In greenhouse eggplant cultivation, fruit growth is a critical determinant of crop yields and depends on the surrounding environment. In this study, to evaluate fruit growth responses to the surrounding environment, we developed a mathematical model for eggplant fruit growth in a greenhouse. We first measured eggplant fruit growth using digital photographs. The growth of eggplant fruits from flowering until harvest was represented accurately by an exponential growth function rather than a commonly used single sigmoid growth function, as eggplant fruits were harvested at a market-specified size during an exponential growth period before maturation. We attempted to construct a statistical model that estimates the relative growth rate (μ) in the exponential function from environmental and plant information (i.e., daily accumulated solar radiation (I), daily average temperature (T_a), daytime average temperature (T_a,day), nighttime average temperature (T_a,night), daytime average CO₂ concentration (C_a), and daily accumulated photosynthesis (P)). This model construction was based on two datasets that recorded the flowering dates, harvest dates, and harvested fruit weights of six plants throughout an entire year. A simple correlation analysis between μ and each of the explanatory variables revealed that correlation with μ was highest with T_a,day (correlation coefficient R = 0.75), while that with C_a showed a negative R value (-0.41). Further, we used principal component regression (PCR) to construct a statistical model for predicting μ while avoiding multicollinearity among the explanatory variables. Different combinations of the explanatory variables were tested to find the most generalizable model. It was confirmed that overfitting occurred to the training data depending on the combination of explanatory variables used in the model, leading to a decrease in the generalization ability of the model. The best combination of explanatory variables for predicting μ was P, T_a,day and T_a,night.

　This study assessed the flowering-date forecast skill of cherry blossom in Tokyo from 2018 to 2023 using seasonal ensemble forecasts from three numerical weather prediction centers: the Deutscher Wetterdienst, the European Centre for Medium-Range Weather Forecasts, and the Météo-France. First, the optimal seven parameters used in the flowering-date estimation model, developed by Maruoka and Itoh (2009), were determined for Tokyo, based on the period from 1994 to 2017, during which the estimation bias was ±1.91 days. Then, flowering dates were predicted using bias-corrected seasonal ensemble forecast of 2 m temperature as a model input. The root-mean-square errors for the flowering-date forecasts initialized on 1st January, February, and March, averaged over all ensemble members, were about ±8.0 days, ±6.2 days, and ±2.3 days, respectively. The best- or worst-performing center is dependent on the specific cases. The grand ensemble forecast, comprising all forecasts from all single-center ensembles, showed better performance in predicting flowering dates of cherry blossoms than the single-center ensemble forecasts alone. These results suggest that the grand ensemble approach at seasonal timescales holds potential for predicting of the growth of flowers and fruits.