Gwendolyn Burke, Ana Deaconu, Jeanette Lim, Hannah-Leigh Varnell

Econ 106: World Food Economy

3/8/12

The World Food Economy to 2050 (5000 words)

Abstract

This paper details the impacts on the three most widely consumed cereal crops (corn, rice, and wheat)

from 2010 to 2050. The impact assessment is performed using a data analysis of the most important

demand and supply factors affecting cereals. Rising global incomes play a large role in the demand

side. Supply is mostly affected by arable land availability and crop yields. This study has implications

for food security, as prices are expected to increase for corn, wheat, and rice, which can leave

vulnerable populations food insecure and malnourished.

Introduction

Defining what the “world food economy” is is the first step towards understanding how it will

change. The world food economy, as defined in this analysis, is the production and trade of the three

most common cereals: corn, rice, and wheat, and the prices at which they are traded. There are an

infinite number of regional and global factors that influence supply and demand and therefore affect

prices.

On the demand side, the most important factors affecting regional demand for the three cereals

are population growth, and income growth leading to changes in food demand. Just as important are the

additive factors that contribute to the quantity demanded, which include the increase in cereal demand

to feed livestock, and the increase in cereal demand for the production of biofuels. The supply side is

affected by changes in agricultural yields and changes in arable land, which include the effects of

technology, resource availability, and climate change.

The regions used for our world food economy analysis are North America, which includes just

the US and Canada because both countries have high incomes and high levels of cereal production.

South America, which includes all of Latin America and the Caribbean, is defined as a region because

income levels in the region are mostly middle and low. Brazil is an outlier in the region of South

America because of its size and its biofuel use and production, contrasting it with the rest of the region.

Asia is its own region. China is an outlier in Asia because of its immense size and because of current

and projected changes, especially in income level, which affect livestock consumption and other

factors. The continent of Africa is its own region. Western and Eastern Europe are separate regions

because the western side is more economically developed than the eastern side. Oceania is the last

world region that is included in our analysis. Adding up demand or supply of each region and outlier

gives us the global demand or supply.

The model functions by equating demand with supply while at the same time accounting for a

price effect, which takes into count price change and elasticities, that will make demand and supply

equal each other. The application Solver is used for this function; the constraint is that demand minus

supply equals zero, and the price change that results in the price effect that fulfills this constraint is

what is solved for. Once price change is found, the 2010 prices are used to project future prices in 2020,

2030, 2040, and finally, 2050. Determining trade is another objective of the analysis, and it is solved

for by subtracting quantity demanded from quantity supplied. Trade is determined separately for each

cereal and region, whereas price is specific to each cereals but regionally non-specific.

World Demands for Corn, Rice, and Wheat to 2050

Total cereal demands can be found by using the current demands for food, fuel in the case of

biofuels, and feed for livestock, and determining how these demands will change with each ten-year

period. Cereal demand changes of food, feed, and fuel are additive effects that result in a certain

quantity demanded, with the price effect and cross-price elasticities with the other cereals also playing

a role. Data for global aggregate initial quantity demanded is defined as domestic supply quantity or

utilization, which is quantified by the following logic: production + imports - exports + changes in

stocks (FAOSTAT 2012). In 2010, global demand for corn, wheat, and rice was 1,982,737,350 metric

tons.

Food Demand

Food demand includes the factors of population and income. Initial food demands were found

under the Commodity Balances’ sub-tab “Crops Primary Equivalent” with the selection “food”

(FAOSTAT: Commodity Balances 2010). The highest demands for food as a portion of the total initial

demand for cereals occur in the developing world regions: Africa, Asia, and South America. The

developed world regions like North America and Western Europe demand less cereals for food due to

Bennet’s Law: the higher one’s income, the less one proportionally spends on starchy staples and the

more one spends on quality food sources (Timmer et al. 1983).

In order to project food demands to 2050, cereal demand in terms of kilocalories was used

(Kruse 2010). Percent changes in food demand were calculated for each world region, and these were

used as a multiplier to the original food demand quantities. The initial quantities had to be converted to

kilocalories to apply the multiplier through each decade to 2050, and then the numbers were converted

back to kg of cereal The conversion rate used is 1500kcal to 1kg cereals.

