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OENO One | By the International Viticulture and Enology Society 2023 | volume 57–1 | 207
*correspondence:
markus.rienth@changins.ch
Associate editor:
Olivier Viret
Received:
17 October 2022
Accepted:
17 January 2023
Published:
23 February 2023
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Effects of biodynamic preparations
500 and 501 on vine and berry
physiology, pedology and the soil
microbiome
Markus Rienth1*, Frederic Lamy1, Clément Chessex1 and Thierry J. Heger1
1 University of Sciences and Art Western Switzerland, Changins College for Viticulture and Enology,
route de Duillier 60, 1260 Nyon, Switzerland
ABSTRACT
In the pursuit of increasing sustainability, climate change resiliency and independence of synthetic
pesticides in agriculture, the interest of consumers and producers in organic and biodynamic
farming has been steadily increasing in recent decennia. This is, in particular, the case for the
vitivinicultural industry in Europe, where more and more producers are converting from organic
to biodynamic farming. However, clear scientic evidence showing that biodynamic farming
improves vine physiology, vine stress resilience, soil quality related parameters and berry
or wine quality is still lacking, despite the growing number of research studies on this issue.
To investigate whether biodynamic farming methods have an impact on vine physiology, berry
quality and the environment, a ve-year experiment was set up in 2016 in a commercial vineyard in
Switzerland. In this trial, the two main biodynamic preparations 500 and 501 were applied and
compared to an organic control. Vine and berry physiology (net photosynthesis, vigour, sugar,
organic acids, berry weight, yield) were assessed from 2016 to 2020. Soil physical properties
(soil bulk density, water holding capacity, soil structural stability, macropore volume) were
analysed from 2017-2020, and, soil fungal communities were analysed by DNA-sequencing in
the last year of the experiment (2020).
None of the parameters related to vine and berry physiology showed signicant differences
throughout the duration of the experiments, except photosynthesis, which was higher when
biodynamic preparations were applied at one time point. Similarly, the soil’s physical properties
were not inuenced by the application of the two biodynamic preparations in all years.
Regarding the soil microbiome, the preparations 500 and 501 neither led to signicant
differences in fungal diversity nor seemed to impact the soil fungal communities. The present
study conrms previous ndings of different research teams that did not observe signicant
differences between organic and biodynamic farming methods in terms of observed soil and
vine parameters.
KEYWORDS: biodynamic viticulture, microbial diversity, organic viticulture, preparations 500 and
501, soil physical properties, vine physiology, berry quality
ORIGINAL RESEARCH ARTICLE
OPEN ACCESS
DOI: https://doi.org/10.20870/oeno-one.2023.57.1.7218
OENO One | By the International Viticulture and Enology Society208 | volume 57–1 | 2023
INTRODUCTION
In recent years, both winegrowers and consumers have shown
a steadily growing interest in organic wine production,
with an estimated winegrowing area of 454 000 ha,
corresponding to 6.4 % of the world’s viticulture surface
certied organic production (OIV, 2021). In the course of
this development, biodynamic grape and wine production
is also receiving increasing attention with some of the most
prestigious wineries converting to organic or biodynamic
viticulture (Castellini et al., 2017; Reeve et al., 2005).
