utilization of climate information for development...

Utilization of climate information for development of early warning system ... 17Indonesian Journal of Agriculture 3(1), 2010: 17-25

1)Article in bahasa Indonesia has been published in Jurnal Tanah danIklim No. 30, 2009, p. 47-60.

ABSTRACT

Pests and diseases are major limiting factors in crop productions.Brown plant hopper (BPH, Nilaparvata lugens) is a major rice pestin Asia since 1970’s. The existence of BPH is depending uponseveral conditions including the pest, the host plants, the physicalenvironment (rainfall, temperature, humidity), and the bioticenvironment (natural enemies, competitor organisms). BPHreproduces rapidly, laying a large number of eggs, and has shortlife cycle (28 days) with fast distribution and incredible attackedforces. The pest is very dynamic and allegedly associates withclimatic conditions of their habitat. It is expected that the climatecan be used as an indicator for an early warning system of BPHattacks as early efforts to control the pest, the area of BPH attacksespecially and in general for crop pest and disease control.Correlations between the pest attack and climate parameters(rainfall, mean temperature, maximum temperature, minimumtemperature, mean humidity, maximum humidity, and minimumhumidity) were analyzed using the multiple regression technique.An early warning system was developed using the MS Access, ArcView, Map Object, and Visual Basic softwares to obtain a dynamicand interactive system. The results showed that the climateparameters were positively correlated with the widespread attackof BPH during the La-Niña year. These climate parameters wererainfall, maximum temperature, maximum temperature at 2 weeksbefore attack, minimum temperature, and minimum temperatureat 2 weeks before the BPH attack. The early warning system isstarted by entering a climate prediction for the next rice croppingseason where the time is subject to prediction at a certain district.After inputting the climate data, the system will provide informationon the potential area of BPH attack. By the information of potentialarea of BPH attack, anticipatory action can be designed earlier sothat the harvest failure can be minimized.

[Keywords: Oryza sativa, Nilaparvata lugens, climate data, earlywarning system]

INTRODUCTION

Although the diversification of food continues to beencouraged, rice is still a strategic commodity that receives

handling priority in agricultural development. Severalattempts have been done to spur increased rice production,such as improvement of irrigation system, provision of abalanced fertilizer, and preparation of planting calendar.Nonetheless, many challenges are still facing, such as highincrease in population number, climate anomalies that oftenoccur such as drought and flood, plant pests and diseases,and reduction of agricultural land area due to landconversion.

Plant pests and diseases are major limiting factors forcrop production. The brown plant hopper (BPH,Nilaparvata lugens) is a major pest of rice crops in Asiasince the early of 1970s. The pest existence is very dynamic.Over a period of 40 years (1969-2008), explosion of BPHattacks occurred once in a while, while in other periodsthere were very few attacks. The BPH attacks were quitehigh in the periods of 1974-1978, 1998-1999, and 2005-2006.In some of the other years, there were BPH attacks but notas wide area as in the years mentioned. West Java was themost heavily affected area by the BPH, higher than otherprovinces in Indonesia. Based on the observation in the1982-1988 period, the total area of BPH attack in West Javareached 2474 ha, in the first two weeks of March 1988, thepeak attack was repeated again in 1998, in which the areaof BPH attacks in three regencies (Karawang, Subang, andIndramayu) reached 40,000 ha (Susanti 2008).

The BPH existence depends on the pest, the hosts, thephysical environment (rainfall, temperature, humidity), andthe biotic environment (natural enemies, competitororganisms). BPH reproduces rapidly and lays a largenumber of eggs. The pest has a short life cycle (28 days),a vast dispersive energy, and ferocious attack power. BPHhas to be controlled because it causes rice leaves turnyellowish orange before turning brown dead. Undercircumstances of high BPH population and susceptiblerice varieties are planted, a hopper burn is frequentlyoccurred. BPH also transmits grassy stunt and ragged stuntviruses that are very destructive to rice plants. Hence, thepresence of BPH could threaten the national food security.

