Some standard content:
ICS 07.060
Meteorological Industry Standard of the People's Republic of China
QX/T107—2009
Grade of waterlogging for winter wheat and rape
Issued on 2009-06-07
China Meteorological Administration
Implemented on 2009-11-01
Terms and Definitions
Winter wheat and rape waterlogging disaster factors and their values Calculation of winter wheat and rape waterlogging grade.
Appendix A (Informative Appendix)
References:
Crop trend yield calculation method,
QX/T107-2009
Appendix A of this standard is an informative appendix.
This standard is proposed by the National Technical Committee for Standardization of Meteorological Disaster Prevention and Mitigation (SAC/TC345). This standard is under the jurisdiction of the National Technical Committee for Meteorological Disaster Prevention and Mitigation (SAC/TC345). This standard was drafted by the China Meteorological Science Academy, with participation from the Anhui Meteorological Science Institute. The main drafters of this standard are: Huo Zhiguo, Sheng Shaoxue, He Nan, and Bai Qinfeng. QX/T107-2009
QX/T107—2009
Waterlogging of winter wheat and rapeseed is one of the main agricultural meteorological disasters in eastern my country, often causing a reduction in yield or quality of winter wheat and rapeseed. At present, when monitoring and evaluating waterlogging of winter wheat and rapeseed, the selected disaster factors, the adopted calculation methods, and the determined grade indicators vary greatly in various regions, making it impossible to make temporal and spatial comparisons, and it is not conducive to the formulation and implementation of national agricultural disaster prevention and mitigation and agricultural structure adjustment countermeasures. The purpose of compiling this standard is to determine the disaster grade standards for waterlogging of winter wheat and rapeseed, standardize the monitoring and evaluation of waterlogging of winter wheat and rapeseed, and provide a scientific basis for national agricultural disaster prevention and mitigation, and adjustment of agricultural layout and structure. Based on the latest research results, this standard selects factors such as precipitation, precipitation days, sunshine hours, etc., adopts comprehensive relative change indicators, constructs a waterlogging index, and divides winter wheat and rape waterlogging into three levels: mild, moderate, and severe. V
1 Scope
Winter wheat and rape waterlogging levels
QX/T107—2009
This standard specifies the definition, indicators and calculation methods, level division, level naming, and usage methods of winter wheat and rape waterlogging. This standard applies to the investigation, statistics, early warning, and assessment of winter wheat and rape waterlogging in southern my country (along the Qinling Mountains and Huaihe River and to its south).
2 Terms and definitions
The following terms and definitions apply to this standard. 2.1
Precipitation
The depth of liquid or solid (after melting) water that falls from the sky to the ground and accumulates on the horizontal surface without evaporation, infiltration or loss, in millimeters (mm).
Note: One decimal is taken in meteorological observations. Precipitation is generally measured with a rain gauge, so precipitation may include a small amount of dew, frost and sleet. 2.2
Precipitation day precipitationday
A precipitation day is when the daily precipitation (or the amount of fog, dew and frost) is ≥0.1mm, in days (d). 2.3
Precipitation days precipitationdays
The sum of precipitation days in a certain period (ten days, month, year), in days (d). 2.4
Sunshine duration sunshineduration
The actual number of hours that the sun shines on the ground in a place (the cumulative time that the ground observation location is subject to direct solar radiation irradiance ≥ 120W/m). The unit is hour (h), take one decimal place. 2.5
Possible sunshine duration maximum sunshineduration The maximum possible sunshine duration in a day (ten days, month, year), that is, the total number of daytime hours, the unit is hour (h), take one decimal place. Note: It depends on the latitude and season of a place. It can be calculated through the common table of ground meteorological observation. 2.6
Field capacity fieldcapacity
The maximum amount of capillary suspended water that can be retained in the soil under the condition of deep groundwater burial, the unit is percentage (%). Note: Capillary suspended water refers to the water retained in the upper layer of the soil by capillary force after gravity water completely infiltrates after rainfall and irrigation. 2.7
Relative soil humidityrelativesoilmoistureThe weight of water in the soil as a percentage of the field water holding capacity, in percentage (%). 2.8
Waterlogging
When the relative soil humidity of farmland is ≥90%, the soil water content is in an over-wet or saturated state, the soil macropores are filled with water, lack of air, and the environmental conditions of crop roots deteriorate, resulting in poor plant growth and development and a decrease in crop yield. An agricultural meteorological disaster. 1
QX/T107—2009
Yield reduction rateyield reduction rate
The negative value of the difference between the actual yield of winter wheat and rapeseed in a certain year and its trend yield (see Appendix A for calculation method) as a percentage of the trend yield. 3. Calculation of disaster factors and their values of winter wheat and rapeseed waterlogging Select precipitation, precipitation days, and sunshine hours to construct the winter wheat and rapeseed waterlogging index (Q). The calculation formula is shown in formula (1) + b
, where:
waterlogging index:
decade precipitation, unit is bright meter (mm);
each maximum precipitation in the past three decades, unit is millimeter (mm); the number of days with precipitation in the decade is day (a);
decade days, unit is d);
decade sunshine hours, unit is hour (h);
decade possible sunshine hours, unit is hour (h). bi, bz, bs
..·(1)
respectively represent the influence coefficients of precipitation, precipitation days and sunshine hours on the formation of waterlogging disasters. There are different methods to calculate the influence coefficients. This standard adopts the principal component analysis method. The reference values b1 is 0.75~1, bz is 0.75, and b is 0.50~0.75.