The biggest international biodynamic association is
Demeter. Demeter-certied agricultural farms have grown
signicantly in number (more than 5900 farms in 2019),
with the certied surface area almost doubling to over
200,000 ha in 63 countries (Santoni et al., 2022). Regarding
viticulture, in 2021, there were a total of 1012 wineries and
17 079 ha of Demeter-certied vineyards across the world
(Sinpfendoerfer and Fischer, 2021)
Biodynamic winegrowers generally claim to produce lower
environmental impact, higher vineyard biodiversity, better
vine health and higher wine quality compared to organic
farming. Nevertheless, this practice is still controversial in
the viticultural industry and, in particular, within the scientic
community. Despite an increasing number of scientic studies
on the effects of organic, biodynamic and conventional
viticulture in recent decennia, there is little evidence
showing unambiguous differences between biodynamic and
organic viticulture in terms of environmental impact, vine
performance and berry quality (Döring et al., 2019). Several
studies have compared the organoleptic quality of wines
from biodynamically and organically grown vines, but no
differences in wine sensory characteristics have been found
(Collins et al., 2015; Parpinello et al., 2015; Patrignani et al.,
2017). However, some studies have reported minor
differences in the preferences for and in the sensory properties
of biodynamic and organic Riesling (Meissner, 2015),
Merlot (Ross et al., 2009) and Sangiovese (Parpinello et al.,
2019) wines. In terms of vine physiology and berry quality,
most studies have found differences between biodynamic
and organic production when comparedto conventional
viticulture, but not when comparing biodynamic with
organic only. In general, organically and biodynamically
managed vines show signicantly lower growth and yield
in comparison to integrated plots (Döring et al., 2015;
Fritz et al., 2021; Meissner et al., 2019; Parpinello et al.,
2019; Parpinello et al., 2015). Soil management and
fertilisation strategies are supposedly responsible for these
differences. As regards soils, Hendgen et al. (2018) found
signicant differences in the fungal community composition
when comparing organic and biodynamic with conventional
production, but again did not nd any differences between
organic and biodynamic management. Similarly, Longa et al.
(2017) showed that the application of the preparations 500
and 501 did not affect microbial communities in the short
term. In a meta-analysis, Christel et al. (2021) found that
biodynamic farming displays higher soil ecological quality
compared to organic farming.
Soustre-Gacougnolle et al. (2018) report a higher expression
of silencing and immunity related genes, and higher
anti-oxidative and anti-fungal secondary metabolite levels
in biodynamically managed vineyards, which suggests that
the sustainability of biodynamic practices probably relies on
ne molecular regulations. However, the latter study was not
conducted using a controlled experimental design, but within
a large network of commercial plots to which each producer
applied his own denition of “biodynamic production”; this
could have introduced biases in gene expression.
Despite the controversial scientic and empirical evidence
of improved vine physiology, berry and wine quality
and less environmental impact, demand for wines from
biodynamically grown vines is growing and putting increasing
pressure on both conventional and organic wine producers
to apply biodynamic principals to vineyard management,
thus increasing production costs (Castellini et al., 2017).
We therefore tested the hypothesis that the application
of the two main biodynamic preparations increases vine
physiological performance and berry quality, leading to
higher production costs.
The present study aimed to evaluate the long term effects of
the biodynamic preparation 500 and 501 on vine physiology,
berry and soil quality and the soil microbiome in a Swiss
winegrowing region, planted with the most emblematic
Swiss autochthonous grape variety, Chasselas (Rienth et al.,
2020).
METHODS
The eld experiment was conducted in a commercial
vineyard in Mont-sur-Rolle, Switzerland (46°28’10.4”N
6°20’33.4”E). The experimental site was 0.76 hectare in size
and planted in 2012 (Vitis vinifera L. cv. Chasselas clone
RAC, grafted on 3309C).
The vines were planted with a spacing of 0.8 m within rows
and 1.8 m between rows within a vertical shoot positioning
system (VSP). Row orientation was north–south. Conversion
to organic viticulture started in 2015 in accordance with
Regulation (EC) No 834/2007 and Regulation (EC) No
889/2008, prior to which the plot had been managed
conventionally with spontaneous interrow grass cover and
under-vine herbicide application.
The experiment was set up in a randomised complete block
design with 18 homogenous blocks, each of which consisting
of 4 rows with a total of 190 vines. Nine blocks were assigned
as control blocks, to which pure water was applied instead
of biodynamic preparation. In the remaining 9 treatment
blocks, the two main biodynamic preparations, Horn Manure
(500) and Horn Silica (501), were applied. Preparation 500
consists of fermented cow manure and is applied to the soil
with the aim of stimulating soil processes and root growth.
Preparation 501 consists of fermented ground silica from
quartz of feldspar and is applied to leaves with the aim of
stimulating plant physiological processes and improving
crop quality (Koepf et al., 1990). 500 was applied twice a
Markus Rienth et al.
