Testing the effectiveness of locally prepared liquid ... · CONTACT Pip Tilbrook, Project Officer DATE February 2019 PUBLICATION DATA Cook, B.A. & Ford, B., 2019. Testing the effectiveness

Testing the effectiveness of locally prepared liquid

biofertilizers for improving soil health in southwestern

Australia

February 2019

Report No CENRM152

Prepared by Barbara Cook & Ben Ford

Centre of Excellence in Natural Resource Management,

University of Western Australia

Centre of Excellence in Natural Resource Management Report CENRM117

Centre of Excellence in Natural Resource Management Report CENRM152 Page i

© Centre of Excellence in Natural Resource Management,

University of Western Australia.

TITLE Testing the effectiveness of locally prepared liquid biofertilizers

for improving soil health in southwestern Australia.

PRODUCED BY Centre of Excellence in Natural Resource Management,

University of Western Australia,

P.O. Box 5771, Albany, WA 6332.

Telephone: (08) 9842 0837

Fax: (08) 9842 8499

PRODUCED FOR Torbay Catchment Group Inc.

CONTACT Pip Tilbrook, Project Officer

DATE February 2019

PUBLICATION DATA Cook, B.A. & Ford, B., 2019. Testing the effectiveness of locally

prepared liquid biofertilizers for improving soil health in

southwestern Australia. Report No CENRM152. Centre of

Excellence in Natural Resource Management, University of

Western Australia.

Cover Photograph Preparation of LAMP

DISCLAIMER This report has been prepared on behalf of and for the exclusive use of the Client, and is subject to and issued in accordance with the agreement between the client and Centre of Excellence in Natural Resource Management. The Centre of Excellence in Natural Resource Management accepts no liability or responsibility whatsoever for it in respect of any use of or reliance upon this report by any third party. In particular, it should be noted that this report is a professional assessment and opinion only, based on the scope of the services defined by the Client, budgetary and time constraints imposed by the Client, the information supplied by the Client (and its agents), and the method consistent with the preceding. Centre of Excellence in Natural Resource Management could not attempt to verify the accuracy or completeness of all information supplied. Copying of this report or parts of this report is not permitted without explicit authorisation of the Client and the Centre of Excellence in Natural Resource Management.

Centre of Excellence in Natural Resource Management Report CENRM152 Page ii

Table of Contents Executive summary ................................................................................................................................. 1

Introduction ............................................................................................................................................ 5

Materials and methods ........................................................................................................................... 6

Study sites ........................................................................................................................................... 6

Preparation of biofertilizer ................................................................................................................. 8

Experimental design............................................................................................................................ 8

Collection of soil samples .................................................................................................................... 9

Soil moisture content .......................................................................................................................... 9

Soil organic carbon, electrical conductivity and pH ............................................................................ 9

Microbial activity............................................................................................................................... 10

DNA extraction, PCR amplification and bioinformatic analysis ........................................................ 10

Statistical analysis of soil chemistry and microbial activity .............................................................. 11

Statistical analysis of bacterial communities .................................................................................... 11

Results ................................................................................................................................................... 13

Soil moisture, electrical conductivity, pH, and total organic carbon ................................................ 13

Pasture/Livestock .......................................................................................................................... 13

Orchards ........................................................................................................................................ 15

Gardens ......................................................................................................................................... 17

Bacterial communities ...................................................................................................................... 22

Full data ......................................................................................................................................... 22

Tilbrook property .......................................................................................................................... 25

Langridge property ........................................................................................................................ 29

Pasfield property ........................................................................................................................... 33

Denmark TAFE ............................................................................................................................... 38

CO2 .................................................................................................................................................... 42

Chromatography ............................................................................................................................... 44

Tilbrook property .......................................................................................................................... 46

Langridge property ........................................................................................................................ 49

Pasfield property ........................................................................................................................... 51

Denmark TAFE ............................................................................................................................... 53

Discussion.............................................................................................................................................. 56

Acknowledgements ............................................................................................................................... 57

References ............................................................................................................................................ 57

Appendices ............................................................................................................................................ 60

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Tables Table 1 Physico-chemical characteristics of soil in paddocks with and without livestock present at

the Tilbrook site treated with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling

event; R2 = 2nd post-treatment sampling event; R3 = 3rd post-treatment sampling event. ................. 14

Table 2 Physico-chemical characteristics of soil from the Langridge property treated with

biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-treatment

sampling event. ..................................................................................................................................... 15

Table 3. Physico-chemical characteristics of soil from the Pasfield property orchard treated with


sampling event. ..................................................................................................................................... 17

Table 4. Physico-chemical characteristics of soil from the Denmark TAFE orchard treated with


sampling event. ..................................................................................................................................... 17

Table 5. Physico-chemical characteristics of soil from the Pasfield property vegetable garden treated

with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-

treatment sampling event. ................................................................................................................... 18

Table 6. Summary of OTU (L6) richness among treatments. ............................................................... 23

Figures Figure 1. Study site locations near Albany in south western Australia. ................................................. 7

Figure 2. Timeline of biofertiliser application and soil sampling events. .............................................. 9

Figure 3. PCA ordinations of soil health variables for pastures with a) horses, b) no livestock, and c)

sheep. .................................................................................................................................................... 19

Figure 4. PCA ordinations of soil heath variables for orchards at a) Denmark TAFE and b) Pasfield

property. ............................................................................................................................................... 20

Figure 5. PCA ordinations of soil heath variables for gardens at a) Denmark TAFE and b) Pasfield

property. ............................................................................................................................................... 21

Figure 6. nMDS of level six OTU community structure across properties, treatments, and times. .... 24

Figure 7. Relative abundances of common bacteria phyla and their orders at the Tilbrook property;

a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment. ........................................ 26

Figure 8. L6 OTU richness over environmental gradients and OTU richness, Shannon-Wiener

diversity, and evenness over treatments and time at the Tilbrook property. ..................................... 27

Figure 9. nMDS ordination of community structure at the Tilbrook property. ................................... 28

Figure 10. Relative abundances of common bacteria phyla and their orders at the Langridge

property; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment. ........................ 30


diversity, and evenness over treatments and time at the Langridge property. ................................... 31

Figure 12. nMDS ordination of community structure at the Langridge property. .............................. 32

Figure 13. Relative abundances of common bacteria phyla and their orders in the Garden at the

Pasfield property; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment. ........... 34

Centre of Excellence in Natural Resource Management Report CENRM152 Page iv

Figure 14. Relative abundances of common bacteria phyla and their orders in the Orchard at the

Pasfield property; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment. ........... 35


diversity, and evenness over treatments and time at the Pasfield property. ...................................... 36

Figure 16. nMDS ordination of community structure at the Pasfield property. .................................. 37

Figure 17. Relative abundances of common bacteria phyla and their orders in the Garden at the

Denmark TAFE; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment. ............... 39


diversity, and evenness over treatments and time at Denmark TAFE. ................................................ 40

Figure 19. nMDS ordination of community structure at Denmark TAFE. ............................................ 41

Figure 20. Associations of CO2 burst test results and measured soil environmental variables. ......... 43

Figure 21. PCA ordination of differences in measured chromatogram variables among locations,

treatments, and sampling events. Plot b) is a zoomed view of plot a). ............................................... 45

Figure 22. Tilbrook chromatogram variables. Different lower-case letters indicate significant

differences. ........................................................................................................................................... 47

Figure 23. PCA ordination depicting relations among chromatogram variables at the Tilbrook

property. TR = "Total radius (mm)", CZR = "Central zone radius (mm)", MZB = "Median zone breadth

(mm)", OZB = "Outer zone breadth (mm)" ........................................................................................... 48

Figure 24. Langridge chromatogram variables. ................................................................................... 50

Figure 25. PCA ordination depicting relations among chromatogram variables at the Langridge



Figure 26. Pasfield property chromatogram variables. Different lower-case letters indicate

significant differences. .......................................................................................................................... 52

Figure 27. PCA ordination depicting relations among chromatogram variables at the Pasfield



Figure 28. Denmark TAFE chromatogram variables. Different lower-case letters indicate significant

differences. ........................................................................................................................................... 54

Figure 29. PCA ordination depicting relations among chromatogram variables at Denmark TAFE. TR

= "Total radius (mm)", CZR = "Central zone radius (mm)", MZB = "Median zone breadth (mm)", OZB =

"Outer zone breadth (mm)" .................................................................................................................. 55

Centre of Excellence in Natural Resource Management Report CENRM152 Page 1

Executive summary Despite the fact that application of biofertilizers can lead to significant improvements in on-

farm fertility and substantial reductions in input costs, the potential of using biofertilizers to

improve soil condition has received sporadic attention to date.

OBJECTIVES OF STUDY - This project is aimed at assessing the potential for improving agricultural soil health using biofertilizers prepared using local area microbes and fungi. Following the production and application of liquid live microbial biofertilizer to agricultural trial plots, selected soil health parameters were measured (soil moisture content, organic carbon, conductivity, pH, microbial activity and DNA sequence diversity) at four sites to test whether the biofertilizer had led to a noticeable improvement in soil health. The specific questions addressed were:

o What effect does the application of a liquid live microbial biofertilizer have on carbon storage, microbial activity and water holding capacity in agricultural soils?

o Does the application of this biofertilizer have the same effect on carbon storage, microbial activity and water holding capacity in paddocks with and without livestock access?

SOIL MOISTURE, CARBON, CONDUCTIVITY AND PH - In a univariate approach, each soil parameter measured was compared among treatments and sampling events. For the pasture sites (Tilbrook and Langridge), soil moisture, organic carbon, conductivity, and pH did not differ between treatments for any sampling events, with or without the presence of livestock. For the orchard sites (Pasfield and DenmarkTafe), both electrical conductivity and pH differed between treatments at the Pasfield property for the R1 sampling event and total organic carbon differed between treatments for the R2 sampling event at the Pasfield site. In the Pasfield property vegetable garden, electrical conductivity, pH, and total organic carbon differed between treatments for the R1 sampling event, with total organic carbon also differing between treatments for the R2 sampling event. In the Denmark TAFE vegetable garden, pH differed between treatments for all sampling events.

