Cultivation method

Abstract

To provide a crop cultivation method enabling avoidance or suppression of disease occurrence due to pathogenic microorganisms.SOLUTION: A crop cultivation method comprising: performing co-occurrence analysis on a microbiome dataset composed of microbiome data obtained by DNA analysis of soil collected from a data collection field; identifying key microorganisms based on a coexistence pattern or a non-coexistence pattern with a target pathogenic microorganism in results of the co-occurrence analysis; determining a threshold based on correlation information between an amount of the key microorganisms present in soil and a threshold-related growth item associated with growth of crops; and determining a cultivation policy for the crops by comparing the amount of the key microorganisms present in cultivation soil of a cultivation target field with the threshold.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a method for cultivating agricultural crops. [Background technology]

[0002] Just as intestinal bacteria affect human health, it is known that microorganisms have a significant impact on the growth of agricultural crops. Attempts have been made to utilize useful microorganisms in the growth of agricultural crops, and so-called microbial materials have been brought to market. For example, a cultivation method has been proposed in which microorganisms of a specific genus are inoculated (Patent Document 1). With the advancement of DNA analysis technology, it has become possible to visualize the microflora (microbial network) in the soil, and to understand the collaborative actions of microorganisms (Non-Patent Document 1). An ecosystem induction method has been proposed that includes a step of analyzing the DNA of the soil in the area where the plants are to be grown and analyzing the microflora (Patent Document 2). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent Publication No. 2021-141886 [Patent Document 2] Patent No. 6899107 [Non-patent literature]

[0004] [Non-Patent Document 1] Toju, H. et al., Core microbiomes for sustainable agroecosystems., Nature Plants 4, 247-257(2018) Summary of the Invention

[0005] One embodiment of the present disclosure provides a method for cultivating agricultural crops, which comprises: performing a co-occurrence analysis on a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected at a data acquisition field; identifying key microorganisms based on patterns of coexistence or non-coexistence with target pathogenic microorganisms in the results of the co-occurrence analysis; determining a threshold based on correlation information between the abundance of the key microorganisms in the soil and threshold-use growth items related to the growth of agricultural crops; and comparing the abundance of the key microorganisms in the cultivation soil collected at a target cultivation field with the threshold to determine a cultivation policy for the agricultural crops. [Brief explanation of the drawings]

[0006] [Figure 1] FIG. 1 is a schematic diagram of a process flow including one embodiment of the present disclosure. [Figure 2] FIG. 2 is a schematic diagram of a process flow including another embodiment of the present disclosure different from that of FIG. [Figure 3] FIG. 3 is a schematic diagram of a process flow including an embodiment of the present disclosure different from that of FIGS. [Figure 4] Figure 4 is an illustration of an example in which the abundance ratio of each microorganism is superimposed on co-occurrence network analysis, which is a co-occurrence analysis using microbiota data. [Figure 5] Figure 5 is an illustration of an example of co-occurrence network analysis using microbiota data, in which the correlation coefficient between the abundance of microorganisms in soil contained in the microbiota data and the data subject to correlation analysis is superimposed. DETAILED DESCRIPTION OF THE INVENTION

[0007] [Problem to be solved by this disclosure] Various types of pathogenic microorganisms can exist in the growing environment of agricultural crops. These pathogenic microorganisms cause diseases, which can significantly reduce crop yields, creating a problem.

[0008] [Effects of this disclosure] The present disclosure provides a method for cultivating agricultural crops to avoid or suppress the occurrence of diseases caused by pathogenic microorganisms, which is particularly useful when the pathogenic microorganisms are difficult to detect.

[0009] [Description of the embodiments of the present disclosure] First, embodiments of the present disclosure will be listed and described.

[0010] (1) According to one embodiment of the present disclosure, a method for cultivating agricultural crops includes: performing a co-occurrence analysis on a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected in a data collection field; identifying key microorganisms based on patterns of coexistence or non-coexistence with target pathogenic microorganisms in the results of the co-occurrence analysis; determining a threshold based on correlation information between the abundance of the key microorganisms in the soil and threshold-use growth items related to the growth of agricultural crops; and determining a cultivation policy for the agricultural crops by comparing the abundance of the key microorganisms in the cultivation soil collected in the target cultivation field with the threshold in order to avoid or suppress the occurrence of diseases caused by the target pathogenic microorganisms.

[0011] (2) A method for cultivating agricultural crops according to another aspect of the present disclosure, different from (1), is a cultivation method for determining a cultivation policy for agricultural crops by comparing the abundance of key microorganisms in cultivation soil collected from a target cultivation field with a threshold value in order to avoid or suppress the occurrence of diseases caused by target pathogenic microorganisms, wherein the key microorganisms are identified based on coexistence or non-coexistence patterns with the target pathogenic microorganisms in a co-occurrence analysis of a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected from a data acquisition field, and the threshold value is determined based on correlation information between the abundance of the key microorganisms in the soil and threshold-use growth items related to the growth of the agricultural crops.

[0012] (3) According to another aspect of the present disclosure, other than (1) and (2), a method for cultivating agricultural crops includes: identifying key microorganisms based on correlation analysis between a microbiome dataset consisting of microbiome data acquired by DNA analysis of soil collected from a data acquisition field; and a correlation analysis target dataset consisting of correlation analysis target data including correlation growth items related to the growth of the agricultural crops; determining a threshold value based on correlation information between the abundance of the key microorganisms in the soil and the threshold value-related growth items related to the growth of the agricultural crops; and comparing the abundance of the key microorganisms in the cultivation soil collected from the cultivation target field with the threshold value to determine a cultivation policy for the agricultural crops in order to avoid or suppress the occurrence of diseases caused by target pathogenic microorganisms.

[0013] (4) A method for cultivating agricultural crops according to another aspect of the present disclosure, different from (1) to (3), is a cultivation method for determining a cultivation policy for agricultural crops by comparing the abundance of key microorganisms in cultivation soil collected from a target cultivation field with a threshold value in order to avoid or suppress the occurrence of diseases caused by target pathogenic microorganisms, wherein the key microorganisms are identified based on correlation analysis between a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected from a data acquisition field and a correlation analysis target dataset consisting of correlation analysis target data including correlation growth items related to the growth of the agricultural crops, and the threshold value is determined based on correlation information between the abundance of the key microorganisms in the soil and the threshold growth items related to the growth of the agricultural crops.

[0014] (5) In the above (1) to (4), the key microorganisms may be identified based on a co-occurrence analysis of the microbiome dataset in addition to the correlation analysis. By combining the correlation analysis and the co-occurrence analysis, key microorganisms that are easy to detect can be identified properly without omission.

[0015] (6) In the above (1) to (5), sparse inverse covariance estimation may be used for the co-occurrence analysis. This is because it is possible to obtain a lot of information by not only observing the simple correlation between the increase and decrease in the abundance of each microorganism, but also by performing a sophisticated analysis of the coexistence and non-coexistence patterns.

[0016] (7) In (6) above, the criteria for identifying the key microorganism may include the covariance between the abundance of the target pathogenic microorganism and the abundance of microorganisms included in the microbiome dataset, and the absolute value of the covariance may be 0.1 or more and 50.0 or less. This is because it is possible to identify key microorganisms that are closely related to the abundance of the target pathogenic microorganism in terms of covariance.

[0017] (8) In the above (1) to (7), the co-occurrence analysis may be an analysis that examines the correlation between the abundance of the target pathogenic microorganism and the abundance of microorganisms included in the microbiota dataset.

[0018] (9) In the above (1) to (8), the criteria for identifying the key microorganism may include a correlation coefficient between the abundance of the target pathogenic microorganism and the abundance of microorganisms included in the microbiome dataset, and the absolute value of the correlation coefficient may be 0.2 or more and less than 1.0. This is because calculating the correlation coefficient between the abundance of the target pathogenic microorganism and the abundance of microorganisms is simple and easy compared to sparse inverse covariance estimation, etc.

[0019] (10) In the above (1) to (9), the criteria for identifying the key microorganism may include the correlation coefficient of the correlation analysis, and the absolute value of the correlation coefficient may be 0.2 or more and less than 1.0. This is because a correlation coefficient of 0.2 or more is recognized as having a certain degree of correlation.

[0020] (11) In the above (1) to (10), the identified key microorganisms may include multiple microorganisms. By identifying multiple key microorganisms, the results of co-occurrence analysis and correlation analysis can be reflected more deeply and broadly in determining the threshold.

[0021] (12) In the above (1) to (11), the identified key microorganisms may include multiple key microorganisms belonging to the same microbial module, because the presence or absence of the microbial module and the degree of its formation can be reflected in determining the threshold.

[0022] (13) In the above (1) to (12), the identified key microorganisms may include microorganisms that belong to the same microbial module as the target pathogenic microorganism. This is because the target pathogenic microorganism can be indirectly detected by detecting key microorganisms that belong to the same microbial module as the target pathogenic microorganism.

[0023] (14) In the above (1) to (13), the identified key microorganism may include a microorganism having at least one average value of 1.0% or more selected from the group of average values ​​consisting of the average value of the abundance ratio in all of the microbiota data in which the target pathogenic microorganism is detected and the average value of the abundance ratio in all of the microbiota data in which the target pathogenic microorganism is not detected. This is because microorganisms with high abundance ratios are easy to detect.

[0024] (15) In the above (1) to (14), the identified key microorganisms may include fungi, because the abundance of fungi can be measured with high accuracy using the meta-ITS method.

[0025] (16) In the above (1) to (15), the correlating growth items may include growth status items, since the growth status items have a direct correlation with the growth of agricultural crops.

[0026] (17) In (16) above, the correlation growth items may include the degree of disease occurrence as an item. This is because the degree of disease occurrence represents the state of growth of agricultural crops and has a strong correlation with the abundance of the target pathogenic microorganisms in the soil, which is directly related to the growth of agricultural crops.

[0027] (18) In the above (16), the correlation growth items may further include at least one item selected from the group consisting of soil characteristics items, climate items, and work management items, because the soil characteristics items, climate items, and work management items may affect the growth of agricultural crops.

[0028] (19) In the above (1) to (18), the location where the correlation analysis target data is acquired may be within 20 km of the soil sampling location where the corresponding microbiome data is acquired. This is because the distance between the target data acquisition location and the soil sampling location is close, and the values ​​of the items included in the correlation analysis target data at both locations are likely to be similar.

