A comprehensive prediction system and method for poplar bark rot based on prediction model

Abstract

A comprehensive prediction system and method for poplar bark rot based on a prediction model, including a data collection unit for collecting N standard plots of associated data in a target area, a data collection unit for performing multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot, an incidence prediction unit for using the linear model of the incidence rate and susceptibility index of poplar bark rot on current associated data to predict the incidence rate and susceptibility index of poplar bark rot to obtain the current predicted data, and a data display unit for displaying associated data, linear models, and predicted data; the associated data include site-related factors, climate-related factors, and biological-related factors. The present invention is based on site-related factors: latitude, slope, climate-related factors: temperature, temperature-humidity ratio, rainfall, biological-related factors: tree species, tree age, and a linear model of the incidence rate and susceptibility index of poplar bark rot to predict the incidence rate and susceptibility index of poplar bark rot.

Description

Technical Field

[0001] The present invention relates to the field of measurement technology, and more specifically, to a comprehensive prediction system and method for poplar bark rot based on a prediction model. Background Art

[0002] Poplars are the most widely distributed and adaptable tree species in the world. They are important ecological and efficient timber species and important afforestation species in my country. They are widely distributed in the "Three Norths", Central China, East China, and Southwest China. The area of ​​poplar plantations in my country accounts for about 73% of the global area of ​​poplar plantations, but the annual timber production of poplar plantations in my country only accounts for 53% of the global annual timber production of poplar plantations, indicating that the growth and output per unit area of ​​poplar plantations in my country are at a relatively low level. Among them, one of the most important constraints is the large-scale and continuous damage caused by pests and diseases in poplar plantations.

[0003] Poplar bark rot is one of the main diseases of poplars. It is a disease of poplar branches and trunks and a host-dominated disease. It is highly contagious and most serious in newly planted forests. It has had a major impact on afforestation and the "Three Norths" shelterbelt. When the trees are growing well, the pathogen is in a latent period. When environmental conditions are not conducive to the growth of poplars, such as drought, frost damage, sunburn, etc., which cause trauma to the poplar trunk and weaken the tree, bark rot will occur on a large scale. In the early stage of the disease, brown edema-like lesions appear on the poplar trunk, the cortex rots, softens, and sinks, and cracks form after water loss. The characteristics of the diseased and healthy junction are obvious, with dark brown edges. In the late stage of the disease, many small needle-like protrusions are formed on the lesions, which are the conidiophores of the pathogen. As the onset time prolongs, the lesions can form a ring cut on the poplar trunk, causing phloem necrosis and tree death, resulting in huge losses.

[0004] In recent years, due to abnormal climate and other reasons, as well as the annual expansion of poplar plantation areas, poplar bark rot has also become more serious, becoming a destructive disease of forests, posing a serious threat to the sustainable development of forestry, ecological and environmental security, and even the development of agricultural production. At present, the prediction of poplar bark rot is based on the establishment of a forecast model based on meteorological factors alone, which is not very accurate.

[0005] Therefore, the problems existing in the prior art need to be further improved and developed. Summary of the invention

[0006] (I) Purpose of the invention: In order to solve the problems existing in the above-mentioned prior art, the purpose of the present invention is to provide a comprehensive prediction system and method for poplar bark rot based on a prediction model.

[0007] (II) Technical solution: In order to solve the above technical problems, this technical solution provides a comprehensive prediction system for poplar bark rot based on a prediction model, including a data acquisition unit, a data analysis unit, an incidence prediction unit and a data display unit. The data collection unit is used to collect the associated data of N standard plots in the target area; the data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot; the incidence prediction unit uses the linear model of the incidence rate and susceptibility index of poplar bark rot to predict the incidence rate and susceptibility index of poplar bark rot on the current associated data to obtain the current predicted data; the data display unit is used to display the associated data, the linear model, and the predicted data; The associated data include site conditions, climatic conditions, biological conditions and the occurrence of poplar bark rot. The site conditions include site-related factors, which include latitude and slope aspect; the climatic conditions include climate-related factors, which include temperature, temperature-humidity ratio, and rainfall; the biological conditions include biological-related factors, which include tree species and tree age.

[0008] The comprehensive prediction system for poplar bark rot based on the prediction model, wherein the data collection unit collects the associated data of the target plots in different climate zones respectively, and collects the site conditions and climate conditions of the warm temperate semi-humid areas, the plateau temperate semi-arid areas, and the mid-temperate arid areas respectively.

[0009] The poplar bark rot comprehensive prediction system based on the prediction model, wherein the investigation of the occurrence of poplar bark rot in target plots in different climate zones may include the following steps: Step 1, the data collection unit randomly sets M survey plots in N standard plots in each different climate zone; Step 2, the data collection unit selects Z sampling points in each survey area; Step 3, the data collection unit selects W sample plants at each sampling point and marks and numbers the sample plants; Step 4, the information collection device of the data collection unit observes and records the conditions of the sample plants within a specified time to obtain observation data; specifically, from the beginning of the disease in mid-March to the end of October, a survey is conducted every 15 days according to the disease grading standard and the grading counting method, that is, each survey is called an investigation period.

[0010] The poplar bark rot comprehensive prediction system based on the prediction model, wherein the data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the poplar bark rot incidence rate and susceptibility index, may include the following steps: Step 1, calculate the incidence and susceptibility index of poplar bark rot in N standard plots in each different climate zone at different survey periods according to the observed data, and draw trend graphs of the incidence and susceptibility index; Step 2, according to the trend graph of the incidence rate and the disease susceptibility index, the correlation between the incidence rate and the disease susceptibility index of poplar bark rot in N standard plots in each different climate zone at different survey periods is analyzed to obtain first analysis data, wherein the first analysis data includes the correlation data between the incidence rate and the disease susceptibility index between different survey years; Step 3, according to the associated data, respectively calculate the standard values ​​of the associated data of N standard plots in each different climatic zone, and analyze the correlation between the incidence rate and the disease susceptibility index of poplar bark rot under different climatic conditions, different site conditions, and different biological conditions, to obtain second analysis data; the second analysis data includes the correlation between the incidence rate and the disease susceptibility index of poplar bark rot under different climatic conditions, different site conditions, and different biological conditions; Step 4, performing a multivariate linear regression analysis on the associated data based on the first analysis data and the second analysis data, respectively constructing a linear model of the incidence rate and susceptibility index of poplar bark rot in each climate zone based on climatic conditions, site conditions, and biological conditions, and respectively constructing an annual linear model of the incidence rate and susceptibility index in each climate zone based on climatic conditions, site conditions, biological conditions, and the previous year; Step 5, performing a multivariate linear regression analysis on the associated data to obtain the correlation data between the climatic conditions, site conditions, biological conditions and the incidence and susceptibility index of poplar bark rot in different climatic zones, and constructing a linear model of the incidence and susceptibility index of poplar bark rot in different climatic zones based on the climatic conditions, site conditions, and biological conditions, as well as constructing an annual linear model based on the climatic conditions, site conditions, biological conditions and the incidence and susceptibility index of the previous year.

[0011] The poplar bark rot comprehensive prediction system based on the prediction model, wherein the incidence rate and susceptibility index calculation method are as follows: Incidence rate (%) = number of diseased plants / number of investigated plants × 100%; Disease susceptibility index = Σ(number of diseased plants at each level × representative value at each level) / (total number of plants surveyed × highest representative value).

[0012] The poplar bark rot comprehensive prediction system based on the prediction model, wherein the step 4, specifically, comprises the following steps: Step 41, constructing linear models of the incidence of poplar bark rot in each climate zone based on climate conditions, site conditions, and biological conditions; Step 42, constructing linear models of poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in each climatic zone; Step 43, constructing a linear model of the annual incidence of poplar bark rot in each climate zone based on climate conditions, site conditions, biological conditions and the incidence rate in the previous year; Step 44, constructing a linear model of the annual poplar bark rot disease susceptibility index in each climate zone based on climate conditions, site conditions, biological conditions and the disease susceptibility index of the previous year.

[0013] The poplar bark rot comprehensive prediction system based on the prediction model, wherein the step 5, specifically, comprises the following steps: Step 51, constructing a linear model of the incidence of poplar bark rot based on climate conditions and site conditions in different climate zones; Step 52, constructing a linear model of poplar bark rot susceptibility index based on climatic conditions and site conditions in different climatic zones; Step 53, constructing a linear model of the annual incidence of poplar bark rot in different climate zones based on climate conditions, site conditions and the incidence rate in the previous year; Step 54, constructing a linear model of the annual poplar bark rot disease susceptibility index in different climate zones based on climate conditions, site conditions and the disease susceptibility index of the previous year.

[0014] The poplar bark rot comprehensive prediction system based on the prediction model, wherein the linear model of the incidence of poplar bark rot based on climatic conditions, site conditions, and biological conditions in different climatic zones is: Z(3) =1.855-0.016*Longi+0.008*Age +0.01*Tl25- 0.012*Td5+ 0.056*Topo- 0.015*P25- 0.089*Ta / Hr20, the importance of factors affecting the incidence of poplar bark rot is longitude>Tl25>Age>Topo= Ta / Hr20>P25>Td5; Among them, L Z(3) The linear model of the incidence of poplar bark rot in different climate zones based on climatic conditions, site conditions, and biological conditions; Z(3) is based on the comprehensive data of three places; Longi is longitude; Age is tree age; Tl is the average daily minimum temperature; Td is the average daily temperature difference; Topo is topography; P is average rainfall; Ta / Hr is temperature-humidity ratio; The linear model of poplar bark rot susceptibility index based on climatic conditions and site conditions in different climatic zones is: Z(3) = 0.503- 0.005*Longi+ 0.005*Age+ 0.003*Tl 5+ 0.004* Aspect- 0.004*P25+0.017*Topo, R= 0.775, R 2 = 0.600, p<0.001, the importance of factors affecting the incidence of poplar bark rot is longitude>Age>Tl5>slope aspect>Topo>P25; Among them, Z Z(3)is a linear model of the poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in different climatic zones; Longi is longitude; Age is tree age; Tl is the average daily minimum temperature; Aspect is slope direction; P is average rainfall; and Topo is topography.

[0015] The comprehensive prediction system for poplar bark rot based on the prediction model, wherein the linear model of the annual poplar bark rot incidence rate based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in different climate zones in warm temperate semi-humid areas and plateau temperate semi-arid areas is: Z(2)Y =-5.504+0.013*Tm20-0.019*P30+1.005*Zc Z(2)Y-1 -0.0157*Ta / Hr20-0.019*Aspect+0.772*Zm Z(2)Y-1 +0.154*Lati, Among them, L Z(2)Y Linear models for annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions, and the incidence rate in the previous year for different climatic zones in warm temperate semi-humid areas and highland temperate semi-arid areas; Z(2)Y The data for the current year are for the warm temperate semi-humid region and the plateau temperate semi-arid region; Z(3)Y-1 is the data of the previous year for the warm temperate semi-humid region and the plateau temperate semi-arid region; Tm is the average daily maximum temperature; P is the average rainfall; Zc is the average rainfall; Z(2)Y-1 is the disease susceptibility index of the previous year in the warm temperate semi-humid area and the plateau temperate semi-arid area; Ta / Hr is the temperature-humidity ratio; Aspect is the slope direction; Zm Z(2)Y-1 is the disease susceptibility index at the end of the previous year's survey in warm temperate semi-humid areas and plateau temperate semi-arid areas; Lati is latitude; The linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in different climate zones in the mid-temperate arid region is: K =-48.292-0.011*Hr20-0.097*Td30-1.116*Lati+1.187*Lati+0.001*Longi+0.008*P5, Among them, L K is the linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in different climatic zones in the mid-temperate arid region; Hr is relative humidity; Td is the average daily temperature difference; Lati is latitude; Longi is longitude; P is the average rainfall; The linear model of annual poplar bark rot incidence in different climate zones based on climate conditions, site conditions, biological conditions and the incidence rate in the previous year is: Z(3)Y= 1.425 - 0.013*Longi + 0.007 * Tm25 + 1.286*Zg Z(3)Y-1 - 0.018*Aspect- 0.013*P30- 0.008*Td5, Among them, L Z(3)Y is the linear model of annual poplar bark rot incidence in different climate zones based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year; Longi is longitude; Tm is the average daily maximum temperature; Zg Z(3)Y-1 is the disease susceptibility index of the peak period of the previous year in warm temperate semi-humid areas, plateau temperate semi-arid areas and mid-temperate arid areas; Aspect is the slope direction; P is the average rainfall; Td is the average daily temperature difference; The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones in warm temperate semi-humid areas and plateau temperate semi-arid areas is: Z(2)Y =-2.012+0.004*Tm20+0.477*Zm Z(2)Y-1 -0.041*Ta / Hr20-0.005*P30+0.002*Age+0.056*Lati, Among them, Z Z(2)Y is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and susceptibility index of the previous year in different climatic zones of warm temperate semi-humid area and plateau temperate semi-arid area; Tm is the average daily maximum temperature; Zm Z(2)Y-1 is the disease susceptibility index at the end of the previous year's survey in warm temperate semi-humid areas and plateau temperate semi-arid areas; Ta / Hr is the temperature-humidity ratio; P is the average rainfall; Age is the tree age; Lati is the latitude; The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones in the mid-temperate arid region is: K = -19.09- 0.001*Hr 30-0.385*Lati +0.435*Longi -0.003*age, Among them, Z K is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones of the mid-temperate arid region; Hr is relative humidity; Lati is latitude; Longi is longitude; Age is tree age; The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climatic zones is: Z(3)Y=0.414 -0.004*Longi + 0.005*Age +0.002*Tm25 +0.229*Zg Z(3)Y-1 + 0.001*P30, Among them, Z Z(3)Y is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climatic zones; Longi is longitude; Age is tree age; Tm is the average daily maximum temperature; Zg Z(3)Y-1 It is the disease susceptibility index in the warm temperate semi-humid area, plateau temperate semi-arid area and mid-temperate arid area during the peak period of disease in the previous year; P is the average rainfall.

[0016] A comprehensive prediction method for poplar bark rot based on a prediction model, using the above prediction system, includes the following steps: Step I: the data collection unit collects relevant data of the target area; Step II, the data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot; Step III, the incidence prediction unit uses the linear model of the incidence and susceptibility index of poplar bark rot disease to predict the incidence and susceptibility index of poplar bark rot disease for the current associated data to obtain the current prediction data; Step IV: the data display unit is used to display the initial data, process data and result data such as the associated data, the linear model and the predicted data.

