Regional Assessments

In addition to estimates of regional yield changes that have been developed in the process of global assessments (e.g., Fig. 10.2), there is also a growing wealth of studies focused on particular regions. Indeed, the literature is too vast to provide an exhaustive review.

2 However, higher emissions scenarios can actually reduce near-term impacts since the CO2 fertilization effect responds instantly to higher CO2 levels while the climate system takes several decades to respond.

ECHAM HADCM2 CGCM1

■ India □ Pakistan

Fig. 10.3 Yield changes in India and Pakistan by 2050 (relative to 1961-1990) simulated with the Agro-ecological Zone (AEZ) models of Fischer et al. (2002) for three different climate model scenarios: ECHAM (Max-Planck Institute of Meteorology), HADCM2 (Hadley Centre for Climate Prediction and Research), and CGCM1 (Canadian Centre for Climate Modelling and Analysis). Not only do average yield changes vary with climate scenario, but the relative impacts in India and Pakistan differ greatly, likely due to the spatial distribution of rainfall change

Fig. 10.3 Yield changes in India and Pakistan by 2050 (relative to 1961-1990) simulated with the Agro-ecological Zone (AEZ) models of Fischer et al. (2002) for three different climate model scenarios: ECHAM (Max-Planck Institute of Meteorology), HADCM2 (Hadley Centre for Climate Prediction and Research), and CGCM1 (Canadian Centre for Climate Modelling and Analysis). Not only do average yield changes vary with climate scenario, but the relative impacts in India and Pakistan differ greatly, likely due to the spatial distribution of rainfall change

Instead we present below a brief summary for four key regions. We focus here on projected impacts in the absence of adaptation, with potential effects of adaptation addressed in Chapter 8.

10.3.1 China

Rice remains the main staple in China as it has for thousands of years, accounting for over one-quarter of the calories and roughly one-sixth of the protein consumed in 2003 (FAO 2007). Wheat, soybean, and maize are also important components of the modern Chinese diet, consumed both directly and indirectly via animal products (Chapter 2). Nearly all rice fields in China are irrigated (Huke and Huke 1997), while the majority of wheat and maize fields are rainfed.

China is commonly viewed as facing relatively benign impacts of climate change on agriculture. For example, Rosenzweig and Parry (1994) projected moderate yield declines for rice and maize but slight increases for wheat and soybean (Fig. 10.2). A main reason for the modest impacts is that most of the rainfed crops are concentrated in Northern China where fairly cool temperatures predominate for most of the growing season. In Southern China, where rice is the dominant crop, widespread irrigation is assumed to prevent any significant losses that would arise from greater water stress.

Even with these moderating factors, warming is expected to harm yields in most assessments. Figure 10.4a summarizes estimates of rice yield changes from various crop-modeling studies of China that considered a range of warming. Studies often differ by a factor of two, depending on the crop model used, the projected change in rainfall, and other factors. Yet all show a negative response for warming. A recent Ricardian analysis of revenues from 8,405 households throughout China also found a negative marginal impact of temperature on the average crop revenues (Wang et al. 2008).

Thus, most projected gains in agriculture in China result from a fertilization effect of CO2 that is simulated to overwhelm climate related losses. This CO2 effect is illustrated for rice in Fig. 10.4 by the arrows, whose lengths indicate that the magnitude of CO2 fertilization also differs considerably by study. Some prominent studies appear to include CO2 effects that are much bigger than suggested by recent field experiments (see Chapter 7). For example, one analysis of impacts by 2050 projected rice, wheat, and maize yield losses under an A2 emission scenario of 12.4, 20.4, and 22.8%, respectively, without CO2 fertilization, but yield gains of 6.2, 20.0, and 18.4% with CO2 fertilization (Lin et al. 2005; Xiong et al. 2007). This corresponds to 18.6, 40.4, and 41.2% yield boost from CO2 concentrations of 559 ppm, reflecting a major role of reduced water stress from stomatal closure in the version of the CERES models used in that study.

China

India

China

1

I

I!

4 ,

• W/OUT CO2 fert.

India

I

1

: I

I

| •

• W/OUT CO2 fert.

