Breeding and management strategies to
`improve reproductive efficiency in dairy
`cattle
`D. J. Ambrose, Alberta Agriculture and Forestry1, University of Alberta2, Canada; and
`J. P. Kastelic, University of Calgary3, Canada
`
`1
`
`Introduction
`
`2 Reproductive efficiency in dairy cattle
`
`3 The oestrous cycle and oestrus behaviour
`
`4 Factors affecting reproductive efficiency
`
`5 Strategies to improve reproductive efficiency in cows
`
`6 Future trends
`
`7 Where to look for further information
`
`8 Acknowledgements
`
`9 References
`
`1 Introduction
`
`Approximately 150 million households around the world produce milk (FAO, 2013) with
`up to one billion people deriving their livelihood from the dairy sector (International Dairy
`Federation, 2016). Cattle, water buffaloes and other livestock contribute to global milk
`production, with >80% of milk from dairy cattle, ~15% from water buffaloes and 5%
`from goats, sheep, camels, yaks and so on. The global value of milk and milk products
`is estimated to exceed US$300 billion annually (International Dairy Federation, 2016).
`Clearly, the dairy industry is of huge importance to the global economy.
`The global dairy industry is continually changing. China is making substantial investments
`to develop its dairy industry (Sharma and Rou, 2014); annual growth of domestic milk
`production was 12.8% from 2001 to 2010, and was projected to be 3.3% between 2011
`and 2020, with a projected 38% increase in dairy consumption by 2022. Similarly, India, the
`
`1. Livestock Research Branch, Alberta Agriculture and Forestry.
`2. Department of Agricultural, Food and Nutritional Science, University of Alberta.
`3. Department of Production Animal Health, Faculty of Veterinary Medicine, University of Calgary.
`
`http://dx.doi.org/10.19103/AS.2016.0005.16
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`2
`
`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`1935
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`Number of dairy cows (x 1000)
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`Figure 1 Changes in the number of dairy cows in Canada over the past 80 years. Source: Statistics
`Canada, accessed through Canadian Dairy Information Centre. http://dairyinfo.gc.ca/index_e.
`php?s1=dff-fcil&s2=farm-ferme&s3=nb.
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`174137
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`42325
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`40000
`20000
`0
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`No of dairy farms
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`Figure 2 Change in the number of Canadian dairy farms over the past five decades. Source: Canadian
`Dairy Information Centre. http://dairyinfo.gc.ca/index_e.php?s1=dff-fcil.
`
`largest milk-producing nation (mostly small farms) is modernizing its dairy industry (Swormink,
`2014). Concurrently, global milk production has only a 1.9% annual growth rate, with 73% of
`the additional global milk production of 150 million tonnes this decade expected to come
`from developing countries, of which 38% is from India and China (Sharma and Rou, 2014).
`Countries where most dairy cows are confined (e.g. Canada, the United States and
`Europe), greatly improved milk yield (per-cow basis), and increased herd size were
`observed. Changes in the number of dairy cows and dairy farms in Canada over the past
`several decades are shown in Fig. 1 and 2).
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`3
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`70000
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`0
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`Number of AI (x 1000)
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`2014
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`Year
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`Figure 3 Growth in use of AI in India. Number (millions) of AI in cattle and water buffalo performed by
`government agencies in India between 1997 and 2014. Source: National Dairy Development Board.
`
`11000
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`10000
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`9000
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`Milk production per cow in kg
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`2015
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`1995
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`1940
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`1935
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`1930
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`1925
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`Year
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`Figure 4 Milk production per cow (kg/y) in the United States from 1925 to 2015. Source: USDA,
`National Agricultural Statistics Service.
`
`The use of artificial insemination (AI) to improve genetic merit of dairy cows started in
`the late 1930s (Foote, 2002). Approximately 90% of dairy cows in Europe, >80% in the
`United States (Gillespie et al., 2014) and >75% in Canada (Van Doormaal and Kistemaker,
`2003) are bred by AI. In India, the use of AI tripled (~20 million to > 60 million) in the
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`4
`
`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`past decade (Fig. 3). Due to the resulting genetic progress (and improved management),
`annual milk production per cow has increased from 2000 kg in 1925 to >10 000 kg as of
`2016 in the United States (Fig. 4; USDA-NASS, 2016) and this currently exceeds 11 400 kg
`in Israel (Flamenbaum and Galon, 2010).
