Genomic prediction (GP) can increase genetic gain by allowing for the selection of traits earlier in the breeding cycle. Spike morphology traits are of interest because of their relationship with grain yield. To better understand the prediction capabilities of agronomic traits and spike architecture traits, a population of 594 soft red winter wheat inbred lines was evaluated for traits including heading date, grain weight spike(-1), thousand kernel weight, and kernel spike(-1) and spike architecture traits spikelets spike(-1) (SPS), spike length (SL), and spike width (SW). Univariate GP models were utilized for each spike trait, and multivariate GP models were used to predict spike architecture traits using each combination of all seven traits for a total of 63 multivariate models per spike trait. Significantly higher cross-validation mean prediction accuracies (p(Z) < 0.05) were observed for 52 models for SPS, 0 models for SW, and 13 models for SL compared with the univariate models of each trait. Methods in this study could be used for GP of spike architecture traits with further analysis of forward validation accuracy.

Spatial variation is a major source of error in agricultural field experiments affecting genotype performance prediction. Implementing statistical models that account for spatial effects can improve the prediction of genotype performance. This study evaluated the impact of the P-spline spatial correction method on the estimation of genetic parameters and AIC values in two distinct crops, wheat and cassava, using four models: Block, Block + Spatial, Block + Marker, and Block + Marker + Spatial. Analyses were performed on data from 115 and 68 trials obtained from the T3/WheatCAP and Cassavabase databases, respectively. As assessed using Cullis heritability estimates and AIC values, the results demonstrated that correcting for spatial variation improved analyses of grain yield, test weight, plant height, powdery mildew, stripe rust, and bacterial streak disease in wheat. Similar improvements were observed in cassava for dry matter content, dry yield, and plant height. However, no improvement was observed for cassava mosaic disease or bacterial blight. These results were consistent whether or not marker effects were fitted in the models. This study demonstrates that incorporating spatial correction into statistical analyses substantially improves the precision of variety evaluation. By accounting for field heterogeneity, spatial modeling complements experimental design and enhances the accuracy of treatment comparisons. Therefore, integrating robust experimental designs with appropriate spatial analyses is essential for achieving optimal precision and reliability in field trial evaluations.

The production of soft red winter wheat (SRWW) (Triticum aestivum L.) in the US southeast (SE) region is important. However, wheat production faces many challenges including many stresses resulting in substantial losses in yield and quality. To address these challenges, developing new cultivars with high yield potential with resistance to major pests in the region and good quality is warranted. The SRWW breeding programs ate the University of Georgia (UGA) and the regional institutions including the Southern Universities GRAINS (SUNGRAINS) programs aims to solve these problems. The release of 'GA071518-16E39' (Reg. no. CV-1210, PI 698826) SRWW in 2019, is among many adapted cultivars developed and released by the UGA College of Agricultural and Environmental Sciences. GA071518-16E39 has broad adaptation to the US SE region, but specifically well fit to the Georgia environments. It is a high yielding cultivar with excellent resistance to most dominant diseases including leaf (caused by Puccinia triticina Erikss.) and stripe (caused by P. striiformis Westend.) rusts, Soil-borne wheat mosaic virus, and Hessian fly insect [Mayetiola destructor (Say)] including major prevalent biotypes (B, C, O, and L) in the region. GA071518-16E39 is moderately resistant to powdery mildew (caused by Erisyphe graminis) and moderate susceptible to Fusarium head blight (caused by Fusarium graminearum Schwabe) which is reflected in relatively lower levels of disease severity and Deoxynivalenol toxin. GA071518-16E39 has excellent grain volume weight and milling and baking quality as a SRWW.

