CARBON AND NITROGEN CYCLING IN INTENSIVE PRODUCTION SYSTEMS:
TRADE-OFFS BETWEEN PRODUCTIVITY AND SUSTAINABILITY
By
Paligwendé Nikièma

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Forestry
2010

ABSTRACT
CARBON AND NITROGEN CYCLING IN INTENSIVE PRODUCTION SYSTEMS:
TRADE-OFFS BETWEEN PRODUCTIVITY AND SUSTAINABILITY
By
Paligwendé Nikièma
A solid understanding of the impacts of agricultural management practices on soil
biology, nitrogen and carbon dynamics and net greenhouse gas (GHG) emissions is essential to
evaluating agro-ecosystem sustainability. I established a suite of experiments at the Michigan
State University’s (MSU) Tree Research Center (TRC) in East Lansing and at two different sites
in Northern Michigan to address the following three hypotheses. Hypothesis 1: Managing Fraserfir plantations with cover crops in a low-input system will increase soil organic carbon, total soil
nitrogen, microbial biomass and functional diversity as well as tree performance compared to a
high-input conventional system. Hypothesis 2: Conversion of grassland to short-rotation woody
crop (SRWC) bioenergy systems will increase nitrate leaching losses and soil emissions of
nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4), undermining the environmental
benefits of substituting biomass energy for fossil fuels. Hypothesis 3: Adding N fertilizer to a
switchgrass bioenergy system will improve the net GHG balance because enhanced CO2 fixation
will exceed direct and indirect emissions of GHGs associated with fertilizer use. To

test

the

first hypothesis, a tree-cover crop intercropping trial involving Fraser fir, two leguminous (Dutch
white clover and alfalfa) and a non-leguminous (perennial ryegrass) cover crops was conducted
at the TRC. For each cover crop, two competition-management practices were evaluated.

The approach used to test the second hypothesis was to set-up experimental plots cleared
of existing grassland vegetation, then cultivated and planted with either willow (Salix
dasyclados) or poplar (Populus nigra). I measured soil greenhouse gas (GHG) fluxes and N
leaching losses from these plots and compared these against undisturbed, reference grassland
plots. I also established a switchgrass fertilization trial to test the third hypothesis. Nitrogen
fertilization treatments included 0, 56 and 112 kg N ha

-1

applied as urea, once early in the

growing season. Direct and indirect GHG fluxes were performed and biomass yield of
switchgrass was evaluated at the end of the growing season. Results indicate that cover crop
treatments increased soil microbial N and available N by 35 to 80% and 1.5- and 2.2-fold,
respectively, relative to the conventional treatment. Tree seedling survival and growth in cover
crop treatments with strips and in the CONV plots were similar. These results demonstrate the
potential for cover crops to quickly increase plantation soil nitrogen fertility and tree growth
response. Converting grassland to SRWC resulted in 13.3 - 17.2-fold increases of N2O, an
additional loss of 3.3 - 9.0 Mg CO2 ha

-1

of CO2 emissions and an extra N leaching loss of 36 -

-1

51 kg N ha , relative to the reference pasture control plots. Grassland conversion to SRWC
systems incurred GHG debts of 9.4 and 14.2 Mg CO2eq ha

-1

for poplar and willow plots,

respectively. Overall, N fertilizer application to a switchgrass contributed little to direct GHG
emissions from soil but substantially increased aboveground biomass production which
sequestered an additional 2.6 - 9.4 Mg ha

-1

of atmospheric CO2 relative to an unfertilized field.

N fertilization of switchgrass in this region could reduce the land base needed for bioenergy
production and reduce pressure on land required for food and forage crop production.

DEDICATION

Dedicated to my
Wife Pauline Kéré,
and sons
Ives Rodrigue Nikièma,
Jonanthan Rayende Nikièma,
Bonaventure Koka Nikièma,
Wend-Lassida Magloire Nikièma

In memory of my
Father, Raogo Denis Nikièma,
Mother, Guesyaoba Rose Kaboré,
Brother, Salif Gustave Nikièma,
Father-in-law, Gabriel Kéré,
Mother-in-law, Regina Sidyandé, and
Sister-in-law, Anne Marie Kéré

iv

ACKNOWLEDGMENTS
The completion of this dissertation would not have been possible without the support and
guidance of exceptional persons and Institutions during the course of my study. I would therefore
like to make use of this opportunity to express my sincere appreciation to these persons and
Institutions.
I am very grateful to Dr. David E. Rothstein who has served as my major supervisor for
this PhD program since 2008. Dr. Rothstein has tremendously guided me, academically and
personally, throughout this program. He generously provided me with financial resources,
laboratory and field research facilities. His invaluable time, inputs, advice, commitment and
patience towards the successful conclusion of this dissertation is gratefully appreciated. I am
equally grateful to the other members of my guidance committee, Drs. Daniel Hayes, Sieglinde
Snapp and Stuart A. Grandy for their tireless guidance, insightful suggestions and critiques of
this study. It has been a blessing to have had the benefit of working under these excellent
committee members, who collectively and individually, have devoted considerable time and
resources into the successful accomplishment of this work.
This study benefited from the financial support of a number of institutions. I am most
grateful to the Fulbright Program and the Institute of International Education (IIE) for awarding
me the prestigious Fulbright fellowship during the first two year of my PhD program. Without
this fellowship, I would not have been able to start this study in fall 2006. The guidance of Mme
Yolande Kabore at the U.S.A’s Embassy in Ouagadougou, Burkina Faso has particularly been
helpful to me when applying for Fulbright Fellowship and a special thank you goes to her. The
various financial supports I received from MSU’s department of Forestry, College of Agriculture
and Natural Resources, Graduate School, Office of International Students and Scholars (OISS)
v

and Office of Diversity and Pluralism is greatly appreciated. Further acknowledgement goes to
the North Central Region - Sustainable Agriculture, Research and Education (NCR-SARE/
USDA) for research funding provided through Project Number No GNC07-080.
I would like to express my deep gratitude to the International Sponsored Student advisor,
Mary M. Gebbia-Portice from the Office of International Students and Scholars (OISS), who has
not only been a great technical advisor, but also a sincere and remarkable counselor. I will
always be grateful for her moral support and encouragement during times of crisis. I would also
like to show my sincere gratitude to two special people, Dr. Frank Fear, Senior Associate Dean
and Dr. Brenda Alston-Mills, Associate Dean and Director of the Office of Diversity and
Pluralism (ODP) for their constant and priceless support, guidance and advice at a time I needed
these most.
I also would like to thank Dr. Phu Nguyen, Ray Miller, Brad Bender and the other lab
and field crews, Emily Scott, Kevin Haynes, Jamie Berlin, Allie Esper, Ben Daly, Andy Mueller
and Christian Kapp for their invaluable role in my data collection and analysis. Part of this work
has been conducted under the supervision of Dr. Nzokou Pascal and his lab which is highly
acknowledged. I would also like to acknowledge the help of Paul Bloese and Randy Klevikas at
the MSU’s Tree Research Center (TRC) for their continuous help during the field work I
conducted at the TRC. I am equally indebted to Mr. Barry Bahrman, the landowner at Skandia,
for his collaboration and providing his land to conduct part of this study.
Finally, I need to thank my wife Pauline and my children for sharing difficult moments
with me during my academic program. They firmly stood behind me, providing a lot of
encouragement and moral support I needed to fearlessly pursue my dreams. Finally, I thank the
almighty God for blessing and protecting me and my family during our stay here in the U.S.A.

vi

TABLE OF CONTENTS
LIST OF TABLES ...........................................................................................................................x
LIST OF FIGURES ..................................................................................................................... xiii
LIST OF ABBREVIATIONS ...................................................................................................... xvi
CHAPTER 1
GENERAL INTRODUCTION ........................................................................................................1
1.1. INTRODUCTION.............................................................................................................2
1.2. BIBLIOGRAPHY .............................................................................................................9
CHAPTER 2
SOIL MICROBIAL BIOMASS AND COMMUNITY CATABOLIC DIVERSITY AS
AFFACTED BY INTERCROPPING COVER CROPS WITH FRASER FIR (ABIES
FRASERI [PURSH] POIR) IN A CHRISTMAS TREE PLANTATION IN MICHIGAN,
U.S.A..............................................................................................................................................10
2.1. ABSTRACT ....................................................................................................................11
2.2. INTRODUCTION...........................................................................................................12
2.3. MATERIALS AND METHODS ....................................................................................15
2.3.1. Site description.........................................................................................................15
2.3.2. Plant materials, experimental design and management ...........................................17
2.3.3. Soil sampling and analysis .......................................................................................18
2.3.4. Microbial biomass analysis ......................................................................................19
2.3.5. Microbial community-level physiological profile analysis .....................................19
2.3.6. Data analysis ............................................................................................................21
2.4. RESULTS AND DISCUSSION .....................................................................................23
2.4.1. Soil properties ..........................................................................................................23
2.4.2. Soil microbial biomass and quotients ......................................................................23
2.4.3. Community-level physiological profile of soil microbial communities ..................28
2.5. CONCLUSIONS .............................................................................................................37
2.6. BIBLIOGRAPHY ...........................................................................................................39
CHAPTER 3
EFFECTS OF GROUNDCOVER MANAGEMENT ON SOIL PROPERTIES, TREE
PHYSIOLOGY, FOLIAR CHEMISTRY AND GROWTH IN A NEWLY ESTABLISHED
FRASER FIR (ABIES FRASERI [PURSH] POIR) PLANTATION IN MICHIGAN, U.S.A. ....43
3.1. ABSTRACT ....................................................................................................................44
3.2. INTRODUCTION...........................................................................................................45
3.3. MATERIALS AND METHODS ....................................................................................48
3.3.1. Location ...................................................................................................................48
3.3.2. Experimental design and plot layout........................................................................51
3.3.3. Plant materials and management .............................................................................52
3.3.4. Seedling growth and physiology..............................................................................52
vii

3.3.5. Soil and plant sampling and nutrient analysis..........................................................53
3.3.6. Statistical analysis ....................................................................................................55
3.4. RESULTS........................................................................................................................56
3.4.1. Cover crop establishment, biomass yield and foliar nutrient concentration ............56
3.4.2. Soil extractable nutrients..........................................................................................59
3.4.3. Seedling survival ......................................................................................................62
3.4.4. Relative diameter and height growth of seedlings ...................................................64
3.4.5. Seedling chlorophyll fluorescence and stem water potential ...................................66
3.4.6. Seedling foliar chemistry .........................................................................................69
3.5. DISCUSSION .................................................................................................................71
3.6. CONCLUSIONS .............................................................................................................76
3.7. BIBLIOGRAPHY ...........................................................................................................79
CHAPTER 4
GREENHOUSE GAS EMISSIONS AND NITROGEN LEACHING ASSOCIATED WITH
CONVERSION OF GRASSLAND TO SHORT-ROTATION WOODY BIOMASS CROPS
IN NORTHERN MICHIGAN, U.S.A. ..........................................................................................82
4.1
ABSTRACT ....................................................................................................................83
4.2. INTRODUCTION...........................................................................................................85
4.3. MATERIALS AND METHODS ....................................................................................88
4.3.1. Study site characteristics ..........................................................................................88
4.3.2. Experimental set-up .................................................................................................90
4.3.3. Measurement of GHG fluxes ...................................................................................91
4.3.4. Measurements of Nitrogen leaching potential .........................................................92
4.3.5. Nitrification Potential...............................................................................................93
4.3.6. Ancillary data ...........................................................................................................94
4.3.7. Nitrogen leaching and GHG flux calculations .........................................................94
4.3.8. Data analysis ............................................................................................................97
4.4. RESULTS........................................................................................................................98
4.4.1. Soil chemical properties ...........................................................................................98
4.4.2. Environmental variables ..........................................................................................98
4.4.3. Soil nitrogen availability ........................................................................................101
4.4.4. Nitrogen leaching ...................................................................................................105
4.4.5. Carbon dioxide fluxes ............................................................................................109
4.4.6. Nitrous oxide fluxes ...............................................................................................109
4.4.7. Methane fluxes .......................................................................................................112
4.4.8. Global warming potential and payback period ......................................................112
4.5. DISCUSSION ...............................................................................................................117
4.6. CONCLUSION .............................................................................................................127
4.7. BIBLIOGRAPHY .........................................................................................................129

viii

CHAPTER 5
NITROGEN FERTILIZATION OF SWITCHGRASS IN NORTHERN MICHIGAN, U.S.A.
INCREASES BIOMASS YIELD AND IMPROVES NET GREENHOUSE GAS BALANCE
......................................................................................................................................................135
5.1. ABSTRACT ..................................................................................................................136
5.2. INTRODUCTION.........................................................................................................137
5.3. MATERIALS AND METHODS ..................................................................................140
5.3.1. Site description and experimental set-up ...............................................................140
5.3.2. Field measurements of direct GHG emissions.......................................................141
5.3.3. Soil sampling and analyses ....................................................................................142
5.3.4. Plant sampling and analyses ..................................................................................143
5.3.5. Indirect GHG emissions and C sequestration ........................................................143
5.3.6. Greenhouse gas flux calculations...........................................................................145
5.3.7. Data analysis ..........................................................................................................145
5.4. RESULTS......................................................................................................................146
5.4.1. Soil C, N, pH, WFPS and temperature ..................................................................146
5.4.2. Soil nitrogen availability ........................................................................................148
5.4.3. Biomass yield and nutrient concentrations ............................................................150
5.4.4. Greenhouse gas flux and global warming potential ...............................................150
5.5. DISCUSSION ...............................................................................................................158
5.6. CONCLUSIONS ...........................................................................................................163
5.7. BIBLIOGRAPHY .........................................................................................................165
CHAPTER 6
SUMMARY AND CONCLUSIONS ..........................................................................................169
APPENDICES .............................................................................................................................173

ix

LIST OF TABLES
Table 2.1

Physical and chemical characteristics of the topsoil (0–15 cm) of the study site ..16

Table 2.2

Microbial Biomass quotients (SMB-C:Corg and SMB-N:Norg) measured at 015, 15-30 and 30-45 cm soil depth for each groundcover management treatment.
†
Treatments are: Conventionally managed (CONV), Dutch white clover with
strips (DWCS), Dutch white clover with no strips (DWCNS), alfalfa with strips
(ALFS), alfalfa with no strips (ALFNS), perennial ryegrass with strips (PRGS),
and perennial ryegrass with no strips (PRGNS). ( n=3) ........................................27

Table 2.3

List of all 31 C-substrates in Biolog EcoPlate , classified by category of
carbon and their relative substrate utilization in each groundcover management
treatment. †Treatments are: Conventionally managed (CONV), Dutch white
clover with strips (DWCS), Dutch white clover with no strips (DWCNS), alfalfa
with strips (ALFS), alfalfa with no strips (ALFNS), perennial ryegrass with
strips (PRGS), and perennial ryegrass with no strips (PRGNS). ( n=3) ................34

Table 3.1

Initial soil chemical characteristics (means ± se; n=3) at three soil depths of the
experimental site, Michigan State University Tree Research Center, East
Lansing, Michigan, US.A. .....................................................................................49

Table 3.2

Cumulative cover crop biomass obtained after five mowing events and biomass
N, P, K, Ca and Mg concentration during the 2008 growing season with
different cover crops for a Fraser fir Christmas tree plantation in Michigan,
U.S.A. BDM= cover crop biomass dry matter yield, BDMN= cover crop
biomass dry matter nitrogen yield..........................................................................57

Table 3.3

Average concentration of Mehlich III extractable P, K, Ca and Mg measured in
2008 at three soil depths with different cover crops in a Fraser fir Christmas tree
plantation in Michigan, U.S.A. Values are means (±se; n=3,). Treatments are:
Conventionally managed (CONV), Dutch white clover with strips (DWCS),
Dutch white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa
with no strips (ALFNS), perennial ryegrass with strips (PRGS), and perennial
ryegrass with no strips (PRGNS). ..........................................................................60

Table 3.4

Average soil available nitrate, available N, nitrification and net N mineralization
-1
(kg ha )at three depths, from samples collected at the end of the growing
season of 2007 and 2008 with different cover crops in a Fraser fir Christmas
tree plantation in Michigan, U.S.A. Treatments are: Conventional (CONV),
Dutch white clover with strips (DWCS), Dutch white clover with no strips
(DWCNS), alfalfa with strips (ALFS), alfalfa with no strips (ALFNS), perennial
ryegrass with strips (PRGS), and perennial ryegrass with no strips (PRGNS). ....61

TM

x

Table 3.5

Relative growth of collar diameter and height of Fraser fir seedlings for the first
growing season (FS), second growing season (SS) and the two growing seasons
(TS) in a low input soil management with different cover crops for Fraser fir
Christmas tree plantation in Michigan, U.S.A. Values are means (± se; n =3).
Treatments are: Conventionally managed (CONV), Dutch white clover with
strips (DWCS), Dutch white clover with no strips (DWCNS), alfalfa with strips
(ALFS), alfalfa with no strips (ALFNS), perennial ryegrass with strips (PRGS),
and perennial ryegrass with no strips (PRGNS). ...................................................65

Table 4.1

Selected soil characteristics measured from 0-10 cm of soil depth in a long-term
grassland as well as in poplar and willow biomass bioenergy plantations in the
central Upper Peninsula of Michigan, U.S.A ........................................................99

Table 4.2

Hydrologic fluxes of N species at 50 cm soil depth in a long-term grassland as
well as in poplar and willow biomass bioenergy plantations in the central Upper
Peninsula of Michigan, U.S.A., from August 2009 to May 2010........................108

Table 4.3

Direct and indirect GHG emissions and total global warming potential estimated
for a long-term grassland as well as in poplar and willow biomass bioenergy
plantations in the central Upper Peninsula of Michigan, U.S.A. Data were
collected during one year period following land conversion ...............................114

Table 4.4

Cultivation induced-cumulative greenhouse gas flux, -global warming potential
and payback time for willow and poplar plantation establishment. Values are
estimated based on raw data of direct GHG fluxes, indirect emissions of N2O
calculated from N leaching data and assumptions of grassland root contribution
to soil CO2 fluxes (see text for full description). Payback periods are calculated
based on scenarios of minimum, average and maximum yields for SRWC in the
region ...................................................................................................................115

Table 5.1

Selected soil characteristics measured beneath switchgrass grown under three
different N fertilization regimes in the central Upper Peninsula, Michigan,
-1
U.S.A. Data were collected on July 29, 2009. Treatments are: N0= 0 kg N ha ;
-1

-1

N50 =56 kg N ha and N100 = 112 kg N ha .................................................. 151
Table 5.2

Yield and nutrient concentrations of aboveground biomass of switchgrass grown
under three N fertilization regimes in the central Upper Peninsula, Michigan,
-1
-1
U.S.A. Treatments are: N0= 0 kg N ha ; N50 =56 kg N ha and N100 = 112
-1

kg N ha ............................................................................................................. 152

xi

Table 5. 3

Total annual CO2, N2O and CH4 fluxes, cumulative GHG emissions and GWP
-1

all expressed in kg CO2eq ha (Total ± se) from switchgrass bioenergy plots
grown with three different levels of N fertilizer in the central Upper Peninsula,
-1
-1
Michigan, U.S.A. Treatments are: N0= 0 kg N ha ; N50 =56 kg N ha and
-1

N100 = 112 kg N ha ......................................................................................... 156
Table 5.4

Greenhouse gas (GHG) flows per hectare of switchgrass bioenergy field in
Northern Michigan, U.S.A. Positive figures represent CO2 emissions and
-1
negative numbers are CO2 sequestration. Treatments are: N0= 0 kg N ha ; N50
-1

-1

=56 kg N ha and N100 = 112 kg N ha .......................................................... 157
Table A.1

Table A.2

Table A.3

Description of groundcover management, competition management and cultural
management practices at the cover crop-Fraser fir intercropping system at the
MSU’s Tree Research Center ..............................................................................173
Description of cultural management practices applied at Skandia willow yield
trial during the first year of the plantation establishment at Skandia, in Upper
Peninsula of Michigan, USA ...............................................................................174
Description of cultural management practices applied at Skandia hybrid poplar
yield trial during the first year of the plantation establishment at Skandia, in
Upper Peninsula of Michigan, USA ....................................................................175

xii

LIST OF FIGURES
Figure 2.1

(a) Soil microbial biomass carbon microbial, (b) biomass nitrogen and (c)
microbial biomass C:N ratio (C) as influenced by groundcover management.
Treatments are: Conventionally managed (CONV), Dutch white clover with
strips (DWCS), Dutch white clover with no strips (DWCS), alfalfa with strips
(ALFS), alfalfa with no strips (ALFNS), perennial ryegrass with strips (PRGS),
and perennial ryegrass with no strips (PRGNS).. ..................................................24

Figure 2.2

(a) Average well color development (AWCD) and (b) average catabolic
TM
diversity obtained from Biolog EcoPlate
incubation of different groundcover
treatments in a Fraser fir plantation. Treatments are: Conventionally managed
(CONV), Dutch white clover with strips (DWCS), Dutch white clover with no
strips (DWCNS), alfalfa with strips (ALFS), alfalfa with no strips (ALFNS),
perennial ryegrass with strips (PRGS), and perennial ryegrass with no strips
(PRGNS).. ..............................................................................................................29

Figure 2.3

(a) Species richness, (b) Shannon index of diversity and (c) Evenness index as
influence by different groundcover management (October/November 2007 data)
†
NS is no strips, S is strips and CONV is conventional treatments .......................30

Figure 2.4

Principal component analysis (PCA) performed on BIOLOGTM Eco Plates from
data microbial community substrate utilization patterns of soil extracts from
different groundcover management at 120 h incubation period. †Treatments are:
Conventionally managed (CONV), Dutch white clover with strips (DWCS),
Dutch white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa
with no strips (ALFNS), perennial ryegrass with strips (PRGS), and perennial
ryegrass with no strips (PRGNS). ..........................................................................32

Figure 3.1

(a) Total monthly precipitation and (b) monthly minimum and maximum
temperatures during the 2007 and 2008 growing seasons compared with the 12year monthly average at MSU’s Horticultural Farm, East Lansing, Michigan,
U.S.A......................................................................................................................50

Figure 3.2

(a) Total aboveground biomass yield (bars) and biomass N content (lines), (b)
tissue N concentration (bars) and C:N ratio (lines) of alfalfa (black bars, black
circles and short lines), Dutch white clover (gray bars, white circles and long
dashed lines) and perennial ryegrass (white bars, down triangles and solid lines)
mowed five times in 2008 at 3 cm above the ground in a Fraser fir Christmas
tree plantation in Michigan, U.S.A. Values are means (± se; n = 3) .....................58

xiii

Figure 3.3

Tree survival in 2007 and 2008 with different cover crops in a Fraser fir
Christmas tree plantation in Michigan, U.S.A. Treatments are: Conventionally
managed (CONV), Dutch white clover with strips (DWCS), Dutch white clover
with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with no strips
(ALFNS), perennial ryegrass with strips (PRGS), and perennial ryegrass with
no strips (PRGNS). Values are treatment means (± se; n = 3). .............................63

Figure 3.4

Average maximum quantum yield (Fv/Fm) measured in 2007 and 2008 with
different cover crops in a Fraser fir Christmas tree plantation in Michigan,
U.S.A. Mean values within each year and the same cover crop species, followed
with the same letters are not statistically different. The long dashed line
represents the threshold of Fv/Fm for healthy foliage. S, NS and CONV are
strips, no strips and conventional treatments, respectively; ns = not significant. ..67

Figure 3.5

Average stem water potential (Ψw) measured in 2007 and 2008 with different
cover crops in a Fraser fir Christmas tree plantation in Michigan, U.S.A. Mean
values within each year and the same cover crop specie, followed with the same
letters are not statistically different. The dashed line represents the threshold of
Ψw at which stomatal opening and photosynthesis in young conifers are
inhibited. S, ST and CONV are strips, no strips and conventional treatments,
respectively ............................................................................................................68

Figure 3.6

Needles (a) nitrogen, and (b) phosphorus concentrations of Fraser fir seedling in
2007 (black bars) and 2008 (white bars) with different cover crops for a Fraser
fir Christmas tree plantation in Michigan, U.S.A. Treatments are:
Conventionally managed (CONV), Dutch white clover with strips (DWCS),
Dutch white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa
with no strips (ALFNS), perennial ryegrass with strips (PRGS), and perennial
ryegrass with no strips (PRGNS).Values are means (±se; n=3). ...........................70

Figure 4.1

Experimental site location in Michigan, U.S.A.”For interpretation to color in
this and all other figures, the reader is referred to the electronic version of this
dissertation.” ..........................................................................................................89

Figure 4.2

(a) Precipitation and average air temperature, (b) soil water filled pore space and
(c) soil temperature measured from a poplar and willow biomass bioenergy
plantations and adjacent grassland during the first year of a long term grassland
conversion to bioenergy systems at Skandia in the Upper Peninsula of
Michigan, USA. Black solid, dashed dot-dotted and long dashed arrows indicate
dates for field application of herbicide, plowing and planting respectively ........100

xiv

Figure 4.3

(a) Soil ammonium concentration, (b) nitrification rates and (c) nitrate
concentration (KCL extraction) measured from 0-10 cm soil depth in the
undisturbed grassland and in the poplar and willow biomass plantations prior
and after cultivation at Skandia in the Upper Peninsula of Michigan, USA.
Black solid, dashed dot-dotted and long dashed arrows indicate dates for field
application of herbicide, plowing and planting respectively ...............................102

Figure 4.4

Concentrations of (a) NH4 -N, (b) NO3 -N and (c) dissolved organic N in
leachate water collected at 50 cm below the soil surface measured in the
undisturbed grassland and in the poplar and willow biomass plantations after
cultivation at Skandia in the Upper Peninsula of Michigan, USA. Black solid,
dashed dot-dotted and long dashed arrows indicate dates for field application of
herbicide, plowing and planting respectively. .....................................................106

Figure 4.5

Fluxes of (a) CO2, (b) N2O and (c) CH4 from soil measured in the undisturbed
grassland and in the poplar and willow biomass plantations after cultivation at
Skandia in the Upper Peninsula of Michigan, USA. Black solid, dashed dotdotted and long dashed arrows indicate dates for field application of herbicide,
plowing and planting, respectively. .....................................................................111

Figure 5.1

Precipitation and average air temperature (a), soil water filled pore space (b) and
soil temperature (c) measured from switchgrass bioenergy yield plots, grown
under three different N fertilization regimes, at Chatham, Upper Peninsula in
Michigan, U.S.A. Black solid indicates the date for field application of Nfertilizer. The black, long dashed arrow indicates date of N fertilizer application
and the solid arrow indicates date of biomass harvest. ........................................147

Figure 5.2

Soil ammonium (a), nitrate (b) and total mineral N (c) concentrations measured
from 0-10 cm soil depth in switchgrass bioenergy yield plots, grown under three
different N fertilization regimes, at Chatham, Upper Peninsula in Michigan,
U.S.A. Black solid indicates date for field application of N-fertilizer. Black,
long dashed arrows indicate date of N fertilizer application and the solid arrow
indicates date of biomass harvest. ........................................................................149

Figure 5.3

Fluxes of CO2 (a), N2O (b) and CH4 (c) from soil, measured in switchgrass
bioenergy yield plots, grown under three different N fertilization regimes, at
Chatham, Upper Peninsula in Michigan, U.S.A. Black solid indicates date for
field application of N-fertilizer. Black, long dashed arrows indicate date of N
fertilizer application and the solid arrow indicates date of biomass harvest .......153

+

-

xv

LIST OF ABBREVIATIONS
ALFNS

: Alfalfa with no strips

ALFS

: Alfalfa with strips

ANOVA

: Analysis of variance

AWCD

: Average well color development

BN

: Biomass nitrogen

BY

: Biomass yield

CEC

: Cation exchange capacity

CLPP

: Community-level physiological profiling

CONV

: Conventional

DM

: Dry matter

DON

: Dissolved organic nitrogen

DWCNS

: Dutch white clover with no strips

DWCS

: Dutch white clover with strips

FBIC

: Forest Biomass Innovation Center

FS

: First growing season

GC

: Gas chromatography

GHG

: Greenhouse gas

GREET

: Greenhouse gases, Regulated Emissions, and Energy use in Transportation

GWP

: Global warming potential

ICP-AES

: Inductively coupled plasma-atomic emission spectrometry

IPCC

: Intergovernmental panel on climate change

KBS

: Kellogg Biological Station
xvi

LCA

: Life Cycle Assessment

MSU

: Michigan State University

NCR-SARE

: North Central Region-Sustainable Research Education

NT

: No strip

OD

: Optical density

PC

: Principal component

PRGNS

: Perennial ryegrass with no strips

PRGS

: Perennial ryegrass with strips

PVC

: Polyvinyl chloride

RC

: Root contribution to soil respiration

RGR

: Relative growth rate

S

: Strip

SMB

: Soil microbial biomass

SOC

: Soil organic carbon

SOM

: Soil organic matter

SRWC

: Short rotation woody crop

SS

: Second growing season

TKN

: Total Kjeldahl nitrogen

TRC

: Tree Research Center

TS

: Two growing seasons

WFPS

: Water filled pore space

xvii

CHAPTER 1

GENERAL INTRODUCTION

1

1.1.