UTILIZATION OF CLIMATE INFORMATION FOR DEVELOPMENTOF EARLY WARNING SYSTEM FOR BROWN PLANT HOPPER

ATTACK ON RICE1)

E. Susantia), F. Ramadhania), T. Juneb), and L.I. Amiena)

a)Indonesian Hydrology and Agroclimate Research InstituteJalan Tentara Pelajar No. 2, Bogor 16111, West Java, Phone: (0251) 8312760, Facs.: (0251) 8312760

Email: [email protected])Faculty of Mathematics and Natural Sciences, Bogor Agricultural University, Kampus IPB Dramaga, Bogor 16680, West Java

18 E. Susanti et al.

Distribution of BPH attacks was influenced by thepresence of susceptible rice varieties grown in the fieldand the rice plant growth stages (Natanegara and Sawada1992). The BPH attacks tend to become broader (Soetartoet al. 2001 in Las et al. 2003; Baehaki 2005) on the climaticanomaly conditions. The increase in pest and disease attackdue to the climate, especially El-Niño, is determined byseveral factors, such as difference in planting times in thesame location, as the El-Niño affects the growth of pestsand diseases, biophysical factors, particularly air humidityand temperature and humidity after the El-Niño period, andmore rain in the dry season. The BPH has what is called abiological clock, in which it can develop well in both wetand dry seasons, as long as there is a stimulant in the formof high rainfall. Climatic factors suspected to affect theinsect pests of rice put forward by Kisimoto and Dyck (1976)were temperature, relative humidity, precipitation, and wind.Climatic factors are important parameters and variables inthe plants, pests and diseases forecasting. The climateparameters are, therefore, used as indicators for an earlywarning system of BPH attacks. A research aimed to developan early warning system to predict the widespread of BPHattack by exploiting the climate information.

MATERIALS AND METHODS

Data collection was took place in Karawang, Subang, andIndramayu Regencies located on the northern coast ofWest Java. These regencies are the rice production centersbut also endemic areas for BPH. Data analysis andpreparation of the early warning system for extensive BPHattacks were done in the Indonesian Hydrology andAgroclimate Research Institute, Bogor, West Java.

Data used in this activity included: (1) bi-weekly dataon BPH attack areas in 1996-2008 from the Plant ProtectionInstitute for Food and Horticulture of the DirectorateGeneral of Food Crops, Ministry of Agriculture; (2) climaticdata and bi-weekly rainfall data in 1996-2006 from theAgency for Meteorology, Climatology and Geophysics(BMKG), and from automatic climate stations of theIndonesian Agency for Agricultural Research andDevelopment (IAARD); (3) digital maps, includingadministrative boundaries from BAPPENAS and maps ofwetland rice of the Indonesian Center for Agricultural LandResources Research and Development. The softwaresused were: (1) Microsoft (MS) Access 2003 as a databasesoftware; (2) Minitab as a software for tabular data analysis;(3) Arc GIS 9.2 and Arc View 2.3 as softwares for analyzingand processing of spatial data; (4) MS Visual Basic 6 andMap Objects 2.4 as softwares for applications of thegeographic information system and simulation model ofBPH attacks.

Correlation between Climate Parameters andBPH Attack

To view the correlation between BPH attack area andclimate parameters, i.e. rainfall, average air humidity,maximum air humidity, minimum air humidity, maximum airtemperature, minimum air temperature, and average airtemperature at the time of the incident, 2 weeks before, andone month before the incident, analysis of correlationcoefficient of Pearson was done using the followingformula:

– –Σ(xi - x)(yi- y)rxy = ——————

(n - 1)sxsy

where :rxy = Pearson’s coefficient of correlation between x and y

variablesx i = data set 1, climate parametersyi = data set 2, BPH attack areasx = average of data set 1y = average of data set 2sx = standard deviation of x variables y = standard deviation of y variable

