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Home » Issue 3 (Summer 2017) » Project Articles » An investigation into the effects of mastitis on fertility in a commercial dairy herd

An investigation into the effects of mastitis on fertility in a commercial dairy herd

Author Names: Jessie Guscott (BSc (Hons) Bioveterinary Science) and Brian Evans

 

Abstract:

Background: During the transition from pregnancy to lactation, changes in nutrient partitioning can leave dairy cows in a negative energy balance, predisposing them to reduced immune competence and making them more susceptible to production diseases, such as mastitis. Mastitis can contribute to poor reproductive performance in infected cows, through alterations in hormone production and essential regulatory elements of the oestrus cycle. Somatic cell count is used to detect immune defence systems within the udder and is an indicator for subclinical mastitis. Both mastitis and infertility are common disease complexes in dairy cattle worldwide, and major reasons for premature culling and decreased profitability of a herd.

Aim: To investigate whether the presence of clinical mastitis during early lactation affects fertility in dairy cows.

Methodology: A retrospective longitudinal study was conducted on lactating cows (n=268) at Home Farm, Hartpury College where fertility events and any mastitis cases were recorded. The timing of mastitis was used to group cows, and statistical analysis was performed to establish whether mastitis had a significant effect on fertility performance.

Key Findings: No significant effects of mastitis upon the days from calving to first service, days to conception and calving interval were found, though when cows with a period greater than 90 days from calving to first service were removed from the ‘no mastitis group’ for analysis, a significant effect of the presence of mastitis on days from calving to first service was found. There was no correlation found between somatic cell count and days to first service or days to conception.

Discussion and Conclusion: No significant effects of the presence of mastitis during early lactation were found on fertility. However, although findings were not initially significant, when there was a possibility to remove confounding factors, significant effects were found, reflecting the multi-factorial nature of fertility.

 

1.0 Introduction

Over the past 50 years there has been a decline in fertility of dairy cows, which has been correlated with an increase in individual milk production (Walsh, Williams and Evans, 2011; Heringstad and Larsgard, 2010 ). Genetic selection for high milk yield and low clinical mastitis frequency has contributed to a decrease in reproductive ability and reduced the likelihood of pregnancy establishment in high-producing cows (Albaaj, Foucras and Raboisson, 2017; Heringstad and Larsgard, 2010). This has been seen in a range of countries and regions that utilise diverse production systems, such as the United Kingdom, North America, New Zealand, and Australia (Dillon et al. 2006). This suggests that genetic selection for production traits is the factor causing undesirable side effects in poor health and fertility, and not a specific management or production system.

AHDB Dairy (2016) report that the average UK milk yield for 2015/16 was 7,912 litres per cow per annum, an increase of 13% from 10 years previously. This increase in milk yield has accompanied a decrease in national herd size, as the same bulk yield is achieved with fewer cows. As the demand for high production from each cow continues to rise, the farm animal welfare committee (FAWC) report that the average number of lactations is also increasing, from 3.3 to 3.6 between 1991 and 2005. They also reported that cows are living longer than in previous years, with an average lifespan of six years, although they still recommend that the target should be to raise welfare and lifespan to eight years (FAWC, 2009). An increased lifespan could be an indicator of improving health, with better veterinary treatments available, however infertility remains the main reason for premature culling, followed by production diseases, such as mastitis (McDougal et al. 2016; FAWC, 2009). Dillon et al (2006) reported economic analysis in an EU milk quota scenario over a 14-year period (1990-2003) showing that despite increasing yields, only 41% of the potential improvement in farm profit was being achieved because of impaired reproductive performance.

 

1.1 Fertility

Fertility can be assessed using a range of key performance indicators (KPI), although these are indirect measures and highly influenced by farm management (Tenghe et al. 2015).

1.1.1 Calving to First Service

Calving to First Service (CFS) describes the sum of days between calving and first natural or artificial insemination (AI) service. Average herd values reported for healthy cows range from 64-67 days (Ahmadzadeh et al. 2009; Santos et al. 2004).

1.1.2 Number of Services

The number of services required to achieve conception (S/C) varies between individuals, due to age, health and lactation number (Baez et al. 2015). While one S/C is the ultimate goal, Mokhtari et al (2015) reported 2.09 to be the mean number of inseminations required for first-parity cows, whereas 1.31 was the mean in heifers. As they are not affected by lactation or associated stress and health problems, conception rates are higher in heifers (Beaz et al.  2015; Kurjogi, Kaliwal and Vanti, 2015; Mokhtari et al. 2015; Chebel et al. 2004). In the UK, conception rate is around 40%. This is lower than it has been for 40 years despite improvements in estrus detection and AI technologies (FAWC, 2009). It could be suggested that this is due to declining reproductive health of dairy cows as breeding selection continues to favour high-yielding cows.

1.1.3 Calving to Conception

Calving to Conception (CC) or ‘Days Open’, is the number of days between calving and successful conception (Rowson and Kirk, 2015). A normal range for a herd is 105-136 days (Krpalkova et al. 2014; Chapinal et al. 2013;  Pinedo et al. 2009). This will depend on the health of the cow, frequency of fertility cycles, S/C, and the interval between services.

1.1.4 Calving Interval

The sum of days between two consecutive calvings is the Calving Interval (CI). Studies report herd averages of 399-410 days (Lehmann et al. 2016; Rowson and Kirk, 2015; Albarran-Portillo and Pollott, 2013; Chapinal et al. 2013). FAWC (2009) report that average CI has extended from 384 to 410 days since the 1980s, supporting suggestions of decreasing reproductive ability of high-producing cows (Albaaj, Foucras and Raboisson, 2017; Heringstad and Larsgard, 2010).

50% of modern dairy cows are seen to have abnormal oestrous cycles postpartum, resulting in a delay in the reestablishment of cyclic activity, increased S/C and an extended CC period (Tenghe et al. 2015; Garnsworthy et al. 2009). Reproductive efficiency is a high priority in all dairy systems. It is a major determinant of herd profitability and associated with milk production, the culling rate, cost of breeding, and value of calves, with each oestrus cycle that does not result in a pregnancy contributing to farmers’ expenses (Albaaj, Foucras and Raboisson, 2017; Tenghe et al. 2015; Walsh, Williams and Evans, 2011). Poor fertility is the most economically important animal health issue, costing £250 per cow, with an extended CI costing an estimated £2 – £2.30 per day (Lavon, 2017a). While infertility is not in itself a welfare problem, it could be an indirect indicator of poor health and welfare (FAWC, 2009).

During the transition from pregnancy to lactation, dairy cows undergo substantial metabolic and physiological adaptation, as this is a critical time for changes in nutrient partitioning and metabolic changes in order to meet the nutrient and energy demands necessary for foetal growth and lactation (Sordillo and Atiken, 2009). These changes result in a negative energy balance and loss of body condition in high-producing animals during the first peak of lactation (Wall, Coffey and Brotherstone, 2007; Wathes et al. 2007). The considerable increase in oxygen requirements during this time results in oxidative stress and is a significant underlying factor to lactating cows being predisposed to reduced immune competence and consequently more susceptible to a variety of metabolic and infectious diseases, including mastitis, mammary oedema, metritis and retained foetal membranes, especially during the transition period (Beaz et al.  2015; Kurjogi, Kaliwal and Vanti, 2015; Sordillo and Aitken, 2009; Goff, 2006; Wilde, 2006; Stefanon et al. 2005). Disease and oxidative stress in the periparturient period can have negative effects on the resumption and normality of oestrous cyclicity, and the success of inseminations (Wall, Coffey and Brotherstone, 2007; Wathes et al. 2007).

 

1.2 Mastitis

Clinical mastitis (defined by recognisable clinical signs of bacterial infection in the bovine mammary gland, when visible abnormalities in milk are observed) is a common and costly widespread production disease among dairy cows worldwide (Levison et al. 2016; Fogsgaard et al. 2012). Mastitis poses a serious welfare issue with pain and discomfort being associated with local and systemic signs, and is the reason for 9% of premature culling of dairy cows (FAWC, 2009). Clinical signs can include fever, loss of appetite, diarrhoea, dehydration, changes in milk composition and yield, and swelling and redness of the infected mammary glands, leading to reduction in mobility due to pain (McDougal et al. 2016; Fogsgaard et al. 2012). Fogsgaard et al (2012) reported that mastitis also results in significant behavioural changes, finding that time spent ruminating and feeding was lower in mastitis afflicted cows, with an overall decrease in lying time and self-grooming behaviour.

