Swine Feed Efficiency: Implications for Swine Behavior, Physiology and Welfare Jessica Diane Colpoys Iowa State University

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2015 Swine feed efficiency: implications for swine behavior, physiology and welfare Jessica Diane Colpoys Iowa State University

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Swine feed efficiency: Implications for swine behavior, physiology and welfare

Jessica D. Colpoys

A dissertation submitted to the graduate faculty

in partial fulfillment of the requirements for the degree of

DOCTOR OF PHILOSOPHY

Major: Animal Physiology (Ethology)

Program of Study Committee: Anna K. Johnson, Co-Major Professor Nicholas K. Gabler, Co-Major Professor Aileen F. Keating Suzanne T. Millman Cheryl L. Morris Jason W. Ross

Iowa State University

Ames, Iowa

2015

TABLE OF CONTENTS

Page

LIST OF TABLES ...... v

LIST OF FIGURES ...... vii

LIST OF ABBREVIATIONS ...... ix

ACKNOWLEDGMENTS ...... xii

ABSTRACT ...... xiv

CHAPTER 1. LITERATURE REVIEW ...... 1

Introduction ...... 1 Measuring Feed Efficiency ...... 2 Biological Factors Contributing to Feed Efficiency ...... 5 Body composition, digestion, and metabolism ...... 6 Activity and feeding behavior ...... 6 Immunological and environmental stress ...... 10 Stress ...... 12 Psychobiology of the stress response ...... 12 Animal stress and feed efficiency ...... 15 Measuring animal stress ...... 17 Animal welfare...... 25 Conclusions ...... 29 References ...... 29

CHAPTER 2. FEEDING REGIMEN IMPACTS PIG GROWTH AND BEHAVIOR ...... 40

Abstract ...... 40 Introduction ...... 41 Materials and Methods ...... 43 Animals and housing...... 43 Performance ...... 45 Whole body composition and tissue accretion...... 45 Behavior ...... 46 Data analysis ...... 46 Results ...... 48 Performance ...... 48 iii

Whole body composition and tissue accretion...... 48 Behavior ...... 49 Discussion ...... 50 Conclusion ...... 55 Acknowledgements ...... 55 References ...... 56

CHAPTER 3. EFFECTS OF GENETIC SELECTION FOR RESIDUAL FEED INTAKE ON BEHAVIORAL REACTIVITY OF CASTRATED MALE PIGS TO NOVEL STIMULI TESTS ...... 68

Abstract ...... 69 Introduction ...... 70 Materials and Methods ...... 72 Animals and housing...... 72 Test methodology and facility ...... 73 Human approach test...... 76 Novel object test ...... 77 Measures ...... 77 Data analysis ...... 77 Results ...... 78 Human approach test...... 78 Stimulus attention ...... 78 Arousal and fear behavior ...... 79 Novel object test ...... 79 Stimulus attention ...... 79 Arousal and fear behavior ...... 80 Discussion ...... 80 Stimulus attention ...... 80 Arousal and fear behavior ...... 81 General discussion ...... 82 Conclusions ...... 83 Acknowledgments...... 83 References ...... 84

CHAPTER 4. FEED EFFICIENCY EFFECTS ON BARROW AND GILT BEHAVIORAL REACTIVITY TO NOVEL STIMULI TESTS ...... 93

Abstract ...... 94 Introduction ...... 95 Materials and Methods ...... 97 Animals and housing...... 97 RFI selection and calculation ...... 98 Test methodology and facility ...... 99 Human approach test...... 102 iv

Novel object test ...... 102 Measures ...... 103 Data analysis ...... 103 Results ...... 104 Human approach test...... 104 Genetic line, barrow, and gilt differences ...... 104 Behavior and RFI relationship...... 106 Novel object test ...... 106 Genetic line, barrow, and gilt differences ...... 106 Behavior and RFI relationship ...... 107 Discussion ...... 108 Genetic line differences ...... 108 Barrow and gilt differences ...... 109 Behavior and RFI relationships ...... 110 General discussion ...... 111 References ...... 112

CHAPTER 5. RESPONSIVENESS OF SWINE DIVERGENTLY SELECTED FOR FEED EFFICIENCY TO EXOGENOUS ADRENOCORTICOTROPIN (ACTH) AND GLUCOSE CHALLENGES ...... 124

Abstract ...... 124 Introduction ...... 125 Materials and Methods ...... 128 Animals and housing...... 128 Adrenocorticotropic hormone challenge and intravenous glucose tolerance test ...... 129 Sample preparation and assay procedures ...... 129 Data analysis ...... 130 Results ...... 131 Response to the ACTH challenge ...... 131 Response to the intravenous glucose tolerance test ...... 132 Discussion ...... 134 Acknowledgements ...... 138 References ...... 138

CHAPTER 6. GENERAL CONCLUSIONS AND FUTURE DIRECTIONS ...... 147

References ...... 162

APPENDIX. AUTHORED ABSTRACTS AND ANIMAL INDUSTRY REPORTS ...... 170

LIST OF TABLES

Page

Table 1.1. Time budget of two genetic lines of grow-finish gilts over the subsequent rounds, in the home pen ...... 7

Table 1.2. Effects of feeding frequency on performance in pigs ...... 9

Table 1.3. Function of autonomic nervous system activation of various organs ...... 14

Table 1.4. Novel object test methodologies of object presentation, social situation, and the location in which the test is performed ...... 20

Table 1.5. Human approach test methodologies of human presentation, social situation in which the test is performed, and the location in which the test is performed ...... 21

Table 2.1. Ethogram of behaviors recorded during week 7 of the study ...... 59

Table 2.2. Overall performance differences collected throughout the 7 week study period (least square means ± SE) in gilts undergoing twice daily (2x; n=24) or ad libitum (n=24) feeding treatments ...... 60

Table 2.3. Pearson correlations of overall performance and overall whole body tissue accretion ...... 61

Table 2.4. Final whole body composition and overall tissue accretion rates (least square means ± SE) of gilts undergoing twice daily (2x; n=12) or ad libitum (n=12) feeding treatments ...... 62

Table 2.5. Frequency (n), duration (min), and rate (g/min) of gilt behaviors (least square means ± SE) while undergoing twice daily (2x; n=24) or ad libitum (n=24) feeding treatments ...... 63

Table 2.6. Spearman rank correlations of behaviors with overall performance and overall whole body tissue accretion ...... 64

Table 3.1. Ethogram of behaviors recorded during human approach and novel object tests ...... 89

Table 3.2. Latency (s), frequency (n), and duration (%; least square means ± SE) of behaviors during the human approach test in castrated male pigs selected for low-RFI (more feed efficient) and high-RFI (less feed efficient) ...... 90 vi

Table 3.3. Latency (s), frequency (n), and duration (%; least square means ± SE) of behaviors during the novel object test in castrated male pigs selected for low-RFI (more feed efficient) and high-RFI (less feed efficient) ...... 91

Table 4.1. Ethogram of behaviors recorded during human approach and novel object tests ...... 117

Table 4.3. Odds ratios of RFI and behavior regressions during the human approach test in barrows and gilts selected for low residual feed intake (more feed efficient) and high residual feed intake (less feed efficient) ...... 118

Table 4.2. Latency (s), frequency (n), and duration (%) of behaviors (least square means ± SE) during the human approach test in barrows and gilts selected for low residual feed intake (RFI; more feed efficient) and high RFI (less feed efficient) ...... 118

Table 4.4. Latency (s), frequency (n), and duration (%) of behaviors (least square means ± SE) during the novel object test in barrows and gilts selected for low residual feed intake (RFI; more feed efficient) and high RFI (less feed efficient) ...... 120

Table 4.5. Odds ratios of RFI and behavior regressions during the novel object test in barrows and gilts selected for low residual feed intake (more feed efficient) and high residual feed intake (less feed efficient) ...... 121

Table 5.1. Plasma cortisol and NEFA concentrations in response to an ACTH challenge in gilts divergently selected for residual feed intake (RFI) ...... 143

Table 5.2. Plasma glucose, insulin and NEFA concentrations in gilts divergently selected for residual feed intake (RFI) and challenged with a IVGTT ..... 144

Table 6.1. Latency (s), frequency (n), and duration (%) of behaviors (least square means ± SE) during the human approach test in 8th and 9th generation ISU RFI selection lines ...... 167

Table 6.2. Latency (s), frequency (n), and duration (%) of behaviors (least square means ± SE) during the novel object test in 8th and 9th generation ISU RFI selection lines...... 168

Table 6.3. Spearman rank correlations between latency to approach zone 1 and other behaviors performed during the human approach- (HAT) and novel object tests (NOT) ...... 169

vii

LIST OF FIGURES

Page

Figure 1.1. Flow chart of the design of selection lines used for the ISU RFI selection lines ...... 4

Figure 1.2. Response to selection for residual feed intake over eight generations ...... 4

Figure 1.3. Contributions of biological mechanisms to variation in RFI as determined from experiments on divergently selected beef cattle ...... 5

Figure 1.4. Hypothalamic-pituitary-adrenal axis ...... 15

Figure 1.5. Three schools of animal welfare ...... 27

Figure 2.1. A latch was mounted to the feeders to prevent pigs from accessing feed during non-meal times ...... 65

Figure 2.2. Weekly body weight (BW, a), average daily gain (ADG, b), average daily feed intake (ADFI, c), and gain:feed ratio (G:F, d) for gilts undergoing twice daily (2x; n=24) or ad libitum (ad lib; n=24) feeding treatments ...... 66

Figure 2.3. Feeding rates of gilts undergoing twice daily (2x; n=12) or ad libitum (ad lib; n=12) feeding treatments ...... 67

Figure 3.1. Arena where pigs were tested using human approach- (HAT) and novel object tests (NOT) ...... 92

Figure 4.1. Arena where pigs were tested using human approach (HAT) and novel object (NOT) tests ...... 122

Figure 4.2. Predicted escape attempt (A) and freezing (B) frequencies across residual feed intake (RFI) in low-RFI (more feed efficient) and high- RFI (less feed efficient) barrows and gilts during the novel object test .. 123

Figure 5.2. Plasma glucose (a), insulin (b), glucose to insulin (G:I) ratio (c), and NEFA concentrations (d) in response to an intravenous administration viii

of glucose (0.25 g/kg BW) at 0 min in gilts divergently selected for residual feed intake (RFI) ...... 146

LIST OF ABBREVIATIONS

2x Twice daily feed treatment

ACTH Adrenocorticotropic hormone

Ad lib Ad libitum

ADFI Average daily feed intake

ADG Average daily gain

AUC Area under the curve

BF Backfat

BMC Bone mineral content

BMD Bone mineral density

BW Body weight

CDKAL1 Cyclin-dependent kinase 5 regulatory subunit associated protein1-

line 1

CRH Corticotrophin-releasing hormone

DXA Dual X-ray absorptiometry

EBV Estimated breeding value

FAO Food and Agriculture Organization of the United Nations

FCR Feed conversion ratio

Fig Figure

FIRE Feed intake recording equipment

G:F Gain to feed ratio

G:I Glucose to insulin ratio x

GDU Gilt developer units

GLP1R Glucagon-like peptide 1 receptor

GWAS Genome wide association study h Hour

HAT Human approach test

High-RFI High residual feed intake

HPA Hypothalamic-pituitary-adrenal

IACUC Institutional animal care and use committee

INRA Institut National de la Recherche Agrionomique

ISU Iowa State University

IVGTT Intravenous glucose tolerance test

LMA Loin muscle area

Low-RFI Low residual feed intake

LPS Lipopolysaccharide

MBW Metabolic body weight

ME Metabolizable energy

Min Minute

NEFA Non-esterified fatty acids

NOT Novel object test

NRC National Research Council

OFFWTDEV Off-weight deviation

ONAGEDEV On-age deviation

ONWTDEV On-weight deviation xi

OR Odds ratios

QTL Quantitative trait locus

RFI Residual feed intake

SA Sympatho-adrenal system

SE Standard error

SID Standardized ileal digestibility

SNP Single nucleotide polymorphisms

Wk Week

xii

ACKNOWLEDGMENTS

I would like to thank my co-major professors, Drs. Anna Johnson and Nick

Gabler, for their guidance and support. Thank you, Anna, for your wisdom and mentoring that will continue to shape my future in animal behavior and welfare. Thank you, Nick, for your compassion and persistence in pushing me towards my potential and instilling me with the confidence to succeed in research. I feel very blessed to have had the opportunity to study under both of you.

To my committee members, Drs. Suzanne Millman, Aileen Keating, Jason Ross, and Cheryl Morris, I thank you for your valuable input and time. Your guidance is much appreciated. I also want to thank other mentors I’ve had through this journey, Drs. Janice

Siegford, Amy Toth, Ken Stalder, and Howard Tyler, for challenging me to think critically about animal behavior, swine production, research, and teaching.

Thank you to all of the undergraduates that put in the hard work and time helping me in the lab and on the farm. Thank you to past and present graduate students, especially

Caroline Mohling, Shawna Weimer, Dr. Monique Pairis-Garcia, Rebecca Kephart, Dr.

Caitlyn Abell, Dana van Sambeek, Wes Schweer, and Shelby Curry for your support and friendship. I would also like to thank Dr. Larry Sadler, Dr. Samaneh Azarpajouh, and

Theresa Johnson for assistance through this process. I also cannot thank Becky Parsons enough for her continual advice and friendship.

I thank my family for their support and instilling me with grit and a passion for agriculture. Finally, I’d like to thank my husband, Ken, for his support, patience, being xiii

my sounding board and my inspiration to keep moving forward in this process. Without you I never would have been brave enough to even begin this journey. xiv

ABSTRACT

The overall goal of this dissertation was to address how improving feed efficiency impacts swine welfare through two objectives: 1) assess how altering feeding behavior impacts pig feed efficiency of lean tissue gains and 2) evaluate how selection for altered feed efficiency impacts pig ability to respond to and cope with stressful events. The results of this dissertation identify a relationship between pig behavior and feed efficiency of lean tissue gains, and suggest that improving feed efficiency did not negatively impact pig welfare in regards to the ability to respond to stress.

Swine feed efficiency and welfare are interrelated and represent both producer goals and consumer concerns. Feed efficiency can be defined as the efficiency at which an animal utilizes dietary nutrients for maintenance and tissue accretion. Increasing swine feed efficiency of lean tissue gains is an important goal that is critical for improving sustainable pork production and profitability. In order to improve feed efficiency, a deeper understanding of the environmental and biological factors underlying feed efficiency is essential. It is also necessary to ensure that feed efficiency modifications do not negatively impact animal welfare, as concerns have specifically been raised in which genetic selection for and improvement in feed efficiency impacts how livestock cope with various forms of stress. Therefore, the overall goal of this dissertation was to address these concerns by evaluating how altering feed efficiency impacts swine welfare in regards to feeding behavior and the stress response. To address this goal, four research chapters (2-5) focused on the following objectives: xv

1) To assess how altering feeding behavior impacts grow-finish pig feed

efficiency of lean tissue gains.

2) To evaluate how selection for altered feed efficiency impacts pig ability to

respond to and cope with stressful events.

In the first research chapter (Chapter 2), we utilized commercial pigs to evaluate behavior and efficiency of lean tissue gains in pigs fed utilizing two divergent feeding patterns: twice daily feeding and ad libitum feed. Altering feeding regimen did not impact feed efficiency or behavioral expression of hunger. However, gilts fed twice daily had a lower fat to protein ratio than gilts fed ad libitum. Research chapters 3-5 utilized two genetic lines of pigs divergently selected for residual feed intake (RFI) as a model to evaluate how genetic selection for feed efficiency may alter the stress response in pigs.

Chapters 3 and 5 evaluated pigs from the 8th generation and Chapter 4 utilized pigs from the 9th generation of the Iowa State University RFI selection lines. Chapters 3 and 4 utilized two novel stimuli tests, the human approach and novel object tests, to evaluate behavioral stress response. In Chapter 3, low-RFI (more feed efficient) barrows expressed fewer stress behaviors than high-RFI (less feed efficient) barrows.

Interestingly, in Chapter 4, few RFI selection line differences were observed and sex

(barrows vs. gilts) had a larger impact on behavioral stress responses during the human approach test than genetic line. Additionally, phenotypic expression of RFI was related to behavior during the novel object test. To further understand the physiological mechanisms underlying feed efficiency, pigs divergent in RFI were subjected to an intravenous glucose tolerance test and an adrenocorticotropin hormone (ACTH) challenge (Chapter 5). More feed efficient (low-RFI) pigs had a greater insulin response xvi

to the glucose tolerance test and a lower cortisol and NEFA response to the ACTH challenge than less feed efficient (high-RFI) pigs. 1

CHAPTER 1

LITERATURE REVIEW

Introduction

Swine feed efficiency and welfare can be interrelated and define producer goals and consumer concerns. Stress can be defined as the nonspecific response of the body to any demand (Selye, 1973). In commercial swine production stressors, defined as stress- producing factors (Selye, 1973), can include handling by humans, novel environments

(Gray, 1979), disease prevalence, high or low temperature, and aggressive pig temperament (Black et al., 2001). While the stress response is essential for animal survival and biological function, it can antagonize swine production goals such as feed efficiency, growth, carcass quality and welfare. Therefore, an improved understanding of the stress response in grow-finish swine production is critical for understanding swine feed efficiency and welfare.

Feed efficiency has been a production goal of interest since the early 1970s and can be defined in grow-finish pigs as the efficiency by which an animal utilizes dietary nutrients and energy for maintenance and tissue accretion (Patience et al., 2015). Due to genetic selection for feed efficiency and lean carcasses, the feed conversion ratio (FCR) has decreased (improved) over the last 35 years from approximately 3.0 to 2.6 (Knap and

Wang, 2012). Swine feed efficiency is an important component of producer profitability and sustainable protein production, as feed is estimated to be 50 to 85% of the variable production costs (McGlone and Pond, 2003). Feed costs can be compounded by 2

competition between animal agriculture, human food, and biofuel industries resulting in increased demand for grain and higher grain prices (Swick, 2011).

In 2012, pork accounted for 36.3% of meat intake and was the most commonly consumed meat globally (FAO, 2015b). Pork production is therefore important for providing nutritious, safe, and affordable animal protein to the growing human population that is expected to reach 9 billion people by 2050 (Swick, 2011). It is expected that the growing human population will limit space available for expanding livestock and crop production (FAO, 2015a). Therefore, we can increase efficient resource usage by decreasing the amount of feed needed per pig for the same rate of growth. This can have important implications for improving producer profitability, industry competitiveness, and environmental sustainability. This literature review will address the biological factors that contribute to feed efficiency in swine, and then focus specifically on stress and it’s interrelationship with swine feed efficiency and welfare.

Measuring Feed Efficiency

Feed efficiency is not a directly measurable trait, but rather a ratio typically calculated from feed input and weight gain (Koch et al., 1963; Herd et al., 2004).

Traditional measures of feed efficiency are gross gain efficiency (gain:feed) and feed conversion (feed:gain) ratios. Gross efficiency can be defined as the ratio between weight gain and feed input per given time, and its inverse, FCR, is defined as the ratio between feed intake and weight gain (Archer et al., 1999). These ratios are typically measured on a live weight basis; however, there has also been discussion on expressing these ratios on a carcass weight basis (Gaines et al., 2012). Additionally, neither ratio accounts for 3

animal size, body composition or basal metabolic rate (Koch et al., 1963). Therefore, alternative calculation methods have been developed to take some of these variables into consideration. Hence, residual feed intake (RFI) has been developed.

Residual feed intake is a term that describes the difference between observed and expected feed intake based on expected requirements for given production and maintenance parameters. The RFI measure can differ based on the production traits used to adjust daily feed intake (Young and Dekkers, 2012). Traditional adjusted production traits may include growth rate, backfat (Cai et al., 2008), milk and piglet production

(Young and Dekkers, 2012). Therefore, RFI captures feed intake as the amount of feed expected for the given level of production and the residual portion deviating from the expected (Koch et al., 1963). Animals which consume less feed than expected for a given population have a lower RFI value, are more feed efficient, and are therefore may be more economically desirable. On the other hand, high RFI animals consume more feed than expected for a given population, are less feed efficient, and they are therefore less economically desirable. Through genetic selection for RFI, lines of livestock can be used as a model to study the genetic and physiological basis of feed efficiency.

In order to gain a greater understanding of feed efficiency in swine, two research groups have used pigs divergently selected for RFI: 1) Iowa State University (ISU) selection lines in Yorkshire pigs (Figure 1.1) and 2) Institut National de la Recherche

Agronomique (INRA) selection lines in Large White pigs. Residual feed intake has been identified as a moderately heritable trait in both ISU (h2=0.29) and INRA (h2=0.24) selection lines (Gilbert et al., 2007; Cai et al., 2008). After eight generations of divergent selection of the ISU RFI lines, the low-RFI line had 241 g/day less RFI, 376 g/day less 4

ADFI, 0.22 g feed/g weight gain less FCR, 79 g/day less ADG, and 2.5 mm less BF compared to high-RFI pigs (Figure 1.2; Young and Dekkers, 2012).

Random allocation of purebred Generation 0 Yorkshire littermates to either line

Low-RFI line Control line Generations 1-4 Parity 1 gilts bred to parity 1 boars, Parity 1 gilts randomly bred to selection based on EBVs for low-RFI parity 1 boars

Low-RFI line Control line Generation 5 Parity 1 gilts bred to parity 1 boars, Parity 1 gilts bred to parity 1 selection based on EBVs for low- boars, selection based on EBVs RFI for high-RFI

Low-RFI line High-RFI line Generation 6-10 Parity 1 gilts bred to parity 1 boars, Parity 1 gilts bred to parity 1 selection based on EBVs for low- boars, selection based on EBVs RFI for high-RFI

Figure 1.1. Flow chart of the design of selection lines used for the ISU RFI selection lines.

Figure 1.2. Response to selection for residual feed intake over eight generations. Line differences are low-RFI – high-RFI. BF = backfat thickness; ADG = average daily gain; FCR = feed conversion ratio; LEA = loin eye area; FI = feed intake; RFI = residual feed intake (Young and Dekkers, 2012).

Biological Factors Contributing to Feed Efficiency

The main biological factors that contribute to differences in feed efficiency have been widely studied using RFI feed efficiency models. These factors been partially quantified in poultry (Luiting, 1990), beef cattle (Richardson and Herd, 2004), and pigs

(Barea et al., 2010) divergently selected for RFI. The biological factors identified in beef cattle that contribute to variation in RFI have been summarized (Figure 1.3) by

Richardson and Herd (2004) and Herd and Arthur (2009). The major categories included physical activity, feed intake patterns and behavior, stress, body composition, nutrient digestibility, protein turnover, and metabolism. A variety of studies utilizing pig RFI selection projects and non-RFI studies investigating the contribution of these aforementioned factors will now be discussed.

Figure 1.3. Contributions of biological mechanisms to variation in RFI as determined from experiments on divergently selected beef cattle (Richardson and Herd, 2004).

Body composition, digestion, and metabolism

In beef cattle, 5% of the difference in RFI is estimated to be due to body composition differences (Richardson and Herd, 2004). In the 5th generation of the ISU lines, low-RFI pigs were reported to have a greater lean percentage (Smith et al., 2011) and the whole carcass had lower fat content compared to high-RFI pigs (Boddicker et al.,

2011). Harris and colleagues (2013) found similar results in 7th generation ISU RFI gilts and reported that low-RFI gilts had reduced backfat and whole body fat compared to high-RFI gilts. Additionally, low-RFI gilts tended to have increased whole body protein accretion and had significantly greater bone accretion compared to high-RFI gilts (Harris et al., 2013).

The ISU RFI selection lines have also identified differences in digestibility. Low-

RFI pigs were reported to have higher energy (gross energy) and nutrient digestibility

(dry matter and nitrogen), use (digestible energy and metabolizable energy), and retention

(nitrogen) compared to high-RFI pigs (Harris et al., 2012). In contrast, Barea and colleagues (2010) found no selection line differences in the digestibility coefficients for organic matter, DM, N, P, or energy in the INRA lines. Low-RFI pigs from INRA lines also had lower heat production related to physical activity and basal metabolic rate compared to high-RFI pigs (Barea et al., 2010).

Activity and feeding behavior

Differences in home pen activity and feeding behavior are reported in the ISU

RFI selection lines. Using the fifth generation of ISU lines, Sadler and colleagues (2011) reported that low-RFI gilts were less active in their home pen than control gilts, except 7

for the day of placement within the pen (Table 1.1). On the day of placement, the low-

RFI gilts had decreased lesion scores from the head to the flank compared to the control gilts. These lesion score results may indicate that low-RFI gilts were involved in less reciprocal fighting which may be due to engaging in fewer or winning more aggressive encounters compared to control gilts.

Table 1.1. Time budget of two genetic lines of grow-finish gilts over the subsequent rounds, in the home pen1 (Sadler et al., 2011).

Results of ISU RFI line studies have suggested differences in feeding behavior.

Young and colleagues (2011) investigated feeding behavior in fourth and fifth generation pigs RFI pigs. They reported that the low-RFI pigs ate faster, spent less time at the feeder, and visited the feeder fewer times per day than control pigs. Additionally, positive correlations were found for RFI with daily feed intake, feed intake per feeder visit, and number of feeder visits per day (Young et al., 2011). 8

Different allele frequencies for single nucleotide polymorphisms (SNPs) near insulin release regulation genes have been identified in ISU RFI selection lines (Onteru et al., 2013). Onteru and colleagues (2013) identified associations of RFI and average daily feed intake with genomic regions containing glucagon-like peptide 1 receptor (GLP1R) and cyclin-dependent kinase 5 regulatory subunit associated protein 1-line 1 (CDKAL1) genes. The GLP1R gene activates the adenylyl cyclase pathway to increase synthesis and release of insulin and CDKAL1 is involved in insulin release through the provision of

ATP and potassium-ATP channel responsiveness (Onteru et al., 2013). Therefore, insulin response may be associated with feeding behavior differences between selection lines.

While ISU RFI selection line studies suggest differences in feeding behavior and insulin response between low- and high-RFI pigs, little work has been done to investigate RFI line differences in hormones associated with appetite regulation.

Other studies have identified that feeding frequency may be an important aspect of feed efficiency in pigs and this has been a research subject of interest over many years

(Ohea and Leveille, 1969). More recently the investigation of feeding frequency’s impact on body composition has gained interest (Le Naou et al., 2014; Newman et al., 2014).

After multiple studies in this area, no clear consensus on the feeding method that has the best effect on performance and body composition has been identified. This may be partially due to experimental differences in pig age, body weight, sex, genetics and experimental methodology (Table 1.2).

Feeding frequency may impact feed efficiency partially due to the endocrine and metabolite feeding response. Le Naou and colleagues (2014) observed a greater rise and fall of plasma glucose and insulin concentrations in response to feeding in gilts fed twice 9

daily compared to those fed 12 times daily. Similarly, Newman and colleagues (2014) observed relatively constant plasma insulin concentrations in pigs fed ad libitum whereas pigs fed twice daily expressed increased post-prandial insulin concentrations.

Table 1.2. Effects of feeding frequency on performance in pigs. Performance Length of Pig Feed Body changeb feed Pig sex body Reference frequencya composition ADG ADFI FEc treatment weight 2X Inc NS Inc NS (Le Naou et 21 days Gilt 30 kg 12X Dec NS Dec NS al., 2014) 2X NS NS NS - (Newman et 23 days Boar 70 kg Ad libitum NS NS NS - al., 2014) 2X NS Dec.d Inc Leaner (Newman et 49 days Boar 41 kg Ad libitum NS Inc.d Dec Fatter al., 2014) 2X NS Dec. NS - (Newman et 44 days Gilt 59 kg Ad libitum NS Inc. NS - al., 2014) 2X Dec NS Dec - Barrows (Schneider 42 days 70 kg 6X Inc NS Inc - & gilts et al., 2011) 1X Dec NS Dec - Barrows (Fanimo et 2X Inc NS Inc - 70 days 16 kg & gilts al., 2003) 3X Inc NS Inc - aMeal frequency per day. bPerformance change. Inc = increased, Dec = decreased and NS = no change. cIncrease in FE notes greater gain:feed or lesser feed:gain. d0.05 < P < 0.10

Differences in the insulin secretory profile may have implications on feeding and satiety behavior as insulin is instrumental for the post-prandial inhibition of food intake

(Gerozissis, 2008). Activity levels are reported to increase when pigs are restrictively fed

(Beattie and O'Connell, 2002) and likely reflect hunger as pigs inherently forage for their food (Stolba and Woodgush, 1989). Group housed pigs fed twice daily were reported to spend less time eating and had lower activity compared to those fed six times daily

(Schneider et al., 2011). However, group housed gilts fed once daily spent less time eating but were more active than gilts fed ad libitum (Brouns et al., 1994). Therefore, 10

more research is necessary to understand how feeding frequency impacts pig behavioral expression of hunger and satiety and in turn the effect on swine welfare.

