This document describes a Bayesian Belief Network model developed to determine the most suitable climate-smart agriculture water management technologies for Tanzania's Southern Agricultural Growth Corridor. The model considers various biophysical, social, and economic factors that influence the suitability of different technologies across the diverse agricultural regions in the corridor. It was parameterized using quantitative data and expert knowledge. The results indicate that drip irrigation has the highest suitability where market access, water availability, and social assets are strong, while charco dams are universally suitable due to their low startup costs and independence from social factors. Water harvesting and rice intensification techniques showed more limited suitability due to greater dependence on capital resources and specific farming systems, respectively. The model is intended

1. 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