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ECOSYSTEM SERVICES PROVIDED BY CONTRASTING GRAZING SYSTEMS IN NORTH FLORIDA
By
LIZA MARIA GARCIA-JIMENEZ
A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
UNIVERSITY OF FLORIDA
2019
© 2019 Liza Maria Garcia-Jimenez
To my lovely family
4
ACKNOWLEDGMENTS
I would like to thank my advisor Dr. Jose Dubeux for giving me the opportunity to
achieve one of my dreams. I am very thankful for his patience, dedication, guidance and
friendship. Also, I extend my thanks to all the members of my committee, Dr. Lynn
Sollenberger, Dr. Joao Vendramini, Dr. Ellis James, and Dr. Holly Ober, for the time and
dedication throughout my project. Thanks for helping me with data analysis and all the
valuable inputs for my study. Also, I would like to thank the Agronomy Department for
this opportunity, especially to Cynthia Hight for her unconditional help. I give special
thanks to the United States Department of Agriculture (USDA) and the North Florida
Research and Education Center (NFREC) for the financial support during my PhD.
I would like to thank my colleagues and office mates Erick Santos and David
Jaramillo, for their invaluable help in the field and with sample analysis. Also, their
patience and friendship is greatly appreciated. It was a pleasure to work with these two
fine fellow researchers and future colleagues. My research was possible thanks to the
help in the field of valuable students and interns. I would like to thank Agustin Lopez,
Marina Bueno, Alejandra Gutierrez, Raul Guevara, Camila Sousa, Elijah Conrad, Daci
Abreu, Luana Dantas, Pierre Yves, Jose Diogenes, Pedro Sueldo, Jose Rolando, Joyce
Patu, Sophia Cattleya, Julie Arnett, Mariana Garcia, Manuel Pena, Caroline Monteiro,
Andre Ferraz, Nubia Epifanio, Michell Siqueira, Lucas Miranda, Lautaro Rostoll, Ana
Carolina Gomez, Gonzalo Barreneche, Luara Canal, Maria Teresa Davidson, Federico
Podversich, Fabio Pinesi, Luana Zagato, Flavia Scarpino van Cleef, Vanessa Longhini,
Tessa Shulmeister, and our passionate lab supervisor, science mentor, and friend, Dr.
Martin Ruiz-Moreno. I am very thankful for the help and encouragement of the NFREC
5
family, especially to the beef unit crew. I express special gratitude to David Thomas,
Tina Gwin and Gina Arnett.
I would like to thank my parents Jose Garcia and Gloria Jimenez for their love
and guidance and for always inspiring me to learn and keep studying. Special thanks is
extended to my mom, my role model, for her unconditional help. I am also grateful for
my maternal grandparents, uncles and aunts for their unconditional love, support and
for always inspiring me. Finally, I would like to thank my two favorite people in this
world, my lovely husband Nicolas DiLorenzo, and my daughter Luciana DiLorenzo. I am
very grateful for their help and companionship during my PhD. Thank you for your love,
patience and support.
6
TABLE OF CONTENTS page
ACKNOWLEDGMENTS .................................................................................................. 4
LIST OF TABLES .......................................................................................................... 10
LIST OF FIGURES ........................................................................................................ 12
LIST OF ABBREVIATIONS ........................................................................................... 15
ABSTRACT ................................................................................................................... 16
CHAPTER
1 OVERVIEW ............................................................................................................ 19
2 LITERATURE REVIEW .......................................................................................... 23
Introduction ............................................................................................................. 23 Ecosystem Services from Grasslands .................................................................... 24
Nitrogen Fertilization in Grasslands ........................................................................ 27 Forage-Livestock Systems ...................................................................................... 29 Forage and Animal Performance on Grass Monocultures ...................................... 30
Warm-Season Grass: Bahiagrass .................................................................... 30
Cool-Season Grasses: Oat and Rye ................................................................ 32 Forage and Animal Performance on Legume-Grass Mixtures ................................ 33
Cool-Season Mixtures with Clovers .................................................................. 34
Warm-season Grass Mixture with Rhizoma Perennial Peanut ......................... 35 Application of Stable Isotopes in Grazing Studies .................................................. 37
Nutrient Cycling in Grasslands ................................................................................ 38 Soil Organic Matter (SOM) in Grasslands ........................................................ 39 Biological Nitrogen Fixation (BNF) and Nutrient Cycling .................................. 41 Nutrient Cycling Via Litter and Animal Excreta ................................................. 41
Greenhouse Gas Emissions from Grasslands ........................................................ 43 Enteric Methane Emissions .............................................................................. 45 Legumes and Methane Emissions ................................................................... 47
Importance of Pollinator Insects .............................................................................. 49 Pollination Services from Grasslands ..................................................................... 50
3 FORAGE AND ANIMAL PERFORMANCE IN N-FERTILIZED OR GRASS-LEGUME PASTURE DURING COOL- AND WARM- SEASON ............................. 52
Introduction ............................................................................................................. 52 Materials and Methods............................................................................................ 54
Experimental Site ............................................................................................. 54
Treatments, Experimental Design, and Management ...................................... 55
7
Herbage Responses ............................................................................................... 56
Herbage Mass, Allowance and Accumulation Rate - Cool Season .................. 56
Nutritive Value – Cool-Season ......................................................................... 58 Biological N2 Fixation – Cool-Season ............................................................... 58 Botanical Composition – Cool-Season ............................................................. 59 Herbage Mass, Herbage Allowance and Herbage Accumulation Rate –
Warm- Season .............................................................................................. 59
Nutritive Value, Biological N2 Fixation and Botanical Composition – Warm-Season .......................................................................................................... 60
Livestock Performance ........................................................................................... 61 Average Daily Gain, Stocking Rate, and Gain Per Area ................................... 61 Fecal and Blood Samples................................................................................. 61
Statistical Analysis ............................................................................................ 62
Results .................................................................................................................... 63
Herbage Responses – Cool-Season ................................................................ 63 Nutritive Value – Cool-Season ......................................................................... 63
Isotopic Composition and Biological Nitrogen Fixation – Cool-Season ............ 64 Animal Responses – Cool-Season ................................................................... 64
Botanical Composition: Cool- and Warm-Season ............................................ 65 Herbage Responses – Warm-Season .............................................................. 66 Nutritive Value – Warm-Season ....................................................................... 67
Isotopic Composition and Biological Nitrogen Fixation – Warm-Season .......... 67 Animal Responses – Warm-Season ................................................................. 68
Discussion .............................................................................................................. 69 Herbage Responses – Cool-Season ................................................................ 69
Nutritive Value – Cool-Season ......................................................................... 70 Isotopic Composition and Biological Nitrogen Fixation – Cool-Season ............ 72
Animal Responses – Cool-Season ................................................................... 73 Botanical Composition – Cool- and Warm-Season .......................................... 75 Herbage Responses – Warm-Season .............................................................. 76
Nutritive Value – Warm-Season ....................................................................... 77 Isotopic Composition and Biological N2 Fixation – Warm-Season .................... 77
Animal Responses – Warm-Season ................................................................. 78 Conclusions ............................................................................................................ 80
4 NUTRIENT EXCRETION FROM CATTLE GRAZING IN N-FERTILIZED GRASS OR GRASS-LEGUME PASTURES IN NORTH FLORIDA ...................... 108
Introduction ........................................................................................................... 108 Material and Methods ........................................................................................... 111
Experimental Site and Treatments ................................................................. 111 Urine Samples ................................................................................................ 112 Fecal Output ................................................................................................... 112 Calculations .................................................................................................... 114 Statistical Analysis .......................................................................................... 115
Results .................................................................................................................. 116 Nutrient Concentration in The Excreta - Cool-season .................................... 116
8
Output per Animal per Day - Cool-season ...................................................... 116
Output per Hectare per Day - Cool-season .................................................... 117
Output per Season - Cool-season .................................................................. 117 Nutrient Concentration in The Excreta - Warm-season .................................. 117 Output per Hectare per Day - Warm-season .................................................. 117 Output per Season - Warm-season ................................................................ 118 Total Annual Nutrient Excretion – Cool and Warm-seasons ........................... 119
Discussion ............................................................................................................ 120 Nutrient Concentration in the Excreta - Cool-season ..................................... 120 Output per Animal per Day - Cool-season ...................................................... 121 Output per Season - Cool-season .................................................................. 122 Nutrient Concentration in The Excreta - Warm-season .................................. 123
Output per Hectare per Day - Warm-season .................................................. 125
Output per Season - Warm-season ................................................................ 125
Total Annual Nutrient Excretion – Cool- and Warm-season ........................... 127 Conclusions .......................................................................................................... 128
5 FORAGE INTAKE AND ENTERIC METHANE EMISSIONS IN N-FERTILIZED OR GRASS-LEGUME PASTURES DURING COOL- AND WARM-SEASON ...... 135
Introduction ........................................................................................................... 135 Materials and Methods.......................................................................................... 136
Experimental Site ........................................................................................... 136
Experimental Design ...................................................................................... 136 Enteric CH4 Emissions from Cattle ................................................................. 137
Dry Matter Intake Measurements ................................................................... 139 Proportion of C3 in Feces and Selection Index .............................................. 141
Calculations .................................................................................................... 141 Statistical Analysis .......................................................................................... 142
Results and Discussion......................................................................................... 142 Cool-season ................................................................................................... 143 Warm-season ................................................................................................. 145 Cool vs. Warm-season ................................................................................... 147
Selection Index – Warm-season ..................................................................... 148 Conclusions .......................................................................................................... 148
6 MANAGING GRASSLAND STRUCTURE TO ENHANCE POLLINATOR HABITAT ............................................................................................................... 156
Introduction ........................................................................................................... 156 Material and Methods ........................................................................................... 159
Experimental Site ........................................................................................... 159
Sampling Procedure ....................................................................................... 160 Statistical Analysis .......................................................................................... 160
Results and Discussion......................................................................................... 161 Bee Species ................................................................................................... 161 Presence of Bees per Trap Color ................................................................... 164
9
Medium and Small Body Size Bees ............................................................... 164
Abundance of Bees ........................................................................................ 166
Species Richness and Diversity ..................................................................... 166 Flower Density ................................................................................................ 168
Conclusions .......................................................................................................... 169
7 SUMMARY ........................................................................................................... 185
LIST OF REFERENCES ............................................................................................. 190
BIOGRAPHICAL SKETCH .......................................................................................... 206
10
LIST OF TABLES
Table page 3-1 Herbage mass, herbage allowance and herbage accumulation rate during
the cool-season of 2016 and 2017. .................................................................... 82
3-2 Nutritive value from hand-plucked samples during the cool-season of 2016 and 2017. ........................................................................................................... 82
3-3 Isotopic composition and biological nitrogen fixation (BNF) of clovers during the cool-season of 2016 and 2017. .................................................................... 83
3-4 List of reference plants and δ15N in the cool-season of 2016 and 2017. ............ 84
3-5 Animal performance during the cool-season of 2016 and 2017.......................... 85
3-6 Herbage mass, herbage allowance and herbage accumulation rate during the warm-season of 2016 and 2017. .................................................................. 85
3-7 Nutritive value of bahiagrass, during the warm-season (2016 and 2017). .......... 86
3-8 Isotopic composition of bahiagrass and biological nitrogen fixation (BNF) of rhizoma peanut during the warm-season of 2016 and 2017. .............................. 86
3-9 List of reference plants and δ15N in the warm-season of 2016 and 2017. .......... 87
3-10 Animal performance during the warm-season of 2016 and 2017. ...................... 89
4-1 Chemical composition from fecal samples collected from beef steers grazing three forage systems during the cool- and warm-season of 2016 and 2017. ... 129
4-2 Chemical composition of urine, N excretion and total N excretion (feces and urine) from beef steers grazing three forage systems during cool- and warm-season of 2016 and 2017. ................................................................................ 130
4-3 Fecal dry matter (DM) and organic matter (OM) output, fecal nutrient excretion, and total (fecal and urinary) N excretion from beef steers grazing three forage systems during the cool-season of 20161 and 20171. .................. 131
4-4 Fecal dry matter (DM) and organic matter (OM) output, fecal nutrient excretion, and total (fecal and urinary) N excretion from beef steers grazing three forage systems during the warm-season of 20161 and 20171. ................ 132
4-5 Fecal dry matter (DM) and organic matter (OM) output, fecal nutrient excretion, and total (fecal and urinary) N excretion from beef steers grazing three forage systems during cool- and warm-season of 2016 and 2017. ......... 132
11
4-6 Total annual nutrient excretion from beef steers grazing three forage systems in 2016 and 2017. ............................................................................................. 133
5-1 Enteric methane sample dates and fecal output collection during the warm and cool-season, from 2016 to 2018. ............................................................... 150
5-2 Forage nutritive value from hand-plucked samples collected during the methane sampling from 2016 to 2018 cool and warm-season. ........................ 150
5-3 Dry matter intake (DMI) and enteric methane emissions from beef steers during the cool-season; 2016 to 2018. ............................................................. 151
5-4 Dry matter intake (DMI) and enteric methane emissions from beef steers during the warm-season; 2016 to 2018. ........................................................... 152
6-1 List of bee species and counts of individuals collected in the grazing trial per treatment from 2016 to 2018. ........................................................................... 171
7-1 Summary of ecosystem services provided in the grazing trial during the cool- and warm season. ............................................................................................ 189
12
LIST OF FIGURES
Figure page 3-1 Herbage mass during the cool-season (kg DM ha-1 d-1). .................................... 90
3-2 Total herbage accumulation rate during the cool-season (kg DM ha-1 d-1). ....... 91
3-3 Crude protein (CP) from cereal rye and oat during the cool-season (2016 and 2017). .......................................................................................................... 92
3-4 In vitro digestible organic matter (IVDOM) of rye and oat in the cool-season of 2016 and 2017. ............................................................................................... 93
3-5 Isotopic composition (δ15N and δ13C) from rye and oat in the cool-season of 2016 and 2017. ................................................................................................... 94
3-6 Isotopic composition (δ15N and δ13C) from feces of steers grazing in the cool-season of 2016 and 2017. .................................................................................. 95
3-7 Isotopic composition (δ15N and δ13C) from plasma of steers grazing in the cool-season of 2016 and 2017. .......................................................................... 96
3-8 Isotopic composition (δ15N and δ13C) from red blood cells of steers grazing in the cool-season of 2016 and 2017. .................................................................... 97
3-9 Botanical composition of the grazing trial in 2016 and 2017, dry weight rank method (DW). ..................................................................................................... 98
3-10 Variation in herbage mass during the warm-season of 2016 and 2017. ............. 99
3-11 In vitro digestible organic matter (IVDOM) concentration of bahiagrass during the warm-season of 2016 and 2017. ..................................................... 100
3-12 Nutritive value of rhizoma peanut during the warm-season of 2016 and 2017. ................................................................................................................ 101
3-13 % N derived from atmosphere (%Ndfa) in the pastures with rhizoma peanut during the warm-season of 2016 and 2017. ..................................................... 102
3-14 Isotopic composition of rhizoma peanut during the warm-season of 2016 and 2017. ................................................................................................................ 103
3-15 Isotopic composition (δ15N and δ13C) from feces of steers grazing in the warm-season. ................................................................................................... 104
13
3-16 Isotopic composition (δ15N and δ13C) from plasma of steers grazing in the warm-season. ................................................................................................... 105
3-17 Isotopic composition (δ15N and δ13C) from red blood cells of steers grazing in the warm-season. ............................................................................................. 106
3-18 Blood urea nitrogen (BUN) mg dL-1 of steers grazing in the warm-season of 2016 and 2017. ................................................................................................. 107
4-1 Treatment × evaluation interaction (P < 0.01) for total (fecal and urinary) N excretion in kg ha-1 d-1 during the cool-season. ................................................ 134
5-1 Dry matter intake (DMI) as % of body weight in cool and warm-season in three grazing systems. ..................................................................................... 153
5-2 Enteric methane emissions in g per kg of average daily gain (ADG)-1 in cool and warm-season in three grazing systems. .................................................... 154
5-3 Selection index, proportion of C3 (rhizoma peanut, RP) in feces, and proportion of rhizoma peanut (RP) dry weight (DW) in the pasture during 3 evaluations in the warm-season of 2016 and 2017 in the Grass+CL+RP treatment. ......................................................................................................... 155
6-1 Monthly average solar radiation (w m2 -1) and temperature from 2016 to 2018 in the experimental area, Marianna, FL. The circles mark the periods of maximum number of bees collected. ................................................................ 172
6-2 Monthly average rainfall mm from 2016 to 2018 in the experimental area, Marianna, FL. The circles denote the dry periods, when rainfall decreased, and a greater number of bees were collected at each evaluation. ................... 173
6-3 Effect of trap color on presence of honey bees per trap from 2016 to 2018. .... 173
6-4 Presence of medium bees per trap color and per evaluation from 2016 to 2018. ................................................................................................................ 174
6-5 Presence of bees per trap color from 2016 to 2018. ......................................... 175
6-6 Presence of honey bees per treatment from 2016 to 2018. .............................. 176
6-7 Presence of small bees per treatment per evaluation from 2016 to 2018. ........ 177
6-8 Abundance of bees per treatment from 2016 to 2018. ..................................... 178
6-9 Total bees comparing the grass monoculture system and the grass legume mixture. ............................................................................................................. 179
6-10 Estimated species richness for each treatment (Chao 1 index). ....................... 180
14
6-11 Estimated species diversity for each treatment (Shannon-Wiener diversity index). ............................................................................................................... 181
6-12 Estimated species diversity for each treatment (Simpson inverse diversity index). ............................................................................................................... 182
6-13 Species accumulation curve. ............................................................................ 183
6-14 Total flower density by treatment during 2017 and 2018. ................................. 184
15
LIST OF ABBREVIATIONS
ADG Average daily gain
BNF Biological nitrogen fixation
BUN Blood urea nitrogen
C Carbon
CP Crude protein
HA Herbage accumulation
HM Herbage mass
IVDMD In vitro dry matter digestibility
IVDOM In vitro organic matter digestibility
MA The Millennium Ecosystem Assessment
N Nitrogen
Ndfa Nitrogen derived from the atmosphere
SOC Soil organic carbon
SOM Soil organic matter
16
Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy
ECOSYSTEM SERVICES PROVIDED BY CONTRASTING GRAZING SYSTEMS IN
NORTH FLORIDA
By
Liza Maria Garcia-Jimenez
August 2019
Chair: Jose C.B. Dubeux Jr. Co-chair: Lynn E. Sollenberger Major: Agronomy
In Florida, beef cattle production is pasture-based, primarily with C4 grasses that
are well adapted to the environment and tolerant to grazing. The success of those
grasses depends on N fertilization, increasing the economic inputs of grazing systems
and environmental risks due to nutrient loss. Forage legumes are a possible alternative
to replace N fertilizers, and rhizoma peanut (RP; Arachis glabrata Benth.) is one of the
few warm-season perennial legumes that have demonstrated persistence under a range
of grazing conditions. Grazing systems provide and support the livelihoods of millions of
people and offer a variety of ecosystem services, such as forage, animal protein, clean
water, cycling and movement of nutrients, climate stability, protection of soil from
erosion, habitat for wildlife and pollination. The objective of this dissertation was to
evaluate ecosystem services from N-fertilized grasses, grasses with low N input, and
grass-legume mixtures during the cool- and warm- season in beef-forage production
systems in Northwest Florida. The experiment was conducted at the University of
Florida, North Florida Research and Education Center (NFREC) during the warm and
cool seasons of 2016 and 2017. Treatments consisted of three livestock production
17
systems as follows: 1) N-fertilized bahiagrass pastures during the warm-season (112 kg
N ha-1), overseeded with a mixture of FL 401 cereal rye (Secale cereale L.) and RAM
oat (Avena sativa L.) during the cool-season + 112 kg N ha-1 (Grass+N); 2) unfertilized
bahiagrass pastures during the warm-season, overseeded with rye-oat and clover
mixture during the cool-season + 34 kg N ha-1 (Grass+clover); 3) Rhizoma peanut-
bahiagrass pastures during the warm-season, overseeded with a similar rye-oat mixture
plus a mixture of clovers during the cool-season + 34 kg N ha-1 (Grass+CL+RP).
Treatments were replicated in three blocks in a randomized complete block design.
Forage and animal performance, nutrient cycling and enteric methane emissions were
evaluated, as well as the evaluation of pollinator biodiversity. The introduction of
legumes during the cool- and warm-season increased the nutritive value of the forage
and extended the grazing period. Animal performance was greater in the Grass+CL+RP
system in the warm-season, and cattle ADG increased by 70% when compared with the
other treatments without rhizoma peanut (P < 0.01). The contribution of BNF was
around 37.07 kg d-1 adding both seasons. The magnitude of fecal nutrients excreted
was less in the Grass+CL+RP, due to the greater digestibility of the rhizoma peanut (P
< 0.01). The proportion of N returning to the pasture via urine vs. feces was greater in
the Grass+CL+RP (P < 0.04). The concentration of N in feces did not show differences
among treatments (P > 0.05) and averaged 18 g kg-1 in the warm-season. Emission
intensity when measured as g of CH4 per unit of ADG, did not differ among treatments
(P > 0.05); however, a 58% decrease in emission intensity was observed for steers
grazing during the cool vs. warm-season. The abundance of bees was greater in the
Grass+CL+RP system compared with the others (P = 0.003). In conclusion, the
18
introduction of legumes enhanced ecosystem services from grasslands in pasture-
based livestock systems in North Florida, with similar or greater animal performance
and forage production to those in a N-fertilized system.
19
CHAPTER 1 OVERVIEW
Grazinglands cover approximately 40% of the global land surface. They
represent a diverse system that provide a wide range of ecosystem services, primarily
as provisioning of agricultural products, supporting, regulating, aesthetic and cultural
(Littlewood et al., 2012). Grasslands play an important role in the global carbon cycle
and grazing livestock have a significant influence on soil carbon storage (Chen et al,
2015). A better understanding of the components of grasslands is important for these
agro-ecosystems to achieve valued ecosystem services such as regulate nutrient
cycling and mitigate climate change.
Beef cow-calf production systems in the southeastern United States are typically
pasture-based. In Florida, there are approximately 4.5 million ha are grasslands
(Vendramini, 2010) and over a million are planted grasslands, where bahiagrass
(Paspalum notatum Flügge) is the dominant forage species (Chambliss and
Sollenberger, 1991). Grass monoculture pasturelands may need some levels of N
fertilizer to be productive, and this leads to a large C footprint of beef-forage production
when life cycle assessments are considered. The increasing cost of N fertilizer over the
past years has led livestock producers to reduce the amount of fertilizer applied in order
to decrease input costs in animal production systems. This in turn has resulted in
degradation of southern grasslands, limiting their potential to provide regulating,
provisioning, and supporting ecosystem services (Sollenberger, 2014).
The introduction into grasslands of forage legumes that have the natural ability to
associate with soil microorganisms and fix atmospheric N2, such as forage legumes,
could increase functionality and production of pastures. Grazing trials with livestock
20
have demonstrated positive results in beef cattle weight gain on forage mixtures versus
monocultures (Sanderson et al., 2013). Furthermore, legumes are an important source
of highly digestible, protein-rich feed for livestock (Muir et al., 2011). In Florida, rhizoma
peanut (Arachis glabrata Benth.) is the most important perennial forage legume
(Sollenberger et al., 2014). The introduction of rhizoma peanut into the livestock system
is associated with a high cost of establishment, and strip-planting has been suggested
as one of the strategies to reduce establishment costs (Castillo et al., 2013).
Agricultural practices and specifically ruminant livestock systems comprise a
direct source of methane (CH4) via enteric fermentation (Lassey, 2007). This source of
CH4 from livestock contributes to greenhouse gas (GHG) emissions and their impact on
climate change. Ruminants produce methane as a result of the complex microbiological
fermentation that breaks down cellulose and other macro-molecules in the rumen
(McGinn et al., 2004). Factors such as feed type, passage rate and pH in the rumen can
explain changes in the amount of methane formed (Janssen, et al., 2010). The inclusion
of legumes in N-fertilized grass-based grazing systems has the potential to decrease
the use of N fertilizer and thus reduce GHG emissions related to N production and
application (Jensen et al., 2012). In addition, the desirable characteristics of legumes
have the potential to improve protein utilization in ruminants (Broderick, 1995). For the
cattle industry, decreasing methane losses can represent an improvement in feed
efficiency, as methane production via enteric fermentation represents a waste of dietary
energy. Thus, mitigating CH4 emissions from cattle has both long-term environmental
and short-term economic benefits (Lassey, 2006).
21
Soils also play an important role in greenhouse gas emissions and are the most
important source of nitrous oxide (N2O), a potent greenhouse gas, representing
approximately 65% of total global emissions. The mechanisms responsible for N2O
emissions from soils are the microbial processes of nitrification and denitrification. In
addition, nitrogen fertilization is a critical factor in N2O soil emissions from agricultural
systems (Jones et al., 2011).
In addition to the benefits of grass-legume systems decreasing GHG emissions,
the recycling of nutrients can have an impact on the productivity of grasslands and on
the environment. Inclusion of forage legumes provides a nutritional benefit to livestock
productivity and wellbeing, as it aids in meeting the animal protein and energy
requirements for growth. Fecal and urine samples collected under each of these
systems can provide information on forage consumption for optimal livestock production
and for optimized management of grasslands (Wagner et al., 1986).
Grassland ecosystems host a variety of beneficial insects, including wild bees
who play an important role in pollination. Wild bees are responsible for the pollination of
wild plants and cultivated crops, and thereby help to maintain biodiversity and food
production (Breeze et al., 2011; Garibaldi et al., 2014). However, pollinators are
threatened by habitat loss, pesticides, climate change and diseases (Potts et al,. 2010;
Goulson et al., 2015). The National Academy of Science report entitled the Status of
Pollinators in North America in 2007, indicated that in the last three decades the number
of colonies of honey bees has been seriously declining. In 1940, around 5.7 million
colonies were reported in the U.S. Today there are only 2.74 million colonies of honey
bees in the country. The introduction of the external parasitic mite (Varroa destructor
22
Anderson and Trueman) and the syndrome of Colony Collapse Disorder (CCD) have
been responsible for most of these colony losses. Beekeepers in the United States must
travel long distances in order to cover the demand of crop pollination in apples,
blueberries, citrus hybrids, cherries, squash, and almonds across several states. The
cost of this practice has been increasing since 2009 for both the beekeeper and the
grower (NRC, 2007). The efforts needed from beekeepers to maintain healthy colonies
is greater and the natural pollination process has been reduced. Furthermore, besides
the honey bees there are around 4,000 species of wild bees in the United States that
are also relevant for pollination. Studies in bumble bees showed that populations have
declined due to introduced pests and diseases (Cameron, 2011). Efforts from the White
House (PHTF, 2015) are committed to determine the current status of insect pollinator
communities, and document shifts in distribution and abundance of various species
(Lebuhn et al., 2013).
Managed grasslands provide reservoirs of biodiversity, which can contribute
greatly to crop, fruits and vegetable production in terms of pollinators’ services. The bee
population in grassland systems adds more economic and environmental value to these
production systems, which is beneficial for producers and the local community. The
overall goal of this project is to quantify and to compare provisioning, regulating, and
supporting ecosystem services for forage-livestock systems based on legume-grass
mixtures, grass with low N inputs, and grass + N fertilized, assessing both cool- and
warm-seasons in Northwest Florida.
23
CHAPTER 2 LITERATURE REVIEW
Introduction
Florida forage-livestock systems are important components of the state
economy, with a total of approximately 18,433 beef cattle and 435 dairy operations that
contribute to a combined revenue of $1,039 million annually in beef and milk sales
(USDA 2012; USDA, 2016, Hodges et al., 2019). In Florida, 5.4 million acres of
improved pastures, rangelands and farm woodlands are used for grazing. The output of
beef cattle farming-ranching increased from $513 million in 2007 to a peak of $1.065
billion in 2014, and then declined to $549 million in 2016 (Hodges et al., 2019). Pasture-
based systems are important components of beef and dairy production in Florida and it
is important to document the additional services that are provided by these ecosystems,
beyond the role of provisioning high-quality animal protein. Florida has the unique
characteristic of having some of the largest cattle operations in the country, which
provide the competitive advantages in economy scale and technology adoption. These
large operations can multiply the impact of grasslands, while modifying the ecosystem
services they provide. For this reason, the management of these grasslands becomes
critical in livestock operations because of the magnitude of the area affected. In
addition, most small-scale cow calf-operations grazed their cattle on their own land,
used production practices to target conventional marketing channels, with lower
technology adoption in breeding and management practices (USDA, 2011). Florida has
4 counties in the top 10 ranked counties with most cattle in the United States:
Okeechobee, Highland, Osceola, and Polk counties are home to a total of 296,000 head
of cattle (USDA, 2015).
24
The tropical and subtropical environment of Florida supports warm-season, cool-
season and tropical grasses that are used mainly for grazing. Bahiagrass (Paspalum
notatum Flueggé) is the most-utilized forage for beef cattle production in Florida
(Vendramini, 2010). Most of the forage production in the state comes from N-fertilized
perennial grass pastures or unfertilized pastures, which may lead to a large carbon
footprint (Lal, 2004) and impact delivery of ecosystem services. The introduction of
legumes into grasslands could increase functionality and production of pastures. The N
that is fixed by legumes could be transferred via animal excreta and litter deposition.
Belowground transfer occurs via nodule turnover, root exudates, mycorrhiza fungi (Ta
and Faris, 1987; Ledgard, 1991; Russelle, 2008), and belowground litter (Rezende et
al., 1999).
Introducing forage legumes such as clovers, rhizoma perennial peanut, peas,
vetches, and many others in a mixture with forage grasses, could increase the
productivity in the livestock system, extend the grazing season, and improve soil quality
(McCormick et al., 2006; Sanderson et al., 2013; Dubeux et al., 2016). Consequently,
the introduction of legumes could reduce the dependence on nitrogen fertilizer in the
livestock production system, with benefits for producers and the environment.
Ecosystem Services from Grasslands
The Millennium Ecosystem Assessment (MA) was organized by the United
Nations in 2000. The MA provided a scientific appraisal of the conditions and the
services of the ecosystems worldwide. The main objective was to assess the
environmental changes in ecosystems for human well-being and introduce the concept
of Ecosystem Services as “the benefits people derive from ecosystems” (MA, 2005).
The categories of ecosystem services have been classified for effective decisions,
25
assumptions of policies, and practices intended to improve them (MA, 2005; Wallace,
2007; Carpenter et al., 2009). There are four categories of ecosystem services: cultural,
provisioning, supporting, and regulating. Cultural services provide recreational,
aesthetic, and spiritual benefits. Provisioning services include food, water, timber, and
fiber, or within the pasture context, the animal products that are used as a food source.
Regulating services are those that affect climate, floods, disease, wastes, and water
quality; and supporting services include soil formation, photosynthesis, and nutrient
cycling (MA, 2005; Palm et al., 2014).
Grasslands are terrestrial ecosystems dominated by herbaceous and shrub
vegetation, and are typically maintained by fire, grazing, drought and/or freezing
temperatures. Grasslands include vegetation cover with an abundance of non-woody
plants and thus combine some savannas, woodlands, shrublands, and tundra (White et
al., 2000; Allen et al., 2011). Grasslands provide an array of goods and services for
human civilization; however, few of them have market value comparable to forage,
meat, milk, wool, and leather (Sala et al., 1997; Lamarque et al., 2011). Other services
provided by grasslands are biodiversity, pollination, carbon storage, nutrient cycling,
climate regulation, water catchment, improving water quality, and tourism and recreation
(White et al., 2000; Lamarque et al., 2011; Palm et al., 2014). Most of these ecosystem
services do not have a market value yet, or their economic value is underestimated.
