Predicting the current and future distributions of major food crop designated geographical indications (GIs) in China under climate change

Abstract

We first predicted the potential climate suitability of food crops with geographical indications (GIs) using the maximum entropy (MaxEnt) model and eight bioclimatic predicators under three shared socioeconomic pathway (SSP) scenarios during 2021–2040. The results showed that highly suitable rice, wheat, and corn areas increased in the three scenarios. Specifically, the growth rate under the SSP2–4.5 scenario was higher than that under the other scenarios. The potentially suitable area of wheat GIs was the largest with the most dramatic change. In contrast, the highly suitable area of millet and soybean GIs decreased under the SSP2–4.5 scenario but increased under the SSP1–2.6 and SSP3–7.0 scenarios. The increased greenhouse gas concentrations and radiative forcing under various scenarios had different effects on the dominant factors, changing the highly suitable areas of various food crop GIs. This study provides scientific evidence for the future cultivation of food crop GIs.

Keywords:

• Food crop geographical indications
• MaxEnt model
• spatial pattern
• climate change

Acknowledgments

We are very grateful to the reviewer for providing us constructive suggestions on the manuscript. We are grateful for the National Geographical Indications Inquiry System for Agricultural Products, Resource and Environmental Science and Data Center of the Chinese Academy of Sciences, WorldClim Climate Database and other databased in this paper for sharing data.

Conflicts of interest

The authors declare no conflict of interest.

Data availability statement

The data presented in this study are openly available in [National Geographical Indications Inquiry System for Agricultural Products (NGIISAP), http://www.anluyun.com; the WorldClim Climate Database, https://www.worldclim.org/].

Contribution

Yuyang Xian: Data curation, Formal analysis, Methodology, Validation, Writing—original draft, Writing—review & editing.

Guilin Liu: Conceptualization, Funding acquisition, Methodology, Supervision, Writing—original draft, Writing—review & editing.

Huizong Yao: Formal analysis, Methodology, Validation.

Additional information

Funding

This research was supported by the National Natural Science Foundation of China (NSFC) (grant number: 41901349), the Science and Technology Program of Guangdong Province, China (Grant number. 2018B020207002 and 2021B1111610001), the Startup Foundation for Talented Scholars in South China Normal University (grant number: 8S0472), and Foundation for Young Innovation Talents in Higher Education of Guangdong, China (Natural Science) (grant number: 2018KQNCX054).