CN110889571A - Forest disease (insect) based index curve group and establishing method and application thereof - Google Patents
Forest disease (insect) based index curve group and establishing method and application thereof Download PDFInfo
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Abstract
The invention discloses a forest disease (insect) base index curve group and an establishment method and application thereof. The invention firstly discloses a method for establishing a curve group with forest disease (insect) base index, which comprises the steps of establishing an equation model of disease or insect pest occurrence degree and key forest stand factors of pure forest, drawing a disease base index main curve or an insect base index main curve, and then expanding the main curve to obtain the disease base index curve group or the insect base index curve group. The forest land disease-based index curve group or pest-based index curve group established by the invention can quantitatively evaluate the general potential condition of a pure forest land with certain disease or pest, provides a theoretical basis for reasonable and effective management of the pure forest land with diseases, and achieves the purpose of preventing and realizing ecological control of forest pests.
Description
Technical Field
The invention relates to a method for establishing a curve group of forest disease (pest) -based index, and also relates to application of the curve group of forest disease (pest) -based index established by the method in evaluating general potential conditions of a pure forest with certain diseases (pests), belonging to the field of evaluation of the damage degree of forest disease (pest) and habitat conditions of corresponding forest.
Background
Forest pests, known as "smokeless fires", are a major factor impeding the development of forestry. In the process of occurrence, outbreak and epidemic of forest diseases and insect pests, besides the adaptability of the population, the interspecific relationship, climatic conditions, habitat conditions, biodistribution pattern, community structure and the like play an important role.
The research on forest diseases and insect pests by many scholars is not limited to the relation between the forest diseases and insect pests and the epidemic factors in a small range, and a great deal of research is focused on the influence of forest stand habitat factors on the occurrence of the diseases and insect pests in a large range, so that the main induction or influence factors of the (class of) diseases and insect pests are analyzed through a series of methods such as model establishment. The habitat factors generally comprise slope position, slope direction, gradient and elevation, and the forest stand factors generally comprise canopy density, tree seed proportion, crown breadth, breast diameter, tree height and the like. The factors of different habitats and forest stands playing a leading role are different due to different types of diseases and insect pests in the forest land. If the occurrence of the anthracnose of the fir, the forest age, the type of the standing land and the slope position play a leading role; the pine wilt disease is similar to the anthracnose of China fir, and the forest age is a key factor in a plurality of community structural characteristic factors. And for another example, by investigating 8 community structural factors of tree height, ground diameter, forest age, density, canopy density, herb coverage, herb height and canopy width of 41 artificial forest plots, regression analysis shows that the forest stand density is a key factor influencing the occurrence of leaf diseases of the dalbergia odorifera artificial forest, and then the forest age, canopy density, herb coverage and canopy width are carried out. The density of forest stand affects the soil fertility of the pure forest land, and further affects the disease (insect) pest susceptibility (resistance) capability of the pure forest tree species.
Forest stand factors and habitat conditions are two important factors influencing the occurrence of diseases (insect pests), and the two factors usually act on a forest ecosystem in a synergistic manner. However, no research has been made on the effect of isolating and quantifying the habitat factors on the degree of disease (insect) occurrence.
In recent years, most scholars obtain a mathematical equation of disease (insect) pest occurrence conditions and habitat conditions in sample plots through models, but the equation can only reflect quantitative calculation results and cannot carry out corresponding and quantitative classification on the severity and habitat level of disease (insect) pest occurrence, so that the accurate judgment on whether a certain forest land is suitable for planting a pure forest is influenced.
In view of the prediction and forecast of the general potential occurrence degree of diseases (pests) of pure forest with relatively consistent forest age, the concept with forest land disease (pest) base index is provided, so that the general potential situation of the habitat diseases (pests) of the pure forest affected by the key forest stand factors is quantitatively evaluated, and the purpose of really preventing and realizing the ecological control of forest pests is achieved.
