14 March 2016. Brussels. DevCo External Cooperation InfoPoint. An overview of the situation of food and nutrition security in the world today was presented. Special emphasis was given to the current situation of El Niño, current droughts in Africa South of the Sahara, and potential policies that need to be put in place in the future to minimize these and associated risks. Introduction: Jean-Pierre Halkin, Head of Unit - DEVCO C1- Rural development, Food security, Nutrition Presentation: Maximo Torrero, Director, Markets, Trade and Institutions Division, International Food Policy Research Institute

1. Hunger and Food Security
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 Evolution in 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 Excessive Volatility
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 but same policies

5. GLOBAL CHALLENGE
Source: Johan Rockstrom: Let the environment guide our development
Growing
Human
Pressure
Climate change
Ecosystem
decline
Surprise

6. 6
Bigger population in urban 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 vs total 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 of childhood malnutrition
(abstract)

9. -
200
400
600
800
1,000
1,200
Africa south of the 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 ON RAINFED 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 2020 2025 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 to 1999-2001 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 Climate Change
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 trend towards
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
Average distance (km) travelled by imported calories
1995 2000 2005 2010

17. Evolution by region of 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 of preferential trade
agreements (PTAs) in force
Note: it includes notified and non-notified PTAs by country group

19. But mega trade deals 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 of 2012 and 2015 GDP growth
projections for 2017 (selection of countries)
Source: World Economic Outlook (2015 and 2012) - IMF

21. Economic Slowdown
World Commodity Price 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 of Effects: Global Poverty
Source: Laborde and Martin (2016)

23. Economic Slowdown
Projections of Effects: 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 of Effects: 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 of Effects: 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: Africa South of the
Sahara

27. Africa in Global Trade
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 on Global
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 to differentiate 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 the Past
• 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 and Analysis
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 Net Agricultural 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-African Trade 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 Barriers for 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
efficiency or shift of
potential frontier

37. Yields are vey low

38. Spatial Patterns (annual avg. 2005-07)
LaborLand
Source: Benin, et.al (2011). Trends and Spatial Patterns in Agricultural Productivity in Africa 1961-2010, ReSAKSS.

39. Intensity of agricultural research spending and
capacity, 1981–2008 (E.g. Zambia)

40. STOCHASTIC PROFIT FRONTIER
C
Production of
maize
Production of
wheat
Frontier of
possibilities of
production
Frontier of
possibilities of
production
increases

41. Challenge 2: We need to
value externalities positive
or negative

42. Pricing water

43. A CONTINUOUS TREND TOWARDS
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: We need 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
Niño episodes
(ONI) since 1950
El Niño Risks

47. El Niño Risks: January to March 2015

48. El Niño Risks: October – December 2015

49. Cereal Production in selected 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 wheat price 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: Is not only
supply!

52. ADDITIONAL DEMAND FOR BIOMASS
Growing population
Growing income
Need for alternative to fossil carbon chains

53. Increased
production
Reduced supply
for final consumers
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. SUPPLY AND DEMAN

56. SUPPLY AND DEMAND
• Huge opportunity for
smallholders
• Huge potential for contract
farming
• But we need an appropriate
regulation framework

57. Challenge 5: Economic
Growth is not enough

58. Prevalence of Undernourishment

59. Economic Growth is not enough
A 10% increase
in GDP/PC
leads to a 6%
reduction in
stunting
Source: Ruel and Alderman, 2013

60. Income Growth Can Have 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. Final Remarks

62. Agriculture is
critical for
Employment
Economic development
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. 63
Features
SSA.foodsecurityportal.org

64. Thanks!