Our initial numbers showed food demand to be 48% of world cereal demand. After applying

the changing demands over the next four decades, the demand for food as a percent of world cereal

demand in 2050 decreases to 41%. Again, only the developing countries experience a noticeable

decrease in grain consumption for food; developed countries tend to decrease on the rate of 1-2% per

decade. The new world food demand in 2050 is projected to be 1,107,409,906 metric tons of cereals.

Population

The factor of population is one of the most important determinants of demand, because more

people on this planet will require more food for their consumption. Population is one of the most

pressing issues of our time. The demographic transition is moving further along each year, with some

regions in the higher growth stages, and some regions with low, zero, or even negative growth. As of

2010, global population is measured to be 6,895,889,018 people (United Nations Department of

Economic and Social Affairs, Population Division). This number has already hit seven billion as of

October 2011. Projections of population through 2050 use data provided by the United Nations

Population Division, the most commonly cited projection source to date. The world food economy

model assumes a medium fertility growth rate, which is utilized for all regions. The methods used in

the 2010 Revision of the World Population Prospects, which measure global population to 2100, are

based on empirical fertility trends for all countries of the world for the period of 1950 to 2010 and

internalize the different stages of demographic transition within each region. So rather than providing

data with three different growth variants, the assumption that all regions are moving towards a

replacement rate of 2.1 children per woman with respect to their current position in demographic

transition is reasonable. Interestingly, all regions expect positive growth with the exception of Eastern

Europe, which will experience a decline in population. Global population, then, is expected to grow

positively, reaching 9,708,595,289 people by the year 2050, a 40% increase in population from the

2010 estimates.

Income

Incomes continue to increase worldwide, in every region. The income effect uses income

elasticity of demand, which is different for each region depending on their preference for cereals over

non-staple foods. Food consumption in developing regions is more affected by the projected income

rise, because people in developing regions tend to spend a larger portion of their income on food,

making an income rise directly correlate to increased food demand (Naylor 2012). In order to find

income projections through 2050, the initial incomes are important, and the 2010 GDP for every

country in the world came from the World Bank (DATASTATISTICS 2010). Like with income

elasticities, an average GDP was taken across the different world regions for our model. Various

sources for income projections through 2050 for each of these world regions helped make predictions.

Using Table 2 of the OECD’s paper on the “Long Run Growth Framework and Scenarios for the World

Economy,” income projections through 2050 for China, Brazil, North America and other main regions

of the world could be found (Duval and de la Maisonneuve 2009). The information was combined with

projections from the Carnegie Endowment Global Think Tank, which projects world and G20 GDP

growth through 2050 (International Economic Bulletin 2009). The projections were comparable across

all world regions. Growth projections allowed the extrapolation of GDP from the initial 2010 values to

2050 values. Most of the developed regions, like North America and Western Europe, have steady

growth rates through the next four decades. China’s GDP is expected to grow by a factor of 1.5 through

2025, then slow to .8 through 2050. Brazil, another converging economy, has better prospects: total

GDP is expected to grow four-fold through 2025 and 2050. Developing world regions like South

America and Africa are also expected to see their GDP increase three to four-fold through 2050.

However, these increases in GDP do not account for increasing populations. After projecting

GDP through each world region through 2050, they were divided by projections for population growth

through 2050 in order to get estimates for GDP/capita. After doing this calculation, it is clear that

GDP/capita does not follow the same projections as GDP. North America’s GDP/capita will grow on

average 31.8% while South America grows 42% and Africa will see 34% growth in the half-century.

Asia, China and Brazil have the biggest projected growth rates of 55%, 80%, and 57% respectively.

Discrepancies between GDP growth and GDP/capita growth may be explained by population growth.

Africa and South America are projected to have 46% and 71% population growth. These population

growth rates are high enough that even three and four-fold increases in GDP will not result in large

increases in GDP/capita. This is also due to the fact that GDP is low in these regions to begin with.

USDA’s Economic Research Service statistics help outline income elasticities of demand for

cereals, meat, milk and other staple foods for 144 countries in the year 2010 (USDA 2005). Each

country was placed into its world regions, and income elasticities for cereals were averaged. In order

to differentiate between the income elasticity of demand for corn, wheat, and rice in each region,

specific elasticities for each cereal were extracted from a paper by the OECD, “Demand Growth in

Developing Countries” (Abler 2010). Sources were cross-referenced to ensure the elasticities were

accurate.

