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Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach

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  • Yuan Gao

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
    The Frederick S. Pardee Center for the Study of the Longer-Range Future, Boston University, Boston, MA 02215, USA)

  • Anyu Zhang

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China)

  • Yaojie Yue

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China)

  • Jing’ai Wang

    (Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
    Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, Beijing Normal University, Beijing 100875, China
    Academy of Plateau Science and Sustainability, Xining 810008, China)

  • Peng Su

    (School of Geographic Science, Qinghai Normal University, Xining 810008, China)

Abstract

Suitable land is an important prerequisite for crop cultivation and, given the prospect of climate change, it is essential to assess such suitability to minimize crop production risks and to ensure food security. Although a variety of methods to assess the suitability are available, a comprehensive, objective, and large-scale screening of environmental variables that influence the results—and therefore their accuracy—of these methods has rarely been explored. An approach to the selection of such variables is proposed and the criteria established for large-scale assessment of land, based on big data, for its suitability to maize ( Zea mays L.) cultivation as a case study. The predicted suitability matched the past distribution of maize with an overall accuracy of 79% and a Kappa coefficient of 0.72. The land suitability for maize is likely to decrease markedly at low latitudes and even at mid latitudes. The total area suitable for maize globally and in most major maize-producing countries will decrease, the decrease being particularly steep in those regions optimally suited for maize at present. Compared with earlier research, the method proposed in the present paper is simple yet objective, comprehensive, and reliable for large-scale assessment. The findings of the study highlight the necessity of adopting relevant strategies to cope with the adverse impacts of climate change.

Suggested Citation

  • Yuan Gao & Anyu Zhang & Yaojie Yue & Jing’ai Wang & Peng Su, 2021. "Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach," Land, MDPI, vol. 10(3), pages 1-31, March.
  • Handle: RePEc:gam:jlands:v:10:y:2021:i:3:p:295-:d:516426
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    References listed on IDEAS

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    1. Banziger, Marianne & Setimela, Peter S. & Hodson, David & Vivek, Bindiganavile, 2006. "Breeding for improved abiotic stress tolerance in maize adapted to southern Africa," Agricultural Water Management, Elsevier, vol. 80(1-3), pages 212-224, February.
    2. Ceballos-Silva, Alejandro & Lopez-Blanco, Jorge, 2003. "Delineation of suitable areas for crops using a Multi-Criteria Evaluation approach and land use/cover mapping: a case study in Central Mexico," Agricultural Systems, Elsevier, vol. 77(2), pages 117-136, August.
    3. Nisar Ahamed, T. R. & Gopal Rao, K. & Murthy, J. S. R., 2000. "GIS-based fuzzy membership model for crop-land suitability analysis," Agricultural Systems, Elsevier, vol. 63(2), pages 75-95, February.
    4. Swastika, Dewa K.S. & Kasim, Firdaus & Sudana, Wayan & Hendayana, Rachmat & Suhariyanto, Kecuk & Gerpacio, Roberta V. & Pingali, Prabhu L., 2004. "Maize in Indonesia: Production Systems, Constraints, and Research Priorities," Maize Production Systems Papers 7647, CIMMYT: International Maize and Wheat Improvement Center.
    5. Di Paola, A. & Caporaso, L. & Di Paola, F. & Bombelli, A. & Vasenev, I. & Nesterova, O.V. & Castaldi, S. & Valentini, R., 2018. "The expansion of wheat thermal suitability of Russia in response to climate change," Land Use Policy, Elsevier, vol. 78(C), pages 70-77.
    6. Detlef Vuuren & Jae Edmonds & Mikiko Kainuma & Keywan Riahi & Allison Thomson & Kathy Hibbard & George Hurtt & Tom Kram & Volker Krey & Jean-Francois Lamarque & Toshihiko Masui & Malte Meinshausen & N, 2011. "The representative concentration pathways: an overview," Climatic Change, Springer, vol. 109(1), pages 5-31, November.
    7. Ran Wang & Yao Jiang & Peng Su & Jing’ai Wang, 2019. "Global Spatial Distributions of and Trends in Rice Exposure to High Temperature," Sustainability, MDPI, vol. 11(22), pages 1-53, November.
    8. Hao Guo & Xingming Zhang & Fang Lian & Yuan Gao & Degen Lin & Jing’ai Wang, 2016. "Drought Risk Assessment Based on Vulnerability Surfaces: A Case Study of Maize," Sustainability, MDPI, vol. 8(8), pages 1-22, August.
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    3. Abigail Fitzgibbon & Dan Pisut & David Fleisher, 2022. "Evaluation of Maximum Entropy (Maxent) Machine Learning Model to Assess Relationships between Climate and Corn Suitability," Land, MDPI, vol. 11(9), pages 1-20, August.

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