THE INFLUENCE OF COW-CALF GRAZING
SYSTEMS ON CARBON FLUX
By
Marília Barbosa Chiavegato

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
Animal Science – Doctor of Philosophy
2014

ABSTRACT
THE INFLUENCE OF COW-CALF GRAZING
SYSTEMS ON CARBON FLUX
By
Marília Barbosa Chiavegato
Grazing management has been identified as the most suitable means to increase forage
production and reduce GHG emissions from the cow-calf sector, while potentially increasing
carbon sequestration. The grazing management applied to pastures determines factors such as
forage growth, residuals accumulation, manure distribution, and soil properties, which create
favorable or unfavorable conditions for microbial populations to develop. Methane, N2O and
CO2 production and consumption in soils are microbial processes. Forage maturity determines
forage quality and CH4 production in the rumen. This study applied a system-based approach to
assess net GHG exchange, in terms of C equivalent, and soil organic C accumulation in pastures
grazed with cow-calf herds under different stocking rate and densities, and non-grazed pasture
sites. Data were collected post-grazing and at a farm-scale and the variability associated to GHG
emissions from pasture soils was very large. CO2 emissions did not differ between systems. Soil
and ambient temperature and soil water content had effects on CO2 emissions. The effect of
grazing was not conclusively observed on CH4 and N2O emissions. In addition, soil and ambient
temperature and soil water content did not conclusively explain CH4 and N2O emissions. Other
soil properties might be better predictors of CH4 and N2O, such as water filled pore space
(WFPP) or soil O2 content. Further research is needed to confirm the effect of WFPS and O2
content on GHG emissions. We did not observe any clear trade-offs between GHG; generally
GHG emissions increased from 2011 to 2013, which was likely associated to weather changes.
Our results indicate that grazing management did not affect daily enteric CH4 emissions from

lactating beef cows. Additionally, CH4 emissions tended to be lower than reported values for
lactating beef cows. The selective grazing allowed cows managed with different grazing
strategies to eat forage with similar qualities that met nutritional requirements with reduced CH4
emissions. Results indicate forage quality might be a better predictor to daily CH4 emissions than
DMI. Grazing systems resulted in higher C equivalent flux than non-grazed pasture sites, which
was a result mainly of enteric CH4 emissions. However, the effect of greater enteric CH4
contribution from high stocking rate systems was offset by GHG exchange from the soil, and C
equivalent flux was not different between grazing systems. High stocking rate, low stocking
density system potentially increased total SOC stock, the addition of SOC to deeper layers and
SOM. However, low stocking rate, high stocking density systems accrue litter on top of the soil.
SOM decomposition rate was slower on the low stocking rate, high stocking density system,
which could allow for greater resilience to adverse conditions. Long-term research is needed to
confirm SOC sequestration potential of these systems and SOM decomposition rates. Grazing
management should be adaptive and farm management decisions are inherent to management.
Both grazing systems have opportunities to improve ecosystems services at the farm level,
including animal production and food provisioning.

ACKNOWLEDGEMENTS

I have many people to thank for their contribution in this dissertation. I would first like to
thank my advisors, Dr. Wendy Powers and Dr. Jason Rowntree, for their guidance and support
during my graduate program, but also for giving me the opportunity to pursue the PhD degree at
Michigan State University. I would also like to thank my guidance committee: Dr. Robert
Tempelman, Dr. Tim Harrigan, Dr. Santiago Utsumi and Dr. Kurt Steinke for their insights and
unique perspective on the topics I worked on.
I would like to thank the staff at the Lake City AgBioResearch Center Doug Carmichael,
Ty Stroud and Tom Brunnick for all their help during the sampling periods and for always
assuring I had everything I needed to complete the field work. I want to extend my appreciation
to the undergraduate students who spent several weeks with me in Lake City: Natalie Palumbo,
Brooke Latack, Kaitlyn Thompson, and Rachel Baumgardner. I am also grateful to the AAQRF
staff; Jolene Roth, Mark Schilling, Andrew Fogiel, Venkata Vadella and Zifei Liu, without their
help my projects could not be completed. Additionally, I want to thank Dr. Steve Bursian and
Kathy Tatro for their help and support.
Special thanks to my dearest friend Paola Piantoni, for her inspiration, support to keep me
going through difficult times and company during the happy times. I want to thank all my
Brazilian friends who have also supported me psychologically. Special thanks to my sorority
sisters at República Alforria. I want to express my deepest gratitude to my parents and siblings,
for their understanding and always being by my side, providing me strength to complete this
journey. Especially I want to thank my fiancé, Guilherme Signorini. I would not be able to
complete this PhD without his help, company, support, love, patience and friendship.

iv

TABLE OF CONTENTS

LIST OF TABLES ........................................................................................................................ vii
LIST OF FIGURES ....................................................................................................................... ix
KEY TO ABBREVIATIONS ......................................................................................................... x
CHAPTER 1 LITERATURE REVIEW ......................................................................................... 1
1.1. Introduction ...................................................................................................................... 2
1.2. Grazing management........................................................................................................ 5
1.3. Grazing management and enteric CH4 emissions ............................................................ 6
1.4. Grazing management and CH4 flux from grassland soil .................................................. 7
1.5. Grazing management and N2O flux from grassland soil.................................................. 9
1.6. Grazing management and soil properties ....................................................................... 10
1.7. Carbon balance in grazing systems ................................................................................ 12
LITERATURE CITED ............................................................................................................. 16
CHAPTER 2 PASTURE-DERIVED GREENHOUSE GAS EMISSIONS IN COW-CALF
GRAZING SYSTEMS.................................................................................................................. 24
2.1. Introduction ........................................................................................................................ 25
2.2. Material and Methods......................................................................................................... 26
2.2.1. Study site and pasture management............................................................................. 26
2.2.2. Soil emission measurements ........................................................................................ 28
2.2.3. Soil gas flux calculation and dependent variable measurements ................................ 31
2.2.4. Statistical analysis........................................................................................................ 32
2.3. Results and Discussion ....................................................................................................... 33
2.3.1. Study site and soil properties ....................................................................................... 33
2.3.2. CO2 emissions.............................................................................................................. 34
2.3.3. CH4 emissions.............................................................................................................. 38
2.3.4. N2O emissions ............................................................................................................. 41
2.4. Conclusion.......................................................................................................................... 45
LITERATURE CITED ............................................................................................................. 53
CHAPTER 3 ENTERIC METHANE FROM LACTATING BEEF COWS ............................... 60
3.1. Introduction ........................................................................................................................ 61
3.2. Material and Methods......................................................................................................... 62
3.2.1. Pasture management .................................................................................................... 62
3.2.2. Enteric methane measurements ................................................................................... 64
3.2.3. Intake determination and forage analyses ................................................................... 65
3.2.4. Statistical analysis........................................................................................................ 67
3.3. Results and Discussion ....................................................................................................... 67
3.3.1. Herbage mass, botanical composition and forage nutritional characteristics .............. 67
3.3.2. Animal performance .................................................................................................... 71
v

3.3.3. CH4 emissions from beef cows .................................................................................... 72
3.4. Conclusion.......................................................................................................................... 75
LITERATURE CITED ............................................................................................................. 82
CHAPTER 4 CARBON FLUX ASSESSEMENT IN COW-CALF GRAZING SYSTEMS ...... 87
4.1. Introduction ........................................................................................................................ 88
4.2. Material and Methods......................................................................................................... 89
4.2.1. Pasture management and GHG collection ................................................................... 89
4.2.2. Soil bulk density determination ................................................................................... 91
4.2.3. Soil organic matter and C and N stocks determination ............................................... 91
4.2.4. C flux calculations ....................................................................................................... 93
4.2.5. Statistical analysis........................................................................................................ 95
4.3. Results and Discussion ....................................................................................................... 96
4.3.1. Soil characteristics ....................................................................................................... 96
4.3.2. SOC and TSN stock and SOM content ....................................................................... 96
4.3.3. Total C equivalent flux .............................................................................................. 102
4.4. Conclusion.................................................................................................................... 107
LITERATURE CITED ........................................................................................................... 117
CHAPTER 5 SUMMARY .......................................................................................................... 123

vi

LIST OF TABLES

Table 2.1. Ongoing and historic precipitation, maximum and minimum air temperature at the
study site and total irrigation applied to paddocks…………………………………………….....47
Table 2.2. Summary of soil characteristics in the study area………………………………….....48
Table 2.3. Soil properties of pasture sites grazed with two different management strategies and
non-grazed pasture sites………………………….………………………………………………49
Table 2.4. CO2 emissions from of pasture sites grazed with two different management strategies
and non-grazed pasture sites………………………………………………..……………………50
Table 2.5. Effects of soil properties on CO2 emissions from pasture sites grazed with two
different management strategies and non-grazed pasture sites…………………………………..50
Table 2.6. CH4 emissions from pasture sites grazed with two different management strategies
and non-grazed pasture sites……………………………………………………………………..51
Table 2.7. Effects of soil properties on CH4 emissions from pasture sites grazed with two
different management strategies and non-grazed pasture sites…………………………………..51
Table 2.8. N2O emissions from pasture sites grazed with two different management strategies
and non-grazed pasture sites……………………………………………………………………..52
Table 2.9. Effects of soil properties on N2O emissions from pasture sites grazed with two
different management strategies and non-grazed pasture sites……………………………..……52
Table 3.1. Forage mass pre and post-grazing and forage disappearance for pastures grazed with
different grazing management strategies…………...……………………………………………76
Table 3.2. Botanical composition of pastures grazed with different grazing management
strategies…………………………………………………………………………………………77
Table 3.3. Nutritional characteristics of pastures grazed with different grazing management
strategies…………………………………………………………………………………………78
Table 3.4. Body weight, dry matter intake, gross energy intake, NDF intake and digestible NDF
intake of cows grazed with different grazing management strategies………………………..….80
Table 3.5. Methane emissions from cows grazed with different grazing management
strategies…………………………………………………………………………………...…….81

vii

Table 4.1. Soil bulk density in pasture soils grazed under two management strategies and nongrazed……………………………………………………………………………………….…..109
Table 4.2. Soil organic carbon and total soil nitrogen stocks in pasture soils grazed with high and
low stocking rates and pasture soils non-grazed………………………………………………..110
Table 4.3. GHG exchange from pasture soils and animal and total C equivalent flux from pasture
sites managed under two different management strategies and non-grazed pasture sites…...…111
Table 4.4. Annual GHG emissions from soil and animal managed under two different grazing
strategies and non-grazed pasture sites………………………………………………….…...…113

viii

LIST OF FIGURES

Figure 4.1. Soil organic matter in pasture soils grazed with two different grazing management
strategies and non-grazed pastures sites.…………………..…………………………………...114
Figure 4.2. Soil carbon stock in pasture soils grazed with two different grazing management
strategies and non-grazed pastures sites.…………………………………...…………………..115
Figure 4.3. Total soil nitrogen stock along the soil profile in pasture soils grazed with two
different grazing management strategies and non-grazed pastures sites.………………………116

ix

KEY TO ABBREVIATIONS

ADF…………………………………………Acid detergent fiber
AT…………………………………………..Ambient temperature
BD…………………………………………..Bulk density
BW………………………………………….Body weight
BW0.75……………………………………….Metabolic body weight
C…………………………………………….Carbon
Ceq………………………………………….Carbon equivalent
Ceqflux……………………………………….Net GHG exchange in terms of C equivalent
CH4………………………………………….Methane
CO2……………………………………….…Carbon dioxide
CP…………………………………...............Crude protein
Cr2O3………………………………………..Chromium oxide
DM………………………………………….Dry matter
DMI………………………………………....Dry matter intake
DNDF……………………………………….Digestible neutral detergent fiber
DNDFI………………………………………Digestible neutral detergent fiber intake
ECD…………………………………………Electron capture detector
FCH4cows……………………………………...C equivalent flux of enteric CH4 from cows
FCH4soil……………………………………….C equivalent flux of CH4 from pasture soil
FCO2………………………………………….C equivalent flux of CO2 from pasture soil
FID…………………………………………..Flame ionization detector

x

FN2O……………………………………….....C equivalent flux of N2O from pasture soil
GC………………………………………..….Gas chromatograph
GE………………………………………..….Gross energy
GEI………………………………………..…Gross energy intake
GHG……………………………………..…..Greenhouse gas
IVTD…………………………………..…….In vitro true digestibility
LU……………………………………..…….Livestock unit
LW……………………………………..……Live weight
N…………………………………………….Nitrogen
N2…………………………………………....Nitrogen gas
N2O………………………………………….Nitrous oxide
NDF………………………………………....Neutral detergent fiber
NDFI………………………………………...Neutral detergent fiber intake
O2……………………………………………Oxygen
SF6…………………………………………...Sulfur hexafluoride
SOC……………………………………….....Soil organic carbon
SOM…………………………………………Soil organic matter
ST……………………………………………Soil temperature
TSN………………………………………….Total soil nitrogen
WC…………………………………………..Water content in the soil
WFPS………………………………………..Water filled pore space

xi

CHAPTER 1
LITERATURE REVIEW

1

1.1. Introduction
The competitive beef market and consumer concern about climate change and global
warming have led the beef industry to search for production systems capable of maintaining beef
supplies while mitigating environmental impacts. In this evolving context, numerous questions
are open for discussion. Research focused on beef production, in particular, has considered what
role grazing systems play when discussions turn to global warming: should grazing systems be
viewed as mitigation options or as active contributors to climate change?
Beef cattle production contributes to global climate change primarily through enteric
methane (CH4) production. The Environmental Protection Agency affirms that the cow-calf
sector of the beef industry is the largest CH4 emitter within U.S. livestock industries, responsible
for 58% of all CH4 emissions compared with 23% from the dairy sector and 19% from the
feedlot and stocker sector (EPA, 2013). The agency suggests that total emissions from the beef
cow-calf sector are high for several reasons such as; diets, consisting mainly of forages and of
varying quality, are generally poorer than diets fed in the dairy or feedlot sectors; the level of
management is typically not as good as in other sectors; and the cow-calf population is
historically very large. In addition to enteric CH4, cow-calf grazing systems are associated with
other greenhouse gas (GHG) emissions. The urine nitrogen (N) breakdown in pasture soil results
in nitrous oxide (N2O) emissions (Flechard et al., 2007), and feces decomposition are associated
to CH4 emissions (Holter, 1997). On the other hand, Bodelier and Laanbroek (2004) have
observed the potential of grassland soils to sink atmospheric CH4, and Conant et al. (2001)
suggested that improved grazing management potentially reduces atmospheric concentrations of
carbon dioxide (CO2) through carbon (C) sequestration on grazing lands.

2

Grazing management has been identified as the most suitable alternative to improve
animal efficiency and reduce GHG emissions from the cow-calf sector (EPA, 2013) while
potentially increasing carbon sequestration. The grazing management applied to pastures
determines factors such as forage growth, residuals accumulation, and manure distribution.
These factors affect soil properties (e.g. soil moisture, temperature, pH, aeration, and density)
which create favorable or unfavorable conditions for microbial populations to develop. Methane,
N2O and CO2 production and consumption in soils are microbial processes. Furthermore, forage
growth determines forage quality and CH4 production in the rumen.
Previous research have identified pasture soils as potential sink for atmospheric CH4
(Bodelier and Laanbroek, 2004). Zhou et al. (2008) have suggested that grazing intensities have
different impact on the structure of the methanotroph community. Nazaries et al. (2011) studying
soil CH4 fluxes from grassland soils observed an increase in CH4 consumption following
changes in soil management. Their work has postulated the hypothesis that methanotroph
communities could be intentionally manipulated with field management to increase their CH4
oxidation activity.
Ruminants excrete on average 85% of the ingested N, and excess dietary N is excreted
mainly in the urine (Castillo et al., 2000). Urine deposition on grazed pasture soil provides
optimal conditions for N2O production. While more research is needed to precisely quantify N2O
emissions from beef cattle production systems and to identify specific options for emissions
mitigation, efficiency improvements can reduce N2O emissions (Eckard et al., 2010). Recycling
and maintenance of N within the system reduce the need for inorganic fertilization and increase
utilization of urine N. de Klein and Eckard (2008) suggested that if animal urine in grazing
systems were spread more evenly across paddocks the effective N application rate from urine

3

deposition would decrease, potentially reducing N2O emissions. Rotational grazing management
has been associated to more evenly distribution of excreta on paddocks (White et al., 2001), but
whether rotational grazing effectively reduces N2O is unknown.
The effects of grazing management on C cycling and distribution have been evaluated
before, however, literature does not yet suggest clear relationships between grazing management
and C sequestration. Some studies have reported no effect of grazing on soil organic C (SOC;
e.g. Milchunas and Laurenroth, 1993), others reported increases (Wienhold et al., 2001) or a
decrease (Derner et al., 1997). The lack of clear relationship between C stocks and grazing
management has been associated to climate, inherent soil properties, landscape position, plant
community composition, and grazing management practices (Reeder and Schuman, 2002);
factors that affect C cycling and sequestration potential on grasslands.
Research outcomes are variable with respect to the potential of grazing management to
mitigate GHG emissions. Studies analyzed the following issues without searching for
interactions within the system: enteric CH4 production (e.g. Jones et al., 2011), N2O emission
from pasture soil (e.g. Horvath et al., 2010), and accumulation of SOC (e.g. Schipper and
Sparling, 2011). However, a holistic view considers different ecological processes and their
relationships, perhaps uncovering new relationships and conclusions.
The primary objective of this work was to apply a system-based approach quantifying
GHG flux from 2 grazing management practices and identify the grazing practice that
contributes the most to climate change mitigation. The next sections review the latest research
conducted in GHG flux, soil properties in grazing systems, pasture management and systemsbased approaches for estimating GHG flux in grazing systems.

