CN113689034A - Method for comprehensively predicting growth suitability of spodoptera frugiperda - Google Patents

Method for comprehensively predicting growth suitability of spodoptera frugiperda Download PDF

Info

Publication number
CN113689034A
CN113689034A CN202110953120.3A CN202110953120A CN113689034A CN 113689034 A CN113689034 A CN 113689034A CN 202110953120 A CN202110953120 A CN 202110953120A CN 113689034 A CN113689034 A CN 113689034A
Authority
CN
China
Prior art keywords
spodoptera frugiperda
temperature
days
larvae
suitability
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202110953120.3A
Other languages
Chinese (zh)
Inventor
鲁韦坤
胡雪琼
李亚红
陈瑶
徐梦莹
陈小华
李湘
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yunnan Climate Center (Yunnan eco meteorological and Satellite Remote Sensing Center)
Yunnan University YNU
Original Assignee
Yunnan Climate Center Yunnan Eco Meteorological And Satellite Remote Sensing Center
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Yunnan Climate Center Yunnan Eco Meteorological And Satellite Remote Sensing Center filed Critical Yunnan Climate Center Yunnan Eco Meteorological And Satellite Remote Sensing Center
Priority to CN202110953120.3A priority Critical patent/CN113689034A/en
Publication of CN113689034A publication Critical patent/CN113689034A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/02—Agriculture; Fishing; Forestry; Mining

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • Tourism & Hospitality (AREA)
  • Theoretical Computer Science (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Animal Husbandry (AREA)
  • Quality & Reliability (AREA)
  • Operations Research (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Game Theory and Decision Science (AREA)
  • Agronomy & Crop Science (AREA)
  • Development Economics (AREA)
  • Marine Sciences & Fisheries (AREA)
  • Mining & Mineral Resources (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Peptides Or Proteins (AREA)

Abstract

The invention relates to a crop pest and disease damage disaster prevention and reduction technology, and particularly discloses a method for comprehensively predicting the growth suitability of spodoptera frugiperda, which comprises the following steps: predicting the first stage of the spodoptera exigua larvae, and binarizing the grid calculation result by taking 305 ℃ as a division point through spatial interpolation and grid calculation; after the first-time period is obtained, the reporting date of the first-time period is calculated as a node, average air temperature data observed by a meteorological station 30 days before the reporting date is utilized, lattice localization is carried out through spatial interpolation, then the growth suitability grade of spodoptera frugiperda is divided according to the previous 30 balance average air temperatures, the method has important guiding significance for the control of spodoptera frugiperda larvae, all weather and agricultural departments can adopt the daily average air temperature data observed by the meteorological station, the first-time date and the growth condition of the spodoptera frugiperda larvae are predicted by applying the method, and the control material preparation and control work of the spodoptera frugiperda larvae is carried out timely according to the prediction result.

