Abstract
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.
Highlights
Rice is the staple food crop for more than half of the world’s population
In the present study, relevant count time series and machine learning techniques were applied to develop the rice gall midge occurrence models based on climatological input variables
The results showed that the INGRACHX and Support Vector Regression (SVR) with exogenous variable (SVRX) models were not suitable for the time series of the gall midge incidence due to the highly nonlinear and heterogeneous nature of the data
Summary
The Asian gall midge, Orseolia oryzae (Wood-Mason) (Cecidomyiidae: Diptera) (Figure 1a), is one of the most common difficult-to-control rice pests in South and Southeast Asia [1,2,3]. In India, it is the third most important rice pest after the stem borer and the plant hoppers [2], affecting 30–70% of the total rice area [4]. It is most prevalent in the states of Andhra Pradesh, Telangana, Tamil Nadu, Kerala, Goa, Karnataka, Maharashtra, Madhya Pradesh, Bihar, Odisha, Assam, Manipur, and in certain niches of West Bengal, and Uttar Pradesh of India [5,6,7,8]. An efficient early warning system based on a robust statistical model to predict gall midge population buildup is of great importance in designing and implementing a proactive and more sustainable site-specific pest control and management strategy
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
