Analysis of Climate and Population Dynamics of White Stem Borer Scirpopagha innotata (Walker) in Sumbawa Regency

ANALYSIS OF CLIMATE AND POPULATION DYNAMICS OF
WHITE STEM BORER SCIRPOPHAGA INNOTATA (WALKER)
IN SUMBAWA REGENCY

HIJJAZ SUTRIADI

DEPARTMENT OF GEOPHYSICS AND METEOROLOGY
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014

STATEMENT OF SOURCES AND COPY RIGHT TRASNFER
AGREEMENT
Hereby I state that this scientific writing titled Analysis of Climate and
Population Dynamics of White Stem Borer Scirpopagha innotata (Walker) in
Sumbawa Regency is truly my own work under guidance by the due supervisor
and never been published in any higher education institution. The sources
originating from published and unpublished works are appropriately cited and
listed in References.
I hereby transfer the copyright of this scientific writing to Bogor

Agricultural University.
Bogor, July 2014
Hijjaz Sutriadi
NIM G24090004

ABSTRACT
HIJJAZ SUTRIADI. Analysis of Climate and Population Dynamics of White
Stem Borer Scirpopagha innotata (Walker) in Sumbawa Regency. Supervised by
YONNY KOESMARYONO.
Preliminary selection is conducted in order to choose which pest that will
be presented in this research. Four consecutive years from 2009 to 2012 is
selected as time range to measure rice harvest losses due to infectious pest
outbreak. The white stem borer (Scirpophaga innotata) has been one of major
pests that cause farmer worries over years in Sumbawa Regency. The climate
dynamics as hypothesized one of important factors to drive pest development is
the underlying to be concerned. Australia CSIRO’s DYMEX agroclimatic
software is employed in constructing a pest dynamics model provided with
biological cycle (egg, larva, pupa and adult) with approach using quantitative
climate parameter. Therefore, the output will be subject for analysis of white stem
borer dynamic population in the regency from 2009 to September 2012. The result

shows that fluctuating involved climate parameters give influence on white stem
borer development at every stage. Meanwhile heat and cold stress, humid stress,
and washing, simultaneously act as limiting factor for each life stage
development. Larva stage as deemed the most perilous stage causing rice plant
damage, lives within considerably suitable range of temperature (23.8ºC –
31.6ºC), relative humidity (65% - 77%), rainfall (0 mm – 30 mm), thus Sumbawa
climate circumstance comforts the pest development. Meanwhile, most
intensifying larva infestation occurs on February 2010, September 2011, and
September 2012 respectively.
Keywords: Scirpophaga innonata, DYMEX, climate dynamics, Sumbawa
Regency

ANALYSIS OF CLIMATE AND POPULATION DYNAMICS OF
WHITE STEM BORER SCIRPOPHAGA INNOTATA (WALKER)
IN SUMBAWA REGENCY

HIJJAZ SUTRIADI

Undergraduate Thesis
as one of requirements to obtain Bachelor of Science degree

from the Department of Geophysics and Meteorology

DEPARTMENT OF GEOPHYSICS AND METEOROLOGY
FACULTY OF MATHEMATICS AND NATURAL SCIENCES
BOGOR AGRICULTURAL UNIVERSITY
BOGOR
2014

Title
Name
ID

: Analysis of Climate and Population Dynamics of White Stem Borer
Scirpopagha innotata (Walker) in Sumbawa Regency
: Hijjaz Sutriadi
: G24090004

Approved by

Prof. Yonny Koesmaryono

Research Supervisor

Approved by

Dr. Tania June
Head of Department of Geophysics and Meteorology

Date of accomplishment:

PREFACE
This research presents climate impact toward population dynamics of selected
pest in Sumbawa Regency. It shows that how living organism and intangible
atmospheric condition in particular is closely related. I am very delighted as this
work has come to end yet it does not fully mean ceasing my curiosity about this
study. Thus, possibility of conducting advanced research in the future is still
present.
This accomplishment has brought me to another series of my timeline, at the
pausing point of study period after my undergraduate education with ample
guidance walking along. Thus I would like to thank those who have contributed to
my research and study by assisting me a lot. For the foremost I name my parents

at the peak of my gratitude, joined by my study and research supervisor, Prof.
Yonny Koesmaryono, lecturers in the Department of Geophysics and
Meteorology, and lecturers in IPB who have guided my path. I also thank to
related institution in my hometown, Sumbawa Regency, namely BAPPEDA
(Bureau of Regional Planning and Development), BPS (Central Bureau of
Statistics), and Department of Agriculture for providing me the data. I surely do
not forget to mention the family members and beloved friends for always
supporting me and for helping me to get rid out of boredom.
Bogor, July 2014
Hijjaz Sutriadi

CONTENTS

ABSTRACT ............................................................................................................ ii
PREFACE................................................................................................................ v
CONTENTS ........................................................................................................... vi
TABLE ................................................................................................................. viii
FIGURES ............................................................................................................. viii
LIST OF APPENDIX ............................................................................................. ix
INTRODUCTION ................................................................................................... 1

