Method for predicting and increasing agricultural yield and bacterial or fungal compositions for improving the soil

Abstract

The present invention relates to a method for predicting agricultural output by determining the percentage of a taxon of a bacterial genus or family, e.g. Hyphomicrobium or Hyphomicrobiceae and / or a fungal species in the soil using 16S sequencing, e.g. in green maize and grass feed. Also claimed is a method of improving the yield by adding a bacterial species and / or fungal species, e.g.Keithomyces, to the soil and compositions comprising the bacterial and / or fungal species.

Description

[0001] METHOD FOR PREDICTING AND INCREASING AGRICULTURAL YIELD AND BACTERIAL OR FUNGAL COMPOSITIONS FOR IMPROVING THE SOIL

[0002] The present invention relates to a method for predicting agricultural output.

[0003] BACKGROUND OF THE INVENTION

[0004] It is assumed that feeding the world's population will face problems in the coming decades due to at least two factors: a growing world population on the one hand and the negative effects of climate change on soil fertility on the other. Therefore, it is elementary to optimize yield on agricultural soils through intelligent management without compromising soil fertility. Soil fertility can be viewed as a function of both physical and chemical, as well as biological parameters.

[0005] In the past, the study of bacteria from a specific habitat presupposed the cultivation in a laboratory. It was then possible to analyze the grown bacterial cultures more precisely and identify them With the development of molecular genetic methods, these laborious procedures are no longer necessary. Today, they are largely replaced by so-called metagenomic methods. With the appearance of high-throughput sequencing technologies, it is possible to determine a significant portion of (micro)biological parameters with a single measurement.

[0006] Therefore, the development of microbiome-based yield prediction would be a promising method to cost-effectively and less laborious optimize yield through targeted field and fruit selection.

[0007] The present inventors have identified microbiome signatures using metagenomic methods which allow the prediction of agricultural output.

[0008] Specifically, the present inventors have extracted bacterial DNA directly from soil samples taken from different agricultural areas. In these different agricultural areas, the inventors also recorded the maize corn yield. The soil samples taken from the different agricultural areas were subsequently sequenced. From the resulting DNA pattern, the present inventors could read which bacteria were contained in the soil sample and associated them with the maize com yield.

[0009] The gene of choice for the metagenomic studies of the present inventors is the so-called 16S rRNA gene, as this gene is ubiquitous in bacteria. It contains some conserved DNA regions that are highly similar in all bacterial taxa, and also contains variable DNA sequences that have been significantly modified in the course of evolution, making it possible to identify the bacteria by sequence diversity.

[0010] Particularly, the present inventors first amplified a part of the 16S rRNA gene from the soil sample. Then, the sequence of individual clones was determined by next-generation sequencing. The 16S rRNA gene sequences, thus, obtained from a sample were then compared with a database to determine the taxonomic affiliation of the bacteria and their abundance in the soil sample. In addition to the 16S rRNA gene sequences of bacteria, the internal transcribed spacer (ITS) of fungi was detected. The abundance of the bacteria and fungi was then correlated with the maize com yield.

[0011] The LASSO models created by the present inventors in this process were able to describe 65% (16S V4), 58% (ITS), and 70% (equally weighted averaging of 16S V4 and ITS) of the yield variance in an exemplary maize corn field. Significant correlations between predicted and observed yields were also obtained for external / published datasets, by utilizing 16S rRNA gene abundances.

[0012] The present inventors could, thus, show that the determined microbiome signatures allow prediction of agricultural output.

[0013] SUMMARY OF THE INVENTION

[0014] In a first aspect, the present invention relates to a method for predicting agricultural output comprising the steps of:

[0015] (i) determining the relative percentage of

[0016] (a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, Candidatus Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Methylorubrum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae,

[0017] (b) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericell opsis, Keithomyces, and Knufia, or (c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspir ilium, Candidatus Solibacter Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia in a soil sample of an agricultural or a prospective agricultural area, and

[0018] (ii) predicting agricultural output on the basis of the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib), or the at least one bacterial taxon and the at least one fungal taxon of (ic) determined in step (i).

[0019] In a second aspect, the present invention relates a (therapeutic) composition comprising

[0020] (a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, JGI 0001001-H03 ', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, and of the genus Lapillicoccus,

[0021] (b) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces, or

[0022] (c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, JGI 0001001-H03 ', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces.

[0023] In a third aspect, the present invention relates to the use of the composition according to the second aspect to increase agricultural output. In a fourth aspect, the present invention relates to the use of a composition comprising phosphates and manganese to stimulate the propagation of a bacterial taxon of the genus Hyphomicrobium .

[0024] In a fifth aspect, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0025] (i) carrying out the method according to the first aspect,

[0026] (ii) applying the composition according to the second aspect onto the (prospective) agricultural area, and

[0027] (iii) planting or cultivating agricultural plants onto the agricultural area.

[0028] In a sixth aspect, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0029] (i) applying the composition according to the second aspect onto an (a prospective) agricultural area, and

[0030] (ii) planting or cultivating agricultural plants onto the agricultural area.

[0031] In a seventh aspect, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0032] (i) applying the composition according to the second aspect onto agricultural plant seeds, thereby coating the agricultural plant seeds with the composition,

[0033] (ii) sowing the coated agricultural plant seeds into the (prospective) agricultural area, and

[0034] (iii) cultivating agricultural plants derived therefrom onto the agricultural area.

[0035] This summary of the invention does not necessarily describe all features of the present invention. Other embodiments will become apparent from a review of the ensuing detailed description.

[0036] DETAILED DESCRIPTION OF THE INVENTION

[0037] Definitions

[0038] Before the present invention is described in detail below, it is to be understood that this invention is not limited to the particular methodology, protocols and reagents described herein as these may vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention which will be limited only by the appended claims. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. Preferably, the terms used herein are defined as described in “A multilingual glossary of biotechnological terms: (IUPAC Recommendations)”, Leuenberger, H.G.W, Nagel, B. and Kolbl, H. eds. (1995), Helvetica Chimica Acta, CH-4010 Basel, Switzerland).

[0039] Several documents are cited throughout the text of this specification. Each of the documents cited herein (including all patents, patent applications, scientific publications, manufacturer's specifications, instructions, GenBank Accession Number sequence submissions etc ), whether supra or infra, is hereby incorporated by reference in its entirety. Nothing herein is to be construed as an admission that the invention is not entitled to antedate such disclosure by virtue of prior invention. In the event of a conflict between the definitions or teachings of such incorporated references and definitions or teachings recited in the present specification, the text of the present specification takes precedence.

[0040] The term “comprise” or variations such as “comprises” or “comprising” according to the present invention means the inclusion of a stated integer or group of integers but not the exclusion of any other integer or group of integers. The term “consisting essentially of’ according to the present invention means the inclusion of a stated integer or group of integers, while excluding modifications or other integers which would materially affect or alter the stated integer. The term “consisting of’ or variations such as “consists of’ according to the present invention means the inclusion of a stated integer or group of integers and the exclusion of any other integer or group of integers.

[0041] The terms “a” and “an” and “the” and similar reference used in the context of describing the invention (especially in the context of the claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context.

[0042] As used herein, the term “about” indicates a certain variation from the quantitative value it precedes. In particular, the term “about” allows a ±5% variation from the quantitative value it precedes, unless otherwise indicated or inferred. The use of the term “about” also includes the specific quantitative value itself, unless explicitly stated otherwise. For example, the expression “about 80°C” allows a variation of ±4°C, thus referring to range from 76°C to 84°C.

[0043] The present inventors have identified microbiome signatures which allow the prediction of agricultural output. Thus, the present invention relates to a method for predicting agricultural output.

[0044] The term “agriculture”, as used herein, encompasses crop production.

[0045] The term “agricultural output”, as used herein, refers to an amount, a quantity, or a proportion of crops obtained from an agricultural area. While inputs in agriculture are seeds, fertilizers, machinery, labor, etc., the outputs of the farming activity are the harvested crops. Thus, agricultural output is a direct measure of the total amount, quantity, or proportion of crops produced.

[0046] The term “low agricultural output”, as used herein, refers to an output which is below the usually expected output and / or average of the last five cultivation periods in relation to the cultivated agricultural land under usual climate conditions in the respective geographical area. In one preferred embodiment, the agricultural output of maize or green fodder is predicted.

[0047] In this regard, a predicted low crop yield of maize is preferably indicative for a crop yield size of < 20 tons / hectare.

[0048] In this regard, a predicted low green fodder yield is preferably indicative for a low green fodder yield size of the respective crop.

[0049] The term “high agricultural output”, as used herein, refers to an output which is above the usually expected output and / or average of the last five cultivation periods in relation to the cultivated agricultural land under usual climate conditions in the respective geographical. In one preferred embodiment, the agricultural output of maize or green fodder is predicted.

[0050] In this regard, a predicted high crop yield of maize is preferably indicative for a crop yield size of > 35 tons / hectare.

[0051] In this regard, a predicted high green fodder yield is preferably indicative for a high green fodder yield size of the respective crop.

