globalfoodemissions.ipynb

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    "# <span style=\"color:indigo\">Exploring Greenhouse Gas (GHG) Emissions from Food Systems</span>\n",
    "\n",
    "*Amanda Knudsen — Summer 2021*\n",
    "\n",
    "### Goal \n",
    "In the notebook below, we explore **GHGs across the lifecycle of food production and consumption** through data. \n",
    "\n",
    "We'll take a look at sources of emissions across stages of global food systems and the production of common food products in order to identify areas with the most significant impact. Using this information, we then zoom in on multiple opportunities for sustainable alternatives, explored in the interactive dashboard version of this project on Tableau public: \n",
    "**[Low Hanging Fruit: Sustainable Food Opportunities to Combat Climate Change](https://public.tableau.com/app/profile/amanda.knudsen/viz/FoodProduction07/LowHangingFruit)**\n",
    "\n",
    "\n",
    "\n",
    "### Context\n",
    "\n",
    "**Contemporary consumers want to know** how the foods they eat contribute to their environmental footprint. Across the global food system, **businesses know there is value and urgency** in creating more sustainable systems with reduced GHGs. Taxes and caps on emissions, increasing temperatures, diminishing availability of land and quality of soil are increasingly a reality for **stakeholders across the food value chain**.\n",
    "\n",
    "- *GHG emissions from human activities are the main driver of climate change and food systems are a significant contributor with one third (~34%) of total global GHG emissions from human activities coming from food systems each year, based on multiple U.N.-backed studies released in 2021. via <a href = https://news.un.org/en/story/2021/03/1086822>United Nations</a>, <a href = http://www.fao.org/news/story/en/item/1402118/icode/>FAO </a>*\n",
    "- *The impacts of climate change such as increasing temperatures and changing precipitation patterns are already affecting the security of food systems and negative impacts are expected to increase based on future projected climate change, resulting in further declines of crop yields and increasingly higher food prices.  via <a href = https://www.ipcc.ch/srccl/chapter/chapter-5/> IPCC </a>*\n",
    "- *As of May 2021, <a href = https://www.pewresearch.org/science/2021/05/26/climate-energy-and-environmental-policy/ps_2021-05-26_climate-and-generations_02-010/>70% of U.S. adults</a> now favor taxing corporations based on their emissions, among other proposals aimed at reducing the effects of climate change that majorities of Americans support. via <a href = https://www.pewresearch.org/science/wp-content/uploads/sites/16/2021/05/PS_2021.05.26_climate-and-generations_REPORT.pdf>Pew Research Center</a>* \n",
    "\n",
    "\n",
    "\n",
    "***Libraries***\n",
    "<ul>\n",
    "  <li> pandas as pd # to process data in a tabular format</li>\n",
    "  <li> numpy as np # for linear algebra </li>\n",
    "  <li> matplotlib as mpl and matplotlib.pyplot # basic plotting library</li>\n",
    "  <li> matplotlib inline # so that graphs are easily included in the notebook</li>\n",
    "  <li> seaborn # aesthetically pleasing plots</li>\n",
    "</ul>\n",
    "\n",
    "***Data sources***\n",
    "<ul>\n",
    "  <li> <a href = https://edgar.jrc.ec.europa.eu/edgar_food#data_download>EDGAR-FOOD</a> global food system GHG emissions from 1990-2015</li>\n",
    "\n",
    "  <li> <a href = https://ourworldindata.org/environmental-impacts-of-food> Our World in Data</a> environmental impacts of foods and original source <a href = https://science.sciencemag.org/content/360/6392/987>article</a> and supplementary <a href = https://science.sciencemag.org/highwire/filestream/710905/field_highwire_adjunct_files/3/aaq0216_DataS2.xls>dataset</a> published in Science</li>\n",
    "\n",
    "  <li> <a href = http://www.fao.org/gleam/results/en/> GLEAM</a> emissions data from Global Livestock Environmental Assessment Model (GLEAM) from the U.N. FAO</li>\n",
    "  </ul>"
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    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mpl \n",
    "import matplotlib.pyplot as plt \n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "import plotly.tools as tls\n",
    "import plotly.io as pio\n",
    "pio.templates.default = \"seaborn\"\n",
    "plt.style.use(\"seaborn\")"
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    "df_edgar = pd.read_csv('../edgarfoodemissions/EDGARfood.csv')\n",
    "df_foods = pd.read_csv('../foodproductemissions/Food_Product_Emissions.csv')\n",
    "df_gleam = pd.read_csv('../gleamlivestockemissions/GLEAM_LivestockEmissions.csv')\n",
    "# df_edgar = food system emissions from EDGAR-FOOD\n",
    "# df_foods = food products global average emissions from Science and Our World in Data\n",
    "# df_gleam = livestock emissions from UN-FAO"
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   "source": [
    "## Exploring GHG emissions in the global food system\n",
    "First, we'll explore data in **EDGAR-FOOD** for the big picture of GHGs in global food systems. "
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      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 128700 entries, 0 to 128699\n",
      "Data columns (total 7 columns):\n",
      " #   Column             Non-Null Count   Dtype  \n",
      "---  ------             --------------   -----  \n",
      " 0   Food System Stage  128700 non-null  object \n",
      " 1   FS Stage Order     128700 non-null  object \n",
      " 2   GHG                128700 non-null  object \n",
      " 3   Country            128700 non-null  object \n",
      " 4   Year               128700 non-null  int64  \n",
      " 5   GHG Emissions      128700 non-null  float64\n",
      " 6   Unit               128700 non-null  object \n",
      "dtypes: float64(1), int64(1), object(5)\n",
      "memory usage: 6.9+ MB\n"
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    "### EDGAR-FOOD overview\n",
    "\n",
    "* GHG is short for 'Greenhouse Gas' and EDGAR-FOOD identifies 4 of the most common GHGs emitted from global food systems: **Carbon dioxide** (CO2), **Methane** (CH4), **Nitrous oxide** (N2O), and **F-gases** (Fluorinated gases)\n",
    "* GHG emissions are measured for each year from **1990 to 2015** globally and for each country. The unit of measurement is **metric tons CO2e** (carbon dioxide equivalents) using GWP-100, AR5 values for all 4 GHGs measured\n",
    "* GHG emissions from the food system are identified from each **food system stage**. There are **8 stages of the global food system**, Land, Farm, Processing, Transport, Packaging, Retail, Consumer, Waste\n",
    "* There are 128700 rows and 7 columns in the EDGAR-FOOD dataset\n",
    "* Data source <a href = https://edgar.jrc.ec.europa.eu/edgar_food#data_download>link</a>"
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       "      <th></th>\n",
       "      <th>Food System Stage</th>\n",
       "      <th>FS Stage Order</th>\n",
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       "      <td>Methane (CH4)</td>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>Land</td>\n",
       "      <td>Stage 1</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>0.600</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Land</td>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <th>4</th>\n",
       "      <td>Land</td>\n",
       "      <td>Stage 1</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>1994</td>\n",
       "      <td>0.600</td>\n",
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       "      <th>...</th>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>2011</td>\n",
       "      <td>113575.820</td>\n",
       "      <td>metric tons CO2e (GWP-100, AR5)</td>\n",
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       "      <td>Waste</td>\n",
       "      <td>Stage 8</td>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2012</td>\n",
       "      <td>114166.770</td>\n",
       "      <td>metric tons CO2e (GWP-100, AR5)</td>\n",
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       "      <td>Waste</td>\n",
       "      <td>Stage 8</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>2013</td>\n",
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       "      <td>Waste</td>\n",
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       "      <td>Zimbabwe</td>\n",
       "      <td>2014</td>\n",
       "      <td>114348.295</td>\n",
       "      <td>metric tons CO2e (GWP-100, AR5)</td>\n",
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       "       Food System Stage FS Stage Order                  GHG      Country  \\\n",
       "0                   Land        Stage 1        Methane (CH4)  Afghanistan   \n",
       "1                   Land        Stage 1        Methane (CH4)  Afghanistan   \n",
       "2                   Land        Stage 1        Methane (CH4)  Afghanistan   \n",
       "3                   Land        Stage 1        Methane (CH4)  Afghanistan   \n",
       "4                   Land        Stage 1        Methane (CH4)  Afghanistan   \n",
       "...                  ...            ...                  ...          ...   \n",
       "128695             Waste        Stage 8  Nitrous oxide (N2O)     Zimbabwe   \n",
       "128696             Waste        Stage 8  Nitrous oxide (N2O)     Zimbabwe   \n",
       "128697             Waste        Stage 8  Nitrous oxide (N2O)     Zimbabwe   \n",
       "128698             Waste        Stage 8  Nitrous oxide (N2O)     Zimbabwe   \n",
       "128699             Waste        Stage 8  Nitrous oxide (N2O)     Zimbabwe   \n",
       "\n",
       "        Year  GHG Emissions                             Unit  \n",
       "0       1990          0.600  metric tons CO2e (GWP-100, AR5)  \n",
       "1       1991          0.600  metric tons CO2e (GWP-100, AR5)  \n",
       "2       1992          0.600  metric tons CO2e (GWP-100, AR5)  \n",
       "3       1993          0.600  metric tons CO2e (GWP-100, AR5)  \n",
       "4       1994          0.600  metric tons CO2e (GWP-100, AR5)  \n",
       "...      ...            ...                              ...  \n",
       "128695  2011     113575.820  metric tons CO2e (GWP-100, AR5)  \n",
       "128696  2012     114166.770  metric tons CO2e (GWP-100, AR5)  \n",
       "128697  2013     115549.805  metric tons CO2e (GWP-100, AR5)  \n",
       "128698  2014     114348.295  metric tons CO2e (GWP-100, AR5)  \n",
       "128699  2015     115716.490  metric tons CO2e (GWP-100, AR5)  \n",
       "\n",
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    "**At this stage of exploratory data analysis, we know:**\n",
    "\n",
    "- CO2e is the standard unit of measure for GHG emissions\n",
    "- Emissions are accounted for through 8 stages in the food system\n",
    "- Emissions (CO2e) are accounted for through 4 GHGs in the food system \n",
    "\n",
    "We can divide the total emissions by food system stage or by GHG, but in order to accurately assess and prioritize emissions reductions we need to first understand the big picture: \n",
    "* Which GHG contributes the highest emissions across the food system in total?\n",
    "* Which food system stage contributes the highest emissions in total?\n",
    "* Are GHG emissions found equally in each food system stage? Are some GHGs emitted more in some stages than others?\n",
    "\n",
    "**Next we'll identify food system emissions by stage and by type of GHG with ...\n",
    " the illustrious *Sankey diagram***"
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       "      <th></th>\n",
       "      <th>GHG</th>\n",
       "      <th>FS Stage Order</th>\n",
       "      <th>Food System Stage</th>\n",
       "      <th>GHG Emissions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 1</td>\n",
       "      <td>Land</td>\n",
       "      <td>1.638427e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 2</td>\n",
