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Relationship between Pacific and Indian Ocean Sea
Surface
Temperature
Variability
and
Rice
Production, Harvesting Area and Yield in Indonesia
Rizaldi Boer1,2, Akhmad Faqih1,2 and Rahmi Ariani2
1
Center for Climate Risk and Opportunity Management in Southeast Asia and Pacific
(CCROM-SEAP), Bogor Agriculture University, Indonesia
2
Department of Geophysics and Meteorology, Bogor Agriculture University, Indonesia
x
E-mail : rizaldiboer@gmail.com .
Abstract— This study assess the impact of ENSO on anomaly of monthly
rainfall and rice production, harvesting area and yield by province and
evaluate the potential use of Sea Surface Temperature Index (SST Index)
covering Pacific and Indian Ocean for predicting the anomaly of production,
area and yield. The SST index representing the dominant variability in the
Pacific and Indian Oceans (30°E - 70°W, 30°S - 30°N) was processed using
Empirical Orthogonal Function (EOF) analysis. This study found that there
was significant relationship between the anomaly of the rice data and the
anomaly of SST also exist in Indian Ocean. The relationships were only
significant in a number of provinces particularly in the south part of Sumatra
(e.g. Bengkulu), Java (West, Central and East Java), and eastern part of
Indonesia (South Sulawesi, Southeast Sulawesi, North Sulawesi, Bali and
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Nusa Tenggara). The use of the EOF(1) as predictor for estimating the
anomaly of rice production and harvesting is not superior to the one that used
only the anomaly of SST NINO-3.4. Considering the simplicity, the use of the
anomaly SST at NINO3.4 alone as a predictor for the estimation of anomaly
of rice data in Indonesia is preferable.
Keywords: Rice, El Nino Southern Oscillation, Sea Surface Temperature
Index, Empirical Orthogonal Function Analysis.
1.
Introduction
Rice is the main food crop commodity cultivated by Indonesian farmers. In 2008,
total rice harvesting area reached about 10.7 million ha, while the harvesting areas of other to
main food crops, i.e. maize and soybean, were only about 4.0 million ha and 0.5 million ha
respectively. Among these crops, rice remains vulnerable to climate variability associated with El
Niño/Southern Oscillation (ENSO) events. From historical national data, it was revealed that during
El Niño years rice crop was consistently affected by drought and causing significant production
loss. While for the other two crops, the impact of El Nino was not consistent. Some studies
suggested that this could be due to crop switching since shortage of water during El Nino
years may force farmers in some regions to change their dry season rice crop to secondary
crops such as maize and soybean (Boer and Subbiah, 2005). Furthermore, the studies also
stated that the rice production loss due to drought in El Nino years may be offset by the
increase in yield of irrigated rice crop due to higher solar radiation.
Many studies on the assessment of the impact of El Nino on crop production used national
statistical data (e.g. Falcon et al., 2004; Boer and Subbiah, 2005). It was found that the anomaly of
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national rice production in the next calendar year in Indonesia can be well predicted from the
anomaly of August sea surface temperature (ASSTAug) at Region Nino 3.4 of the Pacific Ocean
(Falcon et al, 2004). Year-to-year changes in the August ASSTAug explained 94% of the interannual variance of national rice production anomaly. Other studies in Java suggested that the impact
of El Nino on rice production occurred due to its effect on the timing of the onset of rainy season.
Low rainfall in September–December during El Nino years typically delays plantings until
cumulative rainfall is adequate to permit the transplanting of seedlings (Heytens, 1991, Nalyor et al.,
2001, 2007). Therefore, this seasonal rainfall could explain about 84% of the variance in area planted
in September–December and 81% of the variance in area harvested in January–April.
Further study by D’Arrigo and Wilson (2008) suggested that the use of the Nino-3.4 alone in
predicting Sept-Dec rainfall over Java would be less accurate compare to the one using combined
index of Indian Ocean SSTs with the Nino-3.4. Therefore, they suggested to use the combined index
in developing early warning drought forecast of drought and crop failure risk in Indonesia,
particularly in the western part of the country that is most influenced by Indian Ocean climate
variability.
This study will further assess the impact of ENSO on anomaly of monthly rainfall and rice
production, harvesting area and yield by province and evaluate the potential use of SST index
covering Pacific and Indian Ocean for predicting the anomaly of production, area and yield. Based
on this analysis, we developed models that can be used to predict potential impact of sea
surface temperature variability associate with ENSO on rice production by province.