[0029] (20) In the above (1) to (19), the location where the data to be analyzed for correlation is acquired may be within the field to which the soil sampling location where the corresponding microbial flora data is acquired belongs, or within 1 km from the outer edge of the field. This is because in many cases, the soil characteristics, climate, and work management data items within the same field and its neighboring areas can be considered to be comparable.

[0030] (21) In the above (1) to (20), the correlation information may be correlation information obtained from the results of the co-occurrence analysis used to determine the key microorganisms. This is because information on the key microorganisms can be reliably obtained and the effort of searching for the correlation information can be eliminated. Furthermore, if the conditions of the microbial flora in the soil where the microbial flora data is collected are similar to those in the target cultivation field, the correlation information will more accurately reflect the conditions in the target cultivation field.

[0031] (22) In the above (1) to (21), the correlation information may be correlation information obtained from the results of the correlation analysis used to determine the key microorganism. This is because information on the key microorganism can be reliably obtained and the effort of searching for the correlation information can be eliminated. Furthermore, if the state of the microbial flora at the soil sampling site where the microbiome data is obtained is similar to that of the target cultivation field, or if the state of the microbial flora at the location where the correlation analysis data is obtained is similar to that of the target cultivation field, the correlation information will more accurately reflect the state of the target cultivation field.

[0032] (23) In the above (1) to (22), the threshold value may include multiple threshold values. This allows for more flexible cultivation strategies to be determined based on the abundance of key microorganisms in the soil.

[0033] (24) In the above (1) to (23), the threshold value may be a function including variables. Some growth parameters related to the growth of agricultural crops vary depending on the cultivation season and cultivation location, so this allows for flexible response to such variations, enabling more flexible decisions on cultivation policies.

[0034] (25) In (1) to (24) above, the threshold growth item may include at least one growth item selected from the group consisting of the abundance of the target pathogenic microorganism in the soil, the abundance ratio of the target pathogenic microorganism, and the degree of disease occurrence, and the soil abundance of the key microorganism may have a positive or negative correlation with the threshold growth item. If there is a positive correlation, a cultivation policy may be decided to take control measures or change the crop being cultivated if the abundance in the cultivation soil is equal to or greater than the threshold, and if it is less than the threshold, a cultivation policy may be decided to take neither control measures nor change the crop. If there is a negative correlation, a cultivation policy may be decided to take control measures or change the crop being cultivated if the abundance in the cultivation soil is equal to or less than the threshold, and if it is greater than the threshold, a cultivation policy may be decided to take neither control measures nor change the crop.

[0035] (26) In the above (1) to (25), the abundance ratio of the target pathogenic microorganism may be less than 1.0% in the average value of all the microbiota data included in the microbiota dataset, and at least one average value selected from the group consisting of the average value of all the microbiota data in which the target pathogenic microorganism was detected and the average value of all the microbiota data in which the target pathogenic microorganism was not detected may be 1.0% or more. This is because the cultivation method of the present disclosure is particularly useful when it is difficult to detect the target pathogenic microorganism and easy to detect the key microorganism.

[0036] (27) In the above (1) to (26), the abundance ratio of the target pathogenic microorganism in the cultivation soil collected from the target cultivation field may be less than 1.0% in terms of the relative occurrence frequency of reads determined by amplicon sequencing, and the abundance of the key microorganism in the cultivation soil may be 1.0% or more in terms of the relative occurrence frequency of reads determined by amplicon sequencing. This is because the cultivation method of the present disclosure is particularly useful when the target pathogenic microorganism is difficult to detect but the key microorganism is easy to detect.

[0037] (28) In the above (1) to (27), the soil collected in the data acquisition field may contain soil in which the abundance ratio of the target pathogenic microorganism is higher than that of the cultivation soil collected in the target cultivation field. This is because the data acquisition field may include a field where a disease caused by the target pathogenic microorganism has occurred or a field where the abundance of the target pathogenic microorganism is high, thereby increasing the possibility of obtaining data on target pathogenic microorganisms that are difficult to detect.

[0038] (29) In the above (1) to (28), the average value of the abundance ratio of the target pathogenic microorganism in all the microbiota data in the microbiota dataset may be greater than the abundance ratio of the target pathogenic microorganism in the cultivation soil. This is because the data acquisition fields include fields where diseases caused by the target pathogenic microorganism have occurred and fields where the target pathogenic microorganism is abundant, thereby increasing the possibility of acquiring data about target pathogenic microorganisms that are difficult to detect.

[0039] (30) In the above (1) to (29), the target pathogenic microorganism may be a root rot pathogen, and the key microorganism may be a microorganism selected from the group consisting of the genera Fusarium, Cercospora, Mycosphaerella, Alternaria, Allophoma, Mortierella, and Trichoderma. This is because root rot pathogens are difficult to detect even by next-generation sequencing, whereas the key microorganisms are relatively easy to detect.

[0040] (31) In the above (1) to (30), the target pathogenic microorganism may be a root rot pathogen, and the key microorganism may be a microorganism selected from the group consisting of Fusarium and Mortierella. This is because root rot pathogens are difficult to detect even by next-generation sequencing, whereas Fusarium and Mortierella are particularly easy to detect.

[0041] (32) In the above (1) to (31), the target pathogenic microorganism may be a fungus, because the abundance of fungi can be measured with high accuracy by using the meta-ITS method, which limits the target biological species.

[0042] (33) In the above (1) to (32), next-generation sequencing may be used for the DNA analysis because next-generation sequencing is highly sensitive and highly accurate.

[0043] (34) In the above (1) to (33), the DNA analysis may be at least one DNA analysis selected from the group consisting of 16S rRNA gene analysis, 18S rRNA gene analysis, metagenomic shotgun analysis, and meta-ITS method.

[0044] (35) In the above (1) to (34), next-generation sequencing may be used to measure the abundance of the key microorganisms in the cultivation soil, because next-generation sequencing is highly sensitive and highly accurate.

[0045] (36) In the above (1) to (35), the abundance of the key microorganism in the cultivation soil may be measured using at least one analytical method selected from the group consisting of DNA microarray, PCR, and FISH, because these are simple and inexpensive measurement methods.

[0046] (37) In the above (1) to (36), the amount present in the cultivation soil may be measured multiple times to increase the accuracy of the measurement.

[0047] (38) In the above (1) to (37), the amount of the compound present in the cultivation soil predicted based on the results of multiple measurements of the amount of the compound present in the cultivation soil over time may be used as the amount of the compound present in the cultivation soil, and the threshold value may be compared with this amount. This is because preventive and defensive cultivation is possible.

[0048] (39) In the above (1) to (38), the measurement of the amount present in the cultivation soil may include measurement of the amount present in the cultivation soil of soil collected at a point 0 km or more and 5 km or less from the outer edge of the target cultivation field, because this makes it possible to detect the inflow of the target pathogenic microorganism from the surrounding area into the target cultivation field.

[0049] [Description of the embodiments of the present disclosure] Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Figures 1 to 3 are schematic diagrams of process flows. These schematic diagrams may include processes outside the scope of the embodiments of the present disclosure to aid understanding. Figure 4 is a conceptual diagram of an example in which the abundance ratios of each microorganism are superimposed on co-occurrence network analysis, a co-occurrence analysis using microbiome data. Figure 5 is a conceptual diagram of an example in which the correlation coefficient between the soil abundance of microorganisms contained in the microbiome data and the data to be analyzed for correlation analysis is superimposed on co-occurrence network analysis, a co-occurrence analysis using microbiome data. Please note that Figures 4 and 5 are merely conceptual diagrams, and with the primary focus on facilitating understanding, the actual analysis data has been greatly simplified and may contain inaccurate parts. The same or corresponding parts in the figures are designated by the same reference numerals, and their descriptions will not be repeated. Furthermore, at least some of the embodiments described below may be combined in any manner. The present disclosure is not limited to the illustrated embodiments, but is defined by the claims, and all modifications within the meaning and scope of the claims are intended to be included.

[0050] <Process flow> Figure 1 is a schematic diagram of a process flow including one embodiment of the present disclosure, which comprises the following steps: S1: Data collection: Collect soil from the field. S2: Conduct soil DNA analysis, obtain microbiome data, and create a microbiome dataset. S3: Conduct co-occurrence analysis on the above microbiome dataset to identify key microorganisms. S4: Based on the correlation information between the abundance of the key microorganisms in the soil and the threshold growth items related to the growth of agricultural crops, a threshold value for the abundance of the key microorganisms in the soil is determined. S5: Collect cultivation soil from the target cultivation field. S6: Measure the abundance of key microorganisms in the cultivation soil of the target cultivation field. S7: Determine the cultivation policy by comparing the amount present in the cultivation soil with the threshold value.

[0051] 2 is a schematic diagram of a process flow including an embodiment of the present disclosure different from that of FIG. 1. It consists of the following steps: It differs from FIG. 1 in that it includes step S2′ and step S3′ instead of S3. S1: Data collection: Collect soil from the field. S2: Conduct soil DNA analysis, obtain microbiome data, and create a microbiome dataset. S2': Data to be analyzed for correlation analysis is acquired, and a data set to be analyzed for correlation analysis is created. S3': A correlation analysis is performed between the microbiota dataset and the dataset to be subjected to correlation analysis to identify key microorganisms. S4: Based on the correlation information between the abundance of the key microorganisms in the soil and the threshold growth items related to the growth of agricultural crops, a threshold value for the abundance of the key microorganisms in the soil is determined. S5: Collect cultivation soil from the target cultivation field. S6: Measure the abundance of key microorganisms in the cultivation soil of the target cultivation field. S7: Determine the cultivation policy by comparing the amount present in the cultivation soil with the threshold value.

[0052] Figure 3 is a schematic diagram of a process flow including an embodiment of the present disclosure different from Figures 1 and 2. It consists of the following steps. It differs from Figure 1 in that it includes step S4' instead of step S4 and step S6'. S1: Data collection: Collect soil from the field. S2: Perform soil DNA analysis, obtain microbiota data, and create a microbiota dataset. S3: Perform co-occurrence analysis on the above microbiota dataset to identify key microorganisms. S4‘: Based on the correlation information between the soil abundance of the above key microorganisms and the growth items for threshold values related to the growth of crops, determine the threshold value (function including variables) of the soil abundance of the key microorganisms. S5: Collect cultivation soil from the cultivation target field. S6: Measure the abundance of key microorganisms in the cultivation soil of the cultivation target field. S6‘: Obtain the actual values of the variables for determining the threshold value and calculate the threshold value. S7: Determine the cultivation policy by comparing the abundance in the cultivation soil with the threshold value.