Background .......................................................................................................... 1
Objective .............................................................................................................. 1
LITERATURE REVIEW ........................................................................................ 2
White Stem Borer Ecological Distribution .......................................................... 2
Morphology and Lifecycle................................................................................... 2
Egg ................................................................................................................... 2
Larva................................................................................................................. 3
Pupa .................................................................................................................. 3
Adult (Imago) ................................................................................................... 3
DYMEX™ Software ........................................................................................... 4
DYMEX™ Builder and Simulator ................................................................... 4
METHOD ................................................................................................................ 5
Time and Places ................................................................................................... 5
Tools .................................................................................................................... 5
Materials .............................................................................................................. 5
Procedure ............................................................................................................. 5
Model Building ................................................................................................ 5
Model Simulating ............................................................................................. 8
RESULTS AND DISCUSSION ............................................................................ 11
Topography and Climate Condition of Sumbawa Regency .............................. 11

Model Limitation and Considered Assumption ................................................. 13
White Stem Borer Life Stage Development and Mortality ............................... 14
Life Stage Population Production .................................................................. 15
Life Stage Population Mortality ..................................................................... 18
Effects of Climate Parameters on Life Stage Production .................................. 19

Temperature ................................................................................................... 19
Relative Humidity .......................................................................................... 21
Rainfall........................................................................................................... 23
Larva Population Distribution on Climate Parameters ...................................... 25
CONCLUSION AND SUGGESTION ................................................................. 27
Conclusion ......................................................................................................... 27
Suggestion ......................................................................................................... 27
REFERENCES...................................................................................................... 27
APPENDICES ...................................................................................................... 30
CURRICULUM VITAE ....................................................................................... 33

TABLE
Table 1

Table 2
Table 3
Table 4

Slope of development, developmental zero and maximum
temperature of Scirpopagha innotata (white stem borer) in
laboratory condition .......................................................................... 7
Functions and variables of production and mortality ........................ 8
Stage initialization and development trigger ..................................... 9
Quartile, median, mode and mean of climate parameter and
larva population ............................................................................... 26

FIGURES
Figure 1 White stem borer lifecycle ................................................................ 3
Figure 2 Life cycle model as appears on DYMEX™ Builder 3.0 .................. 4
Figure 3 Model flowchart .............................................................................. 10
Figure 4 Thematic map of Sumbawa Regency after image processing
acquired from LANDSAT 7 ETM+ ................................................ 11
Figure 5 Climate circumstance in Sumbawa Regency from January
2009 – September 2012. Panel 1: rainfall; panel 2: maximum

temperature (—) and minimum temperature (—); panel 3:
maximum relative humidity (—) and minimum relative
humidity (—)................................................................................... 12
Figure 6 Pests and diseases affecting rice field in Sumbawa Regency .......... 14
Figure 7 White stem borer productions at various stages from January
2009 – September 2012 in Sumbawa Regency. Panel 1: egg
production; panel 2: larva production; panel 3: pupa
production; panel 4: adult (imago) production ............................... 16
Figure 8 White stem borer mortalities at various stages from 2009 –
September 2012 in Sumbawa Regency. Panel 1: stage
mortality at phase I; panel 2: stage mortality at phase II; panel
3: stage mortality at phase III .......................................................... 18
Figure 9 Temperature trend on each life stage of white stem borer
production pattern ........................................................................... 20
Figure 10 Relative humidity trends on each life stage of white stem
borer production pattern .................................................................. 22
Figure 11 Rainfall impact on soil moisture index ........................................... 23
Figure 12 Rainfall occurrences on each life stage of white stem borer
production pattern ........................................................................... 24
Figure 13 Climate parameter and larva population distribution ...................... 25

LIST OF APPENDIX
Appendix 1
Appendix 2

Pest and disease that infected rice field area in Sumbawa
Regency from 2009-2012
Average monthly minimum temperature, maximum
temperature, maximum relative humidity, minimum
relative humidity, and monthly rainfall in Sumbawa
during 2009-2012

31

31

INTRODUCTION
Background
Preliminary selection is conducted in order to define which pest that be
presented in this research. Four consecutive years of observation started from

2009 to 2012 conducted by Department of Agriculture in Sumbawa has been done
and later has decided to select white stem borer (Scirpophaga innotata) the pest to
be discussed. This finding is based on severity quantity of affected rice field area
which was collected from the level of sub-district. The white stem borer is the
second most perilous pest after rice bug that causes notable harvesting loss. Basic
statistic calculation in order to measure total loss is exercised. It results that white
stem borer excels other infectious pests (caseworm, golden apple snail, armyworm
and rat) and plant diseases (xoo, tungro and blast).
Sumbawa Regency chosen as study case area lies on west of Sumbawa
island in West Nusa Tenggara Province with half of population at working age is
farmer, particularly to cultivate rice field. Affected area due to white stem borer in
2011 as released by Department of Agriculture in Sumbawa is 361.5 ha which
accounts 0.45% out of 79,270 ha cumulative successful harvesting area (BPS
2012). Failure on rice harvesting due to pests and diseases has paid more serious
attention since agricultural activity chiefly contributes to the society income.
The presence of white stem borer having been one of major causes of
farmer worries for years unfortunately still exists. This research attempts to
intertwine climate dynamics and white stem borer (Scirpophaga innonata)
proliferation. The insect development would be important concern as it deals with
climate circumstance. This lead has profoundly suggested that conducting deep
studies under field of agricultural meteorology is fairly necessary. In this research,
Australia CSIRO’s DYMEX is used in order to help figuring out the relationship
between climate circumstance and the selected pest proliferation in timely basis.

