Section 07 – Bovine Functional Traits

Dairy Cattle Health

Technical abstract

Improved health of dairy cattle is of increasing economic importance. Poor health results in greater production costs through higher veterinary bills, additional labour costs, and reduced productivity. Animal welfare is also of increasing interest to both consumers and regulatory agencies because healthy animals are needed to provide high-quality food for human consumption. Furthermore, this is consistent with the European Union animal health strategy that emphasizes disease prevention over treatment. Animal health issues may be addressed either directly, by measuring and selecting against liability to disease, or indirectly by selecting against traits correlated with injury and illness. Direct observations of health and disease events, and their inclusion in recording, evaluation and selection schemes, will maximize the efficiency of genetic selection programs. The Scandinavian countries have been routinely collecting and utilizing those data for years, demonstrating the feasibility of such programs. Experience with direct health data in non-Scandinavian countries is still limited. Due to the complexity of health and diseases, programs may differ between countries. This document presents best-practices with respect to data collection practices, trait definition, and use of health data in genetic evaluation programs and can be extended to its use for other farm management purposes.

Introduction

The improvement of cattle health is of increasing economic importance for several reasons. Impaired health results in increased production costs (veterinary medical care and therapy, additional labour, and reduced performance), while prices for dairy products and meat are decreasing. Consumers also want to see improvements in food safety and better animal welfare. Improvement in the general health of the cattle population is necessary for the production of high-quality food and implies significant progress with regard to animal welfare. Improved welfare also is consistent with the EU animal health strategy, which states that that prevention is better than treatment (European Commission, 2007).

Health issues may be addressed either directly or indirectly. Indirect measures of health and disease have been included in routine performance tests by many countries. However, directly observed measures of health and disease need to be included in recording, evaluation and selection schemes in order to increase the efficiency of genetic improvement programs for animal health.

In the Scandinavian countries, direct health data have been routinely collected and utilized for years, with recording based on veterinary medical diagnoses (Nielsen, 2000; Philipsson & Linde, 2003; Østerås & Sølverød, 2005; Aamand, 2006; Heringstad et al., 2007). In the non-Scandinavian countries experience with direct health data is still limited, but interest in using recorded diagnoses or observations of disease has increased considerably in recent years (Zwald et al., 2006a,b; Neuenschwander et al., 2008; Neuenschwender, 2010; Appuhamy et al., 2009; Egger-Danner et al., 2010, Egger-Danner et al., 2012, Koeck et al., 2012a,b, Neuschwander et al., 2012).

Due to the complex biology of health and disease, guidelines should mainly address general aspects of working with direct health data. Specific issues for the major disease complexes are discussed, but breed- or population-specific focuses may require amendments to these guidelines.

Types and sources of data

Types of data

The collection of direct information on health and disease status of individual animals is preferable to collection of indirect information. However, population-wide collection of reliable health information may be easier to implement for indirect rather than direct measures of health. Analyses of health traits will probably benefit from combined use of direct and indirect health data, but clear distinctions must be drawn between these two types of data:

Direct health information

1. Diagnoses or observations of diseases
2. Clinical signs or findings indicative of diseases

Indirect health information

1. Objectively measurable indicator traits (e.g., somatic cell count, milk urea nitrogen, health biomarkers)
2. Subjectively assessable indicator traits (e.g., body condition score, conformation scores)

Health data may originate from different data sources which differ considerably with respect to information content and specificity. Therefore, the data source must be clearly indicated whenever information on health and disease status is collected and analysed. When data from different sources are combined, the origin of data must be taken into account when defining health traits.

In the following sections, possible sources of health data are discussed, together with information on which types of data may be provided, specific advantages and disadvantages associated with those sources, and issues which need to be addressed when using those sources.

Sources of data

Veterinarians

Content

1. Primarily report direct health data.
2. Provide disease diagnoses (documented reasons for application of pharmaceuticals), possibly supplemented by findings indicative of disease, and/or information on indicator traits.

Advantage

1. Information on a broad spectrum of health traits.
2. Specific veterinary medical diagnoses (high-quality data).
3. Legal obligations of documentation in some countries (possible utilization of already established recording practices).

Disadvantages

1. Only severe cases of disease may be reported (need for veterinary intervention and pharmaceutical therapy).
2. Possible delay in reporting (gap between onset of disease and veterinary visit).
3. Extra time and effort for recording (complete and consistent documentation cannot be taken for granted, recording routine and data flow need to be established).

Producers

Content

1. Primarily direct health data.
2. Disease observations ('diagnoses'), possibly supplemented by findings indicative of disease and/or information on indicator traits.

Advantages

1. Information on a broad spectrum of health traits.
2. Minor cases not requiring veterinary intervention may be included.
3. First-hand information on onset of disease.
4. Possible use of already-established data flow (routine performance testing, reporting of calving, documentation of inseminations).

Disadvantages

1. Risk of false diagnoses and misinterpretation of findings indicative of disease (lack of veterinary medical knowledge).
2. Possible need to confine recording to the most relevant diseases (modest risk of misinterpretation, limited extra time and effort for recording).
3. Extra documentation might be needed.
4. Need for expert support and training (veterinarian) to ensure data quality.
5. Completeness of recording may vary, and may be dependent on work peaks on the farm.

Remarks

1. Data logistics depend on technical equipment on the farm (documentation using herd management software (e.g. including tools to record hoof trimming, diseases, vaccinations,..), handheld for online recording, information transfer through personnel from milk recording agencies.
2. Possible producer-specific documentation focuses must be considered in all stages of analyses (checks for completeness of health / disease incident documentation; see Kelton et al., 1998).
3. Preliminary research suggests that epidemiological measures calculated from producer-recorded data are similar to those reported in the veterinary literature (Cole et al., 2006).

Expert groups (claw trimmer, nutritionist, etc.)

Content

1. Direct and indirect health data with a spectrum of traits according to area of expertise.

Advantages

1. Specific and detailed information on a range of health traits important for the producer (high-quality data),
2. Possible access to screening data (information on the whole herd at a given point in time),
3. Personal interest in documentation (possible utilization of already-established recording practices)

Disadvantages

1. Limited spectrum of traits,
2. Dependence on the level of expert knowledge (certification/licensure of recording persons may be advisable),
3. Extra time and effort for recording (complete and consistent documentation cannot be taken for granted, recording routine and data flow need to be established)
4. Business interests may interfere with objective documentation

Others (laboratories, on-farm technical equipment, etc.)

Content

1. Indirect health data with spectrum of traits according to sampling protocols and testing requests, e.g., microbiological testing, metabolite analyses, hormone tests, virus/bacteria DNA, infrared-based measurements (Soyeurt et al., 2009a,b).

Advantages

1. Specific information on a range of health traits important for the producer (high quality data).
2. Objective measurements.
3. Automated or semi-automated recording systems (possible utilization of already established data logistics).

Disadvantages

1. Interpretation with regard to disease relevance not always clear.
2. Validation and combined use of data may be problematic.

Table 1. Overview of the possible sources of direct and indirect health information.
Source of data	Type of data
	Direct health information	Indirect health information
Veterinarian	Yes	Possibly
Producer	Yes	Possibly
Expert groups	Yes	Possibly
Others	No	Yes

Data security

Data security is a universally important issue when collecting and using field data. However, the central role of dairy cattle health in the context of animal welfare and consumer protection implies that farmers and veterinarians are obligated to maintain high-quality records, emphasizing the particular sensitivity of health data.

The legal framework for use of health data has to be considered according to national requirements and applicable data privacy standards. The owner of the farm on which the data are recorded is the owner of the data and must enter into formal agreements before data are collected, transferred, or analysed. The following issues must be addressed with respect to data exchange agreements:

1. Type of information to be stored in the health database, e.g., inclusion of details on therapy with pharmaceuticals, doses and medication intervals).
2. Institutions authorized to administer the health database, and to analyse the data.
3. Access rights of (original) health data and results from analyses of the data.
4. Ownership of the data and authority to permit transfer and use of those data.

Enrolment forms for recording and use of health data (to be signed by the farmers) have been compiled by the institutions responsible for data storage and analysis or governmental authorities (e.g., Austrian Ministry of Health, 2010).

For any health database it must be guaranteed that:

1. The individual farmers can only access detailed information on their own farm, and for animals only pertaining to their presence on that farm.
2. The right to edit health data are limited.
3. Access to any treatment information is confined to the farmer and the veterinarian responsible for the specific treatment, with the option of anonymizing the veterinary data.

Data security is a necessary precondition for farmers to develop enough trust in the system to provide data. The recording of treatment data is much more sensitive than only diagnoses, and the need to collect and store such data should be very carefully considered.

Documentation

Minimum requirements for documentation:

1. Unique animal ID (ISO number).
2. Place of recording (unique ID of farm/herd).
3. Source of data (veterinarian, producer, expert group, others).
4. Date of health incident.
5. Type of health incident (standardized code for recording).

Useful additional documentation:

1. Individual identification of the recording person.
2. Details on respective health incident (exact location, severity).
3. Type of recording and method of data transfer (software used for on-farm recording, online-transmission).
4. Information on type of diagnosis (first or subsequent).

