Soybean Tolerance to Off-Target Dicamba

CAIO CANELLA VIEIRA Author

04/05/2023 Added

21 Plays

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Assistant Professor of Soybean Breeding, Department of Crop, Soil and Environmental Sciences, University of Arkansas

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[00:00:00.810]The following presentation
[00:00:02.250]is part of the Agronomy and Horticulture seminar series
[00:00:05.850]at the University of Nebraska Lincoln.
[00:00:08.700]Good afternoon, everyone.
[00:00:11.700]It's my pleasure to introduce our distinguished guest today.
[00:00:16.406]Please join me in welcome Dr. Caio Canella Vieira today
[00:00:20.257]So, Dr. Caio joined University of Arkansas
[00:00:22.500]in January this year, 2023.
[00:00:25.560]Brandon leaving the university (indistinct)
[00:00:29.578]Before joining the University of Arkansas
[00:00:30.896]Dr. Caio Vieria obtained his master's and PhD,
[00:00:35.220]where he built genetics and genomics
[00:00:37.890]at the University of Missouri,
[00:00:39.561]where he was involved in development
[00:00:42.360]and released over 30 soybean (indistinct)
[00:00:45.330]and the lab (indistinct) of the soybean breeding problem
[00:00:49.050]by prioritizing the use of data analytics and predict model.
[00:00:53.160]Today, Dr. Caio will be speaking to us about soybean
[00:00:58.089](indistinct) dicamba.
[00:01:00.507]So please join me and give you a warm welcome to Dr. Caio.
[00:01:07.475](mumbling)
[00:01:09.840]Well, thank you.
[00:01:11.162]Thank you very much for the introduction.
[00:01:13.320]A little distinguished, little, little too much,
[00:01:15.540]but thanks for that.
[00:01:18.930]It's, it's a great pleasure to be here.
[00:01:21.262]I've been, you know,
[00:01:22.322]enjoying meeting everybody that I had the chance so far.
[00:01:25.290]Thanks for, for the reception in being welcoming.
[00:01:29.640]I made some my,
[00:01:31.670]my family types in Nebraska. My brother and his wife,
[00:01:34.560]they did their, their PhDs here, so it's nice to,
[00:01:37.650]nice to be back giving a talk here this time.
[00:01:41.160]So, like Marco said, my name is Caio.
[00:01:43.320]I actually just started this tradition at the
[00:01:45.420]University of Arkansas.
[00:01:46.860]I graduated from Missouri in December and been been working
[00:01:50.370]in Arkansas since January in our soybean breed program.
[00:01:53.820]What I'm sharing today is mostly what I've done during my
[00:01:57.270]part of my master's, but mostly during my PhD in Missouri,
[00:02:00.450]which is we were working on soybean natural tolerance 12
[00:02:04.830]target at campus. So just to give a little overall,
[00:02:06.344]the way I, the way we try to do this project,
[00:02:07.744]we have three major components. So the first one,
[00:02:13.650]we wanted to see if the damage caused any yield loss.
[00:02:16.740]There's so many different reports.
[00:02:18.390]We want confirm if there was loss and one was the magnitude
[00:02:22.142]of the yield loss. The second one,
[00:02:24.420]we wanted to use a type of pheno typing
[00:02:26.550]for differentiating the damage.
[00:02:29.220]Takes a lot of work to walk all the plots
[00:02:31.290]and if we could fly the drone, it would be be much easier.
[00:02:35.220]And the last one was to confirm whether this was really
[00:02:37.920]genetic. So we did our genetic analysis, we did our mapping,
[00:02:42.000]and the result of part seemed to be pretty interesting in
[00:02:46.838]to, to continue to pursue that.
[00:02:50.863]I just wanna make sure that I'm not,
[00:02:53.850]I'm not against any technology by any means. I think they,
[00:02:57.390]each of them have their role in agriculture.
[00:03:00.273]What we, what we're doing is, you know,
[00:03:01.800]reporting science in alternatives to any possible damage
[00:03:05.700]that can come from a specific technology.
[00:03:09.780]Okay.
[00:03:10.613]Alright. So just to, again, don't wanna bore everybody
[00:03:13.050]with the introduction, but I just thought it was worth
[00:03:15.690]mentioning this,
[00:03:16.523]what was the rationale of developing dicamba-tolerant crops
[00:03:20.550]way back in the, in the, the two thousands?