OENO One | By the International Viticulture and Enology Society 2023 | volume 57–1 | 209
year: in March or April and in May (May and June in 2016);
meanwhile, 501 was applied three times a year, in May or
June, August and September. All the measurements and
analyses were carried out on the two middle rows, leaving at
least 10 vines at the end of the blocks as buffer.
The organic and biodynamic blocks were both managed
identically, except for the application of the two biodynamic
preparations to the latter blocks. Downy and powdery mildew
were controlled by organic fungicide treatments, depending
on disease pressure, with 7 to 15 treatments being applied
per season. Nitrogen supply of the vineyard was ensured by
soil cultivation and the plowing-in of the cover crop mixture
in every second row shortly before full bloom. Under-vine
management was done mechanically without the use of
herbicides.
1. Vine and berry physiology
Pruning weight was determined during the winter period by
sampling 30 lignied shoots per block; these were obtained
by cutting 1 m of the fruit cane after the second to last bud.
They were then weighed using a standard scale (g per m of
shoot). Leaf nitrogen content was assessed using an N-tester
on 30 leaves per block in August of each season.
Leaf net photosynthesis was evaluated by gas exchange
measurements on three well-exposed adult leaves per block
at midday using a Ciras 3 Portable Photosynthesis System (PP
Systems, USA). For the control of environmental parameters,
photosynthetically active radiation (PAR) inside the leaf
cuvette was adjusted to 1,500 mmol/m2/s, temperature to
30 °C, relative humidity to 80 % and CO2 concentration to
400 ppm
For berry quality, 50 berries per block (i.e., 450 per
treatment) were sampled, 1 to 3 days prior to harvest. Berries
were weighed to determine individual berry weight and
subsequently pressed for further analysis. Organic acids
and sugar were analysed by HPLC, a 1260 Innity Agilent
HPLC system consisting of a G4225A degasser, an isocratic
G1310 pump system, a GT329B autosample injector, a
G1316A column oven, and a G1314F UV-detector (Agilent
Technologies, Santa Clara, CA, USA) connected to a Shodex
RI-101 refractive index detector (Showa Denko, Kawasaki,
Japan) maintained at 50C. The samples were pre-treated by
solid phase extraction using Waters Oasis HLB and 6 cm3
(200 mg) cartridges (Waters Corporation, Milford, MA,
USA), then ltered through 0.2-mm nylon lters (Millipore,
Burlington, MA, USA); 20 μL were directly injected into an
Aminex HPX-87H HPLC column 300 × 7.8 mm, with a 9 μm
particle size (Bio-Rad Laboratories, Hercules, CA, USA).
Separations were carried out under isocratic conditions at
80C using a 0.65 mmol H2SO4 solution, with a mobile phase
ow rate of 0.5 mL/min. Organic acids were detected at
210 nm.
To assess the vine water status photosynthetic carbon isotope
composition, the 12C/13C ratio (also known as δ13C) was
analysed in the sugars of must samples from berries in 2017
and 2018 according to Gaudillere et al. (2002).
Weather data was retrieved from the meteorological station
in Mont-sur-Rolle (46°28’01.1”N 6°19’26.8”E; https://www.
agrometeo.ch/).
2. Soil abiotic properties
Undisturbed soil samples of approximately 100 cm3 were
taken yearly at a depth of 5 to 10 cm from 2017 to 2020.
Sampling took place every spring except in 2020 (autumn).
The samples were taken in the middle of the inter-row in
order to avoid disturbed environments (wheel passage, tillage
under the vine). A total of 72 samples were analysed over the
four years of the experiment (18 blocks*4 years).
To determine the volume of the samples at eld capacity,
the plastic bag method (Boivin et al., 1991) was used after
equilibrium at a matric potential of -60 hPa. The samples were
then oven-dried at 105 °C and the dry mass and the volume
of the coarse fraction (> 2mm) were removed to calculate
the following parameters: apparent density and porosity
at -60hPa, water retention capacity at -60hPa (equivalent
to pore volume smaller than 50 microns in diameter), and
coarse pore volume greater than 50 µm. Structural stability
was determined according to Le Bissonnais (2016).
