Statistical Analysis Report

Customer	Innovation Centre for Organic Farming, Tove Mariegaard Pedersen
Customer ID	DA00204-22
Project	The microbial community of the field, 2. year (2022).
Sample Type	Soil
Number of samples	48 samples
Type of data	Shotgun metagenomics

Introduction to the biostatistical analysis

The Project

The current report describes microbiome profiles of 48 samples collected in the second year of the project, year 2022, across 48 fields. For each field, one sample was collected to represent the field, corresponding to the ‘main’ samples collected for each filed from 2021. These samples were taken for each field based on 16 subsamples taken in a w-pattern throughout the field.

In 2023, the scope will be expanded to include a total of approx. 100 fields (both conventional and organic fields). In this report we aalyse the samples from 2022 alone. Later we will perform joined analysis to both identify robust patterns arcross the years and evaluate if any patterns appear year-specific indicating varying conditions such as whether.
The aim is to evaluate how the microbiome of the fields associate with other field parameters of both agricultural practices and soil indicators of nutrients, type and structure.
Only when the full data set for 2021-2023 is ready, a final analysis can be conducted.

We separate the evaluations into two R3 reports:

The first report presents results for the analysis of microbial taxonomy for samples form 2022, where the samples’ microbiome profiles are related to information on cultivation practices / cultivation conditions.
The second report presents results for the analysis of microbial functional features for samples form 2022, where the samples’ microbiome profiles are related to information on cultivation practices / cultivation conditions.

Analysis

In “Report 3”, biostatistical analyses are performed and the results presented, building on the data generated and evaluated in the 2 prior reports (Report 1: Sequencing and data processing report, Report 2: Microbiome profiling report).
Through biostatistical analysis we relate the microbiome profiles to the key variables selected for year 2022. The focus here is to evaluate how and to what extent the variables shape and relate to the soil microbiome composition and diversity. We therefore focus on the overall structure of the microbiome also called the microbiome composition and the diversity.

The key variables assessed in this report are summarized with summary statistics across the 48 samples in the below table.

Summary Statistics
Variable	N	Mean	Std. Dev.	Min	Pctl. 25	Pctl. 75	Max
JB_value	48
… 1	19	39.6%
… 2	5	10.4%
… 5	1	2.1%
… 6	20	41.7%
… 7	3	6.2%
Earthworm_status	48
… 0	11	22.9%
… 1	37	77.1%
Cold_soil	48
… 0	33	68.8%
… 1	15	31.2%
Compact_soil	48
… 0	40	83.3%
… 1	8	16.7%
field_well_drained	48
… 0	7	14.6%
… 1	41	85.4%
Mulching_of_straw	48
… 0	25	52.1%
… 1	23	47.9%
Clovergrass_within_3_years	48
… 0	36	75%
… 1	12	25%
No_plough	48
… 0	36	75%
… 1	12	25%
ConservationAgriculture	48
… 0	45	93.8%
… 1	3	6.2%
Years_since_plowing	48	3.417	2.988	1	1	4.25	11
Rt	48	6.487	0.489	5.7	6.2	6.75	7.6
Phosphorus	48	3.081	1.301	0.7	2.175	3.925	6
Potassium	48	10.473	6.247	1.5	7.325	14	41
Magnesium	48	6.89	2.577	1.9	5.5	8.15	16
Cobber	48	2.481	0.885	1	1.8	3.1	5.1
Organic_material_perc	48	3.025	1.363	1.16	2.173	3.372	8.74
Clay_perc	48	9.258	4.727	2.4	4.675	12.85	20
Nitrogen_perc	48	0.144	0.052	0.07	0.1	0.17	0.28
Organic_farm	48
… 0	24	50%
… 1	24	50%
Years_since_turning_organic	48	4.521	4.758	1	1	7.25	15
Livestock	48
… 0	20	41.7%
… 1	28	58.3%
Livestock_manure	48
… 0	16	33.3%
… 1	32	66.7%
Commercial.fertilizer	48
… 0	24	50%
… 1	24	50%
Vinasse	48
… 0	46	95.8%
… 1	2	4.2%
Cast	48
… 0	46	95.8%
… 1	2	4.2%
Degassed.fertilizer	48
… 0	35	72.9%
… 1	13	27.1%
Chalked	47
… 0	38	80.9%
… 1	9	19.1%

Table 1: Summary statistics of the key variables selected for evaluation in relation to the fields microbiome profiles in year 2022.

