: ; Study of farmer's behavior in recultivation of abandoned farmland: Example of the Republic of Buryatia - Alexander Prishchepov; Elena Ponkina; Zhanli Sun; Miroslava Bavorova; Olga Yekimovskaja (Prostranstvennaya Ekonomika = Spatial Economics, 2021)
, . . , , . , , - , , . 2018 . 149 , . - , , , , . , - - . , , , , . , , , . , . , , , . – – Despite the looming land scarcity suited for agricultural expansion, farmland abandonment is widespread globally. The recultivation of some abandoned farmlands could unlock the untapped agricultural potential. Yet, little is known about the determinants of recultivation. To better understand the behavioral intentions of farmers regarding the recultivation of abandoned lands, we concentrated on the Buryat Republic in Russia, where agricultural land abandonment is widespread and farmers with different ethnicities carry out diverse agricultural practices. We conducted 149 face-to-face interviews with the farmers in the Buryat Republic in 2018 and filled a structured questionnaire on farm's and farmers' socioeconomic characteristics, perceived corruption, and farmers' behavioral intentions regarding expected land use. We modeled the intention of recultivation with the Bayesian networks. The Bayesian networks analysis showed that perceived corruption was considered to be a barrier to recultivation. Our study also showed that non-Buryat ethnic and young farmers are more likely to recultivate abandoned land. Abandoned farmlands without or with a low degree of afforestation were preferred for recultivation. In sum, our study showed how behavioral aspects of political trust, the farm's and farmer's characteristics, in combination with regional and locational characteristics, may shape farmers' decisions on land use. The results provide an important ground to assess regional land-use policies, which should foster the recultivation of abandoned lands while preserving ecosystem services in the global hotspots of farmland abandonment, such as Russia.
Keywords: C11; Q12; Q15; 630; abandoned agricultural land; behavioral drivers; Bayesian networks; land-use change; land-use modeling; theory of land use