Development of semi dynamic cropping calendar for Indramayu using Pacific sea surface temperature

SUMMARY
DANIEL NAEK CHRISENDO, Development of Semi Dynamic Cropping Calendar for
Indramayu Using Pacific Sea Surface Temperature. Supervised by RIZALDI BOER and RINI
HIDAYATI

Agriculture is a very important sector in Indramayu’s economy. Approximately 53.52%
of Indramayu residents are involved in agriculture. Indramayu has 119.752 Ha (58% of the total
area) cultivated as rice fields which generates the majority of the district’s income. Most of the
farmers use a traditional cropping method called Pranata Mangsa, which is based on periodic
natural events and Primbon, but not climate variability. Climate variability has become a major
obstacle to achieving a successful harvest, because it can affect the timing of planting and length
of the growing season, which leads to drought and flood vulnerability. The planting date and
growing season can be predicted using monthly sea surface temperature (SST) anomalies in Nino
3.4. The August SST anomalies can describe the planting date better than the growing season,
which are demonstrated best in Lohbener with R-Sq = 45% with forecast skill reach = 84% and
92% for advanced and delayed planting date. Knowing the planting date and growing season
length produces a more efficient cropping calendar, which includes details such as when to prepare
the land, plant seeds, and harvest. This cropping calendar is expected to reduce the impacts of
climate variability by providing a more efficient cropping pattern and avoiding potential harvest
failures.

Keywords: Cropping calendar, SST anomalies, Planting date, Growing season

DEVELOPMENT OF SEMI DYNAMIC CROPPING CALENDAR FOR
INDRAMAYU USING PACIFIC SEA SURFACE TEMPERATURE

DANIEL NAEK CHRISENDO

Research report
As one of the requirements to obtain a degree
Bachelor of Science at
Major of Applied Meteorology

DEPARTMENT OF GEOPHYSICS AND METEOROLOGY
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
2011

APPROVAL PAGE
Title

: Development of Semi Dynamic Cropping Calendar for Indramayu Using
Pacific Sea Surface Temperature
: Daniel Naek Chrisendo
: G24062058

Name
Student ID

Approved by,
First Advisor

Second Advisor

Prof. Dr. Ir. Rizaldi Boer, M.Sc.
NIP. 19600927 198903 1 002

Dr. Ir. Rini Hidayati, MS.
NIP. 19600305 198703 2 002

Head of Department of Geophysics and Meteorology

Faculty of Mathematics and Natural Sciences
Bogor Agricultural University

Dr. Ir. Rini Hidayati, MS.
NIP. 19600305 198703 2 002

Graduation date:

CURRICULUM VITAE
Daniel Naek Chrisendo was born in Jakarta, January 21, 1989, the son of Daud Purba and
Marlin Sitorus. He is the second child of four siblings.
On 2006, he graduated from SMAN 2 Bekasi and later was accepted as a student in Bogor
Agricultural University (BAU) through SPMB (Seleksi Penerimaan Mahasiswa Baru). The
following year, Daniel was accepted by the Department of Geophysics and Meteorology.
During his study at Bogor Agricultural University, he was active in various activities. He
joined the International Association of Students in Agricultural and Related Sciences (IAAS) and
was the head of Human Resources Development Department Local Committee-BAU on 20062007. He also joined the IPB Debating Community (IDC) where he took fifth place at the National
English Debating Competition in PIMNAS XX Lampung 2007. Daniel also became a youth
volunteer in the World Bank Youth Group which gave him the opportunity to attend the
International Youth Forum: The Power of Youth for Peace and Luxor International Forum: End

Human Trafficking Now, in Sharm-El-Sheikh and Luxor, Egypt in 2007 and 2010 respectively.
Currently, he is active in the Indonesian Climate Student Forum (ICSF) where he serves as the
coordinator and got an honor as the Earth Hour Competition Winner for his project in the Asia
category held by the British Council, 2009. ICSF also sent him to the OIYP Kaleidoscope 2010 in
New Delhi, India, that allowed him to be involved in the Oxfam International Youth Partnership
(OIYP). He also joined some other organizations for a short period such the Agriaswara Student
Choir and Christian Student Alliance. Through the Alliance, he volunteered at an orphan house for
a time. Upon finishing his undergraduate program, Daniel did an internship for his final research at
the Center for Climate Risk and Opportunity Management in South East Asia and Pacific
(CCROM-SEAP).

PREFACE
Praise God. I finally completed all my responsible for my undergraduate study. For that, I
would like to show my high gratitude to my God Father, Jesus Christ, and Holy Spirit. This report
is specially dedicated to my family, Papa and Mama, also my greatest sisters and brother, Melissa,
Monica, and Davin. I love you.
In this opportunity, many thanks are given for those who that have helped me in this research.
To Rizaldi Boer and Rini Hidayati as advisers, and Bregas Budianto. Also to Kusnomo Tamkani
as the former Head of Agricultural office in Indramayu and the agricultural extensions that have
contributed a huge amount of help in the field and made much of my work easier. Thanks for the

wonderful teamwork in the field from Tamara Sitorus, Jessica Rosen, Daniel Huber, Andrea
Basche, Suciantini, and everybody else in IMHERE project and CCROM. Also, thanks to Willy
Wulansari, Rika Alfiyanti, Sandro Lubis, Rahmi Ariani, Dinda Tri Handayani and friends in the
Laboratory of Climatology who gave frequent input to this research. My gratitude is also delivered
for all lecturers, staffs, classmates, friends, big families, and organizations that can not be
mentioned one by one. The most special thanks is given to the farmers in Indramayu, who already
provide one of the basic needs in our lives.

