Friday, July 22, 2011

The relationship between hunger and petroleum consumption-Part 4

Parts 1 and 2 looked at the relationship between the global hunger index (GHI) and per capita petroleum consumption, and, part 3 looked at the relationship between per capita petroleum consumption and BMI. 

Here in Part 4, I examine the relationship between the food supply energy for individual countries, as estimated Food and Agriculture Organization of the United Nations, and per capita petroleum consumption.

At the outset for new visitors, I feel compelled to say that this is not a dieting blog.  Also, I am not advocating for or against an all-bacon diet or an all-cracker diet, or any kind of diet.  I have no personal experience with either diet—although apparently there are people in America at least who have tried these diets (sad, sadder, and very sad).  If you want to talk about these or other types of diets, this is not the place to be.

Rather, I am interested in the role that petroleum plays in food production.  I am interested in this because I think that we are in the midst of peak oil, where petroleum production declines, and therefore petroleum consumption must also decline.   I think that this will have negative implications for the world’s petroleum-driven food production system.  That is, there will be problems in feeding the world’s growing population as  petroluem consumption declines over the next 10-30 years.  My assumption is that large numbers of people will die of starvation if/when the food production system is deprived of petroleum below a certain critical level.

What I am particularly interested in is trying pin-point at what point the petroleum consumption rate become too low to support the production of food in sufficient quantities to support a country’s population, and, people start to get hungry and then start to die of starvation. 

Data sources and my selection criterion

If you are a food junky, statistical speaking that is, then the Food and Agriculture Organization (FAO) is the place to go pig out.  The FAO’s compilation of statistical data is distributed over several different divisions, and, the data base is simply enormous.  I have spent most of my free time recently at FAOSTAT, the FAO Statistical Database, which contains, “over 1 million time-series records from over 210 countries and territories covering statistics on agriculture, nutrition, fisheries, forestry, food aid, land use and population.” 

I’m telling you reader, give up watching the “Bachelor,” or whatever, FAOSTAT has endless hours of entertainment, if you are only willing to work for it! 

But, I digress...

Among the legions of statistics complied at FAOSTAT are statistics for the Food Supply, defined as:
total and per caput food supplies available for human consumption during the reference period in terms of quantity and, by applying appropriate food composition factors for all primary and processed products, also in terms of caloric value and protein and fat content. Calorie supplies are reported in kilocalories. The traditional unit of calories is being retained for the time being until the proposed kilojoule gains wider acceptance and understanding (1 calorie = 4.19 kilojoules). Per caput supplies in terms of product weight are derived from the total supplies available for human consumption (i.e. Food) by dividing the quantities of Food by the total population actually partaking of the food supplies during the reference period, i.e. the present in-area (de facto) population within the present geographical boundaries of the country.
from, Food Supply Notes (emphasis added)

I added the emphasis to make clear that the FAO’s definition of “Food Supply” is the amount of food that is available for human consumption. This amount is not necessarily equal to the food that is consumed by humans.  I expect that different countries (e.g., USA compared to Ethiopia) waste their food supply to different degrees (e.g., the USA more than Ethiopia), but my main interest is in the total food available for consumption, as this is likely to reflect the petroleum consumed in making the food. 

As a side note, the FAO’s “Food Supply” omits the food supply to feed livestock for subsequent human consumption.  It is possible to include this because “Feed” is listed as a separate category in FAO’s data bases, although it is not reported in energy units.   However, to correct for feed for each of the +100 countries considered here would take a substantial amount of work that I am not willing to take on at this time. 

Food Supply is given in kilocalories/capita/day (kcal/pd), which is calculated, as noted above, by applying appropriate food composition factors for all primary and processed products, in terms of caloric value.  Expressing food in terms of caloric value is a way of allowing all of the different food types consumed to be compared on a same scale.  Yes, yes, it would be nice to have some measured of the quality of the food supply, but I am unaware of such a unitary measure being available for all countries and all years.  So, we are stuck with caloric value for the time being.

FAOSTAT makes estimates of “Food Supply” for every year and nearly every country in the world since 1961—did I mention, hours of entertainment?  

To make my analysis more tractable, and comparable to the analysis I did in Parts 1 and 2, I decided to focus only on 1990 and the most recent data reporting period of 2007. 

Once again, I used per capita petroleum consumption rate (barrels per person per year, b/py), calculated as described in part 1 (from the EIA and US census bureau), for the two years, 1990 and 2007, of interest. 