INTRODUCTION

Achieving greater agricultural productivity without degrading land resources,
environmental quality and ecosystem services has been a major challenge for the scientific
community because of the interconnection and potential tradeoffs between productivity and
resource conservation. Agricultural systems are essentially ecological systems which are
profoundly modified and managed by mankind (e.g. cultivation, fertilizers, and herbicides) to
produce food and fiber, generally with the aim of increasing their productivity. Whereas
agricultural intensification has the potential to achieve maximum crop productivity, impacts of
agricultural management can alter ecosystem processes and services that support sustainability.
Important areas of resource degradation arising from intensive management include: soil
carbon (C) loss through accelerated soil organic matter (SOM) decomposition, groundwater
contamination and surface water eutrophication through nitrate leaching and runoff, biodiversity
decline, and enhanced greenhouse gas (GHG) emissions. Because the degradation of some of
these environmental resources and processes may not be reversible and jeopardize functioning of
the whole system, finding agricultural management options (e.g. low inputs) with the potential of
keeping these trade-offs within reasonable limits appears necessary. To this end, studies on the
impacts of agricultural management practices on ecosystem processes and services could be an
important step towards finding strategies for sustainable land-use. Here, I focused my
investigations on the response of soil microbial community metabolism, pools and fluxes of
carbon and nitrogen, and GHG emissions to agricultural management practices such as tillage, N
fertilization and cover cropping. I addressed the issues separately in three different intensive
production systems: Christmas tree farming, short-rotation woody biomass plantations and
perennial switchgrass bioenergy production systems.
2

Whereas use of anthropogenic inputs of reactive N and intensive tillage can potentially
increase crop production, their inefficient use can lead to a number of problems. For instance,
these agricultural management practices sometimes lead to accelerated soil erosion and depletion
of soil organic matter, pollution as a result of runoff, lower efficiency of nutrient use and lower
profitability. On the other hand, management practices such as cover cropping reduce soil
erosion, add organic matter to the soil, conserve soil humus, improve soil aeration and structure,
and improve soil nutrient status (Broughton 1977). Organic matter in turn acts as substrate for a
variety of soil organisms that immobilize excess nutrients, mineralize them and make them
available to plants as they are needed.
While the potential benefits of incorporating cover crops into cropping systems has been
widely demonstrated for many agronomic, vegetable and orchard crops, to my knowledge,
studies have not yet been conducted to evaluate the potential beneficial contribution of cover
crops to Christmas tree production, which cover approximately 150,000 ha of land in production
in the United States (Vilsack and Clark 2009) and represent one segment of the forestry sector
that uses substantial fertilizer inputs comparable to agronomic systems (Rothstein, 2005). In
chapter 2, Soil microbial biomass and community catabolic diversity as affected by intercropping
cover crops with Fraser fir (Abies fraseri [Pursh] Poir) – in Christmas tree plantation in
Michigan, U.S.A, I investigated the effects of cover crops on soil microbial properties in a Fraser
fir production system in Michigan. The three cover crops used in this study were two legumes
(alfalfa [Medicago sativa] and Dutch white clover [Trifolium pratense]) and a non-legume
(perennial ryegrass [Lolium perenne]). I expected that incorporating cover crops into a Christmas
tree plantation would increase the amount of organic matter returned to the soil, which is a key
driver of microbiological processes. Because legumes have the ability to fix atmospheric N,

3

through symbiotic association with N-fixing bacteria, I also predicted that the leguminous cover
crops would have more labile biomass residues and be more efficient in improving soil microbial
biomass and activity. I used the chloroform fumigation-extraction method to determine soil
microbial biomass C and N and the effectiveness of each cover crop to enhance soil microbial
biomass and quotients, and thus soil fertility (Anderson and Domsch, 1986). I also sought to
TM

measure the functional diversity of the soil microbial community using the Biolog

Eco Plate

system, which is based on the metabolic capabilities of bacteria during growth in the wells of the
TM

Biolog

micro-plates. Finally, I hypothesized that Managing Fraser-fir plantations with cover

crops in a low-input system would increase soil organic carbon, total soil nitrogen, microbial
biomass and microbial diversity compared to a high-input conventional system.
Whereas anthropogenic inputs of reactive N inputs are widely known to have a great
potential for maximizing growth and producing more marketable trees in a shorter period of time
in Christmas tree plantations, recent N studies have raised concerns about N leaching losses from
excessive and exclusive use of inorganic fertilizers in these systems (Rothstein 2005; Pedersen et
al. 2006). This shows the need to seek alternative N management practices with the potential to
improve sustainability and enhance profitability for this system. Because of their several
benefits, cover crops have been introduced successfully in many cereal and vegetable crop
production systems to address similar issues. However, to my knowledge, studies have not been
yet conducted to evaluate the potential beneficial contribution of cover crops to Christmas tree
production.
In Chapter 3, effects of groundcover management on soil properties, tree physiology,
foliar chemistry and growth in a newly established Fraser fir (Abies fraseri [Pursh] Poir)
plantation, in Michigan, U.S.A. I assess whether soil fertility, Fraser fir survival and growth
4

could be improved by incorporating leguminous and non-leguminous cover crops into the
production system. Because nitrogen input from the cover crop systems is from an organic
source which would have to gradually be decomposed through microbial processes before it can
be made available to the trees, I expected that the release of plant available nitrogen would be in
synchrony with the rate of tree uptake, leading to an improved foliar N level and growth of the
trees in the cover crop systems relative to their counterparts in the conventional system. On the
other hand, I predicted that the cover crop would strongly compete with the trees for water and
plant essential nutrients other than N that could lead to reduced growth performance unless
proper competition-management practices are introduced. To test this prediction, I used two
competition-management practices; no strips (NS) by growing each cover crop throughout the
entire plot and strips (S) by creating a 0.61 m-wide bare zone centered on the tree rows. A
conventionally-managed system was used as a control. Finally, I hypothesized that plots with
strips would result in overall better tree foliar nutrition, water use efficiency and growth relative
to the no-strip treatments. I also anticipated that trees in cover crop plots with strips would
perform as well as or even better than their counterparts in the conventional system.
The second production system of interest in these investigations is bioenergy crop
production. Environmental impacts associated with the use of fossil fuels, combined with
increasing concerns about rising fossil fuel prices, depletion of the world fossil fuel supply and
the need to enhance regional and national energy security have made the large-scale
development and use of bioenergy and bio-based products a top priority worldwide. The
potential for biofuel production and use to meet this growing renewable energy targets has been
emphasized by the Intergovernmental Panel on Climate Change (IPCC 1997). Second-generation
cellulosic feedstocks such as perennial grasses (e.g. switchgrass) and wood crops (e.g., poplar

5

and willow) have been suggested as a more sustainable alternative than first generation standbased (e.g. Maize) feedstocks. However, the use of energy from biomass does not necessarily
mean that their production is sustainable. For instance, there are still knowledge gaps, especially
in terms of immediate direct impacts of agricultural management such as land clearing and
cultivating as well as N fertilization for bioenergy crop production on the system net greenhouse
gas (GHG) balance.
In Chapter 4, greenhouse gas emissions and N leaching associated with conversion of
grassland to short-rotation woody biomass crops in northern Michigan; U.S.A. I tested the
assumption that conversion of grassland land to short-rotation woody bioenergy systems would
increase nitrate leaching to ground water and soil emissions of nitrous oxide (N2O), carbon
dioxide (CO2) and methane (CH4) due to increased SOM mineralization. I therefore expected
that this land-use change would incur some levels of GHG “debt” that would undermine the
potential environmental benefits of substituting biomass energy with fossil fuels and require
years for the bioenergy system to payback this carbon debt. In this study, I sought to answer
these two fundamental questions: (i) what is the magnitude of GHG emissions and “debt”
associated with grassland conversion to SRWC systems? and (ii) what is the “payback period,”
the time required for the SRWC systems to overcome this GHG debt and begin providing net
GHG benefits? Based on a set of assumptions regarding the contribution of root respiration to
total CO2 efflux and the yield potential of these bioenergy crops, I made an attempt to estimated
different levels of GHG debt and the corresponding payback times.
In addition to tillage, the impacts of N fertilizer application on biomass yield and the
magnitude of GHG released from agricultural soils have been the subject of a number of

6

investigations in recent years. However, most of these studies have focused on agronomic crops
such as corn, wheat and potatoes which are not only managed differently but also may have
different N requirements than bioenergy crops (Grant et al. 2006; Zebarth et al. 2008). In Chapter
5, Nitrogen fertilization of switchgrass in northern Michigan, U.S.A increases biomass yield and
improves net greenhouse gas balance, I attempted to determine aboveground biomass production
and GHG emission responses to inorganic N fertilizer application for switchgrass. I hypothesized
that added fertilizer input would increase switchgrass production which would in turn allow
offsetting any GHG emissions associated with switchgrass production. The overall goal of this
study was to determine the correlation between N addition, and net GHG benefits in a perennial
switchgrass system.

7

BIBLIOGRAPHY

8

1.2.

BIBLIOGRAPHY

Anderson T.H., Domsch K.H. (1986). Carbon link between microbial biomass and soil organic
matter. Pages 467–471 in F. Megusar and M. Gantar, editors. Perspectives in microbial ecology.
Proceedings of the Fourth International Symposium on Microbial Ecology, Ljubljana, 24–29
August 1986. Slov. Soc. for Microbiol., Ljubljana, Yugoslavia
Broughton W.J. (1977) The effect of various covers on soil fertility under Hevea brasiliansis
(Muell. Arg.) and on growth of the tree. Agro-Ecosyst. 3: 147-170
Grant R.F., Pattey E., Goddard T.W., Krysanowski L.M., Puurveen H. (2006) Modeling the
effects of fertilizer application rate on nitrous oxide emissions. Soil Science of America 70:235248
IPCC (1997) Intergovernmental Panel on Climate Change/Organization for Economic
Cooperation and Development; Guidelines for National Greenhouse Gas Inventories
OECD/OCDE, Paris
Pedersen L.B., Christensen C.J. Ingerslev M (2006) Leaching versus input of nitrogen, potassium
and magnesium in different fertilizer regimes in Christmas tree stands of Abies nordmanniana in
Denmark. Scand. J. For. Res.: 21(2): 130-142
Rothstein E.D. (2005) Nitrogen Management in a Fraser Fir (Abies fraseri [Pursh] Poir.)
Christmas Tree Plantation: Effects of Fertilization on Tree Performance and Nitrogen Leaching.
For. Sci. 51(2):175-185
Vilsack T., Clark C.Z.F. (2009) 2007 census of agriculture-United States-Summary and state
data, Volume 1, Geography Area Series, Part 51, 739p
Zebarth B.J., Rochette P., Burton D.L., Price M. (2008) Effect of fertilizer nitrogen management
on N2O emissions in commercial corn fields; Can. J. Soil Sci. 88: 189-195

9

CHAPTER 2

SOIL MICROBIAL BIOMASS AND COMMUNITY CATABOLIC
DIVERSITY AS AFFECTED BY INTERCROPPING COVER CROPS
WITH FRASER FIR (ABIES FRASERI [PURSH] POIR) – IN A
CHRISTMAS TREE PLANTATION IN MICHIGAN, U.S.A.

10

2.1.

ABSTRACT

Intercropping cover crops with tree crops may have important effects on soil productive
capacity through their influence on soil organic matter and biology. I investigated soil microbial
biomass carbon (SMB-C) and nitrogen (SMB-N) as well as microbial community-level
physiological profiling (CLPP) in an intercropping system involving Fraser fir, two leguminous
(Dutch white clover and alfalfa) and a non-leguminous (perennial ryegrass) cover crops. For
each cover crop, two competition-management practices were evaluated: no strips (cover crop
grown in continuous patch) and with strips (maintenance of bare ground strips centered on the
tree rows). A conventionally-managed plot was used as a control. SMB-C, SMB-N and soil
organic carbon (SOC) and total nitrogen (TKN) levels were assessed at the 0-15, 15-30 and 3035 cm soil depths and CLPP at the 0-15 cm soil depth. At the 0-15cm depth, cover crop
treatments increased SMB-C by 20-50% relative to the conventional plots; whereas changes in
SMB-N ranged between 35 to 80% higher in the cover crop treatments than the CONV plots.
Values for both SMB-C and SMB-N data followed this order: Legume cover crops > nonlegume cover crop > conventional. Groundcover treatments significantly increased both soil
SMB-C: SOC ratio (P<0.001) and SMB-N: TKN ratio (P<0.01) at the surface soil layer (0-15
cm), whereas no significant differences were recorded among treatments at the deeper soil
layers. The legumes also increased microbial catabolic diversity compared to the conventional
treatment. Cover crop treatment with strips had little effects on soil microbial biomass and
diversity relative to the no strip treatments. These results suggest that cover cropping with proper
management can provide a larger, more active, and more metabolically diverse soil microbial
community and be an alternative approach to sustainable tree production.

11

2.2.

INTRODUCTION

In recent years, there has been increasing interest in the use of management practices that
maintain soil productivity and environmental quality while improving farm profitability
(Baumann et al. 2001). Intercropping legume and/or non-legume cover crops is a practice that
has been widely investigated and promoted as a practical way to achieve sustainable production
(Walsh et al. 1996). Cover crops reduce soil erosion, add organic matter to the soil, conserve soil
humus, improve soil aeration and structure, and improve soil nutrient status (Broughton 1977).
Because of these benefits, cover cropping is increasingly becoming a common practice in cereal
and vegetable crop production systems. However, to my knowledge, studies have not yet been
conducted to evaluate the potential beneficial contribution of cover crops to Christmas tree
production, which cover approximately 150,000 ha of land in production in the United States
(Vilsack and Clark 2009) and represent one segment of the forestry sector that uses substantial
fertilizer inputs comparable to agronomic systems (Rothstein 2005).
Soil management in cover cropping systems generally involves the use of mowed, tilled
or killed cover crops, to increase soil organic matter levels and steadily release available
nutrients to the crop plants as the organic matter breaks down (Sanchez et al. 2007). In this
process, the action of soil organisms is a major determinant of nutrient cycling rates and plant
growth. Planting either grass or legume cover crops in the interspaces of plantations increases
plant residue inputs to soils (Dinesh et al. 2004) and therefore may stimulate soil microbial
activity and increase mineralizable C and N (Mendes et al. 1999). Moreover, many studies have
reported plant-induced quantitative and qualitative variations in nitrogen and carbon flow to the
soil as different plant species may maintain a different microbial biomass and activity (Drury et
al. 1991; Haynes and Francis 1993; Groffman et al. 1996; Chantigny et al. 1997; Angers and
12

Caron 1998; Mullen et al. 1998). It is also well documented that farming systems and
management practices greatly influence microbial populations and activities in soil (Bossio et al.
1998; Mendes et al., 1999).
Soil microbial biomass and activity respond rapidly to changes in agronomic practices
and other disturbances (Kennedy and Papendick 1995; Cong et al. 2006; Powlson et al. 1987;
Lundquist et al. 1999), and have been used to ascertain early changes in soil fertility due to
different soil management practices (Doran and Zeiss 2000; Wang and Wang 2008). In fact,
changes in microbial activity and in the ratio of soil microbial biomass to soil organic matter
(soil microbial biomass quotient) are often used as rapid indicators of changes in soil organic
matter content and soil fertility (Ocio et al. 1991). To date, many studies have focused on the
effects of cover crops on soil properties and plant productivity in several agronomic crop
production systems, including vegetable, cereal crops and orchards (Sainju and Singh 1997;
Macdonald et al. 2005; Lehmann et al. 2000; Hanninen 2002; Sanchez et al. 2007). However,
very little information is available concerning the effects of cover crops and management
practices on soil microbial biomass and activity in Christmas tree plantations.
This study was part of a research project designed to investigate the impact of cover
crops on nutrient cycling, soil and water quality and Fraser fir (Abies fraseri [Pursh] Poir)
performance. The objective of the present study was to examine the effects of various
groundcover management practices on soil microbial properties in a Fraser fir production system
in Michigan. Incorporating cover crops into the plantation is expected to increase the amount of
organic matter returned and diversity of metabolic substrates to the soil that are key drivers of
microbiological processes. Consequently, I hypothesized that the overall site chemical and
biological conditions, particularly soil organic carbon and total nitrogen as well as soil microbial

13

biomass and catabolic diversity would be increased in cover crop-Fraser fir intercropping plots
relative to a conventional production system. Additionally, I anticipated that, because plant
residues from leguminous cover crops are more labile (having higher N content and smaller C:N
ratios) than those of non-leguminous cover crops, plots with N-fixing cover crops would yield
significantly higher soil microbial biomass and catabolic diversity than those with a non-N-fixing
cover crop.

14

2.3.

MATERIALS AND METHODS

2.3.1. Site description
A field experiment was established in the spring of 2007 at the Tree Research Center
(TRC) (42.67ºN, 84.46ºW) on the campus of Michigan State University (MSU) in East Lansing,
Michigan. The local climate is characterized by mean temperature of 15.5 °C and -6.6 °C during
summer and winter periods, respectively. Annual mean precipitation is 853 mm with rainfall
distributed fairly evenly throughout the year. Soil at this site is classified as a fine-loamy, mixed,
active, mesic Aquic Glossudalf (USDA/NRCS-MAES, 1992). The general soil chemical
-1

characteristics of the site are: 35% silt + clay, pH 5.6 and 13 cmol kg CEC (Rothstein 2005).
Soil total C and N, inorganic N and Mehlich III extractable nutrients (P, K, Ca and Mg),
measured at the initiation of the plantation establishment are provided in Table 2.1. The site had
been used for crop cultivation, primarily maize with occasional rotations of wheat and soybeans,
for at least the past 30-40 years. The experiment was located in a fenced area to prevent the
impact of deer browsing.

15

Table 2.1

Physical and chemical characteristics of the topsoil (0–15 cm) of the study site

Soil Characteristics

Values

a

Sand (%)

65
a

Silt + Clay (%)

35
a

pH (soil/water ratio of 1:1)

7.24
b

Soil organic C concentration (%)

1.96 ±0.84

b

Soil total N concentration(%)

0.16 ±0.08

-1 b

Mehlich-3 (mg kg )

35.46±4.38

-1 a

Exch. K (mg kg )
a
b

403.91±31.64

Values are obtained from a previous study (Rothstein 2005)
Samples were taken in May 2007, at tree planting and values are means (±se; n = 21)

16

2.3.2.

Plant materials, experimental design and management
Fraser fir transplants (plug + 2) were obtained from a local commercial nursery (Peterson's

Riverview Nursery), and machine-planted (Whitfield planter) at a spacing of 1.8 × 1.8 m. Fraser
fir seedlings were planted into a chisel-plowed and dragged field soil on 8 May 2007. Seeds of
common Dutch white clover, alfalfa (SS 100 brand) and perennial ryegrass (VNS) were
purchased from Michigan State Seeds (Grand Ledge, Michigan, U.S.A.) and hand-seeded on 22
May 2007. The seeding rates used were 28 kg ha

−1

−1

for clover and alfalfa, and 16 kg ha

for

perennial ryegrass.
The experiment was established in a randomized complete block design with three
replications in a field measuring 32.4 m × 50.4 m. Blocks and experimental plots were 10.8 m ×
50.4 m and 7.2 m × 10.8 m, respectively. Each rectangular plot contained a total of 35 trees (5 ×
7 trees). Trees in border rows were used as buffers and not included in measurements, therefore
restricting data collection to the remaining 15 interior trees in each plot. Each block had two
plots per cover crop, managed either with no strips (NS) or with strips (S). The NS treatments
consisted of growing each cover crop continuously over the entire plot. In contrast, in the S
treatments, the assigned cover crop was intercropped between the tree rows while maintaining,
through glyphosate application, a clear strip of 0.61 m wide, centered on the tree row. Control
plots were managed conventionally (CONV); they contained no cover crop and weeds were
−1

completely controlled with glyphosate (active ingredient concentration =1.1kg ha ). Thus, the
treatments were as follows: conventional system (CONV), Dutch white clover with strips
(DWCS), Dutch white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with
no strips (ALFNS), perennial ryegrass with strips (PRGS) and perennial ryegrass with no strips
(PRGNS).
17

No N fertilizer was applied to any plots in 2007 as it may injure first-year seedling roots
-1

(Koelling, 2002). However, during the second growing season, 16 g N per tree (50 kg N ha ) as
ammonium sulfate [(NH4)2SO4] was applied one time on 13 May 2008 to the control plots but
not to the cover crop plots. The cover crop plots were not fertilized. Once the cover crops were
fully established, mechanical mowing was performed at 3 cm above the ground every three to
four weeks in 2007 (2 July, 26 July, 21 August and 18 September) and 2008 (27 May, 24 June,
17 July, 14 August, and 11 September) to control cover crop growth, minimize the competition
with the trees, and add green manure to the soil surface. Glyphosate was sprayed twice during
each growing season (2007 and 2008) to control weeds in the CONV plots and within the tree
rows in the S plots. The whole field was protected with an electric fence to prevent deer
browsing. A description of treatments and management practices applied in 2007 and 2008 can
be found in Appendix –Table A.1.
2.3.3.

Soil sampling and analysis
Fifteen randomly-selected soil sub-samples per plot were collected with a 2.5 cm diameter

corer and composited into one sample per plot. Soil samples were collected in mid-October of
2007 and 2008, corresponding to the end of the first and second growing seasons, respectively.
Of the 15 soil cores collected per plot, in the cover crop plots with strips, nine were taken within
the cover crop zone and 6 within the bare ground zone, proportional to the area of strip zone. Soil
was collected from 0-15, 15-30, and 30-45 cm depths. Samples were placed in double plastic ziplock bags, securely tied, and kept on ice in a cooler before transporting them to the laboratory at
Michigan State University for analysis. Soil moisture content was determined gravimetrically on
oven-dried samples (105ºC for 48hrs). Soil pH (soil:water ratio of 1:1) values were measured

18

from the air-dried soil (passed through a 2 mm sieve) with a HORIBA pH/COND meter (model
D-54; Spectrum Technologies, Inc. Japan). Soil organic C (SOC) and total N (TN) of soil
samples were determined by combustion with an elemental analyzer (Model ECS 4010,
COSTECH Analytical, Valencia, CA).
2.3.4.

Microbial biomass analysis
Soil samples collected in 2007 were used for microbial biomass determination. Soil

microbial biomass C (SMB-C) and N (SMB-N) were assessed from these samples by the
chloroform fumigation-extraction method described by Brookes et al. (1985) and Beck et al.
(1997). Soil solutions obtained from the fumigated and non-fumigated samples were analyzed
for total dissolved C and N by oxidative combustion-infrared analysis and oxidative combustionchemiluminescence, respectively (Shimadzu models TOC-VCPN analyzer and TNM-1 unit,
Kyoto, Japan). Microbial biomass C and N were calculated as the difference between C and N in
the fumigated and non-fumigated samples using 0.45 as a correction factor for SMB-C and 0.54
as a correction factor for SMB-N (Brookes et al. 1985; Beck et al. 1997). Soil microbial
quotients, the ratios of soil microbial biomass to soil organic C or N, were calculated as SMB-C:
SOC and SMB-N: TN.
2.3.5.

Microbial community-level physiological profile analysis
The functional diversity of the soil microbial community was measured on soil samples (0TM

15 cm) collected in 2007, using the Biolog

Eco Plate system (Biolog Inc., Hayward, CA,

USA). The Biolog approach is based on relative changes in carbon source utilization. The Biolog
micro-plates were chosen as they contain 31 of the most useful carbon sources for community
analysis of mixed cultures. Substrate-utilization patterns of the soil microbial population were

19

determined by a procedure adapted from Garland and Mills (1991). Ten grams of field-moist soil
were shaken with 90 ml of a sterilized saline solution (0.85% NaCl, w/v) for 60 min and then
3

brought to 10 final dilutions. A 150 µL aliquot was inoculated into each micro-plate well. The
plates were kept at a constant temperature (25 °C) in the dark. The absorbance of the content of
each well at 595 nm was measured at 0, 24, 96, 120, 168, and 240 h using an automated plate
reader (Dynatech, MR-7000, Dynatech Laboratories - U.S.A.). Readings of the plates at day 0
and readings generated from the control wells were subtracted from subsequent readings to
eliminate background color generated by the substrates and the bacterial suspension.
Microbial activity in each micro-plate, expressed as average well color development
(AWCD), was calculated for each sample at each time point by dividing the sum of the optical
density data by 31 (number of substrates). I used an optical density (OD) of 0.25 as the threshold
for a positive response (Garland 1997) to calculate richness (R), or the total number of oxidized
C substrates, a Shannon-Weaver index (H’) of metabolic diversity and evenness of response (E).
The Shannon-Weaver index was calculated as follows:
•

H’ = -∑pi ln(pi)

Eq.(2.1)

where pi is the ratio of the activity on each substrate (ODi) to the sum of activities on all
substrates (∑ODi). Its value usually ranges from 0.4 to 4 and expresses a greater metabolic
diversity when it is close to 4 and a lower metabolic diversity when close to 0.4 (Frontier and
Pichod-Viale, 1988). Substrate evenness (E), which is a measure of the uniformity of activities
across all substrates, was calculated as follows:
•

E = H’/log(R)

Eq.(2.2)

20

where H’ is the Shannon Weaver diversity index and R is the number of different
substrates used by the community (counting all positive OD readings). E values range between 0
and 1 with lower uniformity of activity when the values are close to 0 and a greater uniformity
when the values get close to 1. Micro-plate readings measured at 0, 24, 96, 120, 168, and 240 h
of incubation were used to calculate AWCD. However, plate readings at 120 h were used to
calculate R, H’ and E since it was the shortest incubation time that allowed the best resolution
among treatments.
2.3.6.

Data analysis
Data were analyzed as a randomized complete block design using Proc Mixed in Statistical

Software Package SAS version 9.1 (SAS, 2002-2003). For soil pH, total C, total N, C:N ratio,
SMB-C and SMB-N data were analyzed taking into account the three sampling depths. The
statistical model thus included one random factor (block) and two (2) fixed factors: groundcover
treatments and depths, with the latter variable considered as repeated measurements. The
potential effect of depth on all parameters in the various ground cover treatments was initially
assessed using an analysis of variance/ covariance (ANCOVA) structure. Because the
interactions between groundcover treatments and depths on some of the parameters (e.g. total N
in soil, SMB-C and SMB-N) were statistically significant, an evaluation of treatment effects was
performed using a simple one-way analysis of variance (ANOVA) only for the 0-15 cm depth. I
used Fisher’s Least Significant Difference (LSD) test to make pair-wise comparisons of
individual treatment means. Significance for the overall treatment effects and pair-wise
comparisons was accepted at α= 0.05.
Microbial community level-physiological profiling data were analyzed using the soil
samples collected from 0-15 cm depth. The AWCD, R, H’ and E data were subjected to a one-

21

way ANOVA in SAS. The standardized OD values obtained from each of the 31 substrates for
each treatment were further analyzed using multivariate techniques (principal component
analysis in Minitab 15.1.1.0) to differentiate among microbial communities based on substrate
utilization profiles (Garland and Mills 1991; Winding 1994; Zak et al. 1994; Grayston and
Campbell 1996).

22

2.4.
2.4.1.

RESULTS AND DISCUSSION

Soil properties
There were no measurable effect of treatments on the soil total C and N concentrations as

well as soil pH (all P>0.05), and therefore, data were not presented here. The lack of treatment
differences in the present study is a common finding in most short-term C change studies. Small
gradual changes in SOM are generally difficult to detect because of high background carbon
level and natural variability of soils (Mendes et al. 1999; Brye et al. 2002).
2.4.2.

Soil microbial biomass and quotients
Relative to the CONV treatment, all cover crop treatments significantly (P<0.001)

enhanced soil microbial biomass (SMB-C and SMB-N) at the 0-15 cm depth (Figs. 2.1-a and
2.1-b). However, no statistical difference was observed among groundcover treatments for both
SMB-C and SMB-N at the deeper soil layers (15-30 and 30-45 cm). At the 0-15cm depth, cover
crop treatments increased SMB-C by 20-50% relative to the CONV plots; whereas changes in
SMB-N ranged between 35 to 80% higher in the cover crop treatments than the CONV plots.
Alfalfa and clover were more effective in improving SMB-C and SMB-N than perennial
ryegrass. Increased organic C input from cover crop biomass is probably the dominant factor
explaining the greater amount of SMB-C and SMB-N in all cover crop plots at the soil surface
layer (0-15 cm) than CONV plots. Franzluebbers et al. (1999) showed that as the total organic C
pool expands or contracts due to changes in C inputs to soil, the microbial pool also expands or
contracts. A continuous uniform supply of C from crop residues serves as an energy source for
microorganisms (Govaerts et al. 2007; Campbell et al. 1997). By mowing cover crops and
leaving residues on the soil, organic carbon accumulates in the topsoil, and microbial substrates
of different quality and quantity are made available (Govaerts et al. 2007).
23

- 1 S MB - C ( m g C k g -1
)
)
S MB - N ( m g N k g
C : N -r a t i o

700

(a)

a a
a b

600

c c

500

d
ns

400
300

ns

200
100
0
110
100
90
80
70
60
50
40
30
20
10
0
16

(b)

bc
a abab

CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS

c
c
d
ns

ns

(c)

ns

14
12
10
8

ns
ns

6
4
2
0
0-15 cm

15-30 cm

S a m p l i n g

Figure 2.1

30-45 cm

de p t h

(a) Soil microbial biomass carbon microbial, (b) biomass nitrogen and (c)
microbial biomass C:N ratio (C) as influenced by groundcover management.
Treatments are: Conventionally managed (CONV), Dutch white clover with strips
(DWCS), Dutch white clover with no strips (DWCS), alfalfa with strips (ALFS),
alfalfa with no strips (ALFNS), perennial ryegrass with strips (PRGS), and
perennial ryegrass with no strips (PRGNS.(ns =not significant)

24

Leaving cover crops residues on the soil surface as green manure also increases microbial
abundance, because of improved conditions for growth and reproduction in the top soil layer
(Govaerts et al. 2007). The fact that the residue is not incorporated into deeper soil layers most
likely explains the similarity in SMB-C and SMB-N among the various treatments at 15-30 cm
and 30-45 cm layers.
Whereas I expected greater cover crop residue inputs from plots with no strips compared to
plots with strips, creating strips did not significantly affect SMB-C or SMB-N in the cover crop
treatments (Fig. 2.2-c). SMB-C and SMB-N in plots managed with strips averaged 558.8 mg C
-1

kg

dry soil and 83.2 mg N kg
-1

dry soil and 79.3 mg N kg

-1

-1

dry soil while plots with no strips averaged 535.8 mg C kg

dry soil, respectively. Legume cover crop treatments showed
-1

significantly (P<0.001) higher SMB-C (average of 587.7 mg C kg ) than the grass cover crop
-1

-1

(466.6 mg C kg ) and CONV treatments (399.0 mg C kg ).

Similarly, SMB-N was
-1

significantly (P<0.001) higher in legume cover crops treatments (87.3 mg N kg ) than in the
-1

-1

non-legume cover crop (69.2 mg N kg ) and the CONV (50.7 mg N kg ) treatments.
Soil microbial biomass C: N ratio did not differ among groundcover treatments at any of
the three soil depths. However, Soil microbial biomass C: N ratio significantly (P<0.05)
increased with soil depth (Fig. 2.1-c), ranging from 6.5-7.1, 7.1-8.8, and 11.1-13.7 at the 0-15,
15-30 and 30-45 cm soil depths, respectively. This trend is the opposite of that observed for soil
total C: N and could indicate a shift in community composition, perhaps from a bacterialdominated community on the top soil layer to a fungal-dominated community in deeper soil
layers. In fact, previous research have reported that a high soil microbial biomass C: N ratio

25

generally indicates higher fungal versus bacterial populations in a soil (Jenkinson 1976; Moore et
al. 2000).
Groundcover treatments significantly increased both soil SMB-C: SOC (P<0.001) and
SMB-N: TKN (P<0.01) at the surface soil layer (0-15 cm), whereas no significant differences
were recorded among treatments at the deeper soil layers (Table 2.2). At the surface soil layer
where differences among treatments occurred, soil microbial quotients were significantly higher
from soil collected under legume cover crop plots than the non-legume cover crop and the
CONV treatments. The contrasting effect observed between legume and non-legume cover crops
on soil microbial quotients might be due to differences in the amount and quality of organic
matter produced by these two categories of cover crops. The relatively lower quotients in the
CONV and perennial ryegrass plots may suggest lower availability of organic substrates in these
environments, whereas a great supply of more labile organic substrate may explain the relatively
higher microbial quotients in the leguminous cover crop plots.

26

Table 2.2

Microbial Biomass quotients (SMB-C:Corg and SMB-N:Norg) measured at 0-15,
15-30 and 30-45 cm soil depth for each groundcover management treatment.
†
Treatments are: Conventionally managed (CONV), Dutch white clover with
strips (DWCS), Dutch white clover with no strips (DWCNS), alfalfa with strips
(ALFS), alfalfa with no strips (ALFNS), perennial ryegrass with strips (PRGS),
and perennial ryegrass with no strips (PRGNS); ( n=3).

Groundcover
treatments
CONV†
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value

SMB-C :Corg

SMB-N :Norg

0-15 cm

15-30cm

30-45cm

0-15 cm

15-30cm

30-45cm

0.020 a
0.029 c
0.025 b
0.031 c
0.025 b
0.022 a
0.021 a
P<0.001

0.018 a
0.022 a
0.019 a
0.026 a
0.020 a
0.020 a
0.021 a
P>0.05

0.033 a
0.049 a
0.024 a
0.042 a
0.028 a
0.033 a
0.045 a
P>0.05

0.032 a
0.051 c
0.049 bc
0.049 bc
0.046 bc
0.037 ab
0.039 abc
P=0.05

0.027 a
0.031 a
0.031 a
0.032 a
0.027 a
0.027 a
0.037 a
P>0.05

0.028 a
0.039 a
0.026 a
0.032 a
0.028 a
0.028 a
0.032 a
P>0.05

27

2.4.3. Community-level physiological profile of soil microbial communities
The color response in a given well is related to the number of microorganisms (functional
diversity) which are able to use the substrate within the well as a sole carbon source, and is
therefore used to assess microbial community structure in a given ecosystem (Garland and Mills,
1991). Average well color development (AWCD) recorded as optical density (OD) and the
number of well responses expressed as the catabolic diversity from all treatments followed the
same pattern (sigmoidal curve) throughout the incubation period (Figs. 2.2-a and 2.2-b), although
the rate of increase varied with different treatments. However, both the AWCD and the catabolic
diversity of communities from CONV plots were lower than the cover crop managed plots. The
high AWCD values recorded from soil collected under the cover crop treatments relative to the
CONV plots may suggest that cover crop treatments enhanced the diversity of microbial
communities, resulting in a higher substrate utilization rate.
The number of well responses (catabolic richness) followed the same pattern as AWCD
throughout the incubation (Fig. 2.2-b). For all treatments, only a few wells showed no color
response after 96 h of incubation. Significant differences among treatments (P<0.01) were found
in catabolic richness, Shannon diversity and evenness (Figs. 2.3-a, 2.3-b and 2.3-c). All cover
crop treatments, both with S and NS, had significantly higher (P<0.001) microbial catabolic
richness than the conventional treatments (Fig. 2.3-a). Similarly, plots managed with both
legume cover crops, had significantly higher H’ values than CONV plots. These results are in
agreement with results of studies conducted elsewhere which found organic farming systems to
significantly enhance soil microbial indexes compared with conventional systems (Mäder et al.
2002; Wu et al., 2008; Drinkwater et al,. 1995; Saison et al., 2006).

