UNIVERSITY OF CALGARY. Detection, Prevention and Control of Lameness and Foot Lesions in Canadian Dairy Herds. Laura Solano Quesada A THESIS

Transcription

1 UNIVERSITY OF CALGARY Detection, Prevention and Control of Lameness and Foot Lesions in Canadian Dairy Herds by Laura Solano Quesada A THESIS SUBMITTED TO THE FACULTY OF GRADUATE STUDIES IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE PROGRAM IN VETERINARY MEDICAL SCIENCES CALGARY, ALBERTA JUNE, 2016 Laura Solano Quesada 2016

2 Abstract Lameness is a multifactorial condition and a clinical sign of painful disorders related to the locomotor system. Furthermore, it is a severe welfare issue and production-limiting disorder, ranking third as the most significant health problem in dairy cattle. Approximately 90% of lameness is caused by foot lesions. Understanding the multifactorial nature and interconnected risk factors of lameness and foot lesions is essential for disease prevention and control. This thesis comprises two parts that explore the epidemiology of lameness and foot lesions across Canada. In Part 1, research focused on estimating prevalence and identifying risk factors for lameness and foot lesions, in addition to evaluating automated measures of lying behavior as a diagnostic tool for lameness. Lameness affected 21% of cows; prevalence was higher on farms with poor comfort of surfaces for standing and lying. Lying behavior was not a sensitive diagnostic tool for lame cows, as it was subject to wide variability due to intrinsic cow factors. The most prevalent foot lesion was digital dermatitis (DD), affecting 15% of cows and 94% of herds in Alberta. Digital dermatitis is an infectious foot lesion that causes painful skin erosions, and has large economic and welfare implications. The use of footbaths is the most common herdlevel approach to control spread of DD. Suboptimal footbath management was an important reason for the high DD prevalence in Alberta. Therefore, in Part 2, an intervention study was conducted to evaluate effectiveness of a standardized footbath protocol in decreasing DD prevalence. The protocol was based on current scientific literature, including footbath design and management. Additionally, a method for simple and reliable detection of DD lesions was validated and proven highly sensitive (92%) and specific (88%). The footbath intervention was effective in achieving and maintaining a lower prevalence of cows with active DD lesions and a ii

3 higher prevalence of cows without DD. In addition, cows with poor leg cleanliness more often had DD. Ultimately, adopting management practices intended to improve cows comfort and hygiene of surfaces for lying and standing, along with improvements in footbath design and protocol, could decrease prevalence of foot lesions and lameness. iii

4 Preface The following manuscripts have been published or submitted for publication. Laura Solano was involved in study design, she collected and analyzed the data, interpreted the results and wrote the manuscripts with guidance from her thesis committee and other collaborators. All authors contributed important intellectual content and provided critical review of the papers. Written permission for reproduction of the article in its entirety for this thesis has been obtained from the publishers and all co-authors. Published manuscripts: Solano, L., H. W. Barkema, E. A. Pajor, S. Mason, S. J. LeBlanc, J. C. Zaffino Heyerhoff, C. G. R. Nash, D. B. Haley, E. Vasseur, D. Pellerin, J. Rushen, A. M. de Passillé, and K. Orsel Prevalence of lameness and associated risk factors in Canadian Holstein-Friesian cows housed in freestall barns. J. Dairy Sci. 98: Solano, L., H. W. Barkema, E. A. Pajor, S. Mason, S. J. LeBlanc, C. G. R. Nash, D. B. Haley, D. Pellerin, J. Rushen, A. M. de Passillé, E. Vasseur, and K. Orsel Associations between lying behavior and lameness in Canadian Holstein-Friesian cows housed in freestall barns. J. Dairy Sci. 99: Solano, L., H. W. Barkema, E. A. Pajor, S. Mason, S. J. LeBlanc, and K. Orsel Prevalence and distribution of foot lesions in dairy cattle in Alberta, Canada. J. Dairy Sci. iv

5 Submitted manuscripts: Solano, L., H. W. Barkema, C. Jacobs, and K. Orsel Validation of the M-stage scoring system for digital dermatitis in the milking parlor. Submitted for publication. Solano, L., H. W. Barkema, C. Pickel, and K. Orsel Effectiveness of a standardized footbath protocol for prevention of digital dermatitis. Submitted for publication. v

6 Acknowledgements Firstly, I would thank my supervisor, Karin Orsel, and co-supervisor, Herman Barkema, for dedicating so much of their time guiding me, providing support, giving me feedback, and a few pushes here and there throughout my PhD. In particular, a big thank you to Karin who challenged me and believed in my ability to undertake such ambitious research projects. Your guidance throughout my field work and my thesis have been invaluable. Thank you for making me a stronger person and a better researcher. Also, a huge thank you to Herman who gave me the opportunity to come to the University of Calgary and managed to convince me that the weather in Calgary was better than in Costa Rica. Thank you for being there to listen and for providing advice and encouragement during difficult times. This was especially beneficial during the writing process. Herman, you have undoubtedly left a lasting mark on my education and career, and I will always be grateful to you. I would also like to thank Steve Mason for his help in setting up, developing and managing my databases. Thank you for letting me bug you all the time, for sharing your ideas, knowledge and showing to me how gratifying it is to work for and with farmers. Steve, your energy and passion for learning are inspiring. Thank you for your mentorship, the occasional home cooked meal, and above all, the kindness and friendship you and Donna have shown me. I would also like to thank my committee members, Ed Pajor and Steven LeBlanc, whose input and counsel certainly strengthened my skills as a researcher and writer and helped to significantly shape this thesis. A special thank you to Dr. John Kastelic whose superb editing helped make my manuscripts and thesis undeniably stronger. I would also like to thank you for the many kind words and encouragement, which seemed to come just when I needed them most. vi

7 A very special thank you to all participating farmers and the 7 hoof trimmers who made this research possible and allowed me to bother them for 4 years. I am sincerely appreciative of Elbert Koster, Rob Geier and Philip Spence s assistance and contributions during my field trials. Working with you was amazing! Thank you for helping me with recruiting farms, collecting data, teaching me about hoof trimming and foot lesions, and keeping a good attitude while enduring the -25C days! Elbert and Rob, these projects would have been impossible without your support and hard work. This gratitude extends to Charlotte Pickel. Your positive attitude, support, friendship and organizational skills kept me going during the footbath project. I m deeply grateful for your help with the database, data collection, and for helping me lead and complete the footbath trial. I do not know what I would have done without you! You are amazing Carlota! There were several people working behind the scenes that made this research and thesis possible. Thank you to Brenda Moore, Kasia Judycki, Sharon Schille, Barbara Banman, Ophira Charikar, and Natasha Reed; an amazing administrative team who helped me with the logistics of the field trials and throughout different stages of my PhD. Thank you Grace Kwong for your assistance with statistical analyses. Many technicians and students were also involved in data collection and analysis. I particularly want thank Guilherme Bond for his invaluable help during the cow comfort trial. Thanks for all those farm visits, long hours of driving and for sharing the difficult times! Thanks to: Fokke S., Mart W., Ilse G., Miranda M., Jan S., Melissa W., Simone K., Jacomien K., Margriet K., Annick B., Anet v.d.b., Casey J. Megan B., Larissa C., Sven B., Stijn v.d.g., and Tanja K. You all went above and beyond and accompanied me for so many hours on dozens of farms, and spent so much time in the office helping with data entry, analyses and reports. I have a lot gained both professionally and personally from working with you. vii

8 I would also like to acknowledge the funding agencies and collaborators throughout Canada. Thank you Dairy Farmers of Canada, Agriculture and Agri-Food Canada, the Canadian Dairy Commission, people involved in the Dairy Research Cluster 1, Alberta Milk, Alberta Livestock and Meat Agency, Growing Forward 2 Biosecurity Funding, and the University of Calgary Faculty of Veterinary Medicine for the Clinical Research Fund and Eyes High. There are not enough words of to express my gratitude to my family. Pa, Ma, Cris, Sil & Ga, you are the reason I am here. Your unconditional support and understanding throughout my vet school, externships, PhD and all the time we have been apart, goes beyond limits. Your advice and guidance throughout ups and downs and knowing that you are always there, no matter the distance, demonstrates the love and strength that only family can give. This gratitude extends to my Southern family, Janie & Blane. Your kindness, support, and amazing advice (academic and non-academic!) have been essential in these years. Thank you for welcoming me as a daughter. I would also like to thank all my friends in Calgary who made these past 5 years an amazing and unforgettable experience. You ve been family to me, contributed to many memories and a lifelong friendship. You all know who you are. And last but certainly not least, thank you to my amazing husband and best friend: Dust. I do not know how you put up with me, let alone with such patience, love and dedication. Thanks for always putting a smile on my face and for making me believe in myself. Thank you for sharing this beautiful life with me. viii

9 ix TO DUSTIN AND MY PARENTS for your unconditional support

10 Table of Contents Abstract... ii Preface... iv Acknowledgements... vi Dedication... ix Table of Contents... x List of Tables... xiii List of Figures and Illustrations... xv List of Symbols, Abbreviations and Nomenclature... xvi CHAPTER ONE: GENERAL INTRODUCTION Canadian Dairy Industry Lameness Relevance of Lameness... 3 Detection of Lameness The use of lying behavior for lameness detection... 4 Prevalence of Lameness Foot Lesions... 7 Detection of Foot Lesions... 7 Prevalence of Foot Lesions... 8 The Alberta Dairy Hoof Health Project... 9 The Importance of Digital Dermatitis Pathogenesis and Risk Factors of Foot Lesions Roles of Cow Housing and Comfort Prevention and Control of Digital Dermatitis Thesis Aim and Overview CHAPTER TWO: PREVALENCE OF LAMENESS AND ASSOCIATED RISK FACTORS IN CANADIAN HOLSTEIN-FRIESIAN COWS HOUSED IN FREESTALL BARNS Abstract Introduction Materials and Methods Farms Cow Selection Lameness Assessment Animal-based Measures General Management Facility Design Statistical Analyses Results Cow-level Variables Herd-level Variables Multivariable Analysis Discussion Conclusions x

11 CHAPTER THREE: ASSOCIATIONS BETWEEN LYING BEHAVIOR AND LAMENESS IN CANADIAN HOLSTEIN-FRIESIAN COWS HOUSED IN FREESTALL BARNS Abstract Introduction Material and Methods Farms Cow Selection Lying Behavior Animal-based Measures General Management Facility Design Statistical Analyses Results Lying Behavior Within and Among Herds Cow and Herd Factors Associated with Lying Behavior Multivariable Analysis Lying Behavior as a Detection Tool for Lameness Discussion Conclusions CHAPTER FOUR: PREVALENCE AND DISTRIBUTION OF FOOT LESIONS IN DAIRY CATTLE IN ALBERTA, CANADA Abstract Introduction Materials and Methods Data Recording Herd and Cow Selection Data Management and Statistical Analysis Results Prevalence and distribution of Foot Lesions Risk Factors for Foot Lesions Discussion Conclusions CHAPTER FIVE: VALIDATION OF THE M-STAGE SCORING SYSTEM FOR DIGITAL DERMATITIS IN THE MILKING PARLOR Abstract Introduction Materials and Methods Farm and Cow Selection DD Lesion Assessment Milking Parlor Inspection Trimming Chute Inspection Statistical Analyses Results Interobserver Agreement xi

12 Trimming Chute Inspection Accuracy of Milking Parlor Inspection Discussion Conclusions CHAPTER SIX: EFFECTIVENESS OF A STANDARDIZED FOOTBATH PROTOCOL FOR PREVENTION OF DIGITAL DERMATITIS Abstract Introduction Materials and Methods Farm and Cow Selection Study Design General Management Original Footbath Protocol Footbath Intervention Assessment of DD and Leg Cleanliness Statistical Analyses Results Farms and Footbath Practices Prevalence of Digital Dermatitis Lesions Discussion Conclusions CHAPTER SEVEN: SUMMARIZING DISCUSSION Summary of Findings Practical Applications of Prevalence Estimates and Animal-based Measures Practical Considerations in Detection Methods for Lameness and Digital Dermatitis Relevance of The Alberta Hoof Health Project Lameness and Foot Lesions Control through Management Practices Digital Dermatitis Control through Footbathing Further Research REFERENCES APPENDIX: COPYRIGHT PERMISSION xii

13 List of Tables Table 2.1. Characteristics (mean ± SD) of 141 freestall dairy farms and the average freestall farm in 3 Canadian provinces Table 2.2. Distribution of continuous and categorical [n (%)] cow-level explanatory variables for non-lame (n=4,162) and lame cows (n=1,094) from 141 Canadian freestall dairy farms Table 2.3. Distribution of continuous (mean ± SD) and categorical [n (%)] herd-level stall management variables for dairy herds with a low, medium or high lameness prevalence Table 2.4. Distribution of categorical [n (%)] herd-level flooring variables for dairy herds with a low, medium or high lameness prevalence Table 2.5. Distribution of continuous (median ± interquartile range, IQR) and categorical [n (%)] footbath management variables for dairy herds with a low, medium or high lameness prevalence that use regular footbathing Table 2.6. Final multilevel logistic regression model for lameness with cow and herd-level factors in 141 Canadian dairy herds (n=4,981) Table 3.1. Characteristics (mean ± SD) of 141 freestall dairy farms and the average freestall farm in 3 Canadian provinces Table 3.2. Distribution (mean ± SD) of lying behavior for 141 dairies assessed using 40 early lactation cows on each farm Table 3.3. Final generalised linear mixed model for 3 measures of lying behavior with cow and herd-level factors in 141 Canadian dairy herds, considering cow (n = 4,790) as the experimental unit Table 3.4. Final generalised linear mixed model for mean daily lying time (h/d) with cow and herd-level factors in 141 Canadian dairy herds, considering cow (n =4,790) within herd as the experimental unit Table 3.5. Distribution of cow-level [median ± interquartile range, IQR (range)] and herdlevel [mean ± SD (range)] explanatory variables of 5,135 cows from 141 dairy herds with a low ( 10%), medium (10-30%) or high ( 30%) lameness Table 3.6. Use of lying behavior as independent predictors of lameness estimated by logistic regression models including parity and days in milk as covariates in 141 farms Table 4.1. Distribution of 156 Alberta dairy herds (28,607 cows) enrolled in the Alberta Dairy Hoof Health Project Table 4.2. Distribution of foot lesions [n (%)] by foot and claw per cow (n=28,607) on 156 Alberta, Canada, dairy farms xiii

14 Table 4.3. Herd- and cow-level prevalence (%) of foot lesions in 156 farms and 28,607 cows as recorded by 7 hoof trimmers in Alberta Table 4.4. Comparison of foot lesion distribution among hoof trimmers and within-herd intraclass correlation coefficients in 28,607 cows on 156 Alberta, Canada, dairy farms Table 4.5. Final multilevel logistic regression models of cow- and herd-level factors associated with the 3 most frequent foot lesions in 23,014 cows in 156 Canadian dairy herds Table 5.1. Interobserver agreement for scoring digital dermatitis on dairy cows with pictures and live in the milking parlor and trimming chute using the 5 M-stage and a simplified scoring system Table 5.2. Distribution (no.; % in parentheses) of anatomical locations of digital dermatitis (DD) lesions identified at trimming chute inspection on 4,161 hind feet of dairy cattle Table 5.3. Agreement (in bold)/disagreement by M-stage pairs (no.; % in trimming chute in parentheses) for scoring digital dermatitis (DD) in the milking parlor compared to DD scoring of cattle examined in a trimming chute Table 5.4. Agreement of digital dermatitis scores on dairy cows examined in the milking parlor and in a trimming chute Table 5.5. Test characteristics for scoring each digital dermatitis stage in the milking parlor, considering scoring in the trimming chute as a gold standard (n = 6,991 feet) Table 6.1. Farm characteristics (mean ± SD) and digital dermatitis (DD) prevalence and management protocol for 9 freestall dairy farms in Alberta, Canada Table 6.2. Characteristics of footbathing practices for lactating cows before intervention on 9 freestall dairy farms in Alberta Table 6.3. Prevalence (%) of each digital dermatitis M-stage identified at trimming chute inspection on 3,956 feet at start of data collection, intervention, and end of data collection Table 6.4. Final repeated measures multilevel logistic regression model for active digital dermatitis lesions detected in the milking parlor before and after intervention with a standardized footbath protocol on 9 dairy farms (n = 3,956 feet) xiv

15 List of Figures and Illustrations Figure 2.1. Causal web of factors hypothesized to affect lameness in dairy cows Figure 2.2. Percentage of lame cows per parity (n=number of cows per category) Figure 2.3. Percentage of lame cows per body condition score category (n=number of cows per category) Figure 3.1. Mean lameness prevalence and mean daily lying time (h/d) on each of 141 farms Figure 3.2. Mean lying bouts (no./d) per parity and month of lactation. *Difference (P < 0.05) between primiparous and multiparous cows Figure 3.3. Mean lying bout duration (min/d) per parity and month of lactation. *Difference (P < 0.05) between primiparous and multiparous cows Figure 4.1. Flowchart of dairy cow study selection process Figure 4.2. Claw zones affected by each type of the most common foot lesions in 28,607 cows on 156 Alberta, Canada, dairy farms. Foot lesion identification used was developed by International Lameness Committee (2008) in cooperation with Zinpro Corporation (Zinpro Corporation, Eden Prairie, MN, USA) Figure 4.3. Percentage of dairy cows with (a) digital dermatitis, (b) sole ulcers, and (c) white line disease per parity and stage of lactation Figure 5.1. Reference card used by observers summarizing the M-stage scoring system for digital dermatitis according to Berry et al. (2012) Figure 5.2. Inspection of digital dermatitis lesions during milking using a simple tool a) mirror glued to a kitchen spatula; b) use of headlamp to improve lighting while scoring lesions; c) showing an M4 stage difficult to detect without a mirror Figure 6.1. Timeline of trial, showing digital dermatitis inspections at milking parlor (every 2 wk) and at trimming chute (start, middle, and end of trial) Figure 6.2. Computerized automated footbath with dimensions based on literature recommendations implemented on 9 dairy farms Figure 6.3. Percentage of feet with various M-stages of digital dermatitis (Berry et al., 2012) before and after intervention with a standardized footbath protocol for (a) farms (n = 6; n = 2,974 feet) with inadequate footbath protocol at baseline, and (b) farms (n = 3; n = 982 feet) with adequate footbath protocol at baseline xv

16 List of Symbols, Abbreviations and Nomenclature Symbol AB β BCS CuSO4 DD d h L m mo ON OR SD QC wk yr Definition Alberta Beta coefficient Body condition score Cooper sulfate Digital dermatitis Day (s) Hour (s) Liter Meter Month (s) Ontario Odds ratio Standard deviation Québec Week (s) Year (s) xvi

17 Chapter One: General Introduction 1.1 Canadian Dairy Industry The Canadian dairy industry has substantially intensified over the past 35 y; during that interval, the number of farms declined from 55,472 to 11,683, and the number of cows declined from 1,772,600 to 953,200. Nevertheless, annual Canadian milk production concurrently increased by almost 9,900,000 hl, reflecting a trend towards fewer but more productive and efficient animals (Barkema et al., 2015; Canadian Dairy Information Centre, 2016). This intensification has also resulted in changes in housing conditions. To increase efficiency in land use by greater stocking rates (Alvarez et al., 2008), cows have been moved from extensive outdoor systems to indoor housing with limited or no access to pasture. In Canada, cows are predominantly housed in tiestalls or freestalls. In freestall housing, the predominant housing system for lactating cows in western Canada (Canadian Dairy Information Centre, 2016), cows can walk freely and have ready access to stalls in which to lie and sources of feed and water (Rushen et al., 2008; Canadian Dairy Information Centre, 2016). Intensive dairy farming can be managed more efficiently and is more cost-effective than extensive farming (Alvarez et al., 2008); however, it is associated with increased risk for health problems, disease transmission, and longevity (Sewalem et al., 2008; Koeck et al., 2014). Numerous studies have assessed impacts of housing on health disorders. For example, prevalence of foot problems and lameness are generally higher in freestall barns compared to other housing and pasture systems (Cook, 2003; Sogstad et al., 2005b; Hernandez-Mendo et al., 2007). Although evidence of impact of various indoor housing systems on prevalence and 1

18 incidence of health problems has been equivocal (Rushen et al., 2008; Simensen et al., 2010), there is increased public concern regarding housing and cows well-being (von Keyserlingk et al., 2009; Barkema et al., 2015). Hence, in response to health, welfare implications, consumers and processors interests, the dairy industry worldwide has developed regulations and requirements promoting sound husbandry practices, with Canada being no exception. The Code of Practice for the Care and Handling of Dairy Cattle (National Farm Animal Care Council, 2009) and the upcoming proaction Initiative (Dairy Farmers of Canada, 2016) are part of a regulatory framework that addresses growing public concerns regarding dairy cattle production, handling and welfare. 1.2 Lameness Lameness is a clinical sign of pain related to the locomotor system, which results in abnormal locomotion or posture. It is a multifactorial condition, predisposed by environmental, management and genetic factors (Greenough, 2007; Van Nuffel et al., 2015a). Approximately 90% of the causes of lameness involve foot lesions (Murray et al., 1996; Shearer and Van Amstel, 2013), although foot lesions do not necessarily result in lameness (Manske et al., 2002b). Lameness that does not originate from the foot can be caused by trauma, hock or knee injuries, joint and neurological diseases, etc. (Desrochers, 2013). In literature, the per definition incorrect term subclinical lameness is often used when a foot lesion is present but the cow is not visibly lame (Bruijnis et al., 2012), although not visibly lame does not guarantee an absence of foot lesions. For the purpose of this thesis, the term lameness is used when cows are visibly 2

19 lame but the cause is unknown, and the term foot lesion is used when lameness status is unknown Relevance of Lameness Lameness is one of the most important welfare, health, and productivity problems in intensive dairy farming worldwide. It is considered the third major health problem (after mastitis and reproductive problems), the third most important reason for culling (USDA, 2007; Canadian Dairy Information Centre, 2016), and was selected by Canadian dairy farmers and their advisors as the highest research priority (Bauman et al., 2016). Most importantly, lameness is a debilitating condition, highly associated with pain (Rushen et al., 2007), and the dairy industry s leading and most visible animal welfare concern (Vermunt, 2007; von Keyserlingk et al., 2009). In addition to reduced well-being and longevity, lameness causes substantial economic losses (Ettema and Ostergaard, 2006) due to reductions in milk production (Warnick et al., 2001; Green et al., 2002) and reproductive performance (Hernandez et al., 2001; Garbarino et al., 2004), in addition to treatment costs. Altogether, economic losses due to lameness for a default farm with 120 cows was estimated at $281 CAD per case (Ettema and Ostergaard, 2006). Detection of Lameness Early detection, prompt and effective treatment of lameness reduces severity, increases response rates to treatment (Thomas et al., 2016), and decreases direct and indirect costs (Bruijnis et al., 2010). On the basis of the Five Freedoms stated in The Brambell Report (Brambell, 1965), early detection and treatment of lameness improves animal welfare by contributing to freedom from pain, injury or disease. 3

20 Lameness assessment is based on visual observation of the cow s locomotion; therefore, it is subjected to observer bias and estimates on its true prevalence remain a challenge (Otten et al., 2013). Numerous locomotion scoring systems have been developed to identify deviations from normal gait (Sprecher et al., 1997; Flower and Weary, 2006; Leach et al., 2009). There are nearly 25 locomotion scoring systems available, differing in the scale, gait characteristics and postures used (Van Nuffel et al., 2015a). Notwithstanding, locomotion scoring has become an industry standard to identify and monitor lameness (at both cow- and herd-levels). Several factors contribute to challenges in detection of lameness: 1) pain is often masked by cows stoic nature, which can explain the low correlation between presence of foot lesions and lameness (Manske et al., 2002b; Bicalho et al., 2007); 2) inaccuracy and subjectivity of detection methods; and 3) increasing herd sizes and farmers failure to notice, acknowledge or lack the time to identify and monitor lame cows (Leach et al., 2010a). Therefore, several automated technologies have been developed for objective and early detection of lameness. Some technologies attempt to detect lameness based on changes in cows locomotion, posture or weight bearing, using force plates (Van der Tol et al., 2003; Neveux et al., 2006), pedometers (Mazrier et al., 2006), accelerometers (Chapinal et al., 2011) and pressure-sensitive walkways (Maertens et al., 2011). Other technologies assess changes in cows behavior, including feeding (Gonzalez et al., 2008), neck motion, ruminating (Van Hertem et al., 2013) and lying (Ito et al., 2010) The use of lying behavior for lameness detection Automated monitoring of lying behavior enables continuous and objective assessment of a behavior that although it is not specific to a single disease, it can differentiate normal versus 4

21 abnormal patterns of lying duration and frequency (Rushen et al., 2008). In addition, the amount of time cows spend lying and the frequency of lying events is an indicator of comfort of the lying surface (Haley et al., 2001; Ito et al., 2014). Most research investigating associations between lameness and lying behavior has been conducted with limited sample sizes, on controlled environments, or focusing on only a few factors that could influence this association (Gomez and Cook, 2010; Ito et al., 2010). However, changes in lying measures recorded by accelerometers have been identified as a potential behavioral indicator of lameness, based on differences in lying responses of lame and nonlame cows (Ito et al., 2010). The limitation with indirect measures of lameness (e.g. lying behavior) is that they are also influenced by other factors such as stage of lactation, parity, and surfaces used for lying and standing. Therefore, compiling reliable data on cow characteristics and interaction of cows and their environment on a large scale should aid in validation of algorithms that accurately detect lame cows, with a long-term goal of promoting adoption on a commercial scale (Van Nuffel et al., 2015a). Prevalence of Lameness Estimates on incidence of lameness using locomotion scores are scarce (Clarkson et al., 1996; Hedges et al., 2001); most research in this area has focused on prevalence estimates. Apparent lameness prevalence estimates in indoor housing systems from the United States ranged from 10 to 63% (Espejo et al., 2006; von Keyserlingk et al., 2012; Lombard, 2015). In Europe, estimates ranged from 16 to 48% (Amory et al., 2006; Dippel et al., 2009; Barker et al., 2010). Reports on apparent lameness prevalence in pasture systems from New Zealand and Chile ranged from 8 to 17% (Flor and Tadich, 2008; Fabian et al., 2014), suggesting that although prevalence is higher in indoor housing systems, lameness in cattle on pasture is also a growing 5

22 concern. Lameness prevalence in those studies was estimated using various locomotion scoring systems (Sprecher et al., 1997; Winckler and Willen, 2001; Barker et al., 2010), scales (e.g. 3-, 4- or 5-point scale), and gait indicators (e.g., back arch, head bob, reluctance to bear weight, stride length). Definitions of lameness varied from presence of limping or short striding to presence of back arched and limping and/or short striding. In addition to the high lameness prevalence reported worldwide and despite increased awareness of its importance, studies indicate limited or no improvement over the past years. For instance, surveys conducted by the National Animal Health Monitoring System in the United States, indicate that lameness prevalence estimates oscillated from 11% in 1996 to 14% in 2007 to 10% in 2014 (USDA, 2007; Lombard, 2015). Similarly, in the United Kingdom lameness prevalence were essentially static at 20% in 1991 to 22% in 2001 (Whay, 2002). Despite increased awareness of lameness as a problem in Canada, prevalence studies are scarce. In Ontario, one study assessed lameness prevalence involving a large sample (n = 317) of tiestall barns. However, lameness was detected based on the presence of back arch (3.2%) and claw rotation (23%), the latter being an uncommonly used indicator of lameness (Zurbrigg et al., 2005). The only study that investigated prevalence of lameness based on locomotion scores was conducted in British Columbia (n = 42) and determined a lameness prevalence of 35%, including 7% that were severely lame (von Keyserlingk et al., 2012). Results of lameness prevalence on those studies were above the target of < 10% of lameness prevalence at herd-level recommended by the Canadian Dairy Code of Practice (National Farm Animal Care Council, 2009). 6

23 1.3 Foot Lesions Foot lesions are commonly categorized according to their etiology into infectious [e.g., digital dermatitis (DD), interdigital dermatitis, heel horn erosion] and noninfectious [e.g. sole ulcer, sole hemorrhage, white line disease (collectively termed claw horn disruption lesions) and toe ulcer] according to the International Lameness Committee (2008) and Potterton et al. (2012). Detection of Foot Lesions Diagnosis of foot lesions is done by visual inspection and involves restraining the cow and lifting the foot for inspection. Unfortunately, this is a time-consuming and labor-intensive approach and as such, is difficult and costly, especially if done at herd-level. Although diagnosis of foot lesions could appear to be a more objective evaluation than locomotion scoring, is also a subjective approach that can lead to misclassification bias. For example, there seems to be low levels of agreement when identifying hemorrhages, white line disease, and infectious lesions other than digital dermatitis (Holzhauer et al., 2006b; Cramer et al., 2008). Thus, over the past years, the dairy industry has improved upon standardizing definition of lesions by visual appearance, location and severity. Two examples are the Dairy Claw Lesion Identification Guide by International Lameness Committee (2008), the nomenclature used in this thesis, and more recently, the ICAR Claw Health Atlas developed in 2015 (Egger-Danner et al., 2015). However, lack of adoption of a standardized lesion identification system among veterinarians, hoof trimmers and researchers worldwide is perhaps the greatest contributing factor for challenges in lesion diagnosis (DeFrain et al., 2013). 7

24 Digital dermatitis detection is another example of challenges involved with the lack of a standardized lesion identification system. Similar to locomotion scoring, there are numerous classification systems for DD, based on visual observation and subject to misclassification bias (Döpfer et al., 1997; Laven, 1999; Manske et al., 2002c; Vink, 2006; Krull et al., 2014). Research could benefit from using a uniform scoring system and case definition for DD stages as it would facilitate comparisons and effectiveness of control plans across regions and countries. Moreover, DD is highly prevalent (more details below) and requires consistent monitoring at the herd level to enable early treatment and successful control. However, the traditional method for detection (feet inspection in the trimming chute) is neither practical nor cheap, contributing to inaccurate and delayed diagnosis and treatment (Relun et al., 2011). Therefore, there is a need for simple, inexpensive and accurate tools for DD detection, likely to be used by researchers but most importantly by farmers, on a regular basis. Prevalence of Foot Lesions Over the past decades, several studies investigated prevalence of foot lesions. In Canada, there is only one study conducted to determine foot lesions prevalence, but estimates were affected by misclassification bias from hoof trimmers (Cramer et al., 2008). Estimates from Ontario indicated that 47 and 26% of cows housed in freestalls and tiestalls, respectively, were affected by at least one foot disorder (Cramer et al., 2008); whereas in the Netherlands, 80 and 55% of cows exposed to concrete flooring and straw yards, respectively, had at least one foot disorder (Somers et al., 2003). Prevalence of specific foot lesions also varied widely across countries and dairy systems. For example, the most prevalent lesion in Ontario was DD (18%) followed by sole ulcer (9%; Cramer et al., 2008). Sweden s most prevalent lesions were heel 8

25 horn erosion (41%) followed by sole ulcer (30%; Manske et al., 2002a), whereas in Chile, white line disease (64%) followed by heel erosion (50%) predominated (Tadich et al., 2010). According to USDA (2009), DD was the most prevalent foot lesion in the United States, affecting 49% of dairy cattle. The Alberta Dairy Hoof Health Project A uniform and standardized system for foot lesion data collection at herd, provincial and national levels can provide baseline information on type, frequency and distribution of foot lesions. Furthermore, these large-scale data can be used for disease surveillance, genetic improvement and benchmarking. Consequently, and in response to increasing concerns about lameness, Alberta Milk (Alberta Milk Inc., Edmonton, AB, Canada) initiated in 2009 the Alberta Dairy Hoof Health Project (Alberta Milk, 2013) to collect foot lesion data using a computerized foot lesion recording system. Data were collected by seven hoof trimmers from 2009 to 2012 from 40,558 cows on 158 dairy farms. Preliminary results indicated that 51% of cows had at least one foot lesion; of these, 43% were affected by DD. The Importance of Digital Dermatitis Digital dermatitis causes ulcerative, painful lesions on the heel bulb region and is associated with reduced milk production, treatment costs and increased risk for culling (Bruijnis et al., 2010; Cha et al., 2010; Ettema et al., 2010). Its high prevalence, the pain it can cause, and related financial losses, make it the foot lesion with the largest economic and welfare impact (Bruijnis et al., 2010; Bruijnis et al., 2012). Due to its infectious nature, DD has expanded to endemic proportions in confined systems worldwide, with Canada being no exception. For 9

26 example, in the Netherlands, it affects 21% of cows (Holzhauer et al., 2006a) and 100% of herds exposed to concrete flooring (Somers et al., 2003). In Ontario, it affects 23% of cows and 92% of herds housed in freestall barns (Cramer et al., 2008), whereas in Alberta, cow-level prevalence of DD was estimated to be 33% (Alberta Milk, 2013). Therefore, DD is not only a major health issue in Alberta and Canada, but its prevalence was similar or even higher compared to other countries. Clearly, identifying prevention and control practices to reduce DD prevalence, along with the economic and welfare implications, is of the utmost importance. 1.4 Pathogenesis and Risk Factors of Foot Lesions Each type of lesion has its own pathophysiology and specific risk factors. However, the etiopathogenesis of many foot lesions has not been fully elucidated (Potterton et al., 2012). Remarkably, there is only one study in Canada that investigated risk factors associated with foot lesions but it did not include individual factors (Cramer et al., 2009b). In addition, no study has investigated risk factors for lameness in Canadian herds. However, there is a large body on literature on risk factors associated with foot lesions. Individual risk factors consistently identified in literature common to many lesions include peak of lactation, high milk yield, low body condition, and the Holstein-Friesian breed. First parity is considered a risk factor for infectious lesions, whereas higher parities (>2) are considered a risk factor for noninfectious lesions (Holzhauer et al., 2008b; Dippel et al., 2009; Sarjokari et al., 2013). Infectious foot lesions affect the foot skin and most are considered of polybacterial nature. As already stated, the main infectious lesions include foot rot, interdigital dermatitis, heel horn erosion and DD, the latter being the foot lesion of focus in this thesis. Bacterial families 10