`
`2 Reproductive efficiency in dairy cattle
`
`Although high reproductive efficiency is critical to sustainable dairy farming, reproductive
`failure is the primary reason for culling dairy cows in many countries (Seegers et al., 1998;
`Rozzi et al., 2007; Swedish Dairy Association, 2009–12; Ahlman et al., 2011; Ansari-Lari
`et al., 2012), and accounts for ~30 and 36.5% of all culling in North America (USDA, 2002;
`CanWest DHI, 2014; Fig. 5) and England (Esslemont and Kossaibati, 1997), respectively.
`Infertility is also the primary reason for culling dairy cattle managed on pasture (Crosse et
`al., 1999, cited by Maher et al., 2006).
`Concurrent with increasing milk production, dairy cow fertility is generally declining
`in North America (Lucy, 2001; Westwood et al., 2002) and elsewhere (Macmillan et al.,
`1996; Royal et al., 2000; Lopez-Gatius, 2003; Kumaresan et al., 2009; Barbat et al.,
`2010; Dochi et al., 2010; Walsh et al., 2011). Annual decreases in conception rate (CR)
`in the United States (Beam and Butler, 1999) and Canada (Bosquet et al., 2004) were
`~0.4% (mid-1970s to 1990s) and 0.5% (1990–2000) and even faster in Europe (Hoekstra
`et al., 1994; Jorritsma and Jorritsma, 2000). Royal et al. (2000) reported that the CR
`to first service (insemination) after calving in the United Kingdom declined from 56%
`to ~40% between 1975 and 1998. Although an antagonistic association between milk
`production and reproductive performance may be inferred, there is no clear evidence of
`
`Dairy cow disposal reasons
`
`3 2 1
`
`5
`
`31
`
`16
`
`15
`
`7
`
`9
`
`11
`
`Reproductive problem
`Low milk production
`Mastitis related
`Sickness
`Feet/leg problem
`Udder breakdown
`Injury/accident
`Old age
`Export/sale
`Bad temperament
`
`Figure 5 Reasons for culling dairy cows in Canada (expressed as per cent). At 31%, reproductive
`problems remain the primary reason for culling. Source: CanWest DHI Ontario Progress Report, 2014.
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`5
`
`a cause-and-effect relationship (Raheja et al., 1989; Bello et al., 2012). In this regard, high
`milk production and high first-service CRs often co-exist in well-managed dairy herds
`(Peters and Pursley, 2002; Lopez-Gatius et al., 2006; Galon et al., 2010; Leblanc, 2010,
`2013).
`The cow-to-person ratio usually increases with herd size, which may reduce reproductive
`efficiency. Therefore, it is essential to assess reproductive performance, identify key
`problems and deficiencies, and plan and deliver corrective actions.
`Due to the widespread use of AI in the dairy industry, accurate oestrus detection and
`timely and skilful performance of AI are of paramount importance to optimize reproductive
`efficiency if cattle are bred following detection of oestrus. Therefore, all persons involved
`in AI should have a very good understanding of the oestrous cycle and of both the primary
`and secondary signs of oestrus.
`It is essential to sustain the motivation of those who are engaged in oestrus detection.
`An excellent attitude, interest and knowledge are extremely important for achieving
`reproductive success in dairy herds. Casual and new employees may not realize the
`importance of reproductive management and could have limited or no interest in oestrus
`detection. So, it becomes all the more important to send such staff to extension meetings
`and workshops dealing with dairy reproductive management so that they could gain first-
`hand knowledge.