Levison et al (2016) reported that the most frequently isolated mastitis pathogens were ‘coagulase-negative staphylococci, Bacillus spp., Streptococcus spp., Staphylococcus aureus, and Escherichia coli.’ Although, it is important to note that this study was conducted in Canada, and prevalence of microorganisms may differ in the United Kingdom. Risk factors for contracting mastitis include parity, milk production, stage of lactation, and breed and teat pathologies, such as teat-end keratosis or injury (Pantoja et al. 2016). Bedding can also have an effect, with deep-bedded sandy material found to be most beneficial. Cow cleanliness is also a factor and is linked to the quality and abundance of bedding (Rowbotham and Ruegg, 2016; Favero et al. 2015). Immunity is also reduced in the presence of infection, meaning cows are more susceptible to other potentially fatal secondary diseases (Sordillo and Aitken, 2009). Mastitis is an economical problem for businesses due to reduced milk production, reduced milk quality, cost of treatment with antibiotics, increased risk of culling and withdrawal of milk (Pinedo et al. 2009; Santos et al. 2004). The cost of a mild case of mastitis is about £169, a severe case is £469, and a fatality due to mastitis can cost £1709 per cow (FAWC, 2009). This soon results in large financial losses, as farmers report that 20% – 40% of their cows suffer mastitis in a year (FAWC, 2009).

There is a positive genetic correlation between mastitis and milk yield (range 0.15–0.68) and consequently high-yielding cows have an increased risk of developing mastitis (Walsh, Williams and Evans, 2011). Early lactation, defined as the first 100 days of lactation, is when milk yield is at its highest, with peak yield seen at approximately day 34 of lactation (McCarthy et al. 2016; Albarran-Portillo and Pollott, 2013). While cases of mastitis occur throughout lactation, the risk is highest during early lactation, with the majority occurring within the first four months and mastitis seen in 23% of cows within 30 days postpartum (Walsh, Williams and Evans, 2011; Kemp et al. 2008). Recent work by Hertl et al (2010) evaluated the importance of the timing of clinical mastitis on conception, although this study was performed on a limited number of similarly managed herds, and therefore did not consider how timing could be a factor in different herds or test this on a large scale.

 

1.3 How Mastitis Can Affect Fertility

Recent studies have shown that both clinical and subclinical mastitis are associated with compromised reproductive performance, such as longer CC intervals, and increased S/C (Ahmadzedeh et al. 2009;  Kemp et al. 2008; Santos et al, 2004; Schrick et al. 2001; Barker et al. 1998). These studies indicated that clinical mastitis, especially in the early postpartum period, had a profound effect on reproductive success. Walsh, Williams and Evans (2011) consider many factors in the early postpartum period that affect subsequent fertility, however fail to mention mastiti in the supplementary poster, a model for poor fertility in dairy cows that accompanies the report. It is stated that this poster is a summary of the events impacting fertility in high-producing dairy cows, but no justification is given as to why mastitis is excluded, and hence the effect of mastitis during the early post-partum period is an area for further research to determine to what extent it impacts on fertility.

The University of Tennessee Dairy Experiment Station research herd at Lewisburg were used by Barker et al (1998) to evaluate a group of 102 Jersey cows with clinical mastitis during the first 150 days of lactation, along with a control group of 103 cows with no clinical mastitis in order to investigate the effect of mastitis on fertility. Schrick et al (2001) obtained data from DHIA records on 752 cows from The University of Tennessee Dairy Experiment Station research herd from 1986 to 1997. While no breed was stated, it could be assumed that this was the same herd as used by Barker et al (1998). Santos et al (2004) used a larger sample size of 1001 Holstein cows, from two commercial herds in the Central Valley of California. They were followed for an entire lactation (320 DIM) and found that mastitis increased culling rates and those affected left the herd earlier than control cows. Incidence of abortions was also found to be 6% higher in cows that suffered mastitis (Santos et al. 2004). While these studies were conducted on different breeds, in different areas of the USA, they used similar groupings to compare the timing of mastitis and effects on KPIs, and hence provide comparable findings, suggesting the negative impact of mastitis on fertility.

Cows with clinical mastitis reportedly had an increased CFS period and required more S/C compared to healthy herd members (Walsh, Williams and Evans, 2011). Santos et al (2004) found that mastitis had more detrimental effects on conception rates when occurring between the first AI and pregnancy diagnosis. This supports Barker et al (1998)’s  findings that S/C was significantly greater (+1.2 services) for cows with mastitis between first service and conception. The same was seen by Schrick et al (2001) finding an increase of 1.5 services. Barker et al (1998) found that the days CFS was significantly greater when mastitis occurred before the first service (+22.6 days). Schrick et al (2001) found that days CFS were not significantly increased in cows, when mastitis occurred between first service and conception (+9.5 days). Ahmadzedeh et al (2009) used records from 967 lactating Holstein cows from a commercial dairy farm in southern Idaho, finding that regardless of the time of occurrence, days CC was greater for cows with mastitis (+52 days) than those without mastitis. These reports agree that the CC interval is significantly extended and S/C higher, in cows experiencing mastitis, especially when occurring between first service and conception.

Figure 1 Guscott

Figure 1: The effect of mastitis on the hypothalamic-pituitary axis (Metcalfe, 2016)

 

Clinical mastitis adversely affects the reproductive performance of dairy cows through the release of inflammatory mediators, resulting in alteration of luteinising hormone (LH) release and follicle stimulating hormone (FSH) activity, which can delay ovulation, alter oocyte development, oestrus cycle, and embryonic function, resulting in a lower chance of conception and higher risk of abortion (Lavon et al. 2016; Kurjogi, Kaliwal and Vanti, 2015; Rowson and Kirk, 2015; Pinedo et al. 2009; Santos et al. 2004). Rahman et al (2012) supported this via observation of milk samples and ovaries from 68 cows collected at slaughter, and concluded that decreased fertility of cows with mastitis is due to an effect on the structure and function of the ovaries and patterns of reproductive hormone secretion, which alters essential regulatory elements of folliculogenesis. Figure 1 shows the impact of inflammation on hormonal balance, where the presence of endotoxins leads to the production of cortisol, a strong inhibitor of the hypothalamic-pituitary axis (Metcalfe, 2016). Cortisol decreases the Gonadotropin-releasing hormone (GnRH) pulse amplitude in the hypothalamus, which in turn reduces the level of LH and FSH released from the pituitary gland. The decrease in basal LH concentration inhibits follicle growth, resulting in the low pre-ovulatory circling oestradiol concentrations seen in 30% of subclinical mastitis cows, which leads to blocked, delayed or no ovulation (Metcalfe, 2016; Lavon et al. 2010). The reduction in conception rate could be further related to the release of inflammatory mediators in mastitis infection, such as prostaglandin F2alpha (PGF) which can alter the inner-estrus interval by causing luteolysis (Lavon et al. 2011; Santos et al. 2004).

 

1.4 Somatic Cell Count

Somatic cell count (SCC) in milk is an indicator of the activity of the cellular immune defence system (Lavon et al. 2011). There is wide variation in SCC though cows with ≥200,000 cells/mL are usually classified as high (Madouasse et al. 2012). A high SCC indicates a high level of white blood cells (leukocytes) in a milk sample, indicating an inflammatory response to infection in the mammary gland (mastitis) (Lomander et al. 2013). Milk from healthy udders can vary between 18,000 and 250,000 cells/mL, whereas cows with subclinical mastitis can have SCC of up to a few million cells/mL (Lavon et al. 2011;FAWC, 2009). Pinedo et al (2009) evaluated the effect of high SCC during early lactation on reproductive performance, using a retrospective cohort study on data collected by a government certified recording system, for a population of 12,000 Holstein cattle, from 157 herds, over a 15-year period. They found that subclinical mastitis, measured by SCC, had a significant impact on reproductive performance in the Chilean dairy cattle, manifested by increased time CFS (+21.8 days) and CC (+48.7 days), and more S/C (+0.5).

