Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds

25 

Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds CHRIS HUDSON, JOHN GEORGE COOK AND RICHARD LAVEN

Introduction Background Control of cow fertility is critical to running a profitable dairy enterprise because of the shape of the cow’s lactation curve. Daily milk production increases after parturition up to a peak (usually at 40–80 days postcalving in mature cows), after which it declines with time. This effect is much less marked in first lactation heifers, which typically have much ‘flatter’ lactation curves, but it is still important. Therefore in order to produce milk efficiently, cows are required to calve at regular intervals throughout their productive lives so that they spend most of their time in the relatively productive part of the lactation curve. Both artificial insemination and natural service are common in the main dairying nations, and many herds use a combination of both. Cows are commonly considered eligible for breeding after a set period of time after calving (the voluntary wait period, VWP) in herds calving year-round and during a specified window of time during the year in seasonal calving herds. The choice of VWP policy is determined by the herd manager and is usually influenced by lactation curve shape and herd replacement policy. Cows that fail to become pregnant sufficiently quickly after becoming eligible for breeding are commonly culled from the herd; the decision on the stage at which this happens typically varies between cows.

Evolution of Dairying and Fertility Management Dramatic changes have occurred to the global dairy industry over recent decades, brought about through improvements to management, feeding, and breeding practices. Genetic advances have led to breeds that are heavier, leaner, and have a higher milk yield potential. Deregulation of supply in many markets has also exposed the dairy producer to the effects of widely fluctuating milk prices, as dairy commodity prices rise and fall in response to global influences as well as local circumstances. Although the future is difficult to predict, it seems likely that there will be a continued general increase in herd size, alongside an increase in the number of farms producing milk aimed at small, but high value, markets. Large herd size offers the potential to reduce overhead costs per litre in exchange for a greater market share, whereas the niche market benefits from a potential higher product value. The dynamics of health and fertility management

are fundamentally different in larger herds, and the challenge for herd owners and veterinary surgeons is to understand how their roles need to change. The obvious and clear incentive that should motivate dairy producers to improve health and fertility is the undoubted financial benefits that can be gained from such improvements, and in larger herds it is logical that these improvements should be carried out in an evidence-based and planned manner. However, in many cases, early ‘health planning’ initiatives simply led to the construction of documents solely designed to comply with the requirements of farm assurance schemes and large retailers. A clear distinction should be drawn between this approach and a more holistic approach to herd health and production monitoring, which should be a continuous, active, and ongoing process involving regular monitoring of key parameters and processes; this requires a detailed knowledge of the farm as well as excellent record keeping. Arguably, there is no reason why a similar approach should not be undertaken for herds of all sizes. Nevertheless, although both large and small dairy herds face the same reproductive management issues, in larger herds the problems become magnified, and the potential economic consequences are larger and accrue faster. Larger herds are characterised by some fundamental differences that go beyond their size and structure, which necessitate a different approach to the problem of reproduction. The provision of labour, both in terms of quantity and quality, is a key area as herds expand in size. Larger herds involve more cows and more staff, but generally fewer people per cow; many large herds operate at around 1 person per 120 cows. Historically, dairy herds have relied on skilled individual staff to retain a high level of individual cow knowledge, but as the herd gets bigger, this level of individual attention to detail becomes impossible to sustain. At a certain herd size, it becomes necessary to replace this day-to-day individual cow knowledge (what is essentially a data capture and storage function) with technology, usually in the form of computer software packages. Technology means that the skill levels of the average farm worker do not need to be as high as in smaller herds, but key skills areas remain that, as yet, cannot be successfully replaced by technology, such as evaluation of calving issues or the carrying out of artificial insemination (AI) procedures. When technology can have an effect, it should go without saying that there should be a cost benefit. For example, the use of pedometers to aid oestrous detection may improve submission rates but potentially could incur increased costs if logistic considerations, such as cow identification and handling and separation facilities, 467

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are suboptimal; as a consequence, greater labour costs may be incurred in physically finding the cows to serve. Increased numbers of staff mean that senior staff must learn to interact and manage subordinate personnel, and there is no doubt that this represents a huge challenge on larger units. Personnel management (and sometimes language) skills are needed that are not necessary in a smaller herd employing maybe a single member of staff. Greater numbers of employees incur a greater staff turnover, and issues with sickness, holiday, and work rotas can be disheartening; although the overall skill level may be reduced, herd performance should not be limited by the least skilled worker. As herds expand, job roles, such as milking, may become more mundane and routine, whereas other roles become more specialised such as, for example, that of parlour manager. Systems will need to be in place to ensure that all staff are adequately trained to allow them to carry out their job role to a given standard and that performance is monitored. Motivation of staff, continuing education, and the opportunity for progression may be crucial for success in many larger herds. The importance of high quality data and data management cannot be overemphasised in larger units. However, as time spent at the computer keyboard increases, it becomes vital that methods of data capture are as automated and speedy as possible, so the time that key managers spend in the cow-centric actions resulting from that data increases, and less time is spent inputting it. Cows in larger herds are often split into multiple management groups, but group sizes of over 200 are common, resulting in more cow-to-cow interactions. However, the effects of behaviour and group sizes on health, welfare, and production are not well understood and will vary depending on herd facilities. Larger herds can consider management strategies, such as milking heifer-only groups, to mitigate some of these effects. Larger groups theoretically may improve oestrous detection, as more cows will be simultaneously sexually active. In reality, differentiating and identifying cows with short duration and less intense oestrous expression can be very difficult in large groups. For the herd owner, changing to an environment with more staff to manage, a reliance on data, and a magnification of cost structures requires considerable adaptation and learning. Senior managers in large herds often have high levels of formal education and an in-depth knowledge of both the biology of the dairy cow and business management; this drastically changes the level and quality of input required from their professional advisors. Veterinary surgeons working with large herds should realise that there is a shift in emphasis towards the use of data and team approaches and a clear focus on profitability. Critical to the management of larger herds is to have systems in place that monitor critical indicators and provide an early warning system of health and production problems. Many of the traditional roles and technical tasks carried out by veterinary surgeons in smaller herds will often be undertaken by trained herd staff; the role of the veterinary surgeon is to provide an independent evaluation of those systems and any changes proposed. As the dairy industry has evolved, the techniques used to manage reproduction have also evolved. Between 1956 and 1967, research took place that largely established the mechanism of luteolysis (see Chapter 1). In 1979 the first prostaglandin (PG) product (Lutalyse, dinoprost trimethamine) was licensed for use in cattle by the United States Department of Agriculture (USDA), becoming the first pharmacological agent used to manipulate reproduction in dairy cows. The first systematic and structured use of these products was termed ‘targeted breeding’, in which cows were injected on either a weekly or a fortnightly basis, and then either inseminated

at observed oestrus or inseminated at a fixed time interval after the injections were administered. The procedure could be further refined by transrectal veterinary palpation or, later, ultrasound examination of cows to identify those animals likely to have a corpus luteum (CL) and therefore thought more likely to respond to treatment. Conception rates from insemination to observed oestrus occurring after prostaglandin administration proved to be acceptable. In contrast, the results of inseminations conducted at a fixed time were disappointing because the timing of ovulation relative to prostaglandin injection was highly variable depending on the stage of follicular wave development at the time of the prostaglandin injection. This led to the development of the ‘Ovsynch’ protocol, which has now become widely adopted globally within the dairy industry as a breeding tool to ensure timely insemination of cows (Pursley et al. 1995; see Chapter 8 for further details). This protocol offered dairy producers the first opportunity to adopt fixed time insemination as a viable management strategy. More recently, it has been shown that conception rates could be improved if the ‘Ovsynch’ protocol could be commenced during a particular part of the cycle (day 4–12); this led to the development of presynchronisation protocols (see Chapter 8 for further details). In some large herds, especially in North America, it has become common practice to breed all cows by timed AI using synchronisation programmes. For example, Souza et al. (2013), based on a survey of over 1 million inseminations from 40 US states, reported that 30% of all inseminations were timed AI after a synchronisation programme. This is for a variety of reasons. First, large herds may find it more convenient and cost effective to organise labour and work routines in a consistent manner so that certain tasks always occur on a certain day of the week. This can allow skilled staff to focus more on certain other aspects of cow care at specific times and, by doing so, generate other health and welfare benefits. Second, in some herds, suboptimal oestrous detection prevents generation of sufficient pregnancies to sustain viable production, leading to adoption of 100% timed AI. However, the routine use of synchronisation programmes, without any attempt being made to identify natural oestrus (which is particularly common when presynchronisation programs are used), combined with conception rates that are usually around 35% (Souza et al. 2013), means that on many farms a high proportion of cows are receiving 10 or more hormone injections or treatments before they become pregnant and some are receiving more than 20. This is undoubtedly an ethical and an animal welfare issue, especially when routine synchronisation is used without addressing the underlying issues causing the need for that synchronisation. This is consistent with the findings of Higgins et al. (2013), who reported that 65 of 93 UK dairy veterinarians judged the longterm routine use of synchronisation programmes as “unacceptable” if they were used at the start of the breeding period without addressing the underlying issues causing the fertility problem. This begs the question that if one of the key underlying causes of the poor fertility in the herd is its genetic make-up, does continuing to breed for production, without a focus on fertility, count as a failure to address the underlying causes? The majority of respondents (> 70%) to Higgins et al. (2013) wanted to see a decrease in the use of hormones to control fertility in dairy cattle. Achieving such a decrease may be difficult. First, there was a considerable variation in attitude across the respondents in the study by Higgins et al. (2013), and it is highly likely that veterinary perspectives on the routine use of hormones may differ across different systems (for example, in North America it is likely that acceptance of routine treatment would be higher

CHAPTER 25  Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds



469

TABLE 25.1  Examples of published levels of reproductive performance worldwide

Nation

Year

Parameter

US

2006

CI CR

Canada

2011

UK

Reported Average

Trend Over Time

Reference

422d 30%

Deterioration in both measures 1996–2006 (CI more marked)

Norman et al. (2009)

21d PR SR CR

14% 38% 37%

Relatively stable for each measure 1999–2011

Le Blanc (2013)

2015

CI CFS CR

410d 80d 32%

Improvement in most measures 2010–2015, CR stable

Hanks & Kossaibati (2016)

Norway

2006

CI CFS

378d 86d

CI relatively stable 1989–2006

Refsdal (2007)

Sweden

2005

CI CFS

403d 92d

Deterioration when 1995 compared with 2005

Löf et al. (2007)

Ireland

2000

CI

395d

Deterioration 1991–2000

Mee (2004)

Australia

2009

6 wk ICR 3 wk SR CR

50% 72% 38%

Slight deterioration in 6 wk ICR 2000–2009 (others not reported)

Morton (2011)

New Zealand

2010

6 wk ICR 3 wk SR CR

67% 81% 48%

Not reported

Brownlie et al. (2014)

CI, calving interval; CR, conception rate/risk; 21d PR, 21-day pregnancy rate/risk; SR, submission/serve rate; CFS, calving to first service interval; 6 wk ICR, 6-week in calf rate; 3 wk SR, submission rate in first 3 weeks of breeding period.

than in the UK, where fixed time AI is used alongside oestrous detection). Second, hormone treatments are a significant source of income for veterinary practices, and it is likely that a focus on management rather than treatment will reduce veterinary incomes (Higgins et al. 2013). For these reasons, any decrease in the use of hormones for routine oestrous synchronisation is likely to come from pressure from outside the industry (as happened in New Zealand when the use of corticosteroids for inductions at the end of pregnancy was banned). In the authors’ opinion, there should be few situations in which the reasons driving the use of synchronisation programmes cannot be addressed and performance improved without necessarily using hormones. Combining good oestrous detection with appropriate synchronisation of target cows provides a comprehensive system of sequential ‘pregnancy traps’ as cows progress through lactation. Using this reasoning, many large herds find that sufficiently high levels of reproductive performance can be achieved and sustained by applying protocols at the herd level and thus removing the need to individually examine each cow before deciding on an appropriate course of action. Substantial cost savings can also be made by abandoning individual cow level decisions. In these situations, it is vital that veterinary surgeons have a thorough understanding of herd level risk factors if their actions and decisions are to remain relevant and add value to a dairy business.

