Apparatus, systems and methods for increasing plant productivity using a fungal microbiome

Abstract

This invention relates to apparatus and methods for selecting a soil microbiome having increasing plant productivity. The apparatus and methods relate to combining an output from a machine learning model, a generalized linear model, and a distance-based multivariate model to execute a donor forest selection tool that is configured to select from a plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

Description

Attorney Docket No.1723.2.WO2 PATENT APPARATUS, SYSTEMS AND METHODS FOR INCREASING PLANT PRODUCTIVITY USING A FUNGAL MICROBIOME CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to International Application No. PCT / US2024 / 036393, filed July 1, 2024, entitled METHODS, SYSTEMS AND RELATED MACHINE LEARNING TOOLS FOR ACCELERATED PLANT PRODUCTIVITY AND ATMOSPHERIC CARBON SEQUESTRATION, and claims priority to U.S. Provisional Application Serial No. 63 / 571,140, filed March 28, 2024, entitled “METHODS, SYSTEMS AND RELATED MACHINE LEARNING TOOLS FOR ACCELERATED PLANT PRODUCTIVITY AND ATMOSPHERIC CARBON SEQUESTRATION,” the disclosures of which are incorporated herein by reference in their entirety. BACKGROUND OF THE INVENTION Field of the Disclosure

[0002] This application relates generally to improved apparatus, methods and systems for selecting a soil microbiome for increasing plant productivity and / or accelerating plant biomass growth and / or plant-mediated sequestration of atmospheric carbon, in particular, for automating selection of microbial drivers thereof. Background

[0003] Commercial applications for soil microbiome melioration have been slow to develop, particularly in scenarios where harnessing the benefits of an entire native community is sought. Several factors contribute to this slow development, such as uncertain methods for identifying the appropriate microbial biodiversity, and due to difficulties in scaling inoculum production and fungal establishment.

[0004] The vast majority of plant species form symbioses with fungi, which significantly influence their access to growth-limiting resources. Recent research has shown, however, that functional characteristics of the soil fungal microbiome as a whole can influence the growth of an entire forest. For example, there is a strong correlation between the fungal microbiome’s functional characteristics and overall forest productivity. Thus, soil fungal biodiversity servesAttorney Docket No.1723.2.WO2 PATENT as a strong bio-indicator of underlying plant growth and health. Additionally, recent research has shown that certain location-specific fungal species are linked to forest performance, measured in terms of tree growth rate and ability to sequester atmospheric carbon.

[0005] Therefore, it is critical to take a holistic and natural approach in developing systems and methods of forest management and regeneration, especially in commercial contexts where biomass productivity is tightly linked to economic profitability. While commercial microbial inoculants are widely available, cross-laboratory studies have shown that many fungal inoculant products are ineffective at increasing tree growth rate, biomass productivity and atmospheric carbon uptake, especially in the long-term. Most commercial inoculants are ineffective for several reasons, most notably due to the limited number of species they contain, most of which can be ill-suited for geographically wide-spread inoculation across a variety of ecological regions. SUMMARY OF THE INVENTION

[0006] In a first aspect, an apparatus for selecting a soil microbiome for increasing plant productivity, comprising a non-transitory computer-readable medium configured to store processor-executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations, comprising: receiving observational data that is used to train a machine learning model, wherein the observational data comprise location- specific soil fungal community structure data, plant productivity data and environmental covariate data, wherein the observational data are collected from a plurality of geographically distinct plant communities; using the machine learning model, determining a plant biomass prediction comprising an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site; receiving experimental data comprising (i) productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soil sample, and (ii) fungal biodiversity data associated with each plant at predetermined times during plant growth; using a generalized linear model, determining a cumulative growth effect size for each of the productivity responses; using a distance-based multivariate model, determining differences between pairwise comparisons of fungalAttorney Docket No.1723.2.WO2 PATENT biodiversity data associated with each plant at each of the predetermined times; and combining an output from the machine learning model, the generalized linear model, and the distance- based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

[0007] In another aspect, a computer-implemented method for selecting a soil microbiome having increasing plant productivity, comprising: receiving, by one or more processors, observational data that is used to train a machine learning model, wherein the observational data comprise location-specific soil fungal community structure data, plant productivity data and environmental covariate data, wherein the observational data are collected from a plurality of geographically distinct plant communities; using the machine learning model, determining via the one or more processors, a plant biomass prediction comprising an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site; receiving, by the one or more processors, experimental data comprising (i) productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soil sample, and (ii) fungal biodiversity data associated with each plant at predetermined times during plant growth; using a generalized linear model, determining via the one or more processors, a cumulative growth effect size for each of the productivity responses; using a distance-based multivariate model, determining differences between pairwise comparisons of fungal biodiversity data associated with each plant at each of the predetermined times; and combining via the one or more processors, an output from the machine learning model, the generalized linear model, and the distance-based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

[0008] In another aspect, a method of accelerating plant productivity and atmospheric carbon sequestration is provided. The method can include, for example, identifying a growth- promoting fungal consortium from a natural fungal microbiome, including: providing at least one sampling kit to a subject at a geographic location, the at least one sampling kit including a sample container configured to receive a soil sample from the geographic location; receivingAttorney Docket No.1723.2.WO2 PATENT the at least one sampling kit, including the soil sample, from the geographic location; extracting nucleic acid material from a first portion of the soil sample; generating a fungal microbiome dataset based on sequencing the nucleic acid material present in the first portion of the soil sample, wherein sequencing further includes a plurality of reagents that enrich for fungal- derived nucleic acids; providing a machine learning tool, wherein the machine learning tool includes a training database, the training database including biotic and abiotic data associated with a plurality of high productivity ecosystems; and inputting the fungal microbiome dataset into machine learning tool, whereby the machine learning tool identifies the growth-promoting fungal consortium including a subset of fungal species present in the first portion of the soil sample and associated with the plurality of high productivity ecosystems; propagating the growth-promoting fungal consortium, including: providing a second portion of the soil sample to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promoting fungal consortium to reproduce and outcompete other organisms present in the second portion of the soil sample; colonizing the feedstock with fungal species and / or strains including the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; and inoculating a plurality of plants present at the geographic location with the inoculum slurry; and monitoring productivity of each of the plurality of plants after each of the plurality of plants has been inoculated with the inoculum slurry, wherein monitoring includes utilizing a plurality of sensors.

[0009] In another aspect, a system for accelerating plant productivity and atmospheric carbon sequestration is disclosed. The system can include, for example, at least one sampling kit, wherein the at least one sampling kit is configured to be sent to and from a geographic location and includes a sample container, the sample container configured to receive a soil sample; a soil sample processing system configured to extract nucleic acid material from a first portion of the soil sample; a nucleic acid sequencing platform configured to sequence the nucleic acid material present in the first portion of the soil sample and configured to generate a fungal microbiome dataset, wherein the nucleic acid sequencing platform further includes aAttorney Docket No.1723.2.WO2 PATENT plurality of reagents adapted to enrich for fungal-derived nucleic acids; a machine learning tool, wherein the machine learning tool includes a training database, the training database included of biotic and abiotic data associated with a plurality of high productivity ecosystems, and wherein the machine learning tool is configured to identify a growth-promoting fungal consortium including a subset of fungal species present in the first portion of the soil sample and associated with the plurality of high productivity ecosystems; a forest bioreactor configured to receive a second portion of the soil sample and to propagate the growth- promoting fungal consortium, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are adapted to promote colonization of the feedstock by the growth-promoting fungal consortium such as to outcompete other organisms present in the soil sample; an inoculum slurry including a mixture of water and a growth-promoting fungal consortium inoculum, wherein the growth-promoting fungal consortium inoculum includes the feedstock substantially colonized by the growth- promoting fungal consortium; a plurality of plants, wherein each of the plurality of plants is inoculated with the inoculum slurry; and a plurality of sensors configured to monitor plant productivity of each of the plurality of plants inoculated with the inoculum slurry.

[0010] In another aspect, a method of producing a fungal inoculum is disclosed. The method may include, for example, providing a forest bioreactor; isolating a growth-promoting fungal consortium; providing the growth-promoting fungal consortium to the forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promoting fungal consortium to grow and to reproduce; colonizing the feedstock with fungal species and / or strains including the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; and harvesting the inoculum slurry.

[0011] In another aspect, a method of increasing plant productivity with reduced plant fertilizer utilization is disclosed. The method may include, for example, isolating a growth- promoting fungal consortium; providing the growth-promoting fungal consortium to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promotingAttorney Docket No.1723.2.WO2 PATENT fungal consortium to grow and to reproduce; colonizing the feedstock with fungal species and / or strains including the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; and inoculating a plurality of plants with the inoculum slurry, wherein the fungal species and / or strains including the growth-promoting fungal consortium enhances nutrient bioavailability without the need for chemical fertilizers.

[0012] In another aspect, a method of generating biodiversity credits is provided. The method includes, for example, isolating a growth-promoting fungal consortium, wherein the growth-promoting fungal consortium includes native fungal species and / or strains; providing the growth-promoting fungal consortium to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promoting fungal consortium to grow and to reproduce; colonizing the feedstock with the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; inoculating a plurality of plants with the inoculum slurry; and establishing a community of the native fungal species and / or strains including the growth-promoting fungal consortium, in symbiosis with the plurality of plants, wherein the biodiversity credits increase in positive correlation to a diversity of the community of the native fungal species and / or strains.

[0013] In another aspect, a method of improving water quality is provided. The method may include, for example, inoculating a plurality of plants with an inoculum slurry, wherein the inoculum slurry includes a mixture of water and a growth-promoting fungal consortium inoculum including a feedstock and a growth-promoting fungal consortium; and establishing a community of the native fungi including the growth-promoting fungal consortium in symbiosis with the plurality of plants, wherein the growth-promoting fungal consortium inoculum includes a plurality of native fungal species and / or strains, and wherein each of the plurality of native fungal species and / or strains is adapted to filter contaminants from a volume of water, thereby improving quality of the volume of water.Attorney Docket No.1723.2.WO2 PATENT

[0014] In another aspect, a method of improving water quality is provided. The method may include, for example, inoculating a plurality of plants with an inoculum slurry, wherein the inoculum slurry includes a mixture of water and a growth-promoting fungal consortium inoculum including a feedstock and a growth-promoting fungal consortium; and establishing a community of native fungal species or strains , the native fungal species or strains being in symbiosis with the plurality of plants, wherein the growth-promoting fungal consortium inoculum comprises the native fungal species or strains, and wherein the native fungal species or strains are adapted to filter contaminants from a volume of water, thereby improving quality of the volume of water.

[0015] In another aspect, a method of remediating soil is provided. The method may include, for example, isolating a growth-promoting fungal consortium; forming a growth- promoting fungal consortium inoculum including a feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; inoculating a volume of soil with the inoculum slurry; and maturing the inoculum slurry within the volume of soil such as to substantially colonize the volume of soil with the growth-promoting fungal consortium.

[0016] These and other aspects of the present invention are set forth in more detail in the description of the invention below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 illustrates an example embodiment of a method of developing a training dataset for a machine learning tool for accelerated plant productivity and atmospheric carbon sequestration.

[0018] Figure 2 illustrates an embodiment of a method of accelerating plant productivity and atmospheric carbon sequestration.

[0019] Figure 3 illustrates an embodiment of a system for accelerating plant productivity and atmospheric carbon sequestration.

[0020] Figure 4 illustrates a method of producing a fungal inoculum with a forest bioreactor.

[0021] Figure 5 illustrates a method of increasing plant productivity with reduced plant fertilizer utilization.Attorney Docket No.1723.2.WO2 PATENT

[0022] Figure 6 illustrates a method of generating biodiversity credits.

[0023] Figure 7 illustrates a method of improving water quality.

[0024] Figure 8 illustrates a method of remediating soil.

[0025] Figure 9 illustrates an example framework for selecting a soil microbiome having increased plant productivity.

[0026] Figure 10A-B illustrate examples of sample locations and ectomycorrhizal fungal species diversity as a function of forest age.

[0027] Figure 11 illustrates example data of asymptotic species richness of ectomycorrhizal taxa in soil in relation to forest age.

[0028] Figure 12 illustrates example data of species richness of plants with and without a fungal inoculant or wild rhizosphere microbiome having increasing plant productivity.

[0029] Figure 13A-E illustrate example results of field trials using a soil microbiome having increased plant productivity. DETAILED DESCRIPTION

[0030] The present invention now will be described hereinafter with reference to the accompanying drawings and examples, in which embodiments of the invention are shown. This description is not intended to be a detailed catalog of all the different ways in which the invention may be implemented, or all the features that may be added to the instant invention. For example, features illustrated with respect to one embodiment may be incorporated into other embodiments, and features illustrated with respect to a particular embodiment may be deleted from that embodiment. Thus, the invention contemplates that in some embodiments of the invention, any feature or combination of features set forth herein can be excluded or omitted. In addition, numerous variations and additions to the various embodiments suggested herein will be apparent to those skilled in the art in light of the instant disclosure, which do not depart from the instant invention. Hence, the following descriptions are intended to illustrate some particular embodiments of the invention, and not to exhaustively specify all permutations, combinations and variations thereof.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for theAttorney Docket No.1723.2.WO2 PATENT purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0032] All publications, patent applications, patents and other references cited herein are incorporated by reference in their entireties for the teachings relevant to the sentence and / or paragraph in which the reference is presented.

[0033] Unless the context indicates otherwise, it is specifically intended that the various features of the invention described herein can be used in any combination. Moreover, the present invention also contemplates that in some embodiments of the invention, any feature or combination of features set forth herein can be excluded or omitted. To illustrate, if the specification states that a composition comprises components A, B and C, it is specifically intended that any of A, B or C, or a combination thereof, can be omitted and disclaimed singularly or in any combination.

[0034] As used in the description of the invention and the appended claims, the singular forms “a,” “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0035] Also as used herein, “and / or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations when interpreted in the alternative (“or”).

[0036] The term “about,” as used herein when referring to a measurable value such as an amount or concentration and the like, is meant to encompass variations of ± 10%, ± 5%, ± 1%, ± 0.5%, or even ± 0.1% of the specified value as well as the specified value. For example, “about X” where X is the measurable value, is meant to include X as well as variations of ± 10%, ± 5%, ± 1%, ± 0.5%, or even ± 0.1% of X. A range provided herein for a measurable value may include any other range and / or individual value therein.

[0037] As used herein, phrases such as “between X and Y” and “between about X and Y” should be interpreted to include X and Y. As used herein, phrases such as “between about X and Y” mean “between about X and about Y” and phrases such as “from about X to Y” mean “from about X to about Y.”

[0038] Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as ifAttorney Docket No.1723.2.WO2 PATENT it were individually recited herein. For example, if the range 10 to 15 is disclosed, then 11, 12, 13, and 14 are also disclosed.

[0039] The term “comprise,” “comprises” and “comprising” as used herein, specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0040] As used herein, the transitional phrase “consisting essentially of” means that the scope of a claim is to be interpreted to encompass the specified materials or steps recited in the claim and those that do not materially affect the basic and novel characteristic(s) of the claimed invention. Thus, the term “consisting essentially of” when used in a claim of this invention is not intended to be interpreted to be equivalent to “comprising.”

[0041] As used herein, the terms “increase,” “increasing,” “enhance,” “enhancing,” “improve” and “improving” (and grammatical variations thereof) describe an elevation of at least about 25%, 50%, 75%, 100%, 150%, 200%, 300%, 400%, 500% or more as compared to a control.

[0042] As used herein, the terms “reduce,” “reduced,” “reducing,” “reduction,” “diminish,” and “decrease” (and grammatical variations thereof), describe, for example, a decrease of at least about 5%, 10%, 15%, 20%, 25%, 35%, 50%, 75%, 80%, 85%, 90%, 95%, 97%, 98%, 99%, or 100% as compared to a control. In some embodiments, the reduction can result in no or essentially no (i.e., an insignificant amount, e.g., less than about 10% or even 5%) detectable activity or amount.

[0043] As used herein, “plant productivity” refers to the rate at which plants in an ecosystem synthesize organic matter through photosynthesis. For example, plant productivity can refer to the rate of biomass increase of a plant and / or the rate of atmospheric carbon sequestration by a plant. In some embodiments, plant productivity can be measured at the level of an individual plant, a plurality of plants, a whole ecosystem (e.g., a forest), and other like groupings of plants of the same or different species. In some embodiments, plant productivity can be quantified as the amount of carbon fixed per unit area per period of time, which can be expressed in terms of mass (e.g., grams of carbon per square meter per year). In some embodiments, plant productivity can be divided into subcategories, including, but not limited to, (1) gross primary productivity (GPP), which is the total rate of photosynthesis inclusive ofAttorney Docket No.1723.2.WO2 PATENT oxygen produced and carbon dioxide consumed by photosynthesis, and (2) net primary productivity (NPP), which is carbon dioxide respired by a plant or group of plants subtracted from the GPP. In some embodiments, NPP can represent the rate of new biomass production available for consumption by heterotrophic organisms (such as animals, fungi, and bacteria) or for accumulation in the form of standing biomass. In some embodiments, plant productivity can be further quantified in terms of biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate.

[0044] As used herein, “carbon sequestration” refers to the process of capturing and storing atmospheric carbon dioxide (CO2) in a stable form. For example, carbon sequestration can be achieved through physical, chemical, or biological processes that remove CO2 from the atmosphere and secure it in natural (e.g., forests) or artificial reservoirs.

[0045] In some embodiments, carbon sequestration can occur through photosynthesis of plants and other photosynthesizing organisms. As used herein, carbon sequestration can refer to the biological conversion of atmospheric CO2into organic carbon compounds and biomass through the process of photosynthesis. In some embodiments, plant photosynthesis can include a process whereby plants utilize specialized enzymes to catalyze the fixation of CO2into organic molecules (e.g., glucose, cellulose, lignin, etc.). In some embodiments, plant photosynthesis can be an effective means of transferring carbon from the atmosphere and stably storing it in living tissue. Advantageously, the stored organic material can contribute to nutrient cycling in ecosystem, for example, when plants die and decompose, sequestered atmospheric carbon can be transferred and recycled in non-gaseous form through carbon pools. In some embodiments, carbon sequestration by natural ecosystems can be a means of mitigating anthropogenically driven levels of CO2in the atmosphere that, e.g., can contribute to climate change. In some embodiments, carbon sequestration by plants can be enhanced by selecting plant species with high growth rates and long lifespans and through managing forests for increased density and longevity.

[0046] As used herein, “growth-promoting fungal consortium” refers to a pool of fungal species and / or strains that increase the plant productivity of an individual plant, and ecosystem, or any subset therebetween. In some embodiments, a growth-promoting fungal consortium refers to a subset of a fungal microbiome. For example, a fungal microbiome can includeAttorney Docket No.1723.2.WO2 PATENT pathogenic, parasitic, commensal species, in addition to mutualistic species, wherein pathogenic and / or commensal species may not be species best suited to accelerate plant growth and / or increase atmospheric carbon sequestration.

[0047] As used herein, “geographic location” refers to any location, point or area that can be defined by latitudinal and longitudinal coordinates. In some embodiments, the geographic location is a natural ecosystem. For example, a natural ecosystem can be an old growth forest, a native grassland, a rainforest, or any other ecosystem that has not been substantially affected by human activities. In some alternative embodiments, a geographic location can be a location involving substantial human activities. For example, farmlands, orchards, gardens, plantations and other locations where plants grow as the direct result of human activities. As used herein, “geographically distinct,” refers to a location or area that is separate from other locations or areas based on physical, political, ecological, spatial, or like boundaries.

