ABSTRACT GRAHAM, JASON ROBERT. Assessment of Crossbred

ABSTRACT

GRAHAM, JASON ROBERT. Assessment of Crossbred Dairy Cattle for Heat Tolerance in a Pasture-Based System. (Under the direction of Dr. Francesco Tiezzi).

Heat stress has a large impact on dairy cattle production and welfare throughout the world and is expected to be exacerbated due to global climate change. Ways to ameliorate heat stress, such as environmental modifications, nutrition and genetic selection have been proposed. Most genetic selection for heat tolerance so far has been performed in purebred dairy cattle through the analysis of routine and novel traits using robust statistical methods. Furthermore, genomic selection and identification of candidate genes for heat tolerance have shown promise in creating more thermotolerant dairy herds. However, inbreeding depression has a negative effect on the fitness of purebred animals, which is of major concern to the dairy industry at large. Crossbreeding has been used in many species to take advantage of hybrid vigor and reverse the detrimental effects of inbreeding. However, crossbreeding of dairy cattle in the United States is still uncommon, but interest is growing due to a rise in organic and pasture-based dairy systems, which are more common in countries such as New Zealand and Ireland. This is because most pasture-based dairy farms are seasonally calving and therefore emphasis is placed on fitness traits. Pasture-based dairy systems are also the most affected by heat stress related production losses because they lack environmental modifications needed to cool lactating dairy cattle in warm weather. Therefore, the primary objective in this thesis was to evaluate crossbred performance for dairy cattle in a pasture- based system to determine if crossbreeding provides resistance to predicted losses due to heat stress. The results from our study demonstrated that the greatest impact of performance when the environment became more challenging was due to the proportion of breed ancestry in the cow.

Dairy cows with greater Holstein ancestry had better performance for milk yield, protein yield, fat yield and SCS, while those with greater Jersey breed ancestry had produced milk with higher

protein and fat percentage. Furthermore, the F1 crossbred animals, particularly those with Jersey dams, were as competitive with the purebred Holstein animals for production yield traits. The results reported in this thesis showed that crossbreeding has potential for maintaining performance when the environment becomes more challenging to dairy cattle in a pasture-based system.

Assessment of Crossbred Dairy Cattle for Heat Tolerance in a Pasture-Based System

by Jason Robert Graham

A thesis submitted to the Graduate Faculty of North Carolina State University in partial fulfillment of the requirements for the degree of Master of Science

Animal Science

Raleigh, North Carolina 2019

APPROVED BY:

______Dr. Francesco Tiezzi Dr. Stephanie Ward Committee Chair

______Dr. Dahlia Nielsen

DEDICATION

First, I dedicate this thesis to my fiancée Shelby, who has been my biggest cheerleader, even when things became difficult and nights became long. To my Father and Grandfather for their financial support. To my Mother, and my late Grandmother, for instilling in me the curiosity, passion and love of biology and animal husbandry at a young age. Finally, I dedicate this thesis to all my fellow graduate students and friends who I have made throughout my journey in graduate school.

BIOGRAPHY

Jason R. Graham was born in Yucaipa, California to Jennifer and Steven Graham. He grew up on a small hobby farm where he learned how to ride horses, raise poultry, goats and other animals as well as tend to fruit trees and vegetables. He moved to North Carolina after high school and began his college career at Stanly Community College in Albemarle, NC, where he graduated with an

Associates in Science. After transferring to North Carolina State University, he graduated summa cum laude with an Animal Science degree. Immediately afterward, he began pursuing his Master of Science in Animal Science degree under the direction of Dr. Francesco Tiezzi.

iii

ACKNOWLEDGMENTS

First, I would like to acknowledge my mentor Dr. Francesco Tiezzi, for always being available and patient when Im trying to understand new concepts and for making me a better scientist. I would like to thank all the professors at NC State that have helped broaden my expertise and understanding. I would like to acknowledge my friends, Sarah Allen, Dr. Jennifer Moore, and

Hannah Wackel for helping proofread portions of my thesis. Finally, I would like to thank my cohorts and colleagues, He Yuqing, Emmanuel Lozada Soto and Dr. Piush “Sweetie Khanal for always having my back and making me laugh when things got tough.

TABLE OF CONTENTS

LIST OF TABLES ...... vi

LIST OF FIGURES ...... vii

Chapter 1. Literature Review ...... 1 Heat Stress ...... 2 The Thermoneutral Zone and Temperature-Humidity Index ...... 3 Heat Stress and Fertility ...... 4 Heat Stress and Production Traits ...... 5 Regional Impact ...... 6 Climate Change ...... 7 Environmental modification ...... 7 Selection for Thermotolerance ...... 8 Organic and Pasture-based systems ...... 12 Selection for Animals in Pasture-Based systems ...... 14 Crossbreeding ...... 16 Hybrid Vigor ...... 17 Breed Utilization ...... 18 Crossbreeding for Thermotolerance ...... 20 References ...... 22

Chapter 2. Evaluation of crossbred dairy cattle performance when exposed to a range of temperature and humidity in a pasture-based system ...... 40 Abstract ...... 41 Introduction ...... 43 Materials and Methods ...... 46 Management and Animals ...... 46 Traits ...... 48 Meteorological-Information ...... 49 Statistical Analyses ...... 50 Results and Discussion ...... 52 Environmental descriptors and effects ...... 53 Effect of Breed and Heterosis ...... 54 Effect of Breed and Heterosis Interaction with the Environment ...... 57 Conclusions ...... 60 References ...... 62

Chapter 3. Conclusions ...... 83

APPENDIX ...... 86

LIST OF TABLES

Table 1. Number of animals observed and genetic expectations for each breed group ...... 70

Table 2. Correlations between genetic covariates of direct Holstein breed (%) and heterosis ...... 71

Table 3. Values used for classifying environmental descriptors ...... 72

Table 4. Descriptive statistics of overall herd ...... 73

Table 5. F-value and significance of fixed effects ...... 74

Table 6. Environmental descriptor and least-squares means ...... 75

LIST OF FIGURES

Figure 1. Change in direct and maternal expected breed percentage (Holstein) and heterosis average by year of bird and year of test date ...... 76

Figure 2. Least-squares means for breed group by environment interaction of four breed groups and regression coefficient estimates (right) of four genetic factors for milk yield (kg/d) across levels of the environmental factor ...... 77

Figure 3. Least-squares means for breed group by environment interaction of four breed groups and regression coefficient estimates (right) of four genetic factors for fat yield (kg/d) across levels of the environmental factor ...... 78

Figure 4. Least-squares means for breed group by environment interaction of four breed groups and regression coefficient estimates (right) of four genetic factors for protein yield (kg/d) across levels of the environmental factor ...... 79

Figure 5. Least-squares means for breed group by environment interaction of four breed groups and regression coefficient estimates (right) of four genetic factors for protein (%) across levels of the environmental factor ...... 80

Figure 6. Least-squares means for breed group by environment interaction of four breed groups and regression coefficient estimates (right) of four genetic factors for fat (%) across levels of the environmental factor ...... 81

Figure 7. Least-squares means for breed group by environment interaction of four breed groups and regression coefficient estimates (right) of four genetic factors for somatic cell score across levels of the environmental factor ...... 82

vii

CHAPTER 1

Literature Review

HEAT STRESS

Because dairy cattle (Bos taurus) are homeothermic animals, they must regulate their body temperature within a narrow range, specifically between 38.6°C and 39.6°C (Radostits et al., 2000). This optimal range is tightly regulated through biological processes that either increase heat production (e.g., shivering) or promote heat loss (e.g., sweating). An animal's metabolism is the sum of the catabolic and anabolic reactions that occur at the cellular level

(Herman, 2016). When these chemical reactions occur, they produce heat as a by-product.

Generally, excess metabolic heat is released into the surrounding atmosphere. However, when this heat cannot be released efficiently, the cows body temperature will rise outside of its optimal range.

Heat stress refers to the biological response that occurs as a result of excess radiant energy acting on an animal in such a way that requires the animal to utilize metabolic energy to keep itself in thermal equilibrium (Polsky & Von Keyserlingk, 2017). This may result in both physiological and behavioral changes in the affected animals. These changes include: increased body temperature, shade-seeking behavior, increased water intake, sweating, panting, pacing, reduced rumination, reduced feed intake, and changes in hormone levels (Da Silva et al., 2007).

These changes occur earlier in lactating dairy animals because of the increased metabolic heat production caused by lactation, which can change over time (Kadzere et al., 2002). Furthermore, dairy animals with greater milk production will also have a higher basal metabolism and an increased internal temperature when compared to less productive animals (Chebel et al., 2004).

For example, Holstein cattle administered recombinant bovine growth hormone (RbST), a synthetic hormone that increases milk production, were found to have elevations in body temperature of 0.2°C and 0.3°C during morning and evening milking, respectively (West et al.

1990). This is due to RbST increasing the metabolic rate, which consequently increases heat production. High basal metabolism as a result of production can cause heat stress to occur more often. Consequently, drops in performance are often observed in the summer months because heat stress has an antagonistic relationship with production and fertility. Selection of dairy breeds for high production has made the dairy herds in the United States more susceptible to production losses caused by an increasing frequency of temperature extremes (Klinedinst, 1993). The annual estimated loss from heat stress to the U.S. dairy industry is between $897 million and $1,500 million (St-Pierre et al., 2003). Therefore, heat stress is an integral factor in the profitability of dairy farms in warmer climates.

The thermoneutral zone and temperature-humidity index

Assessing heat stress requires a standard of measurement, which traditionally includes the thermoneutral zone and the temperature-humidity index. A cows thermoneutral zone (TNZ) is defined as its optimum range of ambient temperature for production, which varies based on the animals metabolic state (Yousef, 1985). Roenfeldt (1998) reported that a lactating dairy cows

TNZ is between 5 and 25°C. Berman et al. (1985) observed the rectal temperature, respiratory frequency, ear-skin temperature, body weight, and milk yield of Israeli Holstein cattle and determined the upper critical level, when mild heat stress begins, to be roughly 25 to 26°C. Dikmen

& Hansen (2009) later observed rectal temperatures from Florida Holstein cattle and reported mild heat stress to begin at roughly 28.4°C. However, temperature alone may not adequately explain the ambient environment that the animals are managed in and is not as useful as other measures when comparing different environments. The temperature-humidity index (THI) is a unitless index that was developed to combine the effect that temperature and humidity have on livestock (NRC,

1971). Ravagnolo et al. (2000) observed a value of 72 THI as the upper critical level of the TNZ

for THI, but more recently, Jeelani et al. (2019) observed a value of 80 THI. When THI exceeds this threshold, dairy animals begin to present the signs of heat stress described previously

(Armstrong, 1994). However, different calculations for the temperature-humidity index better explain the environment that dairy cattle are exposed to during the warmer months in different regions. When comparing milk production losses in the arid southwestern and humid southeastern

U.S., it was reported that different calculations of THI should be used. A THI that places higher emphasis on ambient temperature was reported to better predict heat stress in the southwestern

U.S., while a THI calculation that places a greater emphasis on humidity better predicts heat stress in the southeastern U.S. (Bohmanova et al., 2007). Therefore, the type of THI calculation should be considered when predicting heat stress and subsequent production losses in different environments. Wind speed and solar radiation have also been considered as major factors affecting heat stress, and these parameters may be included in some models, but are less relevant in closed- housing production systems (Mader et al., 2006; Mader et al., 2010; Da Silva et al., 2007). The temperature-humidity index has also been successfully incorporated into evaluations that measure the response of production traits to increasingly stressful environments and is therefore useful for selecting animals that have greater thermotolerance (Ravagnolo & Misztal, 2000).

Heat stress and fertility

One of the negative outcomes typically associated with heat stress is a reduction in fertility.

Specifically, heat stress has been found to reduce the duration and intensity of estrus, reduce the quality of oocytes, decrease uterine blood flow, and increase uterine temperature, all of which increase the probability of embryonic loss (Polsky & Von Keyserlingk, 2017). A retrospective study found that pregnancy rates at first insemination are greater (44.1%) for cooler temperatures compared to lower rates (27.4%) for warmer temperatures (López-Gatius, 2003). This is made

worse by dairy cows being artificially inseminated during the peak of their lactation. Gwazdauskas et al. (1986) reported that the average time until first service was 78 days postpartum. This is when metabolic energy requirements are generally at their greatest. During the first trimester of lactation, dairy cattle are in a negative energy balance, in which they cannot consume enough energy to meet their metabolic needs (Coppock, 1985). Therefore, any metabolic energy that must be used for thermoregulation reduces the already limited energy available for reproductive purposes, which may explain the dropoff in reproductive fitness.

Heat stress and production

In addition to negative impacts on fertility, heat stress negatively affects production traits such as milk, protein, and fat yield. When cows were exposed to ambient temperatures outside their thermoneutral zone of greater than 72 THI, a marked reduction in milk yield (15-25%) was observed (Hansen, 2013). Ravagnolo et al. (2000) reported a decrease of 0.2, 0.012, and 0.009 kg for milk, fat, and protein yields, respectively, when THI increased past the threshold of 72 per unit

THI. When THI breached a threshold of 60, Brügemann et al. (2012) reported a decrease in milk yield of 0.08 kg/THI in an enclosed management setting with a high feed-input system and a decrease of 0.17 kg/THI in a pasture-based system. Therefore, the type of management the cow is under can greatly affect the production response. Stage of lactation can also affect the response to heat stress, with mid-lactation and primiparous cows exhibiting the largest dropoff in milk production (Abeni et al., 2007; Bernabucci et al., 2014). This large dropoff is most likely due to the depletion of energy reserves in the lactating dairy animal during this stage and the negative energy balance of the animal, which reduces its ability to optimally thermoregulate.

Regional impact

Management of heat stress is of critical importance to dairies located in the tropics and subtropics. The major U.S. dairy regions that have extended periods of ambient temperature outside of the upper-range of TNZ of dairy cows are the Southwest, South-central plains and

Southeast, of which the Southwest contains the greatest number of cattle. Most of the Southeast experiences a humid subtropical environment (West, 2003). Because of this, southeastern dairy herds are faced with more environmental challenges than other regions of the United States. For example, Freitas et al. (2006) reported a drop in production of 0.36 kg/THI during the summer months in the Southeast (Alabama), which was nearly twice the reduction of 0.18 kg/THI observed in the Southwest (Arizona). The major contributors to this decline are the ambient environment of the cow and the farms ability to manage it. During the cooler nights in the southwestern U.S., there is a general lack of relative humidity. Because of this, dairy animals in this region are more capable of reducing their body temperature, as metabolic heat is more readily released into the atmosphere (Igono et al, 1992; Correa-Calderon et al., 2004). However, in the southeastern U.S., summer nights have a very high relative humidity, which prevents evaporative cooling. This reduces cow comfort and inhibits the animals ability to rest and ruminate efficiently (Soriani et al., 2013). During early lactation, dairy animals are already in a negative energy balance because production is at its peak, and an additional reduction of rumination reduces the bioavailability of energy from feed, which affects both productivity and welfare (Xu et al., 2018). This production loss reduces the profitability of southeastern U.S. dairies and makes them less competitive with other U.S. regions (Mukherjeet et al., 2013).

Climate change

Climate change is expected to affect agriculture differently depending on the region.

Regions in northern latitudes may see extended growing seasons and increased productivity.

However, these gains are expected to be lost due to a greater frequency of intense weather events such as heat waves, flooding, and droughts (Rosenzweig et al., 2001). In the southeastern region of the U.S., rainfall is expected to decline by 20%, and average yearly temperature is expected to increase by roughly 2°C by 2030. This change is expected to decrease crop yields by roughly 15%

(Ingram et al., 2013). All dairy regions across the United States will be impacted by climate change in some manner, with the largest impact occurring within the southeastern regions due to an extension of duration and intensity of warm weather throughout the year (Klinedinst, 1993).

Because of this, the dairy industry will be greatly affected in this region, particularly those dairies that have intense selective pressure for performance (Nardone, 2006). Heat waves are predicted to be especially detrimental and are expected to increase in occurrence during the summer months

(Frumhoff et al., 2007). Longer and more intense summers have already been reported to cause production losses of 300 kg/cow/season (Klinedinst et al., 1993). Therefore, it will be necessary to take precautions to reduce the losses in production caused by climate change.

Environmental modification

Drops in performance as a result of heat stress can be mitigated by controlling the environmental conditions that the animals are managed in. Animals that have access to artificial cooling systems, such as fans and misters, exhibit a lower internal temperature and greater production and reproductive performance than those without environmental modifications (Her et al., 1988). This occurs as a result of conduction, convection, and evaporation, where the cool water sprayed onto the back of the animal absorbs excess heat, and then evaporative cooling removes

the thermal energy into the atmosphere (Turner et al., 1992). Dairies that use enclosed structures year-round can benefit from greater temperature amelioration via roof insulation, air conditioners, and tunnel ventilation (Sebastian et al., 2017). In pasture-based systems, Davison et al. (2016) reported that a combination of high roofing with sprinklers produced the greatest reduction in body temperature. However, due to the cost of these cooling strategies and water scarcity concerns, many farms may not be able to provide adequate environmental controls.

Selection for thermotolerance

Due to the economic importance of heat stress, the selection of animals that are better adapted to their environment has gained interest in almost all animal production systems. In cattle, it has been widely perceived that Bos taurus indicus breeds are better able to thermoregulate their body because of morphological differences when compared to Bos taurus breeds. These differences include superior hair-shedding ability and excess skin that provides increased surface area for heat exchange (Hansen, 2004). These morphological differences are due to Bos taurus indicus cattle being developed and raised extensively throughout the tropics because of their innate thermotolerance and resistance to parasites that are prolific in a warm, humid environment.

However, Bos taurus indicus breeds have a much lower production potential than Bos taurus for both meat and milk. A review paper on the adaptation of Bos taurus indicus cattle in hot climates discussed that the major factor for increased thermotolerance is the low selective pressure on milk yield in favor of longevity traits. Therefore, the perceived thermotolerance may simply be due to a much lower production potential, rather than any real adaptation (Berman, 2011). If this is the case, more attention should be focused on selecting Bos taurus breeds to be more thermotolerant.

Many studies have evaluated ways to measure thermotolerance (Misztal et al., 2000;

Aguilar, et al., 2009; Santana et al., 2017). Some traits considered for evaluating thermotolerance

include hair shedding, rectal temperature, and blood protein levels. Gray et al. (2011) reported that

Angus cattle who shed their winter coats earlier in the season wean heavier calves. Spiers et al.

(2004) determined rectal temperature to be the best measure of thermotolerance. Dikmen et al.

(2012) used rectal temperature monitors to evaluate dairy cattle in a pasture-based system and reported the heritability for this trait to be 0.17 ± 0.13. Other novel ways of evaluating heat stress have been proposed. Milk temperature was found to have a high correlation (0.78 - 0.99) with body temperature (Allen et al., 1974). Respiration rate has also been a common way of measuring heat stress. Animals with higher rates of respiration were found to have lower thermoregulatory capability (Fordham et al., 1984; Igono et al., 1987; Kadkawa et al., 2012). Analyzing blood samples using metabolomics and proteomics to search for various proteins such as heat shock factors have also been proposed. Min et al. (2016) reported roughly 41 metabolites involved with carbohydrate, amino acid, and microbiome-derived metabolism in animals undergoing heat stress.

In that study, simultaneous decreases in plasma glucose and increases in plasma lactate and lactate dehydrogenase were observed, which suggested an increase in glycolysis in animals undergoing heat stress.

The downside to these novel traits is the cost of collection and impracticality of adding them to a selection index due to a lack of routine collection. Therefore, Misztal et al. (2000) proposed using traits already collected for dairy herd improvement, such as milk yield, fat yield, and protein yield, which are useful in estimating genetic parameters for heat tolerance. Aguilar et al. (2009) reported a heritability ranging from 0.10 to 0.24 for milk fat and protein yields across a range of THI above 72. Bohmanova et al. (2005) reported that sires with a lower estimated breeding value for milk yield showed greater thermotolerance, indicating that there is an

antagonism between milk yield and thermotolerance. Therefore, we must select for more robust animals across differing environments that also retain high production.

To do this, statistical analyses have been adapted to evaluate robustness. For example, reaction norm equations specify the phenotype as a function of the environment, and these equations are commonly used in genotype by environment interaction studies (De Jong & Bijma,

2002). Traditionally, reaction norms have been utilized in biological sciences to observe phenotypic plasticity within a species across different environments. We can use reaction norms as a method of selecting phenotypic plasticity, as described by De Jong (2005), to optimize overall mean fitness. Robust statistical analyses have been developed to estimate genetic correlations across a range of temperatures using either a broken line model, which includes parameters for thermoneutrality threshold and slope of decay after passing this threshold, reported by Bernabucci et al. (2014), or random regression modeling with 1st or 2nd order polynomials to describe the norm of reaction (Brügemann et al., 2014; Menéndez-Buxadera et al., 2012; Carabaño et al., 2014).

Using reaction norms, we can select the animals with the least negative slope, meaning that they have a lower decay of milk production with increasing THI. Therefore, the slope of the curve has been proposed as a selection criterion for heat stress, where the superior animal has the least negative slope (Carabaño et al., 2014).

