Abstract: Meteorological time series are used in modelling agrophysical processes of the soil-plant-atmosphere system which determine plant growth and yield. Additionally, longterm meteorological series are used in climate change scenarios. Such studies often require forecasting or projection of meteorological variables, eg the projection of occurrence of the extreme events. The aim of the article was to determine the most suitable exponential smoothing models to generate forecast using data on air temperature, wind speed, and precipitation time series in Jokioinen (Finland), Dikopshof (Germany), Lleida (Spain), and Lublin (Poland). These series exhibit regular additive seasonality or non-seasonality without any trend, which is confirmed by their autocorrelation functions and partial autocorrelation functions. The most suitable models were indicated by the smallest mean absolute error and the smallest root mean squared error.

Abstract: We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known.

Abstract: Climate and soil data at coarse resolution are often used as input for crop models in order to simulate crop yields at larger scales, e.g. at regional or national level, potentially leading to biased yield estimates. While the response to data resolution differs between crop models, it is unknown how the spatial aggregation of different types of input data interacts and contributes to this so-called aggregation effect. An ensemble of crop models was run with soil and climate input data at different spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. For this purpose, climate time series were averaged spatially and soil data was aggregated by selecting the dominant soil type with a representative soil profile based on a soil map at the scale of 1:50,000. Yields of winter wheat and silage maize were simulated under potential, water-limited and water-nitrogen-limited production conditions. Crop yields from soil and climate aggregation were evaluated separately.Mean of crop yields of the region and over the simulation period were reasonably reproduced by most models regardless of input data resolution, either using aggregated soil or climate as input. However, larger aggregation effects were observed at higher temporal resolution (e.g. annual yields). Models revealed similar spatial patterns in yield. Being distinct for soil and climate aggregation, these patterns indicate a larger impact of soil aggregation on the spatial distribution of simulated crop yield for this region. Additionally, models differed considerably in their susceptibility to input data aggregation. The results reveal the importance of model ensemble assessments and the relevance of data aggregation when short simulation periods are considered. No Label