Introduction
One of the most prominent features of the economic landscape is the intense geographical concentration of economic activity. Economic activities tend to be located in specific locations (for example, near markets or raw materials) and often some industries are concentrated in specific areas. Surveys show that out of 619,000 cattle and calf farms in the country, only 27,000 (4%) operate industrially. However, the share of the number of cows and calves and milk production of industrial farms from the total number of farms in the country is 32% and 58%, respectively. In this paper, the spatial distribution analysis of industrial cattle farming will be performed in two stages. First, the pattern of spatial distribution in the provinces in 2013, 2016 and 2019 has been studied, and then, using spatial regression method, more details of the regional concentration in industrial cattle farming are provided. More precisely, it has been tested to what extent natural and geographical factors, transportation, raw materials, are able to explain the spatial pattern of the geographical concentration of industrial cattle farming.
Methodology
The research is applied in terms of purpose and exploratory-confirmatory in terms of method. The research data are related to the number of active dairy and beef cattle’s divided and showed by provinces and extracted from the results of the census of the industrial cattle farming of the country in 2013, 2016 and 2019. The main determinants of the research are: percentage of barley production, percentage of forage production, average elevation, GDP, average temperature, average rainfall, road network density and population percentage.
Poisson global regression (GPR) and Poisson geographic weight regression (GWPR) were used to model the spatial distribution of industrial cattle farming. The mentioned models are performed in ArcGIS, GWR4 and the maps are prepared in the illustrator software.
Discussion and conclusion
This study aimed to model the spatial distribution of industrial cattle farming and its main determinants in the provinces of Iran. The results showed that the spatial and temporal distribution of industrial farms in the studied periods show little spatial and temporal variability. The results showed that the GWPR model has a better performance compared to the GPR model due to the fact that it shows the spatial variability of variables according to local conditions. The mean height showed a positive relationship. Active industrial farms are mainly concentrated in the central, southern and northeastern regions, where on the one hand it is far from mountainous and high areas and on the other hand the average temperature is high and the average rainfall is lower. But in local modeling, the relationships of these variables change according to local conditions and are not the same throughout the space. The results of this study show that the relationships between the distribution of industrial cattle farming and its determinants among the provinces of Iran both change in direction and intensity.
