Journal of Environmental Biology

Predicting the incidence and severity of

wheat aphids and development of a

web-enabled forewarning system in India

A. Kumar¹, R. Sharma¹, B. Singh², S.D. Patil³, C.P. Srivastava⁴*, G.P. Singh⁵ and A.K. Joshi^6,7,*   

¹Agricultural Knowledge Management Unit, ICAR- Indian Agricultural Research Institute, New Delhi - 110 012, India

²Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana - 141 004, India

³Agricultural Research Station, Niphad, Mahatma Phule Krishi Vidyapeeth, Rahuri - 413 722, India

⁴Department of Entomology and Agricultural Zoology, Banaras Hindu University, Varanasi - 221 005, India

⁵Indian Institute of Wheat and Barley Research, Karnal - 132 001, India

⁶International Maize and Wheat Improvement Center, New Delhi - 110 012, India, India

⁷Borlaug Institute for South Asia, New Delhi - 110 012, India

*Corresponding Author Email : csrivastava63@gmail.com                    *ORCiD: http://orcid.org/0000-0003-3181-7825

Received: 25.05.2022                                                                                           Revised: 31.08.2022                                                                           Accepted: 28.10.2022

Abstract

Aim: Forecasting the incidence and severity of aphids, the major insect pest of wheat, is expected to significantly help in their management. In the present study, a set of weather-based models were developed to predict the timing and severity of Rhopalosiphum maidis infestation at Ludhiana falling under the North Western Plain Zone and R. padi at Niphad in the Peninsular Zone of India.

Methodology: The weather indices-based regression models for two locations, Ludhiana and Niphad, were developed using the aphid population and weather data gathered over eight years (2006–14), and the models' predictive accuracy was successfully tested over four additional years (2014-18). The developed statistical models were transformed into three-tier architecture, web-based system, i.e. Presentation, application and data tier for dissemination of information.

Results: The developed models can predict the crop’s age - when aphids first colonize the plants, when the aphid population attains the peak and the information about the peak intensity of the aphid population. For predicting the crop’s age at which population peaked at Ludhiana, the weighted interaction of the relative humidity (RH) in the evening and the number of hours of sunshine (NHS) along with the weighted interaction of minimum temperature and RH (morning) were important parameters while, at Niphad, the weighted NHS and the interaction of RH (morning and evening) were important. Likewise, for predicting the maximum aphid population at Ludhiana, the weighted interaction of minimum temperature and RH (morning) were important, while at Niphad, the key parameters were the weighted interaction of RH (evening) with the NHS.

Interpretation: A prototype system developed to forecast the location-specific (Ludhiana and Niphad) infestation of wheat crops by aphids is expected to facilitate aphid management through an accurate forewarning at the locations.

Key words: Mean Absolute Percentage Error, Rhopalosiphum maidis, Rhopalosiphum padi, Three-tier architecture, Weather-based regression model

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