[0053] The following will explain each step. <S1: Soil Sampling> In S1, soil is sampled from the data acquisition field. The data acquisition field may include fields where diseases are occurring. This is because by including fields where diseases are occurring, key microorganisms related to the target pathogenic microorganisms can be easily identified.

[0054] The location of soil sampling in the data acquisition field may be a field adjacent to the field, such as a meadow or mountain forest. The number of fields from which soil is sampled may be 5 or more, 10 or more, or 50 or more. This is because as the number of data increases, the accuracy of the results of co-occurrence analysis and correlation analysis increases. The number of soil samples taken from each field may be 2 or more, 3 or more, or 4 or more. This is because as the number of data increases, the accuracy of the results of co-occurrence analysis and correlation analysis increases. Soil may be sampled from the same field on different sampling dates.

[0055] <S2: Microbiota Dataset Acquisition> In S2, DNA analysis of the soil collected in S1 is performed to obtain microbiome data. The microbiome refers to the collection of living microorganisms in an ecosystem. In the present disclosure, microorganisms include viruses, bacteria, archaea, fungi (such as molds (filamentous fungi), yeasts, etc.), and in addition, small protozoa and protists. Microbiome data is data of the microbiome obtained by DNA analysis of soil, and includes at least information on the names (microbial identification markers) and detection amounts (abundance of microorganisms in the soil) of the microorganisms present in the analyzed soil. The microbiome dataset includes a plurality of microbiome data. As described in <S1: Soil Sampling>, a large amount of microbiome data may be obtained from many fields. This is because the accuracy of the results of co-occurrence analysis and correlation analysis increases.

[0056] <DNA Analysis> Examples of methods for DNA analysis and gene base sequence analysis include, but are not limited to, DNA microarray, next-generation sequencing, PCR method, DGGE method, T-RFLP method, FISH method, etc. The DNA analysis may be next-generation sequencing. This is because next-generation sequencing is highly sensitive and has excellent accuracy. Next-generation sequencing includes shotgun metagenomic analysis method, and amplicon sequencing, 16S rRNA gene analysis (DNA metabarcoding method targeting the prokaryotic 16S rRNA region), 18S rRNA gene analysis (DNA metabarcoding method targeting the eukaryotic 18S rRNA region), and fungal ITS (DNA metabarcoding method targeting the fungal ITS (internal transcribed spacer) region). 16S rRNA gene analysis is useful for identifying bacteria and archaea, 18S rRNA gene analysis is useful for identifying various eukaryotes including fungi and internal / external parasitic organisms, and fungal ITS is useful for identifying fungi. Calibration may be performed when analyzing DNA data by adjusting the concentration of a standard sequence fragment such as an artificially designed DNA sequence or λ phage and adding it to the PCR solution to estimate the absolute abundance (nucleic acid concentration) of each biological species. The above analysis methods may be combined.

[0057] <S3: Identification of Key Microorganisms> In S3, co-occurrence analysis is performed on the above microbiome dataset to identify key microorganisms.

[0058] <Co-occurrence analysis> In the co-occurrence analysis of a microbiome dataset for co-occurrence analysis, a statistical analysis of coexistence and non-coexistence patterns between microorganisms is performed using multiple microbiome data. The co-occurrence analysis may be used to identify key microorganisms, or a threshold for the abundance of key microorganisms in soil may be determined. For example, microorganisms that have a strong coexistence pattern relationship with target pathogenic microorganisms or a strong non-coexistence pattern relationship may be identified as key microorganisms. Furthermore, a threshold for the abundance of key microorganisms in soil may be determined based on the coexistence or non-coexistence patterns.

[0059] In co-occurrence analysis, for example, co-occurrence network analysis may be performed to visualize the connections between microorganisms with similar coexistence or non-coexistence patterns (for a simple example, microorganisms with similar abundance in soil), and microbial module 2 may be visualized. This is because microbial module 2 makes it easier to identify key microorganisms. For example, a microorganism that belongs to the same microbial module 2 as the target pathogenic microorganism may be identified as the key microorganism.

[0060] Examples of co-occurrence analysis methods include, but are not limited to, simple methods such as measuring the correlation coefficient of soil abundance between microorganisms, to more advanced methods such as the sparse inverse covariance estimation for ecological association and statistical inference (the Meinshausen and Buhlmann (MB) method, the Glasso method, the sparse and low-rank (SLR) decomposition method, etc.).

[0061] When the correlation coefficient of the abundance of microorganisms in soil is used in the co-occurrence analysis as a criterion for identifying key microorganisms, the absolute value of the correlation coefficient may be 0.2 or more as the criterion. Alternatively, the criterion may be 0.3 or more. The upper limit may be less than 1.0 or 0.5 or more. When sparse inverse covariance estimation of the abundance of microorganisms in soil is used, the absolute value of the covariance may be 0.1 or more as the criterion. Alternatively, the criterion may be 0.5 or more. Alternatively, the criterion may be 1.0 or more. The upper limit may be, for example, 50 or less, 20 or less, or 10 or less, but is not particularly limited.

[0062] FIG. 4 shows an example of an image diagram of a co-occurrence network analysis using microbiome data, in which the abundance ratios of each microorganism are superimposed, according to one embodiment of the present disclosure. Microorganism 1 in the microbiome data used in the co-occurrence network analysis is shown as a black circle. The size of the black circle indicates the abundance ratio. The larger the size, the higher the abundance ratio. Microorganisms that have a covariance relationship of a certain level or more in the co-occurrence network analysis are connected by line segments. Microorganism module 2 is surrounded by a dotted line.

[0063] <Key microorganisms> The key microorganism may be a fungus (mold (filamentous fungus), yeast, etc.), a bacterium, archaea, a small protozoan, a protist, or a virus. The key microorganism may be a fungus (mold (filamentous fungus), yeast, etc.), a bacterium, archaea, a small protozoan, a protist. This is because the abundance of key microorganisms can be measured quantitatively and accurately using the meta-ITS method, 16S rRNA gene analysis method, and 18S rRNA gene analysis method. The key microorganism may be a fungus (mold (filamentous fungus), yeast, etc.). This is because the abundance can be measured accurately using the meta-ITS method, which limits the target biological species compared to 18S rRNA gene analysis method. The key microorganism may be a filamentous fungus. This is because many filamentous fungi are usually present in the soil, which can have a significant impact on crop growth.

[0064] The key microorganism may be a pathogenic microorganism or a functional filamentous fungus, such as a fungus belonging to the genus Cladosporium, Trichoderma, Astraeus, Hyaloscypha, Leptodophora, Malassezia, Cladophialophora, Mortierella, Phialocephala, Penicillium, Pseudeurotium, Linnemannia, Pyricularia, Curvularia, Adineta, or Clonostachys. , Mucor, Nigrospora, Rhizoctonia, Gremmenia, Acrocalymma, Fomitopsis, Colletotrichum, Cadophora, Phialocephala, Meliniomyces, Glutinomyces, and Fusarium, Cercospora, Mycosphaerella, Alternaria, Allophoma.

[0065] Multiple key microorganisms may be identified as key microorganisms. Identifying multiple key microorganisms allows the results of the correlation analysis to be reflected more deeply and broadly in the determination of thresholds. Microbiota contain a wide variety of microbial species, but certain microbial species strongly influence each other and act as a microbial module 2 (a group of microorganisms). For example, even if the abundance of a target pathogenic microorganism in soil is related to the degree of formation of a certain microbial module 2, this relationship does not necessarily hold between the abundance of the target pathogenic microorganism in soil and the individual microorganisms that make up the microbial module 2. For example, even if a certain microbial module 2 has a relationship that suppresses the abundance of the target pathogenic microorganism in soil, it is possible that this relationship will not be manifested when only a certain microorganism belonging to that microbial module 2 is introduced into the soil.

[0066] The key microorganisms may include multiple key microorganisms that constitute the microbial module 2. By including multiple key microorganisms that constitute the microbial module 2, the presence or absence and degree of formation of the microbial module 2 can be reflected in determining the threshold value. This increases the likelihood of obtaining a relationship between the microbial module 2 and the target pathogenic microorganism.

[0067] The key microorganisms may include microorganisms that belong to the same microorganism module 2 as the target pathogenic microorganism. This is because the target pathogenic microorganism can be indirectly detected by detecting microorganisms that belong to the same microorganism module 2 as the target pathogenic microorganism.

[0068] The abundance of the key microorganism in all the microbiome data sets in which the target pathogenic microorganism was detected may be 1.0% or more, 1.5% or more, 2.0% or more, 2.5% or more, or 3.0% or more, in terms of the abundance ratio of the key microorganism. The larger the abundance ratio, the easier the detection. The abundance of the key microorganism in all the microbiome data sets in which the target pathogenic microorganism was not detected may be 1.0% or more, 1.5% or more, 2.0% or more, 2.5% or more, or 3.0% or more, in terms of the abundance ratio of the key microorganism. The larger the abundance ratio, the easier the detection.

[0069] The abundance ratio of a microorganism is an index of the abundance of a microorganism, and is the ratio of the detected amount of that microorganism to the total detected amount of all microorganisms detected by DNA analysis. In other words, it is calculated as follows: "abundance ratio of microorganism A = abundance of microorganism A / total abundance of all detected microorganisms x 100".

[0070] Using the frequency of reads of key microorganisms determined by amplicon sequencing as the detection amount in DNA analysis, the abundance ratio of the key microorganism in all of the microbiome data sets in which the target pathogenic microorganism is detected may be 1.0% or more, 1.5% or more, 2.0% or more, 2.5% or more, or 3.0% or more, in terms of the average relative frequency of reads of key microorganisms determined by amplicon sequencing. This is because the higher the relative frequency, the easier the detection. The abundance ratio of the key microorganism in all of the microbiome data sets in which the target pathogenic microorganism is not detected may be 1.0% or more, 1.5% or more, 2.0% or more, 2.5% or more, or 3.0% or more, in terms of the average relative frequency of reads of key microorganisms determined by amplicon sequencing. This is because the higher the relative frequency, the easier the detection.