28

1.4

(a)

A WC D

1.2
1.0
0.8

ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
CONV

0.6
0.4

Nu mb e r o f we l l r e s p o n s e s

0.2
0.0
30

(b)

25
20
15
10
5
0
0

24

48

72

96

120

144

168

192

216

240

Hours after incubation

Figure 2.2

(a) Average well color development (AWCD) and (b) average catabolic diversity
TM

obtained from Biolog EcoPlate
incubation of different groundcover treatments
in a Fraser fir plantation. Treatments are: Conventionally managed (CONV),
Dutch white clover with strips (DWCS), Dutch white clover with no strips
(DWCNS), alfalfa with strips (ALFS), alfalfa with no strips (ALFNS), perennial
ryegrass with strips (PRGS), and perennial ryegrass with no strips (PRGNS).

29

Numbe r o f we l l r e s po ns e

35

(a)

30

b

b b
a

25

b

b

b

a

a

20
15
10
5
0
5

(b)

4

H'

b

b b

b
a

a

NS
S
CONV
c
b

a

3
2
1

0
0.9
0.8
0.7

E

(c)
ab b a

a

b
a

a

b

a

0.6
0.5
0.4
0.3
0.2
0.1
0.0
Alfalfa

Figure 2.3

white clover

p. ryegrass

(a) Species richness, (b) Shannon index of diversity and (c) Evenness index as
influence by different groundcover management (October/November 2007 data)
†
NS is no strips, S is strips and CONV is conventional treatments

30

In order to determine the extent of differentiation between the conventional and the cover
crop treatments with regard to carbon source utilization, the OD data were subjected to
multivariate analyses (principal component [PC] analysis). The trends observed on soil microbial
biomass and diversity indexes were supported by results from the multivariate analysis.
Contrasting patterns were apparent between the cover crop treatments and the CONV treatment
(Fig. 2.4).
The results showed that PC1 had a high coordinate value (Eigen value of 17.04) and
explained 56% of the total variance in the data; while the second principal component had a
variance of 4.90 and accounted for 16% of the data variability. While PC1 is the axis that
differentiates the striped legumes from the other treatments, PC2 differentiates the control plots
from all of the cover crop treatments. In PC1 space, soils from the two leguminous cover crop
plots, managed with strips, had distinct catabolic responses from the other treatments (i.e.
CONV, PRGS, PRGNS, ALFNS and DWCNS). Carbohydrates and carboxylic acids were the
potential carbon sources used by microorganisms in the leguminous cover crop plots with strips,
while all phosphorylated chemicals and amino acids were the most utilized carbon sources in soil
collected from the CONV plots and to some extent the non-leguminous cover crop plots (Table
2.3). The combination of the “strip effect” and the availability of high labile C substrates from
the leguminous cover crops might have provided an ideal condition in the soil surface for
specific groups of microbial populations with affinity to utilize these substrates.

31

ALFS
ALFNS
CONV
D WC S
D WC N S
P R GS
P R GN S

6
5
4

PC2 (16%)

3
2
1
0
-1
-2
-3
-7.5

-5.0

-2.5

0.0

2.5

PC 1 (56%)

Figure 2.4

PC1:|Loading| >0.90
- N Acetyl Dglucosamine
- Glucose-1-phosphate
- 4-hydroxy Benzoid Acid
- D,L-alpha-Glycerol-phosphate
- D-glucosamine
PC2: |loading| >0.70
- i-Erythritol
- Beta-Methyl-Dglucoside
5.0 - Alpha-D-lactose
- Erythritol

TM

Principal component analysis (PCA) performed on BIOLOG
Eco Plates from
data microbial community substrate utilization patterns of soil extracts from
different groundcover management at 120 h incubation period. †Treatments are:
Conventionally managed (CONV), Dutch white clover with strips (DWCS),
Dutch white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa
with no strips (ALFNS), perennial ryegrass with strips (PRGS), and perennial
ryegrass with no strips (PRGNS).

32

I observed higher rates of utilization of N-acetyl-D-glucosamine in soils collected from
the leguminous cover crops, as compared to the control and the non-legume cover crop plots
(Table 2.3). This could be an indication that the soil microbial decomposer community in these
soils is dominated by fungal mycelia as this particular substrate is an important component of
fungal tissues (Mäder et al. 2002). Fungal biomass generally contains significant amounts of
chitin, which releases N-acetyl-D-glucosamine when enzymatically degraded. Suitable enzymes
for degrading the chitin include chitinases and β-N-acetyl-glucosaminidases, which are generally
secreted from eukaryotic or prokaryotic microorganisms (Bohlmann et al. 2004). The presence of
ample amount of fungal biomass might have stimulated high numbers of microorganisms that
possess these particular enzymes involved in chitin decomposition.

33

Table 2.3

TM

List of all 31 C-substrates in Biolog EcoPlate , classified by category of carbon and their relative substrate utilization
in each groundcover management treatment. †Treatments are: Conventionally managed (CONV), Dutch white clover
with strips (DWCS), Dutch white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with no strips
(ALFNS), perennial ryegrass with strips (PRGS), and perennial ryegrass with no strips (PRGNS). ( n=3)

C-Categories

amines

Amino acids

Carbohydrates

C-Substrates
Phenylethyl-amine
Putrescine
Glycyl-L-glutamic-acid
L-arginine
L-asparagine B1
L-phenylalanine
L-serine
L-threonine
D-cellobiose
D-mannitol
D-xylose
i-erythritol
N-acetyl-D-glucosamine
α-D-lactose
β-methyl-D-glucoside
D-galactonic acid γlactone

Groundcover management treatments
ALFN DWC DWCN
PRGN
CONV† ALFS
PRGS
S
S
S
S
-------------------------------------%------------------------------------4.3
1.6
5.4
2.1
6.0
5.0
4.2
2.5
2.2
2.7
2.1
2.2
2.2
2.5
1.1
1.4
2.3
1.2
0.8
1.8
2.7
7.0
3.5
6.5
4.1
6.0
6.1
6.2
7.9
4.9
6.8
5.0
6.6
6.6
7.5
5.8
2.9
3.8
1.4
5.0
5.0
4.6
1.0
3.6
1.2
3.5
1.4
1.3
1.7
1.0
0.6
1.2
0.5
1.7
1.1
1.5
3.8
6.8
2.7
6.7
2.6
3.0
2.7
5.0
6.1
3.4
5.5
4.0
3.6
4.4
2.8
6.0
3.9
6.1
4.2
3.3
4.0
1.3
2.2
3.1
1.6
2.9
3.2
2.8
0.4
6.9
7.4
7.4
5.5
4.3
5.3
2.4
2.5
3.2
2.8
2.6
3.5
3.1
0.6
4.5
3.3
4.9
4.5
4.0
5.8
2.3

4.9

34

4.1

4.7

3.1

3.4

3.5

Pvalue
0.01
ns
ns
0.001
0.001
<0.001
<0.001
0.001
<0.001
0.001
<0.001
0.01
<0.001
ns
0.002
<0.001

Table 2.3

(Cont’d)

C-Categories

Carboxylic acids

Esters
Phosphorylated
chemicals
Polymers

C-Substrates
4-hydroxy benzoic acid
2-hydroxy benzoic acid
D-galacturonic-acid
D-glucosaminic-acid
D-Malic acid
iItaconic acid
α-Ketobutyric acid
γ-Hydroxybutyric acid

Groundcover management treatments
DWC
PCONV† ALFS ALFNS
DWCNS PRGS PRGNS
S
value
-------------------------------------%------------------------------------1.1
2.9
0.8
2.7
1.0
1.1
0.9
<0.001
0.6
0.6
1.0
0.1
1.1
0.8
0.4
ns
0.5
5.5
2.1
5.4
1.3
1.9
1.5
<0.001
0.1
2.5
1.2
2.0
0.6
0.2
0.3
<0.001
2.2
2.3
2.9
3.6
2.7
2.7
2.3
0.041
2.8
1.7
2.7
2.5
2.1
3.4
2.2
0.006
1.0
0.5
1.5
0.5
1.5
1.7
1.8
<0.001
0.1
0.2
0.4
0.3
0.8
0.4
0.4
ns

Pyruvic acid-methyl ester

5.3

3.1

3.2

2.8

3.1

3.6

4.1

0.027

D,L-α-glycerol phosphate
glucose-1-phosphate

4.0
9.3

3.8
7.1

0.9
3.2

3.7
7.3

1.1
3.8

3.8
7.1

1.9
2.7

<0.001
<0.001

Glycogen
Tween 40
Tween 80
α-Cyclodextrin
Total

8.3
6.4
6.3
2.8
100

2.4
2.6
3.5
0.7
100

5.3
5.6
3.7
4.5
100

2.4
3.4
3.3
0.4
100

5.2
7.0
4.6
5.0
100

4.5
4.6
4.2
2.6
100

5.0
6.2
4.9
2.9
100

<0.001
<0.001
0.021
<0.001

35

Also, Table 2.3 indicates that the rates of utilization of the two (2) phosphorylated
substrates were the highest in soil from the control and the cover crop plots with strips.
Glyphosate (N-phosphonomethyl glycine), a non-selective herbicide used for control of
unwanted vegetation in these targeted areas is reported in previous studies to be rapidly and
completely degradable by specific soil micro-organisms (Jacob et al. 1985; Krzysko-Lupicka and
Sudol 2008; Kremer and Means 2009). The glyphosate applications may have selected specific
microorganisms, most likely fungal species, capable of metabolizing phophorylated chemicals.
For instance, in a recent culture-based study, different strains of a fungal species (Fusarium spp.)
isolated from soil were able to successfully metabolized glyphosate and used it as a phosphorus
source (Castro et al. 2007). In this experiment, although the glyphosate was not intentionally soil
applied, a significant concentration of material might have reached the soil surface during
broadcast; thus making it available to soil micro-organisms with the ability to utilize it as
phosphorus source.

36

2.5.

CONCLUSIONS

This study indicates that the leguminous cover crop systems are better in increasing soil
microbial biomass and catabolic diversity than the non-leguminous cover crop and the CONV
systems, probably due to the quantity and quality of the green manure input into the system.
These preliminary results indicate that inclusion of leguminous or non-leguminous cover crops
into Fraser fir production systems can lead to healthier soil and could be an alternative to the use
of inorganic N fertilizers in Fraser fir Christmas tree plantations. Nitrogen mineralization,
nutrient fluxes and N losses through leaching and their effects on tree nutrition and growth are
being investigated to confirm these trends and determine the overall impact of these management
practices on Fraser fir production.

37

BIBLIOGRAPHY

38

2.6.

BIBLIOGRAPHY

Angers D.A., Caron J., (1998). Plant-induced changes in soil structure: Processes and feedbacks.
Biogeochem. 42:55–72
Baumann D.T., Bastianns L., Kropff M. J. (2001) Competition and crop performance in a leekcelery intercropping system. Crop. Sci. 41: 764-774
Beck T., Joergensen G., Kandeler E., Makeschin E., Nuss H., Oberholzer R., Scheu S., (1997)
An inter-laboratory comparison of ten different ways of measuring soil microbial biomass C.
Soil Biol. and Biochem. 29: 1023–32.
Bohlmann J.A., Schisler D.O., Hwang K., Henning J.P., Trinkle J.R., Anderson T.B., Steinke
J.D., Vanderhoff A. (2004) N-acetyl-D-glucosamine and process for producing N-acetyl-Dglucosamine; United States Cargill Incorporated (Wayzata, MN) Patent Number 6693188 online,
http://www.freepatentsonline.com/6693188.html.
Bossio D.A., Scow K.M., Gunapala K.J., (1998) Determinants of soil microbial communities:
Effects of agricultural management, season, and soil type on phospholipid fatty acid profiles.
Microb. Ecol. 36, 1-12
Brookes P.C., Landman A., Pruden G., Jenkinson D.S. (1985) Chloroform fumigation and the
release of soil nitrogen: a rapid direct extraction method to measure microbial biomass nitrogen
in soil. Soil Biol. Biochem. 17: 837-842
Broughton W.J. (1977) The effect of various covers on soil fertility under Hevea brasiliansis
(Muell. Arg.) and on growth of the tree. Agro-Ecosyst. 3: 147-170
Brye K.R., Gower S.T., Norman J.M. et al. (2002) Carbon budgets for a prairie and agroecosystems: effects of land use and inter-annual variability. Ecol. Appl. 12: 962–979
Campbell C.A., Grayston S.J., Hirst D.J. (1997) Use of rhizosphere carbon sources in sole
carbon source tests to discriminate soil microbial communities J. Microbiol. Methods 30:33-41
Castro V., Peralba M.C.R., Ayub M.A.Z. (2007) Biodegradation of the herbicide glyphosate by
filamentous fungi in platform shaker and batch bioreactor; J. Environ. Sci. Health B 42: 883–886
Chantigny M.H., Angers D.A., Prévost D., Vézina L.P., Chalifour F.P. (1997) Soil aggregation
and fungal and bacterial biomass under annual and perennial cropping systems. Agron. J.
61:262–267
Cong T., Louws F.J., Creamern N.G., Mueller J., Brownie C., Fager K., Bell M., Shuijin H.
(2006) Responses of soil microbial biomass and N availability to transition strategies from
conventional to organic farming systems. Agric. ecosyst. & envir. 113 (1-4):206-215

39

Coupland D., Caseley J.C. (1979) Presence of
from Agropyron repens treated with

14

C activity in root exudates and guttation fluid

14

C-labelled glyphosate. New Phytologist 83:17-22

Dinesh R., Chaudhuri S.G., Ganeshamurty A.N., Pramanik S.C. (2004) Biochemical properties
of soils of undisturbed and disturbed mangrove forests of South Andaman (Indian). Wetlands
Ecol. Manag. 12:309–320
Doran J.W., Zeiss M.R. (2000) Soil health and sustainability: managing the biotic component of
soil quality. Appl. Soil Ecol. 15: 3-11.
Drinkwater,L.E., Letourneau D.K., Workneh F., Van-Bruggen A.H.C., Shennan C. (1995)
Fundamental differences between conventional and organic tomato agroecosystems in
California. Ecol Appl. 5:1098–1112
Drury C.F., Stone J.A., Findlay W.I. (1991) Microbial biomass and soil structure associated with
corn, grasses and legumes. Soil Sci Soc Am J. 55:805–811.
Franzluebbers A.J., Langdale G.W., Schomberg H.H. (1999) Soil carbon, nitrogen, and
aggregation in response to type and frequency of tillage. Soil Sci. Soc. Am. J. 63:349-355
Frontier S., Pichod-Viale D. (1988) Ecosystèmes: Structure – Fonctionnement – Evolution. 2
édition révisée et augmentée. 3

ème

ème

tirage corrigé. DUNOD 286-298

Garland J.L. (1997). Analysis and interpretation of community-level physiological profiles in
microbial ecology. FEMS Microb. Ecol. 24:289-300.
Garland,J.L., Mills .L. (1991) Classification and Characterization of Heterotrophic Microbial
Communities on the Basis of Patterns of Community-Level Sole-Carbon-Source Utilization.
Appl. and Envir. Microbiol. 57 (8):2351-2359
Govaerts B., Mezzalama M., Unno Y., Sayre K.D., Luna-Guido M., Vanherck K., Dendooven
L., Deckers J. (2007) Influence of tillage, residue management, and crop rotation on soil
microbial biomass and catabolic diversity Appl. Soil Ecol. 37, 18-30
Grayston S.J., Campbell C.D. (1996) Functional biodiversity of microbial communities in the
rhizospheres of hybrid larch (Larix eurolepis) and Sitka spruce (Picea sitchensis). Tree Physiol.
16:1031-1038
Groffman P.M., Eagan P., Sullivan W.M., Lemunyon J.L. (1996) Grass species and soil type
effects on microbial biomass and activity. Plant Soil. 183: 61–67
Hanninen K. (2002) Tree - cover crop interactions: Birch growth, competition and soil
properties. Academic Dissertation, University of Oulu; 129p

40

Haynes R.J., Francis G.S. (1993) Changes in microbial biomass C, soil carbohydrate
composition and aggregate stability induced by growth of selected crop and forage species under
field conditions. J Soil Sci. 44: 665–675.
Jacob G.S., Schaefer J., Stejskal E.O., McKay R.A. (1985) Solid-state NMR Determination of
Glyphosate Metabolism in a Pseudomonas sp. The J. of Biological Chem. 260(10):5699-5905
Jenkinson D.S. (1976) The effects of biocidal treatments on metabolism in soil. IV. The
decomposition of fumigated organisms in soil. Soil Biol. Biochem. 8, 203–208.
Kennedy A.C., Papendick R.I. (1995) Microbial characteristics of soil quality. J Soil Water
Conserv. 50:243–24
Koelling M.R. (2002) Fertilization recommendations for Fraser fir: Part II—Established
plantings. Michigan Christmas Tree J. 49:5– 8.
Kremer R.J., Means N.E. (2009) Glyphosate and glyphosate-resistant crop interactions with
rhizosphere microorganisms. Europ. J. Agronomy 31:153–161
Krzysko-Lupicka T., Sudol T. (2008) Interactions between glyphosate and autochthonous soil
fungi surviving in aqueous solution of glyphosate. Chemosphere 71:1386–1391
Lehmann J., Silva J.P.D., Schroth G., Gebauer G., Silva L.F.D. (2000) Nitrogen use in mixed
tree crop plantation with legume cover crop. Plant and Soil 225:63-72
Lundquist E.S., Jackson L.E., Scow K., Hsu C. (1999) Changes in Microbial biomass and
community composition, and soil carbon and nitrogen pools after incorporation of rye into three
California agriculture soils. Soil Biol. Biochem. 31,:221-236
Macdonald A.J., Poulton P.R., Howe M.T., Goulding K.W.T., Powlson D.S. (2005) The use of
cover crops in cereal-based cropping systems to control nitrate leaching in SE England. Plant and
Soil 273 (1-2):355-373
Mäder P., Fliessbach A., Dubois D., Gunst L., Fried P., Niggli U. (2002) Soil fertility and
biodiversity in organic farming Science 296:1694–1697
Mendes I.C., Bandick A.K., Dick R.P., Bottomley P.J. (1999) Microbial biomass and activities in
soil aggregates affected by winter cover crops. Soil Sci Soc Am J. 63:873-881.
Moore, J.M., Klose S., Tabatabai, M.A. (2000). Soil microbial bio mass carbon and nitrogen as
affected by cropping systems; Biol Fertil. Soil 31, 200–210.
Mullen M.D., Melhorn C.G., Tyler D.D., Duck B.N. (1998) Biological and biochemical soil
properties in no-till corn with different cover crops. J Soil and Water Conserv. 53:219-224.
Ocio J.A., Martinez J., Brookes P.C. (1991) Contribution of straw-derived N to total microbial
biomass N following incorporation of cereal straw to soil. Soil Biol. Biochem. 23:655-659
41

Powlson D.S., Brookes P.C., Christensen B.T. (1987) Measurement of microbial biomass
provides an early indication of changes in total soil organic matter due to the straw incorporation.
Soil Biol Biochem. 19:159-164
Rothstein E.D. (2005) Nitrogen Management in a Fraser Fir (Abies fraseri [Pursh] Poir.)
Christmas Tree Plantation: Effects of Fertilization on Tree Performance and Nitrogen Leaching.
For. Sci. 51(2):175-185
Sainju U.M., Singh B.P. (1997) Winter cover crops for sustainable agricultural systems:
Influence on soil properties, water quality, and crop yields. Hort Sci. 32(1):21-28
Saison C., Degrange V., Oliver R., Millard P., Commeaux C., Montange D., Roux X.L. (2006)
Alternation and resilience of the soil microbial community following compost amendment:
effects of compost level and compost-borne microbial community. Environ Microbiol. 8:247–
257
Sanchez J.E., Giayetto A., Cichon A.L., Fernandez D., Aruani M.C., Curetti M. (2007) Cover
crops influence soil properties and tree performance in an organic apple (Malus domestica
Borkh) orchard in northern Patagonia. Plant and Soil 292 (1-2): 193-203
USDA-NRCS & MAES (1992) Soil Survey of Ingham County, Michigan
Vilsack T., Clark C.Z.F. (2009) 2007 census of agriculture-United States-Summary and state
data, Volume 1, Geography Area Series, Part 51, 739p
Walsh B.D., Salmins S., Buszard D.J., MacKenzie A.F. (1996) Impact of soil management
systems on organic dwarf apple orchards and soil aggregate stability, bulk density, temperature
and water content, Can. J. Soil Sci. 76 (2):203–209
Wang Q.K., Wang S.L. (2008) Soil microbial properties and nutrients in pure and mixed Chinese
fir plantations. J. of For. Res. 19 (2):131-135
Winding A. (1994) Fingerprinting bacterial soil communities using Biolog microtiter plates. p.
85–94. In K. Ritz et al. (ed.) Beyond the biomass: Compositional and functional analysis of soil
microbial communities. John Wiley and Sons, Chichester, UK.
Wu T.D., Chellemi O., Rosskopf E.N. (2008) Comparison of Soil Bacterial Communities Under
Diverse Agricultural Land Management and Crop Production Practices. Microb Ecol. 55:293–
310
Zak J.C., WillieM.R., Moorhead D.L., Wildman H.G. (1994) Functional diversity of microbial
communities: a quantitative approach. Soil Biol. & Biochem. 26: 1101-l108

42

CHAPTER 3

EFFECTS OF GROUNDCOVER MANAGEMENT ON SOIL
PROPERTIES, TREE PHYSIOLOGY, FOLIAR CHEMISTRY AND
GROWTH IN A NEWLY ESTABLISHED FRASER FIR (ABIES FRASERI
[PURSH] POIR) PLANTATION IN MICHIGAN, U.S.A

43

3.1.

ABSTRACT

Incorporating cover crops into Fraser fir (Abies fraseri) Christmas tree plantations may improve
soil fertility and tree performance. Dutch white clover (Trifolium pratense), alfalfa (Medicago
sativa) and perennial ryegrass (Lolium perenne) were grown in a newly established Fraser fir
plantation using two competition-management practices; no strips (NS) by growing each cover
crop throughout the entire plot and strips (S) by creating a 61 cm-wide bare zone centered on the
tree rows. A conventionally-managed system (CONV) was used as a control. The cover crop
-

+

aboveground biomass and N content were assessed. Soil available N (NO3 -N and NH4 -N) and
N mineralization were measured at 0-15, 15-30 and 30-45 cm soil depths. Tree survival, height,
diameter, chlorophyll fluorescence (Fv/Fm), stem water potential (Ψw) and foliar nutrients were
also evaluated. Biomass production was as high as 13.9, 10.2 and 5.9 Mg DM ha

-1

yr

-1

for

clover, alfalfa and ryegrass, respectively. Cover cropping increased soil available N by 1.5- and
2.2-fold relative to the CONV treatment in the top soil layer in 2007 and 2008. Tree seedling
survival and growth in the S and CONV systems were similar. In contrast, NS treatments
resulted in poor seedling survival and growth relative to the S and CONV plots. Plant stem Ψw
decreased significantly for seedlings on the NS treatments relative to their counterparts on the S
and CONV plots. Conversely, cover cropping had marginal effects on foliar nutrients and Fv/Fm.
Cover cropping with strips can be an efficient strategy for maintaining productivity in Fraser fir
Christmas plantations.

44

3.2.

INTRODUCTION

In the past two to three decades, increasing crop productivity and conserving the
environment have been major challenges facing the agricultural and forestry sectors. Whereas N
fertilization can maximize Christmas tree growth and produce more marketable trees in a shorter
period of time, excessive use of anthropogenic inputs of reactive nitrogen (N) and subsequent N
losses through runoff and leaching, pose potential pollution risks to many ecosystems (Rothstein
2005). The issue is generally not important in forestry production systems, which receive far less
fertilizer inputs than agronomic and horticultural production systems (Di and Cameron 2002;
Rothstein 2005). It is, however, well documented that Christmas tree production represents one
segment of the forestry sector that uses substantial fertilizer inputs comparable to agronomic
systems, and these inputs can contribute to N pollution (Rothstein 2005; Pedersen et al. 2006).
Given the large scope of the production of this specialty crop and its potential environmental
risks, alternative management systems need to be sought.
In North America, approximately 33 to 36 million Christmas trees are produced each
year. By comparison, the estimated total annual production from Christmas tree farms in Europe
ranges between 50 and 60 million trees (Frampton and McKinley 1999). According to the United
States 2007 census of agriculture, there were an estimated 17,367 Christmas tree farms in the
U.S.A. and Michigan ranks third among all Christmas tree producing states and harvested about
1.6 million trees annually (Vilsack and Clark 2009).
A number of species are grown as Christmas trees in Michigan such as Scotch pine
(Pinus sylvestris), Fraser fir (Abies fraseri [Pursh] Poir), Douglas-fir (Pseudotsuga menziesii),
Colorado blue spruce (Picea pungens) and Balsam fir (Abies balsamea). However, demand for
Fraser fir has increased, primarily due to its overall appearance, needle length, crown
45

architecture and fragrance. In general, Firs have gained market share in the last 10-15 yrs, and
Fraser fir is the most popular Eastern/mid-western fir. By 2000, Fraser fir alone occupied about
one fifth of the total Christmas tree acreage in Michigan and is still expanding (Chastagner and
Benson 2000). On the other hand, Fraser fir is known to be more demanding with respect to soil
fertility, and performs poorly when grown on marginal land that is suitable for other conifers
such as spruces and pines (Leuty 2005). While weed control alone may be enough to adequately
release enough nutrients for other conifer species, Fraser fir requires close attention to nitrogen
availability (Koelling et al. 1998). Because of continued increases in the cost of fertilizers and
the declining price of trees due to a nationwide oversupply (Sidebottom 2009), conventional
fertilization in Fraser fir plantations may not be cost-effective and may reduce overall farm
profitability.
Because of these economic and environmental considerations, it is important to seek
alternative cropping systems for Fraser fir production that can improve sustainability and
enhance profitability. One potentially useful strategy is the incorporation of cover crops (Ruffo
and Bollero 2003). Cover crops offer a valuable source of nitrogen, especially if they are
nitrogen fixers such as Dutch white clover (Trifolium repens) and alfalfa (Medicago sativa)
(Smith et al. 1996). Such cover crops are regularly planted to improve soil fertility, increase crop
nitrogen economy and obtain better growth in cereal, vegetable, and fruit production systems
(Hanninen et al. 1999; Sanchez et al. 2007). However, very little published information is
available concerning the use and potential benefits of cover crops in Christmas tree production
systems.
To fill this gap, this research was established in the spring of 2007 to assess whether soil
fertility, Fraser fir survival and growth could be improved by incorporating leguminous and non-

46

leguminous cover crops into the production system. This paper presents results on the effects of
Dutch white clover, alfalfa and perennial ryegrass on soil fertility, tree physiology, foliar
chemistry, survival and growth for the first two growing seasons following establishment of a
Fraser fir Christmas tree plantation.

47

3.3.

MATERIALS AND METHODS

3.3.1. Location
The study was conducted from May 2007 to October 2008 in a newly established Fraser
fir plantation located at the Tree Research Center (TRC) (42.67ºN, 84.46ºW) on the campus of
Michigan State University (MSU) in East Lansing, Michigan, U.S.A. Soil at this site is classified
as a fine-loamy, mixed, active, mesic Aquic Glossudalf (USDA/NRCS-MAES 1992). The
-1

general soil chemical characteristics of the site are: 35% silt + clay, pH 5.6 and 13 cmol kg

CEC (Rothstein 2005). Soil total C and N, inorganic N and Mehlich III extractable nutrients (P,
K, Ca and Mg), measured at the initiation of the plantation establishment are provided in Table
3.1. The site had been used for crop cultivation, primarily corn with occasional rotations of
wheat and soybeans, for at least the past 30-40 years. Mean monthly temperature and total
monthly rainfall from May to September of 2007, 2008 and the past 12-yr average (1996-2008)
are presented in Figure 3.1.

48

Table 3.1

−1

Initial soil chemical (mg kg )characteristics (means ± se; n=3) at three soil
depths (cm) of the experimental site, Michigan State University Tree Research
Center, East Lansing, Michigan, US.A.
-

Mehlich III extractable nutrients

Soil
depth

Total C

Total N

NO3

0-15
15-30
30-45

21.4 (0.8)
16.0 (0.7)
06.5 (0.8)

1.8 (0.1)
1.4 (0.1)
0.6 (0.1)

8.86 (0.56)
4.44 (0.13)
2.73 (0.15)

+NH4

+

P

49

K

Ca

Mg

49.1 (4.8)
31.7 (4.2)
14.1 (3.1)

201(13)
111 (9)
78 (4)

1260 (53) 183 (7)
1186 (43) 155 (6)
1226 (59) 175 (10)

Pr e c i pi t a t i o n ( mm)

300
250
200

2007
2008
12-yrs-Av.

2007-Max
2008-Max
12-yrs-Av. Max
2007-Min
2008-Min
12-yrs-Av.Min

150
100

(a)

50
0

Te mpe r a t ur e ( o C)

(b)
25
20
15
10
5
0
May

June

July

August September

Month

Figure 3.1

(a) Total monthly precipitation and (b) monthly minimum and maximum
temperatures during the 2007 and 2008 growing seasons compared with the 12year monthly average at MSU’s Horticultural Farm, East Lansing, Michigan,
U.S.A.

50

3.3.2. Experimental design and plot layout
The experiment was established in a randomized complete block design with three
replicates in a field measuring 32.4 m × 50.4 m. Blocks and experimental plots were 10.8 m ×
50.4 m and 7.2 m × 10.8 m, respectively. Each rectangular plot contained a total of 35 trees (5 ×
7 trees). Trees in border rows were used as buffers and not included in measurements, therefore
restricting data collection to the remaining 15 interior trees in each plot. Each block had two
plots per cover crop, managed either with no strips (NS) or with strips (S). The NS treatment
consisted of growing each cover crop continuously over the entire plot. In contrast, in the S
treatment, the assigned cover crop was intercropped between the tree rows while maintaining,
through glyphosate application, a clear strip of 0.61 m wide, centered on the tree row. Control
plots were managed conventionally (CONV); they contained no cover crop and weeds were
−1

completely controlled with glyphosate (active ingredient concentration =1.1 kg ha ). Thus, the
treatments were as follows: conventional system (CONV), Dutch white clover with strips
(DWCS), Dutch white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with
no strips (ALFNS), perennial ryegrass with strips (PRGS) and perennial ryegrass with no strips
(PRGNS). No N fertilizer was applied to any plot in 2007 as it may injure first-year seedling
roots (Koelling 2002). However, during the second growing season, 16 g N per tree (50 kg N ha
1

-

) as ammonium sulfate [(NH4)2SO4] was applied one time on 13 May, 2008 to the control

plots. A description of treatments and management practices applied in 2007 and 2008 can be
found in Appendix –TableA.1.

51

3.3.3. Plant materials and management
Fraser fir transplants (plug + 2) were obtained from a local commercial nursery
(Peterson's Riverview Nursery), and machine-planted (Whitfield planter) at a spacing of 1.8 m ×
1.8 m. Fraser fir seedlings were planted into a chisel-plowed and dragged field soil on 8 May
2007. Seeds of common Dutch white clover, alfalfa (SS 100 brand) and perennial rye (VNS)
were purchased from Michigan State Seeds (Grand Ledge, Michigan, U.S.A.) and hand-seeded
−1

on 22 May, 2007. The seeding rates used were 28 kg ha

−1

for clover and alfalfa, and 16 kg ha

for ryegrass. Once the cover crops were fully established, mechanical mowing was performed at
3 cm above the ground every three to four weeks in 2007 (2 July, 26 July, 21 August and 18
September) and 2008 (27 May, 24 June, 17 July, 14 August, and 11 September) to control cover
crop growth, minimize the competition with the trees, and add green manure to the surface soil.
Glyphosate was sprayed twice during each growing season (2007 and 2008) to control weeds in
the CONV plots and within the tree rows in the S plots. The whole field was protected with an
electric fence to prevent deer browsing.
3.3.4. Seedling growth and physiology
Seedling initial height and collar diameter, recorded at 1 cm from the ground, were
measured two weeks after planting. In addition, in mid-October of 2007 and 2008, I measured
tree survival as well as each seedling’s height and collar diameter. Relative growth rate (RGR)
was calculated from total height and collar diameter data using the formula by Hunt (1982):
RGR = (lnW1-lnW2)/(t1-t2)
where W1 and W2 are tree sizes (height or diameter) measured at times t1 and t2.

