CN110889571B - Woodland disease (insect) base index curve group and establishing method and application thereof - Google Patents
Woodland disease (insect) base index curve group and establishing method and application thereof Download PDFInfo
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- 241001236475 Megastigmus pinus Species 0.000 description 1
- 241000351913 Pestalotiopsis funerea Species 0.000 description 1
- 241000218657 Picea Species 0.000 description 1
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Abstract
The invention discloses a woodland disease (insect) base index curve group and a building method and application thereof. The invention firstly discloses a method for establishing a disease (insect) base index curve group of a woodland, which comprises the steps of establishing an equation model of the occurrence degree of diseases or insect pests and a key woodland factor of a 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 disease-based index curve group or the insect-based index curve group of the forest land built by the invention can quantitatively evaluate the general potential condition of a certain disease or insect pest of the pure forest land, provides a theoretical basis for reasonable and effective management of the pure forest land, and achieves the aims of preventing and realizing ecological control of forest pests.
Description
Technical Field
The invention relates to a method for establishing a woodland disease (insect) base index curve group, and also relates to application of the woodland disease (insect) base index curve group established by the method in evaluating general potential conditions of occurrence of certain disease (insect) damage to a pure woodland, belonging to the field of evaluation of the occurrence hazard degree of the woodland disease (insect) and the corresponding woodland habitat condition.
Background
Forest pest is called "smokeless fire" and is a major factor in impeding the development of forestry. In the process of forest pest occurrence, outbreak and epidemic, besides the adaptability of the population, the relationship among species, climate conditions, habitat conditions, biodistribution patterns, community structures and the like play an important role.
Many scholars' researches on forest pest and disease damage are not limited to the relationship between a small range and epidemic factors, and a great deal of researches are focused on the influence of a forest stand habitat factor on pest and disease damage in a large range, so that main induction or influence factors of the type of pest and disease damage are analyzed through a series of methods such as model establishment and the like. The habitat factors generally comprise slope, slope direction, gradient and altitude, and the stand factors generally comprise closing degree, tree species proportion, crown width, breast diameter, tree height and the like. The various dominant habitats and stand factors will vary with the type of plant diseases and insect pests occurring in the woodland. If the fir anthracnose occurs, the forest age, the type of the site and the slope position play a leading role; similar to fir anthracnose, pine needle rot is a key factor in many community structural feature factors, forest age. For example, 8 community structure factors including tree height, ground diameter, forest age, density, canopy, herb height and crown width of 41 artificial forest lands are investigated, and regression analysis shows that the forest stand density is a key factor affecting the occurrence of leaves diseases of rosewood artificial forest, and the tree age, canopy, herb canopy and crown width are the following factors. The density of the stand influences the soil fertility of the pure Lin Linde, and further influences the disease (pest) pest sensing capability of the pure stand tree species.
The forest stand factors and the habitat conditions are two important factors influencing the occurrence of diseases (insect) and commonly act on the forest ecosystem in a synergistic way. However, no research has been made to isolate the habitat factors and quantitatively analyze the effect of the habitat factors on the occurrence degree of diseases (insect pests).
In recent years, most students obtain a mathematical equation of occurrence conditions and habitat conditions of diseases (insects) in a sample plot through a model, but the equation only can reflect quantitative calculation results, and the severity degree and habitat level of the diseases (insects) cannot be correspondingly and quantitatively classified, so that accurate judgment on whether a certain forest plot is suitable for planting a certain pure forest is affected.
In view of the above prediction and forecast of the general potential occurrence degree of disease (insect) harm to pure forests with relatively consistent ages, the concept of a woodland disease (insect) base index is provided, so that the general potential condition of occurrence of the disease (insect) harm of the living environment where the pure forests are subjected to the influence of key forests factors is quantitatively evaluated, and the aim of truly preventing and realizing ecological control of forest pest is achieved.
Disclosure of Invention
The first technical problem to be solved by the invention is to provide a method for establishing a woodland disease (insect) base index curve group;
the second technical problem to be solved by the invention is to provide a woodland disease (insect) -based index curve group established by the method and the application thereof in quantitative evaluation of general potential conditions of occurrence of certain diseases or insect pests on pure woodland.
