Dynamic Climate Change Yield Impact

The calculations above paint an effect that would result if climate induced yield changes were to occur without adjustment, overnight so to say. In the BLS scenario assumptions, however, yield productivity changes are in- troduced gradually to reach their full impact only after a 70 year period, 1990 to 2060. In scenarios with shortfalls in food production caused by cli- mate change yield impacts, market imbalances cause international prices to change upwards and provide incentives to reallocation of capital and human

Table 1. Static climate change yield impact, in year 2060. crop prices, as observed in the respective climate change yield impact scenar- ios relative to the BLS standard reference scenario. When direct physiolog- ical effects of C 0 2 on plant growth and yields are not included, then major increases in world market prices - four t o nine fold increases of cereal prices depending on GCM scenario - would result. Note that such increases would call for strong public reactions and policy measures to mitigate the negative yield impacts. Hence, the outcome for scenarios without the physiological effects of C 0 2 on yields, as shown in Tables 3 and 4, should be interpreted with care, both for their agronomic as well as economic assumptions.

When physiological effects of 555 ppm C 0 2 on crop growth and yields are included in the assessment, then cereal prices increase on the order of

Table 2. Percent change in world market prices, year 2060. change scenario. Price increases are further reduced when farm level adap- tation is considered in addition. With adaptation measures involving major changes in agricultural practices, adaptation level 2, prices would even fall below reference run levels in the GISS and GFDL scenarios. Note that t h e assumptions underlying adaptation level 2 are hardly consistent with such an economic development, so that the stipulated adaptations would often not be viable.

Table 3 highlights the dynamic impacts of climate change on agriculture resulting after 70 years of simulations with the IIASA general equilibrium world model. According t o these calculations, and with direct physiological effects of 555 ppm COz on crop yields, the impact on global agriculture G D P would be less than 2 percent in all but the UKMO scenarios where decreases range between -2 and -5 percent. Developed countries are even likely t o experience a fair increase in output. In contrast, developing countries are projected t o suffer a production loss in all the analyzed scenarios. It is also important t o note that these changes in comparative advantages between developed and developing regions are likely t o amplify the size of t h e impacts suggested by the static analysis. Figures 1 , 2 and 3 illustrate this observation, showing t h e estimated static climate change yield impact on agriculture vis- a-vis t h e simulated dynamic changes in G D P agriculture. Figure 4 shows t h e spatial distribution of changes in G D P of agriculture obtained for different GCMs assuming some adaptations a t farm level (adaptation level 1).

With less agricultural production in developing countries and higher prices on international markets, it does not come as a surprise that t h e

% change WORLD

r I

Odynamic % change DEVELOPING -5 Wllli AD1 AM WITH with phys. effects of C02 AD1 with phys. effects, adaptation level 1 AD2 with phys. effects, adaptation level 2

Figure 2. GFDL climate change, static and dynamic impact on GDPA.

Figure 3. UKMO climate change, static and dynamic impact on GDPA.

Figure 4a. Dynamic impact on GDPA-GISS GCM (Adaptation Level 1).

&

%

, o , o , o , o S

? ? ? ?

+

Figure 4b. Dynamic impact on GDPA-GFDL GCM (Adaptatic 1).

In Level

Figure 4c. Dynamic impact on GDPA-UKMO GCM (Adaptation Level 1).

Table 3. Dynamic impact of climate change, year 2060.

Cereals production G D P agriculture

(% change) (% change)

GISS GFDL UKMO GISS GFDL UKMO WORLD TOTAL

Without phys. effect of CO2 -10.9 -12.1 -19.6 -10.2 -11.7 -16.4 With phys. effect of CO2 -1.2 -2.8 -7.6 -0.4 -1.8 -5.4 Adaptation level 1 0.0 -1.6 -5.2 0.2 -1.2 -4.4

Adaptation level 2 1.1 -0.1 -2.4 1.0 0.0 -2.0

DEVELOPED

Without phys. effect of CO2 -3.9 -10.1 -23.9 1.1 -6.2 -12.5 With phys. effect of CO2 11.3 5.2 -3.6 11.6 5.1 - 1.9

Adaptation level 1 14.2 7.9 -3.8 13.3 6.5 1.8

Adaptation level 2 11.0 3.0 1.8 11.8 6.5 1.3

WORLD TOTAL

Without phys. effect of C 0 2 -16.2 -13.7 -16.3 -13.9 -13.5 -17.7 With phys. effect of COz -11.0 -9.2 -10.9 -4.4 -4.0 -6.6 Adaptation level 1 -11.2 -9.2 -12.5 -4.1 -3.7 -6.4 Adaptation level 2 -6.6 -5.6 -5.8 -2.6 -2.2 -3.1

Table 4. Impact of climate change on people a t risk of hunger, year 2060.

Additional million people % change GISS GFDL UKMO GISS GFDL UKMO DEVELOPING (excl. China)

Without phys. effect of C 0 2 721 801 1446 112 125 225 With phys. effect of C 0 2 63 108 369 10 17 58

Adaptation level 1 38 87 300 6 14 4 7

Adaptation level 2 - 12 18 119 -2 3 19

Net imports of cereals t o developing countries increase under all scenar- ios. The change in cereal imports, relative t o the standard reference sce- nario, is largely determined by the size of the estimated static yield change, the change in relative productivity in developing and developed regions, the change in world market prices, and changes in incomes of developing coun- tries.

4. Conclusions

The impact of climate change on agriculture and global food supply has been evaluated with a system of linked national models, called the Basic Linked System. Several scenarios of climate induced yield changes have

been derived, based on a large number of site specific yield simulations with IBSNAT crop models. Considerable uncertainty still surrounds the magni- tude and spatial pattern of expected climate change and the resulting impact on crop yields. The effects of changes in climate on crop yields are likely to vary greatly from region t o region across the globe. Under the climatic scenarios adopted in this study, the effects on crop yields in mid and high latitude regions appear t o be positive or less adverse than those in low lat- itude regions, provided the potentially beneficial direct physiological effects of increased COz concentrations on crop growth can be fully realized.

Results of each simulated climate change yield impact scenario are com- pared t o a reference scenario. The latter is a projection of the world food and agriculture system till the year 2060. Under the assumptions of the BLS standard reference scenario, agriculture can satisfy effective demand for food a t prices even lower than observed a t present. However, this scenario also clearly shows that, unless the poor receive a higher income share, there will still be a substantial number of people a t risk of hunger, increasing from an estimated 500 million people in 1980 t o some 600 million in year 2000 and reaching about 640 million in year 2060.

The ability of the world food system t o dynamically absorb negative yield impacts decreases with the magnitude of the impact. Adaptation can largely compensate for moderate impacts of climate change such as under the GISS and GFDL scenarios but not greater ones like under the UKMO scenario.

Relative productivity of agriculture in all climate change yield impact scenarios changes in favor of developed countries. Economic feedback mech- anisms are likely t o emphasize and accentuate the uneven distribution of cli- mate change impacts across the world, resulting in a net gain for developed countries in all but the UKMO scenarios and a noticeable loss t o developing countries. This loss of production in developing countries, together with ris- ing agricultural prices, is likely t o increase the number of people a t risk of hunger, on the order of 5 t o 50 percent depending on the GCM scenario.

It must be realized that the ability t o estimate climate change yield impacts on world food supply, demand, and trade is severely limited 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.

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