Federico Bert

Simulating agricultural land rental markets by combining agent-based models with traditional economics concepts

Land exchange through rental transactions is a central process in agricultural systems. The land tenure regimes emerge from land transactions and structural and land use changes are tied to the dynamics of the land market. We introduce LARMA, a LAnd Rental MArket model embedded within the Pampas Model (PM), an agent-based model of Argentinean agricultural systems. LARMA produces endogenous formation of land rental prices. LARMA relies on traditional economic concepts for LRP formation but addresses some drawbacks of this approach by being integrated into an agent-based model that considers heterogeneous agents interacting with one another. PM-LARMA successfully reproduced the agricultural land tenure regimes and land rental prices observed in the Pampas. Including adaptive, heterogeneous and interacting agents was critical to this success. We conclude that agent-based and traditional economic models can be successfully combined to capture complex emergent land tenure and market price patterns while simplifying the overall model design. LARMA is a land rental market model with endogenous rental price formation.LARMA is embedded into the Pampas agent-based model (PM).LARMA combines traditional economics concepts with agent-based modeling.PM-LARMA successfully reproduces land tenure patterns and rental price dynamics.We discuss the advantages of combining approaches for modeling land rental markets.

Land use as possible strategy for managing water table depth in flat basins with shallow groundwater

ABSTRACT In flat plains groundwater affects agricultural production outcomes and risks. Agricultural land use decisions, however, may strongly impact groundwater levels available for production. This paper explores the scope for managing groundwater levels through land use decisions in a sub-basin of the Salado River in the Argentine Pampas, a very flat area that plays a key role in world agricultural production. A spatially distributed hydrological model implemented with MIKE SHE software was used to establish the impacts of different land uses on groundwater dynamics, and to assess the interdependencies among spatially close decision-makers sharing a water table (WT). Additionally, groundwater level changes in response to climate variability were quantified. We found land use has strong effects on WT levels both for oneself (e.g. pastures can lead to significant decreases (up to 4.5 m) in WT levels) and others, in the form of strong interdependencies that exist between farmers sharing a WT where land use decisions of one farmer effect groundwater level of neighbouring farms and vice versa. However, the effectiveness to control groundwater levels through land use decisions is subject to the rather unpredictable effects of rainfall variability. The results presented in this paper provide key insights in relation to physical and social aspects that should be considered for managing groundwater levels through land use decisions, in order to avoid negative and/or maximize positive effects on agricultural production.

Managing the Triple Bottom Line for Sustainability: A Case Study of Argentine Agribusinesses

Abstract Using surveys and interviews with Argentine agribusiness owners and managers, we examine the relative importance of economic, environmental, and social goals in their planning processes. While in one survey, respondents rate these three objectives as equally important, they also prioritize economic goals over environmental and social targets when assigning points based on the importance of decisions made for various sub-categories. Discussions of specific scenarios illuminate goal importance, but also demonstrate that perceived losses can be valuable for understanding how managers think about sustainability in terms of comparative economic gains, social relationships, and different social and economic outcomes. Subsequent analyses suggest that the three categories of the “triple bottom line” are overly rigid and cannot capture the integration among environmental, economic, and social aspects of sustainability. Given these findings, we suggest future directions for research on losses, time scales, and sustainability.

Agricultural Decision Making in the Argentine Pampas: Modeling the Interaction between Uncertain and Complex Environments and Heterogeneous and Complex Decision Makers

1 Rosenstiel School of Marine and Atmospheric Science, University of Miami, USA gpodesta@rsmas.miami.edu, dletson@rsmas.miami.edu 2 Columbia University, Department of Psychology and Graduate School of Business, and Center for Research on Environmental Decisions (CRED), USA euw2@columbia.edu 3 Facultad de Ingenieŕia, University of Buenos Aires, Argentina clacian@fi.uba.ar 4 Facultad de Agronomia, University of Buenos Aires, Argentina fbert@mail.agro.uba.ar

A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas

Abstract Sensitivity analyses (SAs) identify how an output variable of a model is modified by changes in the input variables. These analyses are a good way for assessing the performance of probabilistic models, like Bayesian Networks (BN). However, there are several commonly used SAs in BN literature, and formal comparisons about their outcomes are scarce. We used four previously developed BNs which represent ecosystem services provision in Pampean agroecosystems (Argentina) in order to test two local sensitivity approaches widely used. These SAs were: 1) One-at-a-time, used in BNs but more commonly in linear modelling; and 2) Sensitivity to findings, specific to BN modelling. Results showed that both analyses provided an adequate overview of BN behaviour. Furthermore, analyses produced a similar influence ranking of input variables over each output variable. Even though their interchangeably application could be an alternative in our bayesian models, we believe that OAT is the suitable one to implement here because of its capacity to demonstrate the relation (positive or negative) between input and output variables. In summary, we provided insights about two sensitivity techniques in BNs based on a case study which may be useful for ecological modellers.

Practical Points for the Software Development of an Agent-Based Model of a Coupled Human-Natural System

Modeling complex natural and human systems to support policy or management decision making is becoming increasingly common. The resulting models are often designed and implemented by researchers or domain experts with limited software engineering expertise. To help this important audience, we present our experience and share lessons learned from the design and implementation of an agent-based model of agricultural production systems in the Argentine Pampas, emphasizing the software engineering perspective. We discuss the models design including the model classes; the activity diagram, and data flow; the package and folder layout; the use of design patterns; performance optimization; initialization approaches; the analysis of results; and model measurement, validation, and verification.

A linked modelling framework to explore interactions among climate, soil water, and land use decisions in the Argentine Pampas

Abstract In flat environments, groundwater is relatively shallow, tightly associated with surface water and climate, and can have either positive and negative impacts on natural and human systems depending on its depth. A linked modelling and analysis framework that seeks to capture linkages across multiple scales at the climate/water/crop nexus in the Argentine Pampas is presented. This region shows a strong coupling between climate, soil water, and land use due to its extremely flat topography and poorly developed drainage networks. The work describes the components of the framework and, subsequently, presents results from simulations performed with the twin goals of (i) validating the framework as a whole and (ii) demonstrating its usefulness to explore interesting contexts such as unexperienced climate scenarios (wet/dry periods), hypothetical policies (e.g., differential grains export taxes), and adoption of non-structural technologies (e.g., cover crops) to manage water table depth.