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

Double haploid (DH) population development is widely used in many crops, including wheat ( Triticum aestivum L.), to rapidly produce fixed germplasm for breeding and genetic studies. The genome shock that takes place during DH induction could induce chromosomal aberrations that can impact genome integrity and subsequently plant fitness and agronomic performance. To evaluate the extent of chromosomal aberrations that exist as a result of the DH process, we studied two wheat DH populations: CDC Stanley×CDC Landmark and KS13H9×SYMonument. We utilized high‐throughput skim sequencing to construct digital karyotypes of these populations to quantify deletions and aneuploidy with high resolution and accuracy, which was confirmed in selected plants by cytological analysis. The two populations studied showed high proportion of abnormal primary DH lines, 55 and 45%, respectively, based on at least one abnormality per progeny. The chromosomal abnormalities are genetically unstable and were observed segregating in the subsequent generations. These observations have important implications for the use of DH lines in genetics and breeding.

The development of next-generation sequencing (NGS) enabled a shift from array-based genotyping to directly sequencing genomic libraries for high-throughput genotyping. Even though whole-genome sequencing was initially too costly for routine analysis in large populations such as breeding or genetic studies, continued advancements in genome sequencing and bioinformatics have provided the opportunity to capitalize on whole-genome information. As new sequencing platforms can routinely provide high-quality sequencing data for sufficient genome coverage to genotype various breeding populations, a limitation comes in the time and cost of library construction when multiplexing a large number of samples. Here we describe a high-throughput whole-genome skim-sequencing (skim-seq) approach that can be utilized for a broad range of genotyping and genomic characterization. Using optimized low-volume Illumina Nextera chemistry, we developed a skim-seq method and combined up to 960 samples in one multiplex library using dual index barcoding. With the dual-index barcoding, the number of samples for multiplexing can be adjusted depending on the amount of data required, and could be extended to 3,072 samples or more. Panels of doubled haploid wheat lines ( Triticum aestivum , CDC Stanley x CDC Landmark), wheat-barley ( T . aestivum x Hordeum vulgare ) and wheat-wheatgrass ( Triticum durum x Thinopyrum intermedium ) introgression lines as well as known monosomic wheat stocks were genotyped using the skim-seq approach. Bioinformatics pipelines were developed for various applications where sequencing coverage ranged from 1 × down to 0.01 × per sample. Using reference genomes, we detected chromosome dosage, identified aneuploidy, and karyotyped introgression lines from the skim-seq data. Leveraging the recent advancements in genome sequencing, skim-seq provides an effective and low-cost tool for routine genotyping and genetic analysis, which can track and identify introgressions and genomic regions of interest in genetics research and applied breeding programs.

The A-genome diploid wheats represent the earliest domesticated and cultivated wheat species in the Fertile Crescent and the donor of the wheat A sub-genome. The A-genome species encompass the cultivated einkorn ( Triticum . monococcum L. subsp. monococcum ), wild einkorn ( T. monococcum L. subsp. aegilopoides (Link) Thell.) and T. urartu . We evaluated the collection of 930 accessions in the Wheat Genetics Resource Center (WGRC), using genotyping-by-sequencing (GBS) and identified 13,089 curated SNPs. Genomic analysis detected misclassified and duplicated accessions with most duplicates originated from the same or a nearby locations. About 56% (n = 520) of the WGRC A-genome species collections were duplicates supporting the need for genomic characterization for effective curation and maintenance of these collections. Population structure analysis confirmed the morphology-based classifications of the accessions and reflected the species geographic distributions. We also showed that the T. urartu as the closest A-genome diploid to wheat through phylogenetic analysis. Population analysis within the wild einkorn group showed three genetically distinct clusters, which corresponded with wild einkorn races α, β, and γ described previously. The T. monococcum genome-wide FST scan identified candidate genomic regions harboring domestication selection signature ( Btr1 ) on the short arm of chromosome 3Am at ~ 70 Mb. We established A-genome core set (79 accessions) based on allelic diversity, geographical distribution, and available phenotypic data. The individual species core set maintained at least 80% of allelic variants in the A-genome collection and constitute a valuable genetic resource to improve wheat and domesticated einkorn in breeding programs. One-sentence summary Genotyping of gene bank collections of diploid A-genome relatives of wheat uncovered relatively higher genetic diversity and unique evolutionary relationships which gives insight to the effective use of these germplasm for wheat improvement.