52

(eq.3.1)

The maximum quantum yield of photo-system II, also referred to as chlorophyll
fluorescence (Fv/Fm), of current year needles was measured using a Handy PEA Chlorophyll
Fluorometer (Hansatech Instruments, England). The stem water potential (Ψw) of new branches
obtained from the upper third of the crown was also measured with a Pressure Bomb (PMS
Instruments Company, Oregon, USA). Both Fv/Fm and Ψw were measured three times a year in
2007 (29 May, 18 July and 27 September) and 2008 (14 May, July 17 and 23 September).
3.3.5. Soil and plant sampling and nutrient analysis
In mid-October of 2007 and 2008, 15 randomly selected soil samples per plot were
collected with 5.2-cm diameter PVC tubes and composited into one sample. In the striped cover
crop plots, nine cores of soil were collected within the cover crop zone and six cores within the
strips (bare ground zones), proportional to the area of the striped and cover crop zones. Soil
samples were collected from 0-15, 15-30, and 30-45 cm depths.
From each field-moist soil sample, about 10 g of soil was used for determination of
gravimetric moisture content by weighing the soil before and after oven drying at 105°C for 24
h. A second subsample was used for inorganic N determination while a third subsample was used
for potential N mineralization determination after 4 weeks of incubation at 25oC and maintaining
-

the samples at 50% of field capacity by periodically adding distilled water. Nitrate (NO3 -N) and
+

ammonium (NH4 -N) concentrations of the non-incubated and incubated soil samples were
extracted with 2 M KCL (5:1 extractant to soil ratio) and extracts were analyzed
spectrophotometrically (Spectrophotometric plate reader, Model ELx Bio Tek Instruments, Inc.
-

+

Winooski, Vermont, U.S.A.) for NO3 -N and NH4 -N following the procedures described by

53

Doane and Horwath (2003) and Sinsabaugh et al. (2000), respectively. Soil macronutrients (e.g.
P, K, Ca and Mg) were extracted with Mehlich III extractant (Mehlich 1984) and analyzed using
an inductively coupled plasma atomic emission spectrometry (ICP-AES; PerkinElmer, Model
Optima 2100DV, Shelton, CT, U.S.A.).
Before each mowing, the aboveground biomass of the cover crops was measured by
using four quadrats of 0.3 m x 0.3 m, randomly placed between the tree rows in each plot.
Vegetation was cut at 3 cm above the ground to mimic a mechanical mowing. Then, clippings
were taken to the laboratory where cover crop fresh material was separated from weeds. Needle
samples were also taken at the end of the growing season, in mid-October of 2007 and 2008 for
determination of nutrient (e.g. N, P) concentrations. During this time, roots are dormant, trees
have produced most of their annual woody growth, and nutrient levels in the trees are stable
(Hart et al. 2004). Samples were collected by pinching five to eight needles of new or current
season growth from different locations on the upper one third of the tree crown. This procedure
was repeated for three to six trees within each plot. All plant tissue samples were gently washed
with a non-ionic detergent and rinsed five times in distilled water before they were oven-dried at
65ºC for 48 hours. Dried weight of cover crop clippings was used for the determination of
herbage yield at each mowing event. Then, each plant tissue sample was ground in a ball-mill
and a sub-sample of 500 mg was acid-digested (4.5 mL of concentrated 70% nitric acid) for
elemental analysis (ICP-AES). Additional subsamples of about 2.5 mg were used for total C and
N determination by combustion using an elemental analyzer (Model ECS 4010, COSTECH
Analytical, Valencia, California, U.S.A.).

54

3.3.6. Statistical analysis
All variables were analyzed with the statistical package Minitab 15 (Minitab Inc. 2006)
using a randomized complete block design with three replications. For the soil data, treatment
effects on measured variables were analyzed taking into account the three sampling depths. The
statistical model thus included one random factor (block) and two fixed factors: groundcover
treatments and depths, with the later variable considered as repeated measurements. Both cover
crop and tree measurement data were subjected to a one way ANOVA. Fisher LSD0.05 range test
was used to separate treatment means found to be significantly different in the ANOVA (Little
and Hill 1978).

55

3.4.

RESULTS

3.4.1. Cover crop establishment, biomass yield and foliar nutrient concentration
Overall, all three cover crops germinated and grew well due to adequate rainfall during
the first two months following seeding. As shown in Figure 3.1, precipitation in May and June of
2007 approximated the 12-year average for the corresponding months which promoted good
establishment of the cover crop stands. Although July and September of 2007 were drier than the
12-year average, this drought had little effect on the successful establishment of cover crop
stands. The three cover crops differed significantly (P=0.013) in their annual aboveground dry
matter production as well as biomass N content (P=0.034) and tissue N concentration (P=0.021)
(Table 3.2). Dutch white clover was the most productive (13.9 Mg DM ha
perennial ryegrass had the lowest dry matter yield (5.9 Mg DM ha
followed the same trend as aboveground biomass yield.

56

-1

-1

-1

-1

yr ), whereas

yr ). Biomass N content

Table 3.2

Cumulative cover crop biomass obtained after five mowing events and biomass
N, P, K, Ca and Mg concentration during the 2008 growing season with different
cover crops for a Fraser fir Christmas tree plantation in Michigan, U.S.A. BY=
cover crop biomass dry matter yield, BN= cover crop biomass dry matter nitrogen
yield

Cover
crop

BY

Alfalfa
Clover

10.2

Ryegrass
P-value

BN

-1

(t ha )

13.9
5.9

b
c
a

0.013

N
-1
(kgha ) (%)
494
563
210

b
b
a

0.034

4.1
4.0
3.4

C:N
ratio
b
b
a

0.021

10.9
11.3
13.4

P
(%)
a
a
b

0.040

57

0.36
0.30
0.30

K
(%)
b
a
a

0.021

3.22
3.32
2.78

Ca
(%)
b
b
a

0.044

1.19
1.15
0.30

Mg
(%)
b
b
a

<0.001

0.31
0.36
0.23

b
c
a

<0.001

200

10
8

100

6
4

0

2

B i o ma s s N ( k g N h a - 1 )

B i o ma s s y i e l d ( Mg h a - 1 )

(a)

12

18

(b)

16

10

14
8

12
10

6

8
4

C: N r a t i o

P l a n t t i s s u e N ( %)

0

6
4

2

2
0

0
120

140

160

180

200

220

240

260

280

Mowing event (Julian day)

Figure 3.2

(a) Total aboveground biomass yield (bars) and biomass N content (lines), (b)
tissue N concentration (bars) and C:N ratio (lines) of alfalfa (black bars, black
circles and short lines), Dutch white clover (gray bars, white circles and long
dashed lines) and perennial ryegrass (white bars, down triangles and solid lines)
mowed five times in 2008 at 3 cm above the ground in a Fraser fir Christmas tree
plantation in Michigan, U.S.A. Values are means (± se; n = 3)

58

The two leguminous cover crops accumulated the highest biomass N. With the exception
of P, foliar nutrient concentrations were significantly (P<0.05 for N and K; P<0.001 for Ca and
Mg) higher for the two legumes than the non-legume cover crop. The distribution of the 2008
biomass yield among mowing events varied in magnitude and rank for the three cover crops. In
general, biomass yields per mowing event ranged from 0.3-3.8, 1.4-4.2 and 0.3-2.2 Mg DM ha

-1

for alfalfa, Dutch white clover and perennial ryegrass, respectively (Fig. 3.2-a).There was a
general trend toward a decrease of biomass yield for each consecutive mowing event. Likewise,
biomass N content followed almost the same pattern as biomass yield, with a general trend of
decline with each subsequent mowing event. Tissue N concentrations per mowing event were in
the range of 3.2-4.8, 3.1-4.5, and 2.7-3.8%, whereas biomass N content ranged between 10-182,
53-187, and 10-80 kg N for alfalfa, Dutch white clover and perennial ryegrass, respectively (Fig.
3.2-a and 3.2-b). C: N ratios ranged from 8.9-13.6, 9.7-14.8 and 11.6-16.7 for alfalfa, Dutch
white clover and perennial ryegrass, respectively (Fig. 3.2-b). Tissue N concentration also tended
to decline with each subsequent mowing event, resulting in an increase of the C: N ratios.
3.4.2. Soil extractable nutrients
Across all three soil depths and groundcover treatments, P was generally significantly
lower in NS than the other treatments (Table 3.3). However, extractable P concentration did not
differ between CONV and S treatments at any of the three soil layers. Soil extractable K
concentration decreased significantly (P<0.001) with increasing soil depth whereas treatment
effect was not significant. Treatment and sampling depth had no significant effects on extractable
Ca concentration (Table 3.3).

59

Table 3.3

Soil
depth

0-15

15-30

30-45

-1

Average concentration of Mehlich III extractable P, K, Ca and Mg (mg/kg ) measured in 2008 at three soil depths
(cm) with different cover crops in a Fraser fir Christmas tree plantation in Michigan, U.S.A. Values are means (±se;
n=3,). Treatments are: Conventionally managed (CONV), Dutch white clover with strips (DWCS), Dutch white clover
with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with no strips (ALFNS), perennial ryegrass with strips
(PRGS), and perennial ryegrass with no strips (PRGNS).
Management
CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value
CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value
CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value

Mehlich III
P
43.4
(2.4) b
40.2
(0.6) b
27.4
(3.6) a
38.3
(3.0) b
29.7
(2.4) a
41.3
(2.0) b
28.0
(3.6) a
0.002
14.4
(2.2) b
17.9
(1.6) b
8.0
(0.7) a
16.5
(0.8) b
9.8
(0.2) a
16.0
(1.6) b
7.3
(0.5) a
<0.001
4.9
(0.7) c
3.1
(0.4) ab
2.2
(0.6) ab
2.2
(0.4) ab
1.1
(0.4) a
3.3
(0.2) b
2.0
(0.1) ab
0.001

K
196
217
144
204
249
228
165
ns
81
139
105
100
122
127
73
ns
80
77
68
67
70
68
98
ns

(61)
(8)
(51)
(13)
(34)
(24)
(54)
(36)
(21)
(10)
(3)
(29)
(3)
(22)
(12)
(5)
(8)
(2)
(20)
(16)
(43)

60

C
1480
1079
1231
1224
1380
1160
1207
ns
973
1061
1232
1352
1169
1048
1128
ns
1481
1079
1459
1204
921
1025
1251
ns

(266)
(169)
(242)
(160)
(215)
(142)
(178)
(234)
(165)
(223)
(79)
(112)
(174)
(311)
(395)
(148)
(168)
(168)
(219)
(119)
(160)

Mg
207
161
173
169
191
174
180
ns
131
140
153
168
145
135
156
ns
191
148
212
190
130
113
207
ns

(35)
(19)
(29)
(19)
(31)
(27)
(22)
(50)
(9)
(22)
(10)
(11)
(21)
(19)
(49)
(6)
(4)
(31)
(36)
(11)
(12)

Table 3.4

Depth
(cm)

0-15

15-30

30-45

-1

Average soil available nitrate, available N, nitrification and net N mineralization (kg ha )at three depths, from samples
collected at the end of the growing season of 2007 and 2008 with different cover crops in a Fraser fir Christmas tree
plantation in Michigan, U.S.A. Treatments are: Conventional (CONV), Dutch white clover with strips (DWCS), Dutch
white clover with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with no strips (ALFNS), perennial ryegrass
with strips (PRGS), and perennial ryegrass with no strips (PRGNS).
Management
practice

NO3 -N
2007

2008

+
(NO3 +NH4 )-N
2007
2008

CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value
CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value
CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value

8.3 a
13.0 b
13.1 b
11.0 b
11.3 b
11.1 b
11.8 b
0.050
3.7 a
7.8 b
6.5 b
5.7 ab
8.0 b
6.0 ab
6.0 ab
0.021
5.4
2.9
4.9
4.9
3.0
6.5
4.6
ns

7.9 a
24.5 c
21.6 bc
24.0 c
20.5 bc
16.4 b
16.5 b
0.004
5.3 a
18.5 bc
20.5 c
19.4 bc
15.1 bc
12.9 abc
10.2 ab
0.038
4.9 ab
7.9 cd
8.5 d
7.2 abcd
7.7 bcd
4.5 a
5.1 abc
0.032

12.4 a
17.8 bc
22.1 c
16.9 b
17.3 b
16.3 ab
15.6 ab
0.010
10.6
11.6
10.4
9.0
10.4
10.1
10.1
ns
6.6
6.5
6.4
6.6
4.8
7.9
6.2
ns

13.6 a
35.5 c
32.3 bc
32.0 bc
26.1 b
26.2 b
24.8 b
0.001
8.1 a
24.9 c
27.3 c
27.5 c
24.5 bc
20.2 bc
15.3 ab
0.005
7.0 a
15.6 c
12.7 bc
13.6 bc
10.1 ab
10.9 abc
11.5 abc
0.022
61

Nitrification

Net N-mineralization

2007

2008

2007

2008

25.8 a
44.3 c
37.8 bc
41.0 bc
32.7 ab
31.9 ab
33.3 ab
0.029
11.4 a
25.0 c
25.8 c
24.4 bc
25.4 c
17.8 abc
15.2 ab
0.027
5.0
8.6
7.1
10.0
13.0
9.5
10.5
ns

28.4 a
98.1 d
98.1 d
101.4 d
77.3 c
57.5 b
67.3 bc
<0.001
14.7 a
39.5 b
46.0 b
68.3 cd
55.6 bc
57.2 bc
79.6 d
<0.001
16.9 a
41.1 bc
24.3 ab
43.5 c
34.7 abc
38.1 bc
29.6 abc
0.021

22.9 a
40.9 c
30.6 ab
36.5 bc
27.9 ab
28.6 ab
31.4 ab
0.021
5.7 a
22.1 bc
22.8 c
22.2 c
24.1 c
14.7 abc
12.1 ab
0.010
4.4
5.6
6.2
9.2
11.5
8.5
9.5
ns

20.0 a
87.4 de
88.5 de
94.1 e
70.2 cd
49.5 b
61.4 bc
<0.001
12.3 a
33.6 b
39.9 b
60.6 cd
46.7 bc
51.0 bc
75.2 d
<0.001
8.9 a
32.5 c
17.4 ab
27.8 bc
31.4 c
33.5 c
23.3 bc
0.012

By the end of the first growing season (2007), cover crop treatments significantly
-

increased soil NO3 -N concentration relative to CONV in the top two soil layers (P=0.05 and
-

0.021, respectively). There was more than 2% increase in soil NO3 -N levels at both 0-15 and
15-30 cm soil depths (Table 3.4). Similarly nitrification rate and net N mineralization increased
substantially in all cover crop treatments relative to CONV at the two top soil depths. In 2008,
-

however, treatment differences were apparent at all three soil depths for soil NO3 -N,
nitrification rate and net N mineralization. In 2007, cover cropping also significantly (P=0.01)
increased soil available N levels relative to the CONV treatment at the top soil layer, whereas
treatments differences were not significant at the two deeper soil layers. In 2008, cover crop
treatments significantly enhanced soil available N by more than 2% at all three soil layers.
3.4.3. Seedling survival
By the end of 2008 growing season, tree survival ranged from 13 to 68% (Fig. 3.3).
PRGNS and DWCNS had the lowest tree survival of all groundcover treatments (13 and 20%,
respectively). In both 2007 and 2008, no significant differences were found between the CONV
and the S plots. After two growing seasons, survival rates within the S plots decreased in the
following order: alfalfa > perennial ryegrass > Dutch white clover. In general, most seedling
mortality occurred during the first growing season. It is noteworthy that on average, 91% of the
seedlings that survived the first year also survived the second year in the CONV and S
treatments. In the NS plots, however, second-year survival rate was 70%, with the lowest
survival (47%) recorded in Dutch white clover plots. In 2007, some dead seedlings were sent for
analysis at MSU’s Center for Integrated Plant Systems and the results indicated that the mortality
was not related to any disease problems.
62

Seedl ing survival (%)

120

CONV
ALFB
ALFNB
DWCB
DWCNB
PRGB
PRGNB

100
b
80

b

b

b

b

b

b

b

b

b

60
a

a

40
a

a

20
0
2007

Figure 3.3

2008

Year

Tree survival in 2007 and 2008 with different cover crops in a Fraser fir
Christmas tree plantation in Michigan, U.S.A. Treatments are: Conventionally
managed (CONV), Dutch white clover with strips (DWCS), Dutch white clover
with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with no strips
(ALFNS), perennial ryegrass with strips (PRGS), and perennial ryegrass with no
strips (PRGNS). Values are treatment means (± se; n = 3).

63

3.4.4. Relative diameter and height growth of seedlings
Relative diameter growth for the first growing season (FS) was not significantly affected
by groundcover treatments. However, relative diameter growth for the second growing season
(SS) and the combined two growing seasons (TS) differed significantly (P=0.003 and 0.009,
respectively) among groundcover treatments. With the exception of ALFNS, seedlings grown in
NS treatments had lower relative diameter growth than their counterparts in the CONV and S
treatments (Table 3.5). In contrast, FS-, SS- and TS-relative height growth did not significantly
differ among groundcover treatments. By the end of the second growing season, relative
diameter growth of seedlings in the ALFNS, DWCNS and PRGNS treatments was reduced by
30, 52 and 70%, respectively, relative to their counterparts in the CONV plots.

64

Table 3.5

Treatment
CONV
ALFS
ALFNS
DWCS
DWCNS
PRGS
PRGNS
P-value

Relative growth of collar diameter and height of Fraser fir seedlings for the first growing season (FS), second growing
season (SS) and the two growing seasons (TS) in a low input soil management with different cover crops for Fraser fir
Christmas tree plantation in Michigan, U.S.A. Values are means (± se; n =3). Treatments are: Conventionally managed
(CONV), Dutch white clover with strips (DWCS), Dutch white clover with no strips (DWCNS), alfalfa with strips
(ALFS), alfalfa with no strips (ALFNS), perennial ryegrass with strips (PRGS), and perennial ryegrass with no strips
(PRGNS).
Relative diameter growth
FS
SS

TS

Relative height growth
FS
SS

TS

0.07 (0.04)
0.10 (0.03)
0.10 (0.04)
0.07 (0.04)
-0.03 (0.04)
0.09 (0.03)
-0.10 (0.03)
ns

0.23 (0.02) c
0.22 (0.02) c
0.16 ( 0.02) bc
0.22 (0.02) c
0.11 (0.02) ab
0.21 (0.02) c
0.07 (0.02) a
0.009

0.15
0.09
0.08
0.12
0.06
0.19
0.07
ns

0.09
0.08
0.05
0.07
0.02
0.08
0.01
ns

0.38 (0.03) b
0.34 (0.02) b
0.20 (0.02) a
0.38 (0.02) b
0.19 (0.02) a
0.33 (0.02) b
0.22 (0.01) a
0.003

65

(0.03)
(0.03)
(0.03)
(0.04)
(0.04)
(0.03)
(0.04)

0.06 (0.03)
0.01 (0.02)
0.01 (0.02)
0.00 (0.02)
0.00 (0.03)
-0.02 (0.02)
-0.05 (0.02)
ns

(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)

3.4.5. Seedling chlorophyll fluorescence and stem water potential
Cover crop treatments had marginally significant effects on seedling Fv/Fm during the
course of the experiment. In 2007, Fv/Fm differed significantly (P=0.048) among groundcover
treatments, with the NS treatments generally having lower Fv/Fm values than the other
treatments (Fig. 3.4). However, in 2008, Fv/Fm did not differ among groundcover treatments.
Similar to seedling survival and Fv/Fm, in both 2007 and 2008, plant stem Ψw were significantly
lower for seedlings on the NS treatments relative to their counterparts in the S and CONV plots
(Fig.3.5). However, mean Ψw of seedlings in the S plots did not differ from that of their
counterparts on the CONV plots. Average Ψw ranged from -2.6 to -1.8 MPa in 2007 and -1.6 to 1.1 MPa in 2008. Mean Ψw values ranged from -2.1 to -1.7 MPa in all S and CONV plots and
from -2.4 to -2.9 MPa for seedlings in all NS treatments in 2007. Although lower mean Ψw
values were recorded in 2008 than in 2007, similar patterns were observed among treatments in
both years.

66

Fv/Fm

1.2

(2007)

S
NS
CONV

1.0
0.8

ab a b

ab a b

b a b

(2008)
ns

ns

ns

0.6
0.4
0.2
0.0

Alfalfa W. clover P. ryegrass

Figure 3.4

Alfalfa W. clover

P. ryegrass

Average maximum quantum yield (Fv/Fm) measured in 2007 and 2008 with
different cover crops in a Fraser fir Christmas tree plantation in Michigan, U.S.A.
Mean values within each year and the same cover crop species, followed with the
same letters are not statistically different. The long dashed line represents the
threshold of Fv/Fm for healthy foliage. S, NS and CONV are strips, no strips and
conventional treatments, respectively; ns = not significant.

67

Stem water potential (MPa)

Alfalfa W. clover P. ryegrass

Alfalfa

W. clover P. ryegrass

0.0

-0.5
-1.0

-2.0
-2.5

a

a

-1.5

b
a

a

a

b

b

c

a

a
b

a

a
b

a

a

S
NS
CONV

b

(2007)

(2008)

-3.0

Figure 3.5

Average stem water potential (Ψw) measured in 2007 and 2008 with different
cover crops in a Fraser fir Christmas tree plantation in Michigan, U.S.A. Mean
values within each year and the same cover crop specie, followed with the same
letters are not statistically different. The dashed line represents the threshold of
Ψw at which stomatal opening and photosynthesis in young conifers are inhibited.
S, ST and CONV are strips, no strips and conventional treatments, respectively

68

3.4.6. Seedling foliar chemistry
Needle N concentration ranged from 1.60 to 2.07% and 1.12 to 1.50% in 2007 and 2008,
respectively (Fig 3.6-a). In both years, PRGNS treatments had the greatest adverse effects on
needle N concentration, although treatment differences were not significant during the first
growing season. In 2007, needle N concentration averaged 1.74 % for seedlings in the NS
treatments, whereas a mean value of 2.00 % was recorded for seedlings in the S and CONV
plots. In 2008, N concentrations averaged 1.30, 1.44 and 1.50 % for seedlings in the NS, S and
CONV plots, respectively. In general, Needle P concentration tended to decrease for seedlings in
the PRGS and PRGNS plots relative to all other treatments but differences were not significant
(Fig 3.6-b).

69

2.5
(a)

2007
2008

N (%)

2.0

1.5

1.0

0.5

0.0
CONV

ALFS ALFNS DWCS DWCNS PRGS PRGNS

0.25
(b)

P (%)

0.20

0.15

0.10

0.05

0.00
CONV

Figure 3.6

ALFS ALFNS DWCS DWCNS PRGS PRGNS

Needles (a) nitrogen, and (b) phosphorus concentrations of Fraser fir seedling in
2007 (black bars) and 2008 (white bars) with different cover crops for a Fraser fir
Christmas tree plantation in Michigan, U.S.A. Treatments are: Conventionally
managed (CONV), Dutch white clover with strips (DWCS), Dutch white clover
with no strips (DWCNS), alfalfa with strips (ALFS), alfalfa with no strips
(ALFNS), perennial ryegrass with strips (PRGS), and perennial ryegrass with no
strips (PRGNS).Values are means (±se; n=3).

70

3.5.

DISCUSSION

In this study, I compared the effects of different cover crops and competitionmanagement on soil chemical properties, tree physiology, foliar chemistry, survival and growth
in a newly established Fraser fir plantation. Over the first two growing seasons, I found that
despite differences in external N fertilizer inputs and competition control, Fraser fir seedling
survival and growth in the S and CONV plots tended to be similar. In contrast, however,
allowing the cover crops to grow continuously over the entire plot led to poor performance of
seedlings. This suggests that incorporating cover crops into Fraser fir Christmas tree production
with partial competition control could be an alternative to the conventional system. On the other
hand, cover cropping with no competition control should not be recommended for Fraser fir
production system, especially at the early stage of the plantation establishment.
The primary benefits of cover crops include their ability to add organic matter to soil and
provide better recycling of plant nutrients. After five consecutive mowing events in 2008, the
two leguminous cover crops outproduced the non-legume cover crop probably because I did not
fertilize the plots, which is in agreement with findings of other studies (Newman et al. 2007;
Torbert et al. 1996). Aboveground biomass yield of alfalfa in this study is comparable to that
obtained by Rock et al. (2009) in Minnesota, U.S. In contrast, biomass yields for white clover
and perennial ryegrass, however, were higher than those reported by Elgersma and Schlepers
(1997) probably due to differences in management, climatic, or edaphic conditions as these
factors highly influence cover crop herbage yield (Odhiambo and Bomke 2001; Cherr et al.
2006).
Tissue N concentration of all three cover crops decreased with each subsequent mowing
event, whereas the C: N ratio increased. These results are congruent with the results of studies
71

conducted elsewhere (Odhiambo and Bomke 2001; Dahlin et al 2005). It is generally agreed that
cover crop amendments with C: N ratios lower than 25:1 lead to rapid N mineralization (Ranells
and Wagger 1996). The C: N ratios for all three cover crops were below this critical value,
suggesting that biomass N would be quickly decomposed and released within weeks for plant
(trees and cover crops) uptake. The high tissue N concentration and low C: N ratios obtained in
this study could be attributed to the short mowing frequency that allowed for the re-growth of
fresh, immature plant material throughout the growing season.
These measurements of soil mineral N and N mineralization in 2007 and 2008 provided
an indication of the fate of N from the cover crop green manures. Although more extensive
research would be needed to follow the dynamic of the cover crop biomass N in the plantation
soil, a single sampling of soil during a growing season may provide an indication of treatment
differences. Although high available N and N mineralization rates were generally obtained in
the S and NS plots, these did not necessarily lead to enhanced tree foliar N nutrition in these
plots relative to the control. On the other hand, I did observe marginal effects of treatments on
soil available nutrients (except for N and P) and seedling foliar nutrition (except for N). The
reason for the lack of response of seedling foliar chemistry to groundcover treatments could be
that water stress might have reduced the ability of the trees to effectively absorb plant available
nutrients from the soil solution.
As indicated by the weather data, July and September of 2007 were drier than the 12-year
average. Similarly, May and August of 2008 received 70 and 75%, respectively, less rainfall than
the 12-yr average. The expected low soil moisture contents during these periods may have
reduced the effective diffusion of elements in the soil solution. In addition, plant water stress
may in turn, have reduced the mass flow and plant uptake of elements. Similar to moisture

72

availability, root volume and nutrient uptake and utilization are linked. Prior to planting,
seedlings were root pruned to 20 cm following standard planting practices. This resulted in
substantial root loss, especially the absorbing roots, and may also limit seedling nutrient uptake.
As reported by Knapp and Smith (1982), root penetration to greater depths is an important factor
determining the survival of conifer seedlings. Although I did not investigate seedling root
development in this study, it is also likely that the root system was still shallow during the first
year of the plantation establishment. The newly transplanted seedlings might have suffered from
a combination of high temperature, drought, root damage and shallow rooting that contributed to
poor nutrition and high mortality of seedlings (Dalton and Messina 1994), especially in the NS
plots. However, most of the seedlings that survived during the first growing season also survived
during the second year. Second year seedlings likely had ample time to restore their root system
to the pre-transplant size, making them less susceptible to environmental stresses.
The presumed reduction of belowground competition between Fraser fir seedlings and
cover crops by creating strips was expected to influence stem Ψw. The strip treatment
substantially raised the mean Ψw of Fraser fir seedlings relative to the NS treatments, suggesting
that seedlings in S plots had less water stress than their counterparts in the NS plots. Despite
differences in the degree of competition control, seedlings in the S plots displayed similar Ψw to
that of their counterparts on the CONV systems. This may be partially due to likelihood that root
systems in the S plots have not grown much beyond the strip cleared of competing vegetation. It
is noteworthy that seedlings in NS plots had their Ψw consistently below the threshold of -2.0
MPa, which is considered severe water stress and critical for stomatal opening and
photosynthesis in seedlings and saplings of a variety of conifers (Tseng et al. 1988). In 2008,
73

mean Ψw was -1.1 MPa for seedlings in S and CONV treatments and -1.5 MPa for their
counterparts in the NS plots. This suggests that seedlings in NS plots suffered more severe water
stress than their counterparts in the S and CONV plots. The lower mean Ψw values of seedlings
recorded during the first growing season versus the second growing season is in agreement with
Muyi and Smith (1991) who also reported increasing Ψw value with increasing age in subalpine
fir (A. lasiocarpa) seedlings.
The capacity to perform photosynthesis, given appropriate environmental conditions, is
reflected by measurements of the maximum quantum yield of photosystem II (Fv/Fm).
Environmental conditions such as extreme temperature, drought or nutrient deficiency can cause
photo-oxidation or photo-inhibition, changing the efficiency of non-photochemical quenching
and decreasing Fv/Fm (Westin et al. 1995; Maxwell and Johnson 2000). Similar to seedling
survival and Ψw, I observed a significant response of Fv/Fm to groundcover treatment in 2007.
Seedlings from NS treatments had their needles Fv/Fm below the 0.8 threshold considered
characteristic of healthy foliage (Lambers et al. 1998) whereas mean Fv/Fm values for seedlings
in the S and CONV treatments were greater or equal to this threshold. Competition management
through creating strips, however, led to enhanced Fv/Fm level comparable to the CONV plots. I
found a positive relationship between Ψw and Fv/Fm in both 2007 and 2008, suggesting that
water stress was responsible for the decline in seedling Fv/Fm on the NS plots. Water stress, in
turn, can reduce the ability of tree foliage to produce energy (carbohydrates), diminish growth,
and leave the tree susceptible to many other environmental stresses. Together, these problems,
74

might have contributed to transplant stress that led to reduced seedling survival and growth in the
NS plots.
Continued root and shoot growth during the establishment period depends on a number of
factors such as the availability of nutrients and moisture, a well-established root system takingup water and nutrients as well as healthy leaves producing high levels of carbohydrates during
the growing season (Lambers et al. 1998). Relative diameter and height growth were not affected
by groundcover treatment during the first growing season probably because root systems were
still in the recovery phase, during which more carbohydrates are allocated for root growth than
shoot growth. During the second growing season, however, unlike height, relative diameter
growth of seedlings in all NS treatments was significantly reduced relative to their counterparts
in the S and CONV plots. As reported in previous studies, height is generally less sensitive than
diameter, and height growth differences may not become evident until the second or third
growing season (Dunlap and Helms 1983; Dalton and Messina 1994).

75

3.6.

CONCLUSIONS

Results of this study not only underline the major role of cover cropping in improving
soil N fertility level but also underscore the overriding importance of competition management
to ensuring survival and improving early growth of newly planted Fraser fir Christmas trees.
-

Cover crop treatments increased soil NO3 -N levels by 1.5- and 2.2-fold relative to CONV in the
top soil layer in 2007 and 2008, respectively. Nitrification rate also increased substantially in all
cover crop treatments relative to the CONV treatment at all three soil depths. Seedlings in the NS
treatment performed poorly compared to their counterparts in the S and CONV plots, and water
stress appeared to be the key factor on the poor performance of seedlings. However, seedlings on
the S treatments performed as well as those on the conventional system, suggesting that
incorporation of cover crops into Christmas tree plantation with partial weed competition control
could be a viable alternative to the conventional system.
Although incorporating cover crops into Christmas tree plantations appeared to be a
promising approach, an economic evaluation of the costs and benefits should be conducted to
determine its profitability before recommending such a practice to growers. The study should
focus on determining the costs and efforts required to purchase and sow the cover crop seed,
periodically mow the cover crop, and maintain the strips. These costs should be compared to the
costs and labor involved in the conventional system such as complete weed control and inorganic
N fertilization. The potential contribution of the cover crops to soil carbon sequestration over the
entire plantation rotation period should also be investigated, so that growers who adopt this
approach could be adequately rewarded for the carbon sequestration services they provide.
Another possible option would be to simply create strips and allow local vegetation to fill
in between the rows and serve as a “wild cover crop.” This would remove the cost of purchasing
76

and establishing a cover crop. While it may not provide as much nitrogen as a leguminous cover
crop, it should approximate the advantages of perennial ryegrass. Additionally, a lysimeter study
of nitrate leaching should be conducted to determine whether the cover crop system reduces
-

NO3 -N leaching below the tree rooting zone relative to the conventional system.

77

BIBLIOGRAPHY

78

3.7.

