Land and Food Systems, Faculty of
DSpace
UBCV
Bauer, Janet Wright
Master of Science - MSc
University of British Columbia
Proactive management practices during the transition period are necessary to reduce the risk and mitigate the effect of transition diseases, such as dystocia and uterine diseases, in dairy cows. The aims of this thesis were to 1) investigate the relationship between the duration of labour, calving assistance, and uterine diseases (metritis and subclinical endometritis), and 2) investigate the test performance of relative changes in activity and lying behaviour using two automated activity monitors (AAM) to predict calving time and uterine disease. Holstein cows (n = 567) were followed from 3wk before to 3wk after calving. Lying behaviour and activity were monitored continuously by the AAMs. Cameras were used to record calving time and duration and calving assistance was recorded. Metritis was diagnosed based on vaginal discharge and body temperature measured at 6 and 12DIM, while subclinical endometritis was based on cytological examination at 42 ± 3DIM. Duration of labour was estimated as time from the appearance of the amniotic sac until the calf was expelled. Within study 1, we determined that there was a quadratic relationship between metritis and duration of labour for assisted cows, where the probability of metritis was greatest at the shortest and longest durations of labour, but the lowest probability of metritis (28.2%) was at approximately 130 min. Probability of metritis in unassisted cows was not associated with duration of labour. Subclinical endometritis was not associated with the duration of labour or calving score. For study 2, a relative decrease of 44% in lying time per bout, 8 h before calving, resulted in the best performance (AUC = 0.76; Se = 67%; Sp = 77%). Distinct changes in activity and lying behaviour were observed prior to calving and within cows diagnosed with uterine disease. Although current technologies show promising results for on-farm detection of calving, they may not be a reliable method for detection of uterine disease. Future research should focus on refining the efficacy of AAMs for the use of health detection.
THE USE OF CALVING BEHAVIOURS AND AUTOMATED ACTIVITY MONITORS TO PREDICT AND DETECT PARTURITION AND UTERINE DISEASES IN HOLSTEIN CATTLE by Janet Wright Bauer B.Sc., The University of British Columbia, 2017 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Applied Animal Biology) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2020 ©Janet Wright Bauer, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: The use of calving behaviours and automated activity monitors to predict and detect parturition and uterine diseases in Holstein cattle submitted by Janet Wright Bauer in partial fulfillment of the requirements for the degree of Master of Science in Applied Animal Biology Examining Committee: Dr. Ronaldo Cerri, Professor, Applied Animal Biology, UBC Supervisor Dr. Wolfgang Heuwieser, Professor, Clinic for Animal Reproduction, Freie Universität Berlin Supervisory Committee Member Dr. Murray Isman, Dean Emeritus, Applied Biology, UBC Additional Examiner Additional Supervisory Committee Members: Dr. Daniel Weary, Professor, Animal Welfare Program, UBC Supervisory Committee Member iii Abstract Proactive management practices during the transition period are necessary to reduce the risk and mitigate the effect of transition diseases, such as dystocia and uterine diseases, in dairy cows. The aims of this thesis were to 1) investigate the relationship between the duration of labour, calving assistance, and uterine diseases (metritis and subclinical endometritis), and 2) investigate the test performance of relative changes in activity and lying behaviour using two automated activity monitors (AAM) to predict calving time and uterine disease. Holstein cows (n = 567) were followed from 3wk before to 3wk after calving. Lying behaviour and activity were monitored continuously by the AAMs. Cameras were used to record calving time and duration and calving assistance was recorded. Metritis was diagnosed based on vaginal discharge and body temperature measured at 6 and 12DIM, while subclinical endometritis was based on cytological examination at 42 ± 3DIM. Duration of labour was estimated as time from the appearance of the amniotic sac until the calf was expelled. Within study 1, we determined that there was a quadratic relationship between metritis and duration of labour for assisted cows, where the probability of metritis was greatest at the shortest and longest durations of labour, but the lowest probability of metritis (28.2%) was at approximately 130 min. Probability of metritis in unassisted cows was not associated with duration of labour. Subclinical endometritis was not associated with the duration of labour or calving score. For study 2, a relative decrease of 44% in lying time per bout, 8 h before calving, resulted in the best performance (AUC = 0.76; Se = 67%; Sp = 77%). Distinct changes in activity and lying behaviour were observed prior to calving and within cows diagnosed with uterine disease. Although current technologies show promising results for on-farm detection of calving, they may not be a reliable method for detection of uterine disease. Future research should focus on refining the efficacy of AAMs for the use of health detection. iv Lay Summary Around time of calving (birthing), dairy cows are susceptible to calving difficulties and illness, such as uterine infections. Often calving difficulties result in the need for human assistance and increased risk of uterine infections. Proactive management practices are needed to reduce the risk of illness and detect illnesses as soon as possible. With advancements in technology, farmers and researchers are able to continuously monitor cowsâ activity, via collared and ankle-strapped monitors, in order to aid in detecting calving and illnesses. My thesis determined that providing assistance too early or too late during calving can increase the risk of uterine infections. In addition, we found that activity monitors can accurately detect a cow that is going into labour; however, activity monitors cannot accurately detect cows with uterine infections. These results may help farmers improve calving management and prevent or mitigate negative effects associated with calving difficulties. v Preface All of the work presented in this thesis was conducted at the University of British Columbiaâs Dairy Education and Research Centre in Agassiz, BC. The projects and associated methods within this thesis were approved by the University of British Columbiaâs Animal Care Ethics Committee [certificate #A14-0019 and #A18-0043]. A version of the material in Chapter 2 will be submitted for publication. Bauer, J.W., T.A. Burnett, A.M.L. Madureira, R.L.A. Cerri. Association between calving factors, duration of labour, and uterine diseases in Holstein dairy cows. Additionally, a version of the material in Chapter 3 will be submitted for publication: Bauer, J.W., T.A. Burnett, A.M.L. Madureira, R.L.A. Cerri. Evaluation of automated activity monitors to predict calving time and uterine diseases in Holstein dairy cows. J.W. Bauer, T.A. Burnett, and R.L.A. Cerri developed the main ideas for the projects. R.L.A. Cerri helped with interpreting material, as well as editing and providing comments on the drafts. T.A. Burnett helped with statistical analysis, providing input and editing drafts. A.M.L Madureira helped by providing input and editing drafts. I was the lead investigator, responsible for all major areas of concept formation, material interpretation, data collection and processing, statistical analysis, and manuscript composition. vi Table of Contents Abstract ........................................................................................................................................ iii Lay Summary ................................................................................................................................ iv Preface ............................................................................................................................................ v Table of Contents .......................................................................................................................... vi List of Tables ................................................................................................................................. ix List of Figures ................................................................................................................................ x List of Abbreviations .................................................................................................................... xi Acknowledgements .................................................................................................................... xiii Chapter 1: Introduction ................................................................................................................ 1 1.1 Setting the scene ................................................................................................................... 1 1.2 The transition period ........................................................................................................... 2 1.3 Parturition ............................................................................................................................ 3 1.2.1 What triggers the onset of parturition? ............................................................................ 3 1.3.2 Stages of labour ............................................................................................................... 4 1.4 Eutocia and dystocia ............................................................................................................ 6 1.4.1 Definition of eutocia and dystocia .................................................................................. 6 1.4.2 Risk factors and types of dystocia ................................................................................... 6 1.4.3 Effects of dystocia on the calf ......................................................................................... 9 1.4.4 Effects of dystocia on the dam ...................................................................................... 10 1.5 Uterine diseases .................................................................................................................. 11 1.5.1 Definition of metritis and endometritis ......................................................................... 11 1.5.2 Diagnosing uterine diseases .......................................................................................... 12 vii 1.5.3 Uterine disease causes and risk factors ......................................................................... 13 1.5.4 Impact of uterine diseases on the dam .......................................................................... 14 1.6 Current management practices ........................................................................................ 15 1.6.1 Preventing dystocia ....................................................................................................... 15 1.6.2 Preventing uterine disease ............................................................................................. 17 1.6.3 Current parturition management practices .................................................................... 18 1.6.4 Current uterine health management practices ............................................................... 19 1.7 Use of automated technology ............................................................................................ 21 1.7.1 Application of activity monitors on farms .................................................................... 21 1.7.2 Application of activity monitors for parturition ............................................................ 21 1.7.3 Other methods for automated detection of calving ....................................................... 24 1.7.4 Application of automated activity monitors for uterine diseases .................................. 25 1.8 Objectives and hypotheses ................................................................................................ 26 Chapter 2: Association between calving factors, duration of labour, and uterine diseases in Holstein dairy cows ...................................................................................................................... 28 2.1 Introduction ........................................................................................................................ 28 2.2 Materials and methods ...................................................................................................... 30 2.2.1 Animals and housing ..................................................................................................... 30 2.2.2 Disease diagnosis .......................................................................................................... 31 2.2.3 Data recorded using video cameras ............................................................................... 33 2.2.4 Statistical analysis ......................................................................................................... 33 2.3 Results ................................................................................................................................. 35 2.4 Discussion ............................................................................................................................ 37 viii 2.5 Conclusion .......................................................................................................................... 41 Chapter 3: Evaluation of automated activity monitors to predict calving time and uterine diseases in Holstein dairy cows ................................................................................................... 44 3.1 Introduction ........................................................................................................................ 44 3.2 Materials and methods ...................................................................................................... 46 3.2.1 Animals and housing ..................................................................................................... 47 3.2.2 Disease diagnosing ........................................................................................................ 48 3.2.3 Data recorded using automated activity monitors ......................................................... 48 3.2.4 Statistical analysis ......................................................................................................... 49 3.3 Results ................................................................................................................................. 52 3.4 Discussion ............................................................................................................................ 56 3.5 Conclusion .......................................................................................................................... 63 Chapter 4: General Discussion ................................................................................................... 71 4.1 Summary ............................................................................................................................. 71 4.2 Limitations .......................................................................................................................... 75 4.3 Future directions ................................................................................................................ 78 Chapter 5: Conclusions ............................................................................................................... 81 References ..................................................................................................................................... 82 ix List of Tables Table 3.1: Activity and lying behaviour measures (least squares mean ± SEM) from a collar-mounted (AAMC; n = 241) and a leg-mounted (AAML; n = 208) monitor for metritic (AAMC = 60; AAML = 70) and non-metritic (AAMC = 181; AAML = 176) Holstein dairy cows during the 2 wk before calving (wk â2 and â1) and 1 wk after calving (prior to diagnosis). .............................................................................................................................. 66 Table 3.2: Activity and lying behaviour measures (least squared means ± SEM) from a collar-mounted (AAMC; n = 220) and a leg-mounted (AAML; n =188) monitor for subclinical endometritic (AAMC = 36; AAML = 36) and non-subclinical endometritic (AAMC = 184; AAML = 152) Holstein dairy cows during the 2 wk before calving (wk â2 and â1) and 3 wk after calving (wk +1, +2, and +3; prior to diagnosis). ............................................................ 67 Table 3.3: Test performance, with 95% CI in parentheses, of optimal cut-off point of relative increases in activity and relative decreases in lying time and lying time per bout from a collar-mounted (AAMC; n = 179) and a leg-mounted (AAML; n = 213) monitor as a predictor of calving within 32, 24, 12, and 8 h. ......................................................................................... 68 Table 3.4: Test performance, with 95% CI in parentheses, of optimal cut-off point of relative change in activity-related behaviours from a collar-mounted (AAMC; n = 225) and a leg-mounted (AAML; n = 204) monitor as a predictor for metritis1 (metritis: AAMC = 59; AAML = 68; no metritis: AAMC = 166; AAML = 136 cows) prior to diagnosis at wk -2, -1, and +1 relative to calving. .................................................................................................................. 69 Table 3.5: Test performance, with 95% CI in parentheses, of optimal cut-off point of relative change in activity-related behaviours from a collar-mounted (AAMC; n = 225) and a leg-mounted (AAML; n = 204) monitor as a predictor for subclinical endometritis1 (subclinical endometritis: AAMC = 59; AAML = 68; no subclinical endometritis: AAMC = 166; AAML = 136 cows) prior to diagnosis at wk -2, -1, +1, +2, and +3 relative to calving. ...................... 70 x List of Figures Figure 2.1: Scatter plot representing the association of calf weight with duration of stage II labour (appearance of amniotic sac until calf expulsion) in Holstein dairy cows (n = 224). ............ 42 Figure 2.2: Proportion cows diagnosed with metritis at 6 or 12 DIM. A total of 96 cows were diagnosed with metritis (76 unassisted and 20 assisted at calving) and 317 cows were not diagnosed with metritis (296 unassisted and 21 assisted). ..................................................... 42 Figure 2.3: Effect of duration of labour in cows assisted (n = 34) and unassisted at calving (n = 194) on the predicted probability of metritis. ......................................................................... 43 Figure 3.1: Least squared means Â± standard error of the mean of activity measured as an index in 2 h time blocks via a collar-mounted automated activity monitor in 192 Holstein cattle during the 72 h before calving. Hours with * differ in activity compared to previous hour. ............ 64 Figure 3.2: Least squared means Â± standard error of the mean of (a) activity (steps/h), (b) lying bout duration (min per bout), and lying time (min) measured in 8 h time blocks via a leg-mounted automated activity monitor in 227 Holstein cattle during the 72 h before calving. Hours with * differ in activity compared to previous hour. ................................................... 