Soft red winter wheat (Triticum aestivum L.; SRWW) is a major crop in the US southeast (SE) region. However, growing successful wheat crop is challenged by many stresses resulting in substantial losses in yield and quality. To alleviate these challenges, developing new cultivars with high yield potential with resistance to major pests in the region and good quality is warranted. This constitutes the major goal of the SRWW breeding programs ate the University of Georgia (UGA) and the regional institutions including the southern universities GRAINS (SUNGRAINS) programs. 'GA09436-16LE12' (Reg. no. CV-1209, PI 700011) SRWW cultivar was among the adapted wheat developed and released by the UGA College of Agricultural and Environmental Sciences in 2019. While GA09436-16LE12 is generally adapted to the US SE region, it specifically well fit to the Georgia environments. It has high yield, very good resistance to most dominant diseases including leaf (caused by Puccinia triticina Erikss.) and stripe (caused by P. striiformis Westend.) rusts; powdery mildew (caused by Erisyphe graminis); and Soil-borne wheat mosaic virus. GA09436-16LE12 has improved Fusarium head blight (caused by Fusarium graminearum Schwabe) which is reflected in lower levels of Deoxynivalenol toxin and Fusarium damaged kernels levels. It also showed moderate field resistance to Hessian fly [Mayetiola destructor (Say)] although it is susceptible to the biotypes B, C, O, and L. GA09436-16LE12 has good grain volume weight and good milling and baking quality as a SRWW.

‘TX17D2337’ (Reg. no. CV-1220, PI 706602) is a soft red winter wheat (SRWW; Triticum aestivum ) released in 2022 by Texas A&M AgriLife Research. This cultivar was developed from the cross of an experimental Louisiana line, LA04041D-63, and an experimental North Carolina line, NC09-22206, in 2012 made by the Louisiana State University small grains breeding program. TX17D2337 is medium maturity, awned, and white-glumed with average height and semi-erect early growth. It was released based on its above-average grain yield and grain volume weight, and good resistance to leaf and stripe rust. This cultivar is widely adapted to the SRWW growing regions of Texas and the wider region of the Gulf Atlantic including Louisiana, Mississippi, Alabama, Florida, Georgia, South Carolina, and North Carolina. Breeder, foundation, registered, and certified seed is authorized for this cultivar in the United States. TX17D2337 will be submitted for US Plant Variety Protection with a certification option.

AbstractWater absorption capacity (WAC) influences various aspects of bread making, such as loaf volume, bread yield, and shelf life. Despite its importance in the baking process and end‐product quality, its genetic determinants are less explored. To address this limitation, a genome‐wide association study was conducted on 337 hard wheat (Triticum aestivum L.) genotypes evaluated over 5 years in multi‐environmental trials. Phenotyping was done using the solvent retention capacity (SRC) test with water (SRC‐water), sucrose (SRC‐sucrose), lactic acid (SRC‐lactic acid), and sodium carbonate (SRC‐carbonate) as solvents. Individuals were genotyped using genotyping‐by‐sequencing to detect single nucleotide polymorphisms across the wheat genome. To detect the genomic regions that underline the SRCs and gluten performance index (GPI), a genome‐wide association study was performed using six multi‐locus models using the mrMLM package in R. Adjusted means for SRC‐water ranged from 54.1% to 66.5%, while SRC‐carbonate exhibited a narrow range from 84.9% to 93.9%. Moderate to high genomic heritability values were observed for SRCs and GPI, ranging from h2 = 0.61 to 0.88. The genome‐wide association study identified a total of 42 quantitative trait nucleotides (QTNs), of which five explained over 10% of the phenotypic variation (R2 ≥ 10%). Most of the QTNs were detected on chromosomes 1A, 1B, 3B, and 5B. Few QTNs, such as S1A_5190318, S1B_3282665, S4D_472908721, and S7A_37433960, were located near gliadin, glutenin starch synthesis, and galactosyltransferase genes. Overall, these results show WAC to be under polygenic genetic control, with genes involved in the synthesis of key flour components influencing overall water absorption.

The water absorption capacity (WAC) of hard wheat (Triticum aestivum L.) flour affects end-use quality characteristics, including loaf volume, bread yield, and shelf life. However, improving WAC through phenotypic selection is challenging. Phenotyping for WAC is time consuming and, as such, is often limited to evaluation in the latter stages of the breeding process, resulting in the retention of suboptimal lines longer than desired. This study investigates the potential of univariate and multivariate genomic predictions as an alternative to phenotypic selection for improving WAC. A total of 497 hard winter wheat genotypes were evaluated in multi-environment advanced yield and elite trials over 8 years (2014-2021). Phenotyping for WAC was done via the solvent retention capacity (SRC) using water as a solvent (SRC-W). Traits that exhibited a significant correlation (r >= 0.3) with SRC-W and were evaluated earlier than SRC-W were included in the multivariate genomic prediction models. Kernel hardness and diameter were obtained using the single kernel characterization system (SKCS), and break flour yield and total flour yield (T-Flour) were included. Cross-validation showed the mean univariate genomic prediction accuracy of SRC to be r = 0.69 +/- 0.005, while bivariate and multivariate models showed an improved prediction accuracy of r = 0.82 +/- 0.003. Forward validation showed a prediction accuracy up to r = 0.81 for a multivariate model that included SRC-W + All traits (SRC-W, Diameter, SKCS hardness and diameter, F-Flour, and T-Flour). These results suggest that incorporating correlated traits into genomic prediction models can improve early-generation prediction accuracy. Genomic prediction can be used to improve end-use quality traits like water absorption capacity in hard winter wheat. Correlated traits enhance prediction accuracy when included in a multivariate prediction model. Multivariate models excel in predicting water absorption capacity compared to univariate models.