4

1.2. Grazing management
The most common grazing management on rangelands is continuous year-round
stocking. Previous research suggests that paddocks grazed continuously exhibit uneven herbage
consumption, where certain plants are heavily grazed while others are lightly grazed or
completely avoided (Witten et al., 2005).
Teague et al. (2004) suggest that the uneven grazing of the paddock initiates a spiral
degradation that is accelerated during periods of below average precipitation. Briske et al. (2008)
affirms rotational grazing is not superior to continuous grazing in terms of forage and animal
productivity and question the efficacy of multi-paddock grazing management for maintaining or
improving rangeland conditions. Teague et al. (2009) criticize that Briske et al. (2008) did not
take into account plant and animal processes at appropriate spatial and temporal scale and the
study was not adaptively managed to achieve desirable soil, vegetation and livestock goals, thus
resulting in incorrect interpretations for rangeland management.
Rotational grazing embraces more than paddocks subdivisions and variable rest periods.
Rotational grazing is conservation-oriented livestock grazing management with the objective to
maintain or improve forage production and forage harvesting efficiency as results of soil
chemical, physical and hydrological properties (Teague et al., 2011). Improvement of forage
production is related to soil water infiltration rates and water-holding capacity (Snyman, 2003).
Conservation-oriented grazing management, when well implemented, is accompanied by several
environmental benefits. When the focus goes beyond animal and forage productivity, there is
evidence that conservation-oriented grazing management is superior to continuous grazing
(Teague et al., 2013). Van der Ploeg et al. (2006) discussed the differences between research
conducted at small-scale and focused on a few related parameters (reductionist research) and

5

systems research involving interaction among elements in whole systems that takes into account
many related parameters. They suggested that reductionist research could result in completely
different understanding of a subject than whole systems research.
1.3. Grazing management and enteric CH4 emissions
When focusing on enteric CH4, grazing animals pose a bigger challenge than feedlot
animals because of difficulty to manipulate the diet. Pasture quality becomes a critical factor for
achieving reduced CH4 emissions from grazing animals (Boadi et al., 2004).
Implementing proper grazing management practices contributes to reduced CH4
emissions from pasture-based systems. Improved grazing management practices that focus on
reduction of fiber content of the sward can increase animal productivity and decrease CH4 per
unit of product with less dietary energy lost as CH4 (Beauchemin et al., 2008). Improving forage
quality tends to increase voluntary intake and reduce the retention time in the rumen, promoting
more energetically-efficient post-ruminal digestion and reducing the proportion of dietary energy
converted to CH4 (Blaxter and Clappert, 1965).
Forage type affects CH4 production; legumes often produce lower CH4 than grasses
(McCaughey et al., 1999; Van Dorland et al., 2007). Legumes have lower proportion of
structural carbohydrates (lower fiber content), presence of condensed tannins, and faster rate of
passage when compared to grasses (Beauchemin et al., 2008). These legumes characteristics
have the potential to shift fermentation patterns towards higher propionate production rather than
acetate (Johnson and Johnson, 1995).
The grazing management debate between continuous and rotational grazing is included in
previous research quantifying enteric CH4 emissions. McCaughey et al. (1997) observed that
CH4 production was greater from steers continuously grazed at low stocking rates (1.1 steers ha–

6

1

; 306.7 L d–1) than from steers grazed continuously at high stocking rates (2.2 steers ha–1; 242.2

L d–1). They did not observe differences in dry matter intake from steers grazed at different
stocking rates. Steers grazing mixed pastures composed of alfalfa and grass emitted 25% less
CH4 than steers grazing grass only pastures (McCaughey et al. 1997).
Methane production tends to increase with forage maturity. Grazing during the beginning
of the growing season, when forage is in vegetative stage, produced emissions from steers that
were 29% to 45% less compared with steers grazing during mid and late season, when forage is
in the reproductive stage (Boadi et al. 2002). Chaves et al. (2006) observed that cattle grazing
alfalfa sward in advanced stage of maturity produced more CH4 as compared with cattle grazing
young grass swards. Further research should focus on holistic systems views to establish the
relationships among improvements in diet quality, dry matter intake, stocking rates, and net CH4
production from different production systems (Eckard et al., 2010).
1.4. Grazing management and CH4 flux from grassland soil
The production and consumption of CH4 in soils results from biological processes and
therefore is directly affected by weather conditions and soil management. Methanogens and
methanotrophs have different requirements for growth on the soil. Methanogens are strictly
anaerobic Archaea that require low redox potential to develop. Methanotrophs are mainly
aerobic bacteria with a unique metabolism that uses CH4 as the only source of C and energy
through oxidation into CO2 (Henneberger et al., 2012). Van den Pol-Van Dasselar et al. (1999)
suggests that the production of CH4 in the soil is mainly controlled by moisture, aeration, type
and quantity of soil organic matter (SOM), pH and temperature. The main factors affecting CH4
consumption are concentrations of CH4 and oxygen (O2) in the soil and size of the
methanotrophs community. Both methanogens and methanotrophs can be found in various soil

7

types, but soil properties might support one type of microbial community over the other (Murrell,
2010). The oxidation of CH4 in aerobic soils represents an important mitigation strategy for
atmospheric CH4 (Lowe, 2006). Saggar et al. (2008) reviewed studies about CH4 consumption in
soils and concluded that forest soils demonstrated the highest potential for CH4 consumption,
followed by pasture soils.
Although grasslands have been identified as a sink for atmospheric CH4, feces deposited
by grazing animals are a CH4 source. Feces are high in moisture content, microbial communities,
and readily available C substrates, which can induce high CH4 production levels immediately
following deposition. Because methanogens are strictly anaerobic, their development in feces is
dependent on maintenance of moisture content. Feces deposited on pastures usually dry quickly
leading to short term CH4 emissions. The pattern of CH4 emissions from feces is quite constant,
beginning with an initial peak of emissions and quick declining over time. However, the duration
of CH4 emissions after feces deposition on pasture soils is dependent on weather conditions and
previous studies have shown different results. Williams (1993) confirmed that CH4 emissions
from feces in a hot, dry climate cease after 2 d during summer and 3 d during winter. Flessa et al.
(1996) found longer emissions lasting approximately 20 d, but decreasing over time as aerobic
conditions predominated. More recent studies have shown high levels of CH4 emissions from
feces during the first 2 week following deposition and decreasing over time, but lasting for a
period of approximately 35 d (Sherlock et al., 2002). Saggar et al. (2008) point out that after
feces decomposition the soil beneath the feces started to oxidize CH4, becoming a sink for
atmospheric CH4.

8

1.5. Grazing management and N2O flux from grassland soil
Intensively managed grasslands are a potential source of N2O due to soil fertilization and
urine deposition by grazing animals (Rafique et al., 2011). Nitrous oxide emission is strongly
affected by the rate and timing of organic and inorganic N applications. However, other factors
that act as controls for N2O production are the availability of O2, N content in the soil, soil
moisture and aeration, temperature, pH, and readily available C content. Soil moisture content
dictates either nitrification or denitrification as the major process producing N2O.
Nitrogen fertilization combined with adequate soil moisture can induce N2O emissions.
Eckard et al. (2010) observed that when keeping the N fertilization rate constant, N2O emissions
were dependent on moisture. Luo et al. (2010) observed that the addition of N in small amounts
at frequent intervals rather than a small number of large applications could reduce N2O
emissions. Uneven deposition of excreted N by grazing animals can result in “hotspots” of N
application equivalent to an application of 400 to 2000 kg N ha-1 year-1 in the small affected area
(Watson et al., 2007). The large amount of N applied in a small area could result in N2O
emissions when combined to appropriate soil conditions to N2O production (Eckard et al., 2010).
Methods to reduce N2O emissions from grasslands have been studied. Eckard et al.
(2010) affirms that restricting grazing on seasonally wet soils not only reduces N input from
urine, but reduces soil compaction, which increases anaerobic conditions on the soil. The
combined effect of reduced N fertilization and reduced grazing time on case study farms resulted
in 10% reduction in total farm N2O emissions (Schils et al., 2006). Rotational grazing with
appropriate rest periods results in restricted grazing and avoids overgrazing that could result in
lower N2O emissions when compared to continuous grazing.

9

Improved grazing management provides an overall decrease in N input at farm-scale, by
increasing N recycling within the system (Oenema et al., 2005). The introduction of legumes
through rotational grazing management can increase soil N, resulting in superior fertility and
decreasing the need for fertilization and potentially reducing N2O emissions (Eckard et al.,
2008).
1.6. Grazing management and soil properties
Soil functioning and ecosystem health of rangeland ecosystems rely on plant and litter
cover, which are important to provide protection from soil loss and allow soil microbes to
function (Bardgett et al., 2009). Plant and litter cover enhance water infiltration, buffer
temperatures and decrease soil water evaporation, keeping moisture levels high. These factors
enhance soil microbial activity, which promotes soils aggregate stability, sustain plant nutrient
status and availability, improve plant growing conditions and result in the incorporation of soil
organic matter (SOM). The soil-building factors that affect microbial activity consequently affect
GHG production or consumption in the soil, as well as C stock and sequestration.
Grazing management practices affect soil-building factors. For instance, excessive
grazing that causes excessive trampling can lead to soil degradation (Herrick and Jones, 2002).
Soil degradation is associated with soil compaction and increased bulk density, elevates
penetration resistance and reduces aggregate stability (Teague et al., 2011). Management that
generates bare ground also causes soil degradation. Bare ground is exposed to the sunlight and
temperature is increased, causing decreased microbial activity and accelerated loss of organic
matter (Thurow, 1991). Elevated soil temperature and soil loss have a direct negative effect on
infiltration rates, soil evaporation, nutrient retention and biological functions that contribute to
ecosystem function (Neary et al., 1999). Soil degradation affects GHG production in the soil;

10

increases loss of C, and decreases potential to sequester C and consume CH4 in grasslands by
decreasing microbial development. Previous research has demonstrated that rotational grazing
results in less bare ground, lower soil temperatures and higher soil C than continuous grazing at
the same stocking rate (Teague et al., 2010).
The type and amount of vegetation cover influences soil physical parameters and
hydrological properties. Plants producing greater amounts of foliage and root biomass increase
organic matter aboveground through litter accumulation and belowground through dead root
biomass decomposition (Milne and Haynes, 2004). Aboveground litter and plant cover create a
more consistent temperature and moisture microenvironment, which in turn favors microbial
activity (Devi and Yavada, 2006). These factors enhance formation of stable soil aggregates that
increase water infiltration and could improve soil fertility and soil C sequestration potential.
Soil organic C constitutes approximately 60% of SOM and has beneficial effects on the
chemical, physical and biological functions of soil (Bardgett et al., 2009). Soil organic C
increases the cation-exchange capacity of the soil and water-holding capacity, and contributes to
soil structure stability. Organic matter increases adsorption of nutrients, cations and trace
elements that are of importance to plant growth. The maintenance of high contents of SOM
contributes to overall soil health, as well as for atmospheric C sink (Lal, 2008).
Grassland SOM can be strongly influenced by management. Losses of SOM have
occurred due to over grazing and poor pasture management (Conant et al., 2001). However,
SOM losses can potentially be reversed and atmospheric C sequestered with good grazing
management. Pastures soil C can be increased by eliminating disturbances to the soil and by
increasing primary production. Management improvements intended to increase forage
productivity usually increase soil C (Conant et al., 2003).

11

1.7. Carbon balance in grazing systems
The major challenge in whole-system C accounting research is the definition of
assumptions and data selection. Often C accounting studies use previous published data and
modeling. In that case, there are two important decisions that need attention from grazing beef
cattle researchers: the inclusion or not of pastures C sequestration potential, and the choice of
enteric CH4 emissions factors.
The majority of models that estimate GHG balance in agriculture assume that established
systems achieve equilibrium conditions in SOC stock and hence do not consider C sequestration
potential of soils (e.g. Freibauer et al., 2004). Previous research has suggested that pastures lands
may in fact sequester C on an on-going basis. Soussana et al. (2007) challenged the concept of C
sink saturation in European grasslands, and Conant et al. (2003) propose that changes in
management strategies may also change SOC dynamics. Although some footprint tools consider
that pastures provide a greater range of ecosystem services than do agricultural croplands
(Pelletier et al., 2001), the exclusion of C sequestration potential may result in a overestimation
of GHG emissions.
Pelletier et al. (2010) assumed that pastures were in equilibrium and therefore not
sequestering atmospheric C for a C accounting of different beef production systems. However,
they performed a sensitivity analysis to include C sequestration potential and verify its impact on
results. Phetteplace et al. (2001) estimates C sequestration of 0.12 Mg C ha-1 year-1 for improved
pastures managed with cow-calf herds and 0.40 Mg C ha-1 year-1 for pastures recently converted
to management-intensive grazing. When Pelletier et al. (2010) included the potential of C
sequestration proposed by Phetteplace et al. (2001) in their accounting, GHG emissions per mass
live weight kg produced would be 8.2 kg less for beef finished on intensively-grazed improved

12

pastures and hay during the transition phase. Grass-finished beef would produce 15% less GHG
than feedlot-finished beef, and their conclusion would have been different. This is just an
example of how results could change with different assumptions. The C sequestration rates
suggests by Phettepplace et al. (2001) are estimates derived from very specific experimental
conditions and other studies have found no C sequestration on pasture soils (Derner et al., 1997).
More research is needed to elucidate C sequestration rates in grasslands ecosystems.
Given the importance of enteric CH4 to overall GHG emissions in beef production, the
choice of CH4 emissions factors can strongly influence modeling outcome. For example,
Pelletier et al. (2010) quantified GHG emissions from feedlots using a CH4 emissions factor of
5.5% instead of the 3% suggested by the Intergovernamental Panel of Climate Change (IPCC),
because their modeled diets did not contain 90% concentrates. They performed a sensitivity
analysis to calculate the impact of changing the CH4 emissions factor and found that estimated
emissions would be 6% lower using the 3% factor. However, the sensitivity analysis considered
the diet in question fed only during the finishing stage, which contributes 30% of total emissions.
Previous studies quantifying whole-system GHG emission from grazing beef cattle
production verified that intensive grazing could reduce emissions compared with less intensive
grazing beef systems (De Ramus et al., 2003). Best management practices in grazing systems
could reduce enteric CH4 emissions by as much as 22% compared to continuous grazing (De
Ramus et al., 2003). The management strategy chosen and entered into the footprint tool
influences greatly the output results. Pasture utilization rates may range from 30% to 90%, with
the highest utilization rates achieved in well-managed pastures where cattle rotated through
paddocks daily (Gerrish et al., 2002). Net emissions from grazing systems management at the
high end of this range were lower and the resource efficiencies were improved compared to

13

grazing systems managed with 60% pasture utilization rates (Pelletier et al, 2010). In addition,
most studies do not consider the potential benefits of organic-managed pastures and feed input
production strategies. Research in Ireland suggested that organic beef production might lower
emissions and improve resource use efficiencies (Casey and Holden, 2006).
Grazing management is also associated with stocking rates. The primary factor that
modifies the C flux returned to the soil by excreta is the grazing pressure, which varies with the
annual stocking rate. Secondary effects of grazing on the C cycle of a pasture include the role of
excreta returns for SOM mineralization and N cycling (especially in nutrient-poor grasslands),
and the role of hoof activity and defoliation by animals which reduce the leaf area and canopy
photosynthesis (Soussana et al., 2010).
An integrated approach that allows the simultaneous quantification of the 3 most
important GHG (CO2, N2O and CH4) is desirable as management choices to reduce emissions
involve potential trade-offs. For example, improving the primary production of grasslands by N
fertilizer supply may favor belowground C storage but likely leads to increased N2O and CH4
emissions (Vuichard et al, 2007). There is a clear need to investigate different grasslands sites to
further reduce uncertainties in C flux and GHG balances.
We propose the use of rotational grazing management practices to originate discussion of
GHG emissions, in terms of C-equivalent (Ceq), and soil C accumulation in cow-calf grazing
systems. A system-based approach is used to assess C accumulation and losses in pastures
grazed with cow-calf management differing in stocking rate and density. We understand that the
C cycle involves a large number of sources and sinks. However, the purpose of this study was to
focus on GHG emissions from pasture soils, CH4 emissions from cows and soil organic C
accumulation in pasture soils. Future studies may add other components to the cycle, such as

14

microbial development and other gases emissions. The central hypothesis of this study was that
rotational grazing with high stocking rate and low stocking density decreases enteric CH4
emissions, but increases GHG emissions from pasture soil and decrease soil organic C stocks in
the soil. The specific objectives of this study were to:


Quantify CO2, N2O and CH4 emissions from pasture soils grazed with cow-calf pairs
managed with different stocking rate and density;



Quantify enteric CH4 emissions from grazing cows managed with different stocking
rate and density;



Quantify C and N stocks in pasture soils grazed with cow-calf pairs managed with
different stocking rate and density;



Apply a system-based approach to determine the net GHG emissions and C flux from
cow-calf pairs managed with different stocking rate and density.

15

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16

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23

CHAPTER 2
PASTURE-DERIVED GREENHOUSE GAS EMISSIONS IN COW-CALF GRAZING
SYSTEMS

24

2.1. Introduction
Grazing management has been identified as the most suitable alternative to improve
production efficiency and reduce GHG emissions from cow-calf systems (EPA, 2007). The
grazing management applied to pastures determines factors such as forage growth, accumulation
of residues, soil compaction, and manure distribution. These factors, in turn, affect soil properties
(e.g. soil moisture, temperature, pH and aeration), which create favorable or unfavorable
conditions for microbial population development. Given that production and consumption of
CH4, N2O and CO2 in soil are microbial processes, the flux of these important GHG from
grassland soils are inter-dependent on grazing management.
Assessing the impact of land use and land use change on emissions requires attention to
the relationships among all GHG (Robertson et al., 2000). In grasslands ecosystems, CO2 is
exchanged with the soil and vegetation, N2O is emitted by soils, and CH4 is emitted by grazing
livestock and exchanged with the soil (Soussana et al., 2004). Management choices to reduce
emissions involve important trade-offs. For example, preserving grasslands and adapting
grasslands management to improve C sequestration may actually increase N2O and CH4
emissions at farm scale (Smith et al., 2001). Within grassland ecosystems, N2O emissions tend to
be highly variable in space and time (Oenema et al., 1997), mainly due to heterogeneous
distribution of urine and variability of soils properties that control soil moisture (Mosier et al.,
1998). Depending on factors that regulate the activity of soil microbes responsible for CH4
production and consumption, grasslands soils can be small CH4 sinks (Chan and Parkin, 2001) or
sources (Allard et al., 2007).
There is a lack of information regarding the simultaneous evaluation of CO2, CH4 and
N2O in pasture soils. The objective of this study was to quantify GHG fluxes from pasture soils

25

grazed with cow-calf pairs managed with different stocking rate and density. The central
hypothesis was that high stocking rate, low stocking density grazing systems resulted in greater
GHG emissions from pasture soils, because of shorter rest periods resulting in more often
manure deposition.
2.2. Material and Methods
2.2.1. Study site and pasture management
This study was conducted at Michigan State University Lake City AgBioResearch Center
(latitude 44°18’N, longitude: 85°11’W; elevation 377 m) located in northwest MI. The region,
developed over glaciated soils, was primarily deciduous and coniferous forest before farming
entered the area in the 1860’s. In the study site, 64% of the area is dominated by the Nester soil
series, comprised of well drained sandy loams containing 1 to 6% slopes. The remaining area is
dominated by the Kawkawlin soil series which is characterized by heavier soil texture and gentle
slope (NRCS, 1999). Ongoing and historic weather data, including precipitation and maximum
and minimum air temperature were obtained from an onsite weather station (NOAA, 2013).
Cow-calf pairs were managed with 2 rotational grazing management practices differing
in stocking rate and density; an intensive system with high stocking rate and low stocking
density, and an extensive system with low stocking rate and high stocking density. The system
with low stocking rate and high stocking density (SysA) consisted of 120 cow-calf pairs rotating
on a total of 120 ha, divided into 0.7 ha paddocks. Cow-calf pairs were moved to a new paddock
3 times daily (at approximately 0600 h, 1200 h and 1700 h). The equivalent stocking rate was 1
cow ha-1 and the stocking density was approximately 100,000 kg live weight (LW) ha-1. The rest
period varied from 60 to 90 d during the course of the growing season depending on plant
growth. Cow-calf pairs grazed each paddock 2 to 3 times per year. The system with high

26

stocking rate and low stocking density (SysB) consisted of 4 cow-calf pairs rotating on 1.6 ha
pasture, divided into 0.08 ha paddocks. Cow-calf pairs were moved to a new paddock once daily
(at approximately 0800 h). The equivalent stocking rate was 2.5 cows ha-1 and the stocking
density was 28,000 kg LW ha-1. The rest period varied from 18 to 30 d during the course of the
growing season depending on plant growth. Cow-calf pairs grazed each paddocks 4 to 5 times
per year. The pasture sites in SysB were irrigated as needed, whereas there was no irrigation
applied to SysA pasture sites. The only fertilization application was on SysB pasture sites that
received urea fertilization (23 kg of actual urea) on June 3rd of 2011 (approximately 30 d before
the start of gas sampling, see dates below).
In addition to these 2 systems, grazing-exclusion pasture sites (GE) were monitored in
order to account for GHG emissions from non-grazed pastures. The use of a non-grazed pasture
site was important to confirm that any differences found between SysA and SysB were attributed
to the grazing management practices implemented.
Soil emission sample collection occurred in paddocks most recently occupied by cows.
Animal management was dependent on forage growth; consequently different paddocks were
assigned as pseudoreplicates for measurements of gas flux during each year and period. The use
of pseudoreplicates might not be ideal, but justification for using this approach centers on longterm value of the grazing systems (Liebig et al., 2010) and monitoring at the farm scale.
Controlled experiments using genuine replication, with all variables held constant except the
treatment, usually deliver most definite results. However, the definition of genuine replications is
not practical when addressing landscape ecological impacts and questions at farm scale
(Hargrove and Pickering, 1992; Teague et al., 2011).

