IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i14p3810-d247589.html
   My bibliography  Save this article

The Spatial-Temporal Characteristics of Cultivated Land and Its Influential Factors in The Low Hilly Region: A Case Study of Lishan Town, Hubei Province, China

Author

Listed:
  • Xuesong Zhang

    (Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Maomao Zhang

    (Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
    These authors contributed equally to this work.)

  • Ju He

    (Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

  • Quanxi Wang

    (College of Management, Gansu Agricultural University, Lanzhou 730070, China)

  • Deshou Li

    (Hubei Province Key Laboratory for Geographical Process Analysis and Simulation, Wuhan 430079, China
    College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China)

Abstract

Cultivated land is a basic resource that is related to the sustainable development of the global economy and society. Studying the spatial and temporal distribution of cultivated land and its influential factors at the township scale is an important way to improve its sustainable use. Based on the land use data in 2009 and 2015, this paper comprehensively uses kernel density estimation, spatial autocorrelation analysis, and the spatial autoregressive model to analyze the spatial distribution characteristics and influential factors of cultivated land. The results show that in 2009 and 2015, the maximum kernel density of cultivated land in Lishan Town was 31/km 2 and 38/km 2 , respectively, and there is an increasing tendency for it in the future. The global spatial autocorrelation Moran’s I of the proportion of cultivated land area in the administrative villages of Lishan Town in 2009 and 2015 was 0.5251 and 0.3970, respectively. Cultivated land has significant spatial self-positive correlation agglomeration characteristics in spatial distribution. Based on spatial error model (SEM) analysis, the regression coefficients of the village were 0.236 and 0.196 in 2009 and 2015, respectively. The regression coefficients of the road were 0.632 and 0.630, respectively. The regression coefficients of the water system were 0.481 and 0.290, respectively. The regression coefficients of the topographic position index were −0.817 and −0.672, respectively. By comparing 2015 with 2009, the regression coefficients of each influential factor have been reduced to varying degrees.

