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Hunger and Food Security
The document discusses the challenges of hunger and food security, highlighting important trends and statistics regarding food prices, agricultural trade, and malnutrition. It emphasizes the impact of climate change on food systems and the need for improved policies to address these issues, particularly in regions like sub-Saharan Africa. The analysis also includes projections for population growth and changes in global trade dynamics related to agriculture.
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Hunger and Food Security
- 1. Hunger and FoodSecurity Major challenges we are facing today Maximo Torero (m.torero@cgiar.org) Monday 14th March, 12:30 – 14:00 Lunchtime Conference External Cooperation Infopoint Rue de la Loi 43, Ground floor
- 2. Real Price Evolutionin US$ 2015 0.00 500.00 1000.00 1500.00 2000.00 2500.00 3000.00 1960M01 1961M01 1962M01 1963M01 1964M01 1965M01 1966M01 1967M01 1968M01 1969M01 1970M01 1971M01 1972M01 1973M01 1974M01 1975M01 1976M01 1977M01 1978M01 1979M01 1980M01 1981M01 1982M01 1983M01 1984M01 1985M01 1986M01 1987M01 1988M01 1989M01 1990M01 1991M01 1992M01 1993M01 1994M01 1995M01 1996M01 1997M01 1998M01 1999M01 2000M01 2001M01 2002M01 2003M01 2004M01 2005M01 2006M01 2007M01 2008M01 2009M01 2010M01 2011M01 2012M01 2013M01 2014M01 2015M01 2015USDperMetricTons Soybeans (US$/mt) Maize (US$/mt)
- 3. Periods of ExcessiveVolatility Note: This figure shows the results of a model of the dynamic evolution of daily returns based on historical data going back to 1954 (known as the Nonparametric Extreme Quantile (NEXQ) Model). This model is then combined with extreme value theory to estimate higher-order quantiles of the return series, allowing for classification of any particular realized return (that is, effective return in the futures market) as extremely high or not. A period of time characterized by extreme price variation (volatility) is a period of time in which we observe a large number of extreme positive returns. An extreme positive return is defined to be a return that exceeds a certain pre-established threshold. This threshold is taken to be a high order (95%) conditional quantile, (i.e. a value of return that is exceeded with low probability: 5 %). One or two such returns do not necessarily indicate a period of excessive volatility. Periods of excessive volatility are identified based a statistical test applied to the number of times the extreme value occurs in a window of consecutive 60 days. Source: Martins-Filho, Torero, and Yao 2010. See details at http://www.foodsecurityportal.org/soft-wheat-price-volatility-alert-mechanism. 2014 Please note Days of Excessive volatility for 2014 are through March 2014 2015
- 4. Different problems butsame policies
- 5. GLOBAL CHALLENGE Source: JohanRockstrom: Let the environment guide our development Growing Human Pressure Climate change Ecosystem decline Surprise
- 6. 6 Bigger population inurban areas will demand more and better food 36% POPULATION GROWTH Change in population by region 2010-2100 (millions) 182 millions 97 millions -63 millions 2,552 millions 432 millions 29 millions Africa: Younger Asia and Europe: Older Source:UN 2011
- 7. Calorie consumption vstotal Cereal Equivalent Consumption 0.511.52 (tons/capita/year) 0 5000 1000015000 0 10000 20000 30000 40000 50000 Real GDP(PPP) per capita in 2005 int. $ 1980-2009 China Calorie Consumption Fitted Calorie Consumption China CE Consumption Fitted CE Consumption Source: Fukase, E. and Martin, W. (2015)
- 8. Different types ofchildhood malnutrition (abstract)
- 9. - 200 400 600 800 1,000 1,200 Africa south ofthe Sahara South Asia Developing Countries Slow decline in malnourishment. Alarming increase in obesity. Stunted children (millions) 0 10 20 30 40 50 60 1990 1995 2000 2005 2010 2015 2020 Overweight & obese children (millions) Source: FAOSTAT3 (http://faostat3.fao.org/download/D/FS/E). Source: UN in de Onis, M, M. Blössner and E. Borghi. 2010. Global prevalence and trends of overweight and obesity among preschool children. American Journal of Clinical Nutrition 92:1257–64. (http://www.who.int/nutgrowthdb/publications/overweight_obesity/en/). Undernourished people (millions) 0 50 100 150 200 250 300 1990 1995 2000 2005 2010 2015 2020 Source: de Onis, M, M. Blössner and E. Borghi. 2011 http://www.who.int/nutgrowthdb/publications/Stunting1990_2011.pdf. Africa Asia Developing Countries Africa Asia Developing Countries
