Computing Competition for Light in the GREENLAB Model of Plant Growth: A Contribution to the Study of the Effects of Density on Resource Acquisition and Architectural Development

Cournède, Paul-Henry; Mathieu, Amélie; Houllier, François; Barthélémy, Daniel; Reffye, Philippe de

doi:10.1093/aob/mcm272

Cited by 69 publications

(59 citation statements)

References 46 publications

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“…In Greenlab, as the biomass production is computed at the individual plant level, a 'local' LAI [11] is defined, corresponding to the leaf surface of the plant multiplied by a coefficient related to the two-dimensional projection of the space occupied by the plant on the ground (see II-A1). In CERES, as the biomass production is computed at the square meter level, a 'global' LAI is constructed from the individual leaf surfaces of the plant, by multiplying by the crop density (see II-A3).…”

Section: A Description Of the Modelsmentioning

confidence: 99%

Evaluation of the predictive capacity of five plant growth models for sugar beet

Baey

Cournède

et al. 2012

2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications

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Abstract-A lot of plant growth models coexist, with different modelling approaches and levels of complexity. In the case of sugar beet, many of them are used as predictive tools, even when they were not originally designed for this purpose.We propose the evaluation and comparison of five plant growth models that rely on the same energetic production of biomass, but with different levels of description (per plant or per square meter) and different biomass repartition (empirical or via allocation): Greenlab, LNAS, CERES, PILOTE and STICS. The models were calibrated on a first set of data, and their predictive capacities were compared on an independent data set from the same variety and similar environmental conditions, using the root mean squared error of prediction (RMSEP) and modelling efficiency (EF) for the total dry matter production and the dry matter of root.All the models tended to overestimate both the total dry matter and the dry matter of root. Greenlab gave the best predictions for the root biomass, and CERES the best total biomass predictions. The overestimation was partly explained by a hail episode that caused a lot of damages to the leaves in the validation year. The five models also provided similar yield prediction errors.

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Section: A Description Of the Modelsmentioning

confidence: 99%

Evaluation of the predictive capacity of five plant growth models for sugar beet

Baey

Cournède

et al. 2012

2012 IEEE 4th International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications

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“…T growth cycles thus correspond to T phyllochrons. We recall the classical GreenLab equation giving Q t , plant biomass production at growth cycle t related to Beer's law [9]:…”

Section: Application To the Greenlab Equation Of Plant Growthmentioning

confidence: 99%

“…Likewise, the extrapolation of individual-based models to the field scale is still at its early stages. It mostly concerns competition for light, by considering radiosity models to compute light interception [8] or empirical functions to describe interactions between neighbours [9]. The latter approach based on the GreenLab model of plant growth has led to model calibration at the population level in different density conditions for maize [10], tomato [11] or sugar beet [12].…”

Section: Introductionmentioning

confidence: 99%

Modeling Inter-individual Variability in Sugar Beet Populations

Reffye

Lemaire

Srivastava

et al. 2009

2009 Third International Symposium on Plant Growth Modeling, Simulation, Visualization and Applications

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Modeling heterogeneity in field crops is a key issue for a better characterization of field production. This paper presents some experimental data on sugar beet illustrating this heterogeneity. Several sources of individual variability within plant populations are identified: namely, initial condition (seed biomass, emergence delay), genetic variability (including phyllochron) and environment (including spacing and competition). A mathematical framework is introduced to integrate the different sources of variability in plant growth models. It is based on the classical method of Taylor Series Expansion, which allows the propagation of uncertainty in the dynamic system of growth and the computation of the approximate means and standard deviations of the model outputs. The method is applied to the GreenLab model of plant growth and more specifically to sugar beet. It opens perspectives in order to assess the different sources of variability in plant populations and estimate their parameters from experimental data. However important issues like optimization of data collection and system identifiability have to be resolved first, since the uncertainty effects may be mixed in an inextricable way or may necessitate a too huge amount of experimental data for their estimation.

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“…It is particularly useful in functional-structural plant models (FSPMs), which explicitly describe the development of the 3D architecture or structure of plants over time as governed by physiological processes and environmental factors [3,4]. In these FSPMs, such as GreenLab [5,6], stem morphology (length and diameter) is an important component because it requires an accurate description of the geometric and topological structure of the plant and canopy. It is well known that plants often display large differences in morphological characters.…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Variability of Internode Allometry within and between Trees for Pinus tabulaeformis Carr. Using a Multilevel Nonlinear Mixed-Effect Model

Jun

Lei

Wang

et al. 2014

Forests

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Allometric models of internodes are an important component of Functional-Structural Plant Models (FSPMs), which represent the shape of internodes in tree architecture and help our understanding of resource allocation in organisms. Constant allometry is always assumed in these models. In this paper, multilevel nonlinear mixed-effect models were used to characterize the variability of internode allometry, describing the relationship between the last internode length and biomass of Pinus tabulaeformis Carr. trees within the GreenLab framework. We demonstrated that there is significant variability in allometric relationships at the tree and different-order branch levels, and the variability decreases among levels from trees to first-order branches and, subsequently, to second-order branches. The variability was partially explained by the random effects of site characteristics, stand age, density, and topological position of the internode. Tree-and branch-level-specific allometric models are recommended because they produce unbiased and accurate internode length estimates. The model and method developed in this study are useful for understanding and describing the structure and functioning of trees. OPEN ACCESSForests 2014, 5 2826 Keywords: internode allometry; mixed-effect models; Pinus tabulaeformis Carr.; variance of random effects

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Computing Competition for Light in the GREENLAB Model of Plant Growth: A Contribution to the Study of the Effects of Density on Resource Acquisition and Architectural Development

Cited by 69 publications

References 46 publications

Evaluation of the predictive capacity of five plant growth models for sugar beet

Evaluation of the predictive capacity of five plant growth models for sugar beet

Modeling Inter-individual Variability in Sugar Beet Populations

Quantifying the Variability of Internode Allometry within and between Trees for Pinus tabulaeformis Carr. Using a Multilevel Nonlinear Mixed-Effect Model

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