Citation
Association between Dry Matter Intake Pre and Postparum and Postparum Diseases in Dairy Cows

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Title:
Association between Dry Matter Intake Pre and Postparum and Postparum Diseases in Dairy Cows
Alternate title:
Association between Dry Matter Intake Pre and Postpartum and Postpartum Diseases in Dairy Cows
Creator:
Perez Baez, Johanny Maribel
Place of Publication:
[Gainesville, Fla.]
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University of Florida
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Language:
english
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1 online resource (100 p.)

Thesis/Dissertation Information

Degree:
Master's ( M.S.)
Degree Grantor:
University of Florida
Degree Disciplines:
Veterinary Medical Sciences
Veterinary Medicine
Committee Chair:
GALVAO,KLIBS NEBLAN ALVES
Committee Co-Chair:
RISCO,CARLOS A
Committee Members:
HERNANDEZ,JORGE A
SANTOS,JOSE EDUARDO
Graduation Date:
8/8/2015

Subjects

Subjects / Keywords:
Calving ( jstor )
Dairy cattle ( jstor )
Diseases ( jstor )
Dry matter intake ( jstor )
Dystocia ( jstor )
Ketosis ( jstor )
Mastitis ( jstor )
Milk ( jstor )
Milk yield ( jstor )
Retained placenta ( jstor )
Veterinary Medicine -- Dissertations, Academic -- UF
dairy-cows -- transition-period
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Electronic Thesis or Dissertation
born-digital ( sobekcm )
Veterinary Medical Sciences thesis, M.S.

Notes

Abstract:
The transition period is characterized by an increase in the demand of nutrients in order to meet the requirements for the finals stages of gestation, and the initiation of lactation. The objectives of this study were to determine the association between dry matter intake (DMI) pre (-14 d) and postpartum (28 d) and postpartum diseases [dystocia, retained placenta (RP), metritis (MET), mastitis (MAST), ketosis (KET), and displaced abomasum (DA)]. Data involving 294 cows from 7 experiments were collected. The data were analyzed with PROC MIXED and GLIMMIX of SAS. Random and repeated variables were cow, and day relative to calving, respectively. Models were adjusted for parity, BCS, evaporating cooling, experiment, and interactions between each disease or disorder and other covariates. P < or equal 0.05 were considered significant. Cows that had dystocia had a tendency to eat more during prepartum (P = 0.09) and ate less on days 11, 12, and 13 postpartum (dystocia x day P = 0.04). Cows that had RP ate less on d -3 and tendency on day -2 (RP x day P = 0.01), and ate less during the postpartum period (P < 0.01). Cows that had MET ate less on d -3, -2, and 1 (MET x day P = 0.03) before calving, and ate less during postpartum period (P < 0.01). Cows that had MAST ate less prepartum from days -5 to -1 and had tendencies on days -6 and -10 (MAST x day P < 0.01), and on d 1, 3, 4, 6, 7, 8, 9, 10, and 13 postpartum with tendencies on d 5, 12, 14 and 15 (MAST x day P<0.01). Cows that had KET ate less during prepartum except on d -8, -10, and -13 (KET x day < 0.01) and during postpartum (P < 0.01). Intake of cows that had DA did not differ prepartum (P = 0.70) but they ate less postpartum (P < 0.01). Cows with at least one disease ate less pre and postpartum (P < 0.01). Collectively, these data suggest that there is an association between DMI pre and postpartum and postpartum diseases. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (M.S.)--University of Florida, 2015.
Local:
Adviser: GALVAO,KLIBS NEBLAN ALVES.
Local:
Co-adviser: RISCO,CARLOS A.
Statement of Responsibility:
by Johanny Maribel Perez Baez.

Record Information

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UFRGP
Rights Management:
Copyright Perez Baez, Johanny Maribel. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Classification:
LD1780 2015 ( lcc )

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ASSOCIATION BETWEEN DRY MATTER INTAKE PRE AND POSTPARUM AND POSTPARUM DISEASES IN DAIRY COWS By JOHANNY MARIBEL PEREZ BAEZ A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUI REMENTS FOR THE DEGREE OF MASTER OF SCIENCE UNIVERSITY OF FLORIDA 2015

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2015 Johanny Maribel Prez Bez

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To God and all my other loved ones

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4 ACKNOWLEDGMENTS I would like to express my sincere gratitude to the chairman of my committee, Dr. Klibs N. Galvo who has been a true support during my Master’s program. He always was available to clarify my doubts and to respond all my questions. He was always willing to share his knowledge and lead me to the right source to look for solutions to the obstacles that were presented during this research. I am truly thankful for all his guidance, suggestions, and the trust that he putted on me during this project. I would also like to give thanks to my committee members composed by Dr. Jos Eduardo P. Santos, Dr. Jorge Hernndez, and Dr. Carlos A. Risco. Their suggestions and guidance during this project have been invaluable. I give thanks to them for sharing their vast knowledge and experiences with me. The value of having their expertise available was much appreciated. I want to acknowledge the people that were involved in the previous projects from which the data of this research came from. These researchers are Dr. Leandro Greco, Dr. Sha Tao, Dr. Gabriel Gomes, Dr. Bruno Amaral, Dr. Izabella Thompson, and Dr. Geoffrey Dahl. I give thanks to each one of the m for allowing me to have access their work and effort, and letting me work with the data collected during their projects. Also, I am grateful for receiving, from the former three researchers, all the answers that I needed about their respective projects. Special thanks to my graduate student colleagues in the College of Veterinary Medicine, Department of Animal Sciences, and College of Public Health at UF. Their friendship and support during this journey was very helpful for me to continue this program. Am ong them I thank to Rodolfo Daetz, Federico Cunha, Chang Yu, Priscilla Ferraz, Myriam Jimenez, Jaime Grijalva, and Lucas Ibarbia.

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5 Likewise, I want to express my appreciations to the team of the Food Animal Reproduction and Medicine Service (FARMS) of the Department of Large Animal Clinical Sciences (LACS). My sincere thanks to Dr. Myriam Jimnez, Dr. Joo Bittar, Dr. Gabriel Gomes, Dr. Soo Jin Jeon, Dr. Carlos Risco, Dr. Owen Rae, Dr. Arthur Donovan, Dr. Fiona Maunsell, Dr. Klibs Galvo, Dr. Jorge Hernnd ez, and the others members of the FARMS’s group, Mrs. Delores Foreman, Mrs. Doe Dee Davis, and Mrs. Laura Neumann for being so helpful and so kind. All of them made my stay at FARMS a great experience. Also, special thanks to Dr. Carlos Risco and the University of Florida for accepting me in the first place as a Master student in LACS and putting me in contact with my other committee members. Last but not least, I would like to give thanks to the Fulbright/LASPAU program and their team for giving me the gr eat opportunity of studying abroad, the economic support, and the assessment needed during this process. Especially, I would like to thank Derek Tavares for finding a solution to every problem that I have encountered and for giving me the best answer or advice needed in each situation. Thanks to Fulbright/LASPAU I had contact with different cultures and nationalities, being able to meet friends from around the world, helping me to grow not only as a professional but also as a person.

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6 TABLE OF CONTENTS p age ACKNOWLEDGMENTS .................................................................................................. 4 LIST OF TABLES ............................................................................................................ 8 LIST OF FIGURE S .......................................................................................................... 9 LIST OF ABBREVIATIONS ........................................................................................... 10 ABSTRACT ................................................................................................................... 13 CHAPTER 1 INTRODUCTION .................................................................................................... 15 2 GENERAL INFORMATION ..................................................................................... 18 Transi tion Period ..................................................................................................... 18 Dry Matter Intake .................................................................................................... 18 Energy Bal ance ...................................................................................................... 20 Immunity ................................................................................................................. 21 Dystocia .................................................................................................................. 23 Parturition in Cows ........................................................................................... 24 Causes ............................................................................................................. 25 Physiopathology ............................................................................................... 25 Epidemiology .................................................................................................... 26 Effects on Milk Yield ......................................................................................... 27 Dystocia and Reproduction .............................................................................. 27 Retained Placenta .................................................................................................. 28 Expulsion of the Placenta ................................................................................. 28 Physiopathology ............................................................................................... 29 Epidemiology .................................................................................................... 30 Effects on Milk Yield ......................................................................................... 31 Metritis .................................................................................................................... 31 The Postpartum ................................................................................................ 32 Causes ............................................................................................................. 33 Physiopathology ............................................................................................... 33 Epidemiology .................................................................................................... 34 Effects on Milk Yield ......................................................................................... 37 Effect on Profitability ......................................................................................... 38 Mastitis .................................................................................................................... 38 Lactogenesis and Lactopoiesis ........................................................................ 3 9 Causes ............................................................................................................. 40 Physiopathology ............................................................................................... 40 Epidemiology .................................................................................................... 42

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7 Effect on Milk Yield ........................................................................................... 43 Ketosis .................................................................................................................... 43 Lipid Mobilization .............................................................................................. 43 Causes ............................................................................................................. 44 Physiopathology ............................................................................................... 44 Epidemiology .................................................................................................... 45 Effect on Milk Yield ........................................................................................... 46 Effect o n Reproduction ..................................................................................... 47 Displaced Abomasum ............................................................................................. 47 Anatomy of the Abomasum .............................................................................. 47 Causes ............................................................................................................. 48 Physiopathology ............................................................................................... 48 Epidemiology .................................................................................................... 49 Effect on Milk Yield ........................................................................................... 50 3 MATERIALS AND METHODS ................................................................................ 51 Measurement of Dry Matter Intake ......................................................................... 52 Diseases ................................................................................................................. 52 Body Weight (BW), and Body Condition Score (BCS) ............................................ 53 Milk Yield and Milk Components ............................................................................. 54 Net Energy Balance ................................................................................................ 55 Statistical Analysis .................................................................................................. 55 Prepartum Data ................................................................................................ 56 Postpartum Data .............................................................................................. 57 Milk Data .......................................................................................................... 57 Net Energy Balance ......................................................................................... 57 Disease ............................................................................................................ 58 4 RESULTS ............................................................................................................... 61 Incidences ............................................................................................................... 61 Dystocia .................................................................................................................. 61 Retained Placenta .................................................................................................. 63 Metritis .................................................................................................................... 63 Mastitis .................................................................................................................... 64 Ketosis .................................................................................................................... 65 Displaced Abomasum ............................................................................................. 66 Disease ................................................................................................................... 67 Logistic Regression Results .................................................................................... 68 5 DISCUSSION ......................................................................................................... 81 6 CONCLUSION ........................................................................................................ 88 LIST OF REFERENCES ............................................................................................... 89 BIOGRAPHIC AL SKETCH .......................................................................................... 100

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8 LIST OF TABLES Table page 3 1 Correlation matrix showing Spearman’s rho correlation coefficient and pvalues. ................................................................................................................ 60 4 1 Frequency table of the diseases or disorders presented among the cows in the study. ............................................................................................................ 69 4 2 Effect of each kilogram (Kg) decrease of dry mater intake on the last seven days postpartum. ................................................................................................ 80

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9 LIST OF FIGURES Figure page 3 1 Calan gates at dairy research unit of University of Florida ................................. 59 4 1 Venn diagram of the frequency of RP, metritis, mastitis, and ketosis during the first 28 days in milk. ...................................................................................... 70 4 2 Association between pre and postpartum dry matter intake (DMI), milk, and n et energy balance with dystocia ........................................................................ 71 4 3 Association between postpartum dry matter intake (DMI), milk, body weight (BW), and net energy balance wi th dystocia in primiparous cows ...................... 72 4 4 Association between postpartum dry matter intake (DMI), milk, body weight (BW), and net energy balance with dystocia in mult iparous cows.. .................... 73 4 5 Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with retained placenta (RP).. ............................................... 74 4 6 Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with metritis. ........................................................................ 75 4 7 Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with mastitis ......................................................................... 76 4 8 Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with ketosis .......................................................................... 77 4 9 Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with displaced abomasum (DA) ........................................... 78 4 10 Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with cows that at least had one disease (RP, metritis, mastitis, ketosis, and DA).. ................................................................................. 79

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10 LIST OF ABBREVIATIONS ACTH Adrenal corticotropin hormone AI Artificial insemination BCS Body condition score BHBA

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11 L Liter Lb Pound LDA Left displaced abomasum MAST Mastitis Mcal Mega calorie MEq Milliequivalent MET Metritis Mg Magnesium MHC 1 Major histocompatibility complex class 1 Mmol Micromole NDF N eutral detergent fiber NE Net energy balance NEB Negative energy balance NEFA N on esterified fatty acids NRC Nutrients requirements of dairy cattle PBMC Peripheral blood mononuclear cell PEB P ositive energy balance

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12 RUP R uminally undegradable protein SCC Somatic cells count SCK Subclinical ketosis SE Standard error TG Triglyceride TMR Total mixed ration TNF

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13 Abstract of Thesis Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Science ASSOCIATION BETWEEN DRY MATTER INTAKE PRE AND POSTPARUM AND POSTPARUM DISEASES IN DAIRY COWS By Johanny Maribel Prez Bez August 2015 Chair: Klibs Neblan Alves Galvo Major: Veterinary Medical Sciences The transition period is characterized by an increas e in the demand of nutrients to meet the requirements for the finals stages of gestation, and the initiation of lactation. The objectives of this study were to determine the association between dry matter intake (DMI) pre ( 14 d) and postpartum (28 d) and postpartum diseases [dystocia, retained placenta (RP), metritis (MET), mastitis (MAST), ketosis (KET), and displaced abomasum (DA)]. Data involving 294 cows from 7 experiments were collected. The data were analyzed with PROC MIXED and GLIMMIX of SAS. Random and repeated variables were cow, and day relative to calving, respectively. Models were adjusted for parity, BCS, evaporating cooling, experiment, and interactions between each disease or disorder and other covariates. P ant. Cows that had dystocia had a tendency to eat more during prepartum ( P = 0.09) and ate less on days 11, 12, and 13 postpartum (dystocia x day P = 0.04). Cows that had RP ate less on d 3 and tendency on day 2 (RP x day P = 0.01), and ate less during t he postpartum period ( P < 0.01). Cows that had MET ate less on d 3, 2, and 1 (MET x day P = 0.03), and ate less during postpartum period (P < 0.01). Cows that had MAST ate less prepartum from days 5 to 1 and had tendencies on days 6 and 10 (MAST x d ay P < 0.01), and

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14 on d 1, 3, 4, 6, 7, 8, 9, 10, and 13 postpartum with tendencies on d 5, 12, 14 and 15 (MAST x day P <0.01). Cows that had KET ate less during prepartum except on d 8, 10, and 13 (KET x day < 0.01) and during postpartum ( P < 0.01). Intake of cows that had DA did not differ prepartum ( P = 0.70) but they ate less postpartum ( P < 0.01). Cows with at least one disease ate less pre and postpartum ( P < 0.01). Collectively, these data suggest that there is an association between DMI pre and postpartum and postpartum diseases.

