Integrated Approaches to White Mold Management


Hi I’m Damon Smith, Extension field crops
pathologist for the University of Wisconsin-Madison. Today we’re gonna talk a little about integrated approaches to white mold management, and specifically
some research to back up some recommendations that we have put
together over the last couple of seasons. First however I want to just start and
talk a little about the white mold cycle on soybean. So we’ll start at this point
of the cycle here, which is after at the end of the season and the beginning of
the the next season where we have these sclerotia which are a hardened survival
structure. The sclerotia are actually fungal material; they’re formed on last
season’s plants, as you can see here. Once the plant is killed you see the formation
of even hundreds of these sclerotia on a single plant. When a combine goes through
the field, they harvest these soybeans and knock the sclerotia
off onto the surface of the soil where they end up being lightly incorporated
in the upper layer. This fungus is pretty unique in that it’s timed to it’s
life cycle in a lot of cases with the life cycle of soybean so when the
conditions are right during the season and during the bloom period in soybean
the formation of these mushrooms actually develops on the sclerotia, or from the
sclerotium. These little mushrooms are termed Apothecia. They are a little cup
shaped mushroom, and on these cup shaped mushrooms form microscopic spores which you can see here. This is all timed very closely with the development of flowers
on soybean, so the infection sitee are the — the time for infection in soybean
really occurs pretty finitely during the bloom period. Here you can see
these spores land on the flowers. They infect through these flowers and then
later in the season we get the the disease development, which usually are
bleached lesions and death of plants. So really the opportunity in season to
actually control this disease occurs during this bloom period, which is very
important. Now in terms of yield reductions, we’ve previously known that
there was traditionally what was termed a linear response in terms of
increasing disease development and decreasing yield. However more recently
we’ve learned with modern soybean varieties, which have a bushy phenotype,
that the yield loss really isn’t linear. Okay, this means that as disease severity
index or those DIX value increases, we actually have a point at which the curve
or relative yield loss is actually fairly static or fairly level. Okay, right
here in this point in the curve. However, as disease gets more intense, we
see that quickly we get into a pretty steep part of this curve here, So what
governs this this flat part versus this steep part? Well it really has a lot to do
with the morphology of the modern soybean. We have these bush phenotypes
now with soybean and we’ve set a lot of yield on these lateral branches. And if
we account in our disease index scoring here for these lateral branch hits, which
we have here in this green box, we can see that most of the yield reduction or
slight reduction that we get in this point in the curve is actually from
these lateral branch hits. When we move into the steeper part of the curve, this
is where we actually have a large number of main stem hits. Obviously these are
much more important in terms of yield reduction,
hence the steep part of this curve. Keep this in mind because this this threshold
right here where we start to break over into the steep part of the curve is right
around 40 percent or 0.4 on this particular scale. And we’ll use this
score later on in this presentation. Now, some questions that we’ve sort of heard
farmers talk about over the last couple of seasons are listed here and we’ll go
through these each in some detail. The first of which is their genetic
resistance to white mold, the second is what fungicides work for managing white
mold, the third is when should I spray for white mold and the fourth what
cultural practices should I use in my integrated management strategy? Before I
begin with each of these questions I just want to sort of jump to the
punchline and tell you that no one management practice really is going to
be complete. You’re going to have to integrate multiple facets of management
into your integrated management program. This might include capturing
variety resistance or limited amounts of variety resistance which we currently
have in the commercial variety selection. Canopy row width and planting population
may be widening out the rows and reducing the planting populations crop
rotation and then maybe chemical and/or biological control. However, note that
even chemical or biological control can be incomplete and timing of application
is extremely important and we’ll talk more about this here in a in a few
slides. So I’ll address the first question which is whether we have
varietal resistance in our commercial soybean varieties. First, we’ll look at
some data and these are data from the 2017 glyphosate tolerance soybean trials
which are conducted by Dr. Sean Conley here at the University of Wisconsin.