Objective
This research aims to analyze population dynamics of white stem borer
Scirpophaga innotata (Walker) in relation with climate circumstance in Sumbawa
Regency.

2

LITERATURE REVIEW
White Stem Borer Ecological Distribution
The presence of pest in ecological life is inevitable although it causes
damage to certain crops. Its existence seems more likely very dynamics as it is
highly influenced by environmental circumstance. In South East Asia where rice
is predominantly most planted, losses in harvest due to pest proliferation still
prevails. This damage is triggered by suitable environment of the region that
comforts pest development.
The white stem borer Scirpopagha innotata (Walker) has been long
standing known as one of major rice pests. Geographically it is mostly distributed
in Indonesia, Philippines, Malaysia and Australia (IRRI 2009). The extent of
white stem borer distribution covers countries such as Papua New Guinea,
Pakistan, Taiwan, Thailand and Vietnam. These countries range between belt of
tropic of capricorn and tropic of cancer. Although the most severe impact occurs
in Indonesia and Northern Territory of Australia (Li 1991)
The damage due to white stem borer has been recorded in previous conducts.
In Indonesia, it is found that white stem borer attacks rice in major irrigated rice
field area in some districts in West Java such as Bekasi, Karawang, Subang,
Indramayu and some parts of Cirebon (Rubia et al 1997). The stem borer has also
been found out that exists in India causing losses in grain production
(Muralidharan and Pasalu 2006). In Sumbawa, within four years, from 2009 to
2012 the presence of white stem borer has been recorded that accounts rice
harvest failure area of 1063.3 ha. However, other factor that triggers its
development is still under study.

Morphology and Lifecycle
White stem borer, locally known as sundep is typically endemic species
that survives to live in range between coastal area and interior of the island at 200
m above sea level with rainfall less than 200 mm (Kartasapoetra 1993). This
species was first time introduced by Walker as it was found in Sarawak, East
Malaysia. The lifecycle itself is mainly divided into four stages, such as egg, larva,
pupa and lastly adult. Under tropical climate circumstance, it could be more
possible for white stem borer to live all year long, ignoring the combating effort
intervened by human and other extreme major factor.
Egg
Female white stem borer is able to lay down egg as many as 100 to 600
placed beneath tip of rice leaf. The egg is grouped into cohort consisted of 50 to
150 eggs for each cohort. The egg size is 0.6 x 0.5 mm, resembling oval form and
is covered by tuft of hair (cilia) with yellowish brown color. Egg itself lasts for
nine days before undergoing transformation into larva (IRRI 1986). Experiment
conducted by Rachman and Khalequzzaman (2004) reveals that egg stage of white
stem borer is able to live within the interval of temperature of 11.3ºC and 35ºC.
However, under certain circumstance it is possible the egg to survive even at 40ºC.

3
Larva
Larva period of white stem borer is started when egg has hatched. Larva
hangs on cilia that previously covers egg. The wind factor can drive the
distribution of larva to reach bud of other rice plant. Later it lurks into deeper stem
then starts to damage the midrib. It takes around 22 days for larva to accomplish
its period, including diapauses. Morphologically larva of white stem borer has
length of 21 mm with yellowish white color (IRRI 1986). Larva can survive at
minimum temperature of 8.7ºC (Rachman and Khalequzzaman 2004).
The stage of larva is believed the most crucial metamorphosis. Rubia et al
(1998) studied the factor based on analysis of cultivar response on development
stage of the white stem borer. It has found that the larva stage predominantly
contributes the most severe cause to injure rice plant development.
Pupa
Pupa takes place in white cocoon in the lower internode of the plant (Li
1991). Pupa stage takes around 11 days to accomplish the metamorphosis before
it transforms into imago or adult white stem borer (IRRI 1986). During this cycle,
temperature of 5.9ºC is considered zero development in which at given point the
development ceases to evolve (Rachman and Khalequzzaman 2004). Pupa is
mostly found around lower stem.
Adult (Imago)
The adults are nocturnal, phototrophic and strong fliers. The mean
fecundity is 142 or 160 eggs with sex ratio 1.0 males/2.3 females (van der Goot
1925 in Li 1991). Adult happens to lay down eggs in the night at 19.00 – 22.00 in
cohorts. It flies as long as 6 – 10 km in normal condition and can reach further
miles away whenever it is driven by wind force. Other factors that also contribute
to imago migration are such as sunlight, rainfall and temperature (IRRI 1986).
Adult is still able to survive at minimum temperature of 10ºC (Rachman and
Khalequzzaman 2004) and lives lasting for 9 days (IRRI 1986).