Furthermore, grasslands are the habitat for domestic and wild herbivores, which use
grasslands for breeding, migratory movement and winter habitat (White et al., 2000).
Agriculture expansion has led to land use change that has increased production
of food and other commodities over grasslands and forests. Annual global food
26
production is expected to increase by 60% and meat production is projected to increase
200 million tonnes (Mt) by 2050, due to human population growth (FAO, 2017; Pogue et
al., 2018). Therefore, the conversion of native grasslands into cropland is a rising
concern because of the potential permanent loss of these ecosystems. Areas converted
to croplands reduce the net capacity of the ecosystems to sequester and store carbon
per unit area of land (White et al., 2000). Biodiversity has also suffered from the
expansion of agriculture because of overexploitation and the competition from invasive
species. Road networks are another factor that have led to high fragmentation of
grasslands, especially in the Great Plains of the United States where 70% of grasslands
cover less than 1000 km2 (White et al., 2000). Furthermore, agriculture intensification
relies on nitrogen fertilizer, liming and other inputs, and these practices tend to
decrease soil and above-ground biodiversity. Consequently, soil degradation may be
reflected in the poor quantity and quality of forage and the reduction of ecosystem
services offered by grasslands (Lamarque et al., 2011). Grazing practices affect soil
biogeochemical and physical responses; for example, light to moderate grazing may
increase carbon storage through plant productivity and excessive hoof trampling can
lead to soil compaction (Byrnes et al., 2017).
Appropriate management strategies in grasslands could contribute to increased
carbon sequestration and improved soil quality (Sanderson et al., 2013), meeting at the
same time the demands for protein from animal products. Frequency and intensity of
grazing influence the biomass and diversity of microorganisms, which consequently
controls soil carbon turnover. Moreover, grazing strategy can influence plant defoliation,
photosynthetic rates, carbon allocation, root/shoot ratios, plant root exudates and root
27
mass, and all these factors play a major role in the biogeochemical cycles in grasslands
(Chen et al., 2015). Proper grazing strategies could reverse the negative impacts on
soils and plants of poorly managed grasslands. The first benefit of improving grazing
management is the enhancement of N cycling and the recovery of N and C losses
(Byrnes et al., 2018). In grasslands, nutrient cycling improves soil fertility, and
production of food, timber and fuel. Therefore, managed grasslands provide significant
provisioning services, while other ecosystem services are enhanced, such as water
quality, regulation and storage of water flows, nutrient cycling, pest control and
pollination (Sanderson et al., 2013).
Nitrogen Fertilization in Grasslands
In grasslands, the factors that limit forage growth are mainly moisture,
temperature, and nitrogen. Thus, farmers have relied on synthetic N fertilizers to
increase yield and crude protein in many types of forages (Ball et al., 2001). Nitrogen
may have a positive effect on the variables defining forage quality, such as forage
digestibility, and in some morphological traits such as greater leaf size, increased
number of tillers from axillary bud sites, stolon elongation and greater growing point
density in stoloniferous species (Cruz and Boval, 2000). Effects of N fertilization could
result in increasing the proportion of stems in the sward, decreasing the nutritive value
of the forage for livestock production. Therefore, grazing with proper adjustment of
stocking rates in relation to herbage growth, or appropriate cutting time, could control
the proportion of stems in the sward (Cruz and Boval, 2000). Regardless of the N
benefits in grasslands, nitrogen is volatile and mobile, leaving the ecosystem through
leaching of inorganic nitrates or dissolved forms of organic N, or through gaseous
emissions to the atmosphere. All those forms of N losses have environmental hazards
28
at local, regional and global level. For example, ammonia volatilization results in soil
acidification and changes in community composition, while nitrification and
denitrification increase greenhouse gas emissions and nitrate leaching, which could
lead to ground water contamination (Crews and Peoples, 2004). Intensive farming relies
on nitrogen fertilizer, and the consumption of fertilizers increase as food demands
increase. For example, the total fertilizer nutrient (N+P2O5+K2O) consumption in 2018
was 200,500,000 Mg, and global N demand is expected to rise by 9.5% in the following
years (FAO, 2015).
Florida soils have low nutrient retention capacity, due to the large presence of
sandy soils (Sigua et al., 2006). Around 51% of Florida soils are dominated by forestry,
beef cattle, citrus, vegetable and dairy operations. Most of the land in FL is poorly
drained, where runoff can be greater, or areas without slope could have limited
horizontal water movement, especially during the rainy season. Furthermore, Florida
soils typically have low soil organic matter (SOM) and low pH (Silveira et al., 2013).
Consequently, Florida soils require greater input of nitrogen fertilizer and liming, and
those are very common strategies by farmers to counteract nutrient deficiency.
Intensive pasture systems require greater N inputs that may lead to greater
losses of nitrogen and carbon. Storage of soil organic carbon (SOC) is important for
increasing nutrient- and water-holding capacities, as well as improving soil aggregation
and structure. Appropriate grazing management of grasslands may increase
sequestration of organic carbon and have a significant influence on potential mitigation
of greenhouse effect from carbon dioxide (CO2) emissions (Franzluebbers et al., 2001;
29
Chen et al., 2015). Managing nitrogen requirements in pastures is crucial for livestock
production and for reducing pollution and climate change.
Forage-Livestock Systems
Pasture is the most extensive form of land cover for animal production. Around
30% of the world’s surface land is used in livestock production, and 2 billion hectares
are used in extensive grazing, which has a massive influence over other ecosystems
(Steinfield and Wassenaar, 2007; Herrero et al., 2009; Phelps et al., 2017). Land
dedicated to animal production is crucial for supporting worldwide dietary needs and the
livelihood of millions of people. However, grazing land area in the United States has
decreased approximately 6% from 2002 to 2012 (Russell et al., 2015). Livestock
systems offer numerous societal benefits, but at the same time use large quantities of
natural sources with local and global impact on the environment. To ensure that
livestock can continue to provide products and services, it is necessary to improve the
sustainability of these agroecosystems (Herrero et al., 2009). Managing grazing to
maintain adequate vegetative cover could minimize the effect of treading on water
infiltration and soil compaction (Russell et al., 2015).
The land used in the United States for grazing or hay production is 32.2%, where
316 million ha are dedicated to grazing, and 64 million ha to hay production (Lubowski
et al., 2006). In the southeastern United States, beef cattle production depends on
perennial grass pastures. These grasses are the primary feed source for beef cattle
operations, and are well adapted to the environment, with greater tolerance under
extensive management (Peters et al., 2013). The persistence and success of the
perennial grasses depends upon significant amounts of N fertilizer, increasing the inputs
30
of the livestock production and the environmental effects (Lal, 2004; Shepard et al.,
2018).
Forage and Animal Performance on Grass Monocultures
In the southern United States, there are approximately 24 million hectares of
perennial pastures and more than 40 species of grasses that potentially could be grown
for different purposes such as hay, grazing or biofuels. The species of choice will differ
according to their adaptation to certain areas, seasonality (warm- or cool- season) and if
they are annual or perennials (Ball et al., 2007). Utilizing winter annuals over perennial
pastures for livestock would provide supplemental forage for grazing or harvesting that
might reduce the cost of livestock winter feeding, providing vegetative cover that
prevents soil erosion and nutrient losses due to runoff or leaching. Livestock producers
most often plant cool-season annuals overseeded into a perennial warm-season grass
such as bermudagrass [Cynodon dactylon (L.) Pers.] or bahiagrass (Paspalum notatum
Flügge) pasture.
Warm-Season Grass: Bahiagrass
Bahiagrass is a perennial, rhizomatous warm-season grass that can be grown
from seed, or established by sod, sprigs or plugs (Trebholm et al., 2015). Bahiagrass is
the main forage used in Florida, and it was introduced by the Bureau of Plant Industry in
1913 from sub-tropical South America (Newman et al., 2011). Bahiagrass pastures
cover approximately one million hectares in the state of Florida (Chambliss and
Sollenberger, 1991; Inyang et al., 2010), highlighting the relevance of this forage
resource in the state. Bahiagrass is very persistent under adverse climate conditions,
and it is widely used as cover for garden turf, land conservation, and as a forage
species for grazing or hay production. Additionally, bahiagrass performs well under
31
grazing, different soil types, and low soil fertility (Twidwell et al., 1998; Newman et al.,
2011; Vendramini et al., 2013). Most of the cow-calf operations in the state are
managed on bahiagrass pastures with continuous stocking at fixed stocking rates
(Inyang et al., 2010). Pensacola and Argentine are the most common bahiagrass
cultivars in these operations. Pensacola is less sensitive to daylength and offers greater
forage production in the fall and early spring. On the other hand, Argentine bahiagrass
has greater crude protein and tolerates frequent grazing (Vendramini et al., 2013).
Bahiagrass can vary widely in herbage accumulation and nutritive value during the
growing season, and that is reflected in animal performance. However, nitrogen
fertilization is used to reduce seasonal variability, even though in the long-term, greater
N accumulation in plants may lead to nutrient imbalances with negative effects in forage
and animal performance (Yarborough et al., 2017). Vendramini et al. (2013) reported
that Argentine bahiagrass has better herbage accumulation that other cultivars under
extensive grazing systems, i.e., continuous stocking with limited N fertilization. Santos
et al. (2018) evaluated monocultures of Argentine bahiagrass during two consecutive
years, fertilized with 90 kg N ha−1 after plots were staged, and then after each harvest.
The authors reported an herbage accumulation of 4,030 kg DM ha−1, and seasonal
differences in crude protein (CP) of 122 g kg-1 in early season compared with 126 g kg−1
in late season. Also, Argentine bahiagrass had seasonal differences in the in vitro
digestible organic matter (IVDOM) concentration, with values of 579 g kg−1 in the late
season of the first year, and 475 g kg−1 in the early season of the second year.
Bahiagrass pastures under different stocking rates (4, 8 and 12 heifers ha-1)
showed a linear decreased in herbage mass of 5.9 to 3.2 Mg ha-1 with increasing
32
stocking rates. Furthermore, CP and IVDOM concentrations of bahiagrass ranged from
104 to 165 g kg-1, and 482 to 578 g kg-1, respectively (Inyang et al., 2010). Stewart et al.
(2007) evaluated bahiagrass and animal performance under three different stocking
rates and three levels of N fertilization (low, 40 kg N ha-1 yr-1 1.2 AU ha -1 target SR;
medium, 120 kg N ha-1 yr-1 2.4 AU ha -1; and high, 360 kg N ha-1 yr-1 3.6 AU ha -1)
during four grazing seasons. Bahiagrass herbage mass decreased from 3.42 to 2.95 Mg
ha-1 with increasing stocking rate, regardless of the greater N application rate. The
authors reported an animal average daily gain (ADG) of 0.34, 0.35 and 0.28 kg hd-1 d-1
for low, medium and high management levels, respectively. Bahiagrass under high
management level had greater CP and IVDOM (140 and 505 g kg-1, respectively)
concentrations in comparison with the low and medium management levels. In contrast,
Sollenberger and Jones (1989) reported an ADG of 0.38 kg hd-1 d-1 across three grazing
seasons for young steers in bahiagrass fertilized with 180 kg N ha-1 yr-1 in a study using
rotational stocking with variable stocking rate. In this study, the authors reported an
average concentration of 116 g kg-1 for CP and 583 g kg-1 for IVDOM (Stewart et al.,
2007).
Cool-Season Grasses: Oat and Rye
Small grains such as oat (Avena sativa L.) and rye (Secale cereale L.) tend to
have greater digestible energy in the dough stage, providing more energy for livestock,
but in later stages of maturity the nutritive value decreases. In addition, greater yield of
cereal forage is produced with high moisture (Lauriault et al., 2004). In addition, winter-
annual forage offers great opportunity to raise calves in pasture systems with high
nutritive value in the southeastern United States (Lauriault et al., 2004, Vendramini et
al., 2006). In a three-year grazing study conducted in Alabama, with monocultures of
33
rye, oat and ryegrass, the ADG was 1.32 ± 0.12 kg hd-1 d-1 in Angus × Continental
crossbred steers, without differences between treatments. Conversely, gain per area
was greater in the oat treatment with 504 ± 15.4 kg ha-1 (Pereira, 2009). Furthermore,
the benefits of using winter grasses such as rye and oat are also observed in corn-
soybean rotation systems. The use of these winter grasses potentially reduces NO3
losses in drainage water by 61%, or 31 kg N ha−1 and accumulated 47.5 kg N ha−1 in
their shoot biomass. The study was conducted in the upper Mississippi River Basin,
where basin flux of N is largely responsible for the hypoxic zone in the Gulf of Mexico
(Kaspar et al., 2012). Furthermore, rye and oat performed well in mixtures with other
cool-season grasses such as annual ryegrass. Dubeux et al. (2016) reported in a two-
year grazing study that rye-ryegrass treatments showed greater herbage accumulation
at the beginning of the grazing season, due to the early growth of cereal rye and those
difference declined over time (25.5 and 31.5 kg of DM ha-1 d-1 of rye-ryegrass and oat-
ryegrass, respectively). Herbage allowance ranged from 0.6 to 1.4 kg DM kg BW−1 with
an ADG across mixtures of 0.9 kg head−1 d−1. The authors reported an herbage N range
from 18 to 47 g kg−1 and IVDOM of 750 g kg−1, confirming that cool-season mixtures are
an alternative with greater nutritive value in forage livestock systems.
Forage and Animal Performance on Legume-Grass Mixtures
Legume rotation was replaced as a source of N by farmers during the 20th
century when the widespread use of N fertilizers became the first source of nitrogen
fertility (Crews and Peoples, 2004). Legumes could fix N2 from the atmosphere and
incorporate it into the soil, making it available to other plants in the community, and with
greater nutritive value than tropical forages (Muir et al., 2011). Legume and non-legume
34
species have different profiles in terms of N use, and the introduction of legumes could
reduce the environmental risk of traditional agricultural production.
Cool-Season Mixtures with Clovers
Beef cattle producers in the southern and Gulf Coast States, taking advantage of
the mild winter, have often used a mixture of winter annuals, typically small grains and
clovers, to provide forage for the cattle through the winter months (Ball et al., 2015). A
mixture of winter annuals requires more management than monoculture pastures, but
the benefit of improving the forage quality through the introduction of legumes is well
worth it (Han et al., 2012). Nyfeler et al. (2011) reported that in mixed swards with
manipulation in the percentage of legumes, the uptake of N from the soil was greater in
the mixtures with greater presence of legumes. Additionally, the use of N was more
efficient in the mixtures to produce greater biomass. This effect of functional diversity in
plant communities contributes to the productivity and efficiency of grasslands.
Clovers are a large genus of legumes with greater agricultural importance as
forage crops in grasslands. Red clover (Trifolium pratense L.) is an important forage
crop widely used as a winter feed, due to its high protein concentration (Ravagnani et al
2013). After 1960, the production of red clover declined thanks to the high demand of
chemical nitrogen fertilizers. In the last decades due to the environmentally negative
impacts of N fertilizer production and usage, the implementation of red clover forage is
gaining attention again. The low persistence of red clover in FL grasslands is
challenging. Main reasons include its susceptibility to cold, flooding, drought and
diseases. In addition, red clover often has difficulties in competing well in mixed swards;
however, it does perform well under frequent cutting. Therefore, the persistence of red
clover is one of the traits to improve when breeding varieties for grazing conditions.
35
Crimson clover (Trifolium incarnatum L.) is another clover that provides early spring
nitrogen for full-season crops, due to its rapid growth. In the South, crimson clover can
produce 3,900 to 6,200 kg DM ha-1 and fix 78 to 168 N kg ha-1 (Boquet et al., 1991). In
Mississippi, crimson clover was found to produce the most DM (6,300 to 6,700 kg ha-1),
when compared with hairy vetch (Vicia villosa R.), bigflower vetch (Vicia grandiflora
Scop.), berseem clover (Trifolium alexandrinum L.), arrowleaf clover (Trifolium
vesiculosum S.), and fixed 111 to 146 N kg ha-1 (Varco et al., 1991). Another cool-
season legume that performs well in mixtures is ball clover (Trifolium nigrescens Viv.).
Ball clover has low growing habit with excellent tolerance to close grazing, because it
produces flowers close to the ground. Ball clover should be planted in mixtures and not
pure stands in order to avoid bloat in livestock. In addition, it has good tolerance of wet,
clay or loam soils and tolerates lower soil acidity better than crimson clover (Abberton
and Marshall, 2005).
Warm-season Grass Mixture with Rhizoma Perennial Peanut
Rhizoma peanut (Arachis glabrata Benth.) was brought to the United States in
1936 from Brazil. It has been cultivated in Alabama, Georgia and Florida, due to its
adaptation to the light sandy soils of the Gulf Coast region (Baker et al., 1999;
Quesenberry et al., 2010). Rhizoma peanut is a useful perennial warm-season legume
in the southeast USA since it is drought tolerant, grows in low fertility soils and has
relatively high forage yield (Quesenberry et al., 2010). Popular forage cultivars are
Arbrook and Florigraze as well as the germplasm Ecoturf (Quesenberry et al., 2010;
Prine et al., 2010; Williams et al., 2014), although new cultivars (UF Tito and UF Peace)
have been released that offer greater herbage accumulation and resistance to diseases
36
(Quesenberry et al., 2010). In Texas, a cold-tolerant cultivar was released and named
Latitude 34 (Muir et al., 2010).
Rhizoma peanut offer greater CP and IVDOM compared with tropical grasses
and may be a good alternative for beef producers. Dubeux et al. (2017) evaluated the
forage potential, belowground biomass, and biological nitrogen fixation (BNF) of seven
rhizoma peanut entries for two years: Latitude 34, UF Tito, UF Peace, Florigraze,
Arbrook, Ecoturf, and Arblick. The authors reported herbage accumulation in the second
year ranging from 7650 (Florigraze) to 12980 kg DM ha−1 yr−1 (Arbrook). Average
IVDOM reported in this study was 713 g kg-1. Annual average N yield (BNF) was 194 kg
N ha−1 in 2014 and 270 kg N ha−1 in 2015, where UF Peace reported the greatest N
yield. Root and rhizome N content in Ecoturf (574 kg N ha−1) was greater than UF
Peace (399 kg N ha−1), Florigraze (228 kg N ha−1), and Arbrook (209 kg N ha−1).
However, the adoption of rhizoma peanut into grazing systems presents some
limitations such as the high cost of vegetative establishment and a long establishment
period. The integration of rhizoma peanut in strips into warm-season grasses has been
suggested as an alternative to decrease the establishment cost and to maintain the
persistence of the legume into the sward (Cook et al., 1993; Whitbread et al., 2009;
Quesenberry et al., 2010; Castillo et al., 2013). In addition, several studies had reported
that the integration of rhizoma peanut into bahiagrass swards results in greater herbage
accumulation, CP and IVDOM (Castillo et al., 2013; Mullenix et al., 2016; Jaramillo et
al., 2018; Santos et al., 2018). Mixtures of Ecoturf and Argentine supported herbage
accumulation ranging from 4090 to 5400 kg DM ha-1, an IVDOM from 402 to 518 g kg-1
and CP from 150 to 170 g kg-1 (Santos et al., 2018). Animal performance also reported
37
better ADG (0.97 kg day-1) in steers grazing rhizoma peanut, compared with an ADG of
0.35 kg day-1 in steers grazing bahiagrass (Sollenberger et al.,1989). In addition,
Williams et al. (2004) reported an increase in ADG of +0.14 kg d-1 in Romosinuano
calves creep-grazing rhizoma peanut compared with the calves grazing bahiagrass.
These findings offer more options in warm-season legumes for producers to adopt
different cultivars in hay and grazing systems.
Application of Stable Isotopes in Grazing Studies
Stable isotopes are atoms with the same number of protons and electrons, but
with different numbers of neutrons. They are energetically stable, they do not decay,
and they are not radioactive (Michener and Lajtha, 2007). The use of isotopes to study
plants and animals has become a standard tool for scientists studying element cycling
in the environment, and for that reason, they are now widely used in agricultural and
ecological research (Svejcar et al., 1990). Isotope abundance is measured by
instruments applying mass spectrometry technology, and this abundance is typically
reported as atom percentages. To express the natural abundance of a stable isotope in
a material, the common notation is to express the ratio of the minor (heavier), over the
major abundant (lighter), for example 13C/12C (Meier-Augenstein and Kemp, 2012).
Isotopic fractionation of CO2 fixation during photosynthesis is well documented. Thus,
the isotopic fraction of the bound carbon as CO2, is approximately -20‰ in plants that
use the Calvin Benson cycle for photosynthesis, and -4‰ in plants that use the Hatch-
Slack cycle (Meier-Augenstein and Kemp, 2012). The difference in the 13C/12C ratio
between C3 and C4 plants is used to estimate the proportion of C3 and C4 species in the
diets of insects and large herbivores, and it is often referred as the δ value (Svejcar et
38
al., 1990). Therefore, the combined use of carbon and nitrogen isotopes allows diet
differentiation in grazing systems with mixed swards containing both C3 and C4 species.
For example, there is a 13C discrimination between dietary and fecal samples, and the
proportion of C3 and C4 species in the diet can be predicted based on δ13C from fecal
samples (Pereira et al., 2019). Jones et al. (1979) reported differences δ13C (-28.7‰) in
feces from cattle fed with C3 (tropical legumes), compared with a δ13C of -13.1‰ in
feces from cattle fed with C4 grasses. In addition, Bennet et al. (1999) reported in a
grazing study with mixed swards, greater intake of C3 plants (rhizoma peanut) than C4
grasses using carbon ratio analysis in feces. In forage, silvopasture, agroforestry and
horticultural systems, determining the N transfer above and below ground of mixed
species could be possible by using 15N approaches (Peoples et al., 2015). The most
common method is to measure the N concentration in the non-legume in a monoculture
and in mixed swards, and the extra N available is assumed to be part of the N transfer
from the legume. This method assumes that the proportion of the contributions are the
same and that the measure is taken from the same part of the plant. Therefore, other
measurements should be taken such as the turnover of N in plant, soil, and microbial
pools, in order to increase the detail in the dynamic of 15N composition of above- and
below-ground plant parts and soluble N fractions in the soil over time (Jalonen et al.,
2009).
Nutrient Cycling in Grasslands
Essential nutrients such as carbon, nitrogen, phosphorus and sulfur reside
temporarily in various reservoirs or different pools in the ecosystem. Main pools in
nutrient cycling include soil, live plant biomass and plant litter, animal tissue, animal
39
excreta, and atmosphere (Dubeux et al., 2007; Vendramini et al., 2014). Grazing
animals obtain carbohydrates, protein, minerals, and vitamins formed by plants via
photosynthesis when they graze pastures, and a portion of the carbohydrates is
incorporated into animal cells. Additionally, some of the carbon is lost to the atmosphere
as carbon dioxide, and some energy is lost as heat during digestion and as the animal
grows and breathes. Carbohydrates and other compounds not used by animals are
returned to the soil in the form of urine and manure, and these materials provide soil
organisms with nutrients and energy. As soil organisms use and decompose organic
materials, they release nutrients that are used by plants for their growth and
reproduction (Bellows, 2001). Nutrient cycling in grazing systems is a complex network
of interactions between plant production, type of livestock grazing, intensity of the
grazing, soil fauna and flora (Sollenberger and Burns, 2001).
Soil Organic Matter (SOM) in Grasslands
The major reservoir of pasture nutrients is SOM, especially for soil organic
carbon (SOC) and soil organic nitrogen (SON). Furthermore, SOM is important in
promoting water retention, infiltration, and reducing water and wind erosion (Dubeux et
al., 2006a; Piñeiro et al., 2010; Vendramini et al., 2014). Grasslands can store more
than 100 and 10 Mg ha-1 of SOC and SON, respectively, in the first meter of the soil
profile. Depending on the grazing strategies, those values could be increased or
decreased (Piñeiro et al., 2009). In SOM, the C:N ratio may shift after grazing and any
changes in SON dynamics may constrain C fluxes and SOC accumulation in the soil.
The greatest C stock sequestered in grasslands is located belowground in the roots,
rhizomes, soil organisms, and soil. Carbon sequestration could be facilitated through
improving grazing regimes that allow plants to accumulate belowground biomass. This
40
is of value both to the health of the plant and because changes in soil carbon storage
have the potential to modify the global carbon cycle with benefits in terms of minimizing
climate change (Conant et al., 2001; Fisher et al., 2007; Byrnes et al., 2018).
Management practices in grasslands that result in greater forage production,
typically lead to greater soil C accumulation under native grassland vegetation (Allard et
al., 2007; Skinner et al., 2016). Light to moderate grazing in grasslands compared with
heavy grazing has led to significant increases in soil C and improvements in soil
structure (Hiernaux et al., 1999; Reeder and Schuman, 2002). Additionally, Conant and
Paustian (2002) concluded that up to 45 Tg C yr−1 could be sequestered globally
through grassland restoration, if grazing intensities were reduced from heavy to
moderate levels.
Plant N and C are added to the organic matter pools through the decay of root
exudates, dead leaves and fragments of roots. In grazed bahiagrass, the estimated
pools of total C and N associated with SOM was 60 and 89%, respectively (Dubeux et
al., 2004). As a response to grazing, root mass and C:N ratio increase, with a potential
limitation of N in the formation of SOM (Dubeux et al., 2014). Nitrogen is mineralized to
ammonium if the C:N ratio decreases, and ammonium N could be nitrified into nitrate
and lost by denitrification or leaching (Elgersma and Hassink, 1997). Tropical
grasslands often have great biomass production with poor forage nutritive value,
resulting in low livestock performance (Leng, 1990). Soil fertility in the tropics is typically
low, therefore, well-managed tropical grasses require greater amount of N fertilizer. The
grass response to N-fertilizer is closely related to the availability of P, K, and other
nutrients in the soil; thus, if there are constraints of other nutrients the result is low N-
41
use efficiency (Serra et al., 2018). Properly managed bahiagrass pastures, which
include adjusting the stocking rate according to the herbage mass and appropriate
fertilizer application, increase the efficiency of nutrient cycling with little potential for
negative impact on the environment (Sigua et al., 2010).
Biological Nitrogen Fixation (BNF) and Nutrient Cycling
Integrating forage legumes into grazing systems provides alternatives to reduce
nutrient limitation in grasslands and to enhance nutrient cycling. Biological nitrogen
fixation offered by legumes is another source of N in the system that can deliver great
advantages. Global BNF in terrestrial ecosystems has been estimated at 128 Tg N yr-1,
supplying ~15% of the N requirement across all biomes (Galloway et al., 2004).
Elgersma and Hassink (1997) conducted a BNF study in ryegrass monocultures and
ryegrass-white clover mixtures. Their findings suggest that BNF was greater, ranging
from 150 to 545 kg N ha-1, in the different mixtures, and the net N mineralization rate
was also greater in the mixtures. Nyfeler et al. (2011) performed a study with mixtures
of grasses and legumes, and reported greater total N, N2 fixation, and N transfer from
legumes to grasses compared with grass monocultures. Jaramillo et al. (2018) reported
that in mixtures, legumes contributed more than 30 kg N ha−1 yr−1, increasing
productivity when compared with unfertilized bahiagrass. The addition of legumes also
increased C storage over time in grazing systems; however, the grazing regime and
intensity influenced the biomass and diversity of microbes, which consequently affected
soil carbon turnover (Chen et al., 2015).
Nutrient Cycling Via Litter and Animal Excreta
The two major pathways of nutrient return in grazing systems are litter and
excreta (Dubeux et al., 2014). Litter influences the net balance between mineralization
42
and immobilization, which in turn influences the availability of N, P, and S (Myers et al.,
1994). Litter quality could be improved with N fertilization or introducing legumes in
grass monoculture pastures (Dubeux et al., 2006; Kohmann et al., 2018). Kohmann et
al. (2018) found that mixtures of bahiagrass and rhizoma peanut have greater N release
than bahiagrass monoculture litter (44 vs. 26 kg N ha-1, respectively). In grazing
systems, one of the major N exchange pathways occur when ruminants graze legumes.
Consequently, N is transformed, assimilated, and returned to the soil via urine and
feces (Dubeux et al., 2007). The amount of nutrients that return to the soil via animal
excreta range from 70 to 90%. However, the entry of nutrients is not uniform through the
pasture, due to animal behavior and the partitioning of nutrients between feces and
urine. Soil nutrients accumulate where grazing animals congregate, and they have the
tendency to spend more time around shade, water, and minerals (Dennis et al., 2012;
Dubeux et al., 2014). Management strategies such as rotational stocking with short
grazing periods are alternatives for a better distribution of the nutrients through the
pasture (Sollenberger et al., 2002; Dubeux et al., 2009; Vendramini et al., 2014). Dong
et al. (2014) conducted a meta-analysis from 49 published studies to calculate urinary
and fecal N (g d-1) excretion from beef cattle. The authors reported N intake ranging
from 52 to 350 g d-1 and divided the level of CP in the diet in three groups (low,
moderate and high). As expected, more N was consumed by cattle fed diets with
greater CP concentration. The range in urinary N excretion varied from 13.7 to 201 g N
d-1, and fecal N excretion ranged from 15.1 to 102 g N d-1. The greatest N excretion
from cattle was in the high CP level group. Khaleel et al. (1980) reported concentrations
of nitrogen and phosphorus in cattle manure of 6 g kg-1 and 2 g kg-1, respectively,
43
suggesting that management strategies are important in order to avoid translocation of
the excess of these components not retained by the plants, into receiving waters,
especially during severe rainstorms.
Greenhouse Gas Emissions from Grasslands
Gases that trap heat in the atmosphere are called greenhouse gases (GHG), and
the main GHG are carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). Each
of these gases can remain in the atmosphere from a few years to thousands of years,
regardless of the source of emissions. For example, methane could last a decade on
average and absorbs more energy than CO2 (Knapp et al., 2014; EPA 2018).
Greenhouse gases are measured in a common unit, named Global Warming Potential
(GWP) and represent how much energy the emissions of 1 kg of gas will absorb over a
period of time, compared with the emission of 1 kg of CO2. This GWP allows
comparisons of the global warming impacts of different gases, which are expressed as
carbon dioxide equivalents (CO2eq; EPA, 2018). However, GWP does not account for
differences in “equivalence” emissions, especially in short-lived gasses. One alternative
is to use the radiative forcing index (RFI), which compares different human and natural
agents causing climate change, but it does not account for the different residence times
of different forcing agents (Forster et al., 2007). Fuglestvedt et al. (2003) proposed to
include climate efficacy data in the GWP, because it includes the efficacy of a forcing
agent. In addition, Shine et al. (2005) suggested the global potential temperatures
(GTP) as an emission metric, that include the ratio between the global mean surface
temperature change at a given future time following an emission of a compound by a
reference gas. Greenhouse gas fluxes related to land use are reported in the
44
Agriculture, Forestry and Other Land Use sector (AFOLU), and comprise approximately
25% (10–12 GtCO2eqyr-1) of anthropogenic GHG emissions (FAO 2011; Tubiello et al.,
2013; Smith et al., 2014). Agriculture expansion and intensification is responsible for 30
to 35% of global GHG emissions (Verge et al., 2007; Foley et al., 2011). In agriculture,
CO2 is released from different sources, including burning plant litter, soil organic matter,
microbial decay, and fossil fuel use and fertilizer production (Janzen 2004; Smith et al.,
2007; McSwiney et al., 2010). The use of synthetic and organic fertilizers for food and
feed production, in addition to livestock manure management and urine excretion from
grazed grasslands, are the major contributors of global soil N2O emissions in
agriculture, accounting for 2.8–6.2 Tg N2O yr–1. This amount represents approximately
20 to 40% of the N2O emissions from all sources (IPCC 2007, Herrero et al., 2013).
