Spatial Patterns and Relationships of Industrial Poultry Farming in Razavi Khorasan Province

Introduction

Rapid population growth and increasing demand for animal protein have made food security one of the major strategic challenges of the twenty-first century. Among livestock activities, industrial poultry farming is considered one of the most efficient and scalable sources of animal protein because of its relatively low production costs, short production cycle, and high capacity for supplying white meat. In Iran, the poultry industry plays an essential role in employment generation and stabilization of the national food system. Razavi Khorasan Province is one of the most important poultry-producing regions because of its large population, strategic location, and extensive production capacity. Nevertheless, the spatial distribution of poultry farms across the province is uneven. Some areas contain strong concentrations of poultry activities, whereas other areas remain weakly developed or excluded from the production network. In the absence of effective spatial planning, such inequalities may increase environmental pressure, intensify spatial imbalances, and reduce long-term sustainability. Therefore, the main objective of this study is to analyze the spatial pattern of industrial poultry farm distribution in Razavi Khorasan Province and identify the environmental, demographic, and infrastructural factors shaping this pattern. The theoretical framework is grounded in New Economic Geography, which emphasizes the role of agglomeration economies, transportation accessibility, and environmental constraints in shaping the spatial organization of productive activities.

Methodology

This study was conducted using a descriptive-analytical approach based on quantitative methods and spatial analysis techniques. The data included the geographic locations of 1,935 industrial poultry farms in Razavi Khorasan Province, together with climatic, topographic, demographic, and infrastructural variables. Spatial information related to poultry farms, slaughterhouses, and feed factories was obtained from the Razavi Khorasan Agricultural Jihad Organization and converted into spatial layers through geocoding in a GIS environment. Climatic variables, including long-term mean temperature and precipitation for the 2000–2024 period, were extracted from the TerraClimate database, while elevation data were derived from the Copernicus Digital Elevation Model. Population and road network data were obtained from the Statistical Center of Iran and the Ministry of Roads and Urban Development and processed in ArcGIS after coordinate system standardization. The study area was divided into 5×5 km grid cells, and all variables were aggregated at the grid level. In the first stage, the spatial pattern of poultry farm distribution was examined using the Average Nearest Neighbor index, Kernel Density Estimation, and the Standard Deviational Ellipse. Subsequently, the relationships between poultry farm density and natural and human-related variables were investigated using Spearman’s rank correlation coefficient and hierarchical clustering analysis. Because many grid cells contained zero values and the dependent variable exhibited strong positive skewness, a spatial hurdle model was employed. In this framework, the processes of poultry farm establishment versus non-establishment and the intensity of poultry farm density were modeled separately. The first stage was estimated using a logistic regression model, while the second stage was implemented through a log-normal regression model. To control residual spatial autocorrelation, Moran Eigenvector Spatial Filtering was applied by incorporating spatial eigenvectors into the regression framework.

Results and Discussion

The results of the Average Nearest Neighbor index indicated that the spatial distribution of industrial poultry farms in Razavi Khorasan Province is clustered and statistically significant (ANN = 0.189, p < 0.001). The main concentrations of poultry farming activities are located along the Mashhad–Quchan, Zabar Khan–Firouzeh, Kashmar–Khalilabad, and Torbat-e Heydarieh–Zaveh corridors. Kernel Density Estimation and the Standard Deviational Ellipse confirmed the existence of a directional pole-axis spatial structure in which areal, linear, and hybrid production poles have developed near transportation corridors and population centers. Spearman’s correlation analysis showed that poultry farm density has the strongest positive correlation with population density, whereas distance from roads, distance from cities, and distance from feed factories have significant negative correlations with poultry farm density. Temperature, precipitation, and slope also showed negative relationships with poultry farm density, indicating the constraining role of environmental conditions. The results of the spatial hurdle model demonstrated that, in the first stage related to poultry farm establishment and non-establishment, population density exerted the strongest positive effect, whereas distance from cities, distance from roads, distance from feed factories, temperature, precipitation, and slope all had significant negative effects. The coefficients for distance from feed factories (-1.41), temperature (-1.20), and distance from cities (-1.13) represented the strongest negative effects on establishment probability. These findings indicate that poultry farms are more likely to become established in areas with better access to transportation infrastructure, feed supply centers, and consumer markets. In the second stage, which examined the intensity of poultry farm density, distance from roads (-1.40), distance from feed factories (-1.02), and temperature (-0.79) showed the strongest negative effects. Environmental variables such as precipitation and slope also had significant negative effects, suggesting that environmental constraints influence both the initial establishment and the subsequent intensity of poultry farming activities.

Conclusion

The findings demonstrate that the spatial heterogeneity of industrial poultry farm distribution in Razavi Khorasan Province results from the simultaneous interaction of infrastructural, demographic, and environmental factors and that the spatial organization of this activity follows a clustered and pole-axis pattern. The concentration of poultry farming activities along transportation corridors and around population centers highlights the central role of market accessibility and feed supply chains in shaping the poultry industry. Conversely, environmental constraints such as temperature, precipitation, and slope limit the expansion of poultry farming activities. The results of the spatial hurdle model further revealed that the determinants of poultry farm establishment are not necessarily identical to the determinants of concentration intensity. Although population density increases the probability of establishment, its effect becomes negative in the intensity stage, probably because of higher land prices, urban land-use pressures, and sanitary restrictions in densely populated areas. Overall, the findings emphasize the need to move beyond uniform policies toward intelligent spatial regulation in which poultry development is managed according to environmental capacity, infrastructural accessibility, sanitary considerations, and spatial sustainability. Furthermore, the integration of spatial econometric methods with GIS-based analyses provided a more comprehensive understanding of the mechanisms shaping the spatial patterns of productive activities across regions.