Phenomics data processing: Extracting temperature dose-response curves from repeated measurements

Temperature is a main driver of plant growth and development. New phenotyping tools enable quantifying the temperature response of hundreds of genotypes. Yet, particularly for field-derived data, the process of temperature response modelling bears potential flaws and pitfalls with regard to the interpretation of derived parameters. In this study, climate data from three growing seasons with differing temperature distributions served as starting point for a wheat stem elongation growth simulation, based on a four-parametric Wang-Engel temperature response function. To extract dose-responses from the simulated data, a novel approach to use temperature courses with high temporal resolution was developed. Linear and asymptotic parametric modelling approaches to predict the cardinal temperatures were investigated. An asymptotic model extracted the base and optimum temperature of growth and the maximum growth rate with high precision, whereas simpler, linear models failed to do so. However, when including seasonally changing cardinal temperatures, the prediction accuracy of the asymptotic model was strongly reduced. We conclude that using an asymptotic model based on temperature courses with high temporal resolution is suitable to extract meaningful parameters from field-based data. Consequently, applying the presented modelling approach to high-throughput phenotyping data of breeding nurseries may help selecting for climate suitability.