Targeting CSA in Southern Tanzania under
multiple uncertainties
Which CSA water management technologies are most
suitable for Tanzania’s SAGCOT?
Chris&ne
Lamanna1,
Todd
S.
Rosenstock1,2,
Eike
Luedeling3
1World
Agroforestry
Centre,
Nairobi,
Kenya;
2CGIAR
Research
Program
on
Climate
Change,
Agriculture
and
Food
Security;
3World
Agroforestry
Centre,
Bonn,
Germany
%HHs
w/
Livestock
Livestock
Density
Highland
Focus
Cereal
Focus
Lowland
Focus
Terrain
Soil
Fer&lity
%HHs
w/
Coffee
%HHs
w/
Maize
%HHs
w/
Paddy
Slope
SOC
Farming
System
Compa&bility
Soil
Resources
Cropping
System
Distance
to
Market
Precipita
&on
Depth
to
Groundwa
ter
Surface
Water
Ground
Water
Water
Resources
Physical
Capital
Natural
Capital
Farm
&
Physical
Biophysical
Factors
%
HH
w/
Tenure
%
HH
w/
Extension
%
pop
illiterate
Land
Tenure
Farmer
Support
Literacy
Rates
Labour
Avail.
Complexity
Start
up
costs
Poverty
Access
to
Credit
Social
Capital
Human
Capital
Financial
Capital
Interven&on
Capital
Social
Factors
Interven&on
&
Social
Human
&
Financial
N/A
N/A
Suitability
%
pop
in
lowest
quar&le
• A
probabilis)c,
graphical
model
that
represents
a
causal
network
• Readily
handles
uncertainty
in
both
data
and
causal
pathways
• Can
incorporate
both
hard
data
and
expert
or
stakeholder
knowledge
Using
the
DFID
Livelihoods
framework
(2000)
and
the
field
of
innova&on
diffusion
(Wejnert
2002),
we
developed
a
BBN
for
the
suitabilty
of
CSA
interven&ons
that
can
be
applied
across
diverse
contexts.
For
modeling
the
suitability
of
water
use
technologies
in
Tanzania,
we
parameterized
the
model
using
quan&ta&ve
data
(pink
ovals)
and
expert
opinion,
and
executed
the
model
in
AgenaRisk
(Fenton
&
Neil
2013).
A Bayesian Belief Network for CSA
Contact:
c.lamanna@cgiar.org
In
order
to
implement
Tanzania’s
Agricultural
Climate
Resilience
Plan
(ACRP),
the
Ministry
of
Agriculture,
Food,
and
Co-­‐opera&ves
(MAFC)
needs
to
know
which
technologies
they
should
invest
in
and
promote
in
the
Southern
Agricultural
Growth
Corridor
of
Tanzania
(SAGCOT).
However,
the
SAGCOT
is
agriculturally,
clima&cally,
and
culturally
diverse,
and
there
is
liale
clear
evidence
on
the
costs
and
benefits
of
water-­‐use
technologies
in
this
region
on
which
to
base
their
decision.
Therefore,
we
developed
a
Bayesian
Belief
Network
for
the
suitability
of
CSA
op&ons
in
the
SAGCOT
to
support
the
MAFC’s
investment
decisions
in
the
face
of
uncertainty
and
variability
in
climate,
demographics,
and
op&on
performance.
References
DFID.
2000.
Sustainable
Livelihoods
Guidance
Sheets;
Fenton
N
and
M
Neil.
2013.
Risk
Assessment
and
Decision
Analysis
with
Bayesian
Networks.
CRC
Press;
Wejnert
B.
2002.
Annual
Review
of
Sociology
28:297-­‐326.
• U&lizes
transporta&on
lines
from
Dar
es
Salaam
to
the
Zambia
Border
• Public/Private
Partnership
for
Agricultural
Development
• 12
poli&cal
regions
• Diverse
farming
systems
from
coffee
to
sugarcane
• Diverse
climate,
infrastructure
and
demographics
The SAGCOT
Scaling Up CSA
38
–
44%
44
–
50%
50
–
56%
56
–
62%
62
–
68%
Drip Irrigation
Sustainable
Harvest
Highest
suitability
with
market
access,
water
availability,
and
social
assets
38
–
44%
44
–
50%
50
–
56%
56
–
62%
62
–
68%
E
Nissen-­‐Petersen
Charco Dams
Universally
high
suitability
due
to
low
start
up
costs
and
low
reliance
on
social
assets
38
–
44%
44
–
50%
50
–
56%
56
–
62%
62
–
68%
Water Harvesting
Sustainable
Harvest
Low
overall
suitability
due
to
high
costs,
and
high
dependence
on
social,
financial
and
human
capital
38
–
44%
44
–
50%
50
–
56%
56
–
62%
62
–
68%
System of Rice
Intensification
AfricaRISING
Highest
suitability
in
rice
growing
regions
ACSAA
COMESA
ECOWAS
CCAFS
*list
not
comprehensive
CCAFS,
under
“CSA-­‐PLAN”,
is
helping
countries
scale
up
CSA
via
The
Alliance
for
CSA
in
Africa,
Regional
Economic
Communi&es
(COMESA,
ECOWAS),
and
na&onal
partners.
Decision
support
tools
including
Bayesian
Belief
Networks
can
aid
in
choosing
CSA
pornolios
that
achieve
the
desired
outcomes
for
each
engagement.
Lead
Partner