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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:title>Envirotyping for Quantitative Genetics and Plant Breeding</dc:title>
  <dc:title>R package EnvRtype version 0.1.3</dc:title>
  <dc:description>An enviromics toolkit for genotype-by-environment analysis and
    envirotype-informed genomic prediction. Collects worldwide daily and hourly
    weather from 'NASA POWER', soil profiles from 'SoilGrids', elevation and
    bioclimatic layers from 'WorldClim', agro-ecological zones from 'FAO GAEZ',
    and downscaled 'CMIP6' climate scenarios, with checkpointed retrieval that
    can resume after an interrupted run. Derives agro-meteorological parameters
    (solar radiation, evapotranspiration, thermal time, atmospheric demand),
    runs a FAO-56 single crop coefficient soil water balance, and maps daily
    weather onto crop developmental stages. Builds environmental covariable
    matrices, envirotype typologies and frequency matrices, mega-environment
    clusters, historical risk profiles and target population of environments
    weights. Reduces interval-resolved envirotypes by principal component
    analysis, delineates soil zones with Gaussian mixture models, and constructs
    linear, Gaussian and arc-cosine environmental relatedness kernels. Fits
    Bayesian reaction-norm kernel models and predicts trained genotypes at
    untested locations and planting dates, with coverage diagnostics, spatial
    mapping and planting-window optimisation. Simulation functions generate
    multi-environment trials and enviromes with known architecture for power
    analysis. Methods are described in Costa-Neto, Galli, Carvalho, Crossa and
    Fritsche-Neto (2021) &lt;doi:10.1093/g3journal/jkab040&gt;, and build on the
    Bayesian genotype-by-environment kernel sampler of Granato, Cuevas,
    Luna-Vazquez, Crossa, Montesinos-Lopez, Burgueno and Fritsche-Neto (2018)
    &lt;doi:10.1534/g3.118.200435&gt;.</dc:description>
  <dc:type>Software</dc:type>
  <dc:relation>Imports: ggplot2, grDevices, graphics, reshape2, stats, tools, utils</dc:relation>
  <dc:relation>Suggests: cluster, curl, geobr, ggrepel, httr, jsonlite, knitr, mclust,
nasapower, parallel, randomForest, rmarkdown, sf, terra,
testthat (&gt;= 3.0.0)</dc:relation>
  <dc:creator>Germano Costa-Neto &lt;germano.cneto@gmail.com&gt;</dc:creator>
  <dc:publisher>Comprehensive R Archive Network (CRAN)</dc:publisher>
  <dc:contributor>Germano Costa-Neto [aut, cre],
  Fernanda Pontes [aut]</dc:contributor>
  <dc:rights>GPL-3</dc:rights>
  <dc:date>2026-10-22</dc:date>
  <dc:format>application/tgz</dc:format>
  <dc:identifier>https://CRAN.R-project.org/package=EnvRtype</dc:identifier>
  <dc:identifier>doi:10.32614/CRAN.package.EnvRtype</dc:identifier>
</oai_dc:dc>