The mean weighted diameter (MWD) results from the
average of the three structural stability tests. The organic
matter content and the pH of the blocks were determined at
the end of the experiment in 2020. The organic matter content
of ne soil (< 2mm) was determined according to Walkley,
A., Black (1934). The pH was measured in a 1:2.5 m/v water
suspension.
3. Soil microbial analysis
Soil sampling was carried out at three sampling time points
(24 June, 21 and 28 July) in 2020 (i.e., four years after the
beginning of the experiment). Sampling was performed
in two adjacent central rows. Within each block, eight
subsamples (5x5x5 cm) were collected from the soil surface
and then pooled in a plastic bag, resulting in a total of
18 samples per sampling date. Samples were stored in soft
coolers containing ice packs and transported to the laboratory
within a day. From each composite sample, a representative
subsample of about 10 g was randomly taken and placed in a
50 ml Falcon tube and kept at -80 °C until DNA extraction.
3.1. DNA extraction and sequencing
DNA extraction was performed with 0.5 g of soil using the
FastDNA SPIN Kit for soil (MP Biomedicals, Solon, OH)
and following the recommendations of the manufacturer.
DNA extracts were quantied using a Quawell q9000
spectrophotometer, adjusted to 20 ng µL-1 in ultra-pure
water and stored at –20 °C. DNA samples (25 µL) were sent
to the Centre for Comparative Genomics and Evolutionary
Bioinformatics (Halifax, Canada) for PCR amplication
and Illumina MiSeq sequencing. The internal transcribed
spacer 2 region was amplied with the primer pair ITS86F
and ITS4R to characterise the fungal communities.
Further information regarding the PCR procedures and
Illumina sequencing are provided in Fournier et al. (2020).
OENO One | By the International Viticulture and Enology Society210 | volume 57–1 | 2023
The amplicon data are available on EMBL European
Nucleotide Archive under project number: PRJEB54862.
3.2. Sequence data processing and taxonomic assignment
The absence of sequencing primers in the dataset was veried
using cutadapt (Martin, 2012). The reads analysis was
carried out with the Divisive Amplicon Denoising Algorithm
(DADA2) software (Callahan et al., 2016). The DADA2
pipeline infers exact amplicon sequence variants (ASVs)
from sequencing data with ltering, dereplication, sample
inference, chimera identication and merging of paired-end
reads. The QIIME2 (Bolyen et al., 2019) was used for the
taxonomy assignment of the ASVs with pre-trained Naive
Bayes classiers (Wang et al., 2007) and the UNITE database
as a reference for fungi (Nilsson et al., 2019). Since our
approach relies on extracted DNA, our data might include
ASVs from extracellular DNA or encysted cells.
3.3. Statistical analyses
Microbial data was analysed by stepwise linear regression
models computed to examine the effect of treatment,
sampling day, pH and soil moisture content on microbial
alpha diversity (Inverse Simpson). To visualise changes in
fungi composition, nonmetric multidimensional scaling
(NMDS) was conducted with the Bray–Curtis distance
using the R function “metaMDS” (Oksanen et al., 2020).
The drivers of the community compositional changes were
then investigated using a permutational multivariate analysis
of variance (PERMANOVA) applied to a Bray-Curtis
dissimilarity matrix and using the R function “ADONIS”
(Oksanen et al., 2020). All analyses were performed on a
rareed dataset (4950 sequences per sample).
Other soil, vine and berry physiology data was analysed
using Excel stats and OriginPro, and using standard t-tests to
test for signicant differences between treatments.
