Soil Organic Carbon Pools in Turfgrass Systems of Ohio

SOIL ORGANIC CARBON POOLS IN TURFGRASS SYSTEMS OF OHIO

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the Graduate

School of The Ohio State University

By Mamta H. Singh, M.Sc.

*****

The Ohio State University 2007

Dissertation Committee: Approved by

Professor Parwinder S. Grewal, Adviser ------

Professor Rattan Lal Adviser

Professor Warren Dick Environment Science Graduate Program

Professor John Cardina

ABSTRACT

Soils are an important component of the global carbon (C) cycle. It is estimated that there are 673,300 ha of turfgrass covered land in Ohio. Various management practices including grass species selection, grass establishment, rate and kinds of fertilizers and pesticides, mowing, and irrigation are used to manage turfgrass systems. As not much is known about C storage and dynamics in turfgrass systems, research will provide greater understanding of the turfgrass management practices that can promote C sequestration in these systems. For this dissertation field studies on experimental plots, and residential lawns at a residential neighborhood block, and regional scale were conducted to assess the impact of the above listed management practices on soil organic carbon (SOC) pools, and assess the variability of SOC pools at a neighborhood block and regional level.

The first study evaluated the effects of long term (15-yrs) applications of nine lawn management programs varying in the rate, type and amount of nitrogen (N) fertilizer and pesticide inputs on SOC and total soil nitrogen (TSN) pools, and turfgrass quality, and biomass in Kentucky bluegrass (Poa pratensis L.). Soil samples were collected from replicated experimental plots established at the TruGreen Technical

Center at Delaware, Ohio. In the 3-6 cm soil depth, the Low-N (fertilizer applied at 98

ii kg N ha-1 yr-1) programs had lower SOC pools (5.3 ± 0.7 Mg C ha-1) than to programs receiving higher N (171 to 245 kg N ha-1 yr-1) and Control, which had greater than 6.4

Mg C ha-1. Similarly, in the top 3-6 cm depth, the Low-N programs had lower TSN pools

(0.56 ± 0.07 Mg N ha-1) than programs receiving higher N and Control, which had greater than 0.67 Mg N ha-1. Turf quality was the lowest in the Control program (< 5, on a visual scale of 1-9) for all four sampling years. The clippings biomass was lowest in the Low-N fertilizer application program (< 54 kg ha-1 week-1) for all 3 sampling years. The turfgrass cover estimated visually was lowest in the Control (56%), but showed the highest cover of weeds (41%), dandelion (Taraxacum officinale) (33%) and white clover

(Trifolium repens) (7%). The results show that the SOC and TSN pools in turfgrass systems can be influenced by the amount of N applied and weeds with their N fixing ability and broad leaf cover can reduce turfgrass aesthetic quality but play an important role in the amount of biomass returned to the soil and therefore contribute to C sequestration.

Our next study quantified the C emissions associated with management practices

(fossil fuels required for the production, transport, storage, and application of fertilizers, pesticides, irrigation and mowing), and measure the sustainability of turfgrass systems to offset C emissions from, 1) the use of nine different long term turfgrass management programs used for urban lawns, and 2) the turfgrass management programs in which N is applied and clippings are either removed or returned. The sustainability indices (SI) were calculated as the gain of C sequestered in turfgrass soil for environment benefit as compared to the loss of C which results in environmental degradation. The SI’s assessed

iii by calculating ratios of gross and net C sequestered to C emitted were >1 for all programs

indicating sustainable management of turfgrass lawns for C, except where high N

fertilizer was used without retuning clippings. However, comparing the nine programs

after 15 years, the SI of control and organic treatments was at least five fold higher than

mineral treatments. Similarly the use of lower amount of N fertilizer by returning

turfgrass clippings showed greater SI’s. We concluded that greater sustainability of

turfgrass systems for C can be achieved by reducing or replacing the use of mineral with

organic fertilizers, replacing the use of chemical pesticides with biological pesticides,

mowing less often, and returning clippings.

Our third study evaluated the effect of grass species selection on C dynamics and

litter decomposition. Turf type tall fescue (Festuca arundinacea Schreb.) (TF) and

perennial ryegrass (Lolium perenne L.) (PR) are commonly used for lawns and golf

courses in the Northeast and Midwestern United States. These species differ in growth

habits, drought tolerance, resistance to herbivory, and form mutualistic associations with

Neotyphodium endophytes (Clavicipitaceae). The fungal endophyte infection enhances

plant fitness, but may have negative effect on litter decomposition due to the production of toxic alkaloids in the plant tissue. We hypothesized that both the grass species and the endophyte infection influence C dynamics and litter decomposition in lawns. Replicated treatment plots with low (<30% of plants) (TF- and PR-) and high (80-95%) (TF+ and

PR+) endophyte infection levels in TF and PR were established in 1999 and were managed by mowing, without any fertilizer and pesticide inputs. In 2006, SOC and its labile fractions, including microbial biomass C (MBC) and dissolved organic C (DOC)

iv pools were measured. Soil surface CO2 flux along with soil temperature and moisture

were also measured seven times between June and September in 2006. Litter bags were

incubated on site to determine the effect of the grass species and endophyte on

decomposition of grass clippings. The C pools did not differ significantly between the

four treatments and averaged 25.9 (±1.8) Mg ha-1 SOC, 257.7 (± 19.6) kg ha-1 MBC, and

62.7 (± 5.4) kg ha-1 DOC in 0-12 cm soil depth. Repeated measures analysis revealed

that grass species and/or endophyte infection had a significant influence on soil surface

CO2 flux. Average CO2 flux values for the sampling period were significantly correlated

to the soil moisture (Pearson r = 0.62; p value = 0.01). Repeated measures analysis

revealed that grass species had a significant effect on litter decomposition with PR

decomposing faster than TF during the first three weeks. But within a month clippings

from both species decomposed equally with no difference in the final C:N ratio. We

conclude that carbon sequestration was not influenced by either the grass species or

endophyte level during the 7 year period after plot establishment.

To gain further understanding in C dynamics in turfgrass systems, we collected

data on the baseline soil C pools in newly constructed lawns, SOC spatial variability

across a heterogeneous neighborhood block in a city, and variability at a regional scale by

measuring SOC pools in urban lawns in Wayne and Holmes Counties in Ohio. Lawns

constructed with top soil (TS) had 4.5 times higher SOC pool than to the sub soil (5.1 ±

v other ground covers, but their SOC pools were not significantly different. Sloped turf had 11.7 ± 1.0 Mg ha-1 less SOC pool than the flat turf areas in the 0-12 cm soil depth.

The SOC was more variable across this heterogeneous city block (CV = 32.6) than across the lawns in the study region (CV = 26.2). The average SOC pool across a heterogeneous city block was 46 ± 2.3 Mg ha-1 and that across the region in lawn soils was 25 ± 4.6 Mg ha-1. Considering the average SOC pool of 25 Mg ha-1in the 0-12 cm depth and the total turf area for Ohio at 673,300 ha, the total Ohio SOC pool is at 16.8 tera gram (Tg) which is 0.03 percent of the estimated 59.4 petagram (Pg) SOC pool in the conterminous 48 states.

DEDICATION

This work is dedicated to my husband Ajay whose love, patience, and

encouragement to persevere made this possible.

vii ACKNOWLEDGMENTS

I wish to thank my adviser, for Dr. Parwinder Grewal for many things, but especially for

accepting to supervise my doctoral studies, shaping my project, and fast and efficient review of all submitted manuscripts. He has been instrumental in fuelling my curiosity, sharpening my critical faculties, and providing immense intellectual support, encouragement and enthusiasm in developing my outlook towards this very new research field. I sincerely thank him for his mentorship.

I also wish to thank Drs Rattan Lal, Warren Dick, and John Cardina for serving

on my dissertation committee. It was my privilege to be guided by these excellent

scientists. Their expertise, guidance, and academic support helped me to understand my

research deeply.

My deep appreciation to all the friends in the department of Entomology, and at

OARDC. I am grateful to Dr. Larry Phelan, Dr. Dan Herms, Dr. Casey Hoy, Dr. Ed

McCoy, Bryant Chambers, Dave McCartney, and Kevin Jewell for field and laboratory

support. I am also grateful to all the members of Dr. Grewal’s lab for providing

thoughtful insights and camaraderie in fulfilling my mission.

To the many friends who boosted my spirits and helped me to feel at home in

Ohio, I am grateful. My deep thanks are also due to great number of people provided

help with other fundamental phases of this work or my stay in the U.S. viii My sincere appreciation to my husband Ajay who stood by me throughout this

challenging ordeal, was a constant source of inspiration and determination, and without

him this dream could not have been achieved. This thesis has also been possible with the love and support of my parents Hari Om and Jamnotri Singh, and my parents-in-law

Chandra Pal and Meena Singh. Their belief, trust, strength and sacrifice has helped me focus on my work and maintain an inner harmony to complete this thesis. My heartfelt thanks to all other members of my family for their love, concern, encouragement, and prayers.

ix VITA

September 22, 1977…………..… Born – Allahabad, India

1997…………………………….. B.Sc. in Zoology (Honors), University of Delhi, New

Delhi

1999………………………….…. M.Sc. in Zoology, University of Delhi, New Delhi

2002-2007……………………… Graduate Teaching Associate and Research Associate,

The Ohio State University.

2006 Intern, United Nations Environment Programme,

Washington DC.

FIELDS OF STUDY Major Field: Environment Science Minor Field: Soil Science

x TABLE OF CONTENTS

Page Abstract...... ii Dedication...... vii Acknowledgements...... viii Vita...... x List of Tables ...... xiv List of Figures...... xviii

Chapters:

1. INTRODUCTION...... 1

1.1 Greenhouse effect and climate change...... 1 1.2 Soil organic carbon pool and sequestration...... 2 1.3 Urban Soils...... 7 1.4 Turfgrass lawn management...... 9 1.5 Fossil fuel use associated with turfgrass management...... 11 1.6 Research goals and objectives………...... 12 1.7 Literature Cited...... 12

2. EFFECT OF LONG TERM MANAGEMENT PROGRAMS ON SOIL ORGANIC CARBON, NITROGEN, TURF QUALITY, WEED INFESTATION, AND BIOMASS IN KENTUCKY BLUEGRASS LAWNS IN OHIO...... 20

2.1 Abstract...... 20 2.2 Introduction...... 21 2.3 Materials and methods...... 23 2.3.1 Experimental design and management programs...... 23 2.3.2 Fertilizer programs...... 24 2.3.3 Pesticide programs...... 25 2.3.4 Soil sampling and analysis...... 26 2.3.5 Turf quality, clippings biomass, and weed infestation...... 27 2.3.6 Data management and statistical analyses...... 27 2.4 Results...... 28 2.4.1 Soil texture and bulk density...... 28 xi

2.4.2 Soil pH...... 29 2.4.3 Soil organic carbon concentration...... 29 2.4.4 Soil Organic carbon pool...... 30 2.4.5 Total soil nitrogen concentration...... 30 2.4.6 Total soil nitrogen pool...... 31 2.4.7 Results from contiguous forest and agricultural field...... 31 2.4.8 Turf quality...... 32 2.4.9 Clipping biomass...... 32 2.4.10 Weed infestation...... 32 2.5 Discussion...... 33 2.6 Acknowledgements...... 36 2.7 Literature cited...... 36

3. SUSTAINABLE LAWN MANAGEMENT TO OFFSET CARBON EMISSIONS....56

3.1 Abstract...... 56 3.2 Introduction...... 57 3.3 Materials and Methods...... 60 3.3.1 Quantification of carbon emissions………………………...... 60 3.3.2 Calculation of sustainability indices...... 64 3.4 Results and discussion...... 64 3.5 Acknowledgements...... 67 3.7 Literature cited...... 67

4. SOIL CARBON DYNAMICS AND LITTER DECOMPOSITION AS AFFECTED BY GRASS SPECIES AND FUNGAL ENDOPHYTE INFECTION...... 74

4.1 Abstract...... 74 4.2 Introduction...... 75 4.3 Materials and methods...... 79 4.3.1 Experimental design and research approach...... 79 4.3.2 Soil sampling and analysis...... 79 4.3.3 Soil surface carbon dioxide flux...... 81 4.3.4 Litter bag decomposition...... 81 4.3.5 Calculations and statistical analyses...... 82 4.4 Results...... 83 4.4.1 Soil bulk density, pH, organic carbon, microbial biomass carbon, and dissolved organic carbon...... 83 4.4.2 Soil surface carbon dioxide flux...... 83 4.4.3 Litter bag decomposition...... 84 4.5 Discussion and conclusions...... 84 4.6 Acknowledgements...... 89 4.7 Literature Cited...... 89

xii

5. SPATIAL VARIABILITY IN SOIL ORGANIC CARBON POOLS IN URBAN LANDSCAPES OF OHIO...... 104

5.1 Abstract...... 104 5.2 Introduction...... 105 5.3 Materials and methods...... 108 5.3.1 Experimental design...... 108 5.3.2 Soil sampling and analyses...... 109 5.3.3 Calculations...... 110 5.3.4 Statistical analyses...... 111 5.4 Results and discussion...... 111 5.4.1 Baseline SOC pool in newly constructed urban lawns...... 111 5.4.2 Assess spatial variability in SOC pool across a heterogeneous urban residential block...... 112 5.4.3 Determine variability in soil organic carbon pool at a regional scale in turfgrass lawns……...... 113 5.5 Acknowledgements...... 115 5.6 Literature Cited...... 115

6. SYNTHESIS AND FUTURE DIRECTIONS...... 124 6.1 Synthesis and future directions...... 124 6.2 Literature Cited...... 128

Bibliography...... 129

Appendices Appendix A. Soil physicochemical properties in a heterogeneous residential block in Wooster,Ohio...... 138

xiii

LIST OF TABLES

Table Page

2.1 Fertilization schedule, fertilizer N-P-K composition, herbicide, and insecticide applications under different commercial lawn care programs in experimental plots of Kentucky bluegrass at Delaware, Ohio (1989-2003)...... 40

2.2 Effect of lawn management programs on soil bulk density (Mean ± SEM) at various soil depths in Kentucky bluegrass experimental plots...... 41

2.3 Effect of lawn management programs on soil organic carbon (Mean ± SEM) concentrations (g kg-1) at various soil depths in Kentucky bluegrass experimental plots...... 42

2.4 P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on soil organic carbon concentrations at various depths in Kentucky bluegrass experimental plots...... 43

2.5 Effect of lawn management programs on soil organic carbon (Mean ± SEM) pools (Mg C ha-1) at various soil depths in Kentucky bluegrass experimental plots...... 44

2.6 P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on soil organic carbon pools at various depths in Kentucky bluegrass experimental plots...... 45

2.7 Effect of lawn management programs on total soil nitrogen (Mean ± SEM) concentration (g kg-1) at various soil depths in Kentucky bluegrass experimental plots……………………………………………...... 46

2.8 P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on total soil nitrogen concentration at various depths in Kentucky bluegrass experimental plots...... 47

xiv

2.9 Effect of lawn management programs on total soil nitrogen (Mean ± SEM) pools (Mg N ha-1) at various soil depths in Kentucky bluegrass experimental plots...... 48

2.10 P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on total soil nitrogen pools at various depths in Kentucky bluegrass experimental plots...... 49

2.11 Effect of lawn management programs on turf quality (Mean ± SEM) on a scale of 1-9, collected on an approximately bi-weekly or monthly basis during 1990 (03-19-1990 to 12-03-1990), 1991 (04-01-1991 to 10-17-1991), 1992 (04-041992 to 11-17-1992), and 1993 (05-14-1993 to 11-03-1993) in Kentucky bluegrass experimental plots...... 50

2.12 P values and least significant differences (LSD) (within brackets)at p<0.05 indicating the effect of lawn management programs and program groups on turf quality in Kentucky bluegrass experimental plots...... 51

2.13 Effect of lawn management programs on clipping dry weight (Mean ± SEM), collected on a weekly basis, produced from 1995 (6-5-1995 to 10-27-1995), 1996 (4-26-1996 to 10-25-1996), and 1997 (04-18-1997 to 05-31-1997) in Kentucky bluegrass experimental plots...... 52

2.14 P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on clippings dry weight in Kentucky bluegrass experimental plots...... 53

2.15 Effect of lawn management programs on percentage plant cover (Mean ± SEM) in Kentucky bluegrass experimental plots...... 54

2.16 P values and least significant differences (LSD) at p<0.05 (within brackets) indicating the effect of lawn management programs and program groups on percentage plant cover in Kentucky bluegrass experimental plots...... 55

3.1 Fertilization schedule, N-P-K composition of fertilizer, herbicide, and insectide applications under eight different commercial lawn care programs. Plots were planted with Kentucky bluegrass and maintained at Delaware, Ohio (1989-2003)...... 69

3.2 Carbon emissions and sustainability indices associated with various management practices. Calculations include SOC to a depth of 0-12 cm and C emissions from fertilizer spraying/spreading and irrigation are not included due to lack of information. Information about mowing is approximated based on the gasoline consumption of a John Deere lawn mower and may differ for different mowers. Emissions calculated for a period of 12 years and for all others for a period of 13 years...... 70

3.3. The CENTURY model predicted optimal N fertilizer rates at two clipping management scenarios based on turf age and the calculated values of carbon emissions associated with the N fertilizer...... 72

3.4. Carbon emissions and sustainability indices (SI) associated with clipping management scenarios...... 73

4.1 Soil bulk density (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……………………………………………………...... 93

4.2 Soil organic carbon concentration (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……………………………………………...... 93

4.2 Total soil nitrogen concentration (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment………………………………………………...... 94

4.4 Microbial biomass carbon (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment...... 94

4.5 Dissolved organic carbon (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment...... 95

4.6. Soil surface CO2 flux (Mean ± SEM) at various field sampling days in high and low endophytic tall fescue and perennial ryegrass experimental plots...... 95

4.7 Proportion of remaining litter (Mean ± SEM) at various field days in high and low endophytic tall fescue and perennial ryegrass experimental plots...... 96

5.1 Mean and range of soil parameters across a heterogeneous urban residential

xvi

block in Wooster, Ohio...... 117

5.2 P values and least significant differences (LSD) at p<0.05 (within brackets) indicating the effect of heterogeneity in slope and vegetation on carbon pools and concentrations...... 117

5.3 Mean and range of soil parameters in urban home lawns of Wayne and Holmes Counties, Ohio...... 118

xvii LIST OF FIGURES

Figure Page

1.1 Soil organic carbon pool and equestration…………………………………….…16

1.2 Factors of soil organic carbon (SOC) formation and stabilization………………17

1.3 Fractional turfgrass area in the United States. ………...... ……………………18

1.4 Turf grass management practices that are aimed at improving turf quality for aesthetic in Midwestern United States...... …………………..19

4.1 Soil organic carbon pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……………………………………………………………………..97

4.2 Total soil nitrogen pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……………………………………………………………………..98

4.3 Soil microbial biomass carbon pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……………………………………………………………………..99

4.4 Dissolved organic carbon pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……………………………………………………………………100

4.5 Soil surface carbon dioxide flux at various dates in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment…………………………………………………………………....101

4.6 Soil temperature at various dates in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……...... 102

xviii

4.7 Volumetric soil moisture content at various dates in high and low endophytic tall fescue and perennial ryegrass experimental plots after seven years of establishment……………………………………………………………………103

5.1 Map of sampling area located in the City of Wooster, Ohio.....………………..119

5.2 Initial soil bulk density (Mg m-3) in turfgrass plots established with top soil or subsoil with or without compost amendment.………...... ……………………..120

5.3 Initial soil organic carbon (%) in turfgrass plots established with top soil or subsoil with or without compost amendment...... ………………………..……121

5.4. Initial soil organic carbon pool (Mg ha-1) in turfgrass plots established with top soil or subsoil with or without compost amendment. ..………………...………122

5.5. Soil organic carbon concentration (%) and pool (Mg ha-1) across a heterogeneous residential block in Wooster, Ohio.…………………………………………….123

xix CHAPTER 1

INTRODUCTION

1.1 Greenhouse effect and climate change

Rapid industrialization and urbanization in the last century have changed atmospheric composition, enhancing the greenhouse effect that has resulted in global climate change.

With a 90 percent certainty these changes in atmospheric composition occur from anthropogenic emissions of greenhouse gases, such as carbon dioxide (CO2) from the burning of fossil fuels, deforestation, and multiple human activities (IPCC, 2007).

Atmospheric CO2 contributes about 25% to this greenhouse effect, while water vapor

(~60%), ozone (O3) (~8%), methane (CH4), and nitrous oxide (N2O) contribute the rest

(Karl and Trenberth, 2003). The CO2 concentration has increased from pre-industrial level of 280 parts per million by volume (ppmv) to 380 ppmv at present (Raupach et al.,

2007). With a 3 percent increase in CO2 emissions in this decade, it is estimated that dangerous levels of 450 ppmv of CO2 concentration will be reached in the next 20 years, increasing temperature by 1-1.5º C above pre-industrial levels (Raupach et al., 2007).

This can lead to various environmental transformations such as disappearance of mountain glaciers, destructions of coral reefs and other ecosystems unless concrete steps to reduce CO2 emissions are taken (IPCC, 2007).

1 Some of ways in which CO2 emissions can be minimized are: (i) increasing energy

efficiency, (ii) increased usage of bio-fuels and renewable energy, and (iii) capture and

secure storage of CO2 from the atmosphere (Carbon sequestration) (USDOE, 1999).

