From aed160d7f0eaf5865e2bd9bf6c4b1c9d7b13d911 Mon Sep 17 00:00:00 2001 From: Ranke Johannes Date: Wed, 31 Jan 2024 13:16:17 +0100 Subject: Reorganise data generation - Use inst/data_generation for R code generating data as in some of my other packages - data/*.RData files were checked using https://github.com/jranke/dotfiles/blob/main/bin/rda_diff contents were not changed - Remove ChangeLog, the history is in the git logs - Update docs and some links contained therein - use \doi{} markup - Move logs to log directory --- docs/reference/soil_scenario_data_EFSA_2015.html | 179 ++++++----------------- 1 file changed, 47 insertions(+), 132 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..abea480 100644 --- a/docs/reference/soil_scenario_data_EFSA_2015.html +++ b/docs/reference/soil_scenario_data_EFSA_2015.html @@ -1,69 +1,14 @@ - - - - - - - -Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfm - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -Properties of the predefined scenarios from the EFSA guidance from 2015 — soil_scenario_data_EFSA_2015 • pfm - - - - - - - - - - - - - + + -
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Properties of the predefined scenarios from the EFSA guidance from 2015

@@ -116,81 +47,65 @@ 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.

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+ 
soil_scenario_data_EFSA_2015
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- -

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.

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Source

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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
+ doi:10.2903/j.efsa.2015.4093

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+ +
+

Examples

+ 
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