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Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize.

Yuntao Ma1, Youjia Chen1, Jinyu Zhu2

  • 1Key Laboratory of Arable Land Conservation (North China), Ministry of Agriculture, College of Resources and Environmental Sciences, China Agricultural University, Beijing.

Annals of Botany
|February 16, 2018
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Summary
This summary is machine-generated.

This study revised the GREENLAB-Maize model to simulate individual kernel growth in maize. The enhanced model accurately predicts kernel dry weight and filling duration, aiding in optimizing crop yield.

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Area of Science:

  • Agricultural Science
  • Plant Physiology
  • Computational Biology

Background:

  • Crop simulation models often overlook kernel growth variation, leading to inaccurate yield predictions.
  • Accurate simulation of maize kernel development is crucial for understanding and optimizing crop yield.

Purpose of the Study:

  • To revise the GREENLAB-Maize model by incorporating source- and sink-limited approaches for simulating individual kernel dry matter accumulation.
  • To develop the GREENLAB-Maize-Kernel model for detailed analysis of maize kernel growth dynamics.

Main Methods:

  • Characterized potential individual kernel growth rates and sink demand.
  • Incorporated remobilization of non-structural carbohydrates from reserve organs to kernels.
  • Validated the model using two years of field experiments with two maize hybrids, varying plant densities and pollination treatments.

Main Results:

  • Simulated individual kernel growth closely matched experimental data for final kernel size, growth rate, and filling duration.
  • The model accurately reproduced observed dry weights of various plant organs.
  • Quantified source-sink dynamics and carbohydrate remobilization, demonstrating their role in kernel filling.

Conclusions:

  • The revised GREENLAB-Maize-Kernel model provides a valuable tool for exploring strategies to optimize maize kernel yield.
  • The model facilitates understanding of maize management effects on yield by considering individual kernel responses.
  • This approach aids in matching maize cultivation practices to environmental conditions for enhanced productivity.