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Coucheney, E.; Buis, S.; Launay, M.; Constantin, J.; Mary, B.; García de Cortázar-Atauri, I.; Ripoche, D.; Beaudoin, N.; Ruget, F.; &rianarisoa, K.S.; Le Bas, C.; Justes, E.; Léonard, J. |
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Title |
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 |
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Journal Article |
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Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
64 |
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177-190 |
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Keywords |
soil-crop model; stics; model performances; plant biomass; soil nitrogen; soil water; remote-sensing data; goodness-of-fit; hydrological model; simulation-models; solar-radiation; regional-scale; climate-change; generic model; data set; validation |
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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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1364-8152 |
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CropM |
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MA @ admin @ |
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4554 |
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Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Sosa, C.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Grosz, B.; Dechow, R.; Kuhnert, M.; Yeluripati, J.; Kiese, R.; Haas, E.; Klatt, S.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Vanuytrecht, E.; Tao, F.; Rötter, R.; Cammarano, D.; Asseng, S.; Weihermüller, L.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
Effects of soil and climate input data aggregation on modelling regional crop yields |
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Conference Article |
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Year |
2015 |
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MACSUR Science Conference |
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MACSUR Science Conference, 2015-04-08 to 2015-04-10, Reading, United Kingdom |
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MA @ admin @ |
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5037 |
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Grosz, B.; Dechow, R.; Gebbert, S.; Hoffmann, H.; Zhao, G.; Constantin, J.; Raynal, H.; Wallach, D.; Coucheney, E.; Lewan, E.; Eckersten, H.; Specka, X.; Kersebaum, K.-C.; Nendel, C.; Kuhnert, M.; Yeluripati, J.; Haas, E.; Teixeira, E.; Bindi, M.; Trombi, G.; Moriondo, M.; Doro, L.; Roggero, P.P.; Zhao, Z.; Wang, E.; Tao, F.; Roetter, R.; Kassie, B.; Cammarano, D.; Asseng, S.; Weihermueller, L.; Siebert, S.; Gaiser, T.; Ewert, F. |
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Title |
The implication of input data aggregation on up-scaling soil organic carbon changes |
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Journal Article |
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Year |
2017 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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96 |
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361-377 |
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Keywords |
Biogeochemical model; Data aggregation; Up-scaling error; Soil organic carbon; DIFFERENT SPATIAL SCALES; NITROUS-OXIDE EMISSIONS; MODELING SYSTEM; DATA; RESOLUTION; CROP MODELS; CLIMATE; LONG; PRODUCTIVITY; CROPLANDS; DAYCENT |
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In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1 km and 100 km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. (C)2017 Elsevier Ltd. All rights reserved. |
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2017-09-14 |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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5176 |
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Kuhnert, M.; Yeluripati, J.; Smith, P.; Hoffmann, H.; Constantin, J.; Coucheney, E.; Dechow, R.; Eckersten, H.; Gaiser, T.; Grosz, B.; Haas, E.; Kersebaum, K.-C.; Kiese, R.; Klatt, S.; Lewan, E.; Nendel, C.; Raynal, H.; Sosa, C.; Specka, X.; Teixeira, E.; Wang, E.; Weihermüller, L.; Zhao, G.; Zhao, Z.; Ogle, S.; Ewert, F. |
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Title |
Effects of climate data aggregation on regional net primary production modelling |
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Conference Article |
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2016 |
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Toulouse (France) |
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8th International Congress on Environmental Modelling and Software (iEMSs), 2016-07-10 to 2016-10-13, Toulouse, France |
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no |
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Call Number |
MA @ admin @ |
Serial |
4896 |
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Author |
Coucheney, E.; Eckersten, H.; Jansson, P.E.; Ewert, F.; Gaiser, T.; Hoffmann, H.; Lewan, E. |
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Title |
The role of spatial patterns of soil types for data aggregation effects in crop modelling |
Type |
Conference Article |
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Year |
2016 |
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Berlin (Germany) |
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International Crop Modelling Symposium iCROPM 2016, 2016-05-15 to 2016-05-17, Berlin, Germany |
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MA @ admin @ |
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4902 |
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