BACKGROUNDThe wheat stem sawfly (WSS, Cephus cinctus) is a major pest of wheat (Triticum aestivum) and can cause significant yield losses. WSS damage results from stem boring and/or cutting, leading to the lodging of wheat plants. Although solid-stem wheat genotypes can effectively reduce larval survival, they may have lower yields than hollow-stem genotypes and show inconsistent solidness expression. Because of limited resistance sources to WSS, evaluating diverse wheat germplasm for novel resistance genes is crucial. We evaluated 91 accessions across five wild wheat species (Triticum monococcum, T. urartu, T. turgidum, T. timopheevii, and Aegilops tauschii) and common wheat cultivars (T. aestivum) for antixenosis (host selection) and antibiosis (host suitability) to WSS. Host selection was measured as the number of eggs after adult oviposition, and host suitability was determined by examining the presence or absence of larval infestation within the stem. The plants were grown in the greenhouse and brought to the field for WSS infestation. In addition, a phylogenetic analysis was performed to determine the relationship between the WSS traits and phylogenetic clustering.RESULTSOverall, Ae. tauschii, T. turgidum and T. urartu had lower egg counts and larval infestation than T. monococcum, and T. timopheevii. T. monococcum, T. timopheevii, T. turgidum, and T. urartu had lower larval weights compared with T. aestivum.CONCLUSIONThis study shows that wild relatives of wheat could be a valuable source of alleles for enhancing resistance to WSS and identifies specific germplasm resources that may be useful for breeding. (c) 2024 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. The study explored wild wheat relatives for enhanced resistance to wheat stem sawfly (WSS). Novel findings highlight germplasm potential for breeding. Antixenosis and antibiosis responses to WSS were assessed across 91 accessions, revealing Aegilops tauschii, Triticum turgidum, and T. urartu as potential sources of resistance. image

Tremendous progress has been made in variety development and host plant resistance to mitigate the impact of Fusarium head blight (FHB) since the disease manifested in the southeastern United States in the early 2000s. Much of this improvement was made possible through the establishment of and recurring support from the US Wheat & Barley Scab Initiative (USWBSI). Since its inception in 1997, the USWBSI has enabled land-grant institutions to make advances in reducing the annual threat of devastating FHB epidemics. A coordinated field phenotyping effort for annual germplasm screening has become a staple tool for selection in public and private soft red winter wheat (SRWW) breeding programmes. Dedicated efforts of many SRWW breeders to identify and utilize resistance genes from both native and exotic sources provided a strong foundation for improvement. In recent years, implementation of genomics-enabled breeding has further accelerated genetic gains in FHB resistance. This article reflects on the improvement of FHB resistance in southern SRWW and contextualizes the monumental progress made by collaborative, persistent, and good old-fashioned cultivar development.

In plant breeding, selecting cross-combinations that are more likely to result in superior lines for cultivar development is critical. This step, however, is subjective with decisions being based on available genomic and phenotypic data for prospective parents. Genomic prediction (GP) provides new opportunities to accelerate genetic gain for a target trait by identifying superior crosses through simulation of progeny performance. In this context, this study deployed GP using the phenotype and genotype of potential parents to predict the progeny genetic variance (VG) and means of overall, inferior 10%, and superior 10% (mu, mu ip, and mu sp, respectively). This retrospective experimental design investigated whether the crosses that produced superior soft red winter wheat breeding lines would have been made if progeny simulations had guided crossing decisions of breeding programs. Here, data from historical wheat breeding lines were used to train GP models and predict VG and means for yield, test weight, heading date, and plant height for all combinations of 217 parents. Predicted and observed data for 670 lines derived from biparental crosses were compared to assess the accuracy of progeny simulations, and low-to-moderate prediction accuracy was observed for the four traits (0.25-0.52). Of the pedigrees that produced lines that were selected and advanced into later stage nurseries, 76% were predicted to give rise to progeny with above-average yield. The moderate correlation found between predicted progeny means and observed line per se performance justifies using cross-combination prediction as a tool to reduce crossing number and focus on segregating populations that harbor future cultivars. In plant breeding, selecting parents to be crossed is critical for developing superior progeny. Historical winter wheat data were used to assess the usefulness of genomic prediction for parental selection. Predicted yield and SunGrains breeders' assessment and selection largely agreed. Simulated progeny performance could allow breeders to focus on the most promising crosses.

