Methods for obtaining NCP values, programs for obtaining NCP values, and methods for creating estimation aid diagrams for obtaining NCP values.

Abstract

This invention provides a method for obtaining NCP (Nature Contribution to People) values ​​that are beneficial to the conservation or promotion of NCPs, a program for obtaining these values, and a method for creating estimation aids for obtaining them. [Solution] The method for obtaining the NCP value of a target area involves the following steps: obtaining the reference area gradient analysis result from the reference area microbiome dataset by performing a reference area gradient analysis; analyzing the reference area gradient analysis result and the reference area NCP value dataset to derive the gradient analysis result-NCP value relationship; obtaining the target area gradient analysis result from the target area microbiome data and the target area microbiome dataset by performing a target area gradient analysis; estimating the target area NCP value from the target area gradient analysis result and the gradient analysis result-NCP value relationship; and outputting the target area NCP value.

Description

Technical Field

[0001] The present disclosure relates to a method for obtaining an NCP value, a program for obtaining an NCP value, and a method for creating an estimation assistance diagram for obtaining an NCP value.

Background Art

[0002] In recent years, there has been a growing awareness that human social and economic activities greatly depend on NCP (Nature Contribution to People), that is, what nature provides, nature's blessings, and ecosystem services. The background is that due to global warming, environmental destruction, etc., NCP is declining globally, and it is feared that society will soon fall into a state where it is difficult to maintain. It is required to take actions while paying attention to the conservation and promotion of NCP in politics, economy, specifically in urban design, forest design, land development, agriculture, forestry, fisheries, all industrial activities, and people's social life.

[0003] In the Kunming-Montreal Biodiversity Framework 2030, in order to reduce the burden on biodiversity and increase positive impacts, it is declared as a goal that businesses evaluate and disclose risks related to biodiversity, dependencies and impacts on biodiversity, and take measures to provide the information necessary for sustainable consumption (Target 15). For example, it is recommended that private businesses disclose natural-related financial information related to NCP in accordance with the framework of TNFD (Task Force on Nature-related Financial Disclosures). By disclosing natural-related financial information, it is expected that companies that are actively engaged in biodiversity conservation will find it easier to raise funds and gain consumer support. That is, an increase in corporate value is expected. Although NCP includes various things, IPBES (The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services) categorizes NCP and lists its item names (Non-Patent Document 1).

[0004] Among NCPs (Non-Consuming Programs), CO2 absorption in particular is widely recognized as a particularly important item as a cause of global warming, an urgent issue for humanity. This is because, even before the TNFD (Targeted Nuclear Financial Disclosures), the TCFD framework required businesses to disclose greenhouse gas emissions. In recent years, however, the inflated figures for CO2 absorption and carbon credits based on inaccurate data have become a problem known as greenwashing.

[0005] To better conserve or promote National Conservative Properties (NCPs), it is crucial to know the NCP level of the target area. Disclosure of nature-related financial information concerning NCPs also requires knowledge of the NCP level of the project site. NCP values ​​are used to indicate the NCP level. Examples of NCP values ​​include the carbon stock and water retention capacity of soil in green spaces. In response to requests for obtaining NCP values, methods for evaluating carbon stock have been reported (Patent Documents 1 and 2). Furthermore, in response to requests for obtaining NCP values, various organizations are conducting research on NCP mapping. For example, the Forestry and Forest Products Research Institute provides forest carbon stock data (forest soil carbon maps) (Non-Patent Document 2). [Prior art documents] [Patent Documents]

[0006] [Patent Document 1] Japanese Patent Publication No. 2012-14371 [Patent Document 2] Japanese Patent Publication No. 2024-126015 [Non-patent literature]

[0007] [Non-Patent Document 1] IPBES (2019): Global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. ES Brondizio, J. Settele, S. Diaz, and HT Ngo (editors). IPBES secretariat, Bonn, Germany. 1148 pages. https: / / doi.org / 10.5281 / zenodo.3831673 [Non-Patent Document 2] National-scale 3D mapping of soil organic carbon in a Japanese forest considering microtopography and tephra deposition, Naoyuki Yamashita, Shigehiro Ishizuka, Shoji Hashimoto (Site Environment Research Area), Shin Ukawa (Kagoshima University), Kazuki Minami (Forest Disaster Prevention Research Area), Yoko Osone (Site Environment Research Area), Junko Iwahashi (Geospatial Information Authority of Japan), Yoshimi Sakai (Kyushu Branch), Motoko Inatomi (National Agriculture and Food Research Organization), Ayumi Kawanishi, Kazuhito Morisada, Nagaharu Tanaka, Shuhei Aizawa, Akihiro Imaya (Site Environment Research Area), Masamichi Takahashi (International Greening Promotion Center), Shinji Kaneko (Kansai Branch), Satoru Miura (Disaster Reconstruction and Radioactive Material Research Center), Keizo Hirai (Site Environment Research Area). Geoderma, 406, 115534, January 2022 DOI:10.1016 / j.geoderma.2021.115534 [Overview of the project] [Problems that the invention aims to solve]

[0008] Obtain NCP values ​​that are beneficial for the preservation or promotion of NCPs. [Means for solving the problem]

[0009] A method for obtaining target NCP values, comprising the steps of: a computer obtaining reference site gradient analysis results from a reference site microbiome dataset by performing reference site gradient analysis; analyzing the above reference site gradient analysis results and the reference site NCP value dataset to derive a gradient analysis result-NCP value relationship; obtaining target site gradient analysis results from target site microbiome data and a target site microbiome dataset by performing target site gradient analysis; estimating target site NCP values ​​from the above target site gradient analysis results and the gradient analysis result-NCP value relationship; and outputting the above target site NCP values. [Effects of the Invention]

[0010] Since NCP values ​​can be obtained, it is beneficial for properly maintaining and promoting NCPs. [Brief explanation of the drawing]

