Crop Science

In plant breeding, it is often necessary to improve a target trait while maintaining other essential traits within desirable ranges. When genetic relationships exist among these traits, improvements in the target trait may lead to undesirable changes in essential traits, complicating cross selections. In such cases, it is critical to select cross-pairs that are expected to produce progeny that satisfy the requirements for all traits. The progeny distribution of each crossing pair can be predicted using the estimated genotypic values and genetic (co)variances of the target and essential traits. By utilizing this distribution, the probability of generating progeny that satisfy predefined trait requirements can be evaluated, allowing a direct comparison of alternative crosses. In this study, we developed Cross Potential Selection for Multiple Traits (CPS-MT), a breeding strategy designed to improve a target trait while maintaining one or more essential traits within desirable ranges. CPS-MT extends the original Cross Potential Selection (CPS) framework to explicitly handle trade-offs between traits under genetic correlations. We evaluated the performance of CPS-MT through simulations involving four types of genetic relationships and two genetic causal factors between traits, resulting in seven scenarios. Across all scenarios, CPS-MT consistently improved the likelihood of obtaining desirable progeny, indicating that CPS-MT provides a practical and effective framework for cross selection under multi-trait constraints in breeding programs. Article SummaryThis study developed Cross Potential Selection for Multiple Traits (CPS-MT), a new breeding strategy designed to improve a target trait while maintaining one or more essential traits within desirable ranges. CPS-MT evaluates crossing pairs by predicting progeny distributions based on estimated genotypic values and genetic covariances, enabling direct comparison of alternative crosses under multi-trait constraints. Through simulations incorporating four types of genetic relationships and two causal factors (seven scenarios), CPS-MT consistently increased the likelihood of obtaining progeny that satisfied the predefined trait requirement. These results indicate that CPS-MT provides a practical, robust framework for target trait improvement under trait constraints.

Modern hybrid maize (Zea mays L.) breeding programs are based on the management of distinct complementary heterotic groups to maximize heterosis in high-performing hybrids. This practice lowers shared genetic segments and increases divergence between groups to limit inbreeding in hybrids. However, most breeding programs have not always enforced strict separation between heterotic groups in the past. Competitor commercial hybrids were notably a common elite germplasm source for inbred development, which would diminish divergence between groups. This study proposes a new haplotype-based approach to assess hybrids residual inbreeding based on parental similarity. The new haplotype method has a stronger significant negative effect on hybrids grain yield than raw SNP data. Evaluation of modern experimental hybrids uncovered related inbreds contributing to superior rates of residual inbreeding. Analysis of these inbreds revealed haplotype transfers between heterotic groups, originating notably from the use of a Stiff Stalk-Iodent commercial hybrid as breeding starts material in both Stiff Stalk and Non-Stiff Stalk breeding populations. The introduction of this intergroup parent generated heterotic-group-specific haplotype migration between crossing pools. These fragments caused significant genome-wide residual inbreeding in experimental hybrids across selection cycles. This study highlights the necessity for accurate evaluation of external sources of diversity to minimize haplotype transfers and admixture between crossing pools. We demonstrate the consequences of using commercial hybrids in inbred development, particularly regarding residual inbreeding, and their effects on hybrid performance. Insights from these results can assist breeders in optimizing the choice of parents for introducing genetic diversity in a reciprocal recurrent selection scheme. KEY MESSAGEHaplotype-based hybrids parental similarity better predicts grain yield than marker-based identity-by-state. Utilization of commercial hybrids as breeding start material resulted in higher hybrid residual inbreeding even after several selection cycles

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.

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.

Lima bean (Phaseolus lunatus L.) is an economically and agronomically important grain legume. Lima beans (or limas) show a range of climatic adaptations with independent domestications in the Andes (large-seeded) and Mesoamerica (small- or medium-seeded). We generated and integrated genotypic and comprehensive field- and laboratory-based phenotypic information for the available accessions in the USDA National Plant Germplasm System collection across multiple environments to inform germplasm utilization in breeding. A total of 810 accessions were genotyped using short-read, low-coverage sequencing. Accession geographic origin and domestication explained population structure. A partially overlapping subset of the panel (n=141-308) was field-evaluated across two years in each of Davis, CA, Central Ferry, WA, and Coachella Valley, CA (the latter was fall-planted for evaluation of photoperiod-sensitive accessions) to assess trait performance in contrasting environments. Agronomic traits such as determinacy and flowering time, and seed traits such as seed coat color and hundred-seed weight, were scored. Macronutrient traits (protein, starch, fat, and ash content) were measured on dry (mature) harvested grain via near-infrared spectroscopy. Genome-wide association analyses identified loci significantly associated with descriptive, agronomic, and seed traits, including orthologs of known genes in common bean and novel candidate regions. Genomic predictive abilities were moderate to high for key traits. Finally, we established a conditional core collection that was constrained to include 211 extensively phenotyped accessions and for which 91 supplemental accessions were selected to maximize genetic diversity from among the genotyped accessions. Overall, these resources provide a foundation to support genomics-assisted breeding of limas.