Prediction Model for BPH Attack Area Based onClimate Parameters

To find out a predictive model for the BPH attack area, aregression analysis was done using the climate parametersas independent variables (Xp) and the BPH attack area asthe dependent variables (Y). In statistics, the linearregression in this study is the relationship between adependent variable Y (BPH attack area) and an independentvariable Xp (climate parameters, time/area, rice varieties),and a random term ε. The equation used for the regressionanalysis was as follows:

Y = β + β1X1 + β2X2 + ... + βp Xp + ε

where:Y = dependent variable (BPH attack area)X = independent variable (climate parameter)β = an intercept (a constant value)β1 = regression coefficient for independent variable X1

β2 = regression coefficient for independent variable X2

βp = regression coefficient for independent variable Xp

X2 = independent variable no. 2Xp = independent variable no. pp = number of parametersε = a random variable.

Utilization of climate information for development of early warning system ... 19

Steps in preparation of the information system on theBPH attack area were as follows: (1) inventory ofinformation needed by the users; (2) determination of theobjectives to be achieved; (3) selection of softwares andhardwares used to achieve the goal; (4) design anddevelopment of a database that stores actual climatic dataand predicted climatic data using the MS Access 2003; (5)modelling management (logical flow) based on the modelof relationship between the BPH attack area and that wasanalyzed using the Minitab; (6) interface dialog manage-ment to bridge the logical flow of data management; (7)preparing for spatial data, including administrative (district,regency, and provincial boundaries), wetland area, climate

stations, roads, and rivers using the Arc View and Arc GIS;(8) creating applications using the MS Visual Basic 6.0 toincorporate the flow of logic, tabular data management,model, and spatial data so that can be used easily by theuser; the component used to display and analyze spatialdata was the Map Object 2.4; (9) dividing output from theapplication into two parts, i.e. the area affected by the BPHand the BPH attack area from the observation, andprediction of the area affected by the BPH and the BPHattack area based on data on the predicted climate; and(10) displaying the output through thematic maps, tables,and graphs (Figure 1).

Data attribute:- Attack area- Climate- Administration

Analysis and design of concepts

Database design- Concept level- Logic level- Physic level

Model management(logical flow)

Inputting data

Prediction model ofBPH attack

(climate and non-climateparameters)

Spatial data- Administration- Rice field- Climate station- Road, river

Datarecorded

Dataobtained

Data management

MS Access 2000

Database scenario

MS Visual Basic 6.0

Output:thematic map, table, graphic

Conclusion and recommendation

Attack areaprediction

Interface/dialog

management

t

t t

t

t

t t

t t

t

t

t

s

t

s

Figure 1. A flowchart of the development of an early warning system for brown plant hopper (BPH) attacks.

Aplication(programming phase)

t

t

Objectives

Prediction ofclimatic data

Research needs- Hardware & software- Selection of prediction methods

Historical data- Attack area- Climate data- Administration

User

Climatic historicaldata

Analysis of userneeds

t

t

t

t

t t

Database


RESULTS AND DISCUSSION

BPH Attacks in Karawang, Subang, andIndramayu

The analyzed BPH attacks were data on bi-weekly BPHattacks area in the period of 1996-2008. Figure 2 showsthat the BPH attacks varied annually; low in some yearsbut suddenly soaring high. The highest attack areaoccurred in 1998-1999. The BPH attack area in 1998 reached39,497 ha and in 1999 was 14,790 ha. The BPH attacksdecreased in 2000-2004 with an average attack area of 1400ha, and then increased in 2005 reaching 7659 ha.

BPH attacks did not occur throughout the year asshown in Figure 3. In normal years, BPH generally strikesin the wet season (circled with spaced line), namely inFebruary-March. In 1998/1999, however, when there was aLa-Niña extreme climatic event, the BPH attack occurred inthe dry season (circled with dotted line), which were inJuly, August, and September. Figure 3 also shows that innormal years (black arrows), the number of districts affectedby BPH were more than during the La-Niña years (grayarrows), but the BPH attack area on the La-Niña years (grayarrows) were significantly higher than that in the normalyears (black arrows). This indicates that extreme climaticevents can trigger pest explosions. It was known that theLa-Niña conditions caused rainfall in the dry season, so itenable to grow rice once, hence there is still possible tohave wide areas of rice crops in the second dry season(DS2). The presence of rice plants as the BPH host and thehumid climatic conditions brought about by the La-Niñaare certainly conducive to the BPH proliferation.

