Natural disasters and their impacts on the silica losses from agriculture in China from 1988 to 2016

Journal Pre-proof Natural disasters and their impacts on the silica losses from agriculture in China from 1988 to 2016 Dexiang Zheng, Heng Zhang, Yuze Yuan, Zhong Deng, Ku Wang, Geng Lin, Yi Chen, Jiangjiang Xia, Shao-Fei Jin PII:

S1474-7065(19)30230-X

DOI:

https://doi.org/10.1016/j.pce.2020.102840

Reference:

JPCE 102840

To appear in:

Physics and Chemistry of the Earth

Received Date: 13 November 2019 Revised Date:

16 January 2020

Accepted Date: 24 January 2020

Please cite this article as: Zheng, D., Zhang, H., Yuan, Y., Deng, Z., Wang, K., Lin, G., Chen, Y., Xia, J., Jin, S.-F., Natural disasters and their impacts on the silica losses from agriculture in China from 1988 to 2016, Physics and Chemistry of the Earth (2020), doi: https://doi.org/10.1016/j.pce.2020.102840. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

CRediT author statement

Dexiang Zheng: Conceptualization, Methodology, Software, Investigation, Supervision; Heng Zhang: Conceptualization, Investigation; Yuze Yuan: Writing- Reviewing and Editing; Zhong Deng: Software; Ku Wang: Data Curation, Resources; Geng Lin: Writing- Reviewing and Editing; Yi Chen: Writing- Reviewing and Editing; Jiangjiang Xia: Investigation, Supervision; Shao-Fei Jin: Conceptualization, Methodology, Software, Validation, Formal analysis, Writing - Original Draft, Writing- Reviewing and Editing, Visualization,Supervision.

1 2

Natural disasters and their impacts on the silica losses from

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agriculture in China from 1988 to 2016

4 5

Dexiang Zhenga#, Heng Zhangc#, Yuze Yuand, Zhong Dengb, Ku Wangb, Geng Lind, Yi Chenb,

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Jiangjiang Xiae, Shao-Fei Jinb*

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a Forestry College, Fujian Agriculture and Forestry University, Fuzhou, 350108, China

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b Department of Geography, Minjiang University, Fuzhou, 350108, China

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c Key Laboratory of Oceanic and Polar Fisheries, Ministry of Agriculture, East China Sea Fisheries

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Research Institute, Chinese Academy of Fishery Sciences, Shanghai 200090, China

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d College of Mathematics and Data Science, Minjiang University, Fuzhou, 350108, China

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e Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric

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Physics, Chinese Academy of Sciences, Beijing 100029, China

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#

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* corresponding author: Shaofei Jin, [email protected]

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Address: Minjiang University, NO.200, XiYuanGong Road, MinHou District, Fuzhou, Fujian, China.

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These authors contributed equally.

20 21

Abstract

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Natural disasters play significant destructive roles in agricultural production. Agriculture has

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substantially altered the biogeochemical silica cycle via the harvest of the grain and straw of

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silica-rich crops. China is a developing agricultural country that experiences frequent natural disasters.

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Although the spatiotemporal changes in the occurrence of natural disasters are well known for

27

individual disaster events, the study of the effects of multiple disasters on cereal crop productivity and

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the resultant silica harvest is still relatively new. To make the connection between natural disasters

29

and the silica biogeochemical cycle, we compiled a dataset of natural disasters and crop production

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and silicon contents in cereal crops (rice, wheat, and maize) in China using province-level data from

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1988 to 2016. Our results show that the area affected by natural disasters declined significantly after

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2000, and changes in the area affected by natural disasters varied at the province level. From 1988 to

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2016, the total silica losses from grain and straw harvests due to natural disasters were 7.14 and 53.10

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million tons, respectively. Half of the silica loss in more than half of the provinces was caused by

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drought. Our study suggests that drought prevention will increase the size of the silica sink and

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thereby increase the size of the carbon sink in China’s agriculture.

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Keywords: Climatic risk; Drought; Hail; Floods; silicon cycle

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1 Introduction

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Natural disasters, defined as inevitable natural events that occur worldwide, can result in substantial

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losses of life or severe damage to property. Natural disasters negatively impact crop production(Lesk

45

et al., 2016) and thus threaten the sustainable development of the global food supply (Lesk et al.,

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2016). Agriculture, a human-dominated system, is highly sensitive to agricultural natural disasters

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(Miraglia et al., 2009; Philpott et al., 2008), including droughts, floods, low-temperature events,

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hailstorms, and typhoons (Guan et al., 2015). Forecasting the intensities of these natural disasters in

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advance can allow communities to take measures to maintain food production, although controlling

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natural disasters is still currently beyond the scope of human ability (Wang et al., 2019). Furthermore,

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in the context of more frequent natural disasters (Lei, 2014), the impact of natural disasters on

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biogeochemical cycles has been investigated, e.g., the carbon cycle (Reichstein et al., 2013) and the

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nitrogen cycle ((Fuchslueger et al., 2014)). Meanwhile, the agricultural system plays a significant role

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in regulating biogeochemical cycles, especially those of plant nutrients, e.g., the silicon cycle (Conley,

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2002; Haynes, 2017; Song et al., 2014a; Song et al., 2013; Song et al., 2014b; Song et al., 2012; Song

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et al., 2014c; Street‐Perrott and Barker, 2008; Struyf et al., 2009).

57 58

Silicon, the second most abundant element in the earth’s crust and a quasi-essential beneficial nutrient

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for plants (Epstein, 2009; Marafon and Endres, 2013; Tripathi et al., 2014), plays significant roles in

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protecting plants from various biotic and abiotic stresses, e.g., drought (Zhu and Gong, 2014) and

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heavy metal toxicity (Epstein, 1994, 2009). Furthermore, silica (silicon in biogeochemistry) has been

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considered to play key roles in ecological and biogeochemical processes, specifically in regulating the

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global carbon cycle via 1) consuming CO2 through mineral weathering (Garrels, 1983); 2) providing

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long-term carbon sinks for occlusion-carbon in phytoliths in soils and sediments (Parr and Sullivan,

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2005); and 3) carbon sequestration in oceanic phytoplankton (Carey and Fulweiler, 2012, 2016;

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Street‐Perrott and Barker, 2008; Struyf et al., 2009). In agriculture, silica is, to date, the only element

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that is never toxic to plants, and it is used to improve crop productivity (Epstein, 1994; Keeping and

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Kvedaras, 2008). Therefore, the silica harvest from agriculture has become a new loop in the global

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silicon cycle (Vandevenne et al., 2012). The global crop harvest accounts for 35% of the total biogenic

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silica in terrestrial ecosystems annually (Carey and Fulweiler, 2012). Crops such as rice, maize, wheat,

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and sugarcane accumulate high silica content (more than 1% of dry weight) in their grains and straw

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(Datnoff et al., 1997; Gocke et al., 2013; Li et al., 2013; Parr and Sullivan, 2011; Song et al., 2014b).