27

Cow-calf pairs in SysB were rotating through 0.08 ha paddocks. In order to use similar
size paddocks as pseudoreplicates for the 3 treatments, 0.08 ha paddocks were randomly selected
in SysA and GE area (3 pseudoreplicates per treatment).
Sample collection started on day 1 post-grazing and continued for 14 d in each paddock.
The 14-d sampling period was repeated twice yearly in each of 3 years (2011 to 2013); at the
beginning of the grazing season and at the end of the grazing season. The GE area was sampled
in 2012 and 2013. During 2011, SysA and SysB paddocks were sampled twice daily; in the
morning starting at 0900 h and ending at approximately 1200 h, and in the afternoon starting at
1500 h and ending at approximately 1800 h. Because the time of day did not have significant
effect on GHG flux daily data were obtained. Based on results from 2011, sampling was
performed once daily in the following years; starting at 0900 h and ending at approximately 1300
h.
The sampling dates were: from July 7th to August 3rd (period 1 - P1) and from August
13th to August 26th (period 2 - P2), 2011; from May 18th to May 31st (P1) and from August 21st to
September 3rd (P2), 2012; from May 20th to June 3rd (P1) and August 26th to September 8th (P2),
2013.
2.2.2. Soil emission measurements
In order to characterize soil types in each paddock, ten 30-cm depth soil samples were
collected randomly per paddock and composited into 1 sample. Samples were dried at 65°C and
analyzed for particle size and pH by the Michigan State University Soil and Plant Nutrient
Laboratory. Particle size analysis (clay, silt, and sand) was assessed using sedimentation as
described by Bouyoucos (1951). Soil pH was measured potentiometrically in a 1:2.5 soil water
suspension, with buffer solutions of pH 4 and 7.

28

Gas samples were collected using the static chamber methodology (Holter, 1997).
Although a well known and largely implemented methodology, studies often modify methods to
fit sampling conditions. Our modifications are within method guidelines for use of static
chambers (de Klein and Harvey, 2013).
Chambers of 7.6 L (base plus cap) were placed in paddocks to collect gas samples.
Within each paddock, a grid was used to evenly distribute 10 static chambers. Within each grid
subdivision (n = 10), chambers were placed randomly (Gilbert, 1987) immediately after grazing.
Sample collection started 24 h after chamber placement to allow soil microbial populations to
stabilize and avoid over-estimation or under-estimation of emissions.
Static chambers were composed of a base (stainless steel ring, 20.3 cm o.d.; 15.2 cm
height) and a cap (PVC, 23.5 cm o.d.; 9.4 cm height). Caps contained a rubber strap around them
to seal chambers when closed and avoid atmospheric air from entering chamber headspace.
Caps contained a vent hole (4 mm o.d.) to avoid pressure perturbations and subsequent mass
flow, and a sampling port. The sampling port was sealed with a rubber stopper (Molded
Thermogreen, 9.5 mm, Supelco, Bellefonte, PA).
Static chambers remained closed for the 20-min gas collection period and remained open
between sample collections throughout the 14-d period. It is generally assumed that molecular
diffusion is sufficiently rapid within the chamber headspace such that homogeneous gas
concentrations exist when sampling (Livingston and Hutchinson, 1995).
Gas sample collection procedures were as follows: at 0900 h the first chamber was closed
(capped) and the first sample of headspace gas (0 min) collected; then samples were collected at
5, 10, and 20 min post-closing to permit calculation of gas fluxes. Eighteen 0-min samples from
each treatment were collected (18 out of 30 chambers per treatment). de Klein and Harvey

29

(2013) propose that 0-min samples are not necessary because this sample is equivalent to
atmospheric concentration of the gases of interest.
Gas was collected from the chamber headspace with 20 mL plastic syringes with LuerLok Tip (BD, Franklin Lakes, NJ) and Precision Glide needles (0.8 mm x 40 mm, BD, Franklin
Lakes, NJ). Syringe contents were transferred to pressurized 20 mL vials with beveled tops and
rounded bottoms (23 mm x 75.5 mm x 12.5 mm, Supelco, Bellefonte, PA). Vials were sealed
with straight plug stopper, natural red rubber septa (20 mm, Weathon, Millville, NJ) or rubber
and silicone septa (20 mm, Leal Technologies, Carrboro, NC). We previously tested the 2 types
of septa described and verified that septa type did not increased or decreased the storage time. In
addition to the septa, vials were sealed with natural aluminum crimp tops (o.d. 20 mm, Weathon,
Millville, NJ). Vials were pressurized at approximately -27.6 kPa (maximum flow: 1.90 cfm;
maximum pressure: 413.7 kPa; maximum vacuum: 15000 kPa , GAST, Benton Harbor, MI).
Vial pressure was checked with a Media Gauge series digital pressure gauge (ultra low pressure
range of ≤ 103.4 kPa, 0.25% full scale accuracy, SSI Technologies, Inc., Janesville, WI). Sample
vials were stored at room temperature and transported to a Michigan State University laboratory
for analyses by gas chromatograph (GC).
The GC (Shimadzu GC-2014) was equipped with electron capture (ECD) and flame
ionization detectors (FID; Shimadzu, Addison, IL). Carrier gas was ultra-purity nitrogen gas,
with total flow of 40 mL min-1 and purge flow of 0.5 mL min-1. The column oven was
maintained at 75°C, FID at 250°C and ECD at 325°C. The GC was equipped with a headspace
automatic sampler (COMBI Pal LEAP Technologies, Carrboro, NC). Calibration curves
contained at least 5 points and were generated with standard gas of concentrations: 3903 ppm
CO2, 20.42 ppm CH4 and 4.015 ppm N2O. Standard gas was diluted with atmospheric air in vials

30

to 25%, 50% and 75% to generate the curve, which also included a 0 point (atmospheric air) and
100% (non diluted standard gas). The gas chromatograph was calibrated at beginning of analyses
and once every 2 weeks.
2.2.3. Soil gas flux calculation and dependent variable measurements
Flux was calculated based on gas concentration determined by chromatography,
atmospheric pressure and chamber volume (Equation 2.1). Chamber volume was measured once
the chamber ring was placed in the ground to account for field variability.
Equation 2.1. Greenhouse gas flux calculation from static chambers placed on pasture soil.
GHGflux = GHG × P × Vch / (R x T)
where GHGflux (µg of gas of interest chamber-1) is the mass of gas of interest (e.g. CO2, CH4 or
N2O) per chamber; GHG (ppm) is the concentration of gas of interest determined by gas
chromatography; P (atm) is atmospheric pressure; Vch (cm3) is the chamber volume; R (atm L
mol-1 K-1) is the ideal gas constant, and T (K) is the ambient temperature inside the chamber.
The following conditions were recorded daily during the data collection period:
atmospheric pressure, soil water content (WC), soil temperature (ST) and ambient temperature
(AT). Atmospheric pressure was measured prior to sample collection with a Druck DPI 705
digital pressure indicator (combined non-linearity, hysteresis and repeatability of ± 0.1% fullscale, maximum torque of 2.259 Nm; GE, Fairfield, CT). Soil moisture measurements were
taken adjacent to each chamber once daily following gas collection, at a 12 cm depth. Soil
moisture was monitored with a soil water content reflectometer (HydroSense system with sensor
CS620, display CD620 and two 12 cm-long rods, accuracy: ± 3.0% volumetric water content
with electrical conductivity < 2 dS m-1; range: 0% to saturation, Campbell Sicentific, Logan,
UT). ST and AT measurements were recorded inside 2 chambers randomly selected per paddock

31

(3 paddocks per treatment; 6 measurements per treatment) once daily. Soil temperature was
monitored at a 5 cm depth with a soil thermometer (Digital thermometer, Taylor, Oak Brook, IL)
immediately before gas collection. AT was the air temperature inside the chamber during sample
collection time and was measured with the same thermometer used for ST. Following ST
measurements, the thermometer was placed inside the chamber from which ST was measured.
Chambers were closed and sampling proceeded; when sampling was finished, AT was recorded
immediately after chambers were opened.
2.2.4. Statistical analysis
Paddock was the experimental unit (3 pseudoreplicates per treatment). Chambers were
considered multiple measurements of each experimental unit. Statistical analyses were conducted
using SAS Software (Version 9.2; SAS Institute, 1987). A completely randomized design
considered the term year × period (year and period compressed) as repeated measure, and
chamber nested within area and treatment as the random term.
The model was as follows:
y = µ + ρi(k*l) + τl + γk + λm + πj + τγlk + τλlm + ejlkmi
where µ is the overall mean, ρi(k*l) is the random effect of the ith chamber (ρi(k*l) ~ N(0, σ2ρ))
nested within the kth year and the lth treatment, τl is the fixed effect of the lth treatment, γk is the
fixed effect of the kth year, λm is the fixed effect of the mth period, πj is the fixed effect of the jth
day, τγlk is the interaction between the lth treatment and the kth year, τλlm is the interaction
between the lth treatment and the mth period, and ejlkmi is the residual term (ejlkmi ~ N(0, ∑)).
Statistical analyses were performed using SAS Software (Version 9.2; SAS Institute, 1987). The
explanatory variables tested were ST, AT and WC. All tests were performed with 95%
confidence (α = 0.05). If the main effect of year was significant means were discussed separately

32

by year. If the main effects of period or treatment were significant the interaction of treatment ×
period was tested.
2.3. Results and Discussion
2.3.1. Study site and soil properties
Ongoing and historical precipitation and minimum and maximum air temperatures for the
studied months are summarized in Table 2.1. The volume of water applied by irrigation on SysB
pasture sites during each year is shown in Table 2.1.
Gas sampling was performed in different pastures sites during each year and period
depending on the animal rotation schedule. The sampling sites in GE were maintained constant
for all sampling occasions. The soil type across grazing systems was predominantly sandy loam.
A summary of particle size fractions and soil pH in each pasture is described in Table 2.2. All pH
values were below the threshold for alkaline soils (Savadogo et al., 2007).
The first sampling year (2011) was conducted in order to verify and adjust the GHG
collection methodology and results were preliminary. For that reason, the GE areas had not been
included yet and no data on soil and ambient properties and GHG emissions are available. In
addition, WC on SysA pasture sites was not monitored during the 2011 grazing season (Table
2.3).
Soil and ambient temperature and WC varied with treatment, year (P < 0.01) and period,
for the 3 grazing seasons (Table 2.3). The interaction treatment × period was not significant for
WC during the 2012 grazing season. The 2011 grazing season was the only year in which a
decrease in ST and AT from P1 to P2 was observed. It was expected that temperatures would
increase as the grazing season progressed and daily temperatures became higher. During 2011
the 2 periods were conducted from July 7th to August 26th, with 10 d between periods. During

33

2012 and 2013 sampling was conducted at the beginning (late May) and at the end of the grazing
season (late August, refer to Section 2.2.1 for dates). The effects of weather changes in ST, AT
and WC are therefore clearer during the 2012 and 2013 grazing seasons.
When ST and AT increased from P1 to P2, there was a decrease in WC. The SysA
pastures sites were irrigated as needed during the 3 years, and higher WC was expected. During
the 2012 grazing season, WC was higher for SysB pastures in both periods; GE and SysA were
not significantly different. However, during the 2013 grazing season, SysA pasture sites had
higher WC during P1, and SysB and GE were not significantly different. During P2, WC was
higher in SysB pasture areas (irrigated) than in GE and SysA pasture areas (non-irrigated).
2.3.2. CO2 emissions
The CO2 emissions varied with year (P < 0.01), treatment, period and treatment × period
(Table 2.4). During the 2011 grazing season, the CO2 emissions pattern changed considerably
between periods. Across treatments, CO2 emissions were higher during P1. During 2011 P1,
SysA pasture sites had higher CO2 emissions than SysB pasture sites. However, during P2, CO2
emissions from SysA were greatly reduced and SysB pasture sites had higher CO2 emissions
than SysA pasture sites. These differences are interesting mainly because during the 2011
grazing season the time in between periods P1 and P2 was only 10-d. These results indicate the
importance of temporal variability and long-term monitoring of CO2 emissions from pasture
soils.
Emissions of CO2 during 2012 did not vary between treatments (P = 0.23). CO2
emissions increased from P1 to P2 for GE and SysB pasture sites (P < 0.01). CO2 emissions from
SysA remained constant throughout the grazing season. During the 2013 grazing season, CO2
emissions considerably decreased from P1 to P2 (pooled by treatments). During P1, CO2

34

emissions were not significantly different among treatments. During P2, CO2 emissions from
SysB pasture sites were greater than from GE and SysA pasture sites.
In this study GHG fluxes were monitored always post-grazing (each period lasted 14 d,
starting on day 1 post-grazing). Grazing results in animal trampling, litter accumulation and
excreta deposition, which affect soil properties and microbial growth. The variability observed in
results might be associated to the changes in soil properties caused by grazing. However, we did
not monitored GHG fluxes before grazing to determine the variability of GHG fluxes.
For all treatments, year and periods, negative CO2 fluxes were observed (data not shown).
Nevertheless, the average CO2 emissions were all positive (Table 2.4), indicating emissions.
Although there was some consumption of CO2 on these pasture soils, on average they were
considered CO2 emitters.
Regarding the year to year variation, generally CO2 emissions increased from 2011
(129.5 mg CO2 m-2 h-1) to 2013 (242.1 mg CO2 m-2 h-1). The only exception are emissions during
P1 that decreased from 2011 to 2012. However, in 2013, emissions during P1 were the highest
observed across years and periods. Generally CO2 emissions decreased from P1 (203 mg CO2 m-2
h-1) to P2 (139 mg CO2 m-2 h-1) across treatments (2011 and 2013).
Short-term studies have shown that grasslands may be a sink for atmospheric CO2 during
peak biomass accumulation periods, but annual data are limited (Frank et al., 2002). Our results
suggest that grasslands, directly after grazing, are a source of CO2. Kim et al. (1992) reported
average hourly CO2 fluxes for grassland sites of 170 mg CO2 m-2 h-1 from May to October. Their
results are comparable to the CO2 fluxes observed in the present study (approximately 171 mg
CO2 m-2 h-1) across treatments and years from May to September. Soussana et al. (2007)
observed CO2 emissions from grasslands, taking into consideration CO2 fluxes from soils and

35

vegetation, and found European grasslands either as sinks of CO2 or near equilibrium. A few
studies have suggested that C budgets of grasslands ecosystems are near equilibrium (e.g. Bruce
et al., 1999). Dugas et al. (1999) found an average annual flux of 239 g CO2 m-2 year-1 for a
tallgrass prairie and interpreted it as near equilibrium, given that an adjacent newly established
perennial pasture produced fluxes 10 times greater. In the present study, CO2 fluxes were greater
than the fluxes observed by Dugas et al. (1999) and Bruce et al. (1999), which could be
associated to monitoring post-grazing. Previously reported annual estimates include CO2 fluxes
from winter and fall, which are lower when compared to grazing season fluxes, resulting in
lower annual average.
Kim et al. (1992) reported that during plant senescence, the net CO2 flux was in balance
with the atmosphere, but during droughts and after plant senescence, fluxes were about -3 g CO2
m-2 d-1. These results contrast the present study because, although negative fluxes were observed
in some instances across years, treatments and periods, mean fluxes were always positive (Table
2.4). Similarly, Gilmanov et al. (2005) indicated that a source activity is possible for mixed
prairie ecosystems in North America, especially during years with lower than usual precipitation.
For non-grazed mixed-grass prairie, Frank and Dugas (2001) estimated annual flux of
167 g CO2 m-2. If CO2 fluxes were monitored during winter and fall in the present study, annual
flux might have been lower. It is not accurate to estimate annual fluxes based on monitoring
performed only during spring and summer seasons due to grazing and weather conditions that
vary by season. Annual CO2 fluxes must be inferred based either on long-term monitoring of
emissions throughout the year, or on modeling, taking into consideration the different grazing
managements and weather and soil conditions of each season. Frank (2002) supports that the
large vegetation diversity in grasslands and annual climatic variability requires that CO2 fluxes

36

be measured across a wide range of grasslands types. Accurate estimates of annual CO2 flux
depend on obtaining precise estimates of dormant season fluxes (Frank, 2002).
Our results did not demonstrate conclusively an increase in CO2 emissions because of
grazing. When differences between treatments were observed, SysB had greater CO2 emissions;
during 2011 (126 mg CO2 m-2 h-1 and SysB was 132 mg CO2 m-2 h-1, for SysA and SysB on
average, respectively) and during P2 of 2013 (151 mg CO2 m-2 h-1 from SysA compared to 220
mg CO2 m-2 h-1 from SysB; Table 2.4). The greater CO2 emissions on SysB could be associated
to faster SOM decomposition rate and C cycling. In SysB cow-calf pairs were rotated thru the
pasture sites 4 to 5 times per year. In SysA animals were rotated thru paddocks 2 to 3 times per
year. We believe that the greater rotation frequency in SysB resulted in (i) greater herbage
defoliation, which stimulated plant and root growth; (ii) more frequent excreta deposition and
faster decomposition rate of excreta, because of more frequent animal trampling impact; (iii)
higher soil water content, because of the need of irrigation to maintain the conditions of this
grazing management (Table 2.3). All these factors contribute to microbial growth, which is
associated to SOM decomposition and C cycling that could have increased CO2 emissions. It was
observed that grasslands release CO2 from the soil as a response to management practices
(Soussana et al., 2004; Ammann et al., 2007). The induction of CO2 emissions by grazing was
associated to microbial activities (Pucheta et al., 2004), and increased soil temperature and
respiration resulting from reduced leaf area and increased penetration of light into the soil
surface (Bremer et al., 1998).
Soil temperature, AT and WC affected CO2 emissions during all years and periods (Table
2.5). This is in agreement with previous papers. Frank (2002) who studied CO2 fluxes from
gazed pasture sites observed that the variation in annual precipitation, short-term droughts

37

periods and temperature stress effects on CO2 fluxes are factors that contribute to the interannual flux variability.
Soussana et al. (2007) modeled CO2 emissions from grazed and non-grazed pastures sites
and indicated that the between year variability was strong for some of their monitoring sites, but
mainly for the ones under grazing management.
2.3.3. CH4 emissions
There was a year effect on CH4 emissions (P < 0.01). Methane emissions were not
significantly different between treatments during P1 of 2011 (Table 2.6). There was a period
effect in 2011 (P < 0.01) even though the periods were close together in time, when compared to
periods in 2012 and 2013. During 2011, period and treatment × period effects were observed,
and during P2 emissions from SysB pasture sites were higher than emissions from SysA pastures
sites (Table 2.6). From P1 to P2 CH4 emissions increased across treatments. Both treatments
consumed CH4 during P1, but SysB became a CH4 source during P2.
During the 2012 grazing season, emissions from SysA and SysB were significantly
different during P1 (0.07 and 0.03 mg CH4 m-2 h-1, respectively, P = 0.02), but CH4 emissions
from SysB and GE were not significantly different (-0.01 mg CH4 m-2 h-1 from GE). During P2,
there was no statistical difference among the 3 treatments. There was also no period effect in
2012.
Similar to CO2 emission, CH4 emissions in 2013 were generally higher. There was no
treatment effect on CH4 emissions during P1. During P2, GE became a sink for CH4 and CH4
emissions increased in SysA and SysB.
Regarding across year variation, 2013 was different (P < 0.01), with higher emissions
during P1 and P2. Observed fluxes were noticeably low and the SEM significantly large. Several

38

studies have been conducted to explain the variability associated to CH4 emissions (e.g.
Wachinger et al., 2000; Van den Pol-Van Dasselaar et al., 1999).
In a recent study comparing grazed and non-grazed pasture sites, Wei et al. (2012)
observed average CH4 flux of -0.06 mg CH4 m-2 h-1. These results are comparable to the uptake
observed during the 2011 grazing season of the present study. However, pasture sites of the
present study did not continue to store CH4 in 2012 or 2013. Wei et al. (2012) noticed that the
lowest uptake occurred during intense rainy days. Factors that increase WC facilitate increased
CH4 production over uptake, because methanogens are predominantly anaerobic whereas
methanotrophs are aerobic microorganisms. The highest uptake observed in the present study
occurred on GE pasture sites (-0.12 mg CH4 m-2 h-1, Table 2.6), when WC was the lowest
(12.5%, Table 2.3). The effect of WC on CH4 flux was expected because it was confirmed in
previous studies (Le Mer and Roger, 2001; Curry, 2007), however in this study the effect of WC
on CH4 emissions was not consistently significant.
Methanogens and metanotrophs can develop in all soil types. It is the balance between
the 2 microbial communities and the net CH4 flux that defines a soil as sink or source of CH4.
Because the present study demonstrated both emissions and consumption of CH4 we postulate
that both microbial communities were present in the soils monitored. Zhou et al. (2008)
suggested that grazing intensities have different effects on the structure of the methanotroph
communities. Abell et al. (2009) demonstrated that methanotroph type II were not influenced by
grazing, while the CH4 oxidation and the abundance of type I metanotrophs increased during
grazing.
CH4 fluxes varied with treatment on 3 occasions: during P2 of 2011, P1 of 2012 and P2
of 2013. However, the differences among systems were not consistent. During P2 of 2011 SysB