Insulin plays an important role in stimulating protein synthesis (McNurlan and

Anthony, 2006) and therefore may also impact protein metabolism in pigs with different feeding frequencies. Le Naou and colleagues (2014) reported that pigs fed twice daily had lower plasma concentrations of urea and α-amino nitrogen compared to pigs fed 12 meals daily. This suggests that less-frequent meals may have decreased protein catabolism. Furthermore, in young pigs it has been reported that leucine pulses enhance protein synthesis, suggesting that meal feeding may be important for lean growth (Boutry et al., 2013). This is supported by Newman and colleagues (2014), who reported that boars fed twice daily were leaner than boars fed ad libitum.

Immunological and environmental stress

Grow-finish pigs are continuously immunologically challenged by pathogenic bacteria, viruses and vaccines. These challenges attenuate feed intake and nutrient utilization that reduce growth rates and therefore compromise feed efficiency and animal welfare (Williams et al., 1997). When undergoing an immunological stress induced by

Porcine Reproductive and Respiratory Syndrome virus challenge, low-RFI pigs tended to have a lower viral load, greater growth, and be more likely to survive compared to high-

RFI pigs over a six week challenge period (Dunkelberger et al., 2015). Rakhshandeh and colleagues (2012) reported that low-RFI gilts had greater apparent fecal digestibility; however, when challenged with Escherichia coli lipopolysaccharide (LPS), low-RFI gilts had a greater decrease in apparent fecal digestibility of crude protein compared to high- 11

RFI gilts. No RFI selection line differences in sickness behaviors (i.e. lying, sitting, standing, eating, and drinking) were reported during the LPS challenge (Azarpajouh et al., 2015).

Grow-finish pigs may also commonly undergo alterations in thermal comfort such as heat stress. Heat stress typically alters the composition of tissue gains and attenuates growth, feed efficiency, and pig welfare (Baumgard and Rhoads, 2013). Renaudeau and colleagues (2013) investigated heat stress in pigs from the INRA RFI selection lines.

Overall thermal heat acclimation did not differ between low- and high-RFI lines. Average daily feed intake was consistent with normal line differences (high-RFI pigs ate more than low-RFI pigs); however, no other physiological and behavioral measures in ability to cope with the stressor were reported.

Grubbs and colleagues (2013) identified ISU RFI selection line differences in the mitochondria protein profile. Heat shock protein 60 and heat shock protein 70, which have been linked to the response to heat stress and anti-apoptotic pathways in the mitochondria, were increased in low-RFI compared to high-RFI pigs from the 7th generation of the ISU RFI selection lines. Endoplasmic reticulum oxidase-1 α, which modulates mitochondrial membrane permeability in response to oxidative stress, was decreased in the longissimus dorsi muscle mitochondria of low-RFI compared to high-

RFI pigs. These results suggest that low-RFI pigs may be less prone to muscular oxidative stress compared to the high-RFI pigs. Furthermore, Mani and colleagues (2013) reported that low-RFI pigs had lower haptoglobin, an acute phase protein that can increase after stress, compared to high-RFI pigs. While these studies identify a link between feed efficiency and stress, few studies have directly evaluated how divergent 12

selection for RFI impacts pig hormonal and behavioral stress response. The following section will expand on stress and how this relates to feed efficiency.

Stress

Stress can be defined as the nonspecific response of the body to any demand

(Selye, 1973). Stress can occur during both positive and negative situations, and Moberg

(2000) defines eustress as a non-threatening stress response and distress as a stress response with deleterious effect on the individual’s welfare. In commercial environments, common stressors for pigs include high stocking density, poor air quality, disease prevalence, high or low temperature, and aggressive pig temperament (Black et al.,

2001). These stressors often antagonize feed efficiency, feed intake, growth rate, and increase carcass fat (Black et al., 2001). Strategies to decrease swine stress and ultimately improve feed efficiency include removing the stressor, reducing the pig’s perception of the stressor and/or altering the pig’s physiological response to stress (Black et al., 2001).

Psychobiology of the stress response

The psychobiological stress response on an animal can be divided into three general stages: 1) the recognition of a stressor, 2) the biological defense against the stressor, and 3) the consequences of the stress response. A combination of biological defenses often occurs in response to the stressor and these may include autonomic nervous system, neuroendocrine, immune, and behavioral responses (Moberg, 2000).

Recognition occurs when a stressor, or a potential threat to homeostasis, is perceived by the sensory organs of an animal and information is sent to the cerebral 13

cortex of the brain. Within the brain, this information is sent to the limbic system and hypothalamus. What is perceived as a threat by each animal varies greatly between individuals of the same species, and may or may not be truly threatening. However, the overall stress response is dependent on how threatening the animal perceives the stressor to be (Moberg, 2000).

The short acting stress response is controlled by the autonomic nervous system

(von Borell, 2000). The autonomic nervous system is the division of the nervous system responsible for controlling the body’s visceral functions and receives inputs from regions of the central nervous system. The autonomic nervous system consists of two parts: the sympathetic and the parasympathetic nervous systems (Table 1.3). The sympathetic nervous system activity was first noted by Cannon (1929), who referred to it as ‘fight or flight’. The sympathetic nervous system activates the sympatho-adrenal system through the adrenal medulla. Epinephrine and norepinephrine are released from neurons within the hypothalamus and increase the supply of glucose to the muscles, preparing the body for immediate action (eg. fight or flee). Energy expenditure is reduced in non-vital organs such as the gastrointestinal tract, and cardiac output is increased, diverting blood

(nutrients and oxygen) to the brain, heart, and skeletal muscles. The parasympathetic nervous system regulates day-to-day tasks such as digestion and contributes to restoring the animal to a state of equilibrium following the stress response. Sympathetic and parasympathetic nervous systems are normally known as reciprocal antagonists; for example, sympathetic stimulation increases the heart rate and parasympathetic stimulation decreases it. However, intense emotion such as extreme fear can involve 14

parasympathetic activation in addition to sympathetic activation and can result in diarrhea, urinary incontinence, and fainting (Toates, 1995).

Table 1.3. Function of autonomic nervous system activation of various organs. Table adapted from Reece (2005). Organ/Structure Sympathetic Action Parasympathetic Action Eye Muscles of iris Contraction of radial muscle Contraction of circular muscle (dilates pupil) (contracts pupil) Heart S-A node Increase in heart rate Decrease in heart rate A-V node Increase in conduction velocity Decrease in conduction velocity Muscle Increase in force of contraction Decrease in force of contraction Intestines Muscle Decreased Increased Secretions Decreased Increased Lungs Bronchi Dilation Constriction Kidney Afferent arteriole constriction None and renin secretion Urinary bladder Bladder wall None Contraction Sphincter Contraction Relaxation Salivary glands Mucus secretion Serous secretion

The longer acting, sustained stress response is controlled by the neuroendocrine system (von Borell, 2000). The neuroendocrine system influences multiple biological functions including immune competence, reproduction, metabolism, and behavior

(Moberg, 2000). The primary neuroendocrine axis studied is the hypothalamic-pituitary- adrenal (HPA; Figure 1.4) axis, first recognized by Selye (1936). The perceived stressor activates a cascade of events initiated by stimulating the hypothalamus to secrete corticotrophin-releasing hormone (CRH). Corticotrophin-releasing hormone stimulates the secretion of adrenocorticotropic hormone (ACTH) from the anterior pituitary corticotrophs. Adrenocorticotropic hormone then stimulates the secretion of 15

glucocorticoids from the adrenal cortex (Matteri et al., 2000). The primary glucocorticoid hormone in pigs, cattle, fish, and humans is cortisol, and in birds and rodents it is corticosterone (Mormède and Terenina, 2012). These glucocorticoids prepare the body for behavioral, autonomic, and metabolic responses by increasing blood glucose through gluconeogenesis (Mormède and Terenina, 2012).

Figure 1.4. Hypothalamic-pituitary-adrenal axis. BNST= bed nucleus of the stria terminalis, CRH= corticotrophin-releasing hormone, VP= vasopressin, ACTH= adrenocorticotropic hormone, CBG= corticosteroid-binding globulin, 11βHSD= 11β=hydroxysteroid dehydrogenase (Mormède and Terenina, 2012).

Animal stress and feed efficiency

Stress can have negative consequences on swine performance as it results in catabolism of body tissues through lipolysis, proteolysis, and glycogenolysis (Weissman,

1990). Additionally, behavioral stress responses of decreased feed intake and altered 16

activity level also alter swine performance (Elsasser et al., 2000). Previous work has investigated the relationship between feed efficiency and the HPA axis. Two differences are hypothesized for the HPA axis of more feed efficient animals; 1) more feed efficient animals have lower baseline cortisol concentrations, and 2) more feed efficient animals have a lower response to a HPA axis challenge.

Baseline glucocorticoids identify differences in coping with everyday stressors that the animals are already undergoing. Baseline glucocorticoid differences have been reported in beef cattle (Richardson et al., 2004) and chickens (Katle et al., 1988) divergently selected for RFI. Katle and colleagues (1988) reported that low-RFI chicks had lower corticosterone compared to high-RFI chicks. In beef cattle, cortisol tended to be lower in low-RFI steers and tended have a positive regression coefficient with sire estimated breeding value (EBV); however, cortisol had a negative correlation with RFI values (r2= -0.40; Richardson et al., 2004). Richardson and colleagues (2004) concluded that differences between genetic line and correlation results were influenced by the stress caused from the housing. The studied steers had been moved from feedlot to indoor housing for recording of feed intake data. Cortisol was analyzed from half of the calves before they were moved to the indoor housing, and from half the calves at the end of the trial inside the indoor housing. The added stress of possibly not adapting to the housing appeared to decrease feed intake (particularly in the high-RFI line), and resulted in a strong negative regression between cortisol and average daily feed intake. This may explain the inconsistency of the cortisol results; however, more work should be done to further investigate these differences. 17

Baseline cortisol has also been examined in relation to feed efficiency, in animals which were not selected for feed efficiency; including sheep (Knott et al., 2008, 2010),

Nile tilapia (Martins et al., 2011), and African catfish (Martins et al., 2006). None of the studies reported significant correlations between baseline cortisol concentrations and RFI or FCR (Martins et al., 2006; Knott et al., 2008, 2010; Martins et al., 2011). These data indicate that baseline glucocorticoid differences may be reflective on genetic selection and therefore selection pressure for feed efficiency.

Differences in the HPA axis may also be due to stress responsiveness. The stress response has been investigated using an ACTH challenge (Knott et al., 2008, 2010) and an insulin challenge (Knott et al., 2010) in sheep and a net test with Nile tilapia (Martins et al., 2011) and African catfish (Martins et al., 2006). These tests were done with animals which had not undergone selection for feed efficiency. All four studies found significant positive relationships between cortisol and RFI following the challenges.

Martins and colleagues (2006) also determined that more feed efficient African catfish had a lower glucose response compared to the less feed efficient conspecifics. In both studies utilizing sheep, Knott and colleagues (2008, 2010) reported lower cortisol responsiveness and that lower RFI had a lower proportion of fat tissue. While these four studies agree that low-RFI animals have a lower HPA response, little work has investigated this relationship in pigs.

Measuring animal stress

Measuring stress in animals is difficult as it can be influenced by handling, prior experience, hormonal status, circadian rhythm, age, and varies between species, 18

individuals, and stressors. Additionally, it is difficult to interpret when stress is perceived as distress; therefore, collecting multiple measures may allow for the best understanding of the response (Cook et al., 2000).

In order to measure the stress response, the animal needs to undergo a stressor.

One way to investigate physiological differences of the stress response in a controlled manner is by stimulating the HPA axis. The HPA axis can be stimulated through exogenous CRH and ACTH challenges and measured through cortisol (Figure 1.4). Due to the negative feedback cortisol has on CRH and ACTH, increased cortisol suggests a greater stress response. Challenging pigs with CRH results in blood cortisol reaching lower peak concentrations compared to using ATCH as the agonist (Lang et al., 2004;

Madej et al., 2005). Therefore, an ACTH challenge may be a more sensitive stimulation test for identifying HPA responses compared to CRH challenges.

Animal behavior can also be utilized to measure stress. Animals use behavioral responses as an attempt to manage and cope with stressors (Moberg, 2000). Activation of the autonomic nervous system mediates behaviors including fighting, fleeing, freezing

(Toates, 1995) and elimination (Hall, 1934). Activation of the HPA axis results in more complex behaviors that are more specific to the stressor than behaviors activated by the autonomic nervous system. These behaviors may include activity, exploration, submission, active- and passive avoidance (Toates, 1995).

Fear tests can be utilized as a method of measuring the behavioral stress response.

Fear is a specific type of stressor which is considered a negative emotional state. It is an integrated response adapted to particular aversive events which threaten homeostasis

(Boissy, 1998). Gray (1979) classified fear stimuli into five categories: 1) dangers the 19

animal has learned to avoid, 2) stimuli that will evoke an unlearned fear response, 3) novel stimuli, 4) physical characteristics of a stimuli such as speed of movements, and 5) stimuli which arise from conspecifics, such as alarm calls. Fear tests utilize fear-eliciting events to measure the stress response. Three tests which are commonly used to test fear in grow-finish pigs are the open field, novel object, and human approach tests.

The open field test was originally developed to test emotionality in rodents (Hall,

1934) and was first applied to pigs by Beilharz and Cox (1965). During the open field test, an individual animal is typically placed in a novel arena measuring five to ten m2 for five to 20 minutes (Forkman et al., 2007). This test provides the fear-eliciting components of novelty (Spinka, 2006), agoraphobia, and social isolation (Prut and

Belzung, 2003). Activity, elimination, time spent within the center of the arena, and time spent close to the walls of the arena are commonly recorded behaviors during this test

(Murphy et al., 2014).

During the novel object test, an individual pig may be tested in a novel or familiar arena. Methods of testing are highly variable (Table 1.4). The novel stimulus is typically visual and brightly colored (Forkman et al., 2007), but unfamiliar odors have also been tested (Jones et al., 2000). The stimulus may be already present when the pig enters the arena, or it may be introduced after a habituation period. This test provides the fear- eliciting components of novelty, social isolation, and suddenness if the object is introduced after a habituation period (Forkman et al., 2007). Latency, frequency, and duration of contacts with the novel object, stimulus avoidance, activity, and elimination are commonly recorded behaviors during this test (Murphy et al., 2014).

Table 1.4. Novel object test methodologies of object presentation, social situation, and the location in which the test is performed. Table adapted from Murphy and colleagues (2014). Object Test Location Social Presentation Situation Home Pen Separate Arena Method Slowly Solitary None (de Sevilla et al., 2009; introduced Hemsworth et al., 1996; Jensen, 1994; Kranendonk et al., 2006; Lawrence et al., 1991; Lind et al., 2005; Pearce and Paterson, 1993; Ruis et al., 2001; Siegford et al., 2008) Group None None Suddenly Solitary (Olsson et al., 1999) (Dalmau et al., 2009; Hessing et introduced al., 1994; Janczak et al., 2003a; Janczak et al., 2003b; Jones and Nicol, 1998; Tönepöhl et al., 2012) Group (Brown et al., 2009; (Magnani et al., 2012) Smulders et al., 2006) Placed by Solitary (Burne et al., 2001; (Hayne and Gonyou, 2003; person Wemelsfelder et al., Morrison et al., 2007) 2000) Group (van Erp-van der None Kooij et al., 2002) Already Solitary None (Dalmau et al., 2009; present Wemelsfelder et al., 2000; Wemelsfelder et al., 2009) Group None None

Human approach tests are often performed either as forced approach tests, where the animal is approached by a human, or voluntary approach tests, where the animal is free to approach a human that remains still (Table 1.5). Human posture and interaction may also differ between seated versus standing and looking at versus ignoring the animal.

The animal may be either familiar or unfamiliar with the human; however, little is known about how well pigs discriminate between people. This test provides more complex fear- 21

eliciting components than the open field or novel object tests, in that it may include novelty, social isolation, previous positive or negative interactions and learned responses to humans, and posture and movement of the human (Waiblinger et al., 2006). Latency, frequency, and duration of contacts with the human, human avoidance, activity, and elimination are commonly recorded behaviors during this test (Murphy et al., 2014).

Table 1.5. Human approach test methodologies of human presentation, social situation in which the test is performed, and the location in which the test is performed. Human Test Location Social Presentation Situation Home Pen Separate Arena Method Human Solitary None (Hemsworth et al., 1986; Miura et approaching al., 1996; Marchant et al., 2001; pig Marchant-Forde et al., 2003) Group (Scott et al., 2009) None Human slowly Solitary (Marchant et al., (Hemsworth et al., 1981; enters then 2001; Janczak et al., Hemsworth et al., 1986; Marchant stationary 2003) et al., 2001; Marchant-Forde et al., 2003; Hayne and Gonyou, 2006; Siegford et al., 2008) Group (van der Kooij et al., None 2002; Brown et al., 2009; Reimert et al., 2014) Stationary Solitary None (Tanida et al., 1995; Miura et al., human present 1996; Terlouw and Porcher, 2005; at test start Pairis et al., 2009) Group None (Pairis et al., 2009)

The foundational fear-eliciting component of the open field, novel object, and human approach tests utilizing an unfamiliar person is novelty. Novelty is referred to as a comparative variable, because in order to recognize something as unfamiliar it needs to be compared with previous experiences (Boissy, 1998). Dantzer and Mormède (1983) reported that novelty elicits behavioral arousal similar to that induced by nociceptive stimulation such as an electric foot shock. When exposed to novelty, pigs are typically 22

motivated to both approach and avoid the stimulus due to curiosity and novelty aversion, respectively. This may result in a negative emotional response due to conflict between motivational systems (Murphy et al., 2014). Social isolation of the animal is another fear- eliciting component, which is present in all three tests. Pigs are a social species and many grow-finish pigs have rarely been isolated from their conspecifics (Waiblinger et al.,

2006); therefore, fear is often greater when tested individually (Brown et al., 2009; Pairis et al., 2009).

An additional component of the human approach test is that it also tests the human-animal relationship, including previous positive, neutral, or negative interactions and learned responses to humans (Waiblinger et al., 2006). This is important because human exposure is one of the most frightening events that farm animals are likely to experience (Boissy, 1995). In swine production, humans may have little interaction with pigs other than situations that may be perceived as negative by the pig. These situations can include medically treating (Weimer, 2012), castrating, tail docking, restraining, and sorting pigs (Waiblinger et al., 2006). With little opportunity to habituate, it is suggested that even domesticated animals may often perceive humans as predators (Suarez and

Gallup, 1982).

During fear tests, behavior is often the primary measure used to interpret emotional state. Primary behavioral indicators of fear include active defense reactions such as attack and threaten, active avoidance reactions such as hiding and escaping, and passive avoidance reactions such as movement inhibition (Boissy, 1998). Activity level often is dependent on the emotional intensity of the threat. In pigs, during a low threat, such as those presented by fear tests (Waiblinger et al., 2006) increased activity is viewed 23

as fearful (Archer, 1979). Furthermore, during human approach and novel object tests, latency, duration, and frequency of stimuli interactions are primary behavioral outcomes.

However, behavioral patterns may be contradictory. Natural selection, in some cases, has favored for dishonest communication, making it difficult to interpret signals (Krebs and

Dawkins, 1984). Therefore, collection of multiple behavioral measures is important to adequately interpret affective states during fear tests.

Behavioral measures have been utilized to determine the relationship between stress and feed efficiency. Braastad and Katle (1989) reported that low-RFI hens spent less time performing pre-laying frustration behaviors including less pacing, escape, and aggressive behavior, and spent more time resting or sleeping compared to high-RFI hens.

Low-RFI hens also had better plumage around the neck and breast compared to high-RFI hens, which may be due to lower activity within the pen or lower stress. However, when older hens were moved into respiration chambers, low-RFI hens showed greater molting, weaker egg shells, and a longer adaptation period (Luiting et al., 1994). Differences in these studies may be in part due to age, generation, and selection line. Braastad and Katle

(1989) used 48 to 53 week old, F3, white leghorn laying hens, where Luiting and colleagues (1994) used 58 week old, F4, white leghorn laying hens; both lines had been divergently selected for RFI which was adjusted for egg mass production, metabolic body weight, and body weight gain. This may also indicate differences in acute (short term stress response) versus chronic stress (long term stress response), as the older hens were housed in the respiration chambers over a seven week period (Luiting et al., 1991).

Behavior differences have also been identified in animals that were not selectively bred for feed efficiency. Pajor and colleagues (2008) found that lambs with calmer 24

temperaments had a higher average daily gain and higher weight at the end of fattening compared to lambs with more nervous temperaments during weighing on a scale and a flight test. Contradictory to this, Merino wethers that were selectively bred for low behavioral reactivity to human approach and box tests were less feed efficient with no difference in average daily gain compared to wethers that were bred for high behavioral reactivity (Amdi et al., 2010). Differences may be in part due to age, sex, and breed.

Pajor and colleagues (2008) used weaned rams and ewes of three different breeds where

Amdi and colleagues (2010) used 14 month old, Merino wethers. Differences may also be due to the nature of the tests. Pajor and colleagues (2008) used a scale and flight test where Amdi and colleagues (2010) reported activity during a box test; however, the wethers had been selected for reactivity to both the human approach and the box test.

Differences may also be due to the methods of the box test, where a sheep is isolated within a solid plywood box and sensors records the amount of vibration from movement and vocalizations made by the sheep. High behavioral reactivity is assigned to the animals which create the greatest amount of vibrations during the test, with the assumption that a higher score is more fearful. This score may not accurately portray fear, however, as it excludes freezing.

Overall, measuring animal stress and fear is important for understanding how an animal utilizes energy for growth versus for responding to a stressor. However, another consequence of the stress response is the animal’s welfare. The animal’s ability to cope with a stressor ultimately impacts their welfare and thus growth performance and feed efficiency.

Animal welfare

While selective breeding for animal production traits, such as feed efficiency, is an important component for animal agriculture, it is critical to understand the influence selective breeding for feed efficiency and higher lean tissue accretion has on animal welfare. Current traits for selective breeding of swine primarily include high growth rate, reduced backfat, low FCR, soundness, and a large litter size (Rauw et al., 1998).

However, the Standing Committee of the European Convention for the Protection of

Animals kept for Farming Purposes recommends that health and welfare are included with breading goals (D'Eath et al., 2010).

Two primary swine welfare concerns when selectively breeding for production traits are hunger and stress (Rydhmer and Canario, 2014). Selection for feed related traits has been shown to alter pig appetite. Increasing pig appetite may increase growth and is typically not a welfare concern in pigs fed ad libitum (Lundgren et al., 2014). However, it can also increase sow appetite which results in welfare concerns when sows are restrictively fed as they may not reach satiety (Appleby and Lawrence, 1987). The concern for swine stress is likely partially a consequence of the halothane allele, which was inadvertently selected for with high growth rate and lean carcasses. Pigs which are homozygous for the allele have Porcine Stress Syndrome, resulting in high transportation losses and pale, soft, and exudative meat (Grandin and Deesing, 1998). Saintilan and colleagues (2011) investigated the influence of selecting pigs using RFI on the halothane gene. They determined that while selection for lower FCR increases the occurrence of halothane alleles, selecting for lower RFI improved FCR while not modifying the frequency of halothane alleles. However, as there are many other factors that can 26

contribute to swine stress, there is still concern raised on RFI pig ability to cope with stress (Rydhmer and Canario, 2014). As little work has been done in to evaluate how grow-finish pig welfare relates to feed efficiency, this is an important area of investigation as it addresses producer and consumer concerns.

In 2003 a telephone interview poll that surveyed 1,005 American adults drew attention to consumer concerns for farm animal welfare. Participants were asked to score if they strongly supported, somewhat supported, somewhat opposed, strongly opposed, or had no opinion on the following four proposals, which were presented in a random order:

1) banning all medical research on laboratory animals, 2) banning all product testing on laboratory animals, 3) passing strict laws concerning the treatment of farm animals, and

4) banning all types of hunting. Conclusions drawn from this survey noted that the U.S. society was becoming more concerned about farm animal treatment. Of the four proposals, 62% of Americans supported passing strict laws concerning the treatment of farm animals, while the highest support for any other category was 38% (Moore, 2003).

Thus, animal agriculture is likely seen as an important area by the public for legislative efforts compared to the others due to the enormous number of animals involved in production (Fraser, 2009).

Public concern for farm animal welfare has developed further than just belief; it is being implemented into legislation globally. The European Union livestock and poultry producers are now mandated to implement food animal production government regulations. In contrast to this, U.S. livestock and poultry producers are still relatively free of mandatory animal welfare standards; however, with public demand, animal welfare legislation is quickly increasing (Swanson, 2008). The Humane Methods of 27

Livestock Slaughter Act (7 USC 1901 et seq.) is currently the primary federal food animal regulation in the U.S. Livestock may also be covered under state anticruelty laws for neglect, abuse, and cruelty. Additionally, growth in public concern for particular production practices has led to state and local laws and self-directed programs across the

U.S. (Swanson, 2008).

Figure 1.5. Three schools of animal welfare (Fraser et al., 1997).

Opinions of animal welfare commonly center around three philosophical views:

1) animal health and biological function, 2) natural living, and 3) affective states (Fraser et al., 1997). The concept of animal health and biological function measures welfare through rates of disease, injury, mortality, and reproductive success. Natural living emphasizes allowing animals to develop and live in ways that are natural for the species; measuring welfare through natural behavior and the strength of animal motivation to perform behaviors. Affective states emphasizes minimizing unpleasant emotions and allowing animals normal pleasures; measuring welfare through indicators of pain, fear, distress, and frustration (Fraser et al., 1997). These concepts are strongly related; 28

however, do not completely overlap (Figure 1.5); therefore, improving welfare requires give and take from all three areas (Fraser et al., 1997).

In 1965 the Brambell Committee recommended that animals should have the freedom to stand up, lie down, turn around, groom themselves and stretch their limbs. As a result of the Brambell Committee’s report, the Farm Animal Welfare Advisory

Committee (now referred to as the Farm Animal Welfare Committee (FAWC)) was created to monitor farm animal welfare. The five freedoms currently refer to the following: 1) freedom from hunger and thirst – by ready access to fresh water and a diet to maintain full health and vigor, 2) freedom from discomfort – by providing an appropriate environment including shelter and a comfortable resting area, 3) freedom from pain, injury and disease – by prevention or rapid diagnosis and treatment, 4) freedom to express normal behavior – by providing sufficient space, proper facilities and company of the animal’s own kind, and 5) freedom from fear and distress – by ensuring conditions and treatment which avoid mental suffering (FAWC, 2013). The five freedoms include aspects from each of Fraser and colleagues’ (1997) three schools of animal welfare; therefore, it should be understood that these define the ideal states, and again may require give and take from each of the freedoms (Appleby, 1999).

The five freedoms have been scrutinized for their overly negative emphasis

(FAWC, 2013). Therefore, to work towards a more positive focus FAWC proposed three quality of life classifications in 2009: 1) a good life, 2) a life worth living, and 3) a life not worth living. The classification of ‘a life worth living’ requires good husbandry, considerate handling and transport, humane slaughter, and stockmen that are skilled and conscientious. The FAWC recommends that the minimum standard of farm animal 29

welfare should be set at ‘a life worth living’ with an increasing number of animals having

‘a good life’ from the animal’s point of view (FAWC, 2009).

Conclusions

Improving grow-finish pig feed efficiency and welfare is important for increasing producer profitability, sustainable protein production, and consumer confidence in pork producer practices. Stress is a common occurrence in grow-finish swine production and can antagonize production efficiency and welfare. Studies evaluating biological differences in feed efficiency suggest an interrelationship with pig stress response and feeding behavior; however, it is unclear if this translates to alterations in pig welfare. As few studies have linked swine performance and behavior, this will be essential for better understanding how this impacts pig welfare. Therefore, this dissertation research will address how improving feed efficiency impacts swine welfare through feeding behavior and ability to cope with stressful events.

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CHAPTER 2

FEEDING REGIMEN IMPACTS PIG GROWTH AND BEHAVIOR

A paper submitted to Physiology & Behavior

Jessica D. Colpoys, Anna K. Johnson, Nicholas K. Gabler*

Department of Animal Science, Iowa State University, Ames, IA, 50011, USA

*Corresponding author: 201E Kildee Hall, Iowa State University, Ames, IA, 50011; E- mail: [email protected] Phone: +1-515-294-7370 Fax: +1-515-294-1399

Abstract

A primary swine production goal is to increase efficiency of lean tissue gains.

While many swine production systems currently utilize ad libitum feeding, recent research suggests that altering feeding patterns may impact feed efficiency. Therefore, the objective of this study was to compare two feeding patterns and evaluate their impact on whole body tissue accretion, feeding behavior and activity in growing pigs. Forty eight individually housed gilts (55.9 ± 5.2 kg on test BW) were assigned into one of two feeding treatments: 1) ad libitum access (ad lib) or 2) twice daily access where gilts were allowed to eat ad libitum between 08:00-09:00 h and again from 17:00-18:00 h (2x). Pig performance was recorded weekly for 55 days and average daily gain (ADG), average

daily feed intake (ADFI), and gain:feed (G:F) was calculated. Body composition was assessed in 12 gilts per treatment using dual X-ray absorptiometry (DXA) at day -3 and

55 of treatment, and tissue accretion rates were calculated. Gilt behaviors were assessed via video analysis during week 7 and included time spent eating, feeding rate, enrichment interaction, postural changes, standing, sitting, and lying behaviors. Gilts fed 2x had lower ADG and ADFI compared to ad lib gilts (P ≤ 0.01); however, no treatment difference in G:F was observed (P = 0.83). At day 55 gilts fed 2x had a lower fat:protein compared to ad lib gilts (P = 0.05). Fat, lean, and protein accretion rates were lower in gilts fed 2x compared to those fed ad lib (P = 0.01). Gilts fed 2x ate less frequently and for a shorter duration of time, interacted with enrichment more frequently (P ≤ 0.005), and tended to have less frequent postural changes compared to ad lib gilts (P = 0.08). No treatment differences were observed in duration of time spent standing, sitting, or lying

(P ≥ 0.39). Although feed regimen did not alter feed efficiency, these data indicate that twice daily feeding reduced gilt adiposity and growth without altering the pig’s behavioral expression of hunger. Therefore, twice daily feeding may be a method of increasing percent of lean tissue without negatively impacting gilt welfare.