Glycosidic nitriles (GN) in barley are precursors to carcinogens formed during distillation, making GN reduction a critical breeding objective for malting and distilling industries. Measurement of GN is time-consuming. Grain must first be malted before GN can be quantified, and generally cannot be completed before selections must be made in a winter barley breeding program. Here, feasibility of same-season genomic selection against GN content was evaluated in elite Virginia Tech winter barley germplasm. In 2023, all 176 elite breeding lines screened for presence of GN were shown to be GN producers. A subset of 95 lines was then quantitatively measured for GN concentration to determine the genetic variability for the trait. Efficacy of genomic selection for GN was first assessed using a divergent selection approach on the remaining 81 predicted lines. The highest 16 and lowest 16 of the predicted lines were chosen for GN quantification. A significant phenotypic difference was found between the predicted high and low group means (0.8 ppm; P = 0.003). An additional 120 lines were quantified the following year to determine repeatability. GN exhibited moderate narrow-sense heritability (h2 = 0.42) and a high genetic correlation (r = 0.79) across years. Moderate predictive ability as was observed in cross-validation (range 0.38 - 0.61), and forward prediction using 2023 to predict 2024 (r = 0.39). A genome-wide scan did not identify any major-effect loci, suggesting GN content is polygenic, thus enabling same-season genomic selection to reduce GN content in this germplasm.

Haplotype-based breeding strategies have emerged as promising approaches to maximize long-term genetic gain by identifying complementary parental combinations while maintaining genetic diversity. However, these methods typically require phased genotypes and more intensive workflow pipelines and skillsets. We developed a novel local genomic estimated breeding value (localGEBV) fitness function with similar intent to the optimal haplotype stacking (OHS) framework fitness function and implemented both in the novel R package, HapSelect. Our aim was to evaluate whether phased haplotypes provide additional benefit over the more easily available dosage-based unphased genotypes in highly inbred crops. A subset of bread wheat nested association mapping (NAM) population comprising 444 lines genotyped with 6,054 DArT-Seq markers was analysed. Marker effects were estimated using rrBLUP, localGEBV and haplotype effects were calculated across linkage disequilibrium-defined haploblocks, and genetic algorithms (GA) were used to identify optimal sets of 30 founders using either a localGEBV derived fitness function with unphased, dosage inputs or the OHS fitness function with phased inputs. Selected parental sets were compared with conventional truncation selection (TS) through 150 generations of forward simulation. The OHS fitness function achieved a marginally greater optimized ultimate GEBV than the localGEBV fitness function during GA optimization, with only 18 of the 30 selected founders overlapped between the two methods. Despite these differences, forward simulations demonstrated nearly identical long-term genetic gain for localGEBV and OHS-selected founders, with both approaches outperforming conventional truncation selection by maintaining greater genetic diversity and delaying the genetic plateau. The minimal difference between localGEBV and OHS is likely attributable to the high homozygosity of the population, where localGEBV and haplotype effects are nearly confounded. These results demonstrate that dosage-based localGEBV provides a practical alternative to phased haplotype approaches for parent selection in inbred crops, substantially simplifying genomic workflows while maintaining long-term breeding performance. Future work should evaluate these methods in more diverse inbred populations and outbred species, where great haplotypic diversity may increase the advantage of true haplotype-based optimizations.

