University of Florida Thesis Or Dissertation Formatting Template

ECOSYSTEM SERVICES PROVIDED BY CONTRASTING GRAZING SYSTEMS IN NORTH FLORIDA

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

To my lovely family

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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.

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

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).

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

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.

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).

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,

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

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

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

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;

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

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

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

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

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

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.

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

(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

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

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

13 proportion of C3 and C4 species in the diet can be predicted based on δ C from fecal samples (Pereira et al., 2019). Jones et al. (1979) reported differences δ13C (-28.7‰) in

13 feces from cattle fed with C3 (tropical legumes), compared with a δ C 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

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

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-

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

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,

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

Agriculture, Forestry and Other Land Use sector (AFOLU), and comprise approximately

-1 25% (10–12 GtCO2eqyr ) 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

–1 agriculture, accounting for 2.8–6.2 Tg N2O yr . 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

-1 -1 grazing cattle might contribute from 0.37 to 1.20 Mg CO2-Ceq ha yr (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).

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

(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)

-1 estimated emissions of enteric CH4 of 6.241 Tg yr (minimum = 4.972 and maximum =

7.511), which is comparable to the current 2011 US EPA estimates of 6.542 Tg yr-1.

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

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%

-1 more CH4 expressed as L kg 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

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

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

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).

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

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

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.

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.

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

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

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):

15 15 15 %Ndfa = (δ N of reference plant - δ N of N2-fixing legume)/( δ N of reference plant –B) × 100 (3-1)

The %Ndfa is the percentage of plant N derived from atmospheric N, and B is the

15 δ N 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

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.

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

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

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

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

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

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).

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

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

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

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

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

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,

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

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,

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,

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.

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).

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

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

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

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.

Table 3-1. Herbage mass, herbage allowance and herbage accumulation rate during the cool-season of 2016 and 2017.

Treatment1 P-value3 Item Grass+ Grass+ Grass+ SE2 Trt Eval T × E N clover CL+RP Herbage mass, kg ha-1 658 755 722 77.1 0.44 0.04 <0.01 Herbage allowance, kg 0.79 0.83 0.81 0.081 0.35 0.18 0.18 DM kg-1 BW-1 Herbage accumulation 21 26 16 1.48 0.27 0.03 <0.01 rate, kg ha-1d-1 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+ Grass+ Grass+ SE2 Trt Eval T × E N clover CL+RP 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.

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+ Grass+ SE2 Trt Eval T × E clover CL+RP 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 51 36 19.0 0.21 - - season-1 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.

Table 3-4. List of reference plants and δ15N in the cool-season of 2016 and 2017.

Scientific name Year Evaluation δ15N, ‰ 푋 ± 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 푋 = 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.

Table 3-5. Animal performance during the cool-season of 2016 and 2017.

Treatment1 Item Grass+N Grass+ Grass+C SE2 P-value clover L+RP 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 -1 -1 Gain per area, kg ha season 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+ Grass+ SE2 Trt Eval T × E clover CL+RP Herbage mass, 2369a 2152b 1728c 224.7 <0.01 <0.01 0.01 kg ha-1 Herbage 1.2 1.2 1.2 0.072 0.61 <0.01 0.90 allowance, kg DM kg BW-1 Herbage 37a 36a 14b 4.03 <0.01 <0.01 0.02 accumulation rate4, kg ha-1d-1 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.

Table 3-7. Nutritive value of bahiagrass, during the warm-season (2016 and 2017).

Treatment1 P-value3 Item Grass Grass+ Grass+ SE2 Trt Eval T × E +N clover CL+RP 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+ Grass+CL SE2 Trt Eval T × E clover +RP 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 - - 0.07 0.02 - 0.024 - evaluation-1 BNF4, kg N ha-1 - - 11.7 - - - - season-1 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.

Table 3-9. List of reference plants and δ15N in the warm-season of 2016 and 2017.

Scientific name Year Evaluation δ15N, ‰ 푋 ± 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

Table 3-9. Continued

Scientific name Year Evaluation δ15N, ‰ 푋 ± 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 푋 = 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.

Table 3-10. Animal performance during the warm-season of 2016 and 2017.

Treatment1 Item Grass+N Grass+ Grass+ SE2 P-value clover CL+RP 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 -1 -1 ab b a Gain per area, kg ha season 211 166 306 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.

2000 * 1800

1600

)

1 1 -

d 1400 1 - * 1200 * * 1000 800 600 400

200 Herbagemass DM (kg ha 0 January January February February March March April April May

Evaluation

Grass+N Grass+clover Grass+CL+RP

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).

) * 1 - ‡ 70 * ‡ 60

10 Herbageaccumulation rate DM (kg ha 0 January January February February March March April April May Evaluation

Grass+N Grass+clover Grass+Cl+RP

Figure 3-2. Total herbage accumulation rate during the cool-season (kg DM ha-1 d-1).

300 *

250 ‡ DM )

1 200

- g kg g 150

100

CrudeProtein ( 50

0 January January February February March March April April May

Evaluation Grass+N Grass+clover Grass+CL+RP

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).

900

800

) 1 1

- 700

600

500

400

300

200 IVDOM IVDOM ryeand oat kg (g 100

0 January January February February March March April April May Evaluation

Grass+N Grass+clover Grass+CL+RP

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.

5 * a 4 3

‰) ‡

N ( 15

δ 1 0 -1 -2

and Rye oat -3 January January February February March March April April May Evaluation (month) Grass+N Grass+clover Grass+CL+RP

0 b January January February February March March April April May -5 -10 Evaluation (month)

-15 ‰) C C ( -20 13 * δ ‡ -25 -30

-35

and Rye oat -40

Grass+N Grass+clover Grass+CL+RP

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.

6 a * 5 * C * * 4 V

‰) N ( N

15 2 δ

Feces 0 January February March June July Evaluation

Grass+N Grass+clover Grass+CL+RP

0 b January February March April May -5 Evaluation

-10 ‰)

-15 C C (

13 δ -20 ‡ -25 *

Feces -30 -35 -40

Grass+N Grass+clover Grass+CL+RP

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.

7 * ‡ a * 6 *

5 ‰) 4

N ( N 15

δ 3

2 Plasma Plasma 1 0 January February March April May Evaluation Grass+N Grass+clover Grass+CL+RP

0 b January February March April May -5 Evaluation -10 ‰)

C C ( 13 -15 δ * -20 ‡

Plasma Plasma -25

-30

Grass+N Grass+clover Grass+CL+RP

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.

6 a * ‡ 5

‰) 4 N ( N

15 δ 3

Red bloodRed 1

0 January February March April May

Evaluation Grass+N Grass+clover Grass+CL+RP

0 b January February March April May -5 Evaluation

‰) -10 C C (

13 δ -15 -20

Red bloodRed -25 -30

Grass+N Grass+clover Grass+CL+RP

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.

‡ ‡ ‡ a b 25 ‡ * * ‡ 100 * * * * * 20 80 * 15 60 * 10 40 5

DW Grass (%) Grass DW 0 20 (%) Weeds DW Jan Feb April June Aug Sept 0 Jan Feb April June Aug Sept Month Month Grass+N Grass+clover Grass+CL+RP Grass+N Grass+clover Grass+CL+RP

c 70 d 60 60 50 50 40 40 30

30 DW RP (%) RP DW 20 20 DW Clover (%) Clover DW 10 10 0 0 Jan Feb April June Aug Sept Jan Feb April June Aug Sept Month Month Grass+clover Grass+CL+RP Grass+CL+RP

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.

5000 † † ‡ 4500 * * ‡ * 4000 ‡

) * ‡ 1 - 3500 † * 3000 * 2500

2000

1500 Herbagemass ha (kg 1000

500

Evaluation

Grass+N Grass+clover Grass+CL+RP

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.

