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
Formerly Arkansas Affiliated with University of Arkansas through 2024; recent publications list University of Arkansas System.
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Igor Kuivjogi Fernandes' research focuses on the application of machine learning to agricultural science, particularly in crop yield prediction and understanding genotype-by-environment interactions. He has investigated how integrating genetic and environmental data can improve predictions for crop yields, using maize as a case study. His work also explores the use of environmental clustering to define breeding zones for crops like rice in Brazil, aiming to enhance adaptation to climate variability.
Fernandes also has an interest in the potential of microalgae and cyanobacteria. His publications include work on their use as biofertilizers to improve soil fertility and plant growth, as well as their techno-functional properties and bioactivity for potential applications in food and health, such as sources of antioxidant enzymes.
Metrics
- h-index: 10
- Publications: 18
- Citations: 350
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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