Levels of Reproductive Performance and Trends over Time Typical levels of reproductive performance vary widely across the dairying nations of the world, with poorer overall performance usually encountered in systems involving high milk yields and better

performance often associated with seasonal calving. Examples of publications describing reproductive performance measurements from samples of herds worldwide are summarised in Table 25.1. Across most areas, there is a medium to long term declining trend in fertility (Brownlie et al. 2014, Hudson et al. 2010, Morton 2011). This is commonly associated with rapidly increasing milk production, which not only may compromise fertility by making cow energy balance more challenging to manage but also as a consequence of genetic selection focused heavily on production merit (Lucy 2001, Royal et al. 2000). However, during the last decade the trend has halted or even reversed in many countries; this may be associated with an increased understanding of the economic importance of fertility, adoption of new technology to assist breeding, and the more widespread use of fertility measurements as part of genetic selection indices.

Evaluation of Herd Fertility Herd Recording Systems Accurate recording of reproductive events is critical, both for individual cow management decisions and to allow effective monitoring of herd reproductive performance. Irrespective of how events are recorded, the approach should be consistent (e.g., between members of staff and over time) and comprehensive. The events that should be recorded are outlined in Table 25.2, including an indication of the minimum level of data recording required for useful analysis, as well as additional information that allows greater insight if collected. In addition to these events, some metrics and monitoring approaches also require evaluation of ‘metadata’ for the herd – for example, the herd’s VWP policy or the date of the planned

470

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Subfertility

TABLE Reproductive events which should be 25.2  recorded in a dairy herd to facilitate

effective monitoring

Event

Minimum Information

Additional Information

Calving

Cow ID, date, parity

Level of intervention, calf outcome

Insemination

Cow ID, date

AI/natural, sire, operator (for AI), observed heat/FTAI

Pregnancy diagnosis

Cow ID, date, result

Reproductive disease

Cow ID, date

Oestrus no serve

Cow ID, date

Other veterinary examination

Cow ID, date, result, treatment

start of mating. Depending on the computer software being used for analysis, the user can often add these additional parameters. A wide variety of different dairy herd management software systems exist that can be used to record and store these data, and this varies widely in different areas of the world. In some cases the herd management system is linked with the milking plant, but specialised stand-alone software is also very common. For most medium to large dairy enterprises, efficient management is very difficult without the use of on-farm computerised records. A major challenge in analysing fertility performance of a herd is the variation in the degree to which the various software systems are able to calculate and present reproductive performance measurements. Commonly, different programmes calculate different metrics and often use different calculations or methods so that different values for the same metric can be derived from the same set of data, depending on which software is used. One approach to this problem is the development of analysis software, which can use data from varied sources and apply analysis in a consistent fashion. Bureau recording systems (in which a third-party organisation collects and stores farm data) represent an alternative or supplement to on-farm software systems. Commonly, this service is provided by organisations also supplying milk quality testing, such as milk recording organisations or dairy herd improvement associations. These systems can usually accept data input either from on-farm paper records or by extracting data from an on-farm software system. Some bureau recording systems also provide an Internet-based portal where farmers can enter event data directly into the central database. Analysis of this collated data is then commonly available via a dedicated website; the degree of reproductive monitoring available through such systems varies greatly between providers and is often more limited than would be accomplished using specialised monitoring software. In addition, some bureau services allow downloading of their data for use by farmers and/ or advisors; this can provide a way to obtain data in a consistent format for analysis, but sometimes data quality is reduced or event detail lost from the original source. Irrespective of the source of the data, it is important to treat it critically and to recognise that poor data quality can influence the results of analysis. The most common problem is missing event records. For example, when not all inseminations are recorded (the

final, successful insemination of the lactation being the most likely to be captured), the herd’s performance in terms of submission rates for service will appear artificially poor, whereas the conception rate will be artificially inflated. There are algorithmic approaches to evaluating fertility data quality, and some software products offer data quality assessment. There is a clear trend throughout the agricultural industry for increased amounts of data to be generated and recorded over time. Examples from the dairy sector would include the more widespread adoption of sensor technology such as activity monitoring as a heat detection aid. The ability to integrate these data with that from other sources and use it to generate additional insight is likely to become ever more important to the veterinary advisor in the future.

Aspects of Reproduction which Should Be Monitored Herd-level reproductive performance can be monitored in a variety of ways, but whichever measures are used, it is important that they cover the relevant areas comprehensively. A hierarchical approach can be useful, in which a small number of measurements of overall reproductive performance are used as the main short-term metrics and a wider set of measurements covering the components of reproductive success used when the headline performance is suboptimal. The two key elements that should be covered are submission (the ability of cows to express oestrous behaviour and the ability of the farm’s system to detect it) and conception (including successful maintenance of pregnancy). When overall performance does not meet targets, it is often useful to decide which of these two areas should be the focus of attention by assessing measures specific to each element. It is also important to monitor the level of reproductive disease a herd is experiencing so that specific problems can be identified and addressed. Clear and transparent terminology should be used when defining reproductive performance; this is often hindered by the myriad of performance measurements available. Each has its own advantages and disadvantages, and it is the responsibility of the veterinarian to identify the best way of reporting reproductive performance for each farm. The measurements chosen should reflect the key issues on the farm and allow comparisons to be made with other similar farms. It is not helped when different names are used for the same measurements (see Boxes 25.1 and 25.2). • BOX 25.1  Risk vs Rate A rate is the frequency of an event per unit population per unit time. The numerator is the number of events, and the denominator is the animaltime units at risk. This is most widely understood in the context of disease incidence rate: for example, 4 cases of endometritis per month in a herd of 100 cows would equate to an incidence rate of 48 cases per 100-cow-years. A risk is the frequency of an event, given a designated number of times when it could happen (‘trials’). It has no units, as the numerator is the number of cases, and the denominator is the number of trials; e.g., in the herd described previously, assuming all cows calve once in 12 months the risk of endometritis is 48/100 or 0.48. Reproductive performance metrics can be broadly categorised into interval- or rate/risk-based measures. Many of the risk/rate-based measures are more accurately described as risks, rather than rates (e.g., conception rate). However, across the industry and worldwide it is far more common to refer to rates rather than risks, so to avoid confusion we have used the term ‘rate’ in this chapter.

CHAPTER 25  Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds



• BOX 25.2  Pregnancy and Conception Rates Conception rate has commonly been used to describe the risk of a cow becoming pregnant after a given insemination. This is technically inaccurate, as the proportion of cows that conceive (i.e., have a fertilised embryo) is much greater than the proportion that are pregnant by the time of pregnancy diagnosis. Pregnancy rate would perhaps be a more biologically accurate term but is commonly used in other measures (such as proportion of eligible cows becoming pregnant per unit time). For clarity, in this chapter, we have used conception rate to signify the risk of pregnancy after insemination, and pregnancy rate for proportion of eligible cows becoming pregnant over a defined period of time.

Date*

Br Elig

Bred

Pct

Pg Elig

Preg

Pct2

Aborts

09/11/06

75

33

44

74

15

20

1

30/11/06

72

34

47

70

10

14

0

21/12/06

74

35

47

72

13

18

0

11/1/07

76

46

61

76

14

18

0

1/2/07

76

32

42

76

7

9

1

22/2/07

80

32

40

80

7

9

0

15/3/07

86

23

27

86

9

10

0

5/4/07

80

48

60

78

12

15

1

26/4/07

73

47

64

73

15

21

0

5/17/07

61

36

59

61

16

26

1

7/6/07

45

22

49

44

8

18

1

28/0/07

35

0

0

35

0

0

0

19/7/07

37

2

5

37

1

3

0

9/8/07

64

1

2

64

1

2

0

30/8/07

116

10

9

116

5

4

0

20/9/07

118

90

76

115

33

29

1

11/10/07

94

58

62

0

0

0

0

1/11/07

72

56

78

0

0

0

0

Total

1168

491

42

1157

166

14

6

• Fig. 25.1  Example 21-day pregnancy rate report generated using the DairyComp 305 software package (using a 50 day VWP). Br Elig: number of cows eligible to be inseminated in this time window; Bred: number of cows inseminated; Pct: percentage of cows eligible to be served that are inseminated (submission rate); PgElig: number of cows eligible to become pregnant (differs from Br Elig as cows die, leave the farm or are declared barren); Preg: number of cows that actually become pregnant; Pct2: number of successful outcomes achieved expressed as a percentage of the maximum possible at the start of the window (21-day PR); Aborts:number of abortion cases subsequently reported. *Start of the 21-day period †Unknown pregnancy status, awaiting pregnancy diagnosis ‡Note that the totals exclude cows from the last two 3-week periods as the outcomes of these events remain unknown until pregnancy is diagnosed.