27 involved in the pathogenesis of DD include Corynebacteriaceae, Porphyromonadaceae, and Dichelobacter; notwithstanding, Treponema spp. is consistently identified in DD lesions (Gomez et al., 2012; Krull et al., 2014; Krull et al., 2016). Host susceptibility (Scholey et al., 2010), unhygienic, wet and muddy conditions (Rodríguez-Lainz et al., 1996; Relun et al., 2013b) have important roles in the pathogenesis of DD. Other herd-level factors associated with increased DD prevalence include access to outside exercise areas, long intervals between successive hoof trimming, introduction of dry cows to the lactating herd before calving, and increased alley scraping frequency (Somers et al., 2005a; Cramer et al., 2009b). Noninfectious lesions are mainly predisposed by metabolic disorders (e.g., laminitis resulting from rumen acidosis), unbalanced weight-bearing forces in overgrown claws, and hormonal changes around calving that loosen the suspensory apparatus (Tarlton et al., 2002; Danscher et al., 2010; Bicalho and Oikonomou, 2013). Recent studies suggest additional factors are involved in pathogenesis of claw horn disruption lesions, including loss of body condition around calving compromising the shock-absorbing capacity due to thinning of the digital cushion (Bicalho et al., 2009; Lim et al., 2015; Randall et al., 2015), and bone development on the third phalanx, with inflammation being a key component in lesion recurrence (Newsome et al., 2016). Roles of Cow Housing and Comfort Barn design and management influence lying and standing behavior, and consequently, claw horn growth, wear and concussion; these are factors that may exacerbate trauma and damage to the foot and lesion severity (Cook et al., 2004b; Cook and Nordlund, 2009). Risk factors associated with noninfectious lesions include flooring type and slipperiness (Somers et al., 2003; Telezhenko and Bergsten, 2005), the amount, cleanliness and type of stall bedding 11

28 (Cook, 2003; Cook et al., 2004b; Ito et al., 2010; Chapinal et al., 2013b), stall dimensions (Sogstad et al., 2005a; Espejo and Endres, 2007; Dippel et al., 2009), access to pasture (Chapinal et al., 2013b), and overgrown claws (Manske et al., 2002a). Housing also has an important role in environmental hygiene, as factors that influence hygiene are associated with infectious lesions. Presence of slatted floors with a manure scraper, and long and wide stalls have been identified as a protective factor for DD (Laven, 1999; Somers et al., 2005a), presumably because drier floors and more comfortable stalls promote lying and reduce exposure of feet to slurry. Furthermore, cow comfort not only has an important role in lameness development, but also in lameness recovery, as a comfortable environment can influence duration and aggravation of foot lesions (Cook and Nordlund, 2009). 1.5 Prevention and Control of Digital Dermatitis Understanding the multifactorial nature and interconnected risk factors for lameness and foot lesions is fundamental for disease prevention (LeBlanc et al., 2006). Economic losses and animal welfare concerns related to lameness have motivated farmers and the dairy industry to focus on prevention rather than treatment. Control strategies targeting specific foot lesions may differ, especially depending on its origin (i.e., infectious or noninfectious); nevertheless, there are strategies which are applicable to non-specific lameness, including monitoring locomotion, routine hoof trimming, and providing a comfortable and hygienic housing (Potterton et al., 2012). The three most common foot lesions causing lameness in intensive farming are sole ulcers, white line disease and DD (Somers et al., 2003; Cramer et al., 2008; Potterton et al., 12

29 2012); therefore, in most cases, prevention programs need to be tailored to control both infectious and noninfectious lesions. For infectious lesions, strategies should be tailored to control the spread of the infectious agents in the herd. This thesis focuses on DD, as it was identified as the most prevalent foot lesion in Canada. Key strategies for DD control include: 1) improve environmental hygiene and dryness of floors and stalls by frequent scraping and bedding, but also by avoiding overstocking as it increases manure build-up and contamination (Relun et al., 2013b); 2) implementing biosecurity measures to prevent entry of infected animals through purchase or spread of bacteria through hoof trimming equipment; as well as to prevent infection of healthy heifers or dry cows (Potterton et al., 2012); 3) reduce the reservoir and spread of treponemes by early and prompt treatment interventions such treatments can be at individual level through topical sprays or foot wraps with antibiotics or disinfectants (Döpfer et al., 2012a); and 4) footbathing practices aimed to prevent aggravation of lesions and potential outbreaks by decreasing prevalence of chronically affected cows (Döpfer et al., 2012a; Relun et al., 2012). The use of footbaths is the most common preventative measure at herd-level for DD (Laven and Logue, 2006; Relun et al., 2013c) due to increasing herd size with limited workforce, along with strong evidence from intervention trials supporting its efficacy (Speijers et al., 2010; Relun et al., 2012; Fjeldaas et al., 2014). However, efficacy of footbathing relies on good management (i.e., type, amount, frequency of product used), optimal footbath design and layout that ensures effective delivery of product to the cows feet (Cook et al., 2012). Despite the high prevalence of DD and the widespread use of footbaths, the labor, time, and cost involved in implementing and maintaining an efficacious footbath regime appear to represent practical challenges for farmers. There is therefore a wide variation in on-farm practices related to footbath management (Cook et 13

30 al., 2012; Relun et al., 2013c). For example, in a survey conducted in five countries, farmers reported a range of 80 to 3000 cow passes through the footbath between product changes, and a median footbath shorter (2 m) and shallower (0.11 cm) than recommended (Cook et al., 2012). French farmers reported the use of footbathing less frequently than what has been recommended (i.e. few days every month or less) and did not have an approximate sense of concentrations of footbath products that they used (Relun et al., 2013c). 1.6 Thesis Aim and Overview The overall aim of this thesis was to investigate epidemiology of lameness and foot lesions in Canada by assessing their prevalence and associated risk factors. Moreover, it includes a longitudinal component to evaluate an intervention measure in controlling DD, the most prevalent foot lesion. The research in this thesis consists of two parts. The first part, based on a cross-sectional study, determined the prevalence of lameness and foot lesions, and aimed to gain insights into their multifactorial nature, encompassing individual, management and environmental-related factors. In Chapter 2, lameness prevalence and associated cow- and herd-level factors were identified. In Chapter 3, factors influencing lying behavior and its association with lameness were investigated to determine whether lying measures can be used to detect lameness. Research in Chapters 2 and 3 was done across three Canadian provinces. In Chapter 4, research focused on Alberta, providing insights into the type, frequency and distribution of foot lesions which were recorded electronically by hoof trimmers. In addition, associations between herd- and cow-level factors and the most frequently identified foot lesions were determined. 14

31 Findings from the first part contributed to the development of the second part of this thesis. The second part, based on a longitudinal, intervention study, sought to identify an accurate and practical DD detection tool and evaluate the effect of optimised footbathing management practices on DD control. Chapter 5 focused on validating a tool to determine the accuracy of detecting DD in the milking parlor. Chapter 6 evaluated effectiveness of a standardized footbath protocol based on current scientific literature in decreasing DD prevalence. Finally, in Chapter 7, main results of all studies presented are discussed, with special emphasis on the implementation of findings and proposed directions for future research. 15

32 Chapter Two: Prevalence of lameness and associated risk factors in Canadian Holstein- Friesian cows housed in freestall barns 2.1 Abstract Lameness is a severe welfare problem and a production-limiting disease in dairy farming. The objectives were to determine prevalence of lameness and investigate cow- and herd-level factors associated with lameness in dairy cows housed in freestall barns in 3 Canadian provinces. A purposive sample of 40 Holstein-Friesian cows was selected from each of 141 dairy farms in Québec, Ontario, and Alberta. In total, 5, 637 cows were scored once for lameness (presence of limping when walking). Data collected included information on individual cows (hock lesions, claw length, body condition score, parity, days in milk, and milk production), management practices (floor and stall cleaning routine, bedding routine, and footbath practices) and facility design (stall dimensions, stall base and bedding type, width of feed alley, flooring type, and slipperiness) hypothesized to be risk factors for lameness. Multilevel mixed logistic regression models were constructed (including farm as a random effect and province as a fixed effect). Herd-level lameness prevalence ranged from 0 to 69% (mean = 21%). Lameness prevalence increased with increasing parity; compared to first parity, cows in parity 2, 3 and 4 had 1.6, 3.3 and 4 times, respectively, higher odds of being lame. Furthermore, the odds of lameness were 1.6 times greater in cows with low body condition score ( 2.5) than in cows with a higher body condition score. In addition, injured hocks and overgrown claws were associated with 1.4 and 1.7-fold increased odds of being lame, respectively, whereas every 1 kg increase in daily milk production was associated with a 3% decrease in the odds of being lame. Lameness prevalence 16

33 was higher in herds with 100 cows, but lower in barns with a sand or dirt stall base, or with bedding 2 cm deep. Cows exposed to very slippery floors had 2 times the odds of being lame compared to cows exposed to nonslippery floors. We attributed the wide range of lameness prevalence to the great variability in facilities and management practices among farms. Finally, we inferred that the prevalence of lameness could be decreased by improving management of multiparous, thin or injured cows and by adopting management practices intended to improve cow comfort, namely the floor s slip resistance and stall s lying surface. 2.2 Introduction Lameness is one of the most important welfare, health, and productivity problems in intensive dairy farming worldwide. Furthermore, it causes pain (Whay et al., 1998; Rushen et al., 2007), reduces longevity (Booth et al., 2004; Canadian Dairy Information Centre, 2016), milk production (Warnick et al., 2001; Green et al., 2002), and reproductive performance (Hernandez et al., 2001; Garbarino et al., 2004), and consequently has a great economic effect (Ettema and Ostergaard, 2006). The prevalence of lameness varies considerably among farms, regions, and housing systems, although it is generally higher in freestall barns compared to tie-stalls (Cook, 2003; Sogstad et al., 2005b), bedded packs (Haskell et al., 2006) and pasture systems (Hernandez-Mendo et al., 2007). In the United States, freestall dairies in Wisconsin and Minnesota had a mean lameness prevalence of 25% (Cook, 2003; Espejo et al., 2006), whereas in California and the northeastern United States, overall lameness prevalence was estimated to be 34 and 63%, respectively (von Keyserlingk et al., 2012). British and German studies reported a 17

34 lameness prevalence of 37 and 48% (Whay et al., 2003; Barker et al., 2010), whereas a prevalence of 16% was reported in the Netherlands (Amory et al., 2006). Estimates of the prevalence of lameness using locomotion scores in Canada (irrespective of housing system) are scarce. In Ontario, 1 study assessed lameness prevalence linked to stall design, but estimated lameness based on the presence of back arch (3.2%) and claw rotation (23%) and only in tie-stall barns (Zurbrigg et al., 2005). Another study assessed a wider range of herd-level factors in both tie-stalls and freestalls, but estimated lameness based on the presence of foot lesions (Cramer et al., 2008). The only study that focused on the prevalence of lameness based on locomotion scores in freestalls was conducted in British Columbia (von Keyserlingk et al., 2012). In that study, 35% of the cows were lame, including 7% that were severely lame. However, no risk factor analysis was performed. Lameness scoring systems differed considerably among these studies; therefore, the variation in lameness prevalence estimates among European and North American studies could partly be due to methodology and diagnostic criteria. Several studies reported associations between lameness and factors such as flooring type and slipperiness (Somers et al., 2003; Telezhenko and Bergsten, 2005), the amount, cleanliness and type of stall bedding (Cook, 2003; Cook et al., 2004a; Ito et al., 2010; Chapinal et al., 2013b), stall dimensions (Sogstad et al., 2005a; Espejo and Endres, 2007; Dippel et al., 2009), access to pasture, and footbath frequency (Chapinal et al., 2013b). Therefore, differences in lameness estimates among studies could also be attributed to management and housing differences across farms that lead to the presence or absence of risk factors for lameness. High lameness prevalence estimates and their variation highlight the need for a better understanding of the multifactorial origins of lameness, and the combination of risk factors 18

35 related to the environment, management and the individual cow (Vermunt, 2007). Despite increased awareness of lameness as a problem in Canada, apparently no epidemiological study has been done to identify prevalence (and associated risk factor analysis) in freestall barns. Therefore, the objectives were to determine: 1) prevalence of lameness; 2) herd-level management and facility design factors related to lameness prevalence; and 3) the association between herd and cow-level factors, as related to the prevalence of lameness. 2.3 Materials and Methods The present study was part of a larger research study examining dairy cow comfort and longevity. Methodology for farm and cow selection, and assessment of animal-based measures, management practices and facility design have been described (Zaffino Heyerhoff et al., 2014; Vasseur et al., 2015). Several standard operating procedures were developed and validated as described on the Canadian Dairy Research Portal ( Based on hypothesized biological cause-and-effect relationships and previous research, a causal diagram was drawn to identify variables to measure on farms and to consider in analyses (Figure 1). Farms A total of 141 freestall dairy farms were enrolled as part of a larger study. Farms were located in 3 Canadian provinces: Alberta [(AB) n = 81], Ontario [(ON) n = 40] and Québec [(QC) n = 20]. Data were collected between May 2011 and July 2012 by trained graduate students and research assistants from the University of Calgary (Calgary, AB, Canada), 19

36 University of Guelph (Guelph, ON, Canada), and Université Laval (Québec City, QC, Canada). Due to practical reasons and availability of students, farms were visited from May 2011 to July 2012 in AB; May to November 2011 in ON; and January to April 2012 in QC. All methods were approved by the Animal Care Committees and Research Ethics Boards of each participating academic institution. Eligible farms received a recruitment request by mail. Those who were interested replied by mail or fax, and had to return a letter indicating willingness to participate in AB and ON, or they were called by a DHI (Valacta Inc.) advisor in QC. They were then contacted by telephone, after which it was determined whether they met the study criteria. To ensure that participating farms were representative of the majority of freestall herds in Canada, farms had to be enrolled in an organized milk recording system provided by CanWest DHI (Guelph, ON, Canada) or Valacta Inc. (Sainte-Anne-de-Bellevue, QC, Canada) and have a herd size 40 Holstein-Friesian lactating cows. In ON and QC, farms were also selected on the basis of longevity and having mean milk production 7,000 kg/cow per yr (Vasseur et al., 2015). Farms were excluded if lactating cows were subjected to uncommon management practices (e.g. access to an outdoor exercise area or pasture for > 2 h/d). To ensure that animal-based measures reflected housing conditions, the current freestall facility for lactating cows had to be in use for at least 1 yr. In AB, farm recruitment was also based on participation in the Alberta Dairy Hoof Health Project (Alberta Milk, 2013), a collaborative study that collected data on foot lesions based on professional hoof trimmers records. The sample of farms (n = 158) enrolled in The Alberta Dairy Hoof Health Project was representative of the average AB dairy farm in terms of herd size, breed, type of dairy barn and longevity (Zaffino Heyerhoff et al., 2014). 20

37 Cow Selection Based on previous work on estimating sample sizes for cow comfort aspects such as lying behavior (Ito et al., 2009), and due to time constraints (Zaffino Heyerhoff et al., 2014), a purposive sample of 40 lactating Holstein-Friesian cows between 10 and 120 DIM was selected on each farm. This is the period with an increased incidence of lameness (Green et al., 2002). If the herd had < 40 cows between 10 and 120 DIM, cows > 120 DIM were sequentially included until 40 cows had been assessed. Conversely, if there were > 40 cows between 10 and 120 DIM, the sample of study cows was balanced to reflect the proportion of primiparous and multiparous cows in the herd and cows were then randomly selected. Considering the average study farm (Table 2.1), this sample size represented an average of 29% (9 100%) of the cows present on each farm. Lameness Assessment Training of observers has been described (Gibbons et al., 2012). Briefly, 2 dairy scientists with experience scoring lameness trained 6 observers in the 3 provinces (3, 2, and 1 observers for AB, ON and QC, respectively) during an intensive 2-wk program. To ensure a high level of agreement (weighted Kappa statistic 0.6), the program included 4 repeatability sessions between trainers and all observers (2 on-farm and 2 with videos) on locomotion scoring. A refresher course and mid-way check (3 to 4 and 5 to 15 wk after initial training, respectively) were done to maintain agreement throughout the study. In addition, 20% of the videos were reanalyzed by the trainers. The percentage exact agreement was calculated as the number of exact agreements / total number of observations x

38 Cows were video recorded while returning from the milking parlour by 1 or 2 people per farm to assess lameness using a simplified version of a numerical rating score (Flower and Weary, 2006), previously validated (Chapinal et al., 2009; Ito et al., 2010). This scoring system aimed to identify cows that were reluctant to bear weight on at least 1 limb (i.e. walked with a limp). A cow was defined as lame if limping was present, which was equivalent to a score of 3 on a 5-point scale numerical rating score. Locomotion was not assessed if the video quality was poor, if the cow was trotting or running, or less than 2 complete strides were recorded (n=381). Animal-based Measures Cows were scored for leg cleanliness, BCS, hock injuries and claw length using standard operating procedures by no more than 2 trained observers per farm. For each of these measures, observers underwent a training program similar to that described above for lameness scoring. Leg cleanliness was assessed using the scoring system adapted from Cook (2006). Cleanliness on the lateral lower hind right leg, from the coronary band to the middle of the tarsal joint, was recorded using a 0-3 scale according to the degree of contamination: 0 = fresh manure for < 50% of the area; 1 = fresh manure for > 50% of the area; 2 = dried caked and fresh manure for > 50% of the area; and 3 = entire area with dried caked manure. The BCS was determined as described (Vasseur et al., 2013), using 0.25 increments. Hock injuries were scored in the milking parlour, in the headlocks or where the cows were free to move, as described (Gibbons et al., 2012). In short, conditions of the lateral surface of the left and right tarsal joints was recorded using a 0-3 scale: 0 = no swelling, no hair missing; 1 = bald area with no swelling or swelling < 1 cm; 2 = medium swelling (1 2.5 cm) and/or lesion on bald area; and 3 = major swelling (> 2.5 cm; Gibbons et al., 2012). Claw length was assessed in the milking parlour by estimating the 22

39 angle of the dorsal surface of the left and right lateral claws in relation to the ground. Claw length was defined as no overgrowth (angle 45o) or overgrowth (angle < 45o). Individual cow data on parity, DIM, and test-day milk production (measured at the most recent monthly milk recording after data collection) for the sampled cows were obtained from CanWest DHI and Valacta Inc. The average interval between data collection and monthly milk recording was 17 d (range, 0 to 51 d). General Management A questionnaire was administered by interview on every farm. Initially, the questionnaire was pretested on 4 farms to evaluate whether it was understood easily and interpreted correctly. Thereafter, the questionnaire was adapted and improved where necessary. Questions were either open-ended (e.g., Describe the footbath product(s) you use, in what concentration and frequency ) or closed-ended (e.g., How often is new bedding added?, How often do you rake out stalls and remove manure? ; scale of answer: once/d, > once/wk, once/wk or < once/wk; Vasseur et al., 2015). The questionnaire is available on the Canadian Dairy Research Portal ( Facility Design Pen Space and Flooring. All environmental measures were collected from all pens where the 40 study cows were housed on the day of the visit. Pens were assessed for type of flooring, width of feed alley, floor cleanliness, and floor slipperiness. Type of flooring was categorized as solid or slatted and concrete or rubber. Feed alley width was measured from the feed bunk to the curb of the stalls. Assessing the cleanliness of the feed alley floor consisted of 23

40 walking the entire length of the feed bunk alley 20 min before and after scraping, and measuring the height of manure that collected on the heel of rubber boots. Floor cleanliness was evaluated as clean ( 0.5 cm manure), a bit dirty (> cm), dirty (1-3 cm), or very dirty (> 3 cm). If the scraping system was manual, feed alley cleanliness was scored at the beginning and at the end of the visit. Slipperiness was assessed using the scoring system adapted from Grandin (2008). Slipperiness was estimated as the proportion of cows that slipped or fell when a minimum of 20 cows were observed, or when the pen was observed for up to 30 min while the herd manager moved the cows to the milking parlour. A slip was noted each time the cow s feet slid as they touched the floor. A fall was noted when at least 1 knee (carpus) or hock (tarsus) touched the floor (Rushen and de Passillé, 2006). Floor conditions were evaluated as nonslippery (no slipping or falling), slightly slippery ( 3% of cows slipped), slippery (< 2% fall or 3 to 15% slip), or very slippery ( 2% fall or 15% slip). Stall Management. Each pen where the study cows were housed was assessed for stocking density, stall dimensions, stall base, stall bedding type, cleanliness, quantity, and dryness. Information on stocking density was obtained as described (Charlton et al., 2014) and estimated as number of cows/usable stalls. Data on 8 dimensions per stall, bedding cleanliness, quantity and dryness were estimated as described (Zaffino Heyerhoff et al., 2014; Vasseur et al., 2015). Briefly, stall dimensions were measured at the end stalls of 3 representative rows in each pen (n = 2 stalls per row). If the pen had < 3 rows, stall dimensions were measured from all rows. Lunge space was considered adequate if no obstruction was present 76 cm forward from the brisket board. If no brisket board was present, this measure was taken from the point of the neck rail and 10 cm above the stall surface. End stalls were usually narrower or wider than the average stall. Therefore, stall width was measured in the middle of each row (minimum 6 stalls per farm) 24

41 as the average width of 3 adjacent stalls. Bedding quantity was evaluated as the bedding depth (in cm) after the stall was raked evenly, and was evaluated as 2 cm (equivalent to 1 kg of chopped straw), or > 2 cm. The type of stall base and bedding were also recorded. If different types of stall bases were present in the same pen, the predominant stall base type was considered. Sand was only recorded as a bedding type when it was also the stall base. Footbath. Length, depth and width were measured for every footbath used. Detailed information was collected from the questionnaire on the frequency of use (times/wk), frequency of changing solutions, as well as products used and their concentrations. Statistical Analyses All statistical analyses were performed using Stata13.1 (StataCorp College Station, TX, USA) and P < 0.05 was considered significant. Descriptive statistics (mean, median, range, interquartile range) were used to describe herd- and cow-level characteristics, using farm and cow, respectively, as the experimental unit. Continuous predictor variables were tested for linearity. Analyses of hock injuries and claw length (cow level) were done using the higher score of the 2 limbs. If the lactating cows were housed in 2 or more pens and these pens differed in flooring or stall characteristics, the pen with the highest number of study cows was selected for analysis. If an equal number of cows was housed in each pen, 1 of the 2 pens was randomly selected. Similarly, if there were 2 footbaths that were not identical, random selection was performed. Categorical variables with low frequency ( 4 farms) were collapsed based on biological reasoning (i.e. an exact scraping frequency could not be determined for farms with a robotic scraper, therefore they were categorized as > 2 times/d). Unusual stall bases, bedding 25

42 types and floorings that could not make their own category in analysis due to a low sample size were considered in a category as other (Zaffino Heyerhoff et al., 2014). If 2 predictors were highly correlated ( r 0.7), the 1 with the strongest association with the outcome or the 1 with the fewest missing observations was chosen. The model building process involved 3 steps. First, univariable analyses were performed to assess associations between the outcome of interest, presence of lameness at cow level, and each predictor variable. Predictors with a univariate association with P 0.25 were considered for the next step of multivariable modelling within 4 categories of explanatory variables: individual cow measures, pen space and flooring, stall management, and footbath management. In the second step, explanatory variables were screened in 4 separate multilevel mixed logistic regression models (GLMM in Stata). In this step, all variables significant at P 0.10 from the 4 models were combined, and a backward elimination process was performed. Variables significant at P < 0.05 were retained in the final model. Additionally, if confounding was present (i.e., removal of any variable resulted in a 30% change in the estimate of any other significant predictor), then that variable was also retained in the final model. Two-way interactions (e.g. bedding quantity and type of bedding; floor slipperiness and type of flooring; parity and DIM; BCS and DIM) were tested among the significant predictors in the main effects model, but none was retained (P > 0.05 in all cases). Farm was included as a random effect and province was forced into the final regression model as a fixed effect. Model comparison among all multivariable models was based on Akaike s information criterion, and the model with the lowest Akaike s information criterion was considered the best model (Akaike, 1973). 26

43 2.4 Results Study farms had on average 124 lactating cows, with daily and 305 d milk production of 36 and 10,238 kg respectively (Table 2.1). Farm characteristics of this study represented the overall population of cows housed in freestalls per province in terms of parity, although herd size and milk production were slightly greater, because our study criteria required herds with 40 cows and cows between 10 and 120 DIM. All farms in QC had only 1 pen, whereas in ON and AB, a total of 70 farms (50%) had 2 or 3 pens from which cows were sampled. The exact agreement between the 2 trainers for locomotion scoring was 82% throughout the study period. The exact agreement for the 6 observers across provinces was 94% (Kw 0.8) for lame versus not lame. A total of 5,637 cows were observed with 5,256 cows having complete lameness scoring. Herd-level lameness prevalence ranged from 0 to 69% with a mean of 20.8% on the 141 farms. There was a larger variation of lameness prevalence among farms than among provinces. Mean herd lameness estimates within province ranged from 2 to 69%, 0 to 62% and 8 to 61% for AB, ON and QC, respectively; mean prevalences were 19% (AB), 22% (ON) and 24% (QC). The 10% of herds with the lowest lameness prevalence had a prevalence < 8%, whereas in the 10% herds with the highest prevalence, > 35% of the cows were lame. Cow-level Variables Lameness prevalence increased with increasing parity and it differed among parities in univariable analysis (P < 0.001, Figure 2). The lowest lameness prevalence (< 14 and < 18%) occurred in first and second parity cows respectively, which combined represented 65% of the study cows. There was a 10% point increase in lameness prevalence from second to third parity 27

44 cows, whereas cows in 4th or higher parity had a lameness prevalence 32% [prevalence was highest (38%) for cows with parities 7]. The majority of cows (93%) included in the study were < 200 DIM. Lameness increased with increasing DIM in the univariable analysis (P = 0.002, Table 2.2). However the difference in mean DIM between lame and non-lame cows was small. Cows with a low BCS had the highest lameness prevalence (Figure 3); 46% of the cows with BCS 2 were lame, whereas approximately 15% of the cows with BCS 3 were lame. Lameness was associated with lower milk production, overgrown claws, and hock injuries (Table 2.2). Herd-level Variables A footbath was routinely used in 122 farms (87%). A range of 1 to 4 products were used per farm, the most common being CuSO4 and formaldehyde, with median concentrations of 4.5% (range, 0.3 to 12.5%) and 5% (range, 1 to 10%). Barn design and lameness-associated management practices varied greatly among farms (Tables 3.3 to 3.5). Lameness prevalence increased with decreasing herd size; herds with 100 and > 100 cows had a mean lameness prevalence of 24 and 19%, respectively (P < 0.001). The majority of farms (70%) had a stocking density 100% (i.e. more stalls than cows) and lameness prevalence was not associated with overstocking (P = 0.32). Scraping system (automatic or manual) and scraping frequency were highly correlated (r = 0.83; P < 0.001) because all farms that used a manual system scraped 3 times/d. Holding pen flooring was correlated with feed alley flooring (r = 0.35; P < 0.001). Herd size was correlated with footbath use (r = 0.43; P < 0.001) because 95% of the farms that did not use a footbath were herds 100 cows. 28

45 Multivariable Analysis Based on univariable analyses, cow-level variables initially included in the multivariable model were: parity, DIM, test-day milk production, claw length, leg cleanliness, hock injury, and BCS. Herd-level variables were: herd size, stall bed length, lunge space, stall base, bedding type, bedding quality and quantity, frequency of bedding and stall cleaning, scraping frequency, feed alley width and flooring type, slipperiness of the flooring, footbath length, frequency of use, and number of footbath products (Table 2.6). A total of 7% of the model variability was at the farm level, whereas 93% of the variation was at the cow level. Cows with low BCS, injured hocks and overgrown claws had a higher prevalence of lameness. Cows in parity 3 had 3 times the odds of being lame compared to primiparous cows. Furthermore, every 1 kg increase in test-day milk production was associated with a 3% decrease in the odds of being lame. Cows in groups with a high prevalence of slips or falls had 2 times greater odds of being lame (Table 2.6). The odds of lameness was higher in cows housed with concrete or rubber stall bases than those housed with sand or dirt stall bases, as well as among cows housed with stalls with 2 cm of bedding, compared to those with stalls with > 2 cm of bedding. 2.5 Discussion Results from this study represented the population of Holstein-Friesian cows housed in freestalls without pasture access in the 3 Canadian provinces that represent 79% of the total Canadian dairy cow population. The lameness prevalence of 21% reported in this study seemed comparable to that reported in Wisconsin, Minnesota, (25%; Cook 2003; Espejo et al., 2006), England (22%; Whay et al., 2003), and Finland (21%; Sarjokari et al., 2013). However, the 29

46 lameness prevalence estimate in the present study was lower than findings in British Columbia, California and the northeastern United States (35, 34 and 63%, respectively; von Keyserlingk et al., 2012), conventional farms in central Germany (48%; Dippel et al., 2009), and the United Kingdom (40%; Barker et al., 2010). Apparent differences in lameness prevalence estimates among regions could be the consequence of factors known to affect prevalence, such as herd size (e.g. mean herd size in California and Germany was 1,796 and 54 cows, respectively) and certain management practices (e.g. 100% of herds in California had deep-bedded stalls, whereas 55% of herds in the northeastern US had stalls with little bedding). Additionally, differences in lameness estimates in these regions could also be the consequence of the lameness scoring system used (e.g. assessment of an arched back versus assessment of a cow s locomotion), inter-observer reliability for lameness scoring (e.g. studies in the UK and Finland did not report inter-observer assessments) and cow selection criteria (e.g. selection based on DIM, milk production, or parity). In the present study, the odds of lameness increased with increasing parity, consistent with other studies (Espejo et al., 2006; Sarjokari et al., 2013). In agreement with Sarjokari et al. (2013), this association may be because older cows are bigger, are predisposed to relapse with certain foot lesions and have been exposed for a longer interval to the housing environment than younger cows. In the present study, greater odds of lameness at a single assessment were associated with lower BCS on the same day and lower test-day milk production. Lameness assessment and milk recording did not always occur on the same day, possibly resulting in under or over estimation of milk production. There have been various hypotheses regarding the causeand-effect relationship between BCS and lameness. Some authors suggested that low BCS was a result of lameness (attributed to a reduction of feed intake that caused BW loss; Espejo et al., 30

47 2006), whereas others suggested that low BCS contributed to lameness due to a decrease in thickness of the digital cushion which caused foot lesions such as sole ulcers and white line disease (Bicalho et al., 2009). Studies also differed on the relationship between milk production and the risk of lameness. In some studies, an increased risk of lameness was present that was associated with either low (Warnick et al., 2001; Green et al., 2002) or high (Amory et al., 2008) milk production, whereas others reported no association (Haskell et al., 2006). However, these relationships may be confounded by stage of lactation and parity, among other variables. We inferred that the complex associations between BCS, milk production and lameness vary depending on underlying conditions (e.g. infectious versus non-infectious foot lesions, or upper leg lameness) and need to be examined in large-scale longitudinal studies to distinguish risk factors for the incidence of new cases and persistence of lameness. In a longitudinal study (Green et al., 2013), BCS was highly variable throughout lactation (typically decreased in early lactation but increased in late lactation); furthermore, cows with low BCS (< 2.5) were more prone to develop lameness caused by non-infectious foot lesions and milk production decreased before cows became clinically lame. The odds of lameness were significantly higher for cows with injured hocks. This association was explained in detail by Zaffino Heyerhoff et al. (2014), in which a subset of the present data was analyzed, suggesting that lame cows had difficulty lying down or getting up, resulting in abrasion of the hock area. However, as the direction of risk cannot be determined in a cross-sectional study, it may also be that hock lesions were painful enough to make the cows lame. The odds of lameness significantly increased for cows with overgrown claws compared to cows with a normal claw angle. In a report on the association between foot lesions and claw measurements (Manske et al., 2002a), lame cows had longer, shallower and more concave claws 31

48 than non-lame cows and claw conformation varied according to the underlying foot lesion. Overgrown claws can also be a result of reduced wear (e.g. exposure to rubber flooring) and inconsistent hoof trimming. Several aspects related to stalls were studied, including stall dimensions, cleanliness, dryness, type of bedding and management practices related to bedding and cleaning frequency. Nevertheless, there were statistically significant associations only between lameness prevalence and bedding depth and sand or dirt stall bases, highlighting the importance of comfort of the lying surface with respect to lameness. However, there was no correlation between the type of bedding and its quantity. Additionally, there was a great variation of bedding type, resulting in a lack of power to study the effect of an interaction between bedding type and quantity. Lower lameness prevalence has been reported in farms with sand bedding and deep bedding stalls compared to mattresses or little bedding (Cook et al., 2003; Ito et al., 2010; Chapinal et al., 2013). It is generally understood that deep bedding provides a comfortable lying surface which affects the lying behavior of lame cows, influencing their recovery and thus decreasing the risk of lameness (Cook et al., 2008). However, the majority of farms in the current study (60%) managed their stalls with small amounts of bedding and a minority (11%) used sand or dirt as stall bases. Certainly, sand bases are not common in Canada, perhaps due to challenges related to manure handling with the use of sand, and aggravated by long and harsh winters. Hence, it is encouraging that deep bedding had a protective effect on lameness in the present study, as this may motivate more farmers to adopt this management practice. Furthermore, depth of bedding was important regardless of stall base, emphasizing that cow comfort has an important role in lameness prevalence. 32

