The early 20th-century discovery of heterosis and the establishment of heterotic groups transformed maize (Zea mays L.) into a keystone of global agriculture. However, maize breeding faces two significant challenges: the gradual decline of general combining ability (GCA) variance within heterotic groups and the impracticality of testing all possible single crosses in the early stages of a breeding program. Here, we developed genomic best linear unbiased prediction (GBLUP)-based multikernel models, using additive and two alternative nonadditive genomic relationship matrices, to estimate the variance components associated with the general combining ability of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability arising from their crosses. We further applied these models to predict the performance of untested single-cross combinations under varying levels of parental information. We showed that the SS and NSS groups retained significant GCA variance across traits in both early- and late-maturity groups. The SS group, in contrast, exhibited no detectable GCA variance in grain yield for the intermediate-flowering subset of hybrids, highlighting a limitation for future genetic improvement. Furthermore, our results showed that GBLUP-based multikernel models effectively identified superior hybrids when parental information was available. In the absence of this information, however, these models underperformed compared to covariance-based approaches. Both nonadditive matrices yielded similar results, indicating that they capture comparable genetic relationship patterns despite their distinct formulations. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multikernel models can improve the efficiency of hybrid breeding programs.

Maize is susceptible to ear rot and at risk for mycotoxin accumulation. Fungal colonization and mycotoxin contamination impact grain quality and pose serious health risks. In North America, the most prevalent maize ear rots are Gibberella ear rot (GER), Fusarium ear rot (FER), and Aspergillus ear rot (AER). FER is associated with fumonisin (FUM) contamination and AER with aflatoxin (AFL) contamination. Weather patterns have shifted, and an updated assessment of ear rots and their drivers will guide prioritization of threats to grain quality. Two hybrid panels with varying levels of resistance to ear rot pathogens were screened in highly replicated trials across a range of North American environments. We evaluated one panel for ear rot incidence (n = 108 hybrids) and the other panel (n = 4 hybrids) for mycotoxin accumulation. FER and FUM were the most prevalent disease and toxin, respectively. FER was more common in southern environments, while GER occurred in northern environments. FER and FUM were present in northern environments, including FUM above advisory levels. Insect damage was related to FER and GER incidence. Cool, moist conditions favored GER development, while warm environments were conducive to FER and FUM. Post-silking environmental conditions were important for GER and AFL, while pre-silking conditions were also important for FER and FUM. Despite different epidemiological drivers, GER and FER were observed in overlapping environments. Breeding for resistance to multiple ear rots, integrating epidemiology and plant breeding, and managing environmental stress are crucial for effective ear rot management.

Integrating multi-omics data, including phenomic, genomic, and environmental inputs, offers a powerful approach for enhancing maize performance and predicting grain yield. In this study, crop health was quantified using temporal NGRDI (Normalized Green Red Difference Index) trajectories collected from 16 unoccupied (unmanned) aerial vehicle or system (UAV or UAS, drones and sensors) flights (from 19 to 117 d after planting) in maize trials conducted in Texas. Crop health indices (CHIs) were calculated through area under the curve (CHIAUC) and functional principal component analysis (CHIFPCA), capturing dynamic plant health responses throughout the growing season. Heritability estimates of NGRDI fluctuated between 0.3 and 0.7, averaging 0.51 ± 0.02, reflecting consistent genetic contributions to growth dynamics. CHIs derived from a favorable (irrigated) trial in Texas effectively separated high- and low-yielding hybrids across 41 environment-tester combinations, achieving significant differentiation in 27 (CHIAUC) and 28 (CHIFPCA1) environments. In comparison, only 21 environments were differentiated when using grain yield alone. Genomic mapping of temporal NGRDI revealed key quantitative trait loci (QTLs) linked to maize growth, containing candidate genes including br2, phyC1, wus1, mads69, cct1, rap2, miR172, and gl15, associated with canopy development, flowering regulation, and drought adaptation. Integrating multi-omics data into phenomic- and environment-informed genomic prediction models improved yield prediction accuracy by approximately 18.5%, particularly for untested genotypes in both tested and untested environments. These findings demonstrate that multi-omics integration provides a scalable framework for enhancing maize performance and advancing grain yield prediction across diverse agricultural systems.