Bogor, March 2011

Daniel Naek Chrisendo

TABLE OF CONTENTS
Page
LIST OF TABLES ...................................................................................................................

i

LIST OF FIGURES..................................................................................................................

ii

LIST OF APPENDICES ..........................................................................................................

iii

I. INTRODUCTION
1.1 Background ............................................................................................................
1.2 Objective ................................................................................................................

1
1

II. LITERATURE REVIEW
2.1 Indramayu ..............................................................................................................
2.2 Crop in Indramayu .................................................................................................
2.3 Semi Dynamic Cropping Calendar ........................................................................
2.4 Growing Sesason and Cropping Pattern ...............................................................
2.5 Field Capacity and Permanent Wilting Point ........................................................
2.6 ENSO .....................................................................................................................

1
2
3
3
4
5

III. RESEARCH METHODS
3.1 Location and Time .................................................................................................
3.2 Tools and Materials................................................................................................
3.3 Methods ................................................................................................................
3.3.1 Climate Data Collection and Farmer Interviews...........................................
3.3.2 Determining the Observation Area ...............................................................
3.3.3 Determining the Daily PET ..........................................................................
3.3.4 Calculating the Water Balance......................................................................
3.3.5 Determining the Planting Date and Growing Season ...................................
3.3.6 Determining the Planting Date and Growing Season Equation Based on
August SST Anomalies ................................................................................
3.3.7 Testing the Forecast Skill Using Relative Operating Characteristics (ROC)

3.3.8 Forming the Cropping Calendar Based on August SST Anomalies..............

6
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9

IV. RESULT AND DISCUSSION
4.1 Social Data for Indramayu .....................................................................................
4.2 Average Condition of Surplus and Deficit .............................................................
4.3 Average Water Balance .........................................................................................
4.4 Planting Date and Growing Season........................................................................
4.5 Correlation between Planting Date and SST Anomalies........................................

4.6 Correlation between Growing Season and SST Anomalies ...................................
4.7 Forecast Skill .........................................................................................................
4.8 Semi Dynamic Cropping Calendar ........................................................................

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V. CONCLUSIONS AND SUGGESTIONS
5.1 Conclusions.............................................................................................................
5.2 Suggestions .............................................................................................................

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REFERENCES.........................................................................................................................

18

APPENDICES .........................................................................................................................

2

i

LIST OF TABLES
Page
1
2
3
4
5

Rice Crop Calendar: Planting and Harvesting Seasons in Top Five Producers ...............
Correlation Factor by Latitude.........................................................................................

Planting date for five clusters in Indramayu 2000-2009 in dekads..................................
Growing season for five clusters in Indramayu 2000-2009 in dekads .............................
Equation to predict the planting date and growing season...............................................

4
7
11
12
14

ii

LIST OF FIGURES
Page
1
2
3
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15
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20
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24
25
26
27
28
29
30
31
32
33

Weather Calendar for Rice ..............................................................................................
Monthly Rainfall Anomaly Relations during the Dry Season in Kawapante, NTT
with SST Anomalies in the Pacific Region (Nino 3.4) ....................................................
The Polygon of Rainfall Area Distribution......................................................................
ROC Curve ......................................................................................................................
Farmer’s Cropping Pattern...............................................................................................
Rice Variety that Used by the Farmers ............................................................................
Average Surplus and Deficit Value for 5 Clusters in Indramayu per Dekad ...................
Average soil moisture content for 5 clusters in Indramayu 2000-2009 per dekad...........
Scatterplot of planting date vs SST anomalies for August in Bondan .............................
Scatterplot of planting date vs SST anomalies for August in Bangkir.............................
Scatterplot of planting date vs SST anomalies for August in Lohbener. .........................
Scatterplot of planting date vs SST anomalies for August in Bulak ................................
Scatterplot of planting date vs SST anomalies for August in Cikedung. .........................
Scatterplot of growing season vs SST anomalies for August in Bondan.........................
Scatterplot of growing season vs SST anomalies for August in Bangkir. .......................
Scatterplot of growing season vs SST anomalies for August in Lohbener ......................
Scatterplot of growing season vs SST anomalies for August in Bulak. ...........................
Scatterplot of growing season vs SST anomalies for August in Cikedung......................
ROC curve for advanced planting date in Bondan ..........................................................
ROC curve for advanced planting date in Bangkir ..........................................................
ROC curve for advanced planting date in Lohbener........................................................
ROC curve for advanced planting date in Bulak .............................................................
ROC curve for advanced planting date in Cikedung .......................................................
ROC curve for delayed planting date in Bondan .............................................................
ROC curve for delayed planting date in Bangkir.............................................................
ROC curve for delayed planting date in Lohbener ..........................................................
ROC curve for delayed planting date in Bulak ................................................................
ROC curve for delayed planting date in Cikedung ..........................................................
Cropping calendar for Lohbener (SST anomaly August = 2) ..........................................
Cropping calendar for Lohbener (SST anomaly August = 1) ..........................................
Cropping calendar for Lohbener (SST anomaly August = 0) ..........................................
Cropping calendar for Lohbener (SST anomaly August = -1).........................................
Cropping calendar for Lohbener (SST anomaly August = -2).........................................

2
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iii

APPENDICES
Page
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16

Research flow chart. ........................................................................................................
Dekads rainfall pattern in every cluster in Indramayu District. .......................................
List of rainfall station in every cluster. ............................................................................
Example of water balance calculation table (Lohbener 2001). ........................................
Average surplus and deficit value for Bulak (1979-2009). ..............................................
Average surplus and deficit value for Bangkir (1979-2009)............................................
Average surplus and deficit value for Bondan (1979-2009). ...........................................
Average surplus and deficit value for Lohbener (1979-2009). ........................................
Average surplus and deficit value for Cikedung (1979-2009) .........................................
Example of water availability calculation table (Lohbener 2001) ...................................
Average water availability for Bulak (1979-2009) ..........................................................
Average water availability for Bondan (1979-2009) .......................................................
Average water availability for Bangkir (1979-2009).......................................................
Average water availability for Lohbener (1979-2009) ....................................................
Average water availability for Cikedung (1979-2009) ....................................................
Example of ROC calculation (Lohbener). .......................................................................