Carbon (C) sequestration occurs through direct and indirect fixation of atmospheric CO2

into various reservoirs such as geologic, oceans, plant biomass, and soils.

1.2 Soil organic carbon pool and sequestration

The element C determines the essential structure and function of all living forms.

Soils are an important component of the global C cycle and serve as a large reservoir of

terrestrial C with a pool of 2500 Gigatons (Gt) (Lal et al., 2004). Promoting soil C

sequestration (maximizing C storage and minimizing C loss) by adopting appropriate

land use, soil, nutrient, water, and plant management is an effective strategy for reducing atmospheric CO2 (Lal et al., 1998).

Direct soil C sequestration occurs by inorganic chemical reactions that convert

CO2 into soil inorganic C compounds such as calcium and magnesium carbonates.

Carbon is present in the atmosphere as CO2 and is fixed by plants via photosynthesis.

Direct plant C sequestration occurs as plants photosynthesize atmospheric CO2 into plant

biomass. Plant material is either passed on to animals or enters the soil through the above

ground biomass or below ground from roots and their exudates all of which is

subsequently transferred to soil microbes via the food chain and turns into soil organic C

(SOC) through the process of decomposition (Figure 1.1). Some of the C is released

back as CO2 via respiration by members of the food chain, and some used for building

2 the body material. But this is not the only source of release of atmospheric C. Under anaerobic conditions CH4 could be produced by microorganisms. Part of the remaining C is leached out as dissolved organic C (DOC) (Figure 1.1). Mineral fertilizers can solubilize the soil organic matter (SOM) thus increasing the soil DOC content

(Hartikainen and Yli-Halla, 1996). This DOC can leach out to lower soil depths and remain protected from further degradation and loss as CO2. This is an important process of SOC sequestration referred to as illuviation.

The amount of SOC is a function of climate, biota, parent material, time and human influence (Jenkinson, 1981; Volk and Loeppert, 1982; Schimel et al., 1994; Lal et al., 1998; Akala, 2000; Rice, 2005). These factors influence the vegetation and the process of decomposition and thus the SOC. Climate, through temperature and moisture regimes affects on plant productivity and rates of decomposition. Biota determines the quantity and quality of vegetation and the conclave of fauna dictated by the physical and chemical conditions of the soil determine the rate and amount of decomposition. Parent material affects mineralogy, soil texture, and pH which in turn influence the ability of soil to form stable soil aggregates. With time and increasing inputs to the soil there will be a gradual buildup of SOC. Human influences in terms of land use that increase C input to the soil will increase SOC but may also cause a reduction by use of management practices that lead to the exposure of SOC to microorganisms and its utilization for respiration. The factors that help in the SOC formation and its stabilization are depicted in figure 1.2.

3 The SOC constitutes three pools; the active pool that consists of microbes and

microbial byproducts and has the least residence time, slow pool represents stabilized decomposition products with an intermediate residence time; and the passive pool which

is recalcitrant SOC with a residence time of hundreds or thousands of years. In the slow

and recalcitrant pools, SOC is stabilized due to physical protection and chemical recalcitrance against further microbial action. Physical protection is provided by

physico-chemical interaction of the soil mineral matrix and a biomolecule. Chemical

recalcitrance is due to the chemical and structural stability of the organic molecule.

These two mechanisms are not independent but work by interaction with each other

(Krull et al., 2003).

Numerous experiments have been conducted to study the structure, and formation

of soil aggregates, and the protection afforded by them to the SOC. A detailed review is

provided by Blanco-Canqui and Lal (2004). The aggregate provides the physical

protection and unless disrupted and exposed to suitable physical factors such as pH,

temperature and moisture that may enable further microbial activity, will protect the SOC

for a long time. This, process of SOC sequestration is referred to as aggregation.

Stabilization of SOC by biochemical recalcitrance is achieved through the

inherent chemical structure of the biomolecule, which is a function of the intra- and inter-

structural bond strengths, the degree of regularity of occurrence of structural units and the

degree of aromaticity (Krull et al., 2003) though dominated more by aliphatic compounds

which are formed from the microbial alteration of plant matter or their own body material

(Balser, 2005). If a protective mechanism such as mineral matrix is absent, such as in

4 mineral free thatch in turfgrass and the soil is biologically active, the residence time and

biological availability of SOC will entirely depend on the degree of recalcitrance defined

by the chemical structure of SOC (Krull et al., 2003). Krull et al., (2003) suggest that in

such a scenario chemical recalcitrance may be the only mechanism for SOC protection

for long time.

The chemical recalcitrance is achieved during the process of humification which

is a part of decomposition and leads to the formation of stable humic substances (HS).

Humification is also an important process of SOC sequestration. But this process takes a long time and so it has not been easy to study its dynamics. The chemical perspective on the role of microbes in humus formation suggests the following pathways of humic substance formation (Balser, 2005): (1) Degradation of plant material and abiotic condensation of the products to form humic substances, (2) Degradation of primary resources and re-sysnthesis into recalcitrant components in the bodies of microbes which remain in the soil after their death, (3) Selective preservation wherein easily degradable plant material is degraded and the large resistant structures form humic substances, and

(4) Microbes participate directly in humus formation by enzymatic activities.

Therefore, from a microbiological perspective humification occurs in three phases

(Balser, 2005): 1) Rapid initial decomposition of primary plant residues; 2) slow

decomposition of primary plant structural components; and 3) alteration of SOC and HS

genesis. Simple and rapid decomposition in phase 1 can be performed by a wide range of

organisms. In phase 2, to access nitrogen (N) bound in the structural components of the

plant, microorganisms need multiple enzymes often from multiple organisms. In phase 3

5 microbial activities by cometabolism can oxidize and alter HS or can generate and polymerize aromatic compounds to form HS. During this phase, direct incorporation of metabolites and biomass components in the HS also occurs (Balser, 2005).

Since humification is controlled by soil microbes, factors such as climate and parent material affecting soil microbes also affect the process of HS formation. If the climatic conditions are such that they do not favor the growth of microorganisms the plant material will be stored in the soil as SOC, which is good only if the climatic conditions do not change and favor the soil microorganisms to degrade that SOC (Balser,

2005). In the Tundra ecosystem, low temperatures limit the microbial utilization of organic matter thus resulting in the accumulation of SOC (Archibold, 1994). But this C is in unhumified state and rise in temperature due to global climate change in this ecosystem may cause the SOM to be lost via microbial decomposition (Balser, 2005). So if C has to be stored in the soil for long term it is important to convert the plant matter into HS.

The SOC can be protected in soil aggregates as long as there is no disturbance.

Once a disturbance is caused this SOC is highly exposed to microbial degradation which may or may not lead to HS formation depending on the microbial community that degrades it. The flow and fate of C inputs in the soil will depend on the fungal or bacterial food web in the soil. Fungi degrade incoming plant litter more completely than bacteria, releasing more C as CO2 (Balser, 2005). Because fungal cell walls are highly recalcitrant, humification by degradation and resynthesis may also be high. In contrast, bacteria may contribute more to humification via selective preservation of

6 macromolecular plant litter compounds (Balser, 2005). Actinomycetes can decompose

more resistant plant polymers such as cellulose, hemicellulose, lignin, and fungal and

insect polymer chitin and thus play a major role in the formation of humus in soil

(Nelson, 1997). Thus humus formation is mostly controlled by microorganisms. The

amount of humus in the soil is a balance between the formation of new humus and the

decomposition of the old. These processes are also governed by the physical and

chemical factors in the soil that will enhance or suppress the microorganisms involved.

1.3 Urban soils

Urban expansion impacts soils in various ways. During the process of

urbanization the vegetation covering the soil is often removed, the soil is scraped and mixed thus destroying its structure, which is the arrangement of aggregates and the pores and the continuity and stability of pores between them. Use of heavy building equipment can lead to soil compaction, loss of SOM and nutrients, availability of pore space for moisture, air and movement of soil organisms. Further, urban soils are also treated with various cultural treatments such as mowing, and application of fertilizer and pesticides.

These cultural practices can be beneficial in maintaining the plant quality and reducing

soil erosion, but can also be detrimental if they lead to runoff and long term accumulation

of chemical substances in the soil.

USDA Natural Resources Conservation Service (USDA-NRCS) has researched

on urban soils in four major areas that include heavy metal toxicity, landscape hydrology,

and related transport of sediments and chemicals, biological transformations of

7 waste/new boundaries, and infiltration from heavy metal use and management (Scheyer,

2007). Because of their large soil cover, however, urban soils should also be evaluated

for their use for C storage.

The conterminous 48 states in the United States cover nearly 760 million (M) ha

of land (USDA-NRCS, 2003). Welterlen (2003) estimated that there are 20 M ha of

irrigated turf, 12 M km of roadside, and 33 M ha of National Park Service land under

turfgrass cover. Milesi et al., (2005) estimated total turfgrass area which includes

residential, commercial, and institutional lawns, parks, golf courses, and athletic fields at

163,800 km2 (± 35,850 km2) (Figure 1.3). Further, the turfgrass land cover is rapidly expanding because of increasing urbanization and adding approximately 700,000 ha of residential property per annum (Robbins and Birkenholtz, 2003). Milesi et al., (2005) estimated that there are 673,300 ha of turfgrass covered land in Ohio.

While direct plant C sequestration occurs continually in turfgrass the information on SOC dynamics is scanty. In turfgrass, there is no soil disturbance due to plowing, and the grass cover prevents erosion and maintains soil structure. Plant biomass is continually added to the soil after mowing and through root growth leading to the accretion of the SOC pool. Synthesis of the historic soil testing data from golf courses in

Denver and Fort Collins, Colorado indicated that SOC sequestration in golf course turf soils occurs at a high rate of 1 Mg ha-1 yr-1 comparable to that under Conservation

Reserve Program land (Qian and Follett, 2002). However information on SOC dynamics

in urban lawn soils is lacking.

8 1.4 Turfgrass lawn management

Turfgrass lawn is the ecosystem closest to people living in urban North America.

It is usually managed for aesthetic reasons such that it becomes a grass monoculture or

has minimal plant diversity, maintained by extensive and intensive use of various inputs.

The various management inputs include turfgrass establishment, grass species, rate and

kinds of fertilizers and pesticides, mowing, and irrigation (Figure 1.4). These

management practices are specifically aimed at maintaining turfgrass quality. According

to the National Turfgrass Evaluation Program (NTEP), turfgrass quality provides a visual

assessment of combination of color, density, uniformity, texture, and disease or

environmental stress.

Bormann et al., (2001) have recognized the environmental and social costs of

intensive lawn care and have proposed 2 kinds of lawns, the Freedom lawn and the

Industrial lawn. The Freedom lawn is independent of chemicals and resources and

mowed only when needed, designed so as to reduce the area actually occupied by the

grass. The authors define the Freedom Lawn as a stretch of land covered in grass, closely

mowed, and located near a house or in a park. This lawn has plant diversity and thus it

has the capacity to tolerate various kinds of stresses. Stresses such as diseases, insects, drought may harm some plants but will not be able to completely wipe out the lawn.

On the other hand, the Industrial lawn is a grass monoculture lawn maintained

with the addition of extensive and intensive management inputs. The authors define this

lawn as a stretch of land covered in grass, closely mowed, and located near a house or in

a park, continuously green and to the greatest possible degree, free of weeds and insect

9 pests. But there are lawns that do not fall in either category but use a range of management practices.

For an industrial lawn, regular fertilizer program combined with mowing, pesticide, and irrigation is considered necessary to maintain turfgrass quality.

Tremendous amount of research has been conducted to select the best fertility treatments that not only include macronutrients like N, but also micronutrients, pesticide, and irrigation quantity. Detailed usage rates for various grass species and the environmental conditions have been made available to the public.

Net primary production and its addition to the soil in the form of plant residue and root exudates and its loss via decomposition are the main biological processes that determine SOC sequestration (Jastrow and Miller, 1998). These processes can be influenced by management practices used to maintain industrial lawns. The quality and quantity of the plant residue as determined by the plant species in interaction with their environment can also greatly influence the organic matter inputs in the soil. In turfgrass systems there is periodic application of turf clippings and root exudates from the dense fibrous turfgrass roots. This can help to maintain the soil structure and thus enhance long term SOC sequestration.

Along with C, N is a major element of SOM and is therefore necessary to convert plant residue into SOM (Himes, 1998). A C:N ratio of 8:1 to 15:1 results in stable SOM.

Liu and Hull (2006) conducted a field study to estimate the N recovery from clippings from 10 cultivars each of Kentucky bluegrass (Poa pratensis L.), perennial ryegrass

(Lolium perenne L.), and tall fescue (Festuca arundinaceae Schreb.) and found that N

10 recovery ranged from 260 to 111 kg N ha-1 yr-1, which exceeded the amount of fertilizer

used in these ecosystems. However, few studies (Qian and Follett, 2002; Pouyat et al.,

2006) have been conducted to assess the effect of long-term management practices on C

sequestration rate or the SOC pools in turfgrass covered soils.

1.5 Fossil fuel use associated with turfgrass management

Households in the US spent about $9.6 billion to personally care for their lawns in 2005 increasing from $8.8 billion in 2004 (NGA, 2005). In 2005, of the 59 million homeowners with do-it-yourself lawn care that participated in the survey conducted by

the National Gardening Association, around 52 million used some kind of fertilizer, and

49 million used some kind of chemical pest control. One average lawn requires 40 hours

of mowing. The EPA estimates that one hour of mowing emits pollution equivalent to driving a car for 32 kilometers (EPA, 2006).

Economic and environmental concerns about the use of turfgrass management inputs are increasing. The major environmental concerns are with regard to the toxicity and run off of the applied inputs (Scheyer, 2007). Heavy application of fertilizers and pesticides on suburban lawns and golf courses has been recognized as one of the ‘top 10 stupid environmental policies’ because of their derogative effect on water quality

(Schnoor, 2004). Bormann et al., (2001) have recognized that there are C costs associated with the use of various management inputs (Figure 1.1). The C costs are from the use of fossil fuels associated with the manufacture, transport, storage, and application

11 of various lawn management inputs. However, they lack quantitative data on the C emissions associated with the inputs.

1.6 Research goals and objectives

The goal of this research was to assess the C sequestration potential of urban lawn soils in Ohio. The specific objectives for the research were as follows:

1. Long term management effects on SOC, N, turf quality, and biomass in Kentucky

bluegrass lawns in Ohio

2. Determine the sustainability of turf management to offset C emissions

3. Soil carbon dynamics and litter decomposition as affected by grass species and

fungal endophyte infection

4. Determine the spatial variability in SOC pools in urban landscapes of Ohio

1.7 Literature cited

Amundson, R. 2001. The carbon budget in soils. Annu. Rev. Earth Planet. Sci. 29:535– 62.

Akala, V.A. 2000. Soil organic carbon sequestration in a reclaimed mineland chronosequence in Ohio. Ph.D. Thesis. The Ohio State University. Columbus, OH. pp 205.

Archibold, O.W.1994. Ecology of world vegetation. Chapman and Hall. New York, NY. pp. 528.

Balser, T.C. 2005. Humification. p. 195-207. In D. Hillel, (ed.) Encyclopedia of Soils in the Environment. Elsevier, Oxford UK.

Blanco-Canqui, H. and R. Lal. 2004. Mechanisms of carbon sequestration in soil aggregates. Critical reviews in plant sciences. 23(6):481–504.

12 Bormann, F.H., D. Balmori, and G.T. Geballe. 2001. Redesigning the American lawn. Yale University Press. New Haven, CT. pp 178.

Environmental Protection Agency (EPA). 2006. Conseravtion and native landscape awards. Retrieved June, 13, 2007, from http://www.epa.gov/greenacres/awards.html.

Hartikainen, H, and M. Yli-Halla. 1996. Solubilitiy of soil phosphorus as influenced by urea. Z. Pflanzenernahr. Bodenkd. 159: 327-332.

Himes, F.L. 1998. Nitrogen, sulfur, and phosphorus and the sequestering of carbon. p. 315-319. In R. Lal, J.M. Kimble, R.F. Follett, and B.A. Stewart (eds) Soil processes and the carbon cycle. Advances in Soil Science. CRC Lewis Publishers, Boca Raton.

Intergovernmental Panel on Climate Change (IPCC). 2007. Climate change 2007: The physical science basis. Contribution of working group i to the fourth assessment report of the intergovernmental panel on climate change. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY.

Jastrow, J.D. and R.M. Miller. 1998. Soil aggregate stabilization and carbon sequestration: Feedbacks through Organomineral Associations. p. 207-223. In: Soil processes and the carbon cycle. R.Lal, J.M. Kimble, R.F. Follett and B.A. Stewart (eds.). CRC Press, Inc., Boca Raton, FL.

Jenkinson, D.S. 1981. The fate of plant and animal residues in soil. p. 505–562. In D.J. Greenland and M. H. B. Hayes (eds.) The chemistry of soil processes. John Wiley & Sons, New York.

Karl, T.R., and Trenberth, K.E., 2003. Modern Global Climate Change. Science, Washington DC. 302:1719-1723.

Krull, E.S., A.B. Jeffrey, and J.O. Skjemstad. 2003. Importance of mechanisms and processes of the stabilization of soil organic matter for modeling carbon turnover. Functional Plant Biology. 30:207-222.

Lal, R. J.M. Kimble, R.F. Follett and C.V. Cole. 1998. The potential of U.S. cropland to sequester carbon and mitigate the greenhouse effect. Ann Arbor Press, Chelsea, MI. pp.128.

Lal, R., M. Griffin, J. Apt, L. Lave, and M. G. Morgan. 2004. Managing soil carbon. Science, Washington DC. 304:393.

13 Liu, H, and R.J. Hull. 2006. Comparing cultivars of three cool-season turfgrasses for nitrogen recovery in clippings. HortScience 41(3):827-831.

Milesi, C., S.W. Running, C.D. Elvidge, J.B. Dietz, B.T. Tuttle, R.R. Nemani. 2005. Mapping and modeling the biogeochemical cycling of turfgrasses in the United States. Environmental Management. 36: 426-438.

National Gardening Association (NGA). 2005. National gardening survey. National gardening association, Burlington, VM.

Nelson, E. 1997. Microbiology of turfgrass soils. Grounds Maintenance. 1 March. EDIA Penton Media Inc.

Pouyat, R. V., I. D. Yesilonis, and D. J. Nowak. 2006. Carbon storage by urban soils in the united states. J. Environ. Qual. 35:1566–1575.

Qian, Y. and R.F Follett. 2002. Assessing soil carbon sequestration in turfgrass systems using long-term soil testing data. Agron. J. 94:930-935.

Raupach, M. R., G. Marland, P. Ciais, C. L. Quéré|, J. G. Canadell, G. Klepper, and C. B. Field. 2007. Global and regional drivers of accelerating CO2 emissions. Proceedings of National Academy of Sciences. 104 (24): 10288-10293.

Rice, C.W. 2005. Carbon cycle in soils. p. 164-170. In D. Hillel, (ed.) Encyclopedia of soils in the environment. Elsevier, Oxford UK.

Robbins, P. and Birkenholtz T. 2003. Turfgrass revolution: measuring the expansion of the American lawn. Land Use Policy. 20: 181-194.

Schnoor, J.L.. 2004. Top 10 stupid environmental policies. Environmental Science and Technology, v. 38 issue 13. p 239 A.

Schimel, D.S., B.H. Braswell, E.A. Holland, R. McKeown, D.S. Ojima, T.H. Painter, W.J. Parton and A.R. Townsend. 1994. Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils. Global Biogeochem. Cycles. 8:279- 293.

Scheyer, J. 2007. Urban Soil Issues. Retrieved June, 14, 2007, from http://soils.usda.gov/use/urban/index.html

United States Department of Energy (USDOE). 1999. Carbon sequestration: State of the Science, A working paper for road mapping future carbon sequestration R & D. USDOE, Washington, D.C.

14 United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS). 2003. Summary Report: 2002. National Resources Inventory. USDA-NRCS, Washington, DC.

Volk, B.G. and R.H. Loeppert. 1982. Soil organic matter. p. 211-268. In V.J. Kilmer (ed.). Handbook of soils and climate in agriculture. CRC Press, Inc., Boca raton, FL.

Welterlen, M. 2003. Time to mow. Grounds Maintenance. 1 May. Penton Media Inc.

Figure 1.1. Soil organic carbon pool and sequestration (Modified from Amundson, (2001) and Bormann et al., (2001)).

16 CLIMATE WATER PLANT MICROBIAL TEMPERATURE MATERIAL COMMUNITY / OXYGEN ACTIVITY CARBON PARENT DIOXIDE MATERIAL (CO2) TEXTURE pH DISSOLVED MINERALOGY SOC ORGANIC CARBON (DOC)

ACTIVE POOL SLOW POOL RECALCITRANT POOL MICROBES, STABILIZED HUMIC SUBSTANCES MICROBIAL DECOMPOSITION (HS) BY-PRODUCTS PRODUCTS

PHYSICAL CHEMICAL PROTECTION + RECALCITRANCE

Figure 1.2. Factors of soil organic carbon (SOC) formation and stabilization (Modified from Rice (2005) and Balser (2005)).

Figure 1.3 Fractional turfgrass area (%) in the United States (Milesi et al., 2005).

Figure 1.4 Turf grass management practices that are aimed at improving aesthetic quality in Midwestern United States.

