---
title: The Swiss Register of Plant Protection Products as a Relational Data Model
author: Johannes Ranke
date: 17 July 2025 (rebuilt `r Sys.Date()`)
bibliography: references.bib
output:
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    toc: true
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  %\VignetteIndexEntry{The Swiss Register of Plant Protection Products as a Relational Data Model}
  %\VignetteEncoding{UTF-8}
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---

```{r setup, message = FALSE, echo = FALSE}
library(knitr)
knitr::opts_chunk$set(tidy = FALSE, cache = FALSE)
options(knitr.kable.NA = '')
```

Since the year 2011, the [Swiss Federal Office of Agriculture
(FOAG)](https://www.blw.admin.ch) and later the [Federal Food Safety and
Veterinary Office](https://www.blv.admin.ch), now responsible for the
authorisation of plant protection products in Switzerland, publish the contents
of the Swiss Register of Plant Protection Products (SRPPP) on their respective
websites in a custom format based on the Extensible Markup Language (XML).

In our 
[group](https://www.agroscope.admin.ch/agroscope/de/home/ueber-uns/organisation/kompetenzbereiche-strategische-forschungsbereiche/pflanzen-pflanzliche-produkte/pflanzenschutzmittel-wirkung-bewertung.html) 
at Agroscope, different solutions have been used to read in, process and use
these data. This package offers a fresh approach to directly read in the data 
into `R`.

The current download location of the latest published XML version of the SRPPP
is stored in the package as `srppp::srppp_xml_url`. Reading in the current data
is as simple as

```{r message = FALSE}
library(srppp)
library(dplyr)
example_register <- try(srppp_dm())
```
In case downloading the current register from the internet fails, we read in the
version from 16 December 2024 that is distributed with the package.

```{r}
if (inherits(example_register, "try-error")) {
  test_data <- system.file("testdata/Daten_Pflanzenschutzmittelverzeichnis_2024-12-16.zip",
  package = "srppp")
  test_xml <- srppp_xml_get_from_path(test_data, from = "2024-12-16")
  example_register <- srppp_dm(test_xml)
}
```

The resulting data object contains a number of related tables with
information on the authorized products and their uses. The relation
between those tables is shown below. The identification numbers of the evaluated
products (`pNbrs`) and the general information associated with them are shown
in dark blue. The tables defining their composition in terms of the most 
important ingredients are shown in orange. Tables with information on the 
authorized uses of the products are shown in dark green. Finally, the tables
giving names and expiration dates of products and parallel imports as well as
an identification number for the authorization holder are shown in light blue.

```{r}
#| fig.height: 9
#| fig.alt: >
#|   Diagram showing the relation between the different tables
#|   (foreign key relationships)
library(DiagrammeR)
dm_draw(example_register)
```

# Substances

At the bottom of the table hierarchy, there is the list of substances.
For each substance, there is a primary key `pk`, a chemical name based
on [IUPAC nomenclature](https://iupac.org/what-we-do/nomenclature/), and
substance names in three of the four official languages of Switzerland. The first 
four entries out of `r nrow(example_register$substances)` are shown 
below.

```{r}
library(knitr)
example_register$substances |> 
  select(pk, iupac, substance_de, substance_fr, substance_it) |> 
  head(n = 4L) |> 
  kable()
```

# Products

There are three tables defining the products, `pNbrs`, `products` and
`ingredients`. The P-Numbers contained in the table `pNbrs` are identifiers of
product compositions. Products with the same P-Number are considered
equivalent in terms of efficacy and risks.
The table `pNbrs` is only there for a technical reason. It simply contains
a column holding the P-Numbers.

## Unique products (P-Numbers) and their composition

The composition of these products in terms of active substances, 
additives to declare, synergists and safeners is 
is defined in the table `ingredients`, giving the
contents in percent weight per weight (`percent`).
For liquid products, a content in grams per litre is also given
(`g_per_L`). If a substance is contained in a form that differs
from the definition given in the substance table, this is documented in
the respective additional columns as
illustrated by the first five rows shown below.

```{r}
example_register$ingredients |> 
  select(pNbr, pk, type, percent, g_per_L, ingredient_de, ingredient_fr) |> 
  head(n = 5L) |> 
  kable()
```

