A method for implementing methane breeding values in Australian dairy cattle
C. M. Richardson
A B , B. Sunduimijid A , P. Amer C , I. van den Berg A and J. E. Pryce A B
A Agriculture Victoria Research, AgriBio, 5 Ring Road, Bundoora, Vic. 3083 Australia.
B School of Applied Systems Biology, La Trobe University, Bundoora, Vic. 3083, Australia.
C AbacusBio Limited, PO Box 5585, Dunedin 9058, New Zealand.
D Corresponding author. Email: caeli.richardson@agriculture.vic.gov.au
Animal Production Science - https://doi.org/10.1071/AN21055
Submitted: 5 February 2021 Accepted: 21 April 2021 Published online: 2 August 2021
Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND
Abstract
Context: There has been a lot of interest in recent years in developing estimated breeding values (EBVs) to reduce methane emissions from the livestock sector. However, while a major limitation is the availability of high-quality methane phenotypes measured on individual animals required to develop these EBVs, it has been recognised that selecting for improved efficiency of milk production, longevity, feed efficiency and fertility may be an effective strategy to genetically reduce methane emissions in dairy cows.
Aim: Applying carbon dioxide equivalents (CO2-eq) weights to these EBVs, we hypothesise that it is possible to develop a genetic tool to reduce greenhouse-gas emissions (GHG).
Methods: We calculated the effect of an EBV unit change in each trait in the Balanced Performance Index on CO2-eq emissions per cow per year. The estimated environmental weights were used to calculate a prototype index of CO2-eq emissions. The final set of EBVs selected for inclusion in the GHG subindex were milk volume, fat yield and protein yield, survival and feed saved, as these traits had an independent effect on emissions. Feed saved is the Australian feed efficiency trait. A further modification was to include a direct methane trait in the GHG subindex, which is a more direct genomic evaluation of methane estimated from measured methane data, calculated as the difference between actual and predicted emissions, for example, a residual methane EBV.
Key results: The accuracy of the GHG subindex (excluding residual methane EBV) is ~0.50, calculated as the correlation between the index and gross methane (using 3-day mean gross methane phenotypes corrected for fixed effects, such as batch and parity and adjusting for the heritability). The addition of the residual methane EBV had a minimal effect with a correlation of 0.99 between the indexes. This was likely to be due to limited availability of methane phenotypes, resulting in residual methane EBVs with low reliabilities.
Conclusions: We expect that as more methane data becomes available and the accuracy of the residual methane trait increases, the two GHG subindexes will become differentiated. When the GHG subindex estimates are applied to bull EBVs, it can be seen that selecting for bulls that are low emitters of GHG can be achieved with a small compromise in the BPI of ~20 BPI units (standard deviation of BPI = 100).
Implications: Therefore, selection for more sustainable dairy cattle, both economic and environmental, may be promptly implemented until sufficient data are collected on methane.
Keywords: methane emission, sustainability, selection index, index weights.
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