[0071] The relative frequency of a microorganism's reads is the ratio of the number of reads for that microorganism to the sum of the number of reads for all microorganisms detected by amplicon sequencing. In other words, it is calculated as follows: "Relative frequency of reads for microorganism A = number of reads for microorganism A / sum of the number of reads for all detected microorganisms x 100."

[0072] <Crops> In the present disclosure, agricultural crops may be any plants that can be cultivated in a field. For example, citrus fruits such as kabosu, sudachi, yuzu, and mandarin oranges, pome fruits such as pears and apples, stone fruits such as peaches and apricots, berries such as blackberries and blueberries, potatoes such as konjac, taro, and potato, root vegetables such as turnips, burdock, and carrots, bulbs such as onions, chives, and leeks, beans such as adzuki beans, broad beans, and edamame, melons such as cucumbers and pumpkins, solanaceae fruit vegetables such as tomatoes, eggplants, and peppers, and oily vegetables such as broccoli and pickled mustard greens. Examples of suitable vegetables include, but are not limited to, corn vegetables, leafy vegetables such as komatsuna, cabbage, Chinese cabbage, coriander, lettuce, chrysanthemum, perilla, spinach, etc., stem vegetables such as butterbur and asparagus, edible flowers such as edible pansies, mushrooms such as shiitake mushrooms, grains such as rice, wheat, millet, corn, and buckwheat, flowers such as morning glory, sunflower, pansy, celery, chrysanthemum, calamus, tulip, and rose, conifers such as cedar and cypress, and broad-leaved trees such as persimmon, chestnut, and zelkova.

[0073] <Diseases and target pathogenic microorganisms> The target pathogenic microorganisms may be determined independently of DNA analysis of soil collected from the data acquisition field, or may be determined based on the results of the DNA analysis. The target pathogenic microorganisms may be fungi (molds (filamentous fungi), yeast, etc.), bacteria, archaea, small protozoa, protozoa, or viruses. The target pathogenic microorganisms may be fungi (molds (filamentous fungi), yeast, etc.), bacteria, archaea, small protozoa, protozoa, or viruses. This is because the abundance of the target pathogenic microorganisms can be quantitatively and accurately measured using the meta-ITS method, 16S rRNA gene analysis method, and 18S rRNA gene analysis method. The target pathogenic microorganisms may be fungi (molds (filamentous fungi), yeast, etc.). This is because the abundance can be measured accurately using the meta-ITS method, which limits the target organism species compared to the 18S rRNA gene analysis method. The target pathogenic microorganisms may be filamentous fungi. This is because there are many types of filamentous fungi and they cause great damage to agricultural crops.

[0074] Examples of diseases caused by pathogenic fungi include powdery mildew, sesame leaf spot, rust spot, rust, bottom rot, sooty mold, sooty spot, sooty mildew, sooty leaf blight, scab, Dumontonia root rot, witches' broom, Pythium damping-off, Fusarium damping-off, Pestalotia, downy mildew, blast, dark spot, dwarf disease, wilt, late blight, yellow rot, yellow spot, flower blight, flower rot, bud rot, gray mold, brown spot, brown rust, brown root rot, brown spot, brown spot, foot blight, foot rot, dry rot, sclerotinia rot, stem rot, pink root rot, red stem blight, black mold, and black rust. , black rust (black leaf blight), black rot, black root rot, black scab, black spot, black spot, black spot, backbone, root rot, root rot (acute ginkgo), root rot leaf spot, branch dieback, purple spot, fruit rot, small spot, blue mold root rot, blue mold, red stain, red spot, red spot, anthracnose, canker, white rust, white blight, white spot, white root rot, half-leaf wilt, spot, spotted leaf, leaf spot, seedling rot, seedling damping-off, edema, rot, young fruit sclerotinia, leaf blight, leaf rot, sooty mold, damping-off, green mold, ring spot, chlorosis, vine splitting, clubroot, black rot sclerotinia, and base rot.

[0075] Examples of diseases caused by pathogenic bacteria include, but are not limited to, crown gall, neck rot, brown rot, black rot, bacterial wilt, canker, soft rot, rot, gall, soft rot, small grain gall symptoms, witches' broom, bacterial spot, heart rot, halo blight, bacterial bud rot, and yellows.

[0076] Examples of diseases caused by pathogenic viruses include, but are not limited to, viral diseases, mosaic diseases, ring disease, ink disease, stem necrosis disease, necrotic dwarf disease, white spot disease, stripe disease, black death, necrotic spot disease, stripe disease, necrotic ring disease, necrotic disease, leaf vein yellowing disease, yellow necrosis disease, cigar disease, mild mosaic disease, and necrotic spot disease.

[0077] Examples of diseases caused by other pathogenic microorganisms include, but are not limited to, leaf blight nematode disease, dwarf disease, white algae disease, potato rot nematode disease, tulip rust mite, root-knot nematode disease, root blight, bulb mites, and Tyrophagus putrescentiae.

[0078] Examples of pathogenic filamentous fungi include fungi of the genus Albugo, Alternaria, Bremia, Botrytis, Cercospora, Cercosporella, Colletotrichum, Fusarium, Glomerella, Mycovellosiella, Pyrenochaeta, Pythium, Phytophthora, Pseudoperonospora, Peronospora, Plasmopara, Puccinia, Rhizoctonia, Sclerotium, and Verticillium. More specific examples include Acremonium sp., Acroconidiella eschscholziae, Aecidium hamamelidis, Aecidium rhaphiolepidis, Albugo candida, Albugo ipomoeae-hardwickii, and Albugo ipomoeae-panduratae. f.sp. lacunosae, Albugo ipomoeae-panduratae f.sp. trilobae, Albugo portulacae, Alternaria alternata, Alternaria dianthi, Alternaria gomphrenae, Alternaria japonica, Alternaria sp., Alternaria tenuissima, Alternaria iridicola, Aphanomyces iridis, Ascochyta aquilegiae, Aspergillus niger, Asteridiella rhaphiolepidis, Asterina camelliae, binucleate Rhizoctonia AG-G, Botrytis cinerea, Bremia taraxaci, Capnodium quercinum, Capnodium salicinum, Ceratobasidium sp., Ceratobasidium sp.Rhizoctonia solani, Cercospora abeliae, Cercospora althaeina, Cercospora begoniae, Cercospora insulana, Cercospora ipomoeae, Cercospora kalmiae, Cercospora rosicola, Cercospora sp., Cercospora violae, Chalara elegans, Ciborinia sp, Cladosporium paeoniae, Coleosporium asterum, Coleosporium clematidis, Coleosporium eupaederiae, Coleosporium pini-asteris, Colletotrichum acutatum, Colletotrichum capsici, Colletotrichum destructivum, Colletotrichum gloeosporioides, Colletotrichum lilii, Colletotrichum sp., Colletotrichum dematium, Colletotrichum gloeosporioides, Corynespora cassiicola, Coniothyrium fuckelii, Curvularia gladioli, Cylindrocladium spathiphylli, Cystotheca wrightii, Diapleella coniothyrium, Diaporthe destruens, Didymelina dianthi, Didymella applanata, Didymellina macrospora, Diplocarpon mali, Diplocarpon rosae, Dumontinia tuberosa, Entyloma cosmi, Entyloma dahliae, Erysiphe aquilegiae var.ranunculi, Erysiphe aquilegiae, Erysiphe cichoracearum, Erysiphe gracilis, Erysiphe heraclei, Erysiphe polygoni, Exobasidium camelliae, Exobasidium cylindrosporum, Exobasidium japonicum, Exobasidium sakishimaense, Exobasidium sp.Exobasidium gracile, Fusarium avenaceum, Fusarium chlamydosporum, Fusarium graminearum, Fusarium oxysporum f.sp. conglutinans, Fusarium oxysporum f.sp. tanaceti, Fusarium oxysporum, Fusarium poae, Fusarium semitectum, Fusarium solani, Fusarium striatum, Fusarium tricinctum, Gibberella zeae, Gloeosporium illicii, Gloeosporium nymphaeae, Gloeosporium sp., Gloeosporium venetum, Gloeosporium yatsude, Glomerella cingulata, Glomerella sp.Colletotrichum dematium, Golovinomyces cichoracearum, Golovinomyces magnicellulata var. magnicellulata, Graphiola phoenics, Guignardia sp., Gymnosporangium asiaticum, Gymnosporangium japonicum, Gymnosporangium yamadae, Haematonectria blight Crown and root rot Nectria blight, Haematonectria ipomoeae, Heterosporium echinulatum, Hyaloperonospora lobulariae, Hypocapnodium camelliae, Hypocapnodium japonicum, Itersonilia perplexans, Kuehneola japonica, Lasiodiplodia theobromae, Leptosphaeria coniothyrium, Limacinia harai, Macrophoma lilii, Macrophomina phaseolina, Macrosporium iridicola, Marssonina coronaria, Marssonina rosae, Massonina mali, Melampsora hypericorum, Meliola butleri, Meliola cyclobalanopsina, Meliola osmanthi, Meliola taityuensis, Microsphaera izuensis f.breviseta, Microsphaera izuensis, Microsphaera querci, Microsphaera sp.Microsphaera alni, Monilia mumecola, Monilinia fructicola, Monilinia kusanoi, Monilinia laxa, Mycochaetophora sp., Mycosphaerella dianthi, Mycosphaerella macrospora, Mycosphaerella rosicola, Nectria heamatococca, Nectria ochroleuca, Nimbya gomphrenae, Ochropsora kraunhiae, Oidium hortensiae, Oidium Reticuloidium subgenus, Oidium sp., Pectobacterium carotovorum, Penicillium cyclopium, Penicillium hirsutum, Penicillium olsonii, Penicillium puberulum, Peronospora danica, Peronospora knautiae, Pestalotiopsis palmarum, Peronospora dianthicola, Peronospora parasitica, Peronospora sp., Peronospora sparsa, Pestalotia sp., Phakopsora artemisiae, Phoma exigua, Phoma sp.Didymella applanata, Phoma sp., Phoma spp., Phomopsis sclerotioides, Phragmidium fusiforme, Phragmidium mucronatum, Phragmidium rosae-multiflorae, Phyllactinia ailanthi, Phyllosticta antirrhini, Phyllosticta cruenta, Phyllosticta harai, Phytophthora nicotianae, Phytophthora cactorum, Phytophthora chrysanthemum sp.nov, Phytophthora citricola, Phytophthora citrophthora, Phytophthora cryptogea, Phytophthora nicotianae, Phytophthora palmivora, Phytophthora sp., Plasmodiophora brassicae, Plasmopara halstedii, Plasmopara obducens, Plectosporium tabacinum, Podosphaera clandestina var. clandestine, Podosphaera balsaminea, Podosphaera sp., Podosphaera xanthii, Protomyces inouyei, Pseudocercospora cymbidiicola, Pseudocercospora eustomatis, Pseudocercospora sp., Pseudonectria pachysandricola, Puccinia arenaria, Puccinia dianthi-japonici, Puccinia hikawaensis, Puccinia horiana, Puccinia kusanoi, Puccinia longicornis, Puccinia sasae, Puccinia tanaceti, Puccinia zoysiae, Pyrenochaeta terrestris, Pythium aphanidermatum, Pythium debaryanum, Pythium dissotocum, Pythium helicoides, Pythium irregulare complex, Pythium irregulare, Pythium myriotylum, Pythium oedochilum Pythium sylvaticum, Pythium sp., Pythium sp.Pythium irregulare, Pythium spinopsum, Pythium splendens, Pythium ultimum var.ultimum, Pythium ultimum, Rhizoctonia solani, Rhizoctonia sp. Chalara elegans, Rhizoctonia sp., Rhizopus necans, Rhizopus stolonifera, Rhytisma ilicis-latifoliae, Rosellinia necatrix, Sawadaea bicornis, Sawadaea negundinis, Sawadaea sp. Sawadaea polyfida, Sawadaea tulasnei, Sclerotinia sclerotiorum, Sclerotinia sp., Sclerotium cepivorum, Sclerotium rolfsii, Selenophoma dendrobii, Septobasidium tanakae, Septoria azalea, Septoria callistephi, Septoria chrysanthemella, Septoria gentianae, Septoria helianthi, Septoria obesa, Septoria violae, Sorataea pruni-persicae, Sphaceloma sp., Sphaerotheca ferruginea, Sphaerotheca fuliginea, Sphaerotheca fusca, Sphaerotheca spiraeae, Stagonospora curtisii, Stemphylium lancipes, Stemphylium lycopersici, Stemphylium sp, Stemphylium vesicarium, Subplenodomus drobnjacensis, Synchytrium minutum, Taphrina wiesneri, Thielaviopsis basicola, Treubiomyces japonicus, Uncinula hydrangeae, Uncinula sp., Uncinula veniciferae, Uromyces, Valsa ambiens, Verticillium dahlia, Verticillium dahliae, Verticillium tricorpus, Zygophiala jamaicensis.