Figure 1

White stem borer lifecycle

4

DYMEX™ Software
DYMEX™ is invented and developed by Hearne Scientific Software that
consists of two main parts, Builder and Simulator (Maywald et al 2007). DYMEX
is platform based software that enables developer to build their own model. The
use of DYMEX has increasingly become popular among scientists who focus on
cohort based model. It deals with plant and insect development in association with
climate circumstance in order to figure out the dynamics and forecasting living
organism growth. DYMEX Builder is platform where user can design the
parameter to be involved in model. Meanwhile DYMEX Simulator acts to run the
model and is as well to display the results either in graph or in table format.
DYMEX™ Builder and Simulator
Model building is a basic conduct in constructing the model by designing
components to be involved. DYMEX Builder comprises of components that deal
with climate parameter items stored in the library known as MetBase. Basically
there are three components of conducts when ones use the DYMEX Builder, such
as module, function and process (Maywald, Kriticos, Sutherst and Bottomley
2007).
Module in builder is presumed as component that provides interconnected
input. This input enable user to select desired parameter. The selection of input
can vary from one modeler to the other, depending on purpose and level of
importance. Function in model is an important factor which is responsible to the
quantity of output. The function in this discuss is somewhat from mathematical
derive applied to the model itself. Meanwhile process in builder is an order of
given modules in which modeler arrange the formation of conduct.

Figure 2

Life cycle model as appears on DYMEX™ Builder 3.0

5

METHOD
Time and Places
This research is conducted in two separated phases. The first phase is data
collecting which was done from January to February 2013 in Sumbawa Regency.
The second phase is model building and simulating and data analyzing at the
Department of Geophysics and Meteorology, IPB in Bogor.

Tools
The main tools utilized in this research are CSIRO DYMEX™ Builder and
Simulator version 3.0 and Microsoft Excel 2010 software.

Materials
In this research, materials presented are those data which were collected
from various sources. In time order, data of pest that infected paddy field area was
the first time acquired which was provided by Department of Agriculture,
Sumbawa. Later administrative data (demography and agriculture) of Sumbawa
Regency was collected from Central Bureau of Statistics (BPS) and Bureau of
Regional Planning and Development (BAPPEDA). Meanwhile climate data of the
region was acquired from Tutiempo.net, a worldwide website climate database
that provides exchange data similar to NCDC-NOAA-USA originating from
World Meteorology Organization (WMO) in which BMKG (Bureau of
Meteorology, Climatology and Geophysics) is one of the members. Climate data
from Sumbawa station is recorded at station number 972600 (WRRS) with
latitude -8.43 (South) and longitude 117.41 (East). The station elevation is 3 m
above sea level.

Procedure
Model Building
In this research, there are nine modules employed such as Timer,
Meteorological Data, Latitude, Daylength, Evaporation, Soil Moisture, Average
Daily Temperature, Daily Temperature Cycle, Species (White Stem Borer).
Timer
Setting the timer is the first step in working with DYMEX. The length of
time indicates period of model run. Timer can be set in daily, weekly, monthly
and yearly basis depending on purpose of use by modeler themselves. However,
in this model the time step chosen is daily basis which includes one additional day
within the leap year 2012. For output, there are three outputs used in this model
such as Day Since Start, Day of Year, and Simulation Date.

6
The time set for this model is January 2009 as the starting day and
September 2012 as the last running time. The selected length of time indicates
period of white stem borer analysis in the region.
Meteorological Data
There are five types of the most significant meteorological parameter
considered in this model, including minimum temperature (Tmin), maximum
temperature (Tmax), rainfall (R), 9am relative humidity (RHmax) and 3pm relative
humidity (RHmin). The use of these parameters is common as previous CSIRO’s
CLIMEX recommends the similar item. Barney and DiTomoso (2010) in
bioclimatic prediction exercised the model by emphasizing the use of temperature
and moisture. Similar conduct is also done by Yonow, Hattingh and de Villiers
(2013) in modeling plant disease distribution by climatic factor. Simulation date is
chosen as input that derives from timer module.
Latitude
The latitude module is involved as it deals with climatic zone. The
geographical position can consider living factor. The latitude is set based on
Sumbawa weather station proximity with code 972600 (WRRS) and situating at
8°43 south. It indicates that Sumbawa is located within tropical layer that regards
to almost constant day length in whole year.
Day Length
As module is designed interconnected, the day length module is thus
derived from preceding modules. The latitude module and day of year from timer
module are chosen as input, which is part of climate zoning. Day length is timely
parameter measured in hour and calculated from sunrise to sunset.
Evaporation
Evaporation module is actually thus formed based on Class A Pan
Evaporation measurement using Fitzpatrick method which emphasizes the use
data of maximum mean temperature and vapor pressure (Fitzpatrick 1963). The
use of this method is more considerable since the evidence of practice in the case
of northern Australia as recommended by Fitzpatrick is effectively for estimating
evaporation, in which the climate condition is more similar to that of Sumbawa. In
this model, input for evaporation is derived from Tmax, Tmin, RHmax, RHmin, and
day length module.
Soil Moisture
Soil moisture, as its role upon plant living factor, is considerably relevant
to affect white stem borer development in which life stages presence highly
depends on plant livelihood. Soil moisture module is composed of evaporation
module and rainfall with additional limit factors of soil moisture capacity,
evapotranspiration coefficient and basal evaporation.
Average Daily Temperature
Average daily temperature module is simply derived from average value
of maximum temperature (Tmax) and minimum temperature (Tmin).

