ferent types and levels of conflict resolution; it can be a powerful analytical tool in cases where a single decision-making criterion fails and where impacts (social, ecological or environmental) cannot be assigned monetary values
. Currently, most agricultural technology aims at resolving environmental problems that occur at the small spatial scale (e.g., the plot and farm level), but broad-scale technologies (Stoorvogel and Antle, 2001) are necessary to reveal impacts that are not perceived with site-specific studies. The importance of information technology increases as we scale-up to undertake problems that occur at broader geographical scales. The integration of maps, remote-sensing images, and data bases into geographic information systems (GIS) is needed to assess, monitor and account critical resources and large-scale agroenvironmental processes. This information base, coupled to models and expert systems (De Koning et al., 1999), can help support the application of participatory approaches and multicriteria analysis to resolve present or potential conflicts. Likewise, these tools become tools to support decision-making on large-scale land-use policies and managerial schemes.
The impact of climate change may exacerbate risks of conflict over resources and further increase inequity, particularly in developing countries where significant resource constraints already exist. An estimated 25 million people per year already flee from weather-related disasters and global warming is projected to increase this number to some 200 million before 2050 (Myers 2002); semiarid ecosystems are expected to be the most vulnerable to impacts from climate change refugees (Myers, 2002). This situation creates a very serious potential for future conflict, and possible violent clashes over habitable land and natural resources such as freshwater (Brauch, 2002), which would seriously impede AKST efforts to address food security and poverty reduction.
6.8 Adaptation to Climate Change, Mitigation of Greenhouse Gases
The effectiveness of adaptation efforts is likely to vary significantly between and within regions, depending on geographic location, vulnerability to current climate extremes, level of economic diversification and wealth, and institutional capacity (Burton and Lim, 2005). Industrialized agriculture, generally situated at high latitudes and possessing economies of scale, good access to information, technology and insurance programs, as well as favorable terms of global trade, is positioned relatively well to adapt to climate change. By contrast, small-scale rainfed production systems in semi-arid and subhumid zones presently contend with substantial risk from seasonal and interannual climate variability. Agricultural communities in these regions generally have poor adaptive capacity to climate change due to the marginal nature of the production environment and the constraining effects of poverty and land degradation (Parry etal., 1999).
AKST will be confronted with the challenge of needing to significantly increase agriculture output—to feed two to three billion more people and accommodate a growing urban demand for food—while slowing the rate of new GHG emissions from agriculture, and simultaneously adapting to the negative impacts of climate change on food production. |
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Agriculture will have to become much more efficient in its production if it is to accomplish this without significantly increasing its climate forcing potential. All of this will have to be achieved in a future where agricultural crops may be in direct competition with crops grown for energy purposes as well as without significant extensification and loss of biodiversity.
6.8.1 AKST innovations
6.8.1.1 Technological (high-input) options
Modeling. Climate simulation models indicate the intensification of the hydrologic cycle, climatic conditions which will significantly challenge efforts to control soil erosion and rehabilitate degraded lands even in well-endowed production environments (Nearing, 2004). Tropical soils with low organic matter are expected to experience the greatest impact of erosion on crop productivity because of the poor resilience of these soils to erosive forces, and the high sensitivity of yields to cumulative soil loss (Stocking, 2003; Nearing, 2004). Evidence of significant soil erosion can often be difficult to detect, and its impact on crop productivity can be masked by use of inorganic fertilizer (Knowler, 2004; Boardman, 2006). Extreme events, which significantly contribute to total erosion, are very likely to increase with climate change (Boardman, 2006), as will climate-induced changes in land use that leave soils vulnerable to erosion (Rounsevell et al., 1999).
The improvement of soil erosion modeling capacity can address the role of extreme events in soil erosion and encompass the influence of socioeconomic factors on land use change (Michael et al., 2005; Boardman, 2006). One new technique estimates the impact of more frequent extreme events under different climate scenarios by using meteorological time series projections (Michael et al., 2005). The effects of extreme events on erosion can be more simply modeled with two-dimensional hill slope approaches (Boardman, 2006); GIS can be used to develop landslide hazard maps (Perotto-Baldiviezo et al., 2004).
Recent developments in modeling techniques show potential for estimating the future impact of extreme events, through downscaling from General Circulation Models. Global climate models, however, will continue to be limited by uncertainties (Zhang, 2005). The lack of quantitative data and the technological complexity of many contemporary models are likely to limit the applicability of soil erosion modeling in less developed steep land regions (Morgan et al., 2002; Boardman, 2006). Better field-level assessments of current erosion under different crops and management practices, and, where possible, through integrating GIS into land-use planning could help developing countries assess the impacts of climate change.
Agroecological zone (AEZ) tools used by FAO (FAO, 2000) to determine crop suitability for the world's major ecosystems and climates has potential to enhance efforts to develop crop diversification strategies. The AEZ methodology, which combines crop modeling with environmental matching, allow assessment of the suitability of particular crop combinations given future climate scenarios. However, the data sets that underlie AEZ need to be improved in order to realize the full potential of these tools for crop diversifica- |