Box 56 Will biotechnology assist agricultural and forest adaptation

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Breakthroughs in molecular genetic mapping of the plant genome have led to the identification of bio-markers that are closely linked to known resistance genes, such that their isolation is clearly feasible in the future. Two forms of stress resistance especially relevant to climate change are to drought and temperature. A number of studies have demonstrated genetic modifications to major crop species (e.g., maize and soybeans) that increased their water-deficit tolerance (as reviewed by Drennen et al., 1993; Kishor et al., 1995; Pilon-Smits et al., 1995; Cheikh et al., 2000), although this may not extend to the wider range of crop plants. Similarly, there are possibilities for enhanced resistance to pests and diseases, salinity and waterlogging, or for opportunities such as change in flowering times or enhanced responses to elevated CO2. Yet many research challenges lie ahead. Little is known about how the desired traits achieved by genetic modification perform in real farming and forestry applications. Moreover, alteration of a single physiological process is often compensated or dampened so that little change in plant growth and yield is achieved from the modification of a single physiological process (Sinclair and Purcell, 2005). Although biotechnology is not expected to replace conventional agronomic breeding, Cheikh et al. (2000) and FAO (2004b) argue that it will be a crucial adjunct to conventional breeding (it is likely that both will be needed to meet future environmental challenges, including climate change).

global cereal production by 2080 falls within a 2% boundary of the no-climate change reference production.

Impacts of climate change on world food prices are summarised in Figure 5.3. Overall, the effects of higher global mean temperatures (GMTs) on food prices follow the expected changes in crop and livestock production. Higher output associated with a moderate increase in the GMT likely results in a small decline in real world food (cereals) prices, while GMT changes in the range of 5.5°C or more could lead to a pronounced increase in food prices of, on average, 30%.

Temperature °C

Figure 5.3. Cereal prices (percent of baseline) versus global mean temperature change for major modelling studies. Prices interpolated from point estimates of temperature effects.

Temperature °C

Figure 5.3. Cereal prices (percent of baseline) versus global mean temperature change for major modelling studies. Prices interpolated from point estimates of temperature effects.

5.6.2 Global costs to forestry

Alig et al. (2004) suggest that climate variability and climate change may alter the productivity of forests and thereby shift resource management, economic processes of adaptation and forest harvests, both nationally and regionally. Such changes may also alter the supply of products to national and international markets, as well as modify the prices of forest products, impact economic welfare and affect land-use changes. Current studies consider mainly the impact of climate change on forest resources, industry and economy; however, some analyses include feedbacks in the ecological system, including greenhouse gas cycling in forest ecosystems and forest products (e.g., Sohngen and Sedjo, 2005). A number of studies analyse the effects of climate change on the forest industry and economy (e.g., Binkley, 1988; Joyce et al., 1995; Perez-Garcia et al., 1997; Sohngen and Mendelsohn, 1998; Shugart et al., 2003; see Table 5.4 and Section 5.4.5).

If the world develops as the models predict, there will be a general decline of wood raw-material prices due to increased wood production (Perez-Garcia et al., 1997; Sohngen and Mendelsohn, 1998). The same authors conclude that economic welfare effects are relatively small but positive, with net benefits accruing to wood consumers. However, changes in other sectors, such as major shifts in demand and requirements for energy production, will also impact prices in the forest sector. There are no concrete studies on non-wood services from forest resources, but the impacts of climate change on many of these services will likely be spatially specific.

5.6.3 Changes in trade

The principal impact of climate change on agriculture is an increase in production potential in mid- to high-latitudes and a decrease in low latitudes. This shift in production potential is expected to result in higher trade flows of mid- to high-latitude products (e.g., cereals and livestock products) to the low latitudes. Fischer et al. (2002b) estimate that by 2080 cereal imports by developing countries would rise by 10-40%.

5.6.4 Regional costs and associated socio-economic impacts

Fischer et al. (2002b) quantified regional impacts and concluded that globally there will be major gains in potential agricultural land by 2080, particularly in North America (20-50%) and the Russian Federation (40-70%), but losses of up to 9% in sub-Saharan Africa. The regions likely to face the biggest challenges in food security are Africa, particularly sub-Saharan Africa, and Asia, particularly south Asia (FAO, 2006).