Methane has natural sources such as wetlands, ocean sediments, natural wildfires, peat
bogs and termites. Anthropogenic methane sources are coal mining, wastewater
treatment, landfill, natural gas production and agriculture (Lassey 2007; Knapp et al.,
2014). The principal agriculture sources of methane are ruminant livestock, stored
manure, and rice grown under flooded conditions (Mosier et al., 1998). Methane
production via enteric fermentation comprises 17% and 3.3% of global CH4 and GHG
emissions, respectively (Knapp et al., 2014). Enteric CH4 represents approximately 70%
of total CH4 emission from agricultural sources in the United States (USDA 2004) and
grazing cattle might contribute from 0.37 to 1.20 Mg CO2-Ceq ha-1 yr-1 (Franzluebbers,
2005). The GHG inventory for the United States reported that agriculture contributes
with 9% of the total GHG in 2016, and have increased by 17% since 1990 (EPA, 2018).
45
Enteric Methane Emissions
Microbial fermentation of dietary carbohydrates in the rumen results in the
production of enteric methane. Methane is produced by obligate anaerobic
methanogens belonging to the phylum Euryarchaeota, and their role in the rumen is to
convert CO2 to CH4 using electrons from the oxidation of H2 or formate (Russell, 2002;
Krause et al., 2003; Buddle et al., 2010). Hydrogen is produced during fiber digestion by
cellulolytic bacteria, anaerobic fungi and ciliated protozoa. Depending on the diet,
methanogens presence in the rumen range from 107 to 109 cells mL-1 (Russell, 2002;
Krause et al., 2003). Methane in ruminants can be eliminated by eructation, the lungs or
the anus, and CH4 accounts for 30 to 40% of gases produced during enteric
fermentation (Leek, 2004). Therefore, the production of methane represents a loss of
gross energy intake (GEI) ranging from 2 to 12%, or from 8 to 14% of the digestible
energy intake in ruminants (Johnson et al., 1993; Russell, 2002). The level of methane
produced in the rumen depends on the characteristics of the feed, extent of feed
degradation and amount of H2 formed by feed degradation (Janseen, 2010). In North
America, livestock diets are comprised of 90 to 100% grazed and harvested forages,
consequently, forage livestock nutritional strategies should focus on high-quality forages
with a greater rate of fiber digestion that reduce ruminal retention time and promote dry
matter intake (Janssen, 2010).
How to assess livestock GHG emissions has been a source of discrepancy,
mainly in terms of how enteric methane emissions from livestock are calculated and
expressed. In particular, the challenges are related to the fact that direct measurements
of methane emissions are not available for all sources (e.g., manure, enteric, soil, etc.).
Estimates of GHG emissions usually are reported using IPCC emissions guidelines
46
(IPCC 2006). Tier 1, includes emission and removal factors and guidance on how to
acquire activity data, while Tier 2 uses the same mathematical structure as Tier 1, but
countries need to provide data specific to their national circumstances. Tier 3 methods
normally involve modelling and higher resolution land use and land-use change data.
Other approach is to use Life Cycle Analysis (LCA), which includes other sources in the
supply chain in the livestock and agriculture sector (IPCC, 2003, 2006; Wolf et al, 2017).
The discrepancy between estimates depends on the approaches used in different
studies. For example, FAO estimates are based in Tier 2 methods for IPCC emissions,
and LCA for other sources. Global and regional emission factors have used Tier 1
approaches, and Tier 3 methods have been used with rumen kinetic models to calculate
enteric methane emissions (Herrero et al., 2016; Wolf et al., 2017).
Wolf et al. (2017) reported global estimates for annual livestock CH4 emissions of
119 ± 18.2 Tg CH4 in 2011 using atmospheric inversion methods. This estimate is 11%
greater than that obtained using the IPCC 2006 emission factors, 15% larger than EPA
estimates, and 4% larger than EDGAR (Electronic Data Gathering, Analysis) global
estimates. Wolf et al. (2017) reported an increase of 8.4% and 36.7% in CH4 produced
by enteric fermentation, and CH4 produced by manure management, respectively. In the
calculations, the authors included revised data on livestock population, diet, weight of
mature cows and grain-finished livestock, and land cover area. In addition, Hristov et al.
(2014) provided estimates of CH4 produced from enteric fermentation for the United
States, based in total cattle inventories, and feed dry matter intake. Hristov et al. (2014)
estimated emissions of enteric CH4 of 6.241 Tg yr-1 (minimum = 4.972 and maximum =
7.511), which is comparable to the current 2011 US EPA estimates of 6.542 Tg yr-1.
47
Furthermore, there is a need for accurate methods in estimating GHG emissions from
livestock in order to develop strategies to mitigate enteric CH4 emissions from livestock
while providing economic and environmental benefits. Strategies that enhance the
efficiency of feed energy use for ruminants by improving forage quality, breeding
practices in order to increase animal productivity, and intensification of the livestock
systems lead to lowering the footprint of animal protein production (Beauchemin et al.,
2008; Herrero et al., 2016; EPA, 2018). In addition, it is necessary to explore
modifications of the rumen microbial population by vaccines, probiotics, or changes in
the gastrointestinal tract by feeding grain, fats, oils, tannins, acids or salts (Cottle et al.,
2011).
Legumes and Methane Emissions
Improving forage quality has been proposed as an option for mitigating enteric
CH4 emissions from livestock (Molano and Clark 2008; Cottle et al., 2011). The cell wall
fraction of plants include cellulose, hemicellulose, lignin, soluble fiber, pectin, β-glucans
and galactans; the latter being found in greater concentration in legumes when
compared with tropical grasses (4-12% vs. 1-2%, respectively). Legumes have C3
photosynthesis pathway, where mesophyll cells are more abundant and readily digested
in comparison to C4 grasses that have a greater proportion of thick-walled bundle
sheath cells. This distinctive sheath has specialized cells surrounding the vascular
tissue, with thick walls that are resistant to degradation by rumen microbes (Moore et
al., 2004). Structural carbohydrates such as cellulose and hemicellulose ferment at a
slower rate than non-structural carbohydrates and yield more CH4 per unit of substrate
digested (McAllister et al., 1996). Furthermore, in most forage grasses, lignin
concentration of leaves increases with advanced stages of maturity, reducing the
48
digestibility and altering voluntary intake. In legumes, leaves remain relatively constant
in composition with advanced stages of maturity, thus digestibility remains stable if
secondary compounds such as tannins have lower concentrations (Moore, et al 2004;
Archimede et al., 2011). The greater digestion rate of legumes can result in decreased
ruminal fill effect, increased dry matter intake (DMI), and greater passage rate
(Beauchemin et al., 2008; Archimede et al., 2011). Archimede et al. (2011) conducted a
meta-analysis comparing effects of C3 and C4 grasses on enteric CH4 emissions from
livestock. Their results indicate that ruminants consuming C4 grasses produce 17%
more CH4 expressed as L kg-1 OM intake, when compared with animals consuming C3
grasses. In addition, animals consuming warm-season legumes produced 20% less
CH4 than those animals consuming C4 grasses (Archimede et al., 2011). However,
livestock consuming legumes do not always have lower CH4 emissions; for example,
Chavez et al. (2006) found greater CH4 emissions in cattle grazing alfalfa compared
with grass pastures, and the emissions were consistent with an in vitro study. Methane
production per unit DMI was 39% lower from heifers consuming grass compared with
heifers grazing alfalfa. The authors concluded that the excessive level of maturity of the
alfalfa and the composition of the stand during the grazing trial, affected the production
of methane, which is supported because of the lower IVDMD for alfalfa. Similarly, Hess
et al. (2003) reported greater CH4 emissions in an artificial rumen when adding the
tropical legume Arachis pintoi into a grass diet, compared with the N-limited tropical
grass control diet. A study was conducted to address the effect of maturity of C3
grasses on CH4 emissions by comparing two maturity stages of ryegrass (Lolium
perenne): vegetative or reproductive (Molano and Clark., 2008). The mean CH4
49
emissions per unit of DMI for reproductive and vegetative stages of forage were not
significantly different (23.7 and 22.9 g kg-1 DMI, respectively) indicating that in good
quality forages the effect of maturity on CH4 may be minimal (Molano and Clark, 2008).
Alternatives to reduce enteric methane emissions that include increasing animal
production and farm profitability are necessary in grazing ruminant production systems.
Importance of Pollinator Insects
Pollinator insects are considered beneficial for their important role in plant
reproduction. Pollinator insects deliver one of the most significant ecosystem services to
maintain wild plant communities and agricultural productivity (Potts et al., 2010).
Pollinators comprise a diverse group of animals dominated by insects, especially bees,
which are responsible for the pollination of over 75% of flowering plants, and they
benefit 35% of global crop-based food production (Klein et al., 2007; NRC, 2006;
Kimoto et al., 2012). The abundance, diversity and health of pollinators and the
provision of pollination are threatened by direct drivers that generate risks to societies
and ecosystems. Reasons for bee decline include land-use change and habitat
fragmentation, agriculture intensification, pesticide application and environmental
pollution, alien species, spread of pathogens, and climate change (Batáry et al., 2010;
Potts et al., 2010). In addition, evidence of decline in pollinators is not well documented.
There are some indicators at local or regional level, but with little information about the
status of pollination function (Kremen et al., 2007). Domestic bees, such as honeybee
(Apis mellifera), have been widely studied in comparison with wild bees, and their
decline status is well documented in USA (NRC, 2006). In the last decades, the
cultivation of pollinator-dependent crops has increased, and the greater yield of those
50
crops is explained using commercial pollinators or hand pollination. The necessity of
renting pollination services increases crop inputs and dependency on healthy colonies
for producers. Economic value of pollination has been underestimated and there is still
a lack of information about how bee pollination contributes to seed, fruit yield and quality
in crops (Gallai et al., 2009; Potts et al., 2010).
Pollination Services from Grasslands
Grasslands are a diverse and extensive ecosystem around the globe.
Invertebrates in grasslands can be abundant and crucial to ecosystem functioning
through their roles in herbivory, nutrient cycling and pollination (Littlewood et al., 2012).
Invertebrate diversity is highly correlated with plant diversity, mainly because they
respond to the same drivers such as temperature and humidity. Furthermore, sward
structure and height are important variables in invertebrate population, because with
greater biomass and complex swards, the range of niches available for invertebrates
increases (Morris, 2000; Woodcock et al., 2009; Dittrich and Helden, 2011).
Consequently, well-managed grasslands support a diverse and abundant bee fauna
(Kimoto et al., 2012), especially wild bees, by offering key resources to meet their
nutrients requirements and nesting habitats (Koh et al., 2016). Koh et al. (2016)
published a model of wild bee abundance in USA, based on local nesting resources and
forage quality on the main land-use types. The model predicts high abundance of wild
bees in chaparral and dessert shrublands, intermediate abundance in temperate forest
and grasslands-rangelands, and lower abundances in agricultural areas. Therefore,
well-managed grasslands are important habitats for wild bees and other pollinators.
Practices such as high fertilizer application rate, re-seeding, and intensive defoliation by
51
grazing or cutting reduce food sources by producing degraded species pools and
homogeneous swards (Potts et al., 2009). Considering that livestock grazing is the most
common use of grasslands, its effect may impact native bees through change in plant
growth, architecture, diversity and quality, as well as soil characteristics (Black et al.,
2011; Kimoto et al., 2012). Potts et al. (2009) assessed the effects of conventional
management practices of grasslands (silage, fertilization, early or late cut, no
disturbance, and sown complex mix) on bumblebee and butterfly biodiversity.
Bumblebee species abundance and richness were greater in the treatments with the
complex mixture of grasses and legumes than in the grass treatments. In addition, the
treatments with low intensity grazing, and a single cut, produced a more heterogeneous
sward structure that favors the presence of butterflies. Yoshihara et al. (2008) compared
three grazing intensities (heavy, intermediate and light) in terms of pollinator richness
and abundance. The lightly grazed treatments had the greatest flower visitation
frequency, richness and abundant pollinator species. Consequently, a greater flower
component in the sward and less disturbance favors the abundance of pollinators in
grasslands. Furthermore, the introduction of legumes into grasslands increase floral
resources that benefit pollinators, native wildlife and a range of ecosystems services
with economic consequences (Gallai et al., 2009; Potts et al., 2009; Woodcock et al.,
2014; Bhandari et al., 2018).
52
CHAPTER 3 FORAGE AND ANIMAL PERFORMANCE IN N-FERTILIZED OR GRASS-LEGUME
PASTURE DURING COOL- AND WARM- SEASON
Introduction
Land dedicated to animal production is crucial for supporting worldwide dietary
needs and the livelihood of millions of people. In the United States 32% of the land is
used for grazing or hay production, where 316 million ha are dedicated to grazing and
64 million ha to hay production (Lubowski et al., 2006). However, grazing land area in
the United States has decreased approximately 6% from 2002 to 2012 (Russell et al.,
2015).
Intensive management of grasslands relies on nitrogen fertilizer, and the
consumption of fertilizers increase as global food demands increase. Soils in the state
of Florida typically have low soil organic matter (SOM) and low pH (Silveira et al., 2013),
consequently, it may be necessary greater amounts of inputs to achieve desirable
production. Integrating forage legumes into grazing systems provides an alternative to
reduce nutrient limitations in grasslands and to enhance nutrient cycling (Kohmann et
al., 2018). Legumes fix N2 from the atmosphere and release it into the soil, making it
available to other plants in the community, and, in many instances, legumes have
greater nutritive value than other forages (Muir et al., 2011). Therefore, biological
nitrogen fixation by legumes is an important source of N to the system that can help
improve the sustainability of livestock-forages systems.
In the southeastern United States, beef cattle production depends on perennial
grass pastures. These grasses are the primary feed source for beef cattle operations
and are well adapted to the environment, often offering greater tolerance of extensive
management than legumes (Peters et al., 2013). Annual grasses also play a role, and
53
over-seeding winter annuals into warm-season perennial pastures can provide
supplemental forage for grazing or harvesting that can reduces the cost of livestock
winter feeding. These species also provide vegetative cover that prevents soil erosion
and nutrient losses due to runoff or leaching. Livestock producers in the southern United
States States may have the opportunity to overseed cool-season annuals into a
perennial warm-season grass such as bermudagrass [Cynodon dactylon (L.)] or
bahiagrass (Paspalum notatum F.).
The predominant warm-season perennial legume in beef forage systems in
Florida is rhizoma peanut (Arachis glabrata B.), since it is drought tolerant, grows in low
fertility soils and is productive (Quesenberry et al., 2010). In addition, rhizoma peanut
offers greater CP and IVDOM when compared with tropical grasses (Sollenberger et al.,
1989) and may be a good alternative to N fertilizer for beef producers. The integration of
legumes by planting them in strips into warm-season perennial grasses has been
presented as an alternative to decrease the establishment cost and to maintain the
persistence of the legume into the sward (Cook et al., 1993; Whitbread et al., 2009;
Quesenberry et al., 2010). This technique has been evaluated for rhizoma peanut
(Castillo et al., 2013; Mullenix et al., 2014).
Livestock systems offer numerous societal benefits, such as a supply of
nutrients and economic inputs. Nevertheless, at the same time use large quantities of
natural resources with local and global impact on the environment. To ensure that
livestock can continue to provide products and services, it is necessary to improve the
sustainability of grassland agroecosystems (Herrero et al., 2009). Well-managed
grasslands may sequester organic carbon in soil and mitigate greenhouse effects
54
derived from carbon dioxide (CO2) emissions (Franzluebbers et al 2001; Chen et al.,
2015). Furthermore, managing nitrogen requirements in pastures is crucial for achieving
satisfactory levels of livestock production and minimizing nitrate leaching and N2O
emissions.
The hypothesis of this study is that the introduction of legumes into perennial
grass-based grazing systems will improve herbage and livestock performance when
compared with N-fertilized systems. Therefore, the objective of this study was to
evaluate plant and animal responses in N-fertilized or grass-legume pastures during
cool- and warm-seasons in a north Florida environment.
Materials and Methods
Experimental Site
A grazing experiment was conducted from January to October of 2016 and 2017
at the University of Florida, North Florida Research and Education Center (NFREC),
located in Marianna, FL (30°52’N, 85°11’ W, 35 m a.s.l.). Soils at the experimental site
were classified as Orangeburg loamy sand (fine-loamy-kaolinitic, thermic Typic
Kandiudults), with a pH average of 5.7. Average Mehlich-I extractable soil P, K, Mg and
Ca concentrations at the beginning of the experiment were 26, 99, 43, and 224 mg kg-1,
respectively. Soil organic matter was 15.4 g kg-1 and estimated cation-exchange
capacity was 3.8 meq 100 g-1. Total rainfall for 2016 and 2017 was 1,378 and 1,271 mm
respectively. Annual average, minimum and maximum temperatures for 2016 and 2017
were 20, -3, and 36, and 20, -4, and 35°C, respectively. Annual average solar radiation
was 192 W m-2 in 2016 and 187 W m-2 in 2017.
55
Treatments, Experimental Design, and Management
Treatments consisted of three year-round forage systems including a summer
and winter component. The first system (Grass+N) included N-fertilized (112 kg N ha-1
yr-1) Argentine bahiagrass pastures during the warm-season, overseeded with a
mixture (45 kg ha-1 of each) of FL 401 cereal rye and RAM oat during the cool-season
with a second application of 112 kg N ha-1 yr-1. Both warm- and cool-season
fertilizations were split in two applications (56 kg N ha-1 each application in the warm-
season; 34 and 78 kg N ha-1 yr-1 for the cool-season). Total annual fertilization for this
treatment was 224 kg N ha-1 yr-1. System 2 (Grass + clover) included unfertilized
bahiagrass pastures during the warm-season, overseeded with a similar rye-oat
mixture, plus a mixture of clovers (14 kg ha-1 of ‘Dixie’ crimson, 5.5 kg ha-1 of ‘Southern
Belle’ red clover, and 2.8 kg ha-1 of ball clover) fertilized with 34 kg N ha-1 during the
cool-season. System 3 (Grass+CL+RP) included ecoturf rhizoma peanut and
bahiagrass pastures during the warm-season, overseeded with a similar rye-oat
mixture, fertilized with 34 kg N ha-1 plus a mixture of clovers (14 kg ha-1 of Dixie
crimson, 5.5 kg ha-1 of Southern Belle red, and 2.8 kg ha-1 of ball clover) during the cool
season.
Before planting, the soils of each pasture were disked and harrowed. The
rhizoma peanut was strip-planted simultaneously with bahiagrass on 12 June 2014 and
pastures were already established by the initiation of this trial. Each year after the first
freeze event, providing that soil moisture was available, the warm-season vegetation
was mowed at 5-cm stubble height, and the cool-season seeds were planting using a
grain drill (Massey Ferguson MF43). In Year 1, the planting date was 13 November and
in Year 2 the planting date was 2 December.
56
All pastures were fertilized three weeks after planting the cool-season forages
with 34 kg N, 19 kg P, 47 kg K, and 13.4 kg S ha-1. In addition, in April of each year all
pastures were fertilized with 93 kg K, 27 kg Mg, 12.1 kg S ha-1 with Kmag (0-22-22-11)
as a fertilizer source and 2.24 kg ha-1 B. Grass treatments received 78 kg N ha-1 in the
form of 50% as polymer coated urea (ESN) and 50% as urea every year in January.
Additionally, in May and July, grass pastures received 56 kg N ha-1 (46-0-0) in the form
of urea. The herbicide Impose (active ingredient ammonium salt of imazapic) was
applied in May, July and August during each of the two years of the trial at a rate of 291
mL ha-1 in the treatments with rhizoma peanut strips (applied only to the peanut strips).
Pastures were continuously stocked with variable stocking rate. Two tester
Angus crossbreed steers (Bos sp.) remained on each pasture throughout the season.
Cattle of similar age, weight, and breed were allocated as needed to maintain similar
herbage allowance among treatments, which was assessed every 14 d according to the
methodology described by Sollenberger et al. (2005). Water, shade, and a mineral
supplement mixture (Ca = min. 150 and max. 190 g kg-1, P = min. 30 g kg-1, NaCl = min.
150 and max. 180 g kg-1, Mg = min. 100 g kg-1, Zn = min. 2800 mg kg-1, Cu = min. 1200
mg kg-1, I = min 68 mg kg-1, Se = 30 mg kg-1 , Vitamin A = 308370 units per kg, Vitamin
D3 = 99119 units per kg Special Mag, W.B. Fleming Company) were available for cattle
in each pasture.
Herbage Responses
Herbage Mass, Allowance and Accumulation Rate - Cool Season
Herbage mass (HM) was quantified every 14 d, using an aluminum disk of 0.25
m2. During the cool-season, 30 random disk height points per pasture were taken for all
pastures, and a calibration equation was developed every 28 d, with the regression of
57
the disk height on actual HM using the double sampling method (Wilm et al., 1944;
Haydock and Shaw, 1975). For the calibration, the disk height was taken in 3
representative sites per pasture in the mixture of rye-oat and clovers (Grass+clover and
Grass+CL+RP treatments). While in the pastures with the cool-season grass mixture
(Grass+N treatment), 6 representative sites per pasture were chosen for disk height
measurements, for a total of 18 points for the calibration equations for grass only and
grass-legume mixture treatments. At each of those disk heights measuring sites, the
grass was clipped at 5 cm above ground and dried at 55°C for 72 h to calculate herbage
mass (Stewart et al., 2007). The r2 of the equations developed to calculate herbage
mask from disk height measurements ranged from 0.65 to 0.85.
Herbage allowance was estimated every 14 d, dividing the HM by the cattle body
weight (BW) for each pasture (Sollenberger et al., 2005). Put-and-take animals were
used in order to maintain similar herbage allowance among treatments. The herbage
allowance during the cool season ranged from 0.6 to 1.5 kg DM herbage kg BW-1.
Herbage accumulation rate was determined using exclusion cages, placed at
random sites of the pasture, using four cages per pasture. Disk height was measured in
the previous and new site every 14 d (Vendramini et al., 2012). The same equation
developed for HM was used to calculate the pre- herbage mass and post-herbage mass
for each cage site. In order to calculate the herbage accumulation rate (kg ha-1 d-1), the
difference between post-herbage and pre-herbage mass was divided by the number of
days the cage was in place, in this case by 14 d (Dubeux et al., 2016). The total
herbage accumulation rate in the pastures with clover was obtained for each component
in the sward by multiplying the herbage accumulation rate by the percentage of
58
presence of grass or clover (only in the legume-containing treatments) in each pasture
obtained from the botanical composition (% of dry weigh).
Nutritive Value – Cool-Season
Forage hand-plucked samples were taken every 14 d for each functional group
(i.e., grass and legume) present in the sward. Samples were dried at 55°C for 72 h and
ground to pass a 2-mm screen using a Wiley Mill (Model 4, Thomas-Wiley laboratory
Mill, Thomas Scientific). After grinding the samples, in vitro digestible organic matter
(IVDOM) was determined for grass and legume hand-plucked samples using the two-
stage technique (Moore and Mott, 1974). Subsamples from these species were ball-
milled in a Miller Mill MM 400 (Retsch, Newton, PA, USA) for 9 min at 25 Hz. They were
analyzed for N using a CHNS analyzer and the Dumas dry combustion method (Vario
Micro Cube, Elementar Inc., Germany) and for isotopic composition (δ15N and δ13C)
using a CHNS analyzer and the Dumas dry combustion method (Vario Micro Cube,
Elementar Inc., Germany), attached to an isotope ratio mass spectrometer (IsoPrime
100 Elementar Inc., UK).Crude protein concentration (CP, g kg-1) was calculated as
total N × 6.25.
Biological N2 Fixation – Cool-Season
Biological atmospheric N2-fixation was measured using the natural abundance
technique (Freitas et al., 2010). Non-fixing reference plants (5) were collected every 28
d and were classified to the species level, dried at 55°C for 72 h, ground to pass a 2-
mm screen, and ball-milled. Biological N2-fixation from legumes was estimated as
follows (Shearer and Kohl, 1986):
%Ndfa = (δ15N of reference plant - δ15N of N2-fixing legume)/( δ15N of reference plant –B) × 100 (3-1)
59
The %Ndfa is the percentage of plant N derived from atmospheric N, and B is the
δ15N of shoots of legumes fully dependent on N2 fixation. In this study, the value B = -
0.94‰ for clovers (Unkovich et al., 2008).
Reference plant δ15N for the cool-season ranged from 0.34 to 5.13 ‰ with a
confidence interval (P < 0.05) of 2.56 ± 0.43 ‰.
Botanical Composition – Cool-Season
The proportion of various species in the pastures were determined using the dry-
weight rank method (Mannetje and Haydock, 1963), three times per season. In each
pasture, 30 random sites were sampled using a 0.25 m2 metallic ring. Visual estimation
(% of dry-weight, DW) was recorded for all species present and classified as either
grass (rye, oat), legume (clovers), or weeds for evaluations during the cool-season. The
presence of the species was estimated as first, second and third place and multiplied by
the following factors: 70.19, 21.08 and 8.73, respectively. The data were tabulated to
give the proportion of % DW of the species present in each pasture.
Herbage Mass, Herbage Allowance and Herbage Accumulation Rate – Warm- Season
In the warm-season, 30 random disk height points per pasture were taken in the
bahiagrass pastures (Grass+clover and Grass+N treatments), and 60 points per pasture
in the rhizoma peanut pastures (Grass+CL+RP treatment), 30 points for each botanical
component (bahiagrass and rhizoma peanut). Similar to the cool-season, the double
sampling method was used to obtain HM (Wilm et al., 1944; Haydock and Shaw, 1975).
For the calibration, the disk height in 3 representative sites per pasture were taken in
pastures with bahiagrass (Grass+clover and Grass+N treatments), while in the pastures
with bahiagrass-rhizoma peanut mixture (Grass+CL+RP treatment), 6 representative
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sites per pasture were chosen for disk height measurements, for a total of 18 points for
the calibration equations. During the warm-season the strips of rhizoma peanut and
bahiagrass were measured every 28 days in the Grass+CL+RP treatments. The HM
obtained was multiplied by the grass or rhizoma peanut area occupied in the pasture
and later combined to obtain the total HM ha-1.
Herbage allowance was estimated every 14 d, as described for the cool-season
(Sollenberger et al., 2005). The herbage allowance during the warm-season ranged
from 0.8 to 2.0 kg DM herbage kg BW-1.
In addition, the area of the strips and the percentage of botanical composition of
each component were also included in the calculations, to obtain total herbage
accumulation in the pastures with bahiagrass and rhizoma peanut (Grass+CL+RP).
Nutritive Value, Biological N2 Fixation and Botanical Composition – Warm-Season
Nutritive value, isotopic composition of forages and biological nitrogen fixation
were measured as described for the cool-season. Reference plant δ15N in the warm-
season ranged from -2.58 to 6.78‰ with a confidence interval (P < 0.05) of 2.82 ±
0.69‰. The B value was 1.41‰ used was reported by Okito et al. (2004) for Arachis
hypogea L.
Botanical composition was also determined using the dry-weight rank method
(Mannetje and Haydock, 1963), three times per season using a 0.25-m2 metallic ring. In
the Grass+CL+RP, 60 random sites were sampled and in the pastures with bahiagrass,
30 random sites were sampled to estimate the percentage of grass, legume and weeds.
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Livestock Performance
Average Daily Gain, Stocking Rate, and Gain Per Area
Methodologies to assess livestock performance were similar for both cool- and
warm- season. The body weight (BW) of the tester steers was evaluated every 21 d
after 16 h withdrawal from feed and water. Average weights at the beginning of each
cool grazing season were 224 ± 26.7 in 2016 and 311 ± 30.8 in 2017. The same
animals were maintained on pastures during the 10 months of the grazing trial each
year, encompassing both the cool- and warm-season. Average daily gain (ADG) was
calculated for each 21-d period by dividing the average weight gain of the two testers
per pasture by the number of days (kg head-1 d-1). Grazing days were calculated by
multiplying the total number of animals in each pasture and sampling period (both tester
and put-and-take) by the number of days within each period, and then adding all the
animal days at the end of each season. Gain per area (kg ha-1 d-1) was calculated by
multiplying ADG by the number of grazing days per hectare within each period.
Fecal and Blood Samples
In order to evaluate forage preference and isotopic composition fecal and blood
samples were collected. Fecal samples were collected by rectal grab individually for
each animal, in the evening prior to animal weighing, and samples were immediately
frozen at -20°C for further analyses. Fecal samples were thawed and dried in a forced-
air oven at 55°C for 72 hours, and ground to pass a 2-mm screen using a Wiley Mill
(Model 4, Thomas-Wiley Laboratory Mill, Thomas Scientific) for a posterior analysis of
C, N and their stable isotopes (Vario Micro Cube and and Isoprime100, Elementar Inc.,
Germany). Blood samples were taken via jugular venipuncture and collected into
62
commercial blood collection tubes (10 ml Vacutainer, Becton Dickinson, Franklin Lakes,
NJ) without any additive, and placed immediately in ice. Blood samples were
centrifuged at 3,000 × g for 15 min at 4°C for plasma separation. One portion of the
plasma was stored in a 2-mL vial at -20°C for subsequent lyophilization in a freeze dryer
(FreeZone Labconco, Kansas City, MO) to analyze total C, N (Vario Micro Cube,
Elementar Inc., Germany) and isotopic composition using an isotope ratio mass
spectrometer (IsoPrime 100 Elementar Inc.). In the solid portion of the blood, the white
blood cells were discarded, and the remaining red blood cells were rinsed 3 times with 4
volumes of saline solution (0.9% NaCl wt/vol). Samples were placed in a shaking
incubator at room temperature for 10 min at 60 rpm and centrifuged 15 min at 3,000 × g.
The remaining liquid was aspirated and replaced by new saline solution, and the
agitation and centrifugation steps were repeated. After the third rinse, the solution was
discarded and 2 mL of packed red blood cells were transferred into a vial and stored at -
20°C for further freeze-drying to analyze total C and N (Vario Micro Cube, Elementar
Inc.) and isotopic composition using an isotope ratio mass spectrometer (IsoPrime 100
Elementar Inc.). Serum concentrations of BUN in the tester cattle were used to assess
the protein nutrition status of the animals. A subsample of the plasma collected and
stored as described previously, was determined to quantify BUN using a quantitative
colorimetric kit (B-7551-120, Pointe Scientific Inc., Canton, MI).
Statistical Analysis
The Mixed Procedure of SAS (SAS Inst., Cary, NC) was used with repeated
measures and pasture as the experimental unit. Warm- and cool-season were analyzed
separately using evaluations within each season as the repeated variable. The model
included the fixed effect of treatment, evaluation period, and their interactions. Block
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and year were considered random effects. The best covariance structure that yielded
the lowest Akaike Information Criterion (AIC) was selected for each variable. Means
were compared using the LSMEANS procedure adjusted using the Tukey’s test (P ≤
0.05). The model significance was declared at P < 0.05.
Results
Herbage Responses – Cool-Season
Herbage mass (P = 0.44), herbage allowance (P = 0.35) and herbage
accumulation rate did not differ among treatments (P = 0.27) in the cool-season (Table
3-1); however, there was a treatment × evaluation interaction (P < 0.01) for herbage
mass (Figure 3-1) and herbage accumulation rate (Figure 3-2). Herbage allowance did
not differ (P = 0.35) among treatments and averaged 0.81 kg DM kg-1 BW during the
cool-season (Table 3-1). The two legume systems (Grass+clover and Grass+CL+RP)
had lesser (P < 0.05) HM in late February and early March, when compared with
Grass+N, and greater HM in late April and early May. The treatment × evaluation
interaction for herbage accumulation rate (Figure 3-2) showed the greatest rate (P <
0.05) for Grass+N in late February and early March, 42 and 64 kg ha-1 d-1, respectively.
Toward the end of the cool-season, herbage accumulation rate in Grass+N declined,
being lesser (P < 0.05) than Grass+clover in early April and being the least (P < 0.05) in
late April.