Far-red light (FR; 700-750 nm) is increasingly incorporated into controlled-environment lighting because it can improve photosynthetic efficiency when combined with comparatively shorter wavelengths. In long-day leafy crops such as spinach, however, FR may also promote the transition from vegetative to reproductive growth and thereby reduce marketable yield. Most studies have evaluated FR fraction, intensity or end-of-day exposure, whereas the developmental timing of FR has rarely been tested, particularly in spinach. Here, we evaluated six commercial spinach cultivars (Amador, Harp, Renegade, Responder, Rubino and Santa Cruz) in an indoor vertical farm under a common red-green-blue background (PPFD 260-264 {micro}mol m-{superscript 2} s-{superscript 1}, 12 h photoperiod, 24 {degrees}C) and four FR timing treatments: no FR (Control), FR throughout production (FullFR), FR during early development only (EarlyFR), and FR during late development only (LateFR). LateFR increased marketable fresh weight relative to Control (244 vs 224 g) and reduced flowering incidence, whereas far-red supplied during early development reduced fresh weight (158 g) and increased flowering. The magnitude of the timing response differed among cultivars: switching from EarlyFR to LateFR recovered 0 % fresh weight in Amador but 107 % in Renegade and Rubino, with the largest penalties occurring in otherwise bolt-resistant cultivars. EarlyFR also increased total chlorophyll and reduced the chlorophyll a:b ratio. These results show that FR response in spinach is strongly conditioned by developmental stage and cultivar. Although LateFR received more total far-red than EarlyFR, it behaved like the Control, indicating that the penalty was set by far-red timing rather than dose. Treatment differences in bolting and yield tracked an estimated phytochrome photostationary-state deficit during early development: a phytochrome-deficit model markedly outperformed a cumulative-dose model ({Delta}AIC = 441), and the deficit x cultivar interaction was strong (p < 0.001), with bolt-resistant cultivars losing most yield when far-red coincided with the early developmental window. We therefore propose that FR should be treated as a genotype-dependent management variable rather than as a fixed spectral input, with late application and bolt-resistant cultivars offering the most favourable combination for vertical-farm spinach production. Framed within the breeders equation, the close match between the trial and production environment and the scope for shorter breeding cycles indoors suggest that genotype and far-red timing can be optimised jointly to accelerate genetic gain.

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.

Structured AbstractO_ST_ABSContext and ObjectiveC_ST_ABSDelayed sowing can expose chickpea crops to stress during the critical period for yield determination, but the effect of yield components and phenology to grain yield variation is not well characterised in diverse germplasm under rainfed conditions. Identifying genetic resources for grain yield improvement requires integration of multi-environment trial and genomic analyses to disentangle direct yield effects from indirect effects of phenology. This study aimed to (1) characterise genotype by environment interaction patterns for grain yield, yield components and phenology across times of sowing and seasons, and (2) identify genomic regions associated with improved grain yield that are present in genebank accessions but absent from current Australian commercial cultivars. MethodsA diversity panel of 141 kabuli chickpea genotypes, including six commercial Australian cultivars and 135 genebank accessions, was evaluated across six rainfed trials at Narrabri, New South Wales over three seasons (2018 to 2020) under typical (MAIN) and delayed (LATE) sowing. Multi-environment trial analyses with factor analytic models partitioned genotype by environment interactions for grain yield, 100-seed weight, seed number, and thermal time to flowering, podding, and maturity. Haplotype block analysis identified high variance blocks associated with seed number, classified by their overlap with high variance thermal time to flowering blocks, to distinguish from effects mediated by phenology. Results and ConclusionsDelayed sowing reduced grain yield by up to 1.04 t ha-{superscript 1}, driven primarily by reductions in seed number rather than 100-seed weight. Accelerated phenology was a key component of adaptation among commercial cultivars. Four haploblocks with high block variance for seed number were identified across all six trials. SignificanceSeed number was the dominant driver of grain yield variation in this diverse kabuli chickpea panel. Targeted introgression of rare superior haplotypes from genebank accessions provides an opportunity to broaden the genetic base of Australian kabuli chickpea and improve yield through higher seed number, with relevance to chickpea production systems facing similar climate variability. HighlightsDelayed sowing reduced grain yield in diverse kabuli chickpea germplasm by up to 1.04 t ha-1 across three years and six trials in northern New South Wales. Seed number, not seed weight, was the dominant driver of grain yield variation, and a shorter phenological duration was associated with higher seed number. Across all six trials, haplotype block analysis identified four genomic regions in high linkage disequilibrium with high variance for seed number and low variance for flowering time. The accession FLIP 94 62C uniquely carried rare superior haplotypes at two chromosome 4 blocks, the haplotype at the 17.0 Mb block was the most superior haplotype in all trials while the haplotype at the 8.5Mb block was most superior only in the most heat stressed environment.