Pregnancies per Unit Population Time ‘Herd pregnancy risk’ is an estimate of the instantaneous probability that an open, eligible cow will become pregnant per unit time in the future. The magnitude of the hazard or risk of pregnancy occurring is determined by the management and environment to which the cow is exposed. In all-year-round calving herds, it is most usefully monitored using the 21-day pregnancy rate (21-d

471

PR) (Ferguson & Galligan 1999, Fetrow et al. 2007). An example of a 21-d PR report generated from the DairyComp 305 software package is shown in Fig. 25.1. For a specific 21-day period, the 21-day PR is calculated as follows: 21d PR =

Number of cows becoming pregnant Number of cows eligible to be become pregnant

∗100

It is important to clearly identify which cows are eligible to be included in the denominator. First, cows are included as eligible if, at the start of the 21-day period for which the PR is being calculated, they had passed their VWP and were not pregnant. Eligible cows also have to have been present in the herd throughout the entire 21-day period (i.e., they must not have been sold or died or otherwise exited the herd), and they must not have been identified as ‘not to be inseminated’ (and therefore not to be retained in the herd). Finally, ideally all eligible cows should have a pregnancy diagnosis or subsequent insemination that clearly identifies their pregnancy status (yes or no) at the end of the 21-day period. There is some variability between software programmes in how cows are handled which are eligible for only part of a 21-day period; this is important to understand in order to interpret results, especially when rates calculated in different packages are compared. Discrepancies between software packages are commonly accounted for by differences in the way programmes handle cows as they pass their VWP, exit the herd during a cycle, or are inseminated after pregnancy loss. The related measurement, 21-day submission rate (or 21-day insemination rate), uses the same definition of an eligible cycle, but ‘has been inseminated at least once during the cycle’ as the outcome; a cow is either categorised as being inseminated (yes) or not (no) during an eligible cycle. The use of 21-d PR has several advantages over other methods of monitoring reproduction, as the effects of lag, bias, momentum, and variation are minimised (Fetrow et al. 2007). A time lag occurs in all measurements of reproductive performance, as there is an inevitable delay between when a cow is inseminated and when she can be examined for pregnancy. However, by using 21-day PR, this is limited to as short a period as possible so that changes in management and environment that affect reproductive performance are reflected quickly. This means that 21-day PR can be easily used to evaluate and compare different groups and interventions. In reproductive calculations, bias occurs when selection of cows for inclusion or exclusion occurs. For some measurements, only cows currently present in the herd or cows diagnosed as pregnant are included, with cows failing to be inseminated or pregnant being ignored. In contrast, the calculation of 21-d PR includes all eligible cows, whether they were inseminated or not, and includes cows that have left the herd (provided it was after the 21-day period being measured). A parameter shows momentum when historic data significantly contribute to its calculation. This means that the effect of recent changes (either positive or negative) can be hidden by historic events. As 21-day PR is based on a specific period of short duration, historical data (such as conception rates 60 days ago) have no influence. This does mean that 21-d PR can vary significantly over a short period of time, and that sampling variation can become a problem in smaller herds (see section later in this chapter on Key Epidemiological Concepts). In such circumstances, other measurements of reproductive performance or averaging 21-d PR over a longer time period may be appropriate, even if this increases lag and momentum. Importantly, the short focus of 21-day PR means

472

Pa rt 4

Subfertility

that it is possible for two herds to have similar 21-day PR yet still have different proportions of pregnant cows with a differing pattern of when they became pregnant. Efficiency of reproduction is influenced by four factors: submission rate (the proportion of eligible cows served per unit time, commonly 21 days); conception rate (the proportion of inseminations leading to a pregnancy); the VWP is and the length of time after calving when it is deemed acceptable to continue inseminating a cow. The latter two factors are both under the direct control of the herd manager. The aim of the VWP, is to provide sufficient time that the chance of pregnancy in an inseminated cow is not compromised by a genital tract that has not fully recovered from the previous calving or a cow in excessive negative energy balance. Typically, on non-seasonal calving farms, cows will not be inseminated (even if they are seen in oestrus) for 35 to 60 days after calving; the exact value depending on the farm situation. On seasonally calving farms the interval between calving and a cow becoming eligible for AI or natural mating is not usually a set period of time, as all non-pregnant cows become eligible for mating when the breeding season starts (usually 85 days after the start of calving). The acceptable time postcalving when a decision is made to stop inseminating a non-pregnant cow (which will then be culled at the end of lactation) is often established for individual cows, with higher producing individuals, those of higher genetic merit, and those with high milk yield persistency being allowed a higher maximum time interval. However, in seasonal or block calving herds, the choice of a maximum interval between calving and insemination is determined at the herd, not the individual, level. In such herds, it is the length of the breeding season that is the key influence on reproductive efficiency. Once these two parameters are set by management, reproductive efficiency is determined by the proportion of eligible cows that are inseminated per unit time (submission rate) and the proportion of inseminated cows that become pregnant (conception rate). If these two figures are calculated over a 21-day period, the 21-d PR can be estimated by multiplying insemination rate by conception rate. When calculating average 21-day PR over a longer time period, it is important that it is calculated as a weighted mean of the sum of the pregnancies divided by the sum of eligible cycles (rather than a simple mean of the 21-d PR in each 21-day period). This is particularly important if timed AI programmes are used, as an unweighted mean will tend to underestimate the value of such programmes as it ignores the relative numbers of cows eligible in each 21-day period. Although the 21-day PR can be calculated from the insemination and conception rates, it can be calculated without any knowledge of either as the only data required are the number of cows which became pregnant during the 21-day period and the number of cows that were eligible to become pregnant during that period. The key advantage of this method of calculation is that it eliminates the need to ensure identical time frames to calculate insemination and conception rates. This metric can therefore also be used effectively in herds relying entirely on natural service by running a bull or bulls with eligible cows. The 21-day PR approach also provides flexibility as it can be calculated by calendar date (the most common approach), days in milk, days since an event, or days with bull. It can therefore be applied to a variety of herd situations. Fig. 25.2 shows an example of a 21-day PR report calculated by days in milk, with the same data displayed graphically in Fig. 25.3. Comparisons of performance between all-year-round calving herds that operate different VWPs

DIM

Br Elig

Bred

Pct

Pg Elig

Preg

Pct2

Aborts

50

624

612

98

614

197

32

27

71

405

249

61

403

73

18

6

92

322

248

77

318

65

20

9

113

241

159

66

239

47

20

2

134

205

154

75

199

32

16

2

155

176

120

68

173

28

16

5

176

159

106

67

155

23

15

8

197

144

94

65

140

28

20

5

218

113

74

65

110

21

19

4

239

88

62

70

81

10

12

1

260

78

55

71

72

6

8

1

281

74

55

74

71

9

13

1

302

67

49

73

62

11

18

0

323

48

32

67

44

4

9

0

344

41

32

78

34

6

18

1

365

27

21

78

23

4

17

1

386

21

15

71

15

2

13

0

407

14

11

79

10

1

10

1

428

10

7

70

6

0

0

0

449

6

4

67

4

0

0

0

470

3

2

67

2

0

0

0

491

2

1

50

1

0

0

0

Total

2868

2162

75

2776

567

20

74

• Fig. 25.2

  Example 21-day pregnancy rate report by days in milk (explanations as for Fig. 25.1 generated using DairyComp 305).

can be made by applying a standard VWP to the calculations for each herd. For example, a herd that operates a VWP of 68 days, because all first inseminations are carried out to a double ‘Ovsynch’ protocol, could have a 21-day PR of 30% when calculated using the 68-day VWP. This would be equivalent to a 24% 21-day PR for a herd breeding to observed oestrus from 50 days. This again illustrates the point that 21-day PR does not provide information on the pattern of when cows become pregnant, either within that 21-day period or relative to calving. When comparisons are made between herds, it is recommended that the 21-day PR should be evaluated in all cases using a 50-day voluntary waiting period and that in herds that choose to operate with a different VWP, herd managers should be constantly aware of the 21-day PR; this should be evaluated for both a 50-day waiting period (i.e., with cows becoming eligible for inclusion in the 21-day PR 50 days after calving) and for the actual VWP used on the farm. The former will allow valid comparisons with other similar herds, whereas the latter provides information on the technical success and performance of the programme. Reproductive performance of a dairy herd is ultimately determined by how rapidly the herd management ‘turns open cows in to pregnant cows’, a process most accurately measured and monitored using 21-day PR. It is for this reason that 21-day PR has become the standard measure of reproductive performance in many herds.

CHAPTER 25  Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds

95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0

40

60

80

100 120 140 160 180 200 220 240 260 280 300 320 340 360 380 Days in milk

Insemination risk

• Fig. 25.3

473

100 95 90 85 80 75 70 65 60 55 50 45 40 35 30 25 20 15 10 5 0

Percentage of herd still open

Percentage



 

Pregnancy rate

95 percent confidence interval for pregnancy rate

Percentage of herd still open

Graphical representation of 21-day pregnancy rate by days in milk (generated using DairyComp 305).

Other Fertility Measurements Measurements of Overall Performance Traditionally, the focus of fertility has been ensuring the production of one calf per cow per year. This led to the adoption of the calving interval (the interval, usually in days, between successive calvings for an individual cow) as a standard measure of fertility. At the herd level, the average calving interval across a cohort of lactations (e.g., the most recent completed lactation for each cow in a herd at a specific point in time) is referred to as the calving index. The traditionally accepted optimum for calving interval is 365 days, although there is increasing evidence that extended intervals can be just as economically efficient (e.g., see Lehmann et al. 2016). Irrespective of the optimum value, the usefulness of calving interval as a fertility measurement is limited by its historic nature, which results from the requirement for two successive calvings. This introduces a significant time lag in assessing fertility as there are typically 270 to 285 days between the successful conception and subsequent calving (and thus the calculation of the calving interval). Calving interval is thus a reflection of what happened over 9 months ago, not what is happening currently. In addition, it has two further deficiencies. First, it ignores heifers, which do not contribute to a calving interval until they reach their second lactation. Second, it ignores cows which fail to become pregnant and are culled for failure to conceive. Thus in a herd with a 25% culling rate and a 25% replacement rate, average calving interval will be based on only 50% of the cows in the milking herd. The calving interval is the sum of two components: the interval from calving to conception and the interval from conception to subsequent calving. The latter component is not really a measure of reproductive efficiency as it is simply the length of gestation and therefore not directly under the control of the herd manager (although this may change with increased availability of quality bulls selected for short gestation length; Jenkins et al. 2016). In contrast, the interval from calving to conception is a reflection of reproductive efficiency as it is a measure of how quickly cattle become pregnant after calving. The calving–conception interval (CCI) is calculated by counting the number of days from calving to the service that resulted in

pregnancy (usually the last recorded service date). So, to calculate the CCI requires three data points per cow: calving date, date of the successful insemination, and a positive diagnosis of pregnancy. To achieve a calving interval of 365 days, the target CCI needs to be 85 days (assuming a gestation length of 280 days). In effect, CCI is simply a way to measure CI with a shorter time lag as the outcome is determined by pregnancy diagnosis rather than by waiting for the subsequent calving, although pregnancy loss can introduce variation between one and the other. A cow’s CCI is composed of two components: the length of the VWP and the number of days between a cow becoming eligible for insemination and becoming pregnant. The first factor is a management decision; the second reflects the reproductive performance given that management decision. In this, it is similar to the 21-day PR. However, a key disadvantage is that, in contrast to 21-day PR, it is an interval-based measurement that does not include data from cows which failed to become pregnant. The failure of the CCI to include data from cows that failed to become pregnant led to the development of the related measurement: ‘days open’. For cows that conceive, days open is equivalent to the CCI, whereas for cows not conceiving, the days open is defined as the time from calving to culling or death, or the maximum interval between calving and insemination. This difference between CCI and days open highlights the inherent issue with using interval data to evaluate fertility at the herd level, which is identifying the point at which it is possible to collate data and create a meaningful figure. For days open, the definition requires that the measurement is calculated only when all the cows in the group under evaluation have either reached the time after calving when they will no longer be inseminated or have been culled/sold. Although the addition of data from cows that failed to conceive reduces the issue of bias, it increases lag time. In systems in which cows can be inseminated up to 300 days after calving, this can significantly delay fertility evaluation. In contrast, for CCI the definition is more flexible, so it can be calculated earlier; however, if it is calculated too early (i.e., at a timepoint when only a limited proportion of the cohort being measured have conceived), then it will be biased and of limited value. But defining ‘too early’ is inherently subjective – clearly if

