Export land model analysis of food production and consumption—deriving food energy conversion factors

As I indicated in the last post, food energy conversion factors are vital for converting quantities of different foods into a common energy unit.  It is this conversion that will allow me to compare the food production, consumption, and import/export rates for different years within the same country as well as between countries or regions. 

As also indicated in my last post the problem, for me, is that FAOSTAT doesn’t present the conversion factors for all of the food types.  Well, actually FAOSTAT, in providing its Food Balance sheets, doesn’t present any conversion factors at all.  It’s just that I can derive conversion factors for certain food types, based on the information presents about human food energy consumption per capita.  However, there are significant types of food used for purposes other that human consumption (i.e., as feed for livestock), and not accounting for these other foods presents a real problem for within- and cross-country comparisons, as well as getting an accurate estimate of total food energy production.

This post discusses how I addressed this problem and derived my own set of conversion factors for all of the food types typically listed in the Food Balance Sheets that FAOSTAT reports.

Part of the answer: the Food Composition Table in Annex 1 of the Handbook

I already mentioned the Food Balance Sheet Handbook as an informative reference in the context of providing definitions for various terms presented in the Food Balance Sheets.  However the Handbook in an appendix also provides a table of food composition energy contents in units of kcal/100 gm for over 500 different food items.  Again for illustrate purposes I will just show a portion of the Food Composition Table (henceforth, "Table") corresponding to various cereal food products.

At first this might seem to solve the problem—we have all the major food types an their energy contents, so we should be able to just plug this value into to appropriate categories in the Food Balance Sheet of interest and thereby derive the totals of food energy production, consumption etc...

The problem with doing this is that many, but not all of the food categories presented in the Food Balance Sheets are actually composites of several different food items.

Again, wheat provides a good example.  In the working example for Australia 2003 shown in the last post there is a single row presented for “wheat.”  As explained in the last post, I derived a food energy conversion factor of 2.97 kcal/gm for the “wheat” category.

However, the Table shows ten different wheat-based food types, and, there energy content ranges from 2.13 to 3.82 kcal/gm (see above table red circle).

From this, I surmise that the energy conversion factor I derived for Australia 2003 is actually a composite of these ten different wheat-based food types.  Moreover it's likely a weighted average, because a simple un-weighted average from the ten wheat-based food items equals 3.52 kcal/gm, and not 2.97 kcal/gm (evidently a heavier weighting to bread or bran of wheat than the others).

My surmise is supported by a document at FAOSTAT that lists various food types that are “aggregated or standardized” a second document that defined the various Food Balance Sheets, and a third document at FAOSTAT that presents various  technical conversion factors, which is done as the latter document explains:

to compile commodity balances and supply/utilization accounts for nearly all the countries in the world. The main scope of these balances and accounts is ultimately to arrive at approximate estimates of the total availability of food in each country, expressed in terms of quantity as well as in terms of calories, protein and fat

To generate the Food Balance Sheets, FAOSTAT has made general estimates of the energy content of a number of aggregated food types. 

However, not every food item listed in the Food Balance Sheets is an aggregate of several different foods.   For instance, bananas, dry beans, honey, plantains, sugar cane, sugar beets, sweet potatoes, yams are among a few dozen single-item foods that are presented in the Food Balance Sheets.  In these cases the conversion factor in the Handbook and the conversion factor derived from Australia 2003 Food Balance Sheet are generally in good agreement.  The only exemption to this is when the food item quantity is so small that there is a rounding error in the conversion because the Food Balance Sheets only presents food items in quantities of 1 thousand tonnes or higher.  It appears that quantities less than this are either not reported or rounded to 1. 

The remaining part of the answer: deriving conversion factors from world food balance sheets

To minimize these rounding errors, and to derive conversion factors for a broader number of weighted and aggregated food items, I decided to look at the Food Balance Sheets for the largest entity reported in FAOSTAT: the world.  Specifically, I looked at world-wide Food Balance Sheets for 1963, 1973, 1983, 1993 and 2003.  Using the human food energy consumption reported for each of these years, I derived conversion factors for most of the food items in the Food Balance Sheets, and then calculated an average.  The procedure I used is exactly the same as described in the last post from Australia 2003.

By considering world-wide values, a number of food items with negligible human food consumption quantities for an individual country, (e.g., like barley and sorghum in Australia), are now in significant quantities, and therefore I could expand the number of derived conversion factors for more food items.  Also taking the average for five different years spread-out by a decade gave me some assurance of the consistence of the derived conversion factor over the 46 year time span that FAOSTAT reports.  

Generally, for food items with larger quantities of human food consumption there was a small variation in the conversion factors within the five years considered (e.g., typically the percent standard deviation < 10%) and no discernable trend for change with time.  For single item food types, the agreement with the value from the Table was generally within a few percent.