SOIL MOISTURE, CARBON, CONDUCTIVITY AND PH - When data were analysed using a multivariate approach, pasture soil health variables were found to differ over sampling events in the presence of livestock. In the pasture containing horses, differences in soil health variables over time were between the R1 and R2 sampling events, and R3 with all other sampling events, while in the pasture containing sheep, soil health variables differed among all sampling events. For the orchard sites, soil health variables did not differ with biofertiliser application or over time in the Denmark TAFE orchard. At the Pasfield property orchard, soil health variables differed according to both biofertiliser application and time. The difference over time in the Pasfield orchard was found between the baseline and R1 sampling events. Soil variables did not differ between treatments for any sampling events in the Denmark TAFE orchard. In the Denmark TAFE garden, soil health variables differed over time, but not with application of biofertiliser. Differences among soil variables were found between the baseline and R2 sampling events. Soil health variables did not differ over time or with biofertiliser application in the Pasfield garden.

BACTERIAL DIVERSITY MEASURED USING DNA SEQUENCING - A total of 955 Operational Taxonomic Units (OTUs) were identified (Level 6) over all properties. Denmark TAFE demonstrated the greatest OTU richness, with only small differences among treatments. Likewise, the Langridge property also demonstrated high levels of OTU richness, with small differences among treatments. The Pasfield and Tilbrook properties demonstrated the greatest increases in OTU richness over time, with R2 values often similar to those of the Denmark TAFE and Langridge property.


BACTERIAL DIVERSITY MEASURED USING DNA SEQUENCING - In a multivariate context, OTU communities differed among properties sampled, time, cultivations, and the presence of livestock, but not between treatments. Separation among properties is strongly evident among baseline samples, however by the R2 sampling event, OTU community composition among properties and treatments had become homogenised.

BACTERIAL DIVERSITY MEASURED USING DNA SEQUENCING - The microbial biota at the Tilbrook property was dominated by the Proteobacteria, Actinobacteria, and Acidobacteria in all samples. Baseline control samples also possessed high relative abundances of Bacteroidetes, which were replaced by Firmicutes in the R2 samples. Treatment samples (both baseline and R2) also contained high relative abundances of Verrucomicrobia. OTU richness demonstrated a decrease with increasing TOC, with no other gradients in OTU richness detected along other measured environmental variables. OTU richness and Shannon-Wiener diversity displayed similar patterns over treatments, with both significantly increasing in the presence of horses, and richness increasing between baseline and R2 sampling events. OTU evenness significantly increased in the presence of livestock, and decreased between baseline and R2 sampling events, with the magnitude of decrease between baseline and R2 samples being greater in the treatment than control samples. No differences in community composition were found among treatments at the Tilbrook property, although OTU community composition does appear to be less variable in the presence of livestock.

BACTERIAL DIVERSITY MEASURED USING DNA SEQUENCING - Proteobacteria was typically the dominant microbial phylum at the Langridge property, followed by Actinobacteria and Acidobacteria. Firmicutes was often also a major phylum present, although replaced by Bacteroidetes in the Treatment R2 samples. OTU richness displayed no patterns with measured environmental variables at the Langridge property, and no differences were found in OTU richness, Shannon-Wiener diversity, or evenness between treatments or sampling events. OTU community composition differed between baseline and R2 sampling events, but not treatment at the Langridge property.

BACTERIAL DIVERSITY MEASURED USING DNA SEQUENCING - Very little differences are evident in the OTU phyla composition at the Pasfield property between times or treatments. Dominant phyla were Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes. No trends in OTU richness with measured environmental variables were evident, however, differences were observed between Orchard and Garden samples with respect to pH, and to a lesser extent, TOC. OTU richness, Shannon-Wiener diversity, and evenness were lower in the Garden than Orchard samples at the Pasfield property. Both OTU richness and Shannon-Wiener diversity increased between baseline and R2 samplesand both Shannon-Wiener diversity and evenness increased in samples with LLM. OTU communities differed between Orchard and Garden samples at the Pasfield property, with no effect of biofertiliser, or time.

BACTERIAL DIVERSITY MEASURED USING DNA SEQUENCING - Relative abundances of OTU phyla and orders at Denmark TAFE were very stable over treatments and time, with assemblages being dominated by Proteobacteria, Actinobacteria, Bacteroidetes, and Acidobacteria. OTU richness decreased with TOC at the Denmark TAFE but did not display trends with any other measured environmental variables. OTU richness, Shannon-Wiener diversity, and evenness were all lower in Orchard than Garden samples. OTU communities differed between both Orchard and Garden samples and baseline and R2 samples.

BACTERIAL ACTIVITY AS MEASURED BY SOLVITA BURST TESTS – Carbon dioxide (CO2) output was positively associated with TOC, and differed among locations and between cultivations. CO2 bursts were greatest for the Tillbrook and Langridge properties, and lowest at the Pasfield property. Similarly, CO2 bursts were greatest in Pasture samples (Tillbrook and Langridge properties), and lowest in Orchard samples, which were composed mostly of samples from the Pasfield property.


CHROMATOGRAPHY - Chromatogram features differed among study properties and sampling events, but not between treatments. Chromatograms from all combinations of property and sampling events were found to differ, with the exception of chromatograms from Denmark TAFE baseline with the Langridge property chromatograms and chromatograms from the Langridge baseline samples with those of the Tilbrook R3 sample chromatograms.

CHROMATOGRAPHY - With the exception of median zone breadth, outer zone breadth, and

number of channels, chromatograms from the Tilbrook property soil samples differed

between sampling events, with no differences between treatments within a sampling event.

Chromatograms from the third sampling event displayed greater central zone radius and

total radius. Greater median zone breadth and outer zone breadth, increased development

of channels and spikes, increased number of spikes and colour intensity were features

associated with chromatograms from the second sampling event. The characteristics of

chromatograms from the baseline samples tended to between that of the second and third

sampling events.

CHROMATOGRAPHY - Individually, no differences were found in chromatogram

characteristics between treatments or sampling events for the Langridge property.

Differences in chromatogram characteristics were found between sampling events when all

characteristics were considered simultaneously. Chromatograms from baseline samples at

the Langridge property typically possessed greater total radius and central zone radius,

which were highly positively correlated, and negatively correlated to spike development.

Chromatograms from the second sampling event tended to possess greater spike

development and number of spikes.

CHROMATOGRAPHY - The channel and spike development characteristics of chromatograms from soil samples from the Pasfield property differed between sampling events and cultivations, but not treatments. In a multivariate context, chromatogram characteristics differed between sampling events and cultivation, with an interaction between the two indicating that the effect of sampling event is dependent on the type of cultivation and vice versa. This interaction is likely due to the lack of a difference between garden baseline and R2 chromatograms, but a difference between orchard baseline and R2 chromatogram structures. Chromatograms from samples taken from the orchard typically exhibited greater numbers of spikes and channels, and increased median zone breadth, total radius and central zone radius. Chromatograms from the garden displayed greater development of spikes and channels, and greater colour intensity. No differences were found between baseline and R2 chromatograms from garden samples. Orchard chromatograms from the R2 samples displayed greater spike and channel development and colour intensity.

CHROMATOGRAPHY - Considering chromatogram characteristics each in isolation, only

channel development and central zone radius was found to differ between both cultivations

and sampling events, but not treatment at the Denmark TAFE. When considered

simultaneously, chromatogram variables also differed between cultivations and sampling

events, but not treatments. Chromatograms from garden samples tended to possess a

greater number of channels and spikes, with increased development of spikes and channels

and colour intensity. Median zone breadth and channel development were typically greater

in R2 than baseline chromatograms, while central zone radius tended to be greater in

baseline samples.


CONCLUSION - The use of a liquid biofertilizer prepared using native microbes and fungi did not result in significant increases in soil organic carbon concentrations, soil water moisture content, or increased diversity and activity of microbial populations at any of the sites examined. While the biofertilizer prepared from native microbes was not effective for increasing soil carbon, it is possible that biofertilisers prepared from animal manure and inoculated with specific microorganisms might prove more effective for increasing soil carbon levels.


Introduction

Prolonged use of inorganic fertilisers along with other agricultural practices can lead to poor soil quality. To counteract this, there has been growing interest in the use of biofertilizers that contain living microorganisms to increase organic matter content and improve fertility of agricultural soils (Mayer et al. 2010; Bhardwaj et al. 2014; Mishra and Dash 2014). In particular, liquid biofertilizers are considered a promising and updated technology (Pindi et al., 2012; Bhardwaj et al., 2014). These liquid formulations are based on the intensive culture of a diversity of microorganisms that can play a vital role in promoting soil health by enhancing nutrient recycling and suppressing soilborne plant pathogens (Zhao et al., 2005). Of advantage is that these natural fertilisers do not produce toxic runoff, thus minimising impacts on receiving waterways. It is possible that successful production and application of biofertilizers can lead to significant improvements in on-farm fertility and substantial reductions in input costs. Despite these potential benefits, the potential of using biofertilizers to improve soil condition has received sporadic attention to date (Debska et al. 2016).

It has been claimed (e.g. Chen, 2006; Mishra and Dash 2014; Debska et al. 2016) that use of biofertilizers can increase the organic matter content of soils (and subsequently, soil organic carbon), and improve the water holding capacity of soils. For example, Wu et al. (2005) showed that application of biofertilizer inoculated with rhizobacteria and mycorrhizae resulted in significant increases in soil organic carbon and nutrients, while El-Hamid (2013) showed an increase in organic matter of irrigated, sandy soils in Egypt when treated with compost and biofertilizers.

Cultivation of soils has led to a reduction of soil organic carbon across the Australian agricultural landscape (Luo et al. 2010); modelling of soil organic carbon change across the Wheatbelt in Western Australia showed a decreasing trend during the period 1960-2010 (Wang et al. 2013). The importance of sequestering more carbon into soils to mitigate greenhouse emissions has been recognised globally, including by the Australian government through its Carbon Farming Initiative program (www.agriculture.gov.au/water/policy/carbon-farming-initiative). Even a small increase in soil organic carbon that might result from improved management practices such as the use of biofertilizers could lead to significant increases in storage of carbon across the agricultural landscape of Australia (Rossel et al. 2014). This has led to a call for improved agricultural practices that include changes in fertilization strategies (Wang et al. 2010).

