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Woolnough, S. (2015). Climate Modelling and Sub-seasonal to Seasonal Prediction: Opportunities and Challenges (Vol. 5).
Abstract: Dr Steve Woolnough is a Principal Research Fellow in the Climate directorate of the National Centre for Atmospheric Science, and leads their Tropical Group. His interests are in the variability of the Tropical Climate System on intraseasonal to seasonal timescales, and the representation of the tropical climate system in weather and climate prediction models. He is a member of three international panels of the WMO including the Steering Group of their sub-seasonal to seasonal prediction project. Dr Woolnough will discuss the current state of climate modelling and introduce some of the uncertainties in prediction of regional climate change, and the opportunities to narrow these uncertainties. He will also discuss the current state of sub-seasonal to seasonal prediction and introduce the WCRP/WWRP Sub-seasonal Prediction Project, a new WMO project to promote research into and application of operational prediction systems. No Label
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Niemi, J. (2016). Framework of stochastic gross margin volatility modeling of crop rotation with farm management practices (Vol. 9 C6 -).
Abstract: DP models with risk aversion through meanvariancespecification is already implemented inLuke and applied in North Savo regionHOWEVER climate change, e.g. changes in mean andvariance of crop yiels, still not yet taken into account– Recently, such crop modelling results have becomeavailble for wheat as well, not only for barley– Still CC impact available for 2 cereals crops only, whilemost farms cultivate more than 2 crops Some early conclusions• The suggested approach is consistent in terms of DPprinciples and mean-variance approach and can provideconsistent results for farm scale risk analysis• It is however hard to utilise the approach except assuming afarm with only few crops (those with crop modelling / otherresults of climate change effects on mean and (co-variance)© Natural Resources Institute Finland• Assuming no change in price (co)variability is a majorsimplification results show farm level (or local) effects ofchanges in mean yields and yield (co)variability only
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Köchy, M. (2013). Strategy for the dissemination of outputs at national, EU, and global levels (Vol. 1).
Abstract: Dissemination of FACCE MACSUR will operate in a tiered manner to the scientific community, funding agencies, decision makers in policy and agri-food businesses, and the general public. Efforts by the MACSUR hub level are targeted at the supra-national level, while individual institutions target regional and national stakeholders.The dissemination approaches will include multiple and various methods of information distribution including• scientific papers and presentations,• conferences, congresses, workshops, and courses targeted to specific stakeholders,• a fully developed and interactive website,• regular newsletters,• flyers,• alignment of methodologies and protocols with other research networks or projects. Dissemination is both passive (web site, publications) and active (flyers, presentations, workshops held jointly with other projects or organizations, Twitter). No Label
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Lacetera, N. (2013). National and transnational dairy cows biometeorological datasets linked to productive, reproductive and health performances data (Vol. 1).
Abstract: Different datasets have been completed and are now available for the analysis of interannual and seasonal variations of productive, reproductive or health data relative to intensively dairy cows and also to establish the relationships between temperature humidity index (THI) and dairy cow performances. Datasets are referred to different European countries (Italy, Belgium, Luxembourg and Slovenia) with different climatic features. All these datasets have data relative to Animal Pedigree (Cow ID, Birth date, Breed, Sire ID and Dam ID), Test-day records (Cow ID, Herd ID, Parity, Calving date, Test date, Milk yield, Milk fat and protein (%), Milk somatic cell score), Reproductive events (Cow ID, Herd ID, Parity, Calving date, AI date, Sire ID, Days Open, NRR-56 day), and Daily meteorological records (Meteo station ID, Zip code of the meteo station, Observation date, Max temperature, Min temperature, Mean temperature, Max relative humidity, Min relative humidity, Mean relative humidity, Solar radiation, Wind speed). The dataset relative to Italy includes also Mortality data (Animal ID, Herd ID, Death date) and Bulk milk quality data (Herd ID, Test date, Fat & protein (%), Somatic cell score, Bacterial count, Herd latitude, Herd longitude, Herd elevation). An additional database is still under construction and will be based on Spanish data from organic dairy farms. No Label
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Barnes, A., & Moran, D. (2013). Modelling Food Security and Climate Change: Scenario Analysis (Vol. 1).
Abstract: Developing scenarios is a common interest within MACSUR researchers. This report outlines the main results of a survey of TRADE-M participants with respect to the scenarios used within modelling, the time frame and the importance of factors in their development. Most researchers are generating their own regionally defined scenarios, though some are basing these on IPCC scenarios. Generally, they adopt a short-term time frame of up to 2020 to estimate impacts. Most see food production as the main driver behind the scenarios followed by climate change mitigation and adaptation. The main weakness seems to be lack of interest in modelling variability due to weather effects, these may be an argument for stronger cross-collaboration between different MACSUR consortia within the crops and animals groups. No Label
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