Sanjeev Kumar Kataria, Paramjit Singh, Bhawana and Jaswinder Kaur

2017; 5(4): 976-983 E-ISSN: 2320-7078 P-ISSN: 2349-6800 JEZS 2017; 5(4): 976-983 2017 JEZS Received: 05-05-2017 Accepted: 06-06-2017 Sanjeev Kumar Kataria Paramjit Singh Bhawana Jaswinder Kaur Population dynamics of whitefly, Bemisia tabaci Gennadius and leaf hopper, Amrasca biguttula biguttula Ishida in cotton and their relationship with climatic factors Sanjeev Kumar Kataria, Paramjit Singh, Bhawana and Jaswinder Kaur Abstract The present study was carried out to find the correlation between population dynamics of whitefly and leafhopper with weather variables from June-November 2013 to June-November 2016 and June- November 2015 to June-November 2016, respectively. The main objectives of the study are to observe the population dynamic and correlate the population of whitefly and leafhopper with various climatic factors. The results revealed that peak population of whitefly was recorded in last fortnight of September in 2013(98.60 whitefly adult per 3leaves), 2014(101.00 whitefly adult per 3leaves) and 2016 (38.42 whitefly adult per 3leaves) whereas it shifted towards early season i.e. from July to August during 2015(Range=28.00-123.60 whitefly adults per 3 leaves). Leaf hopper peak infestation was observed in first fortnight of July during 2015(4.20 leafhopper per 3 leaves) and 2016(4.60 leafhopper per 3 leaves). Simple correlations studies revealed that during 2013 and 2014, the whitefly population was negatively correlated with temperature, relative humidity and rainfall. During 2015, population of whitefly showed a significant positive correlation with minimum temperature (r=0.657) and evening humidity (r=0.833) whereas during 2016, it was negatively correlated with rainfall and non-significant positive correlation with all other factors. Leaf hopper has a non-significant positive correlation with all the three weather variables during 2015 whereas negative correlation with temperature during 2016 and positive correlation with all other weather variables. Keywords: Cotton, population dynamics, whitefly, leaf hopper, correlation, weather variables Correspondence Sanjeev Kumar Kataria 1. Introduction Cotton (Gossypium spp.) being the king of natural fibres is popularly known as 'white gold' and 'silver fibre' [18]. It is the principal cash crop and its every plant part is useful to the farmer in one way or the other [40]. In India, cotton is cultivated over a large area and the country ranks first in the world with respect to area under cotton cultivation [39]. In Punjab, the total area, production and productivity of cotton in 2016-17 was 2.56 lakh hectares, 9.00 lakh bales of 170kg and 598kg lint per hectare respectively [4]. One of the most important reasons for low productivity is the damage done by insect-pests [38]. During growth period of cotton crop, 148 insect pests have been recorded, out of which only 17 species have been recorded as major insect pests of cotton crop [1]. Insect-pests of cotton can primarily be divided into sucking pests, foliage pests and bollworms. Among these, whitefly (Bemisia tabaci Gennadius), leaf hopper (Amrasca biguttula biguttula Ishida), thrip (Thrips tabaci Lindemann), aphid (Aphis gossypii Glover) and mealy bug (Phaenococcus solenopsis Tinsley) are very serious sucking insect pests, tobacco caterpillar (Spodoptera litura Fabricius) is a serious pest of cotton foliage whereas pink bollworm (Pectinophora gossypiella Saunders), spotted bollworm (Earias vitella Fabricius) and American bollworm (Helicoverpa armigera Hubner) are serious pests of cotton bolls [21]. In last few decades, bollworm attack was a serious problem but this problem has been solved to some extent with the introduction of Bt cotton varieties and a significant change in the cropping system in the cotton growing areas have been observed [12]. But the threat caused by sucking pests still remains unsolved. Among the sucking pests, whitefly, Bemisia tabaci Gennadius and leaf hopper Amrasca biguttula biguttula Ishida are of major importance [28]. Whitefly sucks sap from the plants which leads to reduction in growth and vigor of the plants. It also act as vector of many viral diseases of cotton especially cotton leaf curl disease [3]. Nymphs and adults of leaf hopper suck sap from leaves and cause damage to the lower surface ~ 976 ~