65 xi List of Abbreviations AAMâ automated activity monitor AAMC â collar-mounted automated activity monitor AAML â leg-mounted automated activity monitor AUC â area under the curve BCS â body condition score C â Celsius cm â centimeters CO2 â carbon dioxide DIM â days in milk DMI â dry matter intake FPI â feto-pelvic incompatibility h â hour HPA â hypothalamic-pituitary-adrenal kg â kilograms m â meters min â minutes NEB â negative energy balance PGF2a â prostaglandin PMN â polymorphonuclear cells ROC â receiver operating characteristic RP â Retained placenta SD â standard deviation xii Se â sensitivity SEM â standard error of the mean Sp â specificity TMR â total mixed ration VD â vaginal discharge wk â week xiii Acknowledgements First and foremost, I would like to thank my supervisor, Dr. Ronaldo Cerri. I never planned on obtaining a Masterâs degree, especially not related to dairy cattle health and reproduction, but I am extremely grateful he provided me the opportunity. Through constant support and encouragement, he pushed me intellectually, in the class room and at the farm. I am extremely thankful for Tracy Burnett for being the best mentor that I could ask for. Without you I donât know where I would be today. You introduced me to dairy cattle back when I was an undergraduate student. You patiently (sometimes) taught me all you know about dairy cows, research, and statistics. You are the one who sparked my love and passion for cows. Thank you for the endless rants and for being a true friend. I am also thankful to Augusto for pushing me to think more broadly and critically. Every day you challenged me to think deeper and outside the box. Your curiosity for dairy science is a true inspiration. To my parents for their constant love and support through this time. Bryan, thank you for putting up with my stress and always putting a smile on my face. And finally, I cannot express how much my time at the UBC Dairy has meant to me; I have met the most amazing people at that farm. Thank you to the of the Repro Team (current and past) for all the memories. Lastly, thank you to the farmers, Nelson, Brad, Barry, Bill, and Lyndon, for all the laughs, memories, and help. 1 Chapter 1: Introduction 1.1 Setting the scene The transition period, defined as the 3 weeks before to the 3 weeks after parturition, is considered to be critically important for dairy cow health, production, and profitability as most health problems occur during this time (Drackley, 1999). According to LeBlanc et al., (2006), 75% of diseases in dairy cows typically occur within the first 3 weeks postpartum. Due to the occurrence of health problems being disproportionally centered on this relatively short period of time, the transition period and incidence of transition diseases have been deemed âan issue of critical importance to the dairy industryâ by the American Dairy Science Association (Drackley, 1999). Due to calving of dairy cows being an essential feature of the dairy industry for milk production and herd renewal, cows often go through transition periods approximately every 390 to 407 days (Hare et al., 2006). However, evolution, domestication, and selective breeding for increased milk production have resulted in an increase in prevalence of transition diseases and disorders, specifically with regards to dystocia (Mee, 2008). Increased risk of dystocia, or difficulty calving, has occurred due to cows producing relatively larger than normal calves compared with their dams (McClintock, 2004) along with other complications (i.e. young primiparous cows and twinning). The negative economic consequences of dystocia are closely related to welfare and productivity. In the U.S., it has been estimated that the annual cost of dystocia in the beef and dairy cattle industries is more than $400 million (USDA, 2010); however, the economic impact in Canada is unknown, but likely proportional to the issue observed in the U.S.. Dystocia has major impacts on production, fertility, cow morbidity and mortality, as well as increased involuntary 2 culling, veterinary costs, and other management costs (Dematawewa and Berger, 1997; Mee, 2008). Production losses are greatest in high yielding cows during early lactation (Rajala and Gröhn, 1998; Lombard et al., 2003; Mee, 2008) as a result of reduced fluid milk production (Thompson et al., 1983; Rajala and Gröhn, 1998; Berry et al., 2007), as well as fat and protein yields (Dematawewa and Berger, 1997). Furthermore, cows that experience dystocia are at an increased risk of developing uterine diseases, such as metritis, and other transition diseases (Sheldon et al., 2006). Similar to dystocia, uterine disease can result in reduced milk production, reproduction performance, and life expectancy due to an increased risk of culling during the lactation (Bell and Roberts, 2007; Wittrock et al., 2011; Giuliodori et al., 2013; Stangaferro et al., 2016). According to Sheldon et al. (2009), it is estimated that the annual cost of uterine disease in the U.S. dairy industry is $650 million; however, the annual costs in Canada are unknown, but likely proportional to the issue observed in the U.S.. Therefore, there has been an increase in research aiming to understand the biology behind transition diseases and how proactive management practices can prevent their occurrences or allow for early detection. 1.2 The transition period The transition period is a common phrase used to describe the progression from the non-lactating dry period, during late pregnancy, to early lactation post-partum (Drackley, 1999). Although the length of time classified as the transition period has been inconsistently defined throughout the dairy industry, it is commonly understood as the 3 weeks before and 3 weeks after parturition (Grummer, 1995; Drackley, 1999). During this time, a dairy cow undergoes changes in the endocrine and immune systems in preparation for colostrogenesis, parturition, and lactogenesis (Aleri et al., 2016). Such changes include glucocorticoid production, impaired leukocyte and neutrophil activity, and transfer of immunoglobins for colostrum production (Aleri et al., 2016). 3 As a cow undergoes these changes, her nutritional requirements change in order to meet her bodyâs demands. During the prepartum transition period, the nutrient requirements of the fetus reach maximal levels and the dam requires increased energy to accommodate for the onset of milk production and fetal demands (Drackley, 1999); however, dry matter intake (DMI) decreases by 10-30% during this time (Bell, 1995). Although reasons for the decline in DMI before parturition are unclear, it is suggested that changes in blood concentrations of steroid hormones prepartum and restricted rumen volume due to the growing fetus inducing space constraints might be responsible for the depressed feed intake (Forbes, 1977; Grummer et al., 1990). As nutrient requirements increase and DMI decreases, the cow enters a state of negative energy balance (NEB). This NEB in combination with endocrine and immune system changes, causes the cow to become immune suppressed. Due to the NEB and onset of milk production, dairy cows are susceptible to developing metabolic diseases (e.g., ketosis, milk fever, displaced abomasum, retained placenta), infectious diseases (e.g., metritis, mastitis, and endometritis), and other disorders (e.g., dystocia and lameness). For this thesis, I will focus primarily on the calving process, dystocia, metritis, and endometritis. 1.3 Parturition 1.2.1 What triggers the onset of parturition? Parturition in ruminants, and other mammalian species, is an endocrine event dependent upon the activation of the fetal hypothalamus-pituitary-adrenal (HPA) axis (Wood, 1999). Activation of the fetal HPA axis is likely caused by programmed maturation in the fetal hypothalamus (Barth, 2006). Increases in plasma concentrations of fetal cortisol activates placental enzymes (17-hydoxylase and 17,20 lyase) resulting in an increase in biosynthesis of estrogen relative to progesterone (Wood, 1999; Barth, 2006). The production of estrogen stimulates the 4 expression of oxytocin receptors resulting in increased oxytocin sensitivity and the synthesis of prostaglandin (PGF2a). PGF2a is responsible for luteolysis of the progesterone producing corpus luteum on the ovary. When oxytocin receptors, PGF2a and the ratio of estrogen to progesterone increases, the onset of uterine contractions occurs (Wood, 1999). The initial preparation of the birth canal takes place without uterine contractions but in the presence of the prepartum endocrine changes that triggers the onset of parturition (Challis and Lye, 1986). As parturition progresses, the distension of the birth canal causes an increase in the release of oxytocin from the posterior pituitary gland which further accentuates the myometrial contractions (Noakes et al, 2001). Onset of labour can be artificially induced through the use of long- or short-acting corticosteroids, PGF2a injections, or a combination of the two with or without a combination of oxytocin and estrogen injections (Barth; 2006; Mansell et al., 2006). Induced parturitions are often seen as beneficial when gestation is prolonged, to synchronize calvings with grazing seasons, or to prevent excessive udder edema and distention that predisposes cows to mastitis (Barth, 2006). However, the interval between induction and parturition is unpredictable and can result in a number of problems such as decreased calf viability, increased retained placenta (RP), uterine infections, decreased onset of milk production, and cow mortality (Barth, 2006). 1.3.2 Stages of labour The process of parturition, also referred to as labour or calving, is traditionally divided into three separate stages. It is unclear to the human eye when these stages start or stop due to their gradual progression, making it difficult to determine which stage of parturition the cow is in (Proudfoot et al., 2013). The first stage, also known as the dilation phase, includes dilation of the cervix, the onset of myometrial contractions, and the placement of the fetus for expulsion (Noakes et al., 2001). 5 This stage can last up to 6 hours (Schuenemann et al., 2011) and ends with the rupture of the chorioallantois in the vagina (Jackson, 1995). Although dilation of the cervix starts gradually during late gestation, it occurs more rapidly just prior to parturition and during the first stage of labour (Taverne, 1992). Movement of the fetus through the birth canal occurs in response to the myometrial contractions (Noakes et al., 2001). Although abdominal contractions begin in this stage, their appearance is not notable. Restless behaviour (e.g. increased lying and standing bouts) has been observed during this stage (Wehrend et al., 2006). This is likely due to the discomfort caused by uterine contractions and the fetus moving into the birth canal. Other behaviours including vocalization, self-licking, nest building, discharge of feces, sniffing the ground, and tail raising are commonly observed during the first stage of labour (Wehrend et al., 2006; Miedema et al., 2011a). Stage II is characterized by the appearance of notable abdominal contractions, the amniotic sac (water bag) appearance and rupture, and expulsion of the calf (Mainau and Manteca, 2011). During this stage, the cervix becomes fully dilated as the calf moves through the birth canal due to the propulsive forces of the uterine and abdominal contractions (Taverne, 1992; Wehrend et al., 2006). Cows are often observed in a recumbent position after the onset of abdominal contractions (Schuenemann et al., 2011). In a normal, eutocic calving, progress should be made every 15-20 min after the appearance of the calfâs feet, followed by the nose, head, body, and hind legs (Schuenemann et al., 2011). The average duration of this stage (appearance of amniotic sac to calf expulsion) is 70 min, but can range between 30 min and 4 h (Noakes et al., 2001; Mee, 2008; Schuenemann et al., 2011). According to Noakes et al. (2001), calving in heifers is usually longer in duration than in multiparous cows. This is due to the pelvic opening being smaller, in addition to the soft tissue of the reproductive tract having never been expanded (Noakes et al., 2001). 6 The third and final stage consists of the expulsion of the fetal membranes, or placenta. During this stage, myometrial contractions decrease in amplitude but become more frequent and irregular (Mainau and Manteca, 2011). Once a large portion of the placenta becomes detached from the uterine wall and inverted, it forms a mass within the uterus which stimulates reflex contractions of the abdomen (Noakes et al., 2001). Failure to expel the fetal membranes within 24 hours after birth is considered a retained placenta (Kelton et al., 1998), which in turn can cause infection of the uterus (Gröhn et al., 1990). 1.4 Eutocia and dystocia 1.4.1 Definition of eutocia and dystocia The calving process can be considered as eutocic or dystocic. Eutocia, or normal calving, is often defined as a spontaneous calving of normal duration that does not require human assistance (Mee, 2008). Dystocia, or difficulty calving, has been defined as a prolonged duration of calving or assisted extraction of the calf at birth, as a result of prolonged stages I or II of parturition (Mee, 2004). However, over time, the term dystocia has come to be used to only describe cows that required human assistance during calving. This is due to lack of a consistent definition of what defines prolonged labour. Prolonged labour has been defined by other authors as a calving duration or stage II taking longer than 65 min or 120 min after appearance of the amniotic sac or the calfâs feet (Mee, 2004; Gundelach et al., 2009; Schuenemann et al., 2011; Kovács et al., 2016). However, these time references are arbitrary and appear to have little to no basis in research. 1.4.2 Risk factors and types of dystocia According to Noakes et al. (2001), dystocia occurs when there is failure of one or more of the main components of calving: fetal size and presentation, expulsive forces, and birth canal 7 adequacy. Although the prevalence of dystocia is greater in primiparous cows when compared to multiparous cows, the risk factors for and types of dystocia overlap (Mee, 2004). The types of dystocia include feto-pelvic incompatibility (FPI), fetal maldisposition, twinning, incomplete cervical or vulva dilation, uterine inertia, and uterine torsion (Mee, 2004). The most common cause of dystocia in dairy cattle is FPI (Mee, 2008). The primary determinants for FPI are calf weight or size and dam pelvic size (Mee, 2004; 2008). FPI can be absolute or relative: absolute occurs when the calf is oversized and the damâs pelvic area is normal and relative occurs when the calf is normal in size and the damâs pelvic area is small (Mee, 2004). Calf birth weight is influenced by breed, sire, gestation length, and gender, while dam pelvic area is influenced by sire, weight at insemination, and age, weight, and body condition at calving (see review by Mee, 2004). Fetal maldisposition, or abnormal fetal disposition, occurs more frequently in multiparous cows when compared with primiparous cows (Mee, 2004). Types of fetal maldisposition include: posterior malrepresenation, where the calf is backwards and the hind legs are presented at the vulva; foreleg malposture, where one leg is back with the head and other leg presented at the vulva; breech position, where the calfâs pelvis is firmly lodged at the entrance to the maternal pelvis with both hindlegs extended alongside the body; and finally cranial malposture where the head of the calf is back and both fore feet are presented in the maternal pelvis and possibly at the vulva (Noakes et al., 2001). Risk factors for fetal malposition include previous twinning, parity, sire, gender, season, herd management practices, overfeeding prepartum, and high milk production (see review by Mee, 2004). Incomplete dilation of the vulva or cervix can lead to dystocia due to insufficient expansion of the birth canal. Incomplete dilation of the vulva is more common in primiparous cows whereas 8 incomplete cervical dilation is more common in multiparous cows. Assisting before the dam is sufficiently dilated can inadvertently cause this form of dystocia. The risk factors for incomplete cervical or vulvar dilation are different in primiparous and multiparous cows. In primiparous cows, risk factors include periparturient environmental stress and hormonal asynchrony (see review by Mee, 2004). In multiparous cows, incomplete dilation is associated with confinement and environmental disturbances at calving and pre-term calvings (see review by Mee, 2004). Twin calvings, uterine inertia, and uterine torsions are considered to be less common causes of dystocia. The occurrence of twin calvings are rare but often require human assistance as it can result in other forms of dystocia. Risk factors for twinning include parity, season of insemination, herd, previous twinning, high DMI, and high milk yield (see review by Mee, 2008). Uterine inertia is defined as a lack of sufficient myometrial contractions to expel the fetus. Uterine inertia can be classified as primary or secondary with primary uterine inertia occurring when uterine contractions fail to initiate and secondary uterine inertia occurring when uterine contractions cease before labour is complete. Risk factors for primary inertia include milk fever due to a lack of available calcium for proper muscle function, old age, debility or physical weakness, lack of exercise, and pre-term calving while risk factors for secondary inertia consist of prolonged calving duration and twinning (see review by Mee, 2004). The etiology of primary inertia is not well understood; however, in canines, it has been postulated to result from a failure of luteolysis resulting in persistently elevated progesterone concentrations (Davidson, 2011). Uterine torsion is the twisting or rotation of the uterine body. Excessive fetal movement during stage I of labour as the fetus moves into the birth position and possible mechanical factors (i.e. sudden slip or fall or knock by another cow) are the known causative factors for uterine torsions (Frazer et al., 1996; see review by Mee, 2004; 2008). 9 1.4.3 Effects of dystocia on the calf Prolonged or difficult deliveries can have detrimental effects on the calf, including risk of mortality or injury. Prolonged labour due to weak or lack of myometrial contractions and incomplete widening of the cervix, uterus or vulva has been associated with perinatal mortality (Gundelach et al., 2009). Newborns requiring assistance at parturition due to prolonged labour might experience severe acidosis due to oxygen deprivation or hypoxia, with subsequent effects on the function of vital organs and overall vitality (Mainau and Manteca, 2011). Calves that survive dystotic births are often weak and slow to stand and suckle (Odde, 1988), which in turn can negatively affect colostral immunoglobin absorption due to increase blood CO2 concentrations being negatively related to immunoglobin concentrations (Besser et al., 1990). As a result, calves that survive dystocia are at increased risk for disease and mortality within the first 30 days of calving (Mee, 2004; Lombard et al., 2007). Furthermore, excessive traction placed on the fetlocks of the calf during calving assistance can place too much force on the calfâs legs and result in a limb fracture; however, this is rare (Mee, 2004). Stillbirth, defined as a calf born dead after a normal gestation length or that dies within 24 hours after calving, is a major concern associated with dystocia. Tenhagen et al. (2007) reported that the proportion of stillborn calves was significantly higher in dystocic births as well as twin calvings. Malpresented calves are five times as likely as normally presented calved to be stillborn (Mee, 2008). According to Mee (2008), necropsy revealed 75% of all periparturient calf deaths occurred within 1 h of calving, 10% occur prepartum, and 15% occur after 1 h postpartum. Proper parturition management can reduce the frequency of stillbirths (Schuenemann et al., 2011, 2013). Thus, a many stillbirths are preventable and reducing these losses would increase profitability of dairy herds. 10 1.4.4 Effects of dystocia on the dam Dystocia can have detrimental effects on the welfare and productivity of the cow as well. It has been estimated that between 2 and 23% of cows in a herd experience difficult calvings requiring farmer or veterinarian assistance (Mee, 2008). Cows which experience dystocia are more likely to experience it again in a subsequent calving (Mee, 2008) and are more likely to develop respiratory and digestive disorders, as well as reduced DMI, mastitis, and hypocalcaemia in the days following parturition (Zamet et al., 1979; Erb et al., 1985; Benzaquen et al., 2007). Increased risk of rectovaginal injuries, such as lacerations urovagina, and pneumovagina, have also been reported (Farhoodi et al., 2000). These injuries can further lead to contamination in the uterus resulting in the contraction of various uterine diseases (Farhoodi et al., 2000). In addition, dystocia due to delayed delivery or physical stress from human intervention can result in excessive uterine inflammation (Guard, 1999; Sheldon, 2018). This inflammation is suggested to be associated with the formation of edema in the caruncles and cotyledons that lock the placenta into the uterus and is suggested to impede the expulsion of uterine tissues (Guard, 1999). Thus, edema of the caruncle can lead to the development of a retained placenta and/ or uterine disease (Guard, 1999). With dystocia that results in a longer labour and requires more intense forms of human intervention, such as or the introduction of hands within the birth canal or of the use of chains, there is greater hazard for a large influx of bacteria into the uterine lumen increasing the potential for the development of more serious forms of metritis (LeBlanc, 2008). In extreme cases of dystocia, calf and dam mortality, uterine prolapse, hernia, and rupture of the pelvic or abdominal organs can also occur (Noakes et al., 2001). Cows in NEB following parturition have impaired reproductive efficiency (LeBlanc, 2010). The effects of NEB on reproduction can be exacerbated by transition diseases. Dystocia, 11 along with its related diseases, can greatly impair reproductive performance by affecting the length of the next calving interval, delayed timing to first estrus, first service and conception, and overall reproductive efficiency (Rajala and Gröhn, 1998). Delayed uterine involution and onset of luteal activity post-partum, in addition to abnormal progesterone profiles, have been reported following dystocia (Dobson et al., 2001; Mee, 2008). Cows that experienced severe dystocia or had a Caesarian section have been shown to conceive later and are more likely to not become pregnant by 200 DIM (Tenhagen et al., 2007). This is possibly due to the absorption of bacterial components from the uterus which can prevent the luteinizing hormone surge and ultimately prevent ovulation (Sheldon and Dobson, 2004). Also, the first postpartum dominant follicle has a slower growth rate and secrete less estradiol at the end of the growth phase (Sheldon and Dobson, 2004). 