Enhancing septoria leaf blotch forecasts in winter wheat II: model architecture and validation results

Abstract

In precision agriculture, pesticides and other inputs shall be used precisely when (and where) they are needed. European Directive 2009/128/EC calls for respecting the principles of integrated pest management (IPM) in the member states. To clarify the question, when, for instance, fungicide use is needed, the well-established economic principle of IPM may be used. This principle says that pests shall be controlled when the costs of control correspond with the damage the pests will cause. Disease levels corresponding with the costs of control are referred to as control thresholds in IPM. Several models have been developed in plant pathology to predict when epidemics will occur, but hardly any of these models predicts a control threshold directly limiting their usefulness for answering the question when pest control is needed according to the principles of IPM. Previously, we quantified the temporal distance between critical rainfall periods and the breaking of the control threshold of Zymoseptoria tritici on winter wheat as being affected by temperature, based on data from 52 field experiments carried out in Luxembourg from 2005 to 2016. This knowledge was used to construct the ShIFT (SeptorIa ForecasT, https://shift.list.lu/ ) model, which has been validated using external data recorded between 2017 and 2019. Within the efficacy period of a systemic fungicide, the model allowed correct predictions in 84.6% of the cases, while 15.4% of the cases were predicted falsely. The average deviation between the observed and predicted dates of epidemic outbreaks was 0.62 ± 2.4 days with a maximum deviation of 19 days. The observed and predicted dates were closely correlated ( r = 0.92, P < 0.0001). Apart from outliers, the forecast model tested here was reliable within the period of efficacy of current commercial fungicides.

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Vol.:(0123456789)
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Journal of Plant Diseases and Protection
https://doi.org/10.1007/s41348-021-00554-8
ORIGINAL ARTICLE
Enhancing septoria leaf blotch forecasts inwinter wheat II: model
architecture andvalidation results
MarcoBeyer1 · BenedekMarozsak1· DorianeDam1· OlivierParisot1,2· MarinePallez‑Barthel1· LucienHomann1
Received: 29 May 2021 / Accepted: 16 November 2021
© The Author(s) 2021
Abstract
In precision agriculture, pesticides and other inputs shall be used precisely when (and where) they are needed. European
Directive 2009/128/EC calls for respecting the principles of integrated pest management (IPM) in the member states. To
clarify the question, when, for instance, fungicide use is needed, the well-established economic principle of IPM may be used.
This principle says that pests shall be controlled when the costs of control correspond with the damage the pests will cause.
Disease levels corresponding with the costs of control are referred to as control thresholds in IPM. Several models have been
developed in plant pathology to predict when epidemics will occur, but hardly any of these models predicts a control threshold
directly limiting their usefulness for answering the question when pest control is needed according to the principles of IPM.
Previously, we quantified the temporal distance between critical rainfall periods and the breaking of the control threshold
of Zymoseptoria tritici on winter wheat as being affected by temperature, based on data from 52 field experiments carried
out in Luxembourg from 2005 to 2016. This knowledge was used to construct the ShIFT (SeptorIa ForecasT, https:// shift.
list. lu/) model, which has been validated using external data recorded between 2017 and 2019. Within the efficacy period of
a systemic fungicide, the model allowed correct predictions in 84.6% of the cases, while 15.4% of the cases were predicted
falsely. The average deviation between the observed and predicted dates of epidemic outbreaks was 0.62 ± 2.4days with a
maximum deviation of 19days. The observed and predicted dates were closely correlated (r = 0.92, P < 0.0001). Apart from
outliers, the forecast model tested here was reliable within the period of efficacy of current commercial fungicides.
Keywords Crop protection· Integrated pest management· Leaf blotch· Model· Pest control· Precision agriculture
Introduction
The fungal wheat pathogen Zymoseptoria tritici (Desm.)
Quaedvlieg and Crous, 2011 (formerly Septoria tritici Rob.
ex Desm.) causes total annual losses of between 800 and
2,400 million euros in France, Germany and the UK (Fones
and Gurr 2015). Agronomic practices such as tillage, crop
rotation and late sowing dates contribute only marginally
to the control of the disease (Thomas etal. 1989; Gladders
etal. 2001), while growing resistant cultivars is an impor-
tant factor (Karisto etal. 2018). Preventing losses caused
by Z. tritici therefore strongly depends on fungicide use.