of leaves by injecting its toxic saliva into tissues which cause shedding of leaves and young bolls along with reduction in fruiting capacity greatly [32]. Climatic conditions have a great influence on the population, survival, development, out-breaks, reproductive capacity and activity of pest as well as predators and parasites either directly or indirectly [5]. For developing a weather based pest fore-casting system, information regarding population dynamics in relation to prevalent meteorological parameters (temperature, relative humidity, rainfall etc.) is needed. Moreover, the same meteorological parameters also influence the growth and development of crop. Therefore, a thorough understanding of interaction between the crop growth stage and meteorological parameters/pest dynamics is a prerequisite for weather based pest forecasting model [43]. Prediction of peak period of activity of a given pest can enable us to develop suitable control measures that will ultimately add to increase in productivity [11]. The main difficulty in resolving the pest management issue is the inadequate knowledge about the characteristics of the pest dynamics that leads to inadequate pest management conditions. Population dynamics play an important role in integrated pest management module [13]. Furthermore, cotton growers of our country come across severe losses due to pests. To avoid the loss incurred by the farmers and to assist them in better production, successful pest prediction methodology is needed. Pest prediction technology is generally based on population dynamics data of the pest taken over on a large area in the past few years. Hence, with this objective, the present research was carried out to observe population dynamics of whitefly and leaf hopper and its relation to weather variables which will assist the scientist to know the trend of population under those similar weather conditions. Our little effort will be supportive to move one step forward towards protection of cotton crop from sucking insect-pests in the present scenario. 2. Materials and methods A field experiment was conducted at Regional Research Station, Bathinda under the All India Coordinated Cotton Improvement Project (AICCIP) to study the population dynamics of whitefly for consecutive four years i.e. 2013, 2014, 2015 and 2016 and that of leaf hopper for consecutive two years 2015 and 2016 on transgenic cotton cultivar under unprotected conditions and their correlation with climatic factors viz. maximum and minimum temperature, morning and evening humidity and total rainfall. The experiment was conducted in a randomized block design with three replications in a plot size of 500m 2 with spacing of 67.5cm (plant to plant) and 75cm (row to row). The population data was recorded at weekly interval on five randomly selected plants per plot with three leaves per plant before 10.00 a.m. The standard meteorological data i.e. data for temperature, humidity and rainfall was collected from Agro-meteorological Department of Regional Research Station, Bathinda. 