The systematic use and appropriate interpretation of direct and indirect health data requires that information on health status be combined with other information on the affected animals (basic information such as date of birth, sex, breed, sire and dam, farm/herd; calving dates, and performance records). Therefore, unique identification of the individual animals used for the health data base must be consistent with the animal ID used in existing databases.

Widespread collection of health data may benefit from legal frameworks for documentation and use of diagnostic data. European legislation requests documentation of health incidents which involved application of pharmaceuticals to animals in the food chain. Veterinary medical diagnoses may, therefore, be available through the treatment records kept by veterinarians and farmers. However, it must be ensured that minimum requirements for data recording are followed; in particular, it must be noted that animal identification schemes are not uniform within or across countries. Furthermore, it must be a clear distinction made between prophylactic and therapeutic use of pharmaceuticals, with the former being excluded from disease statistics. Information on prophylaxis measures may be relevant for interpretation of health data (e.g., dry cow therapy), but should not be misinterpreted as indicators of disease. While recording of the use of pharmaceuticals is encouraged it is not uniformly required internationally, and health data should be collected regardless of the availability of treatment information.

Standardization of recording

In order to avoid misinterpretation of health information and facilitate analysis, a unique code should be used for recording each type of health incident. This code must fulfil the following conditions:

1. Clear definitions of the health incidents to be recorded, without opportunities for different interpretations.
2. Includes a broad spectrum of diseases and health incidents, covering all organ systems, and address infectious and non-infectious diseases.
3. Understandable by all parties likely to be involved in data recording.
4. Permit the recording of different levels of detail, ranging from very specific diagnoses of veterinarian compared to very general diagnoses or observations by producers.

Starting from a very detailed code of diagnoses, recording systems may be developed that use only a subset of the more extensive code. However, the identical event identifiers submitted to the health database must always have the same meaning. Therefore, data must be coded using a uniform national, or preferably international, scheme before entering information into the central health database. In the case of electronic recording of health data, it is the responsibility of the software providers to ensure that the standard interface for direct and/or indirect health data is properly implemented in their products. When farmers are permitted to define their own codes the mapping of those custom codes to standard codes is a substantial challenge, and careful consideration should be paid to that problem (see, e.g., Zwald et al., 2004a).

A comprehensive code of diagnoses with about 1,000 individual input options (diagnoses) is provided as an appendix to these guidelines. It is based on the code of diagnoses developed in Germany by the veterinarian Staufenbiel ('zentraler Diagnoseschlüssel') (Annex). The structure of this code is hierarchical, and it may represent a 'gold standard' for the recording of direct health data. It includes very specific diagnoses which may be valuable for making management decisions on farms, as well as broad diagnoses with little specificity for analyses which require information on large numbers of animals (e.g. genetic evaluation). Furthermore, it allows the recording of selected prophylactic and biotechnological measures which may be relevant for interpretation of recorded health data.

In the Scandinavian countries and in Austria codes with 60 to 100 diagnoses are used, allowing documentation of the most important health problems of cattle. Diagnoses are grouped by disease complexes and are used for documentation by treating veterinarians (Osteras et al., 2007; Austrian Ministry of Health, 2010; Osteras, 2012).

For documentation of direct health data by expert groups, special subsets of the comprehensive code may be used. Examples for claw trimmers can be found in the literature (e.g. Capion et al., 2008; Thomsen et al.,2008; Maier, 2009a, b; Buch et al., 2011).

When working with producer-recorded data, a simplified code of diagnoses should be provided which includes only a subset of the extensive code (Neuenschwander et al., 2008; USDA, 2010). Diagnoses included must be clearly defined and observable without veterinary medical expertise. Such a reduced code may, for example, consider mastitis, lameness, cystic ovarian disease, displaced abomasum, ketosis, metritis/uterine disease, milk fever and retained placenta (Neuenschwander et al., 2008). The United States model (USDA, 2010) is event-based, and permits very general reports (e.g., This cow had ketosis on this day."), as well as very specific ones (e.g., "This cow had Staph. aureus mastitis in the right, rear quarter on this day.").

Data quality

General quality checks

Mandatory information will be used for basic plausibility checks. Additional information can be used for more sophisticated and refined validation of health data when those data are available.

1. The recording farm must be registered to record and transmit health data.
2. If information on the person recording the data are provided, that individual must be authorized to submit data for this specific farm.
3. The animal for which health information is submitted must be registered to the respective farm at the time of the reported health incident.
4. The date of the health incident must refer to a living animal (must occur between the birth and culling dates), and may not be in the future.
5. A particular health event can only be recorded once per animal per day.
6. The contents of the transmitted health record must include a valid disease code. In the case of known selective recording of health events (e.g., only claw diseases, only mastitis, no calf diseases), the health record must fit the specified disease category for which health data are supposed to be submitted.
7. For sources of data with limited authorization to submit health data, the health record must fit the specified disease category (e.g., locomotory diseases for claw trimmers, metabolic disorders for nutritionists).

Specific quality checks

In order to produce reliable and meaningful statistics on the health status in the cattle population, recording of health events should be as complete as possible on all farms participating in the health improvement program. Ideally, the intensity of observation and completeness of documentation should be the same for all animals regardless of sex, age, and individual performance. Only then will a complete picture of the overall health status in the population emerge. However, this ideal situation of uniform, complete, and continuous recording may rarely be achieved, so methods must be developed to distinguish between farms with desirably good health status of animals and farms with poor recording practices.

Countries with on-going programs of recording and evaluation of health data require a minimum number of diagnoses per cow and year (e.g., Denmark: 0.3 diagnoses; Austria: 0.1 first diagnoses); continuity of data registration needs to be considered. Farms that fail to achieve these values are automatically excluded from further analyses until their recording has improved. However, herd sizes need to be considered when defining minimum reporting frequencies to avoid possible biases in favour of larger or smaller farms. Any fixed procedure involves the risk of excluding farms with extraordinary good herd health, but to avoid biased statistics there seems to be no alternative to criteria for inclusion, and setting minimum lower limits for reporting. Different criteria will be needed for diseases that occur with low frequency versus those with high frequency, particularly when the cost of a rare illness is very high compared to a common one.

Because recording practices and completeness on farms may not be uniform across disease categories (e.g., no documentation of claw diseases by the producer), data should be periodically checked by disease category to determine what data should be included. Use of the most-thoroughly documented group of health traits to make decisions about inclusion or exclusion of a specific farm may lead to considerable misinterpretation of health data.

There are limited options to routinely check health data for consistency on a per animal basis. Some diagnoses may only be possible in animals of specific sex, age, or physiological state. Examples can be found in the literature (Kelton et al., 1998; Austrian Ministry of Health, 2010). Criteria for plausibility checks will be discussed in the trait-specific part of these guidelines.

Keys to long-term success

Regardless of the sources of health data included, long-term acceptance of the health recording system and success of the health improvement program will rely on the sustained motivation of all parties involved. To achieve this, frequent, honest, and open communications between the institutions responsible for storage and analysis of health data and people in the field is necessary. Producers, veterinarians and experts will only adopt and endorse new approaches and technologies when convinced that they will have positive impacts on their own businesses. Mutual benefits from information exchange and favourable cost-benefit ratios need to be communicated clearly.

When a key objective of data collection is the development a of genetic improvement program for health, producers must be presented with a reasonable timeline for events. When working with low-heritability traits that are differentially recorded much more data will be necessary for the calculation of accurate breeding values than for typical production traits. It is very important that everyone is aware of the need to accumulate a sufficient dataset to support those calculations, which may take several years. This will help ensure that participants remain motivated, rather than become discouraged when new products are not immediately provided. The development of intermediate products, such as reports of national incidence rates and changes over time, could provide tools useful to producers between the start of data collection and the introduction of genetic evaluations.

Health reports, produced for each of the participating farms and distributed to authorized persons, will help to provide early rewards to those participating in health data recording. To assist with management decisions on individual farms, health reports should contain within-herd statistics (health status of all animals on the farm and stratified by age and/or performance group), as well as across-herd statistics based on regional farms of similar size and structure. Possible access to the health reports by authorized veterinarians or experts will help to maximize the benefits of data recording by ensuring that competent help with data interpretation is provided.

Trait definition

Most health incidents in dairy herds fit into a few major disease complexes (e.g., Heringstad et al., 2007; Koeck et al., 2010a,b, Wolff, 2012), each of which implies that specific issues be addressed when working with related health information. In particular, variation exists with regard to options for plausibility checks of incoming data including eligible animal group, time frame of diagnoses, and possibility of repeated diagnoses.

Distinctions must be drawn between diseases which may only occur once in an animal's lifetime (maximum of one record per animal) or once in a predefined time period (e.g., maximum of one record per lactation) on the one hand and disease which may occur repeatedly throughout the life-cycle. Assumptions regarding disease intervals, i.e., the minimum time period after which the same health incident may be considered as a recurrent case rather than an indicator of prolonged disease, need to be considered when comparing figures of disease prevalences and distributions. Furthermore, it must be decided if only first diagnoses or first and recurrent diagnoses are included in lifetime and/or lactation statistics. Differences will have considerable impact on comparability of results from health data analyses.

