Page last updated: Monday, September 11, 2006 at 07:48 PM
Contact: Guillermo Podestá (,
Project Objectives
Objective 1 Map key components of the decision landscape in agricultural production systems.

We are constructing decision maps and calendars to identify entry points for climate information as a first step to assess the scope for adaptation in agricultural production systems. These maps will characterize (a) production decisions, (b) their timing, and (c) realistic options and constraints.

We are working with farmers in each target location (Pergamino and Pilar). Though we acknowledge the diversity of agricultural producers (Pucciarelli 1997), we focus on individuals who farm relatively large extensions (say, over 400 ha) that they own or rent (or both). Agriculture in the Pampas is in transition, and smallholders are becoming increasingly scarce. On the other end of the spectrum, large corporations operating farms in multiple locations involve entirely different decision-making structures. These considerations, together with resource constraints, justify our target choice.

Objective 2 Build plausible scenarios of inter-annual and inter-decadal climate variability.

We are implementing approaches to “translate” seasonal climate forecasts (often probabilistic and categorical in nature) into site- and area-specific ensembles of relevant climatic variables (precipitation, temperature).  The tools will then be adapted to generate scenarios of inter-decadal climate fluctuations.

We will be able to generate multiple equally-likely realizations of interannual events (eg, an El Niño), or simulate various plausible decadal trends. For example, we may “run the climate movie backwards,” simulating a return to a drier epoch in the Pampas.

Arrow right See preliminary results from Objective 2.

Objective 3 Assess impacts and outcomes of inter-annual and inter-decadal climate variability, and the scope for adaptive management in response to climate and other contextual factors.

This objective involves a two-pronged modeling effort designed to explore the outcomes of adaptive responses to climate variability and other risk factors.

  • Mechanistic modeling will link synthetic daily weather with process-level crop models and financial models to simulate outcomes (yields, economic returns) of alternative decisions under various climate scenarios. This allows us to gain deep understanding of the complexities of climate and decision making across scales.
  • Simplified modeling will rely on simplified statistical or reduced-form models to describe effects of non-climate factors (eg, commodity prices, tax policies, technology) on decisions. This allows us to place climate within the context of environmental and socioeconomic factors influencing farmers’ decisions.

Arrow right See preliminary results from Objective 3.

Objective 4  Seek to understand how probabilistic climate information and uncertainty about outcomes are received and acted upon.

This objective involves two research lines pursued in parallel, but with continuous interaction.

  • The first line involves the empirical identification of the goals and objectives of farmers’ decisions (objective functions) in the Pampas, and an assessment of the prevalence of decision objectives outside the conventional Standard Expected Utility (SEU) model.
  • The second line involves the estimation of the value of climate information under the assumption of different farmer objective functions.

Arrow right See preliminary results from Objective 4.

Objective 5 Explore best practices for the characterization of uncertainty, and the design and communication of climate information.

There are two steps involved in the task of communicating effectively uncertain climate information. The first one is to quantify uncertainty about future outcomes. The second step is to define how best to communicate the uncertainty to decision makers .

  • A probabilistic treatment of uncertainty in forecasts and projections of agricultural outcomes of seasonal and decadal climate variability will be integrally designed into the project. Uncertainties salient for decision-making are being identified, characterized, and communicated.
  • Uses and users of climate information are heterogeneous: one product does not fit all. The contents and formats of climate information (and, in general, all agricultural information) make implicit assumptions about what farmers are trying to achieve and how such information will be used. At a minimum, these assumptions should be made explicit and put to test.

Arrow right See preliminary results from Objective 5.

Objective 6 Explore environmental consequences of human decisions in agroecosystems.

This objective involves an assessment process to address the “soybean monoculture” issue in particular and agroecosystem sustainability in general. The assessment will (a) develop alternative framings of the sustainability issue (currently perceived only as “soybean versus the other crops”), (b) help identify gaps in scientific knowledge, and (c) draw on the perceptions, and concerns of stakeholders. A variety of stakeholders (academic and governmental researchers, government agencies, farmer groups, NGOs, etc) were engaged from the outset through exploratory focus groups, and scientist-stakeholder workshops.

Arrow right See preliminary results from Objective 6.

Objective 7 Conduct self reflective analyses of factors that promote or impede integrated science research and outreach with stakeholder participation.

Many complex problems can only be understood by pulling together insights and methods from many disciplines (Nissani 1997). Careful and systematic analyses of the determinants of success or failure of interdisciplinary collaboration and stakeholder involvement in integrative science projects still are needed. Few formal studies have explored the paradigms, institutions, and incentives that may nurture or impede the development and sustained productivity of integrative research groups (Schneider 1995).

This objective aims to document in a structured and transparent way the process of designing and conducting interdisciplinary research with stakeholder participation. Our goals are to learn from evaluation and re-analyses, share experiences to avoid repeating mistakes, and ultimately to stimulate theory development.

Arrow right See preliminary results from Objective 7.

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Objective 5
Objective 6
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