`
`3 The oestrous cycle and oestrus behaviour
`
`The oestrous cycle of dairy cattle averages 21 days (range, 18–24) and has four phases
`(Fig. 6). Pro-oestrus, the interval from regression of the corpus luteum (CL) until
`manifestation of behavioural oestrus, lasts 3 to 4 days. Oestrus is the sexually receptive
`(and shortest) phase, and its primary sign is that cows ‘stand’ to be mounted by a bull
`or female herd mate (Fig. 7). For AI, breeding must be done during or shortly after
`the end of ‘standing oestrus’ to maximize CRs (Fig. 8). Duration of standing oestrus
`varies considerably and is longer in heifers than in cows (18–24 vs 8–12 h, respectively;
`O’Connor, 2007) with very short oestrus intervals and few mounts in high-producing cows
`(O’Connor, 2007). Metoestrus begins at the end of oestrus and lasts 3 to 5 days. Ovulation
`occurs during metoestrus, usually 24–32 h after the onset of standing oestrus (O’Connor,
`2007). Vaginal bleeding during metoestrus is more common in heifers than in cows
`(Hansen and Asdell, 1952) and is attributed to extravasation of blood after oestrogen
`concentrations decrease (Hansen and Asdell, 1952; Peter et al., 2009a). Dioestrus, the
`longest phase of the cycle, is characterized by the CL actively secreting progesterone
`to prepare the uterus for implantation. If a viable embryo is present in the uterus, at
`~15 d after oestrus, the elongating embryo produces interferon-tau, which suppresses
`the expression of oestrogen receptor alpha and oxytocin receptor, thereby suppressing
`the oxytocin-dependent pulsatile release of prostaglandin F2α (PGF) and preventing
`luteolysis (Thatcher et al., 1989; Bazer, 2013). Concurrently, activation of numerous genes
`in the conceptus and maternal endometrium facilitate cross talk between the embryo
`and the uterus (Mamo et al., 2012), leading to maternal recognition of pregnancy (i.e.
`maintenance of CL). However, in the absence of a viable embryo, oestradiol from the
`dominant ovarian follicle binds to endometrial oestrogen receptors, resulting in the
`release of PGF, luteolysis and then oestrus.
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`6
`
`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`Figure 6 Phases of the oestrous cycle and events associated with each phase. Lighter and darker
`shades of colour represent lower and higher hormone concentrations. The tapered ends represent
`increasing or decreasing concentrations. Upright triangles with narrow bases represent surge (LH) or
`pulsatile (PGF2a) release patterns. The partial yellow bars represent basal LH concentrations.
`
`Figure 7 ‘Standing oestrus’, the primary sign of oestrus.
`
`Key hormones in regulation of the oestrous cycle include oestradiol, oxytocin, PGF,
`GnRH, FSH, LH and progesterone. The relative presence and action of the various
`hormones at the four stages of the oestrous cycle are summarized (Fig. 6).
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`7
`
`Standing oestrus
`
`Ovulation
`
`24–32 h
`
`Early signs of oestrus
`
`6–12 h
`
`Fertile
`life of
`ovum
`(8–12h)
`
`–24 h
`
`–12 h
`
`0 h
`
`12 h
`
`16 h
`
`24 h
`
`36 h
`
`48 h
`
`Too early
`
`Okay
`
`Ideal
`
`Okay
`
`Too late
`
`Fertile life of sperm (24 h)
`
`Breeding during this window
`may result in poor fertility
`particularly in multiparous cows
`
`Figure 8 Optimum time for AI success. A schematic depiction of timelines associated with oestrus
`and ovulation. The duration of standing oestrus is only 6–12 h in lactating dairy cows. The interval
`from onset of standing oestrus to ovulation is 24–32 h, and the fertile life of an ovum after exiting the
`follicle at ovulation is only 8–12 h. Sperm may live in the female reproductive tract for up to 48 h, but
`viability is reduced beyond 24 h. The best time to breed a cow by AI is 12 h after the onset of standing
`oestrus, although acceptable CRs could be attained if AI occurred anytime from the onset of standing
`oestrus until up to 24 h. Breeding cows closer to ovulation time may result in poor fertility, particularly
`in multiparous cows (Stevenson et al., 2014).
`
`4 Factors affecting reproductive efficiency
`
`Many factors, either independently or through their interactions, can influence
`reproductive efficiency. The main factors affecting reproduction can be broadly grouped
`into four categories, namely human (managerial), animal (intrinsic and extrinsic) nutritional,
`and environmental. Specific examples under each category are summarized and briefly
`discussed in Table 1.
`
`4.1 Human or managerial factors
`Voluntary waiting period (VWP), also referred to as elective waiting period, is the minimum
`interval in days from calving to first insemination. In North America, the VWP is often 60
`days (may range from 50 to >90), with variations among herds and even within herds (e.g.
`based on milk production). Clearly, VWP has great potential to affect reproductive efficiency.