Disclosure of Invention
The first technical problem to be solved by the invention is to provide a method for establishing a curve group with forest disease (insect) base index;
the second technical problem to be solved by the invention is to provide the disease (insect) base index curve group of the forested land established by the method and the application thereof in quantitatively evaluating the general potential condition of certain diseases or insect pests in the pure forest forested land.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the invention firstly discloses a method for establishing a curve group with forest land disease (insect) base index, which comprises the following steps: (1) setting a standard land for the pure forest, and surveying indexes of diseases or insect pests by taking the standard land as an object; (2) investigating forest factors and screening key forest factors; (3) establishing an alternative equation model of the occurrence degree of the diseases or the insect pests and key forest factors, and establishing and drawing a disease-base index main curve or a pest-base index main curve; (4) and determining a key forest stand factor datum point, and expanding the disease-base index main curve or the worm-base index main curve to obtain a corresponding disease-base index curve group or worm-base index curve group.
Wherein, step (1) sets sufficient standard for pure forest in research area, which makes it a complete system capable of representing all habitat and all forest stand elements. The disease index in the step (1) is a disease index; the insect pest index is an insect pest situation index. The forest ages of the pure forest in the step (1) are relatively consistent; preferably, the forest age range of the pure forest is limited to the mean age ± 2 years.
Step (2) surveying forest stand factors by taking a standard place as an object; screening the forest stand factors by a statistical analysis method, and screening out key forest stand factors influencing disease or insect pest indexes. The forest stand factors in the step (2) include but are not limited to: the forest stand density, the forest age, the canopy density, the crown width, the average tree height, the branch height or the breast diameter. In order to quantitatively judge the effect of the forest factors in the occurrence degree of diseases (pests), the habitat factors are hidden in the forest factors. The habitat factor of the invention comprises: any one or more of elevation, slope direction, soil type or soil physicochemical properties.
Step (3) establishing an alternative equation model of the occurrence degree of the diseases or the insect pests and the key forest stand factor, and using Q ═ f (x)i，yj) Represented by the formula: x is the number ofi(i ═ 1, 2, 3 … …, m) represents stand factors m, such as stand density, forest age, canopy density, crown width, etc.; y isj(j ═ 1, 2, 3 … …, n) represents n habitat factors affecting the occurrence of diseases (insect pests), such as altitude, gradient, slope, soil type, soil physicochemical property and the like; q represents the occurrence degree of diseases or insect pests and is represented by a disease index or an insect condition index. In order to quantitatively judge the effect of the forest stand factors in the occurrence degree of diseases (pests), especially to hide the habitat factors in the forest stand factors, the alternative equation model in the step (3) is expressed by Q ═ f (x), wherein x is a certain key forest stand factor or a comprehensive variable composed of several key forest stand factors. During investigation, the forest stand and the habitat factor range are uniformly distributed from the actual minimum value to the maximum value of the forest stand. Fitting with 80% sample data to obtain an alternative equation model, substituting unmodeled (20% sample) data into a regression equation to obtain a predicted value of a disease (insect) condition index, comparing the predicted value with an actual value Q, and selecting an average relative error (MAE), a Root Mean Square Error (RMSE) and a model correlation coefficient (R)2) And evaluating the precision of the model fitting parameters, and selecting the model with higher precision as a disease (insect) basis index equation model. Obtaining a main curve by drawing an equation modelThe main curve represents "the relationship between the occurrence degree of various diseases (insect pests) and key forest stand elements under all habitat conditions".
Step (3) drawing a disease-base index main curve or a pest-base index main curve by taking a certain key forest stand factor of a pure forest or a comprehensive variable composed of a plurality of key forest stand factors as an abscissa and taking the occurrence degree of diseases or pests as an ordinate; and (3) expressing the occurrence degree of the diseases or the insect pests by using disease indexes or insect condition indexes.
