Crop Models for Chrysanthemum

Models of growth and quality in chrysanthemum pot plant and cut flower production can be used as input to IT-based decision support systems (DSSs) for planning and online monitoring, and model-based predictions can be directly input to climate controllers. There is a large variety of models that can be used for these purposes. Mathematical models range from simple regression models, or black box big-data algorithms, to explanatory highly parameterized crop physiology models. Each model type has its advantages and disadvantages: data-based models can be used as reliable predictors within the scope they are trained for. Explanatory models can be used to understand processes and find reasons for cultivation problems, when used as input to a planning or monitoring tool. We explain the differences between model families and guide through a process of creating simple models from scratch. However, we mainly focus on and explain the setup of process-based (explanatory) modeling. The crop itself is central, represented in crop growth models and models for external plant quality. We show how photo-assimilates and quality are related, for example, through leaf area formation (external quality) that has a direct feedback on photosynthesis (growth) and vice versa. Biochemical leaf photosynthesis models are introduced, and the scale-up to the crop level is explained. We discuss how climate variables that influence external quality variables, such as stem length, flower number, and so on, are incorporated in models. Eventually, some possible applications of model implementation into IT-based DSSs and climate controllers are introduced and discussed.