Description

Method for comprehensively predicting growth suitability of spodoptera frugiperda
Technical Field
The invention relates to a crop pest and disease damage prevention and disaster reduction technology, in particular to a method for comprehensively predicting the growth suitability of spodoptera frugiperda.
Background
Spodoptera frugiperda is an international major migratory pest. The species is a tropical region of America, has strong migration capability, can not live through the winter in an environment below zero, but can still migrate to the eastern United states and the southern Canada when the temperature is warmed every year, and the United states has a history of several insect disasters of Spodoptera frugiperda. In 2016, Spodoptera frugiperda disseminated to Africa and Asian countries. Spodoptera frugiperda appears in 2019 in 18 provinces of mainland China and Taiwan island China, and has caused great agricultural loss to China. The Spodoptera frugiperda larvae can gnaw various crops of Gramineae such as grain rice, sugarcane and corn, Compositae, Brassicaceae and the like in a large amount, so that serious economic loss is caused, the development speed of the Spodoptera frugiperda larvae becomes fast along with the rise of air temperature, the Spodoptera frugiperda larvae can be bred for several generations in one year, and more than 1000 eggs can be produced by one female moth. Practice in recent two years proves that the early prevention and control of spodoptera frugiperda larvae can effectively control the major outbreak of spodoptera frugiperda, so that a comprehensive growth suitability prediction method is established, unnecessary labor and material resource prevention and control cost is reduced, and a scientific basis can be provided for the early prevention and control of spodoptera frugiperda.
Disclosure of Invention
The invention mainly aims to provide a method for comprehensively predicting the growth suitability of spodoptera frugiperda, so as to provide a scientific basis for the control of spodoptera frugiperda.
In order to achieve the above purpose, the invention provides the following technical scheme:
a method for comprehensively predicting the growth suitability of Spodoptera frugiperda comprises the following specific steps:
(1) establishing a spodoptera littoralis larva first-stage prediction model, calculating the activity accumulated temperature of more than 10 ℃ 30 days before the date of reporting of each station by using meteorological station observation data, and judging the spodoptera littoralis larva first-stage according to the activity accumulated temperature or occurrence probability;
(2) through spatial interpolation and grid calculation, taking the active accumulated temperature of 305 ℃ as a division point, binarizing a grid calculation result, namely, setting 0 as an area smaller than 305 ℃ to indicate that the accumulated temperature does not reach the condition of first appearance of Spodoptera frugiperda larvae, and setting 1 as an area larger than 305 ℃ to indicate that the accumulated temperature reaches the condition of first appearance of Spodoptera frugiperda larvae;
(3) after the first-time period is obtained, taking the reporting date of calculating the first-time period as a node, utilizing a calculation formula of the relation between the newly added area and the average temperature of the previous 30 balances and a calculation formula of the time required by different temperature generations of spodoptera frugiperda, utilizing average temperature data observed by a weather station 30 days before the reporting date, carrying out lattice localization through spatial interpolation, and then dividing the growth suitability grade of the spodoptera frugiperda according to the average temperature of the previous 30 balances:
the average temperature of the current 30 balances is less than 15 ℃, the temperature is lower than the critical temperature at the moment, the Spodoptera frugiperda grows and stops, no control measures are needed, the value is assigned to 1, and the suitability level is judged to be unsuitable;
when the average temperature of the current 30 balances is between 15 and 20 ℃, the temperature just exceeds the critical temperature, the Spodoptera frugiperda starts to grow slowly at the moment, the generation time is 158-108 days, the newly increased area in one week is within 1000 mu, the value is assigned to 2, and the suitability degree grade is judged to be suboptimal;
the temperature is favorable for growth and propagation of the grassland greedy nights, the generation time is 158-66 days, the newly increased area of 1000-2500 mu in a week is assigned as 3, and the suitability level is judged to be suitable;
the average temperature of the current 30 balances is more than 20 ℃, the temperature is very favorable for growth and reproduction of grassland greedy nights, the generation time is less than 66 days, the newly-increased area of one week is more than 2500 mu, the value is assigned to 4, and the suitability level is judged to be optimum;
(4) and (3) multiplying the result by the result obtained in the step (2) after the work is finished to obtain a comprehensive prediction result of the growth suitability of spodoptera frugiperda, wherein the value range of the result is 0-4, and 0 represents an area in which spodoptera frugiperda can not occur in the prediction of spodoptera frugiperda on the first day.
