About

Michael Gore is a professor of molecular breeding and genetics for nutritional quality and holds the Liberty Hyde Bailey professorship at Cornell University. He is a faculty member in the Plant Breeding and Genetics Section within the School of Integrative Plant Science. Additionally, Michael is a faculty fellow at both the Atkinson Center for a Sustainable Future and the Cornell Institute for Food Systems. He earned his BS and MS degrees from Virginia Tech in Blacksburg, Virginia, and completed his PhD at Cornell University. Prior to joining Cornell's faculty, he worked as a Research Geneticist with the USDA-ARS at the Arid-Land Agricultural Research Center in Maricopa, Arizona. Michael Gore's expertise lies in quantitative genetics and genomics, with a particular focus on the genetic dissection of metabolic seed traits related to nutritional quality. He also develops and applies field-based, high-throughput phenotyping tools to advance plant breeding and genetics research. In his teaching role, he instructs courses such as PLBRG 4070 – Nutritional Quality Improvement of Food Crops and PLBRG 4110 – High-Throughput Plant Phenotyping. Beyond Cornell, he teaches short courses at the Tucson Plant Breeding Institute and other international venues. He serves on the editorial boards of The Plant Phenome Journal, Genetics, and Plant Breeding and Biotechnology, and has served as Chair for the Plant Breeding Coordinating Committee (SCC080), a USDA-sponsored advisory group comprising representatives from land grant universities.

Research topics

• Biology
• Genetics
• Computer Science
• Botany
• Agronomy
• Computational biology
• Ecology
• Machine Learning
• Evolutionary biology
• Artificial Intelligence
• Social Science
• Political Science
• Sociology
• Pedagogy
• Engineering ethics
• Geography
• Biotechnology
• World Wide Web
• Data science
• Public relations
• Medicine
• Engineering

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

• Quantifying yield losses from Bt resilience among maize cultivars in South Africa

  Nature Communications · 2026-04-01

  articleOpen accessSenior author

  Genetically modified crops have provided economic and social benefits since becoming commercially available. One of the most successful and widely used applications is the integration of genes from the soil bacterium Bacillus thuringiensis for protection against damaging pests. Here, we leverage a robust dataset of 85,133 field-trial maize observations spanning all major production regions in South Africa from 1980-2018 to estimate yield gains associated with the first wave of genetically modified cultivars and explore the potential dynamic erosion of these gains since resistance was reported among first wave of single gene Bacillus thuringiensis cultivars. Leveraging the cultivars commercial release year, we find that genetically modified yield gains increased dynamically from their initial introduction in 2000, peaking at approximately 0.55 MT/ha around 2006, after which they significantly eroded to near-zero by 2014. Interestingly, this erosion was followed by a dramatic rebound in gains, reaching an in-sample high of approximately 0.75 MT/ha.

  PublisherOA PDFDOI

• Insights into biomass accumulation and challenges in grain yield prediction of elite breeding materials using UAV‐based vegetation indices in soft red winter wheat

  The Plant Phenome Journal · 2026-03-31

  articleOpen access

  Abstract High‐throughput phenotyping (HTP) techniques have brought new opportunities to understand and evaluate key traits in plant breeding programs. Combining multiple measures through time and random regression models permits a more comprehensive understanding of the genetic and environmental effects on trait expression over time. This study aims to understand the genetic basis of biomass accumulation in winter wheat and how this biomass is related to grain yield using unmanned aerial vehicle (UAV)‐based vegetation indices. A large panel of 596 soft red winter wheat genotypes was evaluated for agronomic performance in six environments to verify the ability of HTPs to predict grain yield using multivariate genomic prediction and random regression with Legendre polynomials to model growth through time. An additional set of 22 breeding lines was directly measured for above‐ground biomass, serving as a ground truth for the HTP‐derived biomass estimates. Cumulative vegetation indices were found to be a reliable method to infer biomass accumulation. Vegetation indices capture reliable phenotypes but exhibit low and inconsistent genetic correlation to grain yield, especially when incorporating residual covariance between traits. Predictive abilities of grain yield increased when using vegetation indices as a secondary trait in a multi‐trait genomic prediction model, but increases were highly variable across environments and growing stages, which may be confounded by micro‐environmental variation and lead to biased estimates of true genetic merit. Our results suggest that UAV‐based vegetation indices can be used to understand genetic parameters of biomass accumulation, but wheat breeders should use caution in their use as proxies for grain yield.