Introduction

Multiple feed management systems are utilized in the U.S. swine industry. In grow-finish systems, ad libitum feeding is the most common regimen, whereas drop feeding and electronic sow feeders are commonly used for restrictive feeding of gestating sows. Differences in feed management systems are typically driven by production goals,

i.e. maximal growth in grow-finish systems, reaching ideal bodyweight and condition at first mating in gilt developer units (GDU), and maintaining ideal body condition in gestating gilts and sows. While production goals vary across production stages, an overall swine industry goal is to improve feed efficiency and thus profitability. Furthermore, animal welfare is an important producer and consumer interest and the impact of feed management on pig hunger is a primary concern (1).

Feeder visit frequency varies across individual pigs, in part due to genetics (2,3), feeder design, feeding program and social status (4). Feeder visit frequency has been related to genetic selection for feed efficiency, with more feed efficient pigs visiting the feeder fewer times compared to less feed efficient pigs (2). However, studies manually altering feeding patterns have reported conflicting results related to feed efficiency and changes in body composition (5-7). Therefore, in order to provide recommendations for a feeding system that is most beneficial for production goals, more conclusive information is necessary.

Surprisingly, few studies have related pig behavior to nutrient utilization and growth. Therefore, understanding how feeding regimen and efficiency of lean growth impacts pig behavior is important, as feeding behavior and activity can be indicators of hunger and satiety (8,9). Furthermore, behavior can partially explain differences in energy expenditure (10). Schneider and colleagues (7) and Brouns and colleagues (11) reported that pigs fed fewer times spent a shorter duration of time eating. However, these authors reported conflicting pig activity results.

Therefore, the objective of this study was to compare two feeding patterns and to evaluate their impact on gilt nutrient utilization and whole body tissue accretion, feeding behavior and activity. Our second objective was to relate gilt feeding behavior and activity to nutrient utilization and whole body tissue accretion. Our hypothesis was that gilts fed ad libitum would utilize nutrients less efficiently for lean tissue gains, spend more time eating and be more active compared to gilts fed less frequently. We also hypothesized that gilt behavior would correlate with nutrient utilization and whole body tissue accretion. This study utilized 56 to 114 kg BW female pigs (gilts) as a model for evaluating two feeding patterns. These results can therefore be applied to gilts within grow-finish systems and GDUs.

Materials and Methods

All experimental procedures were approved by the Iowa State University Animal

Care and Use Committee (IACUC# 8-14-7851-S). This work was conducted at Iowa

State University from October to December, 2014.

Animals and housing

Forty-eight crossbred female pigs (gilts; 55.9 ± 5.2 kg on test BW) were blocked by body weight into two feeding treatments: 1) ad libitum feed access (ad lib; n = 24) or

2) twice daily feed access (2x; n = 24). The 2x treatment allowed gilts to eat ad libitum between 08:00-09:00 h and again from 17:00-18:00 h. The 2x feeding treatment was chosen as an alternative regimen to ad lib feeding based on the results of Young and

colleagues (2) who observed that more feed efficient pigs had fewer feeder visits at the two peak meal times that occurred between 8:00-10:00 and 14:00-18:00 h. All gilts received either feeding treatments for 55 days. Both treatments were fed the same corn- soybean diet in three phases that met or exceeded NRC (12) requirements for this size of pig. The phase 1 diet was formulated to contain 13.88 MJ/kg metabolizable energy (ME) and 0.96% standardized ileal digestible (SID) lysine. The phase 2 diet was formulated to contain 13.89 MJ/kg ME and 0.87% SID lysine, and the phase 3 diet was formulated to contain 13.92 MJ/kg ME and 0.78% SID lysine.

All gilts were housed in individual pens measuring 2.21 m long x 0.61 m wide within nose to nose contact with each other. All pens were located within one climate controlled room, set to the thermoneutral requirements for this size of pig. An electronic recording device (HOBO Pro v2, temp / RH, U23-001, Onset Computer Corporation,

Bourne, MA, USA) was located within the room to record ambient temperature (°C) and relative humidity (%). The mean (±S.D.) ambient temperature was 19.9 (±1.2) °C and relative humidity was 60.8 (±5.7) %. Each pen was located on slatted concrete flooring and contained a polypropylene rope tied to an overhead bar for environmental enrichment, a water nipple, and a single-space feeder with a lid. To achieve the 2x feeding treatment, feeders were latched to prevent gilts from accessing feed during non- meal times (Fig. 2.1). Gilts were acclimated to this housing for three days prior to the commencement of feeding treatments. Within the same room, gilts were stalled next to other gilts of the same treatment and a solid visual barrier was located between pens separating ad lib and 2x treatment to avoid synchronized feeding (13).

Performance

Gilt body weight and feeder weights were measured weekly over 7 consecutive weeks. These data were used to calculate average daily gain (ADG), average daily feed intake (ADFI), and feed efficiency (gain:feed, G:F). Feeding rate was determined during week 7 for each treatment by measuring feed disappearance from feeders at 8:00, 9:00,

17:00, and 18:00 h. Feeding rate was calculated by dividing feed intake by the duration of time spent eating (see section 2.4).

Whole body composition and tissue accretion

Longitudinal whole body composition was assessed in 12 gilts per treatment using a Hologic Discovery A Dual X-ray Absorptiometry (DXA) machine (Bedford, MA,

USA) before (day -3; initial scan) and after (day 55; final scan) the 7 week performance period. To ensure that gilts remained stationary during DXA scanning, gilts were anesthetized using xylazine (4.4 mg per kg; Anased, Lloyd Laboratories, Shenandoah,

IA, USA), ketamine HCl (2.2 mg per kg; Ketaset, Wyeth, Madison, NJ, USA), and tiletamine HCl and zolazepam HCl in combination (4.4 mg per kg; Telazol, Wyeth,

Madison, NJ, USA) prior to the initial DXA scan. Immediately prior to the final DXA scan, gilts were humanely euthanized by captive-bolt and scanned. The DXA output provided information on whole body bone, fat, and lean tissue mass. Scan data was corrected using internally built calibration curves using the following regressions: Live weight, y = 1.0822x-1.826, R2=0.997; Fat, y = 0.9515x-1.06, R2=0.9308; Bone mineral ash, y = 2.1473x-0.1411, R2=0.9219; Lean, y = 1.0668x-0.1411, R2=0.9909; Protein, y =

0.2206x-0.6611, R2=0.9758. Where x = DXA results and y = chemical proximate on an empty whole body (i.e. no luminal, urine or gall bladder contents). Tissue accretion was calculated by determining the net change between final and initial body compositions, divided by the days between scans.

Behavior

To assess gilt behavior, eight color cameras (Panasonic, Model WV-CP-484,

Matsushita Co. LTD., Kadoma, Japan) were positioned above the pens. The cameras were fed into a multiplexer using Noldus Portable Lab (Noldus Information Technology,

Wageningen, The Netherlands) and time-lapse video was collected onto a computer using

HandyAVI (version 4.3, Anderson’s AZcendant Software, Tempe, AZ, USA) at 10 frames/s. Video was continuously analyzed during week 7 for 36 hours starting at 7:00 h and ending at 19:00 h the following day. Video observations were collected using the

Observer software (The Observer XT version 10.5, Noldus Information Technology,

Wageningen, The Netherlands) by four trained observers who had intra- and inter- observer reliabilities of ≥ 80%. Eating, postural changes, standing, sitting, and lying behaviors were collected for all 48 gilts and enrichment interaction was collected on 23 gilts (2x n = 12 and ad lib n = 11; Table 2.1).

Data analysis

All data were evaluated for normality using the Shapiro-Wilk test and Q-Q plots using SAS (version 9.4, SAS Inst., Cary, NC, USA). Performance, body composition,

and tissue accretion data were normally distributed; therefore, were analyzed using the

Mixed procedure of SAS. The model included the fixed effects of treatment and pig as the experimental unit. To account for initial variation in BW the covariate of initial BW was used for all performance, body composition, and tissue accretion measures except for initial BW itself. Initial body composition was used as a covariate for the respective parameter. Weekly performance was analyzed using a repeated measures model with the fixed effects of treatment, week, and their interaction. The covariate of weekly BW was used for all weekly performance measures except for weekly BW itself. Correlations among overall performance and whole body tissue accretion variables were performed using Pearson correlations. Behavior data were not normally distributed; therefore, were analyzed using the Glimmix procedure of SAS. Feeding rate and duration data were analyzed with a gamma distribution and frequency data were analyzed with a Poisson distribution. The model included the fixed effects of treatment, covariate of week 7 BW, and pig as the experimental unit. To directly compare feeding rate during meal times, the model also included the fixed effects of meal time and the treatment by meal time interaction. Correlations of behaviors with overall performance and whole body tissue accretion variables were performed using Spearman rank correlations. The significance level was fixed at P ≤ 0.05 and tendency at 0.05 < P ≤ 0.10 for all data analyses.

Results

Performance

Weekly gilt performance differences are reported in Figure 2.2. Treatment, week, and treatment x week interaction differences were observed for BW (P ≤ 0.05), ADG (P

< 0.0001), ADFI (P ≤ 0.03), and G:F (P ≤ 0.0002). Overall gilt performance differences for the 55 day test period are reported in Table 2.2. Gilts fed 2x had a 0.07 kg/day lower

ADG which resulted in a 4.25 kg lower final BW compared to ad lib gilts (P = 0.01).

These gilts also had a 0.23 kg/day lower ADFI compared to ad lib, which translated into in uptake of 3.1 MJ/day less energy by the 2x gilts (P = 0.005). No overall G:F differences were observed between treatments (P = 0.83). As expected, ADG and ADFI were highly correlated (P < 0.0001) and ADG tended to be weakly correlated to G:F (P =

0.07; Table 2.3).

Whole body composition and tissue accretion

As expected, initial whole body composition (i.e. fat, lean, protein, and bone mineral content) did not differ between treatments (P ≥ 0.54; data not presented).

However, at the end of the study gilts fed 2x had 2.51 kg less fat, 3.45 kg less lean, and

0.73 kg less protein on a whole body basis compared to gilts fed ad lib (P ≤ 0.02). This translated into gilts fed 2x having a lower fat:protein compared to ad lib gilts (P = 0.05).

The whole body percent of bone mineral content (BMC) and bone mineral density

(BMD; P ≥ 0.21) did not differ. Fat, lean, and protein tissue accretion rates were lower in gilts fed 2x compared to those fed ad lib (P = 0.01); however, no treatment differences

were observed in BMC accretion (P = 0.28; Table 2.4). All measures of whole body tissue accretion showed a positive, moderate to strong correlation with other measures of whole body tissue accretion (P ≤ 0.04), ADG (P ≤ 0.01), and ADFI (P ≤ 0.01). Lean and protein accretion showed a positive, moderate correlation with G:F (P ≤ 0.05; Table 2.3).

Behavior

Gilt behavior differences are reported in Table 2.5. Gilts fed 2x ate less frequently and for a shorter duration across the 36 hour behavior observation compared to ad lib gilts (P < 0.0001). Looking at the 36 hour behavior window, overall feeding rate did not differ between feeding regimen (P = 0.15); however, when specifically comparing feeding rate at the two meal times 2x gilts were observed eating 6.52 g/min faster than ad lib gilts (P = 0.005). Regardless of treatment, gilts ate 8.38 g/min slower during the 8:00-

9:00 h meal time than during the 17:00-18:00 h meal time (P = 0.0002). No feeding rate treatment by meal time interaction was observed (P = 0.20; Fig. 2.3). Gilts fed 2x interacted with the enrichment more frequently compared to ad lib gilts (P = 0.0002); however, no treatment difference in duration of time spent interacting with enrichment was observed (P = 0.38). Gilts fed 2x tended to display less frequent postural changes compared to ad lib gilts (P = 0.08). No treatment differences were observed in duration of time spent standing, sitting, or lying (P ≥ 0.39; Table 2.5).

To further understand how behaviors relate to overall performance and whole body tissue accretion a correlation analysis was conducted. Duration of time spent eating showed a positive, moderate correlation with ADG, ADFI, fat tissue accretion (P ≤ 0.04),

and tended to be positively correlated with lean tissue accretion (P = 0.09). Duration of time spent interacting with enrichment showed a negative, moderate correlation with

ADG (P = 0.006) and tended to be negatively correlated with ADFI (P = 0.08). Postural changes tended to be positively correlated with BMC accretion (P = 0.07). Duration of time spent standing was weakly and negatively correlated with ADG and ADFI (P ≤

0.02). Duration of time spent lying showed a positive, weak correlation with ADG (P =

0.02). No other significant behavioral correlations with overall performance and whole body tissue accretion were observed (Table 2.6).

Discussion

Young and colleagues (2) observed that more feed efficient pigs fed ad libitum had fewer feeder visits at two peak meal times occurring between 8:00-10:00 and 14:00-

18:00 h; therefore, 2x feeding was chosen as an alternative regimen to compare to ad lib feeding. This study utilized 56 to 114 kg BW gilts as a model for evaluating two feeding regimen. This size of gilt is commonly utilized in grow-finish systems and GDUs; therefore, these results can be applied to gilts within these systems. However, it is important to note that our gilts were individually penned whereas gilts within grow-finish systems and GDUs are commonly housed in group pens.

Across the seven week trial, the benefit of lower ADFI in 2x compared to ad lib gilts was offset by lower ADG and therefore resulted in no G:F treatment differences.

Average daily feed intake was correlated with duration of time spent eating; therefore, the reduced ADFI is likely due to a lower duration of time spent eating and may also relate to

a lower frequency of eating in 2x compared to ad lib gilts. This may be partially explained by the 60 min time allotted for each 2x meal. Newman and colleagues (5) observed a tendency for boars fed two 60 min meals to eat less than ad lib; however, in a separate experiment, boars fed two 90 min meals ate a similar amount as ad lib. In contrast to the current study, Newman and colleagues (5) did not observe a difference in

ADG between boars fed two 60 min meals and ad lib, and therefore observed improved feed efficiency in boars fed two 60 min meals. Interestingly, at week 2 we observed a

40% lower G:F in 2x compared to ad lib gilts, largely driven by a 38% lower ADG. This suggests an acclimation period for pigs fed the 2x feeding regimen; therefore, differences in study length may partially explain discrepancies between earlier studies (5-7).

Based on pigs fed two versus twelve meals daily, gilts fed less frequent meals were shown to have increased ADG and G:F (6). Therefore, based on the assumption that gilts fed ad libitum would eat more frequently than gilts fed twice daily, we hypothesized that gilts fed ad libitum would utilize nutrients less efficiently for lean tissue gains than gilts fed 2x. The reduced fat, lean, and protein accretion rates observed in 2x compared to ad lib gilts largely reflects ADG differences, and thus we reject this hypothesis. However, when evaluating final whole body composition, 2x gilts had a lower fat to protein ratio.

These differences may be partially explained by ADFI, as ADFI increases fat accretion and also protein accretion which requires 16% less energy than fat (14). Dietary energy is therefore first partitioned towards maintenance needs, then towards lean accretion, and excess energy is partitioned towards fat accretion (15).

Newman and colleagues (5) evaluated insulin concentrations of pigs fed ad libitum versus two meals, where gilts were allowed ad libitum access to feed during the two meal times. These authors reported that twice daily feedings increased post-prandial insulin concentrations, whereas pigs fed ad libitum maintained relatively constant plasma insulin concentrations. As insulin plays an important role in stimulating protein synthesis

(16), feeding regimen may impact protein metabolism. Le Naou and colleagues (6) also evaluated insulin as well as urea, and α-amino nitrogen in pigs fed two versus twelve meals daily. These authors reported twice daily fed pigs had a greater rise and fall of plasma insulin concentrations compared to 12 meals; therefore, pigs fed 12 meals may correspond to ad libitum pigs in work by Newman and colleagues (5) and the current study. Le Naou and colleagues (6) also reported that pigs fed meals twice daily had lower plasma concentrations of urea and α-amino nitrogen compared to pigs fed 12 meals daily.

These data suggest that pigs eating less-frequent meals may have decreased protein catabolism. This notion is further supported by work in young pigs which showed that administration of the amino acid leucine every four hours enhanced skeletal muscle protein synthesis compared to continuous orogastric feeding (17). This suggests that meal frequency is important to support lean tissue growth efficiency and that dietary leucine pulsation is an important regulator of protein translation initiation. However, as this did not translate to increased protein accretion in the 2x gilts of the current study, leucine pulsation may need to occur more than twice daily to enhance protein synthesis.

Access to feed and boredom of pigs can lead to changes in behavior, welfare and productivity (18,19). Therefore, one of our objectives was to evaluate how feeding

frequency alters gilt behavior. Schneider and colleagues (7) reported that pigs fed two meals daily spent less time eating than those fed six meals daily. Therefore, we hypothesize that the ad lib gilts in the current study ate more frequent meals than 2x gilts.

Assuming that gilts are motivated by hunger or reduced satiation to interrupt a behavioral state such as lying (9,20), the tendency of ad lib gilts to have more postural changes than

2x gilts further supports the hypothesis that ad lib gilts ate more frequent meals. Due to the methodology of behavioral collection, feeding frequency represents the number of times the pig’s mouth and nose entered the feeder rather than the number of meals the pig ate. Therefore, the pig’s mouth and nose often exited the feeder in order to drink, root, and chew environmental enrichment rather than signify the end of a meal. However, the duration of time spent eating as well as ADFI clearly indicate feeding differences due to feeding regimen.

Feeding rate is reported to reflect feeding motivation in pigs; with faster rates suggesting increased feeding motivation (21). Therefore, when directly comparing feeding rate at the meal times the increased feeding rate of the 2x gilts suggests that they are more motivated to eat than the ad lib gilts. This is likely a result of the limited feeding time available to the 2x gilts since no difference in overall 36 hour feeding rate was observed. This may suggest that overall feeding motivation did not differ between treatments across the 36 hour behavioral observation period.

Increased standing and reduced lying time has been shown to reflect pig behavioral expression of hunger (8,9,22). In the current study, duration of time spent standing was negatively correlated with ADFI; however, whether this is related to hunger

and/or reduced energy intake is unclear. No treatment differences were observed in the duration of time spent standing, sitting, or lying, suggesting no treatment differences in hunger. These results are in contrast to other studies that observed feeding regimen differences in the duration of time spent standing and lying (7,11). However, these studies evaluated behavior of pigs housed in groups rather than individually. Therefore, future studies investigating implementation of twice daily feeding on pig hunger within group housing systems is warranted. Increased NEFA concentrations have been related to increased hunger in pigs (9). Newman and colleagues (5) did not observe differences in

NEFA concentrations in pigs fed twice daily versus ad libitum. Therefore, this supports the behavioral observation of the current study that gilts fed 2x versus ad lib did not differ in hunger.

Gilts fed 2x interacted with the enrichment more frequently compared to ad lib.

These results agree with Zwicker and colleagues (18) who reported that pigs restrictively fed interacted with straw environmental enrichment more frequently than pigs fed ad lib.

In humans, it has been reported that prolonged mastication reduces self-reported hunger

(23). Additionally, cognitive enrichment in pigs has been shown to reduce stress associated with feeding (24). Therefore, interacting with enrichment may serve as a mechanism for coping with hunger or stress associated with restricted access to feed. In the current study it was observed that the duration of time spent interacting with enrichment tended to negatively correlate with ADFI. This suggests that enrichment interaction may be a redirected foraging behavior or may reduce hunger. Enrichment interaction also appears to be energetically demanding as duration of time spent

interacting with enrichment is negatively correlated with ADG; however, it did not significantly alter G:F. Future studies investigating the use of environmental enrichment as a coping mechanism for restrictively fed pigs is warranted as it may positively impact pig welfare.

Conclusion

Feed efficiency is impacted by a number of factors internal and external to the pig

(15); therefore, modifying feeding regimen may not be a reliable method of improving feed efficiency. Due to observed reduced growth of gilts fed 2x, ad lib feeding may be a better regimen for grow-finish systems seeking to maximize throughput and GDUs seeking to maximize growth rate. However, twice daily feeding reduced the fat to protein ratio. As this is a U.S. grow-finish swine production goal, twice daily feeding may have implications for future developments in precision livestock farming. Reduced percent of fat is often observed when restrictive feeding pigs (15), but restrictive feeding may have negative consequences on gilt welfare as they may not reach satiety (22). The current study did not observe differences in overall behavioral expression of hunger; therefore, twice daily feeding may be a method of increasing percent of lean tissue without negatively impacting gilt welfare.

Acknowledgements

This project was supported by the Agriculture and Food Research Initiative Competitive

Grant no. 2011-68004-30336 from the USDA National Institute of Food and Agriculture.

We would like to thank Stephanie Lindblom, Rebecca Parsons, Megan Righi, and Wes

Schweer for assistance in data collection and animal care, Amber Haritos, Paige Mercer, and Kirsten Springman for assistance with video analysis, Ken Colpoys for designing and mounting latches for the 2x feeders, and Caitlyn Abell for statistical consulting.

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Table 2.1. Ethogram of behaviors recorded during week 7 of the study. Frequency (n) and/or duration (min) of behaviors were collected. Measure Description

Eat (n, min) The feeder lid is up with the pig’s mouth and nose located within the feeder.

Enrichment Oral and/or nasal contact with the environmental interaction (n, min) enrichment rope.

Postural changes (n) Sum of the frequency of stand, sit, and lie postures.

Stand (min) All four hooves are on the pen floor with limbs extended or the pig is walking with limbs in both extension and flexion and moving throughout the pen.

Sit (min) The front limbs are extended and bearing weight and the rear limbs and body are in contact with the pen floor.

Lie (min) The pig’s body and limbs are in contact with the pen floor.

Measure 2x Ad Libitum P-value

Initial BW, kg 55.85 ± 1.07 56.04 ± 1.07 0.90

Final BW, kg 109.40 ± 1.14 113.65 ± 1.14 0.01

ADG, kg/d 0.94 ± 0.02 1.01 ± 0.02 0.01

ADFI, kg/d 2.81 ± 0.05 3.04 ± 0.05 0.005

Average daily ME intake, 39.09 ± 0.74 42.19 ± 0.74 0.005 MJ/day

G:F, kg/kg 0.33 ± 0.004 0.33 ± 0.004 0.83

Table 2.3. Pearson correlations of overall performance and overall whole body tissue accretion. Overall Performance1 Overall Whole Body Tissue Accretion2

ADG ADFI G:F Fat, g/d Lean, g/d Protein, BMC, g/d g/d Overall Performance1

ADG, kg/d 1.00 0.89** 0.27# 0.82** 0.94** 0.90** 0.51**

ADFI, kg/d 1.00 -0.19 0.83** 0.76** 0.73** 0.51**

G:F, kg/kg 1.00 0.12 0.43* 0.40* 0.11

Overall Whole Body Tissue Accretion2

Fat, g/d 1.00 0.70** 0.66** 0.42*

Lean, g/d 1.00 0.98** 0.55**

Protein, g/d 1.00 0.55**

1n=24 gilts per treatment 2n=12 gilts per treatment **Indicates significance at P ≤ 0.01, * Indicates significance at P ≤ 0.05, #Indicates tendency at P ≤ 0.1

Table 2.4. Final whole body composition and overall tissue accretion rates (least square means ± SE) of gilts undergoing twice daily (2x; n=12) or ad libitum (n=12) feeding treatments. Treatment

Measure 2x Ad Libitum P-value

Final whole body composition

Fat, % 21.92 ± 0.34 23.08 ± 0.34 0.03

Fat, kg 25.10 ± 0.60 27.61 ± 0.60 0.008

Lean, kg 86.04 ± 0.98 89.49 ± 0.98 0.02

Protein, kg 15.87 ± 0.20 16.60 ± 0.20 0.02

Fat:Protein 1.58 ± 0.03 1.67 ± 0.03 0.05

BMC, g 3209.11 ± 92.78 3363.24 ± 92.78 0.25

BMD, g/cm2 1.07 ± 0.02 1.11 ± 0.02 0.21

Whole body tissue accretion

Fat, g/d 296.90 ± 11.02 338.90 ± 11.02 0.01

Lean, g/d 672.60 ± 16.66 736.60 ± 16.66 0.01

Protein, g/d 135.90 ± 3.42 149.10 ± 3.42 0.01

BMC, g/d 35.12 ± 1.62 37.64 ± 1.62 0.28

Table 2.5. Frequency (n), duration (min), and rate (g/min) of gilt behaviors (least square means ± SE) while undergoing twice daily (2x; n=24) or ad libitum (n=24) feeding treatments. Video was continuously analyzed during week 7 for 36 hours starting at 7:00 h and ending at 19:00 h the following day. Treatment

Measure 2x Ad Libitum P-value

Eat, n 67.97 ± 1.72 127.04 ± 2.39 <0.0001

Eat, min 168.93 ± 9.74 283.08 ± 16.33 <0.0001

Overall feeding rate, g/min 36.36 ± 2.39 31.59 ± 2.07 0.15

Enrichment interaction, n1 53.25 ± 2.24 39.44 ± 1.95 0.0002

Enrichment interaction, min1 50.30 ± 15.59 76.51 ± 24.85 0.38

Postural changes, n 139.22 ± 2.48 145.86 ± 2.54 0.08

Stand, min 411.47 ± 25.73 380.52 ± 23.80 0.39

Sit, min 116.85 ± 19.71 129.77 ± 21.89 0.67

Lie, min 1606.65 ± 24.73 1618.48 ± 24.92 0.74

1Enrichment interaction was collected on n=12 2x and n=11 ad lib gilts

0.15 0.34* 0.23 0.17 0.12 0.11 0.13

- -

Lie, Lie, min

0.08 0.005 0.08 0.05 0.08 0.07 0.00

- -

Sit, Sit, min

≤ ≤ 0.1

0.26 0.34* 0.35** 0.10 0.10 0.10 0.21

------

Stand, Stand, min

0.15 0.03 0.02 0.07 0.11 0.10 0.38

- -

Postural

changes, changes, n

Indicates tendency at at tendency Indicates

accretion: n=5 2x and n=6 ad lib accretion:ad 2x n=5 n=6 and

0.47 0.55** 0.38 0.28 0.40 0.41 0.51

≤ 0.05, 0.05, ≤

------

Enrichment

interaction, interaction, min

Behavior

0.32 0.20 0.01 0.17 0.31 0.27 0.34

------

Enrichment

interaction, interaction, n

0.21 0.12 0.10 0.07 0.20 0.20 0.05

Indicates significance significance at Indicates

- - -

rate, g/min

Overall Overall feeding

≤ 0.01, 0.01, ≤

0.42* 0.35 0.32 0.13

0.40** 0.42** 0.01

Eat, min

0.06 0.13 0.17 0.22 0.16

0.06 0.09

- -

Eat, n

Spearman rank correlations of behaviors with overall performance and overall whole body tissuebody overall whole and performance overall behaviors of with rank correlations Spearman

2.6

Indicates significance significance at Indicates

Fat, g/d Fat, ADG, kg/d ADFI, kg/d G:F, kg/kg Lean, g/d Protein, g/d BMC, g/d

n=24 gilts per treatment per gilts n=24 treatment per gilts n=12 n=12 tissue body lib; performance: Whole 2x ad n=11 Overall and

Table accretion. ** OverallPerformance Whole body accretion tissue 1 2 3

Figure 2.1. A latch was mounted to the feeders to prevent pigs from accessing feed during non-meal times. From 8:00-9:00 and 17:00-18:00 h clips were manually unhooked allowing pigs access to the feed.

Figure 2.3. Feeding rates of gilts undergoing twice daily (2x; n=12) or ad libitum (ad lib; n=12) feeding treatments. Different superscripts indicate significance at P ≤ 0.05. ‘#’ indicates tendency at P = 0.07.

CHAPTER 3

EFFECTS OF GENETIC SELECTION FOR RESIDUAL FEED INTAKE ON BEHAVIORAL REACTIVITY OF CASTRATED MALE PIGS TO NOVEL STIMULI TESTS

A paper published in Applied Animal Behaviour Science. 2014. 159:34-40.