RESULTS AND DISCUSSION
1. Vintage climatic characterisation
The monthly temperature and precipitation data for the
ve seasons of 2016 to 2020 is provided in Supplementary
Figure S1. The long-term annual rainfall (1981-2010) in
the region was 999 mm/m2 and the growing season (1 April
to 30 September) rainfall was 484 mm/m2. Total annual
rainfall in the ve years of the study was 1256, 883, 1056,
1286 and 1225 mm respectively for 2016 to 2020. Growing
season rainfall (1 April to 30 September) was 623 mm,
406 mm, 359 mm, 538 mm and 565 mm from 2016 to 2020.
The annual average temperature of the region in the period
1981-2010 was 9.3 °C and 14.7 °C for the growing season
from 1 April to 30 September. In the study plot, the mean
annual temperature was 10.3, 11.3, 12.5, 12.0 and 12.5 °C
and the mean growing season temperatures 15.9, 17.1, 18.7,
17.7 and 18.2 °C respectively for 2016 to 2020. Drawing
from this climatic data, it becomes evident that the study
region is undergoing global warming with temperatures
increasing considerably in all the studied years, as compared
to the reference period 1981 to 2010.
With a combination of low rainfall and high temperatures, the
2018 growing season was most affected by global warming, as
shown in other studies in other European growing regions
(Labbé et al., 2019; Rienth et al., 2020).
The main berry quality-determining compounds are
illustrated in Figure 1 and the vine physiological parameters
in Figure 2.
2. Vine physiology
When comparing the plant physiological parameters of the
treated and non-treated vines - such as yield (Figure 1A),
pruning weight (Figure 2B) and N-tester readings
(Figure 2C) - as proxies for vine vigour and general
physiological performance, no signicant differences
induced by biodynamic preparations were observed. This is
in agreement with previous studies, which showed that the
use of biodynamic preparations had little inuence on vine
vegetative growth (Döring et al., 2019).
Meissner et al. (2019) found limited signicant impact
on grapevine vegetative growth (reduced growth using
biodynamic preparations). However, they used a broader
combination of biodynamic preparations; therefore, their
results are not strictly comparable to our ndings.
No yield differences were observed in our study, which
is in agreement with studies comparing organic and
biodynamic treatments with cv. Merlot, cv. Sangiovese,
cv. Cabernet-Sauvignon and cv. Riesling (Botelho et al., 2016;
Collins et al., 2015; Döring et al., 2015; Meissner, 2015;
Reeve et al., 2005). While Reeve et al. (2005) found that
biodynamic treatments had no impact on pruning weights,
they found the yield-pruning ratio to be signicantly lower
under biodynamic management; this difference was due to a
slightly higher yield in the organic treatment, while pruning
weights themselves did not differ between treatments.
However, other studies have not found any differences in
yield-pruning weight ratios between organic and biodynamic
plots (Collins et al., 2015; Döring et al., 2015).
Differences in the yield of other crops have been observed;
for example, in a study of ten biodynamic and organically
managed greenhouses in Southern Germany (Zikeli et al.,
2017) the biodynamic farms were found to produce
signicantly higher yields in tomatoes and cucumbers
compared to the organic farms
Biodynamic preparations are claimed to stimulate soil
nutrient cycling and to promote the photosynthetic activity
of crops and compost transformation (Masson and Masson,
2013). In our study, net photosynthesis was signicantly
different, but to only one measurement point (8 May 2018,
Figure 2D), whereas for all the other measurements we did
not observe any differences between treatments similar to
what was observed in other long terms studies on Riesling
(Botelho et al., 2016; Döring et al., 2015).
Markus Rienth et al.
OENO One | By the International Viticulture and Enology Society 2023 | volume 57–1 | 211
FIGURE1. Berry quality characteristics.
A) Sugar concentration,B) Individual berry weight, C) Malic acid concentration, and D) Tartaric acid concentration. Orange bars: plots
treated with biodynamic preparations 500 and 501. Blue bars: plots treated without biodynamic preparations.
FIGURE2. Yield and vine physiology.
A) Yield per square meter, B) Pruning weight per meter of shoot as a proxy for vigour, C) N-tester, and D) net photosynthesis. Orange
bars: plots treated with biodynamic preparations 500 and 501. Blue bars: plots treated without biodynamic preparations. * indicates
signicant differences between treated and non-treated blocks in the respective year.
