weather: Weather variables
In SoyURT: USDA Northern Region Uniform Soybean Tests Dataset
weather R Documentation
Weather variables
Description
Weather variables obtained from NASA's Prediction of Worldwide Energy
Resource (https://power.larc.nasa.gov/) for the 591 environments in
the historical series analyzed by Krause et al. (2022).
Usage
weather
Format
A data frame in messy format with 504 observations on the following 5 variables:
locationlocations, 63 levels (observed locations in the historical series)
LONlongitude
LATlatitude
DOYday of the year
YYYYMMDDcalendar date in the format YYYY/MM/DD
daysFromStartdays from average planting date
T2Mdaily average temperature at 2 meters
T2M_MAXdaily maximum temperature at 2 meters
T2M_MINdaily minimum average temperature at 2 meters
PRECTOTrainfall precipitation
WS2Mwind speed at 2 meters
RH2Mrelative humidity at 2 meters
T2MDEWdew point at 2 meters
ALLSKY_SFC_LW_DWNdownward thermal infrared (longwave) radiative flux
ALLSKY_SFC_SW_DWNinsolation incident on a horizontal surface
nduration of sunshine in hours
VPDthe deficit of vapor pressure
SPVthe slope of saturation vapor pressure curve
ETPevapotranspiration
PETPdeficit of evapotranspiration
GDDgrowing degree-days
FRUEeffect of temperature on radiation use efficiency
T2M_RANGEdaily temperature range at 2 meters
PTTphotothermal time (GDD \times daylight in hours)
PTRphotothermal ratio (GDD / daylight in hours)
Note
Comprehensive R Archive Network (CRAN) policy limits R package size to 5 Mb.
In order to give the users new opportunities of data analysis, we provide
weather data for all combinations of locations (63) and years (31), resulting in
information for 1,953 environments. If an environment was not observed in a
given year, weather data was retrieved with the average planting and maturity
data based on the empirical data for that location. This data set can be downloaded
here.
Source
Krause, M. D., Dias, K. O. G., Singh, A. K., and Beavis. W. D. (2022). Using large
soybean historical data to study genotype by environment variation and
identify mega-environments with the integration of genetic and non-genetic
factors. bioRxiv, doi: 10.1101/2022.04.11.487885
SoyURT documentation built on June 13, 2022, 9:05 a.m.
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| weather | R Documentation |
Weather variables
Description
Weather variables obtained from NASA's Prediction of Worldwide Energy Resource (https://power.larc.nasa.gov/) for the 591 environments in the historical series analyzed by Krause et al. (2022).
Usage
weather
Format
A data frame in messy format with 504 observations on the following 5 variables:
locationlocations, 63 levels (observed locations in the historical series)
LONlongitude
LATlatitude
DOYday of the year
YYYYMMDDcalendar date in the format YYYY/MM/DD
daysFromStartdays from average planting date
T2Mdaily average temperature at 2 meters
T2M_MAXdaily maximum temperature at 2 meters
T2M_MINdaily minimum average temperature at 2 meters
PRECTOTrainfall precipitation
WS2Mwind speed at 2 meters
RH2Mrelative humidity at 2 meters
T2MDEWdew point at 2 meters
ALLSKY_SFC_LW_DWNdownward thermal infrared (longwave) radiative flux
ALLSKY_SFC_SW_DWNinsolation incident on a horizontal surface
nduration of sunshine in hours
VPDthe deficit of vapor pressure
SPVthe slope of saturation vapor pressure curve
ETPevapotranspiration
PETPdeficit of evapotranspiration
GDDgrowing degree-days
FRUEeffect of temperature on radiation use efficiency
T2M_RANGEdaily temperature range at 2 meters
PTTphotothermal time (GDD \times daylight in hours)
PTRphotothermal ratio (GDD / daylight in hours)
Note
Comprehensive R Archive Network (CRAN) policy limits R package size to 5 Mb. In order to give the users new opportunities of data analysis, we provide weather data for all combinations of locations (63) and years (31), resulting in information for 1,953 environments. If an environment was not observed in a given year, weather data was retrieved with the average planting and maturity data based on the empirical data for that location. This data set can be downloaded here.
Source
Krause, M. D., Dias, K. O. G., Singh, A. K., and Beavis. W. D. (2022). Using large soybean historical data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors. bioRxiv, doi: 10.1101/2022.04.11.487885
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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