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9. The potential effects of climate change on world food production and security

GÜNTHER FISCHER
International Institute for Applied Systems Analysis, Laxenburg, Austria

KLAUS FROHBERG
Institut für Agrarentwicklung in Mittel- und Osteuropa, Halle, Germany

MARTIN L. PARRY
Jackson Environment Institute, University College, London, UK

CYNTHIA ROSENZWEIG
Goddard Institute for Space Studies and Columbia University, USA


Study methods
Crop models and yield simulations
Modelling the world food system
An assessment of the world food system under alternative scenarios
Conclusions
References

Since the late 1950s, global agricultural output has increased at rates and to levels that are unprecedented in human history. Much of the productivity increase is attributed to the breeding of high-yielding crop varieties, intensive use of inorganic fertilizers and pesticides, expansion of irrigation, and capital-intensive farm management.

In the 1970s, the euphoria surrounding the 'Green Revolution' was questioned in the wake of the energy crisis and growing awareness of long-term environmental consequences. Concern over soil erosion, groundwater contamination, soil compaction and decline of natural soil fertility, and destruction of traditional social systems, led to a reappraisal of what were then considered to be the most advanced agricultural production techniques. Since then, agricultural research has expanded its scope to include sustainable and resource-efficient cropping systems and farm management practices.

Since the beginning of the 1980s yet another threat to agriculture has attracted much attention. Many climatologists predict significant global warming in the coming decades due to increasing atmospheric carbon dioxide and other trace gases. As a consequence, major changes in hydrological regimes have also been forecast to occur. The magnitude and geographical distribution of such climate-induced changes may affect our ability to expand food production as required to feed a population of more than 10 000 million people projected for the middle of the next century. Climate change could have far-reaching effects on patterns of trade among nations, development, and food security.

Beyond what is known about greenhouse gases and the climate system, however, lie great uncertainties: How much warming will occur, at what rate, and according to what geographical and seasonal pattern? What secondary processes will the warming trend induce, and what might be the physical and biological impacts of such processes? Will some areas benefit while other areas suffer, and who might the winners and losers be? And, if such damages are unavoidable, what can be done to adapt or modify our systems so as to minimize or overcome them? These are important and complex questions, and we have only begun to understand them and to develop methods for their analysis.

Recent research has focused on regional and national assessments of the potential effects of climate change on agriculture. For the most part this work has treated each region or nation in isolation, without relation to changes in production elsewhere and without paying attention to climate change effects on the world market and their feedbacks. Assessments of potential impacts have been achieved in national studies completed in the United States (Adams et al., 1990, 1994; Smith and Tirpak, 1989), Australia (Pearman, 1988), and the United Kingdom (UK Department of the Environment, 1991). Regional studies have been conducted in high-latitude and semi-arid agricultural areas (Parry et al., 1988). These regional and national studies have been summarized in the IPCC Working Group II Reports (IPCC, 1990b, 1996.

In 1989 the US Environmental Protection Agency (EPA), with additional support provided by the US Agency for International Development (USAID), commissioned a three-year study on the effects of climate change on world food supply. The present study is an initial attempt to arrive at an integrated global assessment of the potential effects of climate change on agriculture and the world food system. The collaborative project was jointly managed by the Goddard Institute of Space Studies (GISS) and the Environmental Change Unit (ECU) in collaboration with the International Institute for Applied Systems Analysis (IIASA) and involved about 50 scientists worldwide.

The aim of this paper is to provide a brief description of the study and an analysis of results obtained from a set of simulation runs carried out with the Basic Linked System of National Agricultural Models (Fischer et al., 1988) constructed by the Food and Agriculture Program (FAP) at IIASA. IIASA's research provided a framework for analysing the world food system, viewing national agricultural systems as embedded in national economies, which in turn interact with each other at the international level.

Study methods

The implementation of the study involved a four-step procedure:

1. Selection of climate change scenarios.

2. Estimation of site-specific potential changes in crop yields.

3. Aggregation of crop modelling results to estimates of potential national/regional productivity changes.

4. Dynamic simulation of climate change yield impacts on the world food system.

Details of the methods are described in Rosenzweig et al. (1995), Rosenzweig and Parry (1994) and Rosenzweig and Iglesias (1994).

CLIMATE CHANGE SCENARIOS

Scenarios of climate change were developed in order to estimate their effects on crop yields and food trade. A climate change scenario is defined as a physically consistent set of changes in meteorological variables, based on generally accepted projections of CO2 (and other trace gases) levels. The range of scenarios analysed is intended to capture the range of possible effects and to set limits on the associated uncertainty. One set of scenarios for this study was created by changing observed data on current climate (1951-1980) according to the results of doubled CO2 simulations of three General Circulation Models (GCMs) (Table 9.1).

The temperature changes of these GCM scenarios (4.0-5.2°C) are at or near the upper end of the range (1.5-4.5°C) projected for doubled CO2 warming by the IPCC (IPCC, 1990a, 1992). The GISS and GFDL scenarios, however, are near the mean temperature change (3.8°C) of recent doubled CO2 experiments documented for atmospheric GCMs with a seasonal cycle and a mixed-layer ocean (IPCC, 1992).

GCMs currently provide the most advanced means of predicting the potential future climatic consequences of increasing radiatively active trace gases. They have been shown to simulate current temperatures reasonably well, but do not reproduce current precipitation accurately; and their ability to reproduce current climate varies considerably from region to region (IPCC, 1990a). Of special importance for agricultural climate change impacts, there is a notable lack of consensus among GCMs in prediction of regional soil moisture changes (Kellogg and Zhao, 1988). Furthermore, GCMs have so far not been able to produce reliable projections of changes in climate variability, such as alterations in the frequencies of drought and storms, even though these could significantly affect crop yields.

For the crop modelling part of this study, climate changes from doubled CO2 GCM simulations are utilized with an associated level of 555 ppm CO2; the assumed timing of the simulations with the world food model is that these conditions will occur in year 2060. Rates of future emissions of trace gases and the point in time when their effects will be fully realized are not certain. Because other greenhouse gases besides CO2 such as methane (CH4), nitrous oxide (N2O), and the chlorofluorocarbons (CFCs), are also increasing, an 'effective CO2 doubling' has been defined as the combined radiative forcing of all greenhouse gases having the same forcing as doubled CO2 (usually defined as ~600 ppm). Level of CO2 is important when estimating potential impacts on crops, because crop growth and water use have been shown to benefit from increased levels of CO2 (Cure and Acock, 1986). A CO2 level of 555 ppm was associated with the effective doubled CO2 climate projections for use in the crop modelling simulations. This was based on the GISS GCM transient trace gas scenario A described in Hansen et al. (1988), in which the simulated climate had warmed to the effective doubled CO2 level of about 4°C by 2060. This level assumes that non-CO2 trace gases contribute ~15% of the change in radiative forcing from 300 to ~600 ppm.

Table 9.1 GCM doubled CO2 climate change scenarios

GCM

Year 1

Resolution (lat x long)

CO2 (ppm)

Change in average global

temp. (°C)

precip. (%)

GISS

1982

7.83° x 10°

630

4.2

11

GFDL

1988

4.4°x7.5°

600

4.0

8

UKMO

1986

5.0°x7.5°

640

5.2

15

1 When calculated.
Note: GFDL: Geophysical Fluid Dynamics Laboratory: UKMO: United Kingdom Meteorological Office.

Two other climate change scenarios were tested. The GISS transient scenario consists of a separate GCM run with gradually increasing atmospheric CO2 levels. The CO2 concentrations in the GISS transient scenario A were assumed to be 405 ppm, 460 ppm and 530 ppm, respectively, in the decades of the 2010s, 2030s and 2050s. Crop modelling experiments were conducted separately for these three time periods.

Another scenario, termed GISS-A, utilized the climate changes projected for the 2030s from the GISS transient run with 555 ppm CO2 for the crop model simulations. This scenario was used to test the consequences of a lower sensitivity of the climate system to increasing atmospheric greenhouse gas concentrations. The GISS-A scenario projects a global temperature rise of 2.4°C and a 5% increase in precipitation.

ESTIMATION OF SITE-SPECIFIC POTENTIAL CHANGES IN CROP YIELDS

Crop models and a decision support system developed by the International Benchmark Sites Network for Agrotechnology Transfer (IBSNAT, 1989) were used to estimate how climate change and increasing levels of carbon dioxide may alter yields of major crops. The simulations represented both major production areas and vulnerable regions at low, mid and high latitudes. The IBSNAT models simulate crop growth and yield formation as influenced by genetics, climate, soils and management practices. Models used were for wheat, maize, rice and soybean.

