Mohammad Abdullah Al Bari
Title: Assistant Professor
Email Address: mabari@nmsu.edu
Research area: Breeding, Genetics, Genomics, Quantitative Genetics, High-Throughput Phenotyping, Machine Learning
Education
PhD in Quantitative Genetics & Breeding, Plant Sciences, North Dakota State University, Fargo, ND. GPA 3.9/4.0
Dissertation: Usefulness of Expired Proprietary (ex-PVP) Maize (Zea mays L.) Germplasm for U.S. Northern Breeding Programs
M.S. in Genetics and Plant Breeding, Bangladesh Agricultural University (BAU), Mymensingh, Bangladesh. GPA 3.9/4.0
Thesis: Genetic Analysis of Yield and Yield-Contributing Characters in Spring Wheat (Triticum aestivum L.) under two Sowing Dates
B.S. in General Agriculture, Bangladesh Agricultural University (BAU), Mymensingh, Bangladesh. First Class Honors.
Research Interests
Our goals are to boost breeding efficiency and enhance cultivar development. We aim for the resilience, productivity, and profitability of Alfalfa and other forage crops through interdisciplinary research to serve growers and relevant stakeholders. We are keen on integrating traditional breeding techniques with cutting-edge tools to improve water-use efficiency, salinity tolerance, and resistance to diseases and pests, while advancing crop yield and forage quality. Our approach includes conducting association analyses, population genomics, and comparative genomic studies to identify and apply genetic markers for marker-assisted breeding. By combining genomic and phenomic data with environmental variables to develop precise predictive analytics. Employing CRISPR for targeted gene editing, leveraging big data, machine learning, and AI, we strive to advance trait genetics and understand genotype-by-environment interactions, thereby creating tools for selection and crop improvement. Additionally, train and educate the next generation of competent plant scientists.
Professional Experience
I served multiple institutions in different capacities. Gained experience in germplasm enhancement, population development, population maintenance, inbred development hybrid production. I have managed large-scale, multi-disciplinary trials and contributed to the variety development process for diverse crops, including maize, wheat, sorghum, and legumes. My proficiency spans analyzing association studies and implementing genomic and phenomic selections for crop improvement. I lead the development of deep learning tools for yield forecasting. My commitment to research excellence is demonstrated by securing competitive grants and mentoring 15 graduate students.
I am dedicated to harnessing state-of-the-art tools to close genetics knowledge gaps and enhance breeding efficiency. My work aims to accelerate genetic gains, meet the demands of growers and end-users, and promote sustainable agricultural practices globally.