39

produced greater CH4 than SysA (0.03 compared to -0.03 mg CH4 m-2 h-1, respectively). During
P1 of 2012 SysA produced greater CH4 than GE, but emissions were not significantly different
than SysB; SysB and GE did not differ (-0.01, 0.07 and 0.03 mg CH4 m-2 h-1 for GE, SysA and
SysB, respectively). During P2 of 2013, grazing systems produced greater CH4 than GE, but
SysA and SysB did not differ (-0.12, 0.14 and 0.14 mg CH4 m-2 h-1 for GE, SysA and SysB,
respectively). Our results are too variable to allow conclusions on the impact of grazing on CH4
fluxes. Given that we observed different emissions patterns in each year, we support the
importance of long-term studies on CH4 flux quantification in grazing systems. Additionally,
because of the period effect on CH4 emissions, we suggest that more research needs to be
conducted to monitor CH4 fluxes throughout the year. The significantly effect of period during
2011 (P < 0.01) highlights the importance of repeated monitoring of CH4 emissions throughout
the grazing season. Periods were 10 d apart during 2011, and CH4 emissions significantly
decreased from P1 to P2 in both systems.
Despite the fact that CH4 fluxes were monitored post-grazing, there was no clear increase
of CH4 emissions from grazing systems as compared to GE. Greater CH4 emissions from pasture
soils shortly after grazing were expected, as it was suggested in previous studies that fecal
decomposition results in CH4 production (Saggar et al., 2004; Flessa et al., 1996). Although on
average systems did not differ, we observed increased CH4 emissions from grazed pasture sites
during the first days of sampling (day 1 to day 4), followed by decreasing fluxes and stabilization
(data not shown).
Contrary to CO2 emissions, the effects of soil characteristics and ambient temperature on
CH4 emissions varied with year. During 2011 and 2013 grazing seasons, both ST and AT had
positive effects on CH4 emissions (Table 2.7). During the 2012 grazing season, none of the

40

variables had positive effects, and the CH4 emissions and the differences observed among
treatments must have been due to variables that were not accounted for. Methane production is
influenced by several factors, such as (i) the aeration of the soil, as methanogenic
microorganisms require anoxic conditions to produce CH4, (ii) the presence of alternative
electron acceptors, such as nitrate and sulphate, (iii) type and amount of available organic matter,
(iv) the size of the methanogenic population, and (v) type of vegetation (Van del Pol-Van
Daselaar et al., 1999)
Methanogens can grow in anaerobic micro pores of aerobic soils, and methanotrophs can
grow in aerobic micro pores of soils with high higher WC. For that reason, we believe that
monitoring water-filled pore space (WFPS) or O2 content in the soil could serve as better
predictors of CH4 production or consumption from grasslands soils. More research is needed to
verify the relationship between WFPS and O2 content and CH4 flux.
Methane production also depends on substrate availability in the soil. Joulian et al. (1996)
reported that organic matter quantity and quality influences CH4 production and that
methanogenesis is influenced more by the availability and composition of the substrate than by
the density of methanogens. Bergman et al. (1998) stressed that substrate availability, rather than
abiotic factors such as temperature and pH, might be a predominant constraint for CH4
productivity under held conditions. The fact that variability on the landscape scale cannot be
explained adequately from easily measurable factors makes it reasonable to ask whether it can be
traced back to processes on smaller scales (Wachinger et al., 2000).
2.3.4. N2O emissions
The effect of year was significant for N2O emissions (P < 0.01). N2O emissions did not
vary between systems during P1 of 2011. During P2 of 2011, SysB had greater N2O emissions

41

(59.8 µg N2O m-2 h-1) than SysA (22.5 µg N2O m-2 h-1; Table 2.8). During the 2012 grazing
season the differences between treatments were not significant (P = 0.66). There were no effects
of period (P = 0.82) nor of the treatment × period interaction (P = 0.11).
The grazing season of 2013 was different compared with 2011 and 2012. Pasture sites
grazed with SysA had considerably greater N2O emissions during P1 (152 µg N2O m-2 h-1) than
GE (29 µg N2O m-2 h-1) and SysB (84 µg N2O m-2 h-1) pasture sites. During P2, although GE had
negative N2O fluxes, no significant differences were observed among treatments (Table 2.8).
The N2O fluxes variability is larger when compared to the other GHG. SEM was greater
than means emissions from GE and SysA during P2 of 2012 (Table 2.8). Based on these results
we cannot conclude that grazing increased N2O emissions as has been previously suggested (Liu
et al., 2011).
According to Flechard et al. (2007), the average N2O fluxes from intensively and
extensively grazed European grasslands were 1.77 and 0.48 kg N ha−1 year−1, respectively. The
range of N2O emissions observed in this study was wider, varying from -2.0 to 13.4 kg N ha−1
year−1 (from GE during P2 and SysA during P1 in 2013 grazing season). However, some of the
pasture sites studied by Flechard et al. (2007) received external N inputs, which likely increased
N2O emissions. N2O fluxes were observed to reach 3 kg N ha−1 year−1 without grazing or
applying fertilizer, while the emission increases up to 20 kg N ha−1 year−1 over a grazed field
with the use of 200 kg N ha−1 yr−1 of nitrogen fertilizer (Colbourn, 1992).
Although some differences were found among treatments, the pattern was not consistent
from year to year. During P1 of 2011, SysB had greater emissions than SysA, but during P1 of
2013, SysB had greater N2O emissions than SysA and GE. Wei et al. (2012) observed different
emissions pattern in different years. Their pasture sites acted as a weak N2O source during 2009

42

and as a weak sink during 2010. The only occasion where we verified N2O sink was by GE in P2
of 2013. However, 2013 was the year when we detected the highest variation in the data
collected, and consequently N2O emissions did not vary among treatments during P2. Studies
agree that soils are generally sources for N2O, and negative N2O values are usually associated to
measurement errors. Chapuis-Lardy et al. (2007) reviewed the potential of agricultural soils to
sink N2O from the atmosphere. The authors suggested that the soil potential to sink N2O needs
further research, mainly regarding elucidation of the microbial communities and processes
involved on N2O sink.
Similarly, the processes involved on N2O production in soils still need elucidation. The
main processes involved in N2O production are nitrification (autotrophic and heterotrophic) and
denitrification (carried out by denitrifiers or nitrifiers). It is difficult to differentiate the
proportion of N2O coming from nitrifier denitrification from the proportion coming from
denitrification, because they are basically the same process. Coupled nitrification-denitrification
has also been identified as an important source of N2O in soils. Coupled nitrification
denitrification occurs under conditions that are favorable to both processes, occurring mainly in
micro sites in the soils. Khdyer and Cho (1983) performed a very interesting experiment to
determine the extent that each process (nitrification and denitrification) occurred after the
addition of urea uniformly mixed under similar and constant O2 availability. Nitrification occurs
mainly at the surface layers where aerobic conditions predominate. Denitrification dominated in
the deeper anaerobic layers. In the intermediate layers, N2O production was highest, where there
was an aerobic-anaerobic interface promoting both nitrification and denitrification to occur
simultaneously. This experiment indicated that, as it was postulated for CH4, N2O production or

43

consumption can happen simultaneously in micro pores anaerobic or aerobic, which also
increases the variation in measurements.
Nitrous oxide emissions from soil usually occur in “hotspots” associated with urine and
fertilizer residue despite diffused nutrient spreading (Flechard et al., 2007). Temporal and spatial
variations contribute to uncertainty in N2O fluxes and the field and annual estimations (Soussana
et al., 2007). The possibility that N2O is formed mainly in hotspots is a contributor to the large
variability, mainly in this study because N2O fluxes were sampled post-grazing.
The effects of ST, AT and WC on N2O emissions were not consistent across year (Table
2.9). During the 2011 grazing season, both AT and ST were positively correlated with N2O
emissions. During the 2012 grazing season, ST and WC were positively correlated to N2O
emissions, but AT was not. In the 2013 grazing season, none of the variables were positively
correlated to N2O emissions.
Soil temperature influenced N2O emissions in 2011 and 2012. Denitrification rates and
soil microbial activity positively relate to ST (Franzluebbers et al., 2002; Sulzman et al., 2005).
N2O soil flux commonly increases with the increased soil temperature (Ludwig et al., 2001).
Moisture content in soils defines either nitrification or denitrification as the major process
producing N2O. However, the presence of anaerobic microsites in dry soils and of aerobic
microsites in wet soils possible make WFPS a better indicator of processes prevailing on N2O
production than soil water content alone. Increased moisture content, or decreased WFPS, shifts
the importance from nitrification to denitrification in terms of N2O and nitrogen gas (N2)
production. A generalized relationship between WFPS and emissions of N2O and N2 from
nitrification and denitrification has been postulated (Granli and Bockman, 1995), and it has been
used since then. When soil water content is low, O2 is plentiful and nitrification proceeds to

44

nitrate, resulting in little emission of N2O and no N2. As soil water content increases,
mineralization increases, and nitrification increasingly produces N2O rather than nitrate. Over the
range of moderate soil water content denitrification becomes more important initially with high
ratio of N2O to N2. When the soil water content is high, nitrification stops and denitrification
proceeds increasingly to N2. This relationship suggests that the maximum production of N2O
occurs when the soil water content is at about field capacity and both nitrification and
denitrification are occurring (coupled nitrification-denitrification). It is likely that WFPS could
be a better predictor of N2O emissions than WC.
2.4. Conclusion
This study was conducted post-grazing and at a farm-scale and the variability associated
to GHG emissions was large. We observed year and period effects. Within each year, treatment
differences were also variable. There was a tendency of greater CO2 emissions from SysB
pasture sites. ST, AT and WC had effects on CO2 emissions. CH4 and N2O emissions were
observed from pasture sites of the 3 systems, but the effect of grazing was not conclusively
observed on CH4 and N2O emissions. In addition, ST, AT and WC did not conclusively explain
CH4 and N2O emissions. We suggest that other soil properties might be better predictors of CH4
and N2O, such as WFPS or O2 content in the soil. Further research is needed to confirm the effect
of WFPS and O2 content on GHG emissions. We did not observe any clear trade-offs between
GHG; generally GHG emissions increased from 2011 to 2013, which was likely associated to
weather conditions, such as higher daily temperature and precipitation events.
This study indicates that GHG emissions from pasture soils are still uncertain when
monitored at a farm scale. The variability observed on the results of this study raise questions
about the current knowledge on GHG fluxes when applied to a farm level. Further research is

45

needed on GHG fluxes monitoring from different grazing systems, on long-term (during
different periods throughout the grazing season, and different years) and before and after
grazing, in order to allow conclusions on the impact of grazing systems on GHG fluxes.

46

Table 2.1. Ongoing and historic precipitation, maximum and minimum air temperature at the
study site and total irrigation applied to paddocks.
Max Temp Min Temp Precipitation Irrigation1
⁰C
cm
2011 grazing season
May
19.5
7.0
5.8
Jun
24.5
11.0
8.2
Jul
29.3
15.1
6.1
Aug
26.4
11.7
9.2
Sep
21.1
6.7
7.9
11.2
2012 grazing season
May
21.8
6.9
13.5
Jun
25.8
11.3
7.7
Jul
31.3
14.4
18.6
Aug
25.8
15.4
5.0
Sep
21.6
5.1
8.8
22.4
2013 grazing season
May
20.9
5.7
7.7
Jun
24.6
10.4
4.7
Jul
27.5
12.7
5.2
Aug
26.6
10.1
10.5
Sep
21.4
6.3
4.2
16.8
1981-2010
May
18.7
4.9
8.2
Jun
24.1
10.3
8.6
Jul
26.5
12.6
7.1
Aug
25.3
11.6
9.4
Sep
20.8
7.1
9.2
1

Total cm of water applied on each 1.6 ha paddock of SysB during the grazing season (SysB: 2.5 cows ha -1 stocking
rate and 28,000 kg LW ha-1 stocking density).

47

Table 2.2. Summary of soil characteristics in the study area.
Systems1
GE
SysA
SysB

Size fractions, %
Sand Silt Clay
70.1 16.0 13.8
55.0 23.5 21.5
52.8 26.8 20.4

1

Soil type

pH

sandy loam
sandy clay loam
sandy clay loam

5.9
6.0
6.2

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density.

48

Table 2.3. Soil properties of pasture sites grazed with two different management strategies and
non-grazed pasture sites.
Systems1
ST3, ⁰C
GE
SysA
SysB
SEM
Source of Variation
Treatment
Period
Treatment x Period

2011 grazing season
P1
P22
22.4a
21.9b

2012 grazing season 2013 grazing season
P1
P2
P1
P2

19.7a*
17.8b*
0.07

<0.01
<0.01
<0.01

17.4a
19.9a*
a
17.1
20.0a*
15.7b
18.6b*
0.07

14.9a
20.8a*
b
15.5
21.4b*
14.5c
19.2c*
0.08

<0.01
<0.01
<0.01

<0.01
<0.01
<0.01

23.3a
27.1a*
22.7a
25.1b*
21.5b
23.9c*
0.17

19.1
23.6a*
19.1
25.4b*
19.5
23.8a*
0.18

<0.01
<0.01
<0.01

<0.01
<0.01
<0.01

15.1a
13.4a*
a
15.0
13.8a*
18.9b
16.5b*
0.43

26.9a
12.5a*
b
32.1
12.8a*
27.9a
17.4b*
0.52

<0.01
<0.01
0.11

<0.01
<0.01
<0.01

AT4, ⁰C
GE
SysA
SysB
SEM
Source of Variation
Treatment
Period
Treatment x Period
WC5, %
GE
SysA
SysB
SEM
Source of Variation
Treatment
Period
Treatment x Period

27.4a
25.9b

23.7a*
20.9b*
0.18

<0.01
<0.01
<0.01
14.6

14.9*
0.28

<0.01
-

1

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density
2
Jul 7th – Aug 3rd (P1), Aug 13th – 26th (P2), 2011; May 18th – 31st (P1), Aug 21st – Sep 3rd (P2), 2012; May 20th –
Jun 3rd (P1), Aug 26th – Sep 8th (P2), 2013.
3
ST: soil temperature.
4
AT: ambient temperature.
5
WC: soil water content.
Mean differences within columns indicated by letter (P < 0.05). Mean differences within rows indicated by symbols
(P < 0.05).

49

Table 2.4. CO2 emissions from of pasture sites grazed with two different management strategies
and non-grazed pasture sites.
Period
P1
P2
-2 -1
mg m h
a
160.1
93.7a*
144.8b
119.5b*
4.6

Systems
2011 grazing season
GE
SysA
SysB
SEM
2012 grazing season
GE
SysA
SysB
SEM
2013 grazing season
GE
SysA
SysB
SEM

Effects
Treatment

Period

Treatment x Period

0.35

<0.01

<0.01

125.2
121.3
111.9

140.6*
126.0
143.0*
4.4

0.23

<0.01

<0.01

313.7
314.1
333.7

119.6a*
151.0a*
220.5b*
9.4

<0.01

<0.01

<0.01

1

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density.
2
Jul 7th – Aug 3rd (P1), Aug 13th – 26th (P2), 2011; May 18th – 31st (P1), Aug 21st – Sep 3rd (P2), 2012; May 20th –
Jun 3rd (P1), Aug 26th – Sep 8th (P2), 2013.
Mean differences within columns indicated by letter (P < 0.05). Means difference within rows indicated by symbols
(P < 0.05).

Table 2.5. Effects of soil properties on CO2 emissions from pasture sites grazed with two
different management strategies and non-grazed pasture sites.
Grazing season

2011
2012
2013
1

Effects1
ST, ⁰C AT, ⁰C WC, %
P-value
<0.01
<0.01
0.02
<0.01
<0.01
<0.01
<0.01
0.02

ST: soil temperature; AT: ambient temperature; WC: soil water content.

50

Table 2.6. CH4 emissions from pasture sites grazed with two different management strategies
and non-grazed pasture sites.
Period
P1

Systems

P2
-2

2011 grazing season
GE
SysA
SysB
SEM
2012 grazing season
GE
SysA
SysB
SEM
2013 grazing season
GE
SysA
SysB
SEM

Effects
Treatment

Period

Treatment x Period

0.14

<0.01

<0.01

0.02

0.88

0.31

<0.01

0.27

<0.01

-1

mg m h
-0.08
-0.03a
-0.10
0.03b*
0.01
-0.01a
0.01
b
0.07
0.05
a
0.03
0.04
0.02
0.03
0.11
0.15

-0.12a
0.14b
0.14b
0.05

1

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density.
2
Jul 7th – Aug 3rd (P1), Aug 13th – 26th (P2), 2011; May 18th – 31st (P1), Aug 21st – Sep 3rd (P2), 2012; May 20th –
Jun 3rd (P1), Aug 26th – Sep 8th (P2), 2013.
Mean differences within columns indicated by letter (P < 0.05). Means difference within rows indicated by symbols
(P < 0.05).

Table 2.7. Effects of soil properties on CH4 emissions from pasture sites grazed with two
different management strategies and non-grazed pasture sites.

Grazing season
2011
2012
2013
1

Effects1
ST, ⁰C AT, ⁰C
0.09
0.62
0.52

P-value
<0.01
0.04
0.03

ST: soil temperature; AT: ambient temperature; WC: soil water content.

51

WC, %
0.78
0.55

Table 2.8. N2O emissions from pasture sites grazed with two different management strategies
and non-grazed pasture sites.
Period
P1
P2
µg N2O m-2 h-1

Effects
Treatment

Period

Treatment × Period

22.5a
59.8b

0.05

0.67

<0.01

3.5

5.6

0.66

0.82

0.11

SysA

9.4

3.0

SysB
SEM
2013 grazing season
GE
SysA
SysB
SEM

6.0

8.6

<0.01

<0.01

<0.01

Systems
2011 grazing season
GE
SysA
SysB
SEM
2012 grazing season
GE

32.3
43.7

-

9.5

2.8
30.2a
-30.1a*
151.3b 13.7a*
98.9c
29.2ab*
13.4

1

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density.
2
Jul 7th – Aug 3rd (P1), Aug 13th – 26th (P2), 2011; May 18th – 31st (P1), Aug 21st – Sep 3rd (P2), 2012; May 20th –
Jun 3rd (P1), Aug 26th – Sep 8th (P2), 2013.
Mean differences within columns indicated by letter (P < 0.05). Means difference within rows indicated by symbols
(P < 0.05).

Table 2.9. Effects of soil properties on N2O emissions from pasture sites grazed with two
different management strategies and non-grazed pasture sites.
Grazing season

2011
2012
2013
1

Effects1
ST, ⁰C AT, ⁰C WC, %
P-value
<0.01
<0.01
0.01
0.38
<0.01
0.01
0.35
0.46

ST: soil temperature; AT: ambient temperature; WC: soil water content.

