The fungus Monosporascus cannonballus is a pathogen that causes a severe disease called vine decline, which is found in areas where melons and watermelons are cultivated worldwide. Nevertheless, few studies have examined the hormones involved in host-pathogen interactions. Thus, this study was conducted to identify and quantify these metabolites. The varieties USA PI 124,104 (resistant) and TAM-Uvalde (susceptible) were inoculated with the pathogen, and the hormones were quantified before inoculation (0 h) and 24, 48, and 72 h after inoculation employing ultra-performance liquid chromatography analysis. The hormone levels varied over time. Peculiarly, gibberellic acid and kinetin were detected 24 and 48 h after inoculation, which may be indicative of their synthesis by the fungus. Likewise, methyl jasmonate was detected 48 h after inoculation, a time at which the pathogen could also have produced it. Additionally, salicylic and jasmonic acids were detected, and their concentrations varied antagonistically over time. Interestingly, jasmonic acid could also have been synthesized by the fungus, suggesting a hemibiotrophic lifestyle. Hence, further studies are needed to identify the origins of kinetin, methyl jasmonate, gibberellic and jasmonic acids.

Abstract Soft red winter wheat (SRWW; Triticum aestivum L.) is a major crop grown in the U.S. Southeast Region (the Southeast) that contributes significantly to wheat growers and the industry. However, wheat is challenged by many stresses, resulting in substantial losses in yield and quality. Therefore, developing new cultivars with high yield potential, resistance to major fungal diseases and pests in the Southeast, and good quality is warranted. This is the main goal of the SRWW breeding programs at the University of Georgia (UGA) and the Southern UNiversities GRAINS programs. The ‘GA 09377‐16LE18’ (Reg. No. CV‐1255, PI 698828) SRWW cultivar is a well‐adapted wheat developed and released by the UGA College of Agricultural and Environmental Sciences in 2019 and licensed to Dyna‐Grow Company under the name “Rutledge.” GA 09377‐16LE18 is overall well adapted to the Southeast and specifically for conditions in Georgia. It is a high‐yielding cultivar with very good resistance to most dominant diseases, including leaf rust (caused by Puccinia triticina Erikss.), stripe rust (caused by P. striiformis Westend.), powdery mildew (caused by Erysiphe graminis ), and soil‐borne wheat mosaic virus. GA 09377‐16LE18 is moderately susceptible to Fusarium head blight (caused by Fusarium graminearum Schwabe) and possesses moderate field resistance to Hessian fly [ Mayetiola destructor (Say)], although it is susceptible to biotypes B, C, and L. Overall, as a SRWW, GA 09377‐16LE18 showed excellent quality attributes, including milling and baking quality.

Abstract Among the major crops grown in the U.S. Southeast Region, soft red winter wheat (SRWW; Triticum aestivum L.) contributes significantly to wheat growers and the industry. However, wheat is challenged by many stresses, resulting in substantial yield and quality losses. Therefore, developing new cultivars with high yield potential, resistance to major pests in the Southeast, and good quality is warranted. Consequently, this is the main goal of the SRWW breeding programs at the University of Georgia (UGA) and the Southeastern UNiversities GRAINS programs. The SRWW cultivar ‘GA 09129‐16E55’ (Reg. No. CV‐1226, PI 698827) is a well‐adapted wheat developed and released by the UGA College of Agricultural and Environmental Sciences in 2019. GA 09129‐16E55 is overall well adapted to the Southeast and, specifically, to conditions in Georgia. It is a high‐yielding cultivar with very good resistance to most dominant diseases, including leaf rust (caused by Puccinia triticina Erikss.), stripe rust (caused by P. striiformis Westend.), powdery mildew (caused by Blumeria graminis ), and soil‐borne wheat mosaic virus . The reaction of GA 09129‐16E55 to Fusarium head blight (caused by Fusarium graminearum Schwabe) is reflected in lower levels of disease severity and deoxynivalenol toxin. It also showed moderate field susceptibility to Hessian fly [ Mayetiola destructor (Say)]; under greenhouse conditions, it was resistant to B and C biotypes but susceptible to O and L biotypes. GA 09129‐16E55 has good grain volume weight and good milling and baking quality as an SRWW.