Evaluation of overall microbiome profiles

We initiate the evaluation of the 10 samples (1 per field) with a stacked barplot of the microbiome profiles in each sample. This allows us to make a first evaluation of the extent of difference in the taxonomic profiles between the fields.

Note that in order to show the organisms with a color scheme that is interpretable, it is necessary to filter the profiles and select a subset of the most abundant clades to be included in the plots. The filtering used is specified in the axis labels of each plot (e.g. >2% in the relative abundance plots mean that a clade must have a relative abundance across samples of more than 2% in order to be included in the plot).

Stacked barplots

The stacked barplots allow us to visually access the stability of the taxonomic profile across the fields, and get a feeling of the level to which individual clades are found across field or more sporadic.

Phylum

Figure 1: Visualization of the microbial community in the samples. Stacked barplots of taxonomic clades in each of the evaluated samples. A) Clade abundance was transformed to relative abundance to sum to 100% in each sample B) Taxa abundance was transformed to absolute abundance. Taxa shown in the total abundance plot are selected to match the taxa that are included in the corresponding relative abundance plot (A).

Class

Figure 2: Visualization of the microbial community in the samples. Stacked barplots of taxonomic clades in each of the evaluated samples. A) Clade abundance was transformed to relative abundance to sum to 100% in each sample B) Taxa abundance was transformed to absolute abundance. Taxa shown in the total abundance plot are selected to match the taxa that are included in the corresponding relative abundance plot (A).

Order

Figure 3: Visualization of the microbial community in the samples. Stacked barplots of taxonomic clades in each of the evaluated samples. A) Clade abundance was transformed to relative abundance to sum to 100% in each sample B) Taxa abundance was transformed to absolute abundance. Taxa shown in the total abundance plot are selected to match the taxa that are included in the corresponding relative abundance plot (A).

Overall microbiome communities related to continuous variables

We now turn to the continuous variables. We continue to use the beta-diversity measure from the microbiome, but use a different analysis model named ENVFIT. We again display the samples in ordination plots but add to these plots arrows representing the association of the variables with the microbiome.

ENVFIT

The function fits environmental variables onto an ordination using multiple regression and thus looks for linear relationships between the axis that drive the main variation in the microbiome and the environmental variables.

The output of the analysis gives the direction of the association in terms of coordinates in the ordination plot (the arrow head). In the plots, the arrows are scaled by their correlation (square root of R2) so that “weak” variables have shorter arrows than “strong” variables. The arrows point in the direction where they have maximal correlations with the ordination axis. The lengths of arrows for fitted vectors are automatically adjusted for the physical size of the plot, and the arrow lengths can thus not be compared across plots, only the arrows within a plot can be compared.

Note that the ENVFIT analysis uses the ordination exes and therefore is restricted to relate the environmental factors to the part of the microbiome that is represented by these axes (and thus not the full microbiome variation). This fact is also relevant when comparing R2 (the squared correlation coefficient) from ADONIS and envfit: ADONIS decomposes the entire dissimilarity matrix into “variance” explained by each covariate. In NMDS (used by ENVFIT), you have reduced the entire dissimilarity matrix to three dimensions and then look for correlations in those three dimensions with the covariates. The “discrepancy” is therefore due to the fact that different amounts of variation are assessed by the two models. Therefore, if ADONIS gives higher R2 values, this suggests that the effect of the variable is on the parts of the dissimilarity matrix not represented well by the 3-d NMDS solution.

R2 - variation explained by the model of multiple regression; the square-root of this value is used to scale lengths of vectors (arrows) in the ordination diagrams (variables with higher squared (R2) are represented by longer arrows).
Pr(>r) - the significance of the multiple regression, calculated by permutation test. Indicates whether the variable is related to ordination axes more than if it is a randomly generated one.

Figure 23: Visualization of the ENVFIT analysis. The association of the evaluated environmental variables with the ordination axis is displayed by arrows, with longer arrows indicating a stronger association.

.	Bray		Jaccard		Euclidean
Variable	R2	p	R2	p	R2	p
Rt	0.288	0.002	0.598	0.001	0.505	0.001
Phosphorus	0.056	0.269	0.049	0.335	0.083	0.149
Potassium	0.143	0.034	0.188	0.016	0.21	0.007
Magnesium	0.375	0.001	0.189	0.009	0.212	0.01
Cobber	0.034	0.444	0.014	0.697	0.034	0.46
Organic material (%)	0.108	0.082	0.178	0.017	0.076	0.186
Clay (%)	0.203	0.005	0.397	0.001	0.477	0.001
Nitrogen (%)	0.212	0.004	0.051	0.333	0.026	0.581

Table 21: Results from ENVFIT analysis across all fields. The table shows results from ENVFIT analyses including samples from all fields. The table shows the obtained R-squared values and the corresponding p-values from the ENVFIT analysis, for each of the assessed variables in each of the three beta-diversity ordinations.