1.5 Uterine diseases 1.5.1 Definition of metritis and endometritis Metritis and endometritis are common uterine diseases that impact up to 50% of lactating cows early postpartum (Markusfeld, 1987; Kelton et al., 1998; Zwald et al., 2004; Sheldon et al., 2009; LeBlanc, 2014; Stangaferro et al., 2016). Due to inconsistent definitions and diagnostic criteria, the incidence rates vary among studies with metritis impacting 2-40% of cows and endometritis impacting 3-53% of cows. The terms metritis, uterine infection or uterine disease are often inconsistently used within dairy cow research (Kelton et al., 1998; Huzzey et al., 2007; Dubuc et al., 2010). Often the terms used to describe and diagnose metritis and endometritis are commonly used interchangeably. However, depending on the stage and length of the infection, different terms can be used to describe this type of disease. Generally, metritis refers to the early post-partum inflammation of the uterus, while endometritis refers to inflammation of the endometrial lining and associated with chronic postpartum infection of the uterus (LeBlanc et al., 12 2002; Urton et al., 2005; Dubuc et al., 2010). Please see Sheldon et al. (2006) for review. Uterine disease can result in reduced milk production, reproductive performance, and life expectancy due to an increased risk of culling during lactation (Bell and Roberts, 2007; Wittrock et al., 2011; Giuliodori et al., 2013; Stangaferro et al., 2016). Therefore, early identification of sick cows is crucial to ensure a healthy herd. 1.5.2 Diagnosing uterine diseases The primary diagnosis is based on evaluation of the uterus and uterine discharge. Metritis is associated with the presence of an enlarged uterus along with foul smelling, pink-brown, watery discharge occurring within the first 21 DIM (Földi et al., 2006; Sheldon et al., 2006, 2009). Uterine discharge is often scored on a 0 to 3, 1 to 4 or 1 to 5 scale, with 0 or 1 healthy (no pus or purulent discharge) and 3, 4 or 5 being severe (full of pus, purulent discharge, and foul smelling; Lewis, 1997; Urton et al., 2005; Williams et al., 2005; Sheldon et al., 2009), on the respective scales. Body temperature is also often measured via rectum to determine if a fever is present, often defined as a temperature over 39.5°C. The cow will be diagnosed with metritis, mild or severe metritis, or clinical or puerperal metritis depending on the discharge score, presence of a fever, and the scale used. Endometritis can be diagnosed as clinical or subclinical. Clinical endometritis is often characterized by the presence of purulent uterine exudate in the vagina after 21 d postpartum (Sheldon et al., 2006). In the absence of purulent material, subclinical endometritis can be diagnosed through measuring the ratio of neutrophils and endometrial cells present on a microscope slide obtained via a cytological examination (Sheldon et al., 2006). Behavioral signs of these uterine diseases include anorexia, fever, depression, dehydration, and decreased milk production (Dubuc et al., 2010; Stangaferro et al., 2016). Currently, there is little evidence linking 13 metritis and endometritis due to the lack of studies standardizing their definition and most studies considering these conditions separately (Dubuc et al., 2010). 1.5.3 Uterine disease causes and risk factors Metritis and endometritis are caused by bacterial contamination of the uterine lumen after parturition (Sheldon et al., 2006). During the gestation period, the uterus is considered âsterileâ due to the formation of the mucus plug and closing of the cervix to prevent bacterial contamination; however, during parturition, the mucus plug is expelled and the cervix dilates. Therefore, the uterine lumen is nearly always contaminated with a wide range of pathogenetic bacteria (Sheldon et al., 2009). Depending on the immune function of the cow, as well as the pathogenetic bacterial load, development of a uterine disease may occur; however, as previously mentioned in section 1.2, transition dairy cows are more susceptible to diseases due to a compromised immune system. The development of a clinical disease is dependent upon the balance of pathogenetic bacteria, the cowâs natural âhealthyâ bacteria, and the immune response from the cow (Sheldon et al., 2006; Sheldon, 2018). If an imbalance occurs and the pathogenetic bacteria outnumber the cowâs âhealthyâ bacteria coupled with a compromised immune system, there is a high risk for the development of a uterine disease (Sheldon, 2018). Common strains of bacteria that can lead to uterine diseases include Escherichia coli, Arcanobacterium pyogenes, Prevotella species, Fusobacterium necrophorum, and Fusobacterium nucleatum (Griffin et al., 1974; Bonnett et al., 1991; Sheldon et al., 2002; Williams et al., 2005). Cows diagnosed with RP are much more likely to develop metritis than cows that expel their fetal membranes within a normal time period (Bruun et al., 2002), due to necrosis of the devitalized placenta along with bacteria and the inflammatory response hastening the necrosis process (Guard, 1999). 14 Metabolic disease can increase a cowâs chance of developing metritis within the first days following parturition (LeBlanc, 2010). Furthermore, reduced feeding time and DMI before parturition have been reported as predictors of metritis (Urton et al., 2005; Huzzey et al., 2007). A cow may not be able to obtain sufficient energy from their feed to meet the demanding needs of lactation and for the maintenance of their body tissues, resulting in a negative energy balance that causes excess body fat to be mobilized (Goff and Horst, 1997). This negative energy balance suppresses normal immune function and causes an impairment of neutrophils (Hussain, 1989; Gilbert et al., 2005). Neutrophils are the main leukocyte type that is involved in maintaining proper bacteria balance within the uterus (Hussain, 1989; Gilbert et al., 2005). It has been suggested that cows that develop a uterine disease undergo a larger NEB and have increased neutrophil impairment (Galvão et al., 2010). Reported risk factors for metritis and endometritis include dystocia, twins, retained placenta, stillbirth, abortion, and prolapsed uterus, while endometritis has additional risk factors such as parity, season, and body condition during the transition period (Markusfeld, 1987; Gröhn et al., 1990; Correa et al., 1993; Kaneene and Miller, 1995; LeBlanc et al., 2002; Kadivar et al., 2014). In addition, metabolic disorders, such as ketosis and displaced abomasum, can increase the risk of metritis and endometritis (Dubuc et al., 2010). 1.5.4 Impact of uterine diseases on the dam Typically, metritis impacts the dam in the first 2 weeks postpartum, while endometritis impacts the dam 21 to 60 DIM (Sheldon et al., 2008). As previously mentioned, uterine diseases can result in reduced milk production, reproduction performance, and life expectancy due to an increased risk of culling during the lactation (Bell and Roberts, 2007; Wittrock et al., 2011; Giuliodori et al., 2013; Stangaferro et al., 2016). Cows diagnosed with uterine disease have delayed 15 resumption of ovarian cycles, lower first service conception rates, longer calving to conception intervals, and lower conception rates (McDougall, 2001; LeBlanc et al., 2002). Impairment of reproductive performance is due to smaller and slower growth of ovarian follicles, lower peripheral plasma oestradiol concentrations, blockage of GnRH production and LH surge, and extended luteal phase (Williams et al., 2001; Sheldon et al., 2002, 2008). 1.6 Current management practices 1.6.1 Preventing dystocia Preventing dystocia is heavily dependent on controlling the risk factors associated with the different types of dystocia mentioned in Section 1.4.2. According to Mee (2004), there are five critical periods in which action can be taken to aid in the prevention of dystocia: choices at the heiferâs birth, pre-service, early gestation, pre-calving, and during calving. Firstly, removing certain heifers from the herd soon after birth can reduce the overall potential for dystocia (Mee, 2004). It is recommended that heifers from dams with a history of dystocia, twinning, perinatal mortality, and milk fever should not remain in the herd as they are at a higher risk of having dystocia in future calvings (Mee, 2004). Heifer calves born with excessively heavy birth weights are at a higher risk of having dystocia due to risk of having heavier calves in future calvings (Colburn et al., 1997). In seasonal calving herds, heifers born late in the calving period are likely to be too small at calving. It is recommended that these heifers be removed from the herd as they may be too small at the time of breeding (Mee, 2004). The second control period is the pre-service or pre-breeding period as choices related to heifer age and size of first service, sire, and sexed semen can prevent dystocia (Mee, 2004). At first service, heifers should be at approximately 65% of their mature weight in order to ensure adequate pelvic size at delivery. Since calf weight and parity are both major predictors of dystocia, 16 selective breeding for smaller calves by using specific bull semen can lower the prevalence of dystocia (Atashi et al., 2012). Since primiparous cows are more prone to experiencing dystocia, the use of sexed semen selected for female calves is often implemented for use on heifers to reduce the potential for calving complications, as female calves generally have lower body weight than male calves (Norman et al., 2010). Difficult calvings were reduced by 28% for heifers and 64% for multiparous cows when sexed semen was implemented (Norman et al., 2010). The third control period occurs during early gestation. Pregnancy exams conducted with an ultrasound at 55-80 days after service allows for fetal gender determination and accurate detection of twin fetuses (Mee, 2004). At this time, the farmer and veterinarian can intervene via induced abortion or allow for a complete gestation and prepare for timely calving intervention; however, this practice is not well researched. The fourth period of dystocia prevention occurs pre-calving, or late gestation (Mee, 2004). An adequate body weight of approximately 85% of the mature body weight for heifers in addition to a BCS of 3.75 at calving will help prevent dystocia due to feto-pelvic incompatibility (Mee, 2004). During the dry period, having appropriate nutritional management helps control BCS. In addition, reducing environmental periparturient stress can aid in the prevention of dystocia. This can be done by moving dams into secluded, individual maternity pens with deep bedding and ample space prior to the onset of parturition (Proudfoot et al., 2014). The final control period to prevent dystocia is during calving (Mee, 2004). By providing adequate maternity pen supervision, calving progress can be closely monitored. Detection of calving difficulties will allow for timely intervention and possible prevention of secondary uterine inertia and stillbirths (Mee, 2004). 17 1.6.2 Preventing uterine disease LeBlanc et al. (2006) describes the epidemiology of a dairy cow as being the balance between risk factors and protective factors that determine the animalâs health status, due to it being nearly impossible to control the pathogenetic bacterial agent. Development of transition diseases, specifically uterine diseases, are increasingly known to be rooted in immune function, and in turn, feed quality and DMI during the transition period. Therefore, focusing on the risk factors associated with the cowâs body, immune system, and DMI in addition to her environment is key for uterine disease prevention. Having appropriate nutritional management prior to and during the dry off period helps control BCS and prevent transition diseases due to over conditioned cows. As previously mentioned, when feed intake is greatly reduced prior to calving, cows are at greater risk of developing metritis post-calving (Urton et al., 2005; Hammon et al., 2006; Huzzey et al., 2007; Neave et al., 2018). To reduce the risk of decreased feed intake, providing adequate feed bunk space can reduce competition for feed and displacements (Huzzey et al., 2007). Due to bacterial contamination being the root cause of developing uterine diseases, it is assumed that the bacteria are acquired through feces and fecal contamination of bedding, skin, and the environment (Potter et al., 2010). However, it has been shown that the hygiene of the calving environment has little influence on the uterine bacteria flora and development of metritis or endometritis (Noakes et al., 1991; Potter et al., 2010). Although environmental cleanliness may not be associated with uterine diseases, it is recommended to provide a clean environment to reduce risk of bacterial contamination and developing other transition diseases such as mastitis (Bradley, 2002). Due to the difficult nature of bacteria and uterine diseases, new approaches such as nutraceuticals or vaccines are needed to aid in the prevention of uterine diseases (Sheldon, 2018). 18 Researchers are investigating how altering the bacterial floral of the vagina via intravaginal infusion of probiotics around parturition can prevent the development of metritis. Probiotics change the bacterial flora of the vagina thus inhibiting specific metritis-causing pathogens from surviving in the vagina and entering the uterus (Otero et al., 2006; Deng et al., 2015; Genís et al., 2018). However, there is currently a lack of research investigating applications and impacts of probiotic infusions beyond uterine diseases and currently are cultured in a lab making it difficult for farms to have easy access. Recently, there has been an increase in research investigating the efficiency of vaccines to prevent uterine diseases (Machado et al., 2014; Freick et al., 2017). Vaccines are designed to immunize the body against certain bacterial strains by introducing inactivated bacterial components into the body (Machado et al., 2014; Freick et al., 2017). Machado et al. (2014) reported successful use of experimental vaccines to prevent metritis and improve reproductive performance. Although, Freick et al. (2017) reported use of an experimental vaccine was not efficacious at reducing uterine diseases or influencing reproduction, this study also reported that there were many limitations in the experimental design and therefore development of proper vaccines may prove to be efficacious at improving uterine diseases prevention. 1.6.3 Current parturition management practices Although most cows experience eutocic calving, close monitoring prior to and during calving is necessary to identify dystocic cows and provide appropriate intervention. Management of calving often consists of visual observations of imminent signs of calving, either in the close-up or calving pen, and providing appropriate intervention when lack of calving progress occurs. This form of management is only successful when the close-up pen is closely monitored at all times; however, this is often not feasible on most dairy farms. According to the USDAâs (2010) 19 NAHMS survey, close-up animals were observed more frequently during the day than at night. During the daytime, 47.2% of the dairy farms checked their close-up cows every 3 or less hours while 17.6% monitored cows every 5 or more hours. During the night, only 17.7% of the dairy farms checked their close-up cows every 3 or less hours while 53.9% monitored cows every 5 or more hours. However, due to the average time from the appearance of the amniotic sac to expulsion of the calf being only 70 min (Schuenemann et al., 2011), risk for too early or late intervention increases as the frequency of close-up observations decreases. Training of farm personnel to recognize the imminent signs of calving, stages of labour, when and how to intervene, and if and when to ask for veterinary assistance is important to mitigate the negative effects of dystocia (Lombard et al., 2007; Schuenemann et al., 2013). Imminent signs of calving often include relaxation of sacrosciatic ligaments, enlarged udder, tail raising, and milk let-down (Miedema et al., 2011a; Proudfoot et al., 2013). Monitoring for behavioural signs of stage I (restlessness, vocalization, self-licking, nest building, discharge of feces, and tail raising) and stage II (frequent abdominal contractions, appearance of the amniotic sac or calfâs feet outside the vulva) is important for inferring if the progression of labour is occurring appropriately. It is recommended that calving progress every 15-20 min should be used as a guideline to determine if or when to intervention (Mee, 2004; Mee et al., 2011; Schuenemann et al., 2011, 2013). 1.6.4 Current uterine health management practices Timely and accurate diagnoses are essential to ensure appropriate management of uterine diseases. On most dairy farms, the identification and treatment of sick animals is often performed by on-farm employees (Espadamala et al., 2016). It is also recommended that cows diagnosed with uterine diseases should be evaluated for metabolic diseases (i.e. ketosis, mastitis, and displaced abomasum) since these conditions are known to be associated with uterine diseases (LeBlanc, 20 2010). According to a survey of 429 dairy farms in Germany, 64% of dairy farms performed fresh cow examinations âonce or twice a dayâ while 28% performed examinations âonly if neededâ (Heuwieser et al., 2010). Most of these dairy farms used subjective criteria such as general appearance (97.0%) and appetite (69.7%), while only 33.6% monitored the objective measure of fever. Although it is unknown if vaginal discharge was monitored in the fresh cows of this study, the authors discussed that without the measurement of body temperature several metritis cases may remain undiagnosed (Heuwieser et al., 2010). In a similar survey of 45 dairy farms in California, health-monitoring exams for early detection of metritis were implemented on 42% of the farms, with 13% based on rectal examination, 22% based on rectal temperature, and 7% based on both rectal temperature and examination (Espadamala et al., 2016). Rectal palpations are commonly used to evaluate vaginal discharge despite this practice being a subjective screening method that lacks sensitivity (LeBlanc et al., 2002; Espadamala et al., 2018). Treatments for uterine diseases often consist of systemic antibiotics or antimicrobials, intrauterine infusions, nonsteroidal anti-inflammatory drugs, or systematic injections of PGF2a (LeBlanc, 2008; Heuwieser et al., 2010). The aim of treatment is to either reduce the load of pathogenic bacteria or enhance uterine defence and repair mechanisms, and thereby stop and reverse inflammatory changes that impair reproductive performance or mitigate the effects of the diseases on the body (LeBlanc, 2008). Recently there has been an increase in pressure to reduce antibiotic usage due to possible antibiotic resistance (Haimerl and Heuwieser, 2014). Delaying systemic antimicrobial treatments might be a desirable strategy as Sannmann et al. (2013) reported that 55.3% of untreated cows showed clinical cure at 14 DIM. Similarly, McLaughlin et al. (2012) reported 21.8% of cows with metritis self-recovered within the first 5 DIM. Close monitoring body of temperature and vaginal discharge is important to notice progression of the disease and provide 21 treatment if needed; however, it is not clear whether systematic programs aimed at early diagnosis and treatment of metritis are effective in preventing progression (LeBlanc, 2008). 1.7 Use of automated technology 1.7.1 Application of activity monitors on farms Due to herds growing in size, shortage of available workers, labour costs, and the decreasing number of farm staff per cow, close monitoring of individual cows is becoming more difficult and less feasible (Rutten et al., 2013; Barkema et al., 2015; Stevenson and Britt, 2017). Automated monitoring systems (AAMs; i.e. electronic pedometers, necklaces, and boluses) are becoming more widely used on farms for estrous and disease detection via changes in physical activity, rumination, body temperature, and/or feeding time; however, monitoring activity is more commonly used on commercial farms for estrous detection (Stevenson and Britt, 2017). Physical activity can be automatically monitored as lying bouts, duration of lying, walking bouts, walking durations, standing bouts, standing durations, standing restlessness (stepping, foot-lifting), and restlessness (ratio of standing to lying bouts). Newly available AAM technologies and sensors allow personnel and researchers to continuously monitor these events as they occur (Stevenson and Britt, 2017). 1.7.2 Application of activity monitors for parturition Changes in lying behaviour and restlessness prior to calving have been commonly observed using data loggers. Data loggers record data over time and require frequent removal from the animals in order to manually access the data and therefore are not feasible for use on commercial farms. AAMs, which are readily available for commercial farms, record and transmit data automatically to a computer for easy viewing and access. The following studies investigating lying 22 behaviour used data loggers to quantify the lying time and lying bouts. As the lying bout frequency increases, lying durations begin to decrease indicating possible restlessness (Miedema et al., 2011b; a; Titler et al., 2015). Miedema et al. (2011a) found that the number of lying bouts increased during the final 6 hours before calving and median duration of lying becomes shorter 6 to 18 hours before calving when compared to the control period. The total daily number of lying bouts increased as the average daily lying duration deceased between the control period and calving day (Miedema et al., 2011a). Miedema et al., (2011b) reported parturient cows had an increased frequency of lying during the 12 hours prior to calving compared to the control period; however, no difference in lying duration was reported. The results of these two studies should be interpreted with caution as the control period for each cow was measured 1 to 4 days prior to calving as it has been reported that changes in lying behaviour start to occur the day before calving (Jensen, 2012), therefore an adequate control period may have not been established. Jensen (2012) used data loggers to find that the duration of lying decreased while the number of lying bouts increased on the day before calving. Furthermore, it was reported that lying duration showed no specific trend over the hours prior to calving (Jensen, 2012). Jensen (2012) claimed that lying duration might not be a good indicator of imminent calving but a possible long-term indicator. Similarly, Titler et al. (2015) reported decreased daily average lying duration and increased daily total lying bouts on the day of calving compared to previous days; however, hourly changes were not reported. Changes in activity and steps occur on the day of calving. When compared to the individual established control period for each cow, duration of steps increased over the 12 hours prior to calving (Miedema et al., 2011b). However, there was a significant decrease in walking duration in the final 2 hours, likely due to the cow being recumbent during stage II labour. Similar observations were seen over the 24-h period before calving (Miedema et al., 2011a; Tilter et al., 2015). Tilter et 23 al. (2015) reported the overall mean number of steps increased during the 24 h period before calving when compared with days -4, -3, and -2; however, hourly changes were not reported. When Miedema et al. (2011a) broke down the 24 h period before calving into shorted periods of 6 hours, no significant differences were observed with the exception to the last 2 hours. Number of steps decreased in the last 2 hours, indicating that walking activity may be a good imminent indicator of calving. Standing activities can be measured in bouts and duration. Huzzey et al. (2005) used data loggers to quantify the number of bouts and total time spent standing and lying per day and found an 80% increase in the number of standing bouts during the calving period. Standing bouts during the period of one day before and after calving were significantly higher than during pre- and post-calving periods. This suggests that the cows were more restless, likely due to the discomfort associated with calving (Huzzey et al., 2005). Huzzey et al. (2005) argued that special attention should be placed on cow comfort during the calving period due to the constant changing of positions. When dairy herd personnel determine that calving time is approaching, further monitoring can occur to observe for indicators and predictors of difficult calving that may require intervention. During the 2 hours before calving, there were increases in lying frequency in cows that required assistance in calving (Miedema et al., 2011b). Proudfoot et al. (2009) found that cows with dystocia had more standing bouts during the 18 hours before calving than those without difficulties, indicating that a high number of standing bouts could be a sign of dystocia. In the future, through the use of automated activity monitors (AAMs), dairy farm personnel could be alerted when activity surpasses a specific threshold, warning of possible calving difficulties. 