OENO One | By the International Viticulture and Enology Society212 | volume 57–1 | 2023
Interseasonal variations such as signicantly lower berry
weight, yield and higher sugar concentrations in 2016 can be
explained by the vines still being in their juvenile phase in
combination with a relatively cool year. In 2018, the slightly
higher hexose concentrations are most likely the result of
the particularly dry and hot conditions which lead to lower
berry weights and thus increased sugar concentration.
Lower malic acid concentrations in the same year can be
explained by increased malic acid respiration due to high
temperatures (Rienth et al., 2016; Rienth et al., 2021a).
This is similar for 2020 which was relatively warm but wetter
than 2018 and the opposite tendency for sugar and malic
acid is observed in in 2019, which was cooler than 2020 and
2018 during the growing season.
No differences in sugar concentration between treatments
was observed, which is in line with most of published
biodynamic studies (Döring et al., 2019). Only Reeve et al.
(2005) found signicantly higher Brix, total phenols and
total anthocyanins in one out of four years in cv Merlot
under biodynamic management when compared to organic
management; however, biodynamic management consisted
in applying additional products as well as 500 and 501,
namely 502, 503, 504, 505, 506, 507 and Barrel compost.
The carbon 13 discrimination analysis of sugars in berries
sampled in 2017 and 2018 gave values of -28.42 ± 0.50
(without biodynamic preparations) and -28.47 ± 0.43
(with biodynamic treatment) in 2017, and -28.62 ± 0.34
(without biodynamic preparations) and -28.45 ± 0.42 (with
biodynamic treatment) in 2018. This suggests that no water
decit was experienced by the vines during berry ripening
(Rienth and Scholasch, 2019) in both years and treatments.
The results of two recent studies on organic and biodynamic
viticulture showed signicantly lower pre-dawn water
potentials in biodynamic plots for cv. Riesling in
Germany (Döring et al., 2015) and cv. Sangiovese in Italy
(Botelho et al. 2015). However, the plots of the latter study
were replicated but not randomised; therefore, the observed
changes in physiological performance cannot be completely
attributed to the treatment in this study due to possible soil
heterogeneity.
In a recent metabolomic study conducted in two vineyards in
the Veneto region in Italy on cv Garganega, Malagoli et al.
(2022) applied 501 on leaves then carried out targeted and
untargeted metabolite analyses of the leaves and berries. They
observed changes in the chlorophyll content of the leaves and
no variation in the free amino acid content of the berries;
however, some individual amino acids were found to have
increased in 501-treated vines, such as cysteine (+ 49.9 %),
methionine (+ 100 %) and phenyl alanine (+ 24.9 %).
Furthermore, the authors observed a higher concentration of
epigallocatechin, and the pigment violaxanthin indicated a
stimulation of the biosynthetic pathways of phenolics in the
leaves and berries due to the application of 501; stimulation
of the biosynthetic pathways has also been reported in a few
studies on other types of crop. Jarienė et al. (2019) observed
differences in total phenolic compound concentrations
(TPCC) and total avonoid concentrations (TFC) in the
leaves of two mulberry (Morus alba L. ) cultivars (Turchanka
and Plodovaja 3) when 500 and 501 were applied: the
FIGURE3. Soil physical properties.
A) Soil bulk density, B) Water holding capacity at -60hPa, C) soil structural stability assessed by Mean Weight Diameter, and
D) Macropores volume at -60 hPa. Orange bars: plots treated with biodynamic preparations 500 and 501. Blue bars: plots treated
without biodynamic preparations. * Indicates outliers.
Markus Rienth et al.
OENO One | By the International Viticulture and Enology Society 2023 | volume 57–1 | 213
Turchanka cultivar showed increased TPCC when only 500
was applied and Plodovaja 3 showed decreased TPCC and
TFC when sprayed with 501. The combination of 500 and
501 had signicant effects on quercetin-acetylhexoside and
kaempferol-acetylhexoside accumulation in the mulberry
leaves of both cultivars.
