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.
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
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.
## 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_2015Zone 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