[0079] Agrobacterium tumefacience and Burkholderia gladioli pv. gladioli, Burkholderia andropogonis, Burkholderia caryophylli, Clavibacter michiganensis subsp. Michiganensis, Curtobacterium flaccumfaciens pv. Aortic Candidatus Phytoplasma asteris、 Dickeya sp. Erwinia chrysanthemi Erwinia pineapple、Erwinia herbicola pv. Milletiae、 Erwinia carotovora subsp. carotovora, Erwinia chrysanthemi, Klebsiella oxytoca, MLO, Pectobacterium carotovorum, Pseudomonas caryophylli, Pseudomonas gladioli, Pseudomonas cichorii, Pseudomonas syringae pv. Phaseolicola、 Pseudomonas marginalis pv. marginalis, Pseudomonas andropogonis, Pseudomonas solanacearum, Phytoplasma, Ralstonia solanacearum, Xanthomonas campestris pv. incanae、 Xanthomonas campestris pv. Campestris、 Xanthomonas campestris pv. Begonia、 Xanthomonas campestris pv. Physalidicola is available in the market.

[0080] Materials include Alfalfa mosaic virus (AMV), Cucumber mosaic virus (CMV), Daphne virus, Alstroemeria mosaic virus (AlMV), Alstroemeria virus X Arabis mosaic virus, Lily symptomless virus Potyvirus, Apple stem grooving virus, Lily mottle virus (LMoV), Tulip breaking virus-L, Bean yellow mosaic virus (BYMV), Cycas necrotic stunt virus, Tobacco ringspot virus, Broad bean wilt virus (BBWV), Clover yellow vein virus, Carnation mottle virus (CarMV), Carnation latent virus (CLV), Carnation necrotic fleck virus (CNFV), Carnation vein mottle virus (CaVMV), Carnation etched ring virus (CERV), Chrysanthemum virus B(CVB), Amazon Lily Mozaic Virus. Iris mild mosaic virus Broad bean wilt virus Youcai mosaic virus Tulip breaking virus Gentian mosaic virus Tobacco mosaic virus (TMV) Dasheen mosaic virus (DsMV) Tomato mosaic virus (ToMV) Cymbidium mosaic virus (CymMV)、 Odontoglossum ringspot virus (ORSV)、 Grapevine Algerian latent virus (GALV) Japanese honeywort mosaic virus Aucuba ringspotvirus, Tomato spotted wilt virus (TSWV), Carla virus, Chrysanthemum stem necrosis virus (CSNV), Tomato bushy stunt virus (TBSV), Lisianthus necrotic stunt virus (LiNSV), Gloriosa fleck virus (GlFV), Gloriosa stripe mosaic virus (GSMV), Helleborus net necrosis virus, Impatiens necrotic spot virus (INSV), Iris yellow spot virus (LYSV) Lisianthus necrotic ringspot virus, Olive latent virus, Tobacco necrosis virus, Tobacco leaf curl Japan virus, Tomato yellow leaf curl virus (TYLCV), Tulip mild mottle mosaic virus, Turnip mosaic virus (TuMV).

[0081] Other pathogenic microorganisms include, but are not limited to, Aphelenchoides ritzemabosi, Aphelenchoides fragariae, Chrysanthemum stunt viroid (CSVd), Cephaleuros virescens, Ditylenchus destructor, Eriophyes tulipae, Meloidogyne arenaria, Meloidogyne hapla, Paraphytoptus kikus, Rhyzoglyphuse chinopus, Rhyzoglyphus robini, and Tyrophagus putrescentiae.

[0082] The target pathogenic microorganism may be a microorganism that is difficult to detect by DNA analysis, particularly next-generation sequencing. Alternatively, the key microorganism may be a microorganism that is easy to detect by DNA analysis. If the target pathogenic microorganism is difficult to detect, its presence will not be known, and as a result, no measures such as control will be taken, resulting in significant damage to crops. However, by detecting a key microorganism that is easy to detect and that is correlated with the abundance of the target pathogenic microorganism in a coexistence or non-coexistence pattern, or a key microorganism that is easy to detect and that is correlated with the data subject to correlation analysis, instead of the target pathogenic microorganism, it is possible to take measures such as control and avoid damage to crops.

[0083] If the ratio of the total detected amount of all detected microorganisms contained in the microbiome data to the detected amount of a specific microorganism, i.e., "detected amount of a specific microorganism / total detected amount of all detected microorganisms x 100," is taken as an abundance ratio, which is an index of the abundance of that specific microorganism, the abundance ratio of the target pathogenic microorganism may be less than 1.0% of the average value for all of the microbiome data in the microbiome dataset, or may be less than 0.5%, or may be less than 0.1%, or may be less than 0.05%. This is because the smaller the abundance ratio, the more difficult it is to detect, and therefore it is more useful to select key microorganisms that are easy to detect.

[0084] If the ratio of the number of reads for a particular microorganism to the sum of the number of reads for all microorganisms included in the microbiome data detected by amplicon sequencing, i.e., "relative occurrence frequency of reads for microorganism A = number of reads for microorganism A / sum of the number of reads for all detected microorganisms x 100," is taken as the relative occurrence frequency of the reads for that particular microorganism, then the average relative occurrence frequency of reads for the target pathogenic microorganism in all of the microbiome data in the microbiome dataset may be less than 1.0%, less than 0.5%, less than 0.1%, or less than 0.05%. This is because the smaller the abundance ratio, the more difficult it is to detect, and therefore selecting key microorganisms that are easy to detect is more useful.

[0085] The target pathogenic microorganism may be the causative agent of basal rot disease, and the key microorganism may be a microorganism selected from the group consisting of fungi of the genus Fusarium, Cercospora, Mycosphaerella, Alternaria, Allophoma, Mortierella, and Trichoderma. The target pathogenic microorganism may be the causative agent of basal rot disease, and the key microorganism may be a microorganism selected from the group consisting of fungi of the genus Fusarium and Mortierella. This is because the causative agent of basal rot disease is difficult to detect by next-generation sequencing, while the above key microorganisms are relatively easy to detect. This is because fungi of the genus Fusarium and Mortierella are particularly easy to detect.

[0086] The abundance of the target pathogenic microorganism in the soil contained in the data acquisition field may be greater than the abundance in the cultivation soil contained in the cultivation target field. In the data acquisition field, a disease caused by the target pathogenic microorganism may occur. This is because in the case of a target pathogenic microorganism that is difficult to detect, the disease often occurs before it can be detected.

[0087] <S2‘: Creation of Correlation Analysis Target Dataset> In S2‘, correlation analysis target data is acquired and a correlation analysis target dataset is created.

[0088] <Correlation Analysis Target Data> The above correlation analysis target data includes at least correlation growth items related to the growth of agricultural crops.

[0089] <Correlation Growth Items> Correlated growth parameters include, but are not limited to, crop growth status parameters such as the degree of disease occurrence, crop growth rate, and the size, number, weight, color, and composition of the crop's fruit, leaves, and stems; soil characteristic parameters such as soil pH, electrical conductivity, phosphate absorption coefficient, nitrogen concentration, nitrate nitrogen concentration, ammonium nitrogen concentration, available phosphate concentration, cation exchange capacity, base saturation, available silica concentration, soil organic matter content, and various element concentrations (nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, manganese, boron, zinc, copper, molybdenum, chlorine, and nickel); climatic parameters such as temperature, precipitation, frost, and amount of sunshine; and work management parameters such as fertilizer type, frequency and amount of fertilizer application, fertilizer application timing, cultivation method, number of cultivations, cultivation timing, pesticide type, frequency and amount of pesticide application, pesticide application timing, number of irrigation applications, irrigation amount, and irrigation timing. Items that are often expressed using qualitative or descriptive labels can also be quantified by setting appropriate standards. For example, in the case of the degree of disease occurrence, a field with diseased crops can be assigned a value of 1 and a field without diseased crops can be assigned a value of 0. Alternatively, the percentage of crops in a field that show disease symptoms can be used as the value for that field.