24 Activities should be monitored in the late gestation period and when the cow is approaching its expected calving day. Decreases in lying bouts (Miedema et al., 2011a; b), along with increases in standing bouts (Huzzey et al., 2005) and walking durations (Miedema et al., 2011a; b), can alert dairy herd personnel that the cow will calve in approximately 6 to 24 hours. Although many of the presented studies measured changes in activity behaviours using data loggers and summarized observed data, there is potential to make an alert system using established thresholds to predict calving events. Ouellet et al. (2016) used data loggers to record and summarize lying behaviour in order to create thresholds for predicting calving within 6, 12, 18, and 24 hour cutoffs. Ouellet et al. (2016) found decreases lying duration and increased lying bouts leading up to calving; however, it was reported that lying bouts and lying duration at low positive predictive values, high negative predictive values, and moderate to low specificity and sensitivity. Due to the large variation in lying behaviour between cows, it was difficult for Ouellet et al. (2016) to calculate a common optimal cut-off points for all the cows. Using commercially available automated activity monitors, and the established baseline to determine optimal cut-off points, may improve sensitivity and specificity of predicting calving events using lying behaviour. More work is needed using commercially available monitors to create an alert system. By creating long-term alerts (i.e. 6 to 24 hours before calving), farm personnel can then examine the cow to assess if the she has started stage I of parturition or not. This can allow the personnel to move the cows to their maternity pens at a time that will not impact the calving process and begin monitoring for short-term indicators of calving. 1.7.3 Other methods for automated detection of calving There are other devices marketed for automatic calving detection, include rumen-reticulum boluses that measure changes in body temperature, inclinometers and accelerometers that detect 25 tail raising, abdominal belts that monitor contractions, intravaginal thermometers detecting a drop in body temperature and changes in light to detect expulsion of the allantochorion, and devices fixed on the vagina/ vulvar lips designed to detect calf expulsion (Saint-Dizier and Chastant-Maillard, 2015). Unfortunately, there is lack of research investigating the reliability and performance of these devices under field conditions. 1.7.4 Application of automated activity monitors for uterine diseases Currently there is a lack of research investigating activity or lying behaviour and cows diagnosed with endometritis. However, several studies have shown how changes in these behaviours can be used as indicators for metritis (Neave et al., 2018; Liboreiro et al., 2015; Tilter et al., 2013; Barragan et al., 2016). Although little research has shown a relationship between postpartum activity and later diagnosis of metritis, Neave et al. (2018) used data loggers to show that cows that were later diagnosed with metritis spent less time lying and had fewer lying bouts during the 2 weeks prior to calving. During the postpartum transition period, there have been reports of decreases in activity around the time of metritis diagnosis. Liboreiro et al. (2015) reported decreases in physical activity in metritic cows during the first 5 days postpartum, however, it was not clearly stated the type of physical activity (i.e. standing, steps, or movement) measured. Liboreiro et al. (2015) used a commercially available activity monitor that reported âactivityâ in arbitrary units. Tilter et al. (2013) reported that cows with metritis took fewer steps, spent more time standing, and had fewer lying bouts during the 1 to 3 days before and after diagnosis when compared with healthy cows. Specifically, cows diagnosed with metritis took fewer steps 1 days prior to diagnosis and continued to decrease following diagnosis. Unlike Tilter et al. (2013), Neave et al. (2018) found that lying time did not increase in the days before diagnosis. Neave et al. (2018) further reported that in the 3 days prior to diagnosis, metritic cows spent more 26 time lying per bout and overall had decreased lying bouts when compared to healthy cows. Neave et al. (2018) suggest that lying time is only affected after clinical symptoms of metritis become apparent. Similarly, Barragan et al. (2018) found that lying time increased after diagnosis; however, data loggers were placed on the cows on the day of diagnosis. In the study presented by Barragan et al. (2016), it is unknow how lying behaviour or activity differed prior to calving or diagnosis. Similar to the literature on changes in activity behaviour prior to calving, the studies presented above, with the exception of Liboreiro et al. (2015), used data loggers to record and summarize changes in behaviour. Although these studies show that activity and lying behaviour change in cows diagnosed with metritis, future work is needed to see if similar results would have been seen using other commercially available monitors. In addition, the previously mentioned studies focus on comparing metritic cows to non-metritic cows rather than intra-animal comparisons. Intra-animal comparisons are necessary for identifying changes that occur within the animal rather than comparing to other animals. Within-cow comparisons are likely to be more sensitive given the large variation in cow daily activity and lying behaviour reported in studies presented above. Furthermore, application of automated activity monitors on farms is only feasible if an accurate alert system is created and therefore more research is needed investigating whether creating cut-offs points or establishing an alert system can accurately detect or predict cows at risk of uterine disease. 1.8 Objectives and hypotheses This thesis reports research investigating if the duration of labour and physical activity, recorded using commercially available automated activity monitors, can be used to predict calving times and cows at risk of developing uterine diseases, in addition to investigating a reference time 27 to provide human assistance during labour based on the health outcome of metritis. We hypothesized that: 1) cows diagnosed with uterine diseases will have a longer duration of unassisted labour when compared with healthy cows; 2) providing assistance too early or too late will increase risk of uterine diseases; 3) cows diagnosed with uterine diseases will be less active and restless before and after calving when compared with healthy cows; and 4) through calculated relative changes in activity and lying behaviour, AAM can accurately predict calving times and uterine disease. 28 Chapter 2: Association between calving factors, duration of labour, and uterine diseases in Holstein dairy cows 2.1 Introduction The transition period, defined as the 3 weeks before to the 3 weeks after parturition, is considered to be critically important for dairy cow health, production, and profitability as most health problems occur during this time (Drackley, 1999). Due to calving of dairy cows being an essential feature of the dairy industry for milk production and herd renewal, cows often go through transition periods approximately every 390 to 407 days (Hare et al., 2006). Dystocia, or difficultly in calving, often results in negative economic impacts on the farm that are closely related to the welfare of the cow (Buckley et al., 2003; Mee, 2008). Dystocia can result in reduced production and fertility, increased cow morbidity, mortality, involuntary culling, and veterinary costs (Dematawewa and Berger, 1997; Mee, 2008). Furthermore, cows diagnosed with dystocia are more likely to develop uterine diseases such as retained placenta, metritis, and endometritis in addition to other transition diseases (Sheldon et al., 2006). Similar to dystocia, uterine disease can result in reduced milk production, reproductive performance, and life expectancy due to an increased risk of culling during the lactation (Bell and Roberts, 2007; Wittrock et al., 2011; Giuliodori et al., 2013; Stangaferro et al., 2016). Because of this, there has been an increase in research aiming to determine risk factors for uterine diseases and dystocia and how proactive management practices can prevent their occurrences or allow for early detection or mitigation of possible negative effects. Parturition consists of three stages: stage I, the cervix and birth canal begin to dilate and the calf moves into position; stage II, the cervix is fully dilated and the calf moves through the birth canal as abdominal contractions begin along with of the appearance of the amniotic sac and 29 calfâs feet and expulsion of the calf; and stage III, expulsion of the placenta (Wehrend et al., 2006). Dystocia has often been defined as a prolonged duration of labour or assisted extraction of the calf at birth as a result of prolonged stages I or II of parturition (Mee, 2004). However, over time, the term dystocia is often used to only describe cows that required human assistance during calving. This is due to the lack of a consistent definition of what prolonged labour is and when to provide assistance. Prolonged labour has been defined by other authors as a calving duration taking longer than 70 minutes (Schuenemann et al., 2011; Kovács et al., 2016), 80 minutes (Mee, 2004), or 120 minutes (Gundelach et al., 2009) after the appearance of the amniotic sac. However, these time references are merely arbitrary numbers and are backed by little to no research explaining why these time references were chosen. Furthermore, there is a lack of research investigating the relationship between duration of labour and development of uterine diseases. According to Noakes et al. (2001), dystocia occurs when there is failure of one or more of the following main components of calving: fetal size and presentation, expulsive forces, and birth canal adequacy. Reported factors that influence dystocia and prolonged labour include calf birth weight, maternal pelvic size, sire, parity, twins, season, milk fever, gestation length, weight at service and calving, and body condition at calving (as reviewed by Mee, 2004). Similarly, reported risk factors for metritis and endometritis include dystocia, twins, retained placenta, stillbirth, abortion, gestation length, and prolapsed uterus, while endometritis has additional risk factors such as parity, season, and body condition during the transition period (Markusfeld, 1987; Gröhn et al., 1990; Correa et al., 1993; Kaneene and Miller, 1995; LeBlanc et al., 2002; Kadivar et al., 2014; Vieira-Neto et al., 2017). In addition, metabolic disorders, such as hypocalcaemia, ketosis and displaced abomasum, can increase the risk of metritis and endometritis (Dubuc et al., 2010). 30 Further understanding of the calving process and how it impacts uterine health can improve calving management practices to prevent the development of uterine disease. Therefore, the objectives of this observational study were to investigate the association between factors around the time of calving, duration of stage II labour, and the development of uterine diseases in dairy cattle. In addition, we aimed to estimate a reference time to be used as a guideline for providing calving assistance based on the health outcome of metritis. We hypothesized that cows diagnosed with uterine disease will have a longer duration of unassisted labour when compared with healthy cows and providing assistance too early or too late will increase the risk of uterine diseases. 2.2 Materials and methods This project was conducted at the University of British Columbiaâs Dairy Education and Research Centre in Agassiz, BC from May 2016 to March 2019. All procedures were carried out in accordance with the University of British Columbiaâs Animal Care Committee. In addition, animals used in this experiment were cared for as outlined by the guidelines provided by the Canadian Council of Animal Care. 2.2.1 Animals and housing A total of 567 Holstein dairy cows, 161 primiparous and 406 multiparous were used in this study. Prior to calving, cows were housed in a pre-partum pen (12.5 m x 9.7 m) containing 12 deep-bedded sand free-stalls. After calving, cows were housed in lactating cow pens, ranging from 12 to 36 deep-bedded sand free-stalls. River sand was replenished once every two weeks. Rubber matting lined the alleys and were scraped approximately 6 times per day using an alley scraper. Cows were fed a total mixed ration (TMR) formulated for transition cows prior to calving and a lactating TMR formulated for high production cows after calving to meet or exceed the 31 requirements of Holstein cows weighing 620 kg, producing 45 kg/day of 3.5% fat corrected milk (NRC, 2001). Prior to calving, fresh feed was delivered once daily and pushed up an additional 4 times daily. After calving, fresh feed was offered twice a day and pushed up twice between feedings. Water and feed were available ad libitum. Lactating cows were dried-off 60 ± 5 days from their expected calving date and moved to a dry pen immediately after the last milking. Cows were then moved into the pre-partum pen approximately 21 d before their expected calving date and moved into an adjacent maternity pen when they showed physical signs of imminent calving (i.e. relaxation of sacrosciatic ligament, tail raising, and enlarged udder; Miedema et al., 2011a; Proudfoot et al., 2013). The maternity pen was bedded with straw, approximately 30 cm deep, with dimensions of 30 m x 3.15 m and cleaned every one to two weeks. Upon enrollment at 21 ± 3 d before the cowsâ expected calving date, body condition and gait were scored. Body condition was scored on a 5-point scale from thin (1) to obese (5) by increments of 0.25 as outlined by Edmonson et al. (1989). Gait scores were given at the same time on a 4-point scale from sound (1) to severely lame (4) in increments of 1 as outlined by Flower and Weary (2006). Calving location, presence of other cows at the time of calving, if the cow was moved during stage II labour, days dry, gestation length, and calf weight were also recorded. Within 24 h after calving, cows were moved to the lactating cow pens. Postpartum cows were milked twice per day, once between 0500 and 0800 h and again between 1500 and 1800 h, in a double twelve parallel milking parlor. 2.2.2 Disease diagnosis Cows with retained fetal membranes after 24 h after calving were diagnosed as having a retained placenta (RP). Treatment for RP was based off discretion of farm management. Vaginal 32 discharge (VD) and temperature was evaluated for each cow at 6 and 12 h postpartum for the diagnosis of metritis. Examinations took place immediately following the morning milking. Before vaginal examination, the vulva was thoroughly cleaned with a soft-bristled brush soaked in an iodine solution (Prepodyne Scrub, Kane Veterinary Supplies, Edmonton, AB) diluted in warm water to remove feces that could introduce bacteria into the vaginal canal. Discharge was evaluated by a Metricheck device (Simcrotech, Hamilton, New Zealand) which was insert into the vaginal canal up to the cervix. The device was cleaned with the iodine solution between uses. The appearance and smell of the VD was evaluated using the following scale adapted from Williams et al. (2005): 1 = no discharge or clear mucus; 2 = cloudy mucus with < 50% purulent; 3 = purulent > 50%; and 4 = putrid (red/brown color, watery, foul smelling). Cows were classified as having metritis if they had at least one VD score of 3 with a fever (â¥ 39.5°C) or VD score of 4 at 6 or 12 h postpartum. The VD of cows that still had an RP at 6 postpartum was not evaluated. At 42 ± 3 days postpartum, following vaginal examination, a cytological examination was performed via an endometrial cytobrush (CooperSurgical Inc., Trumbull, CT). Slides for cytological examination were prepared on-farm by rolling the cytobrush on a clean glass microscope slide, and were stained using a Romanowsky stain (Diff-Quick, Fisher Diagnostics, Middletown, VA); coverslips were applied once dry using slide glue (Fisher Chemicalâ¢ Permountâ¢ Mounting Medium, Fisher Scientific, Fair Lawn, NJ). Each slide was examined at 400x magnification using a light microscope (Nikon Eclipse E200, Nikon, Tokyo, Japan) to perform a differential cell count of 300 (polymorphonuclear and endometrial cells) done in three counts of 100 and then averaged by a trained observer. Subclinical endometritis was defined as the presence of â¥ 10% polymorphonuclear cells (PMN), as defined by Sheldon et al. (2006). All additional health information was collected by the dairy herd personnel with the assistance of the 33 herd veterinarian and confirmed and recorded by the project leader using the on-farm Dairy Comp 305 software (Valley Agricultural Software, Tulare, CA). 2.2.3 Data recorded using video cameras Video was used to record behaviour of a subset of the cows, beginning with the appearance of the amniotic sac until the expulsion of the calf (n = 381; 111 heifers and 270 multiparous cows). Stage II labour, calving assistance, presence of other cows in the maternity pen, calving location, and if cows were moved during labour were recorded continuously via 5 cameras, one placed above the pre-partum pen and four above the maternity pen (CCTV camera, model WV-BP330; Panasonic, Osaka, Japan; with F1.4/2.5-186 mm lenses). The length of stage II of labour was determined as the duration from the appearance of the amniotic sac until the calf was fully expelled. Red lamps were positioned close to the cameras for low light. The pre-partum camera was used to capture cows calving behaviours before being moved into the maternity pen. Delivery of the calf was assisted based off discretion of farm management, in addition to the decision to move the cows into the maternity pen. Multiple observers were used for video observation. To determine the reliability of video observations between observers, intraclass correlations were determined for 5 cows. The intraclass correlation coefficient ranged from 0.92 to 0.99 which is considered to be excellent (Hallgren, 2012). 2.2.4 Statistical analysis A total of 67 cows were excluded from the final analysis due to postpartum illnesses, twinning, or culling, which included cows diagnosed with milk fever (n = 24), displaced abomasum (n = 9), severe lameness (gait = 4; n = 11), twinning (n = 18), culled immediately after calving (n = 2), or multiple conditions (n = 3). All statistical analyses were performed in SAS Studio (version 3.8; SAS Institute Inc., Cary, NC). Prior to all analyses, data were checked for 34 normality using the UNIVARIATE procedure and probability distribution plots; extreme outliers in unassisted calving behavior (greater than 3 interquartile ranges beyond the third quartile of the data set) were considered to be a result of farm management error and were removed (n = 4). Cows were later classified as below average (BCS < 3.25), average (BCS = 3.25 or 3.5) or above average (BCS > 3.5) based on 33rd percentiles. Non-significant variables were eliminated from the models one at a time using the Wald statistic backward selection criterion (P â¥ 0.15). Significance was set at P â¤ 0.05 and tendency at 0.05 < P â¤ 0.1. Duration of labour was rounded to the nearest minute and analyzed using multivariable linear regression. Models including the categorical variables of parity (primiparous or multiparous), BCS (below average, average, above average), calving location (pre-partum pen or maternity pen), presence of other cows in the maternity pen at the time of calving (yes or no), and moved during labour, in addition to the continuous variables of days dry, gestation length (days), and calf weight (kg) were used to assess the factors that influenced the duration of labour. Cows that were assisted during parturition were excluded from the models due to the lack of an established calving protocol for on-farm personnel. The estimates of categorical variables from the models were reported as least squares means ± SEM. Risk factors for RP were analyzed using logistic regression with models including the categorical variables calving assistance (assisted or unassisted), parity, BCS, calving location, in addition to the continuous variables duration of labour, days dry, gestation length, and calf weight. Animals diagnosed with a retained placenta after calving were removed from the metritis and subclinical endometritis models (n = 44). Risk factors for metritis and subclinical endometritis were analyzed using logistic regression with models that included the same variables as the RP models with the addition of the categorical variable of metritis diagnosis included in the subclinical 35 endometritis models. Probability of metritis in relation to the duration of labour in assisted and unassisted cows was calculated with a regression equation consisting of linear, quadratic, and cubic effects of the duration of labour. For RP, metritis, and subclinical endometritis models, odds ratios were reported for risk factor associations. The following groups of variables were assessed separately due to collinearity: calving assistance, calving location, and duration of labour; days dry, gestation length, and calf weight; calving location, presence of other cows in the maternity pen, and moved during labour. 