Using Markov Chains to Predict Productivity of Maize in Iraq for the Period (2019-2025)

L.A.F. Al-Ani and A.D.K. Alhiyali

Applied Economics and Finance, 2021, vol. 8, issue 5, 1-9

Abstract: The research aims to predict the productivity of one of the most important major crops in Iraq, which is Maize, using Markov chains, which is one of the most important predictive methods that depend on relatively recent historical data and based mainly on previous data that is not far away. This is the advantage that Markov chains have, as relying on somewhat old historical data may negatively affect the predicted values. The results of the research showed the superiority of the third state to predict the productivity of Maize depending on the availability of Markov chains prediction conditions for this state. The results of the research also showed the continued decline in productivity for the coming years, as well as the impact of the predictive values on changes in the cultivated area more than changes in production, which confirms the existence of horizontal expansion at the expense of vertical expansion, that is, there is no intensification of production per unit area. The research also found that the actual values of productivity have approached the estimated values of the following years, and the matter applies to the convergence of these results for the subsequent years with the previous years, which confirms the accuracy of the method of Markov chains, in other words that what happened in the recent past had a clear impact in the future near.

Date: 2021
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