For each year, the first XML dump published by the registration authority is used, with few exceptions, where a corrected dump was published shortly after the first one. Please note the use conditions set out by the registration authority for the XML dumps currently published at their website.
Format
list A named list of srppp::srppp_dm objects created with the companion
package 'srppp'. The list elements are named with the years from 2011 to the
current year as a character vector
Use conditions set out by the registration authority
Please consult the use conditions of the XML data files currently published by the Federal Food Safety and Veterinary Office (FSVO). For the the historical data contained in this package, the following points are of particular importance:
In cases of doubt, the definitive source of information are always the original registration documents, for present as well as past authorisations.
Commercial use of the data provided as XML files is not permitted without the written consent of the FSVO.
Additional notes regarding proper use of the data
As we include only historical, not current authorisation data in this package, please note the following:
The descriptions of products and their authorised uses contained in this package refer to past authorisations. Regarding current authorisation, please refer to the Swiss Register of Plant Protection Products, or use the
srppppackage which facilitates reading in the current registration data into R.Products whose authorisation has expired or which have been withdrawn from the parallel import list are present in the historical data until the end of the period during which use by the end user is still permitted ('exhaustionDeadline'). This date and the sell-out period ('soldoutDeadline') are indicated in the
productstable of eachsrppp_dmobject.If you use the historical registration data in the form provided by this package, please cite the package as described by the output of
citation("srppphist").
Examples
names(srppp_list)
#> [1] "2011" "2012" "2013" "2014" "2015" "2016" "2017" "2018" "2019" "2020"
#> [11] "2021" "2022" "2023" "2024"
# In case you are interested in the registered uses of products containing
# a certain active substance, here is some example code
library(dplyr, warn.conflicts = FALSE)
# Step 1: Get the pk number of a certain active substance
pk_active <- srppp_active_substances |>
filter(substance_de == "Cyproconazole") |>
pull(pk)
# Step 2: Get the products (pNbrs) containing that substance in 2018
products_2018 <- srppp_list[["2018"]]$ingredients |>
filter(pk == pk_active)
# Step 3: Get the associated uses
uses_2018 <- products_2018 |>
left_join(srppp_list[["2018"]]$uses, by = "pNbr")
# Step 4: Add additional information, e.g. the cultures
uses_x_cultures_2018 <- uses_2018 |>
left_join(srppp_list[["2018"]]$cultures, by = c("pNbr", "use_nr"))
# Step 5: Application rate in g/ha
uses_x_cultures_2018_rate <- uses_x_cultures_2018 |>
srppp::application_rate_g_per_ha() |>
select(pNbr, use_nr, application_area_de, culture_de, rate_g_per_ha)
# If this should be repeated for all available years, it is convenient
# to define a function that extracts the desired information, apply it
# to the list of yearly product registers, and combine the results in a
# table.
uses_cultures_rates <- function(sr, pk_active) {
sr$ingredients |>
filter(pk == pk_active) |>
left_join(sr$uses, by = "pNbr") |>
left_join(sr$cultures, by = c("pNbr", "use_nr")) |>
srppp::application_rate_g_per_ha() |>
select(pNbr, use_nr, application_area_de,
culture_de, rate_g_per_ha)
}
# Test the function
uses_cultures_rates(srppp_list[["2018"]], 116L)
#> # A tibble: 72 × 5
#> pNbr use_nr application_area_de culture_de rate_g_per_ha
#> <int> <int> <chr> <chr> <dbl>
#> 1 6941 1 Feldbau Gerste 80
#> 2 6941 2 Feldbau Triticale 80
#> 3 6941 3 Feldbau Zuckerrübe 64
#> 4 6941 4 Feldbau Winterroggen 80
#> 5 6941 4 Feldbau Triticale 80
#> 6 6941 5 Feldbau Weizen 80
#> 7 6941 6 Gemüsebau Rande 64
#> 8 6941 7 Feldbau Weizen 80
#> 9 6941 8 Feldbau Weizen 80
#> 10 6941 9 Zierpflanzen Zier- und Sportrasen 80
#> # ℹ 62 more rows
# Create a list of tables
uses_cultures_rates_list <- lapply(srppp_list, uses_cultures_rates, 116L)
# Combine the tables for all years
uses_cultures_rates_all_years <- bind_rows(uses_cultures_rates_list,
.id = "year")
print(uses_cultures_rates_all_years)
#> # A tibble: 972 × 6
#> year pNbr use_nr application_area_de culture_de rate_g_per_ha
#> <chr> <int> <int> <chr> <chr> <dbl>
#> 1 2011 3741 1 Obstbau Steinobst 24
#> 2 2011 3741 2 Obstbau Zwetschge 24
#> 3 2011 3741 2 Obstbau Kirsche 24
#> 4 2011 3741 3 Feldbau Weizen 80
#> 5 2011 3741 4 Feldbau Weizen 80
#> 6 2011 3741 5 Feldbau Roggen 80
#> 7 2011 3741 6 Feldbau Zuckerrübe 80
#> 8 2011 3741 7 Feldbau Weizen 80
#> 9 2011 3841 1 Feldbau Weizen 79.7
#> 10 2011 3841 2 Feldbau Weizen 79.7
#> # ℹ 962 more rows
# Find names of original products and sales permissions (W-Numbers with dash)
uses_cultures_rates_all_years |>
select(year, pNbr) |>
unique() |>
left_join(srppp_products[c("pNbr", "wNbr", "name")], by = "pNbr",
relationship = "many-to-many")
#> # A tibble: 125 × 4
#> year pNbr wNbr name
#> <chr> <int> <chr> <chr>
#> 1 2011 3741 4068 Alto 100 SL
#> 2 2011 3841 4070 Tiptor
#> 3 2011 4442 4374 Atemi 50 SL
#> 4 2011 6814 5787 Fusatox R Fluid
#> 5 2011 6941 5772 Dexter
#> 6 2011 6952 5958 Maxim Star
#> 7 2011 6954 5945 Radius
#> 8 2011 6955 5966 Solitaer
#> 9 2011 7153 5995 Agora
#> 10 2011 7153 5995-1 Desi>proXX
#> # ℹ 115 more rows