Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Record number 495254
Title Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables
Author(s) Zhao, Gang; Hoffmann, Holger; Bussel, L.G.J. Van; Enders, Andreas; Specka, Xenia; Sosa, Carmen; Yeluripati, Jagadeesh; Tao, Fulu; Constantin, Julie; Raynal, Helene; Teixeira, Edmar; Grosz, Balázs; Doro, Luca; Zhao, Zhigan; Nendel, Claas; Kiese, Ralf; Eckersten, Henrik; Haas, Edwin; Vanuytrecht, Eline; Wang, Enli; Kuhnert, Matthias; Trombi, Giacomo; Moriondo, Marco; Bindi, Marco; Lewan, Elisabet; Bach, Michaela; Kersebaum, Kurt Christian; Rötter, Reimund; Roggero, Pier Paolo; Wallach, Daniel; Cammarano, Davide; Asseng, Senthold; Krauss, Gunther; Siebert, Stefan
Source Climate Research 65 (2015). - ISSN 0936-577X - p. 141 - 157.
DOI https://doi.org/10.3354/cr01301
Department(s) Plant Production Systems
PE&RC
Nature Conservation and Plant Ecology
Publication type Refereed Article in a scientific journal
Publication year 2015
Keyword(s) Crop model - Data aggregation - Model comparison - Scaling - Spatial heterogeneity - Spatial resolution
Abstract

We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 processbased crop models and the ensemble mean, we simulated 30 yr continuous cropping systems for 2 crops (winter wheat and silage maize) under 3 production conditions for the state of North Rhine-Westphalia, Germany. The DAE was evaluated for 5 weather data resolutions (i.e. 1, 10, 25, 50, and 100 km) for 3 response variables including yield, growing season evapotranspiration, and water use efficiency. Five metrics, viz. The spatial bias (Δ), average absolute deviation (AAD), relative AAD, root mean squared error (RMSE), and relative RMSE, were used to evaluate the DAE on both the input weather data and simulated results. For weather data, we found that data aggregation narrowed the spatial variability but widened the Δ, especially across mountainous areas. The DAE on loss of spatial heterogeneity and hotspots was stronger than on the average changes over the region. The DAE increased when coarsening the spatial resolution of the input weather data. The DAE varied considerably across different models, but changed only slightly for different production conditions and crops. We conclude that if spatially detailed information is essential for local management decision, higher resolution is desirable to adequately capture the spatial variability for heterogeneous regions. The required resolution depends on the choice of the model as well as the environmental condition of the study area.

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