49 Lameness prevalence was not associated with floor type in the milking parlour, holding pen or feed alley. However, the odds of lameness were much higher on farms with very slippery flooring compared to nonslippery flooring. Evidence on the impact of different types of flooring on lameness prevalence has been equivocal. Concrete flooring is known to have a detrimental effect on foot health when compared to straw yards or pasture (Somers et al., 2003); however, there was no clear evidence of the effect of various types of concrete flooring on lameness. Somers et al. (2003) reported no associations between solid or slatted concrete floors and prevalence of non-infectious foot lesions, whereas Sogstad et al. (2005a) reported a higher risk for white line disease on slatted floors. Although benefits of rubber flooring on the locomotion of lame and non-lame cows (i.e. increased length of steps and speed) have been reported (Telezhenko and Bergsten, 2005), there was no clear evidence on the risk of developing foot lesions in cows exposed to rubber versus concrete flooring. Vanegas et al. (2006) reported that foot lesions did not differ between floor types, although the odds of becoming lame were greater for concrete-exposed cows. In contrast, Kremer et al. (2007) reported a higher incidence of sole ulcers on cows exposed to rubber flooring compared to concrete. It is noteworthy that these findings may be confounded by variables including stall design, bedding type and depth, and stocking density. Manure, slurry and wet flooring conditions are predisposing factors to increased floor slipperiness. In this study, lameness prevalence was not associated with floor cleaning routines nor feed alley cleanliness. However, floor cleanliness was only assessed on 1 occasion. Perhaps direct observation of cows slipping or falling was a better indicator of floor conditions than recording information on floor cleanliness. Notwithstanding, results of the present study highlighted that the type of flooring (rubberized or concrete, slatted or solid) was less relevant 33

50 than the flooring s slip resistance. However, slipperiness indicator in the present study may have been confounded by cattle handling. Poor cattle handling (rushed or aggressive handling) and slippery floors can result in injuries from slipping or falling and may be a predisposing factor for foot lesions due to alterations in cows gait. Lameness prevalence was not associated with footbathing practices, nor with footbath measurements. Besides a possible lack of statistical power, this result may reflect the fact that certain footbathing practices can increase lameness and that high lameness prevalence can also incite certain footbathing practices. The same applies to other management practices such as stall bedding frequency or floor scraping frequency. Therefore, more prospective, longitudinal studies are needed to assess these variables as contributors to the onset, duration, and resolution of lameness. Potential bias introduced by observers was controlled by the SOP developed, the intensive training program, and periodic checks throughout the study; collectively, these apparently contributed to achievement of high inter-observer agreement. However, controlling for observer had no added value, as observers in ON and AB were not specific per farm (i.e. 2 observers were involved per farm) and in QC there was only 1 observer. In the statistical analysis, province did not result as a confounder and it was forced as a fixed effect in the models; furthermore, there was no large difference among provinces overall lameness estimates. Therefore, we were confident of the reliability of the lameness estimate in our study. A strength of the present study was the large number of cows and farms and the comprehensive set of variables assessed. However, the cross-sectional nature of the data collection revealed numerous associations with lameness, but limited our ability to make causal inferences. Several factors may have limited the generalizability of our results. Study farms were 34

51 not randomly selected, they were visited during different seasons, and different farm selection methods were used among provinces. For example, farms in Alberta were selected based on participation in a hoof trimming project. Although trimming practices are known to be an important risk factor for lameness (Cramer et al., 2009b), this was not included as a factor in our analyses because it was linked to the study s selection criteria and therefore could not be assessed in an unbiased manner. Therefore, a potential bias due to selection and seasonality may have affected the lameness prevalence estimate, although probably not the risk factors identified. The method used for cow selection was not random, as it targeted early- to mid-lactation cows (high producing group) that are at high risk for lameness (Green et al., 2002). However, the study findings supported several of the hypotheses in our causal diagram and were in agreement with other studies from North America (Espejo et al., 2006) and Europe (Sarjokari et al., 2013). In that regard, we inferred that our study provided valid and generalizable results to North American freestall dairies without pasture access. 2.6 Conclusions This was apparently the largest study conducted to determine lameness prevalence and associated risk factors in dairy cows in Canada. We attributed the wide range of lameness prevalence to the great variability in facilities and management practices among farms. Improving management of multiparous, thin or injured cows and adopting management practices intended to improve cow comfort, namely the floor s slip resistance and stall s lying surface, should reduce the prevalence of lameness. 35

52 Table 2.1. Characteristics (mean ± SD) of 141 freestall dairy farms and the average freestall farm in 3 Canadian provinces. Average freestall farm 1 Study farms Variable Québec (n = 306) Ontario (n = 788) Alberta (n = 347) Québec (n = 20) Ontario (n = 40) Alberta (n = 81) Herd size (no ± ± ± 76 lactating cows) Daily milk ± 8 36 ± 9 38 ± 9 yield (kg) Parity ± ± ± d milk yield (kg) 9,195 9,645 9, 960 9,506 ± 1,700 10,192 ± 1,932 10,432 ± 2,032 1 Data from Source: CanWest DHI (Guelph, ON, Canada). 36

53 Table 2.2. Distribution of continuous and categorical [n (%)] cow-level explanatory variables for non-lame (n=4,162) and lame cows (n=1,094) from 141 Canadian freestall dairy farms 1. Québec n=734 (14%) Ontario n=1548 (30%) Alberta n=2974 (56%) Total 2 Variable Non-lame Lame Non-lame Lame Non-lame Lame Non-lame Lame Days in milk (median ± IQR 3 ) 99 ± ± ± ± ± ± ± ± 78 Parity (median ± IQR) 2 ± 2 3 ± 2 2 ± 2 3 ± 3 2 ± 2 2 ± 2 2 ± 2 3 ± 2 Daily milk yield, kg (mean ± SD) 33.9 ± ± ± ± ± ± ± ± 9.7 Claw length No overgrowth 439 (79) 119 (21) 993 (81) 226 (18) 1,881 (84) 359 (16) 3,313 (82) 704 (18) Overgrowth 64 (60) 43 (40) 209 (65) 110 (35) 497 (70) 211 (30) 770 (68) 364 (32) Hock injury Not injured (score 0-1) 310 (77) 90 (23) 711 (83) 147 (17) 1,508 (83) 302 (17) 2,529 (82) 539 (18) Injured (score 2-3) 239 (74) 84 (26) 489 (72) 188 (28) 850 (76) 266 (24) 1,578 (75) 538 (25) Leg cleanliness Clean (score 0-1) 512 (76) 166 (24) 962 (78) 271 (22) 2,087 (80) 516 (20) 3,561 (79) 953 (21) Dirty (score 2-3) 44 (85) 8 (15) 241 (78) 67 (22) 279 (83) 56 (17) 564 (81) 131 (19) Body condition score (75) 165 (25) 243 (71) 101 (29) 544 (72) 215 (28) 1,285 (73) 481 (27) (84) 11 (16) 808 (80) 205 (20) 1,514 (84) 298 (16) 2,379 (82) 514 (18) (0) 0 (0) 153 (82) 33 (18) 292 (85) 51 (15) 445 (84) 84 (16) 1 The percentage for categorical variables is reported as the sum of non-lame and lame cows within a province. 2 Percentage of non-lame differs (P 0.05) from percentage of lame cows for every variable except leg cleanliness. 3 Interquartile range. 37

54 Table 2.3. Distribution of continuous (mean ± SD) and categorical [n (%)] herd-level stall management variables for dairy herds with a low, medium or high lameness prevalence. Low ( 10%) 1 n = 24 Herd level lameness prevalence Medium (10-30%) n = 94 High ( 30%) 1 n = 23 Overall n = 141 Variable Stall dimensions (cm) Width 115 ± ± ± ± 6 Bed length 179 ± ± ± ± 12 Brisket board 12 ± 4 13 ± 6 13 ± 4 13 ± 7 Neck rail height rail 117 ± ± ± ± 8 Neck rail to rear curb 173 ± ± ± ± 9 Curb height 22 ± 5 23 ± 4 22 ± 3 22 ± 4 Obstruction in lunge space Yes 15 (62) 53 (56) 13 (56) 81 (57) No 9 (38) 41 (44) 10 (44) 60 (43) Base type [n (%)] Concrete 3 (13) 9 (9) 4 (17) 16 (11) Rubber matt 1 (4) 10 (11) 5 (22) 16 (11) Geomattress 12 (50) 58 (62) 13 (57) 83 (60) Sand / dirt 4 (16) 11 (12) 1 (4) 16 (11) Waterbed 3 (13) 3 (3) 0 (0) 6 (4) Other 1 (4) 3 (3) 0 (0) 4 (3) Bedding type [n (%)] Straw 6 (25) 24 (26) 2 (9) 8 (17) Sawdust 5 (21) 20 (21) 6 (27) 11 (24) Wood shavings 10 (42) 36 (39) 9 (41) 19 (41) Sand 3 (12) 3 (3) 1 (5) 4 (9) Other 0 (0) 8 (9) 4 (18) 4 (9) Bedding quantity [n (%)] 2 cm 9 (38) 61 (66) 15 (65) 85 (60) > 2 cm 15 (62) 32 (34) 8 (35) 55 (40) Bedding quality [n (%)] Clean 21 (88) 85 (90) 19 (83) 125 (89) Dirty 3 (12) 9 (10) 4 (17) 16 (11) Bedding dryness [n (%)] Dry 19 (79) 74 (80) 18 (78) 111 (80) Wet 5 (21) 18 (20) 5 (22) 28 (20) Bedding frequency [n (%)] More than once/wk 11 (46) 48 (52) 11 (48) 70 (50) Once/wk 13 (54) 44 (48) 12 (52) 69 (50) 1 Categories defined by the 20 th and 80 th percentile of herd lameness prevalence on a single assessment of a purposive sample of 40 cows per herd in 141 herds. 38

55 Table 2.4. Distribution of categorical [n (%)] herd-level flooring variables for dairy herds with a low, medium or high lameness prevalence. Low ( 10%) 1 n = 24 Herd level lameness prevalence Medium (10-30%) n = 94 High ( 30%) 1 n = 23 Overall n = 141 Variable Feed alley width (cm) < (17) 21 (22) 8 (35) 33 (23) (83) 73 (78) 15 (65) 108 (72) Scraping frequency (times/d) 2 6 (25) 20 (21) 4 (17) 30 (21) > 2 18 (75) 74 (79) 19 (83) 111 (79) Parlour flooring Solid concrete 13 (54) 67 (71) 15 (65) 95 (67) Solid rubber 11 (46) 27 (29) 8 (35) 46 (33) Holding pen flooring Solid concrete 11 (48) 53 (61) 11 (50) 75 (57) Slatted concrete 2 (9) 6 (7) 2 (9) 10 (8) Solid rubber 8 (34) 23 (26) 5 (23) 36 (27) Other 2 (9) 5 (6) 4 (18) 11 (8) Feed alley flooring Solid concrete 13 (54) 63 (68) 14 (61) 90 (64) Slatted concrete 5 (21) 13 (14) 5 (22) 23 (17) Solid rubber 6 (25) 17 (18) 4 (17) 27 (19) Feed alley cleanliness Clean 14 (61) 55 (59) 12 (55) 81 (59) Dirty 9 (39) 38 (41) 10 (45) 57 (41) Slipperiness (% cows) Nonslippery (no slipping) 16 (70) 48 (57) 10 (45) 74 (57) Slightly slippery ( 3% slip) 4 (17) 22 (26) 6 (27) 32 (25) Slippery (< 2% fall, 3-15% slip) 3 (13) 13 (14) 3 (14) 19 (15) Very slippery ( 2% fall, 15% slip) 0 (0) 1 (1) 3 (14) 4 (3) 1 Categories defined by the 20 th and 80 th percentile of herd lameness prevalence on a single assessment of a purposive sample of 40 cows per herd in 141 herds. 39

56 Table 2.5. Distribution of continuous (median ± interquartile range, IQR) and categorical [n (%)] footbath management variables for dairy herds with a low, medium or high lameness prevalence that use regular footbathing. Low ( 10%) 1 n = 24 Herd level lameness prevalence Medium (10-30%) n = 94 High ( 30%) 1 n = 23 Overall n = 122 Variable Footbath product [n (%)] CuSO4 8 (38) 30 (38) 7 (35) 45 (37) Formaldehyde 1 (5) 13 (16) 4 (20) 18 (15) CuSO4 & formaldehyde 11 (52) 32 (40) 7 (35) 50 (41) Other 1 (5) 6 (6) 2 (10) 9 (7) Number footbath products [n (%)] 1 6 (29) 33 (41) 7 (35) 46 (38) 2 12 (57) 35 (44) 10 (50) 57 (47) 3 3 (14) 12 (15) 3 (15) 18 (15) Footbath frequency of use [n (%)] 2 d/wk 8 (38) 41 (51) 10 (50) 59 (48) > 2 d/wk 13 (62) 40 (49) 10 (50) 63 (52) Footbath dimension (median ± IQR) Length (cm) 186 ± ± ± ± 46 Width (cm) 76 ± ± ± ± 14 Depth (cm) 15 ± 6 16 ± 4 15 ± 3 15 ± 4 1 Categories defined by the 20 th and 80 th percentile of herd lameness prevalence on a single assessment of a purposive sample of 40 cows per herd in 141 dairy herds. 40

57 Table 2.6. Final multilevel logistic regression model for lameness with cow and herd-level factors in 141 Canadian dairy herds (n=4,981). Variable Coefficient Odds ratio 95% CI P-value Parity 1 Referent < < < Daily milk production (kg) < Body condition score 2.5 Referent < < Hock injury Not injured (score 0-1) Referent Injured (score 2-3) < Claw length No overgrowth Referent Overgrowth < Herd size (n. lactating cows) 100 Referent > Stall base Concrete Referent Rubber mattress Geotextile mattress Sand / Dirt Waterbed Other Bedding quantity 2 cm Referent > 2 cm Slipperiness Nonslippery (no slipping) Referent Slightly slippery ( 3% slip) Slippery (< 2% fall, 3-15% slip) Very slippery ( 2% fall, 15% slip)

58 Stall dimensions Figure 2.1. Causal web of factors hypothesized to affect lameness in dairy cows. 42

59 50 Lamenes prevalence (%) n = 1,484 n = 1,914 n = 860 n = 525 n= 233 n = 120 n = Parity Figure 2.2. Percentage of lame cows per parity (n=number of cows per category). 50 n = 67 Lameness prevalence (%) n = 560 n = 1,139 n = 1,335 n = 1,017 n = 541 n = 374 n = 116 n = Body condition score Figure 2.3. Percentage of lame cows per body condition score category (n=number of cows per category). 43

60 Chapter Three: Associations between lying behavior and lameness in Canadian Holstein- Friesian cows housed in freestall barns 3.1 Abstract Lying behavior is an important measure of comfort and well-being in dairy cattle, and changes in lying behavior are potential indicators and predictors of lameness. Our objectives were to determine individual and herd-level risk factors associated with measures of lying behavior, and to evaluate whether automated measures of lying behavior can be used to detect lameness. A purposive sample of 40 Holstein cows was selected from each of 141 dairy farms in Alberta, Ontario and Québec. Lying behavior of 5,135 cows between 10 and 120 days in milk was automatically and continuously recorded using accelerometers over 4 days. Data on factors hypothesized to influence lying behavior were collected, including information on individual cows, management practices and facility design. Associations between predictor variables and measures of lying behavior were assessed using generalised linear mixed models, including farm and province as a random and a fixed effect, respectively. Logistic regression models were used to determine whether lying behavior was associated with lameness. At the cow-level, daily lying time increased with increasing days in milk, but this effect interacted with parity: primiparous cows had more frequent but shorter lying bouts in early lactation, changing to mature-cow patterns of lying behavior (fewer and longer lying bouts) in late lactation. In barns with stall curbs > 22 cm high, the use of sand or > 2 cm of bedding was associated with an increase in average daily lying time of 1.44 and 0.36 h/d, respectively. Feed alleys 350 cm wide or stalls 114 cm wide were associated with increased daily lying time of 0.39 and 0.33 h/d, respectively, 44

61 whereas rubber flooring in the feed alley was associated with 0.47 h/d lower average lying time. Lame cows had longer lying times, with fewer, longer and more variable duration of bouts, compared to non-lame cows. In that regard, cows with lying time 14 h/d, 5 lying bouts per d, bout duration 110 min/bout or SD of bout duration over 4 d 70 min had 3.7, 1.7, 2.5 and 3.0 higher odds of being lame, respectively. Factors related to comfort of lying and standing surfaces significantly affected lying behavior. Finally, we inferred that automated measures of lying behavior could contribute to lameness detection, especially when interpreted in the context of other factors known to impact lying behavior, including those associated with the individual cow (e.g. parity and stage of lactation) or environment (e.g. stall surface). 3.2 Introduction Adequate rest has been positively associated with productivity, health and welfare of dairy cattle. When access to stalls is restricted, cows prioritize lying down over feeding (Munksgaard et al., 2005) and preventing cows from lying down induces stress (Cooper et al., 2008). As a consequence, measures of lying behavior, such as the daily duration, and the frequency and duration of lying bouts, is a measure of cow comfort (Haley et al., 2001; Rushen et al., 2008). Furthermore, changes in lying behavior can be associated with pain and malaise, enabling the use of lying behavior not only as an indicator of present illness, but also as a tool to predict cattle at risk of becoming ill (Weary et al., 2009). These findings contributed to development of automated systems to measure lying time that are less time-consuming than live or video-based observations and that provide a useful measure of health, welfare, and comfort (Rushen et al., 2008; Bewley et al., 2010). 45

62 In freestall systems, lactating cows commonly lie down for approximately 11 h/d (Bewley et al., 2010; von Keyserlingk et al., 2012). However, lying duration varies considerably among dairy systems, with the shortest duration often in pasture systems (6.7 h/d; Botheras, 2006; 8 h/d; Sepúlveda-Varas et al., 2014) and the longest usually in tie-stalls (12.5h/d; Charlton et al., 2015). Typically, cows have 6 to 13 lying bouts daily, averaging 55 to 90 min each (EFSA, 2009). However, lying behavior is influenced by several factors, including housing system (Hernandez-Mendo et al., 2007; von Keyserlingk et al., 2012), stall dimensions (Tucker et al., 2004), stall surface (Cook et al., 2008), stocking density (Fregonesi et al., 2007), flooring (Haley et al., 2001), parity, stage of lactation (Vasseur et al., 2012), lameness (Ito et al., 2010; Thomsen et al., 2012), and heat stress (Cook et al., 2007). Understanding dynamics of lying behavior provides insight into how cows interact with their environment and what management practices may modify this behavior (Rushen et al., 2008). Diseased animals often exhibit abnormal or reduced activity; therefore, changes in lying behavior have been used in dairy cattle as potential indicators and predictors of health issues, including dystocia (Proudfoot et al., 2009), postpartum disorders (i.e. metritis and retained placenta; Sepúlveda-Varas et al., 2014) and lameness (Ito et al., 2010; Blackie et al., 2011; Alsaaod et al., 2012). The latter is one of the most important welfare and productivity problems in the dairy industry. That it causes pain (Rushen et al., 2007) and reduces both milk yield (Green et al., 2002) and reproductive performance (Hernandez et al., 2001) makes it extremely costly (Ettema and Ostergaard, 2006). Early recognition and treatment of lameness is fundamental to mitigate its negative impacts. Therefore, changes in measures of lying behavior have been identified as a potential behavioral indicator of lameness, based on differences in lying responses of lame and non-lame cows (Ito et al., 2010). However, changes in lying time can be 46

63 both a risk factor for and a consequence of lameness, as lameness can be preceded by reduced duration of lying, and once clinically lame, cows tend to have longer lying bouts and longer total lying time per day (Chapinal et al., 2009; Ito et al., 2010). Reports on lame cows lying behavior vary among studies. For example, some authors reported that the length and variability of lying bouts were greater in lame cows compared to non-lame cows (Chapinal et al., 2009; Ito et al., 2010), whereas others reported no difference in bout duration between lame and non-lame cows (Gomez and Cook, 2010). Furthermore, there are interactions of certain stall design features (e.g. stall surface) with the severity of lameness, relative to lying behavior (Cook et al., 2008). Hence, it is expected that lying behavior and its association with lameness are related to housing conditions, as well as management and cow factors. Lameness detection is a challenge for dairy producers; therefore, its prevalence is often underestimated (Espejo et al., 2006). Automated detection systems based on changes in lying behavior could alert the farmer of the onset of lameness or a high probability of the presence of lameness and would be of great benefit to farm productivity and cow well-being (de Mol et al., 2013). Although lying behavior has potential as an indicator of lameness, automated technologies that provide real-time lameness detection based on changes in lying behavior have not proven to be highly accurate (Alsaaod et al., 2012; de Mol et al., 2013). Unfortunately, most research on lying behavior has been conducted with limited sample sizes, on experimental dairy farms, or focused on limited individual (e.g. DIM, parity) or management factors (e.g. stall surface; Bewley et al., 2010; Gomez and Cook, 2010; Ito et al., 2010). Therefore, the objectives of this study were to determine: 1) individual and herd-level risk factors associated with measures of lying behavior; and 2) associations between lying behavior and lameness; to determine whether measures of lying behavior can be used to detect lameness. 47

64 3.3 Material and Methods Farms A total of 141 Canadian freestall dairy farms were enrolled as part of a larger study characterizing dairy cow comfort and longevity (Charlton et al., 2014; Vasseur et al., 2015). Farms were located in 3 Canadian provinces: Alberta [(AB); n = 81], Ontario [(ON); n = 40] and Québec [(QC); n = 20]. Data were collected between May 2011 and July 2012 by 6 trained graduate students and research assistants. Three of the observers were from the University of Calgary (Calgary, AB, Canada), 2 from University of Guelph (Guelph, ON, Canada), and 1 from Université Laval (Québec City, QC, Canada). All methods were approved by the Animal Care Committees and Research Ethics Boards of each participating academic institution. The farm selection process has been described in detail (Zaffino Heyerhoff et al., 2014; Solano et al., 2015). In short, eligible farmers from all 3 provinces were recruited via mail and participation was voluntary. In AB, farms already enrolled in a collaborative study, the Alberta Dairy Hoof Health Project (Alberta Milk, 2013) were invited to participate (n = 158). The subpopulation of farms enrolled in the Alberta Dairy Hoof Health Project was representative of the average AB dairy farm in terms of herd size, breed, type of dairy barn and longevity (Zaffino Heyerhoff et al., 2014). In ON and QC, farms invited to participate were selected on the basis of representative strata of longevity and having mean milk production 7,000 kg/cow per y (Vasseur et al., 2015), which was estimated to be within Canada s lower end of the normal range of milk production per cow per year. Farmers who indicated that they were willing to participate were then contacted by telephone, after which it was determined whether they met the study criteria. To ensure that participating farms were representative of the majority of freestall herds 48

65 in Canada, farms had to be enrolled in an organized milk recording system provided by CanWest DHI (Guelph, ON, Canada) or Valacta Inc. (Sainte-Anne-de-Bellevue, QC, Canada) and have a herd size 40 Holstein-Friesian lactating cows. Farms were excluded if lactating cows were subjected to management practices not commonly used in Canada (e.g. access to an outdoor exercise area or pasture for > 2 h/d). Cow Selection Based on a validation study (Vasseur et al., 2012), a purposive sample of 40 lactating Holstein-Friesian cows between 10 and 120 DIM was selected on each farm. The 10 to 120 DIM interval was chosen as it was characterized by an higher incidence of lameness than in later lactation (Green et al., 2002). If > 40 cows between 10 and 120 DIM were present on a farm, the sample of study cows was balanced to reflect the proportion of primiparous and multiparous cows in the herd and cows were then randomly selected. Lying Behavior Lying behavior was recorded using electronic data loggers (HOBO Pendant G Acceleration Data Loggers, Onset Computer Corp., Pocasset, MA, USA), validated for recording lying and standing positions (Ito et al., 2009; Ledgerwood et al., 2010). Data loggers were attached with bandaging wrap (CoFlex, Andover Coated Products Inc., Salisbury, MA, USA) to the cow s hind leg during milking and were programmed to record the position of the cow at 1- min intervals for 4 consecutive 24-h periods (Charlton et al., 2014). Lying data based on 4 d of continuous sampling was sufficient to obtain a representative herd mean lying time estimate (Ito et al., 2009; Vasseur et al., 2012). Each individual farm was visited twice within a 5 to 10 d 49

66 period. During the second farm visit, data loggers were removed and data downloaded. The total duration of lying and the duration and frequency of individual lying bouts were computed using Excel macros (Microsoft Corp., Redmond, WA, USA) for the 4-d period (Vasseur et al., 2012), from which daily lying time (h/d), bout frequency (bout/d), and bout duration (min/bout) were calculated for each cow. In addition, variation in duration of lying bouts within cow over 4-d was calculated from the standard deviation of bout duration (SD of bout duration), previously identified as being associated with lameness (Ito et al., 2010). Animal-based Measures Cows were video recorded while returning from the milking parlour. Lameness was assessed independently by 1 observer per farm using a binomial (yes/no) simplified version of a numerical gait scoring system (Flower and Weary, 2006), previously validated (Chapinal et al., 2009; Ito et al., 2010). This scoring system aimed to identify cows that were reluctant to bear weight on at least 1 limb (i.e. walked with a limp). A cow was defined as lame if limping was present (i.e. reluctance to bear weight on at least 1 limb), which was equivalent to a score of 3 on the 5-point scale numerical rating score developed by Sprecher et al. (1997). Locomotion was not assessed if the video quality was poor, the cow was trotting or running, or < 2 complete strides were recorded (n = 370). Only cows with complete lameness assessment were included in the analyses. Standard operating procedures were developed and tested to score animal-based measures. Cows were scored ( 2 trained observers per farm) for the presence of hock and knee injuries, using standard operating procedures. These injuries were scored in the milking parlour, in headlocks, or where the cows were free to move, as described (Gibbons et al., 2012; Zaffino 50

67 Heyerhoff et al., 2014). In short, conditions of the lateral surface of the left and right tarsal joints (hock assessment) and carpal joints (knee assessment) were recorded using a 4-point scale: 0 = no swelling, no hair missing; 1 = bald area with no swelling or swelling < 1 cm; 2 = medium swelling (1 to 2.5 cm) and/or lesion on bald area; and 3 = major swelling (> 2.5 cm; Gibbons et al., 2012). Individual cow data on parity, DIM and test-day milk production (measured at the most recent milk recording after data collection) were obtained from CanWest DHI and Valacta Inc. The average interval between data collection and milk recording was 17 d (range, 0 to 51 d). Training of observers for lameness assessment and other animal-based measures have been described in detail (Gibbons et al., 2012; Solano et al., 2015). Briefly, the 6 observers were trained during an intensive 2-wk program. A refresher course and mid-way check (3 to 4 wk and 5 to 15 wk after initial training, respectively) were done to ensure and maintain a high level of agreement [weighted Kappa statistic (Kw) 0.6]. Video recording cows on farm for lameness assessment allowed for interobserver repeatability checks. Of all videos recorded, 20% were reanalyzed by the trainers. The percentage exact agreement was calculated as: (the number of exact agreements/total number of observations) x 100. Exact agreement between the 2 trainers for locomotion scoring was 82% throughout the study period, whereas exact agreement for all 6 observers across provinces was 94% (Kw 0.8) for lame versus not lame (Solano et al., 2015). Lameness scores of reanalyzed videos remained unchanged due to the high interobserver agreement. General Management A questionnaire on management practices ( was conducted on every farm (Vasseur et al., 2015; Solano et al., 2015). Data 51

68 for this study were collected using closed-ended questions related to the timing of feeding relative to milking and the frequency of milking, feeding, feed push-up, alley scraping, stall bedding, stall cleaning etc. Data on other management practices related to feed management (e.g., if 90% of cows had access to feed when checked 4 times with at least 1 h between observations) and milking duration were collected by direct observation. Milking duration was defined as the time from when the first cow was taken out of the pen for milking and the last cow returned to the pen after milking. Facility Design Pen Features. Environmental measures were collected from all pens where the 40 study cows were housed on the day of the visit. Pen length and width were measured, as well as other pen features such as the flooring in the feed alley (categorized as solid or slatted and concrete or rubber) and width (measured from the feedbunk to the stall curb); length of water drinker space (i.e. length or diameter was measured if the drinker was rectangular or circular, respectively); and feedbunk type and length. Stall Management. Each pen where the study cows were housed was assessed for stocking density, stall dimensions, stall base, stall bedding type, cleanliness, quantity and dryness. Information on stocking density was obtained as described (Charlton et al., 2014) and estimated as the number of cows/usable stalls. Data on 8 dimensions per stall, bedding cleanliness, quantity and dryness were estimated as described (Zaffino Heyerhoff et al., 2014; Vasseur et al., 2015). Briefly, stall dimensions were measured at the end stalls of 3 representative rows in each pen (n = 2 stalls per row). If the pen had < 3 rows, stall dimensions were measured from all rows. As end stalls are often narrower or wider than average, stall width was measured 52

69 in the middle of each row (minimum 6 stalls per farm) as the average width of 3 adjacent stalls (Solano et al., 2015). Lunge space was considered adequate if no obstruction was present 76 cm forward from the brisket board. If no brisket board was present, this measure was taken from the point of the neck rail and 10 cm above the stall surface. Bedding quantity was evaluated as the bedding depth (in cm) after the stall was raked evenly, and was evaluated as 2 cm (equivalent to 1 kg of chopped straw), or > 2 cm. Type of stall base and bedding were also recorded. If different types of stall bases were present in the same pen, the predominant stall base type was considered. Statistical Analyses Statistical analyses were performed using STATA 13.1 (StataCorp, College Station, TX, USA). A P-value < 0.05 was considered statistically significant. If the lactating cows were housed in > 2 pens and these pens differed in flooring or stall characteristics, the pen with the highest number of study cows was selected for analysis. If an equal number of cows was housed in each pen, 1 of the 2 pens was randomly selected. Unusual stall bases, bedding types and floorings that did not justify a category in analysis due to a low sample size ( 4 farms) were considered in a category as other (Zaffino Heyerhoff et al., 2014). If 2 predictors were highly correlated ( r 0.7), the one with the strongest association with the outcome (or the one with the least missing observations) was chosen. Daily variation of each measure of lying behavior was tested using repeated measures ANOVA. As there were no significant differences among days when data loggers were recording, outcomes were averaged over the 4-d period. Lying behavior data of cows that only had 3 d of recording lying behavior (n = 50) were included in the analyses, as no significant 53

70 differences were detected if 3 d were randomly selected from a 4-d period. However, cows with < 3 d (n = 46) of recording lying behavior were excluded from analyses. The mean of daily lying time (h/d), bout frequency (bout/d), bout duration (min/bout) and SD of bout duration were calculated for each cow. Correlations (Spearman coefficients) among the 4 lying behavior outcomes were determined. Natural logarithmic transformation was applied to bout frequency, bout duration, and SD of bout duration as they showed a positive skew. Extreme values (outliers 3 times the interquartile range from the first and third quartile: n = 1 for lying duration; n = 25 for bout frequency; n = 24 for bout duration; n = 34 for SD of bout duration) were carefully examined and analyses were performed with and without their presence. Including extreme values did not affect the association or significance of any variables included in the analyses; therefore, all were retained. The model building process to assess cow- and herd-level risk factors associated with variations in lying behavior involved 2 steps. The outcome of interest was each measure of lying behavior measured on a continuous scale (lying duration, log bout frequency, log bout duration, log SD of bout duration), using a separate model for each lying behavior. First, univariable analyses were performed to assess associations between each outcome and predictor variable. Outcomes were assessed at the herd and cow levels, using farm and cow, respectively, as the experimental unit. Predictors with a univariable association with P 0.10 were considered for the next step of multivariable modelling. In the second step, predictors for each of the 4 outcomes were screened in separate generalised linear mixed models (GLMM) that included cow-level variables (e.g. DIM, parity) and herd-level variables (e.g. stall management, flooring characteristics, holding time, feed and water access). The cow was considered as the experimental unit and a backward elimination process was performed. Cow- and herd- level 54