[0017] (III) Beneficial effects: The present invention provides a comprehensive prediction system and method for poplar bark rot based on a prediction model. The prediction model for the occurrence and development of poplar bark rot is determined based on key meteorological factors, geographical factors and biological factors that affect the occurrence and development of poplar bark rot. The occurrence and development of poplar bark rot is effectively predicted from multiple angles such as meteorological factors, geographical factors and biological factors, thereby improving the prediction accuracy and timeliness of the model, and thus improving the prevention and control efficiency of poplar bark rot in different regions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flow chart of a comprehensive prediction system for poplar bark rot based on a prediction model of the present invention; Figure 2 It is a schematic diagram of steps of a comprehensive prediction method for poplar bark rot based on a prediction model of the present invention; Figure 3-1 It is a preferred embodiment of the present invention. The occurrence of poplar bark rot in Xinmi City from 2020 to 2022; Figure 3-1Among them, A is the occurrence of poplar bark rot in the standard land of Yuecun Town from 2020 to 2022; B is the occurrence of poplar bark rot in the standard land of Quliang Town from 2020 to 2022; C is the occurrence of poplar bark rot in the standard land of Liuzhai Town from 2020 to 2022; D is the occurrence of poplar bark rot in the standard land of Chaohua Town from 2020 to 2022; E is the occurrence of poplar bark rot in the standard land of Niudian Town from 2020 to 2022; F is the occurrence of poplar bark rot in the standard land of Xinmi from 2020 to 2022; Figure 3-2 This is a residual graph of a model for predicting the incidence of poplar bark rot in Xinmi City, a preferred embodiment of the present invention; Figure 3-2 In the figure, A is the residual histogram of the prediction model of poplar bark rot incidence in Xinmi City; B is the residual scatter plot of the prediction model of poplar bark rot incidence in Xinmi City; Figure 3-3 This is a residual graph of a prediction model for the susceptibility index of poplar bark rot in Xinmi City, a preferred embodiment of the present invention; Figure 3-3 In the figure, A is the residual histogram of the prediction model of the susceptibility index of poplar bark rot in Xinmi City; B is the residual scatter plot of the prediction model of the susceptibility index of poplar bark rot in Xinmi City; Figure 3-4 This is a residual graph of the annual prediction model of the incidence of poplar bark rot in Xinmi City, a preferred embodiment of the present invention; Figure 3-4 In the figure, A is the residual histogram of the annual prediction model of the incidence of poplar bark rot in Xinmi City; B is the residual scatter plot of the annual prediction model of the incidence of poplar bark rot in Xinmi City; Figure 3-5 This is a residual graph of the annual prediction model of the poplar bark rot susceptibility index in Xinmi City, a preferred embodiment of the present invention; Figure 3-5 In the figure, A is the residual histogram of the annual prediction model of the disease index of poplar bark rot in Xinmi City; B is the residual scatter plot of the annual prediction model of the disease index of poplar bark rot in Xinmi City; Figure 3-6 It is a preferred embodiment of the present invention to analyze the occurrence of poplar bark rot in a sample plot in Golmud City from 2020 to 2022; Figure 3-6 Among them, A is the occurrence of poplar bark rot in the East Exit Standard Site from 2020 to 2022; B is the occurrence of poplar bark rot in the Children's Park Standard Site from 2020 to 2022; C is the occurrence of poplar bark rot in the Hexi Sanlian Standard Site from 2020 to 2022; D is the occurrence of poplar bark rot in the Kunlun Park Standard Site from 2020 to 2022; E is the occurrence of poplar bark rot in the Shachang Station Standard Site from 2020 to 2022; F is the occurrence of poplar bark rot in the Golmud Standard Site from 2020 to 2022; Figure 3-7 This is a residual graph of the model of the incidence rate of poplar bark rot in Golmud City, a preferred embodiment of the present invention; Figure 3-7In the figure, A is the residual histogram of the prediction model of poplar bark rot incidence in Golmud City; B is the residual scatter plot of the prediction model of poplar bark rot incidence in Golmud City; Figure 3-8 This is a residual graph of the disease susceptibility index model of poplar bark rot in Golmud City, a preferred embodiment of the present invention; Figure 3-8 In the figure, A is the residual histogram of the prediction model of the susceptibility index of poplar bark rot in Golmud; B is the residual scatter plot of the prediction model of the susceptibility index of poplar bark rot in Golmud; Figure 3-9 This is a residual graph of the model for predicting the annual incidence of poplar bark ulcer in Golmud City, a preferred embodiment of the present invention; Figure 3-9 In the figure, A is the residual histogram of the annual prediction model of the incidence of poplar bark rot in Golmud City; B is the residual scatter plot of the annual prediction model of the incidence of poplar bark rot in Golmud City; Figure 3-10 This is a residual graph of the annual prediction susceptibility index model of poplar bark rot in Golmud City, a preferred embodiment of the present invention; Figure 3-10 In the figure, A is the residual histogram of the annual prediction model of the disease susceptibility index of poplar bark rot in Golmud City; B is the residual scatter plot of the annual prediction model of the disease susceptibility index of poplar bark rot in Golmud City; Figure 3-11 It is a preferred embodiment of the present invention, the occurrence of poplar bark rot in Karamay sample area from 2020 to 2021; Figure 3-11 Among them, A is the occurrence of poplar bark rot in the Wuzhiqu standard area from 2020 to 2021; B is the occurrence of poplar bark rot in the Jiuzhiqu standard area from 2020 to 2021; C is the occurrence of poplar bark rot in the Pulinlian standard area from 2020 to 2021; D is the occurrence of poplar bark rot in the Baishatan standard area from 2020 to 2021; E is the occurrence of poplar bark rot in the inspection station standard area from 2020 to 2021; F is the occurrence of poplar bark rot in the Karamay standard area from 2020 to 2021; Figure 3-12 This is a residual graph of a prediction model for the incidence of Karamay poplar bark rot in a preferred embodiment of the present invention; Figure 3-12 In the figure, A is the residual histogram of the prediction model of the incidence of Karamay poplar bark rot disease; B is the residual scatter plot of the prediction model of the incidence of Karamay poplar bark rot disease; Figure 3-13 This is a residual graph of a prediction model for the susceptibility index of Karamay poplar bark rot in a preferred embodiment of the present invention; Figure 3-13 In the figure, A is the residual histogram of the prediction model of the susceptibility index of Karamay poplar bark rot disease; B is the residual scatter plot of the prediction model of the susceptibility index of Karamay poplar bark rot disease; Figure 3-14 This is a residual graph of the annual prediction model of the incidence rate of Karamay poplar bark rot in a preferred embodiment of the present invention; Figure 3-14In the figure, A is the residual histogram of the annual prediction model of the incidence of Karamay poplar bark rot; B is the residual scatter plot of the annual prediction model of the incidence of Karamay poplar bark rot; Figure 3-15 This is a residual graph of the annual prediction model of the disease susceptibility index of Karamay poplar bark rot disease in a preferred embodiment of the present invention; Figure 3-15 In the figure, A is the residual histogram of the annual prediction model of the susceptibility index of Karamay poplar bark rot disease; B is the residual scatter plot of the annual prediction model of the susceptibility index of Karamay poplar bark rot disease; Figure 3-16 This is a residual graph of the comprehensive poplar bark rot incidence prediction model in a preferred embodiment of the present invention; Figure 3-16 In the figure, A is the residual histogram of the prediction model of the incidence of poplar bark rot in the three places; B is the residual scatter plot of the prediction model of the incidence of poplar bark rot in the three places; Figure 3-17 This is a residual graph of a comprehensive poplar bark rot disease susceptibility index prediction model in a preferred embodiment of the present invention; Figure 3-17 In the figure, A is the residual histogram of the prediction model of the disease susceptibility index of poplar bark rot in the three places; B is the residual scatter plot of the prediction model of the disease susceptibility index of poplar bark rot in the three places; Figure 3-18 This is a residual graph of the annual prediction model of the comprehensive poplar bark rot disease susceptibility index in a preferred embodiment of the present invention; Figure 3-18 In the figure, A is the residual histogram of the annual prediction model of the incidence of poplar bark rot in the three places; B is the residual scatter plot of the annual prediction model of the incidence of poplar bark rot in the three places; Figure 3-19 This is a residual graph of the annual prediction model of the comprehensive poplar bark rot disease susceptibility index in a preferred embodiment of the present invention; Figure 3-19 In the figure, A is the residual histogram of the annual prediction model of the disease index of poplar bark rot in the three regions; B is the residual scatter plot of the annual prediction model of the disease index of poplar bark rot in the three regions; Figure 3-20 This is a residual graph of the annual prediction model of the disease susceptibility index of poplar bark rot in Golmud and Xinmi in a preferred embodiment of the present invention; Figure 3-20 In the figure, A is the residual histogram of the annual prediction model of the incidence of poplar bark rot in Golmud and Xinmi; B is the residual scatter plot of the annual prediction model of the incidence of poplar bark rot in Golmud and Xinmi; Figure 3-21 This is a residual graph of the annual prediction model of the disease susceptibility index of poplar bark rot in Golmud and Xinmi in a preferred embodiment of the present invention; Figure 3-21 In the figure, A is the residual histogram of the annual prediction model of the disease susceptibility index of poplar bark rot in Golmud and Xinmi; B is the residual scatter plot of the annual prediction model of the disease susceptibility index of poplar bark rot in Golmud and Xinmi. DETAILED DESCRIPTION

[0019] The present invention is further described in detail below in conjunction with preferred embodiments. More details are elaborated in the following description to facilitate a full understanding of the present invention. However, the present invention can obviously be implemented in a variety of other ways different from the description. Those skilled in the art can make similar generalizations and deductions based on actual application situations without violating the connotation of the present invention. Therefore, the protection scope of the present invention should not be limited by the content of this specific embodiment.

[0020] The accompanying drawings are schematic diagrams of embodiments of the present invention. It should be noted that the drawings are only examples and are not drawn to scale, and should not be used to limit the actual protection scope of the present invention.

[0021] A comprehensive prediction system for poplar bark rot based on a prediction model includes a data collection unit, a data analysis unit, an incidence prediction unit, a data storage unit, and a data display unit. The data collection unit is used to collect associated data of a target area, the data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence and susceptibility index of poplar bark rot, the incidence prediction unit uses the linear model of the incidence and susceptibility index of poplar bark rot on the current associated data to predict the incidence and susceptibility index of poplar bark rot to obtain the current predicted data, the data storage unit is used to store the associated data, the linear model, the predicted data and other initial data, process data, and result data, and the data display unit is used to display the associated data, the linear model, the predicted data and other initial data, process data, and result data, such as Figure 1 shown.

[0022] The data acquisition unit includes one or more of an Internet of Things crawling device, an information acquisition device, etc. The information acquisition device may be a shooting drone or other device that can acquire information, which is not specifically limited here.

[0023] The target area includes different climate zones such as warm temperate semi-humid areas, plateau temperate semi-arid areas, and mid-temperate arid areas. The associated data include site conditions, climate conditions, biological conditions, and the occurrence of poplar bark rot. Site conditions include latitude, longitude, altitude, topography, soil type, soil conditions, slope, slope aspect, etc. Climate conditions include daily maximum temperature, daily minimum temperature, average temperature, temperature difference, air humidity, average wind speed, rainfall, and temperature-humidity ratio. Biological conditions include tree species, tree age, etc. Site conditions include site-related factors, and site-related factors include latitude and slope aspect. Climate conditions include climate-related factors, and climate-related factors include temperature, temperature-humidity ratio, and rainfall. Biological conditions include biological-related factors, and biological-related factors include tree species and tree age.

[0024] The data collection unit collects the associated data of the target plots in different climate zones, and collects the site conditions and climate conditions of the warm temperate semi-humid areas, the plateau temperate semi-arid areas, and the mid-temperate arid areas. Specifically, the Internet of Things crawling device collects the site conditions and climate conditions of N standard plots in the warm temperate semi-humid areas, the plateau temperate semi-arid areas, and the mid-temperate arid areas. Among them, the site conditions and climate conditions can also be input through the input unit, and there is no specific restriction here. The information collection device investigates the occurrence of poplar bark rot in N standard plots in the warm temperate semi-humid areas, the plateau temperate semi-arid areas, and the mid-temperate arid areas. N is a positive integer. Here, N is equal to 5 as an example, that is, each different climate zone includes 5 standard plots.

[0025] A comprehensive prediction method for poplar bark rot based on prediction model, such as Figure 2 As shown, the following steps are included: Step I: the data collection unit collects relevant data of the target area; Step II, the data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot; Step III, the incidence prediction unit uses the linear model of the incidence and susceptibility index of poplar bark rot disease to predict the incidence and susceptibility index of poplar bark rot disease for the current associated data to obtain the current prediction data; Step IV: the data display unit is used to display the initial data, process data and result data such as the associated data, the linear model and the predicted data.

[0026] The data collection unit collects the associated data of the target area, including investigating the occurrence of poplar bark rot in target plots in different climate zones, specifically including the following steps: Step 1: The data collection unit randomly sets M survey plots in N standard plots in each different climate zone; wherein M is a positive integer, and here M is equal to 3 as an example, that is, each standard plot includes 3 survey plots.

[0027] Step 2: The data collection unit selects Z sampling points in each survey area; wherein Z is a positive integer. Here, Z equals 5 as an example, that is, 5 sampling points are set in each survey area. Specifically, the midpoint of the diagonal of each survey area is selected as the central sampling point, and then 4 points on the diagonal of the survey area with equal distances from the central sampling point are selected as surrounding sampling points. The central sampling point and the surrounding sampling points constitute the sampling points.

[0028] Step three, the data collection unit selects W sample plants at each sampling point and marks and numbers the sample plants; wherein W is a positive integer, and here W is taken as 6 as an example, that is, 6 sample plants are selected at each sampling point.

[0029] Step 4, the information collection device of the data collection unit observes and records the conditions of the sample plants within a specified time to obtain observation data; specifically, from the beginning of the disease in mid-March to the end of October, an investigation is conducted every 15 days according to the disease grading standard (Table 1) and the grading counting method, that is, each investigation is called an investigation period.

[0030] Table 1 Grading criteria for poplar bark rot

[0031] The data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot, which may include the following steps: Step 1: Calculate the incidence and susceptibility index of poplar bark rot in N standard plots in each different climate zone at different survey periods based on the observed data, and draw trend graphs of the incidence and susceptibility index.

[0032] The data analysis unit calculates the incidence rate and susceptibility index based on the observed data using Excel 2020 software. Specifically, the incidence rate and susceptibility index calculation method is as follows: Incidence rate (%) = number of diseased plants / number of investigated plants × 100% Disease susceptibility index = Σ(number of diseased plants at each level × representative value at each level) / (total number of plants surveyed × highest representative value) Step 2: According to the trend graph of the incidence rate and the disease susceptibility index, the correlation between the incidence rate and the disease susceptibility index of poplar bark rot in different survey years in N standard plots in each different climate zone is analyzed to obtain first analysis data, wherein the first analysis data includes the correlation data between the incidence rate and the disease susceptibility index between different survey years. According to the first analysis data, it is confirmed whether the incidence rate and disease susceptibility index data of the previous year can be used for subsequent model construction.

[0033] In step 2, the correlation between the incidence and susceptibility index of poplar bark rot in different survey years is analyzed, and the correlation can be calculated using the Pearson correlation analysis of IBM SPSS software. Correlation analysis process: The incidence and susceptibility index of each survey period of N standard plots are taken as a column, and the correlation analysis of the incidence and susceptibility index between different survey years is performed.

[0034] Step 3, according to the associated data, the standard values ​​of the associated data of N standard plots in each different climate zone are calculated respectively, and the correlation between the incidence rate and the disease susceptibility index of poplar bark rot under different climate conditions, different site conditions, and different biological conditions is analyzed to obtain the second analysis data, which includes the correlation between the incidence rate and the disease susceptibility index of poplar bark rot under different climate conditions, different site conditions, and different biological conditions. The second analysis data provides a reference for factor selection when constructing the subsequent model.

[0035] The second analysis data includes climate-related factors, site-related factors and biological-related factors. Climate-related factors include temperature, temperature-humidity ratio, and rainfall. Site-related factors include latitude and slope aspect. Biological-related factors include tree species and tree age.

[0036] The step 3 specifically includes: step 31, respectively calculating the standard values ​​of the climate conditions of N standard plots in each different climate zone; step 32, respectively calculating the standard values ​​of the site conditions of N standard plots in each different climate zone; step 33, respectively analyzing the correlation between the incidence rate and the disease susceptibility index of poplar bark rot under different climate conditions and different site conditions according to the standard values ​​of the climate conditions and the standard values ​​of the site conditions, to obtain second analysis data. The order of step 31 and step 32 can be swapped.