2.1 Statistical analysis Various graphs were also formed to observe the population trend of whitefly and leaf hopper with the help of Microsoft excel. The data were subjected to simple correlation between various weather factors and population dynamics of the pests was calculated by using following formula: where Correl (X, Y) is the simple correlation coefficient x is first variable i.e. abiotic component Y is the second variable i.e. population of insect-pest is the mean of first variable is the mean of second variable 3. Result and discussion 3.1 Population dynamics of whitefly and leaf hopper 3.1.1 Whitefly The data for population of whitefly was taken for consecutive four years i.e. 2013, 2014 2015 and 2016 and is depicted in Fig.1. The comparison of data for four years shows that the population of whitefly varies in different years. During 2013, whitefly started its appearance on cotton crop from 25 th standard meteorological week (SMW) but it attained its pest status only during 34 th SMW and maintained its pest status till 42 nd SMW. It attained its peak in 39 th SMW with a population of 98.60 adults per three leaves. In 40 th week, the population declined to 57.20, again increased to 89.30 adults per three leaves in 41 st week, then declined to 34.60 in 42 nd SMW. During 2014, whitefly started its appearance from 26 th standard meteorological week (SMW) but it attained its pest status only in 37 th SMW and maintained it till 42 nd SMW. It attained its peak in 39 th SMW with a population of 101.00 adults per three leaves, declined to 52.00 in 40 th week and then afterwards declined and was found below ETL from 43rd week onwards. During 2015, whitefly attained pest status in 25 th SMW and maintained its pest status till 37 th SMW. The population started increasing, reached to 96.40 adults per three leaves in 30 th SMW and was at its peak (123.60 adults per three leaves) during 31 st SMW. It remained high till 35 th SMW, then started declining reached below ETL from 38 th SMW onwards and then no whitefly was seen on cotton crop from 43 rd week onwards. During 2016, whitefly attained pest status during 30 th SMW with a population of 19.78 adults per three leaves, 20.33 adults per three leaves in 31 st SMW and 18.33 adults per three leaves in 36 th SMW. The highest population of 38.42 adults per three leaves was attained during 37 th week, afterwards the population declined and whitefly lost pest status. A lot of work has been done by various workers on population dynamics of whitefly. Similar observations were made by different scientists on the peak activity period of whitefly [37, 43, 24, 20, 27, 38, 23, 30, 36, 10, 2, 28, 14]. 3.1.2 Leaf hopper The data for population of leaf hopper was taken for the year 2015 and 2016 only. During 2015, the population of leaf hopper was observed from 25 th week onwards but it attained its pest status only during 29 th and 31 st SMW with a population of 2.1 and 4.2 adults per three leaves respectively. In all other SMWs, the population of leaf hopper remained below ETL. During 2016, leaf hopper started attaining pest status during 28 th SMW with a population of 3.2 adults per three plants and maintained it till 35 th SMW with a population of 3.0 adults per three leaves, then declined to 1.8 adults per three leaves and then again increased to 3.5 in 37 th SMW, ~ 977 ~

then declined to 1.8, again increased to 2.0 adults per three leaves and then afterwards declined and no more remained as pest (Fig. 2). Similar finding were observed that peak population of leafhopper during 28 th to 34 th SMW by group of the scientists [24, 25, 23, 2, 20, 38, 28]. Whereas some scientist found that maximum population was recorded in 37 th to 38 th SMW [29, 42] and some recorded leaf hopper throughout out the season [17, 33, 41] which were opposite to this finding. 