spatial-temporal map

The purpose of this research is to create a spatio-temporal map illustrating the expansion of modern irrigation systems across Iran's provinces using the t-map package in R-Studio. The raw data used in this research was obtained from the statistical tables provided by the statistics center of the Ministry of Agricultural Jihad regarding the development of advanced irrigation systems in Iran from 2009 to 2022. First, an Excel file was prepared using the raw data, ensuring its compatibility with the geographical information of Iran's provinces. This data was then integrated with geographic information in R-Studio software, and spatio-temporal maps were generated using the functions of the t-map package. For clustering analysis, functions from the factoextra package were used. This package allows for data standardization before clustering using various methods. Moreover, in this research, Iran's provinces were clustered according to three criteria: the percentage of completion of modern irrigation systems, the percentage of development of irrigation and drainage networks, and the percentage of coverage of traditional irrigation canals in 2022. The findings indicate that out of 6,014,211 hectares of irrigated agricultural land, 2,469,835 hectares have been equipped with modern irrigation systems, reflecting an average implementation rate of 41% nationwide. Furthermore, the development percentages of irrigation and drainage networks and the coverage of traditional canals were found to be 15% and 0.5%, respectively. The spatio-temporal map of modern irrigation system implementation revealed that provinces with the most extensive irrigated agriculture exhibited lower adoption rates of these systems compared to others. For example, Khuzestan and Fars, which cumulatively account for 31% of the country's irrigated lands, have only progressed by 9% and 18%, respectively, in establishing new irrigation systems. It is important to note that while advanced irrigation systems enhance irrigation efficiency at the farm level, their significant effect on levels of groundwater table requires the presence of smart meters for agricultural wells that supply these systems.