Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models

Palosuo, Taru; Kersebaum, Kurt Christian; Angulo, Carlos; Hlavinka, Petr; Moriondo, Marco; Olesen, Jørgen E.; Patil, R. K.; Ruget, Françoise; Rumbaur, Christian; Takáč, Jozef; Trnka, Miroslav; Bindi, Marco; Çaldağ, Barış; Ewert, Frank; Ferrise, Roberto; Mirschel, Wilfried; Şaylan, Levent; Šiška, Bernard; Rötter, Reimund P.

doi:10.1016/j.eja.2011.05.001

Cited by 440 publications

(287 citation statements)

References 49 publications

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“…One approach with crop models has been to identify models that have similar equations for underlying processes, such as photosynthesis. In general, however, it has not been found that structural similarity leads to similarity in simulated values in crop MMEs (Palosuo et al 2011;Martre et al 2015;Li et al 2015). An alternative approach, proposed for climate models (Bishop and Abramowitz 2013), is to examine the covariance in model errors as the measure of model dependence.…”

Section: Evaluating the Degree Of Relatedness Of The Models In A Mmementioning

confidence: 99%

“…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%

“…Ensembles allow one to obtain a probability distribution instead of a point prediction (Harris et al 2010). Furthermore, it has been empirically observed in many fields that ensemble averages or medians often better reproduce observations than even the best individual model (Hagedorn et al 2005;Tebaldi and Knutti 2007;Palosuo et al 2011;Martre et al 2015). Additional benefits from working with ensembles of models arise from the closer collaboration between modeling groups.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Evaluating the Degree Of Relatedness Of The Models In A Mmementioning

confidence: 99%

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

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2016

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“…In other disciplines it is less used (Table 1). However, it has been applied for prediction in ungauged basins in hydrology (Refsgaard and Knudsen 1996) and for testing a range of crop simulation models with explicit restrictions on model calibration (Palosuo et al 2011;Rötter et al 2011). As space-time-substitutions use data from other locations or periods, any test of this projection methodology involves elements of proxy site tests.…”

Section: Validation Methodologiesmentioning

confidence: 99%

“…For crop yield modelling a relevant criterion could be the frequency of yields below a critical level that exceeds the coping range of the particular farming system (Palosuo et al 2011). Similarly, for lake modelling a relevant criterion could be the probability of exceeding a critical threshold for abundance of potentially toxic cyanobacteria.…”

Section: Performance Criteriamentioning

confidence: 99%

A framework for testing the ability of models to project climate change and its impacts

et al. 2013

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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 splitsample 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

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Solar radiation management impacts on agriculture in China: A case study in the Geoengineering Model Intercomparison Project (GeoMIP)

et al. 2014

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Geoengineering via solar radiation management could affect agricultural productivity due to changes in temperature, precipitation, and solar radiation. To study rice and maize production changes in China, we used results from 10 climate models participating in the Geoengineering Model Intercomparison Project (GeoMIP) G2 scenario to force the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. G2 prescribes an insolation reduction to balance a 1% a À1 increase in CO 2 concentration (1pctCO2) for 50 years. We first evaluated the DSSAT model using 30 years of daily observed weather records and agriculture practices for 25 major agriculture provinces in China and compared the results to observations of yield. We then created three sets of climate forcing for 42 locations in China for DSSAT from each climate model experiment: (1) 1pctCO2, (2) G2, and (3) G2 with constant CO 2 concentration (409 ppm) and compared the resulting agricultural responses. In the DSSAT simulations: (1) Without changing management practices, the combined effect of simulated climate changes due to geoengineering and CO 2 fertilization during the last 15 years of solar reduction would change rice production in China by À3.0 ± 4.0 megaton (Mt) (2.4 ± 4.0%) as compared with 1pctCO2 and increase Chinese maize production by 18.1 ± 6.0 Mt (13.9 ± 5.9%). (2) The termination of geoengineering shows negligible impacts on rice production but a 19.6 Mt (11.9%) reduction of maize production as compared to the last 15 years of geoengineering. (3) The CO 2 fertilization effect compensates for the deleterious impacts of changes in temperature, precipitation, and solar radiation due to geoengineering on rice production, increasing rice production by 8.6 Mt. The elevated CO 2 concentration enhances maize production in G2, contributing 7.7 Mt (42.4%) to the total increase. Using the DSSAT crop model, virtually all of the climate models agree on the sign of the responses, even though the spread across models is large. This suggests that solar radiation management would have little impact on rice production in China but could increase maize production.

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Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models

Cited by 440 publications

References 49 publications

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

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

A framework for testing the ability of models to project climate change and its impacts

Solar radiation management impacts on agriculture in China: A case study in the Geoengineering Model Intercomparison Project (GeoMIP)

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