The last step in income projection is income elasticities of demand through 2050, as elasticities

determine the change in food consumption due to a change in income. This type of information is

difficult to find in the academic literature and it makes more sense to calculate the elasticities

mathematically. The percent of each decade as it relates to the previous decade was calculated. For

example, GDP/capita in North America in 2010 was divided by the figure in 2020 (equal to .96). The

calculation continued for all world regions through 2050. The new template of growth percentages

could then be multiplied by the initial income elasticities for 2010. This resulted in projected income

elasticities of demand through 2050 according to expected GDP/capita growth. While these numbers

tended to be small near the end of the half-century, they still followed the growth expectations. Every

country’s income elasticity of demand decreases, meaning they are spending less on cereals as their

income rises. This follows Bennet’s Law: increases in income lead to less consumption of starchy

cereals and higher intake of quality foods (Timmer et al. 1983). China’s income elasticity of demand

for rice is .44 in 2010 and .08 in 2050. Other countries have less dramatic changes: North America goes

from .021 to .014 for wheat. The contrast one would expect is observed between the developing and

developed countries. Developed countries already spend a small portion on food, while developing

countries are still converging to that point.

Livestock

As an additive factor to total demand, feed for livestock is important, especially as we move

towards 2050. As incomes increase, diets become more diversified as per Bennet's Law, and people

start to consume more meat. Increasing urbanization as the world population moves from rural areas to

cities also adds to livestock demand (Falcon 2012). Meat production requires large amounts of cereals

as feed, at very low conversion rates of 4%, 10%, and 20% from cereal to meat protein for beef, pork,

and chicken respectively (Falcon 2012). Initial demand for livestock in 2010 was 650,908,693 metric

tons. This amounts to about 33% of total cereal demand. Feed projections total within a range of 30-

40% (Rapid Response Assessments 2012). Currently, no projections for the use of rice are published

although news media articles suggest that rice is quickly becoming an alternative for feed in Asia as

corn prices rise. Since rice is used at the margins for feed, it is represented in the model, though corn

and wheat overwhelm these numbers. The Maize Profile provided projections of demand for corn and

suggests that North America, China, Europe, and Brazil are the largest users of maize for their

livestock. Globally, about 65% of maize is used on livestock, and only 15% is used as food (UN, Maize

Profile 2006). Wheat estimates were determined roughly as the percentage of total quantities per region

with information provided by the United Nations Environment Programme.

Livestock demand will increase as we move towards 2050. A combination of calculations

between the regional changes in meat consumption per calorie demanded and the conversion between

food calories to meat calories and cereal calories demanded for livestock feed project the demand for

livestock feed through 2050. First, the “Meat Calories Delivered by Region” graph projections were

used to calculate the percent change in meat calorie demands through each decade from 2010-2050

(Kruse 2010). The conversions from cereals to animal feed calories were found in the United Nations

Environment Program publication on world food supply. From the source, “If we assume that 3 kg of

cereals are used per kilogram animal product and each kilogram of animal product contains half the

calories as in one kg cereals (roughly 1,500 kcal per kg meat), this means that each kilogram of cereals

used for feed will give 500 kcal for human consumption” (Rapid Response Assessments 2012). Using

these figures, the consumption in meat calories through 2050 across each world region could be

converted into demand for animal feed in cereals.

The 2050 projection for feed demand is 39% of world cereal demand while the initial demand

was 33% of the world total. Africa, China and Brazil are predicted to have the greatest increases in

livestock feed demand with 2.6, 1.29, and 1.23 factors of increase over the four decades. North

America and Western Europe remained relatively constant with only 1.06 and 1.11 factors of increase.

These changes are intuitive: developed countries have fairly complex livestock feed structures already

in place, while developing countries are still forming their agriculture programs and have more room

for expansion with increases in income and technology. Likewise, converging economies like China

and Brazil are seeing an increasing demand for meat, so their feed demands must match their

population’s preferences.

Biofuels

Biofuels are a hotly debated topic and contribute to the quantity demanded of cereals (Naylor

2012). Demand for energy has been steadily increasing and is expected to increase more as incomes

rise and the world becomes more urbanized (Demirbas 2008). It is hard to say exactly how this will

translate to biofuels demand, but biofuels have become more attractive as climate change is a more

well-known threat and our supplies of fossil fuels are depleting. The environmental merits of biofuels

are the reason that their demand versus other fuel demands will increase in the next decade (Demirbas

2008). Projections of future biofuels demand are based mainly on government mandates. The quantity

of biofuels initially demanded in 2010 was found by many methods, as the exact numbers are elusive in

the academic discussion thus far. FAO recognizes that there is a data gap in publicized information

regarding what share of domestic or regional consumption is due to fuel needs. Local stakeholders,

mostly governments, are cited as the best resource for extracting that information (FAO Impacts of

Bioenergy on Food Security 2012).

