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Properties of the predefined scenarios from the EFSA guidance from 2015

Source: R/soil_scenario_data_EFSA_2015.R
soil_scenario_data_EFSA_2015.Rd

Properties of the predefined scenarios used at Tier 1, Tier 2A and Tier 3A for the concentration in soil as given in the EFSA guidance (2015, p. 13/14). Also, the scenario and model adjustment factors from p. 15 and p. 17 are included.

Format

A data frame with one row for each scenario. Row names are the scenario codes, e.g. CTN for the Northern scenario for the total concentration in soil. Columns are mostly self-explanatory. rho is the dry bulk density of the top soil.

Source

EFSA (European Food Safety Authority) (2015) EFSA guidance document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil. EFSA Journal 13(4) 4093 doi:10.2903/j.efsa.2015.4093

Examples

if (FALSE) {
  # This is the code that was used to define the data
  soil_scenario_data_EFSA_2015 <- data.frame(
    Zone = rep(c("North", "Central", "South"), 2),
    Country = c("Estonia", "Germany", "France", "Denmark", "Czech Republik", "Spain"),
    T_arit = c(4.7, 8.0, 11.0, 8.2, 9.1, 12.8),
    T_arr = c(7.0, 10.1, 12.3, 9.8, 11.2, 14.7),
    Texture = c("Coarse", "Coarse", "Medium fine", "Medium", "Medium", "Medium"),
    f_om = c(0.118, 0.086, 0.048, 0.023, 0.018, 0.011),
    theta_fc = c(0.244, 0.244, 0.385, 0.347, 0.347, 0.347),
    rho = c(0.95, 1.05, 1.22, 1.39, 1.43, 1.51),
    f_sce = c(3, 2, 2, 2, 1.5, 1.5),
    f_mod = c(2, 2, 2, 4, 4, 4),
    stringsAsFactors = FALSE,
    row.names = c("CTN", "CTC", "CTS", "CLN", "CLC", "CLS")
  )
  save(soil_scenario_data_EFSA_2015, file = '../data/soil_scenario_data_EFSA_2015.RData')
}

# And this is the resulting dataframe
soil_scenario_data_EFSA_2015
#>        Zone        Country T_arit T_arr     Texture  f_om theta_fc  rho f_sce
#> CTN   North        Estonia    4.7   7.0      Coarse 0.118    0.244 0.95   3.0
#> CTC Central        Germany    8.0  10.1      Coarse 0.086    0.244 1.05   2.0
#> CTS   South         France   11.0  12.3 Medium fine 0.048    0.385 1.22   2.0
#> CLN   North        Denmark    8.2   9.8      Medium 0.023    0.347 1.39   2.0
#> CLC Central Czech Republik    9.1  11.2      Medium 0.018    0.347 1.43   1.5
#> CLS   South          Spain   12.8  14.7      Medium 0.011    0.347 1.51   1.5
#>     f_mod
#> CTN     2
#> CTC     2
#> CTS     2
#> CLN     4
#> CLC     4
#> CLS     4