Global Futures and Strategic Foresight Analysis at CGIAR: Application of the geo-spatial bio-economic modeling framework to inform decision making

S Nedumaran, G Sika, D Enahoro, Keith Wiebe and Cynthia Bantilan CGIAR Research Program on Policies, Institutions and Markets

On behalf of the CG centers working on GFSFBernardo Creamer, Ulrich Kleinwechter, Guy Hareau, Daniel Mason-D'Croz, Nelgen Signe, Roberto Telleria

ICAE Conference 9-14 August Milan, Italy

Outline

Global Futures and Strategic Foresight (GFSF)

Why foresight analysis?

Framework developed in GFSF

Case studies undertaken by CG centres

ICRISAT – Evaluation of Groundnut Promising technologies

CIP – Evaluation of Potato promising technologies

CIMMYT - Impact of climate change on production and food security of maize systems in SSA

ILRI - Quantification of global livestock futures

Summary and way forward

CG centers partners in GFSF

Why Foresight Analysis?

Global food economy is in a state of FLUX

Growing populationRising incomesChanging dietsRestrictive trade policies

Climate changeNatural Resource degradationFood crops used for bio-fuel

Higher and more volatile food prices and increasing food and nutritional insecurity

Drivers of Change

0

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1971 1973 1975 1977 1979 1981 1983 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013

US

$ p

er M

etri

c to

n

Groundnuts Oil Soybeans Maize Sorghum Rice, Thai 5%

Prices for Agricultural Commodities, 1971-2013

Stable and low

Source: World Bank (2014)

Note: Price are in real 2010 US$.

Foresight Analysis Reveals!

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2010 2015 2020 2025 2030 2035 2040 2045 2050

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Supply Demand

Demand and supply of grain legumes in Low Income Food Deficit Countries (LIFDC)*

Source: IMPACT model projection

Note: Grain legumes - groundnuts, chickpea, pigeonpea and soybeans; *There are 62

countries classified under LIFDC by FAO

Projected population(Millions) under poverty in 2050

Goal of Global Futures and Strategic Foresight

Increasing the yield by developing crop

varieties with promising traits

Increased production, reduces the prices,

increase the consumption

Reduce malnutrition and Poverty

To support increases in agricultural productivity and environmental sustainability by evaluating promising technologies, investments, and policy reforms

Evaluation of selected promising technologies under development in CG centers

Source: Nelson et al., PNAS (2014)

Modeling climate impacts on agriculture:Incorporating economic effects

Strategic Foresight@ICRISAT

• GFP activities integrated in CRP-PIM

• Multidisciplinary team created and institutionalized (14 member team)

• Promising technologies were identified and prioritized for evaluation

• Collaboration with other CRPs and Global Projects like AgMIP (data sharing, model enhancement, capacity building)

Multi-disciplinary team @ ICRISAT

• Cynthia Bantilan, Nedumaran, Kai Mausch, N Jupiter

Economists

• Ashok Kumar, SK Gupta, CT Hash, PM Gaur, P Janila, Ganga Rao, Jana Kholova

Breeders and Physiologists

• P Singh

• Dakshina Murthy

• Gumma Murali Krishna

Crop Modelers/GIS/RS

Imp

act

Ass

ess

men

t

Integrated Model Framework

Hydrology model-WSM

Climate model

Source: Rosegrant et al. (2012)

Crop model

Evaluation of Promising Technologies: Virtual Cultivars

Target of the crop improvement scientists – develop promising technologies with higher yield

Pote

nti

al Y

ield

(K

g/h

a)

2014 2020

Incorporating the traits in elite cultivars

better root system

Extr

act

mo

re w

ater

Crop Model Calibration and Development of Virtual Promising Cultivars

DSSAT Crop Model

Baseline Cultivars selected - JL 24, M 335 and 55-437

Location Anantapur and Junagadh sites in India

Samanko (Mali) and Sadore (Niger) sites in West Africa

Calibrate and validate baseline cultivars

Manipulated the genetic co –efficient of baseline cultivars and developed the virtual promising cultivars for each locations Drought Tolerant