BIBLIOGRAPHY

Chastagner G.A., Benson D.M. (2000) The Christmas tree: Traditions, production, and diseases.
Online. Plant Health Progress doi:10.1094/PHP-2000-1013-01-RV
Cherr C.M., Scholberg J.M.S., McSorley R. (2006) Green manure approaches to crop
production: a synthesis. Agron. J. 98:302-319
Dahlin S., Kirchmann H., Katterer T., Gunnarsson S., Bergstro M.L. (2005) Possibilities for
improving nitrogen use from organic materials in agricultural cropping systems. Ambio 34:288–
295
Dalton C.T. and Messina M. (1994) Water relations and growth of loblolly pine seedlings
planted under a shelterwood and in a clear-cut. Tree Physiol. 15:19-26
Di H.J. and Cameron K.C. (2002) Nitrate leaching in temperate agroecosystems: Sources, factors
and mitigating strategies. Nutr. Cycl. Agroecosys. 64:237-256
Doane T.A., Horwath W.R. (2003) Spectrophotometric determination of nitrate with a single
reagent. Anal Lett 36: 2713–2722
Dunlap J.M., Helms J.A. (1983) First-year growth of planted Douglas-fir and white fir seedlings
under different shelterwood regimes in California. For. Ecol. Manag. 5:255-268
Elgersma A., Schlepers H. (1997) Performance of white clover/perennial ryegrass mixtures
under cutting. Grass and Forage Sci. 52: 134-146
Frampton J., McKinley C. (1999) Christmas trees and greenery in Denmark – Production and
tree improvement. Am. Christmas Tree J. 43(2): 4-11
Hänninen H., Ohtonen R., Huttunen S. (1999) Effects of leguminous ground cover competition
on red birch and soil nutrient status in the nursery. Plant and Soil 216: 129-138
Hart J., Fletcher R., Landgren C., Horneck D., Webster S., Bondi M. (2004) Christmas tree
nutrient management for Western Oregon and Washington-Oregon State University Extension
service-EM 8856-E. Online. http://extension.oregonstate.edu/catalog/pdf/em/em8856-e.pdf.
Hunt R. (1982) Plant growth curves, the functional approach to plant growth analysis. London:
London: Edward and Arnold.
Knapp A.K., Smith W.K. (1982) Factors influencing understory seedling establishment of
Engelmann spruce (Picea engelmannii) and subalpine fir (Abies kzsiocurpu) in southeast
Wyoming. Can. J. Bot. 60:2753-2761.

79

Koelling M.R. (2002) Fertilization recommendations for Fraser fir: Part II-Established plantings.
Michigan Christmas Tree J.49:5-8
Koelling M.R., Hart J.B., Leffers L. (1998) Christmas tree production in Michigan. Michigan
State University Ext. Ag. Exp. Station Special Rep. – SR619201
Lambers H., Chapin F.S., Pons T.L. (1998) Plant physiological ecology. Springer, New York.
540 p
Leuty T (2005) Fraser fir for Christmas Trees and Landscape Transplants.” Ministry of
agriculture, Food and Rural Affairs.
Little T.M., Hills F.J. (1978) Agricultural Experimentation Design and Analysis. U.S.A: John
Wiley and Sons Inc.
Maxwell K., Johnson G.N. (2000) Chlorophyll fluorescence – a practical guide. J. Exp. Bot.
51:659–668
Mehlich A. (1984) Mehlich-3 soil test extractant: a modification of Mehlich-2 extractant.
Commun Soil Sci. Plant Anal. 15(12):1409-16
Minitab, Inc. (2006). MINITAB (Statistical software). Release 14. www.minitab.com
Muyi C. and Smith W.K. (1991) Photosynthesis, water relations and mortality in Abies
lasiocarpa seedlings during natural establishment. Tree Physiol. 8: 37-46
Newman Y.C., Chambliss C.G. (2007) Warm season (summer) forage legume guide. EDIS
publication SS-AGR-48 (http://edis.ifas.ufl.edu/DS126)
Odhiambo J.J.O., Bomke A.A. (2001) Grass and legume cover crop effects on dry matter and
nitrogen content Agron. J. 93:299–307
Pedersen L.B., Christensen C.J. Ingerslev M (2006) Leaching versus input of nitrogen, potassium
and magnesium in different fertilizer regimes in Christmas tree stands of Abies nordmanniana in
Denmark. Scand. J. For. Res.: 21(2): 130-142
Ranells N.N., Wagger M.G. (1996) Nitrogen release from grass and legume cover crop
monocultures and bicultures. Agron. J. 88: 777-782
Rock K.P., Thelemann R.T., Jung H.G., Tschirner U.W., Sheaffer C.C., Johnson G.A. (2009)
Variation due to growth environment in Alfalfa yield, cellulosic ethanol traits, and paper pulp
characteristics. BioEnergy Res. 2(3): 79-89
Rothstein D.E. (2005) Nitrogen management in an Abies fraseri Christmas tree plantation:
effects of fertilization on soil N availability, tree performance and nitrate leaching. For. Sci. 51
(2):175-184
80

Ruffo M.L., Bollero G.A. (2003) Modeling rye and hairy vetch residue decomposition as a
function of degree-days and decomposition days. Agron. J. 95:900–907
Sánchez E.E., Giayetto A., Cichón L., Fernández D., Aruani M.C., Curetti M. (2007) Cover
crops infuence soil properties and tree performance in an organic apple (Malus domestica Borkh)
orchard in northern Patagonia. Plant and Soil. 292:193–203 DOI 10.1007/s11104-007-9215-7
Sidebottom J.R. (2009). Evaluation of the Christmas tree industry in western north Carolina on
surface water quality. Christmas tree production in North Carolina; Online
http://ces.ncsu.edu/Fletcher/programs/xmas/environment/water_summary.pdf
Sinsabaugh R.L., Reynolds H., Long T.M. (2000) Rapid assay for amidohydrolase (urease)
activity in environmental samples, Soil Biol. Biochem. 32 2095–2097
Smith M.W., Shiferaw A., Rice N.R. (1996). "Legume cover crops as a nitrogen source for
pecan." J. Plant Nutrition 19 (7): 1117-1130
Torbert H.A., Reeves D.W. and Mulvaney R.L. (1996) Winter legume cover crop benefits to
corn: rotation vs. fixednitrogen effects. Agron. J. 88: 527-535
Tseng E.C., Seiler J.R. and Chevone B.I. (1988) Effects of ozone and water stress on
greenhouse-grown Fraser fir seedling growth and physiology. Environ Exp. Bot. 28:37-41
USDA-NRCS , MAES (1992) Soil Survey of Ingham County, Michigan
Vilsack T., and Clark C.Z.F. (2009) 2007 census of agriculture- USDA, National Agricultural
Statistics Service Vol. 1. Geographic area series. Part 51 AC-07-A-51. Online.
http://www.agcensus.usda.gov/Publications/2007/Full Report/usv1.pdf.
Westin J., Sundblad L.G., Hällgren J.E. (1995) Seasonal variation in photochemical activity and
hardiness in clones of Norway spruce (Picea abies). Tree Physiology 15: 685–689

81

CHAPTER 4

GREENHOUSE GAS EMISSIONS AND N LEACHING ASSOCIATED
WITH CONVERSION OF GRASSLAND TO SHORT-ROTATION WOODY
BIOMASS CROPS IN NORTHERN MICHIGAN, U.S.A.

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4.1

ABSTRACT

Pastures or grasslands are widely believed to be suitable ecosystems for growing
bioenergy feedstocks as this may avoid conflict with food crop production or impacts to existing
forests. However, there are still considerable knowledge gaps regarding the impacts of
converting such lands into short-rotation woody crop (SRWC) bioenergy production. In the
present study, I measured the short-term effects of converting pastureland to poplar (Populus)
and willow (Salix) bioenergy plantations on soil greenhouse gas (GHG) fluxes and N leaching
losses, on a moderately well-drained fine sandy loam soil, in northern Michigan, U.S.A. The
study site had been cleared of forest ca. 100 y ago, and transitioned into pastureland in 2005. In
spring of 2009, experimental plots were cleared of existing pastureland vegetation, cultivated and
planted with either willow or poplar. Soil GHG fluxes and N leaching losses from these plots
were compared against undisturbed, reference pasture plots. Based on a set of assumptions
regarding the contribution of root respiration to total CO2 efflux, I estimated that the cultivated
-1

poplar and cultivated willow plots lost an extra 3.3 and 9.0 Mg CO2 ha , respectively, compared
to the reference pastureland during their establishment year. Emissions of N2O also increased
markedly following cultivation with rates as high as 250 ng N2O-N cm

-2

-1

hr . Consequently,

cumulative direct N2O emissions were 17.2- fold and 13.3-fold higher in the poplar and willow
plots, respectively relative to the undisturbed pasture reference sites. Similarly, grassland
conversion resulted in 20.3-fold and 14.6-fold increases in cumulative indirect N2O emissions
-

(from NO3 -N leaching loads) in poplar and willow plots, respectively as compared with the
reference plots. Although fluxes of methane were a small component of the net GHG balance,
83

data suggest that the undisturbed plots were a sink for CH4 whereas the cultivated plots were a
-

source for CH4. Moreover, land conversion resulted in marked increases in NO3 -N
concentrations of soil leachate which were negligible under the controls (0.1 ppm), but regularly
exceeded 5 ppm in the plantations. Dissolved organic nitrogen (DON) represented an important
portion (69%) of N leaching losses of both bioenergy plantations. Stimulation of nitrification
-

following cultivation appeared to be the driving factor behind increased N2O and NO3 -N fluxes.
-1

Overall, pastureland conversion incurred GHG debts of 9.4 and 14.2 Mg CO2eq ha

for poplar

and willow plots, respectively during the establishment year alone of the SRWC bioenergy
program. These figures do not include any GHG debts associated with fossil fuel inputs for land
preparation. Often neglected in most LCA studies, these GHG debts compares with estimated
11.3 Mg CO2eq ha

-1

of N2O released from N fertilizer application and leaf litter decomposition

over an entire live cycle of 23 years of SRWC bioenergy systems. Before embarking in a large
scale deployment of SRWC plantations on similar pastureland, alternative cropping practices
such as no-tillage, minimum tillage or high density tree planting that have potential to minimize
GHG emissions and nutrients losses should be implemented for more sustainable feedstock
production.

84

4.2.

INTRODUCTION

There is an increasing interest worldwide in displacing fossil fuels with alternative
renewable energies as the latter can potentially contribute to reduce atmospheric CO2 emissions
while enhancing national energy security (Cook and Beyea 2000; Ravindranath et al. 2008). The
potential for biofuel production and use to meet growing renewable energy targets has been
emphasized by the Intergovernmental Panel on Climate Change (IPCC 2007) methodology and
supported by several life cycle assessment (LCA) studies (Graham et al. 1992; Heller et al. 2003;
Heller et al. 2004; Volk et al. 2004; Keoleian and Volk 2005; Cherubini et al. 2009; Goglioa et
al. 2009). To meet the world bioenergy needs, the promotion of large-scale cultivation of
bioenergy crops on a wide range of grassland ecosystems including permanent pastures,
“marginal or degraded land”, “set-aside or reserved agricultural land”, “idle or abandoned land”,
has been suggested as a sustainable strategy for biomass feedstock (De La Torre Ugarte et al.
2003; Perlack et al. 2005). However, there are still knowledge gaps, especially in terms of
immediate direct impacts of clearing and cultivating such grassland ecosystems for bioenergy
crop production on the net greenhouse gas (GHG) balance. The short-term direct effects of land
use conversion, often neglected by many LCA studies, could result in significant GHG emissions
and, thus, substantially reduce the climate benefits of bioenergy production.
Some researchers have argued that land-use conversion from native ecosystems to biofuel
crop production could result in significant GHG emissions and a negative carbon balance, or
carbon-debt, for many years (Cowie 2006; Pineiro et al 2009). These studies focused primarily
on the issue of CO2 release from ecosystem biomass and soils, but relatively little attention has
been given to the GHG emissions associated with disruptions to the nitrogen (N) cycle associated

85

with land conversion. Based on the conceptual model of molecular soil C dynamics, Grandy and
Neff (2008) ecosystem disturbances (e.g. N fertilization and tillage) which enhances soil organic
matter (SOM) decomposition rates may have a series of direct and indirect effects on C
stabilization in soil, leading to the release of large amounts of CO2 and nitrous oxide (N2O), a
-

greenhouse gas 298 times more powerful than CO2. Furthermore, nitrate (NO3 -N) produced
following cultivation can be lost through leaching which will have implications for the quality of
drinking water and surface water eutrophication.
In the United States, permanent grasslands and pastures represent 58.7% of the total
-1

(1,174 million ha ) agricultural land (Lubowsky et al. 2005). To meet the range of renewable
energy targets (7-20% over various time periods) announced by the different states of the U.S.A.,
the expansion of bioenergy crops is likely to come from these available permanent grasslands
(Ravindranath et al. 2008). In the state of Michigan alone, it is estimated that at least 3 million
hectares of idle agricultural and pasture lands are suitable for growing perennial bioenergy crops
(Kelly 2002). Pastures and hayfields that represent a significant portion of the landscape of
northern Michigan are particularly targeted for bioenergy plantations such as short-rotation
woody crops (SRWC) of willow and poplar. However, grassland soils are generally believed to
have large stores of SOM that may become susceptibility to accelerated mineralization and
nitrification/ denitrification once they are disturbed (Six et al. 1998; Soussana et al. 2004)
through conversion to SRWC production, which involves the use of conventional agricultural
methods, such as soil plowing, disking and herbicide application (Tubby and Armstrong 2002).
Cultivation has widely been recognized to create a soil environment that increases SOM
availability, stimulates aerobic microbial activity, and accelerates organic C oxidation to CO2

86

(Blanco-Canqui and Lal 2007; Yadav and Malanson 2007). In addition, mineralization of SOM
enhance soil N turnover which favors N2O production and emissions in soils directly as a
consequence of both nitrification and denitrification (Smith et al. 2003; Grandy and Robertson
2006). Indirect emission of N2O can also occur when N is lost from soil in forms other than N2O
-

(e.g. NOx, NH3, NO3 -N) and later converted to N2O (Wrage et al 2005). The third most
important GHG associated with land conversion is methane (CH4). Soils can be an important
sink or source for methane. Under normal conditions, native grasslands are reported to be a small
sink for CH4 whereas cultivation of such sites tends to inhibit the activity of CH4 oxidizing
bacteria and reduce natural rates of CH4 consumption (Hutsch 2001; Robertson and Grace 2004).
-

Finally, independent of GHG accounting, NO3 -N leaching is important from a water-quality
perspective because it contributes to aquatic eutrophication and can pose a health risk to humans.
This study was initiated to investigate the potential effects of grassland conversion to
SRWC plantation on the net emissions of three primary GHGs associated with agriculture (CO2,
N2O, and CH4) at a site in the central Upper Peninsula (UP) of Michigan. It was hypothesized
that the initial conversion from a grassland to a SRWC system would incur some level of GHG
“debt” due to increased SOM mineralization, resulting in direct and indirect (through N leaching
and subsequent conversion to N2O) emissions of GHG. In this study, I posed two fundamental
questions: (i) what is the magnitude of GHG emissions and “debt” associated with grassland
conversion to SRWC systems? and (ii) what is the “payback period”, the time required for the
SRWC systems to overcome this GHG debt and begin providing net GHG benefits?
87

4.3.

MATERIALS AND METHODS

4.3.1. Study site characteristics
This experiment was conducted in a former grassland located in the central Upper
Peninsula of Michigan, U.S.A. (latitude 46º36´N, longitude -87º27´E, elevation 290 m above sea
level) (Fig 4.1). It is noteworthy that this is the site of the first commercial SWRC plantation in
Michigan’s Upper Peninsula. In the spring of 2009 all but 2.6 ha of the entire field was converted
from grassland to a willow plantation designed to provide feedstock for a new wood-fired power
plant in the nearby city of Marquette, MI. At the same time a clonal-yield trial was established
in a 2.6 ha portion of the field by the Michigan Agricultural Experiment Station in order to
evaluate the performance of 11 clones of hybrid poplar and 20 clones of willow for use in
bioenergy production. I used this clonal yield trial to measure soil GHG fluxes and N leaching
associated with converting grassland to either hybrid poplar or willow.
The site was cleared of the original hardwood forest approximately 100 years ago and
was used for a variety of purposes including potatoes, small grains, hay, and pasturing cattle.
When the farm was transitioned into intensive grazing in 2005 it was seeded with birdsfoot
trefoil (Lotus corniculatus) along with a small amount of timothy (Phleum alpinum). The current
landowner has owned the site for the past 25 years during which time the land was used
primarily for hay alternating with small grain production and intensive grazing. During that time
period, the site regularly received cow manure applications of about 23 500 L ha

-1

annually.

Prior to establishing the willow and poplar yield trials in spring of 2009, the site had not been
tilled since the spring of 2005. The most abundant plant species in the grassland prior to
conversion to willow and poplar yield trials were birdsfoot trefoil, canary reed grass (Phalaris
arundinacea) and quack grass (Elytrigia repens).
88

Figure 4.1

Experimental site location in Michigan, U.S.A. “For interpretation of the
references to color in this and all other figures, the reader is referred to the
electronic version of this dissertation.”

89

The area has lake-enhanced normal seasonal precipitation of 762 mm, and daily minimum winter
temperatures fall to −18°C or lower an average of 26 times in a season. The growing season
averages 91 frost free days and means annual maximum and minimum temperatures are 11 and 3°C. The soil type is a moderately well-drained Munising fine sandy loam (Eichenlaub et al.
1990).
4.3.2. Experimental set-up
The clonal yield trial was established in the spring of 2009 in a randomized block design
with 5 replicate, 64-tree plots of poplar (19 x 22 m) and 4 replicate, 78-tree plots of willow (6 x 8
m). It was not feasible to investigated differences among all 31 clones so I selected a priori,
promising clones of willow (Salix dasyclados SV1]) and of poplar (Populus nigra x P.
maximowiczii [NM6]). The poplar plots were planted at a spacing of 2.4 m between rows and 2.1
m between trees within rows, while the willow plots were planted in double spacing with 0.8 m
between narrow rows, 1.5 m between wide rows and 0.6 m between trees within rows (Bergkvist
and Ledin 1998). Additionally, four plots situated adjacently (at least 10 m away from the
cultivated and plantation zones) to the poplar and willow trials, in the undisturbed pastureland
were used as controls.
Field management practices for converting the grassland to SRWC started with weed
-1

control on 23 May 2009 which consisted of applying glyphosate (2.11L active ingredient ha ) to
all poplar and willow plots. Next, plots were plowed and disked on 31 May 2009 to about 20-25
cm depth, followed by a second cultivation with a tiller on 12 June 2009 to 15-20 cm depth. On
17 and 18 June, 2009, all willow and poplar plots were again sprayed with the herbicides
-1

-1

simazine (1.13 kg active ingredient ha ) and oxyflurofen (1.13 L active ingredient ha ).

90

Herbicide application rates were determined from local experience at MSU’s Forest Biomass
Innovation Center (FBIC). Descriptions of cultural management practices applied to poplar and
willow yield trials are provided in Appendix –Tables A.-2 &-3. No fertilizers were applied
during this first year of plantation establishment. Planting of willow and poplar cuttings (about
0.6 to 1.3 cm in diameter and 25 cm in length) occurred on 16 and 18 June 2009, respectively
and cuttings were obtained from Hramor Nursery, Michigan. The whole experiment was
protected with an electric fence on 13 July 2009 to prevent the impact of deer browsing. On 2
September 2009, the poplar plantation soil was again shallow-cultivated between tree rows.
Willow shoots were coppiced on 20 October 2009 using a Troy-bilt sickle bar mower powered
by a walk-behind system. The stems after cutting were typically 2.5 to 5.0 cm in height
depending on the micro-relief of the area.
4.3.3. Measurement of GHG fluxes
Emissions of CO2, N2O and CH4 were measured 11 times in 2009 (May –November)
and two times in spring of 2010 using one (26.2 cm – diameter and 26.5 cm tall) static chamber
(high-density polyethylene plastic) per plot. Chambers consisted of bases driven 10 cm into the
soil and gas tight lids fitted with rubber septa to sample the approximately 10 L of headspace
volume. Chambers were left uncovered except during the periods when gas samples were being
collected. Aboveground vegetation in each chamber was clipped immediately prior to sampling
in order to avoid respiratory fluxes of CO2 from aboveground plant parts. Removing plants from
the chamber was performed to solve the problems associated with subtracting the fraction of the
CO2 release originating from the aboveground vegetation which is not taking into account when
calculating global warming potential. Gas samples were collected from the headspace between
91

10.00 and 15.00 h to minimize changes of soil GHG fluxes associated with diurnal cycles.
Headspace gas samples (10 mL) were drawn using a 10 mL syringe at 0, 20, 40 and 60 minutes
following chamber closure, and then immediately over-pressurized to pre-flushed 5.9 mL flatbottom exetainer vials (Labco, Unlimited, Buckinghamshire,U.K.).
The concentration of CO2, N2O and CH4 in the headspace gas samples were determined
using a Gas Chromatograph (GC-2014, Shimadzu Corporation) equipped with electron-capture
and flame-ionization detectors. Gas flux measurements were initiated on 22 May 2009 before
field management and planting took place and continued until 4 May 2010, except during the
winter period when the ground was continually covered with snow. A total of 13 sampling events
were captured over the course of this one year investigation, of which 11 were conducted in 2009
(22 May, 5 and 25 June, 7 and 28 July, 11 and 24 August, 14 September, 6 and 17 October and
12 November ) and two in 2010 (30 March and 4 May).
4.3.4. Measurements of Nitrogen leaching potential
Two pairs of tension lysimeters (Soil Moisture Equipment, Santa Barbara, CA) were
installed within each plot in the poplar and willow plantations, and a single lysimeter in each
undisturbed grassland control plot. Lysimeter cups were installed at 50 cm below the soil
surface; one within a tree row and the second between two consecutive rows of trees. A tension
of -50 kPa was applied to each lysimeter two to three weeks prior to sampling. Water samples
were taken height times the day of gas sampling, starting from 11 August 2009 throughout 4
May 2010 (except during the winter period when the soil was permanently covered with snow).
-

+

Solution samples were analyzed for NO3 –N and ammonium (NH4 –N) spectrophotometrically
(Spectrophotometric plate reader, Model ELx Bio Tek Instruments, Inc. Winooski, Vermont,

92

U.S.A.) following the procedures described by Doane and Horwath (2003) and Sinsabaugh et al.
(2000), respectively. Total dissolved N was determined by oxidative combustionchemiluminescence (Shimadzu model TOC-VCPN analyzer and, Kyoto, Japan). Dissolved
organic nitrogen (DON) was calculated as the difference between total N and mineral N (NO3

-

+

+NH4 –N).

4.3.5. Nitrification Potential
Soil nitrification potentials were assayed according to the “shaken slurry method”
+

developed by Hart et al. (1994) in order to evaluate the activity of the NH4 -N oxidizer
community in soil as affected by grassland conversion to SRWC production. Soil samples
collected one week prior (22 May 2009) and eight weeks after (22 July 2009) land conversion,
were use to measure nitrification potential. The10-cm deep soil cores collected during GHG flux
measurements were kept in plastic zip bags and stored in a cooler or fridge (5ºC) and transported
to the Forest Biogeochemistry laboratory at MSU. Field-moist soils were sieved (<2mm) after
removing all vegetation and coarse root parts, before placing subsamples of 10 g of soil into an
acid-washed Erlenmeyer flasks with 100 mL of buffer solution (0.384 ppm of 1M KH2PO4,
0.614 ppm of 1MK2HPO4 and 15 ppm of (NH4)2SO4 solutions) with the pH adjusted to 7.2.
Then aliquots of 1 mL were taken from the shaken slurry incubation at 2-h, 4-h, 20-h and 24-h
-

period, and 0.1mL of flocculant solution added to it. Samples were analyzed for NO3 -N using
the microplate assay as described in the previous section after centrifuging at 3000 rpm for 10
min.

93

4.3.6. Ancillary data
Climatic data (precipitation and air temperature) were obtained from the weather station
at MSU’s Agricultural Experimental Station at Chatham (Enviro-Weather) located 27 km east of
the field site. Soil temperature measurements were taken at 7 cm soil depth during each sampling
event in close proximity (about 10 cm) to each of the chambers, using a temperature probe (HI145, Hanna Instruments) I used 3.8-cm diameter PVC soil corers to take a 10-cm deep soil core
in close proximity to the gas flux chamber at each sampling period. Gravimetric water content
was determined for each chamber per sampling date, and then multiplied by the bulk density (BD
-3

-1

in Mg m ) to determine soil volumetric water. Total porosity was determined as (1– BD X PD )
−3

while PD is soil particle density assumed to be 2.65 Mg m . The ratio of volumetric soil water
to total porosity was used to calculate soil water-filled pore space (WFPS).
Soil samples collected at each gas sampling point were also used for determination of
+

-

NH4 -N and NO3 -N concentrations after extracting the sieved soil (<2mm) with 2 M KCL (5:1
extractant to soil ratio) and analyzing extracts spectrophotometrically. All soils were processed
within 24 h after collection and stored in a fridge (5 deg ). Soil pH was measured (in a mixture of
soil and 0.01 M CaCl2) from air-dried soil with a Corning pH Meter 430 (Corning Incorporated,
Science Products Division, New York, 14831). Total C and N contents were determined on the
same soil samples by dry combustion with an elemental analyzer (Model ECS 4010, Costech
Analytical, Valencia, CA).
4.3.7. Nitrogen leaching and GHG flux calculations
To estimate nitrogen leaching, I used a modified bucket model (Fetter 1988) to estimate
water yield at 50-cm depth from potential evapotranspiration (modified Penman–Monteith
94

method), rainfall, and soil water holding capacity. On days with a net water yield, I estimated
-2

+

-

leaching losses as the product of water yield (L m ) and soil solution NH4 -N, NO3 -N and
-1

DON concentrations (mg L ). Based on the Inter-governmental Panel on Climate Change (IPCC
1997) methodology, I assumed that 2.5% of the N leached would eventually be denitrified to
N2O in water ways.
GHG fluxes were estimated from the concentration change in the chamber headspace
over the 60 min sampling period. Chamber methods for measuring trace gas fluxes are widely
reported to alter the diffusivity of trace gases at the soil surface following chamber deployment,
leading to an underestimation of actual gas fluxes (Livingston et al. 2006; Rochette and Bertrand
2007). To account for this bias due to the chamber deployment, I used the simplified linear
method for quantifying theoretical underestimation of chamber-based gas fluxes developed by
Venterea et al. (2009) to calculate the GHG emission rates from my experimental plots.
Estimation of cumulative CO2, N2O and CH4 emissions during the study period was done by
successive linear interpolation between adjacent sampling dates assuming that emission followed
a linear trend during the periods when no sample was taken.
To compare the net effect of land use conversion from long-term grassland to SRWC
systems on the atmospheric GHG budget, I used global warming potential factors of 25 and 298
based on a 100-year horizon for CH4 and N2O, respectively, to convert the soil emissions of
these two GHGs to CO2eq (IPCC 2007). The global warming potential (GWP), a measure of
how much a given mass of GHG is estimated to contribute to global warming, of the different
land use types was calculated using the following equation (Watson et al. 1996).

95

Net GHGflux = CO2 + 25*(CH4flux) + 298*(N2Oflux)

Eq. (4.1)

Soil CO2 flux in grasslands is composed of root respiration (or the respiration of
underground portion of green plants) and respiration due to microbial decomposition of SOM
(refers to as heterotrophic respiration). With regard to the CO2-driven greenhouse effect,
however, only SOM decomposition contributes to changes in atmospheric CO2 concentration
(Wang and Fang 2009). Hanson et al. (2000) reviewed published articles on root contribution
(RC) to soil respiration and found that grassland RC to total soil CO2 effluxes ranged between 10
and 90% with an average near 50%. Therefore, I used three scenarios for calculating the net
GWP of the bioenergy systems: the first scenario (best case) assumed grassland root contribution
(RC) total soil CO2 efflux to be 10%; a second scenario (worst case) assumed that grassland RC
is 90% of the total soil CO2 efflux and the third scenario used an average value of 50% for RC to
total soil CO2 efflux. Because trees in the plantations were still young and weeds were
aggressively controlled during the study period, root respiration (from poplar and willow
seedlings and from weeds) was assumed to be negligible in both bioenergy systems. I evaluate
the validity of these assumptions and scenarios in the Discussion section below. To calculate
cultivation induced GHG debt and the payback time, I also made the assumption that yields as
harvestable wood biomass in the region would be in the range of 5-20 Mg DM ha
current average of 11 Mg DM ha

-1

yr

-1

-1

yr

-1

with a

based on results of previous studies (Kelly 2002, Ronald

et al. 2009). Based on Gasol et al. (2009) poplar biomass is 50.3% carbon and I assumed the
same C content in the biomass of willow.
96

4.3.8. Data analysis
I tested for the effect of treatment and sampling date on fluxes of CO2, CH4, N2O and the
net GHG flux from soil. I used a mixed model, repeated measures analyses of variance (Little et
al. 1996) in SAS (SAS Institute, Cary, NC), unequal variance model to test for the effect of land
use type, sampling date and the interaction between land use type and sampling date for each
GHG, soil temperature and WFPS. Mean values of flux rates for N leaching and for each trace
gas and net GHG flux as CO2eq were calculated for each sampling date. Each GHG flux rate
was also converted in terms of CO2eq using the GWP coefficients referenced above. Soil
inorganic N concentration and N species concentrations in leachate water were also analyzed for
differences among treatments with a mixed model, repeated measures analyses of variance,
which included the random effects of treatment. Differences were accepted as significant at the
0.05 level.

97

4.4.

RESULTS

4.4.1. Soil chemical properties
Before tillage on 22 May of 2009, soil total C concentrations in the top 10 cm of soil
were 3.3%, 3.7% and 3.1% in the undisturbed pasture, poplar and willow plots, respectively, and
did not differ between treatments (Table 4.1). Soil total N averaged 0.24%, 0.27% and 0.22% in
the undisturbed pasture, poplar and willow plots, respectively, with no significant difference
between treatments. Similarly, there were no significant differences in soil C: N ratio and pH
before tillage. There were no detectable changes in total soil C and N levels a year following
land cultivation (data not shown here). Soil bulk density, measured on 7 July of 2009 after initial
tillage, was significantly lower in the poplar plantation than in the willow and untilled grassland.
4.4.2. Environmental variables
The annual (from May of 2009 to April of 2010) rainfall was 575 mm which was much
less than the long-term average of 762 mm. Of the amount of rainfall received during the study
period, it was estimated that 225 mm (39%) percolated below the rooting zone (50 cm). During
the first growing season, the driest period was from 30 August to 20 September of 2009 when
there was no rainfall (Fig 4.2-a).
WFPS showed highly significant (F2, 17.6=22.0, P<0.001) main effects of treatment and
was highest in the undisturbed grassland, intermediate in the willow, and lowest in the poplar
plots. Before cultivation on 31 May 2009, WFPS was indistinguishable (F2, 64=1.7, P=0.189)
among treatments. WFPS also varied temporally (F12, 99.3=7.5, P<0.001), the temporal patterns
being influenced by rainfall variability (Figs 4.2-a, and 4.2-b). Most notable was the drop in
WFPS coincident with the dry period in early September, 2009.
98

Table 4.1

Selected soil characteristics measured from 0-10 cm of soil depth in a long-term grassland as well as in poplar and
willow biomass bioenergy plantations in the central Upper Peninsula of Michigan, U.S.A.

