The envirotyping pipeline provided by EnvRtype consists in three modules (1 - Environmental Sensing, 2- Macro-Environmental Characterization and 3 - Enviromic Similarity and Phenotype Prediction). Collectively, the EnvRtyping functions generate a simple workflow to collect, process and integrates envirotyping data into several fields of agricultural research, specially for predictive breeding that may include the use of genomic x enviromic relatedness information.

• EnvRtype 1.1.0 is online (1st June 2022)

• New version of the get_weather() function -- now running in parallel (Jun 2022)

• Coming Soon: get_soil() function and EPA() [environmental-phenotype associations]

• [SOLVED] Date 2021-10-14: NASA POWER server off "Error: Something went wrong with the query, no data were returned. Please see https://power.larc.nasa.gov for potential server issues."

• Join our DISCUSSION FORUM

• [SOLVED] PRECTOT variable (rainfall precipitation) is currently off from NASA POWER

• Coming soon: tutorial for using external sources of environmental data (from field micro-stations)

• [SOLVED] Coming soon (Dec 2021): tutorial for colecting soil data from SoilGrids data base

• The current version of the package is 0.0.2 (May 22th 2021)

• [SOLVED] From December 15th 2020 to January 10th 2021 this page will be under maintence. This means that we are now working in several updates and some changes will be made in some functions.

• The current version of the package is 0.0.1 (Nov 20th 2020)

• Envirotyping pipeline
• Genomic Prediction using Environmental Covariates
• Full examples and R Codes for the G3 Paper

Information

• Authorship
• Acknowledgments
• Publications
• Getting help

Install

Using devtools in R

library(devtools)
devtools::install_github('allogamous/EnvRtype',force=TRUE) # current version:  1.1.0 (June 2022)
# Enter one or more numbers, or an empty line to skip updates: 3
require(EnvRtype)

Manually installing

If the method above doesn't work, use the next lines by downloading the EnvRtype-master.zip file

setwd("~/EnvRtype-master.zip") # ~ is the path from where you saved the file.zip
unzip("EnvRtype-master.zip") 
file.rename("EnvRtype-master", "EnvRtype") 
shell("R CMD build EnvRtype") # or system("R CMD build EnvRtype")
install.packages("EnvRtype_1.1.0.tar.gz", repos = NULL, type="source") # Make sure to use the current verision

Required packages

For some users, it seems that the packages below must downloaded...(I am not a IT guy, I am really don't know why, sorry).

• EnvRtype
• raster
• nasapower
• BGGE
• foreach
• doParalell

install.packages("foreach")
install.packages("doParallel")
install.packages("raster")
install.packages("nasapower")
install.packages("rgdal")
install.packages("BGGE")
              
or
              
#source("https://raw.githubusercontent.com/gcostaneto/Funcoes_naive/master/instpackage.R");
#inst.package(c("BGGE",'foreach','doParallel','raster','rgdal','nasapower'));

install.packages(c("BGGE",'foreach','doParallel','raster','rgdal','nasapower'))

library(EnvRtype)
              

Menu

Authorship

This package is a initiative from the Allogamous Plant Breeding Lab (University of São Paulo, ESALQ/USP, Brazil).

Developer

• Germano Costa Neto, University of Sao Paulo/ Cornell University

Maintence

• Germano Costa Neto

Publications

Last update: 2021-10-10

• Costa-Neto, G., Crossa, J., and Fritsche-Neto, R. (2021). Enviromic Assembly Increases Accuracy and Reduces Costs of the Genomic Prediction for Yield Plasticity in Maize. Frontiers in Plant Science 12. doi:10.3389/fpls.2021.717552.

• Costa-Neto, G., Galli, G., Carvalho, H. F., Crossa, J., and Fritsche-Neto, R. (2021). EnvRtype: a software to interplay enviromics and quantitative genomics in agriculture. G3 Genes|Genomes|Genetics. doi:10.1093/g3journal/jkab040.

• Galli G, Horne DW, Collins SD, Jung J, Chang A, Fritsche‐Neto R, et al. (2020). Optimization of UAS‐based high‐throughput phenotyping to estimate plant health and grain yield in sorghum. Plant Phenome J 3: 1–14.

• Costa-Neto G, Fritsche-Neto R, Crossa J (2020). Nonlinear kernels, dominance, and envirotyping data increase the accuracy of genome-based prediction in multi-environment trials. Heredity (Edinb).

Acknowledgments

• Giovanni Galli

• Humberto Fanelli

• Jose Crossa, Biometrics and Statistic Unit at CIMMYT.

• Roberto Fritsche-Neto

• University of São Paulo (ESALQ/USP)

• Conselho Nacional de Desenvolvimento Científico e Tecnológico for the PhD scholarship granted to the authors of the package

• Pedro L. Longhin for additional support in Git Hub