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This is to certify that the
dissertation entitled

ECOSYSTEM SERVICES FROM AGRICULTURE ACROSS A
MANAGEMENT INTENSITY GRADIENT IN SOUTHWEST
MICHIGAN

presented by
SARA FARR SYSWERDA

has been accepted towards fulfillment
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Doctor of degree in Crop and Soil Sciences and
Philosophy Ecology, Evolutionary Biology,
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ECOSYSTEM SERVICES FROM AGRICULTURE ACROSS A MANAGEMENT
INTENSITY GRADIENT IN SOUTHWEST MICHIGAN

By

Sara Parr Syswerda

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Crops and Soil Sciences,
Ecology, Evolutionary Biology, and Behavior

2009

ABSTRACT

ECOSYSTEM SERVICES FROM AGRICULTURE ACROSS A MANAGEMENT
INTENSITY GRADIENT IN SOUTHWEST MICHIGAN

By
Sara Parr Syswerda

I investigated how agricultural systems can be managed to minimize the
environmental impact of agriculture without sacrificing productivity. Agricultural
systems have a large impact on nutrient cycling, climate regulation, and fresh water and
food provisioning, and I have used nitrate leaching, drainage, carbon sequestration, soil
inorganic nitrogen, greenhouse gas fluxes, annual net primary productivity, and
agronomic yield across a management intensity gradient as a measure of these services.

Research was conducted at Kellogg Biological Station’s Long-Tenn Ecological
Research site in southwest Michigan. Treatments included four annual maize-soybean-
wheat rotations with conventional, no-till, reduced-input, and organic management; three
perennial systems in alfalfa, poplar, and conifers; and four native successional systems
ranging from early successional (~20 years since abandonment from agriculture) systems
to old growth forest. All systems were replicated in the landscape on the same soil series.

Nitrate leaching over an 11 year period ranged from less than 1 kg NO3'-N ha'ly'l
in poplars to 62 kg NO3'-N ha'ly'I in the conventional row-crop system. The no—till,
reduced input, and organic systems leached 34%, 61%, and 68% less nitrogen,
respectively, than did the conventional system. The alfalfa and conifer stands leached
nitrogen at rates similar to the organic system. The successional and poplar systems

leached the least amount of nitrogen. Drainage levels were highest in the no-till annual

system and deciduous forest. Our findings show that long-term water quality is
substantially affected by crops and management practices.

Soil carbon levels 11 years post-establishment differed substantially among
systems in the surface soil, where carbon contents were significantly greater than the
conventional system in the no-till, organic, alfalfa, poplar, conifer, early successional,
never tilled mid-successional, and deciduous forest systems. However, soil carbon levels
in the deeper portions of the soil profile were more variable, and showed very few
significant differences among treatments. Subsequently soil carbon levels in the total
profile to 1 meter differed little among treatments. Deeper soil carbon was much more
variable than carbon in upper soil layers, which suggests the need for more intensive
sampling than has been undertaken to date.

The additional measured ecosystem services also differed by management system.
As systems were managed more intensely, soil carbon levels decreased, nitrate leaching
increased, methane oxidation decreased, and nitrous oxide production increased. Trade-
off curves and flower diagrams provide a means to display these alternative services and
their effects. Analyses of multiple ecosystem services are a first step towards better
understanding the large scale trade-offs that occur with land management decisions.
Trade-offs in multiple ecosystem services can be used to help develop models
incorporating the complexity of the different components of the ecosystem. Future
research might focus on using such models to predict the outcome of individual

management decisions on the services delivered by managed systems.

Copyright by
SARA PARR SYSWERDA
2009

Dedicated to my husband, Tim, who has been so supportive of my educational efforts.
This is also dedicated to Cynthia Van Vleet, who instilled me with a love of science that

went far beyond anything I had imagined before.

ACKNOWLEDGEMENTS

My thesis could not have been completed without the patient help of many caring
individuals. I would first like to thank my advisor, Phil Robertson, who has provided
guidance and much needed advice over the past five years. I would also like to thank my
committee members, Steve Hamilton, Mike Klug, Al Smucker, and Scott Swinton. The
helped me improve my research, gave helpful advice on proposals, and made my
committee meetings quite enjoyable. I would also like to thank my collaborators on this
work, including Bruno Basso, Andrew Corbin, Sasha Kravchenko, Delbert Mokma, and
Jane Tausig. Without them, much of this work would not have been possible.

I would also like to thank the many people who have helped me with both
laboratory analyses and field work. Two undergraduates, Matt Gorentz and Cole Smith,
spent countless hours helping me with my research. Stacey Vanderwulp, Cathy McMinn,
and the entire Kellogg Biological Station Long—Term Ecological Research Site field crew
helped in the collection and analysis of much of the data that is shown in this work. I
would like to particularly thank our data manager, Sven Bohm, for his help, and to our
agronomic staff, especially Joe Simmons and Greg Parker, for their help with field
operations. I would like to thank Barb Fox with all of her help with all sorts of problems.

I am extremely thankful for all the wonderful and supportive people who have
given me help and advice over the past five years. I would like to particularly thank
Claire McSwiney, who has been particularly helpful with my experimental designs and
protocols. I would like to thank my fellow graduate students, including Terry Loecke,
Stuart Grandy, Sigrid Smith, Todd Robinson, and Brook Wilke for their advice, support,

and many laughs during my stay at KBS.

vi

I could not have conducted my work without the help of a wonderfully skillful
and able support staff. I would like to thank Sally Shaw, Alice Gillespie, and Jenny Smith
for all their help navigating the administrative world of Michigan State University. I
would also like to thank Nina Consolatti, John Gorentz, and Stephan Ozminski for their
help with both my teaching and research.

I could not have survived my graduate school experience without the love and
support of my family and friends. My husband Tim has been a constant rock during the
past few years, always making me feel better and offering help. My parents have been
very supportive, always offering me encouragement and motivation. My best friend
Cassie has always been there to listen, even when I’m sure she didn’t want to hear

anything more about nitrogen and carbon cycling.

vii

TABLE OF CONTENTS

LIST OF TABLES .................................................................................... x

LIST OF FIGURES ................................................................................. xii

KEY TO SYMBOLS AND ABBREVIATIONS .............................................. xiii

CHAPTER 1

INTRODUCTION .................................................................................... 1
Background ................................................................................... 1
Organization of the dissertation ............................................................ 2

CHAPTER 2

LONG-TERM NITRATE LOSS FROM NO-TILL, ORGANIC, BIOFUEL, AND
UNMANAGED ECOSYSTEMS.

Abstract ....................................................................................... 3

Introduction ................................................................................... 4

Materials and Methods ....................................................................... 8

Results ....................................................................................... 15

Discussion ................................................................................... 26
CHAPTER 3

ECOSYSTEM MANAGEMENT AND CARBON STORAGE IN DEEP SOIL
LAYERS.

Abstract ...................................................................................... 35

Introduction ................................................................................. 37

Materials and Methods ..................................................................... 39

Results ....................................................................................... 45

Discussion ................................................................................... 61
CHAPTER 4

TRADE-OFFS IN ECOSYSTEM SERVICES AS AFFECTED BY MANAGEMENT
INTENSITY IN AN AGRICULTURAL LANDSCAPE.

Abstract ...................................................................................... 69
Introduction ................................................................................. 70
Materials and Methods ..................................................................... 73
Results ....................................................................................... 78
Discussion ................................................................................... 89
CHAPTER 5
SUMMARY AND FUTURE DIRECTIONS
Summary ..................................................................................... 96
Future Directions ............................................................................ 96

viii

APPENDIX

Chapter 2 Supplementary Materials

Statistical Output for Nitrate Leaching Comparisons .................................. 98
Chapter 3 Supplementary Materials

Table of Coefficients of Variation for Bulk Density and Thickness ................. 99

Table of Coefficients of Variation in Percent and Total Carbon ................... 100
Chapter 4 Supplementary Materials

Statistical Output for Nitrous Oxide Comparisons ................................... 101

Statistical Output for Methane Comparisons .......................................... 102

Statistical Output for Soil Inorganic Nitrogen Comparisons ........................ 103

Statistical Output for Grain Yields ..................................................... 104
BIBLIOGRAPHY ................................................................................. 105

ix

LIST OF TABLES
CHAPTER 2

Table 2.1 Soil profile characteristics at the KBS LTER site. The dominant KBS soil
series are the Kalamazoo (fine-loamy, mixed, mesic Typic Hapludalfs) and Oshtemo
(coarse-loamy, mixed, mesic Typic Hapludalfs) series. Data are from Crum and Collins
(1995). Abbreviation nd denotes missing data ................................................... 9

Table 2.2 Management summaries for cropping systems and successional communities at
Kellogg Biological Station Long-Term Ecological Research Site ........................... 12

Table 2.3 Nitrogen fertilizer applications to the annual grain crops. Detailed information
about annual applications can be found at http://lter.kbs.msu.edu. Lime, N, P, and K were
applied as needed according to Michigan State University Soil Testing Lab
recommendations .................................................................................... 13

Table 2.4 Precipitation and crop timing by cropping year in the annual treatments. The
planting date is when the particular crop was planted (or in the case of 1995, when
observations began for that crop). The harvest date was when the crop was removed from
the field, and the duration is the number of days between the planting of the focal crop
until the next crop is planted ....................................................................... 19

Table 2.5 Nitrate leaching and drainage in the annual cropping systems, by crop. . . . . .....20

Table 2.6 Nitrate leaching in the in-season (when the crop is present) and off season (fallow
period) in the annual cropping systems, by crop, with rates of nitrate leaching per mm of

drainage ................................................................................................... 21
Table 2.7 Annual Drainage and Leaching in Perennial crops ................................. 22
Table 2.8 Nitrate leaching in the perennial crops in the summer growing season (May 1-
Oct. 1) and during the rest of the year ............................................................ 23
Table 2.9 Annual drainage and nitrate leaching in the unmanaged systems ................ 24
Table 2.10 Leaching in the unmanaged systems in the summer growing season (May 1-
Oct. 1) and during the rest of the year ............................................................ 25
CHAPTER 3

Table 3.1 Soil profile characteristics at the KBS LTER site. The dominant KBS soil
series are the Kalamazoo (fine-loamy, mixed, mesic Typic Hapludalfs) and Oshtemo
(coarse-loamy, mixed, mesic Typic Hapludalfs) series. Data are from Crum and Collins
(1995). Abbreviation nd denotes missing data .................................................. 41

Table 3.2 Management summaries for cropping systems and successional communities at
Kellogg Biological Station Long-Term Ecological Research Site ........................... 42

Table 3.3 Cropping system and successional community effects on soil horizon depth and
bulk density to 100 cm soil depth at Kellogg Biological Station Long-Term Ecological
Research Site in 2001. Values are given as mean (standard error). Replication is n=6 plots
for annual crops, alfalfa, poplar, and early successional communities; n=4 mid-
successional never-tilled (NT) sites, and n=3 sites for the conifers, mid-successional
historically tilled (HT) communities, and deciduous forest. Systems with different lower
case letters within columns are significantly different (p_<_ 0.05) ............................. 56

Table 3.4 Cropping system and successional community effects on soil carbon levels to
100 cm soil depth at KBS LTER site in 2001. Values are given as mean (standard
error) .................................................................................................. 58

Table 3.5 Cropping system and successional community effects on soil nitrogen
concentrations and C/N ratios to 100 cm soil depth at the KBS LTER site in 2001. Values

are given as mean (standard error) ................................................................ 59
CHAPTER 4
Table 4.1: Management summary for the KBS LTER treatments ............................ 75

Table 4.2 Ecosystem services in the KBS LTER Systems. Values are listed as mean
(standard error). Statistical significance is indicated by different letters within the same
column ................................................................................................ 81

Table 4.3 Correlation matrix for all measures of ecosystem services. Values are listed as
correlations and p-values ........................................................................... 82

APPENDIX

Table A.1 Coefficients of variation for bulk density and profile layer thickness in KBS
LTER treatments .................................................................................... 99

Table A2 Coefficients of Variation for carbon concentrations and total carbon in KBS
LTER treatments ................................................................................... 100

xi

LIST OF FIGURES

CHAPTER 2:

Figure 2.1 Cumulative drainage in all treatments from November 1995 to October 2006
in millimeters. Total precipitation for the period was 8985 millimeters during this

period ................................................................................................. 17
Figure 2.2 Cumulative nitrate leaching in all treatments from November 1995 to October
2006 in kg No3: ha" ............................................................................... 18
CHAPTER 3:

Figure 3.1: Carbon concentrations in the A/Ap, B/Bt, and Bt2/C horizons in the KBS
LTER treatments (in g C kg soil"). Error bars represent standard errors. For significant
differences among treatments see Table 3.4 ..................................................... 57

Figure 3.2: Total carbon to 1 meter in KBS LTER treatments (in kg C m'z). Error bars
represent standard errors. For significant differences among treatments see Table 3.4. . .60

CHAPTER 4

Figure 4.1: Relationship between maize grain yield and nitrous oxide flux for nine levels
of N addition at the Kellogg Biological Station’s Nitrogen Rate Study (McSwiney and
Robertson 2005) ..................................................................................... 72

Figure 4.2 Relationship between net primary productivity and nitrous oxide fluxes on the
Kellogg Biological Station Long Term Ecological Research Site. The curve represents a
best-fit quadratic equation y=2729.9 Ln(x) + 5329.6 (r2=0.603) ............................. 83

Figure 4.3 Relationship between nitrate leaching and net primary productivity at the KBS
LTER Site. The line describes the regression equation y = 0.0682x -— 302.62 (1'2 =
0.469) ................................................................................................. 84

Figure 4.4 Relationship between grain yield and nitrate leaching at the KBS LTER Site.
The best-fit curve is described by the quadratic equation y2 = 16833657 + -3.29x'2 ( r2 =
0.709) ................................................................................................. 85

Figure 4.5 a. Conventional, b. No-till, c Reduced Input, and (1. Organic Systems. Each
flower represents the bundle of ecosystem services provided by each system relative to
the maximum capacity for each service at the KBS LTER site .............................. 87

Figure 4.6 a Alfalfa, b. Poplar, c. Early Successional, and d. Deciduous Forest System.

Each flower represents the bundle of ecosystem services provided by each system relative
to the maximmn capacity for each service at the KBS LTER site ........................... 88

xii

KEY TO SYMBOLS AND ABBREVIATIONS

 

 

Abbreviation Definition
CH4 Methane, a greenhouse gas
KBS Kellogg Biological Station
LTER Long-Term Ecological Research Station
N20 Nitrous Oxide, a greenhouse gas
NO3' Nitrate, an anion, a form of nitrogen that is in the soil
NPP Net Primary Productivity, the amount of biomass that is produced

afier the organism uses the energy necessary for cellular
respiration and maintenance.

xiii

CHAPTER 1
INTRODUCTION
Background

Agriculture provides ecosystem services that are critical for human health and
welfare. Ecosystem services have been defined in numerous ways, but are generally
considered ecological outcomes that benefit humans (Ecological Society of America
1997, Daily 1997). The challenge for ecosystem managers and policy makers is to
identify those services that are most important, to measure them, to systematically value
them, and to then design and implement a management plan to maximize their delivery.

Ecosystem services from row crops are particularly important since these systems
dominate the landscape, provide important economic benefits, and have well-known
environmental costs. Row crops such as corn, soybean, and wheat are critical to our well-
being and provide most of the US food supply (FAO, 2003). American row crops also
use large amounts of energy and have high levels of chemical consumption and release,
with concomitant environmental impacts that are both regional and global in scope
(V itousek et al. 1997, Pimentel et al. 2005, Swinton et al. 2007).

In part the environmental costs of agriculture are high because agricultural
systems tend to be managed for a single ecosystem service—marketable yields. However,
agricultural systems also provide other services that are valued by humans and important
to the functioning of nearby ecosystems, including provisioning services such as food and
fiber, regulating services such as climate regulation and water purification, cultural
services such as aesthetic beauty and recreation, and supporting services such as nutrient

cycling and soil formation (Robertson and Swinton 2005, Swinton et al. 2007, Millenium

Ecosystem Assessment 2005. In this dissertation, I examine tradeoffs among important
ecosystem services in row crop agriculture in order to provide better knowledge for
policy and farm level decision making.

My overall objective is to investigate how agricultural systems can be managed to
minimize the environmental impact of agriculture without sacrificing productivity—or
conversely, to maximize the ecosystem services provided by agriculture, including
productivity. Agricultural systems have a large impact on nutrient cycling, climate
regulation, and fresh water and food provisioning, so I have chosen to focus on these

services.

Organization of the dissertation

Chapter 2: In collaboration with Bruno Basso, Stephen Hamilton, Jane Tausig,
and G. Philip Robertson, I combined measured values of nitrate concentrations in soil
water with modeled water flux data to estimate nitrate leaching losses across a
management intensity gradient. My findings show that nitrate leaching can change
dramatically under different kinds of management. The poplar trees showed the lowest
leaching losses, while the conventional agricultural system showed the highest losses.
Changes in leaching levels were related to both the amount of drainage going through

each system, as well as the level of nitrogen fertilizer application.

Chapter 3: In collaboration with Andrew Corbin, Delbert Mokma, Alexandra N.
Kravchenko, and G. Philip Robertson, I examined carbon levels in deep soil layers across

a management intensity gradient. My findings show that carbon levels are more variable

at depth than in the surface. Carbon levels to 1 meter were lowest in the reduced-input,
conventional, and poplar systems, and highest in the early successional system and
alfalfa. Our results at the surface were similar to those in other studies at our site, but
differences between treatments were less clear when considering the entire profile than
when considering only the surface horizon.

Chapter 4: In collaboration with G. Philip Robertson, I examined trade-offs
between a variety of ecosystem services across a management intensity gradient. These
included soil quality, water quality, climate regulation, and productivity. My findings
show that while some systems are better for grain production, others are preferable for
other ecosystem services, such as water quality and climate regulation. These findings
suggest that many ecosystem services are produced in tandem, and by impacting the
production of one service (for example, climate regulation), you may also be impacting

the production of several other ecosystem services (for example, water quality).

CHAPTER 2
LONG-TERM NITRATE LOSS FROM NO-TILL, ORGANIC, BIOFUEL, AND

UNMANAGED ECOSYSTEMS.

ABSTRACT

Nitrate loss from intensively farmed cropland in the US Midwest is a long-
standing, recalcitrant environmental problem that contributes to surface and groundwater
pollution and coastal hypoxia. Efforts to identify cropping practices and systems that are
productive and nitrate conservative have been stymied by a lack of information from
well-equilbrated and appropriately replicated long-term experiments. Here we report
nitrate leaching from ten replicated systems along a management intensity gradient in
southwest Michigan, USA. Our systems include four annual com-soybean-wheat
rotations, three perennial crops (alfalfa, poplar trees, and conifer stands), and three
unmanaged successional communities, including an early successional community
analogous to a cellulosic biofuel system and a mature Eastern Deciduous forest.
Measured nitrate concentrations were combined with modeled soil water export to
provide estimates of nitrate leached over 11 years of cropping. Nitrate leached ranged
from less than 1 kg NO3'-N ha'ly'l in poplars to 62 kg NO3’-N ha'ly'l in conventionally
managed corn-soybean-wheat. Among annual row crops, average leaching losses
followed the order conventional management > no-till > low-input > biologically based
organic management. The alfalfa and conifers leached less than most row-crop systems,
and the successional and poplar systems leached least. Our findings suggest that nitrate

leaching could be altered substantially by substituting biological for chemical inputs in

annual grain crops and in biofuel systems by moving from grain-based to cellulosic-based
feedstocks.
INTRODUCTION

Agriculture is the major contributor to reactive nitrogen levels in the biosphere,
and reducing nitrogen loss from agricultural ecosystems is a longstanding environmental
priority (Copeland 2000, Gruber and Galloway 2008). Agricultural nitrogen is derived
from a variety of sources, but primarily from inorganic fertilizer, animal waste, and
biological nitrogen fixation in row crops. Most crops take up only about 50% of nitrogen
applied (Robertson 1997), leaving the remainder available for loss to the larger
environment, including leaching loss to groundwater (Fenn et al. 1998, Sanchez et al.
2004, Basso and Ritchie 2005).

