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Abstract:
Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure globalfood security under climate change. Process-based crop models are effective means to project climate impact on cropyield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functionscurrently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% ofuncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of newtemperature response functions that when substituted in four wheat models reduced the error in grain yield simulationsacross seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improvedtemperature 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.