[00:03:22.590]This is an old technology,
[00:03:24.090]there's nothing really new about it.
[00:03:26.190]It was actually developed here in Nebraska
[00:03:27.780]so it's nice to, to talk about it here, but mainly the, the,
[00:03:31.200]the rationale was to preserve biotechnology.
[00:03:34.770]We manage it. If you recall in the early two thousands,
[00:03:38.100]you only had Roundup Ready and resistance was, you know,
[00:03:41.610]exploding within the weeds.
[00:03:43.650]And so this was a way of developing new technologies that
[00:03:47.153]farmers could deploy to do weed management.
[00:03:50.760]So they found, I don't wanna get too much into it,
[00:03:53.277]but it was this gene from,
[00:03:55.440]from bacteria in the soil where it converts the active
[00:03:58.950]dicamba into no active dicamba.
[00:04:01.230]They put that into the plants and as you can see there,
[00:04:03.930]one of the, the first varieties to be transformed
[00:04:06.023]was a Nebraska variety and another one from Ohio.
[00:04:10.470]And they were doing all the few trials and, you know,
[00:04:14.070]plants were highly resistant.
[00:04:15.750]They tried even higher doses than, than the label rate.
[00:04:19.440]And they were showing resistance, everything looking good,
[00:04:22.200]right? It's a, it's a beautiful paper published,
[00:04:24.180]I believe it's either science or nature.
[00:04:26.640]It's a great technology.
[00:04:28.470]Monsanto bought that technology and you had the first
[00:04:31.950]commercial variety came into market in 2016.
[00:04:35.550]And as you can imagine, there was a big desire for,
[00:04:38.460]for a new herbicide trait and quickly took over the acreage.
[00:04:42.390]So in the first year you have almost, you know,
[00:04:44.335]55% of the acreage coming from dicamba-tolerant sprays.
[00:04:49.320]I wanna add this here,
[00:04:50.280]if anybody's not very familiar with the,
[00:04:52.110]the timeline of the traits, but you know,
[00:04:54.900]you literally had the Roundup Ready varieties,
[00:04:57.690]which they were used for, for quite some time.
[00:05:00.270]But then you have the development of resistance that really
[00:05:03.690]effects things. For many years, all that we could rely on
[00:05:08.460]was Roundup Ready 1 and 2 and the liberty technology.
[00:05:12.480]So for many years there was that gap of new tech, excuse me,
[00:05:17.340]new technologies, hence the need of this dicamba tolerance.
[00:05:21.749]In 2016, like I mentioned,
[00:05:23.250]it was the first year where you had dicamba
[00:05:25.470]and glyphosate within the Xtend technology.
[00:05:28.530]And of course it's a,
[00:05:29.460]it's a competition between companies and then you get Enlist
[00:05:33.780]coming up in the market.
[00:05:35.160]Where we are now is just a com- combination of different
[00:05:39.360]technologies, so what we call the,
[00:05:40.710]the stacking multiple technologies. And that's,
[00:05:43.290]that's gonna be interesting to see where that goes.
[00:05:45.840]But the reason I, I put this here, so if you quite imagine,
[00:05:49.080]again, all the rational
[00:05:50.910]behind the development of this technology
[00:05:53.550]and how available this was at the time,
[00:05:57.390]but not everything is perfect.
[00:06:00.810]And you know,
[00:06:01.950]after this really came into multiple acres across,
[00:06:06.870]especially the Midwest, there was numerous reports of,
[00:06:10.380]of target damage to known dicamba tolerant crops and also
[00:06:15.270]different plant species, different dicot species,
[00:06:18.272]not necessarily only crops.
[00:06:19.500]That's the picture that I, I, I took this at the,
[00:06:23.642]the ASTA meeting and I thought it was,
[00:06:24.810]it was interesting to go it here, the,
[00:06:27.920]the way the advertisement for list was done, that
[00:06:31.770]you're hitting the target, not your, your neighbors view.
[00:06:35.220]But so between 17 and 21,
[00:06:37.560]the APA reported nearly about 10,000 cases of off target
[00:06:42.300]damage. But of course it's a very under reported number.