[0011] [Figure 1] Figure 1 is a schematic diagram of a process flow including one form of implementation of this disclosure. [Figure 2] Figure 2 is a schematic diagram of a process flow including another form of implementation of this disclosure, distinct from Figure 1. [Figure 3] Figure 3 is a schematic diagram of a process flow including another form of implementation of this disclosure, distinct from Figures 1 and 2. [Figure 4] Figure 4 is an example of a schematic diagram of the computer configuration relating to this disclosure. [Figure 5] Figure 5 shows a two-dimensional analysis coordinate diagram of the reference area analysis coordinate dataset and the target area analysis coordinates obtained by PCoA of the reference area microbiome dataset. [Figure 6] Figure 6 is a first estimation aid diagram showing a portion of the reference site analysis coordinate dataset with corresponding carbon storage amounts, and a two-dimensional analysis coordinate diagram of the target site analysis coordinates, where the carbon storage amounts of the reference site are represented by the intensity of the marker colors, and the target site analysis coordinates are shown. [Figure 7]FIG. 7 is a contour map showing the carbon accumulation amount in a two-dimensional analysis coordinate diagram in a contour shape based on the relational expression between the analysis coordinates and the carbon accumulation amount, and is a second estimation auxiliary diagram showing the analysis coordinates of the target area.

Mode for Carrying Out the Invention

[0012] [Description of Embodiments of the Present Disclosure] First, embodiments of the present disclosure will be listed and described.

[0013] (1) One aspect of the present disclosure is a method for obtaining a target area NCP value, in which a computer obtains a reference area group gradient analysis result by reference area group gradient analysis from a reference area group microbiota dataset, analyzes the reference area group gradient analysis result and a reference area group NCP value dataset to derive a gradient analysis result - NCP value relationship, obtains a target area gradient analysis result by target area gradient analysis from the target area microbiota data and a microbiota dataset for the target area, estimates the target area NCP value from the target area gradient analysis result and the gradient analysis result - NCP value relationship, and outputs the target area NCP value.

[0014] (2) Another aspect different from (1) of the present disclosure is a method for obtaining a target area NCP value, in which a computer obtains a target area gradient analysis result by target area gradient analysis from the target area microbiota data and a microbiota dataset for the target area, estimates the target area NCP value from the target area gradient analysis result and the gradient analysis result - NCP value relationship, and outputs the target area NCP value, where the gradient analysis result - NCP value relationship is a relationship derived by the computer analyzing the reference area group gradient analysis result obtained by the computer performing a reference area group gradient analysis on a reference area group microbiota dataset and a reference area group NCP value dataset.

[0015] (3) Another aspect different from (1) and (2) of the present disclosure is the method for obtaining a target area NCP value according to (1) or (2), where the gradient analysis result - NCP value relationship is a gradient analysis result - NCP value relational expression.

[0016] (4) Another aspect different from (1) to (3) of the present disclosure is a method for obtaining the target area NCP value according to any one of (1) to (3), wherein the target area gradient analysis is an indirect target area gradient analysis, and the reference area group gradient analysis is an indirect reference area group gradient analysis.

[0017] (5) Another aspect different from (1) to (4) of the present disclosure is a method for obtaining the target area NCP value according to any one of (1) to (4), wherein the target area gradient analysis is an indirect target area gradient analysis using similarity or dissimilarity, and the reference area group gradient analysis is an indirect reference area group gradient analysis using similarity or dissimilarity.

[0018] (6) Another aspect different from (1) to (5) of the present disclosure is a method for obtaining the target area NCP value according to any one of (1) to (5), wherein the target area gradient analysis and the reference area group gradient analysis are analyses selected from the group consisting of PCA, CA, DCA, PCoA, and NMDS.

[0019] (7) Another aspect different from (1) to (6) of the present disclosure is a method for obtaining the target area NCP value according to any one of (1) to (6), wherein the target area gradient analysis result includes target area analysis coordinates, the reference area group gradient analysis result includes a reference area group analysis coordinate dataset, and the gradient analysis result - NCP value relationship is an analysis coordinate - NCP value relationship.

[0020] (8) Another aspect different from (1) to (7) of the present disclosure is a method for obtaining the target area NCP value according to (7), wherein the step of estimating the target area NCP value from the analysis coordinate - NCP value relationship includes creating an estimation auxiliary diagram based on the analysis coordinate - NCP value relationship, and estimating the target area NCP value from the estimation auxiliary diagram and the target area analysis coordinates, and the target area NCP value output in the step of outputting the target area NCP value includes the estimation auxiliary diagram.

[0021] (9) Another aspect of the present disclosure, apart from (1) to (8), is a method for obtaining the NCP values ​​of the target area as described in (8), wherein the estimation auxiliary diagram is selected from the group consisting of contour maps, bubble diagrams in which the size of the markers changes according to the NCP values, diagrams in which the color of the markers changes according to the NCP values, and diagrams in which the intensity of the color of the markers changes according to the NCP values.

[0022] (10) Another aspect of the present disclosure, apart from (1) to (9), is a method for obtaining the target site NCP value as described in any of (1) to (9), wherein the reference site group microbial community dataset includes the target site microbial community data and the target site microbial community dataset, the reference site group gradient analysis is also the target site gradient analysis, and the reference site group gradient analysis results and the target site gradient analysis results are obtained by the reference site group gradient analysis.

[0023] (11) Another aspect of the present disclosure, apart from (1) to (10), is a method for obtaining a target site NCP value, wherein a computer performs the steps of: obtaining a reference site group gradient analysis result and a target site gradient analysis by performing a gradient analysis of a reference site microbial community dataset including target site microbial community data; analyzing the reference site group gradient analysis result and the reference site group NCP value dataset to derive a gradient analysis result-NCP value relationship; estimating the target site NCP value from the target site gradient analysis result and the gradient analysis result-NCP value relationship; and outputting the target site NCP value.

[0024] (12) Another aspect of this disclosure, apart from (1) to (11), is a method for obtaining the NCP value of a target site as described in any of (1) to (11), wherein the NCP value of the target site is the amount of carbon stocks.