The method has important guiding significance for preventing and controlling Spodoptera frugiperda larvae. In spring, the time for the activity accumulated temperature of more than 10 ℃ in each region of Yunnan to reach 305 ℃ is different, the weather and agricultural departments in each region can adopt daily average air temperature data observed by a weather station, the method is applied to predict the first date and the growth condition of the spodoptera frugiperda larvae, the preparation and prevention and control work of the spodoptera frugiperda larvae on prevention and control materials is carried out in due time according to the prediction result, and unnecessary field observation and pesticide spraying can be reduced. The method is simple and easy to use, has clear physiological significance and high accuracy of the prediction result, and the prediction evaluation conclusion can provide a basis for the pest control decision of the agricultural department.
Drawings
FIG. 1 is a graph showing a comparison of Spodoptera frugiperda prediction accuracy in each ten days;
FIG. 2 is a graph showing the activity accumulated temperature and the occurrence probability of the activity at a temperature of more than 10 ℃ in 3 consecutive days before the start of the reporting;
FIG. 3 is a graph of a regression model of temperature versus generation days;
FIG. 4 is a graph showing the comparison between the population density of Spodoptera frugiperda larvae in Mangifera indica and the average temperature in the day and the average temperature of the previous 30 balances;
FIG. 5 is a graph showing the newly added nightly grassland area in Miscanthus sinensis, compared with the average temperature of the current day and the average temperature of the previous 30 balances;
FIG. 6 is a graph showing the correlation between the population density of Spodoptera frugiperda larvae in Mangifera indica and the average temperature of the first 30 scales;
FIG. 7 is a graph showing the correlation between the newly added area of Spodoptera frugiperda larvae in Mangifera indica stations and the average temperature of the first 30 balances;
FIG. 8 is a graph showing the correlation between population density of Spodoptera frugiperda larvae in Advance station and average temperature of first 30 balances;
FIG. 9 is a graph showing the correlation between the newly increased area of Spodoptera frugiperda in Tengchong station and the average air temperature of the first 30 balances;
FIG. 10 is a graph showing the correlation between population density of Spodoptera frugiperda larvae in the Menglan station and the mean temperature of the first 30 scales;
FIG. 11 is a graph showing the correlation between the newly increased area of the Spodoptera frugiperda larvae in the Menglan station and the average temperature of the first 30 scales;
FIG. 12 is a graph showing the correlation between the population density of larvae of Spodoptera frugiperda and the average temperature of the first 30 scales at three stations;
FIG. 13 is a graph showing the correlation between the newly added Spodoptera frugiperda larva area and the average temperature of the first 30 scales.
Detailed Description
The present invention will be further explained with reference to the drawings and the embodiments, wherein 10 days are used for the first ten days and 30 days are used for the third ten days.
Example 1
Establishment of first-day prediction model of spodoptera frugiperda larvae
And obtaining the first-day prediction index of the spodoptera frugiperda larvae in Yunnan in the future in one ten days by using daily average air temperature data of a meteorological station in Yunnan province from 12 months to 2020 in 2019 and day-by-day observed spodoptera frugiperda finding date data of each county (city and district) and a binary logistic model.
During calculation, for convenience of statistical analysis, classifying the larvae according to the ten-day scale according to the first-seen date of the larvae in 1-5 months in 2020, wherein the classification method is that the value before the ten-day of the undetected larvae is assigned to be 0, the value after the ten-day and the last-day of the detected larvae is assigned to be 1, meanwhile, the activity accumulated temperature of more than 10 ℃ in 3 consecutive days of 12-2020 and 5-months in 2019 of each station is calculated, and is correlated with the first-seen date of the larvae, and a ten-day Spodoptera frugiperda larva monitoring data set is established. The calculation formula of the active accumulated temperature which is higher than a certain critical temperature is as follows:
Figure BDA0003219284830000031
Ti>b is when T isiWhen B is less than or equal to B, Ti＝0
Wherein n is the days of the period; t isiThe average temperature on the ith day; b is critical boundary temperature.
And (3) performing binary logistic regression analysis by using the data, wherein the dependent variable is set as whether the larva is found, and the independent variable is the rolling accumulated temperature of more than 10 ℃ in 3 consecutive days. Taking prediction in 1-month-last ten days of 2020 as an example, setting a dependent variable as whether a Spodoptera frugiperda appears in a data column in 1-month-last ten days, setting an independent variable as an activity accumulated temperature of more than 10 ℃ in 12 months (12-month-last, middle and last three days), applying an entry method, calculating model prediction precision and related parameters, and performing correlation calculationCalculation parameters are shown in tables 1-3, and Hosmer and Lemeshow test χ2<15.507 (chi-square cut-off at df of 8, df equal 8 in the following analysis, not described in detail), P>0.05, the model fitting is better, and the prediction precision of the model is 86.2%.