Correlation between BPH Attack and ClimateParameters

The Pearson’s correlation coefficients can be used todetermine the level of closeness between two parameters.

Table 1 shows a list of the calculated correlation coefficientsbetween the BPH attack area and the climate parameters. Itwas indicated that the climate parameters contributedsignificantly to the widespread attacks of BPH in 1998,while in the period of 1996-2006 the contributions were notsignificant. This shows that the climatic conditions in 1998became one of the triggers for the BPH explosion. Theclimate parameters that significantly affected the BPHwidespread attacks, with correlation values of > 0.4, wererainfall, maximum temperature, maximum temperature at 2weeks before the incident, minimum temperature, andminimum temperature within 2 , 4 and 6 weeks before thepest incidents, maximum humidity, minimum humidity,minimum humidity at 2 weeks prior to the incident, averagehumidity, and average humidity at 2 weeks prior to theincident. This means that those parameters contributed atleast 40% to the BPH attack areas.

Model for Prediction of BPH Attack Areas Basedon Climate Parameters

Prediction model using multiple linear regressions was madefor data from the period of 1996-2006, 1998-1999, and 1998alone. Result of the analysis showed that based on linearrelationship between logarithms of BPH attack area withsome climate parameters under normal circumstances (noextreme climate events), the climate parameters did nottrigger the BPH attack. This is indicated by the value of R2

< 0.3, which means that the contribution of climateparameters was less than 30%. The 30% contribution ofclimate factors triggerred the BPH attack only in the eventof La-Niña in 1998, with a linear regression equation asshown in Table 2.

The values of standard deviation (S) in Table 2 areerrors generated by the model. In general, the smaller the Svalue, the better the equation model. This means that resultof the model is closer to the real data distribution. S valueof the equation model was 0.44; this means that the resultsof the data prediction of the model deviate 44% from thedata distribution.

The coefficient of determination (R2) indicates thecontribution of independent variable (climate factors) tothe dependent variable (BPH attack area). The bigger thevalue of R2, the better results from the model, which meansthe more contribution of the climate factors to the BPHattack areas. The R2 value of 33% means that the climaticfactors contributed 33% to the BPH attack areas.

Prototype of an early warning system for the BPHattack was made in a spatial scale for three districts, i.e.Karawang, Subang, and Indramayu. The early warningsystem was made in a spatial scale so that informationgiven is more easily understood by the policy makers and

120

96 97 98 99 00 01 02 03 04 05 06

Year

Num

ber

of in

cide

nce 100

80

60

40

20

0

No. of incidenceAttack area

Figure 2. Number of incidence and area of brown plant hopper(BPH) attacks of more than 25 ha in the period of1996-2006.

45,000

Atta

ck a

rea

(ha)

40,00035,00030,00025,00020,00015,00010,000 5,0000


Tabel 1. Coefficient of logarithmic correlation between brown plant hopper attack and climate parameters.

Log attack area

Climate parameter 1996-2006 1998-1999 1998

r-Pearson P-value r-Pearson P-value r-Pearson P-value

Rainfall -0.135* 0.015 -0.223* 0.001 -0.379* 0.000Rainfall-1 -0.094 0.089 -0.195* 0.005 -0.293* 0.003Rainfall-2 -0.113* 0.041 -0.155* 0.025 -0.266* 0.007Rainfall-3 -0.111* 0.046 -0.107 0.124 -0.269* 0.006

T-max 0.136* 0.014 0.267* 0.000 0.469* 0.000T-max-1 0.100 0.072 0.174* 0.012 0.359* 0.000T-max-2 0.079 0.156 0.096 0.169 0.101 0.311T-max-3 0.092 0.098 0.018 0.799 -0.131 0.189

