E3S Web Conf.
Volume 389, 2023Ural Environmental Science Forum “Sustainable Development of Industrial Region” (UESF-2023)
|Number of page(s)
|Precision Agriculture Technologies for Crop and Livestock Production
|31 May 2023
Statistical analysis and forecasting of cotton yield dynamics in Kashkadarya region of Republic of Uzbekistan
Tashkent State Agrarian University, University str., 2, 100140, Tashkent province, Uzbekistan
* Corresponding author: Ruzmetovqudrat1967@gmail.com
There are phenomena that are significant to research because of how they grow and change through time in practically every discipline. One could attempt to direct a process, forecast the future using knowledge of the past, or characterize the distinctive aspects of a series using a finite quantity of information. The techniques used to handle time series are heavily influenced by the techniques created by mathematical statistics for distribution series. The most basic to the most complicated time series analysis techniques exist in statistics today. The article discusses the statistical analysis of a time series, specifically the average yield of cotton in the Kashkadarya region, Uzbekistan, and the Republics, using data from the Central Statistical Office of Uzbekistan from 2001 to 2020. The study involved constructing point and interval estimates for the average cotton yield with a 95% guarantee, identifying different types of trends, and predicting future yields for the region. Through the use of the Durbin-Watson statistical criteria, it was discovered that there is an autocorrelation dependence in the average cotton yield, indicating that the yield for the current year is dependent on yields from past years. The methods used in this study can be applied to further research conducted by students and scientists.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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