It must be noted that selecting animals for thermotolerance is difficult because production animals are only exposed to THI outside their thermoneutral zone for a portion of the year. Thus, a larger population is needed to make up for the lack of information. This can be done by utilizing information from multiple farms. Tiezzi et al. (2017) evaluated multi-region dairy production data and reported that different genetic lines of Holstein animals were affected differently by the environment and suggested that cows will have different performance levels depending on the

environmental factors they are managed in. Genomic information has already been used to increase genetic prediction with lower sample sizes. Hayes et al. (2009) determined that using multi-breed reference populations can be used to better estimate genomic breeding values by roughly 13%.

Therefore, genomic evaluation may increase the accuracy of prediction for thermotolerance with fewer animals. Using genomic information, Nguyen et al. (2017) has proposed an Australian genomic breeding value for heat tolerance that utilizes phenotypic information comprised of milk, fat, and protein per unit increase in THI, where single-nucleotide polymorphism effects were calculated separately for each trait. Furthermore, through genome-wide association studies, genomic regions that correlate to thermotolerance have been identified such as BTA5, BTA14,

BTA15, and BTA26 (Sigdel et al., 2019; Dikmen et al., 2013; Macciotta et al., 2017). These regions hold candidate genes that have been correlated to heat stress in dairy cattle. The SLICK hair allele, which was first observed in Senepol cattle and mapped to chromosome 20, was discovered to convey significant thermotolerance in Holstein cattle (Olson, 2003). This is due to a mutation in the prolactin gene that controls for hair growth. Dikmen et al. (2008) reported that

SLICK hair dairy cattle in both indoor (solid roof and climate control) and outdoor (shade only) environments presented lower vaginal temperatures, respiration rates, and sweating rates.

However, when the cattles hair was clipped off, the difference between the control cattle and the

SLICK hair cattle was not significant. This suggests that the reason for the increased thermotolerance is the sweat-wicking properties of the hair coat, which consequently provides a better evaporative cooling ability. Introduction of this allele to dairy populations in warmer environments may effectively reduce the drops in production during the summer months.

However, high humidity also reduces the evaporative cooling ability of cattle, thus requiring environmental modifications to increase air velocity and allow for evaporative cooling (Berman,

2006). There has also not been any work published about animals with SLICK hair dairy cattle in cooler climates. This may be an animal welfare concern in regions that experience warm summers, but short and intense winters. More genome-wide association studies should be used to identify candidate regions for genes that provide increased thermotolerance.

ORGANIC AND PASTURE-BASED SYSTEMS

Dairy intensification has been increasing in the United States, and across the globe, for the past 50 years (Clay, 2020). A shift to larger herd sizes has changed the dairy farming landscape.

In 1992, farms with less than 100 cows made up roughly 49% of the U.S. dairy cattle population, but in 2012, this proportion had decreased to only 17% (MacDonald & Newton, 2014). This means that farms with small herds have had difficulty competing in the fluid milk market, which encourages consolidation and has led to increased interest in intensively managed pasture-based dairy operations returning to the United States. Currently, pasture-based systems in the U.S. are poorly defined. To qualify as pasture-based, dairy animals must simply have access to pasture at some point throughout the year. This means that some dairies may rely entirely on pasture or may heavily supplement most of the herds nutrients with harvested feed (Washburn & Mullen, 2014).

Most dairies that perform some type of intensive pasture management produce organic milk. This is because the USDA requires that cattle have access to pasture for at least 120 days out of the year in order to be classified as organic. (Rineheart et al., 2011). This has been a benefit to smaller dairy farms, as they may demand a higher price for their milk and milk products.

Organic milk production has become one of the fastest growing markets of organic agriculture in the U.S. (Mcbride & Greene, 2009). Interest in organic and pasture-based milk production is the result of many factors, such as concerns over animal welfare and environmental factors, which have led to increased consumer demand for organic products (Taylor & Foltz,

2006). Animal welfare is often a particularly important concern for consumers. Intensive grazing dairies were found to require fewer veterinary services due to lower incidences of hoof issues, eye infections, and clinical mastitis (Parker et al., 1993; Green et al., 2007; Washburn et al. 2002). This increases welfare while also lowering the cost of production for farmers. Another concern that consumers may have is the perceived environmental impact of dairy farms. Generally, concentrated feed operations produce greater amounts of fluid milk as a result of higher-energy feed rations and increased management. However, this comes at an environmental cost due to increased use of herbicide, pesticide, and petroleum-based fuel needed to grow the animal feed, as well as water quality issues due to nutrients being transported to the farm from a distance and applied as manure in high quantities for crops located near the dairy (Thorne, 2007; Heichel et al.,

1976). Pasture-based dairies are generally closed-nutrient systems, where the nutrients in the cows diet are recycled back into the pasture as manure. Therefore, there is less concern for nutrient run- off occurring. For producers, interest in pasture-based systems is often economically motivated because of lower input costs. An economic study performed by Nehring et al. (2009) using USDA's

National Agricultural Statistics Service and Economic Research Service survey data reported that pasture-based systems with greater than 100 cattle were as economically viable as larger intensive feed operations. This is most likely due to a reduction in feed inputs which is roughly one-half of a dairys expenses, the consequence of which reduces overall production (Vandehaar et al., 1998).

When a concentrate supplement was given, milk production was reported to increase linearly by 1 kg of milk for every 1 kg of concentrate (Bargo et al., 2003). This linear growth is due to the energy density in concentrate feed, which has higher digestibility, and therefore, more energy is available for production. Although dairy cattle in pasture-based systems are not able to produce milk to their genetic potential due to a reduced energy availability, the loss in production is offset by the reduced

production cost. This indicates a need to re-evaluate breeding goals for production to determine if selection criteria should change for a pasture-based system.

Selection for animals in pasture-based systems

Pasture-based dairy systems have traditionally been more common in countries with high- quality pasture and prohibitive grain prices. Countries noted for their large numbers of pasture- based dairies are New Zealand and Ireland (Verkerk, 2003; Obrien & Hennessy, 2017). Dairies found in the tropics and equatorial countries also take advantage of the long growing season and increased solar radiation for growing pasture and other forages. However, an intensification of livestock in these countries is also occurring as people begin to move urban cities (McDermott et al., 2010). Many animals in these pastoral dairies are indigenous breeds of Bos taurus indicus and

Bos taurus indicus crossbreds (Kiwuwa et al. 1983; Philipsson et al., 2000). The same production issues occur in these breeds due to a much lower milk production than Bos taurus animals. In more temperate environments, such as New Zealand, Bos taurus dairy breeds have been selected to perform well on pasture (Norman et al., 2006; Piccand et al., 2013).

Differences in Bos taurus breed performance in pasture-based systems have been reported, with emphasis on the comparison of Jersey and Holstein cattle. When observing grazing behavior of the two breeds, Prendiville et al. (2010) did not observe a significant difference between them.

However, anatomical differences that may impact the ability to better mobilize nutrients from forage have been reported. For example, Holstein cattle were found to have roughly 0.80 of the digestive capacity of their Jersey counterparts (Brade, 1992; Smith and Baldwin, 1974; Brade,

1992). This increase in capacity implies that Jersey animals are better able to thrive on the lower- quality forage found in pasture systems. It was also reported that Jersey animals have a greater feed conversion efficiency of 6.2%, which means that they produce higher fat and protein per kg

of dry matter consumed (Grainger and Goddard, 2004). This efficiency difference between

Holstein and Jersey cattle may be greater on a pasture-based diet due to increases in lipogenic precursors in forage that are more readily used by Jersey cattle (Oldenbroek, 1988). However, even though Jersey cattle have a higher fat-corrected energy in their milk, Holstein cattle are still reported to have superior total production, even in a low production environment (Lembeye et al.,

2016). Therefore, selection for Holstein cattle may be warranted in a pasture-based system.

North American Holsteins that have been brought to New Zealand were observed to drop in performance when compared to New Zealand Holstein. Macdonald et al. (2008) reported that

North American Holsteins had higher breeding values for milk components than their New

Zealand counterparts, but failed to meet their genetic potential due to lower total fat and protein yields, body condition scores, and pregnancy rates. This suggests that North American Holsteins may need additional supplementation to meet their genetic potential when compared to New

Zealand Holsteins. This report corroborates the findings of Harris & Kolver (2001) that Holstein cows with lineages from North America did not have the fertility and survivability of their New

Zealand counterparts in an intensive-pastoral system. This has led to studies being conducted to determine if it may be necessary to change breeding goals for pasture-based systems. Boettcher

(2008) reported that the genetic correlation between cows in either conventional or pasture-based systems for all traits, except for fat, was above 0.90. In the same study Boettcher also reported that heritability was larger for production yields (0.35-0.40) in conventional herds when compared to pasture-based herds (0.30-0.35). This is similar to the findings of Kearney et al. (2004), who reported genetic correlations of 0.89, 0.88, and 0.91 for milk, fat, and protein, respectively. These results agree with prior studies performed by Weigel et al. (1999), which reported that current genetic evaluations are accurately predicting the performance of sires with daughters in both

systems, and that selection should occur in conventional systems due to the higher heritability estimates. However, these studies were performed in temperate locations and do not factor in temperature or other extreme environmental conditions. Zwald et al. (2003) reported that between

17 countries, genetic correlations were lower due to environmental factors such as temperature

(0.84) and herd size (0.79). Therefore, the accuracy of predicting breeding values for production traits will be greatest if the pasture-based and conventional dairies are found in similar environments.

CROSSBREEDING

Crossbreeding is a simple way to increase productivity and resilience in many agriculturally relevant species, such as plants and livestock. However, crossbreeding of dairy cattle has failed to become commonplace in the United States and other large milk-producing nations.

Intra-breed selection has traditionally been the norm, where Holstein-Friesen cattle have become the dominant breed across the globe. This is due to the heavy selective pressure placed on milk yield and other production traits, which has created an animal that can outproduce most other breeds. However, crossbreeding has been reported to be rising in the United States with roughly

5% of the total dairy cows being composed of two or more distinct dairy breeds (Norman et al.,

2019). This change could be due to the reported drops in fertility of many purebred Holstein herds, and farmers may be re-evaluating their breeding goals in favor of traits associated with health and longevity (Weigel, 2007). Therefore, crossbreeding should be re-evaluated to improve fertility traits while maintaining production. Holstein cattle crossed with either Normande, Montbeliarde, or Scandinavian Red were reported to have greater reproductive fitness than their purebred counterparts (Heins et al., 2006b; Heins et al., 2006c). However, Bjelland et al. (2011) later concluded that Holstein by Jersey crossbred cattle had lower production with no clear advantage

for health and fertility traits. The viability of crossing two or more breeds should continue to be evaluated as a viable option to combat increases in inbreeding depression and a reduction in fertility traits.

Hybrid vigor

Crossbreeding takes advantage of the phenomenon known as hybrid vigor, which is a reversal of the loss in fitness due to inbreeding, resulting in an increased performance that is above the expected mid-parent mean (Falconer and Mackay, 1996). Hybrid vigor is the opposite of inbreeding depression, which is a quantitative measure of the reduction in fitness due to homozygosity. Therefore, hybrid vigor is mainly caused by an increase in heterozygosity of the genome (Birchler et al., 2006). The non-additive effect is what is observed as hybrid vigor, which includes both the dominance and epistatic effects of dominance, the latter being difficult to calculate and most often ignored (Kinghorn, 1982). Hybrid vigor is observed in many traits, and an improvement above the mid-parent mean of roughly 10% for production traits and 5 to 25% for fertility traits is expected in dairy cattle (Swan & Kinghorn, 1992). Ahlborn-Breier & Hohenboken

(1991) estimated that the hybrid vigor for F1 Holstein by Jersey crosses to be 6.1% for milk yield and 7.2% for fat yield. Overall, the total genetic merit would be increased by roughly 10% due to hybrid vigor (Christensen & Pedersen, 1989; Touchberry, 1992; McAllister et al., 2002). However, hybrid vigor is lost when crossbred animals are mated inter se or to a parental breed. Mating two

F1 animals inter se will reduce the expected heterosis by 50%. Rotational crossbreeding, where each generation is bred to a different breed in succession, is useful in maintaining hybrid vigor while reducing mating complexity. A two-breed rotational cross will result in roughly 67% of the

F1 heterosis after three generations, while a three-breed rotational cross will lead to roughly 86% of the F1 heterosis, and a four-breed rotation increases it to 93% (Sørensen et al., 2008). The small

gains in hybrid vigor in the four-breed rotation may not warrant the complexity of the extra breed.

Therefore, to maximize hybrid vigor efficiently, crossbreds should either be F1, with two separate sire and dam breeds used, or they should utilize a three-breed rotation. Another example of crossbreeding is in meat animal production systems, such as in swine, where a crossbred of two maternal lines is then bred to a sire line, producing a terminal line that goes to market. This allows for maximum gains from heterosis but requires maintenance of purebred lines and would not be practical in the dairy industry. Therefore, three-breed rotational crossing would be the optimal way to gain hybrid vigor and reduce inbreeding depression while reducing complexity in breeding plans.

Breed utilization

Crossbreeding of various landrace Bos taurus indicus cattle has been a common practice in equatorial countries of Sub-Saharan Africa and South America. Cardenas-Medina et al. (2010) reported that Bos taurus indicus cattle have a roughly 10% lower basal metabolic rate than Bos taurus breeds, and therefore, lower maintenance requirements. This has allowed Bos taurus indicus breeds to have greater fertility and livability in relatively poor management situations. However, these cattle have much lower milk production when compared to North American Holstein cattle.

Thanks to recent improvements in artificial insemination, high-producing Holstein cattle have been crossed with local breeds to increase milk production in an attempt to combine the perceived robustness found in the endemic dairy breed with the production of the Holstein breed (Barbosa et al., 2008; Bock et al., 1999). Further studies have been performed to determine the production effects of crossing Holstein cattle with zebu cattle in smallholder farms in Ethiopia, native cattle in Bangladesh, and Gyr cattle in Brazil (Duguma et al. 2012; Hirooka et al., 1995; Barbosa et al.,

2008). The F1 crossbreds of Bos indicus by Bos taurus retain some of the lower maintenance

requirements and reduced heat production of the Bos taurus indicus parent, while also gaining a production advantage (Johnston, 1958; Singh, 2018). However, the loss of milk production is significant in these crosses compared to purebred Holsteins, especially when crossed with Bos taurus indicus cattle. Therefore, crossing well established, high-production Bos taurus breeds could provide increases in performance, without sacrificing as much productivity, especially in the

United States.

An experiment conducted on Holstein and Guernsey crosses between 1949 and 1969 in

Illinois found that F1 crossbred cows had a significant increase in income per cow per year of

11.4% compared to their purebred counterparts (Touchberry, 1992). Another study that evaluated crossbred dairy cattle using selection indices found that Jersey x Holstein and Brown Swiss x

Holstein crosses had a greater selection index net merit (NM$) and cheese merit (CM$) than purebred Holsteins cattle, while Holsteins had a greater fluid merit (FM$) (Vanraden & Sanders,

2003). Malchiodi et al. (2011) reported that Swedish Red by Holstein cattle and three-way crosses between Montbeliarde, Swedish Red, and Holstein cows had greater protein percentage but lower total milk and protein yield than purebred Holstein cows. Determining the general and specific combining ability is important in determining which breeds to use for crossbreeding programs.

The general combining ability is the mean performance of a single genetic line, in this case a breed, when combined with multiple breeds, and the specific combining ability is the expected value of the interaction when combining two specific breeds (Falconer et al. 1996). Utilizing single nucleotide polymorphisms from Holstein and Jersey cattle, Sun et al. (2014) found that the variance of yield traits explained by dominance effects was roughly 5-7% and concluded that adding the effects of dominance in genomic evaluations can improve prediction accuracy and can be useful in taking advantage of the combining ability of breeds. The Council of Dairy Cattle

Breeding has recently created an evaluation for crossbred dairy cattle that contain the breeds:

Holstein, Jersey, Ayrshire, Brown Swiss, and Guernsey. The reference population contained over

32,000 crossbred genotypes. The performance of the effects of the single nucleotide polymorphisms for the purebred population were evaluated, and those estimates were then weighted by the proportion of the breeds termed breed base representations in the crossbred reference population (Wiggans, 2019). This evaluation will be useful for establishing breeding protocols for producers and predicting combining abilities for bulls within breeds.

Crossbreeding and thermotolerance

Crossbreeding may provide the benefit of increased thermotolerance for dairy animals due to reducing the effects of inbreeding depression. Many countries near the equator perform some type of crossbreeding, mainly between Bos taurus x Bos taurus indicus cattle. Utilizing rectal temperature and respiration rate, Lima Da Costa et al. (2015) reported that F1 crossbreds

(Girolando x Holstein) had greater thermotolerance than animals with 75% Holstein breed percentage. Boonkum et al. (2011) evaluated production data from Holstein x Bos indicus crossbreds and reported that Thai Holsteins with less than 87% Holstein breed percentage- maintained productivity under heat stress. However, crossing Bos taurus with Bos indicus leads to a dramatic dropoff in milk production.

Therefore, crossing two or more Bos taurus breeds may be helpful in retaining productivity. Earlier studies have indicated hybrid vigor for crossbred Bos taurus dairy cattle in the warmer months (Ruvuna et al., 1982; McDowell., 1959). Dikmen et al. (2009) reported evidence of hybrid vigor for thermoregulation for F1 Jersey by Holstein and Swedish Red by

Holstein crossbred cattle in Florida using intravaginal temperature recording devices. However, it should be noted that the correlation reported between milk yield and average vaginal temperature

in that study was low (0.06). El-Tarabany et al. (2015) evaluated the performance of Egyptian

Holstein, Brown Swiss, and their subsequent crosses when exposed to increasing levels of THI in a subtropical environment. In the lowest level of THI, they reported that Holstein cattle had the highest daily milk yield at 36.6 kg/day compared to the F1 crossbred at 31.8 kg/day. When THI levels were highest, however, the least-square means for Holsteins were 27.9 kg/day, and they were surpassed by the F1 crossbred at 30.1 kg/day. They also reported that crossbred animals were superior for health traits, with reduced incidence of metritis, clinical mastitis, and feet and leg problems across all levels of THI. While these studies are helpful in evaluating the effects of crossbreeding, the literature is severely lacking in current studies that evaluate the production performance of crossbred dairy cattle, particularly the two most common breeds in the U.S., Jersey and Holstein, when exposed to stressful environments. Therefore, greater investigation into crossbreeding of Bos taurus cattle should be performed to determine viability in sub-tropical and temperate environments.

REFERENCES

Abeni, F., L. Calamari, & Stefanini., L. 2007. Metabolic conditions of lactating Friesian cows

during the hot season in the Po valley. 1. Blood indicators of heat stress. International

Journal of Biometeorology 52(2):87-96.

Ahlborn-Breier, G., & Hohenboken, W. D. 1991. Additive and Nonadditive Genetic Effects on

Milk Production in Dairy Cattle: Evidence for Major Individual Heterosis. J. Dairy Sci.,

74(2), 592–602. https://doi.org/10.3168/jds.S0022-0302(91)78206-4

Allen, T. E., & Donegan, S. M. 1974. Bos indicus and Bos taurus Crossbred Dairy Cattle in

Australia. III* A Climate Room Test of Heat Tolerance Used in the Selection of Young

Sires for Progeny Testing. In Aust. J. Agric. Res (Vol. 25).

Armstrong, D. V. 1994. Heat Stress Interaction with Shade and Cooling. J. Dairy Sci., 77(7), 2044–

2050. https://doi.org/10.3168/jds.S0022-0302(94)77149-6

Barbosa, S. B. P., Ramalho, R. P., Monardes, H. G., Dias, F. M., Santos, D. C. dos & Batista., A.

M. V. 2008. Milk and fat production of crossbred Holstein-Gir cows (Bos taurus taurus-

Bos taurus indicus) in the Agreste region of the Brazilian state of Pernambuco. Genet.

Mol. Biol. 31:468–474.

Bargo, F., Muller, L. D., Kolver, E. S., & Delahoy, J. E. 2003. Invited review: Production and

digestion of supplemented dairy cows on pasture. J. Dairy Sci., Vol. 86, pp. 1–42.

https://doi.org/10.3168/jds.S0022-0302(03)73581-4

Berman, A. 2011. Invited review: Are adaptations present to support dairy cattle productivity in

warm climates? J. Dairy Sci., Vol. 94, pp. 2147–2158. https://doi.org/10.3168/jds.2010-

3962

Berman, A., Folman, M. Kaim, M. Mamen, Z. Herz, D. Wolfenson, A. Arieli, and Y. Graber.

1985. Upper critical temperatures and forced ventilation effects for high-yielding cows

Berman, A. 2006. Extending the Potential of Evaporative Cooling for Heat-Stress Relief. In J.

Dairy Sci (Vol. 89).

Bernabucci, U., Biffani, S., Buggiotti, L., Vitali, A., Lacetera, N., & Nardone, A. 2014. The effects

of heat stress in Italian Holstein dairy cattle. J. Dairy Sci., 97(1), 471–486.

https://doi.org/10.3168/jds.2013-6611

Birchler, J. A., Yao, H., & Chudalayandi, S. 2006. Unraveling the genetic basis of hybrid vigor.

