Does the structure-function model GREENLAB deal with crop phenotypic plasticity induced by plant spacing? A case
Qiaoxue Dong1, Gaëtan Louarn, Yiming Wang
1China Agricultural University, Department of Electronic Information, College of Information and Electrical Engineering, Box 63, 100083, Beijing, China.
Annals of Botany
|January 18, 2008
Summary
This study tested the GREENLAB plant growth model by simulating competition for light in tomato plants. The model accurately simulated biomass and allocation changes, showing potential for predicting plant plasticity.
Area of Science:
- Plant modeling
- Phenotypic plasticity
- Computational biology
Background:
- Plant growth models simulating phenotypic plasticity are crucial for predicting crop yield and quality.
- These models can offer insights into the genetic basis of quantitative traits.
- The GREENLAB model simulates resource-dependent plasticity in plant architecture.
Purpose of the Study:
- To rigorously test the GREENLAB model's ability to simulate plant plasticity under varying light competition.
- To evaluate the model's performance with tomato, a species with a strong photomorphogenetic response.
- To assess the impact of plant spacing on model parameter stability.
Main Methods:
- A greenhouse experiment varied planting densities (0.3, 0.6, 1 m spacing) to create a light competition gradient.
- Detailed records of plant development, architecture, and organ growth were collected.
- Model calibration utilized a multi-fitting statistical optimization procedure.
Main Results:
- The GREENLAB model accurately simulated total biomass production and allocation patterns under different planting densities.
- Some model parameters related to organ sink strengths and light interception showed environment-dependence at high densities.
- Parameter value responses were consistent with growth measurements and prior research.
Conclusions:
- The GREENLAB model structure shows potential for reproducing observed plant plasticity, particularly biomass and allocation responses.
- Model limitations in fully accounting for plasticity under high competition were identified.
- Biologically informed adaptations and advanced fitting tools could enhance model accuracy and robustness.
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