J. Agr. Sci. Tech. (2020) Vol. 22(6): 1501-1510

Fixed-Precision Sequential Sampling Plan of temperatella (Lep., ) 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 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 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 . 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 (: ) 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]

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Tuta absoluta Meyreck (Lep., Spatial Distribution Gelechiidae) on tomato (Cocco et al., 2015), and 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 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 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)

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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  ((an1b / D2 )(1/(2b)) ) Fixed-Precision Sequential Sampling Plan n The optimum sample size of S. Where, T is the 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. temperatella larval densities on Validation of Sampling Plans) software wheat ranged from 5-68 plants and 12-189 based on Naranjo and Hutchison (1997) plants at precision levels of 0.25 and 0.1, respectively.

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Table 1. Spatial distribution statistics of Syringopais temperatella on wheat using Taylor’s power law and Iwao's patchiness regression analyses. Model/Statistics Spatial F N Intercept±SE Slope±SE R2 P distribution regressio Taylor's power law 20 0.627±0.157 1.425±0.182 0.774 clumped 61.492 <0.0001 Iwao's patchiness 20 381.67±662.09 -628.2±553.2 0.017 clumped 0.332 <0.571 regression

Figure 1. Optimum sample size of Syringopais temperatella on wheat at precision levels of 0.25 and 0.1.

Estimated stop lines using Green’s model levels for S. temperatella larvae on wheat for S. temperatella in wheat fields are plant were 0.26 and 0.108 at precision levels presented in Figure 2. Based on the of 0.25 and 0.1, respectively, which were estimated stop lines, numbers of required close to the desired precision (Figure 4). sampled plants to cross the stop lines are significantly changed. The results indicated DISCUSSION

Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 that the sampling of the pest must be continued until the cumulative number of larvae on wheat plant reaches 3.45 and 83.76 In our experiments, Taylor’s power law larvae per plant at precision levels of 0.25 provided a better fit to the data than Iwao’s and 0.1, respectively. patchiness regression. Similar results were obtained for some other Gelechiid leaf miners including T. absoluta on greenhouse Validation of Developed Sampling Plan cucumber (Cocco et al., 2015), and P. operculella on potato (Shahbi and Estimated sample sizes of S. temperatella Rajabpour, 2017). Spatial distribution of S. on wheat according to resampling analysis teperatella on wheat leaves was aggregative. using RVSP software are shown in Figure 3. Similarly, spatial distributions of some The means of sample sizes for S. lepidopteran leaf miners were reported as temperatella on wheat were 140.1 and 389.1 aggregative, including C. ohridella on horse plants at precision levels of 0.25 and 0.1, chestnut (Ferracini and Alma, 2007), P. respectively. citrella on lemon (Liu et al., 2008), and T. For the 10 independent data sets covering absoluta on tomato (Cocco et al., 2015; different densities, the average precision Ghaderi et al., 2018). However, spatial

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D=0.25

45 40 35 30 25 20 15 10

5 Cumulative insect numbersCumulative insect 0 0 5 10 15 20 25 30 35 Number of sampled plants

D=0.1

1200

1000

800

600

400

200

Cumulative insect numbers 0 0 5 10 15 20 25 30 35 Number of sampled plants

Figure 2. Fixed precision sequential sampling stop lines for Syringopais temperatella on wheat at precision levels of 0.25 and 0.1.

Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 distributions of some leaf miner larvae, e.g., determined as aggregative. Therefore, Lyonetia speculella Clemens (Lepidoptera: different sample universe is the main reason Lyontiidae) on apple (Brown, 1989) and for the different results. Erionota thrax L. (Lepidoptera, Optimum sample size to estimate S. Hesperiidae) (Okolle et al., 2006) were temperatella larval densities strongly random. The difference in results may be depended on the desired precision level and due to the differences in host plant. the pest larval density (from 5-68 plants and Moreover, many factors related to host 12-189 plants according to the average of plant, including morphological difference larval density at precision levels of 0.25 and among cultivars (Shahbi and Rajabpour, 0.1, respectively). The number of samples 2017) and weed status (Dinarvand et al., required to attain a certain precision was a 2019), and/or factors related to pest strong function of density; higher sample including oviposition completion patterns size was required at the lower pest density. (Damos, 2018) affect spatial distribution This was due to the relationship between the parameters. Jemsi et al. (2002b) reported mean and the variance of the pest densities spatial distribution of the pest larvae as as expressed by the slope of Taylor's random in wheat fields. In our study, spatial regression (Kapatos et al., 1998). distribution of larvae on wheat leaf was

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Figure 3. Summary of re-sampling validation analysis using 10 independent data sets of Syringopais temperatella on wheat showing the calculated sample size means (±SE) for Green's sequential sampling plan at Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 precision levels of 0.25 and 0.1.