Brazil, the US, and Europe are seen as the most important players in biofuels. However, only

the US, Canada, and Europe produce biofuels from wheat and corn, and no regions produce a rice

biofuel. The Renewable Fuels association publishes numbers for ethanol production in regions of the

world in 2010. North America, Brazil, and Europe have productions on the order of billions of gallons,

whereas the rest of the world regions, including China, have productions on the order of only millions

(Lichts 2011). Therefore, the assumption is that production elsewhere besides North America and

Europe is small enough to be negligible, and quantities demanded for those regions are all zero. If Latin

America and the Caribbean produce ethanol, it is not corn based but rather from sugar cane or

soybeans. Even Brazil is using sugarcane for almost all of its biofuel production, and the demand for

the crops corn, rice, and wheat for fuel is negligible. Developing countries may have biofuel industries,

but they are not likely to use corn, rice, or wheat food products for their biofuel production.

Corn is the cereal most commonly used to produce biofuel, whereas wheat plays a minute role, and rice

plays no role, in the production of biofuels. Corn demand for biofuels in each region was determined.

The United States demand figures for 2010 came from the Department of Energy. The reason the

United States started producing ethanol in the first place was because of a large corn surplus. Corn

ethanol is incredibly impractical in other countries, especially in countries that import corn, because all

the corn they have goes towards food, and sometimes feed. The United States will experience high

growth of corn ethanol in this decade, the highest growth predicted. Canada has wheat ethanol plants

and the production of these plants in 2009 was used as a good approximation of the total wheat demand

of North America for biofuels in 2010 (University of Sasketchwan 2010). Growth in wheat is

substantial, but much more modest than corn's growth, and this is assumed through government

mandates (Biofuels Digest 2011). Exact growth is impossible to predict, but growth projections reflect

reasonable ranges. Numbers for Europe's biofuels production came from a matrix of each countries

production in the year 2006, the most recent data available (Ernsting 2008). The numbers were

converted from liters produced of fuel to metric tons of crop input. Growth in wheat and corn in

Eastern and Western Europe is expected to be modest, as productions are fairly small and biofuels from

grains are not very important to the European agenda.

There is a difference between grain ethanol, which uses crops that could be food, and cellulosic

ethanol, which uses waste biomass, and this is an important distinction to make. Wheat straw wastes,

which do not include parts of wheat that are used for food or livestock feed, are seen as a good energy

source, but wheat itself is not used as much as corn is for biofuel production (Demirbas 2008). Rice is

not used for biofuels, but rice straw is sometimes used, and has potential in Asia and other areas.

However, rice straw is not used for food or feed and is therefore excluded from the calculations. Also,

there is an important distinction to make between first and second generation biofuels. The crop

biofuels discussed here are an example of the first generation, which includes biodiesel. Biodiesel is a

biofuel, but is made from vegetable oils or animals fats, so it does not interact with the crops studied

here. The second generation of biofuels is made from wastes and lignocelullosic biomass. There is even

a third generation of biofuels that is from algae and is currently working towards becoming

commercially viable (Naylor 2012).

Predicted growth of corn and wheat as biofuels inputs, 2010-2050.

Biofuels will continue to see growth in this decade. However, this growth will plateau as

second generation biofuels, as well as renewable energies, are researched more and seen as a better

investment. The FAO agrees that technological advancement in producing biofuels from cellulosic

biomass will contribute to less pressure on food crops to produce biofuels (FAO World Agriculture).

