- Postdoctoral Research Associate
- Supervisor: Lindsey du Toit
Education
- BS, Plant Sciences (Alemaya University)
- MS, Plant Sciences (Alemaya University)
- PhD, Plant Breeding and Genetics (North Dakota State University)
Biography
Sintayehu Daba joined Dr. du Toit’s vegetable seed pathology program at the WSU Mount Vernon NWREC in September 2024 as a postdoctoral researcher for the USDA NIFA Specialty Crops Research Initiative Grant No. 2023-51181-41321. This collaborative project, involving multiple universities and a USDA unit in Salinas, CA, aims to develop molecular breeding methods and phenotyping tools to accelerate spinach cultivar development, focusing on five diseases, including Fusarium wilt, which Sintayehu will research. He will build on the research on spinach Fusarium wilt accomplished by a former postdoctorate in the vegetable seed pathology program, Sanjaya Gyawali. Sintayehu is originally from Ethiopia, where he earned both his BSc in Plant Sciences and MSc in Plant Breeding from Alemaya University. After his BSc, he spent 10 years as a barley breeder at the Ethiopian Institute of Agricultural Research, including three years leading regional barley breeding programs. In 2010, he moved to the U.S. to pursue a PhD in Plant Breeding and Genetics at North Dakota State University (NDSU), focusing on genome-wide association mapping of agronomic, disease resistance, and quality traits in barley accessions from Ethiopia, ICARDA (International Center for Agricultural Research in the Dry Areas), and the U.S. After earning his PhD in 2015, he worked at Purdue University for nearly four years in the soft red winter wheat breeding program, overseeing overall operations of the program, and later briefly returned to NDSU for genomic prediction research in the hard red spring wheat breeding program. In October 2020, he joined the USDA-ARS Western Wheat Quality Lab, where he completed several projects on pea protein and starch quality. His research interests include genetic mapping, proteomics, and developing predictive models such as genomic prediction and NIRS calibration.