IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i11p2090-d1524662.html
   My bibliography  Save this article

Prediction of the Potentially Suitable Areas of Sesame in China Under Climate Change Scenarios Using MaxEnt Model

Author

Listed:
  • Guoqiang Li

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450008, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Areas, Zhengzhou 450008, China)

  • Xue Wang

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450008, China
    College of Agriculture, Henan University of Science and Technology, Luoyang 471023, China)

  • Jie Zhang

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450008, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Areas, Zhengzhou 450008, China)

  • Feng Hu

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450008, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Areas, Zhengzhou 450008, China)

  • Hecang Zang

    (Institute of Agricultural Information Technology, Henan Academy of Agricultural Sciences, Zhengzhou 450008, China
    Key Laboratory of Huang-Huai-Hai Smart Agricultural Technology, Ministry of Agriculture and Rural Areas, Zhengzhou 450008, China)

  • Tongmei Gao

    (Henan Sesame Research Center, Henan Academy of Agricultural Sciences, Zhengzhou 450008, China)

  • Youjun Li

    (College of Agriculture, Henan University of Science and Technology, Luoyang 471023, China)

  • Ming Huang

    (College of Agriculture, Henan University of Science and Technology, Luoyang 471023, China)

Abstract

Sesame ( Sesamum indicum L, flora of China) is an essential oil crop in China, but its growth and development are affected by climate change. To cope with the impacts of climate change on sesame cultivation, we used the Maximum Entropy (MaxEnt) model to analyze the bioclimatic variables of climate suitability of sesame in China and predicted the suitable area and trend of sesame in China under current and future climate scenarios. The results showed that the MaxEnt model prediction was excellent. The most crucial bioclimatic variable influencing the distribution of sesame was max temperature in the warmest month, followed by annual mean temperature, annual precipitation, mean diurnal range, and precipitation of the driest month. Under the current climate scenario, the suitable areas of sesame were widely distributed in China, from south (Hainan) to north (Heilongjiang) and from east (Yellow Sea) to west (Tibet). The area of highly suitable areas was 64.51 × 10 4 km 2 , accounting for 6.69% of the total land area in China, and was primarily located in mainly located in southern central Henan, eastern central Hubei, northern central Anhui, northern central Jiangxi, and eastern central Hunan. The area of moderately suitable areas and lowly suitable areas accounted for 17.45% and 25.82%, respectively. Compared with the current climate scenario, the area of highly and lowly suitable areas under future climate scenarios increased by 0.10%–11.48% and 0.08%–8.67%, while the area of moderately suitable areas decreased by 0.31%–23.03%. In addition, the increased highly suitable areas were mainly distributed in northern Henan. The decreased moderately suitable areas were mainly distributed in Heilongjiang, Jilin, and Liaoning. This work is practically significant for optimizing the regional layout of sesame cultivation in response to future climate conditions.