Total C1
Treatment

-1

1

Total N

-1

1

C:N

NH4+-N

3

-1

-

3

NO3 -N

-1

2

Bulk Density
-3

2

Porosity

(mg kg )
Grassland
Poplar
Willow
1

1

pH

(mg kg )

(Ratio)

(mg kg )

(mg kg )

(g m )

(%)

32.8 (3.5)a
37.2 (3.0)a
31.0 (5.4)a

2.4 (0.4)a
2.7 (0.3)a
2.2 (0.3)a

13.9 (0.2)a
14.2 (0.1)a
13.8 (0.1)a

2.9 (2.5)a
3.0 (3.7)a
2.6 (3.2)a

5.3 (0.9)a
96.1(13.7)b
67.0 (14.7)b

1.13 (0.05)b
0.89 (0.06)a
1.10 (0.04)b

57 (3) a*
66 (2) a
59 (1) a

2

5.24 (0.17)a
5.33 (0.04)a
5.29 (0.12)a

Pre-cultivation; Post-cultivation and 3one year average; *Means in columns followed by the same letter are not significantly
different at the 5% level using the means separation test

99

40

(a)

30
20

30
10
20

0

10

-10

0

Te m p e r a t u r e ( oC )

R a i n f a l l ( mm)

Precipitation
Average air temperature

-20
(b)

W F P S ( %)

80
60
40
Grassland
Poplar
Willow

S o i l t e m p e r a t u r e ( oC )

20
0

(c)
25
20
15
10
5
0
May 09

Jul 09

Sep 09

Nov 09

Jan 10

Mar 10

May 10

Sampling date

Figure 4.2

(a) Precipitation and average air temperature, (b) soil water filled pore space and
(c) soil temperature measured from a poplar and willow biomass bioenergy
plantations and adjacent grassland during the first year of a long term grassland
conversion to bioenergy systems at Skandia in the Upper Peninsula of Michigan,
USA. Black solid, dashed dot-dotted and long dashed arrows indicate dates for
field application of herbicide, plowing and planting respectively

100

However, there was no significant (F24, 97.6=0.9, P=0.655) treatment*date interaction
effect for WFPS. Soil temperatures were significantly (F2, 33.1=67.5, P<0.001) cooler in the
undisturbed grassland compared to the tilled poplar and willow plantation soils. However, soil
temperature was statistically indistinguishable (P=0.112) between the poplar and willow biomass
plantations at any time during the study period. Soil temperature also varied across all sampling
dates; being the greatest in the summer, then decreased over time from autumn to winter (Fig
4.2-c).
4.4.3. Soil nitrogen availability
+

Soil extractable NH4 -N concentration did not differ significantly (F2, 37.5=1.9, P=0.170)
between the undisturbed grassland and the tilled SRWC plantations and was unaffected (F24,
96=1.4, P=0.133) by the interactions between treatment and sampling date. In contrast, soil

NH4+-N levels varied significantly (F12, 97=11.7, P<0.001) across the sampling dates, being
greatest early on in the experiment (June-September) (Fig 4.3-a).
Initial nitrification potentials, measured one week prior to the grassland conversion (22
May, 2009), were statistically indistinguishable between the three sites (Fig 4.3-b). On average,
nitrification rates were 0.1, 0.3 and 0.5 mg N kg

-1

-1

soil day in the willow, grassland and poplar

plots, respectively prior to conversion. However, eight weeks after cultivation, nitrification
potential was significantly enhanced and was seven-fold higher in both SRWC plantations
compared to the control grassland (Fig 4.3.-b).

101

12

NH4+-N (mg kg-1)

(a)

Pasture
Poplar
Willow

10
8
6
4
2
0

May 09

Jul 09

Sep 09

Nov 09
Jan 10
Sampling date

Mar 10

May 10

Figure 4.3 (a) Soil ammonium concentration, (b) nitrification rates and (c) nitrate concentration
(KCL extraction) measured from 0-10 cm soil depth in the undisturbed grassland
and in the poplar and willow biomass plantations prior and after cultivation at
Skandia in the Upper Peninsula of Michigan, USA. Black solid, dashed dot-dotted
and long dashed arrows indicate dates for field application of herbicide, plowing
and planting respectively

102

Nitrification potential (µg N g-1 soil day-1)

3.5
3.0

1-wk pre-conversion
8-wk post-conversion

b

b

2.5
2.0
1.5
1.0
0.5

a
a

a
a

0.0

Pasture

Poplar

Figure 4.3 (cont’d)

103

Willow

(b)

NO3--N (mg kg-1)

50
(c)

Pasture
Poplar
Willow

40
30
20
10
0

May 09

Jul 09

Sep 09

Nov 09

Jan 10

Sampling date

Figure 4.3 (cont’d)

104

Mar 10

May 10

The marked increase in nitrification potential following cultivation was reflected in
-

-

substantial increases in soil extractable NO3 -N pools. Soil NO3 -N was significantly (F2,
43.7=35.4, P<0.001) greater in both SRWC plantations relative to the adjacent grassland control.

Soil nitrate also varied significantly (F12, 99.3=9.9, P<0.001) across sampling dates. For instance,
nitrate levels, measured on 22 May 2009 and on 5 June 200, were consistently low (< 1 mg N kg
1

-

) in all plots, with no significant differences among the three sites (P=0.963 and 0.116). Nitrate

levels remained unchanged (P=0.520) in the undisturbed grassland throughout the study period
-

(Fig 4.3-b). In contrast, soil NO3 -N levels steadily increased after 5 July 2009 through mid-1

September 2009 with peaks as high as 21 and 30 mg kg

of soil in the tilled poplar and willow

plantations, respectively, then declined rapidly through the autumn and the winter, and became
indistinguishable to the grassland controls by spring 2010.
4.4.4. Nitrogen leaching
With the exception of the poplar plots at the first sampling date (11 August 2009),
+

concentrations of NH4 -N in leachate were consistently low (<0.1 mg/L) at all sampling dates,
regardless of treatments (Fig 4.4-a). In contrast, nitrate concentration in soil leachate was
significantly (P<0.001) higher from both cultivated SRWC plantations than the undisturbed
-

grassland control at all sampling dates (Fig 4.4-b). In the grassland, NO3 -N levels were
consistently negligible, ranging from 0.1 to 0.9 mg/L throughout the study period. There were
-

marked increases of NO3 -N concentration in soil leachate from the poplar and willow
plantations due to cultivation, with values as high as 35 and 39 mg/L, respectively (Fig 4.4-b).
105

NO3--N (ppm)

NH4+-N (ppm)

0.6

(a)

0.5
0.4
0.3
0.2
0.1
0.0

20
Pasture
Poplar
Willow

15
10
5
0

40

DON (ppm)

(b)

(c)

30
20
10
0
-10

May 09

Jul 09

Sep 09
Nov 09
Jan 10
Mar 10
Date after land conversion
+

-

May 10

Figure 4.4 Concentrations of (a) NH4 -N, (b) NO3 -N and (c) dissolved organic N in leachate
water collected at 50 cm below the soil surface measured in the undisturbed
grassland and in the poplar and willow biomass plantations after cultivation at
Skandia in the Upper Peninsula of Michigan, USA. Black solid, dashed dot-dotted
and long dashed arrows indicate dates for field application of herbicide, plowing
and planting respectively.

106

Similar to nitrate, DON levels were low in the undisturbed grassland throughout the
sampling period (Fig 4.4-c). In both cultivated willow and poplar plantations, however, DON
levels increased 14.1- and 19.6- fold, respectively relative to the adjacent grassland controls
following soil cultivation. It is noteworthy that, later in the season, DON increased more
-

markedly than nitrate. The marked increase in NO3 -N and DON concentrations following
cultivation was reflected in substantial leaching loads of these two N pools. Annual nitrate
leaching losses were 16 -and 22 -fold higher in the tilled willow and poplar plantations,
respectively, relative to the adjacent undisturbed grassland (Table 4.2). Similarly, annual
leaching loads of DON were 14 –and 20 –fold greater in the tilled willow and poplar plantations,
+

respectively, relative to the grassland controls. Annual leaching of NH4 -N was negligible from
both disturbed and undisturbed plots. For all nitrogen species, leaching losses from the cultivated
-1

poplar and willow plots amounted to 39 and 54 kg N ha , whereas N loss from the undisturbed
-1

grassland was less than 3 kg N ha for the study period.

107

Table 4.2

System

Hydrologic fluxes of N species at50 cm soil depth in a long-term grassland as
well as in poplar and willow biomass bioenergy plantations in the central Upper
Peninsula of Michigan, U.S.A., from August 2009 to May 2010.

-

+

NO3 -N

Dissolved N

NH4 -N
-1

Total N

-1

-----------------------------------(kg ha yr )---------------------------------Grassland
Poplar
Willow

0.8
17.8
12.8

(0.2)
(3.0)
(2.5)

0.02
0.01
0.03

(0.00)
(0.01)
(0.01)

108

1.8
36.0
25.9

(0.4)
(5.1)
(4.4)

2.6
53.9
38.8

(0.5)
(6.0)
(5.2)

4.4.5. Carbon dioxide fluxes
Soil CO2 fluxes were significantly (F2, 28=5.0, P<0.014) affected by land conversion;
soils from both tilled willow and poplar plantations emitted 29 and 42% less CO2 relative to the
adjacent undisturbed grassland controls. Soil CO2 effluxes also varied significantly (F12,
101=28.2, P<0.001) across sampling dates, with rates being higher in the summer and lower in

autumn and winter (Fig 4.5-a). Examining the temporal patterns of CO2 fluxes, before
cultivation on May 22, 2009, CO2 flux rates were low with no significant (P=0.645) differences
between treatments. Then flux rates increased and stayed high during the growing season (June-2 -

September 2009) in both uncultivated and cultivated plots with peaks as high as 480 mg C m h
1

in the uncultivated grassland plots (14 August 2009). Flux rates decreased sharply in autumn

and remained low the following spring. In early spring of 2010, flux rates started rising again
with more pronounced increases in the undisturbed grassland. The interacting effects between
treatment and sampling date were also significant (F24, 91.0=4.5, P<0.001) for CO2 effluxes.
-1

Overall, annual cumulative CO2 emissions from soil were 41.9 and 24.3 and 30.0 Mg CO2 ha
yr

-1

from the undisturbed grassland, the tilled poplar and tilled willow plantations, respectively

(Table 4.3).
4.4.6. Nitrous oxide fluxes
Flux rates of N2O were significantly (F2, 39.6=15.9, P<0.001) higher in the disturbed soil
(poplar and willow plantation) than in the undisturbed grassland control plots. Likewise, flux
109

rates in the disturbed plots were significantly affected by sampling date (F12, 99.6=6.8, P<0.001).
For instance, in both cultivated SRWC plantations, there was an initial pulse of N2O flux rates a
week following the cultivation event which increased until 11 August 2009, followed by a sharp
decrease in mid-September of 2009. Thereafter, a second pulse occurred in both poplar and
willow plantations through October 2009.

110

mg CO2 - C m- 2 h- 1

600

(a)

500
400
300
200
100
0
-100

ug N2 O-N m-2 h-1

(b)
1000

Pasture
Poplar
Willow

800
600
400
200
0

ug CH4 - C m- 2 h- 1

-200

(c)

80
60
40
20
0
-20
-40
-60
-80

May 09

Jul 09

Sep 09

Nov 09

Jan 10

Mar 10

May 10

Date after land conversion
Figure 4.5

Fluxes of (a) CO2, (b) N2O and (c) CH4 from soil measured in the undisturbed
grassland and in the poplar and willow biomass plantations after cultivation at
Skandia in the Upper Peninsula of Michigan, USA. Black solid, dashed dot-dotted
and long dashed arrows indicate dates for field application of herbicide, plowing
and planting, respectively.
111

Similarly, the interaction effects between the two factors (treatment and sampling date)
were significant (F24, 98.4=2.3, P=0.002). Annual cumulative N2O emissions for the undisturbed
grassland and the tilled poplar and willow plantations were 0.3, 5.8 and 4.5 Mg CO2eq ha

-1

-1

yr ,

respectively (Table 4.3).
4.4.7. Methane fluxes
The undisturbed grassland was a small sink for CH4 (annual average consumption rate of
-2 -1

-5.00 µg C m h ) whereas soil disturbance resulted in CH4 emissions of about 10.8 and 9.8 µg
Cm

-2 -1

h from the poplar and willow plots, respectively; however, these differences, were not

statistically significant (F2, 26.2=2.9, P=0.071) (Fig 4.5-c and Table 4.3). In contrast to CO2 and
N2O fluxes, methane flux rates did not differ significantly (F12, 98.8=1.29, P<0.238) over the
experimental period, nor was there a significant (F24, 97.6=0.0.96, P=0.525) interaction between
treatment and sampling date. Over the entire experimental period, undisturbed grassland
cumulatively consumed as much as 10.6 kg CO2eq ha
-1

-1

-1

yr , whereas the tilled poplar and

-1

willow plots emitted 21.1 and 18.3 kg CO2eq ha yr (Table 4.3).

4.4.8. Global warming potential and payback period
In general, the poplar treatment resulted in a lower cultivation induced GWP and shorter payback
period relative to willow. The best case scenario, based on the assumption that the contribution
of roots to soil CO2 effluxes in the grassland plots is 10% results in the reference grassland soil
producing 19 and 7% greater CO2 flux than the poplar and willow bioenergy systems,
112

respectively, for this initial phase of plantation establishment (Table 4.4). Under this scenario,
initial cultivation would have resulted in a net soil C sequestration of 7.0 and 2.8 Mg CO2eq ha
in the poplar and willow bioenergy plantation.

113

-1

Table 4.3

System

Direct and indirect GHG emissions and total global warming potential estimated for a long-term grassland as well as in
poplar and willow biomass bioenergy plantations in the central Upper Peninsula of Michigan, U.S.A. Data were
collected during one year period following land conversion

Direct emission
-1
(CO2eq kg ha )
N2O

CO2
Grassland
Poplar
Willow

42185
24391
30105

Indirect emission
-1
(CO2eq kg ha )

(175)
(117)
(135)

342
5886
4558

NO3 -N +DON

-11
21
18

(7)
(14)
(13)

114

31
629
453

42547
30927
35134

-

CH4
(16)
(236)
(163)

Cumulative GHG
emission
-1
(CO2eq kg ha )

(2)
(22)
(18)

Table 4.4

Cultivation induced-cumulative greenhouse gas flux, -global warming potential and payback time for willow and
poplar plantation establishment. Values are estimated based on raw data of direct GHG fluxes, indirect emissions of
N2O calculated from N leaching data and assumptions of grassland root contribution to soil CO2 fluxes (see text for
full description). Payback periods are calculated based on scenarios of minimum, average and maximum yields for
SRWC in the region

Payback time (year)

Cultivation induced GWP
-1 -1
(Kg CO2eq ha yr )

Poplar

Poplar

Willow

Min.

Avg.

Max.

Min.

Avg.

Max.

Scenario1 (RC=10%)

-7040

-2833

-0.8

-0.3

-0.2

-0.3

-0.1

-0.1

Scenario2 (RC=50%)
Scenario3 (RC=90%)

9835
26709

14042
30916

1.1
2.9

0.5
1.3

0.3
0.7

1.5
3.4

0.7
1.5

0.4
0.8

Scenarios

-1

-1

Willow

Min = expected minimum yield (5 Mg DM ha yr ), Avg. = average yield (11 Mg DM ha
-1

-1

DM ha yr ).

115

-1

-1

yr ), and Max= maximum yield (20 Mg

When RC to soil CO2 efflux in the grassland plots is assumed to be 50%, cultivation
induced GWP as CO2eq is in the magnitude of 9.8 and 14.0 Mg CO2eq ha

-1

for poplar and

willow bioenergy systems, respectively. A willow plantation with yields ranging from 5 to 20
-1 -1

Mg DM ha yr would require 0.5 – 1.5 years to payback this GHG debt. Finally, the worst case
scenario, using a RC of 90% for grassland plots results in a GHG debt as high as 26.7 and 30.9
Mg CO2eq ha

-1

with payback times ranging from 0.7 to 2.9 years and 0.8 to 3.4 years for poplar

and willow bioenergy plantations, respectively.

116

4.5.

DISCUSSION

In the present study, I documented substantial emissions of CO2 and N2O as well as soil
-

and soil leachate NO3 -N following conversion of grassland to poplar and willow bioenergy
plantations. Based on the most likely scenarios for root contribution to soil CO2 flux, I estimate
that the newly-established poplar and willow plantations lost an additional 3.3 and 9.0 Mg CO2
-1

ha , respectively, during the establishment year. Emissions of N2O also drastically increased
following the grassland clearing and cultivation for the SRWC plantation establishment.
Cumulative direct N2O emissions measured in the cultivated poplar and willow plantations were
17.2- fold and 13.3-fold, respectively higher relative to the undisturbed grassland site. Similarly,
land-use conversion resulted in 20.3-fold and 14.6-fold increases of cumulative indirect N2O
emissions (from N leaching losses) in poplar and willow plots, respectively, relative to the
grassland site. Although fluxes of methane were a small component of the GWP of the both
bioenergy plantations, data suggest that the undisturbed grassland was a sink for CH4 while the
cultivated poplar and cultivated willow plantations were sources for methane. Overall, this
estimate of cumulative emissions of all three GHGs suggests that grassland conversion resulted
in a GHG debt of 9.4 and 14.2 Mg CO2eq ha

-1

for poplar and willow plots, respectively in the

establishment year alone. This GHG debt reflects solely the effects of plantation establishment,
which are typically neglected in most LCA studies. In comparison, Keoleian and Volk (2005)
estimated that N2O released from N fertilizer application and leaf litter decomposition over an
-1

entire rotation cycle of 23 years of willow biomass production would total 11.3 Mg CO2eq ha .
117

Moreover, nitrogen leaching increased markedly following grassland conversion, resulting in
-1

additional losses of 51 and 36 kg N ha

in the poplar and willow plots, respectively. These N

leaching loads can potentially contribute to eutrophication of surrounding ground and surface
waters.
Physical disturbance of soil through clearing and cultivation of native ecosystems is
widely recognized to result in the loss of soil organic C (Lal et al. 1998; Reicosky 2003). Tillage
increases top soil aeration, mixes plant residues with the soil, increases substrate availability to
microbial decomposition, thus releasing CO2 into the atmosphere (Reicosky et al 1999; Lal
2004). Therefore, I was expecting that cultivation of the grassland for establishing poplar and
willow bioenergy plantations would result in larger CO2 emissions in the disturbed plots relative
to the undisturbed plots. Instead, based on raw CO2 flux data, I found lower CO2 efflux in the
poplar and willow treatments relative to the undisturbed grassland, suggesting that cultivation of
the grassland for establishing bioenergy crops resulted in a net sink of C. This result is consistent
with two other studies (Linn and Doran 1984; Yamulki and Jarvis 2002) that also found greater
CO2 production from non-tillage grassland soil than from cultivated plots.
However, based on the characteristics of this system, it seems highly implausible that
grassland clearing and cultivation would lead to C sequestration rather than net release from the
soil. An alternative explanation is that greater autotrophic respiration in the reference plots
masked the mineralization of SOC following clearing and cultivation. In both poplar and willow
plots, low occupancy by crop plants, coupled with aggressive weed control, suggests that
heterotrophic rather than autotrophic respiration accounted for virtually all of the CO2 emissions

118

in these sites. In contrast, the high CO2 emissions measured in the undisturbed grassland could
be an indication that root respiration, from the dense and actively growing grasses and forbs,
might have contributed to a substantial share of CO2 released from the control plots.
To address this issue, I used existing data from the literature (Hanson et al. 2000
summary of previous studies) to evaluate the potential influence of root contribution to total soil
CO2 efflux, as direct measurements are exceptionally difficult (Killham and Yeomans 2001).In
the best case (scenario 1) using the minimum RC of 10% from the range provided by Hanson et
al. (2000), I estimated that cultivating the grassland for poplar and willow bioenergy plantation
-1

establishment resulted in a net C sequestration in the soil of about 7.9 - 13.6 Mg CO2 ha .
Assuming that RC is 50% (scenario 2), or near the worldwide average for grassland based on
-1

Hanson et al. (2000), land conversion would release as much as 3.3 and 9.0 Mg CO2 ha in the
poplar and willow plots, respectively. In the worst case (scenario 3), using the upper end value of
RC (90%) suggests that grassland conversion would lead to net emissions of 20.2 and 25.9 Mg
-1

CO2 ha from poplar and willow plots, respectively.
Based on the range of possible yields of the bioenergy crops, estimates from scenarios 2
and 3 suggest the payback time could range from one to three years. A net C sink arising from
grassland cultivation as suggested by scenario 1 (RC is 10%) appears implausible considering
that tillage has extensively been cited to accelerate SOM decomposition and release CO2 to the
atmosphere (Lal et al. 1998; Lal 2004; Reicosky et al. 1999; Reicosky 2003). Furthermore,
Hanson et al. (2000) found that RC values close to the lower end of the range occurred in
grassland ecosystems in which root density was generally low (e.g. old fields and crop studies),
119

whereas high values (at least 50%) were generally recorded from untilled or permanent
grasslands.
In order to get a better approximation of the RC value of the grassland of the present
study site, I compared the root biomass of the studied grassland (measure from the top 10 cm in
mid-summer) to data by Jackson et al. (1996) who conducted an extensive review of the
literature on root distribution and biomass and found that temperate grassland root biomass
averaged 600 g m

-2

in the top 10 cm of the soil. This value is quite similar to root biomass of
-2

undisturbed grassland plots in this study, which averaged to 500 g m . The fact that root
biomass in the control plots approximated that of average values for temperate grassland
suggests that scenario 2 (RC = 50%) is the more plausible for this site. Another assumption
imbedded in my approach is that RC to soil CO2 efflux in the poplar and willow plots is
negligible. In a more recent study on similar soils in the UP we have found that root biomass at
the end of the first growing season averages 0.4 g m

-2

for poplar and 1.3 g m

-2

for willow

(Rothstein unpublished data) – nearly two orders of magnitude lower than that of our
undisturbed grassland plots. Very low root biomass by SWRC crop species, coupled with
aggressive weed control in conventional production practices, suggests that our assumption of
negligible root respiration in willow and poplar plots is reasonable.
The higher CO2 flux from willow compared to poplar plots (scenario 2 and 3) could
possibly be due to differences in site preparation, tree planting density and growth habit. Unlike
willow plots, poplar plots were shallow-cultivated in early September. Additionally, the higher
planting density of willow relative to poplar might have resulted in a denser root development,
thus leading to larger RC to soil CO2 flux from the young willow trees. This may in part explain
120

-

the lower N2O flux and NO3 -N leaching loss found in the willow plots relative to the poplar
-

treatments, as demand for NO3 -N may be greater in willow than in poplar plots.
In contrast to CO2 flux, conversion of grassland to willow and poplar plantings resulted
in clear and dramatic increases in N2O flux. On average, N2O emissions was 2.6 µg N2O-N m
-1

h

-2

in the reference control plots, while in the poplar and willow plots flux rates averaged 238.3

and 174.3 µg N2O-N m

-2 -1

h , respectively. Emission rates of N2O measured from the reference

grassland control plots in this study are comparable to those found (average of 2.9 µg N2O-N m
2

-

-1

h ) in grassland ecosystem at MSU’s Kellogg Biological Station (KBS) (Grandy and

Robertson 2006). However, N2O flux rates in the cultivated plots in the present study were 8-11fold higher than results obtained by Grandy and Robertson (2006) following initial cultivation of
grassland at KBS. It is noteworthy that the tow grassland sites (at KBS and Skandia) have similar
total soil C and N and differences in N2O flux cannot be explained by SOM content of the sites.
The grassland and cultivated plots in present study, however, are located in lowland area and
there is the likelihood that higher moisture condition of the site has resulted in this elevated N2O
flux. Based on this field measurement data, land use conversion has produced an estimated GHG
-1

debt as N2O of 4.6 and 6.1 Mg CO2eq ha for willow and poplar treatments, respectively. Of
this, approximately 90% was attributed to direct N2O emission from soil whereas the remaining
10% was attributed to indirect emissions through N leaching losses and its subsequent
denitrification. The potential for significant GHG emissions due to establishment of SRWC is
121

typically ignored in LCA studies; however, N2O emissions documented in this study over a
-1

single year exceeds the estimate of 4 Mg CO2eq ha

of N2O released from N fertilizer

application over an entire rotation cycle of 23 years of willow bioenergy production (Heller et al.
2003; Koeleian and Volk 2005).
Elevated N2O emissions in the poplar and willow plots relative to that of the undisturbed
-

grassland appeared to be driven primarily by the high availability of soil N. Soil NO3 -N
-1

concentrations averaged 9.6 and 6.7 mg kg

of soil in poplar and willow plots, respectively,
-

-1

whereas in the undisturbed reference control plots, NO3 -N level averaged 0.5 mg kg

of soil.

-

Seasonal N2O emission rates and NO3 -N concentrations followed a similar pattern (Figs 4.3-c
and 4.5-b) with the exception of the low N2O fluxes for sampling dates that occurred in
September 2009. These findings are consistent with results from other studies which also
-

observed that as soil NO3 -N concentration increases following perennial grassland cultivation,
N2O flux also increases; with the initial increase generally following a short lag period (Pinto et
al. 2004; Grandy and Robertson 2006). The low flux rates recorded in September 2009 were
most likely due to low moisture availability at that particular month, demonstrating that elevated
-

soil NO3 -N levels are necessary, but not sufficient for stimulating N2O emissions.
-

Elevated NO3 -N levels in willow and poplar plots likely resulted from a combination of
accelerated mineralization of N from SOM and grass residues, coupled with low vegetation
occupancy of the sites during the planting year. These conditions likely supplied large quantities

122

+

of available NH4 in soil, which, in turn, resulted in dramatic increase in the size or activity of
the nitrifying bacteria community. Nitrifier response to grassland disturbance is clearly
demonstrated in Figure 4.3-b, where soil nitrification potential measured one week before
cultivation was low across the sites, but eight weeks following cultivation increased 7-fold in
both cultivated sites relative to the uncultivated grassland treatments.
I have found that land-use conversion also resulted in large leaching fluxes of N, as both
-

NO3 -N and DON. I estimate that during this one year of poplar and willow establishment phase,
-1

-1

total N leaching losses from land-use conversion were 36 kg N ha and 51 kg N ha willow and
poplar plantations, respectively. These N leaching loads have the potential to contribute to
eutrophication of river systems or negatively impact on the quality of water of the lakes in the
study area. Impacts of N leaching associated with widespread grassland conversion for biofuels
production is of particular concern for this region where nitrate accumulation in the Great Lakes
is of pressing concern (Finlay et al. 2007). Moreover, using the IPCC (2007) approach, I
estimated that indirect N2O emissions associated with N leaching from willow and poplar plots
-1

contributed to 0.4 and 0.6 Mg CO2eq ha , respectively to the overall GHG debt. Nitrate
leaching loads following land-use conversion, measured in this study, are within the range of 11
to 24 kg N ha

-1

reported by Eriksen at al. (2008). Interestingly, DON dominated N leaching in

both undisturbed and disturbed plots (69.2% and 66.8% of total N leaching as DON in
uncultivated and cultivated plots, respectively), although absolute losses in the uncultivated soil
were much lower. The magnitude of DON leaching loads in the cultivated poplar and willow
plots is in agreement with results from Bhogal et al. (2000), who showed that significant DON

123

leaching may occur after grassland cultivation and can represent a significant pathway of N loss
in these ecosystems once they are disturbed (Van Kessel et al. 2009). The late season DON spike
in the cultivated plots was most likely due to a combination of factors such as colder soil,
slowing of SOM mineralization and nitrification and high soil moisture at that sampling period.
Fluxes of methane were a small component of the GWP arising from SRWC plantation
establishment. The undisturbed grassland was a small sink for CH4 while the cultivated poplar
and willow plantations were sources for CH4; however, treatment differences were not
statistically significant. The low P-value for this statistical comparison (P = 0.071), combined
with the fact that similar results have been reported in other studies (Van den Pol-Van Dasselaar
et al 1999; Regina et al. 2010; Steudler et al 1989, Moiser et al 1991), suggests that the transition
from CH4-sink to CH4-source was biologically significant.
The ANOVA performed on total soil C and N data, collected at the top 0-10 cm soil layer
over the sampling period, did not detect any C and N changes (likely due to spatial heterogeneity
in soil), which is a common finding of short-term soil C studies (Cambardella and Elliott 1994,
-

Grandy and Robertson 2006). However, the increase in nitrifier population size and NO3 -N
concentration in soil following cultivation, are evidence of accelerated SOM mineralization in
the cultivated plots, which most likely resulted in a net loss of SOC in these plots. The
decomposition rate of the labile pools of SOM, mainly composed of plant residues in different
stages of decomposition, is reported to increase by 50 – 100% immediately following tillage (Six
et al 1998; Lupwayi et al. 2004) and the preferential decomposition and depletion of these soil C
pools represent a major portion of C loss in cultivated soils (Cambardella and Elliott 1994).

124

Given that fast shoot growth (Cambardella and Elliott 1994), rapid development of dense
root structures, and high nutrient and water uptake (Rytter and Hansson 1996) are some of the
growth traits of willow and hybrid poplar clones, I expect that a new equilibrium between
nutrient release (through SOM mineralization) and plant demand will be reached rapidly as the
trees grow and occupy the plots. The sizeable decrease of C and N fluxes to almost the levels of
the undisturbed grassland controls by the end of this study, suggests that losses will continue to
decrease gradually and perhaps become negligible during the next few years of the tree rotation
cycles until a new disturbance event occurs. Results of other tillage studies also found that SOM
mineralization and subsequent C and N losses following grassland ecosystem disturbance is a
two-stage process with rapid decomposition over the first few months followed by a second
phase of low decomposition with rates several times lower than in the first phase (Aronsson and
Bergstrom 2001; Vertes et al. 2007).
It is widely recognized that accurate estimation of cumulative GHG flux remains a
difficult task as flux rates may be highly variable, both temporally and spatially (Parkin 2008).
The low frequency of measurement points taking in time may have potentially induced biases in
our estimation of cumulative GHG flux for this system. First, by sampling GHG flux every two
to three week time period, we may have not captured all episodic events (e.g. rainfalls,
temperature, etc) which could have considerably contributed to increase the annual GHG
emissions. For instance, Parkin and Kaspar (2006) who measured GHG flux in a corn field
observed that almost 50% of the annual N2O flux was due to two episodic pulses that occurred
during the year. Another temporal uncertainty could come from the lack of GHG samplings
during the winter period (December 2009 to February 2010) when the ground was continually
covered with snow and GHG flux assumed to be negligible. Furthermore, all GHG sampling
125

were performed between 10.00 and 15.00 h when flux rates are supposed to represent the
average daily fluxes. However, this midday time frame is generally the time period when air and
soil temperature as well as biological activity are at their peaks, leading to higher GHG flux rates
than diurnal flux rates which our approach did not capture.
Removing the aboveground parts of the vegetation prior to sampling is another factor that
may have affected our estimation of cumulative GHG flux as maintaining plants in the chamber
could have allowed the inclusion of the effects of growing plants on GHG emissions or uptake
(Mosier et al 2006) in our measurements. However, because our chamber size could not
accommodate the rapid shoot growth of switchgrass throughout the growing season,
aboveground vegetation had to be removed to facilitate chamber installation and lid closure.

126

4.6.

CONCLUSION

The present study has shown that clearing and cultivation of grassland for a bioenergy plantation
led to significant GHG fluxes and nitrogen leaching losses in northern Michigan. The projected
widespread conversion of idle agricultural lands to SRWC systems in the region, coupled with
the magnitude of cultivation-induced GHG emissions emphasizes the need to consider these
impacts explicitly when calculating life-cycle GHG balances for bioenergy systems.
Opportunities clearly exist to mitigate the GHG and eutrophication impacts associated with
SWRC establishment. In particular, I suggest that alternative cropping practices such as notillage, minimum tillage, cover cropping or high-density tree planting may have the potential to
minimize GHG emissions and nutrients losses associated with SWRC establishment.

127

BIBLIOGRAPHY

128

4.7.

