ENSO Effects on Uruguayan Rice Production

The fact that rice is irrigated under Uruguayan conditions theoretically should ameliorate ENSO effects on this crop productivity. Straightforward reasoning will indi-

cate that for an irrigated crop like rice, ENSO phases can have opposite effects than in non-irrigated ones.

Figure 10.1 shows national rice yield average evolution in the last 31 growing seasons (1972-2003). In order to analyze ENSO impacts on rice production the probabilistic impact of ENSO phases on the distribution shifts of crop yields were studied using the same approach as the one used by Baethgen. The detrended national yield crop average data from 1973 to 2003 were divided into quartiles and any given value was defined as being "high" if it was greater than the third quartile (upper 75% of the data), "low" if it was less than the first quartile (lower 25%), and "normal" if its value fell between the first and the third quartile (central 50% of the data). By this way the range of average yield values that corresponded to each quartile were determined. Using these values the shift in the distribution of crop yields were studied for the different ENSO phases (El Niño, La Niña and neutral). The IRI classification of El Niño, neutral and La Niña years was used (http://iri.columbia.edu/climate/ENSO/ enso.html).

Table 10.1 shows the classification of the series of years according to ENSO phases. This analysis showed that the distribution of national relative yield differences (RYD) varied with ENSO phases (Fig. 10.2). For example, the frequency of high rice yield differences was more than two times higher in La Niña years than in neutral years. On the other hand in El Niño years the chances of having high yields were zero. In summary this figure clearly shows that in La Niña years the chance of having high yields increased with respect to the neutral years, while in El Niño years this chance strictly does not exist.

Spatial Variability

The DSSAT v3.5 rice model requires information about: weather (temperature and solar radiation), soil variables, genetic coefficients and crop management. Crop manage-

Fig. 10.1. National rice yield (1972-2002)
Table 10.1. El Niño, La Niña and neutral years

El Niño

Year

La Niña

Year

Neutral

Year

1972-1973

1

1973-1974

1

1978-1979

1

1976-1977

2

1974-1975

2

1979-1980

2

1977-1978

3

1975-1976

3

1980-1981

3

1982-1983

4

1984-1985

4

1981-1982

4

1986-1987

5

1988-1989

5

1983-1984

5

1987-1988

6

1995-1996

6

1985-1986

6

1990-1991

7

1998-1999

7

1989-1990

7

1991-1992

8

1999-2000

8

1993-1994

8

1992-1993

9

2000-2001

9

1996-1997

9

1994-1995

10

2001-2002

10

1997-1998

11

2002-2003

11

Fig. 10.2. National rice yield distribution and ENSO phases (1972-2003)

Fig. 10.2. National rice yield distribution and ENSO phases (1972-2003)

ment and genetic coefficients were uniform for the field since the same cultivar and management practices were applied throughout the studied field. In order to assess the capability of the model in recreating the observed rice yield spatial variability, three different simulations were carried out at each sensor location:

Simulation 1. Weather information (temperature and solar radiation) was extracted from the agrometeorological weather station located at INIA. Soil information was gathered from the soil analyses data that come from the samples extracted at each sensor location. In these simulations all locations have the same weather data but differ in the soil variables data.

Simulation 2. Same as above, but the temperature from the weather station was substituted with each canopy temperature's data registered at each sensor locations. In these simulations each location had its own temperature and soil data and shared the solar radiation data extracted from the weather station.

Simulation 3. Same as Simulation 2, but temperature from the weather station, was substituted with each water temperature data registered at each sensor locations.

Table 10.2 displays the correlation values between observed and simulated yields for all three simulations. Figure 10.3 displays the observed and interpolated predicted yield values for all simulations. In these figures, it can be observed that the crop simulation model was able to capture satisfactorily the spatial variability that was measured in the field. It is important to highlight in these figures that the actual observed spatial variation in yield ranges from 5 000 to 7100 kg ha-1 (2100 kg), while the predicted ones vary in general from 4000-5 250 kg ha-1. This indicates that the model tends to underestimate productivity under these conditions and that the observed spatial variability was indeed larger that what was predicted. The reason for this underprediction should be further investigated.

Table 10.2. Correlation between observed and predicted yield values

Table 10.2. Correlation between observed and predicted yield values

Fig. 10.3. Observed and predicted yield spatial variability for simulations 1-3

Temporal Variability

In order to achieve this, the average simulated yields of the 10 selected locations in the field were compared with the country's national rice yield average evolution in the last 16 growing seasons (1987-1988 to 2002-2003) (Fig. 10.4). For each growing season, the national rice yield average is determined by a large number of environmental situations (i.e. planting dates, fertilization, soils, cultivars, etc.). Differences among growing seasons are caused in part by the differences in the "average" climatic conditions of each growing season. In other words, each growing season can be classified as good or bad from the climatic point of view. The goal of this section of the study was to test if the model was able to capture those good, fair and bad years.

Overall the DSSAT v3.5 CERES-Rice model was able to capture satisfactorily rice yield temporal variability. The model was able to simulate higher or lower production levels in "good" or "bad" growing seasons. The only exceptions of the latter are in the 1990-1991 and 1998-1999 growing seasons when the model determined average yields for the field for these years were lower than in the previous seasons (1989-1990 and 1997-1998) when the national yield averages actually increased during these years with respect to the previous ones.

Spatiotemporal Variability

The model was run, in each of the ten selected locations in the field using the weather data from a series of years (1972-2003) to characterize the spatiotemporal variability. The same soil data, management practices (planting date, seeding rate, fertilization, etc.) and genetic data (El Paso 144) that were used in the studied field for 2002-2003 growing season were applied at each of the ten locations through out all of these years. Specific attention was given to evaluating if different regions within the studied field would react differentially to a given climatic data. Simulated yield data from each of the ten locations and for each 31 growing seasons were spatially interpolated in order to generate yield maps for each growing seasons. Figure 10.5 shows the set of yield maps

from the 1972-1973 through the 2002-2003 growing seasons. In order to be able to display the yield range variability along these 31 growing seasons with a common legend, the whole data set of yield outcomes were and divided into quartiles. These quartiles defined the range of variability of the different yield classes displayed in Fig. 10.5.

Figure 10.5 also categorizes each growing season according to ENSO conditions (El Niño, La Niña and neutral conditions, Table 10.1). This figure shows that in all the growing seasons in which some part of the field presented production levels that fell in the lowest yield class (2968-3 819 kg ha-1, red color), those years corresponded to El Niño years (1986-1987, 1987-1988 and 1990-1991). Conversely in all the years classified as La Niña, the yield variation of the field tended to be in the highest yield classes (green and blue) with the exception of the 1998-1999 and 1974-1975 growing seasons. These results coincide with the previous ones suggesting that La Niña year's climatic conditions are better for rice production.

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