Contact fungicides protect a winter wheat crop for approxi-
mately 16days, while systemic fungicides protect the crop
for approximately 22days (Greiner etal. 2019). With con-
tact fungicides versus leaf blotch recently banned, systemic
fungicides remain available to the farmers in the region
studied here. Since fungicide use is the last resort of farm-
ers to prevent losses, these periods represent the minimum
requirement for the accuracy of leaf blotch forecast mod-
els. The timing of fungicide spraying remains a challenge
for farmers because (1) spraying before the disease occurs
implies the risk of spraying in vain if the disease should not
reach a damaging level, (2) spraying too late cannot reverse
the damage that the disease has already inflicted and (3) Z.
tritici has a long latent period during which the pathogen
* Marco Beyer
marco.beyer@list.lu
1 Agro-Environmental Systems, Environmental Monitoring
andSensing Unit, Environmental Research andInnovation
Department, Luxembourg Institute ofScience
andTechnology (LIST), 41, rue du Brill, L-4422Belvaux,
Luxembourg
2 Data Processing & Statistics, Data Science & Analytics
Unit, IT forInnovative Services Department, Luxembourg
Institute ofScience andTechnology (LIST), 5, avenue des
Hauts-Fourneaux, L-4362Esch-sur-Alzette, Luxembourg

Journal of Plant Diseases and Protection
1 3
spreads without exhibiting visible symptoms that could be
the trigger for countermeasures.
In integrated pest management (IPM), minor disease
incidences are left untreated since they do not justify the
costs of pest control. The control threshold is the disease
incidence for which the loss caused by the disease at the
farm level is approximately equivalent to the costs of disease
control (Zadocks 1985; Beer 2005). Control thresholds are
used to determine the best timing for crop protection actions,
including pesticide application from an economic point of
view at the farm level. In precision agriculture, actions are
taken precisely where and when they are needed. Previous
studies focused on forecasting events with epidemiologi-
cal relevance (Magarey etal. 2005; El Jarroudi etal. 2017;
Lalancette etal. 1988; Giroux etal. 2016; Walter etal. 2016;
Zhao etal. 2018; Molitor etal. 2016), but with often unclear
relevance for precision agriculture. A predictive model for
early warning of septoria leaf blotch on winter wheat was
developed by te Beest etal. (2009) based on data from the
UK. While te Beest etal. (2009) predicted if 5% disease
severity will be reached on the upper three-leaf layers within
a cropping season using the window pane approach (Coak-
ley and Line 1982; Kriss etal. 2010), the model presented
here directly predicts when a spray is due according to the
criterion developed by Beer (2005). Chaloner etal. (2019)
presented two new mechanistic models for predicting sep-
toria leaf blotch considering experimental data on wetness-
dependent germination, growth and death of the causal agent
and found several shortcomings including failure to predict
the observed annual disease and a cumulative overestima-
tion of the disease over the course of a growing season with
one of the two models. We previously used field data to
quantify the temperature dependency of the temporal dis-
tance between critical rain events and the breaking of the
control threshold of Z. tritici on winter wheat in order to
achieve direct applicability in IPM and precision farming
(Beyer etal. 2022).
The objective of the present study was to test whether the
previously found empirical relationship (Beyer etal. 2022)
allows a prediction of when the control threshold of Z. tritici
as defined by Beer (2005) will be reached.
Materials andmethods
Acquisition ofdisease data
Forty plants were marked in winter wheat plots untreated
with fungicide at the beginning of each season and were
monitored repeatedly. The development of leaf necroses
caused by Z. tritici was monitored by visual assessment in
13 environments over the 2017–2019 period at the same
locations described previously (Aslanov etal. 2019, Beyer
etal. 2022). Staff were trained before the visual assessments
using the online tool provided by the Julius-Kühn Institute
(http:// proze ntual er- befall. julius- kuehn. de/ schad bilder.
php? show=5). According to Beer (2005), the point of time
when winter wheat needs to be sprayed versus leaf blotch is
reached when 30% or 10% of the plants express symptoms
on the upper four leaves during growth stages 32–37 and
39–61, respectively. The date on which the control threshold
was reached will subsequently be referred to as the date of
epidemic outbreak and was used for further data analysis
(Table1).
