Risk Assessment of Maize Cold Damage in Heilongjiang Province in Recent 30 Years

Sun Yan-kun, and Yu Lan

College of Resources and Environment, Northeast Agricultural University, Harbin 150030, China

Introduction

With global warming, since 1980s, there had been obviously warming in Heilongjiang Province. In the past 50 years, temperature rises one degree Celsius and Heilongjiang Province becomes the climate change center in China (Panet al., 2003). The frequency and intensity of cold damage reduce (Chen and Zhu, 2004;Wanget al., 2010). But because of the increase in active accumulated temperature, which is caused by temperature rise, the choices of the local varieties are later than their early types (Fanget al., 2005), which offset the effect of accumulated temperature increase to a certain extent. So cold damage to the production safety of crops and grains is still a threat. Heilongjiang Province is one of the major maize producing areas in China. Because it is at high latitude, accumulated temperature is not enough, the interannual variability of the heat in growing season is considerable and maize is vulnerable to the threat of low temperature during its growth process. Cold damage is not only one of the main agrometeorological disasters in this area, but also one of the key factors of instability of maize yield in the area.

At present, there are three principal methods for the risk assessment of agrometeorological disasters.Firstly, a method of comprehensive assessment based on indices, such as the comprehensive indices of meteorological risks of low temperature and cold dew wind, based on intensity factors of low temperature,which are the cold accumulated equivalent temperature, the lasting days of low temperature and hours of process sunshine (Luet al., 2011). Secondly, the method is probability assessment based on data, such as the risk assessment of agricultural cold damage based on information diffusion (Liet al., 2009).Finally, an assessment method based on scenario simu-lations, such as the risk assessment model of maize cold damage, based on improved dynamic models of maize growth and dry matter accumulation, and the new indices and parameters of maize cold damage(Maet al., 2003). At present, some scholars have carried out the risk assessments of maize cold damage in Heilongjiang Province (Liet al., 2013; Zhuet al.,2015). However, after 1980, there had been few reports about the risk assessment of maize cold damage in Heilongjiang Province. In this paper, cold damage of maize growing season in Heilongjiang Province was taken as the research object. Multivariate data, such as meteorological phenomena, planting area and yield of maize and the risk assessment model of maize cold damage in northeast China (Tang and Guo, 2016) were used, during the years from 1986 to 2015 and the risk assessment of maize cold damage in Heilongjiang Province was carried out, to make the relevant risk departments and the agricultural producers had a clearer understanding of maize cold damage.

Materials and Methods

Research materials

The data of daily average temperature of 83 meteorological stations in Heilongjiang Province from May to September from 1986 to 2015 were used, which were from the Meteorological Bureau of Heilongjiang Province. And the data of planting area and yield of maize of 78 counties in Heilongjiang Province from 1986 to 2015 were used, which were from the Statistics Bureau of Heilongjiang Province.

Outcome measures of cold damage year of maize

Hazard referred to the factors and degree of natural variations which caused cold damage, mainly referred to abnormally low temperature and its influential factors (including regional meteorological factors,topographies and latitudes). For maize cold damage,maize was the hazard-affected body, the higher the hazard, the higher the risk of maize cold damage(Zhang, 2009; Zhang and Li, 2007). The interannual variation trend of meteorological yield and that of the sum of monthly average temperature from May to September were basically the same (Zhuet al., 2015).High yield happened in the year that temperature was high during the growing season, otherwise the yield was low. (Zhuet al., 2015). Therefore, the anomalies of the sum of monthly average temperature from May to September from place to place could be used as the indices to judge whether the crop yield was reduced by cold damage in one year (Zhuet al., 2015). Based on the new evaluative criterion of maize cold damage,which was ＜QX/T 167-2012 technical specification for assessment of cold damage to Spring Maize in Northern China＞, promulgated in 2012, the anomalies of the sum of monthly average temperature of maize growing season from May to September were selected as the indices to judge mild cold damage, moderate cold damage and severe cold damage (Table 1).

Table 1 Intensity grade indices of maize cold damage in Heilongjiang Province

Risk assessment model of maize cold damage

The United Nations Office for the Coordination of Humanitarian Affair defined natural disaster risk as the product of the hazard of disasters (natural attribute)and the vulnerability of hazard-affected body (social attribute). For the physical target which suffered from disasters, the larger the exposed range outside, the higher the potential risk of disasters. Hence, the risk of maize cold damage was decided by three factors that were the hazard of maize cold damage, the vulnerability and the exposure of maize. The product of them was the risk index of maize cold damage (Tang and Guo, 2016). The risk assessment model of maize cold damage in northeast China (Tang and Guo, 2016)was used as the following:

Where,Rwas the risk index of maize cold damage,Hwas the hazard of maize cold damage,Vwas the vulnerability of maize, andEwas the exposure of maize.

Index system of risk assessment of maize cold damage

The hazard of causing natural disasters could be decided by the intensity and the happening probability of disasters (Tang and Guo, 2016). The hazard of maize cold damage was equal to the weighted accumulation of the hazard of mild cold damage,moderate cold damage or severe cold damage (Tang and Guo, 2016). Hence, the happening probabilities of maize cold damage and the anomalies of the sum of monthly average temperature from May to September were selected as the assessment indices of the hazard of maize cold damage. The formula (Tang and Guo,2016) was as the following:

Where,H'iwas the assessment index value of the hazard of stationi(i=1, ..., 83),hijwas the hazard of cold damage intensity of gradejof stationi, and

Uijwas the happening probability of cold damage intensity of gradejof stationi.

According to formula (3) (Liet al., 2013), the assessment index values of the hazard were made dimensionless. Then the natural broke classification method (which could make the least difference within groups and the largest difference between groups) (Liet al., 2013) was used to divide the assessment index values of the hazard of the study area into five grades.

The vulnerability index showed the degrees of losses and the regional differences of crop yield influenced by the main meteorological disasters, which in general was depicted by loss indicators, such as the percentage of area covered with natural disasters, the percentage of area affected by natural disasters, (Zhang and Li,2007). A research showed that the yield reduction rate had notable positive correlation with the percentage of area covered with natural disasters (Zhang and Li, 2007). Due to lacking of complete data of area covered by maize cold damage and area affected by maize cold damage over the years, the yield reduction rates of maize of 78 counties (cities) in Heilongjiang Province were selected as the assessment indices of the vulnerability of maize. The formula (Wanget al.,2016) used was as the following:

Where,V'iwas the assessment index value of the vulnerability of county (city)i(i=1, …, 78),Ywiwas the meteorological yield of maize of county (city)i(kg), andYtiwas the trend yield of maize of county(city)i(kg). The actual outputYcould be separated into three parts: the trend yieldYt, which improved with the level of social production, the meteorological yieldYw, which fluctuated with meteorological conditions, and the random noise which was generally ignored (Wanget al., 2016). The method of moving average (Zhouet al., 2014) was used to calculate the trend yield of 78 counties (cities), then separated out crop yield (i.e. meteorological yield) that affected by meteorological disasters, finally the negative values of relative meteorological yield (×100%) were the yield reduction rates (Wanget al., 2016).

According to formula (5) (Liet al., 2013),the assessment index values of the vulnerability were made dimensionless, then the natural broke classification method was used to divide the assessment index values of the vulnerability into five grades.

In order to reflect the relative importance of maize production among counties, the ratios of planting areas of counties to land areas of them (i.e. relatively planting area) (Tang and Guo, 2016) were selected as assessment indices of the exposure of maize. The formula (Tang and Guo, 2016) was as the following:

Where,E'iwas the assessment index value of the exposure of county (city)i(i=1, ..., 78),Siwas the planting area of maize of county (city)i(hm2), andS'iwas the land area of county (city)i(km2). With the improvement of agricultural science and technology,the agricultural industry allocations were planned and the administrative regions were adjusted, in the past 30 years, changes of maize planting areas from place to place in Heilongjiang Province were great (Tang and Guo, 2016). Hence, for the exposure, the data of planting area in recent five years were mainly used.

According to formula (7) (Liet al., 2013), the assessment index values of the exposure were made dimensionless, then the natural breaks classification method was used to divide the assessment index values of the exposure into five grades.

At last, according to formula (1), the risk index values of maize cold damage of all the assessment units were calculated, and the natural breaks classification method was used to divide the risk index values of maize cold damage into five grades.

Results and Discussion

Hazard assessment of maize cold damage

Fig. 1 showed the hazard zoning of maize cold damage in Heilongjiang Province from 1986 to 2015. The results showed that the high hazard areas and the subhigh hazard areas of maize cold damage in Heilongjiang Province were mainly distributed in Lindian County of Daqing City and Sunwu County of Heihe City, which meant that these areas were prone to maize cold damage. The medium hazard areas were mainly distributed in some areas of Daxing'anling Prefecture,a large part of Heihe City and parts of Qiqihar City.The low hazard areas were scattered in Daqing City,Hegang City, Jiamusi City, Qitaihe City and other regions. Most of the rest were the sub-low hazard areas.

Fig. 1 Hazard zoning of maize cold damage in Heilongjiang Province

Vulnerability assessment of maize cold damage

Fig. 2 showed the vulnerability zoning of maize cold damage in Heilongjiang Province from 1986 to 2015.The results showed that the high vulnerability areas of maize cold damage in Heilongjiang Province were scattered in Daxing'anling Prefecture, Qiqihar City,Suihua City and Jixi City, which meant that the maize yields in these areas were affected most by cold damage. The sub-high vulnerability areas were mainly distributed in parts of Daxing'anling Prefecture,parts of Heihe City, parts of Qiqihar City, parts of Suihua City, parts of Daqing City, the eastern part of Heilongjiang Province and other regions. The sublow vulnerability areas were mainly distributed in some areas of Daxing'anling Prefecture, a large part of Yichun City, the southern part of Heilongjiang Province and other regions. The low vulnerability areas were scattered in Daxing'anling Prefecture,Yichun City, Harbin City, and Suifenhe City. Most of the rest were the medium vulnerability areas.

