In this part . . .
Where have you gone, Adam and Eve? Life was so simple then. Two people. One culture. Nice garden. Plenty of room for growth. If somebody else had been around to write about population geography, I’m guessing three paragraphs would have sufficed. Not any more.
Today more than 6 billion people are divided into who knows how many thousands of cultures. And these folks just won’t stay put. Ever since the original twosome got their eviction notice, people have been moving and migrating, rendering population geography into something akin to a restless tide. In the midst of change, however, discernible patterns (constants, if you will) emerge that concern the geographies of population, culture, migration, and control of the planet.
In this part, you will learn some of the key concepts and concerns of human geography. And yes, it takes more than three paragraphs. Indeed, it consumes four chapters that address the topics just mentioned — population, culture, migration, and control of the planet. Even that doesn’t complete the story, for we still have the matter of how people use and misuse the planet. Stay tuned.
In This Chapter
Living in crowded spaces, but not empty quarters
Studying a major league curve
Charting the stages of change
Grappling with the question of overpopulation
N ot long ago, the global population — the number of people worldwide — passed the 6 billion mark. That number has little meaning by itself. But if you consider that 200 years ago the global population was “only” 1 billion, then today’s total gets your attention pretty quickly. A logical first reaction is that the birds and the bees have been working overtime. Indeed, those little critters have a certain way about them. But global population trends involve more than what happens in the privacy of a nest or hive.
The pages ahead focus on population geography, which analyzes the distribution of people and their characteristics over the face of the Earth. Of necessity, this involves a smattering of demography, the science of vital statistics. “Vital” refers here to life, as when medical equipment is used to monitor a patient’s “vital signs.” Thus, demography involves birth rates, death rates, life expectancy, and other numerical indicators of the human condition.
For people who love to calculate statistics, demography is a dream come true. Chances are good, however, that you are not one of those people. So I forego the arithmetic and focus on generalizations and implications that result from it. Most of all, I focus on how humans and some of their vital attributes vary geographically.
Migration is an important factor in population change both internationally and within individual countries. Indeed, I am going to hold off on that subject for now and instead devote the entire next chapter to it because of its importance.
Going by the Numbers
The world’s 6.1 billion people are spread very unevenly across the planet’s surface, as you can see from Figure 11-1. Virtual empty quarters — large and totally uninhabited realms — correspond with the ice caps and tundra of Antarctica, Greenland, and the very high latitudes in general. Similarly, large desert areas often are low on people. Indeed, if you read the chapter on climate (Chapter 10), then it should come as no surprise that the Sahara, Gobi, Arabian, and other desert realms are fairly devoid of people. Also, most of the world’s rainforest realms have low population densities, as the Brazilian interior and central Congo indicate.
But for every desolate area, you must consider the likes of Hong Kong, with some 16,000 people per square mile, or Singapore with its 17,000 people per square mile. Those are small dots on the world map that complement large areas of comparatively high density: the northeastern U.S. and adjoining areas of Canada; much of Western and Central Europe; the Nile valley; north central India; eastern China, and Japan and Java.
Table 11-1 on the world’s most populous countries highlights the dominance of China and India, which respectively are home to 21 percent and 17 percent of all the people on this planet. Given those two population powerhouses, Asia contains some 60 percent of the world’s population — the largest continental percentage by far (as shown in Figure 11-2). The United States is now the third most populous country on Earth, but North America as a whole contains only 8 percent of the human population. All told, the 15 most populous countries account for fully two-thirds of humanity.
Dispersion versus clustering
Two areas can contain the same number of people, yet have a totally different look and feel because of the ways their populations are distributed. For example, the United States is among the minority of countries in which farmers typically live on their farms. That statement may cause you to ask, “Where else would a farmer live?” The answer is, in a village or town, and therein lies a significant difference in the way people are distributed. Rural population geography in the United States generally exhibits dispersion, which entails considerable open space between individual farmsteads. In contrast, the pattern in much of the rest of the world exhibits clustering. That is, farming families tend to live in a compact village, from which they walk or otherwise “commute” to the land that they tend.