  PublisherDOI

• Unlocking the potential of <i>Capsicum</i> Germplasm Collections for Climate Resilience and Fruit Quality

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-03-28

  articleOpen access

  Abstract Climate change increasingly threatens global Capsicum (pepper) production. Accelerating the deployment of climate-resilient cultivars requires effective use of genetic diversity conserved in genebanks. We implement a “turbocharging” strategy in Capsicum by integrating genome-wide association studies and genomic prediction in a core collection (n = 423), followed by genomic prediction across the global collection (n = 10,250) using the core as a training population. We generated genomic estimated breeding values (GEBVs) for 31 high-accuracy traits (r &gt; 0.5) encompassing hyperspectral phenotypes (heat/control), agronomic performance (heat/control) and fruit quality. To enhance accessibility and decision-making, we developed a large language model (LLM) integrated application that enables flexible, preference-based selection of candidates. By narrowing the parental decision space, this framework streamlines screening of large germplasm collections while balancing climate resilience, quality attributes and market demands. Our approach provides a scalable decision-support system to accelerate climate-resilient Capsicum breeding and maximize global genetic resources.

  PublisherDOI

• Drone‐based phenotyping of maize for multiple disease resistance and yield in breeding field trials

  The Plant Phenome Journal · 2026-04-14

  articleOpen access

  Abstract Improving selection for multiple disease resistance (MDR) and yield in maize ( Zea mays L.) requires high‐throughput, objective phenotyping tools, particularly under field conditions where several foliar diseases co‐occur. We evaluated drone‐based multispectral vegetation indices (VIs) for predicting resistance to northern leaf blight (NLB; inoculated), northern leaf spot (NLS; natural), anthracnose top dieback (ATD; natural), and for predicting grain yield across 2 years in near‐isogenic inbreds, near‐isogenic hybrids, and a diverse hybrid panel. VIs showed lower coefficients of variation but broad‐sense heritability ranging from 0.09 to 0.95, compared with 0.30 to 0.99 for visual disease scores and 0.21 to 0.97 for yield. Correlations between VIs and ground traits were strongest in near‐isogenic hybrids, particularly for early‐season yield prediction ( r = 0.98–0.99 in 2018; r = 0.87–0.90 in 2019), and moderate for total disease severity (e.g., r = −0.61 to −0.68 in 2018). Associations were weaker and less consistent in the diverse hybrid panel (yield r = 0.11–0.28). Disease‐specific signals were temporally structured: NLS correlated most strongly with early‐season VIs ( r = −0.59 to −0.75), whereas ATD was best detected mid‐season ( r = −0.63 to −0.66) along with NLB ( r = −0.66 to −0.75). Overall, multispectral VIs captured meaningful canopy variation related to MDR and yield, with predictive performance depending on germplasm structure and flight timing. These findings highlight the potential of drone‐based temporal phenotyping to complement visual assessments and improve selection efficiency in maize breeding programs.

  PublisherDOI

• Genome-wide Association Study of Kernel Carbohydrates in the Wisconsin Sweet Corn Diversity Panel Revealed Loci beyond Classic Endosperm Genes

  Journal of the American Society for Horticultural Science · 2026-04-23

  articleOpen access

  Sweet corn ranks among the top vegetable crops by production value in the United States. The sweetness, tenderness, and texture of sweet corn kernels are the most important traits for eating quality. This study investigated the variation and genetic associations of seven endosperm carbohydrates traits—starch, water-soluble polysaccharides, total polysaccharides, glucose, fructose, sucrose, and total sugar—within a large sweet corn diversity panel. Predicted kernel carbohydrate composition was generated using high-throughput near-infrared spectroscopy. We found that carbohydrate content varied widely across the diversity panel, and endosperm mutation groups differed significantly from one another for most traits. Genotype was the main source of variance of all factors in the mixed linear model for all phenotypes, explaining 48% to 69% of the variation. Through a genome-wide association study of the diversity panel using 86,445 genetic markers, 191 unique markers were identified for the seven carbohydrate traits in addition to the major endosperm genes shrunken2 and sugary1. Of the 191 unique markers, 57 were identified as significant quantitative trait loci through multiple linear regression with backward elimination. The large number of loci suggests that in sweet corn breeding programs, carbohydrates are best selected for using genomic selection.

  PublisherDOI

• Affordable Phenomics special topic—Foreword for <i>The Plant Phenome Journal</i>

  The Plant Phenome Journal · 2026-03-09

  articleOpen accessSenior author

  Abstract The Affordable Phenomics special topic in The Plant Phenome Journal showcased recent advances that expand the accessibility, cost‐effectiveness, and scalability of plant phenotyping technologies. This collection of 15 articles presented innovative approaches, ranging from low‐cost sensors and open‐source analytical pipelines to artificial intelligence–driven image analysis and spectroscopy, that address the financial and technical barriers limiting widespread adoption of plant phenomics. In this foreword, we highlight the contributions featured in the special topic. The foreword also serves as an overview of the state of the art in affordable phenomics by summarizing the vision and perspectives presented in the invited review “Affordable phenomics: Expanding access to enhancing genetic gain in plant breeding.”

