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Modeling and Forecasting of Agricultural Commodity Production under Changing Climatic Condition: A Review

DOI: 10.18805/BKAP362    | Article Id: BKAP362 | Page : 273-279
Citation :- Modeling and Forecasting of Agricultural Commodity Production under Changing Climatic Condition: A Review.Bhartiya Krishi Anusandhan Patrika.2021.(36):273-279
Rahul Banerjee, Pankaj Das, Bharti, Tauqueer Ahmad, Manish Kumar rahuliasri@gmail.com
Address : Division of Sample Surveys, ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110 012, India.
Submitted Date : 26-08-2021
Accepted Date : 3-12-2021

Abstract

India is a country with an agrarian economy in which majority of its population rely on agriculture directly as their source of livelihoof. Climate has a very significant role in agricultural production. It predominantly influences growth of the crop, development of the crop and eventually crop yield. Climate also significantly influences the outbreak of disease and pest; it affects the requirement of water by the crop. Possible changes in weather factors, like precipitation, temperature and CO2 concentration are expected to have a significant impact on crop growth. If farmers are able to predict the weather activities and are aware of the effect of these activities on crop production, then it will be beneficial to them as a feasible plan can be devised synchronizing the crop production activities as per changes in the climatic conditions. In view of tackling the aforementioned problem, this article describes various statistical techniques that can play a crucial role in forecasting production of agricultural commodities changing climatic conditions.

Keywords

Climate change Crop yield forecast etc Production of agricultural commodities Statistical models

References

  1. Birthal, P.S., Khan, M.T., Negi, D.S., Aggarwal, S. (2014). Impact of climate change on yields of major food crops in India: Implications for food security. Agric. Econ. Res. Rev. 27(2): 145-155.
  2. Baier, W. (1977). Crop weather models and their use in yield assessments. WMO Technical Note No. 151. World Meteorological Organization, Geneva, 48.
  3. Basso, B., Cammarano, D., Carfagna, E. (2013). Review of crop yield forecasting methods and early warning systems. In Proceedings of the First Meeting of the Scientific Advisory Committee of the Global Strategy to Improve Agricultural and Rural Statistics, FAO Headquarters, Rome, Italy: 18-19.
  4. Chattopadhyay, C., Agrawal, R., Kumar, A. (2011). Epidemiology and development of forecasting models for White rust of Brassica juncea in India. Arch Phytopathology Plant Protect. 44: 751-763. 
  5. Das, P. (2020). Study on machine learning techniques based hybrid model for forecasting in agriculture. Published Ph.D. Thesis, I.A.R.I. New Delhi, India.
  6. Divisekara, R.W., Jayasinghe, G.J.M.S.R. and Kumari, K.W.S.N. (2020). Forecasting the red lentils commodity market price using SARIMA models. SN Business and Economics. 1(20): 1-13.
  7. Dkhar, D.K., Feroze, S.M., Singh, R., Ray, L. (2017). Effect of rainfall variability on rice yield in north eastern hills of India: A case study. Agric. Res. 6(4): 341-346.
  8. Fisher, R.A. (1925). The influence of rainfall on the yield of wheat at Rothamsted. Philosophical Transactions of the Royal Society of London. Series B, Containing Paper of a Biological Character. 213: 89-142.
  9. Iizumi, T., Shin, Y., Kim, W., Kim, M., Choi, J. (2018). Global crop yield forecasting using seasonal climate information from a multi-model ensemble. Clim. Serv. 11: 13-23.
  10. Kaul, M., Hill, R.L., Walthall, C. (2005). Artificial neural network for corn and soybean prediction, Agricultural System. 85: 1-18. 
  11. Kumar, S., Kumar, V. and Sharma, R.K. (2019). Rice Yield Forecasting Using Support Vector Machine. Int. J. Rec. Tech. and Eng. 8(4): 2588-2593.
  12. Kumar, S.N., Aggarwal, P.K., Rani, S., Jain, S., Saxena, R., Chauhan, N. (2011). Impact of climate change on crops productivity in Western Ghats, Coastal and North Eastern Regions of India. Curr. Sci. 101(3): 332-341.
  13. Lobell, D.B., Gourdji, S.M. (2012). The influence of climate change on global crop productivity. Plant Physiol. 160(4): 1686-1697.
  14. Mishra, G.S. and Dharm, Raj (2020). Neural machine translation using natural language processing, Journal of Critical Reviews. 7(9): 1432-1436.
  15. Prasada, A.K., Chai, L., Singha, R.P., Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. Int. J. Appl. Earth Obs. Geoinf. 8: 26-33.
  16. Paul, R.K., Sinha, K. (2016). Forecasting Crop Yield: ARIMAX and NARX Model. RASHI. 1(1): 77-85.
  17. Sellam, V., Poovammal, E. (2016). Prediction of crop yield using regression analysis. Indian J. Sci. Technol. 9(38): 1-5. 

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