52

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53

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59

CHAPTER 3
ENTERIC METHANE FROM LACTATING BEEF COWS

60

3.1. Introduction
The production of CH4 by cattle has become a subject of scientific debate as the concern
over climate change increases. In order to understand the C flux in grazing systems, quantify and
understand the impact of management on enteric CH4 production is warranted. The primary
factor affecting enteric CH4 production are the quantity and quality of the diet. As forage fiber
content increases, nutrient digestion and passage rate decelerate, increasing the overall time of
ruminal fermentation and subsequent enteric CH4 production. Therefore, implementing
management tools at the farm level that increase pasture quality potentially decreases enteric
CH4 emissions from grazing cows (Beauchemin et al, 2008). Grazing management is the most
suitable option to reduce CH4 emissions from cows. Grazing management is a combination of
stocking rate, density and rest periods. These factors define the relationship between herbage
demand and supply (Animut et al., 2005), herbage utilization efficiency, animal performance and
production per hectare (Pinares-Patino et al., 2007). Despite, the influence of grazing
management strategies on enteric CH4 emissions, especially from beef cows, is not completely
clarified.
The majority of studies measuring CH4 emissions from the beef industry focused on
steers or heifers (Ricci et al., 2013). Previous research has measured emissions from lactating
beef cows fed mixed alfalfa-concentrate diets (Reynolds and Tyrrel, 2000) or grazing alfalfagrass and grass-only pastures (McCaughey et al., 1999). The influence of management decisions
in the context of systems on CH4 emissions from beef cows is missing. The objective of this
study was to compare CH4 emissions from lactating beef cows grazed with different
combinations of stocking rate and density. Our hypothesis was that low stocking rate and high
stocking density grazing management results in lower forage quality, because of longer rest

61

periods that resulting in lower forage quality increases enteric CH4 emissions from lactating beef
cows.
3.2. Material and Methods
3.2.1. Pasture management
All animal procedures were approved by the Michigan State University Animal Care and
Use Committee (protocol no. 04/12-078-00).
Cow-calf pairs were managed with 2 rotational grazing management practices differing
in stocking rates and density; an intensive system with high stocking rate and low stocking
density, and an extensive system with low stocking rate and high stocking density. The system
with low stocking rate and high stocking density (SysA) consisted of 120 cow-calf pairs rotating
on a total of 120 ha, divided into 0.7 ha paddocks. Cow-calf pairs were moved to a new paddock
3 times daily (at approximately 0800 h, 1200 h and 1600 h). The equivalent stocking rate was 1
cow ha-1 and the stocking density was approximately 100,000 kg LW ha-1. The rest period varied
from 60 to 90 d during the course of the growing season depending on plant growth. Cow-calf
pairs grazed each paddocks 2 to 3 times per year. The system with high stocking rate and low
stocking density (SysB) consisted of 4 cow-calf pairs rotating on 1.6 ha pasture, divided into
0.08 ha paddocks. Cow-calf pairs were moved to a new paddock once daily (at approximately
0800 h). The equivalent stocking rate was 2.5 cows ha-1 and the stocking density was 28,000 kg
LW ha-1. The rest period varied from 18 to 30 d during the course of the growing season
depending on plant growth. Cow-calf pairs grazed each paddock 4 to 5 times per year. The
pasture areas in SysB were irrigated as needed, whereas there was no irrigation applied to SysA
pasture sites.

62

The SysA described was simulated in a 1.6 ha pasture closer to the head-gate area, to
facilitate transportation of cow-calf pairs to the head-gate area for sample collection.
Enteric CH4 measurements were collected from respired air of grazed cows, twice during
the grazing season using sulfur hexafluoride (SF6) as a tracer gas technique (Johnson and
Johnson, 1995). Sampling occurred during the grazing seasons of 2012 and 2013, in 2 periods at
the following dates: from June 4th to June 10th (Period 1 – P1), and from September 4th to
September 11th (Period 2 – P2), 2012; from June 5th to June 10th (P1), and from September 11th
to September 17th, (P2) 2013. Methane was collected twice daily for 7 d. The first collection
started at 0830 h and the second collection started at 1530 h daily. Each collection lasted
approximately 1 h. Cows were moved to a chute area for each collection.
Cannulated Angus cows were stratified on weight, age and parity and assigned to each
treatment (n = 6). Groups were randomly assigned to treatments (SysA and SysB) for the first
periods within each sample year. For the second period in each year, groups assignments were
reversed. To achieve the desired grazing density additional cows were added to each herd as
needed.
During P1 of 2012, hematomas developed in a subset of cows and after attending
veterinarian consultation, we attributed the swelling to head-gate pressure used while collecting
samples. During P2 of the 2012 grazing season, 2 cows that still exhibited swelling were
eliminated from the sampling. Therefore, P2 was performed with 10 cows (5 replicates per
treatment). Cows were cannulated in January 2013. Due to complications post-surgery, 1 cow
showed health issues and was euthanized, a replacement cow similar in weight, age and parity
was selected for the second year. During 2013, cows did not show any health issues and both
periods were performed with 12 cows.

63

3.2.2. Enteric methane measurements
Enteric CH4 was quantified with the SF6 as a tracer gas technique. Pre-calibrated
permeation tubes containing SF6 were placed into the reticulum-rumen of each animal via bolus
in 2012 and through the cannula in 2013. Expired gases were collected with a sampling
apparatus containing a collection canister (PVC) and modified halter. The permeation tubes,
canisters and halters were built following protocol described by Johnson et al. (2007).
To collect enteric CH4 and SF6 samples, the canisters were vacuumed to approximately 2.7 kPa with a vacuum pump (2 stages, Robinair, Owatonna, MN). After the collection period,
canisters were connected to a dilution system and the final pressure was recorded. Nitrogen was
then added slowly until canister pressure reached 117.2 kPa. Pressure readings were recorded to
calculate the dilution factor (Johnson et al., 2007). Pressure was measured with a Druck DPI 705
digital pressure indicator (combined non-linearity, hysteresis and repeatability of ± 0.1% fullscale, maximum torque of 2.259 Nm; GE). After pressurization to 117.2 kPa, the contents of the
canisters were transferred under positive pressure to evacuated vials, described in Chapter 2,
section 3. Methane and SF6 concentrations were determined by gas chromatography as described
in Chapter 2, section 2.2.2.
To adjust for background CH4, air samples were collected at 2 points upwind of each
pasture strip, using the canister method. Canisters were placed on fences adjacent to grazed
paddocks. Background canisters were sampled following cow sampling; twice daily for 7-d (first
collection at 0930 h and the second collection at 1630 h daily).
Daily CH4 emissions and the CH4/SF6 ratio of concentrations in breath samples were
calculated after adjustment for background gas concentrations (Johnson and Johnson, 1995).

64

Methane emission rate was calculated from collected SF6 and CH4 concentrations and from SF6
permeation tube release rate according to the Equation 3.1.
Equation 3.1. Methane emissions rate from grazing cows.
QCH4= QSF6 x ([CH4]cows - [CH4]background]) / [SF6]
where QCH4 is the CH4 emission rate (g min-1), QSF6 is the permeation tube SF6 release rate (g
min-1), [CH4]cows is the CH4 concentration quantified from cows (µg m-3), [CH4]background is the
CH4 concentration quantified from background canisters (µg m-3), and [SF6] is the SF6
concentration in samples (µg m-3).
3.2.3. Intake determination and forage analyses
In order to determine intake, chromic oxide (Cr2O3) was used as a marker. Fecal output
was determined from the passage kinetics of Cr2O3 (Fenton and Fenton, 1979). A dosage of 6 g
of Cr2O3 was administered to the animals twice daily for 7 d, via oral bolus in 2012 and cannula
in 2013. Fecal samples were collected during the last 4 d of dosage period. Feces were dried at
65°C until constant weight. Dried feces were ground on a Wiley mill (Carbon steel, 4 adjustable
hard tool steel knives, Thomas Scientific, Swedesboro, NJ) and analyzed by atomic absorption
spectrophotometry. The atomic absorption spectrophotometer was equipped with 357.9 nm
wavelength lamp and air-acetylene flame (Perkin Elmer, Waltham, MA). Fecal Cr2O3
concentrations were used to estimate fecal output according to Equation 3.2 and dry matter
intake (DMI) was calculated based on Equation 3.3. Indigestibility of DM was determined based
on diet apparent digestibility. Cows were weighed at the beginning (d-1) and at the end (d-7) of
each sampling period.
Equation 3.2. Fecal output determination based on marker Cr2O3.
Fecal output, g d-1 = (marker consumed, g d-1) / (marker concentration in feces, g g of DM-1)

65

Equation 3.3. Dry matter intake (DMI) determination based on fecal output.
DMI, kg d-1 = fecal output, g d-1 × 100 / indigestibility of DM (%)
Pre and post-grazing forage samples were collected twice during the 7-d period; on d 2
and d 6. Within each 1.6 ha pasture, 3 forage sampling sites (approximately 0.5 ha) were
designated as forage sampling areas. Each sampling area was equally split into 2 zones (west
and east). The day prior to grazing, the pre-grazing pasture biomass was sampled within each
zone by clipping 3 randomly placed 0.25 m2 quadrats to a 5 cm stubble height with Gardena
8803 battery operated harvest shears (Ulm, Germany). Samples were composited by sampling
zone, weighed and an average wet weight from each zone was recorded. After mean wet weights
were recorded, forage samples from both zones were combined, thoroughly mixed and a 200 g
subsample was collected. Subsamples were oven-dried at 60° C for 48 h weighed and grounded
in a Wiley mill (Carbon steel, 4 adjustable hard tool steel knives, Thomas Scientific,
Swedesboro, NJ). The same sampling process was repeated after cattle grazed the paddock to
determine the post-grazing (residual) biomass. The pre-grazing and post-grazing samples were
sent to DairyOne (Ithaca, NY) for NIR analysis. The parameters analyzed were: crude protein
concentration (CP%), acid detergent fiber (ADF%), neutral detergent fiber (NDF%), lignin
concentration (%) and in vitro total digestibility (IVTD%). Forage gross energy (GE, MJ kg-1)
and digestible NDF (DNDF) were determined based on calculations proposed by the Nutrient
requirements of cattle protocol (NRC, 2001). Botanical composition was determined in each
paddock pre-grazing. Species composition was estimated using the dry-weight-rank method
(Mannetje and Haydock, 1963).

66

3.2.4. Statistical analysis
For the first period in each year cow-calf pairs were randomly assigned to treatments. For
the subsequent periods, a crossover design was implemented in a double repeated measures
design, with period and day as repeated measures. The variance-covariance matrix structure
defined was unbalanced autoregressive to account for the double repeated measures. The model
was as follows:
y = µ + ρj + τl + γk + λm + πi + τγlk + τλlm + ejlkmi
where µ is the overall mean, ρj is the random effect of the jth cow (ρj ~ N(0, σ2ρ)), τl is the fixed
effect of the lth treatment, γk is the fixed effect of the kth year, λm is the fixed effect of the mth
period, πi is the fixed effect of the ith day, τγlk is the interaction between the lth treatment and the
kth year, τλlm is the interaction between the lth treatment and the mth period, and ejlkmi is the
residual term (ejlkmi ~ N(0, ∑)). Statistical analyses were performed using SAS Software (Version
9.2; SAS Institute, 1987). All tests were performed with 95% confidence (α = 0.05).
3.3. Results and Discussion
3.3.1. Herbage mass, botanical composition and forage nutritional characteristics
There was an effect of year (P < 0.01) and means are shown separately for 2012 and
2013. Pre-grazed forage growth was not affected by treatments (Table 3.1). A greater herbage
mass pre-grazing was expected on SysA pasture sites, because of longer rest periods (60 to 90 d
as compared to 18 to 30 d on SysB pasture sites). Irrigation, as needed, and frequent defoliation
on SysB pasture sites might have increased the forage production at these sites, explaining the
lack of difference. Cow-calf pairs grazed SysB pasture sites 4 to 5 times per year, whereas SysA
pasture sites were grazed 2 to 3 times per year. The greater frequency of grazing applied to SysB
pasture sites might have stimulated forage growth. Additionally, SysB had greater amount of

67

legumes during P2 than SysA (Table 3.2) and likely greater amount of N available to plants,
which might also have contributed to forage growth.
Herbage disappearance might not be an accurate predictor of intake, however observed
herbage disappearance was in agreement with the DMI estimated using Cr2O3 as a marker. Dry
matter intake was not different between cows grazed with SysA or SysB (Table 3.4), which is in
agreement with the treatments effects found on herbage disappearance (P > 0.05). Post-grazing
herbage mass was greater for SysA during P2 in both years, 2012 and 2013 (Table 3.1). SysA
pasture sites were given longer rest periods, and forage offered to cows were reproductive and
mature. We believe that cows grazed selectively, trampling down on a great amount of forage
mass, increasing post-grazing herbage mass.
The pastures sites were comprised of mixed grass and legume species. The 3 most
predominant species observed in each pasture site are depicted in Table 3.2. Grasses were
predominant and represented from 67 to 96% of the pastures across periods and treatments.
However, legumes were always found, contributing from 3 to 21% of the pastures across periods
and treatments (Table 3.2). Frequently more than 1 species of legume was present (data not
shown).
The same species were present throughout the year but in different proportions. In SysA
pasture sites orchard grass (Dactylis glomerata) and bromegrass (Bromus inermis) were the
predominant grasses and birdsfoot trefoil (Lotus corniculatus) was the predominant legume, but
red (Trifolium pratense) and white clover (Trifolium repens) were also observed (Table 3.2). In
SysB pasture sites, Kentucky (Poa pratensis) and orchard grass were the predominant grasses
over the 2 years of study (Table 3.2). White clover was the most predominant legume during P1
and red clover was predominant during P2. The grass-legume ratio of pasture sites varied from

68

96:4 (SysA pasture sites during P2 of 2012) to 69:4 (SysB pasture sites during P2 of 2013). Our
results are in agreement with previous research that found few species responsible for large
proportion of the DM production in mixed grass and legumes pastures (Sanderson et al., 2005;
Skinner et al., 2006).
Previous studies have shown that grazing intensity did not affect species composition
(Ren et al., 2012; Dumont et al., 2008; Kruess and Tscharntke, 2002). The proportion of legumes
tended to be higher in SysB pasture sites during from P1 to P2. The SysA pasture sites were
provided longer rest periods, consequently at the end of the grazing season (P2) the plants were
tall and reproductive. The SysA grass height during P2 may have shaded and impaired the
development of legumes, decreasing their proportion. Shorter grazing returns results in frequent
defoliation, which maintains grasses at shorter heights. Shorter grasses have less shading effect,
allowing the development of plants with different growing habits, such as legumes. Alfalfa
(Medicago sativa) has an upright growth habit, red clover and birdsfoot trefoil have upright to
decumbent, and white clover have prostrate growth habit (Hannaway, 2004). Indeed, we
observed the predominance of red and white clover on SysB pasture sites, that with more
frequent defoliation had shorted grasses, allowing the development of prostrate growth habit
legumes (Table 3.2). Another factor that might have contributed to the decreased abundance of
legumes during P2 in SysA pasture sites was dry weather during the summer (end of the season P2). The SysB pastures areas were irrigated as needed, which might have allowed the growth of
legumes throughout the year.
Botanical shift affects forage quality, complicating the estimation of the nutritional value
of a pasture (Skinner et al., 2004; Belesky et al., 1999). Dumont et al. (2009) suggested that the
distribution evenness of plant species is greater under high stocking rates. In order to decrease

69

the error associated with botanical composition on estimation of forage quality, we collected 3
replicates of composited random samples per treatment. However, animals graze selectively and
the forage quality results may not accurately represent the DM consumed by cow-calf pairs.
Grazing animals select their feed and prefer living to dead material, younger to older material,
leaf to stem and legume to grass leaves (Hodgson et al., 1990; Popp et al., 1997).
The nutritional characteristics of the pastures differed by year (P < 0.01), therefore means
are presented separately for 2012 and 2013 grazing seasons (Table 3.3). Period and treatment
effects were observed for forage nutritional characteristics. During P1 forage characteristics were
quite similar between treatment pasture sites. As the grazing season proceeded the impact of
grazing management increased, mostly due to longer rest periods given to SysA pasture sites.
Crude protein content increased in SysA pasture sites from P1 to P2 of 2012, and
remained constant from P1 to P2 during 2013. The change in NDF content was the same in both
years; increased from P1 to P2 in SysA pasture sites but remained constant in SysB pasture sites.
Based on NDF, lignin and CP content forage quality decreased in SysA pasture sites during P2
(Table 3.3).
The larger differences observed (between SysA and SysB pasture sites) during P2 was
expected. At the beginning of the grazing season (P1), forage growth is independent of the
grazing system. Four months later (P2), SysB pasture sites were comprised of mature forages in
the reproductive stage. The SysB pastures sites, managed with shorter rest periods, maintained
young, vegetative forage throughout the season. Irrigation and frequent defoliation might have
contributed to higher forage quality of SysB pasture sites during P2 allowing the development of
legumes during P2 (Table 3.2). The lower grass-legume ratio in SysB pasture sites is reflected in
increased or maintained CP and NDF composition.

70

3.3.2. Animal performance
Cows were maintained in 1 single herd for most of the year, being divided into SysA or
SysB herd approximately 14 d before the sampling for diet adaptation. Therefore, treatment
effects on body weight (BW) cannot be determined. Body weights increased from 2012 to 2013
(P < 0.01) and from P1 to P2 (Table 3.4).
There were no treatment effects on DMI, although SysA cows had lower DMI than SysB
cows during P1. There was a period effect on DMI (P < 0.01), likely associated with decreased
intake of SysA cows during P1 (12.8 kg DMI d-1) compared to SysB cows during P1 (15.2 kg
DMI d-1). DMI of SysA and SysB cows increased from P1 to P2 (4 and 1.1 kg DMI d-1 increase,
respectively). In this study, cows ingested on average 2.6% (SysA) and 2.8% (SysB) of their
BW. These values are in agreement with previous studies. Marston et al. (1998) suggested that
DMI of lactating beef cows varies from 2.3 to 2.7% of BW for cows grazing average or high
quality forages, respectively. Hatfield et al. (1989) observed DMI from 14.8 to 16.1 kg DM d-1
(on average 2.8% of BW) for lactating beef cows with different milk production levels.
Forage or animal management-related factors do not explain the lower intake of SysA
cows during P1. It is possible that the stress associated to sampling, such as the use of canister
and halter, and moving to the head-gate area twice daily, could have decreased DMI (cows were
not halter broken prior to the sampling periods). Another hypothesis is that the decreased DMI
observed could be error associated with the use of Cr2O3 as a marker. Previous research (Smith
and Reid, 1955) reported that the excretion of chromium might not be constant during the day.
Feces were sampled twice daily during the present study in order to decrease the variation of
marker excretion, but in some occasions, fecal collection was not possible because the cows had
defecated on their way to the head-gate area.

