IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i5p670-d1393299.html
   My bibliography  Save this article

Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China

Author

Listed:
  • Chengge Jiang

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Lingzhi Wang

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Wenhua Guo

    (Technology Innovation Center for Territorial & Spatial Big Data, MNR, Beijing 100830, China
    Information Center of Ministry of Natural Resources, Beijing 100830, China)

  • Huiling Chen

    (Technology Innovation Center for Territorial & Spatial Big Data, MNR, Jiangsu Branch, Nanjing 210017, China
    Information Centre of Land and Resources of Jiangsu Province, Nanjing 210017, China)

  • Anqi Liang

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Mingying Sun

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Xinyao Li

    (College of Earth Sciences, Jilin University, Changchun 130061, China)

  • Hichem Omrani

    (Urban Development and Mobility Department, Luxembourg Institute of Socio-Economic Research, University of Luxembourg, 4366 Esch-sur-Alzette, Luxembourg)

Abstract

Cultivated land plays a crucial role as the basis of grain production, and it is essential to effectively manage the unregulated expansion of non-grain production (NGP) on cultivated land in order to safeguard food security. The study of NGP has garnered significant attention from scholars, but the prediction of NGP trends is relatively uncommon. Therefore, we focused on Jiangsu Province, a significant grain production region in China, as the study area. We extracted data on cultivated land for non-grain production (NGPCL) in 2000, 2005, 2010, 2015, and 2019, and calculated the ratio of non-grain production (NGPR) for each county unit in the province. On this basis, Kernel Density Estimation (KDE) and spatial autocorrelation analysis tools were utilized to uncover the spatio-temporal evolution of NGP in Jiangsu Province. Finally, the Patch-Generating Land Use Simulation (PLUS) model was utilized to predict the trend of NGP in Jiangsu Province in 2038 under the three development scenarios of natural development (NDS), cultivated land protection (CPS), and food security (FSS). After analyzing the results, we came to the following conclusions:(1) During the period of 2000–2019, the NGPCL area and NGPR in Jiangsu Province exhibited a general decreasing trend. (2) The level of NGP displayed a spatial distribution pattern of being “higher in the south and central and lower in the north”. (3) The results of multi-scenario simulation show that under the NDS, the area of NGPCL and cultivated land for grain production (GPCL) decreases significantly; under the CPS, the decrease in NGPCL and GPCL is smaller than that of the NDS. Under the FSS, NGPCL decreases, while GPCL increases. These results can provide reference for the implementation of land use planning, the delineation of the cultivated land protection bottom line, and the implementation of thee cultivated land use control system in the study area.

Suggested Citation

  • Chengge Jiang & Lingzhi Wang & Wenhua Guo & Huiling Chen & Anqi Liang & Mingying Sun & Xinyao Li & Hichem Omrani, 2024. "Spatio-Temporal Evolution and Multi-Scenario Simulation of Non-Grain Production on Cultivated Land in Jiangsu Province, China," Land, MDPI, vol. 13(5), pages 1-21, May.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:670-:d:1393299
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/5/670/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/5/670/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuanyuan Chen & Mu Li & Zemin Zhang, 2023. "Does the Rural Land Transfer Promote the Non-Grain Production of Cultivated Land in China?," Land, MDPI, vol. 12(3), pages 1-16, March.
    2. Robert Pontius & Wideke Boersma & Jean-Christophe Castella & Keith Clarke & Ton Nijs & Charles Dietzel & Zengqiang Duan & Eric Fotsing & Noah Goldstein & Kasper Kok & Eric Koomen & Christopher Lippitt, 2008. "Comparing the input, output, and validation maps for several models of land change," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 42(1), pages 11-37, March.
    Full references (including those not matched with items on IDEAS)

