container was separated from the surface 70 mm; then the surface

section was quartered so that each quarter contained 3 of the same litter patches

17

and the roots that had grown into that quarter. The roots were separated from
sand and litter by wet sieving over a1 mm sieve. Approximately 20 to 30 mm of
the primary lateral root most proximal to the shoot was separated from the
remaining root system and not included in the root length determination. We
stored the roots in resealable polyethylene bags with a 10% ethanol solution
sufﬁcient to completely submerge the entire root sample. Root length of each
quarter was then determined by scanning the roots with a ﬂat-bed scanner
(Epson 3180) as the roots ﬂoated in an acrylic pan. We analyzed the images for
total root length with WinRhizo root image analysis system (Regent Instruments,
Quebec, Canada).

We calculated selective root foraging as the proportion of the total root
length in the surface 70 mm of sand that was found in the two quarters
containing the microsites with greater total N (Campbell et al., 1991). We
calculated the mean litter beneﬁt derived from each litter type as the mean
aboveground biomass from each treatment containing that particular litter. The
degree of choice contrast was calculated by the difference between the mean
litter beneﬁt of the two litter choices in a treatment. The choice contrasts were
then ranked from least to greatest difference to establish a contrast gradient
across the choice treatments.

Experiment #2: Selective Root Foraging across an N Fertility Gradient

We conducted this experiment at KBS on the Long Term Ecological

Research (KBS-LTER) site in Michigan, USA (42° 24' N, 85° 24' W). Kalamazoo

(ﬁne-loamy, mixed, mesic Typic Hapludalfs) and Oshtemo (coarse-loamy, mixed

18

mesic Typic Hapluadalfs) soil series co-occur on the site. The soil had been
planted to Medicago sativa L., Glycine max L., and Triticum aestivum L. and
managed without tillage for the previous 18 years. We laid out the experiment in
a Randomized Complete Block Design with three replicates and 6 N application
rates plus a no N fertilizer control. Plots were six - 0.76 m wide rows (4.57 m) by
27.4 m in length and were sprinkler irrigated.

On 18 May 2005, maize (Pioneer ® 35Y54) was planted at 71,000 seeds
ha'1 with a John Deere MaxEmerge. We established the N fertilizer treatments
on 1 July 2005 by applying 0, 3.4, 10.1, 13.4, 16.9, 24.6, and 29.1 g N rn'2 as a
urea ammonium nitrate solution with a knife applicator. The maize plants were at
the V6 growth stage (Hanway 1963) at this time. Irrigation water was applied
from growth stage V10 - R4 to supply 25 mm per week adjusted for precipitation.

In-growth root cores

Root foraging within the N fertility gradient was assessed by comparing
maize root growth into litter-amended and unamended control soil cores (Raich
et al. 1994). We constructed these in-growth soil cores by pounding a 76 mm
diameter x 102 mm length of schedule 30 polyvinylchloride (PVC) pipe with a
sharpened edge into the soil and then removing the soil from within the PVC,
leaving a ca. 510 mL hole and then these holes were then ﬁlled with 630 g of
amended or control soil. The soil used in this experiment was collected from a
nearby unfertilized plot, sieved (2 mm screen), and allowed to air-dry. The litter
amended soil was 20 g litter kg soil ‘ of red clover shoot litter (same as

Experiment #1). We stored both amended and control soils at 4°C for 10 days

19

prior to ﬁeld application. The paired (amended and control) cores were located
3.5 m into the plots, within 0.1 m of each other, and in the center of the inter-
rows. We deployed the cores in the plots on 22 July 2005 and harvested them
on 21 August 2005. The maize plants were at the R1 growth stage when the
cores were deployed. This stage is typically thought to be at or just beyond the
maximum N uptake stage of the plants (Hanway 1963). We harvested soil from
the cores by hammering the sharpened edge of a 64 mm diameter by 100 mm
length of PVC pipe into the center of each core. These intact cores were
transferred to 1-L polyethylene bags and 0.4 L of 10% ethanol solution was
added before storing at 4°C for a maximum of 4 days. The roots were separated
from the soil by wet sieving (2 mm), stored in 10% ethanol, and analyzed for total
root length by core as described above. The samples were dried at 60°C for 72
hrs and weighed. We harvested maize grain on 14 October 2005 from the center
two rows of each plot with a plot-scale combine. Reported grain yields are
adjusted to a standard 150 9 H2O kg grain“.
Statistical Analysis

For the microsite quality choice experiment an analysis of variance
(ANOVA) was conducted on each dependent variable using the mixed linear
model procedures in SAS v.9.1 (Littell et al. 2005) with block, microsite
treatment, and species as independent variables. Multiple comparisons were
conducted using a Tukey-Kramer adjustment procedure. Data were checked for
homogeneity of variance and normality and were natural log transformed to meet

the assumptions of ANOVA as necessary.

20

The maize grain yield data was ﬁtted to a quadratic plus plateau model
(Cerrato and Blackmer 1990) to deﬁne plant N saturation using the non-linear
regression procedures in SAS v.9.1 (Sita 1994). The source code for this model
can be found in Table 2.1.

RESULTS
«Experiment #1: Root Foraging Response to Microsite Quality

Aboveground growth of both B. inermis and A. sativa varied signiﬁcantly
with litter quality treatment (Figure 2.1); however, this response differed between
the two species. The average aboveground B. inermis biomass was greatest in
response to litter treatments containing clover shoots (0.0289 plant"),
intermediate in response to oat leaves (0.0239 plant") and clover roots (0.0209
plant"), and least in response to oat straw (0.0089 plant"). Avena sativa
responded to litter quality choices in the same rank order as B. inermis with an
average of 0.24 9 plant" in response to treatments containing clover shoots,
0.179 plant" in response to oat leaves, 0.169 plant" in response to clover roots,
and 0.109 plant" in response to oat straw.

The total root length of A. sativa in the surface 70 mm of soil varied
signiﬁcantly in response to litter quality treatments, whereas the root length of B.
inermis did not (Figure 2.2). Total root length of A. sativa followed the same rank
order response to the litter treatments as the aboveground biomass. The total
root length was the greatest in response to litter treatments containing clover

shoots (8.99 rn plant"), intermediate in response to oat leaves (7.72 m plant")

21

and clover roots (7.50 m plant") and least in response to treatments containing
oat straw (4.57 m plant").

To understand how patch quality inﬂuence the distribution of roots within
soil, we arranged the choice treatments along a quality contrast gradient. This
gradient was determined from a post-hoc evaluation of the litter treatment rank
order effects on the aboveground biomass. Thus a quality choice of clover roots
and cat leaves represents the weakest choice contrast, because their beneﬁt to
the aboveground plant growth was most similar. Whereas the strongest choice
contrast was between clover shoots and cat straw is contrast (Figure 2.3). The
proportion of total root length of each plant that was located in the two quadrants
of the higher quality litter as a response variable indicates selective foraging.

No root foraging differences were detected with B. inermis in response to
litter quality contrasts (Figure 2.3); however, B. inermis produced more root
length in patches of oat leaves than clover roots (average selectivity was greater
than the 95% conﬁdence interval)(Figure 2.3). In contrast, A. sativa
demonstrated selective root foraging in all of the litter quality contrasts except the
weakest contrast (Figure 2.3). For A. sativa, the rank order of the effect size of
the foraging selectivity generally followed that of the aboveground productivity
ranking. For example, clover roots and cat leaves sponsored similar
aboveground productivity and the roots did not distinguish between the two. In
contrast, the cat straw and clover shoots produced very different resource levels
and the roots appeared to respond accordingly. No differences in A. sativa root

selectivity were detected between the four intermediate litter contrast treatments;

22

however, each of these demonstrated selective foraging. For A. sativa, the
strongest roots selectivity appears to be against oat straw.
Experiment #2: Selective Root Foraging across an N Fertility Gradient

Plot level maize seed production across the N fertilization gradient
increased from 990 i 30 g rn'2 with no N applied to 1320 g rn‘2 at 11.6 9 N rn'2 of
fertilizer according to the ﬁtted quadratic plateau model (Figure 2.4). Mean root
length found in the in-growth root cores over the 21 d deployment period
decreased from a maximum of ca. 26,800 m rn‘2 with no N added to 800 m rn‘2
at N application levels of 17 to 29 9 N rn'2 (Figure 2.5). The speciﬁc root length
(SRL) in the in-growth root cores ranged from 0.3 to 12.7 m g" and averaged 5.3
m 9" across all of the cores (Figure 2.6). The SRL of the plot receiving the
lowest two N application rates was fairly constant, averaging 3.3 m g'1 with a
coefﬁcient of variation (CV) of 16%. At N rates of 10 g N m'2 and above, the
maize SRL was more variable averaging 5.9 m g" with a CV of 48%. No
differences in SRL were detected across the N gradient between the amended
and control in-growth cores.

The proportion of total in-growth core root length found in the amended
core serves as an index of selective root foraging. Maize roots were more
consistently selective for the amended core when grain yields were limited by N
than when maize plants were N saturated (Figure 2.7). Variability in root foraging
increased with increasing maize yields. This increase in variability is likely the
result of the large differences that can occur between samples of relatively rare

occurrences (Hutchings et al. 2000), Le. when the spatial distribution of a

23

population or processes is limited to localized patches. The difference between
the root length in the amended and the control cores is an alternative
representation of selective root foraging (Figure 2.8). The same quadratic
plateau regression model that was used to model the grain yield was also ﬁt to
the root selectivity-difference data across the N fertilization gradient. The ﬁtted
model indicated that the differences in root length between the amended and
controls cores were negligible after approximately 15.8 g N m'z.
DISCUSSION
Experiment #1: Root Foraging Response to Microsite Quality

This experiment demonstrates that plant root foraging for microsites of
varying quality can be related to the resources acquired and that selective root
foraging varies with plant species. Both plant species (A. sativa and B. inermis)
responded to the variation in microsite quality with a similar pattern in
aboveground biomass (Figure 2.1). This indicates that each species was likely
N-limited, because all of the other nutrient requirements should have been met
with the nutrient solution. This is an interesting result given that the range in
aboveground biomass between the two species did not overlap. One possible
explanation deals with the synchrony of plant N uptake and N mineralization from
the microsites. The demand for N by B. inermis should have lagged behind that
of A. sativa due to differences in absolute growth rate, thus N mineralizing from
the N-rich microsites (red clover shoots and cat leaves) had a greater chance to
ﬂow into the N-poorer microsites where it may have been immobilized for the

duration of the experiment. This would have reduced the total available N for B.

24

inermis more than for A. sativa. Wang and Bakken (1997) demonstrated that
plant N demand can alter N immobilization into N-poor microsites.

The differences in root foraging between A. sativa and B. inermis (Figure .
2.3) may be caused by several factors related to species traits and experimental
conditions. Again N availability relative to plant N demand may partially explain
these differences. The N-rich microsites may have been mineralizing inorganic N
before the B. inermis roots could have perceived the patchiness of the inorganic
N. This may have allowed inorganic N from the N-rich microsites to have
dispersed more widely in the soil, thus B. inermis may have experienced a more
uniform distribution of inorganic N than did A. sativa. Several studies have
demonstrated species of different absolute and relative growth rates differ in root
foraging traits (Campbell and Grime 1989, Hutchings and de Kroon 1994,
Fransen et al. 1999, Aanderud et al. 2003). Further work on nutrient dispersion
rates would need to be conducted to understand if these studies may also suffer
from patch dispersion prior to the plant nutrient demand. Fitter et al. (2000) point
out that patch dynamics maybe as important as plant foraging traits; however,
patch dynamics are less intensely researched.

Alternatively, root foraging traits of A. sativa and B. inermis may differ
because of life histories (annual versus perennial, respectively), rooting
' architecture (Bell and Lechowicz 1994), dependence on soil microbial associates
(Hodge 2003), and root turnover rates (Gross et al. 1993, Aanderud et al. 2003).
Avena sativa has not been studied in root foraging trials to our knowledge.

Bromus inermis has been used in several root foraging experiments (Rajaniemi

25

and Reynolds 2004) and has been generally described as a moderately precise
forager (Kembel and Cahill 2005); however, our data suggest that B. inermis’
foraging precision may be context dependent (Figure 2.3). For example, B.
inermis was the most precise root forager of eight old-ﬁeld plant species in
response to a slow release synthetic fertilizer (Rajaniemi and Reynolds 2004),
displayed an intermediate level of foraging compared to three other old-ﬁeld
species in response to a pulse of inorganic nutrient solution (Gross et al. 1993),
and did not demonstrate root foraging in response to a choice of plant litter
patches in this experiment (Figure 2.3). The nutrient patches used in each of
these experiments (Gross et al. 1993, Rajaniemi and Reynolds 2004)(Figure 2.3)
differed in form (inorganic versus organic), concentration, and intend duration
(nutrient solution, slow release nutrient formulation, and organic matter quality,
respectively). Thus it is difﬁcult to speculate if these differences may be due to
patch characteristics, genetic variance in foraging traits, or some genetics by
environmental interaction.
Experiment #2: Selective Root Foraging across an N Fertility Gradient

The co-occurrence of a grain yield plateau and discontinuation of the
selective root foraging suggests that selective root foraging was regulated by the
overall N limitation of the plants. In plots limited by soil N availability roots
proliferated an average 81% of their total core root length into the amended
cores (Figure 2.6). In plots that were not N limited, maize roots did not
demonstrate selectively foraging and root growth into the cores was only 3% of

the most N-limited plot.

26

A criticism of "the in-growth core method used in this experiment is that it
severs intact roots in the portion of the soil where the cores are deployed.
Actively growing roots typically respond to the removal of their apical meristems
by increasing branching just behind the excision site (Hutchings and de Kroon
1994). Thus the magnitude of the root proliferation into our in-growth cores may
be an overestimation of root growth into patches of nutrient rich microsites;
however, the relative degree of selective root foraging should be independent of
the method.

This study was conducted during the reproductive growth phases of R2-
R4 (Hanway 1963). Under non N-limiting soil conditions maize is thought to
reduce N uptake rates following pollen shed (R1) and shift to mostly translocating
N from nonreproductive tissues to meet the developing grain’s N demand
(Hanway 1962). In N-limited conditions, however, plants likely continue N uptake
into the reproductive phase to meet this demand. Our data suggest that root
proliferation into N-rich microsites may be an important component of
reproductive phase N uptake if plant available N is limited and heterogeneous
distribution within the scale of an individual plant’s root system.

The source of plant available N varies with cropping systems, for instance
low input system rely more on organic forms of N (legume cover crops and
animal manure) to meet plant requirements than conventional cropping systems.
The data presented here and elsewhere (Wang and Bakken 1997, Hodge 2006)
suggest that root foraging is likely to occur whenever plants are grown in N

limited patchy soils; however, this root foraging response may be more beneﬁcial

27

to plant N acquisition when the source of N is in a complex organic form (Hodge
et al. 1999). Drinkwater et al (1998) hypothesized that the coupling of C and N
cycling is an important component of the improved N retention they observed in
their organic versus conventional cropping systems. Could the spatial coupling
' of plant roots and N mineralizing plant litter and animal manure be an important
component of this improved N retention? In heterogeneous soils, this spatial
coupling could potentially increase the ﬂux of N from N-rich microsites to plant
roots without increasing the bulk soil inorganic N content (Wang and Bakken
1997), resulting in a decreased potential for this inorganic N to be lost to
denitriﬁcation or hydrologic leaching. The hypothesis that roots stimulate N
mineralization from patches of plant litter does not seem to be important
(Bonkowski et al. 2000) despite the rhizosphere having dramatic effects on N
mineralization of homogenized soil (Herman et al. 2006). Thus the spatial
coupling may be an important component of N conservation in low input organic
based cropping systems, but not likely in conventional mineral fertilizer based
systems.
CONCLUSIONS

The potential for roots to associate with microsites of varying organic
matter quality appears to be dependent on both microsite resource availability
and the overall resource limitation of the entire soil-plant system. Our results
suggest that root proliferation into microsites is likely only an important plant
adaptation when the plant nutrient that is most limiting productivity is

heterogeneously distributed.

28

REFERENCES

Aanderud, Z. T., C. S. Bledsoe, and J. H. Richards. 2003. Contribution of relative
growth rate to root foraging by annual and perennial grasses from
California oak woodlands. Oecologia 136:424-430.

Bell, 6., and M. J. Lechowicz. 1994. Spatial heterogeneity at small scales and
how plants respond to it. Pages 391-414 in M. M. Caldwell and R. W.
Pearcy, editors. Exploitation of Environmental Heterogeneity by Plants.
Academic Press Inc., San Diego.

Berg, 8., M. Muller, and B. Wessen. 1987. Decomposition of red clover (T rifolium
pratense) roots. Soil Biol. Biochem. 19:589-593.

Bonkowski, M., B. Grifﬁths, and C. Scrimgeour. 2000. Substrate heterogeneity
and microfauna in soil organic 'hotspots' as determinants of nitrogen
capture and growth of ryegrass. Appl. Soil Ecol. 14:37-53.

Caldwell, M. M. 1994. Exploiting nutrients in fertile soil microsites. Pages 325-
348 in M. M. Caldwell and R. W. Pearcy, editors. Exploitation of
Environmental Heterogeneity by Plants. Academic Press, San Diego, CA,
USA.

Campbell, B. D., and J. P. Grime. 1989. A comparative study of plant
responsiveness to the duration of episodes of mineral nutrient enrichment.
New Phytol. 112:261-267.

Cerrato, M. E., and A. M. Blackmer. 1990. Comparison of models for describing
corn yield response to nitrogen fertilizer. Agron. J. 82:138-143.

Clarkson, D. T. 1985. Factors affecting mineral nutrient acquisition by plants.
Annu. Rev. Plant Physio. 36:77-115.

Drinkwater, L. E., P. Wagoner, and M. Sarrantonio. 1998. Legume-based
cropping systems have reduced carbon and nitrogen losses. Nature
396:262-265.

29

Einsmann, J. C., R. H. Jones, M. Pu, and R. J. Mitchell. 1999. Nutrient foraging
traits in 10 co-occurring plant species of contrasting life forms. J. Ecol.
87:609-619.

Fitter, A., A. Hodge, and D. Robinson. 2000. Plant response to patchy soils.
Pages 71-90 in A. J. A. Stewart, E. A. John, and M. J. Hutchings, editors.
The ecological consequences of environmental heterogeneity. Blackwell
Science Ltd., Oxford, UK.

Fransen, B., J. Blijjenberg, and H. d. Kroon. 1999. Root morphological and
physiological plasticity of perennial grass species and the exploitation of
spatial and temporal heterogeneous nutrient patches. Plant Soil 2112179-
189.

Gross, K. L., A. Peters, and K. S. Pregitzer. 1993. Fine root growth and
demographic responses to nutrient patches in four old-ﬁeld plant species.
Oecologia 95:61-64.

Hanway, J. J. 1962. Corn growth and composition in relation to soil fertility: lll.
Percentages of N, P, and K in different plant parts in relation to stage of
growth. Agron. J. 54:222-229.

Hanway, J. J. 1963. Growth Stages of Com (Zea mays L.). Agron. J. 55:487-492.

Herman, D. J., K. K. Johnson, C. H. Jaeger, E. Schwartz, and M. K. Firestone.
2006. Root inﬂuence on nitrogen mineralization and nitriﬁcation in Avena
barbata rhizosphere soil. Soil Sci. Soc. Am. J. 70:1504-1511.

Hesselsoe, M., A. Pedersen, K. Bundgaard, K. K. Brandt, and J. Sorensen. 2001.
Development of nitriﬁcation hot-spots around degrading red clover
(T rifolium pratense) leaves in soil. Biol. Fertil. Soils 33:238-245.

Hewitt, E. J. 1966. Sand and water culture methods used in the study of plant
nutrition, 2nd edition. Famham Royal, Commonwealth Agricultural
Bureaux.

Hodge, A. 2003. Plant nitrogen capture from organic matter as affected by spatial
dispersion, interspeciﬁc competition and mycorrhizal colonization. New
Phytol. 157:303-314.

30

Hodge, A. 2006. Plastic plants and patchy soils. J. Exp. Bot. 57:401-411.

Hodge, A., D. Robinson, B. S. Grifﬁths, and A. H. Fitter. 1999. Why plants bother:
root proliferation results in increased nitrogen capture from an organic
patch when two grasses compete. Plant Cell Environ. 22:811-820.

Hutchings, M. J., and H. de Kroon. 1994. Foraging in plants: the role of
morphological plasticity in resource acquisition. Pages 187 in M. Begon
and A. H. Fitter, editors. Advances in Ecological Research. Academic
Press, San Diego, CA.

Hutchings, M. J., E. A. John, and D. K. eresinghe. 2000. The effects of
heterogeneous nutrient supply on plant performance: a survey of
responses, with special reference to clonal herbs. Pages 91-110 in M. J.
Hutchings, E. A. John, and A. J. A. Stewart, editors. The ecological
consequences of environmental heterogeneity. Blackwell Science, Oxford.

Kembel, S. W., and J. F. Cahill. 2005. Plant phenotypic plasticity belowground: A
phylogenetic perspective on root foraging trade-offs. Am. Nat. 1662216-
230.

Littell, R. C., G. A. Milliken, W. W. Stroup, R. D. Wolﬁnger, and O.
Schabenberger. 2005. SAS for Mixed Models, 2nd edition.

Malpassi, R. N., T. C. Kaspar, T. B. Parkin, C. A. Cambardella, and N. A. Nubel.
2000. Oat and rye root decomposition effects on nitrogen mineralization.
Soil Sci. Soc. Am. J. 64:208-215.

Raich, J. W., R. H. Riley, and P. M. Vitousek. 1994. Use of Root-lngrowth Cores
to Assess Nutrient Limitations in Forest Ecosystems. Canadian Journal of
Forest Research-Revue Canadienne De Recherche Forestiere 24:2135-
2138.

Rajaniemi, T. K., and H. L. Reynolds. 2004. Root foraging for patchy resources in
eight herbaceous plant species. Oecologia 141:519-525.

Robinson, D. 1996. Resource capture by localized root proliferation: why do
plants bother? Ann. Bot. 77:179-186.

31

Sita, V. 1994. Catalog of curves for curve ﬁtting. Forest Science Research
Branch Ministry of Forests, Victoria, BC Canada.

Stark, J. M. 1994. Causes of soil nutrient heterogeneity at different scales. Pages
255-284 in M. M. Cladwell and R. W. Pearcy, editors. Exploitation of
environmental heterogeneity by plants. Academic Press Inc., San Diego,
CA.

vanVuuren, M. M. l., D. Robinson, and B. S. Grifﬁths. 1996. Nutrient inﬂow and
root proliferation during the exploitation of a temporally and spatially
discrete source of nitrogen in soil. Plant Soil 178:185-192.

Wang, J., and L. R. Bakken. 1997. Competition for nitrogen during decomposition
of plant residues in soil: effect of spatial placement of N-rich and N-poor
plant residues. Soil Biol. Biochem. 29:153-162.

Wresler, F., and W. J. Horst. 1994. Root growth and nitrate utilization of maize
cultivars under ﬁeld conditions. Plant Soil 163:267-277.

Zhang, H. M., H. L. Rong, and D. Pillbeam. 2007. Signalling mechanisms
underlying the morphological responses of the root system to nitrogen in
Arabidopsis thaliana. J. Exp. Bot. 58:2329-2338.

