Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/5247
Title: FORECASTING WHEAT PRODUCTIVITY IN IRAQ FOR THE PERIOD 2019-2025 USING MARKOV CHAINS
Authors: Alani, L. A. F
Alhiyali, A. D. K
Keywords: cereal crops productivity
stochastic processes
normal distribution test
transitional matrix
Issue Date: 2021
Publisher: Iraqi Journal of Agricultural Sciences
Series/Report no.: 52(2);411-421
Abstract: This research was aimed to reveal the level of wheat crop productivity in Iraq by forecasting it using Markov chains for the period 2019-2022 , also exploring ways to improve the productivity of the crop under investigation by studying recent predictive values that are mainly based on previous data not far away. The problem of the study is the low productivity of wheat crop and its failure to achieve levels comparable to global and regional productivity. As long as it represents a permanent problem, this calls for concern that casts a shadow on other aspects such as self-sufficiency in this crop and endangering food security at risk. The results showed a continued decrease in the productivity of the wheat crop due to the superiority of the changes in the area to the changes in production, which are among the most important factors in determining productivity as well as the other factors that surround them, which should be noted. Accordingly, the research recommended the necessity to follow vertical intensification in agriculture, which has proven effective in influencing the productivity of a unit area, in addition to the need for vertical intensification to be compatible with the provision of other factors, namely the provision of improved seeds, highly efficient fertilizers and the necessary pesticides. As well as the need for all of the above to be consistent with the quality and efficiency of management, which plays an effective role in raising productivity. 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.
URI: http://localhost:8080/xmlui/handle/123456789/5247
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