The two patterns are depicted graphically in the following figures, both of which contain 21 dots that represent homesteads.
Opportunity for livelihood
Trying to fully explain global population geography in a one-liner is impossible, but perhaps “opportunity for livelihood” is a good start. Human population densities tend to be high where opportunity for livelihood is favorable and low where the opposite is true. Opportunity for livelihood takes different forms and therefore, so does the characteristics of regions that support high densities.
Agricultural land in the Nile, Ganges, and Indus River Valleys, plus the valleys and coastal plains of eastern China support large populations (see Figure 11-1) due to their rich alluvial soils. How rich? Well, rich enough that since the beginning of recorded time, people who possess even the most modest agricultural technology have been able to realize sizeable harvests on relatively small acreage. In complete contrast, high densities are also found in countries where industrial and post-industrial economies dominate. Examples include the Northeastern U.S., Western Europe, and Japan.
Because people round the worldview cities as centers of opportunity for livelihood, one of the most significant population trends today is urban growth. I talk about that more fully in Chapters 12 and 17. But for now, you can clearly see its effects on the world population map in the likes of Greater Mexico City, the Sao Paulo–Rio de Janeiro complex in Brazil, and the major cities on the East and West Coasts of the U.S.
Globally, about 46 percent of the human race is categorized as urban, but the figures vary sharply from one continent to the next. The populations of North America, South America, and Europe are each at least 70 percent urbanized. In contrast, the urban population percentages of Asia and Africa are 47 percent and 33 percent respectively, which largely reflect continued heavy reliance on manual labor in the agricultural sector of countries’ economies, plus relative lack of employment opportunities in the service and manufacturing sectors, which globally tend to be more urban-based.
Going Ballistic: Population Growth
For the vast majority of human history, total population was much, much lower than it is today. The United Nations estimates that it was only about the year 1650 that, for the first time, as many as 500 million people were alive at any one time (as illustrated by Figure 11-3). Until then, population growth could be fairly characterized as a gently rising straight line. But around 1650 something began to happen. The line started to curve upward — gently at first, but then ballistically.
Total population passed 1 billion around 1800. Thus, while it took untold eons for human numbers to reach 500 million, a mere 150 years were required to double that number. By about 1925, the figure had doubled again to about 2 billion. In the next fifty years, it doubled again, reaching 4 billion sometime around 1975. And here we are today at slightly more than 6.1 billion.
Global population growth has not stopped. Instead it will continue to rise in the present century before leveling off at about 10 billion in the year 2100. Naturally, that may cause you to ask, “How can demographers be so certain about the future course of the world’s population?” In fact, the experts are not certain. Instead, the future projections are (highly) educated guesses based upon reasonable assumptions concerning the global courses of birth and death.
OK, everybody into Rhode Island!
Rhode Island is the smallest state in the United States. Imagine if every person on Earth went there to participate in a meeting. Would they all fit? And what would be the population density?
Rhode Island contains 1,212 square miles, and about 6.1 billion people currently live on Earth. If all of them went to Rhode Island and stood evenly spaced apart, that would work out to 5,033,003 people per square mile. One square mile (5,280 feet x 5,280 feet) equals 27,878,400 square feet. Dividing that many square feet by 5,033,003 people gives each person 5.54 square feet in which to stand. That works out to a square that is roughly 2 feet, 4 inches on each side.
Get out a foot ruler, measure an area that size on the floor, and stand in it. Now imagine being surrounded for miles and miles by people who are allotted an equal amount of space. Would you feel cramped? Well, people may respond differently to that question. But with 5.54 square feet per person, 6.1 billion people could stand in Rhode Island with little or no physical contact between them. And all of the rest of the world would be completely empty of humans.
This mathematical exercise is just that. It’s not meant to play down the global impact of 6.1 billion people who need to be housed, fed, and otherwise sustained — all of which requires considerably more space than exists in Rhode Island.
