Improved genetics for fertility

According to NZAEL, selective breeding delivers replacement dairy cows that are $11 more profitable (per cow) with each new generation. Genetic merit for fertility is one of several contributors to this annual gain. The measures currently used, calving and artificial breeding information, are cheap and easy to measure. However, heritability is low1, meaning that progress on improving fertility through a selection approach is slow, compared with other more heritable traits such as milk production. Selection for fertility cannot be overlooked, however, and opportunities exist to improve the gains that can be made. 

Can animal evaluation include selection for improved oestrous expression?

In high-yielding dairy systems, there’s a pronounced decline in oestrous expression as milk production increases2, making heat detection more difficult and cows harder to get back in calf3,4. The heritabilities of oestrous length and strength are reported to be less than 10%5. This is consistent with the general observation that fertility is a traditionally difficult trait in which to make rapid gains through genetic evaluation. That’s not because there is a lack of genetic variation in the traits driving mating and calving outcomes. It’s because fertility success can be affected by a high number of other factors, even with the most accurate herd recording5.

Reported estimates of heritability for fertility traits can be wide-ranging. For example, the literature says that the heritability of age at puberty in cows ranges from 9% to 56%. A previous estimate for New Zealand-type dairy cows was at the low range of just 9%6 . Our own recent studies, however, have been able to achieve a robust heritability estimate of 34%7, similar to that for milk production traits. Advances in genetic evaluation methods and greater accuracy in recording these traits may help explain this improved outcome.

Key points

  • A longer and stronger oestrus is more easily detected. 
  • Longer and stronger oestrus is more likely in cows with positive genetic merit for fertility. 
  • Activity-based sensor technology like wearable cow collars and tags provide a reliable and objective measure of oestrous expression. 
  • These ‘wearables’ can also inform other traits of interest relevant to genetic selection decisions (for example, the timing of oestrus after calving). 

The cow being ridden by a herd mate in this photo is a clear indication of her being in oestrus. 

Oestrous expression and detection are critical

Achieving a high level of reproductive performance during artificial breeding (AB) requires an equally high standard of oestrous detection performance. A review of the science and art of detecting oestrus reported previously8 noted the following core elements:

  • Oestrus is the sign that the cow is about to ovulate and is ready for insemination. 

  • The two most common errors people make are missing heats and putting up cows for AB when they’re not in heat. 

  • Stressed cows don’t have strong, easily detected heats. 

  • Put processes in place so people don’t experience ‘oestrous detection fatigue’, as heat detection errors are costly on herd reproductive performance.

Oestrus is longer and stronger in cows with high genetic merit for fertility

In comparing cows with either positive (+5 Fertility BV/Breeding Value) or negative (-5 Fertility BV) genetic merit for fertility, activity collars worn by cows identified more than 2100 oestrous events (Figure 19 ). The average duration of oestrus in the positive-Fertility BV cows was about 13 hours, compared with 11 hours in negativeFertility BV cows. Intensity of oestrus during these times was also greater in those with positive genetic merit for fertility9 . Oestrous expression is a distinguishing feature between cows with high and low genetic merit for fertility. 

An evaluation of whether these oestrous measures have potential for selective breeding is currently underway in DairyNZ ‘scale-up’ trials involving 5000 animals. A key requirement is large-scale accurate data, which was gathered using pedometers on a subset of 2000 of these animals. While the data is yet to be fully analysed, we’ve demonstrated that this technology can be deployed at scale. The behaviours relevant to describing oestrus for genetic evaluation purposes will came from this type of information. 

Timing of oestrus is also important 

In addition to assessing the length and strength of oestrus, wearables can also be used to determine the timing of oestrous events. Two typical examples are when puberty is reached; and when cows begin having oestrous cycles after calving. Both are novel fertility traits with potential to add value as predictors of genetic merit for fertility. The scale-up studies described above will pave the way towards assessing and developing the use of these traits. Age at puberty is likely to be more valuable as a predictor because it can be measured earlier in the animals’ lives, and it has a higher heritability than most fertility-related traits. This enables an earlier accuracy for sire evaluation and greater confidence in the use of younger, genetically superior sires. 

