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Author(s):  Juho Hautsalo, Lauri Jauhiainen,  Asko Hannukkala, Outi Manninen,  Merja Veteläinen,  Leena 
Pietilä, Kirsi Peltoniemi & Marja Jalli 
Title: Resistance to Fusarium head blight in oats based on analyses of multiple field and 
greenhouse studies 
Year: 
Version: 
 2020 
 Publisher's version
Copyright:   The author(s) 2020  
Rights:  CC BY 4.0   
Rights url:  https://creativecommons.org/licenses/by/4.0/  
Please cite the original version: 
Hautsalo, J., Jauhiainen, L., Hannukkala, A. et al. Resistance to Fusarium head blight in oats based 
on analyses of multiple field and greenhouse studies. Eur J Plant Pathol (2020). 
https://doi.org/10.1007/s10658-020-02039-0  
Resistance to Fusarium head blight in oats based
on analyses of multiple field and greenhouse studies
Juho Hautsalo & Lauri Jauhiainen & Asko Hannukkala & Outi Manninen & Merja Veteläinen & Leena Pietilä &
Kirsi Peltoniemi & Marja Jalli
Accepted: 11 June 2020
# The Author(s) 2020
Abstract Fusarium head blight (FHB) and the myco-
toxins produced by its causal agents in oats (Avena
sativa L.) have become a growing problem in northern
countries over the last decades. The development of
resistant cultivars would offer a highly needed and
economical solution to the problem. To tackle the high
genotype×environment interaction of FHB, a combined
analysis was carried out on eight greenhouse and 13
field experiments inoculated with DON-producing Fu-
sarium species. Our data included 406 oat genotypes
consisting of Nordic cultivars, breeding lines and poten-
tially resistant gene bank accessions. High variation in
the DON accumulation estimates in the material shows
that the selection of genotypes with better resistance
would be valuable. The greenhouse and field studies
resulted in significantly different oat genotype suscepti-
bility rankings for both DON and Fusarium infected
kernels. The results obtained from the field experiments
have more practical relevance for farmers and breeders
for the identification of DON resistant cultivars than
greenhouse screenings. Days to maturity and the plant
height of the genotypes both significantly affected the
Fusarium infections and DON in the field. The relation-
ship between Fusarium infected kernels, DONand ger-
mination capacity provide an insight into the composi-
tion of genotypes with resistance. The core set of 30 oat
genotypes, which were phenotyped in several experi-
ments, provides valuable examples of both highly sus-
ceptible and moderately resistant oat genotypes.
Keywords Fusarium resistance . Oats . DON .
Phenotyping . Resistance components .Meta-analysis .
Mycotoxins . Cereals
Introduction
Cultivated oats (Avena sativa L.) are an important ex-
port crop for northern countries such as Finland, Swe-
den and Canada (FAOSTAT 2016). Oats are used both
Eur J Plant Pathol
https://doi.org/10.1007/s10658-020-02039-0
Asko Hannukkala passed away on 10 May 2020.
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s10658-020-02039-0) contains
supplementary material, which is available to authorized users.
J. Hautsalo (*)
Natural Resources Institute Finland, Survontie 9A,
FI-40500 Jyväskylä, Finland
e-mail: juho.hautsalo@luke.fi
J. Hautsalo :A. Hannukkala
Department of Agricultural Sciences, FI-00014 University of
Helsinki, Helsinki, Finland
L. Jauhiainen :A. Hannukkala :M. Jalli
Natural Resources Institute Finland, Tietotie 4, FI-31600,
Jokioinen, Finland
O. Manninen :M. Veteläinen : L. Pietilä :K. Peltoniemi
Boreal Plant Breeding Ltd., Myllytie 10, FI-31600, Jokioinen,
Finland
for food and animal feed and are considered a healthy
cereal due to a number of nutritional compounds includ-
ing β-glucan which has been confirmed to lower blood
cholesterol and reduce the risk of heart disease (EFSA
2009 and 2011). The health benefits of oats have con-
tributed to the development of a range of new oat
products. In addition, Nordic oats are branded for their
high quality. Unfortunately oats are also susceptible to
Fusarium head blight (FHB), a disease which in recent
years has reduced yields and seed germination and has
led to the severe accumulation of the mycotoxin
deoxynivalenol (DON) in all oat production areas
(Hietaniemi 2016, Tekle et al. 2013, Tekauz et al. 2004).
The main causal agent of FHB, Fusarium
graminearum Schwabe sensu stricto, has recently be-
come the most important Fusarium species affecting
cereals in Nordic European countries (Hofgaard et al.
2017, Hietaniemi et al. 2016, Fredlund et al. 2013) and it
is the most prevalent species associated with the disease
in Canada (Tekauz et al. 2004). DON is produced by
both F. graminearum and F. culmorum (Wm. G. Sm.)
which are favoured by different weather conditions.
F. culmorum usually infects cereals in wet and cool
conditions (Xu et al. 2008), whereas F. graminearum
infections occur in warm, rainy and humid conditions
during flowering (Hjelkrem et al. 2017).
Resistance breeding programmes have already gen-
erated some oat cultivars with considerably lower my-
cotoxin accumulation when they are compared to other
cultivars (Tekle et al. 2018, Yan et al. 2010). In addition,
several potential resistance sources from cultivars and
landraces have been identified based on resistance
screening experiments (Tekle et al. 2018, Bjørnstad
et al. 2017, Loskutov et al. 2016, Yan et al. 2010). The
resistance in oats has a quantitative nature (He et al.
2013, Bjørnstad et al. 2017) and the genes behind the
resistance are still unknown. Resistance against FHB in
cereals is commonly divided into at least five separate
components of resistance. Resistance against initial in-
fection and the spreading of infection (Schroeder and
Christensen 1963) are known as type I and II resistance,
respectively, whereas mycotoxin resistance is known as
type III resistance (Miller et al. 1985). Resistance
against kernel infections is called type IV resistance
and tolerance is referred to as type V resistance
(Mesterházy 1995, Mesterházy et al. 1999). However,
the numbering of the types III-V is somewhat inconsis-
tent in the literature (e.g. Mesterházy et al. 1999 vs.
Mesterházy 2002) and therefore we refer to these three
as: resistance to kernel infection, tolerance, and resis-
tance to mycotoxin accumulation, instead of using the
abbreviations. In addition, there are many morphologi-
cal features, such as plant height (Mesterházy 1995),
which may act as disease escape mechanisms and thus
complicate resistance evaluation.
Gathering enough phenotypic information to draw
reliable conclusions on the Fusarium resistance in oats
requires a toolbox of different analyses which measure
different resistance components. An artificial inoculation
system is needed to ensure sufficient infection and rep-
etition reduces the high environmental variation
(Hautsalo et al. 2018). In natural infections the differ-
ences between oat genotypes are often too small to be
reliably distinguished from each other due to the low or
unevenly distributed infection pressure. Moreover, the
mycotoxin accumulation is also dependent on particular
species causing the FHB. Natural infections are often
highly dependent on factors such as the weather
(Hjelkrem et al. 2017), the previous crop (Dill-Macky
and Jones 2000) and the tillage (Kaukoranta et al. 2019).
In wheat, the disease severity can be used as a parameter
for selection (Buerstmayr and Lemmens 2015), but this
is not seen as a reliable method for oats and therefore
more analyses are needed from harvested samples (Tekle
et al. 2018). This all makes the breeding of Fusarium
resistant oat cultivars a time-consuming and expensive
effort. For example Tekle (2018) et al. achieved reliable
cultivar resistance rankings, relying on DON accumula-
tions and germination capacities from a minimum of
4 years of spawn inoculated nursery experiments, where-
as Xue et al. (2015) used inoculated growth chamber
experiments and they were able to separate Canadian oat
genotypes concerning their resistance to FHB based on
DON and infected spikelets.