71

Although forage quality generally decreased in SysA pasture sites during P2, DMI did
not change between systems (during P2). We believe that, despite the poorer forage quality
observed in SysA, cows grazed selectively. The botanical composition analysis indicated the
presence of legumes during P2 in both systems. Herbage mass analysis indicated no treatment
differences. In addition, in SysA cow-calf pairs were moved to a new paddock 3 times daily and
once daily in SysB. We believed that these factors altogether - presence of legumes, high
herbage mass and rotational schedule - provided the opportunity for cow-calf pairs to select what
to eat and match nutritional requirements.
3.3.3. CH4 emissions from beef cows
Emissions are described as daily emissions per head (g CH4 d-1), daily emissions mass
expressed per unit of intake (GE, NDF or DNDF intake; GEI, NDFI and DNDFI respectively), or
unit of metabolic body weight (g CH4 kg BW-0.75).
Treatment effects were observed for CH4 emissions as a percent of GEI and per unit
DNDFI (Table 3.5). The significant difference on CH4 as a percent of GEI is probably a result of
the lower DMI found for SysA cows during P1. The highest daily emissions were observed from
SysA cows during P1, which might have led to greater loss of CH4 as a percent of GEI and
reduced DMI.
Enteric CH4 emissions in this study are within the range reported by others using the SF6
tracer method from yearling heifers, first-calf heifers and mature cows (120 to 255 g CH4 d-1, De
Ramus et al., 2003), and cows and steers (150 to 240 g CH4 d-1, Pavao-Zuckerman et al., 1999).
McCaughey et al. (1999) reported CH4 emissions varying from 267 to 294 g CH4 d-1 from firstcalf, early lactation heifers grazing grass-only and alfalfa pastures. Our results varied from 195 to
249 g CH4 d-1 and were lower than the values found by McCaughey et al. (1999). The authors

72

also observed higher emission rates per unit BW0.75 (2.6 g CH4 kg BW-0.75) and higher loss of
CH4 as a percent of GEI, varying from 7 to 9.5%. Pinares-Patino et al. (2007) conducted an
experiment to compare CH4 emissions from grazing heifers managed under high (2.2 livestock
units; LU) and low (1.1 LU) stocking rates. They observed similar daily emissions (on average
216.6 g CH4 d-1), but higher CH4 emission rate (7% of GEI) compared to the present study.
Johnson et al. (1994) suggested CH4 losses as a percent of GEI varying from 2 to 12% for
cattle fed diets with different composition. Published values for grazing cattle approximate 6%
and represents studies including steers (Kennedy and Charmley, 2012) and heifers (Chaves et al.,
2006; Boadi and Wittenberg., 2002). Cattle eating high forage diets typically release a greater
percentage of their dietary energy as CH4 than cattle eating grains (Freetly and Bown-Brandl,
2013). Enteric CH4 emissions range from 3% for feedlot cattle to 6% of GEI lost as CH4 for
grazing cattle (IPCC, 2006). Kurihara et al. (1999) observed CH4 emissions rate as high as 11%
of GEI. Our values varied from 3.8 to 6.4% of GEI, which could be considered low for grazing
lactating beef cows. The highest value (6.4% for SysA cows during P1) was a result of high daily
emission and low DMI.
Emissions rate is a function of DMI; higher DMI explains lower emission rate as a
percent of GEI. Cows in the present study had comparatively higher DMI than animals from
previous studies focusing on enteric CH4 emissions. McCaughey et al. (1999) observed heifers
with 10 kg d-1 DMI on average. Pinares-Patino et al. (2007) reported heifers consuming on
average 9 kg DM d-1, whereas our cows consumed, on average, 15 kg DM d-1. Daily CH4
production increases with DMI (Boadi et al., 2002). Ricci et al. (2013) studied the correlation
between daily CH4 emissions and several variables such as BW0.75, DMI, GEI, CP, NDF and
lignin. Stronger correlations were observed for DMI and GEI (on average r = 0.83). A weaker

73

correlation was with BW0.75 (r = 0.64). Crude protein and NDF were correlated but not as
strongly (r = 0.04 and 0.20, respectively). Lignin was negatively correlated with CH4 emissions.
These results suggest that intake related variables individually explained a substantial proportion
of the variation in observed CH4. Therefore, we expected to observe higher daily CH4 emissions
due to higher DMI than previous research, and increased from P1 to P2 given that intake
increased and overall forage quality decreased during P2.
Bannink et al. (2010) performed a study using simulations to verify the impact of
fertilization and grass stage of maturity on cattle. They concluded that the quality of the forage
had more effect on CH4 emission than DMI. This concept is applicable to our results. The
rotational grazing management implemented, provided new paddocks every 8 to 12 hours (SysA)
or each day (SysB). The rotational grazing practice allowed cows to select for higher quality
forage at every feeding event. Consequently, despite the higher DMI compared to previous
research, our cows were selecting high quality forage and produced comparatively lower CH4
emissions.
It has been suggested that continuous set stocking allows maximum selective grazing,
which results in higher response per animal than from rotational grazing (Matches and Burns,
1995). De Ramus et al. (2003) have suggested that when forage quality is low, low stocking
density and continuous stocking allow the animals to select “portions of the forage plant” that are
higher in quality. We are in agreement with De Ramus et al. (2003), mainly because continuous
grazing reduces forage diversity, which requires animals to seek portions of the plants with
higher quality, given that they do not have a wide range of plant diversity to choose from.
Selective grazing is the cause of uneven usage of pasture (Teague et al., 2004), which leads to
overgrazing and diversity reduction under continuous grazing practices (Teague et al., 2013). In

74

our pasture sites, forage diversity was wide enough that cows were able to select higher quality
plants, instead of higher quality portions of the same plants.
3.4. Conclusion
Botanical composition analysis showed that both grazing systems implemented in this
study allowed the development of grasses and legumes throughout the grazing season. Grazing
system with shorter rest periods (SysB) had greater proportion of legumes and overall greater
forage quality at the end of the season, than grazing system with longer rest periods (SysA).
However, decreased forage quality at the end of the grazing season did not decrease DMI of
cows in SysA. We believe that both grazing systems implemented in this study provided
opportunities for selective grazing of different plant types, even though overall forage quality
decreased at the end of the grazing season in pastures grazed with low stocking rate and high
stocking density system.
Our results indicate that grazing management did not affect daily CH4 emissions from
lactating beef cows. Additionally, CH4 emissions tended to be lower than reported values for
lactating beef cows. The selective grazing resulted from the management systems implemented
in this study allowed cows managed with different grazing strategies to eat forage with similar
qualities that met nutritional requirements with reduced CH4 emissions. The results in this study
suggest that forage quality might be a better predictor to daily CH4 emissions than DMI. Further
research is needed to confirm that hypothesis.

75

Table 3.1. Forage mass pre and post-grazing and forage disappearance for pastures grazed with
different grazing management strategies.
Systems1

Periods2
P1
P2

SEM

2012 grazing season
Pre-grazing , kg ha-1
SysA
SysB

2278
2670

3824*
3508*

127

Post-grazing, kg ha-1
SysA
SysB

1375
1978

2580a*
1454b

38
32

2013 grazing season
Pre-grazing , kg ha-1
SysA
SysB
Post-grazing, kg ha-1
SysA
SysB

Disappearance, %
SysA
SysB

Disappearance, %
SysA
SysB

Effects
Treatment

Period

Treatment × Period

0.74

P-value
<0.01

0.06

121

0.15

0.10

<0.01

26a
52b

6

0.09

0.30

0.23

3851
4240

3735
3443

157

0.83

0.05

0.14

2776
2963

2167a*
1524b*

82

0.06

<0.01

<0.01

26
30

42*
55

5

0.28

0.03

0.76

1

SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha-1 stocking rate and
28,000 kg LW ha-1 stocking density.
2
Jun 4th - Jun 10th (P1), Sep 4th - Sep 11th (P2), 2012; Jun 5th - Jun 10th (P1), Sep 11th - Sep 17th (P2), 2013.
Mean differences within columns indicated by letters (P ≤ 0.05). Mean differences within rows indicated by symbols
(P ≤ 0.05).

76

Table 3.2. Botanical composition of pastures grazed with different grazing management
strategies.
Periods2
P1
2012 grazing season
SysA
Orchard (Dactylis glomerata)
Birdsfoot trefoil
(Lotus corniculatus)
Dandelion
(Taraxacum officinale)
SysB
Kentucky (Poa pratensis)
Orchard (Dactylis glomerata)
White clover (Trifolium repens)
2013 grazing season
SysA
Kentucky (Poa pratensis)
Orchard (Dactylis glomerata)
Red/white clover
(T. pratense/repens)
SysB
Kentucky (Poa pratensis)
Orchard (Dactylis glomerata)
Red/white clover
(T. pratense/repens)
Systems1

P2

1

%
70

Bromegrass (Bromus inermis)

%
85

17

Orchard (Dactylis glomerata)

11

13
34
54
7

Red clover (Trifolium pratense)
Timothy (Phleum pratense)
Red clover (Trifolium pratense)
Bromegrass (Bromus inermis)

4
70
15
11

50
17

51
26

7
54
30

Bromegrass (Bromus inermis)
Orchard (Dactylis glomerata)
Birdsfoot trefoil
(Lotus corniculatus)
Orchard (Dactylis glomerata)
Kentucky (Poa pratensis)

3

Red clover (Trifolium pratense)

21

9
55
14

SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha-1 stocking rate and
28,000 kg LW ha-1 stocking density.
2
Jun 4th - Jun 10th (P1), Sep 4th - Sep 11th (P2), 2012; Jun 5th - Jun 10th (P1), Sep 11th - Sep 17th, (P2), 2013.

77

Table 3.3. Nutritional characteristics of pastures grazed with different grazing management
strategies.
Systems1

Periods2
P1
P2

Effects
Treatment Period

Treatment × Period

2012 grazing season
CP3, %
SysA
SysB

11.7
12.3

11.4a
15.5b*

<0.01

0.08

<0.01

NDF4, %
SysA
SysB

65.6
65.1

73.9a*
64.1b

<0.01

0.32

<0.01

Lignin, %
SysA
SysB

3.9
3.1

6.1a*
4.8b*

<0.01

<0.01

<0.01

IVTD5, %
SysA
SysB

78.1
80.3

66.7a*
78.9b

<0.01

<0.01

<0.01

18.8
19.5

17.6
17.6

0.64

0.02

0.44

13.2
13.2

8.1a*
11.4b

<0.01

<0.01

<0.01

NDF, %
SysA
SysB

62.5
60.2

66.8a*
62.1b

<0.01

0.04

0.25

Lignin, %
SysA
SysB

3.3
3.1

6.6*
6.4*

0.27

<0.01

0.83

IVTD, %
SysA
SysB

82.5
83.9

58.4a*
65.1b*

<0.01

<0.01

<0.01

GE6, MJ kg-1
SysA
SysB
2013 grazing season
CP, %
SysA
SysB

78

Table 3.3. (cont’d)
Systems1

Periods2
P1
P2

Effects
Treatment Period

Treatment × Period

-1

GE, MJ kg
SysA
SysB

17.6
17.6

17.4
17.3

0.35

1

<0.01

0.05

SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha-1 stocking rate and
28,000 kg LW ha-1 stocking density.
2
Jun 4th - Jun 10th (P1), Sep 4th - Sep 11th (P2), 2012; Jun 5th - Jun 10th (P1), Sep 11th - Sep 17th, (P2), 2013.
3
CP: Crude protein.
4
NDF: Neutral detergent fiber.
5
In vitro total digestibility.
6
Gross energy.
Mean differences within columns indicated by letters (P ≤ 0.05). Mean differences within rows indicated by symbols
(P ≤ 0.05).

79

Table 3.4. Body weight, dry matter intake, gross energy intake, NDF intake and digestible NDF
intake of cows grazed with different grazing management strategies.
Periods2
P1

Systems1
3

P2

Effects
Treatment Period

Treatment × Period

-1

BW , kg head
SysA
SysB
SEM

545.1
571.3*
544.5
561.4*
14.4

0.8

<0.01

0.29

DMI4, kg d-1
SysA
SysB
SEM

12.8a
15.2b

16.8*
16.3*

0.16

<0.01

<0.01

GEI5, MJ d-1
SysA
SysB
SEM

226.3a
268.7b

296.7*
286.6*

0.16

<0.01

<0.01

NDFI6, g kg-1
SysA
SysB
SEM

8.0a
9.4b

11.6a*
9.8b

0.58

<0.01

<0.01

DNDFI7, g kg-1
SysA
SysB
SEM

5.6a
6.8b

5.4
5.5*

0.02

<0.01

<0.01

0.42

7.5

0.28

0.17

1

SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha-1 stocking rate and
28,000 kg LW ha-1 stocking density.
2
Jun 4th - Jun 10th (P1), Sep 4th - Sep 11th (P2), 2012; Jun 5th - Jun 10th (P1), Sep 11th - Sep 17th, (P2), 2013.
3
BW: Body weight.
4
DMI: Dry matter intake.
5
GEI: Gross energy intake.
6
NDFI: Neutral detergent fiber intake.
7
DNDFI: Digestible neutral detergent fiber intake.
Mean differences within columns indicated by letters (P ≤ 0.05). Mean differences within rows indicated by symbols
(P ≤ 0.05).

80

Table 3.5. Methane emissions from cows grazed with different grazing management strategies.
Systems

Periods2
P1

1

Effects
Treatment

Period

Treatment × Period

235.9
206.0

0.13

0.96

0.66

12.1*
14.1

0.17

0.08

<0.01

4.1*
4.2

0.05

0.09

0.02

2.0
1.8

0.29

0.81

0.68

19.2*
21.0

0.06

0.03

0.01

43.0
37.8

0.02

0.41

0.1

P2

-1

g CH4 d
SysA
SysB
SEM

249.3
195.3
20.0
3

-1

g CH4 kg DMI
SysA
SysB
SEM

21.0a
11.3b
1.80

4

CH4 % of GEI
6.4a
3.8b

SysA
SysB
SEM

0.46

g CH4 kg 5BW-0.75
SysA
SysB
SEM

2.2
1.8
0.20
6

-1

g CH4 kg NDFI

32.6a
19.1b

SysA
SysB
SEM

2.22
7

-1

g CH4 kg DNDFI
SysA
SysB
SEM

46.5a
26.7b
3.77

1

SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha-1 stocking rate and
28,000 kg LW ha-1 stocking density.
2
Jun 4th - Jun 10th (P1), Sep 4th - Sep 11th (P2), 2012; Jun 5th - Jun 10th (P1), Sep 11th - Sep 17th, (P2), 2013.
3
DMI: Dry matter intake.
4
GEI: Gross energy intake.
3
BW-0.75:Metabolic body weight.
6
NDFI: Neutral detergent fiber intake.
7
DNDFI: Digestible neutral detergent fiber intake.
Mean differences within columns indicated by letters (P ≤ 0.05). Mean differences within rows indicated by symbols
(P ≤ 0.05).

81

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82

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86

CHAPTER 4
CARBON FLUX ASSESSEMENT IN COW-CALF GRAZING SYSTEMS

87

4.1. Introduction
GHG fluxes from grasslands ecosystems are intimately linked to grazing management. In
grasslands, CO2 is exchanged with the soil and vegetation, N2O is emitted by soils and CH4 is
emitted by animals and exchanged with the soil. When CO2 exchange with vegetation is included
on net GHG exchange calculation, these ecosystems are usually considered GHG sinks
(Soussana et al, 2007; Allard et al., 2007). Similarly, the inclusion of SOC change in net GHG
exchange accounting might result in grasslands with GHG sink potentials (Liebig et al., 2010).
Grasslands management choices to reduce GHG budget may involve important tradeoffs. Allard et al. (2007) and Soussana et al. (2007) studied net GHG exchange from grasslands
including CO2 exchange with the vegetation, and observed net CO2 equivalent sink activity, but
with different trade-offs. Allard et al. (2007) observed that enteric CH4 emissions expressed as
CO2 equivalent strongly affected GHG budget in intensive and extensive managed grasslands
(average 70% offset of total CO2 sink activity). Soussana et al., (2007) observed that addition of
enteric CH4 and N2O emissions from pasture soils to CO2 sink activity of grasslands resulted in
relatively small offset of total CO2 sink activity (19% average). The small trade-off observed by
Soussana et al. (2007) was not enough to affect the CO2 equivalent sink potential of the sites
studied.
Management of grasslands modifies SOC storage (Conant et al., 2001; Schuman et al.,
2002), potentially increasing C sequestration (Follet et al., 2001). Grasslands management
primarily affects SOC storage by modifying C inputs to the soil, including root turnover and C
allocation between roots and shoots (Ogle et al., 2004). Liebig et al. (2010) suggested that the
factors contributing to net GHG exchange decreased in relative impact in the order of SOC
change, soil-atmosphere N2O flux, enteric CH4 emissions, CO2 emissions associated with N

88

fertilizer production and application, and soil-atmosphere CH4 flux. Similarly, Roberston et al.
(2000) observed that SOC change and N2O flux control net GHG exchange in agroecosystems.
In this study we assessed the net GHG exchange (in terms of Ceq flux) of 2 grazing
systems differing in stocking rate and density. We hypothesized that low stocking rate, high
stocking density systems have lower C flux resulting from less animals per area, and higher
accumulation of SOC because of longer rest periods.
4.2. Material and Methods
4.2.1. Pasture management and GHG collection
Cow-calf pairs were managed with 2 rotational grazing management practices differing
in stocking rates and density; an intensive system with high stocking rate and low stocking
density, and an extensive system with low stocking rate and high stocking density. The system
with low stocking rate and high stocking density (SysA) consisted of 120 cow-calf pairs rotating
on a total of 120 ha, divided into 0.7 ha paddocks. Cow-calf pairs were moved to a new paddock
3 times daily (at approximately 0600 h, 1200 h and 1800 h). The equivalent stocking rate was 1
cow ha-1 and the stocking density was approximately 100,000 kg LW ha-1. The rest period varied
from 60 to 90 d during the course of the growing season depending on plant growth. Cow-calf
pairs grazed each paddocks 2 to 3 times per year. The system with high stocking rate and low
stocking density (SysB) consisted of 4 cow-calf pairs rotating on 1.6 ha pasture, divided into
0.08 ha paddocks. Cow-calf pairs were moved to a new paddock once daily (at approximately
0800 h). The equivalent stocking rate was 2.5 cows ha-1 and the stocking density was 28,000 kg
LW ha-1. The rest period varied from 18 to 30 d during the course of the growing season
depending on plant growth. Cow-calf pairs grazed each paddocks 4 to 5 times per year. The
pasture sites in SysB were irrigated as needed, whereas there was no irrigation applied to SysA

89

pasture sites. The only fertilization application was on SysB pasture sites that received urea
fertilization (23 kg of actual urea) on June 3rd of 2011 (approximately 30 d before the start of gas
sampling, see dates below). In addition to these 2 systems, grazing-exclusion pasture sites (GE)
were monitored in order to account for GHG emissions from non-grazed pastures. The use of a
non-grazed pasture site was important to confirm that any differences found between SysA and
SysB were attributed to the grazing management practices implemented. The soil type across
treatments pasture sites was predominantly sandy loam.
SysA and SysB areas were sampled during 3 years (2011 to 2013). Sampling for all
treatments was repeated in 2 periods; at the beginning of the grazing season (period 1 – P1) and
at the end of the grazing season (period 2 – P2). The first year was considered a preliminary year,
for the purpose of adjusting the methodology for GHG from soils collection. For that reason, GE
pasture sites were not sampled, dates of periods monitored were closer together in time as
compared to 2012 and 2013, soil bulk density (BD) was not monitored, soil was sampled to 10
cm depth, and enteric CH4 emissions were not monitored. For details on dates of each period and
methodologies used on GHG emissions from soils and enteric CH4 emissions refer to Chapter 2,
section 2.2 and Chapter 3, section 3.2. Soil texture and pH in each treatment are described in
Table 2.1, Chapter 2.
Soil sample collection occurred in paddocks most recently occupied by cows. Soil
samples were collected from 0.08 ha paddocks (3 pseudoreplicates per treatment). Soil sampling
occurred approximately 20 days post-grazing. The sampling dates were: August 1st, and August
28th, 2011; June 3rd and September 15th, 2012; June 30th and September 28th, 2013.