The crop models account for the beneficial physiological effects of increased CO2 concentrations on crop growth and water use (Peart et al., 1989). Most plants growing in experimental environments with increased levels of atmospheric CO2 exhibit increased rates of net photosynthesis and reduced stomatal openings, thereby reducing transpiration per unit leaf area while enhancing photosynthesis. Crop modelling simulation experiments were conducted for baseline climate (1951-1980) and GCM doubled CO2 climate change scenarios with and without the physiological effects of CO2.

The study also tested the efficacy of farm-level adaptations to climate change, including change in planting date, change of cultivar, irrigation, fertilizer, and change of crop. These measures were then grouped into two levels of adaptation: Level 1 implies little change to existing agricultural systems reflecting relatively easy and low-cost farmer response to a changing climate. Level 2 implies more substantial changes to agricultural systems possibly requiring resources beyond the farmer's means. It must be noted that costs of adaptation and future water availability for irrigation under the climate change scenarios were not considered in the study.

AGGREGATION OF CROP MODELLING RESULTS

Data on crop yield changes expected for different scenarios of climate change had to be compiled for all crop commodities and geographical groupings represented in the IIASA/FAP world food model, the Basic Linked System (BLS).

Crop model results for wheat, rice, maize and soybean from the selected sites were aggregated by weighting regional yield changes, based on current production, to estimate changes in national yields. The regional yield estimates represent the current mix of rainfed and irrigated production, the current crop varieties, nitrogen management and soils. Production data were gathered by scientists participating in the study and from the FAO, the USDA Crop Production Statistical Division, and the USDA International Service.

Changes in national yields of other crops and commodity groups and of regions not simulated with crop models were estimated based on similarities to modelled crops and growing conditions, and previous published and unpublished climate change impact studies. Estimates were made of yield changes for the three GCM scenarios with and without direct effects of CO2. The yield changes with the direct effects of CO2 were based on the mean responses to CO2 for the different crops in the crop model simulations.

In total, 12 climate change yield impact scenarios were developed: for each of three climate models (GISS, GFDL, UKMO) four scenarios are specified (GCM without direct effects of CO2 on yields; with direct effects of CO2 on yields; with direct effects of CO2 and Adaptation Level 1; with direct effects of CO2 and Adaptation Level 2).

SIMULATION OF CLIMATE CHANGE YIELD IMPACTS ON THE WORLD FOOD SYSTEM

The agricultural production components of the national models in the BLS were modified to incorporate the projected changes in yields and crop productivity. Impacts were assessed for the period 1990 to 2060, with population growth and technology trends projected to that year. The simulation results obtained under each of the climate impact scenarios are compared to a 'neutral' point of departure, a BLS reference scenario which assumes that policies, especially those affecting economic and technological development remain more or less unchanged. The reference scenario projects a possible future based on an internally consistent set of assumptions. The difference between the reference scenario and the climate change simulations is the climate-induced dynamic effect. Note that the results of this study are considered to be long-term projections, not forecasts. The considerable length of the projection period makes it impossible to avoid judgement concerning some of the assumptions in the system.

Crop models and yield simulations

The IBSNAT crop models were used to estimate how climate change and increasing levels of carbon dioxide may alter yields of world crops at 112 sites in 18 countries. (Figure 9.1). The crop models used were CERES-Wheat (Ritchie and Otter, 1985; Godwin et al., 1989), CERES-Maize (Jones and Kiniry, 1986; Ritchie et al., 1989), CERES-Rice (Godwin et al., 1993) and SOYGRO (Jones et al., 1989).

The IBSNAT models are comprised of parameterizations of important physiological processes responsible for plant growth and development, evapotranspiration, and partitioning of photosynthate to produce economic yield. The simplified functions enable prediction of growth of crops as influenced by the major factors that affect yields, i.e., genetics, climate (daily solar radiation, maximum and minimum temperatures, and precipitation), soils, and management practices. The models include a soil moisture balance submodel so that they can be used to predict both rainfed and irrigated crop yields. The cereal models simulate the effects of nitrogen fertilizer on crop growth, and these were studied in several countries in the context of climatic change. For the most part, however, the results of this study assume optimum nutrient levels.

The IBSNAT models were selected for use in this study because they have been validated over a wide range of environments (e.g., Otter-Nacke et al., 1986) and are not specific to any particular location or soil type. The validation of the crop models over different environments also improves their ability to estimate effects of changes in climate. Furthermore, because management practices, such as the choice of varieties, planting date, fertilizer application and irrigation, may be varied in the models, they permit experiments that simulate adaptation by farmers to climate change.

Figure 9.1. Crop modelling sites

PHYSIOLOGICAL EFFECTS OF CO2

Ratios were calculated between measured daily photosynthesis and evapotranspiration rates for a canopy exposed to high CO2 values, based on published results (Allen et al., 1987; Cure and Acock, 1986; Kimball, 1983), and the ratios were applied to the appropriate variable in the crop models on a daily basis (see Peart et al., 1989 for a detailed description of methods). The photosynthesis ratios (555 ppm CO2/330 ppm CO2) for soybean, wheat, rice and maize were 1.21, 1.17, 1.17 and 1.06, respectively. Changes in stomatal resistance were set at 49.7/34.4 s/m for C3 crops and at 87.4/55.8 s/m for C4 crops, based on experimental results by Rogers et al. (1983). As simulated in this study, the direct effects of CO2 may bias yield changes in a positive direction, since there is uncertainty regarding whether experimental results will be observed in the open field under conditions likely to be operative when farmers are managing crops. Plants growing in experimental settings are often subject to fewer environmental stresses and less competition from weeds and pests than are likely to be encountered in farmers' fields. Recent field free-air release studies have found overall positive CO2 effects on cotton under current climate conditions (Hendry, 1993).

CROP YIELD SIMULATIONS

Crop modelling simulation experiments were performed for baseline climate (1951-1980), and GCM doubled CO2 climate change scenarios with and without the physiological effects of CO2. This involved the following tasks:

· For the countries studied, geographical boundaries were defined for the major crop production regions; agricultural systems (e.g., rainfed and/or irrigated production, number of crops grown per year) were described, and data on regional and national rainfed and irrigated production of major crops were gathered.

· Observed climate data for representative sites within these regions were obtained for the baseline period (1951-1980), or for as many years of daily data as were available; the soil, crop variety, and management inputs necessary to run the crop models at the selected sites were specified.

· The crop models were validated with experimental data from field trials, to the extent possible.

· The crop models were run with baseline data, and GCM climate change scenarios, with and without direct effects of CO2 on crop growth. Rainfed and/or irrigated simulations were carried out as appropriate to current growing practices.

· Alterations in farm-level agricultural practices that would lessen any adverse consequences of climate change were identified and evaluated, by simulating irrigated production and other adaptation responses, e.g., shifts in planting date and substitution of crop varieties.

FARM-LEVEL ADAPTATIONS

In each country, the agricultural scientists used the crop models to test the possible responses to the worst climate change scenario (this was usually, but not always, the UKMO scenario). These adaptations included change in planting date, change of cultivar, irrigation, fertilizer and change of crop. Irrigation simulations in the crop models assumed automatic irrigation to field capacity when plant available water dropped to 50%, and an irrigation efficiency of 100%. Not all adaptation possibilities were simulated at every site and country: choice of adaptations to be tested was made by the participating scientists, based on their knowledge of current agricultural systems (Table 9.2).

The adaptation simulations were not comprehensive because not all possible combinations of farmer responses were tested at every site. Spatial analyses of crop, climatic and soil resources are needed to test fully the possibilities for crop substitution. Neither the availability of water supplies for irrigation nor the costs of adaptation were considered in this study; these are both critical needs for further research.

Table 9.2 Adaptations tested in crop modelling study

Country

Crop tested

Change of planting date

Change of cultivar/crop

Additional irrigation

Additional N fertilizer

Argentina

m

x

xc,xz

x


Australia

r,w

xx

x

xx


Bangladesh

r


x



Brazil

w,m,s

xx

x,xc

x,xx,xxx

x

Canada

w

x


x,xx


China

r

x

xp,xz



Egypt

m,w

x

x

x


France

m,w

x,xz

x

x


India

w



x


Japan

r,w,m

xx


xx


Mexico

m

x

xc

xxx

x

Pakistan

w

x


x


Philippines

r

xp

xp



Thailand

r


x



Uruguay

b

x

x

x

x

USA

w,m,s

x

x

x


Former USSR

w

x,xz

x



Zimbabwe

m

xp


x,xp


w = wheat
m = maize
r = rice
s = soybean
b = barley.
xc = hypothetical new cultivar
xp = new cultivar and change in planting date
xx = irrigation and change in planting date
x = simple change
xxx = irrigation and increase nitrogen fertilizer
xz = suggested shift in crop production zone.