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15 CHAPTER 1 INTRODUCTION Improvements in dairy cow genetics and management have led to a steady increase in milk production per cow in the last 60 years (USDA NASS, 2015). However, greater milk yield requires more calories that have to be supplied by the diet consumed or body reserves. Throughout the transition period there is a variation in the dry matter intake (DMI). During prepartum there is a decline of DMI that occurs around th e last ten to seven days before calving being more pronounced in the last week of this period (Rhoads et al., 2004). Conversely, after calving, the cow increases her feed intake in an attempt to compensate the nutrients needed for maintenance, activity, lactogenesis, and lactopoiesis. However, this increment in intake is not able to supply the demands for nutrients needed during the first weeks of the new lactation, leading cows to mobilize lipid tissue in order to supply the energy and nutrients needed for this stage. The decrease in DMI and associated increase in metabolites as nonesterified fatty acids (NEFA) and betahydroxybutyrate (BHBA) due to fat mobilization lead to immunosuppression, which lead to increased susceptibility to diseases and disorders during the postpartum period. Diseases that affect the uterine tract and the mammary gland, and metabolic disorders are the most common problems during this period. Among these diseases or disorders we can mention dystocia, retained placenta, metritis, ke tosis, mastitis, and displaced abomasum. As a consequence of the occurrence of these diseases, there will be economic losses. It has been stated that almost 40% of the cows may receive at least one treatment for postpartum diseases in the first 30 days in milk (Berge and Vertenten,

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16 2014). Also, these diseases will reduce milk production, and impair reproductive performance, increasing the losses to the herd. In the same order, cows with diseases are more likely to be culled. Cows with subclinical ketosis, calving abnormality, or abomasal displacement have a risk to be culled or removed from herd ar ound 3 times higher (Raboisson et al., 2014; Vergara et al., 2014) compared to cows without these diseases or disorders whereas the risk to be culled in cows wit h metritis is 1.75, and for retained placenta 1.52 (Raboisson et al. 2014). Moreover, these diseases or disorders also have an impact in reproductive performance. For example, subclinical endometriti s increases days open (Cheong et al. , 2011) whereas puerperal metritis affects cyclicity (Vercounteren et al., 2015), pregnancy rates and calving to conception interval (Giuliodori et al., 2013). Also, mastitis reduc ed conception rates (Santos et al. , 2004) and cows with low calcium and increase NEFA had reduced odds of pregnancy (Chapinal et al., 2012; Martnez et al., 2012). Therefore, it is important to determine factors associated with the increment of disease incidence to be able to reduce economic losses, increase reproductive performance, and decreasing culling. Some studies have shown relationship between DMI, social behavior (Huzzey et al. , 2007) and, body condition ( Urton et al., 2005) and some of the diseases previously mentioned. On the other hand, there are studies also showing the relation between diseases and net energy balance. Ospina et al. (2010) determined a threshold of BHBA and NEFA to predict a postpartum disease and indicated the critical thresholds for the prepartum and postpartum periods. Roberts et al. (2012) determined that t he optimal NEFA threshold for prediction of culling risk within 60 DIM in the last week prepartum

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17 was 0.4 mmol/L and 0.7 mmol/L for BHBA for prediction of early lactation culling risk . During the 2 weeks postpartum the optimal thresholds for NEFA was 0.8 mmol/L , w hereas for BHBA the threshold in the first week postpartum for predicting culling risk within 60 DIM was 1.2 mmol/L . Some studies have linked metabolites concentrations as NEFA and BHBA with immune cells showing that an increase in these metabolites can alter the immune and cell function and consequently predisposing cows to diseases (Galvo et al., 2010; Hammon et al., 2006). On the other hand, cows during the first weeks of postpartum have an increase in postpartum diseases as dystocia, retained placent a, metritis, ketosis, mastitis, and displaced abomasum. Therefore, the DMI during the transition period may be associated with postpartum diseases being that it is associated with levels of NEFA and BHBA concentrations in blood. Other studies have shown th e relationship of DMI and postpartum diseases or disorders, as metritis (Huzzey et al., 2007), dystocia (Proudfoot et al., 2009), and subclinical ketosis (Goldhawk et al., 2009). However, the literature showing this relationship is limited to some diseases or disorders. Therefore, our main objective is to determine if exist an association between dry matter intake pre and postpartum and postpartum diseases as dystocia, retained placenta, metritis, ketosis, mastitis, and displaced abomasum. A second objectiv e is to determine the risk of disease postpartum based on dry matter intake prepartum, and lastly, to determine the association between energy balance and milk yield during the postpartum period.

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18 CHAPTER 2 GENERAL INFORMATION Transition Period The transition period, also called periparturient period, is the period from 3 weeks before parturition to 3 weeks after parturition. During this period the cow will experience abrupt metabolic changes (Grummer, 1995; Drackley, 1999). The periparturient period is characterized by an increase in the demand of nutrients in order to meet the requirements of the final weeks of gestation, and the initiation of lactation. With the initiation of lactation, the energy, protein, and minerals requirements increase but thes e requirements are not met by feed intake (Drackley, 1999). Therefore, to meet nutrients (caloric) demands, cows have to mobilize adipose tissue from its own reserves increasing metabolites as NEFA and BHBA and leading to a state of negative nutrient balance. Another challenge that cows face is immunosuppression. It has been shown that at the end of gestation and early lactation, neutrophils and lymphocytes function will be impaired (Kehrli and Goff, 1989; Hammon et al., 2006). Factors such as stress and a negative nutrient balance may contribute to this impairment (Mallard et al., 1998) putting cows in high risk of postpartum diseases such as metritis, retained placenta, and mastitis or metabolic disorders as displaced abomasum, and ketosis. Dry Matter In take The dry matter intake (DMI) is the consumption feed on a dry matter basis. It is used instead of feed intake because feeds vary dramatically on water content. Therefore, to determine the nutrients needed to support the animal’s health and production i t is important to calculate dry matter of a feed component.

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19 In order to determine the DMI of lactating cows, the nutrients requirements of dairy cattle (NRC, 2001) suggest using the following equation: DMI (kg/d) = (0.372 x FCM + 0.0968 x BW0.75) x (1 e( 0.192 x (WOL+3.67)) , where FCM = percent fat corrected milk (kg/day), BW = body weight (kg), and WOL = week of lactation. The term (1 e( 0.192 x (WOL+3.67)) adjusts for depressed DMI during early lactation. However, during prepartum, DMI decline 30% to 35% (Hayirli et al., 2003; Urton et al., 2005) but this variation occurs according to the breed. French (2006) reported that Holstein cows decreased feed consumption by 35% 3 weeks before parturition while Jerseys decreased only 17%. There are three major categories of factors that affect voluntary DMI in the prepartum period. First, there are the animal factors, like parity, breed, and body condition. Second, it will be the diet factors, as the neutral detergent fiber, ether extract, ruminally undeg radable protein, and ruminally degradable protein. Third, and last, the management factors such as diet changes, feed bunk space, and dry period length (Grummer et al., 1995). However, there are some studies that indicate other factors that contribute to t he decrease in DMI during the prepartum period. Huzzey et al. (2007) indicated that the social hierarchy and aggressive interactions may influence the drop of feed consumption. Santschi et al. (2011) stated that the length of the dry period and the diet of fered during this period affect the decrease in DMI. Also, other factors as leg problems (Santschi et al., 2011), and body condition (Urton et al., 2005) may influence the DMI.

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20 Energy Balance When the amount of energy available differs from the energy required by the body then the cow can be in either a positive energy balance (PEB) or a negative energy balance (NEB) (NRC, 2001). Positive energy balance occurs when the intake of energy exceeds the energy required by the animal. Conversely, NEB occurs when t he energy required for maintenance, activity, and lactation is higher than energy intake. Dry matter intake declines during the prepartum period and start to recover during the first weeks of postpartum. Also, most of the nutrients are used for the new lac tation cycle so are driven for lactogenesis. As a consequence, during these weeks, as a physiological adaptation, cows start to mobilize lipids from adipose tissue to compensate the energy and nutrients demands. By the action of lipase in the adipocytes, l ipids start to be mobilized from the adipose tissue. These tissues start to release lipids as nonesterified fatty acids (NEFA) to the bloodstream and be used as an energy source for several other tissues. The liver uptakes NEFA from the bloodstream and metabolize and transform these metabolites in several products. This process occurs inside the hepatocyte where NEFA is taken by the peroxisomes and is converted to acetyl CoA or taken by the mitochondria where they can be partially oxidized to form ketone b odies as hydroxybutyrate (BHBA), completely oxidized to carbon dioxide (CO2), or can be reesterified as triglycerides (TG) in the cytosol (Emery et al., 1992). Therefore, the concentration of serum NEFA reflects the magnitude of fat mobilization, whereas the concentration of BHBA reflects the flow of NEFA to the liver and the energy requirements of the tissue (LeBlanc, 2010).

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21 However, when NEFA levels increase due to elevated lipid mobilization, these metabolites raise causing problems as ketosis and fat ty liver (McDonald, 2010). On the other hand, during the transition period leptin hormone level varies helping to maintain the net energy balance and its production is associated with DMI (Liefers et al., 2003). Leptin is produced by adipocytes, and its role is to inhibit feed intake. Liefers et al., (2003) determined that during pregnancy leptin hormone concentrations are high and decline at parturition. Similarly, Block et al. (2001) stated that during prepartum there is a PEB which maintains high leptin levels. However, immediately after parturition these levels are reduced by 50% and last up to seven weeks postpartum (Block et al., 2001; Loiselle et al., 2009) and the plasma leptin concentrations are lower in cows on NEB during postpartum which had higher milk yield and consumed less feed and in cows with less weight (Liefers et al., 2003). In summary, cows during prepartum are in a PEB and have less leptin concentrations decreasing DMI but after parturition leptin levels decreases thus increasing DMI. This decrease in leptin during postpartum is coincident with the increase in energy and nutrients demands for lactogenesis (Liefers et al., 2003). Besides, cows with higher milk yield and less DMI had lower leptin concentrations (Liefers et al., 2003) explai ning the relationship between leptin and energy balance. Moreover, overconditioned cows have less feed consumption throughout the transition period (Urton et al., 2005) because cows in PEB have less fat mobilization during postpartum thus the recovery in leptin concentrations is faster compared to cows in a state of NEB (Liefers et al., 2003). Immunity Most cows experience immunosuppression during the transition period which leads to increased susceptibility to periparturent diseases. Some research has b een

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22 done in this area showing the behavior and the role of white blood cells, cytokines, immunoglobulins, DMI and blood metabolites (NEFA and BHBA), and hormones during this period (Hammon et al., 2006; Loiselle et al., 2009; Galvo et al., 2010). Neutrophils are the first nonspecific phagocytes that stop bacterial infections. During peripartum period neutrophils concentration in blood decline (Martnez et al., 2012), and its functions are impaired (Kehrli and Goff, 1989; Kim et al., 2005; Hammon et al., 2006) which leads to increase incidence of postpartum diseases. In cows with metritis, the lysosomal protein myeloperoxidase and polymorphonuclear leukocytes ( PMN ) cytochrome c showed a reduction in cows with metritis (Hammon et al., 2006). In addition, t he PMN glycogen also shows a reduction in cows with metritis compared to healthy cows at calving and continue decreasing during the first two weeks postpartum (Galvo et al., 2010). Therefore, it can be concluded that neutrophil function is impaired during the transition period. The decline on the innate and the acquired immunity can also be affected by external factors as temperature, and diet. The neutrophil phagocytosis activity and IgG production is more reduced in cows with heat stress compared with t he ones that were with evaporating cooling during prepartum (Do Amaral et al., 2011). Also, myeloperoxidase activity is decreased in cows that have less dry matter intake during the prepartum period ( 2wk) and continued to be suppressed until 3 weeks after parturition (Hammon et al., 2006). In addition, during the transition period, blood metabolites such as NEFA and BHBA are increased, which has been shown to impair neutrophil function (Grinberg et al., 2008; Ster et al., 2012). Several studies have shown the relationship between these

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23 metabolites and neutrophil impairment or diseases. NEFA concentration start to be higher in metritic cows during prepartum period impairing PMN myeloperoxidase activity (Hammon et al., 2006), and this elevation is higher in primiparous cows at calving day and at day 7 when started to decline during the following weeks of postpartum period. (Galvo et al., 2010). BHBA levels were also reported as higher in metritic cows (Hammon et al., 2006; Galvo et al., 2010). Furthermore, peripheral blood mononuclear cell ( PBMC ) proliferation and the cytokine production were also reported to be at its lowest during the first week postpartum. (Loiselle et al., 2009). Cows that had metritis had higher levels of IL8 (Bicalho et al., 2014), while the tumor necrosis factor alpha (TNF but subsequently decline (Kim et al., 2005; Loiselle et al., 2009). The immunity on the transition period is regulated for several factors. As said, the immunosuppression that the cow suffers during this period is mostly due to neutrophil impairment and decline due to internal and external factors. Therefore, it can be concluded that this immunosuppression has an important role increasing the incidence of postpartum diseases and/or disorders. Dystocia The definition of dystocia may vary among researchers because some definition may not include some characteristics that are proper for dystocia (Meijering, 1984). For example, an assisted calving does not necessarily means that there is a dystoci a, and some may argue that there is no easy calving (Mee, 2008). However, some researchers have defined dystocia as a difficult parturition due to an impediment of the fetal passage through the birth canal, thus assistance may be required (Bradford, 2002; Meijering, 1984). Mee, (2008) defined dystocia as calving difficulty resulting from prolonged

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24 spontaneous calving or prolonged or severe assisted extraction. Similarly, Schuenemann et al. (2011) reported that unassisted births lasted 45.2 min with a standard deviation of 24.5 min; therefore, 95% of the cows would calve within 70 min. Hence, calving within 70 min could be considered normal (eutocic) and calvings that took longer than 70 min after amniotic sac presence could be considered dystocic. Parturitio n in Cows Parturition is a combination of several physiological events that are initiated by the fetus. It has three stages. In stage 1 there is an initiation of myometral contractions. Stage 2 is characterized by the expulsion of the fetus. And, stage 3 that is when the expulsion of the placenta occurs (Senger, 2003). During the first stage, the anterior pituitary of the fetus releases the hormone adrenal corticotropin (ACTH). Adrenal corticotropin hormone stimulates the production of prostaglandin synthase that will cause the synthesis of prostaglandin F2alpha estradiol (Evans et al., 1981). The decrease in progesterone and increase in estradiol will lead to initiation of myometrial contractions. Because of the increment of the pressure in the uterus, the fetus will rotate and be positioned and presented for parturition. The contraction of the uterus pushes the fetus toward the cervix, which will trigger the release of oxytocin (Ferguson reflex). This hormone will increase uterine contractions which induce more oxytocin release and this positive feedback loop continues until the fetus is expelled (Gilbert et al. 1994). will act in the tissues of the cervix, and pelvic ligaments making them more elastic and helping the calf pass through the birth canal (Gordon et al., 1983). Besides, estradiol causes an increase in the

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25 production of mucus in the cervix, which lubricates the cervical canal helping to ease the parturition. This lubrication is also due to the amniotic and allantoic fluid that will flow after the rupture of the fetal membranes. C auses There are several causes of dystocia as fetal size, weight of the heifers (Civelek et al., 2008) failure of proper fetal rotation, twins, insufficient dilatation of the cervix, uterine torsion, periparturent hypocalcemia, among others (Spensley and Troedsson, 2002). Physiopathology As said above dystocia is a difficult parturition. To evaluate dystocia it is often used a scoring system from 1 to 5. A score equal to 1 means no assistance; 2 equals to assistance by one person without the use of mechanical traction; 3 equals to assistance by 2 or more people; 4 equals to assista nce with mechanical traction; and, 5 equals to fetotomy or cesareansection procedure. Proudfoot et al. (2009) showed an association between feed and water intake, behavior, and dystocia. In this study is shown that 48 hours previous to parturition cows t hat had dystocia decreased their feed intake by 1.9 kg. However, these cows increased their consumption in the 24 hours before calving. The water consumption during the last 24 hours was decreased but was compensated during the first 24 hours postpartum. In addition, in the behavior aspect, cows that had dystocia changed positions from standing to lying more often than cows that did not have dystocia during the 24 hours before calving. A characteristic that had previously been reported (Spensley and Troeds son, 2002), and was later confirmed by others (Schuenemann et al., 2011).