Taking a corresponding key forest stand factor value as a reference point when the disease or insect pest occurrence degree of a disease-based index main curve or an insect-based index main curve is 50, taking the main curve as a center, and respectively stretching 2 curves upwards and downwards by an equal ratio method to obtain a curve group consisting of 5 curves; the values of the 5 curves at the key forest stand factor reference point are respectively 10, 30, 50, 70 and 90, namely the grade difference is 20, and the values represent the potential occurrence conditions of certain forest diseases (insects) in a specific pure forest due to different habitats, namely disease (insect) base indexes. The 5 curves respectively correspond to 5 types of disease or pest occurrence grades, and the general potential occurrence conditions of the disease or pest are quantitatively classified into 5 grades: 10-extremely light diseases or insect pests, 30-light diseases or insect pests, 50-moderate diseases or insect pests, 70-severe diseases or insect pests and 90-extra-heavy diseases or insect pests; or marking the occurrence of I-extremely light disease (insect) damage, II-light disease (insect) damage, III-moderate disease (insect) damage, IV-severe disease (insect) damage and V-extra-severe disease (insect) damage.
Further, on the basis of the curve group, 4 central lines are expanded according to an equal ratio method, and the values of the disease or pest occurrence degree of the 4 central lines at the key forest stand factor reference point are respectively 20, 40, 60 and 80. According to the definition of the disease (insect) base index, all points within the range of 2 middle lines represent the disease (insect) base index of the same grade.
The invention further discloses a woodland disease-based index curve group or a worm-based index curve group established by the method. The disease (insect) base index curve group is shown in the attached figure 2 of the specification.
The invention also discloses application of the disease-based index curve group or pest-based index curve group in the forest land in quantitatively evaluating the general potential condition of certain diseases or pests in the pure forest land.
The general potential occurrence condition of the disease (insect) damage is divided into five grades by the disease (insect) base index curve group established by the invention, and the general potential occurrence condition of a certain pure forest under a specific habitat condition can be quantitatively evaluated by utilizing the disease (insect) base index curve group.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
the method selects pure forests with relatively consistent forest ages as objects, establishes a disease (pest) -based index curve group, judges the general potential situation of the disease (pest) in the habitat of the pure forests according to the curve group diagram and the degree of the disease (pest) in the pure forests under a specific forest stand factor, provides a theoretical basis for reasonable and effective management of the pure forests in the forest, provides a technical basis for proper afforestation of the forest in the forest, and controls the occurrence degree of the disease (pest) at a lower level, thereby achieving the purposes of really preventing and realizing ecological control of forest pests.
Definitions of terms to which the invention relates
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
Forest disease (pest) -based index PBI: based on the basic principle of forest disease (insect) occurrence, the forest disease (insect) occurrence result is attributed to the comprehensive effect of forest stand element values and habitat element values. In order to evaluate the potential effect of the habitat factors on forest disease (insect) damage results, defining an index to quantitatively describe the effect size of the habitat factors is necessary work for quantifying the index, and the index is called a forest disease (insect) base index PBI. The index refers to the potential condition of a certain forest disease (insect) in a specific pure forest under different habitat conditions, and the value range of the index is 0-100.
Key stand factor fiducial points: and the forest stand index value corresponding to the main curvatura (insect) condition index of 50.
Drawings
FIG. 1 is a schematic representation of a disease (insect) -based index master curve;
FIG. 2 is a schematic view of a population of disease (pest) -based index curves;
FIG. 3 is a technical roadmap;
FIG. 4 is a principal curve of red blight disease base index of Pinus densiflora;
FIG. 5 is a plot of the red blight disease basal index curves of Pinus densiflora;
FIG. 6 shows the group of plots (including the middle line) of the indices of red blight disease of Pinus densiflora.
Detailed Description
The invention will be further described with reference to specific embodiments, and the advantages and features of the invention will become apparent as the description proceeds. It is to be understood that the described embodiments are exemplary only and are not limiting upon the scope of the invention. It will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention, and that such changes and modifications may be within the scope of the invention.