Udder health

Mastitis is the qualitatively and quantitatively most important udder health trait in dairy cattle (e.g. Amand et al., 2006; Heringstad et al., 2007, Wolff, 2012). The term mastitis refers to any inflammation of the mammary gland, i.e., to both subclinical and clinical mastitis. However, when collecting direct health data one should clearly distinguish between clinical and subclinical cases of mastitis. Subclinical mastitis is characterized by an increased number of somatic cells in the milk without accompanying signs of disease, and somatic cell count (SCC) has been included in routine performance testing by many countries, representing an indicator trait for udder health (indirect health data).

Cows affected by clinical mastitis show signs of disease of different severity, with local findings at the udder and/or perceivable changes of milk secretion possibly being accompanied by poor general condition. Recording of clinical mastitis (direct health data) will usually require specific monitoring, because reliable methods for automated recording have not yet been developed. Documentation should not be confined to cows in first lactation but include cows of second and subsequent lactations. Optional information on cases that may be documented and used for specific analyses includes

1. Type of clinical disease (acute, chronic).
2. Type of secretion changes (catarrhal, hemorrhagic, purulent, necrotizing).
3. Evidence of pathogens which may be responsible for the inflammation.
4. Location of disease (affected quarter or quarters).
5. Presence of general signs of disease.

Appropriate analyses of information on clinical mastitis require consideration of the time of onset or first diagnosis of disease (days in milk). Clinical mastitis developing early and late in lactation may be considered as separate traits.

Table 2. Udder health trait considerations.
Parameters to check incoming health data	Recommended inclusion criterion	Remarks
Eligible animal group	Heifers and cows (obligatory: sex = female)	Exceptions possible (where appropriate, diagnoses in younger females may be considered separately)
Time frame of diagnoses	10 days before calving to 305 days in milk	Exceptions possible (where appropriate, diagnoses beyond -10 to 305 days in milk may be considered separately; shorter reference periods may be defined)
Repeated diagnoses	Possible per animal and lactation (possibility of multiple diagnoses per lactation)	Definition of minimum time period after which same diagnosis may be considered as recurrent case rather than prolonged disease

Reproductive disorders

Reproductive disorders represents a set of diseases which have the same effect (reduced fertility or reproductive performance), but differ in pathogenesis, course of disease, organs involved, possible therapeutic approaches, etc. To allow the use of collected health data for improvement of management on the herd and/or animal level, recording of reproductive disorders should be as specific as possible.

Grouping of health incidents belonging to this disease complex may be based on the time of occurrence and/or organ involved. Within each of these disease groups, specific plausibility checks must be applied considering, for example, time frame of diagnoses and possibility of multiple diagnoses per lactation (recurrence). Fixed dates to be considered include the length of the bovine ovarian cycle (21 days) and the physiological recovery time of reproductive organs after calving (total length of puerperium: 42 days).

Gestation disorders and peri-partum disorders

Examples:

1. Embryonic death, abortion.
2. Bradytocia (uterine inertia), perineal rupture.
3. Retained placenta, puerperal disease, ... .

Irregular oestrus cycle and sterility

Examples:

1. Cystic ovaries, silent heat.
2. Metritis (uterine infection), ...

Table 3. Reproduction trait considerations.
Parameters to check incoming health data	Recommended inclusion criterion	Remarks
Eligible animal group	Heifers and cows	Minimum age should be consistent with performance data analyses
Time frame of diagnoses	Depending on type of disease	Fixed patho-physiological time frames should be considered (e.g. Duration of puerperium, cycle length)
Repeated diagnoses	Depending on type of disease: maximum of one diagnosis per animal (e.g. Genital malformation), maximum of one diagnosis per lactation (e.g. Retained placenta) or possibility of multiple diagnoses per lactation (e.g. Cystic ovaries)	Definition of minimum time period after which same diagnosis may be considered as recurrent case rather than prolonged disease (e.g. 21 days for cystic ovaries because of direct relation to the ovary cycle)

Locomotory diseases

Recording of locomotory diseases may be performed on different level of specificity. Minimum requirement for recording may be documentation of locomotion score (lameness score) without details on the exact diagnoses. However, use of some general trait lameness will be of little value for deriving management measures.

Because of the heterogeneous pathogenesis of locomotory disease, recording of diagnoses should be as specific as possible.

Rough distinction may be drawn between claw diseases and other locomotory diseases, but results of health data analyses will be more meaningful when more detailed information is available. Therefore, recording of specific diagnoses is strongly recommended. Determination of the cause of disease and options for treatment and prevention will benefit from detailed documentation of affected structure(s), exact location, type and extent of visible changes. Such details may be primarily available through veterinarians (more severe cases of locomotory diseases) and claw trimmers (screening data and less severe cases of locomotory diseases). However, experienced farmers may also provide valuable information on health of limbs and claws.

Care must be taken when referring to terms from farmers' jargon, because definitions are often rather vague and diagnoses of diseases may be inconsistent. Documentation practices differ based on training and professional standards, e.g., claw trimmers and veterinarians, as well as nationally and internationally, and different schemes have been implemented in various on-farm data collection systems. To ensure uniform central storage and analysis of data, tools for mapping data to a consistent set of keys must to be developed, and unambiguous technical terms (veterinary medical diagnoses) should be used in documentation whenever possible.

Claw diseases

Examples:

1. Laminitis complex (white line disease, sole haemorrhage, sole duplication, wall lesions, wall buckling, wall concavity).
2. Sole ulcer (sole ulcer at typical site = rusterholz's disease, sole ulcer at atypical site, sole ulcer at tip of claw).
3. Digital dermatitis (mortellaro's disease = hairy foot warts = heel warts = papillomatous digital dermatitis).
4. Heel horn erosion (erosio ungulae = slurry heel).
5. Interdigital dermatitis, interdigital phlegmon (interdigital necrobacillosis = foot rot), interdigital hyperplasia (interdigital fibroma = limax = tylom).
6. Circumscribed aseptic pododermatitis, septic pododermatitis.
7. Horn cleft, ... .

The expertise of professional claw trimmers should be used when recording claw diseases. In herds with regular claw trimming (by the producer or a professional claw trimmer) accessibility of screening data, i.e., information on claw status of all animals regardless of regular or irregular locomotion (lameness) or absence or presence of other signs of disease (e.g., swelling, heat), will significantly increase the total amount of available direct health data, enhancing the reliability of analyses of those traits. Incidences of claw diseases may be biased if they are collected on based on examinations, or treatment, of lame animals.

Other information about claws which may be relevant to interpret overall claw health status of the individual animal, such as claw angles, claw shape or horn hardness, also may be documented. Some aspects of claw conformation may already be assessed in the course of conformation evaluation. Analyses of claw disease may benefit from inclusion of such indirect health data.

Foot and claw disorders - Harmonized description

Refer to ICAR Claw Atlas for detailed descriptions. The Claw Atlas is available on the ICAR website:

1. As a .pdf file in English here.
2. Translations in twenty other languages here.
3. As a poster in English here.
4. As a poster in German here.

Other locomotory diseases

Examples:

1. Lameness (lameness score).
2. Joint diseases (arthritis, arthrosis, luxation).
3. Disease of muscles and tendons (myositis, tendinitis, tendovaginitis).
4. Neural diseases (neuritis, paralysis), ... .

Low frequencies of distinct diagnoses will probably interfere with analyses of other locomotory diseases involving a high level of specificity. Nevertheless, the improvement of locomotory health on the animal and/or farm level will require detailed disease information indicating causative factors which need to be eliminated. The use of data from veterinarians may allow deeper insight into improvement options. Despite a substantial loss of precision, simple recording of lame animals by the producers may be the easiest system to implement on a routine basis. Rapidly increasing amounts of data may then argue for including lameness or lameness score in advanced analyses.

Table 4. Considerations for locomotion traits.
Parameters to check incoming health data	Recommended inclusion criterion	Remarks
Eligible animal group	No sex or age restriction	Sex- and/or age-dependent differences in intensity of systematic recording should be considered
Time frame of diagnoses	No time restriction	-
Repeated diagnoses	Possibility of multiple diagnoses per animal independent of lactation	Definition of minimum time period after which same diagnosis may be considered as recurrent case rather than prolonged disease (no clear physiological reference period)

Metabolic and digestive disorders

The range of bovine metabolic and digestive disorders is generally rather broad, including diverse infectious and non-infectious disease. Although each of these diseases may have significant impacts on individual animal performance and welfare, few of them are of quantitative importance. Major diseases can broadly be characterized as disturbances of mineral or carbohydrate metabolism, which are caused in the lactating cow primarily by imbalances between dietary requirements and intakes.

Metabolic disorders

Examples:

1. Milk fever (i.e., hypocalcaemia, periparturient paresis), tetany (i.e., hypomagnesiaemia).
2. Ketosis (i.e., acetonaemia), ...

