Future of Rice Yellow Stem Borer Scirpophaga incertulas (Walker) Under Changing Climate

Vennila, S.; Bagri, Manisha; Tomar, Ankur; Rao, M. S.; Sarao, Preetinder Singh; Sharma, Sanjay; Jalgaonkar, VN; Prasannakumar, M. K.; Suresh, S.; Mathirajan, V. G.; Chatterjee, Sushovan; Tanwar, R. K.; Kumari, Anita; Prabhakar, M.

doi:10.1007/s40009-018-0751-x

Cited by 8 publications

(4 citation statements)

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“…The Chinsurah type light trap with a 200-watt bulb was used in the study because it is successfully used in monitoring of rice gall midge and other insect pests in rice ecosystems throughout the year [29,30]. The bulb was illuminated daily from 6:00 pm to 6:00 am.…”

Section: Data Collectionmentioning

confidence: 99%

Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches

et al. 2021

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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.

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Section: Data Collectionmentioning

confidence: 99%

Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches

et al. 2021

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“…It is a fact that both natural and manufactured agroecosystems are vulnerable to the current climate change phenomenon, and the rice ecosystem is no exception (Vennila et al, 2019). In the rice ecosystem, insect pest populations are one of the main pests of rice plants.…”

Section: Introductionmentioning

confidence: 99%

An Overview of the Population Dynamics Model Based on Climate Parameters

Koem

Lahay

Pambudi

2023

Geosfera J. Penelit. Geogr.

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Understanding how the environment, especially climate, affects the growth of pests (life cycle) is very important to create management techniques that reduce or eliminate damage to rice plants and minimize the resulting losses. This study aims to describe population dynamics modeling based on the influence of climate parameters using the DYMEX software. Climate and life cycle characteristics, which can be processed in the DYMEX life cycle module, are two important elements in modeling the rice stem borer. Egg, larval, and imago populations exhibit a variety of climate-related effects. The larval stage is most visible in the month of MJJA, where rainfall intensity is low or during the dry season. This indicates that the larval stage can survive through diapause in the dry season. The correct numbers must be used to initialize and parameterize models created using DYMEX. Overall, DYMEX can produce good simulation results in all life stages of the modeled insects. However, obtaining good, precise simulation results requires the user's accuracy in building the model, especially the values of the input parameters. In addition, to test the results of the DYMEX simulation, it is necessary to validate them with the actual conditions of the population in the field.

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“…level in terms of change in species abundance and seasonal dynamics have only been projected based on phenological models for a few insects on some crops (Vennila et al, 2019a;Rao et al, 2020). Weather based models using heuristic and empirical approaches for different agroeco regions based on datasets of light traps have been found useful to predictthe severity of rice insect pests that providesadvance information on timing and abundance of their population for use in pest management (Anon.2016).…”

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confidence: 99%

Future scenarios of rice brown plant hopper Nilaparvata lugens (Stal.) under changing climate

Vennila

Nisar²,

ISLAM

et al. 2021

J. Agrometeorol.

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Abundance of brown plant hopper (BPH) Nilaparvatalugens (Stål) (Delphacidae: Homoptera) is modulated by prevalent weather conditions of rice growing seasons and locations. Categorization of BPH adults caught in light traps (nos/week/trap) into low, moderate and high and formulation of criteria accounting weather variables [maximum/ minimum/ mean temperature (ÚC), morning/evening/mean relative humidity (%), rainfall (mm) and sunshine hours (h/day) and wind speed (km/h)] during kharif of2011-16 for four locations viz., Ludhiana (Punjab), Chinsurah (West Bengal), Raipur (Chhattisgarh) and Aduthurai (Tamil Nadu) with associated rules for weather based BPH prediction. Validation of BPH predictions for kharif 2017 indicated 96, 87, 73 and 61% accuracies in respect of Aduthurai (TN), Raipur (CG), Ludhiana (PB) and Chinsurah (WB). Future weather based predictions of BPH based on climatic projections of representative concentration pathway (RCP) 4.5 for 2020, 2050 and 2080 indicated absence of high population at Chinsurah (WB) during all time periods of 2020-2080. Progressively reducing BPH abundance from past (2011) to all future periods was noticed at Aduthurai (TN). ‘High’ BPH from 2020 and beyond over 2011 and 2016 at Raipur (CG) and reducing ‘high’ but increasing ‘moderate’ category between 2020-2050 but the reverse in 2080 at Ludhiana (PB) were predicted indicating requirement of continued monitoring strategies put in place at these locations. The observed spatial variability of climate change influence on BPH implied a need for zonation mapping of rice insects including BPH for India.

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Future of Rice Yellow Stem Borer Scirpophaga incertulas (Walker) Under Changing Climate

Cited by 8 publications

References 1 publication

Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches

Climate-Based Modeling and Prediction of Rice Gall Midge Populations Using Count Time Series and Machine Learning Approaches

An Overview of the Population Dynamics Model Based on Climate Parameters

Future scenarios of rice brown plant hopper Nilaparvata lugens (Stal.) under changing climate

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