The time from calving to first oestrus is a heritable trait (about 12%) and slightly greater than reported for most fertility traits5,10. Cows that have prolonged intervals (longer than six weeks) between calving and first oestrus are a major source of infertility in our pasture-based, seasonal dairy system. While pre-mating cycling rates are largely influenced by management factors, there is a genetic component to these intervals that could be further investigated. The most obvious goal would be to prevent any gene flow linked with a prolonged noncycling issue, by using measures captured during the early stage of first lactation.

Future opportunities – a place for ‘wearables’ technology

The availability and use of on-farm wearables technology are increasing. Remote monitoring technologies offer a richer, more objective, and effective way to measure the behaviours, health and wellbeing of all cows. Wearables for oestrous detection are relevant to farm management, while also offering value at a sector level. Obtaining objective measures of oestrous expression traits might enable us to select animals that demonstrate an enhanced expression of oestrus. Information on when and how often oestrus occurs would also be valuable. The critical requirement is to share the data collected from routinely used wearables for sector-wide benefit, for example, for genetic improvement.


DairyNZ conducted this research under the Pillars of a New Dairy System research programme, funded by New Zealand dairy farmers through the DairyNZ levy and the Ministry for Business, Innovation and Employment.


1.     Fleming, A., C. F. Baes, A. A. A. Martin, T. C. S. Chud, F. Malchiodi, L. F. Brito, and F. Miglior. 2019. Symposium review: The choice and collection of new relevant phenotypes for fertility selection. Journal of Dairy Science 102:3722-3734. 

2.     Lopez, H., L. D. Satter, and M. C. Wiltbank. 2004. Relationship between level of milk production and estrous behavior of lactating dairy cows. Animal Reproduction Science 81:209-223. 

3.     Madureira, A. M. L., B. F. Silper, T. A. Burnett, L. Polsky, L. H. Cruppe, D. M. Veira, and R. L. A. Cerri. 2015. Factors affecting expression of estrus measured by activity monitors and conception risk of lactating dairy cows. Journal of Dairy Science 98:7003-7014.

4.     Ismael, A., E. Strandberg, M. Kargo, A. Fogh, and P. Løvendahl. 2015. Estrus traits derived from activity measurements are heritable and closely related to the time from calving to first insemination. Journal of Dairy Science 98:3470-3477. 

5.     Dennis, N. A., K. Stachowicz, B. Visser, F. S. Hely, D. K. Berg, N. C. Friggens, P. R. Amer, S. Meier, and C. R. Burke. 2018. Combining genetic and physiological data to identify predictors of lifetime reproductive success and the impact of selection on these predictors on underlying fertility traits. Journal of Dairy Science 101:3176-3192.

6.     Morris, C. A., and S. M. Hickey. 2004. Heritability of puberty in dairy heifers in commercial herds. Proceedings of the New Zealand Society of Animal Production 64:115-117. 

7.     Stephen, M., S. Meier, M. Price, J. E. Pryce, C. R. Burke, C. V. C. Phyn, and D. Garrick. 2022. Variance parameter estimation for age at puberty phenotypes under two levels of phenotype censorship. Journal of Dairy Science – short communication (in press).

8.     Burke, C. R. 2012. The science and art of detecting oestrus. DairyNZ Technical Series August 6:5-10. 

9.     Reed, C. B., B. Kuhn-Sherlock, C. R. Burke, and S. Meier. 2022. Estrus activity in lactating cows with divergent genetic merit for fertility traits. Journal of Dairy Science 105:1674-1686. 

10.  Løvendahl, P., and M. G. Chagunda. 2009. Genetic variation in estrus activity traits. Journal of Dairy Science 92:4683-4688.

Page last updated:

30 Aug 2022