Corn low-temperature cold damage vulnerability assessment method

Abstract

The invention discloses a corn low-temperature cold damage vulnerability assessment method. The method comprises the following steps: 1, selecting indexes; step 2, constructing a cold damage index; step 3, identifying a cold injury event; 4, simulating the growth condition; 5, establishing a fragile curved surface; in the fourth step, the key physiological index loss rate is obtained by subtracting the key physiological index under the cold injury condition from the key physiological index, dividing the key physiological index under the normal condition and multiplying by 100%; according to the method, from the perspective of a soil-crop-atmosphere continuum, indexes capable of representing the low-temperature cold damage on different scales are selected, the comprehensive cold damage index is formed after weighting, the actual low-temperature cold damage condition of corn can be more accurately represented, a complete cold damage event is identified according to a run-length theory, and the method has the advantages of being high in accuracy and high in accuracy. The three-dimensional vulnerability curved surface is formed in combination with the cold damage duration, crop disaster conditions corresponding to different cold damage degrees can be represented, and cold damage events can be described more visually and comprehensively.

Description

Technical Field

[0001] The invention relates to the technical field of agricultural disaster risk assessment, and in particular to a corn low temperature chilling damage vulnerability assessment method. Background Art

[0002] The Northeast region is located in the corn golden belt and is one of the three major corn producing areas in China. In the past 10 years, the corn planting area and yield in the Northeast region have shown a gradual increasing trend, and the occurrence of low temperature and chilling damage undoubtedly poses a serious threat to the safe production of corn. According to statistics, in my country, low temperature and chilling damage is one of the important disasters that affect the sustainable and stable development of agricultural production. It has the characteristics of large scale, comprehensiveness and regional differences. In a severe disaster year, my country's grain production can be reduced by about 10 billion kilograms. Northeast corn is planted in high latitudes, and the stress of low temperature and chilling damage will be more severe. During the growth period of corn, due to insufficient heat supply and large temperature fluctuations, it is easy to have delayed growth and poor growth, empty shells and barren grains, thereby reducing the yield and quality of corn. Therefore, it is crucial to identify the time when chilling damage occurs and assess the possible losses caused by low temperature and chilling damage.

[0003] At present, the research on corn vulnerability to low temperature and chilling damage has the following defects: First, the identification indicators selected by existing research are generally relatively single. Low temperature and chilling damage affects crops in various ways. A single indicator is difficult to characterize the actual damage to crops and cannot accurately characterize the actual occurrence of chilling damage. Second, the existing research methods ignore the temporal variability of low temperature and chilling damage events. The accumulation of time will also affect the actual damage to crops. The existing research methods are difficult to characterize the damage to crops corresponding to different degrees of chilling damage, and the description of chilling damage events is not intuitive and comprehensive enough. Summary of the invention

[0004] The purpose of the present invention is to provide a method for assessing corn low temperature chilling injury vulnerability to solve the problems raised in the above background technology.

[0005] To achieve the above object, the present invention provides the following technical solution: a method for assessing corn low temperature chilling injury vulnerability, comprising step one, selecting an index; step two, constructing a chilling injury index; step three, identifying chilling injury events; step four, simulating growth conditions; step five, establishing a vulnerability surface;

[0006] In the above step 1, an indicator capable of characterizing corn low temperature chilling injury is selected;

[0007] In the above step 2, the entropy weight method is used to weight the four low temperature chilling damage indicators to construct the comprehensive chilling damage index (ICDI);

[0008] In the above step 3, the run theory is used to identify the chilling damage event, and the chilling damage intensity and chilling damage duration of the chilling damage event are obtained;

[0009] In the above step 4, a crop model (CERES-Maize) is used to simulate the growth of corn and obtain the loss rate of key physiological indicators;

[0010] In the above step five, the intensity of chilling damage, duration of chilling damage and loss rate of key physiological indicators are fitted according to trend surface analysis, and a vulnerability surface is formed through binary nonlinear regression.