Controlled environments and artificial inoculations
are often used for disease resistance research purposes
(Afanasenko et al. 2009). However, for large-scale
screening purposes, a controlled environment is not
always economically or practicable. The results from
experiments carried out in controlled environment may
not always apply in field conditions. The consistency of
results between greenhouse and field trials has varied in
experiments studying FHB resistance in transgenic
wheat lines (Anand et al. 2003, Mackintosh et al.
2007) as well as in studies of biological control agents
against FHB in wheat (Schisler et al. 2002). Green-
houses are generally considered to be the most reliable
environment for the assessment of the resistance against
Eur J Plant Pathol
the spread of infection for wheat, while field studies are
seen to provide more comprehensive estimates of the
overall FHB resistance.
Further studies are needed to better understand how
to utilise greenhouse and field research facilities for
phenotyping the resistance against FHB in the most
efficient way. With special focus on determining the
state of FHB resistance in Finnish oats, this study at-
tempts to carry out a combined analysis of several
artificially inoculated greenhouse and field experiments
assessing FHB resistance in oat material (Nordic culti-
vars, Finnish breeding material and various gene bank
accessions that have shown some resistance in earlier
studies (Gagkaeva et al. 2013)). The aim with the com-
bined analyses is to gain a more comprehensive estimate
of the FBH resistance. In addition, this study highlights
the possible differences between greenhouse and field
environments concerning the resistance rankings and
describes the interaction between resistance components
(DON accumulation, germination capacity and Fusari-
um infected kernels) and typical escape mechanisms
(plant height, maturation time)..
Material and methods
Data description
The data were collected from eight greenhouse (Table 1)
and 13 field FHB resistance screening experiments
(Table 2) conducted in Southern (Jokioinen, 60°48′
55”N 23°28′35″E) and Central Finland (Laukaa,
62°19′22”N 25°59′05″E) by the Natural Resources In-
stitute Finland (Luke). These experiments were de-
signed for various projects to test potential FHB resis-
tance sources in oats, as well as to develop phenotyping
methods for FHB resistance screening. The oat geno-
types studied in these greenhouse and field experiments
included 348 oat breeding lines from Boreal Plant
Breeding Ltd., 40 north European oat cultivars and 16
gene bank accessions from the N. I. Vavilov Research
Institute of Plant Industry (VIR).
Most of the breeding lines were included only in a
few experiments (on average three experiments), but
some oat genotypes (‘BOR31’, ‘Belinda’, ‘Akseli’) that
were used as checks were represented in 14, 16 and 18
experiments, respectively. The ‘Akseli’ and ‘BOR31’
genotypes were included as check cultivars in every
greenhouse experiment since 2013 and the ‘Belinda’
genotype was included in all field experiments. The
‘BOR31’ genotype is a rejected variety that has never
been entered the market and the breeder no longer
maintains the seeds. A core set of 30 oat genotypes
containing cultivars, breeding lines and gene bank ma-
terial were selected to be studied more carefully in 2017
in the field because these genotypes had DON accumu-
lation and germination data from a minimum of three
separate field experiments. The exact number of analy-
ses behind each core genotype’s estimates is provided in
the supplementary material (S1).
Greenhouse experiments
The greenhouse experiment data consisted of eight ex-
periments (Table 1) that were carried out between 2012
and 2016 in greenhouses at the Natural Resources Insti-
tute Finland (Luke) and Boreal Plant Breeding Ltd.,
Jokioinen, Finland. The experimental design as well as
the number of replicates varied in the experiments
(Table 1) according to the purpose of the trial. The spray
inoculation protocol for the greenhouse experiments
was developed by testing different fungal isolates or
inoculum concentrations for which a sufficient number
of replicates were needed. These were all added as
replicates for the oat genotypes.
In the greenhouse trials, the temperature was set at 18
± 2 C during the day and 15 ± 2 C during the night. Five
plants per pot were grown in a fertilized medium con-
taining peat and sand. After spray inoculations, the tem-
perature was raised to 20 C during the day and 18 C at
night and the light intensity was kept above 200 W/m2
16 h per day. Oats were spray inoculated during the main
stem anthesis (GS 65, Zadoks 1974) with a 2 ml per plant
of a suspension containing Finnish F. culmorum isolates
05018 (for the first five experiments, Table 1) or 05015
(for the last three experiments, Table 1) and with
F. graminearum isolate 05011 (for the last four
experiments, Table 1). The concentration of the inoculum
varied between 80,000 and 500,000 spores per ml from
one experiment to another. During the inoculation, mist
irrigation was applied for 2 h before and 6 h after the
inoculation. The main stems (5 panicles per pot) were
harvested at GS92 (Zadoks 1974).
Field experiments
The field experiments were inoculated by spraying them
once during anthesis (GS 65) with F. culmorum isolate
Eur J Plant Pathol
05018 similarly to the greenhouse inoculations in 2012,
2013 and 2014. Since 2015, a spawn inoculation meth-
od (Tekle et al. 2018, Skinnes et al. 2010) was applied
with an isolate mixture of five F. graminearum isolates
(12,007, 12,010, 05011, 05039 and 06249) which have
been isolated from Finnish cereals. The plots in the field
experiments were either small hill plots containing 20
seeds per one oat genotype, or row plots containing six
metre-long rows of which one oat genotype represented
two to three rows per 0.5 to 0.75 m2. All the field
experiments had from one to four replicates. For the
spawn inoculated experiments, a total of 1 h of irrigation
was applied daily during the evening hours between
7 PM and 10 PM from anthesis to yellow maturity (GS
87) from 2015 to 2017. The application of fertiliser and
herbicides was done according to local needs. Fungicides
were not applied in these experiments. From the hill plots
only the main stems were harvested, but from the exper-
iments in rows all the matured panicles were harvested.
Analysed traits
The purpose of each experiment determined which traits
were analysed. The percentage of Fusarium infected
kernels (FIK) was noted for all of the experimental units
(n = 4661). The DON accumulation (DON) was record-
ed for 50.4% of the total number of observations and the
germination capacity was observed from 58.8% of the
harvested samples. Before the analyses, the yield was
threshed to remove most of the small and empty grains.
Additionally, broken or green grains were removed for
the FIK or GC analyses, but not from DON samples.
The FIK was estimated by plating 100 grains/
replicate on a selective PCNB (pentachloronitrobenzene
(Nash and Snydermedium, Nelson et al. 1983)) medium
favouring Fusarium growth instead other fungi or bac-
teria. The FIK was determined either after 3 days or
1 week of incubation at 23 °C, when the variation in
infection rates between genotypes was the most pro-
nounced. DON-mycotoxin contamination was analysed
from milled grain samples using an ELISA kit (R5906
Ridascreen DON 96 test, R-Biopharm, Darmstadt, Ger-
many). A commercial reference sample for DON, TR-
D100 (R-Biopharm, Darmstadt, Germany), was used to
check that our DON analyses gave consistent results. A
typical yield for two row plots was around 200 g from
which a subsample of 1/2 of the yield was milled for a
DON analysis. If the yield was lower than 100 g, the
entire sample was milled for analysis. The germination
capacity (GC) was determined with a standard paper test
from a sample of 100 grains/replicate (ISTA 2006).
Harvested yields were subsampled for analyses either
by hand (greenhouse) or by using a sample divider.
