Impact of spatial soil and climate input data aggregation on regional yield simulations

Publikations-Art

Zeitschriftenbeitrag (peer-reviewed)

Autoren

Hoffmann, H., Zhao, G., Asseng, S., Bindi, M., Biernath, C.,...Heinlein, F.,...Priesack, E., et al.

Erscheinungsjahr

2016

Veröffentlicht in

PLoS ONE

Band/Volume

11/e0151782

DOI

10.1371/journal.pone.0151782

Seite (von - bis)

23 S.

We show the error in water-limited yields simulated by crop models which is associated with
spatially aggregated soil and climate input data. Crop simulations at large scales (regional,
national, continental) frequently use input data of low resolution. Therefore, climate and soil
data are often generated via averaging and sampling by area majority. This may bias simulated
yields at large scales, varying largely across models. Thus, we evaluated the error
associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter
wheat and silage maize were simulated under water-limited production conditions.We
calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for
the state of North Rhine-Westphalia, Germany. Most models showed yields biased by
models using aggregated soil data was in the range or larger than the inter-annual or intermodel
variability in yields. This error increased further when both climate and soil data were
aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a
first step towards an ex-ante assessment of aggregation errors in large-scale simulations.