Impact of a multiple-test strategy on breeding index development for the Australian dairy industry
Michelle Axford![https://orcid.org/0000-0001-5954-2080](/media/client/orcid_16x16.png)
![https://orcid.org/0000-0002-1397-1282](/media/client/orcid_16x16.png)
A DataGene Ltd, 5 Ring Road, Bundoora, Vic. 3083, Australia.
B School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.
C Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Vic. 3083, Australia.
D AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand.
E Corresponding author. Email: maxford@datagene.com.au
Animal Production Science - https://doi.org/10.1071/AN21058
Submitted: 10 February 2021 Accepted: 12 July 2021 Published online: 6 September 2021
Journal Compilation © CSIRO 2021 Open Access CC BY
Abstract
Context: A high level of acceptance and use of breeding indices by farmers and breeding companies that target a National breeding objective is an effective strategy to achieve high rates of genetic gain. Indices require maintenance to ensure that they reflect current economic and genetic trends and farmer preferences. Often, indices are tested on an average herd on the basis of, for example, milk composition and calving pattern. However, this strategy does not differentiate the impact on breeds. Australian dairy farmers routinely make breeding decisions by using the balanced performance index (BPI) or the health weighted index, published by DataGene.
Aims: The aim of the present study was to test new selection indices on the most popular breeds to better understand the genetic progress that each breed is expected to make. Existing economic models were updated to reflect changing trends in input costs and milk income. Consultative processes identified opportunities to improve alignment between farmer preferences and Australia’s National Breeding Objective. In response, more than 20 selection index options were developed and options were discussed with industry.
Methods: Indices were evaluated on three breeds in the following three ways: (1) expected response to selection from the use of each index, (2) index and trait correlations, and (3) relative trait emphasis.
Key results: Farmer trait preferences varied by breed and this information was considered in the development of economic weights. The updated BPI has primary emphasis on production traits (44% in Holstein, 49% in Reds), secondary emphasis on health and fertility (35% in Holstein, 29% in Reds), tertiary emphasis on type, workability and feed saved. The equivalent index for Jerseys is similar, but following stakeholder feedback to multiple tests, it was decided to remove emphasis on the feed saved estimated breeding values, so that the percentage emphasis on trait groups in Jerseys is 51% production, 32% health and fertility and the remainder on type and workability.
Implications: Understanding trait preferences and testing indices on different breeds can change the decisions that are made during index development.
Conclusions: Developing a better understanding of the differences among breeds had a positive impact on farmer engagement and resulted in a modified BPI for the Jersey breed.
Key words: dairy selection index, breeding objective, trait preference.
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