[0037] Specifically, step 31 may be as follows: first, the climatic conditions of N standard plots in each different climatic zone 5, 10, 15, 20, 25 and 30 days before the survey date are counted, including the maximum temperature (Tm), the minimum temperature (Tl), the average temperature (Ta), the temperature difference (Td), the air humidity (Hr), the average wind speed (Ws), the rainfall (P) and the temperature-humidity ratio (Ta / Hr), etc.; and then the average value of the climatic conditions 5, 10, 15, 20, 25 and 30 days before the survey date is calculated using 2020 as the standard value of the climatic conditions.

[0038] Specifically, the step 32 may be, first, taking the standard plot as a unit, respectively summarizing the site conditions of N standard plots in each different climate zone, including latitude, longitude, altitude, terrain, soil type, slope, aspect, etc.; respectively summarizing the biological conditions of N standard plots in each different climate zone, including tree species and tree age; then assigning values ​​to the terrain, aspect, soil type, and tree species in a quantitative manner to obtain the standard values ​​of the site conditions and biological conditions of the N standard plots in each different climate zone. Specifically, the statistical values ​​of latitude, longitude, altitude, and slope in the site conditions are used as their corresponding standard values, and the numerical values ​​obtained by assigning values ​​to the terrain, soil type, soil conditions, and aspect in a quantitative manner are used as their corresponding standard values. The statistical value of the tree age in the biological conditions is used as its corresponding standard value, and the numerical values ​​obtained by assigning values ​​to the tree species in a quantitative manner are used as their corresponding standard values.

[0039] In the site conditions, the specific values ​​of topography, slope aspect, and soil type are: The terrain is divided into three levels: 1. depression; 2. plain; 3. gentle slope; The slope direction is divided into 9 levels: 0, flat slope; 1, northeast; 2, east; 3, southeast; 4, south; 5, southwest; 6, west; 7, northwest; 8, north; Soil types are divided into 6 levels: 1, gravel soil; 2, gray-brown desert soil; 3, sandy soil; 4, sandy loam; 5, loam; 6, brown soil; There are four types of trees: 1. Populus cathayana; 2. Xinjiang Populus; 3. Russian Populus; 4. European and American Populus.

[0040] In step 33, the correlation between the incidence of poplar bark rot and the disease susceptibility index under different site conditions is analyzed. The correlation can be calculated using Pearson correlation analysis of IBM SPSS software to provide a reference for factor selection when constructing subsequent models.

[0041] Step 4, performing a multivariate linear regression analysis on the associated data based on the first analysis data and the second analysis data, respectively constructing a linear model of the incidence rate and susceptibility index of poplar bark rot in each climate zone based on climatic conditions, site conditions, and biological conditions, and respectively constructing an annual linear model of the incidence rate and susceptibility index in each climate zone based on climatic conditions, site conditions, biological conditions, and the previous year; In step 4, the Pearson correlation analysis of IBM SPSS software can be used to perform multiple linear regression analysis to obtain the correlation data between the climatic conditions, site conditions, biological conditions and the incidence and susceptibility index of poplar bark rot in each climatic zone, providing a reference for factor selection when constructing a linear model.

[0042] When constructing the linear model of the incidence and susceptibility index of poplar bark rot in each climate zone, all the associated factors were first used as independent variables, and the incidence or susceptibility index was used as the dependent variable. The stepwise method was used to construct the prediction model to obtain multiple prediction models. For the output multiple prediction models, the following methods were used to select them: (1) the significance of the model p < 0.001; (2) the collinearity (VIF) value of each model coefficient was < 5 (in some cases it could be relaxed to < 10); (3) the model with a higher R2 value was preferred; when the R2 values ​​were similar, the prediction model with fewer variables was selected.

[0043] Specifically, step 4 comprises the following steps: Step 41, constructing linear models of the incidence of poplar bark rot in each climate zone based on climate conditions, site conditions, and biological conditions; Step 42, constructing linear models of poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in each climatic zone; Step 43, constructing a linear model of the annual incidence of poplar bark rot in each climate zone based on climate conditions, site conditions, biological conditions and the incidence rate in the previous year; Step 44, constructing a linear model of the annual poplar bark rot disease susceptibility index in each climate zone based on climate conditions, site conditions, biological conditions and the disease susceptibility index of the previous year.

[0044] In step 41: The linear model of the incidence of poplar bark rot in warm temperate semi-humid areas based on climatic conditions, site conditions, and biological conditions is: L X = -12.951+ 0.002*Alti - 0.02 *Td5 -1.24 * Lati + 0.015 * Tm 10-0.013 * Age -0.014 * P 25- 0.309 * Ws 5 + 0.502 * Longi -0.011 * Hr 30-0.058* Hs30; the model correlation coefficient is R= 0.707, R 2 = 0.499, p<0.001, indicating that the model fit is acceptable and the factors introduced by the model can explain nearly 50% of the overall variation.

[0045] Among them, L X is a linear model for the incidence of poplar bark rot based on climatic conditions, site conditions, and biological conditions in warm temperate semi-humid areas; Alti is altitude; Td is the average daily temperature difference; Lati is latitude; Tm is the average daily maximum temperature; Age is tree age; P is average rainfall; Ws is average wind speed; Longi is longitude; Hr is relative humidity; and Hs is average sunshine hours.

[0046] The linear model of the incidence of poplar bark rot based on climatic conditions, site conditions, and biological conditions in the plateau temperate semi-arid region is: L G = 21.763+ 0.718*longitude- 2.429*latitude+ 0.004*Ta30+ 0.004*Hr10. The model correlation coefficient is R= 0.816, the model correlation coefficient is R= 0.816, R 2 = 0.666, p<0.001, indicating that the model has a good fit and the factors introduced by the model can explain 67% of the overall variation.

[0047] Among them, L G is a linear model for the incidence of poplar bark rot based on climatic conditions, site conditions, and biological conditions in the temperate semi-arid plateau area; Ta is the average daily temperature; Hr is the relative humidity.

[0048] The linear model of the incidence of poplar bark rot in mid-temperate arid areas based on climatic conditions, site conditions, and biological conditions is: L K = -48.292 - 0.011*Hr20 - 0.097*Td30- 1.116*Lati +1.187 *Longi+0.001*Alti+ 0.008*P5, the model correlation coefficient is R= 0.903, R 2 = 0.816, p<0.001, indicating that the model fit is good and the factors introduced by the model can explain more than 90% of the overall variation.

[0049] Among them, L K is a linear model for the incidence of poplar bark rot in mid-temperate arid areas based on climatic conditions, site conditions, and biological conditions; Hr is relative humidity; Td is the average daily temperature difference; Lati is latitude; Longi is longitude; Alti is altitude; and P is the average rainfall.

[0050] In step 42, the linear model of the poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in warm temperate semi-humid areas is: Z X = 18.059+ 0.001 * Alti - 0.522 *Lati + 0.141 * Ta / Hr 25 - 0.056*Ws20- 0.005 Age; the model correlation coefficient is R=0.716, R 2 =0.513, p<0.001, indicating that the model fit is acceptable and the factors introduced by the model can explain 51% of the overall variation.

[0051] Among them, Z X It is a linear model of poplar bark rot susceptibility index based on climatic conditions, site conditions and biological conditions in warm temperate semi-humid areas; Alti is altitude; Lati is latitude; Ta / Hr is temperature-humidity ratio; Ws is average wind speed; Age is tree age.

[0052] The linear model of the poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in the plateau temperate semi-arid region is: Z G = -44.525+ 0.471*Longi + 0.002*Age+ 0.002*Ta25- 0.006*Td20, the model correlation coefficient is R= 0.834, R 2 = 0.695, p < 0.001, indicating that the model fit is good and the factors introduced by the model can explain nearly 70% of the overall variation.

[0053] Among them, Z G is a linear model of the poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in the temperate semi-arid plateau area; Longi is longitude; Age is tree age; Ta is the average daily temperature; and Td is the average daily temperature difference.

[0054] The linear model of poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in mid-temperate arid areas is: Z K = -19.09-0.001*Hr 30-0.385*Lati +0.435*Longi -0.003*age, the model correlation coefficient is R= 0.891, R 2 = 0.793, p<0.001, indicating that the model fit is good and the factors introduced by the model can explain nearly 80% of the overall variation.

[0055] Among them, Z K It is a linear model of poplar bark rot susceptibility index based on climatic conditions, site conditions and biological conditions in mid-temperate arid areas; Hr is relative humidity; Lati is latitude; Longi is longitude; age is tree age.

[0056] In step 43, the linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in warm temperate semi-humid areas is: XY = -0.123 + 1.050*Zm XY-1 + 0.481*Ta / Hr30, the model correlation coefficient is R= 0.687, R 2 = 0.472, p<0.001, indicating that the model fit is poor and the factors introduced by the model can only explain 47% of the overall variation.

[0057] Among them, L XY The linear model of annual poplar bark rot incidence in warm temperate semi-humid areas based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year; Zm XY-1 It is the disease susceptibility index at the end of the survey in the previous year in warm temperate semi-humid areas; Ta / Hr is the temperature-humidity ratio.

[0058] The linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year in the temperate semi-arid plateau region is: GY = -0.237 + 0.618*Lm GY-1 +0.473*Lg GY-1 +0.501*Zc GY-1 +0.003*Tm30, the model correlation coefficient is R = 0.887, R2 =0.787, p<0.001, indicating that the model has a good fit and the factors introduced by the model can explain 79% of the overall variation.

[0059] Among them, L GY is the annual poplar bark rot incidence linear model in the temperate semi-arid plateau region based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year; Lm GY-1 Lg is the incidence rate at the end of the survey in the plateau temperate semi-arid region in the previous year; GY-1 Zc is the peak incidence rate of the disease in the plateau temperate semi-arid region in the previous year; GY-1 It is the initial disease susceptibility index of the survey in the plateau temperate semi-arid area in the previous year; Tm is the average maximum daily temperature.

[0060] The linear model of annual poplar bark rot incidence in mid-temperate arid regions based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year is: KY = -0.097+ 0.006*Tl30+0.917*Lg KY-1 , the model correlation coefficient is R=0.935, R 2 =0.873, p<0.001, indicating that the model fit is very good and the factors introduced by the model can explain 87% of the overall variation.

[0061] Among them, L KY is the annual poplar bark rot incidence linear model in the mid-temperate arid region based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year; Tl is the average minimum daily temperature; Lg KY-1 It is the peak incidence rate of the disease in the mid-temperate arid areas in the previous year.

[0062] In step 44, The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in warm temperate semi-humid areas is: XY = -0.081 + 0.497*Zm XY-1 -0.201*Zg XY-1 + 0.003*Age- 0.131*Ta / Hr 15- 0.007*P 30+ 0.006*Tm 20; the model correlation coefficient is R= 0.792, R 2 =0.627, p<0.001, indicating that the model fit is acceptable and the factors introduced by the model can explain 63% of the overall variation.

[0063] Among them, Z XYThe linear model of annual poplar bark rot susceptibility index in warm temperate semi-humid areas based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year; Zm XY-1 Zg is the disease susceptibility index at the end of the survey in the previous year in warm temperate semi-humid areas; XY-1 is the disease susceptibility index during the peak period of the previous year in warm temperate semi-humid areas; Age is the tree age; Ta / Hr is the temperature-humidity ratio; P is the average rainfall; Tm is the average daily maximum temperature.

[0064] The annual linear model of poplar bark rot susceptibility index in plateau temperate semi-arid regions based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year is: GY = -0.025+ 0.689*Zg GY-1 + 0.55*Zm GY-1 - 0.052*Lc GY-1 + 0.002*Tm 30- 0.008*Ws 5, the model correlation coefficient is R = 0.877, R 2 = 0.769, p<0.001, indicating that the model fit is good and the factors introduced by the model can explain 77% of the overall variation.

[0065] Among them, Z GY Zg is the annual linear model of poplar bark rot susceptibility index in plateau temperate semi-arid regions based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year; GY-1 Zm is the disease susceptibility index in the plateau temperate semi-arid region during the peak period of disease susceptibility in the previous year; GY-1 Lc is the disease susceptibility index at the end of the survey in the plateau temperate semi-arid region in the previous year; GY-1 is the initial incidence rate of the survey in the plateau temperate semi-arid area in the previous year; Tm is the average daily maximum temperature; Ws is the average wind speed.

[0066] The annual prediction model of poplar bark rot susceptibility index in mid-temperate arid areas based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year is: KY =-0.035 + 0.002*Tl25+0.923*Zg KY-1 , the model correlation coefficient is R=0.948, R 2 =0.898, p<0.001, indicating that the model fit is very good and the factors introduced by the model can explain nearly 90% of the overall variation.

[0067] Among them, Z KY is the annual prediction model of poplar bark rot susceptibility index in mid-temperate arid areas based on climatic conditions, site conditions, biological conditions, and the incidence and susceptibility index of the previous year; Tl is the average minimum daily temperature; Zg KY-1It is the disease susceptibility index in the mid-temperate arid region during the peak period of disease susceptibility in the previous year.

[0068] Step 5, performing a multivariate linear regression analysis on the associated data to obtain the correlation data between the climatic conditions, site conditions, biological conditions in different climatic zones and the incidence and susceptibility index of poplar bark rot, to provide a reference for factor selection when constructing a linear model.

[0069] Construct linear models of the incidence and susceptibility index of poplar bark rot in different climate zones based on climate conditions, site conditions, and biological conditions, and construct annual linear models of the incidence and susceptibility index in different climate zones based on climate conditions, site conditions, biological conditions, and the previous year; Specifically, all associated factors were taken as independent variables and the incidence or susceptibility index as the dependent variable. The stepwise method was used to construct a prediction model to obtain multiple prediction models. For the output multiple prediction models, selection was made according to the following method: (1) the significance of the model p < 0.001; (2) the collinearity (VIF) value of each model coefficient was < 5 (which can be relaxed to < 10 in some cases); (3) the model with a higher R2 value was given priority; when the R2 values ​​were similar, the model with fewer variables was selected.

[0070] In step 5, the Pearson correlation analysis of IBM SPSS software can be used to perform multiple linear regression analysis to obtain a linear model for constructing the incidence rate and susceptibility index of poplar bark rot in different climate zones.

[0071] Specifically, step 5 comprises the following steps: Step 51, constructing a linear model of the incidence of poplar bark rot in different climate zones based on climate conditions, site conditions, and biological conditions; Step 52, constructing a linear model of poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in different climatic zones; Step 53, constructing a linear model of the annual incidence of poplar bark rot in different climate zones based on climate conditions, site conditions, biological conditions and the incidence rate in the previous year; Step 54, constructing a linear model of the annual poplar bark rot disease susceptibility index in different climate zones based on climate conditions, site conditions, biological conditions and the disease susceptibility index of the previous year.

[0072] In step 51: The linear model of the incidence of poplar bark rot in different climate zones based on climatic conditions, site conditions, and biological conditions is: L Z(3)=1.855-0.016*Longi+0.008*Age +0.01*Tl25- 0.012*Td5+ 0.056*Topo-0.015*P25- 0.089*Ta / Hr20, the model correlation coefficient is R= 0.691, R 2 = 0.477, p<0.001, indicating that the model fit is poor, and the factors introduced by the model can explain 48% of the overall variation. The standardized coefficients of each factor show (Table 51) that the importance of factors affecting the incidence of poplar bark rot is longitude>Tl25>Age>Topo= Ta / Hr20>P25>Td5.

