Impact of a multiple-test strategy on breeding index development for the Australian dairy industry
Michelle Axford
A B C E , Bruno Santos D , Katarzyna Stachowicz D , Cheryl Quinton D , Jennie E. Pryce B C and Peter Amer D
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.
References
Abdelsayed M, Haile-Mariam M, Pryce J (2018) A multi-trait approach combining clinical mastitis and indicator traits to predict mastitis resistance. In ‘Proceedings of the World Congress on Genetics Applied to Livestock Production’. Electronic Poster Session – Biology & Species – Bovine (dairy); 1, 177.Alchemer 2019 Available at https://www.alchemer.com/
Berry DP, Wall E, Pryce JE (2014) Genetics and genomics of reproductive performance in dairy and beef cattle. Animal 8, 105–121.
| Genetics and genomics of reproductive performance in dairy and beef cattle.Crossref | GoogleScholarGoogle Scholar | 24703258PubMed |
Byrne TJ, Santos BFS, Am PR, Martin-Collado D, Pryce JE, Axford M (2016) New breeding objectives and selection indices for the Australian dairy industry. Journal of Dairy Science 99, 8146–8167.
| New breeding objectives and selection indices for the Australian dairy industry.Crossref | GoogleScholarGoogle Scholar | 27522425PubMed |
Cole JB, VanRaden PM (2018) Symposium review: possibilities in an age of genomics: the future of selection indices. Journal of Dairy Science 101, 3686–3701.
| Symposium review: possibilities in an age of genomics: the future of selection indices.Crossref | GoogleScholarGoogle Scholar | 29103719PubMed |
Cunningham EP, Tauebert H (2009) Measuring the effect of change in selection indices. Journal of Dairy Science 92, 6192–6196.
| Measuring the effect of change in selection indices.Crossref | GoogleScholarGoogle Scholar | 19923623PubMed |
Dairy Australia (2021) Cow and Farms Data. DataGene, Melbourne, Vic., Australia. Available at https://www.dairyaustralia.com.au/industry-statistics/cow-and-farms-data#.YRG9RIgzaUk [Verified 10 August 2021]
DataGene (2020a) National Herd Recording Statistics. Available at https://datagene.com.au/IndustryStatistics [Verified 17 August 2021]
DataGene (2020b) NBO Options Paper May 2020. DataGene, Bundoora, Melbourne, Vic., Australia. Available at https://datagene.com.au/sites/default/files/Upload%20Files/NBO%202020%20Options%20Paper%20May%202020%20GESC.pdf [Verified 26 January 2021]
Falconer DS, Mackay TFC (1996) ‘Introduction to quantitative genetics.’ (Pearson Education Ltd: Harlow, UK; New York, NY, USA)
Fuerst-Waltl B, Fuerst C, Obritzhauser W, Egger-Danner C (2016) Sustainable breeding objectives and possible selection response: finding the balance between economics and breeders’ preferences. Journal of Dairy Science 99, 9796–9809.
| Sustainable breeding objectives and possible selection response: finding the balance between economics and breeders’ preferences.Crossref | GoogleScholarGoogle Scholar | 27692721PubMed |
Haile-Mariam M, Pryce JE (2015) Variances and correlations of milk production, fertility, longevity, and type traits over time in Australian Holstein cattle. Journal of Dairy Science 98, 7364–7379.
| Variances and correlations of milk production, fertility, longevity, and type traits over time in Australian Holstein cattle.Crossref | GoogleScholarGoogle Scholar | 26254520PubMed |
Haile-Mariam M, Bowman PJ, Pryce JE (2013) Genetic analyses of fertility and predictor traits in Holstein herds with low and high mean calving intervals and in Jersey herds. Journal of Dairy Science 96, 655–667.
| Genetic analyses of fertility and predictor traits in Holstein herds with low and high mean calving intervals and in Jersey herds.Crossref | GoogleScholarGoogle Scholar | 23127912PubMed |
Leitch H (1994) ‘Comparison of international selection indices for dairy cattle breeding, Interbull annual meeting.’ Ottawa, Canada. (Interbull). Available at https://journal.interbull.org/index.php/ib/article/view/213 [Verified 5 February 2021]
Martin-Collado D, Byrne TJ, Am PR, Santos BFS, Axford M, Pryce JE (2015) Analyzing the heterogeneity of farmers’ preferences for improvements in dairy cow traits using farmer typologies. Journal of Dairy Science 98, 4148–4161.
| Analyzing the heterogeneity of farmers’ preferences for improvements in dairy cow traits using farmer typologies.Crossref | GoogleScholarGoogle Scholar | 25864048PubMed |
Miglior F, Muir BL, Van Doormaal BJ (2005) Selection Indices in Holstein Cattle of Various Countries. Journal of Dairy Science 88, 1255–1263.
| Selection Indices in Holstein Cattle of Various Countries.Crossref | GoogleScholarGoogle Scholar | 15738259PubMed |
Newton JE, Axford MM, Ho PN, Pryce JE (2021) Demonstrating the value of herd improvement in the Australian dairy industry. Animal Production Science 61, 220–229.
| Demonstrating the value of herd improvement in the Australian dairy industry.Crossref | GoogleScholarGoogle Scholar |
NHIA (2020) Semen Market Survey 2020 Results. National Herd Improvement Association. Available at https://www.nhia.org.au/files/Semen%20Market%20Survey%201%20July%202019%20to%2030%20June%202020%20Results.pdf [Verified 13 January 2021]
Paakala E, Martín‐Collado D, Mäki‐Tanila A, Juga J (2018) Variation in the actual preferences for AI bull traits among Finnish dairy herds. Journal of Animal Breeding and Genetics 135, 410–419.
| Variation in the actual preferences for AI bull traits among Finnish dairy herds.Crossref | GoogleScholarGoogle Scholar | 30334292PubMed |
Poelman A (2020) Breeding the most efficient cow: from tall to well-balanced. Holstein Hub. Holstein International Berver van Amerongen, 4, 1–2.
Pryce JE, Gonzalez-Recio O, Nieuwhof G, Wales WJ, Coffey MP, Hayes BJ, Goddard ME (2015) Hot topic: definition and implementation of a breeding value for feed efficiency in dairy cows. Journal of Dairy Science 98, 7340–7350.
| Hot topic: definition and implementation of a breeding value for feed efficiency in dairy cows.Crossref | GoogleScholarGoogle Scholar | 26254533PubMed |
Schneider S (2019) Worldwide breeding index 2019. Holstein International.
Slagboom M, Kargo M, Edwards D, Sørensen AC, Thomasen JR, Hjortø L (2016) Herd characteristics influence farmers’ preferences for trait improvements in Danish Red and Danish Jersey cows. Acta Agriculturæ Scandinavica. Section A, Animal Science 66, 177–182.
| Herd characteristics influence farmers’ preferences for trait improvements in Danish Red and Danish Jersey cows.Crossref | GoogleScholarGoogle Scholar |
Van der Werf J (2019) ‘Advances in Breeding of Dairy Cattle.’ (Ed. J Pryce) (Burleigh Dodds Science Publishing Ltd: Milton)
Watson P, Watson D (2019) Dairy Australia Animal Husbandry and Herd Genetics Survey report. Dairy Australia, Melbourne, Vic, Australia.
