| Records |
Author  |
Pirttioja, N.; Carter, T.R.; Fronzek, S.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Asseng, S.; Baranowski, P.; Basso, B.; Bodin, P.; Buis, S.; Cammarano, D.; Deligios, P.; Destain, M.F.; Dumont, B.; Ewert, F.; Ferrise, R.; François, L.; Gaiser, T.; Hlavinka, P.; Jacquemin, I.; Kersebaum, K.C.; Kollas, C.; Krzyszczak, J.; Lorite, I.J.; Minet, J.; Minguez, M.I.; Montesino-San Martin, M.; Moriondo, M.; Müller, C.; Nendel, C.; Öztürk, I.; Perego, A.; Rodríguez, A.; Ruane, A.C.; Ruget, F.; Sanna, M.; Semenov, M.A.; Slawinski, C.; Stratonovitch, P.; Supit, I.; Waha, K.; Wang, E.; Wu, L.; Zhao, Z.; Rötter, R.P. |
| Title |
Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces |
Type |
Journal Article |
| Year |
2015 |
Publication |
Climate Research |
Abbreviated Journal |
Clim. Res. |
| Volume |
65 |
Issue |
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Pages |
87-105 |
| Keywords |
climate; crop model; impact response surface; IRS; sensitivity analysis; wheat; yield; climate-change impacts; uncertainty; 21st-century; projections; simulation; growth; region |
| Abstract |
This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop simulation models for sites in Finland, Germany and Spain across a latitudinal transect. The sensitivity of modelled yield to systematic increments of changes in temperature (-2 to +9°C) and precipitation (-50 to +50%) was tested by modifying values of baseline (1981 to 2010) daily weather, with CO2 concentration fixed at 360 ppm. The IRS approach offers an effective method of portraying model behaviour under changing climate as well as advantages for analysing, comparing and presenting results from multi-model ensemble simulations. Though individual model behaviour occasionally departed markedly from the average, ensemble median responses across sites and crop varieties indicated that yields decline with higher temperatures and decreased precipitation and increase with higher precipitation. Across the uncertainty ranges defined for the IRSs, yields were more sensitive to temperature than precipitation changes at the Finnish site while sensitivities were mixed at the German and Spanish sites. Precipitation effects diminished under higher temperature changes. While the bivariate and multi-model characteristics of the analysis impose some limits to interpretation, the IRS approach nonetheless provides additional insights into sensitivities to inter-model and inter-annual variability. Taken together, these sensitivities may help to pinpoint processes such as heat stress, vernalisation or drought effects requiring refinement in future model development. |
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English |
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| ISSN |
0936-577x 1616-1572 |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4662 |
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| Author |
Sándor, R.; Barcza, Z.; Acutis, M.; Doro, L.; Hidy, D.; Köchy, M.; Minet, J.; Lellei-Kovács, E.; Ma, S.; Perego, A.; Rolinski, S.; Ruget, F.; Sanna, M.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
| Title |
Multi-model simulation of soil temperature, soil water content and biomass in Euro-Mediterranean grasslands: Uncertainties and ensemble performance |
Type |
Journal Article |
| Year |
2016 |
Publication |
European Journal of Agronomy |
Abbreviated Journal |
European Journal of Agronomy |
| Volume |
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Issue |
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Pages |
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| Keywords |
Biomass; Grasslands; Modelling; Multi-model ensemble; Soil processes |
| Abstract |
• We simulate biomass, soil water content (SWC) and temperature (ST) in grasslands. • We compare nine models to the multi-model median (MMM) at nine sites. • With model calibration, we obtain satisfactory estimates of ST, less of SWC and biomass. • We observe discrepancies across models in the simulation of grassland processes. • We improve performance with multi-model approach. This study presents results from a major grassland model intercomparison exercise, and highlights the main challenges faced in the implementation of a multi-model ensemble prediction system in grasslands. Nine, independently developed simulation models linking climate, soil, vegetation and management to grassland biogeochemical cycles and production were compared in a simulation of soil water content (SWC) and soil temperature (ST) in the topsoil, and of biomass production. The results were assessed against SWC and ST data from five observational grassland sites representing a range of conditions – Grillenburg in Germany, Laqueuille in France with both extensive and intensive management, Monte Bondone in Italy and Oensingen in Switzerland – and against yield measurements from the same sites and other experimental grassland sites in Europe and Israel. We present a comparison of model estimates from