Crop Science

Relatively small genomic introgressions containing quantitative trait loci can have significant impacts on the phenotype of an individual plant. However, the magnitude of phenotypic effects for the same introgression can vary quite substantially in different environments due to allele-by-environment interactions. To study potential patterns of allele-by-environment interactions, fifteen near-isogenic lines (NILs) with >90% B73 genetic background and multiple Mo17 introgressions were grown in 16 different environments. These environments included five geographical locations with multiple planting dates and multiple planting densities. The phenotypic impact of the introgressions was evaluated for up to 26 traits that span different growth stages in each environment to assess allele-by-environment interactions. Results from this study showed that small portions of the genome can drive significant genotype-by-environment interaction across a wide range of vegetative and reproductive traits, and the magnitude of the allele-by-environment interaction varies across traits. Some introgressed segments were more prone to genotype-by-environment interaction than others when evaluating the interaction on a whole plant basis throughout developmental time, indicating variation in phenotypic plasticity throughout the genome. Understanding the profile of allele-by-environment interaction is useful in considerations of how small introgressions of QTL or transgene containing regions might be expected to impact traits in diverse environments.\n\nKey MessageSignificant allele-by-environment interactions are observed for traits throughout development from small introgressed segments of the genome.

CORE IDEASO_LINixtamalization moisture content can be selected early in breeding programs using NIR spectroscopy. C_LIO_LIYield does not significantly correlate with nixtamalization moisture content in diverse or elite populations. C_LIO_LIAdditive and dominance gene action impact nixtamalization moisture content in hybrid maize. C_LIO_LIGenomic prediction can be used to assess nixtamalization moisture content early in hybrid maize breeding. C_LI Nixtamalization moisture content, a measure of the quantity of water absorbed during the nixtamalization of a grain such as maize, has a large impact on the end-quality of masa-based products. An application to predict nixtamalization moisture content from raw inbred and hybrid maize grain was recently developed, but its utility in a breeding context has not been assessed. Important breeding considerations for nixtamalization moisture content were assessed in diverse maize hybrids, modern commercial hybrids, and historically high-acreage hybrids grown in up to three environments across two years. This study demonstrated that nixtamalization moisture content is heavily influenced by growing conditions, but sufficient genetic variance is present to allow breeders to make gains from selection. Contrary to prior theory, there was no substantial correlation between nixtamalization moisture content and yield suggesting breeders can select for both traits without negatively impacting either trait. Both additive and dominant genetic action was observed and genomic prediction was able to predict nixtamalization moisture content in hybrids with an average Spearmans rank correlation coefficient greater than 0.441 and a root mean square error below 0.006. The findings here suggest that nixtamalization moisture content can be selected for early in breeding cycles, allowing breeders to develop improved food-grade maize germplasm without negatively impacting important traits such as yield. PLAIN LANGUAGE SUMMARYPlant breeders need to understand the biological mechanisms underlying a trait of interest to maximize the efficiency of their efforts. Nixtamalization moisture content is a highly complex trait that is determined by both genetic and environmental factors. In this study, nixtamalization moisture content was assessed in a diverse set of hybrid and inbred maize to understand the biological mechanisms underlying nixtamalization moisture content. The relationship between nixtamalization moisture content and yield was assessed, the genetic architecture and mode of gene action underlying nixtamalization moisture content were evaluated, and the efficacy of genomic prediction in assessing nixtamalization moisture content was determined. The findings of this study will allow breeders to create optimized breeding strategies for nixtamalization moisture content, thus improving the raw materials that are used to produce globally consumed products such as tortillas and tortilla chips.

