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Olesen, J. E., Sharif, B., Plauborg, F., Yin, X., Bindi, M., Doro, L., et al. (2016). Comparison of wheat models and their sensitivity towards tillage and N fertilization with different calibration approaches.. Berlin (Germany).
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Olesen, J. E. (2016). Socio-economic impacts – agricultural systems. In M. Quante, & F. Colijn (Eds.), (pp. 397–407). North Sea Region climate change assessment, Regional Climate Studies.
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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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Nicklin, K., & Challinor, A. (2016). Addressing uncertainty in model input and evaluation data.. Berlin (Germany).
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Nguyen, T. P. L., Seddaiu, G., Virdis, S. G. P., Tidore, C., Pasqui, M., & Roggero, P. P. (2016). Perceiving to learn or learning to perceive? Understanding farmers’ perceptions and adaptation to climate uncertainties. Agricultural Systems, 143, 205–216.
Abstract: Perception not only shapes knowledge but knowledge also shapes perception. Humans adapt to the natural world through a process of learning in which they interpret their sensory impressions in order to give meaning to their environment and act accordingly. In this research, we examined how farmers’ decision making is shaped in the context of changing climate. Using empirical data (face-to-face semi-structured interviews and questionnaires) on four Mediterranean farming systems from a case study located in Oristano (Sardinia, Italy) we sought to understand farmers’ perception of climate change and their behaviors in adjustment of farming practices. We found different perceptions among farmer groups were mainly associated with the different socio-cultural and institutional settings and perceived relationships between climate factors and impacts on each farming systems. The research findings on different perceptions among farmer groups can help to understand farmers’ current choices and attitudes of adaptation for supporting the development of appropriate adaptation strategies. In addition, the knowledge of socio-cultural and economic factors that lead to biases in climate perceptions can help to integrate climate communication into adaptation research for making sense of climate impacts and responses at farm level.
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