EFFECTS ON CROP YIELDS

Depending on present conditions, global warming and CO2 enrichment can have positive or negative impacts. Simulated yield increases in the mid and high latitudes are caused primarily by:

· Positive physiological effects of' CO2 At sites with cooler initial temperature regimes, increased photosynthesis more than compensated for the shortening of the growing period caused by warming.

· Lengthened growing season and amelioration of cold temperature effects on growth. At some sites near the high-latitude boundaries of current agricultural production, increased temperatures extended the frost-free growing season and provided regimes more conducive to greater crop productivity.

The primary causes of decreases in simulated yields are:

· Shortening of the growing period. Higher temperatures during the growing season speed annual crops through their development (especially the grain-filling stage), causing less grain to be produced. This occurred at all sites except those with the coolest growing-season temperatures in Canada and the former USSR.

· Decrease in water availability. This is due to a combination of increases in evapotranspiration rates in the warmer climate, enhanced losses of soil moisture and, in some cases, a projected decrease in precipitation in the climate change scenarios.

· Poor vernalization. Vernalization is the requirement of some temperate cereal crops, e.g., winter wheat, for a period of low winter temperatures to initiate or accelerate the flowering process. Low vernalization results in low flower bud initiation and ultimately reduced yields. Decreases in winter wheat yields at some sites in Canada and the former USSR were caused by lack of vernalization.

CROP YIELDS WITHOUT ADAPTATION

Table 9.3 shows modelled wheat yield changes for the GCM doubled CO2 climate change scenarios (the yield changes include results from both rainfed and irrigated simulations, weighted by current percentage of the respective practice). Climate changes without the direct physiological effects of CO2 cause decreases in simulated wheat yields in all cases, while the direct effects of CO2 mitigate the negative effects primarily in mid and high latitudes.

The magnitudes of the estimated yield changes vary by crop. Global wheat yield changes weighted by national production are positive with the direct CO2 effects, while maize yield is most negatively affected, reflecting its greater production in low-latitude areas where simulated yield decreases are greater. Maize production declines most with direct CO2 effects, probably due to its lower response to the physiological effects of CO2 on crop growth. Simulated soybean yields are most reduced without the direct effects of CO2, but are least affected in the less severe GISS and GFDL climate change scenarios when direct CO2 effects are simulated. Soybean responds positively to increased CO2, but is the crop most affected by the high temperatures of the UKMO scenario.

Table 9.3. Current production and change in simulated wheat yield under doubled CO2 climate change scenarios, with and without the direct effects of CO2 1

1 Results for each country represent the site results weighted according to regional production. The world estimates represent the country results weighted by national production.

Country

Current production

Change in simulated yields (%)

Yield

Area

Prod.

%

climate change alone

with physiological effects

(tonnes/ha)

('000 ha)

('000 tonnes)

GISS

GFDL

UKMO

GISS

GFDL

UKMO

Australia

1.38

11546

15574

3.2

-18

-16

-14

8

11

9

Brazil

1.13

2788

3625

0.8

-51

-38

-53

-33

-17

-31

Canada

1.88

11365

21412

4.4

-12

-10

-38

27

27

-7

China

2.53

29092

73527

15.3

-5

-12

-17

16

8

-0

Egypt

3.79

572

2166

0.4

-36

-28

-54

-31

-26

-51

France

5.93

4636

27485

5.7

-12

-28

-23

4

-15

-9

India

1.74

22876

39703

8.2

-32

-38

-56

3

-9

-33

Japan

3.25

237

722

0.2

-18

-21

-40

-1

-5

-27

Pakistan

1.73

7478

12918

2.7

-57

-29

-73

-19

31

-55

Uruguay (barley)

2.15

91

195

0.0

-41

-48

-50

-23

-31

-35

USA

2.72

26595

64390

13.4

-21

-23

-33

-2

-2

-14

Former USSR


winter

2.46

18988

46959

9.7

-3

-17

-22

29

9

0


spring

1.14

36647

41 959

8.7

-12

-25

-48

21

3

-25

World 2

2.09

231

482

72.7

-16

-22

-33

11

4

-13

2 World area and production x 1 000 000.

The differences among countries in simulated crop yield responses to climate change without the direct effects of CO2 are primarily related to differences in current growing conditions. Higher temperatures tend to shorten the growing period at all locations tested. At low latitudes, however, crops are currently grown at higher temperatures, produce lower yields, and are nearer the limits of temperature tolerances for heat and water stress. Warming at low latitudes thus results in accelerated growing periods for crops, more severe heat and water stress, and greater yield decreases than at higher latitudes. In many mid- and high-latitude areas where current temperature regimes are cooler, increased temperatures, while still shortening grain-filling periods, thus exerting a negative influence on yields, do not significantly increase stress levels. At some sites near the high-latitude boundaries of current agricultural production, increased temperatures can benefit crops otherwise limited by cold temperatures and short growing seasons, although the extent of soil suitable for expanded agricultural production in these regions was not studied explicitly. Potential for expansion of cultivated land is embedded in the BLS world food trade model and is reflected in shifts in production calculated by that model.

The GISS and GFDL climate change scenarios produced yield changes ranging from +30 to -30%. Effects under the GISS scenario are, in general, more adverse than under the GFDL scenario to crop yields in parts of Asia and South America, while effects under the GFDL scenario result in more negative yields in the United States and Africa and less positive results in former USSR. The UKMO climate change scenario, which has the greatest warming (5.2°C global surface air temperature increase), causes average national crop yields to decline almost everywhere.

CROP YIELDS WITH ADAPTATION

The adaptation studies conducted by the scientists participating in the project suggest that ease of adaptation to climate change is likely to vary with crop, site and adaptation technique. For example, at present, many Mexican producers can only afford to use small doses of nitrogen fertilizer at planting; if more fertilizer becomes available to more farmers some of the yield reductions under the climate change scenarios might be offset. However, given the current economic and environmental constraints in countries such as Mexico, a future with unlimited water and nutrients is unlikely (Liverman et al., 1994). In contrast, switching from spring to winter wheat at the modelling sites in the former USSR produces a favourable response (Menzhulin et al., 1994), suggesting that agricultural productivity may be enhanced there with the relatively easy shift to winter wheat varieties.

Adaptation Level 1, simulating minor changes to existing agricultural systems, compensated for the climate change scenarios incompletely, particularly in the developing countries. Adaptation Level 2, implying major changes to current agricultural systems, compensated almost fully for the negative climate change impacts in the GISS and the GFDL scenarios. With the high level of global warming as projected by the UKMO climate change scenario, neither Level 1 nor Level 2 adaptation fully overcame the negative climate change effects on crop yields in most countries, even when the direct CO2 effects were taken into account.

Modelling the world food system

The world food system consists of many actors, some powerful and others dependent. Producers and consumers interact through national and international markets. While there is a trend towards internationalization in the world food system, only ~15% of world cereal production currently crosses national borders (Fischer et al., 1990). National governments shape the system by imposing regulations and by investments in agricultural research, improvements in infrastructure, and education. Although the system does not guarantee stability nor equity, food and fibre have been produced over time in increasingly efficient ways. These efficiencies have generated long-term real declines in prices of major food staples.

THE BASIC LINKED SYSTEM OF NATIONAL AGRICULTURAL MODELS

The Basic Linked System of National Agricultural Policy Models (BLS) consists of some 35 national and/or regional models: 18 national models, 2 models for regions with close economic cooperation (EU and Eastern Europe + former USSR 1), 14 aggregate models of country groupings, and a small component that accounts for statistical discrepancies and imbalances during the historical period. The individual models are linked together by means of a world market module. A detailed description of the entire system is provided in Fischer et al. (1988). Earlier results obtained with the system are discussed in Parikh et al. (1988) and in Fischer et al., (1990,1994).

1 The political changes as well as changes in national boundaries of the very recent past are not captured in the BLS, although the model formulation has been adjusted, away from centrally planned economies to more market-oriented behaviour.

The BLS is a general equilibrium model system. This necessitates that all economic activities are represented in the model. Financial flows as well as commodity flows within a country and at the international level are consistent in the sense that they balance.

