Crop Science

Common bean (Phaseolus vulgaris L.) is the leading grain legume consumed directly by humans and a primary source of nutrients in many communities. This study utilized common bean genotypes with diverse seed coat phenotypes to investigate genotypic and environmental effects on pigmented seed coat area and seed macronutrient (protein, starch, fat, ash, moisture), anti-nutrient (phytate), and mineral nutrient (iron, zinc, calcium, phosphorus, magnesium, potassium, sodium) profiles. Recombinant inbred lines (RILs) that comprise six phenotypic classes for seed coat patterning and nine commercial cultivars were field-evaluated for multiple years across inland, coastal, and intermountain environments in California. A custom near-infrared spectroscopy calibration improved macronutrient prediction accuracy relative to a pre-existing calibration. Environmental effects on macronutrients were pronounced; the 2022 coastal growing environment was the most distinct, characterized by significantly higher starch and moisture content and significantly lower protein content in the RILs relative to any other environments. Across growing years in the RILs, greater consistency was observed at the inland site, where only protein was significantly different; all macronutrient traits significantly differed within the intermountain site. Certain commercial cultivars largely maintained their relative rank for protein content across environments, indicating consistency of genotypic performance, and Black Nightfall ranked among the highest for iron, zinc, phosphorus, and magnesium. Percent pigmented seed coat area was significantly negatively correlated with both calcium and magnesium concentrations. These results underscore the importance of genotype-by-environment field trials for seed coat patterning, seed nutritional composition, and their interplay, to support breeding of common bean among other grain legumes. HighlightsO_LICustom near-infrared spectroscopy (NIRS) calibration improved prediction accuracies C_LIO_LIEnvironmental effects significantly influenced common bean macronutrient composition C_LIO_LICertain cultivars ranked consistently for macronutrient traits across environments C_LIO_LISeed coat pattern was significantly associated with mineral nutrient concentrations C_LI

Maximum utilization of existing genetic variability in a breeding program depends on the efficient classification of the inbred lines into heterotic groups, particularly under stress conditions. This study applied practical breeding approaches to determine the mode of genetic inheritance for Striga resistance and proposes a weighted heterotic grouping method based on the general combining ability of multiple traits (WHGCAMT) and compares its effectiveness with other existing methods in classifying the inbred lines into heterotic groups in Striga-infested and optimum environments. Using Diallel design IV, 300 crosses were generated from 21 inbred lines and 4 standard testers. The crosses, along with six checks, were evaluated in an 18 x 17 alpha lattice design with two replications at two locations, in both artificial Striga-infested and Striga-free environments. The inbred lines were genotyped using DArTtag SNP markers. Phenotypic and genotypic data were analyzed using R. Analysis of variance revealed significant mean squares for hybrid, general combining ability (GCA), specific combining ability (SCA) and their interactions with environment. Significant positive and negative GCA and SCA effects were detected for grain yield and other measured traits. However, a larger proportion of additive gene action than non-additive gene action was observed for grain yield and most measured traits. The analysis of molecular variance also showed substantial genetic differences within and between clusters. Except for HSCA, the mean grain yield between the inter-group and intra-group hybrids was significant for each method. Pairwise comparison of the inter- and intra-group hybrids of all the methods showed significant differences between the WHGCAMT and all other methods in most cases. WHGCAMT consistently produced higher-yielding inter-group hybrids and lower-yielding intra-group hybrids, achieving breeding efficiency improvements of 55.8%, 4.3%, 15.7%, and 11.4% over the HSCA, HSGCA, HGCAMT and molecular marker methods, respectively, under Striga infestation. Thus, WHGCAMT offers more precise, reliable and biologically meaningful heterotic groups among early-maturing maize inbred lines.