2.
Methodology
This study used historical data of rice production, harvesting area and yield in all
provinces in Indonesia (Figure 1) and sea surface temperature (SST) data from NCDC
NOAA called as the Extended Reconstructed Sea Surface Temperature Dataset (ERSST)
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version 3b from 1983-2009 in the Pacific and Indian Oceans (30°E - 70°W, 30°S - 30°N; see
Smith et al. 2008; Xue et al. 2003 for explanation of the ERSST dataset). The SST data was
downloaded
from
the
IRI
Data
Library
website
(link
address:
http://iridl.ldeo.columbia.edu/SOURCES/.NOAA/.NCDC /.ERSST/.version3b/.sst/).
In
addition we also used the calculated sea surface temperature anomaly in all four NINO
regions (NINO-12 (15°S-5°S, 90°W-80°W), NINO-3 (5°S-5°N, 150°W-90°W), NINO-3.4
(5°S-5°N, 170°W-120°W) and NINO-4 (5°S-5°N, 160°E-150°W)) based on HadISST 1.1
data (Rayner et al. 2003).
The long –historical observed rainfall data is taken from the
gridded CRU TS2.1 dataset. This data is chosen due to its long-term record and as substitute
to unavailability of rain gauge stations data from the provinces. The CRU data is a
reconstructed monthly climate observations obtained from meteorological stations that
comprises rainfall data and other climate variables (Mitchell & Jones 2005). The grid data
covers land area with 0.5°x0.5° horizontal grid resolution for the period of 1901-1998.
The analysis was divided into four parts. The first part was to identify relationship
between variability of monthly sea surface temperature of the four NINO regions with
monthly rainfall variability. This result of this analysis was used to select SST of the NINO
regions that can explain most of the variability of monthly rainfall in Indonesia. The second
part was to analyze correlation between the single monthly SST of the selected NINO Region
and seasonal rice production, area and yield anomalies.
This will be used to evaluate the
months of the SST at the selected NINO region that significantly explain the variability of
the rice data anomalies in each province. The third part was to assess spatial correlation
between the SST anomalies of the Pacific and Indian Oceans (30°E - 70°W, 30°S - 30°N) of
the significance months and the rice data anomalies. This analysis was to evaluate whether
the variability of SST in Indian Ocean also explain significantly the variability of the rice
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data anomaly in the provinces. A seasonal SST index that represents the dominant variability
in the Pacific and Indian Oceans was developed if the significant correlation with the Indian
Ocean SST existed. The fourth part was to develop models for predicting the anomalies of
rice data from the SST of the selected NINO region and from the seasonal SST index and to
compare the performance of the prediction models developed from the two predictors.
Schematically the step of the analysis is presented in Figure 2.
In assessing the relationship between variability of the SST at the four NINO regions with the
rainfall variability, we use the monthly data anomaly. The anomaly of monthly rainfall data was
calculated using the following formula:
ARij  Rij  R i
AR(i) and R(i) were anomaly rainfall and monthly rainfall for month-i respectively, and R (i ) the long
term mean of rainfall for month-i. Similar approach was also applied for the SST data of the NINO
regions. Since the data used for the analysis was long-term data, i.e. from 1901-1998, significant
trends may exist in the data, prior to calculation of the anomaly data, the trend in the data was
removed using the regression technique.
The anomaly of rice data was calculated using three methods, namely first differencing, five
years moving average and polynomial regression. The formulas for the three methods are the
following:
1. First Differencing: AD(i) = D(i) – D(i-1)
2. Five years moving average: AD(i) = D(i) – 1/5(D(i-2) + D(i-1) + D(i) + D(i+1) + D(i+2))
3. Polynomial: AD(i) = D(i) – (a0+a1t + a2t2 + a3t3), where a0, …, a3 are coefficient regression that
relate the rice data with time-t.
AD(i) and D(i) were anomaly of the seasonal rice data and rice data for year-i. The seasonal rice data
were January to April (JFMA), May to August (MJJA), and September to December (SOND).
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In order to develop seasonal SST index representing the dominant variability in the Pacific
and Indian Oceans (30°E - 70°W, 30°S - 30°N), the SST data was processed using Empirical
Orthogonal Function (EOF) analysis. EOF is a statistical method commonly used to identify patterns
of simultaneous variation and had been widely used in many climate studies. Detailed descriptions of
the method can be found for example in von Storch & Zwiers (1999), Hannaci (2004) and many
others. The EOF analysis is purely based on mathematical approach, but its results represent
maximum variability of the data that sometimes can be associated with physical processes (von
Storch & Zwiers 1999).
