Modelling plant growth allows us to test hypotheses and carry out virtual experiments concerning plant growth processes that could otherwise take years in field conditions. The visualization of growth simulations allows us to see directly and vividly the outcome of a given model and provides us with an instructive tool useful for agronomists and foresters, as well as for teaching. Functional–structural (FS) plant growth models are nowadays particularly important for integrating biological processes with environmental conditions in 3-D virtual plants, and provide the basis for more advanced research in plant sciences.
In this viewpoint paper, we ask the following questions. Are we modelling the correct processes that drive plant growth, and is growth driven mostly by sink or source activity? In current models, is the importance of soil resources (nutrients, water, temperature and their interaction with meristematic activity) considered adequately? Do classic models account for architectural adjustment as well as integrating the fundamental principles of development? Whilst answering these questions with the available data in the literature, we put forward the opinion that plant architecture and sink activity must be pushed to the centre of plant growth models. In natural conditions, sinks will more often drive growth than source activity, because sink activity is often controlled by finite soil resources or developmental constraints.
This viewpoint paper also serves as an introduction to this Special Issue devoted to plant growth modelling, which includes new research covering areas stretching from cell growth to biomechanics. All papers were presented at the Second International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications (PMA06), held in Beijing, China, from 13–17 November, 2006. Although a large number of papers are devoted to FS models of agricultural and forest crop species, physiological and genetic processes have recently been included and point the way to a new direction in plant modelling research.
Forest management in Europe is committed to sustainability. In the face of climate change and accompanying risks, however, planning in order to achieve this aim becomes increasingly challenging, underlining the need for new and innovative methods. Models potentially integrate a wide range of system knowledge and present scenarios of variables important for any management decision. In the past, however, model development has mainly focused on specific purposes whereas today we are increasingly aware of the need for the whole range of information that can be provided by models. It is therefore assumed helpful to review the various approaches that are available for specific tasks and to discuss how they can be used for future management strategies.
Here we develop a concept for the role of models in forest ecosystem management based on historical analyses. Five paradigms of forest management are identified: (1) multiple uses, (2) dominant use, (3) environmentally sensitive multiple uses, (4) full ecosystem approach and (5) eco-regional perspective. An overview of model approaches is given that is dedicated to this purpose and to developments of different kinds of approaches. It is discussed how these models can contribute to goal setting, decision support and development of guidelines for forestry operations. Furthermore, it is shown how scenario analysis, including stand and landscape visualization, can be used to depict alternatives, make long-term consequences of different options transparent, and ease participation of different stakeholder groups and education.
In our opinion, the current challenge of forest ecosystem management in Europe is to integrate system knowledge from different temporal and spatial scales and from various disciplines. For this purpose, using a set of models with different focus that can be selected from a kind of toolbox according to particular needs is more promising than developing one overarching model, covering ecological, production and landscape issues equally well.
Patterns and variations in concentration of carbon-based secondary compounds in plant tissues have been explained by means of different complementary and, in some cases, contradictory plant defence hypotheses for more than 20 years. These hypotheses are conceptual models which consider environmental impacts on plant internal demands. In the present study, a mathematical model is presented, which converts and integrates the concepts of the ‘Growth–Differentiation Balance’ hypothesis and the ‘Protein Competition’ model into a dynamic plant growth model, that was tested with concentration data of polyphenols in leaves of juvenile apple, beech and spruce trees. The modelling approach is part of the plant growth model PLATHO that considers simultaneously different environmental impacts on the most important physiological processes of plants.
The modelling approach for plant internal resource allocation is based on a priority scheme assuming that growth processes have priority over allocation to secondary compounds and that growth-related metabolism is more strongly affected by nitrogen deficiency than defence-related secondary metabolism.
It is shown that the model can reproduce the effect of nitrogen fertilization on allocation patterns in apple trees and the effects of elevated CO2 and competition in juvenile beech and spruce trees. The analysis of model behaviour reveals that large fluctuations in plant internal availability of carbon and nitrogen are possible within a single vegetation period. Furthermore, the model displays a non-linear allocation behaviour to carbon-based secondary compounds.