[0011] Preferably, in step 1, starting from the soil-crop-atmosphere continuum, indicators that can characterize low temperature damage are selected from three perspectives: remote sensing, meteorology, and crops. The remote sensing indicator is the surface temperature (day / night): LST-Day / LST-Night, and the meteorological indicator is the daily minimum temperature T min , the crop indicator selected is the heat index F(T).

[0012] Preferably, in step one, the surface temperature is obtained from the global daily 0.05° spatiotemporal continuous surface temperature dataset of the National Tibetan Plateau Science Data Center, and the daily minimum temperature is obtained from the National Meteorological Science Data Center.

[0013] Preferably, in step 1, the heat index F(T) takes into account the suitable temperature, lower limit temperature and upper limit temperature of crops at different development stages, and reflects the response of crops to environmental heat conditions and the biological characteristics of crops. The formula is: F(T) = [(T-T1)(T2-T) B ] / [(T0-T1)(T2-T0) B ], Where F(T) is the heat index, T is the daily average temperature; T0, T1, and T2 are the suitable temperature, lower limit temperature, and upper limit temperature of the growth period on a certain day, respectively, and when T≤T1 or T≥T2, F(T)=0.

[0014] Preferably, in step 2, the entropy weight method is used to construct the comprehensive chilling damage index (ICDI), and the calculation formula of the comprehensive chilling damage index is: ICDC = w1LST + Day + w2LST - Night + w3F (T) + w4T min , ICDI is the comprehensive chilling damage index, and w1-w4 are the weights corresponding to the four low temperature chilling damage indicators.

[0015] Preferably, in the step three, the run theory is used to identify low temperature chilling damage events, an interception level is given according to the chilling damage index grading standard, and the discrete sequence that changes with time is intercepted. When the chilling damage index is lower than a certain threshold and the duration exceeds a certain length, the chilling damage time is considered to have occurred. After normalizing the comprehensive chilling damage index, 0.5 is set as the threshold. When the random variable is greater than the intercept level once or more continuously, a positive run occurs, otherwise, a negative run occurs. In the process of identifying the chilling damage operation, the length of the negative run is called the chilling damage duration. The severity of the chilling damage is the area covered by the chilling damage duration and the interception level. According to the level of the chilling damage index, the run theory is used to decompose the indicators in the time series into two characteristic variables, the duration and intensity of the chilling damage event. The use method is implemented by MATLAB software.

[0016] Preferably, in the step 4, the growth stage of corn is defined and divided into five growth stages (S1-S5), namely, the emergence-three-leaf stage, the three-leaf-jointing stage, the jointing-flowering stage, the flowering-milk-ripening stage and the milk-ripening-maturity stage in the vegetative growth period, the vegetative growth and reproductive growth period of corn. The crop model (CERES-Maize) is used for simulation, and the key physiological indicators (leaf area index, dry matter mass) under normal conditions and under chilling damage conditions are calculated, and the loss rate of the key physiological indicators is obtained.

[0017] Preferably, in step 4, the key physiological index loss rate is obtained by subtracting the key physiological index under chilling injury from the key physiological index, dividing the result by the key physiological index under normal conditions and multiplying the result by 100%.

[0018] Preferably, in step five, the vulnerability surface is composed of X-axis: chilling damage intensity, Y-axis: chilling damage duration and Z-axis: loss rate of key physiological indicators of crops, wherein the chilling damage intensity and chilling damage duration are derived from run theory, and the loss rate of key physiological indicators of crops is obtained from crop model simulation values, and a vulnerability surface simulating changes in disaster intensity, disaster duration and loss rate is generated through trend surface analysis.

[0019] Preferably, in step 5, the construction of the fragile surface is based on binary nonlinear regression analysis, and the basic equation is: where a, b, c, d, e and f are coefficients estimated using the least squares method, and the fitting function is used as the analytical expression of the fragility surface model: Where Z is the loss rate of key physiological indicators, X and Y represent the intensity and duration of chilling injury, respectively.

