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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Neutral and Consensus Management
Consensus Management and Optimal
El Niño Years
La Niña Years
Gross Margin (U$S ha
INFLUENCE OF CLIMATE INFORMATION ON DECISION-MAKING IN MAIZE PRODUCTION SYSTEMS OF THE ARGENTINE PAMPAS
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. *email@example.com
(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
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.
METHODOLOGY & RESULTS
Simulation of Maize yields and economic profits
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).
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.