The simulation results corroborate the underlying assumptions of the presented modelling approach for resource partitioning between growth-related primary metabolism and defence-related secondary metabolism. Thus, the dynamical modelling approach, which considers variable source and sink strengths of plant internal resources within different phenological growth stages, presents a successful translation of existing concepts into a dynamic mathematical model.
In traditional crop growth models assimilate production and partitioning are described with empirical equations. In the GREENLAB functional–structural model, however, allocation of carbon to different kinds of organs depends on the number and relative sink strengths of growing organs present in the crop architecture. The aim of this study is to generate sink functions of wheat (Triticum aestivum) organs by calibrating the GREENLAB model using a dedicated data set, consisting of time series on the mass of individual organs (the ‘target data’).
An experiment was conducted on spring wheat (Triticum aestivum, ‘Minaret’), in a growth chamber from, 2004 to, 2005. Four harvests were made of six plants each to determine the size and mass of individual organs, including the root system, leaf blades, sheaths, internodes and ears of the main stem and different tillers. Leaf status (appearance, expansion, maturity and death) of these 24 plants was recorded. With the structures and mass of organs of four individual sample plants, the GREENLAB model was calibrated using a non-linear least-square-root fitting method, the aim of which was to minimize the difference in mass of the organs between measured data and model output, and to provide the parameter values of the model (the sink strengths of organs of each type, age and tiller order, and two empirical parameters linked to biomass production).
The masses of all measured organs from one plant from each harvest were fitted simultaneously. With estimated parameters for sink and source functions, the model predicted the mass and size of individual organs at each position of the wheat structure in a mechanistic way. In addition, there was close agreement between experimentally observed and simulated values of leaf area index.
Functional–structural plant models (FSPM) constitute a paradigm in plant modelling that combines 3D structural and graphical modelling with the simulation of plant processes. While structural aspects of plant development could so far be represented using rule-based formalisms such as Lindenmayer systems, process models were traditionally written using a procedural code. The faithful representation of structures interacting with functions across scales, however, requires a new modelling formalism. Therefore relational growth grammars (RGG) were developed on the basis of Lindenmayer systems.
In order to implement and test RGG, a new modelling language, the eXtended L-system language (XL) was created. Models using XL are interpreted by the interactive, Java-based modelling platform GroIMP. Three models, a semi-quantitative gibberellic acid (GA) signal transduction model, and a phytochrome-based shade detection and object avoidance model, both coupled to an existing morphogenetic structural model of barley (Hordeum vulgare L.), serve as examples to demonstrate the versatility and suitability of RGG and XL to represent the interaction of diverse biological processes across hierarchical scales.
The dynamics of the concentrations in the signal transduction network could be modelled qualitatively and the phenotypes of GA-response mutants faithfully reproduced. The light model used here was simple to use yet effective enough to carry out local measurement of red:far-red ratios. Suppression of tillering at low red:far-red ratios could be simulated.
The RGG formalism is suitable for implementation of multi-scaled FSPM of plants interacting with their environment via hormonal control. However, their ensuing complexity requires careful design. On the positive side, such an FSPM displays knowledge gaps better thereby guiding future experimental design.
AmapSim is a tool that implements a structural plant growth model based on a botanical theory and simulates plant morphogenesis to produce accurate, complex and detailed plant architectures. This software is the result of more than a decade of research and development devoted to plant architecture. New advances in the software development have yielded plug-in external functions that open up the simulator to functional processes.
The simulation of plant topology is based on the growth of a set of virtual buds whose activity is modelled using stochastic processes. The geometry of the resulting axes is modelled by simple descriptive functions. The potential growth of each bud is represented by means of a numerical value called physiological age, which controls the value for each parameter in the model. The set of possible values for physiological ages is called the reference axis. In order to mimic morphological and architectural metamorphosis, the value allocated for the physiological age of buds evolves along this reference axis according to an oriented finite state automaton whose occupation and transition law follows a semi-Markovian function.
