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OCR for page 5921
Proc. Natl. Acad. Sci. USA
Vol. 96, pp. 5921-5928, May 1999
Colloquium Paper
This paper was presented at the National Academy of Sciences Colloquium "Plants and Population: Is There Time?"
held December 5-6, 1998, at the Arnold and Mabel Beckman Center in Irvine, CA.
Global and local implications of biotechnology and climate change
for future food supplies
ROBERT E. EVENSON*
Department of Economics, Yale University, New Haven, CT 06520
ABSTRACT The development of improved technology for
agricultural production and its diffusion to farmers is a
process requiring investment and time. A large number of
studies of this process have been undertaken. The findings of
these studies have been incorporated into a quantitative policy
model projecting supplies of commodities (in terms of area
and crop yields), equilibrium prices, and international trade
volumes to the year 2020. These projections show that a
"global food crisis," as would be manifested in high commod-
ity prices, is unlikely to occur. The same projections show,
however, that in many countries, "local food crisis," as
manifested in low agricultural incomes and associated low
food consumption in the presence of low food prices, will
occur. Simulations show that delays in the diffusion of modern
biotechnology research capabilities to developing countries
will exacerbate local food crises. Similarly, global climate
change will also exacerbate these crises, accentuating the
importance of bringing strengthened research capabilities to
developing countries.
I. Introduction
Projections of food supply have typically been based on past
experience. Economists usually emphasize the continuity and
"momentum" of the development and diffusion of improved
technology in making these projections. Biological scientists,
on the other hand, usually place more stress on the inherent
"limits" to supply growth as reflected in "carrying capacity"
models. In this paper, food supply projections are based on
projections of investments in productivity improvement activ-
ities and on evidence regarding the effectiveness of these
activities. These projections are incorporated into a global
agricultural general equilibrium model tthe International
Food Policy Research Institute (IFPRI)-International Model
for Policy Analysis of Agricultural Commodities (IMPACT)
model), which does consider limiting factors to supply growth.
These projections are first developed for a "base case," where
they may be compared with other projections. The development
of new techniques for developing biological inventions (biotech-
nology) must be incorporated into the base case, because these
techniques are already producing significant inventions. Experi-
ence to date with biotechnology, however, is still too limited to be
used for projections. A recently completed study of rice biotech-
nology elicited subjective probability estimates of research po-
tential and of time to achievement of research potential for a large
number of research problem areas for rice. This study provides a
basis for both the base case projections and for an important
policy simulation dealing with the diffusion of biotechnology to
developing countries.
This paper also introduces a policy simulation for the effects
of global climate change based on three recent studies of
PNAS is available online at www.pnas.org.
climate change impacts on agriculture in the United States,
India, and Brazil. Although these three studies do not provide
comprehensive coverage of global food production, the three
studies do fit into a common global pattern showing that cooler
regions of the world will benefit from global warming, whereas
warmer regions will suffer losses.
Not surprisingly, the base case and policy simulations show
that "local" effects can differ drastically from "global" effects.
The base case computations yield relatively favorable global
effects. A "global food crisis" appears not to be in the offing
in the next 25 years or so. But that does not mean that "local
food crisis" will not continue to exist in 2020. The base case
projections show general improvement in local indexes of
malnutrition, but even under the most favorable simulations,
malnutrition will continue to be a real problem for much of the
developing world. The policy simulations show that delays in
the diffusion of biotechnology to developing countries is likely
to exacerbate local effects. Global warming scenarios also
show a worsening of many local effects.
The IFPRI-IMPACT model computes price projections that
are essentially global. It also computes production, cropped
area, and trade projections that are local (i.e., national). The
cropped area effects have important implications for biodi-
versity because, as cropped area expands, biodiversity habitats
are altered. Finally, the model also computes measures of child
malnutrition based on food consumption projections.
Part II of the paper provides a brief overview of the
IFPRI-IMPACT model. Part III develops the nonprice supply
components of the model. Part IV develops policy scenarios to
address the questions noted above. In part V, scenario calcu-
lations are reported. Part VI concludes.
II. The IFPRI-IMPACT Model
In this section, the IMPACT model is briefly described. The
Appendix provides more detail.
The IMPACT model developed at IFPRI is a computable
equilibrium market model for agricultural products (crops and
livestock; 17 commodities). It is based on 35 country-region
submodelsT. Each submodel consists of equations depicting
the supply for each commodity as a function of price and
nonprice terms. The demand for each commodity is also
Abbreviations: IFPRI, International Food Policy Research Institute;
IMPACT, International Model for Policy Analysis of Agricultural
Commodities; R&D, research and development; ITI, industrial tech-
nology infrastructure; NARs, National Agricultural Research systems;
IARCs, International Agricultural Research Centers.
*To whom reprint requests should be addressed at: 27 Hillhouse, Yale
University, New Haven, CT 06520. e-mail: robert.evenson@yale.edu.
tAgcaoili, M. C., Oga, K. & Rosegrant, M. W. (~1993) Structure and
Operation of the International Food Policy and Trade Simulation
(IFPTSIM) Model. Paper presented at the Second Workshop of the
Research Project on Projections and Policy Implications of Medium-
and Long-Term Rice Supply and Demand [International Rice Re-
search Institute (IRRI), Los Banos, Philippines].
5921
OCR for page 5922
5922 Colloquium Paper: Evenson
described as a function of price, income, population, and
nonprice terms. The submodels are linked through trade,
which may be free or restricted by tariffs. The model solves for
global and submodel equilibrium prices for each commodity
where all markets are cleared. Linkages with other sectors are
built into the model, but these are not sufficiently complex to
describe the model as a global general equilibrium model. It is
an agricultural general equilibrium model.
