Transcript Asymmetric Externalities in Groundwater Extraction

Using long-term outlooks to highlight
constraints, prioritize investments and
evaluate impacts
Siwa Msangi
Environment and Production Technology Division, IFPRI
Meeting on “Thinking Forward: Assessments, Projections & Foresights”
26 January 2010, CIRAD Headquarters, Paris
During the course of this presentation….
We hope to:
 Motivate our approach to answering questions
of policy impact and investment
 Summarize some illustrative scenario results
 Show the use of forward-looking analysis in
assessing programmatic priorities for new CG
 Offer concluding thoughts
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Key questions to answer
• How much harder does agriculture and its
supporting systems have to work to meet
the future challenges of food needs,
bioenergy and climate change?
• What are the sources of growth and
investment that will be needed to meet
these challenges?
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Summary of research approach
• Evaluate key drivers of change (socio-economic,
environmental) along a ‘baseline’ of current policies
and trends – assess the needs for food/feed/fuel along
this path
• Introduce alternative paths for environmental drivers
consistent with plausible trajectories of climate change
– across a variety of modeled climate outcomes
• Assess the impacts on agricultural production in
various regions, given current technologies
• Infer the regions needing urgent interventions and key
activities/crops to target – with implied investments
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Relevant debates and key issues
• Food-vs-fuel tradeoffs
• Does biofuels ‘crowd out’ land needed for food
production or can it actually ‘crowd in’ investments
that can make a difference for the whole sector?
• Question of ‘indirect impacts’ of biofuels
• The changes that growth of biofuels in US/EU
induce in the RoW – mostly in terms of land use
• Some concern about food security impacts too
• What are the priority areas that the new CG should
address itself to? What are the ‘best bets’ for R&D that
should be captured in the new mega-programs
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IMPACT-driven projections for agriculture
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Additional yield growth in cereals to offset malnutrition
impacts of US biofuels target
Global Cereal Yield Growth
Additional (annual average) yield growth
in cereals:
1% in developing world
0.5% in developed world
Malnourished children (0-5)
In other words….
Going from: 1.3%  1.8%
Avg annual yield growth, globally
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Alternative climate outcomes
CSIRO
NCAR
cooler
warmer
CSIRO
NCAR
drier
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wetter
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Impact of climate change on yields
Year 2025
Year 2050
Year
2000
No climate
change
NCAR
No CF
NCAR
CF
No climate
change
NCAR
No CF
NCAR
CF
2.1
3.1
2.1
2.4
4.1
1.1
4.5
2.5
2.6
2.8
3.5
3.5
3.2
5.7
1.6
5.0
3.2
3.2
2.7
3.3
3.4
3.2
5.3
1.6
4.9
3.0
3.1
2.9
3.6
3.4
3.3
5.4
1.7
5.2
3.2
3.3
3.3
3.9
4.3
3.6
6.2
2.3
6.4
3.6
3.6
3.0
3.6
4.2
3.7
4.9
2.2
6.4
3.3
3.3
3.5
4.0
4.2
3.8
5.7
2.4
6.9
3.7
3.8
Rice
SA
EAP
EE/CA
LAC
MENA
SSA
Developed
Developing
World
Note: SA= South Asia; EAP = East Asia and Pacific; EE/CA= Eastern Europe and Central Asia;
LAC= Latin America and Caribbean; MENA= Middle East and North Africa;
SSA=Sub-Saharan Africa
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Impacts on child malnutrition
Millions of children (age 0 to 5)
Year 2025
SA
EAP
EE/CA
LAC
MENA
SSA
Developing
Year 2050
Year
2000
No climate
change
NCAR
No CF
NCAR
CF
No climate
change
NCAR
No CF
NCAR
CF
75.6
23.8
4.1
7.7
3.5
32.7
147.8
66.4
15.9
3.3
7.1
2.1
44.7
140.0
70.7
18.9
4.0
8.1
2.8
50.6
155.7
69.7
18.0
3.9
7.9
2.7
49.1
151.9
52.6
10.2
2.7
5.1
1.1
34.2
106.4
59.4
14.6
3.8
6.5
2.2
45.4
132.3
57.7
13.3
3.6
6.2
1.9
42.5
125.7
Note: SA= South Asia; EAP = East Asia and Pacific; EE/CA= Eastern Europe and Central Asia;
LAC= Latin America and Caribbean; MENA= Middle East and North Africa;
SSA=Sub-Saharan Africa
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Policy scenarios for SRF of new CGIAR
Consider a layering of improvements over time
 Consider reductions in marketing margins (up to 30%)
 Give improvements in natural resource mgmt by:
 Changes in basin efficiency (for irrigated systems)
 Improvements in effective rainfall (for rainfed systems)
 Increases in ag research – in terms of higher crop yield
and animal numbers growth – with enhanced efficiency
 Increases in irrigated area (at expense of rainfed growth)
