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 

 

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

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.

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

Overview of the decision-making process

Outline

1. A simple operative model of decision-making

2. Optimization of alternative objective functions

3. Next steps: An agent-based model

1. A simple operative model of decision-making

Decision-making 1 D

Decision-making 2 D

Decision outcomes D

Assessment of outcomes AMy #&@$! brother in law did better than I did!

Maize prices dropped after I decided to plant maize

Learning and adaptation L

• 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

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

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

Optimization procedure

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.

Utility Theory - Owners

Utility Theory - Tenants

Prospect Theory - Tenants

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

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

3. Next steps: An agent-based model

• Our implemented model & optimizations focused on “one decision maker, one farm”

Social interactions

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

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

Outline

1. A simple operative model of decision-making

2. Optimization of alternative objective functions

3. Next steps: An agent-based model