Transcript Document

Tradeoff Analysis: From Science to Policy
John M. Antle
Department of Ag Econ & Econ
Montana State University
The Challenge: Policy-Relevant Science
How can we link relevant agricultural, environmental and economic
sciences to support informed policy decision making?
E.g., do we know what policies will reduce poverty and encourage
adoption of more sustainable practices in the Machakos region?
• Ag Scientists: improve crop varieties and management
• Environmentalists: need LISA
• Economists: need to “get prices right”
The Challenge: Policy-Relevant Science
The TOA Approach: Agriculture as a complex system…
• interconnected physical, biological and human systems varying over
space and time
- the role of heterogeneity in relevant populations
the fallacy of the “representative unit”
- the role of human decision making
- the role of system dynamics and nonlinearities
- relevant scales of analysis to support policy decisions
200000
180000
Net Returns (Ksh/ha/farm)
160000
140000
120000
100000
80000
60000
40000
20000
0
0
20
40
60
80
100
120
140
Nutrient Depletion (kg/ha/yr)
V1
V2
V3
V1 V4V2
V3
V5
V4 V6 V5
Linear
V6
(V6)
Linear (V1)
Heterogeneity: Nutrient Depletion and Net Returns in Machakos
Variation within and between systems…
160
Coefficient of Variation of Net Returns
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0
50
100
150
200
250
Mean Net Returns ($ per acre)
Base
CC-A
CC-N
CO2-A
CO2-N
CC+CO2-A
CC+CO2-N
Human Behavior: Mean versus coefficient of variation of net returns by
Montana sub-MLRA, for climate change (CC) and CO2 fertilization
scenarios with (A) and without (N) adaptation. (Source: Antle et al., Climatic
Change, 2004).
Production (kg D.M. /ha )
5000
4000
3000
2000
1000
0
0
50
100
150
Thickness A-horizon (cm)
Nonlinearities: The effect of differences in the thickness of the fertile Ahorizon on the dry matter production of potatoes as simulated with the
DSSAT crop model in the northern Andean region of Ecuador.
Increase in carbofuran leaching (g/ha/yr)
600
A: shallow topsoil, low tillage erosion rate
B: deep topsoil, low tillage erosion rate
C: shallow topsoil, high tillage erosion rate
D: deep topsoil, high tillage erosion rate
500
400
300
200
100
0
0
5
10
15
20
25
30
Year
Complexity: The temporal dynamics in carbofuran leaching for 4 different fields
as a result of tillage erosion and management changes in the northern Andean
region of Ecuador. (Source: Antle and Stoorvogel, Environment and
Development Economics, in press).
Designing and Implementing Policy-Relevant Science
How is it done? Coordinated disciplinary research.
How is it implemented: Tradeoff Analysis.
Tradeoff Analysis is a process that can be used to:
• set research priorities according to sustainability criteria
• support policy decision making
• use quantitative analysis tools to assess the sustainability of
agricultural production systems.
Tradeoff analysis process
•Public stakeholders
•Policy makers
•Scientists
It’s not a
linear
process…
e.g. NUTMON
Research priority setting
•Identify sustainability criteria
•Formulate hypotheses as potential tradeoffs
Project design & implementation
•Identify disciplines for research project
•Identify models and data needs
define units of analysis
Communicate
to stakeholders
•Collect
data and implement disciplinary research
TOA is based on an integrated assessment approach to
modeling agricultural production systems, using spatially
referenced data and coupled disciplinary models.
Soils & Climate Data
Crop/Livestock Models
Environmental
Process Models
Environmental
Outcomes
Economic Data
Yield
Economic Model
Land Use &
Management
Economic
Outcomes
Implementing the TOA Approach: the TOA Software
The Tradeoff Analysis model is a tool to
model agricultural production systems by
integrating spatial data and disciplinary
simulation models.
It helps scientific teams to quantify
and visualize tradeoffs between key
indicators under alternative policy,
technology and environmental
scenarios of interest to policy
decision makers and other
stakeholders.