T-min 0.010 0.854 0.007 0.920 -0.407* 0.000T-min-1 0.065 0.240 -0.011 0.875 -0.396* 0.000T-min-2 0.105 0.058 -0.042 0.547 -0.502* 0.000T-min-3 0.145* 0.009 0.004 0.955 -0.398 0.189

T-mean 0.104 0.060 0.200* 0.004 0.239* 0.015T-mean-1 0.109 0.049 0.113 0.106 0.065 0.515T-mean-2 0.122 -0.211 0.030 0.667 -0.242* 0.014T-mean-3 0.147* 0.008 0.001 0.992 -0.308* 0.002

RH-max -0.030 0.592 -0.162 0.019 -0.463* 0.000RH-max-1 0.035 0.531 0.007 0.924 -0.273* 0.005RH-max-2 0.063 0.258 0.109 0.118 -0.150 0.133RH-max-4 0.084 0.130 0.224* 0.000 0.245* 0.013

RH-min -0.064 0.251 -0.229 0.001 -0.526* 0.000RH-min-1 -0.001 0.988 -0.146* 0.036 -0.432* 0.000RH-min-2 0.049 0.049 -0.084 0.231 -0.277* 0.005RH-min-3 0.038 0.047 -0.031 0.656 -0.196 0.049

RH-mean 0.071 0.202 -0.188* 0.007 -0.505* 0.000RH-mean-1 0.004 0.940 -0.100 0.508 -0.400* 0.000RH-mean-2 0.049 0.170 -0.018 0.524 -0.274* 0.005RH-mean-3 0.047 0.399 0.069 0.320 0.128 0.200

*Significant at 95% level.

the data are easily updated. The information presented isBPH attack area data based on extensive observations andpredicted BPH attack area data based on climate dataprediction.

The information presented is intended for researchersand agricultural planners both in the local governmentoffices and in the Ministry of Agriculture. The users canquickly obtain information on: (1) spatial distribution oftwo-weekly BPH attack areas of the 1996-2007 observationperiod in Karawang, Subang, Indramayu, Tegal, andPemalang regencies; (2) graphs on distribution of two-weekly BPH attack areas per district or regency in the periodof observation; and (3) calculation and presentation ofspatial distribution of the predicted BPH attack areas onlocations that have predicted climate data.

The system can also display a graphic description oftwo-weekly the BPH attack area in each district or regencyin the period of observation. Figure 4 shows the BPH attackarea from Karawang, Subang, and Indramayu regencies inthe period of 1996-2006. This figure shows that in the period

Table 2. Stepwise regression equations of brown plant hopperattack areas in the period of 1998.

Regression equation S R2

Log_LS = 10.44 – 0.32 Tmin-2 – 0.00112 CH +0.182 Tmax-1 – 0.27 Tmin 0.44 33.33

Log_LS = 11.05 – 0.32 Tmin-2 + 0.209 Tmax-1 –0.33 Tmin 0.44 33.33

S = standard deviation; R2 = coefficient of determination


Table 3. Predicted climate data of 1998 at Gabuswetan station,Indramayu Regency, West Java.

RainfallTemperature (°C)

Period(mm)

Minimum Maximum Mean

January 1 33 22.4 30.9 26.7January 2 8 22.5 31.3 26.9February 1 5 22.3 31.4 26.9February 2 22 22.4 31.0 26.7November 1 98 22.4 31.1 26.7November 2 114 22.5 31.0 26.8December1 85 22.4 31.1 26.7December 2 71 22.5 30.9 26.7

climate prediction data. Gabuswetan was a place that hashad a climate prediction data of 1998 obtained using theKalman Filter prediction model (Table 3).

The predicted climate data are entered into the systemin the following ways: choose New and then click Entry toenter the data. After the data entry, click Save and thesystem will automatically calculate the predicted BPHattack areas on four scenarios of calculations. To see thespatial distribution, go back to the main menu, then selectthe year 1998 and the predicted months (February 2nd),select the scenario of calculations and then run. The resultswill appear as shown in Figure 5 and 6. Hence, it is predictedthat if the climatic conditions are as shown in Table 3,Gabuswetan district will then be affected by BPH of morethan 500 ha.