Convincing evidence exists that both clinical mastitis and increased SCC can have substantial negative associations with fertility performance (Hudson et al. 2012). Lavon et al (2011) showed that SCC elevation around the time of AI, was associated with a significant reduction in the probability of conception. They found that even a mild elevation in SCC reduced conception rate. Whereas, Albaaj, Foucras and Raboisson (2017) found that the impact of mastitis on conception appeared to be higher for clinical than for subclinical mastitis and for a large SCC increase than for a small increase. The most critical risk period in which mastitis can reduce conception success has been reported to be at the time of, or within the month after service (Hudson et al. 2012; Ahmadzedeh et al. 2009; Pinedo et al. 2009; Schrick et al. 2001). Albaaj, Foucras and Raboisson (2017) supported this finding that a high SCC after AI has a much greater negative association with decreased conception success than a high SCC before AI, finding that the decrease in conception was up to 60% when SCC increased above 200,000 cells/mL threshold during conception period. They also found a linear trend between the severity of udder inflammation, and a greater decrease in conception success. Close relationships are seen between patterns of SCC elevation and intra-mammary infection, and their association to reduced fertility (Lavon et al. 2011; Lavon et al. 2010; Miller et al. 2001; Schrick et al. 2001).

 

1.5 Aims and Objectives

1.5.1 Aim

The aim of this study was to investigate whether the presence of clinical mastitis during early lactation affects fertility in dairy cows.

 

1.5.2 Objectives and Hypotheses

To investigate the effect of mastitis on herd fertility performance by recording key fertility performance indicators and mastitis events for a commercial herd over a period of a year.

–  Research Hypothesis (H1): There will be a significant difference in fertility performance of dairy cows that suffered a mastitis event during early lactation and those that did not.

–  Null Hypothesis (H0): There will not be a significant difference in fertility performance of dairy cows that suffered a mastitis event during early lactation and those that did not.

 

To investigate the link between somatic cell counts (SCC) over two and three months postpartum, and the days calving to first service (CFS) and days calving to conception (CC).

–  H1: There will be a significant correlation between SCC over two months postpartum and CFS interval.

–  H0: There will not be a significant correlation between SCC over two months postpartum and CFS interval.

–  H1: There will be a significant correlation between SCC over three months postpartum and CC interval.

–  H0: There will not be a significant correlation between SCC over three months postpartum and CC interval.

 

2.0 Methodology

A retrospective longitudinal study was conducted to look at mastitis and fertility events that occurred over a lactation, for the whole population of lactating cows at Home Farm, Hartpury College (n=268). Data was collected by calving date, between 01.05.15 and 30.04.16, allowing the most recent complete lactation to be included and time for subsequent fertility events to have occurred at the time of data collection (13th, 18th, 19th, 20th and 21st October 2016). The subjects were all animals that were lactating and being put into calf again, so heifers and first parity cows who had not yet conceived were excluded from the study. Cows that were not being bred again, or that were sold or culled prior to coming back into oestrus, had no recorded fertility events and were also excluded. However, cows that successfully conceived and then died or were culled, were still included for their fertility events up to conception.

All cattle must have an official ear tag in each ear, with a unique identification number (DEFRA, 2013) meaning it was possible to individually identify each animal through data on InterHerd, a herd production and health recording system, used to obtain the required data (InterHerd, 2016). InterHerd is used in conjunction with National Milk Records (NMR) to record full milk production, health and fertility events and trends (NMR, 2016) and hence the data was analysed retrospectively with ease. Purposive sampling  was used. This is a non-random, single stage sampling method, and was suitable, as there was direct access to all information (Research Methodology, 2016; Creswell, 2014).

Barker et al (1998) evaluated 102 cows with clinical mastitis and used balanced and sub-divided groups, dependent on where mastitis had occurred during fertility events. For this study, purposive sampling allowed mastitis groups to be compiled, dependent on the timing of mastitis, using data from InterHerd to select group members. Each cow was observed for their most recent fertility events to obtain a calving date, date of first service, date of conception and date of subsequent calving, as well as S/C. If a subsequent date of calving had not been reached, the system generated due date was used to calculate the CI. Clinical mastitis cases were diagnosed according to the normal practice of the herd (Hudson et al. 2012), in this case, any mastitis event recorded on InterHerd. SCC were also recorded from routine monthly milk tests (Lavon et al. 2011). SCC after calving, before and after first AI service were collected.

 

1.1 Experimental Study

Data were collected from the InterHerd software already utilised on the computer system at Home Farm, Hartpury College. The following information was collected for 268 lactating cows:.

1.     Date of Birth, Lactation and ID number

2.     Any Mastitis Events

                                          i.     Date and DIM on which it occurred

                                         ii.     Any relevant details on location and treatment

3.     SCC after calving, before and after first AI service

4.     Fertility Events

                                          i.     Date of calving

                                         ii.     Date and DIM of first service

                                       iii.     Date and DIM of conception

                                       iv.     Number of services

                                        v.     Date of subsequent calving

 

The collected data was input into a Microsoft Excel spreadsheet, allowing calculation of four fertility parameters:

  1. Number of Services per Conception (S/C)
  2. Days Calving to First Service (CFS)
  3. Days Calving to Conception (CC)
  4. Calving Interval (CI)

 

Cows were then grouped into four groups, as used by Barker et al (1998):

  1. Cows that suffered no mastitis (n=220)
  2. Cows that suffered mastitis between calving and first service (n=20)
  3. Cows that suffered mastitis between first service and conception (n=15)
  4. Cows that suffered mastitis after conception (n=13)

 

2.2 Statistical Analysis

Firstly, descriptive analysis was performed on the data in Excel to calculate mean values for the KPIs and mastitis prevalence, the standard deviation (SD) and standard error of the mean (SEM) for the data. While sometimes used interchangeably, SEM quantifies uncertainty in estimate of the mean, whereas SD indicates dispersion of the data from the mean (Barde and Barde, 2012). Figure 2 shows the equations used to calculate the SD and SEM. Simplistic bar graphs were then produced for mean comparisons using Excel chart function, with error bars to demonstrate SEM. Any missing values for fertility parameters were excluded to avoid skewness of data, for example where a cow may have undergone a first service, but not reached conception. The KPIs were then separated into four parameters and statistical analysis performed using IBM SPSS software.

Figure 2 Guscott

Figure 2: Equations for calculating SD and SEM (Barde and Barde, 2012)

 

The Kolmogorov Smirnov (KS) test was first used to test normality of the data, by comparing the scores in the sample to a normally distributed set of scores with the same mean and standard deviation (Field, 2013). A significant difference was seen when the distribution in question was significantly different from a normal distribution. A non-parametric test was then used to test significance. The significance level was set at P<0.05. KS tests for normality, were performed on the data for each parameter, first as a whole, and then split into separate groups, in order to appropriately test for normal distribution in both groups.

To compare the difference between two independent groups when the data is parametric, an independent samples t-test can be used. This two- tailed test, ensures a 95% certainty that any relationships did not occur by chance (Statistics Solutions, 2013a). The non-parametric alternative is to use the Mann Whitney test to test the difference between two independent groups, by comparing the number of times a score from one sample is ranked higher than a score from the other sample (Statistics Solutions, 2013b; Lunenburg and Irby, 2008).  When there were more than two groups, a Kruskal -Wallis (non-parametric) or ANOVA (parametric) test was used. Where a significant difference was found, further pairwise analysis was then performed to determine where the significant differences were (Field, 2013).

 

2.2.1 Days Calving to First Service

KS test for normality showed that data was non-parametric. A Mann Whitney test was used to compare the days CFS of the two independent groups (cows that suffered mastitis during CFS, and those without mastitis in this period).