The impact of DON on the germination of the oat
genotypes was analysed by dividing the experiments
into two groups based on a low and high DON accu-
mulation. Group 1 included three experiments, 455
observations and it had an average DON of
Table 1 The greenhouse experiments included in the study. The
experiments are categorized by year. Numbers of replications per
experiment (Reps) and oat genotypes (Oats) are provided. For
each experiment the available range and number of samples
analysed for DON accumulation (DON, n = xx) and Fusarium
infected kernels (FIK, n = xx) are shown together with the differ-
ence between our two check genotypes (moderately resistant
Akseli and susceptible BOR31). The number of samples in the
analyses may differ for the number of genotypes. This is because
some genotypes have been used as check cultivars or the yield was
insufficient for some of the analyses. The last column shows the
Spearman’s rank correlation (r) between DON and FIK, while n.s.
indicates not significant and *** is equal to P < 0.001
Year Oats Reps Average (and range)
of DON μg/kg
DON
(n = xx)
Difference between
checks, μg/kg
Average (and
range) of FIK %
FIK
(n = xx)
Difference between
checks, %-units
r (DON,
FIK)
2012 8 6 1646 (686–4199) 8 – 71 (13–100) 48 – n.s.
2013 190 3 – – – 89 (6–100) 607 13 –
190 3 – – – 98 (60–100) 608 17 –
2014 39 4 – – – 55(2–100) 160 10 –
2015 7 16 10,604 (500–45,100) 112 6219 89 (54–100) 112 18 0.49***
10 24 9606 (19–60,000) 185 34,283 66 (2–100) 164 – 0.68***
21 8 7662 (20–60,288) 163 19,850 67 (6–100) 157 74 0.75***
2016 32 8 7614 (30–70,000) 202 28,832 58 (1–100) 243 56 0.72***
Total 234 – 8604 (19–70,000) 670 – 81 (1–100) 2099 – 0.68***
Eur J Plant Pathol
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Eur J Plant Pathol
7525 μg/kg and a germination rate of 60.6%. Group 2
included three experiments, 783 observations and it had
an average DON content and germination rate of
33,185 μg/kg and 66.3%, respectively.
Data from breeder’s experiments (Boreal Plant
Breeding Ltd.) between 2008 and 2017 on the maturity
and plant height (PH) were added to the meta-analysis
data set. We added data based on at least five experi-
ments for 200 oat genotypes. The maturity was rated
from 1 to 5, 1 being early to mature and 5 being late
maturing. In terms of the growing degree days required
for the genotypes to mature, classes 1 and 2 required
from 910 to 960 degree days, while class 3 needed from
960 to 990 degree days, class 4 took between 990 and
1020 degree days and class five required more than
1020 degree days. The heights of the oat genotypes
were classified into three groups. Group 1 included the
shortest and group 3 included the tallest genotypes. For
comparison, we have included accurate measurements
of the days to heading (DTH), days to maturity (DTM)
and plant height (PH, cm) from the soil surface to
panicle tip) for the core data set. The DTH was mea-
sured at the Laukaa site in 2016 and 2017. The DTM
was measured for two separate experiments at the
Jokioinen site in 2017 and PH was measured for the
Laukaa experiment in 2016 and 2017 and from one
experiment at the Jokioinen site in 2017.
Statistical methods
Best linear unbiased estimators (BLUEs) for all geno-
types were estimated using the following statistical
model based on the data collected:
yijk ¼ μ þ triali þ replication trialð Þij þ genotypek þ εijk
whereμ is the intercept, triali is the random effect of the ith
trial (i = 1,…,21; eight greenhouse and 13 field FHB
resistance screening experiments), replication(trial)ij is the
random effect of the jth replication (j = 1,…,4) within the
ith trial, genotypek is the fixed effect of the kth genotype
and εijk is the residual. The model was applied to analyses
in which only a part of the trials (either greenhouse trials or
field trials) were included. This model and the estimated
means for the genotypes were used when comparing two
or more classes of genotypes mutually, e.g. genotypes for
different PH or maturity categories.
When comparing how the responses of the genotypes
varied between environments (i.e. between the
greenhouse and field FHB resistance screening experi-
ments), two fixed effects were added to the original
statistical model. These were environmentl and geno-
type-by-environmentkl interaction effects (l = 1,2). In
addition, the random effect of the trial was nested in
the environment in the new model.
Unbalanced data, in which not all of the genotypes
are present in all of the trials, would lead to biased
estimates if a simple regression model were to be ap-
plied. Therefore a model that includes several sources of
variation is needed. Searle (1987) showed that genotype
effects can be estimated in any well-defined linear
mixed model analysis if the turnover of genotypes tested
is less than 20%. In fact a higher turnover of genotypes
is allowed if some control cultivars are included in
almost all the trials. Our data were not optimal, but most
of the differences between genotypes can be estimated
without bias using a mixed model.
All the models were fitted using SAS-software with
the MIXED-procedure and REML estimation method.
Assumptions about the normal distribution and homo-
geneity of the error variance were checked using box-
plots of residuals and a scatter plot of residual and fitted
values. The DON was normally distributed after a loge-
transformation. However, all the estimates and their
standard errors were transformed back to the original
scale for the presentation of the results. A correlation
analysis was performed using the SAS/CORR-proce-
dure. Relationships between variables were not typically
linear, as a Pearson’s correlation coefficient assumes,
and therefore a Spearman’s rank correlation coefficient
was used to test statistical dependence.
In a statistical power analysis the magnitude of the
least significant difference between two genotypes was
calculated as a function of the sample size required. The
statistical significance criterion was set to 5% and the
statistical power was set to 50%. The sampling error was
calculated separately for the field and greenhouse trials.
The sampling error described how much the difference
between any two genotypes varied between trials and
not the within-trial variation. After that the magnitude of
the effects (i.e. the least significant difference) was
calculated as a function of the sample size. Calculations
were made using the SAS-software. DONwas normally
distributed after a loge transformation and therefore a
power analysis of the DON was carried out on a loge
scale, in which DON 4000 μg/kg was used as a refer-
ence level (loge4000 = 8.29). However, all the presented
results were transformed back to the original scale.
Eur J Plant Pathol
Results
Fusarium associated traits estimated from a variable data
set
In the greenhouse experiments, a high rate of FIK and
DON was achieved, and the two check cultivars were
constantly separated from each other (Table 1, S2). In
the field experiments (Table 2) the average DON, FIK
and GC varied highly from experiment to another,
which also affected the differentiation of the oat geno-
types (Fig. 1). For example, one of the field experiments
in 2013 (Jokioinen II) and another in 2015 (Jokioinen)
had almost no DON, while the experiment in Laukaa in
2017 had a wide variation concerning both DON and
GC. The GCwas much reduced in the 2013 Jokioinen II
trial, but not in the 2015 Jokioinen trial.
The FHB susceptibility based on DON or FIK dif-
fered between entries depending on whether the oat
genotypes were grouped greenhouse or field (Tables 3
and 4). This was verified by significant G × E interac-
tions that were detected for both DON (P < 0.05) and
FIK (P < 0.001).
DON in the field and greenhouse experiments
The average DON did not differ significantly between
the greenhouse (mean ± SD, 8600 ± 11,000 μg/kg) and
Fig. 1 DON accumulation (DON), germination capacity (GC)
and Fusarium infected kernels (FIK) plotted from the field exper-
iments where the traits were measured. The upper figures show the
data for all the genotypes in different field experiments and the
lower figures show the data for seven selected oat genotypes
Table 3 The range and number (N) of oat genotype specific DON
accumulation estimates for different types of accessions based on
the entire data for the greenhouse (8 experiments) and field (13
experiments). We show separate ranges for all estimates and for
estimates that have data collected from a minimum of three
experiments
Type of material All estimates Estimates with data from min. Three experiments
Greenhouse Field Greenhouse Field
Range n Range n Range n Range n
Breeding lines 3279 - 10,531 10 652 - 57,891 212 3279 - 7526 7 5007–17,398 176
Cultivars 805 - 22,059 13 7204–27,382 27 4792 - 22,059 3 7204–20,138 22
Accessions 462 - 9904 14 5026–22,691 14 462 - 9904 14 5026–22,691 14
Eur J Plant Pathol
T
ab
le
4
F
ie
ld
an
d
gr
ee
nh
ou
se
D
O
N
,
Fu
sa
ri
um
in
fe
ct
ed
ke
rn
el
s
(F
IK
)
an
d
ge
rm
in
at
io
n
ca
pa
ci
ty
(G
C
)
es
tim
at
es
an
d
th
ei
r
st
an
da
rd
er
ro
rs
(S
.E
.)
fo
r
th
e
co
re
se
t
of
30
ge
no
ty
pe
s
in
cl
ud
in
g
cu
lti
va
rs
,g
en
e
ba
nk
ac
ce
ss
io
ns
(f
ro
m
V
IR
69
63
to
V
IR
84
79
)
an
d
br
ee
di
ng
lin
es
(f
ro
m
B
O
R
01
to
B
O
R
15
).