Simulations were performed on tomato plants to demostrate how the AmapSim simulator can interface external modules, e.g. a GREENLAB growth model and a radiosity model.
The algorithmic ability provided by AmapSim, e.g. the reference axis, enables unified control to be exercised over plant development parameter values, depending on the biological process target: how to affect the local pertinent process, i.e. the pertinent parameter(s), while keeping the rest unchanged. This opening up to external functions also offers a broadened field of applications and thus allows feedback between plant growth and the physical environment.
Light interception is a critical factor in the production of biomass. The study presented here describes a method used to take account of architectural changes over time in sunflower and to estimate absorbed light at the organ level.
The amount of photosynthetically active radiation absorbed by a plant is estimated on a daily or hourly basis through precise characterization of the light environment and three-dimensional virtual plants built using AMAP software. Several treatments are performed over four experiments and on two genotypes to test the model, quantify the contribution of different organs to light interception and evaluate the impact of heliotropism.
This approach is used to simulate the amount of light absorbed at organ and plant scales from crop emergence to maturity. Blades and capitula were the major contributors to light interception, whereas that by petioles and stem was negligible. Light regimen simulations showed that heliotropism decreased the cumulated light intercepted at the plant scale by close to 2·2 % over one day.
The approach is useful in characterizing the light environment of organs and the whole plant, especially for studies on heterogeneous canopies or for quantifying genotypic or environmental impacts on plant architecture, where conventional approaches are ineffective. This model paves the way to analyses of genotype–environment interactions and could help establish new selection criteria based on architectural improvement, enhancing plant light interception.
Cotton shows a marked plasticity vs. density in terms of branch development and geometry, internode elongation and leaf expansion. This paper proposes interpretations for observed plasticity in terms of light quantity and quality.
3-D virtual plants were reconstructed from field observations and 3-D digitization and were used to simulate the light regime in cotton stands of different densities.
All densities showed the same linear relationship between LAI and the sum of light intercepted by the canopy, from seedling emergence up to flowering. Simulated R : FR ratio profiles can very likely explain (1) the longer first internodes on main stem and branches and (2) the azimuthal re-orientation of branches toward the inter-row.
Simulation tools were used to analyse plant plasticity in terms of light quantity and quality. The methodology applied here at the stand scale will now be continued at the plant scale to further strengthen the above hypotheses.
In grapevine, canopy-structure-related variations in light interception and distribution affect productivity, yield and the quality of the harvested product. A simple statistical model for reconstructing three-dimensional (3D) canopy structures for various cultivar–training system (C x T) pairs has been implemented with special attention paid to balance the time required for model parameterization and accuracy of the representations from organ to stand scales. Such an approach particularly aims at overcoming the weak integration of interplant variability using the usual direct 3D measurement methods.
This model is original in combining a turbid-medium-like envelope enclosing the volume occupied by vine shoots with the use of discrete geometric polygons representing leaves randomly located within this volume to represent plant structure. Reconstruction rules were adapted to capture the main determinants of grapevine shoot architecture and their variability. Using a simplified set of parameters, it was possible to describe (1) the 3D path of the main shoot, (2) the volume occupied by the foliage around this path and (3) the orientation of individual leaf surfaces. Model parameterization (estimation of the probability distribution for each parameter) was carried out for eight contrasting C x T pairs.
The parameter values obtained in each situation were consistent with our knowledge of grapevine architecture. Quantitative assessments for the generated virtual scenes were carried out at the canopy and plant scales. Light interception efficiency and local variations of light transmittance within and between experimental plots were correctly simulated for all canopies studied. The approach predicted these key ecophysiological variables significantly more accurately than the classical complete digitization method with a limited number of plants. In addition, this model accurately reproduced the characteristics of a wide range of individual digitized plants. Simulated leaf area density and the distribution of light interception among leaves were consistent with measurements. However, at the level of individual organs, the model tended to underestimate light interception.