`Poor oestrus detection efficiency is a primary cause of reduced reproductive efficiency
`in dairy herds. In herds using AI, accurate detection of oestrus is extremely important
`for reproductive success. If protocols are used to synchronize ovulation for fixed-time AI
`without detection of oestrus, AI submission rate equals the oestrus detection rate (EDR).
`In Canadian studies, the EDR during the early 1990s was 48% (Kinsel and Etherington,
`1998), but more recently, mean 21-d AI submission rates were 33% (Leblanc, 2005) and
`38% (Ambrose and Colazo, 2007). In the latter study, only 42% of eligible cows had been
`inseminated by 80 days postpartum, and 23% had not been inseminated by 125 days
`postpartum, emphasizing the importance of oestrus detection.
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`8
`
`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`Table 1 Factors affecting reproductive performance of dairy cows
`
`Human (managerial factors)
`
`Animal (intrinsic and extrinsic
`factors)
`
`Nutritional
`
`Environmental
`
`Voluntary waiting period (VWP)
`Oestrus detection frequency and efficiency
`Use of oestrus detection aids
`Use of oestrus/ovulation synchronization protocols
`Semen storage, thawing and handling
`Insemination time and technique
`Pregnancy diagnosis
`Feeding, disease and environmental management
`Attitude, education, knowledge and skill
`
`Intrinsic factors
`Breed/genotype
`Anatomic and physiologic anomalies or barriers
`Age and parity (e.g. heifers vs cows)
`Energy status/body condition
`Level of milk production
`Expression of oestrus (behaviour)
`Low conception rate (CR)
`Embryonic loss
`Susceptibility to infectious disease and metabolic disorders
`Stress
`In natural service herds:
`Bull libido
`Semen quality
`Sperm survivability in female reproductive tract
`
`Extrinsic factors
`Infectious (e.g. metritis, mastitis, neosporosis, leptospirosis)
`Non-infectious (e.g. acidosis, ketosis, lameness, cystic
`ovary)
`Calving-related events (e.g. dystocia, retained placenta)
`
`Fats and fatty acids
`Protein
`Starch
`Amino acids, minerals and vitamins
`
`Ambient temperature (heat and humidity, air quality)
`Extreme cold, wet and windy conditions
`Flooring (natural vs concrete)
`Light/photoperiod
`Contaminants in feed and water (e.g. mycotoxins)
`Stocking density
`Stray voltage
`
`Whether oestrus detection aids such as tail chalk, tail paint, mount detectors, pedometers
`or other electronic activity monitors are used in addition to routine visual observation, or not
`used, will influence reproductive efficiency. Herds that rely only on visual observations are
`likely to have a lower EDR than herds using oestrus detection aids. Similarly, using oestrus or
`ovulation synchronization protocols increases AI submission rates and reproductive efficiency.
`Other human factors that affect reproductive efficiency include storage conditions of
`frozen semen, semen thawing, pre- and post-thaw-handling of semen straws, inseminator
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`9
`
`Figure 9 Conception rate changes in Holstein and Normande cows over 8 years in France (1997–8 to
`2005–6). Reproduced with permission of the Society for Reproduction and Development from Barbat
`et al. (2010). Female fertility in French dairy breeds: Current situation and strategies for improvement.
`J. Reprod. Dev. 56 (Suppl): S15–S21.
`
`skill, insemination technique and timing of insemination relative to oestrus and ovulation.
`Inadequacies in any of these factors can reduce CRs.
`Yet another major determinant of reproductive efficiency is identification of non-
`pregnant cows as soon as possible after breeding. Although trans-rectal palpation of
`uterine contents for pregnancy diagnosis can be used reliably only beyond ~35 d, newer
`technologies make earlier pregnancy determination a reality (discussed in detail under
`Strategies to Improve Reproductive Efficiency).
`Other managerial decisions relating to feeds and feeding, disease management (e.g.
`vaccination) and environmental management (e.g. heat abatement during hot summers)
`can have major impacts on reproductive efficiency. Finally, the attitude, skills and
`knowledge of personnel involved in reproductive management and their willingness to
`learn, can have a major influence on reproductive success. Unmotivated and unskilled
`workers should be considered a liability and if not willing to improve, ideally reassigned to
`chores not involving reproductive management.
`
`4.2 Animal factors
`Both intrinsic and extrinsic factors can influence reproductive outcomes in dairy cattle.
