CLIMATE CHANGE: AGRICULTURAL AND ECONOMIC IMPACTS Intersessional Meeting of the Intergovernmental Group on Tea Agricultural Development Economics (ESA) Food and Agriculture Organization of the United Nations
Long-term downward trend in real agricultural prices though-out the 20 th century 1000 Real prices in 2010 $US per metric ton 900 800 700 600 500 400 300 200 100 Rice (Thai) Wheat (US HWT) Maize (US #2) 0 1960 1962 1964 1966 1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Source: World Bank pink sheet (http://go.worldbank.org/4roccieq50, accessed 7-Jan-2014) and own calculations Note: 4-year leading moving average (last year available = 2013). 2
Slowing population growth, however 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Population, SSP2, million HIC ECA EAP LAC MNA SAA SSA 2,414 1,120 665 241 203 107 67 11 2010 2050 Note: 2010-2050 incremental change indicated in 2050 column. High-income (HIC), Europe & Central Asia (ECA), East Asia & Pacific (EAP), Latin America & Caribbean (LAC), Middle East & North Africa (MNA), South Asia (SAA), Sub-Saharan Africa (SSA). 10,000 9,000 8,000 7,000 6,000 5,000 4,000 3,000 2,000 1,000 0 Population, SSP2 v. SSP3, million Developing countries High-income countries SSP3 SSP2 SSP2 SSP3 2010 2015 2020 2025 2030 2035 2040 2045 2050 3
GDP per capita under SSP2 and SSP3, $2007 70,000 2010 2050 SSP2 2050 SSP3 1.3 60,000 1.1 50,000 40,000 30,000 20,000 10,000 0 2.0 0.8 3.6 2.0 4.8 3.2 World Developing East Asia & Pacific 5.1 3.3 South Asia 2.8 1.8 Europe & Central Asia 2.1 1.3 Middle East & North Africa 3.6 1.6 Sub-Saharan Africa 2.5 1.2 Latin America & Caribbean High-income Note: Growth rates, percent per annum, on top of columns. 4
History vs. projected yield growth, percent per annum 4.5 1970/1990 1990/2010 2010/2030 2030/2050 4.0 3.5 3.0 2.5 2.0 1.5 1.0 0.5 0.0 World Developing High-income World Developing High-income World Developing High-income Wheat Rice Maize Source: 1970/2010 FAOSTAT (accessed 22-Jul-2013), IFPRI s IPRs and own calculations Note: Slight differences in regional aggregations between history and projections. Maize yield projections equivalent to coarse grain definition in GTAP. 5
Variation of world prices across commodities in 2050 2.5 Price index in 2050 (2005=1) 2.0 1.5 1.0 0.5 AGR WHT RIC CGR CR5 Note: All agriculture (AGR), wheat (WHT), rice (RIC), coarse grains (CGR), index for wheat, rice, coarse grains, oil seeds and sugar (CR5). Source: AgMIP global economic runs, February 2013 and own calculations. 6
The climate modeling chain: from biophysical to socioeconomic Climate Biophysical Economic General circulation models (GCMS) Temp Prec Global gridded crop models (GGCMs) Yield (Biophysical) Global economic models Area Yield Cons Trade RCP s Farm practices CO 2 Pop. GDP Source: Nelson et al., PNAS (2013). 7
An extreme climate scenario? RCP 8.5 was selected Currently on path consistent with 8.5 w/m 2 Excludes potentially positive effects of increasing CO 2 concentration And crop models assume constant management practices (e.g. sowing dates) Is this the worst case? Crop models ignore: Tropospheric ozone (spatially differentiated) Pests, weeds and diseases Extreme events 8
Four potential yield outcomes for maize in 2045 under RCP 8.5 Source: Müller and Robertson (2014). Excludes CO 2 effects. 9
Simulated impacts for the four climate scenarios: global average for major crops in 2050 wrt reference 5 Wheat Rice Coarse grains Oil seeds Sugar CR5 0-5 -10-15 -20-25 IPSL/LPJ HADGEM2/LPJ IPSL/DSSAT HADGEM2/DSSAT Source: Shocks from IFPRI as interpreted for use in the ENVISAGE model, Nelson, van der Mensbrugghe et al. (2014). 10
Climate induced changes to yields, land use, production, trade, consumption and prices in 2050 60 40 Percent change 20 0-20 -40-60 n Mean SD YEXO YTOT AR EA PROD TRSH CONS PRICE -0.17-0.11 0.11-0.02-0.01-0.03 0.2 (0.131) (0.166) (0.249) (0.25) (0.264) (0.063) (0.242) Source: Nelson et al., PNAS (2013). 11
Take away messages Long-term price trends depend on population and income growth and evolution of yields. Climate impacts will negatively affect prices, with many of the increases ranging from 5-25%. Analysis is complicated by significant uncertainty climate, impacts of climate changes and future economic structure. 12
Further reading Special issue of Agricultural Economics (2014): http://onlinelibrary.wiley.com/doi/10.1111/agec.2014.45.issue-1/issuetoc von Lampe, Willenbockel et al., Why do global long-term scenarios for agriculture differ? An overview of the AgMIP Global Economic Model Intercomparison Robinson, van Meijl, Willenbockel et al., Comparing supply-side specifications in models of global agriculture and the food system Valin, Sands, van der Mensbrugghe et al., The future of food demand: understanding differences in global economic models Schmitz, van Meijl et al., Land-use change trajectories up to 2050: insights from a global agro-economic model comparison Müller and Robertson, Projecting future crop productivity for global economic modeling Nelson, van der Mensbrugghe et al., Agriculture and climate change in global scenarios: why don t the models agree Lotze-Campen, von Lampe, Kyle et al., Impacts of increased bioenergy demand on global food markets: an AgMIP economic model intercomparison Proceedings of the National Academy of Sciences (PNAS) (2013): http://www.pnas.org/content/early/2013/12/12/1222465110.full.pdf+html Nelson et al., Climate change effects on agriculture: Economic responses to biophysical shocks Special issue 13