Nitrogen is leached from soils primarily in the form of nitrate, and in agricultural
regions, nitrate often reaches high concentrations in ground water and groundwater-fed
surface waters. Excessive nitrate can affect human health when ingested in drinking
water; potential effects include infant methemoglobinemia (blue baby syndrome), cancer,
and gastroenteritis (McCasland et al.1998). Leached nitrate can also cause eutrophication
and associated algal blooms in some surface waters, which can kill fish and other wildlife
(Buck et al. 1997, F rankenberger and Turco 2003) and promote the invasion of exotic
species (V itousek et al. 1997). Moreover, nitrate leached can be incompletely denitrified
in soils, groundwaters or surface waters, resulting in partial conversion to nitrous oxide
(Mulholland et al. 2008, Burgin and Hamilton 2007), an important greenhouse gas. Once

nitrate reaches coastal areas it can contribute to marine hypoxia (NRC 2003, Rabelais et

al. 2001). Costs to mitigate U.S. water quality impairment due to nitrate contamination
have been estimated in the tens of billions of dollars (Ribaudo 2003).

Estimates of nitrate loss from different row crops in the US. vary widely, with
reported values ranging from 25-146 kg N ha'lyr”l in grain and forage systems (Fox et al.
2001, Power et al. 2001 , and Basso and Ritchie 2005). To date, apart fi'om a well-
documented positive relationship between the rate of N applied and the rate of N loss
(e. g. , Groffrnan et al. 1986, Andraski et al. 2000) consistent management-related patterns
in nitrate leaching rates have been hard to detect. For example, comparisons of organic
and conventional systems have shown that organically managed system can leach greater
(Pimental 2005, Basso and Ritchie 2005), similar (Kirchmann and Bergstrom 2001), or
less (Hansen et al. 2001, Kramer et a1. 2006, Drinkwater et al. 1998, Martin et al. 2006)
nitrogen as compared to conventional systems. Likewise, comparisons of no-till and
conventional tillage systems have shown no differences (e. g. Cabrera et al. 1999, Mitsch
et al., 1999; Smith et al., 1990), lower (e. g. Stinner et al. 1979), or higher (Kanwar et al.

1988, Tyler and Thomas 1977, Chichester 1977) rates of leaching.

This ambiguity may have two sources, the first related to management issues.
Many times the cropping systems compared are not on the same rotation, receive
different levels and/or types of nitrogen inputs, and receive different levels of mechanical
tillage (Kirchmann and Bergstrom 2001). Different crop rotations imply different harvest
and planting regimes, fertilization schedules, and tillage patterns, all of which can affect
N use efficiency differently. The second source of ambiguity may be related to
experiment duration. Many studies last only 2-3 years, and often begin shortly after

treatment establishment. During short-terrn experiments, modest variation in interannual

rainfall can mask long—term leaching differences if, for example, systems don’t differ
during periods of low rainfall differ greatly when rainfall is abundant (e. g. Cabrera et al.
1999). Additionally, prior to equilibration it is difficult to know whether even
consistently different patterns will be maintained in the long-term (Rasmussen et al.
1998). Moreover, most studies have been performed in small plots, which cannot readily
account for the effects of spatial variation present at the field scale (Robertson et al.
2007)

Here we report the results of a comprehensive study of the effect of management
intensity on N leaching that avoids many of these shortcomings. This study compares 10
different replicated systems that include annual grain crops (tilled vs. no-till, organic vs.
conventional), perennial crops (alfalfa, poplar, and conifer trees), and unmanaged
communities in different stages of ecological succession (fiom recently abandoned crop
fields to late successional forest). The successional systems provide a reference for
assessing current vs. historical trends of N loss, and for judging the impact of converting
cropland to biofiiel-successional or Conservation Reserve Program (CRP) management.
All of our annual crop systems are on the same rotation to avoid confounding rotation
effects, and in the organic system biological nitrogen fixation provides the only external
nitrogen addition. We sampled these systems for 11 years following an establishment
period of greater than 6 years. Our cropping systems were established in large, one
hectare plots that allowed the use of commercial-scale equipment to simulate on-farm

conditions.

MATERIALS AND METHODS

Site Description

We compared nitrate leaching from a field experiment that was established at the
Kellogg Biological Station (KBS) in 1988. Multiple treatments at the KBS Long-Term
Ecological Research Site (LTER, ww.lter.l_@s.msu.edu) form a management intensity
gradient. KBS is located in SW Michigan, within the northern boundary of the US. corn
belt (85° 24'W, 42° 24'N). The site lies on Kalamazoo (fine loamy) and Oshtemo (coarse
loamy) soils, both mixed, mesic Typic Hapludalfs, which mainly differ in the thickness of
the Bt horizon. The Kalamazoo has a thicker upper Bt horizon than the Oshtemo and
therefore has a slightly greater water holding capacity (Table 2.1). Annual rainfall at KBS
is 920 mm yr'l, distributed evenly through the year. The water holding capacity of the

soil is approximately 150 mm to 1.5 m depth (Crum and Collins 1999, unpublished data).

Experimental Design

Seven of our ten experimental treatments were established in 1989 in replicated 1-
ha plots organized in a complete randomized block design (n=6). Three treatments (mid-
successional field, old-growth deciduous forest, and coniferous forest) had already been
in existence nearby (within 3 km radius) on the same soil series, and experimental plots
were established in these systems as well (n=3). Maps of sites and experimental design
are available at www.1ter.kbs.msu.edu.

Three low tension lysimeters were installed in all treatments in each of three
blocks (10 treatments x 3 blocks x 3 lysimeters = 90 lysimeters total). Cropping systems

include maize (Zea mays)-soybean (Glycine max)-wheat (Triticum spp.) rotations

Table 2.]. Soil profile characteristics at the KBS LTER site. The dominant KBS soil
series are the Kalamazoo (fine-loamy, mixed, mesic Typic Hapludalfs) and Oshtemo
(coarse-loamy, mixed, mesic Typic Hapludalfs) series. Data are fiom Crum and Collins
(1995). Abbreviation nd denotes missing data.

 

 

Horizon Depth Texture Bulk pH
Density
Sand Silt Clay
(gkg‘b (mg mi)

Kalamazoo Series

Ap 0-30 43 38 19 1.6 5.5
B 30-41 39 41 20 1 .7 5 .7
Btl 41-69 48 23 29 1.8 5.3
ZBt2 69-88 79 4 l 7 nd 5.2
2E/Bt 88-152 93 0 7 nd 5.6

Oshtemo Series

Ap 0-25 59 27 14 1.6 5.7
B 25-41 64 22 14 1.7 5.7
Btl 41-57 67 13 20 1.8 5.8
ZBt2 57-97 83 4 13 nd 5.8
2E/Bt 97-152 92 0 8 nd 6.0

 

managed either i) with conventional inputs and tillage, ii) with conventional inputs and
no tillage, iii) with reduced chemical inputs and tillage, or iv) organically with no
chemical inputs and tillage. The latter two treatments include a leguminous winter cover
crop grown following the maize and wheat portions of the rotation to provide N to the
subsequent grain crops. All cropping systems were planted and harvested during the same
periods according to best management practices for each system. Fertilizer application
rates for the conventional input systems were based on soil-test recommendations and are
shown in Table 2.3.

From 1989 to 1992, the conventional till and no-till systems were on a maize-
soybean rotation, and the reduced-input and organic systems were on a maize-soybean-
wheat rotation. Since 1993, all four of the annual grain crops have been in the same
maize-soybean-wheat rotation. The conventional, reduced-input, and organic systems
received primary tillage, which consisted of moldboard plowing in the spring from 1989
to 1998 and chisel plowing in the spring from 1999 onward. Secondary tillage consisted
of disking before wheat planting, field conditioning with a soil finisher prior to soybean
and maize planting, and inter-row cultivation for soybean and maize. The reduced input
and organic systems received additional inter-row cultivation and rotary hoeing as needed
for weed control.

The three perennial systems included alfalfa (Medicago sativa), fast growing
clonal poplar trees (Populus xeuramericana), and conifer stands established c. 1950. The
alfalfa was harvested 3-4 times a year, and was reestablished once during the study
period. Fertilizer (P, K, B, and lime) and pesticides were applied according to Michigan

State University Extension recommendations and soil test results. Poplar trees were

10

planted in 1989, and starter fertilizer (only) was added at that time. Creeping red fescue
was used as a cover crop to prevent soil erosion. Poplar trees were harvested in 1999, and
allowed to coppice (regrow from the cut stems).The conifer stands were 50-70 years old
at the time of this study and were mixed stands containing Red and White Pine (Pinus
resinosa and strobes), Norway spruce (Picea abies), and some understory Black Cherry
(Prunus serotina); herbaceous ground cover was generally sparser under the conifers
than in the other forest treatments.

The three unmanaged successional systems included: 1) an early successional
system that was abandoned from agriculture in 1989, 2) a mid-successional system that
was released from agriculture in the 1950’s, and 3) a late-successional oak-hickory forest
that has never been cleared or plowed. The early successional system has been burned

annually in the spring since 1997 to prevent tree colonization.

Sampling Protocols

Nitrate was sampled in water that leached from these systems using quartz/PTFE
tension samplers (Prenart; Frederiksburg, Denmark) installed in 1995 at a depth of 1.2 m,
approximately 20cm into unconsolidated sand of the 2Bt2 horizon. Sampled water is thus
presumed to represent water that would otherwise freely leave the soil profile. The three
soil water samplers per plot were installed by coring at an angle in locations 3 meters
apart and at least 10 meters from the plot edge. Each soil water sampler was sampled by
applying 0.5 atrn of vacuum for 24 hours, during which time leachate was collected in a
clean flask. Samples were filtered through Pall Type A/E glass fiber filters (East Hills,

NY) and then frozen until analysis. Samples were collected every two weeks April

11

Table 2.2. Management summaries for cropping systems and successional communities
at Kellogg Biological Station Long-Tenn Ecological Research Site.

 

 

 

Tillage Nitrogen Fertilizer“ Weed Control
Annial Crops (M_aize. Soybean; Whth Rotation)
Conventional Conventional Conventional Chemical and
mechanical
No-Till None Conventional Chemical
Reduced Input Conventional 1/3 Conventional 1/3 Chemical and
with cover crop mechanical
Organic Conventional Cover crop Mechanical
Perennial Crops
Alfalfa None None None
Poplar None Starter1 None
Conifers None None None
Unmanaged Communities
Early Successional None None None
Mid-Successional None None None
Deciduous Forest None None None

 

* see Table 2 for Nitrogen Fertilizer Application Rates

' 60 kg N ha" in 1989 only.

12

Table 2.3. Nitrogen fertilizer applications to the annual grain crops. Detailed information
about annual applications can be found at http://lter.kbs.msu.edu. Lime, N, P, and K were
applied as needed according to Michigan State University Soil Testing Lab
recommendations.

 

Cropping Year Conventional“ No-till“ Reduced Input Organic
kg N ha'I
1995-wheat 56 56 34 + covercrop covercrop
1996-maize 163 163 28 + covercrop covercrop
1997-soybeans 0 0 0 0
1998-wheat 56 56 28 + covercrop covercrop
1999-maize 163 163 28 + covercrop covercrop
2000-soybeans 0 0 0 0
2001-wheat 71 71 31 + covercrop covercrop
2002-maize 153 153 28 + covercrop covercrop
2003-soybeans O 0 0 0
2004-wheat 90 90 54 + covercrop covercrop
2005-rnaize 155 155 31 + covercrop covercrop
2006-soybeans 0 0 0 0

 

* MSU Soil Testing Lab recommended rates

13

through October and monthly otherwise, except when soil temperatures were below
freezing. Frozen samples were thawed and analyzed colorimetrically for nitrate on an OI
Analytical Flow Solution IV continuous flow analyzer (01 Analytical, College Station,
TX) with a detection limit of 0.02mg N/L for nitrate. All samples that were found to be
below detection limits were recorded as half the detection limit.

Nitrate concentrations were combined with modeled downward water flux to
provide estimates of nitrate leaching from the root zone. Water flux was modeled using
the Systems Approach for Land Use Sustainability (SALUS) model (Basso et al. 2006).
SALUS is designed to simulate continuous crop, soil, water, and nutrient conditions
under different management strategies for multiple years (Basso et al. 2006). SALUS
accommodates various crop rotations, planting dates, plant populations, irrigation and
fertilizer applications, and tillage practices, and simulates plant growth and soil
conditions every day during both growing seasons and fallow periods. For each
simulation, all major components of the crop-soil-water model are executed, including
management practices, water balance, soil organic matter change, nitrogen and
phosphorus dynamics, heat balance, plant growth, and plant development. For this study,
daily water fluxes were generated.

The SALUS water balance submodel considers surface runoff, infiltration, surface
evaporation, saturated and unsaturated water flow, drainage, root water uptake, soil
evaporation and transpiration (Ritchie and Basso 2008). The soil water balance module is
based on that used in the CERES models (Ritchie 1998) but incorporates a major revision
for calculating infiltration (Basso et al. 2008), soil water export (Suleiman and Ritchie

2004), evaporation (Suleiman and Ritchie 2003), and runoff (Basso et al. 2008). In

14

SALUS, a time-to—ponding (TP) concept is used to replace the previous rtmoff and
infiltration calculations that were based on SCS runoff curve numbers (Basso et al. 2008).
The simulation of soil water export produced by SALUS has been tested extensively at
KBS using monolith drainage lysimeters (Basso 2000, Basso and Ritchie 2005).

We combined measured nitrate concentration data with each treatrnent’s modeled
water flux to estimate total nitrate leaching over the period November 1995 to October
2006. We modeled water fluxes on a daily time step and interpolated daily nitrate
concentrations between lysirneter sample dates. Multiplying daily water flux by

interpolated nitrate concentrations provided a daily nitrate leaching value.

Statistical Analysis

The experiment was analyzed as a completely randomized design (CRD), with 10
treatments and three replicates of each treatment Data were log-transformed to provide a
more normal distribution and more homogeneous variance. Treatments were compared
with analysis of variance (AN OVA) of the 12 year cumulative leaching values for the 3
replicate blocks per treatment. All comparisons were completed using SAS (SAS Version

8.2, SAS Institute 1999).

RESULTS
Patterns of Nitrogen Loss
Total soil water export and associated nitrate leaching over the 11 years of this
experiment are summarized in Figures 1 and 2. The eleven year period corresponded to

three and a half full rotations of the annual crops. Soil water export rates were highest in

15

the coniferous forest, old-growth deciduous forest, and no-till system (Figure 2). Soil
water export from alfalfa was least.

Among the cropping systems, the conventional agricultural system leached the
most nitrate(685 kg NO3'N ha'l), while the organic system leached the least (209 kg NO3'
N ha'l). The no-till and reduced-input systems leached intermediate amounts (458 and
267 kg NO3'N ha", respectively). The perennial poplars leached less nitrate than the
annual systems (<1 kg NO3'N ha", respectively), while leaching in the alfalfa and
coniferous forest was similar to the organic annual treatment (190 kg NO3'N ha'l). The
unmanaged systems leached less nitrate than did the annual systems.

Rates of leaching (in kg“1 NO3'N mm”') were dramatically different for each of the
treatments (Tables 6, 7, and 8). We found the highest rates of leaching in the
conventional annual cropping system, where 186 g of NO3N were lost for each m of
exported soil water. All of the other annual cropping systems showed lower levels of
nitrate leaching, although rates were much higher than were rates in the unmanaged and
perennial systems.

In the annual crops (Table 6), the largest nitrate leaching losses occurred during
the fallow period after corn and prior to soybean. In the conventional system, 52% of all
nitrate lost (357 kg NO3'N ha") was lost during this period. The no-till, reduced-input,
and organic systems also experienced dramatic losses during the fallow period post-com,
contributing to 50, 46, and 42% of their leaching losses, respectively. Leaching losses

were lowest during soybean periods.

16

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Table 2.4. Precipitation and crop timing by cropping year in the annual treatments. The
planting date is when the particular crop was planted (or in the case of 1995, when
observations began for that crop). The harvest date was when the crop was removed from
the field, and the duration is the number of days between the planting of the focal crop
until the next crop is planted.

Crop Crop Planting Harvest Duration Growing Post- Total

Year Date Date Season Harvest Precip
Precip Precip

(daYS) (III!!!) (M) (In!!!)

 

1995 Post 1 1/1/1995 * 199 * 296 296
Wheat
1996 Maize 5/18/1996 10/31/1996 372 396 394 790
1997 Soybeans 5/25/ 1997 10/ 17/ 1997 159 405 28 433
1998 Wheat 10/3 1/ 1997 7/10/1998 560 502 519 1021
1999 Maize 5/ 14/1999 10/5/1999 391 265 468 734
2000 Soybeans 6/8/2000 10/11/2000 146 409 19 428
2001 Wheat 11/1/2000 7/16/2001 561 608 799 1407
2002 Maize 5/16/2002 10/28/2002 379 _ 412 390 802
2003 Soybeans 5/30/2003 10/6/2003 137 315 2 316
2004 Wheat 10/14/2003 7/ 12/2004 577 811 656 1467
2005 Maize 5/13/2005 10/10/2005 396 368 578 947
2006 Soybeans 6/13/2006 10/15/2006 135 422 88 509

 

*The experiment started after wheat had been harvested, so the first entry point is the
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DISCUSSION

Managing agricultural systems to reduce the amount of nitrate leached fiom these
systems could reduce their environmental impact. For example, highest nitrate losses
typically occur when soil nitrogen is available, water is plentiful, and plants are not
present or inactive in uptake of water or nitrogen. Agricultural practices that minimize
available nitrogen when water infiltrates through the soils would thus be expected to
leach less nitrogen.

Nitrate leaching varies both seasonally and throughout the rotation cycle of
cropping systems as a function of available nitrogen concentrations and soil water
mobility (Watts and Martin 1981, Martin et al. 1994, Rasse et al. 1999). Pimentel et al.
(2005), for example, showed that in manure-based and legume-based organic grain
systems nitrate concentration in leachate varied from 0-28 mg N03'-N L’lppm, and was
highest in June and July. Concentrations of nitrate were probably highest when soil
moisture levels were lowest.

However, nitrate leaching is a function of the concentration of nitrate in the soil
solution and the amount of water moving through the profile. Nitrate leaching should
increase during months when soil moisture is highest, since water is needed to carry the
nitrate through the profile. Nitrate leaching should also increase during the months when
plants are absent or inactive, since roots remove both water and nitrate from soil. Nitrate
leaching should thus increase during cool seaseons following nitrogen fixing crops or
crops that are highly fertilized, since this is when most infiltration occurs and leachable
nitrate exists in the soil.

General Patterns of Leaching

26

This study has shown that cropping systems receiving more intensive
management leach more nitrate into groundwater. The systems showing the greatest
amounts of leaching were the annual cropping systems. These systems have higher
nitrogen inputs than the other systems, and the conventional and no-till annual systems
also have longer fallow periods than many of the other systems. The early and mid-
successional and poplar systems showed the lowest amounts of leaching, mainly due to
the low concentrations of nitrate in the leachate.