Winter wheat and rapeseed waterlogging levels
Based on the three levels of winter small
moderate and severe
Growing period
Sowing period
Pre-winter seedling stage
Overwintering period
Jointing period
Boiling period
Heading and filling period
Sowing period
Pre-winter seedling stage
Overwintering period
, the waterlogging level indicators of winter wheat and rapeseed are determined: winter wheat and rapeseed waterlogging are divided into mild, 1).
Table 1 Waterlogging indexes for winter wheat and rapeseed
Occurrence time
Early to mid-April
Late April to mid-May
November to December
January to early December
Disaster level
2-decade average Q≥1.0
2-decade average 1.0>Q≥0.8
3-sentence average Q≥0.7
2 consecutive decades Q≥1.0||tt| |1.1>Q for 2 consecutive decades, Q≥11, of which 0.8. 1 of which 1.3> 1 of which Q≥1.3Q>1.0
0.9>Q≥0 for 2 consecutive decades, 1.2>Q≥0 for 2 consecutive decades, Q≥1.2, of which 8, 1 of which 12>Q9, 1 of which 1.4>Q 1 of which Q≥1.4≥1.0
1.0>Q≥0.8 for 2 consecutive decades, average 1.2>Q. Average Q≥1.2 1.2, among which or 1 ten-day period 1.2>Q≥1.0
2 ten-day period average 0.9>Q%≥0.8
2 ten-day period average Q≥0.9
2 ten-day period average Q≥0.9
0, among which there is 1 ten-day period 1.4>Q there is 1 ten-day period Q≥1.4>1.2
2 ten-day period average Q≥0.9,
Growth period
Bolting period
Flowering period
Grain filling period
Occurrence time|| tt||Mid- to early-March
April-May
Reference value of yield reduction rate (%)
Table 1 (continued)
Ten-day average1.2>Q≥1.0
2-day average1.2>Q≥0.9
2-day average1.0>Q.≥0.8
Disaster level
Ten-day averageQ≥1.2
2-day average1.4>Q≥1.2
2-day average1.3>Q≥1.0
QX/T107—2009
2-decade average Q≥1.4
2-decade average Q≥1.3
QX/T107—2009
A.1 Decomposition of actual crop yield
Appendix A
(Informative Appendix)
Crop trend yield calculation method
The final yield of crops is formed under the combined influence of various natural and non-natural factors. There are many factors that affect the formation of the final yield of crops, and the relationship between them is extremely complex, which is difficult to characterize with quantitative quantitative relationships. So far, most domestic and foreign researchers have divided these factors into three categories according to the nature and time scale of the impact: agricultural technical measures, meteorological conditions and random "noise". Among them, the agricultural technical measures category includes fertilization, business management, pest and disease control, variety improvement and other yield-increasing measures, which reflects the level of social, economic and technological development in a certain historical period; the corresponding yield component is called time-technical trend yield, or trend yield for short. Meteorological conditions refer to the fluctuation of crop yields caused by the difference in meteorological conditions between years, and the corresponding yield component is called meteorological yield. In the random "noise" category, in addition to the random errors generated by general statistics, it also includes other accidental factors that are not taken into account in the first two categories of factors in the specific calculation model, such as social and economic changes, and their impact on yield is basically irregular; the corresponding yield component is called random yield. Corresponding to the above analysis, crop yield can be decomposed into three parts: trend yield, meteorological yield and random yield. In this standard, the actual yield of winter wheat and rapeseed can be decomposed into: y= y+ yw+Ay
Where:
Actual yield of crops, in kilograms per hectare (kg/hm): y:——trend yield of crops, in kilograms per hectare (kg/hm)); yw——meteorological yield of crops, in kilograms per hectare (kg/hm); Ay—random yield of crops, in kilograms per hectare (kg/hm\). ...(A.1)
Since the accidental factors that affect the increase or decrease of crop yields in various places do not occur frequently, and the impact of local accidental factors is not too great, it is generally assumed that △y can be ignored in the decomposition calculation of actual yields. Therefore, formula (A.1) can be simplified to: y=y.+ yw