- 10. WATER STRESS RISK 2.5 US$9.4 TRILLION Source:Veolia Water & IFPRI 2011. BILLION PEOPLE TODAY Total population living in water scarce areas Global GDP generated in water scarce regions US$63 TRILLION Total population living in water scarce areas 4.7 BILLION PEOPLE 90% 570% By 2050 Global GDP generated in water scarce regions 52% 49% 45% 36% 39% 22% population grain production global GDP
- 11. HEAVY TOLL ONRAINFED MAIZE WITH CLIMATE CHANGE Global yields projected 30% lower in 2050 compared to no climate change Source: IFPRI IMPACT simulations.(HadGEM2, RCP 8.5)
- 12. 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 2010 2015 20202025 2030 2035 2040 2045 2050 Source: IFPRI IMPACT 3.2 Projections. FOOD PRICES INCREASE WITHOUT CLIMATE CHANGE; EVEN HIGHER WITH CLIMATE CHANGE No climate change Average with climate change With climate change - range across models (Indexed to 1 in 2010) 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 2010 2015 2020 2025 2030 2035 2040 2045 2050 Cereals Roots/tubers 2010 = 1 2010 = 1
- 13. Sources: 1969-71 to1999-2001 from Alexandratos 2006; 2010-2050 from IFPRI's IMPACT 3.2 Projections. Per capita food consumption grows. Africa and South Asia catching up. 0 500 1000 1500 2000 2500 3000 3500 4000 World Industrial countries Developing countries South Asia Africa south of the Sahara Per capita food consumption (kcal/person/day) 1979/1981 2010 2050
- 14. 0 100 200 300 400 500 600 700 800 900 2010 2050, No ClimateChange 2050, With Climate Change Source: IFPRI IMPACT 3.2 Projections. Improved progress on hunger, but too slow. Climate change increases hunger. Undernourished people (millions) Developing countries South Asia Africa south of the Sahara
- 15. A continuous trendtowards internationalization of food markets 1975 1985 1995 2005 2015 18.2% 13.9% 12.3% 19.1% 16.1% Share of produced calories crossing an international border
- 16. Globalization and/or Regionalization - 2,000 4,000 6,000 8,000 10,000 12,000 Averagedistance (km) travelled by imported calories 1995 2000 2005 2010
- 17. Evolution by regionof the price support through border measure -60.0% -50.0% -40.0% -30.0% -20.0% -10.0% 0.0% 10.0% 20.0% Africa Asia Eastern Europe LAC HIC World Average Nominal Rates of Assistance (NRA) through Border Measure 1975 1985 1995 2005
- 18. Cumulative number ofpreferential trade agreements (PTAs) in force Note: it includes notified and non-notified PTAs by country group
- 19. But mega tradedeals are becoming strategic TPP USA TTIP TISA Vietnam Brunei Singapore Malaysia Australia Canada Taiwan Japan Chile Mexico Peru South Korea New Zealand Norway Switzerland Iceland Israel Uruguay Hong Kong Costa Rica Colombia Panama Paraguay Turkey Austria Bulgaria Belgium France Czech Republic Ireland Denmark Germany Estonia Portugal Romania Poland Netherlands Lithuania UK Sweden Luxembourg Slovenia Hungary Slovenia Italy Spain Greece Finland Malta Croatia Lativia Cyprus
- 20. Economic Slowdown Comparison of2012 and 2015 GDP growth projections for 2017 (selection of countries) Source: World Economic Outlook (2015 and 2012) - IMF
- 21. Economic Slowdown World CommodityPrice Projections (2015) Source: World Economic Outlook (2015 and 2012) - IMF 40 60 80 100 120 140 2013 2014 2015 2016 2017 PriceIndex100=2011 Year Crude Oil (petroleum), simple average of three spot prices; Dated Brent, West Texas Intermediate, and the Dubai Fateh, US$ per barrel Commodity Natural Gas Price Index includes European, Japanese, and American Natural Gas Price Indices Commodity Coal Price Index includes Australian and South African Coal Wheat, No.1 Hard Red Winter, ordinary protein, FOB Gulf of Mexico, US$ per metric tonne Maize (corn), U.S. No.2 Yellow, FOB Gulf of Mexico, U.S. price, US$ per metric tonne Rice, 5 percent broken milled white rice, Thailand nominal price quote, US$ per metric tonne Soybeans, U.S. soybeans, Chicago Soybean futures contract (first contract forward) No. 2 yellow and par, US$ per metric tonne Palm oil, Malaysia Palm Oil Futures (first contract forward) 4-5 percent FFA, US$ per metric tonne Beef, Australian and New Zealand 85% lean fores, FOB U.S. import price, US cents per pound Poultry (chicken), Whole bird spot price, Georgia docks, US cents per pound
- 22. Economic Slowdown Projections ofEffects: Global Poverty Source: Laborde and Martin (2016)
- 23. Economic Slowdown Projections ofEffects: Net and gross movements into and out of poverty, Scenario 1, Percentage points, Total Population. Source: Laborde and Martin (2016) Note: Poverty is defined by the $1.90 PPP 2011 threshold.
