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 todetermine 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 applicabilityacross 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 ofmainstem 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 drymatter 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 acrosslocations, 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.
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