Quantitative Plant Biology

Carbon allocation governs how plants partition assimilates among leaves, roots, stems, and reproductive organs, directly shaping growth and yield. Most crop models represent this partitioning using fixed or empirically derived coefficients, which limits their ability to predict how allocation responds to genotype, environment, or their interaction. Eco-evolutionary optimality theory offers an alternative where the allocation strategy is allowed to emerge from the marginal costs and benefits of investing carbon in each organ. While this approach has been applied successfully to trees, it has not previously been developed for herbaceous plants. Here, we present DAESIM2-Plant, a mechanistic plant growth model that couples physiological processes including photosynthesis, stomatal conductance, plant hydraulics, and canopy radiative transfer, with an eco-evolutionary optimal allocation scheme for leaves and roots. This is applied to wheat by coupling it to a source-sink grain production module. Using a series of idealized sensitivity experiments, we show that the model reproduces well-documented patterns of plasticity in carbon allocation: diminishing returns on leaf investment as canopy closure and water limitation are approached, a shift in allocation toward roots under drier conditions, and a root:shoot response that depends jointly on soil moisture and the plant's existing root:leaf balance. Simulating a full growing season across a gradient of soil moisture levels reveals a threshold-like response in canopy development, biomass accumulation, and grain yield, with yield constrained by the same assimilate supply that governs vegetative growth during the critical period and grain filling. This provides a promising basis for generalizing model behaviour across a wide range of environmental conditions. Evaluating these results against experimental and field trial data remains an important next step.

Chlorophyll fluorescence imaging is a powerful tool used to characterize abiotic and biotic stress response of photosynthesis in plants. However, extracting metrics such as Fv/Fm (maximum quantum yield of photosystem II) from chlorophyll fluorescence images remains a substantial bottleneck in large measurement campaigns, often requiring significant manual effort. To resolve this issue, we developed fvfmPy, a user-friendly program which enables rapid analysis of chlorophyll fluorescence images of arrays of leaf discs. fvfmPy uses algorithmic image segmentation to identify regions of interest and extract fluorescence metrics from tens or hundreds of leaf samples per image with little to no manual input. fvfmPy is developed in Python and includes a graphical user interface enabling intuitive parameter adjustment and real-time quality control checking. We additionally developed fvfmR, an R wrapper package for fvfmPy. Fv/Fm values extracted with fvfmPy were indistinguishable from those obtained using manual methods (r2 > 0.99) but required 94% less time to complete (13 s vs. 200 s per 100 measurements). fvfmPy and fvfmR were built to directly integrate with existing fluorescence software packages and can greatly increase throughput of fluorescence measurements to help address key challenges in agriculture and ecology.

Gene regulatory networks (GRNs) establish the spatio-temporal gene expression patterns necessary for reproducible development of organisms. MicroRNAs have emerged as important regulators of gene expression controlling either target gene levels, spatial patterns or timing. These molecular functions may vary depending on how they are integrated into GRNs. Here we used the Arabidopsis leaf as a model to study the genetic and molecular interactions between the CUP-SHAPED COTYLEDON 2 (CUC2) transcription factor and its negative regulator, miR164A. Quantitative morphometric analyses showed that miR164A acts from early stages during leaf development regulating CUC2 expression dynamic rather than the spatial distribution of CUC2. At the molecular level, CUC2 directly activates MIR164A expression, forming a single negative feedback loop. Further, using a ratiometric approach, we identified a cis-regulatory element (CRE) sufficient to drive CUC2-dependent MIR164A expression at the leaf sinus. Ordinary differential equation (ODE) modelling suggests that CUC2-CRE may control the timely accumulation of CUC2. Accordingly, quantification of CUC2 protein accumulation dynamics using a spatio-temporal mean cartography strategy in CRISPR cis-regulatory variants at the MIR164A locus revealed that CUC2-dependent MIRA164A expression is required to maintain both the precise timing of CUC2 expression and CUC2 levels. Altogether, our work provides experimental evidence for a specific role of the CUC2-MIR164A module in shaping leaf development, highlighting the contribution of network topology to GRN functions.