[0073] Table 51 Coefficients and significance analysis of each factor in the prediction model of the incidence of poplar bark rot in three places

[0074] Among them, L Z(3) The linear model of the incidence of poplar bark rot in different climate zones based on climatic conditions, site conditions, and biological conditions; Z(3) It is based on the comprehensive data of three places; Longi is longitude; Age is tree age; Tl is the average daily minimum temperature; Td is the average daily temperature difference; Topo is topography; P is the average rainfall; Ta / Hr is the temperature-humidity ratio.

[0075] In step 52: The linear model of poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in different climatic zones is: Z Z(3) = 0.503- 0.005*Longi+ 0.005*Age+ 0.003*Tl 5+ 0.004* Aspect-0.004*P25+ 0.017*Topo, R= 0.775, R 2 = 0.600, p<0.001, indicating that the model fit is acceptable and the factors introduced in the model can explain nearly 60% of the overall variation. The standardized coefficients of each factor show (Table 52) that the importance of factors affecting the incidence of poplar bark rot is longitude>Age>Tl5>slope>Topo>P25.

[0076] Table 52 Coefficients and significance analysis of each factor in the prediction model of the susceptibility index of poplar bark rot in three places

[0077] Among them, Z Z(3)is a linear model of poplar bark rot susceptibility index based on climatic conditions, site conditions and biological conditions in different climatic zones; Longi is longitude; Age is tree age; Tl is the average daily minimum temperature; Aspect is slope direction; P is average rainfall; Topo is topography.

[0078] In step 53, The linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in different climatic zones of warm temperate semi-humid areas and plateau temperate semi-arid areas is: L Z(2)Y =-5.504+0.013*Tm20-0.019*P30+1.005*Zc Z(2)Y-1 -0.0157*Ta / Hr20-0.019*Aspect+0.772*Zm Z(2)Y-1 +0.154*Lati, model correlation coefficient R=0.898, R 2 =0.807, p<0.001, indicating that the model fit is good and the factors introduced by the model can explain nearly 81% of the overall variation.

[0079] Among them, L Z(2)Y Linear models for annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions, and the incidence rate in the previous year for different climatic zones in warm temperate semi-humid areas and highland temperate semi-arid areas; Z(2)Y The data for the current year are for the warm temperate semi-humid region and the plateau temperate semi-arid region; Z(3)Y-1 is the data of the previous year for the warm temperate semi-humid region and the plateau temperate semi-arid region; Tm is the average daily maximum temperature; P is the average rainfall; Zc is the average rainfall; Z(2)Y-1 is the disease susceptibility index of the previous year in the warm temperate semi-humid area and the plateau temperate semi-arid area; Ta / Hr is the temperature-humidity ratio; Aspect is the slope direction; Zm Z(2)Y-1 It is the disease susceptibility index at the end of the previous year's survey in warm temperate semi-humid areas and plateau temperate semi-arid areas; Lati is latitude.

[0080] The linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in different climate zones of the mid-temperate arid region is: L K =-48.292-0.011*Hr20-0.097*Td30-1.116*Lati+1.187*Lati+0.001*Longi+0.008*P5 (R=0.903, R 2 =0.816, p<0.001).

[0081] Among them, L Kis a linear model of the annual incidence of poplar bark rot in different climatic zones in the mid-temperate arid region based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year; Hr is relative humidity; Td is the average daily temperature difference; Lati is latitude; Longi is longitude; P is the average rainfall.

[0082] The linear model of annual poplar bark rot incidence in different climate zones based on climate conditions, site conditions, biological conditions and the incidence rate in the previous year is: L Z(3)Y = 1.425 - 0.013*Longi + 0.007 * Tm25 + 1.286*Zg Z(3)Y-1 - 0.018*Aspect- 0.013*P30- 0.008*Td5 (R=0.892, R 2 =0.795, p<0.001).

[0083] Among them, L Z(3)Y is the linear model of annual poplar bark rot incidence in different climate zones based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year; Longi is longitude; Tm is the average daily maximum temperature; Zg Z(3)Y-1 is the disease susceptibility index during the peak period of the previous year in warm temperate semi-humid areas, plateau temperate semi-arid areas and mid-temperate arid areas; Aspect is the slope direction; P is the average rainfall; Td is the average daily temperature difference.

[0084] In step 54, The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones of warm temperate semi-humid areas and plateau temperate semi-arid areas is: Z Z(2)Y =-2.012+0.004*Tm20+0.477*Zm Z(2)Y-1 -0.041*Ta / Hr20-0.005*P30+0.002*Age+0.056*Lati, model R=0.912, R 2 =0.831, p<0.001, indicating that the model fit is good and the introduced factors can explain 83% of the overall variation.

[0085] Among them, Z Z(2)Y is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and susceptibility index of the previous year in different climatic zones of warm temperate semi-humid area and plateau temperate semi-arid area; Tm is the average daily maximum temperature; Zm Z(2)Y-1is the disease susceptibility index at the end of the previous year's survey in warm temperate semi-humid areas and plateau temperate semi-arid areas; Ta / Hr is the temperature-humidity ratio; P is the average rainfall; Age is the tree age; Lati is the latitude.

[0086] The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones of mid-temperate arid regions is: Z K = -19.09- 0.001*Hr 30-0.385*Lati +0.435*Longi -0.003*age (R= 0.891, R 2 = 0.793, p < 0.001).

[0087] Among them, Z K It is a linear model of the annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the disease index of the previous year in different climatic zones in the mid-temperate arid region; Hr is relative humidity; Lati is latitude; Longi is longitude; Age is tree age.

[0088] The linear model of annual poplar bark rot susceptibility index in different climate zones based on climate conditions, site conditions, biological conditions and the disease index of the previous year is: Z Z(3)Y =0.414 -0.004*Longi + 0.005*Age +0.002*Tm25 + 0.229*Zg Z(3)Y-1 +0.001*P30 (R=0.887, R 2 =0.786, p<0.001) Among them, Z Z(3)Y is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climatic zones; Longi is longitude; Age is tree age; Tm is the average daily maximum temperature; Zg Z(3)Y-1 It is the disease susceptibility index in the warm temperate semi-humid area, plateau temperate semi-arid area and mid-temperate arid area during the peak period of disease in the previous year; P is the average rainfall.

[0089] Here, the warm temperate semi-humid area takes Xinmi, Henan as an example, the plateau temperate semi-arid area takes Golmud, Qinghai as an example, and the mid-temperate arid area takes Karamay, Xinjiang as an example. Of course, the warm temperate semi-humid area, plateau temperate semi-arid area, and mid-temperate arid area can also be explained by taking other areas as examples, and there is no specific restriction here.

[0090] Occurrence of poplar bark rot in Xinmi City, Henan Province during different survey periods: The incidence and susceptibility index of poplar bark rot in Xinmi City fluctuated greatly during different survey periods, and the overall trend was 2020>2021>2022 ( Figure 3-1 ). Among them, A, Yuecun Town; B, Quliang Town; C, Liuzhai Town; D, Chaohua Town; E, Niudian Town; F, Xinmi 5 standard sites for comprehensive analysis. 2020-064 represents the survey time is the 64th day of 2020 (March 4, 2020, DOY34), and so on.

[0091] Yuecun Town Sample Site ( Figure 3-1 A) Although the incidence rate fluctuated in each survey period in 2020, the results of variance analysis showed that the differences in the incidence rates in each survey period were not significant (p>0.05); the incidence rate fluctuated significantly in each survey period in 2021 (p<0.05), and the incidence rate was low at the beginning of the survey (<0.1), and began to rise sharply in early July (DOY186), reaching the maximum in mid-August (DOY253), and then gradually declined; the incidence rate in 2022 was always low (<0.1), and the differences in each period were not significant (p>0.05). The differences in the susceptibility index of the Yuecun Town sample plot in each survey period in 2020 were not significant (p>0.05), and the differences in the susceptibility index of each survey period in 2021 and 2022 were significant (p<0.05). In 2021, the susceptibility index showed a significant increasing trend from early July (DOY186) to mid-August (DOY253), and the susceptibility index in 2022 was always low.

[0092] Quliang Town Sample Site ( Figure 3-1 B) Although the incidence rate and susceptibility index fluctuated during the survey period in 2020, the variance analysis results showed that there was no significant difference between the two during different survey periods (p>0.05). The incidence rate and susceptibility index in the survey period in 2021 were extremely significant (p<0.01), which was manifested in a significant decline in late August and late September to mid-October (DOY269-301) ( Figure 3-7 B). The differences in incidence and susceptibility index in each survey period in 2022 were extremely significant (p<0.01).

[0093] Liuzhai Town Sample Site ( Figure 3-1C) The incidence rates in each survey period in 2020 were significantly different (p<0.05), with two obvious peaks in mid-to-late March (DOY98-117) and late July (DOY209); the incidence rates in each survey period in 2021 fluctuated significantly (p<0.05), with a low incidence rate at the beginning of the survey (<0.1), reaching a maximum in mid-August (DOY253), and then declining; the incidence rate in 2022 was always low (<0.1), and there was no significant difference in each period (p>0.05). The susceptibility index of the Liuzhai Town sample plot in each survey period was not significantly different (p>0.05), with 2020 slightly higher than 2021 and 2022, but the susceptibility index was always low in the three years.

[0094] Chaohua Town Sample Site ( Figure 3-1 D) The incidence rate in each survey period in 2020 was always high, ranging from 0.66 to 0.99; the incidence rate was low in early 2021, with a peak from late May to mid-July (DOY140-197), followed by a sharp decline, and the incidence rate in each survey period was extremely significant (p<0.01); there was a peak in late June to early August 2022 (DOY173-223), with an incidence rate of 0.23-0.3, and the peak incidence rate was significantly lower than that in 2020 and 2021 (p<0.05). The susceptibility index of the Chaohua Town sample site in 2020 was always above 0.2, but there was no significant difference in the survey periods (p>0.05); in 2021, with the peak of the disease, the susceptibility index also showed an obvious peak period; there was no significant difference in the susceptibility index in each survey period in 2022 (p>0.05).

[0095] Niudian Town Sample Site ( Figure 3-1 E) Since the survey began in June 2020, there was a significant peak in the incidence rate from mid-June to early August 2020 (DOY168-244), with a peak incidence rate of 0.589-0.733, and then the incidence rate dropped sharply; there was another peak in the incidence rate from mid-June (DOY169) to mid-July (DOY197) in 2021, which was significantly higher than other periods of the year (p<0.05); the incidence rate in 2022 was generally low, and although there was a small peak, the difference in incidence rate between the survey periods was not significant (p>0.05). The trend of the susceptibility index of the Niudian Town sample plot was consistent with the incidence rate, showing 2020>2021>2022.

[0096] Comprehensive analysis of data from five plots in Xinmi City showed that there were two obvious peaks of poplar bark rot in the area in 2020 ( Figure 3-1F), mid-April (DOY117) and late July (DOY209), with an average incidence of 0.417; the peak incidence in 2021 was in early to mid-July (DOY186-197), with an average incidence of 0.168; the average incidence in 2022 was lower, at 0.106. The trend of the disease index in each survey period was similar to that of the incidence, but there was no significant difference between different survey periods in the same year (p>0.05). The average disease index in 2020 was 0.134, and the average disease index in 2021 and 2022 was 0.061 and 0.062, respectively.

[0097] Correlation analysis of the occurrence of poplar bark rot in Xinmi City, Henan Province in different survey years: A correlation analysis was conducted on the incidence rate and susceptibility index of several sample plots in Xinmi City for three years, as shown in the following table (Table 2). The results showed that there was an extremely significant positive correlation between the incidence rate and susceptibility index of poplar bark rot in Xinmi City from 2020 to 2022 (p<0.01).

[0098] Table 2 Correlation analysis between the incidence of skin rot and susceptibility index of poplar plots in Xinmi City in different years

[0099] In the table, X represents Xinmi City, L represents the incidence rate, and Z represents the susceptibility index; * indicates a significant correlation (α=0.05), and ** indicates an extremely significant correlation (α=0.01).

[0100] Correlation analysis between meteorological factors and poplar bark rot in Xinmi City, Henan Province: Based on the meteorological data of Xinmi City, the average daily maximum temperature (Tm), average daily minimum temperature (Tl), average daily temperature (Ta), average daily temperature difference (Td), relative humidity (Hr), precipitation (P), average wind speed (Ws) and temperature-humidity ratio (Ta / Hr) were calculated 5d, 10d, 15d, 20d, 25d and 30d before the investigation date after statistical analysis. The correlation analysis was also conducted between the statistical meteorological data and the incidence rate and susceptibility index of poplar bark rot in each period. The results are shown in Table 3.

[0101] Table 3 Correlation analysis between meteorological factors in Xinmi and the incidence and susceptibility index of poplar bark rot

[0102] Among them, Tm, Tl, Ta, Td, Hr, P, Ws, Hr, Ta / Hr, and Hs represent the daily average maximum temperature (Tm), daily average minimum temperature (Tl), daily average temperature (Ta), daily average temperature difference (Td), average humidity (Hr), average rainfall (P), average wind speed (Ws), average temperature-humidity ratio (Ta / Hr), and average sunshine hours (Hs), respectively; 5-30 represents the average data of meteorological factors 5-30 days before the survey date. ** and * represent significant correlations at the α = 0.01 and 0.05 levels. The same below.

[0103] The incidence rate was extremely significantly positively correlated with the average daily maximum temperature (Tm), average daily minimum temperature (Tl), and average daily temperature (Ta) 5-30 days before the survey date (p<0.01), and extremely significantly positively correlated with the relative humidity (Hr) and precipitation (P) 5 days before the survey date (p<0.05); it was significantly or extremely significantly positively correlated with the temperature-humidity ratio (Ta / Hr) in the previous 15-30 days (p<0.05 or 0.01, Table 3-5); it was significantly or extremely significantly negatively correlated with the average daily temperature difference (Td) and average sunshine time (Hs) in the previous 5 days (p<0.05 or 0.01, Table 3-2); and there was no obvious correlation with other meteorological factors. The susceptibility index was significantly or extremely significantly positively correlated with the average daily maximum temperature (Tm), average daily minimum temperature (Tl), and average daily temperature (Ta) 5-30 days before the investigation date (p<0.05 or 0.01); it was significantly or extremely significantly positively correlated with the temperature-humidity ratio (Ta / Hr) of the previous 10-30 days (p<0.05 or 0.01); it was extremely significantly negatively correlated with the average daily temperature difference (Td) of the previous 5 days (p<0.01); and there was no obvious correlation with other meteorological factors.

[0104] Correlation analysis between geographical factors and poplar bark rot in Xinmi City, Henan Province: The data of geographical factors of five standard areas in Xinmi City are shown in Table 4, and the results of correlation analysis between each geographical factor and the incidence and susceptibility index of poplar bark rot in each period are shown in Table 5. The incidence of poplar bark rot in Xinmi is extremely significantly negatively correlated with longitude and latitude (p<0.01), and extremely significantly positively correlated with altitude, topography, slope, slope aspect, and tree age (p<0.01).