TABLE 1Hosmer and Lemeshow tests
Step (ii) of Square card df Sig.
1 6.036 8 .643
TABLE 2 Classification of tablesa
Figure BDA0003219284830000041
a. Cutting value of.500
Variables in the equations of Table 3
Figure BDA0003219284830000042
a. The variable input in the step 1 is that the activity accumulated temperature is more than 10 ℃ in 12 months.
The probability prediction model based on the first emergence of spodoptera frugiperda larvae with activity accumulated temperature of more than 10 ℃ for 12 months is as follows:
Figure BDA0003219284830000051
wherein P is the probability of first appearing Spodoptera frugiperda larvae, T is the activity accumulated temperature of more than 10 ℃ in 12 months (continuous 30 days), and when P is more than or equal to 0.5, Spodoptera frugiperda larvae are found.
According to the method, the situation of the monitoring data every ten days is verified in sequence, the prediction precision of each ten day is shown in fig. 1, and as can be seen from the graph, the prediction precision of the middle and the last 3 months is lower than 80%, and the prediction precision of the data is higher than 80% in other periods, wherein the highest is the middle and the last 5 months, and the prediction precision reaches 94.5%. The main reason for the low prediction accuracy in the middle and late 3 months is that the larvae have insufficient food sources, so that no larvae are observed.
Removing data in the middle and last 3 months, merging data from the first ten days of month to the middle 5 days of month to obtain 1416 pieces of data, selecting 10% of samples (140 samples) as model test samples by using a random function, and reconstructing a unified prediction model by using the remaining 1276 samples. The model-related parameters are shown in tables 4-6 below, with a model prediction accuracy of 84.1%. When P is more than 0.5, the corresponding activity accumulated temperature is 305 ℃, as shown in figure 2, the model shows that Spodoptera frugiperda larvae are detected when the activity accumulated temperature is more than 10 ℃ and reaches 305 ℃ in the first 3 th day of the forecast.
TABLE 4Hosmer and Lemeshow tests
Step (ii) of Square card df Sig.
1 13.025 8 .111
TABLE 5 Classification of tablesa
Figure BDA0003219284830000052
Variables in the equations of Table 6
Figure BDA0003219284830000053
Figure BDA0003219284830000061
The probability prediction model based on the first emergence of Spodoptera frugiperda larvae with activity temperature accumulation higher than 10 ℃ is as follows:
Figure BDA0003219284830000062
and (3) verifying the model by using 140 randomly reserved samples, wherein the verification result shows that the accuracy of the model is 83.6%.
TABLE 7 model accuracy verification test
Figure BDA0003219284830000063
TABLE 8 comparison table of model accuracy under different temperature indexes
Figure BDA0003219284830000064
Figure BDA0003219284830000071
Second, Spodoptera frugiperda growth suitability prediction model
2.1 time required for different temperature generations of Spodoptera frugiperda
Establishing a regression model of temperature and generation days by using generation days at different temperatures, wherein according to the model, along with the rise of the temperature, the reduction trend of the days required by the spodoptera frugiperda generation is fast before slow, the reduction trend of 20 ℃ is reduced by 39.0 days compared with 15 ℃ generation days, and the reduction trend of 30 ℃ is reduced by only 3.2 days compared with 35 ℃ generation days; relevant research shows that the accumulated temperature of the effective activity of the Spodox night is about 11 ℃, the generation days required by 11 ℃ are 184.1 days according to the regression model, and at most two generations exist in one year; the number of generation days required at 35 ℃ is only 25.2 days, up to 14.5 generations a year. The more generations, the more favorable the growth and reproduction of spodoptera frugiperda, the more specific generation days regression model at different temperatures is shown in fig. 3, and the specific values of generation days corresponding to different temperatures are shown in table 9.
TABLE 9 days of Generation corresponding to different temperatures
Figure BDA0003219284830000081
2.2 relationship between average temperature of front 30 scales and occurrence degree of Spodoptera frugiperda
In order to verify the relationship between the spodoptera frugiperda emergence degree and the air temperature under natural conditions, data such as observation date, density of population of the spodoptera frugiperda, accumulated emergence area and the like in a weekly report provided by a plant protection station, and daily meteorological observation and 30-balance average air temperature data before observation of a corresponding station are selected, and the correlation between the density of the population of the spodoptera frugiperda and the emergence area is analyzed.