[00:06:46.380]And then, you know, there's a branch of USDA
[00:06:48.650]that does a lot of the statistics.
[00:06:50.070]They think the number's under reported by about 25 fold.
[00:06:55.050]If you're walking any farm around,
[00:06:57.180]you know, Missouri, Nebraska, Arkansas, all those regions,
[00:07:00.240]you probably would expect that the number is much higher
[00:07:02.200]than, than only 10,000 reports.
[00:07:06.180]One thing that to me is quite interesting and and again
[00:07:09.720]justify a lot of the research that we've done here is that
[00:07:12.983]all, all the acreage we're a fixed stand.
[00:07:16.890]You have almost 40% of that in every spray dicamba.
[00:07:20.460]So you have nearly half of the growers adopting this
[00:07:24.480]technology, but they never use the herbicide,
[00:07:27.360]which makes you think, are they doing this
[00:07:28.793]so they can protect themselves from all possible damage or,
[00:07:32.490]you know, it's really interesting to think about it.
[00:07:35.400]So there's, there's a little bit of affecting
[00:07:38.550]the freedom of choice of the crop system that you wanna
[00:07:41.910]pursue in your farm and,
[00:07:43.812]you know, it's not up to me to say if it's right or wrong,
[00:07:46.252]but I think it's a good expressive number when you consider
[00:07:49.800]all the (indistinct) wrong with this.
[00:07:52.890]So shouldn't be surprising for, for everybody.
[00:07:56.040]But soybean is highly sensitive to dicamba,
[00:07:59.271]they can be sensitive even to really small doses.
[00:08:01.830]So again, if you imagine this scenario
[00:08:03.930]where you're getting exposed to dicamba
[00:08:06.272]even multiple times during the season, it's,
[00:08:08.520]you know, but by frankly you're gonna expect some, some
[00:08:11.790]degree of yield loss, especially when you're those regions
[00:08:15.510]where you have a lot of damage growing.
[00:08:18.690]So the nice work done by weed scientists,
[00:08:22.410]they show multiple factors affecting the,
[00:08:25.287]the degree of the severity of the damage.
[00:08:28.200]But one major component that was never really considered in
[00:08:31.050]that, again that's where the symptom comes in,
[00:08:33.660]is how much the genetic background of the soybean varieties
[00:08:37.551]do affect severity of the damage.
[00:08:40.710]You know, I like to say that you know, you consider,
[00:08:43.380]imagine in this room you're for giving everybody a specific
[00:08:46.350]drug, the response are gonna be different.
[00:08:49.080]Not everybody responds the same. And when you think about
[00:08:51.270]plants and the amount of genetic diversity that you have,
[00:08:54.540]you would expect that your response is also different.
[00:08:58.500]And I thought it was interesting to add this because this
[00:09:00.780]ended up happening in, in Arkansas.
[00:09:03.084]It goes way beyond just yield loss. It affects social,
[00:09:08.370]social conflicts between neighbors and everything else.
[00:09:10.590]And if you're familiar with the reportings that were
[00:09:13.890]happening at the time, there's even fatal shootings that
[00:09:16.650]happened in just outside of Arkansas, excuse me.
[00:09:27.450]So holidays started for us.
[00:09:29.704]We were located at the time in (indistinct) Missouri.
[00:09:32.850]So that's really a area where you divided in acres between
[00:09:36.330]soybean and cotton. And as you can imagine,
[00:09:38.520]most of the cotton was also dicamba tolerant
[00:09:41.873]and my advisor at the time we were walking plots, you know,
[00:09:44.730]making gradient selections and we would walk by something
[00:09:47.940]like this where you would have the extent variety
[00:09:51.690]obviously no damage,
[00:09:53.460]and then we would've a conventional variety, so no traits,
[00:09:56.940]highly susceptible, you can see all the damage coming
[00:09:59.040]from that camera.
[00:10:00.330]And we would've something like this in the middle where they
[00:10:03.150]seem to have a lower degree of the symptoms from dicamba.
[00:10:07.860]And the first time we observed that it was,
[00:10:09.780]we were quite interested to see is this gen genetics or this
[00:10:13.590]is just differential response to dicamba or different doses
[00:10:17.880]being exposed to each plot.
