Katherine Frels

Cropping systems in the U.S. High Plains are primarily limited by water availability and a shortened growing season due to the region's high elevations. Winter barley ( Hordeum vulgare L.) shares similar agronomic requirements and equipment needs with winter wheat, and Nebraska's large cattle industry provides an established market for barley as feed or grazing, supporting its adoption with minimal infrastructure changes. Experiments were conducted from 2021 to 2024 at two locations in western Nebraska. The experiments focused on optimizing the timing, rates, and types of P and N fertilizers for grain yield to optimize barley grain yield in the High Plains region. Grain yield was 35% higher than the control when applying 60 lb ac ⁻¹ of P and 18 lb ac ⁻¹ of N as monoammonium phosphate (MAP) and urea, especially when combined with spring N topdress (32 lb ac ⁻¹ of N), which resulted in a 43% yield advantage over the control treatment. Nitrogen‐only applications increased grain protein content by 6.5% compared with combined P and N split applications. When N was applied 100% in‐furrow, grain protein content increased by 7.5% compared with split application. Tillering, total aerial biomass, and plant height were 24%, 38%, and 5% higher, respectively, in the P+N treatment compared with the N‐only treatment. Overall, these results suggest that P placement, combined with split N management, plays a key role in optimizing winter barley yield in western Nebraska. This also suggests that, in the short term, the P source matters, with MAP outperforming 40 Rock, although the physiological or agronomic basis for this advantage warrants further investigation.

The University of Nebraska Barley ( Hordeum vulgare L.) Breeding Program is led by doctoral students who aim to develop high‐yielding winter barley varieties. The student breeders are responsible for crossing, data collection, and advancement decisions, which provides firsthand career preparation experience. However, the impact of student leadership on the success of the program is unknown. This study used a historical yield dataset of 302 unique advanced lines grown in Nebraska from 2002 to 2022 to evaluate the realized genetic gain of the breeding program and as a training population for genomic selection (GS). For yield GS, entries from a new observation nursery were used as the testing set. GS models included variations of genomic best linear unbiased prediction, four Bayesian models (Bayes A, Bayes B, Bayes C, Bayes LASSO [Bayesian least absolute shrinkage and selection operator]), and two machine learning approaches (random forest and support vector machine). The realized genetic gain for yield was 62.4 kg/ha/year or 2.2% annual increase. The Bayes A model trained on a single location (Lincoln, NE) achieved the highest predictive accuracy for yield ( r = 0.420). Implementing GS for yield was successful in the Nebraska barley breeding program and can aid future student barley breeders by providing selection continuity between leadership transitions.

An emerging paradigm in public health focuses on enhancing nutrition in existing food staples to reduce chronic disease at the population scale, rather than relying on individuals to change their behavior. This paradigm leverages plant and animal breeding, production practices, and processing to enhance nutrition, whereby foods consumed by millions can be improved at low incremental cost. This article supports and operationalizes this paradigm, illustrating the potential to improve diets through a case study that increases the arabinoxylan fiber content of commodity wheat through classical plant breeding (a non-GMO technology). The approach described in this article proposes to link agricultural and food science with health system implementation to deliver equitable access, improved healthcare outcomes and cost savings, and improved community health. Based on published dose–response relationships, comparative risk modeling indicates that modest fiber increases achieved by the commodity wheat breeding led to reduced population-level risks of 1–3% for cardiovascular disease, 3–4.5% for type 2 diabetes, and 1–3.5% for colorectal cancer, translating into substantial healthcare cost savings when implemented at a national scale. This article outlines possible low-risk pathways for implementing these nutrition increases at the population scale through commodity supply chains and community-level nutrition improvement efforts and evaluates the ranges of potential population-level impacts.

Wheat stem sawfly (WSS, Cephus cinctus Norton) threatens wheat ( Triticum aestivum L.) production in the US Great Plains. Increased stem solidness improves resistance to WSS but developing solid‐stemmed cultivars requires time‐consuming and destructive phenotyping. To expedite development of WSS‐resistant cultivars, a high‐throughput phenotyping method is needed. Therefore, we assessed phenomic prediction using uncrewed aerial systems (UAS) to predict stem solidness, WSS infestation, and yield in wheat. Multispectral and red‐green‐blue UAS data were collected in‐season at two naturally infested locations in western Nebraska from 2023 to 2024. The spectral reflectance indices from the UAS data were compared with agronomic traits (i.e., yield and plant height) and WSS traits (i.e., stem solidness and WSS infestation). Ridge regression, k ‐nearest neighbors (KNN), and random forest (RF) models were then trained to use spectral indices to predict yield, stem solidness, and WSS infestation. Correlations between WSS and spectral traits were temporally and environmentally dependent. The best prediction model depended on the biological trait. RF performed the best for yield ( r = 0.469), KNN for stem solidness ( r = 0.231), and ridge regression for WSS infestation ( r = 0.194). Predicting traits based on spectral data in a new environment was poor for both stem solidness ( r = 0.02–0.38) and WSS infestation ( r = 0.01–0.22). Our ability to predict WSS resistance was low, and UAS‐based phenotyping was not viable with current technology.