700 * ‡ 600 * *

500 )

1 - 400 300 200 100

IVDOM IVDOM kg (g 0

Evaluation

Grass+N Grass+clover Grass+CL+RP

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.

100

a 800

) 700

1 - 600 500 400 300 IVDOM IVDOM kg (g 200 100 0

Evaluation Grass+CL+RP

b 250

)

200

DM 1

150

100

CrudeProteinkg (g 50

Evaluation

Grass+CL+RP

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.

101

80 70 60 50 40 30 Ndfa(%) 20 10 0 May June July August September October Evaluation Grass+CL+RP

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.

102

a 2

1.5 ‰)

1 N ( N

15 0.5

-0.5

-1 -1.5 Rhizomapeanut δ -2

Evaluation

Grass+CL+RP

b 0

-5

‰)

C ( -10 Evaluation 13 -15 -20

-25

-30 Rhizomaδ peanut -35 -40 Grass+CL+RP

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.

103

7 * a 6 * ‡ 5

4 ‰)

3 N (

15 δ 2

Feces 0

Evaluation Grass+N Grass+clover Grass+CL+RP

0 b May June June July July August August Septem October -5

-10 Evaluation -15

C C (‰) -20

13 δ -25

Feces -30

-35

Grass+N Grass+clover Grass+CL+RP

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.

104

8 * * ‡ † a * † † 7 * ‡ * ‡ * 6 5 ‰)

N ( N 4

15 δ 3 2 Plasma Plasma 1 0

Evaluation Grass+N Grass+clover Grass+CL+RP

0 b May June June July July August August Septem October

-5 Evaluation

‰) -10 C C (

13 δ -15

Plasma -20

-25 Grass+N Grass+clover Grass+CL+RP

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.

105

12 a 10

‰) 8

N ( N 15 6 δ 4 2

Red bloodRed 0

Evaluation Grass+N Grass+clover Grass+CL+RP

0 b May June June July July August August Septem Octo -5 Evaluation * * -10 ‡ * ‡ * * ‡ * ‡ * * ‡ ‰) -15 *

C (

13 -20

δ -25

-30

Red bloodRed -35 Grass+N Grass+clover Grass+CL+RP

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.

106

) 20

1 1 -

5 Blood ureaBlood(mg dL N

0 May June June July July August August September October

Evaluation Grass+N Grass+clover Grass+CL=RP

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.

107

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.

108

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;

109

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

110

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

111

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

112

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

113

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

114

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:

13 13 13 13 ƒtotal 1 = (δ Csample- δ C source 2) / (δ C source 1 – δ C source 2) (4-1)

ƒtotal 2 = 1 - ƒtotal 1 (4-2)

13 13 Where ƒtotal 1 represents the fraction of source 1 and source 2, δ Csample is the δ C 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

115

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

116

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

117

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)

118

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.

119

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

120

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

121

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,

122

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

123

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).

124

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

125

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

126

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

127

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.

128

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+ Grass+ SE2 P-value clover CL+RP 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.

129

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.

Treatment1 P-value2 Grass Grass Grass SE3 T E T × E Item +N + +CL clover +RP 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.

130

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+ Grass+ Grass+ SE3 P-value N clover CL+RP 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 -1 -1 Calcium, kg ha season 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.

131

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 Grass+C SE3 P-value over L+RP 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.

132

Table 4-6. Total annual nutrient excretion from beef steers grazing three forage systems in 2016 and 2017.

Treatment1 Item Grass+N Grass+ Grass+ SE2 P-value clover CL+RP 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 -1 -1 Calcium, kg ha yr 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

133

0.5

0.45 a 0.4 ab

) a 1 1

- 0.35 d

1 a - 0.3 b 0.25 b Grass+N

0.2 Grass+clover

0.15 Grass+CL+RP

0.1

0.05 Total N excretion (kg ha (kg excretion N Total 0 January February March April May Evaluation

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.

134

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

-1 -1 cattle might contribute from 0.37 to 1.20 Mg CO2-Ceq ha y (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).

135

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

136

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

137

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.

138

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

139

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

140

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)

-1 Where QCH4 is methane emissions per animal (g d ) and QSF6 is considered SF6 release

-1 rate (mg d ). 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

141

[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:

13 13 13 13 ƒtotal 1 = (δ Csample- δ C source 2) / (δ C source 1 – δ C source 2) (5-2)

ƒtotal 2 = 1 - ƒtotal 1 (5-3)

13 13 Where ƒtotal 1 represents the fraction of source 1 and source 2, δ Csample is the δ C 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

142

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

143

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,

-1 and did not differ among treatments (P = 0.77). When expressed as g of CH4 kg 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),

144

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.

145

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

-1 24.1 g of CH4 kg 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

-1 contained only bahiagrass in the warm-season (24.1 and 24.2 g of CH4 kg 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

146

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

-1 forages (397 vs. 225 g of CH4 kg of ADG 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

-1 emissions, expressed as g CH4 kg of ADG , 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.

-1 warm-season when expressed as g CH4 kg of ADG (147 vs. 357 for cool and warm- season, respectively). This is in agreement with previous reports comparing warm-

147

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

148

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.

149

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 Warm-season Cool-season Warm-season dates dates dates 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 March 10 to 18 June 28 to July 6 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 -1 CP g kg 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.

150

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+ Grass+ SE2 P-value clover CL+RP 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 -1 -1 CH4 g steer d 96 112 90 21.9 0.77 (0.75)-1 CH4 BW 1.4 1.7 1.4 0.3 0.75 -1 -1 CH4 g ha d 358 392 313 78.2 0.77 -1 CH4 g kg of DMI 21.6 22.0 25.0 9.2 0.96 -1 CH4 g kg of ADG 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.

151

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+ Grass+ SE2 P-value clover CL+RP 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 -1 -1 CH4 g steer d 117 113 101 24.8 0.90 -(0.75) CH4 BW 1.4 1.4 1.2 0.73 0.91 -1 -1 CH4 g ha d 548 447 359 96.2 0.40 -1 CH4 g kg of DMI 24.1 24.2 17.4 5.4 0.61 -1 CH4 g kg of ADG 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.

152

3.5

2.5

1.5

Dry matter intake as % of BW of % as intake matter Dry 0.5

0 Cool Warm Grass+N Grass+clover Grass+CL+RP

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.

153

550 500 450

1 400 - 350 300

250 g kg ADGofkg g

4 200

CH 150 100 50 0 Cool Warm

Grass+N Grass+clover Grass+CL+RP

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 -1 mixture of FL 401 cereal rye and RAM oat during the cool-season 112 kg N ha ; 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.

154

70 % C3 Feces %RP DW Selection Index 4.5 4 60 3.5 50 3

40 2.5

30 2

1.5 index Selection 20 1

% C3 in Feces or % RP RP % orDW Feces % inC3 10 0.5

0 0 June August September

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.

155

CHAPTER 6 MANAGING GRASSLAND STRUCTURE TO ENHANCE POLLINATOR HABITAT

Introduction

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

156

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-

157

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.

158

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/).

159

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

160

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

161

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

162

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

163

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

164

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

165

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

166

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

167

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

168

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

169

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.

170

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.

171

250 30

) 1 1

- 25

2 200 C) 20 ° 150 15 100 10

50 ( Temperature

5 Solar radiation (w m (w radiation Solar

0 0

Jul-16 Jul-17 Jul-18

Jan-17 Jan-16 Jan-18

Sep-16 Sep-17 Sep-18

Nov-16 Nov-17

Mar-16 Mar-17 Mar-18

May-16 May-17 May-18 Months

Solar radiation (w/m2) T (°C)

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.

