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Whitefly started to infest cotton soon after planting in favourable weather condition. During November planting mean whitefly population were highest (6.9 whiteflies per 3 leaves) and slowly declined in successive planting dates. It was found that number of population were above ETL during the month of December, January and February. Maximum population were recorded in the month of February depending on the growth stage of the crop. Maximum temperature beyond 35°C, minimum temperature below 8°C and moderate to high rainfall was very much detrimental to successful population build up. The most favourable temperature was ranged in respect of min. temperature and max. temperature was ranged 12–30°C. Simple regression value reflects whitefly population were influenced to the tune of 70.8%, 69.5%, 35.3% and 75.4% in November, December, January and February month respectively. Whitefly population were negatively correlated with temperature (max. and min.), rainfall and relative humidity (max. and min.); while, positively correlated with sunshine hours, but during November planting relative humidity (max. and min.) was positively correlated and sunshine hours were negatively correlated. Thus adjustment of planting dates may be adjusted or suitable plant protection measure may be introduced according to the weather forecast.
Sasya Shyamala Krishi Vigyan Kendra, Ramakrishna Mission Vivekananda Educational and Research Institute, Kolkata, West Bengal, India
*Address all correspondence to: ghosalabhijit87@gmail.com
1. Introduction
Crop productivity primarily gets highly influenced by biotic and abiotic stress. Several biotic fauna influence the growth of which insects are the most limiting factor to obtain the desired yield. On the other hand abiotic factors play an important role for the biotic stress abundance. Survival and thriving at extreme physical conditions require peculiar adaptations and plastic responses. Among abiotic factors, temperature and humidity stand out as the most important ones constraining abundance and distribution of insect. Furthermore, it is well documented that abiotic factors, regulate the ecology of insect communities. Although effects of temperature on survival, development, and reproduction of insects have been exhaustively explored over several decades, there is still a lot of interest on how temperature and other abiotic factors set the limits of distribution and define abundance of insect species.
Cotton, (Gossypium hirsutum L.) is the important cash crop in India due to its high industrial demand. Despite of huge share in areas the productivity of cotton (290 kg/ha) is still very lower than even the world average productivity. It is anticipated that this low production is mainly attributed due to infestation of pest problem. An array of insect pests has been reported to infest the crop rendering the low yield. About 162 species of insects has been known to occur in cotton at various stages of growth, of which 8 are key pests [1]. Among the sap feeders whitefly emerged as most dangerous due to its wide potency to act as vector of plant viruses.
Cotton leaf curl is suggested as a major factor in the decline in cotton production worldwide [2]. Whitefly is active throughout the year on different host plants depending upon the regional and ecological condition, though the activity of this pest is more in dry season. Likewise other insect pest biology of whitefly population is greatly influenced by abiotic factors both positively and negatively as explored by several workers [3]. Sing et al. [4] studied the effect of microclimate on population dynamics of whitefly in cotton and concluded that whitefly population were negatively associated with temperature but directly associated with relative humidity. It is important to understand the relation between the weather parameters and insect population fluctuation to predict and develop a strategic model of pest management in the changing climate. In search of that conclusion the present work has been oriented to study the impact of weather parameters on population of whiteflies in cotton in West Bengal condition.
Field experiment was conducted in experimental plots of Bidhan Chandra Krishi Viswavidyalaya, Kalyani, Nadia, West Bengal, India during rabi season of 2012–2013 and 2013–2014 in randomised block design with three replication. Cotton (var. Bollguard-II) was raised in plots (10 m × 5 m) under recommended package of practices with 50 cm × 50 cm spacing in different days sowing at monthly interval starting from 1st November onwards till February [5]. The field was left as such without any plant protection intervention. Whitefly population were recorded from three leaves per plant top, middle and bottom canopies (randomly sampled tagged 10 plants per plot) from each plot were enumerated at weekly interval [6]. The meteorological data during the study periods was also recorded from the AICRP on Agro-meteorology, Directorate of Research, BCKV, Kalyani, Nadia to establish the correlation and regression co-efficient between whitefly population and weather factors.
The abiotic factors i.e. maximum temperature (X1), minimum temperature (X2), maximum relative humidity (X3), minimum relative humidity (X4), total rainfall (X5) and sun shine hours (X6) and population of whitefly (Y) were arranged as a weekly interval. The inter action between the population in one hand and meteorological data on the other hand had been worked out through correlation, regression and multiple regressions analysis. The data thus obtained were analysed statistically followed by Fisher’s method of analysis of variance [7]. Simple and multiple regression analysis (X1, X2, X3, X4, X5, X6) were worked out and the data were detailed out based on spectrum of regression analysis and equation as Y = a + b1X1+ b2X2 + b3X3+ b4X4+ b5X5+ b6X6. Where, b1…b6 are the regression coefficient of X1 ….X6.
