Dr Michael Bradfield obtained his BSc at the University of the Free State, MSc at the University of Edinburg (Scotland) and PHD at the University of New England in Australia. He heads up BREEDPLAN in Southern Africa and also works as an international consultant in partnership with ABRI in Australia for various International organizations.
082 857 0961 firstname.lastname@example.org www.agribsa.co.za
The saying “You only get out of something that you put into it” is particularly true of performance recording for the genetic evaluation of an animal, in terms of both the volume and quality of data recorded. Some game breeders may choose to performance record only their ‘best animals’ which they plan to select as future sires or dams, or with the aim of bringing up the average performance of the herd. But only recording your ‘best’ animals won’t result in ‘better’ performance figures.
Breeders should be aware that significant problems can arise from choosing to only performance record a subset of animals from a group. If performance records are provided for a subset of a group, it provides any genetic evaluation system with an inaccurate picture of the average performance of the animals in that y group which can result in the performance values being biased.
One of the basic mechanisms of any genetic evaluation analysis is the comparison of animals within a group of animals, i.e. all animals who have had a similar opportunity to perform. In a situation where only a proportion of a group has been recorded, the performance information for an animal will only be compared with the “selection” that has been recorded. If this “selection” is not an accurate reflection of the entire group, then genetic evaluation cannot make fair comparisons and the genetic evaluation produced may be biased.
Biased analysis from selective performance recording might paint a pretty picture, but in reality, the breeder will not be able to identify true problems, for example: the herd looks good but does not breed well. In order to establish if such problems are as a result of poor genetic potential, incorrect mating combinations or management problems, extensive records are required on the whole herd.
With genetic predictions (estimated breeding values) corrections are made for various environmental factors (herd, sex, age of dam, season born etc.). The genetic analysis then predicts the breeding performance of an animal which is not necessarily reflected in the performance of the individual itself, but rather in the offspring. For example an average performing buffalo cow (26” horn spread) with an outstanding breeding value can have the potential to produce top-performing offspring when mated to the correct bull. However, without complete records on the whole herd the cow would only be evaluated on her own performance (26” horn spread) and likely be sold as one of the “poor performing” animals, while this very cow might be responsible for producing your best calves if given the opportunity to enter the breeding herd. This phenomenon is explained in Figure 1.
Selective Performance Recording
Take for example a situation where a producer has only submitted performance information for the ‘best animals’ in a group versus submitting information for all animals in the group (Table 1).
The problem caused by selective performance recorded is demonstrated if we consider animal A2. In Scenario 1, all six animals in the group have a weight recorded at 200 days and A2 is 3kg heavier than the average of the group (238kg vs 235kg). This is an accurate reflection of how this animal has performed compared to its peers. However, in scenario 2, it is below average. This is not a true reflection of its performance because of improper recording. Note, in a herd management or genetic evaluation program the weights of animals are corrected to a standard weight (for example 200 day weight). Age of the mother is also accounted for in an evaluation. A one-year-old mother’s progeny cannot be compared to a five-year-old for example.
|Animal||200 day Weights: Scenario 1||200 day Weights Scenario 2|
Table 1: Selective performance recording scenario for 200 day weight (kg’s) where weights for all animals in a group are submitted (scenario 1) and when only the heaviest animals are recorded (scenario 2). In scenario 2 animal A2 is below the group average because A4 and A5 are excluded. This is not a true reflection of its genetic potential.
Selective recording can also influence the genetic evaluation by reducing the number of animals represented in a contemporary group. Where only a small number of animals are represented in a group, there are fewer animals to which an individual’s performance can be compared, and that performance cannot be used ‘effectively’ by a genetic evaluation system. Selective recording may result in an increased incidence of small groups, rendering performance information that is submitted ineffective.
Whole Herd Recording
Wherever possible, stud game herds should adopt a whole herd recording strategy. This involves recording all animals with the WS2 system (dead or alive) and recording performance for all animals in the group. Because genetic evaluation systems use not just individual performance in the calculation of genetic merit, but also the performance of all known relatives, it is also recommended that performance must be recorded on all available animals i.e. bulls and cows as opposed to just performance recording your few best animals.
The Wildlife Stud Services system will predict the genetic potential (estimated breeding values) for various performance (horn length, weaning weight) and fertility traits (scrotal circumference) once enough data is available. EBV’s (Estimated Breeding Values) are calculated using the animals own measurements, measurements of relatives (based on DNA verified pedigree) and correlations between traits. Whole herd recording is therefore crucial in accurate genetic predictions