Free Standard AU & NZ Shipping For All Book Orders Over $80!
Register      Login
Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE (Open Access)

Using mid-infrared spectroscopy to identify more fertile cows for insemination to sexed semen

Joanna E. Newton https://orcid.org/0000-0002-2686-3336 A * , Phuong N. Ho A and Jennie E. Pryce A B
+ Author Affiliations
- Author Affiliations

A Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, Vic. 3083, Australia.

B School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.


Handling Editor: James Hills

Animal Production Science 64, AN22343 https://doi.org/10.1071/AN22343
Submitted: 20 September 2021  Accepted: 25 January 2023  Published: 23 February 2023

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Broader applications of milk mid-infrared spectral data could add value to milk-recording data. One such application is to rank cows on the probability of conception to first service (MFERT) which could help prioritise cows for insemination with dairy sexed semen (SS).

Aims

This study compared the use of MFERT estimates against two other approaches, to (1) identify most and least fertile dairy cows and (2) prioritise cows predicted to be most fertile for first service insemination with SS.

Methods

Mid-infrared spectral data from first herd test after calving was used to generate 13 379 MFERT predictions for 76 cohorts. Reproduction records were used to calculate reproductive parameters, calf numbers and net benefit, i.e. calf values minus mating costs, for two breeding programs. Breeding program 1 used SS and conventional dairy semen, while Breeding program 2 used SS, conventional dairy and beef semen. Three semen-allocation approaches were compared, namely, allocation via MFERT, calving date (CDATE) or assignment via random number generator (RANDOM).

Key results

MFERT significantly outperformed (1) RANDOM in identifying cows most and least likely to calf after first insemination (P < 0.05), and (2) both CDATE and RANDOM in identifying cows most and least likely to calf overall (P < 0.05). This resulted in up to 1.5 and 4.5 more dairy heifer calves, in Breeding programs 1 and 2 respectively, and up to six fewer dairy-beef calves in Breeding program 2. Differences in net benefit among semen-allocation approaches were modest, although generally favoured MFERT. Few significant differences between MFERT and CDATE were found. However, significant net benefit differences among all three semen-allocation approaches were seen in Breeding program 2.

Conclusions

MFERT outperformed CDATE and RANDOM in identifying most and least fertile cows. Realised net benefits of semen allocation by MFERT over other approaches were modest. Given the impact of semen type and dairy-beef calf prices value proposition will vary.

Implications

Our study confirmed that MFERT can add value to milk recording data by identifying the most and least fertile cows. As MFERT value is sensitive to individual farm parameters, incorporation alongside other fertility parameters into a decision support tool is desirable.

Keywords: artificial insemination, breeding program, dairy-beef, dairy breeding, herd recording, mid-infrared spectroscopy, milk recording, reproduction.

References

Berry DP, Ring SC (2020) Short communication: the beef merit of the sire mated to a dairy female affects her subsequent performance. Journal of Dairy Science 103, 8241-8250.
| Crossref | Google Scholar |

Berry DP, Wall E, Pryce JE (2014) Genetics and genomics of reproductive performance in dairy and beef cattle. Animal 8, 105-121.
| Crossref | Google Scholar |

Butler ST, Hutchinson IA, Cromie AR, Shalloo L (2014) Applications and cost benefits of sexed semen in pasture-based dairy production systems. Animal 8, 165-172.
| Crossref | Google Scholar |

Byrne TJ, Santos BFS, Amer 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.
| Crossref | Google Scholar |

Cabrera VE (2018) Invited review: Helping dairy farmers to improve economic performance utilizing data-driving decision support tools. Animal 12, 134-144.
| Crossref | Google Scholar |

Carthy TR, McCarthy J, Berry DP (2019) A mating advice system in dairy cattle incorporating genomic information. Journal of Dairy Science 102, 8210-8220.
| Crossref | Google Scholar |

DataGene (2020) ‘NBO 2020 Options Paper.’ (DataGene: Melbourne, Vic., Australia)

De Marchi M, Toffanin V, Cassandro M, Penasa M (2014) Invited review: mid-infrared spectroscopy as phenotyping tool for milk traits. Journal of Dairy Science 97, 1171-1186.
| Crossref | Google Scholar |

DeJarnette JM, Nebel RL, Marshall CE (2009) Evaluating the success of sex-sorted semen in US dairy herds from on farm records. Theriogenology 71, 49-58.
| Crossref | Google Scholar |

Dennis NA, Stachowicz K, Visser B, Hely FS, Berg DK, Friggens NC, Amer PR, Meier S, Burke CR (2018) Combining genetic and physiological data to identify predictors of lifetime reproductive success and the effect of selection on these predictors on underlying fertility traits. Journal of Dairy Science 101, 3176-3192.
| Crossref | Google Scholar |

Genetics Australia (2021) Catalogues: 2021 April Dairy Sire Catalogue. Available at https://genaust.com.au/resource/catalogues [Accessed 14 July 2021]

Gaynor RC, Gorjanc G, Hickey JM (2020) AlphaSimR: an R package for breeding program simulations. G3 Genes|Genomes|Genetics [2] 11 1-5.
| Crossref | Google Scholar |

