Plant science modeling branching in cereals (original) (raw)
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Functional-Structural Plant Modelling in Crop Production
2007
The role acquired by modelling in plant sciences includes integration of knowledge, exploration of the behaviour of the plant system beyond the range of conditions covered experimentally and decision support. The purpose of the model determines its structure. Initially process-based models (PBM) were developed separately from structural (or: architectural or morphological) plant models (SPM). Combining PBMs and SPM into functional-structural plant models (FSPM) or virtual plants has become feasible. This adds a dimension to classical crop growth modelling. FSPM are particularly suited to analyse problems in which the spatial structure of the system is an essential factor contributing to the explanation of the behaviour of the system of study. Examples include intra-specific and interspecific competition phenomena, analyses of mechanisms of physiological response to environmental signals that affect allocation of carbon and nitrogen in the plant, and exploration of alternative, manipulated plant architectures on production of fruits or flowers. Good modelling practice involves different steps in model development. These steps are discussed and include the conceptual modelling, data collection, model implementation, model verification and evaluation, sensitivity analysis and scenario studies.
The role acquired by modelling in plant sciences includes integration of knowledge, exploration of the behaviour of the plant system beyond the range of conditions covered experimentally and decision support. The purpose of the model determines its structure. Initially process-based models (PBM) were developed separately from structural (or: architectural or morphological) plant models (SPM). Combining PBMs and SPM into functional-structural plant models (FSPM) or virtual plants has become feasible. This adds a dimension to classical crop growth modelling. FSPM are particularly suited to analyse problems in which the spatial structure of the system is an essential factor contributing to the explanation of the behaviour of the system of study. Examples include intra-specific and interspecific competition phenomena, analyses of mechanisms of physiological response to environmental signals that affect allocation of carbon and nitrogen in the plant, and exploration of alternative, manipulated plant architectures on production of fruits or flowers. Good modelling practice involves different steps in model development. These steps are discussed and include the conceptual modelling, data collection, model implementation, model verification and evaluation, sensitivity analysis and scenario studies.
A Functional-Structural Plant Model—Theories and Its Applications in Agronomy
Crop Modeling and Decision Support, 2009
Functional-structural plant models (FSPM) simulate plant development and growth, usually accompanied with visualization of the plant 3D architecture. GreenLab is a generic and mechanistic FSPM: various botanical architectures can be produced by its organogenesis model, the growth rate is computed from leaf area, and the biomass partitioning is governed by the sink strength of growing individual organs present in plant structure. A distinguished feature of GreenLab model is that, the plant organogenesis (in terms of the number of organs) and growth (in terms of organ biomass) are formulated using dynamic equations, aside simulation software. This facilitates analytical study of model behaviour, bug-proof of simulation software, and application of efficient optimization algorithm for parameter identification and optimal control problems. Currently several levels of GreenLab model exist: (1) the deterministic one (GL1): plants have a fixed rule for development without feedback from the plant growth; (2) the stochastic level (GL2): pant development is probabilistic because of bud activities, which has influence on plant growth; (3) the feedback model (GL3): the plant development is dependent on the dynamic relationship between biomass demand and supply (and in turn the environment). This paper presents the typical GreenLab theories and applications in past ten years: (1) calibration of GL1 for getting sink and source functions of maize; (2) features of GL2 and its application on wheat plant; (3) rebuilt of the rhythmic pattern of cucumber using GL3; (4) optimization of model parameters for yield improvement, such as wood quantity (for trees); (5) the possible introduction of genetic information in the model through detection of quantitative trait loci for the model parameters; (6) simulation of plant competition for light.
A Functional-Structural Plant Model-Theory and Applications in Agronomy
Functional-structural plant models (FSPM) simulate plant development and growth, usually accompanied with visualization of the plant 3D architecture. GreenLab is a generic and mechanistic FSPM: various botanical architectures can be produced by its organogenesis model, and the plant growth is governed by the competition on biomass among growing organs. A distinguished feature of GreenLab model is that, its organogenesis (in terms of the number of organs) and growth (in terms of organ biomass) are formulated with recurrent equations. It facilitates analytical study of model behaviour, bug-proof of simulation software, and application of efficient optimization algorithm for parameter identification or optimal control problems. Currently several levels of GreenLab model exist: (1) the deterministic one (GL1): plants have a fixed pattern for development without feedback from the plant growth; (2) the stochastic level (GL2): pant organogenesis parameters are probabilistic; (3) the feedback model (GL3): the plant development is dependent on the dynamic relationship between biomass demand and supply (and in turn the environment). It makes it possible to deal with different kinds of behaviour observed in real plants. This paper presents recent typical GreenLab applications: (1) calibration of GL1 for getting sink and source functions of maize; (2) fitting of GL2 on organogenesis of wheat plant; (3) rebuilt of the rhythmic pattern of cucumber using GL3; (4) optimization of model parameters to improve yield, such as leaf (for tea) or wood quantity (for trees); (5) the possible introduction of genetic information in the model through detection of quantitative trait loci for the model parameters; (6) simulation of plant competition for light.