[0052] The term “crop yield”, as used herein, refers to the harvested production per unit of harvested area for crop products.

[0053] Crop production depends on the availability of arable land and is affected in particular by yields, macroeconomic uncertainty, as well as consumption patterns; it also has a great incidence on agricultural commodities' prices. The importance of crop production is related to harvested areas, returns per hectare (yields) and quantities produced. In most of the cases yield data are not recorded, but are obtained by dividing the production data by the data on area harvested. The actual yield that is captured on farm depends on several factors such as the crop's genetic potential, the amount of sunlight, water and nutrients absorbed by the crop, the presence of weeds and pests. This indicator is presented for wheat, maize, rice and soybean. Crop production is measured in tonnes per hectare, in thousand hectares and thousand tonnes.

[0054] The term “agricultural productivity”, as used herein, is the ratio of agricultural inputs to outputs. The greater the agricultural output (for a given input), the higher the agricultural productivity of a farm or farm land. In simple terms, agricultural productivity is: output input = productivity.

[0055] While individual products are usually measured by weight, which is known as crop yield, varying products make measuring overall agricultural output difficult. Therefore, agricultural productivity is usually measured as the market value of the final output. This productivity can be compared to many different types of inputs such as labour or land. Such comparisons are called partial measures of productivity.

[0056] Agricultural productivity may also be measured by what is termed total factor productivity (TFP). This method of calculating agricultural productivity compares an index of agricultural inputs to an index of outputs. This measure of agricultural productivity was established to remedy the shortcomings of the partial measures of productivity; notably that it is often hard to identify the factors cause them to change. Changes in TFP are usually attributed to technological improvements.

[0057] Agricultural productivity is an important component of food security. Increasing agricultural productivity through sustainable practices can be an important way to decrease the amount of land needed for farming and slow environmental degradation as well as climate change.

[0058] The term “agricultural area”, as used herein, refers to land devoted to / used for agriculture. It is land used for the production of crops. It is generally synonymous with both farmland or cropland. It is often expressed in hectares (ha). The term “agricultural area”, as used herein, also refers to an agricultural region unit of measurement used in statistics and management, especially for production indicators such as yields. The term “agricultural area” includes, but is not limited to, cropland, specifically for the production of crops, and grass land, specifically for the production of green fodder.

[0059] The term “prospective agricultural area”, as used herein, refers to land that may be devoted to / used for agriculture, e.g. if diverse preconditions such as microbiome status / composition are met.

[0060] The term “agricultural plant”, as used herein, refers to any plant, or part thereof, grown, maintained, or otherwise produced for commercial purposes, including growing, maintaining or otherwise producing plants for sale or trade, for research or experimental purposes, or for use in part or their entirety in another location. In a preferred embodiment, the agricultural plant is maize or grass (e.g. used as green fodder).

[0061] The term “plant seed”, as used herein, refers to an embryonic plant enclosed in a protective outer covering. The formation of the seed is part of the process of reproduction in seed plants, the spermatophytes.

[0062] The term “coating”, as used herein, refers to a covering that is applied to the plant or plant seed, in particular to the surface of the plant or plant seed, to be coated. The coating itself may be an all-over coating, completely covering the plant or plant seed, or it may only cover parts of the plant or plant seed. The term “microbiome”, as used herein, refers to a community of microorganisms that can usually be found living together in a given habitat, e.g. in an agricultural area.

[0063] The term “soil sample”, as used herein, refers to a sample collected in a representative location of an (a prospective) agricultural area. A soil sample may be taken using ASTM El 727, “Standard Practice for Field Collection of Soil Samples for Lead Determination by Atomic Spectrometry Techniques”, or equivalent method.

[0064] The term “soil sampling”, as used herein, refers to a process of extracting a small volume of soil for subsequent analysis at a lab. Soil testing is an essential component of soil resource management. Each sample collected must be a true representative of the area being sampled. Utility of the results obtained from the laboratory analysis depends on the sampling precision. Hence, collection of large number of samples is advisable so that sample of desired size can be obtained by sub-sampling. In general, sampling is done at the rate of one sample for every two- hectare area. However, at-least one sample should be collected for a maximum area of five hectares. For soil survey work, samples are collected from a soil profile representative to the soil of the surrounding area.

[0065] In the past, the study of bacteria from a specific habitat presupposed the cultivation in a laboratory. It was then possible to analyze the grown bacterial cultures more precisely and identify them. With the development of molecular genetic methods, these laborious procedures are no longer necessary. Today, they are largely replaced by so-called metagenomic methods. With the appearance of high-throughput sequencing technologies, it is possible to determine a significant portion of (micro)biological or microbiome parameters with a single measurement.

[0066] The term “metagenomics”, as used herein, refers to a technique by which the genetic material (e.g. DNA) is extracted directly from samples taken from the environment (e.g. soil samples). The genetic material is, after multiplication / amplification, sequenced, e.g. in an approach called next generation sequencing or shotgun sequencing.

[0067] Today, bacterial DNA is extracted directly from a sample taken from its normal habitat. This sample is sequenced. From the resulting DNA pattern, researchers can read which bacteria are contained in the sample. The gene of choice for the metagenomic studies described herein is the so-called 16S ribosomal RNA (16S rRNA) gene. The term “ 16S ribosomal RNA (16S rRNA)”, as used herein, is the RNA component of the 30S subunit of prokaryotic ribosomes. The 16S rRNA gene is found in all bacteria. It contains some conserved DNA regions that are the highly similar in all organisms. The gene can be recognized by them. In addition, the 16S rRNA gene also contains variable DNA sequences that have been significantly modified in the course of evolution, making it possible to identify the bacteria. In general, at least a part of the 16S rRNA gene is first amplified from a sample. Then, either the pool of sequences, thus, propagated is cloned and the sequence of individual clones is determined, or state-of-the-art sequencing equipment is used for sequencing. The 16S rRNA sequences, thus, obtained from a sample can then be compared with a database to determine the taxonomic affiliation of the bacteria and their abundance in the sample.

[0068] The term “internal transcribed spacer (ITS)”, as used herein, refers to a spacer nucleotide (DNA / RNA) sequence situated / found between the small-subunit ribosomal RNA (rRNA) and the large-subunit rRNA genes in the chromosome or the corresponding transcribed region in the polycistronic rRNA precursor transcript.

[0069] The term “16S / ITS amplicon metagenomics sequencing”, as used herein, refers to an ultradeep sequencing method that focuses on sequencing specific target regions (amplicons). Short (<500 bp) hypervariable regions of conserved genes or intergenic regions are amplified by PCR and analyzed by next generation sequencing (NGS) technology to identify and differentiate multiple microbial taxa from complex samples. Amplicon metagenomic sequencing is designated to sequence the target genes of 16S ribosomal RNA (rRNA) and internal transcribed spacer (ITS) rRNA by universal primers, to describe and compare the phylogeny and taxonomy of bacteria (and archaea), and fungi (such as yeasts, molds and etc.), respectively. The relative percentages of the bacteria and / or fungi described herein may be determined by this technique.

[0070] The term “amplifying”, as used herein, refers to any means by which at least a part of a nucleic acid molecule described herein is reproduced, typically in a template-dependent manner, including without limitation, a broad range of techniques for amplifying nucleic acid sequences, either linearly or exponentially. Any of several methods can be used to amplify the nucleic acid molecule. Any in vitro means for multiplying the copies of a target sequence of nucleic acid can be utilized. These include linear, exponential, or other amplification methods.

[0071] Examples of amplification techniques that can be used include, but are not limited to, PCR, quantitative PCR, quantitative fluorescent PCR (QF-PCR), multiplex fluorescent PCR (MF -PCR), real time PCR (RT-PCR), single cell PCR, restriction fragment length polymorphism PCR (PCR- RFLP), hat start PCR, nested PCR, in situ polony PCR, in situ rolling circle amplification (RCA), bridge PCR, picotiter PCR, and emulsion PCR. Other suitable amplification methods include the ligase chain reaction (LCR), transcription amplification, self-sustained sequence replication, selective amplification of target polynucleotide sequences, consensus sequence primed polymerase chain reaction (CP -PCR), arbitrarily primed polymerase chain reaction (AP-PCR), degenerate oligonucleotide-primed PCR (DOP-PCR), and nucleic acid-based sequence amplification (NABSA).

[0072] In various embodiments, nucleotide sequences are sequenced. The term “sequencing”, as used herein, includes any method of determining the sequence of a nucleic acid molecule. Such methods include Maxam-Gilbert sequencing, Chain-termination methods, Shot gun sequencing, PCR sequencing, Bridge PCR, massively parallel signature sequencing (MPSS), Polony sequencing, pyrosequencing, Illumina (Solexa) sequencing, SOLiD sequencing, Ion semiconductor sequencing, DNA nanoball sequencing, Heliscope single molecule sequencing, Single molecule real time (SMRT) sequencing, Nanopore DNA sequencing, sequencing by hybridization, sequencing with mass spectrometry, microfluidic Sanger sequencing, microscopybased techniques, RNAP sequencing, high- throughput sequencing (HTS).