2.3 Results Duration of labour ranged from 11 min to 173 min (mean ± SD = 57.17 ± 31.98 min; n = 238) in unassisted cows and 49 min to 232 min (122.68 ± 51.21 min; n = 44) in assisted cows. The calving factors associated with duration of labour of unassisted cows was parity, calf weight, and days dry. Primiparous cows tended to have a shorter duration of labour when compared to multiparous cows (least squares means ± SEM; 50.43 ± 3.74 vs. 57.61 ± 2.48 min; P = 0.10). As calf weight increased, the duration of labour increased (Figure 2.1; m = slope ± SEM; m = 1.16 ± 0.34; P < 0.001). The mean gestation length and days dry were 278.97 ± 4.68 and 55.95 ± 8.03 (mean ± SD), respectively. Although gestation length was not associated with duration of labour, as the number of days dry increased, there was a tendency for the duration of labour to increase (m = 0.64 ± 0.33; P = 0.07). Duration of labour was not associated with BCS, location of calving, whether the cow was moved during labour or if other cows were present in the maternity pen at the time of calving. The prevalence of RP for the study was 9%. Calf weight and BCS were associated with RP diagnosis. For every 1 kg increase in calf weight, there was a 1.08 greater odds of RP (95% CI = 1.00 â 1.56; P = 0.05). Above average BCS cows were at a 3.5 higher odds of RP when compared 36 with average BCS cows (95% CI = 1.45 â 8.51; P < 0.001), while below average BCS cows were at a 0.36 lower odds of RP when compared with average BCS cows (95% CI = 0.11 â 0.56; P < 0.01). Duration of labour, calving assistance, parity, calving location, gestation length, and days dry had no association with RP. The prevalence of metritis for the study was 28%, where 92% of cows diagnosed with RP were also diagnosed with metritis. Factors associated with metritis diagnosis were calving assistance, duration of labour, BCS, and gestation length. Cows that were assisted during calving were at a 3.69 higher odds of metritis when compared with unassisted cows (Figure 2.2; 95% CI = 1.77 - 7.68; P < 0.001). For every one minute increase in the duration of labour, there was a 1.01 higher odds of metritis (95% CI = 1.00 - 2.02; P = 0.01). Although overall BCS was not associated with metritis, below average BCS cows had a tendency to be at 0.66 lower odds of metritis when compared to average BCS cows (95% CI = 0.37 â 1.15; P = 0.06). Gestation length had a tendency to increase odds of metritis (OR = odds ratio = 1.05; 95% CI = 0.99 â 1.11; P = 0.10). Parity, calving location, days dry, and calf weight had no association with metritis diagnosis. Duration of labour had a quadratic effect on the predicted probability of metritis in assisted animals, where the predicted probability of metritis in assisted cows was greatest at the shortest (49 min; 84.5%) and longest (232 min; 100%) durations of labour, but the lowest probability was found between 133 and 136 min (27.9%; P = 0.02; Figure 2.3). Although probability of metritis in unassisted cows was not impacted by duration of labour, there a positive linear trend as observed in Figure 2.3. The prevalence of subclinical endometritis was 19% for the study, whereas cows diagnosed with RP or metritis had a greater prevalence of 42% (within both groups). The prevalence of clinical endometritis (VD of 3 or 4 and presence of â¥ 10% polymorphonuclear cells) 37 was 4%. Factors associated with subclinical endometritis diagnosis were metritis and parity. Cows diagnosed with metritis at 6 or 12h postpartum were at a 2.29 higher odds of subclinical endometritis (95% CI = 1.22 â 4.29; P = 0.01) compared to non-metritic cows. When compared to primiparous cows, multiparous cows were at a 1.92 higher odds of subclinical endometritis (95% CI = 0.99 â 3.75; P = 0.05). Duration of labour, calving score, calving location, days dry, gestation length, calf weight, and BCS had no association with subclinical endometritis. 2.4 Discussion Although most cows experience normal calving, close monitoring prior to and during calving is necessary to identify calving difficulties in order to provide appropriate intervention. Inappropriate timing of interventions, specifically assisting too early, can have detrimental effects on both the dam and the calf. Little research has investigated the relationship between the duration of labour and the development of uterine diseases; therefore, the objective of this study was to investigate the association between factors around the time of calving, duration of stage II labour, and the development of uterine diseases in dairy cattle. Although a few studies have looked at the impact of improper timing of intervention on stillbirths and injury to the dam (Gundelach et al., 2009; Kovács et al., 2016), the cut-off times chosen for appropriate timing for intervention lack explanation. Results from this study provide evidence that calving assistance should occur at approximately 130 min after the appearance of the amniotic sac in order to reduce the probability of metritis postpartum. Assisting too early during stage II labour could result in an increased risk of metritis. Late intervention may also put cows at additional risk; however, it should be noted that our model has less power to detect this effect as there were an insufficient number of cows with extreme lengths of labour. This time reference is similar to Gundelach et al. (2009) who reported increased risk of stillbirths when assistance was provided after 120 min of stage II labour. 38 Furthermore, Kovács et al. (2016) reported increased vaginal lacerations and RP when assistance was provided too early within stage II labour (prior to 70 min). Previously reported management practices used to prevent metritis include adequate feed intake during the close-up period and appropriate conditioning of dry cows (Urton et al., 2005; Hammon et al., 2006; Huzzey et al., 2007; Neave et al., 2018); however these management practices are long-term goals with limited use for interventions during calving. The current study demonstrated that proper timing of intervention is key to providing immediate aid in preventing or reducing risk of metritis postpartum. Although calving assistance was left up to the discretion of farm management, the mean duration of labour in unassisted cows (57.2 min) was comparable to other studies. Schuenemann et al. (2011) reported the mean duration of stage II labour in unassisted cows to be 45.2 ± 12.2 min (mean ± SD) while Kovács et al. (2016) reported the mean duration to be 77.4 ± 6.2 min (least squares means ± SEM) for cows in individual maternity pens and 52.6 ± 5.5 min for cows housed in a group maternity pen. Although duration of stage II labour and calving score did not have a direct impact on subclinical endometritis, it may be indirectly impacted by timely intervention through the action of metritis, as subclinical endometritis was found to be increased in cows diagnosed with metritis. Moreover, other studies have reported increased odds of endometritis associated with both dystocia and metritis (Dubuc et al., 2010; Potter et al., 2010). These contrasting results are likely due to differences in the management protocols for providing assistance as well as the definition used for dystocia, in addition to different methods of diagnosing endometritis. As calf weight increased, duration of labour increased, and the odds of RP also increased. It has been estimated that the rate of dystocia increases by 13% for every additional kilogram of 39 birth weight above 40.3 kg, in Holstein cows (Johanson and Berger, 2003). Contrary to our results, Guard (1999) suggests that bad timing due to delayed calving assistance can lead to RP due to increased uterine inflammation. The association between calf weight and RP in the current study could be due to larger calves putting more pressure on the uterine wall and therefore causing inflammation, rather than from the calving duration or assistance itself. Although gestation length did not increase duration of labour, days dry tended to increase duration of labour. This is likely due to the increased days dry increasing the weight of the calf (Atashi et al., 2013); however, decreasing the number of days dry to reduce calving difficulties is not recommended as a dry period of 50 to 60 days can optimize milk production and profitability postpartum (Schaeffer and Henderson, 1972). Furthermore, increased gestation length had a tendency to increase the odds of metritis; however, neither gestation length nor days dry were associated with subclinical endometritis or RP. Vieira-Neto et al. (2017) reported primiparous cows with short gestation lengths had higher incidence rates of RP and metritis, whereas multiparous cows with short or long gestation lengths had higher incidence rates of dystocia, RP and metritis. Although it may appear that artificially inducing labour to occur at a normal gestation length of around 280 days could aid in reducing calving and postpartum complications, inducing labour can result in an unpredictable durations of labour and negative consequences on the calf and the dam (Barth, 2006). Primiparous cows had a shorter duration of unassisted labour than multiparous cows. These results contrast to a previous study that reported primiparous cows to have a higher incidence of dystocia and increased odds of prolonged stage II labour when compared to multiparous cows (Gundelach et al., 2009). Schuenemann et al. (2011) reported no differences in parity for duration of unassisted labour. The different results found on the effect of parity on the duration of unassisted 40 labour among the three studies (Gundelach et al., 2009; Schuenemann et al. 2011; current study) is likely due to the definitions of dystocia used. Furthermore, the lack of increased calving difficulties in primiparous cows in the current study is likely associated with management factors specific to our farm, including the age at which heifers are bred (12 â 14 months), and the use of small sires often with sexed-semen to result in smaller, female calves. It is generally accepted that over-conditioning prior to calving predisposes cows to calving difficulties (Noakes et al., 2001), while under-conditioning prior to calving predisposes cows to endometritis (Dubuc et al., 2010) and metritis (Giuliodori et al., 2013). Below average BCS cows were at the lowest odds of RP and metritis when compared to cows with an average or above average BCS cows, whereas above average BCS cows were at the highest odds of RP. Although below average BCS cows performed better in our study (less RP and metritis), this could be due to a lack of truly thin animals in the herd (our lowest category of BCS included cows of BCS < 3.25 with 1% of cows having a BCS â¤ 2.5). A BCS of 3 is considered ideal for dry cows prepartum (Dubuc et al., 2010) and has been suggested to be the target BCS at calving to optimize health and production (Garnsworthy, 2008; LeBlanc, 2010). The presence of other cows in the maternity pen and calving location was not associated with duration of labour in the present study. Kovács et al. (2016) reported a shorter duration of labour in unassisted calving and a lower prevalence of dystocia in cows housed in group maternity pens, such as those used in the current study, compared with individual maternity pens. Contrary to Kovács et al. (2016), Proudfoot et al. (2014) reported that dairy cows in individual maternity pens seek seclusion to calve and therefore calving in a maternity pen without a secluded area is not recommended. Although we found no association between being moved during stage II labour and the duration of labour, Proudfoot et al. (2013) found that moving cows during the late part of 41 the stage I labour caused a delay in the second stage of labor. This difference in results may be due to Proudfoot et al. (2013) moving cows prior to stage I labour rather than moving cows during stage II labour like the current study. Furthermore, location of calving had no association with uterine diseases postpartum. Although hygiene of the calving environments and uterine bacteria flora were not measured in this current study, there is a lack of evidence indicating that hygiene of the calving environment influences the uterine bacteria flora and development of metritis or endometritis (Noakes et al., 1991; Potter et al., 2010), although it is generally accepted that poor hygiene increases the risk of uterine diseases early postpartum. Although no association between calving in free-stalls was found on the development of RP, metritis or subclinical endometritis, it is discouraged due to increased discomfort to the cow and calf, as well as safety risks for the calf and farm personnel, likely explaining why the practice is banned in some countries, such as Denmark (Danish Ministry of Justice Law no. 520, May 26, 2010). 2.5 Conclusion Appropriate timing of calving intervention can aid in the prevention of metritis early postpartum. Specifically, providing assistance too early during stage II labour may result in an increased risk of metritis, therefore we estimate a reference time of 130 mins, or roughly 2 h, after the appearance of the amniotic sac to be used as a reference point for providing calving assistance to reduce the risk of metritis. Although calf weight was associated with duration of labour and RP, we were unable to find associations between uterine diseases and other important calving factors. Furthermore, duration of labour and calving assistance appeared to not be directly associated with subclinical endometritis, although appropriate timing of intervention may indirectly reduce the risk of subclinical endometritis by reducing the risk of metritis early postpartum. 42 Figure 2.1: Scatter plot representing the association of calf weight with duration of stage II labour (appearance of amniotic sac until calf expulsion) in Holstein dairy cows (n = 224; P < 0.001). Figure 2.2: Proportion cows diagnosed with metritis at 6 or 12 DIM (P < 0.001). A total of 96 cows were diagnosed with metritis (76 unassisted and 20 assisted at calving) and 317 cows were not diagnosed with metritis (296 unassisted and 21 assisted). 02040608010012014016018020025 30 35 40 45 50 55 60Duration of labour (mins)Calf weight (kg)y = 1.16x + 6.0280%51%20%49%0102030405060708090Unassisted AssistedProportion (% of cows)No Metritis Metritis 43 Figure 2.3: Effect of duration of labour in cows assisted (n = 34; P = 0.02) and unassisted at calving (n = 194; P = 0.16) on the predicted probability of metritis. 01020304050607080901000 25 50 75 100 125 150 175 200 225 250Predicted probability of metritis (%)Duration of labour (mins)Assisted Unassisted 44 Chapter 3: Evaluation of automated activity monitors to predict calving time and uterine diseases in Holstein dairy cows 3.1 Introduction The close monitoring of individual cows is becoming more difficult and less feasible due to herds growing in size, shortage of available workers, labour costs, and the decreasing number of farm staff per cow (Rutten et al., 2013; Barkema et al., 2015; Stevenson and Britt, 2017). Automated activity monitoring systems (AAMs; i.e., pedometers, accelerometers, thermometer loggers, GPS) are becoming more widely used on-farm for estrous and disease detection via changes in physical activity, rumination, body temperature, and feeding time. However, monitoring activity is more commonly used on commercial farms for estrous detection (Stevenson and Britt, 2017). Newly available AAM technologies and sensors allow personnel and researchers to continuously monitor these physical changes as they occur (Stevenson and Britt, 2017) and therefore prove to be beneficial as proactive farm management practices. Close monitoring prior to and during calving is necessary to identify cows with calving difficulties as early as possible to provide appropriate intervention. Management of calving often consists of subjective visual observations of imminent signs of calving, either in the close-up or calving pen, and appropriate intervention when lack of calving progress occurs. This form of management is only successful when the close-up pen is closely monitored at all times; however, this is often not feasible on most dairy farms. According to the USDAâs (2010) NAHMS survey, close-up animals were observed more frequently during the day than at night. During the daytime, 47.2% of dairy farms monitored cows every 3 or less hours while 17.6% monitored cows every 5 or more hours (USDA, 2010). However, due to the average time from the appearance of the 45 amniotic sac to expulsion of the calf being around only 70 min (Schuenemann et al., 2011), risk for late intervention increases as the frequency of close-up observations decreases. Accurate and timely diagnoses of diseases are essential for proactive management of transition diseases (e.g., metritis and endometritis). On most dairy farms, the identification and treatment of sick animals is often performed by on-farm employees (Espadamala et al., 2016). In a survey of 45 dairy farms in California, health-monitoring exams for early detection of metritis were implemented on 42% of the farms (Espadamala et al., 2016). Often metritis diagnosis is based on assessment of vaginal discharge via per rectum palpations; however this is a subjective screening method that lacks sensitivity (LeBlanc et al., 2002; Espadamala et al., 2018). Currently, subclinical endometritis can only be diagnosed via assessment of the endometrial uterine lining via swab or cytobrush. This method of diagnosis is not feasible for on-farm personnel and therefore proactive endometritis management practices have not been extensively researched. Due to calving and health assessments being subjective and time consuming, an automated device that is commercially available to farmers that could accurately predict calving and disease diagnose would be valuable to minimize the effect associated with calving difficulties and uterine diseases. Changes in lying behaviour and restlessness prior to calving and metritis diagnoses have been commonly observed using data loggers (e.g., Miedema et al., 2011a; Liboreiro et al., 2015; Titler et al., 2015; Barragan et al., 2018; Neave et al., 2018). Although many of the presented studies measured changes in activity behaviours using data loggers and summarized the observed data, there is potential to make an alert system using established thresholds to predict calving events. Ouellet et al. (2016) investigated if lying behaviour, in addition to rumination time and vaginal temperature, could accurately predict the onset of calving within 24, 12, and 6 h. Ouellet et al. (2016) found decreased lying duration and increased lying bouts leading up to calving; 46 however, it was reported that changed lying bouts and lying duration had moderate to low specificity and sensitivity. Although the previously discussed studies have investigated using activity and lying behaviour to indicate calving and metritis diagnosis, these studies made comparisons between groups of cows, thus not aimed at development of alerts, and used data loggers that are not readily available to farmers. To our knowledge, the use of changes within cows via calculated relative changes in activity and lying behaviour to predict calving or uterine diseases has not been studied. In addition, to our knowledge, the test performances of activity and lying behavior as indicators for uterine health diagnosis has not been studied. Therefore, the objective of this study was to investigate the performance of two commercially available activity monitors to predict calving time and uterine disease. Specifically, we aimed to 1) investigate the test performance of relative changes in activity and lying behaviour to predict calving time during four time periods (within 8, 16, 24, and 32 h) before calving and 2) investigate the test performance of relative changes in activity and lying behaviour to predict uterine diseases during five time periods (wk -2, -1, +1, +2, and +3 relative to calving) before diagnosis. We hypothesized that a decrease in lying time and an increase in restlessness would predict calving. We also hypothesized that an increase in lying time postpartum would predict uterine diseases. 