Main Article Content

Abstract

The early shoot borer, top borer, root borer, internode borer are major insect occurs in most of the sugarcane growing areas of the Gujarat and cause extensive damage to the sugarcane crop, which leads to losses in the crop yield. The weather discrepancies acting an important role in development of sugarcane insect and pest. The proper management of cropping practices may leads to overcome on it. Therefore it need to develop weather based approaches for forewarning the insect incidence which helps to farmers takes timely control measures to reduce the damage and yield losses due to this borer complex. Current study, relationship between insects incidence with weekly average weather parameters has been workout by using Karl-Pearson’s correlation approach on the 18 years of the data (2000-01 to 2017-18) in the Navsari district. The some of the weather variables were found significantly correlated with insect incidence. The multiple linear regression (MLR) and discriminant function analysis approach were adopted for statistical forewarning of the insect incidence. It was observed that MLR technique found better than discriminant function analysis for forecasting of insect incidence for forewarning of early shoot borer at 90 DAP and top shoot borer incidence at 5th month of crop season respectively.

Keywords

Forewarning Insect incidence Sugarcane Weather-insect relationship Weather variables

Article Details

Author Biography

Hiral Gundaniya, Department of Agricultural Statistics, B.A. College of Agriculture, Anand Agricultural University, Anand, Gujarat, India

Research Scholar

Department of Agricultural Statistics, B.A. College of Agriculture, Anand Agricultural University, Anand, Gujarat, India.

How to Cite
Garde, Y., Gundaniya, H., Thorat, V., Pisal, R., & Shrivastava, A. (2022). Forewarning of insect incidence based on weather variables for management of cropping practices in Sugarcane. Environment Conservation Journal, 23(1&2), 211–216. https://doi.org/10.36953/ECJ.021913-2166

References

  1. Agarwal, R.C., & Aditya, K. (2012). Use of discriminant function analysis for forecasting crop yield. Mausam, 63(3), 455-458.
  2. Agrawal, R.C. & Mehta, S.C., (2007). Weather based forecasting of crop yield, pest and diseases- IASRI models. Journal of the Indian Society of Agricultural Statistics, 61(2), 255-263.
  3. Anonymous (2020). E&S, DAC, New Delhi, Government of India, Report. Retrieved from https://www.sugarcane.dac.gov.in/pdf/StatisticsAPY.pdf
  4. Chattopadhyay N., Balasubramaniam, R., Attri S. D., Kamaljeet Ray, Gracy John, Khedikar, S., & Karmakar, C. (2019). Forewarning of incidence of Spodoptera litura (Tobacco caterpillar) in soybean and cotton using statistical and synoptic approach. Journal of Agrometeorology, 21(1), 68-75.
  5. Deb, S. & Bharpoda, T.M. (2017). Impact of meteorological factors on population of major insect pests in tomato, Lycopersicon esculentum Mill. Under middle Gujarat condition. Journal of Agrometeorology, 19(3), 251-254.
  6. Dubey S. K., Gavli A. S., Yadav S. K. Sehgal Seema & Ray S. S. (2018). Remote sensing-based yield forecasting for sugarcane (Saccharum officinarum L.) crop in India. Journal of the Indian Society of Remote Sensing, 46, 1823–833.
  7. Garde Y. A., Thorat V. S., Pisal R. R. & Shinde V. T. (2020), Pre Harvest Forecasting of Kharif Rice Yield Using Weather Parameters for Strategic Decision Making in Agriculture. International Journal of Environment and Climate Change, 10(12), 162-170.
  8. Garde, Y.A., Dhekale, B.S. & Singh, S. (2015). Different approaches on pre harvest forecasting of wheat yield. Journal of Applied and Natural Science, 7(2), 839-843. https://doi.org/10.31018/jans.v7i2.693
  9. Gregory, P.J., Johnson, S.N., Newton, A.C., & Ingram, J.S.I. (2009) Integrating pests and pathogens into climate change/food security debate. Journal of Experimental Botany, 60, 2827-2838. https://doi.org/10.1093/jxb/erp080
  10. Hossain, M.M., & Abdulla, F. (2015). Forecasting the sugarcane production in Bangladesh by ARIMA Model. Journal of Statistics Applications & Probability, 4(2), 297-303.
  11. Kiritani, K. (2006) Predicting impacts of global warming on population dynamics and distribution of arthropods in Japan. Population Ecology, 48, 5-12. DOI: 10.1007/S10144-005-0225-0
  12. Kumar, N., Pisal, R.R., Shukla, S.P., & Pandey, S.P. (2014). Crop yield forecasting of paddy, sugarcane and wheat through linear regression technique for south Gujarat. Mausam, 65(3), 361-364.
  13. Kumar, S., Kumar V. & Sharma, R.K. (2015). Sugarcane yield forecasting using artificial neural network models. International Journal of Artificial Intelligence and Applications, 6(5), 51-68. DOI: 10.5121/ijaia.2015.6504
  14. Kumari Prity, Mishra G. C., & Srivastava C. P. (2017). Forecasting models for predicting pod damage of pigeonpea in Varanasi region. Journal of Agrometeorology¸ 19(3), 265-269.
  15. Laxmi, R.R., & Kumar, A. (2011). Weather based forecasting for crops yield using neural network approach. Statistics and Applications, 9(1&2), 55-69.
  16. Marcari, M.A., Rolim, G.S., & Aparecido, L.E.O. (2015). Agrometeorological models for forecasting yield and quality of sugarcane. Australian Journal of Crop Science, 9(11), 1049-56.
  17. Mohankumar KS, Sugeetha G, Pankaja NS, Mahadev J., & Vijayalaxmi (2020). Seasonal incidence of phytophagous mites infesting different varieties of sugarcane crop (Saccharum officinarum: Poaceae). Journal of Entomology and Zoology Studies, 8(4), 2100-2104.
  18. Paswan, S., Chand, H. & Kumar, M. (2017). Forewarning model for borers of sugarcane under Bihar agro ecosystem. Bulletin of Environment, Pharmacology and Life Sciences, 7(1), 66-68.
  19. Priya, K.S.R. & Suresh, K.K. (2009). A study on pre-harvest forecast of sugarcane yield using climatic variables. Statistics and Applications, 7&8 (1&2), 1-8.
  20. Sattar, A., Khan, S. & Kumar, M. (2014). Crop Weather Relationship and Cane Yield Prediction of Sugarcane in Bihar. Journal of Agricultural Physics, 14(2), 150-155.
  21. Sisodia, B.V.S., Yadav, R.R., Kumar, S., & Mehta, M.K., (2014). Forecasting of pre-harvest crop yield using discriminant function analysis of meteorological parameters. Journal of Agrometeorology, 16(1), 121-125.
  22. Srivastava, A.K (2002). Prediction models for sugarcane production system, Indian Farming, 61(11), 61-64.
  23. Williams, J.R., Metcalfe, J.R., Mungomery, R.W., & Mathes, R. (Eds), (1969): Pests of Sugar Cane. Elsevier Publishing Company, Amsterdam, Amsterdam: Elsevier. pp 393.