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only 10% of the selected group are pregnant, then calculating CCI is of limited value, whereas if 90% are pregnant calculating CCI is likely to be useful; thus CCI may be useful depending on the farm situation. The distribution of CI, CCI, and days open is very commonly right (or positively) skewed, i.e., there is a ‘long’ and/or ‘fat’ tail to the right of the peak. This can result in the mean interval being significantly influenced by a small proportion of the animals. Days open is typically even more right skewed because of the inclusion of cows culled for failure to conceive, which will all have high values. When evaluating these figures on a herd basis, the median (i.e., the ‘middle’ interval where the intervals are arranged in ascending order) is therefore generally a more accurate reflection of the performance of the herd (or a typical cow in the herd). It is useful to examine both the mean and the median value as this can give an impression of the degree of skewness of the distribution. Either figure should be used alongside a measure of dispersion, such as the interquartile range, to show how much variability there is in performance between individual cows. Evaluation of the distribution of individual intervals using graphical representations such as histograms and boxplots can provide additional insight. If this approach is being used as a key metric, it can be useful to evaluate days open and CCI at the same time, as this will provide an assessment of performance with and without the potential distorting effect of high values from cows which failed to conceive. The difficulties in determining when to calculate intervals has resulted in the development of rate-based measurements designed to reflect overall reproductive performance. The most important of these is the 21-day PR, which was discussed earlier, but this is not the only rate-based measure. Morton (2003) described three ratios which are commonly used in Australia and elsewhere to summarise reproductive performance: 1) 100-day in-calf rate: the proportion of cows eligible for rebreeding that conceive within 100 days of calving; 2) 200-day not-in-calf rate: the proportion of cows eligible for rebreeding which do not conceive within 200 days of calving; and 3) 6-week in-calf rate: the proportion of cows eligible for rebreeding that conceive within 42 days of the start of the breeding season. The first two are used in all-year-round calving farms, whereas the third is designed for seasonal or block calving. Compared with the 21-day PR, the 100-day in-calf rate and 200-day notin-calf rate have greater lag but may be simpler to calculate and easier for the farm staff to understand. It is important to realise that both measurements, but particularly 100-day in-calf rate, are significantly affected by the choice of VWP. If the VWP is 60 days, then 100-day in-calf rate reflects the success rate during the first 40 days (around two oestrous cycles) after the end of the VWP. If the VWP is then decreased to 40 days, then each cow will have 60 days (around three oestrous cycles) to become pregnant. This is likely to result in an increased 100-day in-calf rate, even though reproductive efficiency has not changed. Ideally, whichever measurement is used to define overall reproductive performance, it should be used alongside an assessment of the proportion of the herd that were culled due to failure to conceive. If the causes of culling are recorded correctly, then this figure is valuable for interpreting measurements (such as CCI) that exclude cattle that did not conceive as well as determining the economic effect of poor fertility. Unfortunately, in many circumstances such records are not available as the reason for culling is not recorded. Furthermore, especially if only one reason for culling

is recorded, the interpretation of the reasons can be difficult; for example, was the cow with a high cell count culled because of the high cell count or because she failed to become pregnant? Ensuring that culls are properly recorded, so that both the timing of the cull and the key reasons for culling (which may be multiple) are known, can be useful for fertility investigations. If good culling records are not available, then the proportion of the herd being culled after an insemination can be used to estimate the number of cows that have been culled for failing to conceive on the principle that most ‘non-fertility’ culls will not be inseminated during the lactation as they are already identified for culling. However, there are clearly other reasons why a cow might leave the herd after being inseminated, and the relative prevalence of this will vary from herd to herd and over time, making such proxy measurements difficult to interpret consistently.

Specific Measurements Underlying Overall Performance For all of the measurements of overall performance, there are two key factors that underlie those figures: i) the interval between cows becoming eligible to be inseminated (either after the VWP or after an unsuccessful insemination) and being inseminated (‘submission’); and ii) the risk of an inseminated cow becoming pregnant (‘conception’). The most commonly used measurement interval reflecting the speed of submission for first service is the calving-to-first-service interval. This measurement, like CCI, is dependent on VWP as: Calving-to-first-service interval = VWP + interval from VWP end to first service The interval from VWP to first service is determined by when cows first show oestrous activity after the end of the VWP, and also the proportion of oestruses that are detected. The length of the VWP is crucial as, even if all cows are cycling normally and all oestruses are detected, then average calving-to-first-service interval will be, at best, 11 days longer than the VWP (based on an average oestrous cycle length of 22 days). So, if the VWP is 60 days, then calving-to-first-service interval will be at least 71 days. However, in reality the calving-to-first-service interval will be more than 11 days longer than the VWP, as not all cows will be cycling and not all oestruses will be detected. As with CCI, the calving-to-first-service interval tends to be right skewed so that the median may better reflect herd performance than the mean. It is also important to calculate a measurement of the spread of the distribution (such as interquartile range), as well as the median. Evaluation of the distribution visually (e.g., using a histogram) is also often useful. As with other interval-based measurements, identifying when it can be calculated for a group of cows is not simple. Alternative rate-based measurements have thus tended to replace the calving-to-first-service interval. The most important of these is the 21-day submission rate (as described in the previous section):

21d submission rate =

Number of eligible cows inseminated in a 21d period Number of cows eligible for insemination in the same 21d period

∗1100

This measurement has the same advantages as the 21-day PR and can be used in the same way to create an ongoing updated rolling average, particularly in herds in which reproduction data is kept on a computer database. The 21-day (or 24-day) submission

CHAPTER 25  Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds



rates can also be calculated for specific inseminations, particularly the first service. First service submission rate (usually defined as the proportion of cows inseminated within the first 24 days after the end of the cow’s VWP), like calving-to-first-service interval, will identify how quickly cows are served once they are eligible for insemination. Unlike calving-to-first-service interval, it lacks bias as it includes all eligible cows not just those that have had a first service. It also has less momentum, so it is better at showing trends over time than the calving-to-first-service interval. However, all cows that fail to be served within 21 or 24 days of the end of the VWP are treated the same; in contrast, calculating calving-tofirst-service interval and its distribution will provide information on how long these ‘failures’ take to be inseminated for the first time. Achieving a high submission rate is dependent on good oestrous detection. Indeed, in some publications, submission rates are referred to as heat or oestrous detection rates. However, especially in the period immediately after the end of the VWP (or start of the breeding season in seasonally calving herds), submission rates are also dependent on the proportion of cows that are cycling normally. If there are high levels of anoestrus and/or endometritis, submission rates can be low even though heat detection is good. It is therefore important, if submission rates are low to distinguish between poor oestrous detection and acyclicity. Unfortunately, without additional monitoring (such as frequent progesterone testing), establishing the actual heat detection rate is not possible. From Table 25.3, the true oestrous detection rate equals a/a + b. However, the only data available on most farms are a + c and b + d. Without additional data, it is not possible to know how many of the oestruses detected were false positives (c), or the number of false negatives (b), the cows that have been in oestrus but have not been detected. However, on farms in which veterinary examination of cows not seen in oestrus is routine, the results of such examinations can provide a useful clue as to the role of acyclical cows in depressing submission rates. This means that apparent oestrous detection rates (and submission rates) can be falsely inflated if farmers or farm staff regularly submit cows for insemination that are not in oestrus. This could easily happen on a farm that heavily focuses on fertility, and as a consequence, the staff are paid bonuses to improve submission rates. One approach to monitoring, which can provide insight into this, is the evaluation of the intervals between successive inseminations. The commonly accepted range for the normal length of a bovine oestrous cycle is between 18 and 24 days (see Chapter 1), so ideally the majority of interservice intervals will fall within this range. Intervals between 36 to 48 days suggest that one oestrus has been missed, and intervals of 54 to 72 days suggest that two oestruses have been missed. Evaluation of the distribution of interservice intervals (for a given time period) can provide useful insights into both the rate

and accuracy of oestrous detection. Intervals are usually categorised in relation to the accepted normal cycle length (see Table 25.4). Relatively high proportions of intervals at less than 18 and 25 to 35 days are indicative of inaccurate heat detection (i.e., a problem with cows being served when not in oestrus); more than 10% of intervals in the former category is sometimes used as a threshold for this. A high proportion of intervals in all categories more than 24 days suggest late embryonic or early fetal death; high proportions of intervals more than 35 days tend to suggest a high proportion of missed oestruses. These suggestions should be supported by evaluating the distribution of interservice intervals, alongside other fertility parameters. A measurement of overall quality of oestrous detection (known as oestrous detection efficiency) is calculated from the percentage of all intervals within each category, as follows (using the categories defined in Table 25.4): ODE =

b+d ∗ 100 a + b + c + 2( d + e )

Recent evidence suggests that in at least some dairy cow populations, 18 to 24 days may not represent the most appropriate choice of the normal range for this type of analysis and that a high proportion of intervals are at 25 and 26 days (Blavy et al. 2016, Remnant et al. 2015). This should be recognised when interpreting interservice intervals and rates derived from them (such as the proportion of returns to service occurring at 18–24 days after a previous insemination, sometimes referred to as returns submission rate). Measuring the risk of an inseminated cow becoming pregnant is much simpler as this can be calculated from the number of inseminations given (note this is not the same as the number of inseminated cattle) and the pregnancy test results for the inseminated cows. Conception rate can be defined as the proportion of inseminations leading to a pregnancy (see Box 25.2). The inverse of conception rate is the average number of services per pregnant cow. Conception rate is a very flexible measurement; although it is commonly calculated and presented on a monthly basis, it can be used for any defined group of inseminations. However, similar to 21-day PR, care must be taken when comparing values between groups if the number of cows per group is small. Examples of groups for which conception rate can be measured include: those based on days in milk (which will identify if specific lactational stages are affecting underlying cow fertility); parity (particularly useful for identifying issues with heifers in their first lactation); fixed-time inseminations (FTAI) after synchronisation programmes (in which conception rate is the best measure of the success of such programmes); and number of inseminations a cow has received.