A.2 Simulation of crop trend yield
...*(A. 2)
In general, especially in large-scale agricultural production, the impact of agricultural technology measures on crop yields is a relatively slow process in time series. The yield between two consecutive years generally does not increase or decrease sharply due to changes in agricultural technology measures. The change of an agricultural technology measure often occurs gradually, expands (promotes), and lasts for many years to be completed. Therefore, in specific processing, the year sequence or other time parameters are usually simply used as "independent variables", and various functional relationships are used to approximate and simulate the impact of stable non-natural factors such as agricultural technology measures on crop yields. It is commonly known as time technology trend yield (trend yield). In fact, in the weather-yield statistical model, the trend yield represents the sum of the contributions of all non-natural and natural factors to the yield other than the factors used in the meteorological yield simulation, that is, in addition to the impact of agricultural technical measures, it also includes the impact of all other natural and non-natural factors that have a similar effect on the yield as agricultural technical measures. In other words, it is the long-term (or low-frequency) fluctuation part of the yield historical evolution curve. A.3 Linear moving average simulation of crop trend yield This is a simulation method that combines a linear regression model with a sliding average. It regards the change of the time series of crop yield in a certain stage as a linear function, which is a straight line. With the continuous sliding of the stage, the straight line constantly changes its position and slides backward, thereby reflecting the change in the historical evolution trend of the yield. The linear regression model in each stage is obtained in turn. The average of the linear moving regression simulation values at each time point is its trend yield.
If the linear trend equation of a certain stage is:
y;= a,+b,t
where:
i=n-K+1, is the number of equations;
...(A.3)
K—sliding step;
n—number of sample sequences;
t—time sequence number.
When=1,=12,3,,K
When i2,t=2,3,4,,K+1
When i=n-K+1,t=n-K+1,n-K+2,n-K+3,,nQX/T107—2009
Calculate the function value y of each equation at point t: (t), so that there are q function values at each point t, and the number of q is related to n and K. When K≤n/2, g=1,2,3,,K,\,K,,3,2,1; the number of times q is K continuously equals n-2(K+1); when K>n/2, q=1,2,3,\,n一K+1,,n一K+1,,3,2,1; the number of times q is n一K+1 continuously equals 2K一n. Then calculate the average value of the 9 function values at each t point:
y,;(t)=-
(G=1,2,*,q)
.....(A. 4)
The historical evolution trend of production can be expressed by connecting the values of each point (t). Its characteristics depend on the value of K. Only when K is large enough can the trend production eliminate the influence of short-term fluctuations. Generally, the K value can be 10a or longer. This standard stipulates that the K value is 11a. The advantage of this yield trend simulation method is that it does not need to subjectively assume (or judge) the type of curve of historical yield evolution, and it does not lose the number of years of sample sequence. It is a better trend simulation method. 5
QX/T107—2009
References
Editorial Committee of Atmospheric Science Dictionary. Atmospheric Science Dictionary [M]. Beijing: Meteorological Press, 1994. 327-328, 526-527. [1]
Editor-in-chief of Chinese Academy of Agricultural Sciences. Chinese Agricultural Meteorology [M]. Beijing: Agricultural Press, 1999. 310-318. [2]
Sheng Shaoxue, Ma Xiaoqun, Chen Xiaoyi. Identification and indicators of waterlogging disasters in winter wheat and rapeseed in Jianghuai region [J]. Journal of Natural Disasters, 2003, 12(2): 175-181.
[4] Huo Zhiguo, Li Shikui, Wang Suyan, etc. Research on the risk assessment technology and application of major agricultural meteorological disasters [J]. Journal of Natural Resources, 2003, 18(6): 692-703.