The population build up of whiteflies in relation to abiotic factors were ascertained through the correlation studies along with simple and multiple regression analysis. The result showed that the population of whitefly was found in its first peak (9.7 whiteflies per three leaves) on 3rd week of December, when max. Temperature was- 26.1°C, min. Temperature was- 12.4°C, RH% was ranged between—61.5–91%, sunshine hour was—4.8 h and with zero rainfall. There were 10.2 whiteflies per three leaves were recorded in 3rd week of January and this was considered as the second peak. The population were greatly fluctuated with the fluctuation of mean temperature and relative humidity (RH%). Whitefly population stroked its highest and third peak during 2nd week of February (6th standard week) with max. Temperature 29.9°C and 13.4°C min. Temperature, relative humidity ranged 87% max.—43.5% min. and with sunshine hours—8 h. Gradually the population decreased with the subsequent advance of crop age (Figure 1). Population of whitefly illustrated non significant negative correlation with the max. Temperature (r = −0.18), min. Temperature (r = −0.30), rainfall (−0.003) and sunshine hours (r = −0.16); while positive correlation with max. Relative humidity (r = 0.54) and min. Relative humidity (r = 0.04). Maximum relative humidity showed significant correlation with the population load during November planting (pooled) (Table 1). Cumulative effect of weather parameters designated that 70.8% population (R2 = 0.708) can be explained by the cumulative effect of the weather parameters (Table 2).
Figure 1.
Population dynamics of whitefly in relation with weather parameters in November planted cotton (pooled).
Regression equation showing quantitative relationship between B. tabaci (Y) and meteorological parameter.
Correlation significant at 0.05 level.
Correlation significant at 0.01 level.
The number of whitefly population varied from 0.7 to 13.2 per three leaves during December planted cotton (Pooled) (Figure 2). Maximum whitefly population (13.2 per three leaves) was recorded during 2nd week of February; at this stage the max. Temperature was- 29.9°C, min. Temperature was- 13.4°C, max. Relative humidity was- 87%, min. Relative humidity was- 43.5%, sunshine hours was 8 h and without any rainfall. But, during 7th standard week the population suddenly lowered down (7.8 whiteflies per three leaves) which was apprehended due to sudden forms of torrential rain during that week. The population again started rebuilding from 8th standard week (9.4 whiteflies per three leaves) with a comfortable weather. After 2nd week of March the population were in a trend to decrease with steady increase of temperature up to the rest of the experiment. The correlation coefficient (r) showed negative trend with maximum and minimum temperature (r = −0.03 and − 0.21, respectively), max. and min. Relative humidity (r = −0.16 and − 0.20, correspondingly) with population of whitefly. Sunshine hours showed positive correlation (r = 0.42) with the whitefly population build up. The effect of weather parameters during the period of infestation failed to establish any significant correlation (Table 1). The combined contribution of the weather factors was 69.5% (Table 2).
Figure 2.
Population dynamics of whitefly in relation with weather parameters in December planted cotton (pooled).
Figure 3 depicts the incidence pattern of B. tabaci during January planted cotton (Pooled). Infestation was initiated (0.5 whiteflies per three 3 leaves) during 3rd standard week after the initiation of third leaf. It was noted that whitefly population were gradually in upward trend with the advances of crop stage and thereby hits its maximum peak (12.6 whiteflies per three 3 leaves) during 9th standard week (1st week of March), with max. temperature—30.8°C, min. temperature—15.3°C, max. RH—88%, min. RH—45%, sunshine hours—8.2 h and zero rainfall. During 10th standard week and afterwards the population were getting dwindled off with the increase of temperature. All the weather parameters were negatively correlated with whitefly population during December planting (pooled) except sunshine hours (r = 0.30) (Table 1). But none of the parameters showed significant relation. Only 35.3% population were influenced by the existing weather factors at this period (Table 2).
Figure 3.
Population dynamics of whitefly in relation with weather parameters in January planted cotton (pooled)
Effect of climatic factors on the incidence of B. tabaci in February planted cotton (Pooled) is presented in Figure 4. The number of whitefly population were varied from 0.5 to 4.2 per three leaves during the course of study. It was observed that max. Temperature above 35°C was unfavourable to the population load. Maximum whitefly population (4.2 per three leaves) was recorded during 1st week of March (max. temperature was—30.8°C, min. temperature was—15.3°C, max. relative humidity was—88%, min. relative humidity was—45%, sunshine hours was 8.2 h and zero rainfall). But, during 12th standard week the population suddenly fall down (1.8 whiteflies per three leaves) which was detained due to sudden forms of torrential rain during that week associated with high temperature. After that the population were in a trend to shrink with steady increase of temperature and rainfall up to the rest of the experiment. Lowest population were recorded in 20th standard week. Minimum temperature (−0.73), min. relative humidity (−0.54) and rainfall (−0.57) exhibited significant effect on the population of whiteflies (Table 1). 75.4% population of whitefly were influenced by the weather factors during this period of investigation (Table 2).
Figure 4.
Population dynamics of whitefly in relation with weather parameters in February planted cotton (pooled).