DOI: 10.1016/j.applanim.2014.06.013

Jessica D. Colpoysa, Caitlyn E. Abellb, Jennifer M. Younga,1, Aileen F. Keatinga,

Nicholas K. Gablera, Suzanne T. Millmanc, Janice M. Siegfordd, Anna K. Johnsona,*

aDepartment of Animal Science, Iowa State University, Ames, IA, 50011, USA

bDNA Genetics, Columbus, NE 68601, USA

cDepartment of Veterinary Diagnostics and Production Animal Medicine, College of

Veterinary Medicine, Iowa State University, Ames, IA 50011, USA

dDepartment of Animal Science, Michigan State University, East Lansing, MI 48824,

USA

1Present address: Department of Animal Sciences, Hultz Hall, Room 123, North Dakota

State University, Fargo, ND 58108, USA

*Corresponding author: 2356-F Kildee Hall, ISU, Ames, IA, 50011, USA; E-mail: [email protected] Phone: +001-515-294-2098 Fax: +001-515-294-4471

Abstract

Increasing feed efficiency in swine is important for increasing sustainable food production and profitability for producers; therefore, this is often selected for at breeding.

Residual feed intake (RFI) can be used for the genetic selection of pigs for feed efficiency. In our selection project, low-RFI pigs consume less feed for equal weight gain compared to their less efficient, high-RFI counterparts. However, little is known about how feed efficiency influences the pig’s behavioral reactivity towards fear-eliciting stimuli. In this study, behavioral reactivity of pigs divergently selected for RFI was evaluated using human approach- (HAT) and novel object tests (NOT). Forty low-RFI

(more feed efficient) and 40 high-RFI (less feed efficient) castrated male pigs (barrows;

46.5 ±8.6 kg) from 8th generation Yorkshire RFI selection lines were randomly selected and evaluated once using HAT and once using NOT over a four week period utilizing a crossover experimental design. Each pig was individually tested within a 4.9 x 2.4 m test arena for 10 min; behavior was evaluated using live and video observations. The test arena floor was divided into four zones; zone 1 being oral, nasal, or facial contact with the human (HAT) or orange traffic cone (NOT) and zone 4 being furthest from the human or cone and included the point where the pig entered the arena. During both HAT and NOT, low-RFI pigs crossed fewer zones (P < 0.0001), had fewer head movements (P

≤ 0.02), defecated less frequently (P ≤ 0.03), displayed a shorter duration of freezing (P =

0.05), and froze less frequently (HAT: low-RFI = 4.9 ± 0.65 vs. high-RFI = 7.5 ± 0.96;

NOT: low-RFI = 4.7 ± 0.66 vs. high-RFI = 7.2 ± 0.96; P < 0.0001) compared to high-RFI pigs. During HAT, low-RFI pigs also attempted to escape less frequently (low-RFI = 0.4

± 0.14 vs. high-RFI = 1.1 ± 0.30; P = 0.001) compared to high-RFI pigs. In contrast, compared to the high-RFI pigs, low-RFI pigs took 48 sec longer during HAT and 52 sec longer during NOT to approach zone 1 (P ≤ 0.04). These results indicate that low-RFI pigs had decreased behavioral reactivity during HAT and NOT compared to high-RFI pigs. This may suggest that reducing a pig’s behavioral reactivity is an important component of improving feed efficiency; however, it may have implications for animal handling and facility design.

Introduction

Increasing feed efficiency is an important objective for livestock production.

Better feed efficiency can improve producer profitability, increase production for feeding a growing world population and improve environmental sustainability (Nkrumah et al.,

2006; Wall et al., 2010). In place of traditional gross efficiency (gain:feed) and feed conversion (feed:gain) ratios, many investigators have begun using residual feed intake

(RFI) as an alternative method to measure feed efficiency (Koch et al., 1963; Cai et al.,

2008). The Iowa State University Yorkshire RFI selection project uses a RFI model that defined the difference between the actual feed intake of an animal and its expected feed intake based on a given amount of growth and back fat. Therefore, pigs that consume less feed than expected for maintenance and growth have a lower RFI, are more feed efficient, and they are therefore economically better for lean production relative to higher RFI pigs

(Young et al., 2011).

Numerous studies have been conducted to examine the physiology of feed efficiency using divergent RFI models. Specifically in pigs, RFI research has focused on feed intake patterns (Young et al., 2011), physical activity (Sadler et al., 2011), body composition (Boddicker et al., 2011a, b), nutrient digestibility (Barea et al., 2010; Harris et al., 2012), immune system activation (Rakhshandeh et al., 2012), skeletal muscle oxidative stress (Grubbs et al., 2013) and protein turnover (Cruzen et al., 2013).

Furthermore, the hypothalamic-pituitary-adrenocortical (HPA) axis has been shown to be an important contributor to feed efficiency in pigs (Hennessy and Jackson, 1987), sheep

(Knott et al., 2008, 2010), and poultry (Katle et al., 1988). These studies have revealed a relationship between higher feed efficiency and a lower glucocorticoid response.

However, there is no consensus and it remains unclear whether improved feed efficiency alters behavior in livestock (Braastad and Katle, 1989; Luiting et al., 1994; Amdi et al.,

2010).

Novelty has been utilized in studies of pig stress responses to a human approach-

(HAT; Hemsworth et al., 1981; Gonyou et al., 1986; Janczak et al., 2003) and novel object test (NOT; Hemsworth et al., 1996; Dalmau et al., 2009; de Sevilla et al., 2009).

An animal’s response to HAT and NOT can help further our understanding of the animal’s responsiveness to stress, which can in turn impact the animal’s welfare during routine handling and husbandry. It was recently suggested that breeding for improved feed efficiency, and particularly for reduced RFI, may decrease the animal’s ability to adapt to stress (Rydhmer and Canario, 2014). Therefore, the objective of this study was to examine the association between long-term divergent selection for RFI and behavioral

reactivity to fear-eliciting stimuli. The hypothesis that low-RFI pigs would be less behaviorally reactive compared to high-RFI pigs was specifically tested by determining if divergent line selection for RFI influenced pigs’ behavioral reactivity to HAT and NOT.

These data will help develop breeding, handling, and management strategies to optimize feed efficiency in swine.

Materials and Methods

All experimental procedures were approved by the Iowa State University Animal

Care and Use Committee. This experiment was conducted over four consecutive weeks from October through November, 2011.

Animals and housing

A total of 80 healthy Yorkshire castrated male pigs (barrows; 46.5 ± 8.6 kg test day body weight) divergently selected for RFI were used. Half (20 low-RFI and 20 high-

RFI) of the pigs were fed a low-fiber, high-energy diet (9.4% neutral detergent fiber,

13.86 MJ of metabolizable energy/kg of feed) and half were fed a high-fiber, low-energy diet (25.9% neutral detergent fiber, 11.97 MJ of metabolizable energy/kg of feed). Both diets met or exceeded NRC (1998) requirements and further information regarding ingredient and nutrient analysis is explained by Colpoys and colleagues (2014). Two genetic line treatments were compared: low-RFI (n=40) and high-RFI (n=40). Divergent line selection criteria were based on estimate breeding values for RFI as explained by Cai and colleagues (2008). The low-RFI genetic line had been selected over eight generations

whereas the high-RFI genetic line had been randomly selected over five generations, and then selectively bred for high-RFI over the next three generations.

This work was conducted at the Lauren Christian Swine Research Center at the

Iowa State University Bilsland Memorial Farm, near Madrid, Iowa, USA. All pigs were housed in a conventional confinement unit within one room containing 12 mixed-sex and mixed-line pens of 15 to 16 pigs/pen; five to eight pigs from each pen were tested. The pigs were moved to this facility 10 days prior to the start of the experiment. Each pen measured 5.6 m long x 2.3 m wide and had a slatted concrete floor. The barn was naturally ventilated with side curtains. Each pen contained an electronic one-space feeder

(FIRE®, Osborne Industries, Inc., Osborne, KS, USA) that recorded the feed intake of each pig, positioned at the front of the pen to provide pigs with ad libitum feed. Water was provided ad libitum through two nipple-type waterers (Edstrom, Waterford, WI,

USA) per pen. One electronic recording device (HOBO Pro v2, temp / RH, U23-001,

Onset Computer Corporation, Bourne, MA, USA) located in the center of the room, 2.2 m from the ground, recorded ambient temperature (°C) and relative humidity (%) every 5 min for the duration of the trial. The mean (±S.D.) ambient temperature was 21.7

(±1.9)°C and relative humidity was 70.5 (±9.8)%.

Test methodology and facility

Pig testing occurred 5 days per week over four consecutive weeks. A testing session consisted of a 10 min period during which the individual pig underwent HAT or

NOT within the experimental arena. All tests were performed between 13:00 and 17:00 h.

A total of 40 pigs (20 low-RFI and 20 high-RFI) were randomly selected using a random number generator (Microsoft Excel 2010, Microsoft Corporation, Santa Rosa, CA, USA) to be tested using HAT first and the remaining 40 pigs (20 low-RFI and 20 high-RFI) experienced NOT first. Pigs then experienced the opposite test 1 week later, utilizing a crossover experimental design. Therefore, each pig was tested a total of two times, once using each test. Genetic line and diet were blocked by time so that within each hour each of the following types were tested in random order: low-RFI high-fiber diet, low-RFI low-fiber diet, high-RFI high-fiber diet, and high-RFI low-fiber diet. Pigs were tested in the same order for both tests and at the same time of day, and the individual pig was the experimental unit.

The HAT and NOT were conducted in a rectangular arena separate from the home pens. The arena setup was adapted from published work by Hemsworth and colleagues

(1989) and Marchant-Forde and others (2003). The arena measured 4.9 m long x 2.4 m wide and had 1.2 m high, black corrugated plastic sides that were attached to gates. In order to hide the human observer visually during NOT, a 1.2 m wide x 2.2 m high black corrugated plastic observation hide was positioned outside the arena. Concentric curves were drawn on the slatted concrete floor using permanent marker one day before the start of testing to divide the arena into four zones in order to measure the location of the pig in proximity to the novel stimulus. Zone 1 was defined as oral, nasal, and/or facial contact with the human or the cone during HAT and NOT, respectively. For consistency with the other zones, pigs that touched the human or the cone will be referred to as entering zone

1. Zone 2 was the area nearest to the novel stimulus and zone 4 was the area where the

pig entered the test arena, furthest from the novel stimulus. Zones 2, 3, and 4 consisted of approximately equal area which allowed the entire body of the pig to fit within the zone.

The concentric curves allowed each zone to measure a consistent distance from the novel stimulus (Fig. 3.1). Located in the center of the arena, 2.3 m from the ground, was one electronic recording device (HOBO Pro v2, temp / RH, U23-001, Onset Computer

Corporation, Bourne, MA, USA) that recorded ambient temperature (°C) and relative humidity (%) every 5 min for the duration of the testing. Throughout the testing period, the mean (±S.D.) ambient temperature was 15.3 (±2.2)°C, 6.4°C cooler than the home pen, and relative humidity was 73.2 (±10.6)%, 2.7% higher than the home pen.

Three color cameras (Panasonic, Model WV-CP-484, Matsushita Co. LTD.,

Kadoma, Japan) were positioned 2.1 m above the test arena. Camera 1 was positioned over zone 1, camera 2 captured zones 2 and 3 and camera 3 captured zone 4. The cameras were fed into a multiplexer using Noldus Portable Lab (Noldus Information Technology,

Wageningen, The Netherlands) and time-lapse video was collected onto a computer using

HandyAVI (HandyAVI version 4.3 D, Anderson’s AZcendant Software, Tempe, AZ,

USA) at 10 frames/s.

One handler removed the pig to be tested from its home pen using a sort board.

Each pig was moved down an alleyway (0.30 m to 12.47 m long x 0.79 m wide) into a weigh scale (1.50 m long x 0.5 m wide; Electronic Weighing Systems, Rite Weigh,

Robert E Spencer Enterprises, Ackley, IA, USA) adjacent to the test arena. The pig remained in the weigh scale for one min while the pig’s weight was collected to create a uniform pre-test environment for every pig. Black corrugated plastic was attached to the

front of the weigh scale so the pigs were not able to see into the test arena. At the conclusion of the min, the weigh scale door was opened and the pig was allowed to enter zone 4 of the arena. If the pig did not enter the arena within 15 s of the scale door opening, the handler gently pushed the pig forward using their hands. The test time began when both front hooves entered zone 4. Following the 10 min testing period, each pig was returned to its home pen by the handler using the same methods as previously described. Feces and urine within the test arena were scraped through the slats following each testing session and the test arena was hosed down with water at the end of each testing day.

Human approach test

Each pig was individually assessed using HAT, which was designed to measure responses to an unfamiliar human stimulus. The human stimulus was the same woman for all tests, and had never previously interacted with the pigs. The human showered into the facility using the same products at the start of each testing day. During testing, the unfamiliar human wore orange coveralls and orange boots, stood silently at the center of the opposite wall (zone 1) holding a clipboard, and did not interact with or move toward the pigs. Minimal arm movement and body shifting occurred during live observation and data collection. At the end of each testing day, coveralls were laundered and boots were hosed off with water.

Novel object test

Each pig was individually assessed using NOT, which was designed to measure responses to an unfamiliar object stimulus, an orange traffic cone. The traffic cone was positioned at the center of the opposite wall (zone 1) and was hosed off with water at the end of each testing day. The same woman from the HAT collected live observations from outside the test arena behind the black corrugated plastic observation hide (Fig. 1).

Measures

Live observations for the frequency of eliminatory behaviors were continuously collected during both tests (Dawkins et al., 2007). Video observations were continuously recorded (Dawkins et al., 2007) using the Observer software (The Observer XT version

10.5, Noldus Information Technology, Wageningen, The Netherlands) to decode approach, head orientation, freezing, and escape attempts (Table 3.1). All live and video observations were collected by the same, trained researcher who was blind to genetic line and diet treatments.

Data analysis

All data were evaluated for normality using the Shapiro-Wilk test and Q-Q plots using SAS (SAS version 9.3, SAS Inst. Inc., Cary, NC). Data were not normally distributed; therefore, data were analyzed using the Glimmix procedure of SAS. All HAT and NOT data were analyzed separately. Pigs fed high-fiber diets were found to defecate more frequently during HAT, attempt to escape more frequently during NOT, and

crossed fewer zone lines during NOT compared to pigs fed low-fiber diets. However, the main effects of diet have been presented previously (Colpoys et al., 2014) and are therefore not presented in the current work. Latency data were analyzed with a gamma distribution, duration data were analyzed with a beta distribution, and frequency data were analyzed with a Poisson distribution. During both tests, one low-RFI pig did not enter zone 1; therefore, was given a latency of 600 s. All behaviors were analyzed using a model with the fixed effects of genetic line and test week, covariate of body weight

(measured prior to each test), and random effect of pen nested within diet. The significance level was fixed at P ≤ 0.05.

Results

Human approach test

Stimulus attention

Low-RFI pigs took 48 s longer to enter zone 1 compared to the high-RFI pigs (P

= 0.04). No differences were observed between lines for total number of zone 1 entrances

(P = 0.16) or duration of time spent within zone 1 (P = 0.93; Table 3.2). Duration of time spent within zone 2 (F1,63 = 0.8, P = 0.37), 3 (F1,63 = 0.01, P = 0.93), and 4 (F1,63 = 0.04, P

= 0.85) did not differ between lines. Furthermore, no line differences were observed in duration of time spent with head in front (F1,63 = 0.2, P = 0.63), side (F1,63 = 2.4, P =

0.13), or back (F1,63 = 0.7, P = 0.40) orientation relative to the human.

Arousal and fear behavior

Compared to high-RFI pigs, low-RFI pigs crossed fewer zone lines (P < 0.0001) and had fewer head movements (P = 0.02). No difference was observed between lines for frequency of urinations (P = 0.43); however, low-RFI pigs defecated fewer times compared to high-RFI pigs (P = 0.002). Low-RFI pigs performed escape attempts less frequently compared to high-RFI pigs (P = 0.001); however, no difference was observed between lines in duration of time spent attempting to escape (P = 0.08). Compared to high-RFI pigs, low-RFI pigs froze less often (P < 0.0001) and spent 2% less time freezing (P = 0.05; Table 3.2).

Novel object test

Stimulus attention

Low-RFI pigs took 52 s longer to first enter zone 1 compared to the high-RFI pigs

(P = 0.02). No differences were observed between lines for total number of zone 1 entrances (P = 0.13) or duration of time spent within zone 1 (P = 0.93; Table 3.3).

Duration of time spent within zone 2 (F1,63 = 1.1, P = 0.31), 3 (F1,63 = 1.3, P = 0.25), and

4 (F1,63 = 0.00, P = 0.99) did not differ between lines. Furthermore, no line differences were observed in duration of time spent with head in front (F1,63 = 0.6, P = 0.44), side

(F1,63 = 0.2, P = 0.68), or back (F1,63 = 0.9, P = 0.34) orientation relative to the cone.

Arousal and fear behavior

Compared to high-RFI pigs, low-RFI pigs crossed fewer zone lines (P < 0.0001) and had fewer head movements (P = 0.01). No difference was observed between lines for frequency of urinations (P = 0.60); however, low-RFI pigs defecated fewer times compared to high-RFI pigs (P = 0.03). No difference was observed between lines in total number of escape attempts (P = 0.21) or duration of time spent attempting to escape (P =

0.35). Compared to high-RFI pigs, low-RFI pigs froze less often (P < 0.0001) and spent

2% less time freezing (P = 0.05; Table 3.3).

Discussion

Stimulus attention

Low-RFI pigs took longer to approach zone 1 compared to high-RFI pigs. These results are similar to those of Hayne and Gonyou (2006), who reported that pigs with a higher average daily gain were slower to approach a human. In the current study, latency to approach zone 1 was a primary outcome, on the assumption that fearful animals would be less likely to approach. However, this assumption does not coincide with other measures of stimuli attention, as the frequency of entrances, the duration of time spent within and oriented towards zone 1 did not differ between lines. Furthermore, this assumption seems to be inconsistent with the arousal and fear behaviors in the current experiment. Two possible, non-mutually exclusive explanations of latency to approach zone 1 will be discussed.

One explanation is that the high-RFI pigs were more fearful of social isolation than the human or cone. Social isolation can be distressing for pigs (Gonyou, 2001) and is likely one of the greatest stressors during HAT and NOT (Forkman et al., 2007; Pairis et al., 2009). A second explanation is that approach latency reflects coping style rather than the level of fear. Hayne and Gonyou (2006) proposed that a fast approach is indicative of an active response whereas a slow approach is indicative of a passive response in pigs. This interpretation is further supported by Hessing and colleagues

(1994), who reported that pigs quicker to approach a novel object were also more resistant to a back test. These two explanations should be considered in future research.

Arousal and fear behavior

Low-RFI pigs were less active compared to high-RFI pigs, as indicated by less frequent zone crossings and head orientation changes. Reduced activity in the low-RFI line was similar to home pen behavior in fifth generation gilts from the same selection line project (Sadler et al., 2011). Likewise, Imrich and colleagues (2012) reported that pigs with increased average daily gain were less active during a habituation (novel arena) test. The opposite relationship has been found in sheep selected for behavioral activity during fearful situations, where less active sheep were less feed efficient than more active sheep (Amdi et al., 2010).

During HAT, low-RFI pigs attempted to escape fewer times compared to high-

RFI pigs. During both tests, low-RFI pigs defecated less often and engaged in fewer freezing postures. This relationship between improved performance traits and reduced

fearfulness is consistent with work by Geverink and colleagues (2004), who reported that gilts with fewer escape attempts during a back test had higher average daily gain and metabolizable energy.

General discussion

Preliminary analysis of the HPA axis in these lines of pigs reported that low-RFI gilts tended to have lower cortisol concentrations both before and after an exogenous adrenocorticotropin hormone challenge compared to high-RFI gilts (Jenkins et al., 2013).

Therefore, the reduced behavioral reactivity seen in low-RFI pigs compared to high-RFI pigs may in part be due to a reduced physiological stress response. Furthermore, eighth generation, high-RFI pigs from first parity sows (in contrast to pigs from second parity sows investigated in the current study) had 241 g/d greater RFI and 2.5 mm greater backfat than low-RFI pigs (Young and Dekkers, 2012). Reduced feed efficiency and increased carcass fat has been observed in stressed pigs within the commercial environment (Black et al., 2001); therefore, may suggest greater stress in high-RFI than low-RFI pigs within the commercial home pen environment.

Improving swine feed efficiency is important for producer profitability, sustainability, and resource allocation. Therefore, improving feed efficiency has become a goal of genetic improvement and management practices in livestock species. However, when selectively breeding pigs for feed efficiency, it is important to take the animal’s welfare into consideration. One aspect of the animal’s welfare which may be influenced by selective breeding is the animal’s stress response, particularly to human interaction

and novel stimuli. Our data presented herein, indicate that low-RFI pigs (increased feed efficiency) had decreased behavioral reactivity during HAT and NOT compared to high-

RFI pigs.

Conclusions

Compared to selection for reduced feed efficiency (high-RFI), selective breeding for increased feed efficiency (low-RFI) appears to have resulted in an animal welfare benefit in terms of calmer pigs that are less reactive to novelty. Nevertheless, the more feed efficient pigs took longer to approach the novel stimuli compared to the less feed efficient pigs, which may have implications for animal handling and facility design.

Furthermore, these results may suggest that reducing an animal’s stress response is an important component of conserving energy for growth and improving feed efficiency.

Acknowledgments

This project was supported by Agriculture and Food Research Initiative Competitive

Grant no. 2011-68004-30336 from the USDA National Institute of Food and Agriculture.

We would like to thank Dr. Jack Dekkers, Dana van Sambeek, Shawna Weimer, Dr.

Monique Pairis-Garcia, and the Lauren Christian Swine Research Center staff for assistance in data collection and animal care. We also thank two anonymous reviewers for helpful comments on earlier drafts of the manuscript.

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Table 3.1. Ethogram of behaviors recorded during human approach and novel object tests. Latency in seconds (s), duration (%), and/or frequency (n) of behaviors collected. Ethogram adapted from Dalmau et al. (2009) and Hemsworth and Barnett (1992). Live observations were utilized to collect elimination and video decoding was utilized to collect all other measures. Measure Description

Approach

Zone 1 (s, n, %) The mouth, nose, and/or face of the pig contact any part of zone 1 (defined as the human or traffic cone). Latency was measured from the start of the test to the first zone 1 entrance

Zone 4, 3, & 2 (%) The base of both the pig’s ears were within the limits of the respective zone and the pig’s mouth, nose, and/or face was not touching zone 1

Zone crossings (n) Sum of the total number of zone 4, 3, and 2 entrances

Head orientation

Front, Side, Back (%) The pig’s snout was pointed towards, perpendicular, or in the opposite direction of zone 1, respectively

Head movements (n) The sum of front, side, and back head orientations

Elimination

Urination (n) Excreting urine

Defecation (n) Excreting feces

Escape attempt (n, %) The front two or all four pig’s hooves were off the arena floor in attempt to remove itself from the test arena. Duration was measured from the removal of the two front hooves from the floor to all four hooves returning to the floor

Freezing (n, %) No movement of any portion of the pig’s body was visible for ≥3 s. Duration was measured from the start of the freeze to any movement of the body

Zone 1, s 132.7 ± 19.67 84.5 ± 12.47 4.5 0.04

Zone 1, n 6.5 ± 0.55 7.4 ± 0.60 2.1 0.16

Zone 1, % 10.6 ± 1.49 10.4 ± 1.47 0.01 0.93

Zone crossings, n 40.6 ± 1.71 48.9 ± 1.99 26.6 <0.0001

Head movements, n 86.7 ± 1.80 92.2 ± 1.87 6.0 0.02

Urination, n 0.5 ± 0.11 0.5 ± 0.12 0.1 0.72

Defecation, n 3.4 ± 0.30 4.9 ± 0.36 10.5 0.002

Escape attempt, n 0.4 ± 0.14 1.1 ± 0.30 11.3 0.001

Escape attempt, % 0.1 ± 0.04 0.2 ± 0.07 3.3 0.08

Freeze, n 4.9 ± 0.65 7.5 ± 0.96 22.8 <0.0001

Freeze, % 4.1 ± 0.71 6.2 ± 0.93 4.1 0.05

Measures F1,63 P-value Low-RFI (n = 40) High-RFI (n = 40)

Zone 1, s 128.1 ± 20.84 76.3 ± 12.31 5.3 0.02

Zone 1, n 7.3 ± 0.63 8.3 ± 0.70 2.4 0.13

Zone 1, % 9.2 ± 1.46 9.1 ± 1.42 0.01 0.93

Zone crossings, n 40.1 ± 1.74 48.4 ± 2.03 28.5 <0.0001

Head movements, n 80.5 ± 2.28 86.2 ± 2.39 7.2 0.01

Urination, n 0.6 ± 0.12 0.5 ± 0.11 0.3 0.60

Defecation, n 3.4 ± 0.30 4.4 ± 0.35 4.7 0.03

Escape attempt, n 0.6 ± 0.18 0.9 ± 0.23 1.6 0.21

Escape attempt, % 0.1 ± 0.05 0.2 ± 0.07 0.9 0.35

Freeze, n 4.7 ± 0.66 7.2 ± 0.96 20.5 <0.0001

Freeze, % 3.9 ± 0.72 6.0 ± 0.92 3.9 0.05

Figure 3.1. Arena where pigs were tested using human approach- (HAT) and novel object tests (NOT). aIndicates the distance of each zone from the human or cone, located in zone 1. Zones 2, 3, and 4 consisted of approximately equal area.

CHAPTER 4

FEED EFFICIENCY EFFECTS ON BARROW AND GILT BEHAVIORAL REACTIVITY TO NOVEL STIMULI TESTS

A paper published in Journal of Animal Science. 2015. 93:1267-1275

DOI: 10.2527/jas2014-8660

Jessica D. Colpoys*, Caitlyn E. Abell†, Nicholas K. Gabler*, Aileen F. Keating*,

Suzanne T. Millman‡, Janice M. Siegford§, Jennifer M. Young*, Anna K. Johnson*2

*Department of Animal Science, Iowa State University, Ames, IA 50011, USA

†DNA Genetics, Columbus, NE 68601, USA

‡Department of Veterinary Diagnostics and Production Animal Medicine, College of

Veterinary Medicine, Iowa State University, Ames, IA 50011, USA

§Department of Animal Science, Michigan State University, East Lansing, MI 48824,

USA

1 This project was supported by Agriculture and Food Research Initiative Competitive

Grant no. 2011-68004-30336 from the USDA National Institute of Food and Agriculture.

We would like to thank Dr. Jack Dekkers, Johanna Sholar, Jennifer Schubert, Ashley

Woodley, Adrianne Kaiser, and the Lauren Christian Swine Research Center staff for assistance in data collection and animal care.

2 Corresponding author: 2356-F Kildee Hall, Iowa State University, Ames, IA 50011,

USA; E-mail: [email protected]; Phone: +001-515-294-2098; Fax: +001-515-294-

4471

Abstract

Increasing feed efficiency in swine is an important goal for improving sustainable food production and profitability for producers. To study feed efficiency, genetic selection based on residual feed intake (RFI) was used to create 2 divergent lines. Low-

RFI pigs consume less feed for equal weight gain compared to their less efficient, high-

RFI counterparts. Therefore, using RFI selection, our objective was to assess how a pig’s behavioral reactivity toward fear-eliciting stimuli related to selection and improvement of feed efficiency. In this study, behavioral reactivity of pigs divergently selected for RFI was evaluated using human approach (HAT) and novel object (NOT) tests. Eighty low- and high-RFI barrows and gilts (n = 20 per line and sex combination) from ninth generation Yorkshire RFI selection lines were randomly selected and evaluated once using HAT and once using NOT over a 2 wk period utilizing a crossover experimental design. Each pig was individually tested within a 4.9 x 2.4 m test arena for 10 min; behavior was evaluated using live and video observations. The test arena floor was divided into 4 zones; zone 1 being oral, nasal, and/or facial contact with the human

(HAT) or orange traffic cone (NOT) and zone 4 being furthest from the human or cone and included the point where the pig entered the arena. During HAT and NOT, low-RFI pigs entered zone 1 less frequently compared to high-RFI pigs (P ≤ 0.03). During NOT,

low-RFI pigs changed head orientation more frequently (P = 0.001), but attempted to escape less frequently (low-RFI = 0.97 ± 0.21 vs. high-RFI = 2.08 ± 0.38; P = 0.0002) and spent 2% less time attempting to escape compared to high-RFI pigs (P = 0.04).

Different barrow and gilt responses were observed during HAT and NOT. During HAT, barrows spent 2% more time within zone 1 (P = 0.03), crossed fewer zone lines (P <

0.0001), changed head orientation less frequently (P = 0.002), and froze less frequently compared to gilts (P = 0.02). However, during NOT, barrows froze more frequently (P =

0.0007) and spent 2% longer freezing (P = 0.05). When the behavior and RFI relationship was examined using odds ratios, decreasing RFI by 1 kg/d decreased the odds of freezing by 4 times but increased the odds of attempting to escape by 5.26 times during NOT (P ≤

0.04). These results suggest that divergent selection for RFI resulted in subtle behavioral reactivity differences and did not impact swine welfare in its response to fear-eliciting stimuli.

Introduction

The Iowa State University (ISU) Yorkshire residual feed intake (RFI) selection project divergently selected pigs for RFI as a model to investigate the physiological and genetic differences in swine feed efficiency. This model defined RFI as the difference between the actual feed intake of a pig and its expected feed intake based on a given amount of growth and backfat (BF). Therefore, pigs that consume less feed than expected for maintenance and growth have a lower RFI, are more feed efficient, and are

economically better for lean protein production relative to higher RFI pigs (Young et al.,

2011).

