Santosha RathodNanda Kumar Reddy, B.2024-05-032024-05-032023-01-28https://krishikosh.egranth.ac.in/handle/1/5810208659Aim of this study was to develop the reliable statistical model to forecast the yellow stem borer pest data. Count time series modelling is a popular statistical approach in which auto-correlated discrete count observations are considered as dependent variables, and the observations are assumed to be derived from Poisson or negative Binomial distributions. Crop pest modelling is one of the major areas of count time series modelling wherein daily or weekly counts of insects (pests) are considered as dependent variables of interest. As the pest population does not occur regularly on a daily basis there used to be many zeros in weekly count data under such conditions the classical count time series models may not yield better results alternatively zero excess count models can be used to model the data with excess zeros. In this study, the rice Yellow Stem Borer (YSB) populations recorded using a light trap with an incandescent bulb along with weather parameters of major centres generated under All India Coordinated Rice Improvement Project (AICRIP) from 2013-2021 were considered. The residuals of the fitted models namely, Integer-valued Generalized Autoregressive Conditional Heteroscedastic (INGARCH), Zero Inflated Poisson Autoregressive (ZIPAR), Zero Negative Binomial Autoregressive (ZINBAR) models were found significant for most of the centres. Therefore, to correct the classical model a two stage count time series methodology is proposed in this study. In the first stage, the YSB populations are modelled using the count time series models and diagnostically tested using the multivariate Box-Pierce test. If this test is not significant, the procedure ends. In the second stage, when the residuals are significant, the BDS test is used to test for the nonlinearity of the residuals. If the residuals are found to be significant, the ANN model is fitted to the residuals to obtain the predicted values. Finally, the ANN fitted residuals are combined with the count time series fitted values to obtain the final YSB population forecast. The proposed two stage methodology performed better thanclassical count time series models in both training and testing data sets, further, two stage zero inflated models outperformed all models which as it provides lowest root mean square error values. The proposed methodology for an efficient early warning system to predict the YSB population could greatly contribute to the sustainable site-specific pest management strategies to avoid significant riceyield losses.EnglishThesis