PhenoFECT: A Python package for forecasting and exploring plant phenology under climate change

Climate change and global warming significantly affect the timing of plant flowering in spring. Accurately predicting these phenological events is crucial for understanding plant biology and agriculture. The chill-day model (CDM) based on seasonal temperature changes has been a reliable model for predicting phenological activities; however, recent global warming has challenged its accuracy due to extreme weather variability, necessitating the adjustment of the CDM parameters to reflect recent climate change. We developed an open-source Python package, PhenoFECT (Phenology Forecasting Exploring ClimaTe), that refines temperature-based phenology models. The package enables efficient phenological analysis for open and user-specified data.

The Python package embeds open meteorological data collected over 53 years (1973-2025) in Korea for cherry (Prunus yedoensis), golden-bell (Forsythia koreana), and azalea (Rhododendron mucronulatum). PhenoFECT was utilized to adjust the parameters of the CDM to reflect recent climate changes. The refined CDM improved the prediction accuracy of flowering at the average root mean square error (RMSE) of 5.74 days for three angiosperms.

PhenoFECT is a versatile tool for accurate forecasting, exploration, and visualization across diverse open meteorological data. Exploring data using PhenoFECT revealed that the CDM can be influenced by geographic features and global climate phenomena.