Similar to the optimum sample sizes, the fixed-precision sequential sampling of S. estimated stop lines of the larvae were temperatella on wheat or other host plants. different based on the desired precision level However, some studies were done to (3.45 and 83.76 cumulative larvae numbers develop the sampling plan for estimating per plant at precision levels of 0.25 and 0.1, population densities of other pests belonging respectively). Totally, the optimum sample to Gelechiidae. For instance, the developed sizes and sampling stop lines were increased fixed precision sequential sampling plan for by increasing the desired precision level estimating population of P. operculella on (from 0.25 to 0.1). Similarly, Afshari et al. different cultivars showed that the required (2009) stated that the optimum sample size optimum sample size ranged from 149 to was flexible and depended upon the aphid 1,054 leaves based on precision level, 0.1 or density and desired level of precision. 0.25, larval population density, and potato The developed sequential sampling could cultivar (Shahbi and Rajabpour, 2017). not be compared with other studies because Moreover, the optimum sample size for there were no previous studies to develop a population monitoring of lineatella

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Figure 4. Summary of re-sampling validation analysis using 10 independent data sets of Syringopais temperatella on wheat showing the calculated precision level means (±SE) for Green's sequential sampling plan at precision levels of 0.25 and 0.1.

Zeller at the precision level of 0.2 varies on spatial distribution parameters of each from 3 to 10 samples according to the larval pest species. Spatial distribution is one of density (Damos, 2018). the most characteristic ecological properties Some studies were performed to develop of species. Therefore, the parameters are fixed precision sequential sampling of wheat different according to pest species. pests (Boeve and Weiss, 1998; Elliott, 2003; Moreover, many factors related to host Parker et al., 2002; Fathi and Bakhshizadeh, plant, environmental conditions, 2014). All of these studies are not competitions, etc. can affect the spatial comparable with our studies since all fixed distribution parameters (Taylor, 1984). precision sequential sampling models, Green's or Kuno's model, basically depend

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CONCLUSIONS Agriculture", (Eds.): Pedigo, L. P. and Buntin, G. R. CRC Press, Boca Raton, USA, PP. 99–115. Spatial distribution of S. temperatella 6. Burgio, G., Lanzoni, A., Masetti, A. and larvae on wheat leaves was aggregative. The Manucci, F. 2005. Spatial Patterns and optimum sample sizes of the larvae ranged Sampling Plan for Liriomyza huidobrensis from 5-68 plants and 12-189 plants (Diptera: Agromyzidae) and Related according to the average of larval density at Parasitoids on Lettuce. Environ. Entomol. precision levels of 0.25 and 0.1, 34(1): 178-183. respectively. The sampling must be 7. Cocco, A., Serra, G., Lentini, A., Deliperi, continued until the cumulative number of S. and Delrio, G. 2015. Spatial Distribution and Sequential Sampling Plans for larvae per plant reaches 3.45 (at precision absoluta (Lepidoptera: Gelechiidae) in level 0.25) and 83.76 (at precision level 0.1) Greenhouse Tomato Crops. Pest. Manag. larvae per plant. Results of this study can be Sci., 71(9): 1311-1323. used in integrated pest management program 8. Damos, P. 2018. Density-Invariant of S. temperatella in wheat fields. Dispersion Indices and Fixed Precision Sequential Sampling Plans for the Peach Twig Borer (Lepidoptera: ACKNOWLEDGEMENTS Gelechiidae). Eur. J. Entomol., 115: 642- 649. The research was financially supported by 9. Dinarvand, N., Rajabpour, A., Sohani, N. Z. Agriculture and Natural Resources and Farkhari, M. 2020. Effect of Weedy University of Khuzestan (Grant no. Culture on Population Densities, Spatial Distributions and Sampling Procedures of 9628402). Spodoptera exigua and Sesamia cretica (Lep., Noctuidae) in Corn Fields. Bul. REFERENCES Entomol. Res. 110: 84-89 10. Elliott, N. C., Giles, K. L., Royer, T. A., Kindler, S. D., Tao, F. L., Jones, D. B. and 1. Afshari, A., Soleyman Nejadian E. and Cuperus, G. W. 2003. Fixed Precision Shishehbor, P. 2009. Population Density and Sequential Sampling Plans for the Greenbug Spatial Distribution of Aphis gossypii Glover and Bird Cherry-Oat Aphid (Homoptera: (Homoptera: Aphididae) on Cotton in Aphididae) in Winter Wheat. J. Econ. Gorgan, Iran. J. Agr. Sci. Tech. 11(1): 27- Entomol. 96(5): 1585-1593. 38. 11. Fathi, S. A. A. and Bakhshizadeh, N. 2014. Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 2. Al-Zyoud, F., Salameh, N.M., Ghabeish, I. Spatial Distribution of Overwintered Adults and Saleh, A. 2009. Susceptibility of of Eurygaster integriceps (Hemiptera: Different Varieties of Wheat and Barley to Scutelleridae) in Wheat Fields of Ardabil Cereal leafminer Syringopais temperatella Province. J. Crop Protect., 3: 645-654. Led. (Lep., Scythrididae) under Laboratory 12. Ferracini, C. and Alma, A. 2007. Sequential Conditions. J. Food Agri. Environ., 7(3 and Sampling Plan for Cameraria ohridella 4): 235-238. (Lepidoptera: Gracillariidae) on Horse 3. Boeve, P. J. and Weiss, M. 1998. Spatial Chestnut Tree. J. Econ. Entomol., 100(6): Distribution and Sampling Plans with Fixed 1910-1915. Levels of Precision for Cereal Aphids 13. Ghaderi, S., Fathipour, Y. and Asgari, S. (Homoptera: Aphididae) Infesting Spring 2018. Population Density and Spatial Wheat. Can. Entomol., 130(1): 67-77. Distribution Pattern of 4. Brown, M. W. 1989. Spatial Dynamics and (Lepidoptera: Gelechiidae) on Different Sampling of Lyonetia speculella Tomato Cultivars. J. Agr. Sci. Tech., 20(3): (Lepidoptera: ), a Leafminer of 543-556. Apple. Environ. Entomol. 18(5), 875-880. 14. Heinz, K. M. and Chaney, W. E. 1995. 5. Buntin, G.D. 1994 Developing a Primary Sampling for Liriomyza huidobrensis Sampling Program. In: "Handbook of (Diptera: Agromyzidae) Larvae and Damage Sampling Methods for in

