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El Niño influence on potential maize yield in Iberian Peninsula

AuthorsCapa-Morocho, Mirian; Rodríguez-Fonseca, Belén ; Ruiz Ramos, Margarita
KeywordsMaize yield
El Niño
La Niña
Crop simulation
Iberian Peninsula
Reanalysis dataset
Issue Date15-Mar-2016
PublisherJohn Wiley & Sons
CitationInternational Journal of Climatology 36(3): 1313-1330 (2016)
AbstractEl Niño phenomenon is the leading mode of sea surface temperature interannual variability. It can affect weather patterns worldwide and therefore crop production. Crop models are useful tools for impact and predictability applications, allowing to obtain long time series of potential and attainable crop yield, unlike to available time series of observed crop yield for many countries. Using this tool, crop yield variability in a location of Iberia Peninsula (IP) has been previously studied, finding predictability from Pacific El Niño conditions. Nevertheless, the work has not been done for an extended area. The present work carries out an analysis of maize yield variability in IP for the whole 20th century, using a calibrated crop model at five contrasting Spanish locations and reanalyses climate datasets to obtain long time series of potential yield. The study tests the use of reanalysis data for obtaining only climate‐dependent time series of crop yield for the whole region, and to use these yield to analyse the influences of oceanic and atmospheric patterns. The results show a good reliability of reanalysis data. The spatial distribution of the leading principal component of yield variability shows a similar behaviour over all the studied locations in the IP. The strong linear correlation between El Niño index and yield is remarkable, being this relation non‐stationary on time, although the air temperature–yield relationship remains on time, being the highest influences during grain filling period. Regarding atmospheric patterns, the summer Scandinavian pattern has significant influence on yield in IP. The potential usefulness of this study is to apply the relationships found to improving crop forecasting in IP.
Publisher version (URL)https://doi.org/10.1002/joc.4426
Appears in Collections:(IGEO) Artículos
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