Evaluating genetic gains over time is essential for assessing the success of breeding programs and refining strategies for ongoing improvement. Hard red spring (HRS) wheat is an important wheat class in the US and is primarily grown in the Northern Great Plains. Despite a long history of breeding efforts in this region, long-term quantification of genetic gains for key traits has remained limited. This study analyzes over sixty years of data from the USDA-coordinated Hard Red Spring Wheat Uniform Regional Nursery (HRSWURN) to evaluate genetic advancements in agronomic traits across multiple phases. A significant positive genetic gain of 0.61% per annum was observed for grain yield in HRS wheat released in the Northern US region, which is lower than the expected gains needed to meet future wheat demand. The change was 0.07% for test weight, -0.04% for days to heading, and -0.16% for plant height. Notably, sustained yield improvements have not affected grain protein levels since they were first measured in 1995, indicating that ongoing selection has effectively balanced grain yield and protein despite their negative correlation (r = -0.31). Assessment of genetic gains over 20-year phases suggested slowing rates of genetic gains for grain yield but did not indicate any plateaus. The realized genetic gains were generally higher for individual breeding programs when breeding for target environments, with the public breeding program in Minnesota observing gains of approximately 1% per annum. These findings highlight the significant impact of long-term breeding efforts and offer valuable insights for refining future breeding strategies.

Computer simulations of breeding strategies are an essential resource for tree breeders because they allow exploratory analyses into potential long-term impacts on genetic gain and inbreeding consequences without bearing the cost, time, or resource requirements of field experiments. Previous work has modeled the potential long-term implications on inbreeding and genetic gain using random mating and phenotypic selection. Reduction in sequencing costs has enabled the use of DNA marker-based relationship matrices in addition to or in place of pedigree-based allele sharing estimates; this has been shown to provide a significant increase in the accuracy of progeny breeding value prediction. A potential pitfall of genomic selection using genetic relationship matrices is increased coancestry among selections, leading to the accumulation of deleterious alleles and inbreeding depression. We used simulation to compare the relative genetic gain and risk of inbreeding depression within a breeding program similar to loblolly pine, utilizing pedigree-based or marker-based relationships over ten generations. We saw a faster rate of purging deleterious alleles when using a genomic relationship matrix based on markers that track identity-by-descent of segments of the genome. Additionally, we observed an increase in the rate of genetic gain when using a genomic relationship matrix instead of a pedigree-based relationship matrix. While the genetic variance of populations decreased more rapidly when using genomic-based relationship matrices as opposed to pedigree-based, there appeared to be no long-term consequences on the accumulation of deleterious alleles within the simulated breeding strategy.

Modern agriculture depends on chemically synthesized nitrogen fertilizer, which ensures high yields but also can carry significant environmental and economic costs. Biological nitrogen fixation (BNF) already supplies nitrogen to legume crops and several avenues of research are underway to extend it to non-legume crops. In maize (Zea mays), aerial roots have been shown to contribute to BNF in some varieties, and both having many aerial roots and large aerial roots contributes to the fixation trait. However, much of the genetics controlling aerial root number and size is still unknown. Here we validate and quantify BNF in maize varieties from Southern Mexico under controlled conditions and evaluate a population of double haploids derived from the elite inbred PHZ51 crossed with these varieties. We find that most aerial root traits (root number, nodes with roots, root size) are reasonably heritable (h2 0.5-0.75) and generally uncorrelated with each other. QTL mapping identifies 5 QTL each affecting nodes with aerial roots and aerial root number per node; in both cases all but 1 QTL show an increase from the landrace allele. We also identify 11 QTL for aerial root diameter, with most positive QTL coming from PHZ51. Between the two populations, only a few QTL overlap, indicating a presumably high diversity of genes affecting aerial root morphology in landrace populations. Combining the best QTL into elite material may provide a path toward meaningful levels of BNF for maize, and additional work is needed to determine how viable this approach will be in field settings.

Breeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy by up to 19%, but it is highly dependent on the genetic architecture of the trait. Differences in prediction accuracy across marker types were more pronounced for traits with high heritability, high number of QTLs, and SVs as causative variants. In these scenarios, using SV markers resulted in better prediction accuracies than SNP markers, especially when predicting untested genotypes across environments, likely due to more predictors being in linkage disequilibrium with causative variants. The simulations revealed little impact of different effect sizes between SNPs and SVs as causative variants on prediction accuracy. This study demonstrates the importance of knowing the genetic architecture of a trait in deciding what markers and marker types to use in large scale genomic prediction modeling in a breeding program. Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.