[0048] As used herein, “nucleic acid material” refers to a polymeric form of nucleotides of any length, either ribonucleotides or deoxyribonucleotides. Thus, these terms include single-, double-, or multi-stranded DNA or RNA. Examples of polynucleotides include a gene or gene fragment, whole genomic DNA, genomic DNA, epigenomic, genomic DNA fragment, exon, intron, messenger RNA (mRNA), regulatory RNA, transfer RNA, ribosomal RNA, non-coding RNA (ncRNA) such as PIWI-interacting RNA (piRNA), small interfering RNA (siRNA), and long non-coding RNA (lncRNA), small hairpin (shRNA), small nuclear RNA (snRNA), micro RNA (miRNA), small nucleolar RNA (snoRNA) and viral RNA, ribozyme, cDNA, recombinant polynucleotide, branched polynucleotide, plasmid, vector, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probe, primer or amplified copy of any of the foregoing. A polynucleotide can include modified nucleotides, such as methylated nucleotides and nucleotide analogs including nucleotides with non-natural bases, nucleotides with modified natural bases such as aza- or deaza-purines. A polynucleotide can be composed of a specific sequence of four nucleotide bases: adenine (A); cytosine (C); guanine (G); and thymine (T). Uracil (U) can also be present, for example, as a natural replacement for thymine when the polynucleotide is RNA. Uracil can also be used in DNA. The term “nucleic acid sequence” can refer to the alphabetical representation of a polynucleotide or any nucleic acid molecule, including natural and non-natural bases.Attorney Docket No.1723.2.WO2 PATENT

[0049] As used herein, “reagents” refers to a selection of chemicals, enzymes, and buffers designed to lyse cells, denature proteins, and solubilize nucleic acid material, facilitating its purification from biological samples. The term “reagents that enrich for fungal-derived nucleic acids” can include reagents that can be collectively referred to as “primer.” As used herein, a primer refers to a short, synthetic oligonucleotide sequences designed to anneal to complementary regions of a target nucleic acid molecule, providing the starting point for enzymatic amplification and subsequent determination of the nucleotide sequence. For example, in the context of identifying fungi found in an environmental sample, a primer can be designed to target an Internal Transcribed Spacer genomic region (ITS), an ITS1 genomic region, an ITS2 genomic region, a Large Subunit rRNA (LSU) genomic region, a small subunit rRNA (SSU) genomic region, an 18S genomic region, a Translation Elongation Factor 1-alpha(TEF1-alpha) genomic region, a Beta- -tubulin) genomic region, an RNAPolymerase II (RPB1 and / or RPB2) genomic region, a Calmodulin (CaM) genomic region, or any other genomic regions that is used in the relevant art to identify fungal species and / or strains. In some embodiments, the primers used can be those as described by Tedersoo et al. (2018), New Phytologist, 217(3), pg. 1370-1385, which is incorporated by reference herein in its entirety.

[0050] As used herein, “forest bioreactor” refers to a sealed or unsealed enclosed environment (e.g., a vessel) that is used to grow a soil microbiome (e.g., a growth-promoting fungal consortium). For example, the forest bioreactor can include a feedstock and / or a plurality of conditions favoring the growth of fungal species and / or strains comprising a soil microbiome. In some embodiments, the bioreactor can be configured such as to suppress growth of undesirable species (e.g., contaminant organisms) present in a sample, such as species that do not include a growth-promoting fungal consortium.

[0051] As used herein, “feedstock” refers to a material or surface thereof, from or on which an organism lives, grows, and / or obtains nourishment. In some embodiments, a substrate provides sufficient nutrition to the organism under target growth conditions such that the organism can live and grow without providing the organism a further source of nutrients. In some embodiments, the substrate is a natural substrate. Non-limiting examples of a natural substrate include a lignocellulosic substrate, a cellulosic substrate, or a lignin-free substrate.Attorney Docket No.1723.2.WO2 PATENT The materials may have a variety of particle sizes and may occur in a variety of forms (e.g., as shavings, pellets, chips, flakes, or flour), or can be in homogeneous form and / or shape.

[0052] As used herein, “optimal environment” refers to an environment that supports the growth of a soil microbiome or one or more species or strain thereof, as would be readily understood by a person of ordinary skill in the art. In some embodiments, the optimal environment may include a gaseous environment of carbon dioxide (CO2), oxygen (O2), and other atmospheric gases including nitrogen (N2), and which is further characterized as having, e.g., a temperature, a relative humidity, a salinity, and a pH.

[0053] As used herein, “inoculum” refers to a solid or liquid composition of any living organism or part thereof, including, but not limited to, fungi and other living material present in a microbiome. “Inoculum slurry” as used herein refers to a liquid inoculum comprising a mixture of a soil microbiome and a volume of water, and optionally further comprising a fungal colonized feedstock or any other suitable liquid and / or nutrient additives. For example, nutrient additives can include, but are not limited to, glucose, sucrose, molasses, yeast extract, one or more salt (e.g., sodium chloride, potassium chloride), peptone, malt extract, soybean meal, corn steep liquor, ammonium sulfate, magnesium sulfate, potassium phosphate, vitamins, trace minerals, agar, and the like.

[0054] As used herein, “fungi,” “fungal species” and / or “fungal strain” refers to an organism classified as Basidiomycota, Ascomycota, and the phylum formerly referred to as Zygomycota. In some embodiments, fungi include all species classified as Cryptomycota, Microsporidia, Chytridiomycota, Blastocladiomycota, Neocallimastigomycota, Mucoromycota, Glomeromycota, Ascomycota and Basidiomycota. In some embodiments, fungi can include a mold, a yeast, and / or a filamentous fungus. For example, a filamentous fungus can include a mycelium, a mycelium further comprising a network of hyphae. In some embodiments a fungal strain can refer to a genetic variant or subtype of a fungal species, characterized by distinct morphological, physiological, or genetic traits that differentiate a strain. In some embodiments, a fungal strain can result from mutations, genetic recombination, or adaptation. For example, a fungal strain can result from adaptation to a specific environmental condition. In some embodiments a strain can result from laboratory experimentation, mutagenesis and / or breeding. In some embodiments, a fungal strain can be characterized as a morphological variant, a life history variant, a genetic variant, an epigeneticAttorney Docket No.1723.2.WO2 PATENT variant, and / or like variations of a type specimen of a fungal species. For example, a fungal strain comprising a genetic variant can be characterized by genetic differences thereof to at least part of the genome of the type fungal species whose at least partial genome is known and / or has been sequenced. In some embodiments, a fungal strain can be characterized as a fungus exhibiting differences in characteristics such as growth rate, pathogenicity, enzyme production, and resistance to antimicrobial agents, from a type sample of a species.

[0055] As used herein, “plant” refers to photosynthetic eukaryotes belonging to the kingdom Plantae, characterized by the presence of chloroplasts containing chlorophyll and cell walls composed of cellulose. In some embodiments, plants can include eukaryotic organisms whose cells contain chloroplasts containing chlorophyll a and chlorophyll b. As used herein, “tree” refers to its plain language meaning as would be customarily understood or as would be understood by one having ordinary skill in the relevant art. In some embodiments, a tree can include any large plant. In some embodiments, a tree can include a plant with a woody structure. For example, a tree can include a plant with a woody structure and whose cells include cellulose and / or lignin.

[0056] In some embodiments, a plant of the present invention may include a plant, plant variety, plant cultivar, or the like, from, for example, but not limited to, the plant family Acanthaceae, Aceraceae, Adoxaceae, Alismataceae, Amaranthaceae, Amaryllidaceae, Anacardiaceae, Apiaceae, Apocynaceae, Aquifoliaceae, Araceae, Araliaceae, Asparagaceae, Aspleniaceae, Asteraceae, Balsaminaceae, Betulaceae, Boraginaceae, Brassicaceae, Bromeliaceae, Cactaceae, Campanulaceae, Cannabaceae, Caprifoliaceae, Caryophyllaceae, Celastraceae, Chenopodiaceae, Cistaceae, Cleomaceae, Convolvulaceae, Cornaceae, Crassulaceae, Cucurbitaceae, Cupressaceae, Cyperaceae, Dennstaedtiaceae, Dioscoreaceae, Droseraceae, Ebenaceae, Elaeagnaceae, Ericaceae, Euphorbiaceae, Fabaceae, Fagaceae, Gentianaceae, Geraniaceae, Grossulariaceae, Hamamelidaceae, Hydrangeaceae, Hypericaceae, Iridaceae, Juglandaceae, Juncaceae, Lamiaceae, Lauraceae, Lentibulariaceae, Linaceae, Liliaceae, Lythraceae, Magnoliaceae, Malvaceae, Melastomataceae, Melianthaceae, Menyanthaceae, Moraceae, Myricaceae, Myrtaceae, Nyctaginaceae, Nymphaeaceae, Oleaceae, Onagraceae, Orchidaceae, Orobanchaceae, Oxalidaceae, Papaveraceae, Passifloraceae, Phytolaccaceae, Pinaceae, Plantaginaceae, Platanaceae, Poaceae, Polemoniaceae, Polygalaceae, Polygonaceae, Portulacaceae, Primulaceae, Proteaceae,Attorney Docket No.1723.2.WO2 PATENT Ranunculaceae, Resedaceae, Rhamnaceae, Rosaceae, Rubiaceae, Rutaceae, Salicaceae, Santalaceae, Sapindaceae, Sarraceniaceae, Saxifragaceae, Scrophulariaceae, Solanaceae, Thymelaeaceae, Urticaceae, Verbenaceae, Violaceae, Vitaceae, or Zingiberaceae. In some embodiments, a plant of the present invention may include a plant, plant variety, plant cultivar, or the like, from, for example, but not limited to, Abies, Acer, Aesculus, Alnus, Betula, Carpinus, Castanea, Carya, Celtis, Cercis, Cornus, Corylus, Crataegus, Fagus, Fraxinus, Gleditsia, Ilex, Juglans, Juniperus, Larix, Liquidambar, Liriodendron, Magnolia, Malus, Morus, Nyssa, Ostrya, Picea, Pinus, Platanus, Populus, Prunus, Pseudotsuga, Quercus, Robinia, Salix, Sassafras, Sorbus, Taxodium, Thuja, Tilia, Tsuga, or Ulmus.

[0057] As used herein, “establish,” “established” and / or “establishing” refers to the successful introduction, colonization, and subsequent growth and proliferation. In some embodiments, one or more fungi can be established in a volume of soil. For example, fungal mycelium, cells, spores, and other like fungal structures can be added to a volume of soil, a feedstock, a substrate, and / or like media, wherein such fungal structures reproduce asexually and / or asexually, thereby increasing the biomass of such fungi. In some embodiments, one or more plants can be established. For example, a seed, a transplanted seedling, or a transplanted plant can be said to have established when the plant has successfully developed a robust root system to support growth, nutrient uptake, water absorption, and like plant physiological processes, without supplemental support beyond normal care. In some embodiments, an established plant has acclimated to a new environment and demonstrates healthy growth, including, but not limited to, new foliage, root development, and like morphological growth. In some embodiments, a growth-promoting fungal consortium can be established. For example, growth-promoting fungal consortium can substantially colonize, fully colonize, and / or fully consume the medium to / on which it is added. In some embodiments, established can refer to growth-promoting fungal consortium inoculum becoming fully associated in symbiosis with a plant. For example, an organism can be qualified as established when the organism persists in a stable and self-sustaining equilibrium.

[0058] As used herein, “native” refers to an organism, population, or biological material that originates, develops, or naturally occurs within a specific geographic region or ecological habitat without human introduction. Native organisms exhibit adaptations to the environmental conditions, ecological interactions, and evolutionary pressures of their respective regions. AsAttorney Docket No.1723.2.WO2 PATENT used herein, a “native plant” refers to a plant species that occurs naturally within a specific geographic region or ecosystem and has evolved in association with local environmental conditions, soil microbiota, and climate factors. A native plant may be distinguished from introduced, cultivated, or invasive species by its historical and ecological presence in a given region, wherein its growth, reproduction, and interactions with surrounding biota occurs without human intervention. As used herein, “native fungi” refers to fungal species or strains that naturally occur within a defined geographic area, ecosystem, or soil environment without anthropogenic introduction. Native fungi may include mycorrhizal, saprophytic, or endophytic species that have co-evolved with local plant communities, contributing to nutrient cycling, plant symbiosis, and soil health. These fungi are distinguished from non-native or introduced fungal species by their natural occurrence and ecological role within the habitat.

[0059] As used herein, a “plant community” refers to a group of coexisting plant species within an environment that may be interacting with each other and their surroundings. A plant community may be characterized by its species composition, abundance, and ecological functions, influenced by factors such as soil, climate, and biotic interactions. A plant community may be classified based on dominant species, functional traits, or successional stage and analyzed using biodiversity metrics or multivariate models. Examples of a plant community include, but are not limited to, a forest, a grassland plant community, a shrubland plant community, a wetland plant community, a riparian plant community, a scrubland plant community, an alpine plant community, a fynbos plant community, a chaparral plant community, a steppe plant community, a prairie plant community, a savanna plant community, a desert plant community, a tundra plant community, early successional plant community, a climax plant community, an invasive plant community, and the like.

[0060] As used herein, “ex situ” refers to a condition, process, or treatment that occurs outside of the original, native or natural environment of a biological, chemical, or physical system. In the context of a biological system, ex situ may refer to the cultivation, propagation, or experimental manipulation of organisms (e.g., a plant, microbial communities, etc.) in a controlled setting, such as a laboratory, greenhouse, nursery, or non-natural plantation. “In situ,” as used herein, refers to a condition, process, or treatment that occurs within the original, native, or natural environment of a biological, chemical, or physical system. In the context ofAttorney Docket No.1723.2.WO2 PATENT a biological system, in situ may refer to the study or observation of organisms, ecological communities, or environmental processes occurring in their native habitat.

[0061] As used herein, a “control plant,” “control seedling,” and like usage, refers to a plant or group of plants cultivated or grown under conditions devoid of any fungal inoculum or donor soil inoculum. A control plant or control seedling may serve as baseline reference(s) against which changes or enhancements due to inoculation can be assessed. For example, species richness of fungi hosted by donor soil-inoculated seedlings may be compared to that of control plant seedlings (e.g., seedlings devoid of donor soil inoculum) as well as to reference forest soils (e.g., “wild” or native soils that act as positive controls). Further, productivity parameters such as increased plant growth, biomass accumulation, and overall plant health in inoculated plants may be measured relative to a control plant or group thereof, thereby demonstrating the efficacy of the fungal inoculum in promoting plant growth and accelerating productivity.

[0062] Embodiments of the disclosure are described herein in the context of “machine learning” and / or “machine learning models,” in particular for selecting a soil microbiome for increasing plant productivity. Machine learning may be embodied in a variety of different ways including, but not limited to, one or more of the following: a multi-layer neural network, a deep learning system, a large language model, a natural language processing system, and / or an artificial intelligence system. Moreover, it will be understood that a multi-layer neural network is a multi-layer artificial neural network comprising artificial neurons or nodes and does not include a biological neural network comprising real biological neurons. The machine learning described herein may be configured to transform a memory of a computer system to include one or more data structures, such as, but not limited to, arrays, extensible arrays, linked lists, binary trees, balanced trees, heaps, stacks, and / or queues. These data structures may be configured or modified through the adjudication process and / or the machine learning training process to improve the efficiency of a computer system when the computer system operates in an inference mode to make an inference, prediction, classification, suggestion, or the like with respect to selecting a soil microbiome having increasing plant productivity.

[0063] A machine learning model may comprise a hardware and / or software architecture having structural hyperparameters defining a model’s architecture and / or one or more parameters (e.g., coefficient(s), weight(s), biase(s), activation function(s) and / or actionAttorney Docket No.1723.2.WO2 PATENT function type(s) in examples where the activation function and / or function type is determined as part of training, clustering centroid(s) / medoid(s), partition(s), number of trees, tree depth, split parameters) determined as a result of training the machine learning model based at least in part on training hyperparameters (e.g., for supervised, semi-supervised, and reinforcement learning models) and / or by iteratively operating the machine learning model according to the training hyperparametsers (e.g., for unsupervised machine learning models).

[0064] In some embodiments, structural hyperparameter(s) may define component(s) of a model’s architecture and / or their configuration / order, such as, for example, a configuration / order specifying which input(s) are provided to one component and which output(s) of a component are provided as input to other component(s) of a machine learning model; a number, type, and / or configuration of component(s) per layer; a number of layers of a model; a number and / or type of input nodes in an input layer of a model; a number and / or type of nodes in a layer; a number and / or type of output nodes of an output layer of the model; component dimension (e.g., input size versus output size); a number of trees; a maximum tree depth; node split parameters; minimum number of samples in a leaf node of a tree; and / or the like. The component(s) of a model may comprise one or more activation functions and / or activation function type(s) (e.g., gated linear unit (GLU), such as a rectified linear unit (ReLU), leaky RELU, Gaussian error linear unit (GELU), Swish, hyperbolic tangent), one or more attention mechanism and / or attention mechanism types (e.g., self-attention, cross-attention), nodes and split indications and / or probabilities in a decision tree, and / or various other component(s) (e.g., adding and / or normalization layer, pooling layer, filter). Various combinations of components (e.g., as defined by structural hyperparameter(s)) may result in different types of model architectures, such as a transformer-based machine learning model (e.g., encoder-only model(s), encoder-decoder model(s), decoder-only models, generative pre- trained transformer(s) (GPT(s))), neural network(s), multi-layer perceptron(s), Kolmogorov- Arnold network(s), clustering algorithm(s), support vector machine(s), gradient boosting machine(s), and / or the like. Structural parameters and components a machine learning model may vary depending on the type of machine learning model.

[0065] Training hyperparameter(s) may be used as part of training or otherwise determining the machine learning model. In some embodiments, training hyperparameter(s), in addition to the training data and / or input data, may affect determining the parameter(s) ofAttorney Docket No.1723.2.WO2 PATENT the target machine learning model. Using a different set of training hyperparameters to train two machine learning models that have the same architecture (i.e., the same structural hyperparameters) and using the same training data may result in the parameters of the first machine learning model differing from the parameters of the second machine learning model. Despite having the same architecture and having been trained using the same training data, a machine learning model may generate different outputs from each other given the same input data. Accordingly, accuracy, precision, recall, and / or bias may vary between machine learning models.

[0066] In some embodiments, training hyperparameter(s) may include a train-test split ratio, activation function and / or activation function type (e.g., in examples like Kolmogorov- Arnold networks (KANs) where the activation function type can be determined as part of training from an available set of activation functions and / or limits on the activation function parameters specified by the training hyperparameters), training stage(s) (e.g., using a first set of hyperparameters for a first epoch of training, a second set of hyperparameters for a second epoch of training), a batch size and / or number of batches of data in a training epoch, a number of epochs of training, the loss function used (e.g., L1, L2, Huber, Cauchy, cross entropy), the component(s) of the machine learning model that are altered using the loss for a particular batch or during a particular epoch of training (e.g., some components may be “frozen,” meaning their parameters are not altered based on the loss), learning rate, learning rate optimization algorithm type (e.g., gradient descent, adaptive, stochastic, etc.) used to determine an alteration to one or more parameters of one or more components of the machine learning model to reduce the loss determined by the loss function, learning rate scheduling, and / or the like.

[0067] “Training database” as used herein refers to a subset of data used to train a machine learning tool by iteratively adjusting the machine learning tool’s parameters based on the input- output pairs provided, aiming to minimize the difference between predicted and actual outcomes.

[0068] In some embodiments, structural hyperparameters and / or training hyperparameters may be determined by a hyperparameter optimization algorithm or based on user input, such as a software component written by a user or generated by a machine learning model. A machine learning model may include any type of model configured, trained, and / or the like toAttorney Docket No.1723.2.WO2 PATENT generate a prediction output for a model input. In some examples, any of the logic, component(s), routines, and / or the like discussed herein may be implemented as a machine learning model.

[0069] A machine learning model may include one or more of any type of machine learning model including one or more supervised, unsupervised, semi-supervised, and / or reinforcement learning models. Training a machine learning model may comprise altering one or more parameters of the machine learning model (e.g., using a loss optimization algorithm) to reduce a loss. Depending on whether the machine learning model is supervised, semi- supervised, unsupervised, etc. this loss may be determined based at least in part on a difference between an output generated by the model and ground truth data (e.g., a label, an indication of an outcome that resulted from a system using the output), a cost function, a fit of the parameter(s) to a set of data, a fit of an output to a set of data, and / or the like. In some embodiments, determining an output by a machine learning model may comprise executing a set of inference operations executed by the machine learning model according to the target machine learning model’s parameter(s) and structural hyperparameter(s) and using / operating on a set of input data.

[0070] Various illustrative logics, logical blocks, modules, circuits and algorithm steps described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. Interchangeability of hardware and software has been described generally, in terms of functionality, and illustrated in the various illustrative components, blocks, modules, circuits and steps described above. Whether such functionality is implemented in hardware or software depends upon the particular application and design constraints imposed on the overall system.

[0071] The hardware and data processing apparatus used to implement the various illustrative logics, logical blocks, modules and circuits described in connection with the aspects disclosed herein may be implemented or performed with a general-purpose single- or multi- chip processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor, or, any conventional processor, controller, microcontroller, or state machine. A processor alsoAttorney Docket No.1723.2.WO2 PATENT may be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some implementations, particular steps and methods may be performed by circuitry that is specific to a given function.

