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1,0. Neutral and Consensus Management. Optimal Simulated. 0,8. 0,5. 0,3. Neutral Years . 1,0. Neutral Management. Consensus Management and Optimal. Simulated. 0,8. Cumulative Probability. 0,5. 0,3. El Niño Years. 1,0. Neutral Management. Consensus Management. Optimal Simulated.

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Bert f e 1 2 g p podest 3 e h satorre 1 2 and c d messina 4


Neutral and Consensus Management

Optimal Simulated




Neutral Years


Neutral Management

Consensus Management and Optimal



Cumulative Probability



El Niño Years


Neutral Management

Consensus Management

Optimal Simulated




La Niña Years









Gross Margin (U$S ha


Bert, F. E.*1,2, G.P. Podestá3, E.H. Satorre1,2 and C. D. Messina4

(1) Cátedra de Cerealicultura, Facultad de Agronomía, UBA. CONICET. Bs. As. Argentina. *fbert@agro.uba.ar

(2) Área de Tecnología, AACREA (Asoc. Arg. Consorcios Reg. de Exp. Agrícola). Bs. As. Argentina.

(3) Rosenstiel School of Marine & Atmospheric Science, University of Miami, Miami, FL, USA.

(4) Pioneer Hi-Bred, IA, USA.

Financial support provided by


  • Some conditions are necessary for a seasonal climate forecast to result in improved decision outcomes (Hansen, 2002; Podestá et al., 2002):

  • Information has to be relevant to, and compatible with, production decisions. In part, this is a function of the existence of entry points for climate information into the decision-making process.

  • Alternative options must exist for a given decision. Furthermore, the alternative actions should show an interaction with expected climate scenarios.

  • Decision-makers should be able to evaluate the outcomes of alternative actions.

  • The forecasts must have useful accuracy and appropriate lead time and geographical and temporal resolutions.

  • Decision makers must be willing and able to modify their actions in response to climate information.

OBJECTIVE: The objective of this study is to explore some of these necessary conditions for effective use of seasonal climate forecasts maize production systems of the Argentine Pampas.


Decision Maps

Simulation of Maize yields and economic profits

  • CERES Maize model

  • Synthetic climate series for Pergamino (990 years to each ENSO phase)

  • 24 management strategies resulting from combination of options identified in the decision maps: two hybrids (DK 752 and DK 615), two sowing dates (Sep 15, Oct 15), two densities (7 and 8 pl/m2) and three fertilization levels (50, 100, and 150 kg ha-1 of N).

  • Characterize the main decisions involved in maize production,

  • Identify those decisions that are sensitive to climate fluctuations, and

  • Assemble a realistic set of options for each decision under different climate scenarios (phases of El Nino-Southern Oscillation, ENSO).

  • A “consensus management” was defined by technical advisors in the study region (Pergamino) during focus groups.

Table 2-Gross margin of each managent under different ENSO phases

Figure 1. Conceptual representation of climate influences on land assignment decisions in an idealized farm in the Pampas.

Figure 3. Cumulative probability distributions for simulated gross margins obtained under various combinations of ENSO phase and crop management strategies. Strategies compared are those resulting from managing crops as in neutral scenarios (ignoring climate information), as suggested from the decision map (consensus management, with incorporation of climate information) and applying optimal management in each ENSO phase as resulted from crop simulations.

Figure 2. Conceptual diagrammatic representation of climate influences on the selection of a maize hybrid.

Table 1. Experts’consensus management (incorporating climate information).


  • The modeling exercise highlighted divergences between (a) options selected to maximize average profits by local experts and (b) simulation results. These differences were most apparent in nitrogen fertilization rates.

  • Future work should consider a realistic description of the farmers’ goals and of the context in which they operate, as it may involve both opportunities and constraints for the use of climate information.

  • The iterative and participatory elicitation process, which involved local researchers, technical advisors, and a limited number of farmers, ensured a realistic description of decisions and viable options in maize production systems in the Argentine Pampas.

  • The decision maps show the influence of climate on several decisions,and a viable set of alternative management options for each ENSO phase.

REFERENCES: Burns, W. J., Clemen, R. T., 1993. Covariance structure models and influence diagrams. Manag. Sc. 39, 3 816–834.- Hansen, J. W., 2002. Realizing the potential benefits of the climate prediction to agriculture: issues, approaches, challenges. Ag. Sys. 74, 309 - 330.- Podestá, G., Letson, D., Messina, C., Royce, F., Ferreyra, A., Jones J., Hansen, J., Llovet, I., Grondona, M., O’Brien, J., 2002. Use of ENSO– related climate information in agricultural decision making in Argentina: a pilot experience. Agric. Sys. 74, 371–392.