3.2 Materials and methods This project was conducted at the University of British Columbiaâs Dairy Education and Research Centre in Agassiz, BC from May 2016 to March 2019. All procedures were carried out in accordance with the University of British Columbiaâs Animal Care Committee. In addition, the cows used in this experiment were cared for as outlined by the guidelines provided by the Canadian Council of Animal Care. 47 3.2.1 Animals and housing The cows used in this study were also used in a study presented in Chapter 2. The cows, housing, and other material and methods are discussed in the previous chapter and outlined below. A total of 567 Holstein dairy cows, 161 primiparous and 406 multiparous, were used in this study. All cows were housed in a free-stall system. Prior to calving, cows were fed a total mixed ration formulated for transition cows. After calving, a TMR formulated for high production lactating cows designed to meet or exceed the requirements of Holstein cows weighing 620 kg, producing 45 kg/day of 3.5% fat corrected milk was used (NRC, 2001). Water and feed were available ad libitum. Cows were moved into the close up pre-partum pen approximately 21 days before their expected calving date and moved into an adjacent maternity pen when they showed physical signs of imminent calving (i.e. relaxation of sacrosciatic ligament and enlarged udder; Miedema et al., 2011a; Proudfoot et al., 2013). Upon enrollment at 21 ± 3 days before the cowsâ expected calving date, body condition and gait were scored. Body condition was scored on a 5-point scale from thin (1) to obese (5) by increments of 0.25 as outlined by (Edmonson et al., 1989). Gait scores were given at the same time on a 4-point scale from sound (1) to severely lame (4) in increments of 1 as outlined by Flower and Weary (2006). Calving assistance was provided based on discretion of farm management. Calving time was recorded as the calf was expelled from the birth canal to the ground via cameras placed above the pre-partum pen and maternity pen (CCTV camera, model WV-BP330; Panasonic, Osaka, Japan; with F1.4/2.5-186 mm lenses). Within 24 h after calving, cows were moved to the lactating cow pens. Postpartum cows were milked twice per day, once between 0500 and 0800 h and again between 1500 and 1800 h, in a double twelve parallel milking parlor. 48 3.2.2 Disease diagnosing The disease diagnosing used in this study is discussed in the previous chapter and outlined below. Retained placenta (RP) was diagnosed as failure to expel fetal membranes within 24 h after calving. Diagnosis of metritis was determined by evaluation of vaginal discharge (VD) and temperature was evaluated for each cow at 6 and 12 days postpartum via a Metricheck device (Simcrotech, Hamilton, New Zealand) inserted into the vaginal canal up to the cervix. The appearance and smell of the VD was evaluated using the following scale adapted from (Williams et al., 2005): 1 = no discharge or clear mucus; 2 = cloudy mucus with < 50% purulent; 3 = purulent >50%; and 4 = putrid (red/brown color, watery, foul smelling). Metritis was classified as at least one VD score of 3 with a fever (â¥ 39.5°C) or VD score of 4 independent on the body temperature. Vaginal discharge of cows that still had an RP at 6 days postpartum was not evaluated. At 42 ± 3 days postpartum, following vaginal examination, a cytological examination was performed via an endometrial cytobrush (CooperSurgical Inc., Trumbull, CT) for analysis of polymorphonuclear cells (PMN). Subclinical endometritis was diagnosed as the presence of â¥ 10% PMN, as defined by Sheldon et al. (2009). All additional health information was collected by the dairy herd personnel with the assistance of the herd veterinarian and confirmed and recorded by the project leader using the on-farm Dairy Comp 305 software (Valley Agricultural Software, Tulare, CA). 3.2.3 Data recorded using automated activity monitors Activity and lying behaviour were recorded via two automated activity monitors (AAM), a leg-mounted (AAML; n = 377; AfiMilk®, Afitagâ¢, Kibbutz Afikim, Isreal) and a collar-mounted (AAMC; n = 333; Heatime®, H-Tags, SCR Engineers, Netanya, Israel). Recorded data from the AAML included lying time, duration of lying time per bout, activity, and restlessness (ratio between 49 steps per h and duration of lying). Recorded data from AAMC included only activity. Tags and collar were mounted to the cows approximately 28 d before the expected calving date in order to establish baseline activity. AAML data was manually saved every 10 d, resulting in data being summarized into 8 h blocks from 00:00 â 7:59, 8:00 â 15:59, and 16:00 â 23:59 h. AAMC data was automatically saved every 7 d, resulting in data being summarized into 2 h blocks. 3.2.4 Statistical analysis A total of 67 cows were excluded from the final analysis due to milk fever (n = 24), displaced abomasum (n = 9), severe lameness (gait = 4; n = 11), twinning (n = 18), culled immediately after calving (n = 2), or multiple conditions (n = 3). All statistical analyses were performed in SAS Studio (version 3.8; SAS Institute Inc., Cary, NC). Cows were later classified according to body condition score (BCS) as thin (BCS < 3.25), average (BCS = 3.25 or 3.5) or moderate (BCS > 3.5) based on 33rd percentiles. Nonsignificant variables were eliminated from the models one at a time using the Wald statistic backward selection criterion (P > 0.15). Significance was set at P â¤ 0.05 and tendency at 0.05 < P â¤ 0.1. Activity data included activity (arbitrary unit; measured as an index) from the AAMC and lying time (min), duration of lying bouts (min/b), activity (steps/h), and restlessness (arbitrary unit) from the AAML. Data was automatically summarized into hour time blocks (AAML: 8 h blocks; AAMC: 2 h blocks). Changes in activity data from 72 h prior to calving to calving time (0 h) were analyzed in a mixed model using the PROC MIXED procedure with hour relative to calving as a repeated measure and cow as a random effect. The fixed effect included hour relative to calving (â72 to 0 h), parity (primiparous or multiparous), BCS (thin, average, or moderate), and the interaction of parity and hour relative to calving. 50 For metritis and subclinical endometritis analysis, activity data from both monitors were summarized into 3 weekly periods, wk â2 (d â14 to â8), wk â1 (d â7 to â1), and wk + 1 (d 0 to d 5), with the additional weekly periods of wk +2 (d 6 to d 11) and wk +3 (d 12 to d 18) for subclinical endometritis analysis only. Changes in activity data between metritic and non-metritic, or subclinical endometritic and non-subclinical endometritic, were analyzed in a mixed model using the PROC MIXED procedure with week relative to calving as the repeated measure and cow as a random effect. Fixed effects included health (metritic or non-metritic; subclinical endometritic or non-subclinical endometritic), parity, calving assistance, and BCS. For the calving, metritis, and endometritis analyses, first order autoregressive, AR(1), covariance structure was used to account for the correlated data structure between the repeated time measurements within animals. The differences in least squares means of the activity data among the 72 h before calving and weeks were computed by including the PDIFF option in the LSMEANS statement. Least squares means and standard errors were reported. To determine test performance of activity and lying behaviour at predicting calving time during four time periods (within 8, 16, 24, and 32 h) before calving, relative changes in activity data variables were calculated. Relative changes were calculated by determining the change in activity data at 8, 16, 24, and 32 h, relative to calving time, in comparison to a baseline calculated specifically for each cow based on the previous 7 days. Following the formula from Madureira et al. (2019), using the example of activity measured via AAML for the time period of 16 h before calving, was used : [(activity/8 h at 16 h before calving â activity/8 h at baseline)/ (activity/8 h baseline)] x 100. Receiving operating characteristic (ROC) analyses for differences were then conducted in logistic regression models using the PROC LOGISTIC procedure to determine the diagnostic performance and cut-off points of changes in activity data. Cut-off points were defined 51 as the threshold calculated for each activity data optimizing both sensitivity (Se) and specificity (Sp) for predicting calving time. The continuous variable was relative change in activity data, whereas the classification variable was the occurrence of calving within 8, 16, 24, or 32 h. A prediction of calving was considered when a relative change in activity data were observed and greater or equal to the cut-off point. Test characteristics of Se and Sp for predicting calving for each cut-off point and the 95% confidence interval (95% CI) were then calculated using frequency tables generated using the PROC FREQ procedure. Area under the curve (AUC) was also determined and 95% CI were calculated. Sensitivity was defined as the proportion of positive events (occurrence of calving within 8, 16, 24, or 32 h) correctly predicted by the test (calving correctly predicted/total calving events). Specificity was defined as the proportion of negative events (absence of calving within 8, 16, 24, or 32 h) correctly diagnosed as being negative by the test (absence of calving correctly predicted/total of absence of calving). To determine test performance of activity and lying behaviour at predicting metritis during three periods (wk -2, -1, and +1) or subclinical endometritis during five periods (wk -2, -1, +1, +2, and +3), relative changes in activity data were calculated. Relative weekly changes were calculated by determining the change in activity data at wk -2, -1, +1, +2, and +3 relative to calving day, in comparison to a baseline calculated specifically for each cow based on the previous summarized week. ROC analyses (similar to the calving ROC analyses) were then conducted using a logistic regression to determine the diagnostic performance and cut-off points of changes in activity related behaviours. Cut-off points were defined as the threshold calculated for each of the activity related behaviours, optimizing both Se and Sp for predicting metritis or subclinical endometritis with the classification variable being one of the uterine disease diagnoses. Test characteristics (Se and Sp) for predicting metritis or subclinical endometritis for each cut-off point, 52 in addition to AUC, and the 95% CI were then calculated. Sensitivity was defined as the proportion of positive events (metritis or subclinical endometritis diagnosis) correctly predicted by the test (diagnoses correctly predicted/total number of metritic or subclinical endometritic cows). Specificity was defined as the proportion of negative events (diagnosed as healthy) correctly diagnosed as being negative by the test (absence of metritis or subclinical endometritis correctly predicted/total of metritis or subclinical endometritis). 3.3 Results Activity and lying behaviour prior to calving An increase in mean AAMC activity in the final 2 h period before calving was observed (F6,982 = 1.8, P < 0.01; Figure 3.1). Mean activity was associated with parity, with primiparous cows having higher activity than multiparous cows during the 72 h before calving (40.1 ± 0.9 vs. 37.5 ± 0.5 arbitrary units; F1,196 = 6.7, P < 0.01). Primiparous cows showed significant differences in activity over the 72 h prior to calving when compared to multiparous cows, specifically, primiparous cows showed a higher increase in activity 2 h period before calving when compared to multiparous cows (5.1 ± 1.9 vs 2.4 ± 1.2 arbitrary units). Body condition score had a significant effect on activity prior to calving, with thin and moderate BCS cows having lower mean activity when compared to average cows, 38.8 ± 0.6, 37.2 ± 1.0, and 40.3 ± 0.8 arbitrary units, respectively (F2,196 = 3.5, P < 0.05). Similar to the AAMC activity, an increase in mean AAML activity during the last two 8 h periods before to calving was observed (F1,472 = 19.9, P < 0.001; Figure 3.2a). Primiparous cows had higher mean activity when compared to multiparous cows during the 72 h prior to calving (161.6 ± 4.2 vs. 134.3 ± 2.7 steps/h; F1,354 = 29.8, P < 0.001). Furthermore, primiparous cows showed significant differences in AAML activity between hourly time blocks prior to calving when 53 compared to multiparous cows, specifically primiparous cows showed a higher increase in activity during the 16 to 24 h periods and 0 to 8 h periods before calving when compared to multiparous cows (18.7 ± 7.2 vs 11.8 ± 4.7; 46.4 ± 7.1 vs 29.0 ± 4.7 steps/hr, respectively). Lying time and lying time per bout decreased during the last two 8 h periods prior to calving (F1,228 = 14.8, P < 0.001, Figure 3.2b; F1,286 = 21.5, P < 0.001, Figure 3.2c). Multiparous cows spent more time lying than primiparous cows during the 72 h prior to calving, 227.5 ± 2.2 min vs. 193.1 ± 3.4 min, respectively (F1,920 = 73.2, P < 0.001). Furthermore, multiparous cows spent more time lying per lying bout than primiparous cows prior to calving, 67.1 ± 1.1 vs. 39.2 ± 1.7 min, respectively (F1,697 = 198.8, P < 0.001). Although multiparous cows showed differences in total lying behaviour when compared to primiparous cows, no distinctive hourly difference in changes in lying behaviour were observed between parity groups prior to calving. Body condition score had no association with AAML activity, lying time, or lying time per bout. Prior to calving, there were no observed changes in restlessness. Parity and BCS had no association with restlessness. Activity and lying behaviour prior to uterine disease diagnosis The prevalence of metritis for the study was 28% while the prevalence of subclinical endometritis was 19%. Cows later diagnosed with metritis tended to have lower mean AAMC activity (F1,240 = 3.6, P = 0.06) and higher mean lying time per bout (F1,221 = 2.9, P = 0.09) when compared to cows not diagnosed with metritis. Specifically, during the wk +1 after calving, cows later diagnosed with metritis had marginally lower mean AAMC activity (P < 0.05) and higher mean lying time per bout (P < 0.01) when compared to cows not diagnosed with metritis (Table 3.1). AAMC and AAML activity and restlessness increased postpartum, while lying time and lying time per bout decreased postpartum. There was no difference in AAML activity, mean lying time, total lying time, and restlessness between metritic and non-metritic cows (Table 3.1). 54 Overall AAML activity did not differ between subclinical endometritic cows and non-subclinical endometritic cows; however, AAMC mean activity was higher prepartum and lower postpartum in subclinical endometritic cows (F1,181 = 2.4, P < 0.05). Subclinical endometritic cows tended to have a lower mean AAMC (P = 0.1) and AAML (P = 0.1) during wk +1 when compared to non-subclinical endometritic cows (Table 3.2). Cows later diagnosed with subclinical endometritis tended to have lower mean lying time when compared to cows not diagnosed with subclinical endometritis (F1,476 = 2.2, P = 0.06). Specifically, during wk +3, cows later diagnosed with subclinical endometritis had lower mean lying time than cows not diagnosed with subclinical endometritis (P < 0.05). Furthermore, cows later diagnosed with subclinical endometritis spent less total time lying during wk -2 prior to calving than cows not diagnosed with subclinical endometritis (75.0 ± 2.4 h vs. 81.0 ± 1.8 h; P < 0.05). Lying time per bout and restlessness did not differ between subclinical endometritic cows and non-subclinical endometritic cows. Test performances in predicting calving and uterine disease diagnosis Among all activity variables, a relative decrease in lying time per bout showed the highest combination of Se and Sp, along with the highest AUC, for calving within the next 24, 16, and 8 h (Table 3.3). Specifically, a relative decrease of 44% in lying time per bout 8 hr before calving showed the highest test performance with a Se of 67%, Sp of 77%, and AUC of 0.76. AAMC and AAML activity showed increased relative changes as calving approached, but had low Se, Sp, and AUC for calving within 32, 24, 16, and 8 h. Similar, low test performances were observed with lying time and restlessness. As calving approached, relative decreases in lying time and relative increases in restlessness were observed over the 32, 24, 16, and 8 h. During the 32, 24, 16, and 8 h as calving approached, lying time showed relative decreases while restlessness showed relative 55 increases. When stratifying by parity to investigate its effect on test performances, results changed marginally and therefore were not reported. A relative change in lying time per bout of â¤ -12% measured by the AAML, showed the highest combination of Se and Sp at wk -2 (Se = 54%; Sp = 71%; AUC = 0.64) followed by a mean lying time of â¥ -7%, measured by the AAML, at wk +1 (Se = 64%; Sp = 64%; AUC = 0.6) when compared to the other activity variables. This indicates that non-metritic cows had a greater increase in lying time per bout before calving and a decrease in mean lying time after calving when compared to metritic cows. Prior to calving, a relative decrease in mean lying time less than or equal to the cut-off was predictive of metritis diagnosis; however, after calving, a relative change in activity greater than or equal to the cut-off was observed. Over the three week periods, AAMC and AAML activity, total lying time, lying time per bout, and restlessness fluctuated week to week between relative increases and decreases being either less than or greater than the calculated cut-offs. In general, total lying time and lying time per bout fluctuated prior to calving; however, after calving a relative change greater than or equal to the cut off was observed indicating that metritic cows had a lower relative change in lying behaviour when compared to non-metritic cows. Overall, relative restlessness increased after calving with metritic cows having a lower relative change in restlessness when compared to non-metritic cows. For predicting subclinical endometritis, a relative change in AAMC activity of â¤ -6% showed the highest combination of Se and Sp at wk +1 (Se = 54%; Sp = 78%; AUC = 0.67). Although a greater relative decrease in activity during wk +1 predicted subclinical endometritis, a greater relative increase in AAMC activity was predictive of subclinical endometritis during the other weeks. Similar to metritis diagnosis cut-offs, AAML activity, mean and total lying time, lying time per bout, and restlessness fluctuated week to week between relative increases and decreases 56 being either less than or greater than the calculated cut-offs. Lying time and restlessness showed large fluctuations across the 5 week periods, indicating inconsistent relative changes in lying time and restlessness between subclinical endometritic and non-subclinical endometritic cows. A greater relative decrease in lying time per bout was predictive of subclinical endometritis before calving. After calving, a greater relative increase in lying time per bout was predictive of subclinical endometritis. 3.4 Discussion Activity and lying behaviour prior to calving Cows exhibited distinctive changes in activity and lying behaviours within the 24 h prior to calving (Figure 3.1 and Figure 3.2). Cows laid down less, spent less time lying per bout, and were more active during the last 16 h before calving with AAML and last 2 h with AAMC when compared to the previous hours. These results suggest that calving may be detected by behavioural changes at least 16 h before calving with AAML and at least 2 h before calving with AAMC. The change in activity measured by AAML can be explained biologically as it coincides with the start of the first stage of labour (i.e. the dilation of the cervix and start of uterine contractions). It is thought that this stage of labour in cattle begins approximately 12 h before expulsion of the calf (Noakes et al., 2001). Although an increase in mean AAMC activity was only observed during the last two 2 h periods prior to calving, an increase in mean AAML activity was observed during the last two 8 h periods prior to calving. The increase in activity prior to calving is similar to previous reports (Miedema et al., 2011a; Jensen, 2012) while the decrease in lying time prior to calving contrasts previous reports (Miedema et al., 2011a; Jensen, 2012; Titler et al., 2015; Ouellet et al., 2016). Although a decrease in lying time on the day of calving has been reported (Titler et al., 2015), 57 previous studies did not identify any difference in lying time between hourly periods (Miedema et al., 2011a; Jensen, 2012; Titler et al., 2015). Ouellet et al. (2016) reported that lying time was numerically lower in the interval of 12 to 6 h before calving. The cause of variation between Ouellet et al. (2016) and the present study might be due to the different housing systems. Ouellet et al. (2016) housed cows in a tie-stall which restrained their movements, which could explain the smaller decrease in lying observed prior to calving; however this does not explain the difference between the other contrasting reports. Furthermore, variation between other studies and this present study might also be due to lack of calibration/validation between monitors. Differences in activity and lying behaviours between primiparous and multiparous cows were observed. The effect of parity on cow behaviour prior to parturition has been described elsewhere (Wehrend et al., 2006; Miedema et al., 2011b; Schuenemann et al., 2011; Titler et al., 2015). In the present study, primiparous cows were more active and spent less time lying and lying per bout during the 72 h before calving when compared to multiparous cows. Similar results were observed by Miedema et al. (2011a) during the last 2 h before calving. Decreases in lying bouts have also been observed in primiparous cows during the 24 h before calving (Titler et al., 2015). Variation in behavioral signs such as frequency of postural changes (Schuenemann et al., 2011; Titler et al., 2015) and non-activity related restlessness such as tail raising and vocalization (Wehrend et al., 2006; Miedema et al., 2011a) have been reported between primiparous and multiparous cows. Although no changes in activity related restlessness by parity prior to calving were observed in this present study, it has been suggested that primiparous cows may not have fully acquired a maternal experience and may be prone to