The frequency of occurrence of the four different ingredient types 
is quite different.


```{r message = FALSE}
library(dplyr)
example_register$ingredients |> 
  select(pk, type) |> 
  unique() |> 
  group_by(type) |> 
  summarize(n = n()) |> 
  kable()
```

Additives to declare are additives that have an effect on
classification and labelling of the product. All substances
occurring as synergists or safeners are listed below.

```{r}
example_register$ingredients |> 
  left_join(example_register$substances, by = "pk") |>
  filter(type %in% c("SYNERGIST", "SAFENER")) |>
  group_by(type, substance_de) |> 
  summarize(n = n(), .groups = "drop_last") |> 
  select(type, substance_de, n) |> 
  arrange(type, substance_de) |> 
  kable()
```

Note that the first two lines in the code could also be replaced by

```{r eval = FALSE}
example_register |> 
  dm_flatten_to_tbl(ingredients) |> 
```

which makes use of the foreign key declaration in the data object. 
However, the more explicit version using `left_join` is probably easier
to understand.

## Registered products (W-Numbers)

The registered products are identified by
the so-called W-Numbers. The relation between P-Numbers
and W-Numbers is illustrated below by showing the first five entries in the
`products` table.

```{r}
example_register$products |> 
  select(-terminationReason) |> 
  head() |> 
  kable()
```

As can be seen in these example entries, several registrations (W-Numbers) of the
same product type (P-Number) can exist. The W-Numbers without a dash
(e.g. `18`) are the original registrations, and the ones containing a dash and 
a trailing number (e.g. `18-1`, `18-2`) are equivalent products with
sales permissions that have a different legal entity as permission holder.

If the product registration has been revoked, the relevant latest dates
for selling the product (`soldoutDeadline`) and for use of the product
(`exhaustionDeadline`) are given in the respective columns.

```{r}
example_register$products |>
  filter(exhaustionDeadline != "") |> 
  select(-terminationReason) |> 
  head() |> 
  kable()
```

```{r echo = FALSE}
n_pNbrs <- nrow(example_register$pNbrs)
n_wNbrs <- nrow(example_register$products)
```
At the build time of this vignette, there were `r n_wNbrs` product registrations
for `r n_pNbrs` P-Numbers in the Swiss Register of Plant 
Protection Products (SRPPP) as published on the website of the Federal Food 
Safety and Veterinary Office.

## Example code for getting a product composition from a product name

If the name of a product is known, the associated
P-Numbers and W-Numbers as well as the product composition can be retrieved by a 
command like the following.

```{r}
example_register$products |>
  filter(name == "Plüsstar") |>
  left_join(example_register$ingredients, by = "pNbr") |>
  left_join(example_register$substances, by = "pk") |>
  select(pNbr, name, substance_de, percent, g_per_L) |> 
  kable()
```

# Uses

For each product type (P-Number), the registered uses (tagged 
as `<Indication>` in the XML file) are
specified in the `uses` table. The use numbers in the column `use_nr`
are generated while reading in the XML file, in order to be able to refer to 
each use by a combination of P-Number (`pNbr`) and use number (`use_nr`).

```{r}
example_register$uses |> 
  filter(pNbr %in% c(6521L, 7511L) & use_nr < 10) |> 
  select(pNbr, use_nr, ends_with("dosage"), ends_with("rate"), units_de,
    waiting_period, time_units_en, application_area_de) |> 
  head(20) |> 
  kable()
```

The columns `min_dosage` and `max_dosage` contain either a range of
recommended product concentrations in the spraying solution in percent,
or, if only `min_dosage` is given, the recommended concentration.
Similarly, if there is a single recommended application rate, it is stored in
`min_rate`. Only if there is a recommended range of application rates,
`max_rate` is given as well. The units of the application rate are given
in the columns starting with `units_`. In addition, a required
waiting period before harvest can be specified, as well as the application
area associated with the use.

## Application rates

Application rates in terms of grams of the active 
substances contained in the products per hectare can be calculated using
the function
`application_rate_g_per_ha()` as illustrated in the example below.