The endogenous variables determined by the model equilib-
rium are:
1. Commodity prices and quantities by country-region.
2. Trade quantities (imports and exports) by country-
region.
3. Cropped area by commodity by country-region.
4. Commodities consumed; calories per capita; percent
children malnourished by country-region.
The model also generates the per capita calorie availability
from food consumed by using standard kilocalorie conversion
values for different foods. Estimates of the relationship be-
tween the percentage of children ages 0-6 years malnourished
as a function of calorie availability are made from pooled
cross-section, time and series data for 61 developing countries
for 1980, 1985, and 1990.t
The exogenous variables in the model are:
1. Population by year, by country-region.
2. Nonagricultural income by year, by country-region (ag-
ricultural incomes are endogenous).
3. Total land area by country-region.
4. Nonprice (productivity) supply growth including contri-
butions from:
a. Farmers' schooling
b. Agricultural extension
c. Public-sector agricultural research
d. Private-sector agricultural research.
Population and income projections are taken from World
Population Prospects (1~. Irrigation projections are based on
IFPRI studies. Nonagricultural income projections are based
on World Bank and Asian Development Bank sources.
Price and income elasticities of demand are taken from
national sources where feasible. Harvested area is a function
of price (price response parameters are taken from national
studies),! total land area, and nonprice factors. Similarly,
yields are a function of both price and nonprice factors. Supply
is area times yield. Supply elasticities are generally low. A
dynamic adjustment process is allowed, consistent with esti-
mates of supply responses in China, India, Indonesia, and other
countries.
The structure of the model allows for baseline projections to
be specified for the exogenous variables. This produces base-
line projections for the endogenous variables. Alternative
policy scenarios can be defined and projections obtained.
These can be compared with the baseline projections.
III. Specifying the Nonprice Supply (Area and Yield) Terms
The nonprice supply component for both area and yield is
specified as an annual rate of change. This trend can be
interpreted as a total factor productivity growth trend. Pro-
ductivity growth is traceable to several sources. These include
improvements in the human capital (schooling) of farmers,
agricultural extension programs, and agricultural research
programs (public and private), all of which produce positive
productivity gains. Resource degradation produces negative
productivity gains.
tWorld Nutrition Database ACC-SCN, 1992.
Proc. Natl. Acad. Sci. USA 96 (1999)
The IFPRI-IMPACT model is not based on trend estimates
or judgements except to achieve continuity with the recent
past. It relies instead on a combination of a detailed ex ante
research contribution study for rice production and an exten-
sive body of ex post productivity decomposition studies. Non-
price supply trends are developed for both yield and area for
5-yr periods: 1995-2000, 2000-2005, 2005-2010, 2010-2015,
and 2015-2020.
The starting point for developing these nonprice trends was
to examine past nonprice trends in yield and area growth. This
was done by first removing price effects from yield and area
data and estimating nonprice trends for each commodity and
country for the 1962-1982 and 1983-1992 periods. In most
cases, these trends were higher in the 1962-1982 "Green
Revolution" period. Part of this slowdown is because of a
relative exhaustion of "Green Revolution" gains, a fact that is
taken into account in the component analysis. Thus, the
projected future trends explicitly account for the slowdown in
yield growth for most commodities in most countries (as well
as accounting for strong performers, such as rice yield growth
in India and pork meat and poultry production in much of
Asia). For the few countries where base or end-of-year values
resulted in trend estimates that were clearly outliers from
trends over the 1983-1992 period, these estimates were mod-
ified to be consistent with nonoutlier trends. Phase-in rules
were used to link these past trends with projected trends.
The first step in making nonprice yield projections was to
break the projection into its components and subcomponents.
The following component structure, based on a study of Indian
crop productivity,§$ was used:
1. Public [International Agricultural Research Centers
(IARC)-National Agricultural Research systems
(NARs)] research
a. Management research
b. Conventional plant breeding
c. Widecrossing-hybridization breeding
d. Biotechnology (transgenic) breeding
2. Private-sector agriculturally related research and devel-
opment (R&D)
3. Agricultural extension farmers' schooling
4. Markets
5. Infrastructure
6. Irrigation (interacting with technology)
The yield growth contribution of modern inputs such as
fertilizers is accounted for in price effects in the yield response
function and as a complementary input with irrigation and
with the modern varieties generated by research. The public
sector research subcomponents are based on a rice research
priority setting study (2).
A. The Public Research Component of Rice. A recently
completed study, Rice Research in Asia: Progress and Priorities
(2),ll provided the basis for subcomponent projections for four
broad rice-producing zones (South Asia, Southeast Asia, East
Asia, and the rest of the world). The priorities study contrib-
uted estimates of crop losses from insect pests, plant diseases,
and abiotic stresses for Eastern and Southern India, Indonesia,
Bangladesh, Thailand, and China. These crop-loss estimates
were treated as estimates of potential gains for specific types
§This accounting framework allows for productivity contributions
from many sources. A number of studies have addressed the contri-
bution of each source.
fRosegrant, M. W. & Evenson, R. E. (1993) Determinants of Produc-
t~vity Growth in Asian agriculture: Past and Future. Paper presented at
the 1993 American Agricultural Economics Association International
Pre-Conference on "Post-Green Revolution Agricultural Develop-
ment Strategies in the Third World: What Next," Orlando, Florida.