We’re looking at data from the Arlington AG Research Station and also the Hancock
AG Research Station and what we’ve done is pulled out some varieties which were
planted at both of those locations. We’ve sorted these varieties by high to low
yield at Arlington and you can see for the most part these lineup as well at
Hancock with just a couple of slight deviations. More importantly, however,
we’ve looked at the rate of these for disease incidents or white mole disease
incidents and you’ll notice that most of these performed fairly well giving us
just slight white mold incidents at the Arlington location. However, and notice at
Hancock we had much higher white mole in this particular trial and you can see
that some of these varieties now do not seem to respond very well–specifically,
if we look at the s21-w8x, you’ll see that at at Arlington it gives
a fairly low disease incident score, but at Hancock gives a very high disease
incidence score. The goal in studying these variety trials is to look for a
consistently performing variety for instance this LS Brand variety here has
done fairly well at both Arlington and Hancock so you’ll want to study these
across locations and try to choose something with the best resistance in
multiple locations now the other thing we’ve tried to do is
is choose for physiological resistance and our soybean varieties and the way we
do this is actually using petiole inoculations so he cut out the fungus
and Hays petri dishes we use these little pipette tips which are commonly
found in any lab and that forms a little incubation chamber and we can actually
inoculate the plants at a node a lower note on the plant sort of like what
happens actually in the field our goal with this type of inoculation is
actually to try to choose or select for a high high level of physiological
resistance and we want to do that mainly on the main stem and you can see in our
in our slide here here’s an example of a lesion that can develop from one of
these inoculations we can then go in and measure this with a digital set of
calipers and we can start to select for things which produce the smallest
lesions here’s an example of one of these trials from February of 2018 in a
greenhouse setting where we’ve taken some of the commercial variety
commercial varieties which performed well in the field and we’re actually
asking them for physiological resistance you can see we have a high high level of
kill in this particular trial this means that while some of these lines look
fairly good in the field they actually don’t give us a high level actual
physiological resistance meaning they don’t actually fight the infection from
the white old fungus however you can see some of these lines in here actually
doing fairly well still surviving this form of inoculation these perhaps have a
high level of physiological resistance we really believe that this two-pronged
approach of of inoculation in the greenhouse and selection of the field
can help us determine which varieties are most resistant the other thing that
happens with white mold and in Sclerotinia and on soybean is that you
can have multiple forms of the fungal population and not once a one soybean
may not respond the same across these different populations and here’s an
example of what I’m talking about here so long this lower axis this
horizontal axis here we have I slit okay nine different isolates you can think
about these as nine different filled populations we then have three different
types of soybean varieties here this is a resistant germ plasm line in our
breeding program this is a moderately resistant germ plasm line and then this
is a susceptible public cultivar and you can see the susceptible overall responds
with higher relative disease scores here if you compare that against the
moderately resistant and the resistant Harvey you’ll note even in the resistant
variety here we do occasionally run into a population which can actually overcome
that resistance in the region in the resistant soybean line and for instance
here this isolate number twenty overcoming the high level of resistance
in 91 one forty five so our goal here is to actually screen varieties across
multiple isolates as well or multiple populations so that we’re sure we have a
line which is also consistent across the locations here’s a subset of germ plasm
lines and also a couple of cultivars which we’ve attempted to perform this
multi prong approach of selection and trying to understand resistance here we
have some very resistant varieties in our in our breeding program here which
are fairly consistent even in the field these are greenhouse data here along
this axis is a relative disease score and area under the disease progress
curve so the higher the bar the more susceptible a particular line is you’ll
see susceptible checks here including this as grow 2031 a commercial variety
and then we have a public variety here at dwight giving us a fairly consistent
susceptible reaction an array of lines in the middle here giving us a
moderately resistant reaction and again our resistant scoring lines down here
now we can run this same set of lines in a field performance evaluation so these
are the exact same varieties you just saw in the previous slide but now we see
some shifting in lines so here we have susceptable checks you see now as growth
2031 is shifted down again we’re shooting for consistency across the
greenhouse trials and the field performance evaluations you can see 91
44 removed itself up Dwight is consistently susceptible Harvey l
noticed some of these lines still looking fairly resistant for us
including this particular line here this 91 38 we’re actually going to release
this as a commercial food grade variety it’s name will be Dane hopefully
available in the 2019 growing season now what is the real value of this high
level of physiological resistance why have I spent so much time talking about
this well this is an example of where the rubber meets the road right here so
here’s our resistance to a be in line 50 to 80 to B we have a susceptible
