Statistical Modelling For Wheat Crop Production

Abstract

The main aim of this present chapter is to investigates the dynamic relationships between area and production of the wheat crop grown during 1989-90 to 2020-2021 in different districts of Gujarat state, India. Statistical analyses play a key role in current research studies on food security, where yield time series analysis is used to estimate past yield trends and to predict future yield trends. Various types of statistical models have been used for the analysis of yield time series. The time-series data on the area and production of wheat crops grown in different districts of Gujarat state from 1995-96 to 2019-2020 have been collected from the website. The Pedroni cointegration test indicates long-run equilibrium relationships between wheat area and yield, while the pairwise Granger causality test verifies cointegration linkages. The results reveal that the intercept and slopes are highly significant, and the model F-statistic is also highly significant, with a remarkably high R² of 92%. This model explains 92% of variations in wheat production. Additionally, for every unit increase in area under the wheat crop, the production is increased by 2.19 %. It is concluded that the long-term coefficient is positive and highly significant, indicating the existence of positive long-run equilibrium relationships between the study variables.