restless behavior due to pain before parturition (Wehrend et al., 2006). Furthermore, increased lying bouts have been described as restless behavior characterized by increased frequency of postural changes (Barrier et al., 2012; 58 Jensen, 2012). Increased restlessness has been commonly observed before calving and has been attributed to discomfort during the process of calving (Huzzey et al., 2005; Schuenemann et al., 2011). However, in the present study restlessness recorded via AAM remained unchanged around time of calving. Although the greatest changes in activity and lying behaviour occurred at 0 h, this result should be interpreted with caution due to the AAMs summarizing the data into multiple hour blocks of time. If a cow calved near the end of the time block, the data recorded would be representative of the cowâs pre-calving behaviours, while if a cow calved near the beginning of the time block the data recorded would be more representative of immediate post-calving behaviour. While this is a limitation of this study, an alert based on activity related behavioural changes at 0 h (i.e. when the calf is fully expelled) has little practical value. Activity and Lying Behaviour Prior to Uterine Disease Diagnosis We found that cows later diagnosed with metritis tended to spend more time lying per bout and were less active during the two weeks prepartum and one week postpartum prior to diagnosis when compared to non-metritic cows. During the wk +1 postpartum, cows later diagnosed with metritis were less active and showed higher mean lying time per bout when compared to cows not diagnosed with metritis. We observed no difference in lying time between metritic and non-metritic cows. During the postpartum transition period, similar reports of decreases in activity around the time of metritis diagnosis have been made. Liboreiro et al. (2015) reported decreases in activity in cows later diagnosed with metritis during the first 5 days postpartum; however, it was not clearly stated the type of physical activity (i.e. standing, steps, or movement) measured. Similar to the present study, Liboreiro et al. (2015) used a commercially available activity monitor that reported activity as an arbitrary unit. Tilter et al. (2013) reported that cows with later diagnosed 59 metritis took less steps, spent more time standing, and had fewer lying bouts during the 1 to 3 day before and after diagnosis when compared with healthy cows. Specifically, cows diagnosed with metritis took significantly fewer steps 1 day prior to diagnosis and continued to decrease following diagnosis (Titler et al., 2013). Similar changes in lying time per bout were observed by Neave et al. (2018) in the 3 day period prior to metritis diagnosis. Neave et al., (2018) reported increased lying time per bout, fewer lying bouts, and no changes in lying time during the same period prior to metritis diagnoses. Although in this present study frequency of lying bouts was not measured, having a longer lying time per bout with no observed differences in lying time indicates few lying bouts. Our results support Neave et al. (2018)âs suggestion that cows may be conserving energy by lying down for longer periods of time, resulting in fewer bouts throughout the day. A recent study showed that lying time increased in cows after metritis diagnosis at 7 ± 3 days postpartum (Barragan et al., 2018); perhaps lying time is affected only after clinical symptoms of metritis become apparent (Neave et al., 2018). Currently there is a lack of research investigating activity related behaviours and cows diagnosed with endometritis. To our knowledge, no other studies have investigated changes in activity related behaviours early postpartum in cows later diagnosed with endometritis or subclinical endometritis. We found that cows later diagnosed with subclinical endometritis were more active and spent less time lying prepartum and were less active and continued to spend less time lying early postpartum when compared to cows not diagnosed with subclinical endometritis. Further research investigating time budgeting, in addition to lying bout and standing behaviour changes during the prepartum is needed to support our findings. During the postpartum period, the observed decrease in activity and lying behaviour may be a result of increased standing time. It 60 has been suggested that a decrease in lying time is representative of an increase in standing time (Neave et al., 2018). Furthermore, a decrease in lying time and increase in standing may be consequence of engaging in other behaviours, such as competitive behaviours at the feed bunk (Neave et al., 2018). According to Neave et al. (2018), cows later diagnosed with metritis were found to be just as competitive as healthy cows at the feed bunk before calving and may have also competed for desirable lying spaces, although this was not measured in their study. Increased activity, decreased lying time, and competitive behaviours are energetically costly behaviors and may have compromised the immune system, making the animal more susceptible to infection postpartum. Further research is required to investigate behavioural changes in cows diagnosed with endometritis. Test performances in predicting calving and uterine disease diagnosis Although distinct changes in activity and lying behaviour were observed prior to calving, metritis diagnosis, and subclinical endometritis diagnosis, the current technology shows promising results for on-farm detection of calving; however, it may not be the most practical and reliable method for detection of uterine disease diagnosis. Test characteristics used to determine the predictability of the two commercially available AAMs were Se, Sp, and AUC. Sensitivity evaluates how good the test is at detecting an outcome while Sp evaluates how likely the used population without said outcome can be correctly ruled out (Zhu et al., 2010). In the present study, the outcome is calving or uterine diseases. The AUC is a useful tool to assess the accuracy of a diagnostic test and to compare the performance of more than one test for the same outcome (Fan et al., 2006; Zhu et al., 2010). The AUC indicates the ability of the test to distinguish between cows that will calve and the cows that will not calve within the designated hours or cows that will be diagnosed with a uterine disease and cows that will not be 61 diagnosed with a uterine disease (Ouellet et al., 2016). Therefore, an AUC of 1 (i.e., Se = 100% and Sp = 100%) indicates that the test can perfectly distinguish between cows with or without the outcome, whereas an AUC of 0.5 (i.e., Sp = 50% and Se = 50%) indicates the test is unable to categorize cows with or without the outcome (Fan et al., 2006; Ouellet et al., 2016). Ideally, the best cut-off point provides both high Se and Sp with high AUC; however it is rare that this can be achieved (Fan et al., 2006). Although several scales are available for AUC interpretation, it is generally accepted that a test with an AUC â¥ 0.75 is clinically useful (Fan et al., 2006) and a test with an AUC â¥ 0.7 is acceptable (Mandrekar, 2010). According to the interpretation of Fan el al. (2006), a relative decrease in excess of 44% in lying time per bout at 8 h before calving is clinically useful (AUC = 0.76; Se = 67%; Sp = 77%). A relative increase in activity and restlessness, in addition to a relative decrease in lying time showed lower performances (e.g., AUC = 0.69 at 16 h and AUC = 0.65 at 24 h for lying time per bout) to predict calving; however, these lower performances are near acceptable according to the interpretation of Mandrekar (2010). With the increase in need for proactive management practices to detect calving, these results show that lying time per bout over the 24 h before calving could be used and refined in on-farm practices. The observed test performances in this current study are similar to those presented by Ouellet et al. (2016); they investigated the test performance of lying time and lying bouts, vaginal temperature, and rumination time at 6, 12, and 24 h before calving. Although lying time per bout was not measured, Ouellet et al. (2016) also observed a decrease in lying time prior to calving. It was reported that Se ranged from 48% to 57%, Sp ranged from 47% to 57%, and AUC ranged from 0.52 to 0.58 over the three calving prediction times. The lower test performances in this current study, as well as Ouellet et al. (2016), are likely due to the large variation in activity and 62 lying behaviour among cows. Ouellet et al. (2016) reported better test performance in measured changes in vaginal temperature with the highest AUC of 0.8, Se of 74%, and Sp of 51% occurring at 24 h before calving based on a cut-off of a 0.1°C/6 h decrease. The authors speculate that physiological changes, such as changes in vaginal temperature, might be more stable among cows than behavioral ones and therefore are more accurate for predicting calving time. Prior to metritis diagnoses, although distinct differences in AAMC and mean lying time were observed between metritic cows and non-metritic cows, relative changes in all AAM variables fluctuated within animals. Poor test performance was observed across all activity and lying behaviours at the three defined time periods with the best performance occurring at wk +1 for mean lying time with a cut-off of â¥ -7% (Se = 64%; Sp = 64%; AUC = 0.6). Similar results were observed prior to subclinical endometritis diagnosis. Although distinctive difference in lying behaviour were observed between health groups, poor test performance was observed across all variables. Activity measured by the AAMC at wk +1 had near acceptable test performance, according to the interpretation of Mandrekar (2010), with a cut-off of â¤ -6% (Se = 54%; Sp = 78%; AUC = 0.67). To our knowledge, this is the only study that has investigated the test performance of two commercially available AAMs to predict uterine disease outcomes. However, Stangaferro et al. (2016) evaluated the performance of a commercially available automated health-monitoring system to identify cows with metritis based on an alert system (health index score) that combines rumination time and physical activity. It was reported that the overall sensitivity of the system was 55% for all cases of metritis but was greater for cows with metritis and another disorder (78%). Furthermore, Stangaferro et al. (2016) reported that the system was more effective at identifying cows with severe metritis than it was for identifying cows with mild cases of metritis. It is 63 suggested that this technology could be a useful tool for identifying cows with metabolic and digestive disorders, and more severe cases of mastitis and metritis. The lack of clinically useful test performances is likely due to the large variation and fluctuation in activity related behaviours between and within animals. Although parity was found to be a significant influencer on activity related behaviours, it only marginally changed the test performances when the analysis was stratified by parity. This indicates that the observed variation between and within animals may not be explained by any of the individual measured fixed effects. Further investigation of AAM and health alerts that include more than one variable (e.g. lying time per bout and activity) to predict calving and disease diagnoses is warranted. As herds grow in size, along with increased shortage of available workers, labour costs, and the decreasing number of farm staff per cow, close monitoring of individual cows is becoming more difficult and less feasible (Rutten et al., 2013; Barkema et al., 2015; Stevenson and Britt, 2017). An automated device that measures physiological change, such as body temperature, in addition to activity and lying behaviours could be a useful tool for proactive calving and health management practices. 3.5 Conclusion Activity, lying time, and lying time per bout measured via two commercially available AAMs showed clear changes associated with the calving time. Differences in activity and lying time per bout were observed in cows later diagnosed with metritis, while differences in lying time were observed early postpartum in cows later diagnosed with endometritis. Although overall test performances for early prediction of uterine diseases were poor, lying time per bout showed promising results for predicting calving. Nonetheless, the results are not sufficient to precisely predict calving with automated technology alone; however, in combination with other proactive management practice, AAMs could be used and refined in on-farm practices. With increasing 64 advancements in technology, these automated devices could be a useful tool for proactive calving and diseases management on commercial dairy farms. Figure 3.1: Least squared means Â± standard error of the mean of activity measured as an index in 2 h time blocks via a collar-mounted automated activity monitor in 192 Holstein cattle during the 72 h before calving (P < 0.01). Hours with * differ in activity compared to previous hour (P < 0.01). 3436384042444648-72 -66 -60 -54 -48 -42 -36 -30 -24 -18 -12 -6 0Activity (arbitrary unit)Hour Relative to Calving* * 65 Figure 3.2: Least squared means Â± standard error of the mean of (a) activity (steps/h; P < 0.001), (b) lying bout duration (min per bout; P < 0.001), and lying time (min; P < 0.001) measured in 8 h time blocks via a leg-mounted automated activity monitor in 227 Holstein cattle during the 72 h before calving (P < 0.01). Hours with * differ in activity compared to previous hour (P < 0.01). A) B) C) 100120140160180200220-72 -64 -56 -48 -40 -32 -24 -16 -8 0Activity (steps/h)* * * 15253545556575-72 -64 -56 -48 -40 -32 -24 -16 -8 0Lying Bout Duration (min/bout) * * * 150170190210230250270-72 -64 -56 -48 -40 -32 -24 -16 -8 0Lying Time (min)Hour Relative to Calving * 66 Table 3.1: Activity and lying behaviour measures (least squares mean ± SEM) from a collar-mounted (AAMC; n = 241) and a leg-mounted (AAML; n = 208) monitor for metritic (AAMC = 60; AAML = 70) and non-metritic (AAMC = 181; AAML = 176) Holstein dairy cows during the 2 wk before calving (wk â2 and â1) and 1 wk after calving (prior to diagnosis). Period Before Calving After Calving Variable wk -2 wk -1 wk +1 AAMC Activity1 Non-Metritic2 38.9 ± 0.8 40.0 ± 0.7 40.8 ± 0.7a Metritic 37.3 ± 1.2 38.0 ± 1.2 38.2 ± 1.2b AAML Activity (steps/h) Non-Metritic 127.3 ± 3.3 133.3 ± 3.3 168.3 ± 3.3 Metritic 131.3 ± 4.6 137.2 ± 4.6 167.3 ± 4.5 Mean Lying Time (min) Non-Metritic 241.1 ± 2.3 231.9 ± 3.0 209.6 ± 3.0 Metritic 238.9 ± 4.1 225.3 ± 4.1 207.5 ± 4.1 Total Lying Time (h/wk) Non-Metritic 80.9 ± 1.2 78.8 ± 1.2 60.9 ± 1.1 Metritic 80.9 ± 1.6 77.8 ± 1.6 61.1 ± 1.6 Lying Time Per Bout (min/bout) Non-Metritic 68.7 ± 1.7 61.8 ± 1.7 55.7 ± 1.7a Metritic 70.6 ± 2.4 64.5 ± 2.4 63.5 ± 2.4b Restlessness (arbitrary unit) Non-Metritic 3.6 ± 0.3 4.1 ± 0.3 5.1 ± 0.3 Metritic 3.9 ± 0.4 4.2 ± 0.4 4.5 ± 0.4 a,b Different superscripts within a column indicate significant difference (P â¤ 0.05) between diagnosis categories. 1AAMC Activity: measured by an index created by the manufacturer. 2Metritis was classified as at least one vaginal discharge (VD) score of 3 with a fever (â¥ 39.5°C) or VD score of 4 at 6 or 12 days postpartum. 67 Table 3.2: Activity and lying behaviour measures (least squared means ± SEM) from a collar-mounted (AAMC; n = 220) and a leg-mounted (AAML; n =188) monitor for subclinical endometritic (AAMC = 36; AAML = 36) and non-subclinical endometritic (AAMC = 184; AAML = 152) Holstein dairy cows during the 2 wk before calving (wk â2 and â1) and 3 wk after calving (wk +1, +2, and +3; prior to diagnosis). Period Before Calving After Calving Variable wk -2 wk -1 wk +1 wk +2 wk +3 AAMc Activity1 Non-Sub. Endo.2,3 37.0 ± 0.8 38.0 ± 0.8 39.2 ± 0.8 37.2 ± 0.8 37.5 ± 0.8 Sub. Endo. 37.5 ± 1.5 39.2 ± 1.4 36.8 ± 1.4 36.5 ± 1.4 37.4 ± 1.4 AAML Activity (steps/h) Non-Sub. Endo. 126.8 ± 3.0 133.1 ± 3.0 169.1 ± 3.0 133.9 ± 3.0 132.1 ± 3.0 Sub. Endo. 130.5 ± 6.1 136.2 ± 6.1 159.1 ± 6.1 136.4 ± 6.1 130.4 ± 6.2 Mean Lying Time (min) Non-Sub. Endo. 242.1 ± 2.9 228.9 ± 2.9 208.4 ± 2.9 218.1 ± 2.9 217.1 ± 2.9a Sub. Endo. 233.2 ± 6.0 229.8 ± 5.9 208.1 ± 5.9 208.4 ± 6.0 203.7 ± 6.0b Total Lying Time (h/wk) Non-Sub. Endo. 81.0 ± 1.3a 77.4 ± 1.2 59.6 ± 1.3 62.0 ± 1.3 72.9 ± 1.3 Sub. Endo. 75.0 ± 2.4b 76.7 ± 2.4 61.2 ± 2.4 59.4 ± 2.4 69.9 ± 2.4 Lying Time Per bout (min/bout) Non-Sub. Endo. 71.3 ± 1.8 64.3 ± 1.8 59.3 ± 1.8 66.1 ± 1.8 64.9 ± 1.8 Sub. Endo. 65.1 ± 3.6 58.9 ± 3.6 58.1 ± 3.6 69.6 ± 3.6 71.3 ± 3.6 Restlessness (arbitrary unit) Non-Sub. Endo. 3.3 ± 0.3 4.1 ± 0.3 4.9 ± 0.3 3.7 ± 0.3 3.7 ± 0.3 Sub. Endo. 4.3 ± 0.6 3.9 ± 0.6 4.0 ± 0.6 3.8 ± 0.6 3.7 ± 0.6 a,b Different superscripts within a column indicate significant difference (P â¤ 0.05) between diagnosis categories. 1AAMC Activity: measured by an index created by the manufacturer. 2Sub. = Subclinical; Endo. = Endometritic. 3Subclinical endometritis was classified as PMN â¥ 10% at 42 ± 3 postpartum. 68 Table 3.3: Test performance, with 95% CI in parentheses, of optimal cut-off point of relative increases in activity and relative decreases in lying time and lying time per bout from a collar-mounted (AAMC; n = 179) and a leg-mounted (AAML; n = 213) monitor as a predictor of calving within 32, 24, 12, and 8 h. Calving Indicator Prediction Time Test Performance1 AAMC Activity2 (arbitrary unit) AAML Activity (steps/h) Lying Time (min) Lying Time Per Bout (min/bout) Restlessness (arbitrary unit) 32 h Cut-off point4 â¥ 36% â¥ 15% â¤ -14% â¤ -31% â¥ 76% Se (%) 18 (16-19) 43 (40-46) 53 (50-56) 53 (50-57) 35 (32-39) Sp (%) 87 (85-88) 69 (66-71) 64 (61-67) 65 (62-68) 81 (78-83) AUC 0.52 (0.50-0.53) 0.57 (0.54-0.59) 0.59 (0.57-0.62) 0.58 (0.56-0.61) 0.60 (0.57-0.62) 24 h Cut-off point â¥ 29% â¥ 11% â¤ -14% â¤ -38% â¥ 11% Se (%) 24 (22-25) 50 (47-54) 56 (52-60) 50 (46-54) 21 (58-65) Sp (%) 81 (80-83) 63 (60-65) 64 (61-66) 73 (70-75) 58 (55-60) AUC 0.53 (0.51-0.54) 0.58 (0.55-0.61) 0.61 (0.58-0.64) 0.65 (0.62-0.68) 0.62 (0.59-0.65) 16 h Cut-off point â¥ 34% â¥ 11% â¤ -22% â¤ -41% â¥ 78% Se (%) 21 (19-23) 53 (49-58) 49 (44-54) 55 (50-59) 42 (37-47) Sp (%) 85 (84-86) 62 (60-65) 73 (71-75) 76 (74-78) 78 (76-81) AUC 0.53 (0.51-0.54) 0.59 (0.56-0.62) 0.61 (0.58-0.64) 0.69 (0.66-0.72) 0.61 (0.59-0.64) 8 h Cut-off point â¥ 30% â¥ 16% â¤ -22% â¤ -44% â¥ 53% Se (%) 28 (25-31) 52 (45-58) 51 (44-58) 67 (60-73) 53 (46-59) Sp (%) 81 (80-82) 66 (64-68) 71 (69-73) 77 (75-79) 70 (68-72) AUC 0.53 (0.51-0.56) 0.59 (0.55-0.64) 0.62 (0.57-0.66) 0.76 (0.73-0.8) 0.63 (0.59-0.67) 1Test performance: Se = sensitivity; Sp = specificity, AUC = area under the curve. 2AAMc Activity: measured by an index created by the manufacturer. 3Cut-off point â threshold of relative change calculated for each indicator optimizing both Se and Sp for predicted calving using ROC curve analysis. 69 Table 3.4: Test performance, with 95% CI in parentheses, of optimal cut-off point of relative change in activity-related behaviours from a collar-mounted (AAMC; n = 225) and a leg-mounted (AAML; n = 204) monitor as a predictor for metritis1 (metritis: AAMC = 59; AAML = 68; no metritis: AAMC = 166; AAML = 136 cows) prior to diagnosis at wk -2, -1, and +1 relative to calving. Variable Prediction Time Test Performance2 AAMC Activity3 (arbitrary unit) AAML Activity (steps/h) Mean Lying Time (min) Total Lying Time (h/wk) Lying Time per Bout (min/bout) Restlessness (arbitrary unit) wk -2 Cut-off point4 â¥ -1% â¥ 9% â¤ 1% â¥ -11% â¤ -12% â¥ -18% Se (%) 72 (60-85) 32 (20-42) 70 (61-79) 70 (59-81) 54 (42-66) 82 (73-91) Sp (%) 33 (24-40) 75 (67-83) 41 (34-48) 40 (32-49) 71 (63-79) 38 (30-46) AUC 0.49 (0.39-0.58) 0.52 (0.43 - 0.61) 0.51 (0.43-0.59) 0.52 (0.43-0.60) 0.63 (0.54-0.72) 0.53 (0.44-0.2) wk -1 Cut-off point â¤ 1% â¤ -4% â¤ -6% â¤ -5% â¥ -26% â¤ -21% Se (%) 57 (44-70) 60 (48-71) 47 (38-57) 54 (42-66) 88 (80-96) 26 (16-36) Sp (%) 59 (51-67) 48 (40-57) 64 (58-71) 55 (46-63) 19 (12-26) 80 (73-86) AUC 0.53 (0.43-0.62) 0.50 (0.41-0.58) 0.55 (0.47-0.63) 0.53 (0.44-0.61) 0.51 (0.43-0.60) 0.51 (0.42-0.60) wk +1 Cut-off point â¥ -8% â¤ 33% â¥ -7% â¥ -25% â¥ -19% â¤ 73% Se (%) 80 (69-90) 69 (58-80) 64 (54-73) 50 (38-62) 78 (68-88) 84 (76-93) Sp (%) 23 (17-30) 40 (31-48) 64 (58-71) 62 (54-70) 30 (23-39) 25 (17-32) AUC 0.46 (0.38-0.55) 0.54 (0.46-0.63) 0.60 (0.52-0.68) 0.52 (0.44-0.61) 0.54 (0.46-0.63) 0.55 (0.46-0.63) 1Metritis was classified as at least one VD score of 3 with a fever (â¥ 39.5°C) or VD score of 4 at 6 DIM or 12 postpartum. 2Test performance: Se = sensitivity; Sp = specificity, AUC = area under the curve. 3AAMC Activity: measured by an index created by the manufacturer. 4Cut-off point â threshold of relative change calculated for each indicator optimizing both Se and Sp for predicted metritis diagnosis using ROC curve analysis. 70 Table 3.5: Test performance, with 95% CI in parentheses, of optimal cut-off point of relative change in activity-related behaviours from a collar-mounted (AAMC; n = 225) and a leg-mounted (AAML; n = 204) monitor as a predictor for subclinical endometritis1 (subclinical endometritis: AAMC = 59; AAML = 68; no subclinical endometritis: AAMC = 166; AAML = 136 cows) prior to diagnosis at wk -2, -1, +1, +2, and +3 relative to calving. Variable Prediction Time Test Performance2 AAMC Activity3 (arbitrary unit) AAML Activity (steps/h) Mean Lying Time (min) Total Lying Time (h/wk) Lying Time per Bout (min/bout) Restlessness (arbitrary unit) wk -2 Cut-off point4 â¥ 1% â¥ -6% â¤ -5% â¤ -6% â¤ -17% â¥ -41% Se (%) 68 (51-85) 88 (77-99) 39 (23-56) 48 (31-66) 42 (26-59) 33 (18-52) Sp (%) 49 (41-57) 38 (30-46) 78 (70-84) 72 (64-79) 76 (69-83) 87 (81-92) AUC 0.55 (0.43-0.67) 0.62 (0.51-0.72) 0.50 (0.38-0.62) 0.57 (0.45-0.69) 0.58 (0.47-0.70) 0.59 (0.48-0.70) wk -1 Cut-off point â¥ 9% â¤ 4% â¥ -11% â¥ 2% â¤ -16% â¤ 26% Se (%) 30 (14-46) 68 (52-83) 85 (73-97) 47 (30-63) 74 (59-88) 79 (66-93) Sp (%) 78 (71-84) 49 (41-57) 34 (26-41) 76 (69-83) 40 (32-48) 36 (29-44) AUC 0.51 (0.40-0.63) 0.54 (0.42-0.65) 0.58 (0.48-0.69) 0.64 (0.53-0.75) 0.57 (0.46-0.67) 0.59 (0.49-0.70) wk +1 Cut-off point â¤ -6% â¤ 1% â¤ -15% â¥ -26% â¥ 18% â¤ -11% Se (%) 54 (38-71) 31 (16-47) 37 (21-53) 57 (41-74) 26 (11-40) 40 (24-56) Sp (%) 78 (72-84) 85 (79-90) 76 (69-82) 61 (53-68) 88 (83-93) 78 (71-85) AUC 0.67 (0.56-0.78) 0.57 (0.46-0.68) 0.50 (0.39-0.62) 0.52 (0.40-0.63) 0.54 (0.43-0.64) 0.55 (0.44-0.67) wk +2 Cut-off point â¥ 1% â¥ -21% â¤ 13% â¤ 13% â¥ -8% â¥ -19% Se (%) 58 (42-75) 74 (60-89) 94 (87-100) 94 (87-100) 97 (92-100) 66 (50-81) Sp (%) 72 (65-79) 50 (42-58) 22 (16-29) 20 (14-27) 13 (8-18) 59 (51-67) AUC 0.65 (0.54-0.75) 0.60 (0.50-0.70) 0.60 (0.50-0.70) 0.56 (0.46-0.67) 0.55 (0.45-0.67) 0.62 (0.52-0.73) wk +3 Cut-off point â¥ 3% â¤ 4% â¤ 3% â¤ 20% â¥ 14% â¥ 16% Se (%) 69 (54-85) 80 (67-93) 77 (63-91) 71 (56-83) 26 (0.11-0.40) 11 (9-22) Sp (%) 43 (36-51) 35 (28-43) 38 (30-45) 40 (32-48) 82 (76-88) 96 (93-99) AUC 0.56 (0.46-0.66) 0.58 (0.48-68) 0.55 (0.45-0.66) 0.55 (0.44-0.65) 0.51 (0.40-0.63) 0.48 (0.37-0.60) 1Subclinical endometritis was classified as PMN â¥ 10% at 42 ± 3 postpartum. 