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Climatic Change

, Volume 104, Issue 3–4, pp 679–701 | Cite as

Climate change impact assessment: the role of climate extremes in crop yield simulation

  • M. Moriondo
  • C. Giannakopoulos
  • M. Bindi
Article

Abstract

This work was aimed at assessing the role of climate extremes in climate change impact assessment of typical winter and summer Mediterranean crops by using Regional Circulation Model (RCM) outputs as drivers of a modified version of the CropSyst model. More specifically, climate change effects were investigated on sunflower (Helianthus annuus L.) and winter wheat (Triticum aestivum L.) development and yield under the A2 and B2 scenarios of the IPCC Special Report on Emissions Scenarios (SRES). The direct impact of extreme climate events (i.e. heat stress at anthesis stage) was also included. The increase in both mean temperatures and temperature extremes under A2 and B2 scenarios (2071–2100) resulted in: a general advancement of the main phenological stages, shortening of the growing season and an increase in the frequency of heat stress during anthesis with respect to the baseline (1961–1990). The potential impact of these changes on crop yields was evaluated. It was found that winter and summer crops may possess a different fitting capacity to climate change. Sunflower, cultivated in the southern regions of the Mediterranean countries, was more prone to the direct effect of heat stress at anthesis and drought during its growing cycle. These factors resulted in severe yield reduction. In contrast, the lower frequency of heat stress and drought allowed the winter wheat crop to attain increased yields with respect to the baseline period. It can be concluded that the impact of extreme events should be included in crop-modelling approaches, otherwise there is the risk of underestimating crop yield losses, which in turn would result in the application of incorrect policies for coping with climate change.

Keywords

Heat Stress Winter Wheat Sowing Date Mean Bias Error Anthesis Stage 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.CNR–IBIMETFlorenceItaly
  2. 2.Institute for Environmental Research and Sustainable DevelopmentNational Observatory of AthensThissioGreece
  3. 3.Department of Plant, Soil and Environmental ScienceUniversity of FlorenceFlorenceItaly

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