On 12th October 2015 the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), East Africa through its regional knowledge sharing platform The Climate and Agriculture Network for Africa (CANA) organized a webinar dubbed Climate-Smart Agriculture Tools for Africa.

2. Agroforestry
What Works, Where:
The CSA Compendium and X-Ray
Nutrition security
Poverty alleviation
Natural resource
Improved
cook-stove
Conservation
agriculture
Increased yields
Soil quality & carbon
Erosion
Dietary
diversity
Intercropping
Participatory
approach
Todd Rosenstock & Christine Lamanna
World Agroforesty Centre (ICRAF) | Nairobi

3. Large Scale Initiatives and Investments
Launched Sept 2014
80+ members
Some CSA initiatives

4. Not
CSA
CSA
What is CSA and what is not CSA?

5. Not
CSA
CSA
Many practices can be CSA somewhere
But none are likely CSA everywhere
Context
What is CSA and what is not CSA?

6. Photo: K. Tully
What works where?

7. Key word search
Abstract/title review
Full text review
Data extraction
144,567
papers
16,254
papers
6,100
papers
~175,000 data points
Systematic review and meta-analysis
68 practices/28 indicators of CSA outcomes

8. Response ratio =
ln(mean(treatment)
/mean(control))
Effect size =
weighted mean of
response ratios
●●
● ●● ● ●●● ●●● ●● ●●
●
● ●● ●●
ent
on
zer
try
−1.0 −0.5 0.0 0.5
Effect size
Agroforestry
Inorganic
fertilizer
Crop rotation
Imp. diets
Impact of select practices on productivity
(N = 9,940)

9. ●●
● ●● ● ●●● ●●● ●● ●●
●
● ●● ●●
ent
on
zer
try
−1.0 −0.5 0.0 0.5
Effect size
Agroforestry
Inorganic
fertilizer
Crop rotation
Imp. diets
● ●●
●●● ●● ●● ●●●
non−Legumionous
Leguminous
−1.0 −0.5 0.0 0.5
Effect size
● ●●
●●● ●● ●● ●●●
non−Legumionous
Leguminous
−1.0 −0.5 0.0 0.5
Effect size
- N fixing trees
+ N fixing trees
● ●
●
Alt. feeds
Inc. protein
−0.2 0.0 0.2 0.4
Effect size
Selecting ‘best bets’ for CSA by practice at
global level
Alt. feeds
Inc. protein

10. Selecting ‘best bets’ for CSA for a place
Productivity
Resilience
−1
0
−1
0
1
2
−1
0
1
2
Crop ManagementDiet ManagementIntercropping AgroforestryNutrient ManagementPostharvest StorageSoil ManagementTree ManagementWater Management
Practice
EffectSize
Country
Tanzania
Uganda
P1 P2 P3 P4 P5 P6 P7 P8
Productivity
Resilience

11. Selecting ‘best bets’ for CSA for a place
Productivity
Resilience
−1
0
−1
0
1
2
−1
0
1
2
Crop ManagementDiet ManagementIntercropping AgroforestryNutrient ManagementPostharvest StorageSoil ManagementTree ManagementWater Management
Practice
EffectSize
Country
Tanzania
Uganda
P1 P2 P3 P4 P5 P6 P7 P8
Productivity
Resilience
Predictable

12. Selecting ‘best bets’ for CSA for a place
Productivity
Resilience
−1
0
−1
0
1
2
−1
0
1
2
Crop ManagementDiet ManagementIntercropping AgroforestryNutrient ManagementPostharvest StorageSoil ManagementTree ManagementWater Management
Practice
EffectSize
Country
Tanzania
Uganda
P1 P2 P3 P4 P5 P6 P7 P8
Productivity
Resilience
Predictable
Less so

13. −1.0
−0.5
0.0
0.5
1.0
−1.0 −0.5 0.0 0.5 1.0
Productivity
SOC
Productivity (Effect size)
Resilience(Effectsize)
11%
15% 56%
SynergiesTradeoffs
Tradeoffs
Synergies and tradeoffs with CSA
19%

14. Practices differ in magnitude of co-benefits

15. Practices differ in magnitude of co-benefits

16. Studies with indicators for at least 1
component of CSA

17. Studies with indicators on 2 or more CSA
objectives
~40% of the available research

18. Studies with indicators for all 3 components
1.5% of the available research

19. Turning data in decision-support
‘CSA X-ray’
Evidence-based
and digestible
assessments of
CSA practices and
places
Figures and icons: Morningstar

