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Contact: Guillermo Podestá (gpodesta@rsmas.miami.edu),
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Abstracts of Peer-Reviewed Publications

 Laciana, C.E. and E.U. Weber. In Press.
Correcting expected utility for comparisons between alternative outcomes: A unified parameterization of regret and disappointment.
Journal of Risk and Uncertainty.

ABSTRACT. We present a unified conceptualization and parameterization of an expected utility model that has been corrected for regret and disappointment effects.  The functional form and parameter values for anticipated regret and disappointment are constrained to conform to a well-known pattern of choices, the common consequence effect, which is a special case the Allais paradox. For choices that are subject to regret and disappointment effects to be consistent with the observed choice pattern, we show that  the function that corrects the utility of the obtained outcome has to have a positive second derivative for its regret component and a negative second derivative for its disappointment component. These hypothesized functional forms make predictions about the relative effect that small vs. large differences between obtained and alternative outcomes should have on people’s experiences of either regret or disappointment.


Hidalgo C., C. Natenzon and G. Podestá.  2007.
Interdisciplina: construcción de conocimiento en un proyecto internacional sobre variabilidad climática y agricultura [Interdisciplinarity: knowledge construction in an international project on climate and agriculture].
Revista Ibero-Americana de Ciencia, Tecnología y Sociedad 9(3): 53-68.

ABSTRACT. The growing need to address complex environmentally and socially relevant problems has led to a renewed focus on interdisciplinary teams as producers of knowledge. This paper reports results from a case study of this emerging model for organizing scientific and technological research. Preliminary findings explore the factors that foster or impede interdisciplinary knowledge production, including the participation of stakeholders. The case study focuses on a multi-disciplinary, multi-institutional, multi-national research team convened to understand and model adaptive management of agricultural ecosystems in the Pampas of central-eastern Argentina in response to climate variability and other sources of risk and uncertainty. The team tended to show two kinds of structures which can prevail at different moments: (a) researchers that formed highly productive teams with frequent and intensive interactions, and (b) individual researchers or units that organized themselves around the project coordinator. This dual structure--which may have responded to a tight project schedule--may have contributed to reducing team integration and effectiveness.

Full article (in Spanish, 433 KB)


Appipatanavis, S., G. Podestá, B. Rajagopalan and R. W. Katz. 2007.
A semiparametric multivariate and multisite weather generator.
Water Resources Research 43, W11401, doi:10.1029/2006WR005714.

ABSTRACT. We propose a semiparametric multivariate weather generator with greater ability to reproduce the historical statistics, especially the wet and dry spells. The proposed approach has two steps: (1) a Markov Chain for generating the precipitation state (i.e., no rain, rain, or heavy rain), and (2) a k-nearest neighbor (k-NN) bootstrap resampler for generating the multivariate weather variables. The Markov Chain captures the spell statistics while the k-NN bootstrap captures the distributional and lag-dependence statistics of the weather variables. Traditional k-NN generators tend to under-simulate the wet and dry spells that are keys to watershed and agricultural modeling for water planning and management; hence the motivation for this research. We demonstrate the utility of the proposed approach and its improvement over the traditional k-NN approach through an application to daily weather data from Pergamino in the Pampas region of Argentina. We show the applicability of the proposed framework in simulating weather scenarios conditional on the seasonal climate forecast and also at multiple sites in the Pampas region.
© 2007 by the American Geophysical Union.

Full article (1755 KB)


 E. M. Furrer, E.M. and R. W. Katz. 2007.
Generalized linear modeling approach to stochastic weather generators.
Climate Research 34: 129-144.

ABSTRACT. Stochastic weather generators are a popular method for producing synthetic sequences of daily weather. We demonstrate that generalized linear models (GLMs) can provide a general modeling framework, allowing the straightforward incorporation of annual cycles and other covariates (e.g. an index of the El Niño-Southern Oscillation, ENSO) into stochastic weather generators. We apply the GLM technique to daily time series of weather variables (i.e. precipitation and minimum and maximum temperature) from Pergamino, Argentina. Besides annual cycles, the fit is significantly improved by permitting both the transition probabilities of the first-order Markov chain for daily precipitation occurrence, as well as the means of both daily minimum and maximum temperature, to depend
on the ENSO state. Although it is more parsimonious than typical weather generators, the GLM-based weather generator performs comparably, particularly in terms of extremes and overdispersion.
© Inter-Research 2007 · www.int-res.com.


Bert, F.B., G.P. Podestá, E.H. Satorre and C.D. Messina. 2007.
Usability of climate information on decisions related to soybean production systems of the Argentinean Pampas. Climate Research 33: 123-134.