[0090] The growth items for correlation may include a growth state item, since the growth state item has a direct correlation with the growth of agricultural crops. The growth items for correlation may include the degree of disease occurrence, since the degree of disease occurrence is strongly correlated with the amount of target pathogenic microorganisms present in the soil. The growth items for correlation may include a plurality of items, since many items affect the growth of agricultural crops. The growth items for correlation may include a growth state item and may further include a soil property item. The growth items for correlation may include a growth state item and may further include a climate item. The growth items for correlation may include a growth state item and may further include a work management item.

[0091] The location for obtaining the data for correlation analysis (target data acquisition location) can be anywhere as long as the values of the items included in the data for correlation analysis can be regarded as being of the same level as those of the soil sampling location (soil sampling location) from which the corresponding microbiota data is obtained. For example, the target data acquisition location may be within 20 km from the soil sampling location. It may be within 10 km. It may be within 5 km. It may be within 1 km. This is because the closer the distance between the target data acquisition location and the soil sampling location, the higher the likelihood that the values of the items included in the correlation analysis target data for both locations are close. The target data acquisition location and the soil sampling location may be locations within the same field or within 1 km from the outer edge of the field to which the soil sampling location belongs. This is because in many cases, soil property items, climate items, and work management data items within the same field and within 1 km from the outer edge of the field can be regarded as being of the same level. The number of target data acquisition locations may be less than the number of soil sampling locations. The greater the number of target data acquisition locations, the higher the accuracy of the results of co-occurrence analysis and correlation analysis. However, if the number of target data acquisition locations is large, the acquisition work becomes burdensome.

[0092] When obtaining the data for correlation analysis (at the time of target data acquisition), it may also be a time when the numerical values of the growth items for correlation included in the target data can be regarded as being of the same level as those at the time of soil sampling (soil sampling time) for obtaining the corresponding microbiota data. Also, when the data for correlation analysis is such that changes in the numerical values of items such as soil property items, climate items, and work management items cause changes in the microbiota, the soil sampling time may be later within the range where changes in the microbiota become apparent, compared to the time of target data acquisition. Also, when the data for correlation analysis is such that changes in the microbiota cause changes in the numerical values of items such as growth state items, the time of target data acquisition may be later within the range where changes in the numerical values of the items become apparent, compared to the soil sampling time.

[0093] <S3‘: Identification of key microorganisms> In S3‘, correlation analysis is performed on the microbiota data set and the data set for correlation analysis to identify key microorganisms.

[0094] <Correlation analysis> Key microorganisms may be identified or a threshold value for the soil abundance of key microorganisms may be determined based on a correlation analysis between the soil abundance of each microorganism included in the microbiome data set and the corresponding correlation analysis target data. When identifying key microorganisms or determining a threshold value for the soil abundance of key microorganisms, the absolute value of the correlation coefficient between the soil abundance of the identified key microorganism and the correlation analysis target data may be 0.2 or more and less than 1.0, or 0.3 or more and less than 1.0. This is because the larger the lower limit of the absolute value, the stronger the correlation between the soil abundance of key microorganisms and the correlation analysis target data. Furthermore, when the correlation analysis target data includes multiple items, the partial correlation coefficient between the soil abundance of each microorganism and the corresponding correlation analysis target data may be used as the correlation coefficient. For example, if the data to be analyzed for correlation analysis includes the degree of disease occurrence, the electrical conductivity of the soil, and the number of tillage cycles, the net correlation, which eliminates the disturbance factors of electrical conductivity and the number of tillage cycles, can be examined by examining the partial correlation coefficient between the amount of each microorganism present in the soil and the degree of disease occurrence, which eliminates the correlation between the amount of each microorganism present in the soil and electrical conductivity or the number of tillage cycles.

[0095] Microbiomes contain a wide variety of microbial species, but certain microbial species strongly influence each other and act as a microbial module 2 (a group of microorganisms). In a correlation analysis between the soil abundance of each microorganism included in the microbiome data set and the corresponding correlation analysis target data, microorganisms with similar correlations can be considered to form the same microbial module 2, and network analysis can be performed to visualize the connections between microorganisms to visualize the microbial module 2. This is because the microbial module 2 makes it easy to identify key microorganisms. For example, a microbial module 2 with a large positive correlation or a large negative correlation with a correlation item of the correlation analysis target data, such as a growth status item, can be identified, and a microorganism within that microbial module 2 that is easy to detect by DNA analysis can be identified.

[0096] A plurality of key microorganisms may be identified as key microorganisms. By identifying a plurality of key microorganisms, the results of the correlation analysis can be reflected more deeply and broadly in determining the threshold value.

[0097] The key microorganisms may include multiple key microorganisms that constitute the microbial module 2. By including multiple key microorganisms that constitute the microbial module 2, the presence or absence of the formation of the microbial module 2 can be reflected in determining the threshold value.

[0098] For example, when the numerical values ​​of the items of correlation analysis target data are considered to be similar within the same field, multiple microbiome data and one correlation analysis target data may be combined for analysis, or one microbiome data and multiple correlation analysis target data may be combined for analysis.

[0099] A single correlation analysis target data may be combined with multiple microbiome data collected at different times for analysis. The causal relationship between changes in the microbiome and changes in the correlation analysis target can be measured, and the causal relationship can be reflected in identifying key microorganisms and determining threshold values ​​for the abundance of key microorganisms in the soil. A single microbiome data may be combined with multiple correlation analysis target data collected at different times for analysis.

[0100] Key microorganisms may be identified based on the results of co-occurrence analysis using the microbiome dataset and correlation analysis between the microbiome dataset and the dataset subject to correlation analysis, or a threshold for the abundance of key microorganisms in the soil may be determined.

[0101] In the co-occurrence analysis, a microbial module 2 to which the target pathogenic microorganism belongs may be identified, or a microbial module 2 to which the target pathogenic microorganism does not belong may be identified, and among the microbial modules 2, a microorganism having a large absolute value of the correlation coefficient with the data to be analyzed for correlation analysis may be identified as a key microorganism.

[0102] FIG. 4 shows an image diagram of an example in which abundance ratios of each microorganism are superimposed on co-occurrence network analysis as co-occurrence analysis using microbiota data according to an embodiment of the present disclosure. Microorganism 1 of the microbiota data used for the co-occurrence network analysis is shown as a black circle. The size of the black circle indicates the abundance ratio. The larger the size, the larger the abundance ratio. Microorganisms having a covariance relationship of a certain level or more in the co-occurrence network analysis are connected by a line segment. Microorganism module 2 is surrounded by a dotted line.

[0103] FIG. 5 shows an image diagram of an example in which correlation coefficients between the abundance of microorganisms in the soil included in the microbiota data and the data to be subjected to correlation analysis are superimposed on co-occurrence network analysis as co-occurrence analysis using microbiota data according to an embodiment of the present disclosure. Microorganisms of the microbiota data used for the co-occurrence analysis are represented by black or white circles. Microorganisms having a covariance relationship of a certain level or more in the co-occurrence network analysis are connected by a line segment. Microorganism 3 having a positive correlation with the characteristic data to be subjected to correlation analysis is represented by a black circle, and the larger the size of the black circle, the larger the correlation coefficient. Microorganism 4 having a negative correlation with the characteristic data to be subjected to correlation analysis is represented by a white circle, and the larger the size of the white circle, the larger the correlation coefficient. Microorganism module 2 is surrounded by a dotted line.

[0104] <S4, S4': Threshold determination> In S4 and S4', based on the correlation information between the abundance of the key microorganism in the soil and the growth item for threshold related to the growth of the crop, the threshold of the abundance of the key microorganism in the soil is determined. In S4', the threshold is a function including variables.

[0105] <Correlation information between the abundance of the key microorganism in the soil and the growth item for threshold related to the growth of the crop> The correlation information between the abundance of the key microorganism in the soil and the item related to the growth of the crop is information regarding the correlation between the abundance of the key microorganism in the soil and the growth item for threshold related to the growth of the crop.

[0106] The correlation information may be obtained from publicly known information such as literature on key microorganisms. The correlation information may also be obtained from correlation analysis between the abundance of key microorganisms in the soil and growth parameters related to crop growth. This is because the results of correlation analysis are highly accurate and quantitative. The correlation information may also be obtained from co-occurrence analysis or correlation analysis used to determine key microorganisms. This is because information on key microorganisms can be reliably obtained and the effort of searching for the correlation information anew is eliminated. Furthermore, if the state of the microbial flora at the soil collection site where the microbial flora data is obtained is similar to that of the target cultivation field, or if the state of the microbial flora at the location where the data to be analyzed for correlation is obtained is similar to that of the target cultivation field, the correlation information will more accurately reflect the conditions of the target cultivation field.

[0107] The soil sampling site where data on the abundance of key microorganisms in soil is obtained to obtain the correlation of the above correlation information may be within 20 km of the soil sampling site where the microbiome data used to determine the key microorganisms was obtained, or within 10 km, or within 1 km, or even within the same field. This is because the closer the distance between the two sampling sites, the greater the similarity of the microbiomes.

[0108] <Growth items for thresholds> The growth parameters for threshold values ​​include, for example, the amount of target pathogenic microorganisms in the soil, the ratio of the amount of target pathogenic microorganisms present, the degree of disease occurrence, the growth rate of agricultural crops, the size, number, weight, color, and composition of the fruit, leaves, and stems of agricultural crops, as well as soil pH, electrical conductivity, phosphate absorption coefficient, nitrogen concentration, nitrate nitrogen concentration, ammonium nitrogen concentration, available phosphate concentration, cation exchange capacity, base saturation, available silica concentration, soil organic matter content, and various element concentrations. These include, but are not limited to, soil characteristics such as nitrogen, phosphorus, potassium, calcium, magnesium, sulfur, iron, manganese, boron, zinc, copper, molybdenum, chlorine, and nickel; climate characteristics such as temperature, precipitation, frost, and sunshine; and work management characteristics such as fertilizer type, frequency of fertilization, amount, and timing of fertilization; cultivation method, number of cultivations, timing of cultivation; pesticide type, frequency of pesticide application, amount, and timing of pesticide application; and irrigation frequency, amount, and timing. Items often expressed using qualitative or descriptive labels can also be quantified by setting appropriate standards. For example, in the case of disease incidence, a field with diseased crops can be assigned a value of 1 and a field without diseased crops can be assigned a value of 0. Alternatively, the percentage of crops in a field that show disease symptoms can be used as the numerical value for that field.