Checking Behind the Curve: Population Change
Population change is a matter of birth, death, and/or migration. That is, in a given year in a given country, some people are born, some people die, some people move in, and some people leave. Demographers have developed a statistically based vocabulary that addresses these issues. Three terms in particular are worth passing along to you at the present time because they appear frequently in the following pages:
Birth rate: The annual number of births per 1,000 population.
Death rate: The annual number of deaths per 1,000 population.
Natural increase: The annual rate of population change as calculated by subtracting the death rate from the birth rate. (Typically, the birth rate exceeds the death rate, so population rises. But occasional short-term calamity such as a plague, war, or economic turmoil may produce the opposite effect.)
Dealing with births and deaths: Natural increase
Just as humans are unevenly spread across the surface of the earth, so, too, is population growth. Indeed, perhaps the single most important demographic reality of our times is that the rate of natural increase differs dramatically in different countries and regions of the world (see Figure 11-4). The highest rates tend to be found in Africa, South Asia, and Latin America. The lowest rates occur in North America, Europe, northern Asia, plus Japan, Australia, and New Zealand.
On the map, a “high” rate of natural increase is considered to be in excess of 2 percent. That may not seem like a lot, a 2 percent rate of natural increase will double a country’s population in 35 years. Therefore, every country in the “high” category on the map doubles its citizens in some time less than that. Indeed, approximately half of the “high” countries have rates of natural increase in excess of 2.5 percent (a doubling time of 28 years) and about half of those have rates of 3.0 percent (23 years) or greater.
Rapid growth, poor country
Rates of natural increase and their associated “doubling times” (see the “On the double!” sidebar) have incredible implications for economic development and social well-being. A 2.5 percent rate of natural increase means there will be about twice as many mouths to feed in 28 years; twice as many people in the schools; twice as many people seeking health care, energy, employment, and transportation. Now, it would be one thing if this pertained to affluent countries that could adequately meet the needs of their citizens. But what about a poor country?
And indeed, that’s the rub. Generally, countries that exhibit the highest rates of natural increase are relatively poor. Stated differently, the highest rates of population growth are generally taking place in countries with the least amount of financial resources to address the needs of their growing population.
Slow growth, affluent country (with some notable exceptions)
Figure 11-4 includes a “low” category for which the rate of natural increase is under 1.0 percent. That corresponds to a population “doubling time” of 70 years, so every country in this category takes at least that long to double its present citizenry (migration not considered). Often, low rates of natural increase coincide with relatively affluent countries. A principal reason is that the economies and livelihoods of well-to-do countries do not require large amounts of manual labor, so families have comparatively few children, and that depresses the rate of natural increase. (Additional reasons are discussed in the next section.)
On the double!
How long does it take a country to double its population given a particular rate of natural increase? The following table provides some answers. To take two examples, if a country has a 1.5 percent rate of natural increase, then it will take 48 years to double its population, assuming the rate of natural increase does not change in the interim. In contrast, a 3.0 percent rate of natural increase is sufficient to double the population in only 23 years. Remember that natural increase equals birth rate minus death rate. It does not take into account either in-migration or out-migration, both of which could be key factors in a country’s population change.
However, many countries have doubling times far longer than 70 years, and some even have negative rates of natural increase. Germany, for example, has a natural increase of –0.1 percent. Russia’s figure is –0.7 percent. That means the death rate is actually exceeding the birth rate in those countries, whose populations may begin to decline should these conditions hold for the foreseeable future.
Most examples of “negative growth” are former Communist countries that are experiencing economic difficulties as they struggle to make the transition to market economies. One way to economize in tough times is not to have children, which is why those countries’ birth rates have dropped below their death rates — negative growth.
Increasing for a reason: The demographic transition model
The geography of natural increase doesn’t “just happen.” Instead, particular rates of natural increase are occurring in particular countries for particular reasons. To help explain these circumstances, demographers have developed a widely applicable set of generalizations (based on the experiences of many countries) called the demographic transition model, which is shown in Figure 11-5. Because the topic is people, demographic makes complete sense. Also, the model begins and ends with nominal population change. But in between a period of transition, characterized by substantial growth, occurs.