Leaching rates ranged from less than 1 kg NO3'N ha"l yr'l in the poplars to 62 kg
N03N ha'l yr'l in the conventional cropping system, similar to rates reported in previous
studies of agricultural systems (Fox et al. 2001, Power et al. 2001, and Basso and Ritchie
2005). Soil water export values ranged fi'om 198 to 422 mm yr", representing 24 to 52
percent of precipitation. Previous studies in KBS agricultural systems had 31 and 36% of
precipitation lost as soil water export (Basso and Ritchie 2005 and Smeenk 2003
respectively). The leaching rate (nitrate lost per unit soil water export) of the systems
showed wide variation, from poplar’s losing virtually no nitrogen for each m of soil
water export (0.38 g N03N mm") to the conventional system’s losing far more (17.6 g
NO3'N mm'l). Most of the differences in the systems were attributable to higher nitrate
concentrations in combination with longer fallow periods, which resulted in higher soil
water exports.

Eflects of Tillage

The no-till system leached 35% less nitrogen than the conventional cropping

system (458 vs. 685 kg NO3'-N ha"l respectively). Leached nitrate N represented 50 and

76%, respectively, of the total N applied to these two systems over the eleven year

27

period. The no-till system showed higher soil water export throughout the period as
compared to the conventional system, as has been found in other studies (Rasse and
Smucker 1999, and Ogden et al. 1999). The leaching rate was reduced by 42% in the no-
till system, with the conventional system losing 187 g NO3'-N mm'l compared to 107 g
NO3'-N mm‘1 in the no-till system.

The environmental impact of grain production, measured as the amount of
nitrogen lost relative to grain yield, also varied among annual crops. The no-till system
had less nitrogen loss per unit yield, losing only 10.9 g NO3'-N kg'I yield compared to
17.6 g N03'-N kg‘l yield in the conventional system. The decreased nitrate losses shown
in the no-till system agreed with some studies (Stinner et al. 1979), but not all (Kanwar et
al. 1988, Tyler and Thomas 1977, Chichester 1977). This difference in soil water export
and nitrate leaching may be due to the well drained nature of KBS soils. Also, the no-till
plots also showed higher yields in some years than the conventional plots (Grandy et al.

2006), which may contribute to reductions in nitrate availability in these soils.

Conventional, Organic, and Reduced Input Systems

The conventional till system also leached more nitrate than the organic cropping
system: 685 vs. 209 kg N03'-N ha", respectively. The organic system had both lower soil
water export than the conventional system as well as lower nitrate concentrations in the
exported soil water throughout the study. Lower nitrate leaching and soil water export in
the organic systems are likely the result of cover crops, which increase evapotranspiration
and take up free nitrogen from the soil. The leaching rate in the organic system was less

than half that of the conventional system (86.5 vs. 186.5 g NO3'-N mm'1 respectively).

28

The organic grain production system had a lower environmental impact per unit
yield than the conventional system, losing only 6.7 g NO3‘-N kg yield’l compared to 17.6
g NO3'-N kg yield'1 in the conventional system. The organic system would be expected to
lose less nitrogen per unit yield, since the system was nitrogen limited. The results found
here are similar to some studies showing less nitrate leaching in organic systems (Hansen
et al. 2001, Kramer et al. 2006, and Drinkwater et al. 1998), but not all (Pimental et al.
2005). Since we did not use manure in our system, our organic system had much lower
nitrogen than many other organic systems.

Our reduced input system showed a large reduction in nitrate leaching compared
to the conventional system (267 kg NO3'-N ha"). This system also showed improvements
in leaching rate (110.4 g N03'-N mm") and efficiency (6.6 g N03'-N kg yield'l). This
system also showed lower soil water export rates than the conventional system due to the
use of cover crops, but had higher nitrogen applications and resultant yields than the

organic system.

Alfalfa

The alfalfa lost less nitrate than the conventional, no-till, and reduced-input
cropping systems but more nitrate than the poplar, early successional, and mid-
successional systems. The majority of the losses in alfalfa were in 2000 and 2001, during
a period when the alfalfa stand was being reestablished following plowing in 2000. About
two thirds of the nitrogen from the system was leached during this period. If 2000 and
2001 were excluded from the analysis, the total leaching would have been 46.1 kg N031

N ha", or 5.1 kg NO3'-N ha’l yr". Nonetheless, periodic re-establishment of alfalfa is

29

typically required due to auto-toxicity from previous plants. Alfalfa had the lowest soil
water export of any of the systems, 2174 mm, and a higher leaching rate than any of the

perennial and unmanaged systems (65g NO3'-N mm").

Poplar and Conifer Stands

The poplar system leached the least nitrogen of all the treatments, losing less than
1 kg N03‘-N ha'l over the 11 year period. The poplar system had intermediate rates of soil
water export, with ofien undetectable levels of nitrate in the exported soil water from this
system. The nitrate loss rate in this system was also the lowest among all of our systems,
only 0.38 g N03'-N mm". The low nitrate leaching in the poplars was most likely due to
the lack of fertilization in these plots. Other studies have shown the potential for nitrogen
loss in irrigated and fertilized poplar stands in the United States (McLaughlin 1985), but
there are many environmental programs, both in the US and abroad, that use poplar to
reduce nitrate availability in riparian areas, waste water treatment, or confined animal
feeding operations (Ball et al. 2005).

The coniferous forest stand lost far more nitrogen than the poplar system: 191 kg
NO3'-N ha'l over the 11 year period. The soil water export in this system was also much
higher (4642 mm) than in the poplars (2507 mm). The overall rate of leaching was 42 g
NO3’-N mm", which is higher than in the poplars and the unmanaged systems, but lower
than in the alfalfa and annual cropping systems. Other researchers have found that there
is the potential for nitrate leaching in nitrogen saturated coniferous forest stands like the

one at KBS (Jussy et al. 2004, Tietema et al. 1997).

30

Unmanaged Successional Systems

The unmanaged systems showed a gradient of leaching, with the early and mid-
succesional systems leaching very little nitrate (12.16 and 9.36 kg NO3'-N ha'l over the
11 year period, respectively), while the old growth forest leached an order of magnitude
more nitrate (121.37 kg NO3'-N ha'l over the 11 year period). The soil water export in the
early and mid-successional systems was also lower than in the old growth forest (2543,
2507, and 4137 mm respectively), implying greater evapotranspiration in these systems.
The old growth forest also had the highest rate of leaching of the three unmanaged
systems, losing 29 g NO3'-N mm'l compared to 4.8 and 3.7 g NO3'-N mrn'l in the early
and mid-successional systems, respectively. Since the early and mid-successional
systems are still accumulating biomass, it would be expected that they would lose very
little nitrogen (V itousek and Reiners 1975). Since the old grth forest would be
expected to be at equilibrium biomass, it would be expected that annual losses would be
in approximate equilibrium with the amount of nitrogen that is deposited on the site
annually through both dry and wet deposition. The KBS site received on average 6 kg N
ha'1 yr"l in wet precipitation during this study (National Atmospheric Deposition Program
2008), so the loss rate of 11 kg NO3'-N ha'l yr”l suggests equilibrium depending on
denitrification and dry deposition, and is well within the range of reported values of
nitrate leaching from forest systems (Borken and Matzner 2004), though higher than

some studies in forested sites (Schleppi et al. 2004).

31

Intra-annual variability and temporal trends

There was a great deal of variability in annual precipitation in our study (Table 7),
ranging from 608 mm of precipitation in 1999 to 1150 mm in 2006. The total
precipitation was 9218 mm from 1996-2006, with an average annual precipitation of 838
mm. Years with higher amounts of precipitation led to higher soil water export in all
systems, with peaks in soil water export during 2006 for the perennial and unmanaged
systems, while the wheat year of 2004 (with 1467 mm of precipitation from planting of
wheat until the subsequent planting of corn) showed the highest soil water export of the
annual systems.

The annual crops showed marked differences in nitrate leaching depending on the
crop that was growing and the amount of precipitation received. Due to the variation in
the amount of time a crop was on the field, management the crop including the amount of
nitrogen received, particular crops contributed disproportionally to the total leaching
losses. The soybeans were on the field the shortest amount of time (144 days on average),
while maize and wheat were in place for much longer periods (385 and 566 days,
respectively). The highest level of leaching occurred during the maize years, representing
53 to 57% of the total nitrate lost in these systems, even though maize only occupied 35%
of the rotation cycle. Most of this loss occurred during the post-harvest period after maize
was removed and before soybeans were planted, which accounted for 42 to 52% of the
total nitrate loss in these systems over the 11 year period. This indicates that cover crop
usage during long fallow periods, like those between corn harvest and soybean planting,

could be very useful for reducing nitrate leaching into groundwater.

32

In the perennial systems, the poplars consistently lost very little nitrate, and
leaching was not responsive to variation in precipitation due to the low nitrate levels in
the exported soil water. The alfalfa showed the greatest losses due to stand re-
establishment in 1999-2001. The coniferous forest showed a positive relationship
between soil water export and the amount of nitrate leached fiom the system, a trend that
has been shown in irrigated forest plots elsewhere (Jussy et al. 2004, Tietema et al. 1997).

In the unmanaged systems, precipitation and soil water export seemed to be less
correlated with nitrate leaching. Only in the mid-successional field was there a trend of
increased nitrate leaching with high soil water export. This may be due to the generally
lower leaching overall in these systems. Particularly in the early and mid-successional
systems, leaching was never more than 4 kg NO3'-N ha"I yr, so changes in leaching rates
may have been more closely tied to changes in N deposition rates or other environmental
factors.

Over the 11 year study, striking patterns emerged when comparing periods when
the plants are actively growing and when they are dormant. Leaching losses during the
off season when no crops were on the field accounted for 67 and 64 % of the total for the
conventional and no-till systems, respectively. In the reduced input and organic systems,
this was reduced to 51 and 46% of losses, respectively, presumably due to the use of
cover crops in these systems. The perennial systems seemed to show a similar pattern,
losing 49, 49, and 60% of their nitrate losses in the alfalfa, conifers, and poplars during
the winter season (Oct-Apr). The unmanaged systems also lost the majority of their
nitrogen during the winter offseason, losing 66, 53, and 71% in the early successional,

mid-successional, and old growth forest, respectively, during this period.

33

Management Implications

This information on nitrate leaching in different management systems has many
potential applications. Most importantly, this research shows that there is a wide range of
nitrate leaching levels from row crop systems, and that leaching can be significantly
reduced by changing management. This research suggests that cover crops, reduced
tillage, and reduced inputs could all help to reduce nitrate leaching. Particularly with the
expansion of biofuel production in the United States and elsewhere, implications for
ground water quality should be considered. With the landscape in North America rapidly
changing, the fate of the remaining fresh water supplies depends on the way in which we
manage the lands around them.

Further research should be conducted looking at the impact of CRP and prairie
land conversion to biofuel crop production on nitrate leaching, and the potential for crops
like poplar to mitigate impaired water quality around the US, particularly in areas with
high nitrogen deposition or manure management issues. Additionally, research should
also look at the temporal management that might be used to alleviate the losses of nitrate

during particularly vulnerable portions of the rotation in annual cropping systems.

34

CHAPTER 3

ECOSYSTEM MANAGEMENT AND CARBON STORAGE IN DEEP SOIL LAYERS

ABSTRACT

Soil carbon sequestration is one of several carbon capture methodologies proposed to
mitigate atmospheric C02 increases, principally through the adoption of no-till or
conservation tillage technologies. Historically, soil carbon sequestration research has
focused on the top 10-30 cm of the soil profile, ignoring deeper portions that might also
change due to management. In this study we sampled soils in eleven treatments along a
management intensity gradient to 1 meter depth. Experimental treatments on the same
soil series included four annual grain cropping systems in a maize-soybean-wheat
rotation, three perennial cropping systems, and four unmanaged successional systems.
The annual grain systems included conventionally tilled and no-till treatments managed
with conventional levels of chemical inputs. A third annual grain system received
reduced-inputs and a fourth, organic system received no chemical inputs; both of these
latter systems used tillage for weed control and leguminous winter cover crops for
nitrogen. Perennial crops included alfalfa, clonal poplar stands, and a mature conifer
plantation. Successional treatments included a 12-year-old early successional community,
two 50-year-old mid-successional communities, and a mature forest.

In surface soils, the no-till and organic systems had significantly higher carbon
concentrations and total carbon than did the conventionally managed cropping system (p
_<_ 0.05). Likewise, the alfalfa, poplar, conifer, early successional, never-tilled mid-

successional, and deciduous forest systems all showed significant gains in both carbon

35

concentrations and total carbon in the surface soil compared to the conventional system
(p 5 0.05). In contrast to surface soil trends, the B/Bt horizon had two times higher
variability in carbon concentrations and total carbon than did surface soils, leading to
very few significant differences among treatments. Likewise, the deeper Bt2/C horizon
soil showed more than three times higher variability in carbon concentrations and total
carbon than did surface soils, and very few significant differences among treatments. In
the total profile to 1 meter, the conventional till maize-soybean-wheat system contained
7.0 kg C m'z. The no-till system contained 1.5 kg C m'2 more carbon, but this gain was
not statistically significant (p=0.17, NS) owing to variability in subsurface horizons. The
reduced-input system contained 0.6 kg C rn'2 less than the conventional system (p=0.55,
NS), while the organic system contained 1.3 kg C m"2 more than the conventional system
(p = 0.16, NS). The alfalfa had 3.5 kg 0 m'2 more total profile carbon than the
conventional agricultural system (p=0.06), while the poplar system had 2.0 kg C m"2
more carbon than the conventional system (p=0.07). The early successional and never-
tilled mid-successional systems showed higher carbon levels, with 1.7 and 2.7 kg C 111'2
more than the conventional, respectively (p 5 0.05). The conifers, historically tilled mid-
successional system, and deciduous forest were not significantly different from the
conventional system. High variability in subsurface soils suggests that detecting
differences among treatments when including deeper soils will be more difficult and will
require more intensive soil sampling and replication. Overall, 1) soil carbon
concentrations were much more variable at depth than at the surface; 2) different
cropping systems distribute their carbon differently throughout the profile; 3) systems

that have more carbon at the surface do not necessarily have more carbon at depth; and 4)

36

future research should focus on defining the distribution and variability of carbon with
depth and under differential management.
INTRODUCTION

Soil carbon sequestration is a process by which plants remove carbon dioxide
from the atmosphere through photosynthesis, to be stored as decaying plant material in
the soil, leaving behind resistant organic matter that persists for variable lengths of time.
Soils hold about 75% of the carbon stored on land, about twice that stored in the
atmosphere, and play a large role in the global carbon cycle (Swift 2001). Carbon is
sequestered when organic matter accumulates faster than it is respired to carbon dioxide
by soil heterotrophs. Soil carbon storage promotes improved drainage, soil structure,
water holding capacity, and other important soil properties that improve agricultural
productivity (Lal et a1. 2004).

Different types of organic matter are differentially resistant to microbial attack,
with some materials showing resistance for hours and others for millennia (Kononova
1975, Schlesinger 1977, VanVeen and Paul 1981). Organic matter can also be protected
by adsorption to clay surfaces and by residing inside soil aggregates, which prevent
microbes from physically accessing the carbon (Six et al. 1998, DeGryze et al. 2004).
Additionally, microbial decomposition requires appropriate environmental conditions—
soils that are too cold or wet inhibit microbial grth and accumulate carbon (Schuur et
al. 2008).

Agricultural soils are particularly important for carbon storage because of their
potential for filture sequestration (Schlesinger 1995, Murty et al. 2002). Conversion of

native ecosystems to agriculture typically leads to 40-60% of soil carbon’s conversion to

37

carbon dioxide as disturbance accelerates heterotrophic activity. Globally, soils have
contributed ca. 124 Pg of carbon to the atmosphere from 1850 to 1990 (Houghton and
Hackler 2001). This contrasts to current rates of atmospheric increase that exceed 4.1 Pg
0 y" (Canadell et al. 2007).

Interest in restoring this lost carbon as a readily deployable, low cost option to
help offset global climate change (IPCC 2001) has led to interest in management
techniques that promote carbon storage. These include reduced or no-tillage technology
to slow decomposition, cover crops that add extra (perhaps recalcitrant) residue to soil,
and cr0p rotations that changes the biochemistry and quantity of crop residue. Other
possible strategies to increase carbon storage include converting cropland from annual to
perennial cropping systems, abandoning agricultural fields to succession, and planting
long-rotation tree crops (West and Post 2002, La] et al. 2004, Angers and Caron 1998,
Martens 2000).

Conversion of cropland to perennial crops or to successional communities have
been estimated to sequester as much as 60 g soil C m"2 y”l (CAST 2004) and conversion
to no-till annual crops have been estimated to sequester C at about half this rate (West &
Post, 2002; Lal, 2003). However, most of these estimates of sequestration capacity are
based on studies of soil carbon change in surface soils, and recent concerns that similar
gains may not be occurring at depth (Baker et al. 2007, VandenBygaart et al. 2003, Carter
2005, Dolan et al. 2006) call into question the overall value of no-till and other
management strategies for storing soil carbon. For example, of 67 studies reviewed by
West and Post (2002) comparing soil carbon sequestration in different management

systems, in only 2 studies were soils sampled below 30 cm. Sampling at depth is more

38

difficult and expensive. It may also be harder to detect carbon changes at depth because
carbon concentrations are lower and may be more variable due to the lack of
homogenization that occurs with tillage.

Prior research on soil carbon at our site in southwest Michigan has shown soil
carbon accumulation in the surface soil under no-till, perennial systems, and successional
systems. Robertson et al. (2000) documented no-till carbon gains of 30 g C m'2 y'1 in the
upper 7 cm of surface soil examined. Grandy and Robertson (2007) reported gains of soil
carbon under no-till, perennial cropping, and successional systems to 5 cm depth.
Subramanian (2008) sampled carbon to 15cm depth, and found carbon levels to be higher
in no-till and organic management systems compared to conventional. But in this case,
the no-till and organic systems had not shown any overall gain of carbon, but had avoided
the loss of carbon that appear to have occurred in the conventional system. Documenting
potential change in the entire soil profile is important for understanding the total effect of
management on carbon sequestration, especially if decreases at depth due to no-till might
offset increases in surface soils. Our objective in this study is to examine changes of
accumulation of carbon at depth (to 1 meter) under different agricultural management
regimes, and additionally to test the hypothesis that carbon gains that might be observed

in surface soils of various treatments are offset by losses lower in the profile.

MATERIALS AND METHODS
Site Description
We collected soil cores to compare soil carbon storage from a field experiment

that was established at the Kellogg Biological Station (KBS) in 1988. Multiple treatments

39

at the KBS Long-Term Ecological Research Site (LTER, http//lter.kbs.msu.edu) form a
management intensity gradient that is well suited to ecosystem comparisons. KBS is
located in SW Michigan within the northern portion of the US. maize belt (85° 24'W,
42° 24'N). The site lies on Kalamazoo (fine loamy) and Oshtemo (coarse loamy) soils,
both developed on glacial outwash and mixed, mesic Typic Hapludalfs, which mainly
differ in the thickness of the B/Bt horizon. The Kalamazoo series has a somewhat thicker
upper B/Bt horizon than the Oshtemo. Annual rainfall at KBS is 920mm/yr, distributed
evenly through the year. The water holding capacity of these soils is approximately 150

mm to 1.5 m depth (Crum and Collins 1995).

Experimental Design

Seven experimental treatments were established in 1989 in replicated l-ha plots
organized in a complete block design (n=6). Additional offsite native deciduous forest
plots were added in 1991 (n=3). For maps of the treatments and experimental design, see
www.1ter.kbs.msu.edu/. Cropping systems include four maize (Zea mays)-soybean
(Glycine max)-wheat (T riticum aestivum) rotations managed either i) with conventional
inputs and tillage, ii) with conventional inputs and no tillage, iii) with tillage and reduced
chemical inputs, or iv) organically with tillage and no chemical inputs. The latter two
treatments include a leguminous winter cover crop grown following the maize and wheat

portions of the rotation to provide N to the following grain crops. All cropping systems

40

Table 3.1 Soil profile characteristics at the KBS LTER site. The dominant KBS soil
series are the Kalamazoo (fine-loamy, mixed, mesic Typic Hapludalfs) and Oshtemo
(coarse-loamy, mixed, mesic Typic Hapludalfs) series. Data are from Crum and Collins
(1995). Abbreviation nd denotes missing data.