Abstract High‐throughput phenotyping (HTP) has the potential to revolutionize plant breeding by providing scientists with exponentially more data than was available through traditional observations. Even though data collection is rapidly increasing, the optimum use of this data and implementation in the breeding program has not been thoroughly explored. In an effort to apply HTP to the earliest stages of a plant breeding program, we extended field‐based HTP pipelines to evaluate and extract data from spaced single plants. Using a panel of 340 winter wheat (Triticum aestivum L.) lines planted in full plots and grid‐spaced single plants for two growing seasons, we evaluated relationships between single plants and full plot yields. Normalized difference vegetation index (NDVI) was collected multiple times through the growing season using an unoccupied aerial vehicle. NDVI measurements during grain filling stage from both single plants and full plots were typically positively associated with their respective grain yield with correlation ranging from ‐0.22 to 0.74. The relationship between single plant NDVI and full plot yield, however, was variable between seasons ranging from ‐0.40 to 0.06. A genome wide association analysis (GWAS) identified the same marker trait associations in both full plots and single plants, but also displayed variability between growing seasons. Strong genotype by environment interactions could impede selection on quantitative traits, yet these methods could provide an effective tool for plant breeding programs to quickly screen early‐generation germplasm. Efficient use of early‐generation, affordable HTP data could improve overall genetic gain in plant breeding.

The growth stage best suitable for wheat yield phenotyping has been a hot topic. This study provides a fresh insight into it, from the perspective of spectral correlation between wheat genotype replications. A number of 340 wheat genotypes and their replication were distributed in separate parts (west and east) of an experiment field (2019-2020) in Ashland, Kansas, USA. Unmanned aerial vehicle based hyperspectral images (400-100 nm) of the experiment field were taken over the late growing season, on 29 May 2020, 5 June 2020, and 12 June 2020, respectively. For each narrow spectral band, we calculated a coefficient of determination (R (2)) between the reflectance of genotype replications. Results suggest that R (2) is relatively stable within visible spectrum (450-700 nm) and within near-infrared (NIR) spectrum (770-1000 nm), though it tends to be higher for the visible bands. Moreover, while the R (2) of the visible bands decreases across the three dates, it increases for the near-infrared bands. These findings suggest that genetic information is better reflected in visible reflectance than in near-infrared reflectance. Among the three dates, the one when highest intra-genotype spectral correlation over visible spectrum was observed might be the best timing to discriminate yield levels of different genotypes.

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at The Plant Phenome Journal. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v1]Field-based Single Plant Phenotyping for Plant BreedingAuthorsJaredCrainiDKevinWangiDByronEversJessePolandiDSee all authors Jared CrainiDCorresponding Author• Submitting AuthorKansas State UniversityiDhttps://orcid.org/0000-0001-9484-8325view email addressThe email was not providedcopy email addressKevin WangiDUniversity of FloridaiDhttps://orcid.org/0000-0002-7144-6865view email addressThe email was not providedcopy email addressByron EversKansas State Universityview email addressThe email was not providedcopy email addressJesse PolandiDKAUSTiDhttps://orcid.org/0000-0002-7856-1399view email addressThe email was not providedcopy email address

Barley yellow dwarf (BYD) is one of the major viral diseases of cereals. Phenotyping BYD in wheat is extremely challenging due to similarities to other biotic and abiotic stresses. Breeding for resistance is additionally challenging as the wheat primary germplasm pool lacks genetic resistance, with most of the few resistance genes named to date originating from a wild relative species. The objectives of this study were to, i) evaluate the use of high-throughput phenotyping (HTP) from unmanned aerial systems to improve BYD assessment and selection, ii) identify genomic regions associated with BYD resistance, and iii) evaluate genomic prediction models ability to predict BYD resistance. Up to 107 wheat lines were phenotyped during each of five field seasons under both insecticide treated and untreated plots. Across all seasons, BYD severity was lower with the insecticide treatment and plant height (PTHTM) and grain yield (GY) showed increased values relative to untreated entries. Only 9.2% of the lines were positive for the presence of the translocated segment carrying resistance gene Bdv2 on chromosome 7DL. Despite the low frequency, this region was identified through association mapping. Furthermore, we mapped a potentially novel genomic region for resistance on chromosome 5AS. Given the variable heritability of the trait (0.211 – 0.806), we obtained relatively good predictive ability for BYD severity ranging between 0.06 – 0.26. Including Bdv2 on the predictive model had a large effect for predicting BYD but almost no effect for PTHTM and GY. This study was the first attempt to characterize BYD using field-HTP and apply GS to predict the disease severity. These methods have the potential to improve BYD characterization and identifying new sources of resistance will be crucial for delivering BYD resistant germplasm.

