Breeding tools that help business decisions
MLA & AWI July 19, 2024
When we know what fit for purpose looks like for a livestock enterprise, we can work to optimise production and profitability by concentrating on the genetics and the environmental conditions the animal is exposed to. Numerous useful breeding tools are available to support decision making. Which of these tools are most helpful will depend upon the business objectives and particular circumstances.
An animal’s performance is influenced by its genetics and environment
How a sheep looks and how it performs (phenotype) is a combination of the genes (genotype) it carries and the environment in which it has been raised and managed.
Genes will express themselves to a varying extent depending on the non-genetic or environmental factors.
Producers can influence the phenotype through selection over time by manipulating the genetics of their flock through breeding and by managing the non-genetic (i.e. environmental) factors, such as feeding and management.
When we talk about ‘environment’ we’re really talking about all the non-genetic factors or aspects that impact the animal’s performance that are not due to their genes. A useful example of this is the variation in fibre diameter when a sheep is raised in one location then moves to a different environment and receives different nutrition, which makes their wool present as broader or finer. The genotype of the sheep hasn’t changed, but the environmental factors are changing how these genes are expressed.
It is important to understand that an animal does not pass on the environmental or non-genetic effects to its progeny. It only passes on its genes.
Many non-genetic factors impact the phenotype, or how an animal looks and performs. In addition to environmental factors (such as climate and feed availability), they include the age of the ewe, whether the lamb is born early or later in the lambing period, how long the lambing period was, whether they’re born or raised as a single or multiple, management groups and disease exposure, to name a few.
Some of the non-genetic factors that impact individuals are known and can be accounted for in genetic benchmarking and sheep classing, while others remain a mystery but still contribute to the natural variation between animals. Some examples include ewe milk quality and quantity, the lamb early weaned itself, the lamb got stuck in a dam or fence, one-off disease impacts, and social interaction in the mob.
An example of the effect of environmental factors is that progeny born during a drought with poor nutrition often look and perform differently to progeny born in a year with plentiful feed, even though they are genetically similar. Those born during a drought may be lighter, cut lower quality wool, have poorer meat-eating quality and express less wrinkle. Despite this difference in performance, if they are genetically identical to animals born in a non-drought year, they will pass on the same genes to their progeny.
Another example is that single-born lambs generally look and perform differently compared to twin-born lambs, even though they may have similar genetic potential, due to the additional nutrition they receive in utero and during lactation. A twin-born lamb may have some inferior phenotypic production traits but may, in fact, be genetically superior to the single-born lamb.
Environmental factors mean how an animal looks is not always an accurate indicator of its genetic merit or what it will pass on to its progeny.
Another example of this is that sheep genetically predisposed to fleece rot may not show fleece rot in a dry year.
Decisions are long term
Genetic improvement takes time and patience but is cumulative. When examining a specific trait or characteristic in any flock, most of the flock will be clustered around the ‘average’. The average represents the flock’s genetic merit for a trait. Genetic improvement or gain occurs when the genetic merit is improved through selection. The improvement in genetic merit refers to the overall improvement in a flock brought about by selection for a number of traits that contribute to the flock’s breeding objective.
While the ram and the ewe contribute 50% of the total gene pool to their offspring, their influence over the whole flock is vastly different, with one individual ram contributing many more lambs to the flock over his lifetime than one ewe. For this reason, one ram has a much greater impact on the long-term genetic profile of the flock than does one ewe.
Furthermore, a ram’s genetic influence within a typical self-replacing flock lasts much longer than the four joinings for which he will normally be used. This is because:
- Breeding ewes with 50% of a sire’s genes (daughters of the sire) are likely to be still breeding 10 years after the ram was first used.
- Breeding ewes with 25% of a sire’s genes (granddaughters of the sire) are still in the system up to 16 years after the ram is first used.
This is why it’s important to make strategic and informed breeding decisions and to select sheep that are fit for purpose — classing, selection and joining decisions have a tangible, long-term impact.
Genetic gain requires dedication and patience. While the amount of gain realised depends on many factors, including the starting point relative to the final goal, gain is typically about 1–2% per year, but is cumulative. Encourage producers to have a plan and stick to it. This is particularly important when they are seeking long-term goals, such as reducing dag or improving reproduction.