       "      <td>Farm</td>\n",
       "      <td>2.251379e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 3</td>\n",
       "      <td>Processing</td>\n",
       "      <td>1.015064e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 4</td>\n",
       "      <td>Transport</td>\n",
       "      <td>1.577650e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 5</td>\n",
       "      <td>Packaging</td>\n",
       "      <td>1.802894e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 6</td>\n",
       "      <td>Retail</td>\n",
       "      <td>6.372152e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 7</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>7.211363e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Carbon dioxide (CO2)</td>\n",
       "      <td>Stage 8</td>\n",
       "      <td>Waste</td>\n",
       "      <td>7.188883e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F-gases (Fluorinated)</td>\n",
       "      <td>Stage 6</td>\n",
       "      <td>Retail</td>\n",
       "      <td>6.010303e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 1</td>\n",
       "      <td>Land</td>\n",
       "      <td>6.087960e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 2</td>\n",
       "      <td>Farm</td>\n",
       "      <td>1.064548e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 3</td>\n",
       "      <td>Processing</td>\n",
       "      <td>2.908529e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 4</td>\n",
       "      <td>Transport</td>\n",
       "      <td>1.738191e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 5</td>\n",
       "      <td>Packaging</td>\n",
       "      <td>1.392743e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 6</td>\n",
       "      <td>Retail</td>\n",
       "      <td>3.698025e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 7</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>2.066795e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Methane (CH4)</td>\n",
       "      <td>Stage 8</td>\n",
       "      <td>Waste</td>\n",
       "      <td>3.238047e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 1</td>\n",
       "      <td>Land</td>\n",
       "      <td>8.742122e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 2</td>\n",
       "      <td>Farm</td>\n",
       "      <td>3.809598e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 3</td>\n",
       "      <td>Processing</td>\n",
       "      <td>5.166065e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 4</td>\n",
       "      <td>Transport</td>\n",
       "      <td>2.467820e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 5</td>\n",
       "      <td>Packaging</td>\n",
       "      <td>8.130522e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 6</td>\n",
       "      <td>Retail</td>\n",
       "      <td>2.428989e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 7</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>1.097962e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Nitrous oxide (N2O)</td>\n",
       "      <td>Stage 8</td>\n",
       "      <td>Waste</td>\n",
       "      <td>1.833595e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      GHG FS Stage Order Food System Stage  GHG Emissions\n",
       "0    Carbon dioxide (CO2)        Stage 1              Land   1.638427e+11\n",
       "1    Carbon dioxide (CO2)        Stage 2              Farm   2.251379e+10\n",
       "2    Carbon dioxide (CO2)        Stage 3        Processing   1.015064e+10\n",
       "3    Carbon dioxide (CO2)        Stage 4         Transport   1.577650e+10\n",
       "4    Carbon dioxide (CO2)        Stage 5         Packaging   1.802894e+10\n",
       "5    Carbon dioxide (CO2)        Stage 6            Retail   6.372152e+09\n",
       "6    Carbon dioxide (CO2)        Stage 7          Consumer   7.211363e+09\n",
       "7    Carbon dioxide (CO2)        Stage 8             Waste   7.188883e+07\n",
       "8   F-gases (Fluorinated)        Stage 6            Retail   6.010303e+09\n",
       "9           Methane (CH4)        Stage 1              Land   6.087960e+08\n",
       "10          Methane (CH4)        Stage 2              Farm   1.064548e+11\n",
       "11          Methane (CH4)        Stage 3        Processing   2.908529e+09\n",
       "12          Methane (CH4)        Stage 4         Transport   1.738191e+09\n",
       "13          Methane (CH4)        Stage 5         Packaging   1.392743e+09\n",
       "14          Methane (CH4)        Stage 6            Retail   3.698025e+08\n",
       "15          Methane (CH4)        Stage 7          Consumer   2.066795e+09\n",
       "16          Methane (CH4)        Stage 8             Waste   3.238047e+10\n",
       "17    Nitrous oxide (N2O)        Stage 1              Land   8.742122e+06\n",
       "18    Nitrous oxide (N2O)        Stage 2              Farm   3.809598e+10\n",
       "19    Nitrous oxide (N2O)        Stage 3        Processing   5.166065e+08\n",
       "20    Nitrous oxide (N2O)        Stage 4         Transport   2.467820e+08\n",
       "21    Nitrous oxide (N2O)        Stage 5         Packaging   8.130522e+07\n",
       "22    Nitrous oxide (N2O)        Stage 6            Retail   2.428989e+07\n",
       "23    Nitrous oxide (N2O)        Stage 7          Consumer   1.097962e+09\n",
       "24    Nitrous oxide (N2O)        Stage 8             Waste   1.833595e+09"
      ]
     },
     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "#grouping Food System Stage, GHG and sum of GHG emissions \n",
    "#We'll use this info to create our sankey diagrams below\n",
    "edgar_sankey = df_edgar.groupby(by=\n",
    "                                   [\"GHG\", \n",
    "                                    \"FS Stage Order\",\n",
    "                                    \"Food System Stage\"]).sum()[[\"GHG Emissions\"]]\n",
    "edgar_sankey = edgar_sankey.reset_index()\n",
    "#edgar_sankey2 = df_edgar.groupby(by= [\"FS Stage Order\", \"Food System Stage\",\"GHG\"]).sum()[[\"GHG Emissions\"]]\n",
    "##same process as above for the second sankey grouping\n",
    "edgar_sankey"
   ]
  },
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   "source": [
    "fig = go.Figure(data=[go.Sankey(\n",
    "    arrangement = \"snap\",\n",
    "    node = dict(\n",
    "      pad = 20,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"grey\", width = 0.5),\n",
    "      label = [\n",
    "          \"Carbon dioxide (CO2)\", \"Methane (CH4)\", \"Nitrous oxide (N2O)\",\n",
    "          \"F-gases\", \"Land\", \"Farm\", \"Processing\",\n",
    "          \"Transport\", \"Packaging\", \"Retail\", \"Consumer\", \"Waste\"],\n",
    "      color = [\"#3d6493\", \"#95ceeb\", \"#308bbc\", \"#86aad1\", \"#58805b\", \n",
    "               \"#98c7a0\", \"#f36e3a\", \"#fba644\", \"#ad5849\", \"#d2c795\", \"#736a62\", \"#b0a08c\"]),\n",
    "    #to match the colors I've been working with in Tableau \n",
    "    link = dict(\n",
    "      source = [0,0,0,0,0,0,0,0,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,3], # corresponds to 4 GHGs (the 'sources')\n",
    "      target = [4,5,6,7,8,9,10,11,4,5,6,7,8,9,10,11,4,5,6,7,8,9,10,11,9], # corresponds to 8 food system stages (the 'targets') \n",
    "      value  = [163842700000,22513790000,10150640000,15776500000,18028940000,6372152000,7211363000,71888830,608796000,106454800000,2908529000,1738191000,1392743000,369802500,2066795000,32380470000,8742122,38095980000,516606500,246782000,81305220,24289890,1097962000,1833595000,6010303000] #the amount per Source to Target\n",
    "  ))])\n",
    "fig.update_layout(\n",
    "    height=580,\n",
    "    title_text=\"GHGs to Stages: Food System Emissions, 1990-2015\", \n",
    "    font_size=14)\n",
    "fig.show()"
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     "end_time": "2021-10-21T16:04:49.324994",
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     "start_time": "2021-10-21T16:04:49.293576",
     "status": "completed"
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    "tags": []
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   "source": [
    "* Carbon dioxide (CO2), Methane (CH4), and Nitrous oxide (N2O) emissions are found from **all eight stages** of global food systems\n",
    "* Despite their ubiquity, the vast majority of CO2, CH4, and N20 emissions occur in **Land** and **Farm** \n",
    "* **F-gases** are identified in one stage only: **Retail**\n",
    "* **Land** and **Farm** account for most food system emissions by stage\n",
    "* **Carbon dioxide** (CO2) and **Methane** (CH4) account for the vast majority of food system emissions by GHG\n"
   ]
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     "start_time": "2021-10-21T16:04:49.357006",
     "status": "completed"
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    "tags": []
   },
   "outputs": [
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    "fig = go.Figure(data=[go.Sankey(\n",
    "    arrangement = \"snap\",\n",
    "    node = dict(\n",
    "      pad = 20,\n",
    "      thickness = 20,\n",
    "      line = dict(color = \"black\", width = 0.5),\n",
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    "          \"Land\", \"Farm\", \"Processing\", \"Transport\", \"Packaging\", \"Retail\", \"Consumer\", \"Waste\", \"Carbon dioxide\", \"Methane\", \"Nitrous oxide\",\n",
    "          \"F-gases\"],\n",
    "      color = [\"#58805b\", \"#98c7a0\", \"#f36e3a\", \"#fba644\", \"#ad5849\", \"#d2c795\", \"#736a62\", \"#b0a08c\",\"#3d6493\", \"#95ceeb\", \"#308bbc\", \"#86aad1\"]),\n",
    "    link = dict(\n",
    "      source = [0,0,0,1,1,1,2,2,2,3,3,3,4,4,4,5,5,5,5,6,6,6,7,7,7,7], # corresponds to 8 food system stages (the 'source') \n",
    "      target = [8, 9, 10, 8, 9, 10,8, 9, 10,8, 9, 10,8, 9, 10,8, 11, 9, 10, 8, 9, 10, 8, 9, 10], #ghg types (the targets)\n",
    "      value  = [1.63842659e+11, 6.08796006e+08, 8.74212200e+06, 2.25137898e+10, 1.06454795e+11,\n",
    "                3.80959812e+10, 1.01506384e+10, 2.90852906e+09, 5.16606457e+08, 1.57765035e+10,\n",
    "                1.73819055e+09, 2.46781984e+08, 1.80289417e+10, 1.39274312e+09, 8.13052248e+07,\n",
    "                6.37215236e+09, 6.01030263e+09, 3.69802473e+08, 2.42898945e+07, 7.21136338e+09,\n",
    "                2.06679458e+09, 1.09796230e+09, 7.18888257e+07, 3.23804654e+10, 1.83359537e+09] #the amount per Source to Target\n",
    "  ))])\n",
    "fig.update_layout(\n",
    "    height=580,\n",
    "    title_text=\"Stages to GHGs: Food System Emissions, 1990-2015\", \n",
    "    font_size=14)\n",
    "fig.show()"
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     "start_time": "2021-10-21T16:04:49.456345",
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   "source": [
    "### Yearly trends in global food system emissions\n",
    "Now that we've seen food system emissions from 1990 to 2015 in total, let's take a look at trends over time.\n",
    "Let's take a look at food system emissions in total and then we'll look per stage and GHG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:49.640843Z",
     "iopub.status.busy": "2021-10-21T16:04:49.640167Z",
     "iopub.status.idle": "2021-10-21T16:04:49.964071Z",
     "shell.execute_reply": "2021-10-21T16:04:49.964611Z",
     "shell.execute_reply.started": "2021-10-21T15:18:40.142370Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.367618,
     "end_time": "2021-10-21T16:04:49.964789",
     "exception": false,