People's Republic of China
Meteorological industry standard
Waterlogging grade of winter wheat and rapeseed
QX/T107—2009
Published by Meteorological Press
No. 46, Zhongguancun South Street, Haidian District, Beijing Postal Code: 100081
Website: http://www.cmp.cma.gov.cn Issuing Department: 010-68409198
Beijing Jingke Printing Co., Ltd. Printed by Xinhua Bookstore
Distributed by Xinhua Bookstores all over the country
Format: 880×1230
First edition in August 2009
Printing sheet, 1
Word count: 22.5 thousand words
First printing in August 2009
Book number: 135029-5448
If there is any printing error
The distribution department of our company will replace it
Copyright reserved
Infringement will be investigated
Report telephone number: (010)68406301
6000201/1 Decomposition of actual crop yield
Appendix A
(Informative Appendix)
Crop trend yield calculation method
The final yield of crops is formed under the combined influence of various natural and non-natural factors. There are many factors that affect the formation of the final yield of crops, and the relationship between them is extremely complex, which is difficult to characterize with quantitative relationships. So far, most domestic and foreign researchers have divided these factors into three categories according to the nature and time scale of the impact: agricultural technical measures, meteorological conditions, and random "noise". Among them, the agricultural technical measures category includes fertilization, business management, pest control, variety improvement, and other yield-increasing measures, which reflects the level of social, economic and technological development in a certain historical period; the corresponding yield component is called time-technical trend yield, or trend yield for short. The meteorological condition category refers to the fluctuation of crop yield caused by the difference in meteorological conditions between years, and the corresponding yield component is called meteorological yield. In the random "noise" category, in addition to the random errors generated by general statistics, there are also other accidental factors that are not taken into account in the first two categories of factors in the specific calculation model, such as social and economic changes, and their impact on yield is basically irregular; the corresponding yield component is called random yield. Corresponding to the above analysis, crop yield can be decomposed into three parts: trend yield, meteorological yield and random yield. In this standard, the actual yield of winter wheat and rapeseed can be decomposed into: y= y+ yw+Ay
Where:
Actual yield of crops, in kilograms per hectare (kg/hm): y:——Trend yield of crops, in kilograms per hectare (kg/hm)); yw——Meteorological yield of crops, in kilograms per hectare (kg/hm); Ay—Random yield of crops, in kilograms per hectare (kg/hm\). ...(A.1)
Since the accidental factors that affect the increase or decrease of crop yields in various places do not occur frequently, and the impact of local accidental factors is not too great, it is generally assumed that △y can be ignored in the decomposition calculation of actual yields. Therefore, formula (A.1) can be simplified to: y=y.+ yw
A.2 Simulation of crop trend yield
...*(A. 2)
In general, especially in large-scale agricultural production, the impact of agricultural technology measures on crop yield is a relatively slow process in time series. The yield between two consecutive years generally does not increase or decrease sharply due to changes in agricultural technology measures. The change of an agricultural technology measure often occurs gradually, expands (promotes), and lasts for many years to be completed. Therefore, in specific processing, the year sequence or other time parameters are usually simply used as "independent variables", and various functional relationships are used to approximate and simulate the impact of stable non-natural factors such as agricultural technology measures on crop yields. It is commonly known as time technology trend yield (trend yield). In fact, in the weather-yield statistical model, the trend yield represents the sum of the contributions of all non-natural and natural factors to the yield other than the factors used in the meteorological yield simulation, that is, in addition to the impact of agricultural technical measures, it also includes the impact of all other natural and non-natural factors that have a similar effect on the yield as agricultural technical measures. In other words, it is the long-term (or low-frequency) fluctuation part of the yield historical evolution curve. A.3 Linear moving average simulation of crop trend yield This is a simulation method that combines a linear regression model with a sliding average. It regards the change of the time series of crop yield in a certain stage as a linear function, which is a straight line. With the continuous sliding of the stage, the straight line constantly changes its position and slides backward, thereby reflecting the change in the historical evolution trend of the yield. The linear regression model in each stage is obtained in turn. The average of the linear moving regression simulation values at each time point is its trend yield.
If the linear trend equation of a certain stage is:
y;= a,+b,t
where:
i=n-K+1, is the number of equations;
...(A.3)
K—sliding step;
n—number of sample sequences;
t—time sequence number.
When=1,=12,3,,K
When i2,t=2,3,4,,K+1
When i=n-K+1,t=n-K+1,n-K+2,n-K+3,,nQX/T107—2009
Calculate the function value y of each equation at point t: (t), so that there are q function values at each point t, and the number of q is related to n and K. When K≤n/2, g=1,2,3,,K,\,K,,3,2,1; the number of times q is K continuously equals n-2(K+1); when K>n/2, q=1,2,3,\,n一K+1,,n一K+1,,3,2,1; the number of times q is n一K+1 continuously equals 2K一n. Then calculate the average value of the 9 function values at each t point:
y,;(t)=-
(G=1,2,*,q)
.....(A. 4)
The historical evolution trend of production can be expressed by connecting the values of each point (t). Its characteristics depend on the value of K. Only when K is large enough can the trend production eliminate the influence of short-term fluctuations. Generally, the K value can be 10a or longer. This standard stipulates that the K value is 11a. The advantage of this yield trend simulation method is that it does not need to subjectively assume (or judge) the type of curve of historical yield evolution, and it does not lose the number of years of sample sequence. It is a better trend simulation method. 5
QX/T107—2009
References
Editorial Committee of Atmospheric Science Dictionary. Atmospheric Science Dictionary [M]. Beijing: Meteorological Press, 1994. 327-328, 526-527. [1]
Editor-in-chief of Chinese Academy of Agricultural Sciences. Chinese Agricultural Meteorology [M]. Beijing: Agricultural Press, 1999. 310-318. [2]
Sheng Shaoxue, Ma Xiaoqun, Chen Xiaoyi. Identification and indicators of waterlogging disasters in winter wheat and rapeseed in Jianghuai region [J]. Journal of Natural Disasters, 2003, 12(2): 175-181.