19 CHAPTER 2

LONG TERM MANAGEMENT EFFECTS ON SOIL ORGANIC CARBON,

NITROGEN, TURF QUALITY, AND BIOMASS IN KENTUCKY BLUEGRASS

LAWNS IN OHIO

2.1 Abstract

This study evaluated the effects of long term (15-yrs) applications of nine lawn

management programs varying in the rate, type and amount of fertilizer and pesticide

inputs on soil organic carbon (SOC) and total soil nitrogen (TSN) pools, and turfgrass

quality, and biomass in Kentucky bluegrass (Poa pratensis L.). Soil samples were

collected from replicated experimental plots established at the TruGreen Technical

Center at Delaware, Ohio. The experimental design was a randomized complete block

design with 4 replications per treatment. In the 3-6 cm soil depth, the Low-N (fertilizer

applied at 98 kg N ha-1 yr-1) programs had lower SOC pools (5.3 ± 0.7 Mg C ha-1) than to programs receiving higher N (171 to 245 kg N ha-1 yr-1) and Control, which had greater

than 6.4 Mg C ha-1. Similarly, in the top 3-6 cm depth, the Low-N programs had lower

TSN pools (0.56 ± 0.07 Mg N ha-1) than programs receiving higher N and Control, which

had greater than 0.67 Mg N ha-1. Turf quality was the lowest in the Control program (<

5, on a visual scale of 1-9) for all four sampling years. The clippings biomass was lowest

in the Low N fertilizer application program (< 54 kg ha-1 week-1) for all 3 sampling years.

The turfgrass cover was lowest in the Control (56%), but showed the highest cover of

weeds (41%), dandelion (Taraxacum officinale) (33%) and white clover (Trifolium

repens) (7%). The results show that the SOC and TSN pools in in turfgrass systems can

be influenced by the amount of N applied and weeds with their N fixing ability and broad

leaf cover can reduce turfgrass aesthetic quality but play an important role in the amount

of biomass returned to the soil and therefore contribute to C sequestration.

2.2 Introduction

Soils are an important component of the global carbon (C) cycle (Lal et al., 2004).

Enhancing soil C pool by adopting appropriate land use, soil, nutrient, water, and plant

management practices is an effective strategy for reducing the net rate of increase in

atmospheric carbon dioxide (CO2) concentration (Lal et al., 1999). The conterminous 48

states in the United States cover nearly 760 million (M) ha of land (USDA-NRCS, 2003).

Milesi et al., (2005) estimated total turfgrass area (which includes residential,

commercial, and institutional lawns, parks, golf courses, and athletic fields) at 163,800

km2 (± 35,850 km2). Welterlen (2003) estimated that there are 20 M ha of irrigated turf

excluding 12 M km of roadside and 33 M ha of National Park Service land under

turfgrass cover. Further, the turfgrass land cover is rapidly expanding because of increasing urbanization (Robbins and Birkenholtz, 2003).

While direct plant C sequestration occurs continually in turfgrass plants the information on soil organic carbon (SOC) dynamics is scanty. In turfgrass, there is no

annual soil disturbance due to plowing, and the grass cover prevents erosion and

maintains soil structure. Plant biomass is continually added to the soil through mowing

and through root growth leading to the accretion of the SOC pool. Synthesis of the

historic soil testing data from golf courses in Denver and Fort Collins, Colorado indicated

that SOC sequestration in golf course turf soils occurs at a high rate comparable to that

under Conservation Reserve Program land (Qian and Follett, 2002).

Turfgrass systems are intensively managed with various inputs to enhance or

maintain turf quality. According to the National Turfgrass Evaluation Program (NTEP),

turfgrass quality provides a visual assessment of combination of color, density,

uniformity, texture, and disease or environmental stress. Turf quality management practices used in home lawns differ in grass species selection, rate and kinds of fertilizers and pesticides applied, mowing frequency, and amount and frequency of irrigation which may influence plant diversity, biomass, and SOC. However, few studies have been conducted to assess the effect of different management practices on rate of C sequestration or SOC pools in turfgrass. To fill this information void, we conducted a study to evaluate the effect of nine different management programs typically used to maintain turf quality in urban and suburban lawns on SOC and total soil nitrogen (TSN) concentrations and pools, and relate them to turf quality, weed infestation level, and clippings biomass in Kentucky bluegrass experimental plots over a fifteen year period.

2.3 Materials and methods

2.3.1 Experimental design and management programs

The field experiment was conducted on Kentucky bluegrass (Poa pratensis L.) turf

established at TruGreen Technical Center in Delaware, Ohio in autumn of 1983. The soil

type is Blount Clay / Silt Loam (Fine, illitic, mesic Aeric Ochraqualf) and this area was

used as farmland before the Technical Center was established. The mean temperature at

the site is 15ºC in spring and 21ºC in summer. The mean precipitation is 253.8 mm in

spring and 303.2 mm in summer.

In spring 1989, a long-term study was initiated to examine differences in lawn turf

response to varying input programs. The study plots were maintained annually from

1989 to 2003 for a total of 15 years. A total of 9 management programs were evaluated

(Table 2.1). Programs were arranged in a randomized complete block design with four

replications. Each plot was 2.4 * 1.2 m. Programs included a Control, two organic-

fertilizer, and six mineral-fertilizer management programs. The programs represented

four levels of N application rate (0 kg ha-1 yr-1, 98 kg ha-1 yr-1, 171 kg ha-1 yr-1, 216-245

kg ha-1 yr-1).

The program details are as follows: 1) Control, 2) Organic a (Oa): with four bi-

monthly granular organic fertilizer applications, high N input, 3) Organic b (Ob): with

four bi-monthly organic fertilizer applications and once each spring a post-emergent

herbicide applied to Control broadleaf weeds, high N input, 4) Mineral High-N a

(MHNa): with monthly applications of either liquid urea-N mineral fertilizer with spring- applied pre-emergent herbicide and post-emergent herbicide applied spring and fall, or a

23 fungicide application in late spring and granular blend of sulfur coated urea (SCU) and urea-N fertilizer in summer and late fall, 5) Mineral High-N b (MHNb): with five applications every six weeks of liquid urea-N mineral fertilizer, pre-emergent herbicide in spring , and post-emergent weed Control each spring and fall, 6) Mineral High-N c

(MHNc): with five applications every six weeks of granular urea-N mineral fertilizer, impregnated with pre-emergent herbicide in spring, and a sequential liquid spray for post- emergent weeds annually in spring and fall, 7) Mineral High-N d (MHNd): with five granular urea-N mineral fertilizer applications, 8) Mineral Medium-N (MMN): with four bi-monthly applications of a consumer-formulated granular urea-N mineral fertilizer alone or impregnated pre-emergent herbicide, post-emergent herbicide, or surface insecticide, and 9) Mineral Low-N (MLN) with liquid urea-N fertilizer and post- emergent herbicide applied each spring and fall. Oa, Ob, and MMN were established in

1990 and all other programs were established in 1989.

2.3.2 Fertilizer programs

Oa and Ob used Richlawn commercial organic fertilizer (Richlawn Turf, Platteville, CO) for the first 9 years and Ringer’s commercial organic fertilizer (Woodstream Corporation,

Lititz, PA) thereafter. All other fertilizer programs contained urea, SCU, ammonium phosphates, and potassium chloride. The programs differed in N-P-K composition and application rate, where ML received lowest N input (98 kg ha-1 yr-1) compared to the other programs that received 171-245 kg ha-1 yr-1. The Control was not fertilized.

2.3.3 Pesticide programs

Pesticide applications were made annually as follows: the pre-emergent herbicide

pendimethalin [N-(1-ethylpropyl)-2,6-dinitro-3,4-xylidine] was applied at active

ingredient (a.i.) rate of 1.7 kg ha-1 yr-1 each spring to programs MHNa, MHNb, MHNc,

MMN, and MLN; broadleaf herbicide containing MCPA + mecoprop + dicamba [2-

Methyl-4-Chlorophenoxy-acetic acid, (+)-R-2-(2-Methyl-4-Chlorophenoxy) propionic

acid, and 3,6-Dichloro-o-Anisic acid] was applied at a.i. rate of 3.5 l ha-1 yr-1 to programs

Oa, MHNa, MHNb, MHNc, and MLN, while a mixture of 2,4-D + mecoprop+Dicamba

[2,4-dichlorophenoxyacetic acid, (+)-R-2-(2-Methyl-4-Chlorophenoxy) propionic acid,

and 3,6-Dichloro-o-Anisic acid] was applied at a.i. rate of 1.7 kg ha-1 yr-1 to program

MMN; the insecticide diazinon [O,O-Diethyl O-(2-isopropyl-4-methyl-6-pyrimidinyl)

thiophosphoric acid] was applied at a.i. rate of 5.6 kg ha-1 yr-1 to program MMN; and the

fungicide bayleton [1-(4-chlorophenoxy)-3,3-dimethyl-1-(1,2,4-triazol-1-yl)-butan-2-

one] was applied at a.i. rate of 0.15 l ha-1 yr-1 to program MHNa.

No programs were applied during 2001 and 2003 and all assessments occurred in

September 2003. Mowing was conducted every week from last week of April to last

week of October and mowing height was set at 5.1 cm up to the year 2000 and 6.25 cm thereafter. The experiment was not designed to assess the effects of specific products,

but to evaluate the net effects of the long-term use of different management programs on

SOC and TSN pools.

2.3.4 Soil sampling and analysis

Soil samples were collected in September 2003 by removing five soil cores (2 cm

diameter, and 15 cm deep) obtained randomly from each plot. Each soil core was divided

into 0-3, 3-6, 6-9 and 9-12 cm depths. The soils were sampled to a depth of 12 cm as

turfgrass root systems are concentrated in the surface 0-15 cm depth due to regular

mowing (Qian et al., 1997). Four separate samples, one from each replicate plot were

obtained for measuring soil bulk density using the same corer as for measuring SOC pool

(Blake and Hartge, 1986; Ellert et al., 2001).

The study plots were in proximity to an agricultural field on one side and a forest on the other at an approximate distance of 500 m on either side. The SOC and TSN pools

in these ecosystems were computed for two points each in the field and in the forest at 5

and 10 m distance from the edge for comparison. Soil samples were obtained and

processed as described for the experimental plots.

Soil samples were air-dried and all visible leaf and root materials were removed.

The entire sample was passed through a 2 mm sieve after grinding using a mortar and

pestle. Soil textural analysis was conducted by the sieving and sedimentation method

(Kettler et al., 2001). Soil pH was measured in a 1:1 solution of soil sample in deionized

water (USDA, 1996). Total C (TC) and N concentrations were determined using 1.5 g

soil sub-sample CN Analyzer (Vario EL, Elementar Analysensysteme GmbH, Hanau,

Germany). The method was calibrated using glutamic acid and standard soil samples of

known C and N (1.6 %, 0.145 %) concentrations. The TC concentration thus measured

was regarded as SOC because the soils were slightly acidic to near neutral with pH < 7.2,

which would indicate absence of any inorganic carbon (Gajda et al., 2001).

2.3.5 Turf Quality, clippings biomass, and weed infestation

Average visual turf quality on a scale of 1-9, assessed on an approximately bi-weekly or

monthly basis, was conducted during the following periods: 03/19/1990 to 12/03/1990,

04/01/1991 to 10/17/1991, 04/04/1992 to 11/17/1992, and 05/14/1993 to 11/03/1993.

Grass clippings were collected, dried, and weighed during three years (6-5-1995 to 10-

27-1995, 4-26-1996 to 10-25-1996, and 04-18-1997 to 05-31-1997) on a weekly basis to

estimate clipping biomass production. A survey of weed infestation was conducted in

September 2003. A visual assessment of percentage cover of turfgrass, weed cover and specifically of weeds dandelion (Taraxacum officinale) and white clover (Trifolium

repens) was recorded.

2.3.6 Data management and statistical analyses

The SOC and TSN pools for each 3 cm soil depth, in mega grams per hectare (Mg C ha-1)

were calculated using the following equation (Lal et al., 1998): Mg C ha-1= [%TC x bulk

-3 4 2 -1 density (Mg m ) x depth thickness (m) x 10 m ha ] / 100. The four depths were

summed up to obtain a cumulative pool for 0-12 cm depth. Statistical assumptions were

tested using MINITAB Release 14 (Minitab Inc. 2003). Further statistical analyses were

performed with SAS statistical software package with PROC GLM (SAS Release 9.1,

SAS Institute, Cary, NC). For SOC and TSN the data were analyzed for different depths for a given program and for the same soil depth between programs.

In addition, the nine programs were categorized into different management groups to perform analysis of variance using the same statistical package. These group analyses were: A) Control, low-N input (program 9), medium-N input (8), and high-N input

(programs 2-7); B) Control, organic-fertilizer management (programs 2 and 3) and mineral-fertilizer management (programs 4-9); C) no-input Control and inputs (all other programs); D) Control, no herbicides (programs 2 and 7) and with herbicides (programs

3-6 and 8-9), and E) Control, with insecticides (program 8) and no insecticide (programs

2-7 and 9).

If an F-test proved significant at p < 0.05, the means for each progam were compared by using the least significant difference (LSD). The data analysis using randomized complete block design (RCBD) provided less explanation as compared to the completely randomized design (CRD); therefore p values from CRD have been presented.

2.4. Results

2.4.1 Soil texture and bulk density: Soil textural analysis indicated a particle-size distribution of 7% clay, 18% sand, and 75% silt. Soil bulk density did not differ significantly among programs for any of the depths (Table 2.2). However for a given program the bulk density significantly increased with soil depth.

2.4.2 Soil pH: Soil reaction was slightly acidic (5.9) to near neutral (7.1) and pH

significantly increased with depth in all the programs (data not shown). Soil pH differed significantly among programs in the 0-3 cm depth (p<0.01). Group analysis revealed that the organic programs had significantly higher pH (6.2 and 6.6) than the mineral programs

(5.9 and 6.3) in 0-3 cm and 3-6 cm depths (p<0.02).

2.4.3 Soil organic carbon concentration: Table 2.3 shows the depth distribution of SOC concentration for the nine management programs. The SOC concentration differed significantly among all programs only in the 0-3 cm and 3-6 cm depth. Table 2.4 shows the analyses in various groups. Group analysis revealed significantly greater SOC concentration in the High-N and Medium-N programs as compared to the Low-N and

Control programs in the 0-3 cm, and in all the programs compared to Low-N in the 3-6 cm soil depth. Soil organic C was also greater in the Input intensive programs as compared to the Control, and with or without Insecticide programs as compared to

Control in the 0-3 cm soil depth. Both Organic and Mineral programs, and Insecticide

and No-Insecticide programs were not significantly different from each other but had

higher SOC than Control in the 0-3 cm soil depth. No-Herbicide programs did not

significantly differ from the Herbicide programs. The significantly greater SOC in

programs with High-N, and Input intensive programs show that the fertilizer N application increases SOC concentration in the 0-3 cm and 3-6 cm soil depth.

Application of equivalent amount of N in either Organic or Mineral form results in no difference in the SOC concentration.

2.4.4 Soil organic carbon pool: Table 2.5 shows the depth distribution of SOC pools for

the 9 management programs. The SOC pools differed significantly among all programs

only in the 3-6 cm depth. Table 2.6 shows the analyses in various groups. Group

analysis revealed significantly greater SOC pool in the High-N and Control programs as

compared to the Low-N in the 3-6 cm soil depth. In the 9-12 cm depth the Low-N had

significantly higher SOC pool as compared to all other program groups. Organic programs had significantly higher SOC pool in the 3-6 cm soil depth as compared to mineral programs. Application of various inputs did not have any influence on SOC pool

as compared to Control. Similarly application of insecticides did not influence SOC

pool. No-Herbicide programs significantly differ from the Herbicide programs for the 3-

6 cm, 6-9 cm, and 0-12 cm depth for SOC pool. The significantly greater SOC in

programs with High-N and No-Herbicide programs show that the fertilizer N application

along with no use of herbicides increases SOC pool. Application of equivalent amount of

N in Organic form results in increase in the SOC pool.

2.4.5 Total soil nitrogen concentration: Table 2.7 shows the depth distribution of TSN

concentration for the 9 management programs. The TSN pools differed significantly

among the nine programs in the 0-3 cm, 3-6 cm depths. High-N programs had

significantly greater TSN as compared to Low-N programs at these depths. Table 2.8

shows the analyses in various groups. Programs with Inputs as compared to No-Inputs,

and with or without Insecticide as compared to Control had significantly greater TSN

concentration only at the 0-3 cm depth. Group analysis revealed significantly greater

TSN in the Organic and Mineral programs, and Insecticide and No-Insecticide programs

as compared to Control in the 0-3 cm soil depth and significantly greater TSN in Organic

as compared to Mineral and Control in the 3-6 cm depth. Programs with Herbicide and

No-Herbicide revealed significantly greater TSN than Control in the 0-3 cm depth.

2.4.6 Total soil nitrogen pool: Table 2.9 shows the depth distribution of TSN pools for

the 9 management programs. The TSN pools differed significantly among the nine

programs in the 3-6 cm depth. Table 2.10 shows the analyses in various groups. Group

analysis revealed significantly greater TSN pool in the High-N and Control programs as

compared to the Low-N in the 3-6 cm soil depth. Organic programs had significantly

higher TSN as compared to Mineral programs in the 3-6 cm soil depth. Application of

various inputs as compared to Control did not significantly affect TSN pools. The results

were similar for Insecticide programs as compared to No-Insecticide programs. No-

Herbicide treatments had significantly higher TSN pools as compared to Herbicide and

Control in the 3-6 cm, 6-9 cm, and 0-12 cm soil depths.

2.4.7 Results from contiguous forest and agricultural field: In the forest soil, the SOC and TSN pools for the 0-12 cm depth were respectively 30 Mg C ha-1 and 2.4 Mg N ha-1

at 5 m distance and 42 Mg C ha-1 and 3.5 Mg N ha-1 at 10 m distance from the edge.

Both SOC and TSN pools, and their ratio in the agricultural field for 0-12 cm were

respectively 26.4 Mg C ha-1 and 2.6 Mg N ha-1 at 5 m distance and 26.1 Mg C ha-1 and

2.6 Mg N ha-1 at 10 m distance from the edge.

2.4.8 Turf quality: The turf quality differed significantly between the nine management programs for all the years (Table 2.11). The lowest turf quality was seen in the Control in all the years. In the group analysis (Table 2.12) High-N as compared to Low-N, Input intensive as compared to No-Inputs, and Herbicide as compared to No-Herbicides, with or without Insecticide as compared to Control revealed significantly higher turf quality.

2.4.9 Clipping biomass: The average clippings dry weight, collected on a weekly basis, for 1995 (June 05 to October 27), 1996 (April 26 to October 26), and 1997 (April 18 to

May 31) differed significantly (p < 0.001) among the nine lawn management programs

(Table 2.13). Group analysis for all the three years revealed Control and High-N as compared to Low-N, and Control and No-Herbicide as compared to Herbicide programs yielded significantly greater clippings biomass (Table 2.14). The presence or absence of insecticides did not have an effect on clippings biomass. For all the three years, the MLN had lower clippings biomass as compared to other 8 programs.

2.4.10 Weed infestation: Weed infestation survey showed that Control, Oa, and MHNd programs had significantly higher weed infestation and significantly lower turfgrass cover (Table 2.15). Dandelion cover was significantly higher in the Control, Oa, and

MHNd programs. Cover of white clover a leguminous plant, was higher in the Control

and Oa programs than other programs. The various group analyses revealed that the

Control had significantly lower turfgrass cover and higher weed cover with highest

numbers of dandelion and white clover plants (Table 2.16). Organic programs had

significantly higher turfgrass cover as compared to Control however lower as compared

to Mineral. Input intensive programs as compared to Control, and with or without

Insecticide as compared to Control increased the turfgrass cover and significantly reduced the presence of weeds. Control and No-Herbicide programs had significantly

higher weed cover including dandelions and white clover as compared to Herbicide

programs.

2.5 Discussion

Kentucky bluegrass is a cool season grass that grows best in spring, late summer and

early autumn. It requires cool and moist weather conditions and ample nutrients. These

conditions existed at the experimental site. The soil bulk density of all the plots was not

statistically different due to the absence of physical disturbance. Although, the soil pH

was lower in programs receiving mineral N fertilizers than those receiving Organic or no fertilizers, the decline in pH was small to have any adverse effect on nutrient availability

(Brady, 1990) and decomposer soil fauna (Baker and Whitby, 2003).

In agricultural systems application of high fertilizer N has been shown to increase

SOC pools especially in the top 0-5 cm soil depth (Bowman and Halverson, 1998; Liebig

et. al, 2002). In our study effect of management inputs on depth distribution of TSN pool

showed a trend similar to the SOC pool. The SOC and TSN pools differed significantly

with fertilizer N application only in the 3-6 cm soil depth. However, in this depth the

SOC and TSN pools were lowest in the Low N fertilizer application rather than Control

with no inputs. In a tallgrass prairie, input of N fertilizer increased plant production and

SOC by 1.6 Mg C ha-1 after 10 years (Rice, 2000). In the bermudagrass pastures of

Southern Piedmont of Georgia, Schnabel et al., (2001) found increased SOC in the 0-30

cm depth after application of high rates of inorganic fertilizers (336 kg N ha-1 yr-1) rather than to low rates (134 kg N ha-1 yr-1). Nitrogen fixation by leguminous plants like alfalfa

has shown to increase soil N, plant biomass and thus SOC (Mortenson 2003).

Combination of these factors of high fertility, and presence of leguminous plants could be

responsible for our SOC and TSN results at this soil depth. Improvement in grassland

management practices by fertilization, grazing management, and sowing of legumes and

grasses has shown to have the greatest SOC increases in the 0-10 cm soil depth (Conant

et al., 2001).