3.
Results and Discussions
3.1.
Relationship between Monthly SST of the NINO Regions and Rainfall
The result of analysis showed that monthly rainfall variability of most of Indonesian
region is significantly influenced by the variability of sea surface temperature in Pacific
Ocean represented by SST at NINO-12, NINO-3, NINO-3.4 and NINO-4 (Figure 3).
With
exception in some part of Sumatra, all Indonesian rainfall was negatively affected by the
increase of sea surface temperature in the NINO regions. The decrease in monthly rainfall
with one degree increase in SST of the NINO regions mostly ranged from 0 to 50 mm/ oC
(Figure 4). The impact was stronger in most of Kalimantan, Sulawesi and part of Java and
Papua (Figure 4). This is consistent with other study as shown in Figure 2. Figure 4
indicates that the decrease in monthly rainfall with one degree increase in SST in these
regions ranged from around 20 and 40 mm, while in other regions were less than 20 mm.
Among the four NINO regions, it is apparent that the SST of the NINO3.4 has
stronger correlation with the rainfall variability in Indonesia (Figure 3). The correlation in
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part of Sulawesi and Kalimantan increased with NINO3.4 compare to other NINO regions.
Based on this result, for subsequent analysis we only used SST of the NINO3.4.
3.2.
Relationship between Rice Data Anomaly and SST Anomaly of NINO3.4
As indicated above rainfall variability has strong influenced on the decrease and
increase of rice production in Indonesia. Since the rainfall is strongly influenced by the
variability of SST in the NINO3.4, the use of the SST anomaly in explaining the rice
production variability in Indonesia has been proposed by many studies (e.g. Falcon et al, 2004,
Naylor et al., 2001; Naylor et al., 2007; Boer, 2009).
In analyzing the relationship between long historical rice data and SST anomaly, the
trend in the data is normally removed. In this study, we evaluated three methods for
removing the trend as defined above, first differencing (FD), moving average (MA) and
polynomial (PN) regression. The results of the analysis from the first differencing method
were slightly different from those of the other two methods. Figure 5 shows that using the
FD, significant correlation between JFMA rice production and SST with 12, 11 and 10 month
lag is observed in many provinces, while using the other two methods the significant
correlation is only detected in a few provinces. The correlation between the JFMA rice
production anomaly and SST anomaly in NINO-3.4 increases as the time lag between these
two is reduced. It was found that SST anomaly on September, October and November gave
highest correlation with JFMA rice production of the following year data compare to other
months (Figure 5).
Based on SST anomaly data at NINO-3.4 from 1950 to present the onset of ENSO
development generally occurred after April and end the cycle in April of the following year
(Figure 6). The development of the ENSO will affect the onset of the rainy season in that
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year and this will influence the total area planted in JFMA of the following year as discussed
above. It is not surprising that the number of provinces having significant correlation
increased after April. In the subsequent analysis, the strength of the relationship between
JFMA rice production anomaly and ENSO phenomenon (representing by the SST anomaly)
was measured by calculating the mean of the correlation of May to December (8 months).
For the MJJA, it was measured by taking the mean of the correlation of May to April (12
months), and for SOND by the mean of the correlation of May-August (4 months). Figure 7
illustrate the time frame for calculating the strength of the relationship.
The result of the analysis suggests that there are 16 and 5 provinces in which the
anomaly of JFMA rice production is negatively and positively affected by the increase of the
SST at NINO-3.4 respectively (Figure 8). Provinces in which their rice production are
consistently negatively affected by the increase of the SST irrespective of the methods used
in calculating the data anomaly are Bengkulu, DKI Jakarta, West Java (Jabar), Central java
(Jateng), East Java (Jatim), DI Yogyakarta, Bali, NTB, East Kalimantan (Kaltim), North
Sulawesi (Sulut), South Sulawesi (Sulsel), Southeast Sulawesi (Sultenggara). While the ones
that are consistently positively affected by the increase of SST are North Sumatra (Sumut)
and Maluku.
Referring to Figure 5, it is clear that in general the anomaly of JFMA rice production
has significant correlation with the August SST anomaly at NINO-3.4. Loss of JFMA rice
production for every one degree increase in August SST anomaly varied across provinces. In
some provinces, the production loss could be more than 20% such as South Sulawesi (Figure
9). In other main rice growing areas of Java, the production loss for every one degree
increase in SST anomaly at NINO-3.4 were generally between 10% and 20%.