Heat Tolerant

Drought + Heat + yield Potential

Estimate the yield change for each technology compare to baseline cultivars in each location

Data source: Breeders yield trial data; NARS trial data

National Bureau of Soil Survey and Land Use Planning, Nagpur India; WISE soil database

India Meteorological Department (IMD); NASA website (http://power.larc.nasa.gov/)

1171

1225

1270

1477

1100

1150

1200

1250

1300

1350

1400

1450

1500

JL 24 DroughtTolerance

Heat Tolerance Drought + Heattolerance +

Yield potential

Kg

/ha

1228

12711246

1451

1100

1150

1200

1250

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1350

1400

1450

1500

JL 24 DroughtTolerance

Heat Tolerance Drought + Heattolerance + Yield

potential

Kg/

ha

Step 1: Simulated Yield of Promising Technologies of Groundnuts

Source: Singh, P., Nedumaran, S., Ntare, B.R., Boote, K.J., Singh, N.P., Srinivas, K., and Bantilan, M.C.S. 2013. Potential

benefits of drought and heat tolerance in groundnut for adaptation to climate change in India and West Africa. Mitigation and

Adaptation Strategies for Global Change.

18%

Yield under Current Climate

26%

Yield under Climate change 2050 (HADCM3, A1B scenario)

Location: Anantapur, India; Baseline Cultivars: JL 24

Step 2: Spatial Change in Groundnut Yield

Baseline cultivar (Current Vs Future Climate)

Promising Technology – Drought Tolerant

Step 3: Technology Development and Adoption Pathway Framework

2012 2018 2035

India60%

Nigeria40%

20372020

Research lag Adoption lag

Promising Technologies of Groundnut development

Technology developmentTechnology dissemination and

adoptionOutcomes and

Impacts

• Change in Production

• Change in prices• Change in

consumption • Poverty level

Nedumaran et al. (2013)

Target countries

Target countriesProduction share (%)

Burkina Faso 1.2

Ghana 1.62

India 12.99

Malawi 0.7

Mali 1.01

Myanmar 3.84

Niger 0.51

Nigeria 10.72

Tanzania 1.73

Uganda 0.76

Vietnam 1.84

Total 36.92

Potential Welfare Benefits and IRR (M US$)

TechnologiesNet Benefits

(M US$)IRR (%)

Heat Tolerant 302.39 30

Drought Tolerant 784.08 38

Heat + Drought + Yield

Potential1519.76 42

Nedumaran et al. (2013)

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Malawi Tanzania Uganda BurkinaFaso

Ghana Mali Nigeria Niger India Myanmar Vietnam

ESA WCA SSEA

M U

S$

Heat Tolerance Drought Tolerance Heat+Drought+yield potential

Evaluation of improved potato varieties for SSA

• Key traits• Higher yield potential

• Late-blight and virus resistance

• Heat tolerance

• Processing quality

• 30% higher yields

• Nine target countries

• Total investment: 9.8m US$ (4.29m NPV, 2000

constant prices)

• Project duration: 12 years

Source: Theisen and Thiele (2008).

EthiopiaUganda

Rwanda

Burundi

DR Congo

Kenya

Tanzania

MozambiqueMalawi

Welfare Benefits and IRR

0.010.020.030.040.050.060.070.0

Net welfare changes (M $)