     "start_time": "2021-10-21T16:04:49.597171",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fkpKC0aNHa65oYjQW14Jvqba2FqmpqXjmmWfY5zStKSWds2zZMvj6+mL79u148cUXYW9vj+DgYKxZs6bVP7YjRozAM888g1WrVqG6uhqDBg26L0nMuHHjMHv2bIhEIsyaNQsPPvggAGDlypV48cUXER0djZCQEMyYMYOddd5Rc+bMQXFxMR555BGIxWKMHDkS69evB6CcYfz+++/jjTfeQHFxMXr37o2tW7ey3Z+ffvop1q9fj48++ghjxoxBQkJCm9e5evUq3nrrLYhEInh6euLll19W+2M8c+ZM/Pvf/1ZL4CKRSLB582ZkZGTA1tYWgwYNYnsqJk2ahP3792PYsGEICgrC3r178d577+GDDz7AlClTUFtbi+DgYCxbtqxT9dKSvs6dnZ2NN954AxUVFXBxccGCBQswfPhwpKent/ndVbX3M+rMtVvz0EMP4bnnnsMTTzzB3gSotsgnT54MALh16xYA5VK3rVu3oqGhAb1798b27dvVWv+bNm1Ceno6O3/jpZdeYl8bPXo0li5dikWLFqGhoQETJ07UmJTpo48+QmVlJfvvA1D2/DTV15YtW7B27VrExMTA398fn3zyCVuW/Px8tcmx/fv3R2BgIP7++2/IZDJ89NFHuHfvHmxsbBAeHo7PP/8c3bp1Y9+flJR0X68KMR0cxpSykOhIXl4eli9fjoMHD0IkEmHSpElITk5u8/0vvfQSHnjgAbbL7ODBgzh//jz7D2TDhg0YOnQoddETvUlJScELL7yAY8eOtdmKJMb1/PPPY/LkyWpDKNbs77//xr59+/Dxxx8buyikDRbfRe/k5ISgoCD88ccfAJRdUu1lXho5ciSSk5NRVVWFqqoqJCcnY+TIkYYoLrFCUqkU3333HR588EEK7iZs8+bNFNxVxMfHU3A3cRbXgl+9ejXOnz8PoVAIT09PrFq1CsOHD8drr72G0tJSyGQyTJkyBStXrkRaWhpWrlyJ6upqCAQCeHl5sUt/du/ezU6SWb58ObvshBBdysjIwJw5cxAZGYnt27fTZCVCiM5YXIAnhBBCiJ676NtLOlNTU4Ply5dj+vTpmDp1KpupDKBEM4QQQkhX6LUFn5KSAgcHB7z44otsFjJVW7duRU1NDV544QVUVFSwk+Hq6uowZ84c/Prrr+BwOJg9ezb27NkDV1dXfRWVEEIIsSh6XSbXWtIZVRwOB7W1tWAYBrW1tXB1dQWPx+t0opnS0hqNr3eUu7sDhMI6nZ7T2lAddh3VYddRHeoG1WPX6boOvb2d23zNqOvgH374YTz55JMYNWoUm2SEy+WaTKIZHs/G4Ne0NFSHXUd12HVUh7pB9dh1hqxDowb45ORk9O7dG9999x1ycnKwePFidhOOznB3d9B55Wm6OyLaoTrsOqrDrqM61A2qx64zVB0aNcDv2bMHjz/+ODgcDkJDQxEUFIR79+7B19dXLQ1mcXExhg4d2u75dN115O3trPNuf2tDddh1VIddR3WoG1SPXafrOtR0s2DURDf+/v7sNpllZWXIzMxEUFAQJZohhBBCukivLXjVpDOjR4/GqlWr2C1FFyxYgKeeegpr167FtGnTwDAM1qxZw+ZIfuqpp9jcyitWrGAn3BFCCCGkfRaV6EbXXUfUHdV1VIddR3XYdVSHukH12HVW00VPCCGEEP2gAE8IIYRYIIvfD94UVFSU45NPtuD69WtwdnaGra0t/vWvRRgzZiwuXbqAn3/+Hu+99xH7/k2bXsOIESMxdux4yGQy7NjxJY4dOwI7O3sAwNix4/Doo0vuu86DD06Dg4MDuFzlUsGBAwfh2Wdf0Lqcy5f/H7Zu/bpD32379q0YMGAQYmKGdehzhBBC9IsCfCvSs4UAdLNWkWEYrF27BpMnT8Vrr20CABQVFSI5+YRWn//qqy9QUVGOb7/9GQKBAHV1tfjpp+/bfP8nn3zZ6QmJHQ3uALB06fJOXYsQQqxNerYQRVVi+LkKDHI9CvCt2JecCQAYFR3S5XNdvJgCW1tbzJz5IPucn58/HnxwfrufbWhowIEDv2H37v0QCJS/EA4Ojliy5IkOlWHlysfRs2cvXLlyGQ0N9Xjlldfx3//uxL17dxEfn4DHH38KAJCQMAqHD59CWVkZXn11LWprayGXy7BmzVr07dsf77zzBtLTb4DD4WDq1OmYN+9htd6GCxfO4/PPP4JcLkdkZBTWrFkLQNmzMHlyIk6fPgmZTIY33ngXoaFhSE29iI8/3gwA4HCAzz//Cg4Ojh36boQQYi72JWfC1tYGqx8aYJDrWVWAP3QuB/tOZ0IskWv1/mnP72v3PQK+DWbEdcOkYa3fDGRm3kPPnr00nuPKlVQ89ti/2OPi4iKMGDESeXm58PX17VDQe/rpJ9gu+smTp2LevIcBADyeLXbs+C9++eUnvPTS89ix43u4uLhg3ryZmDfvX3B1dWPPcfjwIQwdOhyPProEcrkcYnED7ty5jdLSEvz3v78AUO4EqEosFuOtt17HRx/9ByEhoXjjjQ347bfdWLFCeTPi6uqKr7/+AXv27MJPP/0XL720Hj/99D1Wr/43+vcfiLq6OvD5fK2/JyGEmIv0bCG++/MWiiqUydje+eESZo7shshQd71e16oC/J8pOVoHd22JJXL8mZLTZoBvafPmd5GWdhm2trbYvv07AMCAAYPuG4NvTVLSfuza9TOqq6vwxRc74Ovrd9972uqiHzlyNAAgIqI7unULh5eXFwAgICAQJSXFagG+d+8ovP32RshkMowe/QB69OiFgIBAFBTk48MP30Ns7EgMHTpc7fw5Odnw9w9ASEgoAGDy5ETs2bOLfX3MmHgAQK9evXHixDEAQL9+A/Dppx9iwoTJGDNmLHx8fDXUHCGEmKcewa6oqpWwx3NGh6NHsJver2tVs+gnxoRAwNdtrnoB3wYTY9oO7t26heP27Vvs8fPPv4iPP/4ClZXCds8dFBSM4uJi1NXVAgCmTp2OnTt/hKOjIxQKRYfK2dQ65nA4ai1lDocDuVz9pmfgwMH4/POv4O3tg02bXscffxyEi4sLdu78CYMGDcG+fb/inXfe6ND1bW2V17Sx4UIuVyY7WrjwMbz00nqIxQ148sklyM7O6tA5CSHEHKTnVKJerPy7Z8e3wfWsCoNc16pa8JOGhbTb0k5JL0FMpA8A4FZBNXoFuHTpmkOGxODLLz/H3r27MWuWchy+oaFBq8/a2dkhMXE6tmx5Dy+8sA4CgQByuRxSqaxLZWpPUVEhvL19MH36LEilEty+fQuxsSNha8vDAw+MQ0hIKDZu3KD2mZCQUBQWFiAvLxdBQcH488/fMXDgYI3Xyc/PQ0REd0REdEd6+g1kZ2chNDRMj9+MEEIML+VmCft4wrBQBHo6GOS6VhXgtdEU3AFg5IDALmcc4nA4ePvtzfj008348cfv4ObmBjs7ezz55CqtPv/44yvw1VdfYNGieXBwcIBAYIfJk6fCy8u71ferjsFHRHTH+vUbO1zm1NSL+PHH78Dj8WBv74BXXnkdpaUlePvt16FQKBMfPvHECrXPCAQCrFv3Ktavf5GdZDdz5hyN1/nllx9x6dIFcLlchIWFY/jwER0uKyGEmDK5QoFLt0vZ47gBAfB2Msx8I0pVqwGlZew6qsOuozrsOqpD3aB67LjrmRXY/L/LAAB3ZwF2bpiI8nKRzs5PqWoJIYQQI0hJL2YfR/fyAZfLMdi1KcATQggheiCTK3DxVnP3fExvHw3v1j0K8IQQQogepOcIUdugnBTt7ixAeBcnbXcUBXhCCCFED1Rnz8dE+oDLMVz3PEABnhBCCNE5mVx99rzqCi1DoQBPCCGE6Fh6dnP3vIeL4bvnAQrwhBBCiM6dT2/uno/u5QOOgbvnAQrwhBBCiE7J5Aqk3jbe7PkmFOAJIYQQHbqR1dw97+kiQLi/4bvnAQrwhBBCiE5dUO2ejzRO9zxAAZ4QQgjRmftnzxtvG2wK8IQQQoiO3MiqQJ24qXveDt38284Vr28U4AkhhBAdUUtu09t43fMABXhCCCFEJ2RyBS7dKWOPjZHcRhUFeEIIIUQHrmdWoL6xe97L1Q5hfsbrngcowBNCCCE6kZKunnvemN3zAAV4QgghpMukMgVSVbrno43cPQ9QgCeEEEK67HqWaXXPAxTgCSHE4qVnC5GeLTR2MSyaKc2eb8IzdgEIIYTo177kTABAZKi7kUtimaQyBS7fbU5uM9SIyW1UUQueEEIsVHq2EO/8cBG3citxK7cS67adpZa8Hihnz8sBAN5udgjxdTJyiZQowBNCiIWKDHVHv3BP9riooh5yhjFiiSxTSnox+zgm0tckuucBCvCEEGLR/r6Ur3a89bdrKK2sN1JpLI9UJlebPW/s5DaqKMATQoiFyi0RQVgjVnuutkGGT3+9CrFEbqRSWZZr9yrQ0FiXPu72JtM9D1CAJ4QQi3U8tbn1HubnDJ6Nsus4r1SEb/64CYa667ss5ZZpJbdRRQGeEEIsUL1YhjPXi9jjh8Z2xyMTerHH52+W4ND5HGMUzWJIpKbbPQ9QgCeEEIv0z41ithve39MBvULcMHpAAMYOCmTfs/t4Bq5llhuriGbvWmYFW8e+7vYI9jGd7nmAAjwhhFgchmFwTGVy3QODAtmu4wXje6BHkGvj+4Av911HibDOKOU0dxdUcs9Hm1j3PEABnhBCLE5GQTXySkUAAD6Pi7i+fuxrPBsunprZF+7OAgCNk+72XEWDRGaUsporiVSO1Lum2z0PUIAnhBCLo9p6HxrlCwc7W7XXXZ0EWDGrHzvpLr+0Fl8n0aS7jrh6T6V73sPB5LrnAQrwhBBiUUT1UrVtS1XH3FWFB7hg4cTmSXcXbpXi93+y9V4+S6Ge3Mb0uucBCvCEEGJRktMKIZMrACiXxnXzd2nzvaP6B2Dc4CD2eM+Je0jLoEl37ZFI5bhyt7meTLF7HqAATwghFkPBMGpr39tqvauaN647ega7AQAYANv2X0dxBU260+TqvXKIpcrueT8PBwR5Oxq5RK2jAE8IIRbiZpYQJY1paO0FPAzt3f6uZi0n3dWJlZPumvY2J/dTHQIx1e55gAI8IYRYjGMqrfe4vn4Q8G20+pyLIx8rZ/cDz0YZEgrKaNJdW8Rm0j0PUIAnhJB2pWcLTX6bVWGNGJdVsqo9oEX3vKpu/i54dFLzpLuLt0tx8CxNumvpakZz97y/pwMCTbR7HqAATwghGuUU12DnoXTsOn7X2EXR6MTlfCgaW9yRIW4I8Op44Inr54/x0c2T7n47eQ9XVNZ6E/PpngcAXntvqK2txe+//45//vkHRUVFsLOzQ69evTBx4kQMGDDAEGUkhBCDYhgG1zIr8OuJDOQUi9jn3/rvBcweHYHIUHcjlu5+coUCJ68UsMcdbb2remhsd+SViJCeU6mcdHfgOtY/GgM/DwcdlNS8iaVyXMkw7eQ2qjQG+C+//BK7d+/GyJEjMWrUKHh7e0MsFiMjIwNbtmyBQqHAa6+9hoiICEOVlxBC9EYqU+Cf60X4KyUX+WW1970e4OVocsEdAC7fKUelSAJAOZ4+uKd3p8/Fs+Fi+cy+eGNnCsqrxagXy/Hpr2l4ZVG0roprtq5mlEMiVS5BVHbPm15yG1UaA7yLiwv++OMP8Hjqb4uPj8eyZctw7949FBUVUYAnhJg1Ub0Ux1Lz8ffFPFTVStp835lrRZg7tjscW2SGM7bjqXns41H9/dnJcp3l4sDHytn98db3FyGVKVBYXocPf7mMx6b1RYCbXVeLa7bOt+ieN3UafwsWLFhwX3BXFR4ejhEjRui8UIQQYgglwjp8/9ctrPnPaew9eU8tuAv4NkiIDsa/EnrC31PZPS2TMzhyIa+t0xlFcUUdrmcpJwByAIwZGKCT84b6OeOxyZHs8d38anzyv1SdnNsciSVypJl47vmW2h2DB4Dy8nK8/fbbKCwsxA8//ID09HSkpqZiwYIF+i4fIYTo3N28Kvx5PgeXbpei5UIwd2cBxg8JwpiBAWwOd0c7Hr46cAMAcDglFxNigmEv0OrPp94dv9y8NK5/hCe8XO11du7YPn5IvV2KC7dKAQAlwnq8/f1FzBoVbpJDFfr0+7lsSGTK7vlAL0eT754HtJxF/8orr2DIkCGorq4GoGy5//jjj3otGCGE6EJ6thBX75ZBoWBw8VYJNv33At76/iIutgjuwT5OWJrYG+8uj8Xk4aFqG7QM7e0DH3dl4KwTy3D0omm04qUyOZLTCtnjsYM7P7muLU/M6AMv1+Zu+eF9fK0uuAPqG/hEm0HrHdCyBV9cXIwFCxbgf//7HwCAz+eDy6UVdoQQ/Wtaf96RoCJXKNAgkaNBLMcvx+5CLFNALlOwWd5U9Q33wMShIYgKdW9zyZMNl4vE2DB8/ftNAMBfKbkYHx0EO75xW/Ep6SWobVBmnPNytUPfbp46v4YNlwsXRz7KqhoAAGeuFmHsoKB2PmU50rOF2HPyHkT1UvY5TxfzmIeg1W9ny3H46upqynBECDGIX47dRb1YhtEDA9AglisDt0TW+H/Vx83PSRu7UtvCs+FgeJQfJgwNRpCWXa3D+/hi/+lMlFU1sJPyJg8L1cVX7DTVzHVjBgaAy9XPmuzIEHfcK1D24ApFYr1cw1RFhrqjR5Ar7uZXAVDmnh/Z39/IpdKOVgE+ISEBGzZsQG1tLfbs2YMff/wRc+bM0XfZCCFWLD1biF+O3UVWUQ0AYNexjC6f09GOhwcGBWLckCC4OQk69FmeDRdTYkPx3aFbAIA/z+UgfnAQBLbapYPVtZziGmTkK4OuDZeDUf11M7muNdNGhOHP8zmQKxhUVItRJRLDtYP1Z64UDIMTKjkGfD10N8dB37QK8MuWLcP+/ftRXV2NEydOYOHChZgxY4bGz6xduxbHjx+Hp6cnDh48eN/r27dvx4EDBwAAcrkcGRkZOHv2LNzc3BAfHw9HR0dwuVzY2Nhgz549nfhqhBBz1tRyagrwHcHhAHZ8HhiGAc+GA3uBLfw8HPDUzL5a52dvTVxffxw8k4WKajGq66Q4ebkACTHBnT5fVxy/3Bx0hvTyhosjX2/XEvBt0D3QFbdyKwEAN7OFGN7HT2/XMyXXMytQ1zgMYi/gISay/Q18TIXWA0jTp0/H9OnTtT7x7Nmz8cgjj+DFF19s9fWlS5di6dKlAIC///4bO3fuhJubG/v6t99+Cw8PD62vRwixPDdU8r/7uttjWJQv7Pg82AlsYMe3gT2fBzu+jcpzymM+jwsOh4OU9BLERPrA29sZv5/K6FJwBwBbHheTh4Xih8O3AShnVj8wKAC2PMO24uvFMpy9XsQea7MtbFdFhbmzAf5GlvUE+L8vqucYGNHXfL53uzPl9u/fj61btyI9PV3t+S+//FLj52JiYuDq6qpVIZKSkpCYmKjVewkh1qNBLGcfx/bxw8xR4Zg0LAQPDAzE8Cg/DOjuhV4h7gj1c4avuwNcHfkQ2Nqwk+VU1yrrat3y6AH+cHVStparRBKcUpnFbij/XC+CWKKsmwAvR3Y/d32KCmtucN3IrrCKeVgllfVIy2jeOU4fqxT0SWML/v3330dqaiqioqKwbNkyLFmyBI899hgA4NChQ3jiiSe6XID6+nqcOnUK69evV3t+yZIl4HA4mDdvHubNm6fVudzdHcDT8Z20t7ezTs9njagOu84a61AmV6glnpk3MRJODp3vhtZlHc4d1xPb910DABw6n4vZ43rBlmeYlUUMw+DU1ebWe+LIcPj4uOj9uh4ejnCwu4K6BhkqqsWQgIMgC/+9PPBPDruUckikD/r21E33vKH+PWsM8CdOnMDevXtha2uLJ598Ek899RREIhFWrlyps7u3Y8eOYfDgwWrd8z/99BN8fX1RXl6OxYsXIzw8HDExMe2eSyis00mZmnh7O6O0tOPjf6QZ1WHXWWsdFpTVQiZXzoZ3dxagvlaM+trOzeDWdR0O6e6JXxxsUV0nRVllPfYfv4PRA/Q3yU3VnbxKZBUqJ9fxbbnoH+ZusN+PfhFeONc4NJB8KQ/jhljucjmxVI6//slij0f29dNJPev6d1HTzUK7t5y2tspkD56entixYwfOnTuHzZs362yLvKSkJEydOlXtOV9fX/aaCQkJSEtL08m1CCHmQ3WzF1Pbc1tga4OJQ0PY44NnstibEX07rrI0bniULxzsDLcWf6DKJjY3sioMdl1jOH+jmM0x4O1mh37hus8xoG8aA7yTkxNycnLUjr/66iukpaXh9u3bXb54TU0NUlJSMG7cOPa5uro6iEQi9vHp06fRo0ePLl+LEGJe8kubt2kN8jK9tKBjBwfCyV7ZACqrasC5G8V6v2ZNnURtP3JDJ5xRDfDpOZWQKwxzU2NoDMOoZSscOyhIbzkG9Enjrd+LL74IiUR9ZyU7Ozt89dVX2LVrl8YTr169GufPn4dQKMTo0aOxatUqyGTKu6GmHPaHDx9GXFwcHBya9xkuLy/HihUrACiXzyUmJmL06NEd/2aEELOWV2q6LXhAuQwvISYYe0/eA6Bsxcf28dNrIEi+WgiZXDk82s3fBaF+hh0DD/R2gruzAMIaMerFMmQV1SAiQLvJ1OYkI78aOSXKG0w+j2s2iW1a0hjgBw0axD6ur68HwzBwcHAAn8/Hww8/rPHEW7Zsaffis2fPxuzZs9WeCw4Oxv79+9v9LCHEsqm14E10Y49xg4Pw57kc1IllKBbW4/zNYr0tH1MwDE6kNq99f2CQYcb8VXE4HESFueN04yS/G1lCiwzwRy81t96HRfmyPTXmpt0x+AMHDiAhIQGDBw/GkCFDkJCQQAGYEKJXEqkcJUJl3ngOwG7Xamoc7HgYH93cTX7wbDYUelo+diOrgs2l7yDgYWhv4yRcUVsul2l54/BVIjEuqAyDxA8234mEGlvwu3btwrfffov169dj4MCBAIDU1FR88MEHaGhowEMPPWSIMhJCrExheR27PMnH3R58I6WD1UZCTDD+SslFg0SOgrJaXLpVqpfdxlR3M4vr52+0FLlRKpv+3M2vglgi73ICIVNy4koB5Arlb1/3IFeDD4PoksYW/LfffosdO3Zg9OjRcHFxgYuLC8aMGYNt27bh22+/NVQZCSFWJk+le97U9912tLNVWy62/3SWzlvxFdUNuHy3jD02Rvd8E1cnATsnQq5gcDuv0mhl0TWZXKG2SmGcGbfegXYCPMMw7JI1Vf7+5jnhgBBiHvJVJtgFmeAEu5YmxASzLeq8UhGu3Clr5xMds+dkBpruGXqHusPf07h1EhWq0k1vQcvlUu+UoVKknFju6sjHkF7e7XzCtGkM8DKZjF2ypqq6upqdEU8IIbqWV2Y+LXgAcHbgq+WD338mS2fJwGRyBc7daB4TfsAAeefb06dbczf9jSyhhneaF9WlcWMGBoBnY5jshPqisfSzZs3CU089haysLPa5zMxMrFy5ErNmzdJ32QghVkq1BR/oZfoteACYODSYTVebXVSDq/fK2/lE+85dL8JznyazY8I2XA4cBMYf7+4Z7AabxuWAuSUiVNdK2vmE6csrEeF242Y6NlwOxgw0/o1UV2mcZPfEE09ALpdj5syZ4POVOaClUimWLFmikzz0hBDSUm2DFMIaZUpang0HPu7msf+2q5MAYwYG4MgFZSvwwOks9Av37FTWT5lcgb9ScrE/ORMSWXMymTEDA9Cnm/EzqtnxeYgIcMHtvCoAyu1jh0WZzzaqrflbZWnc4J7ecHc2//3uNQZ4DoeDFStWYNmyZcjOzgbDMAgLC2ODPSGE6Jpq693f09GsukknDwvF8dR8yOQMMgqqcSNbiD5hHdv2+nZuJf775y21VL0AEObnDEcTWo8dFebBBvjrWRVmHeDrGqQ4o7L9rqXk2NcY4C9cuAChUIiEhAS1dLGHDx+Gp6cnBg8erPcCEkKsiynnoG+Pu7MAo/oH4FjjTOwDyZlaB/iaOgl2Hc9AcovtZz1d7LB8Rh9EBLqqpak1tqgwD/yWnAlAOdGOYRid7VFiaMlXiyCRKntKgryd0CPIMpL3aLw1/uyzzxAZGXnf871798Ynn3yit0IRQqyXagY7cxl/VzVleCg7Pn07rwq3cjRPQmMYBqfSCvDyV+fUgrvA1gbz47vjneXDERGoDDi62tNeF8L8nWHXuP69olrMJiYyNwqGUeuejx8SaLY3Ki1pDPC1tbUIDg6+7/mgoCBUVFjO0ghCiOlQz0Fv+jPoW/J0tUNcv+Z0tftPZ7X53vxSEd794RK++T0donop+/yQnt7YtGwYJgwNgQ3XNIcoeDZcRIaozqY3z5hwI7OCvTmxF/AQG6WfVMPGoPE3p6qqqs3XGhoadF4YQoh1YximRQ5682vBA8CU2DBwG1uBN7OFuJun/rdULJXj1xMZeO2bFHYcG1B2xz/9YH+smN0PHi52Bi1zZ0SFmf9yOdWlcaP6+1tUVj6NAT4oKAhnzpy57/mzZ88iIMB4mZQIIZapqlbC7sEt4NvA0wyCXGt83OwR26d50tn+M5ns47SMMqzffg5JZ7PVlr9NHh6CN5cOw8DuXgYvb2ep5qW/mS2EQqGfPPz6UlJZj7SM5uWMYweb/9I4VRon2T377LNYvnw55s6di/79+wMA0tLSsGvXLmzdutUgBSSEWA+1DHZejmY9Fjp1RBjOXC8CwwDX7lVg/+lM5JaIcPFWqdr7egS5YuHEXia7Y54m/p4OcHPio1IkQV3j9rHhAS7GLpbWjl/KZ/c86BvuAV9309zUqLM0Bvj+/fvjm2++wfbt23HkyBEAQFRUFL7++utWJ98RQkhXqOegN8/u+SZ+Hg4Y2tsX524UAwB+O5Wp9rqjHQ8Pje2OuP7+bHe+uVFuH+uBM9eato+tMJsAL5bKcSqteftdc8873xqNAR4AevXqhffff98QZSGEWLl8M59g11KfMHc2wKsa2c8fc8dGwNnB/HOKRIW5qwX4xBFhxi2Qls7fKGaHg7zd7NAv3PgJhHSt3QBPCCGGkq+Sgz7IDJfItTSyfwDOp5fg2j3lDHMvVzssmdobvVRmn5s71XH4u/lVEEvlRtvKVlsMw6hNrhs7KAhcrnn2omhCAZ4QYhIUDNMiyY35t+ABINjbCQ1iGewFPIT5uVhUcAcANycBAr0ckV9WC5mcwZ28SvQ1gXS6mmTkVyOnRHkzyedxMbK/Ze6QSgGeEGISyirr2Wxizg62cHE0/+5rAAjzd8Hcsd0BwKQy0elS7zB39ubsRpbQ5AP8UZXENsOifOFkQimAdUmrDAoikQgKhfIf3u3bt5GUlASJxPx3DyKEmA71PeAto/UOqGefM6VMdLqk2k1v6glvqkRiXFC50Yq3wMl1TbQK8IsWLUJDQwNKS0uxZMkS7NmzBxs2bNB32QghViSvzPy2iCVKvYLd2JUAOcUiVNeZbgPwxJUCNv9A9yBXhPo5G7lE+qNVgGcYBg4ODjh+/Dgeeugh7NixA9evX9d32QghViTfgpbIWRt7AQ/hgc3L49KzTTOrnUyuwPHGjYAAy1wap0qrAC8WiyGRSHD69GnExsYqP2ii+ZEJIebJ0pbIWZuoUNPPS596pwyVImXvgosjH0N6eRu5RPqlVZSeMmUK4uLikJeXh8GDB6O0tBQCgUDfZSOEWAmZXIGiijr2mLrozU+fbs3j8NczhWAY00tbq7o07oGBAeDZWHZDVatZ9CtXrsTChQvh7OwMLpcLBwcHfPrpp/ouGyHEShRV1LHjop4udrAX0AIfc9PN3wUCvg3EEjnKqxtQWlkPHxNK/XrqSgFu51YCUOb+HzPQsvLOt0brf0VVVVW4evUq5HI5+5yvr6+GTxBCiHbUu+ep9W6OeDZcRAa74Urj5i03soQmFeD3nLzHPh7c0xvuzpbfC61VgN+8eTN27dqFiIgIduydw+FgzJgxei0cIcQ6WFIOemsWFebBBvjrWRV4YJBxW8k1dRIcTsnFySsFqK6Tss93D3I1YqkMR6sAf+jQIRw5cgROTjTxhRCie5a6Bt7aqO4Pn964fawhU8AqFAwyC6tx9V45rt6rQFZhNVrOBPB1t8f4IZY9e76JVgHe29ubgjshRG9Uc9DTBDvzFeDlCFcnPqpEEtQ2yJBdXINu/vrdXa6qVoJr98px9V45rmdWsBvItEZgy0X3IFez3oa4I7QK8AMHDsTq1asxadIktdnz1EVPCOkqsUSO0soGAACXw4G/p+mM25KO4XA4iAr1wNnrzbvL6SrAN62t7xHsinsFja30jApkF9doKA8QEeAKD2cBJg4LQaifMy7eKtVJecyBVgH+6tWrAID//ve/7HM0Bk8I0YWC8ubueV8Pe9jyTHsnMqJZVJi7SoAXYmpsWJfPyTAMvv/rFqrrpFAoGNSJ226luzry0TfcA/3CPdGnmwcc7dTzzFtquuDWaBXgVQM7Ieai6Y4/MtSydu+yNHklqhPsaCjQ3Knmpb+TVwWJVA5+F7aPvZFVga8O3EBVbevpb7kcDroHuaJfY1AP9nGymi749mi9TO7UqVM4c+YMAGDkyJGIi4vTW6EI0YXfku+hQSzHy4uGUKvQhKluEWsJe8BbO3dnAfw9HVBYXgeZXIE7+VXooxL0O0KuUCD5auF9wd3dWYB+4R7o280TUWEecLCjvAmt0SqNz/bt2/Huu+/CxcUFLi4ueOedd7Bjxw59l42QTknPFuLdHy7hdm4VckpEeO7T0yabG5tQDnpLpLa7XGbn0tbK5Aps3Xcd/1wvZp8L9HLE2EGB+OCpEXhscm9ER/pQcNdAq5rZt28ffv75Z3Ym/cKFC7FgwQIsWbJEr4UjpDMiQ91hy+Ni038vAgDqxDL4etDELVOltoscddFbhKgwdzYt7I2sjt9cS2VyfL73GtIa19QDwNhBgXh4Qk9cvFVKXfBa0joRr+oyOVoyR0zd6auFascZ+VVGKgnRRFQvRVXj5h+2PC583OyNXCKiC5Eh7irbx9agpgPbx4olcny0K00tuE+ICcYjE3qCy+FY1SS5rtIqwPft2xdr167FpUuXcOnSJaxbtw59+/bVd9kI6bSWucwzCijAmyLV7vkAT0eDJkUh+mMv4CE8QLk8jgGQnlOp1efqxTJs+eUybqoMqSWOCMW8+O7Uau8ErQL8+vXr4enpiTfffBNvvvkmPDw8sH79en2XjZBO83CxUzvOyK82UkmIJnmUg95iqWa102b7WFG9FB/8nIo7ec0347NGh2P26AgK7p2k1Rh8YWEh1qxZo/ZcRkYGIiIi9FIoQrqqvKpB7TirqBpSmQK2PMveHtLc5JdRgLdUUWEe2H86C0D7Ab66ToLNP19GrsqSyfnjemBCTLA+i2jxtPpr1zK4t/UcIaairKpe7VgmZ5CjIeMVMQ7VTWYoB71lCQ9wgaBx/XtpZQNKKutbfV+lSIx3f7ikFtwXTexFwV0HNLbgKyoqUFFRAbFYjIyMDDCMMm1/TU0N6urqDFJAQjqjvLrhvucy8qsQEWgdu0iZA4Zh1LeJpTXwFoVnw0WvEDd2styNrAr4tNiDvbyqAe//nIoSoTL4czjA/03pjbh+/gYvryXSGOAPHDiAb7/9FiUlJVi2bBn7vLOzM5YuXar3whHSWS276AHgbkE1JhihLKR1whox6htTjtoLeFaxP7e1iQp1VwnwQjygEuBLhHV4/6dUlFeLAQA2XA6WTYvC0N6+RimrJdIY4B999FE8+uij2Lp1K5YvX26oMhHSJRKpXG3v5ya0VM60tBx/p4lUlieqW3PCm5tZFVAwDLgcDgrKavH+z6nsEkmeDQdPzuiLQT29jVVUi6TVGDwFd2JOVLvnPVwE7DigsEaMila67olx0Pi75Qv0coSLIx8AUNsgQ26xCDnFNXj3x0tq+Q+entOfgrse0JRiYnFUu+d93OzRzd+ZPb5LrXiTQePvlo/D4agtl/vzfA7e/ykVNY09bAJbGzw3dwD6hnsaq4gWjQI8sThlKq10Txc7tYl1tB7edKgG+CBaImexokKbu+n/uVGM2obmeRfPzx9Iuz3qEWXpJxZHtQXv6WqHbv4u7DFltDMNCgWjtg885aC3XKot+CZO9rZ4ft5AhPo5t/IJoitateBXrVqFyspK9lgoFOKZZ57RV5kI6ZJyDS347KIaSGVyYxSLqCiprIdUpgAAuDrx4WRva+QSEX0pEdarJZiy4XIwd2wEBXcD0CrA5+bmws3NjT12d3dHTk6OvspESJeUtWjBO9nbsrvJyRUMsotEbX2UGIhqDnraA96yRYa6IzE2jD1eObsvRvUPMF6BrIhWAV4ul0Mub271SKVSSCTa7w5EiCGpdtF7uSpz0ncPaO6mp4l2xqc2wY665y2eXKHA5GEhmDYiDFl0g20wWo3Bjxw5Es899xwWLVoEAPjuu+8watQovRaMkM6QyRWoFCkTZ3DQvOlMRKArTl8rAkDr4U2B2h7w1IK3eIHeTuw2rynpJUYujfXQKsCvXr0aX375Jd555x0AwAMPPIDHH39crwUjpDOENWI0ZlSGqxMfPBtlJ1V3lXH4uwVVYBiGEqsYkVoXvQ+14C2d6h7utJ+74WgV4G1tbbFy5UqsXLlS3+UhpEtazqBvEuDlCDu+DRokclSJJCivboCXq70ximj1pDI5iiuaNx4J8KQWPCH6oDHA//HHH5g8eTJ++OGHVl9/+OGH9VIoQjqrTG38vTmAc7kchAe44EaWEIByPTwFeOMoLK+DorGbxdvNDgK+jZFLRIhl0hjg79y5g8mTJ+PatWuGKg8hXdJyiZyqiABXNsDfza/CsCja1MIY1HLQe1H3PCH6ojHAP/3001AoFJg0aRLGjBljqDIR0mltddEDaJHRjibaGYtaDnof6p4nRF/aXSbH5XLx0UcfdfjEa9euRWxsLBITE1t9ffv27ZgxYwZmzJiBxMRE9O7dm02mc/LkSUycOBEJCQnYtm1bh69NrJfGFnxg81K53BIRJFJKeGMM6jnoqQVPiL5otQ4+MjISaWlpHTrx7NmzsX379jZfX7p0Kfbt24d9+/Zh9erViImJgZubG+RyOTZu3Ijt27cjKSkJBw8exN27dzt0bWK9yqqaJ2+1bME72tnC37M54U1WUY1By0aU1NfAUwueEH3Rahb99evXsWDBAoSGhsLBwYF9fvfu3W1+JiYmBnl5eVoVIikpiW3pp6WlITQ0FMHBwQCAqVOn4ujRo+jevbtW5yLWS8EwqKgWs8deLVrwgLKbvrC8DoCym75nsJuhikcA1ItlbC+LDZcDPw+Hdj5BCOksrQL8K6+8orcC1NfX49SpU1i/fj0AoLi4GH5+fuzrvr6+He49INapSiSBXKGcne1kb9vq7Ozuga5ITisEQBntjEF1gp2fpwObp4AQontaBfjCwkLMmDFD7bl9+/bppADHjh3D4MGD1XLdd5a7uwN4PN0uufH2pg0RuspQdVgmkrKP/TwdWr1udF9/7PwjHQCQWVgDLy8ns0h4Yym/h5cyytnHEYFuBv1ellKHxkb12HWGqkOtAvzOnTvvC/CtPdcZSUlJmDp1Knvs6+uLoqIi9ri4uBi+vtotZxIK67pcHlXe3s4oLaVx2q4wZB3ezWkOHq4O/Fava8dV7kNdL5ahUiTGjbul8HEz7fXwlvR7mH6v+Wfk6SIw2PeypDo0JqrHrtN1HWq6WdAY4K9evYq0tDQIhUK1ZDcikQhSqVTDJ7VTU1ODlJQUvP/+++xz/fr1Q1ZWFnJzc+Hr64ukpCRs3ry5y9cilk/TErkmXA4HEQEuuJZZAUA5Dm/qAd6SqHbR0y5yhOiXxgBfXFyMa9euob6+Xi3ZjaOjI95++22NJ169ejXOnz8PoVCI0aNHY9WqVZDJZACABQsWAAAOHz6MuLg4tYl7PB4PGzZswNKlSyGXyzFnzhz06NGj01+QWA9tAjygnGinGuBj+/i1+V6iW6pr4AMpBz0heqUxwI8fPx7jx49HcnIyRo4c2aETb9mypd33zJ49G7Nnz77v+TFjxlBiHdJhZSpr4FubQd9EdT18Rn61XstEmlXXSlBTp+z549ty2a18CSH6odUU1iFDhuCjjz7C888/DwDIyMjAkSNH9FowQjpK2xZ8uL8rmqbV5ZaIIJZQwhtDUN1BLtDLEVwzmNxIiDnTKsC/9tprkMlkSE9Xzj728/PDZ599pteCEdIRDMNoHeAd7HgIaBz/VTAMMgupFW8IeZSDnhCD0irA37p1C2vWrIGtrS0A5Ri8QqHQa8EI6YiaeikkMuXvpL3ABg4CzQtE1LrpC2g9vCGo7QFPGewI0TutAjyfz1c7FovFYBq3eyTEFKi13l3s2l3brr7xDLXgDUE9RS214AnRN63WwUdHR2Pr1q2QSCQ4d+4cvvnmG8THx+u7bIRorWWAb093lQB/N78KDMOYRcIbc8UwjHoXPbXgCdE7rVrwzz33HBiGgaOjI95//330798fq1at0nfZCNGa2i5yWszO9vVwgKOd8v5WVC9FibC+nU9Yr/RsIdKzhV06R3l1AzuZ0dGOB1dHfjufIIR0lVYteFtbWzz55JN48skn9V0eQjqlTMsJdk24HA7CA1xxtTGz2t38KvjSxiet2nvqHrgcDiJD3Tt9jjyV7vkgb/NID0yIudMqwDc0NODgwYPIyclhk9UAwL///W+9FYyQjlDtovdy1S4zXUSgCxvgMwqqEdfPXy9lM1fp2ULsPJTO9m5s3JmCh8Z271SgV1siR93zhBiEVgF+5cqV4HK56NOnz30T7ggxBWpd9FqMwQPq4/AZtLPcfSJD3SGTNa+WyS0RIb+sFr1C3DrcAs8vowl2hBia1rvJJSUl6bsshHSatmvgVXXzdwGHAzCMMoVqvVgG+3aW11kThYKBsEbMHssVDH44fBtX75Xj/6b0hksHxtHVZtBTDnpCDEKrSXY9evRASUmJvstCSKfUNchQJ1YOHdnyuHBxsNXqc/YCHptwhWFACW9aqKqVoLXFsGkZ5djw9XlcU9kZThOZXIHCctUxeArwhBiC1l30Dz30ECIjIyEQCNjnP/74Y70VjBBtteye70j3cfdAF3YDlIz8KkSFeei8fOaqQqVeA70d0SfMA3+l5AJQ5pXf8ssVTIgJxpwxEbDltd1WKBHWQyZX3iq4OwvgYKfdDRghpGu0CvD//ve/ER8fj6ioKNjY2Oi7TIR0SGe655tEBLri+OUCAMqJdqSZ6o2Tj5s95o/rgT7dPLAj6SaqayUAgL9ScnEzW4gnpvdh0/+2lE/r3wkxCq0CvFQqxYYNG/RdFkI6pTMT7Jq0nGhHCW+aVVQ3j7831Wu/cE9s/L+h+Pr3m0jLUHbR55aIsHFnCuaP64ExAwPuqz+1FLWUg54Qg9FqDH7gwIG4deuWvstCSKd0pQXv424PJ3tll3FtgwxFFXU6LZs5U71x8lC5cXJx5OOZB/vjX+N7gGej/BMikSnw3Z+38Nmeq6ipk6idJ6+UWvCEGINWLfi0tDTMmTMH3bp1UxuD3717t94KRoi2yqqas9Bp2ge+NRwOBxEBLriS0Zzwxt+TghCgPgbv4SJQe43D4WB8dDAiQ9zx5YHr7Cz51DtlyCw8j6WJUex8BvVNZqgFT4ihaBXgX375ZX2Xg5BO62ia2pYiAl3ZAJ+RX41R/QN0VjZzps3QR5CPE9YvisauYxk4eikPAFApkmDzz5cxaVgIpsaGsYlyOAD8PSlbICGGolWAHzp0qL7LQUinqWex61yAb0JbxzZTHYP30NAzwre1wcMTeqJPuAe+TroJUb0UDIA/zuUgJb2EXWrn424Pvi1N0iXEUDSOwb/55psa178fOXKEEuAQo5JI5aiukwIAbLgcuDkJ2vnE/br5O6NpXlhBaS3qGmSaP2AFxFI5RPXN9erq1H5Sm4HdvfDGkqHo0615qaHqHgHUPU+IYWlswY8YMQJLliyBh4cHBgwYAE9PT4jFYmRmZuLChQsYMWIEnn32WQMVlZD7qXYjuzsLwOV2fAa8HZ+HYB8n5BSLwECZ8EY1SFmjipb1quXKAlcnAZ57aACOpOTil+N3oWjOdIusomqkZwu7tGkNIUR7GgN8fHw84uPjceHCBZw/fx4ZGRmws7PDkCFDsGbNGnh6ehqqnIS0qqP7wLclItAVOcXNCW8owN+/RE5bXA4HE4aGIDLUHZ/tucq24meNDqfgTogBaTUGHx0djejoaH2XhZAOK6vu2vh7k+4Brjh2KR+Acia9tWtriVxHhPg6Y1iUL/JKRRDY2qC0sqH9DxFCdIZ21iBmrStr4FVFBLqwjzMKqqFgGK27pS1RhdrKhI7Pa2gS4uuMOWMiAAAp6bSfBSGGpFWiG0JMVVey2KnydrOHc+MmNfViGQrLrTvhjS5a8AAQE+nT6mNCiP5RgCdmrUxHLXgOh0P7w6voyhg8IcQ0dDjAl5eX4/Lly3ooCiEd19U18KoiKMCzdNWCJ4QYj1YB/l//+hdqampQXV2NmTNn4uWXX8a7776r77IRopFMrkClSNnS5KDrgSgioHkc3pon2ikYRj3JjXPnx+AJIcajVYCvq6uDs7Mzjh07hmnTpuHAgQNITk7Wd9kI0UhYIwbTmCbN1YnPbnzSWWH+LrBpXEdfWF6H2gZpV4tolmrqpJDJlQvYHe14sBfQXFxCzJFWfxElEuXuUOfOnUNcXBy4XC7tC0+MTlcz6JsIbG0Q5NOcbe2ele4PX0Hd84RYBK0C/NChQzFlyhRcvHgRQ4cORXV1Nbhcmp9HjKtMR0luVNFEO90lDyKEGJdWfW+vvvoq0tPTERwcDFtbW9TU1ODNN9/Ud9kI0ahcLcmNvU7OGRHogqMXlY+tNcBr2iaWEGI+tArwHA4HYWFhKCoqQlFREQCAz29/8wlC9EnXXfSAMqNdk4yCaigUTKfy25uzci13kSOEmDatAvwPP/yADz74AG5ubuA0ZvficDg4evSoXgtHiCa6SnKjytPVDq6OfFTVStAgkaOgrFZtXN4aUAueEMugVYD/+uuvcfDgQQQGBuq7PIRorayqnn2sqxY8h8NBRKArLt0uBQDcLaiyvgBfQ2PwhFgCrWbKeXt7U3AnJqXlWm0vHQYitbz0VjgOX05Z7AixCFq14EeMGIH33nsPU6dOhUDQ3GXXvXt3vRWMEE2qRBLIFcpF8E72thDwdbdsU30mvXUtlZPK5KiuVS6L5XI4cHWiuTaEmCutAvxvv/0GADh06BD7HI3BE2PSxwS7JmF+zrDhciBXMCiqqIOoXgone1udXsNUVdQ0t97dnfmwoeWwhJgtrQL833//re9yEAuUni1EUZUYfl3YbrQtZdXN4++67J4HAFueDUJ8nZFZqGy9Z+RXYUB3L51ew1RVVFGSG0IshdY5KO/evYtz584BAIYPH46IiAi9FYqYv+paCT79NQ3gAG8sGabzYKHPFjygHIdnA3yB9QR4Gn8nxHJo1f/222+/YfHixbh58yZu3ryJxYsXY//+/fouGzFT6dlCvPbNedRL5KgXy7Hpu4tIzxbq9Br6DvCq4/BpGeU6P7+pojS1hFgOrZfJ7dmzB97e3gCA0tJSLFmyBNOnT9dr4Yh5CvJxgkhloxa+LReRoe46vUaZahY7PQQi1QCfWyKCXKGwivFo9dwCtAaeEHOm9V+spuDe8jEhLZ24nA+ZjGGPi4X17MxsXdF3C75EWA+ejTKpE8MAG3de0HkvhCmiFjwhlkOrAB8SEoJPPvkExcXFKC4uxmeffYbg4GB9l42YIZlcgb8v5d/3fHqO7oIjwzB6D/CRoe7oH+HJHjva8XTeC2GKaAyeEMuhVYB//fXXkZmZienTp2PGjBm4d+8eNm7cqO+yETN04VYJhCpLrZrosvVbUy+FRKbcr9yObwMHPe1X7urY3EWdnlOJ3BKRXq5jKhiGoRY8IRZEq7+Mnp6e+PDDD/VdFmLmGIbB4ZRc9rh3qDtuNgb2GzoM8Kqtdy9XO3Z/BF2LDHVHda0EFxvT1iadzcLyGX31ci1TIFK5cbIX2MDBTj83ToQQw9D4L/jixYsYMmQITpw40errY8aM0UuhiHnKyK9GZmENAIBnw8WSqb2x7qtzkEjlKBHWo7yqQSfd6Ybarzwm0gfebnZsgE9JL8GsUXXw9XDQ2zWNqYJ2kSPEomgM8Hv37sWQIUOwffv2+17jcDgU4Imavy40t96H9/GFh4sdorp54HJjgEzPESKun3+Xr6M201sP4++qwvxc0LebB65lVoBhgD/OZeOxyb31ek1jUa1XD2cK8ISYO40B/s033wQA/Pe//zVIYYj5Kquqx8VbJezxhGjlJMz+3b3YAH8zWzcBvkzPE+xamhobimuZFQCA01eLMD2um0W2cGmJHCGWRatJdikpKaitrQUA7Nq1Cxs2bEBubm47nyLW5OjFPDCNK+N6h7qzW6wO6NG8pPJmthAMw7T28Q5RH4O37/L52tMz2A3dg5Tr4uUKBofO5+j9msZAE+wIsSxaBfiNGzfCwcEBd+7cwTfffIOAgAC8/PLL+i4bMRP1YhlOXilkjyfENC+hjAh0hX3jLHdhjRjFwvr7Pt9R6i1N/QciDoeDxNgw9vjk5QJU1+l2Xb8pqKAlcoRYFK0CPI/HA4fDwcmTJ7FgwQIsX74c1dXWtY0madvpq4WoF8sAAL4eDuinsn7cxoaLXsFu7PFNHcym1/ca+Nb0C/dASGOvhESmUFstYCnUW/DURU+IudMqwMtkMly5cgWHDx/G8OHDAQByuVyvBSPmQcEwOHIhjz1OiA4Ct8Wytd4qCWK6GuDrGmSoa7yZsOVx4eJgmG1cORwOpo4IY4//vpSHugaZQa5tKIbuGSGE6JdWAf6ZZ57Bhg0bMHDgQPTo0QOZmZkIDQ3Vd9mIGbhytwwllcpudwcBD3F9759Epxrg07OFUHRhHL68xTixvtbAt2ZIT2/4NS6RqxfLcSw1r51PmA+ZXIEqkXLYgQPAzZla8ISYO60C/Pjx47Fv3z689NJLAIBu3brhs88+02vBiHlQ7aoeMzAAAr7Nfe8J8HaEc2NLW1QvRV4XMsK1THJjSFwuB1OGN9/Y/pWSC7HUMnqyhDViNN12uTkLwLOx/I11CLF0Wv0r/uabb1BTo0xg8sILL2DSpElITk7Wa8GI6csprkF6TiUAgMvhYNyQoFbfx+Vw7mvFd5axu5GH9/Fll5DV1Elx8kqBwcugDzT+Tojl0SrA79mzB87Ozvjnn39QUVGBt956C1u2bNF32YiJU229R0d6a1xaFamjcXhjTLBTxbPhYtKw5lb8oXM5kMkVBi+Hrhn7xokQontaBXgbG2W367lz5zBt2jQMHjxYJ+uZifmqEolx7mYxe5wQo3l3QdUW/K3cSsgVnQuKZVXNy+z0sQ+8Nkb192cn9wlrxDh7rcgo5dClckpTS4jF0SrA29nZYdu2bUhKSkJcXBwYhoFUKtX4mbVr1yI2NhaJiYltvufcuXOYMWMGpk6dikceeYR9Pj4+HtOmTcOMGTMwe/ZsLb8KMaRjqfmQyZU3eREBLogIcNX4fh83e7brt0EiR1ZjzvqOMmSa2rbwbW0wYWgIe/z7P9lQKMz7hreCWvCEWBytAvzbb7+N0tJSrFmzBt7e3sjNzcW0adM0fmb27Nmt5rBvUl1djddffx1ffPEFkpKS8PHHH6u9/u2332Lfvn3Ys2ePNkUkBiSVyXEstXnP9/Za74BymVnvkK530xtzkp2qsYMC2W1qi4X1uKCSptccldMYPCEWR6sA361bN7z88suYMGECACAkJARPPPGExs/ExMTA1bXtVt2BAweQkJCAgIAAAMotaYl5+Od6MWrqlD04Hi4CDOnl3c4nlHqHdS3AS6RyVDde14bLgZuT8QKRvYCnNqnw4Jlssx62oix2hFgejZvNvP/++3jhhRfw9NNPt7reuGWruyOysrIgk8mwcOFC1NbWYtGiRZg5cyb7+pIlS8DhcDBv3jzMmzev09chusUwDA6r7Bo3bkgQbLjaLamKVGnB382vglQmhy3v/mV1bVFtZbo7C8DlGm4NfGvGRwfhz5QcSKQK5JWKcCWjHAO7exm1TJ3BMMx9+QUIIeZPY4AfMmQIAGDs2LE6v7BcLsf169exc+dONDQ0YP78+RgwYAC6deuGn376Cb6+vigvL8fixYsRHh6OmJiYds/p7u4AXgcChja8vZ11ej5zd+V2KfJKlRsP2fFtMDu+J5wc+Bo/01SH3t7OCPR2RH5pLaQyBcprZejX3U3ra+eWN0+w8/NyNPrPxhvA5Nhu2HcyAwDwZ0ouxg8P00vyHX1+V1GdBGKJcj2/Hd8GYcHuBk0gZCjG/n2xFFSPXWeoOtQY4OPj4wEAs2bN0vmF/fz84ObmBgcHBzg4OCA6Ohrp6eno1q0bfH19ASi77RMSEpCWlqZVgBcK63RaRm9vZ5SWdm4ymKX65cgt9vGIvn6orxWjvlbc5vtb1mGPIDfkN94gnLmSDz9X7bvZM3Ir2Meu9rYm8bMZ3c8PSafvQSZncCtbiOSLuWpLAnVB37+HOcXN53Z3FqCsrPOJiEwV/VvWDarHrtN1HWq6WdCqb7W8vBwff/wxnn/+eTzzzDPsf10xbtw4XLx4ETKZDPX19UhLS0NERATq6uogEin/wNTV1eH06dPo0aNHl65FdKOoog5pGeUAlOlME6Lbn1zXUlcS3hh7DXxr3J0FanvcJ53NMl5hOqmClsgRYpE0tuCbPPXUU4iKikJsbCy7Jr49q1evxvnz5yEUCjF69GisWrUKMplyc44FCxYgIiICo0aNwvTp08HlcvHggw+iZ8+eyM3NxYoVKwAou/ETExMxevToTn49okuqY+8DunvBtzEve0dEhrixjzMLq1EvlrHbybbHVJOxTB4WgpNXCsAwwPUsITILq9HN38XYxdKaer3SDHpCLIVWf1nr6+vx6quvdujE2mS6W7p0KZYuXar2XHBwMPbv39+haxH9q22Q4vTV5j3fE6JbT0vbHmcHPoJ9nJBbIoJcweBOXhX6R2i3gqLMBFvwAODj7oBhvX3xzw1l4p+DZ7Kwak5/I5dKexU1NMGOEEukVRf9gAEDcOvWrfbfSCzWycsFkEiV2eeCvJ26NM7c2W56U+yibzIltjl9beqdMuSXms84Ni2RI8QyadWCnz9/Ph555BH4+flBIGjuwtu9e7feCkZMh0yuwJGLKnu+xwR1aZZ1ZKg7/mrMY38ju6KddzeXoVKkDEQcAB7OphWIgrydMKiHF1LvlAEAkv7JxuPT+hi5VNqhJXKEWCatAvwLL7yA5cuXIyoqSusxeGI5Lt0uhbBGGVxdHGwxPMq3S+frFewGLocDBcMgt1gEUb0UTva2Gj8jrBGjKY+MqxMftjzT2850amwYG+DP3SjGzFHh8HGzN3Kp2ldBY/CEWCStArxAIMCSJUv0XRZiov5S2TXugUGBHUpO0xp7AQ/d/J2RUVANBsCtHCGG9PLR+BlT7p5vEh7ggqgwd9zIEoJhgEP/ZGPRpEhjF0sjuULB3rwBgLuJ9YwQQjpPq2bQqFGjcPLkSX2XhZigjPwq3CuoBgDwbDgYO7hzk+ta6uj2sWoT7Ey4G3lqbBj7OPlqoVrwNEWVNZLmnhFH0+wZIYR0jlYt+F9++QXbtm2Do6Mj+Hw+GIYBh8PB2bNn9V0+YmSqrffhUX5wddSctU5bvUPdkXQ2G4B2AV51nNjL1XS7vSND3BAR4IKMgmrI5Az+PJ+D+eNMN48Djb8TYrm0CvC//vqrvstBTFB5VQMu3iplj8d3cmlca7oHuoJnw4VMrkBheR2ENWK4O7c9/msOXfSActe8qSPC8MnuNADA8cv5SBwR1u4cA2Oh8XdCLJfG/rimxDSBgYH3/cfn66YlR0zXL8fvQtHYf9s71B0hvrrLn8y3tUH3wOZkMOk5mlvxpprkpjUDIjwR5O0EAJBIFfj56G0jl6ht1IInxHJpDPBz585lHz/11FNqrz3++OP6KRExCQ0SGS6mN+9x3pm0tO3p3YFx+LKq5o1mTLkFDzS24lXWxf9zvRj1YpkRS9Q2WgNPiOXSGOBV97cuKCho8zViWdKzhXj16/NQNP6IbXlc8Pm6n3zVO9RD7ZptUTCMWiDyMoNA5Oxgy05YUzDAum3/dDj3viFQC54Qy6Xxr7ZqMpOWiU0scTtJolTbIENZZfMf/okxwYhSCca6EubvDIGtcsldWVUDSirrW31flUgCeePdhpO9LQR808/FEBXmgelxYexxVa2kze9nTGpj8B3Y2Y8QYvo0BniFQoGGhgbU19erPW46JpbnelYFvtx/DU39M84O+pscxrPhomewG3vcVgvXXCbYtSSVKRDo5cgef//XbWQVVRuxRPcrV91JjtbAE2JRNAb4W7duYdCgQRg0aBBu3bqFgQMHYvDgwRg0aBBu3zbdiUOkc+7mV+HTX9MgkyvDu4+7PTb+31AE63ByXUvajMOXVTe3fM2he75JoLcTXlkUjUBvZZCXyRX4fM81iOqlRi6ZUl2DjJ0bwLPh6vVmjhBieBqXyaWnpxuqHMTIcopr8NEvV9gNZdydBVgzfyBcnQSIidScZa4rWgb4phwLqsy1Bd9Ubytn9cPGb1NQL5ajvLoB2/Zfx7NzB4DLNe4wl+oucp4uAhp2I8TCUNoqgqKKOmz532XUNbbmnB1ssWb+QIMklAn2dYKjnfI+s7pWgoKy2vveU24mWeza4uvhgKWJUezxtcwK7EvONGKJlCpogh0hFo0CvJUrr2rABz+norpO2W1sL+Dh+XkD4e/p2M4ndYPL4SAyRHM3fZlaFjvzDESDengjcUTz0rkDZ7JwuXFjGmOhJXKEWDYK8FasqlaCD35OZf/Q8225eHZuf50mtNFG7zDNAd5cu+hbmjkyHH1UvutXB2+gWFhntPKoL5GjGfSEWBoK8FaqtkGKzT9fRrFQOYGNZ8PBytn90CPIzeBlUR2Hv5VTCYWiOccCwzAWE+C5XA4en96HTQlbL5bh8z1XIZbKjVKeCjPKDkgI6TiNAb64uBiXLl1ij7/55ht89tln+Oyzz5Cbm6vhk8SUNUhk+GjXFeSVigAAHA7wxPQ+6NvN0yjl8fNwgKuTMvVxnViGnJIa9rWaeikkMuXEPzu+DRwEWm2fYLKcHfh4alY/8GyUE9rySmvx3aF0oySOUlsiZ8Y3ToSQ1mkM8B999BHy8vLY4x9//BG1tbUoLy/H1q1b9V44ontSmRyf7bmKjPzm9dj/N6V3u/ux6xOHw1GfTZ/V3E2v2nr3crWziJne3fxd8MiEXuzx2evF+PtSvsHLQS14QiybxgB/48YNTJs2jT12dHTEiy++iFdffRWZmcafBUw6Rq5QYOu+67ihEkD/Nb4H4vr5G7FUSr3bmGhn7jPo2zJ6QABG9W+u95+P3sHdvCqDXV+hYNT2qvfQsJMfIcQ8tZuLXrXF9OKLL7KPa2vvX85ETJeCYfB1UjpSVWZuzxrVDeP1sIlMZ6hOtLudVwmZXNktr7aLnIV1Iz8yoSdC/ZQTGuUKBv/57SqqaiUGuXZVbXP6X2cHW/BtTT/9LyGkYzQG+IaGBkgkzX9wYmNjAQASiQR1dcab/Us6hmEY/Hj4Ns5eL2KfmzQ0BIkjwoxXqBa8XO3h7aYM4BKpAvcKlEMIZRYywa41tjwbrJjVl80DUCmSYOtv1yA3QBpo2mSGEMunMcCPGTMG77zzDuTy5lm+CoUC7733HsaMGaP3whHd2HPyntoY7+gBAZg7NsLkxrNbS1trqV30Tbxc7fHEjD5o+kncyq3E7uMZer8ujb8TYvk0Bvhnn30Wt2/fRkJCAlauXImVK1di/PjxuHHjBp577jlDlZF0wR//ZCPpbDZ7PLS3DxZN7GVywR0AIlsL8GpJbvSfWc8Y+nbzxMzR4ezxn+dzkZJeotdr0hp4QiyfxjVHjo6O+P7773HmzBncuHEDAPCvf/0LI0aMMEjhrFnTzmqqQa+jfj56B3+lNC9n7B/hiaWJUUbPgd4W1Yl2GflVEEvlFrMGvj1TY0ORWVCNy3eVcyS+TrqJQC9HBHjpJ6NgRRVlsSPE0mm1qHjEiBEU1A2oXizDz3/fgVSmwJThoZDJFZDJGUhlisbHymOZXAGpXAFZ4/NSOQO5ynPpOZXsOXsFu+GpmX3BszHd3EauTgIEejkiv6wWcgWDqxnlbH58Wx4XLha82xmXw8HSxN7YuPMCSirrIZYqlzOufzRaL9crpy56QiyexgC/cOFCjV253333nc4LZE0aJDIUlNUhv0yEgrJa5JfVIruoBjV1zduJ7ki62eXr2PFtMGlYiFnMlI4MdUd+44YzyVcL2ec9XCxjDbwmDna2WDG7HzZ9dwESmQJFFXX4+vebeHVZrM6vpToG705d9IRYJI0BfsmSJQCUs7A3bdqEV155xSCFsgSqXexiqRyF5bXIL61lA3lBWa3aDHF9eu6hAUZJQdsZvUPdcfSiMrnS1Xvl7PNeVhKEgn2c8OjkSHx1QDkkdvFWKf6zOw0PPRDezic7hlrwhFg+jQH+gQceYB9//PHHasekdQqGQXZRDXb8fhMNYhkc7Hgoq2xARxKRcgA42dvC0d4WPBsOuvm7gMfjwtaGCxsbDmxtuOA1/mfLa+05Ds7dKIaNDRdujnzcyBKaTYDvFeIGDgAGgGr2Vk8LnWDXmtg+frhXUM3e6Bz6JwsDwt3RK6Tz8zFUiSVy1DYohz5suBy4OPJ1cl5CiGnROrG3pXePdkW9WIbrmRVIyyhH6p1S9o8nALXHLXE5HPh62LOTqQK9nRDg5YjckhoMj/IDAKSklyAmsuNpZMVSBfs5fc/I1iVHO1uE+Dkju6hG7XlLnmDXmoHdPXH6aiEaJMolqp/+ehUrZ/fr0qTLJhU16jPoufRvmxCLZN47dxgJwzAoqqjDlbvlSMsow528KjYrWGs4HMDH3aE5kDf+5+vhAFve/ZPeAlVmTncmuLf8XGfPYSxRoe73BXgvK+tG7tPNE0/N7Istv1wBoLyJDPJx0sm5qXueEOugMcA//fTTbMs9Ly8PzzzzjNrrH3/8sf5KZmKkMjnScyqRdrccaffKUFrZ9vi5LY8LX3d7eLvZw9WRjwXje8CWZ/oT3ExF71B3/HEuR+05a2vBA8Dd/Cq4O/MhrJGAAXD5ThlG9u/6vgEVqrvIUYAnxGJpDPBjx45lH1vb+Ht6thDpedUoLK1B2t0y3MwWstuWtibU1xn9IjwxIMIT5VUNGBrlC0DZPU7BvWN6BLnBhstR6xXxssIAH+jthIToEPxy7C4A4NLtUp0EeNXcAhTgCbFcGgP8rFmzDFUOk1JWVY9Pfk1jxz9bI+DboE+YB/pHeKJfuCfcVXbjigh0ZR+bW/e4KRDwbRAe4II7jbur2XA5cHOyjln0qmIifVAirGMD/LXMCtSLZbAXdG1kTT1NrfXVKyHWQuNfioyMDGRmZmL8+PEAgLfeegs1Ncqx0UWLFqF37976L6GBpWcLsT3pRqvB3dfDAQMiPNE/whM9gtxaHT8nXZeeLVQLQhwOcDu3UicTzMyNj7sDugW4ILOgGjK5AlfvlWNob98unZPG4AmxDhoj1CeffAJb2+bsYSdOnEDfvn0RHh6Obdu26b1wxhAZ6o5Zo5rXHEcEumLB+B54+4nhePvx4Zg/rgeiwjwouOtRZKg7Zo+JYI/DA1ysMrg3ie0XwD6+eKu0y+ejMXhCrIPGFnx2drbarnH29vZ4+OGHAYD9vyUqrazHtBFhsLe3hVgsQ4KJ7JluTYor6hAZ4oaKGjECvHQze9xcjejnjx//TAcApGWUQyqTd3peh4Jh7lsmRwixTBoDvOo2sQCwefNm9nF1dbV+SmQCAr2dEBPpA29vZ/x+Sv9bd5L7BXo7YWZjT4o5rePXhxA/Z/h6OKC4og5iqRzXM4UY2MOrU+eqqZVAJldOXnS048GOTytlCbFUGvuZpVIpRCIRexwRoew2FYlEkEgk+i2ZEZnzGnJLQT+DZhwOB0N6erPHF293/oanvJp2kSPEWmgM8FOnTsW6devUgrxIJMIrr7yCKVOm6L1whBClIb2aA/zlO2WQydtesqlJRTUtkSPEWmgM8E8++ST4fD5GjRqFWbNmYdasWRg1ahR4PB5WrFhhqDISYvXC/JzZpZi1DTLczq3s1HloBj0h1kPjAByPx8MHH3yA7Oxs3Lih3N0qKioKoaGhBikcIUSpqZv+SOMGNBdvlyIqzKPD5ymvpgl2hFgLrWbYhIaGUlAnxMiG9GoO8Jdul+LhhJ4d3iiGlsgRYj1oMTchZqJHkBucHZR5KapEEtwr6PhKFuqiJ8R6UIAnxExwuRwMUlked/FWx2fTV1AXPSFWgwI8IWZkcM/mJYMXb5WCYdreprgliVSOmjopAIDLsc78/oRYEwrwhJiR3qHusBcos9iVVTUgt0TUzieaCWuax9/dnQXgcjs2fk8IMS8U4AkxI7Y8LgZEqHbTa5+bvpx2kSPEqlCAJ8TMqCa9uXS7cwHew5Um2BFi6SjAE2Jm+nbzBL9xN8P8sloUltdq9bkKSlNLiFWhAE+ImRHwbdA33JM91rYVX05pagmxKhTgCTFDqpvPaBvgK2gMnhCrQgGeEDM0oLsnbBpnwWcW1qC8qqGdT6jvJEcteEIsHwV4QsyQg50teoe6s8eX7mhuxTMM06IFTwGeEEtHAZ4QMzVYZTZ9e8vlauqlkMqUW8zaC3iwF2i1DQUhxIxRgCfETA3q4Y2mVDV3citRVStp8700/k6I9dFbgF+7di1iY2ORmJjY5nvOnTuHGTNmYOrUqXjkkUfY50+ePImJEyciISEB27Zt01cRCTFrro589AhyBQAwAC5r6KYvr6Lxd0Ksjd4C/OzZs7F9+/Y2X6+ursbrr7+OL774AklJSfj4448BAHK5HBs3bsT27duRlJSEgwcP4u7du/oqJiFmbXAvldz0GmbTV9ASOUKsjt4CfExMDFxdXdt8/cCBA0hISEBAQAAAwNNTua43LS0NoaGhCA4OBp/Px9SpU3H06FF9FZMQs6a6XO5mlhB1DdJW30dpagmxPkabaZOVlQWZTIaFCxeitrYWixYtwsyZM1FcXAw/Pz/2fb6+vkhLS9PqnO7uDuDxbHRaTm9vZ52ezxpRHXZdW3Xo7e2M7sFuuJtbCbmCQWZJLR4YEnzf+2rFcvZxWKCbVf5MrPE76wPVY9cZqg6NFuDlcjmuX7+OnTt3oqGhAfPnz8eAAQO6dE6hsE5HpVPy9nZGaWmNTs9pbagOu669OhwQ7oG7uZUAgOMXctEnxO2+9xSUNu86Z8uB1f1M6PdQN6geu07XdajpZsFos+j9/PwwcuRIODg4wMPDA9HR0UhPT4evry+KiorY9xUXF8PX19dYxSTE5A1W6aa/eq8cYqn8vvdU1KiOwVMXPSHWwGgBfty4cbh48SJkMhnq6+uRlpaGiIgI9OvXD1lZWcjNzYVEIkFSUhLi4+ONVUxCTJ6/pyMCvBwBABKZAtfuVai9LpUpUCVSLqHjcAA3JwrwhFgDvXXRr169GufPn4dQKMTo0aOxatUqyGQyAMCCBQsQERGBUaNGYfr06eByuXjwwQfRs2dPAMCGDRuwdOlSyOVyzJkzBz169NBXMQmxCIN7eqOgTLmr3KXbJWpbygpFzUvk3JwE4NlQ+gtCrIHeAvyWLVvafc/SpUuxdOnS+54fM2YMxowZo49iEWKRhvT0xsEzWQCAy3fLIZMr2EBeUUUpagmxRnQrT4gFCPF1gperMnjXi2W4mS1kX1PfJpa65wmxFhTgCbEAHA5HbbKdam562mSGEOtEAZ4QC6E67p56pxQKBQOAtoklxFpRgCfEQkQEusLVkQ8AqKmT4k5eJQBqwRNirSjAE2IhuC276Rtz09MYPCHWiQI8IRZEdY/4S7dLwTAMKlS66D1dqQVPiLWgAE+IBekV7AZHO+Xq14pqMa5nVbCZ7QR8GzgIjJadmhBiYBTgCbEgPBsuBnb3Yo8Pp+Sxjz2cBeBwOMYoFiHECCjAE2JhVLvpr94rZx/TBDtCrAsFeEIsTJ8wDwhs7982mZbIEWJdKMATYmH4tjboF+F53/OeNIOeEKtCAZ4QCzREZblcE2rBE2JdKMATYoH6R3iCZ6M+oY7G4AmxLhTgCbFA9gIeosI81J7zoDXwhFgVCvCEWKD0bCFKhHVqz5VU1LXxbkKIJaIAT4gFigx1x2OTI9ljbzd79A2/f+IdIcRyUVorQizUjSwhhkX5oKCsFt38XY1dHEKIgVGAJ8RCBXo7YeaocABASnqJkUtDCDE06qInxELFRPq0+pgQYh0owBNCCCEWiAI8IYQQYoEowBNCCCEWiAI8IYQQYoEowBNCCCEWiAI8IYQQYoEowBNCCCEWiAI8IYQQYoEowBNCCCEWiAI8IYQQYoE4DMMwxi4EIYQQQnSLWvCEEEKIBaIATwghhFggCvCEEEKIBaIATwghhFggCvCEEEKIBaIATwghhFggqwrwa9euRWxsLBITE9nn0tPTMW/ePEybNg3Lly+HSCQCAEgkEqxduxbTpk3D9OnTce7cOfYz165dw7Rp05CQkIA333wT1rTSUFd1uHDhQkycOBEzZszAjBkzUF5ebvDvYiyFhYVYuHAhpkyZgqlTp+Lbb78FAFRWVmLx4sWYMGECFi9ejKqqKgAAwzB48803kZCQgGnTpuH69evsufbu3YsJEyZgwoQJ2Lt3r1G+jzHosg579+7N/h4uX77cKN/HWDpajxkZGZg3bx769u2LHTt2qJ3r5MmTmDhxIhISErBt2zaDfxdj0WUdxsfHY9q0aZgxYwZmz57d9cIxVuT8+fPMtWvXmKlTp7LPzZ49mzl37hzDMAyza9cu5sMPP2QYhmG+//575qWXXmIYhmHKysqYWbNmMXK5nGEYhpkzZw6TmprKKBQKZsmSJczx48cN+0WMSFd1+MgjjzBpaWmGLbyJKC4uZq5du8YwDMPU1NQwEyZMYO7cucO8++67zJdffskwDMN8+eWXzHvvvccwDMMcP36cWbJkCaNQKJjU1FTmwQcfZBiGYYRCIRMfH88IhUKmsrKSiY+PZyorK43zpQxMV3XIMAwzcOBAw38BE9HReiwrK2OuXLnCbNmyhdm+fTt7HplMxowbN47JyclhxGIxM23aNObOnTuG/0JGoKs6ZBiGGTt2LFNeXq6zsllVCz4mJgaurq5qz2VlZSEmJgYAEBcXh7/++gsAcPfuXQwbNgwA4OnpCWdnZ1y7dg0lJSUQiUQYOHAgOBwOZs6ciaNHjxr2ixiRLurQ2vn4+KBPnz4AACcnJ4SHh6O4uBhHjx7FzJkzAQAzZ87EkSNHAIB9nsPhYODAgaiurkZJSQmSk5MRFxcHNzc3uLq6Ii4uDqdOnTLW1zIoXdWhtetoPXp6eqJ///7g8Xhq50lLS0NoaCiCg4PB5/MxdepUq/m7qKs61AerCvCt6dGjB/uLeOjQIRQWFgIAIiMj8ffff0MmkyE3NxfXr19HYWEhiouL4efnx37ez88PxcXFRim7qehoHTZZt24dZsyYgc8//9yqhjlU5eXl4ebNmxgwYADKy8vh4+MDAPD29maHLdr6nWv5vK+vr1X+LnalDgFALBZj9uzZeOihh9g/wtZIm3psC/0uKnWlDpssWbIEs2fPxv/+978ul0f/txAmbtOmTdi0aRP+85//ID4+Hnw+HwAwZ84cZGRkYM6cOQgICMCgQYNgY2Nj5NKaps7U4QcffABfX1+IRCI8/fTT2LdvH3u3ay1qa2vx9NNPY926dXByclJ7jcPhgMPhGKlk5kMXdXjs2DH4+voiNzcXjz76KHr27ImQkBB9Fdkk0e9i1+miDn/66Sf4+vqivLwcixcvRnh4ONs72hlWH+AjIiLw9ddfAwAyMzNx/PhxAACPx8O6devY982fPx9hYWFwcXFBUVER+3xRURF8fX0NWmZT09E6BMDWmZOTExITE5GWlmZVAV4qleLpp5/GtGnTMGHCBADKrruSkhL4+PigpKQEHh4eAJR11drvnK+vL86fP88+X1xcjKFDhxr2ixiRLuqw6TUACA4OxtChQ3Hjxg2rCvAdqce2tKzf4uJiq/q7qIs6BJp/Fz09PZGQkIC0tLQuBXir76Jv6jZRKBT44osvMH/+fABAfX096urqAACnT5+GjY0NunfvDh8fHzg5OeHy5ctgGAa//fYbxo0bZ7Tym4KO1qFMJkNFRQUA5T+M48ePo0ePHsYpvBEwDIOXX34Z4eHhWLx4Mft8fHw8fvvtNwBQ+71qep5hGFy+fBnOzs7w8fHByJEjkZycjKqqKlRVVSE5ORkjR440xlcyOF3VYVVVFSQSCQCgoqICly5dQvfu3Q3+fYylo/XYln79+iErKwu5ubmQSCRISkpCfHy8PotuMnRVh3V1dewKpLq6Opw+fbrLfxetaje51atX4/z58xAKhfD09MSqVatQV1eHH3/8EQCQkJCA559/HhwOB3l5eViyZAm4XC58fX2xadMmBAYGAgCuXr2KtWvXoqGhAaNHj8b69eutpgtLF3VYV1eHRx55BFKpFAqFArGxsVi7dq3VDIFcuHABDz/8MHr27AkuV3mPvXr1avTv3x/PPvssCgsLERAQgI8++ghubm5gGAYbN27EqVOnYG9vj7feegv9+vUDAOzevRtffvklAGD58uWYM2eO0b6XIemqDi9duoRXX30VHA4HDMNg0aJFmDt3rpG/neF0tB5LS0sxZ84ciEQicLlcODg44Pfff4eTkxNOnDiBt956C3K5HHPmzMGTTz5p5G9nGLqqQ6FQiBUrVgAA5HI5EhMTu1yHVhXgCSGEEGth9V30hBBCiCWiAE8IIYRYIArwhBBCiAWiAE8IIYRYIArwhBBCiAWiAE8Iuc/atWvx/vvvqz332GOPscshCSGmjwI8IeQ+69atwx9//IErV64AAH7++WdwOBwsWLCgS+eVyWS6KB4hRAu0Dp4Q0qrTp09j06ZN+Pzzz9nW+7vvvouCggKIxWJMnTqV3T/93Xffxfnz5yGVSuHu7o633noLgYGByMvLw5w5czB79mz8888/eOihh7p8k0AI0Q4FeEJIm1599VUcPHgQa9euRVJSEp566inExMRAIpHgsccew4oVKxAXF4eKigo21/auXbtw5swZfPjhh8jLy8O4cePw4YcfYsqUKUb+NoRYF6vfbIYQ0rYlS5bgjz/+wJQpU/Dqq6+yewgAyt2zMjIyEBcXh5MnT+LHH39EXV3dfd3wAoEAkydPNnTRCbF6FOAJIW3icrngcDhQKBTgcDjYvXs3bG1t1d6Tn5+Pt99+G7t370ZwcDAuXbqENWvWsK/b29tbzV4NhJgSmmRHCGmXk5MThgwZgm3btrHPFRYWorS0FCKRCLa2tvD29oZCocDPP/9sxJISQppQgCeEaOWDDz5ARkYGpk2bhmnTpuG5555DdXU1evXqhUmTJmHKlCmYO3cugoKCjF1UQghokh0hhBBikagFTwghhFggCvCEEEKIBaIATwghhFggCvCEEEKIBaIATwghhFggCvCEEEKIBaIATwghhFggCvCEEEKIBfp/Av8dE615zp8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_edgar_total = df_edgar.groupby(\n",
    "    by=[\"Year\"]).sum()[[\"GHG Emissions\"]]\n",
    "df_edgar_total = df_edgar_total.reset_index()\n",
    "df_edgar_total = df_edgar_total.set_index(\n",
    "    [\"Year\"])\n",
    "\n",
    "ax = df_edgar_total.plot(\n",
    "    title=\"Global food system emissions (1990-2015)\",\n",
    "    ylabel=\"GHG Emissions (metric tons CO2e)\",\n",
    "    linewidth=3, marker='*')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.042542,
     "end_time": "2021-10-21T16:04:50.048465",
     "exception": false,
     "start_time": "2021-10-21T16:04:50.005923",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Total food system GHG emissions have been growing\n",
    "From 1990 to 2015, total GHG emissions were in the range of **~16 to ~18 billion metric tons of CO2e**   \n",
    "Land emissions have declined while Farm emissions have grown, and annual trends for Land emissions mirror the rise and fall of carbon dioxide emissions "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:50.136820Z",
     "iopub.status.busy": "2021-10-21T16:04:50.135702Z",
     "iopub.status.idle": "2021-10-21T16:04:50.538818Z",
     "shell.execute_reply": "2021-10-21T16:04:50.539367Z",
     "shell.execute_reply.started": "2021-10-21T15:18:40.448089Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.449171,
     "end_time": "2021-10-21T16:04:50.539547",
     "exception": false,
     "start_time": "2021-10-21T16:04:50.090376",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
        {
         "hovertemplate": "Food System Stage=Land<br>Year=%{x}<br>GHG Emissions=%{y}<extra></extra>",
         "legendgroup": "Land",
         "line": {
          "color": "rgb(76,114,176)",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "Land",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1990,
          1991,
          1992,
          1993,
          1994,
          1995,
          1996,
          1997,
          1998,
          1999,
          2000,
          2001,
          2002,
          2003,
          2004,
          2005,
          2006,
          2007,
          2008,
          2009,
          2010,
          2011,
          2012,
          2013,
          2014,
          2015
         ],
         "xaxis": "x",
         "y": [
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         ],
         "yaxis": "y"
        },
        {
         "hovertemplate": "Food System Stage=Farm<br>Year=%{x}<br>GHG Emissions=%{y}<extra></extra>",
         "legendgroup": "Farm",
         "line": {
          "color": "rgb(221,132,82)",
          "dash": "solid"
         },
         "marker": {
          "symbol": "circle"
         },
         "mode": "lines",
         "name": "Farm",
         "orientation": "v",
         "showlegend": true,
         "type": "scatter",
         "x": [
          1990,
          1991,
          1992,
          1993,
          1994,
          1995,
          1996,