71 variables significant at P 0.05 were retained in the final model for each outcome. Additionally, if confounding was present (i.e., removal of any variable resulted in a 30% change in the estimate of any other significant predictor), that variable was also retained in the final model. Two-way interactions (e.g. bedding quantity and type of bedding; bedding quantity and stall curb height; stall width and bed length, type of flooring and floor cleanliness; parity and DIM; lameness and milk production) were tested among the significant predictors in the main effects model. Akaike s information criterion (Akaike, 1973) was used to compare models within each of the 4 lying behavior outcomes, and the model with the lowest estimate was considered the best model. Farm was included as a random effect and province was forced as a fixed effect into all models. Logistic regression models were performed to determine whether measures of lying behavior were associated with lameness. The outcome of interest was the presence of lameness, considering cow as the unit of interest and using a separate model for each measure of lying behavior. Interaction terms between different measures of lying behavior (e.g. lying duration and bout frequency, lying duration and bout duration, SD of bout duration and bout frequency) were tested, but none was retained due to collinearity. The nonlinear relationship between the log odds of lameness and lying behavior was visually inspected using scatter plots. Characteristics of lying behavior as a diagnostic test (sensitivity, specificity, positive and negative predictive values) were examined, using cut-off points to define extreme lying behavior based on the log odds of lameness graphs of each lying variable. Parity and DIM were forced into all models, as they are known to influence lying behavior (Vasseur et al., 2012). 55

72 3.4 Results The study population had an average herd size of 124 lactating cows. The average cow in the study was in their second parity producing 36 kg of milk daily. Farms were representative of the overall respective provincial cow population housed in freestalls, in terms of parity, but slightly higher in milk production and herd size (Table 3.1). Lying Behavior Within and Among Herds Of 5,634 cows in the study, usable lying behavior data were obtained from 5,135 cows (2,920, 1,516, and 699 cows for AB, ON, and QC, respectively). In total, 4,790 cows had complete observations on parity, days in milk and milk production. Cows had a median lying bout duration of 63 ± 28 min with 10.2 ± 4.7 bouts daily and a SD of bout duration of 38 ± 18, for a mean total daily lying time of 10.6 ± 2.3 h/d (Table 3.1). Bout duration was correlated with bout frequency (r = 0.71; P < 0.001) and SD of bout duration (r = 0.76; P < 0.001). Mean herdlevel daily lying time ranged from 8.2 to 13.2 h/d (Figure 3.1) and individual daily lying time for cows ranged from 1.3 to 22.1 h/d. Cow and Herd Factors Associated with Lying Behavior Lying behavior was associated with parity and DIM of the cows. Daily duration of lying increased with DIM among all parities. Bout frequency decreased in primiparous cows (P < 0.001) throughout lactation. In multiparous cows, bout frequency remained similar until the seventh month of lactation (P > 0.13), but decreased (P < 0.04) when cows reached 8 mo of lactation (Figure 3.2). Bout duration increased with DIM regardless of parity, but primiparous 56

73 cows had the highest increase (P < 0.001) in relation to multiparous cows. Bout duration of multiparous cows remained similar in the first 3 mo of lactation (P > 0.07), but increased (P < 0.001) when cows reached 8 mo of lactation (Figure 3.3). A number of correlations were found among the independent variables. Milk production was negatively correlated with DIM (r = -0.40; P < 0.001); however, milk production was included in the multivariable analysis as it is known to influence lying time (Fregonesi and Leaver, 2002). Pen area, feeder length and herd size were correlated with each other (r > 0.33; P < 0.001). Linear water space increased as the pen area and feeder length increased (r > 0.55; P < 0.001). The timing of feeding relative to milking was correlated with feed push-up frequency (r = -0.42; P < 0.001) because most of the farms that fed cows around milking time also pushed up feed 2 times/d. Barn design and management practices varied greatly among farms (Table 3.2). Farms with stocking density of 1 cow/stall, pen area of > 9 m 2 /cow or with linear water space of > 9 cm/cow tended to have longer daily lying time than farms with > 1 cow/stall, pen area of < 6 m 2 /cow and linear water space of < 4 cm/cow, respectively (P < 0.10 but > 0.05). However, all measures of lying behavior varied widely and were significantly affected by the stall lying surface and management (P < 0.05). For example, mean herd daily lying time and bout duration ranged from an average of 9.7 h/d and 61 min/bout for farms with waterbeds to an average of 11.3 h/d and 76 min/bout for farms with sand/dirt as the stall base (Table 3.2). Multivariable Analysis Based on descriptive herd-level (Table 3.2) and multivariable analyses, daily lying time was associated with the same risk factors as the other measures of lying behavior. Explicitly, 57

74 daily lying time represents information that bout frequency, bout duration and SD of bout duration provided independently. Results of the hierarchical models for bout frequency, bout duration and SD of bout duration are included in Table 3.3. To simplify the presentation of results, we reported only results related to our daily lying time model (Table 3.4). At the cowlevel, bout duration was longer for cows with injured hocks. Increasing parity and DIM were associated with decreased bout frequency but increased bout duration and SD of bout duration. Furthermore, at the herd-level, bout duration increased in farms with stalls with > 2 cm of bedding and feed alley width > 450 cm. At the cow-level, daily lying time was higher for lame cows, and increased with increasing parity and DIM, but this effect interacted with parity: in that regard, primiparous cows had more frequent but shorter lying bouts in early lactation, whereas mature-cows had fewer and longer lying bouts in late lactation. In addition, lying time decreased with increasing milk yield (Table 3.4). At the herd-level, cows housed with stalls with sand had an increased average daily lying time of 1.44 h/d compared to cows housed in stalls with wood shavings. In barns with stall curbs > 22 cm high, the use of > 2 cm of bedding was associated with an increased average daily lying time of 0.36 h/d. Furthermore, bedding quantity was confounded by stall base, presumably because bedding management practices are associated with the use of certain stall bases. Feed alleys 350 cm wide or stalls 114 cm wide were associated with increased daily lying time of 0.39 and 0.33 h/d, respectively, whereas rubber flooring in the feed alley was associated with 0.47 h/d lower average lying time (Table 3.4). 58

75 Lying Behavior as a Detection Tool for Lameness Lying behavior differed between non-lame and lame cows, and among herds with low, medium or high lameness prevalence (Table 3.5). On average, lame cows had longer lying times, and fewer, longer, and more variable lying bouts compared to non-lame cows. Similarly, herds with high lameness prevalence had longer mean daily lying time, bout duration, and higher SD of bout duration (Table 3.5). Several thresholds in the measures of lying behavior were associated with increased risk of being lame (Table 3.6; P < 0.001). Daily lying time and bout frequency had a non-linear relationship with lameness, which allowed for meaningful cut-off points to be identified. Cows with lying time 14 h/d, bout frequency 5 times/d, bout duration 110 min/bout, or SD of bout duration 70 min had 3.7, 1.7, 2.5, and 3.0 higher odds of being lame, respectively. All thresholds analyzed provided low sensitivity (54-64%) and specificity (59-69%) and positive predictive values (29-33 %), but high negative predictive values (84-87%) for the presence of lameness at the cow-level (Table 3.6). 3.5 Discussion This was the largest study conducted to investigate lying behavior and associated risk factors in dairy cows on commercial farms in Canada. Regarding risk factors associated with measures of lying behavior, some individual factors (i.e. parity and DIM) and herd-level factors related to stall dimensions or flooring surface consistently affected measures of lying behavior. Regarding associations between lying behavior and lameness, cows with lying time 14 h/d, 5 lying bouts/d, or with daily mean bout duration 110 min/bout, were at higher risk of being 59

76 lame. Therefore, identifying lying behaviors beyond these thresholds has potential for automated detection of lameness; however, this must be interpreted in the context of individual and management factors identified in the first objective. In this study, all measures of lying behavior varied considerably among farms. Mean daily lying time (10.6 h/d) was comparable to findings in 42 Danish farms (10.7 h/d; Thomsen et al., 2012) and 121 commercial farms in British Columbia, California and the north-eastern United States (11.0, 10.4 and 10.6 h/d, respectively; von Keyserlingk et al., 2012). However, mean daily lying time in the present study seemed lower than findings on 16 farms in Wisconsin (11.9 h/d, Gomez and Cook, 2010) and 1 farm in the UK (11.7 h/d, Blackie et al., 2011). In the present data, cows had on average 10 lying bouts/d, lasting 63 min each. These results seemed comparable to other reports that frequency and duration of lying bouts ranged from 11 to 13 bouts/d, with a duration of 62 to 72 min (Bewley et al., 2010; Gomez and Cook, 2010; Thomsen et al., 2012) but were higher and shorter, respectively, than reported by Ito et al. (2009; 9 bouts of 88 min), and Watters et al. (2013; 9 bouts of 85 min). Apart from facility design and management factors, apparent differences in lying behavior among regions could be explained by several factors. For instance, the method used to record behavior (e.g. video analysis vs. automated recording systems), cow selection criteria (e.g. studies in the US assessed cows with higher mean DIM which are known to have longer daily lying times), herd selection (e.g. experimental farms with controlled environments vs. commercial farms), and the proportion of lame cows. When assessing cow comfort, most attention has focused on daily lying time. Sufficient amounts of lying time have been suggested for confined dairy cattle. For example, the Canadian Dairy Code of Practice recommends that stalls should allow cows to lay comfortably for at least 60

77 12 h/d (National Farm Animal Care Council, 2009); therefore, by this standard, on the majority of farms (91.5%) in this study, cows were not getting sufficient lying time, indicating a potential welfare issue. However, this recommendation was based on small-scale studies (Cook et al., 2004a), or under conditions with a small sample size (Jensen et al., 2005; Munksgaard et al., 2005). In addition, based on results from the present and previous studies (Ito et al., 2010), while associated, there was not a large effect of herd-average daily lying time on the prevalence of lameness. Moreover, all recommendations about optimum lying times must take into account individual factors of the cow (i.e. parity and DIM), instead of merely focusing on an average number for all cows. Daily lying time increased with increasing DIM, due to decreased bout frequency and increased bout duration, although it varied among parities. These results were consistent with those of Vasseur et al. (2012) and Sepúlveda-Varas et al. (2014), but differed from those of Endres and Barberg (2007), who reported increased bout frequency with DIM in compost barnhoused cows. Given that frequent changes of positions between lying and standing may be attributed to increased comfort (Haley et al., 2001), the change in lying behavior that cows exhibited throughout lactation could have been a response to physical and metabolic adaptations related to comfort. Primiparous cows exhibited the greatest differences in lying bout frequency and duration based on DIM. In the first month after calving, primiparous cows had a high frequency of short duration lying bouts, whereas later in lactation, they decreased the frequency and increased duration of bouts, and their lying behavior became more like older cows. This change in behavior could be an indicator of restlessness due to the stress related to calving for the first time and to adapting to a social structure in a new environment (Blowey, 2005). In addition, primiparous cows have a higher prevalence and more severe udder edema resulting in 61

78 udder distension (Melendez et al., 2006), which could contribute to observed differences in lying time in early lactation and first parity (Vasseur et al., 2012). We inferred that much of the variation in a herd s lying behavior was due to parity and DIM; therefore, these factors must be considered when assessing lying behavior at individual and herd levels. The use of sand or > 2 cm depth of bedding in stalls with high curbs was associated with increased daily lying time. These findings were in agreement with previous studies conducted on commercial farms (Gomez and Cook, 2010; Ito et al., 2014) and the Canadian industry standard (National Farm Animal Care Council, 2009). We inferred that sand and greater bedding quantity promoted lying behavior, perhaps due to greater cushion (Gomez and Cook, 2010). In the present study, bedding quantity interacted with stall curb height, but this was affected by stall base. This relationship probably occurred because a high rear curb not only allows for a greater quantity of bedding (i.e., stalls with 2 and >2 cm bedding had mean higher rear curb of 21 and 23 cm, respectively), but may also help maintain bedding in stalls. In addition, bedding management practices are associated with the presence of certain stall bases. For example, 87% of the farms that used sand as a stall base also used > 2 cm of bedding. Conversely, 100% of the farms that used waterbeds as a stall base used 2 cm of bedding. In regards to stall dimensions, cows spent more time lying down in farms with stalls 114 cm wide. This was in agreement with an experimental study which reported cows lying down for 72 min (1.2 h/d) longer in wider stalls (112 compared to 132 cm wide, Tucker et al., 2004). To our knowledge, this was the first large-scale study to provide evidence of an association between stall width and lying behavior on commercial farms. Cows in pens with rubber flooring in the feed alley spent an average of 29 min/d less lying compared to cows on solid concrete. Perhaps cows may sometimes lie down to avoid 62

79 standing on an uncomfortable floor, or may choose to spend longer time standing on softer floors (Tucker et al., 2006). However, the present data did not allow us to distinguish the extent to which this extra standing time may have been spent in other activities (e.g. eating). Only 9 of the 27 farms with rubberized flooring had > 2 cm of bedding in stalls, suggesting that rubber flooring was not necessarily combined with, presumably, more comfortable stalls. Experimental studies demonstrated that when given a preference, cows stood on soft rubber compared to solid concrete (Telezhenko et al., 2007). This preference could impact the cows time budgets, as when exposed to a soft standing surface, cows may reduce their requirement for rest (Cook and Nordlund, 2009). However, there is also evidence that reduced time spent lying down precedes development of claw lesions (Chapinal et al., 2009). Therefore, in agreement with Cook and Nordlund (2009), the use of rubber should be combined with comfortable stalls to avoid an increased risk for lameness. In the present study, cows exposed to feed alleys 350 cm wide had increased daily lying time. The impact of feed alley space on daily lying time could be related to the display of aggressive or competitive behavior among cows when feeding space is reduced (DeVries et al., 2004). Adequate feed alley space could also ease cow traffic flow, as cows lying in the stalls facing the feed alley would not necessarily be forced by their herd mates to exit their stall. Overstocking, milking duration and frequency are likely to affect lying time. In contrast to previous small-scale studies where overstocking (Fregonesi et al., 2007) or time away from the pen for milking (Gomez and Cook, 2010) reduced lying time for cows, there was no evidence of these associations in the present study. As described by Charlton et al. (2014), the lack of association with stocking density may be due to good management practices, as the majority of farms (98%) met or bettered the recommendation from the Canadian Dairy Code of Practice that 63

80 stocking density must not exceed 1.2 cows/stall (National Farm Animal Care Council, 2009). In regards to milking practices, AB was the province with the longest milking duration, which could be attributed to the herd size but interestingly, it is also the province with the shortest average lying time. The lack of an association with duration and frequency of milking may be due to failure to fully capture the data. First, the measurement did not necessarily reflect individual cows time away from the pen. Secondly, the variation of barn design and milking management practices biased our observation. Some farms had a holding pen where all lactating cows were moved in 1 group or in subgroups. In the absence of a holding pen, other farms allowed their cows to flow freely from stalls to the milking parlour, providing access to a lying space, water and food while waiting to be milked. Therefore, we suggest that measuring the individual cow s time away from pen instead of a herd s milking duration would provide a better estimate. In agreement with other reports, lame cows differed in their lying behavior from nonlame cows, and lame cows had slightly fewer but longer bouts, resulting in longer daily lying time compared to non-lame cows (Ito et al., 2010; Blackie et al., 2011; Sepúlveda-Varas et al., 2014). The locomotion scoring system in the present study did not capture different lameness severities. However, it is expected that severely lame cows will present even longer daily lying time and lying bouts than moderately lame cows, as demonstrated by Ito et al (2010). In agreement with Gomez and Cook (2010) and Ito et al. (2010), lame cows had both extremely high and low lying times, suggesting that the effect of lameness on lying behavior may be twoway. Cows lie down longer because rising is challenging, or they may compromise resting because lying down is challenging, and this in turn may be confounded by stage of lactation, parity and lying surface among other variables. When assessing lameness and lying behavior at 64

81 the herd-level, the results in this study indicated that farms with high lameness prevalence also had longer mean lying time and higher mean SD of bout duration. Therefore, the proportion of lame cows in a farm influenced lying data. Thus, caution must be exercised when evaluating farms based on average daily lying duration and bout duration estimates. The odds of lameness increased as bout frequency decreased and daily lying time, bout duration and SD of bout duration increased. Regardless, the use of lying behavior was not optimal to diagnose lameness. With no true gold standard available to detect lameness and the wide variability of lying behavior regardless of lameness, poor test accuracy is expected. These results were in agreement with Ito et al. (2010) where lying behavior was not a sensitive diagnostic tool for severely lame cows (< 70%); however, careful attention should be paid to cows with extreme lying behaviors. In the present study, low sensitivity and specificity resulted in a low positive predictive value ( 31%), leading to a high proportion of false positive cows that could result in extra treatments. Lying behavior as a screening test only provided moderate negative predictive value (> 84%). A moderate negative predictive value alone is not an extremely useful result from a practical standpoint, as it solely gives high confidence that a cow that does not exhibit extreme lying behavior is truly non-lame. However, results related to predictive values could aid in improving automated detection of lameness and the sensitivity and specificity may increase if additional individual and management factors are included through serial testing (Dohoo et al., 2009). 3.6 Conclusions Lying behavior was associated with individual factors such as parity and stage of 65

82 lactation and herd-level factors related to comfort of lying and standing surfaces. Daily lying time was a good measure that summarized herd-level risk factors associated with lying behavior. In addition, lame cows differed in their lying behavior from non-lame cows, and they exhibited extreme lying behaviors more often. Finally, automated measures of lying behavior may improve lameness detection, especially when interpreted in the context of other factors known to impact lying behavior, including those associated with the individual cow (e.g. parity and stage of lactation) or environment (e.g. stall surface). 66

83 Table 3.1. Characteristics (mean ± SD) of 141 freestall dairy farms and the average freestall farm in 3 Canadian provinces. Average freestall farm 1 Study farms Herd characteristics Alberta (n = 347) Ontario (n = 788) Québec (n = 306) Alberta (n = 81) Ontario (n = 40) Québec (n = 20) Herd size (no. milking cows) ± ± ± 57 Parity ± ± ± 1.6 Daily milk yield (kg) ± 9 36 ± 9 34 ± d milk yield (1,000 kg) ± ± ± 1.7 Days in milk 2 76 ± ± ± 103 Lying time (h/d) ± ± ± 2.4 Bout frequency (n/d) ± ± ± 4 Bout duration (min/bout) ± ± ± 32 SD of bout duration (min) ± 6 41 ± ± 21 Milking duration (h/d) ± ± ± 1.0 Lameness prevalence (%) ± ± ± Data from Source: CanWest DHI (Guelph, ON, Canada). 2 Median ± interquartile range were calculated. 67

84 Table 3.2. Distribution (mean ± SD) of lying behavior for 141 dairies assessed using 40 early lactation cows on each farm. Proportion of farms (%) Lying duration (h/d) Bout frequency (n/d) Bout duration (min/bout) SD of bout duration (min) Herd variables Pen area (m 2 /cow) < ± 1.0 a 10.8 ± ± 10 a 41 ± ± 1.0 a,b 10.8 ± ± 10 a,b 41 ± 7 > ± 0.8 b 10.5 ± ± 10 b 43 ± 7 Stocking density (cows/stall) ± 0.9 a 10.6 ± ± ± 8 a > ± 0.9 b 10.8 ± ± 8 40 ± 5 b Feed alley flooring Solid concrete ± 0.9 a 10.9 ± 1.5 c 65 ± 10 c 41 ± 7 c Slatted concrete ± 1.1 b 10.8 ± 1.2 c 63 ± 7 c 39 ± 6 c Solid rubber ± 1.1 a 10.0 ± 1.1 d 70 ± 10 d 46 ± 8 d Feeding frequency (times/d) > ± ± ± ± ± ± ± 9 41 ± 6 Feed availability Feed for 90% of cows ± ± ± 9 42 ± 7 Feed for < 90% of cows ± ± ± ± 10 Feeder type Post and rail ± 1.0 c 10.5 ± 1.2 c 68 ± 11 c 43 ± 8 c Headlocks ± 0.8 d 11.0 ± 1.5 d 63 ± 8 d 40 ± 6 d Diagonal bars ± 1.1 c 9.7 ± 1.0 c 73 ± 12 c 47 ± 10 c Bunk/troughs ± 0.6 d 9.5 ± 0.2 c 63 ± 4 40 ± 2 Linear water space (cm/cow) < ± 1.0 a 10.5 ± ± 9 a 42 ± ± 0.9 b 10.8 ± 1.4 a 67 ± ± 8 a > ± 0.8 b 10.2 ± 1.1 b 70 ± 6 b 45 ± 6 b a,b Within a column and category, means without a common superscript differed (P < 0.10). c,d Within a column and category, means without a common superscript differed (P < 0.05). 68

85 Table 3.2. Continued No. farms (%) Lying duration (h/d) Bout frequency (no./d) Bout duration (min/bout) SD bout duration (min) Herd variables Milking frequency (times/d) ± ± ± ± ± ± ± ± 16 Stall base Concrete ± 0.7 c 9.8 ± 1.5 c 70 ± ± 9 Rubber mattress ± ±1.3 d 62 ± 9 c 40 ± 6 c Geotextile mattress ± ± 1.3 d 64 ± 9 c 40 ± 7 c Sand/dirt ± 1.2 c 9.8 ± 1.3 c 76 ± 11 d 47 ± 8 d Waterbed ± 0.4 d 10.5 ± ± 4 c 38 ± 4 c Other ± ± ± 6 44 ± 4 Stall bedding Straw ± 1.0 c 10.4 ± ± 8 c 41 ± 6 c Sawdust ± 0.8 c 10.9 ± 1.4 c 65 ± 10 c 41 ± 7 c Wood shavings ± 0.9 c 10.9 ± 1.4 c 64 ± 9 c 40 ± 7 c Sand ± 1.0 d 9.8 ± 1.5 d 77 ± 11 d 48 ± 9 d Other ± ± 1.9 c 67 ± ± 11 Bedding depth (cm) ± 0.8 c 10.9 ± 1.4 a 64 ± 10 c 41 ± 7 > ± 1.0 d 10.5 ± 1.3 b 68 ± 10 d 42 ± 7 Bedding dryness Dry ± ± ± ± 7 Wet ± ± ± 9 42 ± 7 a,b Within a column and category, means without a common superscript differed (P < 0.10). c,d Within a column and category, means without a common superscript differed (P < 0.05). 69

86 Table 3.3. Final generalised linear mixed model for 3 measures of lying behavior with cow and herd-level factors in 141 Canadian dairy herds, considering cow (n = 4,790) as the experimental unit. Log bout frequency (no./d) Log bout duration (min/bout) Log SD of bout duration (min) Predictor Estimate (95% CI) P-value Estimate (95% CI) P-value Estimate (95% CI) P-value Intercept 2.43 ( ) 3.92 ( ) 3.46 ( ) Lame No Ref Ref Ref Yes ( ) ( ) < ( ) <0.001 Hock injury Not injured (score 0-1) - Ref - Injured (score 2-3) ( ) < Knee injury 1 Not injured (score 0-1) Ref - - Injured (score 2-3) 0.01 ( ) Parity 1 Ref Ref Ref ( ) < ( ) < ( ) < ( ) < ( ) < ( ) < Days in milk (DIM) Fresh (1-44) Ref Ref Ref Early (45 99) ( ) < ( ) < ( ) < Mid ( ) ( ) < ( ) < ( ) < Late ( 200) ( ) < ( ) < ( ) < Parity * DIM 1 st parity, fresh DIM Ref Ref Ref 2 nd parity, early DIM 0.17 ( ) < ( ) < ( ) < nd parity, mid DIM 0.19 ( ) < ( ) < ( ) < nd parity, late DIM 0.26 ( ) < ( ) < ( ) < rd parity, early DIM 0.16 ( ) < ( ) < ( ) < rd parity, mid DIM 0.26 ( ) < ( ) < ( ) < rd parity, late DIM 0.39 ( ) < ( ) < ( ) <

87 Table 3.3. Continued Log bout frequency (no./d) Log bout duration (min/bout) Log SD of bout duration (min/bout) Estimate (95% CI) P-value Estimate (95% CI) P-value Estimate (95% CI) P-value Intercept 2.43 ( ) 3.92 ( ) 3.46 ( ) Stall Base Concrete Ref Ref Ref Rubber mattress 0.13 ( ) ( ) ( ) Geotextile mattress 0.11 ( ) ( ) ( ) Sand/dirt 0.03 ( ) ( ) ( ) Waterbed 0.05 ( ) ( ) ( ) Other 0.06 ( ) ( ) ( ) Bedding depth (cm) 2 - Ref - > ( ) Feed alley flooring Solid concrete Ref - Ref Slatted concrete 0.01 ( ) ( ) Solid rubber ( ) ( ) Scrape frequency (times/d) 2 Ref ( ) ( ) Feed alley width (cm) < Ref ( ) > ( ) Milking duration (30 min increase) 0.01 ( ) Scrape frequency confounded by knee injury. 71

88 Table 3.4. Final generalised linear mixed model for mean daily lying time (h/d) with cow and herd-level factors in 141 Canadian dairy herds, considering cow (n =4,790) within herd as the experimental unit. Predictor Estimate 95% CI P-value Intercept Lame No Ref Yes < Parity 1 Ref < Days in milk (DIM) Fresh (1-44) Ref Early (45 99) < Mid ( ) < Late ( 200) < Parity * DIM 1st parity, fresh DIM Ref 2nd parity, early DIM nd parity, mid DIM nd parity, late DIM rd parity, early DIM rd parity, mid DIM < rd parity, late DIM Daily milk yield (kg) < Stall base 1 Concrete Ref Rubber mattress Geotextile mattress Sand/dirt Waterbed Other Stall bedding Wood shavings Ref Straw Sawdust Sand Other Bedding depth (cm) 2 Ref >

89 Table 3.4. Continued Predictor Estimate 95% CI P-value Intercept 9.06 Stall curb height > 22 cm (per 1 cm increase) Bedding quantity * stall curb height 2 cm bedding, stall curb height increase Ref > 2 cm bedding, stall curb height increase Stall width (cm) < 114 Ref Feed alley flooring Solid concrete Ref Slatted concrete Solid rubber Feed alley width (cm) < 350 Ref Confounds bedding quantity. 73

90 Lame Total Low Table 3.5. Distribution of cow-level [median ± interquartile range, IQR (range)] and herd-level [mean ± SD (range)] explanatory variables of 5,135 cows from 141 dairy herds with a low ( 10%), medium (10-30%) or high ( 30%) lameness. Cow-level Herd-level lameness prevalence Non-lame Medium High Total Variable (n = 4,062) 2 (n = 1,073) 2 (n = 24) (n = 94) (n = 23) Parity 2 ± 2 3 ± 2 2 ± 2 (1 11) 2.4 ± ± ± ± 0.4 ( ) Days in milk 81 ± ± ± 72 (1 486) 85 ± ± ± ± 31 (29 188) Milk yield (kg/d) 37 ± ± ± 9 (4 69) ± ± ± ± 4.9 ( ) Lying time (h/d) 10.5 ± ± ± 2.3 ( ) ± ± ± ± 0.9 ( ) Bout frequency 10.2 ± ± ± 4.7 ( ) 10.8 ± ± ± ± 1.4 ( ) (no./d) Bout duration 61 ± ± ± 28 (7 219) 63 ± 8 65 ±9 70 ± ± 10 (46 106) (min/bout) SD of bout duration 38 ± ± ± 18 (8 243) 39 ± ± 6 45 ± ± 7 (27 66) (min) Size of adult herd ± ± ± ± 77 (40 470) 1 Mean ± SD was calculated. 2 Mean of non-lame differed (P 0.05) from mean of lame cows for every variable. 3 Mean was different (P 0.05) from high-lameness prevalence herds. 74

91 Table 3.6. Use of lying behavior as independent predictors of lameness estimated by logistic regression models including parity and days in milk as covariates in 141 farms. Lying behavior OR (95% CI) Se 1 Sp 2 PPV 3 NPV 4 Lying time (h/d) ( ) ( ) ( ) ( ) ( ) Bout frequency (no./d) ( ) ( ) ( ) ( ) Bout duration (min/bout) ( ) ( ) ( ) ( ) SD of bout duration (min) ( ) ( ) ( ) ( ) Note: All associations between lameness and lying behaviors were significant (P 0.01). 1 Sensitivity: Proportion of lame cows identified by the threshold of lying behavior. 2 Specificity: Proportion of non-lame cows correctly classified by the threshold of lying behavior. 3 Positive predictive value: Probability that being above the given threshold of lying behavior, the cow is lame. 4 Negative predictive value: Probability that being below the given threshold of lying behavior, the cow is not lame. 75

92 Lameness prevalence (%) farms Daily lying duration (h/d) Daily lying duration (h/d) Figure 3.1. Mean lameness prevalence and mean daily lying time (h/d) on each of 141 farms. 76

93 Bout frequency (n/d) * * * * Parity Month of lactation Figure 3.2. Mean lying bouts (no./d) per parity and month of lactation. *Difference (P < 0.05) between primiparous and multiparous cows. Bout duration (min/bout) Parity * * * * Month of lactation Figure 3.3. Mean lying bout duration (min/d) per parity and month of lactation. *Difference (P < 0.05) between primiparous and multiparous cows. 77

94 Chapter Four: Prevalence and distribution of foot lesions in dairy cattle in Alberta, Canada 4.1 Abstract The objectives of this cross-sectional study were to determine prevalence and distribution of foot lesions and associated cow- and herd-level risk factors in dairy cows in Alberta, Canada. Foot lesion data were recorded electronically by 7 hoof trimmers on 28,607 cows in 156 dairy farms from June 2009 to November Foot lesion prevalence estimates differed between farms that had the whole herd trimmed at once ( 80% of lactating cows were trimmed; n = 69 farms and 8,020 cows) and farms on which part of the herd was trimmed (selection of cows was determined by farmer and < 80% of lactating cows were trimmed; n = 87 and 20,587 cows), being consistently higher for the latter, likely because farmers presumably prioritized lame cows in partial-herd trims. On farms with whole-herd trims, digital dermatitis was the most common lesion among all housing types, present in 15% of cows and 94% of herds. Sole ulcers and white line disease were detected in 6 and 4% of the cows and 92 and 93% of herds, respectively. Other infectious and claw horn lesions each affected 1 to 2% of cows and 62 to 78% of herds. Intraclass correlation coefficients for hoof trimmers ranged from 0.01 to 0.20 for all lesions, indicating some clustering of recorded lesions by trimmer. Multilevel mixed logistic regression models were constructed (including hoof trimmer as fixed and farm as random effects) for the 3 most frequently identified lesions. Prevalence of digital dermatitis decreased with increasing parity, but this effect interacted with days in milk; primiparous cows had higher odds of digital dermatitis in mid- (100 to 199 DIM) and late-lactation ( 200 DIM) compared to cows at other 78

95 stages of lactation. In contrast, prevalence of sole ulcers and white line disease increased with increasing parity; compared to cows in parity 1, those in parity 4 had 5 or 7 times higher odds of having these lesions, respectively. Cows in mid- and late-lactation had higher odds of sole ulcers and white line disease than cows at other stages of lactation, regardless of parity. Digital dermatitis prevalence was 2 times higher in herds housed in barns with access to an exercise area. The odds of sole ulcers and white line disease were 2 times higher in cows housed in freestalls than those housed in deep-bedded packs. Therefore, preventive measures for control of digital dermatitis merit emphasis, especially in primiparous cows and on farms with exercise areas. In addition, improving housing environment by providing a deep-bedded area for older cows in mid- or late-lactation could reduce prevalence of claw horn lesions. We inferred that foot lesion data recorded by hoof trimmers can provide useful information not only to develop effective foot health programs at herd-level, but also for disease surveillance and genetic improvement at regional and national levels. 4.2 Introduction Lameness is a severe welfare problem in cattle and has a detrimental effect on longevity (Cramer et al., 2009a), productivity (Green et al., 2002), and reproductive performance (Barkema et al., 1994; Garbarino et al., 2004). Consequently, it is among disorders causing the largest economic losses in the dairy industry (Ettema and Ostergaard, 2006). Approximately 90% of the causes of lameness involve foot lesions (Murray et al., 1996; Shearer and Van Amstel, 2013), although foot lesions do not necessarily result in lameness (Manske et al., 2002b). Regardless of whether foot lesions cause lameness, they have a high impact on animal welfare (Bruijnis et al., 79

96 2012) and farm economics. Foot disorders that result in lameness are estimated to cost on average $95, whereas those foot disorders that do not, cost on average $18 per case (Bruijnis et al., 2010). In addition, lameness prevalence is often underestimated by dairy producers (Espejo et al., 2006). Therefore, inspection of foot lesions as a routine management practice facilitates earlier identification and treatment of lesions, plausibly enhancing herd productivity and welfare (Cramer et al., 2008; Chapinal et al., 2009). Prevalence of foot lesions varies considerably among farms, regions, and housing systems. For example, when cows are housed in tiestall barns, prevalence of foot lesions is lower than when cows are housed in freestalls (21% lower in Ontario and 37% lower in Norway, respectively; Fjeldaas et al., 2006; Cramer et al., 2008). In addition, cow-level prevalence of digital dermatitis ranged from 8% in Chile to 23% in Ontario (Cramer et al., 2008; Tadich et al., 2010). Herd-level prevalence of digital dermatitis ranged from 92 to 100% when cows were housed on concrete flooring, whereas when cows were housed in deep-bedded straw yards, 65% of herds were free of digital dermatitis (Somers et al., 2003; Cramer et al., 2008). Each type of lesion has its own pathophysiology and specific risk factors. Foot lesions are commonly categorized according to their etiology into infectious and non-infectious lesions (International Lameness Committee, 2008; Potterton et al., 2012). Infectious lesions include digital dermatitis, interdigital dermatitis, heel horn erosion, and foot rot, whereas the most common non-infectious lesions are sole ulcer, toe ulcer, sole hemorrhage and white line disease. Infectious lesions usually affect foot skin and are influenced by herd-level factors such as wet and unhygienic floor conditions (Bell et al., 2009), scraping frequency (Cramer et al., 2009b), introduction of dry cows into the milking herd, pasture access, and footbathing frequency (Somers et al., 2005a; b). Non-infectious lesions affect the claw horn and their occurrence is 80