[4] Huo Zhiguo, Li Shikui, Wang Suyan, etc. Research on the risk assessment technology and application of major agricultural meteorological disasters [J]. Journal of Natural Resources, 2003, 18(6): 692-703.
People's Republic of China
Meteorological industry standard
Waterlogging grade of winter wheat and rapeseed
QX/T107—2009
Published by Meteorological Press
No. 46, Zhongguancun South Street, Haidian District, Beijing Postal Code: 100081
Website: http://www.cmp.cma.gov.cn Issuing Department: 010-68409198
Beijing Jingke Printing Co., Ltd. Printed by Xinhua Bookstore
Distributed by Xinhua Bookstores all over the country
Format: 880×1230
First edition in August 2009
Printing sheet, 1
Word count: 22.5 thousand words
First printing in August 2009
Book number: 135029-5448
If there is any printing error
The distribution department of our company will replace it
Copyright reserved
Infringement will be investigated
Report telephone number: (010)68406301
6000201/1 Decomposition of actual crop yield
Appendix A
(Informative Appendix)
Crop trend yield calculation method
The final yield of crops is formed under the combined influence of various natural and non-natural factors. There are many factors that affect the formation of the final yield of crops, and the relationship between them is extremely complex, which is difficult to characterize with quantitative relationships. So far, most domestic and foreign researchers have divided these factors into three categories according to the nature and time scale of the impact: agricultural technical measures, meteorological conditions, and random "noise". Among them, the agricultural technical measures category includes fertilization, business management, pest control, variety improvement, and other yield-increasing measures, which reflects the level of social, economic and technological development in a certain historical period; the corresponding yield component is called time-technical trend yield, or trend yield for short. The meteorological condition category refers to the fluctuation of crop yield caused by the difference in meteorological conditions between years, and the corresponding yield component is called meteorological yield. In the random "noise" category, in addition to the random errors generated by general statistics, there are also other accidental factors that are not taken into account in the first two categories of factors in the specific calculation model, such as social and economic changes, and their impact on yield is basically irregular; the corresponding yield component is called random yield. Corresponding to the above analysis, crop yield can be decomposed into three parts: trend yield, meteorological yield and random yield. In this standard, the actual yield of winter wheat and rapeseed can be decomposed into: y= y+ yw+Ay
Where:
Actual yield of crops, in kilograms per hectare (kg/hm): y:——Trend yield of crops, in kilograms per hectare (kg/hm)); yw——Meteorological yield of crops, in kilograms per hectare (kg/hm); Ay—Random yield of crops, in kilograms per hectare (kg/hm\). ...(A.1)
Since the accidental factors that affect the increase or decrease of crop yields in various places do not occur frequently, and the impact of local accidental factors is not too great, it is generally assumed that △y can be ignored in the decomposition calculation of actual yields. Therefore, formula (A.1) can be simplified to: y=y.+ yw
A.2 Simulation of crop trend yield
...*(A. 2)
In general, especially in large-scale agricultural production, the impact of agricultural technology measures on crop yield is a relatively slow process in time series. The yield between two consecutive years generally does not increase or decrease sharply due to changes in agricultural technology measures. The change of an agricultural technology measure often occurs gradually, expands (promotes), and lasts for many years to be completed. Therefore, in specific processing, the year sequence or other time parameters are usually simply used as "independent variables", and various functional relationships are used to approximate and simulate the impact of stable non-natural factors such as agricultural technology measures on crop yields. It is commonly known as time technology trend yield (trend yield). In fact, in the weather-yield statistical model, the trend yield represents the sum of the contributions of all non-natural and natural factors to the yield other than the factors used in the meteorological yield simulation, that is, in addition to the impact of agricultural technical measures, it also includes the impact of all other natural and non-natural factors that have a similar effect on the yield as agricultural technical measures. In other words, it is the long-term (or low-frequency) fluctuation part of the yield historical evolution curve. A.3 Linear moving average simulation of crop trend yield This is a simulation method that combines a linear regression model with a sliding average. It regards the change of the time series of crop yield in a certain stage as a linear function, which is a straight line. With the continuous sliding of the stage, the straight line constantly changes its position and slides backward, thereby reflecting the change in the historical evolution trend of the yield. The linear regression model in each stage is obtained in turn. The average of the linear moving regression simulation values at each time point is its trend yield.