[0072] In some embodiments, functions described may be implemented in hardware, digital electronic circuitry, computer software, firmware, including the structures disclosed in this specification and their structural equivalents thereof, or in any combination thereof. Implementations of the subject matter described in this specification also can be implemented as one or more computer programs, e.g., one or more modules of computer program instructions, encoded on a computer storage media for execution by, or to control the operation of, data processing apparatus.

[0073] If implemented in software, functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The steps of a method or algorithm disclosed herein may be implemented in a processor-executable software module which may reside on a tangible, non-transitory computer-readable medium. Computer- readable medium / media includes both computer storage media and communication media including any medium that can be enabled to transfer a computer program from one place to another. A storage media may be any available media that may be accessed by a computer.

[0074] A software module may reside in random access memory (RAM), flash memory, read only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD ROM, or any other form of storage medium known in the art. A storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. In the alternative, the storage medium may be integral to the processor. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and blue ray disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations of the above should also be included within the scope of computer readable media. A processor and a storage medium may reside in an ASIC. An ASIC may reside in a user terminal. In the alternative, a processor and a storage medium may reside as discrete components in a user terminal.Attorney Docket No.1723.2.WO2 PATENT

[0075] Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and instructions on a machine readable medium and computer- readable medium, which may be incorporated into a computer program product.

[0076] As used herein, a “linear model” refers to a mathematical model that describes the relationship between a dependent variable and one or more independent variables using a linear function. In some embodiments, changes in independent variables correspond to proportional changes in a dependent variable, e.g., as determined by a set of coefficients. A linear model may include an intercept term and an error component to account for variations not explained by independent variables. A linear model may be used for predictive modeling, statistical inference, classification, or optimization and may be implemented in various computational or analytical frameworks. A linear model may include, for example, but not limited to, linear regression, multiple linear regression, generalized linear model (GLM), logistic regression, Poisson regression, linear discriminant analysis (LDA), linear mixed model (LMM), hierarchical linear model, ridge regression, lasso regression, elastic net regression, principal component regression (PCR), partial least squares regression (PLS), autoregressive integrated moving average (ARIMA) with linear components, ordinary least squares (OLS) regression, and the like. Parameters of a model may be determined through methods including, but not limited to, least squares estimation, maximum likelihood estimation, Bayesian inference, and the like. In some implementations, a linear model may be applied to structured or unstructured data, make predictions, or optimize decision-making processes.

[0077] A linear model may include a generalized linear model. As used herein, a “generalized linear model” or “GLM” refers to a statistical modeling framework that extends linear regression by allowing a dependent variable to follow a probability distribution from the exponential family, such as, e.g., the normal, binomial, Poisson, or gamma distribution. A GLM may model the relationship between one or more independent variables and a dependent variable through a linear predictor function, which applies a transformation known as a link function to map the linear combination of independent variables to the expected value of a dependent variable. Parameters of a GLM may be estimated using techniques such as maximum likelihood estimation or iteratively reweighted least squares (IRLS). A GLM may be applied in computational and statistical environments for tasks including, but not limited to, predictive modeling, classification, risk assessment, and decision support systems. ExamplesAttorney Docket No.1723.2.WO2 PATENT of GLMs include, but are not limited to, logistic regression, Poisson regression, gamma regression, and the like, each of which may apply a different link function and probability distribution depending on the nature of the dependent variable.

[0078] As used herein, a “generalized linear mixed model” or “GLMM” refers to an extension of a GLM that incorporates both fixed effects and random effects, allowing for modeling of data with hierarchical, grouped, or correlated structures. A GLMM may accommodate response variables following distributions from an exponential family (e.g., normal, binomial, Poisson, etc.) while accounting for variability across different levels of a dataset through random effects. A GLMM may be applied in computational and statistical environments for predictive modeling, hierarchical data analysis, and repeated-measures data. Parameter estimation in a GLMM may be performed using maximum likelihood estimation, restricted maximum likelihood (REML), or Bayesian inference methods.

[0079] As used herein, “effect size” refers to a quantitative measure of the magnitude of a relationship or difference between variables in a statistical model. Effect size may provide a standardized metric for assessing the strength of an association, the impact of an independent variable on a dependent variable, or the practical significance of a statistical finding. Effect size measures may be absolute (e.g., mean differences, regression coefficients) or standardized (e.g., Cohen’s d, Pearson’s r, odds ratios) and may be computed using various statistical methods depending on the model type and data structure. In the context of a linear model (LM), effect size may refer to the estimated regression coefficients, which represent the expected change in a dependent variable for a one-unit change in an independent variable, assuming all other variables remain constant. Standardized effect sizes, such as beta coefficients (standardized regression coefficients) or Cohen’s f2, may also be used to compare the relative importance of predictors across different scales. In the context of a GLM, effect size refers to estimated model coefficients transformed according to a GLM’s link function to provide interpretable effect measures as would be readily understood by the ordinarily skilled artisan in the relevant art. In the context of a GLMM, effect size includes measures for both fixed effects and random effects. For example, fixed effects can follow the same principles as GLMs, using untransformed or transformed coefficients (e.g., odds ratios, rate ratios) to quantify predictor impact, and random effects can be assessed using variance components, intraclassAttorney Docket No.1723.2.WO2 PATENT correlation coefficients (ICCs), or variance explained by random factors to measure a contribution of hierarchical grouping structures.

[0080] As used herein, “cumulative growth effect size” refers to a quantitative measure of the aggregated impact of one or more independent variables on growth-related outcomes of a plant over a defined period or across multiple experimental conditions. A cumulative growth effect size may account for the combined or sequential influence of predictors on cumulative growth, incorporating both direct and indirect contributions to changes in a dependent variable associated with growth, such as biomass accumulation, height expansion, or productivity increases. Cumulative growth effect size(s) may be computed using statistical models, including, but not limited to, linear models, GLMs, GLMMs, and the like, by estimating a combined effect of predictor variables over time or across experimental treatments. The effect size may be expressed in absolute terms (e.g., total biomass increase per unit predictor change) or standardized terms (e.g., relative effect size compared to baseline conditions). In the context of hierarchical or multilevel data structures, cumulative growth effect size may also include variance components, interaction effects, or aggregated marginal effects to assess overall contribution to growth dynamics.

[0081] As used herein, a “distance-based multivariate model” refers to a statistical modeling framework for analyzing relationships among multiple dependent variables using a distance or dissimilarity matrix rather than direct numerical measurements of individual observations. Such models are configured to assess patterns, differences, or relationships between data points based on predefined distance metrics, including but not limited to Euclidean distance, Bray-Curtis dissimilarity, Jaccard similarity, Mahalanobis distance, and the like. In some embodiments, distance-based multivariate models may employ permutation- based hypothesis testing, eigenvalue decomposition, constrained ordination, or regression-like techniques to quantify variations among predefined groups or explain variations in multivariate data using predictor variables. Examples of distance-based multivariate models include, but are not limited to, Permutational Multivariate Analysis of Variance (PERMANOVA), Analysis of Similarities (ANOSIM), Mantel tests, and Distance-Based Redundancy Analysis (db-RDA), and the like.

[0082] As used herein, a “weighted rank model” refers to a computational framework that ranks a set of items or entities based on multiple input scores, wherein each input score isAttorney Docket No.1723.2.WO2 PATENT assigned a predefined or dynamically calculated weight to determine its relative contribution to the final ranking. In some embodiments, a weighted rank model may process input data from one or more predictive or statistical models, normalize the input scores as needed, apply weighting factors to each score, and compute a combined weighted score for each ranked entity. In some embodiments, weighting factors may be predetermined, adaptively learned, or optimized using statistical or machine learning techniques, including, but not limited to, Bayesian optimization, gradient-based learning, or heuristic ranking adjustments. A weighted rank model may be implemented in various computational applications, including predictive analytics, decision support systems, recommendation engines, multi-criteria optimization frameworks, and the like. Examples of weighted rank models include, but are not limited to, multi-model ensemble ranking systems, machine-learning-driven ranking frameworks, and statistical ranking algorithms that incorporate heterogeneous data sources. A weighted rank model may operate on structured or unstructured data and may support real-time or batch processing implementations.

[0083] According to some embodiments of the present invention, an apparatus for selecting a soil microbiome for increasing plant productivity, comprising a non-transitory computer-readable medium configured to store processor-executable instructions that, when executed by one or more processors, may cause the one or more processors to perform operations, which may include: receiving observational data that is used to train a machine learning model, wherein observational data may comprise location-specific soil fungal community structure data, plant productivity data and environmental covariate data, wherein observational data may be collected from a plurality of geographically distinct plant communities; using the machine learning model, determining a plant biomass prediction comprising an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site; receiving experimental data comprising (i) productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soil sample, and (ii) fungal biodiversity data associated with each plant at predetermined times during plant growth; using a generalized linear model, determining a cumulative growth effect size for each of the productivity responses; using a distance-basedAttorney Docket No.1723.2.WO2 PATENT multivariate model, determining differences between pairwise comparisons of fungal biodiversity data associated with each plant at each of the predetermined times; combining an output from the machine learning model, the generalized linear model, and the distance-based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

[0084] In some embodiments, the operations may include receiving observational data that is used to train a machine learning model, wherein the observational data comprise location- specific soil fungal community structure data, plant productivity data and environmental covariate data, wherein the observational data are collected from a plurality of geographically distinct plant communities. In some embodiments, the location-specific fungal community structure data comprise nucleic acid sequences, operational taxonomic unit (OTU) tables, taxonomic information, guild composition, microbiome diversity, and / or functional characteristics of fungal biomass collected from geographically distinct plant communities.

[0085] In some embodiments, nucleic acid sequences may include genomic DNA, complementary DNA (cDNA), environmental DNA (eDNA), RNA, functional genetic elements, nucleic acid-based identification marker, molecular barcode, genetic identifier sequence, DNA barcode, genomic tag, index sequence, unique molecular identifier (UMI), oligonucleotide tag, molecular genetic markers, polymorphic DNA markers, simple sequence repeats (SSRs), amplified fragment length polymorphisms (AFLPs), restriction fragment length polymorphisms (RFLPs), single nucleotide polymorphisms (SNPs), inter-simple sequence repeats (ISSRs), random amplified polymorphic DNA (RAPDs), sequence- characterized amplified regions (SCARs), diversity arrays technology markers (DArTs), expressed sequence tag markers (ESTs), microsatellites, minisatellites, variable number tandem repeats (VNTRs), insertion-deletion polymorphisms (InDels), sequence-tagged sites (STSs), and the like.

[0086] In some embodiments, OTUs may include a computationally defined cluster of closely related biological sequences (e.g., DNA or RNA) used to classify organisms (e.g., microorganisms), particularly in microbial ecology. For example, OTUs can serve as a proxy for species-level or higher taxonomic groupings when studying complex microbial communities, especially when exact taxonomic assignments are uncertain or unavailable. InAttorney Docket No.1723.2.WO2 PATENT some embodiments, an OTU table may include a data matrix that quantifies abundance of OTUs across multiple biological samples. For example, an OTU table can be used in microbial ecology, metagenomics, and environmental DNA (eDNA) applications to analyze community composition and diversity.

[0087] In some embodiments, taxonomic information may include domain, kingdom, phylum, class, order, family, genus, species, strain, operational taxonomic units (OTUs), amplicon sequence variants (ASVs), ribosomal sequence classifications, phylogenetic lineage assignments, hierarchical taxonomic ranks, clade designations, sequence-based taxonomic identifiers, metagenomic species bins, functional guild classifications, ecological assemblage groupings, and the like.

[0088] In some embodiments, guild composition may include functional group classifications, trophic interactions, ecological roles, metabolic capabilities, substrate utilization patterns, symbiotic relationships, decomposer communities, mutualistic associations, pathogenic assemblages, nutrient cycling functions, carbon sequestration contributions, host-specific microbial consortia, habitat-specific microbial communities, environmental stress response groups, guild diversity indices, and the like. For example, eDNA can be categorized according to guild composition, such as ectomycorrhizal, endomycorrhizal, arbuscular mycorrhizal saprotrophic, endophytic, plant pathogenic, animal pathogenic, lichenized, etc.

[0089] In some embodiments, microbiome diversity may include quantitative and qualitative measures of taxonomic, functional, and phylogenetic variation within and between microbial communities. Ecological diversity may be assessed using alpha diversity (e.g., within-sample diversity) and beta diversity (e.g., between-sample dissimilarity), which may be computed based on species richness, abundance distributions, and phylogenetic relationships. Diversity may be calculated using methods such as species accumulation curves, rarefaction analysis, Hill numbers, and ordination techniques, which quantify the distribution and heterogeneity of microbial taxa across environmental gradients. Additionally, microbiome diversity metrics may be derived from OTU-based, ASV-based, metabarcode or metagenome sequence data to capture taxonomic and functional variability. Alpha diversity metrics may include species richness, Shannon Diversity Index (H’), Simpson’s Diversity Index (D), Chao1 Index, Faith’s Phylogenetic Diversity (PD), and / or other like indices. Beta diversity metricsAttorney Docket No.1723.2.WO2 PATENT may include Bray-Curtis Dissimilarity, Jaccard Index, UniFrac distance, Aitchison Distance and / or other like indices.

[0090] In some embodiments, microbiome diversity may include computational methods such as rarefaction and normalization to standardize sequencing depth across samples. For example, non-dimensional scaling or ordination techniques, including, but not limited to, principal component analysis (PCA), principal coordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS), can be used to observe differences in microbiome structure across samples. In some embodiments, PERMANOVA (permutational multivariate analysis of variance), a form of distance-based multivariate modeling, may be employed to test statistical differences in microbial community composition.

[0091] In some embodiments, functional characteristics may include nutritional mode, symbiotic interactions, ecological roles, enzymatic activity, metabolic pathways, stress tolerance mechanisms, reproductive strategies, and substrate utilization patterns. Nutritional modes may include saprotrophic, mutualistic, parasitic, or pathogenic lifestyles. Ecological roles may include decomposers, nitrogen fixers, carbon sequestration agents, and soil stabilizers. Enzymatic activity may involve cellulases, ligninases, chitinases, proteases, peroxidases, and the like, while metabolic pathways may include lignocellulose degradation, nitrogen cycling, secondary metabolite production, and volatile organic compound (VOC) emission. Stress tolerance mechanisms may include heat resistance, drought adaptation, heavy metal resistance, and antifungal production. Reproductive strategies may involve sexual and asexual sporulation, fruiting body formation, spore dispersal, and dormancy adaptations. Substrate utilization patterns may include wood decay, litter decomposition, and nutrient acquisition from soil, living hosts, or other fungi. It will be appreciated by the ordinarily skilled artisan in the relevant art that a functional characteristic may include any measurable biological, ecological, and biochemical traits of an organism that define its role within an ecosystem, its metabolic capabilities, and / or its interactions with other organisms and the environment.

[0092] In some embodiments, location-specific plant productivity data may comprise plant height, plant diameter, plant biomass, plant survival, age of the geographically distinct plant community, plant density, and the like, collected from each of the geographically distinct plant communities. Plant height and plant diameter measurements may be recorded at predeterminedAttorney Docket No.1723.2.WO2 PATENT time intervals to assess growth trajectories and structural development. Plant biomass may be estimated through direct sampling, allometric equations, or remote sensing techniques, e.g., to provide information on aboveground and belowground nutrient allocation. Plant survival may be monitored to evaluate establishment success, resilience to environmental stressors, and long-term viability within different geographic regions. Age of the geographically distinct plant community may be determined based on stand history, dendrochronology, remote sensing-derived forest succession data, and the like, e.g., to determine factors influencing plant growth potential and ecological interactions. Plant density may be measured as the number of individuals per unit area, and may provide information on competitive interactions, resource availability, and community structure. Additional plant productivity metrics may include leaf area index (LAI), specific leaf area (SLA), chlorophyll content, net assimilation rate, transpiration rate, and canopy closure, which are indicative of photosynthetic efficiency, water- use strategies, and overall plant health. Environmental variables influencing productivity, such as soil nutrient content, microbial associations, precipitation levels, temperature, and light availability, may also be incorporated to refine growth predictions. These data alone or in combination may enable a quantitative assessment of plant performance across multiple geographically distinct plant communities, and may support comparative growth analyses, predictive modeling, and optimization of site-specific plant productivity strategies.

[0093] In some embodiments, location-specific environmental covariate data may comprise soil composition, contemporary climate data, future climate projection data, and the like, e.g., collected from geographically distinct plant communities. For example, location- specific environmental covariate data can serve as input for modeling plant productivity, microbial community dynamics, and ecosystem resilience across diverse landscapes. Soil composition may include physical, chemical, and biological properties that influence plant and microbial interactions. Physical properties may include soil texture (e.g., per cent sand, silt, and clay), bulk density, porosity, water-holding capacity, and the like, which may affect root penetration and water availability. Chemical properties may encompass pH, organic matter content, cation exchange capacity (CEC), concentration of essential macronutrients (e.g., nitrogen, phosphorus, potassium) and micronutrients (e.g., calcium, magnesium, sulfur, iron, and zinc), and the like. Biological properties may include microbial biomass, enzymaticAttorney Docket No.1723.2.WO2 PATENT activity, mycorrhizal colonization rates, presence of beneficial or pathogenic microbial taxa, and the like.

[0094] Contemporary climate data may include temperature, precipitation, humidity, solar radiation, wind speed, atmospheric CO2 concentration, and the like, e.g., collected through on- site meteorological stations, remote sensing platforms, or climate monitoring databases. Contemporary climate data may provide real-time insights into weather patterns, microclimatic variability, and seasonality effects that impact plant growth and microbial processes.

[0095] Future climate projection data may be derived from global climate models (GCMs), regional climate downscaling techniques, probabilistic climate scenario analyses, and the like, e.g., enabling the assessment of long-term trends in temperature shifts, precipitation variability, extreme weather events, and climate-induced stressors. Future climate projection data may enable predictions of ecosystem resilience, plant community shifts, microbial adaptation strategies under changing climatic conditions, and the like. Additional environmental covariates may include elevation, slope, aspect, hydrological features (e.g., water table depth, drainage capacity), vegetation cover, land-use history, disturbance regimes (e.g., fire frequency, logging, or agricultural conversion), and the like, which, e.g., may influence site productivity, species composition, and habitat suitability for soil microbiomes.

[0096] According to some embodiments of the present invention, an apparatus for selecting a soil microbiome having increasing plant productivity may include operations including, using the machine learning model, determining a plant biomass prediction comprising an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site. A machine learning model may be configured to be either a supervised machine learning model or an unsupervised machine learning model. In some embodiments, a supervised machine learning model may be used, e.g., wherein the model is trained on labeled datasets containing known relationships between soil microbiome characteristics, plant productivity metrics, and environmental covariates. In some embodiments, a supervised learning model may be employed to predict biomass based on environmental and biological variables. A supervised machine learning model may be implemented as a boosted regression tree model configured to utilize spatially explicit environmental covariates, biodiversity indexes, and forest productivity data as input features. A supervised machine learning model may be trained at the individual tree level, incorporatingAttorney Docket No.1723.2.WO2 PATENT measurements such as tree height, tree diameter, and species-specific allometric equations to estimate biomass. In some embodiments, a supervised learning model may be optimized using cross-validation techniques, such as k-fold cross-validation, to improve generalization and predictive accuracy. In some embodiments, a k-fold cross-validation may include a 5-fold cross-validation, 10-fold cross-validation, leave-one-out cross-validation (LOO-CV), 3-fold cross-validation, 7-fold cross-validation, 2-fold cross-validation (holdout validation), or the like. A supervised machine learning model may be trained using automated hyperparameter tuning frameworks, e.g., using an algorithm (such as but not limited to the hyperparameter optimization framework) to iteratively refine performance based on predefined optimization metrics. For example, a boosted regression tree model may be trained using a dataset comprising tree-level measurements from geographically distinct forest plots, where tree height, tree diameter, soil nutrient levels, and microbial diversity indices serve as predictive features. By way of further example, the model may be validated using 5-fold cross-validation, ensuring robustness across different environmental conditions. By way of further example, through iterative optimization, the model may refine its ability to predict biomass and inform forestry management strategies by identifying factors contributing to high-productivity forest ecosystems.