    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. Yang, Yuanyuan & Bao, Wenkai & Liu, Yansui, 2020. "Scenario simulation of land system change in the Beijing-Tianjin-Hebei region," Land Use Policy, Elsevier, vol. 96(C).
    2. 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.
    3. Aritta Suwarno & Meine van Noordwijk & Hans-Peter Weikard & Desi Suyamto, 2018. "Indonesia’s forest conversion moratorium assessed with an agent-based model of Land-Use Change and Ecosystem Services (LUCES)," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 23(2), pages 211-229, February.
    4. Yuanyuan Yang & Shuwen Zhang & Jiuchun Yang & Xiaoshi Xing & Dongyan Wang, 2015. "Using a Cellular Automata-Markov Model to Reconstruct Spatial Land-Use Patterns in Zhenlai County, Northeast China," Energies, MDPI, vol. 8(5), pages 1-21, May.
    5. Bonoua Faye & Guoming Du & Edmée Mbaye & Chang’an Liang & Tidiane Sané & Ruhao Xue, 2023. "Assessing the Spatial Agricultural Land Use Transition in Thiès Region, Senegal, and Its Potential Driving Factors," Land, MDPI, vol. 12(4), pages 1-20, March.
    6. Rifat, Shaikh Abdullah Al & Liu, Weibo, 2022. "Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area," Land Use Policy, Elsevier, vol. 114(C).
    7. Jing Yang & Feng Shi & Yizhong Sun & Jie Zhu, 2019. "A Cellular Automata Model Constrained by Spatiotemporal Heterogeneity of the Urban Development Strategy for Simulating Land-use Change: A Case Study in Nanjing City, China," Sustainability, MDPI, vol. 11(15), pages 1-19, July.
    8. Brian Pickard & Joshua Gray & Ross Meentemeyer, 2017. "Comparing Quantity, Allocation and Configuration Accuracy of Multiple Land Change Models," Land, MDPI, vol. 6(3), pages 1-21, August.
    9. Hong Shi & Ji Yang & Qijuan Liu & Taohong Li & Ning Chris Chen, 2024. "Impacts of Climate and Land-Use Change on Fraction Vegetation Coverage Based on PLUS-Dimidiate Pixel Model," Sustainability, MDPI, vol. 16(23), pages 1-18, November.
    10. Ju-Sung Lee & Tatiana Filatova & Arika Ligmann-Zielinska & Behrooz Hassani-Mahmooei & Forrest Stonedahl & Iris Lorscheid & Alexey Voinov & J. Gareth Polhill & Zhanli Sun & Dawn C. Parker, 2015. "The Complexities of Agent-Based Modeling Output Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 18(4), pages 1-4.
    11. Zhang, Yan & Chang, Xia & Liu, Yanfang & Lu, Yanchi & Wang, Yiheng & Liu, Yaolin, 2021. "Urban expansion simulation under constraint of multiple ecosystem services (MESs) based on cellular automata (CA)-Markov model: Scenario analysis and policy implications," Land Use Policy, Elsevier, vol. 108(C).
    12. Margaret Gitau & Nathaniel Bailey, 2012. "Multi-Layer Assessment of Land Use and Related Changes for Decision Support in a Coastal Zone Watershed," Land, MDPI, vol. 1(1), pages 1-27, December.
    13. Xiaoli Hu & Xin Li & Ling Lu, 2018. "Modeling the Land Use Change in an Arid Oasis Constrained by Water Resources and Environmental Policy Change Using Cellular Automata Models," Sustainability, MDPI, vol. 10(8), pages 1-14, August.
    14. Yaya Jin & Jiahe Ding & Yue Chen & Chaozheng Zhang & Xianhui Hou & Qianqian Zhang & Qiankun Liu, 2023. "Urban Land Expansion Simulation Considering the Increasing versus Decreasing Balance Policy: A Case Study in Fenghua, China," Land, MDPI, vol. 12(12), pages 1-21, November.
    15. Charlotte Shade & Peleg Kremer, 2019. "Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies," Land, MDPI, vol. 8(2), pages 1-19, February.
    16. repec:ris:cieodp:2013_019 is not listed on IDEAS
    17. Wu, Wei & Yeager, Kevin M. & Peterson, Mark S. & Fulford, Richard S., 2015. "Neutral models as a way to evaluate the Sea Level Affecting Marshes Model (SLAMM)," Ecological Modelling, Elsevier, vol. 303(C), pages 55-69.
    18. Robert Gilmore Pontius, 2018. "Criteria to Confirm Models that Simulate Deforestation and Carbon Disturbance," Land, MDPI, vol. 7(3), pages 1-14, September.
    19. Kikuko Shoyama, 2021. "Assessment of Land-Use Scenarios at a National Scale Using Intensity Analysis and Figure of Merit Components," Land, MDPI, vol. 10(4), pages 1-13, April.
    20. Holger Cammerer & Annegret Thieken & Peter Verburg, 2013. "Spatio-temporal dynamics in the flood exposure due to land use changes in the Alpine Lech Valley in Tyrol (Austria)," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 68(3), pages 1243-1270, September.
    21. Vaz, Eric & Buckland, Amy & Worthington, Kevin, 2013. "A Regional Spatial-retrofitting Approach (RSRA) to Geovisualise Regional Urban Growth: An Application to the Golden Horseshoe in Canada," Journal of Tourism, Sustainability and Well-being, Cinturs - Research Centre for Tourism, Sustainability and Well-being, University of Algarve, vol. 1(4), pages 229-240.

    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:jlands:v:13:y:2024:i:5:p:670-:d:1393299. 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.