Global Assessments

The first major studies of global agricultural impacts began roughly 20 years ago, as agriculture was one of the first sectors for which impacts of climate change were thought to be important (Kane et al. 1992; Rosenzweig and Iglesias 1994; Rosenzweig and Parry 1994). Then, as now, these efforts focused on linking three basic modeling pieces that had been previously developed and applied independently: (1) models of climate response to higher CO2; (2) models of crop yield responses to climate change, higher CO2, and, in some cases, potential farmer adaptations; and (3) models of adjustments in the world food economy in response to differential yield effects in different regions (Fig. 10.1). Some studies focused solely on changes in worldwide aggregate production and prices, while others also investigated changes in food security, typically measured by the number of malnourished.

One of the first seminal assessments considered the global impacts of doubling CO2 from pre-industrial levels (Rosenzweig and Parry 1994) by utilizing a network of crop modelers from around the world, who provided estimates of yield impacts from locally calibrated models for prescribed climate changes in over 100 sites in 18 countries. These site-level estimates were then used to infer national level

Climate Models

Crop Yield Models

World Food Trade Model

Examples:

GISS, GFDL, UKMO, CSIRO

Examples:

CERES,EPIC, ORYZA, AEZ

Examples: BLS, IMPACT

Fig. 10.1 Outline of a common approach to estimating global impacts of climate change on food prices, production, trade, and hunger. Climate scenarios generated from climate models are used to drive crop yield models for individual locations. The simulated yield responses are then summed for different regions and input into a food trade model, which determines the equilibrium price for each commodity and the associated crop areas, yields, production, and trade for each region. The price changes are also often used to estimate the change in number of people at risk of hunger

1 For example, yield changes in Rosenzweig and Parry (1994) were prescribed by interpolating results from crop model simulations at individual sites, of which the only sites in sub-Saharan Africa were for maize in Zimbabwe.

production changes for all cereals in all countries, based on similarities of agronomic characteristics among crops and agro-ecological environments among countries. The results were then aggregated into regional yield changes according to the regions defined in the Basic Linked System (BLS) model of agricultural trade.

Figure 10.2 presents the yield changes used as input to BLS for the four major commodity groups treated in that study: wheat, rice, coarse grains, and oilseeds. These

■ Coarse grains

Turkey

Thailand

Former USSR + Eastern Europe

Pakistan

Nigeria

New Zealand

N.E.Asia Oil Exporter High Inc. N.E.Asia Med.-Low Inc. Mexico

Latin America Med.-Low Inc.

Latin America High Inc. Cal. Importers

Latin America High Inc. Cal. Exporters

Kenya

Japan

Indonesia

India

F.E.Asia Low Inc.

F.E.Asia High-Med. Inc. Cal. Importers F.E.Asia High-Med. Inc. Cal. Exporters Europe

Egypt + Similar Countries China + Similar Countries Canada

Brazil + Similar Countries

Australia

Argentina

Africa Oil Exporters

Africa Med. Inc. Cal. Importers

Africa Med. Inc. Cal. Exporters

Africa Low Inc. Cal. Importers

Africa Low Inc. Cal. Exporters

Fig. 10.2 Yield changes for doubled CO2 (climate change plus CO2 fertilization effects) used as input into global trade model in Rosenzweig and Parry (1994), based on crop model simulations for 100+ sites. Bars show yield changes using climate scenarios from the NASA GISS climate model, "+" indicates values for GFDL climate model, and "x" for UKMO climate model. The magnitude of CO2 fertilization was 4%, 11%, 12%, and 17% for coarse grains, rice, wheat, and oilseeds, respectively. All yield changes correspond to simulations without any farmer adaptation results exhibit some of the main features of yield changes in most global assessments. First, the net yield impacts of climate change and doubled CO2 tends to be negative for most but not all region-commodity combinations. Second, high latitude countries tend to have lower impacts than tropical countries because they start from a cooler baseline. Third, C3 crops (rice, wheat, and oilseeds) tend to have lower impacts than maize because of greater CO2 fertilization. Fourth, yield impacts varied substantially for different climate model scenarios, three of which were considered in the study.

An important innovation by Rosenzweig and colleagues was to examine the potential impact of adaptation in a systematic way. Each modeling group was asked to perform simulations with no adaptation (i.e. climate change and CO2 effects only, as shown in Fig. 10.2), with "level 1" adaptations, where tactical decisions such as planting date and cultivar choice were optimized, and with "level 2" adaptations, which included more costly adaptations such as development of new irrigation infrastructure and new crop varieties. This design allowed an evaluation of the benefits of both small and large investments in adaptation.

