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<h1>Properties of the predefined scenarios from the EFSA guidance from 2015</h1>
<small class="dont-index">Source: <a href='https://github.com/jranke/pfm/blob/master/R/soil_scenario_data_EFSA_2015.R'><code>R/soil_scenario_data_EFSA_2015.R</code></a></small>
<div class="hidden name"><code>soil_scenario_data_EFSA_2015.Rd</code></div>
</div>
<div class="ref-description">
<p>Properties of the predefined scenarios used at Tier 1, Tier 2A and Tier 3A for the
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.</p>
</div>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p>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. <code>rho</code> is the dry bulk density of the top soil.</p>
<h2 class="hasAnchor" id="source"><a class="anchor" href="#source"></a>Source</h2>
<p>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. <em>EFSA Journal</em> <b>13</b>(4) 4093
doi:10.2903/j.efsa.2015.4093</p>
<h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
<pre class="examples"><span class='co'># NOT RUN {</span>
<span class='co'># This is the code that was used to define the data</span>
<span class='no'>soil_scenario_data_EFSA_2015</span> <span class='kw'><-</span> <span class='fu'>data.frame</span>(
<span class='kw'>Zone</span> <span class='kw'>=</span> <span class='fu'>rep</span>(<span class='fu'>c</span>(<span class='st'>"North"</span>, <span class='st'>"Central"</span>, <span class='st'>"South"</span>), <span class='fl'>2</span>),
<span class='kw'>Country</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"Estonia"</span>, <span class='st'>"Germany"</span>, <span class='st'>"France"</span>, <span class='st'>"Denmark"</span>, <span class='st'>"Czech Republik"</span>, <span class='st'>"Spain"</span>),
<span class='kw'>T_arit</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>4.7</span>, <span class='fl'>8.0</span>, <span class='fl'>11.0</span>, <span class='fl'>8.2</span>, <span class='fl'>9.1</span>, <span class='fl'>12.8</span>),
<span class='kw'>T_arr</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>7.0</span>, <span class='fl'>10.1</span>, <span class='fl'>12.3</span>, <span class='fl'>9.8</span>, <span class='fl'>11.2</span>, <span class='fl'>14.7</span>),
<span class='kw'>Texture</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"Coarse"</span>, <span class='st'>"Coarse"</span>, <span class='st'>"Medium fine"</span>, <span class='st'>"Medium"</span>, <span class='st'>"Medium"</span>, <span class='st'>"Medium"</span>),
<span class='kw'>f_om</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0.118</span>, <span class='fl'>0.086</span>, <span class='fl'>0.048</span>, <span class='fl'>0.023</span>, <span class='fl'>0.018</span>, <span class='fl'>0.011</span>),
<span class='kw'>theta_fc</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0.244</span>, <span class='fl'>0.244</span>, <span class='fl'>0.385</span>, <span class='fl'>0.347</span>, <span class='fl'>0.347</span>, <span class='fl'>0.347</span>),
<span class='kw'>rho</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>0.95</span>, <span class='fl'>1.05</span>, <span class='fl'>1.22</span>, <span class='fl'>1.39</span>, <span class='fl'>1.43</span>, <span class='fl'>1.51</span>),
<span class='kw'>f_sce</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>1.5</span>, <span class='fl'>1.5</span>),
<span class='kw'>f_mod</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>4</span>, <span class='fl'>4</span>, <span class='fl'>4</span>),
<span class='kw'>stringsAsFactors</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>,
<span class='kw'>row.names</span> <span class='kw'>=</span> <span class='fu'>c</span>(<span class='st'>"CTN"</span>, <span class='st'>"CTC"</span>, <span class='st'>"CTS"</span>, <span class='st'>"CLN"</span>, <span class='st'>"CLC"</span>, <span class='st'>"CLS"</span>)
)
<span class='fu'>save</span>(<span class='no'>soil_scenario_data_EFSA_2015</span>, <span class='kw'>file</span> <span class='kw'>=</span> <span class='st'>'../data/soil_scenario_data_EFSA_2015.RData'</span>)
<span class='co'># }</span><div class='input'>
<span class='co'># And this is the resulting dataframe</span>
<span class='no'>soil_scenario_data_EFSA_2015</span></div><div class='output co'>#> 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</div></pre>
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<h2>Contents</h2>
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<li><a href="#format">Format</a></li>
<li><a href="#source">Source</a></li>
<li><a href="#examples">Examples</a></li>
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