Non-linear relationships for continuous environmental variables

The ENVFIT model looks for linear relationships between the main variation of the microbiome (the separation of the samples along the first ordination axes) and the environmental variables. It can also be relevant to look for non-linear relationships - so called smooth surfaces - where the environmental variable for example is highest in the centrally located samples on the ordination plot and lower in samples located towards the edges. We use a mode called Ordisurf to look for such non-linear patterns. Ordisurf internally uses generalized additive models with integrated smoothness estimation, so-called GAMs, to fit the variable. Below is a table of the results from the analysis looking for smooth surfaces for the selected variables and further down are the surfaces illustrated on ordination plots.

Euclidean
	F	P
Rt	7.64	0.00e+00
Phosphorus	0.231	1.40e-01
Potassium	5.844	4.44e-06
Magnesium	4.219	2.16e-05
Cobber	1.088	1.98e-02
Organic_material_perc	4.611	3.34e-05
Clay_perc	5.855	7.48e-07
Nitrogen_perc	3.475	3.06e-04

Table 22: Results from non-linear analysis. The table shows results from the non-linear analyses performed using Ordisurf. The table shows the obtained F values and the corresponding p-values, for each of the assessed variables based on the Aitchison beta-diversity.

Figure 24: Illustration of the non-linear relationship between the ordination plot and the environmental variables. The non-linear relationships or smooth surfaces detected by ordisurf is illustrated on the ordination plots made using Aitchison diversity measures. Note that ordination plot is made per environmental variable.

Differences in alpha-diversity

As described in Report 2, alpha diversity is a measure of the diversity within (or complexity within) one microbiome community (or sample). We here evaluate the two measures of alpha diversity; Shannon and observed species (a measure of richness). The measures are introduced in Report 2.

Alpha-diversity in relation to each environmental variable

In the following plots the measures of alpha diversity and percentage of fungi is evaluated in relation to each key environmental variable. Following the plots is a table with the statistical analysis of the relations.

Grouping variables

JB value

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Figure 25: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Earthworm_status

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Figure 26: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Cold soil

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Figure 27: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Compact soil

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Figure 28: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Field well drained

alt text here )

Figure 29: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Mulching_of_straw

alt text here )

Figure 30: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Clovergrass within 3 years

alt text here )

Figure 31: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

No plough

alt text here )

Figure 32: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Conservation Agriculture

alt text here )

Figure 33: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Years since plowing

alt text here )

Figure 34: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Organic farm

alt text here )

Figure 35: Illustration of the alpha diversity levels across the levels or values of the environmental variable. Note that we have coded the none-organic fields as 0.

Years since turning organic

alt text here )

Figure 36: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Livestock

alt text here )

Figure 37: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Livestock manure

alt text here )

Figure 38: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Commercial fertilizer

alt text here )

Figure 39: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Vinasse

alt text here )

Figure 40: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Cast

alt text here )

Figure 41: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Degassed fertilizer

alt text here )

Figure 42: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Chalked

alt text here )

Figure 43: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Continous variables

Rt

alt text here )

Figure 44: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Phosphorus

alt text here )

Figure 45: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Potassium

alt text here )

Figure 46: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Magnesium

alt text here )

Figure 47: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Cobber

alt text here )

Figure 48: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Organic material (perc)

alt text here )

Figure 49: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Clay (perc)

alt text here )

Figure 50: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Nitrogen (perc)

alt text here )

Figure 51: Illustration of the alpha diversity levels across the levels or values of the environmental variable.

Statistical assessment

An analysis of Variance Model (ANOVA) was used to evaluate if the mean diversity differed significantly between the levels of grouping variables, and a robust linear regression was used to assess the relationship for continuous variables.