 Combine these into an overall comprehensive policy
scenario – and allow for spillovers to other regions too
SRF = Strategy & Results Framework
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Policy scenario definitions for SRF
Scenario Change from CC with CF
Parameters
CC w/CF
Global Average
Livestock
0.44% per year
numbers growth
Livestock yield
growth
Food crop yield
growth
Irrigated area
growth
0.76% per year
1.13% per year
0.23% per year
INC AG RES
w/EFF & IRR
EXP
INC AG RES w/EFF
& IRR EXP + Dev’d
Reg Imp [devg|devd]
+ 30%
+ 30% | + 9%
+ 30% from
2015
+ 50% from
2030
+ 60%
+ 30% | + 9%
from 2015
+ 50% | + 15%
from 2030
+ 30% | + 18%
+ 78% from
2015
+ 78% | + 23.4%
from 2015
+ 90% from
2030
+ 90% | + 27%
from 2030
+ 25%
+ 25% devg only
Rainfed area
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growth
-0.21% per year
- 15%
- 15% devg only
COMP POL_INV
COMP
+ Dev’d Reg Imp
POL_INV
[devg|devd]
+ 30%
+ 30% | + 9%
+ 30%
+ 30% | + 9%
from 2015 from 2015
+ 50% + 50% | + 15%
from 2030
from 2030
+ 60% + 30% | + 18%
+ 78% | +
+ 78%
23.4% from
from 2015
2015
+ 90% + 90% | + 27%
from 2030
from 2030
+ 25% devg
+ 25%
only
- 15%Page
devg
12
- 15%
only
Policy scenario definitions for SRF
Scenario Change from CC with CF
Parameters
Basin water use
efficiency
CC w/CF
Global Average
INC AG RES
w/EFF & IRR
EXP
Trending from
0.51 in 2000 to
0.57 in 2050
Works through
changing
Soil water
effective
holding capacity
precipitation
for an FPU
0.38 average
Marketing
marketing
efficiency
margins
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INC AG RES w/EFF
& IRR EXP + Dev’d
Reg Imp [devg|devd]
COMP POL_INV
COMP
+ Dev’d Reg Imp
POL_INV
[devg|devd]
n.c.
n.c.
Increase
by 0.15
by 2050
(max
0.85)
n.c.
n.c.
+ 20%
+ 20% devg
only
n.c.
n.c.
- 30%
- 30% devg
only
Increase by
0.15 by 2050
(max 0.85)
devg only
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Yield impacts from CC under investments
SSA
Dev’d
4.1
1.1
4.5
2.5
2.6
3.8
5.7
2.4
6.9
3.7
3.8
109.6
38.9
14.0
137.4
-2.9
41.5
39.6
39.7
104.8
41.1
-7.6
137.2
-3.1
42.1
40.1
37.0
39.5
104.7
40.9
-7.7
136.9
13.8
41.9
40.7
COMP POL_INV
44.7
41.8
106.9
43.2
-6.1
146.5
-3.2
46.6
44.5
COMP POL_INV +
DEVD
44.5
41.6
106.8
43.1
-6.2
146.2
13.6
46.5
45.0
SA
EAP
EE/CA LAC MENA
2000 (mt/ha)
2050 NCAR CF
(mt/ha)
2.1
3.1
2.1
2.4
3.5
4.0
4.2
INC AG RES w/ EFF
37.1
38.2
INC AG RES w/ EFF
& IRR EXP
37.1
INC AG RES w/ EFF
& IRR EXP + DEVD
Dev’ing World
Rice
Note: SA= South Asia; EAP = East Asia and Pacific; EE/CA= Eastern Europe and Central Asia;
LAC=
Latin America and Caribbean; MENA= Middle East and North Africa;
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SSA=Sub-Saharan Africa
Impacts on child malnutrition
2050
CC w/CF
2000
IMP MM
Global Average
Millions of children
INC AG RES
INC AG RES
w/EFF & IRR
EXP
COMP
POL_INV
Percent change from CC w/ CF
LAC
76
24
4
8
58
13
4
6
-2
-6
-2
-6
-8
-26
-18
-21
-18
-39
-38
-41
-22
-41
-42
-46
MENA
4
2
-12
-37
-56
-62
SSA
33
43
-6
-24
-53
-59
Developing
148
126
-4
-17
-34
-39
SA
EAP
EE/CA
Note: SA= South Asia; EAP = East Asia and Pacific; EE/CA= Eastern Europe and Central Asia;
LAC=
Latin America and Caribbean; MENA= Middle East and North Africa;
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SSA=Sub-Saharan Africa
Building towards a strategy
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No single model can build a strategy
 Strategy team for CG reform used 3 system-level
results criteria as a starting point
 Greatest impacts can be realized by integrating
productivity-enhancing R&D, NRM and institutional &
policy change [ IMPACT results support this]
 Directing productivity-focused R&D, NRM & policy to
sustainably reduce poverty/hunger most quickly for
the most people
 Recognize dominance of regions by certain
commodities to make research choices (dominant
crops and foods in diets, dietary diversity problems)
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Choosing the ‘Mega Programs’
 The MPs were chosen with a dual focus in mind:
 Identify research on ag productivity, sustainability &
policy that delivers specific outcomes in the form of
IPGs & which contribute to 3 system-level outcomes
 Focus research in ag systems/regions/domains
where research interventions could achieve the
greatest impact on hunger & poverty
 This was done with a combination of model-based
evaluation and spatially-explicit socio-economic and
biophysical mapping products – and consultation
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Sub-national poverty ca. 2005 ($1.25/day)
Prevalence
Number
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Source: Stan Wood et al. (IFPRI) 2009.