TABLE Commonly used categories of 25.4  interservice interval

TABLE Test characteristics of a farm’s 25.3  oestrous detection TRUE STATUS

Farmer record

475

a)

2–17 days

1-day intervals excluded

b)

18–24 days

Normal interval length

In Oestrus

Not in Oestrus

c)

25–35 days

Seen in heat

a

c

d)

36–48 days

Not seen in heat

b

d

e)

> 48 days

Double normal interval length

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When accurate pregnancy test data are not available, non-return to service at a specified timepoint (e.g., 24 days or 56 days postinsemination) may be used as a proxy outcome. Although better than nothing, the non-return rate is not a good measurement of the success of insemination because it is dependent on both the conception rate and the submission rate of non-pregnant, but inseminated, cows. Thus a low non-return rate can indicate either good reproductive performance (high conception rate), or poor reproductive performance (low return to service submission rate). The influence of submission on this measurement is dependent on the timepoint chosen – rates of non-return at later timepoints (e.g., 100-day non-return rate) are likely to be more reflective of true conception rates but introduce an increased lag into the metric. Of the two factors influencing overall reproductive performance, oestrous detection and submission is generally more susceptible to human control than conception rate. On most farms, improving the submission rate so that more cows are inseminated (and thus have the chance to become pregnant) will be more effective than focusing on improving the chance of individual cows becoming pregnant to a single service. Improving the submission rate can result in improved reproductive performance, even if the conception rate is reduced, provided the reduction in conception rate is less than the increase in submission rate. This principle is the basis by which oestrous synchronisation works: more cows are inseminated (often 100% of a group), so more cows become pregnant, even though at the individual cow level the risk of conception may be lower than if the cow was inseminated to an observed normal oestrus. In addition, it is important to recognise that poor understanding of oestrous detection is an important cause of low conception rates. This occurs when an inappropriately high proportion of cows are inseminated when not in oestrus. This problem commonly goes hand in hand with poor submission rates (i.e., herds that are incorrectly identified in oestrus are also more likely to miss true oestrus), although this is not always the case.

Monitoring Reproductive Disease Minimising reproductive disease is essential for optimising fertility. In order to do this, it is essential that reproductive disease is clearly monitored and recorded. The reproductive diseases can be divided into those diagnosed by the farmer and those in which veterinary intervention is required for diagnosis. The key diseases that need to be accurately identified by farm staff are dystocia and retained fetal membranes (RFM) because in most cases veterinary involvement is not necessary. In both cases a clear definition is needed so that, as much as possible, records are consistent within farms (and, if feasible, between them). Dystocia (see Chapters 11–18) can be recorded simply as an assisted calving but ideally should be categorised based on difficulty: (1) no assistance; (2) slight assistance (one person without calving aids); (3) considerable assistance (one person with aids or more than one person); (4) veterinary assistance, including Caesarean operation (Mee et al. 2011). Categorisation will limit the effect of different farmer approaches to dystocia (e.g., interventionist or not); the dystocia risk should be reported as incidence per calving. This is because using time-based measurements, such as cases per cow per year, means that long calving intervals can reduce the apparent rate of dystocia, as fewer cows will calve each year. Comparison between groups can be useful, particularly comparing heifers calving for the first time with older cows, and between animals bred to different bulls, especially when beef and dairy bulls are used on the same farm.

Even though, in the vast majority of cases, treatment of RFM is unnecessary; retention of the membranes is a key risk factor for uterine infection (both metritis and endometritis; see Chapter 25) and thus for future reproductive performance. Recording which cows have had RFM will identify those that need additional observation in the immediate postpartum period, and examination/treatment before being bred, even if there has been no apparent clinical effect. Using consistent definitions is important. The simplest definition of RFM is a cow that retains its membranes for more than 24 hours. This is not necessarily the most accurate definition, but it is easy for farmers to record accurately (Laven & Peters 1996); as with dystocia, RFM should be reported as incidence per calving cow. Metritis and abortion (see Chapters 23 and 24) need to be identified by farm staff, but recording will be more valuable if there is veterinary involvement. For metritis, veterinary involvement in developing the strategies to identify sick cows in the early postpartum period is likely to make them significantly more effective. This is because there is no simple single clinical sign that will identify metritis, so skilled staff are required to identify sick cows in the immediate postpartum period (Burfeind et al. 2014). Again, metritis should be reported on the basis of incidence per calving cow. Loss of the fetus after the diagnosis of pregnancy can be a significant problem. Abortion (loss of one or more calves between 152 and 270 days of gestation) can be diagnosed by the farmer if the aborted fetus is identified. However, abortion incidents appear to be underrecorded on many farms (Lemire et al. 1993). Fetal loss, from the time of pregnancy diagnosis to 152 days, is commonly not recorded as a discrete event by farmers, although measuring the proportion of cows being re-inseminated after a positive pregnancy diagnosis can be a useful guide (Forar et al. 1995). Thus the true level of fetal loss (and to a lesser extent abortion) can be challenging to estimate in the absence of diagnosis of the pregnancy status. The simplest measure of abortion frequency is the incidence risk of abortion, expressed as a percentage of pregnancies. This is useful for quick calculations such as identifying abortion outbreaks. However, it is better to assess abortion risk, particularly over longer periods of time, using a days-at-risk model. This is because abortion data is both right and left censored. The data are right censored principally because, once cows have aborted, they stop contributing days-at-risk. Estimating a simple proportion ignores the effect of early abortions; in addition, pregnant cows can be sold, culled, or die. These animals need to be included in days-at-risk and, ideally, pregnancy tested before removal. The data are left censored as the cow is not ‘at risk’ of detectable abortion until after pregnancy diagnosis has occurred. Early pregnancy diagnosis increases the apparent rate of fetal loss – not only because it increases days at risk but also because most fetal loss occurs early in gestation, and there is a significant negative association between the risk of fetal loss and days pregnant (Forar et al. 1995). Abortion rate per 100 days-at-risk accounts for many of these issues but is more complex to calculate and interpret. Survival analysis, which plots abortion risk against gestational age, is the most useful method as this can take into account differences in the timing of pregnancy diagnosis. The key issue with days-at-risk is identifying when the abortion occurred; to obtain accurate information requires close observation and regular monitoring of pregnancy status. The latter is likely to be feasible only when a problem is suspected as a result of an increased number of apparently pregnant cows failing to calve. Endometritis (see Chapter 23) is one of the most important reproductive diseases in terms of its effect on reproductive performance



CHAPTER 25  Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds

(Gilbert et al. 2005). Although some cows with endometritis will show a vulvar discharge, the majority will not; thus identifying endometritis needs an active rather than a passive approach (Gilbert et al. 2005). Various methods of diagnosing clinical endometritis have been used, but probably the simplest and quickest to use on a routine basis is the Metricheck device (see Chapters 23 and 24). In addition to being quicker, the Metricheck device causes less discomfort to the cow and has a greater sensitivity compared with vaginoscopy, although specificity is reduced (LeBlanc et al. 2002). Whatever method is used, routine examination for endometritis should be undertaken before the breeding period, i.e., before the end of the VWP in all-year-round calving herds or the planned start of mating in seasonal ones. Ideally, it should be done at least 3 weeks before intended breeding as, if treatment is successful, the cow should have at least one oestrus before the start of breeding. The timing of the diagnosis relative to calving is important as early diagnosis and treatment improves reproductive outcomes; but cows should be at least 4 weeks postcalving before being treated (LeBlanc et al. 2002). Endometritis frequency is most usefully measured as an incidence risk per cow calved; if a cow has more than one diagnosis of endometritis during a lactation, it should only contribute once to the numerator.

Monitoring Fertility in Nulliparous Heifers When monitoring reproductive performance, it is important that nulliparous heifers are not excluded. Several studies have demonstrated that replacement heifers calving for the first time at around 22 to 24 months of age are likely to be the most productive and profitable individuals in the herd (Bach 2011, Brickell & Wathes 2011, Sherwin et al. 2016). In order to achieve this, heifers must conceive promptly and maintain pregnancy once they have reached target breeding age or size; breeding from around 13 to 14 months is usually required to achieve the target age at first calving. Monitoring heifer performance can be done using broadly similar approaches to those described for the milking herd. When record keeping for this group is good and herd policy sufficiently clear and consistent (such that every animal becomes eligible for breeding at a specified age and all insemination and pregnancy diagnosis events are recorded accurately), the 21-day pregnancy rate approach can work well. As with adult cows, the proportion of eligible heifers becoming pregnant per unit time can be evaluated over time or by heifer age. This approach can also be useful when only natural service is used, with service dates unrecorded, but periodic early pregnancy diagnosis performed. Again, as in adult cows, interval-based measurements can also be useful. In heifers, age at first calving is the most commonly used, but the interval from target breeding age to first insemination can also be useful. In both cases, it is useful to evaluate the distribution of intervals or at least to calculate both the mean and median rather than relying on a single average value. Interservice intervals can also be used, although it is likely that oestrous cycle lengths tend to be shorter in nulliparous animals (Wolfenson et al. 2004). Conception rates can also be analysed in exactly the same way as in cows, although this is usually only practical in herds using AI.

477

when the denominator is small. Herd size clearly plays an important role here; in very large herds, it is rare for small denominators to be a practical problem. However, even in herds with hundreds of animals, the denominator for a particular performance metric may still be small. For example, in a herd with good fertility performance, only 12% to 18% of cows are likely to be eligible for breeding at any one time. Thus a herd with 200 cows may only have around 25 eligible for service in a given 21-day period. If around 20% of these are expected to become pregnant, this would represent only five expected pregnancies. Clearly, there is potential for this outcome to be influenced by chance, and one or two more (or less) pregnancies will make a relatively large difference to the outcome being measured. If the analysis is restricted to cows eligible for first insemination or at a specified stage of lactation, then the denominator clearly becomes even smaller. Some analysis software gives an indication of the uncertainty in an estimate of a particular performance measurement, for example, by providing p-values for contrasts or showing confidence intervals around estimates of proportions. It can also be useful for the size of the denominator to be displayed alongside the result so that the user has some ability to judge empirically the potential for sampling error to be important. There are no ‘hard and fast’ rules, but the approach outlined in the previous paragraph can be useful (i.e., considering the potential for a small number of fewer or more events to influence the measured outcome to the extent that decision making may be influenced). When this problem is encountered, commonly the most appropriate option is to increase the time frame of the analysis to increase the effective size of the denominator. For example, in the 21-day pregnancy rate example described previously, the 9-week or 12-week rolling average may reflect true performance more accurately than the rate in each individual 3-week period. In this context, it is also interesting to consider the concepts of type I and type II error. These terms describe the potential errors in making an inference from a given result. A type I error refers to accepting or acting on an apparent trend or association, which is actually due to chance. A type II error is the reverse: failing to accept or act on a trend or association which is real. The probability of making either error is based on the level of evidence the decision maker requires before acting; when this is very strong, type I errors become less likely but type II errors more likely. There is an important contrast here between clinical epidemiology at an individual herd level and epidemiological research. In the latter situation, avoiding a type I error (for example, in promoting a research finding which is illusory) is often much more important, so relatively strong evidence is required in order to take action. When working with a single herd’s data to drive herd level decision making, it is usually necessary to act based on weaker evidence, as the cost of failing to act in the face of a genuine trend is larger relative to the cost of acting on false evidence. For example, a p-value less than 0.05 or 0.01 is commonly used to signify statistical significance in a research context – at a herd level, use of this criterion would generally mean that change is rarely advocated.

