GIS–FA for prediction in untested environments
End-to-end pipeline integrating factor–analytic mixed models, environmental data (climate, soil, spectral) and thematic maps to recommend common bean lines in regions without experimental trials.
Projects
NASA POWER agroclimatic variables downloader
Script for downloading agroclimatic variables from NASA POWER, including temperature, radiation, humidity, rainfall and derived environmental indicators for large-scale enviromics workflows.
SoilGrids soil variables downloader
Script for downloading global soil property layers from SoilGrids, including clay, silt, sand, bulk density, organic carbon, nitrogen, cation exchange capacity and other pedological variables at multiple depths.
MODIS spectral bands downloader
Script for downloading MODIS spectral bands and vegetation indices (NDVI, EVI), handling extraction, temporal filtering, and preprocessing for use in enviromics and remote sensing.
Environmental grid generator (GridMaker 2.0)
Script for generating spatial grids used to retrieve climate, soil and remote-sensing environmental covariates, supporting enviromics workflows and multi-environment trial analysis.
Spatial interpolation with kriging
R script for performing spatial interpolation using ordinary and universal kriging, supporting prediction of environmental variables and creation of high-resolution spatial layers for enviromics and G×E studies.
Selected publications
- Blasques, G.M., Dias, L.A.S., Araújo, M.S. et al. (2025). Enhancing enviromics-based predictions in common bean multi-environment trials. Scientific Reports. https://doi.org/10.1038/s41598-025-21882-x
- Pereira, G.R., Araújo, M.S., Chaves, S.F.S., Blasques, G.M. et al. (2025). Exploring genotype-by-environment interactions in tropical soybean multi-environment trials. Euphytica. https://doi.org/10.1007/s10681-025-03576-7
About me
- Biotechnological Engineer | PhD Candidate in Genetics and Plant Breeding at UFV.
- Background in plant tissue culture, with expertise in protoplast technology, mutagenesis, suspension cell culture, and elicitation strategies.
- Research focused on Genotype-by-Environment Interaction, genomics and enviromics, integrating environmental covariates to enhance breeding strategies.
- Skilled in quantitative genetics, statistical modelling, and experimental design to support data-driven breeding decisions.
- Experience in data analysis, predictive modelling, and computational tools (R).
- Committed to bridging genetics, statistics and biometrics to develop high-performing crops, leveraging big data and machine-learning approaches to accelerate genetic gain in plant breeding.