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Presentation from the CCAFS Farm-household Modeling workshop - Amsterdam, 23-35 April 2012TRANSCRIPT
TOA-MD: Tradeoffs Analysis for
Multidimensional Impact Assessment
Roberto O. Valdivia
and
John M. Antle
CCAFS Modeling Workshop
Amsterdam, The Netherlands
April, 2012
What is the TOA-MD Model?
The TOA-MD Model is a unique simulation tool for multi-dimensional impact assessment that uses a statistical description of a heterogeneous farm population
to simulate the adoption and impacts of a new technology or a change in
environmental conditions. TOA-MD is designed to simulate what would be observed if it were possible to conduct a controlled experiment. In this experiment, a population of farms is offered the choice of continuing to use the current or “base” production system (System 1), or choosing to adopt a new system (System 2). In fact it is never possible to carry out such ideal experiments, so TOA-MD is designed to utilize the available data to attain the best possible approximation, given the available time and other resources available to conduct the analysis. Additionally, TOA-MD is designed to facilitate analysis of the inevitable uncertainties associated with impact assessment.
TOA-MD approach: modeling systems used by heterogeneous populations
Systems are being used in
heterogeneous populations
A system is defined in terms of household, crop, livestock and
aquaculture sub-systems
(ω)
0
Map of a heterogeneous region
Opportunity cost, system choice and adoption
Opportunity cost = v1 – v2 follows distribution ( )
v1 = returns to system 1 V2 = returns to system 2
System 1: > 0
(non-adopters)
System 2: < 0 (adopters)
opportunity cost
( ) 100
A useful adaptation shifts the distribution of opportunity cost
and the adoption curve, increasing gains and reducing losses, to give a net gain from
adaptation
r(2)
The difference between the curves is the gain from
adaptation when all farms use the adapted technology
Adoption
rate
Outcome distributions are associated with system choice ◦ Farms select themselves into “non-adopter” and “adopter” sub-
populations, generating corresponding outcome distributions for these sub-populations
Impact indicators are based on system choice and outcome distributions ◦ TOA-MD produces mean indicators and threshold-based indicators
Analysis shows that impacts depend on the correlations between adoption (opportunity cost) and outcomes ◦ Many impact assessments ignore correlations
◦ Yet these correlations are often important for accurate impact assessment!
Adoption, Outcome Distributions and Impact Indicators
Adoption and outcome distributions
Entire Population with adoption: 55% >
r(1,a)% non-adopters
System 1: 20% >
System 1 before adoption: 25% > threshold
System 2: 90% >
Outcome z
(z|1)
r(2,a)% adopters
(z|1,a) (z|2,a)
(z|a)
Components of the Model
System characterization
Adoption rate
Population (Strata)
Impact indicator design
Opportunity cost distribution Outcome distributions
Indicators and
Tradeoffs
Design
Data
Simulation
TECHNOLOGY ADOPTION AND IMPACT ASSESSMENT
The TOA-MD allows users to simulate technology adoption (i.e. adoption rate)
under a variety of conditions defined by the user. The TOA-MD has the
capability of simulate impacts of technology adoption using statistical
relationships between technology adoption and environmental, economic and
social outcomes. Impacts are defined as population means or as the proportion
of the population above or below a threshold (e.g. poverty line). Examples of
technology adoption applications are:
• Introduction of new crop varieties
• Crop and livestock management
• Soil conservation & agroforestry
• Integrated agriculture – aquaculture
Types of application
ECOSYSTEM SERVICES SUPPLY AND PAYMENTS
The TOA-MD can simulate supply curves for ecosystem services associated
with agricultural systems and payments schemes. Examples of these
applications are:
Soil carbon sequestration and GWP
Water quality and quantity
Biodiversity
ENVIRONMENTAL CHANGE
The TOA-MD allows users to assess impacts of any exogenous
environmental change such as climate change on population of farms.
Examples of these applications are:
Simulate impacts of and adaption to climate change
Changes in water quantity and quality
Types of application, cont.
Technology Adoption
Ecosystem services
Environmental change
cv
Economic (e.g. income based
poverty rate, farm income, other
poverty indicators)
Social (e.g. food security
indicators, , health)
Environmental (e.g. soil depletion,
water quality)
cv
Application Impacts
Recent applications
- Preliminary Economic, Environmental and Social Impact Assessment of the EADD Project in Kenya using
Minimum-Data Tradeoff Analysis. Gates Foundation, ILRI
- Integrated Agriculture-Aquaculture in Malawi. –USAID/AQCRSP
- IFAD Projects: Ghana, Bangladesh, Malawi - World Fish Center
- Climate change and adaptation : AgMIP
- Livelihood Strategies and Adoption of Endemic Ruminant Livestock Breeds, ILRI
- Climate change: Kenya (Claessens et al, 2012), CIP-ICRISAT
Final remarks The TOA-MD can: Simulate technology adoption (estimate an adoption rate) under a variety of conditions defined by the user
Assess economic, environmental and social impacts of technology adoption, using population mean and threshold indicators
Simulate supply curves for ecosystem services associated with agricultural systems
Assess impacts of environmental change, such as climate change, with or without adaptation Training in use of the model, and the model software are available from the TOA Team.
Claessens, L., J.M. Antle, J.J. Stoorvogel, R.O. Valdivia, P.K. Thornton, and M. Herrero. 2012. “A minimum-data approach for agricultural
system level assessment of climate change adaptation strategies in resource-poor countries.” Agricultural Systems, Forthcoming.
Antle, J.M. 2011. “Parsimonious Multi-Dimensional Impact Assessment.” American Journal of Agricultural Economics.
Antle J.M. and R.O. Valdivia. "Methods for Assessing Economic, Environmental and Social Impacts of Aquaculture Technology: Integrated
Agriculture-Aquaculture in Malawi.” 9th Annual Fisheries and Aquaculture Forum, Shanghai Ocean University, April 22 2011
Antle, J.M., B. Diagana, J.J. Stoorvogel and R.O. Valdivia. 2010. “Minimum-Data Analysis of Ecosystem Service Supply in Semi-Subsistence
Agricultural Systems: Evidence from Kenya and Senegal.” Australian Journal of Agricultural and Resource Economics 54:601-617.
Claessens, L., J.J. Stoorvogel, and J.M. Antle. 2009. “Economic viability of adopting dual-purpose sweetpotato in Vihiga district, Western
Kenya: a minimum data approach. ” Agricultural Systems 99:13-22.
Nalukenge, I., J.M. Antle, and J.J. Stoorvogel. (2009). “Assessing the Feasibility of Wetlands Conservation Using Payments for Ecosystem
Services in Pallisa, Uganda.” In Payments for Environmental Services in Agricultural Landscapes . Ed. L. Lipper, T. Sakuyama, R. Stringer and D.
Zilberman. Springer Publishing.
Smart, F. 2009. Minimum-Data Analysis of Ecosystem Service Supply with Risk Averse Decision Makers. Ms. Thesis, Montana State University –
Bozeman.
Immerzeel, W., J. Stoorvogel and J. Antle. 2007. "Can Payments for Ecosystem Services Secure the Water Tower of Tibet?" Agricultural
Systems 96:52-63.
Antle, J.M. and J.J. Stoorvogel. 2006. "Predicting the Supply of Ecosystem Services from Agriculture." American Journal of Agricultural
Economics 88(5):1174-1180.
Antle, J.M., Valdivia, R. 2006. “Modelling the supply of ecosystem services agriculture: a minimum-data approach.” Australian Journal of
Agricultural and Resource Economics 50: 1–15.
Key Publications
Thanks..
http://tradeoffs.oregonstate.edu
Developments needed
to better deal with
this attribute
Attribute Covered
in
previous
analyses?
If ‘yes’, which
indicators were
used?
Which indicators
would you like to use
in future to deal with
attribute?
For your
model
For
household
level models
in general
Economic
performanc
e
Yes Poverty rate
Per capita income
Total farm income
Link to
Market
equilibrium
Models
Food self-
sufficiency
Yes - Protein
Consumption
Food
security
Yes Total calorie
consumption, fish
consumption
(WF), dairy
consumption
(EADD)
Developments needed
to better deal with
this attribute
Attribute Covered
in
previous
analyses?
If ‘yes’, which
indicators were
used?
Which indicators
would you like to use
in future to deal with
attribute?
For your
model
For
household
level models
in general
Climate
variability
Yes Change in
poverty,
environment,
other socio-econ
Risk
Yes
Mitigation
Yes
Adaptation
Yes