172

350

300

250

200

150

Rainfall Rainfall (mm) 100

Rainfall, mm

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.

0.25 a

1 -

0.2

0.15

b b

0.1 Honey bees trap trap bees Honey 0.05

Blue White Yellow

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.

173

1 - 20

15 a 10 a a

5 b b b

Medium bees trap trap bees Medium b b b b b 0

Blue White Yellow

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.

174

5 1 - a 4 a

year year a b

1 - 3 b a 2

c b 1

c Small bees trap bees Small

0 Grass+N Grass+clover Grass+CL+RP

Blue White Yellow

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.

175

year year

- Honey bees trap bees Honey

Grass+N Grass+clover Grass+CL+RP

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.

176

Grass+clover 5 a 4.5 Grass+N

1 ab - 4 3.5 Grass+CL+RP 3 b 2.5 2 1.5 1

Small bees treatment bees Small 0.5 0

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.

177

160 a 140

120 b

1 - 100

40 Total bees year year bees Total

0 Grass+N Grass+clover Grass+CL+RP

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.

178

160 a

140

1 - 120 b

100

40 Total bees year year bees Total 20

0 Grass-legume Grass-N

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.

179

Chao 1 1 Chao index 4

0 Grass+N Grass+clover Grass+CL+RP

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.

180

1.2 a ab 1 b

0.8

0.6

Wiener index Wiener - 0.4

0.2 Shannon

0 Grass-N Grass-Clover Grass-RP-CL

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.

181

2.5 a ab b

1.5

0.5 Simpson inverse index inverse Simpson 0 Grass+N Grass+clover Grass+CL+RP

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.

182

3.5

2.5

2 S(est) 1.5 Chao 1

1 ACE

Estimated number of species of number Estimated 0.5

0 0 5 10 15 20 Cumulative number of samples

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.

183

600 2 - 500 a 400 a 300 a number 10 m 10 number ab 200 b b

Flower 100

Grass+N Grass+clover Grass+CL+RP

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.

184

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

185

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

186

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.

187

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.

188

Table 7-1. Summary of ecosystem services provided in the grazing trial during the cool- and warm season.

Treatment1 Ecosystem service Grass+N Grass+ Grass+ clover CL+RP Provisioning ADG Greatest in the warm-season Gain per area Greater than Greater than Grass+clover Grass+clover in the warm- in the warm- season season Supporting Nutrient cycling Fastest nutrient recycling Regulating Enteric methane emissions Similar among Similar among Similar among treatments treatments treatments and lesser in and lesser in and lesser in cool-season cool season cool season Pollination Greater Greater presence of presence of bees 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.

189

LIST OF REFERENCES

Aiken, G. 2014. Cool-season annual grasses interseeded into a bermudagrass with improved cold tolerance for grazing in the upper south. Forage and Grazinglands. 1-6.

Allard, V., J.-F. Soussana, R. Falcimagne, P. Berbigier, J.M. Bonnefond, E. Ceschia, P. D’hour, C. Henault, P. Laville, C. Martin, and C. Pinares-Patino. 2007. The role of grazing management for the net biome productivity and greenhouse gas budget (CO2, N2O, and CH4) of semi-natural grassland. Agric. Ecosyst. Environ. 121:47–58. doi:10.1016/j. agee.2006.12.004.

Allen, V. G., Batello, C., Berretta, E. J., Hodgson, J., Kothmann, M., Li, X., Sanderson, M. 2011. An international terminology for grazing lands and grazing animals. Grass and Forage Science, 66(1), 2–28. https://doi.org/10.1111/j.1365- 2494.2010.00780.

Archimède, H., M. Eugène, C. Marie Magdeleine, M. Boval, C. Martin, D.P. Morgavi, P. Lecomte, and M. Doreau. 2011. Comparison of methane production between C3 and C4 grasses and legumes. Anim. Feed Sci. Technol. 166–167: 59–64.

Ball, D.M., Hoveland, C.S., Lacefield, G.D., 2001. Southern Forages: Modern Concepts for Forage Crop Management International Plant Nutrition Institute, Peachtree Corners, GA.

Bhandari, K., West., C., Longing, S., Brown, C., Green, P., Barkowsky, E. 2018. Pollinator abundances in semi-arid pastures as affected by forage species. Crop sci. 8-15. doi:10.2135/cropsci2018.06.0393.

Batary, P., Baldi, A., Kleijn, D., Tscharntke, T. 2011. Landscape-moderated biodiversity effects of agri-envirnmental management: a meta-analysis. Prc. R. Soc. B. 278:1894-1902.

Black, S. H., M. Shepherd, and M. Vaughan. 2011. Rangeland management for pollinators. Range- lands 33:9–13.

Beauchemin, K., Kreuzer, M., Mara, F., McAllister, T. 2008. Nutritional management for enteric methane abatement: a review. Aust. J. of expe. Agriculture. 48, 21-27.

Boller, B., Nosberger, J. 1987. Symbitically fixed nitrogen from field grown white and red clover mixed with ryegrass at low levels of 15N-fertilization. Plant and soil, 104: 219-226.

Breeze, T.D., Bailey, A.P., Balcombe, K.G, Potts, S.G. 2011. Pollination services in the UK: how important are honey bees? Agri.Ecosy.Serv. Vol 142, 3-4: 137-143.

Brink, G. E., Pederson, G. A., Sistani, K. R., Fairbrother, T. E. 2001. Uptake of selected nutreints by temperate grasses and legumes. Agronomy J. 93, 4:887-890.

190

Broderick, G. 1995. Desirable characteristics of forage legumes for improving protein utilization in ruminants. Journal of Animal Sci. 73 (9):2760-2773.

Burns, J.C., and L.E. Sollenberger. 2001. The conduct of grazing trials: Measurements explaining why animal response differences occur. In: D. Lang, editor, Proc. 56th Southern Pasture Forage Crop Improvement Conf., 21–22 Apr. 2001, Springdale, AR. p. 18–24.

Butler, T.J., J.P. Muir, M.A. Islam, and J.R. Bow. 2007. Rhizoma peanut yield and nutritive value are influenced by harvest technique and timing. Agron. J. 99: 1559–1563.

Byrnes, R. C., Nunez, J., Arenas, L., Rao, I., Trujillo, C., Alvarez, C., Arango, J., Rasche, F., Chirinda, N. 2017. Biological nitrification inhibition by Brachiaria grasses mitigates soil nitrous oxide emissions from bovine urine patches. Soil Biology & Biochemistry 107, 156–163.

Cameron, S.A., J.D. Lozier, J.P. Strange, J.B. Koch, N. Cordes, L.F. Solter, & T.L. Griswold. 2011. Patterns of widespread decline in North American bumble bees. Proceedings of the National Academy of Sciences, 108(2): 662–667.

Cane, J.H., Minckley, R.L., Kervin, J.L. 2000. Sampling bees (Hymenoptera:Apiformes) for pollinator community studies: pitfalls of pan trapping. Journal of the Kansas Entomological Society. 73(4): 225-231.

Cane, J.H. 1991. Soils of ground-nesting bees (Hymenoptera : Apoidea): Texture, moisture, cell depth and climate. Journal of the Kansas Entomological Society 64:406- 413.

Cantalapiedra-Hijar, G., Ortigues-Marty, I., Sepchat, B., Agabriel, J., Huneau, F., Fouillet, H. 2015. Diet -animal fractionation of nitrogen stable isotopes reflects the efficiency of nitrogen assimilation in ruminants. British J. of Nutri. 113: 1158- 1169.