‘B
O
R
31
’i
s
a
re
je
ct
ed
va
ri
et
y.
T
he
es
tim
at
es
ar
e
ca
lc
ul
at
ed
fr
om
th
e
da
ta
ga
th
er
ed
fr
om
12
fi
el
d
an
d
8
gr
ee
nh
ou
se
ex
pe
ri
m
en
ts
,
th
e
ex
ac
t
nu
m
be
rs
of
an
al
ys
es
be
hi
nd
ea
ch
es
tim
at
e
ar
e
re
pr
es
en
te
d
in
S
up
pl
em
en
ta
ry
T
ab
le
(S
1)
.M
at
ur
ity
(1
=
ea
rl
y,
2
=
m
od
er
at
el
y
ea
rl
y,
3
=
av
er
ag
e,
4
=
m
od
er
at
el
y
la
te
,
5
=
la
te
)
an
d
pl
an
t
he
ig
ht
cl
as
si
fi
ca
tio
n
(1
=
m
od
er
at
el
y
sh
or
t,
2
=
av
er
ag
e
he
ig
ht
=
3
=
hi
gh
pl
an
ts
)a
re
al
so
pr
ov
id
ed
to
ge
th
er
w
ith
av
er
ag
es
fo
rt
he
pl
an
th
ei
gh
t(
P
H
,c
m
)b
as
ed
m
ai
nl
y
on
th
re
e
ex
pe
ri
m
en
ts
an
d
av
er
ag
e
nu
m
be
r
of
da
ys
to
he
ad
in
g
(D
T
H
)
an
d
m
at
ur
ity
(D
T
M
)
ba
se
d
on
m
ai
nl
y
tw
o
se
pa
ra
te
ex
pe
ri
m
en
ts
.
T
he
le
as
t
si
gn
if
ic
an
t
di
ff
er
en
ce
w
as
es
tim
at
ed
fo
r
th
e
D
T
H
,
D
T
M
an
d
pl
an
t
he
ig
ht
(c
m
).
T
he
va
lu
es
m
ar
ke
d
w
ith
a
st
ar
sy
m
bo
l
(*
)
ar
e
ba
se
d
on
m
ea
su
re
-
m
en
ts
fr
om
on
ly
on
e
ex
pe
ri
m
en
t.
T
he
va
lu
es
ar
e
so
rt
ed
ac
co
rd
in
g
to
th
e
fi
el
d
es
tim
at
e
of
th
e
D
O
N
G
en
ot
yp
e
M
at
ur
ity
cl
as
s
D
T
H
D
T
M
P
H
cl
as
s
P
H
,c
m
F
ie
ld
es
tim
at
es
G
re
en
ho
us
e
es
tim
at
es
L
SD
=
1.
9
L
SD
=
3.
4
L
SD
=
5.
7
D
O
N
±
S.
E
.
L
SD
=
65
42
FI
K
±
S.
E
.
L
SD
=
8.
1
G
C
±
S
.E
.
L
S
D
=
13
.2
D
O
N
±
S
.E
.
L
SD
=
69
63
F
IK
±
S.
E
.
L
SD
=
21
.4
B
O
R
03
5
55
12
4
2
93
50
07
±
29
78
57
±
8.
6
58
±
8.
5
–
–
B
O
R
15
2
53
11
0
3
97
60
00
±
32
94
46
±
8.
4
74
±
8.
3
–
–
B
O
R
09
2
56
11
1
2
92
70
68
±
35
75
51
±
8.
4
68
±
8.
3
–
–
N
ik
la
s
2
48
*
10
6*
2
99
*
72
04
±
37
03
52
±
8.
4
63
±
8.
8
57
92
±
35
15
82
±
7.
8
B
O
R
12
2
56
11
1
3
97
74
88
±
36
80
47
±
8.
5
63
±
8.
3
85
29
±
45
18
81
±
11
.5
B
O
R
13
2
54
10
7
2
88
84
96
±
39
20
51
±
8.
5
67
±
8.
4
–
–
A
lk
u
2
53
11
0
1
90
89
86
±
39
76
56
±
8.
3
67
±
8.
1
–
86
±
9.
1
B
O
R
06
3
57
11
7
3
98
97
59
±
42
01
50
±
8.
5
63
±
8.
3
–
85
±
10
.8
B
O
R
07
4
57
12
0
3
10
3
98
65
±
42
56
49
±
8.
4
72
±
8.
4
–
84
±
10
.8
O
da
l
3
57
*
12
3
2
10
6
10
,3
35
±
48
46
50
±
8.
6
59
±
8.
8
84
30
±
18
23
78
±
6.
7
B
O
R
01
4
56
12
1
3
96
10
,3
95
±
42
51
53
±
8.
3
64
±
8.
1
32
79
±
11
16
60
±
6.
4
B
el
in
da
4
53
12
1
2
95
10
,4
60
±
40
36
58
±
8.
1
61
±
7.
7
–
87
±
7.
1
V
IR
69
63
4
58
12
1*
3
11
9
10
,5
69
±
46
46
62
±
8.
5
59
±
9.
0
44
62
±
15
53
50
±
7.
1
B
O
R
05
4
57
11
9
3
99
10
,5
81
±
43
74
57
±
8.
4
56
±
8.
4
–
–
V
IR
79
34
4
54
*
12
6
3
11
5
10
,6
36
±
44
19
57
±
8.
4
58
±
8.
4
71
92
±
19
71
63
±
7.
1
B
O
R
11
4
54
11
8
2
99
10
,8
72
±
43
26
47
±
8.
3
65
±
8.
2
–
85
±
10
.8
E
em
el
i
2
54
10
7
1
91
10
,9
53
±
42
94
52
±
8.
3
70
±
7.
9
–
86
±
10
.8
B
O
R
08
2
55
11
2
3
10
0
12
,0
03
±
46
59
52
±
8.
5
69
±
8.
4
–
–
St
ei
na
r
3
59
*
12
0
3
10
2
12
,6
61
±
45
53
56
±
8.
2
57
±
7.
9
–
82
±
10
.8
A
ks
el
i
2
53
11
1
2
93
12
,7
13
±
44
76
54
±
8.
1
67
±
7.
9
47
92
±
13
90
65
±
6.
8
Sy
m
ph
on
y
4
58
*
12
1
3
10
2
13
,5
51
±
49
50
57
±
8.
4
61
±
8.
4
–
10
3
±
13
.1
B
O
R
31
2
52
*
10
7
1
95
13
,5
53
±
48
52
48
±
8.
2
68
±
8.
4
22
,0
59
±
31
63
91
±
6.
4
V
IR
84
79
5
62
12
7
3
12
8
14
,3
62
±
52
78
52
±
8.
6
61
±
8.
9
51
48
±
22
51
64
±
8.
0
B
O
R
10
5
54
12
3
1
93
15
,0
20
±
52
51
58
±
8.
4
58
±
8.
4
–
67
±
9.
1
D
on
na
4
57
12
1
3
10
1
15
,0
99
±
52
65
55
±
8.
5
62
±
8.
