Functional-structural plant models (FSPMs) are advanced plant models that combine a detailed description of the 3-D growth and development of a plant with the ecophysiological processes that drive its growth. FSPMs are a relatively recent development and allow integration of ecophysiological aspects of plant growth on a finer scale than classical crop or ecophysiological models. While this creates a considerable advantage towards understanding plant growth, the downside is the substantial increase in model complexity. To deal with this problem, FSPM model design requires specific focus on the aspects of plant functioning relevant to the model objective, while simplifications can be used for aspects considered less important. FSPMs for studying plant growth focus mainly on carbon availability through photosynthesis but do not adequately consider the contribution of water availability. This is a severe simplification, as it has been shown that the availability of water can lead to a faster and more drastic response in plant growth as opposed to the availability of carbon. However, the computational cost of solving the differential equations required for integrating water flow on a detailed scale in developing plants remains a major hindrance for its integration in these complex models.

The goal of this thesis was to develop an FSPM where turgor pressure in a plant organ, established by the local availability of both water and carbon, is the main driver of plant growth. Soybean is chosen as a model plant to develop this model as it is an internationally important crop of which the potential for local cultivation in Flanders is still under exploration. As a dynamic FSPM for soybean is, to date, not available, the model objective was to evaluate both the feasibility of introducing turgor-driven growth in FSPMs, as well as evaluate the future potential for such a model to contribute to a better understanding of soybean growth. Within this thesis, the different chapters correspond to the stepwise creation of this model.

In a first step (chapter 2), the PhD focussed on the structural aspect of the model. The morphology of the individual plant organs in a soybean plant as well as their topology was analysed. A specific focus was put on the detailed modelling and analysis of leaf shape, as their individual shape influences plant light interception. 3-D recreations of the various plant organs were developed and connected into a static, structural model in the GroIMP modelling platform.

Such a static description of soybean morphology can already provide insight regarding light interception and transmission. However, such a feat can only be achieved with knowledge on the external light conditions and how the light interacts with the plants. In the next step in the model development (chapter 3), an advanced light model was integrated which simulates accurate irradiance based on data, while taking into account its directional component based on the geographical location of the plot, day of the year, time of the day, and daily cloud conditions. While this allows simulation of incident light on individual plant leaves, the actual light absorption in a soybean leaf, a crucial metric towards evaluating photosynthesis, is not constant. Leaf light absorption depends on the spectral characteristics (i.e., the colour) of each individual leaf. During the development of soybean, leaf colour is not constant and changes with both leaf age and plant age which makes it a difficult metric to measure and model. To deal with this problem, a strategy is introduced to capture and model the spectral characteristics of individual leaves using measurements of their chlorophyll content combined with an existing optical leaf model.

In the next step (chapter 4), dynamic growth, photosynthesis and transpiration were integrated in the model. Detailed monitoring of the growth and development of soybean plants under field conditions allowed calibration of growth curves and phenological parameters, as well as insight in plant biomass accumulation. Additionally, measurements of photosynthesis and transpiration revealed a clear trend with leaf chlorophyll content. These dynamics and observations were integrated in the model, which was subsequently expanded with a combined photosynthesis – stomatal conductance – transpiration model. The resulting model described the growth and development of a soybean plant in the way it was observed in the field. While such a model does not yet have a predictive value, it allowed spatio-temporal evaluation of plant characteristics that are difficult to measure, such as the carbon sink priorities between different organ types, and the whole-season evolution of carbon assimilation in the crop canopy. These are important variables to consider when moving towards the calibration of a more complex, predictive model, where the descriptive growth curves are replaced by growth based on the availability of carbon and water, which, in turn, is driven by the environmental conditions of the plant.

Before moving directly towards the integration of the complex mechanisms of water transport in the soybean model, a conceptual model was first designed on a theoretical plant with a simple morphology (chapter 5). In this model, plant-water relations were integrated on the scale of individual plant organs by adapting an existing flow-and-storage model. As a result, the availability of both carbon and water plays a role in establishing the water potential components throughout the plant. The established turgor pressure then drives the growth of individual plant organs in 3-D, resulting in a model of plant growth which is highly responsive towards its growing environment.

In chapter 6, the concepts of this model were integrated in the soybean FSPM and model parameters were calibrated based on measurements. Secondly, the capability of the model in predicting soybean growth under different planting densities was explored. It was concluded that the FSPM, as it stands, was able to capture the observed growth of soybean with a limited number of physiological parameters. Comparison of measurements with simulation results under different planting densities revealed that the model was able to predict some of the observed changes in plant morphology and growth, but was lacking the mechanism of shade-avoidance to fully capture it. The ability to distinguish and diagnose missing interactions is an important advantage resulting from the model design. In essence, the model comprises of a collection of clearly defined, though highly interacting, sub-models for morphology, environment, and physiology (consisting of both the photosynthesis – stomatal conductance – transpiration model and the newly created turgor-driven growth model). This makes the model highly diagnostic, and allows the origin of discrepancies between measurements and simulation results to be dynamically identified which can guide improvement of the model design. Additionally, straightforward reusability of the model components benefits the integration of turgor-driven growth in other FSPMs which could lead to a better understanding of water availability in FSPMs and in plants in general.