Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model
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School of Geography, Beijing Normal University, Beijing 100875, China
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State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
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Department of Geography, University of Florida, Gainesville, FL 32611, USA
4
Department of Geography, Kent State University, Kent, OH 44242, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Marc A. Rosen
Sustainability 2016, 8(12), 1308; https://doi.org/10.3390/su8121308
Received: 18 September 2016 / Revised: 15 November 2016 / Accepted: 2 December 2016 / Published: 13 December 2016
(This article belongs to the Special Issue Sustainable Ecosystems and Society in the Context of Big and New Data)
Crop frost, one kind of agro-meteorological disaster, often causes significant loss to agriculture. Thus, evaluating the risk of wheat frost aids scientific response to such disasters, which will ultimately promote food security. Therefore, this paper aims to propose an integrated risk assessment model of wheat frost, based on meteorological data and a hybrid fuzzy neural network model, taking China as an example. With the support of a geographic information system (GIS), a comprehensive method was put forward. Firstly, threshold temperatures of wheat frost at three growth stages were proposed, referring to phenology in different wheat growing areas and the meteorological standard of Degree of Crop Frost Damage (QX/T 88-2008). Secondly, a vulnerability curve illustrating the relationship between frost hazard intensity and wheat yield loss was worked out using hybrid fuzzy neural network model. Finally, the wheat frost risk was assessed in China. Results show that our proposed threshold temperatures are more suitable than using 0 °C in revealing the spatial pattern of frost occurrence, and hybrid fuzzy neural network model can further improve the accuracy of the vulnerability curve of wheat subject to frost with limited historical hazard records. Both these advantages ensure the precision of wheat frost risk assessment. In China, frost widely distributes in 85.00% of the total winter wheat planting area, but mainly to the north of 35°N; the southern boundary of wheat frost has moved northward, potentially because of the warming climate. There is a significant trend that suggests high risk areas will enlarge and gradually expand to the south, with the risk levels increasing from a return period of 2 years to 20 years. Among all wheat frost risk levels, the regions with loss rate ranges from 35.00% to 45.00% account for the largest area proportion, ranging from 58.60% to 63.27%. We argue that for wheat and other frost-affected crops, it is necessary to take the risk level, physical exposure, and growth stages of crops into consideration together for frost disaster risk prevention planning.
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Keywords:
frost disaster; risk assessment; vulnerability curve; wheat; growth stage; threshold temperature; information diffusion; hybrid fuzzy neural network model; China
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MDPI and ACS Style
Yue, Y.; Zhou, Y.; Wang, J.; Ye, X. Assessing Wheat Frost Risk with the Support of GIS: An Approach Coupling a Growing Season Meteorological Index and a Hybrid Fuzzy Neural Network Model. Sustainability 2016, 8, 1308.
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