Linking data management systems to analytics is an important step in breeding digitalization. Breeders can use this R package to Query the Breeding Management System(s) like BMS, BreedBase, and GIGWA (using BrAPI calls) and help them to retrieve phenotypic and genotypic data directly into their analyzing pipelines developed in R statistical environment.
TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution. The data cover the period from 1958-2020. We plan to update these data periodically (annually).
###Reference: Abatzoglou, J., Dobrowski, S., Parks, S. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci Data 5, 170191 (2018). https://doi.org/10.1038/sdata.2017.191
install.packages("remotes")
remotes::install_github("icarda-git/QBMS")# load the QBMS library
library(QBMS)
# ICARDA Tal-Hadya station, Syria
data <- get_terraclimate(36.016, 36.943, '1979-09-01', '2012-06-30')
View(data$climate[[1]])
View(data$biovars[[1]])
# 1. ICARDA Merchouch station, Morocco
# 2. ICARDA Terbol station, Lebanon
# 3. ICARDA Amlaha station, India
x <- c(-6.716, 35.917, 76.884)
y <- c(33.616, 33.833, 23.111)
# ICARDA stations (WorldClim temporal range)
worldclim_range <- get_terraclimate(y, x, '1970-01-01', '2000-12-31', c('ppt', 'tmin', 'tmax'))
# ?calc_biovars
worldclim_norm <- apply(worldclim_range$biovars[[2]][,1:19], 2, function(x) round(mean(x), 1))
# biovars bio1 bio2 bio3 bio4 bio5 bio6 bio7 bio8 bio9 bio10 bio11
# terraclimate 16.2 13.5 41.4 713.5 33.8 1.0 32.8 8.0 24.3 24.5 7.4
# worldclim/10 15.5 13.6 43.0 631.0 33.0 1.6 31.4 7.3 23.2 23.2 7.3
#
# NOTE: all worldcim biovars derivative from temperature (i.e., bio1-bio11) have a scale factor of 10
# except bio3 because it is a ratio.
#
# biovars(cont) bio12 bio13 bio14 bio15 bio16 bio17 bio18 bio19
# terraclimate 661.0 182.1 0.0 113.5 419.2 0.3 0.9 398.5
# worldclim 592.0 140.0 0.0 99.0 368.0 1.0 2.0 368.0