The number of pollen grains, which carry male gametes in seed plants, has attracted interest in genetics, evolution, and breeding. Rapid evolutionary reductions in pollen number and anther length were reported in selfing species as well as domesticated species, although this poses a challenge for hybrid breeding. Here, we studied the variation of pollen number and anther length of the hexaploid bread wheat (Triticum aestivum) by employing a quick pollen counting method. Pollen numbers in cultivars were lower than those in landraces among 54 lines of diverse geographic origins. Using the year of registration of traditional and modern cultivars, we found a reduction in pollen number over the past 150 years. We detected high heritability and variation among Asian landraces and cultivars. Thus, we conducted QTL mapping of pollen number as well as of anther length using nested association mapping lines in which Norin 61 was the common parent. Genomic loci encompassing Green Revolution genes (Rht-B1, Rht-D1, and Ppd-D1) showed significant effects on pollen number and anther length, but their contributions were relatively minor. Although anther length has often been used as a proxy for pollen number in bread wheat, our data showed that their correlations are not necessarily high. Interestingly, we identified a new QTL of pollen number that was not detected by measuring anther length, and, vice versa, a new QTL specific to anther length. These data suggest that pollen number has reduced rapidly in bread wheat but can be modified using the genetic diversity of landraces. Significance statementWe found that modern cultivars of bread wheat have reduced pollen number and shorter anther length, which are common in domesticated species but can be a challenge for hybrid breeding. Using underutilized Asian landraces and cultivars, we reported that new quantitative trait loci as well as loci used in the Green Revolution, are responsible for the traits, which can be employed to increase pollen numbers.

Stripe rust and leaf rust, caused by Puccinia striiformis f. sp. tritici and P. triticina, respectively, are the most destructive wheat diseases in the southern Great Plains. Green Hammer is a hard red winter wheat (HRWW) cultivar released by Oklahoma State University in 2018 and has demonstrated a stable adult plant resistance to stripe rust and race-specific seedling resistance to leaf rust. To identify and map rust resistance loci, 109 doubled haploid (DH) lines derived from the cross between Green Hammer and another HRWW cultivar, Lonerider, were developed. Lonerider showed adult plant resistance to stripe rust but was susceptible to multiple P. triticina races. The DH lines were evaluated for stripe rust at the adult plant stage in greenhouse and field environments across Oklahoma, Kansas, and Washington, and for leaf rust at the seedling stage against seven U.S. P. triticina races and at the adult plant stage in Oklahoma and Texas. Genotyping-by-sequencing generated 6,078 polymorphic single-nucleotide polymorphisms used for genetic mapping. Quantitative trait loci (QTL) analysis identified 14 stripe rust and 8 leaf rust resistance QTL. For stripe rust, a major QTL in Green Hammer, QYr.osughln-2AS, was identified in the proximity of the 2NvS translocation. Three other major stripe rust resistance QTL were identified in Lonerider on chromosomes 2AL (two QTL) and 2BS (one QTL). For leaf rust, QLr.osughln-1DS and QLr.osughln-2DS.1 were the two major QTL identified in Green Hammer and most likely correspond to the all-stage resistance genes Lr21 and Lr39, respectively. In this study, we identified previously characterized genes as well as unknown genes that can be utilized in wheat breeding programs to enhance resistance to leaf rust and stripe rust.

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.

