Frontiers in Plant Science

The apoplast is a primary location for interactions between plants and invasive pathogens and is the site where many pathogen-secreted effectors are perceived by cell surface immune receptors to activate defence responses. However, our understanding of apoplastic interactions remains limited because protein interactions are difficult to investigate in this harsh extracellular environment. TurboID-based proximity labelling (PL) has emerged as a powerful approach for studying protein interactions in plants, though its application has to date been restricted to intracellular proteins. Here, we designed and validated a TurboID-based PL strategy for investigating protein interactions in the leaf apoplast using the well-characterised interaction between the Phytophthora infestans elicitor INF1 and the receptor-like protein (RLP) REL in Nicotiana benthamiana. Transient expression of SP-INF1-TurboID (INF1-T) induced a cell death (CD) response comparable to that triggered by native INF1, demonstrating that fusion of the TurboID tag did not impair INF1 recognition by REL. Apoplastic localisation of both INF1-T and the control construct SP-eGFP-TurboID (eGFP-T) was confirmed, validating their suitability for PL experiments. Efficient TurboID-mediated biotinylation was achieved in the apoplast using co-infiltration of biotin, ATP and magnesium acetate. Streptavidin-HRP immunoblotting revealed distinct biotinylation profiles for INF1-T and eGFP-T. Furthermore, co-immunoprecipitation demonstrated specific biotinylation of REL by INF1-T, but not by eGFP-T, in wild-type, bak1 and sobir1/sobir1-like N. benthamiana. These findings demonstrate that TurboID-based PL is functional in the apoplast and provides a proof-of-concept for investigating elicitor-receptor interactions in this compartment.

Efficient yield prediction under field conditions remains a key challenge for crop breeders and agronomists. YieldLearn is an interpretable machine-learning pipeline developed to estimate yield-related traits in durum wheat (Triticum durum) using physiological indicators measured by the Hansatech Instruments Pocket PEA. The workflow integrates fluorescence parameters (Area, Fo, Fm, Fv/Fm, PI Inst.) with field-measured agronomic traits (Booting, Height, Maturity, Lodging, Yield kg/plot, and Adjusted Yield kg/ha). Five supervised learning algorithms multinomial logistic regression, random forest, support vector machine (radial kernel), k-nearest neighbors, and decision tree were evaluated using five-fold cross-validation. Models achieved average classification accuracy around 0.57 with AUC values exceeding 0.58. SHAP (SHapley Additive exPlanations) visualizations revealed that high Fv/Fm and PI Inst. values were positively associated with yield potential, whereas elevated Fo indicated stress-induced yield reduction. An interactive Shiny App enables real-time prediction and feature-importance exploration. Given the small sample size (165 plots) and absence of environmental covariates such as temperature or soil moisture, YieldLearn should be regarded as a reproducible exploratory framework rather than a production-ready predictor. Nonetheless, it offers a valuable foundation for guiding experimental design, validating physiological indicators, and supporting the next generation of scalable, interpretable crop-yield models.

Gray leaf spot can cause severe turfgrass damage, particularly in intensively managed sports fields, while disease management remains challenging due to the rapid development of symptoms under favorable environmental conditions. Early detection of Pyricularia oryzae could therefore provide valuable information for anticipating disease development and supporting management decisions. This study aimed to develop a digital PCR (dPCR) molecular tool to support the monitoring and management of gray leaf spot disease in stadium turfgrass. The assay showed high sensitivity, consistently detecting P. oryzae when infected tissue represented only 0.05% of a mixture with healthy turfgrass, and enabled pathogen detection before symptom appearance under both controlled and field conditions. Different matrices were also investigated across 15 stadiums to identify the most relevant material for disease monitoring. Roots showed the highest prevalence of P. oryzae detection, including during asymptomatic periods, highlighting their potential as a relevant matrix for pathogen surveillance. In a stadium monitored several months after a gray leaf spot outbreak, P. oryzae DNA was still detected in roots from 27 of the 32 sampled zones despite the absence of visible symptoms. Overall, these results demonstrate the potential of dPCR for the early detection and monitoring of P. oryzae in stadium turfgrass, particularly through root sampling during periods when environmental conditions are not favorable for gray leaf spot development, and a spatio-temporal surveillance at stadium scale.

