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<p>This study investigates the adoption of precision agriculture technologies (PATs) in the Czech Republic. Using unbalanced panel data from the Czech Farm Accountancy Data Network (FADN) survey spanning the period from 2017 to 2021, it aims to identify the drivers and barriers to the adoption of PATs in Czech field crop production. The estimation of a probit binary choice model with a within-between random effects (WBRE) specification – a novel approach to addressing heterogeneity in panel data choice models – reveals that PATs adoption is significantly influenced by socio-economic factors such as labor intensity, indebtedness, manager education, and farm economic size, as well as environmental factors such as localization and land quality. Furthermore, the adoption of PATs is associated with temporal dynamics in labor intensity, production efficiency, specialization, and land ownership. The findings underscore the need for targeted policy measures to promote the adoption of technology and enhance agricultural efficiency. The contribution of this study lies in deepening the understanding of the determinants and barriers to precision agriculture adoption in the EU context, where empirical research remains relatively scarce.</p>

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Drivers and barriers to precision agriculture adoption in Czech agriculture

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月23日

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  • Zdeňka Žáková Kroupová,
  • Lenka Rumánková,
  • Bartłomiej Bajan,
  • Lukáš Čechura,
  • Zuzana Hloušková,
  • Renata Aulová,
  • Pavel Šimek,
  • Jan Jarolímek

摘要

This study investigates the adoption of precision agriculture technologies (PATs) in the Czech Republic. Using unbalanced panel data from the Czech Farm Accountancy Data Network (FADN) survey spanning the period from 2017 to 2021, it aims to identify the drivers and barriers to the adoption of PATs in Czech field crop production. The estimation of a probit binary choice model with a within-between random effects (WBRE) specification – a novel approach to addressing heterogeneity in panel data choice models – reveals that PATs adoption is significantly influenced by socio-economic factors such as labor intensity, indebtedness, manager education, and farm economic size, as well as environmental factors such as localization and land quality. Furthermore, the adoption of PATs is associated with temporal dynamics in labor intensity, production efficiency, specialization, and land ownership. The findings underscore the need for targeted policy measures to promote the adoption of technology and enhance agricultural efficiency. The contribution of this study lies in deepening the understanding of the determinants and barriers to precision agriculture adoption in the EU context, where empirical research remains relatively scarce.