`Among intrinsic animal factors, breed and genotype have a tremendous impact. For
`example, Holsteins are reported to be less fertile than other dairy breeds like Jersey
`(Norman et al., 2009) and Normande (Barbat et al., 2010; Fig. 9).
`
`4.3 Intrinsic factors
`Anatomical defects in the reproductive tract (e.g. kinked or blind cervix, segmental
`aplasia of the uterus, freemartinism, hydrosalpinx, blocked oviduct) can either hinder AI or
`interfere with gamete transport, fertilization or pregnancy sustenance. Aberrant endocrine
`function could result in abnormal hormone concentrations disrupting normal reproductive
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`10
`
`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`34
`
`30
`
`29
`
`28
`
`26
`
`24
`
`1st parity
`2nd parity
`3rd parity
`4th parity
`5th parity
`6th & higher
`
`40
`
`35
`
`30
`
`25
`
`20
`
`15
`
`10
`
`05
`
`Holstein
`
`Figure 10 Influence of parity on first-service CRs in US Holstein cows (n = 1 032 506) in 2006.
`Source: Norman et al. (2009).
`
`processes such as resumption of cyclicity postpartum (extended anoestrus), expression
`of oestrus behaviour and ovulation, or create a non-conducive environment for gamete
`transport, fertilization and pregnancy establishment (López-Gatius, 2012).
`Age and parity exercise a major influence on fertility in dairy cattle. Whereas nulliparous
`heifers have high CRs of 65–75% (Pursley et al., 1997a; Ambrose et al., 2005; Balendran
`et al., 2008) following AI, CRs in cows are usually considerably lower (40% or lower; Pursley
`et al., 1997a; Dochi et al., 2010). Parity is also a significant factor affecting CRs in dairy
`cows, with primiparous cows having higher CRs than multiparous cows. Although this has
`been widely recognized (Tenhagen et al., 2004; Balendran et al., 2008), a report from the
`United States involving more than 1 million breeding records is one of the largest databanks
`to demonstrate the negative influence of parity on first-service CRs (Norman et al., 2009;
`Fig. 10). Furthermore, lactating cows have high embryonic losses (Santos et al., 2004) and
`greater susceptibility to infections, metabolic disorders and stress (Dobson and Smith, 2000;
`Dobson et al., 2008; Walker et al., 2008), all of which can reduce reproductive performance.
`High milk production can negatively affect oestrus expression (Lucy, 2001; Van
`Eerdenburg et al., 2002; Lopez et al., 2004), as high-producing cows are more likely than
`first-lactation cows to have negative energy balance postpartum (Butler and Smith, 1989).
`Although this can impair fertility, well-managed herds with very high milk production can
`still maintain good fertility (Leblanc, 2010, 2013).
`In herds exclusively using AI, semen should come from reliable sources and be of good
`quality and high fertility. Therefore, effects of male factor on reproductive efficiency in herds
`managed solely by AI will not be discussed. However, in herds using natural service, either
`exclusively or partially, the male factor is relevant. Breeding soundness evaluation should
`be performed by an experienced veterinarian before first using a bull and subsequently
`perhaps once annually (more frequently if there is reduced fertility).
`
`4.4 Extrinsic factors
`
`Calving disorders such as dystocia and retained foetal membranes predispose cattle to
`uterine infections (Kinsel and Etherington, 1998; Fourichon et al., 2000; Opsomer et al.,
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`11
`
`2000). Cows that suffer from metritis, mastitis or other early postpartum diseases typically
`have lower fertility and require a much longer interval from calving to conception (Santos
`et al., 2009). Furthermore, non-infectious conditions and metabolic disorders such as
`acidosis, ketosis, lameness, cystic ovaries and so on are also known to delay the interval
`from calving to first service and CRs in dairy cows (López-Gatius, 2012).
`If bulls are used for breeding, they should be tested and determined to be free of
`sexually transmitted diseases, particularly trichomoniasis and campylobacteriosis
`(Bondurant, 2005; Michi et al., 2016).
`
`4.5 Nutritional factors
`Inadequate energy intake during the early postpartum period is common in high-
`producing dairy cows, resulting in negative energy balance, with mobilization of fat
`and high concentrations of non-esterified fatty acids (NEFA). High NEFA concentrations
`have negative effects on oocyte function and embryo quality, which likely contribute to
`subfertility in dairy cows (Leroy et al., 2005; Van Hoeck et al., 2014). In addition, high-
`protein diets (which result in increased blood urea nitrogen concentrations), fats, long-
`chain polyunsaturated fatty acids, certain vitamins, amino acids and minerals also influence
`reproductive outcomes (Mattos et al., 2000; Ambrose et al., 2006; Bourne et al., 2007;
`Santos et al., 2010; Sinclair et al., 2014; Leroy et al., 2015).