Example 1 establishment of disease (insect) -based index Curve Panels
1. Disease (insect) base index (PBI) quantitative
For forest diseases, the disease base index can be extended from the disease state index; for forest pest damage, the population density standard value of the pest can be calculated, and the value range of the population density standard value is 0-100. In order to represent the difference of potential conditions of a pure forest in generating specific diseases (insect pests) at different habitat levels, the pure forest can be equally divided into 5 grades, which are respectively represented as: 10. 30, 50, 70, 90 or I-extremely light diseases (insect) occur, II-mild diseases (insect) occur, III-moderate diseases (insect) occur, IV-severe diseases (insect) occur, and V-extra-severe diseases (insect) occur. As shown in FIG. 2, if the sample point falls on [ Q ]0,Q20) Within the interval, the disease (insect) base index of the forest land is 10; if the sample point falls on [ Q ]20,Q40) Within the interval, the disease (insect) base index of the forest land is 30; if the sample point falls on [ Q ]40,Q60) Within the interval, the disease (insect) base index of the forest land is 50; if the sample point falls on [ Q ]60,Q80) Within the interval, the disease (insect) base index of the forest land is 70; when the sample point falls on the central line Q80And above, the disease (pest) base index of the forest land is 90.
2. Disease (insect) base index quantitative step in actual operation
(1) And setting a sufficient standard for the research area to be a complete system capable of representing all habitats and all forest stand elements.
(2) Indexes of biological elements and environmental elements are investigated with respect to a standard land, and forest stand indexes (forest stand density, forest age, canopy density, canopy width, and the like) and habitat indexes (altitude, gradient, slope, soil type, and the like) are generally investigated. Screening the forest factors by a statistical method, and taking a selected key factor or a comprehensive variable consisting of several key factors as the abscissa of a disease (insect) base index model.
(3) Taking the corresponding stand index value when the disease (insect) base index is 50 as a reference point.
(4) And establishing a disease (insect) base index alternative equation model. With Q ═ f (x)i，yj) Representing an alternative equation model, in which: x is the number ofi(i is 1, 2, 3 … …, m) respectively represents stand indexes m, such as stand density, stand age, canopy density, crown width and the like; y isj(j ═ 1, 2, 3 … …, n) respectively represents n habitat factors affecting the occurrence of diseases (insect pests), such as altitude, gradient, slope, soil type, soil physicochemical property and the like; q represents the severity degree of the disease (insect) damage and is expressed by a disease index or an insect condition index. In order to quantitatively judge the action effect of the forest stand factors in the occurrence degree of diseases (pests), especially to hide the habitat factors in forest stand elements, an alternative equation model is represented by Q ═ f (x), wherein x is a certain key factor obtained by screening through a statistical method or a comprehensive variable consisting of a plurality of key factors. During investigation, the forest stand and the habitat factor range are uniformly distributed from the actual minimum value to the maximum value of the forest stand, and the alternative equation model is formed by fitting 80% of sample data.
(5) Testing and screening of models
Before establishing a disease (insect) base index curve group, substituting unmodeled (20% of samples) data into a regression equation to obtain a predicted value of a disease condition index, comparing the predicted value with an actual value Q, and selecting an average relative error (MAE), a Root Mean Square Error (RMSE) and a model correlation coefficient (R)2) And evaluating the accuracy of the model fitting parameters, and selecting a model with higher accuracy as a disease (insect) basis exponential equation model. And drawing a main curve through an equation model, wherein the main curve represents the relation between the occurrence degree of various diseases (insect pests) and key forest stand elements under all habitat conditions.
