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Renting-in cropland, machinery use intensity, and land productivity in rural China

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  • Hongyun Zheng
  • Wanglin Ma
  • Xiaoshi Zhou

Abstract

This study examines the impacts of renting-in cropland on machinery use intensity, utilizing an innovative endogenous-treatment Poisson regression (ETPR) model and survey data from wheat farmers in China. We also analyse how machinery use intensity affects land productivity, reflected by wheat yields and net returns, using a two-stage residual inclusion (2SRI) model. Unlike previous studies that consider general machinery use, this study considers self-owned machinery use intensity and purchased machinery service use intensity. The ETPR model results reveal that renting-in cropland significantly increases self-owned machinery use intensity. However, it has a negative and insignificant impact on purchased machinery service use intensity. The 2SRI model estimates show that increasing self-owned machinery use intensity and purchased machinery service use intensity significantly increases wheat yields and net returns. Our findings suggest that it is essential to take stakeholders’ land transfer status into account when designing policies to promote agricultural mechanization and enhance land productivity.

Suggested Citation

  • Hongyun Zheng & Wanglin Ma & Xiaoshi Zhou, 2021. "Renting-in cropland, machinery use intensity, and land productivity in rural China," Applied Economics, Taylor & Francis Journals, vol. 53(47), pages 5503-5517, October.
    • Handle: RePEc:taf:applec:v:53:y:2021:i:47:p:5503-5517
    DOI: 10.1080/00036846.2021.1923642
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    Citations

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    Cited by:

    1. Ma, Wanglin & Vatsa, Puneet & Zheng, Hongyun & Rahut, Dil Bahadur, 2022. "Nonfarm employment and consumption diversification in rural China," Economic Analysis and Policy, Elsevier, vol. 76(C), pages 582-598.
    2. Martinson Ankrah Twumasi & Bright Senyo Dogbe & Ernest Kwarko Ankrah & Zhao Ding & Yuansheng Jiang, 2023. "Assessing Financial Literacy and Farmland Abandonment Relationship in Ghana," Agriculture, MDPI, vol. 13(3), pages 1-18, February.
    3. Hongbo Li & Lewei Chen & Zongyi Zhang, 2022. "A Study on the Utilization Rate and Influencing Factors of Small Agricultural Machinery: Evidence from 10 Hilly and Mountainous Provinces in China," Agriculture, MDPI, vol. 13(1), pages 1-25, December.
    4. Lewei Chen & Zongyi Zhang & Hongbo Li & Xinpu Zhang, 2023. "Maintenance Skill Training Gives Agricultural Socialized Service Providers More Advantages," Agriculture, MDPI, vol. 13(1), pages 1-17, January.
    5. Huaquan Zhang & Zhenyao Yang & Yidan Wang & Martinson Ankrah Twumasi & Abbas Ali Chandio, 2023. "Impact of Agricultural Mechanization Level on Farmers’ Health Status in Western China: Analysis Based on CHARLS Data," IJERPH, MDPI, vol. 20(5), pages 1-20, March.
    6. Adane Tufa & Arega Alene & Hambulo Ngoma & Paswel Marenya & Julius Manda & Md Abdul Matin & Christian Thierfelder & David Chikoye, 2024. "Willingness to pay for agricultural mechanization services by smallholder farmers in Malawi," Agribusiness, John Wiley & Sons, Ltd., vol. 40(1), pages 248-276, January.
    7. Wanglin Ma & Huanguang Qiu & Dil Bahadur Rahut, 2023. "Rural development in the digital age: Does information and communication technology adoption contribute to credit access and income growth in rural China?," Review of Development Economics, Wiley Blackwell, vol. 27(3), pages 1421-1444, August.
    8. Yang, Wei & Vatsa, Puneet & Ma, Wanglin & Zheng, Hongyun, 2023. "Does mobile payment adoption really increase online shopping expenditure in China: A gender-differential analysis," Economic Analysis and Policy, Elsevier, vol. 77(C), pages 99-110.
    9. Huaquan Zhang & Ruijia Jin & Martinson Ankrah Twumasi & Shishun Xiao & Abbas Ali Chandio & Ghulam Raza Sargani, 2022. "How Does the Heterogeneity of Family Structure Affect the Area of Land Transferred Out in the Context of Rural Revitalization?—Experience from CHIP 2013," Land, MDPI, vol. 12(1), pages 1-14, December.

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