cultivar here at Dwight and we planted these in 2017 and a high white mold
environment and we even planted them in two different populations at 160,000
seeds per acre and 120,000 seeds per acre and we sprayed with Endura at r2 or
we didn’t treat you can see right off the bat that 5282 bee is extremely
resistant here this is the white mold index score along this axis here and
we’ve put a reference line here in red you can see the Dwight being the
susceptible cultivar is responding quite well to the Endura fungicide application
with a significant reduction in either of the planting populations here where
we have the interior fungicide our resistance soybean line however not
responding hardly at all to fungicide application indicating it’s high level
of resistance so if we look at yield in the same trial again our resistant
soybean line here in the top graph our susceptible cultivar on the bottom graph
you can see even in the high population here a pretty good response again how
will their susceptible cultivar where we sprayed in Deraa
notice in the upper graph here if we focus on 160,000 seeds per acre as an
example again very little response where we used fungicide here so what’s this
really worth well an enduro a single application
Deraa at the 8 ounce per acre rate would list at about $40 per acre if you don’t
own your own sprayer the average application fee is about $7 per acre and
then if we compare the non sprayed 160,000 seeds per acre yield for our
resistant line and compare that to the sprayed Dwight yield here our resistant
soybean line actually out yielded the sprayed Dwight cultivar here by 4
bushels per acre so if we do some quick math on the back of an envelope that
gives us a total value of about 87 dollars per acre if our soybean prices
is at $10 per bushel so again high value for this type of
resistance if we can get this into our commercial varieties but beware we don’t
have this type of physiological resistance out there commercially
available continue to choose things which respond well across locations but
bear in mind you’re probably going to have to do some other things out in the
field so that brings us to fungicide applications including product and
application timing have we’ve done quite a bit of work with our colleagues across
the north-central US and performed actually what’s called a Network
meta-analysis this is just a fancy term for saying that we have a large number
of data points here actually over 2000 data points in over 25 site years the
beauty in this kind of data is that we can we can look at a particular product
and its response and its timing of application and see how consistent that
is across location and we can also use this information to make predictions on
how well these things will perform in the future we actually looked at 10
common active ingredients here and summing seven common timings so in the
next few graphs here we’ll be looking at a couple of different scores the first
is disease index reduction score and this will be in percent so this will be
the disease index of the treated – the disease index of the non treated in that
in that particular trial and then we’ll look at yield benefit and percent here
again against the non-treated in that in that particular trial the reason why
we’re using percents especially for yield benefit is you can take this
percent score that we we have here and to fix your yield potential on your farm
you’ll see the ten active ingredients over here on the right hand side and
then also the common fungicide timings relative to the growth stage of soybeans
here starting at late vegetative all the way into the late reproductive so let’s
just take a look some data here these are the mean disease index reductions
across the ten active ingredients here we’ll focus on a couple of products up
here in this blue box however I just want to point out what these actually
are so this first one here this lack’d is actually the herbicide Cobra
Bossk is the fungicide in Deraa Pico is the fungicide approach and then
another commonly are well performing a program at least for us here in
Wisconsin historically has been this one here the proline followed by Stratego
yield program again we’ll focus up here you can see the Cobra giving us the best
reductions almost twenty percent reductions here but not statistically
different from indoor or approach in this particular analysis now if we look
at the yield benefits for these three programs that we’ll focus on here you’ll
see now that the the herbicide Cobra actually falls down a little bit below
ten percent you’ll benefit and statistically lower than Endura or
approach so what’s going on here well the application of Cobra at the
recommended timing of the r-1 grouse stage and soybean does cause some phyto
toxicity and injury and that’s probably if we just take the mean yield benefits
here that’s probably what’s giving us this lower score okay so that the story
gets a little clearer if we actually separate the yield benefit scores by low
and high disease pressure situations how do we arrive at a low disease or a high
disease pressure situation well early on when we looked at the yield loss
analysis I showed you that there was a 40% cut off from
we go from subtle you lost a really high yield loss and we use that same cutoff
here in this particular analysis now if we separate low and high disease
pressure now we see that that the Cobra situation sorts itself out a bit where
we look at the high disease here in the red bars we can see that there’s no
statistically significant differences here in terms of yield benefits again
among and dira approach and Cobra however in the low disease situation
again that injury that we get from Cobra applied at the Arwen growth stage does
seem to give us a mean negative yield benefit here in this case note however
that in Deraa and approach still give us a positive yield benefit in this
particular graph now in terms of fungicide application timing you can see