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Conclusions
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Forward-looking analysis for the CGIAR
 During a period of re-evaluation, change and
programmatic re-prioritization, the CGIAR is in need of
tools to evaluate options and target investment & efforts
 Not all scientists within the CGIAR are comfortable with
forward-looking assessment/foresight/projections due to
the inherent uncertainties in future outcomes
 Scenario-based approaches are foreign to some
 The utility of equilibrium, economic models is not shared
by all, and frequently misunderstood (‘black boxes’)
 Yet the complexity of socio-economic & environmental
drivers affecting ag needs a structured approach
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Weaknesses of previous methods
 A common approach that was used to evaluate the
futures for specific ag commodities (in terms of area,
prodn, consn or yield) was straightline projections
based on historical trends
 Single-commodity models, that could drill down into the
details on varieties, prodn systems & policies -- lack
key links to other (competing) ag commodities
 Most agronomists would prefer to use detailed models
of production systems that represent the realities of
farming practices at the field level – but these lack price
response (tech change/innovation)
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Analytical challenges to address
 Equilibrium models tend to remain too rigid in the face
of radical shocks that are outside the range of
estimated parameters – evaluating global change may
require also looking at non-equilibrium situations
 Price formation is at the heart of economic market
models, but can only capture situations where market
prices are relevant. Optimization models can impute
shadow values, but still embody behavioral
assumptions that require knowledge of preferences
 A number of qualitative aspects of agriculture and
behavior which are important cannot be fully quantified
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Concluding Remarks
 The CGIAR needs a framework which has
sufficient detail to cover their mandate
commodities and eco-regions – and which are
key to livelihoods and nutrition
 Biophysical linkages to the environment are
important to understanding how ag & underlying
resource base interact
 Linkages to well-being outcomes are essential to
evaluating policy options for investment and
potential outcomes and impacts
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Continuing work
Some on-going projects seek to address these
challenges and engage with the research/policy
community in a different way
 HarvestChoice project provides a rich information
portal and combines it with analytical work that
helps users better identify the constraints to crop
productivity (for better targetting of technology)
 GlobalFutures project will engage scientists from
key CG centers and important stakeholders to
explore plausible futures for ag R & D
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Thank You!
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Additional Results
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Impact of Climate Change on Yields
Year 2025
Year 2050
Year
2000
No climate
change
NCAR
No CF
NCAR
CF
No climate
change
NCAR
No CF
NCAR
CF
2.5
3.8
2.1
2.5
1.7
1.9
3.4
2.5
2.7
3.9
4.5
3.1
3.2
2.9
2.6
4.0
3.4
3.6
2.8
4.9
2.9
3.3
2.8
2.0
3.9
3.2
3.4
2.9
5.0
3.0
3.4
2.9
2.2
4.0
3.3
3.5
5.4
5.0
4.3
4.0
3.8
3.4
5.5
4.6
4.8
2.7
6.1
3.8
4.2
3.6
2.3
5.3
3.8
4.2
3.0
6.5
4.0
4.4
3.8
2.5
5.6
4.1
4.5
Wheat
SA
EAP
EE/CA
LAC
MENA
SSA
Developed
Developing
World
Note: SA= South Asia; EAP = East Asia and Pacific; EE/CA= Eastern Europe and Central Asia;
LAC= Latin America and Caribbean; MENA= Middle East and North Africa;
SSA=Sub-Saharan Africa
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Yield impacts from CC under Investments
SSA
Dev’d
5.7
1.5
8.6
3.0
4.4
5.2
6.8
2.2
13.6
5.5
7.8
77.2
34.7
9.4
48.9
-12.6
47.2
16.5
52.6
81.8
33.4
6.6
48.7
-12.2
48.1
17.4
5.9
51.9
80.9
32.6
6.2
47.8
-5.7
47.3
20.2
COMP POL_INV
8.3
57.2
84.2
35.0
8.1
52.8
-12.5
50.8
18.4
COMP POL_INV +
DEVD
7.7
56.3
83.3
34.4
7.7
51.9
-6.0
50.1
21.1
SA
EAP
EE/CA LAC MENA
2000 (mt/ha)
2050 NCAR CF
(mt/ha)
1.9
4.2
3.7
3.0
2.5
7.9
8.0
INC AG RES w/ EFF
3.9
52.0
INC AG RES w/ EFF
& IRR EXP
6.5
INC AG RES w/ EFF
& IRR EXP + DEVD
Dev’ing World
Maize
Note: SA= South Asia; EAP = East Asia and Pacific; EE/CA= Eastern Europe and Central Asia;
LAC=
Latin America and Caribbean; MENA= Middle East and North Africa;
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SSA=Sub-Saharan Africa
Harvest Choice
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HarvestChoice Data Portal
Thematic Data Dissemination
http://harvestchoice.org/
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The IMPACT model