'FL16045-25' (Reg. no. CV-1207, PI 704484), a soft red, facultative doubled-haploid wheat (Triticum aestivum L.) cultivar, was developed and tested as FL16045DH-25 by the University of Florida and released in October 2022. FL16045-25 was derived from the cross MD07W478-14-5/GA06112-13EE16. It is well adapted from Texas to Virginia and provides producers with an early-season, facultative (Vrn-A1_short), medium-height, awned, semi-dwarf (Rht2) cultivar that has high yield potential, good straw strength, good grain volume weight, and good end-use quality. It expresses moderate-to-high levels of resistance to most diseases prevalent in the southern United States. Molecular marker analysis confirms the presence of Sbm1, Yr17/Lr37/Sr38, Lr18, Sr36/Pm6, Pm54, and Pm1a-linked disease-resistant genes. The yield average of FL16045-25 from 41 environments during 2020-2022 ranged from 4211 to 5782 kg ha-1, which is competitive with check cultivars that are widely used in the southern part of the United States. The grain volume weight of FL16045-25 ranged from 749 to 785 kg m-3 (32 environments), which was higher than most of the checks. FL16045-25 has soft grain texture with softness equivalence varying from 51.3% to 59.3% and sodium carbonate solvent retention capacity (SRC) ranging from 66.8% to 68.5%. Flour yields on a Quadrumat Senior milling system varied from 68.7% to 69.5%. Flour protein content varied from 8.9% to 9.1%. Cookie spread diameter varied from 19.4 to 19.5 cm. The presence of TaSus2-2B, Sucrose Synthase2 gene on 2B or 2G:2B, was confirmed by marker analysis. FL16045-25 is an early-maturing, facultative, medium-height wheat cultivar broadly adapted to the southern United States. FL16045-25 has high yield potential and good straw strength, grain volume weight, and end-use quality. FL16045-25 demonstrated moderate-to-high levels of resistance to most diseases prevalent in the southern United States. Molecular marker analysis confirms the presence of Sbm1, Yr17/Lr37/Sr38, Lr18, Sr36/Pm6, Pm54, and Pm1a genes. It has above-average resistance to Fusarium head blight disease.

ContextGlobal nutritional health outcomes are directly reliant on agroecosystem nutrient outputs. Appropriately, there is concern surrounding the impacts of a changing climate not only on crop yields, but also on crop nutritional quality (e.g., mineral nutrient concentrations). Quantifying the impacts of elevated CO2 concentrations, elevated temperature, drought stress, edaphic factors, and agronomic management on crop yields and mineral nutrition is critical, yet a systems-level understanding of these interactive factors is poorly developed, limiting our ability to effectively target solutions. Empirical data for climate impacts on crop nutritional quality remain scarce, with much of the research emerging from valuable, but geographically limited, Free-air CO2 Enrichment (FACE) experiments, several of which suggest that human nutrition will be adversely impacted by e[CO2]. Specific concerns center on observed declines in grain protein, iron, and zinc concentrations due to already wide-spread human nutritional deficiencies in these nutrients.ObjectivesAs global change experiments expand to pursue questions regarding interactive climate impacts on crop yields and nutritional quality, it is imperative to interrogate the measurements, data standardization, and metadata needed for unifying synthesis. The data reported for shifts in crop nutritional quality are often incomplete, precluding the generalizability and comparability of results.MethodsWe frame this review around six inter-reliant methods, tools, and practices to support maximally useful experimental datasets to inform questions of global change impacts on crop nutrition and aid in detecting genotypic differences in mineral nutrient density. The bulk of the data and discussion centers on wheat (Triticum aestivum L.) due to the central role this crop plays in human nutrition and sustained biofortification efforts.ResultsTo permit experimental comparability and synthesis, datasets should (1) clearly delineate analytical methods and standards and (2) link mean nutrient concentrations with the covariate of yield. (3) Multi-year, multi-location data is required to identify genotypes with significant deviations in nutrient concentrations, with (4) data normalized for yield within appropriate analytical frameworks. (5) Inclusion of data on soil properties, weather, and abiotic and biotic stresses as well as (6) agronomic practices and nutrient management is essential for understanding global change impacts on nutritional outcomes.ConclusionsCoordinated, multi-dimensional data will permit the syntheses and meta-analyses needed to identify and quantify climate impacts on nutrition.ImplicationsThis work is essential to effectively target nutritional solutions, to develop modeling tools to support nutritional planning, and to identify areas where agronomic management and breeding can minimize climate impacts on nutritional outcomes.