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26 Some metabolites and hormones change in cows that had dystocia. Plasma cortisol was reported to be increased in heifers that experienced dystocia and their calves. Also, cholesterol concentrations were higher in the heifers, while a tendency for increased BHBA was also shown in cows that experienced assisted calving (Melendez et al. 2004; Civelek et al., 2008). Epidemiology United States is one of the countries that has the highest prevalence of dystocia in dairy cattle (22.6% in heifers, and 13.7% in heifers and cows) compared to others countries (Mee, 2008). A recent study (Vergara et al., 2014) showed an overall incidence of 14.1% of abnormal calving in multiparous cows, with a 6.8% incidence of twin calving, and 3.3% of stillborn calves. There are several risk factors for dystocia such as birthweight, twins, sex of the calf, calving season, and parity, among others. The odds of having dystocia increase by 13% for each kilogram of i ncrement of birthweight (Johanson and Berger, 2003; Atashi et al. 2012). While cows that delivered male calves had greater odds to need assistance compared to cows that delivered female calves (Atashi et al., 2012, Johanson and Berger, 2003). Cows that del ivered in winter had 15% higher risk to experience dystocia compared to cows that delivered during summer (Johanson and Berger, 2003). While the ones that delivered in fall season had less chance to develop dystocia compared to the ones that delivered in s pring (Atashi et al., 2012). However, this relationship may be linked to bodyweight because offspring that were born in the fall were lighter (Atashi et al. 2012). Moreover, parity is also a factor to take into account when evaluating the risk of dystocia. Primiparous cows are around 2 times more likely of experiencing abnormal

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27 calving compared to cows with 2 lactations (Grohn et al., 1990; Johanson and Berger, 2003; Dematawewa and Berger, 1996). However, after 2 lactations the risk increased with increasing parity (Grohn et al. 1990). In addition, Santschi et al. (2011) showed that older cows that had a dry period of 65 days had higher chance of having dystocia than cows that had a shorter dry period (35d). Other risk factors reported were stillbirth and pelvic area. Johanson and Berger, (2003) determined that a 10 cm2 increase in the pelvic area decreased the odds of dystocia by 11%. Effects on Milk Yield Some studies have demonstrated the effect of dystocia on milk production. Atashi et al. (2012) reported that cows that experienced dystocia produced 135 kg less than cows that did not have dystocia. Rajala and Grohn (1998) reported that milk yield of primiparous Ayrshire Finnish cows was not affected by dystocia; however, multiparous cows with dystocia had decreased milk yield. Moreover, it was reported that in a 305 days of lactation, cows that had dystocia produced 703.6 kg less milk than cows that did not had dystocia (Dematawewa and Berger, 1996). Dystocia and Reproduction Dystocia has a negative effect on fertility. The odds of becoming pregnant in cows that had dystocia are reduced during the early to midbreeding season compared to late breeding season (Berry et al., 2007). Thus, there is an increase of services per conception in cows that had dystocia. However, the effect of dystocia is affected by parity. Dematawewa and Berger (1996) found that primiparous cows that had extreme calving difficulty required 0.22 more services compared to the ones that did not had

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28 calving difficulty while for multiparous , there was not a consistent trend showing increase in services with increased dystocia. Retained Placenta Retained placenta (RP) is defined as the failure to expel the fetal membranes. Normally, the expulsion occurs within 38 hours after calving (Gross, 1986). The period considered to diagnose RP varies in the literature. Some sources considered a cow with retained placenta if the expulsion does not occur within the first 12 hours (Smith, 2002) and others have considered a cow to have RP when the fetal me mbranes were visible at the vulva or were identified in the uterus or vagina by vaginal examination performed more than 24 h after calving (Kelton et al., 1998). Expulsion of the Placenta The placenta is a transient organ that is present during pregnancy, and its main function is to regulate the metabolic exchange between the mother and the fetus. Cows have a cotyledonary placenta in which the placentomes are the combination of the fetal cotyledon and the maternal caruncles (Senger, 2003). Approximately at day 25 the placentomes will be created by the protrusion of the chorionic villi into the crypts in the caruncular tissue, and will continue growing throughout the gestation. After calving, the body has to recognize and reject the placental tissue in order to separate the fetal membranes and the placenta be expelled (Gunnink, 1984). The mechanism of placental detachment is believed to be multifactorial. Changes in the hormonal environment, activation of the maternal innate immune system, and uterine contrac tions are involved in the release of the placenta (Beagley et al., 2010). Cows have cotyledonary placentas, where the fetal cotyledons envelop the maternal caruncles, forming the placentome. Collagen links the interface

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29 between cotyledons and caruncles; therefore, the breakdown of this collagen is important for placental detachment. The decrease in progesterone and increase of relaxin before calving increase collagenase activity and might promote collagen breakdown at the cotyledoncaruncle interface (Beagl ey et al., 2010). Maternal recognition of fetal major histocompatibility complex Class 1 (MHC 1) antigens and chemotaxis of neutrophils to the sites of placental attachment by the cytokine interleukin8 might work to activate and attract immune cells to help in the release of the placenta (Davies et al., 2004; Beagley et al., 2010). Uterine contractions might be more important for the final removal of the placenta, and primary myometrial dysfunction does not seem to be an important prerequisite for retention of the placenta (Beagley et al., 2010). Physiopathology It has been observed that retention of the placenta was more common when there was MHC Class I compatibility between the dam and the calf (Joosten et al., 1991; Davies et al., 2004). This deficiency in alloreactivity might impair activation of a cell mediated immune response and impair the detachment of the placenta. Regarding the innate immune response, it has been demonstrated that cows that developed or that would develop retained placenta had l ower chemotactic activity of leukocytes toward cotyledon supernatant, lower chemotractant (IL8) concentration in the blood, decreased neutrophil migration to the sites of placental attachment, and decreased neutrophil phagocytosis and killing ability from the period 2 weeks before and 2 weeks after calving (Gunnink, 1984; Cai et al., 1994; Kimura et al., 2002).

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30 Epidemiology Incidence of RP varies widely from herd to herd. Studies have reported average incidence of 8.6 to 18.3% but with a range from 1.3 to 39.2% (Kelton et al., 1998; Grohn et al., 1990; van Werven et al., 1992). It has been reported that the risk of retained placenta in Finnish Ayrshire cows increased with increasing parity (Grohn et al., 1990). However, another study found neither cow’s parity nor the calving season were risk factor for RP. But they identify abnormal calving (Han and Kim, 2005; Moretti et al., 2015), and gestation length as risk factors, reporting that cows that had a short gestation length (< 271 days) had 4.9 times higher risk of developing retained placenta compared with cows that had longer gestation length (Han and Kim, 2005). Other risk factors that have been reported are the delivery of male calves, and twins (Moretti et al. 2015). Also, calcium levels (Melendez et al ., 2004), NEFA, and BHBA. On the other hand, Santschi et al., 2011 showed that cows with three or more lactations and with a dry period of 35 days increased the incidence of RP by two fold compared with cows with 2 lactations. However, the authors did not attribute the incidence increase to nutrition; they also mentioned that may be other possible causes as gestation length, dystocia, and calf size. Moreover, there are studies that have shown no relationship between RP and energy metabolites NEFA, BHBA (Mel endez et al., 2004; Moretti et al., 2015), calcium (Moretti et al., 2015), and glucose (Melendez et al., 2004). Phosphorus, magnesium, and energy related metabolites were not associated with RP (Melendez et al., 2004). On the other hand, retained placenta is a predisposing risk factor for metritis, endometritis, and metabolic disorder (Han and Kim, 2005). Subclinical ketosis increases

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31 the the mean raw risk of placental retention by 1.60 (Raboisson et al., 2014). Also, deficiency on vitamins and minerals are associated with RP incidence. Deficiencies of tocopherol concentrations during prepartum (Qu et al., 2014). Effects on Milk Yield Cows that had RP for more than 12 or 23 hours had a decreased milk production during the first 100 days of lactation (van Werven et al., 1992). Milk loss in cows that had retained placenta varies according to their parity status. Primiparous cows lost 1.4 kg/d during the first 2 weeks but this loss decreased during the following weeks. However, cows that had 2 or more parities lost 3.4 kg/d during the first week and 3.5 kg/d during wk 2 and 4 postpartum. Moreover, cows with 3 parities lost 1.7 kg/d during the first 2 wk after diagnosis (Rajala and Grohn, 1998). Metritis The definition of metritis varies among studies because it differs on time and characteristics of the disease. In 1983, Dohoo et al. (1983) described metritis as reproductive tract infections, and stated that metritis is most commo nly diagnosed within the first 30 days following calving, and is often referred to as primary (within the first 21 days), secondary (from 21 to 60 days) and tertiary (occurring after 60 days postpartum). Another definition provided by Kelton et al. (1998) was that a cow was considered to have metritis if she had a postpartum condition characterized by an abnormal (i.e., not including lochia or clear oestral mucus) cervical discharge, vaginal discharge, or both or uterine content. However, because this variation among studies, Sheldon et al. (200 6) gave definitions to be used by researchers in order to differentiate between puerperal metritis,

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32 clinical metritis, clinical endometritis, subclinical endometritis, and pyometra. They defined puerperal metritis as an animal with an abnormally enlarged uterus and a fetid watery redbrown uterine discharge, associated with signs of systemic illness (decreased milk yield, dullness or other signs of toxemia) and fever >39.5 oC, within 21 days after parturition. Cows wit h clinical metritis are those that are not systemically ill, but have an abnormally enlarged uterus and a purulent uterine discharge detectable in the vagina, within 21 days postpartum. Clinical endometritis is characterized by the presence of purulent (> 50% pus) uterine discharge detectable in the vagina 21 days or more after parturition, or mucupurulent (approximately 50% pus, 50% mucus) discharge detectable in the vagina after 26 days postpartum. Subclinical endometritis on cows is defined by >18% neutr ophils in uterine cytology samples collected 21– 33 days postpartum, or >10% neutrophils at 34 – 47 days. And lastly, pyometra which is defined as the accumulation of purulent material within the uterine lumen in the presence of a persistent corpus luteum and a closed cervix . The Postpartum Immediately after calving start the puerperium period where the reproductive function start to recover in order to be ready for another pregnancy to occur. In dairy cows the time required for uterine involution is around 2 3 days ranging from 13 to 70 in Holstein cows, and for the resumption of ovarian activity is 17 days ranging from 10 to 26 days (Fonseca et al., 1983). Some events have to happen during this period as the expulsion of lochia, endometrium repair, resumption of the ovarian function, and elimination of bacterial contamination. After parturition, by actions of oxytocin, the myometrium begins strong and repeated contractions to discharge fluids lochia, and debris from the uterus. Also, this

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33 contractions help to minimize the occurrence of hemorrhage and to reduce the uterus size. At the same time the uterus suffers a coordinated atrophy that leads to recover a normal nonpregnant size. Moreover, the maternal caruncle undergoes necrosis and repairs itself eventuall y covering with endometrial epithelium (Archbald et al., 1972). Virtually all cows have the reproductive tract contaminated with bacteria postpartum. The uterine contractions, expulsion of lochia, mucus production, pH, and the adaptive and innate immunity of the uterus help eliminate bacterial contamination (Sheldon, 2015). Causes Several studies are being conducted to determine the different bacteria that contaminate the uterus after parturition and which are the responsible for causing metritis. Infection with Escherichia coli (E. coli) has been identified as one of the first steps for the development of metritis. E. coli create the conditions for other bacteria to multiply and cause metritis. Bicalho et al. (2012) found that c ows contaminated with E. coli had a 4.7 times higher odds of developing metritis. Another bacteria identified that were associated with metritis were F usobacterium necrophorum , and A rcanobacterium pyogenes . The latter was related to clinical endometritis when detected at 8– 10 and 34– 36 days postparum ( Bicalho et al., 2012). Physiopathology After uterine contamination, bacteria proliferate leading to a uterine filled with fetid, brownish lochia, and with an incomplete involution. However, discharge of lochia is not abnormal unless the fluid is fetid or the cows develop clinical signs (Sheldon, 2006).

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34 As mentioned above, several studies have shown that cows with metritis have neutrophils with impaired function. Therefore, the elimination of bacteria after contamination will not be wel l performed leading to the proliferation and accumulation of bacteria in the lumen of the uterus causing a purulent discharge that can be seeing during rectal palpation, and can be used as a diagnostic tool (Sheldon, 2006). Besides the immune impairment, a low contractility of the uterus due to low concentrations of calcium (Ca) and magnesium (Mg) in serum (Martnez et al., 2012) disrupt the elimination of bacteria. Consequently, the cases of metritis can be turned into septic metritis in which the cow can suffer symptoms as fever, depression, partial or complete anorexia, and laminitis (Sheldon, 2006). Other factors may induce the onset of metritis, as the low production of mucin which helps protect the mucosa from bacteria impeding to reach the cells and hormones like progesterone and estradiol may play a role in the immunosuppression in cows with metritis (Lewis, 2003; Galvo et al., 2010). Epidemiology Several studies have been done showing the prevalence of the different types of metritis. For subclinic al endometritis, has been determined a prevalence of 26.3% and a range within herd from 4.8 to 52.6%. Incidence of puerperal metritis was 29.7% and of clinical metritis was 9.6% (Cheong et al., 2011). Also, Benzaquen et al. (2007) determined that the overall incidence of puerperal metritis was 21.0% and for clinical endometritis was 24.0%. Another study showed that the overall prevalence of subclinical endometritis was 53% (Gilbert et al., 2005). While Martnez et al. (2012) reported an incidence of 47.3% o f metritis and 20% of puerperal

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35 metritis, and Kelton et al. (1998) from the compilation of 43 studies determined an incidence of metritis of 37.3% that ranged from 2.2% to 37.3% Several studies have identified risk factors for the different types of metri tis. Among them are abnormal calving (RP, twins, dystocia or combination of these conditions), season, and parity. Benzanquen et al. (2007) reported that primiparous cows h a d higher incidence of puerperal metritis during the cold season (October to April) compared to warm season (May to September) while in multiparous was not observed a seasonal effect. Also, factors like retained placenta, dystocia, abortion, over and under conditioned cows are risk factors for metritis (Kaneene and Miller, 1995). Some st udies have identified that those cows with abnormal calving (RP, twins, dystocia or combination of these conditions, and delivery of a dead calf have greater odds of having puerperal metritis and clinical metritis compared to cows with normal calving (Benz anquen et al., 2007; Giuliodori et al., 2013). Ribeiro et al., (2013) reported that calving problems (dystocia, twin birth, stillbirth, and retained placenta) increased the probability of metritis and clinical endometritis. Also, Huzzey et al. (2007) indic ated that the odds of severe metritis increased by 15.8 when calving needed assistance while Hossein Zadeh (2011) indicated that the odds of having metritis were 4.32 times larger in cows that had dystocia, 6.26 times larger in cows that had stillbirth, an d 27.74 times larger in cows that had RP compared to cows without these conditions. On the other hand, season is also a factor that affects the risk of metritis. Primiparous cows that calved during the winter had higher risk of developing puerperal metriti s (Benzanquen et al., 2007). HosseinZadeh (2011) reported that the odds of having metritis were 2.45 times larger in cows that calved in winter season (October to December) compared to

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36 cows that calved during spring (April to June), while a previous study (Brunn, 2002) reported that t he odds of metritis increased by 1.2 times in cows calving during the period November to April compared to the ones that calved during May to October and stated that this may be caused by a lower general health of cows during the cold season. In the case of subclinical endometritis Cheong et al., (2011) reported that diseases as ketosis and acute metritis were risk factors for metritis and also reported an association between parity with milk production in which multiparous cows with lower milk yield and primiparous with higher milk production had a greatest risk of having subclinical endometritis. Nutrition also is associated with the risk of metritis. The odds of cows being diagnosed with severe metritis increased by 1.19 for each kilogram (kg) decrease of the close up diet before calving (Huzzey et al., 2007) which may lead to high levels of NEFA during prepartum increasing more the risk of having metritis (Galvo et al., 2010; Giuliodori et al., 2013). Also, the water consu mption was related to the risk of developing metritis. Huzzey et al., (2007) concluded that the odds of severe metritis increase by 1.21 for every 1kg decrease in water intake during the week before parturition. However, in a different study there was no association between drinking behavior and metritis (Patbandha et al., 2012). Besides, cows that developed metritis had less feeding time during the last week prepartum, and less number of feeding bouts. Also it was determined that cows that spend less than 284.5 min/d feeding were most likely to develop metritis (Patbandha et al., 2012; Patbandha et al., 2013).