32

Table 2.1 SAS code for ﬁtting a quadratic plateau model to the difference in root
mass between the amended and the control cores.

 

dm'output;clear;log;clear;';
OPTIONS NODATE FORMDLIM='_' LS=75 ; TITLE;
options noovp;
data Nroot;
input trt plot ngm Ggpm Amass Cmass Alength Clength Asrl Csrl
Agpm Cgpm Ampm Cmpm Mgpm Mmpm Dgpm Dmpm Prtlen Protms;
y = dmpm; *dmpm = difference in root mass between A and C
cores;
x = ngpm; *ngpm = N rate, 9 N m”;
*A= amended and C: control cores, gpm = 9 m4, length = cm
root per core, srl = g m-l of root, D difference between A
and C, G = grain yield;
cards;
run;
proc sort data=Nroot;
by ngm;
run; quit;
title 'Quadratic Model with Plateau';

proc nlin data=Nroot; /*nonlinear regression procedure in
SAS*/

parms a=3000 b=0.08 c=—0.0001;

x0=-.5*b / c; * Estimate join point;

if x<x0 then * Quadratic part of Model;
model y=a+b*x+c*x*x;

else * Plateau part of Model;

model y=a+b*x0+c*x0*x0;

if _obs_=1 and _iter_ =. then do;
plateau=a+b*x0+c*x0*x0;
put / x0: plateau: ;
end;
output out=yhatdmpm predictedzyp u95m=u95i 195m=195i ;
run;
/* Setup for creating the graph */
legendl frame cframe=ligr label=none cborder=black
position=center value=(justify=center);
axisl label=(angle=90 rotate=0) minor=none;
axisz minor=none;
proc gplot; *plots the model output and the raw data;
plot y*x yp*x /frame cframe=ligr legend=legend1
vaxis=axisl haxis=axi52 overlay ;
run;
QUit;
proc print data=yhatdmpm;
run; quit;

 

 

33

 

50 -
a ab B. inermis

    

4o-

30-

20-

a A. sativa

Aboveground biomass (mg plant")
0

 

 

AB AC AD BC BD CD
Litter Quality Choice Treatments

Figure 2.1. Mean aboveground biomass of B. inermis (top) and A. sativa
(bottom) in response to litter choice of treatments: AB = clover shoots/clover
roots; AC = clover shoots/oat leaves; AD = clover shoots/oat straw; B0 =
clover roots/oat leaves; BD = clover roots/oat straw; CD = oat leaves/oat
straw. Different litter above the bars indicate signiﬁcant differences (P =
0.05).

1'2 - B. inermis
1.0 '
0.3 -
0.6 -

7" 0.4 -

0.2 -

.0
o

‘A. sativa
12 - ab a

10 - ab

Total root length (m plan ")

8- be
6-
4-
2.

 

Litter Quality Choice Treatments

Figure 2.2. Total root length in the surface 70 mm of sand in response to
litter quality choice treatments (same as Figure 1). Vertical bars represent
treatment means +/- one standard error. Different letters above each bar
indicate signiﬁcant differences (alpha = 0.05).

1. - . .
00 B. mermls

95% Cl.

 

Uniform

.0 .o .o
N U! N
01 O 01
l l I

marital:

0.00 4

o

N

01
I

95% Cl.
Uniform

Proportion of total root length in N-rich patch
0
in
o

 

 

BC BD CD AC AB AD

Litter Quality ChoiceTreatments

Figure 2.3. Proportion of root length growth of B. inermis (top) and A. sativa
(bottom) into patches of higher quality litter. Uniform line at 0.5 represents an
even distribution of roots into quadrants of each litter type. The 95% Cl line
represents the upper conﬁdence interval around the uniform distribution line.
Treatment means falling on or below the 95% Cl are statistically
indistinguishable from the uniform root length distribution, is. no selective
foraging. Different letters above the vertical bars indicate signiﬁcant
differences (Tukey-Kramer adjustment for multiple comparisons).

36

1500 ‘
1400 - ' '
1300 l
1200 l ._
1100 -
1000 -

900 -

800 I l I r I I
0 5 1 0 1 5 20 25 30

 

 

 

Corn grain yield (9 m'2)

 

 

 

Nitrogen fertilization rate (9 N m'2)

Figure 2.4. Corn grain yield across an N fertilization gradient. The top and
bottom dotted lines represent the 95% conﬁdence intervals around the ﬁtted
quadratic plateau model (middle solid line). The grain plateau was reached
with 11.69 N rn'2 at a maximum of 1321 g m'2 and a minimum of 986 g m'z.

37

 

 

A 40000 -

or

E

E 30000 J '

1: e

‘6: 20000 ~

5 ° 0

1'5 10000 -

o 8

m 0 - O . O .

 

 

0 5 10 15 20 25 30

Nitrogen fertilization rate (9 N m'2)

Figure 2.5. Mean corn root length in in-growth cores across an N fertilization
gradient.

38

 

1400

 

 

 

7’" e

g 1200 ‘ Q

3 1000 - g

.C

*5, 800 - . 8 e '

2 600 1 8
*‘ e

E 400 - i . g . 3 a
1% 200 - ' e

8 0 - 9
(D

0 5 10 15 20 25 30 35

Nitrogen fertilization rate (9 N m'2)

Figure 2.6. Speciﬁc root length of roots in amended and control in-growth
cores across our N fertilization gradient. The gray circles indicate the litter-
amended cores and the black circles indicate the unamended control cores.

39

 

1.0
.C
*6) ‘ . 0. Ge
5 0.8 " . ..
*6
9 0.6- 3. e
E
.9 0.4 - 0
E
- e
a- 0.2
n. e
0.0 I I l I T

 

 

 

900 1000 1100 1200 1300 1400 1500
Grain yield (9 m'2)

Figure 2.7. Proportion of total in-growth core root length found in amended
cores as a function of the maize grain yield.

40

 

00 b
O O
O O
O O
O O
1 l

 

 

 

 

Difference in root length (m m'z)
N
O
O
O
O

0 5 10 15 20 25 30

Nitrogen fertilization rate (9 N m'z)

Figure 2.8. Selective maize root proliferation into amended and unamended
root in-growth cores across our N fertilization gradient as indicated by the
difference in root length between amended and unamended cores (root
length in amended core minus length in unamended core). The top dotted
line depicts the upper 95% conﬁdence interval, middle line solid depicts the
ﬁtted quadratic plateau model and the lower dotted line indicates the lower
95% conﬁdence interval. The maximum difference occurred at 32,500 m rn'2
+/- 1765 SE. and the plateau of 226 m m'2 was reached at 15.8 g N m'2.

41

Chapter 3. Litter Aggregation has a disproportionate inﬂuence
on Nitrous Oxide Flux

INTRODUCTION

Biogenic greenhouse gas emissions from soils are typically studied under
in-situ conditions or in homogenized laboratory incubations. The former requires
intensive monitoring to obtain realistic estimates of emission rates, whereas the
latter is intended to identify the cellular-level controls on these processes.
Extension of mean in-situ soil conditions to process rates using predictive models
based on the controls identiﬁed under homogeneous conditions has proven
insufﬁcient. This is in part because soil properties and process rates are
temporally and spatially heterogeneous at multiple scales (Groffman and
Robertson, 2007).

Jensen’s Inequality is the fundamental basis for the prediction that
process rates will differ between systems with uniform and heterogeneous
distributions of process controls if the functional response to that control is
nonlinear (Jensen 1906). The soil microbial processes responsible for soil
surface CO2 and N20 ﬂuxes, namely decomposition and denitriﬁcation, have
nonlinear responses to the availability of many resources (e.g., soil moisture,
Linn and Doran 1984). If spatial or temporal resource distribution at microbial
scales has signiﬁcant effects on biogeochemical processing rates then our
understanding of larger scale processes may be challenged.

The incorporation of plant litter into soil is a potentially important source of

microbe-scale resource heterogeneity in many terrestrial ecosystems; however,

42

most studies of litter decomposition and litter’s inﬂuence on N20 ﬂuxes have
uniformly distributed the litter in soil (Magid et al. 2006). The few exceptions to
this have compared the inﬂuence litter distribution, either distributed uniformly or
placed in a horizontal layer, on decomposition and N cycling processes (N
mineralization, N20 emissions, denitriﬁcation, and leaching).

The results from these experiments are mixed. For example, Breland
(1994) compared the decomposition of red clover (Trifolium pretense L.) shoots
when uniformly distributed versus when placed in a single horizontal layer in a
loamy soil. The layered litter initially decomposed more rapidly than the
uniformly distributed. Breland postulated that the difference in litter and soil
physical contact between the distributions provided for different levels of physical
protection from microbial attack. He also suggested that this physical protection
was responsible for the reduced denitriﬁcation rates observed in the uniform litter
distribution. In contrast, Ambus et al. (2001) found that uniformly distributed
wheat (Triticum aestivum L.) and alfalfa (Medicago sativa L.) litter produced 6.5
and 1.6 times more N20, respectively, than layered. By following N
mineralization in response to these two spatial distribution, Ambus et al. (2001)
hypothesized that the observed differences in N20 ﬂuxes were due to a greater N
limitation in the layered litter.

Magid et al (2006) followed decomposition and soil inorganic N in
response to layered and uniformly distributed corn (Zea mays L.) stalks, sheep
(Ovis arias L.) manure, and rapeseed (Brassica napus L.) stems. Each material

initially decomposed more rapidly when uniformly distributed into a loamy sand

43

soil; however, this was followed by a sustained period of more rapid
decomposition in the layered litter. They also suggest that a temporary N
limitation during decomposition of these materials is exacerbated when the
materials are layered due to physical constraints on N03' diffusion from the bulk
soil.

The spatial co-occurrence of soil micro- and mesofauna with decomposing
organic matter (Van Noordwijk et al. 1993) may also explain some of the
differences in C and N cycling in uniformly distributed versus layered litter
(Grifﬁths 1994). For example, Bonkowski et al. (2000) found greater population
densities of bacterial feeding nematodes and protozoans in soils containing
layered fresh Lolium perenne leaves than in uniformly distributed leaves. These
enhanced microbial grazer population densities corresponded to an increase N
mineralization from the litter. The favored hypothesis to explain this response
was that the concentrated resource allows for more trophic levels to spatially co-
occur, and thus mineralize C and N at a greater rate (Clarholm 1985).

A uniform distribution of litter of varying particle sizes may also behave
similar to a gradient in litter aggregation. The larger particle sizes have less soil
to litter contact and are numerically fewer than smaller particles of litter. Fine
grinding of plant litter has both stimulated (Angers and Recous 1997, Ambus et
al. 2001) and inhibited (Breland 1994, Shelp et al. 2000) litter decomposition and
N cycling rates. In particular, N20 emissions have ranged from 50% higher
(Ambus et al. 2001) to 20% lower (Shelp et al. 2000) in soils with ﬁnely-ground

litter versus coarsely chopped litter.

Three main hypotheses have been forwarded to explain how organic
matter aggregation may alter soil microbial activity and CO2 and N20 emissions:
1) resource density dependent expansion of trophic levels to include soil
microfaunal grazers (Clarholm 1985, Bonkowski et al. 2000); 2) release from the
soils physical protection (Breland 1994); and 3) resource diffusional constraints
(e.g., N03" and 02) (Myrold and Tiedje 1985, Magid et al. 2006). Because the
second and third hypotheses have only been tested in systems comparing a
single litter layer versus a uniform distribution it is difﬁcult to extend these results
across gradients in aggregation or environmental conditions.

Our objective is to examine the inﬂuence of resource heterogeneity on
CO2 and N20 emissions by manipulating plant litter across a gradient of
aggregation. By varying the intensity of litter aggregation we are able to alter the
level of physical protection and diffusional constraints to address four questions:
1) does the intensity of plant litter aggregation affect litter decomposition and N20
emissions; 2) does the aggregation effect on decomposition and N20 ﬂuxes vary
with soil moisture and hence diffusion constraints; 3) does plant litter particle size
affect CO2 and N20 emissions similarly when uniformly distributed and
aggregated; and 4) does the presence of growing plants alter the N20 emissions
in response to litter aggregation? We address these questions in two laboratory
studies and a ﬁeld experiment.

MATERIALS AND METHODS

Experiment #1: Litter Aggregation and Soil Moisture

45

We used a 2x4 factorial experiment laid out in a Randomized Complete
Block Design (RCBD) with 5 replicates to address the question of how litter
aggregation and soil moisture content affect decomposition and N20 emissions.
Soil moisture (50% or 80% water ﬁlled pore space - WFPS) and plant litter
aggregation (1, 3, or 9 patches or uniform distribution) in the soil were the
manipulated factors. We mixed coarse sand 1:1 into a composite soil composed
of the surface 0.4 m of soil from the WK. Kellogg Biological Station Long-Tenn
Ecological Research site (KBS-LTER), including both Kalamazoo (ﬁne-loamy,
mixed, mesic Typic Hapludalfs) and Oshtemo (coarse-loamy, mixed mesic Typic
Hapluadalfs) series. The soil mixture was air-dried and stored for ca. 18 months
prior to use. Immediately prior to initiating the experiment ﬁve 10 g subsamples
of soil mixture were extracted with 100 mL of 1M KCI each for inorganic N
concentration determination (Sollins et al. 1999) and indicated that the mixture
contained 21.4 pg N03'-N 9 soil" and 1.0 ug NHi-N 9 soil" (colorimetric
determination on a Ol Alpkem 3550 Flow analyzer).

We used red clover (Trifolium pretense) shoots as the liter. The clover
was grown in sand in the greenhouse, fertilized with a modiﬁed complete
Hoagland’s nutrient solution (Hewitt 1966), and harvested before initiation of the
reproductive phase. The shoots were cut at the sand surface, dried for 4-5 days
at 55°C, coarsely-chopped to pass a 10 mm screen with a WIley mill and then
ﬁnely-ground in a Cyclotec® 1093 sample mill to pass a 1 mm screen. Eight

subsamples of the ﬁnely-ground litter were analyzed for C and N content with a

46

Costech ECS 4010 CHNSO elemental analyzer and found to contain 413 g C kg
litter'1 and 32 g N kg litter‘1 giving it a C:N ratio of 12.9.
Incubation setup

We conducted the litter incubations in 2.6 L (0.15m in height) round
polyethylene containers. The container lids were ﬁtted with two 6.4 mm diameter
threaded polyvinylchloride reducer couplings: one was attached to a three-way
stopcock to allow gas sampling with a 10 mL syringe with, and the other was
attached to a 28 gauge hypodermic needle hub to act as a vent. We sealed the
lids onto the containers only when gas sampling. In between samplings the
containers were covered with 1.0 mil low density polyethylene bags to minimize
moisture loss while allowing O2 and CO2 gas exchange. We incubated the
containers on a bench top out of direct sunlight. Daily minimum and maximum
air temperature was recorded with a digital thermometer placed in the center of
the containers: temperatures ranged from 21.7 to 235°C and averaged 227°C
throughout the 39 day incubation.

A litter aggregation gradient was constructed in each container by
distributing 4.5 g of dry ﬁnely ground red clover shoots into one patch (4.5 g of
litter per patch), 3 patches (1.5 g of litter per patch) or 9 patches (0.5 g of litter
per patch), or uniformly mixed into a band of soil (Figure 3.1). We constructed
the patches of litter by ﬁrst placing 850 mL of soil mix into each container and
then a template for the litter placement was pressed into the soil. The template
was a 140 mm diameter circular sheet of acrylic with one 21.3 mm, three 14.3

mm, or nine 8.3 mm diameter plastic syringe cylinders, less the needle hub end,

47

adhered perpendicular to the sheet. The rubber tips of the syringe plungers were
removed and the plungers were placed in the syringe cylinders as the template
was pressed into the soil to displace consistent volume of soil. The litter was
then placed into the syringe cylinders and topped off with soil. We pushed the
plungers down into the cylinders as the template was raised so that the litter
remained in the soil as individual column shaped patches just below the surface
of the soil. The mean depth of the litter patches was similar across the gradient
(Figure 3.1). An additional 400 mL of soil was then added on top of the litter and
initial soil. The uniform treatment was constructed by ﬁrst adding 250 mL of soil
to the containers, then 600 mL of a litter and soil mixture to which was then
added 400 mL of additional soil. We added soil to all of the containers to bring
each to 1940 9.

Soil moisture treatments were established by adding 260 mL (50% WFPS
equivalent) or 415 mL (80% WFPS equivalent) of water (puriﬁed by reverse
osmosis) slowly to each container. Containers were weighed weekly and water
was added to maintain a constant moisture content.

002 and N20 ﬂux determinations

Carbon dioxide and N20 gas ﬂuxes across the soil surface were
determined by incubating the containers for 35 to 45 minutes with the lids to
allow these gases to accumulate in the headspace above the soil to be sampled
(Holland et al. 1999). Gas samples were collected at four times with 10 to 12
minutes between samplings. The headspace was sampled by transferring 20 mL

of gas from the container headspace to 5.9 mL glass vials outﬁtted with rubber

48

septa using a 10 mL plastic syringe and 22 gauge hypodermic needles. We
determined N20 concentrations on a gas chromatograph (GC) outﬁtted with an
electron capture detector. An infrared gas absorption analyzer (Ll-Cor 820) in
series with the GC was used to determine the CO2 gas concentration. We
regressed the sample CO2 and N20 concentrations with a linear model against
sampling time to determine the gas ﬂux rate.
Experiment #2: Liter Aggregation and Particle Size

To assess the effects of litter aggregation and particle size on
decomposition and N20 ﬂuxes we used the same soil, litter, and incubation setup
as Experiment #1 in a 2x2 RCBD with four replicates per block for a total of 16
experimental units. The litter distribution treatment consisted of the uniform litter
distribution and the 3-patch distribution only, and the particle size treatment was
composed of the ﬁnely-ground and the coarsely-chopped red clover shoots. Soil
moisture was maintained at 50% WFPS throughout the incubation. Gas ﬂuxes
were measured from these containers on 9 sampling dates across the 18 day
incubation. The air temperature during this incubation ranged from 21.9 to
245°C and averaged 233°C.
Experiment #3: Liter Aggregation with Growing Plants

Plant roots alter many of the same soil properties that control microbial
activity in soils. To assess the inﬂuence of litter aggregation on N20 ﬂuxes in the
presence of growing plants in the soil we scaled up the design used in
Experiment #1 to accommodate a growing maize plant in 50-L containers that

were placed in the ﬁeld. We will describe the details of this experimental setup

49

brieﬂy, because they are described elsewhere (Loecke Chapter 5). The
experiment was conducted on the KBS-LTER using a similar soil mixture as
experiments #1 and #2. We used a RCBD with 4 replicates per block and 5 soil
amendment treatments consisting of four litter distributions (8, 24, and 72
patches and uniform) and a control +N fertilizer treatment. Each of the 20
experimental units were composed of a 50-L black plastic container ﬁlled with the
soil mix, amendment, and a single maize plant (Pioneer ® 35Y54).

Litter application

We distributed ﬁnely-ground red clover litter into eight — 4.699 patches,

twenty-four - 1.56 9 patches, seventy-two - 0.52 9 patches or a uniformly
distributed 37.5 9 patch of litter in the soil; however, because we did not want to
disturb the litter patches when planting the maize no patches were placed within
the center 150 mm of the container (Figure 3.2). We constructed the litter
distribution treatments by: placing a temporary circular template of the same
diameter as the inside of the 50-L container on top of 40 L of soil mix; adding the
litter to the template; removing the template; and then adding 10 L more soil mix
on top of the litter. The patches were distributed at a mean depth of 100 mm
below the soil surface and application process lasted three days from May 22-24,
2006 during which no precipitation was allowed into the containers. The
template was a similar to that used in experiments #1 and #2. The uniform
treatment was constructed by mixing 37.5 g of litter with 4 L of soil taken from to
the containers, then adding to the litter-soil mix back into the outer ring of the soil

in the container (Figure 3.2).

50

Planting and fertilizing

The overall design of this experiment is to test N cycling responses to litter
distribution in a system where N was the only soil nutrient limiting plant growth.
To achieve this we applied 250 mL of nutrient solution containing 0.5 P g, 1.25 K
g, 0.25 S-SO4 g, 0.27 Ca 9, and 0.07 Mg 9 to the soil surface of each container
on July 6,2006. In addition the control +N containers received 1 L of solution
containing 1.6 g N as NaNOa and litter treated containers received 1 L of R0
(reverse osmosis) water. Supplemental watering (2 L container") was conducted
on August 13 and 22.

Soil surface N20 ﬂux

We used removable static chambers to measure soil surface N20 ﬂux
(Holland et al. 1999). Each chamber lid sealed around the outside of the
container and the plant stem (Figure 3.2). The chamber lids were modiﬁed 120-L
refuse container and lid with a 60 mm wide slit removed from on edge to the.
center of the lid to accommodate the plant stem. Latex sheeting was secured to
the plant stem and chamber lid to complete an air tight seal. Deployment of the
chamber lid required ~3 minutes and remained on the container for a maximum ‘
of 70 minutes during the sampling. We used a similar sampling and analysis
procedure as above.
Statistical Analysis

All ﬂux data were natural log transformed to meet the analysis of variance
(ANOVA) assumption of homogeneity of variances. For experiments #1, we

used a repeated measure ANOVA on the full factorial design with block,

51

moisture, litter distribution, and sampling date as independent ﬁxed variables with
sampling date as the repeated random variable. The second experiment was
analyzed with block, litter distribution, particle size, and sampling date as the
independent ﬁxed variable with sampling date the repeated measure. We
analyzed Experiment #3 in the same way as Experiment #1. We used Akaike’s
information criteria (Akaike 1974) to choose a ﬁrst-order heterogeneous
autoregressive (ARH) covariance structure to model the repeated measure
variance components using SAS mixed model procedures (Littell et al. 2005) in
both experiments. Where independent variable interactions were signiﬁcant (0 <
0.05), the interacting variables were analyzed separately by sampling date.
Multiple comparisons within sampling dates were conducted using the Tukey-
Kramer protection procedures in SAS. Differences were consider signiﬁcant at
the a = 0.05 level for all ANOVAs.
RESULTS
Experiment #1 : Litter Aggregation 'and Soil Moisture

Litter decomposition in response to aggregation was dependent on the soil
WFPS (aggregation*WFPS; P<0.001, Table 3.1 and Figures 3.2 and 3.3). The
more moist soil (80% WFPS) inhibited litter decomposition relative to the 50%
WPPS soil at all sampling dates (P<0.0001, Table 3.2) and the magnitude of this
difference decreased with incubation time (WFPS*date; P<0.0001, Table 3.1).

At 50% WFPS, litter decomposition through time varied with litter
aggregation (aggregation*date; P<0.0001, Table 3.3). Initially (ﬁrst two sampling

dates), the uniformly distributed litter decomposed at a greater rate'than the

52

average of the aggregated litter (P<0.05, Table 3.4 and Figure 3.2). This was
followed by an abrupt switch were the aggregated litter began decomposing
more rapidly than the uniform distribution at 5 and 7 days into the incubation
(P<0.0001, Table 3.4). On day 18, litter distributed into 9 patches was
decomposing at a more rapid rate than any other distribution. Twenty-three days
into the incubation, the 3 patch treatment was decomposing the fastest, 9
patches and uniform distributions were decomposing at an intermediate rate, and
the single large patch treatment was decomposing at the slowest rate (P<0.05,
Table 3.4).