Carpenter, S., Mooney, H., Agard, J., Capistrano, D., Defires, R., Diaz, S., Dietz, T., Duraiappah, A., Oteng-Yeboah, A., Pereira, H., Perrings, C., Reid, W., Sarukhan, J., Scholes, R., Whye, A. 2009. PNAS. Vol 106-5:1305-1312.

Castillo, M.S., L.E. Sollenberger, A.R. Blount, J. Ferrell, M.J. Williams, and C.L. Mackowiak. 2013a. Strip planting a legume into warm-season grass pasture: Defoliation effects during the year of establishment. Crop Sci. 53(2): 724-731.

Chambliss, C., and L. Sollenberger. 1991. Bahiagrass: The foundation of cow-calf nutrition in Florida. p. 74– 80. In Florida beef cattle short course. Gainesville.

Chao, A., Gotelli, N.J., Hsieh, T.C., Sander, E.L., Ma, K.H., Colwell, R.K. & Ellison, A.M. 2014. Rarefaction and extrapolation with Hill numbers: a framework for sampling and estimation in species diversity studies. Ecological Monographs, 84, 45–67.

191

Change Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M.Tignor and H.L. Miller (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Chen, W., Huang, D., Liu, N., Zhang, Y., Bagery, W., Wang, X., Shen, Y. 2015. Improved grazing management may increase soil carbon sequestration in temperate steppe. Sci. Report. 5:10892, 1-13.

Chizzotti, M.L., Valadares, S.C., Valadares, F. R.F.D., Chizzotti, F., Tedeshi, L. 2008. Determination of creatinine excretion and evaluation of spot urine sampling in Holstein cattle. Livestock Science. Vol.113, (2-3):218-225.

Clark, C., McLeod, K., Glassey, C., Gregorini, P., Costall, D., Betteridge, K., Jago, J. 2010. Capturing urine while maintaining pasture intake, milk production, and animal welfate of dairy cows in early and late lactation. Journal of Dairy Science. Vol 93 (5):2280-2286.

Cole, L., Brocklehurst, S., Robertson, D., Harrinson, W., McCracken, D. 2017. Agriculture, Ecos. and Environ. 246: 157-167.

Coleman, S. W., S. A. Gunter, J. E. Sprinkle, and J. P. S. Neel. 2014. Beef species symposium: Difficulties associated with predicting forage intake by grazing beef cows. J. Anim. Sci. 92:2775–2784. https://doi.org/10.2527/jas.2013-7090

Colwell, R. K., A. Chao, N. J. Gotelli, S.-Y. Lin, C. X. Mao, R. L. Chazdon, and J. T. Longino. 2012. Models and estimators linking individual-based and sample- based rarefaction, extrapolation, and comparison of assemblages. Journal of Plant Ecology 5:3-21

Conant, R.T., and K. Paustian. 2002. Potential soil carbon sequestration in overgrazed grassland ecosystems. Global Biogeochem. Cycles 16:90-1–90-9. doi:10.1029/2001GB001661.

Cononat, R., Paustian, T., Elliot, T. 2001. Grassland management and conversion into grassland: Effects on soil carbon. Ecol. Appl. 11:343–355.

Cook, S.J., R.L. Clem, N.D. MacLeod, and P.A. Walsh. 1993. Tropical pasture establishment. 7. Sowing methods for pasture establishment in northern Australia. Trop. Grassl. 27:335-343.

Cotrufo, M.F., Wallenstein, M.D., Boot, C.M., Denef, K., Paul, E. 2013. The microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter ecomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Global change biol. 19: 988-995.

Crews, T.E., Peoples, M.B. 2004. Legume versus fertilizer sources of nitrogen: ecological tradeoffs and human needs. Agric.Ecos.Environ. 102:279-297.

192

Decourtye, A., E. Mader, and N. Desneux. 2010. Landscape enhancement of floral resources for honey bees in agro-ecosystems. Apidologie 41:264–277

Dick, M., Abreu da Silva, M., Dewes, H. 2015. Mitigation of environmental impacts of beed cattle production in southern Brazil - evaluation using farm-based life cycle assessment. J. of Cleaner Prod. 87:58-67.

Droege, S., Tepedino, V.J, LeBuhn, G., Link, W., Minckley, R.L., Chen, Q., Conrad, C. 2010. Spatial patterns of bee captures in North American bowl trapping surveys. Insect Conserv. Divers 3:15–23.

Droege, S. 2012. The very handy manual: how to catch and identify bees and manage a collection. U.S. Geological Survey, Beltsville, MD. http://www.pwrc.usgs.gov/nativebees/Handy%20Bee%20Manual/Handy%20Bee %20Manual.pdf.

Dubeux, J.C.B., A.R. Blount, C. Mackowiak, E.R.S. Santos, J.D. Pereira Neto, U. Riveros, L. Garcia, D.M. Jaramillo, and M. Ruiz-Moreno. 2017. Biological N2 fixation, belowground responses, and forage potential of rhizoma peanut cultivars. Crop Sci. 57: 1027–1038.

Dubeux, J.C.B., Jr., L.E. Sollenberger, J.M.B. Vendramini, S.M. Interrante, and M.A. Lira, Jr. 2014. Stocking method, animal behavior, and soil nutrient redistribution: How are they linked? Crop Sci. 54:2341–2350. doi:10.2135/cropsci2014.01.0076

Dubeux, J., DiLorenzo, N., Blount, A., Mackowiak, C., Santos, E., Silva, H., Ruiz- Moreno, M., Shulmeister, T. 2016. Animal performance and pasture characteristics on cool-season annual grass mixture in North Florida. Crop Sci. 56:2841-2852.

Dubeux, J.C.B., L.E. Sollenberger, L.A. Gaston, J.M.B. Vendramini, S.M. Interrante, and R.L. Stewart. 2009. Animal behavior and soil nutrient redistribution in continuously stocked “Pensacola” bahiagrass pastures managed at different intensities. Crop Sci. 49: 1503–1510.

Dubeux, J.C.B., L.E. Sollenberger, B.W. Mathews, J.M. Scholberg, and H.Q. Santos. 2007. Nutrient cycling in warm-climate grasslands. Crop Sci. 47:915–928.

Dubeux, J.C.B., L.E. Sollenberger, S.M. Interrante, J.M.B. Vendramini, and R.L. Stewart. 2006a. Litter decomposition and mineralization in bahiagrass pastures managed at different intensities. Crop Sci. 46(3):1305–1310.

Dubeux, J.C.B., L.E. Sollenberger, J.M.B. Vendramini, R.L. Stewart, and S.M. Interrante. 2006b. Litter mass, deposition rate, and chemical composition in bahiagrass pastures managed at different intensities. Crop Sci. 46(3):1299– 1304. Lal, R. 2004. Carbon emission from farm operations. Environ. Int. 30(7): 981–90.

193

Elgersma A., Hassink, J. 1997. Effects of white clover (Trifolium repens L.) on plant and soil nitrogen and soil organic matter in mixtures with perennial ryegrass (Lolium perenne L.). Plant and Soil 197:177-186.

EstimateS 9.1.0. 2013. Colwell, R. Statistical estimation of species richness and shared species from samples. University of Connecticut, Storrs, CT 06869-3043.

Florida Automated Weather Network, https://fawn.ifas.ufl.edu.

Food and Agriculture Organization of the United Nations (FAO). 2010. Survey protocols for monitoring status and trends of pollinators. Global project pollinator protocols. http://www.fao.org/agriculture/crops/thematic-sitemap/theme/spi/gppp/gppp- home/en/

Food and Agriculture Organization of the United Nations (FAO). 2000. Global Action on Pollination Services for Sustainable Agriculture http://www.fao.org/pollination/en/

Forster, P., V. Ramaswamy, P. Artaxo, T. Berntsen, R. Betts, D.W. Fahey, J. Haywood, J. Lean, D.C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz and R. Van Dorland. 2007. Changes in Atmospheric Constituents and in Radiative Forcing. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate.