[0097] In some embodiments, an apparatus for predicting tree biomass and forest growth may employ a supervised learning model to generate biomass, height, and diameter predictions for trees at geographically referenced sites. A supervised learning model may utilize environmental covariates, soil microbiome data, and climate variables to estimate tree growth patterns across a spatially defined region. Predictions may be made at individual site levels or aggregated across larger geographical areas for broader assessments of forest productivity. For example, a supervised machine learning model may be applied to predict tree growth metrics across a geographic area. A supervised learning model may generate site-specific estimates of tree height, diameter, and biomass accumulation based on environmental conditions such as soil composition, precipitation, temperature, elevation, and microbial diversity indices. For example, a supervised learning model may predict forest biomass accumulation across a geographic area with outputs averaged over the entire area to provide a broad-scale assessment of carbon sequestration potential and forest yield. By way of further example, a supervised machine learning model can generate localized predictions within a 100-mile radiusAttorney Docket No.1723.2.WO2 PATENT surrounding carbon project locations, enabling targeted assessments of tree growth in reforestation sites, allowing for precision forestry applications, such as carbon credit validation, optimized planting strategies, and site selection for afforestation projects.

[0098] In some embodiments, an unsupervised machine learning model may be employed to identify patterns, clusters, or latent structures within the dataset without requiring predefined labels. An unsupervised model may apply techniques such as principal component analysis (PCA), k-means clustering, hierarchical clustering, autoencoders, and the like, to group soil microbiomes with similar ecological functions or to detect underlying microbial community structures associated with plant growth.

[0099] In some embodiments, a hybrid approach incorporating both a supervised and an unsupervised machine learning model may be implemented. For example, an unsupervised clustering algorithm may first identify microbiome groupings, and a supervised model may then be trained to predict the impact of each microbiome cluster on plant growth.

[0100] In some embodiments, plant allometry may include quantitative relationships between plant structural traits and overall biomass, growth dynamics, or resource allocation patterns. Plant allometric measurements may be used to estimate biomass accumulation, carbon storage potential, and ecological fitness across different environments. For example, plant allometry may include height-to-diameter ratios, which describe structural stability and growth form, and allometric scaling equations, which predict total biomass based on measured dimensions. Additional allometric traits may include leaf area-to-stem diameter ratios, root-to- shoot biomass ratios, and crown volume estimations, which provide insights into photosynthetic capacity, nutrient allocation, and drought resilience. These relationships may be determined through empirical field measurements, remote sensing techniques, or predictive modeling frameworks to assess plant productivity in forestry, agriculture, and ecological restoration applications.

[0101] According to some embodiments of the present invention, an apparatus for selecting a soil microbiome having increasing plant productivity may include operations including receiving experimental data comprising (i) productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soil sample, and (ii) fungalAttorney Docket No.1723.2.WO2 PATENT biodiversity data associated with each plant at predetermined times during plant growth. An apparatus according to any embodiment disclosed herein may be configured to receive, process, and analyze experimental data, enabling data-driven selection of soil microbiomes associated with increased plant productivity.

[0102] In some embodiments, experimental data may comprise productivity responses of plants grown ex situ relative to a particular geographically distinct plant community, wherein each plant may be inoculated with an individual soil sample harvested from a specific geographically distinct plant community, e.g., ensuring that each plant is exposed to a unique soil microbiome. For example, seedlings may be grown in controlled nursery conditions where soil samples originating from old-growth forests, regenerating forests, or agricultural lands are applied to assess their respective influences on plant biomass accumulation, root development, and survival rates.

[0103] In some embodiments, productivity responses may comprise: plant height, plant root collar diameter, plant rate of height change, biomass accumulation, leaf area index (LAI), growth rate, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate and / or plant cone volume collected from the plants grown ex situ relative to the particular geographically distinct plant community.

[0104] In some embodiments, experimental data may comprise fungal biodiversity data associated with each plant at predetermined times during plant growth, wherein fungal community structure and dynamics may be monitored using nucleic acid sequencing (e.g., high-throughput sequencing of eDNA), DNA metabarcoding, quantitative PCR techniques, and the like. For example, fungal biodiversity can be assessed at early-stage root colonization, mid-growth, and at full plant maturity to determine how microbial composition shifts over time and how different fungal taxa correlate with plant productivity metrics such as height, stem diameter, and biomass allocation. For example, fungal biodiversity can be measured using high-throughput DNA sequencing, such as amplicon sequencing of ITS or 18S rRNA genes from environmental samples, and sequences can be clustered into OTUs or ASVs, with diversity metrics (e.g., Shannon Index, Bray-Curtis dissimilarity) computed to assess taxonomic richness and community composition. For example, fungal biodiversity can be assessed through functional profiling, including enzyme activity assays, metabolite analysis,Attorney Docket No.1723.2.WO2 PATENT or metatranscriptomics to quantify biochemical functions such as lignocellulose degradation, organic acid secretion, and nutrient cycling.

[0105] In some embodiments, fungal biodiversity data associated with each plant may be collected at predetermined times during plant growth. In some embodiments, predetermined times may include a 1-week interval, 2-week interval (biweekly), 3-week interval, 4-week interval (monthly), 6-week interval, 8-week interval (bimonthly), 10-week interval, 12-week interval (quarterly), 16-week interval, 20-week interval, 24-week interval (semiannual), 36- week interval, 48-week interval, 1-year interval (annual). In some embodiments, the predetermined times may include from a 1-week interval to a 3-week interval, from a 2-week interval to a 6-week interval, from a 3-week interval to an 8-week interval, from a 4-week interval to a 10-week interval, from a 6-week interval to a 12-week interval, from an 8-week interval to a 16-week interval, from a 10-week interval to a 20-week interval, from a 12-week interval to a 24-week interval, from a 16-week interval to a 36-week interval, from a 20-week interval to a 48-week interval, from a 24-week interval to a 1-year interval. In some embodiments, the predetermined times are from weekly to annually.

[0106] According to some embodiments of the present invention, an apparatus for selecting a soil microbiome having increasing plant productivity may include operations including, using a generalized linear model, determining a cumulative growth effect size for each productivity response. In some embodiments, a generalized linear (GLM) model may be a generalized linear mixed model (GLMM). In some embodiments, a GLMM may include both fixed effects and random effects to account for hierarchical, grouped, or correlated data structures. It will be understood by one having ordinary skill in the relevant art that GLMMs extend the GLM framework by allowing for random variations across different levels of a dataset, such as plots within a field trial, trees within a forest stand, or microbial communities within distinct soil samples. Fixed effects in a GLMM may represent predictor variables with consistent effects across all observations, such as, e.g., soil nutrient composition, climate variables, or fungal diversity indices. Random effects may capture variation associated with repeated measures, spatial dependencies, or nested experimental designs, such as site-specific growth differences, individual plant responses to inoculation, or variability among geographically distinct plant communities. In some embodiments, a GLMM may be appliedAttorney Docket No.1723.2.WO2 PATENT using logistic, Poisson, negative binomial, gamma, or other link functions, depending on the distribution of the response variable.

[0107] In some embodiments, a GLM may be used to analyze measured plant traits, including, but not limited to, tree height, root collar diameter, change in height, cone volume, as response variables. Model selection criteria may be applied to optimize the top predictive response variables, ensuring the most significant growth indicators are retained. Cumulative growth effect sizes may be derived from repeated measures’ coefficients and tested for statistical significance to assess long-term inoculant impact. In some implementations, seedling survival may be estimated as the proportion of seedlings alive at the end of the study relative to the initial number of inoculated seedlings per source soil treatment. Uncertainty estimation may be performed using bootstrapping techniques, providing confidence intervals for model predictions. In some embodiments, a GLMM may be used to analyze measured plant traits in nursery inoculation experiments. For example, experimental data obtained from a nursery study can be used to evaluate the effects of multiple fungal inoculants on seedling growth by monitoring height and diameter changes over a six-month period. In some embodiments, a GLMM may be used to analyze measured plant traits in experimental field trials, where, e.g., a completely randomized block design is used. A GLMM may account for variability across different experimental blocks, ensuring accurate estimation of treatment effects. In some embodiments, spatially explicit models may be employed to account for georeferenced field data, e.g., incorporating kernel density estimation to model spatial dependencies in tree growth responses. For example, a field trial evaluating tree growth responses to soil inoculation can use a GLMM approach to control for differences in soil composition, climate, and geographic location. Incorporation of spatially explicit modeling may allow for a more refined analysis of localized environmental effects on plant productivity, aiding in the identification of optimal inoculation strategies for large-scale reforestation efforts.

[0108] According to some embodiments of the present invention, an apparatus for selecting a soil microbiome for increasing plant productivity may include operations including, using a distance-based multivariate model, determining differences between pairwise comparisons of fungal biodiversity data associated with each plant at each of the predetermined times. In some embodiments, fungal biodiversity data may be collected and analyzed at multiple predetermined time points to assess microbiome dynamics and their effects on plantAttorney Docket No.1723.2.WO2 PATENT growth. For example, in a reforestation trial, soil microbiomes from different donor forests may be used as inoculants for tree seedlings in a nursery or in field assay, with fungal community composition monitored over time, such as at predetermined times. Collected biodiversity data may be analyzed using permutational multivariate analysis of variance (PERMANOVA) to determine statistically significant differences in fungal community composition across time points and treatment groups. By comparing fungal diversity metrics at each stage, the influence of specific microbiome inoculants on plant productivity, nutrient cycling, and soil microbial interactions may be quantified, informing data-driven selection of optimal soil microbiomes for forestry and ecological restoration applications.

[0109] In some embodiments, operations may include combining an output from a machine learning model, a generalized linear model, and a distance-based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity. In some embodiments, operations may include, using a weighted rank model, combining an output from a machine learning model, a generalized linear model, and a distance-based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity. A weighted rank model may be configured to assign differential importance to predictive variables and model outputs based on their relative contributions to plant growth outcomes, ensuring that microbiome selection is optimized for productivity and ecological stability. A weighted rank model may combine outputs from three key computational models: (i) a machine learning model, which estimates a plant biomass prediction (ii) a generalized linear model (GLM) or generalized linear mixed model (GLMM), which estimates cumulative growth effect sizes, and (iii) a distance-based multivariate model, such as PERMANOVA, which quantifies pairwise differences in fungal biodiversity among soil microbiome samples and identifies clusters of microbiomes associated with higher plant productivity. A weighted rank model may apply scoring algorithms, weighted averaging, or multi-criteria decision analysis (MCDA) to rank soil microbiomes based on plant growth potential, microbiome stability, and biodiversity metrics. Weighting factors may be determined through adaptive learning processes, empirical field validation, or the like. Ranked outputs may be used to execute a donor forest selection tool, which may be configured to identify aAttorney Docket No.1723.2.WO2 PATENT soil microbiome from available geographically distinct plant communities that are predicted to maximize plant growth, resilience, and carbon sequestration potential.

[0110] In some embodiments, a donor forest selection tool may be used to integrate observational and experimental datasets to guide spatial biodiversity transfer for assisted reforestation. A donor forest selection tool may incorporate multiple data inputs, including, e.g., forest age, soil composition, environmental covariates, climate forecasts, and the like, along with results from nursery and field trial experiments and biodiversity metrics. By analyzing such variables, a donor forest selection tool may be configured to select donor soils for use in both controlled experimental trials and large-scale reforestation and restoration projects. For example, observational data on forest age and soil composition may be combined with predictive models trained on nursery and field trial data to identify donor forest sites that harbor microbial communities associated with enhanced plant growth and survival. A donor forest selection tool may use machine learning models, GLMs, GLMMs, and distance-based multivariate analyses to compare biodiversity patterns across geographically distinct donor forests, e.g., ensuring that soil microbiomes selected for transplantation are ecologically compatible with the target reforestation site. Unlike conventional site-specific agricultural input recommendations, this approach may be able to prioritize long-term ecological resilience by selecting donor microbiomes that support sustained plant productivity, soil health, and biodiversity recovery under future environmental conditions.

[0111] In some embodiments, observational data and experimental data may be collected from controlled experiments or from native plant communities (e.g., forests) to assess the effects of different soil microbiomes on plant productivity under standardized conditions. For example, plants can be inoculated with soil microbiomes sourced from geographically distinct donor forests, and growth parameters may be monitored over time. By way of further example, soil and root samples may be periodically collected for fungal biodiversity analysis, including DNA sequencing, metagenomic profiling, or quantitative PCR to determine the relative abundance of ectomycorrhizal fungal taxa. By way of further example, collected data may be fit into statistical and machine learning models to evaluate microbiome effects on plant growth.

[0112] In some embodiments, observational data may be collected from natural plant populations or native plant communities to identify microbial communities associated withAttorney Docket No.1723.2.WO2 PATENT high-productivity ecosystems. Data may be gathered from plant communities of varying ages, climates, and soil types to establish relationships between soil microbiome composition, tree growth metrics, and environmental covariates. In some embodiments, observational data may be used to train a supervised machine learning model, such as a boosted regression tree model, to predict plant biomass based on soil microbiome composition and environmental covariates. Additionally, spatially explicit models incorporating climate data, soil characteristics, and topography may be employed to refine predictions of plant productivity across different ecological conditions.

[0113] In some embodiments, the soil sample may comprise native soil harvested from the particular geographically distinct plant community. For example, native soil can include a volume of soil present in a native plant community that is unmodified in its composition post- harvest. In some embodiments, the soil sample may comprise a slurry comprising a volume of water mixed with soil harvested from the particular geographically distinct plant community. In some embodiments, a soil-to-water dilution ratio may be used to prepare a soil inoculation slurry for large-scale seedling inoculation. The soil inoculant may be diluted to create a slurry that ensures effective microbial delivery while facilitating ease of application. In some embodiments, a soil-to-water dilution ratio may be used to prepare a soil inoculation slurry for large-scale seedling inoculation. The dilution ratio may be 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, to 1:10 (soil to water by volume), wherein higher concentrations maintain greater microbial loads, while more diluted mixtures improve handling and uniform distribution. In some embodiments, the dilution ratio may range from 1:1 to 1:3, 1:2 to 1:5, 1:3 to 1:6, 1:4 to 1:7, 1:5 to 1:8, 1:6 to 1:9, and 1:7 to 1:10 (soil to water by volume). In some embodiments, the inoculation slurry may be applied at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 mL per seedling. In some embodiments, the inoculation slurry may be applied at 1 to 3 mL per seedling, 2 to 4 mL per seedling, 3 to 5 mL per seedling, 4 to 6 mL per seedling, 5 to 7 mL per seedling, 6 to 8 mL per seedling, 7 to 9 mL per seedling, and 8 to 10 mL per seedling.

[0114] In some embodiments, a method of the present invention may include growing plants ex situ relative to the particular geographically distinct plant community. For example, growing plants ex situ relative to the particular geographically distinct plant community can comprise growing plants in a greenhouse, nursery, non-natural plantation, or the like.Attorney Docket No.1723.2.WO2 PATENT

[0115] According to some embodiments of the present invention, a system for generating a fungal inoculant for increasing plant productivity may comprise a forest bioreactor comprising a feedstock, and an apparatus for selecting a soil microbiome having increasing plant productivity as disclosed herein. In some embodiments, a forest bioreactor may be designed to cultivate diverse soil microbial communities by providing a controlled substrate for fungal and bacterial colonization. Unlike traditional industrial bioreactors, which rely on closed-environment incubation, a forest bioreactor may involve placing organic materials within the forest floor, allowing native soil microbiota to colonize said organic materials over time. The bioreactor may be designed to optimize moisture retention, aeration, and nutrient availability, promoting natural proliferation of plant growth-promoting fungi and other microbes that may be used as inoculants for forestry and agricultural applications. In some embodiments, a feedstock for a forest bioreactor may include organic substrates capable of supporting microbial colonization and nutrient cycling. Feedstocks may comprise decomposable plant materials such as, e.g., leaf litter, wood chips, mulch, composted organic matter, nutrient-rich agricultural byproducts, and the like. The selection of feedstocks may be tailored to optimize microbial diversity, with different materials influencing the composition and functional traits of colonizing fungi and bacteria.

[0116] According to some embodiments of the present invention, a computer- implemented method for selecting a soil microbiome having increasing plant productivity may comprise: receiving, by one or more processors, observational data that is used to train a machine learning model, wherein the observational data comprise location-specific soil fungal community structure data, plant productivity data and environmental covariate data, wherein the observational data are collected from a plurality of geographically distinct plant communities; using the machine learning model, determining via the one or more processors, a plant biomass prediction comprising an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site; receiving, by the one or more processors, experimental data comprising (i) productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soil sample, and (ii) fungal biodiversity data associated with each plant at predetermined times during plant growth; using a generalizedAttorney Docket No.1723.2.WO2 PATENT linear model, determining via the one or more processors, a cumulative growth effect size for each of the productivity responses; using a distance-based multivariate model, determining differences between pairwise comparisons of fungal biodiversity data associated with each plant at each of the predetermined times; and combining via the one or more processors, an output from the machine learning model, the generalized linear model, and the distance-based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

[0117] In some embodiments, the method may further comprise sampling the rhizosphere fungal biodiversity of each plant at the predetermined times during plant growth, wherein sampling comprises sampling at the predetermined times of from each week to each year. In some embodiments, the predetermined times may include a 1-week interval, 2-week interval (biweekly), 3-week interval, 4-week interval (monthly), 6-week interval, 8-week interval (bimonthly), 10-week interval, 12-week interval (quarterly), 16-week interval, 20- week interval, 24-week interval (semiannual), 36-week interval, 48-week interval, 1-year interval (annual). In some embodiments, the predetermined times may include from a 1-week interval to a 3-week interval, from a 2-week interval to a 6-week interval, from a 3-week interval to an 8-week interval, from a 4-week interval to a 10-week interval, from a 6-week interval to a 12-week interval, from an 8-week interval to a 16-week interval, from a 10-week interval to a 20-week interval, from a 12-week interval to a 24-week interval, from a 16-week interval to a 36-week interval, from a 20-week interval to a 48-week interval, from a 24-week interval to a 1-year interval. In some embodiments, the predetermined times may include sampling from each week to each year.

[0118] In some embodiments, a rhizosphere fungal biodiversity may include a rhizosphere soil fungal biodiversity of a plant and a bulk soil fungal biodiversity of a plant. Rhizosphere soil fungal biodiversity may refer to the diversity of fungal species, strains, and the like, present in the soil region immediately surrounding and influenced by a plant’s roots. For example, the rhizosphere can be enriched by root exudates and can be inhabited by fungi that interact directly with a plant, e.g., including mutualists, pathogens, and other symbiotic species. Bulk soil fungal biodiversity may refer to a diversity of fungal species, strains, and the like, present in soil that may not be directly influenced by root activity but nevertheless in theAttorney Docket No.1723.2.WO2 PATENT vicinity of a plant rhizosphere. For example, the bulk soil fungal biodiversity can be representative of a background soil microbial community and can partially overlap with, or be entirely different from, a corresponding rhizosphere soil fungal biodiversity. In some embodiments, analysis of both rhizosphere and bulk soil fungal biodiversity may provide insights into plant-microbe interactions and the effects of fungal community composition on plant health and productivity.

[0119] In some embodiments, the computer-implemented method may include location-specific fungal community structure data comprise, the location-specific fungal community structure data may comprise: nucleic acid sequences, operational taxonomic unit (OTU tables, taxonomic information, guild composition, microbiome diversity, and / or functional characteristics of fungal biomass collected from each of the geographically distinct plant communities; wherein the location-specific plant productivity data comprise: plant height, plant diameter, plant biomass, plant survival, age of the geographically distinct plant community, and / or plant density collected from each of the geographically distinct plant communities; and wherein the location-specific environmental covariate data comprise: soil composition, contemporary climate data, and / or future climate projection data collected from each of the geographically distinct plant communities.

