Spatial Autoregressive Modelling of Priorities for Agricultural Development in the Ural Federal District

Abstract

Disparities in spatial development across agricultural sectors are becoming an increasingly urgent issue for regional food security. The study’s hypothesis is that agricultural development should focus on creating new growth poles, forming spatial clusters, and strengthening cooperative ties with surrounding areas. The purpose of this study is to test a methodological approach that supports the identification of promising directions for the development of the agricultural sector in the Ural Federal District (Russia). The proposed approach evaluates the spatial distribution of agricultural production in livestock and crop sectors, identifies centres of localization, emerging clusters, and their zones of influence, and examines direct and inverse spatial interactions between municipalities. These tasks are addressed through spatial autocorrelation analysis following P. Moran’s methodology and L. Anselin’s matrices of local spatial autocorrelation indices, while spatial autoregressive modelling assesses the effectiveness of proposed development priorities. As a result, the study identified priorities for the spatial development of agricultural sectors in the Ural Federal District, including new growth poles in crop production (Beloyarsky and Bogdanovich districts; Kartalinsky, Oktyabrsky, and Argayashsky municipal districts) and livestock farming (Kamyshlov District; Reftinsky, Tavdinsky, and Ketovsky districts), along with stronger cooperative links between existing and emerging growth poles, spatial clusters, and surrounding municipalities. Spatial modelling confirmed the effectiveness of these priorities for crop production and indicated that concentrating livestock production in growth poles is ineffective without the development of cooperative relationships. The findings of the study may be useful to policymakers in setting priorities for the spatial development of agricultural sectors in the Ural Federal District.