BIBLIOGRAPHY

Aronsson P.G., Bergstrom, L.F. (2001) Nitrate leaching from lysimeter grown short rotation
willow coppice in relation to N-application, irrigation and soil type. Biomass and Bioenergy
21:155–164
Bhogal A., Murphy D.V., Fortune S., Shepherd M.A., Hatch D.J., Jarvis S.C., Gaunt J.L.,
Goulding K.W.T. (2000) Distribution of nitrogen pools in the soil profile of undisturbed and
reseeded grasslands. Biol Fertil Soils 30:356–362
Blanco-Canqui H. and Lal R. (2007) Soil structure and organic carbon relationships following 10
years of wheat straw management in no-till, Soil Till. Res. 30:1–15.
Cambardella C.A., Elliott E.T. (1994) Carbon and nitrogen dynamics of soil organic matter
fractions from cultivated grassland soils; Soil Science Society of America Journal, 58:123–130
Cherubini F., Bird N., Cowie A., Jungmeier G., Schlamadinger B., Woess-Gallasch S. (2009)
Energy and GHG-based LCA of biofuel and bioenergy systems: key issues, ranges and
recommendation. Resourc Conserv Recy 53:434–447
Cook J., Beyea J. (2000) Bioenergy in the United States: progress and possibilities. Biomass and
Bioenergy, 18: 441-455
Cowie A.L., Smith P., Johnson D. (2006) Does soil carbon loss in biomass production systems
negate the greenhouse benefits of bioenergy? Mitigation and Adaptation Strategies for Global
Change 11: 979-1002
Davidson E.A. (1991) Fluxes of nitrous oxide and nitric oxide from terrestrial ecosystems 219–
235; In JE Rogers and WB Whitman (ed) Microbial production and consumption of greenhouse
gases: Methane, nitrogen oxides, and halomethanes Am Soc of Microbiol, Washington, DC
De La Torre Ugarte D., Walsh M., Shapouri H., Slinksy S. (2003) “The Economic Impacts of
Bioenergy Crop Production on U.S. Agriculture.” U.S. Department of Agriculture, Office of the
Chief Economist, Agricultural Economic Report 816p
Doane T.A., Horwath W.R. (2003) Spectrophotometric determination of nitrate with a single
reagent, Anal. Lett. 36:713–2722
Eichenlaub VL, Harman JR, Nurberger FV, Stolle HJ (1990) The climatic atlas of Michigan.
Notre Dame Univ. Press, NotreDame, IN
Eriksen J., Askegaard M., Søegaard K. (2008) Residual effect and nitrate leaching in grass-arable
rotations: effect of grassland proportion, sward type and fertilizer history Soil Use and
Management Soil Use and Management (24) 4: 373-382

129

Fetter C.W. (1988) Applied Hydrogeology. 2nd ed. Merrill Publishing Company; Columbus,
OH.
Finlay J.C., Sterner R.W., Kumar S. (2007) Isotopic evidence for in-lake production of
accumulating nitrate in lake superior. Ecological Applications 17:2323-2332.
Gasol C.M., Gabarrell X., Anton A., Rigola M., Carrasco J., Ciria P., Rieradevall J. (2009) LCA
of poplar bioenergy system compared with Brassica carinata energy crop and natural gas in
regional scenario. Biomass and Bioenergy 33(1):119–129
Goglioa P., Owendeb P.M.O. (2009) A screening LCA of short rotation coppice willow (Salix
sp.) feedstock production system for small-scale electricity generation, Biosystems Engineering
(103) 389–394
Graham R.L., Wright L.L, Turhollow A.F. (1992) The potential for short-rotation woody crops
to reduce U.S. CO2 emissions, Climatic Change (22):223-238.
Grandy A.S., Robertson G.P. (2006) Initial cultivation of a temperate-region soil immediately
accelerates aggregate turnover and CO2 and N2O fluxes. Global Change Biology, 12:1507–1520.
Hanson P.J., Edwards N.T., Garten C.T., Andrews J.A. (2000) Separating root and soil microbial
contributions to soil respiration: A review of methods and observations, Biogeochemistry 48:
115–146,
Hart S.C., Stark J.M., Davidson E.A., Firestone M.K. (1994) Nitrogen mineralization,
immobilization, and nitrification. In: Weaver, R.W., Angle, J.S., Bottomley, B.S. (Eds.),
Methods of Soil Analysis. Part 2 Microbiological and Biochemical Properties; SSSA Book
Series, No. 5 SSSA, Madison, WI, pp. 1011–1016.
Heller M.C., Keoleian G.A., Mann M.K., Volk T.A. (2004) Life cycle energy and environmental
benefits of generating electricity from willow biomass, Renewable Energy 29:1023–1042
Heller M.C., Keoleian G.A., Volk T.A. (2003) Life cycle assessment of a willow bioenergy
systems; Biomass and Bioenergy 25:147–165
Hütsch B.W. (2001) Methane oxidation in non-flooded soils as affected by crop production; Eur.
J. Agron 14: 237–260
IPCC (1997) Intergovernmental Panel on Climate Change/Organization for Economic
Cooperation and Development; Guidelines for National Greenhouse Gas Inventories
OECD/OCDE, Paris
IPCC (2007) Climate Change 2007: Impact, Adaptation and Vulnerability. Contribution of
Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate
Change [Parry, Martin L., Canziani, Osvaldo F., Palutikof, Jean P., van der Linden, Paul J., and
Hanson, Clair E. (eds.)]. Cambridge University Press, Cambridge, United Kingdom, 1000 pp
130

Jackson R.B., Canadell J. Ehleringer J.R., Mooney H.A., Sala O.E., Schulze E.D., (1996) A
global analysis of root distributions for terrestrial biomes; Oecologia 108:389-411
Jenkins M.C., Kemp, W. M. (1984) The coupling of nitrification and denitrification in two
estuarine sediments. Limnology and Oceanography, 29, 609-619
Kelly L. (2002). Energy crops and their potential development in Michigan; A report of the
Michigan Biomass Energy Program 33p
Keoleian G.A., Volk T.A. (2005) Renewable energy from willow biomass crops: life cycle
energy, environmental and economic performance, Critical Reviews in Plant Sciences 24: 385–
406.
Killham K., Yeomans C. (2001). Rhizosphere carbon flow measurement and implications: From
isotopes to reporter genes. Plant Soil. 232(1–2): 91–96.
Kim H., Kim S., Dale B.E. (2009) Biofuels, land use change and greenhouse gas emissions:
some unexplored variables. Environ Sci Technol 43:961–967
Lal R. (2004) Review: Soil carbon sequestration to mitigate climate change Geoderma 123: 1–22
Lal R., Kimble J.M., Follett R.F., Cole C.V. (1998) The Potential of U.S. Croplands to Sequester
Carbon and Mitigate the Greenhouse Effect; Ann Arbor Press, Ann Arbor, MI, 128 pp
Linn D.M., Doran, J.W. (1984) Effect of water-filled pore space on carbon dioxide and nitrous
oxide production in tilled and non-tilled soils, Soil Sci. Soc. Am. J. 48: 1267–1272.
Livingston G.P., Hutchinson G.L. (1995) Enclosure-based measurement of trace gas exchange:
Applications and sources of error. p. 14–51. In PA Matson and RC Harris (ed) Biogenic trace
gases: Measuring emissions from soil and water Blackwell Science, Oxford, UK
Lubowski R.N., Vesterby M., Bucholtz S., Baez A., Roberts M.J. (2006) Major Uses of Land in
the United States, 2002 Economic Information Bulletin No. (EIB-14) 54 pp.
http://www.ers.usda.gov/Publications/EIB14/
Lupwayi N.Z., Clayton G.W., O’Donovan J.T. et al. (2004) Decomposition of crop residues
under conventional and zero tillage. Canadian Journal of Soil Science, 84, 403–410.
Moiser R.L., Schimel D., Valentine D., Bronson K., Patron W. (1991) Methane and nitrous oxide
fluxes in native, fertilized and cultivated grasslands; Nature 350,S. 330-332.
Perlack R.D., Wright L.L., Turhollow A.F., Graham R.L., Stokes B.J., Erbach D.C. (2005)
Biomass as feedstock for a bioenergy and bio-products industry: the technical feasibility of a
billion-ton annual supply. Oak Ridge National Laboratory, Oak Ridge, TN, ORLN/TM-2005/66

131

Piñeiro G., Jobbágy E.G., Baker J., Murray B.C., Jackson R.B. (2009) Set-asides can be better
climate investment than corn ethanol, Ecological Applications, 19: 277–282.
Pinto M,. Merino P., Del Prado A. et al. (2004) Increased emissions of nitric oxide and nitrous
oxide following tillage of a perennial pasture. Nutr. Cycl. Agroecosyst 70: 13–22.
Ravindranath N.H., Manavie R., Fargione J., Canadell J.G., Berndes G., Woods J., Watson H.,
Sayather J. (2008) Greenhouse Gas Implications of Land Use and Land Conversion to Biofuel
Crops. Biofuels: Environmental Consequences and Interactions with Changing Land Use.
Proceedings of the Scientific Committee on Problems of the Environment (SCOPE) International
Biofuels Project Rapid Assessment 22–25.
Regina K, Alakukku L. (2010) Greenhouse gas fluxes in varying soils types under conventional
and no-tillage practices. Soil & Tillage Research 109: 144–152
Reicosky D.C., 2003. Tillage-induced CO2 emissions and carbon sequestration: effect of
secondary tillage and compaction. In: Garcia-Torres, L., Benites, J., Martinez-Vilela, A.,
Holgado-Cabrera, A. (Eds.), Conservation Agriculture. Kluwer Acad. Pub., Dordrecht, The
Netherlands, pp. 291–300
Reicosky D.C., Reeves D.W., Prior S.A., Runion G.B., Rogers H.H., Raper R.L. (1999) Effects
of residue management and controlled traffic on carbon dioxide and water loss. Soil and Tillage
Research 52: 153– 165.
Robertson G and Grace P. (2004) Greenhouse gas fluxes in tropical and temperate agriculture:
the need for a full-cost accounting of global warming potentials. Environ Dev Sustain 6: 51–63
Robertson G.P., Paul E.A., Harwood R.R. (2000) Greenhouse gases in intensive agriculture:
contributions of individual gases to the radiative forcing of the atmosphere. Science, 289, 1922–
1925
Rochette P., and Bertrand N. (2007) Soil-surface gas emissions. p. 851–861. In M Carter and EG
Gregorich (ed) Soil sampling and methods of analysis 2nd ed CRC Press, Boca Raton, FL.
Rytter R.M., Hansson A.C. (1996) Seasonal amount, growth and depth distribution of fine roots
in an irrigated and fertilized Salix viminalis L. plantation. Biomass Bioenergy 11: 129–137
Sexstone A.J., Revsbech N.P., Parkin T.B., Tiedje T. (1985) Direct measurement of oxygen
profiles and denitrification rates in soil aggregates, Soil Sci. Soc. Am. J. 49:645-651.
Sinsabaugh R.L., Reynolds H., Long T.M. (2000) Rapid assay for amidohydrolase (urease)
activity in environmental samples, Soil Biol. Biochem. 32:2095–2097
Six J., Elliott E.T., Paustian K., Doran J.W. (1998) Aggregation and soil organic matter
accumulation in cultivated and native grassland soils. Soil Science Society of America Journal
62:1367–1377
132

Smith K.A., Ball T., Conen F., Dobbie K.E., Massheder J., Rey A. (2003) Exchange of
greenhouse gases between soil and atmosphere: interactions of soil physical factors and
biological processes, European Journal of Soil Science 54:779–791
Soussana J.F., Loiseau P., Vuichard N., Ceschia E., Balesdent J., Arrouays D. (2004), Carbon
cycling and sequestration opportunities in temperate grasslands, Soil Use Manage. 20:219–230
Steudler P.A., Bowden J.M., Melillo J.M., Aber J.D. (1989) Influence of nitrogen fertilization on
methane uptake in temperate forest soils. Nature 341: 314–316
Tubby I., Armstrong A. (2002) The establishment and management of short rotation coppice – a
practitioner’s guide. Forestry Commission Practice Note 7; Forestry Commission, Edinburgh
Van den Pol-van Dasselaar A., van Beusichem M.L., Oenema O. (1999) Determinants of spatial
variability of methane emissions from wet grasslands on peat soil, Biogeochemistry 44:221–237
Van Kessel C., Clough T., Van Groenigen J.W. (2009) Dissolved organic nitrogen: an
overlooked pathway of nitrogen loss from agricultural systems? J Environ Qual 38:393–401
Venterea R.T. (2009) Simplied Method for Quantifying Theoretical Underestimation of
Chamber-Based Trace Gas Fluxes, J. Environ. Qual. 39:126–135. doi:10.2134/jeq2009.0231
Vertés F., Hatch D., Velthof G., Taube F., Laurent F., Loiseau P. and Recous S. (2007) Shortterm and cumulative effects of grassland cultivation on nitrogen and carbon cycling in ley-arable
rotations. Grassland Science in Europe 12:227-246
Volk T.A., Verwijst T., Tharakan P.J., Abrahamson L.P., White E.H. (2004) Growing fuel: a
sustainability assessment of willow biomass crops, Frontiers in Ecology and the Environment 2:
411–418
WangW. and Fang, J. (2009) Soil respiration and human effects on global grasslands, Global
Planet Change, 67, 20–28
Watson, R.T., Zinyowera M.C., Moss R. H., Dokken D.J. (1996) Climate change (1995),
impacts, adaptations and mitigatin of climate change. Scientific Technical Report Analyses. In:
Watson, R.T., Zinyowera, M.C., Ross, R.H. (Eds.), Contribution of Working Group II to the
Second Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, New
York
Wrage N., van Groenigen J.W., Oenema O. Baggs E.M, (2005) A novel dual-isotope labelling
method for distinguishing between soil sources of N2O, Rapid Communications in Mass
Spectrometry 19: 3298–3306.
Yadav V., Malanson G.P. (2007) Progress in soil organic matter research: litter decomposition,
modelling, monitoring and sequestration, Prog. Phys. Geog. 31: 131–154.
133

Yamulki S. and Jarvis S.C. (2002) Short-term effects of tillage and compaction on nitrous oxide,
nitric oxide, nitrogen dioxide, methane and carbon dioxide fluxes from grassland, Biol. Fertil.
Soil 36:224–231
Zalesny Jr R. S. , Hall R.B., Zalesny J.A., McMahon B.G, Berguson W.E., Stanosz G.R. (2009)
Biomass and genotype x environment interactions of populous energy crops in the Midwestern
United States; Bioenerg. Res. 2:106-122
Grandy AS, Neff J.C. (2008) Molecular C dynamics downstream: The biochemical
decomposition sequence and its impact on soil organic matter structure and function; Science of
the Total Environment (404):297–307
Petra Bergkvist P., Ledin S. (1998) Stem biomass yields at different planting designs and
spacings in willow coppice systems; Biomass and Bioenergy 14 (2): 149-156

134

CHAPTER 5

NITROGEN FERTILIZATION OF SWITCHGRASS IN NORTHERN
MICHIGAN, U.S.A INCREASES BIOMASS YIELD AND IMPROVES NET
GREENHOUSE GAS BALANCE.

135

5.1.

ABSTRACT

Nitrogen (N) management can affect agro-ecosystem sustainability through impacts on
biomass production and soil greenhouse gas (GHG) fluxes. In the present study, I investigated
the effects of N fertilization on GHG emissions and biomass production of switchgrass grown for
bioenergy production, in the central Upper Peninsula of Michigan, U.S.A. Nitrogen fertilization
treatments included 0, 56 and 112 kg N ha

-1

applied as urea, once early in the growing season.

Field-based measurements of direct GHG fluxes (CO2, N2O and CH4) were performed every
two to three weeks using static chambers. Indirect GHG emissions associated with machinery
operations in field activities, manufacturing of fertilizer and pesticides and transport of chemicals
to the farm were derived from the literature. Biomass yield of switchgrass was evaluated at the
end of the growing season. Overall, N fertilizer application contributed little to direct GHG
emissions from soil. Relative to the unfertilized plots, there were an additional 0.74 and 1.5 Mg
CO2eq ha

-1

yr

-1

of net GHG emissions from soil in the 56 and 112 kg N ha

-1

treatments,

respectively. On the other hand, N fertilization greatly stimulated CO2 uptake by the switchgrass
bioenergy crop, resulting in 1.5- and 2.5-fold increases in aboveground biomass yield in 56 and
-1

112 kg N ha treatments, respectively. Biomass N contents were 26.6, 49.9 and 89.5 kg ha
-1

the 0, 56 and 112 kg N ha

-1

for

treatments, respectively, with a relative recovery of applied N

ranging from 43 - 56% in the fertilized plots. Nitrogen amendments improved the net GHG
-1

benefits by 2.6 and 9.4 Mg CO2eq ha relative to an unfertilized field. These results suggest that
N fertilization of switchgrass in this region could reduce (15-50%) the land base needed for
bioenergy production and decrease pressure on land needed for food and forage crop production.
136

5.2.

INTRODUCTION

Of the many perennial grass species and fast-growing trees that have been intensively
investigated for use as bioenergy crops, switchgrass (Panicum virgatum L.), a warm-season
perennial grass native to North America, is believed to be one of the most promising and
valuable crop for a wide range of sites in the U.S.A. (Alder et al. 2007; Froese et al. 2010; Wang
et al. 2010). Relative to other bioenergy crops, desirable characteristics of switchgrass include its
low establishment costs, high yielding potential, suitability for marginal lands, low nutrient and
water requirements and positive environmental benefits (Sanderson and Alder 2008; McLaughlin
et al. 2005; Wang 2010). However, nitrogen (N) availability has been reported as the most
frequently limiting factor for growth of native grasses suggesting that N fertilization can be an
important management practice for switchgrass biomass production and feedstock quality
(Sanderson and Alder 2008; Wang et al. 2010). At the same time, production and use of
exogenous N fertilizers in agricultural systems can contribute directly and indirectly to
greenhouse gas (GHG) emissions, potentially reducing the net GHG benefits of bioenergy
production (Hill et al. 2006; Lewandowski and Schmidt 2006). Yet, there is still a critical lack of
information regarding biomass yield and net GHG benefit of adding N fertilizer for switchgrass
bioenergy crop production, especially in northern Michigan’s conditions.
The use of renewable energy from biomass has the potential to mitigate atmospheric CO2
increase but achieving this potential will necessitate the development of high yielding feedstock
production systems (McLaughlin et al. 1999). While a number of options are being investigated
to achieve bioenergy crop yield potential (e.g. genetic improvement, irrigation, soil fertility
management), it is most likely that N fertilization will play a central role in contributing to
achieving high biomass production. This is because harvesting switchgrass biomass is likely to
137

remove high levels of N and adequate N inputs may be needed to replace the lost N from the soil.
To date, N fertilization requirements for switchgrass are still not well established. While recent N
fertilization studies suggest that growing switchgrass for use as feedstock without N fertilizer
would be impracticable (Vogel et al. 2002; Sanderson and Alder 2008; Wang et al. 2010), some
other studies have found no yield response to added N suggesting that N fertilization may not be
require for this bioenergy crop (Christian et al. 2002). Mulkey et al. (2006), who conducted a
study on N fertilization for a switchgrass bioenergy crop in South Dakota, recommended
-1

applying no more than 112 kg N ha once annually to optimize biomass production.
Nitrogen fertilizer use in agricultural settings is generally cited as one of the largest
potential sources of GHG emissions, especially if excessive soil N levels stimulate N2O fluxes
(Bouwman 1996; Bouwman et al. 2002; IPCC 2007; Robertson and Vitousek 2009). Although
there is a large body of work that has investigated soil GHG emission response to N fertilizer
addition, most of these studies have focused on agronomic crops such as corn, wheat and
potatoes which are not only differently managed but also may have different N requirement than
switchgrass (Grant et al. 2006; Zebarth et al. 2008). To account for N fertilizer input effects on
GHG fluxes from soil, the IPCC (1996) methodology provides a N2O emission factor as a
percentage (1.325%) of the amount of N fertilizer applied that has been extensively used in most
life cycle assessment (LCA) studies. This emission factor suggests that there is a linear
relationship between N input and N2O release (Bouwman 1996). Because perennial grasses
generally have extensive root systems making them more efficient at taking up large amounts of
soil inorganic N, N2O fluxes may respond to N addition differently in perennial grassland
ecosystems than in agronomic systems where this emission factor was derived. For instance, in a
138

nitrogen management for switchgrass biomass production study, Vogel et al. (2002) reported no
-1

change in soil N level when the N fertilizer rate was less than 120 kg N ha , suggesting that
below this threshold N2O flux is unlikely to be persistent as the crop is taking up all of the N
added to the soil.
In the present study, I investigated switchgrass yield and soil GHG (CO2, N2O, and CH4)
flux responses to different rates of N fertilizer application, at a site in the central Upper Peninsula
of Michigan. I hypothesized that increases of CO2 fixation into switchgrass biomass resulting
from N additions would be more than compensate the direct and indirect GHG emissions
associated with fertilizer production, transport and application. A better understanding of
aboveground biomass production and GHG emission responses to N additions will help improve
further LCA studies for this bioenergy crop.

139

5.3.

MATERIALS AND METHODS

5.3.1. Site description and experimental set-up
This experiment was conducted at Michigan State University’s Chatham Experiment
Station located in central Upper Peninsula of Michigan, USA (46°33′ N, 86°55′ W), on a sandy
loam, mixed, frigid Alfic Haplorthod soil. The study area has lake-enhanced precipitation with
average annual precipitation of 880 mm (last 10 years). The rainfall for the study period (15 May
2009 to 14 May 2010) was 575 mm. Daily minimum winter temperatures generally fall down to
−18°C or lower and there is a mean of 91 d frost free and average annual maximum and
minimum temperatures of 11 and -3°C, respectively (Eichenlaub et al 1990)
The plant material that I used in the present study was switchgrass variety “Cave In
Rock”; selected because of its ability to grow on marginal lands, high yield potential ease of
processing after harvest (Varvel et al. 2008), high quality of pellets for heating or electricity
generation and good conversion efficiency (McLaughlin et al. 1996). Moreover, because
switchgrass is a perennial crop, it does not require annual seeding or soil tillage (Sampson et al.
2000).
The experimental field measured approximately 1.5 ha (200 m long in EW direction and
150 m wide in NS direction). Prior to beginning of the experiment, the field had been used for
silage corn production for the past 30-40 years. Preparation for switchgrass establishment began
-1

on 10 June 2008 when the area was sprayed with 2.5 L ha of glyphosate and 2.5 L of 2-D ester
herbicides. The field was no-till planted on 23 June 2008, and the planting was performed in 18.8
-1

cm row spacing 1.3 cm deep, at a seeding rate of 22.4 kg ha (40% of pure live seeds). The area
was sprayed again on 27 June 2008 with 2.5 L ha

140

-1

of Atrazine 4L (pre-emerge herbicide).

Biomass was not harvested in 2008. On 1 June 2009, the field was sprayed with a combination of
dicamba and atrazine at a rate of 2.5 L ha

-1

for each herbicide. The switchgrass fertilization

treatment was started in the spring of 2009. The experiment was a randomized block design with
treatments consisting of 3 levels of N fertilizer as urea (46-0-0), replicated six times. The N
-1

-1

-1

fertilizer treatments used were N0 = 0 kg N ha , N50 = 56 kg N ha and N100 = 112 kg N ha .
Treatment plots were fertilized on 4 June 2009. Biomass was first mechanically harvested and
baled on 11 November 2009.
5.3.2. Field measurements of direct GHG emissions
Field data collection started on 5 June 2009, a day following fertilizer application and
continued until 4 May 2010. To measure emissions of CO2, N2O and CH4, I used one static nonvented bucket chamber (Nº2 high-density polyethylene plastic - Letica, U.S. Plastic Corp.) per
plot. The buckets used to design the chamber measured 26.2 cm in diameter, and chambers
consisted of bases driven 10 cm into the soil and gas tight lids fitted with rubber septa to sample
the approximately 10 L of headspace volume. To keep the chamber from heating up during the
sampling period and altering GHG fluxes, foil covered bubble wrap (water heater blanket) was
glued to the outside of the chamber. Aboveground vegetation in each chamber was clipped
immediately prior to sampling in order to avoid respiratory fluxes of CO2 from aboveground
plant parts. Removing plants from the chamber was performed to solve the problems associated
with subtracting the fraction of the CO2 release originating from the aboveground vegetation
which is not taking into account when calculating global warming potential. Gas samples were
collected from the headspace between 10.00 and 15.00 h to minimize changes of soil GHG

141

fluxes associated with diurnal cycles. Headspace gas samples (10 mL) were drawn using a 10
mL syringe at 0, 20, 40 and 60 minutes following chamber closure, and then immediately overpressurized

to

pre-flushed

5.9

mL flat-bottom

exetainer vials

(Labco,

Unlimited,

Buckinghamshire,U.K.). After each sampling event, chambers were moved to a new location,
about 5 m away from the previous location, and left uncovered until the periods when gas
samples were being collected on the next sampling date.
The concentration of CO2, N2O and CH4 in the headspace gas samples were determined
using a Gas Chromatograph (GC-2014, Shimadzu Corporation) equipped with electron-capture
and flame-ionization detectors. Gas flux measurements were performed every 2-3 weeks, except
during the winter period when the ground was continually covered with snow. A total of 12
sampling events were captured over the course of this one year investigation, of which 10
samplings were made in 2009 (5 and 25 June, 9 and 29 July, 10 and 25 August, 15 September, 5
and 27 October and 13 November ) and two samplings in 2010 (3 March and 5 May).
5.3.3. Soil sampling and analyses
I used 3.8-cm diameter PVC soil corers to take a 10-cm deep soil core in close proximity
to the gas flux chamber at each sampling period. Gravimetric moisture content was determined
º

from field-moist soil after oven drying at 105 C for 48 hrs. Soil volume was calculated based on
−3

the core size. Soil bulk density (BD in mg m ) was determined by dividing the oven-dried
weight by soil volume after correcting the volume and weight for gravel contents in the sample.
-1

Total porosity was determined as (1– BD X PD ) where PD is soil particle density assumed to be
−3

2.65 mg m . The ratio of volumetric soil water to total porosity was used to calculate soil
water-filled pore space (WFPS).
142

Soil samples collected at each gas sampling point were also used for determination of
+

-

NH4 -N and NO3 -N concentrations after extracting the soil with 2 M KCL (5:1 extractant to
soil ratio) and analyzing extracts spectrophotometrically. Soil pH was measured (in a mixture of
soil and 0.01 M CaCl2) from air-dried soil with a Corning pH Meter 430 (Corning Incorporated,
Science Products Division, New York, 14831). Total C and N contents were determined on the
same soil samples by dry combustion with an elemental analyzer (Model ECS 4010, Costech
Analytical, Valencia, CA).
5.3.4. Plant sampling and analyses
Switchgrass aboveground biomass was sampled on 27 October 2009. Standing
aboveground biomass was sampled by randomly placing two 50×50 cm quadrats in each plot.
Aboveground biomass samples were manually cut to a stubble height of 12.5 cm and all leaves
and stems produced were collected, and a subsample was taken from each sample for moisture
content determination after oven-drying at 65 ◦C for 48 hrs and weighing. All yields are
expressed on a dry weight basis. The aboveground subsamples were chopped, finely ground with
a mini-ball mill for C and N analysis. From the ground samples, other sub-samples of 500 mg
were acid-digested (4.5 mL of concentrated 70% nitric acid) for elemental (P, K, Mg and Mn)
analysis (ICP-AES). Additional subsamples of about 2.5 mg were used for total C and N
determination by combustion using an elemental analyzer (Model ECS 4010, COSTECH
Analytical, Valencia, California, U.S.A.).
5.3.5. Indirect GHG emissions and C sequestration
In this study, an attempt has been made to estimate indirect GHG emission associated
with switchgrass bioenergy production. By indirect GHG emissions, I refer to non-soil related
143

GHG release prior to, and during farming processes, including energy use during farming,
biomass harvesting and transportation, as well as fertilizer and pesticide manufacturing and use.
Greenhouse gas emissions during feedstock transformation and power generation are not
considered here. Farming energy and chemical use for growing switchgrass was based on the
Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) default
values (Wang 1999; Wu et al. 2006). The energy and emissions values for fertilizer
manufacturing and transportation used in this study were obtained from Wang et al. (2003) and
Wells (2001). Carbon dioxide emissions associated with urea, herbicide and lime production are
3.0, 0.4 and 16.2 kg CO2 per element or active ingredient. I assumed that about the same amount
of CO2 will be release during distribution/ transportation of these chemicals to the farm. Fuel
consumption data for farm activities were derived from Bone et al. 1999 (e.g. fertilizer
-1

-1

spreading, herbicide application and liming = 3 L ha ; harvesting = 6 L ha ) and the IPCC
(1996) carbon emission factor of 74.07 Mg of CO2/TJ for the CO2 emissions associated with
farm activities. Because nutrient run-off or leaching data from switchgrass bioenergy crops are
unavailable, I did not include such data in this assessment. Most perennial grasses have a strong
ability to scavenge for soil available N and I expected N leaching losses to be negligible in the
system. Cultivation of switchgrass on crop land leads to a significant soil C sequestration (Liebig
et al. 2008). To account for the improvement in soil C content, I used the default value (48.5 kg
CO2 per Mg of switchgrass aboveground biomass) provided by Wu et al. (2006).

144

5.3.6. Greenhouse gas flux calculations
Fluxes of GHGs were estimated from the concentration change in the chamber headspace
over the 60 min sampling period. I used the simplified linear method for quantifying theoretical
underestimation of chamber-based gas fluxes developed by Venterea et al. (2009). The method
takes into account the effects of chamber geometry and deployment time, the flux-calculation,
and properties of the soil and the gas under consideration. I used linear interpolation between
adjacent sampling dates (assuming that emission followed a linear trend during the periods when
no sample was taken) to calculate cumulative CO2, N2O and CH4 fluxes during the study period.
To compare the net effect of N fertilization on GHG gas budgets from the different
treatments, I used global warming potential factors of 25 and 298 based on 100-y time horizons
for CH4 and N2O, respectively, to convert the soil fluxes of these two GHGs to CO2eq (IPCC
2007).
5.3.7. Data analysis
Chamber-based data were analyzed using ‘‘general linear model-repeated measures of
ANOVA (Minitab 15, Minitab, U.S.A.) to assess the significance of the impacts of N
fertilization, sampling date, and their interactions on the fluxes of CO2, N2O, and CH4, as well as
-

+

soil available N (NO3 and NH4 ), temperature and WFPS in which sampling date was treated as
a within-subject variable and N fertilizer rate was treated as a between-subject variable. For
measurement of biomass yield and biomass nutrient concentration, the significant difference
between the different N fertilization treatment was assessed by one-way ANOVA and least
significance difference (LSD). All significances mentioned in the text are at the 0.05 level.

145

5.4.

RESULTS

5.4.1. Soil C, N, pH, WFPS and temperature
Soil C content did not change with nitrogen application (P=0.417), whereas soil N levels
increased significantly (P=0.033) in both N50 and N100 treatments compared with the N0 plots
(Table 5.1). Soil porosity, bulk density and pH were not affected by N fertilization regimes. The
total annual (from May of 2009 to April of 2010) rainfall was 575 mm and was much less than
the long-term average of 880 mm. There was very little rainfall in June 2009. Also, the first three
weeks of September 2009 were dry (no rainfall) while the last week of September and the entire
month of October 2009 regularly received rainstorms (Fig 5.1-a). Soil temperature and soil
WFPS were unaffected by the N-fertilizer treatments (Fig. 5.1-b & 5.1-c). However, both soil
temperature and soil water content (expressed as WFPS) varied significantly (both P<0.001)
across sampling dates. Soil WFPS levels responded to rainfall patterns over the study period.
Soil WFPS measured at the depth of 0-10 cm varied between 16 to 45% with the lowest values
recorded during the sampling dates that occurred on 25 June and 15 September of 2009.

146

Precipitation
Average air temperature

40

(a)

20
10

30
20

0

10

-10

0

Temperature (oC)

Rainfall (mm)

50

-20
(b)

WFPS ( %)

40
30
-1

0 kg N ha
56 kg N ha-1
112 kg N ha-1

20

Temperature (oC)

10
25
(c)

20
15
10
5
0
Jun 09

Aug 09

Oct 09

Dec 09

Feb 10

Apr 10

Jun 10

Sampling date
Figure 5.1

Precipitation and average air temperature (a), soil water filled pore space (b) and
soil temperature (c) measured from switchgrass bioenergy yield plots, grown
under three different N fertilization regimes, at Chatham, Upper Peninsula in
Michigan, U.S.A. Black solid indicates the date for field application of Nfertilizer. The black, long dashed arrow indicates date of N fertilizer application
and the solid arrow indicates date of biomass harvest.

147

5.4.2. Soil nitrogen availability
+

Soil NH4 -N did not differ between fertilized and unfertilized plots at any sampling date.
-

In contrast, N fertilizer application caused a sharp increase in soil NO3 -N levels following N
fertilization (Fig 5.2-a). For instance, during the sampling date that occurred on 25 June 2009,
-

NO3 -N levels were 2.3- and 2.7-fold higher (P<0.001) in plots fertilized with 56 and 112 kg N
-1

-

ha , respectively, relative to the unfertilized plots. Similarly, on 9 July 2009, NO3 -N
concentrations were 1.6- and 2.0-fold higher (P=0.01) in plots fertilized with 56 and 112 kg N
-1

-

ha . The spike of NO3 -N following after fertilization application was followed by a steady
decrease to the unfertilized soil level by 29 July 2009 (55 days following plots fertilization) (Fig
5.2-b).
Finally, on 5 December 2009, a 1.8-fold increase was noted in both fertilized plots
-

relative to the unfertilized control. With the exception of these above mentioned dates, NO3 -N
concentration did not differ between fertilized and unfertilized plots at any sampling dates. On
the other hand, nitrate level did not change and remained relatively steady in the control. N
mineralization and nitrification, measured twice (5 June 2009 and 28 July 2009) during the
growing season were not significant between treatments and data are not reported here.