[0109] The growth items for threshold may include a growth state item, since the growth state item has a direct correlation with the growth of agricultural crops. The growth items for threshold may include the degree of disease occurrence, since the growth of agricultural crops is strongly correlated with the amount of target pathogenic microorganisms present in the soil. The growth items for threshold may include a plurality of items, since many items affect the growth of agricultural crops. The growth items for threshold may include a growth state item and may further include a soil characteristic item. The growth items for threshold may include a growth state item and may further include a climate item. The growth items for threshold may include a growth state item and may further include a work management item.

[0110] <threshold> The threshold value is for determining the cultivation policy of agricultural crops in comparison with the amount of key microorganisms present in the cultivation soil sampled from the cultivation target field. The threshold value is determined based on the correlation information between the amount of key microorganisms present in the soil and the growth items for threshold values related to the growth of agricultural crops. For example, in the growth of agricultural crops, the amount of key microorganisms present in the soil at a stage where the degree of disease occurrence is extremely low may be used as the threshold value, or the amount of key microorganisms present in the soil at the time when an event that appears prior to the occurrence of the disease appears may be used as the threshold value, but it is not limited to these.

[0111] There may be multiple threshold values. This is because it can handle cases where there are multiple possible cultivation policies depending on the amount of key microorganisms present in the soil. For example, there are two threshold values, S1 and S2 (S2 > S1). When the amount present in the soil is less than S1, the agricultural crops are grown as usual. When it is S1 or more and less than S2, control measures against the target pathogenic microorganisms are taken. When it is S2 or more, the agricultural crops to be cultivated are changed, etc.

[0112] The threshold value may be a function including variables. The above variables may be growth items related to the growth of agricultural crops. Growth items related to the growth of agricultural crops include, for example, growth state items, soil property items, climate items, operation management items, etc., similar to those exemplified in <Growth items for threshold values>, but are not limited to these. Among the growth items related to the growth of agricultural crops, there are those that vary depending on the cultivation period and cultivation location, such as soil property items and climate items. Therefore, these growth items may be used as variables. By obtaining the numerical values to be substituted into the variables at the cultivation target field or its vicinity, for example, at a point within 10 km, 5 km, or 1 km from the outer edge of the cultivation target field, at the cultivation period or a little before the cultivation period, such as six months ago or three months ago, the situation of the cultivation field can be reflected. There may be multiple of the above variables. This is because many factors affect the growth of agricultural crops.

[0113] <S5: Cultivation soil sampling> In S5, cultivation soil is collected from the cultivation target field. The cultivation soil is the soil in the cultivation target field. The collection location of the above cultivation soil may be a field adjacent to the cultivation target field, such as a meadow or mountain forest, as long as the abundance of key microorganisms in the cultivation soil is within the same range. The number of soils to be collected in each field may be 2 or more, 3 or more, or 4 or more. This is because as the number of data increases, it becomes easier to obtain the representative value of the field in the next step, S6. The collection in each field may be performed multiple times on different days. This is because the change over time in the abundance in the cultivation soil can be understood by the measurement in the next step, S6.

[0114] <S6: Measurement of the Abundance of Key Microorganisms in Cultivation Soil> In S6, the abundance of key microorganisms in the cultivation soil of the cultivation target field is measured.

[0115] DNA analysis may be used for the measurement of the abundance in the cultivation soil. As exemplified in the above <DNA analysis>, there are many methods for DNA analysis, but it is not limited to these.

[0116] Next-generation sequencing may be used for the measurement of the abundance in the cultivation soil. This is because next-generation sequencing is highly sensitive and has excellent accuracy. The DNA sequence analysis methods using next-generation sequencing include 16S rRNA gene analysis (DNA metabarcoding method targeting the prokaryotic 16S rRNA region), 18S rRNA gene analysis (DNA metabarcoding method targeting the eukaryotic 18S rRNA region), fungal ITS (DNA metabarcoding method targeting the fungal ITS (internal transcribed spacer) region), and shotgun metagenome analysis method. Calibration may be performed when analyzing DNA data by adjusting the concentration of a standard sequence fragment such as an artificially designed DNA sequence or λ phage and adding it to the PCR solution, and the absolute abundance (nucleic acid concentration) of each species may be estimated. Multiple analysis methods may be combined.

[0117] DNA microarrays, PCR, and FISH can also be used to measure the abundance in cultivation soil, as these are simple and inexpensive methods. PCR can also be used to measure the abundance in cultivation soil, as this has a good balance of accuracy, simplicity, and cost.

[0118] The amount present in the cultivation soil may be measured multiple times, and the average of the measurement results may be used as the amount present in the cultivation soil. This is to increase the accuracy of the measurement. The amount present in the cultivation soil may also be measured for multiple soil samples collected from multiple points within the cultivation field, and the average of the measurement results may be used as the amount present in the cultivation soil. This is to obtain a representative value for the field.

[0119] Measurement of the amount present in the cultivation soil may include measurement of soil collected at a point 0 km or more but not exceeding 5 km from the outer edge of the target cultivation field, or may include measurement of soil collected at a point 0 km or more but not exceeding 1 km from the outer edge of the target cultivation field. This is because it is possible to detect the inflow of the target pathogenic microorganism from the surrounding area of ​​the target cultivation field.

[0120] The amount present in the cultivation soil may be measured for multiple soil samples collected on different days. This allows for the time-dependent change in the amount present in the cultivation soil to be determined. Based on the time-dependent change, the amount present in the cultivation soil in the future from the time of collection of the soil for which the amount present in the cultivation soil was measured may be predicted, and the future amount present in the cultivation soil may be compared with the threshold value. This allows for preventive and defensive cultivation.

[0121] The abundance of the target pathogenic microorganism in the cultivation soil of the target cultivation field may be less than 1.0%, less than 0.5%, less than 0.1%, or less than 0.05% in terms of the relative occurrence frequency of reads determined by amplicon sequencing. This is because the lower the relative occurrence frequency, the more difficult the detection, and therefore the usefulness of selecting key microorganisms that are easy to detect increases. The abundance of the key microorganism in the cultivation soil may be 1.0% or more in terms of the relative occurrence frequency, 1.5% or more, 2.0% or more, 2.5% or more, or 3.0% or more. This is because the higher the relative occurrence frequency, the easier the detection.

[0122] The relative frequency of a microorganism's reads is the ratio of the number of reads for that microorganism to the sum of the number of reads for all microorganisms detected by amplicon sequencing. In other words, it is calculated as follows: "Relative frequency of reads for microorganism A = number of reads for microorganism A / sum of the number of reads for all detected microorganisms x 100."

[0123] The above definition of the relative occurrence frequency of the target pathogenic microorganism or lead of a key microorganism in the cultivation soil of a target cultivation field is the average value of multiple measurement results of soil collected from multiple locations to reduce measurement error and variation within the field. Specifically, if the size of the target field is smaller than 10 ares, the value is the average value of the results of three measurements each of soil samples collected evenly from at least 10 points. If the size of the target field is 10 ares or larger, the value is the average value of three measurements each of soil samples collected at a frequency of at least one point per ares.

[0124] For fungal amplicon sequencing, the meta-ITS method was used. A library was created using two-step PCR with the ITS1-F_KYO01 and ITS2-KYO2 primers, and sequencing was performed using the Iseq100 system and iSeq100 i1Reagent v2 at 1 × 300 bp. For bacterial amplicon sequencing, the 16S rRNA gene analysis method was used. A library was created using two-step PCR with the 515f and 806B primers, and sequencing was performed using the Iseq100 system and iSeq100 i1Reagent v2 at 1 × 300 bp. If the key microorganism is a eukaryote, including endo- and ectoparasites, and is not a fungus, bacterium, or virus, 18S rRNA gene analysis should be performed. A library should be created using NF1_rv and R-N1-Nem18SV8_rv primers in a two-step PCR method, and sequencing should be performed using the Iseq100 system and iSeq100 i1Reagent v2 under the conditions of 1 x 300 bp.

[0125] Note that the above definitions do not impose any restrictions on the implementation details in each step illustrated in FIGS. 1 to 3 of the present disclosure, such as the method for collecting cultivation soil and the method for measuring the abundance in the cultivation soil.

[0126] <S6‘: Obtain actual value of variable, calculate threshold value> In S6‘, the actual value of the variable is obtained to calculate the threshold value for determining the threshold value. S6‘ is the next step after S4‘ that determines the threshold value as a function including the variable. In this step, at an appropriate time according to the content of the item, the actual value of the variable in the cultivation target field or the actual value of the variable is obtained in the neighboring area where it can be regarded as equivalent in determining the threshold value, and the threshold value is determined.

[0127] <S7: Determine cultivation policy> In S7, the cultivation policy is determined by comparing the abundance of the key microorganism in the cultivation soil with the threshold value.

[0128] As the cultivation policy, for example, if it is determined that there is no problem compared with the threshold value, the planting and growth of the agricultural crops may continue as originally planned, or if it is determined that there is a possibility of problems such as diseases, control measures may be taken or the agricultural crops to be cultivated may be changed, but it is not limited thereto. As the control measures, for example, before planting, the soil may be replaced, the cultivation soil may be disinfected with a drug, or the shelter of the target pathogenic microorganism may be physically destroyed by crushed soil or the like. After planting, for example, it may be disinfected with a drug, the diseased agricultural crops and the surrounding agricultural crops may be removed, and the soil in the part where they were planted may be removed, but it is not limited thereto.

