authors: Lenny van Bussel, Frank Ewert, Gang Zhao, Holger Hoffmann, Andreas Enders, Daniel Wallach, Julie Constantin, Hélène Raynal, Christian Klein, Christian Biernath, Florian Heinlein, Eckart Priesack, Fulu Tao, Reimund Rötter; Davide Cammarano, Senthold Asseng, Joshua Elliott, Michael Glotter, Claas Nendel, Kurt-Christian Kersebaum, Xenia Specka, Bruno Basso, Guillermo Baigorria, Consuelo Romero, James Chryssanthacopoulos.
abstract: 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.
journal: Agr For Met (under review).