Digestive disorders

Examples:

1. Ruminal acidosis, ruminal alkalosis, ruminal tympany.
2. Abomasal tympany, abomasal ulcer, abomasal displacement (left displacement of the abomasum, right displacement of the abomasum).
3. Enteritis (catarrhous enteritis, hemorrhagic enteritis, pseudomembranous enteritis, necrotisizing enteritis).

Table 5. Considerations for metabolic traits.
Parameters to check incoming health data	Recommended inclusion criterion	Remarks
Eligible animal group	Depending on type of disease: no sex or age restriction or restriction to adult females (calving-related disorders)	Sex- and/or age-dependent differences in intensity of systematic recording should be considered
Time frame of diagnoses	Depending on type of disease: no time restriction or restriction to (extended) peripartum period	Possible definition of risk periods (where appropriate, diagnoses beyond may be considered separately)
Repeated diagnoses	Depending on type of disease: maximum of one diagnosis per lactation (e.g. Milk fever), possibility of multiple diagnoses per lactation and independent of lactation (e.g. Enteritis)	Definition of minimum time period after which same diagnosis may be considered as recurrent case rather than prolonged disease (no clear physiological reference period)

Others diseases

Diseases affecting other organ systems may occur infrequently. However, recording of those diseases is strongly recommended to get complete information on the health status of individual animals. Interpretation of the effect of certain diseases on overall health and performance will only be possible, if the whole spectrum of health problems is included in the recording program.

Examples:

1. Diseases of the urinary tract (hemoglobinuria, hematuria, renal failure, pyelonephritis, urolithiasis, ...).
2. Respiratory disease (tracheitis, bronchitis, bronchopneumonia, ...).
3. Skin diseases (parakeratosis, furunculosis, ...).
4. Cardiovascular disease (cardiac insufficiency, endocarditis, myocarditis, thrombophlebitis, ...).

Table 6. Considerations for other disease traits.
Parameters to check incoming health data	Recommended inclusion criterion	Remarks
Eligible animal group	No sex or age restriction	Sex- and/or age-dependent differences in intensity of systematic recording should be considered
Time frame diagnoses	No time restriction	-
Repeated diagnoses	Possibility of multiple diagnoses per animal independent of lactation (e.g. Tracheitis)	Definition of minimum time period after which same diagnosis may be considered as recurrent case rather than prolonged disease (no clear physiological reference period)

Calf diseases

Impaired calf health may have considerable impact on dairy cattle productivity. Optimization of raising conditions will not only have short-term positive effects with lower frequencies of diseased calves, but also may result in better condition of replacement heifers and cows. However, management practices with regard to the male and female calves usually differ between farms and need to be considered when analysing health data. On most dairy farms the incentive to record health events systematically and completely will be much higher for female than for male calves. Therefore, it may be necessary to generally exclude the male calves from prevalence statistics and further analyses.

Examples:

1. Omphalitis (omphalophlebitis, omphaloarteriitis, omphalourachitis).
2. Umbilical hernia.
3. Congenital heart defect (persitent ductus arteriosus botalli, patent foramen ovale, ...).
4. Neonatal asphyxia.
5. Enzootic pneumonia of calves.
6. Disturbance of oesophageal groove reflex.
7. Calf diarrhea, ... .

Table 7. Considerations for calf health traits.
Parameters to check incoming health data	Recommended inclusion criterion	Remarks
Eligible animal group	Calves	Sex-dependent differences in intensity of systematic recording should be considered
Time frame of diagnoses	Depending on type of disease (e.g. Neonatal period, suckling period)	Possible definition of risk periods (where appropriate, diagnoses beyond may be considered separately)
Repeated diagnoses	Depending on type of disease: maximum of one diagnosis per animal (e.g. Neonatal asphyxia) or possibility of multiple diagnoses per animal (e.g. Diarrhea)	Definition of minimum time period after which same diagnosis may be considered as recurrent case (no clear physiological reference period)

Use of data

Rapid feedback is essential for farmers and veterinarians to encourage the development of an efficient health monitoring system. Information can be provided soon after the data collection begins in the form individual farm statistics. If those results include metrics of data quality, then producers may have an incentive to quickly improve their data collection practices. Regional or national statistics should be provided as soon as possible as well. Early detection and prevention of health problems is an important step towards increasing economic efficiency and sustainable cattle breeding. Accordingly, health reports are a valuable tool to keep farmers and veterinarians motivated and ensure continuity of recording.

Direct and indirect observations need to be combined for adequate and detailed evaluations of health status. Reference should be made to key figures such as calving interval, pregnancy rate after first insemination, and non-return rate. A short time interval between calving and many diagnoses of fertility disorders is due to the high levels of physiological stress in the peripartum period, and also may indicate that a farmer is actively working to improve fertility in their herd. A low rate of reported mastitis diagnoses is not necessarily proof of good udder health, but may reflect poor monitoring and documentation.

In addition to recording disease events, on-farm system also can be used to record useful management information, such as body condition scores, locomotion scores, and milking speed (USDA, 2010). Individual animal statuses (clear/possibly infected/infected) for infectious diseases such as paratuberculosis (Johne's disease) and leukosis also may be tracked. Such data may be useful for monitoring animal welfare on individual farms.

Improvement of management (individual farm level)

Farmers

Optimised herd management is important for economically successful farming. Timely availability of direct health information is valuable and supplements routine performance recording for early detection of problems in a herd. Therefore, health data statistics should be added to existing farm reports provided by milk recording organisations. Examples from Austria are found in Egger-Danner et al. (2007) and Austrian Ministry of Health (2010).

Veterinarians

The EU-Animal Health Strategy (2007-2013), 'Prevention is better than cure', underscores the increased importance placed on preventive rather than curative measures. This implicates a change of the focus of the veterinary work from therapy towards herd health management.

With the consent of the farmer, the veterinarian can access all available information about herd health. The most important information should be provided to the farmer and veterinarian in the same way to facilitate discussion at eye-level. However, veterinarians may be interested in additional details requiring expert knowledge for appropriate interpretation. Health recording and evaluation programs should account for the need of users to view different levels of detail.

The overall health status of the herd will benefit from the frequent exchange of information between farmers and veterinarians and their close cooperation. Incorrect interpretation or poor documentation of health events by the farmer may be recognised by attending veterinarians, who can help correct those errors. Herd health reports will provide a valuable and powerful tool to jointly define goals and strategies for the future, and to measure the success of previous actions.

Immediate reactions

It is important that farmers and veterinarians have quick access to herd health data. Only then can acute health problems, which may be related to management, be detected and addressed promptly. An Internet-based tool may be very helpful for timely recording and access to data.

Long term adjustments

Less-detailed reports summarizing data over longer time periods (e.g., one year) may be compiled to provide an overview of the general health status of the herd. Such summary reports will facilitate monitoring of developments within farm over time, as well as comparisons among farms on district and/or province level. References for management decisions which account for the regional differences should be made available (Austrian Ministry of Health, 2010; Schwarzenbacher et al., 2010). Definitions of benchmarks are valuable, and for improvement of the general health status it is important to place target oriented measures.

Monitoring of the health status (population level)

Ministries and other organisations involved in animal health issues are very interested in monitoring the health status of the cattle population. Consumers also are increasingly concerned about aspects of food safety and animal welfare. Regardless of which sources of health information are used, national monitoring programs may be developed to meet the demands of authorities, consumers and producers. The latter may particularly benefit from increased consumer confidence in safe and responsible food production.

It is recommended that all information, including both direct and indirect observations, be taken into account when monitoring activity and preparing reports. For example, information on clinical mastitis should be combined with somatic cell count or laboratory results.

It is extremely important to clearly define the respective reference groups for all analyses. Otherwise, regional differences in data recording, influences of herd structure and variation in trait definition may lead to misinterpretation of results. To ensure the reliability of health statistics it may be necessary to define inclusion criteria, for example a minimum number of observations (health records) per herd over a set time period. Such lower limits must account for the overall set-up of the health monitoring program (e.g., size of participating farms, voluntary or obligatory participation in health recording).

Key measures that may be used for comparisons among populations are incidence and prevalence. In any publication it must be clear which of the two rates is reported, and also how the rates have been calculated.

Incidence

Number of new cases of the disease or health incident in a given population occurring in a specified time period which may be fixed and identical for all individuals of the population (e.g., one year or one month) or relate to the individual age or production period (e.g., lactation = day 1 to day 305 in milk).

For example, the lactation incidence rate (LIR) of clinical mastitis (CM) can be calculated as the number of new CM cases observed between day 1 and day 305 in milk.

Equation 1. For computation of lactation incidence rate for clinical mastitis.

Another, and arguably a more accurate incidence rate could be calculated, by taking into account the total number of days at risk in the denominator population. This allows for the fact that some animals will leave the herd prematurely (or may join the herd late) and will therefore not contribute a 'full unit' of time of risk to the calculation.

Equation 2. For computation of lactation incidence rate for clinical mastitis taking account of day as risk.

