Trends of data contained in XML dumps of the Swiss Register of Plant Protection Products

Johannes Ranke

Last change 4 November 2025 (rebuilt 2025-11-04)

knitr::opts_chunk$set(tidy = FALSE, cache = FALSE)
options(knitr.kable.NA = '')
library(srppphist)
library(dplyr)
library(kableExtra)
library(ggplot2)

Trends of numbers of active substances

The figure below shows the number of active substances contained in products occurring in the Swiss Register of Plant Protection Products over the years starting from 2011. Unauthorized products and active substance only contained in such products are filtered out using the function srppp_xml_product_use_no_authorized.

The categories are based on the classification in the register. Each active substance can have more than one category, therefore the total number of active numbers is smaller than the sum of the active substances in each category.

years <- names(srppp_list)
names(years) <- years
actives_by_category <- sapply(years, function(year) {
  sr <- srppp_list[[year]]
  actives_categories <- sr$products |>
    mutate(nonauth = srppp_xml_product_use_not_authorized(name, year, exhaustionDeadline)) |>
    filter(!nonauth) |> 
    left_join(sr$categories, by = "pNbr", relationship = "many-to-many") |>
    select(pNbr, category = category_de) |>
    unique() |>
    left_join(sr$ingredients, by = "pNbr", relationship = "many-to-many") |>
    dplyr::filter(type == "ACTIVE_INGREDIENT") |>
    left_join(sr$substances, by = "pk") |>
    select(pk, category, substance_de) |>
    unique() |>
    arrange(pk, substance_de, category)

  n_actives <- actives_categories |>
    select(pk) |>
    unique() |>
    nrow()

  actives_categories |>
    group_by(category) |>
    summarise(n = n()) |>
    arrange(desc(n))

  n_tibble <- actives_categories |>
    mutate(category = case_when(
      grepl("Herbizid", category) ~ "Herbicides",
      grepl("Fungizid", category) ~ "Fungicides",
      grepl("Insektizid", category) ~ "Insecticides",
      grepl("Saatbeizmittel", category) ~ "Seed dressings",
      grepl("Regulator", category) ~ "PGR",
      grepl("Phytoregulator", category) ~ "PGR", # new in 2024
      grepl("Lebende Organismen", category) ~ "Organisms",
      grepl("Akarizid", category) ~ "Acaricides",
      .default = "Others"
    )) |>
    group_by(category) |>
    select(substance_de, category) |> 
    distinct() |> 
    summarise(n = n())

  n <- n_tibble$n
  names(n) <- n_tibble$category
  n <- c(Total = n_actives, n)
  n <- n[c(
    "Acaricides",
    "Fungicides", 
    "Herbicides", 
    "Insecticides", 
    "Organisms", 
    "PGR", 
    "Seed dressings", 
    "Others", 
    "Total"
    )]
  return(n)
})
actives_by_category |> 
  kbl()

	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025
Acaricides	18	22	20	17	17	18	19	18	16	16	16	16	15	14	14
Fungicides	94	100	96	98	103	103	107	94	94	94	89	85	79	76	78
Herbicides	105	116	108	103	101	98	96	94	91	90	87	85	79	78	75
Insecticides	90	100	99	86	85	85	84	80	77	76	73	72	63	64	64
Organisms	53	53	54	57	60	59	61	56	56	59	59	59	62	71	76
PGR	23	23	26	26	29	29	28	27	27	27	26	26	26	26	26
Seed dressings	34	37	39	39	35	35	35	32	30	30	27	25	23	20	20
Others	80	82	80	79	80	81	84	70	72	67	62	62	65	62	64
Total	425	454	439	420	425	423	426	391	385	380	361	354	335	332	338

actives_by_category_long <- actives_by_category |>
  tibble::as_tibble(rownames = "Category") |>
  tidyr::pivot_longer(cols = where(is.numeric), names_to = "Year", values_to = "Number") |>
  ungroup() |>
  group_by(Category)

actives_by_category_long |>
  ggplot(aes(Year, Number, group = Category, color = Category)) +
  geom_point() +
  geom_line()