Hershberger Lab

Vegetable Breeding and Genetics Research

As the world’s food supply faces the dual threats of population growth and climate change, many have rallied around a call for increased productivity and plant resiliency. While this need is indisputably clear, focusing solely on quantity omits an aspect essential to true food security – quality. To holistically meet the dietary needs of nine billion people in the next thirty years, improved crop varieties must adhere to consumer preferences and provide adequate quantities of micronutrients.

But what makes a vegetable high quality, and how might this assessment differ across stakeholder groups? What is the genetic control of good flavor? How might available genetic resources be used to improve quality both efficiently and effectively?

Using tools from genomics, proximal sensing, and computer programming, the Hershberger lab seeks to explore these questions while breeding flavorful and nutritionally dense vegetables at Clemson University.

Research projects further the breeding process
Research projects further the breeding process

Our work falls under the following aims:

  1. Establish efficient and stakeholder-driven vegetable breeding programs focused on nutritional quality and flavor
  2. Characterize the genetic variation for and control of target traits
  3. Develop and evaluate new phenotyping methods for the quantification of nutritional and consumer preferred quality traits in vegetable crops
High-dimensional phenotype data management
High-dimensional phenotype data management

The Hershberger Lab is leading a new AFRI DSFAS project, High-Dimensional Phenotype Data Management and Analysis Infrastructure for Plant Breeding, in collaboration with Trevor Rife, Lukas Mueller, Jean-Luc Jannink, and Peter Selby.

Green bean variety trials
Green bean variety trials

We partnered with McCall Farms to conduct our first variety trial in 2023 at Clemson’s PDREC. We are excited to continue this work 2024.

More

waves
waves

Originally designed application in the context of resource-limited plant research and breeding programs, ‘waves’ provides an open-source solution to spectral data processing and model development by bringing useful packages together into a streamlined pipeline.

Sweet corn transcriptomics
Sweet corn transcriptomics

We used fresh sweet corn kernel 3’RNAseq for transcriptome-wide association and prediction of carotenoid and tocochromanol content.

Cassava NIRS
Cassava NIRS

Working closely with collaborators at IITA, we successfully predicted root dry matter content with a handheld near-infrared spectrometer.