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RESEARCH ARTICLE (Open Access)

Genetic parameters of blood urea nitrogen and milk urea nitrogen concentration in dairy cattle managed in pasture-based production systems of New Zealand and Australia

Irene van den Berg https://orcid.org/0000-0002-9292-8636 A D , Phuong N. Ho A , Mekonnen Haile-Mariam A , Phil R. Beatson B , Erin O’Connor B and Jennie E. Pryce https://orcid.org/0000-0002-1397-1282 A C
+ Author Affiliations
- Author Affiliations

A Agriculture Victoria, AgriBio, Centre for AgriBioscience, 5 Ring Road, Bundoora, Vic. 3083, Australia.

B CRV Ambreed, PO Box 176, Hamilton, New Zealand.

C School of Applied Systems Biology, La Trobe University, 5 Ring Road, Bundoora, Vic. 3083, Australia.

D Corresponding author. Email: irene.vandenberg@agriculture.vic.gov.au

Animal Production Science - https://doi.org/10.1071/AN21049
Submitted: 4 February 2021  Accepted: 29 April 2021   Published online: 13 July 2021

Journal compilation © CSIRO 2021 Open Access CC BY-NC-ND

Abstract

Context: Urinary nitrogen excretion by grazing cattle causes environmental pollution. Selecting for cows with a lower concentration of urinary nitrogen excretion may reduce the environmental impact. While urinary nitrogen excretion is difficult to measure, blood urea nitrogen (BUN), mid-infrared spectroscopy (MIR)-predicted BUN (MBUN), which is predicted from MIR spectra measured on milk samples, and milk urea nitrogen (MUN) are potential indicator traits. Australia and New Zealand have increasing datasets of cows with urea records, with 18 120 and 15 754 cows with urea records in Australia and New Zealand respectively. A collaboration between Australia and New Zealand could further increase the size of the dataset by sharing data.

Aims: Our aims were to estimate genetic parameters for urea traits within country, and genetic correlations between countries to gauge the benefit of having a joint reference population for genomic prediction of an indicator trait that is potentially suitable for selection to reduce urinary nitrogen excretion for both countries.

Methods: Genetic parameters were estimated within country (Australia and New Zealand) in Holstein, Jersey and a multibreed population, for BUN, MBUN and MUN in Australia and MUN in New Zealand, using high-density genotypes. Genetic correlations were also estimated between the urea traits recorded in Australia and MUN in New Zealand. Analyses used the first record available for each cow or within days-in-milk (DIM) intervals.

Key results: Heritabilities ranged from 0.08 to 0.32 for the various urea traits. Higher heritabilities were obtained for Jersey than for Holstein, and for the New Zealand cows than for the Australian cows. While urea traits were highly correlated within Australia (0.71–0.94), genetic correlations between Australia and New Zealand were small to moderate (0.08–0.58).

Conclusions: Our results showed that the heritability for urea traits differs among trait, breed, and country. While urea traits are highly correlated within country, genetic correlations between urea traits in Australia and MUN in New Zealand were only low to moderate.

Implications: Further study is required to identify the underlying causes of the difference in heritabilities observed, to compare the accuracies of different reference populations, and to estimate genetic correlations between urea traits and other traits such as fertility and feed intake. Larger datasets may help estimate genetic correlations more accurately between countries.

Keywords: nitrogen, environmental stress, animal breeding, quantitative genetics, urea.


References

Alexander DH, Novembre J, Lange K (2009) Fast model-based estimation of ancestry in unrelated individuals. Genome Research 19, 1655–1664.
Fast model-based estimation of ancestry in unrelated individuals.Crossref | GoogleScholarGoogle Scholar | 19648217PubMed |

Ariyarathne HBPC, Correa-Luna M, Blair H, Garrick D, Lopez-Villalobos N (2021) Can Nitrogen Excretion of Dairy Cows Be Reduced by Genetic Selection for Low Milk Urea Nitrogen Concentration? Animals 11, 737
Can Nitrogen Excretion of Dairy Cows Be Reduced by Genetic Selection for Low Milk Urea Nitrogen Concentration?Crossref | GoogleScholarGoogle Scholar | 33800330PubMed |

Beatson PR, Meier S, Cullen NG, Eding H (2019) Genetic variation in milk urea nitrogen concentration of dairy cattle and its implications for reducing urinary nitrogen excretion. Animal 13, 2164–2171.
Genetic variation in milk urea nitrogen concentration of dairy cattle and its implications for reducing urinary nitrogen excretion.Crossref | GoogleScholarGoogle Scholar | 30808431PubMed |