Evenson, R. E. (1998) "Biotechnology Research Priorities for Rice,"
International Rice Research Institute (mimeograph).
OCR for page 5923
Colloquium Paper: Evenson
of research. Insect reduction potentials were based on losses
from 13 major insect pests. Disease reduction potentials were
based on losses for 14 major rice diseases. Abiotic stress
potentials were based on losses (adjusted for the proportion
that can be affected by research) for nine abiotic stresses (heat,
cold, drought, flooding, etc.~. Management potentials were
based on data for management-related pest problems (weeds,
rodents, birds). Biological efficiency potential estimates were
based on scientists' estimates of gains from improved plant
design, improved photosynthesis efficiency, shorter growth
duration, and improved grain quality.
A scientist's rating exercise was carried out with 18 senior
rice scientists in 1995 and followed up in 1997 with a second
rating study with an additional 60 scientists. For each of the
research problem areas for which respondents had scientific
qualifications, four ratings were elicited (ratings were on a
scale of 1 to 5 but were calibrated to percentage achievements
of economic potential) for alternative research techniques
(managerial research, conventional breeding, widecrossing
and hybridization, and biotechnology/transgenic rice and
marker-aided selection). These ratings were:
1. A rating of achievement to date (RA);
2. A rating of potential achievement (RP);
3. An estimate of the number of years required to achieve
25% of the difference between achievement to date and
potential (Yowl;
4. An estimate of the number of years required to achieve
75% of the difference between achievement to date and
potential (Y754.
In developing these estimates, scientists were asked to
presume that both IARC and NARs programs would continue
to be supported at the levels of the past decade in future
periods.
The specification of two ratings, one for achievement to date
and one for potential achievement, forced respondents to focus
on "remaining potential." Ratings of potential minus achieve-
ments to date were summarized and converted to percent of
accomplishments.
Estimates were obtained for each research problem area by
research technique. For purposes of developing projections,
the ratings were scaled into period estimates based on the Y75
estimates. Conventional breeding programs were considered
the core genetic (improvement) programs. Widecrossing and
marker-aided breeding programs were not expected to con-
tribute to productivity gains until their actual potential
achieved exceeded that of conventional breeding. Similarly,
transgenic breeding was not projected to contribute to pro-
ductivity until it exceeded the potential of widecrossing and
marker-aided breeding.
By converting the ratings to actual percentages and multi-
plying them times the units affected (crop losses or yield
potential), rice nonprice yield projections were created for
each region for the public NARs and IARC components. Note
that one can distinguish between biotechnology and conven-
tional breeding components, thus enabling the biotechnology
slowdown policy scenarios noted below.
B. Extension Schooling Components. Several studies of
agricultural extension and schooling have been undertaken (3~.
It is difficult to generalize as to the growth contribution of
extension and schooling, however, because to produce growth,
investments must be made and investments must be produc-
tive. The most comprehensive study of growth experience to
date is the IFPRI study of India (4~. That study indicated that
the extension contribution was roughly two-thirds of the public
research contribution.
The management research contribution is also related to the
extension contribution. The rule for computing the extension
contribution is that extension plus management research is
Proc. Natl. Acad. Sci. USA 96 (1999J 5923
two-thirds of conventional breeding, widecrossing, and bio-
technology research.
C. The Private-Sector R&D Contribution. The private-
sector R&D contribution depends on the stage of industrial
technology infrastructure (ITI). The stages range from little or
no ITI (stage 1A) to the Newly Industrialized Country type ITI
(stage 2C) and developed country ITI (stage D). A study by
Evenson and Westphal (5) defines stages for different coun-
tries (see Appendix). Projections of these stages are based on
the expectation of the continuation of industrial reforms
underway for the past decade. The Appendix summarizes
projections of ITI by country, by period.
The India growth accounting study (4) indicated approxi-
mately a 0.1% growth contribution for India from private-
sector R&D. India is a 2A country moving toward 2B status.
U.S. evidence suggests a 0.2% contribution (6) for developed
countries.
Based on these studies, the following private R&D growth
components by ITI class were assigned:
1B = 0; 1C = .05; 2A = .1; 2B = .15; 2C = .2; D = .2.
D. Markets and Infrastructure Contributions. As with
private-sector R&D, these contributions are tied to ITI class.
Based on the India study (4), the following markets-
infrastructure growth components by ITI class were assigned:
1B = .1; 1C = .1; 2A = .15; 2B = .15; 2C = .2; D = .1.
E. Extension to Other Commodities. Scientist ratings are not
available for other crops to allow estimates similar to those for
rice. However, there is a larger body of evidence from returns
to research studies. From this evidence, it appears that re-
search programs have been effective in all cereal grains.
Public-sector research has also been effective in oilseed crops
(soybeans). It appears that research progress has been slower
in rootcrops than for cereals (the Appendix provides further
detail on returns to research). The management and conven-
tional breeding components for other commodities have been
scaled to the rice estimates by using relative rates of return.