Summary: Eukaryotic genomes harbor many forms of variation, including nucleotide diversity and structural polymorphisms, which experience natural selection and contribute to genome evolution and biodiversity. However, harnessing this variation for agriculture hinges on our ability to detect, quantify, catalog, and utilize genetic diversity. Here, we explore seven complete genomes of the emerging biofuel crop pennycress ( Thlaspi arvense ) drawn from across the species’s current genetic diversity to catalogue variation in genome structure and content. Across this new pangenome resource, we find contrasting evolutionary modes in different genomic regions. Gene-poor, repeat-rich pericentromeric regions experience frequent rearrangements, including repeated centromere repositioning. In contrast, conserved gene-dense chromosome arms maintain large-scale synteny across accessions, even in fast-evolving immune genes where microsynteny breaks down across species but the macrosynteny of gene cluster positioning is maintained. Our findings highlight that multiple elements of the genome experience dynamic evolution that conserves functional content on the chromosome scale but allows rearrangement and presence-absence variation on a local scale. This diversity is invisible to classical reference-based approaches and highlights the strength and utility of pangenomic resources. These results provide a valuable case study of rapid genomic structural evolution within a species and powerful resources for crop development in an emerging biofuel crop.

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 B-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 136 and 68 trials obtained from the T3/WheatCAP and Cassavabase databases, respectively. The results demonstrated that correcting for spatial variation, regardless of marker information, increased the heritability estimate of grain yield, test weight, plant height, powdery mildew, stripe rust, and bacterial streak disease in wheat. Similar increases were observed in cassava for dry matter content, dry yield, and plant height. However, no increase was observed for cassava mosaic disease or bacterial blight. Models incorporating spatial correction in both crops consistently provided the best fit based on AIC values across all traits in wheat and cassava. These results were consistent whether or not marker effects were fitted in the models. This showed the importance of spatial correction in field experiment analysis.

Estimating potential crop yield during the growing season allows growers to adjust inputs, set reasonable harvest expectations, and guide marketing decisions. Therefore, the ability to estimate yield is a valuable yet difficult goal for growers. To evaluate the potential of several methods of wheat (Triticum aestivum L.) grain yield prediction, this experiment used published methods based on phenological characteristics: stand and tiller/spike counts, and newer methods that employ image‐ and reflectance‐based approaches, such as fractional green canopy cover (FGCC) and normalized difference vegetation index (NDVI) readings. This experiment was conducted across six locations (Banner, Box Butte, Cheyenne, Intensively Managed at Cheyenne, Deuel, and Kimball counties) in western Nebraska during 2019–2020, 2020–2021, and 2021–2022 for a total of 11 site‐years. Treatments consisted of seven winter wheat varieties that were evaluated in the Winter Wheat State Variety Trials. Stand count did not show a significant correlation with wheat yield (0.09) nor model fit for yield estimation. Spike count was significantly correlated with yield (0.54), but efforts to use it to estimate final yield were not significant. Due to the inconsistency of yield prediction with historical methods, analyses of novel methods of yield estimation were warranted. NDVI and FGCC readings correlate with wheat yield and model fit efforts were successful. NDVI at Feekes 10 correlated significantly at 0.39, while FGCC had correlations of 0.56, 0.50, and 0.68 at Feekes 2, 4, and 10 (respectively). This experiment suggests that NDVI and FGCC are methods that could be used to replace outdated and laborious approaches.

Wheat (Triticum spp and, particularly, T. aestivum L.) is an essential cereal with increased human and animal nutritional demand. Therefore, there is a need to enhance wheat yield and genetic gain using modern breeding technologies alongside proven methods to achieve the necessary increases in productivity. These modern technologies will allow breeders to develop improved wheat cultivars more quickly and efficiently. This review aims to highlight the emerging technological trends used worldwide in wheat breeding, with a focus on enhancing wheat yield. The key technologies for introducing variation (hybridization among the species, synthetic wheat, and hybridization; genetically modified wheat; transgenic and gene-edited), inbreeding (double haploid (DH) and speed breeding (SB)), selection and evaluation (marker-assisted selection (MAS), genomic selection (GS), and machine learning (ML)) and hybrid wheat are discussed to highlight the current opportunities in wheat breeding and for the development of future wheat cultivars.

Barley (Hordeum vulgare L.) thrives in the arid and semi-arid regions of the world; nevertheless, it suffers large grain yield losses due to drought stress. A panel of 426 lines of barley was evaluated in Egypt under deficit (DI) and full irrigation (FI) during the 2019 and 2020 growing seasons. Observations were recorded on the number of days to flowering (NDF), total chlorophyll content (CH), canopy temperature (CAN), grain filling duration (GFD), plant height (PH), and grain yield (Yield) under DI and FI. The lines were genotyped using the 9K Infinium iSelect single nucleotide polymorphisms (SNP) genotyping platform, which resulted in 6913 high-quality SNPs. In conjunction with the SNP markers, the phenotypic data were subjected to a genome-wide association scan (GWAS) using Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK). The GWAS results indicated that 36 SNPs were significantly associated with the studied traits under DI and FI. Furthermore, eight markers were significant and common across DI and FI water regimes, while 14 markers were uniquely associated with the studied traits under DI. Under DI and FI, three (11_10326, 11_20042, and 11_20170) and five (11_20099, 11_10326, 11_20840, 12_30298, and 11_20605) markers, respectively, had pleiotropic effect on at least two traits. Among the significant markers, 24 were annotated to known barley genes. Most of these genes were involved in plant responses to environmental stimuli such as drought. Overall, nine of the significant markers were previously reported, and 27 markers might be considered novel. Several markers identified in this study could enable the prediction of barley accessions with optimal agronomic performance under DI and FI.