Sequential sampling: planthoppers in rice

CROP PROTECTION

(1986) 5 (5), 319-322

Sequential

sampling:

planthoppers

in rice

M. SHEPARD*, E. R. FERRER*, P. E. KENMORE* AND J. I?. SUMANGIL~ *Department of Entomology, The International Rice Research Institute, PO Box 933, Manila, Philippines, *Food and Agricultural Organization, Suite 104, Josefa Apt. 711, President Quirino Avenue, Malate, Manila, Philippines, and $Bureau of Plant Industry, San Andres, Malate, Manila, Philippines

A sequential sampling plan was developed for the brown planthopper, Nilaparvata lugens ABSTRACT. Stal, and the whitebacked planthopper, Sogatella furcifera HorvPth in rice. The plan was tested in several different areas in the Philippines and compared with a more intensive sampling technique. Results showed a 100% agreement between the two techniques in decision-making, and an 80% saving in time was realized using the sequential sampling plan.

Introduction

plusia ni (Hiibner) on cabbage (Shepard, 1973) and on cauliflower (Harcourt, 1966), velvetbean caterpillar, Anticarsia gemmatalis Hiibner (Strayer, Shepard and Turnipseed, 1977; Shepard, 1980), and potato leafhopper, Empoasca fabae (Harris) (Walgenbach, Wyman and Hogg, 1985). A bibliography of sequential sampling plans for other insect species was published by Pieters (1978). The brown planthopper (BPH), Nilaparvata lugens St%&and whitebacked planthopper (WBPH), Sogatella furcifera Horvath, are major pests of rice in South-East Asia. The bionomics and damage caused by these species are similar; likewise, the distribution pattern and behaviour of these two species are similar. They injure the rice plant by sucking juices from stems: dense populations can cause the plants to die, resulting in a condition called ‘hopperburn’. The use of pesticides is a major control tactic for these insect pests and, although pesticide applications should be made only when pest population exceeds a threshold level, growers often apply these chemicals indiscriminately. This practice is expensive and contributes to pest resistance, resurgence and environmental contamination. To determine the necessity for a chemical treatment, a sampling technique which is reliable, inexpensive and easy to use should be developed. Proper timing of chemical application is cost effective and will also help to conserve natural enemies which are important factors in regulating populations of BPH and WBPH (Kenmore et al., 1984). The objective of this study was: (1) to develop a sequential sampling plan for BPH and WBPH; (2) to

Sequential sampling was developed by Wald (1945) as a rapid method of classifying populations into broad categories such as light, medium and heavy. The ability to do this is useful in pest management in order to determine the necessity for insecticide treatments. As the name implies, with sequential sampling, samples are taken in sequence and the decision to take the next sample depends upon what is found in the one just made. Using this technique it is not necessary to estimate population parameters, and low numbers of samples can be taken when the population density is low or high; this is in contrast to most conventional procedures, which require a fixed number of sampling units regardless of population density. Before sequential sampling plans can be developed, three types of information are necessary: (1) the mathematical distribution of the insect species; (2) the damage threshold, and (3) the level of risk of making a wrong decision. . Sequential sampling plans have been developed for many insect pests including green cloverworm, Plathypena scabra (F.) (Hammond and Pedigo, 1976; Pedigo, Buntin and Bechinski, 1982; Bechinski et al., 1983), bollworm, Heliothis zea (Boddie), in cotton in the USA (Allen, Gonzales and Gokhale, 1972) and for II. armigera Hiibner in Australia (Sterling, 1976), boll weevil, Anthonomus grandis Boheman (Pieters and Sterling, 1975), adult corn rootworm, Diabrotica longicornis (Say) (Foster, Tollefson and Steffey, 1982), cotton fleahopper, Pseudatomoscelis seriatus (Reuter) (Pieters and Sterling, 1974), cabbage looper, Tricho-

