About

Shawn Kaeppler is a Professor in the Department of Plant and Agroecosystem Sciences at the University of Wisconsin-Madison. His research focuses on genetics and breeding, with current interests in maize including lignocellulosic biofuel, abiotic stress tolerance, epigenetics, and seed development and composition using genetic and genomic analysis. He also conducts genomic research in switchgrass to support the development of cultivars for biofuels.

Research topics

• Computer Science
• Biology
• Genetics
• Ecology
• Artificial Intelligence
• Machine Learning
• Geography
• Engineering
• Agronomy
• Political Science
• Data science
• Chemistry
• Engineering ethics
• Horticulture
• Waste management
• Medicine
• Pulp and paper industry
• Botany
• Biochemistry
• World Wide Web
• Organic chemistry
• Environmental science
• Evolutionary biology

Selected publications

• Genomes to fields 2024 maize genotype by environment prediction competition

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

  articleOpen access

  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.

  PublisherDOI

• Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-07

  preprintOpen access

  Abstract Genomic and phenomic analyses suggest additional heritable phenomic features can improve modeling of important end traits like senescence or yield. Field phenotyping generally uses trait values averaged across individual experimental units (plants or numerous plants within plots), ignoring the full distributional pattern of collected measures. Images of plants or plots, as captured by drones (unoccupied aerial vehicles / UAVs / drones), can be viewed as individual distribution functions that capture biological information. This study introduces and validates distributional data analysis in two crops and experiment types – cotton ( Gossypium hirsutum L.) single plant vegetation index (VI) analysis and maize ( Zea mays L.) plot-level yield predictions. In both crops, the concept of within-day variance decomposition was demonstrated. In cotton, genotypes exerted significant influences on temporal quantile functions of VIs. Maize yield prediction using distributional data with elastic-net regression indicated improvements in yield prediction between 12.7%-21.6% with quantiles outside the conventionally used median responsible for added predictive power. A novel data visualization method for per-pixel heritability allowed distributional features to be explainable and interpretable. These results have implications for future plant phenomic studies, indicating that distributional data analysis applied across temporal imagery captures novel, heritable, and interpretable biological signal that is lost when working with conventional measures of central tendency such as mean or median summary values of experimental units. Significance Repeated aerial imaging of agricultural experiments produces image data sets that capture plant development in high spatial and temporal resolutions. Frequently, images are summarized by measures of central tendency, such as mean or median values. Here, functional data distributional methods were applied to cotton ( Gossypium hirsutum L.) and maize ( Zea mays L.) image data, capturing more information than standard approaches. Cotton genotypes significantly impacted distributional spectral data while in maize, distributional data enabled more accurate predictions of grain yield versus models trained with median data alone. Distributional data were more explainable by genetics, with novel data visualization techniques able to shine light on specific parts of plant imagery with high and low genetic variance.

  PublisherOA PDFDOI

• Designing a nitrogen-efficient cold-tolerant maize for modern agricultural systems

  The Plant Cell · 2025-07-01 · 6 citations

  reviewOpen access

  Maize (Zea mays L.) is the world's most productive grain crop and a cornerstone of global food supply. However, in temperate agricultural systems, maize exhibits 2 key anomalies. First, as a tropical species, maize cannot be planted in the cold conditions of early spring when light and natural soil nitrogen are available, resulting in a shorter growing season and creating a seasonal mismatch between nitrogen accessibility and demand. Second, maize kernel protein is a major nitrogen sink, driving fertilizer demand because of the scale of cultivation. This inefficient mismatch stems from modern maize's uses and the modest nutritional value of storage proteins. To address these anomalies, we established the Circular Economy that Reimagines Corn Agriculture initiative. Our vision requires advances in 3 research areas: (ⅰ) developing cold and frost tolerance during germination and early growth to enable the use of spring nitrogen and light resources; (ⅱ) reducing nitrogen allocation to grain by reducing low-quality storage proteins and developing alternative nitrogen sinks; and (ⅲ) stabilizing soil nitrogen by enhancing biological nitrification inhibition. We present blueprints for a nitrogen-efficient, cold-tolerant maize designed to utilize the full growing season, enabling farmers in temperate regions to fully leverage maize's C4 photosynthesis, reduce fertilizer inputs, increase yields, and minimize environmental impact.