Drought and waterlogging are prominent climate change challenges that, alone or in sequence, pronouncedly constrain agricultural crop production. A better understanding of relationships between water availability and nutrition regarding plant functioning is needed to optimise fertilisation under these challenges. We investigated impacts of drought, waterlogging and drought followed by waterlogging under two fertilisation regimes (low, normal) on various traits of wheat (Triticum aestivum). Next to biomass and photosynthesis measurements, cross-platform metabolomics was applied for in-depth metabolic analyses of flag leaves. We found detrimental effects of drought and drought-waterlogging on shoot and root biomass, especially at normal fertilisation, while photosynthetic traits were not or only slightly affected. Foliar nitrogen contents were higher in plants exposed to drought or to drought-waterlogging. Numerous primary and specialised metabolites were modulated by fertilisation and/ or watering. In particular, amino acids, benzoxazinoids and flavonoids were affected by fertilisation. Due to watering especially several amino acids changed in concentrations, with the induction pattern partly depending on fertilisation. Our study deepens the understanding of the interplay between water and nutrient availabilities as well as nutritional demands, growth and metabolism of wheat. This is highly relevant to balance sustainable agricultural nutrient management under the challenges of ongoing climate change.

Cannabis sativa L. cultivation in tropical regions or under indoor conditions requires supplemental lighting to maintain plant yields. Night-break (NB) photoperiods offer a cost-effective alternative to conventional day extension, although genotypic responses have not been clearly described. This study evaluated the effects of continuous and pulsed NB treatments on the growth, phenology, physiology, productivity, and cannabinoid accumulation of three Cannabis sativa genotypes representing two chemotypes. Plants were exposed to a commercial 18-h photoperiod (T1), a continuous 1-h NB (T2), or four 15-min NB pulses (T3) with equal total duration. Continuous NB produced responses comparable to the commercial photoperiod. In contrast, pulsed NB accelerated phenological progression by two to three weeks and reduced final plant height by 15-28%, depending on genotype, without impairing photosynthetic performance or decreasing floral dry biomass. Harvest index increased under both NB treatments, indicating enhanced biomass allocation to reproductive structures, while cannabinoid accumulation remained primarily genotype-dependent with only minor treatment-specific modulation. These findings demonstrate that NB efficacy depends not only on the duration of nocturnal light but also on its temporal distribution, highlighting temporal organization of nocturnal light as a key regulator of photoperiodic responses and a promising strategy for optimizing cannabis cultivation.

The mechanisation of agriculture has achieved major increases in labour productivity but has also led to an erosion of agrobiodiversity. Neglected and underutilized crops such as common buckwheat (Fagopyrum esculentum Moench) offer the potential to diversify food production systems but require major genetic improvement to compete with established crops. Although target traits for breeding such as more homogeneous ripening, reduced height, larger seeds and higher and more stable yields are well known, large-scale and field-based phenotyping data of diverse common buckwheat accessions across multiple environments are still scarce. Here, we tested the agronomic performance of a common buckwheat germplasm collection consisting of 170 accessions of diverse origin. The collection was tested at seven year-sites, and 19 agronomic traits related to phenology, growth habit and yield were assessed. Most traits showed high repeatability (0.31 < R2 < 0.98) and heritability (0.45 < H2 < 0.97), indicating genetic control of these traits. Coefficients of variation ranged between 6.19% and 46.15%, showing the high phenotypic diversity within the collection. By combining multiple traits, distinct ideotypes for different roles in the crop rotation were investigated, and accessions with promising agronomic profiles identified. Genome-wide association studies revealed marker-trait associations for 18 traits, allowing the identification of putative candidate genes influencing these traits such as a putative GRF-INTERACTING FACTOR associated with flowering time and biomass production. Our results show that the collection offers vast phenotypic diversity that can be exploited for the development of improved varieties tailored to meet specific agronomic purposes. The efficient utilization of these genetic resources should be supported by the application of genomics-based breeding approaches. The development of genetic markers and search for putative candidate genes mark important steps towards the establishment of efficient breeding schemes. Thus, these genomic resources provide a foundation for accelerating the genetic improvement of common buckwheat and developing agronomically competitive varieties.

