研究目的
To use a functional-structural plant model to find out the major geometrical traits that influence the efficiency of light interception in apple trees.
研究成果
The study demonstrated that leaf area has the strongest direct effect on light interception efficiency in apple trees, accounting for 40% of the variance in STAR values. However, interactions between traits, particularly leaf area and internode length, were significant, suggesting that optimal combinations could be identified for genetic improvement. The in silico approach using MAppleT and VPlants provided an efficient means to explore these relationships, setting a foundation for future research on ideotype definition and trait optimization in apple breeding programs.
研究不足
The study is an in silico simulation, so results may not fully capture real-world complexities such as indirect lighting from reflections, which was not considered in the Fractalysis module. The model was parameterized for a specific cultivar ('Fuji'), limiting generalizability. Stochastic elements in the MAppleT model introduced variance, and only four geometrical traits were investigated, potentially omitting other influential factors. Future work is needed to analyze sensitivity at lower scales (e.g., branch level) and include additional traits like leaf inclination angles.
1：Experimental Design and Method Selection:
The study used the MAppleT model to simulate apple tree architecture and the VPlants software (specifically the Fractalysis module) to simulate light environments. The STAR (Silhouette To Area Ratio) was employed to evaluate light interception efficiency. Sensitivity analysis was conducted using the FAST (Fourier Amplitude Sensitivity Test) method to quantify the influence of input parameters on output variance.
2：Sample Selection and Data Sources:
Virtual apple trees were generated using the MAppleT model, parameterized for the 'Fuji' cultivar. The architectural traits manipulated were leaf area, internode length, apex radius, and branching angle, with values ranging based on observed genetic variation in apple progeny.
3：List of Experimental Equipment and Materials:
The primary tools were the MAppleT model and the VPlants software package (including the Fractalysis module) within the OpenAlea platform. No physical equipment was used as it was an in silico study.
4：Experimental Procedures and Operational Workflow:
The workflow involved: (a) Manipulating geometrical parameters in MAppleT; (b) Integrating the tree architecture into the simulated light environment to calculate STAR values at the whole tree scale; (c) Analyzing the influence of each trait variation on STAR variance using the FAST method, with simulations run for three years of growth and STAR calculated on June 30 of each year.
5：Data Analysis Methods:
The FAST method was used to compute first-order and total-order sensitivity indexes, which quantify the proportion of output variance attributable to each input parameter and their interactions. Simulations were repeated with increasing sample sizes until indexes stabilized.
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