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Spatial response of cultivated land use efficiency to the maize structural adjustment policy in the "Sickle Bend" region of China: An empirical study from the cold area of northeast

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  • Song, Ge
  • Ren, Gaofeng

Abstract

The changing of regional cultivated land use efficiency (CLUE) plays an important role in understanding the agricultural policy effects. The implementation of the maize structural adjustment policy in the "Sickle Bend" region of China has significantly reduced the maize- growing area, which increases the scale of competitive crops and rotation-fallow. It is worth exploring the CLUE changes in the context of the policy response behavior, however, an in-depth study and discussion of the relationship between CLUE and agricultural supportive policy are currently scarce. Therefore, this study constructed a theoretical framework of the relationship between CLUE and the planting structural adjustment policy response, and we systematically sorted out the related policies of the Cold Area of Northeast in the "Sickle Bend" region of China, where selected the typical 17 counties (districts) as the study case. The Random Forest algorithm was used to measure the CLUE of the study area during the period 2013–2018, and static and dynamic spatial panel models were constructed respectively to analyze the CLUE changes before and after the maize structural adjustment policy implementation, and their spatial response level to this policy. Results show that: (1) CLUE was basically stable at around 70 % during the study period, and slightly downward after policy implementation. (2) The spatial response of CLUE to the maize structural adjustment policy was negative, and there was no significant spatial spillover effect. (3) CLUE was more affected by the time lag than spatial interaction, due to the CLUE impacted in the previous period, the spatial response level of CLUE to the policy reduced. (4) After policy implementation, CLUE had a spatial spillover effect, and a spatial coordinated governance mechanism for cultivated land use was implied in the policy. These findings affirmed the importance of agricultural policy in promoting agricultural factors flow and the factor allocation efficiency. Based on this, we proposed to focus on the strategic synergy among the regional social security (including non-agricultural employment), economic development policy, the cultivated land use. Meanwhile, an adaptation mechanism should be established to carry out the planting structure optimization, as well as to improve farmers' adaptability and efficiency to planting structure changes by strengthening the zonal guidance, the behavioral regulation, and the dynamic feedback on crop planting for different types of planting entities.

Suggested Citation

  • Song, Ge & Ren, Gaofeng, 2022. "Spatial response of cultivated land use efficiency to the maize structural adjustment policy in the "Sickle Bend" region of China: An empirical study from the cold area of northeast," Land Use Policy, Elsevier, vol. 123(C).
    • Handle: RePEc:eee:lauspo:v:123:y:2022:i:c:s0264837722004483
    DOI: 10.1016/j.landusepol.2022.106421
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    1. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2012. "Estimation for spatial dynamic panel data with fixed effects: The case of spatial cointegration," Journal of Econometrics, Elsevier, vol. 167(1), pages 16-37.
    2. Jayne, Thomas S. & Mason, Nicole M. & Burke, William J. & Ariga, Joshua, 2018. "Review: Taking stock of Africa’s second-generation agricultural input subsidy programs," Food Policy, Elsevier, vol. 75(C), pages 1-14.
    3. Lai, Zhaohao & Chen, Meiqiu & Liu, Taoju, 2020. "Changes in and prospects for cultivated land use since the reform and opening up in China," Land Use Policy, Elsevier, vol. 97(C).
    4. Chen, You-hua & Chen, Mei-xia & Mishra, Ashok K., 2020. "Subsidies under uncertainty: Modeling of input- and output-oriented policies," Economic Modelling, Elsevier, vol. 85(C), pages 39-56.
    5. Shiwei Liu & Pingyu Zhang & Ben Marley & Wenxin Liu, 2019. "The Factors Affecting Farmers’ Soybean Planting Behavior in Heilongjiang Province, China," Agriculture, MDPI, vol. 9(9), pages 1-13, September.
    6. Yi, Fujin & Sun, Dingqiang & Zhou, Yingheng, 2015. "Grain subsidy, liquidity constraints and food security—Impact of the grain subsidy program on the grain-sown areas in China," Food Policy, Elsevier, vol. 50(C), pages 114-124.
    7. Wenting Wang & Longbao Wei, 2021. "Impacts of agricultural price support policy on price variability and welfare: Evidence from China's soybean market," Agricultural Economics, International Association of Agricultural Economists, vol. 52(1), pages 3-17, January.
    8. Bola Amoke Awotide & Aziz Karimov & Aliou Diagne & Tebila Nakelse, 2013. "The impact of seed vouchers on poverty reduction among smallholder rice farmers in Nigeria," Agricultural Economics, International Association of Agricultural Economists, vol. 44(6), pages 647-658, November.
    9. Liu, Yansui, 2018. "Introduction to land use and rural sustainability in China," Land Use Policy, Elsevier, vol. 74(C), pages 1-4.
    10. Mockshell, Jonathan & Birner, Regina, 2015. "Donors and domestic policy makers: Two worlds in agricultural policy-making?," Food Policy, Elsevier, vol. 55(C), pages 1-14.
    11. Zhou, Yang & Li, Xunhuan & Liu, Yansui, 2020. "Rural land system reforms in China: History, issues, measures and prospects," Land Use Policy, Elsevier, vol. 91(C).
    12. Lung-fei Lee & Jihai Yu, 2012. "QML Estimation of Spatial Dynamic Panel Data Models with Time Varying Spatial Weights Matrices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 7(1), pages 31-74, March.
    13. Jiarong Qian & Shoichi Ito & Zhijun Zhao, 2020. "The effect of price support policies on food security and farmers’ income in China," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(4), pages 1328-1349, October.
    14. Lee, Lung-fei & Yu, Jihai, 2010. "A Spatial Dynamic Panel Data Model With Both Time And Individual Fixed Effects," Econometric Theory, Cambridge University Press, vol. 26(2), pages 564-597, April.
    15. David J. Pannell & Roger Claassen, 2020. "The Roles of Adoption and Behavior Change in Agricultural Policy," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 31-41, March.
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