21
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22
23
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26
27
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29
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31

1

I.INTRODUCTION
1.1 Background
Indramayu is an administrative district in
West Java province. According to Bureau of
Population and Statistics (BPS) (2009),
Indramayu has 31 sub districts which are
divided to 313 villages. Most of the citizens
get their main income from agriculture. The
total number of citizens is 1.732.674 where
927.308 or around 53.52% are involved in
agricultural sector. Indramayu has a total
land area of 204.011 Ha, with 119.752 Ha
(around 58%) being rice fields.
Climate variability is a climate
phenomenon which is being concerned at
this time because of its big impact to the
agricultural sector, also agricultural in
Indramayu. According to Koesmaryono et
al. (2008), climate variability is the variation
of climate element occurring in the certain
time, includes climate extreme event.
Climate and weather in Indonesia is
often influenced by global phenomenon
which happened in tropical Pacific Ocean,
most known as Walker Circulation. The
disturbance that happened in Walker
Circulation is known as ENSO (El Nino
Southern
Oscillation)
phenomenon.
Indicator which often used to show the
happening of El-Nino phenomenon is the
increasing of sea surface temperature
anomaly in Pacific or the pressure difference
between Tahiti and Darwin excessing its
normal condition.
ENSO is a global climate phenomenon
which is centered in Pacific Ocean and
consist of three fluctuation; normal, El Nino,
and La Nina. ENSO directly gives impact to
the climate condition especially the rainfall.
The rainfall anomaly in Indramayu will
cause drought and flood which will give
impact to the failure of agricultural activity.
The drought which is caused by ENSO will
increase the drought plantation area until 810 times wider than the normal condition. In
the contrary, La Nina increases the flooding
plantation area until 4-5 times wider than the
normal condition.
Nowadays, farmers in Indonesia use
cropping calendar for their agricultural
activity. ASA (1976) and FAO (1996) in
Koesmaryono et al. (2008) said that
cropping calendar is “a sequential summary
of the dates/periods of essential operations,
including land preparation, planting, and
harvesting, for a specific land use; it may
apply to a specific plot, but is frequently

generalized to characterize a specified area”.
Koesmaryono et al. (2008) said that
cropping calendar is one of the agricultural
aspects which is often plotted to understand
the planting schedule, the plants type in
certain area for one year, starting from the
period of land preparation, planting, and
harvesting.
Traditionally, cropping calendar is also
developed by Indonesian farmer long time
ago. The Sundanese call Pranata Mangsa,
which is needed as the determination or
standard for farming. Knowledge about
Pranata Mangsa is gotten hereditary which is
based on regular natural events. Nowadays,
those local wisdoms can not be fully made
as reference in determining the planting date
because of the changing of some systems
and season sign indicator also climate
change. Starts from these analysis and
thought, it is needed to make an adjustment
in growing season and plant rotation, to be
more adaptive with the climate change and
variability.
The sustainable agriculture needs the
changing of cropping calendar, cropping
pattern, also plant rotation in every
agroecology zone because of the climate
change and variability (Viet et al. 2001 in
Koesmaryono et al. 2008). Because of this
reason, it necessary to review how the
existing cropping calendar is based on the
happening ENSO. For the agricultural
success because of ENSO, it necessary to
form the new cropping calendar which
figure on ENSO phenomenon.
1.2 Objective
Form a cropping calendar for Indramayu
by using the pacific sea surface temperature.
II. LITERATURE REVIEW
2.1. Indramayu
Indramayu is an administrative district in
West Java. Geographically, Indramayu is
located on 107°52° - 108°36°E and 6°15° 6°40°S and topographically, most of the
areas are sloping around 0-2%. This
condition influences the drainage, if the
rainfall quite high, in some certain areas will
be excess water. Indramayu is located on the
Java’s north coast and has 31 sub districts
which are divided to 313 villages with has
10 sub districts with 35 villages which are
directly adjacent to the sea with coast line
around 114.1 km (BPS 2009).

2

-10

0
semai

10

Vegetativ
20
30

40

tanam
anakan aktif
anakan maksimum

Kebutuhan air
Kritis
Penting
Cukup penting
Tidak penting

50

Reproduktiv
60
70

Pemasakan
80
90
100
pembugaan

bunting
inisiasi malai

Panen

3

After planting or first fertilizing, the rice
field is not irrigated for three days but left
saturated. 4 until 14 days after planting, the
water is irrigated and gradually inundated
until reach 7cm-10cm. 15-30 days after
planting, the rice field is flooded until it
reaches a height of 3-5 cm. the lack of water
in this phase will reduce the number of
tillers and the too high pool will inhibit the
growth of tillers. 30 days after planting, the
water is removed from the field and left
saturated. 35-50 days after planting, the field
is flooded with the height of 5-10 cm. 50
days after planting, the field is dried up and
left saturated. 55 days after planting, the
field is flooded with a height of 10 cm, until
the flowering period. The lack of water in
this phase will weaken the formation of
panicle and fertilization and cause the empty
grains. After that, water is removed from the
field and for 5 days the field is left saturated.
The inundation is continued in the height of
10 cm until the grain filled. 15 days before
harvesting, the field is dried up (Prasetyo
2002)
2.3 Semi-Dynamic Cropping Calendar
FAO in Koesmaryono (2008) divides the
planting pattern of three major parts; the
cropping calendar, planting intensity, and
planting rotation. Cropping calendar is
defined as “A sequential summary of the
dates/periods of essential operations,
including land preparation, planting, and
harvesting, for a specific land use; it may
apply to a specific plot, but is frequently
generalized to characterize a specified area”.
Cropping calendar is one aspect of
agriculture that are often mapped to
determine the schedule for planting crops in
certain areas during the year, starting from
the time of land preparation, crop planting,
and harvesting. According to Boer (2002),
cropping calendar is a calendar system
which shows the urgency level of relation
between environment conditions with the
plant growth phase. Cropping calendar will
shows the needed environment condition in
certain phase where the plant become
sensitive to that environment condition.
Traditionally, cropping calendar has also
been developed by Indonesian farmers.
Sundanese called Pranata Mangsa which is
required as a determination to grow crops.
This knowledge is gained hereditary by the
farmers. For thousands of years they
memorize the season pattern, climate, and
other natural phenomena, and their ancestors

