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Evaluation approaches and tools for assessing agricultural vulnerability/resilience to climate change in regional contexts (assessments, multi-scale models, place-based analysis with stakeholders)

Andy Jarvis

Theme Leader, Adapting to Progressive Climate Change

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A wicked problem

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Landscape-scale research on food security, natural resources, policy and governance to achieve agricultural resilience to climate change.

Talk about a wicked scale!

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Evaluation approaches and tools for assessing agricultural vulnerability/resilience to climate change in regional contexts (assessments, multi-scale models, place-based analysis with stakeholders)

…a particularly malicious title

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I’m not a wicked scientist…

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Let’s talk about Wicked Solutions

wick·ed  (w k d)adj. wick·ed·er, wick·ed·est1. 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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What do we know?

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1 January 2013

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maize beans tomato Chicken/hens

dairy0

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Lushoto (Tanzania)

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Weather reasons for adapting

a) More erratic rainfallb) ↘ 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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1 January 2013

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Weather related reasons for Δ Crop/ mngt change

↘ overall rainfall (88%)↗ erratic rainfall (75%)↗ frequent droughts (71%)

Adoption of shorter cycle and drought tolerant crop varieties

Earlier start of the rains (77%) Planting earlier (maize) Switching to disease resistant varieties (maize cassava,

bananas)

Later start of rains (65%) Planting crops later (beans and cassava)

↗ overall rain (39%) Maize, beans and tree based crops (peaches, apples and coffee) planted years to utilize the increased moisture

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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

MenWomenBoysGirls

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

MenWomenTogether

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Let’s ask the scientist?

What happens to staples in Africa?

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Impacts on staples in SSA

Crops affected differently.

Regional differences in impacts.

We have uncertainty.

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Cassava gaining while other fail

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Cassava’s role as a substitution cropCassava as a fallback crop under an uncertain climate (risk management)

Cassava as the substitution crop for other staples more sensitive to heat and drought

What are the socio-cultural constraints to a shift in staple, and how can this shift be most effectively made?

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If I were a policy maker or decision maker…………I’d be confused

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Wicked solutions across scales and disciplines

• Global and regional scale problem diagnosis feeding into local and

national lead solutions

• Some examples:

• Vulnerability assessment identifying entry points

• Local learning processes

• Towards better national level plans and strategies

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Led byAdaptation entry points in maize-bean systems

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Led byFarms of the futureThe Concept

Three ongoing pilots

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Led byYamba analogue map + Study Tour Itinerary + Activities - Zoom

-Weather sttin visit - Bean trial visit- Tree nursery visit

LushotoCCAFS site

Farms of the futureJourney to Yamba’s plausible futures

Tanzania

Morogoro

Mwitikilwa

Njombe

Nyombo

Mbinga

Farms of the futureTaking the analogue concept to the field

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Lushoto Mbuzii Yamba

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-StovesMasasi Village:-Water source-Fish pond-BiogasMtama Village: - Bee keeping

-Weather station visit

- Avocado trial

-Banana varieties trial

- Maize fertility mngt

-Market value chain social enterprise visit- Input supply Stockists

-Weather station visit - Bean trial visit- Tree nursery visit

- SACCOS

visit

- Market

visit

Farms of the futureJourney to Yamba’s plausible futures

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Persons and items distribution

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

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Led byDeterminants 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=0.000Alpha = 0.12N=131

Dependent variable = number of adaptation strategies undertaken

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Scalable climate smart technologies….

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Scalable climate smart technologies….

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A MAC style prioritisation framework for CSA?

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Time

Upt

ake

of s

usta

inab

le a

gric

ultu

ral p

racti

ces

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 across scales and disciplines

• Local, landscape, national, regional and global scale – make sense of the trade-offs and drivers between these

• Science stitching pieces together, without getting lost in the complexity –hopping between disciplines and scales

• Plug scientific insights into the policy environment to achieve wide-scale climate smart agricultural adoption