Modern agriculture faces an urgent need to improve nutrient use efficiency while reducing environmental impacts. Here, we show that ancestral traits controlling rhizosphere microbiome functions can be reintroduced into elite maize through targeted teosinte introgressions. Using near-isogenic lines, we mapped microbiome-associated phenotypes (MAPs) derived from teosinte that suppress nitrification and denitrification-key microbial processes contributing to nitrogen loss. These introgressions altered root exudate chemistry, resulting in distinct microbial assemblies and enhanced nitrogen retention. We identified candidate loci and exudate metabolites responsible for suppressive activity and demonstrated their functional effects in vitro. These findings reveal a genetic and biochemical basis for rewilding microbiome-mediated ecosystem services in crops, offering a scalable path toward sustainable nutrient management in global agriculture.

OBJECTIVES: The genomes to fields (G2F) 2024 Maize Genotype by Environment (GxE) Prediction Competition challenged participants to develop and submit their best performing models to predict grain yield for the 2024 maize GxE project field trials, using G2F data collected from 2014 to 2023 and other publicly available data. DATA DESCRIPTION: The G2F Maize GxE Project is a collaborative effort, with all generated data made publicly available. The resource presented here includes the training and test datasets used for the G2F 2024 Maize GxE Prediction Competition. Specifically, data collected from 2014 to 2023 served as the training set to predict grain yield in the 2024 test set. The dataset comprises phenotypic, genotypic, soil, weather, and environmental covariate data, along with metadata describing environments (year-location combinations). It has been curated and lightly filtered for quality control and to ensure consistent naming across years. Competitors also had access to readme files that describe the structure and content of the datasets.

The use of computational and data-driven approaches to accelerate and optimize breeding programs is becoming common practice among plant breeders. Simulations allow breeders to evaluate potential changes in breeding schemes in a time- and cost-efficient manner. However, accurately simulating traits that match empirical trait data remain a challenge. Here we tested if incorporating information about the genetic architecture from genome-wide association studies (GWASs) of maize (Zea mays L.) agronomic traits with varying heritabilities into simulations can improve the concordance between simulated and empirical data in a population of hybrids developed from crosses of 333 maize recombinant inbred lines grown in 4-11 environments. Using at least 200 nonredundant top GWAS hits as causative variants, regardless of statistical significance, resulted in mean correlations between simulated and empirical trait data of 0.397-0.616 within environments and 0.610-0.915 across environments. Reducing the GWAS-estimated marker effect sizes in the simulations further improved concordance with empirical data. This study provides valuable insights into methods for simulating more realistic phenotypes for digital breeding to parallel empirical trait distributions and shows that these simulated traits are highly concordant with observed variance partitioning (i.e., genotype, environment, etc.) and genomic prediction performance.

Biological indicators, including particulate organic matter C (POMC) and N (POMN), potentially mineralizable N (PMN), fluorescein diacetate (FDA) hydrolysis, and permanganate oxidizable C (POXC), may prove useful soil health indicators if they can link management to soil productivity. We evaluated relationships between indicators of soil health and routine soil tests, with management practices, and corn (Zea mays L.) yield in organic grain systems in the US Midwest. For this, we used site-specific covariates and management typologies that describe N fertility, cropping intensity, and crop diversity. Soil samples were collected from 43 fields within 2 weeks of corn planting. Fields amended with animal manure had significantly higher POMC, POMN, PMN, and inorganic N concentrations than those relying only on green manure. Both POMC and potassium concentrations increased with cropping system diversity. Crop diversity was also positively related to POXC, FDA, and calcium when perennials were used in the rotation. Regression models relating soil health indicators to crop yield performed best for POM attributes and ranked POMN > POMC > POM C:N. The best model fit for POMN and crop yield included soil organic carbon, texture, and seasonal temperature as covariates. Results reveal shortcomings in management typologies applied to complex farming systems and demonstrate how regionalized on-farm studies can account for site-specific factors influencing the effects of management on soil health. Results suggest POM has promise as a soil health indicator, and future work should quantify its contributions to soil N supply, soil physical condition, and crop yield.

