Xing Wang¹, Yuhe Ji², Guangsheng Zhou², Sheng Wang³, Xiaoying Yao⁴

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¹ Northwest Regional Climate Center, Lanzhou, China.
² State Key Laboratory of Severe Weather (LASW), Chinese Academy of Meteorological Sciences, Beijing, China.
³ Lanzhou Institute of Aridology, China Meteorological Administration, Lanzhou, China.
⁴ Tianshui Meteorological Bureau, Tianshui, China.
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Abstract: Winter wheat is the main food crop in China. Gansu Province is a traditional winter wheat growing area, and its planting range is limited by the thermal conditions of winter. The average temperature in Gansu Province increased by 0.28°C per decade, higher than the China’s and global average, and the warming in winter was more obvious. Therefore, it is necessary to study the climate suitability and vulnerability of winter wheat planting in Gansu. In this paper, the maximum entropy model Maxent and Arcgis software are used to select six major climatic factors including annual total radiation, annual precipitation, the warmest monthly average temperature, the coldest monthly average temperature, annual average temperature, and annual extreme minimum temperature, which construct winter wheat planting distribution-climate relationship model that studies the climate suitability and vulnerability of winter wheat during the period 1961-2015. Studies have shown that the average cold weather and annual extreme minimum temperature are the most important climatic factors affecting winter wheat in Gansu, which can reflect the low temperature conditions that winter wheat can tolerate. However, the main winter wheat planting areas in Gansu Province are distributed in arid and semi-arid rain-fed agriculture areas. Precipitation and total annual radiation are also very important constraints. At the same time, climate change has little effect on winter wheat in Gansu Province, and the area of suitable area fluctuates slightly. It shows moderate adaptation in each evaluation period.

Keywords: Winter Wheat, Climate Suitability and Vulnerability, Maxent Model, Threshold Curve

Cite this paper: Wang, X. , Ji, Y. , Zhou, G. , Wang, S. and Yao, X. (2019) Climate Suitability and Vulnerability of Winter Wheat Planting in Gansu under the Background of Global Warming. Journal of Geoscience and Environment Protection, 7, 239-250. doi: 10.4236/gep.2019.74016.

References

[1]   Alam, M. M., Siwar, C., Jaafar, A. H. et al. (2013). Agricultural Vulnerability and Adaptation to Climatic Changes in Malaysia: Review on Paddy Sector. Current World Environment, 8, 1-12.
https://doi.org/10.12944/CWE.8.1.01

[2]   Burton, I., Huq, S., Lim, B. et al. (2002). From Impacts Assessment to Adaptation Priorities: The Shaping of Adaptation Policy. Climate Policy, 2, 145-159.

[3]   Deng, Z., Zhang, Q., Xu, J. et al. (2008). Research Progress of Global Climate Warming on the Growth of Crops in Gansu. Advances in Earth Science, 23, 1070-1078.

[4]   Elith, J., Graham, C. H., Anderson, R. P. et al. (2006). Novel Methods Improve Prediction of Species’ Distributions from Occurrence Data. Ecography, 29, 129-151.

[5]   Fernanda, M. S., & Maria, L. F. (2009). Climate Change and Its Marginalizing Effect on Agriculture. Ecologicaleconomics, 68, 896-904.

[6]   Han, L., Zhang, Q., Zhao, H., Huang, T., Jia, J., & Zhang, X. (2016). Characteristics of Agricultural Drought Disaster Loss in Gansu Province and Its Response to Climate Warming. Journal of Desert Research, 36, 767-776.

[7]   Lei, J., & Xu, H. (2010). Prediction of Potential Distribution Area of Solidago canadensis in China Based on MaxEnt. Journal of Ecology and Rural Environment, 26, 137-141.

[8]   Ma, Y., Li, D., Yu, J. et al. (2013). The Relationship between the Distributed Genus and Climatic Factors of Chinese Genus and Woody Genus. Biodiversity, 212, 177-184.
https://doi.org/10.3724/SP.J.1003.2013.08102

[9]   Ning, X. (2016). Research on Environmental Adaptability of Major Food Crops in China under Climate Change. Zhengzhou: Henan University.

[10]   Philips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum Entropy Modeling of Species Geographic Distributions. Ecological Modelling, 190, 231-259.
https://doi.org/10.1016/j.ecolmodel.2005.03.026

[11]   Philips, S. J., Dudik, M., & Schapire, R. E. (2004). A Maximum Entropy Approach to Species Distribution Modeling. In Proceedings of the 21st International Conference on Machine Learning (pp. 655-662). New York, USA: Association for Computing Machinery.

[12]   Ragab, R., & Prudhomme, C. (2002). Climate Change and Water Resources Management in Arid and Semi-Arid Regions: Prospective and Challenges for the 21st Century. Biosystems Engineering, 81, 3-34.
https://doi.org/10.1006/bioe.2001.0013

[13]   Shang, S., Lian, L., Ma, T., Zhang, W., & Han, T. (2018). Temporal and Spatial Variation Characteristics of Temperature and Precipitation in Northwest China in Recent 54 Years. Arid Zone Research, 35, 68-76.

[14]   Sun, W., & Liu, Y. (2010). Research Progress in Risk Analysis of Biological Invasion. Chinese Agricultural Science Bulletin, 26, 233-236.

[15]   Wang, Y., Xie, B., Wan, F. et al. (2007). Application of ROC Curve Analysis in Evaluation of Invasive Species Distribution Model. Biodiversity, 15, 365-372.

[16]   Zhang, Q., Han, L., Jia, J. et al. (2016). Management of Drought Risk under Global Warming. Theoretical & Applied Climatology, 125, 187-196.

[17]   Zhou, G., & Wang, Y. (2003). Global Ecology. Beijing: Meteorological Press.

[18]   Zhou, G., He, Q., & Yin, X. (2015). Adaptability and Vulnerability of Chinese Vegetation/Land Ecosystem to Climate Change. Beijing: Meteorological Press.