Although there are a number of studies that have looked at the effect of commercially produced biofertilizers such as ‘Effective Microorganisms’ (Mayer et al. 2010) and UGmax (Zarzecka et al. 2018) , there are few studies that have addressed the impacts of local, or ‘indigenous’ biofertilizer application on soil properties (Nisha et al. 2007). In potting experiments conducted in India, Nisha et al. (2007) showed that the application of biofertilizer enriched with indigenous cyanobacterial strains isolated from native soil resulted in an increase of 50% in soil organic carbon (initially 0.34%) and an increase of 15% in total Kjeldahl nitrogen after 3-6 months.

Of interest is the promotion of biofertilisers that are prepared using local area microbes and fungi in Australia. At workshops held in Australia and abroad, participating landowners are shown how to produce and apply a liquid live microbial biofertilizer. For example, workshops run by Eugenio Gras of Mashumus in Mexico and other parts of Latin America (http://mashumus.com) and Kym Cruze of Regen Ag (www.regenag.com) have proved popular. Participants at these events are exposed to practical demonstrations of how to produce fermented biofertilizers, including from local area microbes and fungi, and are also shown how to use chromatography for the qualitative assessment of soil health.

In the interest of developing sustainable agricultural practices, it is important to test the feasibility of using these sorts of biofertilizers in regional WA. Successful implementation of biofertilizer use can

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lead to more sustainable on-farm and off-farm practices, protecting soil condition and off-farm habitats such as wetlands and waterways. This project is aimed at assessing the potential for improving agricultural soil health using biofertilizers prepared using local area microbes and fungi. Following the production and application of liquid live microbial biofertilizer to agricultural trial plots, selected soil health parameters were measured to test whether the biofertilizer had led to a noticeable improvement in soil health. The specific questions addressed were:

(i) What effect does the application of a liquid live microbial biofertilizer have on carbon storage, microbial activity and water holding capacity in agricultural soils?

(ii) Does the application of this biofertilizer have the same effect on carbon storage, microbial activity and water holding capacity in paddocks with and without livestock access?

Based on findings of previous studies, it was hypothesized that soils treated with biofertilizer would

have higher levels of carbon, greater water holding capacity and a more diverse and active microbial

community when compared with untreated soils.

Materials and methods

Study sites The study was conducted at four sites varying in soil (DenmarkTafe - karri loam; Langridge - peaty sand; Pasfield - coastal sand; Tilbrook - sand and loam) and climate characteristics (Figure 1, Table S1). Following the workshop and field days, a call for participating landowners was made. Landowners were chosen for their location and differing soil types and land-uses. A total of 4 trial sites was chosen.

1. Denmark Tafe – vegetable beds and orchard in karri loam 2. Pasfield’s Elleker – raised vegetable beds and orchard in coastal sands 3. Langridge’s Bornholm – pasture paddocks used for biodynamic beef and sheep production,

peaty sand 4. Tilbrook’s Torbay – pasture paddocks used for horse grazing and sometimes poultry, sand

and loam Denmark Tafe – with its proximity to the town of Denmark this site was probably cleared about 100 years ago. More recently, it has been used as an organic vegetable garden and orchard.

Langridge – this site was cleared about 100 years ago and was part of a railway siding yard, in recent years it has been used for grazing by cattle and sheep. It is part of a biodynamic property.

Pasfield – this site has only been fairly recently cleared, and is run on organic principles.

Tilbrook – this site has most likely been cleared for about 100 years. Through the late 1980s and 1990s it was part of a dairy, it then moved to beef cattle grazing. From the late 1980s until about 2005 farm inputs were very high, with the former owner stating he used twice the recommended rate of fertiliser. Since then inputs have been very low, with only the addition of lime in the last 7 years. Approximately 18 months ago cattle grazing changed to horses.


Figure 1. Study site locations near Albany in south western Australia.


Preparation of biofertilizer The biofertilizer trialled in this study was produced on site in a two stage process. Stage one involved the production and fermentation of ‘Local Area Microbe Preparation’ (LAMP) made from leaf litter mixed with bran, molasses and milk. Once fermented LAMP is stored and can be kept for many years in the same way that dried or compressed yeast is stored. Stage two of the process involved the production of ‘Liquid Local Microbes’ (LLM) using a small quantity of LAMP, water, molasses and milk for activation of microbes, usually over a four day period depending on ambient temperature.

Preparation of ‘Local Area Microbe Preparation’ (LAMP) was undertaken by participating landowners following attendance at a three day biofertilizer workshop run by RegenAG on 21-23 September 2016. The workshop was hosted by Kym Kruse, a permaculture designer, consultant and educator from Queensland who works with farmers, natural resource management groups, local government and industry representatives, schools, sustainable backyard developers and indigenous communities across a wide range of bioregions, farms, watersheds and urban environments in Australia. Kym’s background is in integrated rice and duck cultivation, permaculture design and regenerative agriculture methodology (www.regenag.com). The content of this workshop was based on similar workshops run by Eugenio Gras of Mashumus in Mexico and other parts of Latin America (http://mashumus.com. Preparation of biofertilizer based on native microbes was undertaken as follows. Sites with thick leaf or other organic litter (usually under trees) were identified and the litter scraped back to expose the soil-litter junction. Participants searched for white streaks and white blobs; these were taken to be an indication of active fungal activity. Litter containing fungus and surrounding soil containing beneficial bacteria was collected and placed onto a flat surface and broken up to form smaller fragments. Hard bits of bark or dry leaves were discarded. The volume of the remaining litter was measured, and an equal quantity of bran was added and the litter and bran combination was well mixed. Whey or milk and molasses was added in the required amounts (2L molasses and 0.2 L milk per 10 L leaf litter and 10 L bran) and mixed thoroughly to form a mix that ‘hung together’ when squeezed tightly in the hand; water was added in cases where the mix was too dry. The mixture was then placed into an air tight container, which was taped during the process to expel excess air. A 5cm air gap was left at the in the top of the container. Containers were then sealed and placed in a warm location for six weeks. Following this period, lids were removed and the progress of the mixture was checked to ensure that it had a strong but pleasant yeasty smell. These LAMP mixtures were kept in sealed containers until needed for biofertilizer application.

Live local microbes (LLM) liquid biofertilizer was prepared by measuring out 2 L of LAMP, placing it into a mesh bag and suspending this bag into a container of 18 L of water stored in a warm location. Molasses (200 ml) and milk (200 ml) was added to this brewing container, and the mixture was well stirred to ensure that all ingredients were well mixed. The container was covered with a loose fitting lid that allowed a passage of air to the mixture. After four days of brewing, the LLM was diluted with rain or dam water, and this was applied to plots using watering cans (Table 1). Biofertilizer was applied at a rate of 1.6 L of LLM (topped up with water to make a total of 10 L) per plot on all application dates for all trial sites with the exception of two application dates (01/06/2017 and 30/06/2017) when 3.2 L of LLM (topped up with water to make a total of 20 L) was applied to each treatment plot at the Tilbrook trial site (Figure 2).

Experimental design The experimental design of the study incorporated ‘treatment’ and ‘control’ replicate plots, with selected soil attributes (organic carbon, moisture, pH, conductivity and microbial activity) measured ‘before’ and ‘after’ in ‘control’ and ‘treatment’ plots (BACI design) over a 12 month period. Two factors, fertiliser application and livestock access were examined in separate single-factor experiments, where biofertilizer was either applied (‘LLM treatment’), or not applied (‘Control’) to

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plots located in paddocks with and without livestock access, orchards and garden beds. All treatments were equally represented with the same numbers of replicates. As there was no prior knowledge of the likely variability that would be encountered, we adopted the ‘rule of 10’ for replication, where 10 replicate plots each for control and treatment were sampled in each paddock, orchard or garden. Whilst increasing the number of replicates substantially beyond this number would have increased our ability to detect differences in treatment means, particularly if treatment means are close together or the variation encountered is relatively large, time and financial constraints made this level of replication unrealistic for this project.

Plot size varied according to the sites (Langridge and Tilbrook: 5X5m; DenmarkTafe and Pasfield: 1X1m in size). In a randomised block design approach, each treatment (‘LLM’ and ‘Control’) was randomised within each replicate block, so that each block thus contained one plot of each treatment in the experiment. One of the most commonly used designs in agricultural field trials, this randomised block design reduced the effect of ‘experimental error’ (unwanted variability; e.g. due to a gradient of soil fertility), and thus this approach improved the capacity of the trial to test experiment effects.

Collection of soil samples For each sampling event (‘baseline’, 1st , 2nd and 3rd post treatment), between three and five soil cores (soil cores of 4.5 cm diameter) were collected randomly at a depth of 0-10cm from each plot and combined as a composite sample in a bucket (Figure 2, Table S1). After being thoroughly mixed, 250 g of soil was collected and placed in sealed bags and stored in a cool place during the soil sampling. In the laboratory, these soil samples were then air dried at 40oC for 24 hours and passed through a 2 mm sieve to separate the coarse materials.

Figure 2. Timeline of biofertiliser application and soil sampling events.

Soil moisture content Soil moisture content was measured in the field with a WaterScout SM100 Soil Moisture Sensor attached to a FieldScout Soil Sensor Reader (catalogue number 6466). The sensor is made up of two electrodes that function as a capacitor, with the surrounding soil serving as the dielectric. An 80 MHz oscillator drives the capacitor and a signal proportional to the soil’s dielectric permittivity is converted to the output signal. The WaterScout SM100 sensor measures volumetric water content. The volumetric water content (VWC) is the ratio of the volume of water in a given volume of soil to the total soil volume.

Soil organic carbon, electrical conductivity and pH Sub samples of field moist soil were oven dried (70º C, 24 hrs) and sieved (<2mm). Portions of the soil fine fraction (ie <2mm) were analysed for electrical conductivity (EC) and pH in both water and 0.01M CaCl2 solution. A further portion of the soil fine fraction was finely ground in a mortar and


pestle and analysed for total organic carbon (expressed as percent C per 100g of soil). These analyses were performed according to the Australian standards (Rayment and Lyons 2011).