Fusarium graminearum is one of the most important plant-pathogenic fungi that causes disease on wheat and maize, as it decreases yield in both crops and produces mycotoxins that pose a risk to human and animal health. Resistance to Fusarium head blight (FHB) in wheat is well studied and documented. However, resistance to Gibberella ear rot (GER) in maize is less understood, despite several similarities to FHB. In this review, we synthesize existing literature on the colonization strategies, toxin accumulation, genetic architecture, and potential mechanisms of resistance to GER in maize and compare it with what is known regarding FHB in wheat. There are several similarities in the infection and colonization strategies of F. graminearum in maize and wheat. We describe multiple types of GER resistance in maize and identify distinct genetic regions for each resistance type. We discuss the potential of phenylpropanoids for biochemical resistance to F. graminearum. Phenylpropanoids are well characterized, and there are many similarities in their functional roles for resistance between wheat and maize. These insights can be utilized to improve maize germplasm for GER resistance and are also useful for FHB resistance breeding and management.Copyright (c) 2025 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Maize (Zea mays) kernel composition is critical for food, feed, and industrial applications. Improving traits such as starch, protein, oil, fiber, and ash requires understanding their genetic basis. We conducted genome-wide association studies (GWAS) and variance genome-wide association studies (vGWAS) analyses using 954 inbred lines from the USDA-ARS North Central Regional Plant Introduction Station collection to identify loci influencing both trait means and variability. We detected 10 significant single nucleotide polymorphisms (SNPs) associated with five kernel traits, some of which colocalized with known genes such as waxy1 and gras7. vGWAS uncovered additional loci not detected by standard GWAS, highlighting its value as a complementary tool. Genomic selection models, including ridge-regression best linear unbiased prediction, reproducing kernel Hilbert space, and random forest, achieved moderate prediction accuracies (0.41-0.55), with parametric and semi-parametric models showing less prediction bias. Although our dataset was derived from unreplicated genebank seed, key findings, particularly for protein and starch, were consistent with results from replicated field trials, supporting the utility of genebank-derived high-quality samples for initial genomic analysis. These results highlight the potential for using existing seed resources and high-throughput phenotyping to identify candidate loci and prioritize traits for future replicated validation.

Fusarium graminearum colonizes the maize ear, causing Gibberella ear rot (GER) and producing harmful mycotoxins, including deoxynivalenol (DON) and zearalenone (ZEA). The disease can be managed in part by breeding and planting resistant maize cultivars. Resistance to GER is a quantitative and complex trait. Evaluation of diverse germplasm to identify regions and candidate genes associated with resistance may be useful for crop improvement efforts. Screening for GER is time-consuming and costly. Thus, identifying other traits that may serve as a proxy for GER resistance may accelerate resistance breeding efforts. We hypothesized that grain phenylpropanoid content and kernel composition are genetically and mechanistically related to GER resistance. We screened a diverse set of maize inbred lines for disease severity, DON, ZEA, ferulic acid, p-coumaric acid, pericarp thickness, and several kernel composition traits. Using a genome-wide association study, we identified markers associated with each phenotype and genomic regions that harbor alleles for both disease and metabolite-related phenotypes. Pathways significantly associated with GER-related traits were related to detoxification, cell wall integrity, and lignin biosynthesis. End-season ferulic acid and p-coumaric acid concentrations are not strong proxies for GER resistance, but secondary metabolites are important components of the maize-Fusarium graminearum pathosystem. Furthermore, lignin-deficient brown midrib mutants exhibited increased susceptibility to GER, underscoring the importance of lignin composition in limiting fungal colonization. The study highlights the multifaceted nature of GER resistance, involving both biochemical and structural defenses. These findings provide valuable targets for breeding programs aiming to enhance GER resistance and reduce mycotoxin contamination in maize.

Plant breeding in the public sector is a multigenerational process that creates new plant varieties intended to meet current and future needs of society. Many public sector plant breeding programs are over a century old, and they continue to curate plant genetic resources that are far older still. While individual breeders serve as temporary leaders of these programs and the plant genetic resources they maintain, it is only their institutions that have the capacity to provide the necessary generational glue, enabling the accrual of long-term value to both breeders and society. Identifying best practices to ensure mutual benefit to both public sector breeders and their institutions is critical to achieving the smooth leadership transitions necessary for the sustainability and long-term impact of public breeding programs. The findings presented here suggest that the successful passing of the torch in such programs depends not only on strategic institutional support but also, critically, on the routine actions and mindset of the breeders entrusted with their leadership.

Participatory breeding and crop selection can satis­fy the needs of underserved groups of farmers (e.g., organic producers, farmers producing spe­cialty grain for niche markets) neglected by the modern global seed industry. Participatory research methods that value local knowledge and facilitate the active involvement of producers, researchers, and other actors involved in the agri-food system are tactics that can help us achieve sustainable agri­culture. Interest in the use of participatory methods to increase the value of U.S. land-grant universities to society has grown rapidly during the last decade. Interest includes re-engagement in the develop­ment of maize hybrids that perform well in a diverse range of heterogeneous growing environ­ments and that are better suited for sustainability-minded producers, buyers, and consumers. Sys­tems-based breeding aimed at protecting the envi­ronment and providing food, fiber, and energy while considering equity issues, has been proposed as a way to overcome the shortcomings of privat­ized approaches. In this article, we consider recent projects that use collaborative methods for hybrid maize breeding, cultivar testing, and genetic research to develop, identify, and deliver traits associated with crop performance, quality, and sus­tainability. Three case studies consider the efforts focused on developing non-GMO varieties for organic and specialty markets. We find that, unlike many successful efforts focused on the improve­ment of other crops, there are few promising mod­els for participatory breeding of hybrid maize. Even though many projects have sought to involve stakeholders with a variety of methods, all have struggled to meaningfully engage farmers in maize hybrid improvement. Still, our reflection of case studies calls for systems-based breeding and sug­gests a path forward. This route would seek to address the needs, perspectives, and values of a broader range of actors participating in the food system by leveraging technologies and infrastruc­ture in service of the public. Land-grant universities are well positioned to play a crucial role in coordi­nating efforts, facilitating partnerships, and sup­porting breeding programs that satisfy societal wants that include health, equity, and care.

