Abstract
Functional-structural plant models (FSPM) aim to replicate the intricate ecophysiological and developmental responses of plants to their environment. These models are valuable for projecting plant behaviour in a changing climate but rely heavily on detailed measurements of structural and ecophysiological traits for their parameterization. However, collecting these measurements simultaneously and consistently at multiple scales remains a challenge, often limiting model parameterization and thorough evaluation, and thereby reducing confidence in model predictions. Here, we propose a comprehensive dataset of biophysical measurements from four oil palm plants grown (Elaeis guinnensis) in controlled environments. The dataset includes detailed reconstructions of the three-dimensional plant structures derived from terrestrial LiDAR point clouds and leaf-scale gas exchange measurements for parameterising leaf physiology. We also provide plant-scale gas exchange measurements (CO 2 and H 2 0) and leaf temperature data under multiple controlled environmental scenarios, including varying CO 2 concentrations, light, temperature and humidity conditions. Our aim is to create a digital twin of the four plants to facilitate FSPM robust evaluation, and help identify sources of model uncertainty.