Plenaries

Survival of the fittest – selecting for sustainable ruminant livestock

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Abstract

Ruminant livestock production systems are facing an era of unprecedented challenge. Past productivity gains are now being threatened by severe and chronic weather perturbations, new disease threats, land-use competition, pressure to lower biogenic methane, reduce nitrogen leaching and N2O emissions, and an increased demand for high quality protein. Increased environmental variability is putting societal, physical and economic strain on production. Finding genetics fit for a new era of environmental challenges without sacrificing the productivity needed to feed the global population is crucial. Management alone cannot overcome maladaptation. Losses in genetic diversity due to population bottlenecks and the increase in monocultures or reliance on the narrow genetic base of one or two breeds place potential barriers for genetic selection for the new set of challenges that livestock sectors face. Despite these challenges, modern livestock populations successfully bred for productivity show genetic variation for resilience to stressors and for enteric methane emissions. We have shown that individuals vary in the composition of the microbial communities that they harbour, which then influence fermentation pathways and methane output. These pathways in turn, control energy sources for the animal and have a downstream effect on metabolic processes such as feed efficiency, tissue deposition and milk composition. All of these processes offer candidates for molecular phenotypes that have potential as predictors of fitness related traits. Advances in digital and molecular technology are accelerating new phenotypes. Variation in metabolic responses to nutritional stresses such as droughts can be captured by changes in methylation status and metabolic profiles. These phenotypes involve large amounts of high-dimensional data and complex physiological networks and interactions. Incorporation of these descriptors to select livestock that are low impact, climate adapted, resilient to stress, resistant to disease and productive will require unprecedented amounts of data to parameterise new statistical algorithms. It is also likely that many of these traits have optima, so traditional selection indices may not find the fittest. Computational advances offer new algorithms to interrogate multiple levels of complexity with the potential to incorporate dynamic evolution and optimize production while increasing tolerance to stressors.  We describe a program of work to determine the impact of genetic selection for increased productivity, decreased methane emissions, correlated changes in downstream metabolites and nutritional stress, and the utility of a range of biological and spectral predictors of fitness in livestock breeding schemes. 

Keywords: 2026

How to Cite:

Bilton, T., Booker, F., Clarke, S., Dodds, K., Hickey, S., Johnson, P., Jonker, A., McEwan, J., McRae, K., Perry, B. & Rowe, S., (2026) “Survival of the fittest – selecting for sustainable ruminant livestock”, World Congress on Genetics Applied to Livestock Production Digital Archive 2026(1): 2286250. doi: https://doi.org/10.31274/wcgalp.23893

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