7

Daily Temperature Cycle
This module consists of three inputs such as Tmin, Tmax, and day length
module. Daily temperature cycle, on the aftermath, will chiefly contribute to each
life stage development as it acts as temperature limit. This module is associated
with daily cycle of selected pest which turns out its output itself.
Lifecycle
Lifecycle module is chained stage of selected species. Lifecycle module is
designed to record history of species development with adjustable features. Life
cycle module in this model is one designed to consist of four prominent parts
which turn out the pest life stages themselves such as egg, larva, pupa and imago
(adult) respectively. To construct life stage model, it is necessary to define the
variables and to give quantitative value to those chosen variables based on
experimental practice. The following table contains variables used in the model.
Table 1 Slope of development, developmental zero and maximum temperature of
Scirpopagha innotata (white stem borer) in laboratory condition
Life
Stage
Egg
Larva
Pupa
Adult
Note:

Slope
(DR)
0.04792
0.01446
0.06333
0.0542

*Degree Day
(DD)
77.83
434.15
143.36
92.25

*Developmental
Zero (K) (ºC)
11.27
8.72
5.92
10

*Topt
(ºC)
15
15
15
15

*Tmax (ºC)
35
35
35
35

*Degree day, *developmental zero, *T opt and *Tmax are adopted from Rahman and
Khalequzzaman (2004)

The slope in the table is actually rate of development (DR), closely linked
to degree-day (DD) which turns out energy required by pest at every stage to
develop. In order to calculate the slope, an equation proposed by Nahrung et al
(2004) and Vojtěch et al (2004) is employed as follows

where DR is slope of linear regression or known as b in, y = bx + a, Topt is
optimum temperature for each life stage development, K is developmental zero
and is known as constant thermal that refers to minimum temperature at which a
given developmental process would cease off. The temperature either maximum
or minimum acts as threshold that limits growth of white stem borer at every

8
stage. It is highly influential in forming the rate curve that involves in
physiological age (Taylor 1981).
Furthermore, each stage of white stem borer in DYMEX Builder
construction is featured with some adjustable components that build up life stage
sub-model. The features are presented in detail based upon life stage order as in
the following table.
Table 2

Functions and variables of production and mortality
Production

Life stage
 Egg
 Larva
 Pupa

Function
2-segment
linear

Mortality
Independent
Variable
Daily
average
temperature

Function
Linear
above
threshold

Combination
Rule
Complement
product

Transfer
Step function

Fecundity
Production
n/a

R=1-(1-r1) x
(1-r2) …
Egg → Larva
Larva → Pupa

Linear
below
threshold

 Adult

2-segment
linear

Daily
average
temperature

Linear
above
threshold

Pupa → Adult

Complement
product

n/a

Linear above
threshold

R=1-(1-r1) x
(1-r2) …

Linear
below
threshold

Linear below
threshold

Combination
rule : Product
R=r1 x r2 …

Model Simulating
Data Preparing
Working with DYMEX requires adjustment in data preparation in order to
fit the placement. The data used in this model is prepared in *.csv format which
consists of climate parameters such as date, minimum temperature (Tmin),
maximum temperature (Tmax), precipitation, maximum relative humidity (RHmax)

9
and minimum relative humidity (RHmin). The executed data starts from 1 January
2009 until 30 September 2013.
Model Running
Before running the model, Latitude component should be set -8.43 based
on reference of Sumbawa weather station coordinate. The negative figure
indicates location of station lying on southern hemisphere. Number of initial
population is also necessary to be given in advance. For this purpose, every stage
is valued with initial population and is run based on farmer’s planting time
decision.
Table 3

Stage initialization and development trigger

Phase

Life stage

Phase I

Egg
Larva
Pupa
Adult
Egg
Larva
Pupa
Adult
Egg
Larva
Pupa
Adult

Phase II

Phase III

Note :

Number
3000
2500
2000
1500
3000
2500
2000
1500
3000
2500
2000
1500

Initialization
Date
11/12/2009
20/12/2009
11/01/2010
22/01/2010
11/10/2010
20/10/2010
11/11/2010
22/11/2010
11/12/2011
20/12/2011
11/01/2012
22/01/2012

Repeat*

Interval (Days)

20
20
20
20
14
14
14
14
6
6
6
6

9
22
11
9
9
22
11
9
9
22
11
9

*

Repeat is calculated from division of total of days since tillering until the last day of
simulation date to white stem borer lifetime (51 days)

When the program is ready, the next step is to acquire climate data through
Meteorological Data component. It is also important to bear in mind that Timer
component is as well needed to be adjusted, the same timing as for meteorological
data. DYMEX Simulator requires user to fetch attachment from third party source.
The source itself is meteorological data in ―.csv” format.
Result Interpreting
Once model run successfully done, the results appear either in table or in
chart format. The results themselves are mainly number of population and
mortality (egg, larva, pupa and adult). In addition, data of day length, evaporation,
moisture, and duration is also available to be acquired. The output of the model is
later partly refigured in graph in accordance to which information is needed to be
elaborated. Some of output is layered with another output in order to figure out
correlated influence among them, such as egg, larva, pupa and adult layered on
climate parameters such as temperature, relative humidity and rainfall. The results
entirely will be somewhat discussed in the given pages.