If the linear trend equation of a certain stage is:
y;= a,+b,t
where:
i=n-K+1, is the number of equations;
...(A.3)
K—sliding step;
n—number of sample sequences;
t—time sequence number.
When=1,=12,3,,K
When i2,t=2,3,4,,K+1
When i=n-K+1,t=n-K+1,n-K+2,n-K+3,,nQX/T107—2009bzxZ.net
Calculate the function value y of each equation at point t: (t), so that there are q function values at each point t, and the number of q is related to n and K. When K≤n/2, g=1,2,3,,K,\,K,,3,2,1; the number of times q is K continuously equals n-2(K+1); when K>n/2, q=1,2,3,\,n一K+1,,n一K+1,,3,2,1; the number of times q is n一K+1 continuously equals 2K一n. Then calculate the average value of the 9 function values at each t point:
y,;(t)=-
(G=1,2,*,q)
.....(A. 4)
The historical evolution trend of production can be expressed by connecting the values of each point (t). Its characteristics depend on the value of K. Only when K is large enough can the trend production eliminate the influence of short-term fluctuations. Generally, the K value can be 10a or longer. This standard stipulates that the K value is 11a. The advantage of this yield trend simulation method is that it does not need to subjectively assume (or judge) the type of curve of historical yield evolution, and it does not lose the number of years of sample sequence. It is a better trend simulation method. 5
QX/T107—2009
References
Editorial Committee of Atmospheric Science Dictionary. Atmospheric Science Dictionary [M]. Beijing: Meteorological Press, 1994. 327-328, 526-527. [1]
Editor-in-chief of Chinese Academy of Agricultural Sciences. Chinese Agricultural Meteorology [M]. Beijing: Agricultural Press, 1999. 310-318. [2]
Sheng Shaoxue, Ma Xiaoqun, Chen Xiaoyi. Identification and indicators of waterlogging disasters in winter wheat and rapeseed in Jianghuai region [J]. Journal of Natural Disasters, 2003, 12(2): 175-181.
[4] Huo Zhiguo, Li Shikui, Wang Suyan, etc. Research on the risk assessment technology and application of major agricultural meteorological disasters [J]. Journal of Natural Resources, 2003, 18(6): 692-703.
People's Republic of China
Meteorological industry standard
Waterlogging grade of winter wheat and rapeseed
QX/T107—2009
Published by Meteorological Press
No. 46, Zhongguancun South Street, Haidian District, Beijing Postal Code: 100081
Website: http://www.cmp.cma.gov.cn Issuing Department: 010-68409198
Beijing Jingke Printing Co., Ltd. Printed by Xinhua Bookstore
Distributed by Xinhua Bookstores all over the country
Format: 880×1230
First edition in August 2009
Printing sheet, 1
Word count: 22.5 thousand words
First printing in August 2009
Book number: 135029-5448
If there is any printing error
The distribution department of our company will replace it
Copyright reserved
Infringement will be investigated
Report telephone number: (010)68406301
6000201/2)
Generally speaking, especially in large-scale agricultural production, the impact of agricultural technology measures on crop yield is a relatively slow process in time series. The yield between two consecutive years will not increase or decrease sharply due to changes in agricultural technology measures. The change of an agricultural technology measure often occurs gradually, expands (promotes), and lasts for many years to be completed. Therefore, in specific processing, the year sequence or other time parameters are usually simply used as "independent variables", and various functional relationships are used to approximate the impact of stable non-natural factors such as agricultural technology measures on crop yield. It is generally called time technology trend yield (trend yield). In fact, in the weather-yield statistical model, trend yield represents the sum of the contributions of all non-natural and natural factors to yield other than the factors used in meteorological yield simulation, that is, in addition to the impact of agricultural technology measures, it also includes the impact of all other natural and non-natural factors that have a similar effect on yield as agricultural technology measures. In other words, it is the long-period (or low-frequency) fluctuation part of the yield historical evolution curve. A.3 Linear moving average simulation of crop trend yield This is a simulation method that combines a linear regression model with a sliding average. It regards the change of the time series of crop yield in a certain stage as a linear function, which is a straight line. With the continuous sliding of the stage, the straight line constantly changes its position and slides backward, thereby reflecting the change of the historical evolution trend of the yield. The linear regression model in each stage is obtained in turn. The average of the linear moving regression simulation values at the time point is its trend yield.
