Publication Abstracts

Van Bussel et al. 2016

Van Bussel, L.G.J., F. Ewert, G. Zhao, H. Hoffmann, A. Enders, D. Wallach, S. Asseng, G.A. Baigorria, B. Basso, C. Biernath, D. Cammarano, J. Chryssanthacopoulos, J. Constantin, J. Elliott, M. Glotter, F. Heinlein, K.-C. Kersebaum, C. Klein, C. Nendel, E. Priesack, H. Raynal, C.C. Romero, R.P. Rötter, X. Specka, and F. Tao, 2016: Spatial sampling of weather data for regional crop yield simulations. Agric. Forest Meteorol., 220, 101-115, doi:10.1016/j.agrformet.2016.01.014.

Field-scale crop models are increasingly applied at spatio-temporal scales that range from regions to the globe and from decades up to 100 years. Sufficiently detailed data to capture the prevailing spatio-temporal heterogeneity in weather, soil, and management conditions as needed by crop models are rarely available. Effective sampling may overcome the problem of missing data but has rarely been investigated. In this study the effect of sampling weather data has been evaluated for simulating yields of winter wheat in a region in Germany over a 30-year period (1982-2011) using 12 process-based crop models. A stratified sampling was applied to compare the effect of different sizes of spatially sampled weather data (10, 30, 50, 100, 500, 1000 and full coverage of 34,078 sampling points) on simulated wheat yields. Stratified sampling was further compared with random sampling. Possible interactions between sample size and crop model were evaluated.

The results showed differences in simulated yields among crop models but all models reproduced well the pattern of the stratification. Importantly, the regional mean of simulated yields based on full coverage could already be reproduced by a small sample of 10 points. This was also true for reproducing the temporal variability in simulated yields but more sampling points (about 100) were required to accurately reproduce spatial yield variability. The number of sampling points can be smaller when a stratified sampling is applied as compared to a random sampling. However, differences between crop models were observed including some interaction between the effect of sampling on simulated yields and the model used. We concluded that stratified sampling can considerably reduce the number of required simulations. But, differences between crop models must be considered as the choice for a specific model can have larger effects on simulated yields than the sampling strategy. Assessing the impact of sampling soil and crop management data for regional simulations of crop yields is still needed.

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BibTeX Citation

@article{va01300f,
  author={van Bussel, L. G. J. and Ewert, F. and Zhao, G. and Hoffmann, H. and Enders, A. and Wallach, D. and Asseng, S. and Baigorria, G. A. and Basso, B. and Biernath, C. and Cammarano, D. and Chryssanthacopoulos, J. and Constantin, J. and Elliott, J. and Glotter, M. and Heinlein, F. and Kersebaum, K.-C. and Klein, C. and Nendel, C. and Priesack, E. and Raynal, H. and Romero, C. C. and Rötter, R. P. and Specka, X. and Tao, F.},
  title={Spatial sampling of weather data for regional crop yield simulations},
  year={2016},
  journal={Agric. Forest Meteorol.},
  volume={220},
  pages={101--115},
  doi={10.1016/j.agrformet.2016.01.014},
}

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RIS Citation

TY  - JOUR
ID  - va01300f
AU  - van Bussel, L. G. J.
AU  - Ewert, F.
AU  - Zhao, G.
AU  - Hoffmann, H.
AU  - Enders, A.
AU  - Wallach, D.
AU  - Asseng, S.
AU  - Baigorria, G. A.
AU  - Basso, B.
AU  - Biernath, C.
AU  - Cammarano, D.
AU  - Chryssanthacopoulos, J.
AU  - Constantin, J.
AU  - Elliott, J.
AU  - Glotter, M.
AU  - Heinlein, F.
AU  - Kersebaum, K.-C.
AU  - Klein, C.
AU  - Nendel, C.
AU  - Priesack, E.
AU  - Raynal, H.
AU  - Romero, C. C.
AU  - Rötter, R. P.
AU  - Specka, X.
AU  - Tao, F.
PY  - 2016
TI  - Spatial sampling of weather data for regional crop yield simulations
JA  - Agric. Forest Meteorol.
VL  - 220
SP  - 101
EP  - 115
DO  - 10.1016/j.agrformet.2016.01.014
ER  -

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