Variability of effects of spatial climate data aggregation on regional yield simulation by crop models

Field-scale crop models are often applied at spatial resolutions coarser than that of
the arable field. However, little is known about the response of the models to spatially aggregated
climate input data and why these responses can differ across models. Depending on the model,
regional yield estimates from large-scale simulations may be biased, compared to simulations
with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models
for the region of North Rhine-Westphalia in Germany. The models were supplied with climate
data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were
used with 2 crops (winter wheat and silage maize ) and 3 production situations (potential, waterlimited
and nitrogen-water-limited growth) to improve the understanding of errors in model simulations
related to data aggregation and possible interactions with the model structure. The most
important climate variables identified in determining the model-specific input data aggregation
on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize).
Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate
input data aggregation changed the mean simulated regional yield by up to 0.2 t ha−1, whereas
simulated yields from single years and models differed considerably, depending on the data
aggregation. This implies that large-scale crop yield simulations are robust against climate data
aggregation. However, large-scale simulations can be systematically biased when being evaluated
at higher temporal or spatial resolution depending on the model and its parameterization.