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Paas, W. (2013). Impacts of climate change and socio-economic drivers on dairy farms in ‘the Baakse Beek’, the Netherlands. M.Sc., M.Sc.. Master's thesis, Wageningen UR, .
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Zhou, Z. (2013). Improving a grass yield model to assess impacts of climate change on grass yields around 2050 at plot level in the Dutch region Baakse Beek. M.Sc., M.Sc.. Master's thesis, Wageningen University, Wageningen.
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Högner, K. (2013). Improving the methodology for global agricultural water availability and identifying hot spots for potential dam sites in East-Africa. M.Sc., M.Sc.. Master's thesis, Potsdam Institute for Climate Impact Research, .
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König, H. (2013). Operationalising sustainability impact assessment of land use scenarios in developing countries : a stakeholder-based approach with case studies in China, India, Indonesia, Kenya, and Tunisia. PhD, PhD. Ph.D. thesis, Leibniz-Zentrum für Agrarlanschaftsforschung e.V., Müncheberg.
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Challinor, A. J., Smith, M. S., & Thornton, P. (2013). Use of agro-climate ensembles for quantifying uncertainty and informing adaptation. Agricultural and Forest Meteorology, 170, 2–7.
Abstract: ► Introduces the special issue on Agricultural prediction using climate model ensembles. ► Discuss remaining scientific challenges. ► Develops distinction between projection- and utility-based ensemble modelling. ► Recommendations made RE modelling and the analysis and reporting of uncertainty. Significant progress has been made in the use of ensemble agricultural and climate modelling, and observed data, to project future productivity and to develop adaptation options. An increasing number of agricultural models are designed specifically for use with climate ensembles, and improved methods to quantify uncertainty in both climate and agriculture have been developed. Whilst crop–climate relationships are still the most common agricultural study of this sort, on-farm management, hydrology, pests, diseases and livestock are now also examined. This paper introduces all of these areas of progress, with more detail being found in the subsequent papers in the special issue. Remaining scientific challenges are discussed, and a distinction is developed between projection- and utility-based approaches to agro-climate ensemble modelling. Recommendations are made regarding the manner in which uncertainty is analysed and reported, and the way in which models and data are used to make inferences regarding the future. A key underlying principle is the use of models as tools from which information is extracted, rather than as competing attempts to represent reality.
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