Advertisement

Empirical Analysis for Crop Yield Forecasting in India

  • S. DharmarajaEmail author
  • Vidyottama Jain
  • Priyanka Anjoy
  • Hukum Chandra
Full-Length Research Article
  • 4 Downloads

Abstract

Several factors, including weather vagaries, possess a serious threat to agricultural crop production in India and also are noteworthy risks to the economy. Crop yield depends on nutrition level of soils, fertilizer availability and cost, pest control, agro-meteorological input parameters like temperature, rainfall and other factors. Further, each particular crop needs specific growing weather conditions. Therefore, prognosticating crop yield is a challenging task for every nation. Statistical models are the most commonly used tools to forecast the crop yield, whereas statistical forecasting model for predicting dynamic behavior of crop yield should be able to take advantage not only of historical data of crop yield, but also the impact of various driving forces of the external environment. This paper describes both the linear regression and time-series models to predict crop yield efficiently and precisely. In particular, Bajra yield data for Alwar district of Rajasthan have been considered for empirical fitting of the models. Additionally, the selection of auxiliary variables, based on the knowledge of crop growth stages, has mediated the outperformance of time-series model.

Keywords

Crop yield Crop growth stages Regression Time series Environment Prediction 

Notes

Acknowledgments

Authors are thankful to the editor and two anonymous reviewers for their valuable suggestions and comments which helped improve the manuscript to a great extent.

References

  1. 1.
    Baier W (1977) Cropweather models and their use in yield assessments. WMO Technical Note No. 151. World Meteorological Organization, Geneva, p 48Google Scholar
  2. 2.
    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, pp 18–19Google Scholar
  3. 3.
    Birthal PS, Khan MT, Negi DS, 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):145–155CrossRefGoogle Scholar
  4. 4.
    Chattopadhyay C, Agrawal R, Kumar A et al (2011) Epidemiology and development of forecasting models for White rust of Brassica juncea in India. Arch Phytopathol Plant Protect 44:751–763CrossRefGoogle Scholar
  5. 5.
    Dickey D, Fuller W (1979) Distribution of the estimators for autoregressive time series with a unit root. J Am Stat Assoc 74:427–431Google Scholar
  6. 6.
    Directorate of Economics and Statistics (2014) Agricultural Statistics at a Glance—2014. Ministry of Agriculture and Farmers Welfare, Government of India. http://eands.dacnet.nic.in/PDF/Glance-2014.pdf
  7. 7.
    Dkhar DK, Feroze SM, 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):341–346CrossRefGoogle Scholar
  8. 8.
    Fisher RA (1925) The influence of rainfall on the yield of wheat at Rothamsted. Philos Trans R Soc Lond, Ser B 213:89–142CrossRefGoogle Scholar
  9. 9.
    Government of Rajasthan (2016) Crop-wise and district-wise fourth advanced estimates. Government of Rajasthan. http://www.agriculture.rajasthan.gov.in/content/agriculture/hi/Agriculture/statistics.html
  10. 10.
    Hyndman RJ, Kostenko AV (2007) Minimum sample size requirements for seasonal forecasting models. Foresight Int J Appl Forecast 6:12–15Google Scholar
  11. 11.
    Kumar SN, Aggarwal PK, 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):332–341Google Scholar
  12. 12.
    Lobell DB, Gourdji SM (2012) The influence of climate change on global crop productivity. Plant Physiol 160(4):1686–1697CrossRefGoogle Scholar
  13. 13.
    Prasada AK, Chai L, Singha RP, Kafatos M (2006) Crop yield estimation model for IOWA using remote sensing and surface parameters. Int J Appl Earth Obs Geoinf 8:26–33CrossRefGoogle Scholar
  14. 14.
    Sellam V, Poovammal E (2016) Prediction of crop yield using regression analysis. Indian J Sci Technol 9(38):1–5CrossRefGoogle Scholar
  15. 15.
    Silver N (2012) The signal and the noise: why most predictions fail—but some don’t. HYPERLINK. Penguin Group, US. https://en.wikipedia.org/wiki/Penguin_Group
  16. 16.
    Silver, N. (2013). The signal and the noise: the art and science of prediction. HYPERLINK. Penguin Group, US. https://en.wikipedia.org/wiki/Penguin_Group

Copyright information

© NAAS (National Academy of Agricultural Sciences) 2019

Authors and Affiliations

  1. 1.Department of MathematicsIIT DelhiNew DelhiIndia
  2. 2.Department of MathematicsCentral University of RajasthanAjmerIndia
  3. 3.ICAR-Indian Agricultural Statistics Research Institute (IASRI)New DelhiIndia

Personalised recommendations