As shown in fig. 4 and fig. 5, taking the city of awn as an example, before 6 months, the population density and the newly added area of the spodoptera frugiperda larvae have a good positive correlation with the average air temperature of the day and the average air temperature of the balance of 30 days, and after 7 months, the temperature is basically kept flat compared with 6 months, but the population density and the newly added area of the larvae are in a descending trend, which is inconsistent with the research result of a laboratory, and the reason is probably related to that the spodoptera frugiperda likes to damage the small-large flare time period (food source) of corn.
As shown in figures 6 and 7, the data such as the observation date, the population density and the accumulated occurrence area of the hundreds of insects in the weekly report table provided by the plant protection station from 1 month to 6 months and 10 days in 2020 in Mangifera, and the data of the daily meteorological observation and the 30-day balance average temperature before observation of the corresponding station are adopted to analyze the correlation between the population density and the occurrence area of the hundreds of insects of Spodoptera frugiperda in Mangifera, and the result shows that the population density and the occurrence area of the hundreds of insects of Spodoptera frugiperda are increased along with the increase of the average temperature of the 30-day balance, wherein the average temperature of the 30-day balance and the R of the head amount of the hundreds of insects are increased20.67, R of the newly added area2Is 0.45, which is substantially consistent with the laboratory incubator test results. The method shows that the density and the occurrence area of the hundreds of insects of Spodoptera frugiperda can be predicted by adopting the average air temperature of the first 30 balances.
As shown in FIGS. 8-11, the same analysis of Tengchong and Mengla by the same method shows that the density and the area of occurrence of the population of the hundred Spodoptera frugiperda are in a positive correlation with the average temperature of the hundred Spodoptera frugiperda in the first 30 days in 2020, 1 month, 1 day to 6 months and 10 days in the second county, wherein the average temperature of the balance 30 before Tengchong and the average temperature of the hundred Spodoptera frugiperda are in a positive correlation20.74, R of the newly added area2R of 0.46, Menglan 30 balance average temperature and head number of hundred plants20.92, R of the newly added area2Is 0.38.
As shown in fig. 12 and 13 and table 10, three stations were combined, and an attempt was made to establish a spodoptera frugiperda growth suitability prediction model based on the population density, newly increased area and first 30 balance mean temperatures, and a regression model showed that although these three stations alone had good correlation coefficients, the population density correlation of the combined larvae decreased significantly, while the correlation coefficient of the newly increased area and the mean temperature of the previous 30 days increased. The reliability of predicting the newly increased area by adopting the average air temperature of the previous 30 days is higher.
TABLE 10 Spodoptera frugiperda newly-increased areas at different temperatures calculated according to regression model
Figure BDA0003219284830000091
2. Method for comprehensively predicting growth suitability of spodoptera frugiperda
The method comprises the following steps of establishing a Spodoptera frugiperda growth suitability comprehensive prediction model by utilizing a Spodoptera frugiperda larva first-present date prediction model and a growth suitability prediction model, and specifically comprises the following steps: (1) firstly, according to a prediction model of the first-appearing date of spodoptera frugiperda larvae, calculating the activity accumulated temperature of more than 10 ℃ 30 days before each station by using meteorological station observation data. (2) calculating by spatial interpolation and grids, taking 305 ℃ as a division point, binarizing a grid calculation result, namely, 0 is an area smaller than 305 ℃, representing that accumulated temperature does not reach the first-appearing condition of Spodoptera frugiperda larvae, and 1 is an area larger than 305 ℃, representing that accumulated temperature reaches the first-appearing condition of the Spodoptera frugiperda larvae. (3) According to the Spodoptera frugiperda growth suitability prediction model, a newly-added area and previous 30-day balance average temperature relation model and the time results required by different temperature generations of Spodoptera frugiperda are utilized, the average temperature data observed in a meteorological station 30 days before the day is reported are utilized, lattice localization is carried out through spatial interpolation, then the Spodoptera frugiperda growth suitability grade is divided according to the following table 11, and 4 grades are reassigned to be 1, 2, 3 and 4. And multiplying the result by the result obtained in the step 2 after the work is finished to obtain a comprehensive prediction result of the growth suitability of the spodoptera frugiperda, wherein the value range of the result is 0-4,0 represents an area where the spodoptera frugiperda cannot be predicted to occur in the first day, 1 represents an area where the growth suitability is inappropriate, and the rest is done in turn.
Table 11 front 30 balance average temperature Spodoptera frugiperda suitability rating Standard
Figure BDA0003219284830000101

