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.

See preliminary results from Objective 2.

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.

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.

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.

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.

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.