If the linear trend equation of a certain stage is:
y;= a,+b,t
Where:
i=n一K+1, is the number of equations;
...(A.3)
K—sliding step;
n—number of sample sequences;
t—time sequence number.
When =1, =12,3,,K
When i2, t=2,3,4,,K+1
When i=n-K+1, t=n-K+1,n-K+2,n-K+3,,nQX/T107—2009
Calculate the function value y:(t) of each equation at point t, so that there are q function values at each point t, and the number of q is related to n, K. When K≤n/2, then g=1,2,3,,K,\,K,,3,2,1; the number of times q is K continuously is equal to n-2(K+1); when K>n/2, then q=1,2,3,\,n一K+1,,n一K+1,,3,2,1; the number of times q is n一K+1 continuously is equal to 2K一n. Then calculate the average value of the 9 function values at each point t:
y,;(t)=-
(G=1,2,*,q)
.....(A. 4)
Connecting the Xi(t) of each point can represent the historical evolution trend of production. Its characteristics depend on the value of K. Only when K is large enough can the trend production eliminate the influence of short-term fluctuations. Generally, the K value can be 10a or longer. This standard stipulates that the K value is 11a. The advantage of this production trend simulation method is that there is no need to subjectively assume (or judge) the curve type of the historical evolution of production, and there is no loss of the number of years of the sample sequence. It is a better trend simulation method. 5
QX/T107—2009
References
Editorial Committee of Atmospheric Science Dictionary. Dictionary of Atmospheric Sciences [M]. Beijing: Meteorological Press, 1994. 327-328, 526-527. [1]
Editor: Chinese Academy of Agricultural Sciences. Chinese Agricultural Meteorology [M]. Beijing: Agricultural Press, 1999. 310-318. [2]
Sheng Shaoxue, Ma Xiaoqun, Chen Xiaoyi. Identification and indicators of waterlogging disasters in winter wheat and rapeseed in the Jianghuai region [J]. Journal of Natural Disasters, 2003, 12(2): 175-181.
[4] Huo Zhiguo, Li Shikui, Wang Suyan, et al. Research on risk assessment technology and application of major agricultural meteorological disasters [J]. Journal of Natural Resources, 2003, 18(6): 692-703.
People's Republic of China
Meteorological Industry Standard
Waterlogging Grade of Winter Wheat and Rape
QX/T107—2009
Published and Distributed by Meteorological Press
No. 46, Zhongguancun South Street, Haidian District, Beijing Postal Code: 100081
Website: http://www.cmp.cma.gov.cn Distribution Department: 010-68409198
Beijing Jingke Printing Co., Ltd. Printed by Xinhua Bookstore
Distributed by Xinhua Bookstores all over the country
Format: 880×1230
First edition in August 2009
Printing sheet, 1
Word count: 22.5 thousand words
First printing in August 2009
Book number: 135029-5448
If there is any printing error
The distribution department of our company will replace it
Copyright reserved
Infringement will be investigated
Report telephone number: (010)68406301
6000201/2)
Generally speaking, especially in large-scale agricultural production, the impact of agricultural technology measures on crop yield is a relatively slow process in time series. The yield between two consecutive years will not increase or decrease sharply due to changes in agricultural technology measures. The change of an agricultural technology measure often occurs gradually, expands (promotes), and lasts for many years to be completed. Therefore, in specific processing, the year sequence or other time parameters are usually simply used as "independent variables", and various functional relationships are used to approximate the impact of stable non-natural factors such as agricultural technology measures on crop yield. It is generally called time technology trend yield (trend yield). In fact, in the weather-yield statistical model, trend yield represents the sum of the contributions of all non-natural and natural factors to yield other than the factors used in meteorological yield simulation, that is, in addition to the impact of agricultural technology measures, it also includes the impact of all other natural and non-natural factors that have a similar effect on yield as agricultural technology measures. In other words, it is the long-period (or low-frequency) fluctuation part of the yield historical evolution curve. A.3 Linear moving average simulation of crop trend yield This is a simulation method that combines a linear regression model with a sliding average. It regards the change of the time series of crop yield in a certain stage as a linear function, which is a straight line. With the continuous sliding of the stage, the straight line constantly changes its position and slides backward, thereby reflecting the change of the historical evolution trend of the yield. The linear regression model in each stage is obtained in turn. The average of the linear moving regression simulation values at the time point is its trend yield.
If the linear trend equation of a certain stage is:
y;= a,+b,t
Where:
i=n一K+1, is the number of equations;
...(A.3)
K—sliding step;
n—number of sample sequences;
t—time sequence number.