20. Financial support: CCAFS, UN FAO, IFAD, CIFOR-EBF
Contributors:
K Tully, C. Corner-Dolloff, E Girvetz, D-G Kim, M Lazaro, A Jarvis,
P Bell, S Chesterman, S MacFatrige, H Strom, A Madalinska, A-S
Eyrich, C Champalle, W English, A Akinleye, A Poultouchidou, A
Kerr, H Neufeldt, A Arshan, J Rioux, F. Atieno, M Ravina, C Zhuo,
S Abwanda, W Zhuo, C Ardilla, P Laderach, D Grunzel, S
Vermuellen, O Bonilla-Findji, K Morris, J Dohn, M Richards, B
Campbell, A Arslan, J Rioux
Thank you, t.rosenstock@cgiar.org
Data will be publically available in 2016

22. Directing Investment in Climate-Smart Agriculture (CSA)
CSA Prioritization Framework
Climate-Smart Agriculture
Tools for Africa Webinar
13 October 2015
Caitlin Corner-Dolloff
CIAT, Decision and Policy analysis
c.corner-dolloff@cigar.org
Miguel Lizarazo (CCAFS-LAM), Andreea Nowak (CIAT), Fanny Howland (CIAT), Nadine
Andrieu (CIAT/CIRAD), Osana Bonilla (CCAFS), Ana Maria Loboguerrero (CCAFS-LAM),
Andy Jarvis (CIAT-CCAFS)
© CIAT/Neil Palmer

23. Alliance for CSA in Africa
Vision
25 x 25
West Africa CSA
Alliance (WACSAA)
Global momentum building for CSA
Map of a selection of CIAT-ICRAF CSA initiatives with CCAFS, WB, USAID from 2014-2105
6 million farmers by 2021
Linking 19 countries
500 million farmers globally
CSA one of 5
priority
investment
areas
Niger, Kenya
200 million in CSA

24. A set of filters for
evaluating CSA options
& establishing
CSA investment portfolios
CSA Prioritization
Framework
Multi-
level
Linkable
Stakeholder
Driven
Flexible
Simple
Intended users
1° National and sub-national
decision makers
2° Donors, NGOs, implementers

25. CSA Prioritization Framework
Filters for selecting CSA investment portfolios
*Identify scope
*Match practices
with context
*Participatory
metrics selection
Long list of
CSA practices
*Ex-ante assessment
based on CSA
indicators
*Stakeholder
workshop
Ranked short
list of priorities
*Economic analysis
– assess costs and
benefits, including
externalities
Ranked short
list based on
CBA
*Integrated analysis
of opportunities &
constraints
* Stakeholder
workshop
CSA investment
portfolios
Pilots
underway
Ethiopia
Ghana
Uganda

26. Workshop 1
Guatemala
Filtering: Indicators of CSA Pillars
Workshop
Literature
review
Expert
interview
+
+
Lessons:
• Participatory indicator selection -
link science with desired change
• Improved communications and
visualization of data key for CSA
decision-making
Ranked long list of possible
CSA Practices
ScoreCSA Practices

27. Guatemala
Filtering: Economic Evaluation
Lesson: Econ analysis in high demand
- data and tools needed to better assess and easily visualize options

28. Prioritized
Practices
Portfolios Designers
Producers Research MoAgr
Agroforestry
systems: live fence 
Varieties tolerant to
pests & diseases 
1: low
resource
farmers
Varieties tolerant to
drought and water
stress
 
1: low
resource
farmers
Conservation
agriculture 
2: FS,
drought
Crop rotation
(maize-beans) 
Reservoirs + Drip
irrigation
X: FS,
drought
Guatemala
Filtering: Integrated Analysis
CSA indicators, CBA, externalities, barriers and opportunities
Lesson:
Prioritization does not
imply one output
• Multi-variate analyses
allow users to create
differentiated
portfolios based on
intended
application and
beneficiaries

29. Lesson:
Process is as important as
the content
• Discussions of data create
space for collaborative
integrated planning
between users
• EU modifying calls based on
results – other potential
applicants linked from
beginning
Mali
CSA at the Regional Level
Policy/Research
forums (AEDD)
Regional
governments
NGOs (C-GOZA,
Sahel Eco)
Donors (EU,
Swedish
Embassy)
CONTEXT
POTENTIALUSERS

30. Lesson: Local ownership is critical to prioritization
• Local communities act as researchers
• Minimize extractive data collection
• Adapt metrics to local context and socialize prior to users.
Training on Survey
Discussion
on indicators
Colombia
CSA at the Local Level
© CIAT/Andreea Nowak

31. CSA-Plan
Uptake of CSA Plan components, including CSA PF,
in 15+ countries in Asia and Africa 2015-2018
ICRAF - T. Rosenstock, C. Lamanna
CIAT - E. Girvetz, C. Corner-Dolloff

32. Ongoing CSA initiatives

33. Caitlin Corner-Dolloff
c.corner-dolloff@cigar.org
additional information at:
ccafs.cgiar.org/climate-smart-agriculture-prioritization-framework
Thank you!