Africa

Yields of grains and other crops could decrease substantially across the African continent because of increased frequency of drought, even if potential production increases due to increases in CO2 concentrations. Some crops (e.g., maize) could be discontinued in some areas. Livestock production would suffer due to deteriorated rangeland quality and changes in area from rangeland to unproductive shrub land and desert.

Asia

According to Murdiyarso (2000), rice production in Asia could decline by 3.8% during the current century. Similarly, a 2°C increase in mean air temperature could decrease rice yield by about 0.75 tonne/ha in India and rain-fed rice yield in China by 5-12% (Lin et al., 2005). Areas suitable for growing wheat could decrease in large portions of south Asia and the southern part of east Asia (Fischer et al., 2002b). For example, without the CO2 fertilisation effect, a 0.5°C increase in winter temperature would reduce wheat yield by 0.45 ton/ha in India (Kalra et al., 2003) and rain-fed wheat yield by 4-7% in China by 2050. However, wheat production in both countries would increase by between 7% and 25% in 2050 if the CO2 fertilisation effect is taken into account (Lin et al., 2005).

5.6.5 Food security and vulnerability

All four dimensions of food security, namely food availability (i.e., production and trade), stability of food supplies, access to food, and food utilisation (FAO, 2003a) will likely be affected by climate change. Importantly, food security will depend not only on climate and socio-economic impacts, but also, and critically so, on changes to trade flows, stocks and food-aid policy. Climate change impacts on food production (food availability) will be mixed and vary regionally (FAO, 2003b, 2005c). For instance, a reduction in the production potential of tropical developing countries, many of which have poor land and water resources, and are already faced with serious food insecurity, may add to the burden of these countries (e.g., Hitz and Smith, 2004; Fischer et al., 2005a; Parry et al., 2005). Globally, the potential for food production is projected to increase with increases in local average temperature over a range of 1 to 3°C, but above this it is projected to decrease. Changes in the patterns of extreme events, such as increased frequency and intensity of droughts and flooding, will affect the stability of, as well as access to, food supplies. Food insecurity and loss of livelihood would be further exacerbated by the loss of cultivated land and nursery areas for fisheries through inundation and coastal erosion in low-lying areas (FAO, 2003c).

Climate change may also affect food utilisation, notably through additional health consequences (see Chapter 8). For example, populations in water-scarce regions are likely to face decreased water availability, particularly in the sub-tropics, with implications for food processing and consumption; in coastal areas, the risk of flooding of human settlements may increase, from both sea level rise and increased heavy precipitation. This is likely to result in an increase in the number of people exposed to vector-borne (e.g., malaria) and water-borne (e.g., cholera) diseases, thus lowering their capacity to utilise food effectively.

A number of studies have quantified the impacts of climate change on food security at regional and global scales (e.g., Fischer et al., 2002b, 2005b; Parry et al., 2004, 2005; Tubiello and Fischer, 2006). These projections are based on complex modelling frameworks that integrate the outputs of GCMs, agro-ecological zone data and/or dynamic crop models, and socio-economic models. In these systems, impacts of climate change on agronomic production potentials are first computed; then consequences for food supply, demand and consumption at regional to global levels are computed, taking into account different socio-economic futures (typically SRES scenarios). A number of limitations, however, make these model projections highly uncertain. First, these estimates are limited to the impacts of climate change mainly on food availability; they do not cover potential changes in the stability of food supplies, for instance, in the face of changes to climate and/or socioeconomic variability. Second, projections are based on a limited number of crop models, and only one economic model (see legend in Table 5.6), the latter lacking sufficient evaluation against observations, and thus in need of further improvements.

Despite these limitations and uncertainties, a number of fairly robust findings for policy use emerge from these studies. First, climate change is likely to increase the number of people at risk of hunger compared with reference scenarios with no climate change. However, impacts will depend strongly on projected socio-economic developments (Table 5.6). For instance, Fischer et al. (2002a, 2005b) estimate that climate change will increase the number of undernourished people in 2080 by 5-26%, relative to the no climate change case, or by between 5-10 million (SRES B1) and 120-170 million people (SRES A2). The within-SRES ranges are across several GCM climate projections. Using only one GCM scenario, Parry et al. (2004, 2005) estimated small reductions by 2080, i.e., -5% (10 [B] to -30 [A2] million people), and slight increases of +13-26% (10 [B2] to 30 [A1] million people).

