Location: Forage Seed and Cereal Research Unit
Project Number: 2072-21000-056-006-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Sep 1, 2024
End Date: Aug 31, 2026
Objective:
The objective of this project is to refine and implement crop models and related simulations to assess the ecological costs, benefits, and impacts of hemp production. Assimilation of remote sensing, simulations, and empirical data will advance our predictive understanding of hemp as part of cropping systems in the pacific northwest.
Approach:
1. Production. We will select germplasm adapted to the latitudinal range of Pacific Western U.S. environments with targeted improved industrial and food use traits. A novel approach will develop new varieties to reduce seed shattering while delivering high grain yields. All of efforts will use a classical plant breeding approach augmented by genomics and machine learning to accelerate progress. In our remote breeding nurseries, we will concurrently develop low-latitude-adapted germplasm that also provides opportunities for Southeastern U.S. farmers participate in this emergent market. Together, these efforts will solve major issues with the currently available genetics that do not satisfy current demand for U.S. produced hemp materials. Select characterized germplasm will be submitted to the National Plant Germplasm System for use by U.S. hemp breeders.
2. Genomic and materials properties characterization. We will incorporate large-scale genome and trait data plus emerging artificial intelligence (AI) approaches to accelerate breeding progress. Genetic line selections will be based on fiber size, density, cellulose, hemicellulose, and lignin contents; microstructure; and thermal-mechanical properties. Grain chemical constituent composition will be determined for protein, lipid, and carbohydrate content and food and beverage products by anti-quality flavor characteristics. Genome sequencing, quantitative trait loci (QTL) analyses, and Genome-Wide Association Studies (GWAS) will identify genes with small-to-large effects on phenotype. GWAS and genomic prediction (GP) models will be extended by deep learning (DL) approaches to understand complex quantitative traits. DL will be applied to genotype and phenotype, environmental (latitude, temperature, production practices) data, and different primary processing methods on industrial materials, grain, and byproduct quality metrics. Genome data generated from this work will be submitted to the National Center for Biotechnology Information (NCBI) database.
3. Materials and by-product utilization. We will use processed bast fibers and hurd byproducts to create biodegradable and compostable packaging products using molded pulp technology and produce nano- or semi-nano scale cellulosic materials for packaging and other applications. Pilot-scale manufacturing systems will produce a wide range of molded-pulp packaging products including nursery pots, fresh produce boxes, and take-away trays. In addition, our biodegradable superhydrophobic coatings will be applied to improve water resistance by reducing hydrophilicity of molded products to meet packaging application needs. We will utilize eco-friendly delignification techniques to produce high-quality cellulosic materials such as nano- or semi-nano scale holocellulose and cellulose fibers for applications in packaging and other areas. Optimization models will examine combinations of genetics, production, and materials utilization to determine whole-systems feasibilities