In a first step, some `uses` need to be selected and joined with 
the information in the `ingredients` table. The names of the
active substances can be joined as well.

```{r}
example_uses <- example_register$products |> 
  filter(wNbr == "6168") |>
  left_join(example_register$uses, by = join_by(pNbr),
    relationship = "many-to-many") |> 
  left_join(example_register$ingredients, by = join_by(pNbr),
    relationship = "many-to-many") |>
  left_join(example_register$substances, by = join_by(pk)) |>
  select(pNbr, name, use_nr,
    min_dosage, max_dosage, min_rate, max_rate, units_de,
    application_area_de,
    substance_de, percent, g_per_L) |> 
  filter(use_nr %in% c(1:5, 12:17))

kable(example_uses)
```

Then, the application rates can be calculated for these uses as illustrated 
below.

```{r}
application_rate_g_per_ha(example_uses) |>
  select(ai = substance_de, app_area = application_area_de,
  ends_with("rate"), units_de, rate = rate_g_per_ha) |> 
  head(n = 14) |> 
  kable()
```


## Culture forms and cultures

In the current SRPPP versions, there are only two culture forms, greenhouse
cultivation and field cultivation.

```{r}
example_register$culture_forms |> 
  select(starts_with("culture")) |> 
  unique() |> 
  kable()
```

For specific uses, e.g. for uses number `1` and `10` of product "Kumulus WG"
with W-Number `4458`, the associated culture form and the registered
cultures can be listed as shown below.
As each use is typically associated with only one culture form, the
culture form and the actual cultures can be joined to the use numbers in
one step.

```{r}
example_register$products |> 
  filter(wNbr == "4458") |> 
  left_join(example_register$uses, by = "pNbr") |> 
  filter(use_nr %in% c(1, 10)) |> 
  left_join(example_register$culture_forms, by = c("pNbr", "use_nr")) |> 
  left_join(example_register$cultures, by = c("pNbr", "use_nr")) |> 
  select(pNbr, use_nr, application_area_de, culture_form_de, culture_de) |> 
  kable()
```

Relations between the cultures are stored as a [data.tree::Node] object in
an attribute named 'culture_tree'. The first entries from that tree
are shown below. A complete culture tree is shown in the [Appendix](#complete-culture-tree).
Note that a culture can be linked to two parent cultures in the
tree. Cultures that are additionally present in another position
of the tree are marked by appending '[dup]' to their name.

```{r}
culture_tree <- attr(example_register, "culture_tree")
print(culture_tree, limit = 30, "culture_id")
```

## Target organisms

The target organisms for each use can be found in the table `pests`. Example
code for retrieving the target organisms for specific uses is given below.

```{r}
example_register$pests |> 
  filter(pNbr == 7105L, use_nr %in% 1:2) |> 
  select(use_nr, ends_with("de"), ends_with("fr")) |> 
  kable()
```

## Unique combinations of cultures and target organisms

In the calculations of mean application rates for the Swiss National Risk 
Indicator [@korkaric_2022; @korkaric_2023], unique combinations
of product, culture, and target organism were termed "indications". Note
that when using this definition of indications, each XML section `<Indication>`
can describe several indications. The relation between uses 
(`<Indication>` sections) and indications as defined in the indicator
project is illustrated below.

```{r}
culture_pest_combinations <- example_register$uses |> 
  filter(pNbr == 6521L) |> 
  left_join(example_register$cultures, by = c("pNbr", "use_nr")) |> 
  left_join(example_register$pests, by = c("pNbr", "use_nr")) |> 
  select(pNbr, use_nr, application_area_de, culture_de, pest_de)

kable(culture_pest_combinations)
```

In this example, there are `r nrow(culture_pest_combinations)` such "indications" 
for the 15 uses.


## Application comments

Sometimes, use specific comments can be found in the `application_comments` table.

```{r}
example_register$application_comments |>
  filter(pNbr == 7105, use_nr %in% 1:2) |> 
  select(pNbr, use_nr, ends_with("de"), ends_with("fr")) |> 
  kable()
```

## Obligations

The use conditions for each use are listed in the table `obligations`. In the 
following example, the column `sw_runoff_points` is selected in the output, as
both use authorisations are conditional on risk mitigation for runoff to
surface water amounting to at least one point.

```{r}
example_register$obligations |>
  filter(pNbr == 7105, use_nr %in% 1:2) |> 
  select(pNbr, use_nr, code, obligation_de, sw_runoff_points) |> 
  kable()
```

# References

<div id="refs"></div>

# Appendix

## Complete culture tree

Note that a culture can be linked to two parent cultures in the
tree. Cultures that are additionally present in another position
of the tree are marked by appending '[dup]' to their name.

```{r}
print(culture_tree, "culture_id", "name_fr", "name_it", limit = 800)
```