Nutritive Value – Cool-Season
Crude protein (CP) concentration of cool-season grasses (rye and oat for all
treatments) had a treatment × evaluation interaction (Table 3-2, P < 0.01; Figure 3-3).
Concentration of CP in rye and oat from Grass+N was greatest (246 g kg DM-1, P <
0.05) in late February, while in April it was less (P < 0.05) than the grass component of
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Grass+clover. The IVDOM of grasses during the cool-season was not different among
treatments (Table 3-2, P = 0.39) and averaged 709 g kg-1 DM. The evaluation date
effect for cool-season grasses (P < 0.01, Figure 3-4) showed a similar IVDOM
concentration for all treatments at the beginning of the season (819 g kg-1 DM), and
then a constant rate of decline after February, to average 515 g kg-1 DM in the last
evaluation of the cool-season at the beginning of May. The Grass+clover had greater
CP concentration when compared with the Grass+CL+RP (255 vs. 234 g kg-1 DM,
respectively). No treatment difference was observed (P = 0.08, Table 3-2) for IVDOM of
clovers in the cool-season (average 756 ± 17.4 g kg-1 DM).
Isotopic Composition and Biological Nitrogen Fixation – Cool-Season
No treatment (P ≥ 0.21) or treatment × evaluation interaction effect (P ≥ 0.15)
was observed for isotopic composition (δ15N and δ13C), %Ndfa, and BNF of clovers
(Table 3-3). The BNF for the two treatments with legumes during the entire cool-season
(126 days in 2016 and 105 days in 2017) was 51 and 36 kg N ha-1 season-1 for
Grass+clover and Grass+CL+RP, respectively, and did not differ between them (P =
0.21, Table 3-3). For the calculation of BNF, more than 30 reference plants were
collected and analyzed for δ15N in the cool-season, comprising 23 species (Table 3-4).
For isotopic composition of grasses, a treatment × evaluation interaction (P < 0.05) was
observed for δ15N and δ13C (Figure 3-5). Values of δ15N for grasses in the cool-season
ranged from -1.3 to 3.4‰, while δ13C values ranged from -25 to -34‰.
Animal Responses – Cool-Season
No treatment effect was observed for stocking rate (P = 0.97), ADG (P = 0.62) or
gain per area (P = 0.62) for steers grazing during the cool-season (Table 3-5). Stocking
65
rate averaged 3.3 ± 0.11 steers ha-1 among treatments during the entire cool-season,
while ADG averaged 0.81 ± 0.064 kg and gain per area 333 ± 24.3 kg ha-1 season-1.
A treatment × evaluation interaction (P < 0.01) was observed for isotopic
composition (δ15N and δ13C) of feces from steers grazing during the cool-season
(Figure 3-6). In February, fecal δ15N was lower for Grass+N when compared with the
other two systems (P < 0.05), while for evaluations in March, April and May, the feces of
steers grazing in the Grass+N system was more enriched in δ15N (P < 0.05) than the
other two systems. For fecal δ13C the Grass+N system was more depleted (P < 0.05)
than the other two systems in evaluation March, and more depleted (P < 0.05) than
Grass+clover in April.
Botanical Composition: Cool- and Warm-Season
The botanical composition in the pastures during the entire grazing season (cool
and warm) of each of the two consecutive years (Figure 3-9 a-d) showed a treatment ×
evaluation effect (P < 0.01) for proportion of grasses (Figure 3-9 a). In that interaction,
for the three evaluations during the cool-season (January, February and April), Grass+N
had a much greater presence of rye and oat when compared to the other two
treatments (P < 0.05), ranging from 81 to 83% of the botanical composition in that
treatment (Figure 3-9 a). During the same three months of the cool-season, both
Grass+clover and Grass+CL+RP had a similar (P ≥ 0.05) proportion of legumes in the
botanical composition, ranging from 22% in January to 51% in April, when both
treatments peaked (Figure 3-9 b). The proportion of weeds was greatest for Grass+N in
April (P < 0.05), peaking at 19% of the botanical composition, and was not different
between Grass+CL+RP and Grass+clover (P ≥ 0.05) in any of the cool-season
evaluations (Figure 3-9 b). Weed proportions were least for Grass+CL+RP (P < 0.05) in
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the August and September evaluations (6 and 8%, respectively; Figure 3-9 b). For the
three evaluations of the warm-season (June, August and September), the proportion of
bahiagrass in botanical composition for June was greatest for Grass+N, intermediate in
Grass+clover and least in Grass+CL+RP, being different among all three treatments (P
< 0.05, Figure 3-9 a). For August and September, the proportion of bahiagrass in
Grass+CL+RP continued to be less than in the other two treatments (P < 0.05). The first
presence of rhizoma peanut in Grass+CL+RP pastures was in April, when it comprised
8% of the botanical composition (Figure 3-9 d). As the warm-season began, the
presence of clover declined in the two treatments containing legumes, while the
proportion of rhizoma peanut increased, reaching a plateau in August, when rhizoma
peanut comprised 45% of the dry weight in the pastures of the Grass+CL+RP treatment
(Figure 3-9 d).
Herbage Responses – Warm-Season
Herbage allowance was not different among treatments in the warm-season (P =
0.61) nor was there a treatment × evaluation date interaction (P = 0.90), averaging 1.2
kg DM kg BW-1 (Table 3-6). A treatment × evaluation interaction was observed for HM
(Table 3-6, Figure 3-10; P = 0.01). Herbage mass increased as the warm-season
advanced, peaking in August for both Grass+N and Grass+CL+RP, while being
significantly different (P < 0.05) in total HM response (4010 vs. 2860 kg ha-1 for
Grass+N and Grass+CL+RP, respectively). The Grass+clover treatment HM peaked in
August with 3630 kg ha-1, differing only from Grass+CL+RP at the same evaluation date
(P < 0.05, Figure 3-10). Herbage mass in Grass+N was greater (P < 0.05) than all other
treatments in July and August, and greater than Grass+CL+RP in July, August and
September (Figure 3-10).
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Nutritive Value – Warm-Season
Bahiagrass IVDOM during the warm-season (Table 3-7) showed a treatment ×
evaluation date interaction (P < 0.01; Figure 3-11). The IVDOM concentration of
bahiagrass in Grass+CL+RP was greater (P < 0.05) than Grass+N in June and July and
was greatest (P < 0.05) in August (522 g kg-1 OM; Figure 3-11). No effect of treatment
(P = 0.36) or treatment × evaluation interaction (P = 0.13) was observed for CP
concentration of bahiagrass during the warm-season, averaging 121 g kg-1 DM (Table
3-7). The IVDOM and CP concentrations of rhizoma peanut showed an evaluation effect
(P < 0.01, Figure 3-12), with season averages of 659 and 171 g kg-1 for IVDOM and CP,
respectively. Both IVDOM and CP concentrations of rhizoma peanut declined at the
beginning of August, to increase again by the middle of August, and remaining steady
until the end of the season (Figure 3-11).
Isotopic Composition and Biological Nitrogen Fixation – Warm-Season
No treatment or treatment × evaluation date interaction effects were observed for
δ15N, δ13C, or C concentration in bahiagrass (P ≥ 0.05); however, an evaluation effect
was observed for δ15N and δ13C (P < 0.01, Table 3-8). Similarly, an evaluation date
effect was observed for δ15N and δ13C in rhizoma peanut, where δ15N steadily increased
(P < 0.05) from late May to early July, and showing a plateau after August that stabilized
at average values of 0.92‰ (Figure 3-14 a). Conversely, the δ13C was enriched (P <
0.05) as the season advanced, from -28.73 in May to -23.61‰ in June, and -19.26‰ in
early August (P < 0.05, Figure 3-14 b).
The biological N fixation of the pastures containing rhizoma peanut in the warm-
season (Grass+CL+RP treatment only) did not show an evaluation effect (P = 0.25),
and accounting for the entire season, rhizoma peanut added 11.7 kg N ha-1 through
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BNF (Table 3-8). Similarly, the percentage of N derived from the atmosphere (%Ndfa) in
pastures with rhizoma peanut remained steady throughout the season, averaging
44.9% Ndfa (Figure 3-13). For the calculation of BNF, more than 55 reference plants
were collected and analyzed for δ15N in the warm-season, comprising 24 species (Table
3-9).
Animal Responses – Warm-Season
A treatment effect (P ≤ 0.01) was observed for stocking rate, ADG, and gain per
area for steers grazing during the warm-season (Table 3-10). Stocking rate was
greatest (P < 0.05) for Grass+N with 4.4 steers ha-1, while Grass+clover and
Grass+CL+RP did not differ (P ≥ 0.05) between them, averaging 3.6 and 3.2 steers ha-1,
respectively. The ADG was greatest (P < 0.05) in Grass+CL+RP (0.56 kg) and did not
differ (P ≥ 0.05) between Grass+N and Grass+clover (0.36 and 0.31 kg, respectively,
Table 3-10). Gain per area was less (P < 0.05) for Grass+clover compared with
Grass+CL+RP (166 vs. 306 kg ha-1 season-1) and neither were different (P ≥ 0.05) from
Grass+N (211 kg ha-1 season-1, Table 3-10).
A treatment × evaluation interaction (P ≤ 0.05) was observed for isotopic
composition (δ15N and δ13C) of feces from steers grazing during the warm-season
(Figure 3-15), where the δ13C from feces of steers grazing Grass+CL+RP was more
depleted when compared with the other two treatments (P < 0.05) at all evaluations
except in May, August and September. Similarly, the fecal δ15N of steers grazing
Grass+CL+RP in the warm-season was the least (P < 0.05) in June and July, and was
more depleted (P < 0.05) than Grass+N in late July and August (Figure 3-15 a,b). Both
δ15N and δ13C in the plasma of steers grazing in the warm-season had a treatment ×
evaluation interaction (P < 0.01, Figure 3-16). Plasma δ13C of steers grazing
69
Grass+CL+RP in the warm-season was the most depleted (P < 0.05) in all evaluations,
ranging from -20 to -22‰ (Figure 3-17 b). Both δ15N and δ13C in the red blood cells of
steers grazing in the warm-season had a treatment × evaluation interaction (P < 0.05,
Figure 3-17). In that interaction, δ15N peaked in July at approximately 10‰ for all
treatments without differing among them (P > 0.05), and only differing between
Grass+N and the other two treatments (P < 0.05) in May (Figure 3-17 a). The δ13C in
red blood cells of steers grazing in the warm-season also peaked in July for all
treatments and was more depleted (P < 0.05) for Grass+CL+RP in late August,
September and October (-23, -22, and -22‰, respectively, Figure 3-17).
Steers grazing during the warm-season showed a treatment effect in blood urea
nitrogen (BUN) concentration (P < 0.001), with values ranging from 12 to 24 mg dL-1
(Figure 3-18). The Grass+CL+RP showed a greater concentration of BUN (21 mg dL-1)
compared with the other two systems (19 and 15 mg dL-1 for Grass+N and
Grass+clover, respectively).
Discussion
Herbage Responses – Cool-Season
Herbage mass in the cool-season fluctuated according with the sward component.
At the beginning of the season, the HM was greater because of the presence of grasses
and in particular the early maturing FL401 rye, which produces abundant forage at the
beginning of the cool-season (Dubeux et al., 2016). As the cool-season advanced to
late February and early March, there was a decline in the herbage mass of the two
systems with legumes. Because the Grass+N system received 62 kg N ha-1 as 50%
ESN and 50% urea in late January/early February of each year, it is highly probable that
70
the response in herbage mass observed was to the N fertilization. Because of the use of
both urea and ESN as fertilizer sources, the availability of N could have been sustained,
even a month after the application, creating the herbage mass differences observed.
Later in the season when the clovers increased their presence in the sward, herbage
mass increased dramatically in those pastures containing legumes, as observed during
April. The Grass+N system displayed earlier growth and changed according with
evaluation, without significant fluctuations through the season.
Herbage mass is a function of multiple factors such as planting date, seeding rate,
precipitation, temperature, and stocking rate (Redmon et al., 1995). Stocking rate is a
major driver of the HM response; therefore, stocking rate was adjusted every 14 days
based on HM and herbage allowance. The target was to maintain similar herbage
allowance among treatments to avoid confounding effects and to avoid a decrease in
HM that could affect animal performance due to nutrient intake limitations (Sollenberger
and Vanzant, 2011). Herbage accumulation was rapid at the beginning of the season in
the Grass+N system and decreased at the end of the season compared with the other
two treatments (Grass+clover, Grass+CL+RP), where the presence of clover increased.
Legumes are more difficult to maintain and grow when compared with grasses, and are
very susceptible to pests and grazing intensity; however, they have been reported to
perform better in mixture with grasses (Brink et al., 2001). The inclusion of legumes in
cool-season grasses could improve the seasonal distribution of forage, potentially
allowing an easier management of the stocking rate through the cool-season.
Nutritive Value – Cool-Season
In the grass component, the CP concentration of rye and oat ranged from 144 to
260 g DM kg-1 with lower concentrations at the end of the season when stem elongation
71
and flowering was abundant. Aiken (2014) reported rye CP concentrations of 141 g kg-1
when it was overseeded into a bermudagrass pasture. Dubeux et al. (2016) also
observed lesser values of CP concentration in rye-ryegrass mixtures at the end of the
season with values ranging from 112 to 294 g DM kg-1. In addition, grass in the clover
treatments showed a greater CP concentration compared with the mixture of grasses
(234 and 255 g DM kg-1 in the Grass+clover and Grass+CL+RP systems, respectively).
Similar studies with mixtures of ryegrass with crimson and white clover reported greater
CP when compared with monoculture grasses and the values ranged from 152 to 252 g
DM kg-1 (Mooso et al., 1990; Weller et al., 2001).
The IVDOM of rye and oat ranged from 498 to 814 g kg-1, showing greater
concentration at the beginning of the season, and later, with more presence of stem in
the sward, the IVDOM concentration declined. In the stage when plant is fully developed
and stem already elongated, IVDOM will likely decline because of loss of cell soluble
compounds, greater cell wall content, and reduced protein concentration (Coleman et
al., 2004). Dubeux et al. (2016) reported IVDOM values greater than 750 g kg−1 in rye-
annual ryegrass and oat-ryegrass, confirming that cool-season mixtures are an
alternative with greater nutritive value in forage livestock systems. Clovers also show
greater concentrations of IVDOM when compared with rye and oat, and this
concentration was affected by evaluation date. The CP concentration of the grasses in
the treatment without legumes was greater than that in the legume-containing systems
(185 vs. 169 g DM kg-1) because of greater N fertilizer application. The results in IVDOM
concentration of grasses in the cool-season are compatible with concentrations reported
72
in other studies with values ranging from 599 to 794 g kg−1 (Terril et al., 1996; Sleugh et
al., 2000).
Isotopic Composition and Biological Nitrogen Fixation – Cool-Season
Plant δ15N varies with environmental conditions and plant characteristics
including soil, moisture, rooting depth, and mycorrhizal associations (Michener et al.,
2007). Most terrestrial plants have δ15N values in the range of -6 to + 5 ‰ (Fry, 1991),
and for plants that fix N δ15N ranges from -3 to +1‰ (Fogel and Cifuentes, 1993). The
range of δ15N of the rye, oat and clovers was within the range mentioned above (-1.3 to
3.4‰ for rye and oats and -0.07 to -0.03‰ for clovers, respectively).
Carbon isotope differences in plants are dictated by the photosynthetic pathway,
and in C3 plants δ13C is highly influenced by environmental factors. Environment affects
δ13C primarily because C3 plants depend on the ratio of intracellular and ambient
concentrations of CO2 (Murphy et al., 2009). Thus, by regulating the stomata opening
C3 plants can regulate CO2 concentrations and water flow in the leaves. When stomata
are closed, there is likely less discrimination and plants become more enriched in 13C
(less depleted). Under conditions of sufficient moisture, C3 plants might fully open their
stomata and discriminate more, thus becoming more depleted in 13C. This stomatal
function is conditioned by external factors such as light and moisture, and may not be
applicable for all C3 plants.
The δ13C for C3 plants ranges from -35 to -22‰ (Michener et al., 2007). In this
study, the δ13C for cool-season grasses and clovers ranged from -34 to -25‰.
The use of grass-legume mixtures can increase plant production by adding
biologically fixed N to the soil and sharing it with grasses via plant litter decomposition
(da Silva et al., 2012). The level of BNF by mixture of pasture legumes can vary greatly,
73
influenced by a wide range of factors, including environmental conditions and
management. In mixture of clovers and grasses, BNF has ranged from 15 to 373 kg N
ha-1 yr-1 (Boller et al., 1987; Ledgard et al., 1992). In the present study, the BNF values
reported were 51 and 36 kg N ha-1 season-1 for Grass+clover and Grass+CL+RP,
respectively during the cool-season. This is within the range reported previously.
Greater grazing intensity can favor increased distribution of BNF through animal
excreta, eventually leading to large N transfer that increases grass growth (Ledgard et
al., 1992).
Animal Responses – Cool-Season
The impact of herbage allowance on animal performance in grazing studies has
been well-documented (Sollenberger et al., 2005). In consequence, by maintaining a
similar herbage allowance among treatments in this study, and because the nutritive
value of the forage in the three systems was similar, no differences in animal
performance during the cool-season were observed. The ADG response from steers
grazing during the cool-season was typical of those observed previously in North Florida
when grazing winter annuals (Dubeux et al., 2016). It is reflective of the excellent
nutritional value of the winter annuals grazed, which averaged 709 g of IVDOM kg-1 OM
and 166 to 185 g of CP kg-1 DM). Both quality and quantity of nutrients provided by the
pastures in each of the three systems studied were similar. Particularly the CP supplied
by the cool season forages in the study was in excess of the amount of protein required
for growing steers when considering the observed forage intake. A steer of 270 kg of
BW, such as those grazing during the cool season, would require 0.846 kg of CP daily
to gain 1.05 kg of BW daily (NASEM, 2016). On average the steers in this trial
consumed 2.63% of their body weight on a DM basis, which considering the average
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CP concentration of the grasses only (174 g kg-1 DM), would translate into 1.24 kg of CP
daily. Thus, CP supplied by the cool season forages in this study was not limiting
animal performance; however, a greater supplied of digestible energy by the forage
could have increased the rate of gain. The magnitude of the ADG response observed
across treatments was expected based on previous research (Dubeux et al., 2016) and
it was likely the result of similar forage digestibility values among treatments in the cool
season. The gains per area observed over the entire cool-season were also similar to
those observed previously in the same area and with similar cool-season forages.
Dubeux et al. (2016) reported an average gain per area of 340 kg ha-1 season-1 over a
2-year study of 112 days in each season. In the present study, after two consecutive
years of either 127 or 105 days in the cool-season, the average gain per area was 333
kg ha-1 season-1.
The isotopic signature of both δ15N and δ13C in the feces of steers grazing in the
cool-season followed a similar pattern throughout the season to that same isotopic
signature of the rye and oats. Only towards the end of the cool-season, the δ13C in rye
and oats slightly increased in all treatments, and this change is not reflected in the fecal
δ13C, which is maintained constant and around -32‰. Very few studies have looked at
grazing cattle and their relationship between fecal and forage isotopic signature. Pereira
Neto et al. (2019) showed that similar to this study, δ13C was more depleted in feces
than in the forage consumed. Based on the relationship between forage and fecal
isotopic signature, particularly δ13C, it may be possible to predict forage intake on mixed
swards based on δ13C as suggested by Pereira Neto et al. (2019). In the cool-season,
75
because all species were C3, it was not feasible to use isotopic signature to
discriminate between clovers and rye and oat intake.
Plasma and red blood cells isotopic composition and its relationship with the
dietary isotopic signature has been suggested as a tool to potentially assess efficiency
of nitrogen use from various pools in ruminants, and to assess potential trophic level
enrichments (Jenkins et al., 2001; Cantalapiedra et al., 2015). In this study, the
relationship between forage isotopic composition and plasma and red blood cells was
not evident in steers grazing in the cool-season. Both plasma and red blood cell δ13C
were much greater when compared with that of the diet in the cool-season, as expected
due to isotopic discrimination as trophic levels increase (Michener et al., 2007). This
isotopic discrimination is reflected in the ∆ value (δ13C in animal - δ13C in diet;
Cantalapiedra et al., 2015) of approximately 10‰. The composition of δ15N in red blood
cells of steers grazing in the cool-season was steady across the season, and was also
greater than in the grazed rye and oat, by a ∆ value of 2‰ or greater. Because of the
similar isotopic signature in the forages consumed in the cool-season, no differences
were observed in the plasma or red blood cell of the steers on the different treatments.
Cantalapiedra et al. (2015) suggested that the ∆15N could be correlated with the
efficiency of use of nitrogen by ruminants, particularly when plasma N is used for the
assessment. Future studies should attempt to correlate ∆15N with efficiency of N use.
Botanical Composition – Cool- and Warm-Season
Through the cool-season and warm-season, the botanical composition showed
variation in grass and legume presence according to the growth pattern of the forage
species. In addition, it was possible to observe the compatibility among species
evidenced by the fact that the botanical composition maintained a certain balance,
76
without a level of competition that deteriorated the presence of one group in particular.
April was an important month because the presence of clovers was greater and the
warm-season forages started to emerge. In May and June, which are transition months
between the two seasons, it was possible to observe the mixture of grasses and
legumes, the transition of the cool-season grasses in senescence, and the warm-
season forage growing. In order to monitor the mixture composition of these grasslands,
the dry weight rank method was appropriate.
Herbage Responses – Warm-Season
Herbage mass in the warm-season showed fluctuations through the season with
greater HM during the month of August, where better growing conditions were present
such as rain, greater temperature, greater light intensity and N-ferilization (Ludlow,
1985). At the beginning of the season, the Grass+N system differed mainly with the
Grass+CL+RP, where the effect of the N fertilizer was pronounced and the rhizoma
peanut was in its early growth stage. Through the entire season, only during the months
of July and August did the Grass+clover system differ from Grass+N, suggesting that
bahiagrass with N inputs from the clovers could have sufficient herbage mass to support
3.6 steer ha-1. Vendramini et al. (2013) evaluated the performance of bahiagrass with
low N inputs and concluded that Argentine bahiagrass with only 60 kg N ha-1 produced
6.4 Mg DM ha-1. This supports the levels of forage production and animal responses
observed in our study when low N fertilizer inputs are applied, highlighting why
bahiagrass is often preferred in forage production systems in Florida. In addition, the
cool-season clovers in the Grass+clovers and Grass+CL+RP systems may have left
residual nitrogen that contributed to the herbage responses during the warm-season.
77
Nutritive Value – Warm-Season
Bahiagrass IVDOM concentration ranged from 433 to 452 g kg-1, which is lower
than reported in previous studies (Stewart et al., 2007; Vendramini et al., 2013). These
differences in IVDOM concentration could be explained by the grazing pressure used in
our study, dictated by the herbage allowance which was perhaps lenient for bahiagrass
and resulted in greater stem-leaf ratio and more mature tissue that decreased
digestibility (Vendramini et al., 2008). In agreement with our findings, Vendramini et al.
(2013) reported bahiagrass CP concentrations ranging from 120 to 132 g kg-1 DM with 2
and 4-wk grazing intervals and across several cultivars.
The IVDOM and CP concentrations for rhizoma peanut (ranging from 525 to 698 g
kg-1, and from 127 to 198 g kg-1, respectively) in the Grass+CL+RP treatment
contributed to increase the overall nutritive value of the pasture grazed in this treatment.
As an example, IVDOM concentration in samples of forages from mixtures of Ecoturf
rhizoma peanut and Argentine bahiagrass ranged from 381 to 474 g kg-1 (Santos et al.,
2018).
Isotopic Composition and Biological N2 Fixation – Warm-Season
The bahiagrass δ15N values did not differ among treatments, and ranged from
0.32 to 0.93‰. Many processes can alter the δ15N values of the nitrogen used by
plants, including factors such as volatilization, nitrification and denitrification or systems
with N limitation or greater nutrient conditions (Michener et al., 2007). The δ15N of
rhizoma peanut in the present study ranged from -0.24 to 1.07‰ and is within the
reported range for plants that fix N2 from the atmosphere (-3 to 1‰). Similar values of
δ15N ranging from -1.15 to -0.41‰ were reported in different rhizoma peanut cultivars by
Dubeux et al. (2017).
78
The δ13C in C4 grasses is more enriched than in C3 plants and is less
susceptible to change due to environmental factors. The δ13C of C4 grasses ranged
from -19 to -9‰ (Michener et al., 2007) and the δ13C for bahiagrass in this study ranged
from -18‰ to -19‰. In addition, δ13C values in rhizoma peanut differed during the
season and ranged from -29 to -19‰. Because of the application of the herbicide
Impose (imazapic) in August, it is possible that this triggered a response in rhizoma
peanut that affected stomata opening and thus causing the greatest value of δ13C
observed in this season.
Nitrogen derived from the atmosphere (%Ndfa) ranged from 37 to 52%. Jaramillo
et al. (2018) reported %Ndfa values for Ecoturf rhizoma peanut ranging from 20 to 87%.
The BNF reported for rhizoma peanut is below that from previous studies conducted in
small plots (Dubeux et al., 2017; Santos et al., 2018; Jaramillo et al., 2018), and
continuous grazing could be a major driver of this response. The amount of N fixed by
forage legumes depends on legume growth and persistence, and it is possible that the
rate of defoliation by grazing animals is faster than the capacity of the plant to respond.
The BNF reported in this study (herbage accumulation rate per day × %DW × area of
rhizoma peanut in the pastures = 14 kg ha-1d-1 × % N = N yield kg ha-1 × %Ndfa = BNF)
is in agreement with that reported by Thomas et al. (1997), where atmospheric nitrogen
fixation ranged from 0.3 to 40 kg N ha-1 season-1 in pastures with mixtures of Brachiara
dictyoneura, Stylosanthes capitata, and Arachis pintoi. Other factors than can affect
BNF are soil type, soil nutrients, pasture age, and grazing (Thomas, 1995).
Animal Responses – Warm-Season
Stocking rates with Grass+N were 29% greater than the other treatments (4.4 vs.
3.4 steers ha-1), however the 70% increase in ADG observed in Grass+CL+RP when
79
compared with the other two treatments (0.56 vs. 0.33 kg) led to a greater gain per area
for Grass+CL+RP compared with Grass+clover (306 and 166 kg ha-1 season-1,
respectively). Gain per area in Grass+N did not differ from either Grass+CL+RP or
Grass+clover. The greater ADG in steers grazing Grass+CL+RP was likely the result of
the increased nutritional value of the forage grazed. While the IVDOM concentration of
the bahiagrass grazed in the summer was, during most of the evaluations, similar
among treatments, in June, July, and August, the grass IVDOM concentration in
Grass+CL+RP was increased when compared with Grass+N. While reasons for this
are not clear from this study, it may be due to effects of N transfer from the rhizoma
peanut to bahiagrass or N transfer via animal excreta, and their effects on improving the
digestibility of bahiagrass, at least at certain time points during the season. However,
the factor that likely contributed the most to the improved ADG was the much greater
IVDOM and CP concentrations in rhizoma peanut, relative to bahiagrass, which have
been widely documented in the past (Santos et al., 2018; Jaramillo et al., 2018).
The relationship between isotopic composition of the diet and the plasma
proteins has been suggested as a potential tool to assess efficiency of N use by
ruminants (Cantalapiedra et al., 2015). Furthermore, when using a calculated ∆ value
(δ15N in animal - δ15N in diet) to assess differences in isotopic signature, Cantalapiedra
et al. (2015) showed a correlation between visceral tissue (splanchnic) amino acid
metabolism and N fractionation. In this study, when comparing blood urea N with δ15N, it
appears that in both variables during some of the first evaluations of the warm-season,
differences were observed between Grass+clover and Grass+N. Whenever a difference
existed, it was always greater BUN and δ15N for Grass+N. Because the δ15N for
80
bahiagrass in both treatments did not differ (and no rhizoma peanut was present in
these treatments), it is fair to assume that animals from both treatments were
consuming a similar δ15N from the forage and it was approximately 0.86‰. Following
this logic, the calculated ∆ value using the plasma δ15N would be greater for Grass+N
when compared with Grass+clover, which according to Cantalapiedra et al. (2015)
would point to a less efficient use of dietary protein. Based on this reasoning, it is
possible that the greater BUN observed in Grass+N compared with Grass+clover is a
result of inefficiencies in converting dietary N intake into animal protein, and thus N is
found circulating in blood, and possibly later excreted. Dietary information provided by
plasma has a turnover of approximately 3 weeks, in contrast with red blood cells, which
offer dietary information over longer periods (Klaassen et al., 2004). Thus, a possible
explanation for the peak in δ15N and δ13C in red blood cells during the month of July,
could be that because of the greater turnover rate of red blood cells, these values may
be reflecting the change between cool- and warm-seasons, and perhaps the intake of
the remains of the C3 at the end of the cool-season. The greater BUN concentrations
observed for steers grazing Grass+CL+RP in several evaluations during the warm-
season, reflect the greater N intake as a result of consuming rhizoma peanut in this
treatment.
Conclusions
The introduction of legumes during the cool-season in a mixture of grasses
increased the nutritive value and extended the grazing period. The latter was due to
differences in their seasons of growth, which were complementary of each other. These
grass-legume mixtures offer greater CP and IVDOM to the diet of the cattle grazing, and
cattle performed similarly to a system with N-fertilized grasses. When rhizoma peanut
81
was included in bahiagrass pastures, ADG of cattle increased by 70%, and the gain per
area was similar to that with systems that included N fertilizer. The contribution of BNF
was 55 kg N ha-1 yr-1 adding both seasons. This level of BNF was associated with better
forage nutritive value during the warm-season, supporting gain per area that was similar
to the system using 224 kg N ha-1 yr-1. Furthermore, overseeding perennial forages with
cool-season mixtures of grasses and clovers did not affect the regrowth of the perennial
grass and legume. The extension of the grazing season provided by introduction of
legumes could reduce feed costs in forage-livestock production systems in North Florida
and decrease the necessity of N fertilizer.
82
Table 3-1. Herbage mass, herbage allowance and herbage accumulation rate during the cool-season of 2016 and 2017.
Treatment1 P-value3 Item Grass+
N Grass+clover
Grass+CL+RP
SE2 Trt Eval T × E
Herbage mass, kg ha-1 658 755 722 77.1 0.44 0.04 <0.01 Herbage allowance, kg DM kg-1 BW-1
0.79 0.83 0.81 0.081 0.35 0.18 0.18
Herbage accumulation rate, kg ha-1d-1
21 26 16 1.48 0.27 0.03 <0.01
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2SE = Standard error of the mean for the treatment effect. 3 Trt = Treatment, Eval = Evaluation month, T × E = Treatment × Evaluation interaction.
Table 3-2. Nutritive value from hand-plucked samples during the cool-season of 2016
and 2017.
Treatment1 P-value3 Item Grass+
N Grass+clover
Grass+CL+RP
SE2 Trt Eval T × E
Cool-season grasses IVDOM, g kg-1 702 723 701 16.3 0.39 <0.01 0.07 CP, g kg-1 DM 185a 172b 166b 17.5 <0.01 <0.01 <0.01 Cool-season clovers IVDOM, g kg-1 - 766 745 7.1 0.08 <0.01 0.37 CP, g kg-1 DM - 255a 234b 16.9 0.03 <0.01 0.74
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2SE = Standard error of the mean for the treatment effect. 3 Trt = Treatment, Eval = Evaluation month, T × E = Treatment × Evaluation interaction. a,b Means differ, P < 0.05.
83
Table 3-3. Isotopic composition and biological nitrogen fixation (BNF) of clovers during the cool-season of 2016 and 2017.