[00:10:20.220]Started seeing that across multiple replications,
[00:10:22.380]multiple environments,
[00:10:24.270]thought it was worth investigating a little bit more.
[00:10:27.540]And then we went to progeny rows.
[00:10:29.280]So if you're familiar with breeding, progeny rows,
[00:10:32.310]you basically cross parent one, parent two,
[00:10:35.220]and then you have your progens.
[00:10:37.200]If you're seeing segregation for a specific trait,
[00:10:39.990]there's a high chance that this a genetic treat,
[00:10:43.140]and this is what we were observing in our oppressions.
[00:10:45.960]So these two had derived from the same parents,
[00:10:49.164]but their response to dicamba is completely different.
[00:10:53.610]And then we were seeing this consistently across different
[00:10:56.250]populations. So that one of the parents were tolerant,
[00:10:58.620]one was susceptive,
[00:11:04.050]and then we decided to investigate if that would be possible
[00:11:09.900]to observe in diverse accessions.
[00:11:11.880]So we brought in soybean accessions from all over the world.
[00:11:15.210]This is supposed to represent most of the genetic diversity
[00:11:18.631]available in soybean.
[00:11:20.940]And as you can see,
[00:11:22.560]they were showing differences the same way.
[00:11:25.020]And I I I like to mention that these are side by side rows,
[00:11:28.650]so it's a really small distance between them.
[00:11:31.230]You would expect that the doses are being exposed pretty
[00:11:34.050]much the same.
[00:11:36.840]When they start breeding for this,
[00:11:38.220]we start advancing varieties with higher tolerance,
[00:11:41.100]some with higher susceptibility.
[00:11:42.840]And this is one of the pictures that we,
[00:11:44.869]that we get one of our most advanced varieties
[00:11:47.760]with a higher tolerance.
[00:11:49.227]And this is just a regular run up ready check,
[00:11:52.410]same maturity, same everything,
[00:11:54.270]but it's almost like you draw a line in the middle,
[00:11:57.242]have have was sprayed, half was not,
[00:11:59.704]but those are being exposed to the same amount of dicamba.
[00:12:01.350]So you can really see at least on the visual aspect
[00:12:05.280]they were showing the difference.
[00:12:07.410]What we wanted to confirm is,
[00:12:08.940]is this visual translating into yield?
[00:12:13.080]The other thing that we've done is we got one of those
[00:12:15.660]diverse successions that we're showing higher tolerance
[00:12:18.210]cross to some of our susceptible elite lines such as lines
[00:12:21.780]from our program.
[00:12:23.370]And again, we started seeing segregation from the same way.
[00:12:26.940]Some of the projects from the cross,
[00:12:29.430]higher tolerance, side by side rows,
[00:12:31.950]grow completely hampered by dicamba.
[00:12:34.080]So every evidence that we were getting over the years
[00:12:37.710]were really confirming that
[00:12:39.030]this would be a true genetic treatment.
[00:12:42.630]Alright, so the first, like I mentioned,
[00:12:44.400]the first step of this work was to define
[00:12:48.420]does the damage cause yield loss?
[00:12:49.860]And if it does, what is the magnitude?
[00:12:52.625]Like I mentioned,
[00:12:53.580]one of the main differences of this work was
[00:12:56.490]relying on genetic diversity. So again,
[00:12:59.520]as much as as available as the previous work done,
[00:13:03.060]those are usually based on a handful of varieties.
[00:13:06.030]Here we used over 550 soybean varieties.
[00:13:09.030]So there's a lot of genetic diversity and we can get a good
[00:13:12.480]distribution of damage across them.
[00:13:15.420]It's also worth mentioning that they're coming from multiple
[00:13:18.531]different populations. So they are, you know, diverse.
[00:13:22.530]It's not all lines from the same parent.
[00:13:25.140]We did this for over three years and multiple locations,
[00:13:28.181]multiple replications. So they end up being a pretty
[00:13:30.450]large data set at the end of the year.
[00:13:35.010]So one of the main questions that we got when we were
[00:13:37.500]presenting the preliminary results was,
[00:13:39.810]how do you know your dicamba exposure?
[00:13:43.290]And it's a very fair question and, and to be honest,
[00:13:45.390]I think it's really hard to define your exposure because
[00:13:50.190]you don't know where it's coming from.
