Reduced Statevariable Tomato Growth Model

A number of physiologically based tomato crop models have been developed for use in studying greenhouseenvironment control. However, these models may have hundreds of state variables and thus cannot be used with optimalcontrol methods to determine how to operate greenhouse environment control equipment over time to maximize profit. Theobjectives of this research were to develop a dynamic tomato growth model with a minimum number of state variables andto determine its applicability across different growing conditions. Our overall approach was to simplify an existing tomatogrowth model (TOMGRO) by reducing its number of state variables to five while retaining much of its physiologicaldetail. The reduced model, with number of mainstem nodes, leaf area index, total plant weight, fruit weight, and maturefruit weight as state variables, contains the same process equations for photosynthesis, respiration, and development asthe comprehensive model, but new leaf area and dry matter growth relationships were developed. Data from twoexperiments and a commercial greenhouse in Florida and an experiment in Avignon, France, were used to evaluate themodel. The model was programmed in a spreadsheet, and parameters were estimated by minimizing RMSE for eachexperiment. Results showed that the reduced state variable model could accurately describe tomato growth and yieldacross 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 invarieties and management. This model could be easily adapted for simulating other greenhouse crops.