In hard-winter wheat (Triticum aestivum L.) breeding, the evaluation of end-use quality is expensive and time-consuming, being relegated to the final stages of the breeding program after selection for many traits including disease resistance, agronomic performance, and grain yield. In this study, our objectives were to identify genetic variants underlying baking quality traits through genome-wide association study (GWAS) and develop improved genomic selection (GS) models for the quality traits in hard-winter wheat. Advanced breeding lines (n = 462) from 2015-2017 were genotyped using genotyping-by-sequencing (GBS) and evaluated for baking quality. Significant associations were detected for mixograph mixing time and bake mixing time, most of which were within or in tight linkage to glutenin and gliadin loci and could be suitable for marker-assisted breeding. Candidate genes for newly associated loci are phosphate-dependent decarboxylase and lipid transfer protein genes, which are believed to affect nitrogen metabolism and dough development, respectively. The use of GS can both shorten the breeding cycle time and significantly increase the number of lines that could be selected for quality traits, thus we evaluated various GS models for end-use quality traits. As a baseline, univariate GS models had 0.25-0.55 prediction accuracy in cross-validation and from 0 to 0.41 in forward prediction. By including secondary traits as additional predictor variables (univariate GS with covariates) or correlated response variables (multivariate GS), the prediction accuracies were increased relative to the univariate model using only genomic information. The improved genomic prediction models have great potential to further accelerate wheat breeding for end-use quality.

The Global Wheat Head Detection (GWHD) dataset was created in 2020 and has assembled 193,634 labelled wheat heads from 4700 RGB images acquired from various acquisition platforms and 7 countries/institutions. With an associated competition hosted in Kaggle, GWHD_2020 has successfully attracted attention from both the computer vision and agricultural science communities. From this first experience, a few avenues for improvements have been identified regarding data size, head diversity, and label reliability. To address these issues, the 2020 dataset has been reexamined, relabeled, and complemented by adding 1722 images from 5 additional countries, allowing for 81,553 additional wheat heads. We now release in 2021 a new version of the Global Wheat Head Detection dataset, which is bigger, more diverse, and less noisy than the GWHD_2020 version.

The development of high-throughput genotyping and phenotyping has provided access to many tools to accelerate plant breeding programs. Unmanned Aerial Systems (UAS)-based remote sensing is being broadly implemented for field-based high-throughput phenotyping due to its low cost and the capacity to rapidly cover large breeding populations. The Structure-from-Motion photogrammetry processes aerial images taken from multiple perspectives over a field to an orthomosaic photo of a complete field experiment, allowing spectral or morphological trait extraction from the canopy surface for each individual field plot. However, some phenotypic information observable in each raw aerial image seems to be lost to the orthomosaic photo, probably due to photogrammetry processes such as pixel merging and blending. To formally assess this, we introduced a set of image processing methods to extract phenotypes from orthorectified raw aerial images and compared them to the negative control of extracting the same traits from processed orthomosaic images. We predict that standard measures of accuracy in terms of the broad-sense heritability of the remote sensing spectral traits will be higher using the orthorectified photos than with the orthomosaic image. Using three case studies, we therefore compared the broad-sense heritability of phenotypes in wheat breeding nurseries including, (1) canopy temperature from thermal imaging, (2) canopy normalized difference vegetation index (NDVI), and (3) early-stage ground cover from multispectral imaging. We evaluated heritability estimates of these phenotypes extracted from multiple orthorectified aerial images via four statistical models and compared the results with heritability estimates of these phenotypes extracted from a single orthomosaic image. Our results indicate that extracting traits directly from multiple orthorectified aerial images yielded increased estimates of heritability for all three phenotypes through proper modeling, compared to estimation using traits extracted from the orthomosaic image. In summary, the image processing methods demonstrated in this study have the potential to improve the quality of the plant trait extracted from high-throughput imaging. This, in turn, can enable breeders to utilize phenomics technologies more effectively for improved selection.