Where N(days) is the total number of days that individual cows were present in the herd when between 1 and 305 days in milk; ie a cow present throughout lactation will add 305 days, a cow culled on day 30 of lactation will only contribute 30 days etc., … (divided by 305 as that is the period of analysis).

Prevalence

Number of individuals affected by the disease or health incident in a given population at a particular point in time or in a specified time period.

Equation 3. For computation of prevalence of clinical mastitis.

Genetic evaluation (population level)

Traits for which breeding values are predicted differ between countries and dairy breeds. However, total merit indices have generally shifted towards functional traits over the last several years (Ducrocq, 2010). Currently, most countries use indirect health data like somatic cell counts or non-return rates for genetic evaluation to improve health and fertility in the dairy population. Direct health information may be used in the future, and already has been included in genetic evaluations for several years in the Scandinavian countries (Heringstad et al., 2007; Østeras et al., 2007; Johansson et al., 2006; Johansson et al., 2008; Interbull, 2010; Negussie et al., 2010).

Trait definitions for genetic analyses must account for frequencies of health incidents, with low incidence rates requiring more records for reliable estimation of genetic parameters and prediction of breeding values. Broader and less-specific definitions of health traits may mitigate this problem, with a possible loss of selection intensity. However, obligatory plausibility checks of data must be performed as specifically as possible, and any combination of traits at a later stage must account for the pathophysiology underlying the respective health traits. Examples of trait definitions found in the literature are given together with the reported frequencies in Table 8.

Many studies have shown that breeding measures based on direct health information can be successful (e.g., Amand, 2006, Zwald et al., 2006a,b; Heringstad et al., 2007). When using indirect health data alone or in combination with direct health data it must be remembered that the information provided by the two types of traits is not identical. For example, the genetic correlations among clinical mastitis and somatic cell count are in the range of 0.6 to 0.7 depending on the definition of the indirect measure of mastitis (e.g., Koeck et al., 2010b). Correlation estimates are lower for fertility traits, with moderately negative genetic correlation of -0.4 between early reproduction disorders and 56-day non-return-rate (Koeck et al., 2010a).

Heritability estimates of direct health traits range from 0.01 to 0.20 and are higher when only first rather than all lactation records are used (Zwald et al., 2004). Results from Fleckvieh and Norwegian Red indicate that heritabilities of metabolic diseases may be higher than heritabilities of udder, locomotory, and reproductive diseases (Zwald et al., 2004; Heringstad et al., 2005). When comparing genetic parameter estimates, methodological differences such as the use of linear versus threshold models need to be considered.

Existing genetic variation among sires with respect to functional traits can be used to select for improved health and longevity. Experience from the Scandinavian countries shows that genetic evaluation for direct health traits can be successfully implemented. For several disease complexes it may be advantageous to combine direct and indirect health data (e.g. Johansson et al., 2006, Johanssen et al., 2008, Negussie et al., 2010, Pritchard et al., 2011 and Urioste et al., 2011; Koeck et al., 2012a,b).

Further information on already-established genetic evaluations for functional traits including considered direct and indirect health information can be found on the Interbull website (http://www.interbull.org/ib/geforms).

Examples of national genetic evaluations (2010)

 

Table 8. Lactation incidence rates (LIR), i.e. proportions of cows with at least one diagnosis of the respective disease within the specified time period.
Breed trait	Time period (parities considered)	LIR (%)	Reference
Danish Red
Udder diseases	-10 to 100 days in milk (1st lactation)	22	Nielsen et al., 2000
Reproductive disturbances		12
Digestive and metabolic diseases		3
Feet and legs disorders		6
Danish Holstein
Udder diseases	-10 to 100 days in milk (1st lactation)	21	Nielsen et al., 2000
Reproductive disturbances		10
Digestive and metabolic diseases		3
Feet and legs disorders		6
Danish Jersey
Udder diseases	-10 to 100 days in milk (1st lactation)	24	Nielsen et al., 2000
Reproductive disturbances		3
Digestive and metabolic diseases		2
Feet and legs disorders		4
Norwegian Red
Clinical mastitis	-15 to 120 days in milk (1st, 2nd, 3rd lactation)	15.8 19.8 24.2	Heringstad et al., 2005
Milk fever	-15 to 30 days in milk (1st, 2nd, 3rd lactation)	0.1 1.9 7.9
Ketosis	-15 to 120 days in milk (1st, 2nd, 3rd lactation)	7.5 13.0 17.2
Retained placenta	0 to 5 days in milk (1st, 2nd, 3rd lactation)	2.6 3.4 4.3
Swedish Holstein
Clinical mastitis	-10 to 150 days in milk (1st, 2nd, 3rd lactation)	10.4 12.1 14.9	Carlén et al., 2004
Finnish Ayrshire
Clinical mastitis	-7 to 150 days in milk (1st, 2nd, 3rd lactation)	9.0 10.6 13.5	Negussie et al., 2006
Fleckvieh (Simmental)
Clinical mastitis	-10 to 150 days in milk	9.6	Koeck et al., 2010a
Early reproductive disorders	0 to 30 days in milk	7.2	Koeck et al., 2010a
Late reproductive disorders	31 to 150 days in milk	8.2	Koeck et al., 2010a
Brown Swiss
Clinical mastitis	-10 to 150 days in milk	8.4	Koeck et al., 2010b

Disease Codes

A full list of disease codes is available:

1. On the ICAR website here - https://www.icar.org/index.php/publications-technical-materials/amendments-recording-guidelines/diseases-codes-for-cows/ and,
2. Can be downloaded as an .xlsx file here - https://www.icar.org/wp-content/uploads/2020/01/ICAR-Claw-Health-Key-coding-20180921.xls

Acknowledgments

This document is the result the ICAR working group on functional traits. The members of this working group at the time of the compilation of this Section were:

1. Lucy Andrews, Holstein UK, Scotsbridge House Rickmansworth, Herts, WD3 3BB United Kingdom; lucyandrews@holstein-uk.org
2. Andrew John Bradley, Quality Milk Management Services, United Kingdom; andrew.bradley@qmms.co.uk
3. John B. Cole, Animal Improvement Programs Laboratory, USA; John.Cole@ARS.USDA.GOV
4. Christa Egger-Danner, ZuchtData EDV-Dienstleistungen GmbH, Austria; egger-danner@zuchtdata.at (Chairperson since 2011)
5. Nicholas Gengler, Gembloux Agricultural University, Belgium; gengler.n@fsagx.ac.be
6. Bjorg Heringstad, Department of Animal and Aquacultural Sciences / Geno , Norwegian University of Life Sciences, Norway; bjorhe@umb.no
7. Jennie Pryce, Victorian Departement of Primary Industries, Australia; jennie.pryce@dpi.vic.gov.au
8. Katharina Stock, VIT, Germany; Friederike.Katharina.Stock@vit.de
9. Erling Strandberg, Sweden (member and chairperson till 2011); Erling.Strandberg@slu.se

Frank Armitage, United Kingdom; Georgios Banos, Faculty of Veterinary Medicine, Greece; Ulf Emanuelson, Swedish University of Agricultural Science, Sweden; Ole Klejs Hansen, Knowledge Centre for Agriculture, Denmark and Filippo Miglior, Canadian Dairy Network, Canada and is thanked for their support and contribution. Rudolf Staufenbiel, FU Berlin, and co-workers is thanked for their contributions to standardization of health data recording.

Female Fertility in Dairy Cattle

Technical abstract

These guidelines are intended to provide people involved in keeping and breeding of dairy cattle with recommendations for recording, management and evaluation of female fertility. Aspects of bull fertility are covered by another set of ICAR guidelines (Section 6), compiled by the ICAR working group for Artificial Insemination. The guidelines described here support establishing good practices for recording, data validation, genetic evaluation and management aspects of female fertility.

To establish a recording scheme for female fertility the following data are desirable:

1. Calving dates.
2. All artificial insemination dates including natural mating dates where possible.
3. Information on fertility disorders.
4. Pregnancy test results.
5. Culling data.
6. Body condition score.
7. Hormone assays.

Other novel predictors of fertility, such as activity based information (pedometer), are also growing in popularity.

This document includes a list of parameters for female fertility and information on recording and validating these data.

Introduction

In broad terms, "fertility" is defined as the ability to produce offspring. In the dairy industry, female fertility refers to the ability of a cow to conceive and maintain pregnancy within a specific time period; where the preferred time period is determined by the particular production system in use. The relevance of certain fertility parameters may therefore differ between production systems, and evaluations of female fertility data have to account for these differences.

There are currently significant challenges to achieving pregnancy in high yielding dairy cows. Accordingly, female fertility has received substantial attention from scientists, veterinarians, farm advisors and farmers. Culling rates due to infertility are much higher than two or three decades ago, and conception rates and calving intervals have also deteriorated. There is no doubt that selection for high yields, while placing insufficient or no emphasis on fertility, has played a role in declining rates of female fertility worldwide, because genetic correlations between production and fertility are unfavourable (e.g. Pryce and Veerkamp 1999; Sun et al., 2010). Most breeding programs have attempted to reverse this situation by estimating breeding values for fertility and including them with appropriate weightings in a multi-trait selection index for the overall breeding objective of dairy cattle.