[0120] In some embodiments, the computer-implemented method may further comprise harvesting the soil sample from the particular geographically distinct plant community and inoculating each of the plants grown ex situ relative to the particular geographically distinct plant community. In some embodiments, the method may further comprise mixing the soil sample with a volume of water to form a slurry, wherein each of the plants grown ex situ relative to the particular geographically distinct plant community are inoculated with the slurry. In some embodiments, the soil sample may comprise a slurry comprising a volume of water mixed with soil harvested from the particular geographically distinct plant community. In some embodiments, a soil-to-water dilution ratio may be used to prepare a soil inoculation slurry for large-scale seedling inoculation. The method may further include diluting a soil inoculant (e.g., with a volume of water) to create a slurry that ensures effective microbial delivery while facilitating ease of application. In some embodiments, a soil- to-water dilution ratio may be used to prepare a soil inoculation slurry for large-scale seedling inoculation. The dilution ratio may be 1:1, 1:2, 1:3, 1:4, 1:5, 1:6, 1:7, 1:8, 1:9, to 1:10 (soil toAttorney Docket No.1723.2.WO2 PATENT water by volume), wherein higher concentrations maintain greater microbial loads, while more diluted mixtures improve handling and uniform distribution. In some embodiments, the dilution ratio may range from 1:1 to 1:3, 1:2 to 1:5, 1:3 to 1:6, 1:4 to 1:7, 1:5 to 1:8, 1:6 to 1:9, and 1:7 to 1:10 (soil to water by volume). In some embodiments, the inoculation slurry may be applied at 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 mL per seedling. In some embodiments, the inoculation slurry may be applied at 1 to 3 mL per seedling, 2 to 4 mL per seedling, 3 to 5 mL per seedling, 4 to 6 mL per seedling, 5 to 7 mL per seedling, 6 to 8 mL per seedling, 7 to 9 mL per seedling, and 8 to 10 mL per seedling.

[0121] According to some embodiments of the present invention, a method of producing a fungal inoculant may include: applying a computer-implemented method according to any embodiment as disclosed herein, wherein the donor forest selection tool is used to select the soil microbiome having increasing plant productivity; and incubating the soil microbiome having increasing plant productivity in a forest bioreactor to produce the fungal inoculum. In some embodiments, the method may further comprise adding a volume of water to the fungal inoculum to produce an inoculant slurry. In some embodiments, a forest bioreactor may be configured to enrich for fungal taxa that increase productivity of a plant relative to a control plant devoid of a fungal inoculum.

[0122] According to some embodiments of the present invention, systems and methods to accelerate plant productivity and atmospheric carbon sequestration. The growth-promoting fungal consortium inoculum that is identified and propagated may be used in various commercial contexts, including, but not limited to, forestry, crop agriculture, horticulture, ecosystem conservation and / or habitat remediation, and like contexts.

[0123] According to some embodiments of the present invention, a method, system, and / or composition according to any embodiment as disclosed herein, which may increase plant (e.g., tree) survival, plant volume (e.g., over a period of time), increase cubic feet of wood per acre (e.g., over a period of time), and / or increased Carbon Dioxide Equivalent (CO2e) per acre as compared to a control plant or control group thereof. In some embodiments, a period of time may be about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, 50, 60, 70, or anywhere therebetween, years. In some embodiments, a period of time may range from about In some embodiments, a period of time may range from 1 to 3 years, 2 to 4 years, 3 to 5 years, 4 to 6 years, 5 to 7 years, 6 to 8 years, 7 to 9 years, 8 to 10 years, 9 to 11 years, 10 to 12 years, 11 to 13 years, 12 to 14Attorney Docket No.1723.2.WO2 PATENT years, 13 to 15 years, 14 to 16 years, 15 to 17 years, 16 to 18 years, 17 to 19 years, 18 to 20 years, 19 to 21 years, 20 to 22 years, 21 to 23 years, 22 to 24 years, 23 to 25 years, 24 to 26 years, 25 to 27 years, 26 to 28 years, 27 to 29 years, 28 to 30 years, 29 to 31 years, 30 to 32 years, 31 to 33 years, 32 to 34 years, 33 to 35 years, 34 to 36 years, 35 to 37 years, 36 to 38 years, 37 to 39 years, 38 to 40 years, 39 to 41 years, 40 to 42 years, 41 to 43 years, 42 to 44 years, 43 to 45 years, 44 to 46 years, 45 to 47 years, 46 to 48 years, 47 to 49 years, 48 to 50 years, 49 to 51 years, 50 to 52 years, 51 to 53 years, 52 to 54 years, 53 to 55 years, 54 to 56 years, 55 to 57 years, 56 to 58 years, 57 to 59 years, 58 to 60 years, 59 to 61 years, 60 to 62 years, 61 to 63 years, 62 to 64 years, 63 to 65 years, 64 to 66 years, 65 to 67 years, 66 to 68 years, 67 to 69 years, 68 to 70 years, or any range therebetween. For example, increase plant survival, plant volume, increase cubic feet of wood per acre, and / or increased CO2e per acre can be monitored or observed each year up to about, or at the end of year about 10, 20, 30, 40, 50, 60, or 70 years after said plant initially germinates or is otherwise planted at a given location. In some embodiments, a plant may be monitored or observed for greater than 70 years, e.g., in the context of ecosystem restoration or remediation.

[0124] It will be understood by the ordinarily skilled artisan in the relevant art that Carbon Dioxide Equivalent, or CO2e, is a standardized unit used to measure the global warming potential (GWP) of various greenhouse gases. CO2e may be used to express the impact of greenhouse gases (e.g., methane, nitrous oxide, and the like) in terms of the amount of CO2 that would have a substantially equivalent global warming impact, e.g., to simplify comparisons and calculations involving different greenhouse gases. CO2e may be expressed as metric tons CO2e (t CO2e). In some embodiments, a plant inoculated with a donor soil (e.g., a donor soil comprising a growth promoting fungal consortium) may result in a CO2e reduction of about 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.08, 0.09, or 0.10 year. In some embodiments, a plant inoculated with a donor soil (e.g., a donor soil comprising a growth promoting fungal consortium) may result in a CO2e decrease of about 0.01 to about 0.03, about 0.02 to about 0.04, about 0.03 to about 0.05, about 0.04 to about 0.06, about 0.05 to about 0.07, about 0.06 to about 0.08, about 0.07 to about 0.09, about 0.08 to about 0.10 metric tons CO2e per tree per year. In some embodiments, at the acre scale, tree planting may decrease by by 40 to 400Attorney Docket No.1723.2.WO2 PATENT a lifespan of a forestry rotation (e.g., over a lifespan of a forestry rotation of from about 20 years to about 40 years).

[0125] In some embodiments, a method of identifying a growth-promoting fungal consortium from a natural fungal microbiome. In some embodiments, a method of identifying a growth-promoting fungal consortium from a natural fungal microbiome may include providing a sampling kit comprising a sampling vessel configured to receive a soil sample from a geographic location. In some embodiments, a sampling kit, including the soil sample, can be received from the geographic location. For example, a soil sample can be taken from a forest in a remote geographic location and the soil sample can thereafter be transported by any suitable means to a central laboratory. In some embodiments, a geographic location can be a natural ecosystem. In some embodiments, once the soil sample is received, nucleic acid material content of a first portion of the soil sample can be extracted. In some embodiments, the extracted nucleic acid material can then be sequenced, wherein sequencing reagents can be selected for their ability to enrich for fungal-derived nucleic acids. For example, the plurality of reagents that enrich for fungal-derived nucleic acids can include one or more primers targeting an Internal Transcribed Spacer genomic region (ITS), an ITS1 genomic region, an ITS2 genomic region, a Large Subunit rRNA (LSU) genomic region, a small subunit rRNA (SSU) genomic region, an 18S genomic region, a Translation Elongation Factor 1-alpha(TEF1-alpha) genomic region, a Beta- -tubulin) genomic region, an RNAPolymerase II (RPB1 and / or RPB2) genomic region, a Calmodulin (CaM) genomic region, and / or any other suitable taxonomic barcodes for identifying fungi from their nucleic acid material. In some embodiments, sequencing results in a fungal microbiome dataset. In some embodiments, a machine learning tool can be used, wherein the machine learning tool can include a training database including biotic and abiotic data associated with a plurality of high productivity ecosystems. For example, the machine learning tool can be trained with data from a training dataset that includes a library of DNA taxonomic barcode sequences that correlate with fungal taxonomic nomenclature, in addition to ecosystem metrics associated with high productivity ecosystems, such as species richness, nutrient content, rainfall, biomass accumulation, yield, growth rate, and other suitable metrics. In some embodiments, the plurality of high productivity ecosystems can include ecosystems with a large number or high biodiversity of photosynthesizing organisms, and with a high rate of atmospheric carbonAttorney Docket No.1723.2.WO2 PATENT sequestration and / or biomass production. In some embodiments, plant productivity can include metrics including, but not limited tom, biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, photosynthesis rate, and other suitable metrics. In some embodiments, the fungal microbiome dataset can be input into the trained machine learning tool to identify the species present in the soil sample such as to identify a growth-promoting fungal consortium, which can include of a subset of fungal taxa present in the soil sample and that are determined by the machine learning tool to promote plant productivity.

[0126] In some embodiments, a method of the present invention may comprise propagating a growth-promoting fungal consortium. In some embodiments, the growth-promoting fungal consortium can include a plurality of fungal species and / or strains native to the geographic location. In some embodiments, a second portion of the soil sample can be provided to a forest bioreactor. For example, the forest bioreactor can be prepared to such as to provide an optimal environment for the growth-promoting fungal consortium to reproduce sexually and asexually and to outcompete other, undesired fungi that are present in the soil sample and are not plant growth-promoting. In some embodiments, the forest bioreactor can include a feedstock, wherein the feedstock can be selected to provide a nutritive source for the growth-promoting fungal consortium such that they more reproduce sexually and / or asexually. In some embodiments, the forest bioreactor can be sealed or unsealed. In some embodiments, the forest bioreactor can additionally include a temperature, an oxygen content, a salinity, a pH and any other suitable condition favoring rapid fungal growth and reproduction. In some further embodiments, the method can include colonizing the feedstock in the forest bioreactor for a time sufficient to create a growth-promoting fungal consortium inoculum. For example, the growth-promoting fungal consortium inoculum can include or can consist essentially of the feedstock and the growth-promoting fungal consortium. In some embodiments, the growth- promoting fungal consortium inoculum includes the feedstock and the growth-promoting fungal consortium. In some further embodiments, the growth-promoting fungal consortium inoculum can be mixed with water to create an inoculum slurry. In some embodiments, the method can include harvesting the inoculum slurry and thereafter inoculating a plurality of plants at the geographic location from which the soil sample was initially taken, with the inoculum slurry.Attorney Docket No.1723.2.WO2 PATENT

[0127] In some embodiments, a method of the present invention comprises monitoring the productivity of each of the plurality of plants after they have been inoculated with the inoculum slurry. In some embodiments, monitoring includes, for example, utilizing a plurality of sensors. For example, the plurality of plants can be monitored for their growth rate and their rate of photosynthesis using sensors configured to measure for either or both. In some embodiments, the plant photosynthesis rate sensor includes a tool for measuring a rate of atmospheric carbon sequestration.

[0128] According to some embodiments of the present invention, a system for accelerating plant productivity and atmospheric carbon sequestration is provided. In some embodiments, the system includes at least one sampling kit, wherein the at least one sampling kit can be configured to be sent to and from a geographic location. In some further embodiments, the geographic location is a natural ecosystem. In some embodiments, the at least one sample kit includes a sample container, and the sample container can be configured to receive a soil sample.

[0129] In some embodiments, a system of the present invention comprises a processing system configured to extract nucleic acid material from a first portion of the soil sample. In some further embodiments, the system includes a nucleic acid sequencing platform. For example, the nucleic acid sequencing platform can be configured to sequence the nucleic acid material present in the first portion of the soil sample and can be configured to generate a fungal microbiome dataset. In some embodiments, the nucleic acid sequencing platform can further include a plurality of reagents that can be adapted to enrich for fungal-derived nucleic acids. For example, the plurality of reagents can include primers, including, but not limited to, primers targeting an Internal Transcribed Spacer genomic region (ITS), an ITS1 genomic region, an ITS2 genomic region, a Large Subunit rRNA (LSU) genomic region, a small subunit rRNA (SSU) genomic region, an 18S genomic region, a Translation Elongation Factor 1-alpha(TEF1-alpha) genomic region, a Beta- -tubulin) genomic region, an RNAPolymerase II (RPB1 and / or RPB2) genomic region, a Calmodulin (CaM) genomic region, and / or other suitable taxonomic barcode regions for fungi.

[0130] In some embodiments, a system of the present invention comprises a machine learning tool. In some embodiments, the machine learning tool can include a training database, the training database may further include biotic and abiotic data associated with a plurality ofAttorney Docket No.1723.2.WO2 PATENT high productivity ecosystems. For example, the plurality of high productivity ecosystems can include ecosystems with a large number and / or high diversity of photosynthesizing organisms, additionally including a high rate of atmospheric carbon sequestration and / or biomass production. In some further embodiments, the machine learning tool can be configured to identify a growth-promoting fungal consortium that can include a subset of fungal species present in the first portion of the soil sample and can be associated with the plurality of high productivity ecosystems.

[0131] According to some embodiments of the present invention, a forest bioreactor is provided. In some embodiments, the forest bioreactor can be configured to receive a second portion of the soil sample and to propagate the growth-promoting fungal consortium. For example, the forest bioreactor can include a sealed or unsealed controlled environment in which the growth-promoting fungal consortium can grow in an optimal environment, including, but not limited to, a temperature, an oxygen content, a salinity, and / or a pH. In some further embodiments, the forest bioreactor can include a feedstock. For example, the feedstock and the optimal environment can be such as to promote colonization of the feedstock by, and / or asexual and sexual reproduction of, the growth-promoting fungal consortium in order to outcompete other organisms present in the soil sample.

[0132] In some embodiments, an inoculum slurry is provided and / or used in a method and / or system of the present invention. In some embodiments, the inoculum slurry can include a mixture of water and a growth-promoting fungal consortium inoculum. In some further embodiments, the growth-promoting fungal consortium inoculum can include the feedstock substantially colonized by the growth-promoting fungal consortium.

[0133] In some embodiments, a plurality of plants can be inoculated with the inoculum slurry. In some further embodiments, the plurality of plants can include a single species. In some alternative embodiments, the plurality of plants can include a plurality of species. In some embodiments the plurality of plants can include one or more tree species. In some alternative embodiments, the plurality of plants can include, for example, a mixture of woody and herbaceous species. For example, a tiered-cropping system can be implemented according to the present invention, wherein plants of two or more different height can be grown together, with the uppermost layer including the tallest-growing species and the lowermost layer including the shortest-growing species.Attorney Docket No.1723.2.WO2 PATENT

[0134] In some embodiments, a plurality of sensors can be configured to monitor plant productivity of each of the plurality of plants inoculated with the inoculum slurry. For example, plant productivity can be monitored and / or measured with respect to biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, photosynthesis rate, and any other metric suitable for measuring plant productivity. In some further embodiments, the plurality of sensors can include at least one of a plant growth rate sensor and a plant photosynthesis rate sensor. For example, the plant photosynthesis rate sensor can include a tool for measuring a rate of atmospheric carbon sequestration.

[0135] In some embodiments, a method can include providing a forest bioreactor. For example, the forest bioreactor can be prepared such as to provide an optimal environment for the growth-promoting fungal consortium to reproduce sexually and asexually and to outcompete other, undesired fungi that are present in a soil sample and are not plant growth- promoting. In some embodiments, the forest bioreactor includes a feedstock, wherein the feedstock can be selected to provide a nutritive source for the growth-promoting fungal consortium such that the fungal species and / or strains included therein reproduce sexually and / or asexually. In some embodiments, the forest bioreactor can be sealed or unsealed. In some embodiments, the forest bioreactor can additionally include a temperature, an oxygen content, a salinity, a pH and any other suitable condition favoring rapid fungal growth and reproduction. In some further embodiments, the method includes colonizing the feedstock in the forest bioreactor for a time sufficient to create a growth-promoting fungal consortium inoculum. For example, the growth-promoting fungal consortium inoculum can include or can consist essentially of the feedstock and the growth-promoting fungal consortium. In some further embodiments, the growth-promoting fungal consortium inoculum can be mixed with water to create an inoculum slurry. In some embodiments, the method can include harvesting the inoculum slurry and thereafter inoculating a plurality of plants at the geographic location from which the soil sample was initially taken, with the inoculum slurry. In some further embodiments, producing a fungal inoculum with a forest bioreactor can include isolating a growth-promoting fungal consortium. In yet some further embodiments, the producing a fungal inoculum with a forest bioreactor can include providing the growth-promoting fungal consortium to the forest bioreactor, the forest bioreactor configured to provide a feedstock andAttorney Docket No.1723.2.WO2 PATENT an optimal environment, wherein the feedstock and the optimal environment can be selected to cause the growth-promoting fungal consortium to grow and to reproduce. In some further embodiments, producing a fungal inoculum with a forest bioreactor can include colonizing the feedstock with fungal species and / or strains comprising the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including or consisting essentially of the feedstock and the growth-promoting fungal consortium. In some further embodiments, producing a fungal inoculum with a forest bioreactor can include mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry. In some further embodiments, producing a fungal inoculum with a forest bioreactor can include harvesting the inoculum slurry. For example, the inoculum slurry can include the growth-promoting fungal consortium, the growth-promoting fungal consortium including one or more fungal species and / or strains that increase plant productivity.

[0136] In some embodiments, a growth-promoting fungal consortium may include a plurality of fungal species and / or fungal strains native to a geographic location. For example, a native fungal species and / or strain can include a species and / or strain that is naturally present in a geographic location. In some embodiments, the geographic location can include a geographic area, e.g., bound by a length and width. In some embodiments, a geographic location includes a volume. For example, a fungal species and / or strain can be native to a circumscribed geographic area, including up to a depth in the soil or up to a certain height or elevation.

[0137] In some embodiments, a method of producing a fungal inoculum with a forest bioreactor using fungal species and / or strains from a geographic location that is a natural ecosystem. In some embodiments, the geographic location can include a high productivity ecosystem. For example, the high productivity ecosystem can include photosynthesizing organisms with a high rate of atmospheric carbon sequestration and / or biomass production. In some embodiments, the growth-promoting fungal consortium can be adapted to maximize plant productivity utilizing metrics including, but not limited to, biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate.

[0138] In some embodiments, a method of the present invention comprises producing a fungal inoculum with a forest bioreactor for which the growth-promoting fungal consortiumAttorney Docket No.1723.2.WO2 PATENT can be identified using a plurality of reagents. In some embodiments, the plurality of reagents is adapted to enrich for fungal-derived nucleic acids. For example, the plurality of reagents can include, but are not limited to, at least a plurality of primers configured to target an Internal Transcribed Spacer genomic region (ITS), an ITS1 genomic region, an ITS2 genomic region, a Large Subunit rRNA (LSU) genomic region, a small subunit rRNA (SSU) genomic region, an 18S genomic region, a Translation Elongation Factor 1-alpha (TEF1-alpha) genomic region,a Beta- -tubulin) genomic region, an RNA Polymerase II (RPB1 and / or RPB2)genomic region, and / or a Calmodulin (CaM) genomic region.

[0139] In some embodiments, a method of the present invention comprises producing a fungal inoculum with a forest bioreactor that can include a sealed environment or an unsealed environment. In some embodiments, the bioreactor can be configured to simulate an optimal environment. For example, the optimal environment can include a temperature, an oxygen content, a salinity, and / or a pH.

[0140] In some embodiments, a method of the present invention comprises producing a fungal inoculum with a forest bioreactor that may include colonizing. In some embodiments, colonizing can include colonizing a feedstock. In some embodiments, colonizing a feedstock includes sexual reproduction and asexual reproduction of each fungal species and / or strain comprising the growth-promoting fungal consortium.

[0141] In some embodiments, a method of the present invention comprises producing a fungal inoculum with a forest bioreactor wherein the growth-promoting fungal consortium inoculum is applied to at least one plant. In some embodiments, the plant includes a plurality of plants. In some further embodiments, the plurality of plants can include a single species. In some alternative embodiments, the plurality of plants can include a plurality of species. In some embodiments the plurality of plants can include one or more tree species. In some alternative embodiments, the plurality of plants includes a mixture of woody species and herbaceous species. For example, a tiered-cropping system can be implemented according to the present invention, wherein plants of two or more different height can be grown together, with the uppermost layer including the tallest-growing species and the lowermost layer including the shortest-growing species.

