Indian Journal of Entomology 83(2021) Online published Ref. No. e20146 DoI No.: 10.5958/0974-8172.2020.00211.4
DISTRIBUTION PATTERN AND SEQUENTIAL SAMPLING PLAN FOR RICE CASEWORM NYMPHULA DEPUNCTALIS (GUENEE)
*Govindharaj Guru-Pirasanna-Pandi, Sujithra M1, Totan Adak, Basana Gowda, Mahendiran Annamalai, Naveenkumar Patil, Aashish Kumar Anant, P C Rath and Mayabini Jena
Division of Crop Protection, ICAR-National Rice Research Institute, Cuttack 753006 1Division of Crop Protection, ICAR-Central Plantation Crop Research Institute, Kasaragod 671124 * Email: [email protected] (corresponding author)
ABSTRACT
Rice caseworm Nymphula depunctalisis (Guenee) is one of the major insect pests that attack rice. Due to its leaf cases, and without taking into account their spatial distribution pattern it is difficult to evolve an IPM strategy against this. Present study is on this and was assessed with field experiments during rainy seasons of 2015, 2016 and 2017. The results obtained with Taylor’s power law (TPL) and Iwao’s mean crowding (IMC) regression show that N. depunctalis followed an aggregation pattern in rice field as revealed by the TPL parameters - Sampling parameter (a) = - 0.023, aggregation parameter (b) = 1.226, R2= 0.851; and IMC parameters- Index of basic contagion (α) = 0.089, and density contagiousness coefficient (β) = 1.204, R2= 0.960). Optimum sample size was estimated with Taylor’s regression coefficients with two precision levels fixed at 0.10 and 0.20, and these revealed that the size increased with TPL parameters with an increased precision level. Sequential sampling decision lines (d = 1n ± 0.38 n) were also determined based on the TPL parameters.
Key words: Rice, Nymphula depunctalis, sampling, spatial distribution, Taylor’s power law, parameters, Iwao’s mean crowding, regression, optimum sample size
Rice Oryza sativa is the major food source and it when insect population attains economic threshold fulfills the nutrient requirement of 2500 million people. level (ETL) and to assess whether the population has Few dozens of insect species had been reported to reached ETL, an efficient sampling plan is needed. damage rice crop but in lowland rice only around twenty Formulation of reliable sampling plan in turn, requires observed regularly and found as major importance (Jena spatial distribution information of the insect, which in et al., 2018). Rice production in India, experiences 25% fact are the most distinguishing ecological properties yield loss in every season due to insect pests attack of species (Taylor’s 1984; Li et al., 2017). Kogan and (Dhaliwal et al., 2010) and it also increases under Haezog (1980) stated that conventional methods of changing climatic condition (Dhaliwal., 2006; Pandi sampling for insect pests depend on a fixed number et al., 2016; 2018). Insect pest status has undergone of sampling units; hence there is a need of faster and a drastic change after green revolution in the rice reliable sampling method required. Fernandes et al. ecosystem (Chander et al., 2003; Jena et al., 2018). (2003) suggested that sequential sampling plan solves Caseworm Nymphula depunctalis (Guenee) had been the above raised concerns and could be an alternative reported in different rice cultivating regions of the world in which the sample size is not fixed and the counts and severe attack takes place in water logged condition acquired are grouped between density classes to (Shroff, 1919; Sisson, 1938; Alum, 1967; Grist, 1969; differentiate the population level (Kao, 1984; Faleiro Chi, 1995; Vromant, 1998). This sporadic pest was and Kumar, 2008). In comparison to other sampling earlier believed to cause < 20% yield loss (Dale, 1994). technique, the saving in time and labour usually However, since the beginning of this century this pest exceeds 50% (Chander and Singh, 2003). Although has attained major status in eastern and north eastern caseworm significantly affects rice production, critical parts of India. Larvae remain inside finely cut leaf cases, information about its spatial distribution pattern and which can float on the water surface. Larval protection suitable sampling plan are lacking. The present study case and semiaquatic habitat make them less susceptible analyses the spatial distribution pattern of case worm to insecticide/ natural enemies. to develop suitable sampling method for assessing its The IPM relies on the use of insecticides only damage in lowland rice. 2 Indian Journal of Entomology 83(2021) Online published Ref. No. e20146