Cassava (Manihot esculenta Crantz) is a critical food security crop for sub-Saharan Africa. Efforts to improve cassava through breeding have expanded over the past decade. At the same time, crop growth models (CGM) are becoming common place in breeding efforts to expand the inference of evaluations of breeding germplasm to environments that have not been tested and to prepare for breeding for adaptation to future climates. We parameterized a CGM, the CROPGRO-MANIHOT-Cassava model in the DSSAT family of models, using data on 67 clones from the International Institute of Tropical Agriculture cassava breeding program evaluated from 2017 to 2020 and over eight locations in Nigeria using trial and error parameter adjustments and the General Likelihood Uncertainty Estimation method. Our objectives were to assess the feasibility of this large-scale calibration in the context of a cassava breeding program and to identify systematic biases of the model. For each cultivar we calculated the Pearson correlation between model prediction and observation across the environments, as well as root mean squared error and d statistics. As a result of calibration, the correlation coefficient increased from -0.03 to +0.08, the RMSE dropped from 21 t ha-1 to 5 t ha-1 while d increased from 0.23 to 0.44. We found that the model underestimated root yield in dry environments (low precipitation and high temperature) and overestimated root yield in wet environments (high precipitation and low temperature). Our experience suggests both that CGM calibration could become a routine component of the cassava breeding data analysis cycle and that there are opportunities for model improvement.

Common bean (Phaseolus vulgaris L.) is the leading grain legume consumed directly by humans and a primary source of nutrients in many communities. This study utilized common bean genotypes with diverse seed coat phenotypes to investigate genotypic and environmental effects on pigmented seed coat area and seed macronutrient (protein, starch, fat, ash, moisture), anti-nutrient (phytate), and mineral nutrient (iron, zinc, calcium, phosphorus, magnesium, potassium, sodium) profiles. Recombinant inbred lines (RILs) that comprise six phenotypic classes for seed coat patterning and nine commercial cultivars were field-evaluated for multiple years across inland, coastal, and intermountain environments in California. A custom near-infrared spectroscopy calibration improved macronutrient prediction accuracy relative to a pre-existing calibration. Environmental effects on macronutrients were pronounced; the 2022 coastal growing environment was the most distinct, characterized by significantly higher starch and moisture content and significantly lower protein content in the RILs relative to any other environments. Across growing years in the RILs, greater consistency was observed at the inland site, where only protein was significantly different; all macronutrient traits significantly differed within the intermountain site. Certain commercial cultivars largely maintained their relative rank for protein content across environments, indicating consistency of genotypic performance, and Black Nightfall ranked among the highest for iron, zinc, phosphorus, and magnesium. Percent pigmented seed coat area was significantly negatively correlated with both calcium and magnesium concentrations. These results underscore the importance of genotype-by-environment field trials for seed coat patterning, seed nutritional composition, and their interplay, to support breeding of common bean among other grain legumes. HighlightsO_LICustom near-infrared spectroscopy (NIRS) calibration improved prediction accuracies C_LIO_LIEnvironmental effects significantly influenced common bean macronutrient composition C_LIO_LICertain cultivars ranked consistently for macronutrient traits across environments C_LIO_LISeed coat pattern was significantly associated with mineral nutrient concentrations C_LI

Variety mixtures provide a potential avenue in US cropping systems to improve yield stability and disease resistance. However, implementation of variety mixtures requires an understanding of the competitive dynamics of the crop. In this study, we examine the effects of plant competition both between and within plots through five unique experiments: 1) 5,000 diverse inbred lines in single-row plots, 2) hybrids in two-row plots developed from the above inbred lines, 3) over 4,000 hybrids measured in 141 locations in two-row plots as part of Genomes to Fields, 4) mixtures of two hybrids within a two-row plot planted across two years and five locations, and 5) mixtures of up to twenty hybrids in four-row plots in three locations. Across all experiments, we find that competitive interactions are extremely limited. Within inbred lines, height of the neighboring plot accounts for 1.2% of the variance in focal plot height. Similarly, neighbor height explains 1.7% of the variance in focal plot yield in hybrids developed from the inbred lines. The genetics of neighboring plots explains 1.55% of the variation in yield across 141 location-year environments, reinforcing the generally modest impacts of neighbor competition. In evaluating mixtures of hybrids in both two and four-row plots, we observe no yield penalty compared to conventional single hybrid plots, even with large height differentials of the hybrids included in the mixture or in mixtures of up to 20 hybrids within a plot. Finally, we observe that mixtures have more yield stability compared to conventional plots, highlighting a new avenue for increased stability in higher risk environments. The lack of yield penalty and stability benefits are promising for future investigations of mixtures that may complement each other in disease resistance or abiotic stress tolerance and increase overall yield stability in the field.