[0142] It is another object of the present invention to provide a method of increasing plant productivity with reduced plant fertilizer utilization. In some embodiments, increasing plantAttorney Docket No.1723.2.WO2 PATENT productivity with reduced plant fertilizer utilization can include isolating a growth-promoting fungal consortium. In some embodiments, increasing plant productivity with reduced plant fertilizer utilization can include providing the growth-promoting fungal consortium to a forest bioreactor. For example, the forest bioreactor can be configured to provide a feedstock and an optimal environment. By way of further example, the feedstock and the optimal environment can be selected to cause the growth-promoting fungal consortium to grow and to reproduce. In some embodiments, increasing plant productivity with reduced plant fertilizer utilization can include colonizing the feedstock with fungal species and / or strains comprising the growth- promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including or consisting essentially of the feedstock and the growth-promoting fungal consortium. In some embodiments, increasing plant productivity with reduced plant fertilizer utilization can include, mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry. In some embodiments, increasing plant productivity with reduced plant fertilizer utilization can include, harvesting the inoculum slurry. In some embodiments, increasing plant productivity with reduced plant fertilizer utilization can include inoculating a plurality of plants with the inoculum slurry. For example, the fungal species and / or strains comprising the growth-promoting fungal consortium can enhance nutrient bioavailability without the need for chemical fertilizers.

[0143] In some embodiments, a method of the present invention comprises generating biodiversity credits. For example, a biodiversity credit can include a market-based method of quantifying conservation and / or restoration efforts. By way of further example, quantifying conservation and / or restoration efforts can include tradable units that correspond to a quantifiable improvement in biodiversity due to conservation efforts, habitat restoration, or sustainable management practices. By way of yet a further example, biodiversity credits can be bought and sold. In some embodiments, generating biodiversity credits can include isolating a growth-promoting fungal consortium. For example, the growth-promoting fungal consortium can include native fungal species and / or strains. In some embodiments, generating biodiversity credits can include providing the growth-promoting fungal consortium to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment. For example, the feedstock and the optimal environment can be selected to cause the growth- promoting fungal consortium to grow and to reproduce. In some embodiments, generatingAttorney Docket No.1723.2.WO2 PATENT biodiversity credits can include colonizing the feedstock with the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including or consisting essentially of the feedstock and the growth-promoting fungal consortium. In some embodiments, generating biodiversity credits can include mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry. In some embodiments, generating biodiversity credits can include harvesting the inoculum slurry. In some embodiments, generating biodiversity credits can include inoculating a plurality of plants with the inoculum slurry. In some embodiments, generating biodiversity credits can include establishing a community of the native fungal species and / or strains comprising the growth- promoting fungal consortium, in symbiosis with the plurality of plants. For example, generating biodiversity credits can include increasing an entity’s biodiversity credits in positive correlation to a diversity of the community of the native fungal species and / or strains. In some embodiments, the biodiversity credits can be carbon credits as would be readily understood by the skilled artisan.

[0144] In some embodiments, a method of the present invention comprises improving water quality. In some embodiments, improving water quality can include inoculating a plurality of plants with an inoculum slurry. For example, the inoculum slurry can include a mixture of water and a growth-promoting fungal consortium inoculum including or consisting essentially of a feedstock and a growth-promoting fungal consortium. In some embodiments, improving water quality can include establishing a community of native fungi including the growth-promoting fungal consortium in symbiosis with the plurality of plants. For example, the growth-promoting fungal consortium inoculum can include a plurality of native fungal species and / or strains. By way of further example, each of the plurality of native fungal species and / or strains can be adapted to filter contaminants from a volume of water, thereby improving quality of the volume of water. In some embodiments, improving water quality can include establishing a community of native fungal species or strains, the native fungal species or strains being in symbiosis with the plurality of plants. For example, the growth-promoting fungal consortium inoculum can include native fungal species and / or strains. By way of further example, the native fungal species and / or strains can be adapted to filter contaminants from a volume of water, thereby improving quality of the volume of water.Attorney Docket No.1723.2.WO2 PATENT

[0145] In some embodiments, a method of the present invention comprises improving water quality by removing at least one contaminant from a volume of water. In some embodiments, the contaminants include, but are not limited to, a heavy metal, an organic molecule, an inorganic molecule, a pharmaceutical, a nutrient, a plastic, a sediment, a radioactive molecule, a pesticide, an herbicide, a detergent, industrial waste, and / or agricultural runoff.

[0146] In some embodiments, a method of the present invention comprises improving water quality of a volume of water. In some embodiments, the volume of water is a naturally occurring volume of water or an artificially formed volume of water. For example, a naturally occurring volume of water can include, but is not limited to, a lake, a pond, a lagoon, an estuary, a bog, a swamp, a marsh, and / or a wetland. By way of further example, a natural body of water can also include, but is not limited to an aquifer and / or groundwater that has permeated soil. In some embodiments, the volume of water is an artificial body of water, a reservoir, a water tank, an artificial lake, a sump, a cesspool, a cistern, or any other like artificial body of water. By way of further example, an artificial volume of water can also include water that has artificially saturated a body of earth, soil gravel or any like material.

[0147] In some embodiments, a method of the present invention comprises remediating soil. In some embodiments, remediating soil can include isolating a growth-promoting fungal consortium. In some embodiments, remediating soil can include forming a growth-promoting fungal consortium inoculum including or consisting essentially of a feedstock and the growth- promoting fungal consortium. In some embodiments, remediating soil can include mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry. In some embodiments, remediating soil can include harvesting the inoculum slurry. In some embodiments, remediating soil can include inoculating a volume of soil with the inoculum slurry. In some further embodiments, remediating soil can include maturing the inoculum slurry within the volume of soil such as to substantially colonize the volume of soil with the growth-promoting fungal consortium.

[0148] In some embodiments, a method of the present invention comprises remediating soil by selecting a volume of soil. In some embodiments, the volume of soil can be selected from, but not limited to,situ soil, ex situ soil, translocated soil, and artificial soil. For example, in situ soil can include soil that is naturally present in an ecosystem, ex situ soil canAttorney Docket No.1723.2.WO2 PATENT include soil collected from a natural ecosystem and placed in a different geographic location, translocated soil can include soil moved within a natural or artificial system, and artificial soil can include man-made blend of various components designed to simulate natural soil properties. In some further embodiments, the volume of soil can include nutritive materials. For example, the nutritive material can provide a nutritive substrate for a growth-promoting fungal consortium to grow and substantially colonize the volume of soil. In some embodiments, the volume of soil can contain at least one contaminant. In some embodiments, remediating a volume of soil that includes a contaminant can include substantially removing the contaminant from the soil. For example, removing can include absorbing from the volume of soil one or more contaminants by at least one fungus of the growth-promoting fungal consortium. In some further embodiments, the at least one contaminant can include a heavy metal, an organic molecule, an inorganic molecule, a pharmaceutical, a nutrient, a plastic, a sediment, a radioactive molecule, a pesticide, an herbicide, a detergent, industrial waste, and / or agricultural runoff.

[0149] Figure 1 illustrates an example embodiment of a method of developing a training dataset for a machine learning tool for accelerated plant productivity and atmospheric carbon sequestration 100. The method 100 outlines a three-step process for understanding the relationship between growth rates of a natural ecosystem and fungal microbiome comprising the natural ecosystem.

[0150] In some embodiments, developing the training database can begin with sampling a plurality of natural ecosystem to measure the fungal microbiome and plant growth rates step 110. For example, extensive sampling can ensure a robust training dataset, which can enable the machine learning tool to comprehensively assess a fungal microbiome dataset input. In some embodiments, step 110 can further include collecting data for the training database on the health and productivity of natural ecosystems, as well as the diversity and role of fungal species within differing natural ecosystems.

[0151] In some embodiments, developing the training data base includes pairing the data collected in step 110 with climate and soil maps step 120. Step 120 can control for environmental variation, allowing for more accurate selection of fungal species and / or strains in the growth-promoting fungal consortium decoupled from other environmental factors, such as soil type, temperature, precipitation, and other climatic conditions.Attorney Docket No.1723.2.WO2 PATENT

[0152] Step 130 describes an embodiment where data from step 110 and analysis from step 120 are combined to construct a machine learning tool 130. For example, the machine learning tool 130 can be used to understand the specific needs of the forest as influenced by the soil fungal microbiome, e.g., in terms of nutrient cycling, disease resistance, and growth optimization.

[0153] Figure 2 illustrates an embodiment of a method of accelerating plant productivity and atmospheric carbon sequestration 200. In some embodiments, the method of accelerating plant productivity and atmospheric carbon sequestration 200 can begin with the overarching goal of identifying a growth-promoting fungal consortium from a natural fungal microbiome 201. In some embodiments, the method of accelerating plant productivity and atmospheric carbon sequestration 200 can include providing a sampling kit comprising a sampling vessel configured to receive a soil sample from a geographic location 202. In some embodiments, the sampling kit, including the soil sample, can be received from the geographic location 203. In some embodiments, once the soil sample is received, the nucleic acid material content of a first portion of the soil sample can be extracted 204. In some embodiments, the extracted nucleic acid material can then be sequenced 205. For example, sequencing reagents can be selected for their ability to enrich for fungal-derived nucleic acids. In some embodiments, sequencing results in a fungal microbiome dataset 205. In some embodiments, a machine learning tool can be used, wherein the machine learning tool includes a training database including biotic and abiotic data associated with a plurality of high productivity ecosystems 206. In some embodiments, the fungal microbiome dataset can be input into the trained machine learning tool to identify the species present in the soil sample such as to identify a growth-promoting fungal consortium, which includes a subset of fungal taxa present in the soil sample and that are determined by the machine learning tool to promote plant productivity 207.

[0154] In some further embodiments, after the growth-promoting fungal consortium can be identified 210, the next overarching goal is to propagate the fungal species and / or strains that can be included in the growth-promoting fungal consortium 220. In some embodiments, a second portion of the soil sample can be provided to a forest bioreactor 221. For example, the forest bioreactor can be prepared to such as to provide an optimal environment for the growth- promoting fungal consortium to reproduce sexually and asexually and to outcompete other, undesired fungi that are present in the soil sample and are not plant growth-promoting. In someAttorney Docket No.1723.2.WO2 PATENT embodiments, the forest bioreactor includes a feedstock, wherein the feedstock can be selected to provide a nutritive source for the growth-promoting fungal consortium such that they more reproduce sexually and / or asexually. In some embodiments, the forest bioreactor can be sealed or unsealed. In some embodiments, the forest bioreactor can additionally include a temperature, an oxygen content, a salinity, a pH and any other suitable condition favoring rapid fungal growth and reproduction. In some further embodiments, the method includes colonizing the feedstock in the forest bioreactor for a time sufficient to create a growth-promoting fungal consortium inoculum 222. For example, the growth-promoting fungal consortium inoculum 222 includes or consists essentially of the feedstock and the growth-promoting fungal consortium. In other embodiments, the growth-promoting fungal consortium inoculum 222 includes the feedstock and the growth-promoting fungal consortium. In some further embodiments, the growth-promoting fungal consortium inoculum can be mixed with water to create an inoculum slurry 223. In some embodiments, the method can include harvesting the inoculum slurry 224 and thereafter inoculating a plurality of plants at the geographic location from which the soil sample was initially taken, with the inoculum slurry 225.

[0155] In yet some further embodiments, the method 200 includes monitoring the productivity of each of the plurality of plants 230 after they have been inoculated with the inoculum slurry. In some embodiments, monitoring includes utilizing a plurality of sensors. For example, the plurality of plants can be monitored for their growth rate and their rate of photosynthesis using sensors configured to measure for either or both. In some embodiments, the plant photosynthesis rate sensor includes a tool for measuring a rate of atmospheric carbon sequestration.

[0156] Figure 3 illustrates an embodiment of a system for accelerating plant productivity and atmospheric carbon sequestration 300. In some embodiments, the system includes at least one kit. For example, the kit can include a sampling kit that can contain a sample container into which a sample of soil is placed. In some further embodiments, no kit is required. For example, a sample of soil can be harvested from a geographic location and subsequently analyzed for the purpose of accelerating plant productivity and atmospheric carbon sequestration. In some embodiments, the system can include a soil sample processing system configured to extract nucleic acid material from the soil sample. For example, the soil sample can be divided into portions for various analyses or applications, such as a first portion of theAttorney Docket No.1723.2.WO2 PATENT soil sample from which nucleic acid material is extracted. In some embodiments, the system can further include a nucleic acid sequencing platform 301. For example, nucleic acid sequencing platform 301 can include a place-specific or geographic location-specific data analysis pipeline. In some embodiments, the system can include nucleic acid sequencing platform 301 that is configured to sequence the nucleic acid material present in the first portion of the soil sample. In some further embodiments, nucleic acid sequencing platform 301 can be configured to generate a fungal microbiome dataset. For example, nucleic acid sequencing platform 301 can include a data analysis pipeline that is configured to receive the nucleic acid material and / or extract the nucleic acid material, and thereafter sequence the nucleic acid material. In some embodiments, the nucleic acid sequencing platform 301 can further include a plurality of reagents adapted to enrich for fungal-derived nucleic acids. In some embodiments, nucleic acid sequencing platform 301 can include a data analysis pipeline that is a machine learning tool. For example, the machine learning tool can include a place-specific or geographic locations-specific training database, the training database being carefully curated to include biotic and abiotic data associated with a plurality of high productivity ecosystems. By way of further example, the machine learning tool can identify a growth-promoting fungal consortium present in the first portion of the soil sample and / or from the nucleic acid material extracted therefrom. For example, the data analysis platform comprising the nucleic acid sequencing platform 301 can be configured to identify a subset of fungal species present in the first portion of the soil sample and associated with the plurality of high productivity ecosystems. In some further embodiments, nucleic acid sequencing platform 301 can be configured to generate place-specific or geographic-location-specific fungal inoculants with the data analysis pipeline. For example, the data analysis pipeline can be configured to receive the fungal microbiome dataset, which can serve as a training set for, the data analysis pipeline including a machine learning tool. By way of further example, the machine learning tool can identify the growth-promoting fungal consortium that includes a subset of fungal species present in the first portion of the soil sample and associated with the plurality of high productivity ecosystems.

[0157] In some further embodiments, the system can include propagation of the growth- promoting fungal consortium 302. For example, a forest bioreactor can be configured to receive a second portion of the soil sample and to propagate the growth-promoting fungalAttorney Docket No.1723.2.WO2 PATENT consortium. In some embodiments, the forest bioreactor can be configured to provide a feedstock and an optimal environment. For example, the feedstock and the optimal environment can be adapted to promote colonization of the feedstock by the growth-promoting fungal consortium such as to outcompete other organisms present in the soil sample. In some embodiments, propagation of the growth-promoting fungal consortium 302 can include a nursery of plant-growing subsystem in which plant seedlings are established by inoculating each of the plant seedlings with the growth-promoting fungal consortium 302. In some further embodiments, the plant seedlings can be established by inoculating each with an inoculum slurry. In some embodiments, the inoculum slurry can include a mixture of water and a growth- promoting fungal consortium inoculum. For example, the growth-promoting fungal consortium inoculum can include the feedstock that is substantially colonized by the growth- promoting fungal consortium.

[0158] In yet some further embodiments, the system includes one or more sensors that can be configured to monitor plant productivity 303 of each of the plurality of plants inoculated with the inoculum slurry. For example, to monitor plant productivity 303 can include to work with existing commercial plant growers, including, but not limited to, persons working in forestry, crop agriculture, horticulture, ecosystem conservation and / or habitat remediation, and like contexts or infrastructure to deploy the established seedlings that have been inoculated with the inoculum slurry, in the field, including, but not limited to, a natural ecosystem or an artificial planting system. For example, an artificial planting system can include a farm, an orchard, a commercial forest, and like contexts. In some embodiments, the one or more sensors can automate monitoring plant productivity 303. For example, the one or more sensors can automatically measure plant productivity with metrics including, but not limited to, biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate.

[0159] In yet some further embodiments, the system can also include tracking plant productivity among inoculated plants relative to non-inoculated plants 304. For example, inoculated plants can be tracked for added growth or plant productivity relative to a control group of plants, the control group of plants including plants that have not been inoculated with the growth-promotion fungal consortium inoculum.Attorney Docket No.1723.2.WO2 PATENT

[0160] In yet some further embodiments, the system can be configured to operate in tandem with a method of generating biodiversity credits 305. For example, the increase in plant productivity generated by inoculating plants with the growth-promoting fungal consortium can generate revenue through sales of verified carbon credits, while also increasing revenue for land partners by increasing crop, biomass, and the like, yield. In some embodiments, accelerated plant growth, for example, tree growth in a forestry context, can be translated into carbon credits 306, which can be bought or sold. In some further embodiments, the system can increase plant productivity, which can correlate with increased biomass volume 307. For example, in a forestry context, the increase in tree growth can result in an increase the quantity of commercial product associated with tree biomass.

[0161] Figure 4 illustrates a method of producing a fungal inoculum with a forest bioreactor 400. In some embodiments, the method of producing a fungal inoculum with a forest bioreactor 400 can include one or more inputs 401. In some embodiments, the method of producing a fungal inoculum with a forest bioreactor 400 can include providing a forest bioreactor 410. For example, the forest bioreactor can include feedstock and an optimal environment, wherein the feedstock and the optimal environment can be adapted to promote colonization of the feedstock by the growth-promoting fungal consortium such as to outcompete other organisms present in the soil sample. In some further embodiments, the method of producing a fungal inoculum with a forest bioreactor 400 can include isolating a growth-promoting fungal consortium 420. In yet some further embodiments, the method of producing a fungal inoculum with a forest bioreactor 400 can include providing the growth- promoting fungal consortium to the forest bioreactor 430. For example, the forest bioreactor can include the feedstock and the optimal environment that are preconfigured to cause the growth-promoting fungal consortium to grow and to reproduce. In some embodiments, the method of producing a fungal inoculum with a forest bioreactor 400 can include colonizing the feedstock with fungal species and / or strains comprising the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum 440. For example, growth-promoting fungal consortium inoculum can include the feedstock and the growth-promoting fungal consortium. In some embodiments, the feedstock can be entirely consumed by the growth-promoting fungal consortium, resulting in a growth- promoting fungal inoculum consisting of or consisting essentially of or comprising fungalAttorney Docket No.1723.2.WO2 PATENT biomass. In some embodiments, the method of producing a fungal inoculum with a forest bioreactor 400 can include mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry 450. In some embodiments, the method of producing a fungal inoculum with a forest bioreactor 400 can include harvesting the inoculum slurry 460.

[0162] Figure 5 illustrates a method of increasing plant productivity with reduced plant fertilizer utilization 500. In some embodiments, the method of increasing plant productivity with reduced fertilizer utilization 500 can include one or more inputs 501. In some embodiments, the method of increasing plant productivity with reduced fertilizer utilization 500 can include isolating a growth-promoting fungal consortium 510. In some embodiments, the method of increasing plant productivity with reduced fertilizer utilization 500 can include providing the growth-promoting fungal consortium to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment 520. For example, the feedstock and the optimal environment can be selected to cause the growth-promoting fungal consortium to grow and to reproduce. In some embodiments, the method of increasing plant productivity with reduced fertilizer utilization 500 can include colonizing the feedstock with fungal species and / or strains that can include the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum including the feedstock and the growth-promoting fungal consortium 530. In some embodiments, the method of increasing plant productivity with reduced fertilizer utilization 500 can include mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry 540. In some embodiments, the method of increasing plant productivity with reduced fertilizer utilization 500 can include harvesting the inoculum slurry 550. In some embodiments, the method of increasing plant productivity with reduced fertilizer utilization 500 can include inoculating a plurality of plants with the inoculum slurry 560. For example, the fungal species and / or strains that can be included in the growth-promoting fungal consortium can enhance nutrient bioavailability without the need for chemical fertilizers. In some further embodiments, the growth-promoting fungal consortium can include a plurality of fungal species and / or strains native to a geographic location. For example, the geographic location can be a natural ecosystem. In some embodiments, plant productivity can be measured with metrics including, but not limited to, biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, photosynthesis rate,Attorney Docket No.1723.2.WO2 PATENT and other like metrics of plant productivity. In some further embodiments, method of increasing plant productivity with reduced fertilizer utilization 500 can include harvesting the growth-promoting fungal consortium from a high productivity ecosystem, the high productivity ecosystem including a plurality of photosynthesizing organisms with a high rate of atmospheric carbon sequestration and / or biomass production.