Suggested Citation

  • Guoqiang Li & Xue Wang & Jie Zhang & Feng Hu & Hecang Zang & Tongmei Gao & Youjun Li & Ming Huang, 2024. "Prediction of the Potentially Suitable Areas of Sesame in China Under Climate Change Scenarios Using MaxEnt Model," Agriculture, MDPI, vol. 14(11), pages 1-17, November.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:11:p:2090-:d:1524662
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/11/2090/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/11/2090/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chloe Margaret Papier & Helen Mills Poulos & Alejandro Kusch, 2019. "Invasive species and carbon flux: the case of invasive beavers (Castor canadensis) in riparian Nothofagus forests of Tierra del Fuego, Chile," Climatic Change, Springer, vol. 153(1), pages 219-234, March.
    2. G. Hurtt & L. Chini & S. Frolking & R. Betts & J. Feddema & G. Fischer & J. Fisk & K. Hibbard & R. Houghton & A. Janetos & C. Jones & G. Kindermann & T. Kinoshita & Kees Klein Goldewijk & K. Riahi & E, 2011. "Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands," Climatic Change, Springer, vol. 109(1), pages 117-161, November.
    3. Richard H. Moss & Jae A. Edmonds & Kathy A. Hibbard & Martin R. Manning & Steven K. Rose & Detlef P. van Vuuren & Timothy R. Carter & Seita Emori & Mikiko Kainuma & Tom Kram & Gerald A. Meehl & John F, 2010. "The next generation of scenarios for climate change research and assessment," Nature, Nature, vol. 463(7282), pages 747-756, February.
    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. Sohl, Terry L. & Wimberly, Michael C. & Radeloff, Volker C. & Theobald, David M. & Sleeter, Benjamin M., 2016. "Divergent projections of future land use in the United States arising from different models and scenarios," Ecological Modelling, Elsevier, vol. 337(C), pages 281-297.
    2. Melania Michetti & Matteo Zampieri, 2014. "Climate–Human–Land Interactions: A Review of Major Modelling Approaches," Land, MDPI, vol. 3(3), pages 1-41, July.
    3. Meiyappan, Prasanth & Dalton, Michael & O’Neill, Brian C. & Jain, Atul K., 2014. "Spatial modeling of agricultural land use change at global scale," Ecological Modelling, Elsevier, vol. 291(C), pages 152-174.
    4. Cai, Yiyong & Newth, David & Finnigan, John & Gunasekera, Don, 2015. "A hybrid energy-economy model for global integrated assessment of climate change, carbon mitigation and energy transformation," Applied Energy, Elsevier, vol. 148(C), pages 381-395.
    5. Chateau, J. & Dellink, R. & Lanzi, E. & Magne, B., 2012. "Long-term economic growth and environmental pressure: reference scenarios for future global projections," Conference papers 332249, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    6. Gerald Nelson & Jessica Bogard & Keith Lividini & Joanne Arsenault & Malcolm Riley & Timothy B. Sulser & Daniel Mason-D’Croz & Brendan Power & David Gustafson & Mario Herrero & Keith Wiebe & Karen Coo, 2018. "Income growth and climate change effects on global nutrition security to mid-century," Nature Sustainability, Nature, vol. 1(12), pages 773-781, December.
    7. Nicole Costa Resende Ferreira & Jarbas Honorio Miranda, 2021. "Projected changes in corn crop productivity and profitability in Parana, Brazil," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(3), pages 3236-3250, March.
    8. Jaewon Kwak & Huiseong Noh & Soojun Kim & Vijay P. Singh & Seung Jin Hong & Duckgil Kim & Keonhaeng Lee & Narae Kang & Hung Soo Kim, 2014. "Future Climate Data from RCP 4.5 and Occurrence of Malaria in Korea," IJERPH, MDPI, vol. 11(10), pages 1-19, October.
    9. Joan Pau Sierra & Ricard Castrillo & Marc Mestres & César Mösso & Piero Lionello & Luigi Marzo, 2020. "Impact of Climate Change on Wave Energy Resource in the Mediterranean Coast of Morocco," Energies, MDPI, vol. 13(11), pages 1-19, June.
    10. Marcinkowski, Paweł & Piniewski, Mikołaj, 2024. "Future changes in crop yield over Poland driven by climate change, increasing atmospheric CO2 and nitrogen stress," Agricultural Systems, Elsevier, vol. 213(C).
    11. Henzler, Julia & Weise, Hanna & Enright, Neal J. & Zander, Susanne & Tietjen, Britta, 2018. "A squeeze in the suitable fire interval: Simulating the persistence of fire-killed plants in a Mediterranean-type ecosystem under drier conditions," Ecological Modelling, Elsevier, vol. 389(C), pages 41-49.
    12. Abhiru Aryal & Albira Acharya & Ajay Kalra, 2022. "Assessing the Implication of Climate Change to Forecast Future Flood Using CMIP6 Climate Projections and HEC-RAS Modeling," Forecasting, MDPI, vol. 4(3), pages 1-22, June.
    13. Tamás Hajdu & Gábor Hajdu, 2022. "Temperature, climate change, and human conception rates: evidence from Hungary," Journal of Population Economics, Springer;European Society for Population Economics, vol. 35(4), pages 1751-1776, October.
    14. Meraj Sarwary & Senthilnathan Samiappan & Ghulam Dastgir Khan & Masaood Moahid, 2023. "Climate Change and Cereal Crops Productivity in Afghanistan: Evidence Based on Panel Regression Model," Sustainability, MDPI, vol. 15(14), pages 1-13, July.
    15. Jiufeng Wei & Hufang Zhang & Wanqing Zhao & Qing Zhao, 2017. "Niche shifts and the potential distribution of Phenacoccus solenopsis (Hemiptera: Pseudococcidae) under climate change," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-17, July.
    16. Schaeffer, Michiel & Gohar, Laila & Kriegler, Elmar & Lowe, Jason & Riahi, Keywan & van Vuuren, Detlef, 2015. "Mid- and long-term climate projections for fragmented and delayed-action scenarios," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 257-268.
    17. Kokou Amega & Yendoubé Laré & Ramchandra Bhandari & Yacouba Moumouni & Aklesso Y. G. Egbendewe & Windmanagda Sawadogo & Saidou Madougou, 2022. "Solar Energy Powered Decentralized Smart-Grid for Sustainable Energy Supply in Low-Income Countries: Analysis Considering Climate Change Influences in Togo," Energies, MDPI, vol. 15(24), pages 1-24, December.
    18. Heinz-Peter Witzke & Pavel Ciaian & Jacques Delince, 2014. "CAPRI long-term climate change scenario analysis: The AgMIP approach," JRC Research Reports JRC85872, Joint Research Centre.
    19. Syed Asif Ali Naqvi & Abdul Majeed Nadeem & Muhammad Amjed Iqbal & Sadia Ali & Asia Naseem, 2019. "Assessing the Vulnerabilities of Current and Future Production Systems in Punjab, Pakistan," Sustainability, MDPI, vol. 11(19), pages 1-13, September.
    20. Alexis S. Pascaris & Joshua M. Pearce, 2020. "U.S. Greenhouse Gas Emission Bottlenecks: Prioritization of Targets for Climate Liability," Energies, MDPI, vol. 13(15), pages 1-28, August.

    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:jagris:v:14:y:2024:i:11:p:2090-:d:1524662. 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.