	Observed				Shannon				Perc. fungi
Variable	Df	Sum.Sq	F.value	P	Df	Sum.Sq	F.value	P	Df	Sum.Sq	F.value	P
JB_value	4	321689.317	4.327	4.97e-03	4	3.643	2.685	4.38e-02	4	9.641	4.858	2.54e-03
Earthworm_status	1	6682.713	0.276	6.02e-01	1	0.039	0.099	7.54e-01	1	0.16	0.239	6.27e-01
Cold_soil	1	5684.802	0.235	6.31e-01	1	0.633	1.655	2.05e-01	1	1.202	1.857	1.80e-01
Compact_soil	1	9475.267	0.392	5.34e-01	1	0.029	0.073	7.88e-01	1	0.956	1.465	2.32e-01
field_well_drained	1	36755.227	1.56	2.18e-01	1	0.35	0.899	3.48e-01	1	0.146	0.217	6.43e-01
Mulching_of_straw	1	25386.264	1.066	3.07e-01	1	0.294	0.755	3.89e-01	1	0.177	0.264	6.10e-01
Clovergrass_within_3_years	1	1167.361	0.048	8.28e-01	1	0.291	0.747	3.92e-01	1	0.186	0.279	6.00e-01
No_plough	1	12656.25	0.525	4.72e-01	1	0.107	0.271	6.05e-01	1	0.526	0.795	3.77e-01
ConservationAgriculture	1	38866.806	1.652	2.05e-01	1	0.097	0.245	6.23e-01	1	0.958	1.468	2.32e-01
Years_since_plowing	1	13450.161	0.559	4.59e-01	1	0.451	1.168	2.85e-01	1	1.717	2.7	1.07e-01
Organic_farm	1	5896.333	0.243	6.24e-01	1	0	0.001	9.81e-01	1	0.147	0.219	6.42e-01
Years_since_turning_organic	1	3402.007	0.14	7.10e-01	1	0.028	0.071	7.91e-01	1	0.369	0.554	4.61e-01
Livestock	1	422.002	0.017	8.96e-01	1	0.561	1.461	2.33e-01	1	1.773	2.793	1.01e-01
Livestock_manure	1	26004.167	1.093	3.01e-01	1	1.343	3.659	6.20e-02	1	0.175	0.262	6.11e-01
Commercial.fertilizer	1	5896.333	0.243	6.24e-01	1	0	0.001	9.81e-01	1	0.147	0.219	6.42e-01
Vinasse	1	4900.612	0.202	6.55e-01	1	0.415	1.072	3.06e-01	1	0.056	0.084	7.74e-01
Cast	1	25271.308	1.061	3.08e-01	1	0.427	1.103	2.99e-01	1	0.727	1.105	2.99e-01
Degassed.fertilizer	1	21748.097	0.91	3.45e-01	1	0.902	2.396	1.29e-01	1	0.069	0.103	7.50e-01
Chalked	1	8113.5	0.334	5.66e-01	1	0.103	0.26	6.12e-01	1	1.693	2.63	1.12e-01

Table 23: Results from ANOVA analysis across all fields. The table shows results from ANOVA analyses including samples from all fields. The table shows the obtained statistical values for each of the environmental variables (rows) and the three microbiome features (columns).

	Observed				Shannon				Perc. fungi
Variable	Estimate	SE	t.value	P	Estimate	SE	t.value	P	Estimate	SE	t.value	P
Rt	99.809	63.566	1.57	1.23e-01	-0.175	0.239	-0.733	4.67e-01	-0.309	0.241	-1.285	2.05e-01
Phosphorus	-4.484	29.902	-0.15	8.81e-01	-0.07	0.075	-0.931	3.57e-01	0.046	0.092	0.503	6.17e-01
Potassium	5.011	4.534	1.105	2.75e-01	0.026	0.025	1.051	2.99e-01	-0.039	0.018	-2.138	3.78e-02
Magnesium	-3.991	13.992	-0.285	7.77e-01	0.082	0.039	2.107	4.06e-02	-0.053	0.046	-1.155	2.54e-01
Cobber	-54.171	40.2	-1.348	1.84e-01	0.129	0.191	0.675	5.03e-01	-0.143	0.134	-1.071	2.90e-01
Organic_material_perc	-2.121	26.788	-0.079	9.37e-01	0.066	0.082	0.805	4.25e-01	-0.131	0.086	-1.527	1.34e-01
Clay_perc	-13.916	6.383	-2.18	3.44e-02	0.026	0.024	1.091	2.81e-01	-0.032	0.025	-1.296	2.01e-01
Nitrogen_perc	-284.116	679.028	-0.418	6.78e-01	2.28	1.703	1.339	1.87e-01	-4.77	2.191	-2.177	3.46e-02

Table 24: Results from linear regression analysis across all fields. The table shows results from the robust regression analyses including samples from all fields. The table shows the obtained statistical values for each of the environmental variables (rows) and the three microbiome features (columns). Note that a robust linear regression was used for the alpha diversity measures while a linear regression was used for fungi percentage. Also note that one sample was removed as outlier for the analysis of potassium.