97 associated with metabolic and hormonal events around calving that weaken the foot suspensory apparatus (Tarlton et al., 2002), low BCS (Green et al., 2013), toe overgrowth, exposure to hard flooring (Manske et al., 2002a; Somers et al., 2003), and thickness of the digital cushion (Bicalho et al., 2009). Despite increased awareness of lameness as a problem in North America, we are only aware of 1 epidemiological study to identify prevalence and distribution of foot lesions and associated risk factors (Cramer et al., 2008; 2009b). Understanding the type, frequency, and distribution of foot lesions will provide insight into their epidemiological background and potentially lead to the identification of trends in the presence of foot lesions and whether they change over time. Therefore, objectives of this study were to determine: 1) prevalence and distribution of foot lesions using observations recorded electronically by hoof trimmers, and 2) association between herd- and cow-level factors and the prevalence of the most frequently identified foot lesions. 4.3 Materials and Methods Data Recording Seven of 17 hoof trimmers in Alberta, Canada agreed to participate in the Alberta Dairy Hoof Health Project, a project developed by Alberta Milk (Edmonton, AB, Canada). The Alberta Dairy Hoof Health Project was designed to collect foot lesion data using a computerized lesion recording system which facilitated routine and consistent data collection. In short, the 7 participating hoof trimmers attended an on-farm workshop demonstration of Hoof Supervisor lesion recording software (KS Dairy Consulting, Dresser, WI, USA). The software was installed 81

98 on damage-resistant touch screen personal computers with a Microsoft Windows XP operating system. The recording method of Hoof Supervisor enabled differentiation between front and back and left and right feet, and between lateral and medial claws. This system also captured the location of lesions in 12 claw and foot zones, using the Claw Lesion Identification in the Dairy Cattle Brochure developed by Zinpro Corporation (D , Zinpro Corporation, Eden Prairie, MN, USA) in cooperation with the International Lameness Committee (2008). To assist in accurate and consistent identification, Hoof Supervisor permits identification of the cow, foot, and claw (in that order). Once the affected claw is selected, the type of foot lesion can be selected from a list of possible lesions specific to each claw zone. Hoof trimmers received a financial incentive of $1 CAD per cow for submitted trim data. Two veterinarians with experience in lameness trained the hoof trimmers to identify foot lesions in a uniform manner during a 1 d hoof health clinic. Digital, coloured photographs were used to identify each foot lesion according to the international classification system adopted during the 15 th International Conference on Lameness held in Finland (International Lameness Committee, 2008). A guide containing photographs of different levels of foot lesion severity was developed to assist hoof trimmers in consistently scoring lesions. One year after field data collection was in progress, another 1 d hoof health clinic was carried out to review and reinforce identification of foot lesions. Herd and Cow Selection A total of 156 dairy farms located in south and central Alberta, Canada, were enrolled. Data were collected between June 2009 and November Hoof trimmers recruited their client farms to participate in the study, regardless of foot lesion status. Participation was 82

99 voluntary and at the farmer s discretion. By participating, farmers signed consent forms agreeing to share their trim records and milk records provided by CanWest DHI (Guelph, ON, Canada). The DHI data (parity, DIM and milk production) from the most recent milk recording test after the hoof trimmer s visit were selected for analyses. Reasons for missing DHI data were that farms dropped out of DHI services (2 farms), cows were sick at milk recording, or failure of producers to report calving dates (Figure 4.1). Information on housing type was collected by the hoof trimmer. An exercise area was defined as an outdoor, unroofed, soil-floored, enclosed area with access to the dairy barn. On farms with partial-herd trims, selection of cows was done by the farmer. To estimate representativeness of the study, data regarding averages on Alberta dairy farms were obtained from 2012 CanWest DHI data (Canadian Dairy Information Centre, 2016). Data Management and Statistical Analysis Trim data were extracted from Hoof Supervisor backup files and imported into a Microsoft SQL Server database through a custom application written in Microsoft Visual Basic (Microsoft Corp., Redmond, WA, USA). The DHI data, provided by CanWest DHI in the form of fixed field length text files, were imported into the database through a second custom Visual Basic application. Farm and hoof trimmer identification data were entered into the database manually. Subsequent data editing and statistical analyses were done using Stata 13.1 (StataCorp, 2013, College Station, TX, USA). The original dataset contained 87,834 cows with data from multiple trimmings (Figure 4.1). To minimize selection bias introduced by the farmer and repeated observations, prevalence was estimated separately for farms that had the whole herd trimmed at once ( 80% of lactating cows were trimmed, n = 69 farms) and farms on which part of the herd was trimmed (< 80% of 83

100 lactating cows were trimmed, n = 87). The value of 80% was selected as a cut-off to define a farm with a whole- or partial-herd trim, using logistic regression models. The outcome of interest was the presence of any foot lesion at cow-level. Herd size, housing type and percentage of lactating cows trimmed per trim session were included as predictors. Various cut-off points (i.e., 60, 70, 80, or 90% of lactating cows trimmed) were tested individually to assess the probability that cows were selected based on presence of foot lesions. Values above 80% were not associated with the odds of presence of a lesion, suggesting that cows were not selected based on their foot lesion status if 80% of lactating cows were trimmed in one session (which could occur over 1 or 3 days). For farms that had the whole herd trimmed, the trim session with the highest percentage of unique cows trimmed was used to calculate foot lesion prevalence at cow and herd levels. As it is common for a herd to be trimmed over several days, trim sessions that occurred within 15 d were considered a single event. For farms on which part of the herd was trimmed, cow- and herd-level prevalence were presented for the first occurrence of a cow in the data set. For each type of foot lesion and hoof trimming routine (whole- and partial-herd trim), prevalence was calculated as the number of affected animals divided by total number examined. Individual cows with duplicate or missing cow identification were removed from the dataset (Figure 4.1). If the same lesion was recorded in 2 different claw zones on the same foot, it was only considered a single occurrence of the lesion. Data were collapsed at the lowest level (foot) on a binary scale. Hoof health status was first collapsed into front and hind limbs, and then into cow. Foot lesions with low frequency ( 1%) were collapsed based on their pathophysiology into other lesions (these included thin sole, corkscrew claw, axial, horizontal, and vertical fissures) or other infectious lesions (heel erosion, interdigital dermatitis and foot rot). A cow 84

101 was considered to have multiple lesions if the same type of lesion affected different feet or if different types of lesions affected the same or different feet. Data were structured at 4 levels: hoof trimmer, herd within-hoof trimmer, cow withinherd, and foot within-cow. Two analyses were performed. In the first analysis, intraclass correlation coefficients (ICC) were used to estimate proportion of correlation of cows within a herd and of observations within a hoof trimmer. The ICC were calculated modelling each foot lesion separately, including hoof trimmer and herd as random effects. In the second analysis, associations between the most prevalent foot lesions and cow- and herd-level predictor variables were assessed using multilevel mixed logistic regression modelling with presence of each foot lesion at the cow level as the outcome of interest and using a backward elimination process. All variables associated with the outcome (P < 0.10) in the univariable analyses were subsequently included in multivariable modelling. Variables significant at P < 0.05 were retained in the final model. For this analysis, presence of other foot lesions were included as predictors and classified into claw horn lesions (sole and toe ulcer, sole hemorrhage and white line disease), other infectious lesions (interdigital dermatitis, foot rot and heel erosion), and other lesions (thin sole, corkscrew claw and horizontal, vertical or axial fissures) (Chapinal et al., 2013a). Digital dermatitis and interdigital hyperplasia were not grouped into one category, instead they were tested independently as predictors. Additionally, if confounding was present (i.e., removal of any variable resulted in a 30% change in the estimate of any other significant predictor), that variable was also retained in the final model. Two-way interactions (e.g. parity and DIM, herd size and housing type) were tested among the significant predictors in the main effects model. Farm was included as a random effect. Hoof trimmer and hoof trimming routine (whole- vs. partial- herd trims) were forced into the final regression model as fixed effects, due to differences in foot 85

102 lesion prevalence estimates between farms with partial- vs. whole-herd trims. Model comparison among all multivariable models was based on Akaike s information criterion, and the model with the lowest Akaike s information criterion was considered the best model. 4.4 Results The sample of farms (n = 156) was representative of the average dairy farm in Alberta in terms of herd demographics (median proportion of cows in third parity or higher: study herds vs. entire province = 36 and 37%; respectively), median annual culling rate (38 and 38%), breed (breed composition of Holstein-Friesian = 97 and 98%), and housing type (freestall barns = 83 and 81%). Farms on which the whole herd was trimmed were representative of the average Alberta farm in terms of herd size, whereas farms where the herd was partially trimmed had a larger herd size (243 cows) than the average Alberta farm (provincial average herd size = 138 cows). Farms on which the herd was partially trimmed had a shorter median interval of hoof trimming sessions (2 mo) than farms on which the whole herd was trimmed (herd size = 139 cows and hoof trimming interval = 6 mo). Median parity of cows trimmed was 2 in herds on which the whole herd was trimmed vs. 1 in partially trimmed herds (Table 4.1). Hoof trimmers recorded lesions on 28,607 unique cows over a 3.5 yr interval (Figure 4.1). A total of 20,587 and 8,020 cows were included from farms where the herd was partially trimmed and where the whole herd was trimmed, respectively. Complete observations on parity and DIM were obtained from 26,974 cows and complete observations on parity, DIM and milk production were obtained from 23,014 cows. 86

103 Prevalence and distribution of Foot Lesions A total of 36% of cows had at least one foot lesion. Of these, 26, 9, and 1% of cows had 1, 2, and 3 legs affected, respectively. Lesion prevalence was higher in rear than in front feet among all types of foot lesions; however, claw horn lesions were more common in front feet compared to digital dermatitis and interdigital hyperplasia. In that regard, 73 to 81% of claw horn lesions occurred in the rear feet, whereas 94% of digital dermatitis lesions occurred in the rear feet. In addition, claw horn lesions were more commonly found in the medial claw of front feet but in the lateral claw of rear feet (Table 4.2). The greatest number of lesions occurred in zone 10 (38%), followed by zone 4 (20%) and zone 3 (13%) and up to 4% of lesions occurred simultaneously in > 1 zone (Figure 4.2). On farms with partial-herd trims, foot lesion prevalence estimates were higher (both overall and foot lesion-specific, except sole hemorrhage) than on farms with whole-herd trims (Table 4.3). Because herds where a partial-herd trim was conducted were less representative of dairy herds in Alberta and a purposive and younger selection of cows was observed in these herds, we chose to focus on farms with whole-herd trims. On these farms, 28% of the cows had at least 1 lesions and 5% of cows had multiple lesions. Digital dermatitis was the most prevalent lesion (15% of cows), followed by sole ulcer (6%) and white line disease (4%). Within-herd prevalence varied greatly; notwithstanding, for all lesions, there were herds with a prevalence of zero (Table 4.3). For example, in 6, 8 and 7% of the herds, no cows were recorded with digital dermatitis, sole ulcers and white line disease, respectively. Furthermore, 6 and 2% of cows with digital dermatitis also had sole ulcers and white line disease, respectively, whereas 45% of cows with interdigital hyperplasia also had digital dermatitis. Presence of foot lesions varied among housing types. 87

104 The 7 hoof trimmers recorded data on a median of 19 herds each (range: 9 to 40). The percentage of farms with whole-herd trims varied among hoof trimmers (range: 8 to 100%). Estimated ICC for hoof trimmers did not exceed 0.20 and were lower than farm-level ICC among all foot lesions (Table 4.4). Risk Factors for Foot Lesions For non-lactating heifers examined (n = 256), 23, 3 and 2% had digital dermatitis, sole ulcers and white line disease, respectively, whereas 21, 5 and 6% of dry cows (n = 1,044) had these lesions. All foot lesions had significant associations with parity and DIM of cows at the time of hoof trimming (Figure 4.3a, b, c). The proportion of cows with digital dermatitis decreased with increasing parity (P = 0.001), but this effect differed for varying stages of lactation. In that regard, primiparous cows had a higher prevalence of digital dermatitis in mid- (100 to 199 DIM) and late-lactation ( 200 DIM), whereas multiparous cows had a higher prevalence of digital dermatitis at peak lactation (45 to 99 DIM). The proportion of cows with sole ulcers or white line disease increased with increasing parity (highest P = 0.003). Fresh cows ( 44 DIM) and cows at peak lactation had a lower prevalence of sole ulcers or white line disease than cows in mid- or late-lactation (Table 4.5; Figure 4.3). Cows with interdigital hyperplasia had 4 times greater odds of having digital dermatitis. Cows with white line disease, toe ulcers, or sole hemorrhages had higher odds of having sole ulcers. The odds of white line disease were 3.5 times higher in cows with other claw horn lesions like thin sole, corkscrew claw and horizontal, vertical and axial fissure (Table 4.5). At herd-level, the prevalence of digital dermatitis was 2 times higher in barns with access to an exercise area than in freestalls without access to an exercise area (Table 4.5). The odds of 88

105 sole ulcers and white line disease were higher in cows housed in freestalls without access to an exercise area than for those housed in deep-bedded packs. The prevalence of digital dermatitis was 2 times higher in herds with > 100 cows than in smaller herds (Table 4.5). 4.5 Discussion Compared to other studies conducted in Canada and Europe, our study included the largest sample of cows used to investigate 1) prevalence of foot lesions in dairy cows, and 2) distribution of foot lesions by claw zone. Digital dermatitis was by far the most common foot lesion, followed by sole ulcers and white line disease. The main risk factors associated with these 3 most frequently recorded lesions included parity and DIM, presence of interdigital hyperplasia and other claw horn lesions, access to an exercise area, and housing type. Selection of cows for trimming determined by the farmer was a potential source of bias for overall prevalence estimation of foot lesions as well as lesion-specific prevalence. To address this potential selection bias, herd- and cow-level prevalence of foot lesions were estimated separately for whole-herd trims and herds that were partially trimmed. Prevalence was consistently higher for the latter herds. This was expected, as it seemed likely that farmers prioritized lame cows in partial-herd trims. Therefore, farms with whole-herd trims were likely less biased by cow selection and had more accurate prevalence estimates than partial-herd trims, because most of the herd was evaluated. Farms with whole-herd trims were also representative of farms in Alberta in terms of herd size (Solano et al., 2015), in contrast to farms with partial-herd trims. For these reasons and for the purpose of this discussion, we focused on prevalence of foot lesions on farms that had the whole herd trimmed. Notwithstanding, neither study farms nor 89

106 cows within farm were randomly selected and only farms with routine hoof trimming were selected. Thus, it is possible that study farms may represent a biased sample with superior management practices for lameness reduction. The study findings regarding the distribution of foot lesions within feet and claw zone support a body of knowledge about the role of anatomy, gait and weight bearing dynamics in the pathogenesis of foot lesions (Toussaint Raven, 1985; Greenough, 2007; Shearer and Van Amstel, 2013). It was not surprising to find most of the claw horn lesions affecting the lateral hind claws and lesions occurring in their typical anatomical site. However, it is possible to misdiagnose lesions when they are in an early stage or too advanced. For example, early-stage toe ulcers in zone 1 could be difficult to differentiate from white line disease in the same zone, which may explain the high prevalence of toe ulcers occurring in zone 1 in our results (Greenough, 2010). Furthermore, information on the prevalence lesions not occurring in the typical claw zone is very valuable, as it can be an indicator of underlying management or environmental factors affecting such zone. For instance, thin sole toe ulcers occurring in zone 5 near junction of zones 1 and 2, can be associated with excessive foot wear from abrasive flooring (Sanders et al., 2009; Shearer et al., 2015). Additionally, white line disease occurring in zone 1 instead of the typical zone 3 can be associated with inappropriate trimming (Shearer and van Amstel, 2008), and sole hemorrhages or ulcers occurring in zone 6 instead of the typical zone 4 can be associated to perching due to short stalls. Thus, knowledge on lesion zone location allows for targeted foot health programs, recovery follow-up, and it helps from a communication stand point, standardizing lesion identification and diagnosis among employees, hoof trimmers, researchers and veterinarians (DeFrain et al., 2013). On farms with whole-herd trims, the prevalence of 28% of cows with 1 foot lesion 90

107 reported in this study seemed comparable to that reported in tiestalls in Ontario (26%; Cramer et al., 2008), but lower than findings in freestalls in Ontario (47%; Cramer et al., 2008), in Norway (40%; Fjeldaas et al., 2006), Sweden (72%; Manske et al., 2002b) and in the Netherlands (80%; Somers et al., 2003). Similar to other studies, digital dermatitis was the most common lesion. The 15% prevalence of digital dermatitis reported in this study seemed lower than in freestalls in Ontario (23%; Cramer et al., 2008) and the Netherlands (21%; Holzhauer et al., 2006b). Unfortunately, the stage of digital dermatitis lesions was not recorded (lesions were only noted as present or absent). Regardless, infectious lesions were the most prevalent lesion and careful attention should be paid to the efficacy and implementation of preventive measures for its control. The prevalence of sole hemorrhage (3%) and other infectious lesions (1%) on farms with whole-herd trims was notably lower than estimates from other studies. Reports on the prevalence of sole hemorrhage and heel erosion in other studies ranged from 11 to 39% and 8 to 41%, respectively (Manske et al., 2002b; Holzhauer et al., 2006b; Cramer et al., 2008). It is possible that minor or slight manifestations of these common foot lesions without a clear effect on lameness were generally underreported. This was supported by the prevalence of sole hemorrhage being lower for farms with partial-herd trims compared to whole-herd trims, suggesting that cows with hemorrhages were not likely to be lame and therefore not selected for trimming. Conversely, prevalence estimates for lesions associated with lameness, such as sole ulcers and white line disease (close to 5% each) were similar to other reports (close to 9% each, Manske et al., 2002b; Cramer et al., 2008). The low prevalence of other lesions such as foot rot was not surprising, due to the cross-sectional nature of the present study, as that is a lesion characterized by acute onset of lameness and rapid treatment (Toussaint Raven, 1985). 91

108 Additionally, wet pasture conditions, not common in Alberta s dairy management systems, are a predisposing factor for foot rot (Greenough, 2007). Apparent differences in foot lesion prevalence estimates among countries could be the consequence of factors known to affect prevalence, such as access to pasture (e.g., in The Netherlands 89% of cows had daily access to pasture and in Norway and Sweden close to 80% of the study herds were at pasture for > 2 mo; Manske et al., 2002a; Sogstad et al., 2005; Holzhauer et al., 2006b), and flooring (e.g., in The Netherlands, cows in deep-bedded straw yards had the lowest prevalence of foot lesions; Somers et al., 2003). Additionally, differences in foot lesion estimates could also be a consequence of cow selection. For example, selection of cows in Ontario was done by the farmer (Cramer et al., 2008), whereas in some European studies the whole herd was examined (Manske et al., 2002b; Holzhauer et al., 2006a). The odds of digital dermatitis decreased with increasing parity, whereas the odds of all other claw horn lesions increased with increasing parity, consistent with other studies (Manske et al., 2002a; Holzhauer et al., 2006a; Barker et al., 2009). This association could be one of the reasons for the higher prevalence of digital dermatitis on farms with partial-herd trims, as 54% of cows presented for trimming in these farms were primiparous compared to 38% in whole-herd trims. The high prevalence of digital dermatitis in fresh primiparous cows in the present study, suggests that infection starts prior to calving; therefore, an appropriate and separate non-lactating heifer rearing environment should be a key consideration for effective management of digital dermatitis (Laven and Logue, 2007; Holzhauer et al., 2012b). In agreement with Somers et al. (2005a) and Holzhauer et al. (2006b), cows immunity increases as parity and lactation stage increase and prevalence of digital dermatitis slowly decreases. In the present study, second-parity cows that were fresh or in peak lactation had a higher prevalence of digital dermatitis at the time 92

109 of trimming compared to primiparous cows that were in the same stage of lactation, similar to a report by Holzhauer et al. (2006b). This could be because primiparous cows were affected later in lactation due to a higher infection pressure from prolonged exposure to wet conditions in the lactating barn. In addition, factors related to management of dry cows (e.g., absence of foot bathing) could be more detrimental for primiparous than for multiparous cows, which can explain the high prevalence of digital dermatitis until mid-lactation of second-parity cows. However, results on lesion occurrence by stage of lactation should be interpreted with caution as the true onset of lesions is unknown. The high odds of claw horn lesions for older cows ( 3 parity) in mid- and late-lactation at the time of trimming, was comparable to other studies (Manske et al., 2002a; Holzhauer et al., 2008b; Barker et al., 2009). This could be explained by a higher risk of recurrence and decreased horn quality due to more prolonged exposure to hard surfaces and external trauma, and to effects of hormonal and metabolic changes around calving, claw horn wear and trauma (Cook and Nordlund, 2009). It is important to highlight that the use of pasture for lactating cows is not common in Alberta, although the majority of farms manage dry cows on pasture or deep-bedded straw yards. These surfaces are known to benefit hoof health and may provide cows a period of recovery from lameness (Hernandez-Mendo et al., 2007). Nevertheless, the high prevalence of claw horn lesions in mid- and late-lactation indicates that these become apparent at this stage; however, this does not necessarily mark the true onset of lesions at this stage of lactation. The association between digital dermatitis and interdigital hyperplasia was consistent with other studies (Manske et al., 2002b; Holzhauer et al., 2006a). In agreement with those authors, development of interdigital hyperplasia can be a response to chronic irritation of the interdigital skin from poor hygiene and infectious foot lesions. Associations between sole ulcers 93

110 or white line disease and other claw horn lesions supports the theory of common pathogenesis of claw horn disruption lesions (Bicalho and Oikonomou, 2013), and was comparable to other studies (Manske et al., 2002b; Holzhauer et al., 2008b). Prevalence of digital dermatitis was higher on farms with access to an exercise area. Studies differed on the relationship between access to an exercise area and risk of digital dermatitis. A beneficial effect of an exercise area outside the barn on digital dermatitis was reported in freestall barns (Haufe et al., 2012), whereas others reported a detrimental effect in tiestall barns (Cramer et al., 2009b). Although specific characteristics of exercise areas were not recorded, a possible explanation for this increased prevalence was wet and unhygienic conditions of these areas. Prevalence of claw horn lesions was lower in cows housed in deep-bedded packs, consistent with other studies (Somers et al., 2003). Perhaps this association was due to exposure to a softer walking surface (Cook and Nordlund, 2009). We inferred that improvements in footbath management, cleanliness and a drier environment on farms with exercise areas could decrease digital dermatitis prevalence. In addition, providing a deep-bedded pack area for cows at high risk (older cows in mid- or late-lactation) could reduce prevalence of claw horn lesions. This was apparently the first epidemiologic study to report foot lesion prevalence and distribution data collected electronically by hoof trimmers, rather than using hand-written reports (Manske et al., 2002b; Fjeldaas et al., 2006; Cramer et al., 2008). To achieve accuracy and consistency of data recording and subsequent analyses, researchers create case definitions and standardized data collection (Kelton et al., 1998), whereas hoof trimmers are usually not as detailed and precise in this process. Notwithstanding, data on foot lesions collected by hoof trimmers are of great value, due to the large number of cows and herds typically inspected (Capion et al., 2008). Electronic record keeping is convenient from a practical perspective as it 94

111 allows hoof trimmers to enter data faster and easier than hand-written reports. It also facilitates summary information of lesions for the farmer that can be used to decrease lameness in their herds (DeFrain et al., 2013). Furthermore, from a data analysis perspective, it can be used for benchmarking and genetic improvement (Chapinal et al., 2013a; Kofler, 2013), whereas from a research standpoint, electronic record-keeping of foot lesions increases accuracy and efficiency of data collection by reducing errors due to transcription and helps in standardizing lesion diagnoses (Shearer and Van Amstel, 2013). All hoof trimmers were professionally trained in hoof trimming. Regardless, there were 2 training sessions for hoof trimmers to standardize foot lesion data collection based on internationally recognized nomenclature. A limitation is that intra- and inter-observer agreements for lesion recognition among hoof trimmers were not evaluated. However, the correlation (ICC) of foot lesion observations within hoof trimmers was low. Therefore, there was little evidence of hoof trimmers over representing certain lesions. Nevertheless, a degree of bias is expected as foot lesion diagnosis and recording is based on visual inspection. 4.6 Conclusions The prevalence of foot lesions differed among housing types, with cows housed in deepbedded packs being the least affected. Digital dermatitis was the most common foot lesion in all housing types and throughout all parities and stages of lactation, followed by sole ulcers and white line disease. Within-herd prevalence varied greatly; notwithstanding, for all lesions, there were herds with a prevalence of zero, good evidence that low prevalence of foot lesions is achievable. 95

112 Preventive measures for the control of digital dermatitis merit emphasis, especially in primiparous cows and on farms with exercise areas. Given that the vast majority of dairy farms in Alberta are freestalls with zero-grazing, improving housing environment by providing a deepbedded area for older cows in mid- or late- lactation could reduce prevalence of claw horn lesions. We inferred that foot lesion data recorded by hoof trimmers provided useful information not only to develop effective foot health programs at herd-level, but also for disease surveillance and genetic improvement at regional and national levels. 96

113 Table 4.1. Distribution of 156 Alberta dairy herds (28,607 cows) enrolled in the Alberta Dairy Hoof Health Project. Farms with partial-herd trims Farms with whole-herd trims (87 farms and 20,587 cows) (69 farms and 8,020 cows) Overall Herd characteristic Herds, n (%) Median ± IQR 1 Herds, n (%) Median ± IQR 1 Herds, n (%) Median ± IQR 1 Herd size (no. milking cows) 243 ± ± ± 182 Parity 1 ± 1 2 ± 2 2 ± 2 Days in milk 141 ± ± ± d milk yield (kg, mean ± SD) 2 10,032 ± 2,097 10,220 ± 1,999 10,090 ± 2,069 Participation duration in project (yr) 3 ± ± ± 0.8 Hoof trimming session interval (mo) 2 ± ± ± 4.8 Percentage of herd trimmed per trim session 18 ± ± ± 72.6 Housing type Freestall (FS) 3 61 (70) 53 (77) 114 (73) Tiestall (TS) 3 6 (7) 1 (1) 7 (4) Deep-bedded pack (DB) 3 7 (8) 5 (7) 12 (8) FS/TS/DB with exercise area 13 (15) 10 (15) 23 (15) 1 Interquartile range was calculated. 2 At the most recent milk recording test after the hoof trimmer s visit. 3 Without pasture or exercise area. 97

114 Table 4.2. Distribution of foot lesions [n (%)] by foot and claw per cow (n=28,607) on 156 Alberta, Canada, dairy farms. Leg Digital Dermatitis 1 Sole ulcer White line Sole hemorrhage Toe ulcer Interdigital hyperplasia Front Left 300 (4.8) 220 (12.0) 238 (16.2) 115 (19.3) 79 (14.7) Lateral 44 (0.7) 41 (2.3) 81 (5.5) 27 (4.6) 30 (5.5) Interdigital 213 (3.4) 14 (3.9) Medial 43 (0.7) 179 (9.8) 157 (10.7) 88 (14.6) 49 (9.2) Right 272 (4.4) 228 (12.5) 260 (17.7) 101 (16.8) 67 (12.5) Lateral 38 (0.6) 57 (3.1) 71 (4.9) 27 (4.4) 25 (4.7) Interdigital 195 (3.1) 8 (2.2) Medial 38 (0.6) 171 (9.4) 189 (12.9) 74 (12.4) 42 (7.9) Rear Left 3,514 (56.3) 837 (45.7) 554 (37.8) 259 (43.1) 185 (34.5) Lateral 203 (3.3) 642 (35.1) 478 (32.6) 214 (35.6) 119 (22.2) Interdigital 3,106 (49.7) 219 (60.5) Medial 205 (3.3) 195 (10.6) 76 (5.2) 45 (7.5) 66 (12.3) Right 3,616 (57.9) 923 (50.4) 555 (37.9) 326 (54.2) 223 (41.6) Lateral 240 (3.8) 734 (40.1) 480 (32.8) 262 (43.7) 152 (28.4) Interdigital 3,149 (50.4) 202 (55.8) Medial 300 (4.8) 220 (12.0) 238(16.2) 115 (19.1) 79 (14.7) Overall cow prevalence 6,244 (21.8) 1,830 (6.4) 1,466 (5.1) 601 (2.1) 536 (1.9) 362 (1.3) 1 Percentages add to >100% due to simultaneous occurrence of lesions in >1 claw per leg. 98

115 Table 4.3. Herd- and cow-level prevalence (%) of foot lesions in 156 farms and 28,607 cows as recorded by 7 hoof trimmers in Alberta. Farms with partial herd trims (n=87) Farms with whole herd trims (n=69) Overall Herd Lesion Cow prevalence Cow prevalence Cow prevalence level Within-herd Within-herd Within-herd Front Rear Cow prev. prevalence 1 Front Rear Cow prevalence 1 Front Rear Cow prevalence feet feet level feet feet level feet feet level 1 (%) 2 None (10.4 to 93.7) (37.4 to 98.2) (10.4 to 98.2) Any lesion (6.2 to 89.6) (1.7 to 62.6) (1.7 to 89.6) Multiple lesions (0.0 to 36.5) (0.0 to 12.2) (0.0 to 36.5) Infectious Digital dermatitis (0.0 to 74.3) (0.0 to 45.9) (0.0 to 74.3) Other (0.0 to 9.8) (0.0 to 13.9) (0.0 to 13.9) Claw horn Sole ulcer (0.0 to 29.8) (0.0 to 16.3) (0.0 to 29.8) White line (0.0 to 17.0) (0.0 to 16.2) (0 to 17) Sole hemorrhage (0.0 to 11.3) (0.0 to 13.7) (0.0 to 13.7) Toe ulcer (0.0 to 11.7) (0.0 to 12.9) (0.0 to 12.9) Interdigital hyperplasia (0.0 to 17.6) (0.0 to 4.7) (0.0 to 17.6) Other lesions (0.0 to 13.3) (0.0 to 8.6) (0.0 to 13.3) Mean (minimum to maximum) were calculated. 2 Herd-level prevalence: percentage of herds affected, calculated as the number of herds with at least 1 animal affected/total number of herds. 3 Includes: heel erosion, interdigital dermatitis and foot rot. 4 Includes: thin sole, corkscrew claw, axial, horizontal and vertical fissures. 99

116 Table 4.4. Comparison of foot lesion distribution among hoof trimmers and within-herd intraclass correlation coefficients in 28,607 cows on 156 Alberta, Canada, dairy farms. Hoof trimmer (HT) Random-intercept models 5 HT level Farm level Variable σ 2 ICC σ2 ICC Farm trimming routine, n (%) Partial-herd trims 7 (44) 2 (13) 0 (0) 22 (92) 27 (68) 18 (55) 11 (58) Whole-herd trims 9 (56) 13 (87) 9 (100) 2 (8) 13 (32) 15 (45) 8 (42) Housing type, n (%) Without exercise area Freestall 12 (75) 14 (93) 8 (89) 20 (83) 31 (78) 18 (55) 11 (58) Tiestall 1 (6) 0 (0) 0 (0) 3 (13) 0 (0) 2 (6) 1 (5) Deep-bedded pack 2 (13) 0 (0) 0 (0) 0 (0) 5 (13) 4 (12) 1 (5) Exercise area 1 (6) 1 (7) 1 (11) 1 (4) 4 (10) 9 (27) 6 (32) Total n (%) of farms 16 (10) 15 (10) 9 (6) 24 (15) 24 (26) 33 (21) 19 (12) Lesion distribution, n (%) 1 No lesion 1,489 (70) 1,851 (62) 901 (75) 3,282 (49) 5,919 (66) 3,295 (57) 1,647 (59) Digital dermatitis 371 (18) 601 (20) 85 (7) 1,967 (30) 1,492 (17) 1,230 (21) 498 (18) Sole ulcer 104 (5) 157 (5) 86 (7) 450 (7) 487 (5) 389 (7) 157 (6) White line disease 72 (3) 85 (3) 69 (6) 309 (5) 444 (5) 282 (5) 205 (7) Sole hemorrhage 12 (1) 142 (5) 18 (1) 134 (2) 87 (1) 118 (2) 90 (3) Toe ulcer 28 (1) 16 (1) 14 (1) 146 (2) 165 (2) 77 (1) 90 (3) Interdigital hyperplasia 20 (1) 1 (0) 2 (0) 77 (1) 152 (2) 96 (2) 14 (1) Other infectious lesions 2 13 (1) 59 (2) 6 (0) 118 (2) 80 (1) 47 (1) 25 (1) Other lesions 3 9 (0) 75 (3) 24 (2) 160 (2) 105 (1) 203 (4) 66 (2) N (%) of cows examined 4 2,047 (7) 2,884 (10) 1,157 (4) 6,121 (21) 8,534 (30) 5,271 (19) 2,593 (9) 1 Lesion distribution percentage was estimated based on the total n of lesions. 2 Includes heel horn erosion, interdigital dermatitis and foot rot. 3 Includes thin sole, corkscrew claw and horizontal, vertical and axial fissure. 4 Total n of cows includes cows with more than one type of lesion. 5 Result of hierarchical models for each foot lesion containing hoof trimmer and farm as random effects. 100

