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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'>&lt;-</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'>#&gt;        Zone        Country T_arit T_arr     Texture  f_om theta_fc  rho f_sce
#&gt; CTN   North        Estonia    4.7   7.0      Coarse 0.118    0.244 0.95   3.0
#&gt; CTC Central        Germany    8.0  10.1      Coarse 0.086    0.244 1.05   2.0
#&gt; CTS   South         France   11.0  12.3 Medium fine 0.048    0.385 1.22   2.0
#&gt; CLN   North        Denmark    8.2   9.8      Medium 0.023    0.347 1.39   2.0
#&gt; CLC Central Czech Republik    9.1  11.2      Medium 0.018    0.347 1.43   1.5
#&gt; CLS   South          Spain   12.8  14.7      Medium 0.011    0.347 1.51   1.5
#&gt;     f_mod
#&gt; CTN     2
#&gt; CTC     2
#&gt; CTS     2
#&gt; CLN     4
#&gt; CLC     4
#&gt; CLS     4</div></pre>
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