In subtropical regions, tomato (Solanum lycopersicum) is mainly produced in autumn and winter. To enhance the off-season production of tomato, summer cultivation has become a prime objective. Grafting tomato scions onto eggplant (Solanum melongena) rootstocks is a key method to overcome the difficulties of tomato cultivation in summer. In this study, we collected seedling growth data over six growing seasons in Taiwan and established growth models by employing three commonly used sigmoid growth curves, namely the Gompertz, Richards, and Logistic curves. Cumulative temperature was introduced as an independent variable and its relationship with plant stem diameter determined. The R2 values of the growth models were 0.74–0.85 and 0.72–0.80 in calibration and validation, respectively. Performance did not differ markedly among models in the same growing season, but notable differences were observed among models for different growing seasons. In addition, the estimates of several model parameters differed significantly among the seasons; hence, separate models should be established for different seasons. The results of this study can be used in prediction of tomato and eggplant seedling growth and arrangement of the grafting schedule to improve the efficiency of seedling production in subtropical countries.

Mathematical modeling has been used to describe the characteristics of crop growth. Establishing a growth model can help to better understand the responses of crops to their environment and improve the efficiency of agricultural production. This study establishes empirical growth models to predict the growth of greenhouse tomato. In this study, we collected beef tomato (Solanum lycopersicum cv. ‘993′) growth data over two crop seasons in Taiwan and established growth models by employing the commonly used Gompertz and Logistic curves. Days after transplanting (DAT) and growing degree-days (GDD) were introduced as independent variables and their relationships with five traits, i.e., plant height, leaf area index, stem dry matter, leaves dry matter, and fruits dry matter were determined. The performances of GDD models were slightly better than those of the DAT models. In addition, we inferred five critical points with biological meaning based on the proposed growth models. The critical points estimated by the Logistic model are closer to our expectation than those of the Gompertz model, and they were applicable for the ‘993′ tomato in Taiwan. These results can be used to predict tomato growth and adjust the fieldwork schedule to improve the efficiency of the greenhouse production of tomatoes.