Using long-term outlooks to highlight constraints, prioritize investments and evaluate impacts

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


• 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


• 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


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


Alternative climate outcomes

wetterdrier

cooler warmer

CSIRO NCAR

NCARCSIRO


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


Impacts on child malnutrition

Year 2000


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


Millions of children (age 0 to 5)


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


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


Policy scenario definitions for SRF

Scenario Change from CC with CF

Parameters CC w/CFGlobal Average


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


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



Impacts on child malnutrition

2050

2000 CC w/CFGlobal Average IMP MM INC AG RES


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



Building towards 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)

No single model can build a strategy


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’


Sub-national poverty ca. 2005 ($1.25/day)

Prevalence

Number

Source: Stan Wood et al. (IFPRI) 2009.



Conclusions


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


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


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


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


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


Thank You!


Additional Results


Impact of Climate Change on Yields

Year 2000


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



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



Harvest Choice


HarvestChoice Data Portal Thematic Data Dissemination

http://harvestchoice.org/


The IMPACT model


• 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


• 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


• 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


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


CGIAR reform & megaprograms


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.



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.



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.


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

Documents

Using long-term outlooks to highlight constraints, prioritize investments and evaluate impacts