Data was then amended, so that for the ‘no mastitis’ group, any values greater than 90 days CFS were removed (n=210) as this could indicate underlying fertility issues (for example, lameness, trauma, having twins, pregnancy or parturition trauma, or illness (Lavon et al. 2016; Rowson and Kirk, 2015) and skew the data. Values over 90 in the ‘mastitis’ group were left. KS test for normality was then followed by a further Mann Whitney test on the adapted values, to test for a statistically significant difference between the two groups.

 

2.2.2 Number of Services

KS test for normality showed non-parametric data, therefore a Mann Whitney test was used to compare S/C in the two independent groups (cows that suffered mastitis during the CC period, and those with no mastitis).

 

2.2.3 Days Calving to Conception

KS test for normality again showed non-parametric data, and a Mann Whitney test was used to compare the days CC of the two independent groups (cows that suffered mastitis during CC, and those that did not).

 

2.2.4 Calving Interval

Following KS tests for normality, a non-parametric Kruskal Wallis test was performed, to compare CI for the four groupings by mastitis occurrence (cows that suffered no mastitis; cows that suffered mastitis between calving and first service; between first service and conception; and after conception).

 

2.2.5 Relationship Between Somatic Cell Counts and Fertility Indicators

The relationship between SCC in the period CFS was also tested using a KS test for normality, again showing data was non-parametric. A Spearman’s correlation was used to determine the relationship between average SCC over two months postpartum, and days CFS.

The relationship between average SCC over three months postpartum, and days CC, was determined to be non-paremetric using a KS test for normality, a Spearman’s correlation was then used.

 

2.3 Ethical Considerations

For all animals on the premises, the Animal Welfare Act (2006) and the Welfare of Farmed Animals (England) Regulations (2007) must be followed (DEFRA, 2012). AI is used for most dairy cows and can only be carried out in line with the Artificial Insemination of Cattle (Animal Health) (England and Wales) Regulations 1985 (DEFRA, 2012). These guidelines ensure that animals are kept to an acceptable level of welfare and correct management practices are upheld. Informed consent ensures that an individual is fully aware of options available to them and any risks involved. In the case of animals, this consent must come from an owner (RCVS, 2015). As this is a retrospective study, where data collected routinely were analysed, no alteration to management or direct contact with the animals was required, and it was only necessary to have consent to access InterHerd and use the data collected.

 

3.0 Results

3.1 Days Calving to First Service

The mean number of days CFS for the herd (n=268) was 57.1 days. Figure 3 suggests that this was higher in cows that suffered mastitis in the CFS period (n=17, mean=64.1 days) than those that did not have mastitis (n=220, mean=56.8 days). However the Mann Whitney test showed that days CFS did not differ significantly (p=0.052, P>0.05) between cows that suffered mastitis in the period CFS, and those that did not.

Figure 3 Guscott

Figure 3: Mean days between calving and first service (error bars for SEM)

 

When cows with a period greater than 90 days CFS were removed from the ‘no mastitis’ group (n=210, mean=54.5 days) the  Mann Whitney test showed that there was a significant difference (p=0.020, P<0.05) in days CFS between cows that suffered mastitis during the period, and those that had not suffered mastitis.

 

3.2 Number of Services

Mean S/C for the whole herd (n=268) was 2.65. When considering the descriptive data, this was slightly higher in cows that suffered mastitis in the CC period (n=23, mean=2.9) and lower in those that did not have mastitis (n=197, mean=2.6) as seen in Figure 4. However, the Mann Whitney test showed that S/C did not differ significantly (p=0.475, P>0.05) between cows that suffered mastitis in the period and those that did not.

 

Figure 4 Guscott

Figure 4: Mean number of services (error bars for SEM)

 

3.3 Days Calving to Conception

The mean number of days CC, for the whole herd (n=268) was 98.5 days. When considering the descriptive data, CC increased by 14.8% in cows that suffered mastitis during the period (n=23, mean=112.2 days) compared to those that did not (n=197, mean=97.8 days), see Figure 5. However a Mann Whitney test showed that days CC, does not differ significantly (p=0.103, P>0.05) between cows that have mastitis during CC, and those that do not.

 

Figure 5 Guscott

Figure 5: Mean days between calving and conception (error bars for SEM)

 

3.4 Calving Interval

For the herd (n=268) the mean CI was 378.5 day. From looking at the descriptive data, this interval could be suggested to be longer in cows that suffered mastitis during the CC period (n=23, mean=392.3 days), see Figure 6. When the groups were divided further (see Figure 7) cows that suffered mastitis between first service and conception had the longest CI (n=10, mean=411.8 days) and cows that suffered mastitis after conception the shortest (n=13, mean=364.5 days). However a Kruskal Wallis test on the four groups, showed that CI was not significantly affected by mastitis (p=0.067, P>0.05).

Figure 6 Guscott

Figure 6: Mean calving interval (error bars for SEM)

 

Figure 7 Guscott

Figure 7: Mean calving interval, with mastitis timings (error bars for SEM)

 

3.5 Relationship between Somatic Cell Counts and Fertility Indicators

3.5.1 Calving to First Service Interval

The mean for the average SCC over the first two months postpartum was 228,000 cells/mL, Figure 8 shows whole herd (n=268) recorded values compared to the days CFS. Non-parametric Spearman’s correlation showed that there was no significant correlation (r= -0.050, p=0.420) between days CFS, and SCC within the first two months postpartum.

Figure 8 Guscott

Figure 8: Relationship between days calving to first service and average SCC of 2 months postpartum

 

3.5.2 Calving to Conception Interval

The mean for the average SCC over the first three months postpartum was 201,000 cells/mL for the whole herd (n=268). These are shown in Figure 9, compared to the days CC. Non-parametric Spearman’s correlation showed that there is no significant correlation (r= -0.028, p=0.671) between days CC, and SCC within the first three months postpartum.

Figure 9 Guscott

Figure 9: Relationship between days calving to conception and the average SCC of 3 months postpartum

 

4.0 Discussion

4.1 Study Population

4.1.1 Lactation Number

For the whole herd studied, the average lactation number was 2.4 (n=268) with subjects ranging from their first to sixth lactation. 31.7% (n=85) of the population were on their first lactation, with only 2.6% (n=7) having reached their sixth. The average number of lactations in the UK was reported to be 3.6 in 2005 (FAWC, 2009) suggesting that the studied herd is not completing as many lactations, and therefore not living as long, compared to national averages. However, Stott (1994) suggested that under UK conditions, the theoretical economic optimum for a cow’s lifespan should be 4.3 to 4.9 lactations. Herds that have a shorter productive lifespan than Stott’s suggestion may indicate a higher prevalence of premature culling, possibly due to poor health or welfare, with endemic and metabolic diseases, and infertility accounting for 57% of culling of dairy cows (FAWC, 2009). The FAWC also recommend that a target for dairy farmers should be to raise welfare and lifespan to eight years, although in the studied herd, only 2.6% are reaching six lactations, and therefore may not be representative of a typical herd.

 

4.1.2 Mastitis Prevalence

The Cattle Health and Welfare Group (2016) reported mastitis prevalence to be approximately 30 cases per 100 cows per year. In the studied population, there were 17.9 cases per 100 cows throughout the studied year, suggesting that mastitis prevalence was lower in the studied herd. This is possibly due to a reduction in mastitis-causing factors, such as environmental pathogens, minimised by using dry, fresh bedding material, good parlour hygiene practices and maintenance of teat health (Pantoja et al. 2016; Rowbotham and Ruegg, 2016; Favero et al. 2015). While mastitis occurred throughout lactation, most cases, 72.9% (n=35) were during 100 days postpartum, supporting previous findings (Walsh, Williams and Evans, 2011). During this early lactation period, the risk of mastitis is increased due to milk yield being at its highest, peaking at approximately day 34 (McCarthy et al. 2016; Kemp et al. 2008). Early lactation is a challenging period for a cow’s metabolism, leaving them more susceptible to a variety of diseases, hence the higher prevalence of mastitis during this time (Sordillo and Atiken, 2009; Goff, 2006).

