Potato breeding

The potato program’s goal is to develop and screen new varieties, advanced clones, and germplasm and to select materials better suited for North Carolina and the Southeastern US. We are working toward this goal through collaborations with the USDA-ARS, Cornell University, the University of Maine, and other breeding programs across the US. The main markets are chipping and table varieties

Cucumber breeding 

The cucumber program’s goal is to develop seedless triploid cucumber varieties, primarily for the pickling industry. A key component of this research is the development of tetraploid female lines, which will serve as parents for producing triploid hybrids. These hybrids are expected to provide desirable fruit quality while producing few or no mature seeds, offering new opportunities for the pickling market.

Genomic selection

Develop new methods and predictive models, and apply them to economically important traits such as yield, disease resistance, and postharvest and processing quality. These approaches will integrate genomic, phenotypic, environmental, and other high-throughput data to better understand trait genetics, improve selection accuracy, and accelerate the development of superior and more resilient cultivars.

HTP – high-throughput phenotyping

Reduces the cost and subjectivity of phenotyping while enabling efficient selection for important traits, such as yield, disease resistance, postharvest quality, and processing quality. It also allows breeders to evaluate larger populations more rapidly and consistently, improving selection accuracy and accelerating genetic gain.

Stochastic simulations

Help identify and compare the most effective breeding schemes and selection strategies by evaluating their expected genetic gain, genetic diversity, and long-term performance under different scenarios.

Enviromics

Integrates historical weather, soil, and crop performance data to better characterize environmental variation, understand G×E interactions, and identify the environments where specific genotypes are most likely to perform well. This information can help optimize field-trial allocation and improve selection efficiency.

Computational tools

Develop new R packages, algorithms, and user-friendly applications to support breeding and quantitative genetics activities. These tools will help breeders efficiently analyze complex datasets, implement advanced statistical and genomic methods, and translate research outputs into practical breeding decisions.