[0105] Table 4 Geographical factor data of Xinmi 5 standard area

[0106] Table 5 Correlation analysis between geographical factors and the incidence and susceptibility index of poplar bark rot in five standard areas of Xinmi City

[0107] Prediction model of poplar bark rot in Xinmi City, Henan Province: A multivariate linear regression analysis was performed on the site factors and the incidence of poplar bark rot, and the linear model of the incidence was finally obtained: L X = -12.951+ 0.002*Alti - 0.02 *Td5 -1.24 * Lati + 0.015 * Tm 10-0.013 * Age -0.014 * P 25- 0.309 * Ws 5 + 0.502 * Longi -0.011 * Hr 30-0.058* Hs30 The model correlation coefficient is R = 0.707, R 2 = 0.499, p<0.001, indicating that the model fit is acceptable and the factors introduced in the model can explain nearly 50% of the overall variation. The variance inflation factors (VIF) of Alti and Hr30 are between 5 and 6, and the variance inflation factors (VIF) of other factors are all <5, indicating that there is no obvious collinearity between the factors. Except for the constant, the p values ​​of the corresponding t-tests of each factor are all <0.001, indicating that each factor has a very significant effect on the model. From the standardized coefficients of each factor (Table 6), it can be seen that the importance of the factors affecting the occurrence of poplar bark rot is Alti>Hr 30>Tm 10>Hs 30>Ws 25>Lati>Longi>Td 5>Age>P25.

[0108] Table 6 Coefficients and significance analysis of each factor in the prediction model of poplar bark rot incidence in Xinmi City

[0109] like Figure 3-2 As shown, A is the residual histogram; B is the residual scatter plot. The standardized residual histogram shows that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-2 A); The standardized residual scatter plot is distributed around the 0 value, and the residual distribution on the left is basically above 0, indicating that the variance homogeneity of the predicted data is slightly poor ( Figure 3-2 B).

[0110] A multivariate linear regression analysis was performed on the site factors and the susceptibility index of poplar bark rot, and the linear model of the susceptibility index was finally obtained: Z X = 18.059+ 0.001 * Alti - 0.522 *Lati + 0.141 * Ta / Hr 25 -0.056*Ws 20- 0.005 Age. The model correlation coefficient is R=0.716, R 2=0.513, p<0.001, indicating that the model fit is acceptable, and the factors introduced in the model can explain 51% of the overall variation. The variance inflation factor (VIF) of each factor is <5, indicating that there is no obvious collinearity between the factors; the p value of the t test of each factor is <0.001 (Age is 0.002), indicating that each factor has a very significant effect on the model. From the standardized coefficients of each factor (Table 7), it can be seen that the importance of the factors affecting the poplar bark rot disease susceptibility index is Alti>Lati>Age>Ta / Hr 25>Ws 20.

[0111] like Figure 3-3 As shown, A is the residual histogram; B is the residual scatter plot. The standardized residual histogram shows that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-3 A); The standardized residual scatter plot is distributed around the value of 0, and is basically symmetrical up and down. The distribution characteristics do not change with the increase of the predicted value, which means that the data variance homogeneity and independence conditions are met ( Figure 3-3 B).

[0112] Table 7 Coefficients and significance analysis of each factor in the prediction model of poplar bark rot susceptibility index in Xinmi City

[0113] Annual prediction model of poplar bark rot in Xinmi City, Henan Province based on site factors and the incidence and susceptibility index of the previous year: Since the incidence and susceptibility index of several samples in Xinmi City in the past three years were significantly correlated (Table 2), we further sorted and analyzed the data of the incidence and susceptibility index at the beginning (Lc, Zc), peak (Lg, Zg) and end (Lm, Zm) of the survey each year, and combined with meteorological data and geographical factors to make annual predictions of the incidence and susceptibility index. The linear model of the incidence was obtained as L XY = -0.123 + 1.050*Zm XY-1 + 0.481*Ta / Hr30, the model correlation coefficient is R= 0.687, R 2 = 0.472, p < 0.001, indicating that the model fit is poor and the factors introduced by the model can only explain 47% of the overall variation. The annual prediction model of disease susceptibility index is Z XY = -0.081 + 0.497*Zm XY-1 -0.201*Zg XY-1 + 0.003*Age- 0.131*Ta / Hr 15-0.007*P 30+ 0.006*Tm 20, R= 0.792, R 2= 0.627, p<0.001, indicating that the model fit is acceptable and the factors introduced by the model can explain 63% of the overall variation.

[0114] The variance inflation factor (VIF) of each factor was <5, indicating that there was no obvious collinearity between the factors (Table 8, Table 9); the p-value of the t-test corresponding to each factor was <0.001, indicating that each factor had a very significant impact on the model (Table 8, Table 9). The normality of the prediction model data was good, and the variance homogeneity was slightly deviated ( Figure 3-4 , Figure 3-5 , where A is the residual histogram; B is the residual scatter plot).

[0115] Table 8 Coefficients and significance analysis of each factor in the annual prediction model of poplar bark rot incidence in Xinmi City

[0116] Table 9 Coefficients and significance analysis of each factor in the annual prediction model of poplar bark rot susceptibility index in Xinmi City

[0117] Occurrence of poplar bark rot in Golmud City, Qinghai Province during different survey periods: The incidence of poplar bark rot in different areas of Golmud City varies greatly. Due to the impact of the epidemic, the survey of various areas will be suspended after August 2022, and the last survey of the year will be conducted on October 29 (DOY301) (except for children's parks). Figure 3-6 As shown, A, East Exit; B, Children's Park; C, Hexi Sanlian; D, Kunlun Park; E, Shachang Station; F, Golmud 5 sample sites morbidity and susceptibility index comprehensive analysis. 2020-094 represents the survey time is the 94th day of 2020 (April 3, 2020), and so on.

[0118] The incidence rate and susceptibility index of the East Exit sample site did not fluctuate significantly during the survey period of the same year (p>0.05), and there was no significant difference in the incidence rate and susceptibility index between different years in the three years of the survey (2020-2022) (p>0.05, Figure 3-6 A). Skin rot has been common in the east exit plot since early April (DOY094), with a high incidence rate of about 0.6-0.8, but the overall degree of infection is relatively mild, with a susceptibility index of about 0.2-0.4 ( Figure 3-6 A).

[0119] The incidence rate of the Children's Park sample site fluctuated significantly during the various survey periods in 2020 (p<0.05, Figure 3-6B), the incidence rate began to increase sharply from the end of May (DOY151), continued until the end of July (DOY213), and then began to gradually decline. During this period, the incidence rate of poplar bark rot exceeded 0.6, which was significantly higher than the same period in 2021 and 2022; there was no significant fluctuation in the incidence rate during the survey period in 2021 and 2022. In the same year, the difference in the disease susceptibility index of poplars in the Children's Park sample plot was not significant (p<0.05), and there was no significant difference in the disease susceptibility index between different years in the three years of the survey (2020-2022) (p>0.05, Figure 3-6 A).

[0120] The incidence rates of the Sanlian plots in Hexi during the different survey periods in 2020 were significantly different (p<0.05, Figure 3-6 C), the incidence rate has been on an upward trend since the beginning of the survey, and began to gradually decline at the end of June (DOY180). During this period, the incidence rate of poplar bark rot was around 0.4-0.8, which was significantly higher than the same period in 2021 and 2022 (p<0.05); the incidence rate did not fluctuate significantly in each survey period in 2021 and 2022 (p>0.05), ranging from 0.2-0.4 ( Figure 3-6 C). In the same year, there was no significant difference in the disease susceptibility index of poplar trees in the Sanlian plot in Hexi, and there was no significant difference in the disease susceptibility index between different years in the three years of the survey (2020-2022) (p>0.05, Figure 3-6 C).

[0121] The incidence of skin rot in poplar trees in Kunlun Park remained relatively stable during the survey period. The incidence was low at the beginning of the 2021 survey and then gradually increased, but there was no significant difference in the incidence during the survey period, and there was no significant difference in the incidence between different years (p>0.05, Figure 3-6 D). There was no significant fluctuation in the disease susceptibility index of poplar trees in the Kunlun Park sample plot during different survey periods, and there was no significant difference in the disease susceptibility index between different years within the three years of the survey (2020-2022) (p>0.05, Figure 3-6 C).

[0122] The incidence of bark rot in poplar trees at Shachang Station fluctuated slightly during the survey period ( Figure 3-6 E). The fluctuation of the incidence rate was mainly due to a small peak in mid-June of 2020 and 2022, after which the incidence rate gradually stabilized. In 2021, the incidence rate of poplar bark rot remained relatively stable, but there was no significant difference in the incidence rate between the survey periods, and there was no significant difference in the incidence rate of bark rot between different years (p>0.05, Figure 3-6 E). The fluctuation of the disease index was mainly caused by the peak of the disease in mid-June 2022, after which the disease index gradually fell and stabilized.

[0123] Comprehensive analysis of data from five sample sites in Golmud City showed that poplar bark rot in the region had a significant peak in 2020, with the occurrence period from early June to mid-August (DOY151-213), and the incidence rate in 2020 was significantly higher than that in 2021 and 2022 (p<0.05, Figure 3-6 F). There was no significant difference in the susceptibility index between different survey periods and different years (p>0.05).

[0124] Correlation analysis of the occurrence of poplar bark rot in Golmud City, Qinghai Province in different survey years: Correlation analysis was conducted on the incidence rate and susceptibility index of several sample plots in Golmud City in different survey years. The results showed that the correlation between the two indicators in each year was significant (p<0.05) or extremely significant (p<0.01) (Table 10, in which G represents Golmud City, L represents the incidence rate, and Z represents the susceptibility index; * indicates significant correlation (α=0.05), and ** indicates extremely significant correlation (α=0.01)).

[0125] Table 10 Correlation analysis between the incidence of bark rot and susceptibility index in poplar plots in Golmud City in different years

[0126] Correlation analysis between meteorological factors and poplar bark rot in Golmud City, Qinghai Province: The meteorological data of Golmud City comes from the meteorological department. After statistical analysis, the average daily maximum temperature (Tm), average daily minimum temperature (Tl), average daily temperature (Ta), average daily temperature difference (Td), relative humidity (Hr), precipitation (P), average wind speed (Ws) and temperature-humidity ratio (Ta / Hr) were calculated 5d, 10d, 15d, 20d, 25d and 30d before the survey date. The statistical meteorological data were analyzed for correlation with the incidence rate and susceptibility index of poplar bark rot in each period. The results are shown in Table 11. ** and * represent significant correlation at the 0.01 and 0.05 levels, the same below.

[0127] Table 11 Correlation analysis between meteorological factors and the incidence and susceptibility index of poplar bark rot in Golmud City

[0128] The incidence rate was significantly positively correlated with the daily average maximum temperature (Tm), daily average minimum temperature (Tl), daily average temperature (Ta) and relative humidity (Hr) 5-30 days before the survey date (p<0.01), and significantly positively correlated with the precipitation 5-10 days before the survey date (p<0.05), and had no significant correlation with other meteorological factors. The susceptibility index was significantly or extremely significantly positively correlated with the daily average maximum temperature (Tm), daily average minimum temperature (Tl), daily average temperature (Ta) 5-30 days before the survey date, precipitation (P) in the previous 20-30 days, average wind speed (Ws) in the previous 30 days, and temperature-humidity ratio (Ta / Hr) in the previous 15-30 days (p<0.05 or 0.01); and significantly or extremely significantly negatively correlated with the daily average temperature difference (Td) in the previous 10-30 days (p<0.05 or 0.01).

[0129] Correlation analysis between geographical factors and poplar bark rot in Golmud City, Qinghai Province: The data of geographical factors of the five standard areas in Golmud City are shown in Table 12, and the results of the correlation analysis between each geographical factor and the incidence and susceptibility index of poplar bark rot in each period are shown in Table 13. As shown in Table 12, the incidence of poplar bark rot in Golmud City is extremely significantly negatively correlated with latitude (p<0.01), extremely significantly positively correlated with longitude and altitude (p<0.01), and significantly positively correlated with soil type (Seil) (p<0.05); while the susceptibility index is extremely significantly negatively correlated with latitude (p<0.01), extremely significantly positively correlated with longitude (p<0.01), and significantly positively correlated with tree age (p<0.05).

[0130] Table 12 Geographical factor data of 5 standard sites in Golmud City

[0131] Table 13 Correlation analysis between geographical factors and the incidence and susceptibility index of poplar bark rot in 5 standard areas of Golmud City

[0132] Prediction model of poplar bark rot based on site factors in Golmud City, Qinghai Province: A multivariate linear regression analysis was performed on the site factors and the incidence of poplar bark rot, and the linear model of the incidence was finally obtained: L G =21.763+0.718*longitude-2.429*latitude+0.004*Ta30+ 0.004*Hr10. The model correlation coefficient is R=0.816, R 2 =0.666, p<0.001, indicating that the model has a good fit and the factors introduced by the model can explain 67% of the overall variation.

[0133] The variance inflation factor (VIF) of each factor was <5, indicating that there was no collinearity between the factors. Except for the constant, the p-values ​​of the t-tests of each factor were <0.001, indicating that each factor had a very significant impact on the model. From the standardized coefficients of each factor (Table 14), it can be seen that the importance of the factors affecting the incidence of poplar bark rot is longitude> latitude> Ta30> Hr10.

[0134] Table 14 Coefficients and significance analysis of each factor in the prediction model of poplar bark rot incidence in Golmud City

[0135] like Figure 3-7 , where A is the residual histogram; B is the residual scatter plot. The standardized residual histogram shows that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-7 A); The standardized residual scatter plot is distributed around the value of 0, and is basically symmetrical up and down. The distribution characteristics do not change with the increase of the predicted value, which means that the data variance homogeneity and independence conditions are met ( Figure 3-7 B).

[0136] A multivariate linear regression analysis was performed on the site factors and the susceptibility index of poplar bark rot, and the linear model of the susceptibility index was finally obtained: Z G = -44.525+ 0.471*Longi + 0.002*Age+ 0.002*Ta25- 0.006*Td20, the model correlation coefficient is R= 0.834, R 2 = 0.695, p<0.001, indicating that the model has a good fit and the factors introduced by the model can explain nearly 70% of the overall variation.

[0137] The variance inflation factors (VIF) of each factor were all <5, indicating that there was no collinearity between the factors. The p-values ​​of the t-tests of each factor were all <0.001, indicating that each factor had a very significant impact on the model. From the standardized coefficients of each factor (Table 15), it can be seen that the importance of factors affecting the poplar bark rot disease susceptibility index is longitude> tree age> Ta25> Td20.

[0138] Table 15 Coefficients and significance analysis of each factor in the prediction model of poplar bark rot susceptibility index in Golmud City

[0139] like Figure 3-8 As shown, A is the residual histogram; B is the residual scatter plot. The standardized residual histogram shows that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-8A); The standardized residual scatter plot is distributed around the value of 0, and is basically symmetrical up and down. The distribution characteristics do not change with the increase of the predicted value, which means that the data variance homogeneity and independence conditions are met ( Figure 3-8 B).

[0140] Annual prediction model of poplar bark rot in Golmud City, Qinghai Province based on site factors and the incidence and susceptibility index of the previous year: Since the incidence rate and susceptibility index of several sample plots in Golmud City have a good correlation in the past three years (Table 10), we further sorted and analyzed the data of the incidence rate and susceptibility index at the beginning (Lc, Zc), peak (Lg, Zg) and end (Lm, Zm) of the survey each year, and combined with meteorological data and geographical factors, we made an annual prediction of the incidence rate and susceptibility index, and obtained the linear model of the incidence rate as L GY = -0.237 + 0.618*Lm GY +0.473*Lg GY-1 + 0.501*Zc GY-1 +0.003*Tm30, the model correlation coefficient is R = 0.887, R 2 =0.787, p<0.001, indicating that the model has a good fit and the factors introduced by the model can explain 79% of the overall variation.