The Economics of Reproduction

Key Epidemiological Concepts

Components of Economic Loss

When analysing any aspect of herd performance data, it is important to keep some basic principles of epidemiology and statistics in mind. Perhaps the most useful is the effect of the size of the denominator population and the potential for sampling variation

It is clear that a herd with very poor reproductive performance will suffer economic losses in a variety of areas, and fertility has commonly been cited as a key driver of efficient milk production (Evans et al. 2006, LeBlanc 2007). Decreased milk production is

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the most obvious source of this loss, with longer intervals between calvings leading to cows spending relatively more of their lives in later lactation, when daily production is lower and feed conversion less efficient. However, this needs to be balanced against the fact that cows with shorter calving intervals will spend an increasing proportion of their lives dry and that high culling rates to maintain a short calving interval can also reduce overall efficiency. Reproductive (or ‘failure to conceive’) culls are the other commonly recognised source of financial loss due to subfertility. Culling cows which fail to become pregnant incurs a cost as the slaughter value of a cull cow is generally substantially lower than the cost of producing or buying a replacement; this is highly variable. In areas where seasonal and year-round calving herds are both common, cows not becoming pregnant during the breeding season are often sold to year-round calving herds, which can decrease the net replacement cost. It is also important to consider the potential increase in herd asset value, when a cull cow is replaced with a heifer. In herds in which replacements are homebred, there is usually a finite pool of animals that can enter the herd per unit time and therefore a fixed maximum number of culls if herd size is to be maintained. In this situation, reproductive culling can utilise replacements that could otherwise be profitably employed replacing culls for poor production or health reasons. However, there are a number of other mechanisms by which poor reproductive performance can have an economic cost. Although it can be considered that calves are a by-product of dairy production, they are also an income source, whether reared as herd replacements or sold for beef production. Clearly, the more frequently cows calve in a herd, the larger the number of calves produced per year. There are also direct costs associated with reproduction, such as labour, semen, medicines, and veterinary time. However, some costs are much less obvious, and are harder for herd managers to appreciate. For example, a substantial association has been demonstrated between the duration of the calving interval and the risk of a cow being culled from the herd in the subsequent lactation (Pinedo & De Vries 2010). There are also some less obvious economic disadvantages associated with shorter calving intervals. Decreasing the number of calvings per unit time (either at cow or at herd level) decreases the risk of diseases associated with calving, including mastitis and lameness, as well as reproductive disease. Pregnancy is also associated with a decrease in milk yield, so cows that become pregnant in early lactation would be expected to have lower yields compared with a similar non-pregnant cow. However, this change is relatively small and has only been demonstrated in the later stages of pregnancy (Roche 2003).

Approaches to Estimating Economic Loss Two main methods have been used to combine the costs described previously and generate estimates of the likely effect of reduced reproductive performance on profitability. The traditional approach has tended to involve estimating the cost per additional day added to a cow’s calving interval, based on the principle that each additional day that the calving interval is extended postpones the subsequent lactation by 1 day, thus effectively substituting it with a day at the end of the current lactation (Esslemont & Kossaibati 2002). Cost in milk production is therefore estimated by subtracting the expected daily yield for the additional day at the end of lactation from the average expected daily yield in the subsequent lactation and multiplying this by margin over feed cost. Daily costs can also be allocated to other components of economic loss and

TABLE An example of a cost estimation using 25.5  a ‘cost per day’ approach

Cost per Day (£)

365–395d

396–425d

> 425d

Lost milk (margin)

1.57

1.79

1.90

Lost calf value

0.25

0.25

0.25

Extra serves and vet treatment

0.70

0.70

0.70

Gain in yield when empty

−0.13

−0.13

−0.13

Reduced disease risk

−0.43

−0.43

−0.43

Total

£1.96

£2.18

£2.29

added to this to provide an estimate. As with other approaches, estimates derived by this route are sensitive to a variety of inputs that can change markedly over time, perhaps most notably milk price. Therefore especially in areas in which milk price is mostly driven by the global commodity market and is highly volatile, it is of limited value to produce even ‘typical’ figures that are widely used across the industry. Estimates should be made at herd level and adjusted to reflect changing market situations. An example of such an estimate is given in Table 25.5. Estimation of the economic effect of reproductive performance also lends itself well to simulation-based approaches, which have also been used extensively in this field. This approach is much more flexible, can account more appropriately for a wider range of considerations, and is generally considered likely to produce more robust results. However, it is inevitably less transparent, so that the results are more difficult to explain and justify. It is also harder to customise or update estimates derived using this approach to reflect a specific farm situation, although tools (such as that described by Giordano et al. 2012) for this are available. It appears common for both approaches to generate broadly similar estimates under most circumstances, although this may change as simulation models become more complex and realistic. Simulation can also be used to evaluate the economic value of fertility in block-calving systems (Shalloo et al. 2014).

Veterinary Management of Reproduction Change in the Role of the Clinician The last 50 years have seen an evolution in the role of the veterinarian in herd reproduction. Initially, the focus tended to be on identification, diagnosis, and management of individual ‘problem’ cows, such as those observed to have vulvar discharge or abnormal oestrous cycles. Subsequently, the emphasis expanded to cover the examination of clinically normal cows, with a view to implementing management strategies solely to reduce the time to conception. This included routine examination of cows not seen in oestrus by a specific point in lactation or presentation of cows for pregnancy diagnosis to identify and treat non-pregnant animals. Routine fertility visits to carry out these examinations have been a mainstay of farm animal veterinary practice worldwide over the past 20 to 30 years. More recently, the focus has moved on, and it is now widely recognised that the veterinarian should also be able to add significant value to herds in monitoring data and giving appropriate management advice. This is now a core part

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479

TABLE Example of groups of cows selected for examination at routine visits with major differential diagnoses and 25.6  key features of suggested examination

Reason for Presentation

Example Criteriaa

Main Focus of Examination

Major Differential Diagnoses

Postnatal examination

• Routine at around 21 DIM • Abnormal vaginal discharge < 21 DIM

• Vaginal examination • +/− uterine ultrasonography

• Normal • Endometritis • Postpartum anoestrus

Oestrus not observed

• No oestrus behaviour observed by 42 DIM • Not inseminated by VWP + 21 days • Not inseminated as expected after previous treatment

• Ovarian palpation/ ultrasonography

• • • • •

Pregnancy diagnosis

• > 30–50 days after last insemination

• Uterine palpation/ ultrasonography • +/- ovarian examination

• Pregnant • Non-pregnant

Repeat breeder

• > 3 unsuccessful inseminations

• Uterine palpation/ ultrasonography • Ovarian examination

• Apparently normal • Identifiable uterine/ ovarian pathology

Unexpected reproductive events

• Abnormal vulvar discharge > 20 DIM • Short or inconsistent cycle lengths • Oestrus behaviour after positive PD

• Determined by reason for presentation

• As appropriate to reason for presentation

Normal Cystic ovarian disease Postpartum anoestrus Abnormally persistent corpus luteum Endometritis

a

Criteria will depend on voluntary wait period, clinician, and farmer preference for pregnancy diagnosis timing and level of veterinary input and should be discussed on commencing visits.

of veterinary involvement in reproduction for many dairy cow clinicians.

Routine Fertility Visits Routine fertility management visits are used by many dairy herds around the world as an essential element in the process of assisting cows to become pregnant, so that they are retained in the herd for a further lactation. However, in the larger more progressive dairy farms typically found in North America, the routine pregnancy diagnosis examination is not part of the process of optimising pregnancy rates but is seen as a key audit point measuring the outcomes of the processes and systems in place. In these circumstances the regular pregnancy diagnostic examination is used as a means to gather information, validating the current process, and evaluating any changes, either intentional or unintentional, which may have occurred. A regular assessment of the success of the reproductive programme in place is also essential to allow the veterinarian to review compliance with the programme and to prescribe the medicines necessary for the programme to be implemented by farm staff. In this context the regular routine visit is truly part of an integrated herd health system, of which reproduction is only one important aspect. In a successful situation the farmer should gain an improved income that exceeds the cost of implementation; the veterinarian profits from the sale of professional skills. In the North American situation, the veterinarian sits as the judge of the reproductive programme that is in place. Generally at each visit, cows will have been selected for examination based on an ‘action list’, often generated by a computer software system using predetermined criteria agreed upon by the herd owner; the categories of animals presented for examination will vary from herd to herd. An example of the groups of cows presented is shown in Table 25.6. For each cow presented, an accurate up-to-date record of all her lactation events should be readily available, and, likewise, all findings and actions taken at

the visit should be transferred back to the main records system in a timely manner. For herds using a high degree of planned synchronisation protocols, the only cows presented may be those for pregnancy diagnosis, as preplanned schedules may dictate the enrolment of cows to those protocols; for ease of allocating and planning labour, the work may occur at a different time of the week. In these situations the prescribing veterinarian should have been involved in the planning and setup of the programme and preferably have a clear knowledge of which cows will receive which treatments and on which day. It is important to understand that a cow appears on an action list because she has fallen outside certain agreed reproduction targets set by the herd management team and therefore may be in a situation in which her profitability is falling to an unacceptably low level. The common practice of a herdsperson editing the ‘action list’ and removing cows before a visit is therefore to be discouraged as those cows removed from the list are only likely to become even less profitable and ultimately hasten their culling from the herd. A major objective of the reproductive programme should be to ensure that every cow has an equal opportunity to become pregnant and be retained in the herd. Similar thinking should apply to cows presented as ‘not seen in oestrus’ or that are overdue for insemination; failure to take action will almost always result in a greater delay in the cow becoming pregnant, with the consequent reduction in her profitability. The frequency of herd visits can also have an effect on the speed with which cows become pregnant as more frequent herd checks means that, on average, cows will be examined for pregnancy earlier and action on open cows is taken sooner. For herds undertaking routine visits, the physical facilities should also allow for calm, quiet, stress-free handling of cows. In situations in which cows must be held in pens away from bedding and feed, it should be for a maximum of only 45 minutes to avoid a significant negative effect on the cow’s daily time budget (Grant & Albright 2001). Handling facilities that are appropriate for