Example: Assessing Impacts of Policy and Technology Options on
the Sustainability of the Machakos Production System
Poverty
Nutrient Dep
Define a tradeoff curve by varying a price (e.g., maize price) for a given
technology and policy environment. What is the form of the tradeoff?
Factors Affecting Slope of Tradeoff Curve:
• Productivity of each system at each site
• Nutrient balance of each system at each site
• Effects of maize price on farmers’ choice of system
at each site (extensive margin)
• Effects of maize price on farmers’ choice of
management at each site (intensive margin)
• Spatial distribution of systems, prices
Technology and Policy Scenarios:
Manure Management, Fertilizer Prices
Poverty
Nutrient Dep
How do these scenarios shift the tradeoff curve?
Do curves differ spatially?
200000
180000
Net Returns (Ksh/ha/farm)
160000
140000
120000
100000
80000
60000
40000
20000
0
0
20
40
60
80
100
120
140
Nutrient Depletion (kg/ha/yr)
V1
V2
V3
V4
V5
V6
Machakos: Base Technology and Prices, Individual Farms
160
86,000
basevv
84,000
82,000
80,000
78,000
76,000
74,000
NRAW
72,000
70,000
68,000
66,000
64,000
62,000
60,000
58,000
56,000
54,000
52,000
50
60
70
80
90
DEP10W
100
110
120
130
Base Technology and Prices, Aggregated by Village
100
90
80
70
Poverty
60
50
40
30
20
10
0
10
60
110
160
210
260
Nutrient Depletion
Base Aggregated
Technology by
andTradeoff
Prices, Point
Aggregated
by Village
and Village
310
360
75
BASET
70
65
60
55
50
45
40
35
30
25
20
40
50
60
70
80
90
DEP10W
100
110
Aggregated by Tradeoff Point
120
130
75
70
65
60
55
50
45
40
35
30
25
20
15
10
30
40
50
BASE
MAIZE PRODUCTIVITY
60
70
MANURE
80
90
DEP10W
100
FERT PRICE
110
120
130
140
150
MANURE + FERT PRICE
Aggregated by Tradeoff Point with Alternative Policy and Technology
Scenarios
Conclusions:
• TOA is a tool that can integrate data and modeling
tools to support informed policy decision making
• The challenges:
• Make the tools available to clients.
• Create a demand for better information.
• Improve the tools:
• lower cost of adoption and use
• expand applicability
Process for Transfer of TOA Tools to Users:
• Informing potential clients (web sites, etc)
• Training (workshops, on-line course)
• Collaborative agreements with clients
• Use by client staff with TOA support
• Follow-up to assess strengths and weaknesses
Key Issue: High adoption (training) and
implementation costs (data)
• Data
• Soils and climate
• Economic: farm surveys
• Model complexity (training)
• DSSAT models
• Economic models
• Environmental models
Solutions
• Data
• Soils and climate: down-scaling techniques
• Economic: minimum data approach
• Linkages to existing data: NUTMON
• Model complexity
• Bio-physical: landscape-scale empirical models
• Economic: minimum data approach
Experience
• Downscaling & linkages: Peru, Senegal, Kenya
• soil & climate data
• adaptation of existing farm survey data
• Kenya: complex model implemented in 3 months
with NUTMON data, but model complexity remains
• Minimum data: Panama
• simple model implemented with 1 week training,
1 month data collection & model development
• but limited applicability
Implications
• Optimal strategy for institutionalization
• utilize minimum data approach for training and
initial applications
• develop more detailed applications if needed as
clients acquire capability, data
Conclusions
• TOA successfully implemented as an operational
tool applied to various policy problems
• environmental & human health impacts of
pesticide use (Ecuador)
• terracing and related conservation investments
(Peru, Senegal)
• soil carbon sequestration (USA, Peru, Senegal,
Kenya)
• nutrient depletion (Senegal, Kenya)
Conclusions (cont.)
• Adoption by national and international institutions is
in progress
• Development of downscaling & minimum data
methods will lower adoption costs
• Further experience needed to fully assess impact
But…note methodological issues to be confronted in
assessing impact of policy research (see Pardey and
Smith, IFPRI, 2004)