In order to solve the technical problems, the invention adopts the following technical scheme:
The invention firstly discloses a method for establishing a woodland disease (insect) base index curve group, which comprises the following steps: (1) Setting a standard land for pure forest, taking the standard land as a target, and investigating disease or insect damage indexes; (2) investigating the stand factors, and screening key stand factors; (3) Establishing an alternative equation model of the occurrence degree of diseases or insect pests and key stand factors, and establishing and drawing a disease base index main curve or a insect base index main curve; (4) Determining a key stand factor datum point, and expanding a disease base index main curve or a pest base index main curve to obtain a corresponding disease base index curve group or pest base index curve group.
The step (1) sets a sufficient standard for pure forest in a research area, so that the pure forest is a complete system capable of representing all habitats and all stand elements. The disease index in the step (1) is a disease index; the insect pest index is an insect pest index. The ages of the pure forests in the step (1) are relatively consistent; preferably, the range of ages of the pure forest is limited to 2 years of average age.
Step (2) taking a standard as a target, and investigating a stand factor; and screening the stand factors by a statistical analysis method to screen out key stand factors influencing disease or insect pest indexes. The stand factors of step (2) include, but are not limited to: any one or more of forest stand density, forest age, canopy density, crown width, average tree height, under-branch height or breast diameter. In order to quantitatively judge the effect of the stand factors in the occurrence degree of diseases (insect pests), the habitat factors are hidden in the stand factors. The habitat factors of the invention include: elevation, grade, slope, soil type, or soil physicochemical properties.
Step (3) an alternative equation model of the occurrence degree of diseases or insect pests and the key stand factor is established, and is represented by q=f (x i,yj), wherein: x i (i=1, 2,3 … …, m) represents m stand factors, such as stand density, age, canopy density, crown amplitude, etc.; y j (j=1, 2,3 … …, n) represents n habitat factors that affect the occurrence of disease (insect) pest, such as altitude, gradient, slope, soil type, soil physicochemical properties, etc.; q represents the occurrence degree of diseases or insect pests, and is expressed by a disease index or an insect pest index. In order to quantitatively judge the effect of the stand factor in the occurrence degree of the disease (insect) pest, the habitat factor is hidden in the stand factor, and the alternative equation model in the step (3) is represented by q=f (x), wherein x is a key stand factor or a comprehensive variable formed by a plurality of key stand factors. During investigation, the range of the above-mentioned forest stand and the habitat factors is uniformly distributed from the actual minimum value to the maximum value of the forest stand. Fitting 80% of sample data to obtain an alternative equation model, substituting the data which are not modeled (20% of samples) into a predicted value of a disease (insect) condition index obtained by a regression equation, comparing the predicted value with an actual value Q, evaluating the precision of model fitting parameters by using average relative error (MAE), root Mean Square Error (RMSE) and model correlation coefficient (R 2), and selecting a model with higher precision as a disease (insect) base index equation model. By drawing an equation model main curve, the main curve represents the relation between the occurrence degree of each sample disease (insect) pest and key stand elements under all habitat conditions.
Step (3) drawing a disease-based index main curve or a pest-based index main curve by taking a certain key stand factor of a pure forest or a comprehensive variable consisting of a plurality of key stand factors as an abscissa and the occurrence degree of diseases or pests as an ordinate; the occurrence degree of the diseases or insect pests in the step (3) is expressed by a disease index or an insect pest index.
Step (4) respectively stretching up and down 2 curves by taking a key stand factor value corresponding to a disease base index main curve or a pest base index main curve when the occurrence degree of diseases or pests is 50 as a reference point and taking the main curve as a center through an equal ratio method to obtain a curve group consisting of 5 curves; the values of the 5 curves at the key stand factor datum points are respectively 10, 30, 50, 70 and 90, namely the level difference is 20, and the values represent the potential occurrence conditions of a certain forest disease (insect) pest in a specific pure forest due to different habitats, namely the disease (insect) base index. The 5 curves respectively correspond to 5 disease or insect pest occurrence grades, and the general potential occurrence conditions of the disease or insect pest are quantitatively classified into 5 grades: 10-very light disease or pest occurrence, 30-light disease or pest occurrence, 50-medium disease or pest occurrence, 70-heavy disease or pest occurrence, 90-extra heavy disease or pest occurrence; the method can also be used for marking the occurrence of I-extremely light disease (insect) pest, II-mild disease (insect) pest, III-moderate disease (insect) pest, IV-severe disease (insect) pest and V-severe disease (insect) pest.
