Reducing greenhouse gas emissions through genetic selection in the Australian dairy industry

Abstract

This research explores possible options to reduce greenhouse gas (GHG) emissions in the Australian dairy industry by (1) including an environmental component in the national breeding program and (2) estimating the economic and environmental impacts of implementation of the subsequent indexes. A total of 12 possible selection indexes were considered. These indexes were developed to predict changes in gross per-animal methane production (using 3 scenarios depending on availability and efficacy of a direct methane trait breeding value prediction) with 4 different carbon prices, integrating them into an augmentation of the current conventional national selection index. Although some economic response is lost with inclusion of the GHG subindexes in the Balanced Performance Index, options do exist where this loss is marginal and, even in scenarios where all selection pressure is based on the environmental weighting, economic progress is still made in all cases. When including environmental traits within an index, if a relatively low percentage of economic gain or index progression is sacrificed, then approximately 40 to 50% of the maximum possible reductions in emissions may be achieved. This concurrent selection of estimated breeding values that have a correlated favorable response in emissions in addition to direct selection on a residual methane trait allows a high level of methane reduction to be achieved with a realized cost to farmers that is far lower than the economic value placed on carbon. By implementing a GHG subindex in the national breeding program, we can achieve up to a 7.9% decrease in residual methane and 9 times the reduction in gross emissions in 10 yr, compared with the current breeding program, with little to no cost to farmers. By 2050, selection based on one of the more moderate index scenarios at a carbon

Improving accuracy and stability of genetic predictions for dairy cow survival

“…Generally, the h 2 estimates for survival analysed as binary scores with a linear model were low in different breeds and across different countries (Sasaki 2013). However, the relative emphasis of survival is large in BPI (Byrne et al 2016) and is almost doubled in the SI due to its relationship with lowering methane emissions (Richardson et al 2022). Hence, improving the accuracy of genetic predictions could lead to greater genetic gain for survival, which has a large economic as well as environmental impact.…”

“…Although survival has a high relative weight or percentage emphasis (~8%) in Australia's national selection index, namely, the balanced performance index (BPI), this weight was almost doubled (~13%) in the sustainability index (SI) launched nationally in Australia in 2022 (DataGene 2022a). The aim of the SI is to reduce methane emission intensity, which is achieved through increasing the relative emphasis on feed saved and survival traits compared with BPI (Pryce et al 2015;Richardson et al 2022).…”

Meta-Analysis and Ranking of the Most Effective Methane Reduction Strategies for Australia’s Beef and Dairy Sector

“…The first 'Shift' strategy was a national carbon price that aims to encourage producers and consumers to shift to low-carbon farming alternatives. Richardson et al [46] measured the effect of including a GHG sub-index into the national breeding program, against carbon prices ranging from AUD 150 to 1000 per t CO 2 e and high-and low-accuracy residual CH 4 traits. The results showed that the current low accuracy of CH 4 prediction would reduce CH 4 by 0%, 0.09%, 0.36%, and 0.71% with a carbon pricing of AUD 150, AUD 250, AUD 500, and AUD 1000 per t CO 2 e, respectively.…”

How does reproduction account for dairy farm sustainability?

“…Although CH 4 production increases as milk yield increases due to genetic selection ( Hossein‑Zadeh, 2022 ), the main should be on CH 4 intensity (g of CH 4 per unit of milk yield). Reducing CH 4 at the expense of milk yield, DMI, or sacrificing economic gains should be avoided ( Richardson et al, 2022 ; Króliczewska et al, 2023 ).…”