Proceedings of the National Academy of Sciences of the United States of America,

103(35), 12957–12958. https://doi.org/10.1073/pnas.0605627103

Bjelland, D. W., Weigel, K. A., Hoffman, P. C., Esser, N. M., Coblentz, W. K., & Halbach, T. J.

2011. Production, reproduction, health, and growth traits in backcross Holstein × Jersey

cows and their Holstein contemporaries 1. J. Dairy Sci., 94(10), 5194–5203.

https://doi.org/10.3168/jds.2011-4300

Boettcher, P. J., Fatehi, J., & Schutz, M. M. 2003. Genotype x environment interactions in

conventional versus pasture-based dairies in Canada. J. Dairy Sci., 86(1), 383–389.

https://doi.org/10.3168/jds.S0022-0302(03)73617-0

Bohmanova, J., Misztal, I., & Cole, J. B. 2007. Temperature-humidity indices as indicators of milk

production losses due to heat stress. J. Dairy Sci., 90(4), 1947–1956.

https://doi.org/10.3168/jds.2006-513

Bohmanova, J., Misztal, I., Tsuruta, S., Norman, H. D., & Lawlor, T. J. 2005. National Genetic

Evaluation of Milk Yield for Heat Tolerance of United States Holsteins.

Bock, R. E., T. G. Kingston, and A. J. De Vos. 1999. Effect of breed of cattle on innate resistance

to infection with Anaplasma marginale transmitted by Boophilus microplus. Aust. Vet. J.

77:748–751.

Boonkum, W., Misztal, I., Duangjinda, M., Pattarajinda, V., Tumwasorn, S., & Sanpote, J. 2011.

Genetic effects of heat stress on milk yield of Thai Holstein crossbreds. J. Dairy Sci., 94(1),

487–492. https://doi.org/10.3168/jds.2010-3421

Brade, W. 1992. Crossbreeding effects in the development of the synthetic Black and White

(SMR) dairy cattle in East Germany. Livestock Production Science, 32(3), 203–218.

https://doi.org/10.1016/S0301-6226(12)80002-9

Brügemann, K., Gernand, E., König Von Borstel, U., & König, S. 2012. Defining and evaluating

heat stress thresholds in different dairy cow production systems. Archives Animal

Breeding, 55(1), 13–24. https://doi.org/10.5194/aab-55-13-2012

Brügemann, K., Gernand, E., Borstel, U. U. von, & König., S. 2011. Genetic analyses of protein

yield in dairy cows applying random regression models with time-dependent and

temperature × humidity-dependent covariates. J. Dairy Sci. 94:4129–4139.

doi:10.3168/jds.2010-4063

Carabaño, M. J., Ramón, M., Díaz, C., Molina, A., Pérez-Guzmán, M. D. & Serradilla., J. M.

2017. Breeding for resilience to heat stress effects in dairy ruminants. A comprehensive

review. J. Anim. Sci. 95:1813–1826. doi:10.2527/jas.2016.1114

Carabaño, M. J., Bachagha, K., Ramón, M., & Díaz, C. 2014. Modeling heat stress effect on

Holstein cows under hot and dry conditions: Selection tools. J. Dairy Sci., 97(12), 7889–

7904. https://doi.org/10.3168/jds.2014-8023

Cardenas-Medina, J. V., Ku-Vera, J. C., & Magana-Monforte., J. G. 2010. Estimation of

metabolizable energy requirements for maintenance and energetic efficiency of weight

gain in Bos taurus and Bos indicus cows in tropical Mexico. J. Anim. Vet. Adv. 9:421–

428.

Chebel, R. C., Santos, J. E. P., Reynolds, J. P., Cerri, R. L. A., Juchem, S. O., & Overton., M.

2004. Factors affecting conception rate after artificial insemination and pregnancy loss in

lactating dairy cows. Anim. Reprod. Sci. 84:239–255. https:// doi .org/ 10 .1016/

j.anireprosci.2003.12.012.

Chase, L. 2006. Climate change impacts in dairy cattle. Available

at http://www.climateandfarming.org/pdfs/FactSheets/III.3Cattle.pdf, accessed on 03 05

2020

Clay, N., Garnett, T., & Lorimer, J. 2020. Dairy intensification: Drivers, impacts and alternatives.

Ambio, Vol. 49, pp. 35–48. https://doi.org/10.1007/s13280-019-01177-y

Correa-Calderon, A., Armstrong, D., Ray, D., DeNise, S., Enns, M., & Howison, C. 2004.

Thermoregulatory responses of Holstein and Brown Swiss Heat-Stressed dairy cows to two

different cooling systems. International Journal of Biometeorology, 48(3), 142–148.

https://doi.org/10.1007/s00484-003-0194-y

Coppock, C. E. 1985. Energy Nutrition and Metabolism of the Lactating Dairy Cow. Journal of

Dairy Science, 68(12), 3403–3410. https://doi.org/10.3168/jds.S0022-0302(85)81253-4

Da Silva, R. G., Morais, D. A. E. F., & Guilhermino, M. M. 2007. Evaluation of thermal stress

indexes for dairy cows in tropical regions. Revista Brasileira de Zootecnia, 36(4

SUPPLEMENT), 1192–1198. https://doi.org/10.1590/s1516-35982007000500028

Davison, T. M., Jonsson, N.N., Mayer, D. G., Gaughan, J. B., Ehrlich, W.K. & McGowan, M. R.

2016. Comparison of the impact of six heat-load management strategies on thermal

responses and milk production of feedpad and pasture fed dairy cows in a subtropical

environment.International Journal of Biometeorology, 60(12), pp. 1961-1968.

(doi:10.1007/s00484-016-1183-2)

De Jong, G. 2005. Evolution of phenotypic plasticity: patterns of plasticity and the emergence of

ecotypes. New Phytologist, 166(1), 101–118. https://doi.org/10.1111/j.1469-

8137.2005.01322.x

De Jong, G., & Bijma, P. 2002. Selection and phenotypic plasticity in evolutionary biology and

animal breeding. Livestock Production Science, 78(3), 195–214.

https://doi.org/10.1016/S0301-6226(02)00096-9

Dikmen, S., Alava, E., Pontes, E., Fear, J. M., Dikmen, B. Y., Olson, T. A., and Hansen., P. J.

2008. Differences in thermoregulatory ability between slick-haired and wild-type lactating

Holstein cows in response to acute heat stress. J. Dairy Sci. 91:3395–3402.

Dikmen, S., Cole, J., Null, D., & Hansen, P. 2012. Heritability of rectal temperature and genetic

correlations with production and reproduction traits in dairy cattle. J. Dairy Sci., 95, 3401–

3405. https://doi.org/10.3168/jds.2011-4306

Dikmen S., Cole J. B., Null D. J., & Hansen P. J. 2013. Genome-wide association mapping for

identification of quantitative trait loci for rectal temperature during heat stress in Holstein

cattle. PLoS One 8, e69202. 10.1371/journal.pone.0069202

Dikmen, S., & Hansen., P. J. 2009. Is the temperature-humidity index the best indicator of heat

stress in lactating cows in a subtropical environment?

Duguma Eticha, B., & Kechero, Y. 2012. Productive and reproductive performance of Zebu X

Holstein-Friesian crossbred dairy cows in Jimma town, Oromia, Ethiopia. Retrieved

from https://www.researchgate.net/publication/290554227

El-Tarabany, M. S., Roushdy, E. M., & El-Tarabany, A. A. 2017. Production and health

performance of Holstein, Brown Swiss and their crosses under subtropical environmental

conditions. Animal Production Science, 57(6), 1137–1143.

https://doi.org/10.1071/AN15809

Falconer D.S., Mackay T.F., Frankham R. 1996. Introduction to quantitative genetics, 4th edn.

Longman, Burnt Mill, Harlow, UK

Fordham, D. P., McCarthy, T. T., & Rowlinson., P. 1984. Oestrus detection in dairy cows by milk

temperature measurement. British Society of Animal Production. Winter Meeting. 26-28

March 1984, Grand Hotel, Scarborough.135 (2).

Fournel, S., Ouellet, V., & Charbonneau, É. 2017. Practices for alleviating heat stress of dairy

cows in humid continental climates: A literature review. Animals, Vol. 7.

https://doi.org/10.3390/ani7050037

Freitas, M., Misztal, I., Bohmanova, J., & Torres, R. 2006. Regional differences in heat stress in

U.S. Holsteins. Proceedings of the 8th World Congress on Genetics Applied to Livestock

Production, Belo Horizonte, Minas Gerais, Brazil, 13-18 August, 2006, 01–11.

Frumhoff, P. C., Mccarthy, J. J., Melillo, J. M., Moser, S. C., & Wuebbles, D. J. 2007. Confronting

Climate Change in the U.S. Northeast Prepared by the Northeast Climate Impacts

Assessment Synthesis Team.

Grainger, C., & Goddard, M. E. 2004. A review of the effects of dairy breed on feed conversion

efficiency - an opportunity lost? Animal Production in Australia (Vol. 25).

Gray, K. A., Smith, T., Maltecca, C., Overton, P., Parish, J. A., & Cassady, J. P. 2011. Differences

in hair coat shedding, and effects on calf weaning weight and BCS among Angus dams.

Livestock Science, 140(1–3), 68–71. https://doi.org/10.1016/j.livsci.2011.02.009

Green, M. J., Bradley, A. J., Medley, G. F., & Browne., W. J. 2007. Cow, farm and management

factors during the dry period that determine the rate of clinical mastitis after calving. J.

Dairy Sci. 90:3764–3776.

Gwazdauskas, F. C., Whittier, W. D., Vinson, W. E., & Pearson, R. E. 1986. Evaluation of

Reproductive Efficiency of Dairy Cattle with Emphasis on Timing of Breeding. J. Dairy

Sci., 69(1), 290–297. https://doi.org/10.3168/jds.S0022-0302(86)80400-3

Hansen, P. J. 2013. Genetic Control of Heat Stress in Dairy Cattle The Growing Problem of Heat

Stress and the Possible Role for Genetic Strategies to Mitigate Its Effects. Retrieved from

http://dairy.ifas.ufl.edu/dpc/2013/Hansen.pdf

Hansen, P. J. 2004. Physiological and cellular adaptations of zebu cattle to thermal stress. Animal

Reproduction Science, 82–83, 349–360. https://doi.org/10.1016/j.anireprosci.2004.04.011

Hayes, B. J., Bowman, P. J., Chamberlain, A. C., Verbyla, K., & Goddard, M. E. 2009. Accuracy

of genomic breeding values in multi-breed dairy cattle populations. Genetics Selection

Evolution, 41(1), 51. https://doi.org/10.1186/1297-9686-41-51

Harris, B. L., & Kolver, E. S. 2001. Review of Holsteinization on Intensive Pastoral Dairy

Farming in New Zealand. J. Dairy Sci. (Vol. 84). https://doi.org/10.3168/jds.S0022

0302(01)70197-X

Heichel, G. 1976. Agricultural Production and Energy Resources: Current farming practices

depend on large expenditures of fossil fuels. How efficiently is this energy used, and will

we be able to improve the return on investment in the future? American Scientist, 64(1),

64-72. Retrieved from www.jstor.org/stable/27847043

Heins, B. J., Hansen, L. B. & Seykora., A. J. 2006b. Calving difficulty and stillbirths of pure

Holsteins versus crossbreds of Holstein with Normande, Montbeliarde, and Scandinavian

Red. J. Dairy Sci. 89:2805–2810.

Heins, B. J., Hansen, L. B. & Seykora., A. J. 2006c. Fertility and survival of pure Holsteins

versus crossbreds of Holstein with Normande, Montbeliarde, and Scandinavian Red. J.

Dairy Sci. 89:4944–4951

Her, E., Wolfenson, D., Flamenbaum, I., Folman, Y., Kaim, M., & Berman, A. 1988. Thermal,

Productive, and Reproductive Responses of High Yielding Cows Exposed to Short-Term

Cooling in Summer. J. Dairy Sci., 71(4), 1085–1092. https://doi.org/10.3168/jds.S0022-

0302(88)79656-3

Herman, I. P. 2016. Physics of the Human Body. https://doi.org/10.1007/978-3-319-23932-3

Hirooka, H., & Bhuiyan, A. K. F. H. 1995. Additive and heterosis effects on milk yield and birth

weight from crossbreeding experiments between holstein and the local breed in

Bangladesh. Asian-Australasian Journal of Animal Sciences, 8(3), 295–300.

https://doi.org/10.5713/ajas.1995.295

Igono, M. O., Johnson, H. D., Steevens, B. J., Krause, G. F. & Shanklin., M. D. 1987.

Physiological, productive, and economic-benefits of shade, spray, and fan system versus

shade for holstein cows during summer heat. J. Dairy Sci.70(5):1069-1079.

Igono, M. O., Bjotvedt, G., & Sanford-Crane, H. T. 1992. meteorology Environmental profile and

critical temperature effects on milk production of Holstein cows in desert climate. In Int J

Biometeorol (Vol. 36). Retrieved from

https://link.springer.com/content/pdf/10.1007%2FBF01208917.pdf

Ingram, K. T., Dow, K., Carter, L., & Anderson, J. 2013. Climate of the Southeast United States:

Variability, Change, Impacts, and Vulnerability.

Jeelani, R., Konwar, D., Khan, A., Kumar, D., Chakraborty, D., & Brahma, B. 2019. Reassessment

of temperature-humidity index for measuring heat stress in crossbred dairy cattle of a sub-

tropical region. Journal of Thermal Biology, 82, 99–106.

https://doi.org/10.1016/j.jtherbio.2019.03.017

Johnson, H.D. 1987. in: H.D. Johnson (Ed.) Bioclimates and livestock. Bioclimatology and the

Adaptation of Livestock. World Animal Science. Elsevier Science Publ. Co., New York;

Johnston, J.E., Hamblin, F.B., & Schrader, G.T. 1958. Factors concerned in the comparative heat

tolerance of Jersey, Holstein and Red Sindhi-Holstein (F1) cattle. J. Anim. Sci.1958;17:473

Kadokawa, H., Sakatani, M., & Hansen, P. J. 2012. Perspectives on improvement of reproduction

in cattle during heat stress in a future Japan. Animal Science Journal, Vol. 83, pp. 439–

445. https://doi.org/10.1111/j.1740-0929.2012.01011.x

Kadzere, C.T., Murphy M.R., Silanikove, N., & Maltz, E. 2002. Heat stress in lactating dairy cows:

a review. Livest Prod Sci. 2002; 77: 59–91. http://dx.doi.org/10.1016/S0301-

6226(01)00330-X

Kearney, J. F., Schutz, M. M., Boettcher, P. J., & Weigel, K. A. 2004. Genotype × Environment

Interaction for Grazing Versus Confinement. I. Production Traits J. Dairy Sci..

Kinghorn, B. P. 1982. Genetic effects in cross breeding I. Models of merit. J. Anim. Breed. Genet

99:59

Klinedinst, P. L., Wilhite, D. A., Hahn, G. L., & Hubbard, K. G. 1993. The potential effects of

climate change on summer season dairy cattle milk production and reproduction. Climatic

Change. https://doi.org/10.1007/BF01092679

Kiwuwa, G. H., Trail, J. C. M., Yousef, M. K., Worku, G., Anderson, F. M., & Durkin, J. W.

1983. Crossbred dairy cattle productivity in Arsi region, Ethiopia.

Lembeye, F., López-Villalobos, N., Burke J.L., Davis S.R., Richardson J., Sneddon N.W., &

Donaghy D.J. 2016. Comparative performance in Holstein-Friesian, Jersey and crossbred

cows milked once daily under a pasture-based system in New Zealand, New Zealand

Journal of Agricultural Research, 59:4, 351-362, DOI: 10.1080/00288233.2016.1207669

Lima Da Costa, A. N., Valmir, J., Péricles, F., Montezuma, A., & Teixeira De Souza, P. 2015.

Rectal temperatures, respiratory rates, production, and reproduction performances of

crossbred Girolando cows under heat stress in northeastern Brazil.

https://doi.org/10.1007/s00484-015-0971-4

López-Gatius, F. 2003. Is fertility declining in dairy cattle? A retrospective study in northeastern

Spain. Theriogenology, 60(1), 89–99. https://doi.org/10.1016/S0093-691X(02)01359-6

Macciotta, N. P. P., Biffani, S., Bernabucci, U., Lacetera, N., Vitali, A., Ajmone- Marsan, P. &

Nardone., A. 2017. Derivation and genome-wide association study of a principal

component-based measure of heat tolerance in dairy cattle. J. Dairy Sci. 100:4683–4697.

doi:10.3168/jds.2016-12249

MacDonald, J. M., & Newton., D. 2014. Milk production continues shifting to large-scale farms.

Accessed Dec. 8, 2019. https://www.ers.usda.gov/amber-waves/2014/december/milk-

production-continues-shifting-to-large-scale-farms.

Macdonald, K. A., Verkerk, G. A., Thorrold, B. S., Pryce, J. E., Penno, J. W., McNaughton, L. R.,

Holmes, C. W. 2008. A comparison of three strains of Holstein-Friesian grazed on pasture

and managed under different feed allowances. J. Dairy Sci., 91(4), 1693–1707.

https://doi.org/10.3168/jds.2007-0441

Mader, T. L., Johnson, L. J., & Gaughan, J. B. 2010. A comprehensive index for assessing

environmental stress in animals. Journal of Animal Science, 88(6), 2153–2165.

https://doi.org/10.2527/jas.2009-2586

Mader, T. L., M. S. Davis, & Brown-Brandl., T. 2006. Environmental factors influencing heat

stress in feedlot cattle. J. Anim. Sci. 84:712–719. https:// doi .org/ 10 .2527/ 2006 .843712x.

Mariasegaram, M., Chase, C. C., Chaparro, J. X., Olson, T. A., Brenneman, R. A., & Niedz, R. P.

2007. The slick hair coat locus maps to chromosome 20 in Senepol-derived cattle. Animal

Genetics, 38(1), 54–59. https://doi.org/10.1111/j.1365-2052.2007.01560.x

Malchiodi, M, Penasa, M, Tiezzi, F, Bittante, G. 2011. Milk Yield Traits, Somatic Cell Score,

Milking Time and Age at Calving of Pure Holstein Versus Crossbred Cow. Retrieved

December 24, 2019, from Agriculturae Conspectus Scientificus

McBride, W. D., & Greene, C. 2009. Economic Research Report Number 82 Characteristics,

Costs, and Issues for Organic Dairy Farming Characteristics, Costs, and Issues for Organic

Dairy Farming. Retrieved from www.ers.usda.gov

McAllister, A. J. 2002. Is crossbreeding the answer to questions of dairy breed utilization? J. Dairy

Sci., 85(9), 2352–2357. https://doi.org/10.3168/jds.S0022-0302(02)74315-4

McDermott, J. J., Staal, S. J., Freeman, H. A., Herrero, M., & Van de Steeg, J. A. 2010. Sustaining

intensification of smallholder livestock systems in the tropics. Livestock Science, 130(1–

3), 95–109. https://doi.org/10.1016/j.livsci.2010.02.014

McDowell, R. E. 1959. Adaptability and performance of cattle under the climatic conditions

existing in the Southern United States. Int. Dairy Congr. XVI 1:333.

Mukherjee, D., Bravo-Ureta, B. E., & De Vries, A. 2013. Dairy productivity and climatic

conditions: econometric evidence from South-eastern United States. Australian Journal of

Agricultural and Resource Economics, 57(1), 123–140. https://doi.org/10.1111/j.1467-

8489.2012.00603.x

Menéndez-Buxadera, A., Molina, A., Arrebola, F., Clemente, I., & Serradilla., J. M. 2012. Genetic

variation of adaptation to heat stress in two Spanish dairy goat breeds. J. Anim. Breed.

Genet. 129:306–315. doi:10.1111/j.1439-0388.2011.00984.x

Min, L., S. Zhao, H. Tian, X. Zhou, Y. Zhang, S. Li, Yang, H., Zheng, N. & Wang., J. 2016.

Metabolic responses and “omics technologies for elucidat-ing the effects of heat stress in

dairy cows. Int. J. Biometeorol. 61:1149–1158. doi:10.1007/s00484-016-1283-z

Nardone, A., Ronchi, B., Lacetera, N., & Bernabucci, U. 2006. Climatic effects on productive

traits in livestock. Veterinary Research Communications, 30(SUPPL. 1), 75–81.

https://doi.org/10.1007/s11259-006-0016-x

National Research Council (NRC) 1971. A guide to environmental research on animals. Natl.

Acad. Sci., Washington, DC, USA

Nehring, R., Gillespie, J., Sandretto, C., & Hallahan, C. 2009. Small U.S. dairy farms: can they

compete? Agricultural Economics, 40, 817–825. https://doi.org/10.1111/j.1574

0862.2009.00418.x

Nguyen, T. T. T., Bowman, P. J., Haile-Mariam, M., Pryce, J. E., & Hayes, B. J. 2016. Genomic

selection for tolerance to heat stress in Australian dairy cattle. Journal of Dairy Science,

99(4), 2849–2862. https://doi.org/10.3168/jds.2015-9685

Norman, H.D., Walton, L.M., Dürr., J.W. 2019. Reasons that cows in Dairy Herd Improvement

programs exit the milking herd. https://queries.uscdcb.com/publish/dhi/curr

ent/cullall.html?