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37 Moreover, dry matter intake before calving has been related to increase the odds of having metritis. Huzzey et al. (2007) concluded that for each k g decrease of DMI the odds of severe metritis increased 2.87 times. Besides, some studies show a relationship between feeding time during the precalving period and risk of metritis in which a decrease in feeding time increases the odds of having metritis ( Urton et al., 2005; Huzzey et al., 2007). Furthermore, diseases like subclinical hypocalcemia, subclinical endometritis, retained placenta, and subclinical ketosis have been reported to increase the risk of metritis (Martnez et al., 2012; Ribeiro et al., 2013; Suthar et al., 2013). Additionally, Ribeiro et al. (2013) reported that the prevalence of metritis increased the prevalence of endometritis and these cows were more likely to have subclinical endometritis. Cows with subclinical hypocalcemia had 3.24 times greater risk of developing metritis, and an 11fold increase in the risk of developing puerperal metritis (Martnez et al., 2012). A threshold for NEFA was identified pre and postpartum by Ospina et al., (2010) in which cows with more than 0.37 mEq/L prepartum were 1.9 more likely to have metritis compared with cows with less or equal than 0.29 mEq/L . In the postpartum, it was determined that cows with NEFA 0. 36 mEq/L w ere 1.4 times more likely to have metritis . However, Martnez et al. (2012) did not find any difference in the concentrations of NEFA and BHBA in cows with or without metritis. Effects on Milk Yield Giuliodori et al. (2013) reported that milk yield is affected in metritic cows during early lactation and, these ones produced more milk during late lactation compared to healthy cows. Healthy cows had higher production by 90 DIM than both the clinical metritis and puerperal metritis cows. Previously, Rajala and Grohn (1998) reported that

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38 cows with metritis in the early stage produced less milk compared with late stage. Overton and Fetrow (2008) showed that cows with metritis that were culled during the first 30 DIM produced 15.1 lb less milk/d while the ones culled during 31 60 DIM produced an average of 9.1 lb less milk/d compared to the ones that did not have metritis. Effect on Profitability Overton and Fetrow (2008) showed that metritis has an effect on profitability affecting culling rate, milk yield, and reproductive performance. They stated that per case of metritis that happened during the first 60 DIM it had an estimated total cost of culling and death of $85 per case and a total estimated milk loss of $83 per each case. While, on the effect on reproductive performance they showed that only 59% of cows that had metritis will become pregnant compared with a 73% of pregnancy on normal cows. Consequently, taking the culling due to infertility attributed to metritis plus the costs of pregnancy programs they determined a loss of $109 per case of metritis. Also, they discussed the treat ment cost which may vary from $53 to $109 according to the use or discarded milk. Therefore, the total estimated cost per case of metritis may range from $329 to $386. Mastitis A cow was considered to have clinical mastitis if she has a visually abnormal m ilk secretion (e.g., clots, flakes, or watery) from one or more quarters. This secretion might or might not be accompanied by signs of inflammation of the udder tissue (e.g., heat, swelling, or discoloration of the skin). Abnormal milk from the same quarter at subsequent milkings or at any time within 8 d of the most recent episode of abnormal milk it is considered to be the same case (Kelton et al., 1998).

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39 Lactogenesis and Lactopoiesis Lactogenesis is the process of milk production by the mammary gland. This process is controlled by hormonal and neural stimulus. Secretion of prolactin and ACTH from the pituitary gland induces secretion from the mammary cell (Akers, 1985) whereas oxytocin contracts myoepithelial cells of the mammary tissue causing milk eject ion (Wagner et al., 1997). The first secretion of the mammary gland is the colostrum and is secreted for the first 2 or 3 days. Its main characteristic is that is rich in immunoglobulins which provide passive immunity protection to the neonate and thus hel p to its survival. After colostrum secretion is finished the mammary cell start to produce milk until the end of lactation (Georgiev, 2008). During the first months of lactation, cows will reach a peak of milk production and after reaching this peak the mi lk secretion start to decrease and mammary cells start to become less functional until the milk secretion stop. These cells undergo atrophy and the cells will remain nonfunctional until the next lactation where all the cycle starts again (Walker et al., 19 86). On the other hand, the mammary gland has several defense mechanisms against bacterial contamination (Sordillo and Streicher, 2002). One of the barriers to avoid penetration of pathogens is the commensal organisms which are nonpathogenic organisms that colonize the teat end preventing mammary gland infection. Another one is the streak or teat canal which is a valve covered by a keratin lining thus protect the entrance to the teat cistern. Besides these barriers, also the mammary gland has its own immune defense which is constituted mainly by PMNs and macrophages. The macrophages are the early detection system in the initiation of inflammation. In addition,

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40 antibodies also protect the mammary gland by serving as toxin neutralizers, bacteriocidins, and opsonins for PMNs and macrophages (Sordillo and Streicher, 2002). Causes The most common bacteria found in the mammary gland of cows that had clinical mastitis was E. coli (Oliveira et al., 2013; Roth et al., 2013). This bacterium was also the most common is olated in the dry period of cows that subsequently had mastitis. Therefore, it is believed that bacteria contamination in the previous lactation can be a cause of clinical mastitis in the following lactation period (Green et al., 2002). The most severe cas es of clinical mastitis are caused by gram negative pathogens. Commons gram negative bacteria found in cases of mastitis are Klebsiella spp, and Enterobacter spp. (Oliveira et al., 2013; Green et al., 2002). Among the gram positive there were identified t he environmental Streptococci , coagulase negative Staphilococci (Roth et al., 2013), and Corynebacterium spp. (Oliveira et al., 2013; Green et al., 2002). Among the Streptococci the most common identified were S. dysgalactiae, S. uberis, and S. faecalis (R oth et al., 2013; Green et al., 2002). Physiopathology Microorganisms enter through the teat sphincter and multiply in the mammary gland starting an inflammatory process. The magnitude of inflammation will vary depending on the microorganism that invades the tissue and its virulence. Also, the inflammation process will depend on the host defense against the pathogen (Harmon, 1994). If the immune cells population of the host is not decreased and their kill ability is not diminished it will fight bacteria contamination preventing infections (Sordillo and Streicher, 2002).

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41 There are two types of mastitis, contagious and environmental. The former has the infected mammary gland quarter as its primary reservoir. Therefore, the transmission occurs from infected quarter to other healthy quarter in the same cow or different cows through a fomite such as milking equipment or contaminated gloves or hands. In environmental mastitis, the microorganisms come from the environment of the cow and get access to the mammary gl and. Thus, the bacteria causing this type of mastitis do not need an infected quarter to be spread. The transmission can occur from feces, bedding, or fomites (Smith, 2002). Mastitis also can be classified as subclinical and clinical mastitis. Subclinical mastitis occurs when the number of leukocytes or somatic cells (SCC) is increased whereas in clinical mastitis, the main characteristic is the abnormal milk and visible inflammation of the mammary gland. This type of mastitis can be also classified in acut e, gangrenous, and chronic mastitis (Smith, 2002). Mastitis can lead to impaired reproductive performance. Roth et al. (2013) indicated that cows with increased number of SCC had less proportion of development of blastocysts which they attributed to a low oocyte quality due to follicleenclosed oocyte sensitivity to endotoxin released during mastitis. Therefore, cows with mastitis may have less pregnancy rates. As indicated above, older cows are more susceptible to mastitis than primiparous cows. Older cow s had less lactoferrin concentration (Hagiwara et al., 2003) which is an antimicrobial component of the immune system that is increased in cases of subclinical mastitis. Hence, multiparous cows which have less lactoferrin cannot fight infections in the udd er as well as primiparous cows.

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42 Epidemiology The reported frequency of clinical mastitis, based on 62 citations from 1982 to 1996 ranged from 1.7% to an annual incidence rate of 54.6% with a median of 14.2%. (Kelton et al., 1998). However, Martnez et al (2012) reported an incidence of 11% during the first 12 DIM. The prevalence of mastitis varies among different herds, microorganism present, and between cities. A study done by Oliveira et al. (2013) showed a prevalence of 22% for E. coli , 12.8% environmental streptococci, 6.9% for Klebsiella spp , and CNS with a prevalence of 6.1%. Also, some risk factors have been reported related with clinical mastitis as the general management, housing conditions, cleaning procedures, management of dry cows, milking proc edures, milk production, among others (Schukken et al., 1990) which may vary among herds and cities. Parity also plays a role in the risk of having mastitis. The incidence in multiparous cows with three or more lactations increased 21.7%, while for second lactation was of 12.4% in a conventional dry period (Santschi et al., 2011). Late dry period infections with major pathogens increased the risk of clinical mastitis with the same organism in the subsequent lactational period. Over 60% of clinical mastitis in quarters, in which the same pathogen was identified during the dry period, occurred within 2 wk of calving and 90% within 150 d of calving (Green et al., 2002). On the other hand, disorders such as subclinical ketosis (SCK) increase the chance for developing mastitis. Raboisson et al. (2014) showed that cows with SCK had 1.64 times higher chance of developing clinical mastitis and of having high SCC

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43 compared with cows without SCK. Therefore, cows with SCK are more susceptible to mastitis compared to cow s that did not have SCK. Effect on Milk Yield In a study done with 3 groups according to SCC records where group 1 had less than 200,000 cell/mL of milk and the 3rd, more than 600,000 cell/ml, there was no difference in milk yield and DIM (Roth et al., 2013). However, several studies have shown the effect of mastitis and subclinical mastitis on milk yield. Santos et al. (2004) showed that cows with clinical mastitis had reduced yields of milk and milk components on early lactations (i.e. prior to first post partum artificial insemination and between first postpartum artificial insemination (AI) and pregnancy diagnosis). Also, they reported that the milk yield was lower for cows that were diagnosed prior to first postpartum AI compare to those that were diagnosed between first postpartum AI and pregnancy diagnosis. Ketosis Ketosis is a condition characterized by abnormally elevated concentrations of hydroxybutyric acid (BHB) in the body tissues and fluids with a bl ood concentration of BHBA greater than 3.0 mmol/L (Oetzel, 2004). Subclinical ketosis (SCK) is defined as the presence of increased blood ketone concentrations without clinical signs with a BHBA concentration of 1.2 mmol/L. Lipid Mobilization As explained in energy balance section, in order to get extra energy the body starts to mobilize fat tissue. For this mobilization, the enzyme lipase acts in the adipose tissue to start the mobilization of lipids as non esterified fatty acids (NEFA) to the bloodstream until it reaches the liver to be converted in several other products by the

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44 hepatocyte where NEFA is taken by the peroxisomes and converted to acetyl CoA or taken by the mitochondria where they can be partially oxidized to form ketone bodies as BHBA, comp letely oxidized to CO2, or it can be synthesized as a TG (Emery et al., 1992). Causes Ketosis is a consequence of the lipid mobilization. Modern dairy cattle have a high milk yield that demands an amount of nutrients and energy that the diet cannot supply . This leads to an increase in lipid mobilization that result in high ketones bodies concentrations in the organism. The ketone body used to measure ketosis is BHBA and blood concentrations of 1.2 mmol/L indicate SCK and concentrations higher than 3.0 mmol /L it is used to indicate clinical ketosis (Oetzel, 2004). Physiopathology Clinical ketosis occurs when there is hypoglycemia due to the increase demand of glucose for milk production. The body tries to maintain normoglycemia by increasing gluconeogenesis from hepatic glycogen and from adipose tissue. As a consequence, hepatic glycogen is depleted, and NEFA are mobilized from fat tissues increasing ketone bodies, and as a consequence causing ketonemia, ketonuria, and ketolactia (Emery et al., 1992). The sym ptoms of ketosis are gradual loss of appetite and decrease in milk production, weight loss, moderate depression, dry feces, and decreased rumen motility. Also, in some cases you may see pica and odor of ketones on the breath and in the milk. In the nervous form, neurologic signs such as circling, head pressing, blindness, excessive salivation, tremors, and tetany may be seen (Smith, 2002).

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45 On the other hand, it has been reported that ketotic cows start changing their behavior in the prepartum period. Itle et al. (2015) reported that cows that were later diagnosed with clinical ketosis stood 20% longer during the week before calving and 35% longer on the day of calving and that this changes occur before changes in blood chemistry. Therefore, ketotic cows may show behavioral signs before the metabolites (NEFA and BHBA) increase. Epidemiology The prevalence of subclinical ketosis varies due to the differences in diagnosis and definition across studies. The reported frequency of ketosis, based on 36 citations fr om 1979 to 1995 ranged from 1.3% to 18.3% with a median of 4.8%. The highest risk for ketosis is within the first 2 months postpartum, after this period, ketosis diagnose is unusual (Kelton et al., 1998) being higher between 2 to 15 days in milk (Suthar et al., 2013). The overall prevalence of subclinical ketosis reported in a study conducted in 5 countries of Europe was of 39% and a herd prevalence of 1.6% for clinical ketosis. In this study they also indicated that the prevalence of ketosis in fresh cows was 41%. However, this percentage varied between countries (Berge, and Vertenten, 2014). Nevertheless, a previous study done in 10 different countries in Europe reported a prevalence of 21.8% (Suthar et al., 2013). Similarly, in New York, Mcart et al. (2012) reported a prevalence of 43.2% of subclinical ketosis. Nutrition has an effect on the risk of having ketosis. Ketosis prevalence was lower in cows that were given forage and concentrate separate compared to those with a total mixed ration (TMR) and part ial mixed ration (PMR). The risk of ketosis in farms that fed PMR had 1.5 times higher risk compared to farms that provide TMR to their

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46 cows (Berge, and Vertenten, 2014). In addition, a short dry period management (35d) fed with a precalving diet without a switching to dry cow ration decreased the incidence of subclinical ketosis (Santschi et al., 2011). A second line of evidence is that cows that are fed a restricted energy ration may also be less susceptible to fatty liver (Grummer et al., 2004). Also, th e environment is related to ketosis incidence. The prevalence of ketosis in cows housed indoors all year was 35% while in cows that were at pasture in the summer or other outdoor alternative housing system had, on average, 44 to 46% of ketosis (Berge, and Vertenten, 2014). Parity also has an effect on subclinical ketosis. Multiparous cows have higher risk of having ketosis than primiparous cows (McArt et al., 2012; Berge, and Vertenten, 2014). This is in accordance with the idea that heifers can withstand higher NEFA concentrations without developing fatty liver than mature cows (Grummer et al., 2004). On the other hand, Berge and Vertenten (2014) reported that the odds of having clinical ketosis were 14.7 for cows that have subclinical ketosis while Rabois son et al. (2014) reported an odds ratio of 5.38. A threshold was identified pre and postpartum by Ospina et al., (2010) in which a cows with BHBA > 0.26 mEq/L prepartum were 1.4 times more likely to have clinical ketosis postpartum. Cows with NEFA postpartum > 0. 57 mEq/L w ere 1.8 times more likely to have clinical ketosis . Effect on Milk Yield Mcart et al. (2012) reported that the milk yield in early lactation decrease 0.5 kg/d for each 0.1 mmol/L increase in BHBA concentration while Raboisson et al. (2014) reported that the loss of milk was approximately 251 kg during the 305 days of lactation.