One of the most important ways that fertility can be improved, through both management strategies and getting better breeding values is by collecting high quality fertility phenotypes. Female fertility is a complex trait with a low heritability, because it is a combination of several traits which may be heterogeneous in their genetic background. For example, it is desirable to have a cow that returns to cyclicity soon after calving, shows strong signs of oestrus, has a high probability of becoming pregnant when inseminated, has no fertility disorders and the ability to keep the embryo/foetus for the entire gestation period. For heifers, the same characteristics except the first one apply. Multiple physiological functions are involved including hormone systems, defense mechanisms and metabolism, so a larger number of parameters may reflect fertility function or dysfunction. However, in initiating a data recording scheme for female fertility it is often not practical (although desirable) to encompass all aspects of good fertility.

The obstacles that exist in adequate recording of fertility measures include: data capture i.e. handwritten notebooks versus computerized data recording and how these data link to a central database used to store data from multiple herds. Although many countries already have adequate fertility recording systems in place, the quality of data captured may still vary by herd. Many farmers are already motivated to improve fertility (as there is global awareness of the decline in dairy cow fertility over recent years). However, what is not always clearly understood is the importance of different sources of fertility data in providing tools that can be used to improve fertility performance.

The principles and type of data that should be recorded are the same regardless of the production system. However, the way in which the data are used i.e. the measures of fertility may vary according to the type of production system. For this reason, we have made a distinction between seasonal and non-seasonal herds:

In seasonal systems cows calve (typically) in the spring, so that peak milk production matches peak grass growth. An alternative is autumn calving herds that use feed conserved from pasture grown in the summer months. True seasonal systems have all cows calving as a tight time frame, i.e. within 8 weeks of the planned start of calvings.

In year-round-systems heifers calve for the first time (predominantly) at a certain age e.g. close to two years of age regardless of the month of year and calvings occur all through the year, so that the calving pattern appears to be reasonably flat.

1.3         Types and sources of data

1.3.1       Types of data

1.3.1.1      Calving dates

Calving dates can be used to calculate the interval between consecutive calvings and to confirm previously predicted pregnancies / conceptions.

To consider: In order to handle bias from culling it is useful to also record culling of cows and the culling reasons.

1.3.1.2      Insemination data

Data on inseminations can be used either alone or in combination with other data e.g. calving dates to define interval traits. Where the measure is initiated by a calving date, it can only be calculated for cows.

Insemination (and calving) dates can be used to calculate the following traits, those that can be measured for cows and/or heifers are indicated in brackets:

a.      Interval from calving to first insemination (cows).

b.      Interval from planned start of mating to first insemination (cows and heifers).

c.      Non-return rate (to first insemination or within a defined time period) (cows and heifers).

d.      Conception rate (to any insemination).

e.      Calving rate within a time period (an individual's phenotype is 0/1) (cows and heifers).

f.       Number of inseminations per lactation or insemination period (cows and heifers).

g.      Number of inseminations per calving or pregnancy.

h.      Interval from first to last insemination (cows and heifers).

i.        Interval between inseminations (cows and heifers).

j.        Interval from calving to last insemination (cows).

There is no best set of traits for evaluation of female fertility, but it is recommended to consider traits which reflect more than one aspect of fertility, e.g. interval from calving to first insemination or interval from calving to first oestrus (return to cyclicity) and non-return rate (probability of conception). For seasonal calving systems, submission rate and calving rate could be alternatives, refer to Table 9. However, calving interval (the interval between two calvings) requires the least data, only calving dates, and is often used as a first step to genetic evaluations for fertility in the absence of insemination or other fertility data. It has to be used with care as highlighted above.

1.3.1.3      Fertility disorders

These data are either diagnoses related to treatments by veterinarians or observations from farmers. Details can be found above in 1.9.1 above.

1.3.1.4      Milk production and composition data

Milk yield is correlated to fertility, and could be used as a predictor (for example in a multi-trait analysis of fertility). However, care should be taken, as the heritability of milk yield is high compared to fertility, the contribution of milk yield to the fertility breeding value could be considerable, making it difficult to identify bulls that are superior for both fertility and milk production. Results from selection based on Total Merit Indices show that it is possible to stabilize fertility if a certain weight is put on fertility.

Recent research confirmed genetic links between fertility and milk composition. In particular, changes of milk fatty acid profiles were identified (Bastin et al., 2011) as useful predictors.

1.3.1.5      Results of pregnancy tests and further hormone assays

Pregnancy status can be determined by veterinary diagnosis, such as uterine palpation or ultrasound or by using information from hormones or circulating peptides associated with pregnancy. The timing of this data is important and should generally be done in consultation with veterinary practitioners. Other hormones, such as progesterone can be used to to determine the post-partum onset of cyclic activity and calculate e.g. interval from calving to first luteal activity (CLA) or other similar traits. The advantage of this trait, is that compared with the interval from calving to first insemination, it is not influenced by the farmer's decision of when to start inseminations. However, it may be costly.

1.3.1.6      Heat strength

Physical activity increases during oestrus, in addition there are other behavioural changes, such as standing heat and mounting behaviour. These signs are used to detect oestrus and can be used to calculate traits such as interval between calving and resumption of oestrus. Tail paint (on the tail head) or colour ampoules attached to the tail head are used in some countries to aid oestrus detection. For larger herds, tail painting is used as a tool to aid insemination rather than resumption of cyclicity, however, on many farms, the decision to inseminate is often made after a defined period between calving and first insemination. In many practical situations it may be unrealistic to expect oestrus (without insemination) data to be collected, however recently there has been innovation in automating heat detection. For example, pedometers and more sophisticated activity monitors are now being used routinely on many farms as part of a management package. As cows become more active when in oestrus, the pedometer information needs to be compared to a baseline for the same cow and algorithms have been developed to interpret the data collected. The efficiency of oestrus detection rate has been reported to range between 50 and 100% depending on the criteria of success (At-Taras and Spahr, 2001). The gold-standard of oestrus detection are still progesterone measurements and imperfect concordance between pedometer and progesterone determined oestrus has been determined because activity monitors will not detect silent behavioural oestrus (Lovendahl and Chagunda, 2010). However, clearly there is an advantage in both progesterone and activity determined oestrus as they do not require farm observations.

1.3.1.7      Culling data

Culling data and culling reasons are important information especially if traits referring to longer time intervals (i.e. particularly those referring to calving dates) are used. Information on cows or heifers culled because of fertility disorders are of use, especially to remove bias arising from cows disappearing from the recording system i.e. a bull can have a biased proof if a lot of his daughters are culled for infertility and this is not recorded.

In the absence of accurate culling data, a useful proxy for monitoring fertility at the herd level is the proportion of animals failing to conceive by 300 days post calving. Cows not served by 300 days most likely reflect non-fertility culls, whereas cows that have been served and fail to conceive are more likely to reflect culls as a result of failure to conceive given that the majority of involuntary culls and decisions on planned culling occur in early lactation prior to the start of the breeding season.

1.3.1.8      Metabolic stress and body condition

Metabolic stress is defined as the degree of metabolic load that distorts normal physiological function. A distortion of normal physiological function may be temporary infertility, where the metabolic load is too great for the cow to invest in reproduction (future pregnancy) when the current lactation is not sustainable. Metabolic load is reflected by the stability of energy balance, which Veerkamp et al. (2001) suggested was related to traits such as milk yield, body condition score (BCS) and live weight (LWT).

By itself live weight is not a particularly good measure of energy balance, as tall thin cows may have weights similar to smaller cows in better condition. Therefore, BCS has been favoured as an indicator for energy balance. Cows with low BCS may have health problems, such as metritis, which may be the underlying problem for poor fertility. However, most studies worldwide have shown that BCS is a good indicator of female fertility, as cows that are mobilize body tissue may be more likely to use this energy to sustain lactation instead of invest in a pregnancy. Therefore, BCS has been found to be suitable to be incorporated into selection indexes for fertility, such as in New Zealand (Harris et al., 2007). BCS is sometimes measured as part of the linear type assessment in pedigree and progeny testing herds it can also be measured by the farmer. However, in some situations, use of BCS as a predictor trait for fertility has been found to be limited (Gredler et al., 2008).

1.3.2       Sources of data

Female fertility data originates from different data sources which differ considerably with respect to information content and specificity; for example from veterinary practices, laboratories, milk recording organisations, breed associations and farms etc. Therefore, ideally, the data source should be clearly indicated whenever information on fertility status is collected and analysed. When data from different sources are combined, the origin of data must be taken into account. Regardless of the data source, it is desirable to have as few steps as possible from initial data recording.

1.3.2.1      Milk-recording

Initiation of lactation requires a calving date to be recorded for a cow. Calving dates are generally collected by organisations that are responsible for recording milk production, based on dates reported by the farmer, or more commonly gathered during the registration of births in countries operating mandatory birth registration systems. Calving dates are the most basic source of data available for evaluation of female fertility and can be used to determine calving intervals (defined as the number of days between two consecutive calvings).