Suggested Citation

  • Xuesong Zhang & Maomao Zhang & Ju He & Quanxi Wang & Deshou Li, 2019. "The Spatial-Temporal Characteristics of Cultivated Land and Its Influential Factors in The Low Hilly Region: A Case Study of Lishan Town, Hubei Province, China," Sustainability, MDPI, vol. 11(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3810-:d:247589
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/14/3810/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/14/3810/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xuesong Zhang & Ju He & Zhen Deng & Jiyue Ma & Guangping Chen & Maomao Zhang & Deshou Li, 2018. "Comparative Changes of Influence Factors of Rural Residential Area Based on Spatial Econometric Regression Model: A Case Study of Lishan Township, Hubei Province, China," Sustainability, MDPI, vol. 10(10), pages 1-14, September.
    2. Huiran Han & Chengfeng Yang & Jinping Song, 2015. "Scenario Simulation and the Prediction of Land Use and Land Cover Change in Beijing, China," Sustainability, MDPI, vol. 7(4), pages 1-20, April.
    3. Luc Anselin, 2003. "Spatial Externalities, Spatial Multipliers, And Spatial Econometrics," International Regional Science Review, , vol. 26(2), pages 153-166, April.
    4. Kawaguchi, Daiji & Yukutake, Norifumi, 2017. "Estimating the residential land damage of the Fukushima nuclear accident," Journal of Urban Economics, Elsevier, vol. 99(C), pages 148-160.
    5. Xie, Hualin & Wang, Wei & Zhang, Xinmin, 2018. "Evolutionary game and simulation of management strategies of fallow cultivated land: A case study in Hunan province, China," Land Use Policy, Elsevier, vol. 71(C), pages 86-97.
    6. Zhijun Feng & Wei Chen, 2018. "Environmental Regulation, Green Innovation, and Industrial Green Development: An Empirical Analysis Based on the Spatial Durbin Model," Sustainability, MDPI, vol. 10(1), pages 1-22, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu Zhang & Na Gong & Huade Zhu, 2023. "Vegetation Dynamics and Food Security against the Background of Ecological Restoration in Hubei Province, China," IJERPH, MDPI, vol. 20(2), pages 1-18, January.
    2. Maomao Zhang & Weigang Chen & Kui Cai & Xin Gao & Xuesong Zhang & Jinxiang Liu & Zhiyuan Wang & Deshou Li, 2019. "Analysis of the Spatial Distribution Characteristics of Urban Resilience and Its Influencing Factors: A Case Study of 56 Cities in China," IJERPH, MDPI, vol. 16(22), pages 1-22, November.
    3. Xiaofu Lin & Hui Fu, 2022. "Spatial-Temporal Evolution and Driving Forces of Cultivated Land Based on the PLUS Model: A Case Study of Haikou City, 1980–2020," Sustainability, MDPI, vol. 14(21), pages 1-16, November.
    4. Yaxin Shi & Yishao Shi, 2020. "Spatio-Temporal Variation Characteristics and Driving Forces of Farmland Shrinkage in Four Metropolises in East Asia," Sustainability, MDPI, vol. 12(3), pages 1-26, January.
    5. Guoming Du & Wenqi Liu & Tao Pan & Haoxuan Yang & Qi Wang, 2019. "Cooling Effect of Paddy on Land Surface Temperature in Cold China Based on MODIS Data: A Case Study in Northern Sanjiang Plain," Sustainability, MDPI, vol. 11(20), pages 1-14, October.
    6. Yanwei Wang & Wei Song, 2021. "Mapping Abandoned Cropland Changes in the Hilly and Gully Region of the Loess Plateau in China," Land, MDPI, vol. 10(12), pages 1-16, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qianru Chen & Hualin Xie & Qunli Zhai, 2022. "Management Policy of Farmers’ Cultivated Land Abandonment Behavior Based on Evolutionary Game and Simulation Analysis," Land, MDPI, vol. 11(3), pages 1-23, February.
    2. Lingzhang Kong & Jinye Li, 2022. "Digital Economy Development and Green Economic Efficiency: Evidence from Province-Level Empirical Data in China," Sustainability, MDPI, vol. 15(1), pages 1-26, December.
    3. Marusca De Castris & Guido Pellegrini, 2015. "Neighborhood Effects On The Propensity Score Matching," Working Papers 0515, CREI Università degli Studi Roma Tre, revised 2015.
    4. Xingwei Li & Jingru Li & Yicheng Huang & Jinrong He & Xiang Liu & Jiachi Dai & Qiong Shen, 2022. "Construction enterprises’ adoption of green development behaviors: an agent-based modeling approach," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-11, December.
    5. Xueyang Wang & Xiumei Sun & Haotian Zhang & Chaokai Xue, 2022. "Digital Economy Development and Urban Green Innovation CA-Pability: Based on Panel Data of 274 Prefecture-Level Cities in China," Sustainability, MDPI, vol. 14(5), pages 1-21, March.
    6. Youjung Kim & Galen Newman, 2019. "Climate Change Preparedness: Comparing Future Urban Growth and Flood Risk in Amsterdam and Houston," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
    7. Carrión-Flores, Carmen E. & Flores-Lagunes, Alfonso & Guci, Ledia, 2018. "An estimator for discrete-choice models with spatial lag dependence using large samples, with an application to land-use conversions," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 77-93.
    8. Polyakov, Maksym & Iftekhar, Md Sayed & Fogarty, James & Buurman, Joost, 2022. "Renewal of waterways in a dense city creates value for residents," Ecological Economics, Elsevier, vol. 199(C).
    9. Sandy Dall'erba & Julie Le Gallo, 2008. "Regional convergence and the impact of European structural funds over 1989–1999: A spatial econometric analysis," Papers in Regional Science, Wiley Blackwell, vol. 87(2), pages 219-244, June.
    10. Ademir Rocha & Fernando Perobelli, 2020. "Spatial distribution of logistics services in Brazil: A potential market analysis," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(1), pages 185-217, February.
    11. Yunfeng Hu & Batu Nacun, 2018. "An Analysis of Land-Use Change and Grassland Degradation from a Policy Perspective in Inner Mongolia, China, 1990–2015," Sustainability, MDPI, vol. 10(11), pages 1-22, November.
    12. Bernard Fosu Frimpong & Frank Molkenthin, 2021. "Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana," Land, MDPI, vol. 10(1), pages 1-21, January.
    13. Andrew Allan & Ali Soltani & Mohammad Hamed Abdi & Melika Zarei, 2022. "Driving Forces behind Land Use and Land Cover Change: A Systematic and Bibliometric Review," Land, MDPI, vol. 11(8), pages 1-20, August.
    14. Jing Tao & Ying Wang & Rong Wang & Chuanmin Mi, 2019. "Do Compactness and Poly-Centricity Mitigate PM 10 Emissions? Evidence from Yangtze River Delta Area," IJERPH, MDPI, vol. 16(21), pages 1-18, October.
    15. David Maddison, 2009. "A Spatio‐temporal Model of Farmland Values," Journal of Agricultural Economics, Wiley Blackwell, vol. 60(1), pages 171-189, February.
    16. Christian Dreger & Reinhold Kosfeld & Yanqun Zhang, 2019. "Determining Minimum Wages In China: Do Economic Factors Dominate?," Review of Urban & Regional Development Studies, Wiley Blackwell, vol. 31(1-2), pages 44-59, March.
    17. Gabriel Lyrio de Oliveira & André Luis Squarize Chagas, 2020. "Effects of a cash transfer programme on origin–destination migration flows," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(1), pages 83-104, February.
    18. Atella, Vincenzo & Belotti, Federico & Depalo, Domenico & Piano Mortari, Andrea, 2014. "Measuring spatial effects in the presence of institutional constraints: The case of Italian Local Health Authority expenditure," Regional Science and Urban Economics, Elsevier, vol. 49(C), pages 232-241.
    19. Jianfei Zhao & Anan Pongtornkulpanich & Wenjin Cheng, 2022. "The Impact of Board Size on Green Innovation in China’s Heavily Polluting Enterprises: The Mediating Role of Innovation Openness," Sustainability, MDPI, vol. 14(14), pages 1-20, July.
    20. You Zheng & Jianzhong Xiao & Jinhua Cheng, 2020. "Industrial Structure Adjustment and Regional Green Development from the Perspective of Mineral Resource Security," IJERPH, MDPI, vol. 17(19), pages 1-18, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:14:p:3810-:d:247589. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.