The non-significant differences in SOC and TSN pools between the 9 programs

for the 0-3 cm, 6-9 cm, 9-12 cm, and 0-12 cm soil depth could be due to several factors.

The perennial nature of the grass cover with continuous input of grass clippings may not

have led to differences in SOC pools in the top 0-3 cm soil depth. Further, experimental

studies with Kentucky bluegrass have shown that high N can be detrimental to root

growth as greater shoot growth diminishes carbohydrates stored in the roots (Dunn and

Diesburg, 2004). Thus, programs with high N fertilizer produced high clippings yield but

probably had less root growth particularly in deeper soil profiles. In contrast, the

programs with less N input produced the same level of SOC pools probably due to higher

root growth and turnover.

Turf quality was lowest in the Control program for all four sampling years, and

was consistently higher in programs with Herbicide inputs, and Input intensive programs.

Proper management, avoidance of injury and maintaining a thick, vigorous turf can prevent weed encroachment (Hull et al., 1994). We found that the turfgrass cover was lowest and weed cover was the highest in the Control program.

There were no differences in turfgrass quality with Insecticide and No Insecticide programs that received fertility but together they were better than Control in overall turf quality. Turf quality mainly represents aesthetics but not biomass. Further, the clippings dry weight in programs with No-Herbicides was consistently higher than those with

Herbicides in all the three years, whereas it was only higher for programs with Inputs as compared to No-Inputs in one year. This suggests that in the absence of N replenishment and herbicide applications, turfgrass systems are colonized by opportunistic weeds with a lower N requirement than the turfgrass (Dunn and Diesburg, 2004), increasing the overall plant biomass production but decreasing the turf quality. Leguminous weeds can fix atmospheric N, which when mowed or naturally senesced, release nitrogen. Returning grass clippings also reduces fertilzer N requirement (Landschoot, 2003). Therefore, weed and turf clippings enhance N availability in the absence of fertilizer input (Bormann,

1993). Plant community effects on soil organic matter and N dynamics were found to be greater than the effects of N supply (Aerts et al., 2003 and Dijkstra et al., 2006). The

plant community composition in our experimental plots probably had a role in C

sequestration in the soil.

In conclusion, this study revealed that in turfgrass systems fertilizer N and N

returned by grass clippings and weed clippings, may have played a role in C

sequestration. Our results suggest that management practices that favor plant diversity in urban lawns are more beneficial for long term SOC sequestration than high N inputs.

2.6 Acknowledgements

We thank Dr. David Shetlar, and Zhiqiang Cheng, and Doug Richmond for helping in the collection of samples. We also thank the Trugreen Technical center for maintaining these lawns for 15 years. This research was funded by the Ohio Lawn Care Association, Ohio

Turfgrass Foundation, and by the Center for Urban Environment and Economic

Development of the Ohio State University.

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Target ControlOrganic a Organic b Mineral Mineral Mineral Mineral Mineral Mineral application High-N a High-N b High-N c High-N d Medium-N Low-N date

April 19 0 48-10-29 † 48-10-29 † 36-4-13 † 48-5-17 † 49-0-0 † 49-0-0 † 46-4-14 † 49-6-14 † ‡ § § ‡ § ‡ § § ‡ May 10 0 - - 25-3-7 † - - - - - ‡ May 31 0 36-7-22 † 36-7-22 † 25-3-7 † 37-4-11 † 36-4-7 † 36-4-7 † 39-5-4 † - # June 23 0 - - 25-3-7 † ¶ - - - - - July 19 0 36-7-22 † 36-7-22 † 24-2-5 † 37-4-11 † 36-4-7 † 36-4-7 † 42-9-6 † - †† August 11 0 - - 24-2-5 † - - - - -

September 4 0 48-10-29 † 48-10-29 † 25-3-7 † 50-6-14† 49-0-0 † 49-0-0 † 44-4-13 † 49-6-14†

40 ‡ ‡ ‡ ‡ September 25 0 - - 37-4-11 † - - - - - October 16 0 48-10-29 † 48-10-29 † 24-0-0 † 49-0-0 † 49-0-0 † 49-0-0 † - - Total per 0 216-44-131 216-44-131 245-24-62 221-19-53 219-8-14 † 219-8-14 † 171-22-37 98-12-28 † annum † † † † †

†N-P-K kg ha-1 yr-1 ‡ Herbicide, Tri-Power 3.5 l ha-1yr-1, Three way broadleaf herbicide, comprises of 2-Methyl-4-Chlorophenoxy-acetic acid, (+)-R-2-(2-Methyl-4-Chlorophenoxy) propionic acid, and 3,6-Dichloro-o-Anisic acid § Herbicide, Pre-M 1.7 kg ha-1 yr-1, Pre emergent herbicide Pendimethalin, N-(1-ethylpropyl)-2,6-dinitro-3,4- xylidine ¶ Fungicide, Bayleton 0.15 l ha-1 yr-1, 1-(4-chlorophenoxy)-3, 3-dimethyl-1-(1,2,4-triazol-1-yl)-butan-2-one # Herbicide, 2, 4-D 3Way 1.7 kg ha-1 yr-1, 2,4-dichlorophenoxyacetic acid, (+)-R-2-(2-Methyl-4-Chlorophenoxy) propionic acid, and 3,6-Dichloro-o-Anisic acid †† Insecticide, Diazinon 5.6 kg ha-1 yr-1, O,O-Diethyl O-(2-isopropyl-4-methyl-6-pyrimidinyl) thiophosphoric acid

Table 2.1. Fertilization schedule, fertilizer N-P-K composition, herbicide, and insecticide applications under different commercial lawn care programs in experimental plots of Kentucky bluegrass at Delaware, Ohio (1989-2003).

Depth (cm)

Treatment φ 0-3 3-6 6-9 9-12

Mg m-3 Untreated Control *** 0.67 ± 0.05 a D 0.94 ± 0.05 a C 1.20 ± 0.03 a B 1.40 ± 0.07 a A Organic a *** 0.63 ± 0.08 a D 0.99 ± 0.07 a C 1.24 ± 0.04 a B 1.48 ± 0.03 a A Organic b *** 0.65 ± 0.01 a C 0.98 ± 0.05 a B 1.14 ± 0.10 a B 1.40 ± 0.07 a A Mineral High-N a *** 0.60 ± 0.05 a C 0.90 ± 0.06 a B 1.13 ± 0.09 a A 1.31 ± 0.06 a A Mineral High-N b *** 0.57 ± 0.04 a D 0.90 ± 0.05 a C 1.27 ± 0.06 a B 1.45 ± 0.03 a A Mineral High-N c *** 0.73 ± 0.03 a D 0.98 ± 0.05 a C 1.19 ± 0.09 a B 1.44 ± 0.02 a A Mineral High-N d *** 0.69 ± 0.04 a D 1.04 ± 0.03 a C 1.28 ± 0.02 a B 1.42 ± 0.03 a A 41 Mineral Medium-N *** 0.64 ± 0.06 a C 0.85 ± 0.04 a B 1.29 ± 0.04 a A 1.43 ± 0.04 a A Mineral Low-N *** 0.65 ± 0.10 a D 0.92 ± 0.05 a C 1.21 ± 0.05 a B 1.45 ± 0.03 a A ***, Significant at the 0.001 probability level. Means followed by same lowercase letters (within columns) and by the same uppercase letter (within rows) are not significantly different.

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.2. Effect of lawn management programs on soil bulk density (Mean ± SEM) at various soil depths in Kentucky bluegrass experimental plots.

Depth (cm)

Treatment φ 0-3* 3-6* 6-9 cm 9-12 cm

g kg-1

Untreated Control*** 37.5 ± 1.6 b A 24.5 ± 1.4 ab A 14.8 ± 0.2 a B 12.0 ± 0.2 a B Organic a *** 41.1 ± 0.6 ab B 28.3 ± 0.9 a A 18.3 ± 0.7 a C 12.8 ± 0.6 a C Organic b * 41.5 ± 1.0 ab A 26 ± 2 ab AB 16.9 ± 2.3 a BC 12.4 ± 0.9 a C Mineral High-N a *** 43.5 ± 1.8 a A 24.8 ± 1.0 ab B 15.0 ± 0.8 a C 11.1 ± 0.5 a C Mineral High-N b *** 43.7 ± 1.7 a A 25.5 ± 1.4 ab B 15.1±.0.9 a C 11.8 ± 0.5 a C Mineral High-N c *** 41.3 ± 0.5 ab A 23.1 ± 1.1 bc B 14.6 ± 0.7a C 11.4 ± 0.3 a C 42 Mineral High-N d *** 42.4± 0.5 a A 25.1 ± 0.9 ab B 16.7 ± 1.3 a BC 12.5 ± 0.9 a C Mineral Medium-N *** 43.3 ± 1.1 a A 25.1 ± 0.4 ab B 14.5 ± 1.0 a C 12.5 ± 1.2 a C Mineral Low-N 37.9 ± 2.8 b A 19.7 ± 3.0 c A 16.2 ± 2.7 a A 14.6 ± 1.7 a A *, ***, Significant at the 0.05, and 0.001 probability levels, respectively. Means followed by same lowercase letters (within columns) and by the same uppercase letter (within rows) are not significantly different.

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.3. Effect of lawn management programs on soil organic carbon (Mean ± SEM) concentrations (g kg-1) at various soil depths in Kentucky bluegrass experimental plots.

Depth 9 regimes A) control, low- B)control,organic, C) no input D) control, no E) control, with (cm) N, medium-N, mineral (control), herbicides, with insecticides, no high-N with inputs herbicides insecticide

0-3 0.046 0.0031 (3.7) 0.043 (3.4) 0.014 (3.4) 0.050 0.030 (3.8) (4.3) 3-6 0.049 0.0175 (4) 0.062 0.92 0.172 0.97 (4.4)

43 6-9 0.56 0.6403 0.105 0.45 0.134 0.42 9-12 0.25 0.055 0.88 0.70 0.83 0.92 Least significant difference (LSD) values for significant results are presented in parentheses.

Table 2.4. P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on soil organic carbon concentrations at various depths in Kentucky bluegrass experimental plots.

Depth (cm)

Treatment φ 0-3 3-6* 6-9 9-12 0-12

Untreated Control*** 7.5 ± 0.6 a A 7.0 ± 0.5 abc A 5.4 ± 0.1 a B 5.1 ± 0.1 a B 24.8 ± 1.0 a

Organic a 7.7 ± 1.0 a A 8.5 ± 0.8 a A 6.9 ± 0.5 a A 5.7 ± 0.2 a A 28.7 ± 1.8 a

Organic b*** 8.0 ± 0.2 a A 7.6 ± 0.6 ab A 5.6 ± 0.3 a B 5.1 ± 0.4 a B 26.5 ± 1.1 a Mineral High-N a *** 7.8 ± 0.6 a A 6.7 ± 0.4 bc A 5.0 ± 0.2 a B 4.4 ± 0.2 a B 23.8 ± 1.4 a Mineral High-N b** 7.4 ± 0.4 a A 6.9 ± 0.6 abc AB 5.8 ± 0.5 a BC 5.1 ± 0.2 a C 25.2 ± 1.0 a Mineral High-N c*** 9.1 ± 0.4 a A 6.8 ± 0.6 abc B 5.2 ± 0.5 a C 5.0 ± 0.1 a C 26.1 ± 1.1 a Mineral High-N d*** 8.7 ± 0.5 a A 7.8 ± 0.3 ab A 6.4 ± 0.6 a B 5.3 ± 0.4 a B 28.1 ± 1.4 a

44 Mineral Medium-N* 8.4 ± 0.9 a A 6.4 ± 0.3 bc B 5.6 ± 0.4 a B 5.3 ± 0.5 a B 25.7 ± 1.0 a

Mineral Low-N 7.4 ± 1.4 a A 5.3 ± 0.7 c A 5.8 ± 0.8 a A 6.4 ± 0.7 a A 24.9 ± 2.2 a *, ***, Significant at the 0.05, and 0.001 probability levels, respectively. Means followed by same lowercase letters (within columns) and by the same uppercase letter (within rows, except for the data in the last column of that row) are not significantly different.

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.5. Effect of lawn management programs on soil organic carbon (Mean ± SEM) pools (Mg C ha-1) at various soil depths in Kentucky bluegrass experimental plots.

Depth 9 regimes A) control, B) control, C) no input D) control, no E) control, with (cm) low-N, organic, (control), with herbicides, with insecticides, no medium-N, mineral inputs herbicides insecticide high-N

0-3 0.76 0.68 0.68 0.43 0.69 0.68 3-6 0.044 (1.7) 0.017 (1.5) 0.023 (1.3) 0.92 0.01 (1.3) 0.61 6-9 0.15 0.84 0.25 0.39 0.009 (0.94) 0.66

9-12 0.08 0.04 (1.0) 0.78 0.61 0.62 0.87

0-12 0.26 0.63 0.17 0.38 0.015 (2.8) 0.65

45 Table 2.6. P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on soil organic carbon pools at various depths in Kentucky bluegrass experimental plots

Depth (cm)

Treatment φ 0-3* 3-6* 6-9 cm 9-12 cm

g kg-1

Untreated Control*** 3.5 ± 0.2 b A 2.5 ± 0.1 ab A 1.6 ± 0 a B 1.4 ± 0 a B Organic a *** 4.0 ± 0.1 ab B 3.0 ± 0.1 a A 2.0 ± 0.1 a C 1.4 ± 0 a C Organic b * 3.9 ± 0.1 ab A 2.8 ± 0.1 ab AB 1.8 ± 0.2 a BC 1.4 ± 0.1 a C Mineral High-N a *** 4.0 ± 0.1 a A 2.6 ± 0.1 ab B 1.7 ± 0.1 a C 1.3 ± 0 a C Mineral High-N b *** 4.0 ± 0.1 a A 2.6 ± 0.1 ab B 1.7±.0.1 a C 1.3 ± 0.1 a C Mineral High-N c *** 3.9 ± 0.1 ab A 2.4 ± 0.1 bc B 1.6 ± 0.1a C 1.3 ± 0.0 a C 46 Mineral High-N d *** 4.0 ± 0 a A 2.6 ± 0.1 ab B 1.8 ± 0.1a BC 1.4 ± 0.1 a C Mineral Medium-N *** 4.1 ± 0.1 a A 2.6 ± 0 ab B 1.6 ± 0.1 a C 1.4 ± 0.1 a C Mineral Low-N 3.4 ± 0.2 b A 2.1 ± 0.3 c A 1.8 ± 0.2 a A 1.6 ± 0.1 a A *, ***, Significant at the 0.05, and 0.001 probability levels, respectively. Means followed by same lowercase letters (within columns) and by the same uppercase letter (within rows) are not significantly different.

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.7. Effect of lawn management programs on total soil nitrogen (Mean ± SEM) concentration (g kg-1) at various soil depths in Kentucky bluegrass experimental plots.

Depth (cm) 9 regimes A) control, low- B) control, C) no input D) control, no E) control, with N, medium-N, organic, (control), herbicides, with insecticides, no high-N mineral with inputs herbicides insecticide

0-3 0.0025 <0.001 0.0280 0.009 0.0159 0.0189 (0.33) (0.28) (0.30) (0.30) (0.29) (0.33) 3-6 0.0052 0.0062 0.008 (0.33) 0.7401 0.0991 0.9237 (0.40) (0.38) 6-9 0.45 0.5074 0.0515 0.367 0.1059 0.3118 9-12 0.35 0.1455 0.9345 0.779 0.6007 0.9621

47 Least significant difference (LSD) values for significant results are presented in parentheses.

Table 2.8. P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on total soil nitrogen concentration at various depths in Kentucky bluegrass experimental plots.

Depth (cm) Treatment φ 0-3 3-6** 6-9 9-12 0-12 Mg N ha-1 Untreated Control 0.70 ± 0.05 a 0.71 ± 0.04 bcd 0.60 ± 0.02 a 0.58 ± 0.03 a 2.6 ± 0.07 a Organic a 0.76 ± 0.09 a 0.88 ± 0.08 a 0.75 ± 0.05 a 0.64 ± 0.03 a 3.0 ± 0.18 a Organic b *** 0.75 ± 0.01 a A 0.84 ± 0.05 ab A 0.61 ± 0.03 a B 0.58 ± 0.01 a B 2.8 ± 0.08 a Mineral High-N a *** 0.72 + 0.05 a A 0.69 + 0.03 bcd A 0.55 + 0.02 a B 0.50 + 0.03 a B 2.5 ± 0.12 a Mineral High-N b 0.68 ± 0.04 a 0.71 ± 0.05 bcd 0.63 ± 0.05 a 0.58 ± 0.01 a 2.6 ± 0.09 a Mineral High-N c *** 0.85 ± 0.03 a A 0.70 ± 0.06 bcd B 0.58 ± 0.05 a B 0.57 ± 0.01 a B 2.7 ± 0.09 a Mineral High-N d * 0.82 ± 0.04 a A 0.82 ± 0.03 abc A 0.69 ± 0.05 a AB 0.62 ± 0.03 a B 2.9 ± 0.13 a Mineral Medium-N 0.78 ± 0.08 a 0.67 ± 0.03 cd 0.62 ± 0.04 a 0.60 ± 0.04 a 2.7 ± 0.10 a 48 Mineral Low-N 0.67 ± 0.11 a 0.56 ± 0.07 d 0.63 ± 0.06 a 0.68 ± 0.06 a 2.6 ± 0.17 a *, **, Significant at the 0.05, 0.01 probability levels, respectively. Means followed by same lowercase letters within columns and are not significantly different

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.9. Effect of lawn management programs on total soil nitrogen (Mean ± SEM) pools (Mg N ha-1) at various soil depths in Kentucky bluegrass experimental plots.

Depth (cm) 9 regimes A) control, B) control, C) no input D) control, no E) control, with low-N, organic, (control), with herbicides, with insecticides, no medium-N, mineral inputs herbicides insecticide high-N

0-3 0.88 0.47 0.78 0.47 0.52 0.71 3-6 0.008 (0.16) 0.01 (0.15) 0.005 (0.13) 0.77 0.01 (0.13) 0.56

6-9 0.12 0.85 0.20 0.40 0..005 (0.09) 0.67

9-12 0.05 0.10 0.72 0.67 0.33 0.91

49 0-12 0.055 0.40 0.055 0.37 0.003 (0.26) 0.63

Table 2.10. P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on total soil nitrogen pools at various depths in Kentucky bluegrass experimental plots.

Treatment φ Year 1990*** Year 1991*** Year 1992*** Year 1993***

Untreated Control 2.8 ± 0.1 f 3.3 ± 0.2 e 3.3 ± 0.2 e 4.8 ± 0.1 e Organic a 4.4 ± 0.1 e 4.8 ± 0.1 d 5.1 ± 0.1 d 5.9 ± 0.2 c Organic b 6.1 ± 0.2 c 6.7 ± 0.2 b 6.6 ± 0.2 a 6.7 ± 0.0 b Mineral High-N a 8.2 ± 0.2 a 7.2 ± 0.1 a 7.0 ± 0.1 a 7.2 ± 0.1 a Mineral High-N b 7.2 ± 0.1 b 6.8 ± 0.1 ab 6.8 ± 0.1 a 6.8 ± 0.2 ab Mineral High-N c 7.1 ± 0.1 b 7.0 ± 0.1 ab 6.6 ± 0.1 ab 6.6 ± 0.1 b Mineral High-N d 6.1 ± 0.1 c 5.8 ± 0.2 c 5.4 ± 0.2 cd 6.0 ± 0.2 c Mineral Medium-N 5.4 ± 0.1 d 6.2 ± 0.1 c 6.2 ± 0.2 b 6.6 ± 0.3 b 50 Mineral Low-N 5.9 ± 0.2 c 5.9 ± 0.3 c 5.7 ± 0.2 c 5.4 ± 0.1 d ***, Significant at the 0.001 probability level. Means followed by same lowercase letters within columns are not significantly different.

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.11. Effect of lawn management programs on turf quality (Mean ± SEM) on a visual scale of 1-9, collected on an approximately bi-weekly or monthly basis during 1990 (03-19-1990 to 12-03-1990), 1991 (04-01-1991 to 10-17-1991), 1992 (04-04-1992 to 11-17-1992), and 1993 (05-14-1993 to 11-03-1993) in Kentucky bluegrass experimental plots.

Year 9 regimes A) control, B) control, C) no input D) control, no E) control, with low-N, organic, (control), herbicides, with insecticides, no medium-N, mineral with inputs herbicides insecticide high-N

1990 0.00 (0.35) 0.00 (1.4) 0.00 (0.98) 0.00 (1.2) 0.00 (1.0) 0.000 (1.3) 1991 0.00 (0.49) 0.00 (1.0) 0.00 (0.78) 0.00 (0.85) 0.00 (0.59) 0.000 (0.97) 1992 0.00 (0.41) 0.00 (0.86) 0.00 (0.71) 0.00 (0.72) 0.00 (0.47) 0.000 (0.83) 1993 0.00 (0.44) 0.00 (0.60) 0.00 (0.62) 0.00 (0.62) 0.00 (0.57) 0.000 (0.71) 51

Table 2.12. P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on turf quality in Kentucky bluegrass experimental plots.

