Your browser doesn't support javascript.
loading
Linking crop yield anomalies to large-scale atmospheric circulation in Europe.
Ceglar, Andrej; Turco, Marco; Toreti, Andrea; Doblas-Reyes, Francisco J.
Afiliação
  • Ceglar A; European Commission, Joint Research Centre, via Enrico Fermi 2749, 21027 Ispra, Italy.
  • Turco M; University of Barcelona, Av. Diagonal 647, Barcelona 08028, Spain.
  • Toreti A; Barcelona Supercomputing Center (BSC), c/ Jordi Girona 29, Barcelona 08034, Spain.
  • Doblas-Reyes FJ; European Commission, Joint Research Centre, via Enrico Fermi 2749, 21027 Ispra, Italy.
Agric For Meteorol ; 240-241: 35-45, 2017 Jun 15.
Article em En | MEDLINE | ID: mdl-28626277
ABSTRACT
Understanding the effects of climate variability and extremes on crop growth and development represents a necessary step to assess the resilience of agricultural systems to changing climate conditions. This study investigates the links between the large-scale atmospheric circulation and crop yields in Europe, providing the basis to develop seasonal crop yield forecasting and thus enabling a more effective and dynamic adaptation to climate variability and change. Four dominant modes of large-scale atmospheric variability have been used North Atlantic Oscillation, Eastern Atlantic, Scandinavian and Eastern Atlantic-Western Russia patterns. Large-scale atmospheric circulation explains on average 43% of inter-annual winter wheat yield variability, ranging between 20% and 70% across countries. As for grain maize, the average explained variability is 38%, ranging between 20% and 58%. Spatially, the skill of the developed statistical models strongly depends on the large-scale atmospheric variability impact on weather at the regional level, especially during the most sensitive growth stages of flowering and grain filling. Our results also suggest that preceding atmospheric conditions might provide an important source of predictability especially for maize yields in south-eastern Europe. Since the seasonal predictability of large-scale atmospheric patterns is generally higher than the one of surface weather variables (e.g. precipitation) in Europe, seasonal crop yield prediction could benefit from the integration of derived statistical models exploiting the dynamical seasonal forecast of large-scale atmospheric circulation.
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2017 Tipo de documento: Article