Developing methodologies in the fields of phenomics and genomic prediction have the potential to increase the production of crop species by accelerating germplasm improvement. The integration of these technologies into germplasm improvement and breeding programs requires evidence that there will be a direct economic benefit to the program. We determined a basic set of parameters, such as prediction accuracy greater than 0.3, the ability to genotype over 7 lines for the cost of one phenotypic evaluation, and heritability levels below 0.4, at which the use of genomic selection would be of economic benefit in terms of genetic gain and operational costs to the Kansas State University (KSU) winter wheat breeding program. The breeding program was then examined to determine whether the parameters benefitting genomic selection were observed or achievable in a practical sense. Our results show that the KSU winter wheat breeding program is at a decision point with regards to their primary means of selection. A few operational changes to increase prediction accuracy would place the program in the parameter space where genomic selection is expected to outpace the current phenotypic selection methodology at a parity of the operation cost and would be of greatest benefit to the program.

Background: Measurement of plant traits with precision and speed on large populations has emerged as a critical bottleneck in connecting genotype to phenotype in genetics and breeding. This bottleneck limits advancements in understanding plant genomes and the development of improved, high-yielding crop varieties. Results: Here we demonstrate the application of deep learning on proximal imaging from a mobile field vehicle to directly estimate plant morphology and developmental stages in wheat under field conditions. We developed and trained a convolutional neural network with image datasets labeled from expert visual scores and used this "breeder-trained" network to classify wheat morphology and developmental stages. For both morphological (awned) and phenological (flowering time) traits, we demonstrate high heritability and very high accuracy against the "ground-truth" values from visual scoring. Using the traits predicted by the network, we tested genotype-to-phenotype association using the deep learning phenotypes and uncovered novel epistatic interactions for flowering time. Enabled by the time-series high-throughput phenotyping, we describe a new phenotype as the rate of flowering and show heritable genetic control for this trait. Conclusions: We demonstrated a field-based high-throughput phenotyping approach using deep learning that can directly measure morphological and developmental phenotypes in genetic populations from field-based imaging. The deep learning approach presented here gives a conceptual advancement in high-throughput plant phenotyping because it can potentially estimate any trait in any plant species for which the combination of breeder scores and high-resolution images can be obtained, capturing the expert knowledge from breeders, geneticists, pathologists, and physiologists to train the networks.

Dedicated bioenergy crops such as perennial warm‐season grasses (WSGs) may reduce soil erosion and improve soil properties while providing biomass feedstock for biofuel. We quantified impacts of perennial WSGs and row crops on soil wind erodibility parameters (erodible fraction, geometric mean diameter of dry aggregates, and aggregate stability) and soil organic carbon (SOC) concentration under a dedicated bioenergy crop experiment in eastern Kansas after 4 and 5 yr of management. Soil properties were measured under switchgrass (Panicum virgatum L.), big bluestem (Andropogon gerardii L.), miscanthus (Miscanthus × giganteus), and annual row crops including continuous corn (Zea mays L.), photoperiod sorghum [Sorghum bicolor (L.) Moench.], sweet sorghum, and grain sorghum. Perennial WSGs reduced wind erodible fraction by 1.08 to 1.16 times compared with row crops. The geometric mean diameter of dry aggregates under switchgrass and miscanthus was 2.8 to 4.5 times greater than under row crops. Dry soil aggregate stability under miscanthus and big bluestem was greater than under row crops. After 5 yr, differences in SOC concentration between WSGs and row crops were not statistically significant for the 0‐ to 15‐cm depth. Photoperiod sensitive and sweet sorghum had greater biomass yield than WSGs. In 2011, miscanthus yielded more biomass than corn by 5.3 Mg ha–1. Overall, growing dedicated bioenergy crops can reduce the soil’s susceptibility to wind erosion but may not significantly increase SOC concentration in this region in the short term.