MATERIALS AND METHODS = 1 and β<1 denotes aggregated, random and uniform distribution, respectively. Using “a” and “b” parameter The field experiment was done at the ICAR- of the TPL, optimum sample size (N) was calculated National Rice Research Institute (NRRI), Cuttack and enumerative sampling plan was developed with (20°45’N,85°93’E, 36 masl) during rainy seasons of two levels (0.10 and 0.20) of precision (Green, 1970; 2015, 2016 and 2017 with Naveen variety. All the Southwood, 1978)- Optimum sample size n = aXb / C2X2, standard agronomic practices were followed except where, b= aggregation parameter, X= mean density and insecticide application. Thirty (30) rice plants in 20 C= desired precision level. The sequential sampling cent land were randomly selected for sampling with N. plan was derived according to Ekborm (1985) following depunctalis infestation at weekly intervals, starting 30 b TPL and equation as d = nm0 ± t (√n a m0 ), where d1 = days after transplanting (DAT). This is corresponding to b b nm0 + t (√n a m0 ) and d0 = nm0 - t (√n a m0 ) indicates, early vegetative growth stage and continued until crop respectively upper and lower sequential sampling maturity. Using the following indices, the population decision lines. d0 and d1 are the lower and upper limit data was analysed for spatial distribution pattern (SDP) of the confidence interval for the cumulative number and most dominant SDP during the peak population of N. depunctalis, respectively; n represent observed period was determined. number of sample units; m0= economic injury level (EIL) of N. depunctalis; t denotes 20% probability level at In the above formulae- S2= variance; X= mean density student’s t-test (t = 1.28); a and b are TPL parameters. of N. depunctalis on randomly selected 30 sampling If the cumulative number of N. depunctalis population plant unit with weekly interval. Besides the above falls between upper and lower decision lines then indices, Taylor’s power law (TPL) described variance maximum number of samples necessary to be inspected (S2) of a proportional to the mean density (X), such as was calculated with the following formulae: n = t2×a S2 = aXb, where a and b are sampling and aggregation max m b/p2 ,where, p= t.Sx (t and Sx represents the value parameter, respectively and both are constant for a 0 of normal deviate and SE of the mean, respectively). species. The b < 1 indicated uniform distribution, b = 1 According to Southwood (2000), 25% standard error indicated random distribution, whereas b >1 indicated (SE) of the mean was considered as acceptable and “t” aggregated distribution. value used was 1.28 at 20% probability level.
Iwao’s mean crowding (IMC) depicted that RESULTS AND DISCUSSION relationship of mean crowding (X*) with mean density (X) as- X* = α+βX, where, α= Index of basic contagion Larval incidence of N. depunctalis was observed on and β= density contagiousness coefficient. The β> 1, β rice during 34th to 40th standard meteorological week
Parameter Formula Spatial distribution pattern Reference Diffusion coefficient (C) C = S2/X C > 1C = 1 C<1, respectively for Cassie, 1962 aggregated, random and uniform distribution Negative binomial K = X2 / (S2 /X) K>0, K<0 and K>8 respectively for Waters, 1959 distribution (k) aggregated, uniform and random distribution Aggregation index (I) I = S2/X-1 I>0, I = 0 and I<0, respectively for David and Moore, 1954 aggregated, random and uniform distribution Cassie index (Ca) Ca = (S2- X)/X2 Ca>0, Ca = 0 and Ca<0, Cassie, 1962 respectively for aggregated, random and uniform distribution Mean crowding (m*) X* = X+(S2/X-1) X*>X, X* = X and X*