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The pattern of the relationship between the anomaly of MJJA Rice production and the
SST is similar to that of JFMA Rice production as shown in Figures 13.
However, the
direction of the relationship changes from negative to positive for some provinces such as
Jambi, Bengkulu and all provinces in Java, and in Sulawesi (Figure 10).
As suggested by
many findings, the occurrence of El Nino will cause the delay in onset of the season and this
will delay the planting time of the first rice crop, thus the total area planted in the wet season
will decrease. This decrease is normally compensated by increasing the planting area in the
second planting (MJJA) as shown in Figure 6. This partly explains why the rice production
of MJJA has positively correlated with the SST.
Different from the JFMA and MJJA, the anomaly of SOND rice production has
negative correlation with the anomaly of MJJA SST in most of provinces except in Aceh and
North Sumatra (Figure 11).
The increase in the SST in MJJA may indicate the occurrence
of El Nino. During the El Nino, most part of Indonesia experienced below normal rainfall
(see Figure 2 and 12). In many cases this condition causes the harvesting failure due to
drought (see Figure 7). It is understandable that the increase in SST of MJJA will have direct
impact to the decrease of rice production in SOND.
The correlation pattern between the anomalies of rice harvesting area and the
anomaly of the SST at NINO3.4 were quite similar to those between the anomalies of the rice
production and the anomaly of the SST (Figure 12 and 13).
This study suggests that, the
ENSO phenomena strongly affect the anomaly of rice production area and harvesting area in
many Indonesian provinces, particularly in provinces located in monsoonal region (Type A;
see Figure 1 and 9).
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3.3.
Spatial Correlation between Rice Data Anomaly and the SST Anomaly
Based on the above finding, we analyze further the spatial correlation between the
anomalies of rice data and SST over Pacific and Indian Ocean (30°E - 70°W, 30°S - 30°N).
This is to evaluate whether the variability of rice data is also affected by the variability of
SST in Indian Ocean. We limit the analysis only for the relationship between the anomaly of
rice data and the anomaly of August-October SST. The result of analysis is presented in
Figures 14-16. It was shown that the significant relationship between the anomaly of the rice
data and the anomaly of SST also exist in Indian Ocean, but the relationship is in different
direction compare to those with the Pacific Ocean. Therefore we evaluate the potential use
of the seasonal SST index representing the variability of the SST in the Pacific and Indian
Ocean.
We process the SST data using empirical orthogonal function. It is very apparent that
the first mode of EOF is the dominant pattern associated with ENSO (Figure 17), and this
first EOF explains most of the variation of the SST over the NINO regions.
In the second
mode of EOFs, the SST anomalies loading patterns seem to indicate either the warming
signals or decadal variations signified by the increasing trends and low-frequency oscillations
found in their PC scores.
3.4.
Relationship between Rice Data Anomaly and the Anomaly of SON SST
at NINO3.4 and the First EOF
The result of the analysis shows that the use of the EOF(1) as predictor for estimating
the anomaly of rice production and harvesting is not superior to the one used the anomaly of
SST NINO-3.4. Figure 18 presents the result of analysis for West Java case. In other
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provinces, the result of the analysis is also the same as that of West Java. The use of the
anomaly SST at NINO3.4 as a predictor for the estimation of anomaly of rice data in
Indonesia is preferable, since we do not need to process of SST data using the EOF before
making prediction. To improve the predictive skill, D’Arrigo and Wilson (2008), proposed to
use of the Nino-3.4 together with the index of Indian Ocean SSTs. The use of the first EOF
as single predictor is not good enough to accommodate the contribution of SST variation in
the Indian Ocean.
4.
Conclusion and Recommendation
The variability of rice production and harvesting area in Indonesia is strongly related
to the variability of the SST at NINO3.4. We can use the anomaly of the SST at NINO3.4
directly for predicting the anomaly of rice production and harvesting area.
The use of the
EOF that represents the variation of SST over Pacific and Indian Ocean do not improve the
skill. To improve the skill of rice anomaly prediction, further study to evaluate the inclusion
of SST anomaly over Indian Ocean is required.
Acknowledgement
The author acknowledges FAO and SEARCA that provided support for this study
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References
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Monitoring and Predicting Agriculture Drought.