[0073] The term “next generation sequencing (NGS)” as used herein, refers to a new method for sequencing nucleotide sequences at high speed and at low cost. Next-generation sequencing (NGS) is, thus, a high-throughput methodology that enables rapid sequencing of the base pairs in DNA or RNA samples. Supporting a broad range of applications, including gene expression profiling, chromosome counting, detection of epigenetic changes, and molecular analysis, NGS is driving discovery and enabling the future of personalized medicine. NGS is also known as second generation sequencing (SGS) or massively parallel sequencing (MPS).

[0074] The term “sequence identity”, as used herein, refers to a measurement which allows to indicate the similarity of nucleotide and amino acid sequences. The percentage of sequence identity can be determined via sequence alignments. Such alignments can be carried out with several art-known algorithms, preferably with the mathematical algorithm of Karlin and Altschul (Karlin & Altschul (1993) Proc. Natl. Acad. Sci. USA 90: 5873-5877), with hmmalign (HMMER package) or with the CLUSTAL algorithm (Thompson, J. D., Higgins, D. G. & Gibson, T. J. (1994) Nucleic Acids Res. 22, 4673-80) or the CLUSTALW2 algorithm (Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, Me William H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG. (2007). Clustal W and Clustal X version 2.0. Bioinformatics , 23, 2947-2948).

[0075] The grade of sequence identity (sequence matching) may be calculated using e.g. BLAST, BLAT or BlastZ (or BlastX). A similar algorithm is incorporated into the BLASTN and BLASTP programs of Altschul et al. (1990) J. Mol. Biol. 215: 403-410. BLAST protein searches are performed with the BLASTP program available e.g. on the web site: http: / / blast.ncbi.nlm.nih.gov / Blast.cgi?PROGRAM=blastp&BLAST_PROGRAMS=blastp&PA GE_TYPE=BlastSearch&SHOW_DEFAULTS=on&LINK_LOC=blasthome

[0076] Preferred algorithm parameters used are the default parameters as they are set on the indicated web site:

[0077] Expect threshold = 10, word size = 3, max matches in a query range = 0, matrix = BLOSUM62, gap costs = Existence: 11 Extension: 1, compositional adjustments = conditional compositional score matrix adjustment together with the database of non-redundant protein sequences (nr). To obtain gapped alignments for comparative purposes, Gapped BLAST is utilized as described in Altschul et al. (1997) Nucleic Acids Res. 25: 3389-3402. When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs are used. Sequence matching analysis may be supplemented by established homology mapping techniques like Shuffle-LAGAN (Brudno M., Bioinformatics 2003b, 19 Suppl 1:154-162) or Markov random fields.

[0078] The term “relative percentage”, as used herein, refers to dividing the read counts of a given gene sequence by the summed read counts of all gene sequences and multiplying it by 100.

[0079] Embodiments of the invention

[0080] The present invention will now be further described. In the following passages, different aspects of the invention are defined in more detail. Each aspect so defined may be combined with any other aspect or aspects unless clearly indicated to the contrary. In particular, any feature indicated as being preferred or advantageous may be combined with any other feature or features indicated as being preferred or advantageous, unless clearly indicated to the contrary.

[0081] The present inventors have identified microbiome signatures which allow the prediction of agricultural output.

[0082] Specifically, the present inventors have extracted bacterial DNA directly from soil samples taken from different agricultural areas. In these different agricultural areas, the inventors also recorded the maize corn yield. The soil samples taken from the different agricultural areas were subsequently sequenced. From the resulting DNA pattern, the present inventors could read which bacteria were contained in the soil sample and associated them with the maize com yield.

[0083] The gene of choice for the metagenomic studies of the present inventors is the so-called 16S rRNA gene, as this gene is ubiquitous in bacteria. It contains some conserved DNA regions that are highly similar in all bacterial taxa, and also contains variable DNA sequences that have been significantly modified in the course of evolution, making it possible to identify the bacteria by sequence diversity.

[0084] Particularly, the present inventors first amplified at least a part of the 16S rRNA gene from the soil sample. Then, the sequence of individual clones was determined by next-generation sequencing. The 16S rRNA sequences, thus, obtained from a sample was then compared with a database to determine the affiliation of the bacteria and their abundance in the soil sample. In addition to the 16S rRNA sequences of bacteria, the internal transcribed spacer (ITS) of fungi was detected. The abundance of the bacteria and fungi was then correlated with the maize corn yield.

[0085] The LASSO models created by the present inventors in this process were able to describe 65% (16S V4), 58% (ITS), and 70% (equally weighted averaging of 16S V4 and ITS) of the yield variance in an exemplary maize corn field. Significant correlations between predicted and observed yields were also obtained for external / published datasets, by utilizing 16S rRNA gene abundances.

[0086] The present inventors could, thus, show that the determined microbiome signatures allow prediction of agricultural output.

[0087] Thus, in a first aspect, the present invention relates a method for predicting agricultural output comprising the steps of:

[0088] (i) determining the relative percentage of

[0089] (a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspir ilium,

[0090] Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Methylorubrum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae,

[0091] (b) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia, or

[0092] (c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, Candidatus Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum,

[0093] Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia in a soil sample of an agricultural or a prospective agricultural area, and

[0094] (ii) predicting agricultural output on the basis of the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib), or the at least one bacterial taxon and the at least one fungal taxon of (ic) determined in step (i).

[0095] For example, the relative percentage(s) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 bacterial taxon / taxa selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter' , Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus is / are determined, the relative percentage(s) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 bacterial taxon / taxa selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC- I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Methylorubrum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae is / are determined, the relative percentage of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 fungal taxon / taxa selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia determined, or the relative percentage(s) of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, or 26 bacterial taxon / taxa selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter' , Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and the relative percentage(s) of at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 fungal taxon / taxa selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia is / are determined. Please note that in step (i), the determination of the relative percentage of at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001- H03', Pseudonocardia, 'Candidatus Udaeobacter', Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspir ilium, Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and the determination of the relative percentage of at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutterellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Methylorubrum, of the family AKIW781, of the family Oxalobacter aceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophil aceae, represent different (alternative) embodiments.

[0096] Preferably, the relative percentage of a bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae is determined.

[0097] In one preferred embodiment, the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib) or the at least one bacterial taxon and the at least one fungal taxon of (ic) is (are) determined by

[0098] (i) isolating microbial DNA from the soil sample of an agricultural or a prospective agricultural area,

[0099] (ii) amplifying the bacterial 16S rRNA genes in case of (ia), the fungal ITS regions in case of (ib), or the bacterial 16S rRNA genes and the fungal ITS regions in case of (ic) comprised in the microbial DNA,

[0100] (iii) sequencing the bacterial 16S rRNA genes in case of (ia), the fungal ITS regions in case of (ib), or the bacterial 16S rRNA genes and the fungal ITS regions in case of (ic) comprised in the microbial DNA,

[0101] (iv) assigning the sequenced bacterial 16S rRNA genes to the at least one bacterial taxon referred to in (ia), the sequenced fungal ITS regions to the at least one fungal taxon referred to in (ib), or the sequenced bacterial 16S rRNA genes to the at least one bacterial taxon and the fungal ITS regions to the at least one fungal taxon referred to in (ic), and

[0102] (v) dividing (normalizing) the read counts of the bacterial 16S rRNA gene of the at least one bacterial taxon referred to in (ia) by the summed read counts of all 16S rRNA genes, the read counts of the fungal ITS region of the at least one fungal taxon referred to in (ib) by the summed read counts of all fungal ITS regions, or the read counts of the bacterial 16S rRNA gene of the at least one bacterial taxon referred to in (ic) by the summed read counts of all 16S rRNA genes and the read counts of the fungal ITS region of the at least one fungal taxon referred to in (ic) by the summed read counts of all fungal ITS regions, thereby determining the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib) or the at least one bacterial taxon and the at least one fungal taxon of (ic), respectively.

[0103] Particularly, the amplification is carried out using a polymerase chain reaction (PCR), and / or the sequencing is carried out using next generation sequencing, Maxam-Gilbert sequencing, Chain-termination methods, Shot gun sequencing, PCR sequencing, Bridge PCR, massively parallel signature sequencing (MPSS), Polony sequencing, pyrosequencing, Illumina (Solexa) sequencing, SOLiD sequencing, Ion semiconductor sequencing, DNA nanoball sequencing, Heliscope single molecule sequencing, Single molecule real time (SMRT) sequencing, Nanopore DNA sequencing, sequencing by hybridization, sequencing with mass spectrometry, microfluidic Sanger sequencing, microscopy-based techniques, RNAP sequencing, or high- throughput sequencing (HTS).

[0104] More particularly, the PCR is selected from the group consisting of conventional PCR or real-time PCR (quantitative PCR or qPCR), and any derivative of both, such as TaqMan qPCR, multiplex PCR, nested PCR, high fidelity PCR, fast PCR, hot start PCR, and GC-rich PCR, and / or the sequencing is next generation sequencing.