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in Celery. Environ. Entomol., 24(2): 204- 24. Okolle, J. N., Mansor, M. and Ahmad, A. H. 211. 2006. Spatial Distribution of Banana 15. Jemsi, G. R. 2006. Determination of Skipper (Erionota thrax L.) (Lepidoptera: Economic Injury Level (EIL) of Cereal Leaf Hesperiidae) and Its Parasitoids in a Miner, Syringopais temperatella Led. (Lep.: Cavendish Banana Plantation, Penang, Elachistidae) in Khuzestan Province. Plant Malaysia. Insect Sci., 13(5): 381-389. Pests Diseas., 74(1): 19-31. 25. Parker, B. L., Costa, S. D., Skinner, M. and 16. Jemsi, G. R., Shojai, M., Radjani, G. R. and Bouhssini, M. U. 2002. Sampling Sunn Pest Ostovan, H. 2002a. Study on Economic (Eurygaster integriceps Puton) in Population Dynamic, Biology, Host Range Overwintering Sites in Northern Syria. Turk. and Economic Threshold of Cereal Leaf J. Agri. For., 26(3): 109-117. Miner in Khuzestan. Agri. Sci., 8(3): 12-21. 26. Pena, J. E. and Schaffer, B. 1997. Intraplant 17. Jemsi, G. R., Shojai, M., Radjani, G. R. and Distribution and Sampling of the Citrus Ostovan, H. 2002b. Spatial Distribution of Leafminer (Lepidoptera: Gracillariidae) on Cereal Leaf Miner Syringopais temperatella Lime. J. Econ. Entomol., 90(2): 458-464. Led. at Larval Stage in Field Conditions. 27. Rajabpour, A. and Yarahmadi, F. 2012. 15th Iranian Olant Protection Conference, Seasonal Population Dynamics, Spatial September, Kermanshah. Distribution and Parasitism of Aphis 18. Kafeshani, F.A., Rajabpour, A., gossypii on Hibiscus rosa-chinensis in Aghajanzadeh, S., Gholamian, E. and Khuzestan. J. Entomol., 9(3): 163-170. Farkhari, M. 2018. Spatial Distribution and 28. Sermsri, N. and Torasa, C. 2015. Solar Sampling Plans with Fixed Level of Energy-Based Insect Pest Trap. Proc-Soc. Precision for Citrus Aphids (Hem: Behav. Sci., 197: 2548-2553. Aphididae) on Two Orange Species. J. 29. Shahbi, M. and Rajabpour, A. 2017. A Econ. Entomol., 111(2): 931-941. Fixed-Precision Sequential Sampling Plan 19. Kapatos, E. T., Stratopoulou, E. T., Tsitsipis, for the Potato Tuberworm Moth, J. A., Lycouresis, D. P. and Alexandri, M. P. Zeller (1998). The Spatial Pattern of Aphis gossypii (Lepidoptera: Gelechidae), on Potato οn Cotton. Entomol. Hellin., 12: 23-30. Cultivars. Neotrop. Entomol., 46(4): 388- 20. Lee, D.H., Park, J.J., Park, H. and Cho, K. 395. 2005. Estimation of Leafminer Density of 30. Shewry, P.R. 2009. Wheat. J. Exp. Bot., Liriomyza trifolii (Diptera: Agromyzidae) in 60(6): 1537-1553. Cherry Tomato Greenhouses Using Fixed 31. Snedecor, G. W. and Cochran, W. G. 1980. Precision Sequential Sampling Plans. J. Statistical Methods. Iowa State University Asia-Pac. Entomol., 8(1): 81-86. Press, Ames, USA. 21. Liu, Z. M., Meats, A. and Beattie, G. A. C. 32. Sokal, R. R. and Rohlf, F. J. 1995. Linear Downloaded from jast.modares.ac.ir at 8:09 IRST on Monday October 4th 2021 2008. Seasonal Dynamics, Dispersion, Regression in Biometry: The Principles and Sequential Sampling Plans and Treatment Practice of Statistics in Biological Research. Thresholds for the Citrus Leafminer, 3rd Edition, New York, USA. Phyllocnistis citrella Stainton (Lepidoptera: 33. Southwood, T. R. E. 1978. Ecological Gracillariidae), in a Mature Lemon Block in Methods with Particular Reference to the Coastal New South Wales, Australia. Aus. J. Study of Insect Populations. John Wiley and Entomol., 47(3): 243-250 Sons, New York, USA. 22. Namvar, P., Safaralizadeh, M. H., 34. Taylor, L. R. 1984. Assessing and Baniameri, V., Pourmirza, A. A. and Interpreting the Spatial Distributions of Karimzadeh, J. 2012. Estimation of Larval Insect Populations. Ann. Rev. Entomol., Density of Liriomyza sativae Blanchard 29(1): 321-357. (Diptera: Agromyzidae) in Cucumber 35. Yaman, I. A. and Jarjes, S. J. 1971. Greenhouses Using Fixed Precision Bionomics of the Wheat Leaf Miner, Sequential Sampling Plans. Afri. J. Biotech., Syringopais temperatella Led., in Iraq. Zeit. 11(9): 2381-2388. Ang. Entomol., 67(1‐4): 266-272. 23. Naranjo, S. E. and Hutchison, W. D. 1997. Validation of Sampling Plans Using a Resampling Approach: Software and Analysis. Am. Entomol., 43: 48-57.