The benefit of using the early warning system for theBPH attack area is that the users can easily obtaininformation on distribution of BPH attack areas. Informationon the extent of observed BPH attack areas providesinformation on endemic areas of BPH that should receivespecial attention, while the predictive information can beused for anticipatory actions. If the BPH attack is predictedto occur, then there must be prevention taken so thatdamages caused by the BPH are not widespread, henceharvest failures could be reduced.

CONCLUSION

The BPH incidence was very dynamic; climate anomaliestriggered the BPH explosions. In West Java, the peak ofBPH attack occurred in 1998, where in La-Niña was precededby a strong El-Niño in 1997. The largest BPH attack occurredin the dry season, i.e. in July and August.

Climate factors that have a coefficient of correlation of> 0.4 with the BPH infestation area were rainfall, maximum

of 1996-2006, Indramayu regency was the most frequentlyaffected by the BPH attacks, followed by Karawang andSubang.

The early warning system also provides informationon the predicted BPH attack areas if a region already has a

Figure 4. Annual variation of brown plant hopper (BPH) attacksin Karawang, Subang, and Indramayu regencies,West Java.


Figure 6. Prediction of brown plant hopper (BPH) attack area on 2 February 2008.

Figure 5. Database system for new data entry and data editing of brown plant hopper attack areas.

temperature, maximum temperature at 2 weeks before theBPH incidence, minimum temperature, minimumtemperature at 2 weeks, 4 weeks and 6 weeks before theBPH incidence, maximum humidity, minimum humidity,minimum humidity at 2 weeks before the BPH incidence,average humidity, and average humidity at 2 weeks beforethe BPH incidence. The extent of BPH attack can bepredicted based on climate information using the equationas follows: Log_LS = 10.44 to 0.32 Tmin - 2-0.00112 + 0.182Tmax CH-1 to 0.27 Tmin or Log_LS = 11.05 to 0.32 Tmin - 2 +0.209 Tmax - 1- 0.33 Tmin.

The prototype of an early warning system forprediction of BPH attack can be useful for predicting broad

attacks of BPH anywhere and help the policy makers insettling up anticipatory action to be done. This can reducethe risk of rice yield losses due to the BPH attacks.

REFERENCES

Baehaki. 2005. Gubernur akan ikut “gropyokan” bersama petaniCirebon. Serangan Wereng Meluas, Kabupaten Bandung Waspada.Pikiran Rakyat, Kamis, 28 Juli 2005.

Kisimoto, R. and V.A. Dyck. 1976. Climate and rice insects. p.367-390. In Proc. Symposium on Climate and Rice(International Rice Research Institute, ed.). IRRI, Los Banos,Philippines.


Las, I., E. Surmaini, N. Widiarta, dan G. Irianto. 2003. Potensidampak anomali iklim, El-Niño dan La-Niña terhadap produksipangan dan kebijakan penanggulangannya. Disampaikan padaSeminar El-Niño dan Implikasinya terhadap PembangunanPertanian, Bogor, 6 Maret 2003. Pusat Penelitian danPengembangan Sosial Ekonomi Pertanian bekerja sama denganESCAP-CGPRT Center, Bogor.

Natanegara F. dan H. Sawada. 1992. Sistem Peringatan Dini dalamUsaha Pengendalian Wereng Batang Coklat di Jalur Pantura.

Taksonomi Musuh Alami Wereng Batang Coklat. KerjasamaTeknis Indonesia-Jepang Bidang Perlindungan Tanaman Pangan(ATA-162). Laporan Akhir Wereng Batang Coklat. DirektoratBina Perlindungan Tanaman, Direktorat Jenderal TanamanPangan, Jakarta.

Susanti, E. 2008. Developing Information System for Climate BasedPotential Area Attack of Brown Plant Hopper (Nilaparvatalugens) in North Coast of West Java. Thesis, Bogor AgriculturalUniversity, Bogor.

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