[0129] When comparing the abundance of key microorganisms in the cultivation soil with a threshold, the growth item for the threshold may include at least one of the abundance of the target pathogenic microorganism in the soil and the degree of disease occurrence, and the abundance of the key microorganism in the soil may have a positive or negative correlation with the growth item for the threshold.If there is a positive correlation, a cultivation policy may be decided to take control measures or change the crop being cultivated if the abundance in the cultivation soil is above the threshold, and if it is below the threshold, a cultivation policy may be decided to take neither control measures nor change the crop.If there is a negative correlation, a cultivation policy may be decided to take control measures or change the crop being cultivated if the abundance in the cultivation soil is below the threshold, and if it is above the threshold, a cultivation policy may be decided to take neither control measures nor change the crop.

[0130] <Example> S1: Soil samples were collected from 66 fields, ranging in area from 5 to 50 ares, at four points in each field, for a total of 264 points. Of the 66 fields, 30 showed no or only mild cases of sweet potato base rot, while 36 showed clear signs of sweet potato base rot.

[0131] S2: Next-generation sequencing (Illumina iSeq100) was used to analyze each soil sample collected in S1 using the meta-ITS method to obtain microbiome data and create a microbiome dataset. For analysis, libraries were created using two-step PCR with primers ITS1-F_KYO01 and ITS2-KYO2. Sequences were generated using the Iseq100 system and iSeq100 i1Reagent v2 at 1 × 300 bp. Detection of Diaporthe species, including the causative agent of sweet potato root rot, was difficult; the relative frequency of reads determined by amplicon sequencing was less than 1.0% in all 264 soil samples. Two samples had a frequency of 0.5% to 1.0%, and four samples had a frequency of 0% to 0.5%.

[0132] S3: A co-occurrence analysis was performed on the above microbiome dataset. Sparse inverse covariance estimation was used for the co-occurrence analysis, and a co-occurrence network was created using a covariance absolute value of 0.1 or greater as the criterion. This identified harmful microorganism module 2, to which the sweet potato base rot causative bacterium belongs. In harmful microorganism module 2, a species of Fusarium genus, whose average relative occurrence frequency in the soil where the sweet potato base rot causative bacterium was detected was 3.5%, was identified as the first key microorganism.

[0133] S2': Data for correlation analysis was obtained using the degree of sweet potato base rot in each field as the correlation target item, and a data set for correlation analysis was created. Data was processed by quantifying the degree of occurrence, with 0 being assigned to cases where no sweet potato base rot was observed or where the disease was present but only mildly severe, and 1 being assigned to cases where the disease was clearly observed.

[0134] S3': A correlation analysis was performed on the above microbiome dataset and the above dataset subject to correlation analysis, and useful microorganisms were identified from microbial module 2 that had a correlation of -0.2 or less with the incidence of sweet potato base rot. Simply put, allowing for some inaccuracy, these were useful microorganisms commonly found in healthy fields where sweet potato base rot had not occurred. Among the above useful microorganisms, a species of the genus Mortierella was identified as the second key microorganism, with a high average relative occurrence frequency of 1.8% for leads in soil where the causative agent of sweet potato base rot was not detected.

[0135] S4: For the first key microorganism, the threshold growth parameter related to crop growth was the abundance ratio of the sweet potato base rot causative microorganism. Based on the results of the co-occurrence analysis in S3, which showed that the average relative occurrence frequency in soil where the sweet potato base rot causative microorganism was detected was 3.5%, the need to detect signs of sweet potato base rot before it clearly occurs, and a safety margin to prevent the error of overlooking the disease signs, the first threshold was set at a relative occurrence frequency of 1.7% for the first key microorganism. For the second key microorganism, the threshold growth parameter related to crop growth was set at the degree of sweet potato base rot occurrence. Based on the results of the correlation analysis in S3', which showed that the average relative occurrence frequency in soil where the sweet potato base rot causative microorganism was not detected was 1.8%, the relative occurrence frequency of the second key microorganism was set at 2.0%. If the relative occurrence frequency of the first key microorganism's lead in the cultivation soil is equal to or greater than the first threshold, control measures are to be taken. Even if it is less than the first threshold, if the relative occurrence frequency of the second key microorganism's lead is equal to or less than the second threshold, the cultivation soil is deemed unhealthy and the occurrence of sweet potato base rot cannot be suppressed, and control measures are to be taken. If the relative occurrence frequency of the first key microorganism's lead is less than the first threshold and the relative occurrence frequency of the second key microorganism's lead is greater than the second threshold, sweet potatoes are to be planted as planned. The control measures are to crush and disinfect the cultivation soil.

[0136] S5: Before sweet potato was planted, four samples of cultivation soil were collected from each of the first and second cultivation fields.

[0137] S6 and S7: The relative occurrence frequency of the first key microorganism and the second key microorganism was confirmed for each of the collected cultivation soils using the same DNA analysis method as in S2. The average of the relative occurrence frequencies of the four points in each cultivation field was used as the relative occurrence frequency for that field. For the first cultivation field, the relative occurrence frequency of the first key microorganism was 2.6%. When compared with the first threshold, it was found to be greater than the first threshold, so control measures were taken. For the second cultivation field, the first key microorganism was not detected, and the relative occurrence frequency of the second key microorganism was 2.3%, which was found to be greater than the second threshold, so sweet potatoes were planted as planned.

[0138] Although the embodiments of the present disclosure have been described above, the above description should be considered to be illustrative in all respects and not restrictive. The scope of the present disclosure is defined by the claims, not by the description of the above embodiments, and is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0139] 1 Microorganisms from the microbiome data used in the co-occurrence network analysis 2 Microbial Module 3 Microorganisms with a positive correlation with the characteristic data subject to correlation analysis 4 Microorganisms with negative correlation with the characteristic data subject to correlation analysis

Claims

1. A cultivation method comprising: performing a co-occurrence analysis on a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected from a data collection field; identifying key microorganisms based on coexistence or non-coexistence patterns with target pathogenic microorganisms in the co-occurrence analysis; determining a threshold based on correlation information between the abundance of the key microorganisms in the soil and threshold-use growth items related to the growth of agricultural crops; and comparing the abundance of the key microorganisms in cultivation soil collected from a target cultivation field with the threshold to determine a cultivation policy for the agricultural crops.

2. A cultivation method for determining a cultivation policy for agricultural crops by comparing the abundance of key microorganisms in cultivation soil collected from a target cultivation field with a threshold value, in which the key microorganisms are identified based on coexistence or non-coexistence patterns with target pathogenic microorganisms in a co-occurrence analysis of a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected from a data acquisition field, and the threshold value is determined based on correlation information between the abundance of the key microorganisms in the soil and growth items for threshold values ​​related to the growth of the agricultural crops.

3. A cultivation method in which key microorganisms are identified based on correlation analysis between a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected from a data acquisition field and a correlation analysis target dataset consisting of correlation analysis target data including correlation growth items related to crop growth, and a threshold is determined based on correlation information between the abundance of the key microorganisms in the soil and the threshold growth items related to crop growth, and a cultivation policy for the crop is determined by comparing the abundance of the key microorganisms in cultivation soil collected from a target cultivation field with the threshold in order to avoid or suppress the occurrence of diseases caused by target pathogenic microorganisms.

4. A cultivation method for determining a cultivation policy for agricultural crops by comparing the abundance of key microorganisms in cultivation soil collected from a target cultivation field with a threshold value in order to avoid or suppress the occurrence of diseases caused by target pathogenic microorganisms, wherein the key microorganisms are identified based on correlation analysis between a microbiome dataset consisting of microbiome data obtained by DNA analysis of soil collected from a data acquisition field and a correlation analysis target dataset consisting of correlation analysis target data including correlation growth items related to the growth of the agricultural crops, and the threshold value is determined based on correlation information between the abundance of the key microorganisms in the soil and the threshold growth items related to the growth of the agricultural crops.

5. The cultivation method according to claim 4 , wherein the key microorganisms are identified based on a co-occurrence analysis of the microbiota dataset in addition to the correlation analysis.

6. The cultivation method according to claim 4 or 5, wherein the correlation growth items include growth status items.

7. The cultivation method according to claim 2 or 5, wherein the correlation information is obtained from the co-occurrence analysis used to determine the key microorganisms.

8. The cultivation method according to claim 4 or 5, wherein the correlation information is obtained from the correlation analysis used to determine the key microorganism.

9. 5. The cultivation method according to claim 2 or 4, wherein the growth parameters for threshold include at least one selected from the group consisting of the abundance of the target pathogenic microorganism in the soil, the abundance ratio of the target pathogenic microorganism, and the degree of disease occurrence, and the abundance of the key microorganism in the soil has a positive correlation or a negative correlation with the growth parameters for threshold, and when there is a positive correlation, if the abundance in the cultivation soil is equal to or greater than the threshold, a cultivation policy is determined to take control measures or change the crop to be cultivated, and when it is less than the threshold, a cultivation policy is determined to neither take the control measures nor change the crop, and when there is a negative correlation, if the abundance in the cultivation soil is equal to or less than the threshold, a cultivation policy is determined to take the control measures or change the crop to be cultivated, and when it is greater than the threshold, a cultivation policy is determined to neither take the control measures nor change the crop.

10. The cultivation method according to claim 2 or 4, wherein the average value of the abundance ratio of the target pathogenic microorganism in all of the microbiota data in the microbiota dataset is less than 1.0%.

11. 5. The cultivation method according to claim 2 or claim 4, wherein the abundance of the target pathogenic microorganism in the cultivation soil is less than 1.0% in terms of the relative occurrence frequency of reads determined by amplicon sequencing.

12. The cultivation method according to claim 2 or 4, characterized in that the soil collected at the data acquisition field contains soil in which the amount of the target pathogenic microorganism present is greater than that of the cultivation soil collected at the target cultivation field.

13. The cultivation method according to claim 2 or 4, characterized in that the target pathogenic microorganism is a root rot pathogen, and the key microorganism includes at least one microorganism selected from the group consisting of Fusarium, Cercospora, Mycosphaerella, Alternaria, Allophomma, Mortierella, and Trichoderma.

14. The cultivation method according to claim 2 or 4, wherein next-generation sequencing is used for the DNA analysis.

15. The cultivation method according to claim 2 or 4, wherein next-generation sequencing is used to measure the abundance of the key microorganisms in the cultivation soil.

16. 5. The cultivation method according to claim 2, wherein the amount of the key microorganism present in the cultivation soil is measured using at least one analytical method selected from the group consisting of DNA microarray, PCR, and FISH.

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