The most effective approach to achieving genetic gain through breeding involves:
- making accurate selection decisions (selection accuracy)
- selecting the best animals more often (selection intensity)
- balancing this with the age structure of the flock (generation interval).
Genetic principles
A number of genetic principles can be exploited to achieve genetic gain through breeding.
Variation
There is natural variation within the traits of a flock. To improve the overall performance of a flock, identify the differences or variation between sheep in the flock and make decisions based on these differences.
Most traits selected for in the sheep industry are what called ‘normally distributed’. That is, they fit into a bell curve shape with most animals around the average and few making up the top and tail of the curve.
Source: AWI ClassiFly
The width of the curve tells us how much variation there is for the trait and the greater the variation, the easier it is to identify the top animals for breeding and to cull the lower merit animals.
The distribution on the curve, or how wide or flat the curve is, will vary from trait to trait and between traits.
Due to variation in traits, we can make progress towards a particular favourable trait by identifying differences in the expression of the trait and choosing the better ewes and rams to produce the next generation.
Heritability
If a trait is heritable, it can be passed onto progeny. Some traits are more heritable than others.
If a trait is highly heritable it means an animal’s genetics has a relatively large impact on the expression of the trait in their progeny and the impact of the non-genetic or environmental factors is relatively small.
Fibre diameter – high heritability
The expression of the trait or what we measure in the progeny is largely (60%) due to the genetic merit of their parents and only 40% due to non-genetic factors. This is why the industry has been able to shift micron relatively easily.
If a trait has low heritability, it means that non-genetic (environmental factors) have a larger impact on the expression of the trait.
Conception – low heritability
This trait is largely influenced by non-genetic or environmental factors (greater than 90%), such as ewe nutrition, which means conception is due only to a small degree to the genetic merit of their parents.
Progress can be made with all heritable traits so it is still worth selecting for more lowly heritable traits that are important profit drivers in an enterprise as genetic gain is cumulative and permanent, producers just need to be more diligent in assessing sheep and know it will take longer to make genetic progress.
Management and husbandry that allows for variation in traits to be expressed are important to making genetic progress, especially for traits with lower heritability. For example, though growth rate is moderately-highly heritable, providing optimal nutrition is still required for genes for high growth rates to be expressed and variability to show.
Correlations
When traits are influenced by the same or closely related genes, they are correlated. This generally means changing one trait will change the other, although the strength of the interaction and impact between one trait and another can vary. Correlated traits can also be used as indicator traits when the trait of interest cannot be easily measured or assessed or is difficult to see. Correlations can be favourable or unfavourable. Favourable correlations occur when two traits both move in the desired direction.
Liveweight and fleece weight — favourable correlation
As liveweight increases, fleece weight tends to increase as well. Unfavourable correlations occur when one trait moves in the desired direction, but another trait moves in an undesirable direction.
Lean meat yield and intramuscular fat — unfavourable correlation
When lean meat yield increases, intramuscular fat generally decreases. Another example of this is as fleece weight increases, fibre diameter does as well, which may not be desirable.
Unfavourable correlations are sometimes called genetic antagonisms.
Source: AWI ClassiFly
Some sheep bend unfavourable correlations and these are the ones to look for if those traits are important profit drivers for an enterprise. Such sheep are sometimes called ‘curve benders’.
Although a trait can be improved indirectly by focussing on a correlated trait, both greater and faster outcomes will be achieved by focussing directly on the trait of interest rather than relying on the correlation.
Decision-support tools
Useful and practical decision-support tools can help direct an investment in the right genetics. Producers have access to many tools to support their decision-making.
Visual assessment
Visual assessment relies on assessing how each trait is phenotypically expressed in an animal; that is, what they look like and how they perform.
Visually assess a number of traits when classing for lifetime productivity and profitability as well as for breeding better progeny.
Sound management protocols are important in assessment, whether this be visual or measured. These should include tight lambing, identifying multiples and maiden progeny, maintaining as few management groups as possible for a drop/mob, the right timing when to take each assessment, a best-practice husbandry and animal health program, and maintaining recommended commercial condition scores.
Raw measurements
Raw data is an assessment of an animal’s actual performance and can include visual assessment, performance records or a piece of information about the animal that will help establish its usefulness to a breeding program.
Use this information when making classing and selection decisions. For example, looking at liveweights or wool test results.
