Introduction

Recent scientific advancements are improving the ability to predict some major elements of climate variability, in advance of the crop-growing season. In selected regions of the world, climate anomalies are linked to the onset and intensity of a warm or cold event of the El Niño-Southern Oscillation (ENSO) phenomenon. Southeast of South America is within the regions of influence of this phenomenon (Ropelewski and Halpert 1989). Hence seasonal weather and climate fluctuations have significant economical impacts on the agricultural production sector of this region.

More recent studies conducted in southeastern South America revealed the existence of a near symmetry between impacts of El Niño and La Niña on precipitation as well as on non-irrigated crop productivity. Positive rainfall anomalies prevail in El Niño years, and negative rainfall anomalies prevail in La Niña years, during the austral spring and/or summer months (Baethgen 1997; Baethgen and Giménez 2002).

While there has been much written about impacts of climate variability, there has been relatively little done in relation to applying knowledge of inherently imprecise climate predictions to modify actions ahead of likely impacts, i.e. applications of climate predictions. Although forecasts make predictions of climate variable behaviors for large regions of the world, these regions are not uniform. Hence in many situations in some areas of these regions forecast recommendations were suitable while in others they were not. A pilot project was then proposed to evolve a system for the effective application of a seasonal climate forecast, which can address the natural spatial variability in growing conditions that control productivity in a rice ecosystem in Uruguay.

Therefore the objectives of this study were: (1) evaluate ENSO effects on Uruguayan rice production; (2) evaluate the capability of crop simulation models in recreating the observed yield spatial variability; and (3) simulate rice yield spatial variability under different seasonal forecast scenarios: El Niño, La Niña and neutral years.

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