Sudden Death Syndrome (SDS), caused by Fusarium virguliforme, can significantly impact soybean yields. To advance the understanding of SDS resistance in plant introductions, we conducted genome-wide association studies (GWAS) on a set of 351 accessions from the USDA Soybean Germplasm Collection. The analysis revealed seven single nucleotide polymorphisms (SNPs) significantly associated with foliar SDS resistance; three significant QTL (on chromosomes 3, 9 and 11) were detected with Fixed and Random Model Circulating Probability Unification (FarmCPU) and four (on chromosomes 5, 6 and two on chromosome 7) with Bayesian-information and Linkage disequilibrium Iterative Nested Keyway (BLINK). Localized linkage disequilibrium blocks were estimated for these regions. Notably, all of them overlapped with regions where QTL associated with SDS resistance had been previously reported and where within, or close to, sequences annotated as implicated in plant defense mechanisms, such as toxin transporters and transcription factors. The architecture of SDS resistance appears quantitative and polygenic, with most loci explaining modest phenotypic variance. Six plant introductions in this study--PI 175188, PI 307889A, PI 323559, PI 578307C, PI 445683, and PI 323558--harbor favorable alleles at all seven loci, providing high-value resources for resistance introgression. This study underscores the value of leveraging the USDA Soybean Germplasm Collection for the discovery of resistance loci and provides a foundation for marker-assisted breeding to enhance SDS resistance in elite soybean lines.

Ensuring the safety and suitability of proteins considered for food and biotechnology applications is an important challenge. Computational approaches can facilitate the prioritisation of candidate proteins for further investigation, particularly given the rapid growth of genomic and metagenomic sequence datasets. In this study, we developed computational models to distinguish edible plant tissue-associated (EPTA) proteins from non-EPTA proteins. The positive class comprised 939 proteins with experimentally validated evidence of expression in plant tissues commonly consumed by humans, hereafter referred to as EPTA proteins. The negative class comprised 939 proteins annotated in UniProt as toxic, allergenic, or antinutritional, hereafter referred to as non-EPTA proteins. Models were evaluated using five-fold cross-validation with a cluster-based partitioning strategy, ensuring that no protein in the validation set shared more than 40% sequence identity with proteins in the corresponding training set. First, composition-based sequence features were evaluated to establish a baseline for EPTA or non-EPTA classification, with the Extra Trees (ET) classifier achieving a validation AUROC of 0.848. To incorporate evolutionary information beyond amino acid composition, PSSM profiles were subsequently evaluated, yielding an AUROC of 0.842 with an SVM classifier. Finally, to capture richer sequence patterns and contextual information using protein language modelling, we evaluated ESM2-based models. The fine-tuned ESM2-T33 model achieved the highest validation AUROC of 0.925 and was selected as the final predictive model. The resulting approach was implemented as the EdiProPred web server (https://webs.iiitd.edu.in/raghava/edipropred/) and standalone software for computational classification of plant proteins. The EdiProPred framework provides a useful computational tool for prioritising candidate plant proteins and facilitating the exploration of novel protein resources for food and biotechnology applications.

Common tansy (Tanacetum vulgare), a significant agricultural invasive species, threatens forage productivity, livestock carrying capacity, and plant biodiversity across North American agroecosystems. Despite being a close relative to the ornamental Asteraceae Chrysanthemum, T. vulgare genomic studies are severely lacking, but remain critical to the development of novel weed management tools and the investigation of plant invasion evolution. In this study, we performed the assembly and annotation of the diploid genome (2n=2x=18) of an invasive North American T. vulgare genotype using long-read PacBio HiFi and Iso-Seq sequencing technology. The assembled haplotype genome comprised 9 pseudomolecules with a total size of ~4.8 Gb and a scaffold N50 of 546 Mb. In addition, both the plastid and mitochondrial genome sequences were assembled. BUSCO analysis revealed a high overall genome completeness with 69,343 predicted protein-coding genes (AED<0.6: 98%). A high-quality North American invasive reference T. vulgare genome, complementing a recent one assembled for a native European accession, is critical for the investigation of plant invasion biology, genome evolution, and the future development of novel genetic biocontrol weed management technologies.