          1997,
          1998,
          1999,
          2000,
          2001,
          2002,
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         ],
         "xaxis": "x",
         "y": [
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Food System Emissions by GHG, 1990-2015\"},\"xaxis\":{\"anchor\":\"y\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"Year\"}},\"yaxis\":{\"anchor\":\"x\",\"domain\":[0.0,1.0],\"title\":{\"text\":\"GHG Emissions\"}}},                        {\"responsive\": true}                    ).then(function(){\n",
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "df_edgar_stages = df_edgar.groupby(\n",
    "    by=[\"FS Stage Order\", \"Food System Stage\", \"Year\"]).sum()[[\"GHG Emissions\"]]\n",
    "df_edgar_stages = df_edgar_stages.reset_index()\n",
    "# Using plotly express to visualize the GHGs emitted in each food system stage\n",
    "fig = px.line(\n",
    "    df_edgar_stages,\n",
    "    x='Year',\n",
    "    y='GHG Emissions', \n",
    "    color='Food System Stage',\n",
    "    title='GHG emissions in Global Food Systems by Stage, 1990-2015')\n",
    "fig.show()\n",
    "\n",
    "df_edgar_ghgs = df_edgar.groupby(\n",
    "    by=[\"GHG\", \"Year\"]).sum()[[\"GHG Emissions\"]]\n",
    "df_edgar_ghgs = df_edgar_ghgs.reset_index()\n",
    "# Using plotly express to visualize each GHG emissions\n",
    "fig = px.line(\n",
    "    df_edgar_ghgs,\n",
    "    x='Year',\n",
    "    y='GHG Emissions', \n",
    "    color='GHG',\n",
    "    title='Global Food System Emissions by GHG, 1990-2015')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.059479,
     "end_time": "2021-10-21T16:04:50.659344",
     "exception": false,
     "start_time": "2021-10-21T16:04:50.599865",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Do any food products create higher emissions than others? \n",
    "Let's look at a dataset of the global average emissions for some common food products to find out. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:50.792508Z",
     "iopub.status.busy": "2021-10-21T16:04:50.787472Z",
     "iopub.status.idle": "2021-10-21T16:04:50.811110Z",
     "shell.execute_reply": "2021-10-21T16:04:50.811654Z",
     "shell.execute_reply.started": "2021-10-21T15:18:40.801799Z"
    },
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     "outputs_hidden": true
    },
    "papermill": {
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     "exception": false,
     "start_time": "2021-10-21T16:04:50.718928",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 43 entries, 0 to 42\n",
      "Data columns (total 11 columns):\n",
      " #   Column                                     Non-Null Count  Dtype  \n",
      "---  ------                                     --------------  -----  \n",
      " 0   Food product                               43 non-null     object \n",
      " 1   Land Use Change                            43 non-null     float64\n",
      " 2   Feed                                       43 non-null     float64\n",
      " 3   Farm                                       43 non-null     float64\n",
      " 4   Processing                                 43 non-null     float64\n",
      " 5   Transport                                  43 non-null     float64\n",
      " 6   Packaging                                  43 non-null     float64\n",
      " 7   Retail                                     43 non-null     float64\n",
      " 8   Total from Land to Retail                  43 non-null     float64\n",
      " 9   Total Global Average GHG Emissions per kg  43 non-null     float64\n",
      " 10  Unit of GHG Emissions                      43 non-null     object \n",
      "dtypes: float64(9), object(2)\n",
      "memory usage: 3.8+ KB\n",
      "Index(['Food product', 'Land Use Change', 'Feed', 'Farm', 'Processing',\n",
      "       'Transport', 'Packaging', 'Retail', 'Total from Land to Retail',\n",
      "       'Total Global Average GHG Emissions per kg', 'Unit of GHG Emissions'],\n",
      "      dtype='object')\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Food product</th>\n",
       "      <th>Land Use Change</th>\n",
       "      <th>Feed</th>\n",
       "      <th>Farm</th>\n",
       "      <th>Processing</th>\n",
       "      <th>Transport</th>\n",
       "      <th>Packaging</th>\n",
       "      <th>Retail</th>\n",
       "      <th>Total from Land to Retail</th>\n",
       "      <th>Total Global Average GHG Emissions per kg</th>\n",
       "      <th>Unit of GHG Emissions</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apples</td>\n",
       "      <td>-0.029</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.096</td>\n",
       "      <td>0.044</td>\n",
       "      <td>0.017</td>\n",
       "      <td>0.357</td>\n",
       "      <td>0.43</td>\n",
       "      <td>kg CO2e per kg food produced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Bananas</td>\n",
       "      <td>-0.025</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.266</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.292</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.678</td>\n",
       "      <td>0.86</td>\n",
       "      <td>kg CO2e per kg food produced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barley</td>\n",
       "      <td>0.009</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.176</td>\n",
       "      <td>0.128</td>\n",
       "      <td>0.035</td>\n",
       "      <td>0.497</td>\n",
       "      <td>0.264</td>\n",
       "      <td>1.109</td>\n",
       "      <td>1.18</td>\n",
       "      <td>kg CO2e per kg food produced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Beef (beef herd)</td>\n",
       "      <td>16.278</td>\n",
       "      <td>1.878</td>\n",
       "      <td>39.388</td>\n",
       "      <td>1.269</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.247</td>\n",
       "      <td>0.164</td>\n",
       "      <td>59.570</td>\n",
       "      <td>99.48</td>\n",
       "      <td>kg CO2e per kg food produced</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Beef (dairy herd)</td>\n",
       "      <td>0.906</td>\n",
       "      <td>2.508</td>\n",
       "      <td>15.689</td>\n",
       "      <td>1.108</td>\n",
       "      <td>0.424</td>\n",
       "      <td>0.268</td>\n",
       "      <td>0.182</td>\n",
       "      <td>21.085</td>\n",
       "      <td>33.30</td>\n",
       "      <td>kg CO2e per kg food produced</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Food product  Land Use Change   Feed    Farm  Processing  Transport  \\\n",
       "0             Apples           -0.029  0.000   0.225       0.004      0.096   \n",
       "1            Bananas           -0.025  0.000   0.266       0.059      0.292   \n",
       "2             Barley            0.009  0.000   0.176       0.128      0.035   \n",
       "3   Beef (beef herd)           16.278  1.878  39.388       1.269      0.346   \n",
       "4  Beef (dairy herd)            0.906  2.508  15.689       1.108      0.424   \n",
       "\n",
       "   Packaging  Retail  Total from Land to Retail  \\\n",
       "0      0.044   0.017                      0.357   \n",
       "1      0.065   0.021                      0.678   \n",
       "2      0.497   0.264                      1.109   \n",
       "3      0.247   0.164                     59.570   \n",
       "4      0.268   0.182                     21.085   \n",
       "\n",
       "   Total Global Average GHG Emissions per kg         Unit of GHG Emissions  \n",
       "0                                       0.43  kg CO2e per kg food produced  \n",
       "1                                       0.86  kg CO2e per kg food produced  \n",
       "2                                       1.18  kg CO2e per kg food produced  \n",
       "3                                      99.48  kg CO2e per kg food produced  \n",
       "4                                      33.30  kg CO2e per kg food produced  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_foods.info()\n",
    "print(df_foods.columns)\n",
    "df_foods.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.060782,
     "end_time": "2021-10-21T16:04:50.932846",
     "exception": false,
     "start_time": "2021-10-21T16:04:50.872064",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Expand or condense any cells throughout this notebook to explore my code!*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:51.059368Z",
     "iopub.status.busy": "2021-10-21T16:04:51.058224Z",
     "iopub.status.idle": "2021-10-21T16:04:52.390834Z",
     "shell.execute_reply": "2021-10-21T16:04:52.391380Z",
     "shell.execute_reply.started": "2021-10-21T15:40:19.145121Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 1.397514,
     "end_time": "2021-10-21T16:04:52.391568",
     "exception": false,
     "start_time": "2021-10-21T16:04:50.994054",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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6Ohq7d+8W4/Ly8lBcXIysrCzI5fJ5v7uysjLcunULhw8fhlQqhVKphMFggFarRW1tLXJzcyGRSCCRSHDu3Dls3LgR5eXluHTpEjo6OhAbG4s9e/b86edKRET0O0ncv2pKQURERER/u1OnTqG0tFT0+QpkampKlEA2NzfDYrHg0aNHv2OJQbHZbKisrERjY6Pf88ePH0dhYaFPz7X/JO8zm52dhV6vR2Zmps8GDv9LFu4wSURERH8/ZpARERER/QOuXr3qd2OBhRoaGtDV1YWfP39i1apVuHHjxm9YXfDGxsZ+uQPj73by5EnMzs7C4XBApVKJzQWIiIiIAmEGGRERERERERERLWls0k9EREREREREREsaA2RERERERERERLSkMUBGRERERERERERLGgNkRERERERERES0pDFARkRERERERERESxoDZEREREREREREtKT9AWmd/9XvGhHrAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x2160 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_foods_sc = df_foods[['Food product', 'Land Use Change', 'Feed', \n",
    "                            'Farm', 'Processing', 'Transport', 'Packaging', \n",
    "                            'Retail', 'Total from Land to Retail']\n",
    "                               ].sort_values(by='Total from Land to Retail', ascending= True)\n",
    "df_foods_sc['Farm and Feed'] = df_foods_sc['Farm'] + df_foods_sc['Feed']\n",
    "# Farm and Feed are aggregated in other datasets, adding here for consistency \n",
    "# df_foods_sc = df_foods_sc.drop(['Total from Land to Retail', 'Farm', 'Feed'], axis=1)\n",
    "df_foods_sc = df_foods_sc.rename(columns= {'Land Use Change': 'Land' })\n",
    "df_foods_sc = df_foods_sc.reset_index()\n",
    "df_foods_sc = df_foods_sc.set_index(['Food product'])\n",
    "\n",
    "## stacked bar chart test\n",
    "fields = ['Land','Farm and Feed','Processing','Transport','Packaging','Retail']\n",
    "colors = ['#1D2F6F', '#8390FA', '#6EAF46', '#FAC748', '#0072BD','#4DBEEE']\n",
    "labels = ['Land', 'Farm', 'Processing', 'Transport','Packaging','Retail']\n",
    "\n",
    "# figure and axis\n",
    "fig, ax = plt.subplots(1, figsize=(20, 30))\n",
    "\n",
    "# plot bars\n",
    "left = len(df_foods_sc) * [0]\n",
    "for idx, name in enumerate(fields):\n",
    "    plt.barh(df_foods_sc.index, df_foods_sc[name], left = left, color=colors[idx])\n",
    "    left = left + df_foods_sc[name]\n",
    "\n",
    "# title, legend, labels\n",
    "plt.title('Global average (mean) GHG emissions for food products from each stage from Land to Retail\\n', fontsize=18)\n",
    "plt.legend(labels, bbox_to_anchor=([0., 1., 1., .102]), loc='lower left', fontsize=12,\n",
    "           ncol=6,  mode=\"expand\", frameon=False)\n",
    "plt.yticks(df_foods_sc.index)\n",
    "plt.xlabel('GHG emissions (kg CO2e) per kg of food produced')\n",
    "# plt.ylabel(df_foods_sc.index)\n",
    "\n",
    "# remove spines\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['bottom'].set_visible(False)\n",
    "\n",
    "# adjust limits and draw grid lines\n",
    "plt.xlim(-3, ax.get_xticks()[-1] + -9.5)\n",
    "plt.ylim(-1, ax.get_yticks()[-1] + 1.25)\n",
    "ax.set_axisbelow(True)\n",
    "ax.xaxis.grid(color='gray', linestyle='dashed')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:52.524551Z",
     "iopub.status.busy": "2021-10-21T16:04:52.523539Z",