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In 2017, the global grain production for wheat, maize, and rice was 0.772, 1.13, and 0.770 billion tons,

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respectively. China’s agriculture accounted for 14.3%, 14.2%, and 25.0%, respectively, of those totals

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(http://www.fao.org/faostat/zh/#data/QC/visualize). Furthermore, the total straw production in China

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was 0.98 billion tons in 2016. The straw production from the three cereal crops above accounted for

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83.51% of China’s total straw production (Shi et al., 2019). The harvest of grain and straw from cereal

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crops (wheat, maize, and rice) in China has been more than 0.46 and 0.81 million tons, indicating that

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these crops make a significant contribution to the agricultural silica harvest.

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China is suffered from natural disaster due to its complex geographical environment and frequent

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occurring East Asian monsoon (Simelton, 2011; Zhou et al., 2013). These frequent disasters have

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disturbed the sustainable development of China’s economy and society. For instance, in 2018, natural

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disasters caused 264.5 billion RMB in damage, affected 130 million people, including 589 deaths, and

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affected 20.8 million hundred hectares (ha) of cultivation area. Therefore, natural disasters have

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negative impacts on food production in China. Although previous studies investigated the

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spatiotemporal patterns of natural disasters and their impacts on grain production (Du et al., 2015; Li

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et al., 2010; Liu et al., 2012; Shi and Tao, 2014; Zhang, 2004), a comprehensive analysis of multiple

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natural disaster events and their impacts on different cereal crops

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et al., 2019). Further, the silica harvest in agriculture has become an inconvenient loop in the global

91

silica cycle (Vandevenne et al., 2012); however, to our knowledge, no studies have estimated the

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losses to the silica harvest due to natural disasters.

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has not yet been performed (Guo

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To address these two research gaps, here, we compiled datasets of natural disasters and crop

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production in China using data at the provincial level from 1988 to 2016 and measured the silicon

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contents of different cereal crops to address the gaps between the climatic disasters and the silicon

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cycle. Further, we asked three questions as follows: 1) How have the effects of natural disasters on

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different cereal crops changed in China over the past three decades? 2) If the above changes were

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detected, how do the effects of natural disasters differ for the different cereal crops in each province?

100 101 102

3) How much silica has been lost due to different natural disasters over the past three decades?

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2 Methods and Materials

104 105

2.1 Study area

106 107

In this study, we investigated the changes in natural disasters and their impacts on silica harvest from

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1988 to 2016 in all of China using province-level data. China (3˚31’’00’’N–53˚33’0”N,

109

73˚29’59.79”E–135˚2’30’’E), located in eastern Asia, is the fourth largest country in the world and is

110

occupied by more than 1.3 billion people. China feeds more than 20% of the world’s population with

111

only 7% of the world’s land area. Therefore, sustainable agricultural production plays a crucial role in

112

food security for China. Due to the large area of the country, multiple types of natural disasters have

113

occurred across the entire country. To investigate the trends in natural disasters more accurately,

114

according traditional definitions (Li and Jin, 2011), this study divided the 34 provinces into six

115

geographical regions (Table 1): Northeast (NE), North China (N), the Changjiang River region (CJ),

116

Northwest (NW), Southwest (SW), and Southeast (SE) (Figure 1 and Table 1). Three regions (Hong

117

Kong, Macao, and Taiwan) were excluded because the data were unavailable. The six geographical

118

regions each have specific characteristics and are described here. The NE region, which includes

119

Heilongjiang, Jilin, and Liaoning, has been identified as the key zone for ecological conservation and

120

food security in China. The N region, including Beijing, Tianjin, Hebei, Henan, Shandong, and Shanxi,

121

is the second largest agricultural development region in China and has thousands of years of

122

agricultural history. The CJ, which includes Shanghai, Jiangsu, Zhejiang, Anhui, Hubei, Hunan, and

123

Jiangxi, is known as the “land of fish and rice” in China. The NW region, which includes Inner

124

Mongolia, Shaanxi, Ningxia, Gansu, Qinghai, and Xinjiang, is well known as the origin place of

125

ancient Chinese agriculture and plays a crucial role in modern agriculture in semi-arid and arid

126

agroecosystems. The SW region, which includes Chongqing, Sichuan, Guizhou, Yunnan, and Xizang,

127

was once known as the “land of abundance”. The SE region, which includes Fujian, Guangdong,

128

Guangxi, and Hainan, is located in the subtropical zone with high temperatures and has been the

129

location for experiments in high-yield agriculture that produce three rice crops per year.

130 131

Figure 1. Map of China’s provincial administrative divisions and geographical regions in this study.

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NE: Northeast; N: North China; CJ: Changing River regions; NW: Northwest, SW: Southwest, and SE:

133

Southeast.

134 135 Code NE N CJ

Table 1. Geographical regions in this study. Provinces Heilongjiang, Jilin, and Liaoning Beijing, Tianjin, Hebei, Henan, Shandong, Shanxi Shanghai, Jiangsu, Zhejiang, Anhui, Hubei, Hunan, Jiangxi

NW SW SE

Inner Mongolia, Shaanxi, Ningxia, Gansu, Qinghai, Xinjiang Chongqing, Sichuan, Guizhou, Yunnan, Xizang Fujian, Guangdong, Guangxi, Hainan

136 137 138

2.2 Data sources

139 140

The natural disaster data were collected from the China Rural Statistical Yearbook

141

(http://data.cnki.net/yearbook/Single/N2019030220). These data included the annual areas of different

142

disaster effect intensities. Disaster intensity was divided into three categories: mild, moderate, and

143

severe. These categories were determined by the grain loss caused by the natural disasters. The mild

144

effect was defined as when the grain loss was between 10% and 30%; the moderate effect was defined

145

as when the grain loss was between 30% and 70%; and the severe effect was defined as when the

146

grain loss was more than 70%. Furthermore, data on the areas affected by natural disasters were

147

collected for four disaster types: floods, droughts, low temperatures, and hail. In this study, the

148

cultivation area and productivity data for three cereal crops, i.e., rice, wheat, and maize, were

149

collected

150

(http://data.cnki.net/yearbook/Single/N2017120001). All data were collected and analysed at the

151

provincial level.

from

the

China

Agriculture

Statistical

Report

152 153

2.3 Methods

154 155

Equations 1-5 are the formulas used to calculate the silica loss due to natural disasters from the grain

156

and straw in the entire study area.