[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: starting from the perspective of the soil-crop-atmosphere continuum, the present invention selects indicators that can characterize low temperature and chilling damage at different scales, and forms a comprehensive chilling damage index after weighting, which can more accurately characterize the actual low temperature and chilling damage conditions suffered by corn, and identify complete chilling damage events according to the run theory, and form a three-dimensional vulnerability surface combined with the duration of chilling damage, which can characterize the crop damage corresponding to different degrees of chilling damage, and can more intuitively and comprehensively characterize chilling damage events. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 is a flow chart of the method of the present invention;

[0022] Figure 2 It is the principle diagram of the stroke theory of the present invention;

[0023] Figure 3 Establishing a flow chart for the vulnerability surface of the present invention;

[0024] Figure 4 This is a diagram showing an application example of a fragile surface in an embodiment. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] Please see attached Figure 1 -Attached Figure 3 The present invention provides a technical solution: a method for assessing corn low temperature chilling injury vulnerability, comprising step one, selecting an index; step two, constructing a chilling injury index; step three, identifying chilling injury events; step four, simulating growth conditions; step five, establishing a vulnerability surface;

[0027] In the above step 1, starting from the perspective of soil-crop-atmosphere continuum, three indicators that can characterize low temperature damage are selected from the perspectives of remote sensing-meteorology-crops. The remote sensing indicator is the surface temperature (day / night): LST-Day / LST-Night, and the meteorological indicator is the daily minimum temperature T minThe crop index selected is the heat index F(T). The surface temperature is obtained from the global daily 0.05° spatiotemporal continuous surface temperature dataset of the National Qinghai-Tibet Plateau Science Data Center. The daily minimum temperature is obtained from the National Meteorological Science Data Center. The heat index F(T) takes into account the suitable temperature, lower limit temperature and upper limit temperature of crops at different development stages, reflecting the response of crops to environmental heat conditions and the biological characteristics of crops. The formula is: F(T) = [(T-T1)(T2-T) B ] / [(T0-T1)(T2-T0) B ], Where F(T) is the heat index, T is the daily average temperature; T0, T1, T2 are the suitable temperature, lower limit temperature and upper limit temperature of the growth period on a certain day, and when T≤T1 or T≥T2, F(T)=0;

[0028] In the above step 2, the entropy weight method is used to construct the comprehensive chilling damage index (ICDI), and the calculation formula of the comprehensive chilling damage index is: ICDC = w1LST + Day + w2LST-Night + w3F(T) + w4T min , ICDI is the comprehensive chilling damage index, w1-w4 are the weights corresponding to the four low temperature chilling damage indicators;

[0029] In the above step three, the run theory is used to identify low temperature chilling events. An interception level is given according to the chilling index classification standard to intercept the discrete sequence that changes with time. When the chilling index is lower than a certain threshold and the duration exceeds a certain length, the chilling time is considered to have occurred. After normalizing the comprehensive chilling index, 0.5 is set as the threshold. When the random variable is greater than the intercept level once or more continuously, a positive run occurs, otherwise, a negative run occurs. In the process of identifying the chilling operation, the length of the negative run is called the chilling duration. The severity of the chilling is the area covered by the chilling duration and the interception level. According to the level of the chilling index, the run theory is used to decompose the index in the time series into two characteristic variables: the duration and intensity of the chilling event. The method is implemented by MATLAB software.

[0030] Wherein in the above step 4, a crop model (CERES-Maize) is used to simulate the growth of corn, and a key physiological index loss rate is obtained. The corn growth stage is delineated and divided into five growth stages (S1-S5) of the emergence-three-leaf stage, the three-leaf-jointing stage, the jointing-flowering stage, the flowering-milk-ripening stage, and the milk-ripening-maturity stage in the corn vegetative growth period, the vegetative growth parallel period, and the reproductive growth period. The crop model (CERES-Maize) is used for simulation, and the key physiological indicators (leaf area index, dry matter mass) under normal conditions and under cold damage conditions are calculated, and the key physiological index loss rate is obtained, which is obtained by subtracting the key physiological indicator under the cold damage condition from the key physiological indicator, dividing it by the key physiological indicator under normal conditions, and multiplying it by 100%;