Treatment φ Year 1995*** Year 1996*** Year 1997***

kg ha-1 Untreated Control 70.8 ± 4.4 bc 54.3 ± 6.8 e 92.7 ± 11.3 b Organic a 83.7 ± 1.5 a 76.3 ± 3.0 a 99.6 ± 5.1 ab Organic b 80.9 ± 1.8 ab 62.7 ± 2.4 cde 69.8 ± 3.2 c Mineral High-N a 68.1 ± 5.8 c 70.8 ± 2.8 abc 96.8 ± 2.7 b Mineral High-N b 69.2 ± 3.5 c 65.2 ± 1.4 bcd 103.8 ± 2.5 ab Mineral High-N c 78.3 ± 4.7 abc 73.4 ± 0.7 ab 89.5 ± 4.9 b Mineral High-N d 88.9 ± 2.9 a 76.2 ± 2.4 a 113.9 ± 6.4 a

52 Mineral Medium-N 71.5 ± 3.5 bc 61.8 ± 1.5 de 69.3 ± 4.8 c Mineral Low-N 52.9 ± 2.6 d 42.9 ± 1.6 f 54.3 ± 2.7 c ***, Significant at the 0.001 probability levels, respectively. Means followed by same lowercase letters within columns are not significantly different.

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.13. Effect of lawn management programs on clipping dry weight (Mean ± SEM), collected on a weekly basis, produced from 1995 (6-5-1995 to 10-27-1995), 1996 (4-26-1996 to 10-25-1996), and 1997 (04-18-1997 to 05-31-1997) in Kentucky bluegrass experimental plots.

Year 9 regimes A) control, low- B) control, C) no input D) control, no E) control, with N, medium-N, organic, (control), with herbicides, with insecticides, no high-N mineral inputs herbicides insecticide

1995 0.00 (10.7) 0.00 (12.0) 0.07 0.60 0.002 (11.0) 0.783 1996 0.00 (8.8) 0.00 (9.2) 0.10 0.05 0.002 (10.7) 0.121

53 1997 0.00 (16.1) 0.00 (20.0) 0.82 0.62 0.005 (19.6) 0.168

Table 2.14. P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on clippings dry weight in Kentucky bluegrass experimental plots.

Treatment φ PTC*** PWC*** PDC*** PWCC***

Untreated Control 56.7 ± 2.3 e 41.9 ± 2.3 a 33.1 ± 3.13 a 7.1 ± 2.61 a Organic a 70.6 ± 3.9 d 27.9 ± 3.9 b 17.1 ± 3.28 b 5.9 ± 2.68 a Organic b 93.9 ± 2.2 ab 5.7 ± 1.9 d 1.4 ± 0.66 c 0.0 ± 0.0 b Mineral High-N a 99.5 ± 0.3 a 0.4 ± 0.4 d 0.0 ± 0.0 c 0.0 ± 0.0 b Mineral High-N b 97.2 ± 1.8 ab 2.4 ± 1.7 d 0.9 ± 0.52 c 0.0 ± 0.0 b Mineral High-N c 98.8 ± 0.5 ab 0.6 ± 0.5 d 0.5 ± 0.42 c 0.0 ± 0.0 b Mineral High-N d 82.0 ± 0.9 c 16.8 ± 0.4 c 12.6 ± 2.01 b 1.8 ± 0.85 b

54 Mineral Medium-N 96.8 ± 1 ab 2.0 ± 1.1 d 1.4 ± 1.21 c 0.4 ± 0.38 b Mineral Low-N 91.3 ± 5.8 b 7.7 ± 5.8 d 5.5 ± 4.04 c 0.0 ± 0.0 b ***, Significant at the 0.001 probability level. Means followed by same lowercase letters within columns are not significantly different. Again say what you have in the table

φ Detailed descriptions of treatments are provided in Table 2.1.

Table 2.15. Effect of lawn management programs on percentage plant cover (Mean ± SEM) in Kentucky bluegrass experimental plots. Abbreviations: PTC – Percent Turfgrass Cover, PWC – Percent Weed Cover, PDC – Percent Dandelion Cover, PWCC – Percent White Clover Cover

Parameter 9 regimes A) control, low-N, B) control, C) no input D) control, no E) control, medium-N, high-N organic, (control), with herbicides, with with mineral inputs herbicides insecticides, no insecticide

PTC 0.00 (7.7) 0.00 (13.3) 0.00 (9.8) 0.00 (11.1) 0.00 (6.5) <0.001 (12.4)

PWC 0.00 (7.6) 0.00 (12.8) 0.00 (9.4) 0.00 (10.7) 0.00 (6.4) <0.001 (11.9) PDC 0.00 (6.4) 0.00 (9.2) 0.00 (7.2) 0.00 (7.6) 0.00 (4.7) <0.001 (8.6)

55 PWCC 0.00 (3.7) 0.01 (3.8) 0.00 (3.0) 0.00 (3.1) 0.00 (2.7) 0.002 (3.6)

Table 2.16. P values and least significant differences (LSD) (within brackets) at p<0.05 indicating the effect of lawn management programs and program groups on percentage plant cover in Kentucky bluegrass experimental plots. Abbreviations: PTC – Percent Turfgrass Cover, PWC – Percent Weed Cover, PDC – Percent Dandelion Cover, PWCC – Percent White Clover Cover

CHAPTER 3

SUSTAINABLE TURF MANAGEMENT TO OFFSET CARBON EMISSIONS

3.1 Abstract

Turfgrass systems have the potential to sequester atmospheric carbon (C).

However, these systems are intensively managed with application of fertilizers, pesticides, mowing and irrigation that can enhance C sequestration through their effect on biomass production but have hidden C costs. The C cost occurs from the use of fossil fuels required for the production, transport, storage, and application of fertilizers, pesticides, irrigation and mowing. The objectives of our study were to quantify the C emissions associated with management practices, and measure the sustainability of turfgrass systems to offset C emissions from, 1) the use of nine different long term turfgrass management programs used for urban lawns, and 2) the turfgrass management programs in which nitrogen (N) is applied and clippings are either removed or returned.

The sustainability indices (SI) were calculated as the gain of C sequestered in turfgrass soil for environment benefit as compared to the loss of C which results in environmental

degradation. The SI’s assessed by calculating ratios of gross and net C sequestered to C

emitted were >1 for all programs indicating sustainable management of turfgrass lawns

for C, except where high N fertilizer was used without retuning clippings. However,

56 comparing the nine programs after 15 years, the SI of control and organic treatments was at least five fold higher than mineral treatments. Similarly the use of lower amount of N fertilizer by returning turfgrass clippings showed greater SI’s. We conclude that greater sustainability of turfgrass systems for C can be achieved by reducing or replacing the use of mineral with organic fertilizers, replacing the use of chemical pesticides with biological pesticides, mowing less often, and returning clippings.

3.2 Introduction

With continued use of fossil energy the atmospheric carbon dioxide (CO2) concentrations will continue to rise and escalate environmental challenges by causing global climate change. Marland et al., (2007) estimated that cement production and fossil fuel use led to the release of 315 Petagram (Pg) of atmospheric carbon (C) since the year 1751. They also estimated that liquid and solid fuels accounted for 77.5% of C emissions in 2004.

Reduction and efficient use of fossil energy consumption, use of renewable energy, and C sequestration have been identified as strategies to mitigate the CO2 emissions (IPCC,

2001).

Throughout the United States, urban ecosystems are expanding and a major part of these ecosystems is turfgrass covered soils. Milesi et al. (2005) estimated total turfgrass area which includes residential, commercial, and institutional lawns, parks, golf courses, and athletic fields at 163,800 km2 (± 35,850 km2). Turfgrass covered soils have the potential to sequester atmospheric C (Qian and Follett, 2002; Singh et al., 2007 unpublished, see chapter 2). However, turfgrass systems are intensively managed with

57 often routine inputs of fertilizers, pesticides, irrigation, and regular mowing. While these inputs enhance the desired aesthetics of urban ecosystems, they have hidden C costs. The

C cost occurs from the use of fossil fuels required for the production, transport, storage, and application of fertilizers, pesticides, irrigation and mowing.

In 2005, of the 59 million homeowners with do-it-yourself lawn care that participated in the survey conducted by the National Gardening Association (NGA), around 52 million used some kind of fertilizer, and 49 million used some kind of chemical pest control (NGA, 2005). Households in the US spent about $9.6 billion to personally care for their lawns in 2005 increasing from $8.8 billion in 2004 (NGA, 2005).

In addition, an average lawn requires about 40 hours of mowing per year. The fossil fuel use related C emissions from these inputs must be calculated to evaluate the benefit of turfgrass systems for C sequestration.

Scheduled application of N fertilizer is a very important component of any turfgrass management program. Nitrogen fertilizer helps in maintaining turf aesthetic quality, and in the production of aboveground plant biomass. Qian et al. (2003) used

CENTURY computer simulation model to assess the impact of long term (100 yr) turfgrass clipping management (clippings returned vs. clippings removed) and N fertilization on plant biomass and SOC content, and further used it as a tool to generate data on turfgrass N requirement based on clipping management and turf age. Their results revealed that plant biomass and SOC content increased at the rate of 0.62 Mg ha-1 yr-1, with addition of high N (150 kg N ha-1 yr-1) fertilizer and returning clippings for the first 50 years. The data presented show that the SOC pool increased from 35 to 70 Mg

58 ha-1 after 100 years with the average rate of SOC sequestration at 0.35 Mg ha-1 yr-1. At low N fertility (75 kg N ha-1 yr-1) with removal of clippings, there was a decline in the

plant biomass production and absence of increase in SOC pool after turfgrass

establishment.

The CENTURY model predictions on turfgrass N requirement generated for

optimal productivity and low N leaching (<2 kg N ha-1 yr-1) revealed that for a clippings

returned management program, N fertilization rates of 150, 100, 75, and 60 kg N ha-1 during 1 to 10, 11 to 25, 26 to 50, and 51 to 100 yr after turfgrass establishment are

required. But, for a clippings removed management program, N fertilization at 200, 150,

and 140 kg N ha-1 yr-1 would be required during the years of 1 to 15, 16 to 50, and 51 to

100 yr after turfgrass establishment.

At this time, it is not known whether turfgrass systems are actually sources or

sinks of C. For turfgrass systems to be an effective CO2 sink, it is important that the C

emissions associated with turfgrass management are lower than the C sequestered in the

turfgrass soils. We define a sustainable turfgrass system to be the one based on its (1)

indefinite benefit to humans for aesthetics and other utilitarian purposes and (2) to the

environment due to optimized efficiency of resources used. According to Lal (2004),

sustainability of a system can be assessed by using a sustainability index (SI) that

evaluates the changes in the gain of the commodity (e.g. C sequestered in turfgrass

covered soil), for environment benefit as compared to the loss of that commodity causing

environmental degradation (C emissions due to various turfgrass management practices).

59 Therefore, our objectives were to quantify the C emissions associated with turfgrass management practices, and measure the sustainability of turfgrass systems to offset C emissions from

1) the use of nine different long term turfgrass management programs used for urban turfgrass systems, and

2) the turfgrass management programs in which nitrogen (N) is applied and clippings are either removed or returned

3.3 Materials and Methods

3.3.1 Quantification of carbon emissions

3.3.1.1 Carbon emissions associated with turfgrass management inputs and the sustainability of turfgrass systems to offset C from the use of nine different long term turfgrass management programs

The field experiment was conducted on Kentucky bluegrass (Poa pratensis L.) turf established at TruGreen Technical Center in Delaware, Ohio in autumn of 1983. In spring 1989, a long-term study was initiated to examine differences in lawn turf response to varying input programs. The study plots were maintained annually from 1989 to 2003 for a total of 15 years. A total of nine treatment programs were evaluated (Table 3.1).

Programs included a Control, two organic-fertilizer, and six mineral-fertilizer management programs. The programs represented four levels of N application rate (0 kg ha-1 yr-1, 98 kg ha-1 yr-1, 171 kg ha-1 yr-1, 216-245 kg ha-1 yr-1).

60 The program details are as follows: 1) Control, 2) Organic a (Oa): with four bi-

monthly granular organic fertilizer applications, high N input, 3) Organic b (Ob): with

four bi-monthly organic fertilizer applications and once each spring a post-emergent

herbicide applied to Control broadleaf weeds, high N input, 4) Mineral High-N a

(MHNa): with monthly applications of either liquid urea-N mineral fertilizer with spring- applied pre-emergent herbicide and post-emergent herbicide applied spring and fall, or a fungicide application in late spring and granular blend of sulfur coated urea (SCU) and urea-N fertilizer in summer and late fall, 5) Mineral High-N b (MHNb): with five applications every six weeks of liquid urea-N mineral fertilizer, pre-emergent herbicide in spring , and post-emergent weed Control each spring and fall, 6) Mineral High-N c

1990 and all other programs were established in 1989.

Fertilizer programs: Oa and Ob used Richlawn commercial organic fertilizer

(Richlawn Turf, Platteville, CO) for the first 9 years and Ringer’s commercial organic fertilizer (Woodstream Corporation, Lititz, PA) thereafter. All other fertilizer programs

61 contained urea, SCU, ammonium phosphates, and potassium chloride. The programs

differed in N-P-K composition and application rate, where ML received lowest N input

(98 kg ha-1 yr-1) compared to the other programs that received 171-245 kg ha-1 yr-1. The

Control was not fertilized.

Pesticide programs: Pesticide applications were made annually as follows: the

pre-emergent herbicide pendimethalin [N-(1-ethylpropyl)-2,6-dinitro-3,4-xylidine] was

applied at active ingredient (a.i.) rate of 1.7 kg ha-1 yr-1 each spring to programs MHNa,

MHNb, MHNc, MMN, and MLN; broadleaf herbicide containing MCPA + mecoprop +

dicamba [2-Methyl-4-Chlorophenoxy-acetic acid, (+)-R-2-(2-Methyl-4-Chlorophenoxy)

propionic acid, and 3,6-Dichloro-o-Anisic acid] was applied at a.i. rate of 3.5 l ha-1 yr-1 to

programs Oa, MHNa, MHNb, MHNc, and MLN, while a mixture of 2,4-D +

mecoprop+Dicamba [2,4-dichlorophenoxyacetic acid, (+)-R-2-(2-Methyl-4-

Chlorophenoxy) propionic acid, and 3,6-Dichloro-o-Anisic acid] was applied at a.i. rate

of 1.7 kg ha-1 yr-1 to program MMN; the insecticide diazinon [O,O-Diethyl O-(2-

isopropyl-4-methyl-6-pyrimidinyl) thiophosphoric acid] was applied at a.i. rate of 5.6 kg ha-1 yr-1 to program MMN; and the fungicide bayleton [1-(4-chlorophenoxy)-3,3-

dimethyl-1-(1,2,4-triazol-1-yl)-butan-2-one] was applied at a.i. rate of 0.15 l ha-1 yr-1 to program MHNa. No programs were applied during 2001 and 2003 and all assessments occurred in September 2003.

62 3.3.1.2 Carbon emissions associated with turfgrass management inputs and the

sustainability of turfgrass systems to offset C emission from the turfgrass management

programs in which N is applied and clippings are either removed or returned

The optimum values for N fertilization under clippings returned and clippings removed management scenarios for a loam site were taken from Qian et al. (2003) (Table 3.3).

For both the objectives estimates of C emission for production, transportation, storage, and transfer of various inputs were taken from the published literature (Lal,

2004) and converted to kg C equivalent (CE). For objective 1, emissions for Organic fertilizers were not calculated because they used Ringer’s organic fertilizer which comes as a by-products of other industries such as feather meal from poultry industry for nitrogen (N), ground bone for phosphorus, and sunflower seed ash for potassium

(Bormann et al., 2001). This does not require the use of fossil fuels. The C emissions associated with the application process for fertilizer, pesticide, and irrigation is not included due to the lack of information. Mowing was calculated for 15 years since mowing was conducted after the establishment of plots, for 26 mowing events in year, and burning gasoline at 3.75 l ha-1 for each mowing event, based on the average

consumption in a John Deere riding mower. The emissions for Mineral Medium N are

calculated for 12 years as they were established in 1990 and for all other programs for 13

years as no programs were applied in the year 2001, and 2003.

63 3.3.2 Calculation of sustainability indices

For objective 1, the SOC pool for each treatment for a depth of 12 cm is taken from

Singh et al. (2007, unpublished, see chapter 2). Sustainability index was calculated by

evaluating the ratio of gross C sequestered and emitted (SIG) and also as the ratio of net C

sequestered and emitted (SIN).

For objective 2, the rate of SOC loss was calculated by calculating the emissions associated with N fertilizer rate for each time period, summing the emissions for 50 and

100 years, and calculating the rate per annum. The rate of SOC gain was taken from

Qian et al. (2003). This data represented the rate of gain of SOC under high N fertility

(150 kg ha-1 yr-1) with the clippings returned management scenario. Sustainability index was calculated by evaluating the ratio of gross rate of C sequestered and rate of emissions

(SIG) and also as the ratio of net rate of C sequestered and rate of emissions (SIN).

3.4 Results and discussion

The calculated C emissions associated with different management programs are shown in

Table 3.2. The C emissions ranged from 59.7 kg CE ha-1 yr-1 in Control to 460.4 kg CE

ha-1 yr-1 in high N fertilizer program. The sustainability indices calculated as either a

ratio of gross C sequestered to C emitted or as net C sequestered to C emitted was >1 for

all programs indicating sustainable lawn management for all. But the SI of control and

organic treatments was at least five fold higher than the mineral fertilizer treatments.

As we lack baseline information on SOC, the comparison of the rates of C

sequestration and emission is not appropriate for our study. However, Qian and Follett

64 (2002) estimate C sequestration in fairways and greens of golf courses to be a linear

increase of 1 Mg ha-1 yr-1 for 25 and 30 years after establishment, respectively. Post and

Kwon (2002) estimate the rate of C sequestration to be 0.33 Mg ha-1 yr-1 on grasslands

established on previously disturbed soils. Comparing Qian and Follett (2002) rates of C

sequestration in turfgrass soils to the rates of C emissions (<0.46 Mg ha-1 yr-1) associated

with our programs we find that our experimental turfgrass soils are sustainable C sinks.

However, overtime the SOC pool may attain equilibrium and the rate of C sequestration decline. If the lawns are still managed with the same intensity they may not be sustainable in offsetting C emissions.

From (Singh et al., 2007, unpublished, see chapter 2) intensive management program inputs did not lead to significant differences in the SOC pools at the 0-12 cm depth after 15 years. So the SOC pools in control and organic programs are not significantly different than any other input intensive treatment suggesting that the best

sustainable C sink is the one with the least amount of management or managed with

organic inputs without herbicides.

The C emissions associated with inputs of optimum fertilizer N at different turf

ages under clippings returned and clipping removed management scenarios are presented

in Table 3.3. In the clippings returned scenario, lowering the use of N fertilizer after 50

years of establishment lowered the C emissions by 2.4 Mg CE ha-1 for the next 50 years.

However, in the clippings removed scenario, lowering the use of N fertilizer after 50

years of establishment lowered the C emissions by 1.3 Mg CE ha-1 for the next 50 years.

Overall for the 100 years of turf establishment and management with high amount of N

65 fertilizer input in clippings removed scenario there was an increase of 9.3 Mg CE ha-1 of

C emissions more than the clippings returned scenario. The SI’s are greater than 1 for

both the scenarios for time periods of 50 years, but less than 1 for the time period of 100

years and clippings removed. The SI’s are greater when the clippings are returned as

compared to clippings removed.

Turfgrass lawns are ecosystems in close interactions with humans in North

America. Quantitative data on C pools and fluxes in these systems will enable their

exploitation as sinks of atmospheric CO2, and can serve as a way to inform and educate

home lawn owners about C management. Thus, research on SOC sequestration in these

ecosystems can be a useful guide towards sustainable management of urban ecosystems.

Turfgrass provides various ecosystem services such as filtering air, regulating the microclimate, preventing soil erosion, noise reduction, rainwater drainage, aesthetics, recreational and cultural values in urbanized North America. These services are

important to maintain the quality of life of the humans in the urban environment. The use

of mineral fertilizers and pesticides contributed to C emissions and thus reduced the

sustainability of the urban lawns. We conclude that greater amount of C can be offset by reducing the use of management inputs from high mineral to zero or organic N inputs, reducing or eliminating the use of pesticides, reducing mowing, and returning clippings back to the turfgrass soils. Because of the large area of land covered by turfgrass in

North America, even a very small change in management practices that reduce C emissions may be an important contribution to the global CO2 level.

66 3.5 Acknowledgements

This research was funded by the by the Center for Urban Environment and Economic

Development of the Ohio State University.

3.6 Literature cited

Bormann, F.H., Balmori, D., Geballe, G.T. (2001). Redesigning the American Lawn: A Search for Environmental Harmony. Yale University Press. New Haven, CT. pp 178.

Intergovernmental Panel on Climate Change (IPCC). 2001. Climate change 2001: mitigation. Cambridge University Press, Cambridge, UK.

Lal, R. 2004. Carbon emissions from farm operations. Environment International. 30:981-990.

Marland, G., T.A. Boden, and R. J. Andres. 2007. Global, Regional, and National CO2 Emissions. In Trends: A Compendium of Data on Global Change. Carbon Dioxide Information Analysis Center, Oak Ridge National Laboratory, U.S. Department of Energy, Oak Ridge, Tenn., U.S.A.

Milesi C., S.W. Running, C.D. Elvidge, J.B. Dietz, B.T. Tuttle, R.R. Nemani. 2005. Mapping and modeling the biogeochemical cycling of turfgrasses in the United States. Environmental Management. 36: 426-438.

National Gardening Association. 2005. National Gardening Survey. National Gardening Association, Burlington (VM).

Post, W.M., and K.C. Kwon. 2000. Soil carbon sequestration and land-use change: Processes and potential. Global Change Biol. 6: 17–327.

