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Challinor, A. (2016). What does the Paris Agreement mean for crop-climate modelling?. Berlin (Germany).
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Nicklin, K., & Challinor, A. (2016). Addressing uncertainty in model input and evaluation data.. Berlin (Germany).
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Wallach, D., Thorburn, P., Asseng, S., Challinor, A. J., Ewert, F., Jones, J. W., et al. (2016). A framework for evaluating uncertainty in crop model predictions.. Berlin (Germany).
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Watson, J., & Challinor, A. (2013). The relative importance of rainfall, temperature and yield data for a regional-scale crop model. Agricultural and Forest Meteorology, 170, 47–57.
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Wang, E., Martre, P., Zhao, Z., Ewert, F., Maiorano, A., Rötter, R. P., et al. (2017). The uncertainty of crop yield projections is reduced by improved temperature response functions. Nature Plants, 3, 17102.
Abstract: Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. Erratum: doi: 10.1038/nplants.2017.125
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