Content

a.      Calving dates.

b.      Culling reasons.

Advantages

a.      Covers both cyclicity and conception.

b.      No additional effort for recording and therefore can be used as an easy first-step into evaluating fertility.

c.      Possible use of already-established data flow (reporting of calving).

Disadvantages

a.      Missing dates for cows with problems around calving that do not enter the herd for milk recording.

b.      Only available for cows, not for heifers.

c.      Calving interval data may be censored, as cows that are infertile are often culled before calving again. If specific culling reasons are available, then information on animals that are culled for infertility can be a very useful addition to calving interval data, as the least fertile cows (i.e. cows culled for infertility) can be distinguished from cows culled for other reasons.

1.3.2.2      AI organisations or producers

AI organisations and other AI operators record insemination dates and the AI sire used for the insemination. Inseminations can either be recorded in a log book and later transferred to a computer or directly into a computer (sometimes handheld device).

Content

a.      Information on inseminations (date of insemination, sire/origin of semen, semen batch, inseminator e.g. technician or member of farm staff).

b.      Sexed semen, embryo transfer, straw splitting etc. should be noted.

c.      Interventions such as synchrony should also be recorded, as it is possible that this may affect analysis results.

Advantages

a.      If logistics for collection of insemination data are established, data can be collected from many farms.

b.      A broad range of measures of fertility can be calculated from insemination dates (often with calving dates) see Table 1. These measures can cover conception and cyclicity.

Disadvantages

a.      If logistics for collection of insemination data are not established, considerable efforts may be needed to set-up recording.

b.      Completeness of recording may vary, especially if there are no legal documentation requirements.

c.      In situations where farmers often use AI for a set period of time followed by natural mating to farm bulls, some mating dates will be missing.

1.3.2.3      Veterinarians

Veterinarians are often involved in monitoring herd fertility. Pregnancy diagnosis or pregnancy testing is practiced and recorded by many veterinary practices to confirm a pregnancy. Uterine palpation per rectum or ultrasonography at around day 60 of conception is a valuable source of data because it is more accurate than non-return rates. Treatment for fertility disorders should also be recorded. From the economic point of view, a cow with good fertility without any treatments needed may be clearly preferred over a cow that was treated several times before it got pregnant.

Content

a.      Pregnancy status.

b.      Diagnoses of fertility disorders.

Advantages

a.      Direct information on fertility, which is not covered by calving and insemination data.

Disadvantages

a.      Veterinary support and training needed to ensure data quality and consistency in diagnosis and definitions.

b.      Completeness of recording may vary depending on work peaks on the farm.

c.      Accurate animal identification may be an issue, as the data may be used (by the veterinary practice) to assess herd-level fertility rather than individual cow fertility.

d.      Data on pregnancy diagnosis may only be available for a subset of the herd.

1.3.2.4      On-farm computer software

Multiple herd management software packages are available for dairy farmers to record their own data. Some of this software interacts with the milk-recording organisations via standard interfaces, i.e. there are automatic exchanges of data between the central database and the computer on the farm. Farmers can enter calving, insemination, culling and pregnancy test information themselves. For genetic evaluation purposes, it is important that all the data is entered. Information on natural matings (if applicable) should also be recorded where possible and practical, which may not be the case for very large herds.

Content

a.      Insemination data.

b.      Calving data.

c.      Pregnancy test results.

Advantages

a.      No additional effort for recording.

b.      Continuous recording.

Disadvantages

a.      Very often only software solutions within farm, difficulties of standardized export of data, although many software packages ensure data exchange with the genetic evaluation unit is possible.

b.      Trait definitions may differ between systems, requiring source-specific data handling.

c.      Incompleteness of insemination data, for example in some cases only the last successful insemination may be recorded for management purposes

1.4         Data security

Data security is a universally important issue when collecting and using field data.

The legal framework for use of fertility data has to be considered according to national requirements and data privacy standards. The owner of the farm on which the data are recorded is the owner of the data, and must enter into formal agreements before data are collected, transferred, or analysed.

1.5         Documentation

Documentation is the precondition of use of fertility data for management and breeding purposes.

Pre-requisite information:

a.      Unique animal identification of both the cow and service sire.

b.      Unique herd identification.

c.      Ancestry or pedigree information (at the very least the cow's sire should be recorded).

d.      Birth registration.

e.      A central database (Often data is recorded on the farm's computer(s) and then uploaded to the milk recording agency who then transfer the data to a central database. Alternatively, data can exchange directly between the farm computer and the central database).

Useful additional documentation:

a.      Individual identification of the recording person.

b.      Details on respective fertility event.

c.      Artificial insemination or natural service.

d.      Type of semen used (e.g. sexed semen, fresh semen).

e.      Type of recording and method of data transfer (software used for on-farm recording, online-transmission).

The systematic use and appropriate interpretation of fertility data requires that different types of information can be combined such as date of birth, sex, breed, sire and dam, farm/herd; calving dates, and performance records. Therefore, unique identification of the individual animals used for the fertility database must be consistent with the animal ID used in existing databases (for more details see the "ICAR rules, standards and guidelines on methods of identification").

Data that can be used to calculate female fertility measures can originate from a number of sources including farm software, milk-recording organisations, veterinarians, breed societies and laboratories. Ideally, as much data as possible should be recorded electronically, as this reduces transcription errors. As long as data is as error free as possible, the origin of data is less important. However, it is preferable for data to be transferred to a central database in as few steps as possible and as quickly as possible. Genetic evaluation of young bulls relies on early information on fertility being available.

1.6         Recording of female fertility

Stepwise decision support for recording fertility

In setting up a recording scheme or using data for genetic evaluation of fertility, the data that is currently captured needs to be considered in addition to implementing strategies for including other data. For example, calving dates and consequently calving interval, is the most basic measure of fertility. Then, insemination dates can be added, to calculate interval traits and non-return rates. Ideally, pregnancy test results should also be recorded as these can be used as early indicators of conception. Finally, or in some cases alternatively, other predictors, such as fertility disorders, type traits, culling reasons and measures derived from hormones assays can also be added.

Figure 1. A flow chart describing the possible steps in developing a recording program for female fertility.

a.      If only data from a milk recording organisation is available, then calving interval can be measured as the interval between 2 successive calvings.

b.      If insemination data is available then days to first service (DFS), non-return (NR), number of services per conception (SPC), first to last service interval (FLI), calving to last insemination (CLI), days open (DOP) can be measured. Conception within 42 days of the planned start of mating and presented for mating within 21 days of the planned start of mating are measures suitable for seasonal systems and require a day when inseminations were started in the breeding season to be identified. Similarly first service submission can be used if a voluntary wait period is defined.

c.      If information about fertility disorders (diagnoses) are available, the information about cows with e.g. cystic ovaries, silent heat, metritis, retained placenta or puerperal diagnoses can be included in an fertility index.

d.      If pregnancy test/diagnosis data is available, then conception or pregnancy to the first (or second) insemination can be calculated, or in seasonal systems, conception within 42 days of the planned start of mating.

e.      If type data is recorded regularly across parities, body condition score (a measure of fatness and metabolic status) can be evaluated. The limitation with condition score as part of a type classification scheme is that it is generally only recorded once, often on only selected cows, and therefore its usefulness may be limited.

f.       If there are research herds or dedicated nucleus herds available, then commencement of luteal activity can be measured on a subset of animals (reference population). If these animals are also genotyped, then a genomic prediction equation can be calculated that can be applied to animals with genotypes but not phenotypes.

1.7         Data quality

1.7.1       General aspects

a.      Recorded data should always be accompanied by a full description of the recording program.

b.      If herds were selected how was this done?

c.      How were the people involved in recording (e.g., veterinarians, and farmers) selected and instructed? Any standardized recording protocol used?

d.      What types of recording forms or (computer) programs were used? - What type of equipment was used?

Is there any selection of animals within herds? Consistency, completeness and timeliness of the recording and representativeness of the data compared to the national population is of utmost importance. The amount of information and the data structure determine the accuracy of the data; measures of this accuracy should always be provided.

1.7.2       General quality checks

National evaluation centers are encouraged to devise simple methods to check for logical inconsistencies in the data. Examples of data checks include:

a.      The recording farm must be registered or have a valid herd-testing identification.

b.      The animal must be registered to the respective farm at the time of the fertility event.

c.      The date of the fertility event must refer to a living animal (must occur between the birth and culling dates), and may not be in the future.

d.      A particular insemination must be plausible. For example are the insemination dates impossible? (e.g. before the calving or birth date)

1.8         Continuity of data flow. Keys to long-term success

Regardless of the sources of fertility data included, long-term acceptance of the recording system and success of the fertility improvement program will rely on the sustained motivation of all parties involved. Quantifying the benefits of data recording of these data is important. For example, data can be useful information for herd management, but also genetic evaluation and integration of these traits into selection programs.

1.9         Trait definition

Refer to Table 9.