Norman H.D., Wright J.R., Powell R.L. 2006. Is there a need for different genetics in dairy grazing

systems? Improving Genetic Predictions in Dairy Animals Using Phenotypic and Genomic

Information View project. Retrieved from

https://www.researchgate.net/publication/228499358

Obrien, B., & Hennessy, D. 2017. Irish Journal of Agricultural and Food Research Scientific

appraisal of the Irish grass-based milk production system as a sustainable source of

premium quality milk and dairy products. https://doi.org/10.1515/ijafr-2017-0011

Oldenbroek, J. K. 1988. The performance of Jersey cows and cows of larger dairy breeds on two

complete diets with different roughage contents. Livestock Production Science, 18(1), 117.

https://doi.org/10.1016/0301-6226(88)90064-4

Olson T. A., Lucena C., Chase C. C., & Hammond A. C. 2003. Evidence of a major gene

influencing hair length and heat tolerance in Bos taurus cattle. J. Anim. Sci. 81, 80–90.

10.2527/2003.81180x

Parker, W. J., Muller, L. D., Fales, S. L., & McSweeny, W. T. 1993. A survey of dairy farms in

Pennsylvania using minimal or intensive pasture grazing systems. Prof. Anim. Sci. 9:159–

165.

Pedersen, J., & Christensen, L.G. 1989. Heterosis for milk production traits by crossing Red

Danish, Finnish Ayrshire and Holstein-Friesian cattle. Livest Prod Sci 23:253-266.

Philipsson., J. 2000. Breeding Strategy Workshop Case study: dairy cattle breeding in less

developed countries.

Piccand, V., Cutullic, E., Meier, S., Schori, F., Kunz, P. L., Roche, J. R., & Thomet, P. 2013.

Production and reproduction of Fleckvieh, Brown Swiss, and 2 strains of Holstein-Friesian

cows in a pasture-based, seasonal-calving dairy system. Journal of Dairy Science, 96(8),

5352–5363. https://doi.org/10.3168/jds.2012-6444

Prendiville, R., Lewis, E., Pierce, K. M., & Buckley, F. 2010. Comparative grazing behavior of

lactating Holstein-Friesian, Jersey, and Jersey × Holstein-Friesian dairy cows and its

association with intake capacity and production efficiency. J. Dairy Sci., 93(2), 764

774. https://doi.org/10.3168/jds.2009-2659

Polsky, L., & von Keyserlingk, M. A. G. 2017. Invited review: Effects of heat stress on dairy cattle

welfare. Journal of Dairy Science, 100(11), 8645–8657. https://doi.org/10.3168/jds.2017-

12651

Ravagnolo, O., & Misztal, I. 2000. Genetic component of heat stress in dairy cattle, parameter

estimation. Journal of Dairy Science, 83(9), 2126–2130.

https://doi.org/10.3168/jds.S0022-0302(00)75095-8

Ravagnolo, O., Misztal, I., & Hoogenboom, G. 2000. Genetic component of heat stress in dairy

cattle, development of heat index function. Journal of Dairy Science, 83(9), 2120–2125.

https://doi.org/10.3168/jds.S0022-0302(00)75094-6

Radostits, O. M., & Done, S. H. 2007. Veterinary medicine: a textbook of the diseases of cattle,

sheep, pigs, goats, and horses. Saunders Elsevier.

Roenfeldt, S., 1998. You cant afford to ignore heat stress. Dairy Manage. 35 (5), 6–12.

Rosenzweig, C., Iglesius, A., Yang, X. B., Epstein, P. R., & Chivian, E. 2001. Climate change

and extreme weather events-Implications for Climate change and extreme weather events

Implications for food production, plant diseases, and pests food production, plant diseases,

and pests. Retrieved from https://digitalcommons.unl.edu/nasapub/24

Ruvuna, F., Mcdaniel, B. T., Mcdowell, R. E., Johnson, J. C., & Brandt, G. W. 1982. Crossbred

and Purebred Dairy Cattle in Warm and Cool Seasons 1,2.

https://doi.org/10.3168/jds.S0022-0302(83)82099-2

Santana, M. L., Bignardi, A. B., Pereira, R. J., Stefani, G., & El Faro, L. 2017. Genetics of heat

tolerance for milk yield and quality in Holsteins. Animal, 11(01), 4–14.

https://doi.org/10.1017/S1751731116001725

Sigdel, A., Abdollahi-Arpanahi, R., Aguilar, I., & Peñagaricano, F. 2019. Whole Genome

Mapping Reveals Novel Genes and Pathways Involved in Milk Production Under Heat

Stress in US Holstein Cows. Frontiers in Genetics, 10.

https://doi.org/10.3389/fgene.2019.00928

Singh, K., & Bhattacharyya, N. K. 2018. Resting heat production in Bos indicus and their F1

crosses with exotic breeds at a thermoneutral environment. British Journal of Nutrition, 53,

301–305. https://doi.org/10.1079/BJN19850037

Smith, N. E., & Baldwin, R. L. 1974. Effects of Breed, Pregnancy, and Lactation on Weight of

Organs and Tissues in Dairy Cattle. J. Dairy Sci., 57(9), 1055–1060.

https://doi.org/10.3168/jds.S0022-0302(74)85008-3

Sørensen, M. K., Norberg, E., Pedersen, J., & Christensen, L. G. 2008. Invited review:

Crossbreeding in dairy cattle: A Danish perspective. J. Dairy Sci., Vol. 91, pp. 4116–4128.

https://doi.org/10.3168/jds.2008-1273

Soriani, N., Panella, G., & Calamari, L. 2013. Rumination time during the summer season and its

relationships with metabolic conditions and milk production. J. Dairy Sci., 96(8), 5082–

5094. https://doi.org/10.3168/jds.2013-6620

Spiers, D. E., Spain, J. N., Sampson, J. D., & Rhoads, R. P. 2004. Use of physiological parameters

to predict milk yield and feed intake in heat-stressed dairy cows. Journal of Thermal Biology,

29(7-8 SPEC. ISS.), 759–764. https://doi.org/10.1016/j.jtherbio.2004.08.051

Sun, C., VanRaden, P. M., Cole, J. B., & OConnell, J. R. 2014. Improvement of Prediction

Ability for Genomic Selection of Dairy Cattle by Including Dominance Effects. PLoS

ONE, 9(8), e103934. https://doi.org/10.1371/journal.pone.0103934

St-Pierre, N. R., Cobanov, B., & Schnitkey., G. 2003. Economic losses from heat stress by US

livestock industries. J. Dairy Sci. 86(E Suppl.):E52–E77.

Swan, A. A., & Kinghorn, B. P. 1992. Evaluation and Exploitation of Crossbreeding in Dairy

Cattle. J. Dairy Sci., 75(2), 624–639. https://doi.org/10.3168/jds.S0022-0302(92)77800-X

Taylor, J., & Foltz, J. 2006. Grazing in the dairy state: Pasture use in the Wisconsin Dairy Industry,

1993–2003. Report provided by the University of Wisconsin, Madison

Teodoro, R., & Madalena, F. 2005. Evaluation of crosses of Holstein, Jersey or Brown Swiss sires

x Holstein-Friesian/Gir dams. 3. Lifetime performance and economic evaluation Economic

evaluation of three-way crosses. In Genetics and Molecular Research (Vol. 4).

Tiezzi, F., de los Campos, G., Parker Gaddis, K. L., & Maltecca, C. 2017. Genotype by

environment (climate) interaction improves genomic prediction for production traits in US

Holstein cattle. J. Dairy Sci., 100(3), 2042–2056. https://doi.org/10.3168/jds.2016 11543

Thorne, P.S. 2007. Environmental health impacts of concentrated animal feeding operations:

anticipating hazards-searching for solutions. Environ. Health Perspect. 115, 296–297.

Touchberry, R. W. 1992. Crossbreeding Effects in Dairy Cattle: The Illinois Experiment, 1949 to

1969. Journal of Dairy Science. https://doi.org/10.3168/jds.S0022-0302(92)77801-1

Turner, L. W., Chastain, J. P., Hemken, R. W., Gates, R. S., Crist, W. L., & Asae, A. 1992.

Reducing heat stress in dairy cows through sprinkler and fan cooling.

Vandehaar, M. J. 1998. Efficiency of Nutrient Use and Relationship to Profitability on Dairy

Farms. J. Dairy Sci., 81(1), 272–282. https://doi.org/10.3168/jds.S0022

0302(98)75576-6

Vanraden, P. M., & Sanders, A. H. 2003. Economic Merit of Crossbred and Purebred US Dairy

Cattle. In J. Dairy Sci (Vol. 86). American Dairy Science Association.

Verkerk, G. 2003. Pasture-based dairying: Challenges and rewards for New Zealand producers.

Theriogenology, 59(2), 553–561. https://doi.org/10.1016/S0093-691X(02)01239-6

Washburn, S., & Mullen, K. 2014. Invited review: Genetic considerations for various pasture-

based dairy systems. J. Dairy Sci., 97, 5923–5938. https://doi.org/10.3168/jds.2014-7925

Washburn, S. P., White, S. L., Green, J. T., & Benson, G. A. 2002. Reproduction, mastitis, and

body condition of seasonally calved holstein and jersey cows in confinement or pasture

systems. J. Dairy Sci., 85(1), 105–111. https://doi.org/10.3168/jds.S0022-0302(02)74058-

Weigel, K. A. 2007. Crossbreeding: A Dirty Word or an Opportunity? Western Dairy Management

Conference, 1–14.

Weigel, K. A., Kriegl, T., & Pohlman, A. L. 1999. Genetic analysis of dairy cattle production traits

in a management intensive rotational grazing environment. J. Dairy Sci., 82(1), 191–195.

https://doi.org/10.3168/jds.S0022-0302(99)75223-9

West, J. W., Mullinix, B. G., Johnson, J. C., Plaln, C., Llftan, S., Ash, K. A., & Taylor, V. N. -

1990. Effects of Bovine Somatotropin on Dry Matter Intake, Milk Yield, and Body

Temperature in Holstein and Jersey Cows During Heat Stress 1. J. Dairy Sci. (Vol. 73).

https://doi.org/10.3168/jds.S0022-0302(90)78977-1

Wiggans, G. R., Vanraden, P. M., Nicolazzi, E. L., Tooker, M. E., Megonigal, J. H., & Walton, L.

M. 2019. Extending genomic evaluation to crossbred dairy cattle – US implementation.

(55).

Xu, W., Vervoort, J., Saccenti, E., van Hoeij, R., Kemp, B., & van Knegsel, A. 2018. Milk

Metabolomics Data Reveal the Energy Balance of Individual Dairy Cows in Early

Lactation. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-34190-4

Yousef, M. K. 1985. Stress physiology in livestock. Volume I. Basic principles. Stress physiology

in livestock. Volume I. Basic principles.217.

Zwald, N. R., Weigel, K. A., Fikse, W. F., & Rekaya, R. 2003. Identification of Factors That Cause

Genotype by Environment Interaction Between Herds of Holstein Cattle in Seventeen

Countries. J. Dairy Sci., 86(3), 1009–1018. https://doi.org/10.3168/JDS.S0022-

0302(03)73684-4

CHAPTER 2

Evaluation of crossbred dairy cattle performance when exposed to a range of temperature and humidity in a pasture-based system.

J. Graham*, S. Ward*, C. Maltecca*, S. Biffani†, F. Tiezzi*

*Department of Animal Science, North Carolina State University, Raleigh, NC.

†Istituto di Biologia e Biotecnologia Agraria, CNR, Milan, Italy.

ABSTRACT

The goal of this study was to compare purebred Jersey, Holstein and subsequent crossbred cows in a pasture-based system to determine differences in performance loss due to heat stress.

Production data were collected between the years 2003 and 2018. Traits analyzed included: milk yield, energy corrected milk, fat yield, protein yield, fat percentage, protein percentage, fat to protein ratio, somatic cell score and weighted somatic cell count. Multiple environmental factors were evaluated to determine which best explained the variation observed in the population for each trait. Two linear mixed models were developed to estimate the breed group effect as well as the direct and maternal genetic effects. For model 1, twelve breed group classes were created based on pedigree information. For model 2, expected direct and maternal breed percentage and heterosis were calculated for each breed combination. The results presented in this study indicate that there was significant re-ranking of breed groups when the environment became more challenging, especially for milk and component yield traits. Breed groups with greater Holstein ancestry had greater milk and component yields, lower component percentages and somatic cell score.

However, these differences decreased when the environment became more challenging, where no significant difference was observed between the breed groups at the most challenging environmental level. The regression of yield traits on the direct breed percentage indicated that animals with the greatest Holstein ancestry were superior to purebred Jerseys for milk, fat and protein by 8.18 kg/d, 0.29 kg/d and 0.16 kg/d, respectively. Depending on breed composition, crossbred animals showed equal or greater performance to the purebred Holstein animals across all environmental levels. Purebred Holsteins had milk yield losses of 3 kg/d when the environment became more challenging, while the Holstein-Jersey and Jersey-Holstein breed groups lost 1.5 kg/d and 0.3 kg/d, respectively. Maternal Holstein breed percentage also had a significant negative

effect on production yields, where the F1 cross with a Jersey dam had 3 kg/d greater milk yield compared to the other reciprocal cross. From this study we can conclude that crossbred animals were competitive across the environmental descriptor levels and show promise in providing yearlong productivity in a subtropical low-input environment.

INTRODUCTION

Heat stress in livestock is a limiting factor of productivity and a concern for animal welfare across the world and is of particular concern to the dairy industry in warmer regions. Dairy cattle

(Bos taurus) must maintain their body temperature within a narrow range of 38.6 to 39.6 °C to avoid disruption of bodily processes and will undergo stress until homeothermic equilibrium is regained (Radostits et al., 2000). Body temperature will rise when the animal undergoes metabolic processes which release heat as a by-product. This metabolic heat must be released into the surrounding atmosphere through conduction, convection and evaporation to allow for adequate thermoregulation (Turner et al., 1992). Dairy cattle have been found to perform best in an ambient thermoneutral zone (TNZ), with an upper critical level of roughly 25–26 °C. When the temperature breaches this critical level, animals begin to present symptoms of mild heat stress such as increased body temperature, shade seeking behavior, increased water intake, sweating, panting, pacing, reduction in rumination, reduced feed intake and changes in hormone levels (Berman et al., 1985;

Da Silva et al., 2007). Oppressive humidity with low air velocity can prevent passive cooling from occurring efficiently. When heat is not lost through passive means, dairy cattle must utilize metabolic energy to maintain thermal equilibrium that could otherwise be used for production

(Polsky & Von Keyserlingk, 2017). Therefore, cattle must be kept in an environment that is conducive to their high metabolism to reduce incidences of heat stress and maintain production.

Ambient temperature alone may not be the best predictor of heat stress because it does not take humidity into consideration. The temperature-humidity index (THI) is a unitless index that can be used to better assess the effect that temperature and humidity have on livestock (NRC,

1971). Dairy cattle have been reported to present symptoms of heat stress when THI exceeds the upper critical level of 72 units. (Armstrong, 1994; Ravagnolo et al., 2000).

Increasing the amount of days past the upper critical level of the cattles TNZ will have a negative economic cost for the dairy industry due to detrimental impacts on milk production, fertility and animal welfare. The annual estimated loss from heat stress to the U.S. dairy industry is between $897 and $1,500 million (St-Pierre et al., 2003). This is due to drops in performance which are observed in the summer months. The southeastern United States is primarily composed of a Köppen climate classification of humid subtropical with cool winters and long warm and humid summers and therefore shows the highest performance losses of any region (West, 2003).

Heat stress is expected to be exacerbated in the southeastern United States because of global climate change which is predicted to cause increases in the intensity and duration of warm weather

(Klinedinst, 1993). Pasture-based dairies in this region will be more affected by climate change than conventional systems due to lack of climatic control (Nardone et al., 2010). In conventional dairy operations, climate control measures that cool the lactating dairy animal when the temperature breaches the thermoneutral zone help prevent drop-offs in milk production due to heat stress, however, this is difficult to provide in pasture-based systems.

Pasture-based dairy systems are currently growing in the United States due to a consumer interest in organic milk. The USDA requires that all certified organic dairy cattle have access to pasture for at least 120 days out of the year. Therefore, pasture-based dairies can also be an economical alternative to scaling-up herds to remain profitable. Nehring et al. (2009) analyzed

USDA's National Agricultural Statistics Service and Economic Research Service survey data and reported that pasture-based systems with greater than 100 cattle were as economically viable as the largest conventional dairy operations in the survey.

Intensive and extensive pasture-based systems are found throughout the developing world, especially near the equator where animals can harvest forage without the need for expensive feed

inputs and mechanization. In westernized countries, pasture-based dairies are common in locations, such as New Zealand, with long forage growing seasons which can supply pasture for extended periods of the year. Therefore, pasture-based dairies would have the highest efficiency in warmer regions of the globe where growing seasons are long, and winters are mild.

Interest in alternative dairying has created a need for cattle that are better able to thermoregulate themselves without the necessity of artificial cooling systems. Milk production was found to be more susceptible to higher temperatures for the Holstein breeds when compared to Jersey (Sharma et al., 1983). This may be due to the selective pressure placed on Holstein cattle for increased milk production, which in turn increases metabolic heat (Chebel et al., 2004).

Therefore, reducing the proportion of representation of the Holstein breed in the herd may help sustain production throughout the year by reducing the impact of milk depression during periods of warm weather. Steady milk production is of most concern to small scale producers who specialize in high-fat milk or cheese production, where having a constant supply of milk components is of more importance than total fluid yield.

Crossbreeding is already prominent in pasture-based dairy systems to take advantage of hybrid vigor, where either Bos taurus breeds (temperate regions) or Bos taurus indicus cattle

(equatorial regions) are utilized in crossbred systems (Lopez-Villalobos et al., 2000). The difficulty with utilization of Bos taurus indicus cattle is that the robustness in the crossbred is overshadowed by milk yield losses (McDowell et al., 1996). Therefore, year-long performance should not be completely compromised for moderate compensation of milk yield depression in the summer.

Crossbreeding has been reported to convey the largest benefits to fertility traits; however, previous studies have found that production traits of crossbred animals can be competitive or outperform

Holstein animals (Dechow et al. 2007; Heins et al. 2008; Heins et al. 2006). Crossbreeding Bos

taurus breeds to take advantage of hybrid vigor could be considered a potential way to make an animal better able to cope with heat stress, while maintaining significant production throughout the year. A study performed by Dikmen et al. (2009) showed evidence of hybrid vigor for thermotolerance of Jersey, Holstein and Swedish Red crosses in a pasture-based environment. El-

Tarabany et al. (2015) evaluated the performance of Egyptian Holstein, Brown Swiss and their subsequent crosses when exposed to increasing levels of THI in a subtropical environment and reported that when THI levels were highest, purebred Holsteins were surpassed by the crossbreds for production traits. Further evaluations of Bos taurus crossbreds should be performed to determine if certain breed combinations show better tolerance to heat stress and to optimize yearlong production in specific management systems.

Therefore, the goal of this current study was to compare production performance of purebred Jersey, Holstein and their crosses in a pasture-based system to determine the loss in performance due to heat stress.

MATERIALS AND METHODS

Management and Animals

Animal welfare approval was not needed for this study because data came from pre- existing databases. All data were recorded at the North Carolina Department of Agriculture and

Consumer Resources Cherry Research Station located in Goldsboro, North Carolina, USA. Data consisting of 15,440 test-day records were collected between the years 1996 to 2018 and provided for this study by the Dairy Records Management System (DRMS, Raleigh, NC). Information pertaining to each individual were provided in the form of Format 4 files

(https://aipl.arsusda.gov/software/bestpred/docs/html/node16.html) and included: animal ID number, sire ID number, dam ID number, birth date, calving date, parity order, stage of lactation,

breed, milk yield, fat percentage, protein percentage and somatic cell score. The cattle in this study were primarily raised on mixed-species pasture with minimal supplementation throughout the summer and with preserved forage given during the winter months.