Synthetic nitrogen fertilizers enhance maize (Zea mays ssp. mays) productivity but impose substantial environmental and economic costs. Some Mexican maize landraces support biological nitrogen fixation (BNF) in aerial root mucilage, raising the question of how much variation exists in this trait and how domestication, geographic spread, and improvement have shaped BNF-related traits across Zea in the Americas. We evaluated aerial root traits and BNF capacity across the maize domestication continuum using 60 Zea accessions grown in field experiments over two years under low-N conditions. Some accessions of wild Zea, landraces from Mexico and South America, and improved varieties had elevated nitrogen derived from the atmosphere (%Ndfa), ranging from 20 to 40%; some of these also had greater %Ndfa than typical improved varieties. Spread and improvement appear to have significantly reduced the number of aerial root-bearing nodes, with wild Zea and Mexican landraces consistently producing more. Nevertheless, the proportion of nitrogen derived from the atmosphere (%Ndfa) does not vary systematically across the domestication continuum. Thus, crop evolution shaped aerial root phenotypes more than associative BNF capacity. The persistence of substantial within-group variation indicates that the genetic potential for enhanced BNF remains within the Zea gene pool, providing opportunities to encourage and improve nitrogen use efficiency in maize cropping systems.

An increasing number of researchers, educators, and at-home growers are turning to hydroponics as an alternative method of food production, diversifying and strengthening the food supply chain. This has led to a substantial increase in the amount of academic work involving hydroponic production of plants for a variety of purposes, including but not limited to a more controlled and customizable environment than conventional production methods, utilizing hydroponics as a method of wastewater treatment, and investigating the vertical production of plants on a much smaller footprint than traditional commercial-scale production. Because so many unique stakeholders are interested in hydroponics for a variety of reasons, academic studies use a wide variety of hydroponic systems with varying levels of technology and automation, making comparisons across studies and systems challenging. A few institutions have sophisticated plant growth and research facilities, like those used by commercial companies, but these systems are often prohibitively expensive, costing upwards of $10,000 USD for a very limited amount of growing space for experimental trials. To enable more researchers, educators, and citizen scientists to conduct experiments and easily compare their results, this study details three pilot-scale hydroponic production systems (Kratky, NFT, and DWC), utilizing entirely off-the-shelf components, for under $5,000 USD each, that are capable of maintaining the desired consistent temperature and relative humidity levels, and have space for 216, 108 (6 irrigation loops), and 64 (16 irrigation bins) plants, respectively. The material lists and prices are provided along with assembly instructions and general guidelines for each hydroponic system. The testing and optimization process of environmental conditions within each growth system is also detailed, with suggested customizations for potential experimental setups. The authors developed these systems during their Masters and Doctoral degree programs, enabling improved hydroponic research at the University of Illinois Urbana-Champaign. They hope that other aspiring hydroponic researchers can benefit from this work in the development and operation of their hydroponic systems, increasing research efficiency and collaboration opportunities in this field of study.