[0025] (13) Another aspect of the present disclosure, apart from (1) to (12), is a method for obtaining NCP values ​​for a target area as described in any of (1) to (12), wherein the analysis is at least one of a multivariate analysis and a correlation analysis.

[0026] (14) Another aspect of this disclosure, apart from (1) to (13), is a method for obtaining NCP values ​​described in any of (1) to (13), obtained by DNA analysis of the above-mentioned target site microbial flora data, the above-mentioned target site microbial flora data set, and the above-mentioned reference site microbial flora data set.

[0027] (15) Another aspect of the present disclosure, apart from (1) to (14), is a method for creating an estimation aid diagram, wherein the computer performs the steps of obtaining a reference geographical analysis coordinate dataset from a reference geographical microbiome dataset, and deriving the analysis coordinate-NCP value relationship from the analysis of the reference geographical analysis coordinate dataset and the reference geographical NCP value dataset, and creating an estimation aid diagram.

[0028] (16) Another aspect of the present disclosure, apart from (1) to (15), is a method for creating an estimation auxiliary figure as described in (15), wherein the estimation auxiliary figure is selected from the group consisting of contour plots, figures in which the size of the markers changes according to the NCP value, figures in which the color of the markers changes according to the NCP value, and figures in which the intensity of the color of the markers changes according to the NCP value.

[0029] (17) Another aspect of the present disclosure, apart from (1) to (16), is a program for causing a computer to perform the following steps: obtaining a reference site gradient analysis result from a reference site microbiome dataset by reference site gradient analysis; analyzing the reference site gradient analysis result and the reference site NCP value dataset to derive a gradient analysis result-NCP value relationship; obtaining a target site gradient analysis result from target site microbiome data and a target site microbiome dataset by target site gradient analysis; estimating the target site NCP value from the target site gradient analysis result and the gradient analysis result-NCP value relationship; and outputting the target site NCP value.

[0030] (18) Another aspect of the present disclosure, apart from (1) to (17), is a program for causing a computer to perform the steps of: obtaining a target site gradient analysis result by performing a target site gradient analysis from target site microbial flora data and a target site microbial flora dataset; estimating a target site NCP value from the above target site gradient analysis result and the gradient analysis result-NCP value relationship; and outputting the above target site NCP value, wherein the above gradient analysis result-NCP value relationship is a relationship derived from the analysis of the reference site group gradient analysis result obtained by performing a reference site group gradient analysis on the reference site group microbial flora dataset and the reference site group NCP value dataset.

[0031] [Details of the embodiments of this disclosure] The embodiments of this disclosure will be described in detail below, with reference to the drawings as appropriate. Figures 1 to 3 are schematic diagrams of the process flow. Figure 3 is an example of a schematic diagram of the configuration of computer 1 according to this disclosure. Figures 5 to 7 are diagrams created in the embodiments, and Figures 6 and 7 are estimated auxiliary diagrams that were output. The same or corresponding parts in the figures are denoted by the same reference numerals and their descriptions will not be repeated. Furthermore, at least some of the embodiments described below may be arbitrarily combined. Also, the figures are merely examples and do not limit the scope of this disclosure. This disclosure is not limited to the figures or the embodiments illustrated, but is indicated by the claims, and all modifications within the meaning and scope of the claims are intended to be included.

[0032] <ncp> NCP is an abbreviation for nature’s contributions to people, which refers to what nature provides, the bounty of nature. The content of NCP, NCP items, includes many things, such as pollination, seed dispersal, water retention, CO2 absorption, provision of habitats and living environments for organisms, genetic resources and medicinal resources, air purification, climate regulation, regulation of ocean acidification, nutrient supply to the ocean, water quality regulation, formation, protection, purification and ground retention of soil and sediment (prevention of soil erosion, landslides, etc.), food supply (crops, fruits, mushrooms, etc.), and suppression of wildlife damage (suppression of the intrusion of wildlife into human living areas, etc.).

[0033] Among NCPs, CO2 absorption in particular is widely recognized as a particularly important item as a cause of global warming, which is an urgent issue for humanity. For example, prior to TNFD, under the framework of TCFD (Task Force on Climate-related Financial Disclosures), businesses are required to disclose their greenhouse gas emissions.

[0034] <NCP value> The NCP value is cited as an indicator for measuring the NCP level. For example, for the NCP of CO2 absorption, there is the NCP value of carbon accumulation amount, and for the NCP of water retention, there is the NCP value of water retention amount. The NCP value does not necessarily have to be a numerical value. It can be a class based on appropriate criteria for the level of NCP, or it can be a diagram, graph, or table showing the relative amount of the NCP value.

[0035] In response to the growing need for easy acquisition of NCP values, various organizations are providing NCP values ​​(measured and estimated values) as open source. For example, regarding CO2 absorption, a particularly important NCP item, the Forestry and Forest Products Research Institute provides forest carbon stock data (forest soil carbon maps) (https: / / www.ffpri.affrc.go.jp / research / saizensen / 2021 / 20211224-02.html). However, open source data is often unavailable for businesses' project sites, such as urban green spaces. Acquiring NCP values ​​requires the collection of various types of data, including multi-perspective data and longitudinal data, from fieldwork conducted by experts knowledgeable in ecology, as well as advanced analysis of this data, making it costly and time-consuming. This hinders businesses from acquiring NCP values ​​and, consequently, the appropriate conservation and promotion of NCPs. Therefore, there is a need for a simple method to acquire NCP values ​​for target sites.

[0036] Regarding CO2 absorption, a particularly important National Control Point (NCP), the inflated figures for CO2 absorption and carbon credits based on inaccurate data have become a problem as greenwashing. Therefore, when project operators present CO2 absorption figures for their project sites, they are required to provide evidence to support their claims.

[0037] <Target area NCP value> The target area NCP value indicates the NCP value of the target area. The target area NCP value may be a numerical value, a class or category representing the NCP level, or an estimation aid diagram such as a figure, graph, or table that clearly indicates the location of the target area. If the output target area NCP value is an estimation aid diagram that clearly indicates the location of the target area, it is likely that a person viewing the estimation aid diagram will be able to recognize it easily visually.

