Résumé
Functional-structural plant models (FSPM) replicate plants' responses to their environment and are useful for predicting behavior in a changing climate. However, they rely on detailed measurements of traits, which are difficult to collect consistently across scales, often limiting model parameterization and thorough evaluation, and thereby reducing confidence in model predictions. Here, we provided a comprehensive dataset of structural and biophysical measurements from four oil palm plants (Elaeis guinnensis) grown under multiple controlled environmental scenarios, including varying CO2 concentrations, light, temperature and humidity conditions. The dataset included detailed reconstructions of the three-dimensional plant structures derived from terrestrial LiDAR point clouds, and enabled the parametrization of biophysical processes at the leaf scale such as photosynthesis and stomatal conductance, as well as the collection of plant-scale measurements (gas exchange measurements of CO2 and H20), which can be compared with FSPM simulations. The tree-dimensional reconstructions effectively represented the architecture of the plants and showed strong correlation with the measured total leaf area. Hence, future comparisons between simulated and observed physiological traits could be used to evaluate the quality of the physiological formalisms independently. By bridging the scales from individual leaves to the entire plant, this database allows modellers to both calibrate their biophysical models at a fine spatial resolution and evaluate their predictive accuracy at the plant scale. The provided data will facilitate benchmarking of biophysical models, help identify sources of model uncertainty, and ultimately enhance model predictions, which can be applied in various fields, from cognitive studies to decision support applications.