Abstract Process-based crop modelling platforms such as DSSAT are potentially valuable tools for crop breeding programmes, with the capacity to predict genotype-by-environment-by-management interactions. However, their application for breeding is challenged by the need to calibrate large numbers of genotypes within populations. In wheat (Triticum aestivum L.), using pre-existing DSSAT-CERES wheat ecotypes can introduce unrealistic parameter compensation during cultivar calibration. To address this, we developed a two-phase sequential calibration framework. This workflow uses phenotypic clustering to first define representative ecotypes using experiment-specific data before proceeding with cultivar-level parameter estimation. We demonstrate the utility of this framework to integrate direct measurements from proximal and remote sensing data collected on 14 genotypes grown under well-watered, drought, and heat stress field conditions. Incorporating experiment-derived ecotypes reduced compensatory adjustments in cultivar coefficients and improved simulation accuracy compared with default or non-representative ecotypes. Time-series data enhanced calibration, although the effect of different data combinations varied with environmental scenario and trait. Model simulations under stress conditions generally captured drought effects on biomass but underestimated heat stress impacts. This framework provides a systematic and scalable approach for integrating high-throughput phenotyping and process-based crop modelling.

Abstract Rare genetic variants play a pivotal role in shaping the genetic architecture of complex traits, yet their identification and characterization remain challenging. Traditionally, genome-wide association studies (GWAS) have focused on common variants, leaving a substantial portion of heritability unexplained—a phenomenon known as the ‘missing heritability problem’. Advances in next-generation sequencing have significantly improved our ability to detect low-frequency variants, shedding light on their contributions to disease susceptibility, evolutionary processes, and agriculturally significant traits. However, the statistical power of traditional GWAS models is often insufficient to capture the impact of rare variants on quantitative trait variability, necessitating the development of alternative approaches. We highlight the limitations of classical association models and evaluate emerging methodologies, including machine learning-driven frameworks, to enhance the detection of rare variants. Despite their importance, rare variants remain underrepresented in human and agricultural genetics due to analytical challenges and the lack of targeted methodologies. By leveraging whole-genome sequencing, population genetics insights, and advanced computational techniques, researchers can better harness the power of rare variants to improve both human health and crop resilience.

Abstract 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.

Abstract Crop phenology informs in-season management practices such as fertilizer and pest and disease controls to optimize final yield. However, tracking crop growth stages across spatiotemporal domains remains challenging, particularly in rainfed broadacre systems subject to climatic variability. This study uses sequential high-resolution Sentinel-2 imagery to estimate phenological stages of wheat and barley across the Australian grain cropping region which comprises >20 million ha of production. An analysis pipeline was developed to estimate main crop growth stages using targeted vegetation indices (VI) and statistical model fitting approaches. Model accuracy was validated against biophysical simulated phenology and field observations from diverse environments. Both parametric models and non-parametric models were evaluated to fill data gaps and capture growth dynamics. The double logistic model was selected for its balance of performance and efficiency. Strong alignment was observed between VI-derived features and simulated phenology. Peak and right shoulder features showed high accuracy for estimating stages of flag leaf (R2 = 0.61, root mean square error (RMSE) = 8.67 days) and flowering (R2 = 0.7, RMSE = 7.66 days). Scalability was evaluated at 110 and 73 fields across Australia, for 2021 and 2022 seasons, respectively, showing moderate to high correlations with recorded phenology (flag leaf R2 = 0.57–0.58; flowering R2 = 0.7–0.85). The method also predicted tillering (R2 = 0.2–0.7), maturity (R2 = 0.73–0.85), and harvest (R2 = 0.55–0.66) where these observations were available. These results demonstrate the utility of high-resolution satellite data for estimating crop phenology and supporting zonal in-season agronomic management.

Abstract Performing global sensitivity analysis on functional-structural plant models (FSP models) can greatly benefit both model development and analysis by identifying the relevance of parameters for specific model outputs. Setting unimportant parameters to a fixed value decreases dimensionality of the typically large model parameter space. Efforts can then be concentrated on accurately estimating the most important input parameters. In this work, we apply the Elementary Effects method for dimensional models with arbitrary input types, adapting the method to models with inherent randomness. Our FSP model simulated a maize stand for 160 days of growth, considering three outputs, namely yield, peak biomass and peak leaf area index (LAI). Of 52 input parameters, 12 were identified as important for yield and peak biomass and 14 for LAI. Over 70 % of parameters were deemed unimportant for the outputs under consideration, including most parameters relating to crop architecture. Parameters governing shade avoidance response and leaf appearance rate (phyllochron) were also unimportant; variations in these physiological and developmental parameters do lead to visible changes in plant architecture but not to significant changes in yield, biomass or LAI. Some inputs identified as unimportant due to their low sensitivity index have a relatively high standard deviation of effects, with high fluctuations around a low mean, which could indicate non-linearity or interaction effects. Consequently, parameters with low sensitivity index but high standard deviation should be investigated further. Our study demonstrates that global sensitivity analysis can reveal which parameter values have the most influence on key outputs, predicting specific parameter estimates that need to be carefully characterized.

