The necessity of coupling the legacy effect with temperature response in crop phenology models

Global climate change has changed vegetation phenology substantially around the world. However, the necessity of coupling legacy effects with temperature responses in phenological models remains unclear. The objective of this study was to demonstrate that legacy effects [based on day of year (DOY) of phenology events] have substantial positive impacts on crop phenology. Data from 1883 crop×site combinations across Germany and China were analyzed. DOY was found to be a temperature-independent factor for both vegetative (VGP) and reproductive growth periods (RGP) (based on variance inflation factor values). Partial correlation analysis suggested that DOY explained almost the same variability in date of phenology events as temperature. Akaike information criterion showed the cost-effectiveness of coupling DOY with temperature in 71.2% and 59.1% of sites in VGP and RGP, respectively. A model that coupled a linear legacy effect and a temperature response mechanism (LETM) improved fitting efficiency by an average of 57%. LETM was observed to outperform the well-calibrated WOFOST and WE models in VGP and RGP for all crops. Averaged over all crops, root mean square errors for WOFOST, WE, and LETM were 4.0, 3.9, and 3.7 d in VGP, respectively, and are 5.3, 4.6, and 4.0 d in RGP, respectively. Our results verified the necessity of coupling the legacy effect with temperature responses in phenology models. Given that the results were consistent for all of the crops investigated, we believe that our conclusions can apply to other field crops. Results of this study expand the knowledge of crop phenology responses to environment, and are helpful for accurately predicting crop growth and development responses under future global climate change. •Phenology data from 14 crop×country and 1883 crop×site combinations were used.•Legacy effect positively affected development rate across various crops and phases.•Coupling the legacy effect in a phenology model generally improved accuracy.