Qian, Y. and R.F Follett. 2002. Assessing soil carbon sequestration in turfgrass systems using long-term soil testing data. Agron. J. 94:930-935.

Qian, Y. L., W. Bandaranayake, W. J. Parton, B. Mecham, M. A. Harivandi, and A. .R. Mosier. 2003. Long-term effects of clipping and nitrogen management in turfgrass on soil organic carbon and nitrogen dynamics. J. Environ. Qual. 32:1694-1700.

67 Singh, M.H., W. A. Dick, R. Lal, K. A. Hurto, H. B. Hamza, D. S. Richmond,, and P.S. Grewal. 2007. Long term management effects on soil organic carbon, nitrogen, turf quality, and biomass in kentucky bluegrass lawns in Ohio, (unpublished, this dissertation, chapter 2).

68 Target ControlOrganic a Organic b Mineral Mineral Mineral Mineral Mineral Mineral application High-N a High-N b High-N c High-N d Medium-N Low-N date

September 4 0 48-10-29 † 48-10-29 † 25-3-7 † 50-6-14† 49-0-0 † 49-0-0 † 44-4-13 † 49-6-14†

69 ‡ ‡ ‡ ‡ September 25 0 - - 37-4-11 † - - - - - October 16 0 48-10-29 † 48-10-29 † 24-0-0 † 49-0-0 † 49-0-0 † 49-0-0 † - - Total per 0 216-44-131 216-44-131 245-24-62 221-19-53 219-8-14 † 219-8-14 † 171-22-37 98-12-28 † annum † † † † †

Table 3.1. Fertilization schedule, fertilizer N-P-K composition, herbicide, and insecticide applications under different commercial lawn care programs in experimental plots of Kentucky bluegrass at Delaware, Ohio (1989-2003).

Table 3.2. Carbon emissions and sustainability indices (SI) associated with various management practices. Calculations include SOC to a depth of 0-12 cm and C emissions from fertilizer spraying/spreading and irrigation are not included due to lack of information. Information about mowing is approximated based on the gasoline consumption of a John Deere lawn mower and may differ for different mowers.

70 Untreated Organic Organic Mineral Mineral Mineral Mineral Mineral Mineral Control a b High-N a High-N b High-N c High-N dMedium-NLow-N

Loss of Carbon (kg CE ha-1)

Fertilizer – – – 4194 3888 3749 3749 2787† 1742

Herbicide – – 399† 1004 1004 1004 – 257† 1004

Fungicide – – – 12 – – – – –

Insecticide – – – – – – – 343† –

Mowing 895.1 895.1 895.1 895.1 895.1 895.1 895.1 895.1 895.1

Total Emissions 895.1 895.1 1294.1 6105 5787 5648 4644 4282 3641

71 (Ce)

Gain of Carbon (kg CE ha-1)

Soil organic carbon 24789 28734 26469 23809 25216 26083 28273 25696 24978 (Cs) Sustainability Index (SI)

SIG = (Cs)/Ce 27.7 32.1 20.5 3.8 4.4 4.6 6.1 6.0 6.9

SIN = (Cs-Ce)/Ce 26.7 31.1 20.2 2.9 3.4 3.6 5.1 5.0 5.9

† Emissions calculated for a period of 12 years and for all others for a period of 13 years. Abbreviations: Ce — Carbon emissions, Cs — Carbon sequestered, SIG — Gross sustainability index, SIN — Net sustainability index.

Clippings Years after Optimal N Loss of Carbon Loss of Carbon for Management turfgass Fertilizer (kg CE ha-1 yr-1) the total specified establishment application number of years rate (kg CE ha-1) (kg ha-1 yr-1) Clippings 1-10 150 195 1950 Returned 11-25 100 130 1950 26-50 75 97.5 2437.5 51-100 60 78 3900 Clippings 1-10 200 260 2600 Removed 11-50 150 195 7800 51-100 140 182 9100

Table 3.3. The CENTURY model predicted optimal N fertilizer rates at two clipping management scenarios based on turf age (Qian et al., 2003) and the calculated values of carbon emissions associated with the N fertilizer.

Clipping Total number Loss of carbon Rate of loss of Rate of gain of SIG = (Cs)/Ce SIN = (Cs-Ce)/Ce Management of years after for the total carbon, Ce carbon, Cs turfgass number of (kg CE ha-1 yr-1) (kg C ha-1 yr-1) establishment years after turfgass establishment (kg CE ha-1) Clippings 50 6337.5 126.8 620 4.9 3.9 Returned 100 10237.5 102.4 350 3.4 2.4 Clippings 50 10400 208 620 3.0 2.0 Removed 100 19500 195 350 1.8 0.8 73

Table 3.4. Carbon emissions and sustainability indices (SI) associated with clipping management scenarios. Calculations for total carbon emissions for respective number of years are from the data presented in Table 3.3. The rate of gain of carbon is taken from Qian et al. (2003). Abbreviations: Ce — Carbon emissions, Cs — Carbon sequestered, SIG — Gross sustainability index, SIN — Net sustainability index.

CHAPTER 4

SOIL CARBON DYNAMICS AND LITTER DECOMPOSITION AS AFFECTED

BY GRASS SPECIES AND FUNGAL ENDOPHYTE INFECTION

4.1 Abstract

Turf type tall fescue (Festuca arundinacea Schreb) (TF) and perennial ryegrass (Lolium perenne L.) (PR) are commonly used for lawns and golf courses in the Northeast and

Midwestern United States. These species differ in growth habits, drought tolerance, resistance to herbivory, and form mutualistic associations with Neotyphodium endophytes

(Clavicipitaceae). The fungal endophyte infection enhances plant biomass, but it may have negative effect on litter decomposition due to the production of toxic alkaloids in the plant tissue. We hypothesized that both the grass species and the endophyte infection have influence on the carbon (C) dynamics and litter decomposition in the lawns.

Replicated plots of PR and TF with low (<30% of plants) (PR- and TF-) and high (80-

95%) (PR+ and TF+) endophyte infection levels were established in 1999 and were managed by mowing, without any fertilizer and pesticide inputs. In 2006, soil organic C

(SOC) and its labile fractions, including microbial biomass C (MBC) and dissolved organic C (DOC) pools were measured separately for depths 0-3 cm, 3-6 cm, 6-9 cm, and

9-12 cm. Soil surface CO2 flux along with soil temperature and moisture were also

measured seven times between June and September in 2006. Litter bags were incubated

on site to determine the effect of the grass species and endophyte on decomposition of

grass clippings. Soil C concentrations and pools did not differ significantly between the

four treatments and averaged 25.9 (±1.8) Mg ha-1 SOC, 257.7 (± 19.6) kg ha-1 MBC, and

-1 62.7 (± 5.4) kg ha DOC in 0-12 cm soil depth. Of the 7 sampling dates for CO2 flux,

the treatments differed on 2 dates on 10 August 2006 (TF > PR), and on 25 August 2006

(PR+ > TF+ > TF-, and PR- was not significantly different from TF). Average CO2 flux values for the sampling period were significantly correlated to the soil moisture (Pearson r = 0.62; p value = 0.01). Repeated measures analysis revealed that grass species had a significant effect on litter decomposition with PR decomposing faster than TF after week

1, week 2, and week 3 sampling dates. But within a month clippings from both species decomposed equally with no difference in the final C:N ratio. We conclude that C sequestration was not influenced by either the grass species or endophyte level within the

7 year period of plot establishment.

4.2 Introduction

Atmospheric carbon dioxide (CO2) content is estimated presently at 730 peta gram (Pg)

increasing by 30 per cent over pre-industrial content of 560 Pg (Zimov et al., 2006). One

of the ways to reduce atmospheric CO2 is to sequester it in the soil. Soil is the second

largest reservoir of Carbon (C) and has a pool of 1550 Pg of soil organic C (SOC) and

750 Pg of inorganic C (Lal, 2001). Turfgrass covered soils are estimated at 2% of the

total land area in the United States (Milesi et al., 2005). Assessment of C sequestration

potential in soils under agriculture, forestry, pastures, and degraded lands has been the

focus of many studies (Lal et al., 1998 a, b, c). However, little is known about the grass

species and management regimes that can enhance SOC sequestration in turfgrass coverd

soils.

Tall fescue (Festuca arundinacea Schreb) (TF), and perennial ryegrass (Lolium perenne L.) (PR) are used extensively in turfgrass lawns in the United States. Both species are colonized by fungal endophyte (Neotyphodium spp). Tall fescue is mutualistic with Neotyphodium coenophialum Morgan-Jones & Gams, and PR is mutualsitic with Neotyphodium lolii Latch, Christensen & Samuels. The endophytic fungi colonize leaf sheaths and stems, and are transmitted only through seed. These fungi do not cause any disease in the grasses (Latch, 1997). The fungi obtain all food resources from the grass host, but in return provide several benefits. Endophytic fungi can provide greater seed survival, germination establishment, competitiveness, drought tolerance, summer survival, and insect and disease resistance to grasses (Latch, 1997). These benefits may lead to enhanced biomass production and thus greater soil C sequestration

(Franzluebbers et al., 1999). Insect resistance is due to the toxic alkaloids produced by fungus that act as deterrents to various plant pests. Salminen and Grewal (2002) and

Salminen et al., (2003) have shown that cultural practices such as mowing frequency and height have an impact on alkaloid production and accumulation in TF and PR. This has implication for reduced pesticide use in turfgrass stands of these grass species thus eliminating C emissions associated with the use of pesticide inputs.

Franzluebbers and Hill (2005) demonstrated the persistence of ergot alkoids in

soil under forage type endophytic TF and demonstrated reduced microbial biomass and

activity. Pastures in Georgia containing high endophytic TF were found to be associated

with greater SOC concentrations and lower potential soil microbial activity than pastures

with low occurrence of endophyte infection (Franzluebbers et al., 1999). This indicates a

positive role of endophytic fungi in soil C sequestration but a negative impact on decomposing microbes. A study conducted by Omacini et al., (2004) demonstrated

reduced microbial activity with endophyte infection in an outdoor microcosm in

Argentina where litter from endophytic Italian ryegrass (Lolium multiflorum)

decomposed slower than from non endophytic grass. Franzluebbers and Hill (2005)

conducted controlled incubation study to test if endophytic TF would reduce soil C and N

mineralization, and microbial biomass. Their results revealed that mineralizable C and

soil microbial biomass C were inhibited by endophytic compared with non endophytic TF

leaves during 32-d incubation. They also found that prior exposure of soil to endophytic

metabolites did not enhance the soil’s ability to decompose freshly added leaf material

and decomposition of ergot alkaloids was lower with previous long-term exposure of soil

to endophytic pasture.

The role of endophyte infection in C sequestration potential in turfgrass covered

soils is not known. Turfgrass systems differ from pastures in management practices and

exposure to vertebrate herbivores. In pastures, plant material is either passed on to

animals or enters the soil through the above ground biomass or below ground from roots and their exudates all of which is subsequently transferred to soil microbes via the food

chain and turns into SOC through the process of decomposition. In turfgass systems the grass is mowed off and does not cycle through the vertebrate herbivores. Turfgrass is also managed intensively with the use of various fertilizers and pesticides, and irrigation that can have an impact on the soil microbes.

Toxic alkaloids in clippings may affect the soil microbial activity thus affect SOC pools and the labile fractions of soil organic matter (SOM) including microbial biomass

C (MBC) and dissolved organic C (DOC). Soil microbial biomass is a measure of the physiologically active part of the soil microbes and thus is a good measure of the effect of toxic alkaloids (Franzluebbers et al., 1999). Dissolved OC is the source of energy for soil microorganisms that are important in energy and nutrient transformations. Non- endophytic or the toxic alkaloid containing endophytic grass clippings can affect the microbial biomass thus affecting the labile organic C fractions of organic matter. Rate of litter decomposition provides an assessment of the below-ground processes that may be altered by the quality of the litter from specific grass species, and endophyte infection.

Soil microbes decompose the returned grass clippings and SOM, and along with root respiration release CO2 in to the atmosphere. The differences in the grass species, and presence of toxic alkaloids in the grass clippings may affect the soil surface CO2 fluxes.

Therefore, this study tested the effects of high and low-endophytic TF and PR on:

1) C sequestration by measuring SOC pool,

2) changes in the labile fractions of SOC by measuring the DOC and MBC,

3) soil surface CO2 flux, and

4) litter decomposition dynamics using litter bags.

4.3 Materials and methods

4.3.1 Experimental design and research approach

Sixteen research plots 6.1 × 6.1 m, four each of TF and PR, with low (<30% of plants) and high (80-95%) fungal endophyte infection, in a completely randomized design were established during September 1999 on the grounds of the Ohio Agricultural Research and

Development Center (OARDC), in Wooster, Ohio which is located at 40°48'33" North, and 81°56'14" West. Thus, there are a total of four treatments, TF with high endophytic infection (TF+), TF with low endophytic infection (TF-), PR with high endophytic infection (PR+), and PR with low endophytic infection (PR-), each with four replications.

Soil at this site is a Wooster silt-loam (fineloamy, mixed, mesic oxyaquic fragiudalphs).

Plots have been managed by mowing under a low maintenance regime, without the application of fertilizers and pesticides. Annual average precipitation, maximum temperature, and minimum temperature is 80 mm, 15 ºC, and 9 ºC respectively for the

Wooster area.

4.3.2 Soil sampling and analysis

Twenty soil cores (2 cm diameter and 15 cm deep) were obtained randomly from each plot in August 2006. Each soil core was divided into 0-3, 3-6, 6-9 and 9-12 cm depths.

Except for 3 soil cores which were used to determine bulk density (see below) all the other soil samples were mixed well and refrigerated.

Soil Bulk Density: Three soil cores per plot were used to measure soil bulk density by the core method (Blake and Hartge, 1986).

Soil pH and Organic Carbon: A small subsample was air-dried, and all visible leaf and root material removed. The sample was passed through a 2 mm sieve after grinding. Soil pH was measured for a 1:1 solution of soil sample in deionized water.

Total C (TC) concentration was determined using 1.5 g soil sub-sample using a CN

Analyzer (Vario EL, Elementar Analysensysteme GmbH, Hanau, Germany). The TC concentration thus measured was regarded as SOC since the soils are slightly acidic to near neutral with pH < 7.2, which indicates absence of any inorganic C.

Microbial Biomass Carbon: Soil microbial biomass C was determined by a fumigation extraction method (Vance et al., 1987, and Jinbo et al., 2006). Organic C in fumigated and nonfumigated soils was analyzed using a Rosemount Dohrman DC-190 total organic C analyzer. The microbial biomass C was calculated by dividing the difference of total extractable C between fumigated and unfumigated samples by the conversion factor 0.45.

Dissolved Organic Carbon: Soil samples at field moisture (equivalent to 10 g oven dry weight) were weighed into 40 mL polypropylene centrifuge tubes. The samples were extracted with 30 mL of distilled water for 60 min on an orbital shaker, and centrifuged for 2 min at 2000 rpm. All the supernatant was filtered through 0.45-µm filter into separate vials for C analysis (Ghani et al., 2003). The extracts were analyzed for C using high temperature combustion using a Rosemount Dohrman DC-190 total organic C analyzer.

4.3.3 Soil surface CO2 flux: Soil surface CO2 flux was measured seven times between

June 16, 2006 and September 6, 2006. Soil surface CO2 flux was measured with a LI-

6200 portable CO2 infrared gas analyzer (Li-Cor Inc., Lincoln, NE) equipped with a 10- cm diameter soil respiration chamber (LI-6200–09). In each of the plots the grass was completely clipped off from the soil surface for approximately 11-12 cm diameter the day before the sampling. This space was then used for installing the chamber to measure the

CO2 flux.

Soil temperature and moisture content: Fluctuations in soil temperature and

moisture can affect soil and root respiration (Fortin et al., 1996; Wagai et al., 1998).

Therefore at each of flux sampling dates soil temperature and moisture content was

measured immediately on the same spot as CO2 flux measurement, at 5 cm below the soil

surface. Soil temperature was measured using a long stem thermometer. Volumetric soil water content was measured using a Theta meter (Delta-T Devices, Cambridge, UK).

4.3.4 Litter Bag Decomposition

Grass was harvested from the individual plots using a hand scissors. Mesh bags made of fiberglass, 10 cm × 10 cm, 1mm × 1mm mesh size, were filled with oven-dried (60 ◦C)

samples of grass litter (leaves and stalks). Initially, one mesh bag per grass type was

filled with litter, and the litter weight calculated. All subsequent mesh bags for each type

were weighed with about the same oven-dried weight as the initial one. Ten bags from

each replicated treatment were placed randomly on June 6, 2006 in the plots at 5 cm

below the soil surface. They were later located using a metal detector and one bag from each plot was extracted on 13 June, 20 June, 27 June, 11 July, 25 July, and 8 August.

After extraction the amount of remaining litter was calculated using the method described by Vazquez et al., (2003). Grass samples were also analyzed for initial and final C:N content at time 0, and after 65 days in the field.

4.3.5 Calculations and statistical analyses

The SOC, MBC, and DOC pool for each 3 cm soil depth, was calculated as mega grams C per hectare (Mg C ha-1) using the following equation (Lal et al., 1998a): Mg C ha-1= %TC x bulk density (Mg m-3) x depth (m) x 104 m2 ha-1 / 100. The data for various depths were added to get a cumulative pool for 0-12 cm soil depth. The soil surface CO2

-2 -1 -2 -1 flux was calculated as mg CO2-C m hr = micromole CO2-C m s * 0.044 * 3600.

Differences among the four treatments were analyzed with SAS statistical software package with PROC GLM (SAS Release 9.1, SAS Institute, Cary, NC). The data were also analyzed separately for the effects of grass species and endophyte infection. The data for soil surface CO2 flux, and litter bag decomposition was analyzed by repeated measures analysis as four separate treatments, and also to analyze the effect of grass species and endophyte infection. If an F-test proved significant at p < 0.05, the means of each treatment were compared using the Fisher’s LSD.

4.4. Results

4.4.1 Soil bulk density, pH, organic carbon, microbial biomass carbon, and dissolved organic carbon

Soil bulk density did not differ between treatments (Table 4.1). The average bulk densities were 0.93 Mg m-3, 1.28 Mg m-3, 1.29 Mg m-3, 1.28 Mg m-3 for 0-3 cm, 3-6 cm,

6-9 cm, and 9-12 cm depth respectively. Soil pH was around 6 and did not differ

between treatments (data not presented). It was below 7.5 for all treatments and therefore the TC was used as SOC. The SOC, TSN, MBC, and DOC concentrations (Tables 4.2,

4.3, 4.4, and 4.5) and pools (Figures 4.1, 4.2, 4.3 and 4.4) did not differ between

treatments. There was an average of 25.9 (±1.8) Mg ha-1 SOC, 257.7 (± 19.6) kg ha-1

MBC, and 62.7 (± 5.4) kg ha-1 DOC in 0-12 cm soil depth.

4.4.2 Soil surface CO2 flux

The results for soil surface are presented in table 4.6. Soil surface CO2 flux did not differ

between treatments except for the sampling dates in August 2006 (Figure 4.5). Results

from repeated measures analysis reveal that the four treatments, grass species and

endophyte infection (p<0.05) had a significant effect on CO2 flux. Soil temperature did

not differ significantly between treatments (Figure 4.6). Soil moisture differed between

treatments for only one sampling date in August 2006 (Figure 4.7). Repeated measures

analysis showed that individually treatment and time had a significant effect on soil

moisture, but not together. Significant differences in soil moisture were observed on

sample date 10 August 2006 with greater moisture content in TF+ as compared to PR-

and TF-. Pearson correlation between soil moisture and CO2 flux was significant (r =

0.62; p = 0.01), but it was not significant for soil temperature and CO2 flux.

4.4.3 Litter bag decomposition

Proportion of remaining litter mass in the 4 treatments at various time intervals is presented in Table 4.7. Repeated measures analysis for the four treatments did not show any significant difference, but time had a significant effect on the amount of litter mass

remaining between treatments (p<0.01). However, the time*treatment effect was not

significant. Repeated measures analysis to determine the effect of grass species showed a

significant effect of grass species, time, and time*grass species interaction (p<0.05) on

the proportion of remaining litter mass. On days 7, 14, and 21 PR litter decomposed

faster than TF litter. However, in the later sampling dates on day 35, 49, and 63, there

was no difference between treatments in the amount of litter mass remaining. Repeated

measures analysis for endophyte infection was not significant. Initially at time 0, PR had

higher (21.8 ± 0.7) C:N ratio than TF (20.3 ± 0.5) but after the end of decomposition

study at day 65, the C:N ratio was not significantly different between grass species and

was on an average of 13:1.

4.5 Discussion and conclusions

Absence of mineral fertilizer-N helped to maintain similar soil pH in all the soil depths.

In the absence of soil disturbance after lawn establishment the bulk density did not differ

between treatments. Franzluebbers et al. (1999) found low bulk density with high

endophyte infection in TF pastures and attributed it to greater return of undigested plant

residue to soil in high endophyte due to lower grazing pressure caused by toxicity. In

turfgrass systems, there is no grazing pressure, and the grass clippings are regularly

returned to the soil after each mowing event. This resulted in similar bulk densities in

our study.

