The old cliché "You can't manage what you don't measure" applies as much to managing herd fertility as it does to managing the production of biscuits.
Unless you record and identify what is happening in terms of your fertility, you won't be able to identify what you need to improve and, if you put in place strategies for improving fertility, you won't be able to accurately identify whether your strategies have been effective.
Recording fertility effectively will allow you to:
So we need to collect data in order to determine what is being achieved in terms of fertility and to identify future targets and strategies for reaching those targets. However, simply collecting data is not sufficient on its own. More data are not necessarily better. Data collection needs to be focussed on activities or outcomes that are crucial for achieving your fertility goals. You need to identify key performance indicators (KPI) that effectively measure your fertility performance so that you can accurately identify how well you are performing and how much you are improving.
KPIs for fertility need to be simple to understand (if you can't explain them to farm staff, they're unlikely to be useful), reflect the economics of fertility (measuring ovary size 12 days after calving may be achievable but is not strongly related to performance), and measured over a relatively short period of time (calving interval is economically important, but the reason your current interval is 410 days is down to issues that occurred when the cow got pregnant over 280 days ago).
Fertility is a complex trait, so a single KPI, such as a Fertility Economic (FERTEX) score, which combines data on the speed with which cows get pregnant with the data on the proportion of cows culled for not getting pregnant, is a good measure of the economics of reproduction but is not useful when investigating fertility performance on farm. However, calculating individual KPIs for every issue will result in data overload, which will mean that reproductive performance will be difficult to track and evaluate. So we need to identify a small group of KPIs which cover the key factors affecting reproductive performance; this means that some will need to be composite measures which cover several different factors. An example of this type of KPI is the proportion of cows which become pregnant within 100 days of calving (as it is affected by both heat detection and conception rate). Calving to conception interval is a similar combined KPI but only includes cows that were served and got in-calf. With composite KPIs, when you identify a problem you then have to 'drill down' and identify which of the underlying components of the KPI are responsible - for example, if a large proportion of cattle are not getting pregnant within 100 days of calving is it a problem with heat detection or conception rate (or both)? The exact mix of KPIs you use will be farm dependent; they should be based on what you need to focus on, not a generic template; although it is likely that some parameters (such as inter-service intervals) will form part of the mix on all farms.
Good quality information on herd fertility can be obtained and KPIs can be calculated from a small number of measures:
1) Cow number: All cows in a herd need to be individually identified. This needs to be clearly and easily readable at a distance. If cow numbers are wrongly recorded, the fertility data are useless.
2) Calving date. This is the starting point for fertility with many KPIs being based on time from calving.
3) Heat dates. Recording these dates is often neglected, particularly when they are not associated with inseminations, but the interval between calving and first heat and inter-service intervals are both extremely useful KPIs, particularly when you are trying to investigate heat detection. For seasonal herds recording heats before the planned start of the breeding season is extremely important.
4) Service dates. These all need to be recorded - not just the one that resulted in pregnancy. The proportion of cows which get pregnant to a service is a crucial piece of information, and conception rate to first service is one of the best KPIs for benchmarking.
5) Pregnancy diagnosis date and result. Without an accurate early pregnancy diagnosis, your ability to measure fertility is severely limited. Non-return rate, often used as a proxy for pregnancy diagnosis, over states the proportion of animals that are pregnant significantly (usually by more than 10%) and its accuracy varies markedly between farms because of differences in quality of heat detection.
Early pregnancy diagnosis is essential. Not only does it allow you to treat and manage the non-pregnant cow effectively, early pregnancy diagnosis is more accurate at determining which insemination a cow is pregnant to - improving the accuracy of conception rate data and is essential if the management of drying off and the transition/calving period are to be optimal.
To get accurate KPIs it is essential that your population of cattle is properly defined - all cows which are eligible for service need to be included, not just those which you think have a good chance of getting pregnant. If you do decide to stop breeding a cow, this needs to be clearly recorded so that such cows don't continue to be included in the eligible population - and once a decision is made that cow should not be bred.
The system you use to record the information can be a simple paper-based record ora computer based system. The key is that the record is permanent - parlour white boards and breeding boards are very useful for day-to-day management and quick one-off assessments but the information gets lost when cows change status or are treated (or the breeding board is knocked off the wall!).