Genomic prediction models that account genotype-by-environment (GxE) have the potential to accelerate the rate of genetic gain for yield and agronomic performance, yet relatively few studies have applied GxE prediction in public soft red winter wheat (Triticum aestivum) breeding programs. In this study, we extended a reaction norm-based genomic prediction framework by integrating weather-based environmental covariates to more effectively capture genotype- environment interactions. Key agronomic traits, including seed yield, plant height, test weight, and heading date, were evaluated across 33 environments (location-year) using over 3,200 breeding lines from the North Carolina State University small grains breeding program. Multiple genomic prediction models were compared using several cross-validation (CV) schemes representing common breeding scenarios. Across traits, the reaction norm M5 model, which incorporates both GxE and genotype-by-environmental covariate interactions (GxO), achieved the highest prediction accuracy (PA) in CV2 (predicting incomplete field trials) and CV1 for yield and test weight (predicting new lines). The highest PA was observed for test weight under CV2 (0.54) and for yield under CV1 (0.41). Under CV0 (predicting new environments), the M3 model incorporating GxE produced highest PA across traits, with the greatest accuracy for plant height (0.45), although differences among M2, M3, and M4 were small. Prediction under CV00 (predicting new lines in new environments) remained more challenging, with PA values 0.10 - 0.20 across traits. Overall, our results demonstrate that integrating environmental covariates into genomic prediction models can improve predictive performance across diverse wheat-growing environments in North Carolina, supporting their utility for applied breeding efforts. CORE IDEASO_LIIntegrating genotype-by-environment (GxE) interactions with environmental covariates improves prediction accuracy across environments. C_LIO_LIModel performance varies by prediction scenario, with different approaches performing best for new lines, incomplete trials, or new environments. C_LIO_LIPrediction of new lines in new environments remains challenging. C_LI PLAIN LANGUAGE SUMMARYThis study explores how adding environmental information to genomic prediction models can improve prediction accuracy in a public winter wheat breeding program. Using data from multi-environment trials conducted across diverse conditions in North Carolina, we evaluated statistical models that capture how different wheat lines respond to changing environments. By incorporating weather data, we improved the ability to predict performance across locations and years. These findings provide practical insights for refining selection strategies and accelerating genetic gain in wheat breeding.

Lucerne is gaining interest as a living mulch in agroecological productions. However, its vigorous growth can lead to competition with cash crops for light and nutrients, necessitating new ideotypes. This study investigated the genetic basis of traits relevant to ideotype breeding: dormancy, spring regrowth, height, growth habit, leaflet size, stem diameter, and plant structure. Individuals from a diversity panel of 27 accessions and a synthetic population were phenotyped in a spaced plant nursery. Over 100,000 SNP markers were used for genotyping. Genome-wide association study (GWAS) and genomic prediction were conducted, considering population structure. Heritability estimates ranged from moderate to high in diversity panel (h{superscript 2} = 0.36-0.70) but were lower in synthetic population (h{superscript 2} = 0.17-0.33), reflecting reduced genetic variance. Trait correlations differed markedly between populations, indicating the possibility of recombining traits to create new ideotypes. GWAS identified a few QTL (r{superscript 2} up to 0.27) for leaflet size, height, growth habit, and plant structure, with candidate genes linked to growth, stress response, and signalling pathways. Genomic prediction was highly accurate in diversity panel, where broad genetic variation allowed reliable estimation of marker effects, with prediction accuracies exceeding 0.8 for heritable traits, including growth habit and leaflet size. In contrast, accuracies were low in synthetic population, reflecting its limited diversity and small size, whether training was based on the synthetic population itself or on the diversity panel. These results highlight the potential to recombine traits and develop lucerne ideotypes using molecular tools such as QTL detection and genomic prediction.

CoverCress is a new winter annual oilseed crop developed from field pennycress within the past 20 years. Field pennycress is commonly considered to be self-pollinated but little basic research has been published and there is some misalignment of conclusions. Our experience working with pennycress plant growth in greenhouse and field conditions over the past 13 years suggests that outcrossing is uncommon. We conducted lab, greenhouse, and field experiments to strengthen the body of work. Pollen viability kinetics analysis showed that longevity of pollen viability is negatively impacted by increasing temperatures and by direct exposure to light. Samples treated at 4C declined to 50% viability in 12 hours while it took just 2.5 hrs at 37C, and 1.6 hrs in full sunlight on a cool early April day. Cross-pollination was absent among greenhouse-grown plants flowering inside an agitated plastic pollen-containment covering. Across greenhouse tests, high rates of cross-pollination occurred only in an emasculation treatment that rendered flowers male sterile and opened the pistil to cross-fertilization. Field trials designed to measure pollen flow distance using a trackable fae1 knockout reporter gene failed to show detectable movement of pollen under field conditions in two locations. This data strongly suggests that domesticated field pennycress may be considered a self-pollinated crop and managed as such.