'FL12034-10' (Reg. no. CV-389, PI 704483), a facultative oat (Avena sativa L.) cultivar, co-developed by the University of Florida and Louisiana State University Agricultural Center, was released in October 2022. FL12034-10 was derived from a three-way cross LA06055SBSBSB-79/FL11048 F1. It is well adapted across the southern United States and provides producers with a medium-tall, mid-season, awnless, white-glumed, dual-purpose oat that has high yield potential, good straw strength, and good forage yield. FL12034-10 was observed to be uniform and stable across environments in the southern United States from 2017 to present. The line possesses a semi-prostrate growth habit, vigorous growth, and high tillering capacity, and has large leaves that are dark green in color. It expresses moderate-to-high levels of resistance to most oat diseases prevalent in the southern United States. The crown and stem rust and Barley yellow dwarf virus ratings (0-9 scale) of FL12034-10 were 1.7, 0.7, and 1.5, respectively, across different environments. The disease ratings were better than most of the checks. The grain yield average of FL12034-10 from 41 environments during 2018-2021 was 6437 kg ha-1, which is competitive with check cultivars that are widely used in the southern part of the United States. The forage yield of FL12034-10 ranged from 2358 to 6617 kg ha-1 (20 environments), which was higher than most of the checks. FL12034-10 demonstrated better lodging and disease resistance, higher grain yield potential, and higher mid-winter to late spring season forage yield potential than Horizon 720 and Legend 567 oats released by University of Florida. FL12034-10 is well adapted across the southern United States. FL12034-10 is a medium-tall, mid-season, dual-purpose oat cultivar. FL12034-10 has high yield potential, good straw strength, and good forage yield. FL12034-10 is semi-prostrate in growth habit with vigorous growth and high tillering capacity. FL12034-10 expresses moderate to high levels of resistance to most oat diseases prevalent in the southern United States.

'FLLA09015-U1' (Reg. no. CV-387, PI 699117) is a new facultative oat (Avena sativa L.) cultivar that was co-developed by the University of Florida and Louisiana State University Agricultural Center and was released in 2019. This line was derived from a single cross of FL0210-J1/MN06203. FLLA09015-U1 has considerable potential for grain and forage yield and for conservation tillage purposes in the southern United States. Exclusive marketing rights for FLLA09015-U1 has been granted to JoMar Seeds and is currently commercialized under the name of Juggernaut. FLLA09015-U1 was developed using selected bulk breeding method and was selected as an F-5:6 head row. The line was evaluated in advanced, regional, and state grain and forage yield trials from 2015 to 2021. FLLA09015-U1 was observed to be uniform and stable across environments in the southern United States from 2015 to present. The line possesses a semi-prostrate growth habit and has large leaves that are dark green in color. It is a mid-maturing, medium to mid-tall height with excellent grain yield and good forage yield and test weight. It has excellent crown rust resistance and very good resistance to Barley yellow dwarf virus and stem rust and demonstrated moderate lodging resistance. It has performed very well in both grain and forage trials. FLLA09015-U1 has broad environmental adaptation and has performed well in Louisiana, Florida, Georgia, Texas, Alabama, and South Carolina. We consider FLLA09015-U1 to be a good dual-purpose type of oat because of its high grain yield potential and vigorous growth and high tillering capacity.

