AUTHOR=Richetti Jonathan , Sadras Victor Oscar , He Di , Leske Brenton , Hu Pengcheng , Beletse Yacob , Cossani C. Mariano , Nguyen Ha , Zheng Bangyou , Deery David Matthews , Dreccer M. Fernanda , Whish Jeremy , Lilley Julianne 

TITLE=Challenges in modelling the impact of frost and heat stress on the yield of cool-season annual grain crops

JOURNAL=Frontiers in Plant Science

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/plant-science/articles/10.3389/fpls.2025.1613432

DOI=10.3389/fpls.2025.1613432

ISSN=1664-462X

ABSTRACT=Frost and heat events at critical growth stages could cause large yield losses. These temperature extremes are increasing in frequency and intensity due to climate change in many parts of the broadacre cropping regions globally, presenting challenges to food production. For cool-season grain-growing regions, where summers are already too hot, heat and frost risks can limit adaptation options. Capturing these stresses in crop models accurately is increasingly important for evaluating the timing, severity, and yield consequences of extreme events. However, most existing process-based models were not designed to simulate short-duration temperature extremes, limiting their ability to assess climate risk and inform adaptation to frost and heat. Yield responses to heat and frost are associated with pollen sterility, grain abortion, accelerated senescence, and grain filling. Six challenges limit current modelling approaches: (1) inadequate spatial and temporal resolution of extreme events, (2) threshold-based and non-linear crop responses, (3) interactions between phenology and management, (4) cumulative and interacting stress effects across development stages, (5) limited representation of genotype-specific sensitivities, and (6) reliance on daily temperature data. Addressing these challenges requires improved use of sub-daily climate data, incorporation of physiological damage mechanisms, and enhanced crop- and genotype-specific parameterisation. These developments are critical for improving crop yield predictions under extreme temperatures in the context of climate change.