In quantitative genetics, candidate SNPs are identified through genotype-phenotype associations inferred with genome-wide association studies (GWAS). In this study, we explore an alternative approach to detect genetic variants with non-neutral effects by tracking temporal trends in allele frequency in a winter wheat (Triticum aestivum L.) breeding population over an eight-year period, from which signals of selection may be inferred. Selection signatures were inferred with a generalized linear model, where we modeled trends in allele frequency as a function of time (crossing year). These signatures of selection were used to prioritize variants. Associations between phenotypic performance and individual load of prioritized variants were then investigated. Furthermore, we assessed whether incorporating selection information into a genomic best linear unbiased prediction (GBLUP) model improves model performance in terms of quality of fit and prediction ability. Our findings indicate that the inferred signals of selection are effective in identifying non-neutral variants. Variants under strong negative selection were associated with a decrease in protein content adjusted for grain yield (p-value < 0.01), while genetic variants that had been under moderate to high levels of positive selection were associated with increased grain yield (p-value < 0.01). However, incorporating selection information did not improve prediction accuracy. In conclusion, temporal trends in allele frequency can be used to detect non-neutral variants. The proposed approach may hence complement traditional quantitative genetic methods for detecting non-neutral genetic variation. This approach may allow breeders to detect non-neutral variants earlier in the breeding cycle, without resorting to phenotypic data.

The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize (Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multi-kernel models, using additive and two alternative non-additive genomic relationship matrices, to estimate the variance components associated with the GCA of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability (SCA) arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multi-kernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both types of non-additive matrices produced similar results, affirming the robustness of the inferred genetic architecture. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multi-kernel models can improve the efficiency of hybrid breeding programs.

Genomic prediction (GP) across diverse environments has a potential to accelerate genetic gain in cotton breeding programs. A major challenge in GP is modelling genotype-by-environment interactions (GEI), which is essential for selecting stable and high-performing genotypes under variable production conditions. However, incorporating GEI into GP models increases the dimensionality and computational complexity, risking complex models that are impractical to use on commercial breeding-scale data sets because of run times and computational demands. This study addresses two primary aims. Firstly, we evaluate the practical benefits of GEI-informed GP for predicting economically important cotton traits. Second, advanced statistical modelling strategies are developed and assessed for integrating genomic and environmental data at scale. We propose a dimensionality reduction approach that combines linkage disequilibrium network analysis with principal component techniques to reduce redundancy while preserving informative variation. Using this reduced dataset, we implement Bayesian linear regression models and, for comparison, deep residual neural networks for genomic prediction. Analyses were conducted on a large multi-environment dataset from the CSIRO cotton breeding program, comprising 3,236 breeding lines, 54 environmental covariates, and 8,049 yield and fibre quality phenotype records collected over 10 years and 9 locations representing 41 year-location combinations. Results demonstrate that generally Bayesian linear regression approaches outperform BG-BLUP models, with all three linear/linear mixed methods providing clearly more reliable performance than the deep learning models. These findings highlight the value of using interpretable statistical models for integrating genomic and environmental information to support selection decisions under diverse environmental conditions.

Neonicotinoid seed treatments protect maize during early growth but can induce phytotoxicity that intensifies during storage. Despite recognized genotypic variation in tolerance, standardized phenotyping methods are lacking. We evaluated nine commercial maize hybrids under three seed treatments (control, one neonicotinoid [1N], and two neonicotinoids [2N]) across two storage periods (0 and 6 months at 25 {degrees}C) using germination, accelerated aging, and cold tests. A Seed Treatment Tolerance Index (STTI) was analyzed through hierarchical clustering, principal component analysis, and multivariate analysis of variance. Results showed a significant triple interaction among genotype, seed treatment, and storage. Hybrids from female line A maintained STTI above 0.95, while female C hybrids showed germination reductions up to 48 percentage points and vigor losses up to 90 percentage points under 2N after six months. Tolerance was associated with hydrogen peroxide regulation by catalase and ascorbate peroxidase. The STTI proved a reliable tool for classifying genotypic tolerance, with direct applications for breeding programs and seed industry logistics.

