Materials and Methods

In this study, the relationship between ENSO 3.4 average total sea surface temperatures (SST) anomalies in October, November and December (OND) and rice yield have been analyzed to evaluate ENSO effects on Uruguayan rice production. Yield data were obtained from the Uruguayan Rice Growers Association (ACA). Yields for any given year were expressed as the relative difference between the observed yield for that year and the yield predicted by the regression model (Eq. 10.1):

where, RYD = relative yield deviation expressed in (%), Yld(n) = observed crop yield for year n, and PYld(n) = yield predicted by regression model for year n.

SST anomalies were obtained from the Climate Prediction Center of NOAA. The anomalies were calculated relative to the period 1950-2003 and aggregated into three-month period means. Rice is normally planted during October-November and harvested during the end of March through May. Therefore any possible relationships found during OND may have significant forecasting applications for this crop.

The study was conducted at a 12 ha rice field located at El Paso de la Laguna Experimental Unit of the National Institute of Agricultural Research (INIA), Uruguay. The cultivar used was El Paso 144 and the planting date was 7-8 November 2002. Seeding rate was 190 kg ha-1. Rice was direct seeded on dry soil. Fertilizer applications were: 170 kg ha-1 15-35-15 (N-P-K) at planting followed by 50 kg ha-1 of urea at flooding time (30 days after emergence) and 50 kg ha-1 of urea at panicle initiation.

Ten locations were selected in this 12 ha rice field in which recording data loggers (Hobo H8 Pro) were fitted. These loggers have an internal temperature sensor that measures ambient air temperature, in this situation representative of canopy temperature, and an external sensor that was used to measure water temperature. The loggers were attached to stakes placed vertically in the field, with the external sensors placed approximately 0.05 m below field water level. As the rice grew, the internal sensors were moved upward along the stake so that they were always near the top of the canopy. Water and canopy temperatures were measured hourly throughout the growing season. Data logger locations were georeferenced using a back-pack differential global positioning systems (DGPS) receiver (Trimble AG 132).

Daily rainfall, temperature and solar radiation data were obtained from the Agrometeorological Weather Station located at El Paso de la Laguna Experimental Unit of the INIA for the period 1973-2003.

At harvest, yield, yield components, and percent blanking were recorded in the vicinity of each sensor. Sensors were removed before harvest. A sample plot (2.5 m x 3.5 m) was harvested with an experimental plot combine at each sensor location. Yields standardized to 14% moisture content were measured. Interpolated yield maps of the field were created using a geographic information system (Arcview, ESRI, Redlands, CA). Yield data from each of the ten locations were spatially interpolated to a fixed 5 m x 5 m grid using inverse distance weighted interpolation with power 2 and number of neighbors 12.

Soil samples were extracted at three different depths: 0-10 cm, 10-20 cm and 20-30 cm at the same locations where sensors were installed. Yield was predicted at each sensor locations using the DSSAT v3.5 CERES-Rice model.

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