3.2 Relationship of population dynamics with weather variables 3.2.1 Whitefly By critically analyzing the data, it was inferred that pest population was abundant when climatic conditions became suitable for its activity. The data presented in table 1 shows that during 2013, population of whitefly crossed ETL during 34 th SMW when the maximum and minimum temperature was 36.1 ºC and 27.0 ºC respectively and morning and evening humidity was 88.9% and 50.4% and rainfall of 2.0mm. Population of the pest reached at its peak of 98.60 during 39 th SMW at a maximum temperature of 34.3ºC, minimum temperature of 24.0 ºC, morning humidity of 89.7%, evening humidity of 48.9% and no rainfall. It remained high till 41 st SMW when the maximum and minimum temperature was 32.5 ºC and 22.7 ºC respectively, morning and evening humidity was 84.4 and 44.9% respectively and rainfall was 5.6mm. During 2014 (Table 2) population build-up of the insect started later in the season as compared to 2013 i.e. during 37 th SMW at 34.6 ºC (max. temp.), 24.5 ºC (min. temp.), 89.0% (morning humidity), 53.1% (evening humidity) and 1.2mm rainfall. It reached at its highest (101.00) during 39 th SMW at a temperature range of 22.9-34.8 ºC, average humidity of 59.0% and no rainfall. Then afterwards, it started decreasing but remained above ETL till 42 nd SMW. The data presented in Table 3 shows that during 2015, whitefly attack started earlier in the season and its population crossed ETL with a population of 29.00 adults per three leaves during 25 th SMW when maximum and minimum temperature was 38.4 ºC and 27.6 ºC respectively, morning and evening relative humidity was 75.7 and 41.7% respectively and rainfall was 0.5mm. It reached at a peak of 96.40 adults per three leaves during 28 th week when maximum and minimum temperature was 33.7 ºC and 26.6 ºC respectively, morning and evening humidity was 92.9 and 63.1% and rainfall was 39.7mm. Whitefly population attained second peak (123.60 adults per three leaves) during 31 th SMW at a temperature of 31.7 ºC (max.), 25.6 ºC (min.), relative humidity of 92.1% (morning), 72.6% (evening) and 44.7mm rainfall. It attained third peak (119.30 adults per three leaves) during 34 th SMW at a temperature of 34.8 ºC (max.), 25.1 ºC (min.), relative humidity of 87.1% (morning), 57.4% (evening) and 44.7 mm rainfall. After this, whitefly population started decreasing and reached below ETL during 38 th SMW at a temperature of 30.7 ºC (max.), 24.9 ºC (min.), relative humidity of 88.9% (morning), 59.6% (evening) and 74.7mm rainfall. After 43 rd SMW onwards whitefly attack on cotton stopped perhaps the conditions for its growth and development were not suitable either due to crop maturity or due to adverse climatic conditions. The data taken for whitefly population with respect to meteorological parameters for the year 2016 is tabulated in Table 4. The data shows that the overall population of whitefly was lowest in 2016. The population attained pest status only during 30 th and 31 st SMW and maintained it for two consecutive weeks with a population of 19.78 and 20.33 adults per three leaves when maximum and minimum temperature was 36.0 ºC, 35.2 ºC and 27.7 ºC, 26.7 ºC respectively, morning and evening humidity was 77.1, 81.0 and 60.7, 64.9% respectively and rainfall of 2.5 and 2.3mm respectively. After this population of the insect declined and reached below ETL, again crossed ETL during 36 th and 37 th SMW when maximum and minimum temperature was 33.9 ºC, 34.6 ºC and 24.1 ºC, 23.9 ºC respectively, morning and evening humidity was 84.9, 80.8% and 64.1, 61.1% respectively and rainfall of 12.2 and 1.2mm respectively. There are some references available in literature on influence [8, 22, of climatic factors on population dynamics of whitefly 34]. 