Franzluebbers, J., Stuedemann, J., Wilkinson, S. 2001. Bermudagrass management in the Southern Piedmont USA: soil and surface residue carbon sulfur. Soil.Sci.Am.J 65: 834-841.

Freitas, A.D.S., E.V.S.B. Sampaio, C.E.R.S. Santos, and A.R. Fer- nandes. 2010. Biological nitrogen fixation in tree legumes of the Brazilian semi-arid caatinga. J. Arid Environ. 74:344–349. doi:10.1016/j.jaridenv.2009.09.018.

Fry, B. 2006. Stable isotope ecology. Coastal Ecology Institute School of the Coast and Environment LSU Baton Rouge, LA 70803. Springer science. 308.

Fuglestvedt, J., Berntsen, T., Godal, O., Sausen, R. 2003. Metrics of climate change: assessing radiative forcing and emission indices. Climate change 58: 267-331.

Gallai, N., Salles, J.M., Settele, J., Vaissiere, B.E., 2009. Economic valuation of the vulnerability ofworld agriculture confronted with pollinator decline. Ecol. Econ. 68, 810–821.

Garcés-Yépez, P., W. E. Kunkle, D. B. Bates, J. E. Moore, W. W. Thatcher, L. E. Sollenberger, 1997. Effects of supplemental energy source and amount on forage intake and performance by steers and intake and diet digestibility by sheep. J. Anim. Sci. 7:1918–1925. https://doi.org/10.2527/1997.7571918x

194

Garibaldi, L.A., Carvalheiro, L.G., Leonhardt, S.D., Aizen, M.A., Blaauw, B.R., Isaacs, R., Kulmann, M., Kleijn, D., Klein, D., Kremen, C., Morandin, L., Scheper, J., Winfree, R. 2014. Enhancing crop yield through wild pollinators. Frontiers in Ecol. And the Env. 12(8): 439-447.

Garibaldi LA, Aizen MA, Klein AM, et al. 2011a. Global growth and stability of agricultural yield decrease with pollinator dependence. P Natl Acad Sci USA 108: 5909–14.

Gotelli, N., Chao, A. 2013. Measuring and estimating species richness, species diversity, and biotic similarity from sampling data. Enciclopedia of Bidi. 5: 195- 211.

Goulson, D., Nicholls, E., Botias, C., Rotheray, E. 2015. Bee declines driven by combined stress from paraasites, pesticides and lack of flowers. Science Vol. 347: 1435- 1444.

Han, K.-J., Alison, M.W., Jr., Pitman, W.D., McCormick, M.E., 2012. Contributions of Overseeded Clovers to Bermudagrass Pastures in Several Environments. Crop Science 52, 431-441.

Harmon-Threatt, A., Chin, K. 2016. Common Methods for Tallgrass Prairie Restoration and Their Potential Effects on Bee Diversity. Natural Areas Journal, 36, 4:400- 411.

Haydock, K.P., and M.H. Shaw. 1975. The comparative yield method for estimating dry matter yield of pasture. Aust. J. Exp. Agric. Anim. Husb. 15:663–670.

Henry, D.D., Ruiz-Moreno, M., Ciriaco, F.M., Kohmann, M., Mercadante, V.R.G., Lamb, G.C., DiLorenzo, N., 2015. Effects of chitosan on nutrient digestibility, methane emissions, and in vitro fermentation in beef cattle. J. Anim. Sci. 93, 3539–3550.

Herrero, M., Henderson, B., Havlik, P., Thornton, P., Conant, R., Smith, P., Wirsenius, S., Hristov, A., Gerber, P., Gill, M., Butterbach-Bahl, K., Valin, H., Garnet, T., Stehfest, E. 2016. Greenhouse gas mitigation potentials in the lisvestock sector. Nature climate change 6: 452-460.

Herrero, M Havlik, P., Valin, H., Notenbaert, An., Rufino, M. 2013. Biomass use, production, feed efficiencies and greenhouse gas emissions, from global livestock systems. PNAS 110, 52: 20888-20893.

Herrero, M., Thornton, P., Gerber, P., Reid, R. 2009. Livestock, livelihoods and the environment: understanding the trade-offs. Environmental Sust. 1: 111-120.

Haynes, R.J., and P.H. Williams. 1993. Nutrient cycling and soil fertility in the grazed pasture ecosystem. Adv. Agron. 49: 119–199.

195

Hiernaux, P., C.L. Bielders, C. Valentin, A. Bationo, and S. Fernández-Rivera. 1999. Effects of livestock grazing on physical and chemical properties of sandy soils in Sahelian rangelands. J. Arid Environ. 41:231–245. doi:10.1006/jare.1998.0475.

Hodges, Alan W., C.D. Court, M. Rahmani, and C. Stair. “Economic Contributions of Beef and Dairy Cattle and Allied Industries in Florida in 2017.” Economic Impact Analysis Program, University of Florida-IFAS, Food & Resource Economics Department, Gainesville, FL, March 2019.

Hoen, P., Tscharntke, T., Tylianakis, JM., Steffan-DeWenter, I. 2008. Functional group diversity of bee pollinators increases crop yield. Proceedings of the Royal Society B. 275: 2283-2291.

IPCC. 2006. 2006 IPCC guidelines for national greenhouse gas inventories. Vol. 4. In H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds. Prepared by the National Greenhouse Gas Inventories Programme. IGES, Hayama, Japan, pp. 10.1–10.86.

Keylock, C. 2005. Simpson diversity and the Shannon Wiener index as special cases of a generalized entropy. OIKOS 109: 203-208.

Kimoto, C., DeBano, S., Thorp, R., Taylor, R., Schmaiz, H., DelCurto, T., Johnson, T., Kennedy, P., Rao, S. 2012. Short-term responses of native bees to livestock and implications for managing ecosystem services in grasslands. Ecosphere. 3, 10: 2-19.

Klaassen, M., Thums, M., Hume, I. 2004. Effects of diet change on carbon and nitrogen stable-isotope ratios in blood cells and plasma of the long-nosed bandicoot (Perameles nasuta) J. of zoolo. 52:635-647.

Klein, A.-M., B. E. Vaissie´re, J. H. Cane, I. Steffan- Dewenter, S. A. Cunningham, C. Kremen, and T. Tscharntke. 2007. Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society B: Biological Sciences 274:303– 313.

Koh, I., Lonsdorf, E., Williams, N., Britain, C., Rufus, I., Gibbs, J., Ricketts, T. 2016. Modeling the status, trends, and impacts of wild bee abundance in the United States. PNAS. 113: 140-145.

Kohmann, M., Sollenberger, L., Dubeux, J., Silveira, M., Moreno, L., da Silva, L., Aryal, P. 2018. Crop sci. 58: 2138-2148.

Krause, D.O., Denman, S.E., Mackie, R.I., Morrison, M., Rae, A.L., Attwood, G.T. and McSweeney, C.S. 2003. Opportunities to improve fiber degradation in the rumen, microbiology, ecology and genomics. FEMS Microbiol. Rev., 27: 663-693.

196

Jaramillo, D.M., J.C.B.B. Dubeux, C. Mackowiak, L.E. Sollenberger, N. DiLorenzo, D.L. Rowland et al. 2018. Annual and perennial peanut mixed with ‘Pensacola’ bahiagrass in North Florida. Crop Sci. 58(2):1–11. doi:10.2135/cropsci2017.09.0542.