The observed population consisted of purebred and rotational backcross combinations of

Holstein and Jersey (n=712) dairy cattle. The breeding system implemented at the farm involved artificially inseminating cows from either purebred Holstein or Jersey sires purchased from major bull studs. Purebred animals were either retained in the herd to generate more purebred progeny or selected for crossbreeding. Cows that were selected for crossbreeding were then bred to either

Holstein or Jersey sires based on their respective breed. Reciprocal F1 crosses were backcrossed to their dams breed. Backcrossed animals were then bred to either Holstein or Jersey sires based on their maternal grandsires breed. However, some animals were mistakenly bred to the incorrect sire breed, which resulted in a population composed of varying breed proportions. For this study, parental breed information of each cow was used to determine breed composition for each individual. Expected genetic factors were then defined: the breed percentage in the cow (i.e. direct additive genetic effect), breed percentage in the dam of cow (i.e. maternal additive genetic effect), heterosis in the cow (i.e. direct heterosis) and heterosis in the dam of cow (i.e. maternal heterosis) as described first by Robison et al. (1981) and later by Dhakal et al. (2013). For simplicity, animals were grouped into 12 classes based on their parental breeds which were: H-H, J-J, H-J, J-H, H-HJ,

H-JH, J-HJ, J-JH, H-HX, H-JX, J-HX, J-JX. Where, H-HX, H-JX, J-HX and J-JX are the classes representing second backcross animals and beyond with varying breed percentages, which were grouped based on the breed composition of their sire and dams. The breed groups, expected genetic factors and the number of animals per breed group are reported in Table 1. A systematic change in breed proportion occurring on the farm was evident (Figure 1) after the year 1997, where the

percentage of animals with high Holstein ancestry dropped dramatically and the rotational backcross scheme did not stabilize until roughly 2003. In the practiced crossbreeding scheme, these four genetic factors should not be correlated. However, to test for collinearity, Pearson correlations between the four genetic factors were calculated and are presented in Table 2. The direct and maternal breed percentage (Holstein) were found to have a large correlation of 0.77.

Animals born before the year 2003 were then removed which reduced the correlation between the two genetic factors to 0.44. This had the consequence of reducing the number of observations from the initial 15,440 down to 8,877. This trade-off between information and collinearity of the genetic parameters was determined to be necessary to adequately estimate the effect that direct and maternal breed (Holstein) percentage had on the trait of interest. To keep the model comparisons consistent, information from the reduced data set was used for both analyses. Another concern was that the weather and management patterns may have changed from the earlier years of the observed period. Therefore, reducing the dataset would have reduced the extraneous variability due to changes in management over the observed period and allowed for better comparison of breed groups.

Traits

Production traits such as milk yield (MY) in kg/d, protein percentage (PP), fat percentage

(FP) and somatic cell score (SCS) were used for this study. Fat yield (FY) in kg/d and protein yield

(PY) in kg/d were created by multiplication of the component percentage traits by milk yield. Fat to protein (F/P) ratio was then calculated by dividing fat yield by protein yield. Energy corrected milk (ECM) was created using the formula, developed by the NRC, (2001):

ECM (kg/d) = 12.55 × FY + 7.39 × (PY + 0.2595 × MY).

The trait weighted somatic cell count (wSCC) was created using the calculation:

(SCS-3) wSCC = log2(100,000× 2 ) × (MY (kg/d) 971 ml))), where observed milk yield was multiplied by the total milliliters in 1 kg of milk and the somatic cell count per ml is multiplied by the total number of ml of MY. This trait was created to compare animals based on total cells delivered by the animal per day, which allows for better comparison between breeds. Records showing milk yield below 2 kg/d were removed. Production records recorded after 365 days in milk were removed due to under-representation of certain breeds in these classes. The stage of lactation was expressed as 12 classes based on 30 days in milk (DIM) intervals (class 1 from 1 to 30 DIM; class 2 from 31 to 60 DIM; class 3 from 61 to 90; class 4 from

91 to 120; class 5 from 121 to 150; class 6 from 151 to 180; class 7 from 181 to 210; class 8 from

211 to 240; class 9 from 241 to 270; class 10 from 271 to 300; class 11 from 300 to 330; class 12 from 331 to 365 DIM). Parity was grouped into 5 classes, where 5th parity included animals that were in their 5th or greater parity. The resulting final data set used for analysis consisted of 8,877 test-day records observed on 449 cows.

Meteorological-Information

The daily maximum, minimum and average dry bulb temperature (DT) and relative humidity % (RH) were collected from the nearest public weather station that had the most accurate and complete records during the observation period (35.8923° N, -78.7819° W). Although the distance of the farm to the station was roughly 83 km, the next closest weather station was missing information for yearlong periods. However, 83 km was close to the average distance found in a similar study (Aguilar et al, 2009). The climate of the farm and weather station presented similar overall weather patterns and share a Köppen classification of humid subtropical. The meteorological variables were used to calculate the minimum, maximum and average temperature- humidity index using the formula (NRC, 1971):

THI = (1.8 x DT C°+ 32) – [(0.55 -0.0055 x % RH) x (1.8 x DT C°- 26.8)]

We chose the THI calculation used in this study due to it being the most common calculation in prior literature and because it had been used previously in the southeastern United States to model the effects of heat stress. (Ravagnolo et al., 2000; Ravagnolo & Misztal, 2000; Kendall & Webster,

2009). Multiple days of challenging meteorological conditions may have a greater impact on production than conditions only occurring on the test-day. Therefore, as suggested by the findings of West et al. (2003) multiple environmental descriptors consisting of meteorological information prior to the test day to assess lag time on production were created. These environmental descriptors were based on the meteorological information (DT, RH or THI) on the test-day and the test-day averaged with preceding days in sequential order, starting from the initial 2-day average and ending with the 7-day average. (e.g., 2-day average of minimum DT, 6-day average of maximum

THI). Information in each environmental descriptor was then assigned to 5 classes based on the first four quintiles (Table 3), where records beginning with the minimum to the first quintile were labeled class 1, records between first and second quintile were labeled as class 2, records between second and third quintile were labeled as class 3, records between third and fourth quintile were labeled as class 4 and records above the fourth quintile were labeled as class 5. Class 1 represented the least challenging environment and class 5 was represented the most challenging environment to the animal.

Statistical Analyses

Data were analyzed using a linear mixed model implemented in the MCMCglmm (V. 2.29) package in R software (Hadfield, 2010). Model 1 was developed to assess variation between breed groups across the range of environmental conditions and was:

yijklmno = + SOLi + Parj + Envk + BGl + (Env BG)kl + t(Env)m:k + a(BG)n:l + ijklmno

Where yijklmno is the trait observation, SOLi is the fixed effect for stage of lactation (i = 1,..,12),

Parj is the fixed effect for parity (j = 1,..,5), Envk is the fixed effect of the environmental descriptor

(k= 1,..,5), BGl is the fixed effect breed group (l = 1,..,5), (EnvBG)kl is the fixed effect for the interaction between the environmental descriptor and the breed group, t(Env)mk is the random effect for the test-day nested within the environmental descriptor effect, a(BG)nl is the random effect for the cow nested within the breed group and ijklmno is the random residual error. The random effects were assumed to be normally distributed with

t~N(0,� ) and

a~N(0,� ) ,

where � and � are the estimated variances of the test-day and cow effect, respectively. The residual variance was given:

� 0 0 0 0 0 � 0 0 0 var() = R = 0 0 � 0 0 , 0 0 0 � 0 0 0 0 0 �

where � is the residual error variance within class k of the environmental descriptor.

The model was run sequentially for each trait (9) against all environmental descriptors (63). This equates to a total of 567 runs, where the Deviance Information Criterion (DIC) was chosen as measure of model fit and the model showing the lowest value for each trait was used. Variance components for the random effects were then fixed to the previously estimate values and solutions were estimated. Least-squares means for the fixed effects were calculated using the “emmeans

(version. 1.4.2) package in R software (Lenth, 2020). Hypothesis testing was performed by calculating F-value for each fixed effect. This was performed in R with a function written

specifically for this study given the lack of a dedicated package. While effects of parity, stage of lactation and the interaction between breed group and environmental descriptor were tested on the residual variance, the effect of the breed group was tested on the random effect of the cow and the effect of the environmental descriptor was tested on the test-day random effect.

A second analysis was performed by replacing the fixed effect of breed group (12 levels) with four covariates representing the genetic effects of breed composition and heterosis. Model 2 was:

d d m m d yijklmnopqr = + SOLi + Parj + Envk + b1G l + b2H m + b3G n + b4H o + (Env G )kl

d m m + (Env H )km + (Env G )kn + (Env G )ko + t(Env)pk + almnoq + ijklmnopqr

Where yijklnopqr is the recorded phenotype, SOLi is the fixed effect for stage of lactation (i = 1,..,12),

Parj is the fixed effect for parity (j = 1,..,5) and Envk is the fixed effect of environmental descriptor

d class (k= 1,..,5) as previously described, G l is the covariate of direct expected breed percentage

d m (Holstein) of the individual, H m is the covariate of direct expected heterosis, G n is the covariate

m of maternal expected breed percentage (Holstein), G o is the covariate of maternal expected

d heterosis, b1, b2, b3, b4 are the regression coefficients for the four covariates, (Env G )kl, (Env

d m m H )km, (Env G )kn and (Env G )ko are fixed interactions between the genetic and the environmental effects, t(Env)pk is the random effect for the test-day nested within the environment, almnoq is the random effect for the cow and ijklmnopqr is the error term where the variance assumptions for the random effects are the same as model 1.

RESULTS AND DISCUSSION

For this herd, average milk yield was at 20.16 kg/d, with 4.65 % Fat and 3.32 % protein, fat yield was 0.97 kg/d, protein yield was 0.68 kg/d, fat to protein ratio was 1.41%, energy corrected milk was 22.62 kg/d, somatic cell score was 2.94 and weighted somatic cell count was

31.07 log2, (Table 4). The low production average observed in the herd can be expected in a pasture-based system and had been found in prior studies performed within production systems with low supplementation (Boettcher et al. 2003; Fontaneli et al. 2005). Hypothesis testing is summarized (Table 5) for model 1. The effect of parity was significant (p < 0.001) for all traits and the effect of SOL was also significant for all, except wSCC. The effect of Env was significant for most traits, except: PY, wSCC and F/P. Traits that were significant at (P < 0.05) for BG were:

FY, PY, SCS, wSCC and F/P. The Env and BG effects appearing non-significant for specific traits are most likely due to the nested structure within the animal and test-day random effects. However, the Env x BG interaction effect was significant for all traits. Hypothesis testing for model 2 is reported in the appendix.

Environmental Descriptors and Effects

The maximum temperature observed on any single test-day was 38.9 °C, while the minimum observed was -7.75 °C, with an overall mean of 14.27 °C. The maximum relative humidity observed on any single test-day was supersaturated at 100.2 %, while the minimum was

13%, with an overall mean of 67.76 %. Evaluating the environmental covariate that best explains the variation in each trait is important to properly understand the impact that heat stress had on lactating dairy animals and how the several genotypes react differently. The average days of prior weather that impacted production on the test-day differed for most traits. This phenomenon was also reported by Linvill et al. (1992), but contrasts with the findings of Bernabucci et al. (2014) who reported the 4 to 5 days prior to test-day were significant for all production traits studied.

Overall, the roughly 5-day average temperature and THI prior to test-day were best at explaining the variation in the traits of interest. The model environmental descriptors with the best fit were as follows: MY and ECM, 5-d prior average of average minimum daily THI; FY, 5-d average of

maximum daily temperature; PY, 5-d average of minimum daily temperature; FP, 1-d average of average daily THI; PP, 7-d prior average of minimum daily temperature; F/P, 4-d average of maximum relative daily humidity; SCS, 1-d average of minimum daily THI; wSCC, 6-d average maximum of relative daily humidity. It should be noted that these environmental descriptors may not be portable to different management systems or regions due to these factors occurring uniquely to this specific farm. The least-squares means (Table 6) were estimated for the environmental effects using model 1. Remarkably, production yield traits of the overall herd in this study did not significantly decrease when the environment became more challenging, which is at odds with previous findings (West et al., 2003; Brügemann et al., 2012). This may be due to the low overall production of the herd described previously or the peculiar genetic composition of the herd.

Conversely, a significant decline in performance for the milk component percentage traits was observed when the environment became more challenging, where PP dropped by 0.23% and FP dropped by 0.57%. Although the same decline was not observed in the yield component traits, a marginal numerical decline occurred of 0.04 kg/d for PY and 0.14 kg/d for FY. Finally, no significant changes were observed among health traits across levels of environmental descriptors.

Effect of Breed and Heterosis

For simplicity, comparisons of purebred and F1 breed groups were performed to evaluate re-ranking across levels of Env (Figure 2-7), while all least-square means are available in the appendix. Overall, the genetic factor that influenced performance for a majority of the traits was the effect of the direct breed percentage (Holstein) (Figures 2-7). The regression of yield traits on

Gd indicated that animals with maximum Holstein ancestry were superior to purebred Jerseys for milk, fat and protein yield by 8.18 kg/d, 0.29 kg/d and 0.16 kg/d, respectively. However, their milk was composed of 0.51% and 0.37% less fat and protein. Therefore, animals with a larger direct

Holstein breed percentage had greater yields overall, while those with lower Holstein breed percentages presented greater milk component percentages, which is similar to the findings in previous studies (De Haas et al., 2013; Bjelland et al., 2011; Malchiodi et al., 2011). For model 1, the breed groups with a Holstein sire appeared to have a notable advantage over the others. This was corroborated by the results of model 2, where the effect of Gd was overall positive for the MY and ECM traits. However, for these traits, the genetic factor Gm had a negative effect, which suggests that breed groups show lower production when their dams had greater Holstein ancestry.

This was evident when we compared the least-squares means of the J-H breed group versus the breed group H-J for milk yield, where the former had an average of 20.9 kg/d, while the latter had an average of 23.05 kg/d. Both breed groups had the same expected direct breed percentage and heterosis, and therefore the dam is the differentiating factor, which may be explained by a possible cytoplasmic effect (Bell et al., 1985). Furthermore, the genetic factor Hd had a significant positive effect for the MY and ECM traits which was apparent when comparing the J-J and F1 breed groups, where crossing a Jersey dam with a Holstein sire increased the average milk yield by 5.86 kg/d.

The effect of Hm was non-significant which may be due to recombination loss (Dechow et al.,

2007).

The component yield traits, FY and PY, followed a similar pattern as MY and ECM. The

d genetic factor G had an overly positive effect when Holstein percentage was maximized, while the genetic factor Gm had an overall negative effect for the PY and FY traits, and therefore breed groups with a Holstein sire and Jersey dam had the highest production. This once again is in accordance with model 1, where the breed group H-J had the largest numerical least-square mean and all crossbred breed groups with a Holstein sire were significantly different than the J-J breed

d group, which had the lowest production overall. The effect of H was positive for PY and FY,

m while the H showed no significant difference from zero. Therefore, only the F1 animals received the full benefit of crossbreeding for these traits.

For the component percentage traits, FP and PP, the effect of Gd was negative, while Gm and Hm were not significant. This corroborates with model 1, where the J-J breed group had the highest least-squares mean for protein percentage, while there was an observed lack of hybrid

d vigor for the F1 breed groups. For FP, the genetic factor H had a slight positive effect and the J-H breed group was significantly different from the H-H breed group, which had the lowest milk fat percentage. The lack of heterosis in these traits may be explained by the high heritability reported in previous studies which means that the phenotypic variance in the animals is due more to additive genetic effects, than dominance (Sun et al., 2014). It may also be due to the fat and protein percentage traits being reported to have an oligogenic architecture with two major genes effecting milk composition DGAT1 on BTA14 and GHR on BTA20, respectively (Ashwell et al., 2004). A study conducted on a New Zealand based population of dairy cattle reported that in the Jersey breed, a major allele for the DGAT1 gene was approaching fixation at a frequency of 0.88, while in the Holstein breed the frequency of the major allele was 0.60 (Spelman et al., 2002). The allelic frequencies in this current studies population are unknown, however, the possibility of similar allelic frequencies in the two breeds across few genes may explain the lack of major hybrid vigor observed.

We did not see an apparent effect for any of the genetic factors for the F/P and wSCC traits.

However, for the SCS trait, only the negative effect of the genetic factor Gd was significantly different from zero. Interestingly, the J-J breed group had the greatest overall SCS when compared to the other groups which is an agreement with Berry et al. (2007) who found Jersey cattle to have

20,000 cells/ml more than Holstein cattle in a pasture-based setting in New Zealand. However,

this is generally in contrast with other studies that found SCS to be similar or lower in the Jersey breed (Caraviello et al., 2005; Lembeye et al., 2016; Prendiville et al., 2010). Jersey cows may have a better immune system and a greater immune response in the mammary gland when subclinical and clinical mastitis is occurring. An earlier study by Bannerman et al. (2008) experimentally induced E. coli infection in Holstein and Jersey animals in a controlled setting and found that Jersey cattle had an earlier and stronger response to infection via pro-inflammatory cytokine induction. This study also reported that pre-infection SCC concentrations were higher in

Jersey animals, but there was no difference between the two breeds post-infection. Another explanation for our results is simply that there may be a dilution effect, where higher producing animals have less cells per milliliter than Jersey animals. When we compared the weighted somatic cell count trait, we observed no breed differences.

Effect of Breed and Heterosis Interaction with the Environment

Breed differences were observed for both the MY and ECM traits, which showed similar patterns of decline when the environment became more challenging. The effect of Gd decreased significantly across Env levels, but remained positive at Env-5, where purebred Holstein animals had an advantage over purebred Jersey animals of 4.19 kg/d and 2.87 kg/d for MY and ECM, respectively. Therefore, maximizing direct Holstein breed percentage was still beneficial. When comparing least-squares means, the breed groups H-H, J-H and H-J were not significantly different from one another across levels of Env and were superior to J-J up until Env-4 and Env-5, where only H-J was superior. Furthermore, the H-H breed group had greater milk production loss from

Env-1 to Env-5 by roughly 3 kg/d, while H-J and J-H had a reduction of 1.5 kg/d and 0.3 kg/d, respectively. This is similar to the finding of El-Tarabaney et al. (2017) who reported that daily milk yield declined more for purebred Holstein animals (36.6 kg/d to 27.9 kg/d), than F1 Holstein-

Brown Swiss (31.8 kg/d to 30.1 kg/d) when THI increased. Since our study was performed in a low-input environment, the loss in performance may not be as dramatic due to nutritional constraints which did not allow for a greater initial milk production. It should be noted that in our study the purebred Jersey animals presented a minor gain in average milk yield of 1 kg/d across environmental classes which could be attributed to greater thermotolerance, a reduction in total milk yield when compared to Holsteins and possible better utilization of forage. Other studies have reported greater production losses of Holsteins than Jersey animals when the environment becomes stressful (Sharma et al., 1983). Our findings corroborate with a previous study by Smith et al.

(2013) who reported that the Holstein breed had greater decreases (p<0.01) in milk yields, while the Jersey breed marginally increased production when THI increased.

The milk component yield traits, FY and PY, followed a similar pattern of depression as

d m MY. The genetic factor G had an overall positive effect on production for PY and G had an overall negative effect for PY, but there is no significant differences across Env levels for either effect, which resulted in no significant re-ranking of breed groups for the PY trait. However, the numerical average for H-J was consistently higher than J-H across levels of Env. The breed groups differed more for FY, where H-J produced significantly more (0.6 kg/d) than J-J at Env-1, but there was no observed difference between the breed groups at Env-5. This corroborates with the results from model 2, where the interaction between the environment and Gd was significant, and a reduction in performance was observed when the environment became more challenging. For this trait, the H-J breed group showed a tendency to be superior to the breed group J-H across levels of Env, which further suggests that there is a potential maternal effect occurring (McAllister,

2002). A similar finding came from a study conducted in a New Zealand pasture-based system

where F1 cows with a Jersey dam produced 10.3 kg fat per lactation above those with Holstein dams (Ahlborn-Breier & Hohenboken, 1991).

It was observed (Figure 5 and 6) that there was less hybrid vigor in the F1 animals across levels of Env for the component percentage traits which is similar to the findings of Ahlborn-

Breier & Hohenboken (1991) who reported significant heterosis for fat yield, but not fat percentage. However, in our study we did observe slight evidence of heterotic effects for the fat percentage trait for some levels of Env. The effect of Hd and Hm were positive for Env-4, which corresponds to the range of 64.39 and 71.26 average THI. This range is below the upper critical level for mild heat stress and therefore the increase in performance may be due to crossbred animals better utilizing the extra nutrition provided by spring-time forbs and grasses. Nevertheless, the influence of the animals breed proportion was much greater, where genetic factor Gd had a strong negative effect on FP, but Gm had no significant effect. This corroborates with the results of model 1, where the H-H breed group was no different than the other breed groups at Env-1,

Env-2 and Env-5, although it had a significantly lower percentage than J-J at Env-3 and both J-H and J-J at Env-4. For the PP trait, there was no significant difference between the genetic factors across environmental descriptors. However, the breed group J-J was superior to only H-H at every level of Env and was superior to all other breed groups at Env-5. It had been well established that milk yield had a high phenotypic correlation with protein yield (0.90) and fat yield (0.75)

(Bourdon, 1999). Therefore, it was unsurprising that milk yield and milk component yield traits would present a similar pattern of depression when heat stress is occurring, compared to the component percentage traits.

For the somatic cell traits, there was a slight decrease observed when the environment became more stressful. The effect of genetic factor Gd was negative when Holstein percentage

was maximized up until Env-4, for which it was not significantly different from zero. This implies that the variation observed in SCS is influenced more by the breed effect when the environment is optimal, but the influence becomes negligible when the environment becomes more challenging. This resulted in the breed group J-J presenting greater SCS than H-H at Env-1, but there were no differences observed at any other Env levels due to large confidence intervals.