0261-2194/86/05/0319-04

$03.00

0

1986 Butterworth

& Co (Publishers)

Ltd

320

Sequential sampling: planthoppers in rice

test the plan in farmers’ fields compared with a more intensive technique referred to as the Surveillance and Early Warning System (SEWS). Materials

The combined mathematical distribution pattern of BPH and WBPH was determined using the field data gathered from the IRRI Experimental Farm during the dry season, 1978. Only those data sets from field sampling when the crop was in the tillering to flowering stage were used. During these stages the crop is most susceptible to damage by WBPH and BPH. Data from field samples representing a wide range of densities were fitted to various distributions using a Fortran program developed by Gates and Ethridge (1972). Formulae for computing for the decision lines (d, and d,) for the sequential plan were found in Waters (1955), Shepard (1980), Southwood (1978) and others. These lines set the boundaries between low, continue sampling, and high categories (see Table 1). For field use, a table containing columns of numbers calculated for di and d2 is appropriate. A simple calculator program was used to expedite the computation of the decision lines d, and dz (Shepard and Grothusen, 1984). Computation of the decision lines (dl and d2) for negative binomial distribution is represented by the following formulae: dl = bn + hl d2 = bn + hz

;pl

q, = 1+p,

=T;

log!%

h, =

42

B ion 7 - l-a

;p2 =3q2

= l+p,

log P!z Pl

plan for hopper

Cumulative/No. of hoppers

pests in rice

Lower limit

Upper limit

3 4 5

<

77

2

120>

;

97

5

140>

.g

160>

’ z E”

199>

6

z

<

7

<116

8

$ *!g


2 is ,g

9

--J <156

g

10


219>

11


239>

12

<215

258>

13

<235

278>

179>

5 ::

/3 = 0 * 20 = the risk of calling a high infestation low k= 5 * 3 = the dispersion parameter of the negative binomial b= slope of the line hl and h, = the intercepts The clumping parameter k was calculated from the approximate value of k formula (Southwood, 1978) using a range of hopper densities of late instars and adults: X2 =s2-x

where S= standard deviation and x= mean.

log a2

Pl

No. of hoppers

k

where n = number of samples

41

sampling

2

of the sequential sampling plan

b=k

Sample IlO.

Sequential

1

and methods

Development

TABLE 1.

42

log -1-B hz=

a

tog= Pl

42

where ml = 17 m2=23 I class limits or economic threshold a= 0 * 20 = the risk of calling a low infestation high

Comparison between sequential sampling and SEWSplans

Field trials were conducted in different locations in the Philippines, as summarized in Table 2. Numbers of plots sampled, rice varieties planted and numbers of sampling occasions also are represented in this table. The study was conducted during the dry season (December 1984 to May 1985). The sequential sampling plan for hoppers was compared with SEWS. The SEWS program was developed by the Bureau of Plant Industry and the Phil-German Crop Protection Programme (MAFBPI, 1984). In each designated area, four plots from 100 m2 to 500 m2 were sampled weekly from 20 days after transplanting, in two plots using SEWS and in two plots using sequential sampling, except in Koronadal, South Cotabato where three plots were sampled using SEWS and three using sequential sampling. For SEWS sampling, weekly samples of 20 hills were selected at random from each plot.

M. SHEPARD et al. TABLE 2. Locations where SEWS and sequential sampling trials were conducted, together with rice varieties planted, number of plots used and number of sampling occasions (December 1984-April 1985) Philippines Number Rice varieties

Locations 1. Quezon, Nueva Ecija 2. Sto. Domingo, Nueva Ecija 3. Zaragoza, Nueva Ecija 4. Pangil, Laguna 5. Victoria, Laguna 6. Abuyog, Leyte 7. Molave, Zamboanga de1 Sur 8. Koronadal, South Cotabato 9. Other locations

