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Hoffmann, H., Zhao, G., Constantin, J., Raynal, H., Wallach, D., Coucheney, E., et al. (2015). Effects of soil and climate input data aggregation on modelling regional crop yields. MACSUR Science Conference.
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Kuhnert, M., Yeluripati, J., Smith, P., Hoffmann, H., Constantin, J., Coucheney, E., et al. (2016). Effects of climate data aggregation on regional net primary production modelling.. Toulouse (France).
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Hoffmann, H., Zhao, G., Asseng, S. A. U. -,, Bindi, M., Cammarano, D., Constantin, J., et al. (2016). Analysing data aggregation effects on large-scale yield simulations.. Berlin (Germany).
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Coucheney, E., Buis, S., Launay, M., Constantin, J., Mary, B., García de Cortázar-Atauri, I., et al. (2015). Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France. Env. Model. Softw., 64, 177–190.
Abstract: Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. (C) 2014 Elsevier Ltd. All rights reserved.
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