- 24. Economic Slowdown Projections ofEffects: Net and gross movements into and out of poverty, Scenario 2, Percentage points, Total Population. Source: Laborde and Martin (2016) Note: Poverty is defined by the $1.90 PPP 2011 threshold.
- 25. Economic Slowdown Projections ofEffects: Net and gross movements into and out of poverty, Scenario 2, Percentage points, Farmer Population. Source: Laborde and Martin (2016) Note: Poverty is defined by the $1.90 PPP 2011 threshold.
- 26. Regional Challenges: AfricaSouth of the Sahara
- 27. Africa in GlobalTrade After the long decline of the ‘70s-’90s, a reversed trend: 0 100 200 300 400 500 600 700 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 BnsUSD SSA TOTAL TRADE Agriculture All goods In 15 years, total trade for SSA has been multiplied by 6, agricultural trade by 4.6. In comparison, global trade multiplied by 3.4 and agricultural trade by 2.9. 0.0% 1.0% 2.0% 3.0% 4.0% 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 SSA SHARE IN GLOBAL TRADE Agriculture All goods
- 28. Heterogeneous Performance onGlobal Agricultural Markets -150% -100% -50% 0% 50% 100% 150% 200% 250% %INCREASEINGLOBALMARKETSHARE Decomposition of export performance (selected countries) between 1995 and 2007 Domestic Performance (competitivness) Geographical Specialization Sectoral Specialization Source: Bouet, Deason and Laborde (2014) Explaining a country’s performance and defining the right benchmark: • Being specialized in the right products? • Being specialized in the booming markets? • Improving its own competitiveness? During this period, exports have: • decreased by 20 M USD for C.A.R (bad performance in absolute and relative terms). • increased by 150 M USD for Uganda (bad performance in relative terms). • increased by 88 M USD for Rwanda (good performance in absolute and relative terms).
- 29. • Need todifferentiate short term variation and medium/long term modification of the trend. • Different policy responses on both energy and agricultural, and macroeconomic policies. • In SSA, weak institutions, capital, financial and insurance markets: incremental costs of volatility. • Energy and food prices: high level of distortions, and huge heterogeneity of policies within the continent. • From energy to food prices: many links (inputs, fertilizers, transports, biofuels). Implications of Changing Prices and Demand for Energy and Food -5.00 -4.00 -3.00 -2.00 -1.00 0.00 1.00 2.00 3.00 4.00 Aggregated welfare impact of a world price shock AGRI -15% Extraction -15% ExtractionAgri -15%
- 30. Looking at thePast • Increased regional integration, especially when looking at the nutritional contents of trade flows. African Imports Africa Asia Europe LAC NorthAmerica Oceania Dollars (value) 1990-1995 6.77% 17.26% 37.90% 9.96% 24.79% 3.31% 2002-2007 12.39% 19.81% 35.23% 15.97% 13.68% 2.93% kCal 1990-1995 3.09% 14.23% 23.81% 10.44% 44.81% 3.62% 2002-2007 7.05% 20.38% 27.06% 19.45% 21.63% 4.43% African Exports Africa Asia Europe LAC NorthAmerica Oceania Dollars (value) 1990-1995 7.99% 16.79% 67.32% 0.61% 6.95% 0.34% 2002-2007 15.15% 14.86% 62.51% 0.53% 6.10% 0.84% kCal 1990-1995 13.80% 26.20% 49.96% 2.99% 6.59% 0.46% 2002-2007 31.41% 29.21% 34.03% 0.92% 4.19% 0.23% 1/3 of the calories exported by Africa, go to Africa Role of African intra-trade over the previous decade has more than doubled. Shift in external suppliers among Americas. Source: Bouet, Deason and Laborde (2014)