finally make the annual calendar not based
on the common calendar or Islam calendar
but based on natural occurrences such as,
rain season, dry season, flowering season,
and the location of stars in the universe, also
the influence of the moon against the tides
of sea water (Koesmaryono 2008)
This culture is not wrong as long as there
is no changing either climate or the landuse.
But the global change is happened. Because
of that reason, it is important to analyze how
the existing cropping calendar can fit to the
climate condition after the changing. To
maintain the sustainable agriculture because
of the climate change, it is needed to make a
new cropping calendar, cropping pattern,
also planting rotation in every agroecology
zone (Vie et al 2001 in Koesmaryono 2008).
2.4 Growing Season and Cropping
Pattern
Adjustment of cropping pattern and
planting date is a strategic approach to
reduce or avoid the impact of climate change
due to a shift in growing season and changes
in rainfall patterns. According to FAO
(1978) in Saleh (2007), growing season is a
lapse of time in a year with rainfall more
than 0.5 PET added by time at the end of
rainy season (rainfall close to 0.5 PET) to
evaporate 100mm of stored ground water.
The water level between field capacity and
permanent wilting point is often referred to
as effective moisture for crop planting or
optimum water content (Sosrodarsono dan
Takeda 1978). For that reason, the water
level on that range is used as the
determination of crop growing season.
According to Heryani (2001), planting
date and growing season is determined
based on the availability of soil moisture.
Soil moisture is the water that is bound by
forces, such as matrix-belt force, osmosis,
and capillary. Growing season period is
periods where soil moisture content is are
not less than 50% of available water. This is
according to Richard (1969) in Perdana
(1995) that said to get good plant growth,
water should be added if 50-80% of water
available has been used up. Oldeman stated
that rainfall equal or more than 200mm per
month can be used as determination of rice
growing season, while for secondary crop, is
based on rainfall equal or more than 100 mm
per month.
Cropping pattern is a setting of a location
and sequence of crops on a plot of land for a
certain period. Thahir (1974) said that

4

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100

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Y=0.848-17.078X
bobot=0.2
50

0

-50
-2

-1

0

1

A
SM
LB
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2

3

6

anomaly for warm (El Niño) events and at or
below the -0.5 anomaly for cold (La Niña)
events. The threshold is further broken
down into Weak (with a 0.5 to 0.9 SST
anomaly), Moderate (1.0 to 1.4) and Strong
(≥ 1.5) events. For the purpose of this report
for an event to be weak, moderate or strong
it much have equaled or exceeded the
threshold for at least three months.
(http://www.cpc.noaa.gov)
The size of the impact caused by the ElNino phenomenon in the area of rice
plantation is closely associated with farmers'
cropping patterns and behaviors in Java
(Boer dan Meinke, 2002). General cropping
pattern followed by farmers in Java are ricerice-rice or rice-rice-fallow. The first rice is
planted in rainy season, around November or
December, the second rice planted in the
beginning of dry season, around March or
April, and the third rice planted around June
or July. Drought-affected rice is usually that
grown in dry season. In 1991/1992, most
farmers finished the planting when the
drought came and make the harvest failure.
In the contrary, in 1997/1998 because of the
formation of El-Nino was very fast and
happened in the beginning of dry season,
made rain in the dry season is not available
so many farmers do not dare to do the
planting. As the result, drought-affected area
becomes less. Commonly, on the dry season
plantation, farmer usually plants the rice if
the rainfall once or twice regardless of
whether it will rain or not in the following
months. On El-Nino 1991/1992, many
farmers were fooled because in the
beginning of dry season, symptoms of
impending the long drought (no rain) hasn’t
been detected, in the contrary of El-Nino
1997/1998.
III. RESEARCH METHODS
3.1 Location and Time
This research was conducted in the
Climatology Laboratory, Department of
Geophysics and Meteorology, Bogor
Agricultural University, Center of Climate
Risk and Opportunity Management in South
East Asia and Pacific (CCROM-SEAP), and
in Indramayu District, from February until
October 2010.
3.2 Tools and Materials
Tools used in this research were:
1. Microsoft Office Word XP 2007 for
documentation,
reporting,
and