[0163] Figure 6 illustrates a method of generating biodiversity credits 600. In some embodiments, the method of generating biodiversity credits 600 can include one or more inputs 610. In some embodiments, the method of generating biodiversity credits 600 can include isolating a growth-promoting fungal consortium 620. For example, the growth-promoting fungal consortium can include native fungal species and / or strains. In some embodiments, the method of generating biodiversity credits 600 can include providing the growth-promoting fungal consortium to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment 630. For example, the feedstock and the optimal environment can be selected to cause the growth-promoting fungal consortium to grow and to reproduce. In some embodiments, the method of generating biodiversity credits 600 can include colonizing the feedstock with the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum the feedstock and the growth- promoting fungal consortium 640. In some embodiments, the method of generating biodiversity credits 600 can include mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry 650. In some embodiments, the method of generating biodiversity credits 600 can include harvesting the inoculum slurry 660. In some embodiments, the method of generating biodiversity credits 600 can include inoculating a plurality of plants with the inoculum slurry 670. In some embodiments, the method of generating biodiversity credits 600 can include establishing a community of the native fungal species and / or strains including the growth-promoting fungal consortium in symbiosis with the plurality of plants. For example, the biodiversity credits can increase in positive correlation to a diversity of the community of the native fungal species and / or strains.

[0164] Figure 7 illustrates a method of improving water quality 700. In some embodiments, the method of improving water quality 700 can include one or more inputs 710. In some embodiments, the method of improving water quality 700 can include inoculating a plurality of plants with an inoculum slurry 720. For example, the inoculum slurry can includeAttorney Docket No.1723.2.WO2 PATENT a mixture of water and a growth-promoting fungal consortium inoculum with a feedstock and a growth-promoting fungal consortium. In some embodiments, the method of improving water quality 700 can include establishing a community of the native fungi including the growth- promoting fungal consortium in symbiosis with the plurality of plants 740. For example, the growth-promoting fungal consortium inoculum can include a plurality of native fungal species and / or strains. By way of further example, each of the plurality of native fungal species and / or strains can be adapted to filter contaminants from a volume of water, thereby improving quality of the volume of water. In some embodiments, the method of improving water quality 700 can include the contaminants, which can include, but are not limited to, a heavy metal, an organic molecule, an inorganic molecule, a pharmaceutical, a nutrient, a plastic, a sediment, a radioactive molecule, a pesticide, an herbicide, a detergent, industrial waste, and / or agricultural runoff. In some embodiments, the method of improving water quality 700 can include the volume of water, the volume of water including, but not limited to, a naturally occurring volume of water or an artificially formed volume of water.

[0165] Figure 8 illustrates a method of remediating soil 800. In some embodiments, the method of remediating soil 800 can include one or more inputs 810. In some embodiments, the method of remediating soil 800 can include isolating a growth-promoting fungal consortium 820. In some embodiments, the method of remediating soil 800 can include forming a growth- promoting fungal consortium inoculum with a feedstock and the growth-promoting fungal consortium 830. In some embodiments, the method of remediating soil 800 can include mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry 840. In some embodiments, the method of remediating soil 800 can include harvesting the inoculum slurry 850. In some embodiments, the method of remediating soil 800 can include inoculating a volume of soil with the inoculum slurry 860. In some embodiments, the method of remediating soil 800 can include maturing the inoculum slurry within the volume of soil such as to substantially colonize the volume of soil with the growth-promoting fungal consortium 870. In some embodiments, the method of remediating soil 800 can include the volume of soil, the volume of soil can be in situ soil, ex situ soil, translocated soil, artificial soil and / or any like medium capable of remediation by a growth-promoting fungal consortium. In some further embodiments, the method of remediating soil 800 can include the volume of soil including nutritive materials, the nutritive material providing a nutritive substrate for the growth-Attorney Docket No.1723.2.WO2 PATENT promoting fungal consortium to grow and substantially colonize the volume of soil. In some embodiments, the method of remediating soil 800 can include the volume of soil including at least one contaminant. In some embodiments, the method of remediating soil 800 can include at least one contaminant substantially removed from the volume of soil and absorbed by at least one fungus comprising the growth-promoting fungal consortium. In some embodiments, the method of remediating soil 800 can include the at least one contaminant, the at least one contaminant including, but not limited to, a heavy metal, an organic molecule, an inorganic molecule, a pharmaceutical, a nutrient, a plastic, a sediment, a radioactive molecule, a pesticide, an herbicide, a detergent, industrial waste, agricultural runoff and / or any like contaminant.

[0166] Figure 9 illustrates an example framework for selecting a soil microbiome having increased plant productivity. Observational data, including, e.g., location-specific plant productivity data 910, location-specific soil fungal community structure data 920, and location- specific environmental covariate data 930, can be received by a machine learning model 940 (e.g., a supervised machine learning model, such as, a boosted regression tree model) that can be configured to determine a plant biomass prediction, which can include, e.g., an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site. The location-specific soil fungal community structure data 920 can include, e.g., after collecting soil microbiome biomass and sequencing nucleic acid content contained therein 921, applying a bioinformatics pipeline 922 to further process or interpret nucleic acid sequence information (e.g., removing primers or tags, enriching for specific gene regions or other markers, removing non-target sequences, etc.). The location-specific soil fungal community structure data 920, e.g., after applying a bioinformatics pipeline 922, can be further processed using a method of determining biodiversity, and optionally thereafter, dimensionally reduced 923. Next, the machine learning model 940 can be executed to generate a plant biomass prediction 950 (e.g., a tree biomass prediction). For example, the plant biomass prediction 950 can include plant height, plant diameter, and plant allometries at a geographically referenced site. The framework can also include receiving experimental data comprising productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soilAttorney Docket No.1723.2.WO2 PATENT sample, and fungal biodiversity data associated with each plant at predetermined times during plant growth. The productivity responses of plants grown ex situ relative to the particular geographically distinct plant community can include greenhouse nursery inoculation assays 960 and / or experimental field trials 970. In some instances, the plant biomass prediction 950 can inform how the greenhouse nursery inoculation assays 960 and / or experimental field trials 970 are designed and / or conducted. Data obtained from greenhouse nursery inoculation assays 960 and / or experimental field trials 970 can be further analyzed as part of a nursery inoculation statistical analysis 961, a nursery inoculation biodiversity data analysis 962, an experimental field trials statistical analysis 971, and an experimental field trials biodiversity data analysis 972. For example, the nursery inoculation statistical analysis 961 and the experimental field trials statistical analysis 971 can include using a generalized linear model to determine a cumulative growth effect size for data obtained in steps 960 and 970 (e.g., productivity responses). Next, output from the plant biomass prediction 950, greenhouse nursery inoculation assays 960 and experimental field trials 970 are combined and analyzed using a weighted rank model 980. The weighted rank model 980 can be used to determine a donor forest selection tool 990, which can be used as a decision-making tool to select an appropriate donor forest for use as a soil microbiome having increased plant productivity. EXAMPLES

[0167] The invention will now be described with reference to the following examples. It should be appreciated that these examples are not intended to limit the scope of the claims to the invention but are rather intended to be exemplary of certain embodiments. Any variations in the exemplified methods that occur to the skilled artisan are intended to fall within the scope of the invention. Example 1:

[0168] Patterns of Soil Biodiversity in Managed Timberlands: Native soil microbial communities associated with over 1,000 Loblolly Pine forests spanning from Texas to Virginia, along the Gulf and Atlantic Coast states, have been examined. To date, a total of 53,008 taxa have been identified within the soil fungal microbiome database, encompassingAttorney Docket No.1723.2.WO2 PATENT 18 phyla, 82 classes, 252 orders, 685 families, 1,737 genera, and 2,267 species. These data are summarized in Table 1.

[0169] Figure 10A-B illustrate sample locations and ectomycorrhizal fungal species diversity as a function of forest age are presented, respectively. As shown in Figure 10B, ectomycorrhizal species accumulation curves demonstrate that the number of species recovered increases as a function of sample number across a range of forests. Data show that ectomycorrhizal fungal diversity increases with forest age, as evidenced by a steeper accumulation curve.

[0170] Fungal Biodiversity Increases with Forest Age: The relationship between ectomycorrhizal fungal species richness and forest age is depicted in Figure 11, demonstrating that forest age significantly influences fungal community diversity with evidence suggesting a potential doubling of ectomycorrhizal species richness as forests mature. Figure 11 illustrates an estimated asymptotic species richness for ectomycorrhizal taxa across different forest age groups, derived from a nonlinear asymptote model fitted to mean species richness data across sites. The model was parameterized to estimate maximum species richness by randomly selecting 20 samples per group to represent mean average richness. The x-axis represents forest age groups, while the y-axis indicates estimated asymptotic species richness, with shapes distinguishing different age groups for visualization. Sampling depth refers to the number of individual observations, measurements, or data points collected from a given sample or study area, representing the thoroughness or resolution of the sampling effort. These findings underscore the importance of long-term forest management strategies in promoting biodiversity recovery and the conservation of mycorrhizal fungi in managed ecosystems.

[0171] Inoculation with donor soil significantly accelerates the rate at which fungal biodiversity can recover within managed forest systems: Figure 12 demonstrates how species richness across control plant seedlings (seedlings devoid of a donor soil inoculum), donor soil inoculated plant seedlings, and reference forest soils (positive control soils as found in nature, or “wild / native soils”). Results indicate that donor soil inoculated plant seedlings host nearly as many fungal species as reference forest soils, demonstrating that inoculating plant seedlings with a growth promoting fungal consortium identified using a donor soil inoculant facilitates soil biodiversity and accelerates plant productivity.Attorney Docket No.1723.2.WO2 PATENT Example 2

[0172] Identifying a “healthy forest microbiome”: To assess fungal biodiversity and its role in tree growth, data were collected from hundreds of forests to document tree growth rates and identify fungal species present using DNA sequencing. For each stand, additional environmental variables, including climate, stand management, and soil characteristics, were incorporated. Machine learning models were applied to integrate these data, controlling for stand factors, soils, and climate, to determine which fungal species are associated with growth promotion.

[0173] The fungal biodiversity data are highly complex, with thousands of species observed across all sampled forests. The health of the forest microbiome was assessed by calculating alpha and beta diversity metrics derived from normalized species relative abundance data obtained through eDNA surveys. These metrics provided insights into both within-sample diversity (alpha diversity) and between-sample diversity (beta diversity). To simplify and interpret this multidimensional dataset, dimensionality reduction techniques were applied (e.g., PCA, PCoA, NMDS, etc.), distilling the information into a single comprehensive metric representing overall microbiome diversity and health.

[0174] Relationship Between Fungal Biodiversity and Forest Productivity: The variation in forest productivity as a function of a key fungal biodiversity indicators, indicates that the fungal microbiome accounts for a substantial proportion of the observed variation in tree growth. Forests with higher values of the fungal biodiversity indicator exhibit increased growth rates, indicating a strong association between fungal diversity and forest productivity. Example 3

[0175] To produce a fungal inoculant containing a consortium of growth promoting fungi, a bioreactor system was designed to facilitate the cultivation and expansion of fungal microbial communities under controlled conditions. To develop a sustainable method for generating wild microbial inoculants from forest environments, an approach utilizing “in-forest bioreactors” was implemented. These bioreactors are established by burying multiple organic substrates at a depth of 10–15 cm beneath the soil surface, allowing microbial colonization over time. Organic substrates serve as feedstock for mycorrhizal fungi and included pine straw, pine mulch, peat moss, and compost.Attorney Docket No.1723.2.WO2 PATENT

[0176] This process allowed natural microbial colonization with minimal environmental disturbance. Shallow areas were excavated and then filling with organic material that included the feedstock, and subsequent left to rest to allow for colonization by local forest microbiota. After colonization, the colonized organic material was harvested as an alternative to directly extracting soil. To ensure sustainability, each batch of removed material is replaced with fresh substrate, thereby maintaining mass balance within the site.

[0177] The concept parallels industrial bioreactors, which are engineered to optimize microbial growth by controlling parameters such as temperature, pH, oxygen levels, and nutrient availability. While industrial bioreactors are effective for cultivating specific microbial strains, they have demonstrated limitations in supporting the growth of diverse, ecologically relevant microbial communities. Research has shown that donor forests naturally support and replicate biodiverse microbial communities, making them suitable environments for in situ microbial propagation.

[0178] Rather than supplying a bioreactor with an external feedstock, organic substrates are introduced into the forest floor, where they are colonized over time by soil microbial communities. Our data indicate that tree roots and native soil fungi will colonize these materials over a period of months, producing an enriched inoculant that can be harvested for application in reforestation efforts.

[0179] Pilot studies have identified multiple organic substrates capable of capturing soil microorganisms and enhancing seedling growth when applied as inoculants. Among the tested materials, longleaf pine straw, a compost-manure mixture, and brown wood mulch were shown to be the most effective as feedstocks. Fungal composition analyses revealed differences in both the structure and function of microbial communities colonizing each substrate. This variation is analogous to how industrial bioreactors selectively cultivate different microbial strains by modifying nutrient and environmental conditions.

[0180] By establishing large-scale forest bioreactors, reliance on soil extraction is minimized, allowing for the collection of forest-derived microbial feedstocks instead. The harvested material is continuously replenished with fresh organic substrate, ensuring the sustainability of inoculant production and maintaining ecological balance at the site. Example 4Attorney Docket No.1723.2.WO2 PATENT

[0181] The example framework for selecting a soil microbiome having increased plant productivity as shown in Figure 9 was used to identify and select candidate donor forests harboring wild, growth-promoting fungi. The efficacy of microbial communities from these donor forests as inoculants was demonstrated through rigorous field trials conducted under real forestry conditions. Each trial was set up as a block-randomized controlled trial, analogous to randomized controlled trials (RCTs) as shown in Figure 13E.

[0182] Field trials consisted of four treatments: three microbial community inoculants selected by the example framework shown in Figure 9, and one control. Each trial encompassed approximately two acres, containing about 1,000-1,200 commercially sourced seedlings. The trial designs were validated through statistical power analyses and approved by the NC State Forest Productivity Cooperative.

[0183] Fifteen field trials have been established across the southern US loblolly pine region as shown in Figure 13D. Data from three, one-year-old field trials are presented below, highlighting only the best-performing fungal community treatments.

[0184] Field Trial 1 (Northern Georgia Piedmont): Tree survival exceeded 90% across all treatments, showing no significant differences. However, the top-performing fungal inoculant produced approximately 60% greater tree volume than controls. Projected gains at the end of a rotation are approximately 3,249 cubic feet of additional wood per acre and 81 additional tons of Carbon Dioxide Equivalent (CO2e) per acre. Results are illustrated as shown in Figure 13A.

[0185] Field Trial 2 (Southern Georgia Coastal Plain): Survival rates also exceeded 90% without detectable differences among treatments. The best fungal inoculant yielded approximately 30% greater tree volume than controls. This translates to approximately 2,881 additional cubic feet of wood per acre and 72 additional tons of CO2e per acre by rotation end. Results are illustrated as shown in Figure 13B.

[0186] Field Trial 3 (Southern North Carolina Coastal Plain): This trial, independently measured by the NC State Forest Productivity Cooperative, experienced substantial flooding, causing an overall survival rate of 67%. The highest-performing fungal inoculant achieved an 80% survival rate compared to a 60% survival rate for controls. Although aboveground volume differences were not observed in the first year, increased survival rates imply significant long-Attorney Docket No.1723.2.WO2 PATENT term benefits, potentially determining the viability of a planting. Results are illustrated as shown in Figure 13C.

[0187] Conclusion: Across all trials, Funga’s microbiome restoration approach substantially increased tree growth or survival rates, comparable to genetic improvement or forest fertilization methods known in the art. These findings align with global studies documented in scientific literature, validating the efficacy of microbial inoculants for enhanced forestry performance. Table 1: List of ectomycorrhizal taxa from top 10 growth performing forests.Attorney Docket No.1723.2.WO2 PATENT

[0188] Various modifications to the implementations described in this disclosure may be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the implementations shown herein, but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein. Additionally, a person having ordinary skill in the art will readily appreciate, the terms “upper” and “lower” are sometimes used for ease of describing the figures, and indicate relative positions corresponding to the orientation of the figure on a properly oriented page, and may not reflect the proper orientation of a feature as implemented.

[0189] While certain embodiments have been described, these embodiments have been presented by way of example only and are not intended to limit the scope of the disclosure. Indeed, the novel methods and systems described herein may be embodied in a variety of other forms. Furthermore, various omissions, substitutions and changes in the systems and methods described herein may be made without departing from the spirit of the disclosure. The accompanying claims and their equivalents are intended to cover such forms or modifications as would fall within the scope and spirit of the disclosure.

[0190] Features, materials, characteristics, or groups described in conjunction with a particular aspect, embodiment, or example are to be understood to be applicable to any other aspect, embodiment or example described in this section or elsewhere in this specification unless incompatible therewith. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. The protection is not restricted to theAttorney Docket No.1723.2.WO2 PATENT details of any foregoing embodiments. The protection extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

[0191] The word “exemplary” is used herein to mean “serving as an example, instance, or illustration.” Any aspect or embodiment described herein as “exemplary” is not necessarily to be construed as preferred or advantageous over other aspects or embodiments. Various aspects of the novel systems and methods are described more fully hereinafter with reference to the accompanying drawings. This disclosure may, however, be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. Rather, these aspects are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art. Based on the teachings herein one skilled in the art should appreciate that the scope of the disclosure is intended to cover any aspect of the novel systems and methods disclosed herein, whether implemented independently of, or combined with, any other aspect described. For example, a method may be practiced using any number of the aspects set forth herein. In addition, the scope of the disclosure is intended to cover such a method which is practiced using other structure, functionality, or structure and functionality in addition to or other than the various aspects of the disclosures set forth herein. It should be understood that any aspect disclosed herein may be embodied by one or more elements of a claim.

[0192] Furthermore, certain features that are described in this disclosure in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Although features may be described above as acting in certain combinations, one or more features from a claimed combination can, in some cases, be excised from the combination, and the combination may be claimed as a subcombination or variation of a subcombination.

[0193] The features and attributes of the specific embodiments disclosed above may be combined in different ways to form additional embodiments, all of which fall within the scope of the present invention. Also, the separation of various system components in the implementations described above should not be understood as requiring such separation in allAttorney Docket No.1723.2.WO2 PATENT implementations, and it should be understood that the described components and systems can generally be integrated together in a single product or packaged into multiple products.

[0194] Moreover, while operations may be depicted in the drawings or described in the specification in a particular order, such operations need not be performed in the particular order shown or in sequential order, or that all operations be performed, to achieve desirable results. Other operations that are not depicted or described can be incorporated in the example methods and processes. For example, one or more additional operations can be performed before, after, simultaneously, or between any of the described operations. Further, the operations may be rearranged or reordered in other implementations. Those skilled in the art will appreciate that in some embodiments, the actual steps taken in the processes illustrated and / or disclosed may differ from those shown in the figures. Depending on the embodiment, certain of the steps described above may be removed, others may be added. Furthermore, the features and attributes of the specific embodiments disclosed above may be combined in different ways to form additional embodiments, all of which fall within the scope of the present invention.

[0195] For purposes of this disclosure, certain aspects, advantages, and novel features are described herein. Not necessarily all such advantages may be achieved in accordance with any particular embodiment. Thus, for example, those skilled in the art will recognize that the disclosure may be embodied or carried out in a manner that achieves one advantage or a group of advantages as taught herein without necessarily achieving other advantages as may be taught or suggested herein.

[0196] Conditional language, such as “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements, and / or steps. Thus, such conditional language is not generally intended to imply that features, elements, and / or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and / or steps are included or are to be performed in any particular embodiment.

[0197] Conjunctive language such as the phrase “at least one of X, Y, and Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z. Thus, such conjunctive language isAttorney Docket No.1723.2.WO2 PATENT not generally intended to imply that certain embodiments require the presence of at least one of X, at least one of Y, and at least one of Z. Thus, as used herein, a phrase referring to “at least one of X, Y, and Z” is intended to cover: X, Y, Z, X and Y, X and Z, Y and Z, and X, Y and Z.

[0198] The headings provided herein, if any, are for convenience only and do not necessarily affect the scope or meaning of the devices and methods disclosed herein.

[0199] Language of degree used herein, such as the terms “approximately,” “about,” “generally,” and “substantially” as used herein represent a value, amount, or characteristic close to the stated value, amount, or characteristic that still performs a desired function or achieves a desired result. For example, the terms “approximately”, “about”, “generally,” and “substantially” may refer to an amount that is within less than 10% of, within less than 5% of, within less than 1% of, within less than 0.1% of, and within less than 0.01% of the stated amount.

[0200] The scope of the present invention is not intended to be limited by the specific disclosures of embodiments in this section or elsewhere in this specification and may be defined by claims as presented in this section or elsewhere in this specification or as presented in the future. The language of the claims is to be interpreted broadly based on the language employed in the claims and not limited to the examples described in the present specification or during the prosecution of the application, which examples are to be construed as non- exclusive.