Table 10.1 summarizes the global production changes that result from the yield changes illustrated in Fig. 10.2, as well as those under different adaptation scenarios. The authors concluded that, assuming the full effects of CO2 fertilization were realized, impacts on global cereal production ranged from negligible to slight declines (<10%) depending on the climate model used. Level 1 adaptations had a fairly small effect on overall impacts, but more expensive level 2 adaptations were effective in minimizing negative outcomes.

The associated changes in cereal prices for doubled CO2 and no adaptation ranged from 25% to 150% for the three climate scenarios, with increases in the number of malnourished by 10-60% (malnourishment prevalence in the BLS model increased by roughly 1% for each 2.5% increase in prices). For adaptation level 2, when global production changes ranged from +1 to -2%, price changes ranged from -5% to +35%, and malnourished populations changed by between -2% and +20%. The role of on-farm vs trade adaptations in these projections are discussed further in Chapter 8.

Many subsequent global assessments have been conducted since the early 1990s (Reilly et al. 1994; Parry et al. 1999; Fischer et al. 2002; Darwin 2004; Parry et al. 2004; Fischer et al. 2005), providing some consensus on several key points:

Table 10.1 The projected impacts of doubled CO2 on global cereal production (% change) for different climate models, adaptation levels, and with and without CO2 fertilization (from Rosenzweig and Parry 1994; see text for details on adaptation levels)

Scenario

GISS

GFDL

UKMO

Climate change only

-11

-12

-20

With CO2 fertilization

-1

-3

-8

With CO2 and adaptation Level 1

0

-2

-6

With CO2 and adaptation Level 2

1

0

-2

Climate models: GISS = Goddard Institute for Space Studies (4.2, 11); GFDL = Geophysical Fluid Dynamics Laboratory (4.0, 8); UKMO = United Kingdom Meteorological Office (5.2, 15). Numbers in parentheses are global average change in temperature (°C) and precipitation (%) for each model.

Climate models: GISS = Goddard Institute for Space Studies (4.2, 11); GFDL = Geophysical Fluid Dynamics Laboratory (4.0, 8); UKMO = United Kingdom Meteorological Office (5.2, 15). Numbers in parentheses are global average change in temperature (°C) and precipitation (%) for each model.

(i) Global price increases associated with a doubling of CO2 range from negligible for moderate climate change to significant for more extreme climate scenarios. As a doubling of CO2 is likely to be reached near mid-century, studies that evaluate transient scenarios out to 2100 generally also consider higher CO2 levels. Long-term price impacts tend to increase as CO2 levels are increased, because the positive effects of higher CO2 are increasingly outweighed by the negative impacts of associated climate changes.2 Equilibrium price changes thus often exceed 10% by the end of the century for most emissions scenarios (Easterling et al. 2007).

(ii) Impacts are generally more negative for developing countries than developed countries. This arises mainly from the fact that most developing countries are in tropical climates with a warmer baseline climate, so that warming more quickly pushes crops beyond their optimum temperature range. In addition, tropical countries tend to rely more on C4 crops like maize, sorghum, and millet that exhibit small CO2 fertilization effects. As a result of more detrimental effects in developing nations, trade models anticipate substantial expansion of trade flows from North to South.

(iii) Although the general North-South gradient in impacts is seen in most models, there can be substantial heterogeneity within regions owing to the specific patterns of rainfall and temperature changes. For example, the United States exhibited among the most severe yield losses out of all nations in a study that used the Hadley Center's HadCM3 model (Parry et al. 1999). Even neighboring countries can exhibit quite different responses depending on details of rainfall simulations. This is illustrated by simulated cereal yield impacts by 2050 in India and Pakistan from Fischer et al. (2002), where impacts ranged from 16% higher to 10% lower in India relative to Pakistan depending on the climate model (Fig. 10.3).

(iv) Adaptations can substantially reduce the impacts of climate change, but relatively easy options such as planting date shifts generally have only a small impact while more expensive changes provide most of the benefit (see Chapter 8). The simulated benefits of adaptations are tempered by two caveats. First, few studies have explicitly incorporated the costs of these adaptations into measures of economic impact, nor have they performed a clear cost-benefit analysis. Second, most studies find that adaptation is likely to proceed more effectively in developed nations, thus exacerbating the North-South gradient in impacts and the trade imbalances that result. Additional issues such as how quickly farmers can actually perceive climate trends are discussed in Chapter 8.

Renewable Energy 101

Renewable Energy 101

Renewable energy is energy that is generated from sunlight, rain, tides, geothermal heat and wind. These sources are naturally and constantly replenished, which is why they are deemed as renewable. The usage of renewable energy sources is very important when considering the sustainability of the existing energy usage of the world. While there is currently an abundance of non-renewable energy sources, such as nuclear fuels, these energy sources are depleting. In addition to being a non-renewable supply, the non-renewable energy sources release emissions into the air, which has an adverse effect on the environment.

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