Claims (2)

1. A method for comprehensively predicting the growth suitability of Spodoptera frugiperda is characterized by comprising the following specific steps:
(1) establishing a spodoptera littoralis larva first-stage prediction model, calculating the activity accumulated temperature of more than 10 ℃ 30 days before the date of reporting of each station by using meteorological station observation data, and judging the spodoptera littoralis larva first-stage according to the activity accumulated temperature or occurrence probability;
(2) through spatial interpolation and grid calculation, taking the active accumulated temperature of 305 ℃ as a division point, binarizing a grid calculation result, namely, setting 0 as an area smaller than 305 ℃ to indicate that the accumulated temperature does not reach the condition of first appearance of Spodoptera frugiperda larvae, and setting 1 as an area larger than 305 ℃ to indicate that the accumulated temperature reaches the condition of first appearance of Spodoptera frugiperda larvae;
(3) after the first-time period is obtained, taking the reporting date of calculating the first-time period as a node, utilizing a calculation formula of the relation between the newly added area and the average temperature of the previous 30 balances and a calculation formula of the time required by different temperature generations of spodoptera frugiperda, utilizing average temperature data observed by a weather station 30 days before the reporting date, carrying out lattice localization through spatial interpolation, and then dividing the growth suitability grade of the spodoptera frugiperda according to the average temperature of the previous 30 balances:
the average temperature of the current 30 balances is less than 15 ℃, the temperature is lower than the critical temperature at the moment, the Spodoptera frugiperda grows and stops, no control measures are needed, the value is assigned to 1, and the suitability level is judged to be unsuitable;
when the average temperature of the current 30 balances is between 15 and 20 ℃, the temperature just exceeds the critical temperature, the Spodoptera frugiperda starts to grow slowly at the moment, the generation time is 158-108 days, the newly increased area in one week is within 1000 mu, the value is assigned to 2, and the suitability degree grade is judged to be suboptimal;
the temperature is favorable for growth and propagation of the grassland greedy nights, the generation time is 158-66 days, the newly increased area of 1000-2500 mu in a week is assigned as 3, and the suitability level is judged to be suitable;
the average temperature of the current 30 balances is more than 20 ℃, the temperature is very favorable for growth and reproduction of grassland greedy nights, the generation time is less than 66 days, the newly-increased area of one week is more than 2500 mu, the value is assigned to 4, and the suitability level is judged to be optimum;
(4) and (3) multiplying the result by the result obtained in the step (2) after the work is finished to obtain a comprehensive prediction result of the growth suitability of spodoptera frugiperda, wherein the value range of the result is 0-4, and 0 represents an area in which spodoptera frugiperda can not occur in the prediction of spodoptera frugiperda on the first day.
2. The method for comprehensively predicting the growth suitability of spodoptera frugiperda according to claim 1, wherein in the step (1), the spodoptera frugiperda larva first-stage prediction model is as follows: the method comprises the following steps of establishing a binary logistic regression model by using the activity accumulated temperature of more than 10 ℃ 30 days before the start of reporting and the first-appearing date of spodoptera frugiperda larvae, and specifically comprises the following steps:
Figure FDA0003219284820000011
wherein P is the probability of the spodoptera frugiperda field inspection, T is the activity accumulated temperature 30-10 ℃ before the reporting date, and when P is more than or equal to 0.5 or T is more than or equal to 305 ℃, the spodoptera frugiperda larvae are inspected within 10 days after the reporting date.
CN202110953120.3A 2021-08-19 2021-08-19 Method for comprehensively predicting growth suitability of spodoptera frugiperda Pending CN113689034A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110953120.3A CN113689034A (en) 2021-08-19 2021-08-19 Method for comprehensively predicting growth suitability of spodoptera frugiperda