Background: Improving grain yield in wheat remains a top priority, requiring integrated breeding and genetic strategies. This complexity poses a major challenge, driven by quantitative polygenic inheritance, environmental influence, and intricate genetic interactions. We investigated genetic factors and their interactions for agronomic and yield traits in two high-yielding winter wheat cultivars adapted to the US Southern Great Plains. Methods: A bi-parental mapping population consisting of 221 F7 recombinant inbred lines (RIL) derived from ‘TAM 204’ and ‘Iba’ was evaluated for three years in 11 Texas environments. Both parents and RIL population were genotyped on Illumina NovaSeq 6000 and sequences were aligned to IWGSC RefSeq v1.0 using Bowtie2 for SNP calling. For QTL analyses, each trait was analyzed by individual environment, across multiple environments and mega-environments. Results: A total of 86 QTL were mapped for five traits and among them 32 were consistent in more than one environment or analysis. Among consistent QTL, four were pleiotropic to more than one agronomic or yield traits mapped on chromosomes 2B (57.18, 59.47 Mb) and 2D (29.34, 40.64 Mb). The consistent QTL on chromosome 2D (29.34 Mb) was pleiotropic to GYLD, DTH, TW, TKW and explained maximum phenotypic variation for all traits, representing photoperiod gene (Ppd-D1). Another QTL on chromosome 2D (40.64 Mb) was pleiotropic to GYLD and TW and based on the physical position comparisons it likely reflects a unique locus in Iba. The pleiotropic consistent QTL Qgyld.tamu.2B.59 from TAM 204 represents Ppd-B1 gene. Moreover, it is more likely that Qdth.tamu.5B.575 represents the Vrn-B1 gene in Iba. A total of 23 digenic epistatic interactions involved consistent QTL for all traits. Amongst these, epistatic interactions between the consistent QTL on 2B (57.18 Mb) and 2D (29.34 Mb) were observed for GYLD, DTH and TKW. Conclusions: Our findings revealed key allelic diversity and interaction effects in elite wheat cultivars, paving the way for marker development for identified pleiotropic loci and implementation in marker-assisted selection and recombination breeding.

Multi-temporal data from unoccupied aerial systems (UAS) offer insights into growth parameters for winter wheat breeding decisions. Weekly UAS data were collected during the 2019 and 2020 growing seasons from dryland and irrigated nurseries at Bushland, Texas, within the Texas A&M Uniform Variety Trials. Canopy cover (CC) was extracted from orthomosaic images and modeled using a double-sigmoid function with a second-order derivative that captured genotypic variation in canopy growth and senescence with high coefficients of determination (R² > 0.99) and low root mean square error (RMSE) values ranging from 1.73 to 4.06. Analysis of variance (ANOVA) revealed highly significant genotypic effects (p < 0.001) for yield, heading, and Green Leaf Area Duration (LAD) in all environments except 2020 dryland, where no significant differences among genotypes were detected. Extracted parameters showed positive correlations with agronomic traits, particularly under rainfed and stress-prone conditions. The end decrease stage (EDS) was correlated with grain yield (r = 0.56 in 2019 dryland, and r = 0.53 in 2020 irrigated, p < 0.001), and the start decrease stage (SDS) was highly correlated with yield (r = 0.53 in 2020 irrigated, p < 0.001). The maximum decrease rate date (MDRD) was positively correlated with yield in 2019 dryland (r = 0.55, p < 0.001), while LAD had a correlation of r = 0.56 in 2019 dryland and r = 0.58 in 2020 irrigated (p < 0.001). These findings demonstrate that the double-sigmoid model provides a powerful, non-invasive framework for quantifying canopy development, senescence timing, and stress responses. By distinguishing genetics from environmental influences on canopy dynamics, this approach enhances selection accuracy and accelerates the development of stress-resilient winter wheat cultivars.

Exposed soil, due to low vegetation cover or in open canopy crops, influences scene reflectance derived from remotely sensed data. An experiment was conducted in College Station, TX, to investigate the potential of six unmanned aerial systems (UASs)-derived and proximally sensed vegetation indices (VIs) in suppressing soil background brightness of four treatments in 2020 and 2021. The treatments were dry soil, dry soil with winter wheat ( Triticum aestivum L.) crop residue, wet soil (WS), and wet soil with winter wheat crop residue (CRWS) in 2020. In 2021, WS and CRWS were replaced with dry sand and dry compost (DC). The VIs were calculated from remotely sensed data of treatment plots. Cotton ( Gossypium hirsutum L.) canopy cover (%) on different dates of UAS flight was extracted using unsupervised classification. Factors such as shadows, crop residue, soil moisture, and uneven canopy growth influenced the scene reflectance. The shadow on the soil decreased the soil background reflectance to <10%. Soil background variations minimally impacted the UAS-derived VIs. Soil wetness resulted in higher normalized difference vegetation index (NDVI) than dry treatment plots at an estimated mean canopy cover > 30% in 2020. Similarly, higher NDVI was observed for DC treatment plots at an estimated mean canopy cover of <35% in 2021. The perpendicular vegetation index was least influenced by canopy cover or soil background variations. The study suggests that UAS can be used for large-scale research without being affected by soil variability when vegetation cover is above 30%.