First generation biofuels hurt biodiversity and food security and only result in a small increase in

developing countries' agricultural value (OFID 2009). First generation biofuels are not viable in the

short run, as limited land resources must go to rising food demand. The transition to second generation

biofuels will occur, and the conversion technologies must be developed to allow for this transition

(OFID 2009). The International Energy Agency predicts first generation biofuels to have a smaller

share of total biofuel production as the next few decades pass (IEA 2008). We remain dependent on

better second generations technologies being created in the next 10-20 years. Currently, second

generation biofuels make up only .1% of ethanol production (IEA 2008). This supports the projection

that corn and wheat biofuels will continue to see growth for the next twenty years, to 2030, as the other

biofuels technologies need time improve and eventually replace food-based biofuels. In addition, the

growth in the price of oil over the next few decades will make alternative fuels more attractive. Oil

prices are very hard to predict, but it is likely that scarcity will increase as we used up for oil, and

higher prices will reflect that scarcity. We will start out increasing our biofuels production, but over

time we will see renewables like wind and solar power as a better option. Or we could switch to

sugarcane ethanol, as it produces more on less land than corn does, and uses up less fossil fuels in its

growth (The Economist 2012). With United States corn ethanol, the largest factor in biofuels of the

crops studied, growth will decline as federal regulations get more stringent and biofuels are required to

come from sources other than corn as per the Energy Independence Act of 2007 (The Economist 2012).

Halving the growth rate for the period 2020 to 2030 is a reasonable assumption. Steady and slow

growth is expected until 2030 for biofuels (International Energy Agency 2008). Demand for corn, rice,

and wheat for biofuel production will plateau from 2030 to 2040, as we phase even more away from

grain-based biofuels, implying a growth rate of zero for that period. Finally, 2040 to 2050 will see

negative growth as we phase completely out of corn and wheat biofuels and demand goes to zero.

These growth rates are all assumptions, but based on forseeable changes in accordance with current

trends and biofuels research.

Price Effect

The price effect was found with own-price elasticities of demand and the percent changes in

price that the application Solver finds. Own price elasticity is a measure of the percentage change in

quantity demanded caused by the percentage change in price. Since the demand function is an inverse

relationship between price and quantity, the coefficient for own price elasticities of demand are always

negative. The model assumes Timmer's Law that poor households are more sensitive to price changes

than richer households, as they spend a huge portion of their budget on food and have less food

substitutes available (Timmer et al. 1983). Therefore, countries with lower incomes are more sensitive

to price changes and have higher demand elasticities since they are much more likely to adjust the

quantity of food they consume as price changes. Conversely, countries with higher incomes will have

more inelastic demand, which results in lower elasticities. Since own price elasticity of demand is a

reflection of regional income, calculations for demand elasticities through 2050 were determined using

the aforementioned logic. Elasticities were compared with data provided by the USDA in order to

determine if estimated elasticities were within the margin of error (USDA 2005).

Cross Price Elasticities between Corn, Rice, and Wheat

The cross-price elasticity of demand measures the percent change in quantity demanded of one

good due to a price change of another good. If two goods are substitutes, consumers will purchase more

of one good when the price of its substitute increases. Conversely, if two goods are complements, we

should see a price increase in one good cause the demand for both goods to fall (Naylor 2012). It is

assumed that the sum of cross price elasticities of demand is lower than its own-price elasticity so as to

not overwhelm the driving elasticity change. If the own price elasticity of a crop changes over time, its

cross price elasticity is altered by a comparable factor. Since corn, rice, and wheat are staples, none of

them are food complements. Furthermore, corn and wheat are substitutes in feed. Cross price

elasticities are numerically derived and represented as the difference between the absolute value of the

own price elasticity and the effect of the price on the crop. The effect of the price on corn B is

determined to be the product of three different variables: the percent of crop A demanded, the total

demand for food or fuel, and the own price elasticity of the crop affected by quantity, crop A.

World Supplies of Corn, Rice, and Wheat to 2050

Supply is represented in the model as the effects of technology, resource availability, and

climate change on agricultural yields and changes in available arable land. Initial supplies were found

for each region as well as China and Brazil and each crop by quantifying the “Production Quantity”

during 2010 on FAOSTAT. Projections are made as far as how supplies of corn, wheat, and rice will

change each decade to 2050. The price effect is important to the quantity supplied, and this price effect

includes elasticities of supply.

Supply Changes

Changes in supply are related to changes in yield and available arable land. Climate change,

technological adaptations, and access to input resources such as water are the most important factors

that can alter yield. Climate change and land use change are predominantly responsible for changes to

arable land area.