117 Table 4.5. Final multilevel logistic regression models of cow- and herd-level factors associated with the 3 most frequent foot lesions in 23,014 cows in 156 Canadian dairy herds. Digital dermatitis Sole ulcer White line disease Predictor Odds ratio Odds ratio Odds ratio P-value P-value (95% CI) (95% CI) (95% CI) P-value Intercept 0.06 (0.03 to 0.14) < (0.01 to 0.03) < (0.01 to 0.03) <0.001 Presence of foot lesions Digital dermatitis (0.60 to 0.81) < (0.42 to 0.60) <0.001 Claw horn lesions (0.45 to 0.55) < (1.49 to 2.01) < (1.26 to 1.76) <0.001 Interdigital hyperplasia 4.00 (3.11 to 5.13) < (0.11 to 0.69) Other lesions (0.34 to 0.56) < (2.69 to 4.43) <0.001 Parity 1 Referent Referent Referent (1.26 to 1.88) < (2.00 to 5.70) < (1.31 to 3.61) (0.97 to 1.61) (2.40 to 7.60) < (2.01 to 5.84) < (0.44 to 0.79) < (4.43 to 12.48) < (3.12 to 8.00) <0.001 DIM Fresh (1-44 d) Referent Referent Referent Peak (45 99 d) 1.51 (1.30 to 1.78) < (2.22 to 5.44) < (1.60 to 3.58) <0.001 Mid ( d) 2.10 (1.82 to 2.42) < (4.31 to 9.84) < (1.77 to 3.73) <0.001 Late ( 200 d) 1.97 (1.73 to 2.25) < (2.69 to 6.14) < (1.79 to 3.67) <0.001 Parity DIM First parity, fresh DIM Referent Referent Referent Second parity, peak DIM 0.70 (0.53 to 0.93) (0.16 to 0.58) < (0.39 to 1.35) Second parity, mid DIM 0.53 (0.42 to 0.69) < (0.15 to 0.47) < (0.44 to 1.40) Second parity, late DIM 0.46 (0.36 to 0.58) < (0.19 to 0.60) < (0.33 to 1.03) Third parity, peak DIM 0.62 (0.43 to 0.88) (0.16 to 0.68) (0.22 to 0.88) Third parity, mid DIM 0.52 (0.38 to 0.72) < (0.17 to 0.60) < (0.36 to 1.24) Third parity, late DIM 0.42 (0.31 to 0.57) < (0.20 to 0.73) (0.31 to 1.05) Fourth parity, peak DIM 0.86 (0.58 to 1.27) (0.20 to 0.71) (0.32 to 1.04)

118 Table 4.5. Continued Digital dermatitis Sole ulcer White line disease Predictor Odds ratio Odds ratio Odds ratio P-value P-value (95% CI) (95% CI) (95% CI) P-value Fourth parity, mid DIM 0.60 (0.41 to 0.87) (0.18 to 0.56) < (0.31 to 0.94) Fourth parity, late DIM 0.61 (0.43 to 0.86) (0.22 to 0.69) (0.28 to 0.82) Daily milk yield (kg) (0.98 to 0.99) < (0.97 to 0.99) <0.001 Farm trimming routine Partial-herd trims Referent Referent Referent Whole-herd trims 0.69 (0.45 to 1.06) (0.47 to 0.96) (0.54 to 0.91) Herd size (no. milking cows) 100 Referent - - > (1.35 to 3.48) Housing type Without exercise area Freestall Referent Referent Referent Tiestall 0.87 (0.33 to 2.27) (0.24 to 1.14) (0.15 to 0.61) Deep-bedded pack 1.29 (0.62 to 2.71) (0.18 to 0.66) (0.30 to 0.79) With exercise area 1.97 (1.10 to 3.53) (0.26 to 0.68) < (0.63 to 1.23) Includes sole and toe ulcer, sole hemorrhage and white line disease. 2 Includes thin sole, corkscrew claw and horizontal, vertical and axial fissure. 102

119 Study population n=91,226 (158 farms) Excluded 2 farms with <5 cows recorded n=5 cows (156 farms) Study population n=91,221 (156 farms) Excluded duplicates (only included first occurrence), n=3,387 - Cows recorded twice in trim sessions <15 d apart, n=341 - Cows recorded twice in the same trim session, n=1,257 - Cows with same lesion recorded in >1 claw zone on same foot, n=1,789 Duplicates excluded n=87,834 (156 farms) Unique cow observations n=28,607 (156 farms) Excluded repeated cows (only included unique cows), n=59,227 Farms that had whole herd trimmed at once ( 80% of lactating cows trimmed, n=69) - Included: trim session with the highest percentage of cows trimmed, n=8,020 - Excluded: other trim sessions, n=16,714 Farms on which only part of the herd was trimmed per visit (<80% of lactating cows were trimmed, n=87) - Included: 1 st occurrence of a cow, n=20,587 - Excluded: repeated occurrence of a cow, n=42,513 Missing data from DHI parameters, n=5,593 - Parity (n=449) and DIM (n=1,633), total n=1,633 - Daily milk production, n=5,207 Complete DHI data n=23,014 (156 farms) Figure 4.1. Flowchart of dairy cow study selection process. 103

120 Digital dermatitis: 85% 3 Sole ulcer: 14% 1 Sole hemorrhage: 7% 2 White line: 83% 4 Digital dermatitis: 0.5% each zone 3 Sole ulcer: 89% Sole hemorrhage: 84% White line: 16% Toe ulcer: 4% 5 Toe ulcer: 51% Sole hemorrhage: 16% White line: 7% Toe ulcer: 52% Digital dermatitis: 18% 1 Percentage of each type of lesion occurring in a specific claw zone. Percentages add to >100% due to simultaneous occurrence in >1 claw zone. 3% of sole ulcers occurred simultaneously in zones 4 and , 2 and 1% of sole hemorrhages occurred simultaneously in zones 4 and 5; 4 and 6; 4, 5 and 6, respectively. 3 4% of digital dermatitis occurred simultaneously in zones 0 and 10. Zone 11 corresponds to the anterior interdigital cleft. 4 1, 1, 1, and 2% of white line lesions occurred simultaneously in zones 1 and 2; 1 and 3; 1, 2, and 3; 2 and 3, respectively. 5 1, 4, and 1% of toe ulcers occurred simultaneously in zones 1 and 2, 1 and 5, and 1, 2, and 5, respectively. Figure 4.2. Claw zones affected by each type of the most common foot lesions in 28,607 cows on 156 Alberta, Canada, dairy farms. Foot lesion identification used was developed by International Lameness Committee (2008) in cooperation with Zinpro Corporation (Zinpro Corporation, Eden Prairie, MN, USA). 104

121 (a) Prevalence of digital dermatitis (%) Fresh ( 44) Peak (45-99) Mid ( ) Stage of lactation Late ( 200) Parity (b) Prevalence of sole ulcers (%) Fresh ( 44) Peak Mid (45-99) ( ) Stage of lactation Late ( 200) Parity 105

122 (c) Prevalence of white line disease (%) Fresh ( 44) Peak Mid (45-99) ( ) Stage of lactation Late ( 200) Parity Figure 4.3. Percentage of dairy cows with (a) digital dermatitis, (b) sole ulcers, and (c) white line disease per parity and stage of lactation. 106

123 Chapter Five: Validation of the M-stage scoring system for digital dermatitis in the milking parlor 5.1 Abstract The need for simple tools for routine digital dermatitis (DD) inspection that can be used by researchers and farmers is highlighted by the high DD prevalence estimates and benefits of early topical treatment. The objective was to determine accuracy of scoring DD lesions on dairy cows in the milking parlor compared to the trimming chute as the gold standard, using the 5 M- stage scoring system. Three observers inspected 6,991 hind feet from 9 farms in the milking parlor using a mirror (glued to a plastic kitchen spatula) and a headlamp, followed by inspection in the trimming chute within 5 d. Interobserver agreement for scoring DD in various settings was 82% (kappa > 0.74; weighted kappa > 0.76). At trimming chute inspections, 68% of cows had at least 1 DD lesion; 19% had 1 hind leg affected and 49% had both hind legs affected. At the foot level, DD prevalence was the same in the milking parlor and trimming chute inspection (mean = 58%), but distribution of M-stages differed. Milking parlor inspection as a means of identifying presence of DD lesions had a sensitivity of 92% and specificity of 88%, with positive and negative predictive values of 91 and 88%, respectively. Agreement between milking parlor and trimming chute inspections was 73% (kappa = 0.58, weighted kappa = 0.66) for the 5 M- stage scoring system and 90% (kappa = 0.80) if only presence of lesion was noted. Test characteristics varied greatly among M-stages, with the highest sensitivity for detecting M4 (81.3%) and M2 (59.9%) lesions, and the lowest for detecting M3 (0.0%) and M1 (5.9%) lesions. A total of 48% of M1 lesions were misclassified as M0, 61% of M3 lesions were misclassified as 107

124 M4, and 68% of M4.1 lesions were misclassified as M4. The majority (87%) of DD lesions were located between the heel bulbs, 10 and 2% of DD lesions affected the interdigital space and the front of the foot, respectively. Sensitivity for detecting presence of a lesion when it occurred between the heel bulbs was 93%, but < 67% if occurred elsewhere on the foot. We concluded that inspection of rear feet in the milking parlor was a reliable, economic and simple method to detect and score DD lesions for researchers and farmers. Although DD scoring in the milking parlor as a routine practice should promote early detection, prompt treatment interventions and herd monitoring, it was not reliable enough to replace definitive identification of M-stages in the trimming chute. 5.2 Introduction Digital dermatitis (DD) is a widespread bacterial foot lesion in cattle that typically develops on the bulb of the heel causing ulcerative lesions that may be uncomfortable or very painful (Cheli and Mortellaro, 1974; Döpfer et al., 1997). It is the most common foot lesion in confined dairy systems, with a prevalence ranging from 15 to 49% (Holzhauer et al., 2006a; USDA, 2009; Solano et al., 2016c), with a large economic impact as a result of the high incidence and prevalence and the related costs due to decreased reproductive performance, increased risk of culling, treatment and labor (Bruijnis et al., 2010; Cha et al., 2010; Gomez et al., 2015). As a consequence of pain, cows affected with DD often change their gait or posture to avoid contact with the floor, exhibiting decreased mobility, lifting or shaking the affected leg, or walking with a toe-down posture (Rodriguez-Lainz et al., 1998; Shearer and Van Amstel, 2013). 108

125 Chronically infected cattle with treponemes (etiologic agent implicated) represent a reservoir of infections and potential source of outbreaks (Döpfer, 2009). Additionally, affected cows can also experience changes in the heel area, favoring persistence and occurrence of other infectious lesions (Gomez et al., 2014a). Thus, DD is a serious animal welfare concern due to painful episodes that can be long-lasting, with recurrent outbreaks (Bruijnis et al., 2012; Döpfer et al., 2012a; Gomez et al., 2015), and increase the probability of developing other foot lesions (Gomez et al., 2014a). Furthermore, DD is of extreme importance to the dairy industry from a public health perspective due to the use of antibiotics, as well as carcinogenic and environmentally detrimental products for treatment and control strategies. Control of DD starts with detection, a critical aspect to enable monitoring and early treatment which are key factors for effective management of the disease (Döpfer et al., 2012a). Numerous classification systems for DD have been developed (Laven, 1999; Manske et al., 2002c; Vink, 2006; Krull et al., 2014), but over the last 15 years, the scientific community has widely used the M-stage scoring system developed by Döpfer et al. (1997) and amended by Berry et al. (2012). Based on visual observation, this scoring system characterizes various clinical stages of DD over the course of the disease, allowing the observation of transitions between active, chronic and healed stages. This information provides researchers, farmers and hoof trimmers a tool to monitor effectiveness of DD control programs at both individual- and herd-levels (Döpfer et al., 2012a). The M-stage scoring system enables macroscopic scoring of DD lesions and is internationally recognized as the most accurate and detailed DD identification system (Greenough et al., 2008), but misclassification bias is expected as diagnosis is based on visual inspection (Relun et al., 2011). Lifting the cow s foot for inspection in the trimming chute 109

126 continues to be the gold standard for DD detection, despite being an expensive, labor-intensive, and stressful procedure for the cattle (Thomsen et al., 2008b; Relun et al., 2011; Stokes et al., 2012). Furthermore, trimming chute inspections are not practical to assess disease prevalence either on a regular basis or for early DD detection and treatment. Consequently, alternative diagnostic tools for DD detection have been developed; for example, inspection of hind feet during milking in the parlor with the assistance of a swiveling mirror (Relun et al., 2011), borescope (Laven, 1999; Vink, 2006; Stokes et al., 2012) or with no specialized tools (Rodriguez-Lainz et al., 1998; Thomsen et al., 2008b). Also, DD is diagnosed during pen walks while cows are confined in headlocks (University of Wisconsin, 2013) or by using blood tests to detect active DD cases (Gomez et al., 2014b). The 6 studies that evaluated the accuracy of DD detection in the milking parlor used various classification systems for DD, and the majority did not distinguish among various DD M-stages. The 1 study that used the M- stage scoring system (Relun et al., 2011) was conducted before the release of the updated scoring system (Berry et al., 2012). In addition, the other 5 studies used a tool that was either expensive or impractical for field conditions (Laven, 1999; Vink, 2006; Stokes et al., 2012) or insufficiently accurate (Rodriguez-Lainz et al., 1998; Thomsen et al., 2008). There is a need for simple, inexpensive and effective tools for routine DD inspection. These inspections can provide a practical and accurate option likely to be used by researchers but most importantly by farmers as a treatment decision tool and as part of the herd s DD control program. Therefore, the objective was to determine the accuracy of detection and scoring DD lesions in the milking parlor, as compared to the trimming chute, using the updated M-stage scoring system. 110

127 5.3 Materials and Methods Farm and Cow Selection A total of 17 freestall dairy farms in Alberta, Canada, clients of 2 hoof trimmers, were contacted by telephone to be enrolled as part of a longitudinal study examining the effectiveness of a standardized footbath protocol for prevention of DD (Solano et al., 2016b). To ensure that participating farms were representative of the majority of farms in Alberta, farms were selected that had freestall housing systems with no access to pasture, and a herd size of 90 Holstein- Friesian lactating cows. Farms were also selected based on convenience criteria: cows had to be milked in a milking parlor and farms had to have 10% DD prevalence, based on hoof trimming records from the past year. Ten farms met all criteria and agreed to participate in the study. One farm withdrew 1 month after field data collection had started due to a change of ownership of the farm. Data were collected between November 2013 and June 2014 by the corresponding author and 2 trained observers from the University of Calgary (Calgary, AB, Canada). All methods were approved in 2013 by the Animal Care Committee and Research Ethics Board of the University of Calgary (AC ). DD Lesion Assessment The DD lesions were scored using the 5-point scale according to Döpfer et al. (1997) and Berry et al. (2012). Briefly, lesions were classified as M0 if skin was normal with no sign compatible with a pre-existing lesion; M1 if a small (< 2 cm in diameter) focal active lesion was observed with red-grey surface and scattered small (~1 mm in diameter) red foci; M2 if there was an ulcerative active lesion 2 cm in diameter with red-grey surface; M3 (healing stage) after 111

128 the M2 lesion has covered itself with a dry brown, scab-like tissue; M4 (chronic stage) if the lesion surface was raised by brown or black tissue, thickened epithelium, proliferative or hyperkeratotic growth; and M4.1 if the chronic stage of M4 had an M1 lesion within its perimeter. The first author conducted a training program with 2 other observers, similar to the one developed by Gibbons et al. (2012) to identify and score DD lesions. The training consisted of a classroom session with a Microsoft Powerpoint (Microsoft Corp., Redmond, WA, USA), presentation where digital, coloured photographs were used to demonstrate each DD stage, along with a detailed description. Each observer received a laminated reference card with a summary table of the scoring system, a characteristic photograph and brief definition of each M-stage (Figure 5.1). Observers used the reference card at all times during both training and data collection. The 3 observers scored 40 photographs of DD lesions. One day later, during a live session, observers scored the hind feet of 110 cows during milking in the parlor. The following day, observers scored a sample of 40 cows (from the 110 cows scored the day before) in the trimming chute. A mid-way check (12 wk after initial data collection) was conducted using the same number of cows but different photographs than the initial training, in order to maintain high agreement throughout the study. Milking Parlor Inspection Each farm was visited 3 times during the study period. Each visit consisted of inspection of the entire lactating herd in the milking parlor, followed by inspection in a trimming chute scheduled within 48 h. The 48-h interval was selected to decrease the possibility of observing a 112

129 different M-stage in the trimming chute than in the milking parlor, which can occur due to the natural rapid transition between DD stages (Nielsen et al., 2012). Cows were scored for DD lesions during milking using a scoring method adapted from Relun et al. (2011). Cows hind feet were washed with water from a hose before examination. Scoring DD lesions was done using a cm (3 x 3 in) mirror [ArtMinds Square Mirror; Michaels Stores ($3.50 CAD for a pack of 5)] glued with contact cement to the flat end of a 25- cm plastic kitchen spatula [Unknown brand; Dollar Store ($1.25 CAD)] and extra lighting was provided by an LED headlamp [Gizmo, Black Diamond, China ($25 CAD; Figure 5.1). Inspection of hind feet only was based on reports that approximately 94% of DD lesions occur in the hind feet and 85% of DD lesions affect the area between heel bulbs (Cramer et al., 2008; Relun et al., 2011; Solano et al., 2016c). A total of 3,756 cows were scored, but information was not recorded on 164 feet. Reasons for missing data included: poor foot conformation that impeded accurate scoring (i.e., low claw heels), feet covered with dried caked manure that did not washed off, and cow behavior (e.g., constant lifting of feet or kicking). Trimming Chute Inspection Inspection in the trimming chute was considered the gold standard. Cows hind feet were thoroughly cleaned with paper towels before being scored for DD. In addition to M-scoring, anatomical location of DD lesions was recorded according to Relun et al. (2011): BH = between the heel bulbs; MH = medial heel; LH = lateral heel; D = around the dewclaws; C = on the coronet; F = on the front; UF = under the foot in the interdigital space; HYP = on an interdigital hyperplasia. All lactating cows were scheduled to be inspected in the trimming chute within 2 d following the milking parlor inspection. However, the interval between milking parlor and 113

130 trimming chute inspection was longer than scheduled (range 0 to 5 d) due to snow storms that impeded driving and trimming chute s malfunctioning as a result of extreme weather conditions (< -25 C / < -13 F). In addition, farms with a large herd size (> 150 cows) required 2 days of trimming inspection. On 9 of 27 farm visits (31% of cows), cows were inspected in the trimming chute 3 to 5 d after milking parlor inspection. Depending on the farm setting and herd size, 1 or 2 trimming chutes were present with 1 observer per chute. The observer at the milking parlor inspection was not necessarily the same as at the trimming inspection. A total of 3,765 cows were inspected in the trimming chute but information was inadvertently not recorded on 18 feet. It was not possible to match data between milking parlor and trimming chute inspections for 180 cows due to missing cow identification. Statistical Analyses Data were entered into Microsoft Access (Microsoft Corp., Redmond, WA, USA), and all statistical analyses were performed using Stata 13.1 (StataCorp, 2013, College Station, TX, USA). For all analyses, a P-value < 0.05 was considered significant. The foot was considered the statistical unit. All analyses were based on 5 M-stages whereas scores were also combined into a simplified scoring system: no lesions (M0), active lesions (M1, M2 and M4.1 were merged) and chronic lesions (M3 and M4 were merged). If > 1 DD lesion was observed on the same foot, the most severe M-stage was used according to the proposed hierarchy: M2 > M4.1 > M1> M4 > M3 (for 5 M-stages) or active > chronic > no lesion (for simplified scoring system; Relun et al., 2011). Interobserver agreement was calculated by assessing DD scores attributed by the 3 observers during the classroom session with pictures and live in the milking parlor and trimming 114

131 chute. Agreement between observers was assessed by the percentage of agreement [number of exact agreements/total number of observations 100, (PAo)], whereas actual agreement beyond chance was assessed by kappa [Cohen, 1960 (Ҡ)]. To quantify the magnitude of discrepancy among raters, linearly weighted kappa for ordinal scores was calculated using a weight matrix according to the hierarchy described above [Cohen, 1968; Fleiss et al., 2003 (Ҡw)]. Agreement as measured using Ҡ was interpreted according to Landis and Koch (1977): 0 = poor, 0.01 to 0.20 = slight, 0.21 to 0.40 = fair, 0.41 to 0.60 = moderate, 0.61 to 0.80 = substantial, and 0.81 to 1.00 = almost perfect. Overall agreement with the gold standard test was estimated by comparing DD scores during milking and at the trimming chute. Sensitivity (Se), specificity (Sp), positive predictive value (PPV), and negative predictive value (NPV) were calculated for absence (M0) and presence (M1, M2, M3, M4 or M4.1) of disease and for specific M-stage. Chi-square tests were used to compare test characteristics between trimming chute inspections that occurred 0-2 and 3-5 d periods after milking parlor inspections. No significant differences were detected between the 2 periods of time; therefore, all data were included in the analyses. Apparent prevalence and accuracy estimates were used to calculate true DD prevalence per M-stage. True prevalence was calculated as: apparent prevalence + (Sp 1) / Se + (Sp 1) (Dohoo et al., 2009). 5.4 Results Average herd size was 151 lactating cows (ranging from 92 to 200), with mean daily milk production of 33 kg/cow and median parity of 2. Three study farms had herringbone milking parlors, whereas 6 farms had parallel parlors. 115

132 Interobserver Agreement Interobserver agreement for scoring DD was substantial to almost perfect (agreement > 82%; κ > 0.74; κw > 0.76) using the 5 M-stages and also for the simplified scoring system throughout the study period (Table 5.1). Agreement at mid-way check improved in the classroom session using the 5 M-stages (κ = 0.77 to 0.83 at first and mid-way check picture session, respectively), and overall when using the simplified scoring system. However, applying κw had little or no effect for the simplified scoring system. Trimming Chute Inspection A total of 6,991 feet were scored in the milking parlor and in the trimming chute. At trimming chute inspections, 67.6% of cows had at least 1 DD lesion; 18.8% had 1 hind leg affected and 48.8% had both hind legs affected. A total of 10.4 and 0.3% of legs affected had 2 and 3 DD lesions on the same foot, respectively (Table 5.2). Within-herd DD prevalence ranged from 10.9 to 72.7% with a mean of 55.7%. The majority (87.0%) of DD lesions were located between the heel bulbs, 9.8% of DD lesions affected the interdigital space (including on an interdigital hyperplasia), and 2.4% of DD lesions affected the front of the foot (Table 5.2). A total of 78.4 and 19.1% of DD lesions occurred in the interdigital space and at the front of the foot, respectively, if > 1 lesion was detected. Accuracy of Milking Parlor Inspection At milking parlor inspections, 66.4% of cows had at least 1 DD lesion. At the foot level, DD prevalence was the same in the milking parlor and trimming chute inspection (mean = 58%), but distribution of M-stages differed. Apparent prevalence at milking parlor inspections for each 116

133 M-stage was 0.3, 5.3, 0.8, 44.0 and 6.4% for M1, M2, M3, M4 and M4.1, respectively. The majority of disagreements between milking parlor and trimming chute inspections per M-stage involved M1, M3 and M4.1 stages (Table 5.3). In the milking parlor, 48% of M1 lesions were not detected (misclassified as M0), 61% of M3 lesions were misclassified as M4, and 68% of M4.1 lesions were misclassified as M4 (Table 5.3). When using the simplified scoring system, 51% of active lesions were misclassified as chronic. Agreement between milking parlor and trimming chute inspections was moderate (κ = 0.58) and substantial (κ w = 0.66) for the 5 M-stage scoring system, and substantial (κ = 0.62, and κw = 0.67) for the simplified scoring system. Agreement improved but was still considered substantial (κ = 0.80) if only presence of lesion was considered (Table 5.4). Milking parlor inspection as a means of identifying presence of DD lesions had a Se of 91.5% and Sp of 88.2%, with PPV and NPV of 91.5 and 88.3%, respectively (Table 5.5). True within-herd prevalence was 1.6, 5.9, 2.4, 34.7 and 12.8% for M1, M2, M3, M4 and M4.1, respectively. Test characteristics varied greatly among DD M-stages, with the highest Se for detecting M4 (81.3%) and M2 (59.9%) lesions, and the lowest for detecting M3 (0.0%) and M1 (5.9%) lesions. Sensitivity for detecting presence of a lesion when it occurred between the heel bulbs was 93%, but < 67% if occurred elsewhere on the foot (Table 5.2). 5.5 Discussion Our study apparently included the largest sample of cows used to investigate the accuracy of M-stage scoring for DD in the milking parlor compared to examination in a trimming chute as 117

134 the gold standard. Scoring DD in the parlor was highly accurate in determining the presence of a lesion, although accuracy varied depending on the M-stage. The training program to identify and score DD achieved substantial agreement among observers that was maintained throughout the study period. Interobserver reliability estimates in the milking parlor seemed higher than those reported by Relun et al. (2011) in a similar study (PAo = 82%, κ = 0.74, κw = 0.76 vs. PAo = 66%, κ = 0.51, κw = 0.63 in Relun et al., 2011). However, the number of observers in the present study was lower (3 versus 5 observers), potentially reducing a source of variation. In addition, intraobserver agreement, another potential source of error, was not evaluated. Perhaps the effectiveness of our training program was a result of implementing 3 consecutive training sessions which were progressively more detailed (i.e., first scoring with pictures, then live in the milking parlor, and finally in the trimming chute), and encouraging observers to use the reference card at all times during scoring. In addition, it is important to check repeatability in different settings and time points to ensure accurate and reliable data collection throughout the study (Gibbons et al., 2012). We inferred that a training program that successfully obtains high repeatability is essential to achieve reliable data and to prevent misclassification bias. Cow-level DD prevalence of 68% detected in this study seemed higher than findings in Denmark and France (27%; Thomsen et al., 2008; 45%; Relun et al., 2011), but this was expected as the study herds were selected based on 10% DD prevalence. It was not surprising that most lesions were located between the heel bulbs; however, 10% of DD lesions occurred in the interdigital space, an area not visible during milking parlor inspections. Additionally, when more than one DD lesion was detected, the vast majority of the second and third lesions detected occurred in the interdigital space. These results and findings of other studies that reported 118

135 interdigital hyperplasia as a risk factor for DD (Holzhauer et al., 2006a; Solano et al., 2016c), indicate that early detection and treatment of feet in a trimming chute should not be neglected, especially for cows with a history of interdigital hyperplasia. Detection of DD in the milking parlor resulted in high test characteristics when determining presence/absence of a lesion. The Se of 92% seemed higher than findings from any other validation study (72%; Rodriguez-Lainz et al., 1998; 67%; Laven, 1999; 65%; Thomsen et al., 2008; 90%; Relun et al., 2011), except Stokes et al. (2012) who reported a Se of 100% in a l study involving only 80 cows. Variation of test characteristics among studies could be the consequence of the scoring system or diagnostic tool used, interobserver reliability, day intervals between test and gold standard evaluation, and DD prevalence. In the present study, the high within-herd mean DD prevalence influenced the test s predictive values. Although Se is not directly affected by prevalence as PPV is, it may be prevalence-related (Greiner and Gardner, 2000). In that regard, Se may vary with the proportion of each M-stage. For example, the farm with the lowest (1%) and highest (17%) prevalence of M2 lesions had a Se of 61 and 74%, respectively, for detecting those lesions in the milking parlor. Because severe cases of DD are easier to diagnose, the Se may increase in farms with a high DD prevalence. Furthermore, with good Se and imperfect Sp for detecting an M-stage, the apparent prevalence of such stage will likely be overestimated (Greiner and Gardner, 2000). Other potential factors that may have affected the current study s test characteristics included: type of milking parlor (i.e., in herringbone parlors, the distance between observer and one of the hind legs may be relatively long; Thomsen et al., 2008), observation conditions (i.e., poor lighting, quantity of manure contamination, water pressure for cleaning feet), foot conformation (i.e., low claw heels in older cows), and lesion location (i.e. under or at the front of the foot). 119

136 Despite the good accuracy of our tool, detecting only presence of DD does not provide enough information for effectively managing the disease. For instance, early detection of M2 lesions followed by rapid treatment can result in increased cure rates, reduced recurrence and spread of infection. Detection of higher prevalence of M4.1 lesions can be an indicator of upcoming outbreaks, suggesting that prevention measures should be undertaken (e.g., adjust footbath program; Döpfer et al., 2012). Thus, accurate detection of M-stages contributes to the development of preventive, control and therapeutic strategies for DD (Döpfer, 2009). In the present study, the Se for all M-stages, except M4, was low (0 to 60%), but Sp for all stages, except M4, was high (96 to 100%). The low Se and high Sp resulted in moderately low PPV (0 to 67%) but high NPV (88 to 100%) specific per M-stage, leading to a low proportion of falsepositive cows. Therefore, milking parlor inspection was a useful screening tool at herd level as it gave high confidence that a cow that did not exhibit any stage of DD was truly disease free, but it could also result in unnecessary individual treatments due to the high proportion of falsepositives. Cows identified positive in the milking parlor could be follow-up closely or selected for further inspection in the trimming chute. The lower Sp for M4 lesions was due to many false-positives, with approximately 10 and 20% of lesions detected as M4 in the milking parlor were truly M0 or M4.1 stages, respectively. Misclassification of M0 as M4 lesions possibly occurred because M4 lesions are characterized by thickened epithelium, and some cows could naturally have a thicker epithelium resembling an M4, or the epithelium could remain thickened after an infection has healed. Additionally, dried manure can be mistaken for scab-like material, which is characteristic of M4 lesions (as well as M3 and M4.1 lesions), when foot inspection is challenged by poor lighting, parlor setting, cow behavior (e.g., constant lifting of feet or kicking), or insufficient washing of feet. 120

137 Misclassification of M4.1 as M4 lesions, could be due to difficulty detecting the small size, active M1 focus as it commonly occurs in the foot s most anaerobic areas such as the posterior end of interdigital cleft. Furthermore, the low Se of M4.1 stage (20%) due to often misclassification with M4 stage may have serious clinical implications as these are lesions that should be treated promptly to prevent aggravation into M2 and potential outbreaks (Döpfer et al., 2012a). The low Se for detecting M2 lesions (60%) and resulting low PPV led to a high proportion of false-negatives. Twenty percent of those false-negatives were M4.1 lesions, meaning that the active M1 focus detected in the milking parlor is in fact larger than it appears (M2) when inspected in the trimming chute. This suggests that control measures for M4.1 lesions should not be underestimated; once detected in the parlor, they should be followed up closely and potentially be treated as seriously as M2 lesions. The lowest Se but highest Sp was for detection of M1 and M3 stages. In the present study, M3 were commonly misclassified as M4 lesions, and M1 were commonly misclassified as M0 or M4 lesions, consistent with the study of Relun et al. (2011). Misclassification of M3 as M4 lesions was not surprising, due to their similar scab-like characteristics and because M3 is a short-lived stage that can rapidly transition to M4. In agreement with Relun et al. (2011), misclassification frequency and infectiousness relevance of the stages can justify merging M3 and M4 into 1 category for practical purposes. Misclassification of M1 as M0 or M4 lesions could be because the majority of M1 lesions (65%) occurred in the interdigital space or on interdigital hyperplasia, an area not visible in the milking parlor. Thus, an M0 or M4, the most predominant M-stages, could have been detected in the milking parlor, whereas the coexistent 121

138 M1 was only observed in the trimming chute. It could also be that M1 lesions were too small to be detected in the milking parlor. Potential bias could have been introduced by observers by not using blinding techniques between milking parlor and trimming chute inspections. However, it was unlikely that data from > 90 cows would be recalled between inspections. In addition, the present study did not record time spent evaluating feet in the milking parlor (30 s to 2 min; Rodriguez-Lainz et al., 1998; Relun et al., 2011) as it was information beyond our objectives. However, farmers consistently reported that observers had no impact on the farms milking routine nor duration. Thus, we believe the practice of scoring feet for DD can be integrated into the milking routine without delaying milking. Unfortunately, our methodology cannot easily nor safely be used to detect DD in pre-calving heifers nor in farms with automatic milking systems. Further research is needed on accurate detection of M-stages and easy monitoring in settings other than the milking parlor. 5.6 Conclusions Inspecting the feet of dairy cows in the milking parlor was a reliable, economic and simple method to detect and score DD lesions for researchers and farmers. Implementation of DD scoring as a routine practice should aid in early detection, prompt treatment interventions, and herd monitoring. Such interventions can be at the individual level, by early topical treatment, or at herd-level by adjusting the frequency and intensity of footbathing. However, this method was not sufficiently reliable to replace thorough foot inspection and definitive identification of M-stages in the trimming chute. 122

139 Table 5.1. Interobserver agreement for scoring digital dermatitis on dairy cows with pictures and live in the milking parlor and trimming chute using the 5 M-stage and a simplified scoring system. Agreement coefficients of 5-M stages 1 Agreement coefficients of simplified system 2 Setting 3 PA o κ 4 (95% CI) 5 κ w (95% CI) PA o κ (95% CI) κ w (95% CI) Classroom with pictures 82.9 ( ) 0.77 ( ) 0.84 ( ) 90.6 ( ) 0.83 ( ) 0.83 ( ) On-farm in milking parlor 82.1 ( ) 0.74 ( ) 0.76 ( ) 86.3 ( ) 0.78 ( ) 0.79 ( ) On-farm in the trim chute 87.5 ( ) 0.82 ( ) 0.89 ( ) 90.9 ( ) 0.86 ( ) 0.91 ( ) Classroom with pictures MW ( ) 0.83 ( ) 0.88 ( ) 93.3 ( ) 0.87 ( ) 0.89 ( ) 1 M0 to M4.1 (Berry et al., 2012). 2 Simplified scoring system: no lesion (M0), active lesion (M1/M2/M4.1), and chronic lesion (M3/M4). 3 Mean overall percent of agreement. 4 Kappa = agreement beyond chance (κ). 5 Weighted kappa (κw) according to hierarchy: M2 > M4.1 > M1 > M4 > M3 (5 M-stage), and active > chronic > no lesion. 6 Mid-way check (12 wk after initial data collection). 123