Background and aims: Perennial root systems develop over decades and exhibit continuous turnover of structural and absorptive components, yet most root architecture models treat woody roots as permanent and neglect root age structure. We developed an empirically constrained modelling framework that explicitly represents age-structured demography, growth regulation and dormancy in perennial root systems. Methods: We extended the functional-structural plant model CPlantBox to simulate perennial root development over decades by integrating age-dependent survival, secondary radial growth and climate-driven dormancy. The model was parameterised using unique three-dimensional root system data from young and mature grapevines, together with dendrochronological root age measurements, and coupled to root hydraulic properties. Key results: Empirical analyses revealed multi-cohort age distributions and substantial turnover of lignified structural roots, contradicting the assumption of a static woody scaffold. The model reproduced age distributions, order-specific survival probabilities and fine-to-structural root length ratios, and generated realistic, genotype-specific three-dimensional architectures. Simulated uptake profiles and hydraulic scenarios showed strong age- and architecture-driven effects. Conclusions: Explicitly representing woody root age distributions and age-dependent processes may fundamentally change predictions of perennial root system architecture and function. The framework advances root modelling beyond annual paradigms and provides a transferable tool for analysing long-term architectural and hydraulic consequences of genotype x climate interactions in perennial crops.

The xylem safety-efficiency trade-off is a central paradigm in plant hydraulics, yet empirical support remains inconsistent. In this study, we highlight how this inconsistency may arise from variation in environmental and evolutionary constraints and from differences in observational scale. We analysed 38 diffuse-porous angiosperm and 24 conifer species grown in a common garden experiment to quantify the relationship between embolism resistance (P50) and specific hydraulic conductivity (Ks) across and within species, and its differences between three species groups differing in evolutionary ancestry and eco-hydrological strategy. When pooling over all species, we found evidence for a safety-efficiency trade-off both across and within species. However, this pattern was driven almost exclusively by the mesic broadleaved species. In contrast, the across-species pattern was suppressed in coniferous species due to the evolutionary constraints on efficiency posed by their tracheid-based wood anatomy, while there was no evidence for a trade-off on either level in hydrophilic broadleaved species, which evolved under conditions that pose reduced selective pressure on embolism resistance. Our results demonstrate that the safety-efficiency trade-off is not universal, but emerges only in specific ecological and evolutionary contexts, highlighting the importance of multi-scale analyses and constraint-based frameworks for interpreting plant hydraulic strategies.

Complex biological behaviours often emerge when a system is faced with mutually incompatible priorities. In such situations, the field of multi-objective optimisation can provide an informative and predictive theoretical foundation for biology. Here we adopt this paradigm in exploring the rich dynamic behaviour of mitochondria inside cells. Using plant cells as a model system, we use physical modelling to characterise the "morphospace" of possible mitochondrial behaviours, and single-cell microscopy with video analysis and network modelling to characterise collective mitochondrial dynamics. We show that wildtype Arabidopsis mitochondrial dynamics near-optimally resolve a tradeoff between maintaining physical spacing and supporting biomolecular exchange. With existing and new experimental data, we show that these dynamics adapt under mutational and chemical challenges to support a rebalanced, but still near-optimal, resolution to this tradeoff under different densities of the mitochondrial population. We also show how an assumption of multi-objective optimisation supports inference of biological mechanisms before any data are observed, and discuss the potential of this multi-objective optimisation paradigm to form a broader theoretical framework of spatial cell biology.