(SMW), which coincides with the maximum tillering the 41st to 43rd SMW during 2015 and 43rd SMW during to flowering stage. The mean incidence during kharif 2016 and 2017 when the diffusion coefficient (C) and 2015, 2016 and 2017 varied from 0.43- 7.71, 0.29- 8.71 patchiness index (X*/X) were <1.00. The K value of the and 0.29- 8.86 larvae/ plant, respectively. The maximum negative binomial distribution, the aggregation index (I) was recorded during 38th and 39th SMW. The aggregation and Cassie index were < 0.00, and the mean crowding indices are as presented in Table 1. Diffusion coefficient (X*) was smaller than the mean density (X), indicating (C) and patchiness index (X*/X) during 35th to 40th that the larvae were in a uniform distribution during the SMW of 2015 and all the observed week for 2016, receding period of the cropping season. 2017 were >1.00 indicating aggregated distribution. The dispersion parameter (K) of the negative binomial However, in the study for the most samples, S2/X distribution, (K), aggregation index (I) and Cassie index (variance to mean ratio) and X* /X (Llyold’s patchiness (Ca) were > 0, indicating aggregated pattern of the pest, index) were observed to be > 1 indicating that the and thus its aggregation behaviour. The mean crowding larvae are distributed in an aggregated pattern (Table 1). (X*) was observed to be > mean density (X), which Variance of the larval incidence was found to be directly further indicated that the larvae are in an aggregated proportional to the pest density and maximum variance distribution. These indices, however, were different in concurred with high pest density. In the initial period, Table 1. Mean, variance and indices of aggregation for the incidence of N. depunctalis
2 2 SMW X S C (S /X) X* X*/X K Ca I kharif 2015 35 0.43 0.62 1.44 2.78 2.03 0.97 1.03 -1.09 36 0.86 0.48 0.56 0.51 0.49 -1.95 -0.51 -3.43 37 2.14 3.81 1.78 1.97 1.36 2.74 0.36 3.34 38 5.57 19.29 3.46 5.19 1.44 2.26 0.44 4.22 39 7.71 22.90 2.97 7.10 1.26 3.91 0.26 3.41 40 4.14 5.81 1.40 3.48 1.10 10.26 0.10 1.85 41 3.71 1.57 0.42 2.82 0.84 -6.43 -0.16 0.58 42 2.43 0.62 0.26 1.53 0.69 -3.26 -0.31 0.43 43 0.57 0.29 0.51 0.46 0.14 -1.16 -0.86 -0.67 kharif 2016 34 0.57 0.62 1.08 1.47 1.15 6.86 0.15 0.08 35 0.86 0.81 0.94 0.96 0.94 -15.43 -0.06 -0.06 36 2.14 3.81 1.78 1.97 1.36 2.76 0.36 0.78 37 6.00 13.67 2.28 5.38 1.21 4.70 0.21 1.28 38 8.71 21.90 2.51 8.00 1.17 5.76 0.17 1.51 39 4.86 8.81 1.81 4.23 1.17 5.97 0.17 0.81 40 3.29 6.24 1.90 2.86 1.27 3.66 0.27 0.90 41 1.29 3.24 2.52 2.24 2.18 0.85 1.18 1.52 42 2.00 2.33 1.17 1.58 1.08 12.00 0.08 0.17 43 0.29 0.14 0.49 -0.04 -1.78 -1.45 -0.69 -0.51 kharif 2017 34 0.43 0.62 1.44 2.80 2.04 0.96 1.04 0.44 35 1.29 1.57 1.22 1.24 1.17 5.79 0.17 0.22 36 3.57 6.29 1.76 3.06 1.21 4.70 0.21 0.76 37 5.29 16.90 3.20 4.89 1.42 2.40 0.42 2.20 38 8.86 21.14 2.39 8.13 1.16 6.39 0.16 1.39 39 6.00 14.33 2.39 5.40 1.23 4.32 0.23 1.39 40 3.29 5.24 1.59 2.77 1.18 5.53 0.18 0.59 41 2.57 2.62 1.21 1.93 1.09 11.56 0.01 0.02 42 1.14 1.48 1.29 1.27 1.26 3.92 0.26 0.29 43 0.29 0.57 2.00 6.29 4.50 0.29 3.50 1.00 SMW= Standard meterological Week; S2 = variance; X= mean density of N. depunctalis on each observed plant; No. of sampling units (N) = 30; X* = Lyold’s mean crowding; X*/X = Lyold’s patchiness index 4 Indian Journal of Entomology 83(2021) Online published Ref. No. e20146 there existed a minimum larval density and with a The N. depunctalis female laid eggs on underside random distribution pattern; but, density increased in the of the leaves in batches of about 20 numbers that led subsequent weeks gradually and changed distribution to aggregated pattern (Sharma et al., 1999). These pattern got changed from random to aggregated. Earlier larvae also had attractive tendency towards each other studies revealed that most of the insect pest populations probably to avoid intraspecific competition; and these were dispersed in aggregated manner (Bisseleua might prefer to move to separate branches of the same and Vidal, 2011; Sujithra and Chander, 2016), and plant after hatching. The 10 and 20 % precision levels relationships between mean and variance were utilized in density estimates were achieved with large variability as aggregation indices (Arnaldo and Torres, 2005). in sample size requirements. An optimum sample size In addition, Taylor and Iwao regression models also ranged from 23 to 563 and 6 to 141 sampling units at depicted the means and variance relationship of insect different pest densities with 10 and 20% precision, population. respectively (Fig. 1). Sample size requirements were inversely related to the pest densities with both the TPL equation for the pooled data of three years precision levels. The sample size requirement increased 2 2 was log S = 1.226 log X + 0.089 (R = 0.851). In S = with precision level as sample size requirement with b 2 1.226 aX form, this could be expressed as S = 1.229X . 10% precision was 4x as high as with 20% precision. The equation revealed that the distribution is in an The sample size with 10% precision thus did not seem aggregated pattern in rice field as the aggregation economical; however, those with 20% precision were parameter b was 1.226. Likewise, IMC regression deemed to be reasonable. It has been observed earlier for the pooled data of three years was established to that the required sample precision in general depends be X* = 0.023 + 1.204 X (R2 = 0.960). The index of upon the purpose of sampling such as density estimates basic contagion or the intercept value was positive (α with 20% precision proved sufficient for decision = 0.021), indicating the attractive tendency among making (Suel et al., 2012). Sampling plans based on the larval population. The density contagiousness parameters of regression models had also been found coefficient (b = 1.204) was significantly greater than to reduce sampling effort and minimize variation of unity further confirming their aggregation behaviour sampling precision (Kuno, 1991; Payandeh et al., 2010). (Table 2). Aggregated distribution has earlier been observed to be a characteristic of arthropod populations, Sequential sampling plans were further formulated while regular distributions rarely occurred, only in so as to have still more efficient method to reliably detect populations with strong intraspecific competition N. depunctalis infestation. Based on the TPL parameter, (Argov et al., 1999). In the present study, distribution viz., aggregation parameter (b =1.226) and sampling pattern of N. depunctalis for the entire season was parameter a = 1.229), EIL as one larvae/ plant (Heinrichs confirmed as aggregated through both TPL and IMC and Viajante, 1987) and tolerable error in decision as models. Wherein, females laid eggs in batches on 20% (t = 1.28), the decision lines of sequential sampling selected rice plants probably due to heterogeneity for N. depunctalis were determined to be: d = 1n ± 0.38 among host plants (Poole 1974; Southwood, 1978; √n. (Fig. 2); lower decision line d0 = 1n-0.38 √n; and
Sujithra and Chander, 2016). It has been observed that upper decision line: d1 = 1n + 0.38 √n. Corresponding to individual species spatial distribution behaviour may be four sample units i.e., four plants in the field, lower and decided by either ecological factors such as intra and upper decision lines showed cumulative larval counts interspecific competition, predation and sexual stimuli of 1 and 7 larva, respectively. These lines would be or abiotic factors such as temperature, humidity, light executed in the following parameter. After observing and wind (Jahnke et al., 2014). two plants, cumulative larval counts <1 and >7 would
Table 2. Taylor’s power law and Iwao’s mean crowding regression for N. depunctalis Crop Sample Taylor’s power law Iwao’s mean crowding regression Season size Sampling Aggregation R2 Sampling Aggregation R2 parameter parameter (b) parameter parameter (b) (a) (a) 2015 9 1.278 1.354 0.733 0.594 1.332 0.942 2016 10 1.390 1.235 0.972 0.227 1.157 0.981 2017 10 1.564 1.135 0.942 0.312 1.158 0.975 Pooled 29 1.229 1.226 0.851 0.023 1.204 0.960 Distribution pattern and sequential sampling plan for rice caseworm Nymphula depunctalis (Guenee) 5 Govindharaj Guru-Pirasanna-Pandi et al.
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(Manuscript Received: May, 2020; Revised: September, 2020; Accepted: September, 2020; Online Published: October, 2020) Online published (Preview) in www.entosocindia.org Ref. No. 20146