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Climate Smart Agriculture for an Inter-

Dependent World: From Dialogue to Action

with the Aid of Science

Andy Jarvis

Director of Decision and Policy Analysis (DAPA)

Theme Leader, CGIAR Research Program on

Climate Change, Agriculture and Food Security

(CCAFS)

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Climate Change, Agricultureand Food Security (CCAFS)

1 January 2013

CGIAR Research Program

Leb by

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1 January 2013

Leb by

15 CG centers and ~70 regional offices

Global alliance

Lead center - CIAT

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1 January 2013

Liderado por

Identify and develop pro-poor adaptation and mitigation practices, technologies and policies for agriculture and food systems.

Support the inclusion of agricultural issues in climate change policies, and of climate issues in agricultural policies, at all levels.

Commit to data availability, cross-centercooperation, and making an impact on both the global and regional level.

Objectives

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1 January 2013

CCAFS Framework

Adapting Agriculture to

Climate Variability and Change

Technologies, practices, partnerships and

policies for:

1. Adaptation to Progressive Climate

Change

2. Adaptation through Managing

Climate Risk

3. Pro-poor Climate Change Mitigation

Improved

Environmental

HealthImproved

Rural

Livelihoods

Improved

Food

Security

Enhanced adaptive capacity

in agricultural, natural

resource management, and

food systems

4. Integration for Decision Making

• Linking Knowledge with Action

• Assembling Data and Tools for Analysis

and Planning

• Refining Frameworks for Policy Analysis

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1 January 2013

Africa del Este

Lider Regional

James Kinyangi

Sur de Asia:

Lider Regional

Pramod Aggarwal

Africa del Oeste

Lider Regional

Robert Zougmoré

Latinoamerica:

Lider Regional

Ana Maria Loboguerrero

Place-based field work

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Urgency and magnitude

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Historical impacts on food security

% Yield impact for wheat

Observed changes in growing season temperature for crop growing regions,1980-2008.

Lobell et al (2011)

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Average projected % change in suitability for 50 crops, to 2050

Our ability to growfood in 2050

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In order to meet global

demands, we will need

60-70%

more food by 2050.

The need for more food

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Livestock products: Developing countries are

hungry for more.

•Growth in animal product consumption has increased more than any other commodity group.1

•Greatest increases in S and SE Asia, Latin America.

-Overall meat consumption in China has quadrupled since 1980 to 119 lbs/person/yr. 2

•Economic and population growth, rising per capita incomes, urbanizationPhoto by: CGIAR

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Source: Erb et al. (2007)

•30-45% of earth’s terrestrial surface is pasture

- 80% of all agricultural land

•1/3 arable land used for feed crop production

•70% of previously forested land in the Amazon = pasture

3 Livestock and GHG

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Led byArable land per person will decrease

Year

• World Population

• Arable land

1950

• 2,500,000,000

• 0.52 ha

2000

6,1000,000

• 0.25 ha

2050

9,000,000

• 0.16 ha

The arable land on the earth is ~3% or 1.5 billion ha

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0

20

40

60

80

100

120

140

160

180

200

Pig Poultry Beef Milk Eggs

kg C

O2

eq

/kg

anim

al p

rote

in

•Livestock alone is 10-18%3 of all global anthropogenic GHG

-Other estimates as high as 51%4,5

•Range arises from methodological differences

-Inventories vs. life cycle assessments, Attribution of land use to livestock, Omissions, misallocations

2 Livestock and GHG

Source: de Vries and de Boer (2009)

Range of GHG intensities for livestock commodities

•Highest variation occurs for beef, due to variety of production systems.

•Ruminants require more fossil energy use, emit more CH4 per animal.6

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A wicked problem

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Let’s talk about Wicked Solutions

wick·ed (w k d)

adj. wick·ed·er, wick·ed·est

1. Evil by nature and in practice: "this wicked man Hitler, the repository and

embodiment of many forms of soul-destroying hatred"(Winston S. Churchill).

2. Playfully malicious or mischievous: a wicked prank; a critic's wicked wit.

3. Severe and distressing: a wicked cough; a wicked gash; wicked driving

conditions.

4. Highly offensive; obnoxious: a wicked stench.

5. Slang Strikingly good, effective, or skillful

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Transformation in agriculture

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Incremental

adaptation

• Farmers are adapting all the time

• But the questions remains if it is at a rate that

is fast enough

• And if the incremental adjustments are in the

right direction to enable the systematic

adjustment

• How we can speeden up incremental

adaptation?

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CCAFS sites Main crops Main livestock

(forages)

Borana(ET)Maize

(96.6%)

Beans

(86.4%)

Wheat

(33.1%)

Beef cattle

(93.2%)

Goats

(77.8%)

Nyando (KE)Maize

(99.2%)

Sorghum

(73.3%)

Beans

(34.4%)

Goats

(66.9%)

Chicken/hens

(61.2%)

Usambara (TZ)Maize

(87.1%)

Beans

(75%)

Tomatoes

(29%)

Chicken/hens

(82.1%)

Dairy cows

(56.4%)

Albertine

Rift (UG)

Cassava

(78.6%)

Beans

(68.4%)

Sweet

potatoes

(59.8%)

Chicken/hens

(82.5%)Pigs (63.1%)

Where do we work?