information publication.
2. Microsoft Office Excel XP 2007 for
statistical analysis.
3. Minitab 14 and Crystal Ball 7.31 for
statistical analysis.
Materials used in this research were:
1. Dekad rainfall data for Bangkir, Bulak,
Bondan, Lohbener, and Cikedung subdistricts from 1979-2009.
2. Dekad
temperature
data
for
Pusakanegara, Subang from 1979-2009.
3. SST anomalies data in Nino 3.4 for
August from 1979-2009
4. Permanent wilting point and field
capacity for Indramayu
5. Social Data for Indramayu
6. Map of Indramayu
3.3 Methods
3.3.1 Climate Data Collection and Farmer
Interviews
Preparation for this research began with
gathering the rainfall data for Indramayu
from 1979-2009, temperature data for
Subang from 1979-2009, monthly sea
surface temperature (SST) anomalies for
August from 1979-2009.
The rainfall data was obtained from the
Water Management Agency for Indramayu.
There are no weather stations in Indramayu
that measures the temperature, so
temperature data for Subang District from
the Indonesian Agroclimate and Hydrology
Research Institute, Bogor, was used with the
assumption that Subang and Indramayu have
the same elevation and are close
geographically which makes the temperature
data for Subang valid for Indramayu. The
August SST anomalies in Nino 3.4 were
obtained from the International Research
Institute
website
(http://iridl.ldeo.columbia.edu). The August
SST anomalies were used because August is
the best and the possible closest time to
predict the planting date which is often
conducted on October, November, and
December. The permanent wilting point and
the field capacity are 210 mm and 350 mm
respectively (Pawitan et al. 1997)
Interviews with farmers were conducted
in three sub districts in Indramyu; Lelea,
Cikedung and Trisi, with a total of 150
farmers interviewed.
3.3.2 Determining the Observation Area
The observation area was determined by
using the rainfall zone of Indramayu

7

M onth
Jan
Feb
M ar
A pr
M ay
Jun
Jul
A ug
Sep
O ct
N ov
D ec





(°) South
6
7
1.06 1.07
0.95 0.96
1.04 1.04
1
1
1.02 1.02
0.99 0.98
1.02 1.02
1.03 1.03
1
1
1.05 1.05
1.03 1.04
1.06 1.07

8

9

miss: forecast said the eventt w
would not
happen and it does happen
from zero to one,
Interval values ranged fro
as the perfect score.
OFD, probability
4. Calculating the FAR (PO
of false detection)

SCUSSION
IV. RESULT AND DISC
yu
4.1 Social Data for Indramayu
that usually
The cropping patterns th
by
conducted by the farmers aree explained
e
figure 5.
5.30%

where,
the event would
false alarm: forecasts said th
pen
happen and it does not happen
ts said the event
correct negative: forecasts
would not happen, and it rreally does not
happen
from zero to one,
Interval values ranged fro
re.
with zero as the perfect score.
table into ROC
5. Plotting the FAR vs HR ta
curve

(Kadarsah 2009)
Figure 4 ROC curve (Ka
ined as follows:
From figure 4 can be explaine
ith the non skill
1. If the line coincide with
line, it means that there is no skill
2. If the line is above the nnon skill line, it
liable)
means positive skill (reliab
3. If the line is below thee nnon skill line, it
on-reliable)
means negative skill (non4. Reliability score is thee aarea below the
squares below the
curve line. Number of squ
curve line is dividedd with the total
core skill.
squares, known as the sco
pping Calendar
3.3.8 Forming the Croppin
Based on August SST Anomalies
ar was compiled
The cropping calendar
lies. The resulting
based on the SST anomalies
cropping calendar shows the recommended
nd seeds, planting
time to prepare the land and
and harvesting.
date, growing season, an
est plant type to
Moreover, it shows the bes
grow.

6%

Rice
Ric - Rice -Fallow
Rice
Ric - Fallow - Fallow
82.67%

Rice
Ric - Secondary Crop Fallow
Fa

ing pattern
Figure 5 Farmer’s cropping
Based on the interviewss that were
2.67% of the
conducted in Indramayu, 82.6
farmers plant rice during the
th first two
growing season and they leavee their land to
on. This trend
fallow during the third season
uperior water
can be attributed to supe
ng the first and
availability from rainfall during
esult of this
second seasons. As a resu
y, 5.3% of the
diminishing water availability,
uring the first
respondents plant rice only dur
their fields to
growing season and leave the
the seasons.
fallow for the remainder off th
However, approximately 6% of the
respondents take the risk to plant a
rd season such
secondary crop during the third
er, longbean,
greenbean, chili, cucumber,
ording to the
cabbage, and soybean. Accord
s, the land and
Indramayu farmer respondents,
ducted during
seed preparation usually condu
lanting date.
the three dekads prior to the plan
sually used by
The rice varieties that usua
ure 6.
the farmers can be seen by figure

29.30%

Ciherang
52%
5

18.70%

Kerbau
Other

Figure 6 Rice variety that used
use by the
farmers
spondents use
Around 52% of the respo
use a
Ciherang rice variety, while 18.7%
1
has not
local variety called Kerbau which
wh

10

Surplus - Deficit (mm)

80
60
Surplus

40

Deficit
20
0
-20
-40

1 4 7 10 13 16 19 22 25 28 31 34

Dekad

11

400

Water Level (mm)

350

FC

300

PWP

250

WHC

200

SMC Bulak

150

SMC Bondan
SMC Bangkir

100

SMC Lohbener

50

SMC Cikedung

0
1 4 7 10 13 16 19 22 25 28 31 34
Dekad

Planting Date
Bondan Bangkir Lohbener Bulak Cikedung Average
79/80
27
27
27
27
27
27
80/81
30
30
31
31
30
30
81/82
31
32
27
27
27
29
82/83
34
33
36
31
31
33
83/84
30
29
30
30
30
30
84/85
29
28
30
24
24
27
85/86
29
27
27
29
29
28
86/87
30
31
31
26
29
29
87/88
33
32
33
33
32
33
88/89
30
29
29
29
29
29
89/90
32
31
32
30
30
31
90/91
33
35
35
34
33
34
91/92
32
31
32
31
32
32
92/93
25
31
29
28
25
28
93/94
29
32
32
32
37
32
94/95
32
33
34
37
32
34
95/96
27
30
30
31
30
30
96/97
28
25
30
30
29
28
97/98
35
35
35
34
35
35
98/99
28
30
29
31
30
30
99/00
29
30
29
32
30
30
00/01
30
31
30
32
32
31
01/02
32
29
32
24
28
29
02/03
33
36
33
36
34
34
03/04
32
33
32
32
29
32
04/05
33
33
33
33
33
33
05/06
33
32
35
33
29
32
06/07
34
35
35
34
36
35
07/08
35
30
32
28
30
31
08/09
34
35
35
30
31
33
09/10
32
32
36
32
33
33
Average
31
31
32
31
31
31
Year

