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The Challenges of Sustainably
Feeding a Growing Planet
By Thomas W. Hertel
In collaboration with Uris L.C. Baldos
Center for Global Trade Analysis
Purdue University
1 Presentation at IFPRI Policy Seminar, January 18, 2017
Overview of the talk
• History is key to understanding the future
• Understanding drivers of global food system:
- Changing relative importance of pop and income
- Key role of technology
- Climate change as a source of uncertainty
• Critical adaptations:
- International trade
- Investments in R&D
Looking back to project forward
• We can draw inspiration from climate
scientists who begin any research on future
climate change with extensive analysis of past
• Important to validate models against history
before attempting to project into future
• If cannot reproduce key elements of past, what
hope do we have of providing useful
information about the future?
Some key historical changes in global food economy:
1961-2006
Source: Hertel and Baldos (2016) under segmented markets and historical data from FAOSTAT (2014) and World Bank GEM Database (2014)
0
40
80
120
160
200
Total Subtotals
Actual Data Model Output
% t
ota
l ch
an
ge
: 1
96
1-2
006
Crop Yield
-100
-75
-50
-25
0
25
50
75
100
Total Subtotals
Actual Data Model Output
Crop Price
0
50
100
150
200
250
Total Subtotals
Actual Data Model Output
% t
ota
l ch
an
ge
: 1
96
1-2
006
Crop Production
-30
-15
0
15
30
45
Total Subtotals
Actual Data Model Output
Crop Land
SIMPLE (a Simplified International Model of Prices,
Land-use and the Environment) reproduces broad
historical changes in global food economy: 1961-2006
Source: Hertel and Baldos (2016) under segmented markets and historical data from FAOSTAT (2014) and World Bank GEM Database (2014)
0
40
80
120
160
200
Total Subtotals
Actual Data Model Output
% t
ota
l ch
an
ge
: 1
96
1-2
006
Crop Yield
-100
-75
-50
-25
0
25
50
75
100
Total Subtotals
Actual Data Model Output
Crop Price
0
50
100
150
200
250
Total Subtotals
Actual Data Model Output
% t
ota
l ch
an
ge
: 1
96
1-2
006
Crop Production
-30
-15
0
15
30
45
Total Subtotals
Actual Data Model Output
Crop Land
SIMPLE allows for attribution across drivers:
Population was key demand driver: 1961-2006
0
40
80
120
160
200
Total Subtotals
Actual Data Model Output
% t
ota
l ch
an
ge
: 1
96
1-2
006
Crop Yield
-100
-75
-50
-25
0
25
50
75
100
Total Subtotals
Actual Data Model Output
Crop Price
0
50
100
150
200
250
Total Subtotals
Actual Data Model Output
% t
ota
l ch
an
ge
: 1
96
1-2
006
Crop Production
-30
-15
0
15
30
45
Total Subtotals
Actual Data Model Output
Crop Land
050100150200250
Total Subtotals
%
cu
mu
la
tiv
e …
Crop ProductionFarm Productivity Population Income
Source: Hertel and Baldos (2016) under segmented markets and historical data from FAOSTAT (2014) and World Bank GEM Database (2014)
Future replicates past!! Population remains a
dominant driver of food demand in naïve forecast
Naïve projections of global crop price to 2050:
SIMPLE model, based on past trends of key drivers
Based on Baldos
and Hertel (2016)
However, population growth is slowing and the
absolute decadal increment is shrinking rapidy
Annual increments to global population (10-year average), 1750-2050: Source: UNPD, 2000, 2011
Population growth is most rapid in Africa:
Per capita food consumption more modest
Extracted from Leslie Roberts, “9 Billion?”, Science vol. 333, 29 July, 2011.