Input variables
We studied the period comprising growth stages 31–65,
because during this period, fungicide use is allowed. Some
fungicides are currently registered for use until growth stage
69. To determine whether the plants are in a susceptible
growth stage, in which fungicide use can be recommended,
the user must state either the date of sowing or the plant
growth stage. If no plant growth stage is available, this will
be estimated from the date of sowing using the relationship
previously published by Beyer etal. (2012). However, enter-
ing observations on the plant growth stage is the preferred
and recommended method.
The susceptibility rank (given, for instance, in national
cultivar assessment lists such as BSA (2017)) of the winter
wheat cultivar grown must be specified by the users. Previ-
ous experimental evidence demonstrated that septoria leaf
blotch epidemics occur about 5.4days earlier per suscepti-
bility rank (Beyer etal. 2022). The cultivar susceptibility
scale ranges from 1 (resistant) to 9 (susceptible). Varieties
that were tested and are recommended for cultivation in Lux-
embourg (https:// www. sorte nvers uche. lu/) can be selected in
a drop-down menu of the software with their susceptibility
rank given in brackets.
The weather input variables that are required for running
the model are the hourly temperature (measured 2m above
the ground as standard weather stations do) and the hourly
precipitation data between 1 March and 30 June. Hours with
precipitation, but temperatures below 6.5°C were discarded,
because no epidemic was observed below that temperature
(Henze etal. 2007).
The date on which the last fungicide spray was applied to
the crop under consideration must be given. The efficacy of
a modern systemic fungicide expires after 22days (Greiner
etal. 2019). During this post-spraying period, no alerts will
be displayed, even if weather conditions were favorable for
disease progress.

Journal of Plant Diseases and Protection
1 3
Estimating therisk ofreaching critical disease levels
/ thecontrol threshold
Dry hours are excluded as potential predictors, because sep-
toria needs water to infect crops. The time between rainfall
and reaching the control threshold is then calculated for all
wet hours according to the previously established empirical
relationship (Beyer etal. 2022, Eq.1):
For each wet hour warmer than 6.58°C, the time shift is
added to the actual day of the year of the precipitation event. A
frequency distribution (histogram) of the resulting data is plot-
ted, reflecting the points of time at which epidemic outbreaks
are expected based on the studies used for the construction of
the model described here.
Outputs
The resulting frequency distribution usually displays several
peaks. Each peak represents a period of time before which
favorable weather conditions for reaching the control threshold
(1)
Time shift (h) =713.144 −31.701
×
current temperature
(◦
C
)
during rainfall event.
occurred frequently. The higher the peak, the higher the risk.
The default output is generated for cultivars with susceptibil-
ity rank 5. For each susceptibility rank, the bars are shifted by
one bar width. Bar width was chosen to match the temporal
shift caused by one susceptibility rank (approx. 5days, Beyer
etal. 2022). The graph for a cultivar with susceptibility rank
4 is shifted to a later date by one bar width, while the graph
for a cultivar with susceptibility rank 7 is shifted to an earlier
date by 2bar widths.