However, this may also may simply be a dilution effect as discussed previously. None of the genetic effects were significant for the wSCC and F/P and there was no difference between breeds for the wSCC and F/P traits across any of the levels of environmental descriptor.

CONCLUSIONS

From this study we can report that most of the environment by breed group interactions occurred because of the animals Holstein ancestry, while hybrid vigor did not significantly decrease when the environment became more challenging. Therefore, if the H-H breed group was superior in the least challenging environment, we did not observe a significant difference between it and the crossbred animals in the most challenging environment. Furthermore, the resulting effect of increasing maternal breed percentage (Holstein) reduced the performance of yield traits (Figures 2, 3 and 4), which corroborates with the results of model 1, however there was no significant maternal heterosis observed. Therefore, in this crossbreeding scenario, having a dam with a reduced Holstein breed percentage provided the maximum benefit in performance for the milk and component yield traits. The results from this study indicates that a crossbreeding scenario where the maternal Holstein percentage is minimized, while direct Holstein percentage and heterosis is maximized would be optimal, but perhaps not practical. Therefore, keeping the herd split between different sire breeds in rotation would keep production maximized. Another option would be to follow a terminal crossbreeding scheme where low producing Jersey cattle

are bred to Holstein sires, the offspring of which are then inseminated with beef semen. This allows for the maximum hybrid vigor to be realized and low producing cattle are culled from the herd. Further evaluation of these breeds and their crosses that include a more robust data set across multiple farms may be needed to better estimate the effects that crossbreeding, and their genetic components have on reducing the deleterious effects of heat stress. However, the crossbred animals were competitive with the purebred Holsteins for the yield traits and therefore can be utilized to increase fertility reported previously, without a serious detrimental impact to production when exposed to an increasingly challenging environment in a pasture-based system.

REFERENCES

Aguilar, I., Misztal, I., & Tsuruta, S. 2009. Genetic components of heat stress for dairy cattle

with multiple lactations. J. Dairy Sci., 92(11), 5702–5711.

https://doi.org/10.3168/jds.2008-1928

Ahlborn-Breier, G., & Hohenboken, W. D. 1991. Additive and Nonadditive Genetic Effects on

Milk Production in Dairy Cattle: Evidence for Major Individual Heterosis. J. Dairy Sci.,

74(2), 592–602. https://doi.org/10.3168/jds.S0022-0302(91)78206-4

Ashwell, M. S., Heyen, D. W., Sonstegard, T. S., Van Tassell, C. P., Da, Y., VanRaden, P. M.,

Lewin, H. A. 2004. Detection of quantitative trait loci affecting milk production, health,

and reproductive traits in Holstein cattle. J. Dairy Sci., 87(2), 468–475.

https://doi.org/10.3168/jds.S0022-0302(04)73186-0

Bell, B. R., Mcdaniel, B. T., & Robison, O. W. 1985. Effects of Cytoplasmic Inheritance on

Production Traits of Dairy Cattle. J. Dairy Sci. (Vol. 68).

https://doi.org/10.3168/jds.S0022-0302(85)81066-3

Boettcher, P. J., Fatehi, J., & Schutz, M. M. 2003. Genotype x environment interactions in

conventional versus pasture-based dairies in Canada. J. Dairy Sci., 86(1), 383–389.

https://doi.org/10.3168/jds.S0022-0302(03)73617-0

Bohmanova, J., Misztal, I., & Cole, J. B. 2006. Temperature-Humidity Indices as Indicators of

Milk Production Losses due to Heat Stress. J. Dairy Sci., 90, 1947–1956.

https://doi.org/10.3168/jds.2006-513

Bourdon, R. 1999. Understanding Animal Breeding, 2nd. (pp. 280). Upper Saddle River, NJ

07458: Prentice Hall.

Bjelland, D. W., Weigel, K. A., Hoffman, P. C., Esser, N. M., Coblentz, W. K., & Halbach, T. J.

2011. Production, reproduction, health, and growth traits in backcross Holstein × Jersey

cows and their Holstein contemporaries 1. J. Dairy Sci., 94(10), 5194–5203.

https://doi.org/10.3168/jds.2011-4300

Berman, A., Folman, M. Kaim, M. Mamen, Z. Herz, D. Wolfenson, A. Arieli, & Graber., Y.

1985. Upper critical temperatures and forced ventilation effects for high-yielding cows in

a subtropical climate. J. Dairy Sci. 68:1488–1495.

Bernabucci, U., Biffani, S., Buggiotti, L., Vitali, A., Lacetera, N., & Nardone, A. 2014. The

effects of heat stress in Italian Holstein dairy cattle. J. Dairy Sci., 97(1), 471–486.

https://doi.org/10.3168/jds.2013-6611

Berry, D. P., Lee, J. M., Macdonald, K. A., Stafford, K., Matthews, L., & Roche, J. R. 2007.

Associations among body condition score, body weight, somatic cell count, and clinical

mastitis in seasonally calving dairy cattle. J. Dairy Sci., 90(2), 637–648.

https://doi.org/10.3168/jds.S0022-0302(07)71546-1

Brügemann, K., Gernand, E., König Von Borstel, U., & König, S. 2012. Defining and evaluating

heat stress thresholds in different dairy cow production systems. Archives Animal

Breeding, 55(1), 13–24. https://doi.org/10.5194/aab-55-13-2012

Carabaño, M. J., Ramón, M., Menéndez-Buxadera, A., Molina, A., & Díaz, C. 2019. Selecting

for heat tolerance. Animal Frontiers, 9(1), 62–68. https://doi.org/10.1093/af/vfy033

Caraviello, D. Z., Weigel, K. A., Shook, G. E., & Ruegg, P. L. 2005. Assessment of the impact

of somatic cell count on functional longevity in Holstein and Jersey cattle using survival

analysis methodology. J. Dairy Sci., 88(2), 804–811. https://doi.org/10.3168/jds.S0022-

0302(05)72745-4

Chebel, R. C., Santos, J. E. P., Reynolds, J. P., Cerri, R. L. A., Juchem, S. O., & Overton, M.

2004. Factors affecting conception rate after artificial insemination and pregnancy loss in

lactating dairy cows. Animal Reproduction Science, 84(3–4), 239–255.

https://doi.org/10.1016/j.anireprosci.2003.12.012

Collier, R.J., R.B. Zimbelman, R.P. Rhoads, M.L. Rhoads, & L.H. Baumgard. 2011. Western

Dairy Management Conference A Re-evaluation of the Impact of Temperature Humidity

Index (THI) and Black Globe Humidity Index (BGHI) on Milk Production in High

Producing Dairy Cows.

De Haas, Y., Smolders, E. A. A., Hoorneman, J. N., Nauta, W. J., & Veerkamp, R. F. 2013.

Suitability of cross-bred cows for organic farms based on cross-breeding effects on

production and functional traits. Animal, 7(4), 655–664.

https://doi.org/10.1017/S1751731112002042

Dechow, C. D., Rogers, G. W., Cooper, J. B., Phelps, M. I., & Mosholder, A. L. 2007. Milk, fat,

protein, somatic cell score, and days open among Holstein, Brown Swiss, and their

crosses. J. Dairy Sci., 90(7), 3542–3549. https://doi.org/10.3168/jds.2006-889

Dhakal, K., Maltecca, C., Cassady, J. P., Baloche, G., Williams, C. M., & Washburn, S. P. 2013.

Calf birth weight, gestation length, calving ease, and neonatal calf mortality in Holstein,

Jersey, and crossbred cows in a pasture system. J. Dairy Sci., 96(1), 690–698.

https://doi.org/10.3168/jds.2012-5817

Dikmen, S., Martins, L., Pontes, E., & Hansen, P. J. 2009. Genotype effects on body temperature

in dairy cows under grazing conditions in a hot climate including evidence for heterosis.

International Journal of Biometeorology, 53(4), 327–331. https://doi.org/10.1007/s00484-

009-0218-3

El-Tarabany, M. S., Roushdy, E. M., & El-Tarabany, A. A. 2017. Production and health

performance of Holstein, Brown Swiss and their crosses under subtropical environmental

conditions. Animal Production Science, 57(6), 1137–1143.

https://doi.org/10.1071/AN15809

Falconer, D.S., Mackay, T.F., & Frankham, R. 1996. Introduction to quantitative genetics, 4th

edn. Longman, Burnt Mill, Harlow, UK

Fontaneli, R. S., Sollenberger, L. E.,. Littell, R. C., & Staples., C. R. 2005. Performance of

lactating dairy cows managed on pasture- based or in freestall barn-feeding systems. J.

Dairy Sci. 88:1264– 1276.

Hadfield, J. D. 2010. MCMC Methods for Multi-Response Generalized Linear Mixed Models:

The MCMCglmm R Package. Journal of Statistical Software, 33(2), 1-22.

http://www.jstatsoft.org/v33/i02/

Heins, B. J., Hansen, L. B., & Seykora, A. J. 2006. Calving difficulty and stillbirths of pure

Holsteins versus crossbreds of Holstein with Normande, Montbeliarde, and Scandinavian

Red. J. Dairy Sci., 89(7), 2805–2810. https://doi.org/10.3168/jds.S0022-0302(06)72357-8

Heins, B. J., Hansen, L. B., Seykora, A. J., Johnson, D. G., Linn, J. G., Romano, J. E., & Hazel,

A. R. 2008. Crossbreds of Jersey × Holstein compared with pure holsteins for production,

fertility, and body and udder measurements during first lactation. J. Dairy Sci., 91(3),

1270–1278. https://doi.org/10.3168/jds.2007-0564

Kendall, P. E., & Webster, J. R. 2009. Season and physiological status affects the circadian body

temperature rhythm of dairy cows. Livestock Science, 125(2–3), 155–160.

https://doi.org/10.1016/j.livsci.2009.04.004

Key, N., S. Sneeringer, & D. Marquardt. 2014. Climate Change, Heat Stress, and U.S. Dairy

Production. SSRN Electron. J. doi:10.2139/ssrn.2506668.

Klinedinst, P. L., Wilhite, D. A., Hahn, G. L., & Hubbard, K. G. 1993. The potential effects of

climate change on summer season dairy cattle milk production and reproduction.

Climatic Change, 23(1), 21–36. https://doi.org/10.1007/BF01092679

Lasley, J. F. 1987. Genetics of livestock improvement. (4th ed.).

Lenth, R. 2020. emmeans: Estimated Marginal Means, aka Least-Squares Means, R package

version 1.4.5. https://CRAN.R-project.org/package=emmeans

Linvill, D. E., & Pardue, F. E. 1992. Heat Stress and Milk Production in the South Carolina

Coastal Plains. J. Dairy Sci., 75(9), 2598–2604. https://doi.org/10.3168/jds.S0022-

0302(92)78022-9

Lopez-Villalobos, N., Garrick, D. J., Blair, H. T., & Holmes, C. W. 2000. Possible effects of 25

years of selection and crossbreeding on the genetic merit and productivity of New

Zealand dairy cattle. J. Dairy Sci., 83(1), 154–163. https://doi.org/10.3168/jds.S0022-

0302(00)74866-1

Malchiodi, M, Penasa, M, Tiezzi, F, & Bittante, G. 2011. Milk Yield Traits, Somatic Cell Score,

Milking Time and Age at Calving of Pure Holstein Versus Crossbred Cow.

McAllister, A. J. 2002. Is Crossbreeding the Answer to Questions of Dairy Breed Utilization? J.

Dairy Sci., 85(9), 2352–2357. https://doi.org/10.3168/JDS.S0022-0302(02)74315-4

Mcdowell, R. E., Wilk, J. C., & Talbolt, C. W. 1996. Economic Viability of Crosses of Bos

taurus and Bos indicus for Dairying in Warm Climates. J. Dairy Sci.

https://doi.org/10.3168/jds.S0022-0302(96)76484-6

Nardone, A., Ronchi, B., Lacetera, N., Ranieri, M. S., & Bernabucci, U. 2010. Effects of climate

changes on animal production and sustainability of livestock systems. Livestock Science,

130(1–3), 57–69. https://doi.org/10.1016/j.livsci.2010.02.011

National Research Council (NRC). 1971. A guide to environmental research on animals. Natl.

Acad. Sci., Washington, DC, USA

Nehring, R., Gillespie, J., Sandretto, C., & Hallahan, C. 2009. Small U.S. dairy farms: can they

compete? Agricultural Economics, 40, 817–825. https://doi.org/10.1111/j.1574-

0862.2009.00418.x

Polsky, L., & von Keyserlingk, M. A. G. 2017. Invited review: Effects of heat stress on dairy

cattle welfare. J. Dairy Sci., 100(11), 8645–8657. https://doi.org/10.3168/JDS.2017-

12651

Prendiville, R., Pierce, K. M., & Buckley, F. 2010. A comparison between Holstein-Friesian and

Jersey dairy cows and their F1 cross with regard to milk yield, somatic cell score,

mastitis, and milking characteristics under grazing conditions. J. Dairy Sci., 93(6), 2741–

2750. https://doi.org/10.3168/jds.2009-2791

Ravagnolo, O., & Misztal, I. 2000. Genetic component of heat stress in dairy cattle, parameter

estimation. J. Dairy Sci., 83(9), 2126–2130. https://doi.org/10.3168/jds.S0022-

0302(00)75095-8

Ravagnolo, O., Misztal, I., & Hoogenboom, G. 2000. Genetic component of heat stress in dairy

cattle, development of heat index function. J. Dairy Sci., 83(9), 2120–2125.

https://doi.org/10.3168/jds.S0022-0302(00)75094-6

Rincon, E. J., Schermerhorn, E. C., Mc Dowell, R. E., & Mc Daniel, B. T. 1982. Estimation of

Genetic Effects on Milk Yield and Constituent Traits in Crossbred Dairy Cattle. J. Dairy

Sci., 65(5), 848–856. https://doi.org/10.3168/jds.S002

Robison, O. W., B. T. McDaniel, & E. J. Rincon. 1981. Estimation of direct and maternal

additive and heterotic effects from crossbreeding experiments in animals. J. Anim. Sci..

52:44–50.

Sharma, A. K., Rodriguez, L. A., Mekonnen, G., Wilcox, C. J., Bachman, K. C., & Collier, R. J.

1983. Climatological and Genetic Effects on Milk Composition and Yield. J. Dairy Sci.

(Vol. 66). https://doi.org/10.3168/jds.S0022-0302(83)81762-7

Smith, D. L., Smith, T., Rude, B. J., & Ward, S. H. 2013. Short communication: Comparison of

the effects of heat stress on milk and component yields and somatic cell score in Holstein

and Jersey cows. J. Dairy Sci., 96(5), 3028–3033. https://doi.org/10.3168/jds.2012-5737

Spelman, R. J., Ford, C. A., McElhinney, P., Gregory, G. C., & Snell, R. G. 2002.

Characterization of the DGAT1 gene in the New Zealand dairy population. J. Dairy Sci.,

85(12), 3514–3517. https://doi.org/10.3168/jds.S0022-0302(02)74440-8

St-Pierre, N. R., Cobanov, B, & Schnitkey., G. 2003. Economic losses from heat stress by US

livestock industries. J. Dairy Sci.. 86(E Suppl.):E52–E77.

Sun, C., P.M. VanRaden, J.B. Cole, & J.R. OConnell. 2014. Improvement of Prediction Ability

for Genomic Selection of Dairy Cattle by Including Dominance Effects. PLoS One

9:e103934. doi:10.1371/journal.pone.0103934.

Turner, L. W., Chastain, J. P., Hemken, R. W., Gates, R. S., Crist, W. L., & Asae, A. 1992.

Reducing heat stress in dairy cows through sprinkler and fan cooling.

VanRaden, P. M., Van Tassell, C. P., Wiggans, G. R., Sonstegard, T. S., Schnabel, R. D., Taylor,

J. F., & Schenkel, F. S. 2009. Invited review: Reliability of genomic predictions for

North American Holstein bulls. J. Dairy Sci., Vol. 92, pp. 16–24.

https://doi.org/10.3168/jds.2008-1514

West, J.W., Mullinix, B.G., & Bernard, J.K. 2003. Effects of hot, humid weather on milk

temperature, dry matter intake, and milk yield of lactating dairy cows. J. Dairy Sci., 86:

232–242.

West, J. W. 2003. Effects of heat-stress on production in dairy cattle. J. Dairy Sci., 86(6), 2131–

2144. https://doi.org/10.3168/jds.S0022-0302(03)73803-X

Table 1. Number of animals observed and genetic expectations for each breed group. Genetic Model2 Breed n13 n24 Gd Hd Gm Hm group1 HH 39 39 1 0 1 0 JJ 60 60 0 0 0 0 HJ 45 45 0.5 1 0 0 JH 56 56 0.5 1 1 0 HHJ 14 14 0.75 0.5 0.5 1 HJH 58 58 0.75 0.5 0.5 1 JHJ 14 14 0.25 0.5 0.5 1 JJH 31 31 0.25 0.5 0.5 1 H-HX 4 HHHJ5 1 0.875 0.25 0.75 0.5 HHJH5 2 0.875 0.25 0.75 0.5 HHJJH5 1 0.8125 0.375 0.625 0.75 H-JX 42 HJHJ6 7 0.625 0.75 0.25 0.5 HJJH6 18 0.625 0.75 0.25 0.5 HJJJH6 1 0.5625 0.875 0.125 0.25 HJHHJ6 1 0.6875 0.625 0.375 0.75 HJHJH6 12 0.6875 0.625 0.375 0.75 HJHJJH6 2 0.6563 0.6875 0.3125 0.625 HJHHJH6 1 0.7188 0.5625 0.4375 0.875 J-HX 61 JHHJ7 3 0.375 0.75 0.75 0.5 JHJH7 36 0.375 0.75 0.75 0.5 JHJHJ7 2 0.3125 0.625 0.625 0.75 JHJJH7 12 0.3125 0.625 0.625 0.75 JHHJH7 2 0.4375 0.875 0.875 0.25 JHJHJH7 4 0.3438 0.6875 0.6875 0.625 JHHJJH7 1 0.4063 0.8125 0.8125 0.375 JHJHJJH7 1 0.3281 0.6563 0.6563 0.6875 J-JX 18 JJHJ8 4 0.125 0.25 0.25 0.5 JJJH8 5 0.125 0.25 0.25 0.5 JJHJH8 5 0.1875 0.375 0.375 0.75 JJHJJH8 1 0.15625 0.3125 0.3125 0.625 JJHJHH8 1 0.1875 0.375 0.375 0.75 JJHHJH8 1 0.2188 0.4375 0.4375 0.875

1The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey. 2Gd and Gm represent the expected direct (d) and maternal (m) breed percentage (Holstein) effect, Hd and Hm represent the expected direct and maternal heterosis effect, respectively. 3Number of animals per breed group, 4Number of animals per breed group after pooling. 5Pooled as H-HX breed groups, X = crossbred. 6Pooled as H-JX breed groups. 7Pooled as J-HX breed groups.8Pooled as J-JX breed groups.

Table 2. Correlations between genetic covariates of direct Holstein breed (%) and heterosis with all data provided by DRMS (upper right triangle) and the reduced data set (lower left triangle).

Direct Holstein Direct Maternal Holstein Maternal (%) Heterosis (%) Heterosis

Direct Holstein - -0.26 0.73 -0.13 (%) Direct Heterosis 0.076 - -0.08 0.27 Maternal Holstein 0.44 0.16 - -0.21 (%) Maternal 0.15 -0.08 0.005 - Heterosis

Table 3. Values used for classifying environmental descriptors.

1Trait Minimum Quintile 1 Quintile 2 Quintile 3 Quintile 4 Maximum MY 30.18 38.2 47.18 57.01 66.15 74.63 2ECM 30.18 38.2 47.18 57.01 66.15 74.63 PY -5.63 1.31 7.26 13.77 19.26 24.34 FY 2.2 11.22 16.44 22.05 26.63 31.48 PP -6.34 1.67 7.59 13.61 19.14 23.99 FP 34.78 44.46 55.58 64.39 71.26 77.32 F/P 65.5 81 86.25 91 94.5 100 SCS 25.67 37.45 47.92 56.98 66.64 75.24 3wSCC 65.17 81.67 87 90.83 93.5 100 1Traits: MY = milk yield; ECM = energy corrected milk; PY = protein yield; FY = fat yield; PP = protein Percent; FP = fat percent; F/P = fat to protein ratio; SCS = somatic cell score; wSCC = weighted somatic cell count. 2ECM = 12.55 × FY + 7.39 × (PY + 0.2595 × MY). 3 (SCS-3) wSCC = log2(100,000× 2 ) × (MY (kg/d) 971 ml))).

Table 4. Descriptive statistics of overall herd of reduced data set (2003-2018). Trait Mean S.D. Min Max Production Yield (kg/d) Milk 20.16 7.65 2.04 49.62 1ECM 22.62 8.54 1.97 54.14 Fat 0.97 0.41 0.04 2.50 Protein 0.68 0.23 0.07 1.62 Milk Composition (%) Fat 4.65 0.93 1.00 7.80 Protein 3.32 0.35 2.00 4.60 Fat/Protein 1.41 0.28 0.27 2.45 2SCS 2.94 1.66 0.10 9.60 3wSCC 31.07 1.60 25.92 37.58 1ECM = energy corrected milk (kg/d) = 12.55 × FY + 7.39 × (PY + 0.2595 × MY). 2SCS = somatic cell score. 3 (SCS-3) wSCC = weighted somatic cell count = log2(100,000× 2 ) × (MY (kg/d) 971 ml))).