Version information

Table 25: List of used software including the used R-programming environment packages.

Package	Version	Package	Version
OS	Ubuntu 20.04.4 LTS	mvtnorm	1.1-3
R	4.2.0	hms	1.1.2
utf8	1.2.2	evaluate	0.15
tidyselect	1.2.0	xtable	1.8-4
Rtsne	0.16	jpeg	0.1-9
munsell	0.5.0	readxl	1.4.2
codetools	0.2-18	compiler	4.2.0
withr	2.5.0	V8	4.3.0
colorspace	2.0-3	crayon	1.5.1
highr	0.9	minqa	1.2.4
rstudioapi	0.14	htmltools	0.5.2
robustbase	0.95-0	mgcv	1.8-40
ggsignif	0.6.3	pcaPP	2.0-1
labeling	0.4.2	tzdb	0.3.0
GenomeInfoDbData	1.2.8	rrcov	1.7-0
mnormt	2.0.2	RcppParallel	5.1.5
hwriter	1.3.2.1	lubridate	1.9.2
farver	2.1.0	DBI	1.1.2
rhdf5	2.40.0	sjlabelled	1.2.0
coda	0.19-4	dbplyr	2.1.1
vctrs	0.5.2	MASS	7.3-57
generics	0.1.2	boot	1.3-28
TH.data	1.1-1	ade4	1.7-19
xfun	0.31	car	3.0-13
timechange	0.2.0	cli	3.6.0
R6	2.5.1	parallel	4.2.0
isoband	0.2.5	insight	0.19.0
bitops	1.0-7	igraph	1.3.1
rhdf5filters	1.8.0	pkgconfig	2.0.3
DelayedArray	0.22.0	xml2	1.3.3
assertthat	0.2.1	foreach	1.5.2
multcomp	1.4-19	svglite	2.1.0
gtable	0.3.0	bslib	0.3.1
sandwich	3.0-1	multtest	2.52.0
rlang	1.0.6	webshot	0.5.3
systemfonts	1.0.4	estimability	1.3
splines	4.2.0	rvest	1.0.2
rstatix	0.7.0	digest	0.6.29
broom	0.8.0	rmarkdown	2.14
yaml	2.3.5	cellranger	1.1.0
reshape2	1.4.4	curl	4.3.2
abind	1.4-5	nloptr	2.0.2
modelr	0.1.8	lifecycle	1.0.3
backports	1.4.1	nlme	3.1-157
tools	4.2.0	Rhdf5lib	1.18.2
ellipsis	0.3.2	carData	3.0-5
jquerylib	0.1.4	viridisLite	0.4.0
biomformat	1.24.0	fansi	1.0.3
plyr	1.8.7	pillar	1.8.1
zlibbioc	1.42.0	fastmap	1.1.0
RCurl	1.98-1.6	httr	1.4.5
ggpubr	0.4.0	DEoptimR	1.0-11
cowplot	1.1.1	survival	3.3-1
zoo	1.8-10	glue	1.6.2
haven	2.5.0	zip	2.2.0
cluster	2.1.3	png	0.1-7
fs	1.5.2	iterators	1.0.14
magrittr	2.0.3	stringi	1.7.6
reprex	2.0.2	sass	0.4.1
tmvnsim	1.0-2	latticeExtra	0.6-29

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.3175	0.002	0.1159	0.181	0.1334	0.018
Class	0.3194	0.001	0.1358	0.019	0.1422	0.002
Order	0.2228	0.003	0.1362	0.018	0.1467	0.001
Family	0.1639	0.021	0.1394	0.003	0.1437	0.001
Genus	0.1628	0.005	0.1377	0.001	0.1399	0.001
ASV	0.1547	0.003	0.1507	0.001	0.142	0.001

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0687	0.055	0.0447	0.089	0.0213	0.424
Class	0.0405	0.142	0.0395	0.056	0.0238	0.283
Order	0.0322	0.174	0.0305	0.116	0.025	0.226
Family	0.0616	0.019	0.0285	0.109	0.0281	0.092
Genus	0.0561	0.013	0.0263	0.127	0.0297	0.036
ASV	0.0428	0.031	0.0385	0.007	0.0398	0.001

