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Food security as a major social concern and a global threat, requires better policy decisions based on empirical studies. This work presents a comparative statistical analysis of different methods to forecast wheat area, productivity, production, and population growth rate in Pakistan. Time series data from 1950 to 2020 were analyzed using various methods such as ARIMA, the compound growth exponential regression model ( CGREM ), Cuddy Della Valle instability index ( CDVI ), and decomposition analysis. The results show that  CGREM  performs better than other models. Periodic compound growth rates indicate that wheat area and yield decrease by about 67.0% and 40.0%, while the population decreases by 31.7%. For the period 2001-2020, the compound growth reaches the level of 0.60% for wheat area, 1.21% for yield, while it is high for the population and amounts to 2.22%. The overall compound growth rate for wheat area and yield (about 1.207%, 2.326%) is lower compared to the population (about 2.839%). The paper presents forecasts for wheat area, yield, and population in Pakistan will rise: 12.7%, 25.5%, 31.8% in 2030 and 43%, 97.8%, and 129% in 2050. The results of this study provide empirical evidence for the necessity of policy decisions addressing the problem of food security in Pakistan.

Forecasting the population growth and wheat crop production in Pakistan with non-linear growth and ARIMA models