`
`4.6 Environmental factors
`Extreme heat and humidity cause severe heat stress-induced fertility impairment in dairy
`cattle (Hansen, 1997; Jordan, 2003) and significant economic losses (St-Pierre et al.,
`2003). Although extreme cold and wet/windy conditions could have a detrimental effect
`on reproductive function, the impact of cold weather on reproductive outcomes is not yet
`extensively studied. However, it is known that severe cold stress can have adverse effects
`on reproductive function (Young, 1983; Gwasdauskas, 1985).
`
`Other environmental factors that can have an indirect influence on reproduction in dairy
`cattle include:
`
`1. Flooring (concrete vs dirt or bedded pack – better footing promotes expression of oestrus)
`2. Type of housing (tie-stall or stanchion-barn vs free-stall or loose-housing – oestrus
`detection is much easier in barns where cows are free-roaming)
`3. Stocking density (subordinate cows may not get access to good quality feed
`in overcrowded barns, putting them at risk of negative energy balance, affecting
`reproductive function)
`4. Wet or dirty stalls (increased risk of mastitis, which can reduce reproductive
`performance)
`5. Poor ventilation (reduces air quality, suppresses the immune system and increases
`susceptibility to disease, with indirect effects on reproduction)
`6. Contaminants in feed and water – dairy cows may be exposed to various contaminants,
`environmental, bacterial or fungal, and toxins (e.g. mycotoxins, plant toxins) including
`gossypol and phytoestrogens (D’Mello, 2004), all of which can affect reproductive function.
`7. Photoperiod (Dahl et al., 2000) and stray voltage (Appleman and Gustafson, 1985)
`may influence reproductive function in dairy cows, although strong evidence directly
`implicating these factors in poor reproductive efficiency is lacking.
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`12
`
`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`5 Strategies to improve reproductive efficiency in cows
`
`Various strategies can be used to improve reproductive efficiency in dairy herds. Managerial
`factors are the easiest to implement; therefore, they will be discussed first. Since poor
`oestrus detection efficiency is a major factor that reduces reproductive inefficiency, the
`first strategy should be to improve oestrus detection efficiency.
`
`5.1 Improving oestrus detection
`Among the many human factors that affect reproductive performance, inefficient oestrus
`detection is the most important; on average, up to 50% of oestrus events are undetected
`(Van Erdenberg et al., 2002). CR is defined as the per cent cows pregnant per insemination,
`whereas pregnancy rate (PR) is the per cent cows pregnant considering all eligible cows
`in the herd intended to be inseminated. For example, if 10 cows are eligible for breeding
`and 7 of the 10 cows are detected in oestrus and inseminated within a 21-day interval,
`then the 21-day EDR is 70% (7/10). Thereafter, if 3 of the 7 inseminated cows are confirmed
`pregnant, the CR is 43% (3/7) and the PR is 30% (3/10). Thus, PR is a product of EDR and
`CR. Increasing EDR can improve PR, even if CR remains constant. For example, if the CR is
`50%, the PR will increase from 15 to 50% as EDR increases from 30 to 100%.
`Based on blood or milk progesterone concentrations, 11–25% of cows are routinely
`inseminated when not in oestrus (Nebel et al., 1987; Ambrose and Colazo, 2007). They
`may be inseminated too early, too late or at a time when they are not even close to being
`in oestrus (e.g. dioestrus phase). This must be borne in mind when detecting oestrus in
`dairy cattle. Furthermore, 5–10% of all pregnant cows exhibit oestrus, usually from mid- to
`late gestation, although this can occur during any stage of pregnancy (Choudary et al.,
`1965; Thomas and Dobson, 1989; Dijkhuizen and Van Eerdenburg, 1997). Ovarian follicular
`growth occurs during pregnancy; large follicles increase blood oestradiol concentrations
`and occasionally trigger oestrus behaviour in pregnant cattle (O’Connor, 2007). Therefore,
`it is essential that farm personnel responsible for oestrus detection and AI are familiar
`with the breeding history of cows to minimize chances for erroneous inseminations. In
`this regard, colour-coded identification schemes (e.g. tail chalk or paint) can be used to
`identify cows that have recently calved, cows due for insemination, inseminated cows
`waiting for pregnancy diagnosis and cows confirmed pregnant. Such a system can help to
`quickly determine the status of cows in oestrus.