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0442	0.138	0.0229	0.368	0.0131	0.784
Class	0.0319	0.217	0.0338	0.104	0.0176	0.666
Order	0.0145	0.575	0.0248	0.284	0.0148	0.882
Family	0.0116	0.758	0.0214	0.447	0.0181	0.738
Genus	0.0161	0.64	0.0176	0.769	0.0193	0.63
ASV	0.0193	0.532	0.0172	0.824	0.0189	0.714

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0084	0.581	0.0117	0.679	0.013	0.796
Class	0.0111	0.54	0.0242	0.349	0.0224	0.375
Order	0.0068	0.866	0.028	0.168	0.0193	0.556
Family	0.0138	0.656	0.0225	0.37	0.0208	0.464
Genus	0.013	0.764	0.0195	0.603	0.0191	0.683
ASV	0.0122	0.898	0.0187	0.685	0.0199	0.576

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0017	0.898	0.044	0.101	0.048	0.021
Class	0.0027	0.891	0.0359	0.086	0.0391	0.02
Order	0.0177	0.478	0.0287	0.188	0.0322	0.077
Family	0.0179	0.518	0.025	0.26	0.0288	0.087
Genus	0.0219	0.421	0.0198	0.566	0.0217	0.407
ASV	0.0188	0.569	0.0286	0.075	0.0268	0.092

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0106	0.537	0.0125	0.669	0.0175	0.571
Class	0.0194	0.34	0.0146	0.75	0.0173	0.685
Order	0.0162	0.499	0.0104	0.948	0.0231	0.323
Family	0.0164	0.564	0.0231	0.325	0.028	0.094
Genus	0.0141	0.695	0.0267	0.124	0.0274	0.08
ASV	0.02	0.466	0.0264	0.142	0.0255	0.141

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0228	0.316	0.012	0.687	0.0117	0.855
Class	0.0157	0.459	0.0181	0.608	0.0161	0.76
Order	0.0096	0.767	0.0286	0.178	0.0254	0.24
Family	0.0123	0.726	0.025	0.258	0.024	0.265
Genus	0.013	0.753	0.0238	0.252	0.0229	0.289
ASV	0.0126	0.874	0.0199	0.556	0.0211	0.433

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0454	0.138	0.035	0.171	0.017	0.587
Class	0.0205	0.325	0.0169	0.62	0.0127	0.923
Order	0.0194	0.361	0.025	0.245	0.0219	0.356
Family	0.025	0.229	0.0215	0.411	0.0207	0.464
Genus	0.0336	0.093	0.0238	0.224	0.0214	0.409
ASV	0.029	0.127	0.0245	0.179	0.0243	0.171

	Bray-Curtis		Jaccard		Aitchison
Taxa level	R2	p	R2	p	R2	p
Phylum	0.0682	0.057	0.0234	0.396	0.0175	0.585
Class	0.0514	0.101	0.0162	0.666	0.0163	0.754
Order	0.0219	0.355	0.028	0.183	0.0185	0.631
Family	0.0171	0.514	0.0205	0.479	0.0194	0.596
Genus	0.0244	0.314	0.0223	0.325	0.0222	0.348
ASV	0.0213	0.401	0.0174	0.785	0.0196	0.593

Statistical Analysis Report

Samples from 2022

Biomcare ApS

31/05/2023

Introduction to the biostatistical analysis

Evaluation of overall microbiome profiles

Stacked barplots

Phylum

Class

Order

Overall microbiome communities related to factor variables

Visualization by ordination (beta-diversity)

Permutational Multivariate Analysis of Variance

JB value

Earthworm status

Cold_soil

Compact soil

Field well drained

Mulching of straw

Clovergrass within 3 years

No plough

Conservation Agriculture

Years since plowing

Organic farm

Years since turning organic

Livestock

Livestock manure

Commercial fertilizer

Vinasse

Cast

Degassed fertilizer

Chalked

Overall microbiome communities related to continuous variables

ENVFIT

Non-linear relationships for continuous environmental variables

Differences in alpha-diversity

Alpha-diversity in relation to each environmental variable

Grouping variables

JB value

Earthworm_status

Cold soil

Compact soil

Field well drained

Mulching_of_straw

Clovergrass within 3 years

No plough

Conservation Agriculture

Years since plowing

Organic farm

Years since turning organic

Livestock

Livestock manure

Commercial fertilizer

Vinasse

Cast

Degassed fertilizer

Chalked

Continous variables

Rt

Phosphorus

Potassium

Magnesium

Cobber

Organic material (perc)

Clay (perc)

Nitrogen (perc)

Statistical assessment

Version information