157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172

where i represents the province; k represents the crop type; j represents the disaster type; Silica loss

173

from Grain (ton) represents the total silica loss from grain in China; Silica loss from Straw (ton)

174

represents the total silica loss from straw in China; Yield represents the yield of each crop per ha;

175

AreaDisa represents the area affected by the disasters; CerealAreaR represents the ratio of different



= ∑ ∑ ! × 0.01 Equation (1)











,

× =

,



×

∑ = ∑ ∑ × 0.01 Equation (2)

! ×

×

! ×





∑

×

×

×

×

∑ % = ∑ ∑ × × × '1 − ) , − )* , + × 0.01 Equation (3) % = ∑ ∑ ! × , × '1 − )





+

,

∑ × × − )* , + × × 0.01 Equation (4)







×



×

×

×

×

% Equation (5)

176

cereal cultivation areas; Silicon represents the silicon contents in grain or straw in different cereal

177

crops; Intensity represents the loss of grain or straw caused by different intensities of natural disasters;

178

GtS represents the ratio of grain to straw in different provinces and cereal crops; STR represents the

179

rate of straw returning in different provinces and for different cereal crops; STB represents the rate of

180

straw burning in different provinces and for different cereal crops; SD represents the standard

181

deviation of the intensities of the different natural disasters; and 0.01 is the conversion factor in the

182

equations. The detailed information for the parameters mentioned above is provided below. The grain

183

to straw ratio is listed in Table 2 and was compiled from the study (Liu and Li, 2017)[Liu and Li 2017,

184

and refs therein]. The straw returning rates and straw burning rates in different regions for wheat,

185

maize, and rice in different study periods are listed in Table 3 and Table 4, respectively [Liu and Li

186

2017, and refs therein]. The intensity of natural disasters is listed in Table 5. The mean silica content

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in wheat, maize, and rice was collected from (National Agricultural Technology Extension Service

188

Center, 1999) and is listed in Table 6.

189 190 Crops Wheat Maize Rice 191 192 193 194

Table 2. Ratio of grain to straw in different regions for wheat, maize, and rice. Region NE 1.56 1.11 0.91

N 1.34 1.23 0.97

CJ 1.39 1.29 1.08

NW 1.1 1.29 1.03

SW 1.49 1.28 0.95

SE 1.36 1.27 0.93

195 196

197 198 199

200

Table 3. Straw returning rates in different regions for wheat, maize, and rice in different study periods. Region NE N CJ NW SW SE 1980s Wheat 0.3 0.27 0.3 0.3 0.31 0.32 Maize 0.3 0.27 0.29 0.29 0.3 0.31 Rice 0.21 0.23 0.21 0.21 0.22 0.23 1990s Wheat 0.12 0.44 0.47 0.3 0.31 0.32 Maize 0.05 0.42 0.29 0.29 0.3 0.31 Rice 0.12 0.38 0.43 0.21 0.22 0.23 2000s Wheat 0.67 0.69 0.42 0.33 0.34 0.32 Maize 0.12 0.5 0.33 0.25 0.32 0.31 Rice 0.28 0.15 0.41 0.16 0.37 0.4 2010s Wheat 0.54 0.87 0.75 0.46 0.41 0.32 Maize 0.27 0.81 0.7 0.5 0.42 0.4 Rice 0.54 0.7 0.75 0.66 0.64 0.77 Table 4. Burning rates of straw in different regions for wheat, maize, and rice in different study periods. Region NE N CJ NW SW SE 1980s Wheat 0.26 0.26 0.26 0.26 0.26 0.26 Maize 0.26 0.23 0.26 0.26 0.26 0.26 Rice 0.49 0.46 0.44 0.24 0.3 0.48 1990s Wheat 0.48 0.4 0.26 0.26 0.26 0.26 Maize 0.45 0.45 0.26 0.26 0.26 0.26 Rice 0.49 0.46 0.44 0.24 0.3 0.48 2000s Wheat 0.4 0.23 0.45 0.3 0.41 0.28 Maize 0.55 0.36 0.49 0.27 0.35 0.47 Rice 0.49 0.46 0.44 0.24 0.3 0.48 2010s Wheat 0.26 0.84 0.16 0.14 0.31 0.28 Maize 0.42 0.12 0.22 0.29 0.31 0.47 Rice 0.17 0.04 0.13 0.04 0.1 0.2

201 202 203 Lower

Table 5. Definition of the intensity categories for natural disasters. Mild Moderate Severe 10% 30% 80%

Mean SD Upper 204 205 206 207

20% 5.10% 30%

55% 12.76% 80%

90% 5.10% 30%

Total silicon content analysis in winter wheat under different water stress conditions

Wheat Maize Rice

Table 6. Mean silica content (% dry weight) in wheat, maize, and rice Si Content (% dry weight) Grain Straw 0.12 3.15 0.06 2.98 0.36 9.45

208 209

2.4 Statistical analysis

210

A linear regression method was applied to identify the changes in silica loss due to natural disasters

211

and the changes in the affected areas in each province from 1988 to 2017. All statistical analysis and

212

plot creation was performed using R programming software (R Core Team, 2019). All raw data and

213

codes for the analyses can be found in Supplementary file 1 and Supplementary file 2, respectively.

214 215

3 Result

216 217

3.1 Trends in the affected areas of natural disasters from 1988 to 2017

218 219

Figure 2 shows the changes in the areas of the three cereal crops affected by the different natural

220

disasters from 1988 to 2016. The changes in the areas affected by disasters of the three intensity levels

221

showed similar patterns (Figure 2). Droughts and floods were the main natural disasters. For maize,

222

the affected area showed an increase before 2003 and then a decrease after 2003. For rice and wheat, a

223

linear decline was observed for the mild effect category from 1988 to 2016; flat trends were observed

224

before 2000 for the moderate effect and the severe effect, and linear declines were observed in these

225

two categories after 2000. Figure 3 shows the corresponding ratios of disaster-affected areas to the

226

total crop cultivation areas over the past three decades. The results showed a similar pattern to that

227

shown in Figure 2. In short, for the mild effect, the mean affected areas for maize, rice, and wheat

228

accounted for 14.07%, 16.24%, and 14.23% of the total cultivation areas, respectively; for moderate

229

effect, the mean affected areas for maize, rice and wheat accounted for 7.34%, 8.50%, and 7.44% of

230

their cultivation areas, respectively; for the severe effect, the mean affected areas for maize, rice and

231

wheat accounted for 1.75%, 2.04%, and 1.79% of their cultivation areas, respectively. In total, the

232

areas of mild effect, moderate effect, and severe effect accounted for 48.50%, 25.31%, and 6.07% of

233

the cereal cultivation area, respectively (Figure 4). Further, the maximum percentages of cultivation

234

area affected by the mild effect, moderate effect, and severe effect were 77.05%, 42.33%, and 11.89%,

235

respectively. Despite the disadvantages caused by natural disasters, the cereal grain yield still

236

continues to grow (Figure 5). In 2016, the mean grain yields for maize, rice, and wheat were 5971.33,

237

6861.74, and 5327.08 kg/ha, respectively.

238 239 240

Maize

Rice

Wheat

25000 20000 Mild

15000 10000 5000

10000 Moderate

Area (Thousand ha)

0 12500

7500 5000 2500 0 3000

Severe

2000 1000 0 1990

2000

2010

1990

2000

2010

1990

2000

2010

Year

241

Drought

Flood

Hail

LowTemp

Total

242

Figure 2. Trends in the area affected by natural disasters from 1988 to 2016. The smooth lines in each

243

panel represent loss trends.

