using long-term outlooks to highlight constraints, prioritize investments and evaluate impacts
Post on 24-Feb-2016
29 Views
Preview:
DESCRIPTION
TRANSCRIPT
Using long-term outlooks to highlight constraints, prioritize investments and
evaluate impacts
Meeting on “Thinking Forward: Assessments, Projections & Foresights”26 January 2010, CIRAD Headquarters, Paris
Siwa MsangiEnvironment and Production Technology Division, IFPRI
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 2Page 2
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
• 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?
Key questions to answer
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
• 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
Summary of research approach
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
• 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
Relevant debates and key issues
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 6
IMPACT-driven projections for agriculture
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Additional yield growth in cereals to offset malnutrition impacts of US biofuels target
Page 7
Global Cereal Yield Growth
Malnourished children (0-5)
Additional (annual average) yield growth in cereals:
1% in developing world
0.5% in developed world
In other words….
Going from: 1.3% 1.8%
Avg annual yield growth, globally
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 8
Alternative climate outcomes
wetterdrier
cooler warmer
CSIRO NCAR
NCARCSIRO
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Impact of climate change on yields
Year 2000
Year 2025 Year 2050No climate
changeNCAR No CF
NCAR CF
No climate change
NCAR No CF
NCAR CF
RiceSA 2.1 2.8 2.7 2.9 3.3 3.0 3.5EAP 3.1 3.5 3.3 3.6 3.9 3.6 4.0EE/CA 2.1 3.5 3.4 3.4 4.3 4.2 4.2LAC 2.4 3.2 3.2 3.3 3.6 3.7 3.8MENA 4.1 5.7 5.3 5.4 6.2 4.9 5.7SSA 1.1 1.6 1.6 1.7 2.3 2.2 2.4Developed 4.5 5.0 4.9 5.2 6.4 6.4 6.9Developing 2.5 3.2 3.0 3.2 3.6 3.3 3.7World 2.6 3.2 3.1 3.3 3.6 3.3 3.8
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Impacts on child malnutrition
Year 2000
Year 2025 Year 2050No climate
changeNCAR No CF
NCAR CF
No climate change
NCAR No CF
NCAR CF
SA 75.6 66.4 70.7 69.7 52.6 59.4 57.7EAP 23.8 15.9 18.9 18.0 10.2 14.6 13.3EE/CA 4.1 3.3 4.0 3.9 2.7 3.8 3.6LAC 7.7 7.1 8.1 7.9 5.1 6.5 6.2MENA 3.5 2.1 2.8 2.7 1.1 2.2 1.9SSA 32.7 44.7 50.6 49.1 34.2 45.4 42.5Developing 147.8 140.0 155.7 151.9 106.4 132.3 125.7
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
Millions of children (age 0 to 5)
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 11
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 12
Policy scenario definitions for SRF
Scenario Change from CC with CF
Parameters CC w/CFGlobal Average
INC AG RES w/EFF & IRR
EXP
INC AG RES w/EFF & IRR EXP + Dev’d Reg Imp [devg|devd]
COMP POL_INV
COMP POL_INV + Dev’d Reg Imp
[devg|devd]
Livestock numbers growth 0.44% per year + 30% + 30% | + 9% + 30% + 30% | + 9%
Livestock yield growth 0.76% per year
+ 30% from 2015
+ 30% | + 9% from 2015
+ 30% from 2015
+ 30% | + 9% from 2015
+ 50% from 2030
+ 50% | + 15% from 2030
+ 50% from 2030
+ 50% | + 15% from 2030
Food crop yield growth 1.13% per year
+ 60% + 30% | + 18% + 60% + 30% | + 18%
+ 78% from 2015
+ 78% | + 23.4% from 2015
+ 78% from 2015
+ 78% | + 23.4% from
2015+ 90% from
2030+ 90% | + 27%
from 2030+ 90%
from 2030+ 90% | + 27%
from 2030Irrigated area
growth 0.23% per year + 25% + 25% devg only + 25% + 25% devg only
Rainfed area growth -0.21% per year - 15% - 15% devg only - 15% - 15% devg
only
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 13
Policy scenario definitions for SRF
Scenario Change from CC with CF
Parameters CC w/CFGlobal Average
INC AG RES w/EFF & IRR
EXP
INC AG RES w/EFF & IRR EXP + Dev’d Reg Imp [devg|devd]
COMP POL_INV
COMP POL_INV + Dev’d Reg Imp
[devg|devd]
Basin water use efficiency
Trending from 0.51 in 2000 to
0.57 in 2050n.c. n.c.
