From d66bd4aa0bf9c4d9b8793a4e308c9e80691b440f Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Sun, 12 Nov 2023 21:40:01 +0100 Subject: Enable links to source, upgrade to bootstrap 5 --- docs/reference/soil_scenario_data_EFSA_2015.html | 245 ++++++++--------------- 1 file changed, 89 insertions(+), 156 deletions(-) (limited to 'docs/reference/soil_scenario_data_EFSA_2015.html') diff --git a/docs/reference/soil_scenario_data_EFSA_2015.html b/docs/reference/soil_scenario_data_EFSA_2015.html index cb3cf14..239596c 100644 --- a/docs/reference/soil_scenario_data_EFSA_2015.html +++ b/docs/reference/soil_scenario_data_EFSA_2015.html @@ -1,196 +1,129 @@ - - - - - - - -Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - +scenario and model adjustment factors from p. 15 and p. 17 are included.">Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfm + Skip to contents + +
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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

- +
+

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

- +
+
+

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
-
- -
+
+

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
+
+
+
- + - - - + -- cgit v1.2.1