Grelet C, Dardenne P, Soyeurt H, Fernandez JA, Vanlierde A, Stevens F, Gengler N, Dehareng F (2021) Large-scale phenotyping in dairy sector using milk MIR spectra: key factors affecting the quality of predictions. Methods 186, 97-111.
| Crossref | Google Scholar |

Healy AA, House JK, Thomson PC (2013) Artificial insemination field data on the use of sexed and conventional semen in nulliparous Holstein heifers. Journal of Dairy Science 96, 1905-1914.
| Crossref | Google Scholar |

Hempstalk K, McParland S, Berry DP (2015) Machine learning algorithms for the prediction of conception success to a given insemination in lactating dairy cows. Journal of Dairy Science 98, 5262-5273.
| Crossref | Google Scholar |

Ho PN, Pryce JE (2020) Predicting the likelihood of conception to first insemination of dairy cows using milk mid-infrared spectroscopy. Journal of Dairy Science 103, 11535-11544.
| Crossref | Google Scholar |

Ho PN, Bonfatti V, Luke TDW, Pryce JE (2019) Classifying the fertility of dairy cows using milk mid-infrared spectroscopy. Journal of Dairy Science 102, 10460-10470.
| Crossref | Google Scholar |

Holden SA, Butler ST (2018) Review: Applications and benefits of sexed semen in dairy and beef herds. Animal 12, s97-s103.
| Crossref | Google Scholar |

Hutchinson IA, Shalloo L, Butler ST (2013) Expanding the dairy herd in pasture-based systems: the role of sexed semen use in virgin heifers and lactating cows. Journal of Dairy Science 96, 6742-6752.
| Crossref | Google Scholar |

Izzo M (2015) Sex-sorted semen: the potential reproductive game changer. In ‘Combined Australian Cattle Veterinarians & Australian Sheep Veterinarians Annual Conference, Hobart, Tas., Australia, 11–13 February 2015’. (Eds DS Beggs, RG Batey) pp. 145–154.

Lou W, Shi R, Ducro B, van der Linden A, Mulder HA, Oosting SJ, Liu L, Wang Y (2022) Classifying the likelihood of conception in dairy cow with milk mid-infrared spectra before the first insemination. In ‘World Congress on Genetics Applied to Livestock Production, Rotterdam, Netherlands, 3–8 July 2022.’ (Wageningen University & Research) Available at https://www.wageningenacademic.com/pb-assets/wagen/WCGALP2022/04_002.pdf

Martin-Collado D, Byrne TJ, Amer 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.
| Crossref | Google Scholar |

Newton JE, Nettle R, Pryce JE (2020) Farming smarter with big data: Insights from the case of Australia’s national dairy herd milk recording scheme. Agricultural Systems 181, 102811.
| Crossref | Google Scholar |

Newton JE, Ho PN, Pryce JE (2021) Using mid-infrared spectroscopy predictions of fertility to optimise semen allocation in dairy herds. Proceedings of the Association for the Advancement of Animal Breeding and Genetics 24, 263-266 Available at http://www.aaabg.org/aaabghome/AAABG24papers/66Newton24263.pdf.
| Google Scholar |

NHIA (2022) ‘Semen Market Survey 2021 Results.’ (National Herd Improvement Association of Australia Inc.: Vic., Australia)

Noonan EJ, Kelly JC, Beggs DS (2016) Factors associated with fertility of nulliparous dairy heifers following a 10-day fixed-time artificial insemination program with sex-sorted and conventional semen. Australian Veterinary Journal 94, 145-148.
| Crossref | Google Scholar |

R Development Core Team (2017) ‘R: a language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria)

Ruelle E, Shalloo L, Butler ST (2021) Economic impact of different strategies to use sex-sorted sperm for reproductive management in seasonal-calving, pasture-based dairy herds. Journal of Dairy Science 104, 11747-11758.
| Crossref | Google Scholar |

Shahinfar S, Page D, Guenther J, Cabrera V, Fricke P, Weigel K (2014) Prediction of insemination outcomes in Holstein dairycattle using alternative machine learning algorithms. Journal of Dairy Science 97, 731-742.
| Crossref | Google Scholar |

Shahinfar S, Guenther JN, David Page D, Kalantari AS, Cabrera VE, Fricke PM, Weigel KA (2015) Optimization of reproductive management programs using lift chart analysis and cost-sensitive evaluation of classification errors. Journal of Dairy Science 98, 3717-3728.
| Crossref | Google Scholar |

Shalloo L, Cromie A, McHugh N (2014) Effect of fertility on the economics of pasture-based dairy systems. Animal 8, 222-231.
| Crossref | Google Scholar |

Stone AE (2020) Symposium review: The most important factors affecting adoption of precision dairy monitoring technologies. Journal of Dairy Science 103, 5740-5745.
| Crossref | Google Scholar |

Walsh DP, Fahey AG, Mulligan FJ, Wallace M (2021) Effects of herd fertility on the economics of sexed semen in a high-producing, pasture-based dairy production system. Journal of Dairy Science 104, 3181-3196.
| Crossref | Google Scholar |