The reasons for no differences between treatments for SOC pools and its labile

fractions MBC and DOC could be as follows. In Italian ryegrass endophyte infection

increased the shoot N content (Omacini, 2001). In such a situation the soil N content

under these grasses can also increase leading to an increase in C and N pools. However

another study (Malinowski and Belesky, 2000) found that changes in N content in plant

tissues depended on soil nutrient levels. Franzluebbers et al. (2005) found that endophyte

infection could play a role in modifying SOC pools in high endophyte TF but this was

seen only at high inputs of fertility. In pastures the plant biomass of high endophyte

infection is not consumed by the grazers, and with greater fertility there is greater return

of plant biomass thus leading to an increase in C pools. In our study, there was no

fertilizer input, and the grass clippings were returned to the soil with each mowing event

equally in each treatment. We also found equal amounts of TSN in our treatments. This

could be a possible reason for non significant differences between C pools.

The CO2 flux rates were higher in the beginning of summer as compared to the

-2 -1 end of summer and ranged from 0.5 to 2.4 CO2-C g m hr . Bremer and Ham (2005)

reported CO2 flux results in PR and TF similar to this study. Using LI-COR 6400 under

-2 -1 -2 -1 sunlit conditions they found CO2 flux rate at 1.7 CO2-C g m hr in PR and 1.4 g m hr in TF under gravimetric moisture content of 22 to 29%. The soil surface CO2 flux was

significantly affected by the treatments. Comparisons between the treatments on specific sampling dates we found that the differences in CO2 flux occurred on the same dates as

the differences in soil moisture content. Soil moisture content has been shown to

influence soil surface CO2 flux (Fortin et al., 1996; Wagai et al., 1998). With a

significant correlation between these parameters in our study the differences in our study

could be the result of soil moisture content.

During soil respiration the C fixed by plants is returned back to the atmosphere by

root respiration and microbial decomposition of plant biomass. Measurement of CO2

flux is important in estimating the C budget of an ecosystem. Herte et al. (1971)

estimated lawn productivity at 2.5 Mg ha-1 yr-1 by measuring only clippings biomass.

Falk (1980) estimated the net primary production (NPP) of lawns to be 10-17 Mg ha-1 yr-1

by measuring both the above and below ground biomass. Singh et al. (2007, unpublished data, see chapter 2) estimated the clippings biomass production in unfertilized control treatment in the period between 6-5-1995 to 10-27-1995 to be at 70 kg ha-1 week-1. From this data we can estimate the clippings biomass production to be 900 kg ha-1 for a period

of 90 days from June to September. Estimating an equivalent amount of belowground

allocation, the total plant biomass that can be available for decomposition could be

approximated to 2 Mg ha-1. From the results presented in figure 4.5, the average soil

-2 -1 surface CO2 flux for all treatments for the entire season is 1.6 g CO2 m hr , which

amounts to a loss of 9.3 Mg C ha-1 in 90 days. Part of the soil respiration would be root

respiration which is estimated to be 17-40% of total soil respiration in grasslands (Kucera

and Kirkham, 1971; Coleman, 1973; Herman, 1977; and Buyanovsky et al, 1987). Qian

and Follett (2003) estimate the rate of C sequestration in turfgrass soils at 0.9 to 1 Mg ha-

1 -1 yr . Comparing the input of plant biomass and soil surface CO2-C flux we find that there is a net loss of C from the soil rather than accumulation for this period.

These results could be due to the following reasons. Soil respiration has been shown to increase with increasing temperature (Raich and Schlesinger, 1992; Rustad and

Fernandez, 1998). The input of NPP could be higher during the spring and autumn when the cool season turfgrasses grow better, and the corresponding C loss may be lower with a decrease in temperature. The opposite is true for summer months with lower inputs and greater loss of C, during which period these results were collected. Use of static chamber in turfgrass systems may have overestimated the soil surface CO2 flux.

Research has shown that the use of static chambers provides overestimated results as

compared to the use dynamic chamber (Nay et al., 1994). Duiker and Lal (2000) reported

a similar overestimation of CO2 flux in no till cropping system using static chamber

technique. Further the area of the chamber (71.6 cm2) used for the study may not be

large enough to be extrapolated on a per hectare basis. Comparative analysis of soil

surface CO2 flux in turfgrass systems, for a full year using both static and dynamic chambers is recommended.

The litter decomposition study shows that within a month of placing litter bags in

the soil, all the treatments decompose litter at the same rate. So there are no negative

effects on the decomposing soil biota by the grass species or the endophyte infection.

This could again result in similar amounts of SOC pools. Greater decomposition of PR in

the first 3 sampling periods could be due to their fine textured leaf blades as compared to

the coarser TF. An argument can be made that this delay in decomposition can overtime build greater SOC pool. However, with the use of a mulching mower that returns smaller pieces of grass clippings as compared to non-mulching mower the fine grass clippings may not make a difference in decomposition and thus eventually the C pools. The initial high C:N ratio of PR was probably not high enough to cause any impact on decomposition.

Endophytes enhance host fitness by providing protection from herbivory and pathogens that cause disease. However the infection may not necessarily protect from these stresses and are very specific to the host-plant genotype (Popay and Bonos, 2005).

Reactions to these stresses can influence the quality and quantity of biomass that is returned to the soil in the form of root exudates or above ground plant biomass. Thus there can be differential impact on the soil bio-physio-chemical parameters.

A very strong contrast between total absence and presence of endophyte infection may be important to see the effect of endophyte infection on SOC pools and litter decomposition. Also the combination of low input conditions and genotypes of grass and fungal endophyte may not have produced high levels of alkaloids to have an effect on litter decomposition and further on the SOC pools. Further, a four year weed population study (Richmond et al., 2006) conducted on these plots during the years 2000 to 2003, showed that endophyte infection did not have an effect on weed encroachment in these plots. However, the grass species did have a significant effect. During the turf establishment phase (2000-2001) TF had more weed cover and less turf cover but later

PR had greater weed cover and less turf cover. This suggests that the amount of plant

biomass returned to the soil in both the grass species would be similar. The weed biomass could compensate for the turf biomass thus providing equal amounts of biomass that can be converted to SOC.

We conclude that C sequestration was not influenced by either the grass species or endophyte level within the 7 year period after plot establishment under the absence of nitrogen fertilization application.

4.6 Acknowledgements

This research was funded by the Center for Urban Environment and Economic

Development of the Ohio State University. We are grateful for the instrument, and technical assistance provided by Dr. Larry Phelan, Dr. Dan Herms, Dave McCartney,

Alfred Alumai, and Bryant Chambers.

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92 Depth (cm) 0-3 3-6 6-9 9-12

Treatment Soil bulk density (Mg m-3) TF- 0.88 ± 0.06 a 1.29 ± 0.03 a 1.28 ± 0.02 a 1.30 ± 0.08 a TF+ 0.99 ± 0.05 a 1.30 ± 0.02 a 1.34 ± 0.05 a 1.30 ± 0.08 a PR- 0.90 ± 0.04 a 1.27 ± 0.04 a 1.27 ± 0.05 a 1.23 ± 0.03 a PR+ 0.93 ± 0.07 a 1.26 ± 0.05 a 1.29 ± 0.03 a 1.27 ± 0.08 a Means followed by same lowercase letters (within columns) and not significantly different.

Table 4.1. Soil bulk density (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

Depth (cm) 0-3 3-6 6-9 9-12

Treatment Soil organic carbon concentration (g kg-1) TF- 32.9 ± 0.4 a 14.8 ± 0.9 a 14.7 ± 1.3 a 14.7 ± 2.3 a TF+ 33.8 ± 3.0 a 16.4 ± 0.8 a 15.1 ± 1.5 a 14.0 ± 1.1 a PR- 30.3 ± 1.5 a 16.1 ± 1.1 a 11.5 ± 1.3 a 12.8 ± 1.0 a PR+ 32.7 ± 0.4 a 16.9 ± 1.2 a 12.8 ± 1.0 a 15.4 ± 1.9 a Means followed by same lowercase letters (within columns) and not significantly different.

Table 4.2. Soil organic carbon concentration (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

Depth (cm) 0-3 3-6 6-9 9-12

Treatment Total soil nitrogen concentration (g kg-1) TF- 3.0 ± 0.0 a 1.6 ± 0.1 a 1.6 ± 0.1 a 1.6 ± 0.2 a TF+ 3.0 ± 0.2 a 1.7 ± 0.1 a 1.6 ± 0.1 a 1.5 ± 0.1 a PR- 2.8 ± 0.1 a 1.7 ± 0.1 a 1.3 ± 0.1 a 1.4 ± 0.1 a PR+ 3.0 ± 0.0 a 1.8 ± 0.1 a 1.4 ± 0.1 a 1.7 ± 0.2 a Means followed by same lowercase letters (within columns) and not significantly different.

Table 4.3. Total soil nitrogen concentration (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

Depth (cm) 0-3 3-6 6-9 9-12

Treatment Microbial biomass carbon (ppm) TF- 321.8 ± 16.0 a 144.9 ± 19.3 a 133.9 ± 19.7 a 125.3 ± 18.5 a TF+ 287.6 ± 29.4 a 152.6 ± 4.3 a 139.6 ± 11.3 a 139.1 ± 17.4 a PR- 276.7 ± 30.9 a 167.2 ± 20.6 a 139.8 ± 7.1 a 144.3 ± 8.7 a PR+ 354.3 ± 31.7 a 198.2 ± 16.0 a 150.5 ± 13.9 a 148.7 ± 9.0 a Means followed by same lowercase letters (within columns) and not significantly different.

Table 4.4. Microbial biomass carbon (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

94 Depth (cm) 0-3 3-6 6-9 9-12

Treatment Dissolved organic carbon (ppm) TF- 48.4 ± 8.0 a 48.1 ± 3.0 a 44.7 ± 4.7 a 38.2 ± 2.8 a TF+ 44.1 ± 3.7 a 42.2 ± 7.0 a 36.8 ± 5.7 a 29.8 ± 7.6 a PR- 48.1 ± 4.8 a 47.2 ± 3.7 a 46.3 ± 1.0 a 35.3 ± 4.5 a PR+ 54.2 ± 3.3 a 50.1 ± 3.1 a 48.9 ± 5.3 a 43.8 ± 4.8 a Means followed by same lowercase letters (within columns) and not significantly different.

Table 4.5. Dissolved organic carbon (Mean ± SEM) at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots after 7 years of establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

Treatment Sampling Date TF- TF+ PR- PR+

-2 -1 CO2 flux (micromol CO2 m s ) 16-Jun-06 9.21 ± 0.54 a 9.75 ± 0.46 a 9.94 ± 0.60 a 9.19 ± 0.54 a 29-Jun-06 11.75 ± 0.46 a 13.14 ± 0.94 a 13.54 ± 0.95 a 14.47 ± 1.22 a 07-Jul-06 12.76 ± 0.32 a 12.66 ± 0.67 a 12.18 ± 0.28 a 13.40 ± 0.39 a 21-Jul-06 13.98 ± 0.52 a 13.81 ± 0.67 a 13.99 ± 0.47 a 14.88 ± 0.52 a 10-Aug-06 * 8.87 ± 0.32 a 9.03 ± 0.26 a 7.22 ± 0.47 b 6.81 ± 0.32 b 25-Aug-06 * 3.86 ± 0.88 c 6.09 ± 0.39 b 4.99 ± 0.46 bc 8.10 ± 0.77 a 06-Sep-06 9.50 ± 0.20 a 8.99 ± 0.25 a 8.84 ± 0.19 a 9.24 ± 0.24 a *, Significant at the 0.05 probability level. Means followed by same lowercase letters (within rows) are not significantly different.

Table 4.6. Soil surface CO2 flux (Mean ± SEM) at various field sampling days in high and low endophytic tall fescue and perennial ryegrass experimental plots. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

0 7 14 21 35 49 63 Litter bag (6 June (13 June (20 June (27 June (11 July (25 July (8 August extraction 2006) 2006) 2006) 2006) 2006) 2006) 2006) days *** ** ** Grass Species Proportion of litter remaining (Mean ± SEM) TF- 0.97 ± 0.01 a 0.78 ± 0.01 a 0.73 ± 0.02 a 0.56 ± 0.03 ab 0.44 ± 0.04 a 0.32 ± 0.04 a 0.29 ± 0.01 a TF+ 0.92 ± 0.03 a 0.79 ± 0.02 a 0.69 ± 0.03 ab 0.57 ± 0.02 a 0.43 ± 0.04 a 0.36 ± 0.05 a 0.36 ± 0.04 a PR- 0.96 ± 0.02 a 0.72 ± 0.03 ab 0.63 ± 0.01 bc 0.51 ± 0.02 bc 0.40 ± 0.01 a 0.34 ± 0.01 a 0.30 ± 0.03 a

96 PR+ 0.93 ± 0.03 a 0.70 ± 0.04 b 0.59 ± 0.02 c 0.47 ± 0.01 c 0.35 ± 0.02 a 0.32 ± 0.03 a 0.25 ± 0.03 a

Table 4.7. Proportion of litter remaining (Mean ± SEM) at various field days in high and low endophytic tall fescue and perennial ryegrass experimental plots. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

30 TF- NS TF+ PR- ) 25 -1 PR+

15 NS 10 NS NS NS

Soil Organic Carbon (Mg ha 5

0 0-3 3-6 6-9 9-12 0-12 Soil Depth (cm)

Figure 4.1. Soil organic carbon pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots 7 years after establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

4 TF- TF+ PR- )

-1 PR+ NS 3

NS 1 NS Total Soil Nitrogen (Mg ha NS NS

0 0-3 3-6 6-9 9-12 0-12 Soil Depth (cm)

Figure 4.2. Total soil nitrogen pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots 7 years after establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

98 TF-

) TF+ -1 300 PR- NS PR+

200

NS 100 NS NS NS Microbial Biomass Carbon (Kg ha

0 0-3 3-6 6-9 9-12 0-12 Soil Depth (cm)

Figure 4.3 Soil microbial biomass carbon pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots 7 years after establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

99 80 TF- NS

) TF+ -1 PR- PR+ 60

NS NS NS NS 20 Dissolved Organic Carbon (Kg ha (Kg Dissolved Organic Carbon

0 0-3 3-6 6-9 9-12 0-12 Soil Depth (cm)

Figure 4.4. Dissolved organic carbon pools at various depths in high and low endophytic tall fescue and perennial ryegrass experimental plots 7 years after establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

100 2.5 TF- TF+ PR- PR+ 2.0 )

-1 a hr 1.5 -2 a a

b b b -C (g m

2 1.0

CO bc

c 0.5

0.0 6 Jun 06 29 Jun 06 7 Jul 06 21 Jul 06 10 Aug 06 25 Aug 06 6 Sep 06 Time

Figure 4.5. Soil surface carbon dioxide flux at various dates in high and low endophytic tall fescue and perennial ryegrass experimental plots 7 years after establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

101 36 TF- TF+ 34 PR- PR+

26 Soil Temperature (degree C) Temperature (degree Soil 24

22 6 Jun 06 29 Jun 06 7 Jul 06 21 Jul 06 10 Aug 06 25 Aug 06 6 Sep 06 Time

Figure 4.6. Soil temperature at various dates in high and low endophytic tall fescue and perennial ryegrass experimental plots 7 years after establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

102 0.40 TF- TF+ 0.35 PR- PR+

0.30

0.25

0.20

a 0.15

Volumetric Soil Moisture 0.10 b b 0.05

0.00 6 Jun 06 29 Jun 06 7 Jul 06 21 Jul 06 10 Aug 06 25 Aug 06 6 Sep 06 Time

Figure 4.7. Volumetric soil moisture content at various dates in high and low endophytic tall fescue and perennial ryegrass experimental plots 7 years after establishment. Abbreviations: TF-: tall fescue with low endophytic infection, TF+: tall fescue with high endophytic infection, PR-: perennial ryegrass with low endophytic infection, and PR+: perennial ryegrass with high endophytic infection

103 CHAPTER 5

SPATIAL VARIABILITY IN SOIL ORGANIC CARBON POOLS IN URBAN

LANDSCAPES OF OHIO

5.1 Abstract

A serious barrier to our understanding of carbon (C) dynamics in urban ecosystems is the

lack of quantified data on the baseline soil C pools and its spatial variability at different

scales. This study addresses these gaps by measuring soil organic C (SOC) pools in

urban landscapes in Wayne and Holmes Counties, Ohio. Our objectives were to evaluate

(1) baseline SOC pool in newly constructed urban lawns, (2) spatial variability in SOC

pool across a heterogeneous urban residential block that includes areas covered by turf

(on sloped or flat land), or other ground covers, and (3) variability in SOC pools at a

regional scale in turfgrass lawns. Soil samples were collected from experimental and

field sites for 0-12 cm soil depth and analyzed for pH, bulk density, and total C. Lawns

constructed with top soil (TS) had 4.5 times higher SOC pool than to the sub soil (5.1 ±

0.1 Mg ha-1). Addition of compost to TS doubled its SOC pool (40 ± 0.3 Mg ha-1), and showed a 7 fold increase in the sub soil SOC pool. Turfgrass areas with slope had significantly lower SOC concentration than to the flat turf areas and those covered with other ground covers, but their SOC pools were not significantly different. Sloped turf 104 had 11.7 ± 1.0 Mg ha-1 less SOC pool than to the flat turf areas in the 0-12 cm soil depth.

The SOC was more variable across this heterogeneous city block (CV = 32.6) than across

the lawns in the study region (CV = 26.2). The average SOC pool across a heterogeneous

residential block was 46 ± 2.3 Mg ha-1 and that across the region in lawn soils was 25 ±

4.6 Mg ha-1. Considering the average SOC pool of 25 Mg ha-1in the 0-12 cm depth and

the total turf area for Ohio of 673,300 ha, the total Ohio SOC pool is at 16.8 tera gram

(Tg) which is 0.03 percent of the estimated 59.4 petagram (Pg) SOC pool in the

conterminous 48 states.

5.2 Introduction

“Attempts to break sprawl’s stranglehold on central Ohio are failing” (Gebolys, 2006).

Milesi et al. (2005) estimated total turfgrass area which includes residential, commercial,

and institutional lawns, parks, golf courses, and athletic fields at 163,800 km2 (± 35,850

km2) in the United States, and 673,300 ha of turfgrass covered land in Ohio. Suburban sprawl is rapidly increasing not only in Ohio but also around many other major cities throughout the United States. This increase is coming at the cost of farmlands, forests, and open lands. In 1990’s the average size of new homes was twice its size in 1950’s

(Simmons, 1997). Sprawl in combination with a preference for a bigger home lot size increases the number of houses and the lawn area that accompanies the house. As the urban lawns are increasing it is important to understand this ecosystem and the various ecosystem services such as soil C sequestration it provides.

105 Before the establishment of the turfgrass lawn, the forest, open land or agrarian

soil undergoes tremendous changes during construction. Urban soils belong to a group of

soils generally referred to as “anthropogenic soils” because of the disturbance caused by

human activity (Penizek and Rohoskova, 2006). It is common practice for builders to

scrape the ground of its organic rich topsoil for commercial purposes, and leave the new

lawn with subsoil. This transport of the topsoil not only disturbs the soil and releases

carbon dioxide (CO2), but leads to further challenges in growing the grass on nutritionally

and structurally poor subsoil. The lawns established on subsoil often require calendar

based fertilizer applications and irrigation thus adding to indirect release of CO2 to the atmosphere. This release is due to the hidden cost of manufacturing, transport, and application of fertilizers and pesticides. However, there are also some ecologically conscious construction companies that use the topsoil excavated from the same area the same place and amend the native or sub soil with compost.

Milesi et al. (2005) concluded that the turf covered areas have the potential to alter nutrient cycles. During the land conversion process for urban use, either the topsoil or subsoil with or without compost amendment is used to establish turf grass. Compost amendment can help to lower the bulk density, increase infiltration, and provide nutrients to the growing turf grass roots (Cogger, 2005). Absence or addition of these amendments can change the soil organic carbon (SOC) pools of the soils initially and can also affect the long term SOC accumulation. Accurate data on the baseline carbon (C) that is present in the soil or subsoil before the establishment of turfgrass after the conversion of land during construction in contemporary urban ecosystem is needed. This will help to

106 estimate the change in the C pools and analyze potential for rate of increase in SOC pool

in urban lawns.

Urban ecosystems are highly heterogeneous in terms of the soil and vegetation

cover even at a scale of a residential block. Urban soils can be subjected to different

levels of human interference causing spatial heterogeneity in soil in terms of its

physicobiochemical properties. To develop greater understanding of C dynamics in urban ecosystems the heterogeneity of urban soils and vegetation should be taken into account while quantifying SOC pools. Assessment of variations in SOC pools at fine- scale heterogeneous soil and vegetation cover in a residential neighborhood, culturally managed in a similar manner will help to make better predictions about C storage in urban ecosystems at a landscape level.

Urban soils represent a huge anthropogenic influence not only before establishment, but also later in terms of managing the urban landscape. Homeowners strive to achieve the best looking lawn that is green, free of weeds and pests, and well trimmed. The homeowners follow various management practices to achieve these results. These cultural practices include variations in the levels of fertilizer, pesticide, irrigation, and mowing. Along with their contribution to improvements in turf quality

(‘the look’) the management practices can have an effect on the amount of turf biomass returned to the soil and hence the amount of the SOC pool. A field assessment of vertical and lateral variation in SOC pools in home lawns under diverse management programs and in soil physio-biochemical characteristics, age, and plant species composition in

107 similar climatic conditions will provide quantitative data to model SOC pool projections

and refine it at a national and global scale.

Therefore, the objectives of this research were to evaluate (1) baseline SOC pool in newly constructed urban lawns, (2) assess spatial variability in SOC pool across a heterogeneous residential neighborhood block, and (3) determine variability in SOC pool at a regional scale in turfgrass lawns under different intensities of management inputs.