140 Table 5.2. Distribution (no.; % in parentheses) of anatomical locations of digital dermatitis (DD) lesions identified at trimming chute inspection on 4,161 hind feet of dairy cattle. Location 1 DD stage 2 No. BH D MH LH F HYP UF Sensitivity st DD lesion 4,161 4,053 (97.4) 3 (0.1) 2 (0.1) 21 (0.5) 17 (0.4) 39 (0.9) 26 (0.6) M (73.0) 1 (0.8) 2 (1.6) 3 (2.4) 2 (1.6) 10 (7.9) 16 (12.7) M (91.8) 1 (0.2) 0 (0) 6 (1.4) 7 (1.6) 15 (3.4) 7 (1.6) M (92.9) 0 (0) 0 (0) 0 (0) 0 (0) 1 (7.1) 0 (0) M4 2,604 2,570 (98.8) 1 (0) 0 (0) 11 (0.4) 8 (0.3) 11 (0.4) 3 (0.1) M (99.7) 0 (0) 0 (0) 1 (0.1) 0 (0) 2 (0.2) 0 (0) 2 nd DD lesion (0.6) 8 (1.7) 91 (18.8) 242 (50.1) 139 (28.8) M (1.5) 4 (2.0) 8 (4.0) 67 (33.5) 118 (59.0) M (1.7) 11 (9.1) 95 (78.5) 13 (10.7) M (0.8) 55 (43.3) 66 (52.0) 5 (3.9) M (2.8) 17 (48.6) 14 (40.0) 3 (8.6) 3 rd DD lesion 13 4 (30.8) 5 (38.5) 4 (30.8) M1 5 1 (20.0) 4 (80.0) M2 5 3 (60.0) 2 (40.0) M4 3 1 (33.3) 2 (66.7) 1 Anatomical areas defined by Relun et al. (2011): BH = between the heel bulbs; D = around the dewclaws; MH = medial heel; LH = lateral heel; F = on the front; HYP = on an interdigital hyperplasia; UF = under the foot in the interdigital space. 2 Scored according to Berry et al. (2012). 3 Sensitivity = proportion of DD lesions affecting a specific anatomical location identified by milking parlor inspection. 124

141 Table 5.3. Agreement (in bold)/disagreement by M-stage pairs (no.; % in trimming chute in parentheses) for scoring digital dermatitis (DD) in the milking parlor compared to DD scoring of cattle examined in a trimming chute. DD M-stage in trimming chute DD M-stage in milking parlor M0 M1 M2 M3 M4 M4.1 Total M0 2,588 (88) 57 (48) 23 (5) 2 (15) 219 (9) 42 (4) 2,931 M1 2 (0) 7 (6) 3 (1) 0 (0) 4 (0) 4 (0) 20 M2 10 (0) 3 (3) 255 (60) 3 (23) 49 (2) 59 (6) 379 M3 7 (0) 0 (0) 2 (0) 0 (0) 41 (2) 8 (1) 58 M4 318 (11) 42 (35) 58 (14) 8 (61) 2,065 (81) 652 (68) 3,143 M4.1 9 (0) 9 (8) 85 (20) 0 (0) 163 (6) 194 (20) 460 Total 2, ,

142 Table 5.4. Agreement of digital dermatitis scores on dairy cows examined in the milking parlor and in a trimming chute. Digital dermatitis stage Agreement (%) κ 1 (95% CI) κw 2 (95% CI) 5 M-stages ( ) 0.66 ( ) Simplified system ( ) 0.67 ( ) Absence vs. presence ( ) 1 Kappa = agreement beyond chance (κ). 2 Weighted kappa (κw) according to hierarchy: M2 > M4.1 > M1 > M4 > M3 (5 M-stages), and active > chronic > no lesion. 3 M0, M1, M2, M3, M4 and M4.1 (Berry et al., 2012). 4 Simplified scoring system: no lesion (M0), active lesions (M1/M2/M4.1), and chronic lesions (M3/M4). 5 Absence (M0) vs presence (M1 to M4.1). 126

143 Table 5.5. Test characteristics for scoring each digital dermatitis stage in the milking parlor, considering scoring in the trimming chute as a gold standard (n = 6,991 feet). Digital dermatitis stage Se 1 (95% CI) Sp 2 (95% CI) PPV 3 (95% CI) NPV 4 (95% CI) Prevalence (95% CI) Presence vs absence ( ) 88.2 ( ) 91.5 ( ) 88.3 ( ) 58.0 ( ) M1 5.9 ( ) 99.8 ( ) 35.0 ( ) 98.4 ( ) 1.7 ( ) M ( ) 98.1 ( ) 67.3 ( ) 97.4 ( ) 6.1 ( ) M3 0.0 ( ) 99.2 ( ) 0.0 ( ) 99.8 ( ) 0.2 ( ) M ( ) 75.8 ( ) 65.7 ( ) 87.6 ( ) 36.3 ( ) M ( ) 95.6 ( ) 42.2 ( ) 88.3 ( ) 13.7 ( ) Active lesion (M1, M2, M4.1) 41.2 ( ) 95.6 ( ) 72.1 ( ) 85.6 ( ) 21.5 ( ) Chronic lesion (M3, M4) 82.8 ( ) 75.5 ( ) 66.0 ( ) 88.4 ( ) 36.5 ( ) 1 Sensitivity = proportion of feet affected with an M-stage of digital dermatitis identified by milking parlor inspection. 2 Specificity = proportion of feet not affected with an M-stage of digital dermatitis identified by milking parlor inspection. 3 Positive predictive value = probability that given an M-stage of digital dermatitis identified in the milking parlor, the foot is affected. 4 Negative predictive value = probability that not given an M-stage of digital dermatitis identified in the milking parlor, the foot is not affected. 5 Absence (M0) vs presence (M1 to M4.1). 127

144 M0 M1 M2 M3 M4 M4.1 Active, Active, large Healing stage Chronic stage small lesion lesion (> 2 cm where M2 Lesion may be (< 2 cm across) has covered proliferative, across) -Ulcerative itself with a firm thickened -Red to (bright red) or scab-like epithelium or gray granulomatous material with scab +Pain (red-gray) (1-2d after tx) --Pain +++Pain +-Pain No sign of preexisting lesion Chronic stage with small active painful M1 +Pain Figure 5.1. Reference card used by observers summarizing the M-stage scoring system for digital dermatitis according to Berry et al. (2012). 128

145 a. b. c. Figure 5.2. Inspection of digital dermatitis lesions during milking using a simple tool a) mirror glued to a kitchen spatula; b) use of headlamp to improve lighting while scoring lesions; c) showing an M4 stage difficult to detect without a mirror. 129

146 Chapter Six: Effectiveness of a standardized footbath protocol for prevention of digital dermatitis 6.1 Abstract A footbath is the most common herd-level approach to control digital dermatitis (DD) in intensive dairy farming. However, wide variation in footbath design and protocols suggests a gap between evidence-based management practices and on-farm implementation. The objective was to evaluate effectiveness of a standardized footbath protocol in decreasing prevalence of active DD lesions in lactating dairy cattle. The protocol was based on current scientific literature, including footbath design and management. A controlled intervention trial was conducted on 9 farms over 22 wk. Each farm served as its own control with data collected for 10 wk before and 12 wk after intervention. A total of 1,978 lactating cattle were assessed biweekly for DD lesions and leg cleanliness in the milking parlor. Lactating cattle were also inspected in the trimming chute at 3 time points: start, intervention, and end of trial. Intervention consisted of implementing an automated footbath that measured 3 m long, 0.25 m wide, 0.15 m high, along with a weekly footbath protocol using 5% CuSO4 for 4 consecutive milkings, with footbath content replaced at a maximum of 200 cow passes. Multilevel logistic regression models for repeated measures were used to evaluate effects of the standardized footbath protocol in preventing active DD lesions. For the purpose of analysis, farms original footbath protocol (at baseline) was assessed and categorized as adequate or not. Farms with an adequate or inadequate baseline footbath protocol at trial outset had a mean cow-level prevalence of active DD lesions of 8% (range, 2 to 13) and 31% (range, 18 to 43), respectively. At milking parlor inspections, 130

147 prevalence of active DD lesions decreased from the time of footbath intervention, but this effect interacted with the farms baseline footbath protocol. In that regard, on farms with an inadequate footbath protocol at baseline, prevalence of active DD lesions decreased after intervention, whereas on farms with an adequate footbath protocol at baseline, prevalence of active DD lesions did not change. At the cow level, poor leg cleanliness was associated with higher prevalence of active DD lesions. At trimming chute inspections, prevalence of active DD lesions decreased from start to the end of the trial (22 and 14%, respectively); concurrently, prevalence of feet with no DD lesions (M0) increased (39 and 48%). We concluded that on farms with an inadequate footbath protocol at baseline, implementation of proper footbath design and improvement of footbathing management will decrease prevalence of active DD lesions and increase prevalence of feet without DD lesions. In addition, improving cow cleanliness will further result in control of active DD lesions. 6.2 Introduction Digital dermatitis (DD) is a widespread infectious foot lesion in cattle that typically causes painful ulcerative lesions (Cheli and Mortellaro, 1974; Read and Walker, 1998). Digital dermatitis has become endemic in intensively managed dairy systems (Blowey, 2005), affecting 70 to 95% of North American dairy farms (Cramer et al., 2008; USDA, 2009; Solano et al., 2016c). The prevalence of DD is higher in cows managed in confinement housing than in pasture-based systems, with the highest within-herd prevalence reported in freestalls (22 to 23% in Canada; Cramer et al., 2008; Solano et al., 2016a) compared to tiestalls (9 to 15% in Canada; Cramer et al., 2008; Solano et al., 2016a) and deep-bedded straw yards (4 to 9%; Somers et al., 131

148 2003; Solano et al., 2016a). In pastured-based systems in New Zealand and Chile, the prevalence of DD was approximately 7% (Chesterton et al., 2008; Tadich et al., 2010). Digital dermatitis lesions can be painful and persist for long intervals, with recurrent outbreaks (Döpfer, 2009; Bruijnis et al., 2012), and cause changes in foot conformation that can lead to other foot lesions (Gomez et al., 2014a) resulting in reduced animal welfare. Furthermore, DD has substantial financial implications associated with decreased milk production, increased risk for culling and treatment costs (Cha et al., 2010; Ettema et al., 2010; Relun et al., 2013a), accounting for the highest cost among all foot disorders due to its high incidence and prevalence (Bruijnis et al., 2010). Digital dermatitis is considered a multifactorial and polybacterial disease, with treponemes consistently isolated from DD lesions (Döpfer et al., 2012b; Gomez et al., 2012; Krull et al., 2016), whereas host susceptibility (Scholey et al., 2010), poor cleanliness and wet conditions (Rodríguez-Lainz et al., 1996; Relun et al., 2013b) are implicated as risk factors. However, the etiology and pathogenesis of DD are not fully elucidated, contributing to challenges in its control. Several control strategies have been recommended, including maintaining a clean, dry environment, individual topical treatment of affected cows, and herd-level strategies, including footbathing (Laven and Logue, 2006; Nuss, 2006; Döpfer et al., 2012a). Early detection and prompt topical treatment of active DD lesions improve cure rates and reduce spread of infection; furthermore, an effective footbath program can prevent occurrence of active lesions (Döpfer et al., 2012a). Footbaths are a common preventative approach, due to the labor involved in treating large numbers of affected cows (Laven and Logue, 2006), along with strong evidence from intervention trials supporting footbath efficacy in reducing DD prevalence compared to negative 132

149 controls (Speijers et al., 2010; Relun et al., 2012; Fjeldaas et al., 2014). However, there is a wide variation in on-farm practices related to footbath management (Cook et al., 2012; Relun et al., 2013c; Solano et al., 2015). In a recent study involving 81 freestall barns in Alberta, 95% of farms used a footbath regularly; nevertheless, no 2 farms had the same protocol, and only 3% of farms met all criteria regarding suggested footbath dimensions. A total of 22 chemical product combinations were recorded, with a range of 1 to 4 chemicals used per farm on a frequency of 0 to 7 d/wk (Solano et al., 2013). These findings suggest a gap between evidence-based management practices and on-farm implementation of footbaths. Most studies have compared chemicals and concentrations used in footbaths, but there is a paucity of research focused on standardization and optimization of footbathing practices for DD prevention (Cook et al., 2012; Potterton et al., 2012). Only 1 study assessed effects of various footbath dimensions on number of foot immersions and recommended long (3 m) and deep (0.28 cm) footbaths to optimize chemical delivery to cows feet (Cook et al., 2012). However, in that study, effects of various footbath dimensions on DD was not estimated. The objective of the present study was therefore to evaluate effectiveness of a standardized footbath protocol in decreasing prevalence of active DD lesions. The protocol was based on current scientific literature, including footbath design and management. 6.3 Materials and Methods Farm and Cow Selection A total of 9 freestall dairy farms in Alberta, Canada, participated in the study. Farm and cow selection process have been described in detail by Solano et al. (2016d). In short, eligible 133

150 farms were clients of 2 hoof trimmers. To ensure participants were representative of Alberta dairies, farms were selected that had freestall housing systems with no access to pasture, a herd size 90 Holstein-Friesian lactating cows, and routine footbathing of lactating cows (Table 6.1). To ensure farms were endemically infected with DD, selected farms had 10% DD prevalence based on hoof trimming records from the past 2 yr. To decrease potential bias introduced by the use of various footbath chemicals, all farms were required to use copper sulfate (CuSO4), as it is the most common chemical used in footbaths in Canada (Solano et al., 2015; Table 6.2). Participating farmers gave consent to share their foot trimming and milk production records provided by hoof trimmers and CanWest DHI (Guelph, ON, Canada), respectively. Individual data on parity, DIM, and test-day milk production (measured at the most recent monthly milk recording after data collection) for sampled cows were obtained from CanWest DHI. Data on the 9 farms were collected between November 2013 and June 2014 by 2 trained observers and the first author. All methods were approved by the Animal Care Committee (AC ) and Research Ethics Board (REB ) of the University of Calgary. Study Design A controlled intervention trial was conducted wherein each farm served as its own control, with data collected before and after intervention. Selection of farms as their own control rather than a comparison with a subgroup of negative control farms was decided after careful examination of factors related to seasonality, power and variability of management practices among farms. All lactating cows were assessed biweekly for DD lesions in the milking parlor for 6 mo (Figure 6.1). The 9 farms were visited 6 times before and 6 times after intervention with the footbath protocol. In addition, the entire lactating herd was inspected in the trimming chute at 3 134

151 time points: start of data collection (Week 1), the day of intervention (Week 10), and end of data collection (Week 22). Before intervention, there were no changes in the farms original footbath and DD protocols. For ethical and welfare reasons, farmers were expected to continue their routine individual treatments for DD (Table 6.1) and they were asked to record cow identification, date and nature of treatment. General Management A questionnaire was administered on every farm. The questionnaire was pretested on 2 farms to evaluate whether it was understood easily and interpreted correctly. Thereafter, the questionnaire was adapted and improved where necessary. Questions were either open-ended (e.g., Describe the footbath chemical(s) you use, in what concentration and frequency ; Table 6.2) or closed ended (e.g., Is the amount of footbath chemical measured/weighed before mixing? ; scale of answer: always, sometimes, or never). Detailed information was collected from the questionnaire on the frequency of use (times/wk), frequency of changing solutions, chemicals used and their concentrations. Farms original footbaths were measured (length, depth, and width) for every footbath used. In addition, data on DD monitoring and treatment, along with hoof trimming routines, were collected. Original Footbath Protocol Farms original footbath protocol (at baseline) was assessed and categorized as adequate or inadequate. To be categorized as adequate, the footbath protocol had to meet at least 3 recommendations from scientific literature on footbath design and management: 1) having a footbath length or depth of 300 cm and 15 cm, respectively (Toussaint Raven, 1985; Cook, 135

152 2006; Shearer and Van Amstel, 2013); 2) frequency of footbath use for 4 consecutive milkings (Speijers et al., 2010); and 3) ensured use of correct amount of chemical by measuring/weighing product before mixing (DairyCo, 2008). Footbath Intervention Footbath. A computerized automated footbath was implemented on all farms, 3 mo after the start of data collection. Stainless steel, split walk-through footbaths were used, consisting of 2 baths separated by a grate (Boumatic, Chinook Dairy Service, Lethbridge, AB, Canada; Figure 6.2). The dimensions were selected based on recommendations from Cook et al. (2012) and Shearer and Van Amstel (2013); each bath was 3 m long, 0.25 m wide, 0.15 m high (9.8 ft 9.8 in 5.9 in) and held a combined volume of 225 L. Footbaths had a center grate, rubber flooring, side walls and a flush door, to prevent manure contamination and ensure good grip, immersion of feet and automatic flushing, respectively. One farm opted to have a footbath without the grate; therefore; the width on this farm s footbath was 0.75 m and it held a volume of 338 L. All footbaths were custom made to fit the farms exit alley dimensions. The footbath system had a liquid and powder dispenser with an auger to store the CuSO4 and a mixing chamber where it was automatically mixed before being dispensed into the footbath. On all farms, footbath software was programmed to prepare a 5% CuSO4 solution and to fill, flush and empty the chemical. The system s software was installed on a damage-resistant touch screen with an operating system where time, day and frequency of use could be adjusted. For the study period, a secure password, only known by the authors, was assigned to all footbath systems so that the assigned protocol could not be modified. In addition, the software offered memory access through a USB port to retrieve data on the chemical, date and time of footbathing 136

153 cycles, which aided in evaluating footbath function between visits. Footbaths were located in the exit lanes, as far away from the milking parlor as farm design allowed. This was to minimize both interruption of cow flow while exiting the milking parlor and manure contamination. Farmers did not have to manually operate nor prepare the footbath, but they were responsible for checking proper functioning of the footbath door drainage, and that there was sufficient CuSO4 stored in the dispenser. Farmers were advised to immediately notify the researchers and the footbath company of any malfunctions. Standardized Footbath Protocol. Before installation of the standard footbath, all farms used CuSO4 as part of their original footbath protocol (Table 6.2). After implementation of the protocol, all farms solely used CuSO4. The standardized protocol consisted of weekly use of CuSO4 at a 5% concentration for 4 consecutive milkings over 2 consecutive days (Speijers et al., 2010; Relun et al., 2012; Speijers et al., 2012). Farmers were asked to pre-wash cows feet in the milking parlor prior to footbathing. The footbath content was automatically flushed and replaced at no more than every 200 cow passes (Cook, 2006) or after 24 h if < 200 cows passed through the footbath. The footbath was also cleaned using an automated system between each use. Follow-up. At each of the 6 visits that followed the footbath intervention, researchers verified: 1) compliance with footbath protocol by checking the software s memory data and confirming with the farmer (i.e., proper functioning of footbath functioning, flushing footbath contents and refilling as required); 2) compliance with other management practices (i.e., cleaning cows feet with water prior to footbath, individual treatments); 3) manure scraper function; and 4) purchase of cattle from other farms. 137

154 Assessment of DD and Leg Cleanliness Training. Training of observers has been described in detail by Solano et al. (2016d). Briefly, 2 observers were trained by the first author to identify and score DD and leg cleanliness in various settings, using 40 photographs and live in the milking parlor (n = 110 cows) and trimming chute (n = 40 cows). A mid-way check (12 wk after initial data collection) using different photographs was done to maintain agreement throughout the study. Interobserver agreement for scoring DD and leg cleanliness in various settings was substantial to almost perfect (percentage of agreement 82%; Kappa > 0.74; Solano et al., 2016b). Digital Dermatitis. Lactating cows were scored for DD every 2 wk in the milking parlor. Inspection of DD in the milking parlor has been described in detail (Solano et al., 2016d). Briefly, cows feet were washed with water from a hose before examination. Feet were inspected using a mirror (glued to a plastic kitchen spatula) and a headlamp. A 5-point scale according to Döpfer et al. (1997) and Berry et al. (2012) was used to score DD lesions. Shortly, lesions were classified as M0 if skin was normal with no sign compatible with a pre-existing lesion; as M1 and M4.1 if an active DD lesion < 2 cm was observed surrounded by healthy skin or by dyskeratotic or proliferative skin alteration, respectively; as M2 if there was an active lesion 2 cm; as M4 (chronic) if a hyperkeratotic or proliferative growth was observed; and M3 (healing stage) if the lesions were a dry brown and scab-like tissue. Additionally, hind feet of all lactating cows were inspected for DD in the trimming chute at 3 time points, 10 and 12 wk apart. Cow s feet were thoroughly cleaned with paper towels before scoring in the trimming chute. For ethical and welfare reasons, cows with M2 lesions were treated according to the hoof trimmers standardized protocol. The protocol consisted of: 1) clean the lesion area with paper towel; 2) pour 2 scoops (approximately 50 g, scoop provided) of 138

155 tetracycline powder (Tetracycline 250, Vétoquinol, Lavaltrie, QC, Canada) on top of the lesion; 3) cover with a disinfectant agent (Dragonhyde Putty, T-Hexx Animal Health, Branchburg, NJ) applied with a small, flat wooden (popsicle) stick; 4) wrap loosely with Vetwrap (Coflex). Leg cleanliness. Along with DD inspection, cows were scored for leg cleanliness every 2 wks in the milking parlor, using the scoring system adapted from Cook (2006) and described by Solano et al. (2015). Namely, cleanliness on the lateral lower hind legs, from the coronary band to the middle of the tarsal joint, was recorded using a 0 to 3 scale according to the degree of contamination: 0 = fresh manure for < 50% of the area; 1 = fresh manure for > 50% of the area; 2 = dried caked and fresh manure for > 50% of the area; and 3 = entire area with dried caked manure. Statistical Analyses Data were entered into Microsoft Access (Microsoft Corp., Redmond, WA, USA), and statistical analyses were performed using Stata 13.1 (StataCorp, 2013, College Station, TX, USA). For all analyses, a P-value < 0.05 was considered significant. The foot within a cow was considered the statistical unit. All descriptive analyses were calculated using the 5 M-stages and also using a simplified scoring system: no lesions (M0), active lesions (M2 and M4.1 were merged) and chronic lesions (M3 and M4 were merged). Merging or excluding M-stages was based on findings by Solano et al. (2016b) that sensitivity was low and misclassification was common for detecting M1 and M3 lesions in the milking parlor compared to the trimming chute as the gold standard. In that regard, M1 lesions were excluded from analyses due to low frequency, common misclassification with M0 and low sensitivity (6%). In addition, M3 was 139

156 merged with M4 lesions due to common misclassification (61%) of M3 lesions as M4 lesions. Furthermore, combining M3 and M4 into 1 category can be justified by the stages infectiousness relevance and clinical implications (Relun et al., 2011). For analyses at cow level, if > 1 DD lesion was observed on the same foot, the most severe M-stage was used according to the proposed hierarchy: M2 > M4.1 > M1> M4 > M3 (Relun et al., 2012; Solano et al., 2016d). The difference in the proportion of lesions at start, middle (intervention), and end of the trial was assessed using McNemar s test. Leg cleanliness was collapsed into clean (score 0 and 1) and dirty (score 2 and 3) due to low frequency of 2 categories. If 2 predictors were highly correlated (e.g., DIM and milk production; r 0.70), the one with the fewest missing observations was chosen. Multilevel logistic regression models for repeated measures were used to evaluate effects of the standardized footbath protocol in prevalence of active DD lesions. Active DD lesions (M2 and M4.1 combined) at foot level were the outcome of interest. Univariable analyses were performed to assess associations between outcome and covariates. Farm was forced into the final regression model as a fixed effect and cow as a random effect. Adequacy of the original footbath protocol was included in the analysis as a herd-level variable. Cow- and herd-level variables significant at P 0.05 were retained in the final model for each outcome. Additionally, if confounding was present (i.e., removal of any variable resulted in a 30% change in the estimate of any other significant predictor), that variable was also retained in the final model. Predictor variables considered included: week of trial, baseline footbath protocol, parity, DIM and leg cleanliness. Two-way biologically relevant interactions (e.g., parity and DIM; parity and leg cleanliness) were tested among the significant predictors in the main effects model, but none was retained (P > 0.05 in all cases). 140

157 6.4 Results Farms and Footbath Practices Average herd size was 152 lactating cows (range, 87 to 209), with mean daily milk production of 33 kg/cow and parity of 2 (Table 6.1). Footbath design and footbathing practices before footbath intervention varied greatly among farms (Table 6.2). Footbaths had a median length of 192 cm (range, 183 to 370) and a mean depth of 15 cm (range, 10 to 21). On average, footbath contents were replaced every 204 cow passes (range, 100 to 360). The majority of farms (n = 6) used a combination of 2 chemicals (mostly with formalin), and chemical concentration was accurately determined on 4 farms who measured/weighed the product before mixing. The original footbathing protocol (at baseline) met the recommendations and was assessed as adequate on 3 of 9 farms (Table 6.1). All farms had an individual DD treatment protocol in place (Table 6.1). However, only 10 cows on 4 farms were treated by the farmer during the study period; of these, 6 cows on 3 farms were treated after the footbath intervention. A total of 8.8, 10.2 and 3.6% of cows received topical treatments during trimming chute inspections at the start, mid- and end of the trial, respectively. None of the farms complied after intervention with pre-washing cows feet prior to footbathing. Prevalence of Digital Dermatitis Lesions A total of 30,672 observations on 1,978 lactating cows (3,956 feet) were collected throughout the study period. At baseline, apparent within-herd prevalence of cows with DD occurring in 1 foot was 69% (range, 15 to 86) and prevalence of cows with active DD lesions 141

158 was 23% (range, 3 to 43; Table 6.1). On farms with an adequate and inadequate baseline footbath protocol, apparent within-herd prevalence of cows with active DD lesions was 8% (range, 3 to 13) and 31% (range, 20 to 43), respectively. Of all cows with DD at baseline, 65% were affected with M4 lesions, and 33% were affected with active DD lesions (Table 6.1). At trimming chute inspections, prevalence of feet with no DD lesions (M0) increased from start to the end of the trial (39 and 48%, respectively), whereas prevalence of active DD lesions decreased (22 and 14%; Table 6.3). This same trend was detected in the milking parlor, but apparent prevalence of M4 lesions was higher at milking parlor inspections compared to the trimming chute. Conversely, prevalence of M4.1 lesions was higher at trimming chute inspections compared to the milking parlor. Presence of M1 lesions was infrequently detected in the milking parlor (apparent prevalence < 1%), but at trimming chute inspections, a decrease in the prevalence of M1 lesions was detected at the end of the trial (Table 6.3). In addition, prevalence of active DD lesions decreased by almost 50% from the time of footbath intervention to the end of the trial as detected in both trimming chute inspections (26 and 14%, respectively) and milking parlor inspections (12 and 6%, respectively). Prevalence of active DD lesions decreased from the time of footbath intervention, but this effect interacted with the farms baseline footbath protocol. In that regard, on farms with an inadequate footbath protocol at baseline, prevalence of active DD lesions decreased after intervention, whereas on farms with an adequate footbath protocol at baseline, prevalence of active DD lesions did not change (P < 0.001, Table 6.4; Figure 6.3). Prevalence and distribution of M-stages was similar at the end of the trial between farms with and without an adequate footbathing management before the footbath intervention (Figure 6.3). 142

159 At the cow level, primiparous cows had a higher prevalence of active DD lesions compared to cows with parity 3 (Table 6.4). Poor leg cleanliness was associated with higher prevalence of active DD lesions; but this association was confounded by farm. 6.5 Discussion In this study, prevalence of active DD lesions was reduced considerably in herds with an inadequate footbathing protocol by implementing a standardized footbath protocol based on current science-based recommendations on footbath design and management. In addition, the use of this standardized footbath protocol increased prevalence of cows without DD lesions. Prevalence of active DD lesions was also affected by individual-cow factors, namely leg cleanliness and parity. In herds with an inadequate footbath protocol, there was a pronounced decrease in active DD lesions and an increase in chronic DD lesions from Weeks 10 to 12 (Figure 6.3). The majority of M2 lesions transition to a chronic or healed stage 2 wk after topical treatment (Holzhauer et al., 2011). This decrease, which was also observed to a lesser extent after the treatment at Week 0 in both herds with an adequate and an inadequate footbathing protocol, was therefore likely due to topical treatment of M2 lesions during trimming chute inspections and not due to the footbath intervention. Despite that, 9 and 10% of cows were treated topically during trimming inspections at the start and at intervention, although the trend was different for prevalence of active DD lesions. Before intervention, prevalence of active DD lesions fluctuated, whereas after intervention, prevalence of active DD lesions remained stable (Figure 6.3). In addition, 8 of the 9 herds had routine partial-herd trims (25 to 45% of lactating cows were 143

160 trimmed) every < 3 mo. Therefore, before the study, a large proportion of the herd was being inspected and topically treated on a regular basis. Regardless, most herds had a high prevalence of active DD lesions at the start of the trial. In addition, although that all farmers purported to have an individual treatment protocol in place and despite the high prevalence of active DD lesions, only 10 cows were treated topically throughout the study period. Thus, due to low frequency of individual treatments performed by farmers, this factor was not further analyzed. Although topical treatments cannot be ruled out as a possible confounder, there is strong evidence that decreased prevalence of active DD lesions on farms with an inadequate protocol at baseline can be attributed to implementation of the standardized footbath protocol rather than topical treatments. Regardless, a follow-up period > 12 wk would have enabled better understanding of the long-term impacts of intervention. The great variation in footbath design and practices reported in the farms before the installation of the automatic footbath was not surprising, given the paucity of research on effective footbath protocols (Laven and Logue, 2006; Cook et al., 2012; Relun et al., 2013c). Guidance on footbathing practices remains quite empirical, as farmers seem to adopt advice from other farmers through word of mouth communication rather than from animal health advisers, thereby increasing variability in management practices (Relun et al., 2013c). Perhaps our descriptive results on some of the farms original footbath protocol were inaccurate as authors relied on the farmers answers about their protocol. For example, most farmers estimated the amount of chemical used, resulting in a potential under- or overestimation of the concentration used. Notwithstanding, it was encouraging to find that farms meeting at least 3 science-based recommendations on footbathing practices had the lowest prevalence of active DD lesions. 144

161 Poor leg cleanliness was associated with higher prevalence of active DD lesions; however, this association was confounded by farm, presumably because farms with a high prevalence of active DD lesions had a high prevalence of dirty legs. This association highlighted the importance of hygiene to limit DD prevalence. It is generally accepted that poor hygiene is a risk factor for DD (Potterton et al., 2012), and leg hygiene is commonly used as a measure to determine footbathing frequency (Cook, 2006). However, few studies have used direct measures of cleanliness in association with the risk of DD (Relun et al., 2012; Relun et al., 2013b). In agreement with Relun et al. (2013b), it was noteworthy that leg cleanliness and DD status were recorded on the same day; consequently, hygiene status when DD lesions developed was unknown. Still, leg cleanliness can reflect environmental hygiene close to the time of DD scoring. We inferred that leg cleanliness can be used as a proxy of environmental hygiene. Most research on footbathing protocols has been conducted on experimental dairy farms (Holzhauer et al., 2008a; Speijers et al., 2010; Speijers et al., 2012), with limited sample sizes (Teixeira et al., 2010; Holzhauer et al., 2012a; Smith et al., 2014), or with relatively short followup periods (Silva et al., 2005; Thomsen et al., 2008a; Smith et al., 2014). Only one large-scale study in France, conducted on 52 commercial farms (n = 4,677 cows) and for a long interval (6 mo) assessed the effectiveness of various DD preventive regimes under different environments and management (Relun et al., 2012). However, intervals between DD inspections were relatively long (4 wk), perhaps allowing feet to undergo more than one transition of DD stages between observations (Nielsen et al., 2012). Nevertheless, this French study demonstrated that conducting intervention studies on several farms with different characteristics may improve reliability of results. 145

162 In the present study, a short (2-wk) interval between DD inspections was selected to decrease the possibility of missing transitions between DD stages (Nielsen et al., 2012). Lifting the cow s foot for inspection is the most accurate method for inspection of DD lesions; however, it is not a practical method that can easily be done on a regular basis. Therefore, a simple and effective method for DD inspection was used in the milking parlor with the assistance of a mirror (glued to a kitchen spatula), as previously described and validated (Solano et al., 2016d). Inspection of feet in the milking parlor was highly accurate and reliable to detect presence of DD lesions; however, accuracy varied among M-stages resulting in different percentages between milking parlor and trimming chute inspections. Additionally, the authors were aware that lesions occurring in the interdigital space were not detected. For these reasons, trimming chute inspections were conducted at 3 key time points to increase accuracy and provide definite identification of M-stages. It was encouraging to detect a similar trend in prevalence of active DD lesions between milking parlor and trimming chute inspections, and a notably higher prevalence of feet without DD lesions at the end of the trial. Thus, effectiveness of the intervention was captured with both DD inspection methods. Components of the standardized footbath protocol implemented in this study were reported to successfully control DD in field trials, and as a whole, it was selected after careful consideration of numerous protocols described in literature (Laven and Logue, 2006; Potterton et al., 2012). The implemented protocol included several changes to the farms original protocol, namely the frequency and interval of use, the chemical s concentration, the footbath s design and automation. Despite clear evidence that the implemented standardized protocol was effective, it is not possible to determine which factor had the largest impact on preventing active DD lesions 146

