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Dono, G. (2015). Awareness of climate change for adaptation of the farm sector (Vol. 4).
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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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Dono, G. (2013). Storylines regarding climate change and scenarios (Vol. 1).
Abstract: WP3 develops the tools for assessing the productive and economic impact of climate change and the potential of mitigation and adaptation strategies. This is achieved by focussing, along with CropM and LiveM, on significant crossing issues in specific geographical areas, natural and human resources, and farming systems. Following, the storylines regarding climate change and scenarios in the hot-spots. No Label
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Dono, G. (2013). Most relevant aspects of climate change in hot-spot analysis (Vol. 1).
Abstract: WP3 develops the tools for assessing the productive and economic impact of climate change and the potential of mitigation and adaptation strategies. This is achieved by focussing, along with CropM and LiveM, on significant crossing issues in specific geographical areas, natural and human resources, and farming systems. Following, the steps for identifying the hot-spots and the basic elements of climate change are shortly described. Next, the main economic and structural characteristics of each hot-spot are described followed by a presentation of the most relevant aspects of climate change, and of their main impacts on farm sector. No Label
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Dono, G., Cortignani, R., Doro, L., & Roggero, P. P. (2014). The adaptation of farm and awareness of ongoing climate change (CC). FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: Farm planning is based on awareness of climate variability, here assumed to depend on experience gained over the years, and to generate expectations on climatic variables. Expectations are based on probability distributions (pdfs) estimated on climate data and used to generate managing choices by means of Discrete Stochastic Programming. The model simulates the income losses in case farmers do not recognize the ongoing CC, and continue to plan assuming climate stability. In particular, the use of resources in 2010 is simulated based on the pdfs of the early 2000s, despite CC has changed the probabilities of the various states of nature. The model, calibrated with Positive Mathematical Programming, generates a 0.9% income increase when is allowed to adapt to 2010 climate pdfs. The model is also calibrated according to pdfs of 2010, i.e. recognizing CC: in this case income falls of 0.7% when farmers are simulated to use their soil mistakenly based of the 2000 pdfs. Given the short period of CC, the differences represent an appreciable error that farmers may be already committing. Properly specifying with the CC at local level can help building farmers’ awareness on it, and to properly manage their resources, recovering profitability.
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