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Dono, G.; Cortignani, R.; Giraldo, L.; Roggero, P.P. |
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Climate change and irrigated farming in the Mediterranean: lower expectation of favorable conditions to profitability rather than harshening of adverse conditions |
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CropM |
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MA @ admin @ |
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2397 |
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Dono, G.; Cortignani, R.; Dell’Unto, D.; Doro, L.; Lacetera, N.; Mula, L.; Pasqui, M.; Quaresima, S.; Vitali, A.; Roggero, P.P. |
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Productive and economic adaptation of Mediterranean agriculture to climate change (Produktive und wirtschaftliche Anpassung der mediterranen Landwirtschaft an den Klimawandel) |
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2014 |
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Jahrbuch der ÖGA |
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24 |
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213-222 |
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24. Jahrestagung der Österreichischen Gesellschaft für Agrarökonomie, 2014-09-25 to 2014-09-26, Vienna |
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MA @ admin @ |
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5027 |
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Dono, G.; Cortignani, R.; Doro, L.; Giraldo, L.; Ledda, L.; Pasqui, M.; Roggero, P.P. |
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Title |
An integrated assessment of the impacts of changing climate variability on agricultural productivity and profitability in an irrigated Mediterranean catchment |
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2013 |
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Water Resource Management |
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Water Resource Manage. |
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27 |
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10 |
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3607-3622 |
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discrete stochastic programming; climate change variability; adaptation to climate change; net evapotranspiration and irrigation requirements; water availability; epic crops model; economic impact of climate change; precipitation; uncertainty; region; series; yield; model; scale; wheat; gis |
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Climate change is likely to have a profound effect on many agricultural variables, although the extent of its influence will vary over the course of the annual farm management cycle. Consequently, the effect of different and interconnected physical, technical and economic factors must be modeled in order to estimate the effects of climate change on agricultural productivity. Such modeling commonly makes use of indicators that summarize the among environmental factors that are considered when farmers plan their activities. This study uses net evapotranspiration (ETN), estimated using EPIC, as a proxy index for the physical factors considered by farmers when managing irrigation. Recent trends suggest that the probability distribution function of ETN may continue to change in the near future due to changes in the irrigation needs of crops. Also, water availability may continue to vary due to changes in the rainfall regime. The impacts of the uncertainties related to these changes on costs are evaluated using a Discrete Stochastic Programming model representing an irrigable Mediterranean area where limited water is supplied from a reservoir. In this context, adaptation to climate change can be best supported by improvements to the collective irrigation systems, rather than by measures aimed at individual farms such as those contained within the rural development policy. |
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English |
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0920-4741 |
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TradeM |
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MA @ admin @ |
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4487 |
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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. |
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Title |
Modelling climate change adaptation in European agriculture: Definitions and Current Modelling |
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Report |
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2017 |
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FACCE MACSUR Reports |
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10 |
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L2.3.2-D |
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Confidential content, in preparation for a peer-reviewed publication. |
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LiveM |
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MA @ admin @ |
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4959 |
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Dono, G.; Cortignani, R.; Doro, L.; Roggero, P.P. |
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Title |
The adaptation of farm and awareness of ongoing climate change (CC) |
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2014 |
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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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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5131 |
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