The demographic transition model considers the relationship between birth rates and death rates over time, and consists of four stages:
Stage 1: High stationary
Stage 2: Early expanding
Stage 3: Late expanding
Stage 4: Low stationary
I am going to make a big deal of them for two reasons. First, if you understand the model, then the factors that gave rise to the historical population growth curve illustrated in Figure 11-3 become crystal clear. Second, different countries of the world are in different stages of the demographic transition. Therefore, if you understand the model, then you can understand why a particular country is experiencing its particular rate of natural increase and its attendant social characteristics. Parenthetically, several developed and developing countries have already completed the transition. Demographers have used the experiences of those countries to predict the course of global population growth into the next century (see Figure 11-3).
Stage 1: High stationary
In this stage, birth rates and death rates are high and about equal (see Figure 11-5). Thus, population growth is stationary (exhibiting little or no change) because the numbers of births and deaths are “canceling out” each other. These conditions were characteristic of global population prior to 1650 (Figure 11-3). The high death rates of those times were products of poor sanitation, tainted water supply systems, faulty food storage, lack of education, and absence of medicines and vaccines. The results included the following:
Infant mortality (the incidence of death before a child’s first birthday) was astonishingly high, meaning that it claimed about 25 percent or more of newborns even in some “advanced” societies.
Average life expectancy (the number of years a newborn could expect to live) was low. How low? Well, today the average citizen of France can expect to live 78 years. But church and cemetery records suggest that in the 1600s, French life expectancy was about 35 years.
On average, therefore, people died young. Many never reached reproductive age, and those who did tended not to live that many years during their fertile time of life.
Human societies have typically responded to high death rates by having high birth rates, and the time prior to 1650 was no exception. Factors that contributed to high birth rates included the following:
Most families farmed for a living, so more children meant more hands to do the manual labor that was necessary in those days before widespread use of machinery.
Retirement pensions, 401(k)s, life insurance, and social security checks were unknown. Having children (and the more, the better) guaranteed there would be somebody to look after you if you were fortunate enough to reach old age.
Given short life expectancy and need for children, people — especially females — married young. Most women were wed by their mid-to-late teens and, not withstanding the often-fatal rigors of childbirth, had been through a couple of pregnancies by age 20.
Virtually no country currently experiences this range of conditions in its entirety. Nevertheless, an understanding of these circumstances is very important because they set the stage for other phases that are very real in the present age.
Stage 2: Early expanding
Birth rates exceed death rates by a widening margin in this stage (see Figure 11-5). When that happens, population does more than simply grow: It increases dramatically. Look again at Figure 11-3 and note the S-shaped curve of population growth. The conditions just described correspond to the bottom — that is, early — half of the curve, when population was expanding at faster and faster rates after years of being stationary.
Hence the name of this stage is early expanding, which nevertheless perplexes many people because, as you can see on Figure 11-5, birth rates and death rates are declining throughout it. The key thing to focus on in that diagram is the widening gap between birth rates and death rates that is characteristic of this stage. Even though both rates are dropping, the gap between them is widening, birth rates being the higher of the two. Thus, population grows (expands).
But why are the rates dropping and the gap widening? Basically, birth rates drop because of a tempering of the last bulleted items of Stage 1, but death rates are declining much faster because of the following:
Basic and widespread improvement in water supply and sanitation are having a very positive effect on public health.
Medicines and vaccines are becoming accessible to more and more people.
Infant mortality is dropping and life expectancy is rising. More people are reaching reproductive age and are reproducing. People are living longer in their reproductive years and are reproducing more.
Death rates are dropping faster than birth rates, so population grows — slowly at first, and more dramatically more recently.
The widening gap between birth rates and death rates results in growing rates of natural increase. Thus, looking again at the global map of natural increase (see Figure 11-4), most countries in the “high” category are in the early expanding stage.