Crop wild relatives can serve as a source of variation for the genetic improvement of modern varieties. However, the realization of this genetic potential depends critically on the conservation of wild populations. In this study, five populations of Zea mays ssp. parviglumis, the closest relative of domesticated maize, were collected in Jalisco, Mexico and planted in a common garden. Eleven traits related to plant fitness were measured and evaluated in the context of genetic diversity and genetic load. Plants whose seed were sourced from larger, less disturbed populations had greater genetic diversity, lower genetic load, and possessed phenotypes associated with higher fitness, while plants sourced from smaller, heavily impacted populations had traits characteristic of lower fitness and increased genetic load. For example, plants from larger populations germinated more quickly, reached anthesis sooner, demonstrated a higher level of photosynthetic activity, and produced more above-ground biomass, suggesting a direct correlation between the fitness of a population, genetic diversity, and genetic load. These results emphasize the importance of preserving the habitat of populations of Zea mays ssp. parviglumis to limit inbreeding depression and maintain the genetic diversity and adaptive potential of this germplasm.

The rising demand for plant-based proteins has intensified interest in pulse crops due to their high protein concentration. However, few studies have evaluated protein and amino acid composition/variability in cultivated lentil (Lens culinaris Medik.). We evaluated protein and amino acid composition using near-infrared reflectance spectroscopy (NIRS) in a diversity panel grown in four site-years in Saskatchewan, Canada, followed by genome-wide association analyses with phenology-related traits as covariates. We found little correlation between days from sowing to flowering, region of origin, cotyledon color, or seed size, and protein concentration. Reproductive period was correlated with protein concentration. We also observed large variability between environments and more variability within market classes than among them. Our results demonstrate the potential for breeders to identify germplasm and select for increased protein and amino acid concentration and quality using a high-throughput NIRS method. We were able to identify numerous molecular markers for use in marker-assisted breeding. Our approach could be replicated by breeders from other regions or with other pulse crops to help meet the demand for plant-based protein and improvements in protein quality.

Models have been developed to account for heterogeneous spatial variation in field trials. These spatial models have been shown to successfully increase the quality of phenotypic data resulting in improved effectiveness of selection by plant breeders. The models were developed for continuous data types such as grain yield and plant height, but data for most traits, such as in iron deficiency chlorosis (IDC), are recorded on ordinal scales. Is it reasonable to make spatial adjustments to ordinal data by simply applying methods developed for continuous data? The objective of the research described herein is to evaluate methods for spatial adjustment on ordinal data, using soybean IDC as an example. Spatial adjustment models are classified into three different groups: group I, moving average grid adjustment; group II, geospatial autoregressive regression (SAR) models; and group III, tensor product penalized P-splines. Comparisons of eight models sampled from these three classes demonstrate that spatial adjustments depend on severity of field heterogeneity, the irregularity of the spatial patterns, and the model used. SAR models generally produce better performance metrics than other classes of models. However, none of the eight evaluated models fully removed spatial patterns indicating that there is a need to either adjust existing models or develop novel models for spatial adjustments of ordinal data collected in fields exhibiting discontinuous transitions between heterogeneous patches.

Background and AimsDomesticated maize (Zea mays ssp. mays) generally forms between two and six seminal roots, while its wild ancestor, Mexican annual teosinte (Zea mays ssp. parviglumis), typically lacks seminal roots. Maize also produces larger seeds than teosinte, and it generally has higher growth rates as a seedling. Maize was originally domesticated in the tropical soils of southern Mexico, but it was later brought to the Mexican highlands before spreading to other parts of the continent, where it experienced different soil resource constraints. The aims of this study were to understand the impact of increased seminal root number on seedling nitrogen acquisition and to model how differences in maize and teosinte phenotypes might have contributed to increased seminal root number in domesticated maize. MethodsSeedling root architectural models of a teosinte accession and a maize landrace were constructed by parameterizing the functional-structural plant model OpenSimRoot using plants grown in mesocosms. Seedling growth was simulated in a low-phosphorus environment, multiple low-nitrogen environments, and at variable planting densities. Models were also constructed to combine individual components of the maize and teosinte phenotypes. Key ResultsSeminal roots contributed about 35% of the nitrogen and phosphorus acquired by maize landrace seedlings in the first 25 days after planting. Increased seminal root number improved plant N acquisition under low-N environments with varying precipitation patterns, fertilization rates, soil textures, and planting densities. Models suggested that the optimal number of seminal roots for nutrient acquisition in teosinte is constrained by its limited seed carbohydrate reserves. ConclusionsSeminal roots can improve the acquisition of both nitrogen and phosphorus in maize seedlings, and the increase in seed size associated with maize domestication may have facilitated increased seminal root number.