 

 

Horizon Depth Texture Bulk pH
Density
Sand Silt Clay
(3 kg") (mg M3)

Kalamazoo Series

Ap 0-30 43 38 19 1.6 5.5
E 30—41 39 41 20 1.7 5.7
Btl 41 -69 48 23 29 1.8 5.3
2Bt2 69-88 79 4 1 7 nd 5.2
2E/Bt 88-152 93 0 7 nd 5.6

Oshtemo Series

Ap 0-25 59 27 14 1.6 5.7
B 25-41 64 22 14 1.7 5.7
Btl 41-57 67 13 20 1.8 5.8
2Bt2 57-97 83 4 13 nd 5.8
2E/Bt 97-152 92 0 8 nd 6.0

 

41

Table 3.2: Management summaries for cropping systems and successional communities
at the Kellogg Biological Station Long Term Ecological Research Site. .

 

 

 

 

Tillage Nitrogen Fertilizerl Weed Control
mud Crops Lmaize—soybean-wheat rotation)

Conventional Conventional Conventional Chemical
(Conventional Rate)
and mechanical

No-Till None Conventional Chemical
(Conventional Rate)

Reduced Input Conventional 1/ 3 Conventional Chemical (1/3

with cover crop Conventional Rate)
and mechanical

Organic Conventional Cover crop Mechanical

Perennial C raps

Alfalfa None None None

Poplar None Starter2 None

Conifer None None None

Unmnaged Communities

Early Successional None None None

Mid-Successional (HT) 3 None None None

Mid-Successional (NT) 3 None None None

Deciduous Forest None None None

¥

1 Conventional refers to the recommended rate based on soil testing and best management

practices.

260 kg N ha" in 1989 only.

3HT = historically tilled, NT = never tilled

42

were planted and harvested during the same periods. Fertilizer application rates for the
conventional input systems were based on soil-test recommendations.

From 1989 to 1992, the conventional tillage and no-till systems were in a maize-
soybean rotation, and the reduced—input and organic systems were in a maize-soybean-
wheat rotation. Since 1993, all four of the annual grain crops have been in a maize-
soybean-wheat rotation. The conventional, reduced-input, and organic systems received
primary tillage, which consisted of moldboard plowing from 1989 to 1998 and chisel
plowing from 1999 onward, all in the spring. Secondary tillage consisted of disking
before wheat planting, field conditioning with a soil finisher prior to soybean and maize
planting, and inter-row cultivation for soybean and maize. The reduced input and organic
systems received additional inter-row cultivation and rotary hoeing as needed for weed
control.

The three perennial systems included alfalfa (Medicago sativa), fast growing
clonal poplar (Populus x euramericana) trees, and conifer stands established before 1950.
The conifer stands were 50-70 years old at the time of this study and were mixed stands
containing red and white pine (Pinus resinosa and P. strobes, respectively), Norway
Spruce (Picea abies), and some understory Black Cherry (Prunus serotina). The alfalfa
was harvested 3-4 times per year, and was reestablished once during the study period.
Fertilizer (P, K, B, and lime) and pesticides were applied according to Michigan State
University Extension recommendations and soil test results. Poplar trees were planted in
1989, and starter fertilizer (only) was added at that time. Creeping red fescue was used as

a cover crop to prevent soil erosion. Poplar trees were harvested in 1999, and allowed to

coppice (regrow from the cut stems).

43

The four unmanaged successional systems included: 1) an early successional
system that was abandoned from agriculture in 1989 (n=6), 2) a historically tilled, mid-
successional system that was released from agriculture in the 1950’s (n=3), 3) a never
tilled pasture that was cleared from forest in 1960 (n=4), and 4) an old-growth oak-
hickory forest that has never been cleared or plowed (n=3). The early successional system
has been burned annually in the spring since 1997, and the never-tilled pasture is mown

every fall with mown biomass left in place.

Soil Sampling and Analysis

Soils from all sites were taken sequentially by replicate from May 31 to October
19, 2001 with a hydraulic sampler (Geoprobe, Salina, Kansas) that collected 6 cm
diameter intact cores to 1 meter depth. Cores were taken using the direct push method,
which uses vibratory driving to avoid compaction. Each core was removed from the soil
in its own acrylic sleeve and taken into the lab for classification into soil horizons. Two

cores were taken at each of five long-term sampling stations within each replicate plot.

Soil profiles were classified according to soil horizon, and each horizon was
measured for length and then split into individual profile segments. Segments were
individually weighed and analyzed for soil carbon and nitrogen. Soil segments were first
passed through a 4mm sieve and mixed. A subsample was then oven dried at 60 degrees
C. Duplicate subsamples from each dried sample were then finely ground in a roller mill,
10 mg was weighed into each of three tin foil cups, and then analyzed for C and N using

a Carlo Erba NA1500 Series II C N Analyzer (Carlo-Erba Instruments, Milan, Italy). The

Coefficient of variation (c.v.) was <5% for all analytical replications; triplicate samples

44

that exceeded 0.05 analytical c.v. were re-analyzed. We used for soil calibration
standards 0519 and 0559 provided by USDA-ARS Pendleton, OR. Subsamples were
reacted with hydrochloric acid to test for the presence of carbonates in 440 of the 800
samples of different treatments and depths. A pressure transducer was used to measure
changes in pressure due to the presence of carbonate minerals (Evangelou et al. 1984,
Loeppert and Suarez 1996), with a detection limit of 0.1% inorganic carbon. No

significant amounts were found.

Statistical Analysis

The experiment was analyzed as a completely randomized design (CRD), with 1 1
treatments and 3 to 6 replicates of each treatment. Treatments were compared based on
bulk density, horizon length, carbon concentrations, and total carbon in each horizon as
well as total carbon to 1 meter. Total carbon was calculated by multiplying the percent
carbon times the average bulk density and horizon length for that depth increment. The
deciduous forest and never-tilled mid—successional systems were analyzed separately due
to their lack of historic lack of tillage. All comparisons were completed using SAS (SAS
Version 8.2, SAS Institute 1999). Proc mixed was used with the lsmeans statement to
determine treatment differences.

RESULTS

Horizon Depth

A/Ap horizon depths ranged from 14.2 cm to 22.3 cm and differed little by

treatment except among plots that differed in plowing history (Table 3.3, Figure 3.1). All

Sites that had been plowed previously showed similar A/Ap horizon depths of 20.8 i 0.3

45

cm, whereas the A/Ap horizons in the never-tilled mid-successional and deciduous forest
systems had significantly different (p g 0.05) mean depths of 14.2 i 0.2 cm and 16.9 j;
1.3 cm, respectively. The annual cropping systems had A/Ap horizons of 19.9 i 2.3, 20.0
i 1.0, 21.6 i 1.4, and 19.5 i 1.4 cm in the conventional, no-till, reduced-input, and
organic systems, respectively. The perennial systems had A/Ap horizons of 21 .2 i 2.0,
21.3 i 2.7, and 20.9 i 1.0 cm in the alfalfa, poplar, and conifer systems, respectively. The
early successional and historically tilled mid-successional system had A/Ap horizons of
22.3 i 1.7 and 20.8 i 2.2 cm, respectively.

The B/Bt horizon lengths were similar in all treatments except the poplars, which
had the smallest B/Bt horizon (30.0 i 2.2 cm) as compared to 35.9 :06 cm in all others.
The annual systems had B/Bt horizons of 35.8 i 3.9, 35.8 i 2.0, 36.2 i 3.2, and 36.7 i
3.4 cm in the conventional, no-till, reduced-input, and organic systems, respectively. The
alfalfa and conifer systems had B/Bt horizons of 35.9 i 2.4 and 34.7 :1; 4.6 cm, and the
early successional, historically tilled mid-successional, never tilled mid-successional, and
deciduous forest systems had B/Bt horizons of 35.8 i 3.4, 33.2 _t 3.9, 35.0 i 1.1, and 40.0
i 4.3 cm, respectively.

All treatments had a similar Bt2/C horizon length of 42.2 i 0.8 cm. The annual
systems had Bt2/C horizons of 43.0 i 2.3, 41.3 i 1.9, 40.1 i 2.8, and 41.6 i 2.4 cm in the
conventional, no-till, reduced-input, and organic systems, respectively. The alfalfa,
poplar, and conifer systems had Bt2/C horizons of 40.5 i 3.3, 46.1 i 4.0, and 43.1 i 6.0
cm, respectively. The early successional, historically tilled mid-successional, never tilled

mid-successional, and deciduous forest systems had Bt2/C horizons of 41 .1 i 2.2, 38.9 i

46

6.5, 47.9 1: 2.8, and 40.6 j; 4.1 cm, respectively. None of the differences were statistically

significant (p 5 0.05).

Bulk Density

Bulk density was generally lower at the surface than in lower profile positions in
all systems (Table 3.2). A/Ap bulk density was less in the never-tilled successional
system (1.2 i 0.03 g cm'3, p50.05) than in all other systems (1.47 g cm'3 i 0.03). The
annual systems had bulk densities of 1.6 i 0.05, 1.5 i 0.05, 1.5 i 0.02, and 1.5 i 0.04 g
cm'3 in the conventional, no-till, reduced input, and organic systems, respectively. The
perennial crops had A/Ap horizon bulk densities of 1.5 j; 0.04, 1.4 i 0.05, and 1.4 i 0.05
g cm'3 in the alfalfa, poplar, and conifer systems, respectively. The early successional,
historically tilled mid-successional, and deciduous forest systems had bulk densities of
1.5 i 0.03, 1.5 i 0.04, and 1.3 t. 0.03 g cm'3, respectively.

Bulk density of B/Bt horizon soils showed very little variation among treatments
(range 1.6-1.8 g cm'3). The annual systems were most similar to one another, with the
conventional, no-till, reduced input, and organic systems having a bulk density of 1.7 i
0.05, 1.6 i 0.03, 1.6 3: 0.03, and 1.6 i 0.04 g cm'3, respectively. The perennial treatments
were equally similar, with 1.7 i 0.05, 1.7 i 0.03, and 1.7 i 0.03 g cm'3 in the alfalfa,
poplar, and conifer systems, respectively. The historically tilled mid-successional system
had higher bulk density (1.8 i 0.09 g cm?) than the early successional (1.6 i 0.04 g cm'
3), never tilled mid-successional (1.6 i 0.02 g cm'3), and deciduous forest systems (1.7 j;

0.08 g cm'3), but not all of these differences were siglificant at the p _<_ 0.05 level: In

47

only the early successional community was B/Bt bulk density significantly different from
that in the conventional annual system (p _<_ 0.05).

In the Bt2/C horizon, bulk densities ranged from 1.5 to 1.7 g cm“3, with no
consistent patterns with management (overall mean 1.64 i 0.07 g cm'3). The annual
treatments had bulk densities of 1.6 i 0.03, 1.6 i 0.03, 1.7 i 0.03, and 1.6 i 0.04 g cm'3
in the conventional, no-till, reduced input, and organic systems, respectively. Of the
perennial systems, the alfalfa, poplar, and conifers had bulk densities of 1.6 i 0.05, 1.7 j;
0.03, and 1.7 i 0.04 g cm'3, respectively. In the successional systems bulk densities were
1.6 i 0.03, 1.7 i 0.01, 1.5 i 0.02, and 1.7 i 0.02 g cm'3 in the early successional,
historically tilled mid successional, never tilled mid-successional, and deciduous forest
system, respectively. As for the B/Bt horizon, in only a few cases were Bt2/C bulk

density differences statistically significant (p 5 0.05).

Soil Carbon Concentrations

Soil carbon concentrations ranged from 0.5 to 29.5 g C kg soil". Concentrations
were higher at the surface than at lower soil horizons, and also showed a pattern of
increasing variability with depth (Table 3.4). We found highest carbon concentrations in
the A/Ap horizons of the never tilled successional community (29.5 i 1.1 g C kg soil'l).
We found the lowest A/Ap carbon concentrations in the conventional and reduced input
row crop systems (10.4 i 0.3 and 11.1 i 0.4 g C kg soil'l, respectively). Among the
annual cropping systems, the no-till (11.5 1- 0.4 g C kg soil'l) and organic (12.2 i 0.4 g C

kg soil") systems had significantly greater C concentrations (p _<_ 0.05) than the

48

conventional system; the reduced input system (11.1 i 1.2 g C kg soil") was intermediate
to these.

In the B/Bt horizon, carbon concentrations ranged from 2.6 i 0.6 g C kg soil'l in
the historically tilled mid-successional field to 4.8 i 0.3 g c kg soil'1 in the early
successional field. Concentrations were statistically similar among all annual and
perennial systems; in the annual systems carbon concentrations were 4.2 i 0.7, 4.4 _-|:
0.0.5, 3.5 i 0.5, and 4.6 i 0.5 g C kg soil'1 in the conventional, no-till, reduced input, and
organic systems, respectively. In the perennial systems concentrations were 4.5 i 0.7, 3.9
i 0.9, and 3.1 j; 1.2 g C kg soil'1 in the alfalfa, poplars, and conifers, respectively. The
early successional, never-tilled mid-successional, and deciduous forest sites had similar
(p > 0.05) carbon concentrations of 4.8 i 0.3, 4.3 i 0.4 and 4.0 i 1.0 g C kg soil",
respectively. In the successional communities only in mid-successional historically tilled
soils were C concentrations significantly different (2.6 j; 0.6g C kg soil'l, p 5 0.05) from
the others.

In the Bt2/C horizon, carbon concentrations ranged from 0.5 i 0.2 g C kg soil" in
the conifers to 6.0 i 2.2 g C kg soil'1 in the alfalfa. The annual treatments all had similar
(p > 0.05) carbon concentrations of 1.8 i 0.2, 3.5 j; 1.4, 1.5 i 0.3, and 2.7 i 0.9 g C kg
soil'I in the conventional, no-till, reduced input, and organic systems, respectively. The
alfalfa and poplar had similar carbon concentrations of 6.0 i 2.2 and 5.2 i 2.3 g C kg
soil'l respectively, which were significantly higher than in the conifer systems (0.5 i 0.2
g C kg soil", p 5 0.05). or the successional treatments, the historically-tilled mid-
successional system (0.9 i 0.1 g C kg soil") had lower soil carbon concentrations than

the early or never-tilled successional systems (1.9 i 0.2 and 1.7 _+_ 0.2 g C kg soil'l,

49

respectively, p_<_0.05), but concentrations were similar to those in the deciduous forest
system (1.3 i 0.4 g C kg soil", p > 0.05).

Variability in carbon concentrations was lowest in the A/Ap horizon, ranging
from a coefficient of variation (CV) of 0.02 in the alfalfa to 0.40 in the historically tilled
mid-successional and poplar systems with a mean CV of 0.18 _-I_~ 0.14. The annual systems
had CVs of 0.07, 0.09, 0.26, and 0.08 in the conventional, no-till, reduced input, and
organic systems, respectively. The perennial crops had a wider range in CVs, with 0.02,
0.40, and 0.24 in the alfalfa, poplars, and conifers, respectively. The successional systems
also had a wide range of CVs, with the early successional, historically tilled mid-
successional, never tilled mid-successional, and deciduous forest systems having CVs of
0.09, 0.40, 0.07, and 0.25, respectively.

In the B/Bt horizon, CVs for carbon concentrations were about twice those in the
A/Ap horizon (mean 0.37 i 0.15), and ranged fiom 0.15 in the early successional system
to 0.67 in the conifers. The annual systems had CVs of 0.41, 0.28, 0.35, and 0.27 in the
conventional, no-till, reduced—input, and organic systems, respectively. The perennials
had CVs of 0.38, 0.57, and 0.67 in the alfalfa, poplar, and conifers, and in the
successional systems, the early successional, historically tilled mid-successional, never
tilled mid-successional, and deciduous forest system had CVs of 0. 1 5, 0.40, 0.19, and
0.43, respectively.

Spatial variability in carbon concentrations was highest in the Bt2/C horizon; CVs
here were about 3.4 times those in the A/Ap horizon (mean 0.61 fig 0.31), ranging from
0.19 in the historically tilled mid-successional system to 1.08 in the poplars. In the annual

crops CVs were 0.27, 0.98, 0.49, and 0.82 in the conventional, no-till, reduced-input, and

50

organic systems, respectively. The perennial crops had CVs of 0.90, 1.08, and 0.69 in the
alfalfa, poplar, and conifers, respectively. In the successional systems, the early
successional, historically tilled mid-successional, never tilled mid-successional, and
deciduous forest system had Bt2/C CVs of 0.26, 0.19, 0.47, and 0.53, respectively.
Throughout the entire profile, the systems with fewer replicates (n=3, conifers, deciduous
forest, and historically tilled mid-successional system) showed higher variation in carbon

concentrations than the other systems (n=6).

Total Carbon

Total soil carbon is a function of bulk density, horizon length, and carbon
concentration. Management changed the distribution of soil carbon throughout the soil
profile (Table 3.4). In the A/Ap horizon, soil carbon levels ranged from 3.0 i 0.5 kg C m'
2 in the poplar system to 5.7 i 1.2 kg C m'2 in the never tilled mid-successional system.
Among annual crops, the no-till and organic systems had significantly more soil carbon
in the A/Ap horizons than did the conventional till system (3.6 i 0.1 and 3.8 i 0.1 vs. 3.2
i 0.1 kg C m'z, p _<_ 0.05); the reduced input system (3.5 i 0.4 kg C m’z) was intermediate
to but not significantly different from the other annual crops (p > 0.05). Among the
perennial systems, the conifers had higher A/Ap carbon levels than the alfalfa or the
poplars (5.4 _t 0.7 vs. 3.6 i 0.3 and 3.0 i 0.5 kg C m'z, p_<_0.05, respectively). And in the
successional communities, the never-tilled mid-successional system had greater A/Ap
carbon levels than the early successional system (5.7 i 0.2 vs. 4.5 i 0.1 kg C m'z,
p50.05), though it was similar to the historically tilled mid-successional and deciduous

forest systems (3.9 i 0.9 and 4.7 i 0.7 kg C m'z, respectively).

51

In the B/Bt horizon, soil carbon levels ranged from 1.5 _+_ 0.4 kg C m”2 in the
historically tilled mid-successional system to 2.8 i 0.2 kg C m’2 in the early successional
system. B/Bt carbon levels in the annual and perennial treatments were all similar, with
the conventional, no-till, reduced input, and organic systems containing 2.4 i 0.4, 2.5 i
0.3, 2.0 i 0.3, and 2.7 i 0.3 kg C m'z, and the alfalfa, poplars, and conifers containing 2.6
i 0.4, 2.3 i 0.5, and 1.8 i 0.7 kg C m'z, respectively. The early successional (2.8 i 0.2 kg
C m'z) and never-tilled mid-successional systems (2.6 i 0.3 kg C m'z) had higher carbon
levels than the historically tilled mid-successional system (1.5 i 0.4 kg C m'z, p50.05),
but none differed significantly (p_<_0.05) from the deciduous forest system (2.5 i 0.6 kg C
m'z).