Plant population density (PPD) influences plant growth greatly. Functional–structural plant models such as GREENLAB can be used to simulate plant development and growth and PPD effects on plant functioning and architectural behaviour can be investigated. This study aims to evaluate the ability of GREENLAB to predict maize growth and development at different PPDs.
Two field experiments were conducted on irrigated fields in the North China Plain with a block design of four replications. Each experiment included three PPDs: 2·8, 5·6 and 11·1 plants m–2. Detailed observations were made on the dimensions and fresh biomass of above-ground plant organs for each phytomer throughout the seasons. Growth stage-specific target files (a description of plant organ weight and dimension according to plant topological structure) were established from the measured data required for GREENLAB parameterization. Parameter optimization was conducted using a generalized least square method for the entire growth cycles for all PPDs and years. Data from in situ plant digitization were used to establish geometrical symbol files for organs that were then applied to translate model output directly into 3-D representation for each time step of the model execution.
The analysis indicated that the parameter values of organ sink variation function, and the values of most of the relative sink strength parameters varied little among years and PPDs, but the biomass production parameter, computed plant projection surface and internode relative sink strength varied with PPD. Simulations of maize plant growth based on the fitted parameters were reasonably good as indicated by the linearity and slopes similar to unity for the comparison of simulated and observed values. Based on the parameter values fitted from different PPDs, shoot (including vegetative and reproductive parts of the plant) and cob fresh biomass for other PPDs were simulated. Three-dimensional representation of individual plant and plant stand from the model output with two contrasting PPDs were presented with which the PPD effect on plant growth can be easily recognized.
This study showed that GREENLAB model has the ability to capture plant plasticity induced by PPD. The relatively stable parameter values strengthened the hypothesis that one set of equations can govern dynamic organ growth. With further validation, this model can be used for agronomic applications such as yield optimization.
Plant growth models able to simulate phenotypic plasticity are increasingly required because (1) they should enable better predictions of the observed variations in crop production, yield and quality, and (2) their parameters are expected to have a more robust genetic basis, with possible implications for selection of quantitative traits such as growth- and allocation-related processes. The structure–function plant model, GREENLAB, simulates resource-dependent plasticity of plant architecture. Evidence for its generality has been previously reported, but always for plants grown in a limited range of environments. This paper aims to test the model concept to its limits by using plant spacing as a means to generate a gradient of competition for light, and by using a new crop species, tomato, known to exhibit a strong photomorphogenetic response.
A greenhouse experiment was carried out with three homogeneous planting densities (plant spacing = 0·3, 0·6 and 1 m). Detailed records of plant development, plant architecture and organ growth were made throughout the growing period. Model calibration was performed for each situation using a statistical optimization procedure (multi-fitting).
Obvious limitations of the present version of the model appeared to account fully for the plant plasticity induced by inter-plant competition for light. A lack of stability was identified for some model parameters at very high planting density. In particular, those parameters characterizing organ sink strengths and governing light interception proved to be environment-dependent. Remarkably, however, responses of the parameter values concerned were consistent with actual growth measurements and with previously reported results. Furthermore, modifications of total biomass production and of allocation patterns induced by the planting-density treatments were accurately simulated using the sets of optimized parameters. These results demonstrate that the overall model structure is potentially able to reproduce the observed plant plasticity and suggest that sound biologically based adaptations could overcome the present model limitations. Potential options for model improvement are proposed, and the possibility of using the kernel algorithm currently available as a fitting tool to build up more sophisticated model versions is advocated.
The dynamical system of plant growth GREENLAB was originally developed for individual plants, without explicitly taking into account interplant competition for light. Inspired by the competition models developed in the context of forest science for mono-specific stands, we propose to adapt the method of crown projection onto the x–y plane to GREENLAB, in order to study the effects of density on resource acquisition and on architectural development.