Fig. 2 Vulnerability zoning of maize cold damage in Heilongjiang Province

Exposure assessment of maize cold damage

Fig. 3 showed the exposure zoning of maize cold damage in Heilongjiang Province from 1986 to 2015.The results showed that the high exposure areas of maize cold damage in Heilongjiang Province were scattered in Suihua City, Qiqihar City and Harbin City.The sub-high exposure areas were mainly distributed in a large part of Suihua City, parts of Qiqihar City,parts of Harbin City and parts of Suihua City. The medium exposure areas were mainly distributed in the Songnen Plain and other regions. The sub-low exposure areas were mainly distributed in the Sanjiang Plain, the middle and the south of Heilongjiang Province and other regions. Most of the rest were the low exposure areas, among them, maize planting areas of Daxing'anling Prefecture, Heihe City, Yichun City and other places were very small, and the index values of the exposure in these places were the lowest,which were mainly related to the local climates and topographies.

Risk assessment of maize cold damage

Fig. 4 showed the risk zoning of maize cold damage in Heilongjiang Province from 1986 to 2015. The results showed that the high risk areas and the sub-high risk areas were in Daqing City and Suihua City. It was mainly because the areas were located in the major production areas of maize in Heilongjiang Province,the planting densities and the yields of maize were high, and at the same time, the degrees of hazard were relatively high. And with the continuous expansion of planting areas, improved yield and the seed selection of late-maturing varieties, the risk index values of maize cold damage in these areas would be increased.The medium risk areas were located in the center of the Songnen Plain and other regions. The sub-low risk areas were located in the Songnen Plain. Most of the rest in the past 30 years were the low exposure areas,the index values of hazard were sub-low grade, and the index values of vulnerability were not high, so they were the low risk areas.

Fig. 3 Exposure zoning of maize cold damage in Heilongjiang Province

Fig. 4 Risk zoning of maize cold damage in Heilongjiang Province

The above analyses showed that the risk assessment of maize cold damage, including the factors of the hazard, the exposure and the vulnerability, was the induction and the summary for what had happened.Due to the fact that the climate had certain stability,the risk assessment of maize cold damage had significance in guiding the future layouts of maize production. The exposure and vulnerability were related to the local planting habits and policies, such as the Sanjiang Plain was mainly growing rice at present,its hazard of maize cold damage, climatic conditions for maize were also very appropriate. However, due to its small planting area of maize, even if the severe cold damage occurred, the economic losses were still relatively small. But if the maize planting area was expanded in the Sanjiang Plain, that was to say, the exposure and the vulnerability were increased, which would make the risk of maize cold damage increased.Therefore, when using the risk assessment results of maize cold damage to guide future maize production,relevant departments should pay attention to it.

Conclusions

The purpose of this study was to analyze the risks of maize cold damage in Heilongjiang Province in recent 30 years. The risk model of maize cold damage included three aspects, which were the hazard of maize cold damage, the vulnerability and the exposure of maize.

The results showed that from 1986 to 2015, for the hazard of maize cold damage, the high hazard areas and the sub-high hazard areas of maize cold damage in Heilongjiang Province were mainly distributed in Lindian County of Daqing City and Sunwu County of Heihe City. The medium hazard areas were mainly distributed in some areas of Daxing'anling Prefecture, a large part of Heihe City and parts of Qiqihar City. The low hazard areas were scattered in Daqing City, Hegang City, Jiamusi City, Qitaihe City and other regions.Most of the rest were the sub-low hazard areas. For the vulnerability of maize, the high vulnerability areas of maize cold damage in Heilongjiang Province were scattered in Daxing'anling Prefecture, Qiqihar City,Suihua City and Jixi City. The sub-high vulnerability areas were mainly distributed in parts of Daxing'anling Prefecture, parts of Heihe City, parts of Qiqihar City,parts of Suihua City, parts of Daqing City, the eastern part of Heilongjiang Province and other regions. The sub-low vulnerability areas were mainly distributed in some areas of Daxing'anling Prefecture, a large part of Yichun City, the southern part of Heilongjiang Province and other regions. The low vulnerability areas were scattered in Daxing'anling Prefecture, Yichun City,Harbin City and Suifenhe City. Most of the rest were the medium vulnerability areas. For the exposure of maize, the high exposure areas of maize cold damage in Heilongjiang Province were scattered in Suihua City, Qiqihar City and Harbin City. The sub-high exposure areas were mainly distributed in a large part of Suihua City, parts of Qiqihar City, parts of Harbin City and parts of Suihua City. The medium exposure areas were mainly distributed in the Songnen Plain and other regions. The sub-low exposure areas were mainly distributed in the Sanjiang Plain, the middle and the south of Heilongjiang Province and other regions.Most of the rest were the low exposure areas.

The risk assessment results showed that in the past 30 years, the high risk areas and the sub-high risk areas were in Daqing and Suihua cities. The medium risk areas were located in the center of the Songnen Plain and other regions. The sub-low risk areas were located in the Songnen Plain. Most of the rest were the low risk areas.

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