Igor Kuivjogi Fernandes
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
CEO
Also affiliated: International Rice Research Institute (2022); University of Arkansas System (2026); Universidade Federal de Goiás (2022–2024)
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Igor Kuivjogi Fernandes is CEO at the University of Arkansas at Fayetteville. His research utilizes machine learning to integrate genetic and environmental data for predicting crop yields, with a recent focus on maize grain yield predictions across multi-environment trials. Fernandes also studies the role of microalgae and cyanobacteria, investigating their potential as biofertilizers and exploring the techno-functional properties and bioactivity of their protein extracts. His work includes analyzing changes in microalgal fatty acid biosynthesis and carbon partitioning in response to nutrient availability and other environmental stressors. Fernandes has a publication record of 18 articles, with 350 citations and an h-index of 10. He has collaborated with researchers including Samuel B. Fernandes and Caio Canella Vieira, both at the University of Arkansas at Fayetteville.
Metrics
- h-index: 5
- Publications: 15
- Citations: 121
Positions
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CEO publications 2024–2026University of Arkansas at Fayetteville Listing
Selected Publications
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Genomic prediction and association mapping of early season flood tolerance in soybean (2025)
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Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay (2024)
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Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions (2024)
Collaboration Network
Top Collaborators
- Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
- Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
- Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay
- Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay
- Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay
- Assessing Soybean Cultivar Resistance to Target Spot Using a Detached Leaf Assay
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Genomic prediction and association mapping of early season flood tolerance in soybean
- Genomic prediction and association mapping of early season flood tolerance in soybean
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