Method for predicting probability or degree of occurrence of plant diseases and insect pests of single forest tree

Abstract

The invention discloses a method for predicting the probability or degree of occurrence of plant diseases and insect pests of a single forest tree. The method comprises the following steps of: predicting the probability or degree of occurrence of the plant diseases and insect pests of the single forest tree by using a cluster index vi of SADIE (Spatial Analysis Distance Indices) as an explanatory variable. The prediction can be carried out on a global scale (the whole investigation area), and can also be carried out on a local scale (a circle taking a single forest tree as a circle center). When local scale prediction is carried out, investigation is carried out on a series of circles with different radiuses, models on different radius scales are established, and then a summary effect of coefficients is obtained by using a Meta analysis method for prediction. The method can provide a suitable prediction method for forest pest control work.

Description

Technical Field

[0001] The invention belongs to the technical field of forest protection, and in particular relates to a method for predicting the probability or degree of occurrence of pests and diseases on individual trees. Background Art

[0002] Prediction and prediction is one of the important contents of integrated pest management. The prevention and control of forest pests and diseases has entered the era of precision management, but there is currently a lack of prediction methods that are compatible with it. The spatial pattern of host trees and their attributes such as size affect the occurrence of forest pests. Therefore, the present invention predicts the probability or degree of pests and diseases in individual trees based on the spatial pattern of the location of the trees (patches, gaps, and gaps between patches). For some pests and diseases that are significantly affected by attributes such as tree size, the present invention uses the spatial pattern of tree breast diameter, tree height and other attributes to predict the probability or degree of pests and diseases in individual trees.

[0003] There are many indicators for measuring spatial patterns, such as the semidifference function in geostatistics, Moran's I in spatial statistics, etc. The present invention uses the clustering index (clustering index) vi of SADIE (Spatial Analysis by Distance Indices) to measure the spatial pattern of individual trees. Continuous variables such as diameter at breast height and tree height can also be rounded for SADIE analysis. The clustering index vi can be used as an explanatory variable with its original value, or it can be converted into a categorical variable (gaps (vi<-1.5), patches (-1.5≤vi≤1.5) and patches (vi>1.5)) and then used as an explanatory variable.

[0004] The model can be divided into a global model and a local model. The global model uses the spatial pattern of a single tree or its attributes such as breast diameter and tree height in the entire survey area. When the local model is established, the spatial pattern is calculated on a circle of a certain radius with a single tree as the center. Since the size of the circle affects the coefficient of the prediction model, the present invention uses multiple radius circles to establish the model, and uses Meta-analysis to deal with the differences in different radius scales. Summary of the invention

[0005] The purpose of the present invention is to provide a method for predicting the probability or degree of occurrence of pests and diseases on individual trees, so as to solve the problem of accurate management of forest pests and diseases.

[0006] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is:

[0007] A method for predicting the probability or degree of occurrence of pests and diseases in individual trees includes two cases: a local model and a global model. The local model includes (1), (3), (5), and (6) in the following steps, and the global model includes (1), (2), and (4) in the following steps. The specific steps are as follows:

[0008] (1) Record the coordinates, diameter at breast height, tree height and other characteristics of each tree and the occurrence of diseases and insect pests using conventional methods;

[0009] (2) Calculate the SADIE clustering index vi for each tree or its attributes such as DBH and tree height in the study area;

[0010] (3) Taking the single host tree as the center, divide the study area into a series of circles with different radii, and calculate the SADIE clustering index vi of the tree at the center of each circle or its DBH;

[0011] (4) The occurrence of pests and diseases on individual trees was used as the response variable, and the SADIE clustering index vi of individual trees or their attributes such as DBH and tree height was used as the explanatory variable, and a prediction model was established using conventional methods.

[0012] (5) Using the method described in (4), establish prediction models at three or more different radius scales;

[0013] (6) Using the meta-analysis method, calculate the summary effect of the coefficients of the predictor variables at each radius scale described in (5), and use this summary effect to make predictions.

[0014] In the above scheme, when calculating the SADIE clustering index vi of the diameter at breast height in step (2), the coordinates of the tree in the study area are used as the spatial position; when calculating the SADIE clustering index vi of a single tree, the study area is first divided into small plots so that the number of plots is not greater than the total number of trees and not less than half of the total number of trees, and then the coordinates of the plot to which the single tree belongs are used as the spatial position of the tree.

[0015] In the above scheme, when calculating the SADIE clustering index vi of the diameter at breast height in step (3), the coordinates of the tree on the circle are used as the spatial position; when calculating the SADIE clustering index vi of a single tree, the circle is first divided into small squares so that the number of squares is not greater than the total number of trees on the circle and not less than half of the total number of trees on the circle, and the number of squares in the row and column with the largest number of squares is an odd number, and then the coordinates of the square to which the single tree belongs on the circle are used as the spatial position of the tree.

