A number of physiologically based tomato crop models have been developed for use in studying greenhouse
environment control. However, these models may have hundreds of state variables and thus cannot be used with optimal
control methods to determine how to operate greenhouse environment control equipment over time to maximize profit. The
objectives of this research were to develop a dynamic tomato growth model with a minimum number of state variables and
to determine its applicability across different growing conditions. Our overall approach was to simplify an existing tomato
growth model (TOMGRO) by reducing its number of state variables to five while retaining much of its physiological
detail. The reduced model, with number of mainstem nodes, leaf area index, total plant weight, fruit weight, and mature
fruit weight as state variables, contains the same process equations for photosynthesis, respiration, and development as
the comprehensive model, but new leaf area and dry matter growth relationships were developed. Data from two
experiments and a commercial greenhouse in Florida and an experiment in Avignon, France, were used to evaluate the
model. The model was programmed in a spreadsheet, and parameters were estimated by minimizing RMSE for each
experiment. Results showed that the reduced state variable model could accurately describe tomato growth and yield
across the different locations and years. Parameters for vegetative growth in the model were consistent across locations,
however, parameters for fruit growth and development varied with location. We attributed these results to differences in
varieties and management. This model could be easily adapted for simulating other greenhouse crops.