The Fernandes Lab is part of the Center for Agricultural Data Analytics (CADA) and the Department of Crop, Soil, and Environmental Sciences at the University of Arkansas, led by Dr. Samuel B. Fernandes, Assistant Professor of Agricultural Statistics and Quantitative Genetics.
We sit at the meeting point of quantitative genetics, statistics, and machine learning. Our work spans genomic prediction, genome-wide association studies, high-throughput phenotyping, and enviromics — integrating genomic and environmental information to predict how crops will perform across diverse environments. We build and freely share the software that makes these methods usable, and we partner with breeding programs in soybean, rice, maize, sorghum, and specialty crops to translate methods into real-world genetic gain.
To connect genotype to phenotype — identifying the genes behind complex traits and predicting how plants perform across environments. By uniting quantitative genetics, statistics, and artificial intelligence, we build predictive models and open-source software that advance plant breeding, biology, and physiology.
A future where we can read the genome to explain how plants grow and perform — where pinpointing causal genes and predicting any genotype in any environment shortens breeding cycles, deepens our understanding of plant biology, and strengthens food security.
Concrete commitments that guide the projects we take on and the people we train.
Develop multi-omics prediction models that integrate genomic, environmental (enviromics), and high-throughput phenotyping data to forecast genotype-by-environment performance.
Push the statistical theory behind multi-trait, multi-environment analysis — from GWAS to genomic selection — so breeders can act on more of their data.
Create and maintain free, well-documented tools (such as our R packages) that the wider breeding and genetics community can use, trust, and extend.
Apply machine learning and AI to extract more value from the data breeding programs already collect — answering the question, "Can we better use the data we have?"
Mentor quantitative geneticists, statisticians, and data scientists for careers in industry and academia.
Collaborate with breeding programs in soybean, rice, maize, sorghum, and specialty crops to turn new methods into measurable genetic gain in farmers' fields.
If our mission resonates with your background in statistics, genetics, engineering, computer science, or agronomy, we'd love to hear from you.
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