F. Nonprice Area Projections. Procedures for estimating
nonprice area growth for 1990-1995 followed the same pro-
cedures described above for yield. In later time periods,
nonprice area components depend on the availability of cul-
tivable land, irrigation, and infrastructure investments and
productivity gains as well as prices. Some of the effects operate
through prices, and to the extent that they do, the price
response parameters within IMPACT, the IFPRI multicom-
modity model, will determine changes in area. But to the
extent that investments expand the effective stock of land, they
are nonprice components. In practice, aggregate land expan-
sion has slowed to very low rates in recent years as the stock
of cultivable land has been exhausted. The chief component of
aggregate area expansion has been investment in irrigation,
which has also slowed dramatically in recent years (7~. Pro-
jections of area expansion in subsequent time periods thus take
estimated nonprice area growth trends estimated for 1990-
1995 and in most cases dampen these to reflect the lagged
effects of declining investments in irrigation. Accordingly,
except for a few crops in a few regions, low rates of nonprice
area expansion are projected. Nonprice growth in livestock
numbers is projected based on recent historical growth and
rates of change in this growth rate from previous periods.
Data on rates of return to agricultural research were used to
scale the nonprice yield and area parameters for other crops to
the rice cases (see Appendix).
IV. Policy Scenarios
The baseline case in the IFPRI-IMPACT model is based on
continued support of research and extension programs at
present levels. Biotechnology contributions are expected to
come on line first for rice then for other commodities, accord
OCR for page 5924
5924 Colloquium Paper: Evenson
ing to scientists. Industrialization and trade liberalization are
expected to continue.
The following alternative policy scenarios are considered:
A. Demographic gift
B. Delayed industrialization
C. Reduced IARC-NARs support
D. A 10-year biotechnology delay
E. Climate change
A. Demographic Gift. The base case population scenario is
based on the U.N. "medium" population projection. The
demographic gift projection reduces this to the U.N. "low"
projection. This is consistent with the demographic gift (8)
associated with reduced population growth. The labor force
growth contributing to production will not be reduced until
after the time lag between birth and labor force entry occurs.
Thus, in the period from 1990 (1998) to 2020, labor force will
grow at the medium projection, but the number of prolabor
force consumers will decline. This is a one-time "gift" in the
sense that the ratio of laborers to population will rise only for
a short period before it returns to normal as labor force growth
slows.
B. Delayed Industrialization. Another realistic scenario is
that of delayed industrialization reforms. The past 10-15 years
have seen considerable progress by a substantial number of
developing economies in improving trade and industrialization
policy. This has enabled many countries to move forward in the
ITI classification. Indonesia, for example, has moved from a 1C
technological infrastructure level to 2A and now to 2B. Con-
tinued reform to 2C status is postulated in the base case.
Similarly, Thailand has moved from 2A to 2C over the past two
decades, while the Philippines has remained at the 2A level.
Latin American countries have generally made improvements
as well.
But this move toward industrialization and the rapid growth
associated with it can be reversed and delayed. This may come
about because of increased local conflicts (by and large,
countries with significant civil strife do not make economic
progress). The recent crisis in Asia demonstrates this effec-
tively. This scenario is one where the ITI class standings for
1995-2000 are maintained through subsequent periods. A
more severe industrialization delay would call for a reversal of
some of the recently attained standings (for example, political
turmoil in Indonesia could cause a reversion to 2A or even IC
status).
C. Reduced IARC-NARs Support. A substantial shift in
international support in terms of loans or grants for NARs and
IARCs over recent decades has taken place. In the l950s,
1960s, and 1970s, bilateral aid agencies supported NARs
building programs and extension programs. U.N. agencies did
as well. Today these agencies support little research. For
practical purposes, support for NARs and most extension
programs is provided by the World Bank (and other banks and
bilateral programs to a lesser extent).
It is possible that World Bank support will be reduced in the
future. The Inter-American Development Bank (IDB) is pro-
posing a regional research fund for Latin America that effec-
tively ends IDB support for IARCs and NARs. NARs in
advanced countries will be little affected, but most weaker
NARs will be substantially affected. Many have reduced their
nonpersonnel budgets (in the interest of saving jobs) under
budget reductions in recent years. This had debilitating effects
on the effectiveness of research.
D. Ten-Year Biotechnology Delay (Developing Countries).
The contributions of biotechnology have been built into the
nonprice terms according to the timing indicated in the
scientist survey. For rice, these contributions begin early next
century and become quite significant by 2015. For other crops,
the timing is delayed. The biotechnology delay scenario delays
Proc. Natl. Acad. Sci. USA 96 (1999'
the biotechnology contribution by 10 years for developing
countries. We are presuming that the antibiotechnology move-
ment will have little or no effect on developed countries, but
it could easily delay access to biotechnology in most developing
countries. The absence of strong intellectual property rights
will also delay access to the "genes for sale" already being
made available in developed countries. This scenario is sup-
ported by some nongovernmental organizations.**
E. Climate Change. A number of projections of climate
change have been made in recent years. Agreement on the
timing and extent of this change has not been reached. The
estimates used here are that mean temperatures will rise by
1°Celsius by the year 2020 and that rainfall will increase by
3.5%.
Three recent studies have provided estimates of the effects
of climate change on agricultural production. The first, by
Mendelsohn, Nordhaus, and Shaw (11), pioneered the use of
the "Ricardian" method for relating climate change to pro-
ductivity through land values. This study for the United States
showed that agricultural productivity in the northern counties
in the U.S. would generally increase as a result of climate
change, while the warmer southern counties would experience
losses.
A second study for Brazil (9) found similar effects in Brazil.
Municipios in the south experienced gains and those in the
warmer north and central regions experienced losses. A third
study for India (10) found similar effects.
The three studies were sufficiently consistent in terms of
fitting into a global "surface" that we believe that extrapola-
tions to other countries (based primarily on latitude) are
justified. These are the basis for the climate change scenarios
(the reader should use caution, given the limited data on which
the scenarios are based).
