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Market proximity and irrigation infrastructure determine farmland rentals in Sichuan Province, China

  • Kristin Leimer*
  • , Christian Levers
  • , Zhanli Sun
  • , Daniel Müller
  • *Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review

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Abstract

A dynamic market for farmland transactions can contribute to an increase in average farm size and to realising economies of scale in farming. China, with more than 200 million small and fragmented farms, has launched a new wave of land tenure reforms that foster land transfers to increase farm size and to improve agricultural efficiency. However, the factors that influence farmland transactions are far from clear. We aim to understand farmland rental determinants in response to a pilot land reform project in China's south-west Sichuan Province. We collected survey data from 410 farm households and used boosted regression trees to quantify the determinants of the land rentals. Our analyses provide three key findings. First, households with more plots equipped with irrigation infrastructure and closer distances to the nearest town were more likely to rent out land. Second, households rented out a larger share of their land when they had more irrigation infrastructure and lived close to Chengdu, the province capital. Third, the land reform pilot provided a modest but positive stimulus for land rentals. In sum, our results suggest that the supply and demand for land crucially hinges upon plot location and plot infrastructure.

Original languageEnglish
Pages (from-to)375-384
Number of pages10
JournalJournal of Rural Studies
Volume94
Early online date5 Aug 2022
DOIs
Publication statusPublished - Aug 2022

Bibliographical note

Funding Information:
This research was conducted within the IAMO China International Research group. We are grateful to Prof. Yuansheng Jiang and Prof. Shemei Zhang at Sichuan Agricultural University for facilitating the data collection and for numerous helpful discussions. Finally, we would like to thank the anonymous reviewers for their constructive engagement and feedback.

Publisher Copyright:
© 2022 Elsevier Ltd

Funding

This research was conducted within the IAMO China International Research group. We are grateful to Prof. Yuansheng Jiang and Prof. Shemei Zhang at Sichuan Agricultural University for facilitating the data collection and for numerous helpful discussions. Finally, we would like to thank the anonymous reviewers for their constructive engagement and feedback.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 2 - Zero Hunger
    SDG 2 Zero Hunger

Keywords

  • Boosted regression trees
  • China
  • Decision making
  • Land market
  • Land reform
  • Machine learning
  • Rural development

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