2Test performance: Se = sensitivity; Sp = specificity, AUC = area under the curve. 3AAMC Activity: measured by an index created by the manufacturer. 4Cut-off point â threshold of relative change calculated for each indicator optimizing both Se and Sp for predicted subclinical endometritis diagnosis using ROC curve analysis. 71 Chapter 4: General Discussion 4.1 Summary During the transition period, cows are at risk of calving difficulties, as well as diseases such as metritis and endometritis. Therefore, proactive management practices are needed to properly detect and mitigate the effects of these issues. Constant monitoring of cows around time of calving for detection of calving difficulties and diseases often requires daily and consistent, non-subjective monitoring. However, in recent years herd sizes have increased while staff numbers per cow have decreased, making it more difficult for close monitoring. Thus, there has been an increase in research of new management strategies, specifically with regards to calving management and the use of technology for early detection of calving or transition diseases. Little research has been done to determine how duration of labour and other calving related factors, such as calf weight, impacts the development of uterine diseases. Furthermore, there is currently a lack of research investigating the optimal time to provide assistance based on a health outcome of the dam. Although a few studies have looked at the impact of improper timing of intervention on stillbirths and injury to the dam (Gundelach et al., 2009; Kovács et al., 2016), the cut-off times chosen for appropriate timing for intervention lack explanation. Further understanding of the calving process and how it impacts uterine health can aid in improving calving management practices to prevent the development of uterine disease. Due to calving and health assessments being subjective and time consuming, an automated device that is commercially available to farmers that could accurately predict calving and disease diagnosis would be valuable to minimize the effect associated with calving difficulties and uterine diseases. Therefore, there has been an increase in research investigating changes in activity prior to parturition and uterine diseases diagnosis; however, much of the existing research 72 has measured changes in activity behaviours using data loggers and summarized observed data for inter-animal comparisons. There is potential to make an alert system using established thresholds to predict calving events and disease detection based on intra-animal comparisons. Furthermore, application of automated activity monitors on farms is only feasible if an accurate alert system is created. Therefore more research is needed investigating whether manually creating cut-off points or establishing an alert system associated with the AAMs can accurately detect or predict cows diagnosed with uterine diseases. The aims of this thesis were to determine 1) the association between factors around the time of calving, duration of stage II labour, and the development of uterine diseases in dairy cattle, and 2) investigate the test performance of relative changes in activity and lying behaviour from two commercially available AAMs to predict calving time and uterine diseases. In this study, duration of labour and timing of calving intervention were related to metritis postpartum. Results from this study provide evidence that calving assistance should be avoided, especially within 130 min or less after the appearance of the amniotic sac. Assisting, especially when early, increases the risk of metritis. Late intervention may also put cows at additional risk; however, it should be noted that our model has less power to detect this effect as we are lacking cows that went without calving intervention with extreme durations of labour. Although duration of stage II labour and calving score did not have a direct impact on subclinical endometritis, it may be indirectly impacted by timely intervention through the action of metritis, as subclinical endometritis was found to be associated with metritis. In addition, we found that primiparous cows had a shorter duration of unassisted labour than multiparous cows. Although this contrasts existing literature, in the current study, proactive heifer breeding management was used resulting in an increase in smaller, female calves for easy first calvings. Furthermore, calf weight has a significant 73 positive association with RP and duration of labour. As calf weight increased, duration of stage II labor increased, and the odds of RP also increased. Increased gestation length tended to increase the odds of metritis; however, neither gestation length nor days dry were associated with subclinical endometritis or RP. Below average BC cows were at the lowest odds of RP and metritis when compared to cows with an average or above average BCS, whereas above average BCS cows were at the highest odds of RP. In the second study, cows exhibited distinctive changes in activity and lying behaviours within the 24 h prior to calving (Figure 3.1 and Figure 3.2). Cows laid down less, spent less time lying per bout, and were more active during the last 16 h before calving with AAML and last 2 h with AAMC when compared to the previous hours. These results suggest that calving may be detected by behavioural changes at least 16 h before calving with AAML and at least 2 h before calving with AAMC. In addition, we found that cows later diagnosed with metritis tended to spend more time lying per bout and were less active during the two weeks prepartum and one week postpartum prior to diagnosis when compared to non-metritic cows. During the wk +1 postpartum, cows later diagnosed with metritis were less active and showed higher mean lying time per bout when compared to cows not diagnosed with metritis. Although in this present study frequency of lying bouts was not measured, having a longer lying time per bout with no observed differences in lying time indicates few lying bouts. Our results support Neave et al. (2018)âs suggestion that cows may be conserving energy by lying down for longer periods of time, resulting in fewer bouts throughout the day. Currently there is a lack of research investigating activity related behaviours and cows diagnosed with endometritis. To our knowledge, no other studies have investigated changes in 74 activity related behaviours early postpartum in cows later diagnosed with endometritis or subclinical endometritis. We found that cows later diagnosed with subclinical endometritis were more active and spent less time lying prepartum and were less active and continued to spend less time lying postpartum when compared to cows not diagnosed with subclinical endometritis. Although distinct changes in activity related behaviours were observed prior to parturition, metritis diagnosis, and subclinical endometritis diagnosis, the current technology shows promising results for calving detection; however, it may not be the most useful and reliable method for detection of uterine diseases. A relative decrease in excess of 44% in lying time per bout at 8 h before calving proved to be clinically useful (AUC = 0.76; Se = 67%; Sp = 77%). A relative increase in activity and restlessness, in addition to a relative decrease in lying per bout showed lower performance in predicting calving with lower Se, Sp, and AUC; however, the lower performances are near acceptable levels for being clinically useful, according to Mandrekar (2010). With the increase in need for proactive management practices to detect calving, these results show that lying time per bout over the 24 h before calving could be used. Poor test performance was observed across all activity and other lying behaviours at the three defined time periods for metritis and subclinical endometritis diagnosis. Although distinctive difference in lying behaviour were observed between health groups, poor test performance was observed across all variables. The lack of clinically useful test performances is likely due to the large variation and fluctuation in activity related behaviours between and within animals. Although parity was found to be a significant influencer on activity related behaviours, it only marginally changed the test performances when the analysis was stratified by parity. This indicates that the observed variation between and within animals cannot be explained by any of the measured fixed effects. 75 This thesis demonstrates that 1) timing of calving intervention is key in reducing risk of metritis early postpartum, 2) transition cow management has a major impact on calving and uterine disease development, and 3) in combination with other proactive management practice, AAM could be used and refined in on-farm practices for predicting calving time. 4.2 Limitations A limitation to this study was the lack of a standardized calving protocol for on-farm personnel. Although the on-farm personnel were trained to look for imminent signs of calving and to provide assistance when needed, this subjective form of calving management resulted in inconsistent timing of assistance. Due to stage II labour ranging from 49 min to 232 min in assisted cows and from 11 min to 173 min in unassisted cows in this study, it is likely that some cows were assisted that did not actually require assistance and some cows that were not assisted may have required assistance. By having a standardized calving protocol in place that required a record of the justification for assisting (i.e., feto-malposition or lack of calving progression), further analysis on difference in activity related behaviours between cows that truly had dystocia and cows that did not could have been made. However, the lack of a strict protocol allowed for a wide range of calving times for analysis on timing of intervention, duration of labour, and its association with uterine diseases. Although there was a wide range in calving times, the mean duration of labour in unassisted cows (57.17 min) was comparable to other studies. Schuenemann et al. (2011) reported the mean duration of stage II labour in unassisted cows to be 45.2 ± 12.25 min (mean ± SD) while Kovács et al. (2016) reported the mean duration to be 77.4 ± 6.2 min (least squares means ± SEM) for cows in individual maternity pens and 52.6 ± 5.5 min for cows housed in a group maternity pen. 76 For this study, we defined duration of labour as the time between the appearance of the amniotic sac to expulsion of the calf. This is also known as stage II labour. Stage II is characterized by the appearance of notable abdominal contractions, the amniotic sac (water bag) appearance and rupture, and expulsion of the calf (Mainau and Manteca, 2011); however stage II of labour actually begins with the rupture of the chorioallantois in the vagina (Jackson, 1995). Due to the methods of the study, we were unable to view the exact transition between stage I and stage II of labour. Often, it is unclear to the human eye when these stages start or stop due to their gradual progression, making it difficult to determine which stage of parturition the cow is in (Proudfoot et al., 2013). Although it is generally accepted that the duration between appearance of the amniotic sac to the expulsion of the calf is representative of the duration of labour, it is not exact. It is likely our recorded duration of stage II labour is shorter that the true duration. Recording the onset of abdominal contractions would give a more accurate reference for the start of active labour; however, this would not clearly indicate whether the animal is in stage I or stage II as abdominal contractions can be seen in both stages. As mentioned in the discussion of Chapter 3, the AAMs summarized the data into multiple hour blocks of time. Due to this limitation, some of the results regarding changes in activity at time of calving should be interpreted with caution. The greatest changes in activity and lying behaviour were observed at 0 h calving; 0 h is not representative of the actual calving time due to the AAMs data being summarized into multiple hour blocks. While this is a limitation of the study, an alert based on activity related behavioural changes at 0 h would not be proactive. Due to the use of commercially available AAMs in this study, the data that was collected by the monitors went through an algorithm prior to being extracted for analysis. We were unable to access the raw data for analysis; however, the use of data loggers allows for the recording and 77 extracting of raw data for analysis. We believe this is why much of the existing literature has used data loggers to investigate changes in activity behaviour prior to calving and diseases; however, the use of loggers for observing changes in activity related behaviour is not efficient and practical for commercial farms. Although data loggers allow for more precise data to be collected, loggers often require a form of bandage for attachment and weekly removal for extraction of data. Commercially available AAMs are commonly used on dairy farms for estrus detection and allow for continuous monitoring of activity relative behaviours that are automatically transmitted to a computer for viewing and extraction. By using commercially available AAMs in research, results can be more easily applied and practiced on commercial farms. Another limitation to this study was the lack of other disease diagnosing by the researchers and on-farm personnel. Although cows diagnosed with lameness, milk fever, and/or a displaced abomasum were recorded and excluded from analysis, other diseases such as ketosis, subclinical hypocalcaemia, and mastitis were not measured or recorded. Several studies have investigated changes in activity related behaviours as an indicator for transition diseases, other than uterine diseases. For example, cows diagnosed with ketosis after calving stood longer before and during calving and changed from standing to lying less often on the day of calving (Itle et al., 2015). However, Kaufman et al. (2016) reported no differences in lying time, number of bouts, or bout duration before diagnosis of ketosis. Other studies have reported behavioral changes relative to the day of diagnosis for mastitis (Fogsgaard et al., 2012; Sepúlveda-Varas et al., 2014). Cows with mastitis had decreased lying time, possibly in reaction to painful pressure on the udder when lying (Fogsgaard et al., 2012). Furthermore, Sepúlveda-Varas et al. (2014) found that primiparous cows diagnosed with more than one clinical health disorder tended to have longer lying bouts on day 3 and 4 after calving compared with healthy cows. Therefore, the results presented in this thesis may 78 have been confounded by an undiagnosed disease (other than lameness; health was not systematically monitored), and this condition might have increased the risk of the animal developing a uterine disease. 4.3 Future directions Although literature on proactive health management practices to aid in the prevention and detection of uterine diseases in dairy cattle is growing quickly, there are still many gaps of knowledge that need to be addressed. As mentioned in the limitations section of this thesis, one crucial area of research for investigation of calving intervention is to determine how timing of intervention with regards to progression of labour impacts the dam, specifically with regards to uterine diseases. In Chapter 2 of this thesis, we concluded that calving assistance should occur at approximately 130 min after the appearance of the amniotic sac in order to reduce the probability of metritis postpartum; however, we did not investigate if progression of labour after the appearance of the amniotic sac had an impact. It has been recommended that monitoring progress during stage II labour is more important than clock-watching, as the onset of stage II is unusually unknown. (Mee, 2004). According to Mee (2004), signs of progress during stage II include a recumbent dam straining intermittently and strongly as well as progressive emergence of the fetal legs and head through the vulva. Further recommendations from Mee (2004) that intervention should occur when progress ceases for more than 30 minutes, or when the calf begins to exhibit signs of reduced vigor (e.g., reduced responsiveness to stimulation); however, it appears that this recommendation is to minimize the risk of severe acidosis of the calf as well as stillbirths. It is unknown how timing of intervention based on the progression of calving impacts the dam. In addition to timing of calving intervention, there has been some debate about the definition of dystocia, resulting in inconsistent reports in the existing literature. Traditionally, 79 dystocia has been defined as prolonged duration of calving or assisted extraction of the calf at birth, as a result of prolonged stages I or II of parturition (Mee, 2004). However, over time, the term dystocia has come to be used to only describe cows that required human assistance during calving. This is due to lack of a consistent definition of what defines prolonged labour. A collective definition of dystocia in which prolonged labour is defined is needed for more accurate comparisons between studies. There has been some debate about the use of commercially available AAMs vs. data loggers to record and monitor changes in activity prior to disease diagnosis. As mentioned in the limitation section of this thesis, raw activity data was summarized into time blocks after being processed through an algorithm. Although some of the results are consistent with previous literature, contrasts in lying behaviour were observed. This is likely due to the type of monitors used as well as how the data was processed and analyzed. More research using commercially available AAMs is needed as these are the monitors that are readily available for farmers to use. A study focusing on the comparison and validation of commercially available activity monitors to data loggers is needed. Another follow-up study could focus on combining different AAM measures, and possibly in combination of physiological parameters measured via commercially AAMs, to see if a more accurate alert could be made for detecting calving and uterine diseases. In the second study, we found large variation and fluctuation in activity related behaviours between and within animals. Further investigation of AAM and health alerts that include more than one variable (e.g. lying time per bout and activity) to predict calving and disease diagnoses may result in better test performance of AAMs. For predicting calving time, Ouellet et al. (2016) found that combining lying time, lying bouts, vaginal temperature, and rumination time improved the test performance; however, no work 80 has been done investigating the combination of commercially available AAMs measurements for predicting calving time and/ or uterine diseases. Furthermore, Ouellet et al. (2016) reported better test performance in measured changes in vaginal temperature to predict calving time. I suggest that that physiological changes, such as changes in vaginal temperature, might be more stable within cows than behavioral ones and therefore are more accurate for predicting calving time. 81 Chapter 5: Conclusions Proactive management practices during the transition period are needed to properly detect parturition and uterine diseases in order to mitigate or prevent possible negative effects from occurring. Appropriate timing of calving intervention can aid in the prevention of metritis early postpartum. Providing assistance too early during stage II labour may result in an increased risk of metritis, therefore we estimate a reference time of 130 mins after the appearance of the amniotic sac to be used as a guideline for providing calving assistance in order to reduce the risk of metritis. Although calf weight was associated with duration of labour and RP, we were unable to find associations between uterine diseases and other important calving factors. Duration of labour and calving assistance appeared to not be directly associated with endometritis, although appropriate timing of intervention may indirectly reduce the risk of endometritis by reducing the risk of metritis early postpartum. In addition, activity, lying time, and lying time per bout measured via two commercially available AAMs showed clear changes associated with the calving time. Decreased activity and increased lying time per bout were observed in cows later diagnosed with metritis. Cows later diagnosed with subclinical endometritis were more active and spent less time lying prepartum and were less active and continued to spend less time lying postpartum when compared with cows not diagnosed with subclinical endometritis. 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Thesis/Dissertation
eng
Applied Animal Biology
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
The use of calving behaviours and automated activity monitors to predict and detect parturition and uterine diseases in Holstein cattle
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