The more raw data collected and used when making decisions, the more accurate classing and selection decisions will be.
Visual Sheep Scores
To help with visual assessment, AWI and MLA developed the Visual Sheep Scores guide that has useful visual representations of a range of traits, scored on a scale of 1-5.
Source: AWI and MLA
Performance records
Performance records are any records that support an understanding of how a breeding enterprise is performing before and after implementing a breeding strategy. Link all performance records to business objectives and profit drivers.
Performance records can include reproductive performance measured through marking or weaning percentages and ewe mortality, and meeting market specifications, measured through carcase data and wool clip data, for example.
Wool production performance
Wool production performance can be measured and monitored through wool clip results. Maintain records of wool production performance overtime and monitoring for changes because of breeding decisions.
Carcase performance
Carcase performance can be measured and monitored through feedback from the abattoir typically in relation to market compliance, age/dentition, intramuscular fat, lean meat yield and disease and defect conditions. Maintain records of carcase performance overtime and monitoring for changes because of breeding decisions.
Reproductive performance
Reproductive performance can be measured and monitored by looking at:
- The number of ewes scanned pregnant compared with the number of ewes joined (ewes scanned in lamb percentage, also called conception rate).
- The number of foetuses scanned in relation to the number of ewes joined (the scanning percentage).
- The number of lambs marked compared with the number of lambs scanned (lamb survival percentage)
- The number of lambs marked compared to the number of ewes joined (lamb marking percentage).
- The number of lambs weaned compared to the number of ewes joined (weaning percentage).
- The number of weaners at 12 months compared to the number of lambs weaned (weaner survival percentage).
These measures will give an accurate idea of the flock’s reproductive performance highlighting areas for potential improvement. Be consistent with the methodology used to measure reproductive performance to gain a true picture of reproductive performance.
Breeding values
Breeding values are a prediction of an animal’s genetic merit for a particular trait. Genetic merit refers to how an animal ranks against other animals for its ability to produce progeny with superior traits. They are an indication of how an animal’s progeny will perform based on the genes they will pass on.
The values are derived from an analysis that adjusts the raw data for non-genetic factors, such as birth type and dam age. It also accounts for known genetic factors including the correlations between traits and heritability.
Commercial tools are available to predict genetic merit for either a flock or individual animals:
RAMPOWER
Commercial Merino flocks can use RAMPOWER, a within-flock analysis program. To use this program, producers collect raw measurements on a group of their sheep that have been managed the same way (no adjustment is able to be made for environmental factors) and analyse this information using well-established industry genetic parameters (e.g., heritability and correlations). RAMPOWER generates a single value (within-group indexes) to allow sheep to be compared to their cohort but is not comparable to other flocks as no environmental adjustment has been accounted for.
Flock breeding values (FBVs)
Flock breeding values benchmark animals run on the same site and mostly within a drop of animals. These are only comparable within the flock and are different to the flock profiling test available for commercial producers. More information on flock profiling as a benchmarking tool will be covered later.
You might see FBVs reported at sire evaluation days or provided for rams at some ram sales.
Sire evaluations
Merino sire evaluations provide an independent comparison of the breeding performance of rams by evaluating their progeny relative to the progeny of other sires and in particular, link sires. Link sires allow sires entered at different sites and in different years to be compared by removing the differences between sites, years and seasons, leaving only the genetics to be evaluated via the progeny. This data is also used to strengthen across-flock linkage, which helps underpin ASBVs.
The progeny of different sires are managed at a range of sites under consistent management protocols and many traits of commercial importance to producers are measured. Ram breeders use the results to benchmark their genetics in a specific environment and ram breeders and buyers alike use them to select the genetics that will add the most profit to their enterprises.
Sire evaluation sites are located throughout the major wool-growing regions of Australia with the progeny of between 12 and 20 sires used at each site. Sires are joined to ewes via artificial insemination (AI).
The ewes at each site are selected to create an even, classed line that is representative of sheep typically run in that environment. An equal number of ewes are joined through AI to each sire. The minimum number of ewes joined to each sire is 50, with some sites choosing to join more.
Evaluation of a sire’s progeny is the key to sire evaluations. Progeny are managed together under the same conditions throughout the period of the trial, with the exception that single and twin bearing ewes can be separated prior to lambing and managed accordingly up until tagging or weaning. All progeny are evaluated to hogget or adult age with no culling, except for welfare purposes, to enhance the understanding of later age stages.
