Preliminary Functional-Structural Modeling on Poplar (Salicaceae)

Poplar is one of the best fast-growing trees in the world, widely used for windbreak and wood product. Although architecture of poplar has direct impact on its applications, it has not been descried in previous poplar models, probably because of the difficulties raised by measurement, data processing and parameterization. In this paper, the functional-structural model GreenLab is calibrated by using poplar data of 3, 4, 5, 6 years old. The data was acquired by simplifying measurement. The architecture was also simplified by classifying the branches into several types (physiological age) using clustering analysis, which decrease the number of parameters. By multi-fitting the sampled data of each tree, the model parameters were identified and the plant architectures at different tree ages were simulated

Comparison between empirical or functional sinks of organs - Application on Tomato plant

International audienceBiomass partitioning among organs depends on their sink strengths, i.e. their capacity to attract assimilates. Using a descriptive approach, where plant development is driven by thermal time, and empirical laws fitted from experimental data, it is possible to trace back by inverse method the dynamics of biomass partitioning among organs. However, the descriptive sink function suffers from the drawbacks that organ development and biomass accumulation are not interactively related. Moreover, many parameters are required and are difficult to be measured accurately. In this paper an alternative organ sink strength definition is proposed, in which the organ sink size is related to the maximum organ biomass, which in turn depends on its primordium size. The sink strength increases proportionally to its size at the early growth stage and decreases by dampening when its mass approaches the final mass. Comparisons to the standard empirical sink function used in the GreenLab model were conducted on tomato plants. The new functional sinks are more biologically relevant and simulated rather adequately the organ biomass evolution. Further improvement is ongoing to increase simulation accuracy

Growth and development simulation based on functional-structural model GreenLab for poplar (Salicaceae)

International audiencePoplar (salicaceae) is one of the widest planted fast-growing trees in the world. It is not only used for timber, but also used as windbreak and ecological protection of forest widely. The architecture of poplar has direct impact on poplar's growth and applications, but poplar's architecture still has not been discussed deeply in previous poplar models because of the difficulties raised by measurement, data processing and parameterization. This paper aimed to collect the biomass and architecture data of poplars of different ages, and construct the functional-structural model of poplar based on GreenLab. The selected poplar variety was poplar 107 (Populus × euramericana cv. Neva). The biomass and architecture data were collected from four trees with 3, 4, 5 and 6 years old, respectively. The architecture was simplified by classifying the branches into several types (physiological age) according to the length and size. Based on GreenLab model, some parameters were obtained and some strong correlation coefficients were got. The comparison between the measured and simulated results was given for the trunk data of all trees. The topological structures of poplar at different tree ages were reconstructed. This paper was a exploration of poplar growth simulation based on GreenLab model, and was a good reference in the Functional-Structural model construction of complex trees

Calibration of topological development in the procedure of parametric identification: application to the stochastic GreenLab model for Pinus Sylvestris var. Mongolica

International audienceArid climate, biophysical conditions and human activities all contribute to the occurrences of ecosystem and environment problems, i.e. water scarcity, desertification, salinization, in arid and semiarid zone of North China. Mongolian Scots pine tree (Pinus sylvestris var. mongolica) is one of the principal species of the windbreak and sand-fixing forest in this area. In this paper, we present the calibration process of stochastic GreenLab model based on experiment data. Specific plant topology and sink–source parameters were estimated for Mongolian Scots pine trees through optimizing procedure. The fitting results showed that the calibration was reasonable and acceptable. The model produces several three-dimensional visual representations of Mongolian Scots pine trees with different topological structures simulated by Monte Carlo methods. This model can be used to describe the plant development and growth in a stand level, taking into accounts the variations in plant topology and biomass

A Functional-Structural Plant Model-Theory and Applications in Agronomy

International audienceFunctional-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

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure