Targeting CSA in Southern Tanzania under multiple uncertainties
1
Targeting CSA in Southern Tanzania under multiple uncertainties Which CSA water management technologies are most suitable for Tanzania’s SAGCOT? Chris&ne Lamanna 1 , Todd S. Rosenstock 1,2 , Eike Luedeling 3 1 World Agroforestry Centre, Nairobi, Kenya; 2 CGIAR Research Program on Climate Change, Agriculture and Food Security; 3 World 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: [email protected] In order to implement Tanzania’s Agricultural Climate Resilience Plan (ACRP), the Ministry of Agriculture, Food, and Coopera&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 wateruse 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 28:297326. • 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 NissenPetersen 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 “CSAPLAN”, 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
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Transcript of Targeting CSA in Southern Tanzania under multiple uncertainties
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: [email protected]
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