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University of Reading, Reading RG6 6BB, U.K., March 24, 2004
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Kaplan, A, 2003, Global analyses of sea surface temperature, sea ice, and night
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no. D14, doi: 4407 10.1029/2002JD002670.
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von Storch H & Zwiers F, 1999, Statistical analysis in climate research, Cambridge
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Figure 1. Name of provinces in Indonesia. Note: Since the rice production data used started from 1979, some of
provinces which have been expanded recently have to be united back. These include Papua Tengah, Papua
Timur and Papua Barat which were united back as Papua Province, and Maluku Utara and Maluku which were
united back as Maluku Province
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Figure 2. Steps of analysis for evaluating the relationship between sea surface temperature variability and rice
production, area and yield anomaly.
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Figure 3. Spatial correlation between monthly rainfall anomaly and of SST anomaly of NINO-12, NINO-3,
NINO-3.4 and NINO-4 regions (10% of significance Level)
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Figure 4. Change in monthly rainfall with the one degree increase in sea surface temperature of NINO-12,
NINO-3, NINO-3.4 and NINO-4 regions
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Rice Production JFMA(t)_FD
Rice Production JFMA(t)_FD
0.7
Mean of Correlation
Number of Provinces
26
24
22
20
18
16
14
12
10
8
6
4
2
0
0.6
0.5
0.4
0.3
0.2
0.1
0
10% Significance Level
5% Significance Level
10% Significance Level
1% Significance Level
0.7
26
24
22
20
18
16
14
12
10
8
6
4
2
0
Mean of Correlation
Number of Provinces
1% Significance Level
Rice Production JFMA(t)_PN
Rice Production JFMA(t)_PN
0.6
0.5
0.4
0.3
0.2
0.1
0
10% Significance Level
5% Significance Level
1% Significance Level
10% Significance Level
Rice Production JFMA(t)_MA
26
24
22
20
18
16
14
12
10
8
6
4
2
0
10% Significance Level
5% Significance Level
1% Significance Level
Rice Production JFMA(t)_MA
0.7
Mean of Correlation
Number of Provinces
5% Significance Level
0.6
0.5
0.4
0.3
0.2
0.1
0
5% Significance Level
1% Significance Level
10% Significance Level
5% Significance Level
1% Significance Level
Figure 5. Number of provinces that show significant correlation between JFMA rice production anomaly and
SST anomaly at NINO-3.4 (left) and mean correlations (absolute values) from May-December (right)
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2.00
2.00
1.50
1.50
Anomlay SST at NINO-3.4
Anomlay SST at NINO-3.4
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1.00
0.50
0.00
-0.50
-1.00
-1.50
-2.00
1.00
0.50
0.00
-0.50
-1.00
-1.50
-2.00
J FM AM J J A S O N D J FM AM J J A S O N D
J FM AM J J A S O N D J FM AM J J A S O N D
Figure 6. Pattern of mean monthly of SST Anomaly of El Nino years and La-Nina Years at NINO3-4 (19502010)
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M
J
J A S O N D J F M A M J J A
May(t-1)-Dec(t-1)
Rice Data(t)
May(t-1)-April(t)
Rice Data(t)
May(t0-Aug(t)
S
O
N
D
Rice Data(t)
Figure 7. Time frame used for calculating the correlation between the anomaly of rice data SST.
