Universities CRED, Columbia University University of Miami Penn State University
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![Page 1: Universities CRED, Columbia University University of Miami Penn State University](https://reader035.fdocuments.in/reader035/viewer/2022070400/56813566550346895d9ccebc/html5/thumbnails/1.jpg)
Modelling Adaptive Management inAgroecosystems in the Pampas in Response to
Climate Variability and Other Risk Factors
Carlos E. Laciana,Federico Bert
University of Buenos Aires
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Universities• CRED, Columbia University • University of Miami • Penn State University• NCAR (National Center for Atmospheric Research)• University of Buenos Aires
NGOs• AACREA (Asociación Argentina de Consorcios Regionales de Experimentación Agrícola)
• CENTRO (Centro de Estudios Sociales y Ambientales)
Government Agencies•SMN (Servicio Meteorológico Nacional)
Project funding: NSF and NOAA of United States.
Project Participants
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Project Objective
To understand and model the workings and interactions of natural and human components in agroecosystems, with…
Special emphasis on assessing the scope for active adaptive management in response to climate variability.
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The study area: Argentine Pampas
• One of the most important agricultural regions in the world
• Agriculture accounts for more than half of exports
• Production systems similar to those in US
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Overview of the decision-making process
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Outline
1. A simple operative model of decision-making
2. Optimization of alternative objective functions
3. Next steps: An agent-based model
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1. A simple operative model of decision-making
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Decision-making 1 D
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Decision-making 2 D
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Decision outcomes D
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Assessment of outcomes AMy #&@$! brother in law did better than I did!
Maize prices dropped after I decided to plant maize
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Learning and adaptation L
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• Objective functions: What farmers are really trying to achieve…
• Standard economic models often consider only maximization of utility
• Wrong assumed objective may imply wrong advice…
• Assumed objective function influences value of climate information
2. Optimization of alternative objective functions
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Objective functions explored
• Expected Utility: – The curvature of the utility function u( . ) is related to a
decision-maker’s risk aversion.
• PT’s Value Function: – Loss aversion: losses are felt more than gains, effect
described by the lambda parameter. – Gains and losses evaluated with respect to a reference
value (specific for an individual)
)()( ii
i wupqEU
)()()( ii
i wvpqV
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Optimization of objective functions
)()(max *xEUxEUx
where is the proportion of land with each crop-management for the optimum of the EU and V.
The optimization is performed using GAMS (Gill et al. 2000).
)()(max *xVxVx
),....,( **1
*mxxx
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Optimization procedure
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Optimization Constraints
• Land owners tend to adhere to a crop rotation (advantages for soil conservation).
• Tenants have no restrictions; the single most profitable crop is chosen.
• Constraints for owners. Land assigned to a given crop had to be:– no less than 25%,– or more than 45% of the farm area.
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Utility Theory - Owners
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Utility Theory - Tenants
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Prospect Theory - Tenants
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Value of climate information
VOI = Economic Benefit with Forecast
- Economic Benefit without Forecast
O.F. Maximized separately for
each ENSO phase
O.F. Maximized for the entire
historical climatic series
• Owners & tenants• UT & PT• Perfect forecasts of ENSO phase
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Value of a Perfect ENSO Phase Forecast
VOI / Owners / Utility
1,71,75
1,81,85
1,9
1,952
risk parameter
$ / h
a 700 $ / ha
1000 $ / ha
1300 $ / ha
1600 $ / ha
2000 $ / ha
VOI / Tenants / Utility
6
8
10
12
14
-0,5 0 0,5 1 1,5 2 2,5 3 3,5 4
risk parameter
$ / h
a
700 $ / ha
1000 $ / ha
1300 $ / ha
1500 $ / ha
VOI / Owners / P.T. / lambda = 2.25
0
1
2
3
4
0,6 0,65 0,7 0,75 0,8 0,85 0,88 0,9 1
alpha parameter
$ / h
a
100 $ / ha
175 $ / ha
500 $ / ha
VOI / Tenants / P.T. / lambda = 2.25
0
5
10
15
20
0,6 0,65 0,7 0,75 0,8 0,85 0,88 0,9 1
alpha parameter
$ / h
a
10 $ / ha
30 $ / ha
60 $ / ha
80 $ / ha
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3. Next steps: An agent-based model
• Our implemented model & optimizations focused on “one decision maker, one farm”
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Social interactions
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Example of interactions
Interaction between agents:
- Formation of land rental price
- Decision by individuals on how much land (rented/owned) to crop
Decision Making
Decision about the proportion of each crop-management
N-1 other agents
Agent "i" with his attributes
Maximization of objective functions
N agents with new attributes
Agent "i" going tothe next step
Endogenousland market
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Interaction between agents
Attributes •Land owned, rented out•Land owned, cropped by self•Land rented in•Available capital•Risk aversion•Others???
Actions •Rent out land to others•Rent out land from others •Stop renting•Crop more of one’s own land
Rules - Potential actors - The actors' selection - Price regulation
RentalMarket Model
Agricultural practices
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Outline
1. A simple operative model of decision-making
2. Optimization of alternative objective functions
3. Next steps: An agent-based model
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