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47 Moreover, it has been shown that high levels of NEFA and BHBA at a herd level decreases milk yield in heifers (McArt et al., 2013). Cows with elevated serum BHBA in week (wk) 1 or 2 postpartum had increased milk fat percentage and decreased milk protein percentage. Estimated milk yield loss of approximately 1 to 2 kg/d. Lactation 2009). Effect on Reproduction Subclinical ketosis affects cows’ cyclicity (Vercouteren et al., 2015). Also, McArt et al. (2012) stated that the time of onset of subclinical ketosis is associated with reproductive performance. They showed that cows with SCK during 3 to 7 DIM were 0.7 times more likely to conceive to first service than cows first testing positive at 8 DIM or later. M oreover, Gillund et al. (2001) reported that cows that did not had ketosis before first insemination were 1.6 times more likely to conceive than cows that were ketotic during the first postpartum period Displaced Abomasum The displacement of abomasum can occur to the right or to the left (LDA), being LDA the most common condition. In the field, a cow is considered to have LDA when she had a decreased appetite accompanied by an audible, high pitched tympanic resonance (ping) produced by percussion of the left abdominal wall between the 9th and 12th ribs (LeBlanc et al., 2005). Anatomy of the Abomasum The abomasum is part of the bovin e digestive tract that is capable of great distension and displacement and it has a capacity of up to 28 L. The parietal surface and part of the greater curvature lie on the ventral abdominal wall and the caudal pat is

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48 separated from intestines by the greater curvature. The lower curvature bends around the omasum and the fundus is continuous with the body of the abomasum. The abomasal folds are rich in gastric glands and start in the omasoabomasal orifice and from the sides of the abomasal groove. The folds diminish toward the pyloric part which begins at the angle of the abomasum. The pyloric sphincter and the torus pyloricus can close off the flow from the abomasum to the duodenum (Trent, 1990). Causes Several factors contribute to displaced abomasum (DA) as hypomotility and gas production due to hypocalcemia or endotoxemia. Also, high concentrate/low forage leads to the production of high concentration of volatile fatty acids (VFA) as propionic acid, acetic acid, and butyric acid and anaerobic microorganis ms will turn carbohydrates to VFA and lactate. Consequently, the high production and accumulation of VFA and the continued microbial fermentation and absorption of VFA plus a low ruminal fill lead to gas accumulation and distension of the abomasum causing hipomotility (Shaver, 1997). Also, disorders as clinical and subclinical ketosis, hypocalcemia, and high BCS are associated with DA. However, high BCS is related to ketosis which may be the real association with DA incidence (Shaver, 1997). Physiopathology The abomasum is suspended by the omenta so it can be displaced to the right or left. In the case of LDA, high concentration of VFA and the microbial fermentation lead to gas production and accumulation distending the abomasum leading to abomasal hypomotil ity. The distended abomasum is displaced upward along the left abdominal

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49 wall lateral to the rumen. Therefore, the fundus and the greater curvature of the abomasum become displaced retaining liquid and gas (Coppock, 1973). Generally, the displacement is partial but interferes with normal digestion. This leads to a partial or total anorexia, dehydration, decreased fecal output, reduced rumen contractions, and reduced milk production. As a consequence, some metabolic alterations may occur such as alkalosis wi th hypochloremia, and ketonuria (Smith, 2002). On the other hand, when LDA is caused by endotoxins related to disease, the endotoxins or the effect of a pyrogen as interleukin1 reduce the gastric muscle tone and the central vagal nuclei which consequentl y may cause abomasal displacement. (Smith, 2002). Epidemiology The incidence reported by Kelton et al. (1998) based on 22 citations was 1.7% ranging from 0.3% to 6.3%. Moreover, in 2005 LeBlanc et al. (2005), reported a lactational incidence risk of 5.1%. Cameron et al. (1998) reported a 6% incidence rate for primiparous cows and 7% for multiparous. Santschi et al., 2011 also reported a higher incidence in older cows. Therefore, it is shown that parity has an effect on DA incidence. Nutrition plays an impor tant role in the incidence of DA. Cows with DA have less feed intake before diagnosis (Van Winden et al., 2003). Therefore, offering an adequate feed bunk with fresh feed availability that provides uniform consumption will decrease the incidence of DA (Cam eron et al., 1998). In addition, cows with increased NEFA prepartum (Cameron et al., 1998; Van Winden et al., 2003; LeBlanc et al., 2005; Ospina

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50 et al., 2010) and BHBA (LeBlanc et al., 2005) had higher risk of DA. Also, cows with NEFA > 1.7 mmol/L had higher risk of DA (Suthar et al., 2013). Subclinical ketosis also increased the odds of developing DA. Mcart et al., 2012 indicated that cows with subclinical ketosis were 19.3 more likely to have DA. A recent study observed that cows with subclinical ketosis were 5.12 more likely to have DA (Raboisson et al., 2014). Other diseases such as metritis, RP, and hypocalcemia have been related to the increase incidence of DA (Shaver, 1997). Other factors that increased the risk of having DA were high BCS, and winter season (Cameron et al. 1998). Although, these factors may be related to others that may be causal. For example, metritis and RP may decrease feed intake and consequently increase NEFA and BHBA in blood, as well hypocalcemia which may be a consequence of i nadequate prepartum diet (LeBlanc et al., 2005) and, also high BCS which is related to ketosis (Shaver, 1997). Effect on Milk Yield Displaced abomasum has a significant effect on milk yield. Cows with DA have been reported to produce an average of 557 kg of milk during the first months of lactation increasing the loss with increment of parity (Detilleux, 1997). Similarly, Van Winden et al. (2003) found that cows with DA produced 6.5 kg less per day compared with healthy cows.

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51 CHAPTER 3 MATERIALS AND METHODS An observational study was performed using the data from seven different experiments conducted by researchers from the Department of Animal Science at University of Florida. Each experiment was conducted at the University of Florida Dairy Unit (DU) , Hague, FL, and all of them were approved by the University of Florida Animal Research Committee. The experiments were conducted in different years, ranging from 2007 to 2011. Six of the seven experiments were performed in summer (JunAug), and cows were provided with only shade or shade plus evaporative cooling (Do Amaral et al., 2009; Tao et al., 2011; Do Amaral et al., 2011; Thompson et al., 2014; Tao, S. et al., 2012; Gomes (2014). However, one of the experiments did not involve the experiment with pre partum cooling and shade Greco et al. (Unpublished)] thus the cows from this experiment were considered as having evaporating cooling. Data from a total of 294 cows were compiled from the above mentioned studies and were used in this study. The cows involv ed in the study were Holstein dairy cows. Sixty one (20.7%) of these cows were primiparous (cows that will become primiparous and will enter their first lactation), and 233 (79.3%) were multiparous (cows that had 1 or more calving and will enter their seco nd or greater lactation). Also, 207 had a body The data from each cow was collected from 14 days prepartum to 28 days postpartum which covers most of the transition period. The data was collecte d during these weeks to not compromise sample size because some of the experiments did not

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52 collect data before day 14 prepartum and it was collected until day 28 postpartum to complete the weeks of postpartum that covers the transition period. Measurement of Dry Matter Intake All the experiments recorded daily dry matter intake for each cow using a system with individual feeding gates (Calan gates, American Calan Inc., Northwood, NH). This system allows researchers to distribute individual diets to the cows . Each cow had one key hanging from the neck, and this key would open a specific feed door preventing cows from eating each other’s ration. Therefore, individual dry matter intake was recorded (Figure 31). Diseases The diseases, or disorders recorded for this present study were dystocia, retained placenta (RP), metritis, mastitis, displaced abomasum (DA), and ketosis. The disease incidence was recorded within 28 days postpartum using the DU records to confirm that the disease or disorder happened during this period. The records were checked according to the calving day registered in each data set of the different experiments. The University of Florida Dairy Unit uses the following definitions for each disease, disorder or condition (Standard Operating Procedures, University of Florida dairy unit, 2012): Dystocia: it is defined as a cow failure to make progress entering into or during labor with no change in labor status 4560 minutes since last observation. The calving difficulty is scored using a scale from 1 to 5. In which 1 = no assistance; 2 = assistance by one person without the use of manual expulsion; 3 = assistance

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53 by 2 or more people; 4 = assistance with mechanic expulsion; and, 5 = fetotomy or cesareansection. Retained placenta: it is characterized by failure to release the placenta within 24 h of parturition. Metritis: it is characterized by the presence of redbrownish watery fetid uterine discharge with or without ever with enlarged uterus. Mastitis: it is characterized by abnormal milk or mam mary gland with the presence of significant flakes and clumps in milk. Ketosis: it is defined as presence of ketone bodies in urine which resulted in a violet color in the ketostick test [5 mg/dL (trace) or higher] in the urine test strip (Ketostix, Bay er) with milk deviation, off milk, licking pipes, unsteady, and nervous attitude. Displaced abomasum: it is characterized by scant pasty manure, ping sound over the 9th to 13th ribs and the cow is usually within 30 days since calving. An additional variable was created that was called disease which is defined as a cow that had at least one of the diseases or disorders previously described, except cows that had dystocia. Dystocia was excluded because does not follow a pathway like the other diseases. A cow can have dystocia due to several predisposing factors but those factors do not lead to signs or symptoms characteristic of disease. Body Weight (BW), and Body Condition Score (BCS) The weight time differed between studies, some projects weighed cows at 46, 32, 18, 0, +14, +28, and +42 (total of 72 cows) some measured weekly (with a total of 111 cows), and some did it daily (111 cows). To obtain an approximate daily weight value of the cows that were measured weekly it was performed a calculation subtracting

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54 the weight of the second week minus the value of the first week divided by 7 days and the value obtained was added to each day of the week. For example, if a cow weighed 550kg in the second week and 500kg in the first week then 550500/7= 7.1 kg. The value obtained (7.1 kg) was added to each day of the week (500 + 7.1 kg= 507.1 (first day); 507.1 + 7.1 = 514.2 (second day), this addition continue until each the last day of that week that have to be as a final value 550 Kg. Conversely, if the cow los t weight then the absolute value was subtracted in each day. All cows in the different studies were weighed using a digital scale (AfiWeight, S.A.E. Afikim). The body condition score (BCS) was evaluated during prepartum and postpartum periods and it was assigned to each cow using a 1 to 5 scale (from 1 = emaciated to 5 = obese) according to Edmonson et al. (1989) and Ferguson et al. considered as a “Normal” body condition cow, and cows with BCS > 3.5 were considered to be “Overconditioned” based on previous studies (Waltner et al., 1990). Milk Yield and Milk Components The milk yield of each cow was measured twice a day using the AfiMilk milk meters (SAE Afikim, Kibbutz Afikim, Isr ael); therefore, the daily milk yield was the average for the two milkings. The milk components were measured using the AfiLab real time milk analyzers (SAE Afikim, Kibbutz Afikim, Israel). Data on milk fat, protein and lactose were collected. In the projects conducted in 2007, and 2008 the milk components were measured once a week at the Southeast Dairy Herd Improvement Association laboratory in Belleview, Florida, because the AfiLab had not used during the experiments done in these years. Lactose was not measured in 2007 and 2008.

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55 The accuracy of the inline milk analyzer was checked monthly by comparing the results from the inline analyzer with that of the Southeast Dairy Herd Improvement Association laboratory. Meters were calibrated as needed. The corr elation between the laboratory test and the inline milk analyzer at this farm has been reported to be 0.59 for milk fat and 0.67 for milk protein, respectively, which indicates strong correlation between the two methods (Kaniyamattam and De Vries, 2014) Net Energy Balance The data necessary to calculate postpartum net energy (NE) balance was available for most of the experiments. Data from projects conducted in 2007 and 2008 were removed because of missing lactose, and daily protein, and fat data. The formula used to calculate EB was: EB = NE intake – (NE maintenance + NE milk) Where NE intake, NE maintenance and NE milk were calculated as follows: Net energy intake = DMI x Net energy of the diet NE maintenance = (POWER BW 0.75 x 0.08) NE milk = (9.35 x Milk Yield x Fat % / 100) + (5.35 x Milk Yield x Protein % / 100) + (3.95 x Milk Yield*Lactose %/100) Net energy balance of the diet was previously calculated for each study. Statistical Analysis A sample size of 294 cows was the total of cows of the seven different studies that were included in this current project. The data were divided in different datasets for better interpretation of results. These datasets were prepartum, postpartum, milk yield, disease, and energy balance.

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56 Prepartum D ata The data were collected from day 14 to day 1 relative to parturition. The PROC MIXED procedure of SAS version 9.4 (SAS Institute Inc., Cary, NC) was used to analyze the data. The outcome of interest was dry matter intake. This outcome was screened for normality and the presence of outliers using PROC UNIVARIATE procedure of SAS version 9.4 (SAS Institute Inc., Cary, NC). The independent variables time (day 1 to day 14), evaporative cooling (EV) (evaporative cooling vs not evaporative cooling), and one of the diseases of the study [dystocia, retained placenta, metritis, mastitis, ketosis, and displaced abomasum (yes vs. no)]. The variable ID was nested within the experiment variable and it was included in the random statement, and time was used as the repeated variable. Different models were created for each disease. For example, for metritis the initial model was: DMI = metritis + time + EV + BCS + parity + metritis x time + metr itis x BCS + metritis x parity + metritis x EV In these models each disease was forced to be in the model and interactions between the disease and parity, body condition score, evaporating cooling (EV), and time were evaluated. A backward elimination was performed by removing explanatory variables and interactions from the model with P > 0.05. An exception was made with the variable DA due to a low number of cases. This model was just controlled for DA and the interaction between DA and Time variable.

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57 Pos tpartum D ata The data were collected from day 1 to day 28 after parturition. The day of calving was excluded because cows were usually moved to a maternity pen without Callan gates; therefore, DMI could not be calculated. In this dataset, 27 cows were removed because of missing DMI data; therefore, 267 cows were used. The outcome, independent variables, and the statistical approach used was similar to the one described for the prepartum period. Milk D ata In this dataset the outcome variable was milk yield. This outcome was also screened for normality and the presence of outliers using PROC UNIVARIATE procedure of SAS version 9.4 (SAS Institute Inc., Cary, NC). The fixed variables, and the statistical method used with this dataset was also similar to the one previously described. In this dataset only 14 cows were removed because of the lack of milk yield for the entirely 28 days remaining a total of 280 cows. Net Energy Balance The outcome in this dataset was net energy balance. The cows of projects done in 2 007 and 2008 were removed because the data to calculate net energy balance was not available leaving a sample size of 230 cows. As similar to what was described before, in this dataset it was used PROC MIXED procedure of SAS version 9.4 (SAS Institute Inc. , Cary, NC) to do the analysis. The fixed variables, and the statistical method used with this dataset was also similar to the one previously described.