In our study, we did not analyse the phenolic compounds
and can therefore not comment on the potential inuences
of biodynamic products on their synthesis. However, we
did not observe any differences in the incidence of downy
and powdery mildew or botrytis between treatments, which
would potentially have been inuenced by higher production
of phenolic compounds, which serve as phytoalexins and
phytotoxins against fungal diseases (Rienth et al., 2021a;
Rienth et al., 2021b).
3. Soil physical properties and analysis
The vineyard parcel is situated on a homogenous colluvic
cambisol soil (FAO/WRB, 2014) with a relatively high eld
capacity of 250 to 300 mm. This data was retrieved from
a previous terroir study (Letessier and Fermond, 2004).
All the analysed soil parameters are shown in Figure 3 A-D
and Table 1. No signicant differences between treated and
control plots were observed from 2017 to 2020. The observed
annual variations are due to soil tillage. Indeed, tillage was
performed on every second inter-row alternately each year
in August. More specically, selected sampling inter-rows
were plowed in August 2017 and 2019. This notably explains
the strong bulk density differences between 2017 and 2018.
Indeed, bulk density decreases after tillage, since the volume
of the coarse pores increases and water retention (ne pores)
decreases. Clearly, these variations were not inuenced by
the application of the biodynamic preparations. In a l ong-t erm
field trial in Germany, Faust et al. (2017) did not nd
preparation 500 to have any additional positive effects
on soil compared to those of composted farmyard manure
fertilisation. However, Reeve et al. (2011) found that soil
pH moderately increased when Pfeiffer eld spray and other
biodynamic preparations were applied.
4. Fungal diversity and community structure
A total of 1,553,659 high-quality soil fungal sequences were
obtained from the nine blocks treated with water (without
biodynamic preparations) and nine blocks reated with
biodynamic preparations 500 and 501 at ve sampling time
points in 2020. The sequences belonged to 1648 fungal ASVs
(Amplicon sequence variants).
No differences in diversity were detected between the
samples treated with biodynamic preparations and those
without biodynamic preparations. The pH was the only factor
showing signicant effects on fungal diversity (Table 2),
while the fungal community composition was signicantly
affected by pH, the sampling date and the soil moisture
(Table 3).
The results of our study indicate that the application of
preparations 500 and 501 does not lead to signicant
differences in diversity and does not seem to impact the soil
fungal communities.
TABLE1. pH and SOC (Soil Organic Carbon) in 2020 from the plots treated with biodynamic preparations (500
and 501) and without biodynamic preparations.
Variable Treatment N Mean Std. Error Minimum Maximum
pH
With biodynamic preparations 9 7.68 0.0596 7.59 7.76
Without biodynamic preparations 9 7.69 0.0636 7.60 7.76
SOC %
With biodynamic preparations 9 2.80 0.474 2.30 3.60
Without biodynamic preparations 9 2.60 0.517 2.00 3.50
TABLE2. Effects of selected variables on inverse Simpson diversity.
Estimate Std. Error t value Pr (> |t|)
(Intercept) 4.08804 0.04591 89.052 < 2e-16***
Sampling day -0.09150 0.04878 -1.876 0.0641
pH 0.10050 0.04878 2.060 0.0424*
TABLE3. Adonis analysis of the effect of sampling day, treatment, pH and soil moisture on fungal communities.
Df Sum of Sqs Mean Sqs F.Model R2 Pr (> F)
Sampling day 1 10.1357 10.1357 68.102 0.42966 0.001***
Treatment 1 0.1270 0.1270 0.853 0.00538 0.402
pH 1 0.4444 0.4444 2.986 0.01884 0.018*
Soil moisture 1 0.3809 0.3809 2.559 0.01615 0.030*
OENO One | By the International Viticulture and Enology Society214 | volume 57–1 | 2023
These results are in line with the study of Hendgen et al.