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smaller herds will be inappropriate for handling larger numbers of cows; for example, a holding pen and crush may be adequate for a 100-cow herd but not for a 1000-cow dairy. When cow handling operations are located away from cow housing and feeding areas, regular inspection of those areas should still be undertaken to assess any effect they may have on reproductive performance or, indeed, other aspects of herd health. Cows presented for routine examinations should always be inspected for general attitude and appearance, body condition, cleanliness, and, when possible, locomotion scoring. It is good practice to keep written records of these observations at each visit for future review. Although it is common practice to examine the reproductive tract and ovaries of cows presented at routine visits in order to formulate a course of treatment based on individual cow findings, there is growing evidence that many problems with oestrous cyclicity that manifest themselves as a number of gross ovarian changes are in fact due to disturbances in follicular growth; these have resulted from metabolic or infectious insults that the cow suffered during the transition phase. Attending veterinarians should also bear in mind that seasonal effects also influence ovarian cyclicity and that, for example, it is normal for the incidence of ovarian cysts to triple during the autumn in the northern hemisphere associated with shortening day length (Nelson et al. 2010). Monitoring reproductive performance (as described in the previous sections) is also a core element of the routine fertility visit and can be included in a variety of ways. For example, a specific additional meeting or visit could be set up on a regular basis, ensuring that appropriate members of the farm management team are present (including those responsible for high level management decisions, day-to-day management of the herd, as well as any other relevant professionals such as the herd’s nutritionist). Alternatively, or additionally, some time could be set aside at the end of routine fertility visits to assess and discuss performance (and, as a result, potential management changes) in a more informal way, with outcomes summarised in a short e-mail or note. The frequency with which monitoring is carried out depends on herd size and seasonality. Larger herds will accumulate higher numbers for monitoring much more quickly, so they are generally more appropriately monitored on a more frequent basis (for example, fortnightly monitoring may be appropriate for a very large, year-round calving herd). This allows changes to be put in place rapidly in response to a perceived change in performance. In smaller herds, making decisions based on a small denominator population (e.g., measuring conception rate based on a small number of inseminations) risks identifying a false trend or association (see Key Epidemiological Concepts section). This means that monitoring in smaller herds is inevitably more retrospective, with decreased ability to react quickly to changes. In order to minimise the temptation to commit this error, it is often appropriate to evaluate data from smaller herds less frequently. For example, a chi-squared test of a change in conception rate from 40% to 30% (a change of highly clinically important magnitude for most herds) produces a p-value of almost 0.28, when there are 25 inseminations in each group (i.e., 25 at 40% and 25 at 30%). This is roughly the number of inseminations that might be expected in a 100-cow herd in each month; the p-value can loosely be interpreted as suggesting a 28% chance that the difference observed is due to chance rather than to a genuine change in performance. The degree of certainty required in order to justify implementing changes will vary between decision makers and different situations, but including two or three months’ worth of inseminations (n = 50, p-value = 0.12; and

n = 75, p-value = 0.06) would make this trend much more convincing. Monitoring conception rate should perhaps be justified in a herd of this size on a monthly basis, always using 2- or 3-month rolling averages as the key metric. In contrast, a 400-cow herd serving around 95 cows per month would be able to have a high degree of confidence that a change from 40% in one month to 30% in the next month would reflect a true change in performance; here, monitoring would be justified on a fortnightly or monthly basis. Even when good quality herd records do not exist, some degree of useful monitoring may be accomplished based on the events that are observed at visits. In this situation the number of positive pregnancy diagnoses should be a key metric. This can be compared with the estimated number required at each visit to represent one pregnancy per cow in the herd per year and gives an impression of the level of overall performance. Again, this should be based on the total number aggregated over an appropriate number of previous visits, based on the size of the herd.

Herd Level Aspects The first step in improving a herd’s reproductive performance should be to employ the monitoring approaches described in previous sections in order to determine whether the focus should be on improving submission rates for insemination (in herds using AI), improving conception rates, or reducing the level of reproductive disease. It is frequently most appropriate to select one of these areas for initial improvement, even when performance is suboptimal in multiple areas; it can be challenging to attempt to improve several aspects at once. Reasons for poor performance in each area and potential management changes that can be implemented to address these are discussed in Chapter 24, but some common problems are highlighted in the following sections.

Improving Submission Rate Rate of submission for service (in herds using AI) is predominantly determined by two elements: the rate or ‘sensitivity’ of the farm’s oestrous-detection system and the ability of the cows to express oestrus. There are often limited clues from data analysis that can help to distinguish between these as potential reasons for a suboptimal submission rate, but strong seasonal patterns tend to suggest an environmental component. Generally, therefore, the approach to a poor submission rate involves assessing both elements to identify risks, and using judgment to determine which area is most likely to be limiting or whether elements of both should be improved. Expression of oestrus is dependent on a number of factors. These broadly fall into those relating to the cow’s environment and management and those directly relating to the individual cow. A number of environmental factors have been associated with oestrous expression, such as lack of appropriate space (especially lack of loafing space in cubicle housing; Pennington et al. 1985), poor quality or badly repaired floor surface, and high temperature and/or humidity. Management factors such as time budget (which can become limited, for example, when a group is milked three times daily with a long milking time) and group size are also common problems. Of the cow factors, lameness is perhaps the most important and frequently encountered. There is a substantial body of evidence associating lameness with decreased reproductive performance at cow level (Sogstad et al. 2006, Machado et al. 2010), although this effect may be relatively small at herd level (Hudson et al. 2014). High milk yield has also been reported to be associated with decreased intensity and duration of oestrus



CHAPTER 25  Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds

(Lopez et al. 2004), but there is little evidence that this association is mediated by negative energy balance. The efficiency of the farm’s system for detection of oestrus clearly depends on the method or methods of oestrous detection employed. When visual detection is used, frequency and timing of detection alongside training of observer are critical. A number of visual signs have been described, among which observing a cow standing to be mounted by another cow is usually considered the most reliable (sometimes described as the ‘primary’ sign). It is widely accepted that observation of this sign alone is sufficient to diagnose oestrus. Less reliable (or ‘secondary’) indicators include mounting other cows (either at head or hindquarters), chin-resting, and vulva sniffing. Van Vliet and Van Eerdenburg (1996) describe a scoring system for visual detection that assigns a number of points to each of the behaviours observed and suggests diagnosis of oestrus when a score threshold is reached. Even when this is not used systematically, this scoring system can be a very useful training aid to help observers rank the relative importance of the various signs. Evidence suggests that modern Holstein cows commonly demonstrate standing oestrus (i.e., standing to be mounted) for an average of around 7 to 8 hours (Sveberg et al. 2011, 2015, Yoshida & Nakao 2005), which means that fewer than three periods of detection spaced evenly through the day may reduce effectiveness substantially. Use of information can also be useful in aiding visual oestrous detection. A number of systems can be used to remind farm staff which cows are likely to be in oestrus on which days (i.e., those 19–26 days postinsemination); recording ‘reference’ heats (when a cow is observed in oestrus during the VWP, so not inseminated); allows anticipation of an ‘expected’ oestrus date for the cow’s first eligible cycle. A number of oestrous detection aids are available, and are widely adopted in most major dairying nations. The simplest of these involve simple physical approaches to detecting when a cow has been mounted by another cow (and is therefore assumed to be in standing oestrus). These devices include tail paint or chalk, self-adhesive scratch cards, and capsule-type systems (see Chapter 24). These have various strengths and weaknesses, and there is no compelling evidence to support use of one over the other. In practice, it appears that one or the other commonly works better in a particular farm situation. Reported sensitivities of these systems vary widely (probably because of differences in cow and environment factors between studies, which are very commonly based on a single herd); they range from around 35% (Holman et al. 2011) to over 90% (Xu et al. 1998). A more sophisticated (and more expensive) approach is the use of sensor technology, either as a standalone system or integrated with milking parlour or voluntary milking systems. Activity monitoring is by far the most common sensing approach used for oestrous detection, and has been relatively widely adopted since the mid-2000s. These systems detect an increase in activity (usually via an accelerometer attached to a leg or neck collar), as cows move about more when demonstrating oestrus. An increase in activity beyond a specified threshold above that expected triggers an alert that the cow may be in oestrus. As with other methods, a range of test characteristics are reported in the literature. Reported sensitivity values are typically in the range 60% to 90%, with positive predictive value generally 70% to 95% (Firk et al. 2003, Holman et al. 2011, Roelofs et al. 2005). Both the algorithms for interpretation of activity data and the sensors themselves are continuously being improved, so it is likely that this method will become increasingly effective in future. Implementing a combination of more than one method is often associated with the highest levels of submission rate, plausibly

481

because different methods detect different oestrous behaviours. Milk progesterone testing can also be useful (greatly facilitated when tests can be done quickly at cow side). It is especially useful when used in parallel with a measurement that would be expected to increase sensitivity but decrease specificity of oestrous detection; for example, reducing the threshold required to trigger an alert on an activity monitor system. Alerts associated with activity levels above the previous (higher) threshold could still be used as a basis for insemination with no further evidence, and alerts between the new and old thresholds screened using milk progesterone, thus inseminating cows when the progesterone level is low. This often detects a higher proportion of oestruses without the drawback of inseminating more ‘false positive’ cows. Effective use of veterinary examinations and fixed-time artificial insemination protocols can also be extremely helpful in improving submission rates; these are discussed elsewhere in this chapter.

Improving Conception Rate As with submission rate, there are a wide variety of possible causes of poor conception rate. Nutrition is one of the most commonly implicated areas; there are a number of possible nutritional factors that may influence conception rate. Perhaps the most common is excessive negative energy balance (NEB) during early lactation (see the review by Roche (2006)). Analysis of body condition score data and estimation of blood ketone and non-esterified fatty acid concentrations can be very helpful in quantifying postcalving energy balance and evaluating the potential importance of this aspect for a particular herd. Energy balance may also play a role in mediating the association between conception rate and temperature and humidity, a key environmental factor influencing conception rates. Micronutrient nutrition can also influence conception rates, although in most systems, this is uncommon compared with the effects of negative energy balance. When NEB is the key factor, data analysis can reveal lower conception rates in early lactation or in higher yielding cows; if micronutrients are involved, conception rates tend to be poor across all inseminations. The role of subacute ruminal acidosis in compromising conception rates remains controversial. A number of diseases are also associated with reduced conception rates. Probably the most important (in this context) are the postcalving reproductive diseases, including retained fetal membranes, metritis, and endometritis. These are strongly associated with decreased fertility and should always be considered when a high rate of such diseases is observed in conjunction with poor conception rates (especially when conception rate is particularly low in early lactation). Herd level infectious diseases, such as bovine viral diarrhoea, infectious bovine rhinotracheitis, leptospirosis, and Johne’s disease, have also been associated with decreased conception rates (see Chapter 24). The evidence for this is especially clear when disease enters a previously naïve herd; the size of association tends to be much smaller and the evidence more equivocal for the effect of such a disease in a herd with a moderate level of endemic infection. Non-infectious endemic diseases, such as mastitis and lameness, have also frequently been associated with reduced conception rates in individual cows but are probably a relatively rare cause of a poor conception rate at herd level (Hudson et al. 2014, 2015). Some venereally transmitted diseases (such as campylobacteriosis) are associated with decreased conception rate in herds using at least some natural service. Finally, there are a number of factors associated with insemination that can result in disappointing conception rates. For artificial insemination, these include poor semen quality or storage, thawing

482

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and handling, as well as inappropriate timing of insemination relative to signs of oestrus, and inaccurate heat detection (i.e., insemination of cows outside of true oestrus). The latter problem can sometimes be identified by a high proportion of interservice intervals of ‘unexpected’ duration (i.e., not multiples of the expected normal oestrous cycle length), especially intervals less than 19 days. Cow-side milk progesterone testing may be a useful tool here; for example, measuring progesterone in the next 50 cows to be inseminated allows calculation of the positive predictive value of the herd’s heat detection (i.e., proportion of apparent heats that represent a true oestrous event). When natural service is used, establishing the semen quality of the bull team is important, alongside factors such as the ratio of bulls to eligible cows and general bull health. In herds using natural service alone (where a bull is running with the eligible cows at all times), it is rational to assume that conception rate is the main limiting factor for fertility in most situations, provided that the bull has been observed to have a normal level of libido and good physical mobility.