V. Policy Simulations
Global effects are summarized in Table 1. The base case
2020/1990 ratios for production, area, trade, and prices are
summarized by commodity. 2020/1990 price ratios are re-
ported for the four policy scenarios plus a "worst-case"
scenar~o.
The base case production scenarios show that global crop
production will increase by approximately 60~o by 2020. Area
planted to crops will expand by roughly 10%. tThis includes
multiple cropping so area in crops will expand by roughly 5~o
(mostly in Africa see below).] Most production gains will
come from yield gains. These yield gains are roughly similar to
the post-Green Revolution period gains.
Animal production will increase more than crop production
and most of this increase will be caused by increased animal
units. For beef, this indicates a significant increase in pasture
land.
World trade will increase for all commodities and this will
take the form of increased exports by developed countries and
increased imports by developing countries (see Table 2~.
Base case price projections indicate continued declines in world
prices for all commodities. These projected declines are highest
for rice and other grains. Table 1 also includes four policy
scenarios for price ratios, and these can be directly compared with
the base case price scenario. Ihe first is the "Demographic Gift"
scenario. This scenario has large price effects (note that the gift
is temporary and would hold only for this period). Because of
reduced demand (number of consumers), prices will fall further
than base case prices for all commodities.
**Altieri7 M. A. (1997) The CGIAR and Biotechnology: Can the Renewal
Keep the Prom~se of a Research Agenda for the Rural Poor? Paper
submitted for consideration by participants of consultation on biotech
nology called by Consultation Group for International Agricultural
Research (CGIAR) Chairman7 April 187 19977 Washington7 DC.
OCR for page 5925
Colloquium Paper: Evenson
Table 1. Global base case and policy scenarios by commodity
Base case 2020/1990 ratios
Proc. Natl. Acad. Sci. USA 96 (1999J 5925
Policy-price 2020/1990 ratios
Demo- Delayed Delayed
Produc- graphic industrial- IARC biotech- Global Worst
Commodity lion Area Trade Prices gift ization phaseout nology warming case
Wheat 1.58 1.06 1.62 .85 .68 .90 .96 .94 .86 1.16
Maize 1.56 1.13 1.36 .77 .62 .81 .85 .90 .78 1.03
Rice 1.66 1.07 1.70 .80 .62 .86 .95 .96 .81 1.17
Other
grains 1.48 1.09 1.60 .75 .63 .81 .85 .82 .75 1.04
Soybeans 1.77 1.14 2.20 .90 .84 .91 .91 .92 .91 .98
Roots/
tubers 3.28 1.15 1.30 .82 .66 .86 .93 .95 .84 1.09
Beef 1.53 1.35 2.87 .94 .85 1.01 1.00 .95 .95 1.31
Pork 1.83 1.53 1.64 .90 .83 1.04 1.06 .91 .91 1.38
Mutton 1.98 1.36 1.84 .96 .89 .99 1.02 .97 .98 1.13
Poultry 1.80 1.53 3.27 .90 .83 .92 .94 .91 .90 1.01
Eggs 1.92 1.06 5.81 .75 .68 .75 .75 .76 .75 .75
Milk 1.53 1.15 3.60 .93 .83 .93 .93 .93 .93 .93
Delayed industrialization, on the other hand, will mean that
prices will be higher than in the base case (by roughly 5%-6%~.
This is because of reduced private-sector R&D spillovers to
agriculture. Reduced IARC-NARs support will have a larger
Table 2. Base case all cereals
Growth rates (%) 1993-2020
Countries/regions
United States
Western Europe
Japan
Australia
Other developed
Eastern Europe
Former USSR
Latin America
Nigeria
Northern Africa
Central-West Africa
Southern Africa
Eastern Africa
Sub-Saharan Africa
West Asia-North
Africa
India
Pakistan
Bangladesh
Other South Asia
Indonesia
Thailand
Malaysia
Philippines
Vietnam
Myanmar
Other Southeast Asia
China
Other East Asia
South Asia
South Asia
(excluding India)
Area/ Produc
no.Yieldlion
0.120.961.08
0.040.420.46
-0~49-0.03-0.52
0.121.751.88
0.071.091.16
0.091.021.11
0.041.181.22
0.441.371.82
1.201.352.56
0.991.552.56
1.381.773.18
1.192.263.48
1.251.773.05
1.171.672.86
Demand Food Feed
0.810.64 0.84
0.390.10 0.53
0.29-0.05 0.62
1.010.84 1.07
1.071.16 0.99
0.24-0.19 0.43
0.49-0.06 0.73
1.631.35 1.92
2.852.92 2.11
3.143.15 2.27
3.063.08 2.65
2.872.90 2.21
2.862.90 2.28
2.963.00 2.25
Southeast Asia
East Asia
Asia
Developed
Developing
World
0.11 1.85
0.07 1.42
0.19 1.54
0.02 1.36
0.12 1.72
0.09 0.98
-0.07 1.00
-0.04 1.00
0.10 2.06
0.00 1.41
0.35 1.58
0.14 2.22
0.02 0.98
-0.47 0.84
0.08 1.43
0.11 1.50
0.08 1.30
0.00 0.98
0.05 1.16
0.06 0.94
0.29 1.20
0.20 1.06
1.96
1.49
1.73
1.39
1.84
1.07
0.93
0.95
2.17
1.41
1.93
2.37
1.00
0.36
1.51
1.97
1.53
2.92
1.65
2.73
1.44
1.39
2.22
2.26
1.43
1.39
2.14
1.32
1.57
1.76
1.61 2.42
1.38 1.61
0.98 1.34
1.22 1.51
1.00 0.57
1.49 1.71
1.27 1.27
1.94 2.12
1.48 3.04
2.92 2.96
1.65 2.41
2.74 2.61
1.18 3.34
0.45 2.77
1.96 2.48
1.88 3.00
1.41 2.29
1.37 2.62
2.14 2.07
0.58 3.22
0.67 2.49
1.72 2.99
2.42 2.88
1.34 2.94
0.59 3.13
1.14 3.09
0.19 0.71
1.43 2.63
1.21 1.40
impact on prices than delayed industrialization. Prices will be
roughly lO~o-15~o higher than in the base case. Delayed
biotechnology for developing countries also has significant
price effects. These are similar to the reduced IARC-NARs
support effects for crops but are smaller in magnitude than for
livestock products. Global climate change effects are quite
small (but see local effects, below). Price effects are only
1%-2% above the base case.