[0201] Any and all cited references are incorporated by reference herein in their entirety.

Claims

Attorney Docket No.1723.2.WO2 PATENT1. An apparatus for selecting a soil microbiome for increasing plant productivity, comprising a non-transitory computer-readable medium configured to store processor- executable instructions that, when executed by one or more processors, cause the one or more processors to perform operations, comprising: receiving observational data that is used to train a machine learning model, wherein the observational data comprise location-specific soil fungal community structure data, plant productivity data and environmental covariate data, wherein the observational data are collected from a plurality of geographically distinct plant communities; using the machine learning model, determining a plant biomass prediction comprising an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site; receiving experimental data comprising (i) productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soil sample, and (ii) fungal biodiversity data associated with each plant at predetermined times during plant growth; using a generalized linear model, determining a cumulative growth effect size for each of the productivity responses; using a distance-based multivariate model, determining differences between pairwise comparisons of fungal biodiversity data associated with each plant at each of the predetermined times; and combining an output from the machine learning model, the generalized linear model, and the distance-based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

2. The apparatus for selecting a soil microbiome of claim 1, wherein the location-specific fungal community structure data comprise: nucleic acid sequences, operational taxonomic unit (OTU) tables, taxonomic information, guild composition, microbiome diversity, and / orAttorney Docket No.1723.2.WO2 PATENT functional characteristics of fungal biomass collected from each of the geographically distinct plant communities.

3. The apparatus for selecting a soil microbiome of any one of claims 1-2, wherein the location-specific plant productivity data comprise: plant height, plant diameter, plant biomass, plant survival, age of the geographically distinct plant community, and / or plant density collected from each of the geographically distinct plant communities.

4. The apparatus for selecting a soil microbiome of any one of claims 1-3, wherein the location-specific environmental covariate data comprise: soil composition, contemporary climate data, and / or future climate projection data collected from each of the geographically distinct plant communities.

5. The apparatus for selecting a soil microbiome of any one of claims 1-4, wherein the productivity responses comprise: plant height, plant root collar diameter, plant rate of height change, biomass accumulation, leaf area index (LAI), growth rate, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate and / or plant cone volume collected from the plants grown ex situ relative to the particular geographically distinct plant community.

6. The apparatus for selecting a soil microbiome of any one of claims 1-5, wherein the soil sample comprises native soil harvested from the particular geographically distinct plant community.

7. The apparatus for selecting a soil microbiome of any one of claims 1-6wherein the soil sample comprises a slurry comprising a volume of water mixed with soil harvested from the particular geographically distinct plant community.

8. The apparatus for selecting a soil microbiome of any one of claims 1-7, wherein the predetermined times are from weekly to annually.Attorney Docket No.1723.2.WO2 PATENT 9. The apparatus for selecting a soil microbiome of any one of claims 1-8, wherein ex situ relative to the particular geographically distinct plant community comprises growing the plants in a greenhouse, nursery, and / or non-natural plantation.

10. A system for generating a fungal inoculant for increasing plant productivity, comprising: a forest bioreactor comprising a feedstock; and the apparatus according to any one of claims 1-9.

11. A computer-implemented method for selecting a soil microbiome having increasing plant productivity, comprising: receiving, by one or more processors, observational data that is used to train a machine learning model, wherein the observational data comprise location-specific soil fungal community structure data, plant productivity data and environmental covariate data, wherein the observational data are collected from a plurality of geographically distinct plant communities; using the machine learning model, determining via the one or more processors, a plant biomass prediction comprising an estimate of plant height, plant diameter, and plant allometries at a geographically referenced site; receiving, by the one or more processors, experimental data comprising (i) productivity responses of plants grown ex situ relative to the particular geographically distinct plant community, wherein each plant is inoculated with an individual soil sample harvested from a particular geographically distinct plant community such that each plant is inoculated with a different soil sample, and (ii) fungal biodiversity data associated with each plant at predetermined times during plant growth; using a generalized linear model, determining via the one or more processors, a cumulative growth effect size for each of the productivity responses; using a distance-based multivariate model, determining differences between pairwise comparisons of fungal biodiversity data associated with each plant at each of the predetermined times; andAttorney Docket No.1723.2.WO2 PATENT combining via the one or more processors, an output from the machine learning model, the generalized linear model, and the distance-based multivariate model to execute a donor forest selection tool that is configured to select from the plurality of geographically distinct plant communities a soil microbiome having increasing plant productivity.

12. The computer-implemented method of claim 11, further comprising sampling the rhizosphere fungal biodiversity of each plant at the predetermined times during plant growth, wherein sampling comprises sampling at the predetermined times of from each week to each year.

13. The computer-implemented method of any one of claims 11-12, wherein ex situ relative to the particular geographically distinct plant community comprises growing the plants in a greenhouse, nursery, and / or non-natural plantation.

14. The computer-implemented method of any one of claims 11-13, wherein the location-specific fungal community structure data comprise: nucleic acid sequences, operational taxonomic unit (OTU) tables, taxonomic information, guild composition, microbiome diversity, and / or functional characteristics of fungal biomass collected from each of the geographically distinct plant communities; wherein the location-specific plant productivity data comprise: plant height, plant diameter, plant biomass, plant survival, age of the geographically distinct plant community, and / or plant density collected from each of the geographically distinct plant communities; and wherein the location-specific environmental covariate data comprise: soil composition, contemporary climate data, and / or future climate projection data collected from each of the geographically distinct plant communities.

15. The computer-implemented method of any one of claims 11-14, wherein the productivity responses comprise: plant height, plant root collar diameter, plant rate of height change, biomass accumulation, leaf area index (LAI), growth rate, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate and / or plant cone volumeAttorney Docket No.1723.2.WO2 PATENT collected from the plants grown ex situ relative to the particular geographically distinct plant community.

16. The computer-implemented method of any one of claims 11-15, further comprising harvesting the soil sample from the particular geographically distinct plant community and inoculating each of the plants grown ex situ relative to the particular geographically distinct plant community.

17. The computer-implemented method of any one of claims 11-16, further comprising mixing the soil sample with a volume of water to form a slurry, wherein each of the plants grown ex situ relative to the particular geographically distinct plant community are inoculated with the slurry.

18. A method of producing a fungal inoculant, comprising: applying the computer-implemented method of any one of claims 11-17, wherein the donor forest selection tool is used to select the soil microbiome having increasing plant productivity; and incubating the soil microbiome having increasing plant productivity in a forest bioreactor to produce the fungal inoculum.

19. The method of any one of claims 11-18, further comprising adding a volume of water to the fungal inoculum to produce an inoculant slurry.

20. The method of any one of claims 11-19, wherein the forest bioreactor is configured to enrich for fungal taxa that increase productivity of a plant relative to a control plant devoid of a fungal inoculum.

21. A method of accelerating plant productivity and atmospheric carbon sequestration, comprising: identifying a growth-promoting fungal consortium from a natural fungal microbiome, comprising:Attorney Docket No.1723.2.WO2 PATENT providing at least one sampling kit to a subject at a geographic location, the at least one sampling kit comprising a sample container configured to receive a soil sample from the geographic location; receiving the at least one sampling kit, including the soil sample, from the geographic location; extracting nucleic acid material from a first portion of the soil sample; generating a fungal microbiome dataset based on sequencing the nucleic acid material present in the first portion of the soil sample, wherein sequencing further comprises a plurality of reagents that enrich for fungal-derived nucleic acids; providing a machine learning tool, wherein the machine learning tool comprises a training database, the training database comprising biotic and abiotic data associated with a plurality of high productivity ecosystems; and inputting the fungal microbiome dataset into machine learning tool, whereby the machine learning tool identifies the growth-promoting fungal consortium comprising a subset of fungal species present in the first portion of the soil sample and associated with the plurality of high productivity ecosystems; propagating the growth-promoting fungal consortium, comprising: providing a second portion of the soil sample to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promoting fungal consortium to reproduce and outcompete other organisms present in the second portion of the soil sample; colonizing the feedstock with fungal species or strains comprising the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum comprising the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; and inoculating a plurality of plants present at the geographic location with the inoculum slurry; andAttorney Docket No.1723.2.WO2 PATENT monitoring productivity of each of the plurality of plants after each of the plurality of plants has been inoculated with the inoculum slurry, wherein monitoring comprises utilizing a plurality of sensors.

22. The method of claim 21, wherein the growth-promoting fungal consortium comprises a plurality of fungal species or strains native to the geographic location; and wherein the geographic location is a natural ecosystem.

23. The method of any one of claims 21-22, wherein monitoring comprises observing.

24. The method of any one of claims 21-23, wherein plant productivity comprises metrics selected from the group consisting of biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate.

25. The method of any one of claims 21-24, wherein the plurality of sensors comprises at least a plant growth rate sensor and a plant photosynthesis rate sensor.

26. The method of claims 25, wherein a plant photosynthesis rate sensor comprises a tool for measuring a rate of atmospheric carbon sequestration.

27. The method of any one of claims 21-26, further comprising sequencing utilizing the plurality of reagents that enrich for fungal-derived nucleic acids, wherein the plurality of reagents comprises at least a plurality of primers targeting an Internal Transcribed Spacer genomic region (ITS), an ITS1 genomic region, an ITS2 genomic region, a Large Subunit rRNA (LSU) genomic region, a small subunit rRNA (SSU) genomic region, an 18S genomic region, a Translation Elongation Factor 1-alpha (TEF1-alpha) genomic region, a Beta-Tubulin -tubulin) genomic region, an RNA Polymerase II (RPB1 or RPB2) genomic region, or a Calmodulin (CaM) genomic region.Attorney Docket No.1723.2.WO2 PATENT 28. The method of any one of claims 21-27, wherein each of the plurality of high productivity ecosystems comprises an ecosystem comprising photosynthesizing organisms with a high rate of atmospheric carbon sequestration or biomass production.

29. The method of any one of claims 21-28, wherein the forest bioreactor is a sealed environment or an unsealed environment, further comprising the optimal environment comprised of a temperature, an oxygen content, a salinity, or a pH.

30. The method of any one of claims 21-29, wherein propagating comprises sexual and asexual reproduction of each fungal species or strain comprising the growth-promoting fungal consortium.

31. The method of any one of claims 21-30, wherein each of the plurality of plants comprises a tree.

32. A system for accelerating plant productivity and atmospheric carbon sequestration, comprising: at least one sampling kit, wherein the at least one sampling kit is configured to be sent to and from a geographic location and comprises a sample container, the sample container configured to receive a soil sample; a soil sample processing system configured to extract nucleic acid material from a first portion of the soil sample; a nucleic acid sequencing platform configured to sequence the nucleic acid material present in the first portion of the soil sample and configured to generate a fungal microbiome dataset, wherein the nucleic acid sequencing platform further comprises a plurality of reagents adapted to enrich for fungal-derived nucleic acids; a machine learning tool, wherein the machine learning tool comprises a training database, the training database comprised of biotic and abiotic data associated with a plurality of high productivity ecosystems, and wherein the machine learning tool is configured to identify a growth-promoting fungal consortium comprising a subset of fungal species present in the first portion of the soil sample and associated with the plurality of high productivity ecosystems;Attorney Docket No.1723.2.WO2 PATENT a forest bioreactor configured to receive a second portion of the soil sample and to propagate the growth-promoting fungal consortium, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are adapted to promote colonization of the feedstock by the growth-promoting fungal consortium such as to outcompete other organisms present in the soil sample; an inoculum slurry comprising a mixture of water and a growth-promoting fungal consortium inoculum, wherein the growth-promoting fungal consortium inoculum comprises the feedstock substantially colonized by the growth-promoting fungal consortium; a plurality of plants, wherein each of the plurality of plants is inoculated with the inoculum slurry; and a plurality of sensors configured to monitor plant productivity of each of the plurality of plants inoculated with the inoculum slurry.

33. The system of claim 32, wherein the growth-promoting fungal consortium comprises a plurality of fungal species or strains native to the geographic location.

34. The system of any one of claims 31-33, wherein the geographic location is a natural ecosystem.

35. The system of any one of claims 31-34, wherein the system is configured to maximize plant productivity utilizing metrics selected from the group consisting of biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate.

36. The system of any one of claims 31-35, wherein the plurality of sensors comprises at least one of a plant growth rate sensor and a plant photosynthesis rate sensor.

37. The system of any one of claims 31-36, wherein a plant photosynthesis rate sensor comprises a tool for measuring a rate of atmospheric carbon sequestration.Attorney Docket No.1723.2.WO2 PATENT 38. The system of any one of claims 31-37, wherein the plurality of reagents adapted to enrich for fungal-derived nucleic acids comprises at least a plurality of primers configured to target an Internal Transcribed Spacer genomic region (ITS), an ITS1 genomic region, an ITS2 genomic region, a Large Subunit rRNA (LSU) genomic region, a small subunit rRNA (SSU) genomic region, an 18S genomic region, a Translation Elongation Factor 1-alpha (TEF1-alpha)genomic region, a Beta- -tubulin) genomic region, an RNA Polymerase II (RPB1 orRPB2) genomic region, or a Calmodulin (CaM) genomic region.

39. The system of any one of claims 31-38, wherein each of the plurality of high productivity ecosystems comprises an ecosystem comprising photosynthesizing organisms with a high rate of atmospheric carbon sequestration or biomass production.

40. The system of any one of claims 31-39, wherein the forest bioreactor is a sealed environment or an unsealed environment, further comprising the optimal environment comprised of a temperature, an oxygen content, a salinity, or a pH.

41. The system of any one of claims 31-40, wherein colonization of the feedstock comprises sexual reproduction and asexual reproduction of each fungal species or strain comprising the growth-promoting fungal consortium.

42. The system of any one of claims 31-41, wherein each of the plurality of plants comprises a tree.

43. A method of producing a fungal inoculum, comprising: providing a forest bioreactor; isolating a growth-promoting fungal consortium; providing the growth-promoting fungal consortium to the forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promoting fungal consortium to grow and to reproduce;Attorney Docket No.1723.2.WO2 PATENT colonizing the feedstock with fungal species or strains comprising the growth- promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum comprising the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; and harvesting the inoculum slurry.

44. The method of claim 43, wherein the growth-promoting fungal consortium comprises a plurality of fungal species or fungal strains native to a geographic location.

45. The method of any one of claims 43-44, wherein the geographic location is a natural ecosystem.

46. The method of any one of claims 43-45, wherein the geographic location comprises a high productivity ecosystem, wherein the high productivity ecosystem comprises photosynthesizing organisms with a high rate of atmospheric carbon sequestration or biomass production.

47. The method of any one of claims 43-46, wherein the growth-promoting fungal consortium is adapted to maximize plant productivity utilizing metrics selected from the group consisting of biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate.

48. The method of any one of claims 43-47, wherein the growth-promoting fungal consortium is identified using a plurality of reagents adapted to enrich for fungal-derived nucleic acids, the plurality of reagents comprising at least a plurality of primers configured to target an Internal Transcribed Spacer genomic region (ITS), an ITS1 genomic region, an ITS2 genomic region, a Large Subunit rRNA (LSU) genomic region, a small subunit rRNA (SSU) genomic region, an 18S genomic region, a Translation Elongation Factor 1-alpha (TEF1-alpha)Attorney Docket No.1723.2.WO2 PATENTgenomic region, a Beta- -tubulin) genomic region, an RNA Polymerase II (RPB1 orRPB2) genomic region, or a Calmodulin (CaM) genomic region.

49. The method of any one of claims 43-48, wherein the forest bioreactor is a sealed environment or an unsealed environment, further comprising the optimal environment comprising a temperature, an oxygen content, a salinity, or a pH.

50. The method of any one of claims 43-49, wherein colonizing the feedstock comprises sexual reproduction and asexual reproduction of each fungal species or strain comprising the growth-promoting fungal consortium.

51. The method of any one of claims 43-50, wherein the growth-promoting fungal consortium inoculum is applied to at least one plant.

52. The method of any one of claims 43-51, wherein the at least one plant is a tree.

53. A method of increasing plant productivity with reduced plant fertilizer utilization, comprising: isolating a growth-promoting fungal consortium; providing the growth-promoting fungal consortium to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promoting fungal consortium to grow and to reproduce; colonizing the feedstock with fungal species or strains comprising the growth- promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum comprising the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; and inoculating a plurality of plants with the inoculum slurry,Attorney Docket No.1723.2.WO2 PATENT wherein the fungal species or strains comprising the growth-promoting fungal consortium enhances nutrient bioavailability without need for chemical fertilizers.

54. The method of claim 53, wherein the growth-promoting fungal consortium comprises a plurality of fungal species or strains native to a geographic location.

55. The method of any one of claims 53-54, wherein the geographic location is a natural ecosystem.

56. The method of any one of claims 53-55, wherein plant productivity comprises metrics selected from the group consisting of biomass accumulation, leaf area index (LAI), growth rate, plant height, yield, net assimilation rate, water use efficiency, nutrient use efficiency, and photosynthesis rate.

57. The method of any one of claims 53-56, further comprising harvesting the growth- promoting fungal consortium from a high productivity ecosystem, the high productivity ecosystem comprising a plurality of photosynthesizing organisms with a high rate of atmospheric carbon sequestration or biomass production.

58. The method of any one of claims 53-57, wherein each of the plurality of plants comprises a tree.

59. A method of generating biodiversity credits, comprising: isolating a growth-promoting fungal consortium, wherein the growth-promoting fungal consortium comprises native fungal species or strains; providing the growth-promoting fungal consortium to a forest bioreactor, the forest bioreactor configured to provide a feedstock and an optimal environment, wherein the feedstock and the optimal environment are selected to cause the growth-promoting fungal consortium to grow and to reproduce;Attorney Docket No.1723.2.WO2 PATENT colonizing the feedstock with the growth-promoting fungal consortium for a period of time sufficient to create a growth-promoting fungal consortium inoculum comprising the feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; inoculating a plurality of plants with the inoculum slurry; and establishing a community of the native fungal species or strains comprising the growth- promoting fungal consortium, in symbiosis with the plurality of plants, wherein the biodiversity credits increase in positive correlation to a diversity of the community of the native fungal species or strains.

60. A method of improving water quality, comprising: inoculating a plurality of plants with an inoculum slurry, wherein the inoculum slurry comprises a mixture of water and a growth-promoting fungal consortium inoculum comprising a feedstock and a growth-promoting fungal consortium; and establishing a community of native fungal species or strains , the native fungal species or strains being in symbiosis with the plurality of plants, wherein the growth-promoting fungal consortium inoculum comprises the native fungal species or strains, and wherein the native fungal species or strains are adapted to filter contaminants from a volume of water, thereby improving quality of the volume of water.

61. The method of claim 60, wherein the contaminants comprise at least a heavy metal, an organic molecule, an inorganic molecule, a pharmaceutical, a nutrient, a plastic, a sediment, a radioactive molecule, a pesticide, an herbicide, a detergent, industrial waste, or agricultural runoff.

62. The method of any one of claims 60-61, wherein the volume of water is a naturally occurring volume of water or an artificially formed volume of water.

63. A method of remediating soil, comprising:Attorney Docket No.1723.2.WO2 PATENT isolating a growth-promoting fungal consortium; forming a growth-promoting fungal consortium inoculum comprising a feedstock and the growth-promoting fungal consortium; mixing the growth-promoting fungal consortium inoculum with water to form an inoculum slurry; harvesting the inoculum slurry; inoculating a volume of soil with the inoculum slurry; and maturing the inoculum slurry within the volume of soil such as to substantially colonize the volume of soil with the growth-promoting fungal consortium.

64. The method of claims 63, wherein the volume of soil is selected from the group consisting of in situ soil, ex situ soil, translocated soil, and artificial soil.

65. The method of any one of claims 63-64, wherein the volume of soil comprises nutritive materials, the nutritive materials providing a nutritive substrate for the growth-promoting fungal consortium to grow and substantially colonize the volume of soil.

66. The method of any one of claims 63-65, wherein the volume of soil contains at least one contaminant.

67. The method of any one of claims 63-66, wherein the at least one contaminant is substantially removed from the volume of soil and absorbed by at least one fungus comprising the growth-promoting fungal consortium.

68. The method of any one of claims 63-67, wherein the at least one contaminant comprises a heavy metal, an organic molecule, an inorganic molecule, a pharmaceutical, a nutrient, a plastic, a sediment, a radioactive molecule, a pesticide, an herbicide, a detergent, industrial waste, or agricultural runoff.

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