The Plant Phenome Journal

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.

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.

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.

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

Non-destructive methods for real-time phenotyping of root growth and development are essential to accelerate the breeding of climate-resilient crops. We present DIRTNet (Digital Investigations of Root Traits Net), a hybrid deep learning model that interprets in-soil signals collected by Fiber Bragg Grating (FBG) sensors. We first evaluated DIRTNet in a controlled simulation study in which metal rods of varying diameters were inserted into soil to emulate root growth. Using augmented training data with an 80:20 train-test split, DIRTNet surpassed baseline models, achieving 0.97 accuracy for root depth prediction and 0.94 for root diameter prediction. We then applied the best-performing models to maize (Zea mays) roots monitored by six FBG sensors placed at different soil location and depths. A control trial and a drought-stress trial yielded 192 samples, split 80:20 into training and holdout sets; the hold-out portion was divided equally into validation and test sets. Augmentation was applied only to the training data. On real maize root data, DIRTNet achieved 0.92 accuracy for both root depth and root width prediction across variable growth periods, covering root mass lengths up to 20 cm and diameters up to 10 cm. The prediction accuracy is independent of the soil moisture and drought condition. In a fixed-interval analysis, accuracy reached 0.97 for days 25-42 after planting and 0.93 for days 25-48, demonstrating robust performance as root complexity increased.

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.

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.

Genomic selection (GS) and trait discovery in crops require reliable marker datasets, yet variant discovery remains constrained by the lack of standardised, high-confidence reference resources. Publicly available plant genomic resources are often based on outdated reference genomes, heterogenous pipelines, and generic variant hard-filtering thresholds derived from human studies. Consequently, implementation of the Genome Analysis Toolkit (GATK) Best Practices framework, including machine learning-based variant quality score recalibration (VQSR), remains challenging in crop genomes. Here, we present a crop-adapted framework for unsupervised variant quality assessment and VQSR enabling advanced SNP and insertion-deletion (INDEL) discovery without predefined high-confidence reference variant sets. Using Brassica napus as a model, we use the Illumina 60k Brassica SNP array to generate a reference-specific truth set for data pre-processing and independent evaluation of an unsupervised machine-learning approach for constructing cohort-specific VQSR resources. Gaussian mixture model (GMM)-based clustering of multidimensional variant annotation profiles identifies high-, intermediate-, and low-quality variant classes, providing flexible training resources for VQSR without requiring labelled training data. We further validate the framework in diploid Brassica oleracea and hexaploid wheat (Triticum aestivum) across cohorts differing in population size, genetic diversity, and genome architecture. Despite population- and species-specific differences in variant annotation profiles, GMM-based clustering consistently identifies informative variant quality classes and enables cohort-specific recalibration of both SNPs and INDELs. Our results demonstrate that unsupervised machine learning can extend GATK Best Practices to crop species lacking comprehensive genomic resources, providing a scalable approach for generating high-quality variant datasets for crop genomics and downstream breeding applications.

Genotype-by-environment interactions can complicate plant breeding because genetic effects may vary across weather regimes, locations, and years. Although genomic prediction models can use dense marker data to predict complex crop traits, they often provide limited information about how marker relevance changes across environmental conditions. This study introduces a graph-based framework for estimating environment-associated marker rankings from multi-environment crop trial data. We introduce an attention-based graph neural network that jointly represents SNP markers and weather-based environment vectors as interacting nodes and is designed to learn environment-associated marker rankings from supervised yield data. The model is trained using yield prediction as the supervised objective, and the learned marker-level attention signals are aggregated to estimate marker rankings overall and within groups of similar environments. We evaluate the framework on two genotype-by-environment datasets. In both datasets, we examine whether learned marker rankings are stable across independent training runs and determine the overlap between high-ranked markers and external GWAS references. In a maize dataset with data from 212 different site-years, we rank markers separately within each weather-defined environment cluster and define a global marker set as the markers shared across all clusters. This provides a structured way to examine how marker rankings vary across weather-defined groups and which markers remain important across environments. We further encode each weather variable with a separate LSTM combined by attention, which makes the contribution of individual weather variables readable, and compare the resulting variable ranking against an independent SHAP-based attribution. Genes located near the top-ranked markers are also examined for functional enrichment. Overall, the results suggest that graph-based, environment-conditioned marker attribution may complement genomic prediction as an exploratory tool for studying context-dependent genomic signals in multi-environment trials.