ABSTRACT. The availability of enhanced climate forecasts offers the potential for farmers to improve responses to climate variability. However, few studies have demonstrated actual effective uses of climate forecasts. Through interaction with regional agricultural experts, we evaluated the opportunities and constraints involved in the use of climate information in decision making regarding soybean farming in the Argentinean pampas. Our results showed that opportunities exist for the successful application of climate information but, consistent with previous research, there is a need to consider the broad and complex context influencing decisions, since climate is just one of the many factors affecting farmers’ decisions. More importantly, we showed that adaptive management strategies proposed by experts in response to hypothetical climate scenarios produced diverging economic outcomes (both positive and negative). We hypothesized that inconsistency of the observed results could be due to a poor understanding by the agricultural experts of the impacts on regional climate of global climate signals (e.g. a given ENSO phase). An alternative hypothesis was that crop consultants had difficulties in anticipating the agronomic outcomes of management decisions made in response to a given climate forecast. Further research is needed to elucidate to what extent these hypotheses are valid. However, our results suggest that the mere availability of climate forecasts will not necessarily benefit growers. In order for there to be an improvement in the use of seasonal forecasts, appropriate interventions are necessary to enhance decision makers’ understanding of the sources and impacts of climate variability, and of the consequences of different responses to a range of climate scenarios.
© Inter-Research 2007 · www.int-res.com.

Full article (216 KB)


Bert, F., C.E. Laciana, G.P. Podestá, E.H. Satorre and A.N. Menéndez. 2007.
Sensitivity of CERES-Maize simulated yields to uncertainty in soil properties and daily solar radiation. Agricultural Systems 94: 141-150.

ABSTRACT. The sensitivity of CERES-Maize yield predictions to uncertainty in a set of soil-related parameters and solar radiation was evaluated in Pergamino, in the Argentine Pampas. Maize yields were simulated for Pergamino using a 31 years climatic record for a range of values of a group of important model input variables. The input variables considered (and the range evaluated) were: soil nitrogen content at sowing (from 20 to 80 Kg ha-1), soil organic matter content (from 1.75% to 4%), soil water storage capacity (from 150 to 200 mm), soil water content at sowing (from 50% to 100% of total available water), soil infiltration curve number (from 76 to 82) and daily solar radiation (from -20% to 12% of the historical values). Then, a sensitivity analysis using a combination of mathematical and graphical approaches was performed to evaluate the model response to changes in the values of the input variables considered. Moreover, a simplified method based on the evaluation of the model sensitivity at extreme values of the input variables is proposed to evaluate the model non-linear responses with a reduced number of runs. Under the scenario evaluated, representative of the typical maize productions systems of the Argentine Pampas, the model results showed higher sensitivity to changes in radiation (normalized sensitivity were -0.69 and 0.45 for rainfed and irrigated conditions, respectively) than for the soil variables (normalized sensitivity ranged from 0.20 to 0.28). The CERES-Maize model was found to have similar sensitivity for the different soil inputs. Furthermore, some of the variables evaluated (soil curve number, soil water content at sowing and radiation under rainfed conditions) showed an important non-linear response.
© 2006 Elsevier Ltd.


Bert, F., E.H. Satorre, F. Ruiz Toranzo, and G.P. Podestá. 2006.
Climatic information and decision-making in maize production systems of the Argentinean Pampas. Agricultural Systems 88: 180-204.

ABSTRACT. In many places, predictions of regional climate variability associated with El Niño Southern Oscillation (ENSO) phenomenon offer the potential to improve farmers' decision-making, i.e., mitigate negative impacts of adverse conditions or take advantage of favorable conditions. However, various conditions must be met for a forecast to result in enhanced decision-making. First, information has to be relevant to, and compatible with production decisions. Second, alternative options must exist for a given decision and these should result in different outcomes under different climate conditions. Third, decision-makers should be able to evaluate the outcomes of alternative actions. In this paper, we explored these conditions as part of a case study targeting maize production systems in the Argentine Pampas. The decision-making process was described via ‘‘decision maps’’ that (a) characterized the main decisions involved in maize production systems and their timing, (b) identified decisions sensitive to climate, and (c) provided a realistic set of options for each decision under different seasonal climate scenarios. Then, we used crop simulation models to assess the outcomes of tailoring crop management to predicted climate conditions. We found differences between the options selected by regional advisors for each climate scenario and those that maximized average profits in the simulation exercise. In particular, differences were most noticeable in preferred nitrogen fertilization rates. Copyright © 2005 Elsevier Ltd.