3.2.2 Leaf hopper The data for population of leaf hopper taken for consecutive two years i.e. 2015 and 2016 is presented in Table 3 and 4 respectively. The data shows that during 2015, the population of leaf hopper remained below ETL during all the meteorological weeks except for 29 th and 31 st SMW when maximum and minimum temperature was 35.3 ºC, 31.7 ºC and 26.6 ºC, 25.6 ºC respectively, morning and evening humidity was 87.4, 92.1% and 60.9, 72.6% respectively and rainfall of 85.6 and 44.7mm respectively whereas during 2016, the population of leaf hopper remained above ETL during most of the season at a temperature range of 32.2-36.0ºC (maximum temperature) and 23.9-27.7 ºC (minimum temperature), humidity of 77.1-90.7% (morning) and 60.7-74.1% (evening) and rainfall of 0.00-122.3mm. similar results were observed in study by different worker [35, 22, 31]. 3.3 Correlation of insect-pest population with weather factors 3.3.1 Whitefly The population data of whitefly was subjected to statistical analysis for finding its correlation coefficient with maximum and minimum temperature, morning and evening humidity and rainfall (Table 5). During 2013 and 2014, the population of whitefly was negatively correlated with maximum and minimum temperature, evening relative humidity and rainfall. Similar observation on correlation between whitefly and rainfall was observed by Kalkal [21], Bhute et al. [9] It was not significantly correlated with morning humidity. During 2015, it was observed that population of whitefly has a significant positive correlation with minimum temperature (r = 0.657) and evening humidity (r = 0.833) whereas no significant correlation with maximum temperature, morning humidity and rainfall. This results of this study during 2015, was opposite to study of another researchers Kadam et al. [19], Ashfaq et al. [7] and Akram et al. [3]. During 2016, population of whitefly has a non-significant positive correlation with maximum and minimum temperature, morning and evening humidity whereas it has a negative correlation with rainfall (Table 5). 3.3.2 Leaf hopper The population data of leaf hopper was also subjected to statistical analysis for finding its correlation with these weather variables. It was observed that population of leaf hopper has a non-significant positive correlation with all the three weather variables i.e. temperature, humidity as well as rainfall during 2015 whereas it has a non significant negative correlation with minimum temperature during 2016 (Table 5). Leaf hopper population had a non-significant positive correlation with all other weather variables during this year. ~ 978 ~

A lot of work has been done on correlation relationship between pest population dynamics and weather variables and similar observation were recorded by Soni and Dhakad [42]. However the study conducted by Kadam et al. [19] also similar with our findings with respect to maximum temperature but results were differing in relative humidity and rainfall. Our study again differ with Hussain et al. [15] findings in term of maximum temperature, rainfall and relative humidity temperature while it was in line with one parameter i.e. minimum temperature and significantly positively. The finding of Laxman et al. [26] does not support our findings as his study showed negative correlation with rainfall. Table 1: Population dynamics of whitefly in relation to weather factors during 2013. Dates Standard Meteorological Temperature ( C) Relative Humidity (%) Total RF Population of whitefly Week (SMW) Tmax Tmin Average M E Average (mm) per three leaves 18 Jun-24Jun 25 40.4 27.6 34.0 60.4 25.7 43.1 0.0 1.20 25 Jun-01 Jul 26 39.8 27.3 33.6 71.4 35.1 53.3 10.4 3.00 02 Jul-08 Jul 27 37.3 27.9 32.6 76.7 43.6 60.1 16.4 2.80 09 Jul-15 Jul 28 38.0 27.6 32.8 77.1 44.7 60.9 9.7 5.00 16 Jul-22 Jul 29 35.3 27.1 31.2 80.6 54.9 67.7 70.6 7.20 23 Jul-29 Jul 30 35.8 26.9 31.4 84.4 54.6 69.5 41.4 8.50 30 Jul-05 Aug 31 35.9 27.7 31.8 83.0 54.4 68.7 36.6 15.40 06 Aug-12 Aug 32 32.3 26.4 29.4 87.4 67.6 77.5 78.8 6.40 13 Aug-19 aug 33 29.6 25.5 27.6 92.9 75.0 83.9 70.2 5.40 20Aug-26 Aug 34 36.1 27.0 31.6 88.9 50.4 69.6 2.0 