Minimising Reproductive Disease A strong association exists in dairy cows between the incidence of inflammatory and metabolic diseases in the first 2 months postpartum and reduced fertility and pregnancy loss during the subsequent lactation (Santos et al. 2010). Calving-related disorders and those that affect the reproductive tract are major contributors to anoestrus or the occurrence of abnormalities of ovarian cyclicity diagnosed later in lactation such as ovarian cysts and anoestrus. It is estimated that approaching half of all cows experience one or more calving-related health disorders in the first 60 days postpartum. Almost all dairy cows are known to experience bacterial contamination of the uterus in the first 2 to 3 weeks postpartum, which, along with the necessary repair of the endometrium during involution (see Chapter 7), means that some degree of uterine inflammation is necessary and is indeed a normal and necessary component of involution (Sheldon et al. 2009). The innate immune system is the main protection for the uterus, which may be overcome by overgrowth of pathogenical bacteria, or the severity and duration may become excessive resulting in the impairment of future fertility. The high incidence of disease is attributable in part to the reduced innate immune function and regulation of inflammation that occurs in all cows from approximately 2 weeks prepartum to 3 weeks postpartum (Kehrli et al. 1999). However, the metabolic adaptations of insulin resistance (IR) and NEB also contribute to the reduced immune defence. Homeorrhetic adaptations to lactation require that every cow experiences a degree of NEB and IR to support lactation, but as with uterine inflammation, the concern is for cows that experience a severity of these states that goes beyond the merely adaptive to become a contributor to disease and impaired reproduction. The point at which reproductive tract inflammation and metabolic status shift from the physiologically normal to the undesirable is likely to involve a number of multifactorial interactions; these remain poorly quantified and understood. It is well known that during the transition period the dairy cow is at particular risk of the effects of NEB, and it is intriguing that it seems likely that the metabolic milieu that accompanies transition may be responsible for the initiation of inflammation without the presence of a pathogen. Although relatively little is known about how reproductive tract disease may be influenced by management, it is known that cows that develop severe metritis will have eaten less than normal cows in the 2 to 3 weeks preceding the onset of the disease; reductions in feed intake are associated with increased circulating concentrations

• BOX 25.3  Suggested Management Practices

to Reduce the Risk of Reproductive Tract Disease

• Dry cows off to ensure a target dry period length of 35 to 60 days for at least 85% of the herd. • Prevent overconsumption of energy above requirement during early part of the dry period. • Provide unrestricted feed access at all times for all dry cow groups. • Ensure feed is provided ad lib, and all cows can eat at the same time when fresh feed is delivered. • Provide a minimum of 75 cm of utilisable trough space per cow. • Provide at least 10 cm of water trough space per cow, sited at a least 2 points in the pen. • Provide adequate cow comfort with cubicles or open yards to ensure at least 11 to 12 hours lying time per cow per day – at least 1 cubicle per cow and for open bedded areas at least 10 m2 per cow plus an additional 1 m2 per additional 1000 kg of milk production over 10,000 kg. • Minimise group and social changes. Aim to restrict grouping changes to a weekly event at most; ideally use an ‘all-in/all-out’ system to maintain the same social group over the dry period. • Ensure each cow spends a minimum of 10 days exposed to an appropriate precalving ration. • Dry cow and fresh pens should have sufficient capacity to cope with variations in calving pattern. Facilities should provide 130% of average monthly capacity in year-round calving systems. • Ensure excellent ventilation of dry and fresh cow buildings. If necessary, provide heat abatement, fans, and sprinklers for when the temperature humidity index (THI) exceeds 68. • Manage overall nutrition for target body condition (BCS) at calving of 3 to 3.25 (on a 5-point scale) and maintain a minimum of 2.5 with no change from dry off to calving.

of NEFAs, which may indirectly or directly inhibit neutrophil function (Hammon et al. 2006, Huzzey et al. 2007, Ster et al. 2012). The high degree of oxidative stress also experienced by early lactation dairy cattle also contributes to a proinflammatory state that may not be helpful for immune defence mechanisms (Sordillo & Aitken 2009). Based on current understanding, there are few management practices that can be recommended with certainty that will prevent inflammatory reproductive tract disease in the postpartum period. Current understanding suggests that the objective should be to support and maintain innate immune function to prevent the risk that the normal postpartum bacterial contamination and inflammation does not progress to infection and disease. Suggested management practices generally recommended for peripartum cows that may reduce the incidence of postpartum reproductive tract disease are shown in in Box 25.3. The aetiologies of cystic ovarian disease and anovulatory anoestrus and associated risk factors are generally considered to be similar to those associated with uterine disease, although less well understood (see Chapter 22). The causes of abortion are generally much better understood and can broadly be categorised as infectious (including neosporosis and brucellosis) and non-infectious (including stress and exposure to fetotoxic agents). These are covered in more detail in Chapter 24. From a herd perspective, it is important to remember that a certain level of abortion is expected; levels higher than 3% to 5% are commonly considered to merit investigation.

CHAPTER 25  Veterinary Control of Herd Fertility in Intensively Managed Dairy Herds



Reproductive Protocols A number of reproductive management protocols, or controlled insemination programmes, have been developed over the years to meet the need of larger dairy herds. As discussed earlier, on many farms, especially in larger herds, hormone-based programmes such as Ovsynch are being increasingly used so that insemination of eligible cows becomes part of the weekly routine and does not interrupt the normal daily workflow of the dairy unit. However, it is the authors’ opinion that an effective oestrous detection system should still be pivotal in an integrated approach to dairy herd reproductive management and should be part of a normal, fixed, daily routine and carried out in a manner specified in written protocols. For example, if a system of tail chalking or tail painting is used, it should be clearly understood by all personnel. Likewise, when telemetric systems of heat detection are in use, data gathered must be checked at least daily so that cows can be inseminated within an appropriate time window (Fig. 25.4). The protocol chosen depends on the time and skills available on the farm to continue to detect oestrus. Programmes that rely on inducing CL-regression, such as those utilising double or multiple injections of prostaglandin, require an element of oestrousdetection to produce acceptable results and are best described as oestrous synchronisation programmes. Variations on programmes that utilise induced CL regression include systems in which a veterinary examination to identify the presence of a CL, either by manual palpation or ultrasound, is done before injection and that the reproductive tract is healthy; this is a system commonly used in smaller herds. A surprisingly large number of herds still simply administer PGF2α every 2 weeks to non-pregnant cows and simply observe for oestrus. This system can work well if a CL is present and is an easily administered programme requiring minimal record keeping; it can be used for cows and heifers. Good oestrous detection is essential for success. Veterinary examination may be seen as an advantage or disadvantage in these programmes as there are unnecessary costs incurred in examining large numbers of cows that do not require examination. In contrast, protocols that rely on induction of ovulation (such as Ovsynch and related programmes) are much more likely to produce better results to TAI without oestrous detection. Such synchronisation protocols are most often used to ensure timely insemination of cows by a target ‘days in milk’ to ensure every cow receives a first insemination at a time in lactation that allows her the opportunity to become pregnant and retain her place in the herd. Cows found not pregnant can

also be enrolled on to synchronisation protocols to ensure timely reinsemination. In some herds that operate very intensive reproductive programmes, the first GnRH of the Ovsynch protocol can be given to all cows 7 days before pregnancy examination without terminating the pregnancy and so shortening the time to reinsemination by 7 days, a practice commonly termed ‘Resynch’. It is worth noting that outcomes from re-synchronisation protocols may be poorer when the interval from the preceding insemination is less than 30 days (Fricke et al. 2003). Perhaps it should also be noted that in herds in which oestrous detection is excellent, aggressive resynchronisation may actually lengthen days open as the initial GnRH treatment may interfere with normal folliculogenesis, thus delaying normal oestrus and ovulation. The success of these programmes is heavily dependent on compliance with the injection schedule, which requires a high degree of staff skill and training. In a three-injection programme, if compliance is 90% (such that 90% of the cows get the correct treatment on the correct day), only 73% (0.9 × 0.9 × 0.9 = 0.73) of the cows will complete the programme correctly. Programme compliance can be checked based on audits of the amount of drug used versus cows injected and/or by periodically sampling some cows on either day 7 of a programme or on the day of insemination for milk progesterone measurement. For herds that use a combination of oestrous detection and synchronisation to ensure that each cow receives a timely first insemination, the question arises as to the ‘days in milk’ when cows should be enrolled on the synchronisation programme, with most herds opting to ensure that every cow receives a first insemination by 75 to 80 ‘days in milk’. The rationale for this timing is based on the information that cows that show an early return to cyclicity and an early first ovulation postpartum are likely to be inseminated to a naturally occurring oestrus before this time and conceive earlier than those that show a delayed return to cyclicity and a delay in the first ovulation postpartum (Galvão et al. 2010). The major factors contributing to the absence of cyclicity observed in some cows are the calving-related disorders of the reproductive tract. The number of cows failing to resume cyclicity by 50 to 65 days in milk in many herds is in excess of 20% (Santos et al. 2010). Thus the percentages of cows requiring synchronisation to achieve a first insemination by this time can be used as a crude assessment of health at calving. In herds that combine oestrous detection with synchronisation to achieve their first insemination by 80 days in milk, it is suggested that a target of no more than 25% of first inseminations should be to synchronised inseminations.

800 700

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• Fig. 25.4

 

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Example of the output from a simple on-farm pedometer activity monitoring system.

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Subfertility

Most synchronisation protocols used in cows (with the exception of periodic injections of PGF2α) are unsuitable for use in heifers due to the difference in follicular dynamics. Addition of an intravaginal, progesterone-releasing device will significantly improve conception rates for fixed-time insemination of heifers (see Chapter 8 for further details). Routine synchronisation of heifers has the same ethical and welfare issues as routine synchronisation of lactating cattle, although the reasons for its use are generally different – namely difficulty in organising oestrous detection in the heifer group versus underlying poor oestrous behaviour in lactating cows. However, in contrast to lactating cows, synchronisation of heifers can have welfare benefits for the treated animals as the compact calving pattern produced allows the creation of heifer groups that, on larger farms, can be kept together after lactation or, even on smaller farms, carefully managed to minimise the effect of the transition from pregnancy to lactation (Boyle et al. 2013, Neave et al. 2017). It should always be borne in mind that, despite the use and development of sophisticated controlled breeding programs, ultimately herd reproductive performance will remain dependent on and restricted by the overall standard of the management of health and nutrition of the herd. Pregnancies resulting from controlled breeding programmes will still be subject to the same level of embryo loss and abortion as those resulting from insemination to spontaneously occurring oestrus. Ultimately, success will depend on an integrated approach to fertility management, with the use of synchronisation protocols being just one aspect of a sound reproductive management programme.

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