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Risk Assessment of Cold Damage to Maize Based on GIS and a Statistical Model



Zhewen- Zhaoa, b, c, Jingfeng- Huanga, b, c, *, Zhuokun- Pana, b, c, Yuanyuan- Chena, b, c
a Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou, 310058, China
b Key Laboratory of Polluted Environment Remediation and Ecological Health, Ministry of Education, College of Natural Resources and Environmental Science, Zhejiang University, Hangzhou, 310058, China
c Key Laboratory of Agricultural Remote Sensing and Information System of Zhejiang Province, Hangzhou, 310058, China


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Creative Commons License
© 2015 Zhao et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Institute of Remote Sensing and Information Application, Zhejiang University, Hangzhou 310058, China; Tel: +86-571-8898-2831; E-mail: hjf@zju.edu.cn


Abstract

Cold damage to maize is the primary meteorological disaster in northwest China. In order to establish a comprehensive risk assessment model for cold damage to maize, in this study, risk models and indices were developed from average daily temperature and maize yield and acreage data in 1991-2012. Three northwest provinces were used to calculate the temperature sum during the growth period, temperature departure over the years and relative meteorological yield in order to obtain the climate risk index, risk sensitivity index and damage assessment index. Using the geographic information system (GIS) and cold damage risk indices obtained from the statistical assessment model, the studied area was divided into four risk regions: low, medium, medium-high and high. Northeast and southwest Gansu were grouped to the high-risk region; west Shaanxi and north NHAR were grouped into to the low-risk region; all other areas fell into medium and medium-high risk regions. Our results can help growers avoid cold damage to maize using local climate data and optimize the structure and layout of maize planting. It is of significance in guiding the agricultural production in the three northwest provinces in China and also can serve as a reference in modeling risk assessment in other regions.

Keywords: Maize, climate, cold damage, risk assessment, geographic information system (GIS).



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