Though currently a minor crop, faba bean is a promising source of plant-based protein as global diets shift towards more plant-based nutrition. To realise this potential, advances in breeding and cultivation are crucial. To exploit heterosis, faba bean breeding frequently utilises synthetic cultivars, which involves open pollination of inbred lines to produce a mixture of F1 hybrid seeds and self-pollinated offspring. Pure F1 hybrid cultivars are currently unavailable due to unstable cytoplasmic male sterility (CMS) systems. An ability to distinguish F1 seeds from their parental inbreds via characteristics associated with xenia effects could change this. The xenia effect refers to the influence of paternal pollen on seed traits, for example seed weight and cotyledon cells in faba bean. In this study, we exploited the xenia effect captured in hyperspectral imaging data to develop machine learning scenarios for discriminating between parental and F1 seeds of open pollinated synthetic combinations (Syn-1). The hyperspectral data were pre-processed using Savitzky-Golay filtering to reduce noise and smooth the spectra. Various machine learning algorithms were applied, incorporating Bayesian hyperparameter optimisation. The scenarios achieved up to 98.9 % accuracy in separating parental components of Syn-1. When including all seeds, the model achieved 40.7 %, indicating moderate detection and classification performance. As the harmonic mean of precision and recall, the F1 score accounts for both the correctness of F1 seed detections and the completeness with which F1 seeds were detected. While this approach does not yet enable the development of full hybrid cultivars, it paves the way for hybrid-enriched cultivars. These could help to streamline breeding for synthetic cultivars and potentially increase yields, for example by increasing the proportion of F1 hybrid seeds in synthetic cultivars. This study extends knowledge of the xenia effect in faba bean and provides a basis for further research aimed at enhancing breeding methods and productivity.

Testing for distinctness, uniformity, and stability (DUS) is a requirement for plant variety registration and based on phenotypic traits, which is time-consuming and sensitive to environmental variation. Advances in genomics allow to complement DUS testing with molecular markers, for which two models in DUS testing were proposed by the Union for the Protection of New Varieties of Plants (UPOV). A use cases was described for maize, but an implementation has been hindered by a lack of suitable markers and validated analytical frameworks. We address these challenges by integrating historical DUS characteristics scores from 352 European hybrid maize varieties with high-density genome-wide single nucleotide polymorphism (SNP) data. Using genome-wide association studies (GWAS), we identified 18 genomic regions and candidate genes associated with 12 DUS characteristics, enabling the development of diagnostic markers consistent with the UPOV model "Characteristic-Specific Molecular Markers". Since most DUS traits are polygenic, we combined GWAS-informed marker selection with XG-Boost-based machine learning to predict notes of DUS characteristics. This approach achieved strong predictive performance across multiple traits (mean accuracy 0.67), demonstrating its potential for managing reference collections under UPOV model "Combining phenotypic and molecular distances in the management of variety collections". Both approaches were validated for two characteristics using independent public USDA-NPGS maize datasets (>1,700 accessions) highlighting the value of public data for method validation. We also identify key limitations of historical DUS data, including imbalanced and sparse trait representation, and discuss mitigation strategies. Despite these constraints, our results demonstrate that molecular markers may improve maize DUS testing, enabling faster, more accurate variety registration and supporting accelerated crop improvement. Key messageHistorical DUS datasets can be used to identify marker-trait associations of DUS characteristics using genome-wide association study (GWAS) and to develop a genomic prediction framework for an accurate prediction of DUS character notes from marker data.

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.

Multi-trait genomic prediction offers a practical route to improve selection for costly, complex traits in clonally propagated crops such as cassava. In a Brazilian breeding panel of 1,078 cassava clones genotyped with 25,923 SNPs and phenotyped for six agronomic traits, we compared single-trait (ST) and multi-trait (MT) GBLUP models. Stage-wise mixed models produced BLUEs that fed into ST and MT-GBLUP. We tested five cross-validation schemes that mimic breeder realities: ST baseline (CV1); naive all-traits MT prediction for unphenotyped candidates (CV2); MT prediction using auxiliary trait phenotypes in the test set (CV3); and two sparse-phenotyping regimes with missingness by trait (CV4) or by clone (CV5) at 25%, 50%, and 75% levels. The main results were that, under the ST baseline (CV1), predictive ability ranged from 0.50 for DMC and 0.45 for FRY down to 0.13 for Le.Dis. A naive full MT model (CV2) performed approximately on par with ST-GBLUP. In contrast, MT designs (CV3) that included informative auxiliary traits, such as shoot yield and combinations with plant vigor and leaf disease severity, yielded small gains for DMC with predictive ability of approximately 0.51 (+2%), while FRY predictive ability increased to approximately 0.65 (+44%), accompanied by RMSE reductions for FRY up to approximately 13.5% (e.g. RMSE approximately 6.2). Sparse-phenotyping simulations (CV4/CV5) demonstrated that MT models sustain or even improve predictive ability under realistic missing-data regimes (PA {approx} 0.62 - 0.65). Selection concordance between MT and ST top-10% sets was generally high (>0.80), and MT configurations produced measurable improvements in expected selection response and genetic gain per cycle for several target traits. These results indicate that strategically implemented MT-GBLUP, using a small set of biologically and operationally informative auxiliary traits and optimized sparse phenotyping, can materially increase predictive accuracy and selection efciency for economically critical cassava traits while reducing phenotyping burden.

