Page 136 - Climate Change and Food Systems

climate change and food systems: global assessments and implications for food security and trade
figure 3
State-of-the-art agricultural impact modelling chain, from climate to crop and economic effects. Abbreviations: Temp, temperature; Prec, precipitation; Cons, consumption. (Reprinted with permission from the National Academy of Sciences of the United States of America; source: Nelson et al., 2014)
section (Section 2.2) as well as in the discussion of uncertainties (Section 4).
2.2 Major shortcomings
Shortcomings in the current methodology and tools for assessing biophysical impacts of CC
on agriculture and food security have been identified and described (e.g. Rötter et al., 2011a; 2013b; White et al., 2011; Asseng et al., 2013; Rosenzweig et al., 2013; Wheeler and von Braun, 2013). These shortcomings basically originate
from two facts: i) existing biophysical models
were not developed for the unprecedented rate and magnitude of currently projected CC and
thus are likely not to represent the expected novel interrelations with agro-ecosystem processes;
and ii) there is increasing demand for specific requirements by integrated assessment tools. The latter can be illustrated by the challenge in Europe to sustainably intensify agricultural production under CC (Soussana et al., 2012), as formulated by the Modelling European Agriculture with Climate Change for Food Security (MACSUR) project. “Key questions to be addressed are how to increase agricultural production and Europe’s share in global food supply security while concurrently reducing greenhouse gas (GHG) emissions from agriculture” (Rötter et al., 2013b, p.556); in other words,
“what land and water resources, efficiency gains, technologies, investments and institutional settings are required” to substantially increase Europe’s agricultural production by 2050 without increasing GHG emissions.
This implies that present-day Integrated Assessment Modelling (IAM) for agriculture under CC, whether at farm, regional or supranational scale, will demand that many biophysical
output variables be considered simultaneously.
To estimate the consequences of CC and management practices, apart from crop yields, models need to provide data on effects of the production process on environmental indicators such as nitrogen leaching, GHG emissions and water use (e.g. Eckersten et al., 2001; Rötter et al., 2013b; Müller and Robertson, 2014). Figure 3 shows only a rough schematic of integrated assessment, to which several environmental and socio-economic dimensions would need to be added.
Furthermore, when considering not only food production trends in the long term, but also the different dimensions of food security – i.e. stability, access and utilization, as well as food supply – at different temporal and spatial scales (Howden et al., 2007; Wheeler and von Braun, 2013), additional critical shortcomings of the biophysical and integrated impact assessment methodology are revealed. These include:
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