  PublisherOA PDFDOI

• Divergence of Bowman-Birk Protease Inhibitor Family into seed-specific and environmentally responsive subfamilies in Legume and Soybean: Implication for Legume Seed Composition Improvement

  bioRxiv (Cold Spring Harbor Laboratory) · 2025-08-27

  preprintOpen access

  Abstract Bowman-Birk inhibitors (BBI) are an ancient class of serine protease inhibitors originating prior to the emergence of the angiosperms. While BBIs have been preserved in the legume (Fabaceae) and cereal (Poaceae) families, they have been lost in many other divergent lineages. However, their underlying molecular evolution and regulation of BBI remain largely uncharacterized. Our study shows that BBIs in legumes and cereals are encoded by two large and divergent gene families. BBI genes in legumes have further diversified into two subfamilies with distinct gene expression patterns. Genes in one legume BBI subfamily are specifically expressed in seeds while BBI genes in the other legume subfamily and cereal do not have significant expressions in any examined tissues including seed, root, leaf and flower. The soybean BBI gene family shows evidence of expansion via whole genome, segmental and tandem duplication. Protein sequence and structural analysis predicts that functional domains for double-headed inhibitory loops and binding abilities to trypsin and chymotrypsin are largely preserved within the soybean BBI family. The seed-specific subfamily genes are specifically expressed at maturation stages and not at embryogenesis stages. The other, non-seed BBI subfamily genes are highly responsive to a distinct spectrum of signals related to abiotic and biotic stresses. Their specific expression under non-essential biological processes for plant growth and development suggests that, although BBIs have been retained in both cereals and legumes, likely due to their role in enhancing plant fitness under natural selection pressures, they are not involved in core developmental processes. This may explain why BBIs were lost in many divergent plant lineages and support their well-established roles in plant adaptation to environmental stress. Having knocked out the seed-specific BBIs through a CRISPR/Cas9 approach, we have successfully generated soybeans which exhibited 69.4 - 73.7% reduction of trypsin inhibitor activity and 76.4 - 79.4% reduced chymotrypsin inhibitor activity. The edited soybean did not show significant changes in key agronomic traits, supporting that the functions of BBIs are not essential. While BBIs in soybean seeds may have a desirable function in natural selection, they are antinutrients from an applied perspective for their use in feed and food. It provides an opportunity to reduce BBIs in seeds for quality improvement. Our findings provide insights into molecular evolution, regulation, and function of BBI in plants, and successfully demonstrate engineering BBI in seeds to result in production of food and feed of higher nutritional value with minimal impacts on the agronomic performance of the plant.

  PublisherOA PDFDOI

• Mitigating NDVI saturation in imagery of dense and healthy vegetation

  ISPRS Journal of Photogrammetry and Remote Sensing · 2025-06-18 · 18 citations

  article
  PublisherDOI

• Integrating GWAS with a gene co‐expression network better prioritizes candidate genes associated with root metaxylem phenes in maize

  The Plant Genome · 2024-07-22 · 1 citations

  articleOpen access

  Root metaxylems are phenotypically diverse structures whose function is particularly important under drought stress. Significant research has dissected the genetic machinery underlying metaxylem phenotypes in dicots, but that of monocots are relatively underexplored. In maize (Zea mays), a robust pipeline integrated a genome-wide association study (GWAS) of root metaxylem phenes under well-watered and water-stress conditions with a gene co-expression network to prioritize the strongest gene candidates. We identified 244 candidate genes by GWAS, of which 103 reside in gene co-expression modules most relevant to xylem development. Several candidate genes may be involved in biosynthetic processes related to the cell wall, hormone signaling, oxidative stress responses, and drought responses. Of those, six gene candidates were detected in multiple root metaxylem phenes in both well-watered and water-stress conditions. We posit that candidate genes that are more essential to network function based on gene co-expression (i.e., hubs or bottlenecks) should be prioritized and classify 33 essential genes for further investigation. Our study demonstrates a new strategy for identifying promising gene candidates and presents several gene candidates that may enhance our understanding of vascular development and responses to drought in cereals.

  PublisherOA PDFDOI

• Trait association and prediction through integrative <i>k</i> ‐mer analysis

  The Plant Journal · 2024-09-11 · 4 citations

  articleOpen access

  Genome-wide association study (GWAS) with single nucleotide polymorphisms (SNPs) has been widely used to explore genetic controls of phenotypic traits. Alternatively, GWAS can use counts of substrings of length k from longer sequencing reads, k-mers, as genotyping data. Using maize cob and kernel color traits, we demonstrated that k-mer GWAS can effectively identify associated k-mers. Co-expression analysis of kernel color k-mers and genes directly found k-mers from known causal genes. Analyzing complex traits of kernel oil and leaf angle resulted in k-mers from both known and candidate genes. A gene encoding a MADS transcription factor was functionally validated by showing that ectopic expression of the gene led to less upright leaves. Evolution analysis revealed most k-mers positively correlated with kernel oil were strongly selected against in maize populations, while most k-mers for upright leaf angle were positively selected. In addition, genomic prediction of kernel oil, leaf angle, and flowering time using k-mer data resulted in a similarly high prediction accuracy to the standard SNP-based method. Collectively, we showed k-mer GWAS is a powerful approach for identifying trait-associated genetic elements. Further, our results demonstrated the bridging role of k-mers for data integration and functional gene discovery.