Validation
To validate the approach outlined above, new data were
recorded (that were not used for the construction of the
model) in the 2017–2019 period in the same region as
above (Beyer etal. 2022). Risk graphs were plotted and
the point of time at which the control threshold accord-
ing to Beer (2005) was reached in the experimental fields
was determined by visual observation on 30–40 plants per
location and assessment date. The actual day of the year
on which the control threshold was reached was compared
with the predicted day. The peaks with the highest predic-
tive power in the risk graphs were identified (for details,
see below). The relationship between predicted and
Table 1 Validation with external data. Observed days when the con-
trol threshold for Z. tritici was reached in winter wheat and predicted
days when the control threshold was reached are given for 13 case
studies that were not used for model construction. The case studies
are identified according to the location of the field where the observa-
tions took place, the wheat cultivar, its susceptibility rank (SR) con-
cerning leaf blotch, the observation year and the plant growth stage
(BBCH code) at which the control threshold was reached. Predictions
were accepted as “correct,” if they fell into the efficacy period of
common commercial fungicides (Greiner etal. 2019), and as “false,”
if epidemic outbreaks were forecasted outside of this time frame. For
details, please see “materials and methods”
a Closest peak to observed
Location Cultivar Year BBCH Observed Predicted Peak heightaPrediction
Date DOY Date DOY
Bettendorf Kerubino 2017 39 21/05/2017 141 22/05/2017 142 30 Correct
Burmerange Kerubino 2017 39 17/05/2017 137 22/05/2017 142 21 Correct
Everlange Kerubino 2017 32 15/05/2017 135 22/05/2017 142 36 Correct
Reuler Kerubino 2017 49 31/05/2017 151 27/05/2017 148 25 Correct
Bettendorf Kerubino 2018 39 14/05/2018 133 17/05/2018 137 15 Correct
Everlange Genius 2018 31 16/04/2018 106 21/04/2018 111 20 Correct
Burmerange Reform 2018 63 28/05/2018 147 08/06/2018 158 25 Correct
Reuler Kerubino 2018 67 04/06/2018 154 03/06/2018 153 22 Correct
Bicherhaff Kerubino 2019 31 15/04/2019 105 11/04/2019 101 19 Correct
Koerich Kerubino 2019 31 29/04/2019 119 03/05/2019 123 15 Correct
Bettendorf Kerubino 2019 32 23/04/2019 113 05/04/2019 95 13 False
Bettendorf Desamo 2019 33 29/04/2019 119 10/04/2019 100 13 False
Weiswampach Kerubino 2019 31 06/05/2019 126 06/05/2019 126 15 Correct

Journal of Plant Diseases and Protection
1 3
observed values was visualized using a Deming regres-
sion that takes, unlike conventional regression, errors in
the x- and y-directions into account (Linnet 1993).
Results anddiscussion
Model architecture
Hourly precipitation (sum) and temperature (average) data
were plotted starting 1 March and ending 15 June (Fig.1).
This period corresponds roughly with plant growth stages
29–69 in winter wheat in the area of observation. We
decided to start before growth stage 31 is reached, because
of the time lag between infection and symptom expres-
sion/reaching of the control threshold. Then, the time shift
between each wet hour and the point of time at which the
control threshold was observed over the period 2015–2016
was estimated using the empirical relationship published
in the first paper of the present series (Beyer etal. 2022).
A histogram was then generated from the time points at
which the reaching of the control threshold was forecasted
(Fig.1A). A graph of the type in Fig.1A will subsequently
be referred to as a risk profile. The more wet hours are
emphasized by the respective temperature conditions on
the same time slice, the higher the bars of the histogram
become. The bar width of the histogram was adjusted to
125h, corresponding to the effect of one rank in the winter
wheat cultivar susceptibility ranks regularly published by
BSA (2017 ff.). For instance, in the period 2005–2016,
a cultivar with susceptibility rank 4 reached the control
threshold on average 125h later than a cultivar with sus-
ceptibility rank 5 (Beyer etal. 2022). The default setting
of the model estimates the risk profile (Fig.1A) for cul-
tivars with susceptibility rank 5 (the most frequent rank
of the cultivars grown in the region of observation). For
cultivars with other susceptibility ranks, the risk profile
is shifted, with the size of the shift corresponding to the
susceptibility rank of the cultivar grown. For instance, for
a cultivar with susceptibility rank 4 (lower than average),
the default histogram is shifted to later dates by one bar
width (= 125h).
The period in which fungicide use is allowed (between
the growth stages 31 and 65–69, depending on fungicide)
is estimated from the sowing date according to Beyer etal.