In the Bt2/C horizon, carbon contents ranged from 0.4 i 0.1 kg C In2 in conifer
soils to 4.1 j; 1.6 kg C m'2 in alfalfa. The annual systems were statistically similar to one
another (p > 0.05), with 1.2 i 0.2, 2.4 i 0.9, 1.0 i 0.2, and 1.9 j; 0.6 kg C m'2 in the
conventional, no-till, reduced input, and organic systems, respectively. The conifers had a
lower Bt2/C soil carbon content (0.4 i 0.1 kg C m'z, p_<_0.05) than either the alfalfa (4.1 i
1.6 kg C m'2) or the poplars (3.6 i 1.5 kg C m'z). The early successional (1.3 i 0.2 kg C
m’z) and never-tilled mid-successional systems (1.2 i 0.3 kg C m'z) had higher carbon
than the historically tilled mid-successional system (0.6 i 0.1 kg C m'2), but all were
statistically similar to the deciduous forest system (0.9 j; 0.3 kg C m'z, p > 0.05).

Over the entire profile to 1 meter, soil carbon levels ranged from 6.1 i 1.3 kg C
m'2 in the historically tilled mid-successional system to 10.4 i 1.5 kg C m'2 in the alfalfa
system. All of the annual treatments were similar to one another, with carbon levels of

6.9 i 0.6, 8.5 i 0.9, 6.5 i 0.8, and 8.3 i 0.8 kg C m'2, respectively, in the conventional,

52

no-till, reduced input, and organic treatments. The perennial systems were also similar,
with 10.4 i 1.5, 8.9 i 0.8, and 7.6 i 1.3 kg C m'z, respectively, in the alfalfa, poplars, and
conifers. The early successional (8.6 i 0.3 kg C m'z) and never-tilled mid-successional
systems (9.6 i 0.7 kg C m'z) had more carbon than the historically tilled mid-
successional system (6.1 i 1.3 kg C m’z, p 5 0.05), but none had statistically different
total carbon contents from the deciduous forest (8.1 i 1.5 kg C m'z).

Variability of total soil carbon was greater at depth than at the surface. In the
A/Ap horizon, CVs for total carbon ranged from 0.05 in the early successional system to
0.41 in the poplars (mean 0.19 i 0.13). The annual systems had CVs of 0.08, 0.07, 0.28,
and 0.06 in the conventional, no-till, reduced-input, and organic system, respectively. In
the perennial systems, CVs were 0.20, 0.41, and 0.22 in the alfalfa, p0plar, and conifers,
respectively. The early successional, historically tilled mid-successional, never tilled mid-
successional, and deciduous forest system had total A/Ap carbon CVs of 0.40, 0.07, 0.26,
and 0.19, respectively.

In contrast, variability in total carbon in the Bth horizon was about twice as high
as in the A/Ap horizon, with CVs ranging from 0.17 in the early successional system to
0.67 in the conifers (mean 0.38 i 0.14). The annual treatments had CVs of 0.41, 0.29,

0.3 7, and 0.27 in the conventional, no—till, reduced input, and organic systems,
respectively. In the perennial crops CVs for total B/Bt horizon carbon were 0.3 8, 0.53,
and 0.67 in the alfalfa, poplars, and conifers, respectively. The successional systems had
B/Bt total carbon CVs of 0.17, 0.46, 0.23, and 0.42 in the early successional, historically
tilled mid-successional, never tilled mid-successional, and deciduous forest system,

respectively.

53

In the Bt2/C horizon, the CVs for total carbon were still higher, more than three
times greater than in the A/Ap horizon, ranging from 0.29 in the historically tilled mid-
successional field to 1.02 in the poplars (mean 0.61 i 0.26). The annual systems all had
high variability, with CVs of 0.41, 0.92, 0.49, and 0.77 in the conventional, no-till,
reduced input, and organic systems, respectively. The alfalfa, poplar, and conifers had
Bt2/C total carbon CVs of 0.96, 1.02, and 0.43 in the alfalfa, poplar, and conifers,
respectively. The successional systems had somewhat lower variability, with CVs of
0.38, 0.29, 0.50, and 0.58 in the early successional, historically tilled mid-successional,
never tilled mid-successional, and deciduous forest system, respectively.

Variation across the entire soil profile in total carbon to 1 meter ranged flour a CV
of 0.09 in the early successional system to 0.37 in the historically tilled mid-successional
system (mean CV = 0.25 i 0.09). The annual systems had CVs for the total profile of
0.21, 0.26, 0.30, and 0.24 in the conventional, no-till, reduced input, and organic systems,
respectively. The alfalfa, poplar, and conifers had total profile carbon CVs of 0.35, 0.22,
and 0.30 in the alfalfa, poplar, and conifers, respectively. The successional systems had
CVs of 0.09, 0.37, 0.15, and 0.32 in the early successional, historically tilled mid-

successional, never tilled mid-successional, and deciduous forest system, respectively.

Percent Nitrogen and C :N ratios

In the A/Ap horizon, all the annual and perennial treatments had similar
concentrations of nitrogen in the soil (ranging from 1.12 to 1.35 g N kg'l soil, see Table
3.5). The never tilled mid-successional field and deciduous forest had significantly more

nitrogen than any of the other treatments, with 2.48 (i 0.16 s.e.) and 2.05 (i 0.19) g N kg'

54

1 soil, respectively. The UN ratio of the A/Ap horizon was highest in the conifers (14.01
Q 0.68)), although the deciduous forest and mid-successional systems also had high C/N
ratios (range 11.51-12.42).

In the B/Bt horizon, the nitrogen concentrations in the soil ranged fi'om 0.45 to
0.73 g N kg" soil. All the annual treatments were similar, while the conifers were
significantly lower than the other perennial crops. The deciduous forest site had similar
nitrogen concentrations to all the other successional treatments, with the historically tilled
mid-successional treatment having the lowest nitrogen concentrations (0.45 g N kg'l
soil), and the never-tilled mid-successional treatment having the highest concentrations of
the successional treatments (0.72g N kg" soil). The C/N ratio of the Bth horizon was
more variable than in the A/Ap horizon, and ranged from 6.43 (i 0.29) in the never-tilled
mid-successional treatment to 10.73 (t 2.33) in the deciduous forest treatment.

In the Bt2/C horizon, the nitrogen concentrations ranged from 0.19 g N kg" soil
in the conifers to 0.53 g N kg" soil in the never-tilled mid-successional system. The C/N
ratio of the Bt2/C horizon was more variable than the A/Ap or B/Bt2 horizons, and
ranged from 4.46 (t 0.84) in the never-tilled mid-successional system to 13.15 in the no-

till annual system.

55

Table 3.3 Cropping system and successional community effects on soil horizon thickness
and bulk density to 100 cm soil depth at KBS LTER Site in 2001. Results are shown as
mean (standard error). Replication is n=6 plots for annual crops, alfalfa, poplar, and early
successional communities; n=4 mid-successional never-tilled (NT) sites, and n=3 sites for
the conifers, mid-successional historically tilled (HT) communities, and deciduous forest.
Systems with different lower case letters within colunms are significantly different (p5

 

 

 

 

 

0.05).
A/Ap B/Bt Bt2/C
Thickness Bulk Thickness Bulk Thickness Bulk
Densi Densit1y Densi
Cm g cm" Cm g cm’ cm g cm'
Annual Crops (Maize-Soybean- Wheat Rotation)
Conventional 19.9 1.6 35.8 1.7 43.0 1.6
(2.30)“ (0.05)3 (3.85)“ (0.05)” (2.31)a (0.03)a
No—till 20.0 1.5 35.8 1.6 41.3 1.6
(0.97)“ (0.05)a (1.99)” (0.03)” (1.86)“ (0.03)a
Reduced Input 21.6 1.5 36.2 1.6 40.1 1.7
(1 .43)8 (0.02)8 (3.24)” (0.03)” (2.76)a (003’”
Organic 19.5 1.5 36.7 1.6 41.6 1.6
(1.43)” (0.04)a (3 .43)” (0.04)” (2.41)” (0.04)”
Perennial Crops
Alfalfa 21.2 1.5 35.9 1.7 40.5 1.6
(1.97)” (0.04)” (2.44)” (0.05)” (3.34)” (0.05)”
Poplar 21.3 1.4 30.0 1.7 46.1 1.7
(2.65)” (0.05)“ (2.21)” (0.03)” (3 .95)a (0.03)”
Conifers 20.9 1.4 34.7 1.7 43.1 1.7
(0.95)” (0.05)“ (4.56)” (0.03)ac (5.96)a (0.04)”
Successional Communities
Early 22.3 1.5 35.8 1.6 41.1 1.6
Succession (1 .67)a (0.03)” (3.39)” (0.04)”c (2.19)a (0.03)a
Mid- 20.8 1.5 33.2 1.8 38.9 1.7
Successional (2.21)” (0.04)” (3.86)” (0.09)a (6.54)a (0.01)”
(HT)
Mid- 14.2 1.2 35.0 1.6 47.9 1.5
Successional (0.20)c (0.03)c (1 .06)a (0.02)” (2.80)3 (0.02)c
(NT)
Deciduous 16.9 1.3 40.0 1.7 40.6 1.7
Forest (1.25)” (0.02)” (4.26)3 (0.08)” (4.13)a (0.02)”

 

56

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57

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60

DISCUSSION

In general we found that surface soil carbon concentrations and total carbon
differed significantly among our different systems, as did the distribution of carbon
through our 1m profiles. We also found more variability in carbon concentrations and
total carbon in the deeper soil horizons than in the surface soil horizons.
Annual treatments

Among the annual cropping treatments, differences in carbon concentrations and
total carbon were only significant in the surface horizon. Surface soils in the no-till and
organic systems had higher concentrations of carbon than in the conventionally managed
system(11.5 and 12.2 vs. 10.4 g C kg soil", 1350.05), while the reduced input system had
intermediate levels not significantly different from others. In total carbon, too, the no—till
and organic systems had greater carbon than the conventional systems (3.6 and 3.8 vs. 3 .2
kg C m’z, p50.05), while the reduced input system had intermediate levels not
significantly different from the other systems. At all other depths, including to one meter,
we found no differences among treatments. Robertson et al. (2000), Grandy and
Robertson (2007), and Subramanian (2008) also found that no-till systems had higher
surface carbon than did the conventionally tilled system at this site, although in these
studies surface soils were defined differently. These findings are consistent with global
meta analyses of other long-term studies that have shown that no-till systems accumulate
surface soil carbon relative to conventional tillage systems (Franzluebbers 2004, Puget
and Lal 2005).

Total profile carbon was also greater in the no-till system than in the

conventionally tilled system, but differences were not statistically significant (change of

61

1.5 kg C m'z, p=0.l7). The no-till system, for example, contained 1.2 kg C m'2 more than
the conventionally tilled system in the Bt2/C horizon, but the carbon level was too
variable among replicate plots to show statistical significance. The source of the
variability in the entire profile is in the B/Bt and Bt2/C horizons, where variability among
plots in total carbon was two to three times greater than in the A/Ap horizon (CV = 0.19
in the A/Ap horizon vs. 0.38 in the B/Bt and 0.61 in the Bt2/C horizon. In light of this
variability plus the relatively small number of replicate plots (n=6), it is not surprising
that we could not document statistically significant changes in carbon at depth. There
either is no change in carbon occurring, or the change in carbon is happening very slowly
in variable locations. In either case, it does not diminish our finding that carbon is
accumulating in these systems in surface soils.

That other studies and reviews have also failed to find whole-profile carbon gains
under no-till (Powlson and Jenkinson 1981, Machado et al. 2003, Baker et al. 2007,
VandenBygaart et a]. 2003, Carter 2005, Dolan et al. 2006) may also be due to lack of
statistical power. Powlson and Jenkinson (1981) examined soils to 40cm at Rothamsted,
UK, and could find no difference between long-term tillage treatments. Machado et al.
(2003) also could find no difference between tillage treatments in a Rhodic Ferrasol in
Brazil.

The carbon gain in our organic system is surprising but consistent with earlier
studies (Robertson et al. 2000, Grandy and Robertson 2007, and Subramanian 2008) at
our site. Even though the soils in this system are exposed to more frequent mechanical
disturbance that breaks apart aggregates and exposes carbon therein to microbial attack

(Grandy and Robertson 2007), the organic system may be gaining carbon due to cover

62

crop composition. The fact that the reduced input system is accumulating carbon more
slowly (or not at all) despite having the same cover crops as the organic system may be
due to an interaction with nitrogen fertilizer that could be accelerating the decomposition
of plant residue relative to rates of decomposition in the unfertilized soils of the organic
system.

It is probably reasonable to assume that soil carbon in the conventional till
treatment is at equilibrium, i.e. that it has already lost all of the carbon it is likely to lose
while row-cropped and is maintaining its current carbon levels, since this system has
been tilled for over a hundred years. If this is the case, then the carbon differences
between the conventional system and the no-till and organic systems represent total
sequestration rates of 145 and 127 g C m“2 y". If, on the other hand, the conventional
system is losing carbon (Subramanian 2008), then differences represent net sequestration.
In this case carbon in the no-till and organic systems are losing carbon more slowly than
in the conventional system. In either case no-till and organic management in these soils
would represent effective C02 mitigation strategies.

Perennials

Our study found that alfalfa contained 3.5 (w: 1.5) kg C 111'2 more carbon to 1
meter than the conventional system (p50.05), and contained as much or more carbon than
any other system we measured. The other perennial systems did not show any significant
gain in carbon compared to the conventional system (p > 0.05). The distribution of
carbon through the profile in the perennial cropping systems was strikingly different from
many of the other systems. The conifers stored most of its carbon in the surface, while the

alfalfa and poplars had a much more even distribution of carbon with depth. The alfalfa

63

and poplars also were the only systems that had their highest amounts of carbon in the
Bt2/C horizon.

Previous studies at this site have shown that the alfalfa and poplar crops have
higher surface carbon levels than the annual cropping systems (Grandy and Robertson
2007, Robertson et al. 2000). While the alfalfa has shown a gain in carbon, most likely
due to its deep rooting depth and high root: shoot ratio, the poplar and conifer systems
have not. The conifer plots are geographically separated from the alfalfa and poplar
treatments, and may have had different initial carbon values than the other treatments.
Thus they could have gained carbon, but since they might have started at a different,
lower level, the carbon levels may not show a net gain when contemporary stores are
compared. The poplar systems, on the other hand, were established at the same time as
the other annual and perennial systems so their low carbon content is a treatment effect.
The poplar stands seem to be nitrogen limited based on patterns of nitrate leaching
(Chapter 2), soil inorganic nitrate (Chapter 4), and nitrous oxide losses (Chapter 4), and
has also have lower rates of aboveground net primary productivity than the other systems
studied here (Chapter 4), and thus could have lower soil carbon levels due to lower
carbon inputs from litterfall and root inputs. Additionally, the lower stores of poplar soil
carbon found here relative to those found in previous studies may be related to
accelerated soil carbon oxidation in the year following cutting: earlier studies reported
carbon contents prior to clearcutting; our findings represent carbon values in summer
2001, about 18 months after harvest in winter 1999. Soils during this period would likely

have been warmer and wetter due to less shading and transpiration prior to canopy

64

closure in 2002, and therefore would have experienced accelerated decomposition of

accumulated inputs.

Successional Communities

The early successional and never-tilled mid-successional systems contained more
total carbon than did the conventional and reduced input annual systems. The historically
tilled mid-successional fields and deciduous forest contained similar levels of total
carbon as compared to all of the annual cropping systems. Previous studies at this site
have shown that the successional systems have higher carbon levels than did the annual
and perennial systems (Grandy and Robertson 2007, Robertson et al. 2000). In all of
these systems, except the early successional field, the number of replicates was smaller
than f for other treatments, and this may have resulted in higher variability than in many
of the other treatments. The variability patterns found in our study were similar to those
shown in Grandy and Robertson (2007). It is also possible that the mid-successional
systems and deciduous forest were not used for agriculture for fertility reasons. This may
be particularly true for the historically tilled mid-successional system that was abandoned
from agriculture in the 1950’s. The complete history of these systems is unknown, but it
seems possible that these were more marginal lands removed fi'om production by
previous land owners. The successional systems also tend to have higher spatial
variability than the cropped systems due to differences in soil disturbance histories
(Robertson et al. 1993 and Table 3.4); more samples should probably have been taken

from these sites to compensate for this variability.

65

Spatial Variability

Soil carbon was more spatially variable at depth in all sites. In the Bt2/C horizon
carbon concentrations were three times more variable than in the A/Ap horizon, which
led to more variability in total soil carbon at depth. Carbon contents tend also to be
smaller at depth. Higher variability combined with lower concentrations at depth leads to
much greater difficulty finding treatment differences. This in turn leads to difficulty in
finding differences between treatments when the entire profile to 1 meter is considered.
In the surface horizon, on the other hand, lower variability combined with higher
concentrations make it easier to find treatment differences.

Overcoming the difficulty of documenting subtle carbon change at depth requires
increasing statistical power. This can be achieved either by increasing the sample size or
increasing the effect size, i.e. the difference between the control and treatment groups. In
the case of soil carbon, both options are important. The effect size can be increased by
waiting more time for carbon levels to change, although for soil carbon this can take
decades. The other option is increasing replication. In our study, a retrospective power
analysis suggests that more than 40 soil cores per plot would have been needed to achieve
a 40% chance of documenting a statistically significant management effect in Bt2/C
horizon soils with a change of 20% in the annual treatments and 30% in the perennial and
successional systems (S. Kravchenko, personal communication).

Additionally, different management systems distributed their carbon differently
within the profile, with some systems (such as conifers) storing the vast majority of their
carbon in the surface, and others (such as the alfalfa and no-till systems) storing their

carbon more evenly throughout the profile. Sampling only at the surface will bias the

66

studies towards those systems that preferentially store their carbon at the surface. This
issue merits further study at other research sites, particularly if soil carbon sequestration
is to be used as a mitigation option for global climate change. However, the absence of
detectable carbon change at depth does not discount the importance of change at the
surface from the standpoint of C02 mitigation.

Future studies should include more extensive sampling at depth so that we might

PAP-

.-

better understand the partitioning and variability of carbon in these soils. It stands to

.."P..'r‘.'r MQIA‘U‘

reason that the partitioning of soil carbon between different depths should vary depending
on the ecosystem as well as the soil type, so documenting these relationships will be L

critical if soil carbon sequestration will be used as a carbon offset.

Conclusions

1) Eleven years post-establishment our no-till and organic systems had higher carbon
concentrations and total carbon in the surface soil (A/Ap horizon) compared to the
conventionally managed system, and showed a non-significant trend towards greater
soil carbon in the total profile.

2) All of our perennial systems had higher surface soil carbon concentrations and total
carbon than the conventionally managed system, and the alfalfa had higher carbon in
the total profile. The poplar and conifers showed a non-significant trend of carbon
accumulation in the total soil profile.

3) The early successional, never tilled mid-successional, and deciduous forest system

had higher soil carbon concentrations and total carbon than the conventionally

67

4)

5)

6)

7)

managed system in surface soils, and the early successional and never tilled mid-
successional system had higher total profile carbon.

In general, soil carbon concentrations were 340% more variable in the Bt2/C horizon
than in the A/Ap horizon;

Different cropping systems distribute their carbon differently throughout the profile,
with perennial cropping systems distributing more carbon at depth than either annual
crops or successional systems;

Systems that have more carbon at the surface do not necessarily have more carbon at
depth, and systems with less carbon at the surface do not necessarily have less total
carbon; and

Future research should focus on defining the distribution and variability of carbon

with depth and under differential management strategies in order to gauge the

potential importance of deep soil carbon for storing carbon.