90

4.2.2. Soil bulk density determination
Soil BD samples were collected with a 7.6 cm diameter and 7.5 cm height brass ring,
avoiding disturbance of soil structure. Samples were weighed, dried at 105°C to constant weight,
and re-weighed. Bulk density was calculated by dividing the dry weight by the soil core volume
(Blake and Hartge, 1986). Soil BD was not assessed during 2011. Soil BD was monitored in
different depths to allow SOC stock calculation (described below). However the distinction of
BD at the 0 to 5 cm and 5 to 10 cm depths was not possible because of the ring height (7.5 cm).
For that reason, BD in the top soil was assessed from 0 to 7.5 cm and it was used to calculate
SOC stock at 0 to 5 cm and 5 to 10 cm depths. SOC stock at 10 to 20 cm was calculated with BD
of 10 to 17.5 cm depth, and SOC stock at 20 to 30 cm was calculated with 20 to 27.5 cm BD.
4.2.3. Soil organic matter and C and N stocks determination
During 2012 and 2013, the soil pool was assessed at different depths: 0 to 5 cm, 5 to 10
cm, 10 to 20 cm, and 20 to 30 cm. SOC and TSN stocks were not monitored during 2011. A 0 to
30 cm depth is often used to report C stocks in soils (Schipper and Sparling, 2011). Previous
studies suggest that changes in soil C and N can extend throughout the soil profile rather than
just in the topsoil (Schipper et al., 2007; Franzluebbers and Stuedemann, 2009). Therefore,
sampling occurred at different depths to illustrate changes along the profile and address the
concern that changes in the surface soil may not represent storage in deeper horizons (BlancoCanqui and Lal, 2008). For each replicate (0.08 ha paddock) 10 soil samples were randomly
collected at each depth and composited per paddock. Soil samples were dried at 65°C separated
in 2 sub samples. One sub sample was sent to the Michigan State University Soil and Plant
Nutrient Laboratory for SOM determination. SOM was determined by wet digestion and
colorimetry (Schulte and Hopkins, 1996). The second sub sample was ground manually with a

91

pestle and mortar and sent to Michigan State University Great Lakes Bioenergy Research Center
Laboratory for analysis of C and N.
Soil OC and total soil N (TSN) from soil samples were determined by an Elemental
Combustion System (ECS 4010 CHNSO Analyzer, Costech, Valencia, CA). The ECS uses
combustion and gas chromatography with thermal conductivity detector and helium as carrier
gas to determine N2 and CO2. We tested for the presence of inorganic C in the soils of the study
area and concluded that no inorganic forms were present, thus total C represents SOC. Carbon:
nitrogen ratio was calculated for 0 to 30 cm depths.
Soil OC and TSN stocks were calculated based on soil layers of fixed depth (Equation
4.1). However, given that we observed high variability on BD between years and among
treatments, we corrected SOC and TSN values for a fixed mass of soil, as suggested by Ellert et
al. (2002; Equation 4.2 to 4.4 use SOC as example of calculations). This approach includes the
selection of a reference soil mass (Mref), which is the lowest soil mass to the prescribed depth
from all sampling sites. The Mref is then used to determine the soil mass to be subtracted from the
deepest core segment (excess mass of soil: Mex) so that mass of soil is equivalent to all sampling
sites
Equation 4.1. Soil organic carbon and nitrogen stock calculated based on soil layers of fixed
volume.
SOCFD = Σ Ci × BDi × Li × 0.1
where SOCFD is SOC stock to fixed depth (Mg ha-1), Ci is organic carbon concentration in depth
i (mg C g-1 dry soil), BDi is the bulk density of soil in depth i (g m-3), and Li is the length of the
depth i (cm).
Equation 4.2. Determination of soil mass in each depth.
Msoil = Σ BDi × Li × 100

92

where Msoil is mass of soil to a fixed depth (Mg ha-1), BDi is bulk density of soil in depth i
(g/m3), and Li is the length of the depth i (cm).
Equation 4.3. Determination of mass of excess soil in each depth.
Mex = Msoil - Mref
where Mex is mass of excess soil (Mg ha-1), Msoil is the mass of soil to a fixed depth (Mg ha-1),
and Mref is the lowest soil mass selected from all sampling sites and depths (Mg ha-1).
Equation 4.4. Determination of SOC stock to fixed mass of soil.
SOCFM = SOCFD – Mex × Cdl/1000
where SOCFM is the SOC stock for a fixed mass of Mref, Mex is mass of excess soil (Mg ha-1), and
Cdl is organic carbon concentration in the deepest depth (mg C g-1 dry soil).
4.2.4. C flux calculations
In this study, fluxes from the ecosystem to the atmosphere are considered a contribution
to the atmosphere budget. Therefore, positive GHG emissions indicate emissions to the
atmosphere and negative GHG emissions indicate sink activity. According to Chapin et al.
(2002) and adapted later by Soussana et al., (2007) the net GHG exchange (NGHGE) of a
managed grassland ecosystem is calculated as:
NGHGE = NEE + FCH4 + FN2O
where NEE is the net ecosystem exchange of CO2 that includes emissions from soil and plant
respiration, FCH4 is the CH4 flux from soil and FN2O is N2O flux from the soil. We adapted the
calculation to obtain the net GHG exchange in terms of C equivalent (Ceqflux). The Ceqflux for
each site was calculated by adding CH4 and N2O emissions to CO2 emissions using the global
warming potential of each of these gases at the 100-year time horizon (IPCC, 2007; GWPN2O =
298 and GWPCH4 = 25), as follows

93

Ceqflux = FCO2 + FCH4soil + FN2O + FCH4cows
where FCO2 is the C equivalent flux of CO2 from the soil, FCH4soil is the C equivalent flux of CH4
from the soil, FCH4cows is the C equivalent flux of enteric CH4 from the cows, and FN2O is the C
equivalent flux of N2O from the soil. In contrast to Soussana et al. (2007) our FCO2 does not
include CO2 lost by plant and animal respiration. The largest part of organic C ingested during
grazing is highly digestible and is respired shortly after intake (Soussana et al., 2007). Additional
C loss (5% of digestible C) occurs through enteric CH4 emissions, which was accounted for by
the term FCH4cows. We did not account for enteric CH4 from the calves. The non-digestible C
(from 25 to 40% of the intake depending on herbage digestibility) is returned to the pasture
mainly as feces (Soussana et al., 2007). We did not differentiate between manure-derived
emissions and soil-derived emissions. Soil emissions sampling was post-grazing and hence we
assume that any emissions from feces or urine decomposition is accounted for in the soil term.
Soussana et al. (2007) and Chapin et al. (2002) included the C lost from the system
through plant biomass export. Because our calculations are limited to the grazing season we
assumed no C loss via herbage cutting and removal from the sampled sites. C loss from herbage
decomposition on top of the soil is assumed to be included in CO2 and CH4 emissions from the
soil, SOM and SOC content. There was no addition of C into our systems by organic fertilization
and hence it is not included on the calculations. We did not account for C leaching from pasture
soils.
In order to allow summation of GHG fluxes from soil and cows and determination of
Ceqflux, FCH4cows (originally in g CH4 cow day-1) was converted to an area basis (g CH4 ha d-1),
using stocking rates of each system: SysA = 1 cow ha-1, and SysB = 2.5 cows ha-1. We monitored

94

only the grazing season and the Ceqflux is shown as daily average flux, because extrapolation to
annual flux would be inaccurate.
SOC stock change was not included in the Ceqflux determination because SOC content
was monitored for a period of 2 years, which is not considered long enough to detect accurate
SOC changes (Schuman et al., 2002). However, we consider SOC stock in our discussion of
Ceqflux because the main objective of this study was to show the importance of looking at
different pools when assessing GHG emissions from grazing systems. SOC stock is an important
pool to consider in any C flux accounting.
4.2.5. Statistical analysis
SOC and TSN stocks data were analyzed as a completely randomized design. Statistical
analyses were performed using SAS Software (Version 9.2; SAS Institute, 1987). Paddocks were
considered experimental units and were treated as the random term, and the compressed term
year × period was considered a repeated measure. We associated the effects of year and period to
the variability of the data, and hence means are shown pooled my year and period. The main
reason for showing pooled means was that the length of this study was not long enough to allow
assessment of SOC change in time, and showing means by year could lead to inaccurate
conclusions. All tests were performed with 95% confidence (α = 0.05). Soil and animal GHG
emissions data were analyzed as described in Chapter 2, Section 2.3.3 and Chapter 3, Section
3.2.3, respectively.
Ceqflux data were analyzed as a completely randomized design. Paddocks were
considered experimental units and were treated as the random term, and the compressed term
year × period was considered a repeated measure. When the main effect of year was significant
differences were discussed separately by year. When the main effects of treatment or period were

95

significant the interaction treatment × period was evaluated and pre-planned comparisons within
treatment and period were performed. All tests were performed with 95% confidence (α = 0.05).
4.3. Results and Discussion
4.3.1. Soil characteristics
Soil sampling was performed in different pasture sites during each year and period
sampled, depending on animal management. The sampling sites in GE were maintained constant
for all sampling occasions. A summary of particle size fractions in each pasture size is described
in Table 2.1, Chapter 2.
Soil BD values were different from 2012 to 2013 (P < 0.01), but did not change from P1
to P2 (P = 0.19). Therefore means are poled by period. Soil BD increased with soil depth but no
treatment effects were observed (Table 4.1). The accumulation of litter over time is a result of
rotational grazing, with adequate rest periods for regrowth. The presence of organic litter
dissipates the animal trampling impact, resulting in less compaction and lower soil BD of the soil
(Sanjari et al., 2008). The accumulation of litter protected grazed soils from compaction,
resulting in no BD differences between grazing systems and GE. Savadogo et al. (2007) and
Franzluebbers and Stuedemann (2009) reported BD values similar to this study.
Soil BD has been found to increase because of grazing in soils with large quantities of
fine soil particles (clay + silt) that are more sensitive to animal traffic and compaction
(Vanhaveren, 1983; Abdelmagid et al., 1987). Our pasture sites were predominantly comprised
of sand particles, and mostly sandy loam.
4.3.2. SOC and TSN stock and SOM content
We observed year and period effects on SOC stocks (P < 0.01 and P = 0.05,
respectively), which are likely associated to spatial and temporal variability. Soil C stocks

96

display high spatial variability, especially in grasslands. Cannell et al. (1999) found a coefficient
of variation of 50% when evaluating spatial variability of C stocks in grasslands as compared to
15% in arable lands. Previous research have associated the variability to sampling at different
depths (Bird et al., 2002), climate (Conant et al., 2001), texture (mainly clay content; Parton et
al., 1987), and lack of evaluation of C distribution within the grazing system (Schumann et al.,
1999). The ability to detect change in SOC stocks depends on the time since the original
sampling, spatial homogeneity of the soil and intensity of sampling (Schipper et al., 2010). In
this study, sampled paddocks (pseudoreplicates) were different at each year and period (see
Section 4.2.), which did not allow spatial homogeneity between soil samples. In addition, Conant
et al. (2001) suggested that periods of 5 to 10 years for a field scale study would be adequate to
detect changes in SOC stock. Therefore, the change observed from 2012 to 2013 cannot be
associated to SOC stock change (i.e. accumulation or loss). However, because the studied
grazing systems were implemented at the study site for 5 years prior to 2012, the relative change
between treatments may be considered.
Table 4.2 illustrates SOC stock means by treatment pooled by year and period. On
average, SOC stock was higher for SysB pasture sites, and the difference between GE and SysA
was not significant (63, 42 and 47.4 Mg C ha-1 for SysB, GE and SysA respectively, P < 0.01). In
SOM, N and C are predominantly covalently bonded (Schipper et al., 2010) and thus the pattern
of TSN accumulation in pasture sites was highly correlated to SOC accumulation (Table 4.2).
SysB pasture sites had higher TSN stocks compared to GE and SysA (4.85, 3.44 and 3.95 Mg N
ha-1, for SysB, GE and SysA respectively, P < 0.01). A similar relationship between C and N
reported by Pineiro et al. (2009).

97

The effects of grazing management on C cycling and distribution has been evaluated
before, however, literature does not yet suggest a clear relationship between grazing management
and C sequestration. Some studies have reported no effect of grazing on SOC stock (e.g.
Milchunas and Laurenroth, 1993), others reported increases (Weinhold et al., 2001) or a decrease
(Derner et al., 1997). Differences in findings between SOC stocks and grazing management has
been associated with factors that affect C cycling and sequestration potential on grasslands, such
as: climate, inherent soil properties, landscape position, plant community composition, and
grazing management practices (Reeder and Schuman, 2002). The management applied to the
land affects soil’s ability to retain organic C. Practices that increase plant productivity and C
inputs to the soil, and decrease soil exposure to sunlight and erosion allow greater C
accumulation (Parton et al., 1987).
Reeder and Schuman (2002) studied the impact of heavy or light grazing on SOC stocks,
compared to non-grazed areas. In their evaluation of the 0 to 30 cm layer, they observed
significantly higher SOC stock in grazed pastures (67 Mg C ha-1) compared to non-grazed
pastures (58 Mg C ha-1). The range of SOC stock observed was from 55 Mg C ha-1 to 100 Mg C
ha-1. We observed wider range of SOC stock values among all treatments (from 25 to 113 Mg C
ha-1; data not shown). The greater variability observed in this study might be associated to the
sampling in different pasture sites at each year and period. Sanjari et al. (2008) observed lower
SOC stock values for rotational grazing, continuous grazing and non-grazed pasture sites in 5
years of monitoring (on average 25 Mg C ha-1). However, increased SOC content in rotational
grazing pasture sites compared to continuous grazing or non-grazed pasture sites was observed
by Sanjari et al. (2008) and associated to greater grass growth and rest periods. Southorn (2002)
attributed the greater SOC accumulation in rotational grazing systems to the larger proportion of

98

plant material being incorporated into the soil. In addition, adequate rest periods is a key driver
in the recovery of grazed species and increase in aboveground organic material, followed by its
subsequent incorporation into the soil, resulting in increased SOC (Gillen et al., 1991).
In this study, SysA pasture sites were given longer rest periods (60 to 90 d) than SysB
pasture sites (18 to 30 d). Nevertheless, the increased SOC stock of SysB pasture sites suggested
that grazing management of SysB is increasing SOC stocks at a faster rate than SysA or GE (P <
0.01; Table 4.2). Naeth et al. (1991) suggested that grazing, such as that in SysB, reduces litter
mass accumulation because animal traffic enhances physical breakdown and incorporation of
litter into the soil. It is likely that more frequent grazing in SysB reduced litter accumulation, and
enhanced physical breakdown increasing litter decomposition and incorporation into the soil.
Frequent grazing also could have stimulated forage and roots development, increased soil water
content and microbial development, enhancing the rate of decomposition of litter and transfer of
C into deeper layers of the soil (Sharif et al., 1994). Root decay, although not measured in this
study, was identified as another reason for increased SOC under rotational grazing systems.
Intensive defoliation under a single grazing event results in cessation of plant respiration, leading
to death of roots within a few hours after grazing, in order to equalize biomass (Sanjari et al.,
2008). In SysB defoliation was intensive and more frequent than in SysA.
In SysA, forage offered to cow-calf pairs was mature and in reproductive stage, which
resulted in selective grazing by cows for higher quality plants (see discussion on Chapter 3,
Section 3.3.3). Forage that was not ingested was trampled down, resulting in greater litter
accumulation on soil surface (Table 3.1, Chapter 3). The significantly lower SOC stock in SysA
and GE compared to SysB might be the result of immobilization of C in excessive aboveground
plant litter, due to longer rest periods (SysA) or non-grazing (GE).

99

Soil organic C constitutes approximately 60% of SOM (Bardgett et al., 2009).
Consequently, the differences in SOM content between treatments were similar to the differences
observed for SOC stocks. SysB had higher SOM content to 30 cm than SysA or GE that did not
differ (4.07%, 3.33% and 3.22%, for SysB, SysA and GE, respectively, P < 0.01). SOM
decreased throughout the soil profile in all treatments (Figure 4.1).
In SysA pasture sites, animal trampling was more intense at each grazing occasion (due
to higher stocking density), but it was less frequent (longer rest periods). The higher stocking
density might have contributed to the formation of litter on soil surface, but without frequent
animal trampling, it is likely that litter decomposition happened at a slow rate. Because of higher
stocking density, cow-calf pairs grazed each paddock of SysA for a short period of time (8 to 12
h). The short time of grazing was likely not prolonged enough to accelerate litter decomposition
and incorporation into the soil. Reeder and Schuman (2002) suggests that a build-up of litter on
the soil surface affects soil temperature and soil water content, which will, in turn, affect plant
residue and SOM decomposition rates.
When observing the SOC distribution along the soil profile, SysB contained higher SOC
content in the 20 to 30 cm layers compared with SysA (P = 0.02) and GE (P = 0.03; Figure 4.2).
It was interesting to find that SysB pasture sites had accumulated C mainly in deeper layers. We
expected that, because of the long rest period and lack of irrigation on SysA, deep-rooted plant
species would develop and significantly contribute to SOC accumulation in deeper layers, as it
was observed before (Fisher et al., 1994). However, botanical composition did not support that
hypothesis (Table 3.2, Chapter 3). Legumes were found to be present on both SysA and SysB
pasture sites, and the same grasses species were found on both systems (although on different
proportions).

100

The surface depth (0 to 10 cm) generally contains the highest levels of labile C, indicative
of rapid turnover. This labile C is important mainly to ecosystem function and microbial
development. It represents the C participating in C cycling within the ecosystem and is not
representative of sequestered C. Carbon sequestered in deeper layers, indicates favorable
conditions for root penetration and high levels of microbial activity. Deeply sequestered C
enhances ecosystem hydrology and nutrient recycling. Additionally sequestration of C on deeper
layers provide long-term benefits, because C is less susceptible to loss from surface-soil
disturbances (Franzluebbers and Stuedemann, 2009). Our data supports earlier findings that
change in soil C can extend throughout the soil profile (Schipper et al., 2010; Schipper et al.,
2007). Schipper et al. (2010) observed that despite the apparent long residence time of soil C in
deep horizons, SOC moves through 1 m-deep horizons more rapidly than previously thought.
The frequent trampling effect caused by the cow-calf pairs in SysB resulted in disruption of
surface soil crust and soil aggregates, increasing SOM decomposition and SOC incorporation in
deeper depths (Liu et al., 2004; Neff et al., 2005). Intensive grazing has been associated to high
rate of SOM decomposition (Sanjari et al., 2008).
TSN concentration was also highly stratified with depth and followed SOC accumulation
(Figure 4.3). Conant et al. (2005) and Franzluebbers and Stuedemann (2009) find that changes in
SOC stock were closely related to changes in TSN stock. There are potential benefits as a result
of coupling between soil C and N changes. For example, the sequestration or loss of 1 Mg C is
associated with approximately 100 kg of N gained or lost (Schipper et al., 2010). There was no
treatment effect on C:N ratio (Table 4.2). The relatively high C:N ratio observed in this study
suggest that C and N immobilization is the dominant processes over mineralization (Du Preez
and Snyman, 1993).

101

4.3.3. Total C equivalent flux
Means are shown separately by year and period for FCO2, FCH4soil, FN2O, FCH4cows and
Ceqflux (year effect P < 0.01; Table 4.3). Daily means are presented in order to allow discussion
on the overall Ceqflux between grazing systems and non-grazed pasture sites (Table 4.4).

Grazing systems versus non-grazed pasture sites - Generally, grazing systems had higher
Ceqflux than GE pasture sites, except during P2 of 2012, when the difference between SysA and
GE was not significant (Table 4.3). The increased Ceqflux from grazing systems was expected
because FCH4cows was considered zero for GE. However, the difference between grazing systems
and GE was substantially small.
The initial hypothesis was that Ceqflux would be increased in grazing systems not only
due to enteric CH4, but also because of manure decomposition in pasture soils. However, during
2012 the difference between grazing systems and GE was approximately 3 kg C ha d-1, which
approximates FCH4cows. This suggests that during 2012, grazing did not increase GHG flux from
the soil. The Ceqflux pooled by treatment during 2012 (average 10.3 kg C ha d-1) was greater
when compared to 2011 (9.6 kg C ha d-1) and 2013 (19.8 kg C ha d-1). The year of 2012 was
relatively dry, with precipitation concentrated in a few days during the grazing season (Table 2.1,
Chapter 2). The low soil moisture content could have decreased GHG flux from the soil in all
pasture sites. The year of 2011 does not include FCH4cows.
During 2013, the difference in Ceqflux between grazing systems and GE was greater
(approximately 8 kg C ha d-1 during P1, and 11 kg C ha d-1 during P2) than the contribution of
FCH4cows (on average 3.3 kg C ha d-1). Generally, during 2013 GE pasture soils had decreased
FCO2, FCH4soil, and FN2O compared to grazing systems. GE pasture sites were the only ones with

102

observed N2O and CH4 sink activities, during the 2013 grazing season. The higher levels of
moisture in the soil (compared to 2012) likely increased microbial activity, resulting in increased
GHG exchange from pasture soils. During P2 of 2013, SysB had greater Ceqflux than SysA and
GE. It was the only occasion when the difference between grazing systems was observed.

SysA versus SysB
During 2011, FCH4cows was not monitored and Ceqflux represents the addition of FCO2,
FCH4soil and FN2O (Table 4.3). FN2O and FCH4soil were not different between treatments in neither
period. During P2, SysB had greater FCO2 than SysA (7.64 and 6.07 kg C ha-1 d-1, respectively),
which resulted in greater Ceqflux from SysB pasture sites than SysA during P2. Pooled by
treatment, Ceqflux decreased considerably from P1 to P2 (11.2 and 8.2 kg C ha-1 d-1, for P1 and
P2, respectively; P < 0.01). Because there were no consistent differences in FN2O and FCH4soil
from P1 to P2, the decrease in Ceqflux is due only to the decrease in FCO2. These results suggest
that, when FCH4cows is not taken into account, FCO2 seems to be the driver of Ceqflux in grazed
pastures.
During 2012, FCH4cows is included in Ceqflux. The differences between systems observed in
FCO2, FCH4soil, FN2O, or FCH4cows were not significant, and consequently the difference between
systems in Ceqflux was likewise not significant (Table 4.3). Despite the greater stocking rate of
SysB (2.5 cows ha-1) compared to SysA (1 cow ha-1), FCH4cows were not significantly different
between grazing systems during P2. We expected greater FCH4cows from SysB because of the
greater number of cows per hectare. However, the results suggest that SysA cows had relatively
high enteric CH4 emissions, during 2012 (Table 4.3)

103

During 2013, SysB had higher Ceqflux when compared to SysA during P2 (22.49 versus
13.40 kg C ha-1 d-1, respectively; P < 0.01). The increased Ceqflux from SysB was a result of
greater FCH4cows compared to SysA during P2 (6.22 versus 1.61 kg C ha-1 d-1, respectively; P =
0.02), because SysB did not have increased GHG emissions from soils compared to SysA (Table
4.3). During P1, again SysB had greater FCH4cows compared to SysA (3.26 versus 1.93 kg C ha-1
d-1, respectively P = 0.03). However, Ceqflux was not different between grazing systems (24.11
and 23.35 for SysA and SysB, respectively, P = 0.13). The decreased FCH4cows in SysA, was
offset by the numerical increased FN2O, which increased Ceqflux of SysA. These results suggest
that the contribution of enteric CH4 to Ceqflux may be not always be the driver of higher GHG
emissions. Robertson et al. (2000) showed that half of the total net CO2 equivalent emissions
from arable sites was contributed by N2O production. Our results indicate that under specific
circumstances this concept might apply to grasslands. Results from Soussana et al. (2007)
indicate that despite the large error in enteric CH4 measuring, the CH4 emission rate would not
lead to a large change in the net GHG exchange of the studied grasslands.

