Thomas Arsouze

Functional-structural plant models simulate plant responses to environmental conditions, but their development and evaluation are often limited by the lack of datasets combining detailed architectural and physiological measurements. Here, we present a comprehensive dataset acquired from four oil palm plants ( Elaeis guinnensis) grown under controlled and contrasting climate scenarios. The dataset includes (i) three-dimensional reconstructions of plant architecture derived from terrestrial lidar point clouds, (ii) leaf-level gas exchange measurements used to parameterize photosynthesis and stomatal conductance models, and (iii) continuous plant-scale measurements of CO ₂ and H ₂ O fluxes obtained in a microcosm under precisely monitored and manipulated environmental conditions (light, temperature, humidity, and CO ₂ concentration) across height climate scenarios. By combining detailed structural data with physiological measurements at both leaf and whole-plant scales, this database has been designed to build and evaluate digital twins (or shadows) of plants functioning under controlled conditions. It provides a valuable resource for calibrating biophysical models (light interception and photosynthesis), benchmarking model predictions across scales, and investigating the consistency between leaf-level parameterization and plant-level fluxes. All data and processing workflows are openly available, facilitating reuse for model development, evaluation, and intercomparison in plant and crop modelling communities.

Evolving agricultural practices and contexts invite to reconsider the way crop and plant models represent agroecosystem processes. Crop models assume spatial homogeneity, which reduces confidence in their predictions for structurally heterogeneous systems, whereas FSPMs’ complexity limits their application to field-scale or large-scale studies. To benefit from strengths of both approaches, we introduce ArchiCrop, a parametric 3D architectural model of cereals that generates plant geometries constrained by crop model dynamics and coordination rules. Inspired by the concept of equifinality, ArchiCrop generates a morphospace of architecturally diverse morphotypes which remain equivalent at crop scale in terms of LAI and height. This multiscale approach enables the comparison of processes computed at different scales on wheat, rice, maize and sorghum. We demonstrate its application evaluating light interception Beer’s formalism in STICS soil-crop model relying on the leaf-resolved radiosity model Caribu for the 3D reference simulations, for a sorghum monocrop. We show that the consideration of the variability of only two plant architectural traits, leaf insertion angle and leaf number, introduces up to 27% of uncertainty in the cumulated absorbed light at the end of the season. A possible outcome from this method is also the definition of metamodels for crop model processes, as exemplified for extinction coefficient of Beer’s law. ArchiCrop can support a range of applications, including crop model uncertainty analysis, model-assisted phenotyping, and ideotype design. Highlights: ArchiCrop is the first 3D+t botany-based parametric generative model for cereals. ArchiCrop downscales crop model dynamics to 3D+t architecture canopies efficiently. ArchiCrop compares big leaf versus leaf-resolved light interception. Plant architectures with same leaf area intercept light with up to 27% variability. ArchiCrop helps ideotyping, crop model evaluation and error propagation analysis. Graphical

Process-based biophysical models are essential for understanding and predicting plant responses to environment, yet their development and application remains hindered by the ‘two-language problem’: models are typically prototyped in interactive, slow languages and then reimplemented in compiled, fast languages for simulation. This workflow requires more human resources, and it may introduce delays, errors, and complexity for model evaluation and prototyping iteration. We present PlantBiophysics.jl, a pure Julia package that unifies model prototyping, calibration, simulation, and evaluation of biophysical models within a single, high-performance and interactive environment. The package currently supports four key processes: light interception, energy balance, photosynthesis, and stomatal conductance. It provides automated model composition and coupling through a modular and extensible architecture. The package supports uncertainty propagation and compatibility with 3D plant structures and dynamic meteorological inputs. We benchmarked PlantBiophysics.jl against the widely used R package plantecophys and the Julia package LeafGasExchange.jl. Simulations ran up to ∼6700× and ∼460× faster, respectively, while improving predictive accuracy (e.g. net assimilation nRMSE: 5% vs. 9% and 12%, respectively). By allowing models to be easily swapped, coupled, or extended, and by supporting simulations at scales ranging from single leaves to 3D plant canopies, PlantBiophysics.jl provides a robust, efficient, and transparent tool for plant ecophysiologists and modellers.

Each year, an average of 45 tropical cyclones affect coastal areas and potentially impact forests. The proportion of the most intense cyclones has increased over the past four decades and is predicted to continue to do so. Yet, it remains uncertain how topographical exposure and tree characteristics can mediate the damage caused by increasing wind speed. Here, we compiled empirical data on the damage caused by 11 cyclones occurring over the past 40 years, from 74 forest plots representing tropical regions worldwide, encompassing field data for 22,176 trees and 815 species. We reconstructed the wind structure of those tropical cyclones to estimate the maximum sustained wind speed (MSW) and wind direction at the studied plots. Then, we used a causal inference framework combined with Bayesian generalised linear mixed models to understand and quantify the causal effects of MSW, topographical exposure to wind (EXP), tree size (DBH) and species wood density (ρ) on the proportion of damaged trees at the community level, and on the probability of snapping or uprooting at the tree level. The probability of snapping or uprooting at the tree level and, hence, the proportion of damaged trees at the community level, increased with increasing MSW, and with increasing EXP accentuating the damaging effects of cyclones, in particular at higher wind speeds. Higher ρ decreased the probability of snapping and to a lesser extent of uprooting. Larger trees tended to have lower probabilities of snapping but increased probabilities of uprooting. Importantly, the effect of ρ decreasing the probabilities of snapping was more marked for smaller than larger trees and was further accentuated at higher MSW. Our work emphasises how local topography, tree size and species wood density together mediate cyclone damage to tropical forests, facilitating better predictions of the impacts of such disturbances in an increasingly windier world.

StormR is an R package allowing the easy extraction of storm track data from a provided database and the generation of surface wind fields (speed and direction) as reconstructed from storm track data and a parametric cyclone model. Then StormR allows us to compute three summary statistics (the maximum sustained wind speed, the power dissipation index, and the duration of exposure to winds reaching a given wing speed along the cyclone life span) and to plot the results. We suggest to use the IBTrACS (International Best Track Archive for Climate Stewardship) database as input (Knapp et al., 2010, 2018). This database provides a fairly comprehensive record of tropical storms and cyclones with a 3-hours temporal resolution since 1841. However any storm track data can be used as long as the mandatory fields are provided.