(6) Establishing a disease (insect) base index curve group. By taking a key forest stand element value when the occurrence degree of the disease (insect) damage is 50 as a datum point, respectively pulling out 2 function models upwards and downwards by taking a process model as a center and adopting an equal ratio method to respectively obtain 5 curves, wherein the values of the 5 curves at the key forest stand element datum point are respectively 10, 30, 50, 70 and 90, namely the grade difference is 20, the values represent the potential occurrence conditions of certain forest disease (insect) in a specific pure forest due to different habitats, namely disease (insect) base indexes, can be directly marked by 10-extremely light disease (insect) occurrence, 30-mild disease (insect) occurrence, 50-moderate disease (insect) occurrence, 70-severe disease (insect) occurrence and 90-extremely heavy disease (insect) occurrence, and can also be marked by I-extremely light disease (insect) occurrence and II-mild disease (insect) occurrence, III-moderate disease (insect) occurrence, IV-severe disease (insect) occurrence, V-specific severe disease (insect) occurrence label (figure 1, figure 2, figure 3). The method comprises the following specific steps: the key forest stand element value and the corresponding disease (insect) situation index are used for solving a main curve as follows:
Q＝f(x) (1)
the 5 curve numbers in the curve group are represented by i ═ I, II, III, IV and V, QiDisease index or insect condition index, Q, representing the ith disease (insect) based index curveDiIndicating the disease index or insect condition index at the benchmark point of the ith disease (insect) base index curve. According to the equal ratio method, each curve and the main curve have a proportional relation of the formula (2):
the expressions of 5 disease (insect) -based index curves obtained from the formula (2) are respectively:
example 2 establishment of Gibberella pinosylvia radicicola index model
1. Method of producing a composite material
1.1 overview of the study region
Kunzea (121 degrees 41 '34' to 121 degrees 48 '04' E, 37 degrees 11 '50' to 37 degrees 17 '22' N) is located in the east of Shandong peninsula, across the Kunzea area of the tobacco terrace and the city of Weihaiwenden. The area belongs to a warm-temperature zone monsoon climate, the climate is mild, the annual average temperature is 12.3 ℃, the annual precipitation is 800-1200 mm, the annual average relative humidity is 62.6%, and the frost-free period is 200-220 d. Most of the soil is brown soil, and most of the soil is sandy loam. The forest types include 6 species of pinus sylvestris, larch/fir wood, conifer-quercus acutissima, conifer-jungle and broadleaf forest. The pinus koraiensis is used as a main group seed of Kunzhou mountain, and is distributed from the foot to the elevation of 800 m. Red pine is mainly infected by pathogens such as pine brown blight (Pestalotiopsis funerea), dieback (Sphaeropsis sapienea) and pine wood nematode (Bursaphelenchus xylophilus) and pests such as wasp Bejohnsonia kummensis (Cerphalunyuanhana), pine moth (Dendrolinius specularis) and pine coccinea (Matsucoccus matsumurae).
1.2 quantification of the gibberellic disease basal index
1.2.1 the pure forest of red pine and red blight of red pine are selected as the research objects.
1.2.2 set 136 temporary plots (30m, guard band width 30m) of relatively consistent forest ages (34 + -2 a) for the study area, and ensure the uniformity and completeness of habitat and all stand element values.
1.2.3 investigation and recording of disease index
The disease index of the Pinus densiflora forest stand is obtained by adopting a five-point method, namely 2 Pinus densiflora plants are respectively taken from 4 corners and the center of each sample plot, 10 Pinus densiflora plants are investigated in total, and then the disease indexes of the 10 Pinus densiflora plants are obtained by averaging. Dividing a single red pine sample wood into an upper layer, a middle layer and a lower layer 3, respectively taking 1 branch in east, south, west and north directions, and counting the morbidity of each branch. The conifer of each sample shoot is considered approximately as a cylinder with the same basal area, so the ratio of the lesion area per conifer to the conifer area can be converted into the ratio of the lengths of the two. The ratio of the length of the needle leaf lesion of each branch to the length of the needle leaf is measured actually, the disease index of the plant is calculated according to a five-level grading weighted average method, and the grading standard of the branch diseases is shown in table 1.
TABLE 1 Classification Standard of branches with diseases due to gibberellic disease
The disease index calculation formula is as follows:
1.2.4 investigation of forest stand and habitat factors
And 6 forest stand factors of the average tree height, the branch height, the breast diameter, the forest stand density, the canopy density and the crown width of the survey plot are investigated. Wherein, the density of the forest stand is only counted for the pinus densiflora with the breast diameter more than or equal to 2cm in the sample plot. And the sample plot is positioned by a GPS, and indexes such as the altitude, the slope position, the slope direction, the slope, the soil physicochemical property and the like of the sample plot are measured and recorded.
