Selecting Heifers by Weight
This week we discussed the weak correlation between our genomic predictions for yearling weight and the raw weight data we’ve recently captured.
For as long as cattle scales have been common place, breeders have been selecting replacement heifers based on weight. Good yearling weight in replacement heifers is critical to optimise pregnancy rates and minimise dystocia. However, it is clear that weights alone are a poor indicator of genetic merit. This is because the noise from environmental factors renders the raw weight data somewhat meaningless. Environmental factors include: health and nutrition, date of birth and age of dam. Considering date of birth alone, in a 6 week joining we might get an 8 week spread of calving dates. 56 days difference in age could easily account for 56kg difference in weight.
As flagged in previous posts, there are other negative effects that result from selecting heifers primarily by weight: We are likely to avoid heifers from heifer dams as they typically wean lighter. And we are likely to select more heifers with big birthweight and/or big mature cow weight.
Critical Mating Weights
Critical mating weight for British bred heifers is usually described as 60% or 65% of mature cow weight (MCW). Our group uses 60% which we think is enough in the High Rainfall Zone in Spring calving herds where winters can be limiting but the Springs are reliably productive. We see strong weight gain during joining (between October and December).
On average, our PTE cows in condition score 2.5 weigh 575 kgs at weaning. That sets an average critical mating weight of 345kgs. HeiferSELECT gives us a genomic prediction for Mature Cow Weight so now that average critical weight of 345 can be a little less rigid. In theory, a heifer with a lower than average MCW can be forgiven from having a joining weight lower than the average. This year, some of the participating producers are giving those light heifers with strong genomic predictions and moderate MCW an opportunity. We look forward to watching how successfully they get pregnant, calve, get back in calf and wean a quality calf.
While not part of our original plan, project participants where keen to compare results. The HeiferSELECT scores provide an effective platform for benchmarking because the scores are based on all the animals (commercial Angus heifers in Australia) evaluated by this particular product. Last time I checked that was around 12,000 heifers.
The variation in the average (mean) scores for key traits proved a fun springboard for a vigorous discussion. Fairly clear-cut back stories about breeding objectives explained most of the variation, and this built a lot of confidence that the HeiferSELECT product accurately reflected the direction each herd had taken historically. It also provided valuable insight for producers considering how to (or whether to) take corrective action on any traits with comparatively lower scores.
For our herd (the blue bars), it was pleasing to see that a consistently implemented breeding objective (without onerous sire selection criteria) was achieving the desired result.
Diving deeper beyond mob averages, further insights were revealed when we looked at trait distribution graphs (histograms). The two histograms above compare two different herds with very different distributions for Total Breeding Value (TBV) which is a multi-trait selection index largely comparable to the Angus Breeding Index (ABI). Farm A uses natural mating with a fairly consistently applied bull selection criteria. Farm B runs an AI program which achieves nearly 50% of all heifers bred by AI sires. In the farm B heifers, there appears to be two fairly distinct groups of heifers – those sired by superior AI sires and those sired by the mop-up bulls.
This example highlighted to the group the value and limitations of AI programs in commercial herds. Of course, that the same polarisation of results can occur in natural breeding programs where a mix of sires without consistent genetic merit have been deployed.
The project is generating a lot of great discussions and I look forward to sharing more insights as we progress.