The "worst-case" calculation is the sum of the delayed
industrialization, reduced IARC-NARs, delayed biotechnol-
ogy, and climate change effects. In this worst case, prices of
most crops will rise over the 1990 levels but not sufficiently to
qualify as a "world food crisis." Global effects, however, are
really quite misleading for policy analysis as Tables 2, 3, and 4
show. In Table 2, base case growth rates for the 1993-2020
period for cereal crop area, yield, production demand, and
food and feed demand are projected by country/region. Trade
effects are the difference between demand and production.
We first note that area expansion is projected to be low in most
developing countries (negative in some). Area expansion will
be high in most Sub-Saharan African countries because these
countries have land on which to expand. This will have
biodiversity habitat effects.
Yield projections are actually higher for developing coun-
tries than for developed countries, reflecting the fact that they
have more "catch-up" potential. Production growth rates
exceed demand growth rates for most developed countries
(excluding Japan). This means that exports will grow at
substantial rates. For most developing countries, demand
growth exceeds production growth. Because of large area
expansion rates, Sub-Saharan Africa countries will not have
large import growth, however.
Table 3 reports the area, yield, and trade 2020/1990 ratios
relative to the base case for cereals by country/region for the
climate change and biotechnology delay scenarios. Here we
note that the local effects of climate change are important even
though global effects were not. In particular, climate change
has minor area effects for developed countries, but signifi-
cantly increased cereals area in a number of South-Southeast
Asian regions. Cereal yields will be higher in developed
countries (by 1.63%) and lower in developing countries (by
1.38% including China, where they will rise). This means that
climate change will produce more exports by developed coun-
tries and imports by developed countries.
The biotechnology delay local effects are roughly similar to
the climate change effects (recall that the base case effects
were also important). Delayed biotechnology diffusion will
lead to increased area cropped in all regions except the U.S.
OCR for page 5926
5926 Colloquium Paper: Evenson
Proc. Natl. Acad. Sci. USA 96 (1999J
Table 3. Cereals: Area, yield, and trade simulations by region relative to the base case
To change
climate change scenario
% change
delayed biotechnology
Area Yield Trade Area Yield Trade
United States.0000.0134.0442 (X)-.0018.0006.0532 (X)
Western Europe.0000.0147.0801 (X)-.0003.0012-.0088 (X)
Eastern Europe.0004.0005.0082 (X).0072.0097.2553 (X)
Former USSR.0004.0279.3955 (X).0098.0103.5187 (X)
Japan.0000.0275.0076 (-I).0014.0011.0070 (I)
Developed.0003.0163.0816.0059.0024.0846 (X)
Latin America.0007-.0203-.2217 (I).0131-.0258-.0939 (I)
Sub-Saharan Africa-.0004-.0558-.1112 (I).0074-.0206-.0282 (I)
West Asia-North Africa.0011-.0370-.0947 (I).0180-.0216-.0183 (I)
India-.0002- .0354-.1132 (I).0012-.0238-.0500 (I)
Pakistan.0006-.0367-.0807 (I).0027- .0221-.0202 (I)
Bangladesh.0042-.0299-.1494 (I).0159-.0193-.0406 (I)
Indonesia.0028.0063.0571 (I).0097-.0255-.0353 (I)
Thailand.0071-.0341-.1374 (X).0236-.0266-.0092 (I)
Philippines.0014-.0337-.1062 (I).0080-.0206-.0164 (I)
Vietnam.0038-.0303-.2521 (X).0147-.0191-.0122 (X)
China.0002.0154.0831 (I).0023- .0292-.3196 (I)
Developing.0007-.0138-.0816 (I).0080-.0259-.0846 (I)
and Western Europe. This will have deleterious habitat con-
sequences. Yields will be higher in developed countries and
lower in developing countries. Developed country exporters
will export more. Developing country importers (including
China) will import more.
Table 4 reports effects of an important local welfare index
in developing countries, the proportion of children 0-6 years
of age who exhibit some degree of malnourishment (see
Appendix for more details). First, note that this measure shows
great variability in 1990 by region. The base case projections
show that the percentage malnourished children will decline
from 34% in 1990 to 25% in 2020 (from 1960 to 1990 it fell
from 45% to 34%~. This is a favorable projection, although it
does vary by region (falling least in East Africa). Clearly,
however, in 2020 serious local problems will remain, and they
form the basis for "local food crises" even if a global food crisis
is unlikely to occur.
The policy scenarios show that reduced research support,
delayed industrialization, delayed biotechnology, and climate
change will delay progress in reducing malnutrition. The
"global" effects are small, but local effects for some countries,
e.g., Bangladesh and Nigeria, are significant.