In the moister soil (80% WFPS), litter decomposition was consistently
higher in response to the uniform distribution than the aggregated distributions
(aggregation*date; P=1) (uniform versus aggregated contrast; P<0.0001, Figure
3.3).

Nitrous oxide ﬂuxes in response to litter aggregation was dependent on
soil WFPS and varied across sampling date (Aggregation*WFPS*date;
P>0.0001, Table 3.5 and Figures 3.2 and 3.3). The 80% WFPSAsoiI treatment
produced a wider range of N20 ﬂuxes (0.5 to 5000 (19 N m‘2 h") than the 50%
WFPS treatment (0.9 to 480 pg N m2 h"). The highest N20 ﬂux rates were
found between 10-15 days into the incubation for the 50% WFPS-and at the ﬁrst
sampling date for the 80% WFPS soils. In both soil moisture treatments, N20
ﬂux response to litter aggregation was dependent on when the ﬂux was

measured (Table 3.6 and 3.7).

53

At 50% WFPS, N20 ﬂuxes from the uniform litter distribution were highest
during the ﬁrst two samplings and consistently lower than or indistinguishable
from the patchy litter distribution treatments throughout the incubation (Table 3.8
and Figure 3.2). Among the patchy litter distributions at 50% WFPS there was
considerable variation in the N20 ﬂuxes throughout the incubation (Figure 3.2
and Table B.6). Statistical differences occurred among the litter aggregation
treatments at 2 days and 7 to 26 days into the incubation under 50% WFPS
(Figure 3.2). The mean N20 ﬂux from the three patchy litter distribution was
greater than the uniform distribution at days 3, 5 and 9 through 26 of the
incubation in the 50% WFPS treatment.

In the moister soil, N20 ﬂuxes decreased with time of incubation (Figure
3.3 and Table 3.8). At this soil moisture, the uniform distribution emitted more
N20 on the last three sampling dates (P>0.05, Table 3.9) than the mean of the
patchy litter distributions and was similar across all other sampling dates.
Experiment #2: Litter Particle Size and Aggregation

The litter decomposition rate in response to aggregation and-particle size
varied with sampling date (Aggregation*Date; P<0.0001 and Size*Date;
P<0.0001, Table 3.10). WIthin 20 hours of adding water to these containers the
decomposition rates of the uniformly distributed litter were higher than the patchy
distributions (P<0.01, Figure 3.4 and Table 3.11). By the fourth day of incubation
this trend was reversed with the patchy litter distribution decomposing more
rapidly than the uniform litter distribution (P<0.05). This pattern continued

through the day 14 with the exception of no differences on day 7 (Figure 3.4).

On the last two sampling dates litter distribution did not affect the decomposition
rate. The litter particle size affected decomposition on four dates. On the sixth
day of the incubation the ﬁnely ground litter decomposed more rapidly than the
coarse litter particles (P<0.05). This trend was observed again during the last
three sampling dates (P<0.05).

The patterns of nitrous oxide ﬂux in response to litter aggregation and
particle size were more complex than were C02 ﬂux patterns
(Aggreg.*Size*Date; P<0.0001, Table 3.12). For seven of the 9 sampling dates
interactions between the two main factors were signiﬁcant (Figure 3.3 and Table
3.13). Initially N20 ﬂuxes from the uniformly distributed coarse size litter were
higher than from the uniformly distributed ﬁne particles (P>0.05) and the N20
ﬂuxes from the patchy distributed litter were intermediate (Figure 3.4 and Table
3.13). By the fourth day of the incubation the patches of litter were emitting more
N20 than the uniform litter (Table 3.13). During the last four samplings N20
ﬂuxes were greatest from the ﬁne sized aggregated litter, intermediate from the
coarse aggregated litter and least from the uniform litter distributions regardless
of particle size (Table 3.13).

Experiment #3: Liter Aggregation with Growing Plants

Nitrous oxide ﬂuxes were highest at the beginning of the sampling period
and varied in response to litter distribution (Aggregation*Date; P<0.001, Table
3.14). Fifteen and 20 days after litter application on DOY 160 and 165, N20

emissions were 4.1 and 3.2 times greater, respectively, from aggregated litter

55

than uniformly distributed litter (Figure 3.6). In contrast on DOY 181, N20 ﬂuxes
were 1.9 times greater from uniformly distributed litter than. aggregated litter.
DISCUSSION
Experiment #1: Litter Aggregation and Soil Moisture

Role of aggregation in litter decomposition

The inﬂuence of litter aggregation on the decomposition rate of ﬁnely-

ground red clover shoots was dependent on soil moisture content. Litter
aggregation at optimal soil moisture for microbial activity (50%WFPS) had
signiﬁcant short term (0-10 days of incubation) effects on litter decomposition
rate (Figure 3.2); in the longer term however, these effects were less important
(Aggregation, P<0.66; Table 3.3). These short term effects were marked by an
initial inhibition of decomposition in the aggregated litter relative to the uniform
distribution. This pattern was more apparent as the aggregate size increased
suggesting that extent of litter aggregation was important. By day 5 of the
incubation the trend had switched such that the most highly aggregated litter was
decomposing at the most rapid rate, the intermediate sized litter aggregates had
intermediate decomposition rates, and the uniform litter distribution had the
lowest decomposition rate. In the more moist soil (80% WFPS), the uniformly
distributed litter consistently decomposed more rapidly than the aggregated litter
(Figure 3.3). Thus increasing the WFPS eliminated the dynamic pattern
observed across the aggregation at optimal soil moisture and caused the uniform

litter distribution to decompose at the greatest rate.

56

The contrast between the CO2 ﬂuxes from the two soil moisture contents
across the litter aggregation gradient is consistent with the hypothesis that 02
diffusion initially limited microbial oxidation of the litter within the aggregates.
Magid et al (2006) observed a similar decomposition pattern in response to
layered maize stalks and sheep manure versus a uniform distribution at optimal
soil moisture. They hypothesized that this pattern was due to N limitation in the
litter that is intensiﬁed when litter is in layers as opposed to uniformly distributed.
This idea that the strength or concentration of a resource sink is related to the
concentration gradient of that limiting resource surrounding that sink is supported
by spatial resource limitation models (Myrold and Tiedje 1985) and empirical
measurements of gradients in C, N, and microbial biomass surrounding
decomposition plant litter (Gaillard et al. 1999). Thus the average inorganic N
concentration experienced by very small patches, i.e. particles uniformly
distributed, can be greater than that of large patches per unit of metabolically
available C.

The temporal differences observed in our data along the aggregation
gradient at optimal soil moisture may be due to N limitation; however, the clover
litter that we used has a low C:N ratio (12.9) and N limitations should diminish as
soil moisture increases. Although it is possible that the aggregated litter was N
limited at 50% WFPS and all of the litter distributions were 02 limited at 80%
WFPS. Overall our results suggest that aggregation plays a signiﬁcant
temporary yet minor role in regulating the decomposition of an N-rich labile plant

litter.

57

Nitrous oxide response to litter aggregation

Nitrous oxide ﬂuxes in response to litter aggregation were dependent on
the sampling date and soil WFPS. At 50% WFPS, N20 ﬂuxes from aggregated
litter differed from uniformly distributed litter on 8 of 13 sampling dates and only
on the ﬁrst sampling date did the uniform distribution emit more N20 than the
aggregated litter (Figure 3.3). The low levels of N20 emitted from all of the litter
treatments under 50%WFPS conditions between 5 and 7 days into the incubation
are likely related to the high rates of CO2 ﬂux at that time. This depression in
N20 ﬂuxes may be the result of a N03' limitation and due to two possible
mechanisms: microbial inorganic N immobilization or N20 reduction to N2. From
our data it is unclear which mechanism may be more important.

Following the dip in N20 production the aggregated litter began emitting
much more N20 than the uniform litter distribution for about 15 to 18 days. In
general, the more aggregated the litter the more N20 was emitted. Across the 39
day incubation, the average N20 ﬂux rates were highest from the two most
aggregated litter distributions of 1 and 3 patches (154 and 146 pg N rn'2 h",
S.E.=9, respectively), intermediate from 9 patches (98 pg N rn’2 h") and lowest
from the uniformly distributed litter (22 pg N m'2 h"). From these results we
conclude that aggregation of N-rich labile plant litter has a substantial inﬂuence
on the N20 emissions. Furthermore, the degree or intensity of aggregation
appears to be the primary factor controlling the magnitude of N20 emitted.

Overall the results of this experiment indicate that spatial aggregation of

labile organic matter in the form of red clover litter can temporally affect

58

decomposition and have substantial effects on N20 emissions than can be
related to the intensity of the aggregation.
Experiment #2: Liter Particle Size and Aggregation

The interaction of particle size and aggregation on decomposition

There was general agreement in patterns of both 002 and N20 ﬂuxes

between Experiments #1 and #2. The litter distribution effect dominated the early
part of the decomposition pattern, whereas following about 14 days into the
incubation particle size became a more signiﬁcant factor with the coarsely-
chopped litter decomposing at a slower rate. There are numerous studies
demonstrating that ﬁnely grinding plant litter removes some of the physical
protection against microbial decomposition provided by cellulose imbedded
lignin. Our data suggest that during the initial phase of decomposition litter
aggregation can provide a functionally similar degree of protection. Overall these
results demonstrate that aggregation affects litter decomposition regardless of
the litter particle size.

N20 emissions from aggregated litter of two particle sizes

The patterns of N20 ﬂux were more complex than the CO2 ﬂux patterns.

The particle size does not appear to inﬂuence N20 ﬂuxes from the uniformly
distributed litter; however, when aggregated the same particle sizes behaved
differently. Through the ﬁrst 8 days of incubation the N20 emissions from ﬁne
and coarse aggregated litter was similar and then starting about day 9 the ﬁnely-
ground aggregated litter began to emit N20 at an accelerated rate similar to the

Experiment #1 .

59

Experiment #3: Liter Aggregation with Growing Plants

The results of this ﬁeld experiment concur nicely with those of the previous
two incubation eXperiments and provide additional support that litter aggregation
inﬂuences N20 emissions for about the ﬁrst 25 to 35 days following incorporation
into the soil. The maize plants in this study were planted from seed 5 days
following litter application (DOY 150) and thus were likely too small to have a
substantial effect on the microbial processes that generate N20 until after the
aggregation effect discontinued. This conclusion is also supported by the
observation that N20 ﬂuxes from unplanted and planted containers were similar
in this experiment (data not shown).
CONCLUSIONS

The aggregation of red clover litter in soil had a transient effect on
decomposition patterns under 50% WFPS. In wetter soils (80 %WFPS) this
short-terrn effect was eliminated, potentially indicating that 02 diffusion into the
litter aggregates was regulating the decomposition of the aggregated litter. Litter
aggregation had substantial effects on N20 emissions regardless of the litter
particle size or if plants were growing in the soil. In fact, the more intensely the
litter was aggregated the greater were the N20 ﬂuxes. This resulted in the most
aggregated litter treatment of our experiments emitting 7 times more N20 than
uniformly distributed litter under 50% WFPS.

This observation that litter distribution alters N20 ﬂuxes has important
implications for how we estimate N20 emissions for greenhouse gas inventories

and mitigation strategies. For example, the IPCC inventory standards estimate

60

that 1.25% of N applied to agricultural soils is lost as N20. Here we show that
the same quantity of litter-N can emit a 7-fold difference in N20 simply depending
on its horizontal distribution in soil. Managing agricultural plant litter in a spatially

explicit manner may offer an effective strategy for migrating N20 emissions.

61

REFERENCES

Akaike, H. 1974. New look at statistical-model identiﬁcation. IEEE Trans. Autom.
Control 19:716-723.

Ambus, P., E. S. Jensen, and G. P. Robertson. 2001. Nitrous oxide and N-
leaching losses from agricultural soil: inﬂuence of crop residue particle
size, quality and placement. Phyton (Austria) 41 :7-15.

Angers, D. A., and S. Recous. 1997. Decomposition of wheat straw and rye
residues as affected by particle size. Plant Soil 189:197-203.

Bonkowski, M., B. Grifﬁths, and C. Scrimgeour. 2000. Substrate heterogeneity
and microfauna in soil organic 'hotspots' as determinants of nitrogen
capture and growth of ryegrass. Appl. Soil Ecol. 14:37-53.

Breland, T. A. 1994. Enhanced mineralization and denitriﬁcation as a result of
heterogeneous distribution of clover residues in soil. Plant Soil 16621-12.

Clarholm, M. 1985. Interactions of bacteria, protozoa and plants leading to
mineralization of soil nitrogen. Soil Biol. Biochem. 17:181-187.

Gaillard, V., C. Chenu, S. Recous, and G. Richard. 1999. Carbon, nitrogen and
microbial gradients induced by plant residues decomposing in soil. Eur. J.
Soil Sci. 50:567-578.

Grifﬁths, B. S. 1994. Microbial-feeding nematodes and protozoa in soil: Their
effects on microbial activity and nitrogen mineralization in decomposition
hotspots and the rhizosphere. Plant Soil 164:25-33.

Groffman, PM. and GP. Robertson. 2007. Nitrogen transformation. Pages 341-
364 in E. A. Paul, editor. Soil Microbiology, Ecology. and Biochemistry.
Academic Press, San Diego, CA.

Hewitt, E. J. 1966. Sand and water culture methods used in the study of plant
nutrition, 2nd edition. Farnham Royal, Commonwealth Agricultural
Bureaux.

62

Holland, E. A., G. P. Robertson, J. Greenberg, P. M. Groffman, R. D. Boone, and
J. R. Gosz. 1999. Soil C02, N20, and CH4 exchange. Pages 185-201 in
G. P. Robertson, D. C. Coleman, C. S. Bledsoe, and P. Sollins, editors. ’
Standard soil methods for long-term ecological research. Oxford Univ.
Press, New York, NY.

Jensen, J. L. W. V. 1906. Sur les fonctions convexes et les inegalites entre les
valeurs moyennes. Acta Mathematica 30:175-193.

Linn, D. M., and J. W. Doran. 1984. Effect of water-ﬁlled pore-space on carbon-
dioxide and nitrous-oxide production in tilled and nontilled soils. Soil Sci.
Soc. Am. J. 48:1267-1272.

Littell, R. C., G. A. Milliken, W. W. Stroup, R. D. Wolﬁnger, and O.
Schabenberger. 2005. SAS for Mixed Models, 2nd edition.

Loecke, T. D. Chapter 5. Liter aggregation alters terrestrial N cycling. Ph.D.
Dissertation. Michigan State University, East Lansing, MI, USA.

Magid, J., A. De Neergaard, and M. Brandt. 2006. Heterogeneous distribution
may substantially decrease initial decomposition, long-term microbial
growth and N-immobilization from high C-to-N ratio resources. Euro. J.
Soil Sci. 57:517-529.

Myrold, D. D., and J. M. Tiedje. 1985. Diffusional constraints on denitriﬁcation in
soil. Soil Sci. Soc. Am. J. 49:651-657.

Shelp, M. L., E. G. Beauchamp, and G. W. Thurell. 2000. Nitrous oxide
emissions from soil amended with glucose, alfalfa, or corn residues.
Comm. Soil Sci. Plant Anal. 31:877-892.

Sollins, P., C. Glassman, E. A. Paul, C. Swanston, K. Lajtha, J. W. Hell, and E. T.
Elliott. 1999. Soil carbon and nitrogen. Pages 85-105 in G. P. Robertson,
D. 0. Coleman, C. S. Bledsoe, and P. Sollins, editors. Standard Soil
Methods for Long-Terrn Ecological Research. Oxford University Press,
New York, NY, USA.

Van Noordwijk, M., P. C. Deruiter, K. B. Zwart, J. Bloem, J. C. Moore, H. G.
Vanfaassen, and S. Burgers. 1993. Synlocation of biological-activity,

63

roots, cracks and recent organic inputs in a sugar-beet ﬁeld. Geoderma
56:265-276.

64

Table 3.1 Analysis of variance of CO2 ﬂux in response to litter aggregation and
soil moisture as water-ﬁlled pore space (VVF PS) across a 39 d incubation at
22°C.

 

 

Source of variation Num DFI Den DF F Value Pr > F
Block 4 338 12.17 <.0001
Aggregation 3 338 21.38 <.0001
WFPS 1 338 553.48 <.0001
Aggreg. x WFPS 3 338 18.89 <.0001
Sampling date ' 12 338 81.85 <.0001
Aggreg. x date 36 338 0.34 0.9999
WFPS X date 9 338 7.82 <.0001
Aggreg. X WFPS*date 27 338 0.29 0.9999

 

1' indicates the numerator (Num) and denominator (Den) degrees of freedom.

65

Table 3.2 Interaction slice of CO2 ﬂux for sampling date by soil moisture
treatment.

 

Main effect slice Date Num DFT Den DF F Value Pr > F
WFPS x Sampling date

2.0 1 338 42.10 <.0001
3.0 1 338 42.57 <.0001
5.0 1 338 116.2 <.0001
7.2 1 338 78.65 <.0001
9.0 1 338 80.95 <.0001
11.0 1 338 77.98 <.0001
15.0 1 338 64.89 <.0001
17.8 1 338 66.92 <.0001
22.9 1 338 18.81 <.0001
36.0 1 338 26.30 <.0001

 

1' indicates the numerator (Num) and denominator (Den) degrees of freedom.

66

Table 3.3 Analysis of variance of CO2 ﬂux response to litter aggregation at
50%WFPS across a 39 d incubation at 22°C.

 

Source of variance Num DF'r Den DF F Value Pr > F

 

Block 4 191 4.16 0.0030
Aggregation 3 191 0.53 0.6636
Sampling date 12 191 767.35 <.0001
Aggreg. x date 36 191 3.90 <.0001

 

1' indicates the numerator (Num) and denominator (Den) degrees of freedom.

67

9.3805 comtmano o_a_=:E ..oEmLxleﬂoxa...
05 3 God 0 3 8:85:9m .8zmzﬂm 8.865 98 mEEEmm 5.53 xac :moE EoEﬁm: 58 Lots whose. E2050
828:8. oz: on; 8E; 38 m5EE8 «we: 05 38.6 258:8. m on: 8.8 m5_aEmm =< h.

 

68

 

 

 

 

m: 9mm m: mHN w: ...mm m: Qmm Nowmd mVNNd wdm
m: mdm m: m.mm m: «on m: «.3 35.0 Ewnd mom
on Ndv m: va m: ﬁrm w: de 55nd Pound mdN
2. 5mm m: on m: 0.2. m: $.50 mmomd vmovd o.oN
no win an ...mm m ode n adv vmhmd moped mNN
n 0: m Qme a No» a 9mm 99.0 3006 QC
m: 52... m: Nam? w: mm: m: 93— mummd mmomd oh?
m... Q»: as $69. .2. w._.o_. m: wdww omNNd mwmod 9:.
no warm a 952 n 9mg m YEN «Rood mmood od
0 odwm n mdnm no Evan m Ndmm 586 58.0 NR
9 05m a make a to: m mdmm 88.0 58.0 Qm
m NNNm on Snow an mwa n QNNN mmvod NFNod 9m
m: mdwm m: ﬁmwm m: wdvm m: 060' named unwed ON
«3 K3
memo—2 «co—Swot. “Tn.
ESE: 8:38 8:83 sound gauge. ﬁotm. 5:885
. - m - m - F .m> ESE: cozmmocmmd. Co 9.28

 

658.83 889019.: ._. E 3833 mcomtmano o_a_=:E :53 88E
EoEamo: cozmmetmmm new 98 m5EE8 .3 EoEamo: weu>> £80m Eo: can. x3: ~00 ho 8:88 Go m_m>_mc< in 03m...

Table 3.5 Analysis of variance of N20 ﬂux in response to litter aggregation and
soil moisture across a 39 d incubation at 22°C.

 

 

Source of variance Num DF1' Den DF F Value Pr > F
Block 4 340 5.29 0.0004
Aggregation 3 340 24.42 <.0001
WFPS 1 340 128.52 <.0001
Aggreg. x WFPS 3 340 57.05 <.0001
Sampling date 12 340 62.09 <.0001
Aggreg. x date 36 340 4.05 <.0001
WFPS x date 9 340 45.31 <.0001
Aggreg. x WFPS x date 27 340 5.70 <.0001

 

1' indicates the numerator and denominator degrees of freedom.

69

Table 3.6 Analysis of variance of N20 ﬂux in response to litter aggregation with
50% WFPS soil across repeated sampling of a 39 d incubation at 22°C.

 

Source of variance Num DFT Den DF F Value Pr > F

 

Block 4 192 3.65 0.0069
Aggregation 3 192 97.06 <.0001
Sampling date 12 192 74.31 <.0001
Aggreg. x date 36 192 14.79 <.0001

 

1 indicates the numerator (Num) and denominator (Den) degrees of freedom.

70

Table. 3.7 Analysis of variance of N20 ﬂux in response to litter aggregation with
80% WFPS soil across repeated sampling of a 39 d incubation at 22°C.

 

Source of variance Num DFT Den DF F Value Pr > F

 

Block 4 144 27.23 <.0001
Aggregation 3 144 22.45 <.0001
Sampling date 9 144 84.95 <.0001
Aggreg.xdate 27 144 2.03 0.0043

 

1' indicates the numerator (Num) and denominator (Den) degrees of freedom.

71

5.82888. .EonomE ._o 8058 Eon: 885E236 new AEazv LBEoEac 85 8.865 ._.

 

 

 

72

 

 

8 ms me «am m: 03 m: Nd 38.0 33.0 mdm
m: Em 8 ad on Ne w: v.9 own to vmmmd won
me ed 8 mm ma mom 8 odm mnmvd mnmwd mdm
9 ON a v.» m 0.2.. a N. E 586 586 com
0 ad a 0.2 m mdm m mum 58.0 300.0 QNN
o md n no: a mom a «.mmm 58.0 58.0 Qt.
0 En a Noe no ﬁmmm m 5.94 58.0 58.0 0.3
a Q: m Omom m Emmv a come 586 58.0 o. 3
0 mom am «Now a m.» 5 n ed? 586 586 06
nm 0.3 m 06¢ a mdn a 0d 836 mmmod Nu
m: mow m: 5.3 m: o9. m: Nam mmmod Fused Qm
a mug a N8 m 0.8m m odvm 98.0 wooed o.»
w: N: m: not an m6? 8 odw 33.0 mmmﬁo od
NF... RE.”
885. E0558; “Tn.
ELSE: 8:28 8:28 52mm cozmmolammes 89cm. c3885
. - m - m - F .m> ESE: 8.88.3.4. eo gap

 

858003 85.519... ._. B 8538 mcomtmano o_a_=:E :53 88E EoEﬁo:
cozmmoammm one 28 mEEEmm >9 EoEumez wen—3 «mom 85 Eo: 9m. .5: ONZ eo 855.8 .6 53.8.4. dd 8.8...

.zm>=oma8: .5082: :0 8850: 585888: ucm 855:5: 8:. 8.865 :.