Spinach (Spinacia oleraceae) is a principal vegetable crop commercially grown in Controlled Environment Agriculture (CEA). Recent research suggests that root morphological and architectural differences among crop species influence yield, resource use efficiency, and environmental stress tolerance. These root traits may be exploited to increase yield, promote efficient nutrient use, and mitigate environmental stressors. This study measured differences between various spinach cultivars in CEA systems to reveal morphological and anatomical variation. We grew three spinach cultivars with different reported growing rates ( Income, Darkside, and El-Majestic) under NFT hydroponic and substrate-based systems in a controlled greenhouse environment over 45 days with destructive harvests at days 15, 30, and 45. Supplemental light (250 {micro}mol/m2/s) with 12-hour photoperiod and periodic fertigation was used. Harvests included the collection of leaf and root biomass, and scanning of root systems in WinRhizo software, measuring ten variables. On day 45, root cross-sections from orders 1-5 were embedded in JB-4 resin, sectioned, stained, and analyzed for diameter, vasculature, and rhizodermis characteristics. Results indicate that in spinach, differences in root system morphology are linked to cultivation systems over cultivar identity. Vascular and root anatomical alterations are minor compared to morphological differences in response to the cultivation system. Hydroponic-style growth systems are associated with the proliferation of fine-root ideotypes compared with substrate-based conditions. Such findings affirm previous studies, which suggest plastic root morphology in response to growth systems, and may be used to help create more resilient, resource-efficient cultivars. HighlightsO_LIIn spinach, root system morphology differences are linked to cultivation systems. C_LIO_LIRoot vascular and anatomical alterations are minor in response to cultivation system. C_LIO_LIHydroponic growth systems are linked to fine-root ideotype proliferation in spinach. C_LIO_LIFine-root ideotype proliferation may be a breeding target for CEA spinach. C_LI

PurposeGenotype-by-environment (G x E) interactions represent a major obstacle to increasing genetic gain in crop breeding, with the underlying physiological drivers often remaining obscured within conventional statistical models. This case study presents a novel framework that transforms the latent factors from Factor Analytic (FA) multi-environment trial (MET) models into heritable quantitative traits, enabling the genetic dissection of adaptive response patterns. MethodsA Factor Analytical Linear Mixed Model (FA-LMM) was fit to plot-level yield data for 1,036 barley genotypes across eight Australian trials. ResultsCorrelation of the factor loadings with APSIM-simulated environmental covariates demonstrated that the second latent factor FA2 was strongly correlated with the Water Stress Index (r = -0.83) during the critical flowering period, establishing water availability as the main biological axis of crossover Gx E. Genotypic scores for the derived traits, Overall Performance (OP) and Water Stress Response (WSR), were subjected to high-resolution haplotype-based mapping using local Genomic Estimated Breeding Values (GEBV). ConclusionThis analysis successfully identified major genomic regions that accounted for a substantial proportion of the additive genetic variance. Gene Ontology enrichment of candidate genes within the top haploblocks implicated fundamental pathways related to energy homeostasis, root development, and stress response, with notable candidates including FTsH11, BPS1, and TDP1. The distribution of favourable Haplotypes of Interest (HOI) in elite cultivars suggested a historical signature of inadvertent selection for these adaptive mechanisms. This framework provides an explicit bridge between statistical modelling and functional genomics, offering breeders actionable genetic targets for accelerated development of climate-resilient cereals.

