MILK DEVELOPMENT COUNCIL

Transcription

1 MILK DEVELOPMENT COUNCIL Genetics of Health and Fertility in Dairy Cattle Project No. 97/R1/04

2 Genetics of Health and Fertility in Dairy Cattle Final Report April 2002 MDC Project Number 97/R1/04 H.N. Kadarmideen¹, M.P. Coffey¹, G. Simm¹, M. Kossaibati², R.J. Esslemont² and R. Thompson³ ¹Animal Breeding and Genetics Department, Animal Biology Division, Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG ²Department of Agriculture, University of Reading, Reading RG6 6AT ³Roslin Institute/IACR-Rothamsted, Harpenden, Hertfordshire AL5 2JQ 1.

3 1.EXECUTIVE SUMMARY There is evidence from previous research on dairy cattle from Scandinavia, the USA, the UK and elsewhere, of unfavourable associations between milk production, fertility and some diseases, including mastitis and lameness. This implies that if selection continues to be mainly for high production alone, there will continue to be a decline in genetic merit for health and fertility. While this decline in genetic merit for health and fertility might be offset on some farms by better management and disease control, it seems unlikely that most farms can rely on this indefinitely. Also, it is probably not the best policy, economically, to rely on increasing management inputs to offset problems caused by narrow selection policies and may be increasingly unacceptable to consumers. Broadening selection to include important health and fertility traits is likely to be a better option. The MDC-funded research reported on here addressed several issues that are vital to halting this decline in genetic merit for health and fertility in the UK dairy herd, by: Assessing the quantity and quality of health and fertility data available from some existing recording schemes (Livestock Services UK, now part of CIS, and DAISY, now part of NMR). Examining trends in health and fertility over time in one of these datasets. Investigating the genetic control of important health and fertility traits and their associations with milk production. Developing appropriate statistical methods of evaluating health and fertility data. Investigating the potential economic impact of including health and fertility traits in future selection indexes. The main findings from the research are that: Selection on indexes including health and fertility, as well as production and lifespan, could increase marginal returns per cow per annum by per cow per year compared to selection on production alone (compared to 4.50). The benefits will occur cumulatively for as long as selection continues, and so represent a potentially major economic benefit to individual farmers and the UK dairy industry as a whole. Achieving these benefits in practice will requires all sectors of the dairy industry to work together to: improve the quality of data collected on health and fertility traits, including: standardisation of coding of events; focussing collection of health and fertility data on farms that can guarantee full recording; adopt standard quality assurance and data editing procedures; introduce genetic evaluations for health and fertility traits, based on the methods applied here; select using broader indexes at all levels in the dairy sector, from breeding company to commercial farm. 1

4 MDC should have a pivotal role in stimulating this co-operation. It will not be easy to achieve, but the potential economic benefits for the UK dairy industry are so large that we strongly recommend that every effort is made to identify a core of farmers, milk recording organisations and breeding companies and researchers that are willing to work together towards achieving these aims. 2

5 2. RECOMMENDATIONS Fertility data quality: Results showed that about half of the records from a national recording scheme (Livestock Services UK (LSUK), now part of CIS) did not have any insemination events - these are crucial for producing fertility proofs based on direct measures. We suspect that this situation is typical of national, voluntary recording schemes, and not specific to LSUK. Stringent data editing is required to achieve sufficient data quality to produce national genetic evaluations. Data quality is higher in the DAISY health and fertility recording scheme (now part of NMR). There are a large number of checks built into data entry in DAISY. But, the quantity of data is insufficient to produce national genetic evaluations. As for the pedigree information, the DAISY recording scheme suffers from the lack of a uniform sire identification system in the database. This results in greater difficulties in creating pedigrees for genetic analysis of DAISY cows. Whatever the trait of interest, this inconsistency in sire identification needs to be rectified within the DAISY scheme if data are to be used fully and effectively for genetic analysis in the future. Improving existing fertility data quality: Reliability of insemination dates, and hence fertility genetic evaluations, could be improved via two approaches. Firstly, by ensuring consistent and uniform recording of insemination dates (e.g. recording all insemination dates at all times) by agreeing guidelines with farmers and MROs. Secondly, by applying editing and validating rules, such as those developed in this project, to eliminate records that have missing insemination dates and have the potential to introduce bias in genetic evaluations. This may be effective if applied at the point of data capture as in DAISY. Future recording for fertility: Future national recording for fertility should aim for a minimum of two fertility measures that address cyclicity and the ability of cows to conceive. Calving interval could be considered a composite trait encompassing both these measures. However, because it requires two successive calvings, it is not available as early as desired to make selection decisions. It is also biased, because it is based on the most fertile cows. Calving interval had the highest genetic correlation with conception success to first service and calving to first service interval in this study (correlation > 0.85). This suggests that calving interval is of value in the first instance. However, in the longer term it would probably be preferable to have direct information on conception success and interval from calving to first heat or insemination. Disease data quality: The volume of disease data is small compared to that for inseminations; wider recording for diseases started in 1986 with DAISY and only in 1994 with LSUK. Because disease events are voluntarily recorded, and recording practices vary from farm to farm, data that are available today are of uncertain quality. Although 3

6 strict data editing can achieve improvements, poor data quality still causes problems in national genetic evaluation for diseases, such as mastitis and lameness. 4

7 Improving existing disease data quality: Sufficient data quality may be achieved by selecting herds only if they had at least one case of disease in a herd-year and/or by obtaining an assessment of the reliability and completeness of records for each herd, directly from MROs, veterinarians or breeding companies. Changes in the way breeding companies gather progeny test daughter records in future would help to assure quality. Future recording for diseases: As for fertility recording, ideally farmers should record all diseases all of the time, with dates of occurrences and any veterinary treatments. A uniform and consistent way of coding diseases across all herds is needed. As this is unlikely to happen nationally in the absence of legislation, it would be useful to identify groups of herds (e.g. progeny test herds, a subset of milk recording herds, a subset of DAISY-Interherd users or a subset of HUKI members) that are willing to undertake more accurate recording to enable progress in national genetic evaluations for diseases. Future selection opportunities: This research has shown very clearly that there are potentially substantial extra economic returns to dairy farmers from basing selection on broader indexes than those available at present. For these potential benefits to be realised, we need: (i) improvement in the quality and consistency of health and fertility data, (ii) implementation of genetic evaluations for the most important health and fertility traits, and (iii) ongoing work, including regular updating of economic values of important traits, to enable calculation and publication of profit indexes. We believe that this project has laid important technical foundations that will help in achieving these aims. Good recording and genetic evaluations are mutually inclusive. A major challenge for the future will be to stimulate partnerships between farmers, recording agencies, breed societies, breeding companies and genetic evaluation agencies to enable these developments in recording and genetic evaluations to take place to the benefit of all parties. MDC should have a pivotal role in this. 5