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Production JFMA vs SST NINO3.4
Papua
Maluku
Sultenggara
Sulsel
Sulteng
Sulut
Kalsel
Kaltim
Kalbar
Kalteng
NTT
Bali
First Differencing
NTB
Jatim
Jateng
Moving Average
DI Yogya
DKI Jakarta
Lampung
Sumsel
Bengkulu
Jambi
Riau
Sumbar
Aceh
Polinomial
Jabar
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
Sumut
Mean Correlation of May(t-1)-Dec (t-1)
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Figure 8. Mean of the correlation between the anomaly of JFMA Rice Production and Anomaly of SST at
NINO3.4
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Yd<10%:
10%<=Yd<15%:
15%<=Yd<20%:
Yd>20%:
Figure 9. Percent JFMA Rice Production Loss for every one degree increase in Anomaly of August SST at
NINO3.4
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Production MJJA vs SST NINO3.4
Mean Correlation of May(t-1)-Apr(t)
0.8
Polinomial
0.6
Moving Average
First Differencing
0.4
0.2
0.0
-0.2
-0.4
-0.6
Papua
Maluku
Sultenggara
Sulsel
Sulteng
Sulut
Kalsel
Kaltim
Kalbar
Kalteng
NTT
Bali
NTB
Jatim
Jateng
DI Yogya
Jabar
DKI Jakarta
Lampung
Sumsel
Bengkulu
Jambi
Riau
Sumbar
Aceh
Sumut
-0.8
Figure 10. Mean of the correlation between the anomaly of MJJA Rice Production and Anomaly of SST at
NINO3.4
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Harvesting Area MJJA vs SST NINO-3.4
Papua
Maluku
Sultenggara
Sulsel
Sulut
Sulteng
Kalsel
Kaltim
Kalbar
Kalteng
First Differencing
NTT
Bali
NTB
Jatim
DI Yogya
Moving Average
Jabar
Lampung
DKI Jakarta
Bengkulu
Jambi
Sumsel
Riau
Sumbar
Aceh
Polinomial
Jateng
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
Sumut
Mean Correlation of May(t-1) to Apr(t
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Figure 11. Mean of the correlation between the anomaly of SOND Rice Production and Anomaly of SST at
NINO3.4
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Papua
Maluku
Sultenggara
Sulsel
Sulut
Sulteng
Kalsel
Kaltim
Kalbar
Kalteng
First Differencing
NTT
Bali
NTB
Jatim
DI Yogya
Jabar
Moving Average
Jateng
Lampung
DKI Jakarta
Bengkulu
Jambi
Sumsel
Riau
Sumbar
Aceh
Polinomial
Sumut
Mean Correlation of May(t-1) to Dec(t-1)
Harvesting Area JFMA vs SST NINO-3.4
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
-0.8
Figure 12. Mean of the correlation between the anomaly of JFMA Rice Harvesting Area and Anomaly of SST
at NINO3.4
2016-02-09
Harvesting Area MJJA vs SST NINO-3.4
Papua
Maluku
Sultenggara
Sulsel
Sulut
Sulteng
Kalsel
Kaltim
Kalbar
Kalteng
First Differencing
NTT
Bali
NTB
Jatim
DI Yogya
Moving Average
Jabar
Lampung
DKI Jakarta
Bengkulu
Jambi
Sumsel
Riau
Sumbar
Aceh
Polinomial
Jateng
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
-0.4
-0.5
-0.6
-0.7
Sumut
Mean Correlation of May(t-1) to Apr(t
26 of 31
Figure 13. Mean of the correlation between the anomaly of JFMA Rice Harvesting Area and Anomaly of SST
at NINO3.4
2016-02-09
27 of 31
Figure 14. Spatial correlation between Anomaly of ASO SST (t-1) and the anomaly of JFMA Rice Production (t).
2016-02-09
28 of 31
Figure 15. Spatial correlation between Anomaly of Aug-Oct SST (t-1) and the anomaly of May-Aug Rice Production (t).
2016-02-09
29 of 31
Figure 16. Spatial correlation between Anomaly of Jun-Aug SST (t) and the anomaly of Sep-Dec Rice Production (t).
2016-02-09
30 of 31
Figure 17. Spatio-temporal of seasonal SST loading factors (SON) over the Pacific and Indian Ocean based on
EOF analysis
2016-02-09
31 of 31
West Java
y = -129.9x + 0.036
R² = 0.362
2.00
3.00
Anomaly Harvested Area
Anomaly JFMA Harvested Area
West Java
3.00
1.00
0.00
-1.00
y = -0.719x + 0.126
R² = 0.431
2.00
1.00
0.00
-1.00
-2.00
-2.00
-3.00
-0.01
-0.005
0
0.005
0.01
0.015
-3.00
-2
First EOF
2.00
1.00
0.00
-1.00
3.00
y = -0.728x + 0.183
R² = 0.467
2.00
1.00
0.00
-1.00
-2.00
-2.00
-3.00
-0.01
3
West Java
y = -137.3x + 0.091
R² = 0.427
Anomaly Harvested Area
Anomaly JFMA Production
West Java
3.00
-1
0
1
2
Anomaly of Aug-Oct SST NINO3.4
-0.005
0
0.005
First EOF
0.01
0.015
-3.00
-2
-1
0
1
2
Anomaly of Aug-Oct SST NINO3.4
3
Figure 18. Regression equations relating the anomaly of rice production and harvesting area with the first EOF
and the anomaly of SST at NINO3.4.
2016-02-09