148

NO3- -N (ug N g-1)

NH4+-N (ug N g-1)

8
(a)

6
4
2
0
-2
6

-1

0 kg N ha
-1
56 kg N ha
112 kg N ha-1

4

(b)

2
0

Min-N (ug N g-1)

-2
10

(c)

8
6
4
2
0
Jun 09

Aug 09

Oct 09

Dec 09

Feb 10

Apr 10

Jun 10

Sampling date
Figure 5.2

Soil ammonium (a), nitrate (b) and total mineral N (c) concentrations measured
from 0-10 cm soil depth in switchgrass bioenergy yield plots, grown under three
different N fertilization regimes, at Chatham, Upper Peninsula in Michigan,
U.S.A. Black solid indicates date for field application of N-fertilizer. Black, long
dashed arrows indicate date of N fertilizer application and the solid arrow
indicates date of biomass harvest.

149

5.4.3. Biomass yield and nutrient concentrations
Switchgrass aboveground biomass yield increased significantly (P=0.005) with increased
level of N fertilizer (Table 5.2). Average biomass production increased by 1.5- and 2.5-fold in
the N50 and N100 treatments, respectively. The N concentration of harvested biomass
significantly (P=0.05) increased with increasing N fertilization (Table 5.2). Consequently,
biomass C:N ratio also significantly decreased with increasing fertilization level. Plant tissue P,
Ca and Mg were unaffected by N addition. However, plants grown in plots that received the
highest N fertilization level had significantly (P<0.01) greater K concentration than their
counterparts in the N0 and N50 treatments.
5.4.4. Greenhouse gas flux and global warming potential
Nitrogen fertilization did not significantly (P=0.519) affect soil CO2 flux as flux rates
were indistinguishable between fertilized and unfertilized treatments. However, CO2 flux
significantly (P<0.001) varied with sampling date, with flux rates being high in the summer and
lower in autumn and winter. The seasonal pattern of CO2 fluxes was similar to that of soil
temperature (Figs 5.1-c and 5.3-a). Soil CO2 fluxes rapidly increased in both fertilized and
unfertilized plots as soil temperature increased, peaked during the month of August, and then
gradually decreased to almost zero by autumn 2009 throughout spring 2010.

150

Table 5.1

Selected soil characteristics measured beneath switchgrass grown under three different N fertilization regimes in the
-1
central Upper Peninsula, Michigan, U.S.A. Data were collected on July 29, 2009. Treatments are: N0= 0 kg N ha ;
-1

N50 =56 kg N ha and N100 = 112 kg N ha

-1

Treatment

Total C
(%)

Total N
(%)

C:N ratio

Bulk density
-3
(g cm )

Porosity
(%)

pH

N0

1.68 (0.03)

0.136 (0.002)

12.37 (0.01)

1.17 (0.01)

56 (0)

6.49 (0.06)

N50

1.71 (0.03)

0.141(0.002)

12.10 (0.05)

1.16 (0.05)

56 (2)

6.59 (0.02)

N100

1.72 (0.02)

0.142(0.002)

12.14 (0.02)

1.16 (0.02)

56 (1)

6.60 (0.10)

P-Value

0.417

0.033

0.939

0.939

0.939

0.540

151

Table 5.2

Yield and nutrient concentrations of aboveground biomass of switchgrass grown under three N fertilization regimes in
-1
-1
the central Upper Peninsula, Michigan, U.S.A. Treatments are: N0= 0 kg N ha ; N50 =56 kg N ha and N100 = 112
kg N ha

-1

Biomass
yield-DM

C

(Mg ha-1)
N0
N50
N100
P-Value

Treatment

N

C:N

P

------------%------------

(Ratio)

-----------------------%----------------------

4.83 (0.21)
7.13 (0.42)
11.93 (0.53)

46.80 (0.06)
46.57 (0.10)
46.98 (0.11)

0.55 (0.03)
0.70 (0.09)
0.75 (0.03)

86 (4)
69 (8)
63 (2)

0.08 (0.01)
0.08 (0.00)
0.08 (0.01)

0.25 (0.02)
0.25 (0.01)
0.35 (0.02)

0.29 (0.01)
0.28 (0.01)
0.27 (0.01)

0.13 (0.01)
0.13 (0.00)
0.14 (0.01)

0.005

0.057

0.055

0.026

0.757

0.009

0.740

0.720

152

K

Ca

Mg

ug CO2-C cm2 hr-1

40
(a)

30
20
10

ng N2O-N cm2 hr-1

0

-1

0 kg N ha
-1
56 kg N ha
-1
112 kg N ha

4

(b)

2
0

ng CH4-C cm2 hr-1

-2
(c)

0
-2
-4
-6
-8

-10
Jun 09

Aug 09

Oct 09

Dec 09

Feb10

Apr 10

Jun 10

Sampling date
Figure 5.3

Fluxes of CO2 (a), N2O (b) and CH4 (c) from soil, measured in switchgrass
bioenergy yield plots, grown under three different N fertilization regimes, at
Chatham, Upper Peninsula in Michigan, U.S.A. Black solid indicates date for
field application of N-fertilizer. Black, long dashed arrows indicate date of N
fertilizer application and the solid arrow indicates date of biomass harvest.
153

Carbon dioxide fluxes were statistically indistinguishable (all P>0.05) between fertilized
and unfertilized plots at all sampling dates. The annual cumulative soil CO2 flux for N0, N50
-1

and N100 treatments were 24.6, 25.3 and 26.0 Mg CO2 ha , respectively. Nitrous oxide flux
rates were not significantly affected by N-fertilization. However, the main effect of sampling
date was significant (P=0.04) whereas the interaction between treatment and sampling date had
no significant effect (P=0.850) on N2O flux. Overall, average N2O fluxes from all three
treatments were very low. The highest flux rate, which occurred in one of the 112 kg N ha
-2 -1

treatments was 15 µg N2O-N m hr

-1

(Fig 5.3-b). There was a trend of decreasing CH4

consumption with increasing N addition; however, this was not statistically significant
-1

(P=0.093). Cumulative N2O fluxes were in the range of 0.2 -0.3 Mg CO2eq ha in the fertilized
and unfertilized plots, but overall there was no significant difference among treatments.
Although CH4 consumption tended to decrease with increasing N fertilization, differences were
not statistically significant between treatments. However, CH4 fluxes were significantly affected
by sampling date and the interaction between treatment and sampling date (P<0.001 and 0.004,
respectively) (Fig 5.3-c). The seasonal flux of CH4 followed almost the same pattern as WFPS,
decreasing with decreasing soil WFPS and increasing with increasing WFPS. Overall,
-1

cumulative CH4 fluxes ranged from -0.06 to -0.07 Mg CO2eq ha in the following order 0 kg N
-1

-1

-1

ha >56 kg N ha >112 kg N ha .
Overall, indirect GHG emissions associated with agricultural input production and
transport and field preparation coupled with direct GHG emissions from soil totaled 1.0, 2.4 and
154

4.1 Mg CO2eq ha

-1

-1

for plots that received 0, 56, 112 kg N ha , respectively (Table 5.4).

Conversely, based on my calculation, CO2 fixed in switchgrass biomass (above and
-1

belowground) and in soil totaled 8.5, 12.5 and 21.1 Mg CO2eq ha
respectively.

155

-1

for 0, 56, 112 kg N ha ,

Table 5.3

-1

Total annual CO2, N2O and CH4 fluxes, cumulative GHG emissions and GWP all expressed in kg CO2eq ha (Total ±
se) from switchgrass bioenergy plots grown with three different levels of N fertilizer in the central Upper Peninsula,
-1
-1
-1
Michigan, U.S.A. Treatments are: N0= 0 kg N ha ; N50 =56 kg N ha and N100 = 112 kg N ha

Fertilization level

CO2

N0
N50
N100

24427
25182
25801

N2O
(4019)
(5564)
(4724)

191
167
302

Cumulative
GHG

CH4
(100)
(125)
(252)

-68.3
-62.9
-56.8

156

(18.5)
(19.7)
(18.2)

24550
25287
26047

(4137)
(5709)
(4995)

GWP

737
1497

Table 5.4

Greenhouse gas (GHG) flows per hectare of switchgrass bioenergy field in
Northern Michigan, U.S.A. Positive figures represent CO2 emissions and negative
-1

numbers are CO2 sequestration. Treatments are: N0= 0 kg N ha ; N50 =56 kg N
-1

ha and N100 = 112 kg N ha

-1

N0

Source/ factor
Indirect emissions
Agricultural input production
- Herbicide manufacture/ transport
- N manufacture/ transport

N50

N100

+0.320
+0.300

+0.320
+0.600

+0.023
+0.121
+0.580

+0.034
+0.178
+0.852

+0.057
+0.238
+1.432

+0.755

Diesel fuel for preparation of switchgrass
- Field management
- Harvest
- transport

+0.320
+0.000

+1.374

Direct emissions from soil
GHG emission from soil (measured)
- CO2
-

N2O

-0.024

+0.111

-

CH4

+0.005

+0.012

-12.200
-0.346

-20.500
-0.579

C sequestration
- Aboveground biomass
- Soil

-8.300
-0.234

Net GHG balance

-7.490

157

-10.126

-16.935

5.5.

DISCUSSION

In this study, I used actual field measurements to determine direct GHG emissions, GWP and
biomass yield associated with N fertilization for a switchgrass bioenergy crop in northern
Michigan. Indirect GHG emissions due to production, transportation, use of N fertilizer and
machinery were derived from the literature. Overall, N fertilization had minimal impact on soil
GHG emissions. Raw data obtained from field measurements indicate that the N50 and N100
treatments had GWP of 0.74 and 1.5 Mg CO2eq ha

-1

-1

yr , relative to the unfertilized baseline.

On the other hand, N fertilization greatly stimulated CO2 assimilation into aboveground biomass
of the switchgrass crop, resulting in 1.5- and 2.5-fold increases in yield in the N50 and N100
treatments, respectively. Taking into account the total mix of activities required for growing
switchgrass and transporting biomass to a processing plant, these estimates indicate that growing
switchgrass on an unfertilized pastureland results in a net GHG sequestration of 7.5 Mg CO2eq
-1

ha . However, fertilizing switchgrass with 56 kg N ha

-1

and 116 kg N ha

-1

increased net GHG

-1

sequestration to 10.1 and 16.9 Mg CO2eq ha , respectively. These results suggest that
application of N fertilizer to switchgrass bioenergy plantings can notably increase feedstock
production and GHG sequestration. This, in turn, would reduce the land area required for a given
level of feedstock production, thus reducing potential conflicts with land required for food and
crop production, as well as avoiding additional GHG emission from land conversion.
Nitrous oxide emissions were not distinguishable between fertilized and unfertilized
plots, suggesting that added N was either quickly taken up by the actively growing switchgrass
plants or the site conditions did not favor N2O production or the combination of both. Although
158

-

soil mineral N, especially NO3 -N, spiked in the spring immediately after N fertilization, this
increase was not accompanied by an enhanced N2O emission in the fertilized plots. A day
+

-

following N fertilization, both NH4 -N and NO3 -N levels in the fertilized plots were
-

indistinguishable from the unfertilized plots. The flush of soil NO3 -N was followed by a sharp
-

decrease of soil available NO3 -N to the unfertilized plot level by 29 July 2009, demonstrating
the ability of switchgrass to scavenge available soil N (Fike et al. 2006; McLaughlin and Kszos
-

2005). The elevated soil NO3 -N which lasted about two months following N addition in the
fertilized treatments implies that applied N must have exceeded the ability of the plant to take it
up, at least, during that period of time, which may increase the risk of increased N2O emissions.
Snyder et al. (2007) reported that N2O emissions usually occur when available soil N, especially
-

-

NO3 -N, exceeds crop demand. In the present study, however, the elevated NO3 -N following N
-

addition was not accompanied by an increase in N2O emissions, suggesting that soil NO3 -N
level is not the single factor require to stimulate soil N2O flux. Soil WFPS at the site was below
-

30% at this period and might explain why NO2 flux did not increase as NO3 -N increased.
Results from Schmidt et al. (2000) study on the relationship between soil variables (including
WFPS) and N2O flux, suggests that minimum WFPS values at which soil N2O flux start to
increase is near 30% with highest fluxes occurring around 70% of WFPS.
In the present study, direct measurements of N2O flux suggest that the N fertilizer rate of
56 kg ha

-1

did not result in any noticeable N2O flux from soil, supporting that there is no linear
159

relationship between N fertilizer addition and N2O flux for this particular system, and that there
will be a critical minimum amount of N rates above which N2O flux occurs. The highest N
-1

-1

fertilizer rate (112 kg ha ) resulted in an additional 0.1Mg CO2eq ha as N2O, corresponding to
an emission factor of 0.24% of the amount of applied N, a value 5.6 times lower than the IPCC
emission factor.
Although the cumulative CO2 flux tended to increase with increasing added N, this
increase was not statistically significant. This result is consistent with Lee et al. (2007) who
studied N fertilization and harvest frequency effects on CO2 fluxes in an established switchgrass
biomass crop field and found no effects of N fertilizer on soil CO2 emission rates. Previous N
fertilization studies have found that added N increased aboveground biomass yield but did not
affect switchgrass root biomass (Ma et al. 2000; Jung 2010), and the lack of significant effect of
N fertilization on soil CO2 flux may suggest that there was no significant increase in root
biomass due to adding N fertilizer.
Because perennial grass ecosystems are generally reported to be a small sink for
atmospheric CH4 (Schmer et al 2006), I expected CH4 flux to make little contribution to net
GHG emissions from switchgrass. Cumulative CH4 measured in the present study indicate that
as N fertilizer amount increases, CH4 consumption decreases; however, overall fluxes were
negligible. Methane oxidation decreasing with added N is in agreement with Moiser et al (1991)
who reported an inhibition of CH4-oxidization with agricultural management practices such as
fertilizer application and tillage. The addition of N to perennial prairie grassland ecosystems has
160

been shown to increase above-ground biomass production (Collins et al. 1998) therefore
suggesting that perennial biomass energy crops including switchgrass may have a similar
response. In this study, increasing nitrogen fertilization linearly increased switchgrass
aboveground biomass yield. In fact, results of previous N fertilization studies for switchgrass
-1

suggested that the highest N fertilization rate (112 kg N ha ) used in the present study is within
the linear response range for this crop. For instance, using almost similar amount of N fertilizer
-1

(126 kg ha ) Bransby et al (1998) obtained 10.4 Mg ha

-1

which is comparable to the biomass

yield achieved with the highest N fertilizer rate in the present study. Muir et al. (2001) and
-1

Mooney et al. (2009) also found switchgrass yields to increase linearly up to 150-168 kg N ha .
Above this threshold, switchgrass yield responses to increasing N fertilizer level start to saturate
(Muir et al. 2001; Lemus et al 2008).
In this study, application of 112 kg N ha

-1

increased tissue K concentration by 40%

relative to the unfertilized crops, suggesting that this level of N addition must have promoted the
uptake of K by the plant (Table 5.3). Guretzky et al. (2010) also reported tissue K increase of a
-1

similar magnitude with comparable amount (134 kg N ha ) of N fertilization, thus suggesting
that N fertilization of switchgrass crops may require more K addition to sustain productivity.
Tissue N concentrations of switchgrass increased in response to increasing N addition thus
agreeing with results obtained by Elbersen et al (2001). Switchgrass accumulated about 26.6 kg
-1

N ha

in the aboveground biomass when grown with no exogenous N added to the field.
-1

Nitrogen content, however, increased to as much as 49.9 and 89.5 kg ha when 56 and 112 kg of
N fertilizer were applied to the plots. This suggests a relative recovery of applied N of 43% and
161

-1

56% in plots that received 56 and 112 kg N ha during the year of application. Although, I did
not find any specific information on N recovery of switchgrass bioenergy crop in the literature,
available data for common agronomic crops (e.g. Maize and wheat) indicate that under
conventional production practices, recovery of applied N is often no greater than 50-60%
(Bransby et al. 1998). In the unfertilized plots, assuming that biomass yield remains constant,
harvesting the biomass for energy production will remove about 26.6 kg N ha

-1

from the soil

annually which, over multiple harvest cycles, could deplete soil N pools if this N removal is not
replaced by application of exogenous N. Therefore, producing switchgrass for bioenergy in the
same field and without N fertilization may lead to yield decline over years (Muir et al. 2001).

162

5.6.

CONCLUSIONS

The results of this study underscore the potential of switchgrass to sequester significant
amounts of C and contribute to reducing atmospheric CO2 levels. Results from this study clearly
support my original hypothesis that increased CO2 fixation into switchgrass biomass resulting
from N fertilizer greatly exceeds the direct and indirect GHG emissions associated with fertilizer
production, transport and application. Thus N fertilization of switchgrass (of at least 112 kg N
-1

ha ), increases net GHG sequestration and reduces the land base needed to meet feedstock
production targets.

163

BIBLIOGRAPHY

164

5.7.

BIBLIOGRAPHY

Adler P.R., Del Grosso S.J., Parton W.J. (2007) Life-cycle assessment of net greenhouse-gas
flux for bioenergy cropping systems, Ecol. Appl., 17: 675–691
Bone I., Galvin M., Moncrieff I., Brennand T., Cenek P., Perrins C., Jones K., Winder P., Hunt
M. (1996) Transport (Part3). In: Energy Efficiency: A guide to current and emerging
technologies, Volume 1, Buildings and Transportation. Centre for Advanced Engineering,
University of Canterbury, Christchurch
Bouwman A.F. (1996) Direct emission of nitrous oxide from agricultural soils. Nutrient Cycling
in Agroecosystems 46:53-70.
Bouwman A.F., Boumans L.J.M., Batjes N.H. (2002) Emissions of N2O and NO from fertilized
fields: Summary of available measurement data. Global Biogeochemical Cycles 16:1058
Bransby D.I., McLaughlin S.B., Parrish D.J. (1998) A review of carbon and N balances in
switchgrass grown for energy. Biomass and Bioenergy 14: 379–384
Christian D.G., Riche A.B. et al. (2002) The yield and composition of switchgrass and coastal
panic grass grown as a biofuel in Southern England. Bioresource Technology, 83:115–124.
Collins S.L., Knapp A.K., Briggs J.M., Blair J.M., Steinauer E.M. (1998) Modulation of
diversity by grazing and mowing in native tallgrass prairie. Science 280:745–747
Eichenlaub V.L., Harman J.R., Nurberger F.V., Stolle H.J. (1990) The climatic atlas of
Michigan. Notre Dame Univ. Press, NotreDame, IN
Elbersen H.W., Christian D.G., El Bassen N., Bacher W. Sauerbeck G., Aleopoulou E., Sharma
NPiscioneri. I., De Visser P., Van Den Berg D. (2001) Switchgrass variety choice in Europe
Aspects of Applied Biology 65:21-28.
Fike J.H., Parrish D.J., Wolf D.D., Balasko J.A., Green J., Rasnake M., Reynolds J.H. (2006)
“Long-term yield potential of switchgrass-for-biofuel systems.” Biomass and Bioenergy 30:198206.
Froese R.E., Shonnard D.R., Miller C.A., Koers K.P., Johnson D.M. (2010) An evaluation of
greenhouse gas mitigation options for coal-fired power plants in the US Great Lakes States.
Biomass and Bioenergy 34 (3):251-262
Grant R.F., Pattey E., Goddard T.W., Krysanowski L.M., Puurveen H. (2006) Modeling the
effects of fertilizer application rate on nitrous oxide emissions. Soil Science of America 70:235248

165

Guretzky J.A., Biermacher J.T., Billy J.C., Kering M.K., Mosali J. (2010) Switchgrass for
forage and bioenergy: harvest and nitrogen rate effects on biomass yields and nutrient
composition; Plant Soil DOI 10.1007/s11104-010-0376-4
Healy, R.W., Striegel R.G., Russell T.F., Hutchinson G.L., G.P. Livingston (1996) Numerical
evaluation of static-chamber measurements of soil–atmosphere gas exchange: Identification of
physical processes. Soil Sci. Soc. Am. J. 60:740–747.
Hill J., Nelson E., Tilman D., Polasky S., Tiffany D. (2006) Environmental, economic, and
energetic costs and benefits of biodiesel and ethanol biofuels, Proc. Nat. Acad. Sci., 103: 11206–
11210,
Hutchinson G.L., Mosier A.R. (1981) Improved soil cover method for field measurement of
nitrous oxide fluxes. Soil Sci. Soc. Am. J. 45:311–316
IPCC (1996) IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual.
International Panel on Climate Change, United Nations, New York.
IPCC (2007) Climate Change 2007: Impacts, Adaptation, and Vulnerability. Contribution of
Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate
Change [Parry, Martin L., Canziani, Osvaldo F., Palutikof, Jean P., van der Linden, Paul J., and
Hanson, Clair E. (eds.)]. Cambridge University Press, Cambridge, United Kingdom, 1000 pp.
Jung J.Y. (2010) Nitrogen Fertilization Impacts on Soil Organic Carbon and Structural Properties
under Switchgrass; PhD Dissertation- Ohio State University 236p
Kim D-G., Hernandez-Ramire G. (2010) Dependency of nitrous oxide emission factors on
nitrogen input rates: A metaanalysis 19th World Congress of Soil Science, Soil Solutions for a
Changing World 1 – 6 August 2010, Brisbane, Australia. Published on DVD
Lee D.K., Doolittle J.J., Owens V.N. (2007) Soil carbon dioxide fluxes in established
switchgrass land managed for biomass mproduction Soil Biology & Biochemistry 39: 178–186
Leep R., Jeranyama P., Min D.H., Dietz T., Bughrara S., Isleib J. (2002) Grazing Effects on
Herbage Mass and Composition in Grass–Birdsfoot Trefoil Mixtures, Agron. J. 94:1257–1262
Lemus R., Brummer E.C., Lee B.C., Moore K. J., Barker M. F., Molstad N. E. (2008) “Effects of
nitrogen fertilization on biomass yield and quality in large fields of established switchgrass in
southern Iowa, USA.” Biomass Bioenergy 32:1187-94.
Lewandowski I., Schmidt U. (2006) Nitrogen, energy and land use efficiencies of miscanthus,
reed canary grass and triticale as determined by the boundary line approach, Agr. Ecosyst.
Environ. 112:335–346
Liebig M.A., Schmer M.R., Vogel K.P., Mitchell R.B. (2008) Soil Carbon Storage by
Switchgrass Grown for Bioenergy; Bioenerg. Res. (2008) 1:215–222
166

Ma Z., Wood C.W., Bransby D.I. (2000) Impacts of soil management on root characteristics of
switchgrass. Biomass Bioenergy 18:105-112.
McLaughlin S.B., Kszos L.A. (2005) “Development of switchgrass (Panicum virgatum) as a
bioenergy feedstock in the United States” Biomass and Bioenergy 285: 15-35.
McLaughlin S.B., Kszos L.A. (2005) Development of switchgrass (Panicum virgatum) as a
bioenergy feedstock in the United States. Biomass and Bioenergy 28: 515–535.
McLaughlin S.B., Samson R., Bransby D., Weislogel A. (1996) Evaluating physical, chemical,
and energetic properties of perennial grasses as biofuels. Proc. Bioenergy 96, Nashville, TN,
Sept. p. 1–8
McLaughlin S.B., Wimmer R. (1999) Calcium physiology and terrestrial ecosystem processes,
New Phytologist 142, pp. 373–417
Moiser R.L., Schimel D., Valentine D., Bronson K., Patron W. (1991) Methane and nitrous oxide
fluxes in native, fertilized and cultivated grasslands; Nature 350,S. 330-332.
Mooney D.F., Roberts R.K., English B.C., Tyler D.D., Larson J.A. (2009) “Yield and Breakeven
Price of 'Alamo' Switchgrass for Biofuels in Tennessee” Agronomy Journal 101:1234-42.
Muir J.P., Sanderson M.A., Ocumpaugh W.R., Jones R.M., Reed R.L. (2001) “Biomass
Production of 'Alamo' Switchgrass in Response to Nitrogen, Phosphorus, and Row Spacing.”
Agronomy Journal 93:896-901.
Mulkey V.R., Owens V.N., Lee D.K. (2006) Management of switchgrass-dominated
Conservation Reserve Program lands for biomass production in South Dakota; Crop Sci. 46:
712–720.
Robertson G.P., Vitousek P.M. (2009) Nitrogen in agriculture: balancing an essential resource.
Annu Rev Energy Environ 34:97–125. doi:10.1146/annurev.environ.032108.105046
Rochette P., Bertrand N. (2007) Soil-surface gas emissions. p. 851–861. In M Carter and EG
Gregorich (ed) Soil sampling and methods of analysis 2nd ed CRC Press, Boca Raton, FL.
Sampson R., Drisdelle M., Mulkins L., Lapointe C., Duxbury P. (2000) The Use of Switchgrass
Pellets as a Greenhouse Gas Offset Strategy. Natural Resources Canada
Sanderson M.A., Adler P.R. (2008) Perennial forages as second generation bioenergy crops.
International Journal of Molecular Sciences 9:768-78
Schmer M.R., Vogel K.P., Mitchell R.B., Moser L.E., Eskridge K.M., Perrin R.K. (2006)
Establishment stand thresholds for switchgrass grown as a bioenergy crop; Crop Sci. 46: 157–
161
167

Snyder C.S., Bruulsema T.W., Jensen T.L. (2007) Greenhouse gas emissions from cropping
systems and the influence of fertilizer management—a literature review. International Plant
Varvel G.E., Vogel K.P., Mitchell R.B., Follett R.F., Kimble J.M. (2008) Comparison of corn
and switchgrass on marginal solids for bioenergy. Biomass Bioenergy 32 18-21.
Velthof G.L., Brader A.B., Oenema O. (1995) Effects of nitrogen fertilization and grazing on the
emission of nitrous oxide from grassland; Studies in Environmental Science, 65(1995):627-630
Venterea R.T. (2009) Simplied Method for Quantifying Theoretical Underestimation of
Chamber-Based Trace Gas Fluxes, J. Environ. Qual. 39:126–135. doi:10.2134/jeq2009.0231
Vogel K.P, Brejda J.J., Walters D.T., Buxton D.R. (2002) Switchgrass biomass production in the
Midwest USA: harvest and nitrogen management. Agron. J. 94: 413–420.
Wang D., Lebauer D., Dietze M. C. (2010) A quantitative review comparing the yield of
switchgrass in monocultures and mixtures in relation to climate and management factors; GCB
Bioenergy (2010) 2, 16–25, doi: 10.1111/j.1757-1707.2010.01035.x
Zebarth B.J., Rochette P., Burton D.L., Price M. (2008) Effect of fertilizer nitrogen management
on N2O emissions in commercial corn fields; Can. J. Soil Sci. 88: 189-195

168

CHAPTER 6

SUMMARY AND CONCLUSIONS

169

This study assessed the impacts of agricultural management practices such as cover cropping,
nitrogen fertilizer inputs and cultivation on soil biology, nitrogen and carbon flows and soil
greenhouse gas emissions. In particular, the study first assessed the possibility of incorporating
cover crops into Fraser fir Christmas tree plantations as an alternative to the conventional use of
N fertilizer to improve and maintain soil fertility and sustain tree growth. The study also
evaluated the impacts of converting pastureland to short-rotation woody (poplar and willow)
bioenergy systems on greenhouse gas emission from soil and nitrogen leaching losses during the
establishment year. Finally, this study evaluated the effects of N fertilization to switchgrass
bioenergy crop on yield and net greenhouse gas benefits. The results of the study are useful in
designing effective strategies and policies to enhance management for these production systems
of interest. My specific conclusions and recommendations are as follows:
1. Inclusion of leguminous or non-leguminous cover crops into Fraser fir production
systems improved soil microbial properties and soil health, and can be used as an
alternative to inorganic N fertilizers in Fraser fir Christmas tree plantations. However, I
recommend that an economic evaluation of the costs and benefits should be conducted to
determine its profitability before suggesting this practice to growers. I also propose that a
study addressing the potential contribution of the cover crops to soil carbon sequestration
over the entire plantation rotation period be conducted.
2. The results also indicate that pasture land-use conversion to SRWC bioenergy systems
contributes to a significant part of the GHG budget of these systems. This implies that
GHG emissions during the establishment year of any SRWC bioenergy systems should in
further be included in any LCA studies for a complete estimate of net GHG budget of
these bioenergy systems. It is also advisable that alternative cropping practices such as
170

no-tillage, minimum tillage or high density tree planting that have potential to minimize
GHG emissions and nutrients losses should be implemented for more sustainable
feedstock production.
3. Nitrogen fertilizer addition up to the levels used in this study markedly increased
switchgrass yield with little effects on GHG emission, which allowed offsetting greater
atmospheric CO2 from fossil fuels. Nitrogen fertilization of switchgrass in this region
could reduce the land base needed for bioenergy production and reduce pressure on land
required for food and forage crop production.

171

APPENDICES

172

APPENDICES
Table A. 1

Description of groundcover management, competition management and cultural management practices at the cover
crop-Fraser fir intercropping system at the MSU’s Tree Research Center

Groundcover

CompetitionManagement

Treatments

Strips (S)

DWCS

No strips (NS)

DWCNS

Strips (S)

ALFS

No strips (NS)

ALFNS

Strips (S)

PRGS

No strips (NS)

Dutch White
Clover
(DWC)

PRGNS

Bare ground

CONV

Alfalfa (ALF)

Perennial Rye
Grass (PRG)

Conventional

Management practice
Year 2007
Year 2008
Cover crop seeded on 22 May 2007 at a rate of N input from the cover crop
−1
28 kg ha ; N input from the cover crop mowed every three to four wks;
mowed every three to four wks; glyphosate glyphosate applied twice along
the tree rows to maintain strips
applied twice along the tree rows (strips)
Cover crop seeded on 22 May 2007 at a rate of
N input from the cover crop
−1
28 kg ha ; N input from the cover crop mowed every three to four wks
mowed every three to four wks
Cover crop seeded on 22 May 2007 at a rate of N inputs from the cover crop
−1
28 kg ha ; N input from the cover crop mowed every three to four wks;
mowed every three to four wks; glyphosate glyphosate applied twice along
the tree rows to maintain strips
applied twice along the tree rows (strips)
Cover crop seeded on 22 May 2007 at a rate of
N input from the cover crop
−1
28 kg ha ; no N input; Cover crop mowed mowed every three to four wks
every three to four wks
Cover crop seeded on 22 May 2007 at a rate of No N input; Cover crop mowed
−1
16 kg ha ; no N input; Cover crop mowed every three to four wks;
every three to four wks; glyphosate applied Glyphosate applied twice along
the tree rows to maintain strips
twice along the tree rows (strips)
Cover crop seeded on 22 May 2007 at a rate of
No N input; Cover crop mowed
−1
16 kg ha ; no N input; Cover crop mowed every three to four wks
every three to four wks
Nitrogen fertilizer applied once
-1
No nitrogen fertilization
at a rate of 50 kg N ha as
ammonium sulfate
173

Table A. 2

Date
5/23/2009
5/31/2009
6/12/2009
6/18/2009
6/18/2009
6/18/2009
7/13/2009
10/20/2009

Description of cultural management practices applied at Skandia willow yield trial
during the first year of the plantation establishment at Skandia, in Upper
Peninsula of Michigan, USA
Management
Weed Control
Weed Control
Weed Control
Weed Control
Planting
Weed Control
Fencing
Coppice Cutting

174

Pesticide
glyphosate

Simazine
Goal

Table A. 3

Date
5/23/2009
5/31/2009
6/12/2009
6/16/2009
6/17/2009
6/17/2009
7/13/2009
9/2/2009

Description of cultural management practices applied at Skandia hybrid poplar
yield trial during the first year of the plantation establishment at Skandia, in
Upper Peninsula of Michigan, USA
Management
Weed Control
Weed Control
Weed Control
Planting
Weed Control
Weed Control
Fencing
Weed control

Pesticide
glyphosate

Pendulum Aqua Cap
Scepter

175