The Asian rice gall midge (Orseolia oryzae (Wood-Mason)) is a major insect pest in rice cultivation. Therefore, development of a reliable system for the timely prediction of this insect would be a valuable tool in pest management. In this study, occurring between the period from 2013–2018: (i) gall midge populations were recorded using a light trap with an incandescent bulb, and (ii) climatological parameters (air temperature, air relative humidity, rainfall and insulations) were measured at four intensive rice cropping agroecosystems that are endemic for gall midge incidence in India. In addition, weekly cumulative trapped gall midge populations and weekly averages of climatological data were subjected to count time series (Integer-valued Generalized Autoregressive Conditional Heteroscedastic—INGARCH) and machine learning (Artificial Neural Network—ANN, and Support Vector Regression—SVR) models. The empirical results revealed that the ANN with exogenous variable (ANNX) model outperformed INGRACH with exogenous variable (INGRCHX) and SVR with exogenous variable (SVRX) models in the prediction of gall midge populations in both training and testing data sets. Moreover, the Diebold–Mariano (DM) test confirmed the significant superiority of the ANNX model over INGARCHX and SVRX models in modeling and predicting rice gall midge populations. Utilizing the presented efficient early warning system based on a robust statistical model to predict the build-up of gall midge population could greatly contribute to the design and implementation of both proactive and more sustainable site-specific pest management strategies to avoid significant rice yield losses.

Yellow stem borer (YSB), Scirpophaga incertulas (Walker) (Lepidoptera: Crambidae), a major monophagous insect pest of rice, causes significant yield losses. The rice–YSB interaction is very dynamic, making it difficult for management. The development of resistant lines has been unsuccessful as there are no effective resistant sources in the germplasm. Genome information is necessary for a better understanding of interaction with rice in terms of its recognition, response, and infestation mechanism. The draft genome of YSB is predicted to have 46,057 genes with an estimated size of 308 Mb, being correlated with the flow cytometry analysis. The existence of complex metabolic mechanisms and genes related to specific behavior was identified, being conditioned by a higher level of regulation. We deciphered the possible visual, olfactory, and gustatory mechanisms responsible for its evolution as a monophagous pest. Comparative genomic analysis revealed that YSB is unique in the way it has evolved. The obvious presence of high-immunity-related genes, well-developed RNAi machinery, and diverse effectors provides a means for developing genomic tools for its management. The identified 21,696 SSR markers can be utilized for diversity analysis of populations across the rice-growing regions. We present the first draft genome of YSB. The information emanated paves a way for biologists to design novel pest management strategies as well as for the industry to design new classes of safer and specific insecticide molecules.

No abstract