The country models are linked through trade, world market prices and financial flows. The system is solved in annual increments, simultaneously for all countries. It is assumed that supply does not adjust instantaneously to new economic conditions. Only supply that will be marketed in the following year is affected by possible changes in the economic environment. A first round of exports from all the countries is calculated for an initial set of world prices, and international market clearance is checked for each commodity. World prices are then revised, using an optimizing algorithm, and again transmitted to the national models. Next, these generate new domestic equilibria and adjust net exports. This process is repeated until the world markets are cleared in all commodities. At each stage of the iteration the domestic markets are in equilibrium. Since these steps are taken on a year-by-year basis, a recursive dynamic simulation results.

An upper bound on land available for cropping and for use as pasture is determined by the availability of land resources as well as economic conditions; e.g., by the economic returns to land. The physical resource limits in developing countries were derived from a FAO assessment of potential arable land (FAO/UNFPA/IIASA, 1983; FAO, 1988). The responsiveness of how much land can be cultivated due to changing economic conditions is rather low since time and investment are needed to bring new land into cultivation.

Technological development is assumed to be largely determined by exogenous factors. Technical progress is included in the models as biological technical progress in the yield functions of both crops and livestock. Rates of technical progress were estimated from historical data and, in general, show a decline over time. Mechanical technical progress is part of the function determining the level of harvested crop area and livestock husbandry. Induced (endogenous) technical progress is not considered for any of these cases or for non-agricultural production.

Information generated by simulating with the BLS consists of a number of variables. At the world-market level these include prices, net exports, global production and consumption. At the country level, the information generated includes: producer and retail prices, level of production, use of primary production factors (land, labour and capital), intermediate input use (feed, fertilizer and other chemicals), level of human consumption, stocks and net trade, gross domestic product and investment by sector, population number and labour force, welfare measures such as equivalent income, and the level of policy measures as determined by the government (e.g., taxes, tariffs).

BLS REFERENCE PROJECTIONS WITHOUT CLIMATE CHANGE

A set of reference scenarios was designed for studying the relative effects of climate change in relation to technology, population and economic growth. The standard reference scenario we describe, termed scenario REF-M, assumes 'business as usual' in the sense that no radical shifts in technological and political trends are included. Protectionist policy measures in agriculture, however, are assumed to be lowered to half the observed historical levels. The transition to such reduced protection of the agriculture sectors is implemented between the beginning of the simulation period and year 2020. Thereafter the respective policy settings are kept constant.

Another reference projection, scenario REF-H, assumes faster economic growth than the standard reference scenario. In the BLS, the dynamics of economic growth can be influenced by adjusting the rate of investment to the amount of technical progress. In scenario REF-H, growth of Gross Domestic Product (GDP) is especially higher for Asian countries compared to scenario REF-M. Similarly, a lower growth simulation run, scenario REF-L, has been created by curtailing investments in favour of consumption.

Additional reference projections with the BLS were based on the standard reference scenario REF-M. Two projections deal with the sensitivity of the world food system with respect to land development and availability of agricultural inputs. In scenario REF-MA, expansion of arable land after year 2000 is constrained to half the expansion in scenario REF-M. Another simulation run, scenario REF-MF, limited the use of fertilizers by implementing a tax on fertilizers of 50% in developed countries and of 33% in developing countries. In addition, a ceiling on fertilizer use per hectare was enforced. Both these scenarios were simulated to test the impact of possible agricultural policies geared towards reducing greenhouse gas emissions from agriculture (specifically, CO2 release from deforestation and N2O emissions from nitrogen fertilizers).

Finally, reference projection REF-MP uses the economic assumptions of the standard reference run REF-M but alters the demographic projections from medium population growth to low population growth.

All the simulations are carried out on a yearly basis from 1980 to 2060.

ASSUMPTIONS ABOUT POPULATION

Population growth rates were obtained from the UN World Population Prospects, Medium Variant (UN, 1989) in all simulations except for scenario REF-MP where the Low Variant was used. Since the UN projected national population levels only up to the year 2025, the remainder of the projection period was covered by growth rates compiled from long-term population projections of the World Bank (Table 9.4) (World Bank, 1990). Labour participation rates are taken from projections of the International Labour Organization. The allocation of total labour force between agriculture and non-agricultural sectors responds to relative prices and incomes.

During the last two decades, population at the global level has been growing at an average rate of about 1.8% per annum, from 3 600 million in 1970 to about 5 300 million people in 1990. In the BLS standard reference projection, the average annual population growth rate is projected to decline gradually from 1.7% per annum during 1980 to 2000 to around 0.5% per annum for the period 2040 to 2060. This would bring global population numbers to 6 100 million in year 2000 and about 10 300 million in year 2060. Most of the increase in population numbers occurs in developing countries, increasing their share in world population from around 73% in 1980 to 78% in year 2000 and some 86% in year 2060. In 2060, almost 9 000 million people are projected to live in developing countries, of which approximately 5 000 million are in Asia, and 2 200 million in Africa. With lower population growth in scenario REF-MP, population reaches 7 200 million in year 2020, and some 8 600 million in year 2060, about 17% less than in scenario REF-M.

ECONOMIC GROWTH

Growth rates in most of the national models of the BLS are determined based on three elements: (a) capital accumulation through investment and depreciation, related to a savings function that depends on lagged GDP levels as well as balance of trade and financial aid flows, (b) dynamics of the labour force as a result of demographic changes, and (c) technical progress. Table 9.5 presents some indicators of economic development derived from the simulation results of the reference projections.

Table 9.4. Estimated population and average annual growth, year 1980-2060

Region

1980 mill.

2000 mill.

2020 mill.

2060 mill.

1980-2000 % p.a.

2000-2020 % p.a.

2020-2040 % p.a.

2040-2060 % p.a.

MEDIUM VARIANT

WORLD

4378

6 125

7883

10315

1.7

1.3

0.8

0.5

Developed

1 186

1 340

1 445

1 470

0.6

0.4

0.1

-0.0


North America

810

915

980

970

0.6

0.4

0.1

-0.1


Western Europe and other developed market economies

428

472

501

505

0.5

0.3

0.1

-0.1


Eastern Europe + former USSR

374

424

465

500

0.6

0.5

0.3

0.1


Pacific OECD countries

134

151

156

145

0.6

0.2

-0.1

-0.2

Developing

3 193

4786

6437

8843

2.1

1.5

1.0

0.6


Africa

412

757

1 274

2240

3.1

2.6

1.7

1.1


Latin America

351

522

695

878

2.0

1.4

0.9

0.3


West Asia

188

325

485

777

2.8

2.0

1.4

1.0


South Asia

898

1 396

1 889

2541

2.2

1.5

0.9

0.6


Centrally Planned Asia

1 086

1419

1 637

1 843

1.4

0.7

0.4

0.2


Pacific Asian countries

258

366

458

564

1.8

1.1

0.7

0.4

LOW VARIANT

WORLD

4380

5968

7215

8565

1.6

1.0

0.5

0.3

Developed

1 185

1 319

1 364

1 348

0.5

0.2

-0.0

-0.0

Developing

3 195

4649

5851

7217

1.9

1.2

0.6

0.4

All regions, developed and developing alike, show a declining rate of GDP growth over time, which is consistent with historical developments. The declining rates of population growth (and the related decline in the growth of the labour force) as well as a general slowdown in productivity increases contribute to this development.

In the standard reference scenario, GDP at the global level increases at an average 2.4% annually during 1980 to 2020. Economic growth declines to about 1.5% per annum during 2020 to 2040. Overall, global GDP increases 4.4 times during the simulation period compared to a 2.4-fold increase in population numbers. This results in an average annual increase in GDP per caput of 1.4 and 1.1% in developed and developing countries, respectively, over the 80-year period from 1980 to 2060. It should be noted, however, that increases in per caput indicators are higher at regional and national levels compared to global figures owing to an aggregation effect induced by the demographic development, giving increasingly higher weights to poorer developing countries.