1.9.1       Calving interval

Calving interval is the number of days between two consecutive calvings. Calving interval covers both return to cyclicity and conception, however its main disadvantage is that it is sometimes biased because cows with the worst fertility are often culled early and hence do not re-calve. Calving interval is also available later than many other measures of fertility, so is not as useful for selection decisions.

1.9.2       Days Open

Days open is the interval between calving and the last insemination date. It is similar to calving interval provided the cow conceives to the last insemination, in which case days open is calving interval minus the gestation length. The USA currently calculates daughter pregnancy rate as 21/(Days Open - voluntary waiting period + 11). The voluntary waiting period is the period after calving that a farmer deliberately does not inseminate the cow.

1.9.3       Non-return rate

Non-return rate is a binary measure of whether a new mating or insemination event occurs after the first insemination within a time period. Frequently studied intervals are 28 days (NR28), 56 days (NR56) or 90 days (NR90). The reference period recommended by Interbull is 56 days. This trait can be evaluated for both heifers and cows.

1.9.4       Interval from calving to first insemination

The number of days between calving and first insemination is sometimes influenced by management aspects and this needs to be considered in fertility evaluations. However, it does provide a measure of return to cyclicity post-calving. However, it does not provide information on conception (Table 9).

1.9.5       Interval between 1st insemination and conception

The number of days between first insemination and positive pregnancy diagnosis.

1.9.6       Conception rate

Success or failure to conceive after each AI (this can be evaluated for heifers and cows)

1.9.7       Calving rate, e.g. 42 or 56 days, from planned start of calving (seasonal systems)

The binary measure of whether a cow returns 42 or 56 days from the herd's planned start of mating. It is generally confirmed by the presence of a subsequent calving date. A herd's planned start of mating is when artificial inseminations for the herd commence.

1.9.8       Number of inseminations per series

The number of inseminations in a lactation or within a certain time period (this can be evaluated for heifers and cows).

1.9.9       Heat strength

A subjective scale is often used for recording of heat strength. This scale could be divided in different ways and could have various numbers of classes, but the classes should be ordered in intensity. As an example, the Swedish system has a five-point scale (very weak, weak, clear signs, strong, very strong heat signs) where each point is described in more detail regarding physical signs of the vulva and mounting/being mounted.

1.9.10   Submission rate

The percentage of cows mated in a fixed number of days after the herd's start of mating. On an individual cow basis, recording is a binary score i.e. AI'd within a period of days from the herd's start of mating.

1.9.11   Fertility disorders - treatments for fertility disorders

Information on specific fertility disorders can provide valuable information for evaluation of female fertility. Recording details can be found in the ICAR Health guidelines.

1.9.12   Body condition score

The Body Condition Score (BCS) measures the fatness of the cow, especially in the region of the loin, hip, pinbone, and tailhead areas. Change in BCS in early lactation may be a better indicator of fertility compared with single observations of BCS per parity. To consider change in BCS it has to be recorded at least twice in early lactation and requires the dates of measurement.

1.9.13   Overview over traits

For monitoring the health status of dairy cows, an assessment of fertility is also useful to ensure that a complete picture of the health of the herd is available. For more information see the ICAR Health Guidelines.

Table 9. Various traits used or possible to use and their potential relation to various aspects of cow fertility.

Ref.	Trait description	Aspect				System
		Return to cyclicity	Oestrus signs	Prob. of conception	Ability to keep embryo	Seasonal	Yearly
1	Interval between two consecutive calvings (calving interval)	+	+	+	+	?	?
2	Days open, interval from calving to conception (or last insemination)	+	+	+	+		?
3	Non-return rate (56, 128, .. days)			++	+	?	?
4	Interval from calving to first insemination	++	+				?
5	Interval from first ins. to conception (or last insemination)		+	++	+		?
6	Conception to 1st insemination (determined with pregnancy diagnosis)			++	+		?
7	Calving rate (e.g. 42 or 56 days) from planned start of calving			++	++	?
8	Number of ins. per series		+	++	+		?
9	Heat strength		+			?	?
11	Treatments for fertility problems	+	+	+		?	?
12	Body condition score, live weight change during early lact., energy balance	+	+	+	+	?	?
	Submission rate: e.g., interval from planned start of mating to first insemination	++	+			?
	Interval from calving to first luteal activity	++				?	?
	Interval between inseminations		+		(+)		?

The number of + indicates how well the measure relates to the aspect of fertility

? indicates the suitability of the measure to the production system

1.10     Use of data

1.10.1   Improvement of management (individual farm level)

Although these guidelines focus mainly on evaluation of female fertility for genetic improvement, information is also very useful for on-farm decision-support. Routinely recording of fertility data allows the presentation of key figures for veterinary herd management.

1.10.2   Farmers

Optimised herd management is important for financially successful farming

Results of recording can be presented per individual animal or about cohorts and distinguish between retrospective "outputs" such as calving index and "inputs" such as number of services, results of pregnancy diagnosis in order to analyze overall performance (Breen et al., 2009).

However, for short term decisions (e.g. whether to continue to inseminate or not) on-farm recording of fertility is probably the only practical solution. More sophisticated decision support may include correction of the observed level for systematic environmental effects (such as parity or stage in lactation) and time analysis. Fertility reports summarizing the fertility performance of age-groups within the dairy herd also allows farmers to benchmark their farm to others.

Timely availability of fertility information is valuable and supplements routine performance recording for optimised fertility management of the herd. Therefore, fertility data statistics should be added to existing farm reports provided by milk recording organisations. Examples from Austria are found in the Austrian Ministry of Health (2010).

Immediate reactions

It is important that farmers and veterinarians have quick and easy access to herd fertility data. Only then can acute fertility problems, which may be related to management, be detected and addressed promptly. An Internet-based tool may be very helpful for timely recording and access to data. Lists of actions with animals ready to be inseminated or pregnancy tested are helpful.

Long term adjustments

Less-detailed reports summarizing data over longer time periods (e.g., one year) may be compiled to provide an overview of the general fertility status of the herd. Such summary reports will facilitate monitoring of developments within farm over time, as well as comparisons among farms on district and/or province level (Breen et al., 2009; Austrian Ministry of Health, 2010). Publication of key figures on female fertility at herd level will provide decision support at the tactical level. A general recommendation is to present recent averages (last year), but also to present trend over several years. If available, it is advised to include a comparison of the averages with a mean of a larger group of (similar) farms. For example, the average days open might be compared with the average days open for all farms in the same region or with the same milk production level.

Farm averages might also be specified for different groups of animals at the farm. For example, days open might be presented as an average for first lactation cows versus later parity animals. This denotes which groups require specific attention in the preventive management.

Definitions of benchmarks are valuable, and for improvement of the general fertility status it is important to place target oriented measures.

1.10.3   Monitoring of the health status (population level)

Government bodies and other organisations involved in animal health issues are very interested in monitoring the health status of the cattle population. Consumers also are increasingly concerned about aspects of food safety and animal welfare. Regardless of which sources of health information are used, national monitoring programs may be developed to meet the demands of authorities, consumers and producers. The latter may particularly benefit from increased consumer confidence in safe and responsible food production.

Fertility data is also important for providing genetic evaluations, both within country and between countries. The following section is from the Interbull website (http://www.interbull.org/ib/idea_trait_codes) and are the traits that the Interbull Steering committee chose in August 2007 to become part of MACE evaluations of fertility. Interbull considers female fertility traits classified as follows:

a.      T1 (HC): Maiden (H)eifer's ability to (C)onceive. A measure of confirmed conception, such as conception rate (CR), will be considered for this trait group. In the absence of confirmed conception an alternative measure, such as interval first-last insemination (FL), interval first insemination-conception (FC), number of inseminations (NI), or non-return rate (NR, preferably NR56) can be submitted.

b.      T2 (CR): Lactating (C)ow's ability to (R)ecycle after calving. The interval calving-first insemination (CF) is an example for this ability. In the absence of such a trait, a measure of the interval calving-conception, such as days open (DO) or calving interval (CI) can be submitted.

c.      T3 (C1): Lactating (C)ow's ability to conceive (1), expressed as a rate trait. Traits like conception rate (CR) and non-return rate (NR, preferably NR56) will be considered for this trait group.

d.      T4 (C2): Lactating (C)ow's ability to conceive (2), expressed as an interval trait. The interval first insemination-conception (FC) or interval first-last insemination (FL) will be considered for this trait group. As an alternative, number of inseminations (NI) can be submitted. In the absence of any of these traits, a measure of interval calving-conception such as days open (DO), or calving interval (CI) can be submitted. All countries are expected to submit data for this trait group, and as a last resort the trait submitted under T3 can be submitted for T4 as well.

e.      T5 (IT): Lactating cow's measurements of (I)nterval (T)raits calving-conception, such as days open (DO) and calving interval (CI).

Based on the above trait definitions the following traits have been submitted for international genetic evaluation of female fertility traits.

Sub-sections

Dairy Cattle Health

Female Fertility in Dairy Cattle

Udder Health in Dairy Cattle

Claw Health Traits

Lameness Traits in Bovine

Calving Traits in Dairy Cattle

Body Condition Score