Functional-structural plant modelling: a new versatile tool in crop science
Journal of Experimental Botany, 2010
Plants react to their environment and to management interventions by adjusting physiological functions and structure. Functional-structural plant models (FSPM), combine the representation of three-dimensional (3D) plant structure with selected physiological functions. An FSPM consists of an architectural part (plant structure) and a process part (plant functioning). The first deals with (i) the types of organs that are initiated and the way these are connected (topology), (ii) co-ordination in organ expansion dynamics, and (iii) geometrical variables (e.g. leaf angles, leaf curvature). The process part may include any physiological or physical process that affects plant growth and development (e.g. photosynthesis, carbon allocation). This paper addresses the following questions: (i) how are FSPM constructed, and (ii) for what purposes are they useful? Static, architectural models are distinguished from dynamic models. Static models are useful in order to study the significance of plant structure, such as light distribution in the canopy, gas exchange, remote sensing, pesticide spraying studies, and interactions between plants and biotic agents. Dynamic models serve quantitatively to integrate knowledge on plant functions and morphology as modulated by environment. Applications are in the domain of plant sciences, for example the study of plant plasticity as related to changes in the red:far red ratio of light in the canopy. With increasing availability of genetic information, FSPM will play a role in the assessment of the significance towards plant performance of variation in genetic traits across environments. In many crops, growers actively manipulate plant structure. FSPM is a promising tool to explore divergent management strategies.
Stochastic, functional and interactive models for plant growth and architecture
The interactions between plant development and plant growth: GL3 Case 4.3. Calibration of GreenLab model on real cultivated plants 4.3.1. CAU experiments 4.3.2. Generalization of the sources and sinks concepts in a plant 4.3.3. Theoretical issues on plant fitting with the generalized least square method 4.4. Optimal control for plants 4.5. From single plant functioning to field functioning 5.
Annals of Botany, 2008
† Background 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. † Scope 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. † PMA06 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.
Functional plant biology, 2008
The canopy structure of grasslands is a major determinant of their use-value, as it affects the quantity and quality of the forage removed when mowed or grazed. The structure of this canopy is determined by individual plant architecture, which is highly sensitive to both environmental variations and management practices such as cutting regimes. This architectural plasticity may partially be mediated by a self-regulation process, i.e. the actual state of the architecture (e.g. length of the pseudostem) may control the determination of morphogenetic processes such as cell production. To test the robustness of this hypothesis, we designed a functional-structural model of ryegrass plant morphogenesis exhibiting this type of cybernetic behavior. The model is based on the L-system formalism. It was able to capture satisfactorily the major quantitative architectural traits of ryegrass under non-limiting growing conditions and under a cutting constraint. From these simulation results it appears that i) self-regulation rules could be of practical use to ryegrass modeling and ii) when activated in an integrated model, they are not markedly incompatible with reality.
Considering plant structure in models of plant growth and development
2040
Mankind is facing several daunting global problems such as food security, climate change, and oil-reserve depletion. The real-world challenges for agriculture today are therefore, breeding for crop genotypes and expressing their potential in target (stressful) environments to produce sufficient quality food, feed, fibre and fuel, while maintaining the sustainability of agro-ecosystems and resource use. These goals can be achieved only via realizing phenotypes of complex traits at the level of the crop-the community of mutually interacting plants. We will discuss the necessity of integrating modern systems biology and traditional crop modelling in order to better assist in achieving the required crop phenotypes.
Modelling kinetics of plant canopy architecture—concepts and applications
European Journal of Agronomy, 2003
Most crop models simulate the crop canopy as an homogeneous medium. This approach enables modelling of mass and energy transfer through relatively simple equations, and is useful for understanding crop production. However, schematisation of an homogeneous medium cannot address the heterogeneous nature of canopies and interactions between plants or plant organs, and errors in calculation of light interception may occur. Moreover, conventional crop models do not describe plant organs before they are visible externally e.g. young leaves of grasses. The conditions during early growth of individual organs are important determinants of final organ size, causing difficulties in incorporating effects of environmental stresses in such models. Limited accuracy in describing temporal source-sink relationships also contributes to difficulty in modelling dry matter distribution and paramaterisation of harvest indices. Functional-architectural modelling aims to overcome these limitations by (i) representing crops as populations of individual plants specified in three dimensions and (ii) by modelling whole plant growth and development from the behaviour of individual organs, based on models of organs such as leaves and internodes. Since individual plants consist of numerous organs, generic models of organ growth applicable across species are desirable. Consequently, we are studying the development of individual organs, and parameterising it in terms of environmental variables and plant characteristics. Models incorporating plant architecture are currently applied in education, using dynamic visual representation for teaching growth and development. In research, the 3D representation of plants addresses issues presented above and new applications including modelling of pesticide distribution, fungal spore dispersal through splashing and plant to plant heterogeneity. #