Effect of weather data aggregation on regional crop simulation for different crops, production conditions, and response variables
2015
Zhao, Gang | Hoffmann, Holger | van Bussel, Lenny G. J. | Enders, Andreas | Specka, Xenia | Sosa, Carmen | Yeluripati, Jagadeesh | Tao, Fulu | Constantin, Julie, J. | Raynal, Helene, H. | 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 | Roetter, Reimund | Roggero, Pier Paolo | Wallach, Daniel, D. | Cammarano, Davide | Asseng, Senthold | Krauss, Gunther | Siebert, Stefan | Gaiser, Thomas | Ewert, Frank | Institute of Crop Science and Resource Conservation [Bonn] (INRES) ; Rheinische Friedrich-Wilhelms-Universität Bonn | Plant Production Systems Group ; Wageningen University and Research [Wageningen] (WUR) | Institute of landscape systems analysis ; Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF) | Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet (SLU) | INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES ; University of Aberdeen | The James Hutton Institute | Agrifood Research Finland | AGroécologie, Innovations, teRritoires (AGIR) ; Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT) | Systems Modelling Team (Sustainable Production Group) ; Plant & Food Research | Johann Heinrich von Thünen-Institut = Thünen Institute | Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS) | Commonwealth Scientific and Industrial Research Organisation [Australia] (CSIRO) | Karlsruhe Institute of Technology = Karlsruher Institut für Technologie (KIT) | Department of Crop Production Ecology ; Swedish University of Agricultural Sciences = Sveriges lantbruksuniversitet (SLU) | Division Soil and Water Management ; Catholic University of Leuven = Katholieke Universiteit Leuven (KU Leuven) | Department of Agri-Food Production and Environmental Sciences ; Università degli Studi di Firenze = University of Florence = Université de Florence (UniFI) | Istituto di Biometeorologia [Firenze] (IBIMET) ; National Research Council of Italy | Consiglio Nazionale delle Ricerche (CNR) | Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE) ; Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS) ; University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF) | University of Florida [Gainesville] (UF) | BMBF/BMELV project on 'Modeling European Agriculture with Climate Change for Food Security (MACSUR)' [2812ERA115] ; Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning [220-2007-1218] ; strategic funding 'Soil-Water-Landscape' from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences) ; Royal Society of New Zealand; Climate Changes, Impacts and Implications for New Zealand Project (CCII)
International audience
اظهر المزيد [+] اقل [-]إنجليزي. We assessed the weather data aggregation effect (DAE) on the simulation of cropping systems for different crops, response variables, and production conditions. Using 13 process-based 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 (Delta), 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., 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.
اظهر المزيد [+] اقل [-]المعلومات البيبليوغرافية
تم تزويد هذا السجل من قبل Institut national de la recherche agronomique
