Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) has been the current topic of research for insect taxonomists and applied entomologists in

Influence of weather variables on occurrence of spiders in pigeon pea across locations of seven agro-climatic zones of India was studied in addition to development of forecast models with their comparisons on performance. Considering the non-normal and nonlinear nature of time series data of spiders, non-parametric techniques were applied with developed algorithm based on combinations of wavelet–regression and wavelet–artificial neural network (ANN) models. Haar wavelet filter decomposed each of the series to extract the actual signal from the noisy data. Prediction accuracy of developed models, viz., multiple regression, wavelet–regression, and wavelet–ANN, tested using root mean square error (RMSE) and mean absolute percentage error (MAPE), indicated better performance of wavelet–ANN model. Diebold Mariano (DM) test also confirmed that the prediction accuracy of wavelet–ANN model, and hence its use to forecast spiders in conjunction with the values of pest–defender ratios, would not only reduce insecticidal sprays, but also add ecological and economic value to the integrated pest management of insects of pigeon pea.

Investigation of the published data of a field experiment on assessment of damage due to leaf webber (Grapholita critica (Meyr.)Torticidae:Lepidoptera) under seven different sowing periods at weekly intervals between June IV to August II weeks of 2013-14 at Gulbarga (Karnataka) and observations on leaf webber damage recorded on weekly basis between 32 and 42 standard meteorological weeks (SMW) was done to demonstrate appropriate analytical methodology for an improved understanding of seasonal dynamics of G. critica damage and its relation to weather. Approach to analyses included reporting of calendar (SMW) based observations of G. critica damage on crop age basis, one way ANOVA for testing differences in damage levels amongst sowing periods, SMWs and crop age besides description of relations of damage with crop age and weather variables. Seasonal damage levels of G. critica for sowing periods were non-significant but significant (P less than 0.05) across SMWs and crop age with reduced damage during early and late crop stages irrespective of sowing periods. Seven and four weeks of higher damage and the best fit of polynomial relations of second order in respect of crop age over calendar based periods signified crop stage dependent damage due to G. critica. While MLR revealed significant influence of all weather and crop age variables (R2: 0.79), non-parametric regression revealed that less than 30°C of maximum temperature and greater than 23°C of minimum temperature to be favourable for G. critica damage. Crop age and calendar based observations have their importance for an area wide and field basis management of G. critica respectively.

In agriculture, methods of crop protection are too many making dissemination difficult. Mobile apps presenting information needs of client solutions and help in wider outreach. Prediction of pest incidence, synthesis of information on integrated pest management and selection cum calculation of appropriate pesticides in the area of crop protection have been used as information base in mobile apps. Mobile apps on pest forewarning (Pest-predict EMS and RBS), insecticide and fungicide calculators (IFCs) for 16 crops and on IPM for 4 crops that are android based have been developed. All the developed apps are hosted under “Google Play Store”. Desired mobile app can be downloaded using search term ‘ICAR-NCIPM’ under “Google Play Store” followed by the selection and installation of required mobile app. Feedback options and additional information are also inbuilt in the mobile apps.