VI. Policy Implications
Global effects of alternative policies are very poor guides to
policy regarding investments and regulations affecting popu-
lation and food supply. Local effects are more relevant.
The simulations reported here do show that population
policy can be very effective in increasing income and reducing
Table 4. Malnourished children simulation "percentage childlren (0-6) malnourished]
Reduced research
support Delayed
I A RC B. iotech in dustriali - Glob al
Countries/ Base case NARs delay zation warming
regions 1990 2010 2020 2020 2020 2020 2020
Latin America 20.40 16.91 14.05 14.47 14.09 14.3 14.05
Nigeria 35.4 30.79 29.52 30.21 29.86 29.89 30.90
North Africa 31.40 29.08 27.93 28.92 28.23 28.48 31.09
Central and West Africa 22.70 22.44 21.10 21.62 21.42 21.37 21.23
South Africa 24.80 22.43 21.24 21.83 21.56 21.54 21.21
East Africa 25.50 25.47 24.77 25.35 25.03 25.08 24.81
West Asia-North Africa 13.40 11.56 9.70 10.05 9.68 9.88 9.67
India 63.00 51.25 45.49 46.91 45.70 46.22 45.49
Pakistan 41.60 36.62 32.40 33.35 32.64 32.91 32.38
Bangladesh 65.80 59.20 52.85 58.12 53.86 55.55 53.14
Other South Asia 37.00 31.62 26.59 27.83 26.90 27.22 26.80
Indonesia 14.00 10.05 7.74 8.01 7.85 7.90 7.78
Thailand 13.00 7.32 5.23 5.33 5.26 5.35 5.25
Malaysia 17.60 12.41 9.88 10.05 10.00 10.06 9.91
Philippines 33.60 25.81 21.29 22.66 21.72 22.24 21.43
Other Southeast Asia 40.00 35.69 32.78 35.21 32.98 34.15 33.11
China 21.80 15.30 13.78 14.26 13.79 14.24 13.78
South Asia 58.50 47.68 41.37 43.03 41.60 42.22 41.40
Developing 34.30 28.01 25.40 26.33 25.83 26.00 25.50
OCR for page 5927
Colloquium Paper: Evenson
Appendix Table At. Industrialization ITI projections
Country 95-00 00-05 05-10 10-15 15-20
United States
EC12
Japan
Other Western Europe
Canada
Australia
New Zealand
Other Developed
Eastern Europe
Russia
Mexico
Brazil
Argentina
Other Latin America
Nigeria
Other Africa
Egypt
Other Near East
India
Pakistan
Indonesia
Thailand
Malaysia
Philippines
China and Taiwan
Singapore
D
D
D
D
D
D
D
D
2B
2B
2B
2B
2B
2A
1C
1B
2A
1C
2A
1C
2B
2C
2C
2A
2C
2C
2C
2C
2C
2C
2C
2B
1C
1B
2B
2A
2B
1C
2C
2C
2C
2B
2C
D
D
D
2C
2C
2C
2B
2A
1C
2C
2B
2C
2A
2C
2C
2C
2C
2C
D
D
2C
2C
2B
2A
2C
2B
2C
2B
2C
D
D
2C
D
2C
2C
2C
2B
D
2B
2C
2C
2C
2C
poverty, provided the demographic gift is accompanied by
effective food supply policy and investment and more generally
Appendix Table A2. Internal rate of return estimates summary
Proc. Natl. Acad. Sci. USA 96 (1999J 5927
by effective technology policy. Although it is probably the case
that the coercion-based demographic gift in China is now
yielding high dividends, it is not at all clear that coercion is
justified in other countries. Certainly it is not justified in
countries unprepared to support the gift with effective eco
. , .
nomlc pOllCy.
In the past 40 years, an effective system of agricultural
research centers has been built. This system has enabled the
very favorable global effects realized over these years. Most
local effects have also been positive, although given the
starting points, indexes of poverty and malnutrition remain
high in many countries in spite of local progress. The simula-
tions reported here indicate that it is vital for local progress to
continue. Shifts in the objectives of research systems and delays
in bringing research technology to developing countries have
high prices in terms of delayed progress on poverty reduction
and land use. Without continued research to improve crop
productivity, cropped area will expand, with biodiversity hab-
itat implications.
Appendix: The IFPRI-IMPACT Model Base Case
IMPACT, developed at the IFPRI, is a partial equilibrium
model covering 17 commodities and 35 countries/regions. It
computes global equilibriums in real prices and is synthetic, in
that it uses price elasticities and nonprice parameters from
other studies. The model incorporates nonagricultural sector
linkages but does not compute equilibriums for markets other
than the 17 commodities.