- 31. Outlook Modeling andAnalysis Important changes within SSA: • Potential evolution in agri-food system value-added in Africa: potential increase by about USD 300 million (constant 2007 USD) between 2013 and 2030 (or 85%) in the business as usual scenario. And beyond: • SSA share in global food trade will reach 4.3% by 2030 (compared to 3% today, and 2.2% in 2000). 0 10 20 30 40 50 60 70 80 90 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 BnsUSD,constant SSA Agri-food exports ECOWAS CEMAC COMESA SACU 0 10 20 30 40 50 60 70 80 90 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 BnsUSD,constant SSA Agri-food imports ECOWAS CEMAC COMESA SACU Source: MIRAGRODEP model simulations, Bouet, Deason and Laborde (2014)
- 32. Per Capita NetAgricultural Trade Flows by Region -150.00 -100.00 -50.00 0.00 50.00 100.00 150.00 2013 2030 2013 2030 2013 2030 2013 2030 2013 2030 2013 2030 AFRICA CEMAC COMESA ECOWAS SACU UMA USDperCapita,Nettradeflows Vegetable Oil Vegetables & Fruits Sugar Fibers Oilseeds Processed Food Cash Crops Meat, white Meat, red Fish Products Dairy Products Cereals Source: MIRAGRODEP model simulations, Bouet, Deason and Laborde (2014) • Complementarity in terms of potential and needs at the continental level shows large potential for intra-trade growth; some targeted initiatives may be needed (vegetable oils, food processing). • The continental agri-business net trade deficit will increase from six dollars per capita to 12 dollars per capita.
- 33. How Will Intra-AfricanTrade Perform? • Under a business as usual scenario? +122% in average • Which levers could we use to reach the CAADP target (+200% from 2014 to 2025, Malabo Declaration)? – Addressing trade policy barriers – Improving infrastructure Source: MIRAGRODEP model simulations, Bouet, Deason and Laborde (2014) CEMAC COMESA ECOWAS SACU 0% 50% 100% 150% 200% SACU ECOWAS COMESA CEMAC % increase in intra-SSA trade between 2013 and 2030 CEMAC COMESA ECOWAS SACU CEMAC 67% 148% 80% 88% COMESA 148% 146% 179% 116% ECOWAS 80% 179% 136% 137% SACU 88% 116% 137% 111%
- 34. Trade Policy Barriersfor Expanding Trade in Africa Huge potential for an ambitious trade facilitation agenda: • Free circulation of goods still not achieved within custom unions (intra-trade still affected by MFN tariffs, double taxation, etc.) • Numerous fees and bribes • Administrative burden • Inefficiency of checkpoints (delays) 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 14.0% 16.0% 18.0% ECOWAS CEMAC COMESA SACU Average import tariffs on agri-food imports Applied to non SSA countries Applied to SSA countries Despite regional integration, intra-African trade still affected by: • significant tariffs; • the need to address between trade barriers between blocs; • external pressure to liberalize markets with third countries (EPA with the EU: SADC and ECOWAS should sign this year); • instability/uncertainty regarding some trade policies 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Rice Wheat Yams Beef (carcass) Chicken (cuts) Milk Powder Tariffs on selected products CEMAC ECOWAS COMESA SACU
- 35. 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 Power Internationalcall Water Roadfreight Internetdial-up Mobiletelephone Ratioofprices Infrastructure Barriers:Several Times More Expensive than Elsewhere Source: AICD – African Infrastructure Country Diagnostic
- 36. Challenge 1: Improve efficiencyor shift of potential frontier
- 37.
- 38. Spatial Patterns (annualavg. 2005-07) LaborLand Source: Benin, et.al (2011). Trends and Spatial Patterns in Agricultural Productivity in Africa 1961-2010, ReSAKSS.
- 39. Intensity of agriculturalresearch spending and capacity, 1981–2008 (E.g. Zambia)
- 40. STOCHASTIC PROFIT FRONTIER C Productionof maize Production of wheat Frontier of possibilities of production Frontier of possibilities of production increases
- 41. Challenge 2: Weneed to value externalities positive or negative
- 42.