[0105] In one more preferred embodiment, the prediction in step (ii) is carried out by inputting the relative percentages of at least two bacterial taxa of (ia), at least two fungal taxa of (ib) or at least one bacterial taxon and at least one fungal taxon of (ic) in a mathematical function where denotes the intercept, n denotes the number of bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa used in the equation, ft to ft denote the slope coefficients of the bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa and x±to xndenote the relative percentages of the bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa, which sums the weighted relative percentages of each bacterial taxon and / or fungal taxon returning a composite score (y) reflective of the predicted agricultural output, or inputting the relative percentage of one bacterial taxon of (ia) or one fungal taxon of (ib) in a mathematical function where ft is the intercept, [f the slope coefficient, and xxrefers to the relative percentage of one bacterial taxon of (ia) or one fungal taxon of (ib), returning a score (y) reflective of the predicted agricultural output.

[0106] Particularly, the (composite) score (y) has a value between 0 and 1, e.g. 0, 0.01, 0.02, 0.03,

[0107] 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21,

[0108] 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6, 0.61 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, or 1. Thus, the (composite)s score (y) can be any n-digit number between 0 and 1, so any value between 0 and 1.

[0109] More particularly, a value close to 0 (e.g. 0, 0.1, 0.2, or 0.3) indicates a predicted low agricultural output, or a value close to 1 (e g. 0.7, 0.8, 0.9, or 1) indicates a predicted high agricultural output.

[0110] In one even more preferred embodiment, the agricultural output includes / represents / is crop yield of maize or green fodder yield.

[0111] Thus, in one even more preferred embodiment, the present invention relates to a method of predicting crop yield of maize.

[0112] In this regard, a predicted low crop yield of maize (as mentioned above) is preferably indicative for a crop yield size of < 20 tons / hectare, e.g. 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, or less tons / hectare, or a predicted high crop yield of maize (as mentioned above) is preferably indicative for a crop yield size of > 35 tons / hectare, e.g. 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or more tons / hectare. In another even more preferred embodiment, the present invention relates to a method of predicting green fodder yield.

[0113] In this regard, a predicted low green fodder yield (as mentioned above) is preferably indicative for a low green fodder yield size of the respective crop, or a predicted high green fodder yield (as mentioned above) is preferably indicative for a high green fodder yield size of the respective crop.

[0114] In one still even more preferred embodiment, the relative percentage(s) is (are) obtained from

[0115] (i) a bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae,

[0116] (ii) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Anaeromyxobacter and, Rummeliibacillus,

[0117] (iii) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus,

[0118] (iv) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03 ', Pseudonocardia,

[0119] Candidates Udaeobacter ', Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, EUin6055, Noviherbaspirillum, 'Candidates Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, or

[0120] (v) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Methylorubrum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae, the relative percentage(s) is (are) obtained from

[0121] (i) a fungal taxon of the genus Knufia,

[0122] (ii) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia and Keithomyces,

[0123] (iii) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia, Keithomyces, and Emericellopsis, or (iv) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia, and / or the relative percentage(s) is (are) obtained from

[0124] (i) a bacterial taxon of the genus Hyphomicrobium and a fungal taxon of the genus Knufia

[0125] (ii) a bacterial taxon of the genus Hyphomicrobium and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia and Keithomyces,

[0126] (iii) a bacterial taxon of the genus Hyphomicrobium and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia, Keithomyces, and Emericellopsis, or

[0127] (iv) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03 ', Pseudonocardia,

[0128] Candidatus Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia.

[0129] For example, the relative percentage(s) is (are) obtained from

[0130] (i) a bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae,

[0131] (ii) bacterial taxa of the genus Hyphomicrobium, Anaeromyxobacter and, Rummeliibacillus,

[0132] (iii) bacterial taxa of the genus Hyphomicrobium, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus,

[0133] (iv) bacterial taxa of the genus Hyphomicrobium, Solirubrobacter, 'JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter', Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter ', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, or

[0134] (v) bacterial taxa of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter llaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methyl obacterium-Methyl or ubrum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae, the relative percentage(s) is (are) obtained from

[0135] (i) a fungal taxon of the genus Knufia,

[0136] (ii) fungal taxa of the genus Knufia and Keithomyces,

[0137] (iii) fungal taxa of the genus Kmifia Keithomyces, and Emericellopsis, or

[0138] (iv) fungal taxa of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia, and / or the relative percentage(s) is (are) obtained from

[0139] (i) a bacterial taxon of the genus Hyphomicrobium and a fungal taxon of the genus Knufia,

[0140] (ii) a bacterial taxon of the genus Hyphomicrobium and fungal taxa of the genus Knufia and Keithomyces,

[0141] (iii) a bacterial taxon of the genus Hyphomicrobium and fungal taxa of the genus Knufia, Keithomyces, and Emericellopsis, or

[0142] (iv) bacterial taxa of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter' , Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter ', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and fungal taxa of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia.

[0143] Please note that the determination of the relative percentage of bacterial taxa of the genus Hyphomicrobium, Solirubrobacter, 'JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter', Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and the determination of the relative percentage of bacterial taxa of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ell aceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methyl 'obacterium-Methylorubrum, of the family AKJW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae, represent different (alternative) embodiments. In the first embodiment, a 26-er signature is used. In the second embodiment, a 12-er signature is used. The markers referred to in the second embodiment are generally applicable, e.g. for the prediction of grass / green fodder yield as well as agricultural plant yield such as crop yield (see example 2 as described herein).

[0144] Knowing the agricultural output and the agricultural input further allows the determination of the agricultural productivity.

[0145] In a second aspect, the present invention relates to a (an agricultural) (therapeutic) composition comprising

[0146] (a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, JGI 0001001-H03 ', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and I Ate de man ne Ila, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sullerellaceae, of the genus Luedemanella, and of the genus Lapillicoccus,

[0147] (b) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces, or

[0148] (c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, JGI 0001001-H03 ', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces.

[0149] The composition may also be designated as (agricultural) cocktail.

[0150] For example, the composition may comprise at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 bacterial taxon / taxa selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, 'JGI 0001001-H03 ', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, at least 1, 2, 3, 4, 5, or 6 bacterial taxon / taxa selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, and of the genus Lapillicoccus, at least 1, 2, 3, 4, 5, or 6 fungal taxon / taxa selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyce, or at least 1, 2, 3, 4, 5, 6, 7, 8, or 9 bacterial taxon / taxa selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, 'JGI 0001001-H03 Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, and at least 1, 2, 3, 4, 5, or 6 fungal taxon / taxa selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces.

[0151] Preferably, the composition comprises a bacterial taxon of the genus Hyphomicrobium (and no other bacterial taxon / taxa) or of the family Hyphomicrobiaceae (and no other bacterial taxon / taxa).

[0152] More preferably, the composition comprises bacterial taxa of the genus Hyphomicrobium, 'JGI 0001001-H03', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella (and no other bacterial taxon / taxa), bacterial taxa of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, and of the genus Lapillicoccus (and no other bacterial taxon / taxa), fungal taxa of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces (and no other fungal taxon / taxa), or bacterial taxa of the genus Hyphomicrobium, JGI 0001001-H03', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera and Luedemannella, and fungal taxa of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces (and no other bacterial taxon / taxa and / or no other fungal taxon / taxa).

[0153] The present inventors found that the above bacterial taxa or fungal taxa or the combination of both positively correlate with the agricultural output (see Figures 3, 5, and 10). Specifically, the agricultural output is increased when the abundance of the above bacterial taxa or fungal taxa or the combination of both is increased (see Figures 3, 5, and 10).

[0154] The composition preferably further comprises phosphates, manganese, iron and / or other trace elements.

[0155] The composition may be in dry (e.g. freeze-dried or powdery) or liquid form. In case the composition is in liquid form, the bacteria are part of / comprised in a solution which stabilizes the bacteria. The solution may comprise a solvent. Solvents suitable for use in the composition include without limitation water, a citrate solution, or combinations thereof. The solvent may be present in an amount sufficient to meet some user and / or process needs. For example, the solvent may be present in an amount of from about 1 wt.% to about 99 wt.%, alternatively about 10 wt.% to about 50 wt.%, or alternatively from about 99 wt.% to about 1 wt.%. Specifically, the composition may be a solution, a suspension, a dispersion, or a powder.

[0156] The composition may also comprise one or more agriculturally acceptable excipients. Said agriculturally acceptable excipients are intended to enhance the agricultural output, e.g. the yield of crops. The agriculturally acceptable excipients comprise, but are not limited to, the use of wheat flour, com starch, gelatine, potato starch, silicon dioxide, citric acid, bicarbonate, polysorbates like Tweens, lactose, soy lecithin, casein, carboxymethyl cellulose or cellulose gum, sucrose esters, mannitol, sorbitans, Pluronic F68, alginate, xanthan gum, PEG (polyethylene glycol), corn syrup, egg, milk, glycerol, fructose, pectins, mineral oil, ester gum, and / or long-chain triglycerides.