Stage 3: Late expanding
Birth rates exceed death rates by a narrowing margin in this stage (see Figure 11-5). When that happens, population grows but at rates that are progressively slowing. Look once at Figure 11-3 and its S-shaped curve of population growth. The conditions just described correspond to the top — that is, late — half of the curve, when population was expanding at slower and slower rates after years of skyrocketing. Hence the name of this stage is late expanding. Because birth rates exceed death rates by a decreasing margin, the result is a lowering of the rate of natural increase. On the map of natural increase (see Figure 11-4), most countries in the “medium” category are experiencing this stage and its attendant social conditions, which include the following:
Improvements continue to be made in public health, resulting in lower infant mortality and longer life spans. Thus, the death rate continues to decline.
As the economy develops, machines perform increasing amounts of work that used to be done manually. Thus, the incentive to produce children strictly for their labor potential drops.
More people gain work in jobs that provide pensions and retirement systems. This lessens another historic incentive to produce children.
Increasing numbers of women encounter career and educational opportunities that have the effect of delaying marriage and child-bearing.
Increasingly, husbands and wives consider the costs of raising and educating children and opt to limit the size of their families.
As a result of the last four factors, the birth rate begins to decline — slowly at first, but then more rapidly as the modern economy encompasses more and more families. As the gap between death rate and the birth rate diminishes, the rate of population increase begins to slow, and the curve exhibits signs of leveling off.
Stage 4: Low stationary
In this final stage birth rates and death rates are low and about equal (see Figure 11-5). Thus, population growth is stationary (exhibiting little or no change) because the numbers of births and deaths are “canceling out” each other. On the map of natural increase (see Figure 11-4), most countries in the “low” category are experiencing this stage. (Global population as a whole, as per Figure 11-3, will probably not experience this stage until early in the next century.) The characteristics of the low stationary stage are as follows:
Continued improvement and increased availability of health care results in continued lowering of the death rate.
The economy is overwhelmingly industrial or post-industrial, resulting in diminished need for manual labor except in those remaining occupations that require a high degree of craftsmanship.
Institutional retirement systems and benefits are widespread, nullifying the need to have children for the sake of social security.
More women defer marriage and motherhood (or opt out entirely) as educational and career opportunities become more widely available and socially acceptable. The effects lower the birth rate.
Average family size continues to decrease as more families factor in the costs of child raising and educating children.
In each stage of the demographic transition model (see the previous section), natural increase is closely related to other demographic variables, each of which can be mapped and analyzed, and thus reveal a broader appreciation of the geography of the human condition. The following sections offer maps and brief discussions of three variables that illustrate the possibilities.
Wealth (Gross National Income [GNI] per capita)
A map of global wealth reveals that the world’s most affluent countries are found in North America, Western Europe, and selected “outlying” places such as Japan, Australia, and New Zealand (illustrated in Figure 11-6). At the other extreme are numerous countries in Africa and Asia. If the overall pattern of rich countries and poor countries on this map looks vaguely familiar, it ought to. As suggested earlier and confirmed here, you can see an inverse relationship between natural increase and wealth. That is, countries that have a high rate of natural increase generally have low average wealth, and vice versa. And again, the most significant implication is this: The highest rates of natural increase are occurring in countries that have the least financial means to see to the needs of their rapidly expanding populations.
Percent of population under 15 years of age
Figure 11-7 — a world map showing percent of population under 15 years of age (by country) — reveals a familiar pattern. The highest category on the map pertains to countries in which more than 40 percent of the population is in that age category. And basically, those countries are found in Africa, plus Central America and Southern Asia. The lowest rates, in contrast, tend to be found in North America, Europe, and East Asia, plus Australia and New Zealand. Thus, the highest percentages of young people tend to be found in countries that have rapidly expanding populations and the least financial wherewithal that can be brought to bear on the education, health, and nourishment of the next generation.