When we impose future population growth rates,
projected change in global crop prices falls sharply…
Relative contribution of population drops sharply by 2050
Based on Baldos
and Hertel (2016)
Global population
growth rate drops
from 1.7 to 0.8%;
Developed regions’
growth drops from
0.6 to 0.1%/yr
But income growth will affect food
consumption: 2006 vs. 2050
0
500
1000
1500
2000
2500
3000
3500
Food consumption (grams/cap/day)
Crops Livestock Processed FoodSource: Baldos and Hertel (2014)
More rapid growth in developing economies translates
into larger impact of income growth on demand
For the first time, income dominates population as a driver of
agricultural demand
Based on Baldos
and Hertel (2016)
Impact of
higher
income
growth in
poor
countries
Productivity growth is critical for future crop prices: Continued fast TFP
growth could lead to a stronger decline; slowdown leads to rising prices
Source: Baldos and
Hertel (2016)
Ludena et al Global
Crops TFP Growth p.a. Years
Baseline 0.94 2001-40
Slow Rates 0.70 2031-40
Fast Rates 1.30 2001-10
Monte Carlo
Analysis: 5,000
different model
predictions
Projections about the future must account for
uncertainty in drivers as well as economic
responses66% of simulations
show a LR price
decline
Source: Hertel, Baldos and van der
Mensbrugghe (2016)
But can’t rule out
significant price
rises if:
- Fertility
declines slow
- Income growth
is stronger
- Productivity
pessimists are
proven right
However, price increases will not be
uniform due to market segmentation
• In SSA region, prices likely
to rise due to strong
population & income; slow
productivity growth
• Tighter market integration
will stem price rise, but will
also expose producers to
greater competition –
potentially massive imports
• Boosting agricultural
productivity growth is of
paramount importance
Source: Hertel and Baldos (2016)
What about climate change? Evidence that
it is already reducing yields of some crops
Source: IPCC, AR5, as presented by CSIRO/Mark Howden for the IPCC Food Security
Summit, Dublin, May 2015. Note: most of underlying studies do not include effects of
elevated CO2 which tends to boost yields
Climate impacts on wheat: 1, 2 and 3 degrees
Celsius warming
Source: Moore, Baldos, Hertel and Diaz, under revision
International trade as adaptation:
Integrated markets moderates nutritional impacts
of more severe climate scenarios in 2050
Source: Baldos and Hertel (2015)
Another important avenue for adaptation is
R&D aimed at drought and heat resilience
But it can take a long time for public R&D to
translate into increased agricultural productivity
On average, the productivity impact of public spending peaks after
two decades; lingers for 50 yearsSource: Baldos, Viens, Hertel and Fuglie, 2016
How much should we be investing in R&D?
Depends on future pop, income and climate
Source: Cai, Golub and Hertel (2017)
Optimal R&D
spending path rises
strongly to 2030,
then slows
R&D as a share of
GDP rises sharply, then
drops after 2030
Conclusions
• Great uncertainty about food system in 2050:
- Fertility rates and population growth
- Income growth
- Climate impacts
- Climate mitigation policies (not discussed here)
• Technological progress is key to food security: particularly critical in SSA region; but depends on R&D which has very long lag
• Adaptability is critical: The value of free and open trade will be greater under climate change
• Baldos, U. L. C, and T. W Hertel. 2013. “Looking back to Move Forward on Model Validation: Insights
from a Global Model of Agricultural Land Use.” Environmental Research Letters 8 (3): 034024.
doi:10.1088/1748-9326/8/3/034024.
• Baldos, U. L. C., and T. W. Hertel. 2014. “Global Food Security in 2050: The Role of Agricultural
Productivity and Climate Change.” Australian Journal of Agricultural and Resource Economics.
doi:10.1111/1467-8489.12048.
• Baldos, Uris Lantz C., and Thomas W. Hertel. 2016. “Debunking the ‘new Normal’: Why World Food
Prices Are Expected to Resume Their Long Run Downward Trend.” Global Food Security 8 (March): 27–
38. doi:10.1016/j.gfs.2016.03.002.
• Baldos, Uris Lantz C., Frederi G. Viens, Thomas W. Hertel, and Keith O. Fuglie. submitted. “R&D
Spending, Knowledge Capital and Agricultural Productivity: A Bayesian Approach.”
• Baldos, Uris L.C., and Hertel, Thomas W. 2015. “The Role of International Trade in Managing Food
Security Risks from Climate Change.” Food Security 7 (2): 275–90.
• Cai, Yongyang, Alla G Golub, and Thomas W Hertel. 2016. “Agricultural Research Spending Must
Increase in Light of Future Uncertainties.” In Review.
• Hertel, Thomas W., and Uris Lantz C. Baldos. 2016. “Attaining Food and Environmental Security in an
Era of Globalization.” Global Environmental Change 41 (November): 195–205.
doi:10.1016/j.gloenvcha.2016.10.006.
• Hertel, Thomas W., Uris Lantz C. Baldos, and Dominique van der Mensbrugghe. 2016. “Predicting Long-
Term Food Demand, Cropland Use, and Prices.” Annual Review of Resource Economics 8 (1): 417–41.
doi:10.1146/annurev-resource-100815-095333.
• Ludena, Carlos E., Thomas W. Hertel, Paul V. Preckel, Kenneth Foster, and Alejandro Nin. 2007.
“Productivity Growth and Convergence in Crop, Ruminant, and Nonruminant Production: Measurement
and Forecasts.” Agricultural Economics 37 (1): 1–17. doi:10.1111/j.1574-0862.2007.00218.x.
• Moore, Frances C., Uris Lantz C. Baldos, Thomas W Hertel, and Delavane Diaz. 2016. “Welfare Impacts of
Climate Change on Agriculture: Evidence from Over 1,000 Yield Studies.” In Review, October.
References