Janssen, P. 2010. Influence of hydrogen on rumen methane formation and fermentation balances through microbial growth kinetics and fermentation thermodynamics. Animal Feed Science and Technology 160:1-2, 1-22.

Jensen, E.S., M.B. Peoples, R.M. Boddey, P.M. Gresshoff, H. Hauggaard-Nielsen, B. Alves, and M.J. Morrison. 2012. Legumes for mitigation of climate change and the provision of feedstock for biofuels and biorefineries. A review. Agron. Sustain. Dev. 32: 329–364.

Johnson DE, Hill TM, Ward GM, Johnson KA, Branine ME, Carmean BR, Lodman DW (1993) Ruminants and other animals. In ‘Atmospheric methane: sources, sinks and role in global change’. (Ed. MAK Khalil) pp. 219–229. NATO ASI Series 1: Global Environmental Change, Vol. 13. Springer-Verlag: Berlin.

Jones, K.S., Famulari, D., Di Marco, F.C., Nemitz, E., Skiba, M., Rees, R., Sutton, A. 2011. Nitrous oxide emissions from managed grassland: a comparison of eddy covariance and static chamber measurements. Atmos. Meas. Tech. 4, 2179– 2194.

Lagerhöf J., Stark J., Svensson B. (1992) Margins of agricultural field as habitats for pollinating insects, Agric. Ecosyst. Environ. 40, 117–124.

Lamarque, P., Tappeiner, U., Turner, C., Steinbacher, M., Bardgett, R., Szukics, U., Schermer, M., Lavorel, S. 2011. Stakeholder perceptions of grassland ecosystem services in relation to knowledge on soil fertility and biodiversity. Reg. Environ. Change. 11:791-804.

Lassey, K.R. 2007. Livestock methane emission: From the individual grazing animal through national inventories to the global methane cycle. Agric. For. Meteorol. 142: 120–132.

Lebuhn, G., Droege, S., Connor, E., Gemmill-Herren, B., Potts, S., Minckley, R., Griswold, T., Jean, T., Kula, E., Roubick, D., Cane, J., Wright, K., Frankie, G., Parker, F. 2013. “Detecting Insect Pollinator Declines on Regional and Global Scales.” Conserv. Biol. 27.1: 113-120.

Ledgard, S., Steele, K. 1992. Biological nitrogen fixation in mixed legume/grass pastures. Plant and soil. 141: 137 -153.

Littlewood, N., Alan, J., Stewart, A., Woodcock, B. 2012. Science into practice – how can fundamental science contribute to better management of grasslands for invertebrates? Insect Conservation and Diversity 5, 1–8.

197

Loncke, C., I. Ortigues-Marty, J. Vernet, H. Lapierre, D. Sauvant, and P. Nozière. 2009. Empirical prediction of net portal appearance of volatile fatty acids, glucose, and their secondary metabolites (beta-hydroxybutyrate, lactate) from dietary characteristics in ruminants: A meta-analysis approach. J. Anim. Sci.87:253–268. doi:10.2527/jas.2008-0939.

Lubowski, R., Vesterby, M., Bucholtz, S., Baez, A. 2006. Major uses of land in the United States. Electronic Report from the Economic Research Service. USDA.

Ludlow, M. 1985. Photosynthesis and dry matter production in C3 and C4 pasture plants, with special emphasis on tropical C3 legumes and C4 grasses. Australian Journal of Plant Physiology 12 (6):557-572.

Macoon, B., L. E. Sollenberger, J. E. Moore, C. R. Staples, J. H. Fike, and K. M. Portier 2003.Comparison of three techniques for estimating the forage intake of lactating dairy cows on pasture. J. Anim. Sci. 81:2357–2366. https://doi.org/10.2527/2003.8192357x.

Mannetje L. and haydock K.P. 1963. The dry- weight-rank method for the botanical analysis of pasture. Journal of the British Grassland Society, 18, 268–275.

McGinn, S. M., Beauchemin, K. A., Coates, T., Colombatto, D. 2004. Methane emissions from beef cattle: Effects of monensin, sunflower oil, enzymes, yeast, and fumaric acid. J Anim Sci. 82:3346-3356.

Michener, R and Lajtha, K. 2007. Stable isotopes in Ecology and environmental science. Blackwell publishing. 350 Main Street, Malden, MA 02148-5020, USA. 566.

Milne, M.L. & Milne. 2000. National Audubon Society: Field Guide to Insects and Spiders. New York, Canada: Alfred A. Knopf, Inc.

Moore, J. E, M. H. Brant, W. E. Kunkle, and D. I. Hopkins. 1999. Effects of Supplementation on Voluntary Forage Intake, Diet digestibility and animal performance. J. Anim. Sci. 77:122-135.

Moore, J.E., and G.O. Mott. 1974. Recovery of residual organic matter from in vitro digestion of forages. J. Dairy Sci. 57(10):1258–1259.

Mooso, G.D., J.I. Feazel, and D.G. Morrison. 1990. Effect of sod- seeding method on ryegrass–clover mixture for grazing beef animals. J. Prod. Agric. 3:470–476.

Muir, J.P., T.J. Butler, W.R. Ocumpaugh, and C.E. Simpson. 2010. “Latitude 34”, a perennial peanut for cool, dry climates. J. Plant Regist. 4(2): 106–108.

Muir, J.P., W.D. Pitman, and J.L. Foster. 2011a. Sustainable, low-input, warm-season, grass-legume grassland mixtures: Mission (nearly) impossible? Grass Forage Sci. 66:301–315.

198

Muir, J.P., 2011b. The multi-faceted role of condensed tannins in the goat ecosystem. Small Ruminant Res. 98, 115–120.

Muir, J.P., W.D. Pitman, J.C.B. Dubeux, and J.L. Foster. 2014. The future of warm- season, tropical and subtropical forage legumes in sustainable pastures and rangelands. African J. Range Forage Sci. 31(3): 187–198.

Mullenix, M.K., L.E. Sollenberger, M.O. Wallau, A.R. Blount, J.M.B. Vendramini, and M.L. Silveira. 2016a. Herbage accumulation, nutritive value, and persistence responses of rhizoma peanut cultivars and germplasm to grazing management. Crop Sci. 56(2): 907–915.

Mullenix, M.K., L.E. Sollenberger, M.O. Wallau, D.L. Rowland, A.R. Blount, J.M.B. Vendramini, and M.L. Silveira. 2016b. Sward structure, light interception, and rhizome-root responses of rhizoma peanut cultivars and germplasm to grazing management. Crop Sci. 56:899-906.

National Academies of Sciences, Engineering, and Medicine (NASEM). 2016. Nutrient requirements of beef cattle. 8th ed. Natl. Acad. Press, Washington, DC.

National Research Council (NRC). 2007. Status of Pollinators in North America. Committee on the Status of Pollinators in North America; Board on Life Sciences; Board on Agriculture and Natural Resources. Division on Earth and Life Studies. Washington, DC: National Academy Press.

Naumann, H.D., L.O. Tedeschi, J.P. Muir, B.D. Lambert, and M.M. Kothmann. 2013. Effect of molecular weight of condensed tannins from warm-season perennial legumes on ruminal methane production in vitro. Biochem. Syst. Ecol. 50: 154– 162.

Neuteboom J.H., Lantinga E.A. and Struik P.C. 1998. Evaluation of the dry weight rank method for botanical analysis of grassland by means of simulation. Netherlands Journal of Agricultural Science, 46, 285–304.

Norman, H., Wilmot, M., Thomas, D., Masters, D., Revell, D. 2009. Stable isotopes accurately predict diet selection by sheep fed mixture of C3 annual pastures and saltbush or C4 perennial grasses. Livestock science. 121:162-172.