It was recently suggested that breeding for improved feed efficiency may decrease the animal’s stress adaptation, thus resulting in adverse effects on livestock behavior and management (Rydhmer and Canario, 2014). Contrary to this, we reported that low-RFI

(more feed efficient) barrows were less reactive to human approach (HAT) and novel object (NOT) tests compared to high-RFI barrows (Colpoys et al., 2014). However, recent research identified differences between barrows and gilts during HAT and NOT

(Reimert et al., 2014) and it is unknown if barrows and gilts from this selection project have different responses to novel stimuli tests. Furthermore, the extent to which behavioral reactivity differences relate to phenotypic expression of feed efficiency is not well understood. In pigs, Cassady (2007) reported significant, yet inconsistent, relationships between time spent struggling during a back test and pre-weaning ADG

(negative relationship) and ADG from 20 to 76 d old (positive relationship).

Using pigs from the ISU RFI selection lines, our first objective was to examine the association between long-term divergent selection for RFI and behavioral reactivity to fear-eliciting stimuli in barrows and gilts. The second objective of this study was to evaluate phenotypic relationships between behavioral responses during HAT and NOT and overall RFI during the grow-finish period.

Materials and Methods

All experimental procedures were approved by the ISU Animal Care and Use

Committee. This experiment was conducted over 2 consecutive weeks from February through March, 2013.

Animals and housing

A total of 80 healthy Yorkshire pigs (101 ± 9 d old) divergently selected for RFI were used. Two genetic line treatments were compared: low-RFI (n=40) and high-RFI

(n=40). Half were barrows (20 low-RFI, 34.83 ± 6.55 kg BW; 20 high-RFI, 30.04 ± 5.21 kg BW) and half were gilts (20 low-RFI, 32.33 ± 6.01 kg BW; 20 high-RFI, 28.96 ± 4.53 kg BW). Body weight was collected using a weigh scale (Electronic Weighing Systems,

Rite Weigh, Robert E Spencer Enterprises, Ackley, IA, USA) three days prior to the start of testing.

This work was conducted at the Lauren Christian Swine Research Center at the

ISU Bilsland Memorial Farm located near Madrid, Iowa, USA. All pigs were housed in a conventional confinement unit within 1 room containing 12 mixed-sex and mixed-line pens of 15 to 16 pigs/pen; 12 to 15 pigs from 6 pens were tested. Each pen measured 5.6 m long x 2.3 m wide and had a slatted concrete floor. The barn was naturally ventilated with side curtains. Each pen contained an electronic single-space feeder (FIRE®, Osborne

Industries, Inc., Osborne, KS, USA) that recorded individual feed intake and was positioned at the front of the pen to provide pigs with ad libitum feed. All tested pigs were fed a corn-soy diet that met or exceeded NRC (1998) requirements. Water was

provided ad libitum through 2 nipple-type waterers (Edstrom, Waterford, WI) per pen.

The pigs were moved to this housing 10 d prior to the start of the experiment. One electronic recording device (HOBO Pro v2, temp / RH, U23-001, Onset Computer

Corporation, Bourne, MA, USA) located in the center of the room, 2.2 m from the ground, recorded ambient temperature (°C) and relative humidity (%) every 5 min for the duration of the trial. The mean (±S.D.) ambient temperature was 22.49 (±2.74) °C and relative humidity was 50.06 (±7.00) %.

RFI selection and calculation

Divergent line selection criteria were based on estimated breeding values for RFI as explained by Cai et al. (2008). The low-RFI genetic line had been selected over 9 generations. The high-RFI genetic line had been randomly selected over 5 generations, and then selected for high-RFI over the next 4 generations.

Feed intake data recorded using the FIRE® feeders were edited following procedures outlined by Casey et al. (2005). Average daily feed intake was calculated as described by Cai et al. (2008). For each pig, ADG was estimated as the slope from a simple linear regression of BW that was recorded every 2 wk. Pigs identified for market had BF and loin muscle area (LMA) at the 10th rib measured using an Aloka 500V SSD ultrasound machine fitted with a 3.5-MHz, 12.5-cm, linear-array transducer (Corometrics

Medical Systems Inc., Wallingford, CT, USA). Metabolic BW (MBW) was estimated as the average BW raised to the 0.75 power. Pigs entered the unit at approximately 90 d of age and 40 kg of BW; therefore, on-age deviation (ONAGEDEV) was calculated by

subtracting 90 d from the age of the pig and on-weight deviation (ONWTDEV) was calculated by subtracting 40 kg from the BW of the pigs when entering the facility. Pigs were removed from the conventional confinement unit at approximately 118 kg of BW; therefore, off-weight deviation (OFFWTDEV) was calculated by subtracting 118 kg from the BW of the pig when removed from the facility. The mixed procedure in SAS 9.3

(SAS Institute Inc., Cary, NC, USA) was used to estimate regression coefficients for

ADG, BF, MBW, ONAGEDEV, ONWTDEV, and OFFWTDEV to calculate RFI. The model used included fixed effects of sex, line, diet, and generation. Covariates included 6 three-way interactions which were the interaction of line and diet with ADG, BF, MBW,

ONAGEDEV, ONWTDEV, and OFFWTDEV. Random effects fitted were dam and pen nested within generation.

Test methodology and facility

Pig testing occurred 5 d/wk over 2 consecutive weeks. A testing session consisted of a 10 min period during which the individual pig underwent HAT or NOT within the experimental arena. All test sessions were performed between 13:00 and 19:00 h. A total of 40 pigs (10 low-RFI barrows, 10 low-RFI gilts, 10 high-RFI barrows, and 10 high-RFI gilts) were selected using a random number generator (Microsoft Excel 2010, Microsoft

Corporation, Santa Rosa, CA, USA) to be tested using HAT first and the remaining 40 pigs experienced NOT first. Pigs then experienced the opposite test 1 wk later, utilizing a crossover experimental design. Therefore, each pig was tested a total of 2 times, once in each test. Genetic line and sex were blocked by time so that within each hour each of the

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following types were tested in random order: low-RFI barrow, low-RFI gilt, high-RFI barrow, and high-RFI gilt. Pigs were tested in the same order for both tests and at the same time of day, and the individual pig was the experimental unit.

The HAT and NOT were conducted in a rectangular arena separate from the home pens. The arena setup followed the same procedures as previously described by Colpoys et al. (2014). The arena measured 4.9 m long x 2.4 m wide and had 1.2 m high, black corrugated plastic sides that were attached to gates. In order to hide the human observer visually during NOT, a 1.2 m wide x 2.2 m high black corrugated plastic observation hide was positioned outside the arena. Concentric curves were drawn on the slatted concrete floor using permanent marker 1 d before the start of testing to divide the arena into 4 zones in order to measure the location of the pig in proximity to the novel stimulus. Zone

1 was defined as oral, nasal, and/or facial contact with the human or the cone during HAT and NOT, respectively. For consistency with the other zones, pigs that touched the human or the cone will be referred to as entering zone 1. Zone 2 was the area nearest to the novel stimulus and zone 4 was the area where the pig entered the test arena, furthest from the novel stimulus. Zones 2-4 consisted of approximately equal area that allowed the entire body of the pig to fit within the zone. The concentric curves allowed a consistent distance from the novel stimulus to be measured in each zone (Fig. 4.1). Located in the center of the arena, 2.3 m from the ground, was an electronic recording device (HOBO Pro v2, temp / RH, U23-001, Onset Computer Corporation, Bourne, MA, USA) that recorded ambient temperature (°C) and relative humidity (%) every 5 min for the duration of testing. Throughout the testing period, the mean (±S.D.) ambient temperature was 13.26

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(±2.16) °C and relative humidity was 69.07 (±10.23) %. This is a 9.23 °C cooler temperature and 19.01% greater relative humidity than in the home pens, due to the test room being heated only from the adjacent pig rooms rather than its own heat source.

Three color cameras (Panasonic, Model WV-CP-484, Matsushita Co. LTD.,

Kadoma, Japan) were positioned 2.1 m above the test arena. Camera 1 was positioned over zone 1, camera 2 captured zones 2 and 3, and camera 3 captured zone 4. The cameras were fed into a multiplexer using Noldus Portable Lab (Noldus Information

Technology, Wageningen, The Netherlands) and time-lapse video was collected onto a computer using HandyAVI (HandyAVI version 4.3 D, Anderson’s AZcendant Software,

Tempe, AZ, USA) at 10 frames/s.

One handler removed the pig to be tested from its home pen using a sort board.

Each pig was moved down an alleyway (0.30 m to 12.47 m long x 0.79 m wide) onto a weigh scale (1.50 m long x 0.5 m wide; Electronic Weighing Systems, Rite Weigh,

Robert E Spencer Enterprises, Ackley, IA, USA) adjacent to the test arena. The pig remained in the weigh scale for 1 min to create a uniform pre-test experience for every pig. Black corrugated plastic was attached to the front of the weigh scale so that pigs were not able to see into the test arena. The total number of pig urinations and defecations during handling and within the weigh scale were recorded. At the conclusion of the minute, the weigh scale door was opened and the pig was allowed to enter zone 4 of the arena. If the pig did not enter the arena within 15 s of the weigh scale door opening, the handler gently pushed the pig forward using their hands. Test time began when both front hooves entered zone 4. Following the 10 min testing period, each pig

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was returned to its home pen by the handler using the described methods. Feces and urine within the test arena were scraped through the slats following each testing session and the test arena was hosed down with water at the end of each testing day.

Human approach test

Each pig was individually assessed using HAT, which was designed to measure responses to an unfamiliar human stimulus. The human stimulus was the same woman for all tests, and she had never previously interacted with the pigs. This person showered into the facility using the same products at the start of each testing day. During testing, the unfamiliar human wore orange coveralls and orange boots, stood silently at the center of the opposite wall (zone 1) holding a clipboard, and did not interact with or move toward the pigs. Minimal arm movement and body shifting occurred during live observation and data collection. At the end of each testing day, coveralls were laundered and boots were hosed off with water.

Novel object test

Each pig was individually assessed using NOT, which was designed to measure responses to an unfamiliar object stimulus, an orange traffic cone. The traffic cone was positioned at the center of the opposite wall (zone 1) and was hosed off with water at the end of each testing day. The same woman who was the stimulus in HAT collected live observations. She was wearing blue coveralls and standing behind the black corrugated plastic observation hide outside the test arena that kept her out of pig sight (Fig. 4.1).

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Measures

Live observations of the frequency of eliminatory behaviors were continuously collected during both tests (Dawkins et al., 2007). Video observations were continuously recorded (Dawkins et al., 2007) using the Observer software (The Observer XT version

10.5, Noldus Information Technology, Wageningen, The Netherlands) to decode approach, head orientation, freezing, and escape attempts (Table 4.1). All video observations were collected by the same, trained researcher who was blind to genetic line treatments. Due to technical difficulties, video of 1 high-RFI gilt during HAT was lost; therefore, video was only collected on 19 high-RFI gilts during HAT. However, live observations and latency and frequency of zone 1 entrances (collected live for cross- validation with video observations) were collected for all 20 high-RFI gilts during HAT.

Data analysis

All data were evaluated for normality using the Shapiro-Wilk test and Q-Q plots using SAS (SAS version 9.3, SAS Inst. Inc., Cary, NC, USA). Data were not normally distributed; therefore, data were analyzed using the Glimmix procedure of SAS. All HAT and NOT data were analyzed separately. Latency data were analyzed with a gamma distribution; duration data were analyzed with a beta distribution; and frequency data were analyzed with a Poisson distribution. During HAT, 1 low-RFI gilt did not enter zone

1; therefore, was given a latency of 600 s. All behaviors were analyzed using a model with the fixed effects of test week, genetic line, sex, and the interaction of genetic line and sex, with the covariate of test day age, and random effect of pen. Frequency of

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urination and defecation models also included the random effect of total number of pre- test urinations and defecations, respectively.

Regressions were analyzed using the same models as previously described; however, linear and quadratic RFI were included as covariates in the model. Odds ratios

(OR) were used to measure the magnitude of effect of the linear and quadratic RFI on the behavior variables. Therefore, OR indicated the multiplicative change in odds of the behavior with 1 kg/d increase in RFI. An OR of 1 indicates no effect, whereas an OR of greater than 1 indicates an increased effect and an OR less than 1 indicates a decreased effect of RFI on the behavior. For ease of interpretation, inverted OR were calculated as 1 divided by OR where necessary. The significance level was fixed at P ≤ 0.05.

Results

Human approach test

Genetic line, barrow, and gilt differences

There were no line, sex, or line × sex differences in latency to enter zone 1 (P ≥

0.25; Table 4.2). Low-RFI pigs entered zone 1 less frequently compared to high-RFI pigs

(low-RFI = 7.03 ± 0.50 vs. high-RFI = 8.45 ± 0.57; P = 0.03; Table 4.2). No sex or line × sex differences were observed in zone 1 entrance frequency (P ≥ 0.35; Table 4.2).

Barrows spent approximately 2% more time within zone 1 compared to gilts (barrow =

6.48 ± 0.61 vs. gilt = 4.69 ± 0.53; P = 0.03); however, line and line × sex did not differ in duration of time spent within zone 1 (P ≥ 0.53; Table 4.2). No line, sex, or line × sex

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differences were observed in duration of time spent within zone 2 (P ≥ 0.22), zone 3 (P ≥

0.27), or zone 4 (P ≥ 0.60; data not presented).

Barrows crossed fewer zone lines (barrow = 49.88 ± 1.82 vs. gilt = 57.35 ± 2.05;

P < 0.0001) and had fewer head movement frequencies than gilts (barrow = 114.24 ±

3.30 vs. gilt = 122.30 ± 3.52; P = 0.002). However, no line or line × sex differences were observed for zone crossing or head movement frequencies (P ≥ 0.26; Table 4.2). No line, sex, or line × sex differences were observed in duration of time spent with head in the front (P ≥ 0.35), side (P ≥ 0.17), or back (P ≥ 0.29) orientation relative to the human

(data not presented). There were no line, sex, or line × sex differences observed in urination or defecation frequency (P ≥ 0.12; Table 4.2). No differences in total number of escape attempts were observed between lines and sexes (P ≥ 0.29; Table 4.2). However, a line × sex interaction was observed for escape attempt frequency (P = 0.007; Table 4.2), whereby low-RFI gilts attempted to escape less frequently than high-RFI gilts (P =

0.008) and high-RFI barrows attempted to escape less frequently than high-RFI gilts (P =

0.02). Low-RFI barrows did not differ from high-RFI barrows (P = 0.23) or gilts of both genetic lines (P ≥ 0.13) for this behavior. Barrows froze less frequently compared to gilts

(barrow = 3.54 ± 0.47 vs. gilt = 4.64 ± 0.60; P = 0.02); however, no line or line × sex differences were observed (P ≥ 0.11; Table 4.2). No line, sex, or line × sex differences were observed for duration of time attempting to escape or freezing (P ≥ 0.30; Table 4.2).

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Behavior and RFI relationship.

Residual feed intake was quadratically associated with zone crossings (OR = 1.12;

P = 0.04) and head movement (OR = 1.08; P ≤ 0.05); however, these variables did not show a linear relationship (P ≥ 0.34; Table 4.3). No other linear or quadratic associations were observed between behaviors and RFI (Table 4.3).

Novel object test

Genetic line, barrow, and gilt differences

There were no line, sex, or line × sex differences in latency to enter zone 1 (P ≥

0.15; Table 4.4). Low-RFI pigs entered zone 1 less frequently compared to high-RFI pigs

(low-RFI = 7.35 ± 0.64 vs. high-RFI = 9.90 ± 0.81; P = 0.0002); however, there was no sex difference observed in zone 1 entrance frequency (P = 0.08; Table 4.4). A line × sex interaction was observed for zone 1 entrance frequency (P = 0.02), where low-RFI gilts entered zone 1 less frequently than high-RFI gilts (P < 0.0001) and barrows of both genetic lines (P ≤ 0.009). Low-RFI barrows did not differ from high-RFI barrows (P =

0.25) or gilts (P = 0.12) in zone 1 entrance frequency. No line, sex, or line × sex differences were observed for duration of time spent within zone 1 (P ≥ 0.11; Table 4.4), zone 2 (P ≥ 0.43), zone 3 (P ≥ 0.22), or zone 4 (P ≥ 0.08; data not presented).

No sex or line × sex differences were observed in zone crossing frequency (P ≥

0.08; Table 4.4); however, low-RFI pigs had more head movements frequencies than high-RFI pigs (low-RFI = 118.68 ± 1.75 vs. high-RFI = 110.43 ± 1.68; P = 0.001). No sex or line × sex differences were observed in head movement frequency (P ≥ 0.29; Table

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4.4). No line, sex, or line × sex differences were observed for duration of time spent with head in the front (P ≥ 0.20), side (P ≥ 0.58), or back (P ≥ 0.11) orientation relative to the cone (data not presented). There were no line, sex, or line × sex differences observed in urination or defecation frequency (P ≥ 0.24; Table 4.4). Low-RFI pigs performed fewer escape attempts (low-RFI = 0.97 ± 0.21 vs. high-RFI = 2.08 ± 0.38; P = 0.0002) and spent approximately 2% less time attempting to escape compared to high-RFI pigs (low-

RFI = 0.17 ± 0.06 vs. high-RFI = 0.41 ± 0.09; P = 0.04). However, no sex or line × sex differences were observed in escape attempt frequency or duration (P ≥ 0.11; Table 4.4).

Barrows froze more frequently (barrow = 4.73 ± 0.58 vs. gilt = 3.15 ± 0.42; P = 0.0007) and spent approximately 2% longer freezing compared to gilts (barrow = 3.99 ± 0.68 vs. gilt = 2.30 ± 0.52; P = 0.05). However, no line or line × sex differences were observed in freezing duration or frequency (P ≥ 0.27; Table 4.4).

Behavior and RFI relationship

Residual feed intake was quadratically associated with frequency of zone 1 entrances (OR = 0.65; P = 0.004), zone crossings (OR = 1.11; P = 0.05), head movement frequencies (OR = 1.15; P = 0.0004), and defecations (OR = 1.57; P = 0.03); however, there were no linear relationships between these variables (P ≥ 0.41; Table 4.5). Residual feed intake was linearly associated with frequency of escape attempts (OR = 0.19; P =

0.04; Fig. 4.2A) and freezing (OR = 4.00; P = 0.0001; Fig. 4.2B); however, there were no quadratic relationships between these variables (P ≥ 0.19; Table 4.5). No other linear or quadratic relationships were observed between behaviors and RFI (Table 4.5).

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Discussion

Genetic line differences

Low- and high-RFI pigs displayed subtle differences in behavioral reactivity in response to fear-eliciting stimuli. No differences were observed between genetic lines in latency to enter or duration of time spent in zone 1, zone crossings, elimination, or freezing during HAT or NOT. During both HAT and NOT, low-RFI pigs entered zone 1 fewer times than high-RFI pigs. A similar line difference was observed in eighth generation barrows but was not significant (Colpoys et al., 2014). Since the decreased zone 1 entrance frequency for the low-RFI pigs did not correspond with a difference in latency to approach or duration of time interacting with the human and cone, we reason that this difference was not related to fearfulness. Alternatively, we hypothesize that the difference observed between genetic lines for zone 1 entrance frequency may be due to the type of interaction, for example rooting and chewing. Due to the camera angles, type of interaction with the human and novel object was not able to be differentiated, thus was not recorded.

In contrast to barrows from the eighth generation (Colpoys et al., 2014), during

NOT, low-RFI pigs in the current study changed head orientation more frequently, suggesting greater fearfulness, compared to high-RFI pigs. Similar to eighth generation barrows (Colpoys et al., 2014), during NOT, low-RFI pigs in the current study attempted to escape for a shorter duration and less frequently, suggesting lower fearfulness, compared to high-RFI pigs. Although the pigs had never previously interacted with the human in HAT, previous experience with humans may have reduced the genetic line

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differences during HAT (Hemsworth et al., 1981; Hemsworth and Barnett, 1992;

Hemsworth et al., 1996). These results do not indicate greater fearfulness in one genetic line; rather, they suggest that low- and high-RFI pigs may differ in coping methods during NOT.

Barrow and gilt differences

Different barrow and gilt responses were observed during HAT and NOT. During

HAT, barrows appeared to be less fearful than gilts. Barrows spent a longer duration of time within zone 1, crossed fewer zone lines, had fewer head movements, and froze fewer times compared to gilts. These results differ from Reimert et al. (2014) who reported no difference in the duration of time spent near the human but observed a tendency for a shorter latency to touch a human in gilts compared to barrows. However, it should be noted that they tested younger pigs (7 wk old) in groups within the home pen which may alter the level of fearfulness compared to older pigs (14 wk old) individually tested within a novel arena in the current study (Forkman et al., 2007; Pairis et al., 2009).

Unlike HAT, barrows spent a longer duration of time freezing and froze more often than gilts during NOT. This may indicate that barrows were more fearful than gilts during NOT, and are in line with findings of Reimert and colleagues (2014) who reached a similar conclusion following NOT. Similarly, Kranendonk et al. (2006) reported that young boars (approximately 25 d old) vocalized during NOT and struggled during a back test more often than gilts. Furthermore, physiological and hormonal differences between barrows and gilts have shown decreased stress in females (Ruis et al., 1997; Lay et al.,

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2002; Baxter et al., 2012), which may be reflected through NOT results of the current study.

Inconsistency of sex differences between tests was unexpected as behavior during

HAT and NOT is reported to be positively correlated (van der Kooij et al., 2002; Janczak et al., 2003). Previous studies found no differences between barrows and gilts during behavioral stress tests (Hessing et al., 1994; De Jong et al., 1998; Siegford et al., 2008); however, comparison between studies is difficult as test methodology, pig ages and genetics vary. Although the human in this study was novel to these pigs, they are exposed to male and female humans during daily chores. Conversely, these pigs had never encountered an orange traffic cone before. Previous studies reported differences between males and females in coping with stressors. In rats, males had faster corticosterone habituation to chronic stress compared to females (Galea et al., 1997). To our knowledge, no swine studies have investigated sex differences in habituation to HAT. It could be speculated from our results that barrows may habituate quicker to humans and better generalize this to unfamiliar humans compared to their gilts counterparts. Further research on understanding the neuroendocrine and behavioral differences between males and females that could contribute to such a differential observation is warranted.

Behavior and RFI relationships

Regardless of genetic line, RFI was not strongly related to behavior during HAT, but may reflect coping style during NOT. Decreasing RFI (increasing feed efficiency) by

1 kg/d increased the odds of attempting to escape by 5.26 times (inverted OR from Table

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4.5), during NOT. Conversely, decreasing RFI (increasing feed efficiency) by 1 kg/d decreased the odds of freezing by 4 times. Interestingly, these phenotypic results are in contrast to genetic line differences in escape attempts described in the current study, and may explain why low-RFI pigs did not show as strong of a linear relationship between predicted escape attempts and RFI as high-RFI pigs (Fig. 4.2A). These results suggest that regardless of genetic line, RFI is phenotypically related to coping styles. More feed efficient (lower RFI) pigs responded to NOT more actively, or through a proactive coping style, by attempting to escape whereas less feed efficient (higher RFI) pigs responded to

NOT more passively, or through a reactive coping style, by freezing (Koolhaas et al.,

1999). These phenotypic relationships are unexpected as it can be assumed that escape attempts require greater energy expenditure than freezing. Therefore, these results may not reflect behavioral coping style within the home pen. We hypothesize that feed efficiency differences may be related to hypothalamic-pituitary-adrenocortical axis responsiveness to stress coping (Koolhaas et al., 1999), whereas more feed efficient pigs may secrete less cortisol in response to stress than less feed efficient pigs (Jenkins et al.,

2013).

General discussion

Increasing swine feed efficiency is a genetic improvement and management goal to facilitate improved producer profitability, sustainability, and resource allocation.

Numerous studies have been conducted to examine the physiology of feed efficiency using divergent RFI models. Specifically in pigs, RFI research has focused on feed intake

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patterns (Young et al, 2011), physical activity (Sadler et al., 2011), body composition

(Boddicker et al., 2011a,b), nutrient digestibility (Barea et al., 2010; Harris et al., 2012), immune system activation (Rakhshandeh et al., 2012), skeletal muscle oxidative stress

(Grubbs et al., 2013) and protein turnover (Cruzen et al., 2013). One aspect of swine welfare which may be influenced by altering feed efficiency is the pig’s stress response

(Jenkins et al., 2013), particularly to human interaction and novel stimuli.

Using RFI selection as a model to study feed efficiency, our data presented herein indicates subtle differences in grow-finish pig behavioral reactivity to fear-eliciting stimuli as it relates to feed efficiency. Moreover, sex had a larger impact on behavioral reactivity during HAT than genetic line. Therefore, overall selection for improved feed efficiency did not negatively impact swine welfare in its response to fear-eliciting stimuli compared to selection for poorer feed efficiency. Furthermore, our data suggests that regardless of genetic line, RFI may be related to coping style as more feed efficient pigs had increased odds of attempting to escape but decreased odds of freezing during NOT.

In conclusion, our data supports that feed efficiency does not decrease a pig’s ability to cope with a stressor as previously suggested (Rydhmer and Canario, 2014), but it could be related to differences in the way pigs cope with stressors, at least in the genetic lines of the present study.

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Rakhshandeh, A., J. C. M. Dekkers, B. J. Kerr, T. E. Weber, J. English, and N. K. Gabler. 2012. Effect of immune system stimulation and divergent selection for residual feed intake on digestive capacity of the small intestine in growing pigs. J. Anim. Sci. 90:233-235.

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Siegford, J. M., G. Rucker, and A. J. Zanella. 2008. Effects of pre-weaning exposure to a maze on stress responses in pigs at weaning and on subsequent performance in spatial and fear-related tests. Appl. Anim. Behav. Sci. 110:189-202. van der Kooij, E. V., A. H. Kuijpers, J. W. Schrama, F. J. C. M. van Eerdenburg, W. G. P. Schouten, and M. J. M. Tielen. 2002. Can we predict behaviour in pigs? Searching for consistency in behaviour over time and across situations. Appl. Anim. Behav. Sci. 75:293-305.

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Young, J. M., W. Cai, and J. C. M. Dekkers. 2011. Effect of selection for residual feed intake on feeding behavior and daily feed intake patterns in Yorkshire swine. J. Anim. Sci. 89:639-647.

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Table 4.1. Ethogram of behaviors recorded during human approach and novel object tests. Latency in seconds (s), duration (%), and/or frequency (n) of behaviors collected1 Measure Description

Approach

Zone 1, s, n, % The mouth, nose, and/or face of the pig contact any part of zone 1 (defined as the human or traffic cone).

Zone 2, 3, & 4, % The base of both the pig’s ears were within the limits of the respective zone and the pig’s mouth, nose, and/or face was not touching zone 1.

Zone crossings, n Sum of the total number of zone 2, 3, and 4 entrances.

Head orientation

Front, Side, Back, % The pig’s snout was pointed towards, perpendicular, or in the opposite direction of zone 1, respectively.

Head movements, n The sum of front, side, and back head orientations.

Elimination

Urination, n Excreting urine.

Defecation, n Excreting feces.

Escape attempt, n, % The front two or all four pig’s hooves were off the arena floor in attempt to remove itself from the test arena. Duration was measured from the removal of the two front hooves from the floor to all four hooves returning to the floor.

Freezing, n, % No movement of any portion of the pig’s body was visible for ≥ 3 s. Duration was measured from the start of the freeze to any movement of the body.

1 Ethogram adapted from Colpoys et al. (2014). Live observations were utilized to collect elimination data and video decoding was utilized to collect all other measures.

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0.60 0.34 0.55 0.35 0.88 0.26 0.26 0.80 0.007 0.11 0.40

Line*Sex

value

Sex

0.03 0.73 0.25 0.76 0.002 0.46 0.34 0.59 0.02 0.45

<0.0001

0.53 0.30 0.50 0.03 0.29 0.58 0.68 0.12 0.29 0.32 0.38

Line

10 74

0.79 0. 0.72 2.43 3.90 0.14 0.38 0.39 0.71 0.

25.05

SE) during the human test human the approach in during SE)

± ± ± ± ± ± ± ± ± ± ±

Gilt

± ±

5.11 8.24 0.41 3.00 1.80 4.81 3.49

RFI gilts n = 19 with regard to the noted RFI noted the to 19 = n gilts regard with

58.18

103.56 120.14

RFI

08 60

High

0.86 0. 0.75 2.17 3.74 0.19 0.42 0.50 0.

0.24

21.99

± ± ± ± ± ± ± ± ± ± ±

Barrow

6.52 8.67 0.70 3.46 0.93 3.05 2.52

90.76 50.84

114.94

; more feed efficient) and high RFI efficient) high RFI (less and more ; feed efficient) feed

07 73

RFI

Genetic Genetic lin

0.71 0. 0.68 2.35 3.98 0.18 0.43 0.22 0.67 0.