Table 9.5. Economic growth indicators under different reference projections (average annual percentage change)


 

REF-L*

REF-M*

REF-MP*

REF-H*

World

Developed

Developing

World

Developed

Developing

World

Developed

Developing

World

Developed

Developing

GDP 1

1980-2020

2.2

2.0

3.1

2.4

2.2

3.3

2.3

2.1

3.1

2.7

2.2

4.0

1980-2060

1.7

1.5

2.3

1.8

1.6

2.4

1.7

1.6

2.3

2.2

1.7

3.2

GDP/CAP 2

1980-2020

0.8

1.5

1.3

1.0

1.7

1.5

1.1

1.8

1.6

1.3

1.8

2.2

1980-2060

0.7

1.3

1.0

0.8

1.4

1.1

0.9

1.4

1.2

1.2

1.5

1.9

AGRICULTURE 3

1980-2020

1.5

0.7

2.0

1.5

0.7

2.0

1.4

0.7

1.9

1.6

0.7

2.1

1980-2060

1.2

0.5

1.5

1.2

0.5

1.6

1.1

0.4

1.4

1.2

0.5

1.6

FOOD PRODUCTION

1980-2020

1.5

1.0

1.8

1.5

1.0

1.9

1.4

0.9

1.7

1.6

1.0

2.0

1980-2060

1.1

0.7

1.4

1.1

0.7

1.4

1.0

0.6

1.2

1.2

0.7

1.5

1 Gross Domestic Product.
2 Gross Domestic Product per caput.
3 Gross Domestic Product of agriculture.
* The terms REF-L, REF-M, REF-MP, REF-H are described in the section on BLS reference projections without climate change.

With faster economic growth, especially in developing countries such as in scenario REF-H, world GDP grows almost 5.6 times between year 1980 and 2060, compared to only 3.9 times as in the lower growth projection REF-L. Note that even with relatively minor variations on the basic economic assumptions, the resulting output in year 2060 varies by some 40%. Similarly, food production (measured in terms of net food energy, i.e., production less feed, seed and waste) as well as overall agriculture growth is projected to exceed population growth throughout the simulation period.

PRODUCTION, DEMAND AND TRADE

The standard reference projection, scenario REF-M, presents the perspective of a world in which the effective demand for food grows substantially owing to higher incomes and larger populations. Technological progress and economic development assumed in the reference scenario allow this increase in demand to be met at somewhat decreasing world market prices for agricultural products, consistent with historical trends. Table 9.6 shows global production of agricultural commodities in the standard reference scenario REF-M and in the higher income scenario REF-H.

Global trade in the reference scenario increases somewhat faster than global agricultural production. For cereals, the share of net exports in global production is estimated to increase from 13% in 1980 to 15% in 2060, with wheat and coarse grains showing an almost three-fold and rice a four-fold increase in trade levels. In general, the share of global trade in global production of commodity aggregates increases gradually over time indicating a growing specialization in production. Increasing demand in developing countries, due to rising incomes and growing populations, leads to a deterioration in the level of agricultural self-sufficiency for this group of countries, which changes from a net surplus of about 3% in 1979/81 into a 1% deficit by the year 2060 caused by increasing deficits in cereals, meat and milk.

RISK OF HUNGER

Finally, we ask where all this leaves the hungry. To evaluate the impact of alternative scenarios on the poor in different countries, it was necessary to generate a consistent hunger indicator in the BLS. Country-wise estimates of the number of undernourished persons have been made by FAO (1984, 1987). To recover the FAO method in a reduced form, suitable for use in the simulation models, a cross-country regression has been estimated explaining the share of people at risk of hunger by a measure of food energy availability relative to nutritional requirements. Food availability, in turn, depends on income and price levels.

Table 9.6. Global production in two BLS reference projections

Commodity

Production in year Scenario REF-M 1

Production in year Scenario REF-H 1

Unit of measurement

1980

2000

2020

2040

2060

2000

2020

2040

2060

Wheat

441

603

742

861

958

658

811

911

1056

million tonnes

Rice 2

249

367

480

586

659

415

545

661

749

million tonnes milled equivalent

Coarse grains

741

1022

1289

1506

1669

1065

1349

1587

1772

million tonnes

Bovine + ovine meat

65

83

105

123

136

84

107

125

139

million tonnes carcass weight

Dairy

470

613

750

877

997

616

758

893

1021

million tonnes whole milk equivalent

Other meat

17

25

33

41

48

25

34

42

49

million tonnes protein equivalent

Protein feed

36

52

64

76

85

52

65

77

87

million tonnes protein equivalent

Other food

225

326

433

538

629

326

436

545

640

million US dollars 1970

Non-food

26

34

41

47

52

34

41

48

53

million US dollars 1970

Agriculture

370

522

676

821

942

533

696

848

977

million US dollars 1970

1 The terms REF-M are described in the section on BLS reference projections without climate change.
2 Production is in milled rice equivalent; conversion factor from paddy is 0.667.

Table 9.7. People at risk of hunger 1 (millions)


 

REF-M

REF-MP

REF-MA

REF-MF

REF-L

REF-H

Year 1980

Year 2020

Year 2060

Year 2060

Year 2060

Year 2060

Year 2060

Year 2060

DEVELOPING

501

715

641

395

727

722

757

498

Africa

116

291

415

305

447

446

441

375

Latin America

36

39

24

13

34

33

32

20

West Asia

28

55

72

46

84

85

78

68

South Asia

265

319

128

30

160

157

202

35

Centrally Planned Asia 2

26

33

0

0

0

0

0

0

Pacific Asian countries

30

6

9

0

3

2

4

0

1 The term 'at risk of hunger' is used following the FAO methodology of the lack of food or income to achieve a dietary intake above 1.4 times the basal metabolic rate.

2 Estimate does not include China.

In the standard reference scenario the incidence of hunger decreases markedly from an estimated 23% of population in developing countries (excluding China) in year 1980 to some 9% in year 2060. Yet, despite this remarkable improvement the estimated number of people at risk of hunger increases somewhat, from about 500 million 2 in 1980 to almost 720 million in year 2020, and some 640 million in the year 2060. The projected number of undernourished people in developing countries is shown in Table 9.7. No estimate was attempted for developed regions. Of course, the projected number of people at risk of hunger is sensitive to the scenario assumption, ranging from less than 400 million under the low population run, about 500 million assuming faster economic development, to about 760 million under the lower growth scenario.

2 FAO has recently estimated (FAO, 1993) that the number of undernourished in developing countries amounted to 941 million people in 1979/81 and to 843 million people in 1988/90. These estimates are based on a threshold food energy level of 1.54 times Basal Metabolic Rate (BMR). The BLS estimates assume a lower threshold of 1.4 times BMR.

While the estimates show an improvement of the food security situation both in relative and absolute terms in Asian countries, the African continent experiences a mixed outcome largely due to the dramatic population increase. The estimated share of people at risk of hunger in total African population declines from 28% in year 1980 to 18% by 2060. However, the number of hungry is projected to increase more than three-fold, from about 120 million people in 1980 to 415 million in 2060, thereby making Africa the region with the largest number of undernourished. With lower population growth, scenario REF-MP, the estimated level of people at risk of hunger is reduced by more than 25%; higher economic development, scenario REF-H, reduces the number of hungry by some 40 million, i.e., approximately 10%.

An assessment of the world food system under alternative scenarios

The evaluation of the potential impact of climate change on production and trade of agricultural commodities, in particular on food staples, is carried out by comparing the results of the climate change scenarios to the reference projections. Various aspects of these reference projections have been presented in the previous section.

The climate change yield impact scenarios devised within the project involve a large number of experiments that relate to:

· different GCM doubled CO2 simulations;

· different assumptions with regard to impacts of climate change on plant growth and yield levels, such as physiological effects of 555 ppm CO2, or time pace of impact;

· different assumptions regarding farm-level adaptation to mitigate yield impacts;

· policy changes to affect both the reference run and climate change experiments, e.g., population growth, trade policies, economic growth, and policies to reduce emission of greenhouse gases from agriculture, such as limitation of arable land expansion, rice cultivation, or use of chemical fertilizers.

Well over 70 experiments have been simulated. Results from six sets of simulation experiments are reported here:

1. Simulations without physiological effects of 555 ppm CO2 on crop growth and yield.

2. Simulations with physiological effects of 555 ppm CO2 on crop growth and yield.

3. Simulations with physiological effects of 555 ppm CO2 on crop growth and yield, and adaptations to mitigate negative yield impacts at the farm-level that would not involve any major changes in agricultural practices (Adaptation Level 1).

4. Simulations with the physiological effects of 555 ppm CO2 on crop growth and yield, and adaptations at the farm-level that, in addition to the former, would also involve major changes in agricultural practices (Adaptation Level 2).

5. Simulations with the physiological effects of 555 ppm CO2 on crop growth and yield, but with temperature and precipitation changes projected by the GISS GCM for the 2030s.