Microbial activity Soil microbial respiration was measured using the Solvita soil CO2 burst test (Haney et al. 2008). This cost effective, rapid method has proved effective in discriminating soils, with microbial activity shown to be significantly correlated with soil organic carbon and microbial abundance (Munoz-Rojas et al. 2016). For this method, 40g of dried soil (< 3% soil moisture) was placed in a 50cc beaker and deionised water added based on the settled volume of the soil until soil was moistened to 50% water-filled pore space. The beaker and a CO2 probe were placed into a sealed glass jar, and after 24 hours, the probe was removed and inserted into a digital color reader. Solvita fertility test color and level of CO2-C expressed in mg/kg (ppm) were recorded.

Microbial activity was also visualized and assessed using Pfeiffer’s Round Filter Chromatography (Follador, 2015). Finely ground soil mixed together with a solution of sodium hydroxide was passed through Whatman #1 150mm diameter filter paper treated with silver nitrate, creating a ‘picture’ reflective of the microbial activity of that soil. Briefly, the filter paper was treated by placing a cylindrical paper wick in a 0.5% solution of silver nitrate, and allowing the circular filter paper to absorb the solution, which spread by capillary action, to 40mm from the centre. The wick was then removed, and the paper dried. The soil tested was mixed with a 0.1 to 1% solution of sodium hydroxide and was left for six hours before the prepared filter paper was allowed to absorb this solution and the substance spread over the paper. When it had spread to 60mm from the centre, the wick was removed and the paper dried. The paper was then exposed to indirect sunlight to let the image develop. The array of forms, colours and patterns that resulted was compared amongst treatments using the approach adopted by Kokornaczyk et al. 2017. Chromatograms were quantitatively assessed through a suite of continuous and ordinal variables. Each chromatogram was divided into 4 quarters and two opposing quarters were selected. Within each of these quarters, five measurements were made of each of Total radius (TR) (mm), Central Zone (CZ) radius (mm), Median Zone (MZ) breadth (mm), Outer Zone (OZ) breadth (mm). In addition, in each quarter, counts were made of the number of channels, spikes, and concentric rings. Channel structure (1=absent, 5=fully developed), Spikes (1=absent, 5=fully developed), and Colour intensity (1=blurred, 5=intense) were also scored for each quarter. Averages were taken over measured variables to characterise each chromatogram.

DNA extraction, PCR amplification and bioinformatic analysis High throughput 16S-rRNA-based sequencing was used to document bacterial communities associated with the biofertilizer treatments in selected samples, where these communities were identified and characterised based on direct targeting, amplification and sequencing of the V3 region of bacterial 16S rRNA genes. DNA was extracted in triplicate from 0.25g of soil samples using a Powersoil-htp96 Soil DNA isolation kit (MO BIO laboratories, CA, USA) following manufacturer’s guidelines with minor modifications (carrying out freeze-thaw cycle (x3) after the addition of solution C1). Extracted DNA was quantified using QUBIT2.0 fluorometer (Life Technologies, USA) and 2 ng µl-1 of DNA was used as template for subsequent PCR amplification using 515F/806R primer set (targeting V4 of 16S rRNA gene for both bacteria and archaeal domains) [34]. See Kumaresan et al. (2017) for detailed PCR reagent and thermal conditions. PCR amplicons were checked for both length and specificity through a 2.5 w/v agarose gel electrophoresis and purified using AMPure (Beckman Coulter, Australia). Equimolar pool containing PCR amplicons from all samples was sequenced using an Ion Torrent PGM platform (316 chip).


All bio-informatic analysis were performed within QIIME platform [35]. Briefly, 1150283 raw reads were filtered (minimum average quality = 20, maximum/minimum sequence length=350/130 Total organic carbon respectively, no primer mismatch or barcode error allowed, maximum length of homopolymers=15 and maximum number of ambiguous bases=6). 193347 filtered sequences were further checked for chimeric sequences using the Usearch algorithm (ver 6.1) rejecting ~11.9%. 177074 high quality reads were then subjected for de novo OTUe picking at 97% sequence identity using Uclust (ver1.2.22q) and subsequent taxonomy was assigned through RDP classifier [36] using the Greengenes database (ver 13.8) [37]. OTU Table rarefied at 2700 reads was then used for multivariate statistical analysis. All amplicon sequences data associated with this report will be deposited in ENA under the project accession number.

Statistical analysis of soil chemistry and microbial activity All statistical analyses were conducted using the statistical software package GenStat v. 18 (VSN International, Hertfordshire, UK) and R. In a univariate approach, one-way and two-way analysis of variance (ANOVA) were used to detect differences in parameters measured. Levene tests were used to check for equality of variances and Shapiro-Wilk tests were used to check whether the residuals were normally distributed. For cases of non-homogeneity of residual variances or evidence of non-normality, data were log-transformed to ensure that the assumptions of ANOVA were met.

Multivariate techniques were employed to assess changes in soil health variables due to biofertiliser application, over time, the presence of livestock, or cultivation technique. Principle Components Analysis (PCA) ordinations were created to visualise the patterns in the data, and Permutational Multivariate Analysis of Variance (PERMANOVA) tests were used to detect significant differences, based on Euclidean distances of the soil health variables, and 9999 permutations of the data. Pairwise comparisons performed where relevant using holm adjustment for multiple comparisons and 9999 permutations of the data. Associations between CO2 output and soil environmental variables were tested for using linear regression. Differences in CO2 output due to treatments was tested for using PERMANOVA based on Euclidean distances and 9999 permutations of the data. For each property, differences in each chromatogram variable between treatments, sampling events, and (if applicable) cultivations (i.e. vegetable garden or orchard) were assessed through Kruskal Wallis tests. Significant differences were investigated through Dunn tests with holm adjustment for multiple comparisons. In a multivariate context, relations among chromatogram variables were visually assessed through principle components analyses (PCA), and differences between treatments, sampling events, and cultivations were tested for through permutational analysis of variance (PERMANOVA), based on Euclidean distances of the data and 9999 permutations.

Statistical analysis of bacterial communities Across all properties, level six operational taxonomic unit (OTU) communities were visually assessed through non-metric multidimensional scaling (nMDS). Tests for difference among locations, treatments, cultivations, and time assessed through PERMANOVA, on Bray Curtis distances of the relative abundance data, using 9999 permutations of the data. Pairwise comparisons were performed on significant terms in the full PERMANOVA test with holm correction for multiple comparisons, again on Bray Curtis distances of the abundance data and 9999 permutations.

For each property, mean relative abundances of the four major OTU phyla and the main lower level (class or order) within each of the phyla were visually compared. Univariate diversity indices of level six OTUs (richness, Shannon-Wiener, and evenness) were calculated for each sample. Due to low levels of replication, univariate PERMANOVA tests were performed on each of the indices to compare values among treatments, time, and cultivation/livestock (if applicable). Univariate


PERMANOVA tests were conducted on Euclidean distances of each the indices, using 9999 permutations of the data. Associations between level six OTU richness and each of soil moisture, electrical conductivity (EC (μS/cm), pH (CaCl2), and total organic carbon (TOC (%)), were tested for using linear regression. OTU community structure was assessed at each property, first visually through nMDS, followed by PERMANOVA tests among treatments, time, and cultivation/livestock (if applicable), based on Bray Curtis distances and 9999 permutations.

Multivariate analyses were performed using R v3.4.3 (R Core Team 2017), and the ‘vegan’ (Oksanen et al. 2018), ‘RVAideMemoire’ (Hervé 2018), and ‘dunn.test’ (Dinno, 2017) packages.


Results

Soil moisture, electrical conductivity, pH, and total organic carbon

Pasture/Livestock No soil variables differed between treatments for any sampling events at the Tilbrook property with or without the presence of livestock (Table 1). Similarly, no soil variables differed between treatments at the Langridge property (Table 2).

In a multivariate context, pasture soil health variables were found to differ over sampling events in the presence of livestock (PERMANOVA - horses, p  <0.001, Figure 3a, Table S2; sheep, p < 0.001, Figure 3c, Table S3), and in the absence of livestock (PERMANOVA, p <0.001, Figure 3b, Table S4). In the pasture containing horses, differences in soil health variables over time were between the R1 and R2 sampling events, and R3 with all other sampling events (Table S5), while in the pasture containing sheep, soil health variables differed among all sampling events (Table S6). In the pasture samples from horse paddocks, soil variables from the R3 sampling event differed from all others (Table S7).


Table 1 Physico-chemical characteristics of soil in paddocks with and without livestock present at the Tilbrook site treated with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-treatment sampling event; R3 = 3rd post-treatment sampling event.

Description Parameter Sampling event Control Treatment F P value

Livestock absent Soil moisture (%) B 10.512 8.892 1.630 0.218 R1 27.220 25.710 0.324 0.576 R2 0.268 0.296 0.023 0.882 R3 4.116 4.217 0.179 0.678

EC (µS/cm) B 139.400 156.300 0.649 0.431 R1 166.400 173.900 0.017 0.899 R2 174.900 178.400 0.018 0.895 R3 9.646 10.258 0.518 0.482

pH (Ca2Cl) B 4.256 4.105 0.411 0.529 R1 4.289 4.152 0.277 0.605 R2 4.138 4.142 0.001 0.976 R3 14.024 14.371 0.020 0.890

TOC (%) B 6.565 7.184 1.776 0.199 R1 6.796 6.647 0.138 0.715 R2 5.523 5.487 0.012 0.916 R3 237.444 287.444 1.819 0.196

Livestock present Soil moisture (%) B 7.506 8.330 0.418 0.526 R1 26.850 42.000 1.550 0.229 R2 1.542 1.206 2.074 0.167 R3 4.724 4.769 0.535 0.475

EC ~ (mu * S/cm) B 163.100 179.700 0.475 0.500 R1 156.900 177.100 0.503 0.487 R2 200.000 209.400 0.093 0.764 R3 6.736 8.132 2.192 0.158

pH ~ (CaCl[2]) B 4.801 4.749 0.463 0.505 R1 4.651 4.739 2.420 0.137 R2 4.723 4.693 0.175 0.681 R3 21.358 23.662 0.165 0.690

TOC (%) B 5.835 6.703 3.630 0.073 R1 5.199 5.713 1.316 0.266 R2 5.161 4.558 2.912 0.105 R3 242.000 272.889 0.062 0.807


Table 2 Physico-chemical characteristics of soil from the Langridge property treated with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-treatment sampling event.