Kernel composition traits in maize, including protein accumulation, are of broad interest. The amount of the most abundant proteins in maize endosperm, the -zeins, can vary dramatically among genotypes and in response to soil nitrogen supply. Targeted reductions in -zein accumulation can improve nitrogen utilization and the nutritional quality of maize grain but have traditionally required expensive and destructive phenotyping methods. The Floury2-RFP (Fl2-RFP) reporter gene enables rapid, non-destructive visualization of -zein accumulation in individual maize kernels under white light. This feature is due to the high expression level programmed by the Fl2 promoter, the stability of zein proteins, and the use of monomeric RFP, which emits fluorescence without the need for multimerization. This study aimed to develop a method to quickly document and quantify Fl2-RFP accumulation using camera or smartphone images of either ears or shelled kernels. Results show images of shelled kernels processed with FIJI software capture the Fl2-RFP reporter phenotype better than images of ears. Fl2-RFP confirms the strong maternal control of -zein accumulation and, like grain protein concentration, responds to soil nitrogen supply. The Fl2-RFP phenotyping pipeline effectively quantified Fl2-RFP accumulation by color features from both camera and smartphone images. Smartphone imaging of Fl2-RFP in a diverse population of inbreds followed by elastic net regression of extracted image features predicted kernel protein concentration, as measured by near-infrared spectroscopy, with moderate accuracy (R2 = 0.68, MAE = 0.76, RMSE = 0.93). The spectral features that were most predictive of kernel protein concentration varied depending on whether the background endosperm color was white or yellow. The integrated analysis of Fl2-RFP intensity and grain protein concentration indicates genetic variation for kernel protein accumulation and N-responsiveness that is distinct from the well-studied -zeins. Our findings highlight the Fl2-RFP reporter gene as a valuable tool for investigating the genetic complexity of grain protein concentration and associated traits in maize.

Current optimum contribution selection (OCS) implementations use point estimates of estimated breeding values (EBVs), potentially leading to suboptimal selections when individuals have uncertain genetic evaluations. We developed a framework assessing how EBV uncertainty affects OCS decisions through MCMC-based approaches using the COSMO optimizer in Julia, evaluated on Norway spruce (Picea abies, n=5,525) and Loblolly pine (Pinus taeda, n=926) populations. Agreement between point estimate (MAP-OCS) and MCMC-OCS was surprisingly low: mean overlap of only 26.6 (4.8) individuals in Norway spruce genotyped subpopulation and 14.1 (3.6) in full pedigree, with Loblolly pine intermediate at 16.0 (9.6). Despite this low individual-level agreement, selection frequency across MCMC iterations corresponded well with EBV rankings (Spearman{rho} = 0.782 for Norway spruce), confirming that higher-EBV individuals were preferentially selected under posterior uncertainty. To comprehensively quantify uncertainty impacts, we employed two complementary metrics: individual robustness scores measuring genetic gain stability upon candidate removal, and population-level contribution distribution metrics capturing concentration of genetic gain across selected individuals. Applying these metrics identified 25 high-risk individuals in Norway spruce and nine in Loblolly pine, and constrained exclusion of these individuals improved individual robustness by 16.5% in Loblolly pine (3.00% genetic gain loss) and 29.8% in Norway spruce (2.14% genetic gain loss). Our uncertainty-aware OCS framework successfully identifies unstable selections that may compromise long-term genetic gain, and we recommend assessing EBV uncertainty through posterior distributions and evaluating population-specific trade-offs when implementing uncertainty-aware selection strategies.

Heat-tolerant rice cultivars are essential for mitigating global warming impacts. Basal anther dehiscence length (BDL) is a promising visible morphological marker for heat tolerance through stable pollination. We investigated the effects of sowing date on anther morphology, pollination, and fertility under controlled high-temperature conditions (35, 37, or 39 {degrees}C at flowering). Three japonica cultivars-- Akitakomachi (early heading), Koshihikari (medium), and Hatsushimo (late)--were sown monthly over 3 months and grown in pots. At heading, the plants were exposed to the temperature treatments for 3 days, and the proportion of florets with [&ge;]10 germinated pollen grains on the stigma (GP10) and seed set were assessed. Among anther traits, BDL showed the greatest variation, with all cultivars from the second sowing exhibiting the shortest BDL. Analysis of variance revealed significant effects of genotype, sowing date, and their interaction on anther traits and fertility. Regression analysis indicated that fertility was associated with GP10, with BDL contributing significantly to GP10 in the late-heading Hatsushimo, together with maximum temperature at flowering. Thus, both genotype and environment shape anther morphology, pollination, and fertility, indicating that BDL plasticity and genotype-specific environmental responses must be carefully considered when using BDL as a breeding marker for heat tolerance. HighlightVariation in sowing date significantly affects anther morphology and heat tolerance in rice. Genotype-specific responses to the growing environment require careful consideration for reliable breeding assessments.

