Abstract
Mathematical modelling is an essential component of understanding the challenge that climate change presents to livestock production systems. State-of-the-art climate change studies typically take a process-based approach that requires (i) selecting one or more modelled projections of future climate, (ii) downscaling these projections, (iii) simulating the agro-ecosystem (soils, plants and animals) under current and future climates and (iv) conducting an economic (and sometimes an environmental) analysis of the modelled outcomes. Within this long chain of models, relatively simple models of animal dynamics (from a nutritionist’s point of view) tend to be used in climate impact analyses; however using a livestock sub-model that is too simple can obscure the vitally important responses of livestock to variable forage supply and to climatic extremes.The analysis of climate change impacts and adaptation in livestock and integrated crop-livestock farming differs in important ways from similar analyses for cropping systems. Animal production systems typically rely on multiple land types and they have many more points of management intervention, increasing the complexity of the agro-ecosystem models that must be constructed. Livestock managers can adapt their management at two or more trophic levels, increasing the space of adaptations that needs to be evaluated. A rigid distinction between “impacts” and “adaptation” is hard to sustain; the human manager is more naturally viewed as a part of a livestock system.Filling 3 knowledge gaps in the modelling of livestock physiology would most advance our ability to assess climate change impacts and adaptation: a better quantification of the differences between animal breeds commonly used in OECD countries and those used in the rest of the world; better models for the behavioural determinants of daily forage intake, particularly under high temperatures; and connecting the cattle, sheep and goat genomes to their phenomes, to enable evaluation of breeding strategies under climate change.