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نمونهبرداری دنبالهای با دقت ثابت از Lep., ( Syringopais temperatella Gelechiidae( در مسارع گندم ایران

د. رشیدی، ع. رجبپور، ن. زندی سوهانی

چکیده

هیٌَز برگ غالت .Lep., Gelechiidae( Syringopais temperatella Led( یکی از آفات هْن گٌذم در بسیاری از هٌاعق جْاى از جولِ ایراى هیباشذ. ًوًَِبرداری دًبالِای با دقت ثابت ) رٍشی هقرٍى بِ صرفِ برای تخویي تراکن جوعیت آفت( برای تَسعِ برًاهِ هَفق هذیریت تلفیقی آفات هَرد استفادُ قرار هیگیرد. در ایي هغالعِ، برًاهِ ًوًَِبرداری دًبالِای با دقت ثابت الرٍّای S. temperatella رٍی گٌذم )رقن ٍریٌاک®( در عَل فصلّای زراعی 8931-8931تَسعِ یافت. برای ایي هٌظَر در هرحلِ اٍل، تَزیع فضایی ایي الرٍّا رٍی برگّای گٌذم با استفادُ از شاخص تایلَر ٍ رگرسیَى آیَاٍٍ تعییي شذ. تَزیع فضایی ایي الرٍُ رٍی برگّای گٌذم تجوعی بَد. بٌابرایي از هذل گریي برای تَسعِ ایي برًاهِ ًوًَِ- برداری دًبالِای با دقت ثابت استفادُ شذ. تعذاد ًوًَِ بْیٌِ الرٍی براساس هیاًگیي تراکن الرٍی بِ ترتیب از 5 تا 81 ٍ از 81 تا 813 گیاُ بِ ترًیب در سغَح دقت D=0.1 ٍ D=25 هتغیر بَد. خغَط تَقف هحاسبِ شذُ ًشاى داد کِ ًوًَِبرداری هیبایست تا زهاًی کِ تعذاد تجوعی الرٍّای آفت بِ D=0.25( 9/55( ٍ D=0.1( 19/85( در ّرگیاُ برسذ، اداهِ داشتِ باشذ. درستی ایي برًاهِ ًوًَِ برداری تَسظ RVSP هَرد تاییذ قرار گرفت.

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