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1 January 2013

Leb by

0

10

20

30

40

50

60

70

80

90

100

Lushoto (Tanzania)

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Weather reasons for adapting

a) More erratic rainfall

b) ↘ overall rainfall (88%)

c) ↗ amount of rainfall (39%)

d) more frequent droughts (71%)

e) earlier start of the rains 77%)

f) Later start of rains (65%)

Drivers

• Availability of high yielding varieties

more resistant to pest and diseases

• More profitable market prices.

• Less productive land

Lushoto (Tanzania)

Changes in land use and crop management

- introduction of new, higher yielding crop varieties of maize, beans

and tomatoes

- switching to disease resistant varieties of cassava, bananas and

maize

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Gender Division of Labor

Examples:

Spraying was reported as a men’s task, and

Weeding mainly as a women’s task

Women’s Reporting Men’s Reporting

Men

Women

Boys

Girls

Overall, men and women tend to report that they themselves do most of the tasks

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Decision-Making

Across all 4 sites:

Women report that men make most decisions

Men report more decisions are taken jointly

Example: Nyando, Kenya

Women’s Reporting Men’s Reporting

Men

Women

Together

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Persons and items distribution

Rash model (Campell, 1963): Attitude towards change = number + difficulty of change made

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Determinants of the degree

of adaptation – Poisson

regression model

Variable Coefficient P-value

Lnage -0.259 0.034**

Help 0.281 0.019**

Years of schooling 0.025 0.014**

Ln total asset value 0.060 0.096*

Government influence 0.364 0.002***

Less land productivity 0.164 0.060*

Ability to hire farm labour 0.231 0.031**

Constant 2.135 0.002***

Wald chi2(20)=104.63; p=0.000

Alpha = 0.12

N=131

Dependent variable = number of adaptation strategies undertaken

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Systemic

adaptation

• Supports incremental adaptation

• But also ensures that the direction farmers take is

along the correct trajectory

• Involves design of suitable policies

• Incentivizing the changes that are needed

• And in some cases, overcoming technological

constraints

• E.g. breeding for a 2030 world

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Why do we need breeding?

For starters, we have novel climates

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Crops biologically

at tipping points

•For example, US maize, soy, cotton yields fall rapidly when exposed to temperatures >30˚C

•In many cases, roughly 6-10% yield loss per degree

Schlenker and Roberts 2009 PNAS

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BeanThe most important food legume in tropical Latin

America and East and southern Africa

Area harvested

Current bean suitability

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Led byChanges in Beans Suitability

• Average global area of suitability for growing beans may be reduced by 6.6% by 2020• But wide range of change in suitability from -87% to +66% across regions.

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Led byWhich climatic constraint affects the most beans?

Major climate constraints: heat stress

drought stress

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Transformational

change

• Different livelihood systems for rural communities

• Different structural make-up of the agricultural and

food system at national and regional scales

• Crucial to plan for transformational change, and not

wait until it happens

• One example where it is needed….

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Suitability in

Cauca

Significant changes to

2020, drastic changes

to 2050

The Cauca case:

reduced coffeee

growing area and

changes in geographic

distribution. Some new

opportunities.

MECETA

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Led byAdaptation entry points in maize-

bean systems

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Led bySilvopastoral systems:A mini-revolution in Colombia and Central AmericaPiedemonte llanero

Estado inicial: Julio 17, 2007

Agosto 15, 2008

13 meses

Octubre 22, 2008

15 meses

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Led byFarms of the futureThe Concept

Three ongoing pilots

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LushotoMbuziiYamba

Morogoro

Mwitikilwa

Nyombo

Njombe

Mbinga

Kinole

FOTF in Tanzania

Analogue study Tour Villages visited Starting point

Sepukila Village: -Matengo pits: Traditional soil and

water conservation technique

-Coffee nursery

-Stoves

Masasi Village:-Water source

-Fish pond

-Biogas

Mtama Village: - Bee keeping

-Market value chain social

enterprise visit

- Input supply Stockists

-Weather station visit

- Bean trial visit

- Tree nursery visit

Farms of the futureJourney to Yamba’s plausible futures

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From dialogue to action…….

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Scalable climate smart technologies….

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Which system is more sustainable?

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A MAC style prioritisationframework for CSA?

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Time

Up

take

of

sust

ain

able

agr

icu

ltu

ral p

ract

ices

Innovation / Identification of practices

Pre-investment (eg, development funds, climate finance)

Implementation at scale / Establishment of institutions

Demonstration of agro-economic and sustainability potential

Policy shifts and large-scale changes in practices, livelihoods and environmental impacts

Demonstration of financial / commercial viability and sustainability outcomes

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Wicked solutions for climate smart

agriculture

• Identifying viable practices, technologies

• Collating costs and benefits for establishment, target

domains

• Prioritisation and screening approaches

• Ensuring the enabling environment

• Piloting and outscaling

• The challenge is very big – reducing emissions from

agriculture, ensuring adaptation

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