 

73

 

 

 

 

m v.3 : ON a N; : o.~ 500.0 moood 9mm
m v.3 : a; a N; : ad moood tweed mdm
m: vd m: md w: No m: Nm Sled mvwod mu:
m: v.3 m: wd m: 04 w: Qm mowed mmo~d Q?
m: NR m: Q. m: Qm m: ed? 836 Soto o. 3
m: ﬂow m: ﬁn m: QNF m: wNN ovumd Smod od
m Emu : On 8 3mm m mdm 5E6 886 Nu
m: odmw w: P.» m: n.2,. m: . mic mmmvd wwvod Qm
w: mum: m: m. KN m: coo m: $3 836 2.5.0 3..
m: mmmv m: 0.53 m: movw m: 8mm N .006 m Sod o.~
N: Rﬁm
88.2 EoEﬁmt. IlllluAaIIIll
882:3. 8:23 - a 8:23 - m :88 - F :Mwumtwrcnd :o_WMwwmm< :wwwmwws

 

85802: :mEm:x->mx:.r 3 8.8.6.: 808253 8.3.3:: 5.3 88:: 5958:
:ozmmmamm ucm Emu mEEEmm .3 E0882. man; .38 85 Eat 9m: .3: Omz :0 85:9 :0 m_m>_m:< m6 835...

Table 3.10 Analysis of variance of C02 ﬂux in response to litter aggregation and
litter particle size across a 18 d incubation at 23°C.

 

Source of variation Num DF'r Den DF F Value Pr > F

 

Block 3 105 3.17 0.0275
Particle size 1 105 0.03 0.8592
Aggregation 1 105 0.69 0.4067
Aggreg. x Size 1 105 1.69 0.1966

Sampling date 105 421.92 <.0001
105 5.79 <.0001
105 6.63 <.0001

105 1.97 0.0579

Aggreg. x Date

Size*Date

oooooooo

Aggreg. x Size x Date

 

1' indicates the nUmerator (Num) and denominator (Den) degrees of freedom,
respectively

74

<>Oz< 85 :_ 888285 8: 883 88:588.: 8588 85:6: 85 a

.8858: @5888 55:5 88:88:88 5:885:98 8.8885 888:: @5328: 8858. 888:2: H
.8288: 8 888888 8:85:88: 8:8 888:8: 85 88.8885 ._.

 

 

 

 

 

 

 

8N : 8» :8 t. 8 now :8 5
cu :8 an : mm 8 cop :8 om
mm :8 F F P : 88 8 cm? 8 n F F
on 8 N8 F : amp : mm P : mm F
:8 8: omm 8: m: 8: mm: 8: :3
mm 8 088 : v ...m :8 Km 8 gm
5: 8: X”: 8: Ev 8: 8:: 8: wow
wow 8: No: 8: 8 E. 8: 8v: 8: 888
mp P :8 mom :8 :3 : mm? 8 a:
88:88.). 88588;
@8580 88:28: - 8. E88: 88:28: - 8 E883
88880 l. 8E“:

omnwd 8888.: 88.: 8.:
3.88.: wwmwd 830.: 8...:
88: ...o 88 5.: «wood 8.?
mo Ed 880.: 889.: 5.8
8888.: 33.: No» to 8.8
vowed 58.: N: 3.: N:
83.0 883.: 3:86 Qv
ommmd mm: P .o Road Nd
5.3.0 mmood 838.: 8.:
:858::o:_
a; m; 8; 8 8 8:
“Km
85 x 85888895. 85 88:8:8>
.8889». 8.858.”. 8 8:88

 

888888 8E8:x-ﬂ8x:._. >: 88.83.88 8:08:8qu8 8853:: 5.3 88:. 8:8 858:
98:88 >: 885885 85888.88 8:8 8~_8 8.858: 85: 2 88:88: :_ 8.8: .3: ~00 8 8o:8_:8> 8 8_8>_8:< 3. m 8.:8...

75

Table 3.12 Analysis of variance of N20 flux in response to litter aggregation and
litter particle size across a 18 d incubation at 23°C.

 

Source of variation Num DFT Den DF F Value Pr > F

 

Block 3 105 5.51 0.0015
Particle size 1 105 16.29 0.0001
Aggregation 1 105 409.49 <.0001
Aggreg. x Size 1 105 184.62 <.0001
Sampling date 8 105 61 .82 <.0001
Aggreg. x Date 8 105 32.69 <.0001
Size x Date 8 105 31.63 <.0001
Aggreg. x Size x Date 8 105 14.20 <.0001

 

1' indicates the numerator (Num) and denominator (Den) degrees of freedom,
respectively.

76

5.8288888: 888885 8 88.888 8.858888 8:8 8.8883: 85 88828:. .8

 

 

 

 

 

 

 

 

P.N : 8N : 8.8 8 F888 8 8.8 588.8 388.8 388.8 8.:
8.8 : o.NF o 8.8 8 P.N8N o 8.N 388.8 588.8 388.8 8.8?
F... : 8.8N o N... 8 8.88 o 84 N588 588.8 8888.8 8.8V
v.8 : 8.8N : 8.8? 8 8.88 o 8.8 588.8 588.8 .88N8d N8
8.8? 8: 8.8.. 8: NNN 8: 8.N8 8: 8.8 8888.8 888.8 8888.8 8.8
8.8 : 8.8N : 8.88 8 8.: o N.“ 588.: 388.8 8888.8 N8
8.8 8 ...8..N : 8.88 8 8.8: u N.NF 8888.8 588.8 8888.8 8.8
8.: 8: 8K8? 8: 8.88 8: 8.88.. 8: 8.8NF E888 885.8 8888.8 N.N
N.8 :8 8.88 8 8.88? :8 8K8 : 8.8 8888.8 888...: 8888.8 8.8

8:885. 888588; :wwwmwws

80888.:
8; 8... .8; 8

888.880

.9880 88:28: - 8 888:: 88:28: - 8 888: “To.
lllll.88:8oo 88.“.
8N_ 8N. 88:8:8>
x .888m8< 88:888. 8.88.888 8 82.88

 

888888 8885888... >: 88.8388 888.8888 8.85.88 5.3 8:888 8:8 888
8:..8888 .5 888885 85888888 8:8 88 8.8.88: 88.. 2 88:88: :_ 8.8: x88 082 8 88:8:8> 8 8.8285. 8F.8 8.:8...

77

Table 3.14 Analysis of variance of N20 flux in response to litter aggregation with
a growingmaize plant across repeated sampling.

Source of variance Num DF'r Den DF F Value Pr > F

 

Block 3 110 2.21 0.092
Aggregation 4 1 10 5.64 0.0004
Sampling date 7 110 28.2 <.0001
Aggreg.*date 28 1 10 2.88 < .0001

 

1' indicates the numerator (Num) and denominator (Den) degrees of freedom,
respectively.

78

.8888 8:..8888 :88; 88888.8 88888.8 8888:. 8:888 8532.8 8888. 8888.8 H
888888 8 888.888 88:88:88 8:8 88:88:: 85 88888:. ..

 

 

8: 8.8 8: 8.8F 8:
8: ﬁN 8: 8... 8:
8: 8.5 8: 8.8 8:
8: 8.8V 8: 8. E 8:
8: 8.8 8: 8.8? 8:
n 8.8— n8 8.5.. 88
8: 8.8.. 8: N. rN 8:
8 8. E. 88 N. 8 8
.2880 8888:

8.8..
8.F

8.8

8.8..
8.N_.
for
...88

8.85..

88:88.2 88:88:...

88:28: - N5

 

 

8: 8. P .. 8: N. .. 888.8 88N
8: N8 8: 8.8 858.8 5 ..N
8: 8.8 8: 8.8 888.8 88N
8: 8.8 8: 8.8 888.8 88N
8: 8.8 8: 8.N.. 588.8 8"
88 5.2 m 8.85 No.8 8: m
8: 8.58 8: 8.88 N 5.8 88v
88 N.88F 888 8.58 8888.8 88?
88>
”To.
8 580
88.888 - 8N 88:888 - 8 +N v.8
88:888>
8.888888.
8 88588

 

8588828 888688888... .3 8888388 8:88.:88888 8.88.88 5.3 8:888
8:8 888 8:..8888 .5 888.8 8:.398 8:8 8.888.888 .88.. o. 88:8888: :. :38 082 8 88:8.:8> 8 8.82898. 8F.8 8.88...

Litter Aggregation Gradient

lggm lggm 150m lggm

 

        

1- 4.59 patch 3- 1.59 patches 9- 0.5g patches Uniform
of clover of clover of clover 4.59 of clover

Figure 3.1 Spatial treatment layout of the litter aggregation gradient. The top
row shows a vertical proﬁle of the litter distribution placement in the 2.6-L
containers. The bottom row of depicts a top-down view of the horizontal
distribution of the litter.

80

  
  

Uniform

removable
gas-tight lid

 
   

Figure 3.2 Schematic depiction of the spatial layout of litter distribution
treatments conducted in 50-L plastic containers used in the ﬁeld experiment.
Containers were set into holes in a ﬁeld and surrounded by maize plants. The
left panel contains a side-cut view of the container components and vertical
layout. The right panel is a top-down view illustrating the spatial distribution of
the litter treatments.

81

 

 
  
 
     

 

 

 

 

 

600‘ t *t t t t t
t it t i t t
:5 * *** t t t i
'1: 500- .
“.‘
g 400- +1-patch
c» 300_ +3-patches
3, +9-patches
>3< 200_ —A—Uniform
u: * Signiﬁcance
<22.
0..
..." 6004
.8
“9
E
0 4004
O)
E
x
8
'3: 200-
N
O
0
0
0

 

Days of incubation

Figure 3.3 002 and N20 ﬂux response to litter aggregation at 50% WFPS

during a 39d incubation. The results of the ANOVA contrast comparing the
treatment means of all the aggregated litter to the uniformly distributed litter
are presented across the top of each graph. *, **, and *** indicate statistical
signiﬁcance at P= 0.05, 0.01, and 0.001 levels, respectively for the contrast.

82

10000‘

 

 

 

 

  
  
 
 

 

 

 

3:; 1000 l

“.‘

E

Z 100 .

a)

3

x 10 -

:r

c

ON 1 -*

2

0.1

A 120 + 1 - patch
‘3: ' + 3 - patches
°.' 1 + 9 - patches
E 00 + —¢u— Uniform
' 0 30 . * Signiﬁcance
E”

v 60 -r

x

8

8: 4o _

N
O
O 20 -
0 l l I l l

o 5 1o 15 20 25 30
Days of incubation

Figure 3.4 C02 and N20 ﬂux response to litter aggregation and at 80% WFPS
during a 38d incubation. The results of the ANOVA contrast comparing the
treatment means of all of the aggregated litters to the uniformly distributed litter
are presented across the top of the N20 ﬂux graph. *, t, and ! indicate statistical
signiﬁcance at the 0.05, 0.01, and 0.001 levels, respectively for the contrast. The
repeated measures ANOVA indicated that the C02 ﬂux was greater (P<0.05) in
the uniform distribution than the mean of the aggregated litter across the entire
incubation. ‘

83

600

 

 

 
 
 
       

 

 

..." Dist I I I I I I
C
“'8 500 ‘Size * t I I I
2| 400 . X i t I I I I I
O
z“ 300 -
8’
v 200 ~
X
.2
ON 100 -
z 0 _
Dist I I I a *

."‘ 1000 "Size * 8 8 t
L: X t
5 800 ' + Fine - 3 patches
.N —0— Fine - Uniform
8 600 - + Coarse - 3 patches
8 —A— Coarse - Uniform
E, 400 _ * Signiﬁcance
3
=
o" 200 -
O

0 I I I I I l I I l

 

0 2 4 6 81012141618

Days of Incubation

Figure 3.5 002 and N20 ﬂux response to litter aggregation and litter particle size
during an 18d incubation. The results of the ANOVA are presented across the top of
each graph. *, **, and *** indicate statistical signiﬁcance at the 0.05, 0.01, and 0.001
levels, respectively for the two main effects, Distribution (Dist) and Particle Size (Size)
and their interaction term (X).

84

 

250 * *

A 2006 * * * + Control
"1: 200 - —O— 8 patches
“9 , + 24 patches
150 - l —A— 72 patches
- + Uniform

 

 

 

Figure 3.6 Nitrous oxide ﬂux response to litter aggregation with growing maize
plants in 50-L containers. The results of the ANOVA are presented across the
top of the graph. * and ** indicate statistical signiﬁcance at the 0.05 and 0.01
levels, respectively, for the contrast between aggregated and uniformly

distributed litter.

85

Chapter 4. Aggregated soil resources enhance primary
productivity

INTRODUCTION

Variation in plant growth responses to soil resource heterogeneity is likely
attributable to soil resource characteristics, experimental conditions, and plant
root foraging traits (Fitter et al. 2000, Hodge 2006). For example, the spatial
aggregation of phosphate fertilizers into patches or strips almost always
improves P use efﬁciency and plant productivity relative to the same quantity of P
distributed uniformly in soil (Kume et al. 2006). Root proliferation into patches of
phosphates increases uptake efﬁciencies because a relatively small mass of
roots can satisfy plant P uptake demands instead of the entire root system
foraging for P (Zhu et al. 2005, Kume et al. 2006). In contrast, the spatial
aggregation of nitrate (NOg'), a more mobile plant nutrient, also often stimulates
root proliferation (Drew 1975) but with inconsistent effects on productivity
(Robinson 1994). One reason for this may be that N03' may act as a signal to
initiate root proliferation (Zhang et al. 2007) because of its association in
unfertilized systems with decomposing organic matter; however, if the N03’ is not
associated with a source of more N03' (i.e., organic matter) then the proliferation
response may require more plant resources than are obtained from the N03'
patch. Hodge et al. (1999) found support for this idea when root proliferation of
two competing plants was only related to plant N capture from patches of

complex organic substrate (plant litter), not patches of inorganic N.

86

Many plant species exhibit compensatory root growth in response to soil
resource heterogeneity (Robinson 1994) - root biomass increases in areas of
enriched soil nutrients and decreases in areas of scarce resources. This can
result in higher nutrient uptake efﬁciency if resources are acquired more
efﬁciently from patches of high resource concentration because more nutrients
are captured with the same quantity of root biomass or less root biomass is
required to meet the nutritional demands of the plants. Thus optimization
principles suggest that the root to shoot biomass ratio (R:S) should decrease in
plant-soil systems that are acquiring soil nutrients more efﬁciently (Hutchings and
John 2004). Because of its easily conﬁrmed plastic R:S, the clonal plant
Glechoma hederacea (Birch and Hutchings 1994) has been used to
demonstrated the predictions of this hypothesis most clearly; however, tests of
this hypothesis with non-clonal plants have yielded mixed results (Robinson
1 994).

In agricultural ecosystems, a shift in the R:S due to changes in nutrient
uptake efﬁciency will be manifested in altered grain yield. We undertook this
study to address the following questions: 1) does the aggregation of labile
organic N inﬂuence the growth of individual maize plants in a system limited by N
availability, 2) is R:S and belowground C allocation altered by resource
aggregation, and 3) does the distribution of aggregated resources inﬂuence
maize productivity?

MATERIALS AND METHODS

87

We conducted two studies at the WK. Kellogg Biological Station (KBS) in
Southwest Michigan (42° 24’N, 85°24’W, elevation 288 m). In the ﬁrst study, we
examined the effect of clustered plant litter aggregation on maize productivity. In
contrast, the second experiment studied the inﬂuence of randomly distributed
litter aggregates on aboveground and belowground plant responses. A 1:1 mix
of coarse sand and a composite of soil taken from the surface 0.4 m soil of a ﬁeld
on the KBS Long-Tenn Ecological Research site was used in each experiment.
Soils of the Kalamazoo (ﬁne-loamy, mixed, mesic Typic Hapludalfs) and
Oshtemo (coarse-loamy, mixed mesic Typic Hapluadalfs) series co-occur on the
site and were both present in the soil used in this experiment. The soil was
excavated from the ﬁeld on May 19, 2005, mixed withan end-loader, placed in a
tarp-covered pile to air dry, power sieved through a 12 mm screen, combined
with coarse sand in a 250 L mixer, and stored in a common covered pile until
use.

Clustered Litter Distribution - Experiment #1

The clustered litter distribution experiment was laid out in a Randomized
Complete Block Design (RCBD) with 6 soil amendment treatments, 5 sampling
dates, and 4 replicates per block. The final sampling date included an additional
no N added control (—N) treatment for a total of 124 experimental units. Each
experimental unit was a 50-L black plastic container ﬁlled with the Soil mix,
amendment, and a single maize plant (Pioneer ® 35Y54) (Figure 4.1). Litter

species (Trifolium pretense and Secale cereale) and plant litter spatial

88

distribution in the soil were the manipulated factors plus NaNOa fertilizer control
(control +N) and no N added control (control -N) treatments.

Litter consisted of Trifolium pretense (red clover) and Secale cereale (rye)
(Michigan State Seed Solutions, Grand Ledge, MI, USA) produced in sand in a
greenhouse, fertilized with a modiﬁed complete Hoagland’s nutrient solution
(Hewitt 1966), and harvested before initiation of the reproductive phase. The
shoots were cut at the sand surface, dried for 4-5 days at 55°C, coarsely-
chopped to pass a 10 mm screen with a Wiley® mill, and then ﬁnely—ground to
pass a 1 mm screen in a Cyclotec® 1093 sample mill. Eight subsamples of each
ﬁnely-ground litter were analyzed for C and N content with a Costech ECS 4010
CHN elemental analyzer. The red clover litter contained 410 g C kg litter‘1 and
29.8 g N kg litter", for a C:N ratio of 13.7, and the rye contained 392 g C kg litter'
1 and 16.1 g N kg litter", for a C:N ratio of 24.4. The red clover litter was
aggregated into 8 or 32 patches or distributed uniformly into the soil, whereas the
rye was aggregated into 8 patches or distributed uniformly (Figure 4.1).

Litter application

Litter distribution treatments were constructed by placing a 150 mm
diameter x 150 mm length polyvinylchloride (PVC) cylinder ﬂat onto a 50 x 185 x
185 mm square Teﬂon® coated baking dish that had one side removed. Into the
PVC cylinder we added 600 mL of soil mix, then a template for theaggregated
litter treatments. The template was a 145 mm diameter circular sheet of acrylic
with four 21.3 mm or sixteen 8.3 mm diameter plastic syringe cylinders (without

the needle hub end) adhered perpendicular to the sheet. We used the syringe

89

plungers to displace a consistent volume of soil within the syringe cylinders
before adding the litter. Around the template we added 300 mL of soil. We
pushed the plungers down into the cylinders as the template was raised such
that the litter remained in the soil as individual column shaped patches just below I
the surface of the soil. After the template was removed we added 400 mL of soil
on top of the litter. Each patch of litter had the same mean depth in the soil
(Figure 4.1). The uniform treatment was constructed by ﬁrst adding 300 mL of
soil to the containers, then 600 mL of a litter and soil mixture that was then
topped off with 400 mL of additional soil. The red clover and rye litter added to
each PVC cylinder was 28.87 g (sixteen 1.80 g or four 7.22 9 patches, or
uniformly 28.879 ) and 36.02 g (4-9.00 g or uniformly 36.02 g), respectively.

Field containers I

We prepared 50-L (370 mm in depth, 390 mm bottom diameter and 440

mm top diameter) containers for this experiment by directing all of the drainage
through a single outlet and adding 3 L of 8 to 10 mm diameter gravel to the
bottom of the container to aid drainage. We lined the container above the gravel
with a medium weight landscaping fabric to keep soil in the containers. Into each
lined-container we added 40 L of soil mix and placed each container in the holes
in the ﬁeld that were created when the soil was originally excavated. The
containers were placed in the ﬁeld such that each was at least 2.5 m from others
and into the soil such that the soil inside and outside the containers were
approximately at the same depth. We chose this container size because it has a

surface area of about 0.15 m2 which is similar to the standard production

90

practices in this region of 0.14 m2 plant". The spaces between the containers
were planted to the same maize hybrid; plants were thinned so as to not shade
out the seedlings in the containers. We covered the drainage holes in the bottom
of the containers with duct tape from the inside and outside, which proved to be
an effective moisture barrier in preliminary tests. A 12 mm circular hole was cut
out of the container at the bottom to allow a standard drainage. For the
containers to be harvested on the second and ﬁnal sampling dates were outﬁtted
' with anion exchange resin columns to capture the anions draining out of the
containers (see Chapter 5).

The anion exchange columns consisted of 30 mL of Type II 16-50 mesh
anion exchange resin (IONAC® A-554 Cl-form ) held in a 250 mm length of 32
mm diameter clear vinyl tubing with 8 pm ﬁberglass (Corning Inc., Corning, NY).
The columns were ﬁtted to the drain hole on the outside of each container with
vinyl ﬁttings. We covered the opening of the drainage hole in the container with a
garden hole screen (~1 mm) to prevent sediment from ﬂowing into the column.
To ensure that all of the drainage water ﬂowed through the column, the column
was secured to the container with rubber gaskets and 100% silicon rubber
sealant (DAP Inc., Baltimore, MD). These containers were tilted about 1%
towards the drain holes to aid drainage.

Fertilization and planting

On June 23-24, 2005, two litter-amended PVC soil cylinders were placed

on the soil surface in each container; each cylinder was placed into about a third

of the surface area of the container (Figure 4.1). We added an additional 4 L of

91

soil around each of the PVC cylinders such that the soil depth in the entire
container was about 20 mm from the top of the container. The PVC cylinders
were raised such that 120 mm of the cylinders were above the soil, thus leaving
the amended soil in contact with the rest of the soil in the container (Figure 4.1).
The PVC cylinders were left in place throughout the season to indicate the
placement of the amended soil and toserve as gas ﬂux chamber bases.

We designed this experiment to test maize N response to litter distribution
and litter species in a system where N was the only soil nutrient limiting plant
growth. On June 28, 2005, we added 250 mL of nutrient solution to the surface
of each container. Each 250 mL dose of solution contained 0.5 P g, 1.25 K g,
0.25 S-SO4 g, 0.27 Ca 9, and 0.07 Mg 9. The control +N treatment was
implemented on July 8, 2005 by adding 1 L of solution containing 1.6 g N as
NaNOa to each container, whereas the -N and litter treatments all received 1 L of
RO (reverse osmosis) water. Supplemental watering (2 L container") occurred
on August 1, 20, and 28.

On June 29, 2005, we transplanted two d old maize seedlings into the
center of each container and placed 6 mm screen hardware cloth exclosures
over the center of the containers until the seedlings were at the V2 growth stage
(Hanway 1963) to protect the seedlings from rodent damage.

