Pattern-oriented modelling of plant architecture: A new approach for constructing functional-structural plant models

Computational modelling is becoming increasingly significant in improving our understanding of natural systems, and in making predictions to manage them. Many computational models have been used in various areas for these purposes. However, it has been suggested that models should not be either too simple or too complex, if they are to be useful. Thus, it is of importance to construct models with an optimised model structure that sufficiently well represents their real world counterparts. To do that, better modelling strategies are needed. The pattern-oriented modelling (POM) strategy is an approach that has been proposed to address these issues. It has been used widely to develop agent-based models (ABMs), aiming to make the models more comprehensive and rigorous, and increasing their predictive power. Functional-structural plant models (FSPMs) can be identified as ABMs, if organs/growth units of a plant are considered as agents. To test the feasibility and demonstrate the value of using the POM strategy for functional-structural plant modelling, this study focuses on modelling of avocado (Persea americana, cv. Hass), because of its clear modular construction and its economic significance to subtropical and tropical horticulture world-wide. Our study focuses on the systematic development of techniques to apply the POM strategy to functional-structural plant modelling. The overall objective was to determine whether the POM strategy could be used to construct FSPMs in order to increase their predictive power. In the present study, a functional-structural plant model of the annual growth module of avocado was constructed using the POM strategy. The model was able to reproduce multiple observed patterns of architecture and shoot growth simultaneously, and to make independent predictions providing insights into branching architecture, which were consistent with independently generated findings of other studies. Comparison of model outcomes to multiple observed patterns of modular construction at different scales, e.g. metamer level, growth unit level and branch level, increases our confidence that the model performed well. Those independent predictions can be strong indicators that the model is structurally realistic.

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