Further, on the basis of the curve group, 4 central lines are continuously expanded according to an equal ratio method, and the values of the occurrence degree of diseases or insect pests of the 4 central lines at the key stand factor datum points are respectively 20, 40, 60 and 80. According to the definition of the disease (insect) base index, all points within the range of 2 midlines represent the same grade of disease (insect) base index.
The invention further discloses a woodland disease base index curve group or an insect base index curve group established by the method. The schematic diagram of the disease (insect) base index curve group is shown in figure 2 of the accompanying drawings.
The invention also discloses application of the woodland disease-based index curve group or the insect-based index curve group in quantitatively evaluating general potential conditions of occurrence of certain diseases or insect pests on the pure woodland.
The disease (insect) -based index curve group established by the invention divides the general potential occurrence condition of disease (insect) injury into five levels, and the general potential condition of certain pure forest for generating specific disease (insect) injury under specific habitat conditions can be quantitatively evaluated by utilizing the disease (insect) -based index curve group.
Compared with the prior art, the technical scheme of the invention has the following beneficial effects:
According to the invention, pure forests with relatively consistent forest ages are selected as objects, a disease (insect) -based index curve group is established, and the general potential condition of disease (insect) injury in the living environment of the pure forests is judged based on the degree of disease (insect) injury of the pure forests under specific forest stand factors according to the curve group, so that a theoretical basis is provided for reasonable and effective management of the pure forests, a technical basis is provided for suitable forests in the forest lands, and the occurrence degree of the disease (insect) injury is controlled at a lower level, thereby achieving the purposes of truly preventing and realizing ecological control of forest pests.
Definition of terms in connection with the present invention
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.
Woodland disease (worm) base index PBI: based on the basic principle of forest disease (insect) pest occurrence, the result of forest disease (insect) pest occurrence is attributed to the combined effect of the forest stand element value and the habitat element value. In order to evaluate the potential effect of a habitat factor on the development of a forest disease (insect) pest result, an index is defined to quantitatively describe the extent of the effect, which is a necessary task for quantifying the index, and the index is called a woodland disease (insect) base index (PBI). The index refers to the potential condition that a certain forest disease (insect) pest occurs under different habitat conditions in a specific pure forest, and the value range of the index is 0-100.
Key stand factor datum point: the index value of forest stand is 50 for main curve disease (insect) condition.
Drawings
FIG. 1 is a schematic diagram of a disease (worm) based index main curve;
FIG. 2 is a schematic representation of a disease (worm) based index profile population;
FIG. 3 is a technical roadmap;
FIG. 4 is a major curve of the base index of red pine with red blight;
FIG. 5 is a population of red pine blight disease base index curves;
FIG. 6 is a population of red pine blight disease base index curves (containing midline).
Detailed Description
The invention will be further described with reference to specific embodiments, and advantages and features of the invention will become apparent from the description. It should be understood that the embodiments described are exemplary only and should not be construed as limiting the scope of the invention in any way. It will be understood by those skilled in the art that various changes and substitutions can be made in the details and form of the technical solution of the present invention without departing from the spirit and scope of the invention, but these changes and substitutions fall within the scope of the present invention.