Also, in addition to considering only those countries for which all both food supply and per capita petroleum consumption data are available (about 175 countries in 2007) I decided to omit all those countries (about 30 countries in 2007) that had a population of less than 1 million people in 2007.  For the most part, this resulted in the omission of a few dozen small island-states and a few city-states, whose food supply system I beleive might not be representative of the bulk of the world’s population.  I could have tried some type of weighting scheme, but simply providing a cut-off was the most expedient way to biasing away from these small states.  That is, that a state with a population of a few 10s of thousands of people should not have the same weighting as state with 10s, 100s, 1000s of millions of population.  This is a somewhat arbitrary cutoff on my part, but, the remaining countries data considered here still accounted for over 98% of the world’s population for the two years considered.

Results and Statistical Analysis

Figure 6 presents a plot of Food Supply (in units of kcal/pd) versus per capita petroleum (b/py) for all 145 countries for which a food supply estimate was made in 2007 and otherwise meeting my criterion: 

As in my previous posts in this series, the solid curve in Figure 6 corresponds to a power equation trend line also shown in the figure.  And, again, as in parts 1-3, I don’t ascribe any particular meaning to the power curve or its best-fit parameter values—but the curve does help show the general trend in the relationship between food supply and per capita petroleum consumption.

Figure 7 shows the analogous plot for 1990:

As illustrated in Figures 6 and 7, the trend is for there to be lower per capita consumption levels for those countries where the available per capita food supply energy is lower than in other countries. 

The average food supply and standard deviation for 1990 and 2007 equaled 2571±573 (n=125) and 2759±521 (n=145), respectively. 

Based on these averages and their standard deviations, I decided to define four different food supply groups, corresponding to the group averages plus or minus one or two standard deviation (sd) values.  The four food supply groups and their corresponding average(±sd) per capita petroleum consumptions, and, the number of countries in each group (n), are presented in the Table below:

Mean per capita petroleum consumption for groups having different degrees available food supply
Food Supply Group
Range of Food Energy Available (kcal/pd)
Per capita petroleum consumption (b/py)
Year =>
G1: Low
< 1998
< 2238
0.56±0.57 (n=21)
0.67±0.71 (n=26)
G2: Moderate
1.8±2.8 (n=49)
2.4±2.7 (n=46)
G3: Good
7.3±6.3 (n=26)
9.4±9.6 (n=50)
G4: High
> 3144
> 3280
14.8±8.7 (n=28)
11.6±6.5 (n=23)

There are a lesser number of countries in 1990 (n=124) compared to 2007 (n=145) mainly due to the birth, or re-birth, of several new countries after the dissolution of the Soviet Union after 1990, and, because some countries had no reported petroleum consumption in 1990, but did report petroleum consumption in 2007.

If the amounts of food energy available in the above table seem high, remember that this is food available for human consumption, and, not the actual amount consumed.   For instance, the FAO also periodical reports a “Minimum Dietary Energy Requirement” for about these same countries. 

The Minimum Dietary Energy Requirement estimated by the FAO tries to account for country-by-country differences in gender and age structure, and other factors, which in turn affects energy requirements for the population.  

For all countries reported, the average Minimum Dietary Energy Requirement equaled 1790±82 kcal/pd for 1990-92 and 1825±82 kcal/pd for 2004-06 (the latest reporting period). These are pretty tight standard deviations, in my opinion, which in turn suggests that for the most part, all populations need about the same minimum amount of food, regardless of which country we are considering.

It makes sense that the food supply energy available would be much higher than minimum food energy required.  Since the food supply within each country is not distributed evenly, if the food supply available was ever close to the minimum food needed, then that would mean that a major fraction of the population would be starving. 

The FAO uses these two numbers, and an ssumed or derived distribution curve (f(x)), to estimate the percentage of a population that is undernourished, such as generically indicated below:

The relative area under the curve on the left-hand-side (red arrow) of the distribution curve corresponds to FAO’s estimate percentage of population that is undernourished in a particular hypothetical population. 

The FAO’s percentage undernourished is actually one of the three hunger-related indicators (i.e., PUN, the proportion of the population that is undernourished) that makes up the GHI, reported by the International Food Policy Research Institute, as discussed in Parts 1 and 2. 

ANOVA of the four groups with different food supply
The four different Food Supply groups tested, G1-G4 were defined in the above table.