(2018), in which the input of biodynamic preparations did
not affect the fungal composition or richness compared to
the organic treatment. However, the bacterial biodiversity
increased in the topsoil under organic management
compared to conventional viticulture, in which mineral
fertilisers, herbicides and synthetic fungicides were applied
(Hendgen et al., 2018).
Morrison-Whittle et al. (2017) quantied fungal communities
in six conventional and six biodynamic vineyards by analysing
samples from several different vineyard “habitats” (i.e., bark,
fruit and soil) using metagenomic techniques; they found
signicantly higher species richness in biodnyamic fruit
and bark communities, but not in the soil. In terms of types
and abundance of fungal species, biodynamic management
has been found to have a signicant effect on soil and fruit
(Morrison-Whittle et al., 2017 in Santoni et al., 2022).
In a metaanalyis on the impact of farming systems on soil
ecological quality, Christel et al. (2021) highlight that in the
reviewed literature, microorganism abundance was enhanced
in biodynamic farming compared to organic farming,
with an increase of 71 % in the abundance measurements.
Microorganism activity was also more stimulated in
biodynamic farming than in inorganic farming: 54 % of the
measurements showed a positive effect and 86 % of the soil
fauna results showed similar effects of biodynamic farming
and organic farming.
Spaccini et al. (2012) characterised the molecular
composition of the biodynamic preparation 500 and found
that it consists of a complex molecular structure, with lignin
aromatic derivatives, polysaccharides and alkyl compounds
as the predominant components. Biodynamic preparations
appear to be enriched with biolabile components and,
therefore, potentially conducive to plant growth stimulation.
In the present study, however, the application of 500 to soil
did inuence fungal diversity or community structure.
CONCLUSIONS
The present study aimed to evaluate the effects of the
application over four years of biodynamic preparations
500 and 501 on vine physiology, berry quality and soil
physical properties and its microbiome. For all assessed
parameters, no signicant differences between the treated
and control blocks were observed during the period of the
experiment. The present study thus conrms the ndings of
several research groups, which showed that the differences
between biodynamic and organic farming were almost never
signicant.
Thus, we could not conrm the empirical observations of the
many biodynamic growers who frequently report that vines
from biodynamic vineyards treated with preparations 500 and
501 are more stress resilient and healthier, and produce higher
quality fruit and thus higher quality wine. Furthermore, the
present study did not conrm the growers’ empirical reports
of higher soil quality in biodynamic vineyards.
However, we cannot rule out that longer trials combined
with the analysis of additional parameters and/or a
different methodological approach, such as epigenetics,
might reveal some differences between organically-
and biodynamically- managed vineyards. Furthermore,
the formulation and manufacturing of the two applied
preparations (500/501), which were commercial standard
preparations, could have inuenced the present results.
FIGURE4. Non-metric multidimensional scaling (NMDS) plots of fungi obtained with the Bray-Curtis dissimilarity
matrix.
Red and green dots represent samples collected from the plots treated with biodynamic preparations (500 and 501) and with water
respectively. For each community cluster, ellipses represent 95% condence intervals around the centroid of each community cluster.
Markus Rienth et al.
OENO One | By the International Viticulture and Enology Society 2023 | volume 57–1 | 215
Due to experimental limitations, we were not able to
conduct a full biodynamic holistic approach involving the
application of not only the two main preparations 500 and
501, but also the different natural products often added by
growers (e.g., different compost preparations and green
manure) and following the lunar cycles; this may have
affected results.
ACKNOWLEDGEMENTS
We would like to thank the Ville de Lausanne (City of
Lausanne) for nancing the study and Enrico Antonioli for
managing the experimental vineyard.
Further thanks go to all the Bachelor and Master students
who worked on the project over the ve years (Noé
Christinat, Pierre-Emile Humbrecht, Louis Essa, Tom Serca,
Thibault Pras, Christopher Bourgeois and Florian Keiser).
We also thank Patrik Schönenberger for his support when
carrying out the vineyard measurements, Marylin Cléroux
and Priscilla Siebert for her help with the HPLC analysis
and Florine Degrune for her help with the bioinformatic
analysis.
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