Bobbo T, Penasa M, Rossoni A, Cassandro M (2020) Short communication: genetic aspects of milk urea nitrogen and new indicators of nitrogen efficiency in dairy cows. Journal of Dairy Science 103, 9207–9212.
Short communication: genetic aspects of milk urea nitrogen and new indicators of nitrogen efficiency in dairy cows.Crossref | GoogleScholarGoogle Scholar | 32773306PubMed |

Brøndum RF, Su G, Janss L, Sahana G, Guldbrandtsen B, Boichard D, Lund MS (2015) Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction. Journal of Dairy Science 98, 4107–4116.
Quantitative trait loci markers derived from whole genome sequence data increases the reliability of genomic prediction.Crossref | GoogleScholarGoogle Scholar | 25892697PubMed |

Gustafsson AH, Palmquist DL (1993) Diurnal Variation of Rumen Ammonia, Serum Urea, and Milk Urea in Dairy Cows at High and Low Yields. Journal of Dairy Science 76, 475–484.
Diurnal Variation of Rumen Ammonia, Serum Urea, and Milk Urea in Dairy Cows at High and Low Yields.Crossref | GoogleScholarGoogle Scholar | 8445100PubMed |

Haile-Mariam M, MacLeod IM, Bolormaa S, Schrooten C, O’Connor E, de Jong G, Daetwyler HD, Pryce JE (2020) Value of sharing cow reference population between countries on reliability of genomic prediction for milk yield traits. Journal of Dairy Science 103, 1711–1728.
Value of sharing cow reference population between countries on reliability of genomic prediction for milk yield traits.Crossref | GoogleScholarGoogle Scholar | 31864746PubMed |

Harris BL, Kolver ES (2001) Review of Holsteinization on intensive pastoral dairy farming in New Zealand. Journal of Dairy Science 84, E56–E61.
Review of Holsteinization on intensive pastoral dairy farming in New Zealand.Crossref | GoogleScholarGoogle Scholar |

Ho PN, Luke TDW, Pryce JE (2021) Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia. Journal of Dairy Science 104, 4467–4477.
Validation of milk mid-infrared spectroscopy for predicting the metabolic status of lactating dairy cows in Australia.Crossref | GoogleScholarGoogle Scholar | 33551158PubMed |

Interbull (2018) Appendix. Available at https://interbull.org/static/mace_evaluations_archive/eval/prod-appen1-141.pdf [Accessed February 2021]

Kauffman AJ, St-Pierre NR (2001) The relationship of milk urea nitrogen to urine nitrogen excretion in Holstein and Jersey cows. Journal of Dairy Science 84, 2284–2294.
The relationship of milk urea nitrogen to urine nitrogen excretion in Holstein and Jersey cows.Crossref | GoogleScholarGoogle Scholar | 11699460PubMed |

Kohn RA, Dinneen MM, Russek-Cohen E (2005) Using blood urea nitrogen to predict nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats. Journal of Animal Science 83, 879–889.
Using blood urea nitrogen to predict nitrogen excretion and efficiency of nitrogen utilization in cattle, sheep, goats, horses, pigs, and rats.Crossref | GoogleScholarGoogle Scholar | 15753344PubMed |

Kohn RA, Kalscheur KF, Russek-Cohen E (2002) Evaluation of models to estimate urinary nitrogen and expected milk urea nitrogen. Journal of Dairy Science 85, 227–233.
Evaluation of models to estimate urinary nitrogen and expected milk urea nitrogen.Crossref | GoogleScholarGoogle Scholar | 11860115PubMed |

König S, Chang YM, Borstel UUV, Gianola D, Simianer H (2008) Genetic and phenotypic relationships among milk urea nitrogen, fertility, and milk yield in Holstein cows. Journal of Dairy Science 91, 4372–4382.
Genetic and phenotypic relationships among milk urea nitrogen, fertility, and milk yield in Holstein cows.Crossref | GoogleScholarGoogle Scholar | 18946143PubMed |

Luke TDW, Nguyen TTT, Rochfort S, Wales WJ, Richardson CM, Abdelsayed M, Pryce JE (2019a) Genomic prediction of serum biomarkers of health in early lactation. Journal of Dairy Science 102, 11142–11152.
Genomic prediction of serum biomarkers of health in early lactation.Crossref | GoogleScholarGoogle Scholar | 31587909PubMed |

Luke TDW, Rochfort S, Wales WJ, Bonfatti V, Marett L, Pryce JE (2019b) Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra. Journal of Dairy Science 102, 1747–1760.
Metabolic profiling of early-lactation dairy cows using milk mid-infrared spectra.Crossref | GoogleScholarGoogle Scholar | 30594377PubMed |

Lund MS, Van Den Berg I, Ma P, Brøndum RF, Su G (2016) Review: how to improve genomic predictions in small dairy cattle populations. Animal 10, 1042–1049.
Review: how to improve genomic predictions in small dairy cattle populations.Crossref | GoogleScholarGoogle Scholar | 26781646PubMed |

Misztal I, Legarra A, Aguilar I (2014a) Using recursion to compute the inverse of the genomic relationship matrix. Journal of Dairy Science 97, 3943–3952.
Using recursion to compute the inverse of the genomic relationship matrix.Crossref | GoogleScholarGoogle Scholar | 24679933PubMed |

Misztal I, Tsuruta S, Lourenco D, Aguilar I, Legarra A, Vitezica Z (2014b) Manual for BLUPF90 family of programs. Available at http://nce.ads.uga.edu/wiki/lib/exe/fetch.php?media=blupf90_all7.pdf.

