Climate change is projected to impact agricultural production. Sugar beet production is an important part of Swiss agriculture, which is why assessing potential climatic impacts is relevant. Since cropping simulation models are useful in such assessments, this thesis evaluated the performance of the World Food Studies (WOFOST) cropping simulation model for simulating the growth of sugar beets under Swiss conditions. The model’s performance was evaluated using yield observations from 20 locations for the period from 1990 to 2022, as well as yield observations from 950 sugar beet fields located across the Swiss Plateau in the year 2020. It was found that WOFOST performed better regarding the temporal variability of the yield observations (1990-2022) compared to the spatial variability (2020). In 2020, Swiss sugar beet production was impacted by the Beet Yellows Virus (BYV), resulting in decreased sugar beet yields. For the 950 sugar beet fields examined, information regarding the level of BYV contamination, alongside the location and harvested yield, was gathered through a survey of sugar beet farmers. A remote sensing analysis was conducted in which 101 different vegetation indices were calculated for the examined sugar beet field to identify the vegetation index that correlates best with the harvested yield and the BYV contamination category. The Chlorophyll Index Green (CIG), the Simple Ratio (SR), and the Green Ratio Vegetation Index (GRVI) showed the strongest correlation with observed yield, while the Anthocyanin Reflectance Index (ARI) demonstrated the best correlation with the BYV categories. Lastly, the combination of WOFOST and remote sensing parts showed that WOFOST predicted more frequent water stress for fields heavily affected by BYV, suggesting that water stress increased the vulnerability to BYV infections.