 

CHAPTER 4
TRADE-OFFS AND SYNERGIES IN ECOSYSTEM SERVICES ALONG A
MANAGEMENT INTENSITY GRADIENT
ABSTRACT

Agricultural ecosystems are primarily managed for marketable yields, but they provide
many other important ecosystem services as well. To assess tradeoffs and synergies
among different services provided by major ecosystems in agricultural landscapes we
measured agricultural yield, aboveground net primary productivity, greenhouse gas
production, soil carbon, soil inorganic nitrogen, drainage, and nitrate leaching in eight
ecosystems along a management intensity gradient on the same soil type in SW
Michigan, USA. Ecosystems included four annual grain systems in a maize-soybean-
wheat rotation, two perennial crops (alfalfa and poplar trees), an early successional
community, and an old-growth deciduous forest. The annual grain systems included
conventionally tilled and no-till treatments, both of which included conventional
chemical inputs. Annual grain systems also included a reduced-input system and a
biologically-based organic system, both of which used tillage for weed control and cover
crops for nitrogen. We constructed trade-off curves to illustrate relationships among the
ecosystem services produced in these systems. Methane oxidation, nitrous oxide
abatement, increased drainage, and carbon sequestration were positively correlated with
one another, indicating that managing for one of these services will provide others and
will synergistically lead to improved overall ecosystem functioning. We also found trade-
offs, including a decrease in soil carbon and methane uptake associated with higher soil

nitrate levels. Flower diagrams illustrate the suite of services provided by each system.

69

Opportunities to manage for multiple ecosystem services should allow farmers and policy

makers to better balance trade-offs between productivity and environmental quality.

INTRODUCTION

Agriculture has many environmental costs, in part because agricultural systems tend
to be managed for a single ecosystem service, marketable yield, with little attention paid
to others. However, agricultural systems also provide other services that are valued by
humans and important to the functioning of nearby ecosystems, including regulating
services such as climate regulation and water purification, cultural services such as
aesthetic attributes and recreation, and supporting services such as nutrient cycling,
pollination, and soil formation (Robertson and Swinton 2005, vainton et al. 2007,
Millenium Ecosystem Assessment 2005).

The measurement of most of ecosystem services in agriculture has mostly been
limited to only one or two services (e. g. Pradel et al. 2005, Guo et al. 2000, Kremen
2005). There have been few attempts to measure and understand multiple services and the
interactions among them, with a small number of notable exceptions. In one of these,
Pimentel eta]. (2005) compared organic and conventional agricultural practices for
energy usage, economic outcomes, and environmental benefits such as organic matter
and retained soil moisture. In two others, Kling et al. (2006) and F eng et al. (2005)
examined trade-offs among economic measures and environmental quality in the Upper
Mississippi River Basin. These latter studies are novel in both their interdisciplinary

nature and their comparisons of multiple economic and environmental indicators. They

70

firrther demonstrate the potential value of understanding the mechanisms that produce
services in order to develop management techniques that will favor their optimization.

Understanding interactions among the services provided by agricultural systems
requires understanding patterns and the individual trade-offs that occur when the delivery
of one service is affected by the delivery of another. While it may be straightforward to
assess trade-offs between two ecosystem services, it is more difficult to evaluate trade-
offs among multiple services (Foley et al. 2005). Trade-off curves (Antle et al. 2006,
Stoorvogel et al. 2001) describe relationships between pairs of sustainability indicators.
The trade—off between yield and the production of nitrous oxide, a greenhouse gas
produced in annual cropping system soils, for example, shows the degree to which one
service (e. g. yield) can be affected by managing for the other (McSwiney and Robertson,
2005; Figure 4.1).

These sorts of analyses are needed for multiple services in order to quantify trade-offs
and synergies. Without doing so it is difficult to know how ecosystem services interact
and provide cumulative benefits, and therefore diffrcult to make informed management
decisions. Here we examine tradeoffs among several important ecosystem services in row
crop agriculture in order to provide better knowledge for policy and farm level decision
making. The services we examined include grain yield, aboveground net primary
productivity, water conservation (measured as drainage), water quality (measured as
nitrate leaching), greenhouse gas flux abatement (measured as nitrous oxide conservation
and methane oxidation), and soil quality (using soil carbon levels and soil inorganic

nitrogen as indicators).

71

80 -
2001 -2003

8

 

 

 

 

.5
O
l

 

 

N20 flux (9 N20—N ha“ day“)

8

 

 

 

 

0 I I fl
4 6 8

Yield (MT grain ha“)

Figure 4.1: Relationship between maize grain yield and nitrous oxide flux for nine levels
of N addition at the Kellogg Biological Station’s Nitrogen Rate Study (McSwiney and

Robertson 2005).

72

Our overall objective is to investigate how agricultural systems can be managed to
minimize the environmental impact of agriculture without sacrificing productivity—or
conversely, to maximize the ecosystem services provided by agriculture, including
productivity. All of the services examined here are important in annual cropping systems
for reducing the environmental impact of these systems, all are to at least some degree
under management control, and all interact—and thus presumably can be optimized vis a

vis production (Heal and Small 2002).

MATERIAL AND METHODS

Site Description

We compared ecosystem services from a field experiment that was established at
the Kellogg Biological Station (KBS) in 1988. Multiple treatments at the KBS Long-
Term Ecological Research (LTER) Site (www.1ter.kbs.msu.edu) form a management
intensity gradient that is well suited to ecosystem comparisons. KBS is located in SW
Michigan, within the northern boundary of the US. corn belt (85° 24'W, 42° 24'N). The
site lies on Kalamazoo (fine loamy) and Oshtemo (coarse loamy) soils, both mixed,
mesic Typic Hapludalfs that mainly differ in the thickness of the Bt horizon. The
Kalamazoo has a thicker upper Bt horizon than the Oshtemo and therefore has a slightly
greater water holding capacity. Annual rainfall at KBS is 920mm/yr, distributed evenly
through the year. The water holding capacity of these soils is approximately 150 mm to

1.5 m depth (Crum and Collins, 1995).

73

Experimental Design

Seven experimental treatments were established in 1989 in replicated l-ha plots
organized in a complete block design (n=6). Additional offsite native deciduous forest
sites were added in 1991 (n=3); for maps of the treatments and experimental design, see
www.1terkbs.msu.edu/. Cropping systems include four maize (Zea mays)-soybean
(Glycine max)~wheat (Triticum aestivum) rotations managed either i) with conventional
inputs and tillage, ii) with conventional inputs and no tillage, iii) with reduced chemical
inputs and tillage, or iv) organically with no chemical inputs and tillage. The latter two
treatments include a legunrinous winter cover crop grown following the maize and wheat
portions of the rotation to provide N to the following grain crops. All cropping systems
were planted and harvested during the same periods. Fertilizer application rates for the
conventional input systems were based on soil-test recommendations.

From 1989 to 1992, the conventional tillage and no-tillage systems were in a
maize-soybean rotation, and the reduced-input and organic systems were in a maize-
soybean-wheat rotation. Since 1993, all four of the annual grain crops have been in a
maize-soybean-wheat rotation. The conventional, reduced-input, and organic systems
received primary tillage, which consisted of moldboard plowing in the spring from 1989
to 1998 and chisel plowing in the spring from 1999 onward. Secondary tillage consisted
of disking before wheat planting, field conditioning with a soil finisher prior to soybean
and maize planting, and inter-row cultivation for soybean and maize. The reduced input
and organic systems received additional inter-row cultivation and rotary hoeing as needed

for weed control.

74

Table 4.1: Management summary for the KBS LTER treatments.

 

 

 

Tillage Nitrogen Fertilizer“ Weed Control
Ann_u_al Crops (Maize. Sovbeam Wheat Rotation)
Conventional Conventional Conventional Chemical and
mechanical
No-Till None Conventional Chemical
Reduced Input Conventional 1/ 3 Conventional 1/3 Chemical and
with cover crop mechanical
Organic Conventional Cover crop Mechanical
Perennial Crops
Alfalfa None None None
Poplar None Starterl None
Unmanaged Communities
Early Successional None None None
Deciduous Forest None None None

 

* Conventional refers to the recommended rate based on soil testing and best

management ractices.
'60 kg N ha' in 1939 only.

75

 

The two perennial systems included alfalfa (Medicago sativa) and fast growing
clonal poplar (Populus x euramericana) trees. The alfalfa was harvested 34 times a year,
and was re-established once during the study period. Fertilizer (P, K, B, and lime) and
pesticides were applied according to Michigan State University Extension
recommendations and soil test results. Poplar trees were planted in 1989, and starter
fertilizer (only) was added at that time. Creeping red fescue was used as a cover crop to
prevent soil erosion. Poplar trees were harvested in winter 1999, and allowed to coppice
(regrow fi'om the cut stems) the following spring.

The two unmanaged systems included an early successional system that was
abandoned fi'om agriculture in 1989 and an old-growth oak-hickory forest that has never
been cleared or plowed. The early successional system has been burned annually since

1997 to prevent tree colonization.

Sampling Protocols

The methodology for the drainage, nitrate leaching, soil carbon sequestration and
total soil nitrogen measurements are described in detail in Chapters 2 (drainage, nitrate
leaching) and 3 (carbon sequestration and total nitrogen). Soil carbon and nitrogen
samples were taken in 2001, and drainage and nitrate leaching data were collected fiom
1996-2007. Methodology for greenhouse gas measurements, collected fi'om 1991-2007,

are described in Robertson et a]. (2000).

All systems were sampled for Aboveground Net Primary Productivity (AN PP).
Additionally, the annual cropping systems were sampled for grain yields from 1993 to

2007 in each of the agronomic plots. Yield was measured on each plot using a John

76

 

 

Deere 9410 combine with a Greenstar yield monitor (John Deere International, York,
NE) as well as by hand harvesting. Hand harvesting was performed at five sampling
stations in each plot by harvesting all the above ground portion of plants that were rooted
within the bounds of a harvest quadrat (1 m2 area). Plant biomass was then dried at 60 ° C
for at least 48 h and weighed. Crops were hand harvested at physiological maturity: for
maize during black layer (early September), for soybeans during pre-leaf drop (early
September), and for wheat when kernels entered dough stage (mid-July). The tissue was

then threshed (Almaco corn or small grain thresher, Nevada, IA) to separate the seed

 

from stover. Seed biomass was recorded and moisture subsampled using a Burrows
Digital Moisture Computer 700 (Burrow Equipment, Evanston, IL). Seed and stover
tissue subsamples were combined over stations by tissue type, and stored for further

analysis.

Alfalfa was harvested three to four times per year, and five 1 m2 samples per
experimental plot were hand harvested to determine yield. Poplar trees were harvested by
clearcut in January 1999 and 2007, and the total woody biomass in each 1 ha plot chipped
and weighed. Vegetation in the early successional system was hand harvested from five
1 m2 sample areas per experimental plot at peak biomass in September. The deciduous
forest annual woody growth increment is estimated by changes in stem diameter for all
trees within a 400 m2 plot area within each of the three stands. Diameters were measured
at three locations per tree to estimate individual tree mass based on allometric equations
available in Tritton and Hombeck (1982). Leaf litter was collected in two 0.8 X 1.2 m
litter traps placed on the forest floor prior to spring leafout. Traps were emptied, dried,

and composited by plot over the season.

77

 

Trade-off curves were constructed for each combination of ecosystem services in
order to assess the degree to which there is a relationship between the provision of one
ecosystem service and another. Each system was also represented with its own flower
diagram. Flower diagrams have been used as a conceptual tool to compare trade-offs in
ecosystem services with different land uses (Foley et al. 2005). We constructed a flower
diagram for each of the management systems using information for each measured
service as a way to compare systems. The relative size of each petal denotes the

proportional delivery of the service it represents.

Statistical Analysis

The experiment was analyzed as a completely randomized design (CRBD) with 7
treatments and 6 replicates of each treatment. Comparisons were completed using SAS
(SAS Version 8.2, SAS Institute 1999). Greenhouse gas fluxes, grain yield, total nitrate
leaching, net primary productivity, soil nitrogen, and soil carbon levels were compared
using proc mixed. Correlation analyses were performed using proc corr.

RESULTS

Individual Ecosystem Services

Nitrate leaching was tracked from 1996-2007. As reported in Chapter 3, highest
leaching rates were found in the conventional row-crop system, which lost 680 (i 104
s.e.) kg NO3'-N ha'l during the 11 year period. The no-till system leached about a third
less nitrogen, losing 455 (i 33) kg NO3'-N ha". The reduced input and organic systems
lost significantly less nitrogen than either of the other annual systems, losing 267 (i 8)
and 209 (i 9) kg N03'-N ha", respectively. The alfalfa lost 141.1 (g 19.8) kg NO3'-N ha'

', mainly during the period of replanting. Both the early successional and poplar systems

78

lost very little nitrogen, 12.3 (1r 4.7) and 0.8 (i 0.4) kg NO3'-N ha", respectively. The
deciduous forest system lost 119.7 (i 46.7) kg NO3'-N ha'l over this period.

Drainage (as an indicator for groundwater replenishment) was highest in the no-
till system and deciduous forest system, with 4.19 and 4.00 m, respectively, draining
through the profile from 1996-2007. The conventional system had 3.62 m drainage. The
reduced-input and organic systems, alfalfa, poplars, and early successional system
drained about half that of the no-till drainage, draining 2.07 to 2.44 m.

As reported in Chapter 4, soil carbon levels to 1 meter depth ranged between 10.5
(i 1.7) kg C In2 in the early successional community to 6.4 (i 0.7) kg C m'2 in the
reduced-input annual system. The baseline conventional system contained 7.0 (i 0.6) kg
C m'z, suggesting that some systems gained as much as 3.5 kg C In2 over the past two
decades. Total soil nitrogen levels were similar in all the annual systems, though the
alfalfa was significantly greater than the organic system, early successional, and
deciduous forest systems.

The no-till system had the highest grain yields, with an average of 4.00 (i 0.68)
Mg ha'1 from 1993-2007. The conventional and low-input systems had lower yields,
averaging 3.66 (i 0.66) and 3.63 (i 0.64) Mg ha", respectively, though these values were
still higher than for the organic system, which averaged 2.79 (i 0.52) Mg ha". These
yields included 5 full rotations of maize-soybean-wheat.

Annual net primary productivity was highest in the reduced input system,
averaging 10.4 (i 0.2) Mg ha"y'l over the period of 1993 to 2007. The other annual
systems all showed lower production, with 9.2 (i 0.3) Mg ha‘ly'l in the organic system,

9.3 (i 0.4) Mg ha"y'I in the conventional system, and 9.7 (i 0.3) Mg ha'ly'l in the no-till

79

 

system. The alfalfa, deciduous forest, and early successional system produced 6.7 (i 0.1),
6.5 (i 0.7), and 6.2 (i 0.3) Mg ha'ly'l respectively, while the lowest production was in
the poplar system with 4.5 (i 0.2) Mg ha'ly‘l.

Nitrous oxide production was highest in the annual systems and in the alfalfa,
which emitted on average from 2.96 to 4.62 g ha'l d"l over the period 1991 to 2007.
Nitrous oxide production was lowest in the poplar system, emitting 0.93 (i 0.15) g N20-
N ha" d'l. Methane oxidation was highest in the deciduous forest system (-9.91 i 0.96 g
CHa ha'l d" on average), and lowest in the conventional, no-till, and reduced-input

system (range -1 .32 to -1.56 g CH4 ha" d")

80

 

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81

Table 4.3 Correlation matrix for all measures of ecosystem services. Values are listed as
correlations and p-values.

 

Nitrate Drain Grain NPP Nitrous Methane Soil N
Leach Yield Oxide
Nitrate 1.0
Leaching
Drainage 0.54 1.0
p500]
Grain Yield 0.53 0.66 1.0
p=0.08 p50.01
NPP 0.65 0.34 0.49 1.0
p500] p=0.02 p=0.02
Nitrous 0.59 -0.04 0.02 0.70 l .0
Oxide p500] p=0.85 p=0.95 p500]
Methane 0.26 -0.39 0.37 0.24 0.31 1.0
p=0.22 p=0.03 p=0.l6 p=0.l9 p=0.09
Soil -0.03 -0.06 0.29 0.04 0.09 0.27 1.0
Nitrogen p=0.91 p=0.70 p=0.l7 p=0.l9 p=0.6l p=0.l3
Soil -0.19 -0.16 0.04 -0.26 -0.06 0.01 0.27
Carbon =0.37 p=0.28 P=O.85 p=0.09 p=0.74 p=0.96 p=0.07

82

 

 

 

Trade-off Curves

Soil carbon levels were higher in systems with lower nitrous oxide emissions,
more methane oxidation, and higher net primary productivity. Nitrate leaching was found
in systems with higher drainage, and leaching levels greater than 200 kg NO3'-N ha'l
during the 11 year period were only found in the annual cropping systems. Higher

methane oxidation was associated with higher soil carbon, and lower nitrous oxide fluxes.

 

 

 

12000
572' ‘>'~ 80004
E g “'2 6000 g
.5125 a, 4000 +4
z n. =5 2000 ~
0 fi """I‘ " i I '
0 2 4 6 8

Nitrous Oxide Flux (9 ha" d")

Figure 4.2 Relationship between net primary productivity and nitrous oxide fluxes on the
Kellogg Biological Station Long Term Ecological Research Site. The curve represents a
best-fit quadratic equation F2729.9 Ln(x) + 5329.6 (12:0.603).

83

1000

 

5 .2

§ 2. 500 <

_I 'n o

3 c2’ 0 ~ ,___ ~ 10 +1 ~ 8 a
5.5 3’ 0 2000 4000 6000 8000 1000012000
2 " -500 -~

Net Primary Productivity
(kg ha-1 yr-1)

Figure 4.3 Relationship between nitrate leaching and net primary productivity at the KBS
LTER Site. The line describes the regression equation y = 0.0682x -— 302.62 (1'2 = 0.469).

84

 

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maximum ecosystem service provisioning possible at our site, while a smaller petal
represents a decrease in ecosystem service provisioning relative to that service as

delivered in another ecosystem.

86

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88

DISCUSSION

Ecosystems in this agricultural landscape vary markedly in their delivery of
ecosystem services, even within broad community types such as grain-based row crops or
successional communities. Within row crops, for example, management leads to big
differences in the capacity of each system to deliver enhanced soil quality, climate
regulation, groundwater recharge, and primary production even with similar levels of
crop productivity.

Under conventional management, the maize-soybean—wheat grain-based system
delivered moderate to high levels of groundwater recharge, net primary productivity, and
grain yield, and little water quality protection or climate protection. Grain production was
high, as was annual net primary productivity and soil quality as indicated by soil
inorganic nitrogen, but soil carbon and methane oxidation were low and nitrous oxide
production was high.

Under no—till management, the grain-based system delivered moderate to high
levels of groundwater recharge, annual net primary productivity, and grain yield, with
more water quality protection than the conventional system. Grain production was high,
as was annual net primary productivity and soil quality as indicated by soil nitrogen, but
soil carbon and methane oxidation were low and nitrous oxide production was high.

In contrast, the reduced-input system delivered high grain yields, annual net
primary productivity, and soil quality as measured by soil nitrogen, but had less
groundwater recharge than the conventional and no-till systems. The reduced-input
system also provided greater water quality protection than either the conventional or no-

till systems. The organic system had lower grain yields than the other annual crops,

89

 

however, although annual net primary productivity and soil quality as measured by soil
nitrogen were both still quite high. The organic system delivered greater water quality
protection and methane oxidation than did any of the other annual cropping systems. The
organic system also provided less groundwater recharge than the conventional or no-till
systems.

The perennial crops excelled at the delivery of water quality protection and soil
quality as measured as carbon content, though they did not produce any grain and had
lower annual net primary productivity and soil nitrogen than the annual systems. The
alfalfa had the highest soil carbon contents of any of the systems, and had lower nitrate
leaching than any of the annual systems. The poplar also had high soil carbon levels, and
had lower leaching levels than any other system. The poplars also showed a reduction in
nitrous oxide emissions compared to the annual crops.

The successional systems delivered improved climate protection and water quality
protection, although productivity and soil quality as indicated by soil inorganic nitrogen
levels decreased. The early successional system showed reduced net primary
productivity. However the early successional system did show improved climate and
water quality protection through reductions in both nitrous oxide emissions and nitrate
leaching. Soil quality was lower than in the annual systems in terms of soil nitrogen, but
soil carbon levels were higher than in the conventional and reduced input annual systems.
The deciduous forest had high levels of water quality protection, groundwater recharge,
and climate protection. The deciduous forest also had higher methane oxidation than any
other system, and only produced about half as much nitrous oxide as the annual systems.

