Management can be defined as the sequence of three operations: planning, implementation and control. The planning operation sets up the strategy which encompasses the goals assigned to the cropping system and the means to achieve these goals. Implementation performs the translation from the strategy into actions, while control ensures the proper applications of these actions by constantly monitoring the process and revising the mode of application of the action. The decision process leading to the determination of the actions to be taken is complex. It depends on uncontrolled external factors, on complex interactions between the crop and its environment and on the knowledge of the crop state.
In view of this, the first application of crop models is to provide information that is otherwise not readily accessible to the grower, either because no measurement system is available or because the cost of obtaining the information would be prohibitive. The second application is to represent crop processes in optimisation routines. In the following subsections an overview is presented of current works using models as information providers (crop management and protection) and as process representations (climate and fertigation control).
The demand for yield prediction varies with the tomato cultivation system. In field production, determinate cultivars are selected to obtain fruits ripe for a single harvest. The expected time of harvest and expected amount of product are predicted to enable an integrated planning of production and processing. For example, Wolf et al.85 estimated the times of emergence, flowering, turning stage and harvesting of tomatoes for processing based on the heat sums. McNeal et al.86 went a step further and predicted the mass of fruits at harvest using a greenhouse tomato crop model (TOMGRO) adapted to field conditions. In greenhouse production, yield is planned for a long period of time. In negotiations with the product buyers, growers must be able to announce their weekly production for the next couple of months. For this purpose, a simple tomato crop model named TOMPOUSSE was developed to predict the weekly yield and average fruit grade from information available on the farm.49 The same model can be used as a simulator to evaluate different strategies of crop management (truss pruning, CO2 enrichment, changes in stem density). De Koning55 used a similar approach in a model of dry matter partitioning to optimise shoot density and number of fruits per plant.
These crop models, used to evaluate the biological consequences of policies of crop management, are still far from real decision support systems (DSS). For this purpose, the models should describe not only the dynamics of the crop and of its physical environment (greenhouse climate and/or soil), but also the decision-making process itself and its interactions with the biophysical system. For example, the GX/Sim system87 is a greenhouse simulation platform that can specify the decision rules the grower uses to adapt the climate settings to the current climate and crop conditions.
In the CONSERTO project,88 a dynamic model of the greenhouse production system has been designed with three components: the decision system, the instructions-to-actions system and the biophysical system. The decision system describes the management strategy (climate, manual operations such as fruit and leaf pruning, training and harvesting) applied over a cultivation period to realise production objectives. The instructions-to-actions system converts these decisions into actions via automatons (the climate and fertigation control system) and workers. The biophysical system comprises a greenhouse climate and a tomato crop model (TOMGRO)16,59 implemented in an object-oriented framework.89 The outputs provide not only information on physical and biological performances of the system under a set of actions but also indicators (e.g. the plant vigour or predictions of important events such as flowering or fruit maturity) useful for the decision system.
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