Parameter Sampling event Control Treatment F P value

Soil moisture (%) B 37.020 38.663 0.361 0.555 R1 15.460 17.330 0.191 0.668 R2 0.410 0.413 0.011 0.916

EC (µS/cm) B 126.200 117.100 0.288 0.598 R1 118.700 128.300 0.672 0.423 R2 160.400 164.600 0.070 0.795

pH (Ca2Cl) B 4.696 4.716 0.011 0.917 R1 3.939 4.099 0.882 0.360 R2 4.571 4.607 0.036 0.852

TOC (%) B 5.595 5.500 0.003 0.957 R1 4.637 4.908 0.341 0.567 R2 6.027 6.477 0.631 0.437

Orchards Both electrical conductivity and pH differed between treatments at the Pasfield property for the R1 sampling event. Total organic carbon differed between treatments for the R2 sampling event in the Pasfield property orchard samples (


Table 3).

Soil health variables did not differ with biofertiliser application or over time in the Denmark TAFE orchard (PERMANOVA - Treatment, p = 0.755, Time, p = 0.406; Figure 4a, Table S8). At the Pasfield property orchard, soil health variables differed according to both biofertiliser application and time (PERMANOVA - Treatment, p = 0.030, Time, p = 0.004; Figure 4b, Table S9). The difference over time in the Pasfield orchard was found between the baseline and R1 sampling events (Table S10). Soil variables did not differ between treatments for any sampling events in the Denmark TAFE orchard (Table 4).


Table 3. Physico-chemical characteristics of soil from the Pasfield property orchard treated with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-treatment sampling event.



EC (µS/cm) B 60.800 92.600 1.932 0.182 R1 42.700 61.400 19.373 <0.001 R2 59.200 53.300 2.328 0.144

pH (Ca2Cl) B 3.377 3.927 4.818 0.042 R1 3.167 3.607 23.231 <0.001 R2 3.598 3.610 0.013 0.910

TOC (%) B 2.207 2.305 0.076 0.785 R1 2.245 2.115 0.292 0.596 R2 1.963 3.031 14.146 0.001

Table 4. Physico-chemical characteristics of soil from the Denmark TAFE orchard treated with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-treatment sampling event.



EC (µS/cm) B 197.500 168.000 1.782 0.211 R1 293.500 275.167 0.060 0.811 R2 225.500 249.600 1.359 0.282

pH (Ca2Cl) B 6.515 6.368 1.226 0.294 R1 6.245 6.307 0.665 0.434 R2 6.293 6.332 1.903 0.210

TOC (%) B 4.907 5.375 1.892 0.199 R1 6.660 6.142 3.778 0.081 R2 6.015 5.932 0.037 0.852

Gardens In the Pasfield property vegetable garden, electrical conductivity, pH, and total organic carbon differed between treatments for the R1 sampling event, with total organic carbon also differing between treatments for the R2 sampling event (). In the Denmark TAFE vegetable garden, pH differed between treatments for the baseline sampling event, and total organic carbon differed between treatments for the R2 sampling event (Error! Reference source not found.).

In the Denmark TAFE garden, soil health variables differed over time, but not with application of biofertiliser (PERMANOVA - Treatment, p = 0.953, Time, p = 0.025; Figure 5a, Table S11). Differences


among soil variables were found between the baseline and R2 sampling events (Table S12). Soil health variables did not differ over time or with biofertiliser application in the Pasfield garden (PERMANOVA - Treatment, p = 0.061, Time, p = 0.274; Figure 5b, Table S13).

Table 5. Physico-chemical characteristics of soil from the Pasfield property vegetable garden treated with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-treatment sampling event.



EC (µS/cm) B 251.800 555.000 0.203 0.663 R1 168.600 254.167 6.178 0.035 R2 247.200 403.333 4.249 0.069

pH (Ca2Cl) B 6.194 5.842 0.605 0.457 R1 5.714 6.082 18.433 0.002 R2 5.976 6.102 2.441 0.153

TOC (%) B 3.832 4.180 0.105 0.753 R1 3.190 4.680 14.660 0.004 R2 2.930 6.380 31.949 <0.001

Table 6. Physico-chemical characteristics of soil from the Denmark TAFE vegetable garden treated with biofertilizers. B = Baseline sampling; R1 = 1st post-treatment sampling event; R2 = 2nd post-treatment sampling event.


Soil moisture (%) B 20.083 18.310 0.751 0.397 R1 29.890 23.337 4.159 0.056

R2 14.560 13.360 0.000 0.996

EC (µS/cm) B 166.600 180.000 0.852 0.368 R1 364.900 327.300 0.000 0.984

R2 238.900 246.800 0.136 0.717

pH (Ca2Cl) B 6.177 6.505 52.835 <0.001 R1 6.284 6.357 4.414 0.050

R2 6.126 6.349 38.447 <0.001

TOC (%) B 3.638 3.931 4.207 0.055 R1 4.515 4.659 0.504 0.487

R2 3.852 4.159 1.906 0.184


Figure 3. PCA ordinations of soil health variables for pastures with a) horses, b) no livestock, and c) sheep.


Figure 4. PCA ordinations of soil heath variables for orchards at a) Denmark TAFE and b) Pasfield property.

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Figure 5. PCA ordinations of soil heath variables for gardens at a) Denmark TAFE and b) Pasfield property.

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Bacterial communities

Full data A total of 955 OTUs were identified (L6 taxonomy) over all properties. Denmark TAFE demonstrated the greatest OTU richness, with only small differences among treatments. Likewise, the Langridge property also demonstrated high levels of OTU richness, with small differences among treatments (Table 6). The Pasfield and Tilbrook properties demonstrated the greatest increases in OTU richness over time, with R2 values often similar to those of the Denmark TAFE and Langridge property (Table 6).

In a multivariate context, OTU communities differed among properties sampled, time, cultivations, and the presence of livestock, but not between treatments (PERMANOVA - Treatment, p = 0.242, Time, p = 0.007, Cultivations, p <0.001, livestock, p = 0.042; Figure 6, Table S14). Separation among properties is strongly evident among baseline samples, however by the R2 sampling event, OTU community composition among properties and treatments had become homogenised (Figure 6).

The significant PERMANOVA interaction between property and sampling event (Table S15) is likely attributable to features such as the OTU community composition of Denmark TAFE baseline samples being different from all other samples, while no differences were detected among Tilbrook and Pasfield OTU communities (Table S16). The interaction detected between property and cultivation is a result of OTU communities not differing between Tilbrook and Langridge pastures or between the Denmark TAFE orchard and garden. (Table S17)


Table 6. Summary of OTU (L6) richness among treatments.

Property Treatment Cultivation Livestock Sampling event

Mean richness

S.E.

Denmark TAFE Control Garden - Baseline 325 12.53

Garden - R2 327 5.292

Orchard - Baseline 283.5 10.5

Orchard - R2 317 NA

LLM Garden - Baseline 322.667 8.686

Garden - R2 318.333 2.728

Orchard - Baseline 261 NA

Langridge Control Paddock Present Baseline 257.8 7.946

Present R2 276 10.383 LLM Present Baseline 269.6 11.382 Present R2 280.2 9.335

Pasfield Control Garden - Baseline 199.667 13.296 Garden - R2 292.5 12.5

Orchard - Baseline 136.6 25.254 Orchard - R2 252.25 13.555 LLM Garden - Baseline 192.333 3.18

Garden - R2 335.333 15.301 Orchard - Baseline 151.5 24.808 Orchard - R2 286 9.292

Tilbrook Control Paddock Absent Baseline 225 26.102 Absent R2 269.333 18.774 Present Baseline 267.5 26.5

Present R2 302 2

LLM Absent Baseline 175.333 9.404

Absent R2 272.333 2.963

Present Baseline 253 44

Present R2 291.5 0.5


Figure 6. nMDS of level six OTU community structure across properties, treatments, and times.


Tilbrook property The microbial flora/fauna at the Tilbrook property was dominated by the Proteobacteria, Actinobacteria, and Acidobacteria, in all of baseline, R2, treatment, and control samples. Baseline control samples also possessed high relative abundances of Bacteroidetes, which were replaced by Firmicutes in the R2 samples. Treatment samples (both baseline and R2) also contained high relative abundances of Verrucomicrobia (Figure 7).

L6 OTU richness demonstrated a decrease with increasing TOC, with no other gradients in OTU richness detected along other measured environmental variables (Figure 8). OTU richness and Shannon-Wiener diversity displayed similar patterns over treatments, with both significantly increasing in the presence of horses, and richness increasing between baseline and R2 sampling events (PERMANOVA – Rich - Treatment, p = 0.207, Time, p = 0.002, Livestock, p = 0.015; SW - Treatment, p = 0.666, Time, p = 0.078, Livestock, p < 0.001; Figure 8, Table S18 & Table S19). OTU evenness significantly increased in the presence of livestock, and decreased between baseline and R2 sampling events, with the magnitude of decrease between baseline and R2 samples being greater in the treatment than control samples (PERMANOVA – Treatment, p = 0.292, Time, p = 0.048, Livestock, p = 0.009; Figure 8, Table S20).

No differences in community composition were found among treatments at the Tilbrook property, although OTU community composition does appear to be less variable in the presence of livestock (Figure 9, Table S21).


Figure 7. Relative abundances of common bacteria phyla and their orders at the Tilbrook property; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment.


Figure 8. L6 OTU richness over environmental gradients and OTU richness, Shannon-Wiener diversity, and evenness over treatments and time at the Tilbrook property.


Figure 9. nMDS ordination of community structure at the Tilbrook property.


Langridge property Proteobacteria was typically the dominant microbial phylum at the Langridge property, followed by Actinobacteria and Acidobacteria. Firmicutes was often also a major phylum present, although replaced by Bacteroidetes in the Treatment R2 samples (Figure 10).

OTU richness displayed no patterns with measured environmental variables at the Langridge property (Figure 11), and no differences were found in OTU richness, Shannon-Wiener diversity, or evenness between treatments or sampling events (Table S22 - Table S24).