The empirical production equation of GREENLAB is extrapolated to stands by computing the exposed photosynthetic foliage area of each plant. The computation is based on the combination of Poisson models of leaf distribution for all the neighbouring plants whose crown projection surfaces overlap. To study the effects of density on architectural development, we link the proposed competition model to the model of interaction between functional growth and structural development introduced by
The model is applied to mono-specific field crops and forest stands. For high-density crops at full cover, the model is shown to be equivalent to the classical equation of field crop production (
Morphogenetic plasticity may be as important as physiological plasticity in determining plant adaptability to changing environmental conditions. This study examines the importance of crown plasticity of trees in stands.
A three-dimensional forest simulator is used to explore the impact of crown shape plasticity on tree growth. Crown deformation is mediated through the local response to light and overall allometric constraints governing tree dimensions. By altering shape response parameters of Hevea brasiliensis the impact of increased or decreased plasticity is explored in a variety of competitive environments defined by various combinations of tree density and relative frequency of different strategies. The possible interactions between plasticity and growth rate and plasticity and below-ground competition are also explored.
Crown plasticity confers competitive superiority in all cases studied. Interactions with other processes may downplay or enhance this competitive advantage.
Simulation results strongly suggest that crown plasticity does have a significant impact on tree performance in nature and that commonly observed crown shape deformation response of trees is of adaptive value.
To model plasticity of plants in their environment, a new version of the functional–structural model GREENLAB has been developed with full interactions between architecture and functioning. Emergent properties of this model were revealed by simulations, in particular the automatic generation of rhythms in plant development. Such behaviour can be observed in natural phenomena such as the appearance of fruit (cucumber or capsicum plants, for example) or branch formation in trees.
In the model, a single variable, the source–sink ratio controls different events in plant architecture. In particular, the number of fruits and branch formation are determined as increasing functions of this ratio. For some sets of well-chosen parameters of the model, the dynamical evolution of the ratio during plant growth generates rhythms.
Cyclic patterns in branch formation or fruit appearance emerge without being forced by the model. The model is based on the theory of discrete dynamical systems. The mathematical formalism helps us to explain rhythm generation and to control the behaviour of the system. Rhythms can appear during both the exponential and stabilized phases of growth, but the causes are different as shown by an analytical study of the system. Simulated plant behaviours are very close to those observed on real plants. With a small number of parameters, the model gives very interesting results from a qualitative point of view. It will soon be subjected to experimental data to estimate the model parameters.
Prediction of phenotypic traits from new genotypes under untested environmental conditions is crucial to build simulations of breeding strategies to improve target traits. Although the plant response to environmental stresses is characterized by both architectural and functional plasticity, recent attempts to integrate biological knowledge into genetics models have mainly concerned specific physiological processes or crop models without architecture, and thus may prove limited when studying genotype x environment interactions. Consequently, this paper presents a simulation study introducing genetics into a functional–structural growth model, which gives access to more fundamental traits for quantitative trait loci (QTL) detection and thus to promising tools for yield optimization.
The GREENLAB model was selected as a reasonable choice to link growth model parameters to QTL. Virtual genes and virtual chromosomes were defined to build a simple genetic model that drove the settings of the species-specific parameters of the model. The QTL Cartographer software was used to study QTL detection of simulated plant traits. A genetic algorithm was implemented to define the ideotype for yield maximization based on the model parameters and the associated allelic combination.
By keeping the environmental factors constant and using a virtual population with a large number of individuals generated by a Mendelian genetic model, results for an ideal case could be simulated. Virtual QTL detection was compared in the case of phenotypic traits – such as cob weight – and when traits were model parameters, and was found to be more accurate in the latter case. The practical interest of this approach is illustrated by calculating the parameters (and the corresponding genotype) associated with yield optimization of a GREENLAB maize model. The paper discusses the potentials of GREENLAB to represent environment x genotype interactions, in particular through its main state variable, the ratio of biomass supply over demand.