[0141] The annual prediction model of disease index is Z GY = -0.025+ 0.689*Zg GY-1 + 0.55*Zm GY-1 - 0.052*Lc GY-1 + 0.002*Tm 30- 0.008*Ws 5, the model correlation coefficient is R = 0.877, R 2 = 0.769, p<0.001, indicating that the model fit is good and the factors introduced by the model can explain 77% of the overall variation.

[0142] The variance inflation factors (VIF) of the two models were all <5, indicating that there was no collinearity between the factors; the p values ​​of the t-tests of the factors were all <0.001, indicating that each factor had a very significant impact on the model (Table 16, Table 17). From the standardized coefficients of the factors, it can be seen that the importance of the factors affecting the annual predicted incidence of poplar bark rot is Lm GY-1 >Lg GY-1 >Zc GY-1 >Tm 30 (Table 16); the most important factor affecting the annual prediction of poplar bark rot disease susceptibility index is Zm GY-1 >Zg GY-1 >Tm 30>Lc GY-1 >Ws 5 (Table 17).

[0143] like Figure 3-9 , Figure 3-10 As shown, A is the residual histogram; B is the residual scatter plot. The standardized residual histogram shows that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-9 A, Figure 3-10 A); The standardized residual scatter plot is distributed around the value of 0, and is basically symmetrical up and down. The distribution characteristics do not change with the increase of the predicted value, which means that the data variance homogeneity and independence conditions are met ( Figure 3-9 B, Figure 3-10 B).

[0144] Table 16 Coefficients and significance analysis of each factor in the annual prediction model for the incidence of Golmud poplar bark rot

[0145] Table 17 Coefficients and significance analysis of each factor in the annual prediction model of poplar bark rot susceptibility index in Golmud City

[0146] The occurrence of poplar bark rot in Karamay, Xinjiang at different survey periods. Due to the impact of the epidemic, Karamay conducted only five surveys from April to early June in 2020 (DOY 95-156), and investigated the occurrence of bark rot from April to August in 2021 (DOY 103-231). The occurrence of bark rot in poplar trees in different standards is as follows: Figure 3-11 As shown, A, Wuzhiqu; B, Jiuzhiqu; C, Pulin; D, Baishatan; E, checkpoint; F, comprehensive analysis of 5 sample plots in Karamay.

[0147] Except for the checkpoint sample plot, the incidence rate in other standard plots in 2021 was higher than that in 2020 ( Figure 3-11 AE), but the susceptibility index in 2020 was higher than that in 2021, indicating that the disease severity in 2020 was more serious than that in 2021. The results of pairwise ANOVA on the incidence and susceptibility index data of the two years showed that there was no significant difference between the two years (p>0.05). A variance analysis was performed on the incidence and susceptibility index of all sample plots in each survey period in the same year. The results showed that there was no significant difference in the incidence and susceptibility index in different survey periods in the same year (p>0.05). A comprehensive analysis of the five sample plots also showed that the incidence rate in Karamay in 2021 was slightly higher than that in 2020 (p>0.05), and the susceptibility index in 2020 was higher than that in 2021 (p>0.05).

[0148] Correlation analysis of poplar bark rot in Karamay, Xinjiang at different investigation periods: A correlation analysis was conducted on the incidence rate and susceptibility index of several sample plots in Karamay in two years. The results showed that there was a significant correlation between the incidence rate and the susceptibility index in 2020 and 2021 (p<0.01), but there was no significant correlation between the incidence rate and the susceptibility index in the same year (p>0.05) (Table 18, in the table, K represents Karamay, L represents the incidence rate, and Z represents the susceptibility index; * indicates a significant correlation (α=0.05), and ** indicates an extremely significant correlation (α=0.01)).

[0149] Table 18 Correlation analysis between the incidence of skin rot and susceptibility index in different years in Karamay poplar plots

[0150] Correlation analysis between meteorological factors and poplar bark rot in Karamay, Xinjiang: The meteorological data of Karamay comes from the local meteorological department. After statistical analysis, the daily average maximum temperature (Tm), daily average minimum temperature (Tl), daily average temperature (Ta), daily average temperature difference (Td), relative humidity (Hr), precipitation (P), average wind speed (Ws) and temperature-humidity ratio (Ta / Hr) were calculated 5 days, 10 days, 15 days, 20 days, 25 days and 30 days before the survey date. The statistical meteorological data were analyzed for correlation with the incidence rate and susceptibility index of poplar bark rot in each period. The results are shown in Table 19. The incidence of Karamay poplar bark rot disease was significantly or extremely significantly positively correlated with the average daily maximum temperature (Tm), average daily minimum temperature (Tl), average daily temperature (Ta), and temperature-humidity ratio (Ta / Hr) 5-30 days before the survey date (p<0.05 or 0.01); and was significantly positively correlated with the precipitation 5-10 days and 25-30 days before the survey date (p<0.05); and was significantly negatively correlated with the average daily temperature difference (Td) in the previous 10-15 days and the relative humidity (Hr) in the previous 15-30 days (p<0.05).

[0151] Table 19 Correlation analysis between meteorological factors in Karamay and the incidence and susceptibility index of poplar bark rot

[0152] Note: ** and * represent that the correlation is significant at the α=0.01 and 0.05 levels, the same below.

[0153] The susceptibility index was significantly or extremely significantly positively correlated with the average daily maximum temperature (Tm), average daily minimum temperature (Tl), average daily temperature (Ta), average wind speed (Ws) 5-30 days before the investigation date, the average daily temperature difference (Td), and temperature-humidity ratio Ta / Hr in the first 10-30 days (p<0.05 or 0.01); it was extremely significantly negatively correlated with the relative humidity (Hr) in the first 10-30 days (p<0.01); and had no significant correlation with precipitation (P) (p>0.05).

[0154] Correlation analysis between geographical factors and poplar bark rot in Karamay, Xinjiang: The data of geographical factors of five standard areas in Karamay are shown in Table 20, and the results of correlation analysis between each geographical factor and the incidence and susceptibility index of poplar bark rot in each period are shown in Table 21. The incidence of poplar bark rot in Karamay was extremely significantly positively correlated with longitude and tree age (p<0.01), and significantly positively correlated with slope, soil type and tree species (p<0.05); while the susceptibility index was extremely significantly negatively correlated with latitude and altitude (p<0.01), extremely significantly positively correlated with longitude and tree age (p<0.01), and significantly positively correlated with slope and tree species (p<0.05).

[0155] Table 20 Geographical factor data of Karamay 5 standard areas

[0156] Table 21 Correlation analysis between geographical factors and the incidence and susceptibility index of poplar bark rot in 5 standard areas of Karamay

[0157] Prediction model of poplar bark rot based on site factors in Karamay, Xinjiang: A multivariate linear regression analysis was performed on the site factors and the incidence and susceptibility index of poplar bark rot, and the linear model of the incidence was finally obtained: L K = -48.292 - 0.011*Hr20 - 0.097*Td30- 1.116*Lati +1.187 *Longi+ 0.001*Alti+ 0.008*P5, the model correlation coefficient is R= 0.903, R 2 = 0.816, p<0.001, indicating that the model fit is good, and the factors introduced by the model can explain more than 90% of the overall variation. The variance inflation factor (VIF) of each factor is <5, indicating that there is no collinearity between the factors (Table 22). Except for the constant, the p value of the t test of each factor is <0.001, indicating that each factor has a very significant effect on the model. From the standardized coefficients of each factor, it can be seen that the importance of the factors affecting the occurrence of poplar bark rot is Hr20>Td30>latitude>longitude>P5 before the survey date (Table 22).

[0158] Table 22 Coefficients and significance analysis of each factor in the prediction model of Karamay poplar bark rot incidence

[0159] like Figure 3-12As shown in Figure 1, A is a residual histogram; B is a residual scatter plot. The standardized residual histogram shows that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-12 A); The standardized residual scatter plot is distributed around the value of 0, and is basically symmetrical up and down. The distribution characteristics do not change with the increase of the predicted value, which means that the data variance homogeneity and independence conditions are met ( Figure 3-12 B).

[0160] A multivariate linear regression analysis was performed on the site factors and the susceptibility index of poplar bark rot, and the linear model of the susceptibility index was finally obtained: Z K = -19.09- 0.001*Hr 30-0.385*Lati +0.435*Longi -0.003*age, the model correlation coefficient is R = 0.891, R 2 = 0.793, p<0.001, indicating that the model fit is good and the factors introduced by the model can explain nearly 80% of the overall variation.

[0161] The variance inflation factor (VIF) of each factor was <5, indicating that there was no collinearity between the factors. The p-values ​​of the t-tests of each factor were <0.001, indicating that each factor had a very significant impact on the model. From the standardized coefficients of each factor (Table 23), it can be seen that the importance of each factor affecting the susceptibility index of Karamay poplar bark rot is Hr30> latitude> longitude> tree age.

[0162] Table 23 Coefficients and significance analysis of each factor in the prediction model of the susceptibility index of Karamay poplar bark rot

[0163] like Figure 3-13 As shown in Figure 1, A is the residual histogram; B is the residual scatter plot. The standardized residual histogram shows that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-13 A); The standardized residual scatter plot is distributed around the value of 0, and is basically symmetrical up and down. The distribution characteristics do not change with the increase of the predicted value, which means that the data variance homogeneity and independence conditions are met ( Figure 3-13 B).

[0164] Annual prediction model of poplar bark rot in Karamay, Xinjiang based on site factors and the incidence and susceptibility index of the previous year: Since there is a very significant correlation between the two-year incidence rate and the two-year susceptibility index in Karamay (Table 18), considering that the two-year survey period in Karamay is incomplete, only the peak period (Lg, Zg) data of the annual incidence rate and susceptibility index are combined with meteorological data and geographical factors to make annual predictions of the incidence rate and susceptibility index. The linear model of the incidence rate is L KY = -0.097+ 0.006*Tl30+0.917*Lg KY-1 , the model correlation coefficient is R=0.935, R 2 =0.873, p<0.001 (Table 24), indicating that the model fit is very good and the factors introduced by the model can explain 87% of the overall variation. The annual prediction model for the disease index is Z KY =-0.035 + 0.002*Tl25+0.923*Zg KY-1 , the model correlation coefficient is R=0.948, R 2 =0.898, p<0.001 (Table 25), indicating that the model fit is very good and the factors introduced by the model can explain nearly 90% of the overall variation.

[0165] Table 24 Coefficients and significance analysis of each factor in the annual prediction model for the incidence of Karamay poplar bark rot

[0166] Table 25 Coefficients and significance analysis of each factor in the annual prediction model of the susceptibility index of Karamay poplar bark rot

[0167] like Figure 3-14 , Figure 3-15 As shown, A is the residual histogram; B is the residual scatter plot. The standardized residual histograms of the two models show that the residual mean is close to 0, the standard deviation is close to 1, and the data is normally distributed, indicating that the model meets the normality condition requirements of linear regression ( Figure 3-14 A, Figure 3-15 A); The standardized residual scatter plot is distributed around the value of 0, and is basically symmetrical up and down. The distribution characteristics do not change with the increase of the predicted value, which means that the data variance homogeneity and independence conditions are met ( Figure 3-14 B, Figure 3-15 B).

[0168] Correlation analysis between meteorological factors and poplar bark rot in three climate zones: The correlation analysis between the meteorological factors in the three climate zones and the incidence and susceptibility index of poplar bark rot in different periods was performed, and the results are shown in Table 26. The incidence rate was extremely significantly negatively correlated with the relative humidity (Hr) 5-30 days before the survey date (p<0.01), and was significantly or extremely significantly negatively correlated with the average daily maximum temperature (Tm), the average daily temperature difference (Td) and the precipitation (P) in the first 15-30 days before the survey date (p<0.05 or 0.01); it was extremely significantly positively correlated with the average wind speed (Ws) and the temperature-humidity ratio (Ta / Hr) 5-30 days before the survey date (p<0.01).

[0169] The susceptibility index was significantly or extremely significantly negatively correlated with the average daily maximum temperature (Tm), average daily minimum temperature (Tl), average daily temperature (Ta), relative humidity (Hr) 5-30 days before the survey date, and precipitation (P) in the previous 10-30 days (p<0.05 or 0.01); it was extremely significantly positively correlated with the average wind speed (Ws) and temperature-humidity ratio (Ta / Hr) 5-30 days before the survey date (p<0.01); and it was not correlated with the average daily temperature difference (Td) in the previous 5 days (p>0.05).

[0170] Table 26 Correlation analysis between meteorological factors and the incidence and susceptibility index of poplar bark rot in three places

[0171] The correlation results between the meteorological factors in the three climate zones and the incidence rate and susceptibility index were quite different, which may be caused by the large differences in meteorological factors in the three climate zones. In order to solve this interference, the key factors were screened through subsequent multiple regression analysis.

[0172] Correlation analysis between geographical factors and poplar bark rot in three areas: The results of the correlation analysis between various geographical factors and the incidence and susceptibility index of poplar bark rot at different periods are shown in Table 27. The incidence and susceptibility index of poplar bark rot are significantly positively correlated with latitude, altitude and tree species (p<0.01), and significantly negatively correlated with longitude, terrain and soil type (p<0.01). Similar to meteorological factors, after the data of the three climate zones were integrated, the results of the correlation analysis between various geographical factors and the incidence and susceptibility index of poplar bark rot at different periods were quite different from the correlation results within each climate zone, which may also be caused by the obvious geographical differences among the climate zones.

[0173] Table 27 Correlation analysis between geographical factors and the incidence and susceptibility index of poplar bark rot in three areas

[0174] Prediction model of poplar bark rot in three places based on site factors: A multivariate linear regression analysis was performed on the site factors and the incidence of poplar bark rot, and the linear model of the incidence was finally obtained: L Z(3) =1.855-0.016*Longi+0.008*Age +0.01*Tl25- 0.012*Td5+ 0.056*Topo-0.015*P25- 0.089*Ta / Hr20 (where Z(3) represents the comprehensive data based on the three places, the same below), the model correlation coefficient is R=0.691, R 2 = 0.477, p<0.001, indicating that the model fit is poor, and the factors introduced by the model can explain 48% of the overall variation. The standardized coefficients of each factor show (Table 28) that the importance of factors affecting the incidence of poplar bark rot is longitude>Tl25>Age>Topo= Ta / Hr20>P25>Td5.

[0175] Table 28 Coefficients and significance analysis of each factor in the prediction model of poplar bark rot incidence in three places

[0176] The multiple linear regression analysis of each site factor and the susceptibility index of poplar bark rot was carried out. The linear model of the susceptibility index was: Z Z(3) = 0.503- 0.005*Longi+ 0.005*Age+ 0.003*Tl 5+ 0.004* Aspect- 0.004*P25+0.017*Topo, R= 0.775, R 2 = 0.600, p<0.001, indicating that the model fit is acceptable and the factors introduced in the model can explain nearly 60% of the overall variation. The standardized coefficients of each factor show (Table 29) that the importance of factors affecting the incidence of poplar bark rot is longitude>Age>Tl5>slope>Topo>P25.

[0177] Table 29 Coefficients and significance analysis of each factor in the prediction model of poplar bark rot susceptibility index in three places

[0178] like Figure 3-16 , Figure 3-17 As shown, A is the residual histogram; B is the residual scatter plot. The variance inflation factors (VIF) of the two linear models are both <5, indicating that there is no obvious collinearity between the factors. The t-test p value of Ta / Hr20 in the incidence model is <0.05, and the p values ​​of the corresponding t-tests of other factors are all <0.01, indicating that each factor has a significant or extremely significant effect on the model. The histogram and scatter plot of the residuals of the prediction model show that the normality and variance homogeneity of the prediction data are good ( Figure 3-16 , 3-17 ).