6. Simulations with the GISS transient run A and associated gradual increases in CO2 level.

METHODS

Data on crop yield changes estimated for the different scenarios of climate change were compiled for 34 countries or major regions of the world. Most models included in the BLS distinguish between yield and acreage functions. The yield response functions of major crops use the level of fertilizer application and a term related to technology as explanatory variables. While technical progress is specified relative to a time trend, the level of fertilizer application is derived from optimality conditions, i.e., by equating the marginal value product of fertilizer to its price. Yield variations caused by climate change were introduced into the yield response functions by means of a multiplicative factor applied to the relevant parameters in the mathematical representation. This implies that both average and marginal fertilizer productivity are affected by the imposed yield changes. Alternative schemes for introducing yield changes are conceivable, e.g., with an additive term in the response functions rather than a multiplier. More empirical knowledge with regard to the effect of climate-induced yield changes on marginal productivity is needed to select the most appropriate implementation.

Since additional country and/or crop specific information to suggest explicit modifications of extents suitable for crop cultivation due to impacts of climate change was not available 3, the land allocation is only indirectly influenced through the implied changes in overall performance of the agricultural sector as well as changing comparative advantage of the competing crop production activities. It should be noted, however, that the BLS is equipped to handle explicit area constraints in the resource allocation module of the agricultural production component.

3 For instance, an assessment of agro-ecological zones (AEZ) under altered climatic conditions as currently being conducted for several countries could provide such information.

The adjustment processes taking place in the different scenarios are the outcome of the imposed yield changes triggering changes in national production levels and costs, leading to changes of agricultural prices in the international national markets. This in turn affects investment allocation and labour migration between sectors as well as reallocation of resources within agriculture. Time is an important aspect in this assessment: the yield modifications due to climate change are assumed to start occurring in 1990, reaching their full impact in year 2060. This allows the economic actors in the national and international food system to adjust their behaviour over a 70-year period. Yet, the dynamic impacts in some of the scenarios are sizeable.

For the GISS transient scenario A, climate change yield impacts were phased in linearly between the climate 'snapshots', i.e., the yield change multiplier terms incorporated in the yield response functions of the BLS are being built up gradually as a function of time between 1990-2010, 2010-2030 and 2030-2050, so as to fully reach the specified impact levels respectively in years 2010, 2030 and 2050. Beyond year 2050 the yield change multiplier is extrapolated extending the trend of the period 2030-2050.

CLIMATE CHANGE YIELD IMPACTS WITHOUT ECONOMIC ADJUSTMENTS

Before assessing the impacts of introducing a set of climate-change-induced yield modifications through simulation with the BLS, we may ask what distortion such a change in agricultural productivity would imply for the world food system. This measure of distortion is termed the static climate change yield impact, as it measures the hypothetical effect of yield changes without adjustments of the economic system taking place over time. It refers to a state of the system that is not in equilibrium. As such it is only of theoretical interest, but helps in the understanding and quantification of the nature and magnitude of adjustments taking place due to changing economic conditions.

Table 9.8 shows static climate change yield impacts estimated for the world and for developed and developing countries. The estimates of static climate change yield impacts, without assuming direct physiological effects of 555 ppm CO2 on crop growth and yields, represent a fairly pessimistic outlook, with decreases in crop productivity on the order of 20 to 30%. Such an assumption is not regarded as very probable and will not be further discussed in the analysis.

When direct physiological effects of CO2 on yields are included, the magnitude and even the direction of the aggregate static impact at the world level varies with GCM climate scenario and with the assumptions regarding farm-level adaptation. In all cases the most negative effects are obtained in scenarios using the UKMO climate change scenario, which has the highest mean global warming, 5.2°C. Results derived from the GISS scenario show only small negative effects or even gains at the global level.

The impacts are, however, quite unevenly distributed. At the aggregate level, developed countries experience an increase in productivity in all but the UKMO scenario. In contrast, developing regions suffer a loss in productivity in all estimates presented here. Table 9.9 shows the continental-level results of scenarios assuming direct physiological effects of increased atmospheric CO2 concentrations (555 ppm) and, where applicable, some farm-level adaptations (Adaptation Level 1). Under the GISS and GFDL climate projections, crop productivity in developed regions benefits substantially, especially in the former USSR and in the Pacific OECD countries (Australia, Japan and New Zealand). Impacts on developing regions are all negative, except for the group of Centrally Planned Asia that includes China. Under the GISS and UKMO GCM scenarios, countries in Central and South America are most affected. The GFDL GCM estimates are worst for West Asia, South Asia, and Africa. Static impacts derived for the GISS-A climate change scenario are mostly positive (except for the GISS-A scenario estimates which take into account the physiological effects of 555 ppm CO2 but assume only modest climate sensitivity to increased atmospheric greenhouse gas concentrations) amounting to ~10% globally. Note, however, that the global increase is estimated to be more than twice the gain in developing countries.

CLIMATE CHANGE YIELD IMPACTS WITH ECONOMIC ADJUSTMENTS

The calculations above discuss an effect that would result if climate-induced yield changes were to occur without agronomic and economic adjustment. In the scenario assumptions, however, yield productivity changes are introduced gradually to reach their full impact only after a 70-year period, from 1990 to 2060. In scenarios with shortfalls in food production caused by climate change, market imbalances push international prices upwards and provide incentives for reallocation of capital and human resources. At the same time, consumers react to price changes and adjust their patterns of consumption.

Table 9.8. Simulated static impact using three GCMs (GISS, GFDL, UKMO)


 

Cereals production % change

Crop production % change

GDP agriculture % change

GISS

GFDL

UKMO

GISS

GFDL

UKMO

GISS

GFDL

UKMO

WORLD TOTAL

without phys. Effect of CO2

-22.1

-21.8

-22.4

-25.4

-24.4

-25.0

-33.6

-33.0

-33.5

with phys. Effect of CO2

-5.1

+2.8

-0.1

-9.0

+0.3

-2.8

-18.2

-8.9

-12.2

Adaptation Level 1

-1.7

+2.8

+0.9

-5.5

+0.3

-1.7

-12.9

-8.3

-10.1

Adaptation Level 2

+1.4

+4.6

+3.2

-1.1

+2.3

+1.0

-6.1

-3.3

-4.4

DEVELOPED

without phys. Effect of CO2

-13.9

-6.1

-10.3

-21.3

-15.3

-18.6

-30.4

-27.1

-28.9

with phys. Effect of CO2

+2.6

+18.6

+10.6

-5.1

+9.6

+2.1

-15.8

-3.2

-9.8

Adaptation Level 1

+7.8

+18.6

+13.1

+0.1

+9.9

+5.0

-6.7

-0.1

-3.6

Adaptation Level 2

+7.8

+18.7

+13.1

+3.3

+9.9

+6.4

-2.8

+1.4

-0.8

DEVELOPING

Without phys. effect of CO2

-28.5

-25.3

-26.5

-28.6

-26.4

-27.1

-36.2

-34.3

-35.1

With phys. effect of CO2

-11.2

-0.7

-3,7

-12.0

-1.8

-4.5

-20.1

-10.2

-13.0

Adaptation Level 1

-9.2

-0.7

-3.2

-10.0

-1.8

-3.9

-17.8

-10.1

-12.3

Adaptation Level 2

-3.6

+1.4

-0.1

-4.5

+0.6

-0.8

-8.7

-4.3

-5.6

Table 9.9. Static climate change impact (%), Adaptation Level 1, year 2060


 

GISS

GFDL

UKMO

GISS-A

Cereals

Other

All crops

Cereals

Other

AH crops

Cereals

Other

All crops

Cereals

Other

All crops

DEVELOPED

North America

+2.7

+12.6

+5.9

-3.8

+3.6

-0.7

-10.8

-9.8

-10.1

+6.6

+22.0

+12.1

Western Europe and other developed market economies

+6.2

+13.5

+10.3

+4.3

+9.6

+7.1

+2.7

+7.3

+5.1

+18.7

+22.2

+20.5

Eastern Europe + former USSR

+17.7

+25.9

+22.8

+2.6

+13.5

+8.6

-8.3

-0.3

-4.0

+14.0

+16.9

+15.9

Pacific OECD countries

+8.3

+13.9

+11.0

+8.5

+10.5

+9.1

+7.4

+8.0

+7.4

+17.6

+17.2

+17.0

DEVELOPING

Africa

-20.6

+0.8

-3.0

-24.0

-4.7

-8.1

-25.6

-7.0

-10.3

-4.7

+7.4

+5.4

Latin America

-16.7

-6.1

-8.7

-14.5

+0.2

-3.2

-22.7

-15.8

-17.7

+3.1

+16.6

+13.8

West Asia

-12.2

-4.6

-6.5

-17.4

-9.2

-11.1

-22.5

-15.0

-16.9

+8.8

+13.5

+12.3

South Asia

-9.8

-4.0

-6.7

-10.7

-6.6

-8.4

-28.8

-26.1

-26.6

+1.8

+7.8

+5.0

Centrally Planned Asia

+3.3

+9.4

+6.9

+1.4

+7.2

+5.0

-0.8

+4.7

+2.6

+1.9

+13.2

+9.7

Pacific Asian countries

-14.9

-11.1

-11.4

-5.6

-3.7

-3.2

-14.8

-5.1

-8.1

+6.1

-3.2

-3.6

Table 9.10. Percentage change in world market prices, year 2060


 