During the development of multicellular organisms, cells are capable of interacting with each other through a range of biological and physical mechanisms. A description of these networks of cell–cell interactions is essential for an understanding of how cellular activity is co-ordinated in regionalized functional entities such as tissues or organs. The difficulty of experimenting on living tissues has been a major limitation to describing such systems, and computer modelling appears particularly helpful to characterize the behaviour of multicellular systems. The experimental difficulties inherent to the multitude of parallel interactions that underlie cellular morphogenesis have led to the need for computer models.
A new generic model of plant cellular morphogenesis is described that expresses interactions amongst cellular entities explicitly: the plant is described as a multi-scale structure, and interactions between distinct entities is established through a topological neighbourhood. Tissues are represented as 2D biphasic systems where the cell wall responds to turgor pressure through a viscous yielding of the cell wall.
This principle was used in the development of the CellModeller software, a generic tool dedicated to the analysis and modelling of plant morphogenesis. The system was applied to three contrasting study cases illustrating genetic, hormonal and mechanical factors involved in plant morphogenesis.
Plant morphogenesis is fundamentally a cellular process and the CellModeller software, through its underlying generic model, provides an advanced research tool to analyse coupled physical and biological morphogenetic mechanisms.
The Finite Element Method (FEM) has been used in recent years to simulate overturning processes in trees. This study aimed at using FEM to determine the role of individual roots in tree anchorage with regard to different rooting patterns, and to estimate stress distribution in the soil and roots during overturning.
The FEM was used to carry out 2-D simulations of tree uprooting in saturated soft clay and loamy sand-like soil. The anchorage model consisted of a root system embedded in a soil block. Two root patterns were used and individual roots removed to determine their contribution to anchorage.
In clay-like soil the size of the root–soil plate formed during overturning was defined by the longest roots. Consequently, all other roots localized within this plate had no influence on anchorage strength. In sand-like soil, removing individual root elements altered anchorage resistance. This result was due to a modification of the shape and size of the root–soil plate, as well as the location of the rotation axis. The tap root and deeper roots had more influence on overturning resistance in sand-like soil compared with clay-like soil. Mechanical stresses were higher in the most superficial roots and also in leeward roots in sand-like soil. The relative difference in stresses between the upper and lower sides of lateral roots was sensitive to root insertion angle. Assuming that root eccentricity is a response to mechanical stresses, these results explain why eccentricity differs depending on root architecture.
A simple 2-D Finite Element model was developed to better understand the mechanisms involved during tree overturning. It has been shown how root system morphology and soil mechanical properties can modify the shape of the root plate slip surface as well as the position of the rotation axis, which are major components of tree anchorage.
The contribution of vegetation to shallow-slope stability is of major importance in landslide-prone regions. However, existing slope stability models use only limited plant root architectural parameters. This study aims to provide a chain of tools useful for determining the contribution of tree roots to soil reinforcement.
Three-dimensional digitizing in situ was used to obtain accurate root system architecture data for mature Quercus alba in two forest stands. These data were used as input to tools developed, which analyse the spatial position of roots, topology and geometry. The contribution of roots to soil reinforcement was determined by calculating additional soil cohesion using the limit equilibrium model, and the factor of safety (FOS) using an existing slope stability model, Slip4Ex.
Existing models may incorrectly estimate the additional soil cohesion provided by roots, as the spatial position of roots crossing the potential slip surface is usually not taken into account. However, most soil reinforcement by roots occurs close to the tree stem and is negligible at a distance >1·0 m from the tree, and therefore global values of FOS for a slope do not take into account local slippage along the slope.
Within a forest stand on a landslide-prone slope, soil fixation by roots can be minimal between uniform rows of trees, leading to local soil slippage. Therefore, staggered rows of trees would improve overall slope stability, as trees would arrest the downward movement of soil. The chain of tools consisting of both software (free for non-commercial use) and functions available from the first author will enable a more accurate description and use of root architectural parameters in standard slope stability analyses.