To reduce reliance on pesticides in potato production, alternative partial control methods are needed, yet their efficacy and reliability are seldom quantified over multiple years. Canopy architecture is a potential lever against late blight, caused by Phytophthora infestans, a pathogen whose infection and development are highly driven by humidity and temperature. However, its practical value under field conditions in pure crop stands remains poorly documented. We compared two commercial potato cultivars with contrasting canopy architectures but similar leaf tissue susceptibility to P. infestans, in replicated field trials conducted over six years (2011 2016) under irrigation, and over three of these years also without irrigation. Disease and canopy development (height, closure, leaf area, stem number) were monitored throughout the growing seasons and analysed using non linear growth models. Monalisa, an erect and ramose cultivar, consistently slowed epidemic progress relative to Bintje, a semi-erect and leafy cultivar. This effect was significant in every non irrigated trial but in only half of the irrigated ones. No single canopy trait explained this suppression across all years and conditions. However, canopy closure at the time of inoculation was the most consistent correlate, supporting the hypothesis of a microclimate mediated effect. Disease reduction seldom translated into a yield benefit, except under irrigation, where Monalisa occasionally out yielded Bintje. Our results confirm that canopy architecture can complement other levers within integrated late blight management. Nevertheless, its significant year to year variability and dependence on environmental conditions must be accounted for if it is to be deployed as a reliable control method.

Flowering time is a trait of broad interest and importance to both plant breeders and plant biologists. Unlike many other traits, flowering time cannot be scored accurately from a single observation; its measurement requires repeated observations of the same experiments over time. Both classification- and object-detection-based approaches have demonstrated the potential to estimate flowering time from Unmanned aerial vehicle (UAV) imagery rather than by manual observation, but they still require data collection at many time points. Here, we train and deploy a regression-based framework for scoring flowering time. Using a dataset of more than 200,000 UAV images and associated flowering-time records collected across 27 environments, we demonstrate that this approach enables the prediction of flowering time from as few as one observation per field experiment and successfully generalizes to field experiments in environments not represented in the training dataset. These results indicate that regression-based approaches for predicting flowering time from sparse UAV observations have the potential to substantially reduce the data-collection burden in field experiments

Breeding programmes choose genomic prediction models by accuracy rankings, and the metric behind a ranking decides which model wins. Selecting the best genotypes within a trial depends only on the order of the predictions, so a metric that guides the choice must be unchanged by any order-preserving change of the predictions and respond when their order is reversed. Of the 22 official metrics of the Genomes to Fields maize competition, one meets both conditions, the within-environment rank correlation; the within-environment Pearson correlation is protected against rescaling only; and twenty fail, including the root mean squared error that decided the 2022 ranking. Switching between the competitions two official metrics reverses 27 % of team pairs, and an exact decomposition shows why pooled metrics mislead: in three constructed panels, 94-95 % of the attainable square of pooled correlation is carried by the environment means. The accuracy difference a trial can resolve falls as about 3/{surd}N with its N genotype-environment cells: at least 0.03 at the competitions design and 0.04-0.12 once calibrated on real panels, against 0.003 between the first two teams; tests that two correlations differ declare far smaller differences, which do not reliably change selection. In soybean, where the two most accurate methods stand clear of most others by more than the trial resolves, ranking by either within-environment correlation selects better material, 98-99 % of the attainable selection gain in held-out environments against 13-34 % for error-magnitude rankings; in common bean and a Chinese maize population, where the leading methods lie within the resolvable gap, all rules pick material of similar value. GPverdict runs these checks on any programmes cross-validated predictions.