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The Bread & Butter of IMPACT
• Much of the past work of IMPACT has centered
around providing a forward-looking perspective on
what’s needed to meet future food needs, and the
implications for key CGIAR mandate commodities
• It was designed to look at the medium-to-long term
periods, that aren’t covered by short- to mediumterm models of USDA, OECD, FAO
• Used for projections and not prediction – which
implies that you’re more interested in percentage
changes from a starting point, or in terms of
deviations from a baseline, under alternative
scenarios
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Typical IMPACT-driven scenarios
• Looking at the implications of expansion in
(irrig/rainfed) area and increased yields on key
indicators of:
• Production (area/yield), Demand (total/food/
feed/other), Net Trade, Prices (int’l/national)
• Per capita calorie availability from all foods
• Implied changes in child (under 5) malnutrition
• Looking at the implications of the growth in irrigated
area and yield, mentioned above, on increased
investments in agricultural research and rural roads
investments
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Typical IMPACT-driven scenarios
• Looking at the implications of socio-economic growth
(income, population) on food/feed demand and other
indicators mentioned above
• Looking at the implications of higher factor prices
(fertilizer, labor) on crop yield – and production
• Fairly simple trade liberalization or protection
scenarios (with phased changes over time)
• Looking at implications of improved socio-economic
conditions ( access to clean water, girls secondary
schooling, rural roads ) on child malnutrition
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The linkages of relevance to modeling
Policy
drivers
Other
Demand
Demand
Agric.
Trade
Imports/
policy
Trade
Equilibrium
Balance
Domestic
Biofuel Prodn
Feed
Socioeconomic
Food
Drivers
child
Price
Calorie
Availability
exports
Area
Supply
Yield
Climate
change
Irrigation
investments
Rural
Roads
[investments]
malnutrition
Clean water
access
Female
education
Ag R&D
investments
Environmental driver
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CGIAR reform & megaprograms
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Mega Program portfolio (1)
1. Agricultural Systems for the Poor and Vulnerable —
Research integrating promising crop, animal, fish, and
forest combinations with policy and natural resource
issues in the domains where high concentrations of the
world’s poor live and which offer agricultural potential.
2. Institutional Innovations, and Markets —Knowledge to
inform institutional changes needed for a wellfunctioning local, national, and global food system that
connects small farmers to agricultural value chains
through information and communications technologies
and facilitates policy and institutional reforms.
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Mega Program portfolio (2)
3. Genomics and Global Food Crop Improvements—Genetic
improvement of the world’s leading food crops’ productivity
and resiliency (i.e. rice, wheat, maize) , building on the
success of the CGIAR, including its crucial role in
conservation of genetic resources.
4. Agriculture, Nutrition, and Health —Research to improve
nutritional value of food and diets, enhance targeted
nutrition and food safety programs, and change
agricultural commodities and systems in the medium term
to enhance health outcomes.
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Mega Program portfolio (3)
5. Water, Soils, and Ecosystems —Harmonization of agricultural
productivity and environmental sustainability goals through
policies, methods, and technologies to improve water and soil
management.
6. Forests and Trees —Technical, institutional, and policy
changes to help conserve forests for humanity and harness
forestry and biomass production potentials for sustainable
development and the poor.
7. Climate Change and Agriculture —Diagnosis of the directions
and potential impacts of climate change for agriculture and
identification of adaptation and mitigation options for
agricultural, food, and environmental systems.
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Cross-Cutting Platforms
• Gender : Facilitate strong attention to gender issues and
research cooperation on these issues across MPs. Expected
results:
• increased involvement and income of women in agriculture
• reduced disparities in their access to productive resources
and control of income
• Capacity-building : Strengthen capacity of CGIAR and
partners. Expected result:
• dynamic knowledge-creating and -sharing system, strong
independent NARS, and other research partners sharing
knowledge resources and applications
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