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|Title:||Using ERA-INTERIM for Regional Crop Yield Forecasting in Europe|
|Authors:||DE WIT Allard; BARUTH Bettina; BOOGARD Hendrik; VAN DIEPEN Kees; VAN KRAALINGEN Daniel; MICALE Fabio; TE ROLLER Johnny; SUPIT Iwan; WIJNGARD Raymond|
|Citation:||CLIMATE RESEARCH vol. 44 no. 1 p. 41-53|
|Type:||Articles in periodicals and books|
|Abstract:||Agrometeorologic systems for regional crop yield forecasting have traditionally relied on weather data derived from weather stations for crop simulation and yield prediction. In recent years, Numerical Weather Prediction (NWP) models have become an interesting source of weather data with the potential to replace observed weather data. This is a result of the steadily decreasing NWP grid sizes (currently in the order of 0.25°x0.25°) and the availability of long and consistent time-series through the so-called reanalysis projects. Within this study we evaluate the ERA-INTERIM reanalysis dataset from ECMWF for regional crop yield forecasting. Crop simulations were carried out using two identical model implementations: one implementation using interpolated observed weather, the other using weather data derived from ERA-INTERIM. Output from both systems was generated for the EU27 countries and 17 crops, aggregated to national level and validated using reported crop yields from the European Statistical office (EUROSTAT). The results indicate that both systems perform very similar in terms of crop yield forecasting skill. In 38.5% of the crop/country combinations both systems can reduce the error on the yield forecast by more then 10% of the baseline forecast (the trend only). In almost 20% of the crop/country combinations both systems can reduce the error on the yield forecast by more then 25% of the baseline forecast. The results demonstrate that the ERA-INTERIM dataset is highly suitable for regional crop yield forecasting over Europe and may be used for implementing regional crop forecasting over data sparse regions.|
|JRC Institute:||Institute for Environment and Sustainability|
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