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A dataset of major agricultural disasters and disaster losses in China (1949 – 2015)
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: 2017 - 11 - 28
: 2018 - 01 - 02
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Abstract & Keywords
Abstract: As a big agricultural country, the People’s Republic of China has experienced a series of natural disasters such as the Great Famine, the 1998 floods and the 2008 snow storm since its founding. We present a dataset summarizing the affected area, disaster area and the area of total crop failure at provincial level in China, with a temporal coverage from 1949 to 2015. Based on the agricultural planting structure and natural disaster data, data of grain disaster loss are also presented through a grainloss assessment model. The dataset plays an important role in the prediction, prevention and reduction of agricultural disasters in future.
Keywords: natural disasters; grain disaster loss; provincial; China; 1949 – 2015
Dataset Profile
TitleA dataset of major agricultural disasters and disaster losses in China (1949 – 2015)
Corresponding authorMao Kebiao (maokebiao@caas.cn,)
Data authorsGuo Jingpeng, Chen Huiqian, Zhao Yinghui, Mao Kebiao, Li Ning, Zhu Liang
Time range1949 2015
Geographical scopeChina
Spatial resolutionprovincial
Data volume0.7 MB
Data format*.xlsx
Data service system<http://www.sciencedb.cn/dataSet/handle/540>
Sources of fundingNational Natural Science Foundation of China (No.41571427), and Innovation Research Group Project (Grant No. Y2017JC33)
Dataset compositionThe data set consists of two parts: natural disaster data and grain disaster loss data. The data set consists of two data files: Natural_disaster.zip, Grain_loss.zip.
(1) Natural_disaster.zip is a 0.45 M disaster data set made up of three xlsx files.
●crop_affected_area.xlsx: It presents the areas with less than 30% of the yield expected with temperature and precipitation equal to long-term average values due to natural disasters.
●crop_disaster_area.xlsx: It presents the areas with less than 70% of the expected yield due to natural disasters.
●crop_failure_area.xlsx:It presents the areas with less than 100% of the expected yield due to natural disasters.
(2) Grain_loss.zip is a 0.25M grain loss data set made up of two xlsx files:
●Grain_loss_amount.xlsx
●Grain_loss_ratio.xlsx
1.   Introduction
With its vast territory, diverse climatic types, fragile ecosystems and diverse types of disasters, China is one of the countries that suffered from the most serious natural disasters in the world. Agricultural disasters are the main natural disasters in China, mainly including flood, drought, low temperature, hail and typhoon, especially flood and drought.13 In June and July, 2017, there are 11 provinces in southern China that suffered from floods, which affected more than 11 million people, including 78 people who died or disappeared and 27 thousand houses that collapsed. The direct economic losses reached 25.27 billion yuan, and the official disaster relief was up to 1.88 billion yuan. Researches on the temporal-spatial laws, driving mechanism, risk assessment, and regional regularity of natural disasters, risk assessment and control measures need the support of historical disaster datasets.34 Here we sorted, summarized and evaluated the major disaster data from 1949 to 2015.
Firstly, the dataset summarizes disaster affected area, disaster area, crop failure area and the total area of agricultural disasters from 1949 to 2015 (flood, drought, low temperature, hail, typhoon). Secondly, based on the agricultural planting structure and natural disaster data, we estimate data of grain disaster losses by grain loss assessment model.
2.   Data acquisition and processing
2.1   Overview
Due to limited data availability, it should be noted that Hong Kong, Macao and Taiwan were excluded in the dataset. 31 provinces/municipalities in China were covered by the study, and there are five main disaster types in China: flood, drought, hail, low temperature and typhoon. The data are collected over the period 1949 2015 via Python scripts (Figure 1) from the website of the Ministry of Agriculture of China (MAC) and China Statistical Yearbooks Database (CSYD). A detailed description of the dataset is given in Table 1.


Figure 1   Flowchart illustrating the formation of the agricultural disaster and loss dataset
Table 1   Description of the dataset
DatasetVariableDisaster typeTime spanGeographical scope
Natural disaster data setcrop affected area (10*4ha )five major disaster types1949 201531 provinces/municipalities in China
crop disaster area (10*4ha )five major disaster types1949 201531 provinces/municipalities in China
crop failure area (10*4ha )five major disaster types1949 201531 provinces/municipalities in China
Grain loss data setGrain loss amount (t)five major disaster types1949 201531 provinces/municipalities in China
Grain loss rate (%)five major disaster types1949 201531 provinces/municipalities in China
2.2   Methods
At the end of the 20th century, based on grain yield statistics, Chinese scholars constructed a statistical model for estimating the amount of grain loss and obtained certain research results. However, studies of grain loss in China have been mainly conducted using the proportion of grain loss to estimate the amount of grain loss in recent years.46 This section uses the proportion method to estimate the amount of grain loss based on the following formula:
\[S_{c}= \sum_{i-1}^{n}S_{ci}= \sum_{i-1}^{n}\left ( R _i\times A_{i1}\times Y_{i}\times P_{1}+R_{i}\times A_{i2}\times Y_{1}\times P_{2}+R_{i}\times A_{i2}\times Y_{i}\times P_{2} \right) \]
(1)
where Sc is the amount of grain loss; n is the number of provinces and municipalities; Sci is the grain disaster loss in province i; Ri is the ratio of the grain crop area to the crop sown area; Ai1, Ai2, and Ai3 are the values of crop areas affected by mild, moderate and severe disasters, respectively (mild: 10 30% grain losses due to natural disasters; moderate disaster: 30% 70% grain losses; severe disaster: over 70% grain losses); and yi is grain yield of province i per ha in the same year. The values of P1, P2 and P3 are 20%, 50% and 85%, respectively, using the median method and according to the definitions of the crop affected area and crop failure area.