The forest had lower productivity and soil nitrogen levels compared to the annual crops.

90

 

 

These ecosystem service differences among systems provide a basis for
describing trade-offs among particular services. Trade-off curves provide insights into
how management can optimize the delivery of multiple services. For some services (c. g.
methane oxidation and nitrate leaching) there is a direct trade-off with food production,
while the delivery of other services synergistically increase as food production increases
(e. g. water use conservation). Even within the annual cropping systems, the particular
management of each system drives the production of ecosystem services. This is
particularly true for the use of nitrogen fertilizer, cover crops, and tillage, which seem to
play a dominant role in determining the state of ecosystem services in these systems.

Many of these trade-offs are what one would expect based on current knowledge
of ecosystem functioning. However, some of these relationships, while obvious, do not
appear to be linear. In the deciduous forest system, for example, methane oxidation is
very high. However in all other systems methane oxidation is less than one fifth of rates
in the deciduous forest. This may be due to shifts in microbial populations that occurred
when these systems were tilled and fertilized. Yet even in the early successional system,
which was last tilled twenty years ago, this service has shown little recovery. When
changing from one type of management to another, managers must be aware that it is
possible to encounter these large shifts in ecosystem service provisioning which do not
appear to be reversible in fairly long period of time. Other services can also take years to
develop or recover — soil carbon and its associated services, for example, may take
decades after the cessation of tillage to recover to levels that provide significant fertility

benefits. Conversely, carbon sequestration (climate mitigation) benefits will be provided

91

immediately but eventually will diminish as soil carbon saturates 50-100 years post-
tillage.

When increasing management intensity, there is a trade-off between the yield in
the systems and some of the other ecosystem services provided. By changing crops and
management, microbial and plant communities change, and thus the ftmctioning of these
systems change. Similar trade-offs were noted at our site by McSwiney and Robertson
(2005), who found that additional nitrogen fertilization improved yields but at a cost to
climate regulation in the form of increased nitrous oxide production.

Knowing that trade-offs exist and understanding the relationship between land-
use types and the services they offer presents an opportunity for implementing land use
strategies that balance the ecosystem services that are provided across the landscape.
Because no one land use is optimal for all ecosystem services, having a multifimctional
landscape will help to balance ecosystem service provisioning. However, implementing
such a mixed landscape will be difficult and probably require legislation, particularly in
those portions of the world dominated by millions of hectares of monoculture crops.
Diversifying the landscape may help offset the imbalances these types of landscapes

create.

Management Implications

There are many trade-offs among competing goals in agriculture, in particular
between the economic goals of farmers and the food and environmental goals of society.
A better understanding of the key biological, biogeochemical, and ecological processes

that function in agricultural systems should lead to a scenario in which food production

92

can increase without an increase in environmental degradation (Tilman et al. 2002).
Managing for multiple ecosystem services (Robertson and Harwood 2001) is an idea that
is entirely foreign to almost all commercial agricultural producers today, and it is a
process that will take further research to support informed management decisions
(Robertson and Swinton 2005).

There is a growing body of research on changing the suite of ecosystem services
produced by ecosystems (Antle and Capalbo 2002, Maier and Shobayashi 2001) and on
using a systems approach to analyze the complex mechanisms driving the ecosystem
services (Robertson et al. 2004). Particularly in agricultural systems, soil biological,
chemical, and physical processes are driving the production of many of the ecosystem
services that are important in these systems. A systems approach is necessary to
understand the complex interactions between all the components of the system. This sort
of approach lends itself well to modeling, and sites like KBS are ideal for this sort of
research where many of the individual components of the system have been well studied
and could be used to parameterize models of ecosystem firnction.

Further research should focus on not only understanding the way ecosystems
function, but also on the way farm managers make decisions. With trade-offs between
competing goals, it is important to understand the priorities of managers and how they
make choices between alternative outcomes. Knowing the costs of producing various
ecosystem services will allow policy makers to adjust incentives to correct imbalances in
the provisioning of different ecosystem services. Further research will be needed on the

valuation of ecosystem services to give common currency to the multitude of services

93

provisioned, in order for decision makers to compare the relative suites of ecosystem

services produced in different systems.

Conclusions

In conclusion we found that

1.

Differences in the provision of services are marked in ecosystems typical of
agricultural landscapes.

The management of each system, and in particular the use of nitrogen
fertilizer, cover crops, and tillage, seems to play a dominant role in
determining the state of ecosystem services in these systems.

In our ecosystems differences including greater production of provisioning
services (food production and fresh water replenishment) in the conventional
and no-till systems, great production of regulating services (climate protection
and water quality) in the poplar successional systems, and increased
supporting services (soil quality) in the annual systems (soil inorganic
nitrogen) and perennial systems (soil carbon).

Tradeoff curves showed both linear and nonlinear relationships among
alternative services, with some services such as methane oxidation and nitrous
oxide abatement being synergistic, and others such as yield vs. nitrate
leaching being antagonistic.

Management for services should include the suite of services available from
specific systems in a landscape, and diversification of landscapes could be

used to diversify and enrich the variety of services provided.

94

 

6. Future research is needed to both identify the full suite of services available
from alternative management and cropping systems and to value services in

order to provide a means to compare services in the same currency.

95

 

CHAPTER 5

SUMMARY AND FUTURE DIRECTIONS

Summary
Ecosystem services at Kellogg Biological Station’s Long Term Ecological

Research (KBS LTER) Site were responsive to changes along a management intensity
gradient. As systems were managed more intensely, methane oxidation decreased, while
nitrate leaching, net primary productivity, and nitrous oxide production all increased.
These sorts of analyses, involving multiple ecosystem services, are a first step towards
better understanding the large scale trade-offs of land management decisions.
Future Directions

Research is needed in the area of the impact of individual crops, and particularly
cover crops, on the rates of nitrate leaching and drainage from particular soil types. This,
in addition to research on the impacts of tillage and nitrogen fertilization levels in
particular soils, would be useful in order to provide information necessary to design
models that land managers and agricultural producers could use to make decisions about
the crops they would grow, rates of fertilizer application, and the type of tillage they
would use on their particular farms, considering their particular soil type and climate.
This would also allow modelers to integrate at a landscape scale the effects of land-use
changes (such as deforestation, afforestation, etc.) on water resources. Water quality and
quantity is an emerging issue that will inevitably necessitate the conservation of water
resources. Improved crop management is necessary to protect the ground water that

nourishes vast portions of the global population.

96

 

Further research on carbon sequestration should focus on determining the
distribution of carbon through out the soil, and the patterns of variability with depth. This
is particularly important for crops with seep rooting depths, such as trees, alfalfa, grasses,
and corn. Assessing this change in carbon distribution with depth in different soil types,
crops, and climates is necessary for the accurate prediction of the potential for
agricultural soils to be used as a mitigation strategy to deal with the current climate crisis.

Future research in multiple ecosystem service trade-offs should focus on using
the data we have gathered at the KBS LTER to help with the development and
improvement of ecological models to enable them to better predict multiple outcomes of
management decisions. The SALUS model is one such model that is already in use at the
KBS LTER that has the potential to include many of the parameters that were discussed
in this work. I am particularly interested in modeling nitrous oxide and methane fluxes so
that policy makers have better information and tools to use when trying to make decisions
about global climate change policies and their impacts. Other avenues of research will
include looking at the impact of biodiversity in agricultural systems, particularly on the
impact of greater taxonomic and genetic diversity in a system on the provisioning of

ecosystem services.

97

APPENDIX
CHAPTER 2 SUPPLEMENTARY MATERIALS

SAS Output on nitrate leaching values using the proc mixed procedure.

Covariance Parameter Estimates
Cov Parm Estimate

Residual 4. l 693

Fit Statistics

-2 Res Log Likelihood 96.3
AIC (smaller is better) 98.3
AICC (smaller is better) 98.5
BIC (smaller is better) 99.3
The Mixed Procedure

Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr> F

Trt 9 20 47.15 <.OOOl

98

I It. ._._A.) may. V
L ' :
;

 

APPENDIX
CHAPTER 3 SUPPLEMENTARY MATERIALS

Table A.1 Coefficients of Variation for bulk density and profile layer thickness in KBS
LTER Treatments

 

 

A/Ap B/Bt Bt2/C

Thickness BD ' Thickness BD Thickness BD
cv cv cv cv cv cv

Conventional 0.28 0.08 0.26 0.07 0.13 0.05

Nn-n" 0.12 0.08 0.14 0.05 0.1 1 0.05

Reduced

Input 0.16 0.03 0.22 0.05 0.17 0.04

Orgamc 0.18 0.07 0.23 0.06 0.14 0.06

Alfalfa 0.23 0.07 0.17 0.07 0.20 0.08

Poplar 0.30 0.09 0.18 0.04 0.21 0.04

Conifers 0.08 0.06 0.23 0.03 0.24 0.04

Early

Successional 0.18 0.05 0.23 0.06 0.13 0.05

Mid-

Succesional '

(HT) 0.18 0.05 0.20 0.09 0.29 0.01

Mid-

Successional

(NT) 0.03 0.05 0.06 0.03 0.12 0.03

Deciduous

Forest 013 0.03 0.18 0.08 0.18 0.02

Average 0.17 0.06 0.19 0.06 0.17 0.04

 

99

 

Table A2 Coefficients of Variation for carbon concentrations and total carbon in KBS
LTER Treatments.

A/Ap BE! _BtZ/C M.
Conc. Total Con Total Conc. Total Total
CV CV 0. CV CV CV CV

CV

Conventional 0.07 0.08 0.41 0.41 0.27 0.41 0.21
NO-till 0.09 0.07 0.28 0.29 0.98 0.92 0.26

Reduced Input 0.26 0.28 0.35 0.37 0.49 0.49 0.30

Organic 0.08 0.06 0.27 0.27 0.82 0.77 0.24
Alfalfa 0.02 0.20 0.38 0.38 0.90 0.96 0.35
Poplar 0.40 0.41 0.57 0.53 1.08 1.02 0.22
Conifers 0.24 0.22 0.67 0.67 0.69 0.43 0.30
Early 0.09 0.05 0.15 0.17 0.26 0.38 0.09
Successional

Mid- 0.40 0.40 0.40 0.46 0.19 0.29 0.37
Successional

(HT)

Mid- 0.07 0.07 0.19 0.23 0.47 0.50 0.15
Successional

(NT)

Deciduous 0.25 0.26 0.43 0.42 0.53 0.58 0.32
Forest

Average 0.18 0.19 0.37 0.38 0.61 0.61 0.25

100

APPENDIX
CHAPTER 4 SUPPLEMENTARY MATERIALS
Statistical Output for Nitrous Oxide using the mixed procedure in SAS.
The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
Rep 0.004888
CS Trt*Rep 0.007611
Residual 1.7664
Fit Statistics
-2 Res Log Likelihood 19584.8
AIC (smaller is better) 19590.8
AICC (smaller is better) 19590.8
BIC (smaller is better) 19588.9
Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr> F

Trt 6 20.5 51.22 <.0001

Year 15 5596 90.81 <.0001
Trt*Year 90 5596 2.92 <.0001

101

 

Statistical Output for Methane using the mixed procedure in SAS.

The Mixed Procedure
Covariance Parameter Estimates
Cov Parm Subject Estimate
Rep 0.03324

CS Trt*Rep 0.02828
Residual 9.2719

Fit Statistics

-2 Res Log Likelihood 26443.2
AIC (smaller is better) 26449.2
AICC (smaller is better) 26449.2
BIC (smaller is better) 26447.3

Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr>F

Trt 6 20.6 3.33 0.0188

Year 14 5119 20.87 <.0001
Trt*Year 84 5119 1.71 <.0001

102

Statistical Output for Soil Inorganic Nitrogen using the mixed procedure in SAS.
The Mixed Procedure

Covariance Parameter
Estimates

Cov Parm Estimate

rep 0

Residual 0.41 3 1

Fit Statistics
-2 Res Log Likelihood 85.9
AIC (smaller is better) 87.9
AICC (smaller is better) 88.0
BIC (smaller is better) 87.7
Type 3 Tests of Fixed Effects

Num Den
Effect DF DF F Value Pr>F

trt 7 32 67.03 <.0001

103

Statistical Output for Grain Yields using the mixed procedure in SAS.

The Mixed Procedure

Covariance Parameter
Estimates

Cov Parm Estimate

rep 1.4963
CS 10.6025
Residual 0.8278

Fit Statistics

-2 Res Log Likelihood 1667.8
AIC (smaller is better) 1667.8
AICC (smaller is better) 1667.8
BIC (smaller is better) 1667.8

Type 3 Tests of Fixed Effects

Num Den
Effect DF DF FValue Pr>F

trt 3 288 142.06 <.0001

date 14 288 282.92 <.0001
date*trt 42 288 13.69 <.0001

104

 

BIBLIOGRAPHY

Andraski, T.W., L.G. Bundy, and KR. Brye. 2000. Crop management and corn nitrogen
rate effects on nitrate leaching. Journal of Environmental Quality 29(4): 1095-1103.

Angers, D.A., and J. Caron. 1998. Plant-induced changes in soil structure: Processes and
feedbacks. Biogeochemistry 42:55-72.

Antle, J .M., and RD. Valdivia. 2006. Modelling the supply of ecosystem services from
agriculture: a minimum-data approach. Australian Journal of Agricultural and Resource
Economics. 50(1): 1 — 15.

Antle, J .M and SM Capalbo. 2002. Agriculture as a Managed Ecosystem: Policy
Implications. Journal of Agricultural and Resource Economics 27(1):1-15.

Baker, J .M., T.E. Ochsner, R.T. Venerea, and T.J. Griffis. 2007. Tillage and soil carbon
sequestration—What do we really know? Agriculture, Ecosystems and Environment 118:
1-5.

Ball, J ., J. Carle, and A. Del Lungo. 2005. Contributions of poplars and willows to
sustainable forestry and rural development. Unasylva 56 (2). Accessed online at
http://www.fao.org/docrep/OO8/a0026e/a0026e02.htrn on July 22, 2008.

Basso, B. 2000. Digital terrain analysis and simulation modeling to assess spatial
variability of soil water balance and crop production. Dissertation. Michigan State
University, Department of Crops and Soil Sciences, East Lansing, Michigan.

Basso, B. and J. T. Ritchie. 2005. Impact of compost, manure and inorganic fertilizer on
nitrate leaching and yield for a 6-year maize-alfalfa rotation in Michigan. Agriculture,
Ecosystem and Environment 108: 329-341.

Basso, B., M. Bertocco, et al. 2006. Analyzing the effects of climate variability on spatial

pattern of yield in a maize-wheat-soybean rotation. European Journal of Agronomy 26:
82-91.

Basso, B., J .T. Ritchie, P.R. Grace, and L. Sartori. 2006. Simulation of tillage systems
impacts on soil biophysical properties using the SALUS model. Italian Journal of
Agronomy 4: 677-688.

Basso, B., J .T. Ritchie, 1.A. White, and A. Gerakis. 2008. Incorporating seasonal rainfall
intensity and soil properties into a daily surface runoff model. Submitted to Water
Resources Research.

Borken, W., and W. Matzner. 2004. Nitrate leaching in forest soils: an analysis of long-

terrn monitoring sites in Germany. Journal of Plant Nutrition and Soil Science 167(3):
277-283.

105

 

Buck, E. H., C. Copeland, et al. 1997. Pfiesteria and related harmful blooms: natural
resource and human health concerns, CRS Report for Congress.

Burgin, A.J. and SK. Hamilton. 2007. Have we overemphasized the role of
denitrification in aquatic ecosystems? A review of nitrate removal pathways. Frontiers in
Ecology and the Environment 5: 89-96.

Cabrera,M.L., Endale, D.M., Radcliffe, D.E., Steiner, J .L., Vencill, W.K., Lohr,
L.,Schomberg, H.H.Tillage and fertilizer source effects on nitrate leaching incotton
production inSouthern Piedmont. pp49-50. In J. E. Hook (ed.) Proc., 22nd Annual
Southern Conservation TillageConference for Sustainable Agriculture.Tifton,GA.July 6-
8, l999.GeorgiaAgricultural ExperimentStation Special Publication 95.Athens,GA.

Calderia, K., Morgan, M.G., Baldocchi, D., Brewer, P.G., Chen, C.T.A., Nabuurs, G-J.,
Nakicenovic, N., and Robertson, GP. 2004. A Portfolio of Carbon Management Options.
The Global Carbon Cycle. C. B. Field and M. R. Raupach. Washington, DC, USA, Island
press 103-129.

Canadell P, et al. 2007 Contributions to accelerating atmospheric C02 grth from
economic activity, carbon intensity, and efficiency of natural sinks. Proc Natl Acad Sci
USA 104:18866—18870.

Carter, M.R 2005. Long-term tillage effects on cool-season soybean in rotation with
barley, soil properties, and carbon and nitrogen storage for fine sandy loams in the humid
climate of Atlantic Canada. Soil Tillage Res. 81: 109-120.

CAST. 2004. Climate change and greenhouse gas mitigation: challenges and
opportunities for agriculture. Ames, Iowa, USA: Council for Agricultural Science and
Technology (CAST).

Chichester, F.W., 1977. Effects of increased fertilization rates on nitrogen content of
run off and percolate from monolith lysimeters. J. Environ. Qual. 6:211-217.

Copeland, C. (2000). Water Quality: Implementing the Clean Water Act. Washington,
DC: Congressional Research Service. The National Council for Science and the
Environment.

Crum, J .R., and HP. Collins. 1999. KBS soils. Available at
www.lter.kbs.msu.edu/soil/characterization (verified 20 July 2008). WK.
Kellogg Biol. Stn. Long-Term Ecol. Res. Project, Michigan State Univ., Hickory
Corners, MI.

Daily, G.C. 1997. Nature’s Services: Societal Dependence on Natural Ecosystems. Island
Press, Washington. 392pp.

106

 

DeGryze, S., J. Six, K. Paustian, S.J. Morris, E.A. Paul, and R. Merckx. 2004. Soil
organic carbon pool changes following land-use conversions. Global Change Biology 10:
1120-1132.

Dolan, M.S., C.E. Clapp. R.R. Allmaras, J .M. Baker, and J .A.E. Molina. 2006. Soil
organic nitrogen in a Minnesota soil as related to tillage, residue, and nitrogen
management. Soil Tillage Res. 89: 221-231.

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

Ecological Society of America. 1997. “Ecosystem Services: Benefits Supplied to Human
Societies by Natural Ecosystems.” Issues in Ecology No. 2, Spring 1997.
http://www.esa.org/sbi/sbi_issues/ (URL updated 12/03)

Evangelou et a1. 1984. An automated manometric method for quantificative
determination of calcite and dolomite. Soil Science Society of America Journal 48: 1236-
1239.

Feng, H., L. A. Kurkalova, C. L. Kling, and P. W. Gassman, 2005. Economic and
Environmental Co-benefits of Carbon Sequestration in Agricultural Soils: Retiring
Agricultural Land in the Upper Mississippi River Basin. Centre for Agricultural and
Rural Development, Iowa State University, Ames Iowa. WP 05-WP 384.

Penn, M.E., M.A. Poth, J .D. Aber, J .S. Baron, B.T. Bormann, D.W. Johnson, A.D.
Lemly, S.G. McNulty, D.F. Ryan, and R. Stottlemyer. 1998. Nitrogen Excess in North
American ecosystems: Predisposing factors, ecosystem responses, and management
strategies. Ecological Applications 8(3): 706-733.