Computer records are clearly the best as they can be quickly and easily used to calculate and regularly update KPIs; -. Simple spreadsheets such as Excel can be used but bespoke programmes which are specifically designed to automatically calculate KPIs can significantly reduce the time taken to get KPIs, which is particularly useful for weekly updates.
They also allow linkage of fertility to other records such as disease or milk production, and they also allow the automatic generation of action lists (e.g. ensuring that all cows that have not been seen in heat 60 days after calving are presented for veterinary examination). Computer systems for recording fertility can also be directly linked to advisors such as your veterinarian, which aids prompt effective identification of problems.
If computer systems are to be used it is essential that:
1) One person is responsible for ensuring data entry and support.
2) Data entry is simple as possible
3) The programme used is flexible enough to meet changing requirements and alterations in focus - for example allowing different voluntary wait periods,
Paper records have their disadvantages but diaries - either daily or three-week diaries - have the key advantage of being simple and easy to use which means that information is likely to be recorded. It doesn't matter how sophisticated your analysis is, if you don't have the data your analysis is pointless. With paper records it is a good idea for one person to be responsible for entering the data on to a database for further analysis. This can be done on a regular basis by veterinary practice staff, so that farm staff do not have to deal with computer entry and database upkeep at all.
CuSum graphs are simple plots of outcome which can be very useful for looking at trends and changes of KPIs; it is particularly useful for those that are proportions such as submission rate and conception rate.
They can be computer generated or, because it's a simple system, created on a paper record. They may also be provided by your milk recording service. CuSums can be continuously updated so provide a timely assessment of reproductive performance. They can highlight issues with groups (such as spring-calving cows compared to winter-calving ones) and because they can identify when problems with fertility started they allow those problems to be linked to feeding and management changes (e.g. the opening of a new silage clamp).
A downloadable entry sheet for a CuSum graph is available at http://tinyurl.com/DairyCoCuSum
For conception rate, a CuSum requires accurate early pregnancy diagnosis; for heat detection (i.e. whether a cow is submitted for insemination), a CuSum simply requires a target interval between calving and first insemination and a record of first insemination.
CuSums are focussed on individual parameters and, on their own, do not provide a complete fertility record, but they are a useful first step, and can often be of significant value when investigating problems because they provide time-based data.
Poor fertility impacts on economic performance by delaying conception and by increasing the proportion of cows which are culled due to failure to conceive. Much of the recording of fertility is aimed at identifying issues with when cows get pregnant; these measures are related to culling rates - reduced conception rates are associated with increased culling due to failure to conceive - but some can be manipulated to reduce culling; for example, the mean interval between calving and conception is often increased in order to reduce the proportion of cows culled because they are not in calf. Without good culling data it can be difficult to properly assess the economic impact of poor fertility.
To get an accurate picture of the cost of culling for poor fertility it is essential to record properly why cows are culled, as a high overall culling rate can occur for reasons other than poor fertility (such as intensive control of Staph. aureus mastitis). Too many farms simply record a cow as being culled, rather than collecting information on why it was culled - if cows are culled for multiple reasons these should all be recorded.
However, although it is a useful economic indicator, like calving interval, it is too historic in nature to be a useful KPI for improving reproductive performance. A focus on more up-to-date KPIs such as the proportion of inseminated cows being diagnosed as pregnant over the last 6 weeks, the proportion of eligible cows submitted for insemination in the last 24 days, or for a recent cohort of cows which were eligible for insemination, the proportion which became pregnant in a 21-day period (21-day pregnancy rate), will pay dividends in terms of reducing culling due to failure due to conceive as well as reducing days between calving and conception.
Good records are essential for monitoring reproductive performance. Records need to be easy and simple to collect, but also need to be able to put in a form where they can easily analysed. Trying to collect too much data will often mean data quality is compromised. High quality assessments can be made with data on calving, heats, services, pregnancy diagnoses and culling information.
Choose the recording system that suits your farm - computer recording is best as that simplifies and speeds analysis, but using a paper-based system can result in more data being recorded, provided there is someone with the time to input the data so that they can be analysed.
Choose the KPIs that match your goals for fertility and focus on strategies to improve these KPIs.
NADIS hopes that you have found the information in the article useful. Now test your knowledge by enrolling and trying the quiz. You will receive an animal health certificate for this subject if you attain the required standard.