22.60 27 Aug-02 Sept 35 36.0 26.3 31.2 77.9 43.3 60.6 0.0 24.80 03 Sept-09 Sept 36 35.5 24.7 30.1 78.6 45.4 62.0 0.0 34.00 10 Sept-16 Sept 37 36.1 24.4 30.2 82.4 39.4 60.9 0.0 33.40 17 Sept-23 Sept 38 34.7 22.3 28.5 84.9 41.1 63.0 0.8 21.40 24 Sept-30 Sept 39 34.3 24.0 29.1 89.7 48.9 69.3 0.0 98.60 01 Oct-07 Oct 40 33.3 22.7 28.0 86.0 44.7 65.4 0.0 57.20 08 Oct-14 Sept 41 32.5 22.7 27.6 84.4 44.9 64.6 5.6 89.30 15 Oct- 21 Oct 42 33.5 18.6 26.1 80.0 31.0 55.5 0.0 34.60 22 Oct-28 Oct 43 31.2 15.6 23.4 81.1 31.4 56.3 0.0 17.90 29 Oct-04 Nov 44 30.3 14.3 22.3 78.9 21.1 50.0 0.0 8.20 Table 2: Population dynamics of whitefly in relation to weather factors during 2014. Dates Standard Meteorological Temperature ( C) Relative Humidity (%) Total RF Population of whitefly Week (SMW) Tmax Tmin Average M E Average (mm) (per three leaves) 18 Jun-24Jun 25 40.4 28.3 34.4 70.6 35.6 53.1 13.4 0.00 25 Jun-01 Jul 26 37.2 26.2 31.7 82.9 45.0 63.9 7.2 0.20 02 Jul-08 Jul 27 37.4 27.7 32.6 80.7 43.9 62.3 13.2 0.00 09 Jul-15 Jul 28 41.4 29.5 35.4 67.1 32.7 49.9 0.0 2.40 16 Jul-22 Jul 29 37.1 27.0 32.0 82.7 48.1 65.4 4.6 2.60 23 Jul-29 Jul 30 35.6 27.7 31.7 80.7 51.7 66.2 0.0 4.90 30 Jul-05 Aug 31 35.1 27.2 31.2 86.3 58.3 72.3 3.6 6.20 06 Aug-12 Aug 32 37.4 27.5 32.4 81.4 50.9 66.1 27.8 8.80 13 Aug-19 aug 33 36.3 27.2 31.8 78.6 46.1 62.4 0.0 7.60 20Aug-26 Aug 34 38.1 26.2 32.2 80.9 38.9 59.9 0.0 6.60 27 Aug-02 Sept 35 33.6 24.9 29.3 86.7 55.1 70.9 5.2 11.30 03 Sept-09 Sept 36 32.1 24.1 28.1 94.7 65.9 80.3 157.2 12.20 10 Sept-16 Sept 37 34.6 24.5 29.6 89.0 53.1 71.1 1.2 26.50 17 Sept-23 Sept 38 35.3 24.0 29.7 86.9 46.1 66.5 0.0 43.00 24 Sept-30 Sept 39 34.8 22.9 28.9 77.0 41.0 59.0 0.0 101.00 01 Oct-07 Oct 40 36.9 23.5 30.2 83.1 34.6 58.9 0.0 52.00 08 Oct-14 Sept 41 32.9 18.8 25.9 80.1 30.4 55.3 0.0 23.50 15 Oct- 21 Oct 42 30.5 15.0 22.7 89.4 31.4 60.4 0.0 18.60 22 Oct-28 Oct 43 31.7 18.0 24.9 83.4 36.1 59.8 0.0 11.90 29 Oct-04 Nov 44 30.3 14.8 22.6 86.6 29.7 58.1 0.8 7.20 ~ 979 ~

Dates Table 3: Population dynamics of whitefly & leaf hopper in relation to weather factors during 2015. Standard Meteorological Week (SMW) Temperature ( C) Relative Humidity (%) Tmax Tmin Average M E Average Total RF (mm) Population of whitefly per three leaves Population of leaf hopper per three leaves 18 Jun-24Jun 25 38.4 27.6 33.01 75.7 41.7 58.71 0.5 29.00 1.00 25 Jun-01 Jul 26 38.6 25.7 32.15 81.9 41.6 61.71 4.7 31.30 0.80 02 Jul-08 Jul 27 36.5 26.9 31.71 77.4 49.4 63.43 20.5 28.00 0.90 09 Jul-15 Jul 28 33.7 25.4 29.57 92.9 63.1 78.00 39.7 96.40 0.00 16 Jul-22 Jul 29 35.3 26.6 30.94 87.4 60.9 74.14 85.6 84.60 2.10 23 Jul-29 Jul 30 34.3 26.2 30.26 86.9 61.1 74.00 6.2 104.00 1.10 30 Jul-05 Aug 31 31.7 25.6 28.62 92.1 72.6 82.36 44.7 123.60 4.20 06 Aug-12 Aug 32 33.3 28.2 30.71 94.1 66.4 80.29 65.6 94.60 1.60 13 Aug-19 aug 33 34.8 26.8 30.82 91.3 62.6 76.93 0.0 99.70 0.20 20Aug-26 Aug 34 34.8 25.1 29.96 87.1 57.4 72.29 1.0 119.30 0.20 27 Aug-02 Sept 35 36.5 26.1 31.27 85.7 51.3 68.50 0.0 89.60 0.40 03 Sept-09 Sept 36 35.8 23.0 29.44 82.1 41.0 61.57 0.0 33.70 1.70 10 Sept-16 Sept 37 37.3 23.5 30.42 90.4 40.4 65.43 0.0 21.50 1.40 17 Sept-23 Sept 38 30.7 24.9 27.78 88.9 59.6 74.21 74.7 7.40 1.00 24 Sept-30 Sept 39 33.3 21.1 27.21 88.7 47.0 67.86 2.0 7.00 1.20 01 Oct-07 Oct 40 35.3 19.8 27.56 92.1 38.3 65.21 0.0 9.20 1.40 08 Oct-14 Sept 41 35.1 20.8 27.99 90.9 40.9 65.86 0.0 5.60 1.70 15 Oct- 21 Oct 42 34.1 19.8 26.98 86.4 39.6 63.00 0.0 3.00 0.00 22 Oct-28 Oct 43 30.2 16.6 23.40 91.3 39.3 65.29 13.0 0.00 0.00 29 Oct-04 Nov 44 28.6 13.5 21.02 91.7 43.1 67.43 0.0 0.00 0.00 Dates Table 4: Population dynamics of whitefly & leaf hopper in relation to weather factors during 2016 Standard Meteorological Week (SMW) Temperature ( C) Relative Humidity (%) Tmax. Tmin. Average M E Average Total RF (mm) Population of whitefly per three leaves