Red crown rot (RCR), caused by Calonectria ilicicola, is an emerging soybean disease in the U.S. Midwest for which scalable approaches to characterize within-field disease distribution are lacking. This study evaluated high-resolution PlanetScope satellite imagery for mapping RCR-affected soybean canopies across 15 commercial fields in Illinois surveyed during the 2024 and 2025 growing seasons. A total of 2,921 georeferenced canopy plots were classified as asymptomatic or RCR-affected and paired with six multispectral bands and seven vegetation indices. Spectral differences between classes were evaluated using linear mixed-effects models, and seven machine-learning classifiers representing linear, tree-based, neural-network, kernel, and probabilistic approaches were compared using spatially independent leave-one-field-out cross-validation. RCR-affected canopies exhibited increased reflectance in the visible and red-edge regions, reduced near-infrared reflectance, and lower vegetation-index values relative to asymptomatic canopies. All classifiers showed strong discrimination, with ROC-AUC values ranging from 0.963 to 0.982. Regularized logistic regression achieved the highest overall performance, with an accuracy of 0.945, balanced accuracy of 0.945, F1-score of 0.948, and ROC-AUC of 0.982 at the optimized decision threshold. Permutation analysis identified EVI, NDVI, and red reflectance as the most influential predictors across representative model architectures. Satellite-derived probability and classification maps generally corresponded with symptomatic canopy patterns observed in high-resolution UAV imagery, although mixed pixels reduced precision near disease-patch boundaries. These results demonstrate the potential of high-resolution satellite imagery for within-field mapping of RCR-associated canopy symptoms across independent commercial soybean fields.

Amaranthus tricolor is a climate-resilient orphan crop whose betalain pigments contribute to nutritional quality and stress tolerance. However, breeding efforts have traditionally relied on field yield trials due to the limited availability of genomic information and genetic resources. To address this gap, we first used the Illumina and PacBio sequencing platforms to assemble a novel reference genome of A. tricolor, using the Japanese cultivar Bayam. By combining this reference genome with a core collection developed in our previous study, we aimed to identify genomic regions associated with pigmentation-related traits across winter and summer seasonal environments. Betalains are antioxidant phytonutrients that contribute to abiotic stress tolerance, making the regulation of their biosynthesis under heat stress particularly relevant for developing climate-resilient crops such as A. tricolor. As a result, ATR1.0CH03_5131103, which is associated with ATR1.0ch03g000565, was identified under the winter seasonal environment. Meanwhile, transcriptomic profiles in this study revealed that ATR1.0ch03g000565 was differentially expressed under both control and heat stress conditions, suggesting that it may be associated with the regulation of CYP76AD1 expression under optimal conditions. Under the elevated-temperature treatment, transcript levels of CYP76AD1 and 5GT decreased in Am087, coinciding with lower betacyanin-dominated absorbance indices. cDOPA-like homologs were upregulated; however, pigmentation-related absorbance remained reduced. Further molecular experiments, such as CRISPR-Cas9 knockouts or overexpression studies, are necessary to validate the regulatory mechanisms of CYP76AD1 and 5GT under heat stress.

Climate change is increasing the frequency and intensity of heatwaves and droughts, threatening medicinal and aromatic plants (MAP) production. Temporary shading during the hottest part of the day may reduce heat and radiative stress while limiting the reduction in light availability. We evaluated this strategy in a field experiment using three MAP species with contrasting ecological requirements and harvested organs: lavandin (Lavandula x intermedia), curly parsley (Petroselinum crispum) and valerian (Valeriana officinalis). Vertical shade nets provided midday shading and effects on crop microclimate, leaf temperature, water status, PSII photochemical efficiency, plant growth and biomass and essential oil production were assessed over two growing seasons. Midday shading reduced daily photosynthetically active radiation by 19.9-55.9 %, with weak effects on air temperature and vapor pressure deficit. In contrast, leaf temperature at solar noon decreased by 6.9 degrees C, 5.6 degrees C and 3.0 degrees C in lavandin, valerian and parsley, respectively. Midday shading improved light-adapted and maximum PSII photochemical efficiencies in all three species and reduced branch dehydration in lavandin during heatwave periods. However, agronomic responses were species-dependent. Floral biomass decreased in lavandin, whereas parsley leaf biomass and valerian root biomass increased. Essential oil concentration was unaffected in lavandin and valerian. Overall, our results show that midday shading primarily reduced leaf-level thermal and radiative stress rather than modifying the surrounding air microclimate, and suggest that its agronomic effects may reflect a balance between stress alleviation and reduced carbon acquisition, that varies with species ecology and production objectives.