[0038] <Process Flow> Figure 1 is a schematic diagram of a process flow including one embodiment of the present disclosure. It consists of the following steps performed by a computer: S1: Perform a reference site gradient analysis on the reference site microbiome dataset and obtain the reference site gradient analysis results. S2: Analyze the relationship between the reference land group gradient analysis results obtained in S1 and the reference land group NCP value dataset to derive the gradient analysis result-NCP value relationship. S3: Perform a local gradient analysis on the target site's microbiome data and the microbiome dataset for the target site, and obtain the local gradient analysis results. S4: The NCP value of the target site is estimated from the relationship between the gradient analysis results obtained in S2 and the target site gradient analysis results obtained in S3. S5: Output the NCP value for the target area obtained in S4.

[0039] Figure 2 is a schematic diagram of a process flow including another embodiment of this disclosure, distinct from Figure 1. It consists of the following steps performed by a computer: S1': Perform a reference geographical gradient analysis on the reference geographical microbiome dataset to obtain the reference geographical analysis coordinate dataset. The relationship between the reference geographic group analysis coordinate dataset obtained in S2':S1' and the reference geographic group NCP value dataset is analyzed to derive the analysis coordinate-NCP value relationship. S3': Perform a local gradient analysis on the target site's microbiome data and the microbiome dataset for the target site to obtain the local analysis coordinates. Based on the relationship between the analytical coordinates and NCP values ​​obtained in S4A:S2', create an estimation aid diagram using at least two analytical coordinate axes. S4B: The NCP value of the target area is estimated from the estimation auxiliary map obtained in S4A and the target area analysis coordinates obtained in S3'. S5': Outputs the target NCP value obtained in S4B.

[0040] Figure 3 is a schematic diagram of a process flow including another implementation of this disclosure, distinct from Figures 1 and 2. It consists of the following steps performed by a computer: S6: A gradient analysis (which is both a reference area group longitude analysis and a target area gradient analysis) is performed on the reference area group longitude analysis data, including the target area microbiome data, to obtain the results of the reference area group longitude analysis and the target area gradient analysis. "S2": Analyze the relationship between the reference site group gradient analysis results obtained in S6 and the dataset of reference site group NCP values to derive the gradient analysis result - NCP value relationship. "S4": Estimate the NCP value of the target site from the gradient analysis result - NCP value relationship obtained in S2 and the target site gradient analysis result obtained in S6. "S5": Output the NCP value of the target site obtained in S4.

[0041] The following explains each step and related phrases.

[0042] <S1, S1': Gradient analysis of the reference site group microbiota dataset> S1: Perform a reference site group gradient analysis on the reference site group microbiota dataset to obtain the reference site group gradient analysis results. S1': Perform a reference site group gradient analysis on the reference site group microbiota dataset to obtain the reference site group analysis coordinate dataset.

[0043] <Microbiota data> The microbiota 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. Microbiota data is data on the microbiota obtained by DNA analysis of soil samples and includes information on the names (microbial identification markers) and detection amounts (abundance of microorganisms. It may also be the abundance ratio) of the microorganisms present in at least the analyzed soil samples.

[0044] <DNA analysis, next-generation sequencing> Microbiota data is preferably obtained by DNA analysis. Examples of DNA analysis and gene sequence analysis methods include, but are not limited to, DNA microarrays, next-generation sequencing, PCR, DGGE, T-RFLP, and FISH. DNA analysis may also be performed using next-generation sequencing because it is highly sensitive and accurate. Examples of next-generation sequencing include shotgun metagenomic analysis and amplicon sequencing, such as 16S rRNA gene analysis (DNA metabarcoding method targeting the 16S rRNA region of prokaryotes), 18S rRNA gene analysis (DNA metabarcoding method targeting the 18S rRNA region of eukaryotes), 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 a variety of eukaryotes including fungi and endoparasitic organisms, and fungal ITS is useful for identifying fungi. When analyzing DNA data, calibration can be performed by adding standard sequence fragments, such as artificially designed DNA sequences or lambda phages, to the PCR solution after adjusting their concentration, thereby estimating the absolute abundance (nucleic acid concentration) of each species. The above analytical methods may be combined.

[0045] <Target area, reference area group> The target area is a plot of land from which microbial flora data has been obtained, from which NCP values ​​are to be acquired. The reference plot group is a collection of multiple plots of land (reference plots) from which microbial flora data and NCP value data have been acquired, in order to acquire reference gradient analysis results or to derive the relationship between longitude analysis results and NCP values. The reference plot group may consist only of reference plots that have both microbial flora data and NCP value data. The reference plot group may consist of reference plots that have both microbial flora data and NCP value data, and reference plots that have only microbial flora data. The number of plots of land included in the reference plot group is preferably 50 or more, preferably 100 or more, more preferably 150 or more, and more preferably 200 or more. Preferably 10,000 or less. The larger the number of reference plots, the higher the likelihood of including land with a wide range of characteristics, and the higher the likelihood of increased accuracy in the relationship between gradient analysis results and NCP values. If the number of reference plots is too large, data processing will take a long time.

[0046] The reference area group may include various types of land with different land characteristics, such as slope, latitude, distance from water sources, topography, sunshine intensity, average temperature, average precipitation, average humidity, and land use history (changes in land use patterns such as forests, residential areas, rural areas, factories, and commercial areas). From the reference area group with various characteristics, a portion of the reference area group that has characteristics similar to the target area in terms of land characteristics, such as slope, latitude, distance from water sources, topography, sunshine intensity, average temperature, average precipitation, average humidity, and land use history (changes in land use patterns such as forests, residential areas, rural areas, factories, and commercial areas) may be selected and used as the reference area group.

