BACKGROUND AND AIMS Using 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. METHODS A 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 RESULTS Our 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. CONCLUSIONS The 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.

中文翻译：

---

内部营养压力，植物发育的调节器？应用于咖啡树的随机功能结构植物生长模型的见解

背景和目的近年来，使用内部营养压力作为调节变量来模拟植物生长功能结构模型中器官发生、同化物产生和分配之间的复杂相互作用回路，引起了越来越多的兴趣。然而，这种方法受到以下事实的阻碍：内部营养压力是一个不可测量的量，只能通过模型​​参数估计来评估，为此该方法并不简单，尤其是当模型是随机的时。方法 开发了植物生长的随机 GreenLab 模型（称为“GL4”），具有内部营养竞争的反馈效应，以生物量供求比 (Q/D) 表示，对器官发生具有反馈作用。提出了一种参数估计方法并将其应用于 15 棵两年生咖啡树的数据集。根据拟合结果，根据器官发生参数的估计变化重建和分析 Q/D 的变化。主要结果 我们的随机追溯模型能够真实地模拟幼苗结构的渐进式设置和树枝修剪效果。使用咖啡树的真实数据进行的参数估计提供了对内部营养动态的访问。这些动态与建立阶段的器官发生概率相关。结论 该模型可以令人满意地再现测量数据，从而为进一步将此原始程序应用于其他实验数据开辟了有希望的途径。