Herein we report the impacts of applying five selection methods across 40 cycles of recurrent selection and identify interactions among factors that affect genetic responses in sets of simulated families of recombinant inbred lines derived from 21 homozygous soybean lines. Our use of recurrence equation to model response from recurrent selection allowed us to estimate the half-lives, asymptotic limits to recurrent selection for purposes of assessing the rates of response and future genetic potential of populations under selection. The simulated factors include selection methods, training sets, and selection intensity that are under the control of the plant breeder as well as genetic architecture and heritability. A factorial design to examine and analyze the main and interaction effects of these factors showed that both the rates of genetic improvement in the early cycles and limits to genetic improvement in the later cycles are significantly affected by interactions among all factors. Some consistent trends are that genomic selection methods provide greater initial rates of genetic improvement (per cycle) than phenotypic selection, but phenotypic selection provides the greatest long term responses in these closed genotypic systems. Model updating with training sets consisting of data from prior cycles of selection significantly improved prediction accuracy and genetic response with three parametric genomic prediction models. Ridge Regression, if updated with training sets consisting of data from prior cycles, achieved better rates of response than BayesB and Bayes LASSO models. A Support Vector Machine method, with a radial basis kernel, had the worst estimated prediction accuracies and the least long term genetic response. Application of genomic selection in a closed breeding population of a self-pollinated crop such as soybean will need to consider the impact of these factors on trade-offs between short term gains and conserving useful genetic diversity in the context of the goals for the breeding program.

1Plant breeding programs must adapt genomic selection to an already complex system. Inbred or hybrid plant breeding programs must make crosses, produce inbred individuals, and phenotype inbred lines or their hybrid test-crosses to select and validate superior material for product release. These products are few, and while it is clear that population improvement is necessary for continued genetic gain, it may not be sufficient to generate superior products. Rapid-cycle recurrent truncation genomic selection has been proposed to increase genetic gain by reducing generation time. This strategy has been shown to increase short-term gains, but can quickly lead to loss of genetic variance through inbreeding as relationships drive prediction. The optimal contribution of each individual can be determined to maximize gain in the following generation while limiting inbreeding. While optimal contribution strategies can maintain genetic variance in later generations, they suffer from a lack of short-term gains in doing so. We present a hybrid approach that branches out yearly to push the genetic value of potential varietal materials while maintaining genetic variance in the recurrent population, such that a breeding program can achieve short-term success without exhausting long-term potential. Because branching increases the genetic distance between the phenotyping pipeline and the recurrent population, this method requires sacrificing some trial plots to phenotype materials directly out of the recurrent population. We envision the phenotypic pipeline not only for selection and validation, but as an information generator to build predictive models and develop new products.