Seed ecophysiology is essential for understanding plant regeneration and developing effective conservation and ecological restoration strategies, yet knowledge remains scarce for most threatened Cerrado species. We characterised the germination niche, desiccation tolerance and storage behaviour of Xyris paradisiaca (Xyridaceae), an Endangered species endemic to Central Brazil, to assess whether its restricted distribution is associated with narrow germination requirements and to inform seed-based conservation. Germination was tested across light regimes, constant temperatures, decreasing water potentials and short-duration heat shocks, while thermal- and hydro-time models were used to quantify thermal and hydric thresholds. Seeds exhibited an absolute light requirement but broad abiotic tolerances. Germination remained >87% between 15 and 40 {degrees}C, with estimated cardinal temperatures of Tb = 8.94 {degrees}C, To = 33.13 {degrees}C and Tc = 45.53 {degrees}C. Germination remained similar to the control down to -0.6 MPa and exceeded 40% at -1.0 MPa, with a median base water potential of {Psi}b = -1.03 MPa. Germination also remained high after 1-min heat shocks up to 200 {degrees}C. Seeds were desiccation tolerant, with 100% germination after drying and a viability loss index of -0.04, and germination remained >90% after 24 months of ambient storage. Thus, the highly restricted distribution of X. paradisiaca is not driven by a narrow physiological germination niche. Its broad environmental tolerances, desiccation tolerance and favourable storage behaviour also highlight its unexplored potential for seed-based restoration, propagation and ex situ conservation.

RNA-guided nucleases (RGNs), such as Cas9 from Streptococcus pyogenes (SpCas9), are widely used for plant genome editing. Previous surveys for off-targeting, the modification of unintended targets with similarity to the intended target, indicate high specificity of SpCas9 in plant cells. However, off-targeting has not been assessed for efficiency-optimized editing systems combined with extensive multiplexing, which might increase the likelihood of cleavage at unintended sites. We therefore analyzed Arabidopsis thaliana lines that had been extensively mutagenized using zCas9i and up to 29 gRNAs addressing >45 target sites over several rounds of editing. Genomes were sequenced by short- and long-read technologies, and genome-wide variants were catalogued. Our pipeline for variant calling reliably detected RGN-induced mutations at on-targets. When excluding these on-target modifications, variants were detected in edited lines at frequencies similar to those previously reported for spontaneous mutations. In further analyses, we did not find any evidence for an origin of these variants from RGN activity. Our data are thus consistent with high specificity of SpCas9. In contrast, we detected genomic reorganization events upon editing at two complex loci, RPP1 and RPP7, encompassing multiple homologous genes, and also identified an allele by WGS that had escaped detection by amplicon sequencing. We conclude that, while off-targets may efficiently be avoided by selection of specific gRNAs, on-target modifications may be more extensive than intended, especially at complex loci and/or during multiplexing.

Knowledge of the influence of particle size, blending, and processing temperature on flour functionality is critical for the utilisation of emerging grains. This study investigated the flour pasting behaviour of four Australian native grains - Button Grass, Curly Mitchell Grass, Native Millet, and Weeping Grass - in relation to particle size, wheat flour substitution at various levels (0-30%), and elevated RVA temperatures (95-140 degrees Celcius). The pasting properties of flour fractions (fine, medium, and wholegrain) were analysed using a Rapid Visco Analyser, and water absorption capacity was also determined. Particle size effects were species-specific. Native Millet and wheat had higher viscosity in the finer fractions, whereas Weeping Grass exhibited maximum viscosity in the medium fraction. Button Grass and Curly Mitchell Grass showed limited sensitivity to particle size, indicating a stronger influence of intrinsic structure. Increasing substitution with native flours generally reduced viscosity, except for Weeping Grass, which was less affected and maintained stable viscosity. High-temperature rapid visco analysis revealed distinct thermal responses: most grains showed reduced final viscosity at elevated temperatures, whereas Weeping Grass maintained relatively high final viscosity across most temperatures. These findings demonstrate the diverse functional behaviour of Australian native grains and provide insights into their potential application in gluten-free and composite flour systems.