Repeated harvest

On July 6 and 20, August 3 and 23, and October 6, 2005, we harvested

four replicates of each treatment. The aboveground shoots were cut at the soil

surface, dried (4 days at 60°C), and weighed. We hauled the containers to a

92

laboratory to determine soil moisture contents in the surface 190 mm and the
bottom 180 mm of each container. We sieved soil and roots were sieved to pass
a 6 mm screen, weighed wet, and sub-sampled for soil moisture. We dried (4 d
at 65°C) and weighed the sub-sample. On the ﬁnal harvest date we harvested
two containers for aboveground biomass for each experimental unit and the
control -N treatment. For the control +N treatment, we separated the roots from
the soil during the dry sieving process. We stored the roots in a 10% v.v. ethanol
solution at 4°C until processing to remove mineral particles and then we dried
(60°C for 4 days) and weighed the roots.
Random Litter Distribution - Experiment #2

This experiment utilizes the same basic litter patch size distribution as
Experiment #1, but here the patches are randomly distributed throughout a
similar depth of soil. Additionally, we quantiﬁed belowground biomass and root
respiration as well as the aboveground response. This experiment was
conducted in the 50-L containers during the 2006 growing season in the same
ﬁeld as above and is laid out in a RCBD with 4 replicates, 5 soil amendment
treatments, and 7 sampling dates. The same soil preparation procedure and
container placement in the ﬁeld was used as earlier except it was not necessary
to use landscape fabric. Red clover was produced in the greenhouse, as
described earlier. We distributed the litter (shoots) into 8, 24, or 72 patches or
uniformly in the soil except no patches were placed within the center 150 mm of

the container (Figure 4.1) to avoid disturbing the litter patch when planting the

93

maize. The red clover litter contained 413 g C kg litter”1 and 31.0 g N kg litter’1
giving it a C:N ratio of 13.3.
Litter application
The litter application method was modiﬁed from earlier. We applied the
litter directly to soil in the containers with a template that was the same diameter
as the-inside of the container (Figure 4.1). The number and sizes of the litter
patches were eight 4.69 9 patches, twenty-four 1.56 9 patches, seventy-two 0.52
9 patches, and the uniform distribution of 37.5 g of litter. The patches were
distributed at a mean depth of 100 mm below the soil surface. The litter
application process lasted three days from May 22-24, 2006.
Planting and fertilizing
On May 25, 2006, we transferred the containers to the ﬁeld and on May 26
we placed the anion exchange columns on the containers to be harvested on the
third and last harvest dates. We planted two maize seeds (Pioneer 35Y54) to a
depth of ~50 mm into the center of the each container on May 31 and covered
with hardware cloth exclosures. In between the containers we planted maize
seeds at standard production densities (70,000 plants ha"). After 14 days, we
thinned the plants to one plant container“. We fertilized as earlier on July 6,
2006. Supplemental watering (2 L container“) occurred on August 13 and 22.
Repeated harvests
We conducted seven repeated harvests for this experiment. Four of the
harvests were used to quantify aboveground plant growth and the other three

used to quantify belowground plant growth in response to the litter distribution

94

treatments. On June 22, July 20, August 22, and October 12, 2006, we
harvested four replicate containers from each treatment and processed the
shoots and the soil as in Experiment #1. To quantify belowground biomass we
harvested containers on July 7, August 14, and September 26, 2006. The
aboveground biomass was processed in the same manner as above, but the
roots were separated from the soil through a combination of ﬂotation and wet
sieving over a 1 mm screen. The entire root system was collected into 4-L
sealable plastic bags, covered with a 10% v.v. ethanol solution, and stored at
4°C. We used a secondary processing to separate the maize roots from other
detritus, soil particles, and any remaining clover litter then we dried (60°C for 4 d)
and weighed the roots.
Soil surface C02 ﬂux

Plant C allocated belowground can have several fates including root
biomass, root metabolic respiration, soil microbial biomass, soil microbial
respiration, and deposition into the soil matrix. Carbon respired by plant roots
and soil heterotrophs makes up most of the C02 that is emitted from the soil
surface. To distinguish the CO2 derived from roots versus soil organic C (SOC)
oxidation we measured the CO2 ﬂux from containers and with and without
growing maize plants (Hanson et al. 2000). To accomplish this an additional set
of non-planted containers with and without uniformly distributed litter were added
to each replicate. The difference between CO2 ﬂux from planted and non-planted

containers within each replicate is our proxy for total root derived respiration.

95

We used removable static chambers to measure soil surface CO2 ﬂux
(Holland et al. 1999). Each chamber lid sealed around the outside of the
container and the plant stern (Figure 4.2). The chamber lids were modiﬁed 120-L
refuse container and lid with a 60 mm wide slit removed from on edge to the
center of the lid to accommodate the plant stem. Latex sheeting was secured to
the plant stem and chamber lid to complete an air tight seal. Deployment of the
chamber lid required about 3 minutes and remained on the container for a
maximum of 70 minutes during the sampling. We sampled the chamber
headspace by using a 10 mL plastic syringe to transfer 20 mL of gas from the
chamber to 5.9 mL glass vials outﬁtted with rubber septa. We used an infrared
gas absorption analyzer (LI-Cor 820) to determine the CO2 gas concentration.
The sample C02 concentrations were regressed with a linear model against
sampling time to determine the gas ﬂux rate (Holland et al. 1999).

Statistical Analysis

For Experiment #1, we used a repeated measure analysis of variance
(ANOVA) for each response variable with block and soil amendment treatment
(litter species, litter distribution, and controls) as independent ﬁxed variables with
sampling date as the repeated random variable. We analyzed Experiment #2 ‘
similarly with block and litter distribution as the independent ﬁxed variables and
sampling date being the repeated random variable. We used Akaike’s
information criteria (Akaike 1974) to choose a ﬁrst-order heterogeneous
autoregressive (ARH) covariance structure to model the repeated measure

variance components using SAS mixed model procedures (Littell et al. 2005) in

96

both experiments. Data from the ﬁrst sampling date of Experiment #2 was
omitted from the biomass response analyses because the ARH model would not
converge due to the lack of variance in biomass at this early sampling date. Soil
surface CO2 ﬂux data were natural log transformed to meet the homogeneity of
the variance assumption of ANOVA. When the interaction of main effects and
sampling date were signiﬁcant (0 < 0.05) the interacting variables were analyzed
separately by sampling date. Single degree of freedom contrasts were used to
determine effects of species identity and litter distribution for each date for
Experiment #1 and to determine the effect of litter distribution for each date in
Experiment #2. Differences were consider signiﬁcant at the a < 0.05 level for all
ANOVAs.
RESULTS
Experiment #1: Plant Growth Response to Clustered Litter Distribution

In Experiment #1, when the amended litter was clustered within 23% of
the soil surface area (Figure 4.1), the aboveground maize response to soil
amendments varied with sampling date (soil amend. x date; P<0.0001, Table 4.1
and Figure 4.2). There were no differences in aboveground biomass until the
ﬁnal harvest (DOY 279) of 2005 (Table 4.2). On this date, aboveground biomass
response to litter distribution was dependent on the litter species used as the soil
amendment (litter species*distribution; P<0.027, Figure 4.3). All of the litter
amendment treatments enhanced maize productivity relative to the control -N
treatment and appeared to be N limited relative to the control -N treatment. The

aggregation of clover litter into 8 and 32 patches enhanced aboveground

97

biomass by 13% (119 :l: 4.9 9 plant") relative to the uniform distribution (105 :I:
4.9 g plant")(Figure 4.3). The distribution of the rye litter did not affect
aboveground biomass at the ﬁnal sampling (P>0.49).
The root biomass in Experiment #1 varied throughout the season

(F igure4.6); R:S ranged from 0.48 at the ﬁrst sampling to 0.17 at the last
sampling date. Soil moisture content in the surface 190 mm and in the bottom
180 mm varied from 0.13 to .07 9 H20 9 soil'1 and 0.18 to 0.08, respectively,
throughout the season, but was unaffected by soil amendment treatment on any
sampling date (P>0.05) (data not shown).
Experiment #2: Plant Response to Random Litter Distribution

Growing season average aboveground biomass was greater in treatments
with aggregated litter (8, 24, and 72 patches) than to uniformly distributed litter
(P<0.025; Table 4.3). Most of the treatment occur during the grain ﬁlling growth
stage (Figure 4.4 and Table 4.4). At physiological maturity, aboveground maize
biomass response to aggregated litter (315 9 container“) was similar to the
control +N (P<0.061), whereas the plants growing in the containers treated with a
uniform distribution were 25% smaller than the control +N (P<0.001) and 16%
smaller than those in the aggregated litter treatments (P=0.025) (Figure 4.5 and
Table 4.3).

Root biomass increased with time and litter distribution effects were only
detectable at the ﬁnal root biomass sampling (Figure 4.7). After physiological
maturity, the root biomass in response to uniformly distributed litter was less than

the mean root response to the aggregated litter distributions (P<0.006). The R:S

98

was similar at each sampling date in 2006 with a mean of 0.54 at DOY 188, 0.28
at DOY 226, and 0.18 at DOY 269.

Soil moisture varied throughout the second experiment in the surface 190
mm from 0.04 to 15.2 9 H20 9 soil‘1 and in the bottom of the containers from 0.03
to 18.8 9 H20 9 soil"on DOY 234 and 285, respectively, and was unaffected by
soil treatment (P>0.05).

Root induced soil surface CO2 ﬂux

The soil surface CO2 ﬂux was on average greater from planted containers
treated with a uniform litter distribution than from containers with an aggregated
litter distribution (P<0.004; Table 4.5). The contributions to the ANOVA variance
from individual sampling dates were fairly consistent across the season with F-
test values ranging only from 0.25 to 2.96, indicating that the difference in CO2
ﬂux between the litter amendments was relatively consistent (soil amendment x
date: P<0.402; Table 4.5). Soil surface C02 ﬂux from containers without maize
plants was on average 3.7 mg C rn‘2 h" greater from soil amended with litter than
from the control soil treatment (P>0.047; Figure 4.8). lnterpolated across the
entire sampling period (DOY 160 to 233), this represents a difference of 0.97 g C
container" or 6.3% of the total litter C added.

The difference in soil surface C02 between planted and unplanted-
containers is an indication of the plant effect on C cycling in this model system.
The interpolated average difference between the planted and unplanted control
+N was 41 mg C m'2 h", whereas the difference in soil surface CO2 ﬂux between

litter amended containers was greatest in response to the uniform distribution (73

99

mg C rn‘2 h") and lower in response to 8, 24, and 72 patches (44, 43, 40 mg C m’
2 h", respectively). Most of the differences between soil surface CO2 ﬂux of
planted and unplanted containers occurred during the last four sampling dates
(DOY 200, 208, 217, and 233)(Figure 4.8) when 76% of the CO2 ﬂux on average
was attributable to the plant effect.
DISCUSSION

Our results demonstrate that spatial heterogeneity of labile organic N
resources within the reach of individual maize plants can inﬂuence above and
belowground productivity of ﬁeld grown maize; however, the plant growth
response to resource heterogeneity was dependent on the resource substrate
(litter species). Plant productivity has been found to be enhanced, suppressed,
and remain unaffected by soil resource spatial heterogeneity (Hutchings et al.
2003). Experiments manipulating resource heterogeneity in single and multiple
plant systems suggest the characteristics of both the resource and the plant roots
and root associates can contribute to the idiosyncratic productivity responses
(Fitter et al. 2000).
Resource Properties

The form, duration, spatial distribution, size, and intensity of soil resource
patches versus a uniform distribution of that resource all potentially contribute
plant productivity responses to resource heterdgeneity (Fitter et al. 2000). In the
research presented here we aggregated plant litter, a biochemically
heterogeneous substrate, into patches surrounded by soil. The litter aggregates

ranged in size from 0.52 (Experiment #2) to 7.2 g of clover litter (Experiment #1)

100

and up to 9.0 g of rye litter (Experiment #1). Across this range in patch sizes we
did not observe any changes maize productivity. The only other study to
distribute plant litter into multiple patches (1 or 4 patches) also found no
differences across patch sizes (Bonkowski et al. 2000).

It is interesting to consider the scale of the resource patches relative to the
organisms involved in this plant-soil system. We estimate that each of ﬁnely-
ground red clover particles is about 10 pg. Thus if we consider the uniform
distribution in our study as about 4 million very small (10 ug) patches, than there
are about 55,000 times more patches in the uniform distribution than in the 72
patch treatment. This is potentially a functionally signiﬁcant jump in patch size
and number for plant productivity and microbial habitat. 0n the microbial scale a
10 or 100 pg patch of substrate is quite large; however, on the plant root scale
patches in this size range may be indistinguishable from the background soil. In
our previous work on the inﬂuence patch size on litter decomposition of this same
soil-clover litter combination, we observed a 3 to 5 d lag in the maximum
decomposition rate when litter is aggregated relative to uniformly distributed
(Chapter 3). This indicates that the patch size had at least a transient effect on
microbial processes, although, it is unclear if this microbial response is linked to
the plant growth response.

Although we cannot directly test for the effect of the spatial distribution of
the litter aggregates into clustered or random distributions, the similar maize

response in both experiments indicates that this is was not a signiﬁcant factor.

101

The duration a resource patch remains distinguishable from the
background soil by a plant root is likely a product of the dispersal and
consumption rate of the resource (Fitter et al. 2000). The dispersal rate of a
resource is in turn controlled by its solubility in the soil solution, movement of the
soil solution, and diffusion rate. Plant litter-N is mostly contained in insoluble
organic compounds and thus to be assimilated by plants must be mineralized
into more soluble forms such as NH], N03', and perhaps amino acids. The N
mineralization rate from plant litter in a given soil environment is largely
dependent on the N, C, phenolic, and lignin content of the litter. Measureable N
mineralization can continue for months to decades depending on plant litter
characteristics (Parton et al. 2007). For the relatively N-rich litters that we used
in these experiments, N mineralization during the ﬁrst year following soil
application likely varied from 30 to 60% of the initial litter-N (Harris et al. 1994,
Honeycutt 1999). Thus even at the end of the ﬁrst growing season about half of
the original litter-N should remain in the litter (Chapter 5).

Plant root proliferation into patches of limiting resources varies with patch
composition (Hodge 2006). In a review of plant root responses to resource
patches, Robinson (1996) asked why do plants bother proliferating roots into
patches of highly mobile ions such as NO; given that often times the N03' is
depleted before a root biomass proliferation is evoked. Furthermore, he pointed
out that roots proliferate to a similar degree in patches of nitrate and phosphate
ions (Drew 1975), despite their differences in soil mobility. Phosphate ions are

typically at too low a concentration in the soil solution for plants to meet their P

102

needs by bulk ﬂow alone (Kovar and Barber 1989) and thus diffusion of P ions is
required to meet plant P demands. Hence proliferating roots into patches of
higher than background phosphate ions is advantageous because it limits the
distance that phosphate ions have to diffuse to be taken up by the plant root.
Likewise, plant root proliferation in response to patches of N02; and NH...+
appears more advantageous if the resource patch has limited solubility and
considerable duration as is the case if the inorganic N is derived from a patch of
mineralizing organic matter (Hodge et al. 1999). Furthermore, root proliferation
into a patch of organic matter under N limited conditions potentially alters plant-
plant (Hodge 2006) and plant-microbe N (Wang and Bakken 1997) competition
because the distribution of N-limited plants and soil microbes are also likely
heterogeneously distributed. Thus intercepting patch derived inorganic N ions
before other plants or N-Iimited microbes improves plant ﬁtness under either wise
generally N-Iimited conditions.

We are unaware of other studies that have used more than one type of
complex organic N (e.g., plant litter) as the heterogeneously distributed resource,
so we have no comparison to draw on to explain why the two litter species used
in Experiment #1 induced different responses from maize plants.

Aboveground and Belowground Biomass and C Allocation

Root to shoot biomass ratio (R:S) was unaffected by litter distribution
during Experiment #2; however, there was a signiﬁcant alteration of the soil
surface C02 ﬂux in response to the litter distribution that is likely associated with

root C allocation. Optimization principles suggest that R:S should vary to

103

allocate resource acquiring structures (roots and leaves) to match the distribution
of growth limiting resources. However, resource heterogeneity within the scale of
individual plants may alter plant resource acquisition efﬁciencies (Hutchings and
John 2004). Several studies have indicated that under heterogeneous resource
conditions (soil or light) plants will maximize resource capture from sites rich in
resources other than from resource-scarce sites (see review by Hutchings and
John 2004). This leads to greater resource acquisition efﬁciencies because
fewer plant resources are required to acquire nutrients from concentrated
resources than from diffuse resources (Kovar and Barber 1989). In contrast, root
detection of resource pools in a spatially and temporally heterogeneous
environment is likely complex and imperfect. Therefore morphological responses
(e.g., root proliferation) to ﬂeeting resource pools may have a negative impact on
resource acquisition efﬁciencies because the costs may out weigh the beneﬁt
(Robinson 1996).

As soil resources acquisition structures, plant roots have both
physiological and morphological responses to most soil nutrients. Physiological
responses to resource availability are thought to precede any changes in
morphological root structure. We saw no differences in morphological allocation
of plant biomass among litter distributions; however, the soil surface CO2 ﬂux
differences may be partly explained by changes in physiological proCesses
occurring in the roots (Figure 4.8). More work has been conducted on
physiological responses to soil N03“ than to NH4+ or amino acids so we will only

speculate on the role of N03' here. In general, physiological root responses to

104

low soil N03‘ concentrations include the induction of high afﬁnity transport
systems speciﬁc for N03' and N03‘ reduction in roots (T ischner 2000). Could the
up regulation of these or other physiological processes partly explain the
differences in C02 soil surface ﬂux among the litter distribution treatments?
Further work needs to be conducted in this area because if the uniform litter
distribution induces increased root respiration than this can have major
implications for interpreting C02 ﬂux in many ecosystems.

In contrast, the differences in soil surface C02 ﬂuxes among the litter
distribution treatment of Experiment #2 may be attributed to greater rhizosphere
induced oxidation of the litter or soil organic matter, the so called priming effect.
Cheng et al. (2003) found that rhizosphere priming was sensitive to plant
phenology and species; however, it was not sensitive to fertilization. Although
many hypotheses have been put fowvard to explain the priming effect there is still
little actually known of its importance to ecosystem level processes (Kuzyakov
2002).

The heterogeneity of agronomically managed soils is well characterized at
scales ranging from 1m to 1 km scales (Robertson et al. 1993); however, less
attention has been paid to spatial scales within the inﬂuence of individual crop
plants (Franklin and Mills 2003, Han et al. 2007). Maize plants are known to
selectively forage for patches of soil P (Zhu et al. 2005) and are potentially more
sensitive to the spatial distribution of P fertilizer than to the quantity of P applied
(Kume et al. 2006). Under N limited conditions we have shown that maize

productivity is sometimes positively affected by the aggregation of plant litter

105

against an otherwise homogeneous soil background. Although it is unclear how
the heterogeneity of agricultural soils compares to our experimental conditions, it
is likely that management operations alter the distribution of crop and cover crop
litters in the range that we manipulated the litter distribution. The duration of the
litter patches as N-rich patches is likely a key component.
CONCLUSIONS

Aggregated red clover litter led .to a 14% greater production of maize
aboveground biomass relative to uniformly distributed litter regardless of whether
the litter was clustered into 23% of the soil or randomly distributed throughout
88% of the soil. In contrast, the distribution of rye litter had no effect on maize
growth. Litter distribution did not affect the biomass root to shoot ratio, but
belowground C allocation to roots was substantially greater in the uniform litter
distribution than in the aggregated distribution. The changes in belowground C
allocation appeared to precede changes in the aboveground and belowground
biomass and thus may provide a mechanistic link between plant response to
resource heterogeneity and the functional signiﬁcance of heterogeneity on

ecosystem processes.

106

REFERENCE

Akaike, H. 1974. New look at statistical-model identiﬁcation. IEEE Trans. Autom.
Control 19:716-723.

Birch, C. P. D., and M. J. Hutchings. 1994. Exploitation of patchily distributed soil
resources by the clonal herb Glechoma hederacea. J. Ecol. 82:653-664.

Bonkowski, M., B. Grifﬁths, and C. Scrimgeour. 2000. Substrate heterogeneity
and microfauna in soil organic 'hotspots' as determinants of nitrogen
capture and growth of ryegrass. Appl. Soil Ecol. 14:37-53.

Cheng, W. X., D. W. Johnson, and S. L. F u. 2003. Rhizosphere effects on
decomposition: controls of plant species, phenology, and fertilization. Soil
Sci. Soc. Am. J. 67:1418-1427.

Drew, M. C. 1975. Comparison of effects of a localized supply of phosphate,
nitrate, ammonium and potassium on growth of seminal root system, and
shoot, in barley. New Phytol. 75:479-490.

Fitter, A., A. Hodge, and D. Robinson. 2000. Plant response to patchy soils.
Pages 71-90 in A. J. A. Stewart, E. A. John, and M. J. Hutchings, editors.
The ecological consequences of environmental heterogeneity. Blackwell
Science Ltd., Oxford, UK.

Franklin, R. B., and A. L. Mills. 2003. Multi-scale variation in spatial heterogeneity
for microbial community structure in an eastern Virginia agricultural ﬁeld.
FEMS Microbiol. Ecol. 44:335-346.

Han, G. X., G. S. Zhou, Z. Z. Xu, Y. Yang, J. L. Liu, and K. 0. Shi. 2007. Biotic
and abiotic factors controlling the spatial and temporal variation of soil
respiration in an agricultural ecosystem. Soil Biol. Biochem. 39:418-425.

Hanson, P. J., N. T. Edwards, C. T. Garten, and J. A. Andrews. 2000. Separating
root and soil microbial contributions to soil respiration: a review of
methods and observations. Biogeochem. 48:115-146.

Hanway, J. J. 1963. Growth stages of corn (Zea mays L.). Agron. J. 55:487-492.

107

Harris, G. H., 0. B. Hestennan, E. A. Paul, S. E. Peters, and R. R. Janke. 1994.
Fate of legume and fertilizer nitrogen-15 in a long-term cropping systems
experiment. Agron. J. 86:910-915.

Hewitt, E. J. 1966. Sand and water culture methods used in the study of plant
nutrition, 2nd edition. Farnham Royal, Commonwealth Agricultural
Bureaux.

Hodge, A. 2006. Plastic plants and patchy soils. J. Exp. Bot. 572401-411.

Hodge, A., D. Robinson, B. S. Grifﬁths, and A. H. Fitter. 1999. Why plants bother:
root proliferation results in increased nitrogen capture from an organic
patch when two grasses compete. Plant Cell Environ. 22:811-820.

Holland, E. A., G. P. Robertson, J. Greenberg, P. M. Groffman, R. D. Boone, and
J. R. Gosz. 1999. Soil C02, N20, and CH4 exchange. Pages 185-201 in
G. P. Robertson, D. C. Coleman, C. S. Bledsoe, and P. Sollins, editors.
Standard soil methods for Iong-tenn ecological research. Oxford Univ.
Press, New York, NY.

Honeycutt, C. W. 1999. Nitrogen mineralization from soil organic matter and crop
residues: ﬁeld validation of laboratory predictions. Soil Sci. Soc. Am. J.
63:134-141.

Hutchings, M. J., and E. A. John. 2004. The effects of environmental
heterogeneity on root growth and root/shoot partitioning. Ann. Bot. 94:1-8.

Hutchings, M. J., E. A. John, and D. K. Vlﬁjesinghe. 2003. Toward understanding
the consequences of soil heterogeneity for plant populations and
communities. Ecology 84:2322-2334.

Kovar, J. L., and S. A. Barber. 1989. Reasons for differences among soils in
placement of phosphorus for maximum predicted uptake. Soil Sci. Soc.
Am. J. 53:1733-1736.

Kume, T., N. Sekiya, and K. Yano. 2006. Heterogeneity in spatial P-distribution
and foraging capability by Zea mays: Effects of patch size and barriers to
restrict root proliferation within a patch. Ann. Bot. 98:1271-1277. ’

108

Kuzyakov, Y. 2002. Review: factors affecting rhizosphere priming effects. J. Plant
Nutr. Soil Sci. 165:382-396.

Littell, R. C., G. A. Milliken, W. W. Stroup, R. D. Wolﬁnger, and O.
Schabenberger. 2005. SAS for Mixed Models, 2nd edition.