Abstract In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis, a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models.

Abstract Plant growth and development involve the integration of numerous processes, influenced by both endogenous and exogenous factors. At any given time during a plant’s life cycle, the plant architecture is a readout of this continuous integration. However, untangling the individual factors and processes involved in the plant development and quantifying their influence on the plant developmental process is experimentally challenging. Here we used a combination of computational plant models (CPlantBox and PiafMunch) to help understand experimental findings about how local phloem anatomical features influence the root system architecture. Our hypothesis was that strong local phloem resistance would restrict local carbon flow and locally modify root growth patterns. To test this hypothesis, we simulated the mutual interplay between the root system architecture development and the carbohydrate distribution to provide a plausible mechanistic explanation for several experimental results. Our in silico experiments highlighted the strong influence of local phloem hydraulics on the root growth rates, growth duration and final length. The model result showed that a higher phloem resistivity leads to shorter roots due to the reduced flow of carbon within the root system. This effect was due to local properties of individual roots, and not linked to any of the pleiotropic effects at the root system level. Our results open a door to a better representation of growth processes in a plant computational model.

Abstract Improving crop yields is one of the main goals of agronomy. However, yield is determined by a complex interplay between Genotypic, Environmental and Management factors (G$\times$E$\times$M), which varies across time and space. Therefore, identifying the fundamental relations underlying yield variation is a principal aim of agricultural research. A narrow, and not necessarily appropriate, set of statistical methods tends to be used in the study of such relations, which is why we aim to introduce a diverse audience of agronomists, production ecologists, plant breeders and others interested in explaining yield variation to the use of graphical models. More specifically, we wish to demonstrate the usefulness of copula graphical models for heterogeneous mixed data. This new statistical learning technique provides a graphical representation of conditional independence relationships within data that is not necessarily normally distributed and consists of multiple groups for environments, management decisions, genotypes or abiotic stresses such as drought. This article introduces some basic graphical model terminology and theory, followed by an application on Ethiopian maize and wheat yield undergoing drought stress. The proposed method is accompanied with the R package heteromixgm https://CRAN.R-project.org/package=heteromixgm.

Abstract Plant phenotypes are often descriptive, rather than predictive of crop performance. As a result, extensive testing is required in plant breeding programmes to develop varieties aimed at performance in the target environments. Crop models can improve this testing regime by providing a predictive framework to (i) augment field phenotyping data and derive hard-to-measure phenotypes and (ii) estimate performance across geographical regions using historical weather data. The goal of this study was to parameterize the Agricultural Production Systems sIMulator (APSIM) crop growth models with remote-sensing and ground-reference data to predict variation in phenology and yield-related traits in 18 commercial grain and biomass sorghum hybrids. Genotype parameters for each hybrid were estimated using remote-sensing measurements combined with manual phenotyping in West Lafayette, IN, in 2018. The models were validated in hybrid performance trials in two additional seasons at that site and against yield trials conducted in Bushland, TX, between 2001 and 2018. These trials demonstrated that (i) maximum plant height, final dry biomass and radiation use efficiency (RUE) of photoperiod-sensitive and -insensitive forage sorghum hybrids tended to be higher than observed in grain sorghum, (ii) photoperiod-sensitive sorghum hybrids exhibited greater biomass production in longer growing environments and (iii) the parameterized and validated models perform well in above-ground biomass simulations across years and locations. Crop growth models that integrate remote-sensing data offer an efficient approach to parameterize larger plant breeding populations.

Abstract The sugarcane stalk, besides being the main structural component of the plant, is also the major storage organ for carbohydrates. Previous studies have modelled the sucrose accumulation pathway in the internodal storage parenchyma of sugarcane using kinetic models cast as systems of ordinary differential equations. To address the shortcomings of these models, which did not include subcellular compartmentation or spatial information, the present study extends the original models within an advection–diffusion–reaction framework, requiring the use of partial differential equations to model sucrose metabolism coupled to phloem translocation. We propose a kinetic model of a coupled reaction network where species can be involved in chemical reactions and/or be transported over long distances in a fluid medium by advection or diffusion. Darcy’s law is used to model fluid flow and allows a simplified, phenomenological approach to be applied to translocation in the phloem. Similarly, generic reversible Hill equations are used to model biochemical reaction rates. Numerical solutions to this formulation are demonstrated with time-course analysis of a simplified model of sucrose accumulation. The model shows sucrose accumulation in the vacuoles of stalk parenchyma cells, and is moreover able to demonstrate the upregulation of photosynthesis in response to a change in sink demand. The model presented is able to capture the spatio-temporal evolution of the system from a set of initial conditions by combining phloem flow, diffusion, transport of metabolites between compartments and biochemical enzyme-catalysed reactions in a rigorous, quantitative framework that can form the basis for future modelling and experimental design.