Xylella fastidiosa is one of the most important plant pathogens worldwide, infecting more than 700 plant species and causing major economic losses in affected agricultural regions. In the absence effective curative treatments under field conditions, experimental research is essential. However, research efforts are severely constrained by strict phytosanitary regulations and the pathogen's restricted geographic distribution, as well as its demanding culture requirements. To the best of our knowledge, no studies have compared different host infection models under controlled growth-chamber conditions. To address these limitations, we developed and compared a microinjection-based inoculation protocol across herbaceous and woody hosts maintained in biosafety growth chambers. Stems of Vitis vinifera, Nicotiana tabacum, N. benthamiana, and Medicago sativa were inoculated with X. fastidiosa by microinjection and maintained under controlled growth-chamber conditions. Pathogen establishment was assessed by qPCR analysis of petiole tissue, together with disease progression, plant growth, and leaf scorch development. Approximately one month after inoculation, bacterial detection rates reached 100% in V. vinifera and 80% in N. tabacum. Both species subsequently exhibited measurable disease development, whereas bacterial establishment and phenotypic responses were less consistent in N. benthamiana and M. sativa.

Global change has a major impact on rivers and their associated terrestrial compartment, the riparian forest. The black poplar (Populus nigra L.), the dominant species of the European riparian forest, is considered an indicator of the biodiversity dynamics of these ecosystems, but is threatened with extinction in several parts of its range. The species is dioecious and shows wide variability in many traits, enabling it to adapt to a broad range of environmental conditions. A detailed characterization of this natural variability is therefore a key issue both for the conservation of natural populations and for biological engineering aimed at restoring riverbanks. Phenology is both an indicator of climate change and an adaptation lever for populations, particularly in perennial species such as trees. In this study, we characterized the spatial and temporal variability in flowering phenology by studying 13 natural black poplar populations along the Loire River, France. Flowering date and duration showed substantial variability both within and between populations, and males generally flowered earlier than females. Despite this variability, the ranking of individual trees' flowering dates was highly consistent across years. Most male-female pairs shared several days of overlapping flowering each year, and this overlap was moderately stable across years. Flowering dates advanced by about five to six days per degree Celsius during early spring, with no detectable difference in sensitivity between sexes. This work contributes to the characterization of flowering variability in an economically important tree species and may inform both the conservation of genetic resources and breeding.

Enhancing grain carotenoid concentration to increase yellowness and provitamin A content in sorghum (Sorghum bicolor [L.] Moench) is a major breeding objective across sub-Saharan Africa. Carotenoid breeding is constrained by costly post-harvest phenotyping and can be accelerated through genetic markers that capture functional variation in key carotenoid biosynthesis genes. Zeaxanthin epoxidase (ZEP) is a major gene controlling sorghum grain carotenoid accumulation, and SNP-based KASP markers facilitate selection of high-carotenoid grain, although quantitative variation remains among lines carrying the favorable allele. We hypothesized that additional functional variation within ZEP and other carotenoid biosynthesis pathway genes contributes to this variation and can be revealed by a pangenome-informed analysis. Using a 33-member pangenome reference, we characterized sequence and structural variation at ZEP and other key carotenoid biosynthesis genes and applied pangenome-derived genotyping to study marker diversity associated with carotenoid accumulation. Evaluation of ZEP in the pangenome reference revealed previously uncharacterized structural variation that is absent from the BTx623 primary reference genome. A pangenome-based association analysis identified genetic markers associated with the accumulation of carotenoids which were specific to particular pangenome reference members, including in RTx430 and SRN39, both yellow endosperm lines. Machine learning identified markers in ZEP, {beta}-OH, ZDS, and Z-ISO as the most predictive for all carotenoid traits, suggesting a multi-locus genetic architecture underlies the accumulation of carotenoids in sorghum grain. By integrating sorghum pangenomic resources with machine learning, this study establishes a framework for pangenome-accelerated trait discovery and identifies new genetic targets for carotenoid biofortification in sorghum.