individual models to the multi-model ensemble (represented by multi-model median: MMM). With calibration (seven out of nine models), the performances were acceptable for weekly-aggregated ST (R² > 0.7 with individual models and >0.8–0.9 with MMM), but less satisfactory with SWC (R² < 0.6 with individual models and < ∼ 0.5 with MMM) and biomass (R² < ∼0.3 with both individual models and MMM). With individual models, maximum biases of about −5 °C for ST, −0.3 m3 m−3 for SWC and 360 g DM m−2 for yield, as well as negative modelling efficiencies and some high relative root mean square errors indicate low model performance, especially for biomass. We also found substantial discrepancies across different models, indicating considerable uncertainties regarding the simulation of grassland processes. The multi-model approach allowed for improved performance, but further progress is strongly needed in the way models represent processes in managed grassland systems. |
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1161-0301 |
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LiveM |
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no |
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MA @ admin @ |
Serial |
4768 |
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Sándor, R.; Ehrhardt, F.; Basso, B.; Bellocchi, G.; Bhatia, A.; Brilli, L.; Migliorati, M.D.A.; Doltra, J.; Dorich, C.; Doro, L.; Fitton, N.; Giacomini, S.J.; Grace, P.; Grant, B.; Harrison, M.T.; Jones, S.; Kirschbaum, M.U.F.; Klumpp, K.; Laville, P.; Léonard, J.; Liebig, M.; Lieffering, M.; Martin, R.; McAuliffe, R.; Meier, E.; Merbold, L.; Moore, A.; Myrgiotis, V.; Newton, P.; Pattey, E.; Recous, S.; Rolinski, S.; Sharp, J.; Massad, R.S.; Smith, P.; Smith, W.; Snow, V.; Wu, L.; Zhang, Q.; Soussana, J.F. |
| Title |
C and N models Intercomparison – benchmark and ensemble model estimates for grassland production |
Type |
Journal Article |
| Year |
2016 |
Publication |
Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
| Volume |
7 |
Issue |
03 |
Pages |
245-247 |
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2040-4700 |
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| Notes |
LiveM, ft_macsur |
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no |
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MA @ admin @ |
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4868 |
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Sandor, R.; Ehrhardt, F.; Grace, P.; Recous, S.; Smith, P.; Snow, V.; Soussana, J.-F.; Basso, B.; Bhatia, A.; Brilli, L.; Doltra, J.; Dorich, C.D.; Doro, L.; Fitton, N.; Grant, B.; Harrison, M.T.; Kirschbaum, M.U.F.; Klumpp, K.; Laville, P.; Leonard, J.; Martin, R.; Massad, R.-S.; Moore, A.; Myrgiotis, V.; Pattey, E.; Rolinski, S.; Sharp, J.; Skiba, U.; Smith, W.; Wu, L.; Zhang, Q.; Bellocchi, G. |
| Title |
Ensemble modelling of carbon fluxes in grasslands and croplands |
Type |
Journal Article |
| Year |
2020 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
| Volume |
252 |
Issue |
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Pages |
107791 |
| Keywords |
C fluxes; croplands; grasslands; multi-model ensemble; multi-model; median (mmm); soil organic-carbon; greenhouse-gas emissions; climate-change impacts; crop model; data aggregation; use efficiency; n2o emissions; maize; yield; wheat; productivity |
| Abstract |
Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs – C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake [grazed or harvested biomass]). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods). |
| Address |
2020-06-08 |
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LiveM |
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MA @ admin @ |
Serial |
5230 |
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| Author |
Sándor, R.; Ma, S.; Acutis, M.; Barcza, Z.; Ben Touhami, H.; Doro, L.; Hidy, D.; Köchy, M.; Lellei-Kovács, E.; Minet, J.; Perego, A.; Rolinski, S.; Ruget, F.; Seddaiu, G.; Wu, L.; Bellocchi, G. |
| Title |
Uncertainty in simulating biomass yield and carbon–water fluxes from grasslands under climate change |
Type |
Journal Article |
| Year |
2015 |
Publication |
Advances in Animal Biosciences |
Abbreviated Journal |
Advances in Animal Biosciences |
| Volume |
6 |
Issue |
01 |
Pages |
49-51 |
| Keywords |
grassland productivity; carbon balance; model simulation; uncertainty; sensitivity |
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English |
Summary Language |
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Abbreviated Series Title |
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Series Issue |
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2040-4700 |
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Article |
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| Notes |
CropM, LiveM, ft_macsur |
Approved |
no |
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MA @ admin @ |
Serial |
4651 |
| Permanent link to this record |