Background and Aims Nitrogenous fertilizers provide a short-lived benefit to crops in agroecosystems, but stimulate nitrification and denitrification, processes that result in nitrate pollution, N2O production, and reduced soil fertility. Recent advances in plant microbiome science suggest that genetic variation in plants can modulate the composition and activity of rhizosphere N-cycling microorganisms. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the rhizosphere nitrifier and denitrifier microbiome under "real-world" conventional agricultural conditions. Methods To capture an extensive amount of genetic diversity within maize we grew and sampled the rhizosphere microbiome of a diversity panel of germplasm that included ex-PVP inbreds (Z. mays ssp. mays), ex-PVP hybrids (Z. mays ssp. mays), and teosinte (Z. mays ssp. mexicana and Z. mays ssp. parviglumis). From these samples, we characterized the microbiome, a suite of microbial genes involved in nitrification and denitrification and carried out N-cycling potential assays. Results Here we are showing that populations/genotypes of a single species can vary in their ecological interaction with denitrifers and nitrifers. Some hybrid and teosinte genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbred genotypes stimulated/did not inhibit these N-cycling activities. These potential differences translated to functional differences in N2O fluxes, with teosinte plots producing less GHG than maize plots. Conclusion Taken together, these results suggest that Zea genetic variation can lead to changes in N-cycling processes that result in N leaching and N2O production, and thereby are selectable targets for crop improvement. Understanding the underlying genetic variation contributing to belowground microbiome N-cycling into our conventional agricultural system could be useful for sustainability.

Key message We 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. Abstract 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, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.

OBJECTIVES:The Genomes to Fields (G2F) 2022 Maize Genotype by Environment (GxE) Prediction Competition aimed to develop models for predicting grain yield for the 2022 Maize GxE project field trials, leveraging the datasets previously generated by this project and other publicly available data.DATA DESCRIPTION:This resource used data from the Maize GxE project within the G2F Initiative [1]. The dataset included phenotypic and genotypic data of the hybrids evaluated in 45 locations from 2014 to 2022. Also, soil, weather, environmental covariates data and metadata information for all environments (combination of year and location). Competitors also had access to ReadMe files which described all the files provided. The Maize GxE is a collaborative project and all the data generated becomes publicly available [2]. The dataset used in the 2022 Prediction Competition was curated and lightly filtered for quality and to ensure naming uniformity across years.

Overuse of synthetic nitrogen fertilizers in agroecosystems causes environmental pollution and human harm at a global level. Nitrogenous fertilizers provide a short-lived benefit to crops in the agroecosystem, but stimulate microbially-mediated nitrification and denitrification, processes that result in N pollution, greenhouse gas (GHG) production, and reduced soil fertility. Recent advances in plant microbiome science suggest that plants can modulate the composition and activity of rhizosphere microbial communities. These rhizosphere communities act as an extended phenotype, primed by genetic variation in the plant host. Genetic variation in traits (e.g., plant secondary metabolites, root architecture, immune system, etc.) act as mechanistic selective agents on the composition of the microbiome. Here we attempted to determine whether genetic variation exists in Zea mays for the ability to influence the extended phenotype of rhizosphere soil microbiome composition and function. Specifically, we determined whether plants’ influence on soil nitrogen cycling activities was altered by plant genetics and thereby allowing it to be incorporated into breeding practices. To capture an extensive amount of genetic diversity within maize we sampled the rhizosphere microbiome of a germplasm chronosequence that included ex-PVP inbreds, hybrids, and teosinte ( Z. mays ssp . mexicana and Z. mays ssp. parviglumis ). We observed that potential N cycling processes were influenced by plant genetics. Teosinte and some hybrid genotypes supported microbial communities with lower potential nitrification and potential denitrification activity in the rhizosphere, while inbreds stimulated/did not inhibit these undesirable N-cycling activities. These potential differences translated to functional differences in N 2 O production, with teosinte plots producing less GHG than maize plots. Furthermore, within these Zea cultivars we found that plant genetics explained a significant amount of variation in the microbiome, particularly among different nitrification and denitrification functional genes within the community. We found that potential nitrification, potential incomplete denitrification, and overall denitrification rates, but not abundance of N-cycling genes of rhizosphere soils were influenced by growth stage and plant genetics. Taken together, these results suggest that crop selection can lead to changes in root phenotypes that could suppress unsustainable N-cycling processes. Reintroducing stress-adapted and “wild” root characteristics into modern germplasm may be a way to manipulate soil microbiomes at both a composition and functional level to improve sustainability.

