weather {SoyURT}

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:

location

locations, 63 levels (observed locations in the historical series)

LON

longitude

LAT

latitude

DOY

day of the year

YYYYMMDD

calendar date in the format YYYY/MM/DD

daysFromStart

days from average planting date

T2M

daily average temperature at 2 meters

T2M_MAX

daily maximum temperature at 2 meters

T2M_MIN

daily minimum average temperature at 2 meters

PRECTOT

rainfall precipitation

WS2M

wind speed at 2 meters

RH2M

relative humidity at 2 meters

T2MDEW

dew point at 2 meters

ALLSKY_SFC_LW_DWN

downward thermal infrared (longwave) radiative flux

ALLSKY_SFC_SW_DWN

insolation incident on a horizontal surface

n

duration of sunshine in hours

VPD

the deficit of vapor pressure

SPV

the slope of saturation vapor pressure curve

ETP

evapotranspiration

PETP

deficit of evapotranspiration

GDD

growing degree-days

FRUE

effect of temperature on radiation use efficiency

T2M_RANGE

daily temperature range at 2 meters

PTT

photothermal time (GDD \times daylight in hours)

PTR

photothermal 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