When =1, =12,3,,K
When i2, t=2,3,4,,K+1
When i=n-K+1, t=n-K+1,n-K+2,n-K+3,,nQX/T107—2009
Calculate the function value y:(t) of each equation at point t, so that there are q function values at each point t, and the number of q is related to n, K. When K≤n/2, then g=1,2,3,,K,\,K,,3,2,1; the number of times q is K continuously is equal to n-2(K+1); when K>n/2, then q=1,2,3,\,n一K+1,,n一K+1,,3,2,1; the number of times q is n一K+1 continuously is equal to 2K一n. Then calculate the average value of the 9 function values at each point t:
y,;(t)=-
(G=1,2,*,q)
.....(A. 4)
Connecting the Xi(t) of each point can represent the historical evolution trend of production. Its characteristics depend on the value of K. Only when K is large enough can the trend production eliminate the influence of short-term fluctuations. Generally, the K value can be 10a or longer. This standard stipulates that the K value is 11a. The advantage of this production trend simulation method is that there is no need to subjectively assume (or judge) the curve type of the historical evolution of production, and there is no loss of the number of years of the sample sequence. It is a better trend simulation method. 5
QX/T107—2009
References
Editorial Committee of Atmospheric Science Dictionary. Dictionary of Atmospheric Sciences [M]. Beijing: Meteorological Press, 1994. 327-328, 526-527. [1]
Editor: Chinese Academy of Agricultural Sciences. Chinese Agricultural Meteorology [M]. Beijing: Agricultural Press, 1999. 310-318. [2]
Sheng Shaoxue, Ma Xiaoqun, Chen Xiaoyi. Identification and indicators of waterlogging disasters in winter wheat and rapeseed in the Jianghuai region [J]. Journal of Natural Disasters, 2003, 12(2): 175-181.
[4] Huo Zhiguo, Li Shikui, Wang Suyan, et al. Research on risk assessment technology and application of major agricultural meteorological disasters [J]. Journal of Natural Resources, 2003, 18(6): 692-703.
People's Republic of China
Meteorological Industry Standard
Waterlogging Grade of Winter Wheat and Rape
QX/T107—2009
Published and Distributed by Meteorological Press
No. 46, Zhongguancun South Street, Haidian District, Beijing Postal Code: 100081
Website: http://www.cmp.cma.gov.cn Distribution Department: 010-68409198
Beijing Jingke Printing Co., Ltd. Printed by Xinhua Bookstore
Distributed by Xinhua Bookstores all over the country
Format: 880×1230
First edition in August 2009
Printing sheet, 1
Word count: 22.5 thousand words
First printing in August 2009
Book number: 135029-5448
If there is any printing error
The distribution department of our company will replace it
Copyright reserved
Infringement will be investigated
Report telephone number: (010)68406301
6000201/
[4] Huo Zhiguo, Li Shikui, Wang Suyan, et al. Research on risk assessment technology and its application of major agricultural meteorological disasters [J]. Journal of Natural Resources, 2003, 18(6): 692-703.
People's Republic of China
Meteorological industry standard
Waterlogging grade of winter wheat and rapeseed
QX/T107—2009
Published by Meteorological Press
No. 46, Zhongguancun South Street, Haidian District, Beijing, Postal Code: 100081
Website: http://www.cmp.cma.gov.cn Publishing Department: 010-68409198
Beijing Jingke Printing Co., Ltd. Printed by Xinhua Bookstore
Distributed by Xinhua Bookstores all over the country
Format: 880×1230
First edition in August 2009
Printing sheet, 1
Word count: 22.5 thousand words
First printing in August 2009
Book number: 135029-5448
If there is any printing error
The distribution department of our company will replace it
Copyright reserved
Infringement will be investigated
Report telephone number: (010)68406301
6000201/
[4] Huo Zhiguo, Li Shikui, Wang Suyan, et al. Research on risk assessment technology and its application of major agricultural meteorological disasters [J]. Journal of Natural Resources, 2003, 18(6): 692-703.
People's Republic of China
Meteorological industry standard
Waterlogging grade of winter wheat and rapeseed
QX/T107—2009
Published by Meteorological Press
No. 46, Zhongguancun South Street, Haidian District, Beijing, Postal Code: 100081
Website: http://www.cmp.cma.gov.cn Publishing Department: 010-68409198
Beijing Jingke Printing Co., Ltd. Printed by Xinhua Bookstore
Distributed by Xinhua Bookstores all over the country
Format: 880×1230
First edition in August 2009
Printing sheet, 1
Word count: 22.5 thousand words
First printing in August 2009
Book number: 135029-5448
If there is any printing error
The distribution department of our company will replace it
Copyright reserved
Infringement will be investigated
Report telephone number: (010)68406301
6000201/
Tip: This standard content only shows part of the intercepted content of the complete standard. If you need the complete standard, please go to the top to download the complete standard document for free.