Still, there were still about two dozen food items (e.g., sugar beets, sunflower and cotton seed, palm kernels, crustaceans, cephalopods, mollusks, aquatic mammal meat, aquatic plants) whose world-wide human consumption was relatively small, and therefore, either the conversion factors could not be derived from the world-wide human energy consumption, or in my judgment, there were likely to be rounding errors.  For those items, I used the single item value, or simple average of multiple items, as presented in the Table.

Summary of Conversion Factors

Alright, here is my summary of my best estimates of the food energy conversion factor either drived from the five world-wide Food Balance Sheets ("D") or taken from the Annex 1 Table from the Handbook ("T").

Table 1:  Summary of Food Energy Conversion Factors for Food Items Listed in FAOSTAT Food Balance Sheets (http://crash-watcher.blogspot.com/)
Food Balance Sheet Item	Food Item Code	Best estimate CF (kcal/gm)	Source
Population
Grand Total + (Total)
Vegetal Products + (Total)
Animal Products + (Total)
Cereals - Excluding Beer + (Total)
Wheat	2511	2.864	D
Rice (Milled Equivalent)	2805	3.676	D
Barley	2513	2.507	D
Maize	2514	3.021	D
Rye	2515	2.655	D
Oats	2516	2.133	D
Millet	2517	2.997	D
Sorghum	2518	3.052	D
Cereals, Other	2520	2.824	D
Starchy Roots + (Total)
Cassava	2532	0.956	D
Potatoes	2531	0.672	D
Sweet Potatoes	2533	0.984	D
Yams	2535	1.000	D
Roots, Other	2534	1.081	D
Sugar crops + (Total)
Sugar Cane	2536	0.306	D
Sugar Beet	2537	0.7	T
Sugar & Sweeteners + (Total)
Sugar, Non-Centrifugal	2541	3.535	D
Sugar (Raw Equivalent)	2542	3.574	D
Sweeteners, Other	2543	3.161	D
Honey	2745	3.622	D
Pulses + (Total)
Beans	2546	3.388	D
Peas	2547	3.352	D
Pulses, Other	2549	3.463	D
Treenuts + (Total)	2912	2.581	D
Oilcrops + (Total)
Soyabeans	2555	3.371	D
Groundnuts (Shelled Eq)	2556	5.469	D
Sunflowerseed	2557	3.08	T
Rape and Mustardseed	2558	4.81	T
Cottonseed	2559	2.53	T
Coconuts - Incl Copra	2560	1.341	D
Sesameseed	2561	7.148	D
Palmkernels	2562	5.14	T
Olives	2563	1.42	T
Oilcrops, Other	2570	3.487	D
Vegetable Oils + (Total)
Soyabean Oil	2571	8.740	D
Groundnut Oil	2572	9.085	D
Sunflowerseed Oil	2573	8.796	D
Rape and Mustard Oil	2574	8.933	D
Cottonseed Oil	2575	8.455	D
Palmkernel Oil	2576	9.649	D
Palm Oil	2577	8.652	D
Coconut Oil	2578	8.599	D
Sesameseed Oil	2579	11.531	D
Olive Oil	2580	8.870	D
Ricebran Oil	2581	8.840	T
Maize Germ Oil	2582	9.319	D
Oilcrops Oil, Other	2586	9.050	D
Vegetables + (Total)
Tomatoes	2601	0.185	D
Onions	2602	0.364	D
Vegetables, Other	2605	0.242	D
Fruits - Excluding Wine + (Total)
Oranges, Mandarines	2611	0.282	D
Lemons, Limes	2612	0.266	D
Grapefruit	2613	0.67	T
Citrus, Other	2614	0.404	D
Bananas	2615	0.621	D
Plantains	2616	0.868	D
Apples	2617	0.445	D
Pineapples	2618	0.355	D
Dates	2619	1.542	D
Grapes	2620	0.649	D
Fruits, Other	2625	0.438	D
Stimulants + (Total)
Coffee	2630	0.324	D
Cocoa Beans	2633	2.078	D
Tea	2635	0.345	T
Spices + (Total)
Pepper	2640	2.76	T
Pimento	2641	3.453	D
Cloves	2642	3.23	T
Spices, Other	2645	3.303	D
Alcoholic Beverages + (Total)
Wine	2655	0.694	D
Beer	2656	0.448	D
Beverages, Fermented	2657	0.516	D
Beverages, Alcoholic	2658	2.910	D
Alcohol, Non-Food	2659	0.6	T
Meat + (Total)
Bovine Meat	2731	1.525	D
Mutton & Goat Meat	2732	2.019	D
Pigmeat	2733	2.469	D
Poultry Meat	2734	1.416	D
Meat, Other	2735	1.258	D
Offals + (Total)	2945	1.103	D
Animal Fats + (Total)
Butter, Ghee	2740	7.432	D
Cream	2743	1.939	D
Fats, Animals, Raw	2737	7.170	D
Fish, Body Oil	2781	9.02	T
Fish, Liver Oil	2782	9.02	T
Eggs + (Total)	2949	1.425	D
Milk - Excluding Butter + (Total)	2948	0.558	D
Fish, Seafood + (Total)
Freshwater Fish	2761	0.727	D
Demersal Fish	2762	0.598	D
Pelagic Fish	2763	0.975	D
Marine Fish, Other	2764	0.706	D
Crustaceans	2765	0.996	T
Cephalopods	2766	1.496	T
Molluscs, Other	2767	1.322	T
Aquatic Products, Other + (Total)
Meat, Aquatic Mammals	2768	1.46	T
Aquatic Animals, Others	2769	0.77	T
Aquatic Plants	2775	1.937	T
Miscellaneous + (Total)	2928