Example 1 establishment of disease (insect) based index Curve group
1. Quantitative determination of disease (worm) base index (PBI) index
For forest diseases, the disease base index can be extended from the disease index; for forest insect pests, the pest base index can be deduced from the normalized value of the pest population density, and the value of this value ranges from 0 to 100. In order to represent the difference of the potential conditions of a certain pure forest with specific disease (insect) damage at different habitat levels, the same proportion can be divided into 5 grades, which are respectively expressed as: 10. 30, 50, 70, 90 or I-very light disease (insect) pest, II-mild disease (insect) pest, III-moderate disease (insect) pest, IV-severe disease (insect) pest, V-severe disease (insect) pest. As shown in fig. 2, if the sample point falls within the [ Q 0,Q20 ] interval, the disease (pest) base index of the woodland is 10; if the sample point falls within the [ Q 20,Q40 ] interval, the disease (pest) base index of the woodland is 30; if the sample point falls within the [ Q 40,Q60 ] interval, the disease (pest) base index of the woodland is 50; if the sample point falls within the [ Q 60,Q80 ] interval, the disease (insect) base index of the woodland is 70; when the sample point falls on the midline Q 80 and above, the disease (pest) base index of the woodland is 90.
2. Step of quantifying index of disease (insect) base in actual operation
(1) Sufficient standards are set for the research area to make the research area a complete system capable of representing all habitats and all stand elements.
(2) Taking a standard ground as a target, biological element and environmental element indexes are investigated, and forest stand indexes (forest stand density, forest age, canopy density, crown amplitude and the like) and habitat indexes (altitude, gradient, slope direction, soil type and the like) are generally investigated. The stand factors are screened by a statistical method, and the selected key factor or the comprehensive variable consisting of a plurality of key factors is used as the abscissa of the disease (insect) base index model.
(3) Taking the corresponding forest 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. The alternative equation model is represented by q=f (x i,yj), where: x i (i=1, 2,3 … …, m) respectively represent m stand indexes, such as stand density, age, canopy density, crown width and the like; y j (j=1, 2,3 … …, n) respectively represents n habitat factors that affect occurrence of disease (insect) pest, such as altitude, gradient, slope direction, soil type, soil physicochemical properties, etc.; q represents the severity of the occurrence of the disease (insect) pest, and is expressed by the disease index or the insect pest index. In order to quantitatively judge the effect of the stand factors in the occurrence degree of diseases (insect pests), the habitat factors are hidden in the stand factors, and the alternative equation model is expressed by Q=f (x), wherein x is a certain key factor or a comprehensive variable formed by a plurality of key factors obtained after screening by a statistical method. During investigation, the range of the environmental factors from the actual minimum value to the maximum value of the stand is uniformly distributed, and the alternative equation model is formed by fitting 80% of sample data.
(5) Inspection and screening of models
Before the disease (insect) base index curve group is established, data which are not modeled (20% of samples) are substituted into a regression equation to obtain a predicted value of a disease index, the predicted value is compared with an actual value Q, average relative error (MAE), root Mean Square Error (RMSE) and model correlation coefficient (R 2) are selected for evaluating model fitting parameter precision, and a model with higher precision is selected as a disease (insect) base index equation model. And drawing a main curve by an equation model, wherein the main curve represents the relation between the occurrence degree of each sample disease (insect) pest and the key stand elements under all habitat conditions.
(6) Establishment of disease (insect) -based index curve group. The key forest stand factor value when the occurrence degree of the disease (insect) is 50 is taken as a reference point, an equation model is taken as the center, 2 function models are respectively pulled up and down by adopting an equal ratio method, 5 curves are obtained, the values of the 5 curves at the key forest stand factor reference point are respectively 10, 30, 50, 70 and 90, namely the level difference is 20, the values represent the potential occurrence condition of a certain forest disease (insect) caused by different habitats in a specific pure forest, namely the disease (insect) base index, and the method can be directly marked by 10-extremely light disease (insect) pest occurrence, 30-mild disease (insect) pest occurrence, 50-moderate disease (insect) pest occurrence, 70-severe disease (insect) pest occurrence and 90-severe disease (insect) pest occurrence, or by I-extremely light disease (insect) pest occurrence, II-mild disease (insect) pest occurrence and III-moderate disease (insect) pest occurrence, IV-severe disease (insect) pest occurrence, V-severe disease (insect) pest occurrence (figures 1,2, 3 and 2). The method comprises the following specific steps: the main curve is obtained by key forest stand element values and corresponding disease (insect) condition indexes, and is as follows:
Q＝f(x) (1)
With i=i, ii, iii, iv, v, 5 curve numbers in the curve group, Q i representing the disease index or insect condition index of the ith disease (insect) base index curve, Q Di representing the disease index or insect condition index at the reference point of the ith disease (insect) base index curve. According to the equal ratio method, each curve and the main curve have the proportional relation of the formula (2):
the expressions of 5 disease (insect) base index curves obtained by the formula (2) are respectively:
QIII＝f(x)，/> and/>
Example 2 construction of a Chisong-akkui disease-based index model
1. Method of
1.1 Overview of study area
Kunzhan (121 DEG 41 '34' to 121 DEG 48 '04' E,37 DEG 11 '50' to 37 DEG 17 '22' N) is located in the eastern part of the Shandong peninsula, straddling the smoke table Kunzhon region and Wisea on sale. The area belongs to warm-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. Forest types are 6 types of red pine, black pine, japanese larch/spruce, conifer-quercus acutissima and conifer-miscellaneous and broadleaf. Pinus sylvestris is used as the main colonic seed of Kunzhen mountain, and is distributed from foot to altitude of 800 m. Pinus densiflora is mainly infected by pathogenic matters such as red blight (Pestalotiopsis funerea), tip blight (Sphaeropsis sapinea) and pine wood nematode (Bursaphelenchus xylophilus) of pine and pests such as Kunzhen flat leaf bees (CEPHALCIA KUNYUSHANICA), pine caterpillars (Dendrolinmus spectabilis) and pine meadow (Matsucoccus matsumurae) of pine.
1.2 Quantitative determination of the Focus of Alternaria alternata
1.2.1 Selecting pure forest and red blight of red pine as study subjects.
1.2.2 Set up 136 temporary plots (30 m x 30m, guard band width 30 m) of relatively consistent forest ages (34±2a) for the study area, and ensure uniformity and completeness of the values of the elements of the living environment and all forest stand.
1.2.3 Investigation and recording of disease index
The disease index of the red pine stand adopts a five-point method, namely 2 plants are respectively taken from 4 corners and centers in each sample area, 10 plants of red pine are all investigated, and then the disease index of the 10 plants is obtained averagely. And (3) taking 1 branch from each of the upper, middle and lower 3 layers of the single pinus koraiensis-like wood in the east, south, west and north directions, and counting the morbidity of each branch. The needles She Jinshi of each sample branch are considered as cylindrical with the same base area, so the ratio of the area of each needle leaf lesion to the area of the needle leaf can be converted into the ratio of the two lengths. The ratio of the length of the needle leaf disease spots of each branch to the length of the needle leaf is actually measured, the plant disease index is calculated according to a five-stage grading weighted average method, and the grading standard of the branch diseases is shown in table 1.
TABLE 1 Red blight disease shoot classification criteria
The disease index is calculated as follows:
1.2.4 investigation of forest stand and habitat factors
6 Stand factors including average tree height, branch height, breast diameter, stand density, canopy density and crown width of the survey sample area. Wherein, the density of the stand is only counted to count the Pinus koraiensis with the diameter of the breast diameter more than or equal to 2cm in the sample area. And (3) positioning the sample plot by using a GPS, and measuring and recording the indexes such as the altitude, the slope position, the slope direction, the gradient, the physical and chemical properties of soil and the like of the sample plot.
1.2.5 Screening of Key stand factors
Screening the stand factors by a stepwise regression method, and taking the selected key factors or the comprehensive variables consisting of the key factors as the abscissa of the disease-based index model.
1.2.6 Establishing an alternative equation model of the disease occurrence degree and the stand factors
The model is fitted with 80% of sample data, and the alternative equation model is expressed as q=f (x), where x is a complex variable composed of a certain key factor or a certain number of key factors after stepwise regression screening.
1.2.7 Determination of reference points
The reference point has very remarkable influence on the disease-based index model, and incorrect selection can cause deviation on the evaluation of the occurrence degree of diseases. The invention defines the datum point as the forest stand index value corresponding to 50 of the disease index of the main curve.
Inspection and screening of 1.2.8 models
Before the disease-based index curve group is established, the unmodeled data are substituted into a regression equation to obtain a predicted value of the disease-based index, the predicted value is compared with an actual value Q, the average relative error (MAE), the Root Mean Square Error (RMSE) and the model correlation coefficient (R 2) are selected for evaluating the model fitting parameter precision, a model with higher precision is selected as a disease-based index equation model, and a disease-based index main curve is drawn.