Background: Plant defensins are small cysteine-rich antimicrobial peptides. In Arabidopsis thaliana, the inducible Class I gene AtPDF1.2a is widely used as a transcriptional marker of jasmonate/ethylene immunity, whereas the constitutively expressed Class II members have received less functional attention. AtPDF2.2 was compared with AtPDF1.2a during infection of Arabidopsis by the vascular fungal pathogen Verticillium longisporum, the necrotrophic fungus Sclerotinia sclerotiorum and the bacterium Pseudomonas syringae pv. tomato DC3000. Results: Profiling of the AtPDF family showed that AtPDF2.2, together with the Class II genes AtPDF2.3 and AtPDF2.5, was significantly reduced during V. longisporum infection, whereas AtPDF1.2a was induced. A promoter-reporter line showed a corresponding decrease in AtPDF2.2 promoter activity during V. longisporum infection, strongest in infected roots. KD-pdf2.2 plants contained more V. longisporum DNA, whereas OE-PDF2.2 plants contained less. The same reciprocal disease pattern was observed during S. sclerotiorum and Pst DC3000 infection. Histochemical Prom::AtPDF2.2::GUS reporter signal decreased during S. sclerotiorum infection but increased following Pst DC3000 infection. Basal AtPDF2.2 expression was lower in JA and ET-signaling mutants and higher in the SA-deficient sid2 background, while the analyzed promoter lacked the GCC-box that mediates inducible AtPDF1.2a regulation. AtPDF1.2a overexpression reduced symptoms across the three pathosystems, but loss of AtPDF1.2a increased disease only during V. longisporum infection under the conditions tested and did not alter Pst DC3000 DNA abundance. AtPDF2.2 overexpression was not associated with reduced vegetative growth under the conditions tested, whereas AtPDF1.2a overexpression reduced rosette size and leaf number. Conclusions: AtPDF2.2 is a constitutively expressed Class II defensin whose expression level is associated with disease outcome across two fungal pathogens and a bacterium. Its promoter shows tissue-dependent regulation during V. longisporum infection, while histochemical reporter signal decreases during S. sclerotiorum infection and increases during Pst DC3000 infection. This regulatory behavior differs from that of the inducible reference gene AtPDF1.2a. The reciprocal disease phenotypes of the AtPDF2.2 transgenic backgrounds support a protective association that may involve direct antimicrobial activity, modulation of host responses or both. The absence of reduced vegetative growth in OE-PDF2.2 under the conditions tested supports further evaluation of PDF2-type genes in Brassica.

Anticipating and managing the impact of herbivorous pests requires evidence-based knowledge about the interactions between pests and their natural enemies. Knowledge of species' ecologies and biological interactions are typically disseminated in unstructured text across hundreds of thousands of scientific articles spanning a rapidly growing scientific literature, but manually screening this literature is time-consuming and impractical at scale. Taxonomic and geographic coverage thus remains sparse, and structured data on interactions between individual species are lacking, particularly for invertebrates. Large language models (LLMs) have recently emerged as a novel and powerful text-mining tool for knowledge extraction and evidence synthesis in a wide range of scientific disciplines, including ecology and conservation. In this study, we use an open-weight, pre-trained LLM to analyse abstracts of a corpus of published material on biological pest control to obtain a global Database of Pest Herbivores and their Natural Enemies (DAPHNE). This method resulted in the extraction of 175,404 species interactions from 112,830 published pest control studies, containing16,755 unique animal taxa resolved at the species level. Interactions include herbivory (granivory, frugivory and gall-formation), predation, parasitism and hyperparasitism, and comprise a range of additional information, such as species' taxonomy, pest status, pest importance, natural enemy importance, provision of biocontrol, associated plants and industries, invasiveness and the vectoring of pathogens and diseases. Validation of the results shows that most data columns were extracted with high recall and precision (>90%) and comparison of DAPHNE with other sources shows a high degree of agreement, but also highlights that the dataset contains information that may have been missed by other sources. In addition to the identification of important crop pests and their natural enemies, DAPHNE may be used for a variety of use-cases, including the identification of invasive species and pathogen vectors, trophic network analysis and literature search and discovery.