A study by Parry et al. (2004) provides projections in

yield changes due to the effects of climate change. It provides projections for 2020, 2050, and 2080

under differing emissions scenarios, and also conditions the projections on whether or not carbon

dioxide is expected to act as a fertilizer and increase productivity. The worst-case emissions scenario,

HadCM3 SRES A1F1, was chosen to be represented in the analysis, under the condition that carbon

dioxide acts as a fertilizer and can therefore improve agricultural production in some areas (Parry et al.

2004). The "worst-case" emissions scenario was chosen because of the conservative nature of these

scenarios. The carbon dioxide fertilization scenario was a cautious decision in the supply analysis.

Although studies show that carbon dioxide's effects as a fertilizer are limited by plants' genetic growth

potential as well as availability of water and other resources, the faster growth afforded by carbon

dioxide fertilization could benefit agriculture by decreasing growing time and allowing for more crop

cycles per year. Parry et al.'s study also accounts for each country’s ability to innovate in order to avoid

the difficulties of a changing climate. In doing so, technological innovation as a supply factor is

represented in the analysis. The relevant figure from the study was used to calculate a weighted mean

for “percent change in yield” for each continent and the outliers. Grain production from 2010 by

country determined each country’s “weight” in the weighted mean (FAOSTAT 2012).

This study uses

1990 as its baseline for its

projected changes, as there

were no newer studies that

were as strong. However,

because most of the

impacts of climate change are at their beginning stages, there is not such a strong difference in yield

owing to climate change between 1990 and 2010. Thus, the 1990 baseline is still valid. Further, this

method does not allow for the differentiation between effects on the different crops corn, rice, and

wheat. This is not likely to be a problem because, as the figure above demonstrates, the responses of

maize, wheat, and rice to a changing climate will be similar (Parry et al. 2004).

Changes in arable land are a sum of two effects: climate and land use change. Climate allows

some regions to become more amenable to production while removing other regions through

desertification or sea level rise. Land use change, in this case, refers to land that is put in or taken out

of agricultural production. Because increasing demand leaves few incentives to take land out of

production, most regions will experience more land put into production. This is especially true of South

America, where deforestation of the Amazon allows more cropland to be created. A study by Haberl et

al. (2011) determines changes to arable land both according to climate as well as land use change.

Because Haberl et al. use different regions of analysis, grain production by country were used to

calculate a weighted mean for our study regions. Our findings are summarized in the graph below.

Arable land predictions are hard to pinpoint. The level of variability between different climate

models is substantial. Therefore, it is difficult to determine which combination of sea level rise,

desertification, and changes in temperature and precipitation will most affect the amount of arable land.

Further, Haberl et al.'s study does not account for possible conservationist measures, such as Amazon

protection.

Price Effect

Supply elasticities for corn, rice, and wheat were needed to determine the effect price has on the

quantity produced as the effect is dependent on the elasticities. Available data for supply elasticites in

the academic discussion as of now was incredibly sparse, and many assumptions were made. Supply

elasticities were estimated using economic reasoning and some available data. Clues were found to

point towards a range of reasonable numbers to represent supply elasticities. For example, corn was

said to have a “high elasticity of supply” (NewsBank 2009). Analysis of elasticities by Nerlove (1956)

provided a range to fall within, and cotton's supply elasticity of .3 helped determine exact elasticities.

Supply elasticities are always positive, because as the price of a good increases, producers want to

supply more of it (Naylor 2012). How much producers are physically able to change their production in

response to price change was the main factor in determining elasticities. For a crop like rice, is is very

hard to switch production to growing other crops, so production is more inelastic, resulting in a lower

elasticity. Rice is grown in paddies and sometimes with terracing in a specific climate, and other crops

are hard to grow on a rice plot. However, it is easier to switch to growing another crops from wheat or

corn. Wheat and corn have the same supply elasticity because they can generally be grown on the same

parcel of land. The developed world exhibits supply elasticities of .35 for corn and wheat and .25 for

rice. Developing world farming changes these numbers so that their elasticities are .05 less because it is

harder to change inputs. Developing nations have a more inelastic supply of corn, rice, and wheat, as

they are more restricted in their resources than developed nations. There are less resources such as

fertilizers, restricted market access, and an information gap that results in price signals being missed by

farmers in developing nations. Therefore, these farmers can't change their quantity much to account for

a price change, resulting in their supply being more inelastic.