OTU community composition differed between baseline and R2 sampling events, but not treatment at the Langridge property (PERMANOVA - Treatment, p = 0.511, Time, p = 0.012; Figure 12, Table S25). The separation in OTU community structure over time is not strongly evident in Figure 12, this may be a result of the relatively high stress in the ordination, suggesting it may not be an accurate two-dimensional depiction of community structure, particularly with few sample numbers.


Figure 10. Relative abundances of common bacteria phyla and their orders at the Langridge property; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment.


Figure 11. L6 OTU richness over environmental gradients and OTU richness, Shannon-Wiener diversity, and evenness over treatments and time at the Langridge property.


Figure 12. nMDS ordination of community structure at the Langridge property.


Pasfield property Very little differences are evident in the OTU phyla composition at the Pasfield property between times or treatments. Dominant phyla were Proteobacteria, Actinobacteria, Acidobacteria, and Bacteroidetes (Figure 13).

No trends in OTU richness with measured environmental variables were evident, however, differences were observed between Orchard and Garden samples with respect to pH, and to a lesser extent, TOC (Figure 15). OTU richness, Shannon-Wiener diversity, and evenness were lower in the Garden than Orchard samples at the Pasfield property (PERMANOVA – Rich - Treatment, p = 0.054, Time, p <0.001, Cultivation, p= 0.003; SW - Treatment, p = 0.041, Time, p <0.004, Cultivation, p= 0.001; Even - Treatment, p = 0.032, Time, p  = 0.789, Cultivation, p= 0.001; Figure 15, Table S26 - Table S28). Both OTU richness and Shannon-Wiener diversity increased between baseline and R2 samples (Figure 15, Table S26 & Table S27), and both Shannon-Wiener diversity and evenness increased in samples with LLM (Figure 15, Table S27 & Table S28).

OTU communities differed between Orchard and Garden samples at the Pasfield property, with no effect of biofertiliser, or time (PERMANOVA - Treatment, p = 0.255, Time, p = 0.028, Cultivation, p <0.001; Figure 16, Table S29).


Figure 13. Relative abundances of common bacteria phyla and their orders in the Garden at the Pasfield property; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment.


Figure 14. Relative abundances of common bacteria phyla and their orders in the Orchard at the Pasfield property; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment.


Figure 15. L6 OTU richness over environmental gradients and OTU richness, Shannon-Wiener diversity, and evenness over treatments and time at the Pasfield property.


Figure 16. nMDS ordination of community structure at the Pasfield property.


Denmark TAFE Relative abundances of OTU phyla and orders at Denmark TAFE were very stable over treatments and time, with assemblages being dominated by Proteobacteria, Actinobacteria, Bacteroidetes, and Acidobacteria (Figure 17).

OTU richness decreased with TOC at the Denmark TAFE but did not display trends with any other measured environmental variables (Figure 18). OTU richness, Shannon-Wiener diversity, and evenness were all lower in Orchard than Garden samples (PERMANOVA – Rich - Treatment, p = 0.640, Time, p  = 408, Cultivation, p= 0.002; SW - Treatment, p = 0.899, Time, p <0.046, Cultivation, p= 0.004; Even - Treatment, p = 0.883, Time, p  = 0.014, Cultivation, p= 0.004; Figure 18, Table S30 - Table S32). The interaction between Treatment and Cultivation found for evenness may be an artefact of unbalanced replication, with only one sample being treated with LLM from the Orchard. Both OTU Shannon-Wiener diversity and evenness increased between baseline and R2 sampling events (Figure 18, Table S31 & Table S32).

OTU communities differed between both Orchard and Garden samples and baseline and R2 samples (PERMANOVA - Treatment, p = 0.631, Time, p <0.001, Cultivation, p = 0.013; Figure 19, Table S33). As with the interaction in the evenness results, the interactions found between Treatment and Sampling event with Cultivation for the OTU community structure are likely to be due to single samples in a factor combination.


Figure 17. Relative abundances of common bacteria phyla and their orders in the Garden at the Denmark TAFE; a) baseline control, b) baseline treatment, c) R2 control, d) R2 treatment.


Figure 18. L6 OTU richness over environmental gradients and OTU richness, Shannon-Wiener diversity, and evenness over treatments and time at Denmark TAFE.


Figure 19. nMDS ordination of community structure at Denmark TAFE.


CO2 CO2 output was positively associated with TOC (Figure 20). CO2 output differed among locations and between cultivations (PERMANOVA - Treatment, p = 0.770, Locations, p <0.001, Cultivation, p= 0.006; Figure 20, Table S34). CO2 bursts were greatest for the Tillbrook and Langridge properties, and lowest at the Pasfield property. Similarly (and likely to be related to location), CO2 bursts were greatest in Pasture samples (Tillbrook and Langridge properties), and lowest in Orchard samples, which were composed mostly of samples from the Pasfield property (Figure 20).


Figure 20. Associations of CO2 burst test results and measured soil environmental variables.


Chromatography In a multivariate context, the chromatogram features differed among study properties and sampling

events, but not between treatments (PERMANOVA - Treatment, p = 0.517, Time, p <0.001, Location,

p <0.001; Figure 21, Table S35). Chromatograms from all combinations of property and sampling

events were found to differ, with the exception of chromatograms from Denmark TAFE baseline with

the Langridge property chromatograms and chromatograms from the Langridge baseline samples

with those of the Tilbrook R3 sample chromatograms (Table S36).


Figure 21. PCA ordination of differences in measured chromatogram variables among locations, treatments, and sampling events. Plot b) is a zoomed view of plot a).


Tilbrook property With the exception of median zone breadth, outer zone breadth, and number of channels,

chromatograms from Tilbrook property soil samples differed between sampling events, with no

differences between treatments within a sampling event (Figure 22). Similarly, in a multivariate

context, chromatograms of soil samples from the Tilbrook property differed among sampling events,

but not treatments (PERMANOVA - Treatment, p = 0.717, Time, p <0.001; Figure 23, Table S37).

Chromatograms from the third sampling event displayed greater central zone radius and total

radius. Greater median zone breadth and outer zone breadth, increased development of channels

and spikes, increased number of spikes and colour intensity were features associated with

chromatograms from the second sampling event. The characteristics of chromatograms from the

baseline samples tended to between that of the second and third sampling events (Figure 22 &

Figure 23).


Figure 22. Tilbrook chromatogram variables. Different lower-case letters indicate significant differences.


Figure 23. PCA ordination depicting relations among chromatogram variables at the Tilbrook


(mm)", OZB = "Outer zone breadth (mm)"


Langridge property Individually, not differences were found in chromatogram characteristics between treatments or

sampling events for the Langridge property (Figure 24). Differences in chromatogram characteristics

were found between sampling events when all characteristics were considered simultaneously

(Figure 25, Table S38).

Chromatograms from baseline samples at the Langridge property typically possessed greater total

radius and central zone radius, which were highly positively correlated, and negatively correlated to

spike development (Figure 25). Chromatograms from the second sampling event tended to possess

greater spike development and number of spikes (Figure 24 & Figure 25)


Figure 24. Langridge chromatogram variables.


Figure 25. PCA ordination depicting relations among chromatogram variables at the Langridge



Pasfield property The channel and spike development characteristics of chromatograms from soil samples from the

Pasfield property differed between sampling events and cultivations, but not treatments (Figure 26).

In a multivariate context, chromatogram characteristics differed between sampling events and

cultivation, with an interaction between the two indicating that the effect of sampling event is

dependent on the type of cultivation and vice versa (PERMANOVA - Treatment, p = 0.588, Time, p =

0.003, Cultivation, p <0.001; Figure 27; Table S39). This interaction is likely due to the lack of a

difference between garden baseline and R2 chromatograms, but a difference between orchard

baseline and R2 chromatogram structures (Table S40).

Chromatograms from samples taken from the orchard typically exhibited greater numbers of spikes

and channels, and increased median zone breadth, total radius and central zone radius.

Chromatograms from the garden displayed greater development of spikes and channels, and greater

colour intensity (Figure 26 & Figure 27). No differences were found between baseline and R2

chromatograms from garden samples (Table S40). Orchard chromatograms from the R2 samples

displayed greater spike and channel development and colour intensity (Figure 26 & Figure 27).


Figure 26. Pasfield property chromatogram variables. Different lower-case letters indicate significant differences.


Figure 27. PCA ordination depicting relations among chromatogram variables at the Pasfield



Denmark TAFE Considering chromatogram characteristics each in isolation, only channel development and central

zone radius was found to differ between both cultivations and sampling events, but not treatment at

the Denmark TAFE (Figure 28). When considered simultaneously, chromatogram variables also

differed between cultivations and sampling events, but not treatments (PERMANOVA - Treatment,

p = 0.772, Time, p <0.001, Cultivation, p <0.001; Figure 29, Table S41).

Chromatograms from garden samples tended to possess a greater number of channels and spikes,

with increased development of spikes and channels and colour intensity (Figure 28 & Figure 29).

Median zone breadth and channel development were typically greater in R2 than baseline

chromatograms, while central zone radius tended to be greater in baseline samples (Figure 28 &

Figure 29).


Figure 28. Denmark TAFE chromatogram variables. Different lower-case letters indicate significant differences.


Figure 29. PCA ordination depicting relations among chromatogram variables at Denmark TAFE. TR = "Total radius (mm)", CZR = "Central zone radius (mm)", MZB = "Median zone breadth (mm)", OZB = "Outer zone breadth (mm)"


Discussion

The use of a liquid biofertilizer prepared using native microbes and fungi did not result in significant

increases in soil organic carbon concentrations at any of the sites examined. This finding is in

contrast to those of some other studies. For example, Nisha et al. (2007) were able to demonstrate

a significant (50%) increase in soil organic carbon after 3-6 months in pots treated with biofertilizer

powder made from three indigenous cyanobacterial strains (Anabaena doliolum HH-209,

Cylindrospermum sphaerica HH-202 and Nostoc calcicola HH201) isolated from native soil. Similarly,

a study conducted in Egypt (El-Hamid et al. 2013) demonstrated significant increases (1.37-1.79

times) in soil organic matter content 4-6 months after treatment with a biofertilizer inoculated with

nitrogen-fixing Cyanobacteria (Bacillus polymxa). The experimental soils used by these authors were

reported to have an organic matter content of 0.19%.