SEWS

of plots

used

Sequential sampling

Number of sampling occasions

IR36

2

2

16

BP1 Ri-10 IR56 IR60 IRl917-3-17 IRl917-3-17 IR60

2 1 1 2 2 2

2 1 1 2 2 2

16 7 7 20 20 12

BP1 Ri-10 IR60 IR64 IR60, IR62, BP1 Ri-10

2 1 2 1

2 1 2 1

12 10 10 31

Total

161

Sequential sampling was carried out by traversing an X pattern across the field. After taking four samples, the sequential table was consulted and the decision to treat, continue sampling or not to treat was made. The sample unit was the hill which was inspected visually and only third-instar nymphs to adult hoppers were counted. To use the sequential sampling table (Table l), the following instructions were followed: 1. After sampling each hill, record numbers of hoppers. 2. Accumulate hopper counts in column 2. 3. Begin consulting the sequential table on the fourth sample. If the decision was to ‘continue sampling’ the table was consulted after each additional sample. No more than 13 samples were taken. If after the thirteen sample, ‘cumulative hopper count’ was within the ‘continue sampling’ zone, the decision was made to treat. In almost all locations where trials were conducted, resistant rice varieties were used, except in Pangil and Victoria, Laguna where a susceptible genotype IR1917-3-17 was grown. There was a total of 161 sampling occasions. Results

321

Preliminary trials conducted at IRRI Farm and in Victoria, Laguna revealed 93% agreement in decisionmaking with conventional intensive sampling (IRRI, 1985). There was 100% agreement in decision-making between sequential sampling and SEWS for the 161 sampling occasions in all the locations in the Philippines. On all occasions, the ‘don’t treat’ decision prevailed, which prevented unnecessary pesticide applications. The usual farm practice is for farmers to make from two to five insecticide applications per cropping season in the Philippines. The number of samples taken by sequential sampling and SEWS is shown in Figure 1. An 80% saving in time was realized using the sequential sampling technique, compared with the SEWS where a constant number of 20 samples (hills) were taken per plot per sampling occasion. The flexibility of the sample size and reliability of sequential sampling made it practical for use. These results revealed that a farmer or scout could sample as few as four hills and arrive at a decision about whether or not to treat. However, pest population densities never reached economically damaging levels in all fields sampled. Thus, although the plan was not tested at high pest densities, the most important aspect of the plan is the significant saving in time required to reach a treatment decision accurately when BPH and WBPH population densities are sparse. As mentioned earlier, using sequential sampling, few samples are required to reach a decision when population density is low or high. This sequential sampling plan may be a logical first step toward developing methods which provide information from which intelligent decisions about insecticide applications can be made; however, it should be tested at a range of pest densities. In addition, we have obtained encouraging results from preliminary experiments using sequential sampling plans in which major predators are considered in addition to insect pests.

and discussion

The mathematical distribution for WBPH and BPH populations fitted the negative binomial. This was true at all insect densities and rice growth stages. The clumping parameter K was 5 -3. Calculation of the decision lines of 4 and d2 was possible using the values below, where n is the sample number: d, = 19 - 7 1n - 2 l-59 (lower boundary) d2 = 19 * 7 1n + 2 l-59 (upper boundary)

FIGURE 1. Comparison of total number of samples per location intensive sampling (SEWS) (0) and sequential sampling (0). Dry (December 1984-April 1985), Philippines.

using season

322

Sequential sampling: planthoppers in rice

References ALLEN,J., GONZALES, D. ANDGOKHALE,D. V. (1972). Sequential sampling plans for the bollworm, Heliothis zea. Environmental Entomology 1, 771-780. BECHINSKI, E. J., BUNTIN, G. D., PEDIGO, L. P. AND THORVILSON,H. G. (1983). Sequential count and decision plans for sampling green cloverworm (Lepidoptera: Noctuidae) larvae in soybean. Journal of Economic Entomology 76, 806-812. FOSTER,R. E., TOLLEFSON,J. J. AND STEFFEY,K. L. (1982). Sequential sampling plans for adult corn rootworms (Coleoptera: Chrysomelidae).Journal of Economic EntomoIogy 74,791-793. GATES,C. E. AND ETHRIDGE,G. F. (1972). A generalizedset of discretefrequency distributions with Fortran program. Journal of International