163 as they were not tested independently. Therefore, we attributed the effectiveness of our protocol to the combination of all management changes. To our knowledge, this was the first study to demonstrate the efficacy of automated footbaths. The aim of footbath automation was to eliminate human error, to consistently achieve a correct chemical concentration and to ensure accurate frequency of use, emptying and refilling. Despite the widespread use of footbaths and their demonstrated efficacy (Laven and Logue, 2006), footbathing is a costly measure mostly due to labor costs (Bruijnis et al., 2013). Hence, although automated footbath systems can initially be expensive, they can be cost-effective as they remove most of the labor. In addition, the benefits of ensuring consistent and accurate footbath protocols can result in decreased DD prevalence, and consequently lower costs related to hoof trimming, topical treatments and a high welfare benefit. The study findings on lack of compliance from farmers with other management practices after footbath intervention (i.e., clean cows feet with water prior to footbath, topical treatments) highlighted the challenges of implementing measures that require labor effort or routine adjustment. To encourage farmers to adopt and maintain new strategies, these must be easy to implement and benefits must be readily apparent (Bruijnis et al., 2013; Relun et al., 2013c). The selected farms were a convenience sample, possibly affecting DD prevalence estimates, although probably not results of the intervention. Potential bias introduced by not blinding observers was controlled by the intensive training program and mid-way check which contributed to achievement of high interobserver agreement (Solano et al., 2016d). In the present study, farms served as their own control rather than a comparison with a subgroup of negative control farms. Being aware of potential bias introduced by the lack of a negative control group, the authors assessed the possible benefits and risks with the proposed 147

164 study design. In that regard, potential bias introduced by seasonality was tested in advance, by analyzing electronic hoof health records on 87,834 cows collected by hoof trimmers from 2009 to 2012 through the Alberta Dairy Hoof Health Project (Solano et al., 2016c). Results indicated fluctuation of DD prevalence throughout the year; however, there was no clear decrease or increase of DD prevalence in specific months that would suggest seasonal confounding. In addition, detection of spontaneous recovery resulting from individual or environmental factors would have been difficult without a negative control group. Therefore, the length of the baseline (pre-intervention) period was the same as the post-intervention period and all observations preintervention were included in the statistical analyses to adjust for the baseline level (Dohoo et al., 2009). With this in mind, it was decided to allocate farms as their own control for 3 reasons: 1) include a larger sample of farms with an intervention, thus enhancing the power of the study; 2) minimize confounding from differences in herd characteristic due to wide variation in management practices across farms; and 3) encourage farmers to participate, knowing that an intervention would be applied on their farms if they enrolled. 6.6 Conclusions Implementation of proper a footbath design and improvement of footbath management through standardization of a protocol based on scientific literature decreased prevalence of active DD lesions and increased prevalence of feet without DD lesions. The footbath design entailed an automated footbath that measured 3 m long, 0.25 m wide, 0.15 m high, with a weekly footbath protocol using 5% CuSO4 for 4 consecutive milkings, replaced at a maximum of 200 cow passes. Effectiveness of the footbath intervention was evident on farms with an inadequate footbath 148

165 protocol at baseline, but not on farms that already had in place a footbath protocol meeting literature recommendations; therefore, we concluded that following science-based recommendations on footbathing practices was effective. In addition, improving cow cleanliness would further result in control of active DD lesion. 149

166 Table 6.1. Farm characteristics (mean ± SD) and digital dermatitis (DD) prevalence and management protocol for 9 freestall dairy farms in Alberta, Canada. Farm Herd size (no. lactating cows) Parity 305-d milk yield (kg) ± ± ,048 ± 1,414 DD individual treatment protocol by farmer Topical product + bandaging in trim chute Trimming session interval and approximate no. cows trimmed Every 2 to 3 mo 55 cows Cow-level DD prevalence (%) at baseline 1 M0 M1 M2 M3 M4 M * 124 ± ± ,557 ± 1,421 Topical product + bandaging in trim chute Every 1.5 mo 40 cows ± ± 0.9 9,389 ± 1,254 Spray lesions in milking parlor Every 2 mo 50 cows ± ± 1.3 9,307 ± 1,916 Spray lesions in milking parlor Every 1.5 mo 40 cows ± ±1.7 9,911 ± 1,591 Topical product + bandaging in trim chute Every mo 45 cows ± ± ,742 ± 1,836 Topical product + bandaging in trim chute Every 3 mo 50 cows * 125 ± ± ,552 ± 1,546 Topical product + bandaging in trim chute Every mo 40 cows * 97 ± ± 1.2 8,950 ± 1, ± ± 1.1 9,960 ± 1,795 1 Apparent prevalence detected in milking parlor. Topical product + bandaging in trim chute Topical product + bandaging in trim chute Spray lesions in milking parlor Every 6 mo 100 cows Every 2 mo 50 cows * Farms with an adequate footbath protocol at baseline. 150

167 Table 6.2. Characteristics of footbathing practices for lactating cows before intervention on 9 freestall dairy farms in Alberta. Farm 1 Quantity. of footbaths 2 footbaths, 1 per exit lane Footbath dimensions (length width height in cm) each 2 * 1 footbath footbaths, placed lengthwise footbath footbath * 2 footbaths, 1 per exit lane 2 footbaths, 1 per exit lane Chemicals and concentration 2% CuSO 4 3% Formalin 4% CuSO 4 4% Zinc 5 and 4% CuSO 4 in short and long footbath, respectively 2% CuSO 4 2% Formalin 3% CuSO 4 and 2% HealMax (products mixed) 6 and 2% CuSO 4 in short and long footbaths, respectively 2% CuSO 4 2% Formalin Chemical measured or estimated Estimated Measured Estimated Estimated Estimated Frequency and interval of use (d and milkings per wk) CuSO 4: 2 consecutive milkings, 1 d/wk Formalin: 2 consecutive milkings, 2 d/wk, 3 d apart Alternating products weekly CuSO 4: 8 consecutive milkings, 4 d/wk Zinc: 8 continous milkings, 4 d/wk 2 consecutive milkings, 2 d/wk, 3 d apart CuSO 4: 1 milking, 1 d/wk Formalin: 1 milking, 2 d/wk CuSO 4 mixed with HealMax: 2 consecutive milkings, 1 d/wk Cow passess between chemical changes Measured 1 milking, 3 d/wk, 1 d apart 100 Measured CuSO 4: 4 consecutive milkings, 2 d/wk for 3 wk Formalin: 4 consecutive milkings, 2 d/wk for 1 wk * 9 2 footbaths, 1 per exit lane 2 footbaths, placed lengthwise each * Farms with an adequate footbath protocol at baseline. 3% CuSO 4 3% Formalin 2 and 4% CuSO 4 in shallow and deep footbaths, respectively Measured Alternates products randomly CuSO 4: 4 consecutive milkings, 2 d/wk, every 2 wk Formalin: 4 consecutive milkings, 2 d/wk every 2 wk 200 Estimated 2 consecutive milkings, 1 d/wk

168 Table 6.3. Prevalence (%) of each digital dermatitis M-stage identified at trimming chute inspection on 3,956 feet at start of data collection, intervention, and end of data collection. Trimming chute inspection Milking parlor inspection 1 Time point inspection M0 M1 M2 M3 M4 M4.1 M0 M1 M2 M3 M4 M4.1 Start (Week 1) 38.6 a 2.9 a 8.8 a 0.3 a 36.7 a 12.7 a 40.1 a 0.4 a 8.5 a 2.1 a 40.6 a 8.3 a Intervention (Week 10) 41.6 a 5.0 b 10.3 a 0.0 a 27.7 b 15.4 b 45.1 b 0.4 a 5.5 b 0.2 b 42.3 b 6.5 b End (Week 22) 47.9 b 1.9 c 3.6 b 0.3 a 35.8 a 10.5 c 47.4 b 0.1 a 2.6 c 0.1 b 46.0 c 3.8 c 1 Apparent prevalence identified in the milking parlor. a-c Within a column, prevalence without a common superscript differed (P < 0.05). 152

169 Table 6.4. Final repeated measures multilevel logistic regression model for active digital dermatitis lesions detected in the milking parlor before and after intervention with a standardized footbath protocol on 9 dairy farms (n = 3,956 feet) 1. Variable Coefficient Odds ratio 95% CI P-value Intercept Week of trial 0 (baseline) Referent to to 0.51 < to to 0.57 < (intervention) to to to 0.49 < to 0.27 < to 0.49 < to 0.47 < (end) to Baseline footbath protocol Adequate Referent Inadequate to Week of trial Baseline footbath protocol 0 (baseline), adequate Referent 2, inadequate to , inadequate to , inadequate to , inadequate to (intervention), inadequate to , inadequate to 0.24 < , inadequate to 0.26 < , inadequate to 0.41 < , inadequate to 0.39 < , inadequate to 0.49 < (end), inadequate to 0.40 < Parity 1 Referent to to Leg cleanliness 2 Clean Referent Dirty to Farm was forced into the final regression model as a fixed effect and cow as a random effect. 2 Cleanliness scored on lateral lower hind legs, from the coronary band to the middle of the tarsal joint. Clean = area partially covered with fresh manure; dirty = dried caked manure for 50% of the area. 153

170 Start of trial Trim chute inspections Mid-trial End of trial 2 w 4 w 6 w 8 w 10 w 12 w 14 w 16 w 18 w 20 w 22 w 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th Start of footbath intervention Milking parlor inspections Figure 6.1. Timeline of trial, showing digital dermatitis inspections at milking parlor (every 2 wk) and at trimming chute (start, middle, and end of trial). 154

171 Figure 6.2. Computerized automated footbath with dimensions based on literature recommendations implemented on 9 dairy farms. 155

172 (a) Digital dermatitis lesions )%) Footbath intervention Week of trial M0 M2, M4.1 M3, M4 (b) Digital dermatitis lesions (%) Footbath intervention Week of trial M0 M2, M4.1 M3, M4 Figure 6.3. Percentage of feet with various M-stages of digital dermatitis (Berry et al., 2012) before and after intervention with a standardized footbath protocol for (a) farms (n = 6; n = 2,974 feet) with inadequate footbath protocol at baseline, and (b) farms (n = 3; n = 982 feet) with adequate footbath protocol at baseline. 156

173 Chapter Seven: Summarizing Discussion The focus of this thesis was to gain insights into the epidemiology of lameness and foot lesions in Canadian dairy herds. In Part 1, cow- and herd- level prevalence of lameness and foot lesions were estimated, along with associated risk factors. Lying behavior, as a measure of cow comfort, was used to predict lameness. In Part 2, a simple method for detection of digital dermatitis (DD) lesions was validated to assess impacts of an intervention intended to decrease DD prevalence. In this final chapter, implications of these studies and possibilities where further research could improve foot health in dairy cows will be discussed. 7.1 Summary of Findings This was the first epidemiological study in Canada that determined lameness prevalence, described lying behavior, and identified their associated risk factors in freestall barns. We determined that lameness was highly prevalent in Canada, affecting on average 21% of cows within a herd (Chapter 2). This prevalence seemed lower than the 35% reported by the only other Canadian study that used locomotion scoring to assess lameness (von Keyserlingk et al., 2012), perhaps due to differences in cow selection criteria. Lameness prevalence varied slightly among Canadian provinces (Alberta = 19%, Ontario = 22%, and Québec = 24%), but varied widely among farms (range, 0 to 69%), and so did facilities and management practices. Wide variation in lameness prevalence provided evidence that a low prevalence is feasible and that facilities and management practices have important roles in lameness occurrence. Effects of stall base, 157

174 bedding quantity and floor slipperiness on lameness prevalence were particularly relevant, as they provided insights into the role of cow comfort in lameness (Chapter 2). The underlying association between lameness and cow comfort was further explored in Chapter 3 by investigating lying behavior. Lying behavior has been used as an indicator of comfort at individual cow and herd levels and as a detection tool for lameness (Cook and Nordlund, 2009; Ito et al., 2010; Alsaaod et al., 2012). Results from the present and previous studies (Ito et al., 2010) indicated that lame cows not only have different lying behavior than nonlame cows (i.e., an average longer lying time), but also have both extremely long and short lying times. Our study was unique in that lying behavior was assessed in conjunction with various environmental, management and individual-animal factors. Lying behavior varied considerably among herds (herd lying time = 10.6 h/d, range, 8.2 to 13.2), but varied even more for cows within herds (individual lying time = 10.6 h/d, range, 1.3 to 22.1), mostly as a result of individual factors related to parity and stage of lactation. Due to the strong association between extreme lying behaviors (lying time 14 h/d, 5 lying bouts per d, bout duration 110 min/bout or SD of bout duration 70 min) and lame cows, we concluded that measures of lying behavior have potential as a diagnostic tool for lameness, with the caveat that they are evaluated in a longitudinal manner and interpreted in the context of other factors. Furthermore, with a comprehensive set of facility- and management-related variables assessed, we consistently identified the comfort of lying and standing surfaces as factors that influenced both lameness and lying behavior (Chapter 2 and 3). This underlines the importance of assessing lameness from a multifactorial perspective, combining environmental and management factors, in addition to cow characteristics (Vermunt, 2007). 158

175 In Chapter 4, prevalence and distribution of foot lesions were determined for Alberta dairy farms. Data in this study originated from the Alberta Dairy Hoof Health Project, a project primarily intended to address lameness by standardizing data collection on foot lesions in collaboration with hoof trimmers. This study was the first of its kind to report data on foot lesions collected electronically by hoof trimmers rather than using hand-written reports (Holzhauer et al., 2006b; Cramer et al., 2008). Digital dermatitis was the most prevalent foot lesion (affecting 15% of cows and 94% of herds), followed by sole ulcers and white line disease. In addition to individual-animal factors (i.e., parity and stage of lactation), housing type had an impact on the prevalence of foot lesions. For instance, prevalence of DD was higher on farms with an exercise area, consistent with findings from Ontario, Canada (Cramer et al., 2009b), but in direct contrast to reports from Europe (Haufe et al., 2012). Noninfectious causes of foot lesions (e.g., sole ulcers and white line disease) were less prevalent in farms with deep-bedded packs. This finding supported evidence from other studies that a claw and concrete floor interaction contributes to development of claw horn lesions (Somers et al., 2003; Van der Tol, 2004). Determining that DD was the most prevalent lesion in Alberta (Chapter 4) allowed us to develop hypotheses about preventive approaches to reduce its prevalence. Given that 95% of farms used footbaths regularly and the wide variability of footbathing management practices (Chapter 2), we hypothesized that suboptimal use of footbaths contributed to a high DD prevalence. Previous studies also reported a wide variability of footbathing management practices (Cook et al., 2012; Relun et al., 2013c) and knowledge gaps on effective footbath protocols have been identified (Laven and Logue, 2006; Potterton et al., 2012). We therefore evaluated effects of an intervention with a standardized footbath protocol aimed to decrease DD 159

176 prevalence (Chapter 6). Our protocol was based on recommendations from current scientific literature, including footbath design and management. A simple method for DD detection in the milking parlor was validated and proven highly sensitive (92%) and specific (88%; Chapter 5); therefore, it was used for monitoring DD lesions on nine farms for 6 mo (Chapter 6). Individual topical treatments during trimming chute inspections affected prevalence of active lesions at the beginning of intervention. However, implementing the standardized footbath protocol was effective in maintaining a lower prevalence of cows with active DD lesions and a higher prevalence of cows without DD (Chapter 6). In addition, poor leg cleanliness negatively affected DD prevalence, highlighting the importance of hygiene to limit spread of DD. 7.2 Practical Applications of Prevalence Estimates and Animal-based Measures Prior to this study, there was limited knowledge on prevalence of lameness, foot lesions and other animal-based measures (e.g. lying behavior, claw length, leg cleanliness, body condition score and body injuries) in Canadian dairy cattle managed in freestall barns. Animalbased measures are increasingly being used worldwide in assessment protocols, as they provide information on cows response to their environment and management (EFSA, 2009; Heath et al., 2014). As the research described in Chapters 2 and 3 was part of a larger study examining cow comfort and longevity (Dairy Research Cluster 1); one of the latter study s objectives was to deliver benchmarking reports to each participating farmer. These benchmarking reports were based on 12 areas of comfort and animal-based measures outlined by the Canadian Dairy Code of Practice. Based on informal communications, we were aware that farmers embraced the 160

177 concept with great enthusiasm, as it was the first time they could compare their herds performance to others at provincial and national levels. Thus, benchmarking is a valuable tool for farmers and the dairy industry as it can be used to establish farm- and region-specific goals, identify areas in need of improvement, and evaluate continuous improvement over time (von Keyserlingk et al., 2012). The prevalence of lameness estimated in this study was 21%. The Canadian Dairy Code of Practice (National Farm Animal Care Council, 2009) recommends a target of < 10% for lameness prevalence at a herd level; unfortunately, the vast majority of farms in our study exceeded the target. It is noteworthy that this target will also be used by the upcoming proaction quality assurance program (planned to be fully implemented in September 2017; Dairy Farmers of Canada, 2016) as their Excellent threshold in the Animal Care module. However, proaction will set other thresholds (e.g., Green = Good, Yellow = Caution, Red = Action ) on targets for animal-based measures based on population estimates after 2 yr of data collection. Hence, prevalence estimates on lameness and other animal-based measures identified in this thesis, along with the benchmarking reports, can be used by Canadian dairy farmers as a reference point to evaluate their herds and start working towards improvement before implementation of audits. As the dairy industry evolves towards implementation of regulatory frameworks, determining prevalence estimates is of extreme importance, but so is evaluating animal-based measures accepted as targets for industry standards. For example, a target of 12 h/d of lying time has been recommended by researchers (Munksgaard et al., 2005; Gomez and Cook, 2010) and The Canadian Dairy Code of Practice (National Farm Animal Care Council, 2009). However, results reported in Chapter 2 demonstrated that mean daily lying time for lame cows was closer to 12 h/d than that for nonlame cows (lame cows = 11.1 h; nonlame cows = 10.5 h; P 0.05) and 161

178 that herds with high ( 30%) lameness prevalence had higher average daily lying time than herds with low (<10%) lameness prevalence (high = 11.0 h; low = 10.4 h; P 0.05). In addition, there was wide variation of within-herd lying time. Therefore, we inferred that lying was a complex behavior, subject to wide variability due to various intrinsic factors (e.g. parity and stage of lactation) and further confounded by presence of lameness. These findings then, call into question the accuracy of using lying time as an animal-based measure of cow comfort in a crosssectional manner. However, if measured longitudinally, deviations from normal behavior withincow may be an appropriate indicator of comfort and welfare (Mattachini et al., 2013). 7.3 Practical Considerations in Detection Methods for Lameness and Digital Dermatitis Lameness data are not readily available to dairy farmers, as locomotion scoring is a timeconsuming process which can be complicated by farm characteristics, including barn layout or flooring (Van Nuffel et al., 2015a). Consequently, most farmers do not know the prevalence of lameness on their farms and they generally underestimate it (Espejo et al., 2006; Leach et al., 2010a). Based on our results from the management questionnaire, farmers in Alberta estimated lameness to be only half of that estimated by observers (12 versus 21%, respectively; Chapter 2). In addition, farmers do not easily identify early signs of lameness (Leach et al., 2010a; Van Nuffel et al., 2015a); therefore, these animals are not treated in a timely matter (Whay, 2002). It would be ideal if lameness data were collected automatically and provided to farmers in real time, facilitating early diagnosis and treatment. Lying behaviour, which can be recorded using electronic data loggers (Chapinal et al., 2009; Ito et al., 2010), was not an optimal tool to diagnose lameness, as it had low sensitivity, 162

179 specificity, and positive predictive value (Chapter 3). These results were possibly due to the cross-sectional nature of the study. Notwithstanding, cows with extreme lying behavior were at higher risk of being lame. Similar results have been reported (Chapinal et al., 2009; Ito et al., 2010), but no other studies evaluated numerous individual and management factors in such a large sample of cows and farms. The importance of approaching test validation from an epidemiologic perspective is the increased evidence accumulated, as we move from experimental settings and evaluation of only a few risk factors. The current study provided evidence that lying behavior has potential for lameness detection if interpreted in context of other factors, including parity, stage of lactation and comfort of lying surface. To be applicable in a practical sense, these factors could be included in algorithms when assessing day-to-day deviations from normal behavior. This could improve accuracy of real-time lameness detection (de Mol et al., 2013; Alsaaod et al., 2015). The gold standard for DD detection is lifting the cow s foot in the trimming chute, as that provides an opportunity for thorough examination of the foot. However, it is an expensive, laborintensive, and apparently stressful procedure for cattle, and therefore, not a practical method to be used on a regular basis for disease monitoring (Relun et al., 2011). Consequently, a simple and inexpensive diagnostic tool for routine DD detection was developed and validated in Chapter 5. Scoring DD presence in the milking parlor using a mirror (glued to a plastic kitchen spatula) and a headlamp had a high sensitivity and specificity compared to the trimming chute as the gold standard. Test characteristics varied greatly among M-stages of DD (Döpfer et al., 1997; Berry et al., 2012), with the lowest sensitivity for M1 (6%) and M3 (0%) lesions, and approximately 10% of DD lesions went undetected as they occurred in the interdigital space (that is not visible using this method). These results were comparable to the only other study that validated the M-stage 163

180 scoring system in the milking parlor (Relun et al., 2011). If the objective is to follow-up DD in cows with history of interdigital hyperplasia or to detect M1 lesions, this method was not reliable enough to replace trimming chute inspection. However, if the objective is to determine herdlevel DD prevalence and routine monitoring, this method was adequately reliable, especially considering that estimation of true herd prevalence is possible, due to validation (Chapter 5). Thus, benefits of routine DD detection in the milking parlor outweigh reductions in accuracy due to misclassification; our tool is inexpensive (approximate total cost = $4), easy to make and simple to use compared to borescopes or swiveling mirrors used in other studies (Relun et al., 2011; Stokes et al., 2012). These characteristics make our tool and method more easily adopted by farmers, as it can be readily implemented without interfering with routine farm management. Implementation of this method by farmers, ideally as a (bi-)weekly monitoring tool (Nielsen et al., 2012), will facilitate early detection and treatment. Alternatively, waiting days or weeks for the hoof trimmer to detect and treat cows will hamper DD control. As stated in the introduction of this thesis, benefits of prompt and effective treatment translate into a higher response rate to treatment, less chronic cases, decreased economic losses and improved animal welfare. Therefore, regular DD detection should form an integral part of DD prevention and control programs. 7.4 Relevance of The Alberta Hoof Health Project In Chapter 4, the importance of consistent diagnosis of foot lesions among hoof trimmers by means of a standardized method was addressed. Results of low intraclass correlation coefficient from hoof trimmers indicated little evidence that trimmers overrepresented certain 164

181 lesions; therefore, results can be considered consistent among trimmers. In contrast, in other studies, hoof trimmers used hand-written lesion reports and there were high intraclass correlation coefficients (Holzhauer et al., 2006b; Cramer et al., 2008). Although hoof trimmers in those studies went through an extensive training program, the high correlation coefficients suggested that presence or absence of a foot lesion depended more on the observer than on herd-level factors. It is likely that our electronic method of record keeping added precision to diagnosis of foot lesions, as it is a standardized method based on case-definitions limited to specific claw zones. Increased uniformity in diagnoses of foot lesions should improve identification of risk factors and increase reliability of inferences and conclusions. The Alberta Hoof Health Project enhanced dialogue and communication among hoof trimmers, researchers and industry. There are numerous opportunities to continue research for clinical trials or longitudinal studies and to continue building on the benefits of a multidisciplinary approach towards improvement of hoof health. The initiative of the Alberta Hoof Health Project has been extended to other provinces (British Columbia and Ontario ; Québec 2013) and will be expanded to Atlantic Canada in the near future. These research initiatives are contributing to development of a national database utilizing CanWest DHI (Guelph, ON, Canada) and Valacta (Sainte-Anne-de-Bellevue, QC, Canada) as the reporting interface for foot-health data. The goal is, in the near future, to include foot health data in the herd management reports that these two organizations provide to farmers. In addition, this national database could be used for genetic improvement and disease surveillance. 165

182 7.5 Lameness and Foot Lesions Control through Management Practices The prevalence of lameness is high (ranging from 16 to 63%) in Canadian herds, as well as in herds from other parts of the world (Chapter 2, Amory et al., 2006; von Keyserlingk et al., 2012). Despite the high prevalence, in contrast to mastitis, there is no well-established lameness prevention and control program, perhaps because lameness has not been studied as intensively as mastitis. To illustrate this, a Pubmed search on May 13, 2016 for lameness AND dairy resulted in 731 manuscripts, compared to 4595 results for mastitis AND dairy. Risk factors identified in this thesis can assist in developing control and prevention plans. The cross-sectional nature of our data in Chapters 2 to 4 limited our ability to make causal inferences. Nothwithstanding, our findings provide evidence that housing comfort affected lameness, lying behavior, and foot lesions. For instance, the use of sand or > 2 cm of bedding in stalls were associated with increased daily lying time and decreased lameness prevalence, whereas slippery floors were associated with increased lameness prevalence. Wide feed alleys and stalls were associated with increased daily lying time. Deep-bedded areas were associated with lower prevalence of claw horn lesions. Perhaps farmers re not using optimal conditions (e.g. more bedding or floors with good traction) due to cost, time, or availability of product or service. It could also be that farmers do not perceive lameness as an important problem in their herds, or they lack recognition of the benefits of implementing these practices. Regardless, our results justify shifting attention and raising awareness towards improving comfort of lying and standing surfaces. Improvements in these areas are feasible, as they mostly involve changes in management rather than facilities. In context with proaction s Animal Care Module, changes to comfort of lying and standing surfaces should be considered when corrective actions or a 166

183 corrective action plan is necessary for lameness reduction. In addition, changes in management of these areas could further result in reduction of the occurrence of other diseases. For example, increasing bedding quantity could result in increased cow cleanliness and subsequent reduction of infectious diseases, e.g. DD (Relun et al., 2013b), mastitis (Barkema et al., 1999), and Johne s disease (Wolf et al., 2016). Thus, it can be inferred that comfort of lying and standing surfaces should form an integral part of lameness and foot lesion prevention and control programs in Canada, and this can improve control of other diseases. Although identification of bestmanagement practices is the first step towards control, it does not necessarily result in adoption. To be effective, a control program requires an integrated approach: identify farmers knowledge, attitudes and behavior towards lameness; develop customized communication strategies; and assess cost-effectiveness of changes in management practices among others (Jansen and Lam; Bruijnis et al., 2013; Roche et al., 2015). 7.6 Digital Dermatitis Control through Footbathing The high herd prevalence of DD (15%; Chapter 4) reduces animal welfare (Bruijnis et al., 2012) which justifies implementation of control strategies to reduce prevalence of DD, at herd and within-herd levels (Chapters 6). A thorough understanding of baseline conditions and management practices at regional- or even farm-levels will impact effectiveness of intervention measures (Bell et al., 2009; Main et al., 2012). We were aware that DD had a high prevalence (Chapter 4) and that the prevention strategy most commonly used (footbaths) was flawed due to poor implementation of scientific knowledge (Chapter 1). Results from the present study (as presented in Chapter 6) indicated that a proper footbath design and improvement of footbathing 167

184 practices through standardization of a protocol was feasible and likely to decrease prevalence of active DD lesions, consequently improving control of DD. Increasing cow cleanliness would further result in control of active DD lesions (Chapter 6). Therefore, a proper footbath design and protocol based on scientific literature, in addition to leg cleanliness scoring as a proxy of floor and stall hygiene, should always be part of DD control programs. Our research in Chapter 6 used a novel approach for evaluating footbath effectiveness. Most studies were limited to comparisons of footbath chemicals and concentrations (Speijers et al., 2010; Teixeira et al., 2010; Relun et al., 2012). Only one study had assessed effects of various footbath dimensions on the number of foot immersions; however, it did not estimate the effect on DD (Cook et al., 2012). Therefore, ours was the first study to assess the impact of footbath design and management on DD prevalence, and to address controlled management through automation of the footbath. We evaluated several recommendations from scientific literature that could be used to support dairy producers implementing footbath protocols. Our results were promising and contributed to fill a gap on how to optimize the use of footbaths (Potterton et al., 2012). It became clear that on farms with adequate footbathing practices, an automatic footbath did not improve the overall prevalence of DD nor prevalence of active DD lesions. However, on farms with inadequate footbathing practices, an automatic footbath reduced the impact of DD. This was encouraging, as farms can have a manageable DD situation by implementing appropriate footbathing management. Also, farms that are not willing to take the time or the effort to do footbathing adequately in a manual way, could reach the same acceptable level of DD through implementation of an automatic footbath. 168

185 7.7 Further Research Several knowledge gaps were filled with the research conducted in this thesis. However, many opportunities for future directions were also apparent. The comfort of lying and standing surfaces were identified in Chapter 2 and 3 as factors that influenced both lameness and lying behavior, consistent with findings in other studies (Somers et al., 2003; Chapinal et al., 2013b). Furthermore, there is an opportunity to go beyond risk factor assessments and move into longitudinal controlled studies at herd-level. An example of this could be prospective cohort studies assessing areas of barn design and management practices such as bedding depth, floor slipperiness, manure scraping frequency, stall width, and deep-bedded areas for older cows. As it is difficult to do controlled trials at the herd level, a viable alternative could be assessing farms that plan to change facilities and/or management (e.g. grooving the floor or changing flooring type), and determining what difference the change makes. Several associations were identified among lameness, lying behavior and cow comfort, and automated measures of lying behavior were identified as potential predictors of lameness (Chapter 3). However, much remains to better understand the causal relationship between lameness and lying behavior and possible influencing factors, which can only be explored by appropriate longitudinal studies. Based on the wide within-herd variation of lying behavior, automated lameness detection based on deviation from normal behavior within-cow on a day-today basis seemed to be the most accurate tool currently available (Alsaaod et al., 2012; de Mol et al., 2013). However, only a few studies have developed algorithms to accurately distinguish lame from nonlame cows in real-time, and have not gone beyond experimental settings. Currently, 169

186 automated lameness detection systems are not commercially available in field settings (Van Nuffel et al., 2015b). Therefore, longitudinal studies on a larger sample of farms, conducted to test accuracy of real-time lameness detection at a larger-scale should contribute to assess validity of these systems. The practical feasibility and cost of early lameness detection systems and the probability that farmers would take immediate action should also be taken into consideration. Streams of cow-level data generated in automatic milking systems (Barkema et al., 2015) provide an excellent opportunity to evaluate lying behavior, which combined with data on milking intervals, could further increase accuracy of lameness detection. Environmental hygiene, poor leg cleanliness (Chapter 6) and biosecurity have been identified as risk factors for DD (Rodriguez-Lainz et al., 1999; Relun et al., 2013b). There is however, a paucity of research on herd-level intervention studies related to environmental hygiene or biosecurity measures. Evidence on how DD prevalence is impacted by, for example, prolonged standing times in slurry, floor cleanliness, scraping frequency, sanitation of hoof trimming tools, boots or used equipment remains uncertain. Findings from these kinds of studies should enhance development of an evidence-based DD control plan. A proper footbath design and a standardized protocol decreased DD prevalence (Chapter 6). Several measures were implemented concurrently (i.e. footbath design and automation, chemical concentration, frequency of use and replace); thus, a cost-benefit analysis would fill an important knowledge gap, in that the combined effect of taking multiple measures or actions at the same time could be quantified. The economic impact could be measured on costs related directly to the footbath and individual treatments, but also on herd productivity and DD relapse. Automation of footbaths facilitated accurate chemical concentration and consistent use; however, their cost is certainly a limiting factor for its implementation. There remains a 170

187 knowledge gap concerning the role of automated footbaths in DD control and their cost-benefit. If proven to be beneficial, the economic limitation could be addressed through financial support provided by producer organizations and/or government. Numerous studies have evaluated long-term efficacy of several treatment and prevention strategies for DD, with the outcome being cure or relapse of lesions at M2 or other stages (Nielsen et al., 2009; Berry et al., 2012; Relun et al., 2012). However, little is known regarding how treatment or prevention strategies affect M-stage dynamics over time in individual cows. For example, it is likely that an active lesion transitions into chronic after topical treatment, but which M-stages occur between events? How long do they last? Does this vary depending on treatment? Understanding how lesions change over short intervals and how is this influenced by treatment will provide insight into progression of DD and promote development of control strategies. Finally, to be effective, control programs for lameness and foot lesions at provincial or national levels should integrate effective communication strategies with farmers (Jansen and Lam). Understanding farmers mindset, motivation, perceived efficacy of preventive measures and barriers that prevent them from improving or adopting known effective practices will help identifying effective strategies to increase the possibility of making on-farm changes (Leach et al., 2010a; Roche et al., 2015). Several questionnaires have been conducted investigating farmers behavior towards lameness (Bell et al., 2006; Leach et al., 2010b). However, an indepth analysis involving qualitative and quantitative methods (e.g., focus groups, in-depth interviews) rather than questionnaires has not been done in the area of lameness. This kind of research would aid in understanding farmers behavior, consequently facilitating implementation of changes through effective communication. 171

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