244

Maize

Rice

Wheat

Mild

0.1 0.0 0.15 0.10

Moderate

0.05 0.00 0.04 0.03

Severe

Ratio of disaster−affected area to total cultivation area

0.2

0.02 0.01 0.00 1990

2000

2010

1990

2000

2010

1990

2000

2010

Year

245

Drought

Flood

Hail

LowTemp

Total

246

Figure 3. Trends in the ratio of the natural disaster-affected area to the total cultivation area for

247

different disaster intensities and different crops from 1988 to 2016.

248 249

0.8 0.6 Mild

0.4 0.2

0.4 0.3

Moderate

Ratio of disaster area

0.0

0.2 0.1 0.0 0.100

Severe

0.075 0.050 0.025 0.000 1990

2000

2010 Year

250

Drought

Flood

Hail

LowTemp

Total

251

Figure 4 Trends in the ratio of the area affected by each natural disaster type to the total cultivation

252

area from 1988 to 2016.

253

7000

6000

Yield (kg/ha)

5000

4000

3000

2000 1990

2000

2010 Year

254 255

Maize

Rice

Wheat

Figure 5. Changes in the yield of rice, wheat, and maize in China from 1988 to 2016.

256 257

3.2 Trends in the affected areas for different natural disasters at the provincial level

258 259

Changes in the area affected by mild (Figure 6), moderate (Figure 7), and severe (Figure 8) natural

260

disasters were observed at the provincial level. In terms of mild natural disasters (Figure 6), for maize,

261

drought was the main disaster and occurred in NE, N, and Inner Mongolia in NW; for rice, drought

262

and flooding were the main disasters impacting the cultivation area in CJ and SE; for wheat, drought

263

was the main impact factor in N. For moderate disasters (Figure 7), a similar pattern was found as that

264

for mild disasters at the provincial level for maize and rice, while the affected area for wheat showed a

265

dramatic decline after 2003. For severe disasters (Figure 8), maize in the regions of N and Inner

266

Mongolia in NW was impacted by drought; the provinces of Jiangxi and Hunan showed the obvious

267

impact of flooding on rice; and wheat in the provinces of Shandong, Henan, and Shanxi was strongly

268

impacted by drought.

269 270

In addition, linear changes in the cereal cultivation areas for each province are shown in Tables 7-9.

271

For maize (Table 7), the areas of mild effects in Jilin, Henan, and Shandong decreased by 41.95, 41.15,

272

and 37.74 thousand ha annually, respectively, while a growth rate of 59.78 thousand ha annually was

273

found in Inner Mongolia. Furthermore, the affected areas in Inner Mongolia increased annually by

274

35.74 and 10.07 thousand ha for the moderate and severe effects, respectively. For rice, the area

275

affected by natural disasters decreased in most provinces except Heilongjiang Province. The increase

276

rates for areas of mild, moderate, and severe effects were 23.34, 14.16, and 1.6 thousand ha annually,

277

respectively. For wheat, the area affected by natural disasters decreased in most provinces except

278

Xinjiang Province, where the increase rates for the areas of mild, moderate, and severe effects were

279

11.72, 10.42, and 1.36 thousand ha annually, respectively. The statistically significant values from the

280

linear analysis are shown in supplementary file 3.

281 282

Flood

Hail

LowTemp

Total

Maize Rice

Province

Drought

Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang

Wheat

1990 2000 2010

1990 2000 2010

1990 2000 2010

1990 2000 2010

1990 2000 2010

Year

Area (10^3 ha)

283 284 285 286

0

500 100015002000

Figure 6. Changes in the area affected by different mild natural disasters for rice, maize, and wheat in each province from 1988 to 2016.

287 288 Flood

Hail

LowTemp

Total

Maize Rice

province

Drought

Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang

Wheat

1990 2000 2010

1990 2000 2010

1990 2000 2010

1990 2000 2010

1990 2000 2010

year

Area (10^3 ha)

289 290

0

500 100015002000

Figure 7 Changes in the area affected by different moderate natural disasters for rice, maize, and

291

wheat in each province from 1988 to 2016. Flood

Hail

LowTemp

Total

Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang

Maize Rice

province

Drought

Wheat

1990 2000 2010

1990 2000 2010

1990 2000 2010

1990 2000 2010

1990 2000 2010

year

Area (10^3 ha)

292 293 294

0

250 500 750 1000

Figure 8 Changes in the area affected by different severe natural disasters for rice, maize, and wheat in each province from 1988 to 2016.

295

296 297

Table 7 Linear changes in the maize cultivation area affected by different natural disasters from 1988 to 2016. Mild Region

Moderate

Severe

Province Drought Flood Hail LowTemp Total Drought Flood Hail LowTemp Total Drought Flood Hail LowTemp Total Heilongjiang 11.66

