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Abstract. Crop models routinely use meteorological variations to estimate crop yield. Soil moisture, however, is the primary source of water for plant growth. The aim of this study is to investigate the intraseasonal predictability of soil moisture to estimate silage maize yield in Germany. We also evaluate how approaches considering soil moisture perform compare to those using only meteorological variables. Silage maize is one of the most widely cultivated crops in Germany because it is used as a main biomass supplier for energy production in the course of the German Energiewende (energy transition). Reduced form fixed effect panel models are employed to investigate the relationships in this study. These models are estimated for each month of the growing season to gain insights into the time-varying effects of soil moisture and meteorological variables. Temperature, precipitation, and potential evapotranspiration are used as meteorological variables. Soil moisture is transformed into anomalies which provide a measure for the interannual variation within each month. The main result of this study is that soil moisture anomalies have predictive skills which vary in magnitude and direction depending on the month. For instance, dry soil moisture anomalies in August and September reduce silage maize yield more than 10 %, other factors being equal. In contrast, dry anomalies in May increase crop yield up to 7 % because absolute soil water content is higher in May compared to August due to its seasonality. With respect to the meteorological terms, models using both temperature and precipitation have higher predictability than models using only one meteorological variable. Also, models employing only temperature exhibit elevated effects.

Physical climate changes due to greenhouse gas emissions are well understood. However, quantifying the economic consequences remains a major challenge. Nevertheless, such quantification is crucial for the development of effective climate protection and adaptation strategies. Especially at local and regional levels, there is insufficient knowledge about the multiple impacts of climate change on economic sectors and regions. This is particularly true for the agricultural sector, which is considered to be vulnerable to the effects of global climate change. Since climate change not only changes temperature but also precipitation patterns in space and time, a higher variability of individual weather and the resulting extreme events (e.g. storms, flooding or droughts) is expected. Accurate models that depict the weather and crop yields are important not only for projecting the effects of agriculture, but also for projecting the impact of climate change on the associated economic and ecological consequences and thus for mitigation and adaptation policies. There are various methodological approaches to modelling climate impacts on agriculture. On the one hand, there are holistic approaches such as integrated assessment models. On the other hand, there are process-based or mechanistic models that capture the relevant biophysical relationships. Finally, there are empirical or statistical models that explain the relationship between meteorological variables and agricultural yields. These modelling approaches are rooted in very different disciplines and involve different emphases and assumptions, often resulting in a lack of consistency. Based on this scientific discussion, the thesis aims at the design of statistical approaches in order to allow a convergence of the results of the different methods. The aim is to identify missing aspects in current statistical approaches, such as the absence of important variables (e.g. soil moisture) and addressing the timing of the occurrence of extreme events that affect plant growth. In addition, new statistical approaches from the field of machine learning will be introduced to complement the existing methods, which are mainly based on econometrics. Furthermore, the approach presented here enables a Germany-wide impact assessment for the main crops. Finally, the development of such statistical damage functions promotes the management of the effects of extreme events on the agricultural sector on several time scales and can be used for climate change impact assessment. The work is cumulative and consists of three scientific articles.