Title: Assessing the impact of climate variability on crop yields in Europe
Authors: CEGLAR ANDREJTORETI ANDREANIEMEYER Stefan
Citation: International work-conference on Time Series vol. 2 p. 816
Publisher: Universidad de Granada
Publication Year: 2014
JRC N°: JRC93682
ISBN: 978-84-15814-97-4
URI: http://publications.jrc.ec.europa.eu/repository/handle/JRC93682
Type: Articles in periodicals and books
Abstract: Today already climate variability has an impact on crop yield. Under future climate change, not only average climate conditions will change but also the variability is expected to further increase. Therefore it is crucial to gain an appropriate understanding of the impact of climate variability on crop yield under current climatic conditions. This knowledge will help to identify adaptation strategies to cope with the impact of future climate change. This study identifies the key climate factors influencing the inter-annual variability of maize and winter wheat yields in Europe. In addition, it aims to find the large-scale atmospheric configurations most risky for crop loss under present climate conditions. Crop yield time series, obtained from national statistical offices in Europe, are used in this study. The time series are quality controlled and cover the last 12 to 25 years. Meteorological data is retrieved from the MARS Crop Yield Forecasting System (MCYFS) database, established and maintained by the Joint Research Centre for the purpose of crop yield monitoring and forecasting. Since crop yield is strongly influenced by changes and improvements in agro-management practice, a de-trending procedure (based on LOESS) is applied to crop yield time series. Thus, the filtered time series can be used to estimate the influence of climate variability on the inter-annual variability of crop yield. Partial least square regression (PLSR) analysis is applied to identify those climate factors with the highest impact on the variability of crop yields. PLSR aims at optimising the covariance between response and explanatory variables and is particularly useful when the number of predictors is large compared to the number of observations. De-trended crop yield time series are used as dependent variable and several different climate factors as independent variable. For this purpose, monthly values of cumulated precipitation, total global radiation and mean air temperature are calculated for each month of the grain maize growing season (from April to September) and the winter wheat growing season (from December to July). The second part of this analysis focuses on the identification and characterisation of the large scale atmospheric patterns that are the most risky for crop loss under present climate conditions. The frequency of prevailing weather regimes during the growing period is analysed in years with low yields and compared to the frequency in years with normal or above-average yields. The results reveal that the most influential climate factors and the timing of impact are highly dependent on the region where the crops are grown. Grain maize and winter wheat yield are significantly influenced by weather conditions during the period of flowering. The weather conditions at the beginning of the growing season have substantial impact on grain maize yield especially over regions with continental climate. This can be explained by the high influence of weather on sowing conditions and therefore also on the length of growing period. Finally, we discuss how these findings can be used in the assessment of climate change impacts.
JRC Directorate:Sustainable Resources

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