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Bartley, D. J., Skuce, P. J., Zadoks, R. N., & MacLeod, M. (2016). Endemic sheep and cattle diseases and greenhouse gas emissions. Advances in Animal Biosciences, 7(03), 253–255.
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Topp, K., Eory, V., Bannink, A., Bartley, D. J., Blanco-Penedo, I., Cortignani, R., et al. (2017). Modelling climate change adaptation in European agriculture: Definitions and Current Modelling (Vol. 10).
Abstract: Confidential content, in preparation for a peer-reviewed publication.
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Bartley, D. (2013). Identification of datasets on climate change in relation to livestock productivity (production and fitness traits) and livestock infectious disease (Vol. 1).
Abstract: Datasets from Germany and the United Kingdom containing information on geographic (European Union 27 countries), climatic, meteorological, host and infectious agents’ parameters (figure 2) have been completed and are now available for preliminary analysis relating to data quality and consistency. Data set information will continue to be added over the next 12 months. No Label
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Bartley, D. (2014). Do modellers dream of electric sheep? – Practical to mathematical and back again. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Disease agents, whether viral, bacterial or parasitic, infecting grazing domestic animals represent a significant threat to livestock health and welfare and to food security, globally. In addition, inefficiency in production due to sub-clinical disease adds significantly to a farm’s environmental footprint. Projected climatic changes over the short-medium term have implications for livestock pests and pathogens, both directly and indirectly, and will result in changing disease patterns e.g. incidence, seasonality and geographic spread. An area where interdisciplinary collaboration is mutually beneficial, and essential in order to gain a better understanding of the interactions between climatic change, pathogen dissemination and livestock productivity is between ‘fundamental’ or ‘practical’ livestock researchers and modellers. To facilitate this collaboration, there needs to be a dialogue between both parties on the data depth, quality and format required to populate different models to ensure relevant and appropriate outputs. An example of where this type of collaboration has been used is work using an Intergovernmental Panel on Climate Change (IPCC)-compliant model (CPLANv2) to calculate greenhouse gases (GHG) associated with fattening lambs over five consecutive grazing seasons. The results demonstrated that effective control of sub-clinical/clinical parasitic gastroenteritis resulted in a ~10% reduction in GHG emissions/kg live weight gain (Kenyon et al., 2013).
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Kipling, R. P., Topp, C. F. E., Bannink, A., Bartley, D. J., Blanco-Penedo, I., Cortignani, R., et al. (2019). To what extent is climate change adaptation a novel challenge for agricultural modellers. Env. Model. Softw., 120, Unsp 104492.
Abstract: Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers’ views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.
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