Loecke, T. D. Chapter 3. Litter aggregation has a disproportionate inﬂuence on
nitrous oxide ﬂux. Ph.D. Dissertation. Michigan State University, East
Lansing, MI.

Loecke, T. D. Chapter 5. Litter aggregation alters terrestrial N cycling. Ph.D.
Dissertation. Michigan State University, East Lansing, MI, USA.

Patton, W. J., W. L. Silver, l. C. Burke, L. Grassens, M. E. Harmon, W. S. Currie,
J. Y. King, E. C. Adair, and L. A. Brandt. 2007. Global-scale similarities in
nitrogen release patterns during long-term decomposition. Science
315:361-364.

Robertson, G. P., J. R. Crum, and B. G. Ellis. 1993. The spatial variability of soil
resources following long-term disturbance. Oecologia 96:451-456.

Robinson, D. 1994. The responses of plants to non-uniforrn supplies of nutrients.
New Phytol. 127:635-674.

Robinson, D. 1996. Resource capture by localized root proliferation: why do
plants bother? Ann. Bot. 77:179-186.

Tischner, R. 2000. Nitrate uptake and reduction in higher and lower plants. Plant
Cell Environ. 23:1005—1024.

Wang, J., and L. R. Bakken. 1997. Competition for nitrogen during decomposition
of plant residues in soil: effect of spatial placement of N-rich and N-poor
plant residues. Soil Biol. Biochem. 29:153-162.

Zhang, H. M., H. L. Rong, and D. Pillbeam. 2007. Signalling mechanisms
underlying the morphological responses of the root system to nitrogen in
Arabidopsis thaliana. J. Exp. Bot. 58:2329-2338.

109

Zhu, J. M., S. M. Kaeppler, and J. P. Lynch. 2005. Topsoil foraging and
phosphorus acquisition efﬁciency in maize (Zea mays). Funct. Plant Biol.
32:749-762.

110

Table 4.1 Repeated measures analysis of variance to determine effects of
clustered soil amendments on aboveground maize biomass during Experiment
#1.

 

Source Num. dff Den. dft F value P>F

Block 3 68 1 .31 0.2796
Soil Amendment 5 68 7.65 <0.0001
Sampling Date 3 68 1187 <o.ooo1
Soil Amend. x Date 15 68 3.82 <0.0001

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively. ' '

111

.m>0n_ 550 m5 :9: $2 96 .3 m_ mam 909 ._mm; u_o >8 mEEEmm .62— Eouom: .0 320% 5358050 9: H
29:033.. .93. n. 5 new: Eouomt ho «620% .oﬁEEocmu ucm 8.90:5: 05 90:3 “6 :00 new 5 E32 ._.

 

 

 

hNod Nod omod de mamd 00.0 02.6 . Nﬁo m_. F .85 x .066QO 5::

mmwd omd mend mod Nmmd cod vmwd and 9. F .65 5:: 0?.

mNod had msod med mood cod mmmd cod 9. _. .35 5:: L3.30.0
JED .03: I mummbcou

voodv omd mine Nmé 2.2. mm... 2:. m...N mp m EmEucmE< ._ow

mwmd wmé mood id 0 5.0 No.0 mend Nmé 9. m x005
“Tn. u. “Tn. n. “Tn— “. “Tn. u. tn .cmo tn .E:Z 850w

mum «mam m FN vowllll

 

 

 

two» he Nag 9mm WEEEmm

 

620QO 5:: 92 05 mcoEm ccm EBUSEEV 5923
5:39:65 53: ESE: v.38? BE. umﬁmmﬁom *0 Soto cmmE on. 23:60 33:50 .2“ 2.95595 9E3 Emu
mEEEmm some .8 «$603 oumE 2505925 no mEgEucoEm =3 “3 ﬂoats mEEQwu o. occur? “.0 m_w>_mc< Nd 03m...

112

Table 4.3 Repeated measures analysis of variance to determine effects of
random litter distribution on aboveground maize biomass during Experiment #2.

 

Source Num. df" Den. df‘ F value P>F
Block 3 81 0.30 0.822
Litter Distribution 4 81 3.76 0.008
Sampling Date 5 81 825 <0.001
Litter Dist. x Date 20 81 1.45 0.126

Contrast
Uniform vs. Aggregated ' 1 81 5.22 0.025
Uniform vs. +N control 1 81 11.27 0.001
Aggregated vs. +N control 1 81 3.61 0.061

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

113

Table 4.4 Partition of the variance attributable to litter distribution by sampling
date (DOY) interaction effect of a repeated measures analysis of variance to
determine effects of random litter distribution on aboveground maize biomass
duringExperiment #2.

 

 

Source DOY Num. dff Den. dfl F value P>F
Litter dist. x DOY 188 4 81 0.41 0.801
201 4 81 0.40 0.811
226 4 81 0.40 0.806
234 4 81 0.21 0.932
269 4 81 3.43 0.012
285 4 81 2.73 0.035

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

114

Table 4.5 Repeated measures analysis of variance to determine effects of soil
amendments of randomly distributed litter aggregates and +N control treatments
on soil surface CO2 ﬂux from containers with maize plants during Experiment #2.

 

Source Num. df'r Den. df'r F value P>F
Block 3 1 10 2.35 0.0761
Soil Amendment 4 110 3.42 0.0113
Sampling Date 7 110 75.4 <0.0001
Amendment x Date 28 110 1.06 0.4016
Contrast — Litter Distribution
Uniform vs. Amated 1 110 8.76 0.0038

 

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

115

Clustered Distribution

   
  
 

plastic container
pea gravel

anion resin

Clustered
Uniform

:. Clustered
32 patches

:. Clustered
® 8 patches

69

Random Distribution

     
  

removable
gas-tight lid

‘ litter

amendment
plastic container

pea gravel
, anion resin

Random
' Uniform

Random
72 patches

Random
24 patches

Random
8 patches

Figure 4.1 Schematic depiction of the spatial layout of litter distribution treatment
for the clustered (Experiment #1) and random (Experiment #2) distribution
experiments conducted in 50-L plastic containers set into pits in the soil and

surrounded by other maize plants.

In the top panels are side-cut views of the

container components and vertical layout. At the bottom top-down views
illustrating the spatial distribution of the litter treatments.

A

 

160
+ Clov Uniform *
140 - + Clov 32 patches '

120 _ + Clov8 patches
—6— Control +N

100 . -I— Rye Uniform
—l— Rye 8 patches
80 -

60~
404
20‘

0

   
   

 

 

 

 

Aboveground biomass (9 plant"

Day of Year

Figure 4.2 Aboveground biomass response to the distribution of rye and red
clover litter and +N control soil amendments during Experiment #1.

Symbols above data from each sampling date represents statistical
signiﬁcance, ns = P>0.05 and * = P<0.05. The arrow on the x-axis indicates
the date of litter application to soil in the ﬁeld containers.

117

 

 

A 140 Control + N
120 .
100 ~
80 -
60 -
40 -

20-

Aboveground biomass (9 plant "

    

 

8 32 Uniform 8 Uniform
Red Clover Rye
Litter Distribution

 

Figure 4.3 Final harvest aboveground biomass response to red clover and rye
litter distributed into 8, or 32 patches or uniformly and 8 patches or uniformly,
respectively, during Experiment #1. Vertical bars represent treatment means
:I: 1 standard error of the mean. The two horizontal lines represent
aboveground biomass in response to containers fertilized with 1.6 g N
container" as KN03 (control + N) or not fertilized (control — N).

118

A 400

 

+ Control +N
+ 8 patches

300 + —v— 24 patches
—A— 72 patches

+ Uniform
200 -

1004

 

Aboveground biomass (9 plant’1

0
l

 

  
 
 
  
 

 

I

1

1 160 180 200 220 240 260 280 .300

Day of Year

Figure 4.4 Aboveground biomass response to red clover litter distributed
into 8, 24, or 72 patches or uniformly and +N control soil amendments
during Experiment #2. Symbols above data from each sampling date
represents statistical signiﬁcance, ns = P>0.05 and * = P<0.05. The arrow

on the x-axis indicates the date of litter application to soil in the ﬁeld

containers.

119

 

400

 

 

300 -

200 -

100 -

Aboveground biomass (9 plant")

    

 

 

0 8 ' 24 72 Uniform
Red Clover Litter Distribution

Figure 4.5 Final harvest aboveground biomass response to red clover litter
distributed into 8. 24, or 72 patches or uniformly from Experiment #2. Vertical
bars represent treatment means :I: 1 standard error of the mean. The
horizontal line represents ﬁnal aboveground biomass in response to
containers fertilized with 1.6 g N container" as NaNOa (control + N).

120

 

 

 

 

' 0.6
25 _—I— Root mass
..." + R:S
SE! 20 _ - 0.5
O.
3’ 15 -
a I" 0.4
g 10 -
:3 5 .. - 0.3
‘6
o
‘r 0 ‘ - 0.2
180 200 220 240 260 280
Day of Year

Figure 4.6 Root biomass and root to shoot ratio response to control +N
treatment during Experiment #1 .

121

Biomass rootzshoot

 

\I
O

 

   
 

 

 

 

*
..." 60 -
‘8
a 50 ‘
O)
7; 40 a
(D
‘“ 30
g 7 + Control +N
2E 20 _ + 8 patches
3 —v— 24 patches
m 10 - ”S —A— 72 patches
0 + Uniform
160 180 200 220 240 260 280 300
Day of Year

Figure 4.7 Root biomass response to red clover litter distributed into 8, 24,
or 72 patches or uniformly and +N control soil amendments during
Experiment #2. Symbols above data from each sampling date represents
statistical signiﬁcance, ns = P>0.05 and * = P<0.05.

122

 

250

   
   

 

 

 

-®— No Plant Control
A -O— No Plant Uniform
‘7; 200 I v —V— Planted Control
0,1 ‘ —V— Planted Uniform
E -I- Planted Aggregated
o 150 -
c»
15..
x 100 1
3
c
8 50 -
\ \ r.‘.~. \.
t 160 180 200 220 240 260 280 300
Day of Year

Figure 4.8 Soil surface C02 ﬂux with and without maize plants and with no
litter, uniform litter, or aggregated liter (8, 24, and 72 patches) during
Experiment #2. Error bars denote one standard error of the mean CO2 ﬂux.
The repeated measures ANOVA of the containers with growing plants
indicated no signiﬁcant litter distribution x sampling date interaction and a
signiﬁcantly greater CO2 ﬂux from uniform vs. aggregated litter. The arrow
on the x-axis denotes the time of container deployment to the ﬁeld.

123

Chapter 5: Litter Aggregation Alters Terrestrial Nitrogen Cycling

INTRODUCTION

Nitrogen mineralized from plant litter is the primary source of plant
available N in both managed and unmanaged ecosystems. The spatial
aggregation of plant litter in soil can potentially alter terrestrial N cycling by
several mechanisms. The mineralization of litter-N into soluble N compounds
and inorganic ions can be stimulated (Breland 1994) or inhibited (Magid et al.
2006) by its aggregation. Physical and chemical interactions between plant litter
and soil mineral surfaces or soil organic matter can decrease soil microbial
access to the litter and inhibited its decomposition. In contrast, aggregated litter
has less physical contact with soil and hence potentially less protection from soil
microbial degradation (Breland 1994). Alternatively, the diffusion of resources
limiting heterotrophic activity (e.g., 02 or N03') is more likely to inhibit
decomposition and N mineralization in aggregated litter than uniformly distributed
litter (Myrold and Tiedje 1985, Magid et al. 2006).

As litter-N is mineralized to NH: its fate can be altered by the distributions
of: 1) nitriﬁer communities surrounding the litter aggregate (Nielsen and
Revsbech 1998), 2) plant roots proliferating into the litter aggregates (Wang and
Bakken 1997), and 3) proximal N-poor microsites where NH4+ may be
assimilated into microbial biomass (Schimel and Bennett 2004). In most soils,
N03’ is more mobile than NH4+ and can be readily transported with hydrologic
ﬂow to plant roots or below the plant rooting zone. Nitrate can also be used as

an electron acceptor during denitriﬁcation under anoxic soil conditions, which are

124

more likely to occur when labile organic matter is aggregated rather than
uniformly distributed due to diffusional constraints of O2 (Parkin 1987).

Plant root systems have plastic morphological and physiological
responses allowing for selective foraging into resource rich microsites (Drew
1975). However, some plants only beneﬁt nutritionally from proliferating roots
into microsites of complex organic matter and not from microsites of inorganic
nutrients (vanVuuren et al. 1996, Hodge et al. 1999), despite the fact that plants
primarily meet their nutritional demands by acquiring inorganic nutrients from soil.
FurtheMore plant roots modify soil conditions by consuming 02, water, and
inorganic nutrients while exuding reduced C compounds and stimulate C and N
mineralization from soils (Cheng et al. 2003, Herman et al. 2006). As roots
forage for heterogeneously distributed soil resources they potentially modify C
and N cycling processes in resource-rich microsites more than in resource-poor
microsites.

Our previous research indicates that plant litter aggregation has a minor
inﬂuence on litter decomposition, thus we predict that the coupled process of
litter-N mineralization will also be minimally affected. However, the interactions
between root foraging and microbial responses to heterogeneous resource
distributions may alter ecosystem level N cycling in ways not predicted from
microbial responses alone. In this chapter, we distributed 15N-labeled plant litter
across an aggregation gradient in soil with growing plants to address these

questions: 1) does resource aggregation inﬂuence above and belowground plant

125

N acquisition; 2) does resource aggregation inﬂuence net N mineralization; and
3) does resource aggregation inﬂuence whole system N retention?
MATERIALS AND METHODS

We conducted this study at the W. K. Kellogg Biological Station (KBS) in
Southwest Michigan (42° 24’N, 85°24’W, elevation 288 m). We used a 1:1 mix of
coarse sand and a composite of soil taken from the surface 0.4 m soil of a ﬁeld
on the KBS Long-Terrn Ecological Research site in this experiment. Kalamazoo
(ﬁne-loamy, mixed, mesic Typic Hapludalfs) and Oshtemo (coarse-loamy, mixed
mesic Typic Hapluadalfs) soil series co-occur on the site and are both present in
the soil used in this experiment. We excavated soil from the ﬁeld, mixed with an
end-loader, placed in a tarp covered pile to air dry, and after 12 months we .
power sieved the soil through a 12 mm screen, combined with the sand in a 250-
L mixer, and stored in a common covered pile until use.

We used a Randomized Complete Block Design with 5 soil amendment
treatments with plants and two amendment treatments without plants, 7 sampling
dates, and 4 replicates per block. The 5 amendment treatments consisted of four
litter distributions (8, 24, and 72 patches and uniform) and control +N fertilizer
treatment. Each experimental unit consisted of a 50-L black plastic container
. ﬁlled with the soil mix, amendment, and a single maize plant (Pioneer 0 35Y54)
(Fig. 1). We used Trifolium pretense (red clover) (variety Michigan Medium Red,
Michigan State Seed Solutions, Grand Ledge, MI) shoots as the plant litter

amendments.

126

We produced the clover in sand in a greenhouse, fertilized it with a
modiﬁed complete Hoagland’s nutrient solution containing ~ 8 atom % ‘5N-KN03
as the N source (Hewitt 1966), and harvested it before initiation of the
reproductive phase. The shoots were cut at the sand surface, dried for 4-5 days
at 55°C, coarsely-chopped to pass a 10 mm screen with a Wiley® mill, and then
ﬁnely-ground to pass a 1 mm screen in a Cyclotec® 1093 sample mill. Eight
subsamples of ﬁnely-ground litter were analyzed for C and N content with a
Costech ECS 4010 CHN elemental analyzer to determine application rate. The
red clover litter used in this experiment contained 413 g C kg litter" and 31.0 g N
kg litter" (5.81 atom% 15N) for a C:N ratio of 13.3.

Field containers

The 50-L (370 mm in depth, 390 mm bottom diameter and 440 mm top
diameter) containers were prepared by cutting 13 mm drainage hole in the
bottom of the containers, adding 3 L of 8 to 10 mm diameter gravel to the bottom
of the container to aid drainage, adding 40 L of soil mix to the container, adding
the soil amendment, and 10 L more of soil mix, and then placing the containers
into the holes in the ﬁeld that were created when the soil was originally
excavated. We placed the containers in the ﬁeld ﬂush with the soil surface and
at least 2.5 m from one another. Containers have a surface area of about 0.15
m2, which is similar to the standard production practices in this regiOn of 0.14 m2
plant". We planted the rows between the containers with the same maize

hybnd.

127

The containers to be harvested on the third and ﬁnal sample dates were
outfitted with anion exchange resin columns to capture N03' draining out of the
containers. Anion exchange columns consisted of 30 mL of Type II 16-50 mesh
anion exchange resin (IONAC® A-554 Cl-forrn) in a 250 mm length of 32 mm
diameter clear vinyl tubing with 8 pm ﬁberglass on each end (Corning Inc.,
Corning, NY). Preliminary analysis showed that this column design had an anion
exchange capacity in excess of 250 mg N-N03‘ in a soil solution matrix
(unpublished data) and that N03‘ held on the exchange resin was stable (no
detectable denitriﬁcation losses) under 4°C and 22°C conditions for at least 60
days. The columns were ﬁtted to the outside of each container’s drain hole with
vinyl ﬁttings and rubber gaskets and sealed with 100% silicon rubber sealant
(DAP Inc., Baltimore, MD). The opening 0f the ﬁtting into the container was
covered with a garden hole screen (~0.5 mm) to keep sediment from ﬂowing into
the column, and the containers were tilted about 1% towards the drain holes to
aid drainage.

Litter application

The ﬁnely-ground clover litter was distributed into each container into one
of 4 conﬁgurations: eight 4.699 patches, twenty 1.56 9 patches, seventy-two 0.52
9 patches, or a uniformly distributed 37.5 9 patch. To avoid disturbing the
patches when planting the maize litter was kept from center 150 mm of each
container (Figure 5.1). The litter distribution treatments were constructed by
placing a temporary circular template of the same diameter as the inside of the

50-L container on top of the initial 40 L of soil-mix, adding the litter to the

128

template, removing the template, and then adding 10 L more soil mix on top of
the litter to a mean depth of 100 mm. Litter application took three days from May
22-24, 2006 during which the containers were protected from precipitation.

The template was a 440 mm diameter circular sheet of acrylic with eight
21.3mm, twenty-four 12.1 mm, or seventy-two 8.3 mm diameter plastic syringe
barrels adhered perpendicular to the sheet. The “needle end” of the syringe
barrels and the rubber tips of the plungers were removed. We used the syringe
plungers to displace a consistent volume of soil within the syringe cylinders
before adding the litter. We pushed the plungers down into the cylinders as we
raised the template such that the litter remained in the soil as individual column
shaped patches just below the surface of the soil. After the template was
removed we added 10 L of soil on top of the litter. Each patch of litter had the
same mean depth in the soil. The uniform treatment was constructed by mixing
37.5 g of litter with 4 L of soil taken from to the containers, then adding to the
litter-soil mix back into the outer ring of the soil in the container (Figure 5.1).

Planting and fertilizing

On May 25, 2006, the containers were transferred to the ﬁeld and on May
26 the anion exchange columns were installed. We place two maize seeds
(Pioneer 35Y54) to a depth of ~50 mm into the center of each container on May
31 and covered with hardware cloth exclosures to prevent rodent damage.
Maize seeds were also planted between the containers at standard production
densities (70,000 plants ha"). After 14 days the seedlings were thinned to one

plant container" and the exclosures were removed.

129

We designed this experiment to test maize response to litter distribution in
a system where N is the only soil nutrient limiting plant growth. On July 6, 2006,
250 mL of nutrient solution containing 0.5 P g, 1.25 K g, 0.25 S g, 0.27 Ca 9, and
0.07 Mg 9 was added to the soil surface of each container. The control +N
treatment was also implemented on July 6, 2006 by adding 1 L of solution
containing 1.6 g N as NaNO3 to each container of the control +N treatment,
whereas the control -N and litter treatments all received 1 L of RO (reverse
osmosis) water. Supplemental watering (2 L container") occurred on August 13
and 22.

Repeated harvest

We conducted seven repeated whole container harvests. Four of the
harvests (June 22, July 20, August 22, and October 12, 2006) were made to
quantify maize shoot N content and soil N pools and three others (July 7, August
14, and September 26, 2006) to quantify maize shoot and root N content. The
aboveground shoots were cut at the soil surface, dried for 4 days at 60°C,
weighed, ground to pass a 1-mm screen, and analyzed for total N and atom%
15N by mass spectrometry at the U of California - Davis Stable Isotope Facility.
We transported the containers to a laboratory for processing to determine soil
moisture and total N contents in the surface 190 mm and the bottom 180 mm of
each container. Inorganic N also was determined in the surface 190 mm of soil.
The soil and roots were sieved to pass a 6 mm screen, homogenized, weighed,

and three sub-samples were dried for 3 days at 65°C and reweighed. A sub-

130

sample of the dried soil was then pulverized with a roller mill and analyzed for N
and 15N content.

Three 20 g fresh soil sub-samples of the homogenized soil were extracted
from the surface soil of each container with 100 mL of 2M KCI, shaken, allowed
to equilibrate for 24 h, shaken again, allowed to settle for 1 h and then ﬁltered
(Type NE Glass Fiber Filter, Pall Life Sciences, Ann Arbor, MI, USA). Filtered
extracts were frozen until analyzed for NH4+ and N03' on an Alphem Flow
Injection Analyzer (NH4+ via diffusion colorimetry technique and N03' via
cadmium reduction and colorimetry) (OI Analytical, College Station, TX, USA).
The inorganic N contained in the KCI extracts were reduced to NH3 (Devarda’s
alloy) and diffused onto acidiﬁed (H2804) 10 mm diameter glass ﬁber ﬁlter paper
during a 14 d incubation at 40°C with MgO to obtain a solid sample to analyze for
15N content (as above) (Burke et al. 1990).

We followed the fate of the litter-derived N throughout the season into
maize roots and shoots, soil inorganic N, leached N03’, total soil N pools by 15N
tracer methodologies (Powlson and Barraclough 1993). Atom % 15N excess is
equivalent atom % 15N minus 0.3663. The percentage of litter -‘5N recovered in
each N pool was calculated as described in Hauck (1982) and Jackson (2000)
using the equation:

Percentage recovery of Iitter-15N = ([{N(Ao/100)} - {N(Ai/100)}]I15NL)*100
where N is mg“’"5N container" of a sampled pool (inorganic N, total soil N,
shoot N, root N, or leached N), A0 is the atom % 15N excess of a sampled pool, A,

is the atom % 15N excess of the initial pool in the control treatment, and ”M is

131

mg litter-”N container" added to the soil. The portion of each N pool that was
derived from the litter-N was calculated as:

Portion derived from litter-N = {N(Ao — Ai)}/A._

where AL is the atom % 15N excess of the litter added to the soil. The mg litter-
derived N container" was the product of the portion derived from litter-N and the
mg N container" of each pool.