Low adoption Medium adoption High adoption

00.10.20.30.40.50.60.70.8

IRR

Low adoption Medium adoption High adoption

Positive production impacts in target countries

Positive net welfare effects and high ROI in target countries

Comparable with findings from previous impact evaluations of improved varieties

Investment in improved potato varieties justified from economic point of view

Strategic Foresight @ CIMMYT:Impact of Climate change on Maize

Production in SSA

GCM monthly gridded data

Regional/global crop productivity under various climate models and

technologies

Evaluated DSSAT model

DSSAT Crop

Model

Site/farm level simulation

Site soil

Daily site

climate

Crop

Crop management

Model calibration

Model evaluation

27 FAO soil groups

daily pixel climate

Cropmanagement

Weather generator

Crop per MME

Evaluated DSSAT model

Evaluated IMPACT model

GIS

GIS

Projections on population and income growth

Trade-offs (elasticities) on inputs, production and consumption patterns

Projections on trade barriers

Projected world and domestic prices

Projected demand, supply and net trade

Nutrition results

DSSAT Spatial DSSAT IMPACT

Bio-economic modelling framework

Changes in yield and area of maize under low N level in SSA by 2050 (a & b) and 2080 (c & d) relative to the baseline (2000) using climate projection from CSIRO and MIROC global circulation models under the A1B emission scenario

Impact of Alternate Climate Scenarios on Maize

SSA

Eastern SSA

Southern SSA

Central SSA

Western SSA0

10

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40CSI-A1 vs. Base2050

MIR-A1 vs. Base2050

Ch

an

ge in

# o

f p

eo

ple

at

risk o

f h

un

ger

(mil

.)

Caloric intake in 2050 under MIR-A1 scenario

1500 2000 2500 3000 3500 4000

Ch

ang

e in

pe

op

le a

t ri

sk o

f h

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r (m

il.)

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ANG

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EQG GAB

BEN

BUFGAM

GHA

GUIGUBIVC

LIB

MAL

MAUNiger

Nigeria

SEN

SLETOG

Effect of Climate Change on Food Security in SSA

Summary

Maize production in SSA

Reduction of up to 12% and 20% by 2050 and 2080, respectively

Sahel and southern Africa: reduction in maize yields due to increasing temperatures and decreasing rainfall

Highlands in eastern Africa: increase in maize production due to small changes in rainfall and increasing temperature

Food security in SSA: hardest-hit is eastern Africa; DRC in central Africa and Nigeria in western Africa

Tesfaye et al., 2015

Strategic Foresight @ ILRIGlobal Livestock Futures

Global Livestock FuturesObjective: Improve representation of livestock sector in IMPACT model to:

Better account for (agro-ecological and management system) barriers to sector growth

Better assess potential for sector expansion

Improve capacity to simulate response, growth and recovery to shocks - including climate change

Enhance model usefulness as policy assessment tool for livestock sector development

Original Specification Suggested Updates

Supply response is relatively homogenous

within countries

Livestock supply disaggregated by system

types (intensive/extensive)

Livestock feed basket composed only of

internationally-traded feeds (mostly coarse

grains and meals)

Pasture grasses, crop residues and occasional

feeds added to livestock feeding possibilities

Yield is exogenously determined, and does

not respond to quantity or quality of fed

rations

Meat and milk yield response functions are

endogenous, responding to changes in feed

quantities and nutritive values

Total herd size includes milk-producing and

slaughtered meat animals only

Total herd count includes replacement and/or

follower herds in dairy and meat production

Animal productivity only indirectly affected

but not affected by feed availability through

price effects

Explicit feed-availability constraints imposed

on animal productivity

Source: Msangi et al., 2014

Suggested Enhancements to Livestock Sector

representation in IMPACT

Source: Msangi et al., 2014

Baseline Projections of Meat Production to 2030 for Key Countries

Baseline Projections of Milk Production to 2030 for Key Countries

Baseline Results

Summary

More dynamic growth for meat and lamb production in China, milk in India; Brazil meat production to surpass US by 2030

Supply-side response to growing demand for livestock products is more constrained in the enhanced model

Growth in feed demand and pressure on land resources more apparent, with important implications for the more extensive production systems

Way forward

Evaluate the additional promising technologies (biotic stress tolerant and management options) with current GF/PIM Strategic foresight tool

Provide evidence to better targeting of technology and inform priority setting for CG centres and CRPs

Identify and collaborate with pest and diseases modelling team

Consider linking results from global models with household data for ex ante impact assessment at lower scales

Gender lens in foresight analysis and technology evaluation

Development of ‘stand alone’ module in IMPACT with enhanced representation of livestock

Test current and alternative (technology and policy) strategies for livestock sector development under a range of plausible future scenarios - including global climate change

Thank you!