Predicting Ram Fertility

Spanner, Eloise
University of Sydney


The following is a summary submitted with the application. IThe student’s abstract/thesis has not been received.

Within the Australian wool industry there is a strong desire to predict a ram’s fertility before his use in breeding programs. At present, fertility prediction measures are conducted via physical examination of the animal (the 4Ts) and in some cases semen samples can be examined subjectively for motility, morphology, and sperm numbers. However, these characteristics are not able to accurately or effectively predict ram fertility or successful artificial insemination and debate on minimum semen standards continues.

The key outcome of this project is to solve these issues. Namely to determine the characteristics of ram semen that explain fertility (whether it be high or low), thus developing a prediction model able to assess the likely fertility for a given ram or straw of semen. In doing so, such a model would also facilitate a new ‘minimum standard’ for use of semen in an AI program.

These scientific outcomes will translate into genuine benefits for the wool industry via an improvement in and more consistent artificial insemination success rates. Increased confidence in the use of and success of AI in the wool industry will have positive effects on genetic and productivity gains for the national flock as the spread of elite genetics is accelerated through the national flock.