The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations

Martre, Pierre; Kimball, Bruce A.; Ottman, Michael J.; Wall, Gerard W.; Asseng, Senthold; Ewert, Frank; Cammarano, Davide; Maiorano, Andrea; Aggarwal, Pramod K.; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Challinor, Andrew J.; Sanctis, Giacomo De; Doltra, Jordi; Dumont, Benjamin; Fereres, E.; García-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, L. A.; Izaurralde, R. C.; Jabloun, Mohamed; Jones, Curtis D.; Kassie, Belay T.; Kersebaum, Kurt Christian; Koehler, Ann‐Kristin; Müller, Christoph; Kumar, Soora Naresh; Lobell, David B.; Nendel, Claas; O’Leary, G.; Olesen, Jørgen E.; Palosuo, Taru; Priesack, Eckart; Rezaei, Ehsan Eyshi; Ripoche, Dominique; Rötter, Reimund P.; Semenov, M. A.; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter J.; Waha, Katharina; Wang, Enli; Wolf, J.; Zhao, Zhigan; Zhu, Yan

doi:10.18174/odjar.v4i0.15830

Cited by 11 publications

(5 citation statements)

References 10 publications

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“…In some cases, the results of past multi-model ensemble studies have been documented in data journals (e.g. Martre et al, 2018). Those studies could be revisited in order to estimate parameter and structure uncertainty.…”

Section: Discussionmentioning

confidence: 99%

Parameter variability makes a major contribution to uncertainty of crop phenology simulations

Wallach

Palosuo

Mielenz

et al. 2023

Preprint

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Crop phenology has a major influence on crop yield and is a major aspect of crop response to global warming. Process-based models of phenology are often used to predict the effect of weather on the development rate of crops through their growth phases, but such models are associated with large uncertainties, as demonstrated by the large variability between simulation results of different models. The purpose of this study is to estimate the relative importance of model structure uncertainty (due to uncertainty in the model equations) and model parameter uncertainty. Previous studies have assumed that the choice of model parameters to be fitted to data is fixed, and have evaluated the effect of uncertainty in those parameters. This underestimates parameter variance, as it ignores uncertainty in many aspects of the calibration approach, in particular the choice of parameters to estimate, as well as uncertainty in the parameters not fitted by calibration. Here, we propose and apply two approaches for estimating parameter variance that take into account uncertainty in all aspects of calibration, and, for one approach, uncertainty in parameters not fitted by calibration. Both approaches are based on previously reported large multi-model studies. The first approach uses components of variance analysis, which is possible because these studies included different modeling groups using the same model structure. The second approach is based on a study where multiple modeling groups each applied two different calibration procedures. The variance calculated from the two calibration approaches gives an estimate of parameter variance. Both methods give an overall result of 69% of total variance coming from parameter variance, for those simulated variables for which there are observed data. We conclude that in order to reduce uncertainty in crop phenology predictions, improving and sharing calibration procedures is as or more important than improving model structure.

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Section: Discussionmentioning

confidence: 99%

Parameter variability makes a major contribution to uncertainty of crop phenology simulations

Wallach

Palosuo

Mielenz

et al. 2023

Preprint

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“…The simulation results reported here are for both steps. For step one modelers had access to all the experimental data for the check cultivar, Bacanora, for the five growing seasons at Valdivia, Chile (2008-2009, 2009-2010, 2012-2014and 2014-2015, one growing season at Buenos Aires, Argentina (2009Argentina ( -2010 and four growing seasons at Ciudad Obregon, Mexico (2009-2010, 1990-1991, 2015-2016and 2016-2017 for a total of 10 site-year-treatment combinations. The data at Ciudad Obregon, Mexico in 1990-1991 were obtained from a previous AgMIP study (Asseng et al 2015;Martre et al 2017).…”

Section: Simulation Of Field Experimentsmentioning

confidence: 99%

A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

Guarin

Martre²,

Ewert³

et al. 2023

ODJAR

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Grain production must increase by 60% in the next four decades to keep up with the expected population growth and food demand. A significant part of this increase must come from the improvement of staple crop grain yield potential. Crop growth simulation models combined with field experiments and crop physiology are powerful tools to quantify the impact of traits and trait combinations on grain yield potential which helps to guide breeding towards the most effective traits and trait combinations for future wheat crosses. The dataset reported here was created to analyze the value of physiological traits identified by the International Wheat Yield Partnership (IWYP) to improve wheat potential in high-yielding environments. This dataset consists of 11 growing seasons at three high-yielding locations in Buenos Aires (Argentina), Ciudad Obregon (Mexico), and Valdivia (Chile) with the spring wheat cultivar Bacanora and a high-yielding genotype selected from a doubled haploid (DH) population developed from the cross between the Bacanora and Weebil cultivars from the International Maize and Wheat Improvement Center (CIMMYT). This dataset was used in the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Phase 4 to evaluate crop model performance when simulating high-yielding physiological traits and to determine the potential production of wheat using an ensemble of 29 wheat crop models. The field trials were managed for non-stress conditions with full irrigation, fertilizer application, and without biotic stress. Data include local daily weather, soil characteristics and initial soil conditions, cultivar information, and crop measurements (anthesis and maturity dates, total above-ground biomass, final grain yield, yield components, and photosynthetically active radiation interception). Simulations include both daily in-season and end-of-season results for 25 crop variables simulated by 29 wheat crop models.

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“…For the parameterization of SLA, data analysis was conducted on the "Hot Serial Cereal" experiment (Martre et al, 2018). Wheat was grown in different periods of the year and the impact of temperature on growth and development was examined.…”

Section: Leaf Dynamicsmentioning

confidence: 99%

New modelling technique for improving crop model performance - Application to the GLAM model

Droutsas

Challinor

Swiderski

et al. 2019

Environmental Modelling & Software

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A B S T R A C TCrop models simulate growth and development and they are often used for climate change applications. However, they have a variable skill in the simulation of crop responses to extreme climatic events. Here, we present a new dynamic crop modelling method for simulating the impact of abiotic stresses. The Simultaneous Equation Modelling for Annual Crops (SEMAC) uses simultaneous solution of the model equations to ensure internal model consistency within daily time steps; something that is not always guaranteed in the usual sequential method. The SEMAC approach is implemented in GLAM, resulting in a new model version (GLAM-Parti). The new model shows a clear improvement in skill under water stress conditions and it successfully simulates the acceleration of leaf senescence in response to drought. We conclude that SEMAC is a promising crop modelling technique that might be applied to a range of models.

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The Hot Serial Cereal Experiment for modeling wheat response to temperature: field experiments and AgMIP-Wheat multi-model simulations

Cited by 11 publications

References 10 publications

Parameter variability makes a major contribution to uncertainty of crop phenology simulations

Parameter variability makes a major contribution to uncertainty of crop phenology simulations

A high-yielding traits experiment for modeling potential production of wheat: field experiments and AgMIP-Wheat multi-model simulations

New modelling technique for improving crop model performance - Application to the GLAM model

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