Increasing the diversity of biofuel crops can help meet energy demands while also stabilizing the domestic biofuel market. Sorghum bicolor is a promising feedstock for bioethanol production due to its sugar accumulation and storage in the stem in addition to its cellulosic biomass. Sorghum also exhibits high tolerance to abiotic stresses like extreme temperatures and drought. However, sorghums sugar production falls short when compared to current bioethanol feedstocks like maize and sugarcane. Therefore, to improve sorghum for the bioethanol market, an autotetraploid sorghum line was induced using colchicine treatments to increase cell size for greater sugar production and storage. Induced autotetraploid sorghum lines were validated with flow cytometry and screened using stomatal prints to detect larger stomatal cells. Two separate autotetraploid sorghum lines that were derived from the same M1 plant were characterized and evaluated for sugar production in a two-year field trial. The two autotetraploid lines displayed equal or improved performance when compared to their diploid equivalents for multiple juicing traits. Altogether, the data illustrate sorghums tolerance for autopolyploidy induction in an inbred background and suggest an opportunity for further improvements through progressive heterosis. SIGNIFICANCE STATEMENTPolyploidy has played a significant role in the improvement of some crop species. The characterization of a novel autotetraploid sweet sorghum line demonstrates the potential of increased sugar production in polyploids for biofuel applications.

Creeping bentgrass (Agrostis stolonifera) is a turfgrass species established on golf course surfaces but is criticized for high irrigation requirements. While genetic variation for water deficit stress tolerance exists between cultivars, the lack of defined critical soil water potential thresholds (Soil {Psi}crit) for this species complicates precise irrigation strategies and benchmarks for plant breeding. This study utilized a polyethylene glycol (PEG) infused agar-based system to simulate water potential reductions and determine the water potential threshold ({Psi}crit) for seedling root elongation. Creeping bentgrass cv Pure distinction seedlings were subjected to six water potentials ({Psi}) ranging from -0.36 MPa (no PEG applied) to -1.72 MPa. Daily digital imaging was used to measure root elongation over 5 days. Results across two experiments demonstrated that creeping bentgrass seedlings are highly sensitive to mild reductions in {Psi}. A reduction to -0.61 MPa significantly decreased root length and growth rates by over 50% compared to the control. Regression models predicted that a {Psi}crit of approximately -0.45 MPa reduced daily root growth by 25%, while upwards {Psi} of -1.0 MPa resulted in a 75% reduction of seedlings root growth. Furthermore, seedlings exposed to the lowest water potentials were predicted to require an additional 30 to 46 days to achieve the same root length as control plants. These findings establish specific {Psi}crit benchmarks for water deficit stress tolerance using a PEG-based system to induce dehydration. These methods can be used in breeding programs, and will help develop more accurate experiments examining the mechanisms of water deficit stress tolerance.

Accurate verification of transgenic plant materials is essential for maintaining scientific integrity and ensuring experimental reproducibility. As the number and diversity of transgenic constructs continue to expand, there is a growing need for practical and scalable methods that enable routine confirmation of transgene presence and identity. Reliable detection systems are particularly important for laboratories handling large numbers of genetically modified lines or distributing materials across research groups. To address this need, we developed two complementary methods for efficient detection of commonly used transgenes. The first method, fDET, is a higher-throughput system capable of simultaneously detecting 15 transgenes and three endogenous genes in a single multiplex PCR reaction followed by capillary electrophoresis. This approach provides rapid, high-resolution detection suitable for high-volume or time-sensitive applications. The second method, DET, offers a more accessible workflow that detects 10 transgenes and one endogenous gene using four multiplex PCR reactions followed by agarose gel electrophoresis. Because DET requires only standard molecular biology equipment, it can be readily implemented in a wide range of laboratory environments without specialized instrumentation. Together, these methods provide flexible and practical solutions for verifying the genetic status of both transgenic and non-transgenic plant materials. By enabling efficient and comprehensive transgene detection, they support reproducible experimentation, facilitate quality control in plant research, and streamline the management and exchange of genetically modified lines. These approaches contribute to more reliable and transparent use of transgenic resources across the plant science community.