Infant mortality is a sensitive indicator of public health and education. Figure 11-8 shows the highest rates of infant mortality are occurring in countries that have the highest rates of natural increase, which also tend to be poor. That lack of wealth is largely responsible for the poor states of health care and sanitation that produce high rates of infant death. Co-occurrence of high rates of infant mortality with high rates of people under 15 years of age suggests a societal preference for high birth rates to offset high death rates. It likewise suggests existence of characteristics described in the high stationary and early expanding stages of the demographic transition model.
Population pyramids provide a graphic means of depicting and comparing the populations of different countries. In the diagrams below, the vertical axis shows age groups, while the horizontal axis indicates the percent of a country’s population that is in each of those groups. Most countries have more young people than old people, so the graph typically has a wide base that tapers upward — rather like the shape of a pyramid. But the width of the base may vary substantially. Pyramids of developing countries, which typically have high rates of natural increase and therefore large percentages of their populations in the younger age groups, tend to have wide bases. In contrast, the pyramids of developed countries, with their low rates of natural increase, tend to have narrower bases.
Population pyramids are particularly useful for contrasting dependents and non-dependents. These are terms demographers use to characterize people in the under-15 and 65-and-over age groups (dependents) and the middle aged people (non-dependents) on whom they must generally rely for their sustenance. Typically, a low percentage of dependents is desirable because it means a high percentage of non-dependents is available to see to the needs of the young and the old. And in fact, that tends to be the case in well-off countries. In developing countries, in contrast, a high percentage of dependents relative to non-dependents is the norm. The population pyramids above make these differences very apparent.
Given the billions who live in poverty, poor health, and crowded conditions, people sometimes suggest that Earth is overpopulated. This controversial subject is made all the more problematical because it has proven difficult, if not impossible, to define. The clear implication of “overpopulation” is “too many people.” But how many is too many, and is that number the same everywhere, or is it dependent on local conditions?
Reputable demographers agree there is no “magic number” of people or of people per square mile beyond which a country or region is overpopulated. Being statistically inclined, however, they do look to numerical data and analyses to gain perspective. Perhaps their most intriguing concept is carrying capacity — the number of people that a country or region can sustain at an acceptable level of well being given its prevailing technology.
As you will see in the “Regarding overpopulation and carrying capacity” sidebar, one can argue that technologically advanced societies have higher carrying capacities than developing nations that lack similar expertise. But differences in culture, life experience, and personal preference have generally rendered inconclusive mathematical attempts to precisely determine carrying capacities. At best, and in response to the questions above, demographers’ numerical exercises suggest it is impossible to determine how many is too many, and that thresholds — if and as near as they can be determined — do vary from place to place depending on local conditions.
Lack of conclusive definition and indicators of overpopulation — statistical and otherwise — has not prevented people from taking sides on this issue. Some argue passionately that Earth or parts thereof are overpopulated and espouse policies to rectify the perceived problem. Others argue just as passionately that overpopulation does not exist and espouse policies aimed at relieving malnourishment, poor health, and its other would-be symptoms. Proponents of these viewpoints occasionally and respectively are referred to as neo-Malthusians and cornucopians.
If you hail from the camp of neo-Malthusians, your camp leader is Thomas R. Malthus (1766-1834), an English political economist and theologian, who believed population increase is a prelude to disaster. In his famous “Essay on the Principle of Population” (1798) he stated that “population increases in a geometric ratio, while the means of subsistence increases in an arithmetic ratio.” In other words, human population is increasing and at a rate much faster than food supply.
Regarding overpopulation and carrying capacity
Below are selected demographic data for two honest-to-goodness countries. If I showed only the first two rows of data and then asked which country could be considered overpopulated, I suspect most people would say Country Y. After all, it has about 50 percent more people than Country X, and has a population density (people per square mile) that is more than 47-times greater than its counterpart’s. But when you throw in the last three rows of data, a different picture emerges.