[0016] Beneficial effects of the present invention:

[0017] (1) The present invention provides a method for predicting the probability or extent of occurrence of pests and diseases on individual trees, which can promote the precise management of forest pests and diseases.

[0018] (2) The method of the present invention can also be used to guide the establishment of artificial forests, especially to guide the spatial arrangement of trees and other plants in the creation of gardening and forest cities. DETAILED DESCRIPTION

[0019] In order to better understand the present invention, the content of the present invention is further explained in conjunction with the following examples, but the content of the present invention is not limited to the following examples. The examples given are only for illustrating the present invention, not for limiting the scope of the present invention.

[0020] The following examples are provided as a guide for further improvements by those skilled in the art and are not intended to limit the present invention in any way.

[0021] Example 1

[0022] (1) A rectangular research area was selected in the ash forest, and each ash tree was surveyed, recording information such as coordinates, DBH, and whether it was infected by the tricholoma;

[0023] (2) Select a single plant and use it as the center of a circle with a radius of 1 m between 6 and 25 m. Use the R epiphy package to calculate the SADIE clustering index vi of the DBH of each plant on each circle. At different radius scales, the selection of single plants should ensure that the circle is a complete circle. The DBH is multiplied by 10 before calculation to convert it into an integer;

[0024] (3) The SADIE clustering index vi calculated in (2) was converted into a categorical variable (gap (vi < -1.5), inter-patch (-1.5 ≤ vi ≤ 1.5), and patch (vi > 1.5)) and then used as the explanatory variable. Whether the ash tree was infected (a binary categorical variable) was used as the response variable. A generalized linear model was established using R language to obtain the coefficients of the SADIE clustering index vi at each radius scale (Table 1);

[0025] Table 1 Coefficients on various radius scales

[0026]

[0027]

[0028] (4) The coefficients and standard errors of the SADIE clustering index vi at each radius scale obtained in (3) were used to obtain a summary effect value, and predictions were made based on this summary effect value (Table 2).

[0029] Table 2 Meta-analysis results

[0030]

[0031] Example 2

[0032] (1) A rectangular research area was selected in the ash forest, and each ash tree was surveyed, with the coordinates and information such as whether it was infected by the tricholoma.

[0033] (2) The study area was divided into small plots so that the number of plots was no more than the total number of ash trees and no less than half of the total number of trees. The epiphy package of R was used to calculate the SADIE clustering index vi of each small plot in the study area and assign it to each individual tree number;

[0034] (3) The SADIE clustering index vi calculated in (2) was converted into a categorical variable (gap (vi<-1.5), inter-patch (-1.5≤vi≤1.5), and patch (vi>1.5)) and then used as the explanatory variable. Whether the ash tree was infected (a binary categorical variable) was used as the response variable. Discriminant analysis and prediction were performed using the MASS package in R language. Of the 241 trees, 170 were correctly predicted, with an accuracy rate of 71%.

[0035] Obviously, the above embodiments are merely examples for clear explanation, and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from this are still within the protection scope of the invention.

Claims

1. A method for predicting the probability or degree of occurrence of pests and diseases on individual trees, comprising the following steps: (1) Record the coordinates, diameter at breast height, tree height and other attributes of each host tree and the occurrence of pests and diseases using conventional methods; (2) Calculate the SADIE clustering index vi for each host tree or its attributes such as DBH and tree height; (3) Taking the occurrence of pests and diseases on individual trees as the response variable and the SADIE clustering index vi of individual trees or their attributes such as DBH and tree height as the explanatory variable, conventional methods are used to establish a global prediction model or a local prediction model.

2. The method for predicting the probability or degree of occurrence of pests and diseases in individual trees according to claim 1, characterized in that: When establishing the global prediction model as described in step (3), the SADIE clustering index vi is calculated over the entire study area.

3. The method for predicting the probability or degree of occurrence of pests and diseases in individual trees according to claim 1, characterized in that: When establishing the local prediction model in step (3), the study area is divided into circular areas with a certain radius with a single host tree as the center, and the SADIE clustering index vi on the circular study area is calculated.

4. The method for predicting the probability or degree of occurrence of pests and diseases in individual trees according to claim 1, characterized in that: When calculating the SADIE clustering index vi of a single tree in step (2), the scheme for miniaturizing the study area is that the number of plots is not greater than the total number of trees and not less than half of the total number of trees, and then the coordinates of the plot to which the single tree belongs are used as the spatial position of the tree; if it is a circular study area as described in claim 3, the number of plots in the rows and columns with the largest number of plots must also be an odd number.

5. The local prediction model according to claim 3, characterized in that: Local prediction models were built at at least three radius scales, and meta-analysis was then used to obtain summary effect sizes.

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