4
60
42
±
35
90
75
±
8.
5
Eur J Plant Pathol
field (mean ± SD, 22000 ± 21,000 μg/kg) environments
(P = 0.13). Both the greenhouse and field screening
experiments separated oat genotypes which had high
differences in their DON (P < 0.001, Tables 3 and 4).
When the estimates for DON from the field experiments
were plotted with the DON from the greenhouse exper-
iments (S3), only the hulless oat genotypes could be
differentiated as a more resistant group in both environ-
ments. Whereas, when comparing the DON estimates,
we found that the rejected variety ‘BOR31’ stood out as
an outlier with a clearly higher DON in the greenhouse
experiments than any other line. However, in field its
estimate was only slightly higher than average estimates
(Table 4, Fig. 2d, S3). The variation in DON within
breeding lines was higher than the variation for the
cultivars or gene bank accession. However, it should
be noted that when the best linear unbiased estimators
(BLUEs) based on a minimum of three experiments are
considered (Tables 3 and 4), the lowest DON for the
field experiments can be found among the breeding lines
and the highest for the breeding lines are lower than the
highest estimates for the cultivars or gene bank
accessions.
Fusarium infected kernels in the field and greenhouse
experiments
The average FIK for the greenhouse (82%) and field
(55%) experiments differed significantly (P < 0.05). If
all the FIK samples would have been counted similarly
(after 7 days of incubation), the respective percentages
would be 83% for the greenhouse experiments and 92%
for the field experiments. Nevertheless, both the green-
house and field screening experiments also sepa-
rated oat genotypes with high differences and
those with differences in their Fusarium infected
seeds (P < 0.001, Table 4). According to a statisti-
cal power analysis (S4) and the standard errors for
FIK in Table 4, the greenhouse analysis for FIK
was more sensitive than the field analysis in find-
ing genotypic differences. Two greenhouse experi-
ments were enough to separate two genotypes with
less than a 20%-unit difference in the FIK, but for
the field samples at least three experiments were
needed.
The FIK variable had a highly positive correlation
with DON in the greenhouse experiments (Table 1), but
in the field experiments the correlations were lower
(Table 2). However, when the correlation between theT
ab
le
4
(c
on
tin
ue
d)
G
en
ot
yp
e
M
at
ur
ity
cl
as
s
D
T
H
D
T
M
P
H
cl
as
s
P
H
,c
m
F
ie
ld
es
tim
at
es
G
re
en
ho
us
e
es
tim
at
es
L
SD
=
1.
9
L
SD
=
3.
4
L
SD
=
5.
7
D
O
N
±
S.
E
.
L
SD
=
65
42
FI
K
±
S.
E
.
L
SD
=
8.
1
G
C
±
S
.E
.
L
S
D
=
13
.2
D
O
N
±
S
.E
.
L
SD
=
69
63
F
IK
±
S.
E
.
L
SD
=
21
.4
B
O
R
04
4
55
12
3
2
94
15
,1
49
±
52
34
60
±
8.
4
55
±
8.
3
56
80
±
33
26
86
±
11
.2
V
IR
77
66
5
60
12
6
3
12
3
16
,0
37
±
54
26
56
±
8.
5
51
±
8.
4
46
40
±
15
68
56
±
7.
3
R
oc
ky
4
59
*
12
3
1
94
17
,4
90
±
55
40
64
±
8.
3
54
±
8.
1
–
–
O
be
lix
4
51
12
3
2
92
18
,1
68
±
57
32
60
±
8.
3
50
±
8.
4
–
71
±
13
.1
M
ir
el
la
4
56
12
2
3
10
0
20
,1
38
±
58
81
60
±
8.
4
55
±
8.
1
–
84
±
10
.8
Eur J Plant Pathol
BLUEs of the genotypes (Fig. 2b) was studied there
were clear correlations in field experiments both for all
genotypes (r = 0.43, P < 0.001) and for the core set of 30
oat genotypes (r = 0.52, P < 0.01). In contrast, in the
case of the greenhouse experiments there was no
significant correlation detected between the oat
genotype estimates for DON or FIK (Fig. 2d).
On average, the gene bank accessions had signif-
icantly lower rates of FIK both in the greenhouse (P
< 0.001) and field (P < 0.001) experiments when
compared to the cultivars and breeding material.
The hulless genotypes stood out with low FIK values
both in the field and greenhouse experiments.
When the FIK estimates were plotted for the field
and greenhouse experiments there was no clear
relationship between them, but the most resistant
genotype, ‘VIR11012’ stood out as an outlier with
clearly lower FIK rates in both environments (S3).
Germination capacity and its interaction with DON
and FIK.
The average germination time for oats in the Fusarium
inoculated field trials (mean ± SD, 58 ± 25%) was sig-
nificantly affected by the oat genotype (P < 0.001) fac-
tor. According to a statistical power analysis (S4) the
least significant difference concerning the germination
capacity in the core set was less than 15%-units. Thus
we can also see clear variation within this trait. DON
had a highly negative correlation with the GC variables
(Table 2). The BLUEs for the GC within the core set
correlated well with both the BLUEs for the FIK (r =
−0.73, P < 0.001) and DON (r = −0.61, P < 0.001) (Fig.
2). When plotting the GC values with the DON or FIK
(Fig. 2a and b)2c) values there was good linearity within
the core set of estimates, but for all estimates the corre-
lations for the GC were weaker (r = −0.21, P < 0.001 for
Fig. 2 The relationships between estimates (BLUEs) for DON
accumulation (DON), Fusarium infected kernels (FIK) and ger-
mination capacity (GC) in the field experiments (2a-2c) and
greenhouse experiments (2d). Blue dots represent all the geno-
types’ estimates and triangles represent the estimates for the core
set of 30 oat genotypes
Eur J Plant Pathol
DON and r = 0.17, P < 0.001 for FIK). Good estimates
of the GC often coincide with low DON or FIK esti-
mates, but the ranking of DON estimates does not
completely follow the ranking of the FIK or the GC
estimates (Table 4, Fig. 2). The germination rankings of
47 oat genotypes changed significantly in one group of
experiments with a high level of DON contamination
compared to another group of experiments with a low
level of DON contamination (P < 0.001).
Passive resistance mechanisms
Hulless oats were more resistant than hulled accessions
(P < 0.001). The most resistant gene bank accession
among all the studied genotypes was the hulless landra-
ce ‘VIR11012’ with a very low estimate for DON in the
greenhouse (462 μg/kg) experiments and a moder-
ately low DON in the field (7525 μg/kg) experi-
ments. The other four hulless oat genotypes in-
cluded in our data also had good estimates ranging
between 5025 and 11,692 μg/kg and between 1720
to 5600 μg/kg in the field and greenhouse exper-
iments, respectively.
In the field experiments, the PH and maturity classes
had significant impacts on DON (P < 0.001) and FIK
estimates (P < 0.001, Table 5). PH also impacted the GC
(P = 0.01, Table 5). The shortest plants (height class 1)
had significantly more DON (mean 19,042 μg/kg) than
the intermediate height plants in class 2 (16,740 μg/kg,
P < 0.01). The tallest oat genotypes (class 3, mean
17,928 μg/kg) did not significantly differ either from
the lowest or the intermediate class in their DON. Class
3 included several lines from VIR with measured
heights of around 120 cm (Table 4). The shortest plants
(height class 1) had more FIK (69%) than the tallest
plants in class 3 (66%, P < 0.05). Intermediate height
plants (class 2) had lower GC than the shortest plants
(52 vs. 55%, P < 0.05), but these did not differ from the
tallest plants.
The early and average maturing oat accessions had
significantly less DON than the late maturing oats
(Fig.3). The FIK estimates for different maturity classes
were similar to the DON estimates, but their differences
were relatively small. Late and very late maturing geno-
types did not differ significantly from each other
concerning their FIK (at 57–58%), whereas the earliest
maturing (the 1st column in Fig. 3) genotypes had FIK
estimates of 51%.
