Title: Impact of meteorological drivers on regional inter-annual crop yield variability in France
Authors: CEGLAR ANDREJTORETI ANDREALECERF REMIVAN DER VELDE MarijnDENTENER Franciscus
Citation: AGRICULTURAL AND FOREST METEOROLOGY vol. 216 p. 58-67
Publisher: ELSEVIER SCIENCE BV
Publication Year: 2016
JRC N°: JRC93641
ISSN: 0168-1923
URI: http://dx.doi.org/10.1016/j.agrformet.2015.10.004
http://publications.jrc.ec.europa.eu/repository/handle/JRC93641
DOI: 10.1016/j.agrformet.2015.10.004
Type: Articles in periodicals and books
Abstract: The impact of intra-seasonal climate variability on interannual variation in winter wheat and grain maize yields over 92 French administrative regions is assessed. Observed monthly time series of temperature, precipitation and solar radiation during the growing season are analyzed together with reported annual crop yields with a statistical approach based on Partial Least Square Regression. Results highlight remarkable spatial differences in the contribution of the main meteorological drivers to crop yield variability and in the timing of the maximum impact. Overall, temperature and global solar radiation are identified as the most important variables influencing grain maize yields over the southern, eastern and northern parts of France, while rainfall variability dominates yields over the central and north-western parts of the country. Positive rainfall anomalies during the summer months lead to an increase in maize yields, while positive temperature and radiation anomalies have the opposite effect. Extensive irrigation suppresses the rainfall signal in dry years. Winter wheat yields are predominantly influenced by temperature variations in eastern France and by rainfall variations over the northern, north-western and south-eastern France. In general, variation in global radiation plays a more important role in the southern than in the northern part of the country. Our study contributes to a better understanding of the impact of intra-seasonal climate variability on crop yields. Potential applications of the inferred models are discussed, especially in terms of seasonal crop yield forecasting and validation of dynamic crop model simulations.
JRC Directorate:Sustainable Resources

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