[0157] The above composition allows to increase agricultural output. Specifically, the above composition allows to increase agricultural output by at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% compared to a situation where no composition is used. Especially, the crop yield of maize or green fodder yield is increased.

[0158] In a third aspect, the present invention relates to the use of the (agricultural) (therapeutic) composition according to the second aspect to increase agricultural output.

[0159] Specifically, the use of the (agricultural) composition increases the agricultural output by at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% compared to a situation where no (agricultural) composition is used. Especially, the crop yield of maize or green fodder yield is increased. In case of maize, the crop yield of maize may be increased to > 35 tons / hectare, e.g. 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, or more tons / hectare.

[0160] In a fourth aspect, the present invention relates to the use of a (an agricultural) composition comprising phosphates and manganese to stimulate the propagation of a bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae . The composition may further comprise iron and optionally other trace elements.

[0161] Specifically, the bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae is propagated / found in an agricultural area or prospective agricultural area. Thus, the (agricultural) composition allows the treatment of an agricultural area in order to increase the population of the bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae therein. This has a positive effect on the agricultural output of this agricultural area, e.g. with respect to crop yield of maize or green fodder yield.

[0162] For example, the use of the (agricultural) composition increases the population of the bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae by at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% compared to a situation where no (agricultural) composition is used. In a fifth aspect, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0163] (i) carrying out the method according to the first aspect,

[0164] (ii) applying the composition according to the second aspect onto the (prospective) agricultural area, and

[0165] (iii) planting or cultivating agricultural plants onto the agricultural area.

[0166] In one preferred embodiment - by carrying out the method according to the first aspect - a composite (score) having a value close to 0 is obtained which is indicative for a predicted low agricultural output. In this case, the composition according to the second aspect is applied onto the (prospective) agricultural area. Subsequently, agricultural plants are planted or cultivated onto the agricultural area. In this way, the agricultural output can be increased.

[0167] Thus, in one particular embodiment, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0168] (i) carrying out the method according to the first aspect, thereby obtaining a (composite) score having a value close to 0 which is indicative for a predicted low agricultural output,

[0169] (ii) applying the composition according to the second aspect onto the (prospective) agricultural area, and

[0170] (iii) planting or cultivating agricultural plants onto the agricultural area.

[0171] In this way, the agricultural area with low agricultural output can effectively be treated with the composition in order to increase said output.

[0172] The composition may be in liquid or solid form. In embodiments in which the composition is in liquid form, it may be sprayed or poured onto the agricultural area. In aspects in which the composition is in solid (e.g. freeze-dried or powdery) form, it may be spread onto the surface of the agricultural area or it may be mixed into the soil which is part of the agricultural area.

[0173] Specifically, the use of the composition increases the agricultural output by at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% compared to a situation where no (agricultural) composition is used.

[0174] In one more preferred embodiment, the agricultural plants are maize or grass / green fodder. In this case, the crop yield of maize or grass / green fodder yield is increased by the application of the composition.

[0175] In a sixth aspect, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0176] (i) applying the composition according to the second aspect onto an (a prospective) agricultural area, and

[0177] (ii) planting or cultivating agricultural plants onto the agricultural area. The composition may be in liquid or solid form. In embodiments in which the composition is in liquid form, it may be sprayed or poured onto the agricultural area. In aspects in which the composition is in solid (e.g. freeze-dried or powdery) form, it may be spread onto the surface of the agricultural area or it may be mixed into the soil which is part of the agricultural area.

[0178] Particularly, the application is carried out by spraying the composition onto the (prospective) agricultural area or by (finely) distributing the composition onto the (prospective) agricultural area.

[0179] Specifically, the use of the composition increases the agricultural output by at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% compared to a situation where no (agricultural) composition is used.

[0180] In one preferred embodiment, the agricultural plants are maize or grass / green fodder. In this case, the crop yield of maize or grass / green fodder yield is increased by the application of the composition.

[0181] In a seventh aspect, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0182] (i) applying the composition according to the second aspect onto agricultural plant seeds, thereby coating the agricultural plant seeds with the composition,

[0183] (ii) sowing the coated agricultural plant seeds into the (prospective) agricultural area, and

[0184] (iii) cultivating agricultural plants derived therefrom onto the agricultural area.

[0185] Alternatively, the composition may be contacted with a part of a plant that is above the ground, for example, the leaves, flowers, fruit or stem.

[0186] In this case, the present invention relates to a method of increasing agricultural output comprising the steps of:

[0187] (i) applying the composition according to the second aspect onto a part of a (an agricultural) plant that is above the ground, thereby coating the part of the (agricultural) plant that is above the ground with the composition, and

[0188] (ii) cultivating the (agricultural) plant onto / in the agricultural area.

[0189] The composition may be in liquid or solid form. In embodiments in which the composition is in liquid form, it may be sprayed or poured onto the seeds as well as plants or parts thereof. In aspects in which the composition is in solid (e.g. freeze-dried or powdery) form, it may be spread onto the surface of the seeds as well as plants or parts thereof.

[0190] Particularly, the application of the composition onto the agricultural plant seeds may take place / be carried out via dip coating or spray coating. Alternatively, the application of the composition onto the part of the (agricultural) plant may take place / be carried out by spray coating.

[0191] Preferably, the coating covers at least 1 %, more preferably at least 50%, even more preferably at least 80%, and most preferably at least 90% or even 100%, of the plant or plant seed, e.g. at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,

[0192] 27, 28 ,29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,

[0193] 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78,

[0194] 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99%, or 100% of the plant or plant seed.

[0195] Specifically, the use of the composition increases the agricultural output by at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100% compared to a situation where no (agricultural) composition is used.

[0196] In one preferred embodiment, the agricultural seeds are maize or grass / green fodder seeds or the agricultural plants are maize or grass / green fodder plants. In this case, the crop yield of maize or grass / green fodder yield is increased by the application of the composition.

[0197] In a further aspect, the present invention relates to a method of predicting agricultural output comprising the steps of: determining the DNA content within a soil sample of an agricultural or a prospective agricultural area, wherein a high DNA content is indicative for a high agricultural output and wherein a low DNA content is indicative for a low agricultural output. Specifically, the threshold needs to be determined de novo from the area under investigation. In this respect, a high DNA content preferably means > 20 ng / pl and / or a low DNA content preferably means < 10 ng / pl.

[0198] The present invention is summarized as follows:

[0199] 1. A method for predicting agricultural output comprising the steps of:

[0200] (i) determining the relative percentage of

[0201] (a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, 'JGI 0001001-H03', Pseudonocardia, Candidatus daeobacter , Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea,

[0202] Anaeromyxobacter, and Rumrneliibacillus, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Me thy lor brum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae,

[0203] (b) at least one fimgal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia, or

[0204] (c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, 'JGI 0001001 -HO 3', Pseudonocardia, 'Candidatus Udaeobacter', Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, Candidatus Solibacter Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea,

[0205] Anaeromyxobacter, and Rummeliibacillus, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia in a soil sample of an agricultural or a prospective agricultural area, and

[0206] (ii) predicting agricultural output on the basis of the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib), or the at least one bacterial taxon and the at least one fungal taxon of (ic) determined in step (i). The method of item 1, wherein the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib) or the at least one bacterial taxon and the at least one fungal taxon of (ic) is (are) determined by

[0207] (i) isolating microbial DNA from the soil sample of an agricultural or a prospective agricultural area,

[0208] (ii) amplifying the bacterial 16S rRNA genes in case of (ia), the fungal ITS regions in case of (ib), or the bacterial 16S rRNA genes and the fungal ITS regions in case of (ic) comprised in the microbial DNA,

[0209] (iii) sequencing the bacterial 16S rRNA genes in case of (ia), the fungal ITS regions in case of (ib), or the bacterial 16S rRNA genes and the fungal ITS regions in case of (ic) comprised in the microbial DNA, (iv) assigning the sequenced bacterial 16S rRNA genes to the at least one bacterial taxon referred to in (ia), the sequenced fungal ITS regions to the at least one fungal taxon referred to in (ib), or the sequenced bacterial 16S rRNA genes to the at least one bacterial taxon and the fungal ITS regions to the at least one fungal taxon referred to in (ic), and

[0210] (v) dividing (normalizing) the read counts of the bacterial 16S rRNA gene of the at least one bacterial taxon referred to in (ia) by the summed read counts of all 16S rRNA genes, the read counts of the fungal ITS region of the at least one fungal taxon referred to in (ib) by the summed read counts of all fungal ITS regions, or the read counts of the bacterial 16S rRNA gene of the at least one bacterial taxon referred to in (ic) by the summed read counts of all 16S rRNA genes and the read counts of the fungal ITS region of the at least one fungal taxon referred to in (ic) by the summed read counts of all fungal ITS regions, thereby determining the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib) or the at least one bacterial taxon and the at least one fungal taxon of (ic), respectively.

[0211] 3. The method of item 2, wherein the amplification is carried out using a polymerase chain reaction (PCR).

