Abstract

Crop phenology, which tracks cyclical events in plant development, is crucial for both agriculture and natural ecosystems. Chilling Hours (CH) and Growing Degree Days (GDD) are key parameters that influence the growth cycles of various plant species. As meteorological variability increases, accurate estimation of these parameters becomes essential for effective agricultural management and climate change adaptation. Traditional methods for calculating CH often struggle to account for diverse climatic conditions. Additionally, Polynomial estimations for accumulated GDD face divergence issues at boundary conditions. This study presents three new methods based on sigmoid functions for calculating daily CH and estimating monthly CH and GDD accumulations. The results enhance decision-making processes for climate adaptation through Digital Agriculture tools. When compared to traditional approaches in a specific region, the proposed models demonstrated robust performance across varying temperature ranges. The findings suggest that sigmoidal models provide more reliable and adaptive data for agricultural planning, making them better suited to dynamic climatic conditions and supporting informed resilient decision-making in agricultural management.