Drought stress significantly reduces tomato (Solanum lycopersicum L.) productivity, and early detection is critical to minimize yield losses through timely interventions. In this study, we developed Drought-Spec-Net, a hybrid 1D convolutional neural network that integrates local and global spectral feature extraction to detect early drought stress from visible and near infrared (Vis NIR) spectra data of tomato seedlings. The model was trained on 378 samples using an 80:20 train test split, with 20% of the training set reserved for validation. DroughtSpecNet outperformed the evaluated baseline and state of the art models, achieving 97% accuracy, 95% precision, 98% recall, and an F1 score of 97%. To improve the agronomic interpretation of the model outputs, predicted drought probabilities were converted into a literature-informed potential yield impact indicator using a maximum impact level of 60%. On the test set (76 samples), mapped potential yield-impact values ranged from 0% to 60%, with an average reduction of 12.97%. We also conducted an initial experiment using our greenhouse RGB dataset, collected daily from drought treated and well-watered tomato plants at West Virginia State University (WVSU). From this dataset, 44 images were selected for ilastik-based canopy segmentation, producing plant-level drought severity indices (DSI) with a mean of 0.28, median of 0.14, and range of 0.01 to 0.91. Additionally, we trained and fine-tuned a large language model (LLM) based on PLLaMA7BInstruct, called AgriLLaMA, for automated agronomic report generation from Drought-Spec-Net outputs. The generated reports summarize predicted stress levels, mapped potential yield impacts, and preliminary management considerations. This integrated approach not only improves early drought stress detection but also delivers quantitative and interpretable estimates of potential productivity losses, providing a complete framework connecting physiological stress detection to actionable agricultural outcomes.

- C2 photosynthesis reduces photorespiratory carbon loss by shuttling glycine from mesophyll (M) to bundle sheath (BS) tissues, where glycine decarboxylase (GDC) releases CO2 that accumulates around rubisco. C2 photosynthesis has been considered a constitutive character, but as a metabolic trait, it may acclimate to the rate of photorespiratory glycine production. - We grew eleven species from two eudicot and two grass lineages spanning C3 to C2 phenotypes at 32 {degrees}C days and 200 or 800 {micro}mol mol-1 CO2. Acclimation was assessed using leaf gas exchange, GDC P-protein immunolocalization, M and BS ultrastructure, photosynthetic enzyme activity, and photosynthesis modelling. - Low relative to high growth CO2 enhanced C2-related phenotypes in sub-C2 and C2 species but not C3 or proto-Kranz species. Sub-C2 species positioned chloroplasts closer to mitochondria and had lower modelled BS conductance. C2 species increased rubisco capacity and GDC allocation to the BS. Aspartate aminotransferase activity increased in both sub-C2 and C2 species. - Acclimation to greater photorespiratory conditions strengthens C2 photosynthesis, but only in species whose BS has crossed a threshold of organelle and GDC investment. We hypothesize that acclimation could expose C2-related traits to selection and facilitate the origin of C4 photosynthesis.

SUMOylation is an essential and evolutionarily conserved post-translational modification that enables rapid cellular adaptation to environmental stress. Despite its importance, how the SUMOylation machinery controls its dynamics remains poorly understood. Here, we integrated a mathematical model of the plant SUMO cycle with quantitative imaging and biochemical data to decode the regulatory logic of SUMOylation dynamics. The model reveals a fundamental principle: steady-state SUMOylation levels are determined not by the full complement of SUMO-cycle components, but by the abundance of SUMO modifier proteins and E1 enzymes, together with SUMO protease abundance and activity. Strikingly, when we integrated fluorescence measurements from reporters for SUMO-cycle components in Arabidopsis roots after three hours of abiotic stress, the model predicted decreased SUMOylation levels across several tissues, contrary to prevailing evidence. Motivated by this unexpected prediction, we performed time-resolved imaging to better characterize the dynamics of key SUMO-cycle components after salt and osmotic stress. These data revealed highly dynamic component levels within the first three hours of stress that were not captured in the previous measurements. Incorporating this temporal data into the model uncovered a twin-peaked SUMOylation response to salt stress: an early surge underpinned by protease dynamics, followed by a decline and a second peak driven by elevated SUMO modifier and E2 levels. Western blot analysis revealed a corresponding twin-peaked pattern of SUMOylation. Together, our findings demonstrate that the plant SUMO cycle is a highly dynamic system that achieves tissue- and stress-specific control of SUMOylation through the coordinated interplay of multiple SUMO-cycle components.