Univariate genomic selection (UVGS) is an important tool for increasing genetic gain and multivariate GS (MVGS), where correlated traits are included in genomic selection, which can improve genomic prediction accuracy. The objectives for this study were to evaluate MVGS approaches to improve prediction accuracy for four agronomic traits using a training population of 351 soft red winter wheat (Triticum aestivum L.) genotypes, evaluated over six site-years in Arkansas from 2014 to 2017. Genotypes were phenotyped for grain yield, heading date, plant height, and test weight in both the training and test populations. In cross-validations, various combinations of traits in MVGS models significantly improved prediction accuracy for test weight in comparison to a UVGS model. Marginal increases in predictive accuracy were also observed for grain yield, plant height, and heading date. Multivariate models which were identified as superior to the univariate case in cross-validations were forward validated by predicting the advanced breeding nurseries of 2018 and 2020. In forward validation, consistent increases in accuracy were observed for test weight, plant height, and heading date using MVGS instead of UVGS. Overall, MVGS models improved prediction accuracies when correlated traits were included with the predicted response. The methods outlined in this study may be used to achieve higher prediction accuracies in unbalanced datasets over multiple environments.

Abstract Wheat (Triticum aestivum L.) is crucial to global food security but is often threatened by diseases, pests, and environmental stresses. Wheat‐stem sawfly (Cephus cinctus Norton) poses a major threat to food security in the United States, and solid‐stem varieties, which carry the stem‐solidness locus (Sst1), are the main source of genetic resistance against sawfly. Marker‐assisted selection uses molecular markers to identify lines possessing beneficial haplotypes, like that of the Sst1 locus. In this study, an R package titled “HaploCatcher” was developed to predict specific haplotypes of interest in genome‐wide genotyped lines. A training population of 1056 lines genotyped for the Sst1 locus, known to confer stem solidness, and genome‐wide markers was curated to make predictions of the Sst1 haplotypes for 292 lines from the Colorado State University wheat breeding program. Predicted Sst1 haplotypes were compared to marker‐derived haplotypes. Our results indicated that the training set was substantially predictive, with kappa scores of 0.83 for k‐nearest neighbors and 0.88 for random forest models. Forward validation on newly developed breeding lines demonstrated that a random forest model, trained on the total available training data, had comparable accuracy between forward and cross‐validation. Estimated group means of lines classified by haplotypes from PCR‐derived markers and predictive modeling did not significantly differ. The HaploCatcher package is freely available and may be utilized by breeding programs, using their own training populations, to predict haplotypes for whole‐genome sequenced early generation material.

Introduction:Polyphenol oxidases (PPO) are dual activity metalloenzymes that catalyse the production of quinones. In plants, PPO activity may contribute to biotic stress resistance and secondary metabolism but is undesirable for food producers because it causes the discolouration and changes in flavour profiles of products during post-harvest processing. In wheat (Triticum aestivum L.), PPO released from the aleurone layer of the grain during milling results in the discolouration of flour, dough, and end-use products, reducing their value. Loss-of-function mutations in the PPO1 and PPO2 paralogous genes on homoeologous group 2 chromosomes confer reduced PPO activity in the wheat grain. However, limited natural variation and the proximity of these genes complicates the selection of extremely low-PPO wheat varieties by recombination. The goal of the current study was to edit all copies of PPO1 and PPO2 to drive extreme reductions in PPO grain activity in elite wheat varieties. Results:A CRISPR/Cas9 construct with one single guide RNA (sgRNA) targeting a conserved copper binding domain was used to edit all seven PPO1 and PPO2 genes in the spring wheat cultivar 'Fielder'. Five of the seven edited T1 lines exhibited significant reductions in PPO activity, and T2 lines had PPO activity up to 86.7% lower than wild-type. The same construct was transformed into the elite winter wheat cultivars 'Guardian' and 'Steamboat', which have five PPO1 and PPO2 genes. In these varieties PPO activity was reduced by >90% in both T1 and T2 lines. In all three varieties, dough samples from edited lines exhibited reduced browning. Discussion:This study demonstrates that multi-target editing at late stages of variety development could complement selection for beneficial alleles in crop breeding programs by inducing novel variation in loci inaccessible to recombination.