Cereals

All crops

GISS

GFDL

UKMO

GISS-A

GISS

GFDL

UKMO

GISS-A

without phys. effect of CO2

306

356

818

81

234

270

592

70

with phys. effect of CO2

24

33

145

-21

8

17

90

-25

Adaptation Level 1

13

22

98


2

10

67


Adaptation Level 2

-4

2

36


-8

-3

25


Table 9.10 contains changes in world market prices for cereals and an overall crop price index, as observed in the climate change scenarios relative to the standard reference projection. When direct physiological effects of CO2 on plant growth and yields are not included, major increases in world market prices result in four- to nine-fold increases of cereal prices depending on GCM scenario. Apart from the scientific evidence of the beneficial physiological effects of elevated CO2 levels on crop yields, such increases would elicit strong public reaction and policy measures to mitigate the negative yield impacts. Hence, the outcome for scenarios without the physiological effects of CO2 on yields are probably unrealistically extreme.

When the physiological effects of 555 ppm CO2 on yields are included in the assessment, cereal prices increase on the order of 24 to 145% relative to the standard reference projection. The index of crop prices increases by 8 to 90%, depending on GCM climate change scenario. Changes caused by GISS and GFDL scenarios are rather modest, resulting in an increase of about 25 to 33% in cereal prices, and an increase of less than 20% in overall crop prices. Only the yield impacts derived under a climate change as projected by the UKMO GCM produced large agricultural price increases. On the other hand, under the GISS-A climate scenario, where impacts are dominated by positive physiological effects of CO2, major price decreases occur.

Price changes are further reduced when farm-level adaptation is considered. The crop price index rises less than 10% in both GISS and GFDL simulation runs. The UKMO projection, however, still produces a two-thirds crop price increase. Figure 9.2 compares the level of crop prices to that generated in the BLS standard reference projection. Results are shown for three simulation runs with physiological effects of 555 ppm CO2 and farm-level adaptation: GISS and GFDL doubled CO2 runs and a simulation run based on GISS transient scenario A. Note that in the GISS doubled CO2 and GISS transient run, positive crop impacts dominate for about half the simulation period. In the GISS transient run the crop price index falls initially (around year 2010) by as much as 10% below the level of the reference run. Then, as negative impacts in developing countries increase and beneficial impacts in developed countries level off, prices return to and finally exceed the price index of the reference scenario.

With adaptation measures involving major changes in agricultural practices, i.e., Adaptation Level 2, prices would even fall below reference run levels in the GISS and GFDL scenarios. Note that the assumptions underlying Adaptation Level 2, sometimes requiring major investments, may not be economically viable. Scenarios with low-cost adaptation measures, i.e., Adaptation Level 1, appear to be more realistic.

Table 9.11 highlights the estimated dynamic impacts of climate change on agriculture resulting after 70 years of simulation with economic adjustment. According to these calculations, which include an optimistic assessment of direct physiological effects of 555 ppm CO2 on crop yields, the impact on global agriculture GDP would be between -2 and +1 % in all but the UKMO scenarios where it ranges between -2 and -6%. Developed countries are likely to experience some increase in agricultural output. On the contrary, developing countries are projected to suffer a production loss in most scenarios. Table 9.12 lists the simulated regional impacts considering physiological effects of CO2 on crop growth and some farm-level adaptation, Adaptation Level 1. It also includes results from the GISS-A scenario. Among developed regions, simulated positive impacts on agricultural output are largest for Europe, the former USSR and the Pacific OECD countries. Dynamic impacts in developing regions are mostly negative except for Centrally Planned Asia which benefits in all these scenarios.

Figure 9.2. Impact of climate change on crop prices (direct CO2 effects and farm-level adaptation taken into account)

It is important to note that these changes in comparative advantage between developed and developing regions are likely to accentuate the magnitude of the static impacts suggested by the analysis without economic adjustment. Winners are likely to gain more, and losers to lose even more. We can distinguish two prototypical situations in these scenario results. (1) When global supply is only marginally affected, there is little impact on prices. Then the shift in relative productivity from developing to developed regions dominates the adjustment process. For instance, in the GISS and GFDL scenarios with farm-level adaptation, agriculture production shifts somewhat from developing to developed countries taking account of the differences in projected yield changes. (2) When global crop yields are strongly affected, as in the UKMO scenario, the supply gap is so substantial that massive price increases result. These in turn provide production incentives to both regions to recover more than half the production forgone due to climate change (according to static crop model estimates).

Table 9.11. Impact of climate change with economic adjustment, year 2060


 

Cereals production % change

Crop production % change

GDP agriculture % change

GISS

GFDL

UKMO

GISS

GFDL

UKMO

GISS

GFDL

UKMO

WORLD TOTAL

without phys. effect of CO2

-10.9

-12.1

-19.6

-11.5

-12.8

-18.0

-10.2

-11.7

-16.4

with phys. effect of CO2

-1.2

-2.8

-7.6

-0.5

-1.7

-6.4

-0.4

-1.8

-5.4

Adaptation Level 1

0.0

-1.6

-5.2

+0.2

-1.0

-5.0

+0.2

-1.2

-4.4

Adaptation Level 2

+1.1

-0.1

-2.4

+1.1

+0.2

-2.3

+1.0

0.0

-2.0

DEVELOPED

without phys. effect of CO2

-3.9

-10.1

-23.9

+3.8

-5.5

-12.7

+1.1

-6.2

-12.5

with phys. effect of CO2

+11.3

+5.2

-3.6

+15.6

+7.6

-0.9

+11.6

+5.1

-1.9

Adaptation Level 1

+14.2

+7.9

+3.8

+17.6

+9.1

+4.0

+13.3

+6.5

+1.8

Adaptation Level 2

+11.0

+3.0

+1.8

+15.1

+8.6

+2.2

+11.8

+6.5

+1.3

DEVELOPING

without phys. effect of CO2

-16.2

-13.7

-16.3

-16.6

-12.8

-19.8

-13.9

-13.5

-17.7

with phys. effect of CO2

-11.0

-9.2

-10.9

-5.8

-4.9

-8.2

-4.4

-4.0

-6.6

Adaptation Level 1

-11.2

-9.2

-12.5

-5.6

-4.4

-8.1

-4.1

-3.7

-6.4

Adaptation Level 2

-6.6

-5.6

-5.8

-3.6

-2.7

-3.9

-2.6

-2.2

-3.1

Table 9.12. Dynamic impact of climate change (%), Adaptation Level 1, year 2060


Cereal production

Crop production

GDPA 1

GISS

GFDL

UKMO

GISS-A

GISS

GFDL

UKMO

GISS-A

GISS

GFDL

UKMO

GISS-A

DEVELOPED

North America

+10.6

+5.9

-5.2

+4.1

+9.3

+4.8

-3.2

+2.0

+7.5

+3.2

-3.0

+0.8

Western Europe and other developed market economies

+6.5

+7.7

+12,2

+14.7

+10.7

+6.7

+11.7

+14.5

+8.0

+5.2

+7.9

+10.9

Eastern Europe + former USSR

+24.6

+7.6

+6.0

+19.9

+30.7

+12.7

-1.3

+20.1

+26.8

+11.2

-2.7

+17.3

Pacific OECD countries

+19.6

+31.7

+53.2

+4.2

+16.3

+24.0

+52.0

-1.1

+4.0

+5.5

+12.1

+0.2

DEVELOPING

Africa

-23.7

-24.5

-16.2

-8.2

-4.1

-9.3

+1.4

-3.3

-2.2

-7.6

+1.4

-2.8

Latin America

-25.0

-17.8

-14.5

-10.3

-13.8

-2.6

-11.1

+4.0

-10.9

-3.2

-7.9

+1.3

West Asia

-13.6.