Most mastitis cases, 41.7% (n=20) occurred during the CFS period, 31.2% (n=15) between first service and conception, and 27.1% (n=13) occurred after conception. In comparison to a study by Barker et al (1998) which used the same groupings, mastitis prevalence was similar in the period CFS (47.1%), less than half in the period between first service and conception (13.7%), and considerably higher in the time after conception (39.2%). However, Barker’s study had a longer mean number of days CFS, but a shorter mean number of days CC, suggesting the period between first service and conception would have been shorter than for the population in this study, possibly accounting for the fewer mastitis cases.

 

4.2 Key Performance Indicators

4.2.1 Days Calving to First Service

Reported average number of days CFS is 64-67 days for a herd of healthy cows (Ahmadzadeh et al. 2009; Santos et al. 2004) however some herds average as high as 92.8 days (Pinedo et al. 2009). The herd in this study, averaged 57.1 days for this period, lower than any study suggests, with some cows being served just 36 days after calving. An early return to oestrus could be a sign of good health and a rebalancing of the negative energy balance associated with parturition. However, as this is a management controlled, indirect indicator of fertility (Tenghe et al. 215) it could be proposed that the cows are coming back into oestrus and unsuccessful AI is being performed too early, possibly accounting for the increased S/C required in this herd. Factors such as timing of the service, and fertility of the cow and semen may also affect conception success (Chenweth and McPherson, 2016).

Days CFS was lower in the group of cows with no mastitis (56.8 days) but increased on average by 7.3 days, in cows that suffered mastitis during the period CFS (64.1 days) although it is important to note that this difference was not significant. Compared to similar studies this increase was small, where days CFS increased on average by 22.6 days (71.0 to 93.6) and 9.5 days (67.8 to 77.3) in cows with mastitis during CFS, for Barker et al (1998) and Schrick et al (2001), respectively. This is in line with previous literature that explains mastitis delays ovulation and the onset of oestrus behaviour, thus extending the period CFS (Lavon et al. 2016; Rowson and Kirk, 2015; Kurjogi, Kaliwal and Vanti, 2015). Schrick et al (2001) also found that mastitis did not have a significant effect on days CFS, when it occurred between first service and conception, 70.6 days compared with 67.8 in their control group. In this investigated population, a slight increase of the same magnitude was seen, to 62.2 days.

Fertility has a multi-factorial nature, and there are many confounding factors that could affect results (Leroy et al. 2012). Values were adapted to account for confounding factors by removing cows with a period greater than 90 days CFS from the ‘no mastitis’ group. Mean days CFS for this group decreased further, to 54.5 days, and there was a significant difference in days CFS between cows that suffered mastitis during the CFS period, and those that had no mastitis. Lactating cows, especially during the transition period from pregnancy to lactation, are predisposed to reduced immune competence and therefore more susceptible to a variety of metabolic and infectious diseases (Beaz et al.  2015; Sordillo and Aitken, 2009). In addition, underlying health issues such as lameness, trauma, having twins or illness, could all contribute to a delayed onset of oestrus, thus extending the period CFS meaning cows went more than 90 days before first AI (Lavon et al. 2016; Kurjogi, Kaliwal and Vanti, 2015; Rowson and Kirk, 2015).

 

4.2.2  Number of Services

Pinedo et al (2009) studied 157 herds of Holstein cattle over 15 years, and determined that conception rate at first service was 47.8% and mean S/C was 1.8. In the current study, 27.3% of cows held at first service, almost half that reported, and the herd on average required more S/C, at 2.6. S/C ranged from one to ten, with 34.7% of cows requiring more than three. Herd practice was to cull cows that were not pregnant after ten AI services, so data for cows requiring more S/C were not available, potentially skewing the data.

A study by Walsh, Williams and Evans (2011) found that cows with clinical mastitis required more S/C. This is in line with previous studies, especially when mastitis occurred between first service and conception, where S/C increased on average by 1.2 and 1.5, for Barker et al (1998) and Schrick et al (2001), respectively. The data in the present study, although not significant, shows an increase of 1.1 S/C, from normal cows requiring 2.6, to 3.7 in cows suffering mastitis in the CC period. Given that the whole herd had a higher mean number of S/C, it could be suggested that the effect of mastitis on this fertility parameter, is of the same magnitude as other studies. Cows with mastitis occurring between first service and conception, would have already undergone one unsuccessful service, and with the onset of mastitis and associated negative effects (Rahman et al. 2012) these changes potentially lead to a greater time between services and more S/C being required, especially in the critical period between first service and conception.

Home Farm, Hartpury use a Reproductive Management System (RMS) meaning they have heat detection, AI services by a highly-trained technician and fertility data management from a company for a set subscription price (Genus, 2012). As such, they do not pay for AI services on an individual basis, perhaps encouraging them to inseminate cows sooner after calving and more frequently, as there are no additional costs associated with increased S/C on this system. This would account for the higher number of services seen in the herd.

 

4.2.3 Days Calving to Conception

Reported values for mean CC interval for a healthy herd, are 105-136 days (Krpalkova et al. 2014; Chapinal et al. 2013; Pinedo et al. 2009). For the population studied, the mean was shorter (98.5 days) showing that conception occurs sooner after parturition. This could have a positive impact on the production of the herd, as CI is subsequently lower. However, across the herd, days CC ranged from 36-257, suggesting high variability in this measurement. This variation could be due to factors of individual fertility, herd health, and timing of service, where this period is extended in cows requiring more S/C. Cows with no mastitis had fewer days CC, 97.8 days, while this was extended by 14.4 days in cows that suffered mastitis during the CC period, to 112.2 days, though it is important to note that the difference was not significant. Previous studies found a more significant increase, where days CC increased on average by 44.5 days (92.1 to 136.6) and 58.1 days (85.4 to 143.5), for Barker et al (1998) and Schrick et al (2001), respectively.

However, when the CC period is compared to the shorter CFS interval of this herd, the time between first service and conception was on average 42 days, double that of reported values, 21 and 18 days (Schrick et al. 2001; Barker et al. 1998). The extended period between first service and conception again could be due to the multi-factorial nature of fertility, with post-parturition disease, such as milk fever and retained placenta, being associated with a lower conception rate (Chebel et al. 2004). This period could also be extended in cows that successfully conceive, but later abort the foetus, coming back into oestrus, and being served again. These cows would have a longer period CC and require more S/C.

 

4.2.4 Calving Interval

For most herds, the average CI is between 399 and 410 days (Rowson and Kirk, 2015; Albarran-Portillo and Pollott, 2013; Chapinal et al. 2013; FAWC, 2009). In the studied herd this was slightly shorter, with a mean CI of 378.5 days. However, this had a range of 311-535 days, again suggesting a high level of variability within the herd, potentially leading to poor profitability, as some cows had a CI of up to 18 months. Improving the health of cattle with extended CC periods, would enable the herd to aim for optimal CI of 365 days (Lavon, 2017a). Although the difference was not significant, cows that suffered mastitis during CC, had an increase of 14.4 days in their CI, compared to those with no mastitis. This was the same increase as seen in the CC period, demonstrating how closely they are linked.

When the groups were divided further, it was possible to see that the most influential effect of mastitis, was again when it occurred in the critical period between first service and conception (411.8 days) extending days CC and hence increasing CI, more so than when mastitis occurred in the CFS period (377.3 days). This supports findings by Santos et al (2004) that mastitis had more detrimental effects on conception rates when occurring between first AI and pregnancy diagnosis. Cows with mastitis after conception had a shorter average CI (364.5 days) than the no mastitis group, suggesting that after conception, mastitis does not affect fertility.

 

4.2.5  Somatic Cell Count

Pinedo et al (2009) found that subclinical mastitis, as measured by high SCC, had a significant impact on the reproductive performance of Chilean dairy cattle, leading to an increased time CFS, CC and more S/C. Previous studies also found that high SCC in the proximity of fertility events had a detrimental effect on conception (Hudson et al. 2012; Pinedo et al. 2009). Some have also shown that SCC elevation around the time of AI, especially in the period after AI, was associated with a reduction in conception probability, and that even a mild elevation can have a negative impact, but that a greater impact is seen for a large SCC increase (Albaaj, Foucras and Raboisson, 2017; Lavon et al. 2011). However, in the studied population, there was no correlation between SCC and the time to first service or conception, and no trend was visible to support data of a high SCC having a negative impact on fertility. While close relationships are demonstrated between patterns of SCC elevation, intra-mammary infection, and their association to reduced fertility (Lavon et al. 2011; Lavon et al. 2010; Miller et al. 2001; Schrick et al. 2001) this does not appear to be the case in the studied population.