- 43. A CONTINUOUS TRENDTOWARDS INTERNATIONALIZATION OF FOOD MARKETS 1975 1985 1995 2005 2015 18.2% 13.9% 12.3% 19.1% 16.1% Share of produced calories crossing an international border Are we pricing the water? We need to recognize carbon as a global externality and value carbon through carbon trade
- 44. Challenge 3: Weneed to be resilient to climate change and weather shocks
- 45. https://www.climate.gov/news-features/blogs/enso/november-el- ni%C3%B1o-update-it%E2%80%99s-small-world Ranking of August-October El Niñoepisodes (ONI) since 1950 El Niño Risks
- 47. El Niño Risks:January to March 2015
- 48. El Niño Risks:October – December 2015
- 49. Cereal Production inselected regions (million tons) 2012/13 2013/14 2014/15 2015/16* 2015 / Average 2012-2014 (% change) World 2,267.0 2,474.9 2,501.1 2,467.5 2.2 SSA 121.4 123.1 124.9 117.2 -4.8 Central Africa 7.5 6.7 6.7 6.9 -1.4 East Africa 38.1 38.9 42.1 40.0 0.8 SouthernAfrica 28.5 31.1 29.5 23.7 -20.2 West Africa 47.3 46.4 46.5 46.6 -0.4 NorthAfrica 32.2 36.2 32.6 36.5 8.6 East Asia 495.0 508.9 512.3 526.2 4.1 SouthAsia 324.7 332.4 333.4 318.8 -3.4 SoutheastAsia 144.8 148.0 146.5 145.2 -0.8 Europe 292.0 318.3 343.6 323.2 1.7 Central America&Caribbean 6.5 6.8 6.3 6.5 -0.7 Middle East 57.9 65.9 55.5 66.0 10.5 NorthAmerica 436.9 533.1 527.2 517.3 3.7 SouthAmerica 169.9 168.0 173.9 167.5 -1.8 Others 185.8 234.2 244.9 242.9 9.6 Source:USDA, *= forecastedestimates
- 50. Maize and wheatprice data in selected markets Region Market CurrentPrice ($/KG) Currentprice comparedto 2012-14 average (%change) East Africa Ethiopia,AddisAbaba, Wheat 0.465 15.3% Ethiopia,AddisAbaba 0.217 -25.6% Uganda, Kampala 0.240 -4.4% LAC El Salvador,SanSalvador 0.413 22.8% Guatemala,GuatemalaCity 0.350 4.3% Honduras,National Average 0.430 38.2% Nicaragua,National Average 0.400 29.5% Southern Africa Malawi,National Average 0.253 6.5% Mozambique,Maputo 0.300 -30.1% SouthAfrica,Randfontein 0.223 -4.7% Zimbabwe,Harare 0.565 -32.6% Source:Authors’calculationbasedonFAOGIEWSdata Notes:All pricesformaize unlessotherwise indicated. Real prices usingIMFCPIdeflator,pricesare averagesforthe period Octoberto Decemberforall yearspresented.
- 51. Challenge 4: Isnot only supply!
- 52. ADDITIONAL DEMAND FORBIOMASS Growing population Growing income Need for alternative to fossil carbon chains
- 53. Increased production Reduced supply for finalconsumers Reduced supply for intermediate consumers New Demand for crops Increase in yield and area, extension of cropland, and reduction of other crops GROWING DEMAND Additional food demand Additional Bioenergy demand Additional industrial Hunger? Substitution effects Feed Other sectors (agrifood, cosmetics) Substitution effects Biomass demand
- 54. OVERALL IMPACT By 2020:illustration with biofuels 1st generation 23.2% 22.1% 2.7% 10.0% 43.3% 15.7% 15.0% 10.1% 2.0% 13.9% 1.4% 2.4% 7.7% 1.7% 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% MAIZE SUGAR CROPS WHEAT PALM OIL RAPESEED OIL SOYBEAN OIL SUNFLOWER OIL Share of the crop (all use) in total HARVESTED cropland Production devoted to biofuels Source: Laborde, 2011But only 16% of world area devoted to biofuels
- 55.
- 56. SUPPLY AND DEMAND •Huge opportunity for smallholders • Huge potential for contract farming • But we need an appropriate regulation framework
- 57. Challenge 5: Economic Growthis not enough
- 58.
- 59. Economic Growth isnot enough A 10% increase in GDP/PC leads to a 6% reduction in stunting Source: Ruel and Alderman, 2013
- 60. Income Growth CanHave Unintended Consequences of Increasing Risks of Overweight and Obesity A 10% increase in GDP/PC leads to a 7% increase in overweight and obesity in women Source: Ruel and Alderman, 2013
- 61.
- 62. Agriculture is critical for Employment Economicdevelopment Food Security Important changes in key drivers Demand drivers changing rapidly Land constraints Water constrains Climate change Huge opportunity But we need proper regulatory environment Gains in efficiency and potential Increase value added SAI Needs to be inclusive
- 63.
- 64.