By global standards, soils in Western Australia are low in carbon, with soils in southwestern Australia

characterised by levels of SOC ranging from 0.1 to 14.8% (in 0–10 cm samples), with a mean of 2.3%

(Griffin et al. 2013). Across all sites and sampling events in this study, mean soil organic carbon

ranged from 4.56 to 6.70%, falling into this reported range. While the biofertilizer prepared from

native microbes was not effective for increasing soil carbon, it is possible that biofertilisers prepared

from animal manure (as recommended by Wang et al. 2010) and inoculated with specific

microorganisms might prove more effective for increasing soil carbon levels. For example, Wu et al.

(2005) demonstrated an improvement in soil organic carbon in pot experiments carried out in Hong

Kong using biofertilizer manufactured (by V-mark Resources) from chicken manure, powder of

phosphate rock and peat moss inoculated with a nitrogen-fixing bacterium (Azotobacter

chroococcum), P and K solubilizers (Bacillus megaterium and B. mucilaginous) and arbuscular

mycorrhizal fungi.

Soil treated with biofertilizer did not have significantly higher levels of soil moisture when compared

with soils from untreated plots. This finding was in contrast to that of El-Hamid et al. (2013) who

demonstrated increased levels of soil moisture in sandy soil in Egypt that had been treated with

biofertilizers only, and even greater improvement in levels of soil moisture when the biofertilizer

was combined with compost and humic acid.

Testing of other commercially prepared biofertilizers such as ‘UGmax’ and ‘Effective

Microorganisms’ has revealed mixed results in terms of their effectiveness for improving soil

condition. Both of these commercially produced biofertilizers are a mix of lactic acid and

photosynthetic bacteria, Azotobacter, Pseudomonas, yeast and actinomycetes (Zarzecka et al. 2018),

with the details of this make up often unknown due to commercial secrecy. Some studies have

reported no improvement in soil condition as a result of applying UGmax, other have reported an

improvement in soil health after applying the biofertilizer (see Zarzecka et al.2018 and references

therein).

In conclusion, in our study, liquid biofertilisers were not applied together with other organic

amendments such as compost. El-Hamid et al. (2013) showed that mixing biofertilisers together

with organic amendments such as compost and humic acids was more effective in improving soil

condition than applying each of these soil conditioners alone.


Acknowledgements We would like to express our gratitude to the landowners who allowed us to carry out this research

on their properties – without their consent and participation, this investigation could not have been

undertaken. This project was supported by funding from the Western Australian Government’s

State NRM Program.

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Appendices

Table S1. Details of study locations and dates for application of biofertilizer and soil sampling events.

Trial site Type Plot size Biofertilizer application dates

Baseline sampling 1st post-treatment sampling

2nd post-treatment sampling

DenmarkTafe Orchard, garden bed 1X1m 21/06/2017 21/06/2017 1/11/2017 16/02/2018 12/09/2017 13/12/2017

Langridge Pasture paddock 1X1m 19/07/2017 19/07/2017 8/11/2017 14/02/2018 18/09/2017 24/10/2017

Pasfield Orchard, vegetable beds 1X1m 24/05/2017 24/05/2017 8/11/2017 15/02/2018 06/06/2017 1/07/2017 10/07/2017 16/11/2017

Tilbrook Pasture paddock 5X5m 10/05/2017 10/05/2017 25/10/2017 20/02/2018 01/06/2017 30/06/2017 27/05/2018 1/10/2018 12/11/2018


Table S2. PERMANOVA results for pasture containing horses.

df ss ms F p value

Treatment 1 1.243 1.243 0.762 0.485

Time 3 190.018 63.339 38.83 <0.001

Treatment * Time 3 2.555 0.852 0.522 0.849

Residuals 70 114.183 1.631 0.371

Total 77 308 1

Table S3. PERMANOVA results for pasture containing sheep.

df ss ms F p value

Treatment 1 58 58.2 0.050 0.930

Time 2 35424 17712.2 15.084 < 0.001

Treatment * Time 2 937 468.7 0.399 0.729

Residuals 54 63409 1174.2

Total 59 99829

Table S4. PERMANOVA results for pasture containing no livestock.

df ss ms F p value

Treatment 1 0.449 0.449 0.506 0.638

Time 3 244.563 81.521 91.908 <0.001

Treatment * Time 3 0.9 0.3 0.338 0.953

Residuals 70 62.089 0.887 0.202

Total 77 308 1

Table S5. Pair wise PERMANOVA results among sampling events for pasture containing horses.

Baseline R1 R2

R1 0.168 - -

R2 0.168 0.034 -

R3 0.001 0.001 0.001


Table S6. Pair wise PERMANOVA results among sampling events for pasture containing sheep.

Baseline R1

R1 0.033 -

R2 < 0.001 < 0.001

Table S7. Pair wise PERMANOVA results among sampling events for pasture containing no livestock.

Baseline R1 R2

R1 0.330 - -

R2 0.330 0.330 -

R3 0.001 0.001 0.001

Table S8. PERMANOVA results for the Denmark TAFE orchard.

df ss ms F p value

Treatment 1 943 943 0.026 0.755

Time 2 62525 31262 0.874 0.406

Treatment * Time 2 4018 2009 0.056 0.900

Residuals 27 965301 35752

Total 32 1032786

Table S9. PERMANOVA results for the Pasfield orchard.

df ss ms F p value

Treatment 1 3319 3319.1 2.644 0.030

Time 2 7203 3601.5 2.869 0.004

Treatment * Time 2 3668 1834.2 1.461 0.174

Residuals 54 67792 1255.4

Total 59 81982

Table S10. Pair wise PERMANOVA results among sampling events for the Pasfield orchard.

Baseline R1

R1 0.022 -

R2 0.061 0.213



Table S11. PERMANOVA results for the Denmark TAFE garden.

df ss ms F p value

Treatment 1 596 596 0.014 0.953

Time 2 304008 152004 3.542 0.025

Treatment * Time 2 7922 3961 0.092 0.920

Residuals 54 2317135 42910

Total 59 2629661

Table S12. Pair wise PERMANOVA results among sampling events for the Denmark TAFE garden.

Baseline R1

R1 0.090 -

R2 < 0.001 0.220

Table S13. PERMANOVA results for the Pasfield garden.

df ss ms F p value

Treatment 1 270137 270137 3.201 0.061

Time 2 226462 113231 1.342 0.274

Treatment * Time 2 67306 33653 0.399 0.770

Residuals 27 2278861 84402

Total 32 2842766


Table S14. Permanova results comparing level six OTU communities across locations, treatments, time, cultivation, and presence of livestock.

Df SS MS F p value

Location 3 2.544 0.848 9.911 < 0.001 Treatment 1 0.103 0.103 1.204 0.242

Sampling event 1 0.286 0.286 3.341 0.007

Cultivation 1 1.393 1.393 16.284 < 0.001

Livestock 1 0.192 0.192 2.245 0.042

Location * Treatment 3 0.209 0.070 0.813 0.684

Location * Sampling event 3 0.449 0.150 1.751 0.033

Treatment * Sampling event 1 0.066 0.066 0.769 0.600

Location * Cultivation 1 0.597 0.597 6.980 < 0.001

Treatment * Cultivation 1 0.053 0.053 0.621 0.781

Sampling event * Cultivation 1 0.085 0.085 0.997 0.375

Treatment * Livestock 1 0.085 0.085 0.989 0.376

Sampling event * Livestock 1 0.083 0.083 0.965 0.401

Location * Treatment * Sampling event 3 0.187 0.062 0.727 0.816

Location * Treatment * Cultivation 1 0.078 0.078 0.915 0.446

Location * Sampling event * Cultivation 1 0.114 0.114 1.336 0.194

Treatment * Sampling event * Cultivation 1 0.108 0.108 1.266 0.214

Treatment * Sampling event * Livestock 1 0.058 0.058 0.683 0.705

Residuals 56 4.792 0.086

Total 82 11.483


Table S15. Permanova results comparing level six OTU communities across locations, treatments, time, cultivation, and presence of livestock.

Df SS MS F p value

Location 3 2.544 0.848 9.911 < 0.001 Treatment 1 0.103 0.103 1.204 0.242

Sampling event 1 0.286 0.286 3.341 0.007

Cultivation 1 1.393 1.393 16.284 < 0.001

Livestock 1 0.192 0.192 2.245 0.042

Location * Treatment 3 0.209 0.070 0.813 0.684

Location * Sampling event 3 0.449 0.150 1.751 0.033


Location * Cultivation 1 0.597 0.597 6.980 < 0.001





Location * Treatment * Sampling event 3 0.187 0.062 0.727 0.816

Location * Treatment * Cultivation 1 0.078 0.078 0.915 0.446

Location * Sampling event * Cultivation 1 0.114 0.114 1.336 0.194



Residuals 56 4.792 0.086

Total 82 11.483

Table S16. Pair wise PERMANOVA results for location * sampling event interaction.

DenmarkTafe Baseline

DenmarkTafe R2

Langridge Baseline

Langridge R2

Pasfield Baseline

Pasfield R2

Tilbrook Baseline

DenmarkTafe R2

0.048 - - - - - - Langridge Baseline

0.003 0.005 - - - - -

Langridge R2 0.005 0.003 0.103 - - - -

Pasfield Baseline

0.034 0.096 0.030 0.095 - - -

Pasfield R2 0.036 0.103 0.043 0.167 0.422 - -

Tilbrook Baseline

0.003 0.015 0.167 0.422 0.167 0.167 -

Tilbrook R2 0.003 0.003 0.048 0.422 0.070 0.129 0.422

Table S17. Pair wise PERMANOVA results for location * cultivation interaction.

DenmarkTafe Garden

DenmarkTafe Orchard

Langridge Paddock

Pasfield Garden

Pasfield Orchard DenmarkTafe

Orchard 0.093 - - - -

Langridge Paddock

0.002 0.005 - - -

Pasfield Garden 0.002 0.021 0.002 - -

Pasfield Orchard 0.002 0.002 0.002 0.002 -

Tilbrook Paddock

0.002 0.005 0.080 0.002 0.002


Table S18. PERMANOVA test for changes in OTU richness among treatments at the Tilbrook property.