Abstract Crown rust (CR), caused by Puccinia coronata f. sp. avenae, is a major constraint to oat (Avena sativa L.) production in the southern United States. We dissected the genetic architecture of CR resistance and assessed genomic prediction in 234 winter and facultative oat lines adapted to the southern United States. Using multi‐environment phenotyping across five CR‐prone sites and 8,234 high‐quality single‐nucleotide polymorphisms, we conducted genome‐wide association study (GWAS) and genomic prediction analyses. GWAS detected 13 significant loci, nine of which co‐localized with reported quantitative trait loci or contained plausible resistance candidates such as leucine‐rich repeat (LRR), nucleotide‐binding site‐LRR, and serine/threonine kinases. A stable locus on chromosome 3D was repeatedly identified in Baton Rouge, LA (2016); Castroville, TX (2016); Winnsboro, LA (2017); and in the combined analysis, indicating cross‐environment consistency. Additional loci were supported on chromosome 4C (Citra and Quincy, FL, in 2017, and the combined analysis) and 2A (Winnsboro, LA, in 2017 and the combined analysis). Allele stacking was associated with large phenotypic gains. Lines with five or more favorable alleles showed >56% lower crust and 63% lower Crust_Per than lines with none, supporting marker‐assisted pyramiding. Genomic prediction models achieved high prediction accuracy. Parametric models, particularly BayesA and RRBLUP, performed strongly across cross‐validation schemes; random forest was broadly comparable; and gradient boosting was lower. These results underscore the effectiveness of using GWAS and genomic prediction to enhance breeding for durable CR resistance in oat and provide a genomic framework for developing resilient cultivars suited to southern United States growing conditions.

Abstract Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language Summary Temporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.

'GoWheat 9216H' (Reg. no. CV-1224, PI 708101) is a hard red winter wheat (Triticum aestivum L.) that was bred and released by the Texas A&M AgriLife Research Wheat Improvement Program in 2021. GoWheat 9216H is an F4-derived line advanced from the cross 'X09A440S' ( = TX07A001482/TAM 401)/'Duster' that was made in Bushland, TX, in 2010. GoWheat 9216H is a medium-maturing, semi-dwarf, awned and white glumed wheat that has demonstrated high grain yield potential across many Texas environments in both irrigated and dryland conditions. GoWheat 9216H is resistant to stem rust (Sr; Puccinia graminis Pers.:Pers f. sp. tritici Erikss. & E. Henn.), leaf rust (Lr; P. triticina Erikss.), and stripe rust (Yr; P. striiformis Westend. f. sp. tritici Erikss.), with marker data suggesting it carries Lr34, Lr37, Lr68, Yr17, Yr18, YrM1225, and Sr38. It is moderately resistant to Hessian fly [Mayetiola destructor (Say)]. This cultivar shows good baking and milling quality traits equivalent to high-quality checks and further features large seeds and high grain volume weight. Its height is similar to that of recently released Texas A&M cultivars, but it has a later maturity date. GoWheat 9216H is poised to perform well under both irrigated and dryland conditions in the Texas Rolling Plains, South and Central Texas, and the Blacklands, as well as in other regions across the state with similar adaptation zones.

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.

Climate change poses an increasing threat to agricultural productivity in the Texas High Plains (THP), a semi-arid region facing both warming trends and declining groundwater resources. This study integrates process-based crop modeling with geospatial analysis to identify spatial zones of climate vulnerability and adaptive potential for four major crops; winter wheat, cotton, maize, and grain sorghum under future climate scenarios. Using the Decision Support System for Agrotechnology Transfer (DSSAT) model, historical (1991–2020) and future yields (2031–2060 and 2070–2099) were simulated across 48 counties under Representative Concentration Pathway 4.5 and 8.5 (RCP 4.5 and RCP 8.5). Spatial clustering techniques, including Global Moran’s I and Getis-Ord Gi* statistics, were applied to classify counties into vulnerable, adaptive, stable, and more stable zones based on projected yield changes. Results revealed that wheat vulnerability was concentrated in southern counties, with projected yield decreases of 10–30

