Adaptation and Validation of a Dynamic Plant Surface Microclimate Model (PSCLIMATE) for Greenhouse Tomatoes

The microclimate within a canopy and at the plant surface has a significant influence on the physiological processes of plants and the epidemiology of pathogens. However, it is usually not measured in routine greenhouse climate monitoring and control. We have developed a plant surface climate model for greenhouse cucumbers (PSCLIMATE-CUCUMBER) to predict the vertical microclimate profile within crop canopy, based on principles of energy balance and heat and mass transfer theory in a previous study. This study further adapted the dynamic model to greenhouse tomatoes (PSCLIMATE-TOMATO), the most important greenhouse vegetable crop. Physiological and architectural functions and parameters of tomato plants were developed and incorporated into the model. The PSCLIMATE-TOMATO model was then validated with microclimate data collected in a greenhouse tomato experiment from November 2002 to February 2003. The wind speed profiles within the canopy, heating system configurations, and greenhouse structural parameters determined experimentally or derived from the literature were used as simulation parameters. The calculated model efficiencies (EF) were 0.87, 0.86, and 0.47 for predicting air temperature, relative humidity, and global solar radiation, respectively, and the corresponding average prediction accuracies were 97.7%, 94.7%, and 64.46%, respectively. The model accurately predicted microclimate variables within the canopy and at the leaf surface except at solar noon when solar radiation was high. At noon hours on sunny days, the model had some overestimation of solar radiation and air temperature, and underestimation of relative humidity. Further modification of the model by incorporating the effects of light distribution (both direct and diffuse), leaf temperature, and CO2 concentration on leaf stomatal conductance and more accurate measurement of solar radiation with line sensors may improve the microclimate prediction at noon hours on sunny days.