Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models

Rötter, Reimund P.; Palosuo, Taru; Kersebaum, Kurt Christian; Angulo, Carlos; Bindi, Marco; Ewert, Frank; Ferrise, Roberto; Hlavinka, Petr; Moriondo, Marco; Nendel, Claas; Olesen, Jørgen E.; Patil, R. K.; Ruget, Françoise; Takáč, Jozef; Trnka, Miroslav

doi:10.1016/j.fcr.2012.03.016

Cited by 294 publications

(211 citation statements)

References 58 publications

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“…These values (Table 4) are lower than those described for wheat (12.6 days) and barley (11.5 days) using nine simulation models (APES-ACE, CROPSYST, DAISY, DSSAT-CERES, FASSETT, HERMES, MONICA, STICKS and WOFOST) (Rotter et al, 2012), confirming the better performance of the model used in the present study.…”

Section: Resultsmentioning

confidence: 44%

Number of leaves and phenology of rice hybrids simulated by the SimulArroz model

Ribas

Streck

Duarte

et al. 2017

Rev. bras. eng. agríc. ambient.

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A B S T R A C TProcess-based-models can predict leaf and reproductive development of agricultural crops. The objective of this study was to calibrate and evaluate the SimulArroz model in simulating the number of leaves on the main culm and the phenology of three hybrid rice cultivars in Rio Grande do Sul, Brazil. Field experiments were conducted in Santa Maria, Cachoeirinha, Uruguaiana and Santa Vitória do Palmar, in Rio Grande do Sul state, in two growing seasons. Observations were made to determine the number of leaves in the main culm through the Haun scale and the phenology of three hybrid cultivars (PRIME CL, INOV CL and QM 1010 CL) and a conventional rice cultivar used as a control (IRGA 424). The calibration of the SimulArroz model was efficient for simulating the number of leaves and phenology as a function of the sowing date and maturity group of three hybrid rice cultivars in Rio Grande do Sul.Número de folhas e fenologia de híbridos de arroz simulado pelo modelo SimulArroz R E S U M O Modelos matemáticos baseado em processos podem prever o desenvolvimento folhar e reprodutivo de cultivos agrícolas. Objetivou-se, com este trabalho, calibrar e avaliar o modelo SimulArroz em simular o número de folhas e a fenologia de três cultivares de arroz híbrido no Rio Grande do Sul. Foram conduzidos experimentos de campo em Santa Maria, Cachoeirinha, Uruguaiana e Santa Vitória do Palmar, no Rio Grande do Sul, em dois anos agrícolas. Foram realizadas observações para determinar o número de folhas do colmo principal através da escala da Haun e a fenologia de três cultivares híbridas (PRIME CL, INOV CL e QM 1010 CL) e uma convencional de arroz usada como testemunha (IRGA 424). A calibração do modelo SimulArroz utilizada foi eficiente em simular o número de folhas e a fenologia em função da época de semeadura e do grupo de maturação de três cultivares híbridas de arroz irrigado no Rio Grande do Sul.

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Section: Resultsmentioning

confidence: 44%

Number of leaves and phenology of rice hybrids simulated by the SimulArroz model

Ribas

Streck

Duarte

et al. 2017

Rev. bras. eng. agríc. ambient.

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“…The need for quality criteria to exclude poorly performing models has been discussed (Palosuo et al 2011;Rötter et al 2012). Asseng et al (2013) removed those models with the highest and lowest 10 % of simulated values in some of their analyses, but do not discuss that choice.…”

Section: Choosing the Participants In A Multi-model Ensemble (Mme)mentioning

confidence: 99%

Lessons from climate modeling on the design and use of ensembles for crop modeling

et al. 2016

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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically Climatic Change (2016) 139:551-564 DOI 10.1007/s10584-016-1803-1 Highlights ?•Ensembles of crop models can improve uncertainty estimates, impact projections, and collaboration.•The climate modeling community has identified and studied many of the questions related to design and analysis of ensembles.•Much of the climate experience could be adapted to crop modeling. oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

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“…While uncertainty analyses of models addressing the first point are usually using combinations of stochastically distributed inputs by using, e.g., Monte-Carlo simulations (e.g., [22]), for the other two aspects recent studies have shown that the application of ensembles of complex simulation models is a valuable tool to assess the uncertainty in the estimation of climate impact on crop growth [23][24][25][26][27] and water consumption [28]. To assess the uncertainty of model based assessments of WF an ensemble of different crop models was applied to field data sets from five locations from across Europe.…”

Section: Introductionmentioning

confidence: 99%

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

Kersebaum

Kroes

Gobin

et al. 2016

Water

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Crop productivity and water consumption form the basis to calculate the water footprint (WF) of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%-19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%-39%. Models responded differently to elevated CO 2 in a FACE (Free-Air Carbon Dioxide Enrichment) experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%-49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%-68% of the total actual evapotranspiration.

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Simulation of spring barley yield in different climatic zones of Northern and Central Europe: A comparison of nine crop models

Cited by 294 publications

References 58 publications

Number of leaves and phenology of rice hybrids simulated by the SimulArroz model

Number of leaves and phenology of rice hybrids simulated by the SimulArroz model

Lessons from climate modeling on the design and use of ensembles for crop modeling

Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

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