Treatment1 P-value3 Item Grass+
clover Grass+CL+RP
SE2 Trt Eval T × E
δ15N -0.07 -0.03 0.244 0.90 < 0.01 0.15 δ13C -33.8 -33.8 0.97 0.90 < 0.01 0.92 C, g kg-1 427 427 42 0.99 0.68 0.55 % Ndfa4 85 85 3.9 0.95 0.28 0.98 BNF5, kg N ha-1 d-1 0.79 0.50 0.45 0.24 0.14 0.86 BNF5, kg N ha-1 season-1
51 36 19.0 0.21 - -
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2 SE = Standard error. 3 Trt = Treatment, Eval = Evaluation month, T × E = Treatment × Evaluation interaction. 4 %Ndfa = % N derived from atmosphere. 5 BNF = Biological N2–fixation. The cool-season in 2016 had 126 days and in 2017 had 105 days.
84
Table 3-4. List of reference plants and δ15N in the cool-season of 2016 and 2017.
Scientific name Year Evaluation δ15N, ‰
𝑋 ± SD
Capsella bursa-pastonis 2016 1 3.72 ± 0.37
Stellaria media 2016 1
Youngia Japonica 2016 1
Lamium amplexicaule 2016 1
Flaveria linearis 2016 2 1.48 ± 0.34
Raphanus raphanistrum 2016 2
Geranium carolinianum 2016 2
Lamium amplexicable 2016 2
Oenothera laciniata 2016 3 3.53 ± 1.39
Centaurea cyanus 2016 3
Geranium carolinianum 2016 3
Centaura solstitialis 2016 3
Gnaphalium spicatum 2016 4 1.33 ± 0.81
Eupatorium capillifolium 2016 4
Geranium carolinianum 2016 4
Flaveria linearis 2016 4
Gnaphalium spicatum 2017 1 2.97 ± 1.20
Lamium amplexicaule 2017 1
Brassica rapa 2017 1
Gnaphalium spicatum 2017 1
Gnaphalium spicatum 2017 1
Lamium amplexicaule 2017 2 4.11 ± 1.37
Geranium carolinianum 2017 2
Gnaphalium spicatum 2017 2
Henbit Lamium amplexicaule 2017 2
Gnaphalium spicatum 2017 2
Pyrrhopappus carolinianus 2017 3 2.28 ± 0.87
Geranium carolinianum 2017 3
Ranunculus sardous crantz 2017 3
Phyrrhoppus carolinianus 2017 3
Lamium amplexicable 2017 3
Sinapis arvensis 2017 4 2.69 ± 1.09
Sonchus asper 2017 4
Geranium carolinianum 2017 4
Gnaphalium spicatum 2017 4
Gnaphalium spicatum 2017 4
𝑋 = Average from δ15N (‰) of all the reference plants in the same evaluation. SD = Standard deviation from δ15N (‰) of all the reference plants in the same evaluation.
85
Table 3-5. Animal performance during the cool-season of 2016 and 2017.
Treatment1 Item Grass+N Grass+
clover Grass+C
L+RP SE2 P-value
Stocking rate, steer ha-1 3.3 3.3 3.3 0.11 0.97 ADG, kg 0.80 0.86 0.77 0.064 0.62 Gain per area, kg ha-1 season-1
322 352 324 24.3 0.62 1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2SE = Standard error. The cool-season in 2016 had 126 days and in 2017 had 105 days.
Table 3-6. Herbage mass, herbage allowance and herbage accumulation rate during the warm-season of 2016 and 2017.
Treatment1 P-value3 Item Grass+N Grass+
clover Grass+CL+RP
SE2 Trt Eval T × E
Herbage mass, kg ha-1
2369a 2152b 1728c 224.7 <0.01 <0.01 0.01
Herbage allowance, kg DM kg BW-1
1.2 1.2 1.2 0.072 0.61 <0.01 0.90
Herbage accumulation rate4, kg ha-1d-1
37a 36a 14b 4.03 <0.01 <0.01 0.02
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2SE = Standard error. 3 Trt = Treatment, Eval = Evaluation month, T × E = Treatment × Evaluation interaction. 4 For Grass+N and Grass+clover systems = Herbage accumulation rate × %DW (botanical component). For Grass+CL+RP system = Herbage accumulation rate (bahiagrass or rhizoma peanut) × %DW (botanical component) × rhizoma peanut area or bahiagrass area. a,b,c Means differ, P < 0.05.
86
Table 3-7. Nutritive value of bahiagrass, during the warm-season (2016 and 2017).
Treatment1 P-value3 Item Grass
+N Grass+clover
Grass+CL+RP
SE2 Trt Eval T × E
Warm-season bahiagrass
IVDOM, g kg-1 433 438 452 14.1 0.18 <0.01 <0.01 CP, g kg DM-1 123 116 124 18.2 0.36 <0.01 0.13
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2SE = Standard error. 3 Trt = Treatment, Eval = Evaluation month, T × E = Treatment × Evaluation interaction.
Table 3-8. Isotopic composition of bahiagrass and biological nitrogen fixation (BNF) of rhizoma peanut during the warm-season of 2016 and 2017.
Treatment1 P-value3 Item Grass+N Grass+
clover Grass+CL
+RP SE2 Trt Eval T × E
δ15N 0.93 0.79 0.32 0.212 0.06 <0.01 0.09 δ13C -18.8 -18.8 -20.0 3.860 0.46 <0.01 0.15 C, g kg-1 427 430 429 5.52 0.45 0.06 0.14 BNF4, kg N ha-1 evaluation-1
- - 0.07 0.02 - 0.024 -
BNF4, kg N ha-1 season-1
- - 11.7 - - - -
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2 SE = Standard error. 3 Trt = Treatment, Eval = Evaluation month, T × E = Treatment × Evaluation interaction. 4 BNF = Biological N2–fixation. The warm-season in 2016 and 2017 had both 168 days.
87
Table 3-9. List of reference plants and δ15N in the warm-season of 2016 and 2017.
Scientific name Year Evaluation δ15N, ‰
𝑋 ± SD
Eupatorium capillifolium 2016 1 0.78 ± 0.01
Taraxacum officinale 2016 1
Ambrosia artemisiifolia 2016 1
Vicia Sativa 2016 1
Verbena urticifolia 2016 2 3.54 ± 2.01
Amaranthus spinosus 2016 2
Erigeron quercifolius 2016 2
Vicia Sativa 2016 2
Ipomoea purpurea 2016 2
Tribulus terrestris 2016 3 5.34 ± 1.5
Ipomoea purpurea 2016 3
Mollugo verticillata 2016 3
Chamaesyce hyssopifolia 2016 3
Cenchrus spinifex 2016 3
Senna occidentalis 2016 4 3.2 ± 0.91
Amaranthus spinosus 2016 4
Ipomoea purpurea 2016 4
Geranium robertianum 2016 4
Amaranthus viridis 2016 4
Amaranthus spinosus 2016 5 3.13 ± 0.84
Verbena brasiliensis 2016 5
Ipomoea purpurea 2016 5
Cenchrus sp 2016 5
Erigeron canadensis 2016 5
Amaranthus spinosus 2016 6 4.42 ± 1.72
Ipomoea purpurea 2016 6
Verbena brasiliensis 2016 6
Solanum viarum 2016 6
Cenchrus spinifex 2016 6
Erigeron canadensis 2017 1 1.39 ± 0.63
Cynodon dactylon 2017 1
Gnaphalium americanum 2017 1
Erigeron canadensis 2017 2 1.48 ± 0.38
Balsamorhiza sagittata 2017 2
Ipomoea purpurea 2017 2
Ipomoea purpurea 2017 2
Erigeron canadensis 2017 2
Eupatorium capillifolium 2017 3 2.45 ± 0.79
Ipomoea purpurea 2017 3
Eupatorium capillifolium 2017 3
Cynodon dactylon 2017 3
88
Table 3-9. Continued
Scientific name Year Evaluation δ15N, ‰
𝑋 ± SD
Cynodon dactylon 2017 3
Ipomoea purpurea 2017 4 3.51 ± 1.1
Ambrosia artemisiifolia 2017 4
Digitaria sanguinalis 2017 4
Digitaria sanguinalis 2017 4
Cynodon dactylon 2017 4
Acanthospermum hispidum 2017 5 3.42 ± 0.83
Dactyloctenium aegyptium 2017 5
Digitaria sanguinalis 2017 5
Ipomoea purpurea 2017 5
Brachiaria plantaginea 2017 6 2.04 ± 0.59
Cyperus sp 2017 6
Cynodon dactylon 2017 6
Ipomoea purpurea 2017 6
Brachiaria plantaginea 2017 6
𝑋 = Average from δ15N (‰) of all the reference plants in the same evaluation. SD = Standard deviation from δ15N (‰) of all the reference plants in the same evaluation.
89
Table 3-10. Animal performance during the warm-season of 2016 and 2017.
Treatment1 Item Grass+N Grass+
clover Grass+ CL+RP
SE2 P-value
Stocking rate, steer ha-1 4.4a 3.6b 3.2b 0.11 <0.01 ADG, kg 0.36b 0.31b 0.56a 0.05 0.01 Gain per area, kg ha-1 season-1
211ab 166b 306a 30.6 0.01 1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2SE = Standard error. a,b Means differ, P < 0.05. The warm-season of 2016 had 168 days and in 2017 had 168 days.
90
Figure 3-1. Herbage mass during the cool-season (kg DM ha-1 d-1). Treatment × evaluation P < 0.01. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. * = Grass+N differ from the other two treatments (P < 0.05).
0
200
400
600
800
1000
1200
1400
1600
1800
2000
January January February February March March April April May
He
rbag
e m
ass
(kg
DM
ha
-1d
-1 )
Evaluation
Grass+N Grass+clover Grass+CL+RP
*
*
**
91
Figure 3-2. Total herbage accumulation rate during the cool-season (kg DM ha-1 d-1). Treatment × evaluation P < 0.01. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. * = Grass+N differ from the other two treatments (P < 0.05); ‡ = Grass+N differs from Grass+clover (P < 0.05).
0
10
20
30
40
50
60
70
80
January January February February March March April April May
He
rbag
e a
ccu
mu
lati
on
rat
e (
kg D
M h
a-1
)
Evaluation
Grass+N Grass+clover Grass+Cl+RP
*
‡
‡*
92
Figure 3-3. Crude protein (CP) from cereal rye and oat during the cool-season (2016 and 2017). Treatment × evaluation P = 0.008. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. * = Grass+N differ from the other two treatments (P < 0.05); ‡ = Grass+N differs from Grass+clover (P < 0.05).
0
50
100
150
200
250
300
January January February February March March April April May
Cru
de
Pro
tein
(g
kg-1
DM
)
Evaluation
Grass+N Grass+clover Grass+CL+RP
*
‡
93
Figure 3-4. In vitro digestible organic matter (IVDOM) of rye and oat in the cool-season
of 2016 and 2017. Evaluation P < 0.01, treatment × evaluation P = 0.08. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
100
200
300
400
500
600
700
800
900
January January February February March March April April May
IVD
OM
rye
an
d o
at (
g kg
-1 )
Evaluation
Grass+N Grass+clover Grass+CL+RP
94
Figure 3-5. Isotopic composition (δ15N and δ13C) from rye and oat in the cool-season of
2016 and 2017. (a) δ15N of rye and oat in the cool-season of 2016 and 2017, treatment × evaluation P = 0.005. ‡ = Grass+N differs from Grass+CL+RP (P < 0.05). * = Grass+N differ from the other two treatments (P < 0.05). (b) δ13C of rye and oat in the cool-season of 2016 and 2017, treatment × evaluation P = 0.04. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
-3
-2
-1
0
1
2
3
4
5
January January February February March March April April May
Rye
an
d o
at δ
15 N
(‰)
Evaluation (month)
Grass+N Grass+clover Grass+CL+RP
a
-40
-35
-30
-25
-20
-15
-10
-5
0
January January February February March March April April May
Rye
an
d o
at δ
13 C
(‰)
Evaluation (month)
Grass+N Grass+clover Grass+CL+RP
b
*
‡
* ‡
95
Figure 3-6. Isotopic composition (δ15N and δ13C) from feces of steers grazing in the cool-season of 2016 and 2017.
(a) δ15N from feces, treatment × evaluation P < 0.0001. * = Grass+N differ from the other two treatments (P < 0.05). (b) δ13C from feces, treatment × evaluation P = 0.004. * = Grass+N differ from the other two treatments (P < 0.05); ‡ = Grass+N differs from Grass+clover (P < 0.05). Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
1
2
3
4
5
6
January February March June July
Fece
s δ
15 N
(‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
a
* *
-40
-35
-30
-25
-20
-15
-10
-5
0
January February March April May
Fece
s δ
13 C
(‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
b
* *CV
* ‡
96
Figure 3-7. Isotopic composition (δ15N and δ13C) from plasma of steers grazing in the
cool-season of 2016 and 2017. (a) δ15N from plasma, treatment × evaluation P < 0.0001. * = Grass+N differ from the other two treatments (P < 0.05); ‡ = Grass+CL+RP differs from Grass+clover (P < 0.05). (b) δ13C from plasma, treatment × evaluation P = 0.012. * = Grass+N differ from the other two treatments (P < 0.05); ‡ = Grass+N differs from Grass+clover (P < 0.05). Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
1
2
3
4
5
6
7
January February March April May
Pla
sma
δ1
5 N (‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
a * *
-30
-25
-20
-15
-10
-5
0
January February March April May
Pla
sma
δ1
3 C (‰) Evaluation
Grass+N Grass+clover Grass+CL+RP
b
‡
* ‡
*
97
Figure 3-8. Isotopic composition (δ15N and δ13C) from red blood cells of steers grazing in the cool-season of 2016 and 2017.
(a) δ15N from red blood cells, treatment × evaluation P < 0.02. * = Grass+N differs from Grass+clover (P < 0.05); ‡ = Grass+N differ from the other two treatments (P < 0.05). (b) δ13C from red blood cells, evaluation P < 0.001, treatment × evaluation P = 0.12. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
1
2
3
4
5
6
January February March April May
Re
d b
loo
d δ
15 N
(‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
a
-30
-25
-20
-15
-10
-5
0
January February March April May
Re
d b
loo
d δ
13 C
(‰) Evaluation
Grass+N Grass+clover Grass+CL+RP
b
* ‡
98
Figure 3-9. Botanical composition of the grazing trial in 2016 and 2017, dry weight rank
method (DW). (a) Grass, percentage of rye-oat during the cool-season and bahiagrass during the warm-season, treatment × evaluation P < 0.0001. *Grass+N differ from Grass+CL+RP treatment (P < 0.05). ‡ = Grass+CL+RP differs from Grass+clover (P < 0.05). (b) Weeds percentage during warm and cool-season, treatment × evaluation P < 0.0001. *Grass+CL+RP differ from the other two treatments (P < 0.05). ‡ = Grass+N differs from Grass+clover (P < 0.05). (c) Clover percentage during the cool-season, evaluation P < 0. 0001. (d) Rhizoma peanut (RP) percentage during the warm-season, evaluation P < 0.0001. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
20
40
60
80
100
Jan Feb April June Aug Sept
DW
Gra
ss (
%)
Month
Grass+N Grass+clover Grass+CL+RP
a
0
5
10
15
20
25
Jan Feb April June Aug Sept
DW
We
ed
s (%
)
Month
Grass+N Grass+clover Grass+CL+RP
b* ‡
0
10
20
30
40
50
60
70
Jan Feb April June Aug Sept
DW
Clo
ver
(%)
Month
Grass+clover Grass+CL+RP
c
0
10
20
30
40
50
60
Jan Feb April June Aug Sept
DW
RP
(%
)
Month
Grass+CL+RP
d
‡ * **
* * * * *
‡ ‡ ‡
99
Figure 3-10. Variation in herbage mass during the warm-season of 2016 and 2017. Treatment × evaluation P < 0.01. *Grass+N differ from Grass+CL+RP treatment (P < 0.05). † Grass+clover differ from Grass+N (P <0.05). ‡ Grass+clover differs from Grass+CL+RP treatment (P < 0.05). Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000H
erb
age
mas
s (k
g h
a-1
)
Evaluation
Grass+N Grass+clover Grass+CL+RP
*†
† †
‡‡
‡
*
* * *
*
‡
100
Figure 3-11. In vitro digestible organic matter (IVDOM) concentration of bahiagrass
during the warm-season of 2016 and 2017. Total herbage accumulation from all the bahiagrass pastures plus the rhizoma peanut strips kg DM ha-1 P = 0.018. *Grass+CL+RP differ from Grass+N treatment (P < 0.05). ‡ = Grass+CL+RP differs from Grass+clover (P < 0.05). Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
100
200
300
400
500
600
700IV
DO
M (
g kg
-1 )
Evaluation
Grass+N Grass+clover Grass+CL+RP
** *
‡
101
Figure 3-12. Nutritive value of rhizoma peanut during the warm-season of 2016 and
2017. (a) In vitro digestible organic matter (IVDOM) concentration of rhizoma peanut g kg-1, evaluation P = 0.008. (b) Crude protein (CP) of rhizoma peanut g kg DM-1, evaluation P = 0.001. Error bars denote standard error. Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
100
200
300
400
500
600
700
800IV
DO
M (
g kg
-1 )
Evaluation
Grass+CL+RP
a
0
50
100
150
200
250
Cru
de
Pro
tein
(g
kg-1
DM
)
Evaluation
Grass+CL+RP
b
102
Figure 3-13. % N derived from atmosphere (%Ndfa) in the pastures with rhizoma peanut during the warm-season of 2016 and 2017.
%Ndfa, evaluation P = 0.022. Error bars denote standard error. Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
10
20
30
40
50
60
70
80
May June July August September October
Nd
fa (
%)
Evaluation
Grass+CL+RP
103
Figure 3-14. Isotopic composition of rhizoma peanut during the warm-season of 2016
and 2017. (a) δ15N from hand-plucked samples in the warm-season of 2016 and 2017. δ15N, evaluation P < 0.25. (b) δ13C from hand-plucked samples in the warm-season of 2016 and 2017. δ13C, evaluation P < 0.001. Error bars denote standard error. Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
Rh
izo
ma
pe
anu
t δ
15 N
(‰)
Evaluation
Grass+CL+RP
a
-40
-35
-30
-25
-20
-15
-10
-5
0
Rh
izo
ma
pe
anu
t δ
13 C
(‰)
Evaluation
Grass+CL+RP
b
104
Figure 3-15. Isotopic composition (δ15N and δ13C) from feces of steers grazing in the warm-season.
(a) δ15N from feces of steers grazing in the warm-season of 2016 and 2017, treatment × evaluation P < 0.001. *Grass+clover differ from Grass+N treatment (P < 0.05). ‡ = Grass+CL+RP differs from Grass+clover (P < 0.05). (b) δ13C from feces cells of steers grazing in the warm-season of 2016 and 2017, evaluation P < 0.001, treatment × evaluation P = 0.06. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
1
2
3
4
5
6
7
Fece
s δ
15 N
(‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
a
‡
-35
-30
-25
-20
-15
-10
-5
0
May June June July July August August Septem October
Fece
s δ
13 C
(‰
)
Evaluation
Grass+N Grass+clover Grass+CL+RP
b
*
*
105
Figure 3-16. Isotopic composition (δ15N and δ13C) from plasma of steers grazing in the
warm-season. (a) δ15N from plasma of steers grazing in the warm-season of 2016 and 2017, treatment × evaluation P < 0.001. *Grass+CL+RP differ from Grass+N treatment (P < 0.05). ‡ = Grass+CL+RP differs from Grass+clover (P < 0.05). † Grass+clover differs from Grass+N treatment (P < 0.05). (b) δ13C from plasma of steers grazing in the warm-season of 2016 and 2017, evaluation P < 0.001, treatment × evaluation P = 0.12. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
1
2
3
4
5
6
7
8
Pla
sma
δ1
5 N (‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
a* ‡ †
* ‡ * ‡ *
-25
-20
-15
-10
-5
0
May June June July July August August Septem October
Pla
sma
δ1
3 C (‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
b
* † * †
106
Figure 3-17. Isotopic composition (δ15N and δ13C) from red blood cells of steers grazing
in the warm-season. (a) δ15N from red blood cells of steers grazing in the warm-season of 2016 and 2017, evaluation P < 0.001, treatment × evaluation P = 0.06. (b) δ13C from red blood cells of steers grazing in the warm-season of 2016 and 2017, evaluation P < 0.001, treatment × evaluation P < 0.001. *Grass+CL+RP differ from Grass+N treatment (P < 0.05). ‡ = Grass+CL+RP differs from Grass+clover (P < 0.05). Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1 ; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
2
4
6
8
10
12
Re
d b
loo
d δ
15 N
(‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
a
-35
-30
-25
-20
-15
-10
-5
0
May June June July July August August Septem Octo
Re
d b
loo
d δ
13 C
(‰)
Evaluation
Grass+N Grass+clover Grass+CL+RP
b
* *
*
* * ‡
* ‡ * ‡ * ‡ * ‡
107
Figure 3-18. Blood urea nitrogen (BUN) mg dL-1 of steers grazing in the warm-season of
2016 and 2017. BUN mg dL-1, treatment P < 0.001, evaluation P < 0.001, treatment × evaluation P = 0.46. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1 ; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover with 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
5
10
15
20
25
30
May June June July July August August September October
Blo
od
ure
a N
(m
g d
L-1 )
Evaluation
Grass+N Grass+clover Grass+CL=RP
108
CHAPTER 4 NUTRIENT EXCRETION FROM CATTLE GRAZING IN N-FERTILIZED GRASS OR
GRASS-LEGUME PASTURES IN NORTH FLORIDA
Introduction
In grazing systems, nutrient cycling is a complex network of interactions between
plant production, type of livestock grazing, intensity of the grazing, soil fauna and flora
(Sollenberger and Burns, 2001). Nutrients such as carbon, nitrogen phosphorus and
sulfur, reside temporarily in various reservoirs or different pools in the ecosystem.
Nutrients cycle among pools including soil, live plant biomass and plant litter, animal
excreta, and the atmosphere (Dubeux et al., 2007; Vendramini et al., 2014). Grazing
animals obtain carbohydrates when they graze on pastures, and a portion of these are
digested and incorporated into animal cells. Carbohydrates and other compounds not
used by animals are returned to the soil in the form of urine and feces, providing soil
organisms with nutrients and energy. As soil organisms use and decompose organic
materials, they release nutrients that are used by plants for their growth and
reproduction (Bellows, 2001). Recycling of nutrients are an important ecosystem service
offered by grasslands and may be affected by pasture structure and function, as well as
management aspects such a stocking rate and N fertilization.
Management practices in grasslands that result in greater forage production,
typically lead to greater soil C accumulation under native grassland vegetation (Allard et
al., 2007; Skinner et al., 2016). Light to moderate grazing in grasslands compared with
heavy grazing has led to significant increases in soil C and improvements in soil
structure (Hiernaux et al., 1999; Reeder and Schuman, 2002). Grasslands can store
more than 100 and 10 Mg ha-1 of SOC and SON, respectively in the first meter of their
soil profile. Different grazing strategies can greatly affect the size of those pools (Piñeiro
109
et al., 2009), highlighting the importance of proper grazing management to enhance
nutrient cycling. In SOM, the C:N ratio may shift after grazing, and any changes in SON
dynamics may constrain C fluxes and SOC accumulation in the soil. The greatest C
stock sequestered in grasslands is located belowground in the soil organic matter,
roots, rhizomes, and soil organisms. Changes in soil carbon storage have the potential
to modify the global carbon cycle with benefits in terms of climate change mitigation
(Conant et al., 2001; Fisher et al., 2007; Byrnes et al., 2018).
Plant N and C are added to the organic matter pools through the decay of root
exudates, dead leaves, and fragments of roots. The total C and N pools associated with
SOM was estimated to be 60 and 89%, respectively, in grazed bahiagrass (Dubeux et
al., 2004). As a response to grazing, root mass and C:N ratio increase, with a potential
limitation of N in the formation of SOM (Dubeux et al., 2006). Nitrogen is mineralized to
ammonium if the C:N ratio decreases, and ammonium N could be nitrified into nitrate
and lost by denitrification or leaching (Elgersma and Hassink, 1997). Properly managing
bahiagrass pastures, which includes adjusting the stocking rate according to the
herbage mass and appropriate fertilizer application, increases the efficiency of nutrient
cycling with little potential for negative impact on the environment (Sigua et al., 2010).
Integrating forage legumes into grazing systems provides alternatives to reduce
nutrient limitation in grasslands and to enhance nutrient cycling. Biological nitrogen
fixation by legumes is a very important influx of N in the system, that offers great
advantages, such as a reduction in the need of N fertilizer inputs, among many others.
The addition of legumes also increases C storage over time in grazing systems;
110
however, the grazing regime and intensity influences the biomass and diversity of
microbes, which consequently controls soil carbon turnover (Chen et al., 2015).
The two major pathways of nutrient return in grazing systems are litter and
excreta (Dubeux et al., 2014). Litter influences the net balance between mineralization
and immobilization, which in turn influences the availability of N, P, and S (Myers et al.,
1994). Litter quality could be improved with N fertilization or introducing legumes in
grass monoculture pastures (Dubeux et al., 2006; Kohmann et al., 2018). In grazing
systems, one of the major N exchange pathways occur when ruminants graze legumes.
The consumed N is transformed, assimilated, and returned to the soil via urine and
feces (Dubeux et al., 2007). The amount of nutrients that return to the soil via animal
excreta ranges from 70 to 90% of the total intake (Williams and Haynes, 1990).
However, the distribution of nutrients is not uniform through the pasture, due to animal
behavior and the partitioning of nutrients between feces and urine. Soil nutrients
accumulate where grazing animals congregate, and they have the tendency to spend
more time around shade, water, and minerals (Dennis et al., 2012; Dubeux et al., 2014).
Management strategies such as stocking method such as rotational stocking with short
grazing periods are alternatives for a better distribution of the nutrients through the
pasture (Sollenberger et al., 2002; Dubeux et al., 2009; Vendramini et al., 2014). We
hypothesize that the inclusion of legumes in forage-livestock systems will enhance
nutrient cycling by reducing losses associated with N fertilization and decreasing fecal N
excretion due to greater digestibility. The objective of this study was to determine the
nutrient excretion via urine and feces in cattle grazing three systems; grass mixtures
with N fertilizer (Grass+N), mixture of grasses with cool-season legumes and low N
111
fertilization (Grass+clover), and mixtures of grasses with both warm- and cool-season
legumes with low N fertilization (Grass+CL+RP).
Material and Methods
Experimental Site and Treatments
The grazing trial was conducted from January to October 2016 and 2017, at the
University of Florida, North Florida Research and Education Center (NFREC).
Treatments consisted of three grazing systems as follows: 1) N-fertilized (112 kg N ha-1
yr-1) ’Argentine’ bahiagrass pastures during the warm-season, overseeded with a
mixture (45 kg ha-1 of each) of FL 401 cereal rye and RAM oat during the cool-season
with a second application of 112 kg N ha-1 yr-1. Both warm- and cool-season
fertilizations were split in two applications (56 kg N ha-1 each application in the warm-
season; 34 and 78 kg N ha-1 yr-1 for the cool-season). Total annual fertilization for this
treatment was 224 kg N ha-1 yr-1 (Grass+N); 2) unfertilized Argentine bahiagrass
pastures during the warm-season, overseeded with a similar rye-oat mixture, plus a
mixture of clovers [14 kg ha-1 of Dixie crimson, 5.5 kg ha-1 of ‘Southern Belle’ red clover
and ball clover 2.8 kg ha-1], fertilized with 34 kg N ha-1 during the cool-season
(Grass+clover); 3) rhizoma peanut and Argentine bahiagrass pastures during the warm-
season, overseeded with a similar rye-oat mixture, fertilized with 34 kg N ha-1 plus a
mixture of clovers (14 kg ha-1 of ‘Dixie’ crimson, 5.5 kg ha-1 of ‘Southern Belle’ red, and
2.8 kg ha-1) during the cool-season (Grass+CL+RP).
Treatments were distributed in a randomized complete block design with three
replicates, for a total of nine experimental units. Two tester Angus crossbred steers
were continuously stocked on each pasture throughout the season. Stocking rate was
adjusted every 14 d with cattle of similar age, weight, and breed, in order to maintain a
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similar herbage allowance among treatments (Sollenberger et al., 2005). Water, shade,
and a mineral supplement mixture (Ca = min. 150 and max. 190 g kg-1, P = min. 30 g
kg-1, NaCl = min. 150 and max. 180 g kg-1, Mg = min. 100 g kg-1, Zn = min. 2800 mg kg-
1, Cu = min. 1200 mg kg-1, I = min 68 mg kg-1, Se = 30 mg kg-1, Vitamin A = 308370
units per kg, Vitamin D3 = 99119 units per kg Special Mag, W.B. Fleming Company)
were available for cattle in each pasture.
Urine Samples
Steers were weighed every 21 d after a 16-h fasting period. When steers arrived
at the working facilities for the fasting period, urine samples were collected in plastic
cups after manual stimulation to induce urination from tester steers. Urine samples were
transferred into 50 mL conical tubes containing sulfuric acid solution 200mL L-1 and
stored at -20°C (Chizzotti et al., 2008). Creatinine concentration in urine was analyzed
by a colorimetric method based on the reaction with alkaline picrate, reading
absorbance at 500 nm (Item No. 500701, Cayman Chemical, Ann Arbor, MI). The
creatinine concentration from the sample was determined using a creatinine standard
curve. In addition, N concentration was measured in 50 μL of urine adding an absorbant
for running non-volatile liquid samples (Chromosorb w 30-60 mesh acid washed 10 gm,
Elemental Microanalysis, Pennsauken, NJ) for a posterior analysis in a CHNS analyzer
using the Dumas dry combustion method (Vario Micro Cube and Isoprime100,
Elementar Inc., Germany).
Fecal Output
Total fecal output was determined by the marker dilution technique using Cr2O3
and TiO2 as indigestible external markers. On day 0 (beginning of total fecal output
collection period), the steers were dosed with a gelatin capsule containing 5 g of Cr2O3
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and 5 g of TiO2 using a balling gun, twice daily at 0700 and 1600 h. Dosing of the
markers continued until the morning of day 8, and from day 5 to 8 fecal samples were
collected by rectal grab at the time of bolus dosing. Fecal samples were frozen
immediately at -20°C and later dried in a forced-air oven at 55°C for 72 hours. Fecal
samples were ground to pass a 2-mm screen using a Wiley Mill (Model 4, Thomas-
Wiley laboratory Mill, Thomas Scientific) and composited within steer to measure Cr2O3
and TiO2 concentrations. The samples were analyzed in duplicate and repeated if the
coefficient of variation between sample and duplicate was greater than 10%. For
concentrations of Cr, approximately 0.5 g of ground feces were dried in a forced-air
oven at 100˚C for 24 h to determine sample dry matter (DM), and ash at 550°C for 3 h
to determine organic matter (OM). The method of Williams et al. (1962) was used to
digest Cr2O3 in the samples. Concentration of chromium was determined by atomic
absorption spectrophotometry, reading absorbance at 358 nm with an air-plus-acetylene
flame (AAnalyst 200; Perkin Elmer, Walther, MA). For concentrations of TiO2,
approximately 0.5 g of ground feces were dried in a forced-air oven at 105°C for 24 h to
determine sample DM, and ash at 550°C for 3 h to determine OM. Titanium dioxide
samples were analyzed using a modification of the method developed by Titgemeyer et
al. (2001). Briefly, TiO2 in the samples was digested by bringing 10 mL of 7.4 M sulfuric
acid to a gentle boil for approximately 30 min (or until translucent) using a hot plate
under a fume hood. After the samples had been cooled, the contents of each beaker
were rinsed into tared 120-mL sample cups. Ten milliliters of 30% H2O2 was added and
the weight of each cup was brought to 100 g using distilled water. Samples were then
mixed and filtered (Fischerbrand P8 Grade, Fisher Scientific, Pittsburgh, PA) and
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analyzed for concentration of TiO2 measuring absorbance at 405 nm of wavelength in a
Beckman DU-530 Spectrophotometer (Beckman Coulter, Palo Alto, CA).
