Modelling Adaptive Management in Agroecosystems 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 A My 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)
Optimization of objective functions 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).
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
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 to the next step Endogenous land 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 Rental Market 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