Second, the magnitude of these climate impacts will be small compared with the impacts of socio-economic development (e.g., Tubiello et al., 2007b). With reference to Table 5.6, these studies suggest that economic growth and slowing population growth projected for the 21st century will, globally, significantly reduce the number of people at risk of hunger in 2080 from current levels. Specifically, compared with FAO estimates of 820 million undernourished in developing countries today, Fischer et al. (2002a, 2005b) and Parry et al. (2004, 2005) estimate reductions by more than 75% by 2080, or by about 560700 million people, thus projecting a global total of 100-240 million undernourished by 2080 (A1, B1 and B2). By contrast, in A2, the number of the hungry may decrease only slightly in 2080, because of larger population projections compared with other SRES scenarios (Fischer et al., 2002a, 2005b; Parry et al., 2004,2005; Tubiello and Fischer, 2006). These projections also indicate that, with or without climate change, Millennium Development Goals (MDGs) of halving the proportion of people at risk of hunger by 2015 may not be realised until 2020-2030 (Fischer et al., 2005b; Tubiello, 2005).

Third, sub-Saharan Africa is likely to surpass Asia as the most food-insecure region. However, this is largely independent of climate change and is mostly the result of the projected socioeconomic developments for the different developing regions. Studies using various SRES scenarios and model analyses indicate that by 2080 sub-Saharan Africa may account for 4050% of all undernourished people, compared with about 24% today (Fischer et al., 2002a, 2005b; Parry et al., 2004, 2005); some estimates are as high as 70-75% under the A2 and B2 assumptions of slower economic growth (Fischer et al., 2002a; Parry et al., 2004; Tubiello and Fischer, 2006).

Fourth, there is significant uncertainty concerning the effects of elevated CO2 on food security. With reference to Table 5.6, under most future scenarios the assumed strength of CO2 fertilisation would not greatly affect global projections of hunger, particularly when compared with the absolute reductions attributed solely to socio-economic development (Tubiello et al., 2007a,b). For instance, employing one GCM, but assuming no effects of CO2 on crops, Fischer et al. (2002a, 2005b) and Parry et al. (2004, 2005) projected absolute global numbers of undernourished in 2080 in the range of 120-380 million people across SRES scenarios A1, B1 and B2, as opposed to a range of 100-240 million when account is taken of CO2 effects. The exception again in these studies is SRES A2, under which scenario the assumption of no CO2 fertilisation results in a projected range of 950-1,300 million people undernourished in 2080, compared with 740-850 million with climate change and CO2 effects on crops.

Finally, recent research suggests large positive effects of climate mitigation on the agricultural sector, although benefits, in terms of avoided impacts, may be realised only in the second half of this century due to the inertia of global mean temperature and the easing of positive effects of elevated CO2 in the mitigated scenarios (Arnell et al., 2002; Tubiello and Fischer, 2006). Even in the presence of robust global long-term benefits, regional and temporal patterns of winners and losers are highly uncertain and critically dependent on GCM projections (Tubiello and Fischer, 2006).

Table 5.6. The impacts of climate change and socio-economic development paths on the number of people at risk of hunger in developing countries (data from Parry et al., 2004; Tubiello et al., 2007b). The first set of rows in the table depicts reference projections under SRES scenarios and no climate change. The second set (CC) includes climate change impacts, based on Hadley HadCM3 model output, including positive effects of elevated CO2 on crops. The third (CC, no CO2) includes climate change, but assumes no effects of elevated CO2. Projections from 2020 to 2080 are given for two crop-modelling systems: on the left, AEZ (Fischer et al., 2005b); on the right, DSSAT (Parry et al., 2004), each coupled to the same economic and food trade model, BLS (Fischer et al., 2002a, 2005b). The models are calibrated to give 824 million undernourished in 2000, according to FAO data.

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