Feed intake was estimated as proposed by Pinares-Patino et al. (2016), by using
the IVOMD from composited hand-plucked samples from each pasture. In the warm-
season, proportions of rhizoma peanut and bahiagrass were estimated based on
isotopic analyses of fecal samples, using δ13C from bahiagrass and δ13C from rhizoma
peanut to estimate the %C3 and C4 in feces. It was assumed that the proportions in the
feces were similar to the proportions in the diet. Samples with similar proportions of
rhizoma peanut and bahiagrass were then incubated to determine IVDOM. Total fecal
excretion was calculated by the marker dilution technique using Cr2O3 and TiO2 as
indigestible external markers. Excreta output and chemical composition were
determined to assess the pathways of nutrient return within each production system.
Fecal samples were analyzed for chemical composition (N, P, K, Ca, Mg) by inductively
coupled plasma mass spectrometry by the IFAS Analytical Research Laboratory
(University of Florida, Gainesville) to determine nutrient deposition in each system.
Calculations
Total urinary excretion was determined based on the daily excretion of creatinine
and cattle live weight, following the approach of Chizzotti et al. (2008). The estimation of
the daily creatinine excretion was obtained from the multiplication of the conversion
factor of 24.4 by the body weight of each steer, as detailed by Chizzotti et al. (2008).
Then, the concentration of creatinine in the spot urine sample, as determined by the
colorimetric kit, was multiplied by the daily excretion of creatinine by each steer to
obtain final urinary volume excreted daily. Total N excreted was calculated based in N
concentration and urinary volume. The estimation of total urinary volume and total N
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excreted were calculated by multiplying individual animal output by the stocking rate
corresponding to each period and year. Fecal output and total nutrient excretion were
determined by the marker dilution technique, using the average of the excretions
calculated with Cr2O3 and TiO2 as indigestible markers, based on previous studies
showing no differences between TiO2 and Cr2O3 when used as external markers to
assess fecal output (Titgemeyer, 1997; Henry et al., 2015; Guzman et al., 2017). In
order to calculate total fecal output based on the marker dilution technique, the total
amount of marker dosed during the collection period was divided by the concentration of
the marker in the composite fecal sample of each steer to determine total fecal output in
kg d-1. The total amount of fecal DM excreted per animal was multiplied by the
concentration of each nutrient in the feces to determine total amounts in kg per steer
returning to the pasture in each of the systems. The stocking rate corresponding to the
evaluation date of urine and fecal collection was used in the calculations of total N
excreted via urine, and N and C excreted in feces to estimate the nutrient return per
area.
The proportion of rhizoma peanut in the feces was estimated using a two-pool
mixing model (Fry, 2008) as follows:
ƒtotal 1 = (δ13Csample- δ13C source 2) / (δ13C source 1 – δ13C source 2) (4-1)
ƒtotal 2 = 1 - ƒtotal 1 (4-2)
Where ƒtotal 1 represents the fraction of source 1 and source 2, δ13Csample is the δ13C in
feces. Source 2 is the δ13C of bahiagrass, and source 1 is the δ13C of rhizoma peanut.
Statistical Analysis
Data for fecal and urinary output were analyzed using pasture as the
experimental unit. The Mixed Procedure of SAS (SAS Inst., Cary, NC) was used and
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the model included the fixed effects of treatment, sampling date, and their interaction.
Block and year were considered random effects. For chemical composition of urine and
N excretion, sampling date was considered the repeated measure, using the repeated
procedure of SAS. The best covariance structure that yielded the lowest Akaike
Information Criterion (AIC) was selected for each variable. The excretions of the two
tester steers in each pasture were averaged for the statistical analyses. Means were
compared using the LSMEANS procedure adjusted using the Tukey’s test (P ≤ 0.05).
The model significance was declared at P < 0.05.
Results
Nutrient Concentration in The Excreta - Cool-season
Fecal mineral concentrations of P, K, Ca and Mg (Table 4-1) did not differ among
treatments (P > 0.05). Concentrations of N and C in the feces ranged from 27 to 32 g kg
-1, and from 384 to 398 g kg -1, respectively. The C:N in the feces of steers grazing the
different systems was similar across treatments, and ranged from 12.3 to 14.1.
Output per Animal per Day - Cool-season
Concentration of N in the feces, and creatinine concentration in urine, did not
differ among treatments (P > 0.05). Urinary creatinine concentration ranged from 55.1 to
58.2 mg dL-1 (Table 4-2). In addition, no differences (P > 0.05) were reported in N
excretion in feces, nor in urinary volume excreted daily, either as per animal or per
hectare. Urinary volume excreted per steer per day ranged from 16.8 to 19.1 L, while
excretions per area ranged from 56.3 to 69.8 L ha-1 (Table 4-2). The only difference
observed during the cool-season was in total (feces and urinary combined) N excretion
expressed as kg ha-1 d-1, where a treatment × evaluation interaction (P < 0.01) was
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detected (Table 4-2 and Fig. 4-1). This interaction occurred because total N excretion in
the cool-season did not differ among treatments in any of the evaluations, except for
February, where steers grazing Grass+N had greater total N excretion (P < 0.05) than
the other two treatments (Fig. 4-1).
Output per Hectare per Day - Cool-season
Fecal DM output per steer did not differ among treatments (P = 0.07) (Table 4-3);
however, fecal OM output in kg d-1 differed among treatments (P = 0.05). Fecal OM
output in kg d-1 was greater for Grass+N when compared with Grass+clover (P < 0.05)
and did not differ between Grass+N and Grass+CL+RP (P ≥ 0.05).
Output per Season - Cool-season
The number of days in the cool-season varied between years, with 126 and 105
days for 2016 and 2017, respectively. There were no differences in mineral excretions
(P, K, Ca, Mg, N) among treatments in the cool-season, and neither in total N excreted
and % of N excreted via urine (P ≥ 0.07; Table 4-3).
Nutrient Concentration in The Excreta - Warm-season
Fecal P and Mg concentrations differed among treatments (P < 0.01; Table 4-1),
where the Grass+CL+RP system had the greatest concentration of Mg (P < 0.05), and
greater P concentration when compared with Grass+N (P < 0.05). For concentration of
C in feces, differences were observed (P < 0.01), where the two grass systems
(Grass+N and Grass+clover) had the least (P < 0.05) concentration of fecal C when
compared with the grass-legume system (Grass+CL+RP).
Output per Hectare per Day - Warm-season
Differences were observed in treatment and evaluation (P < 0.05) in urinary N
concentration, where the Grass+CL+RP system showed a greater concentration (4.41 g
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kg-1) when compared with Grass+N (3.17 g kg-1; P < 0.05; Table 4-2). Fecal N
concentration for steers grazing the Grass+clover system (3.24 g kg-1) did not differ (P ≥
0.05) from the other two systems. There was no treatment × evaluation interaction (P =
0.32) for urinary N concentration in the warm-season. It is noteworthy that the urinary
volume excreted in the summer ranged from 122 to 182 L ha-1 during the warm-season,
and more than doubled that in the cool-season, which ranged from 56 to 70 L hd-1 ha-1
(Table 4-2).
During the warm-season (Table 4-4), differences were observed in fecal DM and
OM output (P = 0.03 and P = 0.02, respectively). Steers grazing on the Grass+CL+RP
system had decreased fecal output (2.8 kg hd-1 d-1 and 2.2 kg hd-1 d-1 for DM and OM,
respectively) when compared with those grazing on Grass+N (3.8 kg d-1 and 3.2 kg d-1
for DM and OM, respectively; P < 0.05), but did not differ from Grass+clover (P ≥ 0.05).
Output per Season - Warm-season
The number of days during the warm-season evaluated were 168 for both 2016
and 2017. Fecal P excretion was greater (P < 0.05) in the Grass+N system (5.6 kg ha-1
season-1) when compared with Grass+CL+RP (3.0 kg ha season-1) (Table 4-4);
however, it did not differ from the Grass+clover system (P ≥ 0.05). Fecal K excretion
was less in Grass+CL+RP (3.8 kg ha-1 season-1) when compared with Grass+N and
Grass+clover systems (6.8 and 4.4 kg ha-1 season-1, respectively; P < 0.05). Fecal Ca
excretion differed among treatments (P = 0.04), such that Grass+CL+RP had decreased
excretion (6.2 kg ha-1 season-1) when compared with Grass+N (P < 0.05), not differing
from Grass+clover (P ≥ 0.05). Differences were observed in Mg fecal excretion among
treatments (P = 0.01), where Grass+N had the greatest excretion (7.3 kg ha-1 season-1)
when compared with the other two systems (P < 0.05). Fecal N excretion (Table 4-4)
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was different among all three treatments (P < 0.05) and was greatest for Grass+N,
intermediate for Grass+clover, and least for Grass+CL+RP. Total N excretion (both
urinary and fecal) was not different among treatments (P = 0.06). The percentage of N
excreted via urine (Table 4-4) differed among treatments (P = 0.04), where
Grass+CL+RP was greatest (71.4%), differing from Grass+N (58.2%; P < 0.05) but not
from Grass+clover (59.4%, P ≥ 0.05).
When comparing nutrient excretion in the warm and cool-seasons and across
treatments (Table 4-5), an effect of treatment was observed for fecal DM, OM, P and
Mg (P < 0.05), and those effects are shown in tables 4-3 and 4-4. A season × treatment
interaction (P ≤ 0.02) was observed for fecal excretion of K, Ca, and N. Additionally, the
percentage of N excreted via urine was also greater (P < 0.01) in the warm-season
when compared with the cool-season (66.1 vs. 44.8%) but did not differ between
treatments (P = 0.10).
Total Annual Nutrient Excretion – Cool and Warm-seasons
Total excretion of nutrients during the entire year (cool and warm-season 2016
and 2017), showed treatment differences (P ≤ 0.05, Table 4-6) in fecal P, K, Mg, N and
in total N excretion (fecal and urine combined). Excretion of fecal P was less (P ≤ 0.01)
in Grass+CL+RP systems compared with Grass+N and Grass+clover systems. In
addition, excretion of fecal K was greater (P ≤ 0.01) in the Grass+N system compared
with Grass+CL+RP system. Fecal Mg excretion was greater (P ≤ 0.01) in Grass+N
system compared with Grass+Clover and Grass+CL+RP systems. Fecal N
concentration was different among the three system (P ≤ 0.01). Total N excretion
tended to be greater in the Grass+N system when compared with Grass+clover (P =
0.06) and with Grass+CL+RP (P = 0.09) systems.
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Discussion
Nutrient Concentration in the Excreta - Cool-season
Fecal mineral concentrations were similar among treatments during the cool-
season, when grasses and grass-legume systems were all based on C3 plants.
Because all systems had rye and oat as the predominant forages, may explained the
lack of variation observed in the nutritional profile of the excreta. The predominance of
the grasses in the botanical composition of the sward during the cool-season is likely
the main reason for the similarities in the profile of nutrients in the excreta. While two of
the three treatments had clovers present in the cool-season, perhaps the timing of the
fecal sampling collection is not reflecting the contribution of the legume. The fecal
samplings were done in March of each year, when clovers where not as abundant as
they were in April or May. The botanical composition (Dry weight rank method, DW)
data showed the presence of clover at the end of February was 20 and 15% in 2016
and 2017, respectively. In contrast, grass participation was 68 and 72% at the same
time.
Variation in the chemical composition of pastures is due to changes in maturity
and season, therefore the nutrients ingested by the animals grazing are not at the same
concentration through the season and nutrient release via feces and urine also is
different (Rotz et al., 2005). The concentration of P in forages ranges from 1.5 to 3 g kg-
1 of DM being greater in early growth than in mature growth. Forage such as
bermudagrass and clovers can bioaccumulate P under adequate N fertilization (Silveira
et al., 2007). Dillard et al. (2015) reported P output in feces from cattle grazing grasses
with clovers from 7.1 to 29.5 g d-1, greater range that 4.8 and 5.5 g kg-1 reported in this
study. Metson and Saunders (1978) reported concentrations in clover ranging from 10
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to 14 g kg-1 Ca, 22 to 35 g kg-1 K, and 2 to 16 g kg-1 of Mg during cool and warm-
seasons. In contrast, the concentration of K, Ca and Mg found in feces were lesser as
an indication of the predominance of grasses in the sward. Furthermore, cereals have
lower concentrations of Ca and K (Kuusela, 2004). Kuusela (2006), reported lower
concentrations of Ca and Mg in the herbage when the proportion of clover decrease in
the sward.
The C concentration in the feces of animals grazing in this study was lower than
the C concentration of animals consuming alfalfa silage or hay where the C
concentration ranged from 439 to 474 g kg-1. These differences may be related to the
digestibility of the cool season forages relative to that in hay or alfalfa silage. However,
the N concentration and C:N reported similar concentrations with animals grazing and
with a diet of alfalfa silage or hay (Powell et al., 2006).
Output per Animal per Day - Cool-season
Urinary chemical composition during the cool-season did not vary greatly among
treatments. The concentration of creatinine in urine is typically used as an indicator of
urinary volume in cattle. When creatinine was used to quantify urinary volume in this
study, the values obtained during the cool-season (average of 17.7 L hd-1 d-1), are
similar to those reported by Chizzotti et al. (2008) in lactating cows (21.6 L hd-1 d-1) and
Bruce et al. (2008) in brahman-cross steers (ranged from 7.7 to 22.4 L hd-1 d-1). When
urinary volume excreted was expressed in L ha-1 d-1, the total amount of urine returning
to the pasture was similar across treatments. Clark et al. (2010) reported cattle grazing
perennial ryegrass and clover urinate an average of 14 times per day and the estimated
N in urine was 85.6 mmol L-1, which should be equivalent to approximately 1.2 g kg-1 of
urine. This is lower than the values reported in our study, however animal category and
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forage intake differences may account for the discrepancy in both studies. When
analyzing the treatment × evaluation date interaction for total (urinary and fecal) N
excretion per unit of area in the cool-season, during the second evaluation (February),
the Grass+N system showed greater total N excretion when compared with the other
treatments. Dong et al. (2014) reported N excretion in urine from 0.013 to 0.20 kg d-1 for
cattle consuming diets with different levels of N. In the present study the N excreted in
urine ranged from 0.05 to 0.06 kg d-1. In the study by Dong et al. (2014), the amount of
N excreted was found to be related to N intake, which may be the reason for the greater
total N excreted in February by steers grazing Grass+N. The increased excretion of
fecal OM in Grass+N when compared with Grass+clover (2 vs. 1.4 kg d-1, respectively)
may also support this observation, and may be explained by the greater digestibility of
the sward in Grass+clover, because of the presence of legumes (IVDOM of legumes in
Grass+clover was 766 g kg-1, while that of the grasses in the Grass+N system was 702
g kg-1).
Output per Season - Cool-season
When expressing nutrients excreted per unit of area during the entire cool-
season, the excretion of nutrients was similar across treatments. This can be explained
due to the relative homogeneity of the swards in the various pastures, where six
pastures included the same mixture of grasses and clovers and the other three pastures
had the same grasses with N fertilizer. In addition, the differences in stocking rates
across treatments that were necessary in order to maintain similar herbage allowance
among treatments, affected the amount of nutrient excreted in each system. In grazed
pastures, the dominant source of mineral nutrients recycling is animal dung and urine.
For example, Saunders (1984) showed the effects of dung and urine on growth rate,
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botanical composition and mineral composition of plant and soil in early spring. Under
the effect of dung and urine, forages were taller with greater concentrations of K, P and
Mo in the herbage, and the levels in the soil of available P, K, Ca, and Mg were greater
than the treatments with lower incidence of urine and dung. Dillard et al. (2015)
evaluated different N regimes and P intake and excretion by grazing cattle in a mixture
of triticale and clover into tall fescue and bermudagrass pastures. The authors reported
that P concentration in forage, and P intake were not affected by N fertilization
treatment or season. However, P in feces increased as N increased in the cool-season
but not in the warm-season.
Nutrient Concentration in The Excreta - Warm-season
Findings on the profile of nutrient excretion in the warm-season contrast with
those in the cool-season. The increased concentrations of P and Mg in the excreta of
steers grazing Grass+CL+RP when compared with the Grass+N treatment can be
attributed to the presence of rhizoma peanut, which represents a significant proportion
of the forage consumed. According to Terrill et al. (1996), concentration of P in rhizoma
peanut ranges from 2.2 to 2.6 g kg-1. Stewart et al. (2005) reported P concentration in
bahiagrass from 1.5 to 2.5 g kg-1 in rotationally stocked pastures, and greater
concentrations of P and Mg have been reported in legumes when compared with C4
grasses such as bahiagrass (Kuusela, 2006; Yarborough et al., 2017). Concentrations
of P in feces of steers consuming bahiagrass and mixtures of bahiagrass with rhizoma
peanut were 0.8 and 1.0 g kg-1, respectively (Kohmann, 2017). These P concentrations
are less than those observed in the current study (4.1 and 5.5 g kg-1 steers consuming
bahiagrass and mixture of bahiagrass and rhizoma peanut, respectively) , as were fecal
K concentrations relative to those from the Grass-CL-RP system in this study (K = 1.6 g
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kg-1 in bahiagrass and 0.8 g kg-1 in mixture). In terms of P concentration in the feces,
Grass+CL+RP did not differ from Grass+clover, and this may be related to the presence
of residual legumes from the cool-season in Grass+clover pastures during the first
evaluations of the warm-season. This is particularly evident in the contrast between
Grass+CL+RP and Grass+N, where the lack of presence of legumes in Grass+N
significantly reduced the concentration of P in the feces of the grazing steers. The
concentration of C in the feces of steers grazing Grass+CL+RP was reduced when
compared with other treatments, and this can be related to the combined effects of the
composition of the forages consumed and their digestibility. Rhizoma peanut has
potential to increase soil C and N pools, due to its greater N concentration and lower
C:N ratio (Sainju et al., 2006). However, previous studies reported that increasing
management intensity in bahiagrass pastures increase C and N accumulation, reducing
potential N losses (Dubeux, 2005). In legumes, N concentration ranged from 30 to 50 g
kg-1, and N concentration in plants is proportionally lower relative to C than is the case
for microbial biomass. It is because of this difference that plants with greater N
concentration are degraded faster at the initial stage of litter decomposition, and they
decompose slowly in latter stages with more presence of biomass. The early-stage leaf
and root decomposition contribute to a large amount of C primarily from microbial
compounds, which are the largest contributors to stable SOM (Knicker, 2011). The
concentration of C does not change widely in plant litter during the decomposition
period; however, the allocation of the different compounds may change and may control
litter decay rates. The combination of high N with high C quality will decompose litter
faster (Cotrufo et al., 2013).
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Output per Hectare per Day - Warm-season
The volume of urinary excretion doubled in the warm-season (average across
treatments = 37 L hd-1 d-1), likely because of an increased water intake and increased
body weight relative to the cool-season. Steers increase water intake and urination
frequency during hot versus cool days (Betteridge et al., 1986). Betteridge et al. (2009)
reported urine volumes between 7.6 and 51.2 L in grazing steers, and the data from this
study are inside this range. When urinary volume was expressed in L ha-1 d-1, the total
amount of urine returning to the pasture was similar across treatments for both cool and
warm-seasons, reflecting the impact of stocking rate on total urine excretion. The
greatest total N excretion that occurred in the Grass+N system may be detrimental from
an environmental perspective if the excess N not captured by the forage root system
and leaches into the water table (Haynes and Williams, 1993; Zaman et al., 2012).
Furthermore, not only is there a greater amount of N returning to the soil via urine and
feces in the Grass+N treatment, but Grass+N pastures are also fertilized with 224 kg N
ha-1 per year. In combination, the risk of N leaching to underground water is likely much
greater, when compared with systems that incorporate legumes during the cool-season
such as Grass+clover or both season such as Grass+CL+RP. However, N leaching is
more sensitive to variations in urinary N concentration than in volume, and N leaching
and losses due to ammonia volatilization can increase if urine patches are overlapped
through the pasture (Li et al., 2012).
Output per Season - Warm-season
The magnitude of fecal nutrients excreted during the warm-season, was least for
the Grass+CL+RP system. This may be related to the greater digestibility of the forage
consumed (IVDOM of 522, 465, and 439 g kg-1 for Grass+CL+RP, Grass+clover, and
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Grass+N, respectively, in July, during the fecal output assessment), mainly because of
the contribution of the rhizoma peanut (Santos et al., 2018; Jaramillo et al., 2018). In
particular, the amount of fecal N excreted in the Grass+CL+RP system was less than
half of that in Grass+N (10.7 vs. 24.0 kg ha-1 season-1). Despite the decrease in N
excreted via feces in Grass+CL+RP, the total amount of N excreted was similar across
treatments. When calculating the % of N excreted via urine, cattle grazing
Grass+CL+RP had greater proportion of the total N excreted to the pasture via urine
(71.4% vs. 58.2% for Grass+CL+RP and Grass+N, respectively). The amounts and
forms of the nutrients returning to the pasture can influence the vegetation responses
because cattle excreta are the major component of the nutrient recycling processes in
livestock-forage systems (Rotz et al., 2005). Furthermore, cattle feces contain soluble C
that stimulate soil respiration and mineralization processes (Hatch et al., 2000), which
then can lead to increased forage production. Grazing animals influence the nutrient
dynamics in grasslands through the deposition of urine and feces in the pasture. In a
study conducted with Pensacola bahiagrass subjected to various frequencies of dung
and urine depositions applied separately, CP and IVDOM concentrations were greater
in the treatments that received urine when compared with dung (White-Leech et al.,
2013). The return of Ca, Mg and micronutrients is mainly in feces, and the balance and
distribution of the nutrients on the pasture is influenced by stocking rate and method.
Differences in the form in which N returns to the pasture may have important
environmental implications. Most of the N found in feces is in the organic form, which
requires long mineralization periods to provide a pool of NH4+ for nitrification and NO3
-
for denitrification (Selbie et al., 2015). White-Leech et al. (2013) reported lesser dry
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matter harvested in Pensacola bahiagrass after dung when compared with urine
applications, due to the high proportion of organic N in dung resulting in slow nutrient
release for plant uptake. Conversely, N excreted in the urine is mostly comprised of
urea, which needs to be hydrolyzed to NH4+ in order to be assimilated by the soil
microorganisms (Selbie et al., 2015; Chadwick et al., 2018). The greater volatilization of
N in the urine when compared with that in feces, can reduce the potential risk of nitrate
leaching and thus underground water contamination; however, it may lead to greater
emissions of N2O (Russelle, 1996; Laubach et al., 2012). The amount of N returning via
dung and urine can be significant and often concentrated in certain areas. For example,
Selbie et al. (2015) reported N loading rates being equivalent to 345 kg N ha-1 in beef
cattle urine patches. Overall, particularly for the case of N, the form by which N returns
to the pasture can lead to a decreased environmental risk when rhizoma peanut is
included as a forage component during the warm-season.
When comparing the cool vs. warm-season in terms of nutrient excretion by
cattle, greater excretions of OM, P, Mg and N were observed in the warm-season.
Particularly in the case of total N excreted (urine and feces), the amount returning to the
pasture in the warm-season was more than double that in the cool-season, which can
be attributed to the combined effect of greater individual animal liveweight and greater
stocking rate during the warm-season.
Total Annual Nutrient Excretion – Cool- and Warm-season
Nutrient concentrations of feces in cattle may vary within days and seasons,
reflecting changes in the diets (e.g., plant species). Fecal N returning to the pastures
was least in Grass+CL+RP, which impacted the overall tendency to a decreased total N
excretion when compared with the Grass+N system. This is likely a function of forage
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digestibility and stocking rate and may have implications on the total amount of N
returning to the system, which could impact forage production. Depending upon the
protein concentration of the sward, livestock type and age, deposition of N in urine can
represent an application rate of 200 to 2000 kg N ha-1 (Selbie et al., 2015).Considering
the cost of nitrogen and phosphorus fertilizer the return of nutrients to the pasture can
be of economic importance (8.6 kg ha-1 yr-1 of P in the Grass+N system).
Conclusions
The introduction of legumes such as clovers and rhizoma peanut in forage
livestock system had positive effects on nutrient cycling, particularly during the warm-
season. The inclusion of rhizoma peanut increased the proportion of N returning to the
pasture via urine vs. feces, when compared to N-fertilized bahiagrass monocultures,
likely reducing N losses by leaching but potentially increasing ammonia volatilization
and denitrification. Increasing the return of N via urine vs. dung when cattle are grazing
pastures with rhizoma peanut, can lead to a decreased risk of nitrate leaching into
underground water. While increased urinary excretions may lead to greater N2O
emissions, these are beneficial for forage growth by increasing the N readily available
for plant absorption. This, coupled with the reduction in N fertilizer inputs, makes the
inclusion of legumes in grass pastures a viable alternative in order to enhance the
nutrient cycling ecosystems services from beef-forage systems.
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Table 4-1. Chemical composition from fecal samples collected from beef steers grazing three forage systems during the cool- and warm-season of 2016 and 2017.
Treatment1 Item Grass+N Grass+
clover Grass+ CL+RP
SE2 P-value
Cool-season Phosphorus in feces, g kg-1 5.1 5.5 4.8 0.51 0.67 Potassium in feces, g kg-1 11.6 10.6 8.3 1.75 0.43 Calcium in feces, g kg-1 7.6 10.0 8.6 0.73 0.09 Magnesium in feces, g kg-1 9.7 8.7 8.2 0.72 0.34 N concentration in feces, g kg-1 28.1 31.8 27.3 1.9 0.08 C concentration in feces, g kg-1 398 394 384 12.1 0.13 C:N in feces 12.8 13.5 13.9 6.80 0.31
Warm-season Phosphorus in feces, g kg-1 4.1b 5.1ab 5.5a 0.29 <0.01 Potassium in feces, g kg-1 13.0 15.2 10.6 1.56 0.12 Calcium in feces, g kg-1 8.8 7.9 11.4 1.63 0.28 Magnesium in feces, g kg-1 5.3b 5.9b 8.8a 0.56 <0.01 N concentration in feces, g kg-1 17.2 18.5 19.0 0.51 0.08 C concentration in feces, g kg-1 345a 342a 332b 43.2 <0.01 C: N ratio in feces 18.2 19.0 16.7 21.8 0.08
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat Grass+clover mixture + 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2 SE = Standard error. a,b Means differ, P < 0.05.
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Table 4-2. Chemical composition of urine, N excretion and total N excretion (feces and urine) from beef steers grazing three forage systems during cool- and warm-season of 2016 and 2017.
Item
Treatment1 P-value2
Grass+N
Grass+
clover
Grass+CL +RP
SE3 T E T × E
Cool-season N concentration, g kg-1 3.09 3.06 3.22 0.416 0.94 0.13 0.17 Creatinine, mg dL-1 55.7 55.1 58.2 14.09 0.90 0.28 0.66 N excretion, kg hd-1 d-1 0.06 0.05 0.06 0.015 0.51 0.81 0.22
Total N excretion, kg ha-1 d-1 0.24 0.19 0.20 0.090 0.43 0.98 <0.01 Urine volume, L hd-1 d-1 19.11 16.8 16.8 2.60 0.46 0.51 0.94 Urine volume, L ha-1 d-1 69.8 59.5 56.3 18.76 0.32 0.38 0.06
Warm-season N concentration, g kg-1 3.17b 3.24ab 4.41a 0.577 0.03 0.02 0.32 Creatinine, mg dL-1 35.5 33.3 34.4 3.72 0.63 0.06 0.06 N excretion kg hd-1 d-1 0.10 0.11 0.14 0.020 0.27 0.39 0.05 Total N excretion, kg ha-1 d-1 0.48 0.35 0.49 0.090 0.45 0.23 0.43 Urine volume, L hd-1 d-1 48.5 30.7 31.9 8.48 0.09 0.54 0.47 Urine volume, L ha-1 d-1 182 122 122 42.9 0.30 0.35 0.56
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat Grass+clover mixture + 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2 T = treatment, E = evaluation, T × E = treatment × evaluation interaction 3 SE = Standard error for the effect of treatment. a,b Means differ, P < 0.05.
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Table 4-3. Fecal dry matter (DM) and organic matter (OM) output, fecal nutrient excretion, and total (fecal and urinary) N excretion from beef steers grazing three forage systems during the cool-season of 20161 and 20171.
Treatment2 Item Grass+
N Grass+clover
Grass+CL+RP
SE3 P-value
Fecal output, kg DM hd-1 d-1 2.6 1.8 2.1 0.20 0.07 Fecal output, kg OM hd-1 d-1 2.0a 1.4b 1.5ab 0.15 0.05 Phosphorus, kg ha-1 season-1 3.0 2.4 2.3 0.30 0.23 Potassium, kg ha-1 season-1 6.8 4.4 3.8 1.04 0.13 Calcium, kg ha-1 season-1
3.9 4.3 3.9 0.35 0.72 Magnesium, kg ha-1 season-1 5.2 3.8 3.8 0.47 0.07 Fecal N, kg ha-1 season-1 15.7 13.6 12.8 1.30 0.30 Total N, kg ha-1 season-1 30.0 27.3 28.9 10.57 0.83 % of N excreted via urine 40.1 46.0 48.1 9.31 0.28
1 Grazing days: 2016 had 126 days, and 2017 had 105 days. 2 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat Grass+clover mixture + 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 3 SE = Standard error. a,b Means differ, P < 0.05.
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Table 4-4. Fecal dry matter (DM) and organic matter (OM) output, fecal nutrient excretion, and total (fecal and urinary) N excretion from beef steers grazing three forage systems during the warm-season of 20161 and 20171.
Treatment2 Item Grass+N Grass+cl
over Grass+C
L+RP SE3 P-value
Fecal output, kg DM hd-1 d-1 3.8a 3.5ab 2.8b 0.25 0.03 Fecal output, kg OM hd-1 d-1 3.2a 2.9ab 2.2b 0.21 0.02 Phosphorus, kg ha-1 season-1 5.6a 4.5ab 3.0b 0.45 < 0.01 Potassium, kg ha-1 season-1 18.7a 13.9a 6.3b 1.91 < 0.01 Calcium, kg ha-1 season-1 11.8a 7.3ab 6.2b 1.52 0.04 Magnesium, kg ha-1 season-1 7.3a 5.1b 4.8b 0.53 0.01 Fecal N, kg ha-1 season-1 24.0a 16.5b 10.7c 1.50 < 0.01 Total N, kg ha-1 season-1 58.6 41.2 41.6 5.22 0.06 % of N excreted via urine 58.2b 59.4ab 71.4a 3.81 0.04
1 Grazing days: 2016 had 168 days and 2017 had 168 days. 2 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat Grass+clover mixture + 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 3 SE = Standard error. a,b,c Means differ, P < 0.05.
Table 4-5. Fecal dry matter (DM) and organic matter (OM) output, fecal nutrient
excretion, and total (fecal and urinary) N excretion from beef steers grazing three forage systems during cool- and warm-season of 2016 and 2017.