NE

-1.22

10.33

-2.88 2.95

-0.38

10.58

-0.3

-0.47 0.65

-0.12

-0.14

-15.94 -19.39 -2.43

-5.15

-41.94 -6.39 -12.74 -1.89

-2.89

-25.71 -1.38

-4.22 -0.44

-0.45

-7.25

Liaoning

-3.32

-4.98 -2.4

-1.46

-10.34

0.56

-3.69 -1.15

-0.9

-4.11

1.81

-1.81 -0.13

-0.15

-0.63

Beijing

-2.46

-0.1 -0.52

0.01

-3.07

-1.06

0.04 -0.11

0.01

-1.14

-0.18

-0.01 -0.03

0

-0.26

Tianjin

-2.95

-0.13 -0.54

0.05

-3.55

-1.23

0.07 -0.17

0.01

-1.32

-0.28

-0.04 -0.03

0

-0.39

Hebei

-18.28

0.08 -1.38

1.79

-18.09 -6.95

-1.01 -1.92

1.13

-8.95

-1.12

-0.84 -0.57

0.2

-2.86

Henan

-20.94 -4.02 -0.28

-1.03

-41.15 -13.83 -2.03 -0.42

-0.43

-16.65 -2.62

-0.83 -0.51

-0.14

-6.11

Shandong

-31.05

-4.1 -4.92

0.53

-37.74 -13.97 -3.78 -2.19

0.1

-19.14 -3.08

-1.36 -0.59

0.01

-5.24

Shanxi

1.79

0.52 1.78

3.33

7.28

-0.16

0.13

Shanghai

-0.21

-0.26 -0.09

-0.03

-0.59

-0.06

Jiangsu

-4.36

-4.84 -0.98

-0.51

-10.64

Zhejiang

-0.11

-0.37 -0.12

0.19

Anhui

-2.94

-0.48 -0.22

Hubei

-2.53

1.7 -0.31

Hunan

-0.04

Jiangxi

-0.22

Inner Mongolia

40.5

Jilin

-8.85 4.2

9.31

N

CJ

Shaanxi NW

2.99

-0.06

-0.09 0.24

0.56

0.38

-0.15 -0.04

-0.01

-0.26

-0.01

-0.04 -0.01

0

-0.06

-2.1

-2.02 -0.53

-0.21

-4.83

-0.4

-0.56 -0.09

-0.02

-1.06

0.23

-0.04

-0.22 -0.05

0.06

0.04

-0.03

-0.07 -0.01

0

-0.01

0.18

-3.06

-2.24

-1.37 -0.37

0.05

-3.72

-0.39

-0.24 -0.08

-0.01

-0.62

1.6

0.41

-1.78

0.77 -0.16

0.58

-0.61

-0.45

0.04 -0.06

0.05

-0.49

1.09

2.23

-0.02

0.29

0.61

0.92

0.1

-0.04 0.01

0.1

0.18

-0.13 -0.05

0.02

-0.37

-0.07

-0.09 -0.02

0.01

-0.17

-0.01

-0.06 -0.01

0

-0.08

6.68 8.32

4.39

59.78 25.01

3.06 5.28

2.45

35.74

7.34

1.27

0.57

10.07

-14.98 -1.64 0.73

0.64

-15.39 -7.42

-1.45 0.41

0.17

-8.35

-2.21

-0.23 0.07

0.11

-2.49

0.1

0

1.2

Ningxia

3.51

0.24 0.64

0.92

5.29

1.89

0.12 0.37

0.29

2.65

0.37

0.03 0.14

0.1

0.59

Gansu

9.98

1.83 2.51

4.7

18.87

5.73

1.35 1.62

2.1

10.71

0.73

0.18

0.3

0.25

1.39

Qinghai

0.39

0.1

0.32

0.22

1.03

0.17

0.05 0.23

0.09

0.55

0.01

0.01 0.05

0.01

0.08

Xinjiang

3.27

1.49 5.74

3.36

14.54

2.17

1.07

2.15

10.04

0.21

0.21 1.04

0.29

1.61

Chongqing -11.68 -3.77 -0.97

0.02

-18.2

-5.95

-2.69 -1.16

-0.21

-10.69 -1.26

-0.43 -0.41

-0.03

-2.29

0.31

-20.89 -6.53

-2.45 -1.21

-0.02

-9.59

-1.25

-0.74 -0.27

-0.01

-2.47

0.17

1.62

0.38

2.99

Sichuan SW

1.55

1

1.6

-14.07 -3.94 -3.13

4.7

Guizhou

0.71

-1.15 -0.71

1.49

0.19

2.05

-0.94 -0.57

0.45

0.92

1.8

-0.2

0

Yunnan

10.91

-0.13 1.34

3.54

15.94

7.49

-0.26 0.7

1.29

9.41

2.43

-0.03 0.26

Xizang

-0.18

-0.02 -0.01

-0.01

-0.21

-0.05

-0.01

0

0

-0.07

-0.01

0

0

0

-0.01

Fujian

-0.08

-0.16 -0.01

0.09

0.1

-0.03

-0.07

0

0.04

0.05

-0.01

-0.02

0

0.01

0.01

Guangxi

-4.48

-2.21 -0.55

0.93

-3.63

-2.71

-1.44 -0.36

0.34

-3.34

-0.67

-0.66 -0.09

-0.01

-1.54

Hainan

-0.34

-0.36 -0.27

0

-0.78

-0.05

-0.18 -0.14

0

-0.28

-0.01

-0.05 -0.03

0

-0.04

Guangdong

-0.26

-0.09 -0.14

0.14

1.37

-0.11

-0.11 -0.07

0.04

0.51

-0.02

-0.05 -0.02

0.01

0.11

SE

298 299 300

Table 8. Linear changes in the rice cultivation area affected by different natural disasters from 1988 to 2016. Mild Region

Moderate

Severe

Province Drought Flood Hail LowTemp Total Drought Flood Hail LowTemp Total Drought Flood Hail LowTemp Total