`Allowing cows to interact with their herd mates in an open area on natural (dirt) footing
`rather than concrete floor improves the likelihood that cows will mount and oestrus
`detected (Table 2).
`
`Table 2 Oestrus activity (mounting and standing to be
`mounted) of Holstein cows on dirt versus concrete flooring
`
`Measure
`
`Dirt floor
`
`Concrete floor
`
`Duration of oestrus (h)
`
`Total number of mounts
`
`Total number of stands
`
`13.8
`
`7.0
`
`6.3
`
`9.4
`
`3.2
`
`2.9
`
`Source: Britt et al. (1986).
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`13
`
`Efficiency of oestrus detection can be improved with aids (e.g. tail chalk, paint or Kamar
`heat mount detector). Applying chalk or paint to the tail head of cows and using a simple
`scoring system (e.g. 5 to 0; no change in colour = 5; complete loss of colour = 0) will
`enhance oestrus detection efficiency. Cows with a paint score of 2 may have been in
`standing oestrus after the last observation period; they should be watched carefully and
`bred upon confirming oestrus.
`In larger herds where oestrus detection is poor, an electronic oestrus detection system
`may be cost-effective. The efficacy of HeatWatch, a radiotelemetric mount detecting
`system, has been reported (Bailey, 1997; Dransfield et al., 1998; Xu et al., 1998; Nebel
`et al., 2000), but the company is no longer in business. In studies that used the HeatWatch
`system, duration of oestrus in lactating Holstein cows was much shorter than previously
`documented. For instance, >70% of cows remained in oestrus for 12 h (30% for 6 h),
`with an average duration of oestrus of 8.5 h and ~10 mounts (Dransfield et al., 1998;
`Nebel et al., 2000). Some cows had less than three mounts, each lasting ~2 s. Most of the
`oestrus activity occurred when cows were on dirt surfaces, and travelling to and from the
`milking parlour, regardless of time of day.
`
`Figure 11 Presence of mucus on the vulva, tail and hindquarters are secondary signs of oestrus
`in cattle.
`
`© Burleigh Dodds Science Publishing Limited, 2017. All rights reserved.
`
`Exhibit 1017
`Select Sires, et al. v. ABS Global
`
`

`

`14
`
`Breeding and management strategies to improve reproductive efficiency in dairy cattle
`
`There are various activity-monitoring systems that are currently marketed for detection
`of oestrus in dairy cattle (Fricke et al., 2014; Madureira et al., 2015; Neves and LeBlanc,
`2015). Although their efficacy varies, in one study, overall reproductive performance was
`similar or better than in herds that used timed-AI programmes (Neves and LeBlanc, 2015).
`Oestrus detection matters! Improved oestrus detection efficiency can dramatically
`improve reproductive performance. Therefore, it is important to invest time into systematic
`detection of oestrus in dairy herds. Simple approaches such as increased frequency of
`observation, dedicated observation time and the use of oestrus detection aids can improve
`results. The recommended frequency of observation is 3 or 4 times a day, for 20 min
`at each occasion. While observing cows for oestrus, do not concurrently perform other
`tasks like cleaning, mixing or delivering feed. Observing cows for signs of oestrus from a
`short distance (preferably positioned above ground level to improve the view), or gently
`walking through the barn or animal pen without unduly disturbing the cows (encouraging
`cows to move slowly will generate new interactions that can stimulate mounting activity)
`provide the greatest opportunity to detect cows in oestrus. Walking through the animal
`pen facilitates visual inspection of the vulva, hindquarters, tail and tail head for secondary
`signs of oestrus such as vulvar oedema, mucus discharge, ruffled hair coat, fresh mucous
`on the tail and hindquarters (Fig. 11), excessively friendly cows and other tell-tale signs of
`imminent, current or recent oestrus.
`
`5.2 Adopting oestrus synchronization
`The next recommended strategy is to use oestrus synchronization. Inducing oestr