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58 Disease The model used for this dataset was GLIMMIX procedure of SAS version 9.4 (SAS Institute Inc., C ary, NC). The outcomes in this model were each disease or disorder (Dystocia RP, Metritis, Mastitis, Ketosis, and DA), and the variable Disease. The fixed variables were week 2 (defined as the average of dry matter intake consumed by the 2 weeks previous to parturition), week 1 (defined as the average of dry matter intake on the week before calving), drop (refers to the subtraction of the average dry matter intake between week 2 and week 1), parity (primiparous vs. multiparous), and body condition score ( were EV, to control for those cows that received evaporative cooling during prepartum, and study to control for the different studies included in this project. Interactions between parity, EV, and BCS with t he other variables (week 2, week 1, and drop) were analyzed. As a random variable ID was used. In the case of DA a simplest model was created in which the RANDOM statement was removed. Similar to the previous models, a backward elimination was performed by removing explanatory variables and interactions from the model with P > 0.05 according to Waldstatistics criterion. A Receiver Operating Characteristic (ROC) curve was performed using PROC LOGISTIC of SAS version 9.4 (SAS Institute Inc., Cary, NC) to te st the sensitivity and specificity of the statistical model of each disease. Also, a correlation matrix was performed showing Spearman’s rho correlation coefficient. To evaluate the correlation it was used PROC CORR of SAS version 9.4 (SAS Institute Inc., Cary, NC), and the variables included were EV, parity, study, BCS, dystocia, RP, metritis, mastitis, ketosis, DA, and disease (Table 3.1).

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59 Figure 31. Calan gates taken at dairy research unit of University of Florida. (Photo courtesy of author).

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60 Table 3 1. Correlation matrix showing Spearman’s rho correlation coefficient and p values. Study EV Parity BCS Dystocia RP Metritis Mastitis Ketosis DA Disease Study 1 0.37 <.01 0.38 <.01 0.19 <.01 0.08 0.21 0.06 0.31 0.13 0.03 0.34 <.01 0.008 0.89 0.06 0.28 0.17 <.01 EV 0.37 <.01 1 0.06 0.35 0.13 0.03 0.09 0.14 0.05 0.38 0.03 0.59 0.09 0.12 0.16 <.01 0.09 0.12 0.10 0.09 Parity 0.38 <.01 0.06 0.35 1 0.23 <.01 0.05 0.41 0.03 0.62 0.02 0.70 0.17 <.01 0.30 <.01 0.08 0.15 0.28 <.01 BCS 0.19 <.01 0.13 0.03 0.23 <.01 1 0.02 0.76 0.07 0.26 0.01 0.82 0.04 0.46 0.23 <.01 0.05 0.38 0.15 <.01 *Dystocia 0.08 0.21 0.09 0.14 0.05 0.41 0.02 0.76 1 0.14 0.02 0.14 0.02 0.04 0.49 0.03 0.63 0.03 0.67 0.04 0.55 4 RP 0.06 0.31 0.05 0.38 0.03 0.62 0.07 0.26 0.14 0.02 1 0.37 <.01 0.10 0.09 0.17 <.01 0.04 0.49 0.22 <.01 *Metritis 0.13 0.03 0.03 0.59 0.02 0.70 0.01 0.82 0.14 0.02 0.37 <.01 1 0.01 0.84 0.16 <.01 0.004 0.95 0.48 <.01 *Mastitis 0.34 <.01 0.09 0.12 0.17 <.01 0.04 0.46 0.04 0.49 0.10 0.09 0.01 0.84 1 0.03 0.63 0.02 0.75 0.43 <.01 *Ketosis 0.008 0.89 0.16 <.01 0.30 <.01 0.23 <.01 0.03 0.63 0.17 <.01 0.16 <.01 0.03 0.63 1 0.22 <.01 0.65 <.01 5 DA 0.06 0.28 0.09 0.12 0.08 0.15 0.05 0.38 0.03 0.67 0.04 0.49 0.004 0.95 0.02 0.75 0.22 <.01 1 0.14 0.02 6 Disease 0.17 <.01 0.10 0.09 0.28 <.01 0.15 <.01 0.04 0.55 0.22 <.01 0.48 <.01 0.43 <.01 0.65 <.01 0.14 0.02 1 EV = Evaporative cooling. Cows that had evaporative cooling were coded as 0, and without evaporative cooling were coded as 1. Primiparous cows were coded as 0, and multiparous cows were coded as 1. BCS = Body condition score. 4RP = Retained placenta. Cows with RP were coded as 1, and without RP were coded as 0. 5DA = Displaced Abomas um. Cows with DA were coded as 1, and without DA were coded as 0. 6Cows that had at least one disease (RP, metritis, mastitis, ketosis, or DA) were coded as 1, and cows without disease were coded as 0. *= Cows that had that particular disease or disorder w ere coded as 1, and without that disease or disorder were coded as 0.

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61 CHAPTER 4 RESULTS Incidences The total of cows gathered from the different experiments that were included in this study was 294. The frequencies of diseases observed in the present study and the average of days in milk that each disease was diagnosed are depicted on Table 41. The disease frequencies are overlapped. For example, from 19 cows that had retained placenta, 16 also had metritis (Figure 41). Dystocia During pr epartum, cows that had dystocia had a tendency ( P = 0.09) for increased DMI compared with cows that did not have dystocia (10.54 0.39 vs. 9.84 0.20 kg/d). Overall, there was no interaction ( P = 0.78) between dystocia and time (Figure 4 2 A). In the 4 weeks of the postpartum period cows that had dystocia had similar DMI compared with cows that did not have dystocia (14.52 0.54 vs. 15.34 0.27 kg/d; P = 0.16). However, there was an interaction between dystocia and time ( P = 0.04). Particularl y, DMI was decreased at day 11 ( P < 0.01), and 12 (P < 0.01), and on day 13 (P = 0.05) and tended to decrease on day 10 ( P = 0.09) on cows that had dystocia compared with cows that did not have dystocia (Figure 42 B). Cows that had dystocia had similar mi lk yield compared with cows that did not have dystocia (25.41 1.11 vs. 25.76 0.61 Kg/d; P = 0.86). However, the interaction between dystocia and time was significant ( P < 0.01). Although, only day 2 showed a tendency ( P = 0.09) of greater milk yield on cows with dystocia compared to cows that did not have dystocia (Figure 42 C).

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62 Cows that had dystocia did not have a significant difference in net energy balance compared with cows that did not have dystocia ( 3.20 0.90 vs. 1.90 0.45 Mcal/d; P = 0.1 9) (Figure 4 2 D), and the interaction between dystocia and time was not significant ( P = 0.58). However, there was an interaction between net energy balance and parity (P = 0.05). In order to explain this interaction it was also analyzed interactions wit h parity and DMI postpartum, milk, and body weight (BW). Though the interaction between parity and dystocia in postpartum DMI was not statistically significant ( P = 0.82) neither was the interaction in milk yield ( P = 0.64) but there was a tendency for BW ( P = 0.09). On primiparous that had dystocia the DMI postpartum was not statistically significant compared to the ones that did not have dystocia (13.35 0.92 vs. 14.37 0.49 Kg/d; P = 0.76) (Figure 43 A). Neither differed in milk yield (17.69 2.06 vs . 16.74 1.31 Kg/d; P = 0.96) (Figure 43 B) nor in BW (510.7 19.4 vs. 491.4 10.2 Kg/d; P = 0.38) (Figure 4 3 C). However, primiparous cows that had dystocia had less energy that the ones that did not have dystocia ( 1.65 1.43 vs. 1.56 0.76 Mcal/d ; P = 0.04) (Figure 4 3 D). Whereas multiparous cows that had dystocia did not differ neither on DMI postpartum compared with the ones that did not have dystocia (15.62 0.65 vs. 16.35 0.26 Kg/d; P = 0.72) (Figure 44 A) nor on milk yield (26.84 1.33 vs. 27.00 0.97 Kg/d; P = 0.91) (Figure 4 4 B). For BW it showed a tendency (P = 0.10) in which multiparous cows hat had dystocia had less body weight compared to cows that did not have dystocia (634.0 14.6 vs. 660.1 6.0 Kg/d) (Figure 44 C). Also, fo r net energy balance there was no statistical difference between multiparous cows that had dystocia

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63 and the ones that did not have dystocia ( 4.77 1.07 vs. 5.37 0.45 Mcal/d; P = 0.63) (Figure 4 4 D). Retained Placenta Cows that had retained placenta (RP) had similar DMI prepartum compared with cows that did not have RP (9.21 0.98 vs. 9.98 0.19 kg/d; P = 0.87). The interaction with time was significant (P < 0.01). Particularly, on day 3 ( P < 0.01) and a tendency on day 2 ( P = 0.07) the DMI for co ws that had RP was lower than for cows without RP (Figure 4 5 A). Cows that had RP had less DMI during postpartum compared with cows that did not have RP (12.36 0.82 vs. 15.35 0.24 kg/d; P < 0.01). Overall, the interaction with time was not significant ( P = 0.14) (Figure 45 B). Cows that had RP produced less milk than cows that did not experienced RP (19.57 1.57 vs. 25.34 0.49 kg/d; P < 0.01). The interaction with time was also significant ( P < 0.01). The milk production in cows that had retained placenta was lower than cows that did not have retained placenta almost during the entire study period except on days 1 ( P = 0.27), and day 2 ( P = 0.20) (Figure 45 C). Cows that had retained placenta (RP) had similar net energy balance postpartum compared with cows that did not have RP ( 2.99 1.37 vs. 2.05 0.41 Mcal/d; P = 0.50). The interaction of RP with time was not significant ( P = 0.52) (Figure 45 D). Metritis Cows that had metritis had similar DMI prepartum compared with cows that did not have metritis (9.54 0.33 vs. 10.10 0.23 kg/d; P = 0.12). However, the interaction between metritis and time was significant ( P = 0.03). Cows that had metritis ate less o n

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64 days 1 ( P = 0.04), 2 ( P < 0.01), and 3 ( P < 0.01) than cows that did not have metritis (Figure 4 6 A). Cows that had metritis had lower DMI postpartum than cows that did not have metritis (13.34 0.45 vs. 15.67 0.25 kg/d; P < 0.01). Also, the inter action with time was statistically significant ( P < 0.01), which showed that DMI was not different on days 25 ( P = 0.14), 26 ( P = 0.11), and 27 ( P = 0.44) (Figure 46 B). Milk yield was lower for cows that had metritis during the 4 weeks postpartum compared with the cows that did not have metritis (22.43 0.91 vs. 26.48 0.57 Kg/d; P < 0.01). The interaction between metritis and time was also statistically significant ( P < 0.01), showing a tendency on day 1 ( P = 0.07) Figure (46 C). Cows that had metriti s had similar net energy balance postpartum compared with cows that did not have metritis ( 2.68 0.67 vs. 1.96 0.44 Mcal/d; P = 0.39). However, the interaction between metritis and time was statistically significant ( P = 0.02). Net energy balance was less for cows that had metritis than for cows that did not have metritis on day 5 ( P = 0.04), and showed a tendency on days 2, 7, 8, and 19 ( P = 0.07) (Figure 46 D). Mastitis Cows that had mastitis had lower DMI prepartum than those that did not have mastitis (9.28 0.36 vs. 10.12 0.19 kg/d; P = 0.03). The interaction with time was statistically significant ( P < 0.01). Particularly, DMI for cows that had mastitis was lower from day 5 to day 1 ( P < 0.05) and tendencies on days 6 and 10 ( P = 0.06) (Figure 47 A). Cows that had mastitis had a tendency ( P = 0.08) of having lower DMI postpartum than cows that did not have mastitis (14.41 0.51 vs. 15.34 0.25 kg/d).

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65 However, the interaction between mastitis and time was statistically significant ( P < 0.01). Particularly, DMI for cows that had mastitis was lower on days 1 ( P = 0.05), 3 ( P < 0.01), 4 ( P < 0.01), 6 ( P = 0.01), 7 ( P < 0.01), 8 ( P = 0.02), 9 ( P < 0.01), 10 ( P < 0.01), and 13 ( P = 0.05), and tended to be lower on days 5 ( P = 0.09), 12 ( P = 0.06), 14 ( P = 0.09), and 15 ( P = 0.09) (Figure 47 B). Cows with mastitis produced less milk compared to cows that did not have mastitis (22.97 0.79 vs. 26.26 0.58 Kg/d; P = 0.03). The interaction with time was also stat istically significant ( P < 0.01). The milk production was less on cows that had mastitis throughout the 28 days postpartum except on days 1 ( P = 0.11) and day 27 ( P = 0.10) (Figure 47 C). Cows that had mastitis did not differ on net energy balance with cows that did not had mastitis in the postpartum period ( 1.91 0.85 vs. 2.69 0.50 Mcal/d; P = 0.39). However, the interaction of the effect of mastitis over time was statistically significant ( P = 0.04). Particularly on days 21 ( P = 0.02), and 23 ( P = 0 .02), and tendency on day 24 ( P = 0.08) the energy was higher on cows with mastitis compared with the rest of the 28 days of postpartum (Figure 47 D). Ketosis Cows that had ketosis had lower DMI prepartum than cows that did not have ketosis (9.16 0.31 v s. 10.23 0.19 Kg/d; P < 0.01). The interaction between ketosis and time was statistically significant ( P < 0.01), which showed that DMI was not different on days 8 ( P = 0.14), 10 ( P = 0.11), 13 ( P = 0.23) (Figure 48 A). Cows that had ketosis had lower DMI postpartum compared to cows that did not have ketosis (13.41 0.39 vs. 15.80 0.25 Kg/d; P < 0.01). The interaction between

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66 ketosis and time was significant ( P < 0.01), which showed that DMI was not different on day 1 (P = 0.65) postpartum (Figure 4 8 B). Cows with ketosis had similar milk yield compared with cows that did not have ketosis (25.79 0.86 vs. 25.57 0.60 Kg/d; P = 0.81). However, the interaction between ketosis and time was significant ( P < 0.01). Particularly on days 2 ( P < 0.01), and 3 ( P < 0.01) cows that had ketosis had higher milk production compared with cows that did not have ketosis (Figure 48 C). Cows that had ketosis had lower net energy balance postpartum than cows that did not have ketosis ( 5.55 0.66 vs. 0.87 0.41 Mcal/d; P < 0.01). Although the interaction between ketosis and time was statistically significant ( P < 0.01), all the time points were different (Figure 48 D). Displaced Abomasum Cows that had displaced abomasum (DA) had similar DMI prepartum compared w ith cows that did not have DA (11.49 1.07 vs. 10.48 1.01 Kg/d; P = 0.44). Also, the interaction between DA and time was not significant ( P = 0.77). (Figure 49 A) In contrast, cows that had DA had lower DMI postpartum than cows that did not have DA (8. 95 1.95 vs. 15.77 0.21 Kg/d; P < 0.01). There was an interaction between DA and time on DMI postpartum ( P < 0.01), which showed that DMI did not differ on days 1 (P = 0.92), 2 (P = 0.41), and 3 ( P = 0.56), 6 ( P = 0.07), 16 ( P = 0.07), 20 ( P = 0.12) and 28 ( P = 0.09) (Figure 4 9 B). Cows that had DA had lower milk yield than cows that did not have DA (19.92 3.85 vs. 27.85 0.48 Kg/d; P = 0.04). Also, the interaction between DA and time was significant ( P = 0.02). Cows with DA had lower milk yield on days 14 (P = 0.03), 16 (P = 0.03), 17 (P = 0.04), 18 (P < 0.01), 19 (P < 0.01), 20 (P = 0.02), 22 (P = 0.03), 23 (P =

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67 0.03), 26 (P = 0.04), 27 (P = 0.02), and 28 (P = 0.03) and a tendency for lower milk yield on days 8 (P = 0.09), 9 (P = 0.07), 11 (P = 0.06), 12 (P = 0.08), 21 (P = 0.06), 24 (P = 0.07) (Figure 49 C). Cows that had displaced abomasum had similar net energy balance postpartum compared with cows that did not have DA ( 8.95 3.39 vs. 3.56 0.41 Mcal/d; P = 0.12). There was no interaction between DA and time on net energy balance postpartum ( P = 0.57) (Figure 49 D). Disease Cows that had at least one disease or disorder had lower DMI prepartum compared with cows that did not have any disease or disorder (9.35 0.24 vs. 10.49 0.22 Kg/d; P < 0.01). The interaction between disease and time was also statistically significant ( P < 0.01). Particularly there was a decrease on DMI during the entire prepartum period except on day 13 ( P = 0.30) and a tendency on days 10 ( P = 0.06), 11 ( P = 0.07) , and 14 ( P = 0.07) (Figure 410 A). Cows with at least one disease or disorder had lower DMI postpartum than cows that did not have any disease or disorder (13.91 0.32 vs. 16.22 0.29 Kg/d; P < 0.01). Although the interaction between disease and time was highly significant ( P < 0.01), DMI was significantly different ( P < 0.05) at all time points (Figure 410 B). Cows that developed at least one disease or disorder had lower milk yield than cows that did not have any disease or disorder (24.43 0.56 vs . 26.71 0.51 Kg/d; P < 0.01). The interaction between disease and time was statistically significant ( P < 0.01). Particularly there was no statistical difference on days 1 ( P = 0.48), 2 ( P = 0.42), 3 ( P = 0.50), and a tendency on days 4 ( P = 0.06), 20 ( P = 0.10) and 28 ( P = 0.07). Differences were significant in all other time points (Figure 410 C).

