State of the art in total pulse production in major states of India using ARIMA techniques

Pradeep Mishra; Aynur Yonar; Harun Yonar; Binita Kumari; Mostafa Abotaleb; Soumitra Sankar Das; S G Patil

doi:10.1016/j.crfs.2021.10.009

State of the art in total pulse production in major states of India using ARIMA techniques

Curr Res Food Sci. 2021 Oct 28:4:800-806. doi: 10.1016/j.crfs.2021.10.009. eCollection 2021.

Authors

Pradeep Mishra¹, Aynur Yonar², Harun Yonar³, Binita Kumari⁴, Mostafa Abotaleb⁵, Soumitra Sankar Das⁶, S G Patil⁷

Affiliations

¹ College of Agriculture,Powarkheda,Jawaharlal Nehru Krishi Vishwa Vidyalaya, Hoshangabad, Madhya Pradesh, India.
² Department of Statistics, Faculty of Science, Selçuk University, Konya, Turkey.
³ Department of Biostatistics, Faculty of Veterinary Medicine, Selçuk University, Konya, Turkey.
⁴ Department of Agricultural Economics, Rashtriya Kisan (PG) College, Shamli (affiliated to Chaudhary Charan Singh University, Meerut), India.
⁵ Department of System Programming, South Ural State University, Chelyabinsk, Russia.
⁶ Birsa Agricultural University, Kanke, Ranchi, Jharkhand, India.
⁷ Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India.

Abstract

Pulses are staple protein-rich food for Indian vegetarians, and India is one of the largest producers in the world. The present investigation is an attempt to study the trend in the production of total pulses in India using the autoregressive integrated moving average (ARIMA) method. For stochastic trend estimation, yearly data were used for the period from 1961 to 2019. On the basis of the performance of several goodness of model fit criteria, the most suitable ARIMA model is chosen to capture the trend of pulse production. Forecasting for the 10 years from 2020 to 2029 is done, and it is observed that India has the highest forecast value (31.03302 million tonnes) in 2029. This study will play an important role in determining the gap between production of and demand for pulses in the future.

Keywords: Autocorrelation function; Autoregressive integrated moving average; Forecasting; Modeling; Production.