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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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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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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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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
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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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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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