5.3 Materials and methods

5.3.1 Experimental design

5.3.1.1 Objective 1: Baseline SOC pool in newly constructed urban lawns

The site for the study is located on the campus of the Ohio Agricultural and Research development Center in Wooster, Ohio. The site was an unused land area covered by natural vegetation. The surface area of the entire study area was 25 m by 34 m. In April

2006, the topsoil, 0-15 cm, from the surface was excavated using a front end loader. The subsoil required for the study was excavated from the nearby area. One half of each of the topsoil and subsoil excavated was mixed with compost at a 1:4 ratio and then immediately filled in the research plots.

Four main plot treatments were established on subsoil (SS), subsoil+compost

(SS+C), topsoil (TS), and topsoil+compost (TS+C). Each plot was 1.7 m by 2.1 m, with buffer area between plots to minimize contamination. All plots were defined by wooden frames raised about 6-10 cm higher than their original surface to maintain each plot at a

4-degree (3.8 to 4.2) cross-angle slope for water, nutrient, and pesticide runoff research.

108

5.3.1.2 Objective 2: Assess spatial variability in SOC pool across a heterogeneous urban residential block

This study was conducted in a residential neighborhood block in the City of Wooster,

Wooster, Ohio (Figure 5.1). The entire block was mapped out and the site was divided into 20 feet by 20 feet grids. The starting points for grids were the edge of College

Avenue on either side east and west. The sampled grids were selected so as to cover the various ecotypes including sloped areas, flat areas, with and without turf cover, wooded area, shrubbery, and perennial planting beds. In total 46 sites were sampled within the neighborhood. Description and values for the various parameters at each sampling location are presented in Appendix A.

5.3.1.3 Objective 3: determine variability in SOC pool at a regional scale in turfgrass lawns

Forty home lawns in Wayne and Holmes Counties in Ohio were included in this study for obtaining samples (Figure 5.2). We sent an email request to home owners to participate in this study. The selected lawns differed in age, vegetation cover, management inputs, slope, and shade.

5.3.2 Soil sampling and analyses

Soil samples were collected in 2006 for objective 1, 2005 for objective 2, and 2003 and

2004 for objective 3. Soil samples were collected before seeding for objective 1, from

109 each of the selected grids in objective 2, and from front lawns of homes for objective 3.

Soils were cored to a depth of 0-12 cm using a 2 cm diameter soil corer. For objective 1

and 2, composite 0-12 cm depth soil cores were collected and analyzed for the following

parameters: texture (sieving and sedimentation) (Kettler et al. 2001), pH (1:1 soil

solution), bulk density (Blake and Hartge, 1986), and total C (TC) with 1.5 g soil sub

sample (Vario EL, Elementar Analysensysteme GmbH, Hanau, Germany). Soil samples

were air-dried and all visible leaf and root materials were removed before analysis. The

TC concentration thus measured was regarded as SOC because the soils were slightly

acidic to near neutral with pH < 7.2, which would indicate absence of any inorganic C

(Gajda et al., 2001). For objective 3 the soil cores were divided into 4 depths of 0-3 cm,

3-6 cm, 6-9 cm, and 9-12 cm and then analyzed for the above listed parameters. Of the

40 lawns sampled for objective 3, 29 were analyzed for soil texture, 30 were analyzed for

bulk density, and all 40 were analyzed for soil pH and TC, and 30 for SOC pool. These

differences in the number of observations were due to inability to obtain permissions

from homeowners for a second attempt at soil sampling.

5.3.3 Calculations

The SOC pools in mega grams per hectare (Mg C ha-1) were calculated using the

following equation (Lal et al., 1998): Mg C ha-1= [%TC x bulk density (Mg m-3) x depth

4 2 -1 thickness (m) x 10 m ha ] / 100. For objectives 1 and 2, an average value of the bulk

density for the 0-12 cm depth was used to obtain the SOC pools. For objective 3, bulk

110 density for each soil was depth was used to calculate the SOC pools and then summed to

obtain the total pool for the 0-12 cm soil depth.

5.3.4 Statistical analyses

For objectives 1 and 2 statistical assumptions were tested using MINITAB Release 14

(Minitab Inc. 2003). Further statistical analyses were performed with SAS statistical

software package with PROC GLM (SAS Release 9.1, SAS Institute, Cary, NC). In

order to describe the variation in individual sampling sites in objective 2 and 3, the

number of observations (N), mean, coefficient of variation (CV), minimum, and

maximum values were calculated for all the parameters. For objective 2 various ecotypes

were placed in groups depending broadly on slope, and plant cover type. These groups

were A) Areas covered with turf without slope (Flat Turf), B) Areas covered with turf

with slope (Sloped Turf), and C) Groups A and B together (Turf), and D) Areas covered

with mulched or non mulched low to medium height bushes (Ground Cover). GLM

ANOVA was performed on the data based on these groups.

5.4 Results and discussion

5.4.1 Objective 1: Baseline SOC pool in newly constructed urban lawns

Soil pH was significantly higher in SS+C treatment with an average of 6.65 (data not

presented) compared to the other 3 treatments which had pH between 6.26 and 6.42. The

soil bulk density was the highest in sub soil; however addition of compost (SS+C) lowered its bulk density equivalent to the top soil (Figure 5.2). The addition of compost

111 to top soil further lowered its bulk density. There was significant difference between the treatments in SOC concentration (Figure 5.3). Top soil had 5 times SOC concentration than to the sub soil. Addition of compost to TS doubled its SOC concentration, and resulted in an 8 fold increase in the subsoil SOC concentration. The SOC pools followed a trend similar to SOC concentration.

5.4.2 Objective 2: Assess spatial variability in SOC pool across a heterogeneous urban residential block

Table 5.1 summarizes the variations in parameters analyzed. The C concentrations and pools are more variable than pH and bulk density across this landscape. At two sampling locations the bulk density could not be determined as the physical nature of the soil did not provide intact soil cores. The statistical analysis between the various groups is presented in Table 5.2. Soil bulk density did not differ between the groups. Turfgrass areas with slope had significantly low SOC concentration than to the flat turf areas and those covered with other ground cover (Figure 5.5 A), but their SOC pools were not significantly different. When the turfgrass covered soils with or without slope were grouped together, they had significantly lower SOC concentration than the areas covered with ground cover (Figure 5.5 B). Sloped turf had 11.7 ± 1.0 Mg ha-1 less SOC pool than

to the flat turf areas in the 0-12 cm soil depth (Figure 5.5 C).

112 5.4.3. Objective 3: Determine variability in SOC pool at a regional scale in turfgrass lawns

Table 5.3 summarizes the variations in the parameters analyzed in lawns located in

Wayne and Holmes counties in Ohio. The C concentration and pool was the most variable parameter. At this regional scale, the sand and silt concentrations were more variable than to the clay content. The variability in soil pH was lower than all the other parameters except for soil clay content and was similar at all depths. The soil bulk density was more variable at the top 0-3 cm depth (CV = 24.1). However the SOC concentrations and pools were more variable as the depth from the soil surface increased.

This study provides basic information on heterogeneity of SOC pools in urban lawns. In our study of baseline SOC pools we found that the soil pH did not differ much with treatments, however the C concentrations and pools were significantly different with each treatment. This could be one of the reasons that can explain the wide variability in the C pools in urban lawns at the regional level. Lawns established on topsoil and subsoil will have different SOC pools to begin with and these differences may remain for a long period.

Soil pH was more variable across a residential block than to the various homelawns across a region. Soil bulk density was less variable when a composite soil

corer was used to assess the bulk density for a complete depth of 0-12 cm than to specific

depth assessment in homelawns. Variability in SOC across a residential block in City of

Wooster was greater than the lawns in Wayne and Holmes Counties. Pouyat et al. (2003)

found that at regional and global scale the urban landscape might be similar in vegetation

113 and soil and be variable at the smaller scales. Our results support this study for SOC

pools in urban lawn soils.

Considering the average SOC pool of 25 Mg ha-1in the 0-12 cm depth and the turf

area provided by Milesi et al. (2005) for Ohio, the total SOC pool in Ohio turfgrass soil

was computed to be 16.8 Tg. This is 0.03 percent of the estimated 59.4 petagram (Pg)

SOC pool in the conterminous 48 states.

Urban land is established on soils that have been exposed to wide changes in

establishment and management practices. It is further managed at various small scales by

land managers. At these small scales there can be wide variability in plants and other

biota that lives there, and the degree of anthropogenic influence. These can have impact

on the biogeochemical cycling at these small scales. Together all these can have a long

term impact on the SOC pools. Models used for predicting the biogeochemical cycles in

urban areas should take into account these variations.

Over the next 50 years C sinks such as undisturbed soils in urban lawns that have

the potential to sequester C, could have a significant impact on reducing atmospheric CO2.

Increasing SOC pools in urban lawns can be an important strategy to reduce atmospheric

CO2 at present, until much advanced technologies can help in reducing this pollutant.

Land managers can keep an inventory by monitoring SOC changes in these lawns with some economic investment. This can help to strengthen the database required for modeling or analyzing changes in SOC pools over time. As these lawns are a major part of the people’s lives in urban areas, they should be used to their maximum potential as C sinks.

114 5.5 Acknowledgements

This research was funded by the Center for Urban Environment and Economic

Development of the Ohio State University. We would also like to thank Zhiqiang Cheng,

and members of the lab for establishment of the research plots and in soil sampling.

5.6 Literature cited

Blake, G.R., and K.H. Hartge. 1986. Bulk Density. p. 363-375. In A. Klute (ed.) Methods of Soil Analysis, Part I. 2nd ed. Agronomy Monographs 9. ASA and SSSA, Madison, WI.

Cogger C. G. 2006. Potential compost benefits for restoration of soils disturbed by urban development compost science & utilization. 13: 4, 243-251.

Gajda, A.M., J.W. Doran, T.A. Kettler, B.J. Wienhold, J.L. Pikul, Jr., and C.A. Cambardella. 2001. Soil quality evaluations of alternative and conventional management systems in the Great Plains. p. 381-400. In R. Lal, J.M. Kimble, R.F. Follet and B. A. Stewart (ed.) Assessment methods for soil carbon. Lewis publishers, Boca Raton, FL.

Gebolys, D. 2006.10 years and still sprawling. The Columbus Dispatch. Retrieved June 20, 2007, from http://columbusretrometro.typepad.com/columbus_retrometro/urbanism/index.ht ml

Kettler, T.A., J.W. Doran., and T.L. Gilbert. 2001. Simplified method for soil particle- size determination to accompany soil-quality analyses. J. Soil Sci. Soc. Am. 65:849-852.

Minitab Inc. 2003. Meet MINITAB Release 14 for Windows. Minitab Inc., State College, PA.

Penizek V., and M., Rohoskova. 2006. Urban soils: a part of man's environment. p 213- 220. In K.C. Donnelly and L. H. Cizmas (eds.). Environmental health in Central and Eastern Europe. Springer. Netherlands.

115 Pouyat R.V., J. Russell-Anelli, I.D. Yesilonis, P.M. Groffman. 2003. Soil carbon in urban forest ecosystems. In: J.M. Kimble, L.S. Heath, R.A. Birdsey, R. Lal (eds) The potential of U.S. forest soils to sequester carbon and mitigate the greenhouse effect. CRC Press, Boca Raton, FL, pp 347-362.

SAS Institute. 2004. The SAS system for Windows. Release 9.1. SAS Inst., Cary, NC.

Simmons P.A. 1997.Housing statistics of the United States. Lanham: Bernan Press.

116

Variable N Mean CV (%) Minimum Maximum pH 46 6.34 13.5 4.5 7.9

BD (Mg m-3) 44 1.2 14.1 0.62 1.34

C Concentration (g kg-1) 46 36.1 30.7 19.0 63.6

SOC Pool (Mg ha-1) 44 46.9 32.6 22.8 99.2

Table 5.1: Mean and range of soil parameters across a heterogeneous urban residential block in Wooster, Ohio. Abbreviations: BD, Bulk Density; C, Carbon; CV, Coefficient of variation; N, Number of observations; SOC, Soil organic carbon.

SOC Concentration (%) SOC Pool (Mg ha-1)

Turf, Ground cover 0.007 (0.6) 0.46 Flat turf, Slope turf, Ground cover 0.003 (0.78) 0.17 Flat turf, Slope turf, 0.012 (0.71) 0.007 (8.3) Flat turf, Ground cover 0.10 0.9 Slope turf, Ground cover 0.001 (0.89) 0.14

Table 5.2: P values and least significant differences (LSD) at p<0.05 (within brackets) indicating the effect of heterogeneity in slope and vegetation on carbon pools and concentrations.

117

Variable N Mean CV (%) Minimum Maximum

Texture SAND (%) 29 25.7 20.17 15 37 SILT (%) 29 12.5 18.64 8 18 CLAY (%) 29 61.8 8.85 5 71

pH 0-3 cm 40 5.8 10.6 4.4 7.4 3-6 cm 40 6.0 11.6 4.5 7.6 6-9 cm 40 6.2 11.3 4.6 7.7 9-12 cm 40 6.4 11.2 4.7 7.8

BD (Mg m-3) 0-3 cm 30 0.87 24.1 0.41 1.25 3-6 cm 30 1.13 12.7 0.89 1.54 6-9 cm 30 1.33 15.0 0.93 1.72 9-12 cm 30 1.33 13.1 0.95 1.61

C Concentration (g kg-1) 0-3 cm 40 36.6 33.9 16.1 93.4 3-6 cm 40 19.8 39.1 10.4 47.9 6-9 cm 40 14.7 50.9 5.5 47.5 9-12 cm 40 12.7 64.6 4.4 43.8

SOC Pool (Mg ha-1) 0-3 cm 30 9.1 26.0 3.8 14.2 3-6 cm 30 6.4 27.5 3.4 10.7 6-9 cm 30 5.4 29.8 2.7 10.7 9-12 cm 30 5.1 55.4 2.6 15.3 0-12 cm 30 25.3 26.2 15.7 43.6

Table 5.3: Mean and range of soil parameters in urban home lawns of Wayne and Holmes Counties, Ohio. Abbreviations: BD, Bulk Density; C, Carbon; ; CV, Coefficient of variation; N, Number of observations; SOC, Soil organic carbon.

118 119

Figure 5.1 Map of sampling area located in the City of Wooster, Ohio.

3.0 Top Soil

) Top Soil + Compost -3 2.5 Sub Soil Sub Soil + Compost 2.0 a 1.5 c b b 1.0

0.5 Soil Bulk Density (Mg m BulkSoil (Mg Density 0.0

Figure 5.2. Initial soil bulk density (Mg m-3) in turfgrass plots established with top soil or sub soil with or without compost amendment.

120 6 Top Soil Top Soil + Compost 5 Sub Soil a Sub Soil + Compost

4 b 3 c 2

1 d

0 Soil Organic Carbon Concentration (%) Concentration Carbon Organic Soil

Figure 5.3. Initial soil organic carbon concentration in turfgrass plots established with top soil or sub soil with or without compost amendment.

121 60 ) Top Soil -1 Top Soil + Compost 50 Sub Soil a Sub Soil + Compost 40 b

30 c

20 d 10

Soil Organic Carbon (Mg ha Carbon(Mg OrganicSoil 0

Figure 5.4. Initial soil organic carbon pool (Mg ha-1)in turfgrass plots established with top soil or sub soil with or without compost amendment.

122 A ) -1 50 a Ground Cover 45 Flat turf 40 a Sloped turf 35 30 b 25 20 15 10 5 0

Soil organic carbon concentration(g kg B ) -1 50 a Ground Cover 45 Turf 40 b 35 30 25 20 15 10 5 0

Soil organic carbon concentration (g kg (g concentration carbon Soil organic

60 )

-1 Plain turf 55 a Slope turf 50 45 40 b 35 30 25 20 15 10 5 Soilorganic carbon pool (Mg ha 0

Figure 5.5. Soil organic carbon concentration and pool across a heterogeneous urban residential block in Wooster, Ohio. A. Soil organic carbon concentration in three ecotypes grouped by slope and plant cover, B. Soil organic carbon concentration in two ecotypes grouped by plant cover, and C. Soil organic carbon pool in two ecotypes grouped by slope.

123 CHAPTER 6

SYNTHESIS AND FUTURE DIRECTIONS

6.1 Synthesis and future directions

Our research was an early field scale study based on some existing information (Qian and

Follet, 2002) to obtain information, and identify opportunities for further large scale

research on turfgrass soil carbon (C) sequestration. Turfgrass systems are characterized

by absence of physical disturbance, drastic changes in soil physicochemical parameters

such as soil moisture and temperature regimes, and by consistent addition of biomass

through mowing and root growth have the potential to enhance soil quality and C

sequestration.

Along with assessing soil organic carbon (SOC) pools in urban lawn soils, we

computed C emissions associated with turfgrass management inputs. The 3-6 cm soil

depth was influenced by treatment inputs as well as plant cover in turfgrass systems.

Results for the top 0-12 cm soil depth were not influenced by nitrogen fertilization.

Because of the carbon cost by the use of fossil fuel in the production of nitrogen fertilizer

and pesticides, their use had a negative effect on C sequestration thus reducing the sustainability of the lawns that were intensively managed. From our results we found that plant diversity, and organic management programs without herbicides and returning 124 clippings are more sustainable than mineral management programs with application of

herbicides for soil carbon sequestration (chapter 2 and 3).

Our results from chapter 4 suggest that specific combinations of grass and

endophyte genotypes should be selected that can enhance grass fitness and plant biomass.

Unique interactions between the genotypes resulting in unique physiologies especially

with regard to alkaloid production may lead to differences in C sequestration. Currently little research have focused on studying the role of microorganisms (bacterial and fungal) on C sequestration in agricultural systems (Balser, 2005; Six et al., 2006). The role of combination of grass species and endophyte infection on SOC pools has been explored only in pasture type tall fescue (Franzluebbers et al., 1999; Franzluebbers and

Stuedemann, 2005; Franzluebbers and Hill, 2005). Addition of fertilizer N was important in influencing SOC pool in endophyte infected grasses. This area of grass species selection and the interaction of grass and microbes will require further research. In this study (chapter 4) we found a significant correlation between soil moisture and soil surface CO2 flux. Turfgrass management programs include irrigation of urban lawns.

Further research on CO2 flux associated with frequent irrigation should be evaluated.

Before designing an urban landscape it is important to collect all the basic

information about the soil that is going to support the turfgrass landscape. We have

shown that high spatial variability exists in SOC pools in an urban landscape and in urban

lawns in a region (chapter 5). It is important to account for this while using management

inputs and using models to predict SOC storage in urban soils. We recommend that

urban landscapes be assessed on a site per site basis for cultural input requirements.

125 Since these soils are anthropogenically altered during construction, the physical, chemical, and biological properties of the soil should be understood before implementing management programs. This can be an expertise intensive, time consuming, and expensive process but can have great benefits in terms of sustainability of urban landscapes in the long run. It can help to identify specific soil conditions and problems that can be solved before designing the lawn landscape. The questions to be answered would be concerning the overall health of the anthropogenically altered soil, its capacity to provide moisture and nutrients, and support plant and microbial growth and activity and thus enhance SOC pool. With this information turfgrass established on SOC depleted subsoil (chapter 5) can be managed to enhance SOC sequestration through adoption of various management practices such as selection of grass spices, and allowing establishment of plants with greater biomass production. Incorporation of this strategy may cost additional revenue but considering that the urban lawn care industry is a multi billion dollar industry, this strategy will save revenue in terms of inputs that may not be necessary while enhancing the urban environment.

Overall, we think that there should be transfer of energy and nutrients within the various components of the landscape such that it becomes self sustainable and does not require external inputs of fertilizers and pesticides. In an urban setting a landscape is mainly used for aesthetic and recreational purposes, but specific set of ecological objectives and goals should be formulated and the design should meet those objectives.

The overall goal should be to make the landscape self sustainable. The specific objectives could be to reduce energy use, minimize waste, recycle waste within the

126 landscape, prevent pollution (air, water, and soil), increase fauna and flora biodiversity,

increase soil C pools, and create aesthetically pleasing and educational landscapes for the

benefit of residents. These objectives can be met by designing appropriate management

programs, development of a holistic approach to sustainable turfgrass management, and

creation of an ecological landscape in turfgrass systems. This will not only be

environmentally beneficial but an economically cost effective strategy for reducing

atmospheric carbon dioxide.

Further, we recommend research in the following areas to enhance C sink capacity in urban lawns,

¾ evaluate the net ecosystem production associated with monoculture of

grass species, and with species rich and diverse turfgrass systems,

¾ evaluate the residence time of SOC,

¾ evaluate the chemical recalcitrance of SOC in urban lawns,

¾ evaluate methods to protect C through various bio physical mechanisms,

¾ evaluate losses of CO2 and other green house gases associated with

application of organic and mineral inputs to urban landscape

¾ controlled experiments to evaluate C sequestration with various

management programs under increasing atmospheric CO2

¾ dynamics of root derived carbon

¾ model predictions using the data from our results

¾ evaluation of organic and mineral management programs for C pools and

fluxes and other green house gases such as methane, and NOx.

127 6.2 Literature cited

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Qian, Y. and R.F Follet. 2002. Assessing soil carbon sequestration in turfgrass systems using long-term soil testing data. Agron. J. 94:930-935.

Six, J., S. D. Frey, R. K. Thiet, and K. M. Batten. 2006. Bacterial and Fungal contributions to carbon sequestration in agroecosystems. Soil Sci. Soc. Am. J. 70:555-569.

128

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