"An Economic Analysis for Productivity of Rice Crop in Iraq and Forecasting it for The Period (2019- 2025) Using Markov Chains"

"The agricultural sector is still facing a problem represented by the low level of productivity of most crops, including the rice crop, despite the natural conditions that helped increase its productivity, but it remains a permanent problem, casting shadows on other aspects such as self-sufficiency in this crop and endangering food security at risk, in addition, the concept of productivity is closely related to the efficient use of the resources associated with its production conditions, which makes the process of forecasting this phenomenon very important. The research aims to study and predict the productivity of rice crop in Iraq using Markov chains for the period 2019-2025, The research also aimed at ways to improve the productivity of the crop in question by studying recent predictive values that are mainly based on previous data not far away. The results showed that the productivity of the rice crop was recorded at good rates that are relatively high, but remained below the global rates. The reason for recording these good rates is due to the superiority of changes in production over changes in area, which are among the most important factors in determining productivity as well as other factors that surround them, which should be noted It out. Accordingly, the research recommended the need for full coordination between what is planned to grow the crop with the plans of the Ministry of Agriculture that are developed depending on the water plans and natural conditions that the ministry takes into consideration as well as attention to the areas of concentration in this crop as they represent the areas of supply and distribution of this crop with attention directed towards the areas of concentration With regard to the provision of advisable agriculture requirements while addressing the problems that these areas are exposed to exclusively. From a statistical point of view, the research recommends adopting the Markov chains method in forecasting because it needs less stringent assumptions than other methods, including a few historical past observations series and fewer statistical tests"