Breedbase ( is a comprehensive, web-based, open-source breeding management and analysis software for plant breeding that was introduced to address gaps with existing software for plant breeding data. Breedbase is continually improved and developed, with new features being added as required by the breeding community. Since its inception, the capabilities of Breedbase were improved to better serve the diverse data modeling needs of additional crops beyond our original ensemble of cassava, banana, yam, sweet potato, tomato, rice, and wheat. We describe here major updates that have been made in the past 4 years, including (1) improved handling of repetitive measurements; (2) a redesigned experimental treatments interface; (3) a more fine-grained user permission system; (4) improved trial quality control workflows; (5) intercropping support; (6) the addition of a decision meeting feature that helps decision makers make the best selection with the available data; (7) transgenics and tracking tools and finally, (8) an improved field layout tool which consolidates several trials in one field. These new features integrate seamlessly with the previous feature set of Breedbase - making it one of the most feature rich breeding management softwares available.

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.

Ordinal scoring of plant disease severity by human raters compresses variation in lesion color, size, and number. Inter-rater variability further complicates comparisons and integrated analyses across environments. We developed a low-cost portable imaging chamber to rapidly image large numbers of leaves under standardized lighting, orientation, and backdrops in the field and employed this system to image more than 11,000 leaves across three states. Embeddings from vision encoders predicted human-assigned disease severity scores. No significant GWAS hits were identified using human-assigned or vegetation-index-based disease severity scores, but GWAS using embeddings identified twelve genomic hotspots controlling leaf appearance. Nine were linked to variation in disease symptom severity. Five hotspots corresponded to previously characterized sorghum genes: all three hotspots not linked to disease and two of the nine that were. Roughly one-third of tested embedding--hotspot associations replicated across at least two states, and twenty replicated across all three. eQTL, PheWAS, and large-effect variant analyses identified single candidate genes with plausible mechanistic links to disease symptom severity for six of the seven hotspots not mapping to characterized genes. These results demonstrate the power of combining scalable, standardized leaf imaging with pretrained image encoders to capture genetically controlled variation in diverse disease symptoms that human ordinal scoring misses.

In common wheat (Triticum aestivum L.), variation at the Vrn1 and Ppd1 loci changes plant phenology, resulting in differential adaptation useful to breeders. Because the effects of phenology on grain yield are conditional on environmental factors, the incorporation of markers for Vrn1 and Ppd1 alleles into breeding approaches has proven difficult. Historic phenotypic, genotypic, and environmental data can provide insight into the relationship between major phenology alleles and grain yield as mediated by environmental variables. Analyses of eight years of breeding trials (1,038 lines at 219 site-years) determined that weak vrn1 alleles and Ppd1 insensitivity alleles have variable effects. These effects change depending on an environment's winter temperature and latitude, respectively, but the environmentally-driven changes in effect of vrn1 alleles are much more variable than effects of Ppd1 alleles. Mediation analyses showed phenologically-dependent environmental variables are one mechanism through which phenological changes created by vrn1 and Ppd1 variants alter yield across multiple developmental stages. Environmentally-driven relationships between flowering time and yield were modeled to estimate yield allele effects as a function of effects on phenology and environmental variables. Weak winter alleles at vrn1 may play a stabilizing role on yield, due to the correlation of winter conditions that drive larger effects with later-season warm temperatures that penalize later maturity. A better mechanistic understanding of how these loci drive environment-specific yield effects may facilitate utilization of markers for these alleles and improve predictive modeling of yield.