1.2.5 screening of Key forest stand factors
Screening the forest factors by a stepwise regression method, and taking a selected key factor or a comprehensive variable consisting of several key factors as the abscissa of the disease-base index model.
1.2.6 establishing an alternative equation model of disease occurrence degree and forest stand elements
And fitting the model by using 80% of sample data, wherein the alternative equation model is expressed as Q ═ f (x), and x in the formula is a certain key factor or a comprehensive variable consisting of a plurality of key factors after stepwise regression screening.
1.2.7 determination of fiducial points
The influence of the benchmark point on the disease-based index model is very obvious, and the misselection causes deviation on the evaluation of the disease occurrence degree. The invention defines the reference point as the forest stand index value corresponding to the disease index of the main curve being 50.
1.2.8 testing and screening of models
Before establishing a disease-based index curve group, data which are not modeled are substituted into a regression equation to obtain a predicted value of a disease condition index, the predicted value is compared with an actual value Q, and an average relative error (MAE), a Root Mean Square Error (RMSE) and a model correlation coefficient (R) are selected2) And evaluating the accuracy of the model fitting parameters, selecting a model with higher accuracy as a disease-base index equation model, and drawing a disease-base index main curve.
1.2.9 Pinus densiflora pure forest disease base index model group establishment
By adopting an equal ratio method, taking a forest stand element value when the disease occurrence degree is 50 as a reference point and taking a main model as a center, respectively fitting 2 function models upwards and downwards by an equal ratio method to obtain 5 function models in total, the values of the 5 function models at the reference point of the forest stand element values are respectively 10, 30, 50, 70 and 90, the values represent the potential occurrence conditions of red blight of the Pinus densiflora in the pure Pinus densiflora due to different habitats, the disease base index can be directly marked by 10-extremely light disease occurrence, 30-mild disease occurrence, 50-moderate disease occurrence, 70-severe disease occurrence and 90-extra-severe disease occurrence, and can also be marked by I-extremely light disease occurrence, II-mild disease occurrence, III-moderate disease occurrence, IV-severe disease occurrence and V-extra-severe disease occurrence.
2. Data processing
Experimental data were processed using Microsoft Excel 2007, stepwise regression screening of dominant factors using SPSS software (version 22.0), origin8.0 fitting of master curves and construction of curve populations.
3. Analysis of results
3.1 screening of Key forest stand factors
And (4) selecting a stepwise regression method to screen forest factors influencing the disease indexes of the red pine. The results are shown in Table 2, and the leading factors affecting disease index size include forest stand density and branch height of 2, which can explain the change of disease index of 57.1% (R)20.571), and the effect on disease index is also very significant (F86.704, P0.000). Wherein forest stand density accounts for 54.9% change in disease index (r)20.549), suggesting that stand density is a key factor in stand factors that affects disease index.
TABLE 2 stepwise regression equation of red blight and forest stand factor
3.2 establishment of the Main Curve
Fitting out 3 forest stand density-disease index relational models shown in Table 3 by using the nonlinear fitting function in origin8.0, and synthesizing R of each fitting equation2MAE, RMSE and the actual rule of occurrence of diseases in forest lands along with density, and the main curve equation for determining forest stand density-disease index is Q-57.40/(1 +49.46 e)-0.0031x) Wherein Q represents disease index, and x represents stand density. R20.89, 9.52% MAE, 3.68% RMSE. The calculated stand density datum point is 1875 plants/hm2It means that the severity of red blight of red pine occurring in the red pine forest plot was 50 at this stand density. According to the main curve, when the stand density is less than 500 plants/hm2The disease index slowly rises along with the increase of the density of the forest stand, which shows that the density of the forest stand has little influence on the disease index before the Pinus densiflora clogs; when the density of the forest stand is 500 plants/hm2And 2300 strains/hm2In between, disease index increases significantly with increasing stand density; when the density of the forest stand is more than 2300 plants/hm2The increase of disease index tends to be gentle, which indicates that the forest stand density has little or no effect on disease index (fig. 4).
