Location: Plant Germplasm Introduction and Testing Research
Project Number: 2090-21000-037-029-S
Project Type: Non-Assistance Cooperative Agreement
Start Date: Aug 1, 2025
End Date: Jul 31, 2028
Objective:
The funds will be used to pay part of the annual salary and benefits of a Washington State University research assistant professor, and of student laborers, under the supervision of the cooperator. The increasing global demand for sustainable, nutritious, and stress-tolerant food sources has positioned lentil as a key crop in the expanding plant-based protein market. Lentils are rich in seed protein, essential minerals, and dietary fiber, and are naturally non-GMO, making them highly attractive to health-conscious and environmentally aware consumers. However, high temperatures during the growing season pose a major threat to lentil production, particularly when seeds are forming, leading to reductions in yield and nutrition in the seeds. This project aims to identify and characterize lentil genotypes that maintain high yield, seed protein, and essential minerals when grown under heat stress. We will use drones and robots to measure useful traits on plants grown under hotter than usual conditions and look for genes that allow them to grow well despite the heat. We will run further tests to make sure these genes do make lentil plants more heat resistant and make these tools available to breeders, who will use them to breed new, high yielding and healthy lentil cultivars for U.S. farmers.
Approach:
This effort will complement ongoing USDA ARS initiatives aimed at improving the nutritional and agronomic performance of pulse crops. This project will assess the natural variation in heat stress tolerance among diverse lentil genotypes over multiple years; characterize genetic architecture underlying seed protein concentration and mineral (Fe, K, and Mg) accumulation in lentil under heat stress conditions, compared to non-stress conditions, by integrating genome-wide association studies (GWAS) with metabolic pathway analysis; and integrate marker development, genomic prediction, gene expression, and mineral profiling, to validate GWAS results and create resources to allow the rapid selection of lentil lines for heat stress tolerance and improved nutritional quality. To achieve these goals, advanced AI-based machine learning methods will be implemented to accelerate the assessment of complex traits using high-throughput phenotyping technologies. Expected outputs include breeder-friendly KASP markers and genomic prediction models; breeding lines with high agronomic and yield performance under heat stress, and with high nutritional content in the seeds; and fast and efficient field phenotyping techniques.
Two diverse lentil panels will be grown over three years under normal and heat stressed conditions and agronomic and seed nutritional components will be measures. High-throughput laboratory and field phenotyping tools will be used, including a Near Infrared (NIR) sensor to measure protein, Unmanned Aerial Vehicle (UAV)-mounted multispectral imaging and a ground robot with three cameras with LED illumination, two light detection and ranging (LiDAR) sensors, FTK GPS, and other onboard sensors to assist in navigation. Agronomic, yield, and nutritional components will be measured under heat stress and normal conditions on all entries of the two lentil panels plus check varieties in an augmented field design over three years. These panels have been previously genotyped, so we will test genomic selection models and GWAS to identify and validate genes and markers linked to heat stress resistance. This will characterize the genetic architecture underlying seed protein concentration and mineral accumulation (Fe, K, and Mg) in lentil under heat stress conditions. Each panel will be used to validate genetic results in the other panel, and quantitative RT PCR will be used as further validation that identified genes are important in heat stress. These resources will be made publicly available for lentil breeders.