Table 5. F-value and significance of fixed effects included in the analysis for milk yield and composition traits. 4Trait Parity 1SOL 2,5BG 3,6Env BG x Env

Milk Yield (kg/d) 1209.7*** 299.52*** 940.67* 326.77* 14.73*** Fat (%) 18.12*** 17.39*** 546.85* 1883 * 3.24*** Protein (%) 9.11*** 406.55*** 779.84* 712.89* 3.51*** Fat Yield (kg/d) 889.25*** 137.73*** 227.88 534.93* 9.55*** Protein Yield (kg/d) 1332.85*** 126.86*** 112.53 133.57 5.92*** SCS 24.83*** 26.73*** 158.29 201.22* 6.46*** 7ECM (kg/d) 1139.12*** 188.7*** 524.36* 854.02* 11.86*** 8wSCC 192.01*** 1.31 72.79 150.86 3.39*** F/P 11.02*** 31.23*** 18.06 89.87 2.3*** Statistical significance is given as: *P < 0.05; **P < 0.01; ***P < 0.001. 1SOL = Stage of lactation. 2BG = Breed group. 3Env = Environmental descriptor. 4Traits: SCS = somatic cell score; ECM = energy corrected milk; wSCC = weighted somatic cell count; F/P = fat to protein ratio. 5,6Breed group effect was tested on the cow within-breed variance and environmental descriptor was tested on the test-day variance. 7ECM = 12.55 × FY + 7.39 × (PY + 0.2595 × MY). 8 (SCS-3) wSCC = log2(100,000× 2 ) × (MY (kg/d) 971 ml))).

Table 6. Environmental descriptor and least-squares mean for each trait at every environmental descriptor level. 1Trait 2Environmental Descriptor Env-1 Env-2 Env-3 Env-4 Env-5

MY 5 d avg. of. min. THI 20.9 21.5 21.9 21.1 21.0 3ECM 5 d avg. of min. THI 23.8 24.0 23.6 22.1 21.9 PY 5 d avg. of min. temp. 0.71 0.77 0.73 0.69 0.67 FY 5 d avg. of max. temp. 1.04 1.07 0.99 0.96 0.90 PP 7 d avg. of min. temp. 3.45b 3.46b 3.39b 3.31ab 3.22a FP 1 d avg. of avg. THI 4.97b 4.71ab 4.57a 4.47a 4.40a F/P 4 d avg. of max. relative hum. 1.42 1.38 1.37 1.37 1.37 SCS 1 d avg. of min. THI 3.50 3.45 3.19 3.26 3.17 4wSCC 6 d avg. max. of relative hum. 31.3 31.1 31.1 31.2 31.2

1Traits: MY (kg/d) = milk yield; ECM (kg/d) = energy corrected milk; PY (kg/d) = protein yield; FY (kg/d) = fat yield; PP = protein percent; FP = fat percent; SCS = somatic cell score; wSCC (log2) = weighted somatic cell count; F/P =fat to protein ratio. 2Environmental Descriptors: temp. = temperature; THI = temperature-humidity index; relative hum. = relative humidity; Env = environmental descriptor. 3ECM = 12.55 × FY + 7.39 × (PY + 0.2595 × MY). 4 (SCS-3) wSCC = log2(100,000× 2 ) × (MY (kg/d) 971 ml))).

Figure 1. Change in direct and maternal expected breed percentage (Holstein) and heterosis averaged by year of birth (grey) and year of test-date (black). Where expected breed percentage does not deviate from 0, meaning that a second breed does not appear until 1998. Breed proportions do not stabilize at 0.5 until roughly 2003.

Figure 2. Least-squares means and 95% highest posterior density confidence interval (95% HPD CI) (left) for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H), where environmental descriptor level is on the x axis and the least- square mean of milk yield (kg/d) is on the y axis. Regression coefficient estimates and 95% HPD CI (right) of four genetic factors (direct breed (Holstein)percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for milk yield (kg/d) across levels of the environmental factor.

Figure 3. Least-squares means and 95% highest posterior density confidence interval (95% HPD CI) (left) for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H), where environmental descriptor level is on the x axis and the least- square mean of fat yield (kg/d) is on the y axis. Regression coefficient estimates and 95% HPD CI (right) of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for fat yield (kg/d) across levels of the environmental factor.

Figure 4. Least-squares means and 95% highest posterior density confidence interval (95% HPD CI) (left) for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H), where environmental descriptor level is on the x axis and the least- square mean of protein yield (kg/d) is on the y axis. Regression coefficient estimates and 95% HPD CI (right) of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein)percentage, direct heterosis and maternal heterosis) for protein yield (kg/d) across levels of the environmental factor.

Figure 5. Least-squares means and 95% highest posterior density confidence interval (95% HPD CI) (left) for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H), where environmental descriptor level is on the x axis and the least- square mean of protein (%) is on the y axis. Regression coefficient estimates and 95% HPD CI (right) of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for protein (%) across levels of the environmental factor.

Figure 6. Least-squares means and 95% highest posterior density confidence interval (95% HPD CI) (left) for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H), where environmental descriptor level is on the x axis and the least- square mean of fat (%) is on the y axis. Regression coefficient estimates and 95% HPD CI (right) of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for fat (%) across levels of the environmental factor.

Figure 7. Least-squares means and 95% highest posterior density confidence interval (95% HPD CI) (left) for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H), where environmental descriptor level is on the x axis and the least- square mean of somatic cell score is on the y axis. Regression coefficient estimates and 95% HPD CI (right) of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for somatic cell score across levels of the environmental factor.

CHAPTER 3

Conclusion

The research presented in this thesis conveys the need for greater evaluation of how crossbreeding impacts the performance of dairy cattle in challenging environments, especially in low-input environments. This research is of interest to dairy producers who require knowledge on the best practices for improving performance in their specific management setting. In the first chapter of this thesis, we investigated the published literature on heat stress in dairy cattle to get a clearer understanding of the underlying effects, how to manage it and how to potentially select dairy cattle for greater thermotolerance. We also found literature involving pasture-based dairy systems and selection for animals in low-input management settings. Finally, we determined the utilization of crossbreeding, the effects that it has in dairy cattle and ways to employ it to reduce the impacts of heat stress on production. In the second chapter, we analyzed production data from purebred Jersey and Holsteins along with their subsequent crosses in a pasture-based system to determine if there were differences in production for nine different traits. We did this by developing two statistical models, which we used to determine the meteorological factor that best explained the variability within the herd. The analysis ultimately resulted in the direct breed proportion of the cow having the largest impact for production traits across environments, where animals with greater Holstein ancestry had the highest production overall, but lower performance for component percentage traits. These animals also had a greater drop off in performance when the environment became more challenging. We also found that F1 cows with a Jersey dam had almost equal if not greater performance to the purebred Holstein breed group. Therefore, retention of the best performing Jersey cattle in the herd, while replacing lower performing animals with F1 crossbreds, may have potential for increasing performance in more challenging environments and reducing the burden of heat stress. Further investigation into the impact that

crossbreeding has on production in increasingly challenging environments across multiple herds should be performed to validate our findings.

APPENDIX

Table S1. F-statistic for all traits and explanatory variables for model 2. F-statistics for model 2 Dir. HO x Dir. He x Mat. HO x Mat. He x Trait Par SOL Env Dir. HO Dir. He Mat. HO Mat. He Env Env Env Env MY 1190.29*** 299.36*** 168.65 5446.2** 332.18* 614.64* 2.68 88.38*** 0.42 2.28 2.93* FP 17.85*** 17.93*** 28.63 1079.63* 61.14 164.75* 187.07* 0.73 6.47*** 5.33*** 0.74 PP 9.17*** 406.09*** 35.91 2100.25* 2.94 120.73 61.9 32.07*** 1.91 4.08** 0.73 FKG 872.82*** 136.5*** 71.29 1739.83* 387.34* 209.19* 27.87 80.72*** 1.71 2.73* 2.33 PKG 1309.91*** 127.3*** 21.48 855.69* 283.9* 169.04* 16.76 40.01*** 2.51* 0.42 1 SCS 27.04*** 26.24*** 114.87 360.84* 26.57 6.47 1.92 19.63*** 5.16*** 8.86*** 1.87 ECM 1118.05*** 187.81*** 140.91 3706.41* 568.9* 400.95* 16.51 77.82*** 0.72 1.34 2.52* TotSCC 195.41*** 1.41 85.04 37.48 0.04 19.29 3.85 10.01*** 2.62* 2.24 6.46*** F:P 11.12*** 30.09*** 6.91 31.58 4.53 21.01 59.24 9.82*** 5.33*** 0.97 1.77 Statistical significance is given as: P < 0.05; P < 0.01; P < 0.001; Dir. HO = Direct Holstein proportion, Dir. He = Direct heterosis, Mat. HO = Maternal Holstein proportion, Mat. He = Maternal heterosis, Par = Parity, SOL = Stage of Lactation, Env = Environmental Descriptor; Traits: MY (kg/d) = milk yield, ECM (kg/d) = energy corrected milk, PY (kg/d) = protein yield, FY (kg/d) = fat yield, PP = protein percent, FP = fat percent, SCS = somatic cell score, wSCC (log2) = weighted somatic cell count; F/P =fat to protein ratio

Table S2. Milk Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 23.261 21.471 25.102 23.277 21.544 25.002 23.444 21.810 25.167 21.360 19.579 23.026 20.643 18.863 22.428 H-HJ 23.487 21.214 25.790 23.544 21.268 25.996 23.966 21.598 26.337 23.579 21.421 25.797 21.592 19.329 23.796 H-HX 23.431 19.800 27.095 24.074 20.257 28.139 25.204 21.627 28.720 24.053 20.719 27.490 24.181 20.828 27.756 H-J 23.486 21.776 25.206 23.306 21.600 24.963 23.937 22.308 25.565 22.473 20.822 24.131 21.994 20.330 23.711 H-JH 23.365 21.710 24.990 23.370 21.827 24.972 23.612 22.044 25.168 22.238 20.678 23.779 21.803 20.213 23.474 H-JX 21.230 19.458 23.025 22.952 21.235 24.746 23.253 21.485 24.932 22.354 20.664 24.076 22.071 20.329 23.801 J-H 20.500 18.851 22.202 21.191 19.611 22.769 21.775 20.215 23.281 20.728 19.159 22.327 20.288 18.691 21.916 J-HJ 19.677 17.654 21.793 20.518 18.464 22.585 20.589 18.600 22.589 20.206 18.278 22.229 19.882 17.808 21.850 J-HX 20.147 18.444 21.815 20.487 18.819 22.087 20.322 18.724 21.958 19.873 18.215 21.470 20.169 18.466 21.749 J-J 17.161 15.417 18.821 17.296 15.687 18.803 18.410 16.847 19.946 18.066 16.446 19.638 17.971 16.298 19.557 J-JH 17.838 16.026 19.785 19.418 17.503 21.215 19.471 17.583 21.228 18.680 16.890 20.482 19.549 17.722 21.383 J-JX 17.618 15.078 20.233 18.806 16.364 21.505 19.429 16.130 23.047 18.822 16.172 21.460 21.988 19.706 24.450 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S3. Fat (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction.

Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 4.667 4.405 4.929 4.438 4.208 4.679 4.198 3.959 4.430 4.029 3.766 4.285 4.171 3.926 4.415 H-HJ 4.892 4.550 5.231 4.633 4.287 4.978 4.424 4.087 4.783 4.461 4.120 4.791 4.308 3.976 4.644 H-HX 4.777 4.223 5.289 4.567 3.847 5.290 4.416 3.853 4.984 4.366 3.861 4.886 3.788 3.273 4.304 H-J 4.875 4.640 5.110 4.676 4.451 4.902 4.566 4.343 4.788 4.457 4.230 4.686 4.410 4.185 4.642 H-JH 4.941 4.716 5.158 4.609 4.402 4.827 4.506 4.301 4.714 4.449 4.243 4.654 4.308 4.083 4.516 H-JX 4.879 4.636 5.130 4.538 4.296 4.799 4.460 4.212 4.696 4.462 4.233 4.700 4.435 4.196 4.689 J-H 5.068 4.851 5.304 4.771 4.552 4.971 4.556 4.340 4.753 4.642 4.425 4.857 4.477 4.261 4.696 J-HJ 5.048 4.755 5.334 4.905 4.602 5.210 4.813 4.523 5.101 4.622 4.355 4.918 4.612 4.322 4.903 J-HX 5.078 4.853 5.297 4.827 4.589 5.057 4.589 4.371 4.814 4.722 4.504 4.937 4.613 4.386 4.843 J-J 5.069 4.825 5.316 4.761 4.554 4.973 4.756 4.543 4.959 4.570 4.341 4.806 4.489 4.254 4.703 J-JH 5.244 4.978 5.503 4.918 4.667 5.178 4.793 4.552 5.044 4.607 4.349 4.883 4.585 4.340 4.853 J-JX 5.167 4.739 5.590 4.914 4.531 5.304 4.782 4.388 5.192 4.297 3.680 4.896 4.623 4.253 5.023 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S4. Protein (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction.

Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 3.258 3.135 3.372 3.255 3.148 3.365 3.185 3.072 3.296 3.106 2.997 3.222 2.996 2.877 3.114 H-HJ 3.384 3.238 3.532 3.392 3.242 3.539 3.344 3.199 3.493 3.213 3.062 3.364 3.162 3.009 3.311 H-HX 3.314 3.074 3.552 3.257 3.005 3.499 3.213 2.983 3.452 3.064 2.824 3.304 2.952 2.710 3.207 H-J 3.391 3.280 3.500 3.428 3.319 3.530 3.352 3.246 3.455 3.293 3.186 3.400 3.185 3.073 3.296 H-JH 3.379 3.276 3.485 3.377 3.275 3.474 3.313 3.209 3.410 3.207 3.103 3.306 3.103 2.990 3.205 H-JX 3.384 3.273 3.503 3.429 3.317 3.541 3.323 3.210 3.431 3.249 3.139 3.362 3.156 3.034 3.267 J-H 3.471 3.369 3.581 3.487 3.384 3.587 3.411 3.308 3.511 3.304 3.204 3.409 3.187 3.076 3.293 J-HJ 3.532 3.396 3.662 3.614 3.485 3.750 3.511 3.378 3.636 3.476 3.345 3.609 3.405 3.270 3.538 J-HX 3.513 3.400 3.619 3.534 3.430 3.644 3.479 3.377 3.583 3.406 3.302 3.512 3.311 3.202 3.421 J-J 3.575 3.470 3.684 3.621 3.522 3.720 3.540 3.434 3.638 3.468 3.365 3.572 3.409 3.302 3.521 J-JH 3.552 3.434 3.674 3.562 3.449 3.681 3.514 3.399 3.628 3.462 3.348 3.584 3.380 3.259 3.503 J-JX 3.638 3.475 3.789 3.566 3.403 3.716 3.518 3.318 3.712 3.430 3.255 3.602 3.354 3.197 3.518 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S5. Fat Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction. Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 1.081 0.956 1.210 1.103 0.978 1.225 0.975 0.853 1.088 0.904 0.787 1.028 0.842 0.717 0.964 H-HJ 1.130 0.972 1.295 1.131 0.974 1.294 1.069 0.903 1.217 1.044 0.891 1.193 0.931 0.785 1.088 H-HX 1.139 0.892 1.365 1.210 0.956 1.466 1.079 0.851 1.311 1.068 0.854 1.288 0.937 0.714 1.174 H-J 1.147 1.028 1.271 1.146 1.031 1.265 1.083 0.969 1.194 1.020 0.900 1.132 0.938 0.821 1.059 H-JH 1.150 1.037 1.271 1.162 1.056 1.284 1.031 0.920 1.133 1.010 0.898 1.118 0.923 0.815 1.043 H-JX 1.054 0.925 1.177 1.082 0.959 1.209 1.009 0.893 1.128 1.011 0.888 1.131 0.953 0.831 1.076 J-H 1.038 0.918 1.155 1.081 0.965 1.194 0.996 0.888 1.106 0.943 0.825 1.055 0.869 0.756 0.985 J-HJ 1.005 0.860 1.149 1.060 0.922 1.205 0.987 0.851 1.121 0.920 0.777 1.052 0.915 0.780 1.055 J-HX 1.026 0.904 1.145 1.011 0.895 1.128 0.968 0.862 1.082 0.937 0.818 1.050 0.902 0.784 1.020 J-J 0.867 0.747 0.987 0.907 0.795 1.021 0.859 0.749 0.967 0.855 0.740 0.968 0.773 0.659 0.891 J-JH 0.931 0.800 1.063 1.041 0.910 1.165 0.874 0.756 1.000 0.893 0.770 1.021 0.873 0.746 1.000 J-JX 0.901 0.722 1.086 0.961 0.780 1.132 0.899 0.677 1.127 0.929 0.757 1.114 0.993 0.833 1.150 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S6. Protein Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction. Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 0.717 0.621 0.815 0.784 0.689 0.881 0.738 0.643 0.830 0.669 0.575 0.761 0.626 0.531 0.723 H-HJ 0.780 0.663 0.898 0.818 0.701 0.936 0.788 0.668 0.903 0.761 0.644 0.874 0.691 0.575 0.808 H-HX 0.764 0.583 0.931 0.819 0.626 1.006 0.793 0.626 0.963 0.754 0.594 0.925 0.739 0.565 0.903 H-J 0.785 0.693 0.880 0.824 0.731 0.916 0.784 0.694 0.877 0.740 0.652 0.834 0.705 0.611 0.801 H-JH 0.771 0.679 0.863 0.813 0.722 0.903 0.775 0.687 0.862 0.716 0.629 0.802 0.687 0.593 0.778 H-JX 0.707 0.613 0.807 0.808 0.713 0.907 0.761 0.663 0.852 0.726 0.632 0.818 0.703 0.606 0.799 J-H 0.703 0.610 0.798 0.761 0.671 0.852 0.731 0.637 0.814 0.681 0.595 0.770 0.648 0.553 0.739 J-HJ 0.695 0.583 0.802 0.767 0.664 0.879 0.714 0.611 0.819 0.692 0.587 0.794 0.667 0.563 0.776 J-HX 0.706 0.606 0.795 0.751 0.661 0.850 0.703 0.611 0.793 0.669 0.581 0.763 0.664 0.574 0.761 J-J 0.614 0.522 0.707 0.662 0.574 0.750 0.642 0.553 0.728 0.619 0.532 0.707 0.596 0.508 0.691 J-JH 0.648 0.546 0.746 0.721 0.627 0.823 0.670 0.572 0.766 0.642 0.545 0.739 0.648 0.550 0.751 J-JX 0.637 0.510 0.757 0.687 0.565 0.816 0.642 0.496 0.794 0.631 0.504 0.756 0.720 0.600 0.839 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S7. Somatic Cell Score Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction. Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 3.100 2.609 3.587 3.153 2.662 3.624 2.694 2.174 3.143 3.128 2.666 3.612 3.182 2.698 3.693 H-HJ 3.317 2.615 4.077 3.738 2.977 4.464 3.147 2.394 3.869 3.453 2.748 4.182 3.451 2.705 4.193 H-HX 3.582 2.366 4.856 3.564 2.174 4.955 3.332 2.079 4.591 3.276 2.079 4.479 3.101 1.807 4.314 H-J 3.145 2.703 3.597 3.117 2.659 3.541 2.978 2.547 3.440 3.263 2.843 3.708 3.145 2.697 3.604 H-JH 3.307 2.905 3.711 3.031 2.639 3.447 2.900 2.496 3.311 3.112 2.716 3.491 3.052 2.653 3.504 H-JX 3.626 3.185 4.107 3.296 2.819 3.795 3.301 2.816 3.748 3.149 2.690 3.595 2.873 2.406 3.378 J-H 3.694 3.286 4.122 3.380 2.964 3.782 3.274 2.872 3.697 3.320 2.926 3.738 3.174 2.740 3.605 J-HJ 3.528 2.907 4.120 3.840 3.223 4.462 3.324 2.727 3.941 3.234 2.600 3.795 3.253 2.608 3.851 J-HX 3.332 2.935 3.760 3.245 2.784 3.655 2.968 2.569 3.383 2.978 2.580 3.380 2.743 2.309 3.164 J-J 4.121 3.706 4.554 3.916 3.513 4.304 3.654 3.242 4.056 3.947 3.548 4.341 3.762 3.305 4.195 J-JH 3.856 3.330 4.363 3.548 3.041 4.065 3.341 2.827 3.856 3.577 3.076 4.089 3.422 2.885 3.981 J-JX 3.372 2.542 4.190 3.669 2.894 4.428 3.341 2.565 4.117 2.701 1.632 3.792 2.887 2.058 3.710 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S8. Energy Corrected Milk Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction. Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 25.144 23.071 27.146 24.742 22.847 26.721 23.925 21.998 25.760 21.223 19.364 23.133 20.829 18.834 22.797 H-HJ 26.078 23.410 28.582 25.741 23.080 28.334 25.296 22.561 27.809 24.561 22.164 27.001 22.391 19.885 24.783 H-HX 25.870 22.115 30.126 27.320 22.740 31.723 26.010 22.161 29.772 24.642 21.112 28.271 23.827 19.999 27.476 H-J 26.221 24.272 28.170 25.647 23.829 27.503 25.851 23.993 27.594 23.482 21.623 25.145 22.765 20.871 24.652 H-JH 26.233 24.415 28.033 25.797 24.024 27.518 25.190 23.509 26.912 23.191 21.499 24.878 22.396 20.621 24.201 H-JX 23.928 21.836 25.804 24.821 22.834 26.774 24.408 22.544 26.396 23.323 21.462 25.103 22.902 20.933 24.785 J-H 23.624 21.900 25.548 23.820 21.988 25.521 23.934 22.255 25.727 21.732 20.053 23.470 21.061 19.307 22.890 J-HJ 22.907 20.642 25.204 23.510 21.170 25.738 23.293 21.170 25.520 21.519 19.402 23.620 21.515 19.362 23.798 J-HX 23.421 21.524 25.239 23.000 21.031 24.735 22.543 20.765 24.294 21.318 19.608 23.138 21.494 19.614 23.276 J-J 20.017 18.090 21.922 19.786 18.054 21.581 20.854 19.196 22.630 19.358 17.530 21.038 18.979 17.173 20.858 J-JH 21.058 19.005 23.189 22.534 20.520 24.587 21.579 19.619 23.649 20.127 18.182 22.054 20.846 18.848 22.844 J-JX 20.683 17.634 23.601 21.472 18.480 24.371 20.762 16.866 24.869 20.744 17.959 23.824 23.617 20.957 26.199 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S9. Fat/Protein Ratio Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction. Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 1.418 1.305 1.524 1.381 1.275 1.488 1.353 1.245 1.451 1.343 1.245 1.449 1.387 1.283 1.490 H-HJ 1.418 1.281 1.563 1.353 1.215 1.491 1.398 1.264 1.531 1.369 1.239 1.498 1.378 1.242 1.508 H-HX 1.485 1.284 1.691 1.452 1.227 1.661 1.265 1.063 1.475 1.377 1.186 1.572 1.361 1.161 1.562 H-J 1.400 1.293 1.506 1.381 1.278 1.482 1.385 1.284 1.482 1.377 1.280 1.473 1.385 1.285 1.483 H-JH 1.442 1.342 1.549 1.398 1.304 1.498 1.386 1.291 1.481 1.382 1.287 1.470 1.382 1.290 1.477 H-JX 1.404 1.293 1.515 1.405 1.297 1.519 1.348 1.245 1.451 1.372 1.272 1.470 1.391 1.293 1.489 J-H 1.421 1.316 1.523 1.398 1.298 1.493 1.379 1.288 1.477 1.399 1.304 1.491 1.377 1.283 1.473 J-HJ 1.412 1.286 1.537 1.371 1.248 1.502 1.366 1.251 1.485 1.343 1.226 1.456 1.368 1.251 1.481 J-HX 1.405 1.294 1.509 1.397 1.294 1.505 1.381 1.286 1.479 1.375 1.282 1.473 1.384 1.291 1.478 J-J 1.366 1.262 1.470 1.377 1.282 1.480 1.367 1.271 1.463 1.316 1.222 1.411 1.317 1.220 1.411 J-JH 1.426 1.311 1.541 1.412 1.304 1.523 1.395 1.280 1.495 1.356 1.253 1.461 1.358 1.251 1.464 J-JX 1.404 1.225 1.578 1.258 1.060 1.469 1.429 1.279 1.588 1.415 1.260 1.565 1.362 1.238 1.501 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S10. Weighted Somatic Cell Count (log2) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group by Environment Interaction. Environmental Descriptor Classes Env-1 Env-2 Env-3 Env-4 Env-5 Env-5 BG LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper LS Lower Upper Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD Means HPD HPD H-H 30.955 30.496 31.410 30.744 30.295 31.182 31.004 30.560 31.470 30.852 30.391 31.295 31.128 30.658 31.587 H-HJ 31.670 30.992 32.397 31.132 30.405 31.817 31.473 30.746 32.175 31.420 30.739 32.088 31.413 30.743 32.126 H-HX 31.304 30.094 32.506 31.480 30.212 32.712 31.619 30.375 32.768 31.459 30.246 32.679 31.210 30.006 32.331 H-J 31.165 30.732 31.563 30.952 30.560 31.370 31.139 30.708 31.546 30.921 30.510 31.336 31.349 30.923 31.766 H-JH 31.125 30.755 31.507 30.949 30.573 31.322 31.030 30.668 31.417 30.927 30.549 31.282 31.120 30.748 31.497 H-JX 31.378 30.942 31.830 31.166 30.667 31.636 31.241 30.787 31.699 31.090 30.676 31.532 30.964 30.534 31.387 J-H 31.292 30.926 31.691 31.092 30.723 31.492 31.216 30.835 31.610 31.045 30.674 31.429 31.227 30.845 31.624 J-HJ 31.434 30.849 32.006 31.158 30.532 31.768 31.077 30.493 31.675 31.203 30.635 31.776 31.059 30.489 31.601 J-HX 31.017 30.614 31.413 30.995 30.566 31.430 30.846 30.452 31.243 30.710 30.346 31.088 30.675 30.308 31.030 J-J 31.461 31.082 31.847 31.445 31.041 31.819 31.488 31.102 31.884 31.226 30.826 31.617 31.439 31.029 31.834 J-JH 31.406 30.899 31.877 31.076 30.572 31.560 31.347 30.824 31.842 31.024 30.556 31.524 31.256 30.754 31.755 J-JX 31.258 30.361 32.140 30.912 29.797 31.921 30.867 29.864 31.905 30.930 30.246 31.590 31.063 30.352 31.793 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; Env = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S11. Milk Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 22.387 21.229 23.544