Each country/region submodel has a set of equations for
supply, demand, and prices for each commodity and for
intersectoral linkages with the nonagricultural sector. Crop
Number
of IRRs
reported
Percent distribution Approx.
median
21-40 41-60 61-80 81-100 100+ IRR
0-20
Extension
Farm observations 16 .56 0 .06 .06 .25 .06 .18
Aggregate observations 29 .24 .14 .07 0 .27 .27 .80
Combined research and extension 36 .14 .42 .28 .03 .08 .06 .37
By region
Developed countries 19 .11 .31 .16 0 .11 .16 .50
Asia 21 .24 .19 .19 .14 .09 .14 .47
Latin America 23 .13 .26 .34 .08 .08 .09 .46
Africa 10 .40 .30 .20 .10 0 0 .27
All extension 81 .26 .23 .16 .03 .19 .13 .41
Applied research
Project evaluation 121 .25 .31 .14 .18 .06 .07 .40
Statistical 254 .14 .20 .23 .12 .10 .20 .50
Aggregate programs 126 .16 .27 .29 .10 .09 .09 .45
Commodity programs
Wheat 30 .30 .13 .17 .10 .13 .17 .51
Rice 48 .08 .23 .19 .27 .08 .14 .60
Maize 25 .12 .28 .12 .16 .08 .24 .56
Other cereals 27 .26 .15 .30 .11 .07 .11 .47
Fruits and vegetables 34 .18 .18 .09 .15 .09 .32 .67
All crops 207 .19 .19 .14 .16 .10 .21 .58
Forest products 13 .23 .31 .68 .16 0 .23 .37
Livestock 32 .21 .31 .25 .09 .03 .09 .36
By region
Developed countries 146 .15 .35 .21 .10 .07 .11 .40
Asia 120 .08 .18 .21 .15 .11 .26 .67
Latin America 80 .15 .29 .29 .15 .07 .06 .47
Africa 44 .27 .27 .18 .11 .11 .05 .37
All applied research 375 .18 .23 .20 .14 .08 .16 .49
Pretechnology science 12 0 .17 .33 .17 .17 .17 .60
Private sector R&D 11 .18 .09 .45 .09 .18 0 .50
Ex ante research 83 .11 .36 .16 .07 .01 .05 .44
OCR for page 5928
5928 Colloquium Paper: Evenson
Proc. Natl. Acad. Sci. USA 96 (1999J
production is determined by area and yield response functions.
Area functions include price responses and a nonprice trend
reflecting remaining land availability and technology. Yield is Class 2B: Transition to modern capacity. Reverse engineer
a function of the price of commodity and prices of inputs, and ing capacity, sciences developed.
a total factor productivity change term. Livestock commodities Class 2C: Export competitiveness, adaptive invention, intel
are similarly modeled.
Domestic demand is the sum of food, feed, and industrial use
demand. Food demand is a function of prices (of all commod
ities), per capita income, and population. Country-specific
population in growth rates are based on U.N. projections (1)
Income growth is partially endogenous to the model and
agriculture-nonagriculture links are specified. Feed and in
dustrial use demands are derived from final demands.
Prices are endogenously determined. Domestic prices are
linked to global equilibrium prices via exchange rates, and
producer-consumer subsidies and trade restrictions are al
lowed. Other policy instruments (acreage restrictions) are
considered. Trade is determined by net supply-demand equi
librium conditions.
Malnourished children projections for children (ages 0-6
years) are based on weight-for-age standards set by the U.S.
National Center for Health Statistics. Data for 61 developing
countries for 1980, 1985, and 1990! were used to link mal
nourished children proportions to per capita calorie consump
tion (determined in the model).
The nonprice total factor productivity terms are based on a
study of Indian productivity (2), a classification of industrial
technological infrastructure (5), and a study of rates of return
to agricultural research and extension (R.E.E., unpublished
data).
The ITI classification of Evenson and Westphal (5) included
the following classes:
Class 1A: Traditional ITI. Economics lack basic infrastruc
ture. Government influence limited.
Class 1B: First emergence. Some direct foreign investment.
Class 1C: Partial modernization. Agricultural sector well
developed. No R&D in producing firms.
Class 2A: Mastery of conventional technology. Market skills
well developed. R&D in firms.
lectual property rights developed.
Class D: Developed country capabilities.Append~x Table A1
reports the ITI projections used in the base case. Evenson
(R.E.E., unpublished data) reviews the rates of return studies
used in constructing the base case. These rates of return are
summarized inAppend~x Table A2. The relative median rate of
return ratios for commodities and regions were used to scale
nonprice terms to the rice base case terms.
1. United Nations (1992) World Population Prospects (U.N., New
York).
2. Evenson, R. E., Herdt, W. & Hossain, M. (1996) Rice Research
in Asia: Progress and Priorities (CAB International, Wallingford,
U.K.~.
Birkhaeuser, D., Evenson, R. E. & Feder, G. (1991) Economic
Development and Cultural Change 39~3), 607-650.
4. Evenson, R. E., Pray, C. E. & Rosegrant, M. W. (1999) "Agri-
cultural Research and Productivity Growth in India," Research
Report 109 (International Food Policy Research Institute, Wash-
ington, DC).
5. Evenson, R. E. & Westphal, L. (1994) "Technological Change
and Technology Strategy" in the Handbook of Development
Economics, eds. Srinivasan, T. N. & Behrman, J. (North-Hol-
land, Amsterdam), Vol. 3.
6. Huffman, W. & Evenson, R. E. (1993) Science for Agriculture
(Iowa State University Press, Ames, IA).
Rosegrant, M. W. & Svendsen, M. (1993) Food Policy 18~2),
13-32.
8. Bloom, D. E. & Williamson, J. G. (1998) The World Bank Econ.
Rev. 12, 419-456.
9. Sanghi, A., Alves, D., Evenson, R. & Mendelsohn, R. (1997)
Economia Aplicado 1~1), 7-34.
10. McKinsey, J. W. (1998) Ph.D. dissertation (Yale University, New
Haven, CT).
Mendelsohn, R., Nordhaus,
Rev. 84~4, 88), 753-771.
7.
W. D. & Shaw, D. (1994)Am. Econ.
Representative terms from entire chapter:
policy scenarios
