Weather based prediction model for beet armyworm (Spodoptera exigua Hubner) in chickpea

Kamakshi, N; Sagar, D.; Jayalakshmi, V.; Chander, Subhash

doi:10.22438/jeb/40/1/mrn-833

Cited by 5 publications

(7 citation statements)

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“…The burning of fuels releases these kinds of exhaust into the environment ( Kiran et al, 2015;Mahla et al, 2017). Some typical industrial wood painting plants produce high volatile organic carbon (Sinha and Shivgotra, 2012;Kamakshi et al, 2019). In feeding of earthworm casts also leads to emission of air pollutants like NH , H S (Jae-Sung Yoon 3 2and Byung-Sung, 2016; Jae-Suk Choi et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Assessment of air quality index of urban area and epidemiological investigations in Chennai

Mariselvam¹,

Kumar²,

Dharmaraj³

et al. 2019

JEB

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To study air quality of Chennai city by comparing the air quality index (AQI) with the National Ambient Air Quality Standard (NAAQS) values. In urban area, a major straddling problem is air p o l l u t i o n. A n a l y s e s w a s conducted during Jan 2016-Dec 2017 for PM , SO , NO and CO 2.5 2 2 at different seasons in Manali, Chennai. Fine particulate sampler and Lutron CO meter were used to estimate PM and 2.5 CO levels. Due to different climate changes in India, higher concentration of pollution occurred during summer and m o n s o o n s e a s o n. T h e commercial and residential area experienced lower pollution compared with industrial areas. Case-control study showed that the result of odds ratio was significant. Commercial and traffic may have significant influence on air quality in Chennai. Air Quality Index, Air Pollutants, Epidemiological investigation, Industrial area. Assessment of air quality index of urban area and epidemiological investigations in Chennai

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Section: Introductionmentioning

confidence: 99%

Assessment of air quality index of urban area and epidemiological investigations in Chennai

Mariselvam¹,

Kumar²,

Dharmaraj³

et al. 2019

JEB

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“…Similar symptoms were reported on onion by Arulkumar et al (2017). Kamakshi et al (2019) observed the peak incidence of S. exigua in chickpea during the 45th standard meteorological week (SMW).…”

Section: Resultsmentioning

confidence: 99%

Population Dynamics of Spodoptera exigua (F.) and S. litura (F.) in Soybean

Babu

Singh²

2021

ije

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This study on the population dynamics of Spodoptera exigua (F.) and S. litura (F.) in soybean was done with observations made from 10 days after germination of the crop for two years. The results revealed that these pests are more abundant from July to August- Spodoptera litura (F.) occurred from the first week of July with occurrence of adults increasing with the growing season, and ending with the maturity of the crop. Amongst the current, 1st lag and 2nd lag week weather parameters, the occurrence of adults of S. exigua revealed a significant positive correlation with minimum temperature (r=0.716), evening relative humidity- RH (r=0.535), sunshine (0.621) and wind speed (0.661). Maximum temperature and wind speed exhibited a significant negative correlation with incidence of males of S. litura whereas, morning and evening RH were positively correlated with the incidence at 1-lag and 2-lag weeks. The incidence of larvae of S. litura was significantly influenced by morning RH (r=0.783) in the 2-lag week. The regression equation indicated that an increase in 1 mm rainfall decreased the male moth catches of S. exigua; while, incidence of larva was significantly affected with increase in 1% of morning RH; and incidence of S. litura larvae was related with maximum temperature of 1oC.

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“…However, in mean model, weekly mean data over the years of each district from first emergence week to first peak week used for Zonal models (Vidarbha, Madhya Maharashtra and Marathwada zones). The models were fitted by second degree polynomial equation taking percent damage per meter row length due to girdle beetle as dependent variable and maximum temperature (TMax), minimum temperature (TMin), relative humidity (RH) and rainfall (RF) pertaining to current, 1 st and 2 nd lag week as independent variables (Vannila et al, 2011;Kamakshi et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…The models were developed to evaluate the relative significance of different meteorological variables on the basis of coefficient of determination, R 2 (Kamakshi et al, 2018), RMSE, PRESS statistics. Accuracy of the models were validated using cross-validation approach (LOOCV -Leave One Out Cross-Validation) that is R 2 Pred (Patel et al, 2019b;Montgomery, et al, 2011);comparison of observed and predicted values using RMSE, standardized residual, and two samples 't-test' have been used.…”

Section: Model Evaluation and Validationmentioning

confidence: 99%

GIS based pest-weather model to predict the incidence of Girdle beetle (Oberiopsis brevis) in Soybean crop

Patel

Sharma

2021

J. Agrometeorol.

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Girdle beetle (Oberiopsis brevis) is an important insect of soybean that can cause up to 42.2% yield loss in severe infestation during flowering stage. The infestation of girdle beetle is prevailed by congenial environmental conditions, which leads girdle beetle to be the severe pest of soybean. The present study assesses the relevant weather variables that can cause the peak infestation. Crop Pest Surveillance and Advisory Project (CROPSAP) survey data of girdle beetle incidence were analyzed with weather variables using correlation and regression techniques. The girdle beetle infestation had significantly positive correlation with relative humidity of current and 2nd lag week (RH0, RH-2); and with rainfall of 2nd lag week (RF-2) but significantly negative correlation with maximum temperature of 1st lag week (TMax-1). The multiple regression technique was used to develop the forewarning models for three zones (Vidarbha, Madhya Maharashtra and Marathwada zones) and overall Maharashtra, the developed models could explain 80.30%, 94.62%, 73.56% and 79.56% variation in girdle beetle infestation, respectively. The congenial conditions for the peak infestation of girdle beetle on soybean have been worked out and validated, which were TMax0, RH0, RF0, RH-1, RF-1, TMax-2, and RF-2 ranged between 28.6-31.6 ºC, 85.2- 91.8 %, 31.8-119.2 mm, 86.3-92.6 %, 38.1-76.4 mm, 27.7-30.8ºC, and 23.3-60.7 mm, respectively. The insect forewarning would be useful in devising the integrated management strategies for protecting the crop from insect in the incidence region.

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Weather based prediction model for beet armyworm (Spodoptera exigua Hubner) in chickpea

Cited by 5 publications

References 0 publications

Assessment of air quality index of urban area and epidemiological investigations in Chennai

Assessment of air quality index of urban area and epidemiological investigations in Chennai

Population Dynamics of Spodoptera exigua (F.) and S. litura (F.) in Soybean

GIS based pest-weather model to predict the incidence of Girdle beetle (Oberiopsis brevis) in Soybean crop

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