Women live twice as long in Country Y, and infant mortality is nearly 25-times less prevalent. Also, per capita GNI data suggest personal income is astronomically higher in Country Y. The implication, therefore, is that citizens of Country Y are generally well off while those of Country X are not. This leads to consideration of carrying capacity — the number of people a country can sustain (carry) at an acceptable level of well-being. Review of the data suggest Country Y has a high carrying capacity. Though densely populated, it clearly has the capacity to carry its relatively large population at a high level of well-being. The opposite might be said of Country X. Even though the country is lightly populated, it appears not to have sufficient resources to sustain its population at a high level of well-being. Therefore, one may argue that Country X has exceeded its carrying capacity. What is certain, however, is that overpopulation cannot be determined simply on the basis of a country’s population or its density. By the way, Country X is Niger, and Country Y is The Netherlands.
During Malthus’s time, agricultural technology offered limited prospects for improved harvests. Thus, the only real option, as Malthus saw it, was for humans to have fewer babies. Artificial means of birth control were available, but Malthus opposed them on theological grounds. He preached restraint, but conceded that human passions were not likely to be held in check by “Just say no.” Thus, he saw no solution save the grim reaper. Population would continue to increase faster than food supply until, ultimately, large-scale famine and starvation rectified the imbalance.
The world has changed a lot since Malthus. Human numbers have exceeded his wildest dreams, but so has agricultural productivity. Also, in Malthus’s time, transportation technology was such that food had to be produced fairly close to consumers. Nowadays, however, food travels hundreds — even thousands — of miles to get to your supermarket. Thus, a local crop failure or poor harvest need not have the devastating effects of yesteryear because food can be brought in from somewhere else.
Today few reputable scholars espouse the literal word of Malthus. But lots of neo-Malthusians believe the old bloke basically got it right. Namely, many countries suffer from too many people (see Figure 11-9). And while technology may someday improve the average welfare, population reduction is the most effective and reliable way to achieve a better balance between people and resources.
This term cornucopia recalls “the horn of plenty,” that curly-cued overflowing basket of food one tends to associate with Thanksgiving decorations. If you camp with cornucopians, the viewpoint is that the world has a food supply problem, not a people supply problem. And in their view, the solutions are not futuristic. Rather, means are available now to greatly increase global food supply and therefore improve carrying capacity throughout much of the world. They include:
Greater use of green revolution know-how. Green revolution refers to a number of agricultural innovations designed to increase food production in developing countries. Chief among them are varieties of rice and wheat that have been genetically engineered to increase their yields as well as their resistance to crop diseases. Increasing access to these relatively inexpensive strains could greatly help developing countries to increase their carrying capacities.
Improved grain storage. In several countries a significant portion of grain harvests are lost to vermin due to poor storage. Modest expenditures on secure storage could substantially increase available food.
Improved transportation. Modest investment in the most basic forms of infrastructure could greatly enhance food supply and well-being. Roads in parts of many developing countries are little more than dirt tracks that may be nearly impassable during a rainy season or other time of year. This diminishes access to markets and to goods that might improve agriculture. Modest investment in road-building could lead to a much improved picture.
Greater distribution of food surpluses. Some developed countries have massive food surpluses that are merely stored, as well as policies that pay farmers not to grow crops (in order to maintain decent price levels). Cornucopians regard these as ethically unconscionable and readily available sources of food.
Cornucopians, in short, believe that a number of means are available that can significantly improve global carrying capacity. What is lacking, in their view, is the will to do the right thing.
Applied Geography: Census-taking from above
In developing countries census-taking is sometimes inhibited by the remoteness of villages and the reluctance of their inhabitants to be enumerated. Intent on conducting the best possible head count, some nations have successfully overcome these impediments through careful use of two geographic techniques: spatial sampling and aerial photography. Specifically, inhabitants in a number of accessible and representative villages throughout the country are surveyed (spatial sampling) with special emphasis on determining the average number of people per house or hut. Afterwards, aircraft fly over remote villages and photograph them. Photo interpreters then examine the pictures, taking special care to identify and count the houses and huts. That number is then multiplied by the average number of people per household, the result being the estimated population of the photographed areas. Geographic variation in house-types and social structure may complicate matters. But adequate sampling coupled with skillful photo interpretation may result in a reasonably accurate census.