To produce genetic gain, hybrid crop breeding can change the additive as well as dominance genetic value of populations, which can lead to utilization of heterosis. A common hybrid breeding strategy is reciprocal recurrent selection (RRS), in which parents of hybrids are typically recycled within pools based on general combining ability (GCA). However, the relative performance of RRS and other possible breeding strategies have not been thoroughly compared. RRS can have relatively increased costs and longer cycle lengths which reduce genetic gain, but these are sometimes outweighed by its ability to harness heterosis due to dominance and increase genetic gain. Here, we used stochastic simulation to compare gain per unit cost of various clonal breeding strategies with different amounts of population inbreeding depression and heterosis due to dominance, relative cycle lengths, time horizons, estimation methods, selection intensities, and ploidy levels. In diploids with phenotypic selection at high intensity, whether RRS was the optimal breeding strategy depended on the initial population heterosis. However, in diploids with rapid cycling genomic selection at high intensity, RRS was the optimal breeding strategy after 50 years over almost all amounts of initial population heterosis under the study assumptions. RRS required more population heterosis to outperform other strategies as its relative cycle length increased and as selection intensity decreased. Use of diploid fully inbred parents vs. outbred parents with RRS typically did not affect genetic gain. In autopolyploids, RRS typically was not beneficial regardless of the amount of population inbreeding depression. Key MessageReciprocal recurrent selection sometimes increases genetic gain per unit cost in clonal diploids with heterosis due to dominance, but it typically does not benefit autopolyploids.

High-quality malt is influenced by three primary factors: barley genotype, environmental conditions, and malting process. To effectively evaluate malting barley breeding material and assess how environmental changes influence malt quality, it is essential to have laboratory- scale malting methods that can produce malt approximating that produced by commercial malting operations. However, existing laboratory-scale malting procedures often demand large quantities of grain, rely on specialized equipment, and are costly. To overcome these challenges, we developed a small sample-scale benchtop malting method utilizing standard laboratory equipment and components available at hardware stores. We validated the method by conducting standard malt quality tests including diastatic power, -amylase activity, total malt protein, and wort composition (soluble protein, wort soluble/total malt protein, {beta}-glucan, free amino nitrogen, and malt extract). Our findings indicate that the benchtop malting method yields quality metrics comparable to those obtained from established small-scale and full-scale malting protocols. Furthermore, a key innovation of this system is the use of separate Erlenmeyer flasks for malting each sample. Unlike conventional shared malting systems, this design enables precise measurement and comparison of treatment effects across samples malted simultaneously. This reliable, low-cost, and efficient method provides a valuable tool for screening malt quality traits in breeding lines with limited sample sizes and for testing malting regimes aimed at improving malt quality and efficiency. Additionally, it offers an accessible solution for producing high-quality, research-scale malt in laboratories without dedicated quality assurance facilities.

Maximum utilization of existing genetic variability in a breeding program depends on the efficient classification of the inbred lines into heterotic groups, particularly under stress conditions. This study applied practical breeding approaches to determine the mode of genetic inheritance for Striga resistance and proposes a weighted heterotic grouping method based on the general combining ability of multiple traits (WHGCAMT) and compares its effectiveness with other existing methods in classifying the inbred lines into heterotic groups in Striga-infested and optimum environments. Using Diallel design IV, 300 crosses were generated from 21 inbred lines and 4 standard testers. The crosses, along with six checks, were evaluated in an 18 x 17 alpha lattice design with two replications at two locations, in both artificial Striga-infested and Striga-free environments. The inbred lines were genotyped using DArTtag SNP markers. Phenotypic and genotypic data were analyzed using R. Analysis of variance revealed significant mean squares for hybrid, general combining ability (GCA), specific combining ability (SCA) and their interactions with environment. Significant positive and negative GCA and SCA effects were detected for grain yield and other measured traits. However, a larger proportion of additive gene action than non-additive gene action was observed for grain yield and most measured traits. The analysis of molecular variance also showed substantial genetic differences within and between clusters. Except for HSCA, the mean grain yield between the inter-group and intra-group hybrids was significant for each method. Pairwise comparison of the inter- and intra-group hybrids of all the methods showed significant differences between the WHGCAMT and all other methods in most cases. WHGCAMT consistently produced higher-yielding inter-group hybrids and lower-yielding intra-group hybrids, achieving breeding efficiency improvements of 55.8%, 4.3%, 15.7%, and 11.4% over the HSCA, HSGCA, HGCAMT and molecular marker methods, respectively, under Striga infestation. Thus, WHGCAMT offers more precise, reliable and biologically meaningful heterotic groups among early-maturing maize inbred lines.