NE

Heilongjiang

15

2.06 3.34

0.21

23.34

9.1

1.72 2.12

0.26

14.16

0.39

0.66 0.46

0.02

1.6

Jilin

-2.53

-3.64 -0.38

-0.97

-7.33

-0.95

-2.4 -0.32

-0.56

-4.58

-0.22

-0.79 -0.08

-0.09

-1.32

Liaoning

-4.37

-1.8 -1.05

-0.59

-7.28

-1.66

-1.32 -0.5

-0.35

-3.51

0.08

-0.61 -0.07

-0.06

-0.82

Beijing

-0.39

-0.08 -0.16

0

-0.63

-0.17

-0.03 -0.07

0

-0.28

-0.03

-0.01 -0.02

0

-0.06

Tianjin

-1.02

-0.15 -0.27

0

-1.44

-0.42

-0.06 -0.12

0

-0.6

-0.09

-0.02 -0.02

0

-0.16

Hebei

-1.99

-0.32 -0.46

-0.02

-2.82

-0.9

-0.22 -0.33

0.01

-1.45

-0.17

-0.08 -0.08

0

-0.37

Henan

-5.07

-0.99 -0.14

-0.25

-10.17 -3.24

-0.5 -0.13

-0.11

-3.94

-0.61

-0.19 -0.12

-0.03

-1.45

Shandong

-1.6

-0.3 -0.29

0

-2.12

-0.74

-0.23 -0.13

0

-1.07

-0.16

-0.08 -0.03

0

-0.28

Shanxi

-0.22

-0.04 -0.02

-0.02

-0.3

-0.11

-0.02 -0.01

-0.01

-0.15

-0.02

-0.01

0

-0.03

Shanghai

-6.23

-7.53 -2.62

-0.95

-17.18 -1.62

-4.52 -1.29

-0.3

-7.66

-0.39

-1.25 -0.28

-0.06

-1.8

Jiangsu

-21.76 -25.47 -4.81

-2.43

-54.16 -10.46 -10.69 -2.65

-1.02

-24.63 -2.02

-2.98 -0.44

-0.12

-5.47

Zhejiang

-15.78 -26.67 -6.52

2.46

-39.64

-15.08 -2.85

0.76

-20.48 -2.09

-4.19 -0.7

0.03

-5.32

Anhui

-21.87 -12.37 -2.73

-0.71

-36.64 -12.63 -12.04 -2.44

-0.21

-26.81 -2.33

-3.14 -0.52

-0.07

-5.69

Hubei

-30.81 -9.58 -3.8

3.49

-40.99 -17.05 -5.38 -1.67

1.08

-23.1

-3.66

-2.09 -0.53

-0.03

-6.84

Hunan

-37.35 -23.22 -3.72

13.74

-49.34 -20.26 -17.55 -2.33

8.22

-31.2

-2.71

-6.76 -0.31

1.42

-8.36

Jiangxi

-53.11 -11.81 -7.33

4.17

-66.19 -19.42 -7.77 -2.9

2.15

-27.18 -3.85

-5.66 -0.73

0.74

-10.07

N

CJ

NW

SW

-6.1

0

Inner Mongolia

0.08

-0.09 0.03

0.02

0.03

0.14

-0.13 0.05

0

0.05

0.11

-0.04 0.01

0.01

0.06

Shaanxi

-2.86

-0.49 -0.06

-0.01

-3.45

-1.43

-0.33 -0.02

-0.02

-1.83

-0.38

-0.08 -0.01

0

-0.49

-0.01 0.03

Ningxia

0.41

0

0.04

0.12

0.56

0.28

0

0.04

-0.01

0.29

0.03

0.01

0.04

Gansu

-0.03

-0.01

0

0.02

-0.02

-0.01

0

0

0.01

0

-0.01

0

0

0

-0.01

Qinghai

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

Xinjiang

0.03

0.05 0.23

0.28

0.72

0.1

0.06 0.33

0.19

0.68

0

0.01 0.08

0.02

0.09

Chongqing

-18.4

-6.32 -1.57

-0.04

-29.06 -9.35

-4.4 -1.8

-0.35

-16.96 -1.98

-0.73 -0.63

-0.05

-3.63

Sichuan

-27.51 -9.13 -6.01

0.08

-42.67 -12.64 -5.25 -2.36

-0.23

-19.5

-2.41

-1.54 -0.52

-0.05

-4.9

Guizhou

-2.41

-2.61 -1.44

0.79

-5.84

0.36

-1.71 -0.95

0.14

-2.23

1.29

-0.4 -0.09

0.11

0.71

Yunnan

6.13

-1.11 0.64

2.29

8.11

4.81

-0.75 0.36

0.8

5.33

1.64

-0.16 0.16

0.26

1.83

Xizang

-0.05

-0.01

0

-0.07

-0.02

0

-0.02

0

0

0

Fujian

-8.96 -19.62 -1.77

0.19

-27.75 -3.43

-9.88 -0.8

-0.25

-13.67 -0.72

-2.54 -0.18

0.11

-3.18

Guangxi

-22.86 -11.52 -2.79

3.48

-24.61 -13.74 -7.16 -1.75

1.28

-18.56 -3.22

-3.04 -0.43

-0.06

-7.34

Hainan

-10.98 -12.25 -8.91

-0.03

-25.11 -1.59

0

-9.74

-0.3

-1.63 -1.06

0

-1.6

Guangdong

-27.8 -21.07 -9.2

-1.18

-43.73 -11.24 -12.37 -4.3

-0.8

-20.99

-2.2

-3.54 -1.01

-0.17

-5.35

0

0

0

0

0

SE

301 302 303

-6.04 -4.75

Table 9. Linear changes in the wheat cultivation area affected by different natural disasters from 1988 to 2016. Mild

Moderate

Severe

Region Provicine Drought Flood Hail LowTemp Total Drought Flood Hail LowTemp Total Drought Flood Hail LowTemp Total Heilongjiang -21.45 -20.14 -1.7 NE