10

Figure 3

Model flowchart

11

RESULTS AND DISCUSSION
Topography and Climate Condition of Sumbawa Regency
Sumbawa Regency covering most western part of Sumbawa island lies on
south-central Indonesia. It is administratively part of West Nusa Tenggara
province, Indonesia with monsoonal rain type (Bayong 1999). In Köppen climate
classification, Sumbawa is classified Aw type along with northern coast of
Australia (Peel, Finlayson and McMahon 2007) with a pronounced dry season in
which driest month having precipitation less than 60 mm and also less than one
per twenty-fifth the total annual precipitation (McKnight and Hess 2000).
Sumbawa Regency situated between 116º42 E - 118º22 E and 8º8 S - 9º7 S with
elevation ranging from 0 m to 1730 m above sea level (BPS 2012). This study
area is typically mountainous with less plateau seizure all out of total coverage.

Figure 4

Thematic map of Sumbawa Regency after image processing
acquired from LANDSAT 7 ETM+

During period of January 2009 – September 2012, there has been recorded
that the lowest temperature event occurred at 12.1ºC on 8 August 2012,
meanwhile the highest temperature recorded at 37.4ºC on 8 November 2009. For
daily rainfall record, it only once occurred to reach 118 mm on 11 December
2010. Even though Sumbawa is very well known standing for its dry condition,
record of highest relative humidity was once achieved at 97% on 3 March 2012.

12

Figure 5

Climate circumstance in Sumbawa Regency from January 2009 –
September 2012. Panel 1: rainfall; panel 2: maximum temperature
(—) and minimum temperature (—); panel 3: maximum relative
humidity (—) and minimum relative humidity (—)

Figure 5 shows condition of climate dynamics in Sumbawa Regency
during modeling period that ranges from January 2009 until September 2012.
Sumbawa is classified in monsoonal rainfall type, the graph shows that peak of
rainfall emerges from end of previous year to beginning of upcoming year.
Meanwhile in the mid of year during running period, there is almost no significant
rainfall recorded.
Temperature, which basically is very dynamic at every hour, was recorded
to create significant extreme between high record and low record in daily basis.
Average minimum temperature calculated is 22.6ºC. Meanwhile average
maximum temperature is 32.3ºC. From this data we can obtain information that in
Sumbawa averagely there is around interval ±10ºC between day temperature and
night temperature.
The dynamics of relative humidity or water content in the air in Sumbawa
during 2009 – 2012 is clearly present. Recorded high RH mostly found during
end-early of year that follows the trend of rainfall. During mid of year, the trend
tends to bowling that reaches average low record. This condition is highly
influenced by Indo-Australian Monsoon that triggers the migration of vapor mass
in the air massively. End to early of year period is the time when maritime
continent monsoon flows down southeastern passing through Sumbawa along
with enriched mass of vapor, resulting increasing RH record and as well triggering
rain downpour. Otherwise during mid of year, Australian monsoon reverts to flow
westerly with very least content of vapor after passing by Great Victoria dessert of
Australia. It causes dry season over Sumbawa with fairly low RH percentage
recorded.

13

Model Limitation and Considered Assumption
Constructing a model in this research is no exception from limitation that
may hinder possible influential factors other than ones included. The selected
parameters involved in building up the model, however, have been considered
carefully under suggestion and guideline from some sources. In Figure 3, there
reveals involving parameters, processes, and linkages which are those entitled to a
nexus. From the top of network, timer module consisting of simulation date and
day of year along with climate parameters comprising of temperature, relative
humidity and rainfall, are two of most important basis that construct the model in
first place. Day length is later considered the second level of module as it is
derived from day of year of timer module and latitude input from independent
parameter. Day length as mostly known limiting factor for plant growth represents
duration of sun light exposure on surface which is closely related to thermal
energy. Length of day varies in each place at different latitude. Sumbawa, a region
lies at 8⁰43 S situated within tropical zone, in which mostly characterized by
constant day length of averagely 12 hours for whole year.
The third level of model sequence as appears in Figure 3 is set for
evaporation and daily average temperature module. Both of these modules are
derived from day length module. Evaporation, however, is not solely influenced
by day length. In addition, its entity consists of climate parameters such as
temperature and relative humidity based on class A Pan Evaporation from
Fitzpatrick method. Meanwhile daily average temperature module is constructed
from temperature parameter only.
The fourth level is filled by soil moisture module. It is set from derivation
of evaporation module and is as well additionally constructed by independent
factors outside inter-dependent thread in the nexus (Figure 3), of which such as
soil moisture capacity, evapotranspiration (ET) coefficient and basal evaporation.
From above level and its associated modules, all of them act as limiting
factor for rice plant growth and thus subsequently affect white stem borer
development. Separately, the white stem borer development is directly limited by
timely dimension and climate parameters such as temperature, relative humidity
and rainfall. Furthermore, influential factors that limit each life stage of white
stem borer is distinct among them.
Despite of varying limiting factor at every stage, these two independent
variables are generally applicable to any stage, of those are complement product
function (R=1-(1-r1) x (1-r2) …) responsible for mortality rate, meanwhile
development rate is derived from 2-segment linear function and daily average
temperature module. The development rate itself is in the meantime limited by
value of temperature threshold that follows slope of 2-segment linear function. At
the stage of adult (imago), it involves additional variables, progeny and fecundity.
Progeny production, in particular, is limited by temperature and rainfall. The
production itself is constructed based on product function (R=r1 x r2) that
multiplies its cohort under suitable condition, living within range of suitable
temperature and under certain rainfall intensity and quantity.
In this model, rice planting time is necessarily determined as rice plant
hosts white stem borer livelihood. Planting time is decided based on farmers’

