Policy decisions are often taken at the regional scale, while crop models, supporting these decisions, have been developed for individual locations, expecting location-specific, spatially homogeneous input data. Crop models are able to account for the variation in climatic conditions and management activities and their effects on crop productivity. However, regional applications require consideration of spatial variability in these factors. Several studies have analyzed effects of using spatially aggregated climate data on model outcomes. The effects of spatially aggregated sowing dates on simulations of crop phenological development have not been studied, however. We investigated the impact of spatial aggregation of sowing dates and temperatures on the simulated occurrence of ear emergence and physiological maturity of winter wheat in Germany, using the phenological model of AFRCWHEAT2. We observed time ranges for crop emergence exceeding 90 d, whereas for harvesting this was more than 75 d. Spatial aggregation to 100 km × 100 km reduced this range to less than 30 and 20 d for emergence and harvest, respectively. Differences among selected regions were relatively small, suggesting that non-climatic factors largely determined the spatial variability in sowing dates and consecutive phenological stages. Application of the AFRCWHEAT2 phenology model using location-specific weather data and emergence dates, and an identical crop parameter set across Germany gave similar deviations in all studied regions, suggesting that varietal differences were less important among regions than within regions. Importantly, bias in model outcomes as a result of using aggregated input data was small. Increase in resolution from 100 km to 50 km resulted in slight improvements in the simulations. We conclude that using spatially aggregated weather data and emergence dates to simulate the length of the growing season for winter wheat in Germany is justified for grid cells with a maximum area of 100 km × 100 km and for the model considered here. As spatial variability of sowing dates within a region or country can be large, it is important to obtain information about the representative sowing date for the region.
- different sowing dates
- process-based model
- winter-wheat crops
van Bussel, L. G. J., Ewert, F., & Leffelaar, P. A. (2011). Effects of data aggregation on simulations of crop phenology. Agriculture, Ecosystems and Environment, 142(1-2), 75-84. https://doi.org/10.1016/j.agee.2010.03.019