Crop system models are generally parametrized with daily air temperatures recorded at 1.5 or 2 m height. These data are not able to represent temperatures at the canopy level, which control crop growth, and the impact of heat stress on crop yield, which are modified by canopy characteristics and plant physiological processes Since such data are often not available and current simulation approaches are complex and/or based on unrealistic assumptions, new methods for integrating canopy temperatures in the framework of crop system models are needed. Based on a forward stepwise-based model selection procedure and quantile regression analyses, we developed empirical regression models to predict winter wheat canopy temperatures obtained from thermal infrared observations performed during four growing seasons for three irrigation levels. We used daily meteorological variables and the daily output data of a crop system model as covariates. The standard cross validation revealed a root mean square error (RMSE) of ~0.8 °C, 1.5–2 °C and 0.8–1.2 °C for estimating mean, maximum and minimum canopy temperature, respectively. Canopy temperature of both water-deficit and fully irrigated wheat plots significantly differed from air temperature. We suggest using locally calibrated empirical regression models of canopy temperature as a simple approach for including potentially amplifying or mitigating microclimatic effects on plant response to temperature stress in crop system models.
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