Aromatic rice (Oryza sativa) cultivation is economically essential, but its successful production depends on genotype adaptability and stability across different environments. In Texas, where environmental conditions can vary substantially between regions, it is essential to develop aromatic rice varieties that deliver high yields and maintain stability in diverse growing conditions. This study aimed to assess the performance and adaptability of 120 aromatic rice genotypes across two distinct environments, Beaumont and Eagle Lake, and to identify superior genotypes for each location. The study was conducted at the Texas A&M AgriLife Research Center in Beaumont and Eagle Lake, TX, and was motivated by the need to develop rice varieties with improved yield and stability under diverse environmental conditions. We assessed diverse morphological and agronomic traits and used genotype main effect plus genotype-by-environment interaction (GGE) biplot analysis to elucidate genotype-environment interactions. Our results revealed significant variations in several characteristics, including days to heading, plant height, and grain yield, among genotypes and across locations. GGE biplot analysis allowed us to identify the best-performing genotypes for each environment, with G85 and G98 excelling in Beaumont and G73 and G90 performing well in Eagle Lake. Furthermore, the analysis provided insights into genotype stability, revealing G27 as a highly stable genotype with above-average grain yield. Cluster analysis categorized the genotypes into four distinct groups based on their overall trait performance. This study highlights the importance of multi-environment trials in aromatic rice breeding programs. It demonstrates the utility of GGE biplot and cluster analysis for identifying superior genotypes with high yield potential and adaptability to specific environments. The findings can be valuable for developing region-specific cultivars and enhancing rice production in diverse agro-ecological zones.

Background and Objectives The end-use quality attributes for wheat are important aspects of breeding and processing activities and are estimated based on rheological properties on gluten hydration and aggregation. GlutoPeak was used to profile gluten performance of commercial flour types and popular wheat cultivars grown in Texas as an alternative tool to Mixograph. GlutoPeak was also used for characterization of gluten strength of hard red winter wheat breeding lines from three locations over 2 years.Findings The results showed that GlutoPeak parameters are consistent and yet distinct for samples of variable gluten strength. GlutoPeak torque values also followed similar trends for different locations, growing years, and test methods. GlutoPeak time to maximum torque (PMT) negatively and significantly correlated with Mixo midline Peak width (r = -0.46) and positively correlated with Mixo midline peak time (r = 0.67) and mixing tolerance (r = 0.57). The GlutoPeak maximum torque value positively and significantly correlated with Mixo midline peak width (r = 0.44), midline peak height (r = 0.52), and Mixo water absorption (r = 0.48).Conclusions The GlutoPeak maximum torque (BEM) exhibited stronger correlation with other GlutoPeak parameters. There was a negative correlation between BEM and PMT (r = -0.58) and a strong positive correlation between BEM versus protein content, water absorption, and wet gluten content (all with r = 0.89). The observed data and trends from GlutoPeak in comparison and association with SCKS, NIR, and Mixo present a potential screening capability at both breeding and industrial end-use quality monitoring endeavors. GlutoPeak might be a useful tool for rapidly screening breeding lines. Further research might be needed to standardize test conditions for wheat samples of various classes and flour samples for specialized applications.Significance and Novelty GlutoPeak might be a useful tool for a new rapid end-use quality analysis for wheat breeding programs and quick quality monitoring applications at small- and large-scale milling and bakery industries. The profiles of different classes of wheat (hard, soft) and various commercial flour samples might guide protocol development and optimization.

Leaf rust is a major biotic factor affecting wheat yield globally. However, the visual scoring technique to assess fungal disease in breeding programs requires significant expert manual labor and time. Unmanned aerial systems have the potential to scan large acreage in a short time for disease screening. An experiment was conducted at College Station and Castroville, TX, in 2018–2019 and 2019–2020 to assess the performance of normalized difference vegetation index (NDVI), normalized difference red edge index (NDRE), and green chlorophyll index (GCI) in detecting leaf rust infection. Other measurements included proximal canopy temperature, grain yield, and visual screening for infection type and severity. A significant positive relationship ( p < 0.001; R 2 = 0.42–0.62) of grain yield with all three vegetation indices (VIs) was observed in mid‐April 2019 at College Station. At College Station, the highest leaf rust severity coincided with the senescence stage in mid‐April 2020. No relationship between the VIs and grain yield was observed. In mid‐April 2020, when the leaf rust infection was high, the VIs showed a significant negative relationship ( p < 0.05; R 2 = 0.27) with grain yield at Castroville. All three VIs showed a significant linear negative relationship with canopy temperature at College Station ( p < 0.05; R 2 = 0.3–0.34) and Castroville ( p < 0.001; R 2 = 0.52–0.54) in mid‐April 2020. At high leaf rust severity, the repeatability of GCI was less than NDVI and NDRE at both locations in 2019 and 2020. These results may differ if multiple factors affect winter wheat simultaneously.

