appendix
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food_energy.pdf
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food_energy.pdf
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@ -103,7 +103,8 @@ As figure \ref{ag_yields} shows, the epoch of ``Better Living Through Chemistry'
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USDA yields over time
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USDA yields over time
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The data plotted comes from the USDA
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The data plotted comes from the USDA
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\url{https://www.nass.usda.gov/Statistics_by_Subject/index.php}
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\url{https://www.nass.usda.gov/Statistics_by_Subject/index.php}
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The idea for this plot came from an online blog, \cite{math_encounters}
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The idea for this plot came from an online blog, \cite{math_encounters}.
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Details for recreating this plot are given in \ref{how_yield_plot_is_made}.
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}
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}
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\label{ag_yields}
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\label{ag_yields}
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\end{figure}
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\end{figure}
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@ -150,8 +151,47 @@ The work was prompted in part by discussions with John Deming, Carl Ferkinhoff,
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%a simple heading of ‘Appendix’ use the code \section*{Appendix}. If it contains
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%a simple heading of ‘Appendix’ use the code \section*{Appendix}. If it contains
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%numbered equations, figures or tables the command \appendix should precede it and
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%numbered equations, figures or tables the command \appendix should precede it and
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%\setcounter{section}{1} must follow it
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%\setcounter{section}{1} must follow it
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%\appendix
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\clearpage
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\%section{Introductory Food Energy Questions}
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\appendix
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\section{Creating the historical kcal/acre figure from USDA data}
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\label{how_yield_plot_is_made}
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The United States Department of Agriculture (USDA) provides historical crop information via the National Agricultureal Statistics Service, \url{https://www.nass.usda.gov/Statistics_by_Subject/index.php?sector=CROPS}. Data was downloaded in spreadsheet csv format and then combined and plotted via a Python Jupyter notebook.
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Each crop has its own bespoke units, for example potatoes are sold by hundredweight (CWT) but sugar beets are measured by the ton.
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Every imaginable agricultural product seems to be tracked in the NASS site, for example Maple Syrup production is tracked and given in gallons of syrup per (tree) tap!
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Conversion factors used are summarized in Table \ref{conversions}.
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Calorie (kcal) density for each crop was taken from \url{https://fdc.nal.usda.gov/fdc-app.html}. Within this database, foods are identified by an FDC ID.
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An example calculation (implemented in the Jupyter notebook) follows for Corn.
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In 2022 the USDA reported an average production of 172.3 bushels of corn per acre of farmland.
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\be
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172.3\frac{bu}{acre}\cdot\frac{56lbs~corn}{bu}\cdot\frac{453.592~grams}{lbs}\cdot\frac{365~kcal}{100~grams} = 15,974,657 \frac{kcal}{acre}
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\ee
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Obviously the result is only reasonable to two signifigant figures!
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%grams_per_lbs=453.592
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%corn_lbs_per_bu=(56.0/1.0)
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%corn_kcal_per_gram=(365/100)
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%corn_kcal_per_acre = corn_bu_per_acre*corn_lbs_per_bu*grams_per_lbs*corn_kcal_per_gram
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\begin{table}
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\caption{\label{label}
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A summary of units and conversions used to create figure \ref{ag_yields} from USDA NASS data. $1cwt$ is a hundred pounds of potatoes. A bushel, $1bu$, is a volume unit of about 35liters and corresponds to about 60lbs of grain. Calorie content per 100 gram mass of food is taken from the USDA's ``Food Data Central'' database. It isn't clear from any of these resources if lb is pound-force (lbf) or pound-mass (lbm) and so I am ignorantly treating them as ``grocery store units'' where $1 lbs = 453.592 grams$.
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}
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\begin{indented}
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\item[]\begin{tabular}{@{}lllll}
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\br
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Crop&per acre unit&production unit&kcals per 100gram & FDC ID\\
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\mr
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Corn & bu/acre & $1bu=56lbs$ & 365 &170288 \\
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Potatoes & cwt/acre & $1CWT=100lbs$ & 77 & 170026 \\
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Soybeans & bu/acre & $1bu=60lbs$ & 446 & 174270 \\
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Sunflowers & lbs/acre & & 584 & 170562 \\
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Wheat & bu/acre & $1bu=60lbs$ & 327 & 168890 \\
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\br
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\end{tabular}
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\end{indented}
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\label{conversions}
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\end{table}
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