35. Climate Smart
Agriculture
Rapid
Appraisal
(CSA-RA)
Caroline Mwongera, Leigh
Winowiecki, Kelvin
Mashisia, Jennifer Twyman,
Peter Laderach, Edidah
Ampaire,
Steve Twomlow
13 October 2015

36. Climate Smart Agriculture Rapid Appraisal
(CSA-RA)
• Combine socio-economic and biophysical
realities across scales in order to prioritize,
implement and out-scale CSA
A tool for Prioritization of Climate Smart Agriculture
across Landscapes
PRA Tools Scale
1. Village
resource
maps
2. Climate
calendars
3. Historical
calendars
4. Cropping
calendars
5. Organizatio
n mapping
using Venn
diagrams
Household-
farm
Community-
landscape
Sub-regional
scales
 Gendered
lens
 climate
focus

37. CSA-RA Methodology
Participatory Approach
1. Farmers’
Workshops
2. Expert
Interviews
3. Farm
visits
(interviews
/
transect
walk)
Gender
disaggregated
Site-specific
targeting of CSA
interventions
Expert opinion Socio-
economic data
1. Crop &
Livestock
listing/uses/ge
nder
association
2. Community/
village
resource maps
3. Cropping
calendar
4. Historical
calendar
5. Climate
calendar
6. Institutional
mapping
 Challenges
 Current
practices
 Community
resources
 Climate impacts
 Local
organizations
for:
 Women
 Men
 Youth (< 30
yrs.)
 Farming
systems
 Current
practices
 Recommend
ations on
site-specific
CSA
intervention
s
 Barriers and
constraints
to adoption
 HH size, farm size
 HH food sufficiency
 Labor (HH & hired)
 Production
(crop/livestock)
 Yield
 HH
consumption
 Sales
 Off farm income
 Remittances,
donations,
savings
 HH expenses
 Use of agricultural
inputs
 Current practices
CSA
Prioritization
o Awareness
and use of
agricultural
o Prioritization
of practices by
gender & AEZ
o Ranking
indicators
considered in
adopting a
practice
o Demonstratio
n plots
o Practic
es
o Sites
3.
Prioritizatio
n
Workshops

38. Cropping calendar
Identifies most
important crops by
gender, division of
responsibilities and
different crop
management
activities
Crop management activities by month for groundnut, cassava and sesame as
detailed by the male participants in the farmer workshop in March 2014 in Gulu
district of Uganda. Logograms indicate whether men or woman undertake the
activity
Crop management activities by month for beans, cassava and sesame as
detailed by the female participants in the farmers workshop in March 2014
in Gulu district of Uganda. Logograms indicate whether men or woman
undertake the activity.

39. Organization mapping
Organization mapping and linkages as detailed by the female participants (left panel) and male participants
(right panel) in the farmers workshop in September 2014 in Mbarali district of Tanzania. Blue circles denote
those ranked as of high importance, yellow circles of medium importance, and pink circles of low
importance. Acronyms represent the organizations.
Indicate
organization
linkages, as well
as gendered
differences in
their ranking

40. Climate calendars
Reveal climate
variability
perceptions over
time, gendered
impacts and
vulnerability
Organization mapping and linkages as detailed by the female and male participants in the
farmers workshop in September 2014 in Mbarali district of Tanzania. Blue circles denote
those ranked as of high importance, yellow circles of medium importance, and pink circles of
low importance. Acronyms represent the organizations.

41. CSA Prioritization
Prioritization of agricultural practices in Anaka, Northern
Uganda by gender and by agro-ecological zone

42. Targeting & Out-scaling site-specific
CSA practices
• Guide agricultural
investments
• PRELNOR Project (IFAD)
• Select project sites
• Socio-economic surveys
• Land Health Surveys
• Select location of CSA
demonstration sites
• Institutional support
• Local
stakeholders/organizations

43. Manual and Reports
Available at CCAFS Harvard
Dataverse:
https://dataverse.harvard.edu/datas
et.xhtml?persistentId=doi:10.7910/
DVN/28703
Output for the CIAT-led, project “Increasing Food
Security and Farming System Resilience in East Africa
through Wide-Scale Adoption of Climate-Smart
Agricultural Practices” funded by IFAD

45. Participatory Scenario Planning: A
decision support approach for
Climate-Smart Agriculture
Adaptation Learning Programme – CARE International
CSA Tools in Africa
CCAFS, CARE Webinar
13th October 2015

46. Known and unknown?
Changing climate and
weather patterns.
Growing challenge for
smallholder farmers,
pastoralists, VCA.
Future climate risks,
opportunities?
Future climate impacts -
agricultural productivity,
incomes, vulnerable
communities, women, men?
WWW.CARECLIMATECHANGE.ORG

47. What needs to be done?
• Adaption in agriculture & building resilience to climate
(CSA)…How?
• Community-based adaptation: social decision-making
processes + support to technical adaptation strategies
WWW.CARECLIMATECHANGE.ORG
• Climate informed decision
making and planning…
But:
Uncertain climate
information – planning for
inexact is challenging
Large vs local scale

48. Participatory Scenario Planning (PSP)
WWW.CARECLIMATECHANGE.ORG
Multi-stakeholder forum for:
• Accessing, understanding seasonal climate forecasts and
• Collectively interpreting them – locally relevant, actionable
information for decision making and planning.