Moderately resistant and highly susceptible oat
genotypes in the core set of genotypes
The genotype estimates for DON, FIK and GC with
only one or two experiments suffered from high varia-
tion (Table 3) and low accuracy (S4). Thus, the
core set of estimates was formed with a higher
number of experiments (Table 4 and Fig. 2). The
core set contained estimates from genotypes which
were included in a minimum of three field exper-
iments and two greenhouse experiments (S1). The
breeding line ‘BOR03’ presented in the core set
had the lowest DON in field of all the studied
genotypes (Fig. 2). Contrastingly, ‘BOR03’ had a
relatively low GC, whereas the breeding line
‘BOR15’ had the second lowest DON in combina-
tion with better germination capacity (Table 4).
These two lines differed mostly in how early they
matured, since ‘BOR03’ had a higher DTM re-
quirement. ‘BOR03’ is an exception since late
maturity was commonly followed by high DON
contents in the CORE set (Table 4). When the
rank correlation between Fusarium associated traits
and DTM, DTH and PH were studied in the core set
only DTM showed significant correlations. The coeffi-
cients of correlation were − 0.76 (P < 0.001), 0.48
(P < 0.01) and 0.58 (P < 0.001) for the GC, DON and
FIK estimates, respectively.
The cultivar ‘Niklas’ had the lowest DON accumu-
lation (estimate 7204 μg/kg) among the tested cultivars.
The cultivars ‘Rocky’, ‘Obelix’ and ‘Mirella’ were
identified as highly susceptible to DON (estimates
17,490, 18,168 and 20,138 μg/kg, respectively)
(Table 4). These oat genotypes with the highest
DON values also had the lowest GC in the Fusar-
ium inoculated field trials (Table 4). When the
most susceptible cultivar ‘Mirella’ and the most
resistant cultivar ‘Niklas’ are compared in Table 4 it
can be seen that the greatest difference between these
lines is in the DTM (Table 4).
From Table 4 it can be seen that hulled gene bank
accessions also contained promising genotypes such as
‘VIR6963’, which had relatively low DON estimates
both in the field and greenhouse experiments and had
the lowest estimate for FIK of the hulled genotypes in
the greenhouse (Table 4). Contrastingly, a gene bank
accession ‘VIR7766’, with a similar PH and maturity to
‘VIR6963’ had similar DON values in the greenhouse,
but a very high DON in the field (Table 4).
Eur J Plant Pathol
Discussion
Analysing unbalanced data
The primary aim of this investigation was to study data
from various experiments in order to test whether a better
understanding of the variation ofFusarium infections and
DON in oat genotypes in various experimental situations/
environments could be achieved. A combination of field
and greenhouse experiments were carried out without
reducing any experiments from the data set in order to
see how FHB resistance can be estimated based on such
diverse data. High variations in the original DON data
(from 19 to 126,000 μg/kg) produced a range of oat
genotype-specific mycotoxin estimates varying from less
than one thousand to 58,000 μg/kg in the field experi-
ments and less than one thousand to 22,000 μg/kg in the
greenhouse experiments. Even with estimates based on
more than three field trials, the difference between the
lowest and highest DON estimates was more than
15,000 μg/kg. Our data support the claim by Bjørnstad
et al. (2017) that the separation oats with varying degrees
of susceptibility to DON with a significant difference of
at least below 3000 μg/kg of DON can be achieved by
conducting a sufficient number of inoculated field or
greenhouse experiments.
Our method involved an analysis of available exper-
iments together instead of a step-wise approach (Piepho
et al. 2012) in whichmulti-environment experiments are
first analysed individually and then the larger set is
studied based on the best linear unbiased predictors
(BLUPs). Since our research focused on estimating
how the selected set of oat genotypes behaved in these
specific field experiments and greenhouse experiments,
the use of BLUEs instead of BLUPs was necessary to
consider the genotypes as fixed factors. Unbalanced
data is recognized as one of the major problems when
linear methods or other traditional methods are applied
in agricultural sciences. In the case of unbalanced data,
major treatments (e.g. genotypes) dominate minor treat-
ments, leading to bias in the estimated means and their
standard errors. When comparing different genotypes
over a range of many years, the set of genotypes cannot
be fixed at the outset of the study. Typically, new
interesting genotypes are constantly added and some
of the older ones will be found to be no longer relevant.
Table 5 The results from analyses of variance on the impact of the plant height and maturity on the classification of the three Fusarium
resistance parameters: DON accumulation (DON), Fusarium infected kernels (FIK), germination capacity (GC) in field conditions
Trait DON FIK GC
Plant height df = 1402 F = 9.70 P < 0.001 df = 2256 F = 8.03 P < 0.001 df = 1213, F = 3.81, P = 0.01
Maturity class df = 1397 F = 27.95 P < 0.001 df = 2245 F = 13.40 P < 0.001 df = 1222, F = 2.02 P = 0.09
Fig. 3 The estimates and
standard error for the DON values
and infection percentages
according to the maturity class
(1 = early, 2 =moderately early,
3 = average, 4 =moderately late,
5 = late). Classes 1, 2 and 3 differ
significantly (P < 0.05) from
classes 4 and 5 which also differ
significantly from each other
(P < 0.05). The number of oat
genotypes (n) within classes is
indicated behind the class
numbers
Eur J Plant Pathol
Searle (1987) suggested that the genotype effects can be
estimated in any well-defined linear mixed model anal-
ysis if the turnover of genotypes tested is less than 20%.
Careful monitoring of the annual turnover is needed.
After that, a statistical analysis can be made without
bias. The protocol that was applied here resulted in valid
estimates and their standard errors for all genotypes. The
REML method used here was very similar to the one
recommended by Piepho and Moehring (2006) to avoid
bias caused by selection during breeding trials. In this
study we have shown one possible way of analysing
unbalanced data and demonstrated the potential effec-
tiveness of utilizing data between greenhouse and field
trials.
High range of DON estimates
The range of DON estimates in different types of mate-
rial indicates that considerable variation in the resistance
response already exists in breeding programmes and in
the cultivars used in Finnish oat production. The com-
parison of greenhouse and field estimates also under-
lines the role of a suitable PH and DTM in disease
resistance, since the resistant gene bank accessions with
generally good estimates in the greenhouse experiments
all had estimates ranging from low to very high DON in
the field. In Nordic field conditions a robust stem and
early maturation seemed to play an important role in
FHB resistance. Additionally, selecting for yield under
high Fusarium incidence typical to Nordic conditions
may have maintained some resistance of the breeding
populations since Fusarium infections can have signif-
icant impacts on oat yields (Martinelli et al. 2014) and
also yields of other cereals (Nganje et al. 2004). This
hypothesis is also supported by similar results from
screenings of Norwegian and Canadian germplasm
(Tekle et al. 2018, Yan et al. 2010).
The overall good performance of the gene bank
accessions in the greenhouse suggests that unused resis-
tance sources exist within these weakly adapted acces-
sions. The use of exotic germplasm in breeding is com-
plicated due to the linkage drag of undesirable traits, and
it can also be practically constrained by weak seed
setting between crosses. Hulless gene bank accessions
were excluded from our experiments after 2016 because
hulless oats are very marginally produced and the resis-
tance in hulless oats is suggested to be mainly related to
the absence of hulls (Tekle et al. 2018). Since the hulless
genotypes included in our experiments also had
undesirable agronomical characteristics such as high
rates of lodging, it was not seen useful to study these
accessions further as potential resistance sources. Nev-
ertheless, the reduction of genetic diversity during the
shift from landraces to modern varieties is significant
(He and Bjørnstad 2012). Therefore, pre-breeding
through crossings between exotic accessions and mod-
ern genotypes followed by backcrossing could be a
valuable initiative which could increase both the diver-
sity and Fusarium resistance of oat breeding popula-
tions. However, in the near future the quickest results in
resistance can be achieved within breeding populations
through efficient phenotyping.