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68 Cows with at least one disease or disorder had lower net energy postpartum than cows that did not have any disease or disorder ( 3.43 0.56 vs. 0.85 0.51 Mcal/d; P < 0.01). The effect of disease and its interaction with time was statistically significant ( P < 0.01). Differences were statistically significant from day 2 to 15 ( P < 0.01), and days 17 ( P = 0.01), 19 ( P = 0.01), 20 ( P < 0.01), and 22 ( P = 0.04) (Figure 410 D). Logistic Regression Results The average DMI one week prepartum was not predictive of dystocia, RP, , it was predictive for disease and ketosis. For each kg decrease in the average of DMI one week prepartum there was an increase in the odds of having ketosis in the postpartum period by 25% ( P <0.01). Also, for each kg less of dry matter intake on prepartum period increased the odds of postpartum disease by 24% ( P < 0.01). But for diseases or disorders as dystocia, retained placenta, metritis, and displaced abomasum the effect was not significant (Table 42). However, only for dystocia the effect of the drop in dry mater intake had a tendency to be significant ( P = 0.06) (Data not shown). The area under the curve obtained from the ROC analysis was 0.62 for dystocia; for RP was 0.61, for metritis, 0.73, for mastitis was 0.82; for ketosis, the area was 0.77; f or DA was 0.58, and lastly for the variable Disease, was 0.67.

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69 Table 41. Frequency table of the diseases or disorders presented among the cows in the study. Disease/Disorder Frequency Percentage (%) DIM (Range) Dystocia Retained Placenta Metritis Mas titis Ketosis Displaced Abomasum Diseased cows* Cows < 3.5 Primiparous cows Total of cows in the study 50 19 70 60 109 8 170 207 61 294 17.0 6.5 24.0 20.4 37.1 2.7 57.8 70.0 20.7 100 3 (1 7) 7 (3 12) 12 (1 – 28) 8 (1 – 28) 13 (624) *Cows that had at least one disease or disorder except dystocia DIM = Days in milk in which the disease or disorder was diagnosed Note: disease frequency was recorded for day 1 to 28 postpartum

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70 Figure 41. Venn diagram of the frequency of RP, metritis, mastitis, and ketosis during the first 28 days in milk.

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71 A. B. Time (days) -14 -12 -10 -8 -6 -4 -2 0DMI (Kg/day) 0 2 4 6 8 10 12 14 16 Time (days) 051015 20 25 30DMI (Kg/day) 0 5 10 15 20 25 C. D. Time (days) 051015202530Milk (Kg/day) 010203040 Time (days) 0510 15 20 25 30Net Energy Balance (Mcal/day) -14 -12 -10 -8 -6 -4 -2 0 2 4 Figure 42. Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with dystocia. A) Least squares means ( SEM) of DMI (Kg/day) during prepartum period in cows that eventually experienced dystocia and the ones that did not experienc ed dystocia. (10.54 0.39 vs. 9.84 0.20 kg/d; P = 0.09). (n=294). B) Least squares means ( SEM) of DMI (Kg/day) during postpartum period in cows that experienced dystocia and the ones that did not experienced dystocia. (14.52 0.54 vs. 15.34 0.27 kg /d; P = 0.16). (n=267). C) Least squares means ( SEM) of milk yield (Kg/day) in cows that experienced dystocia and the ones that did not experienced dystocia. (25.41 1.11 vs. 25.76 0.61 Kg/d; P = 0.86). (n=280). D) Least squares means ( SEM) of net energy balance (Mcal/day) during postpartum period in cows that experienced dystocia and the ones that did not experienced dystocia. ( 3.20 0.90 vs. 1.90 0.45 Mcal/d; P = 0.19). (n=230).

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72 A. B. Time (days) 0510152025 30DMI (Kg/days) 0 5 10 15 20 25 Time (days) 05 10 15 20 25 30Milk (Kg/day) 0 10 20 30 40 C. D. Time (days) 0 5 10 15 20 25 30Body Weight (Kg/day) 400 450 500 550 600 650 700 Time (days) 051015202530Net Energy Balance (Mcal/day) -10 -5 0 5 Figure 43. Association between postpartum dry matter intake (DMI), milk, body weight (BW), and net energy balance with dystocia in primiparous cows. A) Least squares means ( SEM) of DMI (Kg/day) postpartum in primiparous cows that experienced dystocia and the ones that did not experienced dystocia (13.35 0.92 vs. 14.37 0.49 Kg/d; P = 0.76) (n=267). B) Least squares means ( SEM) of milk yield (Kg/day) in primiparous cows that experienced dystocia and the ones that did not experienced dystocia (17.69 2.06 vs. 16.74 1.31 Kg/d; P = 0.96) (n=280). C) Least squares means ( SEM) of BW (Kg/day) in primiparous cows that experienced dystocia and the ones that did not experienced dystocia (510.7 19.4 vs. 491.4 10.2 Kg/d; P = 0.38) (n=294). D) Least squares means ( SEM) of net energy balance (Mcal/day) during the postpartum period in primiparous cows that experienced dystocia and the ones that did not experienced dystocia ( 1.65 1.43 vs. 1.56 0.76 Mcal/d; P = 0.04) (n=230).

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73 A. B. Time (days) 05 10 15 20 25 30DMI (Kg/day) 0 5 10 15 20 25 Time (days) 0 5 10 15 20 25 30Milk (Kg/day) 0 10 20 30 40 C. D. Time (days) 0 5 10 15 20 25 30Body Weight (Kg/day) 400 450 500 550 600 650 700 Time (days) 0 5 10 15 20 25 30Net Energy Balance (Mcal/day) -14 -12 -10 -8 -6 -4 -2 0 2 4 Fi gure 44. Association between postpartum dry matter intake (DMI), milk, body weight (BW), and net energy balance with dystocia in multiparous cows. A) Least squares means ( SEM) of DMI (Kg/day) postpartum in multiparous cows that experienced dystocia and the ones that did not experienced dystocia (15.62 0.65 vs. 16.35 0.26 Kg/d; P = 0.72) (n=267). B) Least squares means ( SEM) of milk yield (Kg/day) in multiparous cows that experienced dystocia and the ones that did not experienced dystocia (26.84 1.33 vs. 27.00 0.97 Kg/d; P = 0.91) (n=280). C) Least squares means ( SEM) of BW (Kg/day) in multiparous cows that experienced dystocia and the ones that did not experienced dystocia (634.0 14.6 vs. 660.1 6.0 Kg/d; P = 0.10) (n=294). D) Least squares means ( SEM) of net energy balance (Mcal/day) during the postpartum period in multiparous cows that experienced dystocia and the ones that did not experienced dystocia ( 4.77 1.07 vs. 5.37 0.45 Mcal/d; P = 0.63) (n=230).

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74 A. B. Time (days) -14 -12 -10 -8 -6 -4 -2 0DMI (Kg/day) 0 2 4 6 8 10 12 14 16 Time (days) 0 5 10 15 20 25 30DMI (Kg/day) 0 5 10 15 20 25 C. D. Time (days) 0 5 10 15 20 25 30Milk (Kg/day) 0 10 20 30 40 Time (days) 05101520 25 30Net Energy Balance (Mcal/day) -14 -12 -10 -8 -6 -4 -2 0 2 4 6 Figure 45. Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with retained placenta (RP). A) Least squares means ( SEM) of DMI (Kg/day) during prepartum period in cows that eventually experienced RP and the ones that did not experienced RP (9.21 0.98 vs. 9.98 0.19 kg/d; P = 0.87). (n=294). B) Least squares means ( SEM) of DMI (Kg/day) during postpartum period in cows that experienced RP and the ones that did not experienced RP (12.36 0.82 vs. 15.35 0.24 kg/d; P < 0.01). (n=267). C) Least squares means ( SEM) of milk yield (Kg/day) in cows that experienced RP and the ones that did not experienced RP (19.57 1.57 vs. 25.34 0.49 kg/d; P < 0.01). (n=280). D) Least squares means ( SEM) of net energy balance (Mcal/day) during postpartum period in cows that experienced RP and the ones that did not experienced RP ( 2.99 1.37 vs. 2.05 0.41 Mcal/d; P = 0.50). (n=230).

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75 A. B. Time (days) -14 -12 -10 -8 -6 -4 -2 0DMI (Kg/day) 0 2 4 6 8 10 12 14 16 Time (days) 0 5 10 15 20 25 30DMI (Kg/day) 0 5 10 15 20 25 C. D. Time (days) 0 5 10 15 20 25 30Milk (Kg/day) 0 10 20 30 40 Time (days) 0 5 10 15 20 25 30Net Energy Balance (Mcal/day) -14 -12 -10 -8 -6 -4 -2 0 2 4 Figure 46. Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with metritis. A) Least squares means ( SEM) of DMI (Kg/day) during prepartum period in cows that eventually experienced metritis and the ones that did not experienced metritis (9.54 0.33 vs. 10.10 0.23 kg/d; P = 0.12). (n=294). B) Least squares means ( SEM) of DMI (Kg/day) during postpartum period in cows that experienced metritis and the ones that did not experienced metritis (13.34 0.45 vs. 15.67 0.25 kg/d; P < 0.01). (n=267). C) Least squares means ( SEM) of milk yield (Kg/day) in cows that experienced metritis and the ones that did not experienced met ritis (22.43 0.91 vs. 26.48 0.57 Kg/d; P < 0.01). (n=280). D) Least squares means ( SEM) of net energy balance (Mcal/day) during postpartum period in cows that experienced metritis and the ones that did not experienced metritis ( 2.68 0.67 vs. 1.96 0.44 Mcal/d; P = 0.39). (n=230)

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76 A. B. Time (days) -14-12-10-8-6-4-20DMI (Kg/day) 0246810121416 Time (days) 051015202530DMI (Kg/day) 0510152025 C. D. Time (days) 051015202530Milk (Kg/day) 010203040 Time (days) 0 5 10 15 20 25 30Net Energy Balance (Mcal/day) -14 -12 -10 -8 -6 -4 -2 0 2 4 Figure 47. Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with mastitis. A) Least squares means ( SEM) of DMI (Kg/day) during prepartum period in cows that eventually experienced mastitis and the ones that di d not experienced mastitis (9.28 0.36 vs. 10.12 0.19 kg/d; P = 0.03). (n=294). B) Least squares means ( SEM) of DMI (Kg/day) during postpartum period in cows that experienced mastitis and the ones that did not experienced mastitis (14.41 0.51 vs. 15.34 0.25 kg/d; P = 0.08). (n=267). C) Least squares means ( SEM) of milk yield (Kg/day) in cows that experienced mastitis and the ones that did not experienced mastitis (22.97 0.79 vs. 26.26 0.58 Kg/d; P = 0.03). (n=280). D) Least squares means ( S EM) of net energy balance (Mcal/day) during postpartum period in cows that experienced mastitis and the ones that did not experienced mastitis ( 1.91 0.85 vs. 2.69 0.50 Mcal/d; P = 0.39). (n=230)

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77 A. B. Time (days) -14 -12 -10 -8 -6 -4 -2 0DMI (Kg/day) 0 2 4 6 8 10 12 14 16 Time (days) 0 5 10 15 20 25 30DMI (Kg/day) 0 5 10 15 20 25 C. D. Time (days) 0510152025 30Milk (Kg/day) 0 10 20 30 40 Time (days) 0 5 10 15 20 25 30Net Energy Balance (Mcal/day) -14 -12 -10 -8 -6 -4 -2 0 2 4 Figure 48. Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with ketosis. A) Least squares means ( SEM) of DMI (Kg/day) during prepartum period in cows that eventually experienced ketosis and the ones that did not experienced ketosis (9.16 0.31 vs. 10.23 0.19 Kg/d; P < 0.01). (n=294). B) Least squares means ( SEM) of DMI (Kg/day) during postpartum period in cows that experienced ketosis and the ones that did not experienced ketosis (13.41 0.39 vs. 15.80 0.25 Kg/d; P < 0.01). (n=267). C) Least squares means ( SEM) of milk yield (Kg/day) in cows that experienced ketosis and the ones that did not experienced ketosis (25.79 0.86 vs. 25.57 0.60 Kg/d; P = 0.81). (n=280). D) Least squares means ( SEM) of net energy balance (Mcal/day) during postpartum period in cows that experienced ketosis and the ones that did not experienced ketosis ( 5.55 0.66 vs. 0.87 0.41 Mcal/d; P < 0.01). (n=230).

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78 A. B. Time (days) -14 -12 -10 -8 -6 -4 -2 0DMI (Kg/day) 0 2 4 6 8 10 12 14 16 Time (days) 0 5 10 15 20 25 30DMI (Kg/day) 0 5 10 15 20 25 C. D. Time (days) 0 5 10 15 20 25 30Milk (Kg/day) 0 5 10 15 20 25 30 35 40 Time (days) 0 5 10 15 20 25 30Net Energy Balance (Mcal/day) -20 -10 0 10 Figure 49. Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with displaced abomasum (DA). A) Least squares means ( SEM) of DMI (Kg/day) during prepartum period in cows that eventually experienced DA and the ones that did not experienced DA (11.49 1.07 vs. 10.48 1.01 Kg/d; P = 0.44). (n=294). B) Least squares means ( SEM) of DMI (Kg/day) during postpartum period in cows that experienced DA and the ones that did not experienced DA (8.95 1.95 vs. 15.77 0.21 Kg/d; P < 0.01). (n=267). C) Least squares means ( SEM) of milk yield (Kg/day) in cows that experienced DA and the ones that did not experienced DA (19.92 3.85 vs. 27.85 0.48 Kg/d; P = 0.04). (n=280). D) Least squares means ( SEM) of net energy bal ance (Mcal/day) during postpartum period in cows that experienced DA and the ones that did not experienced DA ( 8.95 3.39 vs. 3.56 0.41 Mcal/d; P = 0.12). (n=230).

