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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
Published on: March 28, 2025
Parameter optimization and field validation of the functional-structural model GREENLAB for maize at different
Yuntao Ma1, Meiping Wen, Yan Guo
1Key Laboratory of Plant-Soil Interactions, Ministry of Education, College of Resources and Environment, China Agricultural University, Beijing 100094, China.
Annals of Botany
|October 9, 2007
Summary
The GREENLAB model accurately predicts maize growth across various plant population densities (PPDs), demonstrating its ability to capture plant plasticity and offering potential for optimizing crop yields.
Area of Science:
- Agricultural Science
- Plant Physiology
- Computational Biology
Background:
- Plant population density (PPD) significantly impacts plant development and growth.
- Functional-structural plant models (FSPMs) like GREENLAB are valuable tools for simulating plant responses to environmental factors, including PPD.
Purpose of the Study:
- To evaluate the efficacy of the GREENLAB model in predicting maize growth and development under varying plant population densities.
- To assess the model's capability to simulate PPD effects on plant functioning and architectural behavior.
Main Methods:
- Conducted field experiments with three distinct PPDs (2.8, 5.6, and 11.1 plants m⁻²) in irrigated North China Plain.
- Collected detailed measurements of above-ground plant organ dimensions and fresh biomass throughout the growing seasons.
- Utilized in situ plant digitization to create geometrical symbol files for 3D model output visualization.
Main Results:
- GREENLAB simulations showed good agreement with observed maize growth, with linearity and slopes near unity.
- Key parameters like biomass production and internode relative sink strength varied with PPD, indicating model sensitivity.
- 3D representations visualized the impact of contrasting PPDs on individual plants and overall plant stands.
Conclusions:
- The GREENLAB model successfully captures plant plasticity in response to varying plant population densities.
- Stable parameter values support the hypothesis of a unified set of equations governing dynamic organ growth.
- The model shows promise for agronomic applications, including yield optimization, following further validation.
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