soil_scenario_data_EFSA_2015. pfm 0.3-1

+ +

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')
+# ## End(Not run)
+
+# 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