Using Sigmoid Growth Models to Simulate Greenhouse Tomato Growth and Development

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