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Improving The National Breeding Programme

02 January 2012

Teagasc, in association with the Irish Cattle Breeding Federation and Sheep Ireland, has been making strides in the development of the national genetic evaluations for cattle and sheep in Ireland. However access to reliable data is hindering results.

The ultimate aim of any successful breeding programme is to increase farm profitability in a sustainable manner. This article describes the various components of a national breeding programme and uses a recent research project investigating animal price and live weight in cattle as an example.

Food Harvest 2020(DAFF, 2010) has set out clear and ambitious targets for the beef, dairy, and sheep sectors. Genetics researchers at Teagasc work in close collaboration with industry, and are using Food Harvest 2020and the Teagasc Roadmaps to prioritise future research strategies. Nonetheless, genetics is only one component of the entire production system, and it is important that the type of animal bred is suited to the production systems of the future, which will rely heavily on exploiting Ireland’s competitive advantage in grazed grass.

Breeding objective

For a trait to be considered for inclusion in a breeding objective it must be either economically, socially (e.g., animal welfare) or environmentally important. Teagasc has made important contributions to the development of national breeding objectives for beef cattle (Suckler Beef Value –SBV), dairy cattle (Economic Breeding Index – EBI) and sheep (Sheep Value Index).

All of the traits included in these objectives are of economic importance to Irish production systems and are optimally weighted within the objectives. The indexes include revenue-generating traits, such as carcase traits, and cost traits, such as fertility.

Inclusion of the cost traits in the EBI has doubled genetic gain in overall profitability; if exploited optimally this is worth over €26 million annually. All three breeding objectives are routinely reviewed in the light of expected changes in prices and costs, as well as the availability of new data.

Obviously, the price a farmer receives for his animal will affect farm revenue. Following consultation with industry, it was decided to prioritise research on the feasibility of including animal price in the national breeding objectives for dairy and beef cattle.

Trait measurement

The feasibility of breeding for a given trait is dictated by the availability of data, either for the trait itself (e.g. carcase lean meat yield), or a genetically related trait (e.g. ultrasound muscle depth as a predictor of carcass lean meat yield). Ideally the data should be measurable early in life, preferably also across genders, and be available at a low marginal cost.

In Ireland, an excellent cattle movement tracking system is in place; this unique system of tracking movements is captured in the Irish Cattle Breeding Federation (ICBF) database. Livestock marts remain an important marketing outlet for cattle. Approximately 1.5 million cattle, or 66.7 per cent of total cattle movements, are sold through livestock marts annually and these data are available for genetic evaluation. No data are available on individual sheep prices.

Genetic evaluation

A large component of genetic research is estimating the genetic variation present among traits, as well as the genetic relationships among traits. The latter is to determine if the accuracy of identifying genetically elite animals can be augmented by supplementation from alternative data sources; knowledge of the interrelationships among traits is also important to quantify the expected responses to selection – including any possible negative consequences of selection.

In order to determine if a trait is under genetic control, estimation of genetic parameters must be undertaken. This procedure requires an excellent understanding of animal science and the source of the data. As errors can occur through the recording of data, the first step of the genetic analysis involves the editing of the data (e.g., animals with unrealistic live weights or ages are removed from the data).

Once the data are edited, a statistical model is developed, which reflects the underlying biology, and accounts for on-farm managerial effects and temporal trends in prices across marts, as well as other factors, such as the age of the animal. If all effects in the model are deemed to be statistically significant, then genetic analysis of the trait can be undertaken. Genetic analysis of the collected data on animal price and live weight across many thousands of animals clearly showed that genetic variation existed among animals.

The proportion of differences among animals in price attributable to their genetic merit varied from 10 per cent to 34 per cent. Other traits routinely available, such as linear type traits, were identified as useful predictors of genetic merit for animal price, thereby increasing the accuracy of estimated genetic merit. Hence, because:

  1. animal price makes a large financial contribution to the profit on farm;
  2. routine data are freely available;
  3. genetic differences among animals exist, animal price will now be included in the national breeding objectives for beef and dairy.

 

Breeding scheme

The fourth step in designing a successful breeding programme is identifying genetically elite parents of subsequent generations and mating these animals appropriately to ensure sustainable long-term genetic gain with minimal accumulation of inbreeding.

A progeny testing scheme is the traditional breeding scheme for the genetic improvement of beef and dairy cattle and, in Ireland, is called G€N€ IR€LAND®. For sheep, two different breeding initiatives were established in Ireland: the Maternal Lamb Producers (MALP) and the Central Progeny Test (CPT) flocks. The MALP flocks were established to test the robustness of the genetic evaluations across different land types and production systems, but also to provide a demonstration of the range of genetic merit among a group of rams.

The CPT flock involves mating rams of various breeds and from a diverse population of performance recording flocks to a central group of ewes. Information on the resulting progeny produced that are managed in a commercial environment feeds back into the genetic evaluations and provides predictions of the genetic merit of the pedigree rams used and also their relatives.

The inclusion of animal price and live weight in genomic selection breeding programmes for dairy cattle was examined using simulations. Results showed that the use of genomic information on females had the potential to increase the rate of genetic gain three-fold. Subsequent economic analysis revealed an annual cumulative benefit for the Irish dairy herd of €20 million with a cost of €0.25 million per year.

Decision support and dissemination

Finally, the key to any successful breeding programme is to ensure that there is widespread understanding and uptake of the national evaluations by the end users. This is being achieved through dissemination of researcher results through the national press, open days, farm walks, industry meetings and the Teagasc Advisory Service.

Coupled with dissemination, decision support tools are being developed that will ensure that farmers are not overwhelmed with figures but are given clear, concise recommendations to increase genetic gain. One such decision support tool that is under development at Teagasc uses herd- level animal growth profiles as a benchmarking tool that allows farmers to compare the growth performance of their herd with contemporaries, while simultaneously adjusting for the genetic merit of the herd.

So where to now?

Teagasc genetic research will continue to focus on all of the components of a successful breeding programme and will exploit the skills developed in one sector for use in other sectors. A good example is genomic selection, which is already implemented for dairy cattle but which can be relatively easily modified and implemented in beef; implementation in sheep is some time off.

However, a prerequisite for a successful genomic selection programme is accurate breeding objectives and genetic evaluations, as well as optimal breeding programmes to exploit the technology and knowledge among the end user to implement. Therefore, most research in the short to medium term will focus on getting the basics correct.

Access to large quantities of accurately recorded data is one of the main hindrances to accurate genetic evaluations in Ireland and, therefore, consideration is being given to alternative approaches for data generation.

Such approaches are utilising skills from other disciplines, such as engineering; for example, following research at Teagasc and the ICBF, data from video image analysis of carcases will soon be included in Irish cattle genetic evaluations. The foundations for the implementation of successful national breeding programmes exist in Ireland. Research must now focus on harnessing these resources to realise the targets set out in Food Harvest 2020.

January 2011

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