Full article (264 KB)


Mercau, J.L., J.L. Dardanelli, D.J. Collino, J.M. Andriani, A. Irigoyen and E.H. Satorre. 2007.
Predicting on-farm soybean yields in the Pampas using CROPGRO-Soybean. Field Crops Research 100: 200-209.

ABSTRACT. Soybean is the main rainfed crop in a wide range of latitudes and sowing dates of the Argentine Pampas. It is sown alone or as a second crop after other winter and summer crops. Modelling approaches have proved to be helpful in the decision making process. The on-farm evaluation of CROPGRO is rather difficult since input data are scarce and frequently of worse quality than those from experimental works. Moreover, CROPGRO simulation of water dynamic processes and their relation with biomass production has not been comprehensively evaluated in soybean crops. The aims of this study were (i) to evaluate the CROPGRO13 Soybean performance, with emphasis on water demand and supply and biomass production under water limited conditions, (ii) to generate a revised CROPGRO model improving those aspects, and (iii) to compare simulations outputs using the original and the revised CROPGRO models, with on-farm crop data set. In the revised model, we multiplied potential evapotranspiration by 1 to 1.22 when LAI increased from 0 to >= 4.0. We set a root extension rate of 4.0 cm/thermal day and a maximum rooting depth of 2.5 m. Finally, we included a nonlinear equation to simulate the relationship between relative transpiration and relative gross photosynthesis. The ability of the revised CROPGRO-Soybean to simulate water content depletion and biomass production was tested against several experiments with an imposed drought period. We also calibrated cultivar parameters using “ad hoc” tests in a range of environments (combinations of sowing dates and locations). The models were evaluated with data from 155 commercial farms. V% (root mean square error as percentage of the observed mean) for the total cycle length, vegetative period, and reproductive phase simulations were 7, 13 and 15%, respectively. The revised CROPGRO was more accurate in simulating crop yield, biomass, harvest index and yield numeric components. V% values ranged from 11 to 17% (original version) and from 13 to 22% (revised version). Besides, V% values for yield were 16% with the revised model vs. 32% with the original one, considering only paddocks with higher water stress level. The robust prediction of phenology, biomass and yield components obtained with the revised model across different environmental conditions support its use in the decision making process of the soybean crop at the farm scale.
Copyright © 2006 Elsevier.

Full article (616 KB)


Hidalgo, C. 2006. Reflexividades. Cuadernos de Antropología Social 23: 45-56. ISSN: 0327-3776.

ABSTRACT. The paper reviews different positions about reflexivity contrasting the "communicational" approach posed by Gérard Althabe with "objectivist" proposals by Pierre Bourdieu and the "subjectivist" position of feminist thinkers. The author outlines the connection of Althabe's notion of reflexivity with Gadamer's theses. Finally, these distinctions are addressed in the study of the interdisciplinary interaction of an international team that has included among its research objectives self-reflection about the factors that promote or impede an integrated scientific production and outreach with participation of stakeholders involved in an extended peer community.

Full article (196 KB)


Weber, E.U. 2006. Experience-based and description-based perceptions of long-term risk: why global warming does not scare us (yet). Climatic Change 77: 103-120.

ABSTRACT. It should come as no surprise that the governments and citizenries of many countries show little concern about climate change and its consequences. Behavioral decision research over the last 30 years provides a series of lessons about the importance of affect in perceptions of risk and in decisions to take actions that reduce or manage perceived risks. Evidence from a range of domains suggests that worry drives risk management decisions. When people fail to be alarmed about a risk or hazard, they do not take precautions. Recent personal experience strongly influences the evaluation of a risky option. Low-probability events generate less concern than their probability warrants on average, but more concern than they deserve in those rare instances when they do occur. Personal experience with noticeable and serious consequences of global warming is still rare in many regions of the world. When people base their decisions on statistical descriptions about a hazard provided by others, characteristics of the hazard identified as psychological risk dimensions predict differences in alarm or worry across different classes of risk. The time-delayed, abstract, and often statistical nature of the risks of global warming does not evoke strong visceral reactions. These results suggest that we should find ways to evoke visceral reactions towards the risk of global warming, perhaps by simulations of its concrete future consequences for people’s home or other regions they visit or value. Increased concern about global warming needs to solicited carefully, however, to prevent a decrease in concern about other relevant risks. The generation of worry or concern about global warming may be a necessary but not sufficient condition for desirable or appropriate protective or mitigating behavior on part of the general public.
Copyright © 2006 Springer.

Full article (224 KB)

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