1. Thermal performance curves (TPCs) are widely used to quantify temperature dependence of biological traits, but estimating them requires experiments across multiple temperatures. Because these experiments can be costly, researchers must balance the number and placement of temperature treatments against replication within treatments. Evenly spaced temperatures are a common default, but may allocate effort inefficiently when thermal responses are nonlinear or strongly asymmetric. 2. We developed TPCdesign, an R package for planning temperature-trait experiments. TPCdesign uses a pseudo-Bayesian (robust) D-optimal experimental design to identify sets of temperatures expected to provide the most information about a TPC, while allowing users to incorporate prior knowledge of thermal limits and curve shape, uncertainty in that knowledge, available numbers of treatments, and practical restrictions on temperature spacing. 3. Using simulation-based worked examples, we show that optimized designs can recover TPCs more accurately than evenly spaced designs, particularly for asymmetric curves and when the number of available temperature treatments is modest. Optimization primarily improves resolution of regions where performance changes rapidly and can substantially improve estimation of upper thermal limits while retaining information across the rest of the curve. 4. TPCdesign provides a practical framework for allocating limited experimental resources according to the biological response researchers seek to estimate. Rather than prescribing a universal set of temperatures, it allows experimental designs to reflect existing biological knowledge, uncertainty, and logistical constraints, improving the information gained from costly thermal performance experiments.

Anion exchanger 4 (Ae4), encoded by solute carrier family 4 member 9 (Slc4a9), is required for normal salivary secretion, but how it supports fluid secretion remains poorly understood. Nearly a decade ago, we developed a mathematical model that attributed reduced secretion following Ae4 knockout primarily to Ae4 mediated monovalent cation transport. Here, we reassess that interpretation by developing a dynamic model of salivary fluid secretion informed by subsequent experimental findings. The model incorporates {beta}-adrenergic regulation of Ae4 and an additional apical chloride conductance shared by the control and knockout. To isolate the contribution of Ae4, the NKCC1 and Ae2 cycle rates calculated from the evolving control state are also prescribed in the knockout. Under these conditions, the knockout model secretes 20% less fluid over 600 seconds despite identical apical chloride conductances. The control begins with a larger intracellular chloride store and continues to acquire chloride through Ae4 during stimulation. The intracellular chloride difference increases during stimulation, whilst the weaker apical electrochemical driving force in the knockout reduces chloride export and fluid secretion. The analysis also identifies the alkalinity requirement of sustained Ae4 transport and quantifies the additional NKCC1 and Ae2 transport needed to compensate for its loss. These results support intracellular chloride availability as a mechanism linking Ae4 loss to reduced salivary secretion.

Although mitochondrial DNA (mtDNA) is subject to damage and high mutation rates in many eukaryotes, plants have a variety of mtDNA repair pathways that somehow result in exceptionally low mutation rates. However, the stoichiometry of biomolecules in plant mitochondria raises challenges for the DNA repair process. The protein complexes responsible for repair pathways contain subunits present at low copy numbers per mitochondrion. Mitochondria on average contain less than a full genome, limiting the capacity for templated repair, and raising the risk that repair machinery is localised to a DNA-empty mitochondrion where it cannot be used. Here we build on recent experimental characterisation of the copy numbers and physical processes involved to model the capacity for DNA repair in plant mitochondria. We find that targeted import of protein machinery, biased fusion of mitochondria bearing DNA, and exchange of biomolecules upon fusion can improve repair capacity through different mechanisms. Specifically, our model indicates that base excision repair can keep ~95% of mtDNA intact even without targetting or exchange rules and that targetting protein import to DNA-bearing mitochondria or exchanging protein content drives this to effectively 100%. In contrast, template repair cannot keep a majority of mtDNA intact unless mtDNA is shared across mitochondria through fusion, in which case its efficacy increases. Exchanging of metabolons (repair protein complexes) increases repair efficiency across all circumstances. We discuss the implications of our model for plant development and organelle inheritance.

