Preliminary Functional-Structural Modeling on Poplar (Salicaceae)

TL;DR

This paper develops a simplified functional-structural model of poplar trees using GreenLab, calibrated with age-specific data, to better understand their architecture and growth patterns.

Contribution

It introduces a novel calibration approach for GreenLab on poplar, incorporating simplified measurements and branch classification to improve modeling accuracy.

Findings

Successfully calibrated GreenLab with poplar data at multiple ages

Simulated plant architectures across different tree ages

Reduced model complexity through branch classification

Abstract

Poplar is one of the best fast-growing trees in the world, widely used for windbreak and wood product. Although architecture of poplar has direct impact on its applications, it has not been descried in previous poplar models, probably because of the difficulties raised by measurement, data processing and parameterization. In this paper, the functional-structural model GreenLab is calibrated by using poplar data of 3, 4, 5, 6 years old. The data was acquired by simplifying measurement. The architecture was also simplified by classifying the branches into several types (physiological age) using clustering analysis, which decrease the number of parameters. By multi-fitting the sampled data of each tree, the model parameters were identified and the plant architectures at different tree ages were simulated.