[0031] In the above step 5, the intensity of chilling damage, duration of chilling damage and loss rate of key physiological indicators are fitted according to trend surface analysis, and a vulnerability surface is formed through binary nonlinear regression. The vulnerability surface consists of X-axis: chilling damage intensity, Y-axis: chilling damage duration and Z-axis: loss rate of key physiological indicators of crops. The intensity of chilling damage and duration of chilling damage are derived from the run theory, and the loss rate of key physiological indicators of crops is obtained from the simulation value of the crop model. The vulnerability surface simulating the changes in disaster intensity, disaster duration and loss rate is generated through trend surface analysis. The construction of the vulnerability surface is based on binary nonlinear regression analysis, and the basic equation is: where a, b, c, d, e and f are coefficients estimated using the least squares method, and the fitting function is used as the analytical expression of the fragility surface model: Where Z is the loss rate of key physiological indicators, X and Y represent the intensity and duration of chilling injury, respectively;

[0032] Using the method provided in the above embodiment, refer to the attached drawings, the field application of corn low temperature chilling injury vulnerability assessment in the three northeastern provinces;

[0033] (1) From the perspective of the soil-crop-atmosphere continuum, we selected indicators that can characterize low temperature and chilling damage from three perspectives: remote sensing, meteorology, and crops. The remote sensing indicator selected land surface temperature (day / night): LST-Day / LST-Night, and the meteorological indicator selected the daily minimum temperature T min The crop index selected is the heat index F(T). The surface temperature is obtained from the global daily 0.05° spatiotemporal continuous surface temperature dataset of the National Qinghai-Tibet Plateau Science Data Center. The daily minimum temperature is obtained from the National Meteorological Science Data Center. The heat index F(T) takes into account the suitable temperature, lower limit temperature and upper limit temperature of crops at different development stages, reflecting the response of crops to environmental heat conditions and the biological characteristics of crops. Its formula is:

[0034] F(T)=[(T-T1)(T2-T) B] / [(T0-T1)(T2-T0) B ]

[0035]

[0036] Where: F(T) is the heat index, T is the daily average temperature; T0, T1, T2 are the suitable temperature, lower limit temperature and upper limit temperature of the growth period of a certain day, and when T≤T1 or T≥T2, F(T)=0. The three base point temperatures of each growth stage of corn are shown in Table 1;

[0037] Table 1 Three base point temperatures of corn at different growth stages

[0038]

[0039] According to the above, the spatial distribution of four chilling injury indicators in each growth stage was obtained (average from 2002 to 2022);

[0040] (2) The comprehensive chilling damage index (ICDI) was constructed using the entropy weight method. The calculation formula of the comprehensive chilling damage index is:

[0041] ICDC=w1LST+Day+w2LST-Night+w3F(T)+w4T min

[0042] Where: ICDI is the comprehensive chilling damage index, w1-w4 are the weights corresponding to the four low temperature chilling damage indicators, w1-w4 are 0.16, 0.34, 0.38 and 0.12 respectively;

[0043] (3) Use the run theory to identify low temperature chilling events. According to the chilling index classification standard, an interception level is given to intercept the discrete sequence that changes with time. When the chilling index is lower than a certain threshold and the duration exceeds a certain length, the chilling event is considered to have occurred. After normalizing the comprehensive chilling index, 0.5 is set as the threshold. When the random variable is greater than the intercept level once or more continuously, a positive run occurs. Otherwise, a negative run occurs. In the process of identifying chilling operations, the length of the negative run is called the chilling duration. The severity of chilling is the area covered by the chilling duration and the interception level. According to the level of the chilling index, the run theory is used to decompose the indicators in the time series into two characteristic variables: the duration and intensity of the chilling event. The method is implemented using MATLAB software.