Increase by 0.15 by 2050
(max 0.85)
Increase by 0.15 by 2050 (max 0.85) devg only
Soil water holding capacity
Works through changing effective
precipitation for an FPU
n.c. n.c. + 20% + 20% devg only
Marketing efficiency
0.38 average marketing margins
n.c. n.c. - 30%- 30% devg
only
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Yield impacts from CC under investments
SA EAP EE/CA LAC MENA SSA Dev’d Dev’ing WorldRice2000 (mt/ha) 2.1 3.1 2.1 2.4 4.1 1.1 4.5 2.5 2.62050 NCAR CF (mt/ha) 3.5 4.0 4.2 3.8 5.7 2.4 6.9 3.7 3.8
INC AG RES w/ EFF 37.1 38.2 109.6 38.9 14.0 137.4 -2.9 41.5 39.6
INC AG RES w/ EFF & IRR EXP 37.1 39.7 104.8 41.1 -7.6 137.2 -3.1 42.1 40.1
INC AG RES w/ EFF & IRR EXP + DEVD 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
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 15
Impacts on child malnutrition
2050
2000 CC w/CFGlobal Average IMP MM INC AG RES
INC AG RES w/EFF & IRR
EXP
COMP POL_INV
Millions of children Percent change from CC w/ CFSA 76 58 -2 -8 -18 -22EAP 24 13 -6 -26 -39 -41EE/CA 4 4 -2 -18 -38 -42LAC 8 6 -6 -21 -41 -46MENA 4 2 -12 -37 -56 -62
SSA 33 43 -6 -24 -53 -59
Developing 148 126 -4 -17 -34 -39
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 16
Building towards a strategy
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 17Page 17
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)
No single model can build a strategy
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 18Page 18
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
Choosing the ‘Mega Programs’
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Sub-national poverty ca. 2005 ($1.25/day)
Prevalence
Number
Source: Stan Wood et al. (IFPRI) 2009.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 24
Conclusions
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 25Page 25
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
Forward-looking analysis for the CGIAR
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 26Page 26
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)
Weaknesses of previous methods
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 27Page 27
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
Analytical challenges to address
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 30
Thank You!
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 31
Additional Results
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Impact of Climate Change on Yields
Year 2000
Year 2025 Year 2050No climate
changeNCAR No CF
NCAR CF
No climate change
NCAR No CF
NCAR CF
WheatSA 2.5 3.9 2.8 2.9 5.4 2.7 3.0EAP 3.8 4.5 4.9 5.0 5.0 6.1 6.5EE/CA 2.1 3.1 2.9 3.0 4.3 3.8 4.0LAC 2.5 3.2 3.3 3.4 4.0 4.2 4.4MENA 1.7 2.9 2.8 2.9 3.8 3.6 3.8SSA 1.9 2.6 2.0 2.2 3.4 2.3 2.5Developed 3.4 4.0 3.9 4.0 5.5 5.3 5.6Developing 2.5 3.4 3.2 3.3 4.6 3.8 4.1World 2.7 3.6 3.4 3.5 4.8 4.2 4.5
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
Yield impacts from CC under Investments
SA EAP EE/CA LAC MENA SSA Dev’d Dev’ing WorldMaize2000 (mt/ha) 1.9 4.2 3.7 3.0 5.7 1.5 8.6 3.0 4.42050 NCAR CF (mt/ha) 2.5 7.9 8.0 5.2 6.8 2.2 13.6 5.5 7.8
INC AG RES w/ EFF 3.9 52.0 77.2 34.7 9.4 48.9 -12.6 47.2 16.5
INC AG RES w/ EFF & IRR EXP 6.5 52.6 81.8 33.4 6.6 48.7 -12.2 48.1 17.4
INC AG RES w/ EFF & IRR EXP + DEVD 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
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
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 34
Harvest Choice
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 35
HarvestChoice Data Portal Thematic Data Dissemination
http://harvestchoice.org/
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 36
The IMPACT model
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
• 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 medium-term 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
The Bread & Butter of IMPACT
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
• 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
Typical IMPACT-driven scenarios
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
• 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
Typical IMPACT-driven scenarios
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 40
The linkages of relevance to modeling
child
malnutrition
Trade Equilibrium Balance
Rural Roads
Feed
Food
Price
Female educationSupply
Demand
Ag R&D investments
Domestic Biofuel ProdnOther
Demand
Policy drivers
Yield
Socioeconomic
Drivers
Climate change
Irrigation investments
Agric.
Imports/
exports
Area
Calorie Availability
Clean water access
Environmental driver
[investments]
Trade
policy
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE Page 41
CGIAR reform & megaprograms
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
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 well-functioning 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.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
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.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
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.
INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE
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
top related