  PublisherOA PDFDOI

• Modeling the impact of resource allocation decisions on genomic prediction using maize multi‐environment data

  Crop Science · 2024-07-23 · 3 citations

  articleOpen accessSenior authorCorresponding

  Abstract In a hybrid maize ( Zea mays L.) breeding program that utilizes genomic selection, resource allocation used in phenotypic data acquisition must be balanced between population size, number of environments, and the number of testers used for generating hybrids. Plant breeders evaluate newly developed inbred lines using multi‐environment trials to account for genotype‐by‐environment interaction effects. The replication of hybrids across environments in these trials impacts the training data accuracy for developing genomic prediction models. This study examined the impact of resource allocation scenarios on genomic prediction accuracy using a multi‐environment trial dataset generated using inbred lines crossed to multiple testers. A total of 369 Stiff Stalk double haploid lines from a synthetic mapping population were testcrossed to three non‐Stiff Stalk inbred lines as testers, PHZ51, PHK76, and PHP02, and evaluated across 34 environments by the Genomes to Fields Initiative in 2020 and 2021. Resource allocation scenarios significantly impacted site‐specific genomic prediction accuracy for unobserved hybrids in unobserved environments. A training set with three to five environments that had the highest quality data produced similar prediction accuracy as data from 10 random environments for both observed and unobserved hybrids, indicating that strong prediction models can be built with a limited set of environments for both grain yield and plant height. We found that resource‐efficient prediction models that use data from one tester and three to five environments can effectively conduct selection of untested hybrids and in untested environments. Public research programs are often limited in testing capacity, and this study provides support for genomic selection in resource‐limited breeding programs.

  PublisherOA PDFDOI

• Impact of genotype × environment interaction and selection history on genomic prediction in maize (<i>Zea mays</i> L.)

  Crop Science · 2024-10-15 · 3 citations

  articleOpen accessSenior authorCorresponding

  Abstract Breeders made remarkable progress in improving productivity and stability of cultivars. Breeding progress relies on selecting favorable alleles for performance and stability to produce productive varieties across diverse environments. In this study, we analyzed the Genomes to Fields Initiative 2018–2019 genotype by environment interaction (G × E) dataset, focusing on three populations of double haploid (DH) lines derived from crossing inbrexpired Plant Variety Protection (ex‐PVP) inbred line PHW65 with inbred lines PHN11, Mo44, and MoG. PHW65 is an Iodent/Lancaster‐type inbred; PHN11 is an Iodent type ex‐PVP line; Mo44 is a tropical‐derived inbred; and MoG is an agronomically poor line derived from the variety Mastadon. Hybrids were produced by crossing the resulting DHs with Stiff Stalk testers PHT69 and LH195. The study's objective was to determine the donor inbreds' relative value and understand the impact of selection history on genomic prediction. We conducted a two‐stage analysis to compare hybrid performance and G × E variance of the populations. G × E variance for yield was significantly lower in the PHW65 × PHN11 population relative to the PHW65 × MoG population. The reduced G × E variance of the PHN11 population led to increased indirect prediction accuracy (when training and testing data are drawn from the same population but different environments). In cross‐validation, the PHN11 population had the greatest indirect prediction accuracy 45% of the time, followed by the Mo44 population (30%) and the MoG population (25%). Results demonstrate that prediction accuracy was greater in the population with the longest history of selection for favorable alleles (PHN11), contributing to greater yield stability.

  PublisherOA PDFDOI

• Remembering Ronald L. Phillips: Pioneer in crop biotechnology, mentor, and humanitarian

  Proceedings of the National Academy of Sciences · 2024-01-18 · 2 citations

  articleOpen access1st authorCorresponding

  Micro-nano plastics originating from the prevalent usage of plastics have raised increasingly alarming concerns worldwide. However, there remains a fundamental knowledge gap in nanoplastics because of the lack of effective analytical ...Plastics are now omnipresent in our daily lives. The existence of microplastics (1 µm to 5 mm in length) and possibly even nanoplastics (<1 μm) has recently raised health concerns. In particular, nanoplastics are believed to be more toxic since their ...

  PublisherOA PDFDOI

Frequent coauthors

• Natalia de León

  University of Wisconsin–Madison

  227 shared

• C. Robin Buell

  150 shared

• Nathan M. Springer

  Bayer (United States)

  110 shared

• Heidi F. Kaeppler

  University of Wisconsin System

  88 shared

• Candice N. Hirsch

  University of Minnesota

  74 shared

• Brieanne Vaillancourt

  Applied Genetic Technologies (United States)

  71 shared

• Michael D. Casler

  University of Wisconsin–Madison

  70 shared

• Ignacio A. Ciampitti

  Kansas State University

  69 shared

Education

• Ph.D., Plant Pathology

  University of Wisconsin-Madison

  1992

• M.S., Plant Pathology

  University of Wisconsin-Madison

  1988

• B.S., Botany

  University of Wisconsin-Madison

  1985