[0212] 4. The method of item 3, wherein the PCR is selected from the group consisting of conventional PCR or real-time PCR (quantitative PCR or qPCR), and any derivative of both, such as TaqMan qPCR, multiplex PCR, nested PCR, high fidelity PCR, fast PCR, hot start PCR, and GC-rich PCR.

[0213] 5. The method of any one of items 2 to 4, wherein the sequencing is next generation sequencing.

[0214] 6. The method of any one of items 1 to 5, wherein the prediction in step (ii) is carried out by inputting the relative percentages of at least two bacterial taxa of (ia), at least two fungal taxa of (ib) or at least one bacteriual taxon and at least one fungal taxon of (ic) in a mathematical function where / ?0denotes the intercept, n denotes the number of bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa used in the equation, denote the slope coefficients of the bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa and x to xndenote the relative percentages of the bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa, which sums the weighted relative percentages of each bacterial taxon and / or fungal taxon returning a composite score reflective of the predicted agricultural output, or inputting the relative percentage of one bacterial taxon of (ia) or one fungal taxon of (ib) in a mathematical function where ?0is the intercept, ff the slope coefficient, and x}refers to the relative percentage of one bacterial taxon of (ia) or one fungal taxon of (ib), returning a score reflective of the predicted agricultural output.

[0215] 7. The method of item 6, wherein the (composite) score has a value between 0 and 1.

[0216] 8. The method of item 7, wherein a value close to 0 indicates a predicted low agricultural output, or a value close to 1 indicates a predicted high agricultural output.

[0217] 9. The method of any one of items 1 to 8, wherein the relative percentage(s) is (are) obtained from

[0218] (i) a bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae,

[0219] (ii) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Anaeromyxobacter and, Rummeliibacillus,

[0220] (iii) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus,

[0221] (iv) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, Candidates Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspir ilium,

[0222] Candidates Sohbacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, or

[0223] (v) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Methylorubrum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae .

[0224] 10. The method of any one of items 1 to 9, wherein the relative percentage(s) is (are) obtained from

[0225] (i) a fungal taxon of the genus Knufia,

[0226] (ii) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia and Keithomyces,

[0227] (iii) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia, Keithomyces, and Emericellopsis, or

[0228] (iv) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia.

[0229] 11. The method of any one of items 1 to 10, wherein the relative percentage(s) is (are) obtained from

[0230] (i) a bacterial taxon of the genus Hyphomicrobium and a fungal taxon of the genus Knufia,

[0231] (ii) a bacterial taxon of the genus Hyphomicrobium and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia and Keithomyces,

[0232] (iii) a bacterial taxon of the genus Hyphomicrobium and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia, Keithomyces, and Emericellopsis, or

[0233] (iv) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03 , Pseudonocardia, Candidates Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum,

[0234] Candidates Solibacter ', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia. 12. The method of any one of items 1 to 11, wherein the agricultural output includes / represents / is crop yield of maize or green fodder yield.

[0235] 13. A composition compri sing

[0236] (a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, 'JGI 0001001-H03', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, and of the genus Lapillicoccus,

[0237] (b) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces, or

[0238] (c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, JGI 0001001-H03', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces.

[0239] 14. The composition of item 13, wherein the composition is in dry or liquid form.

[0240] 15. Use of the composition of items 13 or 14 to increase agricultural output.

[0241] 16. The use of item 15, wherein crop yield of maize or green fodder yield is increased.

[0242] 17. Use of a composition comprising phosphates and manganese to stimulate the propagation of a bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae .

[0243] 18. The use of item 17, wherein the bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae is propagated in an agricultural area or prospective agricultural area.

[0244] 19. A method of increasing agricultural output comprising the steps of:

[0245] (i) carrying out the method of any one of items 1 to 12(, thereby obtaining a (composite) score having a value close to 0 which is indicative for a predicted low agricultural output),

[0246] (ii) applying the composition of items 13 or 14 onto the (prospective) agricultural area, and (iii) planting or cultivating agricultural plants onto the agricultural area.

[0247] 20. The method of item 19, wherein the agricultural plants are maize or grass / green fodder.

[0248] 21. A method of increasing agricultural output comprising the steps of:

[0249] (i) applying the composition of items 13 or 14 onto an (a prospective) agricultural area, and

[0250] (ii) planting or cultivating agricultural plants onto the agricultural area.

[0251] 22. The method of item 21, wherein the agricultural plants are maize or grass / green fodder.

[0252] 23. The method of items 21 or 22, wherein the composition is in liquid or solid, preferably powdery, form.

[0253] 24. The method of any one of items 21 to 23, wherein the application is carried out by spraying the composition onto the (prospective) agricultural area or by (finely) distributing the composition onto the (prospective) agricultural area.

[0254] 25. A method of increasing agricultural output comprising the steps of:

[0255] (i) applying the composition of items 13 or 14 onto agricultural plant seeds, thereby coating the agricultural plant seeds with the composition,

[0256] (ii) sowing the coated agricultural plant seeds into the (prospective) agricultural area, and

[0257] (iii) cultivating agricultural plants derived therefrom onto the agricultural area.

[0258] 26. The method of item 25, wherein the agricultural plants are maize or grass / green fodder.

[0259] 27. The method of items 25 or 26, wherein the composition is in liquid form.

[0260] 28. The method of items 25 to 27, wherein the application is carried out by dip coating or spray coating.

[0261] Various modifications and variations of the invention will be apparent to those skilled in the art without departing from the scope of invention. Although the invention has been described in connection with specific preferred embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention which are obvious to those skilled in the art in the relevant fields are intended to be covered by the present invention.

[0262] BRIEF DESCRIPTION OF THE FIGURES

[0263] The following Figures are merely illustrative of the present invention and should not be construed to limit the scope of the invention as indicated by the appended claims in any way. Figure 1: Schematic representation of the process used to develop a maize crop yield prediction model.

[0264] Figure 2: Prediction of maize yield score by bacterial microbiome signature (16S V4). The solid line represents the axis bisector (identity function).

[0265] Figure 3: Standardized weights, of genera included in the LASSO model (16S V4), predicting maize corn yield score.

[0266] Figure 4: Prediction of maize corn yield score by fungal microbiome signature (ITS). The solid line represents the axis bisector (identity function).

[0267] Figure 5: Standardized weights, of genera included in the LASSO model (ITS), predicting maize corn yield score.

[0268] Figure 6: Prediction of maize yield score by combining bacterial and fungal microbiome signatures (equally weighted averaging, ensemble). The solid line represents the axis bisector (identity function).

[0269] Figure 7: Scatter plot showing correlation between predicted yield score via the herein established bacterial 16S microbiome signature and the observed yield metric. Data were obtained from Hu et al., 2021

[0011] and include 16S microbiome data from grassland soils with corresponding belowground net primary productivity estimates, the latter of which were used as yield (or output) metric to be correlated to the predicted yield score. R refers to spearman rank correlation coefficient, with its corresponding p-value.

[0270] Figure 8: Scatter plot showing correlation between predicted yield score via the herein established bacterial 16S microbiome signature and the observed yield metric. Data were obtained from Delgado-Baquerizo et al., 2018

[0010] and include 16S microbiome data from grassland soils with corresponding net primary productivity estimates, the latter of which were used as yield (or output) metric to be correlated to the predicted yield score. R refers to spearman rank correlation coefficient, with its corresponding p-value.

[0271] Figure 9: Scatter plot showing correlation between predicted yield score via the herein established bacterial 16S microbiome signature and the observed yield metric. Data were obtained from Wilhelm et al., 2022 [8] and include 16S microbiome data from agricultural soils with corresponding soil health estimates, the latter of which were used as yield (or output) metric to be correlated to the predicted yield score. R refers to spearman rank correlation coefficient, with its corresponding p-value.

[0272] Figure 10:_Analysis workflow and results obtained therefrom. A: The analysis workflow for obtaining taxa correlated to yield-related variables is depicted. B: Plot shows the hits (taxa) obtained by the analysis workflow and their correlation to yield-related variables throughout the analyzed datasets. Figure 11: Multiple linear regression models were fitted either with all, but one dataset (leave one study out approach) and the predictive performance of the models assessed on the left out dataset or with each dataset individually and the predictive power of the model assessed. In both cases the coefficient of determination (R2) is reported.

[0273] Figure 12: The four test sets were categorized into high and low yield groups and tested whether the yield predictions were higher in the high yield groups. Welch two-sample t-tests were performed.

[0274] Figure 13: The biserial rank correlation (Cliff s Delta) between taxon abundance and high or low yield group was assessed for the four test sets for each of the 12 selected taxa.

[0275] EXAMPLES

[0276] The examples given below are for illustrative purposes only and do not limit the invention described above in any way.

[0277] EXAMPLE 1 :

[0278] Materials & Methods

[0279] Yield measurement

[0280] Yield mapping (local yield measured by volume flow + GPS coordinates) was provided by the cooperating farm in Grabko, Brandenburg. These yield data, measured in tons per hectare, were transformed into a corn yield score between 0 and 1 via min-max normalization.