[0044] (4) Using the crop model (CERES-Maize) to simulate the growth of corn, the loss rate of key physiological indicators was obtained, and the growth stage of corn was delineated, which was divided into five growth stages (S1-S5) of corn vegetative growth period, vegetative growth and reproductive growth period, namely, the emergence-three-leaf period, the three-leaf-jointing period, the jointing-flowering period, the flowering-milky period, and the milky-mature period. The crop model (CERES-Maize) was used to simulate and calculate the key physiological indicators (leaf area index, dry matter mass) under normal conditions and under chilling damage, and the loss rate of key physiological indicators was obtained. The loss rate of key physiological indicators was obtained by subtracting the key physiological indicators under chilling damage from the key physiological indicators under normal conditions, dividing by the key physiological indicators under normal conditions, and multiplying by 100%;

[0045] (5) According to the trend surface analysis, the intensity of chilling damage, duration of chilling damage and loss rate of key physiological indicators were fitted, and the vulnerability surface was formed through binary nonlinear regression. The vulnerability surface consists of X-axis: chilling damage intensity, Y-axis: chilling damage duration and Z-axis: loss rate of key physiological indicators of crops. The chilling damage intensity and duration of chilling damage are obtained by run theory, and the loss rate of key physiological indicators of crops is obtained by crop model simulation value. The vulnerability surface simulating the changes of disaster intensity, disaster duration and loss rate was generated through trend surface analysis. The construction of the vulnerability surface is based on binary nonlinear regression analysis, and the basic equation is:

[0046]

[0047] Where: a, b, c, d, e and f are coefficients estimated using the least squares method, and the fitted af are: 9.529, 1.668, -3.792, -0.2834, 1.194, 1.012;

[0048] The analytical expression using the fitting function as the fragility surface model is:

[0049]

[0050] Where: Z is the loss rate of key physiological indicators, X and Y represent the intensity and duration of chilling damage, respectively. The vulnerability surface is obtained by MATLAB software.

[0051] Based on the above, the advantages of the present invention are: starting from the perspective of the soil-crop-atmosphere continuum, the present invention selects indicators of different scales that can characterize low temperature and chilling damage, and forms a comprehensive chilling damage index after weighting, which can more accurately characterize the actual low temperature and chilling damage status of corn. Compared with the existing research methods, the evaluation is more comprehensive, covering the various ways in which low temperature and chilling damage act on crops, and has more reference value. According to the run theory, the complete chilling damage event is identified, and a three-dimensional vulnerability surface is formed in combination with the duration of chilling damage. It can characterize the crop damage corresponding to different degrees of chilling damage, and more intuitively and comprehensively characterize the chilling damage event. Moreover, this method has universality and can be promoted as a disaster risk assessment method for other disasters and other crops.

[0052] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for assessing corn chilling injury vulnerability, comprising step 1, selecting an index; step 2, constructing a chilling injury index; step 3, identifying chilling injury events; step 4, simulating growth conditions; step 5, establishing a vulnerability surface; characterized in that: In the above step 1, an indicator capable of characterizing corn low temperature chilling injury is selected; In the above step 2, the entropy weight method is used to weight the four low temperature chilling damage indicators to construct the comprehensive chilling damage index (ICDI); In the above step 3, the run theory is used to identify the chilling damage event, and the chilling damage intensity and chilling damage duration of the chilling damage event are obtained; In the above step 4, a crop model (CERES-Maize) is used to simulate the growth of corn and obtain the loss rate of key physiological indicators; In the above step five, the intensity of chilling damage, duration of chilling damage and loss rate of key physiological indicators are fitted according to trend surface analysis, and a vulnerability surface is formed through binary nonlinear regression.

2. A corn low temperature chilling injury vulnerability assessment method according to claim 1, characterized in that: In the step 1, starting from the soil-crop-atmosphere continuum, three indicators that can characterize low temperature damage are selected from the perspectives of remote sensing, meteorology, and crops. The remote sensing indicator is the surface temperature (day / night): LST-Day / LST-Night, and the meteorological indicator is the daily minimum temperature T min , the crop indicator selected is the heat index F(T).