Season1 P-value2 Item Cool Warm SE3 S T S × T
Fecal output, kg DM hd-1 d-1 2.2 3.4 0.15 <0.01 0.02 0.14 Fecal output, kg OM hd-1 d-1 1.6 2.8 0.12 <0.01 <0.01 0.20 Phosphorus, kg ha-1 season-1 2.5 4.4 0.27 <0.01 <0.01 0.15 Potassium, kg ha-1 season-1 5.0 13.0 0.93 <0.01 <0.01 0.02 Calcium, kg ha-1 season-1 4.0 8.4 0.62 <0.01 0.04 0.02 Magnesium, kg ha-1 season-1 4.3 5.8 0.34 <0.01 <0.01 0.65 Fecal N, kg ha-1 season-1 14.0 17.1 0.79 <0.01 <0.01 <0.01 Total N, kg ha-1 season-1 28.8 64.0 15.16 0.03 0.44 0.39 % of N excreted via urine 44.8 66.1 2.86 <0.01 0.10 0.85
1 Cool-season was considered from January to middle May, while warm-season was from middle May to October in both 2016 and 2017. 2 S = season, T = treatment, S × T = season × treatment interaction 3 SE = Standard error.
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Table 4-6. Total annual nutrient excretion from beef steers grazing three forage systems in 2016 and 2017.
Treatment1 Item Grass+N Grass+
clover Grass+CL+RP
SE2 P-value
Excretion via feces Phosphorus, kg ha-1 yr-1 8.6a 7.0a 5.3b 0.75 <0.01 Potassium, kg ha-1 yr-1 25.5a 18.4ab 10.1b 3.14 <0.01 Calcium, kg ha-1 yr-1
15.7 11.6 10.1 1.51 0.06 Magnesium, kg ha-1 yr-1 12.6a 8.9b 8.6b 0.99 <0.01 Fecal N, kg ha-1 yr-1 39.7a 30.0b 23.5c 4.00 <0.01
Excretion via urine Urine N, kg ha-1 yr-1 48.9 38.5 47.1 9.36 0.36
Total N excretion, kg ha-1 yr-1 88.6x 68.6y 70.5y 12.42 0.05 1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season +112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat Grass+clover mixture + 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season. 2 SE = Standard error. a,b,c Means differ, P < 0.05. x, y Means differ, P < 0.10
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Figure 4-1. Treatment × evaluation interaction (P < 0.01) for total (fecal and urinary) N excretion in kg ha-1 d-1 during the cool-season.
Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat Grass+clover mixture + 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clovers fertilized with 34 kg N ha-1 during the cool-season. The bars represent the standard error of the treatment means.
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
January February March April May
Tota
l N e
xcre
tio
n (
kg h
a-1d
-1 )
Evaluation
Grass+N
Grass+clover
Grass+CL+RP
a
b
aba
a
b
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CHAPTER 5 FORAGE INTAKE AND ENTERIC METHANE EMISSIONS IN N-FERTILIZED OR
GRASS-LEGUME PASTURES DURING COOL- AND WARM-SEASON
Introduction
Agricultural greenhouse gas (GHG) fluxes are complex and have a considerable
impact on climate change. Methane is produced from ruminant livestock, mainly through
enteric fermentation and stored manure (Niu et al., 2017). Methane production via
enteric fermentation comprises 17% and 3.3% of global CH4 and GHG emissions,
respectively (Knapp et al., 2014). Enteric CH4 represents approximately 70% of total
CH4 emission from agricultural sources in the United States (USDA 2004), and grazing
cattle might contribute from 0.37 to 1.20 Mg CO2-Ceq ha-1 y-1 (Franzluebbers, 2005).
The GHG inventory for the United States reported that agriculture contributed
approximately 9% of the total GHG emissions in 2016, and these emissions have
increased by 17% since 1990 (EPA, 2018).
In addition to be an environmental hazard, enteric methane production
represents a significant loss of dietary energy in ruminants (DeRasmus et al., 2003).
The main factors affecting CH4 production by ruminants are diet composition and intake,
however, assessing intake in grazing systems remains one of the greatest challenges
for researchers. In order to develop strategies to mitigate methane emissions, these
need to be reported per unit of product, feed intake, or any other variable that is directly
related with the output of animal protein production in the systems evaluated. Livestock
systems in the tropics and subtropics rely on forages, particularly on tropical grasses as
a principal feed source (Archimede et al., 2011). Improving grazing management may
increase the potential and efficiency of forage utilization while decreasing methane
emissions by cattle (Johnson and Johnson, 1995).
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Strategies such as increased feed utilization efficiency by improving the
digestibility of forages, or the inclusion of species that may have secondary plant
compounds that inhibit methanogen populations in the rumen, could be viable
alternatives in grazing systems. Therefore, the hypothesis of this study is that the
inclusion of legumes will decrease enteric methane emissions and intensity (i.e.,
emissions per unit of animal product) in grazing beef cattle. The objective of this study
was to assess enteric methane emissions and its relationship with forage intake in three
grazing systems of monocultures grasses or grass-legume mixtures.
Materials and Methods
Experimental Site
The experiment was conducted at the University of Florida, North Florida
Research and Education Center (NFREC) during the warm- and cool-season for three
consecutive years (2016-2018). Methane emissions were measured in the two tester
steers from each experimental unit (pasture) in the grazing trial. Cattle processing
facilities at NFREC were used to restrain the animals and allow placement of the
collection devices during the training periods and the time of collection.
Experimental Design
Treatments consisted of three year-round forage systems including a summer
and winter component. The first system (Grass+N) included N-fertilized (112 kg N ha-1
yr-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture
(45 kg ha-1 of each) of FL 401 cereal rye and RAM oat during the cool-season with a
second application of 112 kg N ha-1 yr-1. Both warm- and cool-season fertilizations were
split in two applications (56 kg N ha-1 each application in the warm-season; 34 and 78
kg N ha-1 yr-1 for the cool-season). Total annual fertilization for this treatment was 224
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kg N ha-1 yr-1. System 2 (Grass + clover) included unfertilized bahiagrass pastures
during the warm-season, overseeded with a similar rye-oat mixture, plus a mixture of
clovers (14 kg ha-1 of ‘Dixie’ crimson, 5.5 kg ha-1 of ‘Southern Belle’ red clover, and 2.8
kg ha-1 of ball clover), fertilized with 34 kg N ha-1 during the cool-season. System 3
(Grass+CL+RP) included ecoturf rhizoma peanut and bahiagrass pastures during the
warm-season, overseeded with a similar rye-oat mixture and a mixture of clovers (14 kg
ha-1 of Dixie crimson, 5.5 kg ha-1 of Southern Belle red, and 2.8 kg ha-1 of ball clover)
during the cool-season.
All pastures were fertilized three weeks after planting the cool-season grasses
and legumes with 34 kg N, 19 kg P, 47 kg K, and 13.4 kg S ha-1. In addition, in April of
each year all pastures were fertilized with 93 kg K, 27 kg Mg, 12.1 kg S ha-1 with Kmag
(0-22-22-11) as a fertilizer source and 2.24 kg ha-1 B.
Enteric CH4 Emissions from Cattle
The sulfur hexafluoride (SF6) tracer technique was used to measure cattle CH4
emissions (Johnson et al., 1994). This technique was first deployed in 1994 (Johnson et
al., 1994) and since then has been widely used and more than 100 peer reviewed
manuscripts have been published using this technique (Williams et al., 2016). The SF6
is used as a tracer gas, because its concentration in the atmosphere is low, and
because it behaves similarly to CH4. By knowing the release rate of the SF6 tracer gas
in the rumen and sampling the cattle breath to quantify SF6 and enteric CH4
concentrations, the daily CH4 emissions can be calculated.
Measurements were conducted on the two tester steers in each pasture, twice a
year, during the cool and warm-season (Table 5-1), for three consecutive years (2016-
2018). Steers were dosed intraruminally with a permeation tube containing
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approximately 1.7 g of SF6. Permeation tubes consisted of a brass body (length = 4.4
cm outside diameter; inside diameter = 0.79 cm; inside depth = 3.8 cm; final volume =
1.86 mL) with a Teflon- membrane, secured by a porous (2 μ) stainless-steel frit and a
SwageLok nut. The permeation rate was measured for each tube during 4 consecutive
weeks using a digital analytical balance before dosing, and the average of SF6 release
rate was 1.8 ± 1.02, 1.21 ± 0.08 and 1.30 ± 0.15 mg d-1 for each year, respectively.
Permeation tubes were dosed via balling gun in each steer on d 0 of the first methane
emissions collection period. Collection of breath samples analyzed for CH4 and SF6 took
place over a minimum of five continuous days during the spring and summer periods.
Steers were fitted with a halter and a polyvinyl chloride (PVC) collection canister (yoke)
with a volume of 2 L. Canisters were under an initial vacuum of 16.7 kPa to sample
gases continuously for a period of 24 h from the mouth-nostril area. The halter had a
capillary tube with restrictive flux in order to take breath samples close to the nostril
area by a hose plastic loop. All steers were adapted to the canisters for a period of 5 to
7 days prior to collection. Canisters were replaced daily and analyzed for CH4 and SF6
concentration by gas chromatography (Agilent 7820A GC, Agilent Technologies, Palo
Alto, CA) using a flame ionization and an electron capture detector, and a capillary
column (Plot Fused Silica 25 m × 0.32 mm, Coating Molsieve 5A, Varian CP7536). Daily
enteric CH4 emissions were determined by calculating the ratio of SF6 to CH4, knowing
the release rate of SF6 from the permeation tube in the rumen (Lassey, 2007). At the
same time during the collection days, three collection canister and capillary tubes were
used to determine environmental CH4 and SF6.
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During this sampling period, a composite hand-plucked sample was collected
from each pasture to determine forage nutritive value. Neutral detergent fiber (NDF), in
vitro digestible organic matter (IVDOM), and crude protein (CP) concentration were
determined in the forage samples to assess any potential relationship between the
nutritional quality of the diet and the enteric CH4 emissions by the steers. For NDF
analyses, composite forage samples were weighed into F57 filter bags (Ankon
Technology, Macedon, NY) and analyzed sequentially in an Ankom 200 Fiber Analyzer
(Ankom Technology) using sodium sulfite and heat-stable α-amylase.
Dry Matter Intake Measurements
Additionally, feed intake was estimated as proposed by Pinares-Patino et al.
(2016), by using the IVDOM from composited hand-plucked samples from each pasture,
(δ13C from bahiagrass and δ13C from rhizoma peanut, % C3 and C4 in feces, g of
consumed DM) and the total fecal excretion calculated by the marker dilution technique
using Cr2O3 and TiO2 as indigestible external markers. In order to assess the IVDOM
representative of the pasture to increase the precision in the estimation of intake, the
forage diet consumed by steers grazing Grass+CL+RP in each pasture and within each
year was reconstituted based on fecal isotope composition. A composite sample of
dried forage was created by mixing proportions of rhizoma peanut and bahiagrass
based on fecal isotopic signature, in order to represent the diet consumed, and
minimize associative effects during IVDOM determination. This reconstituted diet was
then ground at 2 mm and incubated in vitro to determine IVDOM as previously
described. This process was repeated for each year of assessment.
The total fecal excretion was calculated by the marker dilution technique using
Cr2O3 and TiO2 as indigestible external markers. On day 0 (beginning of the CH4
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emissions and total fecal output collection period) the steers were dosed with a gelatin
capsule containing 5 g of Cr2O3 and 5 g of TiO2 using a balling gun, twice daily at 0700
and 1600 h. Dosing of the markers was continuing until the morning of day 8, and from
day 5 to 8, fecal samples were collected by rectal grab at the time of bolus dosing.
Fecal samples were frozen immediately at -20°C and later dried in a forced-air oven at
55°C for 72 hours. Fecal samples were ground to pass a 2-mm screen using a Wiley
Mill and composited within steer to measure Cr2O3 and TiO2 concentration. For
concentrations of Cr, approximately 0.5 g of ground feces were dried in a forced-air
oven at 100˚C for 24 h to determine sample DM, and ashed at 550°C for 3 h to
determine OM. The method of Williams et al. (1962) was used to digest Cr2O3 in the
samples. Concentrations of chromium were determined by atomic absorption
spectrophotometry (358 nm with an air-plus-acetylene flame; AAanlyst 200; Perkin
Elmer, Walther, MA). For concentrations of TiO2, approximately 0.5 g of ground feces
were dried in a forced-air oven at 100°C for 24 h to determine sample DM, and ashed at
550°C for 3 h to determine OM. Titanium dioxide samples were analyzed using a
modification of the method developed by Titgemeyer et al. (2001). Briefly, TiO2 in the
samples was digested by bringing 10 mL of 7.4 M sulfuric acid to a gentle boil for
approximately 30 min (or until translucent) using a hot plate under a fume hood. After
the samples had been cooled, the contents of each beaker were rinsed into tared 120-
mL sample cups. Ten milliliters of 30% H2O2 was added and the weight of each cup was
brought to 100 g using distilled water. Samples were then mixed and filtered
(Fischerbrand P8 Grade, Fisher Scientific, Pittsburgh, PA) and analyzed for
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concentration of TiO2 measuring absorbance at 405 nm wavelength in a Beckman DU-
530 Spectrophotometer (Beckman Coulter, Palo Alto, CA).
Proportion of C3 in Feces and Selection Index
Every 14 d, hand-plucked samples of grass and legume were collected. In
addition, every 21 d fecal samples were collected by rectal grab and frozen immediately
at -20°C. Samples were later dried in a forced-air oven at 55°C for 72 hours and ground
at 2 mm using a Wiley Mill (Model 4, Thomas-Wiley laboratory Mill, Thomas Scientific).
Subsamples of forage and feces were ball-milled in a Miller Mill MM 400 (Retsch,
Newton, PA, USA) for 9 min at 25 Hz, to determine total C and isotopic composition
(δ13C), using the Dumas dry combustion method in a CHNS analyzer (Vario Micro
Cube, Elementar Inc., Germany), attached to an isotope ratio mass spectrometer
(IsoPrime 100 Elementar Inc., UK). The proportion of grass and legume in the pasture
during the warm-season was calculated using the data from the botanical composition
evaluations, with the dry-weight (DW) rank method (Mannetje and Haydock, 1963). This
proportion was multiplied by the area of each forage component per pasture. Later the
selection index was calculated using a ratio of the proportion of C3 from the feces and
the proportion of rhizoma peanut in the pasture based on the DW rank.
Calculations
Enteric methane emissions produced by steers were quantified using the
following equation:
QCH4 = QSF6 × ([CH4]γ – [CH4]β) ÷ ([SF6] γ – [SF6] β) (5-1)
Where QCH4 is methane emissions per animal (g d-1) and QSF6 is considered SF6 release
rate (mg d-1). In addition, [CH4]γ is the concentration of CH4 in the animal’s collection
canister and [CH4]β is the concentration of CH4 in the environmental canisters. The term
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[SF6]γ is the concentration of SF6 in the animal’s collection canister, while [SF6]β is the
concentration of SF6 in the environmental collection canister.
The proportion of rhizoma peanut in the feces was estimated using a two-pool mixing
model (Fry, 2008) as follows:
ƒtotal 1 = (δ13Csample- δ13C source 2) / (δ13C source 1 – δ13C source 2) (5-2)
ƒtotal 2 = 1 - ƒtotal 1 (5-3)
Where ƒtotal 1 represents the fraction of source 1 and source 2, δ13Csample is the δ13C in
feces. Source 2 is the δ13C of bahiagrass, and source 1 is the δ13C of rhizoma peanut.
The selection index for legume preference in the Grass+CL+RP was calculated as
follows: Selection index = % of C3 in feces / % of rhizoma peanut in the pasture
botanical composition.
Statistical Analysis
Methane emissions and intake from cattle were analyzed using PROC Mixed of
SAS (SAS Inst., Cary, NC) with treatment and season as fixed effects, and block and
year as random effects. Pasture was considered the experimental unit, and the
emissions and intake from the two tester steers per pasture were averaged for the
statistical analyses.
Results and Discussion
In the cool-season, the IVDOM of grasses ranged from 709 to 766 g kg-1, while in
the warm-season it ranged from 447 to 479 g kg-1 (Table 5-2).These differences are
explained by the species present in each of the sampling seasons, whereby bahiagrass
was most abundant in the warm-season, and oat and rye were the main grasses during
the cool-season. The concentration of CP in grasses during the cool-season ranged
from 135 to 174 g kg-1, while in the same season, the CP concentration in legumes was
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235 and 225 g kg-1 for Grass+clover and Grass+CL+RP, respectively, which were the
only treatments to which legumes were added (Table 5-2). The concentration of CP in
rhizoma peanut during the warm-season was 196 g kg-1 for Grass+CL+RP. The
nutritive value reported for grasses and legumes in this study are within the ranges of
those previously observed in the same geographic area (Jaramillo et al., 2018; Santos
et al., 2018).
Cool-season
Dry matter intake either as kg d-1 or as percentage of BW did not differ among
treatments (P > 0.10) (Table 5-3). Steers consumed 8.1, 7.0 and 8.2 kg DM d-1 in
Grass+N, Grass+clover, and Grass+CL+RP, respectively, which translated to an intake
of 2.60, 2.45, and 2.84% of BW for the same three treatments, respectively. These
values are in agreement with previously reported intake measurements in grazing cattle.
Wagner et al. (1986) reported a range of intake from 2.2 to 2.8% of BW in cows grazing
cool-season forages in Montana, when using the marker dilution technique, Cr2O3 as a
marker, and esophageal cannulas to sample the forage consumed to conduct IVDOM.
Using the same techniques (esophageal cannulation and Cr2O3 as a marker), Redmon
et al. (1995) reported an average OM intake of 2.2% of BW in beef steers grazing winter
wheat over two consecutive years, which is in agreement with the range of OM intake
observed in this study (2.40, 2.21, and 2.56% of BW for Grass+N, Grass+clover, and
Grass+CL+RP, respectively; data not shown). The lack of differences in forage intake
during the cool-season was expected because the herbage allowance and IVDOM were
similar across treatments (Table 5-2). Redmon et al. (1995) observed that steers
grazing winter wheat with varying herbage allowances over two consecutive years, did
not differ in forage intake when herbage allowance was maintained above 0.21 kg of
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DM kg BW-1. The average herbage allowance during the cool-season in this trial ranged
from 0.79 to 0.83 and was not different (P = 0.35) across treatments (Chapter 3, Table
3-1).
Methane emissions by steers grazing in the cool-season (Table 5-3) were 96,
112, and 90 g steer-1 d-1 for Grass+N, Grass+clover, and Grass+CL+RP, respectively,
and did not differ among treatments (P = 0.77). When expressed as g of CH4 kg-1 of
DMI, emissions were 21.6, 22.0, and 25.0 for Grass+N, Grass+clover, and
Grass+CL+RP, respectively, and also did not differ among treatments (P = 0.96).
Methane emissions intensity, expressed as g of CH4 per kg of ADG, did not differ
among treatments (P = 0.74), nor did emissions per kg of metabolic BW (P = 0.75). The
lack of differences among treatments in CH4 emissions during the cool-season was
expected, and may result from the similar intake and digestibility of the pastures grazed
across treatments. Even with the presence of clovers in two of the treatments, the
nutritive value of the grasses in this season was sufficiently great that it likely
compensated for contributions from the legumes. This was confirmed by the animal
performance during the cool-season, where ADG and gain per area did not differ among
treatments (Chapter 3, Table 3-5). Furthermore, Archimède et al. (2011) reported in a
meta-analysis of in vivo studies that enteric CH4 emissions from livestock grazing C3
grasses or cool-season legumes did not differ. Methane emissions averaging 121 g d-1
were reported by Boland et al. (2013) for heifers grazing perennial ryegrass under two
herbage mass treatments. In that study, despite significant differences between
treatments in CP (217 vs. 156 g kg-1 for low and high herbage mass, respectively) and
ADF concentrations (255 vs. 261 g kg-1 for low and high herbage mass, respectively),
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no differences in CH4 emissions or emission intensity were detected (Boland et al.,
2013).
Warm-season
No effect of treatment (P ≥ 0.18) was observed on DMI, CH4 emissions, or
methane emissions intensity (Table 5-4). The DMI of steers grazing during the warm-
season was 1.79, 1.67, and 2.04% of BW, for Grass+N, Grass+clover, and
Grass+CL+RP, respectively. Very few studies have reported grazing intake in growing
cattle consuming bahiagrass-based pastures in order to compare with the results
observed in this study, and reason for the limited number of studies with such
measurements have been discussed extensively (Moore et al., 1999; Macoon et al.
2003; Coleman et al., 2014). Garcés-Yépez et al. (1997) reported a DMI of 1.99% of
BW in yearling steers and heifers fed bermudagrass hay in Florida. These observations
of bermudagrass grazing intake reported by researchers in Florida (Garcés-Yépez et
al., 1997) are very similar to the average of the grazing intake across all treatments
observed in our study (1.83% of BW). Despite the challenges of the marker dilution
technique associated with its labor intensity, it appears that dosing the markers twice
daily and using IVDOM to assess forage digestibility, may yield reasonable estimates of
grazing intake.
Very few studies have reported in vivo methane emissions from cattle grazing
bahiagrass. DeRamus at al. (2003) measured CH4 emissions from heifers grazing
bahiagrass over two consecutive years using the SF6 tracer technique, and reported
values ranging from 86 to 166 g d-1, and from 1.21 to 1.86 g kg BW -0.75. These values
of daily emissions, are within the range of those reported in this study with steers of
similar BW, also grazing bahiagrass pastures as the primary warm-season forage.
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Furthermore, when assessing the emissions per g of forage DM consumed, the values
observed in this study are well within the range of those reported for high-forage diets in
studies where intake was measured. Archimède et al. (2011) performed a meta-analysis
of 64 trials conducted in large ruminants to assess enteric CH4 emissions by cattle
grazing cool- and warm-season grasses and legumes and reported an average value of
24.1 g of CH4 kg-1 of DMI for cattle grazing warm-season grasses. Interestingly, this is in
agreement with the emissions observed in this study in the two treatments that
contained only bahiagrass in the warm-season (24.1 and 24.2 g of CH4 kg-1 of DMI for
steers grazing Grass+N and Grass+clover, respectively). The authors of the meta-
analysis acknowledged the limited contribution of experiments with warm-season
forages in the data set when compared to cool-season ones, highlighting the need for
more studies addressing emissions in warm-season climates (Archimède et al., 2011).
The possibility of including legumes in forage systems may have multiple
environmental benefits: a reduction in inorganic fertilizer needs due to the biological N
fixation, and potentially a reduction in CH4 emissions due to the presence of tannins. A
life-cycle assessment conducted in southern Brazil to review the impact of N fertilizer
use on GHG emissions showed that pasture management strategies that include
legumes reduced GHG emissions by 11.8 and 12.5 times when compared with N
fertilizer systems under two different scenarios (Dick et al., 2015). This life-cycle
assessment did not consider any potential mitigation due to presence of tannins, and
strictly relied on N fertilizer reductions; however, GHG reductions could have an additive
effect if the legumes incorporated have the potential to decrease daily emissions without
affecting performance. Archimède et al. (2011) reported that enteric methane emissions
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form cattle grazing tropical legumes with tannins decreased by 20% when compared
with those in cattle grazing C4 grasses. In our study, despite the numeric reduction of
nearly 44% in emissions intensity when including rhizoma peanut in the warm-season
forages (397 vs. 225 g of CH4 kg of ADG-1 for Grass+N and Grass+CL+RP,
respectively; P = 0.18), no effects were observed in daily methane emissions. Most
likely the lack of effect on CH4 methane emissions observed in our study when including
rhizoma peanut in the pastures may be related with the fact that unlike other tropical
legumes, rhizoma peanut does not contain significant concentrations of tannins
(Naumann et al., 2013).
Cool vs. Warm-season
Comparing forage intake and GHG emissions between cool and warm-season
can be useful for potential extrapolation of this findings to systems with different
duration of each season, and for year-round GHG emissions calculations. No season ×
treatment interaction was observed (P = 0.99), however there was a marked effect of
season for DMI as % of BW, where steers grazing in the cool-season had a 44%
greater forage intake (2.63 vs. 1.83% of BW; P = 0.01) (Fig. 5-1). Reid et al. (1988)
reported that the decreased forage intake in livestock grazing warm vs. cool-season
forages can be the result of decreased fiber digestibility, thus increasing gut fill and
decreasing intake. Fig. 5-2 shows the effect of season on the intensity of CH4
emissions, expressed as g CH4 kg of ADG-1, where no season × treatment interaction
was observed (P = 0.36), while a strong season effect was found (P < 0.001). A 58%
decrease in emissions intensity was observed for steers grazing during the cool vs.
warm-season when expressed as g CH4 kg of ADG-1 (147 vs. 357 for cool and warm-
season, respectively). This is in agreement with previous reports comparing warm-
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season vs. cool-season forages, and has been associated with the ruminal fermentation
profile under each type of forage. Ruminants grazing cool-season forages had a greater
production of propionic acid and a resulting decrease in the acetate:propionate ratio,
which has been associated with decreased enteric methane production (Archimede et
al., 2010; Loncke et al., 2009; Henry et al., 2015).
Selection Index – Warm-season
The preference for rhizoma peanut is observed by steers grazing in the
Grass+CL+RP treatment (Figure 5-3). The proportion of rhizoma in the pastures ranked
from 14% to 23% approximately in the 3 evaluations of the botanical composition during
the warm-season of 2016 and 2017. Even though the presence of the legume did not
equal the proportion of bahiagrass in the pastures, the proportion of C3 obtained from
the feces ranged from 43 to 45%, showing a greater preference from grazing steers for
rhizoma peanut. This preference is evidenced by the fact that the selection index is
greater than 1 for all the evaluations.
Conclusions
Enteric methane emissions and emissions intensity where not modified by the
inclusion of legumes in the system in the cool or warm-seasons. Presence of tannins in
warm-season legumes have been associated with methane emissions reductions,
however the lack of tannins in rhizoma peanut may have been responsible for these
findings. Emission intensity when measured as g of CH4 per unit of ADG, did not differ
among treatments; however, a 58% decrease in emission intensity was observed for
steers grazing during the cool vs. warm-season. This reduction in emission intensity
was likely driven by the quality of the forage consumed in the cool-season causing an
increased ADG, because there was a 44% greater forage DMI intake in steers grazing
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in the cool-season (2.63 vs. 1.83% of BW for cool vs. warm-season, respectively),
which could have contributed to greater emissions. Data generated in this study can be
useful in generating carbon budgets across an entire year of grazing on different
systems, to assess the carbon footprint of beef production in livestock-forage systems
typical of the southeastern US.
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Table 5-1. Enteric methane sample dates and fecal output collection during the warm and cool-season, from 2016 to 2018.
Enteric methane emissions Fecal output Year Cool-season
dates Warm-season
dates Cool-season
dates Warm-season
dates
2016 March 18 to 26 July 5 to 13 March 18 to 26 July 5 to 13 2017 March 10 to 18 June 28 to
July 6 March 10 to 18 June 28 to
July 6
2018 March 25 to 31 August 6 to 18
- -
Table 5-2. Forage nutritive value from hand-plucked samples collected during the
methane sampling from 2016 to 2018 cool and warm-season.
Treatment1 Item Grass+N Grass+clover Grass+CL+RP
Cool-season IVOMD2 g kg-1 Grasses 709 ± 54 765 ± 37 766 ± 62
IVOMD g kg-1 Legumes 810 ± 20 805 ± 33
CP3 g kg-1 Grasses 139 ± 30 174 ± 39 135 ± 23
CP g kg-1 Legumes 235 ± 43 225 ± 35
NDF4 g kg-1 Grasses 477 ± 81 377 ± 57 386 ± 83
NDF g kg-1 Legumes 187 ± 07 174 ± 21
Warm-season IVOMD g kg-1 Bahiagrass 450 ± 71 479 ± 43 447 ± 77
IVOMD g kg-1 Rhizoma peanut 699 ± 53
CP g kg-1 Bahiagrass 127 ± 3.9 115 ± 27 97 ± 27
CP g kg-1 Rhizoma peanut 196 ± 16
NDF g kg-1 Bahiagrass 640 ± 31 630 ± 37 633 ± 64
NDF g kg-1 Rhizoma peanut 300 ± 14 1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1 ; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season; Grass+CL+RP = Rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season. ± SD = Standard deviation from the observations in 3 consecutive years (2016, 2017 and 2018). 2 IVOMD = invitro digestible organic matter. 3 CP = crude protein. 4 NDF = neutral detergent fiber.
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Table 5-3. Dry matter intake (DMI) and enteric methane emissions from beef steers during the cool-season; 2016 to 2018.
Treatment1 Item Grass+N Grass+
clover Grass+ CL+RP
SE2 P-value
DMI3, kg d-1 8.1 7.0 8.2 1.0 0.62 DMI3, as % of BW 2.60 2.45 2.84 0.4 0.77 CH4 g steer-1 d-1 96 112 90 21.9 0.77 CH4 BW(0.75)-1 1.4 1.7 1.4 0.3 0.75 CH4 g ha-1 d-1
358 392 313 78.2 0.77 CH4 g kg of DMI-1 21.6 22.0 25.0 9.2 0.96 CH4 g kg of ADG-1 177 140 123 49.5 0.74
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season; Grass+CL+RP = Rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season. 2 SE = Standard error. 3 Dry matter intake was measured only during 2016 and 2017, using Cr2O3 and TiO2 as fecal output markers.
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Table 5-4. Dry matter intake (DMI) and enteric methane emissions from beef steers during the warm-season; 2016 to 2018.
Treatment1 Item Grass+N Grass+
clover Grass+ CL+RP
SE2 P-value
DMI3, kg d-1 6.8 6.3 7.6 0.54 0.24 DMI3, as % of BW 1.79 1.67 2.04 0.150 0.25 CH4 g steer-1 d-1 117 113 101 24.8 0.90 CH4 BW-(0.75) 1.4 1.4 1.2 0.73 0.91 CH4 g ha-1 d-1
548 447 359 96.2 0.40 CH4 g kg of DMI-1 24.1 24.2 17.4 5.4 0.61 CH4 g kg of ADG-1 397 448 225 85.1 0.18
1 Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season; Grass+CL+RP = Rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat clover mixture fertilized with 34 kg N ha-1 during the cool-season. 2 SE = Standard error. 3 Dry matter intake was measured only during 2016 and 2017, using Cr2O3 and TiO2 as fecal output markers.
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Figure 5-1. Dry matter intake (DMI) as % of body weight in cool and warm-season in
three grazing systems. Treatment × season, P = 0.99; Season effect, P = 0.01. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season; Grass+CL+RP = Rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season.
0
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Figure 5-2. Enteric methane emissions in g per kg of average daily gain (ADG)-1 in cool
and warm-season in three grazing systems. Treatment × season, P = 0.36; Season effect, P < 0.001. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season 112 kg N ha-1; Grass+clover = unfertilized bahiagrass pastures during the warm-season, overseeded with similar rye-oat and clover mixture fertilized with 34 kg N ha-1 during the cool-season; Grass+CL+RP = Rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat clover mixture fertilized with 34 kg N ha-1 during the cool-season.
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Figure 5-3. Selection index, proportion of C3 (rhizoma peanut, RP) in feces, and
proportion of rhizoma peanut (RP) dry weight (DW) in the pasture during 3 evaluations in the warm-season of 2016 and 2017 in the Grass+CL+RP treatment.
Selection index = % of C3 in feces / % botanical composition, dry weight (DW) of rhizoma peanut in the pasture. For feces C3 proportion, evaluation effect, P = 0.98. For %RP DW, evaluation effect, P < 0.01. For Selection index, evaluation effect, P = 0.21. Error bars denote standard error. Grass+CL+RP = Rhizoma peanut and bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
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CHAPTER 6 MANAGING GRASSLAND STRUCTURE TO ENHANCE POLLINATOR HABITAT
Introduction
Pollinators comprise a diverse group of animals dominated by insects, especially
bees, which are responsible for the pollination of over 75% of flowering plants, and they
benefit 35% of global crop-based food production (Klein et al., 2007; NRC, 2006;
Kimoto et al., 2012). The abundance, diversity, and health of pollinators, particularly
bees, are threatened by direct drivers that generate risks to societies and ecosystems,
by reducing or affecting pollination services. Reasons for bee decline include land-use
change and habitat fragmentation, agriculture intensification, pesticide application and
environmental pollution, alien species, spread of pathogens, and climate change
(Batáry et al., 2010; Potts et al., 2010).