The ratio of grain losses to total grain output in the same year was defined as the rate of grain losses.8 It can be represented as follows:
\[R=S_C/G_M *100\%\]
(2)
where R is the ratio of grain loss; Sc is the amount of grain loss; Gm is the amount of total grain output.
3.   Sample description
crop_affected_area.xls includes six worksheet (tag name: Flood, Drought, Hail, Low_temperature, Typhoon). Each table is a 2 * 2 matrix form; the first column of the worksheet is the name of the province/municipality; time dimension (1949 2015) is given in the first line, and matrix values in units of 104 ha.


Figure 2   Crop affected area at provincial level
4.   Quality control and assessment
Data quality assurance checking involved detailed inspection and exploration of patterns to detect any typographical or other data errors. The data provided herein were re-processed from the raw data supplied by MAC and CSYD, and as such depended on the quality and reliability of the data sources. Based on the MAC data, the above two data sources are mutually validated and supplemented. Unfortunately, the typhoon data is for 2001 2015. The results that Zhao et al. (2017) made based on the above dataset were consistent with the findings of Guan et al. (2015) and Du et al.(2015).25
5.   Usage notes
The dataset is released via Science Data Bank (http://www.sciencedb.cn/dataSet/handle/540), and the Resource Service System of the CERN Data Center (http://www.cnern.org.cn), where users can log into the system, click data resources and select data to enter the download page. There are no copyright or proprietary restrictions for the dataset.
Acknowledgments
This work was supported by National Natural Science Foundation of China (No.41571427), and Innovation Research Group Project (Grant No. Y2017JC33).
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Du X, Jin X, Yang X et al. Spatial-temporal pattern changes of main agriculture natural disasters in china during 1990 – 2011.Journal of Geographical Sciences 25 (2015): 387 – 398.
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Gu X, Zhang Q & Zhang S. Spatio-temporal properties of flood/drought hazards and possible causes and impacts in 1961 – 2010. Scientia Geographica Sinica 36 (2016): 439 – 447.
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Zhao Y, Guo J, Mao K et al. Spatio-temporal distribution of typical natural disasters and grain disaster losses in China from 1949 to 2015. Journal of Geographical Sciences 72 (2017): 1261 – 1276.
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Guan Y, Zheng F, Zhang P et al. Spatial and temporal changes of meteorological disasters in China during 1950 – 2013. Natural Hazards 75 (2015): 2607 – 2623.
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Shi W & Tao F, Spatio-temporal distributions of climate disasters and the response of wheat yields in China from 1983 to 2008. Natural Hazards 74 (2014): 569 – 583.
7.
Yang J, Huo Z, Wu L et al. Indicator-based evaluation of spatiotemporal characteristics of rice flood in Southwest China. Agriculture, Ecosystems and Environment 230 (2016): 221 – 230.
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Li W, Qin Z & Lin L. Quantitative analysis of agro-drought impact on food security in China. Journal of Natural Disasters19 (2010): 111 – 118.
Data citation
1. Guo J, Chen H, Zhao Y et al. A dataset of major agricultural disasters and disaster losses in China (1949 – 2015). Science Data Bank. DOI: 10.11922/sciencedb.540
稿件与作者信息
How to cite this article
Guo J, Chen H, Zhao Y et al. A dataset of major agricultural disasters and disaster losses in China (1949 – 2015). China Scientific Data (under review).
Guo Jingpeng
motivation of study, data analysis, writing.
Master's student; research area: remote sensing monitoring of agricultural disasters.
Chen Huiqian
manuscript translation.
Master's student; research area: climate change, agricultural economic assessment.
Zhao Yinghui
thesis supervision and modification.
PhD, Associate Professor; research area: environmental changes in urban and rural areas.
Mao Kebiao
motivation of study.
87791863@qq.com
PhD, Professor; research area: remote sensing of environment, landscape effects.
Li Ning
data analysis.
Master's student; research area: remote sensing monitoring of agricultural disasters.
Zhu Liang
data collection.
Master's student; research area: agricultural regional economy.
National Natural Science Foundation of China (No.41571427), and Innovation Research Group Project (Grant No. Y2017JC33).
出版历史
I区发布时间:2018年1月2日 ( 版本EN4
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