Relationships between management, soil health indicators, routine soil tests, and corn yield in organic grain systems

Agronomy Journal · 2026-01-01

Abstract 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.

Optimizing population simulations to accurately parallel empirical data for digital breeding

Crop Science · 2026-01-01

Abstract 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.

Genomes to fields 2024 maize genotype by environment prediction competition

BMC Research Notes · 2026-02-09 · 1 citations

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. 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.

Dissecting genetic variance structure and evaluating genomic prediction models for single-cross hybrids derived from Stiff Stalk and Non-Stiff Stalk maize heterotic groups

bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-13

Abstract 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 multi-kernel models, using additive and two alternative non-additive genomic relationship matrices, to estimate the variance components associated with the GCA of Stiff Stalk (SS) and Non-Stiff Stalk (NSS) heterotic groups and the specific combining ability (SCA) 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 multi-kernel 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 types of non-additive matrices produced similar results, affirming the robustness of the inferred genetic architecture. Overall, this study sheds light on the future use of US maize commercial germplasm and demonstrates how GBLUP-based multi-kernel models can improve the efficiency of hybrid breeding programs.

Inter-variety competition dynamics in US inbred and hybrid maize

bioRxiv (Cold Spring Harbor Laboratory) · 2026-02-28

ABSTRACT 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.

Environmental Drivers of Maize Ear Rots and Mycotoxin Accumulation Across North America

Plant Disease · 2026-04-21

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.

Lost and found: Rediscovering microbiome-associated phenotypes that reshape agricultural sustainability

Science Advances · 2026-01-01 · 2 citations

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.

Including prior design intent information to enhance contour extraction robustness for CD-SEM metrology

2026-02-20

Sustaining public plant breeding programs across generations

Crop Science · 2025-05-01

Abstract 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.

Mean and variance heterogeneity loci impact kernel compositional traits in maize

The Plant Genome · 2025-10-09

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.