Stability of Maize Phenology Predictions by Using Calendar Days, Thermal Functions, and Photothermal Functions

Accurate prediction of crop phenological stages is essential for effective crop management. Such a prediction provides the timing of phenological stages, thus aiding in scheduling management practices, understanding the potential risks of adverse weather at critical phenological stages, and adjusting sowing dates. Temperature is the dominant climatic factor affecting maize (Zea mays L.) development, with photoperiod serving as a secondary influence. This study used maize field data with recorded flowering and maturity dates to evaluate the stability of phenological stage predictions obtained using the calendar days method, thermal functions, and photothermal functions. These methods were used to calculate the number of days, accumulated temperature, and accumulated photothermal units from sowing to flowering and from flowering to maturity. Results showed that thermal functions produced the most stable predictions, with the lowest average coefficient of variation (CV) being 8.37%. The thermal functions were further categorized as empirical linear, empirical nonlinear, and process-based. Within each category, the functions with the lowest average CVs were growing degree days (GDD8,34; 9.12%), thermal leaf unit (GTI; 7.74%), and agricultural production system simulator (APSIM; 8.26%), respectively. Among them, GTI had the lowest CV, indicating its superior stability in predicting maize phenological stages. These results provide a basis for selecting thermal models in maize phenology research and can support improved decision-making in crop scheduling and management.