Nyfeler, D., O. Huguenin-Elie, M. Suter, E. Frossard, and A. Lüscher. 2011. Grass– legume mixtures can yield more nitrogen than legume pure stands due to mutual stimulation of nitrogen uptake from symbiotic and non-symbiotic sources. Agric. Ecosyst. Environ. 140:155–163.

Okito, A., B.J.R. Alves, S. Urquiaga, and R.M. Boddey. 2004. Isotopic fractionation during N2 fixation by four tropical legumes. Soil Biol. Biochem. 56:907–915.

199

Palm, C., Blanco-Canqui, H., DeClerck, F., Gatere, L., Grace, P. 2014. Conservation agriculture and ecosystem services: an overview. Agri. Ecosys. And Envirnm. 187:87-105.

Parkin, T.B., and R.T. Venterea. 2010. Chamber-based trace gas flux measurements. p. 3–39. In Sampling Protocols.

Peters, M., M. Herrero, M. Fisher, K-H. Erb, I. Rao, G.V. Subbarao, et al. 2013. Challenges and opportunities for improving eco-efficiency of tropical forage- based systems to mitigate greenhouse gas emissions. p. 1251-1260. In Proc. 22nd Int. Grassl. Cong.

Pereira Neto., J., Dubeux, J., Carvhalo, F., Ferreira, V., Ruiz-Moreno, M., Santor, E. 2019. Tracing back sheep diet composition feeding grass-legume mixture using fecal δ13C. Small ruminant research. 175: 7-14.

Pinares-Patiño, C.S., Franco, F.R., Molano, G., Kjestrup, H., Sandoval, E., MacLean, S., Battistotti, M., Koolaard, J., Laubach, J., 2016. Feed intake and methane emissions from cattle grazing pasture sprayed with canola oil. Livest. Sci. 184, 7- 12.

Pineiro, G., Paruelo, J., Jobbagy, E. Jackson, D., Oesterheld, M. 2009. Grazing effects on belowground C and N stocks along a network of cattle exclosures in temperate and subtropical grasslands of South America. Global Biogeochemical Cycles.

Pineiro, G. Paruelo, J, Oesterheld, M., Jobbagy, E. 2010. An Assesment of grazin effects on soil carbon stock in grasslands. Rang. Ecol. Mnage 63: 01-05.

Pogue, S., Krobel, R., Janzen, R., Beauchemin, K., Legesse, G., Maia de Souza, D., Iravani, M., Selin, C., Byrne, J., McAllister, T. 2018. Beef production and ecosystem services in Canada’s prairie provinces: A review. Agricultural systems. 166:152-172.

Pollinator health task force. 2015. National Strategy to Promote the Health of Honey bees and Other Pollinators. The White House. 1-64.

Potts., S., Jacobus, C., Kremen, C., Neumann, P., Schweiger, O., Kunin, W. 2010. Global pollinator declines:trends, impacts and drivers. Trends in Ecol. & Evol. Vol. 25 (6): 345- 353.

Potts., S., Woodcock, B., Roberts, S., Tscheulin, T., Pilgrim, E., Brown, V., Tallowin, J. 2009. Enhancing pollinator biodiversity in intensive grasslands. J. of applied ecol. 46: 369-379.

Quesenberry, K.H., Blount, A., Mislevy, P., French, C.E., Williams, J.M., Prine, M.G. 2010. Registration of “UF Tito” and “UF Peace” rhizoma peanut cultivars with

200

high dry matter yields, persistence, and disease tolerance. J. Plant Regist. 4(1): 17–21.

Quintero, C., Morales, C., Aizen, M. 2010. Effects of anthropogenic habitat disturbance on local pollinator diversity and species turnover across a precipitation gradient. Biodivers Conserv 19:257–274

Rafique, R., Hennessy, D., Kiely, G. 2011. Nitrous oxide emission from grazed grassland under different management systems. Ecosystems 14(4): 563–582.

Reeder, J.D., and G.E. Schuman. 2002. Influence of livestock grazing on C sequestration in semi-arid mixed-grass and short-grass rangelands. Environ. Pollut. 116:457–463. doi:10.1016/S0269-7491(01)00223-8.

Redmon, L. A., F. T. McCollum III, G. W. Horn, M. D. Cravey, S. A. Gunter, P. A. Beck, J. M. Mieres, and R. San Julian. 1995. Forage intake by beef steers grazing winter wheat with varied herbage allowances. Journal of Range Management 48:198-201.

Reid, R. L., G. A. Jung, and W. V. Thayne. 1988. Relationships between nutritive quality and fiber components of cool-season and worm season forages: a retrospective study. J. Anim. Sci. 66:1275-1291.

Rezende, C.D.P., R.B. Cantarutti, J.M. Braga, J.A. Gomide, J.M. Pereira, and E. Ferreira. 1999. Litter deposition and disappearance in Brachiaria pastures in the Atlantic forest region of the South of Bahia, Brazil. Nutr. Cycl. Agroecosytems 54:99–112.

Riddle, C., Mizell, R. 2016. Use of crape myrtle, Lagerstroemia (Myrtales: Lythraceae), cultivars as a pollen source by native and non-native bees (Hymenoptera: Apidae) in Quincy, Florida. FL. Entom. 99: 38-46.

Roulston TH, Smith SA, Brewster (2007) A comparison of pan trap and intensive net sampling techniques for documenting bee (Hymenoptera: Apiformes) fauna. J Kans Entomol Soc 80:179–181.

Russell, J., Bisinger, J. 2015. Improving soil health and productivity on grasslands using manages grazing of livestock. Symposium titled “Use of Marginal Lands and Fibrous Byproducts in Efficient Beef and Dairy Production Systems” at the Joint Annual Meeting, July 20 to 24, 2014, Kansas City, Missouri.

Russell, N., Ikerd. H., Droege, S. 2005. The potential conservation value of unmowed powerline strips for native bees. 124: 133-148.

Russell, J. B. 2002. Rumen Microbiology and its role in ruminant nutrition, Ithaca, NY.

Russelle, M.P. 2008. Biological Dinitrogen Fixation in Agriculture. Am. Soc. Agron. Crop Sci. Soc. Am. Soil Sci. Soc. Am. 1: 281–359.

201

Sanderson, M., Archer, D., Hendrickson, J., Kronberg, S., Liebig, M., Nichols, K., Schmer, M., Tanaka, D., Aguilar, J. 2013. Diversification and ecosystem services for conservation agriculture: Outcomes from pastures and integrated crop– livestock systems. Renewable Agriculture and Food Systems: 28(2):129–144.

Sala, O., Paruelo, J. 1997. Nature’s services: societal dependence on natural ecosystems. Chapter 13. Island Press. 392.

Santos, E.R.S., J.C.B. Dubeux, L.E. Sollenberger, A.R.S. Blount, C. Mackowiak, N. DiLorenzo et al. 2018. Herbage responses and biological N2 fixation of bahiagrass and rhizoma peanut monocultures compared with their binary mixtures. Crop Sci. 58:2149–2163. doi:10.2135/cropsci2018.02.0128.

Sardinas, H., Kremen, C. 2014. Evaluating nesting microhabitat for ground-nesting bees using emergence traps. Basic and applied Ecol. 15, 2: 161-168.

SAS Institute. 2009. SAS statistics user’s guide. Release version 9.4. SAS Inst. Inc., Cary, NC.

Schulte, R., Neuteboom, J. 2002. Advanced analysis of dry-weight-rank data to discriminate direct and indirect interactions between white clover and grasses in a multi-species pasture under a range of management strategies. Grass and For. Sci. 57, 2:113-123.

