soil_scenario_data_EFSA_2015. pfm 0.3-8

- -

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

- -

Description

- -

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.

- -

Examples

## Not run: ------------------------------------
-#   # 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
-

- -

Properties of the predefined scenarios from the EFSA guidance from 2015

Format

Source

Description

Examples