that the maximal responses are typically centered right around the the bloom
period again this makes sense because this fungus is infecting the plants
during that bloom period and having a fungicide applied during that time helps
protect those flowers from from invasion by the fungus you can see that the to
spray programs an r1 followed by an r2 application or an r1 followed by an r3
typically give us the best responses however we don’t have statistically
significant differences against some of these other single applications here now
we will look at the yield benefits here again the same sort of story where we
have basically the maximal yield benefits centered right around that
bloom period with some of these single applications giving us almost as good
yield benefit as the to spray programs here specifically here with r1 or r2
applications now we can take all these means and the variability about the
means and we can actually start to fix some probabilities of breaking even so
we’ll take the low pressure situation and the high pressure situation here and
we’ll focus here at $10 per bushel soybeans if we use that this math here
we can estimate that the the probability of breaking even at least in a low
disease pressure situation in this case for Indira here in the blue bar or
approach in the purple bar are somewhere around 40% with Cobra being almost 60%
the reason why Cobras so much more attractive in terms of the probability
of breaking even is because it’s it’s quite a bit cheaper than the two
fungicide programs now if you look at the high-pressure situation here things
change quite dramatically now we get over 80 percent for all three programs
however cobra still over still being the best at over 90 percent even in this
case but no here Indira and and approach still performing well above 80 percent
now that we understand which products to apply and what timings are really
important how do we really make that decision in season well we can’t see the
fungus and we don’t understand what’s going on relative to the bloom period
and is it is the weather really going to be favorable for for these infections to
take place we’ve been working on this and in my lab and trying to actually
predict the formation of these little cup shaped mushrooms again we call these
apathy SIA and here you can see that little hardened structure which forms on
on the previous soybean crop hear that little sclerotium and one sclerotium can
actually form multiple mushrooms cup shaped mushrooms here what we have tried
to do in my lab is actually understand the weather information that drives the
formation of these caught mushrooms if we align that weather information during
the bloom period we can actually make predictions on whether we need a
fungicide application based on the probability of presence of these
mushrooms we can then fix that weather modelling if you will on a gridded
weather input scale and that’s what we’ve done here in these maps so this
weather information was made available through the I pipe program and we’ve run
these animated maps for 2016 and 2017 and you can see 2017 being a much better
epidemic year for little for the white mold fungus we see a lot more red and
yellow indicating high to moderate risk through the bloom period 2016 not quite
as quite as high risk but still quite a bit of white mold in that particular
season we can also make recommendations in season during that bloom period and
and tell growers whether they should be whether there’s a high risk of these
apathy SIA forming in the field or not and that gives us an informed
recommendation on whether we should spray or not so the goal here is they
actually be able to predict the end of season disease relative to what’s going
on during that bloom period and we’ve done some validations both in research
trials and also commercial fields with over with around 60 commercial fields
being scouted in 2016 and 2017 depending on what disease incidents threshold we
use in these field situations whether it be five percent or ten percent again
these would be well below the the yield loss or where we would start to see you
loss in a particular field we can see that we’re correct about 75 to 80
percent of the time okay so that’s better than then a coin flip or 50
percent so we believe we have a model here that can help us predict the end of
season disease by actually running this earlier in the season during the bloom
period to see how this might actually work in a real field situation here’s an
example of a strip trial that was conducted up in Marathon County
Wisconsin and 2017 here we’re using the fungicide approach we have the standard
to pass program where we come in at nine ounces per acre at r1 we come back at
our three and apply a second application at nine ounces per acre this will be the
control treatment out here and my lab rated this on average at about twelve to
thirty three percent across this this particular trial with about 56 bushels
per acre yield you can see the same treatment out here on this side of the
field we then had strips where we didn’t apply fungicide where we had 66% disease
incidents and we averaged about 41 bushels per acre across the strip we had
a single application at r1 here of approach not doing quite as well as our
to pass program here but giving us some reduction relative to the non treated
and yielding about forty seven and a half bushels notice here we had our
three application so again a single application at that are three soybean
grow stage here we had 26% disease incidents and about fifty five and a
half bushels break or yield and then we had a three pass program
r1 r3 and our five about 27% disease and incidents in 58 bushels per acre notice
the the addition of this third pass really didn’t give us much advantage in
terms of yield however or any advantage really in the reduction in disease
incidents now why did this r3 application the single application r3
work so well well if we look at the estimated probabilities and our disease