Foley, J .A., R. DeFries, G.P. Asner, C. Barford, G. Bonan, S.R. Carpenter, F.S. Chapin,
MT. Coe, G.C. Daily, H.K. Gibbs, J .H. Helkowski, T. Holloway, E.A. Howard, C.J.
Kucharik, C. Monfreda, J .A. Patz, I.C. Prentice, N. Ramankutty, and PK. Snyder. 2005.
Global Consequences of Land Use. Science 309: 570-574.

Food and Agriculture Organization of the United Nations (FAO). 2003. The state of food
insecurity in the world. Rome, Italy. Accessed at
httpz/lwww.fao.org/docrep/006/j0083e/j0083e00.HTM.

Fox, R.H., Zhu, Y., Toth, J.D., Jemison, J .M., Jr., and Jabro, J .D. 2001. Nitrogen
fertilizer rate and crop management effects on nitrate leaching from an agricultural field
in central Pennsylvania. In Optimizing Nitrogen Management in Food and Energy

Production and Environmental Protection: Proceedings of the 2"d International Nitrogen
Conference on Science and Policy. The Scientific World 1(82): 181-186.

Frankenberger, J. and R. Turco. 2003. Hypoxia in the Gulf of Mexico: A Reason to
Improve Nitrogen Management. Animal Issues Briefing. Purdue University.

107

 

Franzluebbers, A.J. 2004. Tillage and residue management effects on soil organic
matter.p. 227—268. In. F. Magdoff and RR. Weil (ed.) Soil organic matter in sustainable
agriculture. CRC Press. Boca Raton, FL.

Grandy, A. S., T. D. Loecke, S. Parr, and G. P. Robertson. 2006. Long-term trends in
nitrous oxide emissions, soil nitrogen, and crop yields of till and no-till cropping systems.
Journal of Environmental Quality 35: 1487-1495.

Grandy, AS, and Robertson GP. 2006. Aggregation and organic matter protection
following cultivation of an undisturbed soil profile. Soil Sci Soc Am J 70:1398—1406.

Grandy, AS, and Robertson, GP. 2007. Land-Use Intensity Effects on Soil
Organic Carbon Accumulation Rates and Mechanisms. Ecosystems 10: 58-73.

Groffrnan, P. M., G. J. House, P. F. Hendrix, D. E. Scott and D. A. Crossley Jr. 1986.

Nitrogen Cycling as Affected by Interactions of Components in a Georgia Piedmont
Agroecosystem. Ecology, Vol. 67(1): 80-87

Gruber, N., and Galloway, J. 2008. An Earth-system perspective of the global nitrogen
cycle. Nature 451: 293-296.

Guo ZW, Xiao XM, and Li DM. 2000. An assessment of ecosystem
services: water flow regulation and hydroelectric power production.
Ecol Appl 10: 925—36.

Hansen, 8., HF. Alroe and KB Steen. 2001. Approaches to assess the environmental
impact of organic farming with particular regard to Denmark. Agriculture, Ecosystems
and Environment. 83(1-2): 11-26.

Houghton, R. A., and J. L. Hackler. 2001. Carbon Flux to the Atmosphere from Land-
Use Changes: 1850 to 1990. ORNL/CDIAC-l31, NDP-OSO/Rl. Carbon Dioxide
Information Analysis Center, US. Department of Energy, Oak Ridge National
Laboratory, Oak Ridge, Tennessee, USA.

House, G. J ., B. R. Stinner, D. A. Crossley, Jr and E. P. Odum. Nitrogen Cycling in
Conventional and No-Tillage Agro-Ecosystems: Analysis of Pathways and Processes.
1984. The Journal of Applied Ecology, Vol. 21(3): 991-1012

International Panel on Climate Change. (IPCC). 2001. Contribution of Working Group III
to the Third Assessment Report of the Intergovernmental Panel on Climate Change
(IPCC). Bert Metz, Ogunlade Davidson, Rob Swart and J iahua Pan (Eds.). Cambridge
University Press, UK.

108

Jussy, J .H., M. Colin-Belgrand, E. Dambrine, J. Ranger, B. Zeller, and S. Bienaime.
2004. N Deposition, N transformation, and N leaching in acid forest soils.
Biogeochemistry 9(2): 241-262.

Kanwar, R.S., J .L. Baker, and D.G. Baker. 1988. Tillage and split N-fertilization effects
on subsurface drainage water quality and crop yields. Trans. Of ASAE. Vol. 31
(2):453-461.

Kirchmann, H., and L. Bergstrom. 2001. Do organic farming practices reduce nitrate
1eaching?. Communications in Soil Science and Plant Analysis, 32(7):997-1028.

Kling, C., M. Secchi, et al. 2006. Upper Mississippi River Basin modeling system part 3:
Conservation practice scenario results. Highland Ranch, Colorado, Water Resources
Publication.

Kononova, M. M. 1975. Humus of virgin and cultivated soils. Pages 475-526 in J. E.
Gieseking, editor. Soil components. Volume 1. Springer, New York, New York, USA.

Kramer, S. B., J. P. Reganold, et al. 2006. Reduced nitrate leaching and enhanced
denitrifier activity and efficiency in organically fertilized soils. PNAS 103: 4522-4527.

Kremen C. 2005. Managing ecosystem services: what do we need to
know about their ecology? Ecol Lett 8: 468—79.

Lal, R., 2003. Global potential of soil carbon sequestration to mitigate the greenhouse
effect. Crit. Rev. Plant Sci. 22 (2), 151—184. ‘

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

Loerppert, RH. and D.L. Suarez. 1996. Carbonate and gypsum. Pp. 437-475 in: Sparks,
D.L. [ed.], Methods of soil analysis, Part 3-Chemical Methods. SSSA Book Series No. 5.

Machado, P.L.O.A., S.P. Sohi, and J .L. Gaunt. 2003. Effect of no-tillage on turnover of
organic matter in a Rhodic F errasol. Soil Use Manag. 19: 250-256.

Maier, L., and M. Shobayashi. 2001. “Multifunctionality: Towards an Analytical
Framewor ”, Paris (OECD Publications Service).

Marriott, E.E., and M.M. Wander. 2006. Total and labile soil organic matter in organic
and conventional farming systems. Soil Sci. Soc. Am. J. 70:950—959.

Martens, DA. 2000. Plant residue biochemistry regulates soil carbon cycling and carbon
sequestration. Soil Biology and Biochemistry 32: 361-369.

Martin, E.C., T.L. Loudon, J .T. Ritchie and A. Werner. 1994. Use of Drainage

Lysimeters to Evaluate Nitrogen and Irrigation Management Strategies to Minimize
Nitrate Leaching in Maize Production. Trans. of the ASAE. 37(1):79-83.

109

Martin, E.K., A. Tanksley, D.C. Slack, and B. Basso. 2006. Effects of fresh and
composted dairy manure applications on alfalfa yield and the environment in Arizona.
Agronomy Journal 98: 80-84.

McCasland, M., N.M. Trautrnann, K.S. Porter, and R.J. Wagenet. 1998. Nitrate: Health
Effects in Drinking Water. Cornell Cooperative Extension Service, Fact Sheet Page
400.02.

McLaughlin, RA. 1985. Biomass and nitrogen dynamics in an irrigated hybrid poplar
plantation. PhD Thesis. Purdue University, West Lafayette, Indiana.

McSwiney, C. and G. P. Robertson. 2005. Nonlinear response of N20 flux to incremental
fertilizer addition in a continuous maize (Zea mays) cropping system. Global Change
Biology 11: 1-8.

Millenium Ecosystem Assessment; 2005. Ecosystems and human well being: A
framework for assessment. Washington D.C.: Island Press.

Mitsch, W.J., J .W. Day, J .W. Gilliam, P.M. Groffman, D.L. Hey, G.W. Randall, and
nitrogen. Wang. 1999. Reducing nitrogen loads, especially nitrate-nitrogen, to surface
water, groundwater, and the Gulf of Mexico: Topic 5 report for the integrated assessment
on hypoxia in the Gulf of Mexico. NOAA Coastal Ocean Program Decision Analysis
Series No. 19. NOAA Coastal Oceans Program, Silver Spring, MD. lllpp.

Mulholland, P.J., A.M. Helton, G.C. Poole, R.O. Hall, S.K. Hamilton, B.J. Peterson, J .L.
Tank, L.R. Ashkenas, L.W. Cooper, C.N. Dahm, W.K. Dodds, S.E.G. Findlay, S.V.
Gregory, N.B. Grimm, S.L. Johnson, W.H. McDowell, J .L. Meyer, H.M. Valett, J .R.
Webster, C.P. Arango, J .J . Beaulieu, M.J. Bemot, A.J. Burgin, C.L. Crenshaw, L.T.
Johnson, B.R. Niederlehner, J .M. O’Brien, J .D. Potter, R.W. Sheibley, D.J. Sobota, and
SM. Thomas. 2008. Stream denitrification across biomes and its response to
anthropogenic nitrate loading. Nature 452(7184): 202-204.

Murty. D., M.U.F. Kirschbaum, R.E. McMurtrie, H. McGilvray. 2002. Does forest
conversion to agricultural land change soil organic carbon and nitrogen? A review of the
literature. Global Change Biology 8, 105-123.

National Atmospheric Deposition Program. 2008. Kellogg Biological Station Monitoring
Station M126 Data. Available at
http://nadp.sws.uiuc.edu/sites/siteinfo.asp?neFNTN&id=M126.

National Research Council (NRC). 2003. Frontiers in agricultural research. Food, health,
environment, and communities. National Academy Press, Washington, DC, USA.

110

Ogden, C.B., H.M. van Es, R.J. Wagenet, and TS. Steenhuis. 1999. Spatial-temporal
variability of preferential flow in a clay soil under no-till and plow-till. J. Envir. Qual.
28:1264-1273.

Pimentel, D., P. Hepperly, J. Hanson, D. Douds, and R. Seidel. 2005. Environment,
energetic, and economic comparisons of organic and conventional farming systems.
BioScience 55: 573-581.

Power, J .F., R. Wiese, and D. Flowerday. 2001. Managing farming systems for nitrate
control: a research review from management systems evaluation areas. Journal for
Environmental Quality. 30: 1866-1880.

Powlson, D.S., and D.S. Jenkinson. 1981. A comparison of the organic matter, biomass,
adenosine triphosphate, and mineralizable nitrogen contents of ploughed and direct-
drilled soils. J. Agric. Sci. 97: 1 108-1113.

Pradel, W., D. Yanggen, et a1. 2005. Tradeoffs between economic returns and methane
greenhouse gas emissions in dairy production systems in Cajamarca, Peru, International
Potato Center.

Puget, P., and R. Lal. 2005 Soil organic carbon and nitrogen in a Mollisol in central Ohio
as affected by tillage and land use. Soil Till. Res. 80: 201—213.

Pulleman, M.M., J. Bouma, E.A. van Essen, and E.W. Meijles. 2000. Soil organic matter
content as a function of different land use history. Soil Sci. Soc. Am. J. 64:689—693.

Rabelais, N.N., E. E. Turner and W.J. Wiseman, Jr. 2001. Hypoxia in the Gulf of
Mexico. J. Environ. Qual. 30:320-329.

Rasmussen, P.E., Goulding, K.W.T., Brown, J.R., Grace, P.R., Janzen, H.H., and M.
Korschens. 1998 Long-Term Agroecosystem Experiments: Assessing Agricultural
Sustainability and Global Change. Science 282 (5390), 893.

Rasse, Daniel P., Ritchie, Joe T., Peterson, W. Richard, Loudon, Ted L., Martin, Edward
C. 1999. Nitrogen Management Impacts on Yield and Nitrate Leaching in Inbred Maize
Systems. Journal of Environmental Quality 28: 1365-1371

Rasse, DP. and A.J.M. Smucker. 1999. Tillage effects on soil nitrogen and plant
biomass in a corn alfalfa rotation. J. Env. Qual. 28:873-880.

Ribaudo, M., N. Gollehon, M. Aillery, J. Kaplan, R. Johansson, J. Agapoff, L.
Christensen, V. Breneman, and M. Peters (2003). Manure Management for Water
Quality: Costs to Animal Feeding Operations of Applying Manure Nutrients to Land.
AER-824, US. Dept. Agr., Econ. Res. Serv., June.

111

Ritchie, J .T. 1998. Soil water balance and plant stress. In G.Y. Tsuji, G. Hoogenboom,
and PK. Thorton (Eds), Understanding Options for Agricultural Production. Kluwer
Academic Publishers, Dordrecht, The Netherlands, pp. 41-54.

Ritchie, J .T., and B. Basso. 2008. Water use efficiency is not constant when crop water
supply is adequate or fixed: The role of agronomic management. European Journal of
Agronomy 28: 273-281.

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

Robertson, GP. 1997. Nitrogen use efficiency in row crop agriculture: crop nitrogen use
and soil nitrogen loss. Pages 347-365 in L. Jackson, ed. Ecology in Agriculture,
Academic Press, NY.

Robertson, G. P., E. A. Paul, and RR. Harwood. 2000. "Greenhouse gases in intensive
agriculture: contributions of individual gases to the radiative forcing of the atmosphere."
Science 289: 1922-1925.

Robertson, G. P., and R. R. Harwood. 2001. Sustainable agriculture. Pages 99-108 in S.
A. Levin, ed. Encyclopedia of Biodiversity. Academic Press, New York.

Robertson, G. P., J. C. Broome, E. A. Chomesky, J. R. Frankenberger, P. Johnson, M.
Lipson, J. A. Miranowski, E. D. Owens, D. Pimentel, and L. A. Thrupp. 2004.

Rethinking the vision for environmental research in US. agriculture. BioScience 54: 61-
65.

Robertson, G. P., and S. M. Swinton. 2005. Reconciling agricultural productivity and
environmental integrity: A grand challenge for agriculture. Frontiers in Ecology and the
Environment 3: 38-46.

Robertson, G. P., L. W. Burger, et al. 2007. New approachs to environmental
management research at landscape and watershed scales. Managing Agricultural
Landscapes for Environmental Quality. M. Shnepf.

Sanchez, J. E., R. R. Harwood, et al. (2004). "Managing Soil Carbon and Nitrogen for
Productivity and Environmental Quality." Agronomy Journal 96: 769-775.

SAS Institute. 1999. SAS/STAT user’s guide. Version 7. SAS Inst.,
Cary, NC.

Schleppi, P., F. Hagedom, and I. Providoli. 2004. Nitrate leaching from a mountain forest
ecosystem with gleysols subjected to experimentally increased N Deposition. Water, Air
and Soil Pollution: Focus, 4(2-3): 453-467.

Schlesinger, W.M. 1977. Carbon balance in terrestrial detritus. Ann. Rev. Ecol. 8:51—81.

112

Schlesinger, W.M. 1995. An overview of the C cycle. p. 9—26. In R. Lal et al. (ed.) Soils
and global change. CRC Press, Boca Raton, FL.

Schuur EAG, Bockheim J, Canadell JG, Euskirchen E, Field CB, et al. (2008)
Vulnerability of Permafrost Carbon to Climate Change: Implications for the Global
Carbon Cycle. BioScience: Vol. 58, No. 8 pp. 701—714.

Six, J ., B.T. Elliot, K. Paustian, and J .W. Doran. 1998. Aggregation and soil organic
matter accumulation in cultivated and native grassland soils. Soil Science Society of
America Journal 62: 1367-1377.

Smeenk, J. 2003. The impacts of continuous corn and a com-soybean-wheat rotation
grown under various management schemes on nitrate leaching, soil physical
chracteristics and net returns. Ph. D. Dissertation East Lansing, MI, USA, Michigan State
University.

Smith, S.J., J .S. Schepers, and L.K. Porter. 1990. Assessing and managing agricultural
nitrogen losses to the environment. Adv. In Soil Sci. 14:1-43.

Stockdale, E.A., N.H. Lampkin, M. Hovi, R. Keatinge, E.K.M. Lennartsson, D.W.
Macdonald, S. Padel, F.H. Tattersall, M.S. Wolfe, and CA. Watson. 2001. Agronomic
and environmental implications of organic farming systems. Adv. Agron. 70:261—327.

Stoorvogel, J .J .; Antle, J .M.; Crissman, C.C.; Bowen, W. 2001. The tradeoff analysis
model version 3.1: a policy decision support system for agriculture. Wageningen, the
Netherlands : Lab. of Soil & Geology, 2001. - (Tradeoff Analysis Model ; 3.1). - ISBN
90—6754-660-7 - p. 93.

Subramanian, SK. 2008. Effect of topography and soil properties on spatial variability of
soil carbon (C) loss in different crop management systems in a long-term experiment.
Doctoral Dissertation. Department of Crops and Soil Sciences, Michigan State
University, East Lansing, Michigan.

Suleiman, A.A., and J .T. Ritchie. 2003. Modeling soil water redistribution during second-
stage evaporation. Soil Science Society of America Journal 67: 377-3 86.

Suleiman, A.A., and J .T. Ritchie. 2004. Modifications to the DSSAT vertical drainage
model for more accurate soil water dynamics estimation. Soil Science 169(11)745-757.

Swift,R.S. 2001. Sequestration of carbon by soil. Soil Science 166 : 835—858.
Swinton, S. M., F. Lupi, G. P. Robertson, and S. K. Hamilton. 2007. Ecosystem services

and agriculture: cultivating agriculture ecosystems for diverse benefits. Ecological
Economics 64: 245-252.

113

Teasdale, J .R., C. B. Coffrnan, and R W. Mangum 2007. Potential Long-Term Benefits of
No-Tillage and Organic Cropping Systems for Grain Production and Soil Improvement
Agron. J. 2007 99: 1297-1305.

Tietema, A., C. Beier, P.H.B. de Visser, B.A. Emmett, P. Gunderson, OJ. Kjonaas, and
C.J. Koopmans. 1997. Nitrate leaching in coniferous forest ecosystems: The European
field-scale manipulation experiments NITREX (nitrogen saturation experiments) and
EXMAN (experimental manipulation of forest ecosystems). Global Biogeocherrrical
Cycles 11(4): 617-626.

Tilman, D., K.G. Cassman, P.A. Matson, R. Naylor, and S. Polasky. 2002. Agriculture
sustainability and intensive production practices. Nature 418: 671-677.

Tritton, L.M. and J .W. Hombeck . 1982. Biomass equations for major tree species of the
northeast. Broomall, PA: Northeast. For. Exp. Stn.; USDA For. Serv. Gen. Tech. Rep.
NE-69.

Tyler, DD, and G.W. Thomas. 1977. Lysimeter measurements of nitrate and chloride
losses from soil under conventional and no-tillage corn. J. Environ. Qual. 6:63—66.

VandenBygaart, A.J., B.G. Gregorich, and DA. Angers. 2003. Influence of agricultural
management on soil organic carbon: a compendium and assessment of Canadian studies.
Can. J. Soil Sci. 83: 363-380.

VanVeen, J .A., and Paul, EA. 1981. Organic carbon dynamics in grassland soils. 1.
Background information and computer simulation. Canadian Journal of Soil Science. 61:
185-201.

Vitousek, P. M.; Reiners, W. A. 1975. Ecosystem succession and nutrient retention: a
hypothesis. BioScience 25: 376—381.

Vitousek, P. M., J. D. Aber, R. W. Howarth, G. E. Likens, P. A. Matson, D. W.
Schindler, W. H. Schlesinger, and D. G. Tilman. 1997. Human alteration of the global
nitrogen cycle: causes and consequences. Ecological Applications 7(3): 737-750.

Watts, D.G., and D.L. Martin. 1981. Effects of water and nitrogen management on nitrate
leaching loss from sands. Trans. ASAE. 24:911—916.

West, T.O., and Post, W.M. 2002. Soil organic carbon sequestration rates by tillage and

crop rotation: a global data analysis. Soil Science Society of America Journal 66: 1930-
1946.

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