Plants have a surprising capacity to alter their environmental conditions to create adequate niches for survival and stress tolerance. This process of environmental transformation, commonly referred to as “extended phenotypes” or “niche construction”, has historically been studied in the domain of ecology, but this is a process that is pervasive across the plant kingdom. Furthermore, research is beginning to show that plants’ extended phenotypes shape the assembly and function of closely associated microbial communities. Incorporation and understanding the role that plant-extended phenotypes play in agriculture may offer novel, bioinspired methods to manage our arable soil microbiomes. Here, we review the challenges agriculture faces, the plant extended phenotypes we know to shape the microbiome, and the potential utilization of this knowledge to improve the environmental impact of agriculture. Understanding how plant extended phenotypes shape microbial communities could be a key to creating a sustainable future with both plants and microbiomes in consideration.

Understanding how plants adapt to specific environmental changes and identifying genetic markers associated with phenotypic plasticity can help breeders develop plant varieties adapted to a rapidly changing climate. Here, we propose the use of marker effect networks as a novel method to identify markers associated with environmental adaptability. These marker effect networks are built by adapting commonly used software for building gene coexpression networks with marker effects across growth environments as the input data into the networks. To demonstrate the utility of these networks, we built networks from the marker effects of ∼2,000 nonredundant markers from 400 maize hybrids across 9 environments. We demonstrate that networks can be generated using this approach, and that the markers that are covarying are rarely in linkage disequilibrium, thus representing higher biological relevance. Multiple covarying marker modules associated with different weather factors throughout the growing season were identified within the marker effect networks. Finally, a factorial test of analysis parameters demonstrated that marker effect networks are relatively robust to these options, with high overlap in modules associated with the same weather factors across analysis parameters. This novel application of network analysis provides unique insights into phenotypic plasticity and specific environmental factors that modulate the genome.

ObjectivesThis release note describes the Maize GxE project datasets within the Genomes to Fields (G2F) Initiative. The Maize GxE project aims to understand genotype by environment (GxE) interactions and use the information collected to improve resource allocation efficiency and increase genotype predictability and stability, particularly in scenarios of variable environmental patterns. Hybrids and inbreds are evaluated across multiple environments and phenotypic, genotypic, environmental, and metadata information are made publicly available.Data descriptionThe datasets include phenotypic data of the hybrids and inbreds evaluated in 30 locations across the US and one location in Germany in 2020 and 2021, soil and climatic measurements and metadata information for all environments (combination of year and location), ReadMe, and description files for each data type. A set of common hybrids is present in each environment to connect with previous evaluations. Each environment had a collaborator responsible for collecting and submitting the data, the GxE coordination team combined all the collected information and removed obvious erroneous data. Collaborators received the combined data to use, verify and declare that the data generated in their own environments was accurate. Combined data is released to the public with minimal filtering to maintain fidelity to the original data.

Objectives This report provides information about the public release of the 2018–2019 Maize G X E project of the Genomes to Fields (G2F) Initiative datasets. G2F is an umbrella initiative that evaluates maize hybrids and inbred lines across multiple environments and makes available phenotypic, genotypic, environmental, and metadata information. The initiative understands the necessity to characterize and deploy public sources of genetic diversity to face the challenges for more sustainable agriculture in the context of variable environmental conditions. Data description Datasets include phenotypic, climatic, and soil measurements, metadata information, and inbred genotypic information for each combination of location and year. Collaborators in the G2F initiative collected data for each location and year; members of the group responsible for coordination and data processing combined all the collected information and removed obvious erroneous data. The collaborators received the data before the DOI release to verify and declare that the data generated in their own locations was accurate. ReadMe and description files are available for each dataset. Previous years of evaluation are already publicly available, with common hybrids present to connect across all locations and years evaluated since this project’s inception.