Reproductive stage drought disrupts flag leaf metabolism in rice (Oryza sativa), limiting photoassimilate translocation to developing grains and reducing grain yield (GY). How natural variation in this response relates to GY remains poorly understood. This study combines untargeted metabolomics of flag leaves from 271 indica rice accessions grown under well-watered (WW) and reproductive-stage water-limited (WL) field conditions with genome-wide association studies of 89 annotated polar metabolites. A total of 180 metabolite quantitative trait loci (mQTLs) were identified: 93 detected only under WL, 68 only under WW, and 19 under both conditions, indicating a predominant stress-dependent genetic control; mapping of GY itself detected only four marginal QTLs, none overlapping the mQTLs. Four high-density regions (HDRs) of mQTLs were identified: HDR1-1, detected only under WW conditions and associated mainly with raffinose family oligosaccharides; HDR1-2, HDR4, and HDR11, detected only under WL conditions and associated mainly with tricarboxylic acid cycle intermediates and amino acids. After correcting for genetic relatedness, accessions carrying the favourable haplotype at both HDR1-2 and HDR4 yielded 21.5% more under WL conditions than accessions carrying the unfavourable haplotype at both loci and lost 11.4 percentage points less GY to stress. Accessions carrying the favourable haplotype at only one locus showed a comparable reduction in yield loss. Haplotype-guided variant detection against the 3,000 Rice Genomes Project panel identified major-impact variants in candidate genes, including a polyphenol oxidase at HDR1-2 and tandem cytochrome P450s at HDR4. These hubs are promising, metabolically defined targets for breeding drought-resilient rice.

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.

Boron toxicity constrains grapevine (Vitis) production in irrigated arid and semi-arid regions, yet most available rootstocks are sensitive to excessive boron. Although underutilized Vitis species may harbor sources of adaptation, the genetic basis of natural variation in boron exclusion remains poorly understood, limiting the development of boron-tolerant rootstocks. We screened 267 wild Vitis accessions representing 16 species for their ability to limit leaf boron accumulation under excess boron conditions. Leaf boron concentration varied 190-fold among accessions and was highly heritable (entry-mean H^2 = 0.71). Its correlation with stomatal conductance was modest (r = 0.34), indicating that transpiration rate explained only a small proportion of the variation in leaf boron accumulation. Leaf boron was correlated with leaf chloride (r = 0.36), but their largely non-overlapping association profiles supported distinct genetic control. Genome-wide association analysis identified a suggestive association on chromosome 11. Independent quantitative trait locus mapping in a full-sib F1 population (n = 158) derived from Longii-9018 (V. acerifolia) x GRN3 (commercial rootstock) identified qBEx11.1 in the same syntenic region on chromosome 11, which explained 19.5% of the phenotypic variance. Positional candidate genes near the association peak included a pectinesterase and a MATE efflux transporter, suggesting roles for cell wall modification and membrane transport. Leaf spectral reflectance was associated with leaf boron concentration in both populations, particularly at visible and red-edge wavelengths, indicating potential for high-throughput phenotyping. Together, these findings identify qBEx11.1 as a priority locus for boron exclusion and support integrated genetic and spectral approaches for breeding boron-tolerant grapevine rootstocks.

Forward genetics is a powerful approach for gene discovery, but identifying causal mutations becomes difficult when mutants are maintained in heterogeneous populations with uncertain pedigrees. This is exemplified by classical tasselseed (ts) mutants, which have long served as a genetic model for studying sex determination and carpel suppression. Decades of repeated outcrossing to diverse inbred lines have created substantial genetic heterogeneity, limiting the effectiveness of conventional bulked-segregant analysis sequencing (BSA-Seq). To address this, we developed a BSA-Seq framework that integrates flexible experimental designs, multiple reference genomes, and complementary statistical methods tailored for genetically heterogeneous populations. Applying this framework revealed that reference genome selection is critical for mapping success and that Euclidean distance raised to the fourth power (ED4) outperformed homozygosity mapping (HM). Furthermore, the framework enables simultaneous mapping of multiple mutations within a single population, eliminating the need for additional mapping populations. Applying this framework to 26 ts mutant stocks from the Maize Genetics Cooperation Stock Center, we successfully mapped 24 mutants to genomic intervals containing known ts genes, while the remaining mutants mapped to distinct genomic intervals, defining novel candidate regions underlying carpel suppression. Together, these results demonstrate that historical mutant collections represent an underutilized resource for gene discovery and establish a generalizable mapping strategy for unlocking their genetic potential across diverse species.

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.

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.