Applications of the multi-sigmoidal deterministic and stochastic logistic models for plant dynamics Di Crescenzo, Antonio Paraggio, Paola Román Román, Patricia Torres Ruiz, Francisco De Asís We consider a generalization of the classical logistic growth model introducing more than one inflection point. The growth, called multi-sigmoidal, is firstly analyzed from a deter- ministic point of view in order to obtain the main properties of the curve, such as the limit behavior, the inflection points and the threshold-crossing-time through a fixed boundary. We also present an application in population dynamics of plants based on real data. Then, we define two different birth-death processes, one with linear birth and death rates and the other with quadratic rates, and we analyze their main features. The conditions under which the processes have a mean of multi-sigmoidal logistic type and the first-passage- time problem are also discussed. Finally, with the aim of obtaining a more manageable stochastic description of the growth, we perform a scaling procedure leading to a lognor- mal diffusion process with mean of multi-sigmoidal logistic type. We finally conduct a detailed probabilistic analysis of this process. 2024-01-25T10:37:52Z 2024-01-25T10:37:52Z 2020-12-05 journal article Published version: Antonio Di Crescenzo, Paola Paraggio, Patricia Román-Román, Francisco Torres-Ruiz. Applications of the multi-sigmoidal deterministic and stochastic logistic models for plant dynamics. Applied Mathematical Modelling 92 (2021), 884-904. https://doi.org/10.1016/j.apm.2020.11.046 https://hdl.handle.net/10481/87265 10.1016/j.apm.2020.11.046 eng http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Elsevier