[0281] Collection and storage of soil samples

[0282] Soil samples were taken from a total of 80 locations in a maize field in the village of Grabko, Brandenburg, and their GPS positions were documented (Dr. Bruno Steinkraus). Following collection, these samples were stored for several days at -20°C and, after subsequent transfer to Soilytix GmbH, at -60°C until DNA isolation.

[0283] Isolation of DNA from soil samples

[0284] For DNA isolation, 250 mg of soil was used per sample. DNA isolation was performed using the DNeasy PowerLyzer PowerSoft Kit (Qiagen) according to a standard laboratory protocol (deposited in the experimental electronic laboratory notebook). The 80 samples were prepared randomly in batches of up to 12 samples / batch. Sequence library preparation & sequencing

[0285] Sequence libraries of 16S V4 (bacteria) and ITS (fungi) were prepared according to a standard protocol deposited in the experimental electronic laboratory notebook. Sequencing of samples was performed using an Illumina iSeq 100 sequencer.

[0286] Bioinformatic analysis of the sequencing data

[0287] Analysis of the sequencing data was performed using RStudio statistical software [1,2]. Essentially, the dada2 [3] and microeco [4] software packages were used to process the sequencing data. For machine learning, the software package caret [5] was used. Taxonomic assignment was performed against the SILVA database (silva_nr99_vl38.1; 16S V4) [6] or against the UNITE database (sh_general_release_dynamic_29.11.2022; ITS) [7],

[0288] Results

[0289] The following describes the procedure that was used to identify the microbiome signature predictive of maize yield. A schematic for this is shown in Figure 1. Soil samples were taken from the Grabko maize field under study at a total of 80 positions. The yield to each of these positions was interpolated based on yield mapping over the geographically closest yield point and translated into a com yield score between 0 and 1. Genomic DNA was isolated from the soil samples and used as input for sequence library construction of the 16S V4, as well as the ITS region to measure the bacterial and fungal microbiome. Sequencing of the sequence library pool was performed by the Illumina iSeq 100 sequencer. Sequencing data were processed using RStudio and aggregated via the dada2 software package into sequence variants, which were taxonomically assigned using the Silva database (16S V4) or the UNITE database (ITS). The relative proportions (% abundance) of the 100 most abundant genera on average were used as input to a Least Absolute Shrinkage and Selection Operator (LASSO)-regression based machine learning algorithm to predict maize yield score.

[0290] The cross-validated algorithm achieved an R2of 0.65 for 16S V4 (see also Figure 2), and an R2of 0.58 for ITS (Figure 4). The feature selection inherent in the LASSO regression thereby selected 26 out of 100 (16S V4), or 9 out of 100 (ITS) genera, each of which was used as input. Their standardized weights, as well as positive or negative contribution in the model, are shown in Figures 3 and 5. Equally weighted averaging of the two models (ensemble) was able to increase the R2to 0.70 (see Figure 6).

[0291] Summary This invention describes the use of microbiome signatures through machine learning to predict com crop yield. The LASSO models created in this process are able to describe 65% (16S V4), 58% (ITS), and 70% (equal-weighted averaging of 16S V4 and ITS) of the yield variance in an exemplary com field.

[0292] EXAMPLE 2:

[0293] Material & Methods

[0294] Yield and yield-related variable measurement For this meta-analysis, published datasets were aggregated, which measured the prokaryotic soil microbiome (16S) and, for which yield, or yield-related measurements were either directly available from the study or which could be retrieved through the geographical coordinates, by subsequent remote sensing data retrieval using the MODIS satellite to obtain Normalized Difference Vegetation Index (NDVI) data. Table 1 summarizes the datasets used for this meta- analysis.

[0295] Table 1: Summary of the datasets used for the meta-analysis. Bioinformatic analysis of sequencing data

[0296] The processing of the high-throughput sequencing data was performed within RStudio [1,2]. Essentially, the software packages dada2 [3] and microeco [4] were used for processing of the sequencing data. For taxonomic classification the SILVA database was used (silva_nr99_vl38.1) [6], Machine learning was done using the caret package [5],

[0297] Results

[0298] For this meta-analysis, among our herein presented study (Grabko), seven published studies were further included, and one of these studies was split into two parts, separating cropland and grassland samples, giving rise to a total of 9 datasets. After processing the sequencing reads and assigning them to taxonomies, a part of the data was split off, giving rise to test sets for later model evaluation. The remaining data was used for feature selection and model building. Within this training data, we obtained a total of 1617 families and genera, from which 251 were present in all the 9 datasets, and these were defined as our initial feature set. A correlation analysis was carried out, in which for each study and each feature the Pearson’s product moment correlation coefficient with the study-specific response variable was obtained. We defined hits as features which showed statistically significant (p < 0.05) correlations in only one direction (i.e. either strictly positive or negative), which had at least four statistically significant correlations (among the 9 datasets), and where the mean of the correlation coefficients was statistically different from zero (p < 0 05). This resulted in a total of 14 hits, from which two genera were removed due to high correlation with their respective families. Figure 10 summarizes the workflow (A), as well as the results from this meta-analysis (B).

[0299] To obtain a predictive model, for each study the study-specific response variable, along with the 12 selected features were z-transformed. First, a “leave one study out” approach was performed, wherein multiple linear regressions model with the z-transformed response, as well as features were fitted using all, but one dataset and the predictive power of the models tested on the left-out dataset, which resulted in an average R2of 0.18. Similarly, the predictive power of the 12 selected features for each study was assessed by building multiple linear regression models for each individual study, which gave rise to an average R2of 0.31 to out of bag samples. The results are summarized in Figure 11.

[0300] Finally, a multiple linear regression model was fit using the 12 selected features and the data from all 9 datasets. This final model was evaluated on completely unseen data. For this purpose, four different test sets were used. One with held out samples from the NorthEurope. Grass dataset, corresponding to grassland samples in south Europe, held out samples from the Agroscope dataset from south Europe, desert samples from the China dataset and, additionally, samples from another 31 project wherein soil microbiome data from high and low yield fields in eastern Germany (Biickwitz) was available. For all these four test sets, we categorized samples into either high or low yield groups depending on geographical location or aridity. The grassland samples from south Europe (low yield) from the test data were compared against the grassland samples from north Europe (high yield) from the training data; from the Agroscope held out data, grassland samples from Portugal (low yield) were compared against grassland samples from the Azores (high yield); and the held out desert samples (low yield) from the China data were compared against the grassland data (high yield) from the training data. For these four test datasets, it was tested if the final model predicts higher yields for the respective high yield categories. In all four cases, we obtained highly statistically increased predictions for the high yield groups (Figure 12), and with corresponding rank-biserial correlations (Cliff’s Delta) of 0.73 (Biickwitz), 0.74 (Europe.Grassland), 0.41 (Agroscope) and 0.81 (China).

[0301] Moreover, we tested for each of the 12 selected features, if they show a rank-biserial correlation (Cliffs Delta; high vs. low yield categories) in the same direction that was observed for the correlation between the quantitative yield-related variable with the respective feature. For 9 of the 12 features, in at least 3 of 4 cases, respectively, a statistically significant (p < 0.05) rank-biserial correlation was found and in all these cases in the same direction as observed for the respective correlations (Figure 13, and compare to Figure 10).

[0302] Summary

[0303] This example describes the discovery of a set of 12 prokaryotic families and genera in soil that show consistent responsiveness to a variety of agricultural yield-related variables, and are capable of classifying high and low yield areas. Therefore, these taxa are ideal for utilizing them as markers for predicting agricultural outputs such as crop yield or green fodder yield.

[0304] REFERENCES

[0305] [1] R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https: / / www.R-project.org / .

[0306] [2] RStudio Team (2021). RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA URL http: / / www.rstudio.com / .

[0307] [3] Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016). “DADA2: High-resolution sample inference from Illumina amplicon data.” _Nature Methods_, *13*, 581- 583. doi: 10 1038 / nmeth.3869 (URL: https: / / doi.org / 10.1038 / nmeth.3869). [4] Chi Liu, Yaoming Cui, Xiangzhen Li, Minjie Yao, microeco: an R package for data mining in microbial community ecology, FEMS Microbiology Ecology, Volume 97, Issue 2, February 2021, fiaa255.

[0308] [5] Max Kuhn (2022). caret: Classification and Regression Training. R package version 6.0-93. https : / / CRAN R-proj ect. org / package=caret

[0309] [6] Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, Glockner FO (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Opens external link in new windowNucl. Acids Res. 41 (DI): D590-D596.

[0310] [7] Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, Kennedy P, Picard K, Glockner FO, Tedersoo L, Saar I, Koljalg U, Abarenkov K (2018) The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research, DOI: 10.1093 / nar / gkyl022

[0311] [8] R. C. Wilhelm, H. M. van Es, and D. H. Buckley, Predicting measures of soil health using the microbiome and supervised machine learning11, Soil Biology and Biochemistry, Bd. 164, S. 108472, Jan. 2022, doi: 10.1016 / j. soilbio.2021.108472.