TABLE 3 results of fitting of the respective master curves
3.3 formation of Curve groups
From the main curve Q57.40/(1 +49.46 e)-0.0031x) Obtaining:
QⅠ＝11.48/(1+49.46*e-0.0031x)
QⅡ＝34.44/(1+49.46*e-0.0031x)
QⅢ＝57.40/(1+49.46*e-0.0031x)
QⅣ＝80.36/(1+49.46*e-0.0031x)
QⅤ＝103.32/(1+49.46*e-0.0031x)
QⅠ、QⅡ、QⅢ、QⅣand QⅤThe disease indexes of 5 disease-based index curves in the curve group are respectively shown in FIG. 5. As shown in the curve group, the habitat condition reflected by the morbid base index 90 is the worst, and the red blight occurs the most seriously; when the density is close to 2300 strains/hm2The disease index of the plot is 100, which shows that the forest stand density has a large influence on the red blight disease in the plot with the different habitat levels. For the forest land with the disease base index of 10, the habitat conditions are optimal, and the habitat quality plays a greater role in influencing the severity of disease occurrence, namely the disease index is closely tied by forest standsThe influence of the degree is minimal; disease index is less than 2000 strains/hm2There is a small increase in range followed by a flattening.
Example 3 application of Gibberella pinosylvia radicicola index model
1. Basis of application of red blight basic index model of Pinus densiflora
According to the definition of disease-base index, all the points in the range of 2 central lines represent the disease-base index of the same grade. In order to more accurately and simply judge the disease base index condition of a pure forest land of a certain Pinus densiflora (34 +/-2 a) in a disease base index curve group graph, on the basis of the disease base index curve group (figure 5), 4 middle lines (figure 6) are expanded according to an equal ratio method, and the expression of the 4 middle lines is as follows:
Q20＝22.96/(1+49.46*e-0.0031x)
Q40＝45.92/(1+49.46*e-0.0031x)
Q60＝68.88/(1+49.46*e-0.0031x)
Q80＝91.84/(1+49.46*e-0.0031x)
wherein Q is20、Q40、Q60And Q80The disease indices at the baseline of stand density were 20, 40, 60 and 80, respectively. I.e. if the sample point falls on Q20When the index is below, the disease base index of the red pine forest land is 10; if the sample point falls on [ Q ]20,Q40) Within the interval, the disease base index of the Pinus densiflora is 30; if the sample point falls on [ Q ]40,Q60) Within the interval, the disease base index of the Pinus densiflora is 50; if the sample point falls on [ Q ]60,Q80) Within the interval, the disease base index of the Pinus densiflora is 70; when the sample point falls on the central line Q80And above, the disease base index of the Pinus densiflora is 90.
2. Application example of red blight disease basal index model of Pinus densiflora
If the forest stand density of a pure forest land of a certain Pinus densiflora (34 +/-2 a) is investigated to be 1250 strains/hm2The disease index is 57.15, and the disease index is based on the curve group (including the midline) of the red blight disease basal index of Pinus densiflora (FIG. 6), which falls on the midline Q80Above, what is needed isBased on the application of the disease-base index curve group diagram, the potential red blight disease occurrence condition of the disease-base index curve group diagram is 90, namely the disease-base index is V grade; if the forest stand density of the investigated forest land is 1500 plants/hm2If the disease index is 20, the point falls on the midline [ Q ]20,Q40) In the interval, the potential red blight of the sample is indicated to be 30, namely, the disease index is II grade.
Claims (10)
1. A method of establishing a population of curves having a woodland disease-based index or a worm-based index, comprising the steps of: (1) setting a standard land for the pure forest, and surveying indexes of diseases or insect pests by taking the standard land as an object; (2) investigating forest factors and screening key forest factors; (3) establishing an equation model of the occurrence degree of the diseases or the insect pests and the key forest factors, and drawing a disease-base index main curve or a pest-base index main curve; (4) and determining a key forest stand factor datum point, and expanding the disease-base index main curve or the worm-base index main curve to obtain a corresponding disease-base index curve group or worm-base index curve group.