[0047] <Target area microbial community data, reference area microbial community dataset> Target site microbial community data refers to microbial community data obtained from soil samples collected at the target site. A microbial community dataset is a collection of data consisting of multiple microbial community datasets. A reference site group microbial community dataset is a collection of microbial community data obtained from soil samples collected at a reference site group. If the target site is part of a reference site group that does not have NCP value data, the reference site group microbial community dataset may include the target site microbial community data.

[0048] <Slope analysis> Gradient analysis is an exploratory statistical method used in multivariate data analysis to reduce the number of dimensions and make the data easier to interpret by summarizing multivariate data (extracting the trends that characterize the whole). It is broadly classified into direct gradient analysis and indirect gradient analysis. Gradient analysis may also be indirect gradient analysis. Gradient analysis may also use similarity indices or dissimilarity indices. In indirect gradient analysis, multivariate data is summarized and plotted on axes using similarity indices, etc. Examples of indirect gradient analysis include PCA (principal component analysis), CA (correspondence analysis), DCA (detrended correspondence analysis), PCoA (principal coordinate analysis), and NMDS (nonmetric multidimensional analysis), but it is not limited to these. Examples of direct gradient analysis include CCA (canonical correspondence analysis) and RDA (redundancy analysis), but it is not limited to these. Gradient analysis may also be PCoA or NMDS. Gradient analysis may also be PCoA. PCoA and NMDS allow for the arrangement (ranking) of microbiome datasets in an n-dimensional space using similarity and dissimilarity, such that locations with high similarity (dissimilarity) are close together and locations with low similarity (dissimilarity) are far apart. Gradient analysis can also be used to arrange them in two or three dimensions. Two-dimensional and three-dimensional arrangements are easier for humans to understand. Two-dimensional arrangements, in particular, are easy for humans to understand and easy to visualize on paper.

[0049] <Reference ground group gradient analysis results, reference ground group analysis coordinates> The reference site group gradient analysis result is the result obtained by the gradient analysis of the reference site group microbiota dataset. The reference site group analysis coordinates are the coordinates in the n-dimensional space obtained by the gradient analysis of the reference site group microbiota dataset. They may be coordinates in a two-dimensional space or a three-dimensional space. Two-dimensional or three-dimensional spaces are easier for people to recognize. In particular, two-dimensional space is easier for people to recognize and easier to illustrate on paper. The reference site group gradient analysis result may also be the result obtained by the gradient analysis of the target site microbiota data and the reference site group microbiota dataset. The reference site group analysis coordinates may also be the coordinates in the n-dimensional space obtained by the gradient analysis of the target site microbiota data and the reference site group microbiota dataset.

[0050] <Similarity> As a method for calculating the similarity between microbiotas, a statistical analysis of the similarity pattern may be performed using a similarity index or a dissimilarity index. Examples include, but are not limited to, the percentage index, Bray-Curtis index, Jaccard index, Chao index, chi-square distance, etc. A dissimilarity index may be obtained, and the similarity may be calculated based on that value. For example, when the dissimilarity can take a value from 0 to 1, a method of taking the value obtained by subtracting the dissimilarity from 1 as the similarity is exemplified, but it is not limited to this.

[0051] <S2, S2”: Derivation of the relationship between the gradient analysis result and the NCP value, S2’: Derivation of the relationship between the analysis coordinates and the NCP value> S2: Analyze the relationship between the reference site group gradient analysis result obtained in S1 and the dataset of reference site group NCP values to derive the gradient analysis result - NCP value relationship. S2’: Analyze the relationship between the reference site group analysis coordinate dataset obtained in S1’ and the dataset of reference site group NCP values to derive the analysis coordinate - NCP value relationship. S2”: Analyze the relationship between the reference site group gradient analysis result obtained in S6 and the dataset of reference site group NCP values to derive the gradient analysis result - NCP value relationship.

[0052] <Reference site group NCP value dataset> The reference site group NCP value dataset is a set of NCP value data of the reference sites.

[0053] <Gradient analysis results - NCP value relationship, analysis coordinates - NCP value relationship> The gradient analysis results - NCP value relationship is the relationship between the reference area group gradient analysis results and the reference area group NCP value dataset, and the analysis coordinates - NCP value relationship is the relationship between the reference area group analysis coordinates and the reference area group NCP value dataset. To derive the gradient analysis results - NCP value relationship or the analysis coordinates - NCP value relationship, multivariate analysis or correlation analysis may be performed between the reference area group gradient analysis results or reference area group analysis coordinates and the reference area group NCP value dataset. A relational expression between the reference area group gradient analysis results or reference area group analysis coordinates and the reference area group NCP value dataset may also be derived. The reference area group gradient analysis results or reference area group analysis coordinates do not necessarily have to use all of the reference area group gradient analysis results or reference area group analysis coordinates obtained at S1 and S1', and at least a part of them may be used. If NCP values are not obtained for all of the reference areas where the reference area group longitude analysis results or reference area group analysis coordinates are obtained, then naturally a part of the reference area group longitude analysis results or reference area group analysis coordinates of the reference area group for which NCP values are obtained will be used. The reference area group gradient analysis results or reference area group analysis coordinates of a reference area that is close in terms of the characteristics of the target area and the land, such as latitude, gradient, distance from a water vein, terrain, sunlight intensity, temperature and humidity, and land use history (changes in the usage form such as forest, residential land, satoyama, factory, commercial land, etc.), may be used. Similarly, it is not necessarily required to use all of the reference area group NCP value dataset.

[0054] <S3: Acquisition of target area analysis results, S3': Acquisition of target area gradient analysis coordinates> S3: Perform gradient analysis of the target area microbiota data and the target area microbiota dataset for the target area to obtain the target area gradient analysis results. S3': Perform gradient analysis of the target area microbiota data and the target area microbiota dataset for the target area to obtain the target area analysis coordinates. Note that in S1 or S1', the target area may be included in the reference area group, that is, the target area microbiota data may be a part of the reference area group microbiota dataset. S1 and S1' will include S3 and S3'. An example of the flow in this case is shown in FIG. 3 and will also be described in the column of S6 described below.

