Models for Predicting Pineapple Flowering and Harvest Dates

Abstract

The growing-degree-days (GDD) model for pineapple was developed to predict flowering and harvest dates; however, it has not been adapted to the climate in Japan’s growing regions, where air temperatures fluctuate over a wide range, and the prediction accuracy is low. The present study aimed to develop models for predicting flowering and harvest dates with high accuracy by analyzing a large phenological dataset from Japan’s main (Nago) and warmer (Ishigaki) production areas. The number of days between budding and flowering decreased at air temperatures of up to approximately 25°C and remained constant above 25°C. The number of days between flowering and harvest decreased until approximately 23°C. The effect of day length on both days to flowering and harvest was small. The relationship between air temperature and the developmental rate after budding to flowering and after flowering to harvest was modeled using the GDD and exponential function models, both with upper limits. The GDD model with an upper limit temperature was more accurate at predicting flowering and harvest dates compared to the conventional GDD model. In particular, the prediction accuracy of the harvest date was dramatically improved. Because the relationship between the developmental rate until flowering and the air temperature was exponential rather than linear, the exponential function model provided a more accurate prediction of the flowering date. The root-mean-square errors of the most accurate models were 3.7–6.1 days for predicting the flowering date and 6.1–10.2 days for the harvest date. We believe that these models will be useful for planning shipments of pineapple in regions with wide temperature ranges, such as Japan, and for cultivation management in response to climate change.