     "iopub.status.idle": "2021-10-21T16:04:52.725600Z",
     "shell.execute_reply": "2021-10-21T16:04:52.726106Z",
     "shell.execute_reply.started": "2021-10-21T15:39:27.717748Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.270129,
     "end_time": "2021-10-21T16:04:52.726307",
     "exception": false,
     "start_time": "2021-10-21T16:04:52.456178",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_foodstop = df_foods[\n",
    "    ['Food product', 'Total from Land to Retail']\n",
    "].sort_values(by='Total from Land to Retail', ascending=False)\n",
    "df_foodstop = df_foodstop.set_index('Food product')\n",
    "df_foodstop = df_foodstop.head(5)\n",
    "\n",
    "df_foodstop.plot(kind='bar')\n",
    "plt.ylabel('GHG emissions (kg CO2e per kg produced)', fontsize=12)\n",
    "plt.title('Top 5 food products by global average GHG emissions from Farm to Retail', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.065746,
     "end_time": "2021-10-21T16:04:52.858344",
     "exception": false,
     "start_time": "2021-10-21T16:04:52.792598",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Similar to trends in top GHGs by food system stage on a global scale, Farm and Land are the highest stages for most food products\n",
    "- **Animal sourced food products** tend to have higher emissions than **food products sourced from plants** across all stages of food production  (4 of the top 5 in total are foods sourced from animals)\n",
    "- Food products sourced from **ruminants**, which includes **cattle** and **sheep**, account for significantly higher emissions across all stages, particularly in **land** and **farm** stages (* Cattle (beef) is in the top spot at more than double the average GHGs from producing 1 kg of Lamb & Mutton)\n",
    "- Some plant-sourced products like **coffee** and **chocolate** have higher emissions than some animal products like **poultry**, primarily related to the high average emissions for coffee and chocolate from the **land** stage and **farm** stage often related to heavy fertilizer use and land clearing practices\n",
    "- The land stage tends to be higher for food products from larger animals like cattle due to common occurence of land degredation and deforestation for production of feed like soy and land-clearing for production systems that are based on pasture-grazing \n",
    "- The land stage can be negative for some plant-sourced products like Nuts, due to the production process providing an emissions \"sink\" that can sequester carbon, but the land stage can be high for other plant products like **chocolate**, due to production practices like deforestation \n",
    "- The farm stage can be high even for plant products, especially when those products use intensive fertilizers like for coffee"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.066384,
     "end_time": "2021-10-21T16:04:52.990669",
     "exception": false,
     "start_time": "2021-10-21T16:04:52.924285",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Now that we see trends emerging for food products overall, we'll dive into differences among the same food products in different production systems and commodity types \n",
    "\n",
    "We'll specifically focus on **Cattle and chicken** global GHG emissions from the U.N. GLEAM dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:53.159178Z",
     "iopub.status.busy": "2021-10-21T16:04:53.126102Z",
     "iopub.status.idle": "2021-10-21T16:04:53.169606Z",
     "shell.execute_reply": "2021-10-21T16:04:53.170136Z",
     "shell.execute_reply.started": "2021-10-21T15:24:15.738047Z"
    },
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.114125,
     "end_time": "2021-10-21T16:04:53.170329",
     "exception": false,
     "start_time": "2021-10-21T16:04:53.056204",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 583 entries, 0 to 582\n",
      "Data columns (total 22 columns):\n",
      " #   Column                                           Non-Null Count  Dtype  \n",
      "---  ------                                           --------------  -----  \n",
      " 0   Region                                           583 non-null    object \n",
      " 1   Animal species                                   583 non-null    object \n",
      " 2   Production system                                583 non-null    object \n",
      " 3   Commodity                                        583 non-null    object \n",
      " 4   Emission Intensity (kg CO2e per kg protein)      583 non-null    float64\n",
      " 5   Production (kg protein)                          543 non-null    float64\n",
      " 6   Total GHG emissions (kg CO2e)                    543 non-null    float64\n",
      " 7   Total CO2 emissions (kg CO2e)                    543 non-null    float64\n",
      " 8   Total CH4 emissions (kg CO2e)                    543 non-null    float64\n",
      " 9   Total N2O emissions (kg CO2e)                    543 non-null    float64\n",
      " 10  Feed, CO2 (kg CO2e)                              543 non-null    float64\n",
      " 11  Feed, CH4 (kg CO2e)                              96 non-null     float64\n",
      " 12  Feed: fertilizer & crop residues, N2O (kg CO2e)  543 non-null    float64\n",
      " 13  Feed: applied & deposited manure, N2O (kg CO2e)  543 non-null    float64\n",
      " 14  LUC: soy & palm, CO2 (kg CO2e)                   440 non-null    float64\n",
      " 15  LUC: pasture expansion, CO2 (kg CO2e)            8 non-null      float64\n",
      " 16  Enteric fermentation, CH4 (kg CO2e)              425 non-null    float64\n",
      " 17  Manure management, CH4 (kg CO2e)                 543 non-null    float64\n",
      " 18  Manure management, N2O (kg CO2e)                 528 non-null    float64\n",
      " 19  Direct energy, CO2 (kg CO2e)                     503 non-null    float64\n",
      " 20  Indirect energy, CO2 (kg CO2e)                   512 non-null    float64\n",
      " 21  Postfarm, CO2 (kg CO2e)                          491 non-null    float64\n",
      "dtypes: float64(18), object(4)\n",
      "memory usage: 100.3+ KB\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Animal species</th>\n",
       "      <th>Production system</th>\n",
       "      <th>Commodity</th>\n",
       "      <th>Emission Intensity (kg CO2e per kg protein)</th>\n",
       "      <th>Production (kg protein)</th>\n",
       "      <th>Total GHG emissions (kg CO2e)</th>\n",
       "      <th>Total CO2 emissions (kg CO2e)</th>\n",
       "      <th>Total CH4 emissions (kg CO2e)</th>\n",
       "      <th>Total N2O emissions (kg CO2e)</th>\n",
       "      <th>...</th>\n",
       "      <th>Feed: fertilizer &amp; crop residues, N2O (kg CO2e)</th>\n",
       "      <th>Feed: applied &amp; deposited manure, N2O (kg CO2e)</th>\n",
       "      <th>LUC: soy &amp; palm, CO2 (kg CO2e)</th>\n",
       "      <th>LUC: pasture expansion, CO2 (kg CO2e)</th>\n",
       "      <th>Enteric fermentation, CH4 (kg CO2e)</th>\n",
       "      <th>Manure management, CH4 (kg CO2e)</th>\n",
       "      <th>Manure management, N2O (kg CO2e)</th>\n",
       "      <th>Direct energy, CO2 (kg CO2e)</th>\n",
       "      <th>Indirect energy, CO2 (kg CO2e)</th>\n",
       "      <th>Postfarm, CO2 (kg CO2e)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Global</td>\n",
       "      <td>Cattle</td>\n",
       "      <td>Aggregated</td>\n",
       "      <td>Aggregated</td>\n",
       "      <td>160.3</td>\n",
       "      <td>2.916310e+10</td>\n",
       "      <td>4.674630e+12</td>\n",
       "      <td>9.449299e+11</td>\n",
       "      <td>2.648727e+12</td>\n",
       "      <td>1.080973e+12</td>\n",
       "      <td>...</td>\n",
       "      <td>1.740732e+11</td>\n",
       "      <td>7.419864e+11</td>\n",
       "      <td>4.687954e+10</td>\n",
       "      <td>3.870062e+11</td>\n",
       "      <td>2.509275e+12</td>\n",
       "      <td>1.394522e+11</td>\n",
       "      <td>1.649130e+11</td>\n",
       "      <td>4.215542e+10</td>\n",
       "      <td>1.585653e+10</td>\n",
       "      <td>9.955932e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Global</td>\n",
       "      <td>Cattle</td>\n",
       "      <td>Aggregated</td>\n",
       "      <td>Milk</td>\n",
       "      <td>86.7</td>\n",
       "      <td>1.888089e+10</td>\n",
       "      <td>1.637519e+12</td>\n",
       "      <td>2.756914e+11</td>\n",
       "      <td>9.676442e+11</td>\n",
       "      <td>3.941834e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>7.166208e+10</td>\n",
       "      <td>2.543885e+11</td>\n",
       "      <td>1.497421e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.856352e+11</td>\n",
       "      <td>8.200906e+10</td>\n",
       "      <td>6.813278e+10</td>\n",
       "      <td>2.993485e+10</td>\n",
       "      <td>3.252889e+09</td>\n",
       "      <td>8.929544e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Global</td>\n",
       "      <td>Cattle</td>\n",
       "      <td>Aggregated</td>\n",
       "      <td>Meat</td>\n",
       "      <td>295.4</td>\n",
       "      <td>1.028222e+10</td>\n",
       "      <td>3.037111e+12</td>\n",
       "      <td>6.692385e+11</td>\n",
       "      <td>1.681083e+12</td>\n",
       "      <td>6.867892e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>1.024111e+11</td>\n",
       "      <td>4.875979e+11</td>\n",
       "      <td>3.190534e+10</td>\n",
       "      <td>3.870062e+11</td>\n",
       "      <td>1.623640e+12</td>\n",
       "      <td>5.744310e+10</td>\n",
       "      <td>9.678019e+10</td>\n",
       "      <td>1.222057e+10</td>\n",
       "      <td>1.260364e+10</td>\n",
       "      <td>1.026388e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Global</td>\n",
       "      <td>Cattle</td>\n",
       "      <td>Grassland systems</td>\n",
       "      <td>Aggregated</td>\n",
       "      <td>206.3</td>\n",
       "      <td>1.033817e+10</td>\n",
       "      <td>2.133054e+12</td>\n",
       "      <td>5.628062e+11</td>\n",
       "      <td>1.052965e+12</td>\n",
       "      <td>5.172829e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>5.253660e+10</td>\n",
       "      <td>4.267612e+11</td>\n",
       "      <td>8.194330e+09</td>\n",
       "      <td>3.870062e+11</td>\n",
       "      <td>9.969096e+11</td>\n",
       "      <td>5.605552e+10</td>\n",
       "      <td>3.798508e+10</td>\n",
       "      <td>1.417326e+10</td>\n",
       "      <td>4.303272e+09</td>\n",
       "      <td>3.773092e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Global</td>\n",
       "      <td>Cattle</td>\n",
       "      <td>Grassland systems</td>\n",
       "      <td>Milk</td>\n",
       "      <td>95.0</td>\n",
       "      <td>6.940654e+09</td>\n",
       "      <td>6.592844e+11</td>\n",
       "      <td>9.286140e+10</td>\n",
       "      <td>3.713461e+11</td>\n",
       "      <td>1.950770e+11</td>\n",
       "      <td>...</td>\n",
       "      <td>2.156126e+10</td>\n",
       "      <td>1.587838e+11</td>\n",
       "      <td>2.412100e+09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.369018e+11</td>\n",
       "      <td>3.444428e+10</td>\n",
       "      <td>1.473188e+10</td>\n",
       "      <td>1.078605e+10</td>\n",
       "      <td>9.998435e+08</td>\n",
       "      <td>3.462338e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Region Animal species  Production system   Commodity  \\\n",
       "0  Global         Cattle         Aggregated  Aggregated   \n",
       "1  Global         Cattle         Aggregated        Milk   \n",
       "2  Global         Cattle         Aggregated        Meat   \n",
       "3  Global         Cattle  Grassland systems  Aggregated   \n",
       "4  Global         Cattle  Grassland systems        Milk   \n",
       "\n",
       "   Emission Intensity (kg CO2e per kg protein)  Production (kg protein)  \\\n",
       "0                                        160.3             2.916310e+10   \n",