For the herd, the mean SCC average over two months postpartum was 228,000 cells/mL, and 201,000 cells/mL over three months. Milk from healthy udders can vary between 18,000 and 250,000 cells/mL (Lavon et al. 2011; FAWC, 2009) suggesting that the herd on average are at the higher end of the normal range. However, cows with ≥200,000 cells/mL are usually classified as high (Madouasse et al. 2012) and hence, a large proportion of the herd have a high SCC. The range of data was again very variable, 10,000-5,315,000 cells/mL average SCC over the first two months postpartum, and 10,000-3,555,000 cells/mL over three. Extreme outliers could skew the data, potentially explaining why no trend was seen. In future this could be corrected by excluding any extremely high values. A high SCC indicates an inflammatory response to infection in the mammary gland (Lomander et al. 2013). This suggests that the studied population had a high level of subclinical mastitis, demonstrated by raised SCC being seen frequently throughout the herd. While clinical signs were not seen and the cases not noted or treated, the overall herd health would be affected.

 

4.3 Limitations of the Study and Future Research Suggestions

The main limitation of this study was the use of retrospective data and thus relying on the accurate record keeping of others for the secondary data collected. Although the data collected on the cows should have been recorded to InterHerd as routine, in line with the running of the farm, there is the possibility that some data was recorded incorrectly or omitted. The herd size also limited data collection. Having a larger study population in future may prevent data being skewed by any anomalies, potentially allowing for significant findings. The differing sizes of the mastitis groups may have been a limiting factor. Having a larger population would have enabled the random selection of cows for the no mastitis groups, and so allowed for more equally sized groups, such as those used by Barker et al (1998). Data collected were only for cows who had undergone parturition, and hence fertility events for heifers were not included as they would not be affected by mastitis or stresses associated with lactation, and would therefore be more fertile, requiring fewer S/C and a shorter time CC (Beaz et al.  2015; Mokhtari et al. 2015; Wathes et al. 2007). This could mean the data collected was skewed, as including heifers would affect fertility parameters for the herd.

A major drawback to the approach of comparing a population of cows that experienced mastitis, with a population that did not, is that it makes it difficult to account fully for potentially confounding factors in the relationship between mastitis and fertility (Hudson et al. 2012). Factors such as illness were not considered, nor were trauma, lameness, or genetics, all potentially affecting fertility or predisposing a cow to mastitis infection. Higher-yielding cows are also at a higher risk of developing mastitis, but tend to have poorer fertility (Metcalfe, 2016). This could potentially confound the assessment of the relationship between mastitis and fertility. By excluding values greater than 90 days CFS for cows without mastitis, individual health issues were not considered, such as lameness, mastitis, trauma or having had twins, all of which could have an indirect negative effect on fertility.

To prevent these issues in future study, confounding factors should be highlighted at the time of data collection, allowing for analysis of their effect on mastitis and fertility. It would also then be possible to remove cows with underlying issues likely to affect fertility and prevent skewing of the data. This would ensure that only mastitis is affecting the mastitis group, and a no mastitis group here would represent a healthy group of animals. This study would also need to be conducted on a larger population, incorporating multiple farms, to allow for differences between herds and achieve larger, more even group sizes. This would mean the effects of mastitis would be clearer and possibly allow significant effects to be found in the data collected.

 

5.0 Conclusion

In the studied population, whilst no significant effects of mastitis during early lactation were found on fertility, descriptive detrimental effects were observed, manifested as an increased period CFS, CC, CI, and more S/C. The effect was greatest when mastitis occurred in a critical period between first service and conception. After conception, mastitis had no further effect on fertility. When using adapted values to account for confounding factors, it was possible to establish that the days CFS were significantly extended when mastitis occurred within the period. Future studies should consider the multi-factorial nature of fertility, and create a more sophisticated way to identify and account for confounding factors.

No correlation was found between SCC and fertility parameters, suggesting SCC were not an effective measure of fertility performance in this population. SCC for the whole herd were high, suggesting that the population had a high level of subclinical mastitis, possibly accounting for the longer CC period, and greater S/C seen in this herd, compared to other studies.

Producers should be aware of the critical period for mastitis, between first service and conception, when the effect on fertility is greatest. While farmers should take measures to reduce mastitis at all times, it is of particular importance during this period, due to the adverse implications for health and fertility. Further work should focus on using larger sample sizes over multiple farms, eliminating confounding factors, and promoting to farmers the detrimental effect that mastitis can have on fertility in dairy cows.

 

References

Agriculture and Horticulture Development Board (AHDB) (2016) Symptoms of Mastitis. Available from: http://dairy.ahdb.org.uk/technical-information/animal-health-welfare/mastitis/symptoms-of-mastitis/.VtSd1uaD6So [Accessed 29 February 2016].

AHDB Dairy (2014) Fertility Index Fact Sheet [online]. Available from: https://dairy.ahdb.org.uk [Accessed 22 January 2017].

AHDB Dairy (2016) Average UK Milk Yield [online]. Available from: https://dairy.ahdb.org.uk [Accessed 01 February 2017].

AHDB Dairy (2017) Mastitis Control Plan [online]. Available from: http://www.mastitiscontrolplan.co.uk/what-is-the-plan [Accessed 01 February 2017].

Ahmadzadeh, A., Frago, F., Shafii, B., Dalton, J.C., Price, W.J. & McGuire, M.A. (2009) Effect of clinical mastitis and other diseases on reproductive performance of Holstein cows. [online]. Animal reproduction science. 112 (3-4), pp. 273–282.

Albaaj, A., Foucras, G. & Raboisson, D. (2017) High somatic cell counts and changes in milk fat and protein contents around insemination are negatively associated with conception in dairy cows [online]. Theriogenology. 88 (1), pp. 18–27.

Albarrán-Portillo, B. & Pollott, G.E. (2013) The relationship between fertility and lactation characteristics in Holstein cows on United Kingdom commercial dairy farms. [online]. Journal of dairy science. 96 (1), pp. 635–646.

Baez, G.M., Barletta, R. V., Guenther, J.N., Gaska, J.M. & Wiltbank, M.C. (2015) Effect of Uterine Size on Fertility of Lactating Dairy Cows [online]. Theriogenology. 85 (8), pp. 1357-1366.

Barde, P. & Barde, M. (2012) What to use to express the variability of data: Standard deviation or standard error of mean? Perspectives in Clinical Research. 3 (3), pp. 113–116.

Barker, A.R., Schrick, F.N., Lewis, M.J., Dowlen, H.H. & Oliver, S.P. (1998) Influence of clinical mastitis during early lactation on reproductive performance of Jersey cows. [online]. Journal of dairy science. 81 (5), pp. 1285–1290.

Bernabucci, U., Ronchi, B., Lacetera, N. & Nardone, A. (2005) Influence of body condition score on relationships between metabolic status and oxidative stress in periparturient dairy cows. [online]. Journal of dairy science. 88 (6), pp. 2017–2026.

Bittar, J.H.J., Pinedo, P.J., Risco, C.A., Santos, J.E.P., Thatcher, W.W., Hencken, K.E., Croyle, S., Gobikrushanth, M., Barbosa, C.C., Vieira-Neto, A. & Galvão, K.N. (2014) Inducing ovulation early postpartum influences uterine health and fertility in dairy cows. [online]. Journal of dairy science. 97 (6), pp. 3558–3569.

Burfeind, O., Suthar, V.S., Voigtsberger, R., Bonk, S. & Heuwieser, W. (2014) Body temperature in early postpartum dairy cows. [online]. Theriogenology. 82 (1), pp. 121–131.

Carroll, S. (no date) Dissertation Statistics. Available from: http://www.dissertation-statistics.com/ [Accessed 27 February 2016].

Cattle Health and Welfare Group (CHAWG) (2016) Third Report, GB Cattle Health and Welfare Group. Available from: http://www.mastitiscontrolplan.co.uk/images/Public/CHAWG-Third-Report-2016-051216.pdf [Accessed 05 March 2017].