To meet the growing demand for precision and intelligent agricultural management, crop simulation models offer substantial potential for optimizing farm planting strategies. By simulating crop growth processes and assessing the effects of different management practices, these models provide a scientific basis for planting decision-making. In this study, the DSSAT model was first used to optimize the planting strategies of Farm X in 2023. Based on the optimized plans, the model was further applied to predict crop yields per unit area for 2024 and to establish the relationships among yield, planting density, and fertilizer application rate. Subsequently, SPSS was employed to develop a regression model describing the relationship among net profit per unit area, planting density, and fertilizer application rate. A genetic algorithm was then used to identify the optimal solutions under different scenarios, generating prescription maps for the optimal planting density and fertilizer application rate for each plot of Farm X in 2024. The results provide a scientific reference for the mechanized and automated implementation of field management practices and support the dual optimization of economic returns and resource use efficiency. This study not only conducted a systematic optimization of Farm X planting strategies for 2023, but also provided detailed predictions and optimized prescriptions for 2024 in a visual and practical form. The proposed approach offers a scientific decision-support tool for farm planting strategy formulation and lays a foundation for the intelligent and automated development of modern agriculture.

Pennycress (Thlaspi arvense L.) is an intermediate winter oilseed crop that has only recently been domesticated for agronomic use. Improving agronomic traits requires sources of genetic variation, and mutagenesis is frequently used to help overcome the limitations of natural populations. We investigate the impact of Ethyl methanesulfonate (EMS) on genetically effective cells (GECs) to characterize the intra-individual genetic variation of EMS mutagenesis in pennycress. We identified that pennycress contains at least 4 GECs which, when treated with EMS, create unique mutations across different branches within the same individual plant. We then propagated the M2 plants for whole genome sequencing, providing extensive characterization of the EMS mutation profile and developing a gene index as a resource for future reverse genetic screenings. Article SummaryPennycress is an emerging winter oil seed crop in the American Midwest. Domestication efforts have advanced rapidly through a combination of genetic techniques. One of the most successful methods has been the use of a mutant gene index, a large collection of pennycress seed where new genetic variation has been created through Ethyl methanesulfonate (EMS). EMS mutations are not uniform however, and a single treated seed can have wide genetic variation within the resulting plant. We investigate the role of genetically effective cells on EMS variation, and present the full EMS population as a resource for further pennycress domestication efforts.

Securing sustainable crop production requires the genetic improvement of abiotic stress tolerance. Due to the broad range of environmental factors causing abiotic stress and complex genotype-by-environment interactions, it is crucial to understand the genetic basis of crop yield under suboptimal conditions. Here, we developed a dent maize Multi-parent Advanced Generation Inter-Cross (MAGIC) population comprising 388 doubled haploid (DH) lines. The population was derived from eight founders with varying stress tolerance, selected from a dent diversity panel evaluated for yield performance across a wide range of European environments. The MAGIC DH lines were genotyped via whole-genome sequencing ([~]5X coverage) and evaluated in seven testcross and 14 line per se trials, for grain dry matter yield, leaf senescence, leaf rolling, anthesis-silking interval, and six additional agronomic traits. Genetic dissection identified 22 grain yield QTL, explaining 45% of the genetic variance. Under heat and drought stress, testcross grain yield correlated significantly with leaf senescence and leaf rolling measured in line per se trials. Bivariate multi-trait analysis showed that alleles for delayed senescence and reduced rolling at detected QTL generally exhibited positive effects on grain yield, suggesting that accumulating these favorable alleles could enhance yield performance. Incorporating these proxies into multi-trait genomic prediction models improved yield prediction accuracy, although gains were constrained by modest trait correlations. Given the comprehensive data, we also provide recommendations for optimizing sequencing depth and QTL mapping strategies in experimental maize populations. Key messageThis eight-founder MAGIC population represents a powerful resource for dissecting complex traits in maize, assessing the utility of drought proxy traits, and optimizing low-coverage whole-genome sequencing approaches.