24.61

± ± ± ± ± ± ± ± ± ± ±

Gilt

RFI gilt was lost. for Therefore, high was RFI gilt

4.31 7.40 0.64 3.72 0.83 4.48 3.52

56.53

101.69 124.50

high

RFI

Low

08 73

0.86 0. 0.64 2.12 3.70 0.17 0.45 0.30 0.62 0.

16.27

± ± ± ± ± ± ± ± ± ± ±

Barrow

6.43 6.68 0.57 4.04 1.31 4.10 3.57

67.13 48.94

113.54

Latency (s), frequency (n), and duration (%) of behaviors (least means of duration (n), frequency (%) square and (s), Latency

2. 2.

1 4.

Measures

Due to a technical problem, 1 of video technical a to Due

Table intake ( residual low feed for selected and barrows gilts behaviors Zone 1, % Zone % Escape attempt, Zone 1,Zone s 1,Zone n crossings,Zone n Head movements, n Urination, n Defecation, n Escape attempt, n Freeze, n Freeze, % 1 1

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Measures OR P-value OR P-value

Zone 1, s 2.33 0.30 1.51 0.31

Zone 1, n 0.78 0.40 1.09 0.57

Zone 1, %1 0.74 0.55 0.62 0.10

Zone crossings, n1 0.90 0.39 1.12 0.04

Head movements, n1 0.92 0.34 1.08 0.05

Urination, n 0.86 0.88 0.75 0.57

Defecation, n 0.64 0.25 1.21 0.38

Escape attempt, n1 1.32 0.69 0.55 0.11

Escape attempt, %1 1.61 0.65 0.45 0.19

Freeze, n1 0.68 0.35 0.81 0.29

Freeze, %1 2.08 0.24 0.78 0.51

1 Due to a technical problem, video of 1 high-RFI gilt was lost. Therefore, for high-RFI gilts n = 19 with regard to the noted behaviors.

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0.42 0.02 0.64 0.08 0.54 0.97 0.24 0.92 0.77 0.63 0.92

Line*Sex

test in test

value

Sex

0.68 0.08 0.11 0.10 0.29 0.47 0.93 0.11 0.35 0.0007 0.05

Line

0.15 0.0002 0.30 0.27 0.001 0.86 0.49 0.0002 0.04 0.27 0.99

0.96 1.77 2.38 2.38 0.15 0.44 0.50 0. 0.52 0.72

12.66

SE) during the novel object novel the object during SE)

± ±

± ± ± ± ± ± ± ± ± ± ±

Gilt

6.68 0.46 3.77 2.46 3.27 2.27

63.31 10.11 59.81

112.40

RFI

High

0.93 2.28 2.22 2.34 0.17 0.40 0.39 0. 0.72 0.96

13.73

± ± ± ± ± ± ± ± ± ± ±

Barrow

9.69 0.58 3.24 1.76 5.19 4.04

68.55 11.08 53.88

; more feed efficient) and high RFI efficient) high RFI (less and more ; feed efficient) feed

108.50

RFI

09 73

Genetic Genetic line

0.69 1.65 2.24 2.45 0.15 0.43 0.29 0. 0.49 0.

19.98

± ± ± ± ± ± ± ± ± ± ±

Gilt

6.28 5.78 0.44 3.55 1.13 3.04 2.34

99.92 54.76

119.25

RFI

07 95

Low

0.86 1.91 2.25 2.45 0.17 0.45 0.24 0. 0.63 0.

15.64

± ± ± ± ± ± ± ± ± ± ±

Barrow

8.59 7.70 0.54 4.04 0.84 4.31 3.93

78.06 55.01

118.11

Latency (s), frequency (n), and duration (%) of behaviors (least means of duration (n), frequency (%) square and (s), Latency

. .

4.4

Measures

Table intake ( residual low feed for selected and barrows gilts Zone 1,Zone s 1,Zone n 1,Zone % crossings,Zone n Head movements, n Urination, n Defecation, n Escape attempt, n Escape attempt, % Freeze, n Freeze, %

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Measures OR P-value OR P-value

Zone 1, s 1.05 0.93 0.87 0.71

Zone 1, n 1.02 0.94 0.65 0.004

Zone 1, % 0.53 0.50 0.50 0.18

Zone crossings, n 0.96 0.71 1.11 0.05

Head movements, n 1.01 0.84 1.15 0.0004

Urination, n 1.60 0.63 1.30 0.63

Defecation, n 0.73 0.41 1.57 0.03

Escape attempt, n 0.19 0.04 1.66 0.19

Escape attempt, % 0.17 0.21 1.53 0.58

Freeze, n 4.00 0.0001 1.04 0.84

Freeze, % 4.49 0.07 1.02 0.96

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Figure 4.1. Arena where pigs were tested using human approach (HAT) and novel object (NOT) tests. aIndicates the distance of each zone from the human or cone, located in zone 1. Zones 2, 3, and 4 consisted of approximately equal area.

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Figure 4.2. Predicted escape attempt (A) and freezing (B) frequencies across residual feed intake (RFI) in low-RFI (more feed efficient) and high-RFI (less feed efficient) barrows and gilts during the novel object test.

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CHAPTER 5

RESPONSIVENESS OF SWINE DIVERGENTLY SELECTED FOR FEED EFFICIENCY TO EXOGENOUS ADRENOCORTICOTROPIN (ACTH) AND GLUCOSE CHALLENGES

Prepared for submission to Domestic Animal Endocrinology

Jessica Colpoysa, Dana Van Sambeeka, Caitlyn Abellb, Anna Johnsona, Jack Dekkersa,

Frank Dunsheac, Nicholas Gablera1

aDepartment of Animal Science, Iowa State University, Ames, IA, 50011, USA

bDNA Genetics, Columbus, NE 68601, USA cAgriculture and Food Systems, The University of Melbourne, Parkville, Victoria 3010,

Australia

1Correspondence address: 201E Kildee Hall, ISU, Ames, IA, 50011; E-mail: [email protected] Phone: +1-515-294-7370 Fax: +1-515-294-1399

Abstract

Increasing the feed efficiency of lean tissue gains is an important goal for improving sustainable pork production and profitability for swine producers. To study feed efficiency, genetic selection based on residual feed intake (RFI) was used to create two divergent lines. Low-RFI pigs consume less feed for equal weight gain compared to their less efficient, high-RFI counterparts. As cortisol and insulin are important regulators

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of energy control and growth, our objective was to evaluate the role of the adrenocorticotropic hormone (ACTH)-cortisol and the glucose-insulin axes in pig divergently selected for RFI. Adrenocorticotropin hormone (0.2 IU/kg BW)-stimulated cortisol and NEFA concentrations and intravenous glucose (IVGTT; 0.25 g/kg BW)- stimulated glucose, insulin, and NEFA concentrations were assessed in six low-RFI and six high-RFI gilts (68 + 5.2 kg). Prior to the ACTH challenge, low-RFI gilts tended to have lower baseline plasma cortisol (P = 0.08) but no difference in NEFA concentrations

(P = 0.63) compared to high-RFI gilts. Following the ACTH challenge, low-RFI gilts had lower cortisol (P = 0.04) and NEFA concentrations (P = 0.05) compared to high-RFI gilts. Glucose, insulin, and NEFA concentrations did not differ between genetic lines prior to the IVGTT (P ≥ 0.16). Following glucose stimulation, low-RFI gilts had higher insulin concentrations (P = 0.003) but did not differ in glucose or NEFA concentrations compared to high-RFI gilts (P ≥ 0.22). These results indicate that genetic selection for reduced RFI (improved feed efficiency) resulted in a reduced stress response to an ACTH challenge and an increased insulin response following glucose stimulation. These data have implications for identifying and selecting more feed efficient pigs and for understanding the physiological mechanisms underlying feed efficiency.

Introduction

Increasing feed efficiency of lean tissue gains is an important objective for livestock production. In place of traditional gross efficiency (gain:feed) and feed conversion (feed:gain) ratios, many investigators have begun using residual feed intake

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(RFI) as an alternative method to select and measure feed efficiency in livestock [1,2]. To study and understand the genetic and physiological differences defining feed efficiency in pigs, Iowa State University has developed a multigenerational Yorkshire pig RFI selection project. These low- and high-RFI lines are defined by the difference between the actual feed intake of a pig and its expected feed intake based on a given amount of growth and back fat [3]. Therefore, pigs that consume less feed than expected for maintenance and growth have a lower RFI, are more feed efficient, and they are therefore economically better for protein production relative to their higher RFI counterpart pigs.

The main biological factors that contribute to differences in RFI have been partially quantified in pigs [4], poultry [5], and beef cattle [6]. Specifically in pigs, these key factors include physical activity [7], feed intake patterns [3], body composition [8,9], nutrient digestibility [10], protein turnover [11], skeletal muscular oxidative stress [12], heat production [4] and behavioral stress response [13,14]. Furthermore, previous work in pigs [15], beef cattle [16], sheep [17,18], poultry [19,20], and Nile tilapia fish [21] has revealed a relationship between improved feed efficiency and a lower adrenocorticotropic hormone (ACTH) and glucocorticoid response. The hypothalamic-pituitary- adrenocortical (HPA) axis is a primary endocrine stress response pathway and plays an important role in metabolism regulation and survival. Increased HPA axis responsiveness can negatively impact pig growth and feed efficiency [15]. Further, plasma cortisol response to ACTH is highly heritable in young pigs [22], and increased cortisol has been reported to increase blood glucose and insulin [23], and result in increased adiposity [24].

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Within the Iowa State University RFI selection lines, the low-RFI pigs are reported to have leaner carcasses, lower feed intake [8,9] and greater energy digestibility, use, and retention [10] compared to high-RFI pigs. Further, low-RFI barrows expressed fewer stress behaviors compared to high-RFI barrows during human approach and novel object testing [13]. In the same selection lines, Onteru and colleagues [25] identified associations of RFI and average daily feed intake with genomic regions containing glucagon-like peptide 1 receptor (GLP1R) and cyclin-dependent kinase 5 regulatory subunit associated protein 1-line 1 (CDKAL1) genes. The GLP1R gene increases synthesis and release of insulin through the activation of the adenylyl cyclase pathway and CDKAL1 is involved in insulin release through the provision of ATP and potassium-

ATP channel responsiveness [25]. Altogether, these data suggest differences in adrenocortical axis and glucose-insulin sensitivity between the RFI selection lines.

Therefore, the objective of this study was to evaluate the cortisol response to ACTH and the insulin response to glucose in gilts divergently selected for RFI. We hypothesized that more feed efficient (low-RFI) gilts would be less responsive to an ACTH challenge, while being more responsive to an intravenous glucose tolerance test (IVGTT) compared to less feed efficient gilts. This would allow for the more efficient use of nutrient and energy resources to be utilized for lean accretion.

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Materials and Methods

All experimental procedures were approved by the Iowa State University Animal

Care and Use Committee (IACUC #1-11-7058-S). This experiment was conducted from

October to December, 2011.

Animals and housing

Twelve Yorkshire gilts (68 + 5.2 kg) divergently selected for RFI were utilized for this study. Divergent line selection criteria were based on estimated breeding values for RFI as explained by Cai and colleagues [2]. Gilts were selected for low-RFI over 8 generations. High-RFI gilts were randomly selected over 5 generations, and then selectively bred for high-RFI over the next 3 generations. Eight generations of selection resulted in the low-RFI line having lower RFI (-241 g/d), ADFI (-376 g/d), feed conversion ratio (-0.22 g/g), backfat (-2.5 mm), and a larger loin eye area (+1.5 cm2) compared to the high-RFI line [26].

Low-RFI (n = 6; more feed efficient) and high-RFI (n = 6; less feed efficient) treatments were compared and the individual gilt was the experimental unit. This work was conducted at the Swine Nutrition Farm at Iowa State University. All gilts were housed in individual pens measuring 2.21 m long x 0.61 m wide, within sight and/or nose to nose contact of each other. They had been acclimated to this setting for one week prior to the placement of a non-surgical catheter. All gilts had free access to water and were fed a corn-soybean diet that met or exceeded NRC [27] requirements for this size of pig.

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Adrenocorticotropic hormone challenge and intravenous glucose tolerance test

Gilts were anesthetized using xylazine (4.4 mg per kg; Anased, Lloyd

Laboratories, Shenandoah, IA, USA), ketamine HCl (2.2 mg per kg; Ketaset, Wyeth,

Madison, NJ, USA), and tiletamine HCl and zolazepam HCl in combination (4.4 mg per kg; Telazol, Wyeth, Madison, NJ, USA), and a non-surgical catheter (Mila International

Inc., Erlanger, KY, USA) was inserted into the jugular vein. Gilts were then allowed to recover and two days later the gilts were fasted overnight and underwent an ACTH challenge. During the ACTH challenge, gilts received 0.2 IU/kg BW of exogenous porcine ACTH (Sigma-Aldrich, St. Louis, MO, USA) injected intramuscularly. Serial blood samples were collected at -30, -15, and -1 min before and 30, 45, 60, and 90 min after the ACTH administration. Following the challenges, all gilts had free access to feed.

A day later, all gilts were again fasted overnight and then underwent the IVGTT the next morning. During the IVGTT, gilts received 0.25 g/kg BW of glucose injected intravenously. Serial blood samples were collected at -60, -30, -15, and -1 min before and

2, 5, 10, 15, 20, 30, 45, and 60 min after glucose administration.

Sample preparation and assay procedures

Blood (3 mL) was collected using a 10 mL syringe (BD Bioscience, San Jose,

CA, USA) and immediately transferred immediately to a 5 mL blood collection tube containing 6 mg potassium-EDTA (Sigma-Aldrich, St. Louis, MO, USA). Samples were stored on ice for no longer than 3 hours prior to centrifugation for 10 min at 2,000 x g and

4°C. Plasma was separated and stored at -80°C until the ACTH challenge samples were

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assayed for cortisol and non-esterified fatty acids (NEFA), and the IVGTT samples were assayed for glucose, insulin, and NEFA.

Plasma cortisol concentrations were determined using a DPC Immulite assay

(Siemens Healthcare Diagnostics Inc., Flanders, NJ, USA). Plasma glucose and insulin concentrations were measured in duplicate using a Wako Diagnostics Autokit Glucose

(Wako Chemical USA Inc., Richmond, VA, USA) and a Porcine Insulin ELISA kit

(Mercodia AB, Uppsala, Sweden), respectively. Glucose intra- and inter-assay variation averaged 2.35% and 8.22%, respectively. Insulin intra- and inter-assay variation averaged

1.44% and 4.06%, respectively. Plasma NEFA concentrations were measured in triplicate using a Wako Diagnostics NEFA-HR (2; Wako Chemical USA Inc., Richmond, VA,

USA). NEFA intra- and inter-assay variation averaged 3.69% and 5.98%, respectively.

Plates were read with a Synergy 4 plate reader using Gen 5 software (BioTek Instruments

Inc., Winooski, VT, USA).

Data analysis

All data were evaluated for normality using the Shapiro-Wilk test and Q-Q plots using SAS 9.4 (SAS Institute Inc., Cary, NC, USA). Data were normally distributed and were analyzed using the mixed procedure of SAS. A repeated measures model was used to evaluate concentrations over time and recovery time. Repeated measures models included the fixed effects of genetic line, time, and the line x time interaction. Average pre- and post-challenge concentration, peak response, change between the peak and average pre-challenge concentrations, and area under the curve (AUC) were calculated

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for each pig. These parameters were analyzed using a model with the fixed effect of genetic line. Date of challenge was used as a random effect for all models, body weight was used as a covariate for baseline and recovery time models, and average baseline and body weight were used as covariates for all other models. The significance level was fixed at P ≤ 0.05 and tendency at 0.05 < P ≤ 0.10.

Results

Response to the ACTH challenge

Low-RFI gilts tended to have 26% lower pre-ACTH (P = 0.08) and a 27% lower post-ACTH plasma cortisol concentrations compared to high-RFI gilts (P = 0.04; Table

5.1). A difference in line (P = 0.03) and time (P = 0.0006), but no line x time interaction

(P = 0.53) was observed in ACTH induced plasma cortisol concentration across the entire

ACTH challenge (Fig. 5.1a). Cortisol concentrations peaked 30 min post-ACTH in low-

RFI gilts, whereas they peaked 45 min post-ACTH in high-RFI gilts (Fig. 5.1a). Low-RFI gilts tended to reach a peak cortisol concentration 18% lower and have a lesser change in cortisol from basal to peak concentration post-ACTH compared to high-RFI gilts (P =

0.08). Low-RFI gilts tended to have a 21% smaller area under the curve from pre-ACTH to 90 min post-ACTH (AUC0-90) and a 23% smaller AUC30-90 compared to high-RFI gilts

(P = 0.07); however, genetic lines did not differ in AUC0-30 (P = 0.31; Table 5.1). Low-

RFI plasma cortisol concentrations returned to baseline by 60 min post-ACTH (P = 0.14), whereas high-RFI cortisol concentrations returned to baseline by 90 min post-ACTH (P =

0.88; Fig. 5.1a).

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Pre-ACTH NEFA concentrations did not differ between genetic lines (P = 0.63); however, low-RFI gilts had 29% lower post-ACTH NEFA concentrations compared to high-RFI gilts (P = 0.05; Table 5.1). A line (P = 0.01) and a tendency for a time difference (P = 0.07), however no line x time interaction (P = 0.19) was observed in

ACTH induced NEFA concentration across the ACTH challenge period. Plasma NEFA concentrations peaked 60 min post-ACTH in low-RFI gilts, whereas they peaked 90 min post-ACTH in high-RFI gilts (Fig. 5.1b). Low-RFI gilts reached a peak NEFA concentration 29% lower and had a lesser change in NEFA from basal to peak concentration post-ACTH compared to high-RFI gilts (P = 0.03). Compared to high-RFI gilts, low-RFI gilts tended to have a 25% smaller AUC0-90 and AUC0-30, and tended to have a 24% smaller AUC30-90 (P = 0.06; Table 5.1).

Response to the intravenous glucose tolerance test

Pre- and post-IVGTT plasma glucose concentrations did not differ between genetic lines (P ≥ 0.32; Fig. 5.2a and Table 5.2). However, as expected plasma glucose concentrations transiently changed across time post-IVGTT challenge (P < 0.0001) and no line x time interaction was observed (P = 0.11). Plasma glucose concentrations peaked

2 min post-IVGTT for both lines (Fig. 5.2a). There was no difference between RFI lines for peak time or change in glucose post-IVGTT (P = 0.66). There were no RFI line effects reported for any plasma glucose AUC measures (P ≥ 0.41; Table 5.2). Low-RFI plasma glucose concentrations returned to baseline 30 min post-IVGTT (P = 0.86),

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whereas high-RFI plasma glucose concentrations returned to baseline 20 min post-

IVGTT (P = 0.16; Fig. 5.2a).

Pre-IVGTT insulin concentrations did not differ between genetic lines (P = 0.57); however, low-RFI gilts had a 25% greater post-IVGTT insulin concentration compared to high-RFI gilts (P = 0.003; Table 5.2). A difference in line (P < 0.0001), time (P <

0.0001), and a line x time interaction (P = 0.04) was observed for plasma insulin concentration across the IVGTT challenge period (Fig. 5.2b). Plasma insulin concentrations peaked 10 min post-IVGTT for both lines (Fig. 5.2b). The low-RFI gilts had a 29% higher peak plasma insulin concentration and had a greater change in insulin from basal to peak concentrations compared to high-RFI gilts (P = 0.03). The insulin

AUC0-60 was 19% larger and AUC0-30 was 28% larger in low-RFI compared to high-RFI gilts (P ≤ 0.006); however, genetic lines did not differ in AUC30-60 (P = 0.69; Table 5.2).

Insulin concentrations returned to baseline 30 min post-IVGTT in both lines (P ≥ 0.55;

Fig. 5.2b).

Pre- and post-IVGTT glucose to insulin (G:I) ratios did not differ between genetic lines (P ≥ 0.35; Table 5.2). Across the IVGTT, low-RFI gilts expressed a 16% lower G:I ratio than high-RFI gilts (P = 0.04) and a time difference was observed (P < 0.0001).

However, no line x time interaction was observed in G:I ratio across the IVGTT (P =

0.88). G:I ratio peaked 2 min post-IVGTT for both lines (Fig. 5.2c). There was no difference between lines for peak (P = 0.32), change (P = 0.19), or any measures of AUC

(P = 0.41; Table 5.2).

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Pre- and post-IVGTT plasma NEFA concentrations did not differ between genetic lines (P ≥ 0.16). A difference across time (P < 0.0001) but no line difference or line x time interaction was observed in NEFA concentrations throughout the IVGTT (P ≥ 0.20).

Plasma NEFA concentrations peaked 2 min post-IVGTT in low-RFI gilts, whereas they peaked 60 min post-IVGTT in high-RFI gilts (Fig. 5.2d). There were no differences between genetic lines for peak (P = 0.32), change (P = 0.32), or any measures of AUC (P

≥ 0.68; Table 5.2).

Discussion

In both genetic lines, cortisol and NEFA concentrations increased in response to exogenous ACTH. Baseline cortisol concentrations tended to be lower in the low-RFI compared to high-RFI gilts, indicating that selection for low-RFI may have resulted in reduced endogenous HPA axis activity. In response to exogenous ACTH, the low-RFI gilts expressed a lower cortisol and NEFA response compared to high-RFI gilts. As pre- and post-ACTH cortisol concentrations are highly correlated and heritable in pigs [22], these results are as expected. These data suggest that selection for low-RFI (better feed efficiency) resulted in reduced stress responsiveness to ACTH compared to high-RFI gilts. These results agree with our hypothesis and confirm results in other species [16-

18,20,21,28].

Comparable responses were observed by Hennessy and Jackson [29], who reported that pigs with lower cortisol responsiveness to an ACTH challenge had significantly improved feed conversion efficiency compared to those with higher ACTH

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responsiveness. However, unlike the current work, these pigs had not undergone any genetic selection for feed efficiency. In layer chicks divergently selected for feed efficiency, low-RFI chicks had significantly lower baseline corticosterone compared to high-RFI chicks [20]. Furthermore, Luiting and colleagues [30] reported that low-RFI hens had a lower corticosterone response to exogenous ACTH, but maintained a maximum response for longer compared to the high-RFI hens. This is in agreement with our data in which low-RFI pigs had the lower baseline cortisol and reduced ACTH challenge response compared to high-RFI pigs. Contradictory to Luiting and colleagues

[30], our high-RFI pigs maintained the high levels of cortisol longer than low-RFI pigs.

As the HPA axis is a primary endocrine stress response pathway, it plays an important role in pig behavior. In barrows of the same generation as in the current study

(8th generation), we reported that low-RFI barrows expressed fewer stress behaviors than high-RFI barrows in response to novel human and object stimuli [13]. In 9th generation barrows and gilts, we reported that regardless of genetic line, pigs with phenotypically lower RFI had increased odds of attempting to escape but decreased odds of freezing during a novel object test [14]. This may suggest that phenotypic RFI relates to coping style, as more feed efficient pigs responded more actively whereas less feed efficient pigs responded more passively. The results of the current study support this finding as active copers (phenotypically lower RFI pigs) typically have a lower HPA axis reactivity than passive copers [31].

The reduced cortisol response in the low-RFI gilts further agrees with other studies evaluating stress in these RFI selection lines. When undergoing a Porcine

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Reproductive and Respiratory Syndrome viral stress challenge, low-RFI pigs tended to have a lower viral load, greater growth, and be more likely to survive compared to high-

RFI pigs [32]. Low-RFI pigs were also reported to have decreased haptoglobin, an acute phase protein that can increase after stress [33], decreased endoplasmic reticulum oxidase-1 α in the longissimus dorsi muscle mitochondria, which modulates mitochondrial membrane permeability in response to oxidative stress, and increased heat shock protein 60 and heat shock protein 70, which have been linked to anti-apoptotic pathways in mitochondria, compared to high-RFI pigs [34]. Furthermore, as low-RFI pigs are leaner [8,9], the decrease in basal cortisol may partially explain RFI selection line differences in body composition [24].

Insulin and glucose play important roles in feeding-induced stimulation of skeletal muscle protein synthesis in pigs [35,36]. As insulin is a major anabolic hormone, increased insulin responsiveness would allow these pigs to be more efficient at utilizing nutrients and energy for lean tissue growth. Therefore, our second hypothesis was that the low-RFI gilts would be more responsive to an intravenous glucose tolerance test

(IVGTT) compared to less feed efficient gilts. Irrespective of genetic line, glucose, insulin, G:I ratio, and NEFA concentrations were increased in response to the glucose infusion. However, no genetic line differences in glucose, G:I ratio, or NEFA concentrations were observed in response to the IVGTT. In support of our hypothesis, the low-RFI gilts had greater insulin concentrations in response to the IVGTT compared to high-RFI gilts. Glucose concentrations took longer to return to baseline in the low-RFI gilts with significantly more insulin compared to the high-RFI gilts, suggesting some

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level of insulin resistance in this more feed efficient line; however, there was no evidence of hyperinsulinemia.

Insulin is identified as an adiposity signal that reduces food intake [37,38]; therefore, this increased insulin responsiveness may be associated with reduced daily feed intake in low-RFI pigs [3]. These results support work in chickens divergently selected for RFI, which reported increased insulin levels in low-RFI cockerels following an oral

GTT compared to high-RFI cockerels [39]. In contrast, Kolath and colleagues [40] reported that low-RFI Angus steers which had not undergone selection for RFI had reduced baseline glucose concentrations while Richardson and colleagues [16] reported a tendency for Angus steers selected for low-RFI to have reduced insulin concentrations.

Improving feed efficiency of lean tissue gains is an important objective for livestock production. Our data presented herein, indicates that long term genetic selection for reduced RFI (improved feed efficiency) resulted in a reduced stress response to an

ACTH challenge and an increased insulin response following glucose stimulation. These neuroendocrine and metabolism axes differences may partially explain selection line differences in behavior [3,13,14], immune response [32], body composition [8,41], and feed efficiency [26]. Furthermore, these data may have implications for selection techniques and reducing swine stress though handling and facility design in order to improve feed efficiency.

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Acknowledgements

This project was supported by Agriculture and Food Research Initiative

Competitive Grant no. 2011-68004-30336 from the USDA National Institute of Food and

Agriculture. We would like to thank the Gabler lab, the RFI project group, and the swine farm staff for assistance in data collection and animal care.

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Table 5.1. Plasma cortisol and NEFA concentrations in response to an ACTH challenge in gilts divergently selected for residual feed intake (RFI). Parameter Low-RFI1 High-RFI1 Pooled SEM P-value

Pre-ACTH 246 331 41.5 0.08

Post-ACTH 453 617 63.0 0.04

Peak 681 829 70.6 0.08

Cortisol, nmol/L Change 384 531 70.6 0.08

AUC0-90 39843 50575 4920.3 0.07

AUC0-30 13447 15460 1804.9 0.31

AUC30-90 26748 34816 3614.3 0.07

Pre-ACTH 0.27 0.22 0.09 0.63

Post-ACTH 0.34 0.48 0.05 0.05

Peak 0.41 0.58 0.06 0.03

NEFA, mmol/L Change 0.17 0.33 0.06 0.03

AUC0-90 29.20 39.01 4.15 0.06

AUC0-30 26.98 35.87 3.75 0.06

AUC30-90 24.76 32.74 3.38 0.06

1Gilts tested were selected for RFI (low-RFI gilts are more feed efficient and high-RFI gilts are less feed efficient). n=6 gilts per line.

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Table 5.2. Plasma glucose, insulin and NEFA concentrations in gilts divergently selected for residual feed intake (RFI) and challenged with a IVGTT. Parameter Low-RFI1 High-RFI1 Pooled SEM P-value Pre-IVGTT 4.33 4.62 0.28 0.32 Post-IVGTT 7.24 6.92 0.55 0.58 Peak 12.80 12.48 0.69 0.66 Glucose, mmol/L Change 8.47 8.15 0.69 0.66

AUC0-60 345.70 337.98 37.75 0.84

AUC0-30 229.30 210.52 21.20 0.41

AUC30-60 116.41 127.46 18.49 0.57 Pre-IVGTT 214.70 224.10 15.64 0.57 Post-IVGTT 400.48 299.02 20.42 0.003 Peak 614.16 436.56 62.65 0.03 Insulin, pmol/L Change 398.73 221.14 62.65 0.03

AUC0-60 19502 15991 849.9 0.006

AUC0-30 13251 9578 827.2 0.004

AUC30-60 6289 6390 244.1 0.69 Pre-IVGTT 0.02 0.02 0.0007 0.50 Post-IVGTT 0.14 0.13 0.01 0.35 Glucose : Insulin Peak 0.02 0.03 0.004 0.32 Ratio Change 1.00 1.00 0.0005 0.19 mmol/L : pmol/L AUC0-60 1.08 1.20 0.14 0.41 AUC0-30 0.53 0.60 0.08 0.41 AUC30-60 0.55 0.61 0.07 0.41 Pre-IVGTT 0.82 0.54 0.18 0.16 Post-IVGTT 0.47 0.56 0.07 0.22 Peak 1.06 0.93 0.12 0.32 NEFA, mmol/L Change 0.39 0.26 0.12 0.32

AUC0-60 28.46 30.39 4.46 0.68 AUC0-30 27.28 28.12 3.95 0.84 AUC30-60 27.66 28.88 4.12 0.78 1Gilts tested were selected for RFI (low-RFI gilts are more feed efficient and high-RFI gilts are less feed efficient). n=6 gilts per line.