The health of pollinators and their link to food security is a global concern; this is
the reason why the Food and Agriculture Organization (FAO) created the International
Pollinator Initiative (FAO, 2000). The basic premise of this initiative is that the global
food security is threatened by the decline of managed honey bees (Apis mellifera) and
loss of wild pollinators. In addition, agricultural management practices need to improve
in terms of habitat management for wild pollinators in order to preserve this valuable
service. The International Pollinator Initiative recognized animal-mediated pollination as
a regulating ecosystem service of vital importance for nature, agriculture, and human
well-being. Furthermore, global agriculture has become pollinator dependent,
particularly for wild pollinators (Garibaldi et al., 2011). Besides marketable products,
pollinators provide other non-monetary benefits for human well-being such as a source
of inspirations for art, religion, tradition and recreational activities (FAO, 2012). The
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value of wild or managed pollinators in commercial crops has been well documented
and continues to be studied in many countries using different methodologies. The
aggregate value of pollination services provided by managed and wild pollinators is a
growing segment; however, it focuses mainly on the transactions between growers and
beekeepers without assessing the economic value of the service provided. For
example, depending of the methodology used for the assessment, honey bee annual
values in USA are between US$1.6 billion and US$14.6 billion (Allsopp et al., 2008).
This wide range in the value assigned to that service illustrates current methodological
challenges that can lead to under-estimated or overestimated values of pollination
services, and this is not accounting for the wild pollination services. Further attempts
that estimate welfare losses for consumers of crops that would result from loss of
pollinators rely on knowledge of agronomic practices (Sumner, 2010). Gallai et al.
(2009) estimated that the annual consumer welfare loss that would result from the loss
of all pollinators is approximately US$216 billion. Honey bees are used extensively as
an agricultural input because they are excellent generalist pollinators (Allsopp et al.,
2008). The success of honey bees as the primary domesticated crop pollinator is
accounted for by the large size of their colonies, and the portability of their nests
(Sumner, 2010). However, the need for pollinator diversity is emerging as important in
the production of more nutritious and higher value pollinator dependent crops (Riddle et
al., 2016).
Grasslands support a diverse and abundant bee fauna (Kimoto et al., 2012),
especially wild bees, by offering key resources to meet their nutrient requirements and
nesting habitats (Koh et al., 2016). Practices such as high fertilizer application rate, re-
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seeding, and intensive defoliation by grazing or cutting reduce food sources by
producing degraded species pools and homogeneous swards in grasslands (Potts et
al., 2009). Considering that livestock grazing is the most common use of grasslands, its
effect may impact native bees through change in plant growth, architecture, diversity
and quality, as well as soil characteristics (Black et al., 2011; Kimoto et al., 2012).
Factors such as intensity of grazing, types of grazers, and species composition of the
sward could have a positive or negative effect on bee communities (Kimoto et al.,
2012). Consequently, a greater flower component in the sward and less disturbance
favors the abundance of pollinators in grasslands (Yoshihara et al., 2008). Moreover,
the introduction of legumes into grasslands increases floral resources that benefit
pollinators, native wildlife, and a range of ecosystem services with positive economic
consequences (Gallai et al., 2009; Potts et al., 2009; Woodcock et al., 2014; Bhandari
et al 2018). The hypothesis of this study was that the introduction of legumes in forage-
livestock systems would increase bee abundance and diversity. This increased bee
abundance and diversity could lead to increased environmental services provided by
bees.
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Material and Methods
Experimental Site
A grazing experiment was conducted from January 2016 to October 2018 at the
University of Florida, North Florida Research and Education Center (NFREC), located in
Marianna, FL (30°52’N, 85°11’ W, 35 m a.s.l.).
The experimental site had nine pastures of approximately 0.85 ha each.
Treatments were allocated in a randomized complete block design with three
replications per treatment. Treatments consisted of three livestock production systems
as follows: (1) Grass+N, (2) Grass+clover, and (3) Grass+CL+RP. The Grass+N
treatment consisted of N-fertilized (112 kg N ha-1) argentine bahiagrass (Paspalum
notatum F.) pastures during the warm-season, overseeded with a mixture (45 kg ha-1 of
each) of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1. The
Grass+clover treatment consisted of unfertilized argentine bahiagrass pastures during
the warm-season, overseeded with rye-oat mixture and clovers (14 kg ha-1 of Dixie
crimson, 5.5 kg ha-1 of ‘Southern Belle’ red clover and 2.8 kg ha-1 of Ball clover) + 34 kg
N ha-1 during the cool-season. The Grass+CL+RP consisted of ecoturf rhizoma peanut
and argentine bahiagrass pastures during the warm-season, overseeded with similar
rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers (14 kg ha-1 of Dixie
crimson, 5.5 kg ha-1 of Southern Belle, and Ball clover 2.8 kg ha-1) during the cool-
season. For this study, the average rain from 2016 to 2018 was 118 mm, the average
temperature was 20°C and solar radiation was 180 w m2 -1 (Figure 6-1 and 6-2). The
data were obtained from the Marianna weather station (Florida Automated Weather
Network, https://fawn.ifas.ufl.edu/).
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Sampling Procedure
The presence and abundance of bees were assessed using colored bowl traps.
Yellow, white and blue plastic bowl traps (Creative Converting 28102151 Touch of Color
Plastic Bowls, 12oz) filled with a mix of water and dish detergent were set on the ground
in two clusters of three bowls (one of each color) per pasture. Bowl traps were set in the
morning and collected after 24 hours (Cane et al., 2000; Droege et al., 2010; FAO,
2010) once per month over three consecutive years (2016-2018). A temporary electric
fence was placed on the day of the bowls deployment, to prevent cattle from tampering
with the collection devices. Bees collected in bowl traps were strained through a small
fish net and placed in glass vials, and specimens were preserved in 70% isopropyl
alcohol. Bees were later pinned in the laboratory, dried, and identified to species using
the Discover Life online identification matrix (www.discoverlife.org; Droege, 2012).
Additionally, bee body size was classified as small (2 to 8 mm), medium (> 8 mm to 20
mm), and large (> 20 mm to 40 mm) for body size analysis purposes (Warzecha et al.,
2015). Flower abundance was measured at the time of bee collection in 10 quadrants (1
m × 1 m each) per pasture (FAO, 2010), and inside of each quadrant the number of
flowers was counted and averaged by pasture.
Statistical Analysis
Biodiversity indexes (Chao 1, Shannon-Wiener and Simpson Inverse index) were
analyzed using the software EstimateS (Colwell, R. V 9.1.0. University of Connecticut,
USA, 2019). Chao 1 was used as estimator of total species; this is a nonparametric
asymptotic estimator that uses information on the frequency of rare species in a sample
in order to estimate the number of undetected species (Gotelli and Chao, 2013). The
indices of species diversity used were Shannon-Wiener index and Simpson Inverse
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index; these indices combine information on richness and relative abundance (Gotelli
and Chao, 2013). In addition, a species accumulation curve was performed with Chao 1
and abundance-based coverage estimator (ACE). This curve assumed that all the
species will eventually be sampled as long as the area is homogeneous, and it is
calculated from the estimated variance of 1000 random draws. The common species
are detected quickly at the beginning of the curve, and at a certain point the curve will
be flatten when no more new species are detected (i.e., it will reach an asymptote). The
x-axis is the number of individuals sampled and y-axis is the number of species
observed (Colwell et al., 2012; Gotelli and Chao, 2013).
Total flowers, presence of bees, and abundance of bees per treatment were
analyzed using the Mixed Procedure of SAS (SAS Inst., Cary, NC), and the model
included the fixed effect of treatments, evaluation period and trap color. Block and year
were considered random effects and evaluation period was considered a repeated
measure. Means were compared using the LSMEANS procedure adjusted using the
Tukey’s test (P ≤ 0.05). The model significance was declared at P < 0.05.
Results and Discussion
Bee Species
In total, 2,847 bees were collected from the three treatments using bowl traps,
comprising 18 species (Table 6-1). Lassioglosum sp. and Melissodes communis were
the most frequent species collected in the three treatments (Table 6-1). From the
eighteen species collected, seventeen are native species and Apis mellifera is the only
one that was introduced to North America. In the bee collection, there are eight species
of the Apidae family: Apis mellifera, Bombus bimaculatus, Bombus pensylvanicus,
Melissodes communis, Melissodes bimaculate, Melissodes trinodis, Ptilothrix
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bombiformis, Eucera rosae. The most social bees in the United States are from the
genus Apis and Bombus (Wilson et al., 2016). Apis mellifera is naturally found in Africa
and Europe with more than 20 subspecies and was introduced into the United States for
economic purposes (Winston, 1992). European honey bees prefer habitats that have an
abundant supply of suitable flowering plants, such as meadows, open wooded areas,
and gardens. However, they can survive in grasslands, deserts, and wetlands if there is
enough water, food, and shelter (Milne and Milne, 2000; Winston, et al., 1981). The
experimental grazing site at the NFREC is surrounded by a county route, crops, and a
large irrigated area that involves a rotation of crops and perennial grasses. Because
honey bees can travel long distances, it may be possible that the presence of legumes
and mixture of grasses from the grazing trial offered foraging sources to some colonies
of Apis mellifera in the area. The small number of specimens collected from the Bombus
genus confirms that the bowl trap method is not efficient at attracting big size bees,
which are typically collected by other methods such as the net or blue vane traps
(Kimoto et al., 2012; Lettow et al., 2018).
In contrast to the social lifestyles of honey bees and bumble bees, most of the
species of bees collected in the grazing trial are solitary, meaning a single female
excavates a nest, lays her eggs, and collects pollen and nectar provisions for her larvae
without any cooperation from other bees (Winston, 1992). Bees from the genera
Mellisodes are medium body size, and they are solitary ground-nesters. However, there
are some species that nest communally with several individuals using one burrow
(Wilson et al., 2016). The species of bees collected in the grazing trial exhibit the main
characteristic of being generalist foragers, solitary and ground nesting. Grazing systems
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can increase bare ground availability and litter material that provided nesting resources.
Ground nesting bees display a wide range of nesting preferences, some are tolerant to
soil compaction, other species prefer softer soils. Solitary bee nests are often found in
aggregations, but a single female occupies each nest. In addition, the eggs of the
solitary bees emerge in early spring if weather conditions are optimum, and they prefer
to forage in sunny days and when the temperature is above 13°C (Winston, 1992).
Figure 6-1 and 6-2 show the monthly temperature, solar radiation and rainfall during the
sampling period. When temperature increased in April and May, and rainfall decreased
compared to previous months, more bees where collected. Other species collected in
the grazing trial belong to the family Halictidae, one of the largest family of bees with
wide range of distribution (Wilson et al., 2016). To this family belongs the following
genus: Agapostemon, Augoclorella, Augochlora, Halictus, and Lassioglosum (Table 6-
1). In the Halictidae family, many of the bees are commonly called “sweat bees”,
because they are known to be attracted to human sweat, which they drink for its salt
content. Halictus and Agapostomen are considered medium body size bees and the
other genus are small body size bees. Lassioglosum are often the most common bees
in a habitat, with small body size, and they are a mix of specialist and generalist
foragers. Lassioglosum includes species that exhibit the full range of bee social
behaviors, including solitary, communal, and social habits. Small body size bees such
as Lassioglosum forage in a range approximately 300 m from their nest, therefore
nesting resources could affect native bees in a given area (Sardiñas et al., 2014).
Furthermore, some species of bees can share the same area with grazing animals.
Previous studies have reported that abundance and diversity of sweat bees were
164
unaffected by grazing animals, while, on the contrary, bumble bee’s diversity and
richness was negatively affected when grazing activity increased (Kimoto et al., 2012).
Presence of Bees per Trap Color
The purpose of the study was not to evaluate the efficacy of the pan trap method;
however, a color preference was observed, according with the body size of the bees
collected. Honey bees showed a preference for the white bowl trap (Figure 6-3), and
there was a trap color effect (P = 0.001) on number of honey bees. Medium size bees
showed a trap color × evaluation period interaction (P < 0.001), where in the months of
April, May, June and July, a preference was observed for the blue bowl trap (Figure 6-
4), while no color preference was observed in the rest of the year. Small bees showed a
treatment × color interaction (P < 0.001) as showed in Figure 6-5. In the Grass+N and
Grass+CL+RP treatments, the preference was for the blue bowl trap, and in the
Grass+clover treatment, blue and white color bowl trap were selected. It should be
mentioned that in the yellow bowl traps few specimens were collected during the three
years. Pan traps could provide a taxonomic bee bias due to the difference in attraction
to the traps by different species (Cane et al 2001; Roulston et al., 2007); however, they
are an adequate method for comparative, abundance and diversity studies on bees
(Quintero et al., 2010). In addition, pan traps are an effective method in bee monitoring
programs conducted mainly in open land-cover ecosystems, because it detects
changes in total abundance of local bee community through time and species richness
(Lebuhn et al., 2013).
Medium and Small Body Size Bees
There was no treatment effect (P = 0.62) for number of honey bees collected
(Figure 6-6), neither with other medium size bees (P = 0.05). Small bee presence
165
showed a treatment × evaluation interaction (P = 0.009), where in the month of August,
the Grass+CL+RP treatment was greater than the Grass+N treatment (Figure 6-7).
Grass+clover system did not differ from Grass+N and Grass+CL+RP in the presence of
small bees (P > 0.10; Figure 6-7). Body size represents a quantity and quality
component of the bee diversity, with large bees delivering the pollen, favoring quantity
and small bees spreading the pollen more evenly on the stigma, thus increasing the
quality of pollination (Aizen et al., 2009). Consequently, wild bee assemblage is
influenced by local scale factors such as landscape composition and farming practices
(Kennedy et al., 2013). Habitat fragmentation in modern agricultural landscapes is very
common and it is a significant filter in habitat loss of diverse wild species. One of the
major drivers of loss of pollination is habitat fragmentation and there is evidence that
small species are more susceptible than large species (Warzecha et al., 2016). When
habitat fragmentation increases, there is a shift in the composition of local bee
communities, and this is because small size bees commute between foraging and
nesting habitats (Kleijn and van Langevelde 2006). Thus, if the distance of the bee
activity exceeds its radius, small bees are more affected than bigger size bees. The
consequence of loss or change in the bee community is reflected in the pollination
process, such as frequency in flower visitation, and a decreased diversity of the pollen
collected. It is important to mention, that our grazing trial offered foraging and nesting
habitat resources mainly to M. communis, a medium size bee and Lassioglosum spp., a
small size bee. Over 80% of bee families are ground nesters, thus, practices such as
tillage, mowing, and grazing have the potential to disrupt existing nests and change soil
166
characteristics; texture, compaction, hardness, humidity and proportion of bare ground
(Cane 1991; Harmon-Threatt et al., 2016).
Abundance of Bees
The abundance of bees was greatest in the Grass+CL+RP system in comparison
with the other two systems (P = 0.003) and the treatment effect is showed in Figure 6-8.
In addition, when comparing the two systems with legumes with the Grass+N system
(Figure 6-9), the orthogonal contrast analysis revealed that the two legume systems had
greater bee abundance than did the N-fertilizer system (P = 0.01). The flower diversity
and the different phenology of the legume flowers likely offered more foraging resources
to the bee community in the area of the grazing trial. Therefore, the Grass+CL+RP
system offered different blooming periods throughout the year, favoring the presence of
bees. The introduction of legumes increased the productivity of livestock systems and,
at the same time, increased habitat heterogeneity for bees that need different feed
requirements (e.g., pollen vs. nectar) throughout their life span (Cole et al., 2017). Bees
are a ubiquitous and functionally important group of pollinators in agricultural and
natural ecosystems. Healthy bee populations depend on landscapes with ample and
nutritious sources of pollen and nectar yielding flowers (Decourtye et al., 2010), and
legumes are among the most frequently visited plant families by many bee species
(Lagerhöf et al., 1992).
Species Richness and Diversity
Species richness was characterized using Chao 1 (Figure 6-10) estimator, which
is based on abundance and corrects for undetected species. There was no treatment
effect (P = 0.05) and species richness ranged from 6 to 9. Russell et al. (2005) reported
167
no difference in species richness using Chao 1 when comparing a grazing system
dominated by clovers with unmowed powerlines, with a total of 107 species collected.
The Shannon-Wiener diversity index (Figure 6-11) showed a treatment effect (P
= 0.007), where the Grass+N system differed from Grass+CL+RP. No difference was
observed (P > 0.10) between the Grass-clover system with the other two systems. The
Shannon-Wiener Diversity index ranged from 0.89 to 0.97. The values reported in this
study are lesser than those reported in ecological studies, where the Shannon index is
generally between 1.5 and 3.5. In addition, the Simpson Inverse diversity index (Figure
6-12) showed a treatment effect (P = 0.004), where the Grass+N system differed from
Grass+CL+RP. There was no difference observed between Grass+clover with the other
two systems (Grass+N and Grass+CL+RP). Both Shannon and Simpson Inverse
species diversity indices suggested that diversity increased in the Grass+clover system,
but the differences were not dramatic. All the treatments in the grazing trial were
adjacent to one another; the distance between them was limited. In this study the
decreased species richness and diversity observed might have been influenced by the
lack of diversity in the small area sampled, which contrasts with ecological studies
performed in a more diverse landscape, a larger sampling area, and with greater flower
component and sample intensity. Kearns et al. (2008), reported no difference between
both diversity indices, Shannon and Simpson, when comparing bee diversity among
grasslands plots with different levels of urbanization. However, the authors reported that
the abundance of native bees decrease with increased grazing. Diversity indices are a
function of the relative frequency of the different species in a community and both
168
indices calculated here are based in entropy measures providing useful and practical
tool in ecology (Keylock, 2005).
The accumulation curve is showed in Figure 6-13, where the cumulative number
of bee species (y-axis) is plotted as a function of the cumulative number of samples (x-
axis). This curve indicates the expected number of species randomly chosen if the
sampling increases to five additional samples in the grazing trial. The total number of
species recorded in the accumulation curve increases and the curves represent 95%
confidence intervals for interpolates and extrapolated richness estimates as more
individuals are sampled (Colwell et al., 2012). The accumulation curve showed all the
possible combinations of bee assemblage and the accumulation rate in the area,
indicating that the maximum of three new species will be collected if the sampling period
is extended. The accumulation curve is strongly influenced by the distribution of species
among the samples and the spatial relationship of the samples that are randomized
(Ugland et al., 2003).
Richness and visitation rate of wild pollinators are strongly correlated across
agricultural fields globally (Garibaldi et al. 2014). Therefore, practices that enhance
habitats to promote species richness are also expected to improve the aggregate
abundance of pollinators, and vice versa.
Flower Density
A treatment × evaluation interaction was observed (P < 0.0001) for flower density
evaluated over two years (2017 and 2018). In April, a greater density of flowers (P <
0.05) was observed for Grass+clover and Grass+CL+RP when compared with
Grass+N, while in May, a greater density of flowers was only observed in Grass+CL+RP
169
when compared to Grass+N (Figure 6-14). No other differences were observed between
treatments in any of the other evaluation months. Those observations are supported by
the botanical composition data, which showed greater presence of clover in April,
averaging 62% of the dry matter (DM), while in July the presence of the inflorescence of
bahiagrass was greater than in other months averaging 73% of bahiagrass DM. There
are correlative evidence links in flower diversity and pollinator species richness that
leads to a pollination success that enhances crop yield without the use of managed
honey bees (Hoen et al., 2008). Functional interaction between flower and pollinators
offer ecosystem services that contribute to the stability, productivity and sustainability of
landscapes (Riddley et al., 2016). Furthermore, the month when more bees were
collected was May and June (Figures 6-4 and 6-7), showing a strong positive
relationship between the presence of bees and floral resources available. May and June
are the months of the transition between cool and warm-season forages, thus, the
Grass+CL+RP system had the mixture of flowers from clovers, rhizoma peanut and the
remaining inflorescences of rye and oat, plus the blooming period of bahiagrass.
Conclusions
Most of the species collected in the grazing trial were small bees, indicating that
the bees were foraging and had nests in the grazing trial or in a short radius. Medium
body size bees were in majority belonging to the species Melissodes communis, and
their number equal the number of small bees that belong to Lassioglosum spp.
therefore even in the homogeneity of the landscape of the grazing trial foraging and
nesting resources are offered for different species. The presence of flower diversity and
the phenology of the Grass+CL+RP system favored the presence of bees, which was
evident by their greater abundance. Well managed grasslands increase landscape
170
structure and flower resources providing stability to bee communities. In our study, the
inclusion of legumes in livestock-forage system was successful at enhancing ecosystem
services related to pollination.
171
Table 6-1. List of bee species and counts of individuals collected in the grazing trial per treatment from 2016 to 2018.
Treatment1
Bee species Grass+N Grass+clover Grass+CL+RP
Apis mellifera 19 34 26
Bombus bimaculatus 3 1 15
Bombus pensylvanicus 1 0 0
Melissodes communis 362 458 531
Melissodes bimaculata 10 15 10
Melissodes trinodis 1 1 0
Ptilothrix bombiformis 2 0 0
Eucera rosae 0 1 0
Lassioglosum spp. 411 370 533
Augochlorella aurata 0 1 3
Augochlora pura 0 1 1
Augocholorpsis metallica 1 0 0
Agapostemon splendens 0 1 1
Halictus rubicundus 1 0 0
Halictus poeyi 7 10 11
Andrena perplexa 0 1 1
Andrena cressonii 2 0 0
Megachile relativa 0 1 0 1 Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat plus a mixture of clover + 34 kg N ha-1 during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
172
Figure 6-1. Monthly average solar radiation (w m2 -1) and temperature from 2016 to
2018 in the experimental area, Marianna, FL. The circles mark the periods of maximum number of bees collected.
0
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Months
Solar radiation (w/m2) T (°C)
173
Figure 6-2. Monthly average rainfall mm from 2016 to 2018 in the experimental area,
Marianna, FL. The circles denote the dry periods, when rainfall decreased, and a greater number of bees were collected at each evaluation.
Figure 6-3. Effect of trap color on presence of honey bees per trap from 2016 to 2018. Treatment effect, P = 0.001. a,b,c Means differ, P < 0.05.
0
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174
Figure 6-4. Presence of medium bees per trap color and per evaluation from 2016 to
2018. Trap color × evaluation, P < 0.001. a,b Within month, means differ, P < 0.05.
0
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diu
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ee
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175
Figure 6-5. Presence of bees per trap color from 2016 to 2018. Treatment × Trap color, P < 0.001. a,b,c Within treatment, means differ, P < 0.05 Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
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Grass+N Grass+clover Grass+CL+RP
Smal
l be
es
trap
-1ye
ar -1
Blue White Yellow
b
b
a
c
a
b
c
a
a
176
Figure 6-6. Presence of honey bees per treatment from 2016 to 2018. Treatment effect P = 0.62. Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
Ho
ney
be
es
trap
-1ye
ar -1
Grass+N Grass+clover Grass+CL+RP
177
Figure 6-7. Presence of small bees per treatment per evaluation from 2016 to 2018. Treatment × evaluation, P = 0.009. a,b Within month, means differ, P < 0.05. Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
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Grass+CL+RP
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Figure 6-8. Abundance of bees per treatment from 2016 to 2018. Treatment effect, P = 0.003, a,b,c Means differ, P < 0.05 Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
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160To
tal b
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bb
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179
Figure 6-9. Total bees comparing the grass monoculture system and the grass legume
mixture. Contrast, P = 0.01, a,b Means differ, P < 0.05.
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Grass-legume Grass-N
Tota
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180
Figure 6-10. Estimated species richness for each treatment (Chao 1 index). Treatment effect, P = 0.05, (Grass+clover vs. Grass+CL+RP, P = 0.07). Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argetnine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
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Grass+N Grass+clover Grass+CL+RP
Ch
ao 1
ind
ex
181
Figure 6-11. Estimated species diversity for each treatment (Shannon-Wiener diversity
index). Treatment effect, P = 0.007. a,b Means differ, P < 0.05. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season +112 kg N ha-1 ; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
0
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ann
on
-Wie
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Grass-N Grass-Clover Grass-RP-CL
ba ab
182
Figure 6-12. Estimated species diversity for each treatment (Simpson inverse diversity
index). Treatment effect, P = 0.004, a,b Means differ, P < 0.05. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
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Sim
pso
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183
Figure 6-13. Species accumulation curve. This curve was generated using the software EstimateS. Chao 1 and abundance-based coverage estimator (ACE), were used as non-parametric methods to predict number of species.
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S(est)
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Figure 6-14. Total flower density by treatment during 2017 and 2018. Treatment × evaluation P <0.0001. a,b Means differ, P < 0.05. Error bars denote standard error. Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season, overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season + 112 kg N ha-1; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
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CHAPTER 7 SUMMARY
The introduction of legumes into grazing systems and livestock production in
North Florida could greatly enhance ecosystem services they provide, and some of
those services were documented and discussed in this dissertation (Table 7.1). When
legumes are included in the cool-season and are compared with systems based on N
fertilization (such as Grass+N from the studies conducted in this dissertation), the
advantage in terms of improvements on nutritive value is limited. Because of the greater
digestibility and crude protein concentration of winter-annual grasses, the extra CP
provided by the clovers included in the pastures did not provide an advantage in animal
performance. However, it is important to highlight that the systems that included
legumes in the cool-season were able to maintain a similar beef production level than
those that relied on N fertilizer, averaging 322, 352, and 324 kg ha-1 season for
Grass+N, Grass+clover, and Grass+CL+RP, respectively. This may result in an
economic advantage for the systems that included clovers in the cool-season,
considering the increasing cost of fertilizer in contrast with clover seed and planting
costs. Perhaps more important is the contribution of clovers in terms of biological N
fixation, which averaged 43 kg N ha-1 season-1 in the cool-season and the systems with
the legume were able to replace 79 kg N ha-1 during the cool-season and resulting with
similar steer gains. In terms of sustainability of forage-livestock systems in North
Florida, the amount of N fixed by clovers is perhaps the most important ecosystem
service of these legumes in the cool-season. The inclusion of rhizoma peanut as a
warm-season legume greatly enhanced animal performance. Integrating rhizoma
peanut increased ADG by 70% when compared with systems without legumes, and
186
increased gain per area when compared with a system with neither warm-season
legumes nor N fertilizer. The amount of beef produced in the Grass+CL+RP was similar
to that in a system with 224 kg N ha-1 instead of legumes (gain per area of 306 and 211
kg ha-1 season-1, respectively). Combining the gain per area in both seasons, the
system that includes rhizoma peanut and clovers resulted in 630 kg BW ha-1 year-1,
which is quite substantial considering the inputs to this system.
Regarding ecosystem services related to nutrient cycling, the inclusion of
legumes, especially rhizoma peanut during the warm-season, resulted in lesser
nutrients returned via animal excreta because of lower stocking rate, and greater
proportion of N returned via urine. This likely enhanced N losses via ammonia
volatilization and denitrification, but reduced nitrate leaching, which is a major problem
in the Jackson Blue Spring Basin. On average, the Grass+CL+RP returned 70 kg N ha-1
yr-1 to the pasture when combining urinary and fecal excretions from the grazing steers.
The total amount of N returning to the soil in Grass+CL+RP tended to be less than that
returning to the soil in Grass+N. Considering fertilizer costs, it could be arguedd that the
excess N that returns from the steers grazing the fertilizer-based systems is an
expensive way to provide N to the system because of the inefficiencies associated with
the conversion of fertilizer N to pasture and livestock gain, to then return to the soil. The
overall reduced amount of N returned in the rhizoma peanut system can be considered
both more environmentally friendly and more cost effective. Perhaps the greatest
challenge of the Grass+CL+RP system would be to manage grazing in the rhizoma
peanut strips in a manner that balances the risk of overgrazing the legume, with the
need to consume as much of the bahiagrass as possible. Future studies should focus
187
on determining optimal herbage allowance to maximize livestock and forage
performance in the long term.
Very few studies have been conducting assessing greenhouse gas emissions in
forage-based systems in the southeastern U.S. The inclusion of legumes did not reduce
enteric CH4 emissions or emissions intensity in either grazing season. However, the
enteric CH4 emissions intensity in the cool-season were decreased by 58% because of
the greater weight gains by steers. The data generated in this dissertation in terms of
enteric methane emissions under grazing conditions could be useful in future
assessments of the carbon footprint of beef production in the southeast. Perhaps the
inclusion of warm-season legumes containing secondary metabolites such as tannins
could be beneficial in future studies to decrease CH4 emissions as has been
documented elsewhere. However, this needs to be contrasted with any effects on
performance and thus overall productivity of the system, because it has been
documented that tannin-containing legumes can have a negative impact on forage
intake and protein digestibility.
The impact of agricultural practices on pollination services is becoming more
important as studies continue to emerge documenting the impact of pollinators on crop
yields. The inclusion of legumes both in the cool and warm-season, led to a greater
abundance of pollinators, mainly medium-body size bees. This study provided evidence
of the type of pollinators present in these systems, which were for the most part bees
from the species Melissodes communis, which seemed to forage and nest in a relatively
small radius relative to the grazing trial. The presence of flower diversity and the
phenology of the Grass+CL+RP system in particular, favored the presence of bees.
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Because of the findings in the chapter related to pollinator’s presence and diversity, it
would be interesting in future studies to assess the economic impact of this particular
ecosystem service. The development of a multidisciplinary approach that includes
economists, entomologists and agronomists, may be needed in order to fully assess the
economic value of this particular ecosystem service, which appears to be becoming
more relevant every day.
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Table 7-1. Summary of ecosystem services provided in the grazing trial during the cool- and warm season.
Treatment1 Ecosystem service Grass+N Grass+
clover Grass+ CL+RP
Provisioning ADG Greatest in the
warm-season Gain per area Greater than
Grass+clover in the warm-season
Greater than Grass+clover in the warm-season
Supporting Nutrient cycling Fastest
nutrient recycling
Regulating Enteric methane emissions Similar among
treatments and lesser in cool-season
Similar among treatments and lesser in cool season
Similar among treatments and lesser in cool season
Pollination Greater presence of bees
Greater presence of bees
1Grass+N = N-fertilized (112 kg N ha-1) argentine bahiagrass pastures during the warm-season,
overseeded with a mixture of FL 401 cereal rye and RAM oat during the cool-season +112 kg N ha-1 ; Grass+clover = unfertilized argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season; Grass+CL+RP = rhizoma peanut and argentine bahiagrass pastures during the warm-season, overseeded with similar rye-oat mixture fertilized with 34 kg N ha-1 plus a mixture of clovers during the cool-season.
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BIOGRAPHICAL SKETCH
Liza Maria Garcia graduated with a degree in marine biology from Jorge Tadeo
Lozano University, in Colombia in 2001. During her undergraduate studies, she worked
in research projects with coral reefs and wetlands. She was awarded an internship at
the Smithsonian Tropical Research Institute (STRI) in Panama from 2005 to 2006,
where she had the opportunity to interact with international researchers and collaborate
in multiple projects in phylogeny. In 2010 she graduated from her M.Sc. degree in
biological sciences from Texas Tech University. Through her M.Sc. degree she worked
in research projects in the area of population genetics. From 2014 to 2015 she worked
as a research assistant at the North Florida Research and Education Center in a project
funded by the USDA to monitor the impact of bermudagrass stem maggot in producers’
fields across the Florida Panhandle. In 2016 she started her PhD program in the
Agronomy Department at the University of Florida under the direction of Dr. Jose
Dubeux. Her research involved evaluating ecosystem services from grasslands. She
completed her Ph.D. in August of 2019.