[0055] <Microbiota dataset for a target area> The microbiota dataset for a target area is a dataset consisting of microbiota data of a plurality of lands, which is used to perform a gradient analysis together with the microbiota data of the target area and obtain the gradient analysis result (target area gradient analysis result) of the target area. The microbiota dataset for a target area may include at least a part of the microbiota dataset of the reference area group, or the microbiota dataset for a target area may be a part of the microbiota dataset of the reference area group. This is because the obtained target area longitude analysis result is likely to follow the gradient analysis result - NCP value relationship and the analysis coordinate - NCP value relationship derived in S2 or S2'. The microbiota data of the target area and the microbiota dataset for the target area may be a part of the microbiota dataset of the reference area group, or the sum of the microbiota data of the target area and the microbiota dataset for the target area may be the microbiota dataset of the reference area group. Since the reference area group longitude analysis becomes the target area longitude analysis, the step of estimating the NCP value is simplified, and the target area NCP value can be obtained efficiently and simply.

[0056] <S4, S4B, S4”: Estimation of the target area NCP value, S4A: Creation of an estimation auxiliary diagram> S4: Estimate the NCP value of the target area from the gradient analysis result - NCP value relationship obtained in S2 and the target area gradient analysis result obtained in S3. S4A: Create an estimation auxiliary diagram using at least two analysis coordinate axes based on the analysis coordinate - NCP value relationship obtained in S2'. S4B: Estimate the NCP value of the target area from the estimation auxiliary diagram obtained in S4A and the target area analysis coordinates obtained in S3'. S4”: Estimate the NCP value of the target area from the gradient analysis result - NCP value relationship obtained in S2” and the target area gradient analysis result obtained in S6.

[0057] <Estimation of NCP value> Estimating the NCP value refers to introducing the subject site gradient analysis results into the gradient analysis result-NCP value relationship, or introducing the subject site analysis coordinates into the analysis coordinate-NCP value relationship. Methods for estimating the NCP value include, but are not limited to, introducing the subject site gradient analysis results into the relationship equation representing the gradient analysis result-NCP value relationship, introducing the subject site analysis coordinates into the relationship equation representing the analysis coordinate-NCP value relationship, or introducing the subject site analysis coordinates into the estimation auxiliary diagram based on the analysis coordinate-NCP value relationship. Furthermore, the estimation of the NCP value may be performed multiple times, for example, by introducing the subject site analysis coordinates into the relationship equation representing the analysis coordinate-NCP value to calculate the NCP value, and then introducing the subject site analysis coordinates by displaying them as markers in the estimation auxiliary diagram based on the analysis coordinate-NCP value relationship.

[0058] <Estimated auxiliary diagram> The estimation aid diagram can be any diagram that shows the relationship between at least some of the analysis coordinates of the reference area group and the corresponding NCP values. For example, a diagram in which the coordinate axes of the indirect gradient analysis described above are used as the coordinate axes of the diagram, and in which the analysis coordinates of the reference area group and the NCP values ​​or the magnitude levels of the NCP values ​​can be read. The coordinate axes of the estimation aid diagram may be two axes (two-dimensional coordinate diagram) or three axes (three-dimensional coordinate diagram). When a two-dimensional or three-dimensional coordinate diagram is output, a person viewing the coordinate diagram can visually recognize the NCP values. Two-dimensional coordinate diagrams are particularly easy to illustrate, and when output, a person can easily visually recognize the NCP values.

[0059] The estimation aid diagram may be a diagram in which the size, shape, color, and intensity of markers attached to the analysis coordinates of the reference area group or the target area analysis coordinates change according to the NCP value. It may also be a diagram in which the corresponding NCP value is written next to the marker.

[0060] The estimated auxiliary diagram may be a diagram illustrated in a manner capable of estimating the NCP value based on the relationship between the analysis coordinates and the NCP value. For example, it may be a contour map showing contour lines for the NCP value, or a diagram showing contour lines according to the NCP value by color shading. By placing the analysis coordinates of the target area on the estimated auxiliary diagram, when it is output, a person who views the coordinate diagram can easily recognize the NCP value. It may also be a diagram in which a numerical value of the NCP value obtained by introducing the analysis coordinates of the target area into the relational expression of the analysis coordinates - NCP value is described beside the marker indicating the analysis coordinates of the target area.

[0061] The NCP value of the estimated auxiliary diagram may be the carbon accumulation amount. The gradient analysis may be an indirect gradient analysis using similarity or dissimilarity. The inventor found that when the carbon accumulation amount of each reference area included in the reference area group was overlaid on the analysis coordinates of the indirect gradient analysis of the reference area group microbial community dataset with two axes as the coordinate axes of the indirect gradient analysis using similarity or dissimilarity, surprisingly, the carbon accumulation amount is not random, but has a simple regularity such as increasing along a certain direction. Because it has a simple regularity, for example, even if the contour lines of the carbon accumulation amount are shown, it does not become complicated, and the carbon accumulation amount can be easily estimated from the analysis coordinates of the target area. In the examples described later, the linear contour lines are arranged substantially parallel, but the contour lines are not necessarily linear.

[0062] <S5, S5’, S5”: Output of the target area NCP value> S5: Output the target area NCP value obtained in S4. S5’: Output the target area NCP value obtained in S4B. S5”: Output the target area NCP value obtained in S4”.

[0063] <Output of the target area NCP value> The form of output may be transmission via wireless communication, wired, the Internet, etc. of electronic data, transfer or replication to a storage medium, printing on paper, etc., or display on a display means.

[0064] <S6: Gradient analysis of the reference area microbial community dataset including the target area microbial community data> S6: Perform gradient analysis (which is both the reference area group longitude analysis and the target area gradient analysis) on the reference area microbiota data set containing the target area microbiota data, and obtain the reference area group longitude analysis result and the target area gradient analysis result.