our our apathy steel prediction model here you can see that at the r1 growth
stage we were at we just started we were in the high probability of apathy
co-presence category here but notice that the real part the probabilities
really started to ramp up when we got to the r3 Gro stage here this application
r3 performed very well because that weather was so much more conducive for
the fungus at that particular time and so we’re not only able to make a
prediction what we might see at the end of the season but we can help actually
tie in the application of these fungicides more closely with when the
weather is going to be highly favorable for the development of this disease
we’ve taken these models and we put them into a smartphone application and here’s
an example of what the smartphone application will actually look like on
the first screen you’ll enter the field name you’ll also tell us weather which
what row spacing you have either 15 or 30 inch and whether the crop is
irrigated or not if you’re physically standing in the field it’ll actually use
the GPS on your smartphone to access the exact location you’ll hit save that’ll
actually get saved into your field profile for access for the remainder of
this season the next screen you’ll answer another piece of information on
whether the crop is flowering at that time while you’re standing in in the
field and then you’ll run the model the model or the application will actually
access remote weather information pull that down to the smartphone and it will
run all the math for you based on our models and give you a prediction and
whether you should spray or not spray on that particular day you can rerun these
models as many times during the day as you like or as many times during the
week if you like where you are actually running or developing these apps right
now and they should be available during the end time
for the growing season and we’d like to get information from you on how well
these perform as this will be the first time that we’ll actually be running
these in season just to finish up with another couple pieces of information we
get a lot of questions on what things should we be doing in terms of row
spacing and also planting population in our integrated management strategy for
white mold control we’ve started to look at some of this actually revisiting both
row spacing and planting population simultaneously and then layering on
fungicide applications on top of that so this is just some information and how
our trials are set up I’ll show you just one location but we are using 15 and 30
inch row spacings and we have planting populations ranging from one hundred and
ten thousand seeds to two hundred thousand seeds per acre we’re applying
fungicide based on the prediction models and we’re comparing that versus our non
treated or up or a standard growth stage application timing here’s just one
location from Hancock where we’ve run this trial in 2017 along the vertical
axis here we have the disease severity index and percent in the top graph we
have the 30 inch row spacing and on the bottom graph we have the 15 inch row
spacing I’ve affixed a reference line here at around 20% in both graphs and
you can see in the 30 inch row spacing we have almost 50% less white mold than
we do in the 15 inch row spacing here now as you know farmers would rather
plant in a 15 inch row spacing because they gain on average about 5 bushels per
acre and that narrow row spacing however notice we have much higher white mold in
this particular trial in that narrow row spacing now if we look at yield you can
see that the relative yield scores between either the 30-inch row spacing
or the 15 inch row spacing are actually very similar very little advantage
actually in the 15 inch row spacing again that’s because we had 50% more
white mold actually in the 15 inch row spacing so that took that 5 bushels away
from us that we would have gained in that narrow row
spacing so the moral of the story here is in a high white mold situation you
might want to lean more towards a 30-inch row spacing to help reduce the
level of white mold in terms of planting population we did see a little bit of
advantage here in the higher planting populations with slightly higher yield
scores here they were marginally significant especially in the 30 inch
row spacing but not very comparable or not very significant here in the 15 inch
row spacings fungicide unfortunately in this particular trial they give us much
advantage however the variety used here was quite susceptible
which means our fungicide just couldn’t overcome that that susceptibility so
just to sum all of this up really you know and your integrated management
program is going to be multi-pronged here you’re gonna have to look at a
number of different things to really get suitable control in these high white
mold environments remember to look for resistant varieties study multiple
locations and look for varieties which are consistent across multiple locations
remember that fungicide isn’t perfect in some cases we’re going to have to reduce
our expectation on the performance of these products remember in the strip
trial even our best-performing product still had some white mold in those
strips timing is very important when it comes to those when it comes to those
fungicide applications so either plan on spraying two applications to cover
yourself during that bloom period or consider using our white mold apps to
make that prediction on whether you should spray or not finally remember
that wider row spacings can help reduce your white mold issues and also lower
planting populations may also give you marginally lower white mold scores as
well and again don’t forget those resistant varieties with that I’d like
to just acknowledge all the funders and collaborators which have helped us with
all of this work and for more information you can consult me on
twitter or access my website here or feel free to reach out either via phone
or

Leave a Reply

Your email address will not be published. Required fields are marked *