[0312] [9] A. Lanzen et al., „The Community Structures of Prokaryotes and Fungi in Mountain Pasture Soils are Highly Correlated and Primarily Influenced by pH11, Front. Microbiol., Bd. 6, Nov. 2015, doi: 10.3389 / fmicb.2015.01321.

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[0010] M. Delgado-Baquerizo et al., „A global atlas of the dominant bacteria found in soil11, Science, Bd. 359, Nr. 6373, S. 320-325, Jan. 2018, doi: 10.1126 / science.aap9516.

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[0011] W. Hu et al., „Aridity-driven shift in biodiversity-soil multifunctionality relationships11, Nat Commun, Bd. 12, Nr. 1, S. 5350, Sep. 2021, doi: 10.1038 / s41467-021-25641-0.

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[0012] A. Durrer et al., „Organic farming practices change the soil bacteria community, improving soil quality and maize crop yields11, PeerJ, Bd. 9, S. el 1985, Sep. 2021, doi: 10.7717 / peerj.11985.

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Claims

CLAIMS1. A method for predicting agricultural output comprising the steps of:(i) determining the relative percentage of(a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, 'JGI 0001001-H03', Pseudonocardia, Candidatus Udaeobacter , Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea,Anaeromyxobacter, and Rummeliibacillus, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Me thy loru brum, of the family AKIW781, of the family Oxalobacteraceae, of the genus Flavisolibacter, of the family Solirubrobacteraceae, and of the family Geodermatophilaceae,(b) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia, or(c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, 'JGI 0001001-H03', Pseudonocardia, Candidatus Udaeobacter', Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum, 'Candidatus Solibacter ', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea,Anaeromyxobacter, and Rummeliibacillus, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia in a soil sample of an agricultural or a prospective agricultural area, and(ii) predicting agricultural output on the basis of the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib), or the at least one bacterial taxon and the at least one fungal taxon of (ic) determined in step (i).

2. The method of claim 1, wherein the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib) or the at least one bacterial taxon and the at least one fungal taxon of (ic) is (are) determined by(i) isolating microbial DNA from the soil sample of an agricultural or a prospective agricultural area,(ii) amplifying the bacterial 16S rRNA genes in case of (ia), the fungal ITS regions in case of (ib), or the bacterial 16S rRNA genes and the fungal ITS regions in case of (ic) comprised in the microbial DNA,(iii) sequencing the bacterial 16S rRNA genes in case of (ia), the fungal ITS regions in case of (ib), or the bacterial 16S rRNA genes and the fungal ITS regions in case of (ic) comprised in the microbial DNA,(iv) assigning the sequenced bacterial 16S rRNA genes to the at least one bacterial taxon referred to in (ia), the sequenced fungal ITS regions to the at least one fungal taxon referred to in (ib), or the sequenced bacterial 16S rRNA genes to the at least one bacterial taxon and the fungal ITS regions to the at least one fungal taxon referred to in (ic), and(v) dividing (normalizing) the read counts of the bacterial 16S rRNA gene of the at least one bacterial taxon referred to in (ia) by the summed read counts of all 16S rRNA genes, the read counts of the fungal ITS region of the at least one fungal taxon referred to in (ib) by the summed read counts of all fungal ITS regions, or the read counts of the bacterial 16S rRNA gene of the at least one bacterial taxon referred to in (ic) by the summed read counts of all 16S rRNA genes and the read counts of the fungal ITS region of the at least one fungal taxon referred to in (ic) by the summed read counts of all fungal ITS regions,thereby determining the relative percentage(s) of the at least one bacterial taxon of (ia), the at least one fungal taxon of (ib) or the at least one bacterial taxon and the at least one fungal taxon of (ic), respectively.

3. The method of claims 1 or 2, wherein the prediction in step (ii) is carried out by inputting the relative percentages of at least two bacterial taxa of (ia), at least two fungal taxa of (ib) or at least one bacteriual taxon and at least one fungal taxon of (ic) in a mathematical functionwhere ff denotes the intercept, n denotes the number of bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa used in the equation, ft to ft denote the slope coefficients of the bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa and x to xndenote the relative percentages of the bacterial (ia), fungal (ib) or bacterial and fungal (ic) taxa, which sums the weighted relative percentages of each bacterial taxon and / or fungal taxon returning a composite score reflective of the predicted agricultural output, or inputting the relative percentage of one bacterial taxon of (ia) or one fungal taxon of (ib) in a mathematical functionwhere ft is the intercept, ft the slope coefficient, and x, refers to the relative percentage of one bacterial taxon of (ia) or one fungal taxon of (ib), returning a score reflective of the predicted agricultural output.

4. The method of claim 3, wherein the (composite) score has a value between 0 and 1, wherein preferably a value close to 0 indicates a predicted low agricultural output, or a value close to 1 indicates a predicted high agricultural output.

5. The method of any one of claims 1 to 4, wherein the relative percentage(s) is (are) obtained from(i) a bacterial taxon of the genus Hyphomicrobium or of the family Hyphomicrobiaceae,(ii) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Anaeromyxobacter and, Rummeliibacillus,(iii) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus,(iv) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, Candidates Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspirillum,Candidates Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, or(v) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutterellaceae, of the genus Luedemanella, of the genus Lapillicoccus, of the genus Methylobacterium-Methylorubrum, of the family AKIW781, of the family Oxalobacteraceae , of the genus Flavisolibacter , of the family Solirubrobacteraceae, and of the family Geodermatophilaceae, the relative percentage(s) is (are) obtained from(i) a fungal taxon of the genus Knufia,(ii) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia and Keithomyces,(iii) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia, Keithomyces, and Emericellopsis, or(iv) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellul opsis, Emericellopsis, Keithomyces, and Knufia, and / or the relative percentage(s) is (are) obtained from(i) a bacterial taxon of the genus Hyphomicrobium and a fungal taxon of the genus Knufia,(ii) a bacterial taxon of the genus Hyphomicrobium and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia and Keithomyces,(iii) a bacterial taxon of the genus Hyphomicrobium and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Knufia, Keithomyces, and Emericellopsis, or(iv) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, Solirubrobacter, JGI 0001001-H03', Pseudonocardia, 'Candidatus Udaeobacter Bradyrhizobium, Rhodoplanes, Nitrospira, Terrabacter, Reyranella, Bacillus, Ellin6055, Noviherbaspir ilium,Candidatus Solibacter', Aeromicrobium, Microvirga, Oryzihumus, Streptosporangium, Streptomyces, Pajaroellobacter, Paenisporosarcina, Aquisphaera, Luedemannella, Flavitalea, Anaeromyxobacter, and Rummeliibacillus, and at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Papiliotrema, Oidiodendron, Absidia, Endophoma, Gibellulopsis, Emericellopsis, Keithomyces, and Knufia.

6. The method of any one of claims 1 to 5, wherein the agricultural output includes / represents / is crop yield of maize or green fodder yield.

7. A composition comprising(a) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, JGI 0001001-H03', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, or at least one bacterial taxon selected from the group consisting of a bacterial taxon of the family Hyphomicrobiaceae, of the family SC-I-84, of the genus Pseudolabrys, of the family Sutter ellaceae, of the genus Luedemanella, and of the genus Lapillicoccus,(b) at least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellulopsis, Emericellopsis, and Keithomyces, or(c) at least one bacterial taxon selected from the group consisting of a bacterial taxon of the genus Hyphomicrobium, 'JGI 0001001-H03', Terrabacter, Reyranella, Oryzihumus, Streptosporangium, Pajaroellobacter, Aquisphaera, and Luedemannella, andat least one fungal taxon selected from the group consisting of a fungal taxon of the genus Marquandomyces, Oidiodendron, Endophoma, Gibellul opsis, Emericellopsis, and Keithomyces.

8. Use of the composition of claim 7 to increase agricultural output.

9. The use of claim 8, wherein crop yield of maize or green fodder yield is increased.

10. A method of increasing agricultural output comprising the steps of:(i) carrying out the method of any one of claims 1 to 6,(ii) applying the composition of claim 7 onto the (prospective) agricultural area, and(iii) planting or cultivating agricultural plants onto the agricultural area.

11. The method of claim 10, wherein the agricultural plants are maize or grass / green fodder.

12. A method of increasing agricultural output comprising the steps of:(i) applying the composition of claim 7 onto an (a prospective) agricultural area, and(ii) planting or cultivating agricultural plants onto the agricultural area.

13. The method of claim 12, wherein the agricultural plants are maize or grass / green fodder.

14. A method of increasing agricultural output comprising the steps of:(i) applying the composition of claim 7 onto agricultural plant seeds, thereby coating the agricultural plant seeds with the composition,(ii) sowing the coated agricultural plant seeds into the (prospective) agricultural area, and(iii) cultivating agricultural plants derived therefrom onto the agricultural area.

15. The method of claim 14, wherein the agricultural plants are maize or grass / green fodder.

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