Population leaf hopper per three leaves 18 Jun-24Jun 25 28.6 39.4 35.69 78.3 49.0 46.29 5.5 9.67 1.0 25 Jun-01 Jul 26 28.4 39.2 33.04 81.7 50.4 58.14 16.8 14.33 1.5 02 Jul-08 Jul 27 28.0 36.7 34.01 83.3 57.5 63.64 16.9 16.67 0.6 09 Jul-15 Jul 28 27.7 35.6 33.79 83.2 67.3 66.06 63.3 9.33 3.2 16 Jul-22 Jul 29 27.1 35.3 32.31 80.2 64.8 70.41 24.5 12.33 4.1 23 Jul-29 Jul 30 27.7 36.0 31.65 77.1 60.7 75.24 2.5 19.78 4.6 30 Jul-05 Aug 31 26.7 35.2 31.23 81.0 64.9 72.52 2.3 20.33 2.3 06 Aug-12 Aug 32 27.0 34.7 31.85 82.7 70.9 68.90 63.7 17.67 2.0 13 Aug-19 aug 33 26.4 34.6 30.93 85.1 66.4 72.94 40.9 7.78 2.5 20Aug-26 Aug 34 25.7 33.0 30.81 85.9 72.9 76.79 85.4 7.00 2.7 27 Aug-02 Sept 35 24.2 32.2 30.51 90.7 74.1 75.73 122.3 15.11 3.0 03 Sept-09 Sept 36 24.1 33.9 29.35 84.9 64.1 79.42 12.2 18.33 1.5 10 Sept-16 Sept 37 23.9 34.6 28.18 80.8 61.1 82.39 1.2 38.42 3.5 17 Sept-23 Sept 38 24.4 34.7 28.99 86.1 63.1 74.49 0.1 14.70 1.8 24 Sept-30 Sept 39 24.0 34.5 29.24 86.6 63.9 70.95 0.0 13.40 2.0 01 Oct-07 Oct 40 24.0 35.4 29.57 80.0 55.8 74.59 0.0 11.60 1.8 08 Oct-14 Sept 41 20.4 35.0 29.23 75.7 47.7 75.26 0.0 5.40 1.3 15 Oct- 21 Oct 42 16.9 34.6 29.73 69.1 50.1 67.88 0.0 3.80 1.0 22 Oct-28 Oct 43 16.0 33.3 27.69 76.9 41.9 61.69 0.0 5.80 0.5 29 Oct-04 Nov 44 13.6 30.5 25.75 90.8 41.2 59.57 0.0 2.90 1.3 Table 5: Correlation matrix (r) of population of sucking pests and weather variables during different years Year Insect-pest Maximum temperature Minimum temperature Morning humidity Evening humidity Total RF ( C) ( C) (%) (%) (mm) 2013 Whitefly -0.257-0.227 0.391-0.019-0.389 2014 Whitefly -0.180-0.251 0.033-0.123-0.110 2015 Whitefly 0.092 0.657* 0.165 0.833* 0.295 Leaf hopper 0.048 0.288 0.089 0.348 0.386 2016 Whitefly 0.427 0.171 0.084 0.381-0.061 Leaf hopper 0.429-0.069 0.135 0.607* 0.329 Table value of r at 5% probability = 0.444, at 1% probability = 0.561 at df=18 where df refers to degree of freedom ~ 980 ~

Fig 1: Population dynamics of whitefly with respect to standard meteorological weeks during consecutive four years Fig 2: Population dynamics of leaf hopper with respect to standard meteorological weeks during consecutive two years 4. Conclusion From the above discussion, it is concluded that whitefly and leaf hopper remained active throughout crop season with peak population of whitefly was recorded in last fortnight of September (38 th & 39 th SMW) in 2013, 2014and 2016 whereas it shifted towards early season i.e. from July to August during 2015. Leaf hopper peak infestation was observed in first fortnight of July during 2015and 2016. During 2013 and 2014, the whitefly population was negatively correlated with temperature, relative humidity and rainfall. However population of whitefly showed a significant positive correlation with minimum temperature and evening humidity in 2015. During 2016, it was negatively correlated with rainfall and non-significant positive correlation with minimum, maximum temperature and relative humidity. Leaf hopper has a non-significant positive correlation with temperature, humidity as well as rainfall during 2015 whereas ~ 981 ~ negative correlation with temperature during 2016 and positive correlation with all other weather variables. Research works are planned to understand pest dynamics with the use of systematic procedures on pest surveillance data sets. This research work can further be used to understand pest dynamics forecasting models which would help the farmers in the pest management strategies. 5. Acknowledgement We gratefully acknowledge the Punjab Agricultural University Regional Research Station Bathinda for providing the facilities. 6. References 1. Abbas MA. General Agriculture. 2 nd Ed., Emporium Publisher, Pakistan. 2001, 295-301. 2. Aggarwal N, Brar DS, Butter GS. Evaluation of Bt and

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