[0179] Annual prediction model of poplar bark rot based on site factors and the incidence and susceptibility index of the previous year in three locations: The annual incidence rate and susceptibility index were further combined with meteorological data and geographical factors to make annual predictions of the incidence rate and susceptibility index in the three places. Since only two years of data were surveyed in Karamay, Xinjiang, and the survey period was incomplete, only the incidence rate and susceptibility index (Lg, Zg) during the peak period of the previous year were considered when making annual predictions for the three places. The linear model of the incidence rate was finally obtained as L Z(3)Y = 1.425 - 0.013*Longi + 0.007 * Tm25 + 1.286*Zg Z(3)Y-1 -0.018* Aspect- 0.013*P30- 0.008*Td5 (where Z(3)Y Represents the comprehensive data of the three places in that year, Z(3)Y-1 represents the comprehensive data of the three places in the previous year, the same below), the model correlation coefficient is R=0.892, R 2 =0.795, p<0.001, indicating that the model has a good fit and the factors introduced by the model can explain nearly 80% of the overall variation.

[0180] The annual prediction model of disease index is Z Z(3)Y =0.414 -0.004*Longi + 0.005*Age +0.002*Tm25+ 0.229*Zg Z(3)Y-1 + 0.001*P30, the model correlation coefficient is R=0.887, R 2 =0.786, p<0.001, indicating that the model has a good fit and the factors introduced by the model can explain nearly 79% of the overall variation.

[0181] The variance inflation factors (VIF) of the two models were all <5, indicating that there was no obvious collinearity between the factors; the p-values ​​of the t-tests corresponding to each factor were all <0.01, indicating that each factor had a significant or extremely significant effect on the model (Table 30, Table 31). The histogram and scatter plot of the residuals of the prediction model showed that the normality and variance homogeneity of the prediction data were good, such as Figure 3-18 , Figure 3-19 As shown, A is the residual histogram; B is the residual scatter plot.

[0182] Table 30 Coefficients and significance analysis of each factor in the annual prediction model of the incidence of poplar bark rot in three places

[0183] Table 31 Coefficients and significance analysis of each factor in the annual prediction model of poplar bark rot susceptibility index in three places

[0184] If the data of Karamay in Xinjiang is not considered and only the data of Golmud and Xinmi are considered, combining the incidence and disease index data (Lm, Zm) at the initial stage (Lc, Zc), peak stage (Lg, Zg) and end stage of the previous year's investigation with meteorological data and geographical factors for the annual prediction of incidence and disease index, the linear model of the incidence in Golmud and Xinmi is finally obtained as L Z(2)Y =-5.504 + 0.013 * Tm20 - 0.019 * P30 + 1.005 * Zc Z(2)Y-1 -0.0157 * Ta / Hr20 - 0.019 * Aspect + 0.772 * Zm Z(2)Y-1 + 0.154 * Lati (where Z(2)Y represents the data of the current year in Golmud and Xinmi, Z(3)Y-1 represents the data of the previous year in Golmud and Xinmi, and the same below), the correlation coefficient of the model R = 0.898, R 2 = 0.807, p < 0.001, indicating that the model has a good fitting degree, and the factors introduced by the model can explain nearly 81% of the total variation.

[0185] The annual prediction model of the disease index is Z Z(2)Y =-2.012 + 0.004 * Tm20 + 0.477 * Zm Z(2)Y-1 -0.041 * Ta / Hr20 - 0.005 * P30 + 0.002 * Age + 0.056 * Lati, the model R = 0.912, R 2 = 0.831, p < 0.001, indicating that the model has a good fitting degree, and the introduced factors can explain 83% of the total variation.

[0186] Except for the latitude (5 < VIF < 10), the variance inflation factor (VIF) of each factor in the two linear models is < 5, indicating that there is no obvious collinearity between the factors; the p-values of the corresponding t-tests of each factor are all < 0.01, indicating that each factor has a significant or extremely significant impact on the model (Tables 32 and 33). The histograms and scatter plots of the residuals of the prediction model show that the normality and homoscedasticity of the prediction data are both good, as Figure 3-20 、 Figure 3-21 shown, where A is the residual histogram; B is the residual scatter plot.

[0187] Table 32 Coefficient and significance analysis of each factor in the annual prediction model of poplar canker incidence in Golmud and Xinmi

[0188] Table 33 Coefficient and significance analysis of each factor in the annual prediction model of poplar canker disease index in Golmud and Xinmi

[0189] The incidence rate and susceptibility index of the three climate zones were extremely significantly positively correlated with the incidence rate and susceptibility index of the previous year (p<0.01), and were significantly positively correlated with the average maximum temperature (Tm), average minimum temperature (Tl), and average daily temperature (Ta) 5-30 days before the survey date (p<0.05); the incidence rate was significantly positively correlated with the average rainfall (P) 5 days before the survey date; the susceptibility index was significantly positively correlated with the temperature-humidity ratio (Ta / Hr) 15-30 days before the survey date (p<0.05); the incidence rate in the three climate zones had no significant correlation with the geographical factors of each climate zone, and the susceptibility index was significantly negatively correlated with latitude (p<0.05). Compared with the analysis of each climate zone separately, the correlation between the site factors and the incidence rate and susceptibility index in the comprehensive analysis of the data of the three climate zones was quite different, which may be due to the small differences in site factors within the three climate zones and the large differences in site factors between the climate zones.

[0190] Since the survey data of the early and late stages of the disease in Karamay, Xinjiang for two years are incomplete and the prediction effect of the individual prediction is better than the mixed prediction, it is recommended to establish a separate prediction model for the incidence rate and susceptibility index in the region. The prediction model is: L K = -48.292-0.011*Hr20-0.097*Td30-1.116*Lati+1.187 *Lati+ 0.001*Longi+ 0.008*P5 (R= 0.903, R 2 = 0.816, p < 0.001) Z K = -19.09- 0.001*Hr 30-0.385*Lati +0.435*Longi -0.003*age (R= 0.891, R 2 = 0.793, p < 0.001) The prediction model for the incidence and susceptibility index of poplar bark rot disease in Xinmi, Henan and Golmud, Qinghai is: L Z(2)Y =-5.504+0.013*Tm20-0.019*P30+1.005*Zc Z(2)Y-1 -0.0157*Ta / Hr20-0.019*Aspect +0.772*Zm Z(2)Y-1 +0.154*Lati(R=0.898,R 2 =0.807, p<0.001).

[0191] Z Z(2)Y =-2.012+0.004*Tm20+0.477*Zm Z(2)Y-1-0.041*Ta / Hr20-0.005*P30+0.002*Age+0.056*Lati (R=0.912, R 2 =0.831, p<0.001).

[0192] Based on the data of the three climate zones, the comprehensive prediction model of poplar disease incidence and susceptibility index was established as follows: L Z(3)Y = 1.425 - 0.013*Longi + 0.007 * Tm25 + 1.286*Zg Z(3)Y-1 - 0.018*Aspect- 0.013*P30- 0.008*Td5 (R=0.892, R 2 =0.795, p<0.001).

[0193] Z Z(3)Y =0.414 -0.004*Longi + 0.005*Age +0.002*Tm25 + 0.229*Zg Z(3)Y-1 +0.001*P30 (R0.887, R 2 =0.786, p<0.001).

[0194] Among them, L and Z represent the incidence rate and susceptibility index respectively; c, m, and g represent the data at the beginning of the year, the end of the year, and the peak period respectively; K On behalf of Karamay, Y and Y-1 Represent the current year and the previous year respectively. Z(2) Represents the comprehensive data of Xinmi and Golmud, Z（3） represents the comprehensive data of Xinmi, Golmud and Karamay; Lati represents latitude; Longi represents longitude; Tm, Tl, P, Ta / Hr represent the average daily maximum temperature, average daily minimum temperature, average rainfall, and average temperature-humidity ratio, respectively; 5-30 represents the average data of meteorological factors 5-30 days before the survey date; Age represents tree age; Aspect represents slope direction.

[0195] Factors affecting the incidence and susceptibility index of poplar bark rot in different climatic zones: The incidence and susceptibility index of poplar bark rot in the three climatic zones were significantly positively correlated with the average maximum temperature (Tm), average minimum temperature (Tl), and average daily temperature (Ta) 5-30 days before the survey date (p<0.05); the incidence was significantly positively correlated with the average rainfall (P) 5 days before the survey date; the susceptibility index was significantly positively correlated with the temperature-humidity ratio (Ta / Hr) 15-30 days before the survey date (p<0.05); the incidence in the three climatic zones had no significant correlation with the geographical factors of each climatic zone, and the susceptibility index was significantly negatively correlated with latitude (p<0.05).

[0196] The morbidity prediction unit may also verify the constructed linear model of morbidity and / or linear model of susceptibility index, thereby ensuring the accuracy of the prediction of the linear model of morbidity and / or linear model of susceptibility index.

[0197] The linear model of incidence includes any of the above-mentioned linear models of incidence, for example: the linear model of poplar bark rot incidence based on climatic conditions, site conditions, and biological conditions in each climatic zone, the linear model of the annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence in the previous year in each climatic zone, the linear model of the poplar bark rot incidence based on climatic conditions, site conditions, and biological conditions in different climatic zones, and the linear model of the annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence in the previous year in different climatic zones.

[0198] The linear model of disease susceptibility index includes any one of the linear models of the above-mentioned disease susceptibility index, for example: the linear model of poplar bark rot incidence index based on climatic conditions, site conditions, and biological conditions in each climatic zone, the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the disease susceptibility index of the previous year in each climatic zone, the linear model of poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in different climatic zones, and the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the disease susceptibility index of the previous year in different climatic zones.

[0199] The time series point graph can be a two-dimensional graph or a three-dimensional graph. When the time series point graph is a two-dimensional graph, the time series point graph includes an origin, an X-axis, and a Y-axis. The X-axis represents the predicted corresponding period, and the Y-axis represents the incidence rate of poplar skin rot or the incidence index of poplar skin rot; when the time series point graph is a three-dimensional graph, the time series point graph includes an origin, an X-axis, a Y-axis, and a Z-axis. The X-axis represents the predicted corresponding period, the Y-axis represents the incidence rate of poplar skin rot, and the Z-axis represents the incidence index of poplar skin rot, and the meanings of the Y-axis and the Z-axis can be interchanged.

[0200] The disease prediction unit verifies the constructed linear model of disease incidence and / or linear model of disease susceptibility index, specifically including the following steps: The morbidity prediction unit plots the prediction results of any morbidity linear model and / or susceptibility index linear model in a time series point diagram in chronological order; specifically, the prediction results of any morbidity linear model and / or susceptibility index linear model in different periods, and the corresponding periods and prediction results (morbidity and / or morbidity index) are plotted in the time series point diagram corresponding to the morbidity linear model and / or susceptibility index linear model; The incidence prediction unit plots the monitoring values ​​of different periods in the time series point diagram corresponding to the linear model of the incidence rate and / or the linear model of the susceptibility index in the time series point diagram. At this time, the time series point diagram corresponding to the linear model of the incidence rate and / or the linear model of the susceptibility index is obtained; in the time series point diagram, the time series points corresponding to each period include prediction time series points and monitoring time series points, the prediction time series points refer to the corresponding points drawn in the time series point diagram according to the prediction results, and the monitoring time series points refer to the corresponding points drawn in the time series point diagram according to the detection values; the prediction time series points and the detection time series points are distributed in the time series point diagram according to the corresponding periods; the prediction time series points and the detection time series points can be drawn by points with different GRB values, so as to facilitate the distinction between the prediction time series points and the detection time series points; The onset prediction unit calculates the distance between the predicted time point and the monitored time point in the same period in the time point diagram to obtain a predicted evaluation distance; the predicted evaluation distance is an arbitrary number greater than or equal to zero; The accuracy of the linear model of the incidence rate and / or the linear model of the disease susceptibility index is verified according to the predicted evaluation distance; when the predicted evaluation distance is less than or equal to the first standard evaluation distance, the accuracy of the linear model of the current incidence rate and / or the linear model of the disease susceptibility index is greater than 95%; when the predicted evaluation distance is greater than the first standard evaluation distance and less than the second standard evaluation distance, the accuracy of the linear model of the current incidence rate and / or the linear model of the disease susceptibility index is greater than 85%; when greater than the second standard evaluation distance, the accuracy of the linear model of the current incidence rate and / or the linear model of the disease susceptibility index is abnormal; the data analysis unit of the comprehensive prediction system for poplar bark rot performs multivariate linear regression analysis on the latest data collected by the data acquisition unit, and updates the linear model of the poplar bark rot incidence rate and the disease susceptibility index.

[0201] The disease prediction unit includes a model selection tool, which performs correlation data of the poplar bark rot disease incidence rate and disease susceptibility index prediction area according to needs. After selecting the target linear model in the linear model of the above-mentioned skin rot disease incidence rate and disease susceptibility index, the disease prediction unit uses the target linear model to predict the poplar bark rot disease incidence rate and disease susceptibility index for the current correlation data to obtain the current prediction data.

[0202] The incidence prediction unit divides the obtained linear model of the above-mentioned skin ulcer incidence rate and susceptibility index into two model layers to obtain a first-level linear model layer: a first linear model layer and a second linear model layer, and the first linear model layer is placed on the upper layer of the second linear model layer, that is, the first linear model layer and the second linear model layer are arranged from top to bottom in the vertical direction.

[0203] The first linear model layer includes linear models of the incidence and susceptibility index of poplar bark rot in each climate zone and different climate zones based on climate conditions, site conditions, and biological conditions. The second linear model layer includes linear models of the annual incidence and susceptibility index of poplar bark rot in each climate zone and different climate zones based on climate conditions, site conditions, biological conditions, and the incidence / susceptibility index of the previous year.

[0204] The associated data also include the incidence rate and / or morbidity index in the previous year.

[0205] The model selection tool selects a first-level linear model layer in the first linear model layer and the second linear model layer according to whether the associated data includes the incidence rate and / or the incidence index of the previous year.

[0206] The incidence prediction unit encapsulates the linear models of the incidence rate of skin rot and the susceptibility index in the first-level linear model layer in different model layers to obtain a second-level linear model layer. Specifically, the incidence prediction unit encapsulates the linear models of the incidence rate of poplar skin rot and the susceptibility index in the first linear model layer in different model layers to obtain a second-level linear model layer of the first linear model layer. The incidence prediction unit encapsulates the linear models of the incidence rate of poplar skin rot and the susceptibility index in the second linear model layer in different model layers to obtain a second-level linear model layer of the second linear model layer.

[0207] The secondary linear model layer includes at least linear models of the incidence and susceptibility index of poplar bark rot disease adapted to warm temperate semi-humid areas, plateau temperate semi-arid areas, mid-temperate arid areas, and three areas. Specifically, the secondary linear model layer of the first linear model layer includes at least linear models of the incidence and susceptibility index of poplar bark rot disease adapted to warm temperate semi-humid areas, plateau temperate semi-arid areas, mid-temperate arid areas, and three areas based on climatic conditions, site conditions, and biological conditions. The secondary linear model layer of the second linear model layer includes at least linear models of the annual incidence and susceptibility index of poplar bark rot disease adapted to warm temperate semi-humid areas, plateau temperate semi-arid areas, mid-temperate arid areas, and three areas based on climatic conditions, site conditions, biological conditions, and the incidence / susceptibility index of the previous year.