Df SS MS F p value

Treatment 1 1805 1805 1.794 0.207 Livestock 1 8875 8875.2 8.823 0.015

Sampling event 1 16245 16245 16.149 0.002

Treatment * Livestock 1 141 140.8 0.140 0.715

Treatment * Sampling event 1 1378 1377.8 1.370 0.260

Livestock * Sampling event 1 1401 1400.8 1.393 0.262

Treatment * Livestock * Sampling event

1 711 710.5 0.706 0.408

Residuals 12 12071 1005.9 0.283

Total 19 42626 1

Table S19. PERMANOVA test for changes in OTU Shannon-Wiener diversity among treatments at the Tilbrook property.

Df SS MS F p value

Treatment 1 0.002 0.002 0.188 0.666 Livestock 1 0.418 0.418 32.037 < 0.001

Sampling event 1 0.048 0.048 3.704 0.078



Livestock * Sampling event 1 0.053 0.053 4.061 0.070

Treatment * Livestock * Sampling event 1 0.000 0.000 0.019 0.890

Residuals 12 0.156 0.013 0.229

Total 19 0.682 1

Table S20. PERMANOVA test for changes in OTU evenness among treatments at the Tilbrook property.

Df SS MS F p value

Treatment 1 0.000 0.000 1.245 0.292 Livestock 1 0.003 0.003 9.350 0.009

Sampling event 1 0.002 0.002 5.064 0.048



Livestock * Sampling event 1 0.000 0.000 0.509 0.498

Treatment * Livestock * Sampling event 1 0.000 0.000 0.671 0.425

Residuals 12 0.004 0.000 0.374

Total 19 0.012 1


Table S21. PERMANOVA results for OTU community structure at the Tilbrook property.

Df SS MS F p value

Treatment 1 0.067 0.067 0.696 0.715 Sampling event 1 0.153 0.153 1.597 0.114

Livestock 1 0.192 0.192 2.008 0.057





Residuals 12 1.148 0.096

Total 19 1.856

Table S22. PERMANOVA test for changes in OTU richness among treatments at the Langridge property.

Df SS MS F p value

Treatment 1 320 320 0.660 0.430 Sampling event 1 1036.8 1036.8 2.140 0.163


Residuals 16 7752.8 484.55 0.844

Total 19 9181.8 1

Table S23. PERMANOVA test for changes in OTU Shannon-Wiener diversity among treatments at the Langridge property.

Df SS MS F p value



Residuals 16 0.596 0.037 0.903

Total 19 0.660 1

Table S24. PERMANOVA test for changes in OTU evenness among treatments at the Langridge property.

Df SS MS F p value



Residuals 16 0.009 0.001 0.920

Total 19 0.010 1


Table S25. PERMANOVA results for OTU community structure at the Langridge property.

Df SS MS F p value



Residuals 16 1.439 0.090

Total 19 1.782

Table S26. PERMANOVA test for changes in OTU richness among treatments at the Pasfield property.

Df SS MS F p value

Treatment 1 5652 5652 4.178 0.054 Cultivation 1 17262 17262 12.761 0.003

Sampling event 1 99656 99656 73.672 < 0.001

Treatment * Cultivation 1 111 111 0.082 0.776

Treatment * Sampling event 1 1574 1574 1.164 0.290

Cultivation * Sampling event 1 76 76 0.056 0.816

Treatment * Cultivation * Sampling event 1 387 387 0.286 0.601

Residuals 19 25701 1353 0.171

Total 26 150420 1

Table S27. PERMANOVA test for changes in OTU Shannon-Wiener diversity among treatments at the Pasfield property.

Df SS MS F p value

Treatment 1 0.709 0.709 4.814 0.041 Cultivation 1 2.689 2.689 18.269 0.001

Sampling event 1 1.563 1.562 10.615 0.004



Cultivation * Sampling event 1 0.197 0.197 1.336 0.263

Treatment * Cultivation * Sampling event 1 0.004 0.004 0.026 0.875

Residuals 19 2.797 0.147 0.346

Total 26 8.079 1


Table S28. PERMANOVA test for changes in OTU evenness among treatments at the Pasfield property.

Df SS MS F p value


Sampling event 1 0.000 0.000 0.075 0.789




Treatment * Cultivation * Sampling event 1 0.000 0.000 0.000 0.985

Residuals 19 0.042 0.002 0.406

Total 26 0.103 1

Table S29. PERMANOVA results for OTU community structure at the Pasfield property.

Df SS MS F p value


Cultivation 1 1.913 1.913 20.567 < 0.001





Residuals 19 1.767 0.093

Total 26 4.324

Table S30. PERMANOVA test for changes in OTU richness among treatments at the Denmark TAFE.

Df SS MS F p value

Treatment 1 49.8 49.8 0.245 0.640 Cultivation 1 4522 4522 22.266 0.002

Sampling event 1 160.3 160.3 0.790 0.408



Cultivation * Sampling event 1 458 458 2.255 0.174

Residuals 9 1827.8 203.1 0.239

Total 15 7658 1


Table S31. PERMANOVA test for changes in OTU Shannon-Wiener diversity among treatments at the Denmark TAFE.

Df SS MS F p value


Sampling event 1 0.037 0.037 5.171 0.046




Residuals 9 0.065 0.007 0.182

Total 15 0.355 1

Table S32. PERMANOVA test for changes in OTU evenness among treatments at the Denmark TAFE.

Df SS MS F p value


Sampling event 1 0.001 0.001 9.488 0.014




Residuals 9 0.001 0.000 0.168

Total 15 0.004 1

Table S33. PERMANOVA results for OTU community structure at the Denmark TAFE.

Df SS MS F p value

Treatment 1 0.042 0.042 0.864 0.631 Sampling event 1 0.128 0.128 2.636 < 0.001

Cultivation 1 0.098 0.098 2.024 0.013




Residuals 9 0.438 0.049

Total 15 0.977


Table S34. PERMANOVA results for CO2 burst tests.

Df SS MS F p value

Location 3 105390 35130 26.232 < 0.001 Treatment 1 111 111 0.083 0.770

Cultivation 2 15317 7659 5.719 0.006

Livestock 1 2 2 0.002 0.967

Location * Treatment 3 2589 863 0.645 0.592

Treatment * Cultivation 1 3076 3076 2.297 0.141

Treatment * Livestock 1 15 15 0.011 0.918

Residuals 30 40176 1339

Total 42 166677

Table S35. PERMANOVA results for chromatogram variables over all properties.

Df SS MS F P value

Location 3 613.14 204.381 30.631 <0.001 Sampling event 2 240.1 120.052 17.993 <0.001 Treatment 1 5.5 5.496 0.824 0.517 Location * Sampling event 3 37.12 12.372 1.854 0.032 Location * Treatment 3 5.64 1.88 0.282 0.999 Sampling event * Treatment 2 5.67 2.836 0.425 0.953 Location * Sampling event * Treatment 3 11.56 3.854 0.578 0.902 Residuals 225 1501.27 6.672 0.620 Total 242 2420 1

Table S36. Pairwise comparison p values among properties and sampling events for chromatogram variables

Denmark B

Denmark R2

Langridge B

Langridge R2

Pasfield B

Pasfield R2

Tilbrook B

Tilbrook R2

Denmark R2 0.003 - - - - - - - Langridge B 0.145 0.003 - - - - - - Langridge R2 0.145 0.006 0.011 - - - - - Pasfield B 0.003 0.003 0.003 0.003 - - - - Pasfield R2 0.003 0.003 0.003 0.003 0.037 - - - Tilbrook B 0.003 0.003 0.024 0.003 0.003 0.003 - - Tilbrook R2 0.003 0.003 0.003 0.003 0.003 0.003 0.003 - Tilbrook R3 0.003 0.003 0.102 0.003 0.003 0.003 0.003 0.003


Table S37. PERMANOVA results for chromatogram variables at the Tilbrook property

Df SS MS F P value

Treatment 1 5.29 5.286 0.628 0.717 Sampling event 2 207.07 103.535 12.295 <0.001 Treatment * Sampling event 2 6.59 3.295 0.391 0.976 Residuals 107 901.06 8.421 0.805 Total 112 1120 1

Table S38. PERMANOVA results for chromatogram variables at the Langridge property

Df SS MS F P value

Treatment 1 1.021 1.021 0.104 0.999 Sampling event 1 31.569 31.569 3.226 0.004 Treatment * Sampling event 1 2.546 2.546 0.260 0.979 Residuals 24 234.864 9.786 0.870 Total 27 270 1

Table S39. PERMANOVA results for chromatogram variables at the Pasfield property

Df SS MS F P value

Treatment 1 6.14 6.138 0.772 0.588 Sampling event 1 27.71 27.705 3.482 0.003 Cultivation 1 79.31 79.309 9.968 <0.001 Treatment * Sampling event 1 7.76 7.763 0.976 0.432 Treatment * Cultivation 1 15.84 15.836 1.990 0.066 Sampling event * Cultivation 1 24.27 24.267 3.050 0.009 Treatment * Sampling event * Cultivation 1 2.98 2.984 0.375 0.921 Residuals 46 366 7.956 0.691 Total 53 530 1

Table S40. Pairwise comparison p values between cultivations and sampling events for chromatogram variables at the Pasfield property.

Garden B Orchard B Garden R2

Orchard B 0.001 - - Garden R2 0.118 0.001 - Orchard R2 0.001 0.001 0.005


Table S41. PERMANOVA results for chromatogram variables at the Denmark TAFE property

Df SS MS F P value

Treatment 1 4.64 4.639 0.578 0.772 Sampling event 1 52.77 52.773 6.572 <0.001 Cultivation 1 63.93 63.926 7.961 <0.001 Treatment * Sampling event 1 4.53 4.532 0.564 0.775 Treatment * Cultivation 1 3.68 3.677 0.458 0.865 Sampling event * Cultivation 1 15.79 15.786 1.966 0.070 Treatment * Sampling event * Cultivation 1 3.46 3.456 0.430 0.882 Residuals 40 321.21 8.03 0.683 Total 47 470 1

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Testing the effectiveness of locally prepared liquid ... · CONTACT Pip Tilbrook, Project Officer DATE February 2019 PUBLICATION DATA Cook, B.A. & Ford, B., 2019. Testing the effectiveness