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110953120.3A CN113689034A (en) 2021-08-19 2021-08-19 Method for comprehensively predicting growth suitability of spodoptera frugiperda

Publications (1)

Publication Number Publication Date
CN113689034A true CN113689034A (en) 2021-11-23

Family

ID=78580647

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110953120.3A Pending CN113689034A (en) 2021-08-19 2021-08-19 Method for comprehensively predicting growth suitability of spodoptera frugiperda

Country Status (1)

Country Link
CN (1) CN113689034A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116402177A (en) * 2022-11-28 2023-07-07 中化现代农业有限公司 Method and system for predicting occurrence degree of athetis lepigone

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105894131A (en) * 2016-04-28 2016-08-24 广东石油化工学院 Fruit-piercing moth rapid early-warning method
CN108764542A (en) * 2018-05-16 2018-11-06 贾翔 The remote sensing prediction method of phase and occurrence quantity occur for diversiform-leaved poplar spring looper
JP2019054771A (en) * 2017-09-22 2019-04-11 株式会社富士通エフサス Prediction apparatus, prediction method, and prediction program
CN111832441A (en) * 2020-06-28 2020-10-27 广东省农业科学院植物保护研究所 Spodoptera frugiperda prevention and control system and method

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN105894131A (en) * 2016-04-28 2016-08-24 广东石油化工学院 Fruit-piercing moth rapid early-warning method
JP2019054771A (en) * 2017-09-22 2019-04-11 株式会社富士通エフサス Prediction apparatus, prediction method, and prediction program
CN108764542A (en) * 2018-05-16 2018-11-06 贾翔 The remote sensing prediction method of phase and occurrence quantity occur for diversiform-leaved poplar spring looper
CN111832441A (en) * 2020-06-28 2020-10-27 广东省农业科学院植物保护研究所 Spodoptera frugiperda prevention and control system and method

Non-Patent Citations (9)

* Cited by examiner, † Cited by third party
Title
叶延琼;李韵;章家恩;秦钟;李逸勉;: "GIS支持下的广东省水稻种植生态适宜性评价", 湖南农业大学学报(自然科学版), no. 02 *
唐红艳;王惠贞;: "草地螟2代幼虫发生的气象条件等级预报方法――以兴安盟为例", 安徽农业科学, no. 07 *
张红梅;尹艳琼;赵雪晴;李向永;王燕;刘莹;陈福寿;谌爱东;: "草地贪夜蛾在不同温度条件下的生长发育特性", 环境昆虫学报, no. 01 *
杨梅;樊晓梅;邓邦智;马英;张光署;: "凉山州美姑县玉米粘虫发生与气候条件的关系浅析", 福建农业, no. 10 *
赵飞;李捷;贺润平;孔维娜;邢鲲;高建武;: "关键环境因子对桃小食心虫出土羽化动态的影响", 植物保护学报, no. 03 *
路正文;王德锦;王建花;: "临清棉铃虫气象预测模型", 现代农业科技, no. 12 *
雷军成;徐海根;: "外来入侵植物假高粱在我国的潜在分布区分析", 植物保护, no. 03 *
高迎娟;齐金;徐丽萍;: "吉林通化地区玉米螟发生程度气象等级预测预报模型的建立", 中国农学通报, no. 21 *
黄芊;凌炎;蒋婷;符诚强;钟勇;吴碧球;黄所生;李成;黄凤宽;龙丽萍;: "运用有效积温法则预测草地贪夜蛾在广西的发生世代数", 南方农业学报, no. 06 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116402177A (en) * 2022-11-28 2023-07-07 中化现代农业有限公司 Method and system for predicting occurrence degree of athetis lepigone
CN116402177B (en) * 2022-11-28 2024-01-26 中化现代农业有限公司 Method and system for predicting occurrence degree of athetis lepigone

