On-farm demonstration of insecticide for the control of pod borer (Helicoverpa armigera Hubner) on chickpea in Bale zone, Ethiopia

2018 ◽  
pp. 371-376
Keyword(s):  
Helicoverpa Armigera ◽  
Pod Borer ◽  
Helicoverpa Armígera ◽  
On Farm ◽  
Helicoverpa Armigera Hubner ◽  
Bale Zone

Comparative Efficacy of Various Insecticides to Control Gram Pod Borer (Helicoverpa armigera Hubner) on Chickpea

2003 ◽  
Vol 2 (4) ◽  
pp. 403-405 ◽  
Author(s):  
Abdul Rashid ◽  
Habib Ahmad Saeed . ◽  
Lal Hussain Akhtar . ◽  
Sabir Zameer Siddiqi . ◽  
Muhammad Arshad .
Keyword(s):  
Helicoverpa Armigera ◽  
Comparative Efficacy ◽  
Pod Borer ◽  
Helicoverpa Armígera ◽  
Helicoverpa Armigera Hubner

scholarly journals Evaluation of Indoxacarb 14.5 SC against Green Gram Pod Borer, Helicoverpa armigera (Hubner) Management through Front Line Demonstrations

2021 ◽  
Author(s):  
Mamta Devi Choudhary ◽  
Arjun Singh Jat ◽  
Sumitra Devi Bamboriya ◽  
Anop kumari
Keyword(s):  
Broad Spectrum ◽  
Helicoverpa Armigera ◽  
Foliar Application ◽  
Green Gram ◽  
Front Line ◽  
Net Return ◽  
Pod Borer ◽  
Helicoverpa Armígera ◽  
The Given ◽  
Helicoverpa Armigera Hubner

Front Line Demonstration on foliar application of broad spectrum insecticide Indoxacarb 14.5 SC for control of pod borer, Helicoverpa armigera in green gram was conducted on farmers field in Nagaur district during 2019-20. Through the demonstration of the technology, 35.1% increase in green gram production has been observed with the production of 6.35 q/ha as compared to farmer’s practice (4.7 q/ha). Under demonstrated practices farmer were advised to alternate spray of the given insecticide on appropriate time. Under demonstrated practices, farmers got maximum net return of 21,667 Rs./ha, whereas, it was 12,285 Rs./ha in farmer’s practices.

scholarly journals Management of Pod Borer (Helicoverpa armigera Hubner) Infesting Marigold (Tagetes erecta)

2017 ◽  
Vol 6 (9) ◽  
pp. 142-148
Author(s):  
Suheel Ahmad Ganai Hafeez Ahmad ◽  
Sonika Sharma Nadeya Khaliq ◽  
Thanlass Norboo Divya Chaand ◽  
Shallu Raina
Keyword(s):  
Helicoverpa Armigera ◽  
Tagetes Erecta ◽  
Pod Borer ◽  
Helicoverpa Armígera ◽  
Helicoverpa Armigera Hubner

scholarly journals Prediction of Helicoverpa armigera (Hubner) larval population using weather based forewarning model in soybean

2021 ◽  
Vol 21 (4) ◽  
pp. 494-498
Author(s):  
RAM MANOHAR PATEL ◽  
A.N. Sharma ◽  
PURUSHOTTAM SHARMA
Keyword(s):  
Helicoverpa Armigera ◽  
Training Dataset ◽  
Maximum Temperature ◽  
Validation Dataset ◽  
Larval Population ◽  
Pod Borer ◽  
Significant Difference ◽  
Helicoverpa Armígera ◽  
Predicted Values ◽  
Helicoverpa Armigera Hubner

Weekly larval populations of pod borer, Helicoverpa armigera (Hubner) collected from 20 districts of Maharashtra under Crop Pest Surveillance and Advisory Project (CROPSAP) during 2010-2015 were analyzed to work out the effect of weather variables on the incidence of this insect on soybean. The appearance of larva was observed throughout the soybean growing season, and the peak incidence was observed during third week of August to first week of September (34-35 SMW). The correlation analysis of the data revealed that larval population was significantly and positively influenced by the minimum temperature and rainfall of current week as well as previous two weeks. For the development of forewarning model, data for the period from 2010 to 2013 were used as training dataset and two year (2014-2015) data as validation dataset. The mean model used for forewarning the incidence of pod borer larval population was developed by using step-wise multiple regression analysis in polynomial form. The results of mean model revealed that the significant variables affecting the pod borer larval population in soybean were maximum temperature (current and 1st lag week), and rainfall (current week) and the model explained 48.93% variation. The pre-disposing conditions for the incidence of larvae have been worked out as maximum temperature ranging from 26.1 to 31.47 ºC and rainfall ranging from 6.63 to 141.46 mm with low or medium rainfall in previous weeks followed by high in current week. The model was validated with 2014-15 independent dataset with predicted R2 (R2 ) value 28.13%. Two sample t-test showed no Pred significant difference between observed and predicted values (p = 0.3691 > 0.05).

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