ArticleFrontiers in plant science2018
Estimating Sink Parameters of Stochastic Functional-Structural Plant Models Using Organic Series-Continuous and Rhythmic Development.
Article in Frontiers in plant science, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Improving the 3D representation of plant architecture and parameterization efficiency of functional-structural tree models using terrestrial LiDAR data.AoB PLANTS · 2025Article
- Effects of calcium phosphate and phosphorus-dissolving bacteria on microbial structure and function during Torreya Grandis branch waste composting.BMC microbiology · 2024Article
- Functional-Structural Plant Model "GreenLab": A State-of-the-Art Review.Plant phenomics (Washington, D.C.) · 2024Article
- Coupling Plant Growth Models and Pest and Disease Models: An Interaction Structure Proposal, MIMIC.Plant phenomics (Washington, D.C.) · 2023Article
- Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery.Plant phenomics (Washington, D.C.) · 2023Review
- Two decades of research with the GreenLab model in agronomy.Annals of botany · 2021Review
- Internal trophic pressure, a regulator of plant development? Insights from a stochastic functional-structural plant growth model applied to Coffea trees.Annals of botany · 2020Article
- Two decades of functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology.Annals of botany · 2020Review
- Size development of tomatoes growing in trusses: linking time of fruit set to diameter.Journal of the science of food and agriculture · 2020Article
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6 authors.
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Abstract
Functional-structural plant models (FSPMs) generally simulate plant development and growth at the level of individual organs (leaves, flowers, internodes, etc.). Parameters that are not directly measurable, such as the sink strength of organs, can be estimated inversely by fitting the weights of organs along an axis (organic series) with the corresponding model output. To accommodate intracanopy variability among individual plants, stochastic FSPMs have been built by introducing the randomness in plant development; this presents a challenge in comparing model output and experimental data in parameter estimation since the plant axis contains individual organs with different amounts and weights. To achieve model calibration, the interaction between plant development and growth is disentangled by first computing the occurrence probabilities of each potential site of phytomer, as defined in the developmental model (potential structure). On this basis, the mean organic series is computed analytically to fit the organ-level target data. This process is applied for plants with continuous and rhythmic development simulated with different development parameter sets. The results are verified by Monte-Carlo simulation. Calibration tests are performed both
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