  PublisherDOI

• Genomic sampling and population structure of farmer-maintained varieties reveal previously uncharacterized diversity of <i>Theobroma cacao</i> L. in Costa Rica

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-01

  articleOpen access

  Abstract Understanding crop genetic diversity is essential for conservation and breeding, yet farmer-maintained germplasm remains largely underrepresented in genomic studies. Theobroma cacao L. has a complex domestication history and extensive global diversity, and cacao currently cultivated in Central America, particularly in Costa Rica, has been understudied compared to South American and Mexican cultivars despite cultural and historical importance. In this study, we investigate the genetic diversity of cacao from farmer-managed systems across Costa Rica to search for Criollo germplasm and identify and characterize any unique local genetic groups. Ninety-four trees were sampled from 17 farms across four regions of the country and sequenced using whole genome resequencing. Farmer materials were analyzed alongside 166 previously characterized reference accessions representing major cacao genetic groups. Population structure analyses, phylogenetic reconstruction, and network approaches revealed that Costa Rican cacao encompasses multiple known genetic groups, including Criollo-derived lineages, while also harboring locally distinct diversity not fully represented in current global reference collections. Analyses revealed close kinship between many accessions with no clear geographic patterns corresponding to the observed population differentiation, reflecting the effects of farmers in creating dominant patterns of gene flow through seed-saving, clonal propagation, and sharing genotypes among farms. Heterozygosity levels varied substantially among individuals, consistent with a mixture of highly inbred Criollo trees and more heterozygous, admixed genotypes. We find that farmer-managed cacao systems are reservoirs of genetic diversity, including possibly rare or historically important lineages, underscoring the value of these farming systems for effective conservation and management of genomic resources for cacao resilience and improvement.

  PublisherDOI

• Total tocopherol levels in maize grain depend on chlorophyll biosynthesis within the embryo

  BMC Plant Biology · 2025-03-13 · 5 citations

  articleOpen accessSenior author

  BACKGROUND: Tocopherols are a class of lipid-soluble compounds that have multiple functional roles in plants and exhibit vitamin E activity, an essential nutrient for human and animal health. The tocopherol biosynthetic pathway is conserved across the plant kingdom, but source of the key tocopherol pathway precursor, phytol, is unclear. Two protochlorophyllide reductases (POR1 and POR2) were previously identified as loci controlling the natural variation of total tocopherols in maize grain, a non-photosynthetic tissue. POR1 and POR2 are key genes in chlorophyll biosynthesis yet the contribution of the chlorophyll biosynthetic pathway to tocopherol biosynthesis is still not understood. RESULTS: We took two approaches to alter the activity of these two POR genes within kernel tissue, physiological treatments and CRISPR/Cas9-mediated knockouts, to determine the role of chlorophyll biosynthesis for tocopherol content. Since light is required for POR enzymatic activity, we imposed a dark treatment on developing kernels, which reduced chlorophyll a and tocopherols levels in embryo tissue by 92-99% and 87-90%, respectively, compared to the light treatment. In CRISPR/Cas9-mediated knockouts, the levels of chlorophyll a and tocopherols in embryos of the por1 por2 double homozygous mutant were reduced by 98-100% and 76-83%, respectively, compared to WT. CONCLUSION: These findings demonstrate that tocopherol synthesis in maize grain depends almost entirely on phytol derived from chlorophyll biosynthesis within the embryo. POR1 and POR2 activity play crucial roles in chlorophyll biosynthesis, underscoring the importance of POR alleles and their activity in the biofortification of vitamin E levels in non-photosynthetic grain of maize.

  PublisherOA PDFDOI

• Prioritizing parents from global genebanks to breed climate-resilient crops

  Nature Climate Change · 2025-05-29 · 12 citations

  article
  PublisherDOI

Recent grants

• NRI: Deep Learning Unmanned Aircraft Systems for High-Throughput Agricultural Disease Phenotyping

  NSF · $1.1M · 2015–2019

Frequent coauthors

• Edward S. Buckler

  Cornell University

  166 shared

• Alexander E. Lipka

  University of Illinois Urbana-Champaign

  53 shared

• Élodie Gazave

  Cornell University

  47 shared

• David D. Fang

  Agricultural Research Service

  45 shared

• Richard G. Percy

  United States Department of Agriculture

  41 shared

• Rebecca Nelson

  Cornell University

  40 shared

• Don C. Jones

  Cotton (United States)

  38 shared

• James Frelichowski

  Southern Plains Agricultural Research Center

  38 shared

Labs

• The Gore LabPI

Awards & honors

• Public Sector Plant Breeding Impact Award (2024)
• National Association for Plant Breeding Cotton Genetics Rese…
• Joint Cotton Breeding Committee Fellow (2022)
• American Association for the Advancement of Science Fellow (…
• Crop Science Society of America President’s Awards for Excel…