H-HJ 23.238 21.536 24.957

H-HX 24.177 21.243 27.223

H-J 23.039 22.003 24.168

H-JH 22.875 21.921 23.893

H-JX 22.365 21.222 23.499

J-H 20.902 19.873 21.861

J-HJ 20.170 18.741 21.697

J-HX 20.200 19.168 21.239

J-J 17.779 16.807 18.788

J-JH 18.994 17.695 20.194

J-JX 19.337 17.524 21.180 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; HPD = Highest Posterior Density Confidence Interval.

Table S12. Fat (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 4.301 4.140 4.470

H-HJ 4.545 4.299 4.802

H-HX 4.384 3.928 4.818

H-J 4.597 4.446 4.749

H-JH 4.563 4.431 4.699

H-JX 4.556 4.397 4.722

J-H 4.703 4.567 4.837

J-HJ 4.801 4.587 5.005

J-HX 4.766 4.618 4.908

J-J 4.729 4.590 4.871

J-JH 4.829 4.660 5.011

J-JX 4.758 4.479 5.049

The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; BG = Breed Group; HPD = Highest Posterior Density Confidence Interval.

Table S13. Protein (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 3.159 3.083 3.240

H-HJ 3.299 3.184 3.422

H-HX 3.159 2.946 3.373

H-J 3.330 3.261 3.406

H-JH 3.276 3.207 3.341

H-JX 3.308 3.226 3.384

J-H 3.372 3.304 3.441

J-HJ 3.508 3.404 3.607

J-HX 3.448 3.376 3.520

J-J 3.523 3.454 3.589

J-JH 3.494 3.409 3.579

J-JX 3.502 3.384 3.627

Table S14. Protein Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 0.707 0.646 0.765

H-HJ 0.767 0.683 0.855

H-HX 0.774 0.622 0.915

H-J 0.768 0.712 0.825

H-JH 0.752 0.700 0.806

H-JX 0.741 0.682 0.803

J-H 0.705 0.651 0.759

J-HJ 0.707 0.631 0.779

J-HX 0.699 0.643 0.754

J-J 0.626 0.574 0.680

J-JH 0.666 0.601 0.730

J-JX 0.664 0.574 0.752

100

Table S15. Fat Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 0.981 0.903 1.060

H-HJ 1.060 0.948 1.176

H-HX 1.086 0.898 1.279

H-J 1.067 0.995 1.141

H-JH 1.055 0.989 1.122

H-JX 1.022 0.945 1.099

J-H 0.985 0.921 1.058

J-HJ 0.978 0.882 1.079

J-HX 0.969 0.900 1.040

J-J 0.853 0.783 0.919

J-JH 0.923 0.842 1.006

J-JX 0.936 0.817 1.059

101

Table S16. Somatic Cell Score Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 3.052 2.664 3.446

H-HJ 3.422 2.763 4.030

H-HX 3.371 2.220 4.479

H-J 3.129 2.774 3.485

H-JH 3.079 2.766 3.400

H-JX 3.250 2.883 3.637

J-H 3.367 3.039 3.696

J-HJ 3.435 2.931 3.971

J-HX 3.055 2.721 3.372

J-J 3.881 3.554 4.196

J-JH 3.549 3.128 3.989

J-JX 3.191 2.567 3.858

102

Table S17. Energy Corrected Milk Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 23.175 21.987 24.482

H-HJ 24.810 22.951 26.623

H-HX 25.540 22.477 28.759

H-J 24.798 23.624 25.939

H-JH 24.566 23.526 25.632

H-JX 23.878 22.657 25.098

J-H 22.836 21.778 23.923

J-HJ 22.553 21.057 24.162

J-HX 22.369 21.237 23.463

J-J 19.806 18.732 20.894

J-JH 21.237 19.834 22.510

J-JX 21.466 19.416 23.376

103

Table S18. Fat/Protein Ratio Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 1.376 1.309 1.442

H-HJ 1.383 1.291 1.486

H-HX 1.388 1.218 1.553

H-J 1.385 1.325 1.449

H-JH 1.398 1.342 1.456

H-JX 1.384 1.318 1.450

J-H 1.395 1.337 1.451

J-HJ 1.373 1.286 1.453

J-HX 1.388 1.329 1.451

J-J 1.349 1.293 1.407

J-JH 1.389 1.323 1.462

J-JX 1.374 1.267 1.477

104

Table S19. Weighted Somatic Cell Count (log2) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Breed Group.

BG LS Means Lower HPD Upper HPD

H-H 30.937 30.560 31.310

H-HJ 31.423 30.799 31.993

H-HX 31.414 30.320 32.479

H-J 31.105 30.761 31.440

H-JH 31.029 30.725 31.332

H-JX 31.170 30.809 31.537

J-H 31.175 30.869 31.491

J-HJ 31.186 30.683 31.687

J-HX 30.849 30.538 31.162

J-J 31.413 31.104 31.717

J-JH 31.220 30.819 31.636

J-JX 31.004 30.376 31.632

105

Table S20. Milk Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 20.922 19.494 22.427

2 21.514 20.094 22.900

3 21.945 20.535 23.259

4 21.039 19.644 22.440

5 21.016 19.573 22.426 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table 21. Fat (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 4.975 4.780 5.155

2 4.713 4.534 4.903

3 4.571 4.394 4.743

4 4.473 4.293 4.642

5 4.401 4.208 4.576 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

106

Table S22. Protein (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 3.449 3.358 3.545

2 3.460 3.370 3.546

3 3.392 3.300 3.477

4 3.306 3.219 3.397

5 3.216 3.128 3.313 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S23. Protein Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 0.711 0.624 0.796

2 0.768 0.689 0.856

3 0.729 0.645 0.811

4 0.691 0.608 0.772

5 0.674 0.593 0.765 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

107

Table S24. Fat Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 1.039 0.928 1.144

2 1.074 0.969 1.175

3 0.987 0.886 1.081

4 0.961 0.858 1.064

5 0.904 0.800 1.013 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S25. Somatic Cell Score Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 3.500 3.214 3.794

2 3.457 3.152 3.760

3 3.188 2.907 3.488

4 3.260 2.985 3.552

5 3.169 2.860 3.470 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

108

Table S26. Energy Corrected Milk Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 23.770 22.139 25.341

2 24.025 22.475 25.584

3 23.637 22.050 25.119

4 22.098 20.614 23.681

5 21.892 20.301 23.540 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S27. Fat/Protein Ratio Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 1.417 1.322 1.511

2 1.382 1.290 1.472

3 1.371 1.286 1.456

4 1.369 1.287 1.451

5 1.371 1.293 1.459 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

109

Table S28. Weighted Somatic Cell Count (log2) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Environment.

Env. LS Means Lower HPD Upper HPD

1 31.288 31.019 31.559

2 31.092 30.794 31.357

3 31.196 30.920 31.453

4 31.068 30.813 31.316

5 31.159 30.907 31.396 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Env. = Environmental Descriptor; HPD = Highest Posterior Density Confidence Interval.

Table S29. Milk Yield (kg/d) Count Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 16.445 15.688 17.126

2 20.302 19.542 21.005

3 22.169 21.401 22.932

4 23.277 22.485 24.081

5 24.257 23.400 25.090 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

110

Table S30. Fat (%) Count Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 4.549 4.455 4.642

2 4.675 4.581 4.778

3 4.654 4.548 4.756

4 4.681 4.570 4.790

5 4.579 4.456 4.695 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

Table S31. Protein (%) Count Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 3.352 3.303 3.399

2 3.388 3.341 3.438

3 3.372 3.323 3.423

4 3.365 3.316 3.420

5 3.346 3.290 3.402 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

111

Table S32. Protein Yield (kg/d) Count Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 0.553 0.511 0.594

2 0.686 0.644 0.729

3 0.743 0.702 0.787

4 0.782 0.739 0.827

5 0.809 0.765 0.857 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

Table S33. Fat Yield (kg/d) Count Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 0.757 0.701 0.806

2 0.954 0.899 1.006

3 1.038 0.981 1.090

4 1.097 1.037 1.151

5 1.120 1.060 1.180 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

112

Table S34. Somatic Cell Score Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 3.163 2.978 3.347

2 3.110 2.920 3.300

3 3.252 3.060 3.461

4 3.472 3.258 3.679

5 3.579 3.352 3.816 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

Table S35. Energy Corrected Milk Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 17.715 16.944 18.525

2 22.139 21.320 22.944

3 24.078 23.237 24.931

4 25.370 24.515 26.278

5 26.138 25.180 27.064 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

113

Table S36. Fat/Protein Ratio Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 1.363 1.319 1.407

2 1.388 1.342 1.433

3 1.387 1.342 1.436

4 1.398 1.348 1.446

5 1.373 1.323 1.425 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

Table S37. Weighted Somatic Cell Count (log2) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Parity.

Par. LS Means Lower HPD Upper HPD

1 30.645 30.476 30.816

2 30.880 30.710 31.063

3 31.170 30.983 31.356

4 31.457 31.259 31.653

5 31.647 31.441 31.867 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; Par. = Parity; HPD = Highest Posterior Density Confidence Interval.

114

Table S38. Milk Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 23.209 22.406 24.010

151-180 21.699 20.919 22.508

181-210 20.360 19.569 21.146

211-240 19.166 18.365 19.977

241-270 18.252 17.476 19.105

271-300 17.302 16.456 18.127

301-330 16.802 15.948 17.701

31-60 26.912 26.122 27.759

331-365 17.043 16.079 17.961

61-90 25.773 24.966 26.581

91-120 24.382 23.591 25.205

01-30 24.558 23.692 25.407 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

115

Table S39. Fat (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 4.581 4.467 4.689

151-180 4.665 4.553 4.780

181-210 4.696 4.582 4.808

211-240 4.681 4.564 4.796

241-270 4.701 4.586 4.822

271-300 4.730 4.610 4.852

301-330 4.795 4.663 4.928

31-60 4.351 4.231 4.466

331-365 4.861 4.716 5.008

61-90 4.462 4.343 4.571

91-120 4.480 4.370 4.596

01-30 4.523 4.399 4.646 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

116

Table S40. Protein (%) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 3.263 3.212 3.316

151-180 3.317 3.267 3.371

181-210 3.377 3.323 3.429

211-240 3.442 3.389 3.496

241-270 3.530 3.475 3.583

271-300 3.628 3.575 3.683

301-330 3.677 3.620 3.733

31-60 3.005 2.954 3.058

331-365 3.711 3.653 3.769

61-90 3.078 3.025 3.129

91-120 3.176 3.122 3.227

01-30 3.174 3.122 3.230 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

117

Table S41. Protein Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 0.759 0.716 0.804

151-180 0.723 0.681 0.767

181-210 0.695 0.652 0.739

211-240 0.671 0.627 0.715

241-270 0.655 0.612 0.700

271-300 0.633 0.589 0.677

301-330 0.618 0.575 0.667

31-60 0.823 0.778 0.868

331-365 0.629 0.582 0.677

61-90 0.800 0.756 0.844

91-120 0.777 0.732 0.819

01-30 0.792 0.745 0.836 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

118

Table S42. Fat Yield (kg/d) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 1.071 1.013 1.129

151-180 1.022 0.965 1.077

181-210 0.972 0.914 1.026

211-240 0.913 0.856 0.969

241-270 0.874 0.815 0.930

271-300 0.830 0.773 0.889

301-330 0.817 0.754 0.876

31-60 1.200 1.143 1.262

331-365 0.828 0.761 0.891

61-90 1.160 1.102 1.219

91-120 1.101 1.045 1.161

01-30 1.129 1.070 1.192 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

119

Table S43. Somatic Cell Score Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 3.132 2.909 3.335

151-180 3.230 3.018 3.447

181-210 3.315 3.099 3.531

211-240 3.354 3.127 3.568

241-270 3.505 3.287 3.737

271-300 3.642 3.401 3.857

301-330 3.765 3.525 4.018

31-60 2.782 2.549 2.989

331-365 3.930 3.685 4.214

61-90 2.951 2.732 3.165

91-120 3.070 2.850 3.274

01-30 3.107 2.873 3.332 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

120

Table S44. Energy Corrected Milk Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 24.970 24.070 25.861

151-180 23.704 22.778 24.560

181-210 22.470 21.591 23.340

211-240 21.209 20.285 22.084

241-270 20.341 19.433 21.250

271-300 19.393 18.429 20.285

301-330 18.921 17.982 19.908

31-60 27.937 27.057 28.917

331-365 19.174 18.051 20.189

61-90 27.043 26.152 27.991

91-120 25.783 24.904 26.722

01-30 26.091 25.070 27.065 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

121

Table S45. Fat/Protein Ratio Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 1.409 1.363 1.462

151-180 1.410 1.360 1.458

181-210 1.393 1.342 1.441

211-240 1.361 1.312 1.411

241-270 1.329 1.279 1.380

271-300 1.304 1.254 1.357

301-330 1.307 1.253 1.361

31-60 1.455 1.408 1.508

331-365 1.307 1.249 1.366

61-90 1.457 1.407 1.506

91-120 1.415 1.364 1.461

01-30 1.436 1.384 1.487 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

122

Table S46. Weighted Somatic Cell Count (log2) Least-Squares Means and 95% Highest Posterior Density Confidence Interval for Stage of Lactation.

SOL LS Means Lower HPD Upper HPD

121-150 31.182 30.992 31.388

151-180 31.166 30.974 31.370

181-210 31.155 30.960 31.362

211-240 31.067 30.872 31.275

241-270 31.117 30.907 31.322

271-300 31.133 30.918 31.346

301-330 31.150 30.922 31.383

31-60 31.058 30.854 31.260

331-365 31.320 31.065 31.568

61-90 31.165 30.972 31.373

91-120 31.206 31.004 31.403

01-30 31.196 30.973 31.400 The first letter indicates breed of sire and the proceeding letters represent breed or breed composition of dam; H = Holstein, J = Jersey, X = Crossbred; SOL = Stage of Lactation; HPD = Highest Posterior Density Confidence Interval.

123

Figure S1. Milk yield least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

124

Figure S2. Protein yield least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

125

Figure S3. Fat yield Least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

126

Figure S4. Fat (%) least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

127

Figure S5. Protein (%) least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

128

Figure S6. Somatic cell score least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

129

Figure S7. Energy corrected milk least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

130

Figure S8. Weighted somatic cell count least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

131

Figure S9. Fat/protein ratio least-squares means and 95% highest posterior density confidence interval for breed group by environment interaction of four breed groups (H-H, J-J, H-J, J-H).

132

Figure S10. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for milk yield (kg/d) across levels of the environmental factor.

133

Figure S11. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for protein (%) across levels of the environmental factor.

134

Figure S12. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for fat (%) across levels of the environmental factor.

135

Figure S13. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for protein yield (kg/d) across levels of the environmental factor.

136

Figure S14. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for fat yield (kg/d) across levels of the environmental factor.

137

Figure S15. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for somatic cell score across levels of the environmental factor.

138

Figure S16. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for weighted somatic cell count across levels of the environmental factor.

139

Figure S17. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for energy corrected milk across levels of the environmental factor.

140

Figure S18. Regression coefficient estimates and 95% highest posterior density confidence interval of four genetic factors (direct breed (Holstein) percentage, maternal breed (Holstein) percentage, direct heterosis and maternal heterosis) for fat/protein ratio across levels of the environmental factor.

141