-2.36

-45.65 -10.1

-9.89 -0.67

-1.3

-21.97 -1.92

-2.94 -0.15

-0.23

-5.32

Jilin

-1.32

-0.83 -0.22

-0.22

-2.61

-0.65

-0.52 -0.13

-0.11

-1.48

-0.15

-0.17 -0.03

-0.02

-0.4

Liaoning

-2.37

-1.1

-0.21

-4.02

-1.14

-0.72 -0.23

-0.12

-2.21

-0.24

-0.28 -0.04

-0.02

-0.61

Beijing

-2.22

-0.31 -0.74

0

-3.28

-0.97

-0.1 -0.3

0

-1.37

-0.18

-0.02 -0.08

0

-0.32

Tianjin

-2.75

-0.26 -0.61

0.03

-3.58

-1.14

-0.04 -0.23

0.01

-1.4

-0.26

-0.05 -0.05

0

-0.39

-0.35

N

Hebei

-29.9

0.63

-19.47 -2.25

-1.28 -1.07

0.1

-5.16

-59.22 -12.35 -2.82

-3.23

-115.1 -36.72 -6.11 -2.04

-1.27

-45.94 -6.98

-2.22 -1.37

-0.34

-15.93

-58.12 -11.8 -10.19

-0.24

-78.24 -25.92 -8.34 -4.42

-0.08

-37.94 -5.46

-2.71 -1.13

-0.01

-9.8

Shanxi

-20.08 -4.13 -0.94

-0.25

-25.55 -10.51 -2.05 -0.1

-0.3

-13.08 -2.06

-0.66 -0.08

0.16

-3.03

-1.71

-2.08 -0.72

-0.25

-4.72

-1.23 -0.35

-0.08

-2.09

-0.12

-0.34 -0.08

-0.02

-0.49

Jiangsu

-20.99 -25.64 -4.75

-2.49

-53.7 -10.03 -10.85 -2.62

-1.04

-24.39 -1.94

-3.02 -0.44

-0.12

-5.42

Zhejiang

-2.09

-3.53 -0.84

0.09

-5.98

Anhui

-19.04 -9.41 -2.25

-0.28

Hubei

-15.92

-5.3

-2.03

Hunan

-2.62

Jiangxi

-1.49

-0.44

0

-3

-0.27

-0.54 -0.09

-0.01

-0.75

-29.83 -11.49 -10.1 -2.16

-0.08

-23.27

-2.1

-2.51 -0.46

-0.05

-4.78

1.56

-21.82 -8.77

-2.95 -0.9

0.44

-12.23 -1.87

-1.12 -0.28

-0.04

-3.59

-2.49 -0.36

-0.06

-5.56

-1.41

-1.51 -0.17

-0.02

-3.13

-0.27

-0.46 -0.03

-0.01

-0.79

-0.8

-0.23

-0.09

-2.62

-0.56

-0.48 -0.09

-0.04

-1.18

-0.11

-0.18 -0.02

-0.01

-0.35

-15.38 -4.81

-2.3

-1.5

-24.04

-8.4

-3.59 -0.83

-1.1

-13.95

-1

-1.14 -0.2

-0.1

-2.84

Shaanxi

-32.17 -5.92 -1.27

-0.46

-40.01 -16.12 -3.87 -0.57

-0.4

-21.05 -4.17

-0.92 -0.13

0.01

-5.59

Ningxia

-2.43

-0.43 -0.67

-0.5

-4.08

-0.79

-0.2 -0.25

-0.5

-1.77

-0.29

-0.09 -0.01

-0.06

-0.57

-11.46 -2.96 -1.34

3.07

-13.03 -4.18

-1.06 -0.25

1.28

-4.46

-1.29

-0.48 -0.04

0.21

-1.86

Qinghai

-2.79

-0.57 -0.56

0.68

-3.29

-1.14

-0.1 -0.24

0.27

-1.22

-0.38

-0.03 -0.1

0.04

-0.52

Xinjiang

0.15

1.15

5.05

3.85

11.72

1.15

1.18 5.49

2.52

10.42

-0.11

1.27

0.29

1.36

Chongqing -13.21 -6.73

Gansu

SW

-38.44 -12.69 -2.89 -4.32

Henan

Inner Mongolia

NW

0.68

Shandong

Shanghai

CJ

-3.41 -5.49

-0.81

-1.96 -0.36

0.2

-1.3

-0.58

-23.64 -6.38

-4.19 -1.31

-0.44

-13.01 -1.32

-0.74 -0.46

-0.06

-2.74

Sichuan

-21.64 -8.35

-4.7

-0.26

-35.06 -9.92

-4.65 -1.91

-0.3

-15.92 -1.87

-1.32 -0.42

-0.06

-3.93

Guizhou

-3.88

-3.17

-1.6

-0.15

-8.95

-1.25

-1.86 -0.97

-0.18

-4.32

0.18

-0.46 -0.17

0

-0.56

Yunnan

0.71

-1.61 -0.17

0.53

-0.6

1.3

-0.95 -0.06

0.14

0.4

0.55

-0.22 0.02

0.08

0.36

Xizang

-2.91

-0.36 -0.14

-0.19

-3.62

-0.89

-0.2 -0.09

-0.08

-1.26

-0.18

-0.04 -0.01

0

-0.25

Fujian

-0.75

-1.63 -0.17

-0.16

-2.89

-0.3

-0.84 -0.07

-0.09

-1.41

-0.06

-0.22 -0.02

-0.02

-0.33

Guangxi

-0.25

-0.15 -0.02

0

-0.45

-0.14

-0.09 -0.01

0

-0.25

-0.03

-0.03

0

0

-0.07

Hainan

0

0

0

0

0

0

0

0

0

0

0

0

0

Guangdong

-0.71

-0.43

-0.2

-0.06

-1.46

-0.3

-0.02

-0.67

-0.06

0

-0.16

SE

304 305

0

0

-0.23 -0.1

-0.06 -0.02

306 307

3.3 Agricultural silica loss due to natural disasters

308

Due to the high levels of silica in the main cereal crops, we calculated the loss of silica caused by

309

natural disasters. Figure 9 shows the loss of silica in the grain harvest from the different natural

310

disasters from 1988 to 2016. There was no significant change in the amount of silica lost silica during

311

the entire study period. Rice accounted for the most silica lost due to the disasters. Over the entire

312

study period, for mild disasters, the annual mean silica loss in maize, rice, and wheat was 7.89, 65.07,

313

and 11.25 thousand tons, respectively; for moderate disasters, the annual mean silica loss in maize,

314

rice, and wheat was 11.41, 90.34, and 14.64 thousand tons, respectively; for severe disasters, the

315

annual mean silica loss in maize, rice, and wheat was 4.58, 35.30, and 5.78 thousand tons, respectively.

316

Further, Figure 10 illustrates the loss of silica in the straw harvest due to different natural disasters

317

from 1988 to 2016. In recent years, the silicon loss in maize straw was similar to that in rice straw,

318

while the silica loss in wheat straw showed a decreasing trend after 1997. Finally, the changes in the

319

total silica loss from the different cereal crops are illustrated in Figure 11. Over the entire study period,

320

for mild disasters, the annual mean total silica loss in maize, rice, and wheat was 0.16, 0.44, and 0.09

321

million tons, respectively; for moderate disasters, the annual mean total silica loss in maize, rice, and

322

wheat was 0.24, 0.44, and 0.13 million tons, respectively; for severe disasters, the annual mean total

323

silica loss in maize, rice, and wheat was 0.10, 0.24, and 0.05 million tons, respectively. In addition, the

324

total annual silica loss due to natural disasters is presented in Figure 12. Significant declines (all P <

325

0.05) were observed in the total silica loss from 1988 to 2016. The decrease in silica loss from the

326

grain and straw harvests was 3.15 and 37.17 thousand tons, respectively. Over the entire study period,

327

the total silica loss from the grain and straw harvests was 7.14 and 53.10 million tons, respectively. At

328

the provincial level, the total silica loss caused by natural disasters is shown in Figure 13. For mild

329

disasters, the silica loss caused by drought accounted for more than half of the silica loss from maize,

330

rice, and wheat in 15, 17, and 18 of the 31 provinces, respectively. For moderate disasters, the silica

331

loss from drought accounted for more than half of the silica loss from maize, rice, and wheat in 13, 16,

332

and 14 of the 31 provinces, respectively. For severe disasters, the silica loss from floods accounted for

333

more than half of the silica loss from maize, rice, and wheat in 10, 10, and 12 of the 31 provinces,

334

respectively; and the silica loss from drought accounted for more than half of the silica loss from

335

maize, rice, and wheat in 14, 14, and 10 of the 31 provinces, respectively.

336 337 Mild

Moderate

Severe

1e+05 Drought

1e+04 1e+03 1e+05

Flood

1e+03 10000 3000 1000 300

Hail

1e+05 LowTemp

Silicon loss in grain (ton)

1e+04

1e+04 1e+03 1e+02

Total

1e+05 3e+04 1e+04 3e+03 1990

2000

2010

1990

2000

2010

1990

2000

2010

Year Maize

338

Rice

Wheat

339

Figure 9. Trends in silica loss from the grain harvest in maize, rice, and wheat due to different natural

340

disasters from 1988 to 2016. The shaded regions represent one standard deviation of the silica loss in

341

the grain.

342

Mild

Moderate

Severe

3e+05 1e+05 3e+04 1e+04 3e+03 1e+05

Flood

3e+04

Hail

1e+04 3e+03 1e+05

LowTemp

Silicon loss in straw (ton)

Drought

3e+05 1e+05 3e+04 1e+04 3e+03

1e+04 1e+03 1e+06

Total

1e+05 1e+04

1990

2000

2010

1990

2000

2010

1990

2000

2010

Year Maize

343

Rice

Wheat

344

Figure 10 Trends in silica loss from the straw harvest in maize, rice, and wheat due to different natural

345

disasters from 1988 to 2016. The shaded regions represent one standard deviation of the silica loss in

346

the straw.

347 348 349

Mild

Moderate

Severe

3e+05 1e+05 3e+04 1e+04 3e+03 1e+05

Flood

3e+04

Hail

Total silicon loss (ton)

Drought

3e+05 1e+05 3e+04 1e+04 3e+03

1e+04 3e+03

LowTemp

1e+05 1e+04 1e+03

Total

1e+06 3e+05 1e+05 3e+04 1990

2000

2010

1990

2000

2010

1990

2000

2010

Year Maize

350

Rice

Wheat

351

Figure 11 Trends in silica loss from agricultural production in maize, rice, and wheat due to different

352

natural disasters from 1988 to 2016. The shaded regions represent one standard deviation of the silica

353

loss in China’s agriculture.

354

Silica loss (millioin tons)

3.0

1.0

0.3

1990

2000

2010 Year

Grain

Straw

Total

355 356

Figure 12. Trends in silica loss caused by natural disasters in China’s agricultural areas from 1988 to

357

2016. Lines represent the fitted linear regression trends.

358 359

Moderate

Severe

Maize Rice

Provicne

Mild

Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang Guangdong Hainan Guangxi Fujian Xizang Yunnan Guizhou Sichuan Chongqing Xinjiang Qinghai Gansu Ningxia Shaanxi Inner Mongolia Jiangxi Hunan Hubei Anhui Zhejiang Jiangsu Shanghai Shanxi Shandong Henan Hebei Tianjin Beijing Liaoning Jilin Heilongjiang

Wheat

0

25

50

75

100 0

25

50

75

100 0

25

50

75

100

Relative silica loss (%)

360 361

Flood

Hail

LowTemp

Drought

Figure 13 Cumulative relative silica loss due to different natural disasters from 1988 to 2016 for each

362

province. The vertical line represents the 50% contribution to the total loss. Due to the high silica

363

losses from drought and flood, if the bar for drought or flood overlaps with the vertical lines, more

364

than 50% of the silica loss was caused by that type of disaster.