Genomic prediction models that account genotype-by-environment (GxE) have the potential to accelerate the rate of genetic gain for yield and agronomic performance, yet relatively few studies have applied GxE prediction in public soft red winter wheat (Triticum aestivum) breeding programs. In this study, we extended a reaction norm-based genomic prediction framework by integrating weather-based environmental covariates to more effectively capture genotype- environment interactions. Key agronomic traits, including seed yield, plant height, test weight, and heading date, were evaluated across 33 environments (location-year) using over 3,200 breeding lines from the North Carolina State University small grains breeding program. Multiple genomic prediction models were compared using several cross-validation (CV) schemes representing common breeding scenarios. Across traits, the reaction norm M5 model, which incorporates both GxE and genotype-by-environmental covariate interactions (GxO), achieved the highest prediction accuracy (PA) in CV2 (predicting incomplete field trials) and CV1 for yield and test weight (predicting new lines). The highest PA was observed for test weight under CV2 (0.54) and for yield under CV1 (0.41). Under CV0 (predicting new environments), the M3 model incorporating GxE produced highest PA across traits, with the greatest accuracy for plant height (0.45), although differences among M2, M3, and M4 were small. Prediction under CV00 (predicting new lines in new environments) remained more challenging, with PA values 0.10 - 0.20 across traits. Overall, our results demonstrate that integrating environmental covariates into genomic prediction models can improve predictive performance across diverse wheat-growing environments in North Carolina, supporting their utility for applied breeding efforts. CORE IDEASO_LIIntegrating genotype-by-environment (GxE) interactions with environmental covariates improves prediction accuracy across environments. C_LIO_LIModel performance varies by prediction scenario, with different approaches performing best for new lines, incomplete trials, or new environments. C_LIO_LIPrediction of new lines in new environments remains challenging. C_LI PLAIN LANGUAGE SUMMARYThis study explores how adding environmental information to genomic prediction models can improve prediction accuracy in a public winter wheat breeding program. Using data from multi-environment trials conducted across diverse conditions in North Carolina, we evaluated statistical models that capture how different wheat lines respond to changing environments. By incorporating weather data, we improved the ability to predict performance across locations and years. These findings provide practical insights for refining selection strategies and accelerating genetic gain in wheat breeding.

Phenomic prediction (PP), a novel approach utilizing Near Infrared Spectroscopy (NIRS) data, offers an alternative to genomic prediction (GP) for breeding applications. In PP, a hyperspectral relationship matrix replaces the genomic relationship matrix, potentially capturing both additive and non-additive genetic effects. While PP boasts advantages in cost and throughput compared to GP, the factors influencing its accuracy remain unclear and need to be defined. This study investigated the impact of various factors, namely the training population size, the multi-environment information integration, and the incorporations of genotype x environment (GxE) effects, on PP compared to GP. We evaluated the prediction accuracies for several agronomically important traits (days to flowering, plant height, yield, harvest index, thousand-grain weight, and grain nitrogen content) in a rice diversity panel grown in four distinct environments. Training population size and GxE effects inclusion had minimal influence on PP accuracy. The key factor impacting the accuracy of PP was the number of environments included. Using data from a single environment, GP generally outperformed PP. However, with data from multiple environments, using genotypic random effect and relationship matrix per environment, PP achieved comparable accuracies to GP. Combining PP and GP information did not significantly improve predictions compared to the best model using a single source of information (e.g., average predictive ability of GP, PP, and combined GP and PP for grain yield were of 0.44, 0.42, and 0.44, respectively). Our findings suggest that PP can be as accurate as GP when all genotypes have at least one NIRS measurement, potentially offering significant advantages for rice breeding programs. Authors SummaryThis study explores the interest of phenomic selection within the context of rice breeding. Unlike genomic selection, phenomic selection utilizes near-infrared spectroscopic (NIRS) technology to predict genotypes performance. The importance of this methodology lies in its capacity to reduce the costs and enhance the genetic gains of breeding programs, particularly in developing countries where genomic information is not always easily accessible (cost, availability, ease of use). Also, NIRS technology is often already available, even in resource-constrained breeding programs. By focusing the study on rice, a staple food for billions, our research aims to demonstrate the applicability of phenomic selection compared to genomic selection. By investigating the influence of various factors on phenomic prediction accuracy (training population size, incorporation of multiple environment information, consideration of genotype x environment effects in the prediction models), we are contributing to the optimization of this novel breeding method, which could potentially lead to significant improvements in agricultural productivity and food security.