Cross Price Elasticity of Supply

There is very limited data on cross-price elasticities of supply. Since this is one of the last data

sets collected in the project, trends for different regions and crops allowed for some liberties to be taken

on these numbers. Cross price data was found for corn on wheat (-.05) and corn on rice (-0.0076) in

North America (Carey 1992, Rajagopal et al. 2009). Using this as a base comparison, estimate for

cross price data for all other regions could be made. Corn and wheat are more interchangeable than

corn and rice or wheat and rice, so their cross-prices were greater than those for rice. Likewise, North

America and Western Europe are typically more elastic than other regions, while Africa has the most

inelastic numbers (-0.085 for corn on wheat, -0.0088 for corn on rice). However, since the cross prices

are so small, they do not affect the model with huge significance. The significance of these numbers is

the idea that most are substitutes for each other because they display negative cross-price elasticities.

Corn and wheat are much more reasonable substitutes than corn and rice or wheat and rice, so they are

more elastic.

Discussion

Exclusion of Soy

Soy was not included in the analysis. The main reason is that soy is not a cereal crop, it is a

legume; it is an outlier if it is grouped with corn, rice, and wheat. Soy is a staple good, but it is only the

sixth-most produced staple good, behind cassava, potatoes, and the cereals in this analysis. Soy

production in 2010 is around 250 million metrics tons globally, whereas production for wheat, corn,

and rice are from 650 to 850 million metric tons (FAOSTAT 2012). The scales do not compare very

well; thus soy was not seen as an important enough factor in world food production to necessitate

including it. Also, growth projections at around 2 percent are fairly tame, suggesting that soy is going

to continue to have lower production numbers than corn, rice, and wheat up to 2050.

It is important to note how soybeans would affect our model had they been included. They are

used as food, feed, and fuel. As food, they are a source of protein for vegetarians, and the increase in

meat consumption predicted by increasing incomes will result in a slower demand growth rate for soy

as a food product. Soybean meal is fed to livestock, so demand for it will increase as meat

consumptions rises. Regarding fuel, biodiesel is commonly produced from soybeans in the United

States, as we have high soybean production here. Yet quantities of soy biodiesel and corn ethanol are

not comparable, as ethanol production is more than thirty times the volume of soy biodiesel production.

Soybean production is also important to Brazil (South America Biodiesel Program). Biodiesel is a first-

generation fuel just like ethanol, so growth will plateau and go to zero as we make the switch to second

generation biofuels and renewable energies. Soybeans would interact with corn, rice, and wheat by

being a substitute for corn and wheat in livestock feed.

Conclusions

Prices of corn, rice, and wheat are determined by the model to the year 2050. As a whole, price

increases for all three cereals from 2010 to 2050, but there are interesting dynamics in these decades.

Because biofuels growth plateaus and then total demand goes to zero, there is less total demand for

corn, which is clear in the model, as prices decrease from 2030-2040 and decrease further from 2040-

2050, from a high of $280.31 in 2030. Wheat and rice see steady increases in price from 2010 to 2050.

Inflation is not accounted for in the analysis as it is impossible to predict, so there is little we can say

about the future affordability of these commodities.

The trade matrix above quantifies the corn, wheat, and rice traded in millions of metric tons

between regions in each year, according to the model. Negative numbers imply net imports; positive

numbers imply net exports. North America and Western Europe are expected to become importers by

2050. Other notable trends are that Brazil becomes a net exporter by 2050, and that China imports more

and more each year. Africa becomes a net exporter of cereals, which could have implications on

incomes and development there. South America experiences a similar transformation at a much smaller

scale.

Although the model makes several assumptions in order to make predictions about the future,

the assumptions are sound and the predictions can be used normatively to inform international and

domestic policies. The scale of the analysis is huge; every complexity of corn, rice, and wheat cannot

be explained by the model, but it does a good job of basic future predictions. The numbers the model

emits are not exact, yet they do their purpose of explaining trends in the world food economy.

Understanding world cereal trends, on the global and regional level, is imperative for good

policymaking, not only to reduce food insecurity, but to deal with things like biofuels, climate change,

and technological innovation in agriculture. The implications of our model are that we can see, in 2050,

how we can meet a worldwide nutritional demand and ensure food security. The great challenges the

world food economy, and thus the world, will face, caused by factors like the use of food products for

energy and for growing meat consumption, can be helped by good policy and an awareness of the

issues. The model prediction the world is entirely a positive analysis; it is up to us to consider the world

food economy in a normative way, and to act.

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