12

36
34
Year
79/80
80/81
81/82
82/83
83/84
84/85
85/86
86/87
87/88
88/89
89/90
90/91
91/92
92/93
93/94
94/95
95/96
96/97
97/98
98/99
99/00
00/01
01/02
02/03
03/04
04/05
05/06
06/07
07/08
08/09
Average

Bondan
23
22
20
20
20
21
25
23
22
26
17
18
21
30
21
18
22
21
20
29
26
24
19
19
16
25
20
23
12
20
21

Bangkir
24
21
16
21
29
31
28
23
23
32
18
17
36
23
18
25
19
25
24
26
26
24
19
17
17
12
14
21
13
18
22

Growing Season
Lohbener Bulak
20
24
17
20
22
22
18
23
36
22
29
25
28
26
23
22
22
22
26
28
20
23
16
13
25
22
25
18
18
18
23
9
20
18
20
17
20
20
27
25
27
10
18
17
19
26
19
16
14
20
20
10
13
18
21
22
18
21
6
24
21
20

32
Cikedung Average
22
23
27
21
21
20
22
21
24
26
35
28
20
25
22
23
16
21
29
28
22
20
18
16
23
25
24
24
13
18
25
20
19
20
20
21
24
22
20
25
12
20
22
21
21
21
17
18
18
17
17
17
22
17
21
22
20
17
23
18
21
21

30
28
26
y = 1.9298x + 30.608
R² = 0.2817

24
22
20
-2

-1

38
36
34
32
30
28
26
24
22
20

0

1

2

3

y = 2.0628x + 30.772
R² = 0.3097
-2

-1

0

1

2

3

38
36
34
32
30
28

y = 2.5485x + 31.125
R² = 0.45

26
24
-2

-1

0

1

2

3

13

35

38
36
34
32
30
28
26
24
22
20

30
25
20
15
y = 1.9081x + 30.288
R² = 0.1874

y = -1.3412x + 21.682
R² = 0.0651

10
5
0

-2

-1

0

38
36
34
32
30
28
26
24
22
20

1

2

-2

3

-1

0

1

2

3

40
35
30
25
20
y = 1.9563x + 30.117
R² = 0.2244

15
10

y = -1.224x + 22.227
R² = 0.0227

5
0
-2

-1

0

1

2

-2

3

-1

0

1

2

3

40
35
30
25
20
15
y = -2.1094x + 21.392
R² = 0.0709

10
5
0
-2

-1

0

1

2

3

14

30
25
20

Cluster

Planting Date

Growing Season
y = -1,3412x + 21,682

Bondan

y = 1,9298x + 30,608

15

Bangkir

y = 2,0628x + 30,772

y = -1,224x + 22,227

10

Lohbener

y = 2,5485x + 31,125

y = -2,1094x + 21,392

Bulak

y = 1,9081x + 30,288

y = -1,4473x + 20,302

Cikedung

y = 1,9563x + 30,117

y = -0,7415x + 21,438

y = -1.4473x + 20.302
R² = 0.0444

5
0
-2

-1

0

1

2

3

40
35
30
25
20

1.000

15

0.800

10

y = -0.7415x + 21.438
R² = 0.0137

5

0.600

0
-2

-1

0

1

2

3

0.400
0.200
0.000
0.000

0.200

0.400

0.600

0.800

1.000

0.200

0.400

0.600

0.800

1.000

1.000
0.800
0.600
0.400
0.200
0.000
0.000

15

1.000
0.800
0.600
0.400
0.200
0.000
0.000

0.200

0.400

0.600

0.800

1.000
1.000
0.800

1.000
0.600
0.800
0.400
0.600
0.200
0.400
0.000
0.200

0.000

0.200

0.400

0.600

0.800

1.000

0.200

0.400

0.600

0.800

1.000

0.000
0.000

0.200

0.400

0.600

0.800

1.000

1.000
0.800

1.000

0.600

0.800
0.600

0.400

0.400

0.200

0.200

0.000
0.000

0.000
0.000

0.200

0.400

0.600

0.800

1.000

16

1.000

1.000

0.800

0.800

0.600

0.600

0.400

0.400

0.200

0.200

0.000

0.000

0.000 0.200 0.400 0.600 0.800 1.000

0.000 0.200 0.400 0.600 0.800 1.000

1.000
0.800
0.600
0.400
0.200
0.000
0.000 0.200 0.400 0.600 0.800 1.000

Dekad
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Dekad
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Dekad
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