Because we did not sample root and soil N pools on the same harvest
dates we estimated the root biomass on DOY 173, 201, 234, and 285 by linear
interpolation of the root to shoot ratio from the root harvest dates. We also
estimated the root 15N and N concentration in same manner. The product of the
root biomass and the 15N and N concentration were then used to estimate the
root N and 15N content on DOY 173, 201, 234, and 285. The mineralization of
litter-derived N was estimated as the sum of the litter-derived N in the shoot, root,
soil inorganic N, and leached N pools on DOY 173,201, 234, and 285.
Statistical Analysis

A repeated measure analysis of variance (ANOVA) was performed for
each response variable with block and soil amendment treatment as independent
ﬁxed variables with sampling date the repeated random variable. We used
Akaike’s information criteria (Akaike 1974) to choose a ﬁrst-order heterogeneous
autoregressive covariance structure to model the repeated measure variance
components for all of the response variables except the litter-derived root and
leachate N. All statistical tests were conducted using SAS mixed model

procedures (Littell et al. 2005). Natural log transformations of the data were

132

performed prior to analysis when required to meet the assumption of ANOVA.
Differences were considered signiﬁcant at the a < 0.05 level 'for all ANOVAs.
RESULTS
Soil Inorganic N and Litter—derived Soil Inorganic N

The extractable inorganic N in the surface 190 mm of soil in response to
the litter distribution varied with sampling date throughout the growing season
(distribution x date; P<0.0001, Table 5.1 and Figure 5.2). The soil inorganic N
and the proportion of this inorganic N that was derived from the litter-N generally
decreased as the season progressed (Figure 5.2). By the ﬁrst sampling date (28
days after soil application of red clover litter) the soil inorganic N was increased
in response to all of the litter distribution treatments to a similar degree (P<0.68,
averaging 440 mg N container" or 20.5 pg 9 soil") relative to the yet unfertilized
control (Table 5.2 and Figure 5.2). After N fertilization on DOY 201, the soil
inorganic N pool of the control +N treatment was higher than the litter amended
soils. 0n DOY 201 and 285, the soil amended with aggregated litter (8, 24, and
72 patches) averaged 54% (P<0.032) and 15% (P<0.007) less soil inorganic N,
respectively, than soils treated with a uniform litter distribution (Table 5.2). The
litter-derived soil inorganic N in response to litter distribution varied with sampling
date early in the season (distribution x date; P<0.0001, Table 5.3) and was not
determined during the last two sampling dates due to the small pool sizes (Figure
5.2).

Plant N and litter-derived N content

133

Maize shoot N varied in response to litter distribution treatments
throughout the season (distribution x date; P<0.007, Table 5.5 and Figure 5.3)
and did not differ on any individual sampling date (Table 5.6). By the end of the
season maize plants on average accumulated 2.8 g N shoot" with litter
amendments and 3.3 g N shoot" with the control +N fertilization. The plants
grown in soils amended with litter aggregated into 8, 24, and 72 patches on
average acquired a greater proportion (18%, P<0.0001) of their shoot N from the
litter than did plants grown in soils amended with a uniform litter distribution
(Figure 5.3 and Table 5.7).

Maize root N response to litter distribution also varied throughout the
growing season (distribution x date; P<0.01, Table 5.8 and Figure 5.4). At the
end of the growing season (DOY 269) root N accumulation was 27% greater in
the aggregated litter distributions (8, 24, and 72 patches) (0.38 g N container")
than in the uniformly distributed litter treated soils (0.30 g N container")(P<0.016,
Table 5.9 and Figure 5.4). The proportion of the litter-derived N in the roots was
33% greater on average in plants grown in soils amended with aggregated litter
(8, 24, and 72 patches) than the uniform litter distribution (P<0.0001, Table 5.10 4
and Figure 5.4).

Leachate and litter-derived nitrate-N

The nitrate-N leached from the containers was low regardless of how the
litter was distributed in the soil or when sampling occurred, averaging 21 mg N
container" across all treatments and both sampling dates (Table 5.11 and Figure

5.5). The proportion of the N03' leached that was litter derived, however, was

134

affected by the time of sampling such that a greater proportion of the N03'
leached during the entire season (DOY 285) than after 56 days in the soil (DOY
201) (date; P<0.016, Table 5.12).
Accumulated litter-derived mineralized N

The mineralized litter-derived N from litter distribution varied throughout
the season and accumulated from an average of 175 on DOY 173 to 379 mg
litter-N container" on DOY 285 (distribution x date; P<0.008, Table 5.13 and
Figure 5.6). The aggregated litter distributions accumulated 28, 8, 21 and 24%
more litter-derived mineralized -N on DOY 173, 201, 234, and 285 (P<0.046,
0.218, 0.010, and 0.0003), respectively, than did soils amended with a uniform
litter distribution (Table 5.14 and Figure 5.6).

Percent litter-”N recovery

The majority (50 to 60%) of the litter-N remained in the soil, presumably in
an organic form, at the end of the growing season (Table 5.15). No differences
in this pool were observed among distribution treatments (P<0.14). The maize
plants (shoots and roots) were the second most important sink for the litter-N,
accounting for ~30% of the total litter-N added to the soil. At the ﬁnal harvest
litter-derived N in the maize tissue was 24% greater in response to the aggregate
litter distributions (8, 24, and 72 patches) than the uniform litter distribution
(P<0.0003). The total litter-derived NOa' leached during the season was less
than 0.1% of the litter-N for each litter application treatment. Likewise, the soil

inorganic N pool at the end of the growing season only contained 0.1 to 0.2% of

135

the litter-N added. The balance of the litter-N (the unaccounted for mass of litter
15N) varied from 12 to 19% across the litter treatments at the end of the season.
DISCUSSION

Our results demonstrate that resource heterogeneity at spatial scales
inﬂuenced by individual plant root systems can signiﬁcantly alter N cycling rates.
The spatial distribution of plant litter in soil had a statistically signiﬁcant but minor
inﬂuence on maize root N content and a dramatic but short-term effect on soil
inorganic N in the surface soil layer containing the litter. In contrast, litter
distribution had a major effect on the fate of litter-derived N. Plants grown in soils
with red clover litter distributed into aggregates obtained more of their tissue N
from the litter despite only min0r N limitations.

Why was litter-N cycling affected by litter aggregation?

The distribution may have inﬂuenced the litter-N fate by a number of
possible mechanisms, both microbial and plant related. In the short term,
following application of litter to soil there is a potential for litter aggregation to
affect decompbsition (Chapter 3), N mineralization, nitriﬁcation, N20 emissions
(Ambus et al. 2001, Chapter 3), and denitriﬁcation rates (Breland 1994, Nielsen
and Revsbech 1998) occurring in and around the litter aggregates. The
aggregation of plant litter has been shown to both stimulate (Breland 1994) and
inhibit (Magid et al. 2006) decomposition rates relative to a uniform litter
distribution. Our previous work incubating the same litter distribution-soil
treatment combinations without plants revealed a 3 to 5 d delay in the maximum

decomposition rate of the aggregated litter relative to the uniformly distributed

136

litter, but no longer-term changes in the decomposition rate (Chapter 3). Thus in
the short-term (<15 days) it is unlikely that the soil-litter contact (Breland 1994)
provided signiﬁcant physical protection of the litter regardless of distribution.
Likewise, microbial predation by nematodes and protozoa (Grifﬁths 1994) was
unlikely to have responded quickly enough to litter distribution within the 3 to 5 d
to act as a major controller of the litter decomposition in our system, though
these mechanisms may be important later in the season. In contrast, litter
distribution had a substantial effect on the N20 emissions from litter amended
soils without growing plants (Chapter 3), suggesting a signiﬁcant alteration of N
cycling in response to litter distribution.

Our data indicated that soil inorganic N pools liberated from the litter were
substantially affected by the litter distribution suggesting that N cycling was
dramatically different across the litter aggregation gradient (Figure 5.2). For
example, 56 days after litter application (DOY 201), the average soil inorganic N
content in response to aggregated litter (patches of 8, 24, and 72) was 54% less
than the uniform litter distribution (151 vs. 329 mg N container"). This difference
in soil inorganic N is likely due to both differences in processes producing (gross
mineralization) and consuming (gross microbial immobilization, plant N uptake,
denitriﬁcation, and hydrologic N losses) inorganic N.

Gross N mineralization rate, the transformation rate of organic N into
inorganic N, is often assumed to be related to the heterotrophic respiration rate of '
a given substrate of known C:N (Luxhoi et al. 2006). We saw only a short-term

inﬂuence of litter aggregation on respiration rates in the incubation study

137

(Chapter 3) and no detectable differences in the litter decomposition rate
(Chapter 4), we thus have no indication that the gross N mineralization was
inﬂuenced by the litter distribution. Plant accumulated N and leachate N03' were
similar at this point in the season (DOY 201) among litter distribution treatments
and thus should not account for any difference in soil inorganic N. In both the
laboratory incubations and this container experiment (Loecke Chapter 3), N20
emission were considerably higher from the aggregated litter than the uniform
litter; however, it is unclear how well N20 emissions relate to total gaseous N
losses (Mathieu et al. 2006).

The difference in soil inorganic N among the litter distribution treatments
observed at DOY 201 occurred just prior to the most rapid plant grow ratesof the
season (Chapter 4) and thus had the potential to substantially affect maize
growth and N acciuisition (Blackmer et al. 1989). However, this early season
plant available N pool and succeeding plant N accumulation were poorly
correlated (Figures 5.2, 5.3, and 5.4). It was not until plant physiological maturity
that the plant N content differed among the litter treatments. Surprisingly, this
plant N difference was in the opposite direction as predicted from the scil
inorganic N pool from earlier in the season, because soil inorganic N early in the
season is often used as an indication of the plant available N for the entire
season (Blackmer et al. 1989). Also at the end of the growing season, the soil
inorganic N content in the surface 190 mm was 15% less in response to the

aggregated treatments than the uniform litter distribution. This may indicate a

138

greater risk of post-harvest NOa' leaching from the uniform litter distribution as
well as less complete utilization of available resources.

Maize root N content increased in response to aggregated litter, but shoot
N content was unaffected by litter distribution. Other studies have found both
negative (Hodge 2003) and positive (e.g., Bonkowski et al. 2000) effects of litter
aggregation on the plant N uptake of individual plants. Several processes may
explain why litter distribution altered plant N uptake and the proportion of litter-
derived plant N. First, the plants may have only been weekly or not all N-limited
at the point in the season when the differences in the soil inorganic N existed.
Two observations support this supposition: 1) the root N content did not start to
differentiate among litter treatments until after the soil inorganic N had dropped to
insigniﬁcant levels after DOY 234; and 2) the control +N treatment only increased
plant N content by 19% more than the average litter treatment. Alternatively, the
pools of N accessed by the plant roots may have differed among the litter
treatments, thus inﬂuencing the total plant N uptake and the litter-derived plant N.
The proportion of litter-derived N in the maize roots and shoots was on average
and especially early in the season was greater in response to the aggregated
litter than the uniformly distributed litter. This implies that there was a greater
spatial and temporal coupling of the plant N uptake and litter-N mineralization in
the soils amended with aggregated litter than the uniform litter distribution.

Wang and Bakken (1997) hypothesized that the spatial coupling of litter-N
mineralization and plant N uptake improves the synchrony of these two

processes by more directly transferring the mineralized litter-N to plant roots

139

when the roots are spatially associated with the litter. This implies that root
foraging for N-rich microsites can effectively alter plant-microbe competition for N
if N-rich and N-poor microsites are sufﬁciently heterogeneously distributed at a
scale at which plant roots can selectively proliferate into N-rich microsites and
avoid N-poor microsites. Furthermore, rhizosphere induced N mineralization
may have increased litter-N mineralization of the litter where the roots and litter
spatially co-occur (Herman et al. 2006). Maize root systems are known to
selectively proliferate into microsites rich in N (Chapter 2) and P (Kume et al.
2006); however, the scales at which maize roots perceive differences in microsite
quality, intensity, duration, and size is unknown. We did not quantify root
proliferation into the aggregated litter in this experiment because we wanted to
preserve the 15N balance of the system; however, we visually observed intense
root proliferation into the litter aggregates.

' Litter distribution had a substantial effect on litter-derived N mineralization
in our study. Here we deﬁne litter-N mineralization as the transfer of litter-derived
15N into plant tissue and into soil inorganic N, and leachate N pools. Litter-N
mineralization was on average 20% greater in response to aggregated litter than
to unifonnly distributed litter. This is despite the observation that on two of the
four dates measured, the pool of soil inorganic N was greater in response to the
uniform distribution then the aggregated litter and were no different on the other
two sampling dates. This may indicate a signiﬁcant alteration of the extent that
plants access N from labile organic matter pools. A 20% increase in litter-N

mineralization is comparable to the effects of other basic alterations of soil

140

biological conditions. For example, the addition of microfauna grazing
protozoans to denuded soil increased litter-N mineralization by a third
(Bonkowski et al. 2000). From our previous work on the decomposition of
aggregated litter, we predicted that the distribution of litter in soil would have only
a minor inﬂuence on the mineralization of litter, thus either the plant and plant-
microbe interaction or microbial dynamics that were not observable during the
incubation played a signiﬁcant role in altering N cycling in response to resource.
heterogeneity.
Fate of aggregated litter-N

At the end of the season most of the litter-N (as indicated by the 15N label)
was retained in the soil matrix (50 to 60%), presumably as organic N, regardless
of the spatial distribution of the litter (Table 5.15). In contrast, the litter-derived
plant N was affected by the litter distribution and accounted for about 30% of the
litter-N added to the soil. This is a higher recovery of litter-N into maize tissue N
then Harris et al. (1994) at 17% recovery of alfalfa litter N during the ﬁrst season
post application. Our higher recovery of litter-N in plant tissue may be because
we used less fertile soil than Harris et al. Both leachate N and soil inorganic N at
the end of the season were insigniﬁcant portions of the litter-N added. The
unaccounted for litter-N was also not noticeably different among the litter
distributions; however, it was a signiﬁcant fate of the litter-N averaging 16% of
the total litter-N applied. This missing N is likely due to denitriﬁcation losses. It is

interesting to note that we were able to detect 6 to 7 fold differences in N20

141

emissions across the aggregation gradient (Chapter 3) but were not able to
detect differences in total gaseous N losses.
CONCLUSIONS

Litter aggregation substantially altered N cycling in our system. The litter
aggregation gradient had statistically signiﬁcant but minor effects on total plant N
content; however, plants growing in soil with aggregated litter derived 20% more
of their N from the litter than plants grown in soils with a uniformly distributed
litter. This difference in litter-derived plant-N is in contrast to the greater plant
available inorganic N pools observed in response to the uniform distribution on
two dates. Taken together these two observations suggest that a closer spatial
coupling of roots and aggregated litter allowed for greater litter-N uptake while

minimizing the risk of N03' leaching.

142

REFERENCE

Akaike, H. 1974. New look at statistical-model identiﬁcation. IEEE Trans. Autom.
Control 19:716-723.

Ambus, P., E. S. Jensen, and G. P. Robertson. 2001. Nitrous oxide and N-
Ieaching losses from agricultural soil: inﬂuence of crop residue particle
size, quality and placement. Phyton (Austria) 41 :7-15.

Blackmer, A. M., D. Pottker, M. E. Cerrato, and J. Webb. 1989. Correlations
between soil nitrate concentrations in late spring and corn yield in Iowa. J.
Prod. Agric. 22103-109.

BonkowSki, M., B. Grifﬁths, and C. Scrimgeour. 2000. Substrate heterogeneity
and microfauna in soil organic 'hotspots' as determinants of nitrogen
capture and growth of ryegrass. Appl. Soil Ecol. 14237-53.

Breland, T. A. 1994. Enhanced mineralization and denitriﬁcation as a result of
heterogeneous distribution of clover residues in soil. Plant Soil 166:1-12.

Burke, l. C., A. R. Mosier, L. K. Parker, and L. A. O'Deen. 1990. Diffusion of soil
extracts for nitrogen and nitrogen-15 analyses by automated
combustion/mass spectrometry. Soil Sci. Soc. Am. J. 54.

Cheng, W. X., D. W. Johnson, and S. L. Fu. 2003. Rhizosphere effects on
decomposition: controls of plant species, phenology, and fertilization. Soil
Sci. Soc. Am. J. 67:1418-1427.

Drew, M. C. 1975. Comparison of effects of a localized supply of phosphate,
nitrate, ammonium and potassium on growth of seminal root system, and
shoot, in barley. New Phytol. 752479-490. ’

Grifﬁths, B. S. 1994. Microbial-feeding nematodes and protozoa in soil: Their
effects on microbial activity and nitrogen mineralization in decomposition
hotspots and the rhizosphere. Plant Soil 164:25-33.

143

Harris, G. H., O. B. Hesterrnan, E. A. Paul, S. E. Peters, and R. R. Janke. 1994.
Fate of legume and fertilizer nitrogen-15 in a Iong-terrn cropping systems
experiment. Agron. J. 86:910-915.

Hauck, R. D. 1982. Nitrogen-isotope ratio analysis. Pages 735-779 in A. L. Page,
editor. Methods of soil analysis. ASA, CSSA, and SSSA, Madison, WI.

Herman, D. J., K. K. Johnson, C. H. Jaeger, E. Schwartz, and M. K. Firestone.
2006. Root inﬂuence on nitrogen mineralization and nitriﬁcation in Avena
barbata rhizosphere soil. Soil Sci. Soc. Am. J. 70:1504-1511.

Hewitt, E. J. 1966. Sand and water culture methods used in the study of plant
nutrition, 2nd edition. Farnham Royal, Commonwealth Agricultural
Bureaux.

Hodge, A. 2003. Plant nitrogen capture from organic matter as affected by spatial
dispersion, interspeciﬁc competition and mycorrhizal colonization. New
Phytol. 157:303-314.

Hodge, A., D. Robinson, B. S. Grifﬁths, and A. H. Fitter. 1999. Why plants bother:
root proliferation results in increased nitrogen capture from an organic
patch when two grasses compete. Plant Cell Environ. 22:811-820.

Jackson, L. E. 2000. Fates and losses of nitrogen from a nitrogen-15-labeled
cover crop in an intensively managed vegetable system. Soil Sci. Soc.
Am. J. 64:1404-1412.

Kume, T., N. Sekiya, and K. Yano. 2006. Heterogeneity in spatial P-distribution
and foraging capability by Zea mays: Effects of patch size and barriers to
restrict root proliferation within a patch. Ann. Bot. 98:1271-1277.

Littell, R. C., G. A. Milliken, W. W. Stroup, R. D. Wolﬁnger, and O.
Schabenberger. 2005. SAS for Mixed Models, 2nd edition.

Loecke, T. D. Chapter 2. Root foraging is responsive to soil microsite resource
quality and availability. Ph.D. Dissertation. Michigan State University, East
Lansing, MI.

144

Loecke, T. D. Chapter 3. Litter aggregation has a disproportionate inﬂuence on
nitrous oxide ﬂux. Ph.D. Dissertation. Michigan State University, East
Lansing, MI.

Loecke, T. D. Chapter 4. Aggregated soil resources enhances primary
productivity. Ph.D. Dissertation. Michigan State University, East Lansing,
MI.

Luxhoi, J., S. Bruun, B. Stenberg, T. A. Breland, and L. S. Jensen. 2006.
Prediction of gross and net nitrogen mineralization-immobilization-tumover
from respiration. Soil Sci. Soc. Am. J. 70:1121-1128.

Magid, J., A. De Neergaard, and M. Brandt. 2006. Heterogeneous distribution
may substantially decrease initial decomposition, long-ten’n microbial
growth and N-immobilization from high C-to-N ratio resources. Euro. J.
Soil Sci. 57:517-529.

Mathieu, 0., J. Leveque, C. Henault, M. J. Milloux, F. Bizouard, and F. Andreux.
2006. Emissions and spatial variability of N20, N-2 and nitrous oxide mole
fraction at the ﬁeld scale, revealed with N-15 isotopic techniques. Soil
Biology & Biochemistry 38:941-951.

Myrold, D. D., and J. M. Tiedje. 1985. Diffusional constraints on denitriﬁcation in
soil. Soil Sci. Soc. Am. J. 49:651-657.

Nielsen, T. H., and N. P. Revsbech. 1998. Nitriﬁcation, denitriﬁcation, and N-
Iiberation associated with two types of organic hot-spots in soil. Soil Biol.
Biochem. 30:611-619.

Parkin, T. B. 1987. Soil microsites as a source of denitriﬁcation variability. Soil
Sci. Soc. Am. J. 51:1194-1199.

Powlson, D. S., and D. Barraclough. 1993. Mineralization and assimilation in soil-
plent systems. in R. Knowles and T. H. Blackburn, editors. Nitrogen
isotope techniques. Academic Press, San Diego, CA.

Schimel, J. P., and J. Bennett. 2004. Nitrogen mineralization: challenges of a
changing paradigm. Ecology 85:591-602.

145

. vanVuuren, M. M. I., D. Robinson, and B. S. Grifﬁths. 1996. Nutrient inﬂow and
root proliferation during the exploitation of a temporally and spatially
discrete source of nitrogen in soil. Plant Soil 178:185—192.

Wang, J., and L. R. Bakken. 1997. Competition for nitrogen during decomposition
of plant residues in soil: effect of spatial placement of N-rich and N-poor
plant residues. Soil Biol. Biochem. 29:153-162.

146

Table 5.1 Repeated measures analysis of variance to determine effects of litter
distribution on soil inorganic N (N03‘ and NH4”).

 

 

Source Num. off Den. clfT F value P>F

Block 3 50 23.98 <0.0001
Litter distribution 4 50 2.61 0.0467
Sampling Date 3 50 465.1 <0.0001
Distribution x Date 12 50 6.07 <0.0001

 

T Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

147

00.00 02:02.00 .050 02. 202. 0.02. 020 .3 0. SUN C60. .00.. .0 >00 02....E00 .0. 2.00002 .0 000.000 .0.02_E0200 02. ...
202.0300. 0.00. n. 2. 0002 E0000.. .0 000.000 .0.02_E0200 020 0.0.02.2: 02. 0.0200 .0 200 020 .0 2.22 ._.

 

 

 

 

 

 

 

 

Bod N. F F can... «0.. ~86 80 ~00... m ..o o. F 00000.8( .0> E322...
..05 .0.._.. I 0.002200

mood Rd 2.5.0 9.0 wood ~00 Sod n. .. e o. 0 202222.05 .0.._._

50.0 «.3. 2.0.0 o ..m 50d and «mm... mm... or 0 0.02m
“Tn. 2. “Tn. n. “Tn. n. “Tn. ... ...0 .200 ..0 2.22 00.:0w

1|an ..va SN mt
C00» .0 >00. 0.00 02_.0E0m

202222.00

.02.. 22022: 0:0.0> .00.. 00.000500 .0 .0020 2002 0... 0.00E00 0.002200 02... .200000 02.2.0.0 0... 02.50 00.22.00
0.00 2000 .0. £12 + -002. 2 0.20902. ._00 20 200222.00 .0..__ .0 0.0020 05:20.00 0. 002020> .0 0.02020. N0 0.20...

148

Table 5.3 Repeated measures analysis of variance to determine effects of litter
distribution on litter-derived soil inorganic N (NOa' + NHX).

 

 

Source Num. df'r Den. dft F value P>F
Block 3 17 1 .62 0.223
Litter distribution 3 17 1 .91 0.166
Sampling Date 1 17 81.7 <0.0001
Distribution X Date 3 17 4.69 0.015

 

1' Num df and Den clf denote the numerator and denominator degrees of freedom
used in F tests, respectively.