1.2.9 Establishing a group of pure forest disease-based index models of Pinus densiflora
The method is characterized in that an equal ratio method is adopted, a forest stand factor value when the disease occurrence degree is 50 is taken as a reference point, a main model is taken as a center, 2 function models are respectively fitted upwards and downwards through the equal ratio method, 5 function models are taken as the reference point of the forest stand factor value, the values of the 5 function models are respectively 10, 30, 50, 70 and 90, the values represent potential occurrence conditions of red-pine red-spot disease in red-pine pure forest due to different habitats, namely disease base indexes, and can be directly marked by 10-extremely light disease occurrence, 30-slight disease occurrence, 50-moderate disease occurrence, 70-severe disease occurrence and 90-extremely heavy disease occurrence, or I-extremely light disease occurrence, II-slight disease occurrence, III-moderate disease occurrence, IV-severe disease occurrence and V-extremely heavy disease occurrence.
2. Data processing
Experimental data were processed using Microsoft Excel 2007, stepwise regression screening of dominant factors was performed using SPSS software (version 22.0), origin8.0 fitting the main curve and constructing the curve group.
3. Analysis of results
3.1 Screening of Key stand factors
And (5) screening the stand factors influencing the disease index of the red pine by adopting a stepwise regression method. As shown in table 2, the dominant factors affecting the disease index size were woodland density and 2 branches high, both of which can explain the 57.1% change in disease index (R 2 =0.571), and the effect on disease index was also very significant (f=86.704, p=0.000). Wherein the stand density can account for 54.9% change in disease index (r 2 =0.549), indicating that stand density is a key factor affecting disease index in stand factors.
TABLE 2 stepwise regression equation for akkuli disease and stand factors
3.2 Establishment of the Main Curve
By using a nonlinear fitting function in origin8.0, fitting a relation model of 3 stand density-disease indexes shown in table 3, and combining R 2, MAE and RMSE of each fitting equation and actual rules of occurrence of diseases in the forest land along with the density, determining a main curve equation of the stand density-disease indexes as Q= 57.40/(1+49.46× -0.0031x), wherein Q represents the disease index, and x represents the stand density. R 2 =0.89, mae=9.52%, rmse=3.68. The calculated stand density benchmark was 1875 strains/hm 2, indicating that at this stand density, the severity of red pine wilt was 50 for red pine woods. As can be seen from the main curve, when the density of the stand is less than 500 plants/hm 2, the disease index slowly rises along with the increase of the density of the stand, which indicates that the density of the stand has less influence on the disease index before the density of the stand is Lin Yubi; when the stand density is between 500 strains/hm 2 and 2300 strains/hm 2, the disease index is obviously increased along with the increase of the stand density; when the stand density is greater than 2300 strain/hm 2, the increase of the disease index tends to be gentle, which means that the effect of the stand density on the disease index is smaller or approximately no effect (fig. 4).
TABLE 3 results of Main Curve fitting
3.3 Formation of Curve group
From the main curve q= 57.40/(1+49.46 e -0.0031x):
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)
Q Ⅰ、QⅡ、QⅢ、QⅣ and Q Ⅴ represent the disease index of 5 disease base index curves in the curve group, respectively, as shown in FIG. 5. As shown in the curve group, the habitat condition reflected by the disease base index 90 is worst, and the red blight is most serious; when the density is close to 2300 strain/hm 2, the disease index of the plot is 100, which indicates that the effect of the forest stand density on the akkulate disease is large in the plot with the different habitat levels. For the forest land with disease base index 10, the environmental condition is optimal, and the quality of the environment plays a greater role in influencing the severity of disease occurrence, namely the disease index is least influenced by the density of the forest stand; the disease index increases slightly in the range of less than 2000 strain/hm 2 and then becomes gentle.
