Igor Kuivjogi Fernandes

Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions

This paper integrates environmental data with genetic data using machine learning to improve genomic prediction models for maize grain yield, showing increased predictive ability and computational efficiency.

Enviromic prediction is useful to define the limits of climate adaptation: A case study of common beans in Brazil

This paper develops an enviromic prediction approach using GAM to analyze climate impacts on common bean yield in Brazil, identifying key environmental drivers and optimal conditions for different regions and seasons.

Data-driven machine learning for pattern recognition supports environmental quality prediction for irrigated rice in Brazil

The paper uses machine learning to predict environmental quality for irrigated rice in Brazil by clustering yield data and analyzing genotype-environment interactions.