2. The method of claim 1, wherein: the disease index in the step (1) is a disease index; the insect pest index is an insect pest situation index.
3. The method of claim 1, wherein the forest factor of step (2) comprises: any one or more of forest stand density, forest age, canopy density, crown width, average tree height, branch height or breast diameter;
and (2) screening the forest stand factors by a statistical analysis method, and screening out key forest stand factors influencing disease or insect pest indexes.
4. The method of claim 1, wherein: the forest ages of the pure forest in the step (1) are relatively consistent; preferably, the forest age range of the pure forest is the average age plus or minus 2 years.
5. The method of claim 1, wherein: step (3) drawing a disease-base index main curve or a pest-base index main curve by taking a certain key forest stand factor of a pure forest or a comprehensive variable composed of a plurality of key forest stand factors as an abscissa and taking the occurrence degree of diseases or pests as an ordinate;
and (3) expressing the occurrence degree of the diseases or the insect pests by using disease indexes or insect condition indexes.
6. The method of claim 1, wherein: the equation model in the step (3) is expressed by Q ═ f (x); wherein, x is a certain key forest stand factor or a comprehensive variable composed of a plurality of key forest stand factors; q represents the occurrence degree of diseases or insect pests and is represented by a disease index or an insect condition index.
7. The method of claim 1, wherein: taking a corresponding key forest stand factor value when the occurrence degree of diseases or insect pests is 50 as a reference point, taking a disease-base index main curve or an insect-base index main curve as a center, and respectively stretching 2 curves upwards and downwards by an equal ratio method to obtain a curve group consisting of 5 curves; the values of the 5 curves at the key stand factor reference point are 10, 30, 50, 70 and 90 respectively;
the 5 curves respectively correspond to 5 types of disease or pest occurrence grades, and the general potential occurrence conditions of the disease or pest are quantitatively classified into 5 grades: 10-extremely light diseases or insect pests, 30-light diseases or insect pests, 50-moderate diseases or insect pests, 70-severe diseases or insect pests and 90-extra-severe diseases or insect pests.
8. The method of claim 7, wherein: on the basis of a curve group consisting of the 5 curves, continuously expanding 4 middle lines according to an equal ratio method, wherein the values of the 4 middle lines at the key forest stand factor datum point are respectively 20, 40, 60 and 80.
9. A woodland disease-based index curve population or a worm-based index curve population established by the method of any one of claims 1 to 8.
10. Use of the woodland disease-based index curve group or pest-based index curve group of claim 9 for quantitatively evaluating the general potential of pure forest woodland to develop a certain disease or pest.
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| CN112616610A (en) * | 2020-12-30 | 2021-04-09 | 中国林业科学研究院森林生态环境与保护研究所 | Ecological prevention and control method for alleviating harm of pine wilt and pinus densiflora |
| CN115841193A (en) * | 2023-02-17 | 2023-03-24 | 航天宏图信息技术股份有限公司 | Method and device for predicting forest pests |
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| US5719795A (en) * | 1995-07-26 | 1998-02-17 | Westvaco Corporation | Method to provide consistent estimated growth and yield values for loblolly pine plantations |
| CN106034973A (en) * | 2016-05-31 | 2016-10-26 | 北京林业大学 | Disaster prevention control method for man-made Cunninghamia lanceolata forest |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| US5719795A (en) * | 1995-07-26 | 1998-02-17 | Westvaco Corporation | Method to provide consistent estimated growth and yield values for loblolly pine plantations |
| CN106034973A (en) * | 2016-05-31 | 2016-10-26 | 北京林业大学 | Disaster prevention control method for man-made Cunninghamia lanceolata forest |
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| CN112616610A (en) * | 2020-12-30 | 2021-04-09 | 中国林业科学研究院森林生态环境与保护研究所 | Ecological prevention and control method for alleviating harm of pine wilt and pinus densiflora |
| CN115841193A (en) * | 2023-02-17 | 2023-03-24 | 航天宏图信息技术股份有限公司 | Method and device for predicting forest pests |
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