| Records |
| Author |
Tao, F.; Roetter, R.P.; Palosuo, T.; Hernandez Diaz-Ambrona, C.G.; Ines Minguez, M.; Semenov, M.A.; Kersebaum, K.C.; Nendel, C.; Specka, X.; Hoffmann, H.; Ewert, F.; Dambreville, A.; Martre, P.; Rodriguez, L.; Ruiz-Ramos, M.; Gaiser, T.; Hohn, J.G.; Salo, T.; Ferrise, R.; Bindi, M.; Cammarano, D.; Schulman, A.H. |
| Title |
Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments |
Type |
Journal Article |
| Year |
2018 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
| Volume |
24 |
Issue |
3 |
Pages |
1291-1307 |
| Keywords |
barley; climate change; Europe; impact; super-ensemble; uncertainty; Nitrogen Dynamics; Multimodel Ensembles; Simulation-Models; Change; Scenarios; Yield; Rice; Weather; Growth; Wheat; Maize |
| Abstract |
Climate change impact assessments are plagued with uncertainties from many sources, such as climate projections or the inadequacies in structure and parameters of the impact model. Previous studies tried to account for the uncertainty from one or two of these. Here, we developed a triple-ensemble probabilistic assessment using seven crop models, multiple sets of model parameters and eight contrasting climate projections together to comprehensively account for uncertainties from these three important sources. We demonstrated the approach in assessing climate change impact on barley growth and yield at Jokioinen, Finland in the Boreal climatic zone and Lleida, Spain in the Mediterranean climatic zone, for the 2050s. We further quantified and compared the contribution of crop model structure, crop model parameters and climate projections to the total variance of ensemble output using Analysis of Variance (ANOVA). Based on the triple-ensemble probabilistic assessment, the median of simulated yield change was -4% and +16%, and the probability of decreasing yield was 63% and 31% in the 2050s, at Jokioinen and Lleida, respectively, relative to 1981-2010. The contribution of crop model structure to the total variance of ensemble output was larger than that from downscaled climate projections and model parameters. The relative contribution of crop model parameters and downscaled climate projections to the total variance of ensemble output varied greatly among the seven crop models and between the two sites. The contribution of downscaled climate projections was on average larger than that of crop model parameters. This information on the uncertainty from different sources can be quite useful for model users to decide where to put the most effort when preparing or choosing models or parameters for impact analyses. We concluded that the triple-ensemble probabilistic approach that accounts for the uncertainties from multiple important sources provide more comprehensive information for quantifying uncertainties in climate change impact assessments as compared to the conventional approaches that are deterministic or only account for the uncertainties from one or two of the uncertainty sources. |
| Address |
2018-03-08 |
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| Language |
English |
Summary Language |
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Series Issue |
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Edition |
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| ISSN |
1354-1013 |
ISBN |
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Notes  |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5194 |
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| Author |
Liu, B.; Martre, P.; Ewert, F.; Porter, J.R.; Challinor, A.J.; Mueller, C.; Ruane, A.C.; Waha, K.; Thorburn, P.J.; Aggarwal, P.K.; Ahmed, M.; Balkovic, J.; Basso, B.; Biernath, C.; Bindi, M.; Cammarano, D.; De Sanctis, G.; Dumont, B.; Espadafor, M.; Rezaei, E.E.; Ferrise, R.; Garcia-Vila, M.; Gayler, S.; Gao, Y.; Horan, H.; Hoogenboom, G.; Izaurralde, R.C.; Jones, C.D.; Kassie, B.T.; Kersebaum, K.C.; Klein, C.; Koehler, A.-K.; Maiorano, A.; Minoli, S.; San Martin, M.M.; Kumar, S.N.; Nendel, C.; O’Leary, G.J.; Palosuo, T.; Priesack, E.; Ripoche, D.; Roetter, R.P.; Semenov, M.A.; Stockle, C.; Streck, T.; Supit, I.; Tao, F.; Van der Velde, M.; Wallach, D.; Wang, E.; Webber, H.; Wolf, J.; Xiao, L.; Zhang, Z.; Zhao, Z.; Zhu, Y.; Asseng, S. |
| Title |
Global wheat production with 1.5 and 2.0 degrees C above pre-industrial warming |
Type |
Journal Article |
| Year |
2019 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
| Volume |
25 |
Issue |
4 |
Pages |
1428-1444 |
| Keywords |
1.5 degrees C warming; climate change; extreme low yields; food security; model ensemble; wheat production; Climate-Change; Crop Yield; Impacts; Co2; Adaptation; Responses; Models; Agriculture; Simulation; Growth |
| Abstract |
Efforts to limit global warming to below 2 degrees C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2 degrees C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0 degrees C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5 degrees C scenario and -2.4% to 10.5% under the 2.0 degrees C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2 degrees C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade. |
| Address |
2019-04-27 |
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English |
Summary Language |
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Edition |
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| ISSN |
1354-1013 |
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Article |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5219 |
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| Author |
Yin, X.; Kersebaum, K.-C.; Beaudoin, N.; Constantin, J.; Chen, F.; Louarn, G.; Manevski, K.; Hoffmann, M.; Kollas, C.; Armas-Herrera, C.M.; Baby, S.; Bindi, M.; Dibari, C.; Ferchaud, F.; Ferrise, R.; de Cortazar-Atauri, I.G.; Launay, M.; Mary, B.; Moriondo, M.; Öztürk, I.; Ruget, F.; Sharif, B.; Wachter-Ripoche, D.; Olesen, J.E. |