4. Effect of different dates of sowing on the incidence of whitefly
Wide fluctuation of population were noted in different dates after planting, which was varied from 0.9–7.5 mean whiteflies per three leaves. Lowest population were recorded in 21 DAS, and then slowly but steadily increased with the advance of crop age up to 63 DAS. These findings may be ascertained due to the favourable stage of the crop for successful multiplication of whitefly. It was recorded that population maintained uniform pattern of incidence during 49 DAS to 98 DAS. Accordingly the population were in decreasing trend and thus during 119 DAS only 2.6 numbers of whiteflies were recorded from three leaves (Table 3).
Crop stage
Mean whitefly population/3 leaves on different dates of sowing (mean of two years)
Mean
1st November
1st December
1st January
1st February
21 DAS
0.3
0.7
0.5
1.9
0.9
28 DAS
1.4
1.0
1.1
4.2
1.9
35 DAS
4.8
1.8
2.9
3.4
3.2
42 DAS
5.0
2.2
5.9
3.1
4.1
49 DAS
9.7
5.1
5.4
1.8
5.5
56 DAS
7.5
6.1
9.7
2.4
6.4
63 DAS
7.0
8.9
12.6
1.6
7.5
70 DAS
6.0
13.2
7.9
1.2
7.1
77 DAS
10.2
7.8
6.7
0.9
6.4
84 DAS
7.3
9.4
4.1
0.8
5.4
91 DAS
9.5
7.4
3.9
0.7
5.4
98 DAS
13.4
4.9
2.8
0.9
5.5
105 DAS
7.2
2.8
2.6
0.5
3.3
112 DAS
7.3
1.3
1.6
0.7
2.7
119 DAS
6.5
1.3
1.5
1.0
2.6
Mean
6.9
4.9
4.6
1.7
-
Table 3.
Effect of dates of sowing on the incidence of whitefly (B. tabaci) in cotton.
Each and every biological organisms are responsive towards the climatic factors. Biology of herbivore insects are greatly influenced by the weather parameters as these parameters exerted direct impact on the life cycle as well as the plant itself in which the insect used to feed and grow have potential impact on the population build up of the particular insects. It is apparent from this experimental findings that whitefly population were influenced by the weather parameters in different dates of planting of cotton. Population of whitefly in four different planting dates at monthly interval from November to February showed that the population of whiteflies decreased in successive planting. Population build up of whiteflies were high during spring season, as whitefly population were strongly affected by high temperature as well as low temperature; though the population of whitefly were greatly varied with the favourable growth stages of the crop. It was recorded that max. temperature beyond 35°C and min. temperature below 8°C was very detrimental for the population build up. The most favourable temperature was ranged in respect of min. temperature and max. temperature was 12–30°C depending on the favourable vegetative stage of the crop. Dry period greatly favoured the population build up, while rainfall exerted negative effect on the population size because the population of whiteflies were washed out as well as mortality of adult population were noticed. Our result is in confirmation with the findings of Sing et al. [4] and Banjo et al. [8]. It was also observed that population of whiteflies were maximum in 63 days after sowing and maintained its uniform pattern of incidence up to 98 days after sowing, which suggests that whiteflies prefer to feed the crop at early growth stages of the crop. Similar findings were reported by Meena et al. [9]. Correlation matrix of whitefly population and weather factors showed few sort of inconsistency based on the weather parameters recorded on that growing period of the crop. Whitefly population were negatively correlated with maximum and minimum temperature, relative humidity and rainfall; while positively correlated with sunshine hours; which were in agreement with Kataria et al. [10] and Latif and Akhter [11]. During November planting (pooled) maximum and minimum relative humidity was positively correlated with the population dynamics of whitefly, whereas, rainfall showed non significant positive correlation with the whitefly population during December planted cotton (pooled); which was at par with the result of Dahiya et al., [12]. Fluctuation in correlation between weather parameters and whitefly population build-up in different planting dates may be due to inconsistency of weather parameters as an effect of global warming in recent days or may be associated with the other ecological factors influencing whitefly incidence.
In the changing climate it is much very difficult to manage the pest in field condition. The interaction of crop and herbivore are greatly influenced by the meteorological parameters. Now a days thus pest forecasting has gained an importance in world agriculture which strongly depends on the study of population of the biotic species build up in relation with the abiotic factors like temperature, rainfall, humidity etc. In our present study we have noticed that the population of B. tabaci were negatively correlated with maximum and minimum temperature, relative humidity and rainfall; while positively correlated with sunshine hours during November plating while the population showed non significant positive relation with the rainfall during December planting, which denotes that during winter season the light rainfall helps to increase the soil temperature as well as temperature of the micro climate of the field. Temperature is the key meteorological factor in relation to population abundance of whitefly. It is apparent that the population build up of whiteflies in Indian context was favourable in between the temperature range 12–30°C depending on the favourable vegetative stage of the crop, early growth stages of the plant favours the population build up. Thus plant protection measure should be adopted during the early growth phase depending on the temperature, humidity and rainfall. At high temperature and moderate to high rainfall as the population of the whiteflies get affected thus avoid spraying the crop.
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Written By
Abhijit Ghosal
Submitted: 11 January 2022Reviewed: 02 February 2022Published: 31 March 2022
Open access peer-reviewed chapter
