This R package assists breeders in linking data systems with their analytic pipelines, a crucial step in digitizing breeding processes. It supports querying and retrieving phenotypic and genotypic data from systems like EBS, BMS, BreedBase, and GIGWA (using BrAPI calls). Extra helper functions support environmental data sources, including TerraClimate and FAO HWSDv2 soil database.
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).
TerraClimate is a climate and climatic water balance dataset of 14 variables for global terrestrial surfaces from 1958 to 2022 (updated annually) comparing to 7 climate variables for 1970-2000 in WorldClim dataset. All data have monthly temporal resolution and a ~4-km (1/24th degree) spatial resolution.
You can retrieve TerraClimate data using the QBMS R package in two modes, Online using API calls (minimizing download size), or Offline by downloading required NetCDF rasters first (minimizing query time). QBMS also calculate the 19 derivatives bioclimatic variables on-fly.
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
# 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# ?ini_terraclimate
ini_terraclimate('2018-09-01', '2019-06-30', c('ppt', 'tmin', 'tmax'))
# Get TerraClimate data for a given coordinate(s)
data <- get_terraclimate(y, x, '2018-09-01', '2019-06-30', c('ppt', 'tmin', 'tmax'), offline = TRUE)If you encounter a timeout error during the execution of the
ini-terraclimate() function, you can extend the timeout
duration of your environment with the following command (e.g., to 5
minutes, which is 300 seconds, instead of the default 60
seconds):
The default timeout of 60 seconds is often not sufficient for downloading large files (over 50 MB). Additionally, expecting download times of less than 1MB per second is unrealistic.