149

.>.0>..00000. 0.00. n. 2. 000: 220000.. .0 000.000 .0.02.E0200 020 .0.0.0E:2 02. 0.0200 .0 200 020 .0 E22 .F

 

mo F .o $0.0 who... 00.0

... F 00.000.00< .0> 220.23

.85 .02: .. 0.8.250

 

0N F.o mod 5 F .0 mod N. m 20.59505 .03...
mmmd Fm. F Nmﬁd $0.0 n m 0.005
“Tn. u. “Tn. n. ..0 .205 ..0 .E:Z 00.:ow

i Fomlil limb Fli

 

e00> .0 >00. 0.00 02..0E0w

 

22.328... a... 55......

0:0.0> .00.. 00009000 .0 .0020 2002. 0.... 0.00260 0.002200 02» .200000 02.2.0.0 0.... 02.50 0.00 02.32.00 2000
.0. .....Iz 020 -002. 2 0.20002. ..00 0020020... 20 20..:2...0.0 .0.... .0 0.00..0 05220.00 0. 002020> .0 0.0>.02< 2.0 0.20...

150

Table 5.5 Repeated measures analysis of variance to determine effects of litter
distribution on maize shoot N content.

 

 

Source Num. dfT Den. df* F value P>F
Block 91 2.03 0.115
Litter distribution 91 6.60 <0.0001
Sampling Date 91 272.3 <0.0001
24 91 2.08 0.007

Distribution x Date

 

1 Num df and Den df denote the numerator and denominator degrees of freedom

used in F tests, respectively.

151

.>.0>..00200. 0.00. h. 2. 0002 E0000.. .0 000.000 .0.02.E0200 020 5.0.02.2: 02. 0.0200 .0 200 020 .0 E22 ._.

 

00F... mwmd omFd 000.0 wwod 000.0 med 2 F 00.000.00<.0> E.0..2D

..05 .0....— l 0.09.200

 

 

00 F .0 Food wood momd 000.0 . oomd owmd OF 0 20:22....05 .0....
00 F .o owmd mmod Fhod thd 0 F06 mm F.o o F m 2005
“To. ..0 .200 ..0 .E22 00.200

new mom 0mm omm Fom on F mu F

 

C00> .0 >00. 0.00 02.2.2.0.0

 

 

.20..22...0.0 .0.... 220222 020.0> .0.... 00.002000 .0 .0020 2002.. 02. 0.00280 0.002200 02... .200000 02.2.0.0 02. 02220
0.00 02222.00 2000 .0. .2228 2 .0020 0202. 20 20..22...0.0 .0.... .0 0.0020 05220.00 0. 002020> .0 0.0>.0c< 0.0 0.20...

152

Table 5.7 Repeated measures analysis of variance to determine effects of litter
distribution on the proportion of maize shoot N derived from red clover litter.

 

Source Num. dfT Den. dfr F value P>F
Block 3 74 4.90 0.004
Litter distribution 3 74 7.52 0.0002
Sampling Date 6 74 21.1 <0.0001
Distribution x Date 18 74 1.60 0.081
Contrast

Uniform vs. Aggregated 1 74 16.46 0.0001

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

153

Table 5.8 Repeated measures analysis of variance to determine effects of litter
distribution on maize root N content.

 

Source Num. clf'r Den. dff F value P>F
Block 3 38 0.33 0.805
Litter distribution 4 38 4.76 0.003
Sampling Date 2 38 575.0 <0.0001
Distribution x Date 8 38 3.05 0.010

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

154

.>_0>..00200. .0.00 02..2E00 2000 .0. E0000.. .0 000.000 .0.02.E0200 02. H
.>.0>..00200. .0.00. H. 2. 0002 220000.. .0 000.000 .0.02.E0200 020 5.0.02.2: 02. 0.0200 .0 20m. 020 .0 E22 F

 

05.2 .0... .5... 00.2 000... S... «.....0 . 8.80.020. 2.5.22

..05 .0....— I 0.002200

05.0 006 ode o0.F 000.0 00.0 NF .5. .0 v 22.222.05 .0....

 

.000 00.0 80... ~00 000.2 00.0 N. ... .0 m 0.8.0
.2 2 .4. . .4. n. t... .202 .... .252 8:50
1.1081! imam well:

 

 

e00> .0 >00. 0.00 02.32.00

 

 

202222.00 .0.... 220222 020.0> .0.... 00.000.000 .0 .00..0 200E 02. 0.02E00 0.002200 02 .2 .200000 02.2.0.0 02. 02220
0.00 02._2E00 2000 .0. .20.:00 z .00. 00.0.2 20 202222.00 .0.... .0 0.00..0 02.E.0.00 0. 002020.. .0 0.0>.02< 0.0 0.20..

155

Table 5.10 Repeated measures analysis of variance to determine effects of litter
distribution on the proportion of maize root N derived from red clover litter.

 

 

Source Num. dt’f Den. df'r F value P>F
Block 3 30 1 .90 0.370
Litter distribution ' 3 30 10.6 <0.0001
Sampling Date 2 30 4.92 0.014
Distribution x Date 6 30 1.43 0.238

Contrast
Uniform vs. Aggregated 1 30 24.5 <0.0001

 

T Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

156

Table 5.11 Repeated measures analysis of variance to determine effects of litter
distribution on nitrate leached from the containers.

 

Source Num. dft Den. df'r F value P>F
Block 3 18 1.94 0.251
Litter distribution 3 18 1.30 0.305
Sampling Date 1 18 2.20 0.155
Distribution x Date 3 18 0.66 0.589

Contrast I
Uniform vs. Aggregated 1 18 0.04 0.850

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

157

Table 5.12 Repeated measures analysis of variance to determine effects of litter
distribution on litter-derived N03' leached from the containers.

 

Source Num. df‘ Den. clfT F value P>F
Block 3 18 0.61 0.617
Litter distribution 3 18 2.62 0.083
Sampling Date 1 18 7.13 0.016
Distribution x Date 3 18 1.73 0.199

Contrast
Uniform vs. Aggregated 1 18 0.91 0.353

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

158

Table 5.13 Repeated measures analysis of variance to determine effects of litter
distribution on net litter-N mineralization.

 

Source Num. dfi Den. df’ F value P>F
Block 3 33 1 .50 0.231
Litter distribution 3 38 1 1 ..7 <0.0001
Sampling Date 3 38 176.8 <0.0001
Distribution x Date 3 ' 38 3.03 0.008
Contrast

Uniform vs. Aggregated 1 38 31.0 <0.0001

 

1' Num df and Den df denote the numerator and denominator degrees of freedom
used in F tests, respectively.

159

.>.0>..00000. .0.00 02:02.00 2000 .0. 28000.. .0 000.000 .0.02.2.0200 02. 0.
5.0280000. .0.00. n. 2. 0002 50000.. .0 000.000 .0.02.2.0200 020 5.0.0252 02. 0.0200 .0 200 020 .0 E22 ..

 

 

0000.0 N00 0 5.0 0. E. 0 :00 00. _. 000.0 0N0 0.0.0.0 .. 00.000.00< .0> E.o..:D
.00..200
N000 0.0.. 0 :00 50.5 000.0 05.0 ..50.0 00.0 0.0.0.0 0 22.00.00.0— .033
500.0 NW: :000 00.0 00N.0 00. .. 0 ..5.0 50.0 0.0.0.0 0 0.00.0
“Tn. u. uAn. n. 0A0. n. “Tn. n. t... .000 ..0 ._...22 00.200
llil00Nllil- 100m; 11:00 05.1.11

 

0005 .0 >00. 0.00 02:02.00

 

 

.20..20...0.0 .0.... 0.20.22 020.0> .0.... 00%.:000 .0 .00..0 2002. 0... 0.00..200 0.00..200 0...... .200000 02.2.0.0 02. 02.50
0.00 02:02.00 2000 .0. 00~..0.02.E 2.0.... .02 20 20..20...0.0 .0.... .0 0.0000 02.E.0.00 0. 0020..0> .0 0.05.02... 0.0 0.00..

160

 

 

 

 

.00. 5.. .00. e0. .00. 0.: .00. «.0. z 0050.20.22
..00. ~00 .000. 00.0 ..00. 00.0 .000. ~00 2 00503
.00. 0.0 .00. 0... .00. 0.0 ....0. ..0 z .8. 3.0.2
.00. 0.00 .00. 0.00 .00. 0.00 .0. .. «..N z .00..0 8.22
.000. N0 .000. N0 .000. 0.0 .000. .0 2 250.0... __00
.00. 0.00 ... .. 0.00 ....0. :0 .00. 0.00 z 0.2.00.0 ._00
E0500. 20.00.... .0.. .000 2000.22
228.20 0020.00 N5 0020.00 00 0020.00 0
0003.000 .02:

 

.200000 02.2.0.0 02. .0 020 02. .0 .020 00020.0 020 « 20..20...0.0 .0.... 0. 0020000. 2. 000. z 002.0.0x022
020 .2 002000. .2 0.20082. 020 0.2090 ..00 .0.00. 020 0.0020 00.02. 0.2. E0500. 20.00.... 200.00 0.0 0.00...

161

1' Uniform

72 patches

 

removable
gas-tight lid
an utter 24 patches

   

pea gravel
anion rain

 

Figure 5.1 Spatial layout of litter distribution treatments conducted in 50—L
plastic containers set into holes in a ﬁeld and surrounded by maize plants.
The left panel contains a side-cut view of the container components and
vertical layout. The right panel is a top-down view illustrating the spatial
distribution of the litter treatments.

162

700

 

+ 8 patches
—0— 24 patches
+ 72 patches
+ Uniform
—-l— Control

600 -

 
    
  

500 -

400 -

300 -

200 .

100 -

 

 

0.4 0

0.3 -

0.2 -

0.1 -

 

 

Proportion litter derived soil inorganic N 30" inorganic N (mg N container")

0.0

 

160 180 200 220 240 260 280 300
Day of Year

Figure 5.2 Total soil inorganic N (N03' and NH?) in the surface 190 mm of
soil and proportion of soil inorganic N derived from soil amended red clover
litter (bottom panel) in response to litter distribution. Error bars denote :t one
standard error.

163

 

+ 8 patches
+ 24 patches

   

 

 

‘75 4000 - + 72 patches
g + Uniform
E 3000 4 + Control
2
0)
g 2000 -
2
§ 1000 -
(n
0 I J I 1 I l 1
0.22 '

0.20 - 5‘
0.18J /‘ ‘

‘ 3
0-16- . ‘0‘
0.14 . » ‘ \
0.12 . ‘
0.10 ‘
0.08 . 1
0.06 -

0.04

Proportion litter derived shoot N

 

 

 

160 180 200 220 240 260 280 300
Day of Year

Figure 5.3 Maize shoot N content (top panel) and proportion of shoot N
derived from soil amended red clover litter (bottom panel) in response to litter
distribution. Error bars denote :t one standard error.

164

 

500

A

O

O
1

300 -

200 4

100 -

Root N (mg N container")

+ 8 patches
—0— 24 patches
+ 72 patches
-—A— Uniform

+ Control +N

 
 
  
 

 

 

 

 

0.22 -
0.20 -
0.18 -
0.16 q
0.14 -
0.12 -
0.10 -

Proportion litter derived root N

 

 

 

 

0.08

Figure 5.4 Root N content (top panel) and proportion of root N content derived

160 180 200 220 240 260
Day of Year

 

j

280 300

from soil amended red clover litter (bottom panel) in response to litter

distribution.

Error bars denote :I: one standard error.

165

100

 

 

 

 

 

 

 

 

 

- 8 patches
- 24 patches
1 30 ‘ - 72 patches
3 .3 :3 Uniform
t0
5 E 60 -
g. 3
.9 2.
g on 40 -
2 Z
O)
E. 20 4
0 .—
0.10 ‘
'0
g g 0.08 -
£3 33
.5 g 0-06-
1: 0‘:
O ' on
30 0.04 -
0 z
0.00 -
201 285
Day of Year

Figure 5.5 Nitrate leached and proportion of nitrate leached derived from

litter-N in response to red clover litter distribution. Error bars denote one
standard error.

166

 

500
+ 8 patches

450 ‘ + 24 patches

- + 72 patches
400 --6— Uniform
350 -

300 1
250 d
200 -
150 -
100

 
  

 

 

Mineralized litter N (mg N container“)

 

160 180 200 220 240 260 280 300
Day of Year

Figure 5.6 Estimated net mineralized litter-N in response to litter
distribution. Error bars denote :l: one standard error.

167

Chapter 6. Summary of Resource Aggregation Effects and
Implications

SUMMARY

Spatial heterogeneity of soil resources is common whereas uniformity is
rare. Jensen’s Inequality predicts that process rates will differ between systems
with uniform and heterogeneous distributions of process controls if the functional
response to that control is nonlinear. Nonlinear microbial and plant functional
responses to resource availability are prevalent in soils. Nitrogen is the most
common soil derived resource limiting plant productivity in terrestrial ecosystems
and plant litter is typically the most important source of soluble N compounds that
are available for plant and microbial assimilation. Thus heterogeneous
distributions of plant litter are likely to affect terrestrial C and N cycling differently
than uniform distributions of plant litter.

Many factors that cause resource heterogeneity in soil (e.g., tillage, animal
burrowing, and root distributions) also inﬂuence many other aspects of the soil
environment, and thus it is difﬁcult to attribute differences in soil process rates in
natural or managed ecosystems to resource heterogeneity per se. The primary
objective of my dissertation was to address the general question of how does the
spatial distribution of soil microbial and plant resources within the scale of
individual plants inﬂuence rates of C and N cycling. My general approach for
addressing this question was to isolate the effect of resource heterogeneity by
manipulating the spatial distribution of plant litter across an aggregation gradient

in soil and then following speciﬁc microbial and plant responses to this

168

manipulation. In addition, I varied other aspects of the litter or soil environment
while holding the litter aggregation constant to more fully understand the
properties of litter aggregation that determine the microbial and plant
mechanisms involved in these responses.

Many soil organisms have evolved adaptations to avoid, tolerate, and
exploit soil resource heterogeneity. For example, plants can selectively
proliferate roots or forage into microsites rich in limiting resources. Plant species
vary in their root proliferation response to microsites of inorganic N; however, it is
unclear if species vary in their root proliferation response to microsites of
complex organic matter. Furthermore, it is unknown if plant N demand has the
same control over root proliferation into microsites of organic N as into microsites
of inorganic N. In Chapter 2, l addressed three questions with the overall
objective to examine the soil conditions that plant root proliferation may play an
important role in regulating ecosystem level C and N cycling. Speciﬁcally, I
asked: 1) does root proliferation vary with microsite quality; 2) does the
proliferation response correspond to increased productivity; and 3) how does the
plant demand for N inﬂuence root proliferation in patches of N-rich organic
matter? I addressed these questions with two experiments: one in the
greenhouse and one in the ﬁeld.

To address questions 1 and 2 of Chapter 2, I grew Bromus inermis and
Avena sativa in sand with the only N source a choice of microsites composed of
plant litter of varying N mineralization potential. The results of this experiment

showed that both plant species had a similar aboveground biomass response to

169

the litter treatments; however, only in A. sativa did this response correspond to
root foraging. In other words, A. sativa proliferated more roots into microsites
that stimulated overall plant growth, whereas the root distribution of B. inermis
was not related to the microsite resource quality.

Plant root foraging for microsites of inorganic N is thought to be controlled
by plant genetics, overall soil N availability, and the plant demand for N. To
address question 3 of chapter 2, I examined maize root proliferation into N-rich
microsites composed of labile organic matter across an N fertility gradient. The
results of this experiment showed that at about the same N fertility level that
maize plants were N sufﬁcient, as indicated by the grain yield plateau, root
foraging for the microsites discontinued. Together these two experiments
demonstrate that root foraging for microsites of complex organic matter can vary
by plant species and that root foraging is not likely to occur for soil nutrients that
the plant does not need regardless of whether the microsite holds organicor
inorganic N.

' The emissions of 002 and N20 from litter in soils are controlled by several
nonlinear functional microbial responses and thus are likely to differ between
uniformly and heterogeneously distributed litter. Additionally, any alteration of C
and N cycling in response to litter aggregation will potentially affect plant
acquisition of litter-N. In chapter 3, I examined the inﬂuence of resource
heterogeneity on microbial-derived 002 and N20 emissions by manipulating
plant litter across an aggregation gradient. Within this gradient we addressed

four questions: 1) does the intensity of plant litter aggregation affect litter

170

decomposition and N20 emissions; 2) does the aggregation effect on
decomposition and N20 ﬂuxes vary with soil moisture and hence diffusional
constraints; 3) does plant litter particle size affect C02 and N20 emissions
similarly when uniformly distributed and aggregated; and 4) does the presence of
growing plants alter N20 emissions in response to litter aggregation? l
addressed these questions in two laboratory incubations and one ﬁeld
experiment.

Results showed that litter aggregation temporarily delays litter
decomposition by 5 to 7 days regardless of whether the litter is ﬁnely ground or
cropped into ~ 5 mm long pieces. By incubating the litter under near-water-
saturated conditions (80% WFPS), l was able to infer that 02 supply to the
interior of the litter aggregates was likely limiting the initial decomposition of the
aggregated litter relative to the uniform litter distribution. The most signiﬁcant
ﬁnding of these experiments showed that litter aggregation stimulated N20
emissions by an average of 7 fold relative to the uniform distribution.

The microbial responses to litter aggregation suggests that N availability
for plant uptake and productivity may be negatively affected by litter aggregation.
In Chapter 4, l employed two litter aggregation gradients to address these
questions: 1) does the aggregation of plant litter inﬂuence the growth of an
individual maize plant in an N-limited system; 2) is the root to shoot ratio and
belowground C allocation altered by resource aggregation, and 3) does the
distribution of aggregated resources inﬂuence maize productivity? l addressed

these questions in two ﬁeld experiments.

171

First, maize productivity was stimulated by the aggregation of red clover
litter in both experiments. Whereas cereal rye litter aggregation did not affect
maize productivity. The litter distribution did not inﬂuence root to shoot biomass
allocation of maize; however, the root-induced soil respiration was decreased as
a result of litter aggregation. The changes in belowground C allocation appeared
to precede changes in the aboveground and belowground biomass. I suggest
that this extra expenditure of plant C may have caused the plants growing in soil
with the uniform litter distribution to be smaller at the end of the season.

Second, to understand how litter aggregation alters plant N acquisition
and N retention in soils l distributed 15N-labelled red clover litter across an
aggregation gradient in soil and followed the fate of the litter-N into the plants,
soil inorganic N pools, soil organic N pools, and leachate N. With this approach I
addressed three speciﬁc questions: 1) does resource aggregation inﬂuence
above and belowground plant N acquisition; 2) does resource aggregation
inﬂuence litter-derived net N mineralization; and 3) does resource aggregation
inﬂuence whole system N retention.

Results showed that litter aggregation substantially altered N cycling in
this system. The litter aggregation gradient had statistically signiﬁcant but minor
effects on total plant N content; however, plants growing in soil with aggregated
litter derived 20% more N from the litter than plants grown in soils with a
uniformly distributed litter. This difference in litter-derived plant-N is in contrast to
the greater plant available inorganic N pools observed in response to the uniform

distribution on two dates. Taken together these two observations suggest that a

172

closer spatial coupling of roots and aggregated litter allowed for greater litter-N
uptake while minimizing the risk of N03' leaching.
IMPLICATIONS

The overall results of these experiments suggest that the aggregation of
plant litter has important effects on soil microbial processes and plant productivity
that lead to signiﬁcant alterations of C and N cycling. Although, litter aggregation
had a positive effect on plant productivity it also stimulated N20 emissions, and
thus presents a tradeoff in services provide by agricultural ecosystems. These
general implications logically follow: ﬁrst, these results should inform our
experimental approaches; second, results have important implications for how
we manage ecosystem services; and third, these results can help us predict how
changes in resource distribution and availability at spatial scales of less than 1 m
will alter ecosystem level process rates in managed and natural ecosystems.

We commonly study plant-soil interactions and their inﬂuence in
ecosystem level processes'in one of two manners: 1) under homogenized soil
conditions so we can conﬁdently conclude that the factor we manipulated has a
consistent effect on the plant or soil processes across each our experimental
replicates, or 2) under in-situ conditions where many different factors are varying
at the same time. This balance between deterministic manipulation and realism
is difﬁcult and is also part of what makes ecology experimentally challenging. My
deterministic isolation of a single component of soil heterogeneity, i.e. litter
aggregation, is a small step towards explaining the inﬂuence of heterogeneity in

the environment. My results; however, suggest that even at the sub plant scale

173

homogenized environmental conditions may lead to erroneous conditions
regarding ecosystem level C and N cycling. The physical description of my
system should be translatable into a mathematical model to be used to further
explore the inﬂuence of resource aggregation on C and N cycling.

The temporal scale of my experiments was on the order of days to
growing seasons. This time scale may have important implications for my
interpretations. For example, from 40 day incubations I concluded litter
aggregation had only minor effects on decomposition; however, on the time scale
of years to decades the litter distribution may alter C storage in manners not
predicted from the ﬁrst weeks of decomposition. For instance, the distribution of
litter in soil'may have differential effects on mycorrhizal associations. I would
predict that a uniform litter distribution would promote mycorrhizal associations
because the perceptual scale at which fungal hyphae detect nutrient rich
microsites is likely smaller than that of plant roots. Increased mycorrhizal
dependence has been associated with greater C sequestration rates, so
potentially a longer term study would demonstrate that soil organic C may
increase under management to promote uniformity.

Management of agricultural inputs has become more spatially
sophisticated during the last two decades with the coupling of global positioning
systems and spatial data on soil properties. Most of this work has focused on
spatial scales of 5 m and larger. Management practices that manipulate sub
plant scale resource distribution are as old as agriculture itself. Modern day

management of crop, cover crop, and weed litters starts with the chopper box

174

inside the combine, followed by ﬂail choppers and mowers, then tillage and
planting followed by interrow cultivation. Each of these operations alters the
litters' particle size and horizontal and vertical distribution. Because so many of
these management strategies are conducted to achieve multiple objectives, it is
difﬁcult to relate any one practice to the differences yield or N20 emission that my
results suggests might be related to litter distribution. However, a ﬁrst step
towards understanding this possibility would start by increasing or decreasing the
intensity of one of these practices.

In unmanaged ecosystems, my results suggest that heterogeneity of
resource distribution may manifest itself in many ways. For example,
herbivorous insects within the same community can vary in body size by several
orders of magnitude, for example aphids and gypsy moths in oak-hickory forests.
Lets assume that we have a 1000 aphids m“2 and 5 gypsy moths rn'2 and the
biomass of each is the same. As these insects feed, their frass falls to the soil
surface as clumps in proportion to their mass body. This frass then becomes a
resource for soil organisms and potentially for the plants that the insects
originally feed upon. From my dissertation research, I would predict that the
aphid frass although of equal resource quantity to the soil microbes would be
less likely to stimulate N20 emissions than the decomposing gypsy moth frass
because smaller clumps of frass are less likely induce anaerobic conditions. 1
Furthermore, if the plants in this community varied in their root foraging for
patches of different resource quantity then the plant species may differentially

beneﬁt from the presence of one herbivore over the other through an indirect

175

interaction of their decaying frass. If we had assumed that mean resource
quantity per unit of area controlled the N20 emissions rate instead of the
distribution of that resource then we would have underestimated the N20

emissions from forests with gypsy moths and aphids versus that forest without

gypsy moths.

176

ljlllllllﬂljllllljjeIlﬂlljlgll