365

366

Discussion

367 368

In this study, we attempted to link natural disasters and the silicon biogeochemical cycle in

369

agricultural ecosystems using China as an example. Following the questions proposed, we found that

370

1) the area affected by natural disasters showed significant declining trends after 2000; 2) the changes

371

in the area affected by individual types of natural disasters varied at the provincial level; and 3) from

372

1988 to 2016, the total silica loss from the grain and straw harvest due to natural disasters was 7.14

373

and 53.10 million tons, respectively.

374 375

To address the connection between natural disasters and biogeochemical cycles, we first analysed the

376

frequency and intensity of natural disasters affecting different cereal crops, because cereal crops

377

accounted for 92.7% of the total crop production in 2016 and contain high levels of silica (Li et al.,

378

2013; Song et al., 2014c; Vandevenne et al., 2012). However, few studies have focused on the

379

changes in the area of main crop cultivation regions that is affected by natural disasters due to data

380

limitations. To address this, we estimated the affected area for the different crops using the cultivation

381

ratio, which mirrors the relative changes for the different crops. This method is acceptable for use

382

because the changes in the yields of the cereal crops showed similar trends over the past years (Figure

383

5). Further, we proposed a proxy, the maximum index, for the ratio of the affected area to the total

384

cultivation area, because experiencing the maximum impact of a natural disaster can challenge and

385

improve the ability of governments to face these disasters. Here, we calculated that the maximum

386

percentage of the cultivation area affected by mild, moderate, and severe natural disasters was 77.05%,

387

42.33%, and 11.89%, respectively. All of these maximum effects occurred at the beginning of the

388

2000s; after that, the area affected by natural disasters declined. This implied that the experience of

389

overcoming natural disasters in China can be promulgated to developing agricultural countries.

390 391

Although previous studies have fully investigated the characteristics of individual natural disasters in

392

China (Chen and Yang, 2013; Hao et al., 2012; He et al., 2013; Li et al., 2012), studies at the national

393

scale could obscure the detailed impacts of natural disasters at smaller scales, e.g., the provincial scale.

394

Our study showed that changes in the area affected by individual natural disasters varied by province

395

over the past three decades. Several natural factors, e.g., the vast geographic regions and their

396

ecological characteristics, could explain these variations. Although the quantitative impacts (linear or

397

nonlinear trends) of different disasters on rice and wheat production have been described using spatial

398

and temporal analysis (Chen et al., 2018; Shi and Tao, 2014; Wang et al., 2016; Zhang et al., 2018;

399

Zhang et al., 2014a; Zhang et al., 2014b; Zhang et al., 2014c), we performed a comprehensive analysis

400

of the impacts of natural disasters on different crops. Three provinces, Inner Mongolia, Heilongjiang,

401

and Xinjiang, are notable because of the increasing trends in the yield of their dominant crops.

402 403

Our study evaluated silica loss due to natural disasters in China over the past three decades. Due to the

404

high silica content in cereal straw, the total silica loss from straw was 7.5 times that from grain,

405

indicating the importance of straw in the silicon cycle. From 1988 to 2016, the total silica loss from

406

the grain and straw harvest in China was 7.14 and 53.10 million tons, respectively. The annual global

407

agricultural silica harvest is still unclear. Matichenkov and Bocharnikova 2001 (Matichenkov and

408

Bocharnikova, 2001) estimated that approximately 220 million tons of silica are produced annually

409

from agriculture, while Carey and Fulweiler (2012) (Carey and Fulweiler, 2012) calculated that the

410

annual silica production was approximately 52.8 million tons from four main crops (maize, rice, wheat,

411

and sugarcane). According to a similar method used in a later study, the silica loss from natural

412

disasters in China’s agriculture accounted for 3.93% of the world’s silica harvest. Silica loss has a

413

dual nature; the loss increases the soil silicon supply but also decreases the silica sink capability of the

414

agricultural system (Vandevenne et al., 2012). Furthermore, silica loss from drought accounted for

415

more than half of the total silica loss found in more than half of the provinces in China. Previous

416

studies have proven that silicon fertilizer applications can alleviate drought stress (Gong et al., 2005;

417

Gong et al., 2003; Zhu and Gong, 2014). Further, this study implies that drought prevention will be

418

beneficial for increasing the silica sink and thus potentially improving the carbon sink (Song et al.,

419

2014a; Song et al., 2014b).

420 421

There were several limitations in this study. The first one is the model parameters used in calculating

422

the silicon loss due to natural disasters. In this study, regional parameters (grain to straw ratio, straw

423

returning and burning rate) were used rather than the province-level data due to the lack of observed

424

data, and the silicon contents in grain and straw were assessed using average data from across the

425

entire study area. Therefore, key parameters at the provincial level are needed for a more precise

426

analysis in the future. The second limitation is the specific affected area for each individual crop,

427

because there were no detailed data on the affected areas of maize, rice, and wheat. In this study, we

428

derived the affected area for each crop by assuming the ratio of individual cultivation to the total

429

cultivation area. Therefore, a particular dataset for the specific affected area for each crop must be

430

compiled in the future. The third limitation is the silica input from applied fertilizers, especially the

431

organic fertilizer produced from the straw silage Due to the limitation of available dataset observed for

432

the utilization chain of straw in China’s agriculture, precise tracking for the straw utilization should be

433

carried out in future.

434 435 436

437

Conclusion

438

Our work shows that the effects of natural disasters on China’s agriculture lessened significantly after

439

2000 but showed various change patterns at the provincial level. Further, this study estimated that

440

from 1988 to 2016, the total silica loss due to natural disasters from the grain and straw harvest was

441

7.14 and 53.10 million tons, respectively. Although we made the connection between natural disasters

442

and the silicon biogeochemical cycle in this study, moving forward, against the backdrop of global

443

warming, we must explicitly estimate the changes in silica pump and the contribution of silica to the

444

agricultural effects of natural disasters on the carbon budget.

445 446

Acknowledgements

447 448

This study was supported by the National Key Research and Development Program of China (Grant

449

No. 2016YFA0602503), the National Natural Science Foundation of China (Grant Nos. 41701099,

450

2019J1648; 2018-S-110), and the Open Project Program of the Research Center of Data Science,

451

Technology and Applications, Minjiang University, China (No. MJXY-KF-2019002).

452 453

454 455 456

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Highlights Cultivation area affected by natural disasters declined significantly after 2000 in China.

Changes in affected areas of natural disasters varied at provincial level in China from 1988 to 2016.

Annual silica loss due to natural disasters from grain and straw harvest was 0.24 and 1.83 million tons, respectively.

Declaration of interests ☐ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.