Adapting a process-oriented cold hardiness model to conifers

Plant phenology has been and continues to be impacted by climate change. Process-oriented modeling of phenology reveals biological characteristics through interpretation of model results and parameter values. This paper aims to implement a cold hardiness model using historical Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) data, determine if model results improve with data clustering by seed source or growing location, and interpret model results into biological meaning. These interpretations have applications for reforestation and studies of plant phenological response to climate change.Cold hardiness data were compiled through literature review. A dynamic process-oriented cold hardiness model driven by thermal time based on daily temperatures was applied to multiple data clustering scenarios. Model results were analyzed for goodness of fit to determine model error, efficiency, and bias. A sensitivity analysis was performed to determine parameter sensitivity, and further validate the model calibration.Data clustering by seed source improved fit compared to clustering by growing location or no clustering, when applied to the full dataset. Using only temperature inputs, model results had low error when data modeled were similar. Results show that for cold hardiness acclimation a linear growing degree function with a threshold of 10 degrees C was adequate across testing data, as was an upper limit cold hardiness temperature of -3 degrees C.Interpretation of model results show that both acclimation to growing sites and seed source genetics impact cold hardiness response, though clustering by seed source improved model performance. This model could be applied to mitigate cold related risks to seedlings during production and establishment, and serve as a template for predicting phenological responses in simulated future climate scenarios.