Chapinal, N., von Keyserlingk, M.A.G., Cerri, R.L.A., Ito, K., Leblanc, S.J. & Weary, D.M. (2013) Short communication: Herd-level reproductive performance and its relationship with lameness and leg injuries in freestall dairy herds in the northeastern United States. [online]. Journal of dairy science. 96 (11), pp. 7066–7072.

Chebel, R.C., Santos, J.E.P.P., Reynolds, J.P., Cerri, R.L.A.A., Juchem, S.O. & Overton, M. (2004) Factors affecting conception rate after artificial insemination and pregnancy loss in lactating dairy cows [online]. Animal Reproduction Science. 84 (3–4), pp. 239–255.

Chenoweth, P.J. & McPherson, F.J. (2016) Bull breeding soundness, semen evaluation and cattle productivity. Animal Reproduction Science. 169pp. 32–36.

Creswell, J.W. (2014) Research Design. 4th ed. London: SAGE.

Dairy Co (2010) Farmer Intentions Survey [online]. Available from https://dairy.ahdb.org.uk [Accessed 01 February 2017].

Dale, A.J., Hunter, B., Law, R., Gordon, A.W. & Ferris, C.P. (2016) The effect of early lactation concentrate build-up strategy on milk production, reproductive performance and health of dairy cows [online]. Livestock Science. 184pp. 103–111.

Department of Environment, Food and Rural Affairs (DEFRA) (2012) Beef cattle and dairy cows: welfare regulations. Available from: https://www.gov.uk/guidance/cattle-welfare-regulations#dairy-cows  [Accessed 28 February 2016].

DEFRA (2013) Cattle Identification, Registration and Movement. Available from: https://www.gov.uk/guidance/cattle-identification-registration-and-movement [Accessed 28 February 2016].

Dillon, P., Berry, D.P., Evans, R.D., Buckley, F. & Horan, B. (2006) Consequences of genetic selection for increased milk production in European seasonal pasture based systems of milk production. Livestock Science. 99 (2-3), pp. 141–158.

Fávero, S., Portilho, F.V.R., Oliveira, A.C.R., Langoni, H. & Pantoja, J.C.F. (2015) Factors associated with mastitis epidemiologic indexes, animal hygiene, and bulk milk bacterial concentrations in dairy herds housed on compost bedding [online]. Livestock Science. 181pp. 220–230.

FAWC (Farm Animal Welfare Committee) (2009) Opinion on the welfare of dairy cattle [online]. Available from: https://www.gov.uk/government/publications [Accessed 01 February 2017].

Field, A. (2013) Discovering Statistics Using IGM SPSS Statistics. 4th ed. London: SAGE.

Fogsgaard, K.K., Røntved, C.M., Sørensen, P. & Herskin, M.S. (2012) Sickness behavior in dairy cows during Escherichia coli mastitis. [online]. Journal of dairy science. 95 (2), pp. 630–638.

Gantt (2016) What is a Gantt Chart? Available from: http://www.gantt.com/index.htm [Accessed 29 February 2016].

Garmo, R.T., Ropstad, E., Havrevoll, O., Theun, E., Steinshamn, H., Waldmann, A. and Reksen, O. (2009) Commencement of Luteal Activity in Three Different Selection Lines For Milk Yield and Fertility in Norwegian Red Cows. The Journal of Dairy Science [online]. 92 (5), pp. 2159-2165.

Garnsworthy, P.C., Fouladi-Nashta, A.A., Mann, G.E., Sinclair, K.D. & Webb, R. (2009) Effect of dietary-induced changes in plasma insulin concentrations during the early post partum period on pregnancy rate in dairy cows. Reproduction. 137 (4), pp. 759–768.

Genus (2012) Reproductive Management Systems. Available from: http://www.genusbreeding.co.uk/?p=1615 [Accessed 08 March 2017].

Goff, J.P. (2006) Major advances in our understanding of nutritional influences on bovine health. [online]. Journal of dairy science. 89 (4), pp. 1292–1301.

Hartpury College (2017) Home Farm. Available from: http://www.hartpury.ac.uk/university-centre/facilities/agriculture/home-farm/  [Accessed 21 February 2017].

Heringstad, B. & Larsgard, A.G. (2010) Correlated selection responses for female fertility after selection for high protein yield or low mastitis frequency in Norwegian Red cows. [online]. Journal of dairy science. 93 (12), pp. 5970–5976.

Hertl, J.A., Gröhn, Y.T., Leach, J.D.G., Bar, D., Bennett, G.J., González, R.N., Rauch, B.J., Welcome, F.L., Tauer, L.W. & Schukken, Y.H. (2010) Effects of clinical mastitis caused by gram-positive and gram-negative bacteria and other organisms on the probability of conception in New York State Holstein dairy cows. Journal of Dairy Science. 93 (4), pp. 1551–1560.

Hudson, C.D., Bradley, A.J., Breen, J.E. & Green, M.J. (2012) Associations between udder health and reproductive performance in United Kingdom dairy cows. [online]. Journal of dairy science. 95 (7), pp. 3683–3697.

InterHerd (2016) Introducing and using InterHerd on the farm [online]. Available from: https://www.nmr.co.uk/Interherd [Accessed 26 February 2016].

Kemp, M.H., Nolan, a M., Cripps, P.J. & Fitzpatrick, J.L. (2008) Animal-based measurements of the severity of mastitis in dairy cows. The Veterinary Record. 163 (6), pp. 175–179.

Krpálková, L., Cabrera, V.E., Kvapilík, J., Burdych, J. & Crump, P. (2014) Associations between age at first calving, rearing average daily weight gain, herd milk yield and dairy herd production, reproduction, and profitability. [online]. Journal of dairy science. 97 (10), pp. 6573–6582.

Kurjogi, M., Kaliwal, B. & Vanti, G. (2015) Bovine Mastitis Impressions on Reproductive Performance of Cow [online]. International Journal of Livestock Research. 5 (8), pp. 66.

Lavon, Y., Leitner, G., Voet, H. & Wolfenson, D. (2010) Naturally occurring mastitis effects on timing of ovulation, steroid and gonadotrophic hormone concentrations, and follicular and luteal growth in cows. Journal of Dairy Science. 93 (3), pp. 911–921.

Lavon, Y., Ezra, E., Leitner, G. & Wolfenson, D. (2011) Association of conception rate with pattern and level of somatic cell count elevation relative to time of insemination in dairy cows [online]. Journal of Dairy Science. 94 (9), pp. 4538–4545.

Lavon, Y., Kaim, M., Leitner, G., Biran, D., Ezra, E. & Wolfenson, D. (2016) Two approaches to improve fertility of subclinical mastitic dairy cows. [online]. Journal of dairy science. 99 (3), pp. 2268–2275.

Lavon, R. (2017a) National Animal Disease Information Service (NADIS). Fertility in Dairy Herds – Advanced Part 1: What does poor fertility cost? [online]. Available from: http://www.nadis.org.uk/bulletins/fertility-in-dairy-herds-advanced/part-1-what-does-poor-fertility-cost.aspx? [Accessed 08 March 2017].

Lavon, R. (2017b) National Animal Disease Information Service (NADIS). Fertility in Dairy Herds – Advanced Part 5 : The impact of mastitis and lameness on fertility [online]. Available from: http://www.nadis.org.uk/bulletins/fertility-in-dairy-herds-advanced/part-5-the-impact-of-mastitis-and-lameness-on-fertility.aspx  [Accessed 22 January 2017].

Lehmann, J.O., Fadel, J.G., Mogensen, L., Kristensen, T., Gaillard, C. & Kebreab, E. (2016) Effect of calving interval and parity on milk yield per feeding day in Danish commercial dairy herds. [online]. Journal of dairy science. 99 (1), pp. 621–633.

Leroy, J.L.M.R., Rizos, D., Sturmey, R., Bossaert, P., Gutierrez-Adan, A., Van Hoeck, V., Valckx, S. & Bols, P.E.J. (2012) Intrafollicular conditions as a major link between maternal metabolism and oocyte quality: A focus on dairy cow fertility. Reproduction, Fertility and Development. 24 (1), pp. 1–12.