14
decision that mostly start to seed in the beginning of distinct rainfall occurrence of
wet month. It is assumed that between January 2009 and September 2012 there
are three planting times, such as November 2009, September 2010, and November
2011 respectively. This consideration is also supported by the fact that in
Sumbawa rice farming most likely depends on rainfall rather than irrigation. Thus,
rice farming activity most possibly merely occurs once a year in the regency.
The emergence of every white stem borer life stage in this model is
importantly considered. Thus, initialization time is given in order to meet
condition of parasitism symbiosis as conceived between rice plant and pest. Time
of initialization is started after 40 days of seeding, a moment when leaves emerge
during vegetative phase. This condition suits egg development in which grows
under leaf tip of rice plant. Initialization time applies to every farming phase as
shows in Table 3.
This white stem borer model built using DYMEX generally applies to
abide by condition mentioned above. It is conditionally in a state of determined
control by specific parameters, life flow and chain, numeral value based on
experiment and spatial boundary. Sumbawa as chosen study area, any related
information and sources from that place are considerably involved. Some of it for
instance farmer’s planting time decision, climate data, rice harvesting losses,
spatial map, and other related quantitative data are those appropriately in the
model. Further qualitative information that relates to model output will be thus
somewhat additionally for analysis.

White Stem Borer Life Stage Development and Mortality
The presence of white stem borer in Sumbawa Regency has been identified
as proved by recorded infectious area in the region. From January 2009 –
September 2012, based on time order, it is observed that 147.8 ha in 2010; 272 ha
in 2010; 361.5 ha in 2011; 282 ha in 2012 (January – September) are considerably
attacked by white stem borer proliferation. However, the real amount of
population remains no clue since there was no species basis measurement on field.
Therefore, a model is constructed with DYMEX that is expected to become
possible to interpret the real condition through its output.

2000
1500

Figure 6

rat

armyworm

golden apple snail

2010
caseworm

0

blast

2011
xoo

500
tungro

2012

rice bug

1000

white stem borer

Infected Area (Ha)

2500

2009

Pests and diseases affecting rice field in Sumbawa Regency

15
The cause of harvest losses in Sumbawa Regency is not solely generated
by the presence of white stem borer alone. In fact, other pests and plant diseases
are accordingly alleged to damage rice plant over the regency. Total infectious
area as of 2009 is 724.8 ha. In the upcoming year of 2010, the figure significantly
increases up to 4,225 ha. The main contributors to this notable increment are
mostly blast and rat. Meanwhile a year afterward, number of affected area
decreases to about a half that of previous year observation data which accounts
2,441.25 ha. The latest measurement ends up on September 2012 with recorded
accumulative infected area during the due time is observed 2,292.5 ha, a slight
decrement prevalence compared to preceding record. However, in this discuss it
will be narrowed to merely analyze the allegation of white stem borer as one of
major factors to damage the rice plant in Sumbawa Regency in association with
climate circumstance in the region.
Life Stage Population Production
Figure 7 is acquired from the model output that shows the dynamics of
white stem borer production at various stages. Three consecutive planting phases
during four-year running model are the time basis trigger that create dynamic
population graph. Each planting phase enables white stem borer to exist on
hosting plant for a limited period before or during succeeding phase takes place.
First phase of rice planting as conducted by farmer in Sumbawa occurs on
November 2009 when seeding activity is started. However, eggs are yet present at
the moment. Later, 40 days after seed germination leaves start to emerge. Thus, at
this first phase, eggs are present on 11 December 2009 as many 3,000 as first time
initialized in the model. Egg population still grows at almost constant rate in the
upcoming weeks since first initialization until it reaches its peak production on
June 2010. Afterward, declining production begins to incur at the same rate.
However, egg population in the first planting phase still remains its number even
the second phase of planting has been started. This trend, however, follows the
conceived function created in the model which is 2-linear function.