I started off with a one-way analysis of variance (ANOVA) to test the hypothesis: are the mean per capita petroleum consumption rates of the four groups all equal (i.e., G1=G2=G3=G4).  This hypothesis was rejected at p<0.05.  That is, the per capita petroleum consumption rates of the four different food supply groups are not all equal. 

A subsequent multiple group comparison for the 2007 data, using the Tukey test (p=0.05), revealed that the per capita petroleum consumption rates between groups significantly differed in following respects:
1) G1 is different than G4 and G3;
2) G2 is different than G4 and G3;
3) G3 is not different than G4; and
4) G2 is not different than G1.

A subsequent multiple group comparison for the 1990 data, using the Tukey test (p=0.05), revealed that the per capita petroleum consumption rates between groups significantly differed in following respects:
1) G1 is different than G4 and G3;
2) G2 is different than G4 and G3;
3) G3 is different than G4; and
4) G2 is not different than G1.

Statisticians like to summarize such finding graphically, where the continuous line represents group means that are not statistically different, and, a break in the line means a significant group difference exists:

Is the change in food supply from 1990 to 2007 consistent with the expected change in per capita petroleum consumption?

Once again I have matched pairs of Food Supply data for a number of countries (n=124).  This allows me to test to see if changes in the Food Supply from 1990 to 2007 are consistent with my expectations of how the per capita petroleum consumption should change.  That is, if the Food Supply when up from 1990 to 2007, did the per capita petroleum consumption also go up?  Conversely, if the Food Supply when down from 1990 to 2007, did the per capita petroleum consumption also go down?  This is what I would expect if Food Supply and per capita petroleum consumption are causally linked.  Or, at least if the direction of change in these two parameters are random, then it is harder to see how there could be a causal link.

To test this hypothesis, I once again did a SIGNS test similar to that described in Part 2 of this series.

To set up the data properly, for the 124 countries for which we have data in both 1990 and 2007, if Food Supply increased and per capita consumption increased, or, if the Food Supply decreased and per capita consumption decreased, then I assigned a value of "1."  For the opposite scenario, if the Food Supply increased and per capita consumption decreased, or, if the Food Supply decreased and per capita consumption increased, then I assigned a value of "0."

The SIGN test is a test of the null hypothesis.  That is, the hypothesis is that probability of assigning a value of "1" and "0" for a particular country are equal.  That is, if the proposed parallel relationship between Food Supply and per capita consumption is not true, then there should be an equal numbers of 1s and 0s (i.e., 62, in either group for a population of 124 countries).

My analysis showed that, of the 124 countries with paired data, 79 followed the proposed relationship and 45 did not.  The lower and upper criterion values (i.e., a two-tailed SIGNS test) at a p < 0.01 are 48 and 76, respectively, and since 79 is outside of this range, the null hypothesis is rejected. 

In other words, there is a less than 1% chance that proposed direction of change in Food Supply with an parallel direction of change in per capita consumption is due to random chance alone. 

I took this analysis one step further, and repeated the SIGNS test of the null hypothesis for two subgroups.

The first subgroup was all the countries (n=59) with a Food Supply of greater than the average 2759 (i.e., the mean Food Supply in kcal/p/d for the 124 countries considered here) in 2007; the second subgroup was all countries (n=65) with a Food Supply of 2759 or lower (i.e., the average or below average food supply).

The null hypothesis was rejected for subgroup of countries with the above average Food Supply at p<0.01.  For subgroup of countries with the average or below Food Supply, the null hypothesis was rejected—but only at p<0.1.  

This suggests that the expected parallel relationship between the Food Supply energy per capita and per capita petroleum consumption holds for both higher and lower levels of food supply, although the level of uncertainty is greater for the later than the former.


As illustrated in Figures 6 and 7, regardless of the year studied, there is a rapid decrease in per capita petroleum consumption as we look at countries with high to good food supply energy per capita, to countries with a moderate to low food supply energy per capita.

The trend lines basically show the same trends for both 1990 and 2007—the lower the food supply per capita, the lower the per capita petroleum consumption, with a sharp transition occurring at about 1 barrel per year per person.

There are statistically significant differences in the per capita petroleum consumption rates between the two lowest food supply groups (G1 and G2) and the two highest food supply groups (G2 and G4).  And, for 1990, the second highest (G3) and highest (G4) food supply groups had significantly different per capita petroleum consumption rates.

These results are similar to what I observed in Parts 1-3, using two different types of hunger measures (GHI and BMI). 

What does this all mean? 

I will offer my summary and thoughts in the next, and final installment of this series.

1 comment:

  1. Houston, we have a problem.......


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