Similar Documents

Publication Publication Date Title
Law et al. Flowering phenology of myrtaceous trees and their relation to climatic, environmental and disturbance variables in northern New South Wales
Mac Nally et al. Collapse of an avifauna: climate change appears to exacerbate habitat loss and degradation
Rao et al. Climate variability and change: Farmer perceptions and understanding of intra-seasonal variability in rainfall and associated risk in semi-arid Kenya
Bennett et al. Tree hollows as a resource for wildlife in remnant woodlands: spatial and temporal patterns across the northern plains of Victoria, Australia
Coates et al. Effects of disturbance on population dynamics of the threatened orchid Prasophyllum correctum DL Jones and implications for grassland management in south-eastern Australia
Mupangwa et al. Start, end and dry spells of the growing season in semi-arid southern Zimbabwe
Alvarenga et al. Patch size and isolation effects on epiphytic and epiphyllous bryophytes in the fragmented Brazilian Atlantic forest
Fan et al. Habitat and food choice of the critically endangered cao vit gibbon (Nomascus nasutus) in China: implications for conservation
Tsuji et al. Spatial patterns in the diet of the J apanese macaque M acaca fuscata and their environmental determinants
Chung et al. Temporal aspects of the fine-scale genetic structure in a population of Cinnamomum insularimontanum (Lauraceae)
CN112215716A (en) Crop growth intervention method, device, equipment and storage medium
Geib et al. Tracing impacts of partner abundance in facultative pollination mutualisms: from individuals to populations
Ménard et al. Impact of human pressure and forest fragmentation on the endangered Barbary macaque Macaca sylvanus in the Middle Atlas of Morocco
CN107067683A (en) A kind of transmission line forest fire clusters quantitative forecast method and system
Marjuki et al. Observed trends and variability in climate indices relevant for crop yields in Southeast Asia
Makinde et al. Multivariate analysis of genetic divergence in twenty two genotypes of groundnut (Arachis hypogaea L.)
Ferretti et al. Joint effects of weather and interspecific competition on foraging behavior and survival of a mountain herbivore
CN113689034A (en) Method for comprehensively predicting growth suitability of spodoptera frugiperda
Picó et al. The flowering pattern of the perennial herb Lobularia maritima: an unusual case in the Mediterranean basin
Debski et al. Ecological processes maintaining differential tree species distributions in an Australian subtropical rain forest: implications for models of species coexistence
Barrows Habitat relationships of the Coachella Valley fringe-toed lizard (Uma inornata)
Nikolov et al. Habitat requirements of Boreal Owl (Aegolius funereus) and Pygmy Owl (Glaucidium passerinum) in rear edge montane populations on the Balkan Peninsula
Mutze et al. Mouse plagues in South Australian cereal-growing areas. II. An empirical model for prediction of plagues.
Hemrová et al. The effects of large herbivores on the landscape dynamics of a perennial herb
Saatkamp et al. Plant traits and population characteristics predict extinctions in a long-term survey of Mediterranean annual plants

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20211209

Address after: No.77 Xichang Road, Xishan District, Kunming, Yunnan 650000 (Yunnan Meteorological Bureau)

Applicant after: Yunnan Climate Center (Yunnan eco meteorological and Satellite Remote Sensing Center)

Applicant after: Yunnan University

Address before: No.77 Xichang Road, Xishan District, Kunming, Yunnan 650000 (Yunnan Meteorological Bureau)

Applicant before: Yunnan Climate Center (Yunnan eco meteorological and Satellite Remote Sensing Center)

TA01 Transfer of patent application right