PhenoFECT: A Python package for forecasting and exploring plant phenology under climate change
- 1Division of Life Science, College of Life Sciences and Biotechnology Korea University Seoul 02481 Republic of Korea
- 2FablessBio Inc. and Department of Biotechnology, College of Life Sciences and Biotechnology Korea University Seoul 02481 Republic of Korea
Abstract
Premise: 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.
Methods and Results: 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.
Conclusions: 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.
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BibTeX
@article{kim2026phenofect,
title = {{PhenoFECT: A Python package for forecasting and exploring plant phenology under climate change}},
author = {Songwon Kim and In‐Geol Choi},
journal = {Applications in plant sciences},
year = {2026},
month = oct,
doi = {10.1002/aps3.70088},
url = {https://europepmc.org/article/PMC/PMC13722615},
note = {CC BY},
}
RIS
TY - JOUR
TI - PhenoFECT: A Python package for forecasting and exploring plant phenology under climate change
AU - Songwon Kim
AU - In‐Geol Choi
PY - 2026
DA - 2026/10/30
JO - Applications in plant sciences
DO - 10.1002/aps3.70088
UR - https://europepmc.org/article/PMC/PMC13722615
AB - Premise: 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. Methods and Results: 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. Conclusions: 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.
SN - 2168-0450
ER -