ArticleAnnals of botany2020
Internal trophic pressure, a regulator of plant development? Insights from a stochastic functional-structural plant growth model applied to Coffea trees.
Article in Annals of botany, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- Organic amendment composition and sowing depth in coffee Arabica: Effects on seedling growth biometrics.Heliyon · 2024Article
- Functional-Structural Plant Model "GreenLab": A State-of-the-Art Review.Plant phenomics (Washington, D.C.) · 2024Article
- Towards modelling emergence in plant systems.Quantitative plant biology · 2023Article
- 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 functional-structural plant modelling: now addressing fundamental questions in systems biology and predictive ecology.Annals of botany · 2020Review
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5 authors.
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Abstract
BACKGROUND AND
aimsUsing internal trophic pressure as a regulating variable to model the complex interaction loops between organogenesis, production of assimilates and partitioning in functional-structural models of plant growth has attracted increasing interest in recent years. However, this approach is hampered by the fact that internal trophic pressure is a non-measurable quantity that can be assessed only through model parametric estimation, for which the methodology is not straightforward, especially when the model is stochastic.
methodsA stochastic GreenLab model of plant growth (called 'GL4') is developed with a feedback effect of internal trophic competition, represented by the ratio of biomass supply to demand (Q/D), on organogenesis. A methodology for its parameter estimation is presented and applied to a dataset of 15 two-year-old Coffea canephora trees. Based on the fitting results, variations in Q/D are reconstructed and analysed in relation to the estimated variations in organogenesis parameters. KEY
resultsOur stochastic retroactive model was able to simulate realistically the progressive set-up of young plant architecture and the branch pruning effect. Parameter estimation using real data for Coffea trees provided access to the internal trophic dynamics. These dynamics correlated with the organogenesis probabilities during the establishment phase.
conclusionsThe model can satisfactorily reproduce the measured data, thus opening up promising avenues for further applying this original procedure to other experimental data. The framework developed can serve as a model-based toolkit to reconstruct the hidden internal trophic dynamics of plant growth.
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