       "1                                         86.7             1.888089e+10   \n",
       "2                                        295.4             1.028222e+10   \n",
       "3                                        206.3             1.033817e+10   \n",
       "4                                         95.0             6.940654e+09   \n",
       "\n",
       "   Total GHG emissions (kg CO2e)  Total CO2 emissions (kg CO2e)  \\\n",
       "0                   4.674630e+12                   9.449299e+11   \n",
       "1                   1.637519e+12                   2.756914e+11   \n",
       "2                   3.037111e+12                   6.692385e+11   \n",
       "3                   2.133054e+12                   5.628062e+11   \n",
       "4                   6.592844e+11                   9.286140e+10   \n",
       "\n",
       "   Total CH4 emissions (kg CO2e)  Total N2O emissions (kg CO2e)  ...  \\\n",
       "0                   2.648727e+12                   1.080973e+12  ...   \n",
       "1                   9.676442e+11                   3.941834e+11  ...   \n",
       "2                   1.681083e+12                   6.867892e+11  ...   \n",
       "3                   1.052965e+12                   5.172829e+11  ...   \n",
       "4                   3.713461e+11                   1.950770e+11  ...   \n",
       "\n",
       "   Feed: fertilizer & crop residues, N2O (kg CO2e)  \\\n",
       "0                                     1.740732e+11   \n",
       "1                                     7.166208e+10   \n",
       "2                                     1.024111e+11   \n",
       "3                                     5.253660e+10   \n",
       "4                                     2.156126e+10   \n",
       "\n",
       "   Feed: applied & deposited manure, N2O (kg CO2e)  \\\n",
       "0                                     7.419864e+11   \n",
       "1                                     2.543885e+11   \n",
       "2                                     4.875979e+11   \n",
       "3                                     4.267612e+11   \n",
       "4                                     1.587838e+11   \n",
       "\n",
       "   LUC: soy & palm, CO2 (kg CO2e)  LUC: pasture expansion, CO2 (kg CO2e)  \\\n",
       "0                    4.687954e+10                           3.870062e+11   \n",
       "1                    1.497421e+10                                    NaN   \n",
       "2                    3.190534e+10                           3.870062e+11   \n",
       "3                    8.194330e+09                           3.870062e+11   \n",
       "4                    2.412100e+09                                    NaN   \n",
       "\n",
       "   Enteric fermentation, CH4 (kg CO2e)  Manure management, CH4 (kg CO2e)  \\\n",
       "0                         2.509275e+12                      1.394522e+11   \n",
       "1                         8.856352e+11                      8.200906e+10   \n",
       "2                         1.623640e+12                      5.744310e+10   \n",
       "3                         9.969096e+11                      5.605552e+10   \n",
       "4                         3.369018e+11                      3.444428e+10   \n",
       "\n",
       "   Manure management, N2O (kg CO2e)  Direct energy, CO2 (kg CO2e)  \\\n",
       "0                      1.649130e+11                  4.215542e+10   \n",
       "1                      6.813278e+10                  2.993485e+10   \n",
       "2                      9.678019e+10                  1.222057e+10   \n",
       "3                      3.798508e+10                  1.417326e+10   \n",
       "4                      1.473188e+10                  1.078605e+10   \n",
       "\n",
       "   Indirect energy, CO2 (kg CO2e)  Postfarm, CO2 (kg CO2e)  \n",
       "0                    1.585653e+10             9.955932e+10  \n",
       "1                    3.252889e+09             8.929544e+10  \n",
       "2                    1.260364e+10             1.026388e+10  \n",
       "3                    4.303272e+09             3.773092e+10  \n",
       "4                    9.998435e+08             3.462338e+10  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(df_gleam.columns)\n",
    "df_gleam.info()\n",
    "df_gleam.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:53.316737Z",
     "iopub.status.busy": "2021-10-21T16:04:53.315626Z",
     "iopub.status.idle": "2021-10-21T16:04:53.542956Z",
     "shell.execute_reply": "2021-10-21T16:04:53.542437Z",
     "shell.execute_reply.started": "2021-10-21T15:26:17.037725Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.30446,
     "end_time": "2021-10-21T16:04:53.543108",
     "exception": false,
     "start_time": "2021-10-21T16:04:53.238648",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#select columns we need and filter for global, filter out 'aggregated' rows (duplicates) \n",
    "df_gleamglobal = df_gleam[\n",
    "    ['Region', 'Animal species', 'Production system', 'Commodity', \n",
    "     'Emission Intensity (kg CO2e per kg protein)', 'Production (kg protein)', \n",
    "     'Total GHG emissions (kg CO2e)', 'Total CO2 emissions (kg CO2e)', \n",
    "     'Total CH4 emissions (kg CO2e)', 'Total N2O emissions (kg CO2e)']].loc[\n",
    "    (df_gleam['Region']=='Global') & \n",
    "    (df_gleam['Production system']!= 'Aggregated') & \n",
    "    (df_gleam['Commodity']!= 'Aggregated')]\n",
    "\n",
    "# now we'll filter out just the animals of interest\n",
    "df_gleamglobal = df_gleamglobal[df_gleamglobal['Animal species'].isin(['Cattle', 'Chicken'])]\n",
    "df_gleamglobal = df_gleamglobal.reset_index()\n",
    "\n",
    "df_group1 = df_gleamglobal.groupby(\n",
    "    by=['Animal species']).sum()[['Total GHG emissions (kg CO2e)','Total CO2 emissions (kg CO2e)',\n",
    "                                  'Total CH4 emissions (kg CO2e)','Total N2O emissions (kg CO2e)',]]\n",
    "df_group1 = df_group1.reset_index()\n",
    "\n",
    "labels = ['Total GHG emissions', 'Carbon dioxide emissions', 'Methane emissions', 'Nitrous oxide emissions']\n",
    "cattle_totals = [4.674630e+12, 9.449299e+11, 2.648727e+12, 1.080973e+12]\n",
    "chicken_totals = [7.897593e+11, 5.487263e+11, 2.421301e+10, 2.168199e+11]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(x - width/2, cattle_totals, width, label='Cattle')\n",
    "rects2 = ax.bar(x + width/2, chicken_totals, width, label='Chicken')\n",
    "\n",
    "ax.set_ylabel('Annual Emissions (kg CO2e)')\n",
    "ax.set_title('Much higher emissions for Cattle than Chicken (all GHGs, especially Methane)', fontsize=12)\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend()\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "papermill": {
     "duration": 0.069629,
     "end_time": "2021-10-21T16:04:53.681011",
     "exception": false,
     "start_time": "2021-10-21T16:04:53.611382",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*Context on filtering out 'aggregated' rows:  aggregated values in either production system or commodity are duplicates. Example using the below*:\n",
    "- A) *Animal species: Cattle*, *Production system: Grassland systems*, *Commodity: Aggregated* \n",
    "- B) *Animal species: Cattle*, *Production system: Grassland systems*, *Commodity: Milk*  \n",
    "- C) *Animal species: Cattle*, *Production system: Grassland systems*, *Commodity: Meat*\n",
    "\n",
    "*The value of A in the emission intensity column is the average of B and C. \n",
    "The value of all other columns is the aggregate value of B and C.*\n",
    "*Anything that's identified as an 'Aggregate' production system or commodity is literally an aggregate, so they'd only represent duplicate data for our purposes.*\n",
    "\n",
    "Cattle meat production creates higher emissions from grassland systems and overall, as it contributes the highest level of emissions of any food product seen here and across other measures and food system stages. This and other sources of data help illuminate areas for producers to reduce emissions through different production systems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:53.824540Z",
     "iopub.status.busy": "2021-10-21T16:04:53.823844Z",
     "iopub.status.idle": "2021-10-21T16:04:54.113100Z",
     "shell.execute_reply": "2021-10-21T16:04:54.112561Z",
     "shell.execute_reply.started": "2021-10-21T15:30:28.125676Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.361456,
     "end_time": "2021-10-21T16:04:54.113251",
     "exception": false,
     "start_time": "2021-10-21T16:04:53.751795",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gleamcattle = df_gleam[df_gleam['Animal species'].isin(['Cattle'])]\n",
    "df_gleamcattle_agg = df_gleamcattle.loc[\n",
    "    (df_gleamcattle['Production system']== 'Aggregated') & \n",
    "    (df_gleamcattle['Commodity']== 'Aggregated')]\n",
    "df_gleamcattle_agg = df_gleamcattle_agg.drop(['Animal species','Production system',\n",
    "                                              'Commodity','Feed, CH4 (kg CO2e)'], axis=1)\n",
    "\n",
    "emissions_prod = df_gleamglobal[['Animal species', 'Production system', 'Commodity', \n",
    "                                      'Total CO2 emissions (kg CO2e)', 'Total CH4 emissions (kg CO2e)', \n",
    "                           'Total N2O emissions (kg CO2e)', 'Total GHG emissions (kg CO2e)']].loc[\n",
    "    (df_gleamglobal['Animal species']== 'Cattle') & (df_gleamglobal['Commodity']== 'Meat')].set_index(\n",
    "    'Production system').sort_values(by='Total GHG emissions (kg CO2e)', ascending=True).plot(kind='barh')\n",
    "plt.title('Global emissions are highest from cattle produced in grassland systems', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-21T16:04:54.262658Z",
     "iopub.status.busy": "2021-10-21T16:04:54.261909Z",
     "iopub.status.idle": "2021-10-21T16:04:54.924297Z",
     "shell.execute_reply": "2021-10-21T16:04:54.923754Z",
     "shell.execute_reply.started": "2021-10-21T15:31:07.805166Z"
    },
    "jupyter": {
     "source_hidden": true
    },
    "papermill": {
     "duration": 0.740879,
     "end_time": "2021-10-21T16:04:54.924454",
     "exception": false,
     "start_time": "2021-10-21T16:04:54.183575",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x396 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_gleamcattle_aggT = df_gleamcattle_agg[['Region', 'Total GHG emissions (kg CO2e)', 'Total CH4 emissions (kg CO2e)', \n",
    "                                          'Total CO2 emissions (kg CO2e)', 'Total N2O emissions (kg CO2e)']].loc[\n",
    "    (df_gleamcattle_agg['Region']!= 'Global')].set_index('Region').sort_values(\n",
    "    by= 'Total GHG emissions (kg CO2e)', ascending=True).plot(kind='barh')\n",
    "plt.title('Total GHGs from Cattle production is highest in Latin America & the Caribbean',fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "df_gleamcattleT = df_gleamcattle_agg[['Region', 'Emission Intensity (kg CO2e per kg protein)']].loc[\n",
    "    (df_gleamcattle_agg['Region']!= 'Global')].set_index('Region').sort_values(\n",
    "    by= 'Emission Intensity (kg CO2e per kg protein)', ascending=True).plot(kind='barh')\n",
    "plt.title('Cattle Emission Intensity (kg of emissions CO2e per kg protein) is highest in Sub-Saharan Africa',fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
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     "duration": 0.073582,
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     "start_time": "2021-10-21T16:04:54.997218",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "*To interact with the data and discover more insights, head over to my project on Tableau public:*\n",
    "## [Low Hanging Fruit: Sustainable food opportunities to combat climate change](https://public.tableau.com/app/profile/amanda.knudsen/viz/FoodProduction07/LowHangingFruit)"
   ]
  },
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   "source": []
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