[0065] S6 is a step in which the target area microbiota data and the microbiota data set for the target area are included in the reference area microbiota data set, the reference area group gradient analysis is also the target area gradient analysis, and the reference area group gradient analysis result and the target area gradient analysis result are obtained by the reference area group gradient analysis. S6 is a step that may be included in the descriptions such as <S1, S1’: Gradient analysis of the reference area group microbiota data set><Microbiota data><Reference area group, Target area><Gradient analysis><Reference area group gradient analysis result, Reference area group analysis coordinates><S3: Obtaining the target area analysis result, S3’: Obtaining the target area gradient analysis coordinates><Microbiota data set for the target area>. The process flow described in FIG. 3 is a process flow derived from the descriptions of the above steps and the process flows shown in FIGS. 1 and 2. However, for the sake of facilitating understanding, both S6 and FIG. 3 are explicitly described. As an aspect of the present disclosure related to FIG. 3, an example of a method for obtaining the target area NCP value in which a computer performs the steps of obtaining the reference area group gradient analysis result and the target area gradient analysis result by reference area group gradient analysis from the reference area group microbiota data set containing the target area microbiota data, analyzing the reference area group gradient analysis result and the reference area group NCP value data set to derive the gradient analysis result - NCP value relationship, estimating the target area NCP value from the target area gradient analysis result and the gradient analysis result - NCP value relationship, and outputting the target area NCP value is illustrated.

[0066] <Configuration of the computer for performing each step> FIG. 4 shows an example of a configuration schematic diagram of the computer 1 according to the present disclosure. The computer 1 includes an electronic data input means 11, a storage means 12, an arithmetic means 13, an electronic data output means 14, a display means 15, and a display selection means 16.

[0067] Computer 1 may be a workstation, personal computer, mainframe, supercomputer, or client computer, or it may be a PDA, tablet, or smartphone. The means that are normally provided to Computer 1, which are omitted in Figure 4, may of course be provided.

[0068] The electronic data input means 11 is a means for inputting electronic data such as target site microbial flora data, target site microbial flora datasets, reference site group microbial flora datasets, reference site group NCP value datasets, and programs necessary for implementing this disclosure. The electronic data input means 11 may be equipped with a media input section for USB, HDD, CD-ROM, DVD, etc., and electronic data may be input via the above media. It may also be equipped with a wireless communication or wired communication receiving function to receive electronic data. It may also be equipped with an internet communication receiving function to receive electronic data.

[0069] The storage means 12 is a means for storing programs that cause the computer 1 to perform desired processing, electronic data input from the electronic data input means 11, and electronic data processed and generated by the calculation means 13, and may be a memory or a hard disk.

[0070] The arithmetic means 13 is an arithmetic means that drives, controls, and performs calculations for the computer 1. For example, it may be a CPU, a GPU, or, from another perspective, an integrated circuit (IC, LSI, etc.).

[0071] The electronic data output means 14 is one of the output means, and is a means for outputting electronic data processed and generated by the calculation means 13, such as the target NCP value. The electronic data output means 14 may be equipped with a media input section for USB, HDD, CD-ROM, DVD, etc., and may output electronic data via the above media. It may also be equipped with a wireless communication or wired communication receiving function and transmit electronic data. It may also be equipped with an internet communication receiving function and transmit electronic data. It may transmit electronic data to a printer connected via wireless communication, wired communication, or internet communication and print the NCP value etc. on paper.

[0072] The display means 15 is one of the output means and is a means for displaying electronic data processed and generated by the calculation means 13, such as the NCP value of the target area. The display means 15 may be a flat display screen, a projection mechanism for displaying content, a glasses-type or goggle-type display, or a head-mounted display.

[0073] The display selection means 16 is a means for selecting an option when the display content is displayed on the display means 15 in a selectable format. The display selection means 16 may be an audio input device. It may be a device that senses the movements of the operator's body. It may be a dedicated device, a general-purpose touch panel device, a keyboard, a mouse, or a device that senses the movements of the eyes, fingers, or arms.

[0074] <Examples> The carbon sequestration amount was used as the NCP value, and the NCP values ​​for the target sites were obtained. The target sites were two locations in Nagoya City (Target Site 1 and Target Site 2) and one location in Tokyo (Target Site 3).

[0075] The reference site group consisted of 146 locations in Nishiawakura Village, Okayama Prefecture, 51 locations in Otsu City and Kusatsu City, Shiga Prefecture, and a total of 272 locations including locations in Nagoya City, Aichi Prefecture and the 23 wards of Tokyo. Soil samples were obtained from 5-10 cm above ground level for both the reference site group and the target site, and next-generation sequencing (amplicon sequencing) was performed using Illumina Inc.'s Iseq100 to obtain microbial community data for both the reference site group and the target site.

[0076] The following tasks were performed using a workstation (Lepton WS4000TRX50A), which is a computer 1, equipped with an electronic data input means 11, a storage means 12, a calculation means 13, an electronic data output means, a display means 15, and a display selection means 16.

[0077] The reference area microbiome dataset, consisting of the reference area microbiome data and the target area microbiome data obtained in Iseq100, was stored in the storage means 12 of the workstation using the electronic data input means 11. Subsequently, computer 1 performed PCoA using the similarity index (Bray-Curtis index) on the reference area microbiome dataset to obtain the two-dimensional analysis coordinates of the reference area (reference area analysis coordinate dataset) and the two-dimensional analysis coordinates of the target area (target area analysis coordinates). Figure 5 shows the reference area analysis coordinate dataset and the target area analysis coordinates represented in a two-dimensional analysis coordinate diagram.

[0078] The carbon stock amounts of the reference sites, investigated using the Forest Soil Carbon Map provided by the Forestry and Forest Products Research Organization (https: / / www.ffpri.affrc.go.jp / research / saizensen / 2021 / 20211224-02.html), were stored in the storage means 12 of the workstation using the electronic data input means 11 as a reference site carbon stock dataset. The carbon stock amount was measured using OCS:Soil organic carbon stock. The unit is ton / ha. The number of reference site locations included in the reference site carbon stock dataset is 127 locations in Nishiawakura Village, Okayama Prefecture, 44 locations in Otsu City and Kusatsu City, Shiga Prefecture, and a total of 184 locations including others. Computer 1 performed a correlation analysis between the reference site carbon stock dataset of the above 184 locations and a portion of the corresponding reference site carbon stock dataset to obtain a relationship between the analysis coordinates and the carbon stock amount.