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a) 1000 # Low-RFI * 800 High-RFI

600 # 400

200

Plasma cortisol, nmol/L Plasmacortisol, 0 0 15 30 45 60 75 90 b) 0.7 0.6 * # 0.5 * 0.4 0.3 0.2 0.1

Plasma NEFA, mmol/L NEFA, Plasma 0 0 15 30 45 60 75 90 Minutes

Figure 5.1. Plasma cortisol (a) and NEFA (b) responses over time following an exogenous intramuscular injection of adrenocorticotropin hormone (ACTH; 0.2 IU/kg BW) at 0 min in gilts divergently selected for residual feed intake (RFI). Data are the LS means of the more feed efficient, low-RFI gilts (n=6) and the less feed efficient, high-RFI gilts (n=6). Within time point, ‘*’ represents significance at P ≤ 0.05 and ‘#’ for a tendency at P ≤ 0.10.

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a) c) 16 0.04 Low-RFI /L 14 /L # High-RFI

12 pmol # 0.03 *

mmol 10 /L : /L 8 0.02

6 mmol 4 0.01 2

0.00 Plasma glucose, glucose, Plasma

0 Ratio, G:I 0 10 20 30 40 50 60 0 10 20 30 40 50 60 b) d) 700 1.0 * * 600 * * 0.8 500 400 * 0.6 # * 300 0.4 200

100 0.2 Plasma insulin, pmol/L insulin, Plasma 0 mmol/L NEFA, Plasma 0.0 0 10 20 30 40 50 60 0 10 20 30 40 50 60 Minutes Minutes Figure 5.2. Plasma glucose (a), insulin (b), glucose to insulin (G:I) ratio (c), and NEFA concentrations (d) in response to an intravenous administration of glucose (0.25 g/kg BW) at 0 min in gilts divergently selected for residual feed intake (RFI). Data are the LS means of the more feed efficient, low-RFI gilts (n=6) and the less feed efficient, high-RFI gilts (n=6). Within time point, ‘*’ represents significance at P ≤ 0.05 and ‘#’ for a tendency at P ≤ 0.10.

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CHAPTER 6

GENERAL CONCLUSIONS AND FUTURE DIRECTIONS

Swine feed efficiency and welfare can be interrelated and represent both producer goals and consumer concerns. Feed efficiency in growing pigs can be defined as the efficiency by which a pig utilizes dietary nutrients and energy for maintenance and tissue accretion, particularly lean tissue. Therefore, increasing swine feed efficiency of lean tissue gains is an important aim for improving producer profitability, maintaining industry competitiveness, decreasing demand on global feed resources, and improving environmental sustainability (Patience et al., 2015). In order to work towards these aims, a deeper understanding of the environmental and biological factors underlying feed efficiency is essential. It is also necessary to ensure that feed efficiency modifications do not negatively impact animal welfare, and concern has specifically been raised on how feed efficiency impacts their ability to cope with various forms of stress. The overall goal of this dissertation was to evaluate how altering feed efficiency impacts grow-finish pig welfare in regards to feeding behavior and stress response. To address this goal, four research chapters (2-5) focused on the following objectives:

1) To assess how altering feeding behavior impacts grow-finish pig feed

efficiency of lean tissue gains.

2) To evaluate how selection for altered feed efficiency impacts pig ability to

respond to and cope with stressful events.

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The main biological factors that contribute to differences in feed efficiency have been partially quantified in poultry (Luiting, 1990), beef cattle (Richardson and Herd,

2004), and pigs (Barea et al., 2010). In beef steers the biological factors identified as major variables in energy utilization include physical activity, feed intake behavior and patterns, stress, body composition, nutrient digestibility, protein turnover, and metabolism (Richardson and Herd, 2004; Herd and Arthur, 2009). Therefore, to address this dissertation’s objectives, both conventional pigs and pigs divergently selected for residual feed intake (RFI) were utilized. Feed efficiency is typically calculated as gross efficiency, defined as the ratio between weight gain and feed input, and its inverse, feed conversion ratio, defined as the ratio between feed intake and weight gain (Archer et al.,

1999). Residual feed intake is an alternative method of measuring feed efficiency.

Residual feed intake is defined as the difference between observed and expected feed intake based on expected requirements for production and maintenance, and is typically adjusted for growth and backfat in grow-finish swine. Therefore, pigs that consume less feed than expected have low-RFI, are more feed efficient and are therefore more economically desirable than their high-RFI conspecifics (Young and Dekkers, 2012).

Results from the ISU Yorkshire pig RFI selection project suggested differences in feeding behavior. Young and colleagues (2011) investigated feeding behavior in fourth and fifth generation pigs fed ad libitum, and observed that more feed efficient pigs ate faster, spent less time at the feeder and visited the feeder fewer times per day than control pigs. It was also observed that more feed efficient pigs had fewer feeder visits at the two peak meal times occurring between 8:00-10:00 and 14:00-18:00 h. Therefore, Chapter 2

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utilized a similar feeding pattern as a model of feeding behavior and evaluated two meal regimen in a commercial line of pigs: 1) ad libitum feed access (ad lib) and 2) twice daily feed access (2x). The objective of Chapter 2 was to evaluate the impact of two feeding regimen on pig nutrient utilization and whole body tissue accretion, feeding behavior and activity. The hypothesis of this study was that pigs fed 2x would express similar attributes to low-RFI pigs, and therefore would utilize nutrients more efficiently for lean tissue gains, spend less time eating and be less active compared to pigs fed ad lib. Due to facilities and RFI pigs not being available, conventional pigs were utilized.

In support of our hypothesis, gilts fed 2x ate less frequently and for a shorter duration than ad lib; a feeding pattern similar to low-RFI pigs (Young et al., 2011).

However, one drawback of this study was that the behavioral collection method utilized did not allow us to categorize meals, and rather measured the number of times the pigs mouth and nose entered the feeder. No treatment differences were observed in the duration of time spent standing, sitting, or lying. Increased standing and reduced lying time reflects hunger in pigs (Beattie and O'Connell, 2002; Bolhuis et al., 2010); therefore, the results of Chapter 2 suggest no differences in behavioral expression of hunger. These results were in contrast to other studies that observed an effect of feeding regimen on duration of time spent standing and lying (Brouns et al., 1994; Schneider et al., 2011); however, these studies evaluated pig behavior when housed in groups rather than individually. Le Naou and colleagues (2014) and Newman and colleagues (2014) did not observe differences in NEFA concentrations in individually housed pigs fed different feed regimen. Therefore, behavior of the pigs in the current study may reflect feeding

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regimen similarities in the internal energetic balance without alterations caused by the social environment. However, future studies investigating the implementation of twice daily feeding on pig behavior and appetite regulating neuropeptides and hormones within group housing systems is warranted.

Contrary to our hypothesis, altering feeding regimen did not cause a commercial line of pigs to have nutrient utilization, whole body tissue accretion, and feed efficiency benefits. The benefit of lower ADFI in 2x compared to ad lib gilts was offset by lower

ADG and therefore resulted in no G:F treatment differences. A lower ADG in the 2x gilts translated into reduced tissue accretion rates compared to ad lib gilts. In young pigs it has been reported that administration of the amino acid leucine every four hours enhanced skeletal muscle protein synthesis compared to continuous orogastric feeding (Boutry et al., 2013). This suggests that meal frequency is important to support lean tissue growth efficiency and that dietary leucine pulsation is an important regulator of protein translation initiation. However, as this did not translate to increased protein accretion in

Chapter 2, leucine pulsation may need to occur more frequently than twice daily to enhance protein synthesis.

A pitfall to Chapter 2 was that we may have needed more frequent feedings to enhanced protein synthesis and more efficient utilization of nutrients and energy by the pigs. Also, Boutry and colleagues (2013) examined younger pigs compared to those in

Chapter 2, with pigs receiving leucine via the jugular vein. Therefore, future research regarding leucine pulsation frequency in grow-finish pigs would give insight to the most efficient feeding frequencies. However, similar to low-RFI pigs, 2x gilts had a lower

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whole body fat to protein ratio compared to ad lib. This body composition difference is likely partially explained by ADFI as dietary energy is partitioned towards lean accretion before fat (Patience et al., 2015). Reduced percent of fat is also often observed when restrictive feeding pigs (Patience et al., 2015), but restrictive feeding may have negative consequences on gilt welfare as they may not reach satiety (Bolhuis et al., 2010). The current study did not observe any differences in overall behavioral expression of hunger; therefore, twice daily feeding may be a method of increasing percent of lean tissue without negatively impacting gilt welfare.

As the majority of studies focus directly on swine behavior or performance, an additional objective of Chapter 2 was to evaluate relationships between behavior and performance. Spearman rank correlations were used to evaluate the relationship of behaviors with overall performance and whole body tissue accretion. As expected, duration of time spent eating showed a positive, moderate correlation with ADFI and growth (ADG and fat tissue accretion). Duration of time spent standing showed a negative, weak correlation with ADG and ADFI and duration of time spent lying showed a positive, weak correlation with ADG. These relationships suggest that reduced activity is likely an important aspect of conserving energy for growth. One unexpected finding was that postural changes tended to be positively correlated with bone mineral content accretion. Studies primarily performed using rat and rabbit models have identified the importance of short periods of exercise separated by rest periods as being more effective in osteogenic stimulation than a single sustained exercise session (reviewed by Burr et al., 2002). This may explain why frequency of postural changes, but not duration of time

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standing, tended to correlate with bone mineral content accretion. To our knowledge, this has not previously been evaluated in pigs and may have important implications for swine bone health, particularly when pigs are housed within gestation stalls. Therefore, research to improve our understanding of how behavior impacts swine bone health, general health, longevity and welfare is needed.

Access to feed and boredom of pigs can lead to changes in behavior, welfare and productivity (Hill et al., 1998; Zwicker et al., 2013). Environmental enrichment, defined as biologically relevant additions or modifications to the environment (Würbel and

Garner, 2007), can be used to provide an outlet for exploratory behaviors and therefore improve pig welfare (Tuyttens, 2005). In Chapter 2, gilts were provided with a polypropylene rope tied to an overhead bar for environmental enrichment. It should be noted that other outlets for exploratory behavior were available within the gilt’s stall, such as the latch mounted to 2x feeders, feeder lids, pen floors and bars. However, due to placement of the cameras behaviors oriented towards objects other than the polypropylene rope could not be reliably collected. Thus, interaction with the rope enrichment was the only exploratory behavior recorded. Future studies investigating how feeding regimen impacts displaced foraging behaviors and the development of these across the study timeframe would be beneficial.

Gilts fed 2x interacted with the rope enrichment more frequently compared to ad lib. In humans, it has been reported that prolonged chewing reduces self-reported hunger

(Miquel-Kergoat et al., 2015). Additionally, cognitive enrichment in pigs has been shown to reduce stress associated with feeding (Zebunke et al., 2013). Therefore, interacting

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with enrichment may serve as a mechanism for coping with hunger or stress associated with restricted access to feed. In Chapter 2, it was observed that the duration of time spent interacting with enrichment tended to negatively correlate with ADFI. This suggests that enrichment interaction may be a redirected foraging behavior or may reduce hunger. Enrichment interaction also appears to be energetically demanding as duration of time spent interacting with enrichment is negatively correlated with ADG; however, it did not significantly alter G:F. Future studies investigating the use of environmental enrichment as a coping mechanism for restrictively fed pigs is warranted as it may positively impact pig welfare. Additionally, it would be beneficial to investigate environmental enrichment that is less energetically demanding.

It was recently suggested that breeding for improved feed efficiency, and particularly for reduced RFI, may decrease the animal’s ability to cope with stress

(Rydhmer and Canario, 2014). Human approach- (HAT; Hemsworth et al., 1981) and novel object tests (NOT; Hemsworth et al., 1996) have been utilized to evaluate pig stress responses. Therefore, the primary objective of Chapters 3 and 4 was to examine the association between long-term divergent selection for RFI and behavioral reactivity to fear-eliciting stimuli such as HAT and NOT assessments. Contrary to Rydhmer and

Canario’s (2014) suggestion, we hypothesized that low-RFI pigs would be less behaviorally reactive to HAT and NOT compared to high-RFI pigs. This hypothesis was based on previous work with the ISU RFI selection lines which reported that low-RFI pigs may be less prone to muscular oxidative stress (Grubbs et al., 2013) and immunological stress (Dunkelberger et al., 2015).

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In agreement with our hypothesis, 8th generation low-RFI barrows displayed fewer behaviors indicative of stress (i.e. less frequent zone crossings, head movements, and defecations, less frequent and shorter duration of freezing (HAT and NOT), and less frequent escape attempts (NOT)) compared to high-RFI barrows (Chapter 3). However,

9th generation low- and high-RFI pigs expressed few selection line differences (Chapter

4). Moreover, sex (barrows vs. gilts) had a larger impact on behavioral stress during HAT than genetic line. Therefore, it was concluded that overall genetic selection for improved feed efficiency (low-RFI) compared to selection for poorer feed efficiency (high-RFI) did not negatively impact swine welfare in regards to the response to fear-eliciting stimuli.

An interesting finding of Chapter 4 was that barrows and gilts responded differently to HAT and NOT, indicating a sex effect. During HAT, barrows were less fearful than gilts and spent a longer duration of time within zone 1 (closest to the object), crossed fewer zone lines, had fewer head movements, and froze fewer times compared to gilts. However, during NOT barrows froze more often and spent longer durations in a frozen position than gilts. This latter finding agrees with Reimert and colleagues (2014) who reached a similar conclusion following NOT in pigs of a similar age (13 weeks

(Reimert et al., 2014) vs. 14 weeks (Chapter 4)). Similarly, Kranendonk and colleagues

(2006) reported that young boars (approximately 25 d old) elicited greater stress behaviors such as vocalizations during NOT and struggled more during a back test than gilts. Furthermore, 12 to 24 week old barrows have been reported to have greater basal cortisol concentrations compared to gilts (Ruis et al., 1997); therefore, this may be reflected through NOT results of the current study. Behavioral responses to HAT and

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NOT are typically positively correlated (van der Kooij et al., 2002; Janczak et al., 2003); therefore, inconsistency of the sex effect between HAT and NOT was unexpected.

Previous studies reported differences between males and females in coping with stressors.

In rats, males had faster corticosterone habituation to chronic stress compared to females

(Galea et al., 1997). To our knowledge, no swine studies have investigated sex differences in habituation to HAT. It could be speculated from our results that barrows may habituate quicker to humans and better generalize this to unfamiliar humans compared to their gilt counterparts. Further research on understanding the neuroendocrine and behavioral differences between males and females that could contribute to such a differential observation is warranted.

As the majority of studies focus directly on swine behavior or performance, an additional objective of this dissertation was to evaluate relationships between these traits.

Chapter 4 utilized odds ratios to measure the magnitude of effect of linear and quadratic

RFI on the behavior variables. Interestingly, regardless of genetic line, RFI was phenotypically related to coping style as pigs with lower RFI had increased odds of attempting to escape but decreased odds of freezing during NOT. Lower RFI pigs responded to NOT more actively, or through a proactive coping style, by attempting to escape whereas higher RFI pigs responded to NOT more passively, or through a reactive coping style, by freezing. These phenotypic relationships were unexpected as it can be assumed that escape attempts require greater energy expenditure than freezing. Therefore, we hypothesize that feed efficiency differences may be related to hypothalamic-pituitary- adrenocortical (HPA) axis responsiveness to stress coping (Koolhaas et al., 1999),

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whereas more feed efficient pigs may secrete less cortisol in response to stress than less feed efficient pigs (Chapter 5).

Future research investigating the relationship of behavior with carcass composition, meat quality, and sensory characteristics would be beneficial for understanding how behavior relates to the overall value of the pig. The ISU RFI selection project has taken a multidisciplinary approach to investigating differences between low- and high-RFI pigs. This provides us with the unique opportunity to evaluate relationships between traits that are not typically evaluated such as meat quality and behavior. Arkfeld and colleagues (2015) evaluated carcass composition, meat quality, and sensory characteristics in 8th generation ISU RFI selection line pigs and many of the same pigs were also evaluated using HAT and NOT (Chapter 3). Therefore, this provides us with the opportunity to evaluate if behavior during HAT and NOT relates to carcass composition, meat quality, and sensory characteristics.

Combining data from Chapters 3 and 4 provides us with a unique opportunity to evaluate two generations of selection for feed efficiency (Table 6.1 and 6.2). These results showed a similar pattern to the odds ratio analysis in Chapter 4; whereas the 9th generation responds more actively (i.e. increased zone crossings, head movements (HAT and NOT), and escape attempts (NOT)) and the 8th generation responds more passively

(i.e. increased freezing). This gives light to the confusion underlying Rydhmer and

Canario’s (2014) statement that breeding for improved feed efficiency may decrease an animal’s ability to cope with stress, as a pig performing escape attempts may be interpreted as more stressed than a freezing pig. Future work investigating how coping

157

style relates to feed efficiency in pigs from different populations would be beneficial.

Furthermore, investigating the consistency of coping style across situations would be important before utilizing this as a selection technique.

A surprising result of Chapter 3 was that low-RFI barrows took longer than high-

RFI barrows to first contact the novel human and cone. During HAT and NOT, latency to approach the novel human and cone are primary outcomes, on the assumption that fearful animals would be less likely to approach. However, this assumption did not coincide with other measures of stress behavior during HAT and NOT. One hypothesis discussed in

Chapter 3 is that the high-RFI pigs were more fearful of social isolation than the human or cone. Social isolation can be distressing for pigs and is likely one of the greatest stressors during HAT and NOT (Forkman et al., 2007; Pairis et al., 2009). In order to better understand the underlying motivation behind latency to first approach the human or cone Spearman rank correlations were performed with the data from Chapter 3 and 4

(Table 6.3). Latency to approach zone 1 was negatively correlated with frequency of zone

1 entrances, duration of time spent within zone 1, and frequency of zone crossings. This is as expected, since these measures likely depend on the speed of the pig’s movements and the amount of time spent interacting with zone 1. However, these three significant correlations do not answer questions regarding the underlying motivation. Sholar and colleagues (2015) analyzed vocalizations in 9th generation barrows and gilts during HAT and correlated these measures with data from Chapter 4. Latency to first approach zone 1 was negatively correlated with duration of low calls and tended to be negatively correlated with the total number of high calls. Schrader and Todt (1998) suggested that

158

pigs vocalize to regain social contact and this may be an indicator of social stress; therefore, this supports the hypothesis that the high-RFI pigs were more fearful of social isolation than the human or cone.

A second hypothesis discussed in Chapter 3 is that approach latency reflects coping style rather than the level of stress. Hayne and Gonyou (2006) proposed that a fast approach is indicative of an active response whereas a slow approach is indicative of a passive response in pigs. This interpretation is further supported by Hessing and colleagues (1994), who reported that pigs quicker to approach a novel object were also more resistant to a back test. However, correlations were weak and not significant between latency to approach zone 1, escape attempts and freezing. As escape attempts and freezing are typical measures of different coping styles, it is unlikely that coping style differences caused the line differences in latency to approach zone 1.

Considering that latency to approach the novel human and object is a primary outcome during HAT and NOT, research investigating the underlying motivational mechanisms is essential for validating HAT and NOT (Forkman et al., 2007). Since very few studies have observed latency to approach conflicting with other measures of stress behavior this may be due to underlying genetic differences between selection lines. The latency of time to first touch a human during HAT has been previously identified as heritable in pigs (h2 = 0.38); however, the specific genetic basis and its connection to selection for feed efficiency is unknown (Hemsworth et al., 1990). Behavior during fear tests have been demonstrated to be genetically linked, and previous work in mice identified RGS2, a regulator of G-protein signaling, as a primary gene associated with

159

emotional reactivity (measured through avoidance, autonomic activation, and behavioral inhibition) during an open field test (Willis-Owen and Flint, 2006). Furthermore, a quantitative trait locus (QTL) study in cattle identified DRD4, a gene linked to novelty seeking behavior in humans, and RGS2 as candidate genes associated with behavior during a human approach and open field test (Gutierrez-Gil et al., 2008).

Due to the multidisciplinary aspect of the ISU RFI selection project, DNA has been isolated from the tail tissue from low- and high-RFI pigs for genetic analysis. Many of these pigs are the same pigs that were tested using HAT and NOT (Chapters 3 and 4).

This provides us with the unique opportunity to identify different genomic regions associated with pig behavior during HAT and NOT using a genome wide association study (GWAS). This would aid in investigating the underlying motivation to approach and avoid novel stimuli. Furthermore, a GWAS would improve our understanding of the genetic link between feed efficiency and behavior in pigs and could have implications for future genetic selection methods.

To understand how genetic selection for feed efficiency impacts physiological stress, pigs divergent in RFI were subjected to an adrenocorticotropin hormone (ACTH) challenge (Chapter 5). The objective of Chapter 5 was to evaluate the role of the ACTH- cortisol axis in feed efficiency using gilts divergently selected for residual feed intake.

Based on the results of Chapters 3 and 4, we hypothesized that low-RFI gilts would be less responsive to an ACTH challenge compared to high-RFI gilts. In agreement with our hypothesis, low-RFI gilts expressed a lower cortisol and NEFA response during the

ACTH challenge compared to high-RFI gilts, suggesting that selection for low-RFI

160

resulted in reduced stress responsiveness to ACTH compared to high-RFI gilts. A tendency for lower pre-ACTH cortisol in low-RFI compared to high-RFI gilts indicated that basal stress also contributes to feed efficiency in swine. This supports the behavioral results of Chapters 3 and 4, further suggesting that low-RFI pigs are less stressed compared to high-RFI pigs. Additionally, this supports differences in coping styles as active copers (hypothesized to be phenotypically lower RFI pigs) typically have a lower

HPA axis reactivity than passive copers (Koolhaas et al., 1999). Future research measuring epinephrine and norepinephrine under stressful situations would be beneficial for understanding genetic line differences in the sympatho-adrenal system (SA). It has been previously suggested that rodents with an active coping style have increased SA activation whereas passive coping style rodents have decreased SA activation (Koolhaas et al., 1999). Therefore, investigating SA activation will help us better understand this relationship in pigs which will translate to a better understanding of how improving feed efficiency relates to coping style.

In the ISU RFI selection lines, Onteru and colleagues (2013) identified different allele frequencies for single nucleotide polymorphisms (SNPs) near insulin release regulation genes. Therefore, Chapter 5 also evaluated the role of the glucose-insulin axis in feed efficiency using gilts divergently selected for RFI. Insulin and glucose play important roles in feeding-induced stimulation of skeletal muscle protein synthesis in pigs (O'Connor et al., 2003; Jeyapalan et al., 2007). As insulin is a major anabolic hormone, increased insulin responsiveness would allow these pigs to be more efficient at utilizing nutrients and energy for lean tissue growth. Therefore, our hypothesis was that

161

the low-RFI gilts would be more responsive to an intravenous glucose tolerance test

(IVGTT) compared to less feed efficient gilts. In support of our hypothesis, low-RFI gilts showed a greater insulin response to the IVGTT compared to high-RFI gilts. This is likely due to differences between RFI selection lines in the insulin release regulation genes (Onteru et al., 2013). As insulin has an anorexigenic effect on food intake, the increased insulin response in low-RFI gilts may be associated with reduced daily feed intake. Increased growth hormone has been observed to decrease adipose tissue sensitivity to insulin (Gopinath and Etherton, 1989); therefore, future work should evaluate somatotropin in RFI selection lines as it may partially explain the insulin response to the IVGTT and the decreased lipid deposition in low-RFI pigs. However,

Bunter and colleagues (2010) have also shown that decreased juvenile IGF-I, an analogue of GH, is associated with leaner, more efficient animals and is moderately heritable.

The overall goal of this dissertation was to address how improving feed efficiency impacts grow-finish pig welfare through two objectives: 1) assess how altering feeding behavior impacts grow-finish pig feed efficiency of lean tissue gains and 2) evaluate how selection for altered feed efficiency impacts pig ability to respond to and cope with stressful events. The results of this dissertation identify a relationship between pig behavior and feed efficiency of lean tissue gains, and suggest that improving feed efficiency did not negatively impact grow-finish pig welfare in regards to their ability to respond to stress.

162

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indicate treatment treatment indicate

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Latency (s), Latency

Measures

Data were analyzed using Proc Proc Glimmix using analyzed were Data superscripts Different

human approach test in in test approach human Table diet(generation), sex(generation), line*diet(generation), line, of line*generation, generation, effects and week(generation). line*sex(generation), Zone 1, % Zone % Escape attempt, Zone 1,Zone s 1,Zone n crossings,Zone n Head movements, n Urination, n Defecation, n Escape attempt, n Freeze, n Freeze, % 1 2

168

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73.90 ± 10.30 8.86± 0.49 10.44 ± 1.28 49.07 ± 1.15 86.78 ± 1.53 0.51± 0.12 4.34± 0.34 1.07± 0.18 0.25± 0.06 7.75± 0.46 5.10± 0.85

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Low

120.4016.47 ± 6.98± 0.42 7.74± 1.05 40.38 ± 1.01 81.87 ± 1.44 0.60± 0.12 3.46± 0.30 0.75± 0.14 0.20± 0.05 4.74± 0.35 4.16± 0.73

test in test

Latency (s), Latency

6.2

Measures

novel object novel

Data were analyzed using Proc Proc Glimmix using analyzed were Data superscripts Different

Table the diet(generation), line*diet(generation), of line*generation, line, generation, fixedeffects week(generation). and line*sex(generation), sex(generation), Zone 1,Zone s 1,Zone n 1,Zone % crossings,Zone n Head movements, n Urination, n Defecation, n Escape attempt, n Escape attempt, % Freeze, n Freeze, % 1 2

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Table 6.3. Spearman rank correlations between latency to approach zone 1 and other behaviors performed during the human approach- (HAT) and novel object tests (NOT)1. Measures HAT P-value NOT P-value

Zone 1, n -0.30 0.0001 -0.44 <0.0001

Zone 1, %1 -0.24 0.003 -0.39 <0.0001

Zone crossings, n -0.21 0.007 -0.33 <0.0001

Head movements, n -0.06 0.44 0.02 0.79

Urination, n 0.01 0.88 0.15 0.06

Defecation, n 0.07 0.41 0.06 0.42

Escape attempt, n -0.06 0.45 -0.08 0.31

Escape attempt, % -0.06 0.44 -0.08 0.32

Freeze, n 0.01 0.92 -0.03 0.71

Freeze, % -0.004 0.96 0.003 0.97

1Data were analyzed using Proc Corr of SAS.

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APPENDIX

AUTHORED ABSTRACTS AND ANIMAL INDUSTRY REPORTS

Authored Abstracts Related to PhD Dissertation Research

Colpoys, J. D., A. K. Johnson, N. K. Gabler. 2015. The effects of feeding frequency on pig performance, behavior and tissue accretion rates. J. Anim. Sci. 93(E- Suppl.s3):252.

Colpoys, J. D., N. K. Gabler, C. E. Abell, A. F. Keating, S. T. Millman, J. M. Siegford, A. K. Johnson. 2014. Selection and breeding for improved feed efficiency alters gilt behavioral responsiveness to a novel object. J. Anim. Sci. 92(E-Suppl. 2):25.

Colpoys, J. D., D. M. van Sambeek, L. L. Anderson, J. C. M. Dekkers, A. K. Johnson, F. R. Dunshea, N. K. Gabler. 2014. Response of swine divergently selected for feed efficiency to a glucose tolerance test. J. Anim. Sci. 92(E-Suppl. 2):160-161.

Sholar, J. F., J. D. Colpoys, N. K. Gabler, A. F. Keating, S. T. Millman, J. M. Siegford, A. K. Johnson. 2014. Gilt approachability to a human when selected for feed efficiency. J. Anim. Sci. 92(E-Suppl. 2):114.

Colpoys, J. D., N. K. Gabler, A. F. Keating, S. T. Millman, J. M. Siegford, A. K. Johnson. 2013. Barrow approachability to a human when selected for feed efficiency. J. Anim. Sci. 91(E-Suppl. 2):689-690.

Colpoys, J. D., N. K. Gabler, A. F. Keating, S. T. Millman, J. M. Siegford, A. K. Johnson. 2013. Barrow approachability to a novel object when selected for feed efficiency. J. Anim. Sci. 91(E-Suppl. 2):278.

Jenkins, J. D., A. K. Johnson, L. L. Anderson, J. C. M. Dekkers, N. K. Gabler, F. R. Dunshea. 2013. Response of swine divergently selected for feed efficiency to an exogenous adrenocorticotropin hormone (ACTH) challenge. J. Anim. Sci. 91(E- Suppl. 2):9.

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Authored Animal Industry Reports Related to PhD Dissertation Research

Sholar, J. F., J. D. Colpoys, N. Gabler, A. Keating, S. Millman, J. Siegford, A. Johnson. 2015. Approachability to a human in gilts divergently selected for feed efficiency. Animal Industry Report, Iowa State University. R3016.