Local Temperature Change (C) Local Temperature Change (C)

Fig. 10.4 Crop model estimates of rice yield changes for different levels of warming for (a) China and (b) India, as reported in various studies. Black dots indicate effects without CO2 fertilization, and gray dots with CO2 fertilization, with arrows connecting points from the same study. The only difference between points connected by arrows is thus the simulated effect of CO2. Values were derived from three studies for China (Matthews 1995; Lin et al. 2005; Tao et al. 2008), and five for India (Matthews 1995; Lal et al. 1998; Saseendran et al. 2000; Aggarwal and Mall 2002; Krishnan et al. 2007)

Most yield impact assessments for China to date assume a constant supply of irrigation water. Given the crucial role that irrigation plays in Chinese agriculture, however, the potential of climate change to increase or decrease water availability and demand is also of concern. Tao et al. (2008) estimated water use in rice at five stations in China for various climate scenarios, and found in nearly all cases that a shortened growing season resulted in overall lower crop evapotranspiration and water use. Water use was further curtailed when CO2 effects on stomatal closure were taken into account, even when yield gains were simulated. As a result of lower water use, irrigation demand was reduced in nearly all cases except where precipitation was projected to decline. These results suggest that shorter seasons and lower ET rates will tend to diminish water use in agriculture, but the demand for (and surely the availability of) irrigation water will also depend on uncertain precipitation trends.

10.3.2 India

Indian agriculture is characterized by a wide range of crops, most prominent among them rice (44 Mha harvested in 2007), wheat (28 Mha), and millet (11 Mha) (FAO 2007). In contrast to China, most crops in India are grown in relatively hot conditions. Spring and summer temperatures commonly exceed 40°C even in the current climate. Thus, one would expect crops to be more sensitive to warming. Indeed, a survey of rice crop modeling studies indicate that even with CO2 fertilization, warming above 2°C is likely to lower Indian rice yields (Fig. 10.4b). Only a single study with nearly a 40% boost from higher CO2 (Matthews 1995) shows yield gains for more than 2°C warming.

The sensitivity of Indian crops to warming is also evident in statistical analysis of time series data (Table 10.2). Using the approach outlined in Chapter 5 to estimate impacts by 2030, many important crops are anticipated to incur yield losses (Lobell et al. 2008). Combined with the fact that India possesses the highest population of undernourished people in the world (Chapter 2), food security in India appears particularly vulnerable to climate change.

In a Ricardian study, net farm revenues in India were similarly found to respond negatively to warming, with a 12% reduction in revenue for a scenario with 2°C warming and a 7% increase in rainfall (Dinar et al. 1998). Thus, whether using crop models, time series based models, or panel based methods, the expected effects of warming are negative for most crops. In the near term, CO2 benefits may counteract these losses, although not in the case of prominent C4 crops such as millet and sugarcane.

Like China, India is heavily reliant on irrigation and thus the future reliability of water resources will likely play a crucial role in determining the net impact of climate change. Declines in irrigation water, whether resulting from climate change or other factors such as increased urban demand, could greatly increase the sensitivity of crops to higher temperatures. For example, crop model simulations for wheat in Northwest India suggest that the net impact of 1°C warming is roughly double

Table 10.2 Estimated sensitivity of average national yields of Indian crops to a 1°C rise in average growing season temperature, based on time series analysis of 1961-2002 data (adapted from Lobell et al. 2008). The model included both average growing season temperature and rainfall. In several cases (e.g., millet) most of the model's predictive skill came from rainfall, while temperature sensitivities were not significant

Table 10.2 Estimated sensitivity of average national yields of Indian crops to a 1°C rise in average growing season temperature, based on time series analysis of 1961-2002 data (adapted from Lobell et al. 2008). The model included both average growing season temperature and rainfall. In several cases (e.g., millet) most of the model's predictive skill came from rainfall, while temperature sensitivities were not significant

Crop

Percentage contribution to calories in Indian diet

Model r2

Mean

Standard deviation

Wheat

22

0.27

-2.6

0.7

Rice

27

0.63

-4.0

2.0

Sugarcane

14

0.03

-0.1

2.2

Millet

3

0.63

-4.2

4.4

Sorghum

2

0.14

0.8

6.5

Maize

2

0.16

-3.6

2.5

Soybean

2

0.11

-7.4

5.5

Groundnut

2

0.67

-3.4

5.5

Rapeseed

2

0.45

-7.4

2.5

for a scenario reflecting severe water conservation than under business as usual irrigation (Lal et al. 1998). Unfortunately, the future effects of climate change on water resources in South Asia have not, to our knowledge, been closely examined as of this writing.

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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