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

Abstract


 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).