Using ERA-INTERIM for regional crop yield forecasting in Europe

Climate Research

Bettina Baruth¹  Kees van Diepen¹  Johnny te Roller¹  Iwan Supit¹  Raymond van den Wijngaart¹  Fabio Micale¹  Hendrik Boogaard¹  Daniel van Kraalingen¹  Allard de Wit¹ 

关键词: Crop simulation models;    Crop yield;    Regional scale;    ERA-INTERIM;    Europe;   

DOI  :  10.3354/cr00872

来源: Inter-Research Science Publishing

【 摘 要 】

ABSTRACT: Agrometeorological 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 and the availability of long and consistent time-series through the so-called reanalysis projects. We evaluated the ERA-INTERIM reanalysis data set from the European Centre for Medium-range Weather Forecasting for regional crop yield forecasting. Crop simulations were carried out using 2 identical model implementations: one using interpolated observed weather, the other using weather data derived from ERA-INTERIM. Output for both sources of weather variables was generated for the EU27 and neighbouring countries and 14 crops, aggregated to national level and validated using reported crop yields from the European Statistical Office. The results indicate that the system performs very similar in terms of crop yield forecasting skill for both sources of weather variables. In 38% of the crop–country combinations, the forecasting error can be reduced by more than 10% of the baseline forecast (the trend only) for both sources of weather variables. In almost 20% of the crop–country combinations, the forecasting error can be reduced by more than 25% of the baseline forecast. The results demonstrate that the ERA-INTERIM data set is highly suitable for regional crop yield forecasting over Europe and may be used for implementing regional crop forecasting over data-sparse regions. Finally, we conclude that there is a need to improve the crop calendar and/or calibration for some of the modelled crops.

【 授权许可】

Unknown