A virtual plant that responds to the environment like a real one: the case for chrysanthemum

Authors

  • MengZhen Kang,

    1. State Key Laboratory of Management and Control for Complex Systems, LIAMA, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
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  • Ep Heuvelink,

    1. Horticultural Supply Chains group, Wageningen University, PO Box 630, 6700 AP Wageningen, the Netherlands
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  • Susana M. P. Carvalho,

    1. Horticultural Supply Chains group, Wageningen University, PO Box 630, 6700 AP Wageningen, the Netherlands
    2. CBQF – College of Biotechnology, Portuguese Catholic University, Rua Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
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  • Philippe de Reffye

    1. Cirad-Amis, UMR AMAP, TA 40/01 Avenue Agropolis, F-34398 Montpellier, Cedex 5, France
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Author for correspondence:
MengZhen Kang
Tel: +86 10 62647457
Email: mengzhen.kang@ia.ac.cn

Summary

  • Plants respond to environmental change through alterations in organ size, number and biomass. However, different phenotypes are rarely integrated in a single model, and the prediction of plant responses to environmental conditions is challenging. The aim of this study was to simulate and predict plant phenotypic plasticity in development and growth using an organ-level functional–structural plant model, GreenLab.
  • Chrysanthemum plants were grown in climate chambers in 16 different environmental regimes: four different temperatures (15, 18, 21 and 24°C) combined with four different light intensities (40%, 51%, 65% and 100%, where 100% is 340 μmol m−2 s−1). Measurements included plant height, flower number and major organ dry mass (main and side-shoot stems, main and side-shoot leaves and flowers). To describe the basipetal flowering sequence, a position-dependent growth delay function was introduced into the model.
  • The model was calibrated on eight treatments. It was capable of simulating multiple plant phenotypes (flower number, organ biomass, plant height) with visual output. Furthermore, it predicted well the phenotypes of the other eight treatments (validation) through parameter interpolation.
  • This model could potentially serve to bridge models of different scales, and to link energy input to crop output in glasshouses.

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