| Title |
Uncertainties in simulating N uptake, net N mineralization, soil mineral N and N leaching in European crop rotations using process-based models |
Type |
Journal Article |
| Year |
2020 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
| Volume |
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Issue |
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Pages |
107863 |
| Keywords |
multi-model ensemble; crop rotations; catch crops; N cycling; N export |
| Abstract |
Modelling N transformations within cropping systems is crucial for N management optimization in order to increase N use efficiency and reduce N losses. Such modelling remains challenging because of the complexity of N cycling in soil–plant systems. In the current study, the uncertainties of six widely used process-based models (PBMs), including APSIM, CROPSYST, DAISY, FASSET, HERMES and STICS, were tested in simulating different N managements (catch crops (CC) and different N fertilizer rates) in 12-year rotations in Western Europe. Winter wheat, sugar beet and pea were the main crops, and radish was the main CC in the tested systems. Our results showed that PBMs simulated yield, aboveground biomass, N export and N uptake well with low RMSE values, except for sugar beet, which was generally less well parameterized. Moreover, PBMs provided more accurate crop simulations (i.e. N export and N uptake) compared to simulations of soil (N mineralization and soil mineral N (SMN)) and environmental variables (N leaching). The use of multi-model ensemble mean or median of four PBMs significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15% for yield, aboveground biomass, N export and N uptake. Multi-model ensemble also significantly reduced the MAPE for net N mineralization and annual N leaching to around 15%, while it was larger than 20% for SMN. Generally, PBMs well simulated the CC effects on N fluxes, i.e. increasing N mineralization and reducing N leaching in both short-term and long-term, and all PBMs correctly predicted the effects of the reduced N rate on all measured variables in the study. The uncertainties of multi-model ensemble for N mineralization, SMN and N leaching were larger, mainly because these variables are influenced by plant-soil interactions and subject to cumulative long-term effects in crop rotations, which makes them more difficult to simulate. Large differences existed between individual PBMs due to the differences in formalisms for describing N processes in soil–plant systems, the skills of modelers and the model calibration level. In addition, the model performance also depended on the simulated variables, for instance, HERMES and FASSET performed better for yield and crop biomass, APSIM, DAISY and STICS performed better for N export and N uptake, STICS provided best simulation for SMN and N leaching among the six individual PBMs in the study, but all PBMs met difficulties to well predict either average or variance of soil N mineralization. Our results showed that better calibration for soil N variables is needed to improve model predictions of N cycling in order to optimize N management in crop rotations. |
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Edition |
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| ISSN |
0378-4290 |
ISBN |
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article |
| Area |
CropM |
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| Notes |
CropM, ft_macsur |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
5235 |
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| Author |
Refsgaard, J.C.; Madsen, H.; Andréassian, V.; Arnbjerg-Nielsen, K.; Davidson, T.A.; Drews, M.; Hamilton, D.P.; Jeppesen, E.; Kjellström, E.; Olesen, J.E.; Sonnenborg, T.O.; Trolle, D.; Willems, P.; Christensen, J.H. |
| Title |
A framework for testing the ability of models to project climate change and its impacts |
Type |
Journal Article |
| Year |
2014 |
Publication |
Climatic Change |
Abbreviated Journal |
Clim. Change |
| Volume |
122 |
Issue |
1-2 |
Pages |
271-282 |
| Keywords |
simulation-models; shallow lakes; predictions; calibration; ensembles; terminology; uncertainty; temperature; adaptation; validation |
| Abstract |
Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections. |
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English |
Summary Language |
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| ISSN |
0165-0009 1573-1480 |
ISBN |
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Article |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4688 |
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| Author |
Mansouri, M.; Destain, M.-F. |
| Title |
Predicting biomass and grain protein content using Bayesian methods |
Type |
Journal Article |
| Year |
2015 |
Publication |
Stochastic Environmental Research and Risk Assessment |
Abbreviated Journal |
Stoch. Environ. Res. Risk Assess. |
| Volume |
29 |
Issue |
4 |
Pages |
1167-1177 |
| Keywords |
crop model; particle filter; prediction; ensemble kalman filter; parameter-estimation; particle filters; decision-support; state estimation; model; nitrogen; navigation; tracking; systems |
| Abstract |
This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. |
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English |
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1436-3240 1436-3259 |
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| Notes |
CropM |
Approved |
no |
| Call Number |
MA @ admin @ |
Serial |
4664 |
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