(2012). Therefore, users of the forecast model need to give
the sowing date of the winter wheat. The precision of the
estimates can be enhanced by manually entering the cur-
rent growth stage of the crop, thus requiring it to be deter-
mined in (each) field concerned. Users can decide whether
Fig. 1 Graphical model output. The time shift between hours with
temperature > 6.5°C and rain (B) and the point of time at which the
control threshold was reached was estimated based on the empirical
relationship depicted in Eq.(1). The graphical output (A) is a histo-
gram of the time points with suitable infection conditions after the
temperature-dependent time shift between rain and exceeding the
control threshold was considered. High bars indicate that the control
threshold will be reached and that a fungicide with efficacy against
Z. tritici should be sprayed to prevent economic losses at the farm
level. The bar width in (A) is 125h and corresponds to the effect of
one rank in the cultivar susceptibility ranks published by the BSA
(2017). A histogram of the height of the peaks closest to the observed
epidemics showed a clear maximum of 15 (Fig.3). Therefore, bars
exceeding “15” on the “Urgency to spray” axis are marked red. The
yellow area indicates the period between plant growth stages 31 and
65, when fungicide use is allowed
Fig. 2 Histogram of the peak heights displayed by the ShIFT model
outputs (Fig. 1A) based on weather and cultivar susceptibility
data from eight years and four locations. The closest peaks to each
observed Z. tritici epidemic between the growth stages 31 and flower-
ing were considered

Journal of Plant Diseases and Protection
1 3
they want to enter the sowing date or the growth stage of
the winter wheat, although the growth stage is preferred
due to better precision. During the period when fungicide
use is permitted, spraying is recommended, before a red
bar is reached (Fig.1).
Validation
A typical risk profile (Fig.1A) contains several peaks. Each
peak represents a period of time, during which it is highly
likely that the control threshold for Z. tritici will be reached.
No warning is displayed for peaks that occur before or after
fungicide spraying is allowed (for an example, see Fig.1A).
A histogram was generated, showing the peak heights from
the period in which fungicide use was allowed (Fig.2). A
clear maximum was observed at 15 (Fig.2). Hence, we
assume that a peak height of 15 or higher indicates the
reaching of the control threshold and thus the need to apply a
fungicide spray. Among the six peaks that were smaller than
15 but associated with a breaking of the control threshold
(Beer 2005), four were observed in the year 2014 (Fig.2).
The pattern used to construct the model was detected
in data acquired in field experiments during the period
2005–2016 (Beyer etal.2022). Data from 2017–2019 were
used for external validation. If a predicted point of time was
within ± 11days (being equivalent to 50% of the efficacy
duration of a systemic fungicide (Greiner etal. 2019)) of
the observed point of time, the prediction was accepted as
correct, splitting the risk of applying either too early or too
late. If predicted times were outside of the period speci-
fied above, the predictions were categorized as false. Of 13
cases, 2 were identified as false with external data (Table1).
This corresponds to 84.6% correct predictions. Predicted and
observed dates were closely related (r = 0.92, P < 0.0001,
Fig.3).
Critical evaluation ofmodel performance
In contrast to previous models, ShIFT forecasts the time for
which a fungicide spray is needed directly. Users do not
need to interpret epidemiological outputs to identify a suit-
able time frame for a fungicide spray based on the control
threshold concept of integrated pest management.
The winter wheat crop is particularly sensitive toward
septoria leaf blotch between plant growth stages 31 and 65.
Therefore, peaks occurring before GS31 or after GS65 indi-
cate favorable weather conditions for epidemics but do not
indicate a need for fungicide use. Note that in many coun-
tries, including EU countries, fungicide spray application
outside of the mentioned time frame is illegal due to the
registration conditions of the products.
In the year 2014 (that was part of the data set used to
detect the pattern used for the model), a massive yellow
rust epidemic was observed in Luxembourg for the first
time (Dam etal. 2020), probably due to the spread of more
aggressive strains throughout Europe at that time (Aslanov
etal. 2019). The poor performance of the model in that year,
as indicated in Fig.2, suggests that other diseases can seri-
ously interfere with the percentage of correct model outputs
at high disease levels. Thus, the model described here should
not be used in other regions without locally validating model
outputs with field observations, particularly if fungal plant
pathogens other than Z. tritici are dominant.
The model allowed correct predictions in 84.6% of cases,
while 15.4% of the cases were predicted falsely. The average
deviation between the observed and predicted dates of rel-
evant epidemic outbreaks was 0.62 ± 2.4days with a maxi-
mum deviation of 19days. Observed and predicted dates
were closely correlated (r = 0.92, P < 0.0001). The model
demonstrated considerable prognostic power when being
tested with independent new data, and however, the pos-
sibility of outliers being falsely classified cannot be denied.