17

Dekad
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

Dekad
22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21

=

Preparation

=

Growing Season

=

Harvest

=

Planting Date

18

5.2 Suggestions
Further research is expected to gain a
more complete cropping calendar with more
complete inputs in the model. Those can be
reached if all aspects of the society take the
role in make it success. The government and
agencies are expected to provide more
complete
information
such
rainfall,
temperature, and others. Also they should
have the desire to spread the information and
teach the farmers about the cropping
calendar. The farmers are expected to give
data about the agricultural more accurate
and realistic. Other predictors beside SST
anomaly in pacific also needed to make the
cropping calendar fitter to the real condition,
i.e. IOD which also influence the cropping
time in Indramayu and has the opposite
effect with the pacific SST anomaly. The
future researcher are expected to enter more
inputs and considering other aspects such
productivity, field are, harvest, and others.
All of these are needed to make this
cropping calendar better in order to help the
farmers for successful farming.
REFERENCES
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National
Oceanic
and
Atmospheric Administration. 2010. Sea
Surface Temperature.
http://www.cpc.noaa.gov/data/indices/sst
oi.indices. [11 April 2010]
[IRI]. International Research Institute. 2010.
NOAA NCDC ERSST version3b sst 0.0
meters Data Files.
http://iridl.ldeo.columbia.edu/SOURCES
/.NOAA/.NCDC/.ERSST/.version3b/.sst/
T/%28Jan%201979%29%28Oct%20201
0%29RANGEEDGES/Y/%284S%29%2
84N%29RANGEEDGES/X/%28170W%
29%28120W%29RANGEEDGES/?help
+datafiles [9 November 2010]
BPS. 2009. Indramayu dalam Angka Tahun
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Boer R, Arianto MA, Las I and Notodiputro
KA. 1999. Keragaman Produksi Kedelai
pada Kondisi Iklim Ekstrim di FloresNTT. Seminar Proceedings Climate
Variability. BPPT (In Press).
Boer R. 2002. Analisis Risiko Iklim untuk
Produksi Pertanian. Paper presented in
Sumatra and Kalimantan University
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Environmental
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and
Simulation. Bogor, July 1-13 2002.

Boer R and Meinke. 2002.
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Pertumbuhan
Kapan akan
di Indonesia.
of Primary

Boer R. 2003. Fenomena Enso dan
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Mathematics and Natural Sciences,
Bogor Agricultural University.
Boer R. 2003. Penyimpangan Iklim di
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Meteorological,
Climatological, and Geophysics Agency,
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Haryoko U. 2008. Pewilayahan Hujan untuk
Menentukan Pola Hujan (Contoh Kasus
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Sulawesi Selatan Berdasarkan Analisis
Neraca Air Tanah. In Reorientasi
Pendayagunaan Sumberdaya Tanah,
Iklim, dan Pupuk. National Seminar
Proceedings. Bogor: The Indonesian Soil
and Hydrology Research Institute
Kadarsah. 2009. Uji Kehandalan Model
Predeiksi dengan Menggunakan ROC
(Relative Operating Characteristics).
http://kadarsah.wordpress.com/2009/04/0
2/uji-kehandalan-model-prediksi-dengan
-menggunakan-rocrelative-operating-cha
racteristics/ [23 February 2011]
Koesmaryono Y, Las I, Aldrian E,
Runtunuwu E, Syahbudin H, Apriyana
Y, Ramadhani F, and Trinugroho W.
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Terhadap
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Dipole Mode) di Daerah Monsunal dan
Equatorial. Bogor: LPPM-IPB.

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Narapusetty B, Delsole T, and Tippett K.
2010. Optimal Estimation of the
Climatological Mean. Calverton: Center
for Ocean Land Atmospheric Studies.
Pawitan H, Las I, Suharsono H, Boer R,
Handoko,
Baharsjah
J.
1997.
Implementasi Pendekatan Strategis dan
Taktis Gerakan Hemat Air. In Sumber
Daya Air dan Iklim dalam Mewujudkan
Pertanian Efisien. Jakarta: Departemen
Pertanian.
Perdana S. 1995. Analisis Neraca Air Lahan
untuk Menetukan Pola Tanam dan
Periode Tanam Tanaman Padi dan
Palawija di Pulau Sumbawa. Bogor:
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Prasetyo Y. 2002. Budi Daya Padi Sawah
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Sumatera Selatan. Palembang: Faculty of
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Siregar H. 1989. Budidaya Tanaman Padi di
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Pengairan.
Yogyakarta:
Pradnya Paramita.
Tawang A, Ahmad TA, and Abdullah MY.
2002.
Stabilization
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White M and Host G. 2002. An Assessment
of Potential Evapotranspiration for the
Northern Lake States. Duluth: University
of Minnesota.

APPENDICES

21

Appendices 1 Research flow
w chart

Appendices 2 Dekads rainfall
fall pattern in every cluster in Indramayu District (Haryoko
ko 2008)

22

23

Month Dekad
1
Jan
2
3
1
Feb
2
3
1
Mar
2
3
1
Apr
2
3
1
Mei
2
3
1
Jun
2
3
1
Jul
2
3
1
Ags
2
3
1
Sep
2
3
1
Okt
2
3
1
Nov
2
3
1
Des
2
3

RF PET RF-PET APWL SMC ∆ SMC
10
47
-37
-37
315
0
7
47
-40
-78
280
-34
59
52
7
0
350
70
2
47
-45
-45
308
-42
187 48
139
0
350
42
6
38
-32
-32
319
-31
0
47
-47
-79
279
-40
137 47
90
0
350
71
35
52
-17
-17
333
-17
91
47
44
0
350
17
13
48
-35
-35
317
-33
0
48
-48
-82
277
-40
0
47
-47
-129 242
-35
16
48
-32
-161 221
-21
30
52
-22
-183 207
-14
16
47
-31
-214 190
-18
43
48
-5
-219 187
-2
17
48
-31
-249 172
-16
0
47
-47
-297 150
-22
0
48
-48
-344 131
-19
0
52
-52
-397 113
-18
0
47
-47
-444
98
-14
0
48
-48
-491
86
-13
0
52
-52
-544
74
-12
38
47
-9
-553
72
-2
0
48
-48
-601
63
-9
0
48
-48
-649
55
-8
0
47
-47
-696
48
-7
0
48
-48
-743
42
-6
0
53
-53
-796
36
-6
13
47
-34
-831
33
-3
262 47
215
0
350
317
142 48
94
0
350
0
49
47
2
0
350
0
52
47
5
0
350
0
14
53
-39
-39
313
-37

AET Deficit
47
0
41
6
52
0
44
3
48
0
37
1
40
7
47
0
52
0
47
0
46
2
40
7
35
12
37
11
44
9
34
14
45
2
33
15
22
26
19
28
18
34
14
33
13
35
12
40
40
7
9
38
8
40
7
40
6
41
6
47
16
31
47
0
48
0
47
0
47
0
51
2

