Statistical models for forecasting pigeonpea yield in Varanasi region

Authors

  • PRITY KUMARI Section of Agricultural Statistics, Institute of Agricultural Sciences, Banaras Hindu University ,Varanasi-221005, India
  • G.C.MISHRA Section of Agricultural Statistics, Institute of Agricultural Sciences, Banaras Hindu University ,Varanasi-221005, India
  • C.P. SRIVASTAVA Departmentof Entomology and Agricultural Zoology, Institute of Agricultural Sciences, Banaras Hindu University ,Varanasi-221005, India

DOI:

https://doi.org/10.54386/jam.v18i2.956

Keywords:

Artificial neural network (ANN),, autoregressive integrated moving average (ARIMA) model, regression model, pigeon pea yield

Abstract

Present study deals with different linear and non-linear statistical models like multiple linear regression (MLR) model, autoregressive integrated moving average (ARIMA) model and artificial neural network (ANN) for forecastingpigeon pea yield grown in Varanasi region of Uttar Pradesh using 27 years of data (1985-86 to 2011-12). The performance of the model was assessed by root mean squared error (RMSE). On the basis of empirical studies, ANN was found to be best suitable model having lowest RMSE with forecasted yield during the year 2012-13 for Varanasi region. 

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Published

01-12-2016

How to Cite

PRITY KUMARI, G.C.MISHRA, & C.P. SRIVASTAVA. (2016). Statistical models for forecasting pigeonpea yield in Varanasi region. Journal of Agrometeorology, 18(2), 306–310. https://doi.org/10.54386/jam.v18i2.956

Issue

Vol. 18 No. 2 (2016): December

Section

Research Paper

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