Considerations onfungicide efficacy
Greiner etal. (2019) determined the period of fungicide effi-
cacy for Bravo 500 (a representative of contact fungicides
containing chlorothalonil as active ingredient), Epoxion
(a representative of systemic azole fungicides containing
epoxiconazole as active ingredient) and Imbrex (a repre-
sentative of systemic succinate dehydrogenase inhibitors
containing fluxypyroxad as active ingredient) at full dose
rates. The effective period of the fungicides ranged from
16days for the contact fungicide Bravo to 22days for the
systemic fungicide Imbrex. Due to the ban of chlorothalonil
and epoxiconazole, the situation in Luxembourg was greatly
simplified, such that only products from the group with an
effective period of approximately 22days remained on the
market and were therefore considered in the ShIFT model.
Fig. 3 Day of the year (DOY) on which it was observed that the con-
trol threshold of leaf blotch on winter wheat had been reached versus
the predicted DOY for reaching the control threshold. The solid line
represents a Deming regression; the dotted lines represent the 95%
confidence interval

Journal of Plant Diseases and Protection
1 3
Considerations onthenumber ofsprays
recommended–potential forpesticide savings
Under weather conditions that continuously allow for infec-
tions and disease development, three sprays may be applied
for the best protection of the upper three-leaf layers that are
largely responsible for grain filling, namely one spray after
the formation of each of the leaf layer. Under weather condi-
tions that do not allow for infections and disease develop-
ment continuously, the number of sprays and thereby costs
may be reduced without giving rise to an epidemic. ShIFT
recommended on average 1.4 sprays on winter wheat per
season against Zymoseptoria tritici (Fig.4). In a survey
with 108 participants, Luxembourgish farmers responded
that they believed that on average, 1.6 fungicide applica-
tions are needed per season in winter wheat (Beyer etal.
2019). In neighboring Germany, 2.3 sprays are applied on
average per season (https:// papa. julius- kuehn. de/ index. php?
menuid= 46). The difference between the 1.4 sprays per sea-
son recommended by ShIFT, the 1.6 sprays mentioned by
Luxembourgish farmers and the 2.3 sprays applied on Ger-
man farms can probably be attributed to the need for con-
trolling other diseases besides leaf blotch. However, most
commercial fungicides show efficacy against several fungal
pathogens, and therefore, an additional spray is needed only
if the temporal distance between the occurrences of the dis-
eases is larger than the period of efficacy of the fungicide.
Besides leaf blotch, yellow rust has also often been observed
since 2014 in Luxembourg (Aslanov etal. 2019). In the
2005–2017 period, leaf blotch and yellow rust reached their
respective control thresholds in 17 of 62 cases. The break-
ing of the control threshold for both diseases was within the
period of a systemic fungicide in 14 cases. In 3 of 62 cases
(= 5%), the temporal distance between the occurrence of the
two diseases was too large to control both diseases with the
same spray. In approximately one out of five years, weather
conditions for Fusarium head blight were favorable enough
to allow for at least local mycotoxin contamination (Pallez
etal. 2021). The 1.4 sprays per season recommended by
ShiFT against leaf blotch in winter wheat seem to be roughly
realistic with regard to farmers’ opinions and factual use,
leaving little room to further reduce fungicide use in winter
wheat without accepting avoidable losses.
Availability ofthemodel
ShIFT is freely available in the year 2022 for a test period in
English, French, German and Luxembourgish at https:// shift.
list. lu/ (Identifier: JPDP, Password: DPG_2022).
Acknowledgements We thank Rufat Aslanov, Moussa El Jarroudi,
Mélanie Gollier, Louis Kouadio, Jasmin Mahboubi, Abdeslam Mah-
tour, Benedek Marozsák, Bertrand Martin, Farid Traoré and Virginie
Schyns for their excellent technical assistance, Lindsey Auguin for
language editing and the Administration des Services Techniques de
l’Agriculture of Luxembourg for financially supporting the Sentinelle
project.
Declarations
Conflict of interest The authors declare that they have no conflict of
interest.
Human and animal rights This article does not contain any studies
undertaken on human or animal subjects by any of the authors.
Open Access This article is licensed under a Creative Commons Attri-
bution 4.0 International License, which permits use, sharing, adapta-
tion, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article's Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
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