Surplus
0
0
0
0
97
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
94
2
5
0

24

100

Surplus - Deficit (mm)

80
60
40
Surplus
20

Deficit

0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
-20
-40

Dekad

100

Surplus - Deficit (mm)

80
60
40
Surplus
20

Deficit

0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
-20
-40

Dekad

25

80

Surplus - Deficit (mm)

60
40
20
Surplus
0

Deficit
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

-20
-40
-60

Dekad

120

Surplus - Deficit (mm)

100
80
60
40

Surplus

20

Deficit

0
-20 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
-40
-60

Dekad

26

80

Surplus - Deficit (mm)

60
40
20
Surplus
0

Deficit
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35

-20
-40
-60

Dekad

27

Month Dekad
1
Jan
2
3
4
Feb
5
6
7
Mar
8
9
10
Apr
11
12
13
May
14
15
16
Jun
17
18
19
Jul
20
21
22
Aug
23
24
25
Sep
26
27
28
Oct
29
30
31
Nov
32
33
34
Dec
35
36

FC
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350
350

PWP WHC SMC SMC-WHC
210 280 315
35
210 280 280
0
210 280 350
70
210 280 308
28
210 280 350
70
210 280 319
39
210 280 279
-1
210 280 350
70
210 280 333
53
210 280 350
70
210 280 317
37
210 280 277
-3
210 280 242
-38
210 280 221
-59
210 280 207
-73
210 280 190
-90
210 280 187
-93
210 280 172
-108
210 280 150
-130
210 280 131
-149
210 280 113
-167
210 280
98
-182
210 280
86
-194
210 280
74
-206
210 280
72
-208
210 280
63
-217
210 280
55
-225
210 280
48
-232
210 280
42
-238
210 280
36
-244
210 280
33
-247
210 280 350
70
210 280 350
70
210 280 350
70
210 280 350
70
210 280 313
33

28

400

Kolom air (mm)

350
300
250
KAT

200

KL

150

TLP

100

WHC

50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Dekad

400

Kolom air (mm)

350
300
250
KAT

200

KL

150

TLP

100

WHC

50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Dekad

29

400

Kolom air (mm)

350
300
250
KAT

200

KL

150

TLP

100

WHC

50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Dekad

400

Kolom air (mm)

350
300
250
KAT

200

KL

150

TLP

100

WHC

50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Dekad

30

400

Kolom air (mm)

350
300
250
KAT

200

KL

150

TLP

100

WHC

50
0
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35
Dekad

31

ATAS NORMAL (AN)
Threshold Value

DATA INPUT

Event

A

Not Event TA

Delayed Normal Advanced
Tahun
1979
1980
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009

Obs
A
N
A
B
A
A
A
N
B
A
N
B
N
A
N
B
A
A
B
A
A
A
N
B
N
B
B
B
N
B
B

Sim
N
A
A
B
A
A
A
N
B
A
A
N
B
N
N
B
A
N
B
A
A
A
N
B
B
B
N
B
A
A
B

B
28
20
11
65
18
22
11
46
91
3
15
45
69
35
37
54
14
25
94
5
3
15
33
69
49
367
40
48
14
29
59

N
26
25
19
18
23
23
18
27
5
8
25
25
17
26
27
23
24
26
4
11
10
23
27
17
25
15
26
26
23
25
22

A
41
50
66
11
53
50
65
21
1
87
55
25
10
34
31
17
57
43
1
79
83
56
34
9
20
11
27
23
58
41
14

OUTPUT
Contigency Table
12
0
10
9

Sim
A
A
A
TA
A
A
A
A
TA
A
A
A
TA
A
A
TA
A
A
TA
A
A
A
A
TA
TA
TA
A
A
A
A
TA

Event

B

Not Event TB

10

OUTPUT
Contigency Table
11
0
17
3

AA A-TA TA-A TA-TA

BB B-TB TB-B TB-TB

HR

HR

1.00 FAR

CAR 0.55
Obs
A
TA
A
TA
A
A
A
TA
TA
A
TA
TA
TA
A
TA
TA
A
A
TA
A
A
A
TA
TA
TA
TA
TA
TA
TA
TA
TA

BAWAH NORMAL (BN)
Threshold Value

20

AA
1
0
1
0
1
1
1
0
0
1
0
0
0
1
0
0
1
1
0
1
1
1
0
0
0
0
0
0
0
0
0

A-TA
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

0.53

MR

0

TA-A
0
1
0
0
0
0
0
1
0
0
1
1
0
0
1
0
0
0
0
0
0
0
1
0
0
0
1
1
1
1
0

TA-TA
0
0
0
1
0
0
0
0
1
0
0
0
1
0
0
1
0
0
1
0
0
0
0
1
1
1
0
0
0
0
1

1.00

CAR 0.39
Obs
TB
TB
TB
B
TB
TB
TB
TB
B
TB
TB
B
TB
TB
TB
B
TB
TB
B
TB
TB
TB
TB
B
TB
B
B
B
TB
B
B

Sim
B
B
B
B
B
B
B
B
B
TB
B
B
B
B
B
B
B
B
B
TB
TB
B
B
B
B
B
B
B
B
B
B

BB
0
0
0
1
0
0
0
0
1
0
0
1
0
0
0
1
0
0
1
0
0
0
0
1
0
1
1
1
0
1
1

B-TB
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

FAR

0.85

MR

0

TB-B
1
1
1
0
1
1
1
1
0
0
1
0
1
1
1
0
1
1
0
0
0
1
1
0
1
0
0
0
1
0
0

TB-TB
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
1
0
0
0
0
0
0
0
0
0
0