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Wageningen Universiteit (2007)

Regional crop yield forecasting using probalistic crop growth modelling and remote sensing data assimilation

Wit, A. de

Titre : Regional crop yield forecasting using probalistic crop growth modelling and remote sensing data assimilation

Auteur : Wit, A. de

Université de soutenance : Wageningen Universiteit

Grade : PhD thesis 2007

Résumé partiel
Information on the outlook of yield and production of crops over large regions is essential for government services dealing with import and export of food crops, for agencies playing a role in food relief, for international organizations with a mandate in monitoring the world food production and trade, as well as for commodity traders. In Europe, such information is provided by the MARS (Monitoring Agriculture with Remote Sensing) Crop Yield Forecasting System operated by the Joint Research Centre. An important component in the MARS Crop Yield Forecasting System is the so-called Crop Growth Monitoring System (CGMS). This system employs the WOFOST crop growth model to determine the influence of soil, weather and management on crop yield with a spatial resolution of 50×50 km grid. Aggregated CGMS results are used as predictors for crop yield at the level of EU member states.

CGMS is being applied successfully within the framework of the MARS crop yield forecasting system. Nevertheless, there are large uncertainties related to applying WOFOST over large areas such as poorly known sowing dates and soil parameters, application of irrigation and the effect of drought due to limited weather station density. This thesis focuses on developing and testing methods for quantifying and reducing uncertainty in crop model simulations with a focus on reducing the uncertainty related to drought. The uncertainty in crop model simulations is quantified through the variability within an ensemble of models, while it is reduced by combining crop model simulations with satellite-derived information through an ensemble Kalman filter (EnKF). A key aspect in this approach is that the uncertainty of the different components of the system can be estimated. The ultimate goal is to improve the accuracy and timeliness of regional crop yield forecasts. It was demonstrated that the uncertainty in the interpolated meteorological forcings is important, particularly the uncertainty in precipitation fields. Therefore, a method was developed to generate equiprobable realisations of precipitation inputs which can be used as input in the crop simulation model.

It was demonstrated that the statistical properties of the precipitation field were reproduced reasonably well in the realisations, while the deviations from the target statistics that were found are of minor importance for crop models. Further, an ensemble Kalman filter was used to assimilate satellite observations of root-zone soil moisture for Spain, France, Germany and Italy over the period 1992–2000 for winter-wheat and grain maize. It was demonstrated that the assimilation of satellite observations lowered the error on a linear regression model between crop simulation model output and EUROSTAT winter-wheat yield statistics for 66% of the administrative regions. For grain maize the improvement was less evident because improved relationships could be found for 56% of the regions. At national level, the results of the regression only improved for Spain, but not for Germany, France and Italy. Although the results at national level were somewhat disappointing, it is encouraging that the results did improve for Spain where crop production is most affected by water limitation and thus the potential for improvement is greatest.

Mots clés : crop yield / yield forecasting / remote sensing / crops / growth / weather / models / crop growth models

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Page publiée le 27 mars 2008, mise à jour le 2 juin 2022