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Related Experiment Videos

A flexible sigmoid function of determinate growth.

Xinyou Yin1, Jan Goudriaan, Egbert A Lantinga

  • 1Department of Plant Sciences, Wageningen University, PO Box 430, The Netherlands. Xinyou.Yin@wur.nl

Annals of Botany
|January 28, 2003
PubMed
Summary

A new beta growth function accurately models determinate growth patterns in plants. This function provides unique insights into growth dynamics, aiding crop scientists in understanding environmental and genetic influences on plant development.

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Area of Science:

  • Agricultural Science
  • Plant Physiology
  • Mathematical Modeling

Background:

  • Determinate growth, characterized by a sigmoid pattern, is crucial for understanding plant development.
  • Existing growth functions (logistic, Gompertz, Weibull, Richards, expolinear) have limitations in accurately describing determinate growth dynamics.
  • Accurate modeling of determinate growth is essential for crop simulation and understanding environmental/genotypic influences.

Purpose of the Study:

  • Introduce and validate a new empirical equation, the beta growth function, for modeling determinate growth.
  • Compare the performance of the beta growth function against classical and expolinear growth equations.
  • Assess the suitability of the beta growth function for crop simulation models and characterizing growth traits.

Main Methods:

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  • Developed the beta growth function with parameters: time of maximum growth rate (t(m)), time of growth cessation (t(e)), and maximum weight (w(max)).
  • Compared the beta growth function with logistic, Richards, Gompertz, Weibull, and expolinear equations using empirical data.
  • Evaluated flexibility, parameter stability, initial condition handling, and estimation accuracy of w(max).

Main Results:

  • The beta growth function successfully described sigmoid dynamics in seed filling, plant growth, and crop biomass.
  • Differences were observed among equations in estimating w(max), with the beta function offering more reasonable estimates for final quantity and duration.
  • The beta function uniquely predicts zero growth rate at the start and end of determinate growth periods.
  • Its symmetrical form is a cubic polynomial, offering flexibility for asymmetrical sigmoid patterns.

Conclusions:

  • The beta growth function is a robust and flexible tool for modeling determinate growth, outperforming existing models in certain aspects.
  • It is particularly suitable for process-based crop simulation models due to its defined growth period and parameter interpretability.
  • The function's parameters align with key growth traits, making it valuable for analyzing environmental and genotypic effects on plant development.