Sigua, G.C., C.C. Chase Jr., and J. Albano. 2014. Soil-extractable phosphorus and phosphorus saturation threshold in beef cattle pastures as affected by grazing management and forage type. Environ. Sci. Pollut. Res. iIternational 21: 1691– 1700.

Silveira, M., Vendramini, J., Sellers, B., Monteiro, F., Artur, A., Dupast, E. 2013. Bahiagrass response and N loss from selected fertilizer sources. Grass and Forage Sci. 70:154-160.

Sleugh, B., Moore, K., George, J., Brummer, E. 2000. Binary legume-grass mixture improve forage yield, quality and seasonal distribution. Agronomy J. 92: 24-29.

Smith, K.R. et al., 2014: Human Health: Impacts, Adaptation, and Co-Benefits. In: Climate Change 2014: 23 Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of 24 Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 25 [Field, C.B., V.R. Barros, D.J. Dokken, K.J. Mach, M.D. Mastrandrea, T.E. Bilir, M. Chatterjee, 26 K.L. Ebi, Y.O. Estrada, R.C. Genova, B. Girma, E.S. Kissel, A.N. Levy, S. MacCracken, P.R. 27 Mastrandrea, and L.L. White (eds.)]. Cambridge University Press, Cambridge, United Kingdom and 28 New York, NY, USA, pp. 709-754.

Sollenberger, L. 2015. Challenges, opportunities and applications of grazing research. Crop. Scie. 55: 2540-2549.

202

Sollenberger, L.E. 2014. Changing emphases in soil-plant-animal research in pastures. In Sociedade Brasileira de Zootecnica, Brasilia, B. (ed.), 51st Proc. Brazilian Soc. Animal Sci. Mtg. Barra dos Coqueiros. p. 1–38

Sollenberger, L.E., J.C.B. Dubeux, Jr., and J.P. Muir. 2014. Establishment and management of legume-grass pastures. p. 135-177. In O.G. Pereira et al. (ed.) 7th Symposium on Strategic Management of Pasture. 13-15 Nov. 2014. Viçosa, Brazil.

Sollenberger, L., Vanzant, E. 2011. Interrelationships among forage nutritive value and quantity and individual animal performance. Crop. Science. 51: 420-432.

Sollenberger, L., Moore, J., Allen, V., Pedreira, C. 2005. Reporting forage allowance in grazing experiments. Crop Sci. Vol. 45, 3:896-900.

Stewart, R.L., Sollenberger, L.E., Dubeux, J.C.B., Vendramini, J.M.B., Interrante, S.M., Newman, Y.C. 2007. Herbage and animal responses to management intensity of continuously stocked bahiagrass pastures. Agron. J. 99:107–112.

Ta, T., and Faris, M. 1987. Nitrogen transfer form legumes to grasses. Journal of Plant and Soil. 274 (817):265-274.

Terril, T., Gelaye, S., Mahotiere, E., Amoah, E., Miller, S., Gates, R., Windham, W. 1996. Rhizoma peanut alfalfa productivity and nutrient composition in central Georgia. 88: 485-488.

Thomas, R.J., N.M. Asakawa, M.A. Rondon, and H.F. Alarcon. 1997. Nitrogen fixation by three tropical forage legumes in acid-soil savanna of Colombia. Soil Biol. Biochem. 29:801–808. doi:10.1016/S0038-0717(96)00212-X.

Titgemeyer, E.C., C.K. Armendariz, D.J. Bindel, R.H. Greenwood, and C.A. Loest. 2001. Evaluation of titanium dioxide as a digestibility marker for cattle. J. Anim. Sci. 79:1059–1063.

Ugland, K., Gray, J., Ellingsen, K. 2003. The species accumulation curve and estimation of species richness. Journal of animal ecol. 72: 888-897.

Unkovich, M., Herridge, D., Peoples, M., Cadish, G., Boddey, B., Giller, K., Alves, B., Chalk, P. 2008. Measuring plant-associated nitrogen fixation in agricultural systems. Published by the Australian Centre for International Agricultural Research (ACIAR) GPO Box 1571, Canberra ACT 2601, Australia. 259.

United States Department of Agriculture. 2015. 2012 Census of agriculture highlights.

United States Department of Agriculture (USDA). 2011. Small-scale U.S. cow-calf operations.

203

Varco, J. J., Sanford J.O., Hairston, J.E. 1991. Yield and nitrogen content of legume cover crops grown in Mississippi. Research Report. Mississippi Agricultural and Forestry Experiment Station 16:10.

Vendramini, J.M.B., J.C.B. Dubeux, and M.L. Silveira. 2014. Nutrient cycling in tropical pasture ecosystems. Rev. Bras. Ciencias Agrar. 9(2): 308–315.

Vendramini, J.M.B., L.E. Sollenberger, G.C. Lamb, J.L. Foster, K. Liu, and M.K. Maddox. 2012. Forage accumulation, nutritive value, and persistence of ‘Mulato II’ brachiariagrass in Northern Florida. Crop Sci. 52:914–922.

Vendramini, J.M.B. 2010. Forage evaluation and quality in Florida. p. 92–106. In The 21st Annual Florida Ruminant Nutrition. Gainesville.

Verge, X., De Kimpe, C., Desjardins, R. 2007. Agricultural production, greenhouse gas emissions and mitigation potential. Agric. For. Meteorol. 142, 255–269.

Wagner, W.M., Havstad, M.K., Doornbos, E.D., Ayers, L.E. 1986. Forage intake of rangeland beef cows with varying degrees of crossbred influence. J. Anim.Sci. Vol. 63, 5:1484-1490.

Wallace, K. 2007. Classification of ecosystems services: Problems and solutions. Biol. Conserv. 139:235-246.

Willm, H., Costello, D., Klipple, G. 1944. Estimating forage yield by the double-sampling method. J of American socie. Agronomy. 194-203.

Williams, C.H., David, D.J., Iismaa, O. 1962. The determination of chromic oxide in feces samples by atomic absorption spectrophotometry. J. Agric. Sci. Vol.59:381- 385.

Williams, P., Haynes, R. 1990. Influence of improved pastures and grazing animals on nutrient cycling within New Zealand soils. New Zealand Journal of Ecology, 14:49-57.

Wilson, J., Messinger, O. 2016. The bees in your backyard. A guide to North America’s bees. Princenton University Press. 288.

Winston, M., Katz, S. 1981. Longevity of cross-fosteres honey bee workers (Apis mellifera) of European and Africanized races. Canadian J. of Zool. 59, 8: 1571- 1575.

White, R., Murray, S., Rohweder, M. 2000. Pilot analysis of global ecosystems, grasslands ecosystems. World Resources Institute. Wahington D.C. 69.

White-Leech, R. Liu, K., Sollenberger, L., Woodard, R., Interrante, S. 2013. Excreta deposition on grassland patches. I. Forage harvested, nutritive value, and nitrogen recovery. Crop Science, Vol 53:688-695.

204

Whitbread, A.M., C.A. Hall, and B.C. Pengelly. 2009. A novel approach to planting grass–legume pastures in the mixed farming zone of southern inland Queensland, Australia. Crop Past. Sci. 60:1147–1155.

Woodcock, B., Savage, J., Bullock, M., Nowakoswski, M., Orr, R., Tallowin., Pywell, R. 2014. Enhancing floral resources for pollinators in productive agricultural grasslands. Biol. Conserv. 171: 44-51.

Yoshihara, Y., B. Chimeddorj, B. Buuveibaatar, B. Lhagvasuren, and S. Takatsuki. 2008. Effects of livestock grazing on pollination on a steppe in eastern Mongolia. Biological Conservation 141: 2376–2386.

205

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.

206