Abstract Improvements in trait phenotyping are needed to increase the quantity and quality of data available for genetic improvement of crops. In this study, we used moderate throughput image analysis and machine learning as a pipeline for phenotyping a key wheat spike characteristic: spikelet number per spike. A population of 594 soft red winter wheat inbred lines was evaluated in the field for 2 years and images of wheat spikes were taken and used to train deep‐learning algorithms to predict spikelet number. A total of 12,717 images were used to train, test, and validate a basic regression convolutional neural network (CNN), a visual geometry group application regression model, VGG16, the ResNet152V2 model, and the EfficientNetV2L model. The EfficientNetV2L model was the most accurate, having the lowest mean absolute error, second lowest root mean square error, and highest coefficient of determination (mean absolute error [MAE] = 0.60, root mean square error [RMSE] = 0.79, and R2 = 0.90). The ResNet152V2 model was slightly less accurate with a slightly better fit (MAE = 0.61,m RMSE = 0.78, and R2 = 0.87), followed by the basic CNN (MAE = 0.75, RMSE = 1.00, and R2 = 0.74) and finally by the VGG16 (MAE = 1.51, RMSE = 1.29, and R2 = 0.076). With an average error of just above one half of a spikelet, utilizing image analysis and machine learning counting methods could be used for multiple breeding applications, including direct selection of spikelet number, to provide data to identify quantitative trait loci, or for training whole genome selection models.

'FLLA11019-8' (Reg. no. CV-386, PI 700040) is new facultative oat (Avena sativa L.) cultivar for the southern United States for forage, grain, cover, and wildlife food crop uses. It was co-developed by the University of Florida and Louisiana State University Agricultural Center and was released in 2020 under the SunGrains, a cooperative small grain breeding program among seven Southern Universities. This line was derived from a single cross between two advanced breeding lines, FL0564-Ab13 and LA06071SBSB-S1. Exclusive marketing rights were granted to Ragan & Massey, Inc., and the line is currently commercializing under the names of RAM Forage Oats and PlotSpike Forage Oats. The University of Florida is the lead institution in this release. FLLA11019-8 (originally named FLLA11-19S-8) was developed using the selected bulk breeding method and was selected as an F-5:6 head row. The line was evaluated in observation, preliminary, advanced, regional, and state grain and forage yield trials from 2016 to 2021. FLLA11019-8 was released based on the merits of its broad adaptation, excellent grain yield, volume weight, forage potential, and winter survival. It is resistant to crown rust and stem rust and moderately resistant to Barley yellow dwarf virus. It is a mid-maturing and mid-tall height variety. FLLA11019-8 has semi-prostrate plant type with vigorous early-season growth and high tillering capacity. It has performed very well in both grain and forage trials and is broadly adapted to the southern and southeastern United States.

Fusarium head blight (FHB) is an economically and environmentally concerning disease of wheat (Triticum aestivum L). A two-pronged approach of marker-assisted selection coupled with genomic selection has been suggested when breeding for FHB resistance. A historical dataset comprised of entries in the Southern Uniform Winter Wheat Scab Nursery (SUWWSN) from 2011 to 2021 was partitioned and used in genomic prediction. Two traits were curated from 2011 to 2021 in the SUWWSN: percent Fusarium damaged kernels (FDK) and deoxynivalenol (DON) content. Heritability was estimated for each trait-by-environment combination. A consistent set of check lines was drawn from each year in the SUWWSN, and k-means clustering was performed across environments to assign environments into clusters. Two clusters were identified as FDK and three for DON. Cross-validation on SUWWSN data from 2011 to 2019 indicated no outperforming training population in comparison to the combined dataset. Forward validation for FDK on the SUWWSN 2020 and 2021 data indicated a predictive accuracy r ≈ 0.58 $r \approx 0.58$ and r ≈ 0.53 $r \approx 0.53$ , respectively. Forward validation for DON indicated a predictive accuracy of r ≈ 0.57 $r \approx 0.57$ and r ≈ 0.45 $r \approx 0.45$ , respectively. Forward validation using environments in cluster one for FDK indicated a predictive accuracy of r ≈ 0.65 $r \approx 0.65$ and r ≈ 0.60 $r \approx 0.60$ , respectively. Forward validation using environments in cluster one for DON indicated a predictive accuracy of r ≈ 0.67 $r \approx 0.67$ and r ≈ 0.60 $r \approx 0.60$ , respectively. These results indicated that selecting environments based on check performance may produce higher forward prediction accuracies. This work may be used as a model for utilizing public resources for genomic prediction of FHB resistance traits across public wheat breeding programs.