A conceptual sap flow and turgor-driven growth model was introduced for functional-structural plant modelling. It is applicable to any plant architecture and allows visual exploration of the diel patterns of organ water content and growth. Integrated in existing FSPMs, this new concept fosters an array of possibilities for FSPMs, as it presents a different formulation of growth in terms of local processes, influenced by local and external conditions.

Abstract Background and Aims Turgor pressure within a plant cell represents the key to the mechanistical descriptiion of plant growth, combining the effects of both water and carbon availability. The high level of spatio-temporal variation and diurnal dynamics in turgor pressure within a single plant make it a challenge to model these on the fine spatial scale required for functional–structural plant models (FSPMs). A conceptual model for turgor-driven growth in FSPMs has been established previously, but its practical use has not yet been explored. Methods A turgor-driven growth model was incorporated in a newly established FSPM for soybean. The FSPM simulates dynamics in photosynthesis, transpiration and turgor pressure in direct relation to plant growth. Comparisons of simulations with field data were used to evaluate the potential and shortcomings of the modelling approach. Key Results Model simulations revealed the need to include an initial seed carbon contribution, a more realistic sink function, an estimation of respiration, and the distinction between osmotic and structural sugars, in order to achieve a realistic model of plant growth. However, differences between simulations and observations remained in individual organ growth patterns and under different environmental conditions. This exposed the need to further investigate the assumptions of developmental and environmental (in)sensitivity of the parameters, which represent physiological and biophysical organ properties in the model, in future research. Conclusions The model in its current form is primarily a diagnostic tool, to better understand and model the behaviour of water relations on the scale of individual plant organs throughout the plant life cycle. Potential future applications include its use as a phenotyping tool to capture differences in plant performance between genotypes and growing environments in terms of specific plant characteristics. Additionally, focused experiments can be used to further improve the model mechanisms to lead to better predictive FSPMs, including scenarios of water deficit.

Background and Aims Leaflet shapes of tomato plants (Solanum lycopersicum) have been reduced to simple geometric shapes in previous functional–structural plant models (FSPMs) in order to facilitate measurements and reduce the time required to reconstruct the plant virtually. The level of error that such simplifications introduce remains unaddressed. This study therefore aims to quantify the modelling error associated with simplifying leaflet shapes. Methods Realistic shapes were implemented in a static tomato FSPM based on leaflet scans, and simulation results were compared to simple geometric shapes used in previous tomato FSPMs in terms of light absorption and gross photosynthesis, for both a single plant and a glasshouse scenario. Key Results The effect of simplifying leaflet shapes in FSPMs leads to small but significant differences in light absorption, alterations of canopy light conditions and differences in photosynthesis. The magnitude of these differences depends on both the type of leaflet shape simplification used and the canopy shape and density. Incorporation of realistic shapes requires a small increase in initial measurement and modelling work to establish a shape database and comes at the cost of a slight increase in computation time. Conclusions Our findings indicate that the error associated with leaflet shape simplification is small, but often unpredictable, and is affected by plant structure but also lamp placement, which is often a primary optimization goal of these static models. Assessment of the cost–benefit of realistic shape inclusion shows relatively little drawbacks for a decrease in model uncertainty.

The process of leaf elongation in grasses is characterized by the creation and transformation of distinct cell zones. The prevailing turgor pressure within these cells is one of the key drivers for the rate at which these cells divide, expand and differentiate, processes that are heavily impacted by drought stress. In this article, a turgordriven growth model for grass leaf elongation is presented, which combines mechanistic growth from the basis of turgor pressure with the ontogeny of the leaf.Drought-induced reductions in leaf turgor pressure result in a simultaneous inhibition of both cell expansion and differentiation, lowering elongation rate but increasing elongation duration due to the slower transitioning of cells from the dividing and elongating zone to mature cells. Leaf elongation is, therefore, governed by the magnitude of, and time spent under, growth-enabling turgor pressure, a metric which we introduce as turgor-time. Turgor-time is able to normalize growth patterns in terms of varying water availability, similar to how thermal time is used to do so under varying temperatures. Moreover, additional inclusion of temperature dependencies within our model pioneers a novel concept enabling the general expression of growth regardless of water availability or temperature.

I., De Swaef T. (2018). A flexible geometric model for leaf shape descriptions with high accuracy. Silva Fennica vol. 52 no. 2 article id 7740. 14 p. https://doi.org/10.14214/sf.7740 Highlights• A method for assessing leaf shape for 3D plant models is proposed.• The model is highly flexible and fits a large variety of shapes.• It allows analysis of shape differences within and between leaf datasets. AbstractAccurate assessment of canopy structure is crucial in studying plant-environment interactions. The advancement of functional-structural plant models (FSPM), which incorporate the 3D structure of individual plants, increases the need for a method for accurate mathematical descriptions of leaf shape. A model was developed as an improvement of an existing leaf shape algorithm to describe a large variety of leaf shapes. Modelling accuracy was evaluated using a spatial segmentation method and shape differences were assessed using principal component analysis (PCA) on the optimised parameters. Furthermore, a method is presented to calculate the mean shape of a dataset, intended for obtaining a representative shape for modelling purposes. The presented model is able to accurately capture a large range of single, entire leaf shapes. PCA illustrated the interpretability of the parameter values and allowed evaluation of shape differences. The model parameters allow straightforward digital reconstruction of leaf shapes for modelling purposes such as FSPMs.