Crop modelling for integrated assessment of risk to food production from climate change

By Frank Ewert, R. Rötter, M. Bindi, H. Webber, M. Trnka, K. Kersebaum, Jørgen Eivind Olesen, Martin K. van Ittersum, Sander Janssen, M. Rivington and 12 more. Environmental Modelling & Software, 2015

311 citations recorded by the source corpus.

The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches. Extreme events and future climate uncertainty represent risk for food production.Crop models are largely able to simulate crop response to climate factors.Adaptations are best evaluated in integrated assessment models (IAM).Key limitations for crop models in IAM are low data availability and integration.Cross-scale nature of IAM suggests novel modelling approaches are needed.

Subfields: Ecology, Evolution, Behavior and Systematics, Plant Science, Soil Science

View Paper: https://www.semanticscholar.org/paper/19f219258a239e13b9c8607ca55912853e2e760c

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Citations in the corpus

Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change (2017, unknown): “A wide range of dynamic crop growth simulation models have been developed over the past few decades, many of which are being applied to study impacts of climate change (Asseng et al., 2014; Challinor et al., 2014; Ewert et al., 2015; Jones et al., 2017a, 2017b; White et al., 2011).”
Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science (2017, background): “Ewert et al. (2014) reviewed crop models relative to their adequacy in performing integrated assessments of climate change impacts, and pointed out important limitations in most crop models.”
Temperature increase reduces global yields of major crops in four independent estimates (2017, background): “2% per degree Celsius) were observed in wheatwarming experiments in Nanjing, China, where rising temperatures reduce damage from frost and heat stress during the early and late experimental wheat growing seasons, respectively (16) —factors that are captured less well in crop models (17).”