The performance of progeny may be evaluated at many stages in the trial, depending on the traits of interest.
Evaluation involves both measured traits and visual assessment.
Reports are published for each site which provide information on traits including:
- wool, growth and carcase traits
- breech and conformation traits
- internal parasite resistance.
The results are presented as adjusted sire means for each sire, which adjust the raw data for effects such as being born or raised a single or a twin, as well as FBVs.
Australian Sheep Breeding Values (ASBVs)
Australian Sheep Breeding Values, commonly referred to as ASBVs, have been developed to take some of the complexity out of decision making and help producers make selection decisions more accurately. ASBVs go hand in hand with visual assessment of animals and help improve selection accuracy.
Ram breeders report information to Sheep Genetics (a centralised database) on the performance of their sheep for a wide range of economically important traits on a regular basis. This data is analysed along with genomic information to benchmark animals across flocks considering the known non-genetic factors, such as ewe age, birth type and rear type, so selection decisions can be more accurate.
ASBVs compare the genetic performance of sheep across flocks and have been developed for Merinos (MERINOSELECT), Dohnes (DOHNE) and Terminal and Maternal breeds (LAMBPLAN). These figures are calculated and then published by Sheep Genetics and provide an estimation of an animal’s genetic merit or what they will pass onto their progeny. They do this in consideration of the animal’s own performance data as well as the performance data of any known relatives in the database, including parents, siblings, progeny and other relatives.
Genomic testing involves analysing an individual animal’s DNA, along with phenotypic data, to more accurately predict ASBVs. This is useful for better understanding the exact DNA contributed to an animal from its relatives. It is useful if there is limited data measurement available on young rams as genomic testing can significantly increase the accuracy of ASBVs on younger animals, potentially allowing for faster genetic gain.
ASBVs also account for factors such as heritability and correlations, as well as non-genetic factors (known environmental factors), such as birth type, to determine an animal’s genetic merit for a range of important traits.
Most ASBVs are reported and displayed with the same units used to measure the trait. For example, the ASBV for fibre diameter (FD) is reported in microns, the ASBV for weight (WT) is reported in kilograms and the ASBVs for traits measured in visual scores are reported using scores. Some traits, like clean fleece weight (CFW) and fibre diameter coefficient of variation (FDCV) are provided as percentages.
Selection indexes
Sheep Genetics report a significant number of ASBV traits and many of these are correlated to other traits to varying degrees. When there are many traits of importance producers can use selection indexes to select animals for use within a breeding program. Indexes combine important production traits into a single value and are a useful way to rank animals quickly and easily.
Using indexes to make ram purchasing decisions allows producers to make balanced genetic progress towards more profitable sheep for their production system. A ram with a higher index will produce progeny that are more profitable in that production system. There are a number of indexes available for each of the analyses.
It’s important to use the index that:
- is most relevant to the production system and breeding objective, and
- puts emphasis on economically important traits.
Always consider individual trait ASBVs even when using an index to select genetics that will meet the breeding objective.
For example, if eating quality is a priority and the enterprise is located in an environment where internal parasites are an important economic trait, the Lamb Eating Quality (LEQ) index, which takes into account worm egg count (WEC), would better align with the production goals than the Eating Quality (EQ) index, which does not include WEC.
The Sheep Genetics Index guides can help producers to determine the most appropriate index for their business. These list the characteristics of the production system in each chosen index and explain what the change in direction will be if used to make selection decisions.
There may be traits of interest that are not included in an index or that require different prioritisation. After choosing the index that is the closest fit for the breeding objectives, use this a drafting gate before focussing further on the traits of interest.
When it comes to using an index in purchasing decisions:
- Rank animals using the chosen index.
- Consider the individual ASBVs that are important in the breeding objective to further shortlist animals.
- Make sure those animals meet structural and type requirements through visual assessment.
Flock profile (via DNA testing)
If rams do not have ASBVs, flock profiling can be used to establish a flock’s position in ASBV terms.
Flock profiling is a service offered by Sheep Genetics for Merino flocks where a DNA test is carried out on 20 randomly selected ewe lambs to benchmark the flock against the industry average. Results are provided as ASBVs.
Flock profiling requires bloodlines to be well linked in the ASBV database to generate accurate results.