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#' Parameters for expected yields in Switzerland
#'
#' @description
#' The dataset contains the parameters *a* and *b* used to model the expected
#' gross dry matter yield (in t / ha) as a function of altitude (in m.a.s.l.) as
#' yield = a + b * altitude.
#'
#' @details
#' Lookup Table of expected yield as functions of height and management
#' intensity after table 1a in Olivier Huguenin et al.
#'
#' @format A data.frame with 4 rows and 3 variables:
#' \describe{
#' \item{intensity}{Management intensity}
#' \item{a}{Offset *a* in t / ha}
#' \item{b}{Slope *b* in t / ha / m}
#' }
#'
#' @references
#' \insertRef{huguenin2017GrundlagenDuengung}{growR}
#'
#' @md
"yield_parameters"
#' Management practices for Swiss grasslands
#'
#' @description
#' Expected yields, uncertainties and average number of cuts as function of
#' altitude and management intensity.
#'
#' @details
#' Data after table 1b in
#'
#' Olivier Huguenin et al.
#'
#' @format A data.frame with 15 rows and 5 variables:
#' \describe{
#' \item{intensity}{Management intensity}
#' \item{altitude}{Altitude in m.a.s.l.}
#' \item{n_cuts}{Average number of cuts}
#' \item{yield}{Expected gross dry matter yield in t / ha}
#' \item{sigma_yield}{Uncertainty on yield in t / ha}
#' }
#'
#' @references
#' \insertRef{huguenin2017GrundlagenDuengung}{growR}
#'
#' @md
"management_parameters"
#' Example Weather Data
#'
#' @description
#' Datasets containing the weather input parameters as used by growR. The
#' same data is made available as plain text files by the package and
#' automatically found in the `input` directory created by [setup_directory()]
#' if the `include_examples` option is set to `TRUE` (default).
#'
#' @details
#' For use in growR, a [WeatherData] object has to be created from a plain
#' text file. Therefore, this dataset is only provided for convenient
#' inspection. In order to run growR, use the plain text files provided by
#' the package. Use `system.file("extdata", package = "growR")` to locate them.
#'
#' The *snow* column is not actually used by growR but rather calculated
#' through precipitation and temperatures in [WeatherData]`$read_weather()`.
#'
#' Likewise, the *rSSD* column is deprecated, currently unused and only kept
#' for backwards compatibility.
#'
#' @seealso [setup_directory()], [WeatherData]
#'
#' @format A data.frame with 3652 rows and 10 variables:
#' \describe{
#' \item{year}{Year as an integer}
#' \item{DOY}{Day of year as an integer}
#' \item{Ta}{Average temperature of the day in degree Celsius}
#' \item{Tmin}{Minimum temperature of the day in degree Celsius}
#' \item{Tmax}{Maximum temperature of the day in degree Celsius}
#' \item{precip}{Daily precipitation in mm}
#' \item{rSSD}{Relative sunshine duration in percent}
#' \item{SRad}{Sun irradiance in J / s / m^2. This can be converted into
#' photosynthetically active radiation (PAR) in MJ / m^2 as:
#' PAR = SRad * 0.47 * 24 * 60 * 60 / 1e6}
#' \item{ET0}{Evapotranspiration in mm.}
#' \item{snow}{Precipitation in the form of snow in mm}
#' }
#' @md
#'
"posieux_weather"
#' Example results of a parameter scan
#'
#' @description
#' The function [run_parameter_scan()] can take a significant time to
#' execute, as it typically requires a few dozen model evaluations or more.
#' In order to still showcase what its output can look like, and to
#' facilitate testing and giving examples in the documentation of tools that
#' make use of the output of [run_parameter_scan()] (such as e.g.
#' [analyze_parameter_scan()]), this example dataset is provided.
#'
#' @details
#' The input for the parameter scan that produced this output was:
#' - ```param_values = list(w_FGA = seq(0.25, 1, 0.25),
#' w_FGB = seq(0.25, 1, 0.25),
#' w_FGC = seq(0, 0.25, 0.25),
#' w_FGD = c(0),
#' NI = seq(0.75, 1.0, 0.25)
#' ```
#' - `eps = 2e-2`
#' )
#'
#' @format A list containing an entry for each supplied parameter set
#' in *param_values*. Each entry is itself a list containing the following
#' keys:
#' \describe{
#' \item{params}{The parameter set that was used to run growR for this
#' entry.}
#' \item{data}{A list containing for each simulated year a [ModvegeSite]
#' object which was run for the respective year and therefore carries the
#' respective results.}
#' }
#'
#' @seealso [run_parameter_scan()]
#'
#' @md
"parameter_scan_example"
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