Not very scintillating reading, I know, but a fair amount of work to come up with this.

Some Preliminary Testing

Now that I have a decent set of conversion factors, let's go back to my example of the 2003 Food Balance Sheet for Australia and see what kind of food energy totals are obtained for the main headings listed in the sheet.

To review, for each food item listed in the Food Balance Sheet, I multiplied the quantity by the conversion factor in Table 1 above, made the appropriate conversion to Peta Joules, and then added up all the items to derive a grand total food energy estimate for each of the headings: production, imports, exports, feed, seed etc....  I also decided to break the grand total down into subtotals of plant and animal food energy.

Here's a summary of my calculations as applied to the 2003 Food Balance Sheet for Australia:

 My estimate of human food energy being equal to 101 PJ is fairly close to the estimate of 95 PJ that I made in the previous post, which in turn, was based on the food energy per capita values reported by FAOSTAT in the 2003 Food Balance Sheet for Australia. 

In terms of food energy, the total production of 790 Peta Joules per year comprises about 90% plant product and 10% animal product food energy, although the food energy embodied in animal Feed (125 PJ) is about 16% of the total food energy produced.  Clearly, animal product production is energy expensive—the production of 77 PJ worth of animal food energy takes 125 PJ worth of plant energy just in the form of feed.  It is interesting that the total food energy for humans (101 PJ) is actually less that the total food energy for feed of 125 PJ.  Also interesting is that of the total food energy for humans, roughly 1/3 comes from animal products (34 PJ).   Australia, in 2003, was clearly a net exporter (i.e., 270 PJ of food energy, exporting 270 PJ of food energy versus importing only 32 PJ of food energy—the food exports amount to about 1/3 of the total food energy produced.

Finally, how do these numbers compare to that Table 1 of in Arizpe?  

Unfortunately, Arizpe doesn't define exactly what the terms "Gross Food Production" and "Gross Food Production" as used in Table 1 actually mean, or how they were calculated, so a comparison isn't that easy to make.  I asked the contact author, Mario Giampietro, if he could define these terms but his response didn't clarify this. In a reply email, Professor Giampietro told me that his student made the calculation so he wasn't certain, but he thought "gross production" was referring to the energy of the plant production used both as feed and used as food. 

Well, in my opinion, total human food energy plus total feed energy would seem to be a better of a measure of food consumption than food production. 

Indeed, the sum of human plant food (67 PJ) plus plant feed (121 PJ), equal to 188 PJ, is much closer to "Gross Food Consumption" than "Gross Food Production" in Table 1 of Arizpe (615 PJ).   Similarly, the sum of total human food energy (101 PJ) and total feed energy (124 PJ) equals 225 PJ is a bit higher than Arizpe's 210 PJ, and, Arizpe's "Gross Food Production" of 615 PJ is much less that the either the Grand total of food energy (790 PJ) or plant food energy (713 PJ) in my Table 2. 

After comparing a few other countries in the Arizpe's Table 1 (Argentina and Spain) and getting about the same discrepancies, and, getting no response to a second inquiry email, I gave up trying compare my calculations Arizpe's and decided to move on.

Of course, I think that my numbers are correct—and I could be just comparing apples-to-oranges since Arizpe didn't define his terms or describe how he made his food energy energy estimates in Table 1.  In any event, I hope that I have provided enough details that, if it is my error, then it should be possible for someone to detect it.   At least my numbers should be internally consistent, since I will be applying the same conversion factors throughout my analysis in subsequent posts.

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Okay, that is it for the preliminary work, it is time to move on and look at some country or region data—but, where to start? 

Let's start with the world.