Egg Production

80000
60000
40000
20000

Larva Production

0
80000
60000
40000
20000
0

16

Pupa Production

6000
4000
2000

Adult Production

0
80000
60000
40000
20000

1/1/2009
15/2/2009
1/4/2009
16/5/2009
30/6/2009
14/8/2009
28/9/2009
12/11/2009
27/12/2009
10/2/2010
27/3/2010
11/5/2010
25/6/2010
9/8/2010
23/9/2010
7/11/2010
22/12/2010
5/2/2011
22/3/2011
6/5/2011
20/6/2011
4/8/2011
18/9/2011
2/11/2011
17/12/2011
31/1/2012
16/3/2012
30/4/2012
14/6/2012
29/7/2012
12/9/2012

0

Figure 7

White stem borer productions at various stages from January
2009 – September 2012 in Sumbawa Regency. Panel 1: egg
production; panel 2: larva production; panel 3: pupa
production; panel 4: adult (imago) production

Larvae begin to emerge after 9 days since first egg cohort seizing the leaf
tip of rice plant. Larva stage as of egg in this first phase grows constantly until it
reaches the same timing of egg maximum growth. Nevertheless, the rate of larva
itself is lower. The trend, however, is changing afterward in the middle of year.
Larva population tends to grow at faster rate until the end of 2010. Later, it
experiences declining pace, simultaneously when second phase of planting has
already been begun.
Transformation from larva to pupa in the first phase of planting takes place
for first time on 11 January 2010. However, the pupa population output as
produced by model is not appropriate enough. Thus, the concern now only focuses
on trend of its growth instead of quantitative measurement. The trend of pupa
population growth is relatively constant for upcoming months until 12 May 2010.
Though, two outlying records are found that occur on 7 March and 20 April 2010.
Larva stage during the first planting phase undergoes a short diapause for around a
month and half that lasts until 12 June 2010. Larvae as product of pupa
transformation later re-exist again breaking its diapause until September 2010 that
completes its presence during the first planting phase period.
Adult (imago) white stem borer as initialized in the model on 22 January
2010 in the first planting phase shows a pronounced trend. During this phase,
there are two peaks of population production that take place on 6 June and on 9
August 2010. Subsequently the graph lowers to reach lowest point during the first
planting phase, the time when second planting phase is about beginning.

17
As the model set to consist of three planting phases, the output as
interpreted on graph in Figure 7 is thus an overlaid production. This overlay is
selected from the highest value of those two phases so that the lowest value is
then eliminated or unnecessarily figured on graph.
The second planting phase is started on September 2010, two months
earlier than previous year. This is due to shifting wet month in which of such
natural sign farmer makes decision. In this second phase, the emergence of egg is
thus earlier. However, egg population of previous phase is still present. Such
prevalence is somehow provoking a question whether farming activity is still
taking place in other area simultaneously with the beginning of new planting
phase or eggs are living beneath infectious hays from the last planting.
During second phase of planting, egg population increases at constant rate
since initial emergence on 11 October 2010, 40 days after rice plant enters
vegetative phase. The egg production is later no change with constant number for
almost three months until 7 May 2011. Nevertheless, constant production upon its
quantity is somehow the highest record during second phase of planting. It
subsequently lowers to reach zero production on 18 September 2011. Egg stage
later undergoes termination moment for three months. New cohorts of egg emerge
as third planting time takes place. This last phase of three within four years
observation begins on November 2011. Egg production shows a similar pattern of
graph as its two preceding phases, constant rate of increment then followed by
constant production and lastly decreasing to reach zero growth. Even though, in
the third egg emergence, number of production is the lowest among records.
Larva population in the second planting phase is a transformation from
egg stage of the same phase that emerges on 20 October 2010. It reaches its peak
when the date is 18 September 2011. Later the trend line declines until it meets
cross point where emerging production of larva from the third planting phase
inclines up which occurs on 23 February 2012. The larva production of last
planting phase begins to emerge on 23 December 2011. The highest point of this
phase occurs on 5 September 2012.
Pupa production, as mentioned previously, does not show an appropriate
graph thus it is merely discussed from its trend. In the second planting phase, as
pupae are product of transformation form larvae, start to emerge as soon as it is
initialized on 11 September 2010. However, it shows that number of larva
production is not relevant to previous contributing life stage number. This
difference remains big notable lag. The reason may be caused by defect model
process that does not well translate the functions and variables into dynamic
quantitative output value. Nevertheless, the trend at least is the only one resource
that enables us interpreting its dynamic process. As an overlying production
incurs on previous life stages between two phases, the same prevalence applies on
larva stage.
Adult production during the second and third planting phase has shown a
distinct pattern compared to that of any life stage. The production pace exhibits
more dynamic slope with varying uneven pattern. The record of highest
population production occurs on a period of 26 July 2011 to 14 August 2011.
Meanwhile the lowest record occurs during consecutive four-day from 1 to 4
September 2011. Therefore, this extreme is the most significant prevalence of any
trends during four years observation. The trend of adult population production is

18
inherently due to dynamic characteristic as shown by preceding life stages that
reasonably correlated. This linkage of life stage entirely completes a life cycle of
white stem borer.
Life Stage Population Mortality
In the model, it was previously set that mortality of each life stage of white
stem borer should be present. At given limited factors, the development is thus
restricted by considering white stem borer stress due to unfavorable weather
circumstance in daily basis. This condition, however, applies to any living
organism. Since white stem borer is a cold blooded organism, climate factor plays
a pivotal role in determining its livelihood (Kisimoto and Dyck 1974).

Figure 8

White stem borer mortalities at various stages from 2009 –
September 2012 in Sumbawa Regency. Panel 1: stage mortality at

19
phase I; panel 2: stage mortality at phase II; panel 3: stage
mortality at phase III
The three planting phases assumed in the model have resulted in three
different outputs as shows in Figure 8. The population mortality is therefore
concentrated on given tim