49. Why PSP?
• Scenarios: planning for likely & less certain outcomes
• Earlier, better informed: advisories to take advantage of
opportunities, reduce risks
WWW.CARECLIMATECHANGE.ORG
• Learning now to continually
manage seasonal climate
variability, risks and
uncertainties […] provide
potential pathways for
strengthening stakeholders’
adaptive capacities to
manage climate change in the
long term (Niang, et al., 2014)

50. Step 1. Designing
the PSP process
Developing a well
thought out, locally
relevant and
appropriate PSP
process, including
deciding the level
(national,
county/province,
district etc.) at which
to conduct PSP and
forming partnerships
for sustainability of
the process
Step 2. Preparing
for a PSP
workshop
Engaging
stakeholders,
bringing out their
information needs for
the coming season
and using this to
plan for targeted
workshop outcomes.
Step 3. Facilitating
a PSP workshop
Multi-stakeholder
forum – access,
understanding &
combining
meteorological &
local seasonal
forecasts;
interpretation into
locally relevant and
actionable
information for
seasonal decision
making & planning.
Step 4.
Communicating
advisories from a
PSP workshop
Reaching all actors
who need to use the
information, in good
time to inform
decisions and plans.
Step 5. Feedback,
monitoring and
evaluation
Two-way
communication and
feedback between
producers,
intermediaries and
users of climate
information enabling
continuous, iterative
and shared learning
and improving the
PSP process and
outcomes.
PSP is an iterative learning process
The PSP process

51. Value of PSP in climate-smart agriculture
WWW.CARECLIMATECHANGE.ORG
Building adaptive capacity & resilience…

52. Value of PSP in Climate-Smart Agriculture
WWW.CARECLIMATECHANGE.ORG
Building adaptive capacity…
• Institutions, entitlements and governance – multi-stakeholder
dialogue, responsiveness & accountability
• Regular planning – informed by changing risks, vulnerability,
capacity, resources, knowledge and information

53. Way forward?
• Projects, programmes: e.g.
Kenya Agriculture Sector
Development Support
Programme – link with VCA
platforms
• Development plans, budgets:
e.g. N. Ghana DMTDP; Kenya
Garissa County CIDP,
Agriculture work plan
• Policy: e.g. Malawi
Meteorology Policy
WWW.CARECLIMATECHANGE.ORG
Integration of PSP in…

54. Thank
You!
Adaptation Learning Programme (ALP) www.careclimatechange.org/adaptation-initiatives/alp
alp@careclimatechange.org
Joto Afrika Special Issue 12 on Climate communication for adaptation:
http://www.alin.net/Joto%20Afrika
Building resilience to climate change and enhancing food security in north eastern Kenya:
http://www.careclimatechange.org/files/stories/ALP_Kenya_Noor_Aug2012_final.pdf
Facing Uncertainty: the value of climate information for adaptation, risk reduction and resilience in
Africa: www.careclimatechange.org/files/Facing_Uncertainty_ALP_Climate_Communications_Brief.pdf
Coming soon “Climate information for resilient agricultural decision-making and planning in rural
communities: A Guide to Participatory Scenario Planning”
WWW.CARECLIMATECHANGE.ORG
ALP is supported by

56. targetCSA
- a decision support tool to target CSA practices -
Patric Brandt, Marko Kvakić, Klaus Butterbach-Bahl and Mariana Rufino
March, 3 2014

57. Key elements
• National - regional scale
• Spatially explicit
• Combining vulnerability indicators
& CSA practices
• Participatory process
• Consensus oriented
?

58. targetCSA – the framework

59. Vulnerability indicators CSA practices
Example

60. targetCSA – the framework

61. Expert opinions
• Stakeholder preferences on prioritizing:
• Vulnerability indicators
• CSA practices
• Consensus = minimized dissent
NGOGO
Sci. Priv.
Optimization model

62. targetCSA – the framework

63. Spatial indices
Aggregated & consensually weighed by stakeholder opinions
+
Maps are based on example data.
majority vs. minority
Identifying regions of high vulnerability & CSA suitability

64. targetCSA: Take home
• Problem structuring & complexity reduction
• Spatial indices built on consensus & evidence
• Exploring consensus scenarios may lead to
higher acceptance
• Demand-based assessment of CSA potential
• Transferability & flexibility

65. Asanteni sana!