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79 A. B. Time (days) -14 -12 -10 -8 -6 -4 -2 0DMI (Kg/days) 0 2 4 6 8 10 12 14 16 Time (days) 051015202530DMI (Kg/day) 0510152025 C. D. Time (days) 051015202530Milk (Kg/day) 010203040 Time (days) 0 5 10 15 20 25 30Net Energy Balance (Mcal/day) -10 -8 -6 -4 -2 0 2 4 Figure 410. Association between pre and postpartum dry matter intake (DMI), milk, and net energy balance with cows that at least had one disease (RP, metritis, mast itis, ketosis, and DA). A) Least squares means ( SEM) of DMI (Kg/day) during prepartum period in cows that eventually experienced at least one disease or disorder and cows that did not experienced any disease or disorder (9.35 0.24 vs. 10.49 0.22 Kg/d; P < 0.01). (n=294). B) Least squares means ( SEM) of DMI (Kg/day) during postpartum period in cows that experienced at least one disease or disorder and cows that did not experienced any disease or disorder (13.91 0.32 vs. 16.22 0.29 Kg/d; P < 0.01) . (n=267). C) Least squares means ( SEM) of milk yield (Kg/day) in cows that experienced at least one disease or disorder and cows that did not experienced any disease or disorder (24.43 0.56 vs. 26.71 0.51 Kg/d; P < 0.01). (n=280). D) Least squares m eans ( SEM) of net energy balance (Mcal/day) during postpartum period in cows that experienced at least one disease or disorder and cows that did not experienced any disease or disorder ( 3.43 0.56 vs. 0.85 0.51 Mcal/d; P < 0.01). (n=230).

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80 Table 42. Effect of each kilogram (Kg) decrease of dry mater intake on the last seven days postpartum. Disease/Disorder Comparison Odds ratio (OR) 95% CI *P value Dystocia Retained Placenta Metritis Mastitis Ketosis 1DA 2Diseased cows 1 Kg decrease 1 Kg decrease 1 Kg decrease 1 Kg decrease 1 Kg decrease 1 Kg decrease 1 Kg decrease 1.18 1.04 1.07 1.08 1.25 1.07 1.24 0.67 1.69 0.861.22 0.951.19 0.941.21 1.131.37 0.821.32 1.131.34 0.54 0.66 0.23 0.27 <0.01 0.6 <0.01

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81 CHAPTER 5 DISCUSSION The incidences of the diseases and disorders found in the present study are within the ranges found in previous studies (Kimura et al., 2002; LeBlanc et al., 2005; Benzaquen et al. 2006; Mee, 2007; Cheong et al., 2011; Santschi et al., 2011; Martnez et al., 2 012; Ribeiro et al., 2013; Oliveira et al., 2013; Vergara et al., 2014). During prepartum there is a physiological drop in DMI starting approximately on day 10 before parturition (Rhoads et al., 2004). However, cows that eventually had metritis, subclinica l ketosis, and dystocia spent less time eating, which decreased the DMI prepatum (Huzzey et al., 2007; Goldhawk et al., 2009; Proudfoot et al., 2009). Similarly, the results of this study indicate that DMI prepartum is associated with most of diseases and disorders that occur postpartum such as RP, met ritis, ketosis, and mastitis . Cows that eventually experienced RP, metritis, mastitis, and ketosis had decreased DMI prepartum compared with cows that did not experience these diseases or disorders with the ex ception of dystocia and DA. In fact, cows that had dystocia tended to have an increased DMI 14 prepartum. A hypothesis for this association was that increased DMI prepartum would lead to increased BCS and thus increasing the risk of dystocia because of inc reased fat deposition in the birth canal and increased birth weight of the calf (Grunert, 1979). However, the spearman correlation between BCS and dystocia was not significant ( rs = 0.02, P = 0.76). Besides , the results of this current project contrast the findings of Proudfoot et al. (2009) in which they reported that cows with dystocia had 12% less DMI and less eating bouts during the last 48 hours before calving, and consumed 24% less during the 24 h before calving.

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82 Cows that eventually developed mastiti s and ketosis had a sharp decrease in DMI star t ing one week prepartum, whereas cows that developed RP and metritis had a sharp drop in DMI on days 3 and 2 only. Our findings corroborate previous observations where cows that had mild and severe metritis had a drop in DMI prepartum (Huzzey et al., 2007), which may be a result of spending less time eating (Urton et al., 2005, Huzzey et al., 2007). The drop in DMI prepartum probably leads to a drop in energy intake needed for maintenance, growth, activity, and the growth of the fetus. A deficit in energy leads to lipid mobilization. In the adipocytes, lipase hydrolyzes triglycerides and releases nonesterified fatty acids (NEFA), which bind to albumin in the blood and are transported to the liver where it can be turned into triglycerides, completely oxidized and turned into CO2, or be partially oxidized and turned into ketone bodies such as acetoacetate, acetone and especially BHBA (Emery et al., 1992). Thus , the drop in DMI increases NEFA and BHBA in blood. C oncentrations of NEFA greater than 0.3 mEq/L were found to be a risk factor for DA, metritis, RP, and clinical ketosis (Ospina et al., 2010). Subclinical and clinical ketosis have been found to be associated with postpartum diseases such as RP, metritis (Suthar et al., 2013), mastitis (Raboisson et al., 2014), and DA (Mcart et al., 2012), which corroborate our observations. Increased blood NEFA concentrations have been associated with impaired neutrophil function (Hammon et al., 2006). Concentrations of BHBA similar to those of cows with subclinical ketosis impaired neutrophil phagocytosis, extracellular trap formation, and killing of bacteria i n vitro (Hoeben et al., 1997; Grinberg et al., 2008), which may predispose cows to metritis. Cows with RP have also been found to have decreased neutrophil

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83 chemotaxis to the sites of placental attachment (Kimura et al., 2002), which indicates that cows wit h RP also have an impairment in neutrophil function. This impairment may also help explain the high correlation between RP and metritis (HosseinZadeh, 2011). Impairment in innate immunity would hinder bacterial clearance and hence predispose to other infectious diseases such as mastitis (Burvenich et al., 2007). Dry matter intake prepartum was predictive of ketosis and one disease. For each kg decrease in the average DMI one week prepartum there was an increase in the odds of having ketosis by 25 % and for disease by 24%. Ketosis seemed to have contributed heavily for the significance of the variable disease as 64% of diseased cows had ketosis; therefore the pattern of DMI for diseased cows and cows that had ketosis was very similar. A previous study focus on time spent at the feed bunk, and observed that for each 10 minutes decrease in feeding time there was an increase in the odds of having ketosis of 90% (Goldhawk et al., 2009). For diseases RP and metritis it is possible that the logistic regression results were not significant because DMI in the last 3 days prepartum was not offered to the model; therefore, a further analysis should be performed. In contrast to our findings, Huzzey et al., (2007) observed that for each kg decrease in DMI in the last week prepartum there was an increase in the odds of severe metritis of 72%. Postpartum, DMI was lesser in cows that had all the diseases included in this study (RP, metritis, mastitis, ketosis, and DA) except for dystocia. Interpreting the results of dystocia it has to be done with care because the variable dystocia used in this study included from manual assistance to fetotomy during parturition According to Bareille et al. (2003), the cumulative loss of DMI for cows having twins was 13 kg, for

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84 cows having ces arean section or fetotomy it was 43 kg, and assisted calving with hard pull it was 37 kg. Therefore, further analysis looking at varying degrees of dystocia is needed. The drop in DMI from days 1013 in cows with dystocia may be a consequence of metritis development in cows that had dystocia as dystocia is a risk factor for metritis (Bruun et al., 2002). The correlation between dystocia and metritis was significant ( rs = 0.14; P = 0.02). The decrease in DMI seen in cows with metritis and mastitis may be partly explained by the induction of an inflammatory process. Mounting an inflammatory response to infectious agents has been shown to increase the energy used for maintenance by 40% (Plank and Hill, 2000). Furthermore, proinflammatory cytokines such as IL1, IL6 and TNF NEFA in blood (Gifford et al., 2012). These cytokines also act in the central nervous system (CNS) and induce anorexia (PlataSalaman et al., 1996). Anorexia may worsen the energy balance and lead to fat mobilization and ketone body production which may further decrease feed intake (Visinoni et al., 2012). This vicious cycle may explain why ketosis had such a negative effect on DMI postpartum. Together these data indicate that, in diseased cows, proinflammatory cytokines plus metabolites from lipid mobilization (NEFA and BHBA) decrease DMI postpartum. Thus, as all these diseases are correlated, ketosis may play a big role in decreasing DMI intensifying the effects of infectious dise ases such as metritis and mastitis. The effect of RP may be explained by its high correlation with metritis (rs = 0.37, P < 0.01) . Besides, in the case of DA the drop in DMI may be due to obstruction and a decrease in rumen passage rate (Coppock, 1973). Besides, DA has an association with ketosis and diseases such as RP, metritis,

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85 and twins (LeBlanc et al., 2005). However, to evaluate if DMI postpartum is a risk for postpartum diseases further investigation will be needed in which the time point of each dis ease is recorded which was not done in this study. Milk yield was lower in cows that had RP, metritis, mastitis, and DA. This reduction may have been caused by the reduction on DMI during the postpartum period in diseased cows. Reduction in milk yield in c ows affected with RP, metritis, mastitis, and DA has been previously reported (van Werven et al., 1992; Rajala and Grohn, 1998; Huzzey et al., 2007; Galvo et al., 2010; Van Winden et al., 2003). The milk production in cows with RP was affected around day 6 as RP is highly correlated with metritis and metritis is diagnosed on 7 DMI this decrease may be due by the effects of metritis. For mastitis, it becomes clear that the affected udder will produce less. One of the cardinal signs of inflammation of the u dder is the loss of function. Infected quarters of cows with moderate mastitis had a 60% decrease in milk yield whereas uninfected quarters had an 11% decrease. In cows with severe mastitis the decrease was of 90% in infected quarters and 78% in uninfected quarters (Heyneman et al., 1990). In this present study milk yield was not affected by ketosis, except on days 2, and 3 of lactation in which ketotic cows produced more milk. This lack of difference or even higher milk production in ketotic cows may be ex plained by genetic merit for milk production. Ketotic cows may have a higher genetic merit for milk production which may drive milk yield despite lower DMI. Therefore, ketotic cows resort to fat mobilization to maintain milk yield. Therefore, a further analysis controlling for genetic merit is warranted. Nonetheless, others have reported lower milk yield in ketotic cows (Chapinal et al., 2012).

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86 In cows with diseases or disorders as dystocia, RP, metritis, mastitis, and DA the net energy balance did not dif fer compared to cows that did not have any of these diseases. Energy balance is influenced by DMI and milk production. Therefore, the possible reason that cows with these diseases did not vary between diseased cows and cows without the diseases included in this study is because the drop in DMI was accompanied by a drop in milk yield. Therefore, the drop in milk yield compensate the energy balance. In contrast, ketotic cows do show a sharp decrease on net energy balance compared to non ketotic cows supporti ng that high producing cows are trying to maintain milk yield despite lower DMI. A similar behavior in the plot was seen in cows that had at least one disease which could be due to the high proportion of ketotic cows among the diseased cows. Ketotic cows h ad lower net energy balance during the entire study period. It is clear that cows with ketosis have lower net energy balance because ketosis is just a consequence of the lack of glucose which provides energy to maintain the body. The fat mobilization produces ketone bodies that go to the bloodstream. These ketone bodies are converted in NEFA and BHBA and high levels of these metabolites means more mobilization of fat which means higher NEB and consequently higher BHBA. Cows are more prone to have NEB in the first days of postpartum because the demand of energy is higher due to the onset of lactation. In the case of DA, a lot of variation occurred even though the net energy balance was not significant during the postpartum period. Several studies have shown t hat high NEFA increases the risk of having DA (Cameron et al. 1998; Van Winden et al., 2003; Ospina et al., 2010; Suthar et al., 2013) which can explain the difference on net energy balance. However, further

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87 analysis may be done to verify the effect of DA on net energy balance due to the small number of cases. Therefore, it cannot be concluded by this results that DA is not related to net energy balance. There was an interaction between dystocia and parity in which primiparous cows that had dystocia had low er net energy balance compared to the ones that did not had dystocia. Higher body condition in primiparous cows may increase the risk of dystocia (Johanson and Berger, 2003) and body fat will lead to an increase in leptin which will lead to satiety and hen ce a decrease in DMI (Liefers et al., 2003). However, multiparous cows that had dystocia had lower BW compared to multiparous cows that did not have dystocia.

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88 CHAPTER 6 CONCLUSION According to the results of this study, prepartum dry matter intake is associated with some postpartum diseases such as retained placenta, metritis, mastitis, ketosis, and for the variable disease. The possible pathway may be that the drop in dry matter intake increase lipid mobilization in the form of NEFA which led to an increase in BHBA which lead to immunosuppression which increases the odds of having any of these postpartum diseases. Conversely, in cows that had dystocia it may seem that an increase in dry matter intake increases fat deposition increasing the odds of dyst ocia. Also, postpartum dry matter intake is associated with postpartum diseases. Cows that experience one or more of the postpartum diseases or disorders discussed in this study will have symptoms that will affect the intake. Therefore a drop in the intake is likely to occur during the course and recovery of the disease. Moreover, dry matter intake prepartum was a significant explanatory variable for postpartum disease. A decrease in the feed intake during the last week prepartum increased the chances of h aving postpartum disease. This was not true for all the diseases when were treated indiv idually. However, it was significant for diseases in general and ketosis. Altogether, diseased cows with the exception of dystocia had decreased DMI prepartum and DMI one week prepartum was a significant predictor of disease postpartum, particularly ketosis. Maintaining the DMI prepartum, especially in the last week may help maintain herd health.

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100 BIOGRAPHICAL SKETCH Johanny Maribel Prez Bez was born in Santo Domingo, the capital of Dominican Republic in 1982. She is the third of four siblings. Her mother and father were born in rural areas from the north part of Dominican Republic. Johanny pursue d her degree in veterinary medicine at the Universidad Autnoma de Santo Domingo (UASD) in 2005. After her graduation she worked in small animal clinics where she practice her medicine knowledge and experienced how was to work with clients on real life cases. On 2009 she joined UASD staff working in two research studies done by this university. During the development of these studies she received several trainings in reproduction biotechnologies in the Virginia Maryland College of Veterinary Medicine. On 2011 she started to work as a lecturer and as a teacher assistant in reproduction courses in the Veterinary School of UASD. Working in UASD has taught Johanny how to work with students, and have given her experience in projects development. On 2012, Johanny was accepted in the Fulbright program. During this program, Johanny was sent to the Virginia Tech Language and Culture Institute where she improved her English skills and had the opportunity to have contact and learn about other cultures . After that she was accepted to the Master of Science program of v eterinary Medical Science at the College of Veterinary Medicine of University of Florida, Department of Large Animal Clinical Science. During this program she has been awarded as an Outstanding International Student and Excellence in Basic Science Research. She graduate d in summer 2015 from the master’s program, and after this her next goal is to pursue a doctoral degree at University of Florida.