Different estimates from field and greenhouse
Due to higher variation, the field experiments need to be
replicated more than the greenhouse experiments. This
also appears in the statistical power analyses (S4), where
two greenhouse experiments are sufficient to separate
genotypes, but in the field similar difference require
more than three experiments to become significant.
However, the different resistance estimates obtained
from the field and greenhouse trials indicate that these
platforms should be applied for different purposes.
Hulless lines were pointed out as very resistant in
both the field and greenhouse estimates which could
encourage the use of the phenotyping of Fusarium
resistance in controlled environments searching for po-
tentially superior resistance sources for pre-breeding
material. However, it is likely that some of the QTLs
contributing to resistance or susceptibility are missed if
greenhouse screening alone is applied. In controlled
conditions and with targeted inoculations the Fusarium
spores are more likely to reach susceptible plant tissues
in all different host genotypes at the most susceptible
time. The impact of the environment on the infection
pressure is somewhat similar during all the infection
occasions in the greenhouse because of the directed
and standardized inoculum and stable moisture, light
and temperature conditions. When all the panicles are
inoculated directly by spraying during anthesis, the im-
pact of escape factors such as the PH and flowering time
become clearly less significant than in field conditions.
This makes greenhouse testing an efficient tool to eval-
uate resistance that occurs once the Fusarium spores
have reached the floral tissue and resistance mecha-
nisms start to act. This could be used as supplementary
information for field testing results to confirm that the
Eur J Plant Pathol
resistance of crossing parents also contains cell related
mechanisms, for example.
In an open field the infection pressure may be con-
tinuous, and infections occur from beginning of
flowering to yellow maturity (Tekle et al. 2012). Espe-
cially with the spawn inoculation method for oats (Tekle
et al. 2013, Tekle et al. 2018) that was used in most of
the field experiments of this study, the environment is
kept in a continuously disease promoting state and in-
oculum is left on the field to spread the disease. Sec-
ondary infections and continuous spore release from the
original inoculum (Tekle et al. 2013, Tekle et al. 2018)
allow all resistance mechanisms, from the evasion of
initial infection to pathogen induced systemic defences
(Walter et al. 2010), to play a role. For this reason the
results from field experiments are better at reflecting the
situation that exists on farmers’ fields in Finland, espe-
cially in the worst disease years. The Finnish cereal
committee compared oat cultivars and their mycotoxin
levels based on analyses reported by cereal traders and
the food industry between 2014 and 2017 (Finnish
cereal committee’s website, accessed 6.2.2019). The
median values presented for DON, especially from the
heavy FHB year of 2016, agree with our findings show-
ing that the cultivars ‘Rocky’ and ‘Obelix’ were the
most susceptible ones. Moreover, the cultivars that had
an average or moderately low DON in our data do not
raise concerns in that study either. Similarly, (Gagkaeva
et al. 2013) found that most of their oats which had been
screened for resistance under artificially inoculated field
conditions behaved similarly in a site under natural
infection conditions. The results of this study also agree
with the rankings obtained from Norwegian spawn in-
oculated field trials (Tekle et al. 2018), e.g. the cultivars
‘Odal’, ‘Akseli’ and ‘Mirella’ rank very similarly, al-
though the ‘Odal’ cultivar was not as distinctive in our
study as it was in Norway.
DON and Fusarium infected kernels
In wheat studies the relationship between infection traits
such as Fusarium damaged kernels or symptomatic
spikelets and DON can be seen in both field and green-
house conditions (Devkota et al. 1999, Jin et al. 2014),
although differences in the infection pressure, for exam-
ple, can affect the relationships between these two en-
vironments (Jin et al. 2014). In our study, the correla-
tions between FIK and DON analyses were high in the
greenhouse, but in the field the correlation between the
variables was usually lower and more variable but still
significant. Table 2 shows clear Spearman rank correla-
tions between FIK and DON and FIK and GC. Howev-
er, estimating the correlation across all trials does not
give reliable results for the correlation between DON
and FIK. One reason for this may be that there are very
different numbers of entries in different trials which
make the ranks less compatible. Figure 1 shows that
the negative correlations between field FIK and DON
samples in Table 1 are most likely to be a result stem-
ming from the difference in the assessment of the FIK
samples. The FIK values from the most severe infection
years were calculated after three days of incubation,
whereas in the other experiments the calculation was
done after one week and this is why there are two peaks
in Fig. 1 which plots DON against FIK.
There were no clear relationships between DON
observed in the field and greenhouse or the FIK ob-
served in the field and the greenhouse even in the core
set of estimates (S3). The above mentioned differences
in variation of climatic factors and the external infection
pressure between field and greenhouse environments
can explain this weak relationship. In addition, the
amount of DON can be considered as an outcome of
the host and pathogen interaction. However, the FIK
ratings that have been derived from the seeds in this
study mainly reflect the incidence of infections without
giving any information on how superficial they were.
The variations in timing, duration and severity of Fu-
sarium infections are likely to impact the results. Con-
tinuous infection in the field leads to a higher proportion
of infected seeds within panicles and thus it can increase
the quantity of mycotoxins. However, if the conditions
are not optimal for infection there may be a high number
of mildly infected seeds with lower mycotoxin accumu-
lations, but these still may have a good chance of not
germinating (Tekle et al. 2013, Tekle et al. 2018). De-
spite a strong correlation between FIK and DON in the
greenhouse, there are several examples indicating that
also DON can be dependent on other factors than the
number of infections (Table 4, Fig. 2). For example
comparison of genotypes such as ‘BOR04’ ,
‘VIR8479’ or ‘VIR7766’ and ‘VIR6963’ can show
differing responses in their traits depending on
environment.
Since the interpretation of FIK can be cumbersome,
especially when infections reach 100%, we would rec-
ommend quantitative PCR to be used for quantifying the
incidence and severity of Fusarium infection in future.
Eur J Plant Pathol
Góral et al. (2019) compared different FHB resistance
parameters in wheat and found fungal biomass to be the
best predictor for DON. In farm samples of oats the
correlation between the F. graminearum biomass and
DON may be more significant than the infection inci-
dence and DON (Yli-Mattila et al. 2017). Species-
specific primers may also increase the accuracy when
compared to the counting of seeds with Fusariummyce-
lium and may enable the comparison of other Fusarium
species and mycotoxins such as F. langsethiae and T2/
HT2 toxin (Edwards et al. 2012).
DON and germination capacity
The germination capacity does not completely reflect
the FHB severity in the experiments, since dead light
weighted kernels can be partially lost during threshing
and viable fungi also from superficial infections can
harm the germ (Tekle et al. 2013). There were both high
and low correlations between FIK and GC estimates for
oats depending on the experiment, which indicates that
this relationship is also strongly affected by the environ-
ment. A cool and wet autumn in 2017 affected the
germination % in Finland and is likely to explain the
high range of GC in our experiments that year.
In Tekle’s studies, the correlations between DON and
GCwere stronger in drier rather than in wet years and in
spray inoculated rather than spawn inoculated experi-
ments (Tekle 2014). These results indicate that infection
during anthesis is more likely to lead to severe infection
also affecting the GC and DON, whereas in wet years,
the GC is affected also by the weather and by the
increased inoculum, which increases superficial infec-
tions after anthesis. In wet years, there is also higher
infection pressure from other pathogens than Fusarium
ssp. contributing to lower GC, which may be the case in
those experiments that had low GC and low DON. The
highly negative correlation between GC and DON is a
result from both the proliferation of the fungal biomass
which affects the germ and also damage caused by the
infection and mycotoxins. Even relatively low amounts
of DON have been shown to cause abnormal seedling
growth in oats (Tekle et al. 2013) which would not be
regarded as healthy germinated seeds in our germination
assays. The dead kernels killed by infection are not
germinating seeds either. Thus resistance against infec-
tion, tolerance to mycotoxins and tolerance to infection
can all protect the germination capacity from Fusarium
infections.