[0079] The analysis coordinates (X:PCo1, Y:PCo2) of the acquired sites were as follows: The analysis coordinates for site 1 were X:-0.361 Y:-0.051. The analysis coordinates for site 2 were X:-0.212 Y:-0.100. The analysis coordinates for site 3 were X:0.084 Y:-0.012. Computer 1 estimated the carbon storage amount of each site based on the above relationship between the analysis coordinates and carbon storage amount, and the results were that the carbon storage amount for site 1 was 69.1, for site 2 was 68.2, and for site 3 was 75.6.

[0080] Computer 1 removed data for reference sites that did not contain carbon storage from the two-dimensional analysis coordinate diagram of the reference site group analysis coordinate dataset shown in Figure 5, and created a first estimation auxiliary diagram in which the amount of carbon storage for each reference site was represented by the intensity of the color of a circle-shaped marker at the analysis coordinate point of each reference site, and an X-shaped marker was attached to the analysis coordinate point of the target site, and output this to the display means 15. This is shown in Figure 6. The first target site is labeled Field_1, the second target site is labeled Field_2, and the third target site is labeled Field_3. A simple regularity between the analysis coordinate value and the amount of carbon storage can be seen, where the color of the circle-shaped marker becomes darker along approximately the positive Y-axis, and the amount of carbon storage increases. Figure 6 shows the amount of carbon storage for each target site based on the above simple regularity and the carbon storage values ​​of the surrounding markers.

[0081] Computer 1, based on the relationship between the analysis coordinates and the amount of carbon storage, created a second estimation aid diagram in the two-dimensional analysis coordinate diagram, representing the amount of carbon storage as contour lines and attaching X-shaped markers to the analysis coordinate points of the target area, and output it to the display means 15. The contour lines are set in increments of 3 from 66 to 87. This is shown in Figure 7. The amount of carbon storage is indicated by the intensity of the color. A simple regularity can be seen in that the color becomes darker roughly along the positive direction of the Y axis, and the amount of carbon storage increases. Figure 7 shows the amount of carbon storage for each target area based on the above simple regularity and contour lines. From Figure 7, a person can easily recognize that the amount of carbon storage for target area 1 is 69-70, for target area 2 it is 68-69, and for target area 3 it is 75-76. [Explanation of symbols]

[0082] 1 Computer 11. Electronic data input means 12 Memory means 13 Calculation means 14. Electronic data output means 15 Display means 16 Display selection means< / ncp>

Claims

1. Computers The process involves obtaining the results of a reference geographic gradient analysis from a reference geographic microbiome dataset, and The steps include: analyzing the aforementioned reference group gradient analysis results and the reference group NCP value dataset to derive the gradient analysis results-NCP value relationship; The process involves obtaining the results of a site gradient analysis by performing a site gradient analysis using site microbiome data and a site-specific microbiome dataset. The steps include: estimating the NCP value of the target site from the gradient analysis results of the target site and the relationship between the gradient analysis results and the NCP value; A method for obtaining target NCP values, which involves executing the step of outputting the aforementioned target NCP values.

2. A method for obtaining a target site NCP value according to claim 1, wherein the target site gradient analysis is a target site gradient analysis using similarity or dissimilarity, and the reference site group gradient analysis is a reference site group gradient analysis using similarity or dissimilarity.

3. A method for obtaining NCP values ​​for a target site according to claim 1, wherein the target site gradient analysis result includes target site analysis coordinates, the reference site group gradient analysis result includes a reference site group analysis coordinate dataset, and the gradient analysis result-NCP value relationship is an analysis coordinate-NCP value relationship.

4. The step of estimating the NCP value of the target area from the aforementioned analysis coordinate-NCP value relationship is: A method for obtaining the NCP value of a target site according to claim 3, comprising the steps of creating an estimation auxiliary diagram based on the aforementioned analysis coordinate-NCP value relationship, and estimating the NCP value of the target site from the estimation auxiliary diagram and the analysis coordinate of the target site.

5. The method for obtaining the NCP value of a target site according to claim 1, wherein the target site microbial flora data and the target site microbial flora dataset are included in the reference site group microbial flora dataset, and the target site longitude analysis results are also obtained by the reference site group gradient analysis, so that the step of obtaining the reference site group gradient analysis results also serves as the step of obtaining the target site gradient analysis results.

6. The method for obtaining a target NCP value according to claim 1, wherein the reference group NCP value dataset is a dataset of carbon stock amounts of the reference group, and the target NCP value is the carbon stock amount of the target.

7. The method for obtaining NCP values ​​for a target area according to claim 1, wherein the target area microbial flora data, the target area microbial flora dataset, and the reference area microbial flora dataset are obtained by DNA analysis.

8. On the computer, The steps include obtaining a reference geographical analysis coordinate dataset from a reference geographical microbiome dataset, A method for creating an estimation aid diagram for obtaining NCP values, comprising the steps of deriving the relationship between analysis coordinates and NCP values ​​from the analysis of the aforementioned reference geographical group analysis coordinate dataset and reference geographical group NCP value dataset, and creating an estimation aid diagram.

9. On the computer, The process involves obtaining the results of a reference geographic gradient analysis from a reference geographic microbiome dataset, and The steps include: analyzing the aforementioned reference group gradient analysis results and the reference group NCP value dataset to derive the gradient analysis results-NCP value relationship; The process involves obtaining the results of a site gradient analysis by performing a site gradient analysis using site microbiome data and a site-specific microbiome dataset. The steps include: estimating the NCP value of the target site from the gradient analysis results of the target site and the relationship between the gradient analysis results and the NCP value; A program for causing the step of outputting the NCP value of the target area.

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