Levison, L.J., Miller-Cushon, E.K., Tucker, A.L., Bergeron, R., Leslie, K.E., Barkema, H.W. and Devries, T.J. (2016) Incidence Rate of Pathogen-specific Clinical Mastitis on Conventional and Organic Canadian Dairy Farms. Journal of Dairy Science [online]. 99 (2), pp. 1341-1350.

Lomander, H., Svensson, C., Hallén-Sandgren, C., Gustafsson, H. & Frössling, J. (2013) Associations between decreased fertility and management factors, claw health, and somatic cell count in Swedish dairy cows. [online]. Journal of dairy science. 96 (10), pp. 6315–6323.

Lunenburg, F.C. and Irby, B.J. (2008) Writing a Successful Thesis Or Dissertation [online]. California: Corwin Press. [Accessed 28 February 2016].

Madouasse, A., Browne, W.J., Huxley, J.N., Toni, F., Bradley, A.J. & Green, M.J. (2012) Risk factors for a high somatic cell count at the first milk recording in a large sample of UK dairy herds [online]. Journal of Dairy Science. 95 (4), pp. 1873–1884.

McCarthy, M.M., Yasui, T., Felippe, M.J.B. & Overton, T.R. (2016) Associations between the degree of early lactation inflammation and performance, metabolism, and immune function in dairy cows. [online]. Journal of dairy science. 99 (1), pp. 680–700.

McDougall, S., Abbeloos, E., Piepers, S., Rao, A.S., Astiz, S., van Werven, T., Statham, J. & Pérez-Villalobos, N. (2016) Addition of meloxicam to the treatment of clinical mastitis improves subsequent reproductive performance [online]. Journal of Dairy Science. 99 (3), pp. 2026–2042.

Metcalfe, L. (2016) Mastitis and the link to infertility. Veterinary Ireland Journal. 6 (2), pp. 95–100.

Miller, R.H., Clay, J.S. & Norman, H.D. (2001) Relationship of Somatic Cell Score with Fertility Measures [online]. J. Dairy Sci. 84 (11), pp. 2543–2548.

National Milk Records (NMR) (2016) Home Page [online]. Available from: https://www.nmr.co.uk/ [Accessed 27 February 2016].

Pantoja, J.C.F., Almeida, A.P., dos Santos, B. & Rossi, R.S. (2016) An investigation of risk factors for two successive cases of clinical mastitis in the same lactation [online]. Livestock Science. 194pp. 10–16.

Pinedo, P.J., Melendez, P., Villagomez-Cortes, J.A. & Risco, C.A. (2009) Effect of high somatic cell counts on reproductive performance of Chilean dairy cattle. [online]. Journal of dairy science. 92 (4), pp. 1575–1580.

Rahman, M.M., Mazzilli, M., Pennarossa, G., Brevini, T.A.L., Zecconi, A. & Gandolfi, F. (2012) Chronic mastitis is associated with altered ovarian follicle development in dairy cattle. [online]. Journal of dairy science. 95 (4), pp. 1885–1893.

Research Methodology (2016) Purposive Sampling. Available from: http://research-methodology.net/sampling/purposive-sampling/ [Accessed 27 February 2016].

Roche, J.F. (2006) The effect of nutritional management of the dairy cow on reproductive efficiency. [online]. Animal reproduction science. 96 (3-4), pp. 282–296.

Rowbotham, R.F. & Ruegg, P.L. (2016) Associations of selected bedding types with incidence rates of subclinical and clinical mastitis in primiparous Holstein dairy cows [online]. Journal of Dairy Science. 99 (6), pp. 4707–4717.

Rowson, A. & Kirk, D. (2015) Dairy Cow Immunity Impacts Reproductive Performance. Available from: http://www.theomnigendifference.com/resource/dairy-cow-immunity-impacts-reproductive-performance-advisory [Accessed 27 February 2016].

Royal College of Veterinary Surgeons (RCVS) (2015) Communication and Consent. Available from: http://www.rcvs.org.uk/advice-and-guidance/code-of-professional-conduct-for-veterinary-surgeons/supporting-guidance/communication-and-consent/ [Accessed 29 February 2016].

Santos, J.E.P., Cerri, R.L.A., Ballou, M.A., Higginbotham, G.E. & Kirk, J.H. (2004) Effect of timing of first clinical mastitis occurrence on lactational and reproductive performance of Holstein dairy cows. [online]. Animal reproduction science. 80 (1-2), pp. 31–45.

Schrick, F.N., Hockett, M.E., Saxton, A.M., Lewis, M.J., Dowlen, H.H. & Oliver, S.P. (2001) Influence of subclinical mastitis during early lactation on reproductive parameters. [online]. Journal of dairy science. 84 (6), pp. 1407–1412.

Sordillo, L.M. & Aitken, S.L. (2009) Impact of oxidative stress on the health and immune function of dairy cattle. [online]. Veterinary immunology and immunopathology. 128 (1-3), pp. 104–109.

Sordillo, L.M., O’Boyle, N., Gandy, J.C., Corl, C.M. & Hamilton, E. (2007) Shifts in thioredoxin reductase activity and oxidant status in mononuclear cells obtained from transition dairy cattle. [online]. Journal of dairy science. 90 (3), pp. 1186–1192.

Statistics Solutions (2013a) Data analysis plan: Independent Sample t-Test. Available from: https://www.statisticssolutions.com/data-analysis-plan-independent-sample-t-test/ [Accessed 27 February 2016].

Statistics Solutions (2013b) Data analysis plan: Mann Whitney U Test. Available from: https://www.statisticssolutions.com/data-analysis-plan-mann-whitney-u-test/ [Accessed 27 February 2016].

Stefanon, B., Sgorlon, S. & Gabai, G. (2005) Usefulness of nutraceutics in controlling oxidative stress in dairy cows around parturition. Veterinary Research Communications. 29 (SUPPL. 2), pp. 387–390.

Stott, A.W. (1994) The Economic Advantage of Longevity in the Dairy Cow. Journal of Agricultural Economics [online]. 45 (1), pp. 113-122.

Tenghe, A.M.M., Bouwman, A.C., Berglund, B., Strandberg, E. & Veerkamp, R.F. (2014) Genetic Parameters for Endocrine Fertility Traits from In-line Milk Progesterone Records in Dairy Cows. Proceedings, 10th World Congress of Genetics Applied to Livestock Production. pp. 151.

Tenghe, A.M.M., Bouwman, A.C., Berglund, B., Strandberg, E., Blom, J.Y. & Veerkamp, R.F. (2015) Estimating genetic parameters for fertility in dairy cows from in-line milk progesterone profiles. [online]. Journal of dairy science. 98 (8), pp. 5763–5773.

Tiezzi, F., Maltecca, C., Cecchinato, A., Penasa, M. & Bittante, G. (2012) Genetic parameters for fertility of dairy heifers and cows at different parities and relationships with production traits in first lactation. [online]. Journal of dairy science. 95 (12), pp. 7355–7362.

Wall, E., Coffey, M.P. & Brotherstone, S. (2007) The relationship between body energy traits and production and fitness traits in first-lactation dairy cows. [online]. Journal of dairy science. 90 (3), pp. 1527–1537.

Walliman, N. (2011) Your Research Project. 3rd ed. London: SAGE.

Walsh, S.W., Williams, E.J. & Evans, A.C.O. (2011) A review of the causes of poor fertility in high milk producing dairy cows. [online]. Animal reproduction science. 123 (3-4), pp. 127–138.

Wathes, D.C., Fenwick, M., Cheng, Z., Bourne, N., Llewellyn, S., Morris, D.G., Kenny, D., Murphy, J. & Fitzpatrick, R. (2007) Influence of negative energy balance on cyclicity and fertility in the high producing dairy cow. [online]. Theriogenology. 68 (1), pp. 232–241.

Wilde, D. (2006) Influence of macro and micro minerals in the peri-parturient period on fertility in dairy cattle. [online]. Animal reproduction science. 96 (3-4), pp. 240–249.