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.

The Hessian fly, Mayetiola destructor (Say) (Diptera: Cecidomyiidae), is a major insect pest of wheat (Triticum aestivum L.) worldwide. Although parasitoids are frequently observed in Hessian fly puparia, their integration into integrated pest management (IPM) remains challenging. This study surveyed Hessian fly parasitoids and their parasitism rates in a wheat field in McLennan County, Texas, over two spring seasons (2023 and 2024). The Hessian fly puparia were collected monthly from March to June in a susceptible wheat cultivar 'TAM 205'. A high parasitism rate (above 80%) began in May, a month earlier than previously reported 30 years ago. In June 2023, 100% parasitism was recorded at harvest, and this may have contributed to a reduction in the subsequent Hessian fly infestation rates from 78% in 2023 to 36% in 2024. High-resolution macrophotography aided the identification of three parasitoid wasp species: Homoporus destructor (Say) (Hymenoptera: Pteromalidae), Brasema allynii (French) (Hymenoptera: Eupelmidae), and Trichomalopsis subapterus (Riley) (Hymenoptera: Pteromalidae). Homoporus destructor was the most abundant, comprising 67% of emerged parasitoids in 2023 and 100% in 2024. Trichomalopsis subapterus and B. allynii accounted for 23% and 10% of the 2023 parasitoid population. These findings offer valuable insights into the seasonal dynamics of Hessian fly parasitoids in Central Texas. The high parasitism rate observed highlights the need for broader studies across multiple locations and wheat varieties, as well as strategies to enhance the populations of parasitoids and strengthen the biological control efforts in the wheat IPM programs. Additionally, the high-resolution macrophotography images provided in this study will support future field observations by extension specialists and producers.

‘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.

Hessian fly (Mayetiola destructor) is a worldwide pest of wheat (Triticum spp.) causing significant yield losses. Utilizing resistant varieties of wheat is the most effective method of control, particularly in mild climates. The effectiveness of resistance genes is lost over time due to genetic diversity within fly populations, and most cultivars have only one resistance gene making them vulnerable to this loss of resistance. Thirty-seven resistance genes have been mapped along with four Hessian fly response genes and 11 novel quantitative trait loci (QTL). The majority of these exhibit antibiotic resistance, but new research suggests two or more novel QTL are associated with a valuable tolerant response. Studies suggest that fitness costs associated with resistance can be overcome after the initial infestation, but this may not hold true with repeated fly attacks and should be considered when pyramiding genes. Several of these genes do show temperature sensitivity, which should also be considered during breeding. Resistance mechanisms include increased levels of salicylic acid, upregulation of oxo-phytodienoic acid reductase genes and genes responsible for lipid and protein mobilization, and increased synthesis of polyphenols, among others. These mechanisms are part of an effector-triggered response. Resistant genes and QTL with KASP markers for use in creating gene pyramids include QHf.hwwg-6BS, Qhara.icd-6B, h4, H7, H32, H33, H34, H35, and H36. GWAS has been used to screen large numbers of elite breeding lines for resistance to Hessian fly. Marker trait associations discovered through GWAS can potentially be used in creating gene pyramids with multiple pest and pathogen resistance genes.

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.

Melon is an important crop worldwide. However, this crop has many problems, such as high production costs, pests, diseases, and sensitivity to abiotic stresses. Among the diseases that negatively impact its production, vine decline disease (VDD), caused by the fungus Monosporascus cannonballus, is very dangerous because it affects the plant when the fruit is almost ready to be harvested, which causes huge losses to growers. In addition, this disease is widely spread in areas where melons are grown all over the world, and its control through soil fumigation is costly and polluting. Thus, cheap and sustainable ways to combat it must be found. Therefore, this study was conducted with the objectives of identifying and quantifying phenolic compounds produced during the interaction of melon plants with the fungus M. cannonballus, which can be useful in controlling VDD, ameliorating cultural practices of this crop, and consequently reducing production costs. Two varieties were grown and inoculated with the fungus M. cannonballus: TAM-Uvalde (susceptible) and USDA PI 124104 (resistant). Their roots were sampled before inoculating with the fungus (0 h) and 24, 48, and 72 h after inoculation. The root chemicals were then analyzed using high-performance liquid chromatography. Our results indicate that phthalic acid was induced in the roots after inoculation in both varieties, which suggests that it is produced by the plant as a defensive compound. Also, the production of phthalic acid varied over time according to the variety used, which suggests its allelopathic function. [Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .