Fixed-Precision Sequential Sampling Plan of Syringopais Temperatella (Lep., Gelechiidae) in Wheat Fields of Iran
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J. Agr. Sci. Tech. (2020) Vol. 22(6): 1501-1510 Fixed-Precision Sequential Sampling Plan of Syringopais temperatella (Lep., Gelechiidae) in Wheat Fields of Iran D. Rashidi1, A. Rajabpour*1, and N. Zandi-Sohani1 ABSTRACT Cereal leaf miner, Syringopais temperatella Led. (Lep., Gelechiidae), is an important wheat pest in many regions of the world, including Iran. Fixed precision sequential sampling plan, a cost-efficient method for estimating pest population density, has been used for developing a successful IPM program. In this study, the fixed precision sequential sampling plan of S. temperatella larvae was developed on wheat, cultivar Verinac®, during 2017-2019 growing seasons in Iran. For this purpose, first, spatial distribution of the larvae on wheat leaves was determined using Taylor's power law and Iwao's patchiness regression. The spatial distribution of the larvae was aggregative on the wheat leaves. Taylor’s power law provided a better fit for the data than Iwao’s patchiness regression. Therefore, Green's model was used for developing the fixed precision sequential sampling plan. The optimum sample sizes of the larvae ranged from 5- 68 plants and 12- 189 plants according to the average of larval density at precision levels D= 0.25 and D= 0.1, respectively. Estimated stop lines showed that the sampling must be continued until the cumulative number of the pest larvae reaches 3.45 (D= 0.25) and 83.76 (D= 0.1) per plant. Accuracy of the sampling plan was validated by RVSP software. Keywords: Cereal leaf miner, Green model, Integrated pest management, Taylor's power law. INTRODUCTION plants by mining into the leaves, feeding on cells of the internal tissues of the Wheat, Triticum aestivum L., is the blades and leaving the epidermis dominant crop in temperate countries, transparent. Infested leaves are including Iran, being used for human food conspicuous by their light brown color and livestock feed (Shewry, 2009). The (Yaman and Jarjes, 1971). Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 cereal leaf miner, Syringopais Fixed precision sequential sampling plan temperatella Led. (Lep., Gelechiidae), in of some leaf miners were studied. For one of the economically important pests in instance, fixed sequential sampling of many countries, especially in the Middle Liriomyza huidobrensis Blanchard (Dip., East (Al-Zyoud et al., 2009). The pest is Agromyzidae) on celery (Heinz and considered as an important wheat pest in Chaney, 1995) and Lettuce (Burgio et al., west, southwest, and south provinces of 2005), Phyllocnistis citrella Stainton Iran (Jemsi et al., 2002a; Jemsi, 2006). (Lep., Gracillaridae) on lime (Pena and The pest has one generation in southwest Schaffer, 1997), L. sativa Blanchard on of Iran and its activity was recorded cucumber (Namvar et al., 2012), L. trifolii during 4.5 to 5 months on various cereals Burgess on tomato (Lee et al., 2005), in Khuzestan Province, southwest Iran Cameraria ohridella Deschka & Dimic (Jemsi, 2002a). The pest larvae damage (Lepidoptera: Gracillariidae) on horse chestnut tree (Ferracini and Alma, 2007), _____________________________________________________________________________ 1 Department of Plant Protection, Faculty of Agriculture, Agricultural Sciences and Natural Resources University of Khuzestan, Mollasani, Ahvaz, Islamic Republic of Iran. * Corresponding author; e-mail: [email protected] 1501 _______________________________________________________________________ Rashidi et al. Tuta absoluta Meyreck (Lep., Spatial Distribution Gelechiidae) on tomato (Cocco et al., 2015), and Phthorimaea operculella Zeller Taylor’s power law and Iwao’s patchiness (Lep. Gelechidae) on potato (Shahbi and regression were used to evaluate spatial Rajabpour, 2017) were studied. distribution of S. temperatella larvae on There has not been any effort to develop wheat. Taylor’s power law describes the a fixed precision sequential sampling plan regression between logarithm of in the case of S. temperatella on wheat (10) ® population variance and logarithm (10) of (cultivar Verinac ). Therefore, the population mean according to the following objective of this study was to develop equation: fixed-precision sequential sampling plan Log(s2 ) a bLog(X ) of the pest larvae in wheat fields. (1) Where, S2 is the larval population MATERIALS AND METHODS variance, X is larval population mean, a is the Y-intercept, and b is the slope of regression line. The regression slope "b" is Experimental Design an index of species spatial pattern. When b< 1, b= 1, and b> 1 spatial distribution pattern The experiments were performed during of the larvae are uniform, random, and two growing seasons, 2017/2019, in an aggregated, respectively (Southwood, 1978, experimental wheat field, two hectares, in Rajabpour and Yarahmadi, 2012; Kafeshani Masjed Soleiman District, Khuzestan et al., 2018). Goodness of fir of Taylor’s Province, southwest of Iran (31° 45' 03.3" N power law was obtained by calculating 49° 28' 04.8" E). Seeds of a commercial regression coefficient. Two-tailed t-test at wheat cultivar, Verinac®, were cultivated n−2 degrees of freedom was performed to (≈80 plants per m2) in the experimental field. determine if slope and regression coefficient Cultural practices were carried out according values of the regression relation were to practical advisements of Khuzestan significantly different from 1 and 0, Agricultural Organization and no respectively (Snedecor and Cochran, 1980). insecticides were applied during the study. Iwao’s patchiness regression was used to quantify the relationship between mean Sampling crowding index (X*) and mean by using Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 the following equations: * The first sampling was started when the X X (2) first moth of S. temperatella was trapped by Where, solar energy-based pest trap (Raha Andish 2 * S Kavan Company, Tehran, Iran) (Sermsri and X X ( ) 1 Torasa, 2015). For this purpose, two traps X (3) were randomly placed in the experimental Different linear regressions were tested for field. Samplings were usually carried out at heterogeneity of slopes (Sokal and Rohlf, weekly intervals. At each sampling date, 1995) by Analysis Of Covariance twenty plants were randomly selected by (ANCOVA) of data collected from different walking in an X-shaped pattern through the growing years using SPSS software (Version field. From each selected plant, three leaves 16.0). from top, middle, and bottom tillers of the Student’s t-test can be used to determine if plant were chosen and number of larvae was the colonies are randomly dispersed: recorded. In Taylor’s power law: t= (b−1)/SEb, b= 1 (4) 1502 Fixed-Precision Sequential Sampling Plan_______________________________________ In Iwao’s patchiness regression: t= method. The software requires independent (β−1)/SEβ, β= 1 (5) data sets to serve as validation data sets Where, SEb and SEβ are the Standard (Shahbi and Rajabpour, 2017; Kafeshani et Errors of the slope for Taylor’s power law al., 2018). Hence, ten independent data sets and Iwao’s patchiness regression models, with a range of low, medium, and high respectively. Calculated value of t is density levels were randomly selected from compared with tabulated value of t with n−2 a total of 35 data sets, which were collected degrees of freedom. If the calculated t (tc)< in two growing seasons. The mean densities t-table (tt), the null hypothesis (b= 1) would of these data sets for the pest larvae ranged be accepted and the spatial distribution from 0.02 to 3.33 larvae per plant. The would be random. If tc> tt, the null sample size of each data set consisted of 20 hypothesis would be rejected, and if b> 1 or plants. The data was not used in Taylor’s b< 1, the spatial distribution would be power law regression. Simulations were aggregated or uniform, respectively carried out using 500 re-samplings without (Kafeshani et al., 2018). replacement. Fixed-Precision Sequential Sampling Plan The optimum sample size (n) needed to RESULTS estimate S. temperatella density at two levels of fixed precision, 0.25 and 0.1, was calculated using the following equation: Spatial Distribution aX b-2 n 2 Spatial distribution patterns and D , (6) parameters of S. temperatella according to Where, D is a fixed precision level, and a Taylor’s power law and Iwao's patchiness and b are coefficients obtained from the regression on wheat are presented in Table regression of Taylor’s power law (Buntin, 1. 1994). Precision levels of 0.25 and 0.1 are Taylor’s power law provided a significant generally acceptable for sampling in IPM relationship between variance and mean and research purposes, respectively density of the pest on wheat. No significant (Southwood, 1978). relationship was observed between mean Due to fitted data with Taylor’s mean- crowding and mean density of S. variance model, the Green’s method was temperatella on wheat according to Iwao's used to calculate stop lines of fixed- Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 patchiness regression. The aggregation precision sequential sampling (Naranjo and incidence (b) of Taylor’s power law was Hutchison, 1997). The stop lines of S. significantly more than 1, which indicates temperatella in wheat fields were estimated aggregative dispersions of S. temperatella as: larvae on wheat. (7) T ((an1b / D2 )(1/(2b)) ) Fixed-Precision Sequential Sampling Plan n The optimum sample size of S. Where, T is the insect cumulative number n temperatella at fixed precision levels of 0.25 in n samples, and a and b are the Taylor and 0.1 in wheat fields are shown in Figure coefficients, D is the Desired precision level. 1. With increasing larval densities, the optimum sample size dramatically Validation of Sampling Plan decreased. Also, at precision level of 0.25, the optimum sample size was always lower The sequential sampling plan was than 0.1. The optimum sample size to validated by RVSP (Resampling for estimate S.