Association

of Mathematics

and GeoIogy 4,

l-7. HAMMOND,R. B. AND PEDIGO,L. P. (1976). Sequential sampling plans for the green cloverworm in Iowa soybeans.3ournal of Economic Entomology 69, 181-185. HARCOURT,D. G. (1966). Sequentialsampling for usein control of the cabbage looper in cauliflower. Journal of Economic Entomology 59, 1190-l 192. IRRI (1985). International Rice Research Institute Annual Reportfor 1984. Los BaZos,Laguna, Philippines: IRRI. KENMORE,P. E., CARINO,F. O., PEREZ,C. A., DYCK,V. A. AND GUTIERREZ,A. P. (1984). Population regulation of the rice brown planthopper (Nilnparvata ZugensSt&l)within rice fields in the Philippines. Journal of Plant Protection in the Tropics 1, 19-37. MAF-BP1 (1984). PHIL-GERMAN Crop Protection Programme: Implementing Guidelines for the Surveillance and Early Warning System in Masagana. Revised edn, 1983184. Manila,

Philippines: Ministry of Agriculture and Food/Bureau of Plant Industry. 45 pp. PEDIGO,L. P., BUNTIN, G. D. AND BECHINSKI,E. J. (1982). Flushing technique and sequential-countplan for green cloverworm (Lepidoptera: Noctuidae) moth in soybean. Environmental Entomology 11, 1223-1228.

PIETERS,E. I’. (1978). Bibliography of sequential sampling plans for insects.Bulletin of the Entomological Society of America 24, 372-374.

PIETERS,E. P. AND STERLING,W. L. (1974). A sequential sampling plan for the cotton fleahopper Pseudatomoscelis seriatus. Environmental Entomology 3, 102-106. PIETERS,E. l?. ANDSTERLING,W. L. (1975). Sequentialsampling cotton squaresdamagedby boll weevilsor Heliothis spp. in the coastal bend of Texas. Journal of Economic Entomology 68, 543-545.

SHEPARD,M. (1973). A sequential sampling plan for treatment decisions on the cabbage looper on cabbage. Environmental Entomology 2, 901-903. SHEPARD,M. (1980). Sequential sampling plans for soybean arthropods. In: Sampling Methods in Soybean Entomology, pp. 79-93 (ed. by M. Kogan and D. C. Herzog). New York, Heidelberg, Berlin: Springer Verlag. SHEPARD,M. AND GROTHUSEN,J. (1984). A simple calculator program for flexible sequentialsampling of insects.Bulletin of the Entomological Society of America 30, 35-36.

SOUTHWOOD, T. R. E. (1978). Ecological Methods, second edn. London: Methuen. 524 pp. STERLING,W. L. (1976). Sequentialdecisionplans for the management ofcotton arthropods in SoutheastQueensland.Australian Journal of Ecology 1, 265-274.

STRAYER,J., SHEPARD,M. AND TURNIPSEED,S. G. (1977). Sequential sampling for management decisionson the velvetbean caterpillar on soybeans. Journal of the Georgia Entomological Society 12, 220-227.

WALD, A. (1945). Sequential tests of statisticalhypothesis.Annals of Marhematics and Statistics 16, 117- 186. WALGENBACH,J. F., WYMAN, J. A. AND HOGG, D. B. (1985). Evaluation of sampling methods and development of sequential sampling plan for potato leafhopper (Homoptera: Cicadellidae)on potatoes.Environmental Entomology 14,231236.

WATERS,W. E. (1955). Sequential sampling in forest insect surveys.Forest Science 1,68-79. Accepted 12 November 1985