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

Yuan Gao, Anyu Zhang, Yaojie Yue, Jing’ai Wang and Peng Su
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Yuan Gao: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Anyu Zhang: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Yaojie Yue: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Jing’ai Wang: Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Peng Su: School of Geographic Science, Qinghai Normal University, Xining 810008, China

Land, 2021, vol. 10, issue 3, 1-31

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.

Keywords: climate change; maize; land suitability; big data; Kullback–Leibler divergence; law of the minimum (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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