3. A method for assessing corn chilling injury vulnerability according to claim 2, characterized in that: In step 1, the surface temperature is obtained from the global daily 0.05° spatiotemporal continuous surface temperature dataset of the National Tibetan Plateau Science Data Center, and the daily minimum temperature is obtained from the National Meteorological Science Data Center.

4. A method for assessing corn chilling injury vulnerability according to claim 2, characterized in that: In step 1, the heat index F(T) takes into account the suitable temperature, lower limit temperature and upper limit temperature of crops at different development stages, and reflects the response of crops to environmental heat conditions and the biological characteristics of crops. The formula is: F(T) = [(T-T1)(T2-T) B ] / [(T0-T1)(T2-T0) B ], Where F(T) is the heat index, T is the daily average temperature; T0, T1, and T2 are the suitable temperature, lower limit temperature, and upper limit temperature of the growth period on a certain day, respectively, and when T≤T1 or T≥T2, F(T)=0.

5. The method for assessing corn chilling injury vulnerability according to claim 1, characterized in that: In the step 2, the entropy weight method is used to construct the comprehensive chilling damage index (ICDI), and the calculation formula of the comprehensive chilling damage index is: ICDC = w1LST + Day + w2LST-Night + w3F(T) + w4T min , ICDI is the comprehensive chilling damage index, and w1-w4 are the weights corresponding to the four low temperature chilling damage indicators.

6. A method for assessing corn chilling injury vulnerability according to claim 1, characterized in that: In the step three, the run theory is used to identify low temperature chilling events, an interception level is given according to the chilling index grading standard, and the discrete sequence that changes with time is intercepted. When the chilling index is lower than a certain threshold and the duration exceeds a certain length, the chilling event is considered to have occurred. After the comprehensive chilling index is normalized, 0.5 is set as the threshold. When the random variable is greater than the intercept level once or more continuously, a positive run occurs, otherwise, a negative run occurs. In the process of identifying the chilling operation, the length of the negative run is called the chilling duration. The severity of the chilling is the area covered by the chilling duration and the interception level. According to the level of the chilling index, the run theory is used to decompose the index in the time series into two characteristic variables, the duration and intensity of the chilling event. The method of using is implemented by MATLAB software.

7. A method for assessing corn chilling injury vulnerability according to claim 1, characterized in that: In the step 4, the growth stages of corn are defined and divided into five growth stages (S1-S5), namely, the emergence-three-leaf stage, the three-leaf-jointing stage, the jointing-flowering stage, the flowering-milk-ripening stage, and the milk-ripening-maturity stage in the vegetative growth period, the vegetative growth and reproductive growth period of corn. The crop model (CERES-Maize) is used for simulation, and the key physiological indicators (leaf area index, dry matter mass) under normal conditions and under cold damage conditions are calculated, and the loss rate of the key physiological indicators is obtained.

8. A method for assessing corn chilling injury vulnerability according to claim 7, characterized in that: In step 4, the key physiological index loss rate is obtained by subtracting the key physiological index under chilling injury from the key physiological index, dividing the result by the key physiological index under normal conditions and multiplying the result by 100%.

9. A method for assessing corn chilling injury vulnerability according to claim 1, characterized in that: In the step five, the vulnerability surface consists of the X-axis: cold damage intensity, the Y-axis: cold damage duration and the Z-axis: loss rate of key physiological indicators of crops, wherein the cold damage intensity and cold damage duration are derived from the run theory, and the loss rate of key physiological indicators of crops is obtained from the crop model simulation value. The vulnerability surface simulating the changes in disaster intensity, disaster duration and loss rate is generated through trend surface analysis.

10. A method for assessing corn chilling injury vulnerability according to claim 9, characterized in that: In step 5, the construction of the fragile surface is based on binary nonlinear regression analysis, and the basic equation is: where a, b, c, d, e and f are coefficients estimated using the least squares method, and the fitting function is used as the analytical expression of the fragility surface model: Where Z is the loss rate of key physiological indicators, X and Y represent the intensity and duration of chilling injury, respectively.

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