Chris Reberg-Horton
Department of Crop and Soil Sciences
Resilient Agricultural Systems Platform Director, Professor
NC State Extension
3322 Plant Sciences Building
Bio
Dr. Reberg-Horton grew up in Fairview, North Carolina, a small mountain community. He earned a B.S. in Environmental Science from the University of North Carolina, Chapel Hill. Dr. Reberg-Horton earned his M.S. from the University of California, Davis, where he worked on crop modeling in the Department of Agronomy. After working with Cooperative Extension in California and North Carolina, Dr. Reberg-Horton returned to graduate school at NC State under Dr. Nancy Creamer. Dr. Reberg-Horton’s thesis topic looked into the possibility of developing cereal rye to be more allelopathic to improve weed control from this common cover crop. Following graduation, he took a position with the University of Maine as an Assistant Professor of Sustainable Agriculture, where he worked with dairy farmers on grain and forage production.
Currently, Chris co-leads the Precision Sustainable Agriculture network consisting of on-farm and on-station experiments that utilize the latest developments in sensors, IoT platforms, and Machine Learning to collect and standardize field data from a wide range of agricultural environments. The network operates in 25 states and has more than 120 locations each year. Chris also serves as the Platform Director for Resilient Agricultural Systems at the Plant Sciences Initiative at NC State University. As such, he supports teams addressing climate change, food security, and the use of new technologies for adaptive management of farms.
Publications
- A Multiple Instance Learning Approach to Study Leaf Wilt in Soybean Plants , AGRICULTURE-BASEL (2025)
- Hairy vetch (Vicia villosa Roth) germplasm contains a cryptic second species (Vicia varia Host) , CROP SCIENCE (2025)
- Seed size has a major impact on fall seedling vigor in the cover crop hairy vetch (Vicia villosa Roth) , Crop Science (2024)
- Evaluating Chinese fiber hemp (Cannabis sativa L.) varieties and planting dates in North Carolina , AGROSYSTEMS GEOSCIENCES & ENVIRONMENT (2024)
- Genetic and environmental drivers of legume cover crop performance: Hairy vetch , CROP SCIENCE (2024)
- Mapping predicted biomass in cereal rye using 3D imaging and geostatistics , WEED SCIENCE (2024)
- Photorealistic Arm Robot Simulation for 3D Plant Reconstruction and Automatic Annotation using Unreal Engine 5 , 2024 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION WORKSHOPS, CVPRW (2024)
- Spaceborne imaging spectroscopy enables carbon trait estimation in cover crop and cash crop residues , PRECISION AGRICULTURE (2024)
- Understanding the yield impacts of alternative cover crop families and mixtures: Evidence from side-by-side plot-level panel data , AGROSYSTEMS GEOSCIENCES & ENVIRONMENT (2024)
- Addressing biases in replacement series: the importance of reference density selection for interpretation of competition outcomes , WEED SCIENCE (2023)
Grants
The remote sensing team will evaluate relevant geospatial data for Missouri to determine cover crop establishment, along with termination time periods and any evidence of cover crop grazing activity. We will start by using existing algorithms for evaluating living plant cover and adjust for Missouri conditions, along with verification of on the ground activity of cover crops from other team members. The initial two years of the project will involve further refinements in our evaluation and acreage tracking system with the geospatial data. For the last three years of the project, we will utilize the developed remote sensing techniques to continue providing updated assessments of annual cover crop acreage. As part of the work, we will use the remote sensing data as one tool among others (by other team members) for verifying practice implementation of cover crops for farmers signed up with cover crop incentives. Fields signed up by farmers will be matched with the remote sensing data based on CLU or other relevant geographic data layers.
The proposed project will look to develop and analyze systems that would allow for earth observation data (i.e., remote sensing and satellite data sets) to strengthen US agriculture resilience against cliamte change.
Project is in support of PSI: The overarching goal of this project is to accelerate development of a scientifically-based and economically-sound cover crop seed industry. We expect this systems-oriented project will make improved cover crop varieties available to 250,000 American farmers who use cover crops to protect and improve soil on 50 million acres by 2030. Work from this project will contribute significantly to substantial growth in the U.S. cover crop seed industry, more than doubling in size to become a billion-dollar a year industry by the end of the decade. Objective 1. Accelerate cover crop germplasm improvements and seed yield potential to advance the U.S. cover crop seed industry. (S, M, L) Objective 2. Evaluate traits contributing to improved performance of cover crop cultivars (S, M) Objective 3. Establish a National Cover Crop Variety Trial Program to determine optimal regional adaptation of improved cultivars. (S, M)
NRCS has an interagency collaboration with ARS to develop national decision support tools with the regional cover crop councils. The SCCC will initiate an collaborative agreement with NRCS to receive funds for train-the-trainer activities regarding these tools. Furthermore, new funds will be used to enhance regional outreach and extension activities in the SCCC as well as expand content and functionality of the SCCC website. Collectively, this work will train agricultural professionals and provide technical assistance to growers on cover crop management.
The objectives of this cooperative agreement are to: 1. Perform experiments and analyses related to development of high-throughput methods focused on utilizing machine learning tools for integrating into breeding programs. 2. Organize research projects to expand research in this field corresponding to multiple cropping systems, including but not limited to: cotton, soybean, blueberry, peanut and turf. 3. Work on development of genomics based tools within commodity groups to set the stage for future genomic selection projects.