Daily Ceqflux pooled by year and period
In order to allow the comparison between treatments across years and periods, we pooled
daily means (Table 4.4). It is important to keep in mind that we sampled only during the grazing
season. By not monitoring Ceqflux during the winter, early spring or late fall, the pooled daily
means cannot be extrapolated to annual means.
Daily Ceqflux from grazing systems was higher than non-grazed pasture sites by
approximately 5.8 kg C ha-1 d-1 (P < 0.01). The largest contributor for the greater Ceqflux from
grazing systems compared to GE was FCH4cows. However, pooled across years grazing systems

104

also had higher FN2O and FCH4soil than GE. Between grazing systems the difference in Ceqflux (P =
0.60) was not significant. The only flux that was different between grazing system was FCH4cows;
SysB had greater FCH4cows than SysA (4.91 versus 2.09 kg C ha-1 d-1, respectively; P < 0.01).
The increased FCH4cows from SysB was a consequence of higher stocking rate, because daily
enteric CH4 emissions were not difference between systems across years (Table 3.5, Chapter 3).
The contribution of FCH4cows in SysB was not large enough to increase Ceqflux.
Typical N2O emissions from grasslands soils converted into C equivalent range between
0.3 and 3 kg C ha-1 d-1 (Machefert et al., 2002). Freibauer et al. (2004) observed N2O fluxes of
0.7 kg C ha-1 d-1 from grasslands. On the other hand, Soussana et al. (2007) studied grasslands
GHG flux throughout the year and found N2O emissions varying from -0.08 to 2.4 kg C ha-1 d-1.
In the present study, we observed FN2O from 0.06 to 1.35 kg C ha-1 d-1.
Regarding FCH4soil, we observed sink activity (FCH4soil range was from -0.16 to 0.14 kg C
ha-1 d-1, whilst Soussana et al. (2007) when monitoring CH4 fluxes throughout the year obtained
higher emissions (0.2 to 1.3 kg C ha-1 d-1). They associated the lower sink activity observed to
the presence of grazers, suggesting that grazing reduces the on-site sink activity for CH4. In fact,
the negative mean of FCH4soil in the present study was from GE pasture sites (Table 4.3).
Deposition of excreta by animals is expected to produce CH4 emissions at a very low level (as
compared to application of organic fertilizers; Jarvis et al., 2001), but may increase N2O
emissions (Smith et al., 2001).
In the present study, very low FCH4soil was observed and when differences between
treatments were observed they were due to FCO2, FN2O or FCH4cows (Table 4.3). Liebig et al. (2010)
suggested that factors contributing to net GHG exchange in grasslands were decreased in relative

105

impact order of SOC change, soil-atmosphere N2O flux, enteric CH4 emissions and soilatmosphere CH4 flux.
We did not include SOC change in Ceqflux determination, and the differences in N2O
fluxes were not significant between grazing treatments, which resulted Ceqflux differences that
were not significant between grazing systems. Liebig et al. (2010) including SOC change in the
GHG exchange determination, observed negative net GHG from heavily and moderately grazed
grasslands. Allard et al. (2007) and Soussana et al. (2007) also observed negative GHG exchange
from grasslands, because CO2 exchange with the vegetation was included on the determination
of net GHG exchange. The annual mean Ceqflux from SysB was lower than the annual mean
Ceqflux from SysA (Table 4.4), although means were not statistically different. However, if SOC
change was included on Ceqflux these results and conclusions could change. SOC stock results
suggested that potentially SysB is accumulating higher SOC than SysA (Table 4.2), but longterm monitoring of SOC stock in the study is needed to allow incorporation of SOC change in
Ceqflux determination.
Generally, the higher stocking rate in SysB increased FCH4cows, but did not affect FCH4soil
and FN2O. We believe that the lower stocking density in SysB and irrigation allowed shorter rest
periods, frequent herbage defoliation, faster return of nutrients to soils from excreta deposition,
increased plant growth and roots development. These factors, in addition to greater TSN content
in SysB, might have contributed to microbial development and faster nutrient cycling,
decreasing GHG emissions from soils. It was demonstrated in Section 4.3.2 that SysB is
potentially increasing SOC stocks at a faster rate than SysA or GE. Similarly, SOM content was
higher in SysB compared to SysA and GE, which suggests faster litter decomposition. SOC
accumulation on deeper layers (20 to 30 cm) was greater in SysB, which also suggests potential

106

of C sequestration. In addition, SysB gives the producer more flexibility in terms of animal
production. Because of shorter rest periods and frequent defoliation forage quality remained
high and constant throughout the grazing season (Table 3.3, Chapter 3). The maintenance of
forage quality permits the production of different types of animals, such as finishing steers for
instance, which permits the producers to aggregate value to their final product according to
market changes.
In SysA there was a decrease in forage quality from P1 to P2 (Table 3.3, Chapter 3) but
FCH4cows was not increased, which was associated to selective grazing. We observed the
development of legumes in both systems, indicating that the grazing management is not
depleting the development of specific plant species, and selective grazing is allowed in both
systems. SysA does not need irrigation and longer rest periods results in litter accumulation on
the top soil, with slow decomposition rate. It is possible that the SOM slower decomposition
rate of SOM in SysA could provide greater resilience to SysA compared to SysB.
It is important to remember that we monitored GHG exchange during the grazing season
only. We did not account for emissions in other periods other than post-grazing, and hence
annual emissions may not be accurate. Similarly, we are assuming that the grazing seasons of
both systems were of the same duration. If one system allowed prolonged or shortened grazing
season, Ceqflux would change.
4.4. Conclusion
Grazing systems had greater Ceqflux than non-grazed pasture sites. The largest contributor
to increased Ceqflux from grazing systems was enteric CH4 emissions. However, on an annual
basis, grazing systems also had increased N2O and CH4 emissions from pasture soils, compared
to non-grazed pasture sites. Non-grazed pasture sites were the only sites with CH4 sink activity.

107

The effect of greater enteric CH4 contribution from SysB, due to higher stocking rate than
SysA, was offset by GHG exchange from the soil. Hence, our results indicate no clear
difference in C equivalent flux between the grazing systems studied, when SOC change is not
incorporated. SysB potentially increased total SOC stock, the addition of SOC to deeper into the
soil horizon and SOM content to 30 cm. SysA, with longer rest periods, allowed litter
accumulation on the top soil, resulting in slower SOM decomposition rate, which can result in
greater resilience in the long-term.
Grazing management should be adaptive and farm decisions are inherent to grazing
management. Both SysA and SysB have opportunities to improve ecosystems services at the
farm level, including animal production and food provisioning. Long-term research is needed to
confirm SOC stock and SOM decomposition rates of these systems. The incorporation of C
sequestration into the determination of Ceqflux could change results and possibly differentiate the
grazing systems studied.

108

Table 4.1. Soil bulk density in pasture soils grazed under two management strategies and nongrazed.
Soil depth, cm
2012 grazing season
0 to 5
5 to 10
10 to 20
20 to 30
SEM
Source of Variation
Treatment
Depth
Treatment x Depth
2013 grazing season
0 to 5
5 to 10
10 to 20
20 to 30
SEM
Source of variation
Treatment
Depth
Treatment x Depth

GE
1.27
1.27
1.57
1.43

Systems1
SysA
SysB
-3
g cm
1.20
1.25
1.20
1.25
1.25
1.35
1.47
1.44
0.05

0.11
<0.01
0.11

1.46
1.46
1.65
1.65

1.57
1.57
1.58
1.59
0.04

1.39
1.39
1.62
1.57

0.14
<0.01
0.36

1

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density.

109

Table 4.2. Soil organic carbon and total soil nitrogen stocks in pasture soils grazed under two
management strategies and non-grazed.
Systems1

Stocks
SOC2
Mg ha

TSN3

C:N

-1

GE

42.0a

3.44a

21.0

SysA

47.4a

3.95a

18.7

b

4.85
0.2

b

19.4

<0.01

0.06

SysB
63.0
SEM
3.8
Source of Variation
Treatment
<0.01
1

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density.
2
SOC: soil organic carbon.
3
TSN: total soil nitrogen
Means differences within columns indicated by letters (P < 0.05).

110

Table 4.3. GHG exchange from pasture soils and animal and total C equivalent flux from pasture sites managed under two different
management strategies and non-grazed pasture sites.

Systems

1

2011 grazing system
GE
SysA
SysB
SEM
Source of Variation
Treatment
Period
Treatment × Period
2012 grazing season
GE
SysA
SysB
SEM
Source of Variation
Treatment
Period
Treatment × Period

Soil emissions
FCO22

FN2O3

P1

P1

P2

P2

FCH4soil4
P1
P2
-1 -1
kg C ha d
-0.18
-0.07
-0.21
0.06*
0.04

Animal Emissions
FCH4cows5

Total emissions
Ceqflux6

P1

P1

P2

10.54 6.07a*
9.74
7.64b*
0.41

1.16 0.80
1.19 1.59
0.32

0.28
<0.01
<0.01

0.07
0.96
0.08

0.25
0.02
0.04

8.24
8.04
7.11

0.11 0.05
0.44 0.08
0.31 0.19
0.11

0.01a
0.003
b
0.14
0.08
a
0.08
0.07
0.04

0
3.28
4.89

0.19
0.09
0.33

<0.01
0.38
0.51

0.12
0.03
0.68

0.43
0.15
0.07

9.13
8.31
9.26*
0.50

111

-

-

P2

11.35
6.77a*
10.69
9.57b
0.64
0.03
<0.01
<0.01

0
2.26
3.43
0.63

8.38a
9.18a
12.06b 10.75ab
12.17b 12.73b
0.57
<0.01
0.97
0.06

Table 4.3. (cont’d)

Systems1

2013 grazing season
GE
SysA
SysB
SEM
Source of Variation
Treatment
Period
Treatment × Period

Soil emissions
FCO22

FN2O3

P1

P1

P2

FCH4soil4
P2

19.96 8.57a*
19.72 10.75ab*
21.49 14.97b*
1.36

P1
P2
-1 -1
kg C ha d
a
0.96 -0.88
0.20
-0.17
b
4.75 0.35*
0.23
0.33b
3.23b 0.82
0.26
0.35b
0.70
0.18

<0.01
<0.01
0.04

<0.01
<0.01
0.03

<0.01
0.78
0.02

1

Animal Emissions
FCH4cows5

Total emissions
Ceqflux6

P1

P1

P2

P2

1.93a
1.61a
3.26b
6.22b
0.84

20.77a
7.71a*
26.13ab 13.40b*
28.13b
22.49c
1.96

0.02
0.11
0.05

<0.01
<0.01
<0.01

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha-1 stocking rate and 28,000 kg LW ha-1
stocking density.
2
FCO2: C equivalent flux of CO2 from the soil.
3
FN2O: C equivalent flux of N2O from the soil.
4
FCH4soil: C equivalent flux of CH4 from the soil.
5
FCH4cows: C equivalent flux of enteric CH4 from the cows.
6
Ceqflux: net GHG exchange in terms of C equivalent.
Means differences within columns indicated by letters (P < 0.05). Means differences within rows indicated by symbols (P < 0.05).

112

Table 4.4. Daily GHG emissions from soil and animal managed under two different grazing
strategies and non-grazed pasture sites.

Systems

1

GE
SysA
SysB
SEM
Source of Variation
Treatment

Soil emissions
FCO22 FN2O3
9.87a
10.03a
11.47b
0.66

0.25a
1.56b
1.17b
0.32

0.17

<0.01

Animal
Emissions
FCH4cows5

FCH4soil4
kg C ha-1 d-1
-0.09a
0
b
0.13
2.09a
0.10b
4.91b
0.08
1.09
<0.01

1

0.02

Total
emissions
Ceqflux6
8.88a
13.96b
15.34b
0.74
<0.01

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows
ha-1 stocking rate and 28,000 kg LW ha-1 stocking density.
2
FCO2: C equivalent flux of CO2 from the soil.
3
FN2O: C equivalent flux of N2O from the soil.
4
FCH4soil: C equivalent flux of CH4 from the soil.
5
FCH4cows: C equivalent flux of enteric CH4 from the cows.
6
Ceqflux: net GHG exchange in terms of C equivalent.
Means differences within columns indicated by letters (P < 0.05).

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Figure 4.1. Soil organic matter in pasture soils grazed with two different grazing management
strategies and non-grazed pastures sites.

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha1
stocking rate and 28,000 kg LW ha-1 stocking density.

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Figure 4.2. Soil carbon stock in pasture soils grazed with two different grazing management
strategies and non-grazed pastures sites.

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha1
stocking rate and 28,000 kg LW ha-1 stocking density.

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Figure 4.3. Total soil nitrogen stock along the soil profile in pasture soils grazed with two
different grazing management strategies and non-grazed pastures sites.

GE: grazing exclusion; SysA: 1 cow ha-1 stocking rate and 100,000 kg LW ha-1 stocking density; SysB: 2.5 cows ha1
stocking rate and 28,000 kg LW ha-1 stocking density.

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CHAPTER 5
SUMMARY

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The main objective of this study was to apply a system-based approach to determine the
influence of grazing systems on C flux. We monitored CO2, CH4 and N2O emissions from
pasture soils, enteric CH4 emissions from cows and SOC and TSN stocks in pasture soils. We
understand that the C cycle involves a large number of sources and sinks. Future research may
add other components to the C flux determination initiated in this study, such as C leaching,
microbial development, other gaseous emissions, CO2 exchange with the vegetation and from
cows respiration, and CH4 emissions from calves.
This study was conducted at a farm-scale, following animal rotation schedule, which did
not allow the use of true replication. The variability associated to CO2, CH4 and N2O emissions
from pasture soils was considerably high. We observed different results in each of the years and
periods studied. GHG emissions from pasture soils are associated to manure decomposition in
the field. In this study, GHG emissions were monitored always post-grazing, which might also
have contributed to the variability of results. There was a tendency that SysB increased CO2
emissions from pasture soils. Grazing systems did not conclusively influence CH4 and N2O
emissions from pasture soils, and none clear trade-offs were observed between GHG emissions.
Generally, GHG emissions increased from 2011 to 2013, which could be associated to different
weather conditions in each year. On average, pooled by year, grazing resulted in greater
emissions of N2O and CH4 when compared to non-grazed pasture sites. Methane sink activity
was observed only on non-grazed pasture soils.
The soil and ambient properties monitored were soil temperature, ambient temperature
and soil water content. These properties affected CO2 emissions, but did not conclusively explain
CH4 and N2O emissions. The soil and ambient properties monitored were chosen based on
previous studies that found relationships between these properties and GHG emissions. The

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results of this study did not support previous findings, and suggest that at a farm-scale, CO2, CH4
and N2O fluxes could be mainly influenced by other soil properties. We suggest WFPS or O2
content in the soil as possible better predictors of GHG from pasture sites at farm scale. Further
research is needed to confirm the effect of WFPS and O2 content on GHG emissions. This study
indicates that GHG emissions from pasture soils are still uncertain when monitored at a farm
scale.
The DMI of cows observed was relatively high when compared to previous studies.
However, enteric CH4 emissions observed tended to be lower than reported values. Previous
research suggested that enteric CH4 production increased with increased DMI. We hypothesized
that the grazing management implemented in this study provided opportunities for selective
grazing of different plant types, allowing cows to ingest high quality diets, while producing low
amounts of enteric CH4. These results suggest that diet quality might be a better predictor to
daily CH4 emissions than DMI, mainly due to methodological limitations to accurately determine
DMI from grazing animals. Additionally, the differences observed in daily CH4 emissions
between grazing systems were not significant at 5%. Possibly, the selective grazing resulted from
the management systems implemented in this study allowed cows managed with different
grazing strategies to eat forage with similar qualities that met nutritional requirements with
reduced CH4 emissions. However, further research is suggested to determine the effects of
selective grazing on enteric CH4 emissions from grazing cows.
When combining GHG emissions from pasture soils and enteric CH4 emissions from
cows to determine the net C equivalent flux, our results showed that grazing systems had greater
C equivalent flux than non-grazed pasture sites, and the differences observed between grazing
systems were not significant. It is possible that the effect of greater enteric CH4 contribution

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from higher stocking rate systems was offset by GHG exchange from the soil, resulting in nonsignificant difference between grazing systems. Intensive grazing (high stocking rate, low
stocking density) potentially increased total SOC stock, the addition of SOC to deeper into the
soil horizon and SOM content to 30 cm when compared to extensive grazing (low stocking rate,
high stocking density).
Although SysB allowed for more animals per hectare, we did not observed a significant
difference between grazing systems in daily C-equivalent flux, when expressed as kg of C
equivalent per hectare. However, these results are limited to the assumptions and limitations of
this work. The implementation of SysA requires more land than SysB, hence if the results were
expressed in a land basis, conclusions could change. Additionally, an important assumption of
this work was that both grazing systems would allow for the same duration of grazing season. If
one system allowed for longer grazing season, and C equivalent flux was expressed in terms of
total length of grazing season (instead of daily values), conclusions could change. The inclusion
of other components of the C cycle on the calculation of C equivalent flux could potentially
change conclusions. For instance, CO2 exchange with the vegetation and C sequestration
potential. SOC stock was greater in SysB; if the potential of SysB to increase C sequestration in
the soil is confirmed, it could result in a significant difference of C equivalent flux between
systems. Results obtained in this work are only valid within the assumptions and limitations of
the study and further research is encouraged. Additional topics that still deserve future research
include the relationships between GHG fluxes from soils and microbial communities, the role of
nitrogen in the C equivalent flux, processes involved on CH4 and N2O sink activity and the sink
potential of grasslands soils.

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Both systems studied have benefits and could be implemented under specific conditions
and objectives of the farmer. SysA could be implemented in marginal lands, where irrigation is
not possible and crop production is difficult. Additionally, SysA represents a low input
opportunity to reverse land degradation in the farm. High stocking densities grazing systems
(such as SysA) were associated to attenuation or reversal of land degradation in arid ecosystems.
SysB, with shorter rest periods and frequent defoliation of the forage, allowed the development
of greater forage quality throughout the grazing season. The maintenance of high quality forage
provides flexibility of operations to the farmer, according to market prices. For instance, SysB
allows the production of finishing steers, as well as cow-calf operations. This flexibility of
operation aggregates value to the final product of the farm.
Concerning the total C equivalent flux from grazing systems versus non-grazed pasture
sites, indeed the former had greater C equivalent flux. The greater C equivalent flux from the
grazing systems was associated to greater GHG emissions from pasture soils and enteric CH4
emissions. However, the difference between grazed and non-grazed pasture sites observed in this
study was considerably small (approximately 6 kg C ha-1 d-1). The benefits of grazing systems,
such as animal production and food provisioning, overcome the small increase in C equivalent
flux when compared to non-grazed pasture sites.
This study is unique because it included monitoring at the field of CO2, CH4 and N2O
from pasture soils and enteric CH4 emissions from cows simultaneously. Other similar studies
did not monitor the 4 emissions sources and used reported values to determine C equivalent flux.
Observed results raise questions about the current knowledge on C equivalent flux when applied
to a farm level. Small-scale, reductionist research do not focus on interactions among all
elements of a system and could result in completely different results than a system-based

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research. Nevertheless, system-based C flux accounting of grazing systems are very limited and
needed.

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