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Author Bartley, D.J.; Skuce, P.J.; Zadoks, R.N.; MacLeod, M. url  doi
openurl 
  Title Endemic sheep and cattle diseases and greenhouse gas emissions Type Journal Article
  Year 2016 Publication Advances in Animal Biosciences Abbreviated Journal Advances in Animal Biosciences  
  Volume 7 Issue 03 Pages 253-255  
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  ISSN 2040-4700 ISBN Medium  
  Area Expedition Conference  
  Notes LiveM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 4865  
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Author Topp, K.; Eory, V.; Bannink, A.; Bartley, D.J.; Blanco-Penedo, I.; Cortignani, R.; Del Prado, A.; Dono, G.; Faverdin, P.; Graux, A.-I.; Hutchings, N.; Lauwers, L.; Özkan Gülzari, Ş.; Rolinski, S.; Ruiz Ramos, M.; Sandars, D.L.; Sándor, R.; Schoenhart, M.; Seddaiu, G.; van Middelkoop, J.; Weindl, I.; Kipling, R.P. url  openurl
  Title Modelling climate change adaptation in European agriculture: Definitions and Current Modelling Type Report
  Year 2017 Publication FACCE MACSUR Reports Abbreviated Journal  
  Volume 10 Issue Pages L2.3.2-D  
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  Abstract Confidential content, in preparation for a peer-reviewed publication.  
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  Notes LiveM Approved no  
  Call Number MA @ admin @ Serial 4959  
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Author Bartley, D. url  openurl
  Title Identification of datasets on climate change in relation to livestock productivity (production and fitness traits) and livestock infectious disease Type Report
  Year 2013 Publication FACCE MACSUR Reports Abbreviated Journal  
  Volume 1 Issue Pages D-L1.1  
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  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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  Notes Approved no  
  Call Number MA @ admin @ Serial 2255  
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Author Bartley, D. url  openurl
  Title Do modellers dream of electric sheep? – Practical to mathematical and back again Type Conference Article
  Year 2014 Publication Abbreviated Journal  
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  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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  Series Editor Series Title Abbreviated Series Title FACCE MACSUR Mid-term Scientific Conference  
  Series Volume 3(S) Sassari, Italy Series Issue Edition  
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  Area Expedition Conference FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy  
  Notes Approved no  
  Call Number MA @ admin @ Serial 5073  
Permanent link to this record
 
 
Author Kipling, R.P.; Topp, C.F.E.; Bannink, A.; Bartley, D.J.; Blanco-Penedo, I.; Cortignani, R.; del Prado, A.; Dono, G.; Faverdin, P.; Graux, A.-I.; Hutchings, N.J.; Lauwers, L.; Gulzari, S.O.; Reidsma, P.; Rolinski, S.; Ruiz-Ramos, M.; Sandars, D.L.; Sandor, R.; Schoenhart, M.; Seddaiu, G.; van Middelkoop, J.; Shrestha, S.; Weindl, I.; Eory, V. doi  openurl
  Title To what extent is climate change adaptation a novel challenge for agricultural modellers Type Journal Article
  Year 2019 Publication Environmental Modelling & Software Abbreviated Journal Env. Model. Softw.  
  Volume 120 Issue Pages Unsp 104492  
  Keywords Adaptation; Agricultural modelling; Climate change; Research challenges; greenhouse-gas emissions; farm-level adaptation; land-use; food; security; adapting agriculture; livestock production; decision-making; change impacts; dairy farms; crop  
  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.  
  Address 2020-02-14  
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  Language English Summary Language Original Title  
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  ISSN 1364-8152 ISBN Medium Article  
  Area Expedition Conference  
  Notes LiveM, ft_macsur Approved no  
  Call Number MA @ admin @ Serial 5223  
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