Improving rice (Oryza sativa L.) yield requires a balanced enhancement of both sink size and source capacity. While many QTLs for sink size have been identified, only a few are known for source capacity, which is essential for achieving high yield. Here we identified qHP10 as a major QTL for increased photosynthetic rate by using chromosome segment substitution lines derived from a cross between the high-yielding indica cultivar Takanari and the average-yielding japonica cultivar Koshihikari. High-resolution mapping combined with CRISPR/Cas9-induced mutagenesis revealed that the causative gene underlying qHP10 is Mitogen-Activated Protein Kinase 4 (OsMPK4). A near-isogenic line carrying the OsMPK4Takanari allele (NIL-OsMPK4) had a 15-25% higher photosynthetic rate than Koshihikari. NIL-OsMPK4 also had higher stomatal conductance than Koshihikari but similar stomatal pore size and density, indicating that increased stomatal aperture increases photosynthetic rate. This enhancement is likely attributable to the down-regulation of OsMPK4 expression, which increases stomatal conductance and thus promotes CO2 uptake. Our findings demonstrate that OsMPK4 is a promising genetic target for increasing source capacity and, potentially, rice yield through molecular breeding. (175 words)

Lodging in sorghum presents a significant challenge for plant breeders due to the trade-off between lodging resistance and grain yield. Manually measuring lodging across thousands of plots is time-consuming, expensive, and error-prone, making selection for lodging resistance challenging in breeding programs. Unmanned Aerial Vehicle (UAV) derived metrics offer a potential high-throughput, cost-effective alternative for lodging phenotyping. This study developed a framework for predicting plot-level lodging from UAV imagery across 2,675 sorghum breeding plots. Multi-temporal canopy height data were collected at two critical time points: maximum crop height and at manual lodging assessment. Height percentiles were extracted from UAV derived point clouds generated using photogrammetric algorithms. These data were used to develop parametric, non-parametric, and ensemble prediction models, which were evaluated using three statistical metrics. The ensemble model, averaging predictions from all models, achieved the highest accuracy with Pearson correlations of r = 0.80-0.84 and lowest residual mean square error (RMSE=16-18), explaining 64-70% of variation in manual lodging counts. Model diagnostics and iterative refinement, including inspection of UAV imagery and dataset curation, had minimal impact on model performance, demonstrating the robustness of the approach. Model performance was consistent across sites, with minimal effects of stratified sampling on accuracy, confirming the ensemble approach as optimal for plot-level lodging assessment. This study demonstrates that integrated multi-temporal UAV imagery offers a practical alternative to labor-intensive manual evaluation methods by enabling high-throughput lodging assessment suitable for implementation in sorghum breeding programs.

Improving photosynthesis is a promising approach to enhance sugar beet productivity. However, genetic variation in leaf photosynthesis and its relationship with disease resistance remain underexplored. We evaluated 98 sugar beet genotypes representing different breeding categories, including commercial F1 hybrids, seed-parent lines, and pollinator lines, in Hokkaido, northern Japan. Leaf gas exchange was measured during early growth under field conditions around the infection period of Cercospora leaf spot (CLS). To account for fluctuating irradiance during large-scale phenotyping, we applied a multilevel mixed-effects light-response model to estimate genotype-specific photosynthetic characteristics. Substantial genotypic variations in photosynthetic characteristics were detected. F1 hybrids exhibited higher photosynthetic capacity than breeding lines, whereas differences among breeding categories were unclear due to large within-category variation. Some breeding lines exhibited photosynthetic rates higher than those of hybrids, indicating exploitable genetic resources within the present genetic panel. We did not detect statistically significant trade-off between leaf photosynthesis and CLS resistance among 98 genotypes; in a subset of 19 genotypes analysed in detail, the relationship was even synergistic. Our results highlight the genetic diversity of leaf photosynthesis and its category-dependent structure, and suggest that selection for enhanced photosynthesis can proceed without substantial trade-off with CLS resistance. HighlightLeaf photosynthesis of 98 sugar beet genotypes showed significant genetic variation and dependence on breeding category. Active photosynthesis incurred minimal trade-off with Cercospora leaf spot resistance.