Blooms of the toxic dinoflagellate Ostreopsis cf. ovata can cause respiratory irritation in beach users, yet their environmental drivers and temporal dynamics remain poorly understood. Here, we use a dynamical-systems approach to investigate how environmental conditions shape bloom dynamics from long-term time series of algal abundance and abiotic variables. Using the global modelling framework GPoM, we reconstruct nonlinear ordinary differential equation systems and identify candidate models capable of reproducing key features of the observed dynamics, including chaotic behaviour. Topological and statistical analyses reduce the candidate models to two systems that best capture the observed distributions and dynamical patterns. These models reveal distinct combinations of environmental conditions associated with bloom occurrence and amplitude, providing a framework for interpreting bloom trajectories and a potential basis for short-term prediction.

Photosynthesis is a key determinant of crop productivity, yet conventional gas-exchange measurements are labor-intensive and unsuitable for high-throughput assessment of dynamic photosynthetic acclimation across large genotype panels. Consequently, genetic variation in photosynthetic acclimation remains poorly characterized and difficult to exploit in breeding programs. Here we present a computational pipeline that integrates rapid optical sensing (chlorophyll meters and hyperspectral reflectance) with a mechanistic model of photosynthetic protein-turnover to estimate dynamic nitrogen allocation among photosynthetic components. The pipeline was demonstrated using 60 winter wheat (Triticum aestivum L.) cultivars sampled at 10 time points spanning leaf emergence to senescence. Uncertainties associated with each pipeline component were quantified and benchmarked against gas-exchange ground-truth measurements under three controlled-environment light and temperature regimes. Dynamic nitrogen allocation to light harvesting, electron transport, and carboxylation was characterized using three biologically interpretable parameters: maximum synthesis rate, degradation rate, and age-dependent decline in synthesis. Although hyperspectral models showed moderate predictive accuracy for photosynthetic capacity, prediction errors were predominantly random, allowing robust parameter estimation when observations across the leaf lifespan were integrated. The pipeline successfully resolved genotype-by-environment interactions in photosynthetic acclimation, with environmental and interaction effects contributing more strongly than genotype main effects to nitrogen dynamics. These results demonstrate a scalable, uncertainty-aware sensor-to-trait architecture for dynamic physiological phenotyping that enables high-throughput germplasm screening across diverse environments and provides novel physiological selection targets for improving crop adaptation under variable climates.

International travel can increase infection risk and spread antimicrobial resistance (AMR), yet clinical systems rarely capture travel history in a structured format. Extracting this information from free-text clinical documentation at scale could improve alerts for high-consequence infections and AMR surveillance. We developed an annotation framework covering key aspects of travel history, including destination, exposures, travel duration, and multiple temporal variables. The framework was used to annotate a corpus of 100 clinical case reports by five clinicians, yielding over 5,000 clinician annotations. Several LLMs were evaluated on the same extraction task, with their outputs compared against clinician consensus. Agreement among clinicians was high for straightforward variables such as presence of travel and destination, but dropped substantially for temporal variables such as time since return and symptom onset relative to return. LLMs showed a similar pattern. They performed well on high-level variables but struggled with the same temporal fields that challenged clinicians. Model errors were also concentrated in cases where clinicians disagreed. Across models, outputs were more sensitive to sampling temperature than to prompt wording. A smaller open-weight model performed similarly to substantially larger models on several variables but remained weaker on temporal fields. Overall, LLMs could extract some travel history variables, but reliable extraction of temporal information remained difficult.