Our core set’s ranking in GC reveals oat genotypes
with tendencies towards high or low GC under severe
infection pressure. There are also clear outliers such as
‘BOR03’ with low DON and relatively low GC values.
This abnormality could be explained by the wet weather
in 2017 and the longer DTM requirements this entails.
However, ‘BOR03’ also has a relatively high FIK esti-
mate indicating that it has been highly infected in many
experiments. Thus, the mechanism in this case may be
resistance to DON or detoxification. The significant
interaction from the analysis made for GC in two groups
of experiments with different DON levels suggests that
some of these genotypes are able to tolerate DON better
than others. The experimental data were not sufficient to
calculate significant differences for the oat genotypes
between these two groups, but nevertheless this indi-
cates that different mechanisms can affect the germina-
tion of Fusarium infected oats. The inconsistence be-
tween DON levels (4x) and GC levels (similar) between
these groups of experiments may also result from envi-
ronmental conditions and from the different infection
pressure. The correlations in different field experiments
(Table 2) and their visualization in Fig. 1 also support
this. In future, the development of high-throughput au-
tomatic germination scoring methods (Joosen et al.
2010) could make germination analyses more cost-
efficient and widely used for FHB resistance as well.
Digital image analysis has shown promise also as an
alternative to visual scorings of FHB severity in triticale
(Ollier et al. 2020).
The role of passive resistance mechanisms
Differences in the mycotoxin accumulation in oats seem
to be largely affected by the severity of the initial infec-
tion. Passive resistance mechanisms can increase type I
resistance through different mechanisms leading to the
prevention or evasion of infection. Prolonged growing
times from emergence to flowering, increased PH and
increased grain numbers per panicle all led to reduced
incidences of Fusarium infections in surface sterilized
grains in the screening of the VIR gene bank material
(Loskutov et al. 2016). In our data the shortest plants
had the highest FIK and DON, but some of the tallest
plants differed less from the shortest plants than the
intermediate height class 2 plants did. These tall plants
were mainly gene bank accessions, with a high rate of
lodging (data not shown). Moreover, the tallest plants
also had a long growing period especially from
Eur J Plant Pathol
flowering to maturity which may have increased their
susceptibility, as was also observed in the study by
Loskutov et al. (2016). A long stem can provide protec-
tion from Fusarium infections, but it should also be
robust, and the increased PH should not increase lodg-
ing. The oat genotype ‘BOR31’was relatively short and
it was also one of the earliest flowering and maturing oat
genotypes in this study. Early maturation may partly
explain why ‘BOR31’ was less susceptible in the field
than in the greenhouse. Late maturing oats are more
likely to face moister autumn weather, which can pro-
mote the accumulation of DON toxins especially when
they are suitably timed with the right temperature
(Hjelkrem et al. 2017, Kaukoranta et al. 2019). Howev-
er, again the breeding line ‘BOR03’ is an outlier in
Table 4 and an example of an oat with good mycotoxin
resistance combined with late maturity.
In addition to the PH, early maturation and lodging,
there are also other interesting traits that could be stud-
ied. For example, Tekle (2014) suggest that future stud-
ies should investigate the importance of anther extrusion
as a passive resistance mechanism. There is evidence
that oat genotypes with relatively high or relatively low
anther extrusion are shown to be less prone to accumu-
lating Fusarium mycotoxins (Herrmann et al. 2020).
More detailed studies of different active and passive
resistance parameters, including for example anther ex-
trusion and also yield parameters to determine tolerance,
could further enlighten the structure of FHB resistance
in oats. The core set of this study is included in our on-
going studies, and thus more detailed information will
be provided in the future.
Conclusions and future prospects
The common phenomenon that the impact of Fusarium
infection varies between different environments was
shown. Different oat genotypes were shown to have
highly differing responses depending on the resistance
parameter measured, which may be a sign of different
genes and gene interaction impacting the resistance.
Therefore, several check cultivars representing the var-
iation of different resistance components should be in-
cluded in trials as mentioned by Bjørnstad et al. (2017).
In the greenhouse environment, the proportion of infect-
ed seeds could be used as a light screening method to
select potentially resistant and susceptible lines and
parents for pre-breeding. However, in order to achieve
lines with a working resistance the phenotyping needs to
be confirmed in field conditions. In addition, testing
should at least include measurements of DON, Fusari-
um content and germination percentage of the kernels.
Moreover the variation in PH and maturation time in the
field should be considered and if possible dealt with in
the experimental design either by studying the resistance
on oat genotypes of a similar PH and maturation time
together or by including these factors into the statistical
model used for analysis.
The results of this study on the suitability of methods
and the need for repetition to assess FHB infections in
oats can be used to improve phenotyping for Fusarium
resistance. Proper phenotyping is needed to utilize geno-
mic selection in the development of more resistant culti-
vars. Genomic prediction is based on a training popula-
tion in which the genotypes are thoroughly phenotyped
and genotyped. A genomic prediction approach has been
successfully used when selecting Fusarium resistance
for winter wheat (Rutkoski et al. 2012, Arruda et al.
2015) and for spring wheat (Dong et al. 2018). A similar
approach could well work with oats, where Fusarium
resistance is likely to be controlled by numerous loci
with small effects (Bjørnstad et al. 2017, He et al. 2013).
However, because visual scoring for resistance is not
feasible for oats (Tekle et al. 2018); other reliable eval-
uation methods are needed. Moreover, there is still a
need for cost efficient methods to measure DON content
or germination, since both of these methods become very
costly for large populations. The use of hyperspectral
imaging in the prediction of DON contamination has
been suggested as a potential method for oats (Tekle
et al. 2015) and also for wheat (Barbedo et al. 2016).
The disadvantage of these imaging methods may be that
these methods estimate DON indirectly via kernel traits,
which may vary largely in different environments and
with different genetic backgrounds. Further research to
understand the interaction between the host and the
pathogen will be valuable for determining additional
factors contributing to Fusarium resistance in oats.
Acknowledgements The Raisio plc Research Foundation,
Makera funding of Finnish Ministry of Agriculture and Forestry
(diary number: 1692/03.01.02/2015) and Tekes (Grant 1565/31/
2015 to Boreal Plant Breeding Ltd.) are acknowledged for funding
this research. Boreal plant breeding Ltd. played an essential role in
conducting many of the field trials to produce the data and the
company also supported the project financially by providing sup-
plementary data, knowledge and conducting all of the mycotoxin
analyses. From Luke the authors would like to warmly thank the
technical staff of the Vitrinia building in Jokioinen, especially Auli
Kedonperä, Tuula Viljanen, Aila Siren and Marjaana Virtanen.
Eur J Plant Pathol
Senior Scientist Päivi Parikka is acknowledged for her generous
advice especially on the Fusarium isolates and greenhouse inocu-
lations. Technicians Hannu Tiainen and Jorma Moilanen at the
Laukaa research station and farmer Mauri Räkköläinen are ac-
knowledged for helping us with the Laukaa field experiments.
Funding Information Open access funding provided by Natu-
ral Resources Institute Finland (LUKE).
Compliance with ethical standards
Conflict of interests The authors declare that they have no
conflict of interests.
Ethical statement The research presented in this manuscript did
not involve any animal or human participants.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in anymedium 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 Com-
mons licence, unless indicated otherwise in a credit line to the
material. If material is not included in the article's Creative Com-
mons 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 copy of
this licence, visit http://creativecommons.org/licenses/by/4.0/.
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