[0208] Each secondary linear model includes a corresponding regional mark, and the incidence prediction unit sorts the secondary linear models in the same primary linear model layer from top to bottom according to the number of regional marks. When the number of regional marks is the same, the secondary linear models with the same number of regional marks are arranged horizontally on the horizontal line corresponding to the number of regional marks. For example, when the number of regional marks is 1, in the same primary linear model layer, there will be at least 3 linear models of poplar bark rot incidence and / or susceptibility index, namely, linear models of poplar bark rot incidence and / or susceptibility index based on climatic conditions, site conditions, and biological conditions corresponding to warm temperate semi-humid areas, plateau temperate semi-arid areas, and mid-temperate arid areas, respectively. The incidence prediction unit arranges the linear models of poplar bark rot incidence and / or susceptibility index based on climatic conditions, site conditions, and biological conditions corresponding to warm temperate semi-humid areas, plateau temperate semi-arid areas, and mid-temperate arid areas, respectively, horizontally in the position of the secondary linear model corresponding to one regional mark.

[0209] The model selection tool screens the regions in the associated data to be predicted, and after obtaining the regions to which the associated data to be predicted belongs, in the selected first-level linear model layer, according to the number of region tags, in the number of target region tags, a linear model including the corresponding region tag is searched to obtain the target linear model. Then, the incidence prediction unit uses the target linear model to predict the incidence and susceptibility index of poplar bark rot disease for the current associated data to obtain the current predicted data.

[0210] The model selection tool screens regions in the correlation data to be predicted, and can screen based on site correlation factors and / or climate correlation factors.

[0211] The incidence prediction unit uses a model selection tool to quickly select the most matching linear model from the linear models of the above-mentioned skin rot incidence and susceptibility index, and then predicts the poplar skin rot incidence and / or susceptibility index, thereby improving the accuracy of the predicted data and ensuring the response speed of the prediction.

[0212] A comprehensive prediction system and method for poplar bark rot based on a prediction model, using statistical methods such as multivariate linear regression, through the analysis of a large amount of data, identified the key factors affecting the occurrence and development of poplar bark rot; determined the impact of the incidence and susceptibility index of poplar bark rot in the previous year on the incidence and susceptibility index of the current year; determined the factors affecting the incidence and susceptibility index of poplar bark rot in different climate zones; established a comprehensive prediction model for the incidence and susceptibility index of poplar bark rot applicable to different climate zones, and at the same time, the prediction model involves warm temperate semi-humid areas, plateau temperate semi-arid areas, and mid-temperate arid areas, covering the main climate areas where poplars are distributed in my country, effectively filling the gap in the comprehensive prediction of poplar bark rot in my country; the regional model prediction accuracy of the constructed comprehensive prediction model for poplar bark rot is above 95%, and the accuracy of the comprehensive prediction model is above 85%. The forestry and grassland departments can take timely prevention and control measures based on the prediction results of the model and in combination with climate change, effectively reducing the harm of poplar bark rot, significantly reducing the incidence and susceptibility index, and achieving remarkable results, greatly reducing the volume loss of poplars caused by bark rot. Predict high-risk areas in advance, implement precise prevention and control, reduce the occurrence of poplar bark rot, transform from the traditional "passive response" to a modern management model of "active prevention and scientific governance", and continue to provide reliable technical guarantees and support for maintaining the balance of the ecosystem and promoting sustainable development.

[0213] The above content is an explanation of the preferred embodiments of the present invention, which can help those skilled in the art to more fully understand the technical solution of the present invention. However, these embodiments are merely illustrative, and it cannot be determined that the specific implementation methods of the present invention are limited to the description of these embodiments. For ordinary technicians in the technical field to which the present invention belongs, without departing from the concept of the present invention, several simple deductions and transformations can be made, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A comprehensive prediction system for poplar bark rot based on a prediction model, characterized in that: It includes data collection unit, data analysis unit, disease prediction unit and data display unit. The data collection unit is used to collect the associated data of N standard plots in the target area; the data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot; the incidence prediction unit uses the linear model of the incidence rate and susceptibility index of poplar bark rot to predict the incidence rate and susceptibility index of poplar bark rot on the current associated data to obtain the current predicted data; the data display unit is used to display the associated data, the linear model, and the predicted data; The associated data include site conditions, climatic conditions, biological conditions and the occurrence of poplar bark rot. The site conditions include site-related factors, which include latitude and slope aspect; the climatic conditions include climate-related factors, which include temperature, temperature-humidity ratio, and rainfall; the biological conditions include biological-related factors, which include tree species and tree age.

2. A comprehensive prediction system for poplar bark rot based on a prediction model according to claim 1, characterized in that: The data collection unit collects the associated data of target plots in different climate zones respectively, and collects the site conditions and climate conditions of warm temperate semi-humid areas, plateau temperate semi-arid areas, and mid-temperate arid areas respectively.

3. The comprehensive prediction system for poplar bark rot based on the prediction model according to claim 1 is characterized in that: When investigating the occurrence of poplar bark rot in target plots in different climate zones, the following steps may be included: Step 1, the data collection unit randomly sets M survey plots in N standard plots in each different climate zone; Step 2, the data collection unit selects Z sampling points in each survey area; Step 3, the data collection unit selects W sample plants at each sampling point and marks and numbers the sample plants; Step 4, the information collection device of the data collection unit observes and records the conditions of the sample plants within a specified time to obtain observation data; specifically, from the beginning of the disease in mid-March to the end of October, a survey is conducted every 15 days according to the disease grading standard and the grading counting method, that is, each survey is called an investigation period.

4. The poplar bark rot comprehensive prediction system based on the prediction model according to claim 1 is characterized in that: The data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot, which may include the following steps: Step 1, calculate the incidence and susceptibility index of poplar bark rot in N standard plots in each different climate zone at different survey periods according to the observed data, and draw trend graphs of the incidence and susceptibility index; Step 2, according to the trend graph of the incidence rate and the disease susceptibility index, the correlation between the incidence rate and the disease susceptibility index of poplar bark rot in N standard plots in each different climate zone at different survey periods is analyzed to obtain first analysis data, wherein the first analysis data includes the correlation data between the incidence rate and the disease susceptibility index between different survey years; Step 3, according to the associated data, respectively calculate the standard values ​​of the associated data of N standard plots in each different climatic zone, and analyze the correlation between the incidence rate and the disease susceptibility index of poplar bark rot under different climatic conditions, different site conditions, and different biological conditions, to obtain second analysis data; the second analysis data includes the correlation between the incidence rate and the disease susceptibility index of poplar bark rot under different climatic conditions, different site conditions, and different biological conditions; Step 4, performing a multivariate linear regression analysis on the associated data based on the first analysis data and the second analysis data, respectively constructing a linear model of the incidence rate and susceptibility index of poplar bark rot in each climate zone based on climatic conditions, site conditions, and biological conditions, and respectively constructing an annual linear model of the incidence rate and susceptibility index in each climate zone based on climatic conditions, site conditions, biological conditions, and the previous year; Step 5, performing a multivariate linear regression analysis on the associated data to obtain the correlation data between the climatic conditions, site conditions, biological conditions and the incidence and susceptibility index of poplar bark rot in different climatic zones, and constructing a linear model of the incidence and susceptibility index of poplar bark rot in different climatic zones based on the climatic conditions, site conditions, and biological conditions, as well as constructing an annual linear model based on the climatic conditions, site conditions, biological conditions and the incidence and susceptibility index of the previous year.

5. A comprehensive prediction system for poplar bark rot based on a prediction model according to claim 4, characterized in that: The calculation methods of incidence rate and susceptibility index are as follows: Incidence rate (%) = number of diseased plants / number of investigated plants × 100%; Disease susceptibility index = Σ(number of diseased plants at each level × representative value at each level) / (total number of plants surveyed × highest representative value).

6. The poplar bark rot comprehensive prediction system based on the prediction model according to claim 4, characterized in that: Specifically, step 4 comprises the following steps: Step 41, constructing linear models of the incidence of poplar bark rot in each climate zone based on climate conditions, site conditions, and biological conditions; Step 42, constructing linear models of poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in each climatic zone; Step 43, constructing a linear model of the annual incidence of poplar bark rot in each climate zone based on climate conditions, site conditions, biological conditions and the incidence rate in the previous year; Step 44, constructing a linear model of the annual poplar bark rot disease susceptibility index in each climate zone based on climate conditions, site conditions, biological conditions and the disease susceptibility index of the previous year.

7. The poplar bark rot comprehensive prediction system based on the prediction model according to claim 4, characterized in that: Specifically, step 5 comprises the following steps: Step 51, constructing a linear model of the incidence of poplar bark rot based on climate conditions and site conditions in different climate zones; Step 52, constructing a linear model of poplar bark rot susceptibility index based on climatic conditions and site conditions in different climatic zones; Step 53, constructing a linear model of the annual incidence of poplar bark rot in different climate zones based on climate conditions, site conditions and the incidence rate in the previous year; Step 54, constructing a linear model of the annual poplar bark rot disease susceptibility index in different climate zones based on climate conditions, site conditions and the disease susceptibility index of the previous year.

8. A comprehensive prediction system for poplar bark rot based on a prediction model according to claim 7, characterized in that: The linear model of the incidence of poplar bark rot in different climate zones based on climatic conditions, site conditions, and biological conditions is: Z(3) =1.855-0.016*Longi+0.008*Age +0.01*Tl25- 0.012*Td5+ 0.056*Topo- 0.015*P25-0.089*Ta / Hr20, the importance of factors affecting the incidence of poplar bark rot is longitude> Tl25> Age> Topo= Ta / Hr20> P25> Td5; Among them, L Z(3) The linear model of the incidence of poplar bark rot in different climate zones based on climatic conditions, site conditions, and biological conditions; Z(3) is based on the comprehensive data of three places; Longi is longitude; Age is tree age; Tl is the average daily minimum temperature; Td is the average daily temperature difference; Topo is topography; P is average rainfall; Ta / Hr is temperature-humidity ratio; The linear model of poplar bark rot susceptibility index based on climatic conditions and site conditions in different climatic zones is: Z(3) =0.503- 0.005*Longi+ 0.005*Age+ 0.003*Tl 5+ 0.004* Aspect- 0.004*P25+ 0.017*Topo,R= 0.775,R 2 = 0.600, p<0.001, the importance of factors affecting the incidence of poplar bark rot is Longi>Age> Tl5> Slope aspect> Topo> P25; Among them, Z Z(3) is a linear model of the poplar bark rot susceptibility index based on climatic conditions, site conditions, and biological conditions in different climatic zones; Longi is longitude; Age is tree age; Tl is the average daily minimum temperature; Aspect is slope direction; P is average rainfall; and Topo is topography.

9. The comprehensive prediction system for poplar bark rot based on the prediction model according to claim 7, characterized in that: The linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in different climate zones in warm temperate semi-humid areas and plateau temperate semi-arid areas is: Z(2)Y =-5.504+0.013*Tm20-0.019*P30+1.005*Zc Z(2)Y-1 -0.0157*Ta / Hr20-0.019*Aspect+0.772*Zm Z(2)Y-1 +0.154*Lati, Among them, L Z(2)Y Linear models for annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions, and the incidence rate in the previous year for different climatic zones in warm temperate semi-humid areas and highland temperate semi-arid areas; Z(2)Y The data for the current year are for the warm temperate semi-humid region and the plateau temperate semi-arid region; Z(3)Y-1 is the data of the previous year for the warm temperate semi-humid region and the plateau temperate semi-arid region; Tm is the average daily maximum temperature; P is the average rainfall; Zc is the average rainfall; Z(2)Y-1 is the initial susceptibility index of the previous year in the warm temperate semi-humid area and the plateau temperate semi-arid area; Ta / Hr is the temperature-humidity ratio; Aspect is the slope direction; Zm Z(2)Y-1 is the disease susceptibility index at the end of the previous year's survey in warm temperate semi-humid areas and plateau temperate semi-arid areas; Lati is latitude; The linear model of annual poplar bark rot incidence in different climate zones in the mid-temperate arid region based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year is: K =-48.292-0.011*Hr20-0.097*Td30-1.116*Lati+1.187*Lati+0.001*Longi+0.008*P5, Among them, L K is the linear model of annual poplar bark rot incidence based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year in different climatic zones in the mid-temperate arid region; Hr is relative humidity; Td is the average daily temperature difference; Lati is latitude; Longi is longitude; P is the average rainfall; The linear model of annual poplar bark rot incidence in different climate zones based on climate conditions, site conditions, biological conditions and the incidence rate in the previous year is: Z(3)Y = 1.425 - 0.013*Longi + 0.007 * Tm25 + 1.286*Zg Z(3)Y-1 -0.018*Aspect- 0.013*P30- 0.008*Td5, Among them, L Z(3)Y is the linear model of annual poplar bark rot incidence in different climate zones based on climatic conditions, site conditions, biological conditions and the incidence rate in the previous year; Longi is longitude; Tm is the average daily maximum temperature; Zg Z(3)Y-1 is the disease susceptibility index of the peak period of the previous year in warm temperate semi-humid areas, plateau temperate semi-arid areas and mid-temperate arid areas; Aspect is the slope direction; P is the average rainfall; Td is the average daily temperature difference; The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones in warm temperate semi-humid areas and plateau temperate semi-arid areas is: Z(2)Y =-2.012+0.004*Tm20+0.477*Zm Z(2)Y-1 -0.041*Ta / Hr20-0.005*P30+0.002*Age+0.056*Lati, Among them, Z Z(2)Y is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and susceptibility index of the previous year in different climatic zones of warm temperate semi-humid area and plateau temperate semi-arid area; Tm is the average daily maximum temperature; Zm Z(2)Y-1 is the disease susceptibility index at the end of the previous year's survey in warm temperate semi-humid areas and plateau temperate semi-arid areas; Ta / Hr is the temperature-humidity ratio; P is the average rainfall; Age is the tree age; Lati is the latitude; The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones in the mid-temperate arid region is: K = -19.09- 0.001*Hr 30-0.385*Lati +0.435*Longi -0.003*age, Among them, Z K is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climate zones of the mid-temperate arid region; Hr is relative humidity; Lati is latitude; Longi is longitude; Age is tree age; The linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climatic zones is: Z(3)Y =0.414 -0.004*Longi + 0.005*Age +0.002*Tm25 + 0.229*Zg Z(3)Y-1 + 0.001*P30, Among them, Z Z(3)Y is the linear model of annual poplar bark rot susceptibility index based on climatic conditions, site conditions, biological conditions and the susceptibility index of the previous year in different climatic zones; Longi is longitude; Age is tree age; Tm is the average daily maximum temperature; Zg Z(3)Y-1 It is the disease susceptibility index in the warm temperate semi-humid area, plateau temperate semi-arid area and mid-temperate arid area during the peak period of disease in the previous year; P is the average rainfall.

10. A comprehensive prediction method for poplar bark rot based on a prediction model, applied to the prediction system according to any one of claims 1 to 9, characterized in that: The following steps are included: Step I: the data collection unit collects relevant data of the target area; Step II, the data analysis unit performs a multivariate linear regression analysis on the associated data to obtain a linear model of the incidence rate and susceptibility index of poplar bark rot; Step III, the incidence prediction unit uses the linear model of the incidence and susceptibility index of poplar bark rot disease to predict the incidence and susceptibility index of poplar bark rot disease for the current associated data to obtain the current prediction data; Step IV: the data display unit is used to display the initial data, process data and result data such as the associated data, the linear model and the predicted data.

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