Abstract
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.
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© 2009 Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg
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Kang, MZ., Cournède, PH., Mathieu, A., Letort, V., Qi, R., Zhan, ZG. (2009). A Functional-Structural Plant Model—Theories and Its Applications in Agronomy. In: Cao, W., White, J.W., Wang, E. (eds) Crop Modeling and Decision Support. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01132-0_16
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DOI: https://doi.org/10.1007/978-3-642-01132-0_16
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