Mitchell RG, Rogers GW, Dechow CD, Vallimont JE, Cooper JB, Sander-Nielsen U, Clay JS (2005) Milk urea nitrogen concentration: heritability and genetic correlations with reproductive performance and disease. Journal of Dairy Science 88, 4434–4440.
Milk urea nitrogen concentration: heritability and genetic correlations with reproductive performance and disease.Crossref | GoogleScholarGoogle Scholar | 16291635PubMed |

Mucha S, Strandberg E (2011) Genetic analysis of milk urea nitrogen and relationships with yield and fertility across lactation. Journal of Dairy Science 94, 5665–5672.
Genetic analysis of milk urea nitrogen and relationships with yield and fertility across lactation.Crossref | GoogleScholarGoogle Scholar | 22032390PubMed |

O’Callaghan P, Kelly-Quinn M, Jennings E, Antunes P, O’Sullivan M, Fenton O, Huallacháin DÓ (2019) The Environmental Impact of Cattle Access to Watercourses: Review. Journal of Environmental Quality 48, 340–351.
The Environmental Impact of Cattle Access to Watercourses: Review.Crossref | GoogleScholarGoogle Scholar | 30951116PubMed | )

Sargolzaei M, Chesnais JP, Schenkel FS (2014) A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478
A new approach for efficient genotype imputation using information from relatives.Crossref | GoogleScholarGoogle Scholar | 24935670PubMed |

Spek JW, Dijkstra J, Van Duinkerken G, Bannink A (2013) A review of factors influencing milk urea concentration and its relationship with urinary urea excretion in lactating dairy cattle. The Journal of Agricultural Science 151, 407–423.
A review of factors influencing milk urea concentration and its relationship with urinary urea excretion in lactating dairy cattle.Crossref | GoogleScholarGoogle Scholar |

Stoop WM, Bovenhuis H, Van Arendonk JAM (2007) Genetic parameters for milk urea nitrogen in relation to milk production traits. Journal of Dairy Science 90, 1981–1986.
Genetic parameters for milk urea nitrogen in relation to milk production traits.Crossref | GoogleScholarGoogle Scholar | 17369239PubMed |

van den Berg I, Boichard D, Lund MS (2016a) Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds. Journal of Dairy Science 99, 8932–8945.
Comparing power and precision of within-breed and multibreed genome-wide association studies of production traits using whole-genome sequence data for 5 French and Danish dairy cattle breeds.Crossref | GoogleScholarGoogle Scholar | 27568046PubMed |

van den Berg I, Boichard D, Lund MS (2016b) Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle. Genetics, Selection, Evolution. 48, 83
Sequence variants selected from a multi-breed GWAS can improve the reliability of genomic predictions in dairy cattle.Crossref | GoogleScholarGoogle Scholar | 27809758PubMed |

van den Berg I, Ho PN, Luke TDW, Haile-Mariam M, Bolormaa S, Pryce JE (2021) The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers. Journal of Dairy Science 104, 2008–2017.
The use of milk mid-infrared spectroscopy to improve genomic prediction accuracy of serum biomarkers.Crossref | GoogleScholarGoogle Scholar | 33358169PubMed |

van den Berg I, Xiang R, Jenko J, Pausch H, Boussaha M, Schrooten C, Tribout T, Gjuvsland AB, Boichard D, Nordbø Ø, Sanchez MP, Goddard ME (2020) Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds. Genetics, Selection, Evolution. 52, 37
Meta-analysis for milk fat and protein percentage using imputed sequence variant genotypes in 94,321 cattle from eight cattle breeds.Crossref | GoogleScholarGoogle Scholar | 32635893PubMed |

VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.
Efficient methods to compute genomic predictions.Crossref | GoogleScholarGoogle Scholar | 18946147PubMed |

Yang J, Lee SH, Goddard ME, Visscher PM (2011) GCTA: a tool for genome-wide complex trait analysis. American Journal of Human Genetics 88, 76–82.
GCTA: a tool for genome-wide complex trait analysis.Crossref | GoogleScholarGoogle Scholar | 21167468PubMed |