Example 3 application of Red pine and Red blight disease base index model
1. Basis for application of red pine blight disease base index model
According to the definition of disease base index, all points within 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-based index condition of a certain pinus koraiensis (34+/-2 a) pure woodland in the disease-based index curve group diagram, 4 midlines (figure 6) are continuously expanded according to an equal ratio method on the basis of the disease-based index curve group (figure 5), and the expression of the 4 midlines 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 20、Q40、Q60 and Q 80 represent indices of illness at the stand density reference points of 20, 40, 60 and 80, respectively. I.e. if the sample point falls below Q 20, the disease base index of the pinus sylvestris is 10; if the sample point falls within the [ Q 20,Q40) interval, the disease base index of the Pinus sylvestris is 30; if the sample point falls within the [ Q 40,Q60) interval, the disease base index of the Pinus sylvestris is 50; if the sample point falls within the [ Q 60,Q80) interval, the disease base index of the Pinus sylvestris is 70; when the sample point falls on the midline Q 80 and above, the disease base index of the pinus woodland is 90.
2. Application example of red pine and red blight disease base index model
If the forest stand density of a certain red pine (34+/-2 a) pure Lin Linde is 1250 plants/hm 2 and the disease index is 57.15, based on a red pine red blight disease base index curve group (including a central line) graph (figure 6), the point falls above the central line Q 80, so according to the application basis of the disease base index curve group graph, the situation that the site is potential to generate red blight is 90, namely the disease base index is V grade; if the forest stand density of the investigated forest land is 1500 plants/hm 2 and the disease index is 20, the point falls in the interval of the midline [ Q 20,Q40 ], which indicates that the situation of potential occurrence of the red blight of the sample land is 30, namely the disease base index is grade II.
Claims (6)
1. A method of establishing a population of woodland disease base index or insect base index curves, comprising the steps of: (1) Setting a standard land for pure forest, taking the standard land as a target, and investigating disease or insect damage indexes; (2) investigating the stand factors, and screening key stand factors; (3) Establishing an equation model of the occurrence degree of diseases or insect pests and key stand factors, wherein the equation model is represented by Q=f (x); wherein x is a key stand factor or a comprehensive variable formed by a plurality of key stand factors; q represents the occurrence degree of diseases or insect pests and is represented by disease index or insect pest index;
Drawing a disease-based index main curve or a pest-based index main curve by taking a certain key stand factor of a pure forest or a comprehensive variable consisting of a plurality of key stand factors as an abscissa and the occurrence degree of diseases or pests as an ordinate; (4) Taking a key forest stand factor value corresponding to the occurrence degree of the disease or insect pest as a reference point, taking a disease base index main curve or an insect base index main curve as a center, respectively stretching out 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 datum points are 10, 30, 50, 70 and 90 respectively;
The 5 curves respectively correspond to 5 disease or insect pest occurrence grades, and the general potential occurrence conditions of the disease or insect pest are quantitatively classified into 5 grades: 10-very light disease or pest occurrence, 30-light disease or pest occurrence, 50-medium disease or pest occurrence, 70-heavy disease or pest occurrence, 90-extra heavy disease or pest occurrence;
And continuously expanding 4 central lines according to an equal ratio method on the basis of a curve group formed by the 5 curves, wherein the values of the 4 central lines at key stand factor datum points are respectively 20, 40, 60 and 80.
2. A method according to claim 1, characterized in that: the disease index in the step (1) is a disease index; the insect pest index is an insect pest index.
3. The method of claim 1, wherein the stand factor of step (2) comprises: any one or more of forest stand density, forest age, canopy density, crown width, average tree height, under-branch height or breast diameter;
and (2) screening the stand factors by a statistical analysis method to screen out key stand factors influencing disease or insect pest indexes.
4. A method according to claim 1, characterized in that: the ages of the pure forests in the step (1) are relatively consistent.
5. The method of claim 4, wherein: the forest age range of the pure forest is average age + -2 years.
6. A method according to claim 1, characterized in that: the occurrence degree of the diseases or insect pests in the step (3) is expressed by a disease index or an insect pest index.
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| 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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| 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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| 彭龙慧 ; 许永青 ; 唐艳龙 ; 温小遂 ; 王丽娜 ; .林分因子与萧氏松茎象危害程度的风险评估.江西农业大学学报.2007,29(05),第746-750页. * |
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