Jenkins, J. D., A. Johnson, N. Gabler, A. Keating, S. Millman, J. Siegford. 2013. Behavioral fear response to a novel or human stimuli in barrows selected for feed efficiency. Animal Industry Report, Iowa State University. R2810.

Jenkins, J. D., N. Gabler, L. Anderson, J. Dekkers, A. Johnson, F. Dunshea. 2013. Evaluation of the responsiveness of swine divergently selected for feed efficiency to an exogenous adrenocorticotropin hormone (ACTH) challenge. Animal Industry Report, Iowa State University. R2814.

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Authored Abstracts Not Related to PhD Dissertation Research

Colpoys, J. D., N. K. Gabler, C. E. Abell, A. F. Keating, S. T. Millman, J. M. Siegford, A. K. Johnson. 2015. Barrow behavioral reactivity to a human or novel object when fed low versus high fiber diets. J. Anim. Sci. 93(E-Suppl.s3):623-624.

Low energy, higher fiber diets (HFD) are becoming more prevalent in the U.S. swine industry due to fluctuating corn-soy diet prices. In sows, HFD are reported to increase satiety and reduce stereotypic behavior, aggression, and activity. However, little is known about how fiber content in diets contributes to behavioral reactivity in grow- finish pigs. Therefore, the objective of this study was to determine if diet influences behavioral reactivity using a human approach test (HAT) and novel object test (NOT). We hypothesized that pigs reared on HFD would be less reactive to HAT and NOT compared to pigs reared on high energy, low fiber corn-soy diets (CD). Forty Yorkshire barrows (48 ± 8 kg BW) were randomly allocated to two treatments: HFD (n=20) and CD (n=20). The barrows were evaluated once using HAT and once using NOT utilizing a crossover experimental design. Each pig was individually tested within a 4.9 x 2.4 m test arena for 10 min between 1300 and 1900 h. Behavior was evaluated using live and video observations. The video was watched continuously by one trained observer for latency, frequency, and duration of human and novel object (orange traffic cone) touches, frequency of escape attempts, frequency of freezing postures, activity (number of arena line crossings), urination, and defecation. Data were analyzed using the Glimmix procedure of SAS with fixed effects of diet and test week, covariate of body weight, and random effect of pen. Diet did not alter latency to first touch, touch frequency, or duration of touches with the human or novel object (P>0.10). Similarly, frequency of escape attempts, freezing, activity, and urination did not differ between diets during HAT or NOT (P>0.10). Barrows fed HFD defecated more during NOT (P=0.01), and tended to defecate more during HAT compared to CD barrows (P=0.06). Differences in defecations are likely due to high fiber content of HFD resulting in more waste excretion. These results suggest that feeding high fiber diets did not alter grow-finisher barrow behavioral reactivity.

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Azarpajouh, S., J. D. Colpoys, A. Rakhshandeh, J. C. M. Dekkers, A. K. Johnson, N. K. Gabler. 2015. Residual feed intake selection: Effect on gilt behavior in response to a lipopolysaccharide challenge. J. Anim. Sci. 93(E-Suppl.s3):15.

Increasing feed efficiency in swine is important for increasing sustainable food production and profitability for producers. However, it is unknown if selection for improved feed efficiency impacts the expression of sickness behavior. The objective of this study was to characterize gilt behaviors and postures when challenged with lipopolysaccharide (LPS). This work was conducted with seven low residual feed intake (LRFI; more feed efficient) and eight high RFI (HRFI; less feed efficient) gilts (63±4 kg BW) from the 8th generation of the ISU Yorkshire RFI selection lines. All gilts were individually housed in metabolism crates. Gilts were challenged I.M. with 30 µg/kg BW Escherichia coli O5:B55 LPS at 10:00±1 h. Gilts were video recorded one day before LPS challenge (baseline) and on the treatment day (LPS challenge). Video was analyzed using a 1-min scan sample interval at two time points; 1) for two hours starting at the time of treatment injection and 2) for one hour starting at the evening feeding time (~17:00h). Standing, sitting, lying, eating, and drinking were recorded. Data were analyzed using the GLIMMIX procedure of SAS. The model included line, treatment, time, and the interaction, with a random effect of pig nested within replicate. There was no line by treatment interaction for behaviors and postures (P≥0.32). There were no selection line behavioral and postural differences in response to the LPS challenge (P≥0.45). Regardless of selection line, after the LPS challenge gilts laid more (P<0.0001) and stood less (P<0.0001). For the other behaviors and postures there were no treatment differences (P≥0.16). In conclusion regardless of divergent selection for RFI, the LPS challenge affected lying and standing behavior in gilts in the same way. This project was supported by USDA-AFRI Grant no. 2011-68004-30336.

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Robinson, A. L., J. D. Colpoys, G. D. Robinson, E. A. Hines, L. L. Timms, E. M. Edwards, K. J. Stalder, A. K. Johnson, H. D. Tyler. 2015. The effect of antiseptic compounds on umbilical cord healing and infection rates in neonatal piglets from a commercial facility. J. Anim. Sci. 93(E-Suppl.s3):272.

Umbilical cord antiseptics are often not used in swine production systems. The objective of this study was to determine if treating the umbilical cord with antiseptics reduces infection and enhances healing within the first 48 h after birth in newborn piglets. A total of 421 mixed sex commercial piglets from a breed-to-wean sow farm were enrolled. Piglets were alternately assigned by birth order within a litter to 4 treatment groups; (1) iodine (2%), (2) trisodium citrate (10%), (3) a dry dip created using an antibacterial peptide (nisin) mixed with talc, and (4) no treatment. All treatments were applied within 1 h of birth. At birth, stall conditions (wet/dry and clean/dirty) were evaluated on a 3-point scale (3 = most dirty or most wet and 1= dry or clean). Prior to treatment, diameter of the umbilical cords (as an indicator of cord drying and healing) was determined using digital calipers. As a potential indicator of umbilical infections, surface temperature of the umbilical stump, along with a reference point at the midpoint of the sternum, was measured using a dual laser infrared thermometer. These measurements were repeated at 24 ± 1 h of age and at 48 h of age. In addition, umbilical stump redness and swelling (indicators of infection) were evaluated visually at 24 and 48 h. All data were analyzed using mixed model methods. Models included the fixed effects of umbilical diameter at birth, sex (female or male), stall conditions and treatment. No treatment differences were noted between dips on change in diameter of the umbilical cord during the first 24 h (6.60 ± 0.057 mm at birth vs. 3.25 ± 0.072 mm at 24 h). There was no difference in umbilical cord stump surface temperature, redness or swelling at 24 h or 48 h. Stall conditions at birth did not affect the change in umbilical diameter, surface temperature of the umbilical stump, or visual indications of infection. In conclusion, there was no benefit observed when applying an antiseptic treatment on piglet umbilical cords to improve healing or reduce the incidence of infections during the first 48 h of life.

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Colpoys, J. D., N. K. Gabler, A. K. Johnson. 2015. Evaluation of novel rope flavors as environmental enrichment for stalled gilts. Proceedings from the 2015 International Conference on Pig Welfare.

Developing effective devices and approaches for environmental enrichment is important for improving pig welfare and productivity. Therefore, our objective was to evaluate the use of flavored ropes as environmental enrichment for individually housed gilts. Twelve crossbred gilts (112±12 kg) were individually penned and provided ad libitum feed and water. Four rope treatments were evaluated which included ropes soaked in 1) water (n=5), 2) salt water (n=6), 3) sugar water (n=6) and 4) apple juice (n=7). A randomized crossover design was utilized so that gilts were tested on two of the four treatments. Cotton rope (1.2 m) was soaked in the assigned treatment solution for 30 minutes on day 1. The rope was tied to an overhead bar at 10:00 hours on day 1 and was removed at 19:00 hours on day 2. Gilts were video recorded one day before treatments were given (day -1) and throughout the study. Video was analyzed using a 2-minute scan sample interval between 07:00 and 19:00 hours. Oral/nasal contact with the rope, standing and lying postures were recorded. Postures collected on day -1 and 07:00 to 10:00 hours on day 1 are referred to as baseline. Data were analyzed using the Glimmix procedure of SAS including the fixed effects of treatment, day, their interaction, and the random effect of treatment order. The apple juice treatment resulted in gilts standing more than baseline, salt and sugar water treatments (P=0.03). Gilts with apple juice, salt and sugar water treatments were observed lying less than baseline (P=0.02). Oral/nasal contact was not different between rope treatments (P=0.87). Regardless of treatment, gilts had less oral/nasal contact with the rope on day 2 than day 1 (P<0.01). Overall, these results suggest that flavored rope enrichment does not alter oral/nasal contact, but may impact activity levels in individually penned gilts.

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Pairis-Garcia, M. D., A. K. Johnson, J. D. Colpoys. 2015. Swine welfare education and training – In the American setting. Proceedings from the 2015 International Conference on Pig Welfare.

An understanding of animal welfare is essential to animal agriculture professionals, including students, producers, and youth. Educational opportunities exist in formal and informal courses. These can be delivered through traditional methods via in- person or online. In the US, an increasing number of students with non-agricultural backgrounds are enrolling in formal animal and veterinary science programs and these “non-traditional” students are presenting new challenges for instructors. Therefore, it is important to adjust pedagogical styles to better fit student needs. Such pedagogical styles include; interaction i.e. on-farm visits, wet laboratories, and case studies. In addition, development of animal welfare educational resources is imperative for producers and youth working directly with livestock and within the agricultural industries. In the US, several assessment programs have been developed including the Pork Quality Assurance Plus® program (PQA plus), Beef Quality Assurance Program® (BQA) and the National Dairy Farmers Assuring Responsible Management® (FARM). These tools provide educational material and hands-on consulting for farmers to improve in areas such as animal health, animal handling and on-farm record keeping. Youth programs developed by Land Grant Universities such as The Ohio State University and Iowa State University provide a beneficial platform to teach animal welfare to younger generations, inspiring students to learn that in turn helps ensure future sustainability of animal welfare programs within Universities.

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Authored Animal Industry Reports Not Related to PhD Dissertation Research

Myers, S., J. D. Colpoys, J. Sholar, N. Gabler, S. Millman, A. Johnson. 2015. Barrow and gilt vocalizations during a human approach test. Animal Industry Report, Iowa State University. R3018.

Summary and implications The objective of this study was to investigate differences between barrow and gilt vocalizations during a fear test. Twenty barrows and 20 gilts were tested over two consecutive weeks between 1300 and 1900 hours using a human approach test (HAT). Throughout the test, vocalizations were recorded. Gilts expressed a greater number of low calls compared to barrows; however, barrows expressed a greater number of high calls compared to gilts. Further research should be done to better understand vocalization differences between barrows and gilts during a HAT.

Introduction Swine vocalizations may provide information on affective states. Vocalizations are often reported as call frequency (Hz), total number and duration of calls. Previous studies have reported that increased total number of high calls (≥1000 Hz) may be an indicator of negative affective states in male pigs. However, few studies have investigated if vocalizations differ between male and female pigs during negative affective states. Therefore, the objective of this study was to investigate differences between barrow and gilt vocalizations during a fear test.

Materials and methods The protocol for this experiment was approved by the Iowa State University Institutional Animal Care and Use Committee. The experiment was conducted between February and March, 2013. A total of 40 Yorkshire barrows and gilts with a mean (±SD) age of 101 (±9) days, selected for high-RFI (n=20 barrows and n=20 gilts) were tested. Animals and housing: This work was conducted at the Lauren Christian Swine Research Center at the Iowa State University Bilsland Memorial Farm, near Madrid, Iowa. Barrows and gilts were housed in mixed sex groups (15 to 16 pigs/pen) and each pen contained one Osborne single spaced electronic feeder (FIRE®, Osborne Industries, Inc., Osborne, KS) positioned at the front of the pen. Fear test: All pigs were tested using a human approach test (HAT). Testing occurred over two consecutive weeks between 1300 and 1900 hours. The pigs were tested individually within a 4.9 x 2.4 m test arena. Arena sides were lined with black corrugated plastic at a height of 1.2 m. During testing, pigs were individually moved from their home pen to the test arena, which was located in a different room within the same building. Each individual pig was allowed to habituate for one minute in a weigh scale where it could not see the arena. At the conclusion of the one minute the weigh scale door was opened into the back corner of the test arena and an unfamiliar human wearing orange coveralls was standing still at the center of the opposite wall. Each pig was assessed for 10 minutes.

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Table 1. Definitions for collected vocalizations Measure Definition Unit Peak frequency Frequency with the highest power Hz

Duration Length of the vocalization which contains 90% of the S energy

Number of Total number of vocalizations made by the pig during Count vocalizations the human approach test

Data analysis: Data were analyzed using proc glimmix of SAS (SAS Institute Inc., Cary, NC, USA). The model included the fixed effects of genetic line and test week, random effect of pen, and covariate of age on the day of testing. The significance level was fixed at P ≤ 0.05 and tendency at P ≤ 0.10.

Results and discussion Low calls: Gilts had a greater number of low calls than barrows (P < 0.01). No differences were observed between barrows and gilts for peak frequency or duration (P ≥ 0.27; Table 2). High calls: Gilts tended to have a higher peak frequency of high calls than barrows (P = 0.08). Barrows had more high calls compared to the gilts (P < 0.01); however no differences were observed between barrows and gilts in duration of high calls (P = 0.47; Table 2). Behavioral analysis of these pigs showed that gilts were more active, attempted to escape more often, and froze more often compared to barrows (unpublished data); suggesting greater fearfulness in gilts than barrows. The increased total number of low calls and higher peak frequency of high calls observed in gilts compared to barrows suggest greater fearfulness in gilts and agrees with the behavioral analysis. However, the longer high call duration and increased total number of high calls observed in barrows compared to gilts was unexpected as it suggests greater fearfulness in barrows. Further

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research should be done to better understand vocalization differences between barrows and gilts during a HAT.

Acknowledgements This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2011-68004-30336 from the USDA National Institute of Food and Agriculture.

Table 2. Peak frequency, duration, and total number of low and high calls (least square means ± SE) of barrows and gilts during a human approach test. Sex Parameters P-value Barrow Gilt Low Calls Peak Frequency 238.56 ± 17.36 212.50 ± 15.46 0.27 Duration 0.47 ± 0.02 0.45 ± 0.02 0.47 Total Number 257.50 ± 3.59 297.70 ± 3.86 <0.01 High Calls Peak Frequency 1722.25 ± 105.48 2012.67 ± 117.42 0.08 Duration 0.53 ± 0.03 0.46 ± 0.02 0.47 Total Number 28.40 ± 1.23 14.24 ± 0.83 <0.01

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Sholar, J. F., J. D. Colpoys, S. Myers, N. Gabler, S. Millman, A. Johnson. 2015. Association of vocalizations and swine behavior during a human approach test. Animal Industry Report, Iowa State University. R3017.

Summary and implications The objective of this study was to determine if the duration and total number of pig vocalizations when divided into low and high call categories was related to pig behaviors during a fear test. Twenty barrows and 20 gilts were tested over two consecutive weeks between 1300 and 1900 hours using a human approach test (HAT). Throughout the test, vocalizations and behavior were recorded. These results suggest that while high calls are typically the primary measure of stress vocalizations, low calls are also meaningful measures during the stressor of HAT.

Introduction Swine vocalizations may provide information on affective states. Vocalizations are often reported as call frequency (Hz), total number and duration of calls. Previous studies have reported that increased total number of high calls (≥1000 Hz) may be an indicator of negative affective states. However, few studies have investigated if low calls reflect affective states. Therefore, the objective of this study was to determine if the duration and total number of pig vocalizations when divided into low and high call categories was related to pig behaviors during a fear test.

Materials and methods Experimental design: The protocol for this experiment was approved by the Iowa State University Institutional Animal Care and Use Committee. The experiment was conducted between February and March, 2013. A total of 40 Yorkshire barrows and gilts with a mean (±SD) age of 101 (±9) days, selected for high-RFI (n=20 barrows and n=20 gilts) were tested. Animals and housing: This work was conducted at the Lauren Christian Swine Research Center at the Iowa State University Bilsland Memorial Farm, near Madrid, Iowa. Barrows and gilts were housed in mixed sex groups (15 to 16 pigs/pen) and each pen contained one Osborne single spaced electronic feeder (FIRE®, Osborne Industries, Inc., Osborne, KS) positioned at the front of the pen. Fear test: All pigs were tested using a human approach test (HAT). Testing occurred over two consecutive weeks between 1300 and 1900 hours. The pigs were tested individually within a 4.9 x 2.4 m test arena. Arena sides were lined with black corrugated plastic at a height of 1.2 m. During testing, pigs were individually moved from their home pen to the test arena, which was located in a different room within the same building. Each individual pig was allowed to habituate for one minute in a weigh scale where it could not see the arena. At the conclusion of the one minute the weigh scale door was opened into the back corner of the test arena and an unfamiliar human wearing orange coveralls was standing still at the center of the opposite wall (Figure 1). Each pig was assessed for 10 minutes.

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Figure 1. Arena where pigs received the human approach test.

Vocalization collections: Digital audio recordings of pig vocalizations during HAT were captured with a Marantz PMD 661 recorder (Marantz Corp., Kanagawa, Japan) and a Crown PZM185 microphone (Crown Int., Elkhart, IN). The recorder digitized the audio into a wav file at 16 bit and a sampling rate of 48 kHz. Raven software (Raven Pro 1.5, The Cornell Lab of Ornithology, Ithaca, NY) was used to produce spectrograms (Hanning window, window size of 1024 samples and overlap at 75%; time grid size of 256 samples; frequency grid size of 46.9 Hz) and manually identify vocalizations. Vocalization measures: Two call categories were developed based on published literature: low defined as <1000 Hz and high defined as ≥1000 Hz. Within these call categories duration and total number of vocalizations were calculated. Duration was defined as the length of the vocalization which contained 90% of the energy. The total number of vocalizations within the low and high call categories were counted for each pig. Behavioral collection: Three color cameras (Panasonic , Model WV-CP-484, Matsushita Co. LTD., Kadoma, Japan) were placed above the test arena for video collection. Video was collected onto a computer using Handy AVI (HandyAVI version 4.3 D, Anderson’s AZcendant Software, Tempe, AZ, USA) at 10 frames/sec. Continuous observation of video was done by one observer using Observer software (The Observer XT version 10.5, Noldus Information Technology, Wageningen, The Netherlands). Behaviors analyzed were touch, escape attempt, and freeze (Table 1). Data analysis: Data were analyzed using Proc Corr to calculate spearman correlations using SAS (SAS Institute Inc., Cary, NC, USA). The significance level was fixed at P ≤ 0.05 and tendency at P ≤ 0.10.

Results and discussion Low calls: Low call duration was negatively correlated with the latency to first touch (P = 0.03). This may suggest that longer low calls may be weakly related to increased approach motivation. Total number of low calls were positively correlated to the number of escape attempts (P = 0.05); suggesting, that increased number of low calls was weakly related to pig fearfulness during HAT. No other measures of low calls were related to other behaviors collected (Table 2). High calls: Total number of high calls tended to be positively correlated to the frequency of touches and negatively correlated to the latency to first touch. This may suggest that pigs with more frequent high calls have higher motivation to approach an unfamiliar human. Total number was positively related to the frequency of escape attempts;

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suggesting that increased total number of high calls is weakly related to an increase in pig fearfulness during HAT. No other measures of high calls were related to other behaviors collected (Table 2). While high calls are typically the primary measure of stress vocalizations, these results may suggest that low calls are also meaningful measures during the stressor of HAT.

Acknowledgements This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2011-68004-30336 from the USDA National Institute of Food and Agriculture.

Table 1. Definitions for collected behaviors Measure Definition Unit Touch Total number of times touching the human during the human Frequency; approach test and total length of time to first touch the human; Latency (s) touch was considered interaction of the mouth, nose, and/or face of the pig touching any part of the human

Escape Total number of times the pig had either both front hooves or Frequency attempt all four hooves off the arena floor in an apparent attempt to remove itself from the test arena

Freeze Total number of times the pig did not move any portion of its Frequency body for ≥3 s

Table 2. Spearman rank correlations of behaviors with low and high vocalizations. (**) indicates significance (P ≤ 0.05) and (*) indicates tendency (P ≤ 0.10). Low Call Total Number High Call Total Number Duration of Low Calls Duration of High Calls Frequency of Touches 0.09 0.02 -0.27 0.28* Latency to First Touch -0.34** -0.08 -0.01 -0.27* Frequency of Escape 0.15 0.32** 0.31 0.34** Frequency of Freezes 0.05 0.04 0.04 0.14

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Colpoys, J. D., A. Johnson, N. Gabler, A. Keating, S. Millman, J. Siegford. 2014. Barrow behavioral responsiveness to a human or novel object when fed low versus high energy diets. Animal Industry Report, Iowa State University. R2915.

Summary and implications The objective of this study was to determine whether diet influences behavioral responsiveness to novel stimuli as assessed by human approach (HAT) and novel object tests (NOT). Eighty Yorkshire barrows were fed a high fiber, low energy diet or a low fiber, high energy diet. Testing occurred over four consecutive weeks between 1300 and 1700 h. Barrows were tested individually within a 4.9 x 2.4 m test arena. Throughout the test, zone activity, escape attempts, freezing, urination, and defecation behaviors were recorded. The results suggest that dietary fiber reduces overall activity and may modify fear responsiveness while undergoing human approach and novel object tests in swine.

Introduction Low energy, high fiber diets are becoming more prevalent in the U.S. swine industry due to the increasing price of high energy, corn-soy diets. Previous work has reported that increased dietary fiber results in reduced physical activity in pigs. However, little is known about how diets high in fiber contribute to behavioral responsiveness in grow-finisher pigs, particularly while undergoing a stressful situation such as human approach (HAT) and novel object tests (NOT). During these two tests, pigs are isolated from pen-mates and are placed into an unfamiliar situation, which may be perceived by the pig as threatening. These tests are often utilized to quantify an animal’s behavioral response to a novel stimulus. Therefore, the objective of this study was to determine if diet influences behavioral responsiveness during HAT and NOT. Such research can further our understanding of how dietary fiber may influence pig behavior.

Materials and methods The protocol for this experiment was approved by the Iowa State University Institutional Animal Care and Use Committee. Housing. This work was conducted at the Lauren Christian Swine Research Center at the Iowa State University (ISU) Bilsland Memorial Farm, near Madrid, Iowa. All barrows were housed in groups (15 to 16 /pen) and each pen contained one Osborne Fire Feeder (FIRE®, Osborne Industries, Inc., Osborne, KS) positioned at the front of the pen. Experimental design. The experiment was conducted from October to November, 2011. Eighty Yorkshire barrows (46.5 ± 8.6 kg) from the ISU Residual Feed Intake (RFI) selection project were tested, half of the pigs were low-RFI and half of the pigs were high-RFI. The low- and high-RFI pigs were equally allocated to two treatments: high fiber, low energy diet (HFD, n=40) and a control, low fiber, high energy diet (CD, n=40, Table 1). Forty barrows (n=20 HFD, n=20 CD) were randomized to the HAT first and the remaining 40 barrows (n=20 HFD, n=20 CD) experienced the NOT first. Upon completion of this cycle barrows then experienced the opposite test one week later; creating a crossover experimental design. Testing occurred over four consecutive weeks between 1300 and 1700 h. Diet was blocked by time; therefore, within each testing hour,

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two HFD and two CD barrows were tested in random order. Barrows were tested in the same order for both tests at the same time of day. The pen of pigs was the experimental unit and the individual pig was the observational unit.

Table 1. Composition and nutrient analysis of the experimental diets, as-fed basis. Diet, % Ingredient HFD CD Corn, yellow dent 36.39 73.83 Soybean meal 13.76 22.90 Soybean hulls 20.00 - Corn bran 7.00 - Wheat middling’s 20.00 - L-lysine HCL 0.25 0.25 DL-methionine 0.03 0.04 L-threonine 0.07 0.07 L-tryptophan Monocalcium phosphate 0.83 1.14 Limestone 0.86 0.98 Salt 0.50 0.50 ISU vitamin premix 0.15 0.15 ISU trace mineral premix 0.15 0.15

Human approach and novel object tests. Barrows were tested individually within a 4.9 x 2.4 m test arena. Arena sides were lined with black corrugated plastic 1.2 m high. The arena floor was divided into four zones (Figure 1). Three color cameras (Panasonic, Model WV-CP-484, Matsushita Co. LTD., Kadoma, Japan) were placed above the test arena for video collection. Video was collected onto a computer using HandyAVI (HandyAVI version 4.3 D, Anderson’s AZcendant Software, Tempe, AZ) at 10 frames per second. One observer collected live observations throughout the testing. During HAT, the human observer was located in zone 1. During NOT the observer was located behind zone 4, outside the test arena, with corrugated black plastic blocking the pig’s view of the observer. During both tests, barrows were individually moved from their home pen to the test arena, which was located in a different room within the same building. Each barrow was weighed and allowed to habituate for one minute on a weigh scale. Following the habituation, the weigh scale door was opened into the back corner of the test arena and each barrow was tested for 10 minutes. Measures. Continuous observation of video was done by one experienced observer using Observer software (The Observer XT version 10.5, Noldus Information Technology, Wageningen, The Netherlands). Behaviors scored from the video included zone 1 touches, total zone line crossings, escape attempts, and freezing. Urinations and defecations were collected through live observations (Table 2).

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Figure 1. Arena where barrows received human approach and novel object tests aIndicates the distance of each zone from the human.

Table 2. Definitions for collected behaviors. Latency (s) and/or frequency (n) of behaviors were collected. Behavior Definition Zone 1 (s, n) The mouth, nose, and/or face of the barrow touch any part of zone 1. Zone crossing Total number of times zone 2, 3, and 4 lines crossed, defined as the (n) base of both ears of the barrow crossing each line. Escape attempt Either both front or all four legs of the barrow off the arena floor in (s, n) attempts to remove itself from the test arena. Freeze (s, n) No movement of any portion of the barrow’s body was visible for ≥3 sec. Urination (s, n) Excreting urine. Defecation (s, n) Excreting feces.

Statistical analysis. All data were evaluated for normality before analysis using a Univariate procedure of SAS (SAS Institute Inc., Cary, NC). Since data were not normally distributed, data were analyzed using the Glimmix procedure of SAS. Latency data were analyzed with a gamma distribution and frequency data were analyzed with a poisson distribution. The fixed effects included in the models were diet (HFD and CD) and previous experience within the arena, while body weight was used as a covariate. The significance level was fixed at P≤0.05.

Results and discussion Latency. During HAT, HFD barrows tended to take longer to engage in their first escape attempt (P=0.06); however, HDF barrows took less time for first defecation compared to CD barrows (P=0.03). No differences were observed between diets for any other latency measures during HAT or NOT (Figure 2). Frequency. During HAT, HFD barrows tended to urinate fewer times (P=0.09); however, they defecated more times compared to CD barrows (P=0.03). During NOT, HFD barrows crossed fewer zones compared to CD barrows (P<0.01). Additionally, HFD barrows engaged in more escape attempts (P=0.03) and tended to freeze and

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defecate more times compared to CD barrows during NOT (P=0.07). No differences were observed between diets for any other frequency measure during HAT or NOT (Table 3). Differences in defecations are likely due to high fiber content resulting in more waste excretion. Fewer zone crossings and increased freezing during NOT, may be expressions of reduced activity of HFD barrows, explained through the lower dietary energy and composition differences of the HFD. A diet high in fiber may result in reduced overall barrow activity in response to novel stimuli; which could be beneficial for feed efficiency by reducing energy expenditure. In turn, the increased number of escape attempts and freezing expressed by the HFD barrows during the NOT may be expressions of fear. As HFD and CD barrows did not differ in escape attempts and freezing during the HAT, further investigation should be done to determine if pigs fed a diet high in fiber are more fearful of novel stimuli.

Acknowledgements This project was supported by Agriculture and Food Research Initiative Competitive Grant no. 2011-68004-30336 from the USDA National Institute of Food and Agriculture. We would also like to thank Dana van Sambeek, Shawna Weimer, and Monique Pairis-Garcia for assistance in data collection.

Figure 2. Latency (least square means ± SE) or total time to first perform behaviors during the human approach and novel object tests. ‘*’ indicates difference (P=0.03) and ‘#’ indicates tendency for difference (P=0.06).

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Table 3. Frequency (least square means ± SE) or total number of times behaviors are performed during the human approach and novel object tests. Human approach test Novel object test Measures, Diet P- Diet P- total HFD CD value HFD CD value number Zone 1 6.78 ± 0.43 7.15 ± 0.44 0.56 7.41 ± 0.45 8.16 ± 0.47 0.26 Zone 44.78 ± 1.10 44.48 ± 1.10 0.85 40.53 ± 1.04 47.96 ± 1.13 <0.01 crossing Escape 0.84 ± 0.15 1.06 ± 0.17 0.36 1.19 ± 0.18 0.68 ± 0.13 0.03 attempt Freeze 6.46 ± 0.41 7.01 ± 0.43 0.36 6.93 ± 0.43 5.82 ± 0.39 0.07 Urination 0.35 ± 0.09 0.63 ± 0.13 0.09 0.53 ± 0.12 0.56 ± 0.12 0.86 Defecation 4.66 ± 0.36 3.59 ± 0.31 0.03 4.29 ± 0.34 3.44 ± 0.30 0.07