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