-17.0

-18.6

+2.8

-7.9

-11.5

-12.4

+6.3

-5.6

-8.7

-9.5

+5.8

South Asia

-11.9

-8.5

-26.8

-3.5

-7.8

-7.2

-25.1

+2.0

-6.2

-5.2

-20.0

+2.2

Centrally Planned Asia

+4.1

+2.6

+2.1

+1.6

+3.8

+2.5

+1.7

+3.0

+3.3

+2.1

+1.4

+1.9

Pacific Asian countries

-12.3

+0.3

-1.6

-12.8

-14.4

-2.3

-2.3

-11.3

-12.8

-3.1

-3.6

-9.1

1 Gross Domestic Product from Agriculture

The magnitude of the impacts and these different responses at aggregate regional level are shown in Figure 9.3 and Figure 9.4, for cereals and total crops, respectively. Figure 9.5 illustrates the impacts projected by cereal commodity (with physiological effects of 555 ppm CO2 and farm-level adaptation). Accordingly, global wheat production is less likely to suffer negative climate impacts than other cereal commodities; it increases in GISS and GFDL scenarios and declines less than other cereals in the UKMO scenario. This is likely due to the location of major wheat-producing regions at mid and high latitudes, where yield declines are projected to be lower.

Net imports of cereals into developing countries increase under all scenarios. The change in cereal imports, relative to the standard reference projection, is largely determined by the magnitude of the estimated yield change, the change in relative productivity in developing and developed regions, the change in world market prices, and changes in real incomes of consumers in developing countries. For example, under the GISS climate change scenarios, productivity is altered in favour of developed countries with relatively small changes in incomes and prices, resulting in pronounced increases of net cereal imports into developing countries.

With less agricultural production in developing countries and higher prices on international markets, the estimated number of people at risk of hunger is likely to increase. This occurs in all but one scenario (Table 9.13). The largest increase is to be expected from the UKMO scenario without CO2 physiological effects; the smallest change, a decline of 2%, occurred in the GISS scenario considering physiological effects of increased CO2 and Adaptation Level 2.

Conclusions

The distortions of the world food system simulated in the climate change scenarios fall well within the range of estimates obtained from the different reference projections. For instance, the decline in cereal production even under the worst climate change scenario based on the UKMO GCM experiment (assuming physiological effects of increased atmospheric CO2 concentrations and some farm-level adaptation), amounts to less than half the difference between the cereal production levels simulated in the higher and lower economic growth reference projections, scenarios REF-H and REF-L. However, the ability of the world food system to absorb negative yield

Table 9.13. Impact of climate change on people at risk of hunger, year 2060


Additional million people

% change

DEVELOPING (excl. China)

GISS

GFDL

UKMO

GISS-A

GISS

GFDL

UKMO

GISS-A

Without phys. effect of CO2

721

801

1446

265

112

125

225

41

With phys. effect of CO2

63

108

369

-84

10

17

58

-13

Adaptation Level 1

38

87

300


6

14

47


Adaptation Level 2

-12

18

119


-2

3

19


Figure 9.3. Impact of climate change on regional cereal production, year 2060, with physiological effects of 555 ppm CO2 and farm-level Adaptation 1

Figure 9.4. Impact of climate change on regional crop production, year 2060, with physiological effects of 555 ppm CO2 and farm-level Adaptation 1

Figure 9.5. Impact of climate change on cereal commodities, year 2060, with physiological effects of 555 ppm CO2 and farm-level Adjustment 1 impacts decreases with the magnitude of the impact. Economic adaptation can largely compensate for moderate yield changes such as the GISS and GFDL scenarios, but not greater ones such as the UKMO scenario.

The effects of changes in climate on crop yields are likely to vary greatly from region to region across the globe. The results of the scenarios tested in this study indicate that the effects on crop yields in mid- and high-latitude regions appear to be positive or less adverse than those in low-latitude regions, provided the potentially beneficial direct physiological effects of CO2 on crop growth can be fully realized. From a development perspective, the most serious concern relates to the apparent difference in incremental yield impacts between developed and developing countries. The scenario results suggest that if climatic change were to retard economic development beyond the direct effects on agriculture in the poorer regions, especially in Africa, then overall impacts could be sizeable.

In all climate change scenarios, relative productivity of agriculture changes in favour of developed countries, with implications on resource allocation. Economic feedback mechanisms are likely to emphasize and accentuate the uneven distribution of climate change impacts across the world, resulting in net gains for developed countries in all but the UKMO scenarios and a noticeable loss to developing countries. As a result, net imports of cereals into developing countries increase in all scenarios, on the order of 20 to 50% compared to trade in the reference scenario.

Including direct physiological effects of CO2 on crop yields, world cereal production is estimated to decrease between 1 to 3% under GISS and GFDL scenarios, and 7% in the UKMO climate scenario based projections. Assuming adaptation to climate change at farm-level, cereal production would still be reduced between 0 to 2% and 5%, respectively, for the GISS/GFDL and UKMO scenarios. The largest negative changes would occur in developing countries, averaging around -10%. This loss of production in developing countries, together with rising agricultural prices, is likely to increase the number of people at risk of hunger, in the order of 5 to 15 % in the less severe climate scenarios, and ~50% in the UKMO based projections. Under a possibly more realistic climate change scenario with a lower climate sensitivity to increasing greenhouse gas concentrations, aggregate crop productivity at global level increases by ~10% until year 2060, i.e., the end of the simulation period considered in the analysis. Impacts are assessed to be positive in almost all of the 10 aggregate world regions reported in the study. However, the simulated percentage increases in developed countries are about twice the increases in developing regions.

The analysis also shows that the yield impacts do not only vary with geographic region, but are also unequally distributed over time. Results of scenarios based on the GISS transient scenario A demonstrate that benefits from physiological effects due to increasing atmospheric CO2 levels may outweigh negative impacts from changing temperature and precipitation regimes at least in the near-term. The yield-increasing factors in that scenario dominate possible negative impacts until year 2020. Understanding the biophysical processes of CO2 and climate change effects on crops remains an important research area.

It must be realized, however, that the ability to estimate climate change yield impacts on world food supply, demand and trade is surrounded by large uncertainties regarding important elements, such as the magnitude and spatial characteristics of climate change, the range and efficiency of adaptation possibilities, the long-term aspects of technological change and agricultural productivity, and even future demographic trends. Also, the adoption of efficient adaptation techniques is far from certain. In developing countries there may be social, economic or technical constraints, and adaptive measures may not necessarily result in sustainable production over long time-frames.

Determining how countries, particularly developing countries, can and will respond to reduced yields and increased costs of food is a critical research need arising from this study. Will such countries be able to import large amounts of food? Will the burden for adaptation be passed on to the poorest? From a political and social standpoint, the results of the study indicate the potential for a decrease in food security in developing countries. The study suggests that the worst situation arises from a scenario of severe climate change, low economic growth, continuing large population increases, and little farm-level adaptation. In order to minimize possible adverse consequences, like production losses, food price increases, environmental stresses, and an increase in the number of people at risk of hunger, the way forward is to encourage the agricultural sector to continue to develop crop breeding and management programmes for heat and drought conditions, in combination with measures taken to preserve the environment, to use resources more efficiently, and to slow the growth of the human population of the world. The latter step would also be consistent with efforts to slow emissions of greenhouse gases, and thus the rate and eventual magnitude of global climate change.

In the face of these uncertainties, both national and international organizations should encourage the development of new approaches likely to be effective in preparing for climate change. Agricultural research would benefit from increased attention to both macroclimate and microclimate in all experiments and variety trials. Another climate change impact potentially significant for future agricultural production is soil organic matter loss due to soil warming. Considering the vulnerability of agricultural production to the occurrence of climate extremes, research should be directed to determine what are the heat-tolerance limits of currently grown and of alternative crops and varieties. At what threshold values of air or soil temperature do severe problems begin? What agronomic methods are the best to moderate the thermal regime affecting crop growth?

To the extent that the progressive greenhouse effect cannot be prevented in practice, policies should be devised to facilitate the adjustment of agriculture to the likelihood of environmental change. Such adjustments may include modification of agronomic practices, adoption of crops known to be heat-resistant and drought-resistant, increased efficiency of irrigation and water conservation, and improved pest management. Such adjustments are worthy of being implemented in any case, be it with or without climatic change.

Although some countries in the temperate zone may reap some benefits from climate change, many countries in the tropical and subtropical zones appear to be more vulnerable. Particular hazards are the possibly increased flooding of low-lying areas, the increased frequency and severity of droughts in semi-arid areas, and potential decreases in attainable crop yields. It happens that the latter countries tend to be the poorest and the least able to make the necessary economic adjustments. Much of the expected change in global climate is due to the past and present activities of the industrial countries; so it is their responsibility to commit themselves to, and to play an active role in, a comprehensive international effort to prepare for the likely consequences.

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