[00:13:52.410]You know, it may head with the immersion,
[00:13:54.510]you're getting dicamba from multiple directions
[00:13:57.180]and different timing of exposure, different doses.
[00:14:00.000]It's something really hard. You know,
[00:14:01.710]when you think about the statistics,
[00:14:03.630]the dosage doesn't necessarily matter that much as long as
[00:14:07.020]the exposure is homogeneous in a specific field. So we will,
[00:14:11.310]what we wanted to do is kind of like define a yield map
[00:14:14.370]of the performance of a known (indistinct) check
[00:14:18.780]with the (indistinct) check.
[00:14:20.490]So think about it like this.
[00:14:25.598]If you have two checks with the same yield potential,
[00:14:29.310]if there's no dicamba, they should be yielding the same.
[00:14:31.890]So the proportion between them is hundred percent.
[00:14:34.440]So that's what we've done.
[00:14:36.000]If there is dicamba and that proportion goes down,
[00:14:38.610]we cannot attribute that difference to the exposure
[00:14:39.562]to the dicamba.
[00:14:42.780]So that's kind of how the few layouts would look like.
[00:14:45.720]We would've, you know, a plot of a Roundup Ready customer.
[00:14:50.699]We got the proportion for the nearest extend plot.
[00:14:55.440]And then in between those we did the interpolation to really
[00:14:58.890]design that yield map distribution.
[00:15:02.310]And as you confirm if it was homogeneous or not,
[00:15:05.610]we used the Poisson model,
[00:15:07.170]which can account for a spatial distribution.
[00:15:10.200]Surprisingly or not,
[00:15:11.940]we found that distribution was highly homogeneous,
[00:15:14.160]which is what we've been observing over time. So it's,
[00:15:17.130]it's not necessarily a only a section of the field that is
[00:15:21.330]being exposed by dicamba, it's really almost like a cloud
[00:15:25.200]falling over your field. As you see,
[00:15:27.297]in the different fields that we tested,
[00:15:29.790]the distribution's pretty much homogeneous.
[00:15:31.980]So it shows that for, for what matters here,
[00:15:35.520]we know that the distribution was homogeneous.
[00:15:39.000]The other question that we got a lot was
[00:15:42.720]how do you know that your scoring is is accurate, right?
[00:15:45.980]We we basing this on visual scores.
[00:15:49.020]So we use two different metrics to decide if our phenotype
[00:15:52.590]was high quality or not.
[00:15:54.870]One of them is a simple correlation on among replications in
[00:15:58.080]among environments.
[00:15:59.670]As you can see the correlation was pretty high,
[00:16:01.901]which means that if one genotype is getting a low score,
[00:16:05.880]it's getting a low score everywhere. It's getting a high
[00:16:07.800]score, it's getting a high score everywhere.
[00:16:09.930]The other thing that we've done is the Cronbach's alpha.
[00:16:12.990]This is highly used on health science and medical field
[00:16:17.016]where they try to see the consistency
[00:16:18.930]of a specific response.
[00:16:20.700]For us here, the response was the dicamba damage.
[00:16:24.090]And this goes from zero to one.
[00:16:27.360]Obviously one is the highest consistency,
[00:16:30.000]zero no consistency.
[00:16:31.590]There's some quantitative geneticists that they're even
[00:16:33.570]considering this as a alternative credibility.
[00:16:37.320]Familiar with Rex Bernardo, he,
[00:16:38.870]he talks about this a little bit and for us the scores have
[00:16:42.960]very high consistency.
[00:16:45.480]So they're not only correlated among environments that we
[00:16:48.150]showing high consistency theme their own observations.
[00:16:51.390]So again, the two main limitations that we were faced,
[00:16:56.160]one was, is the damage homogeneous,
[00:16:58.707]and the second one is the scoring reliable.
[00:17:02.100]We're able to prove that using statistics.
[00:17:07.260]So when it comes to the damage, right?
[00:17:09.480]I've been been talking too much, but not getting to the
[00:17:11.460]point here, in a one to four scale we found that
[00:17:14.970]for every increment in that scale, you would see
[00:17:17.640]about 9% of yield loss. And again, I,
[00:17:21.000]I wanna reinforce that, but the size of this data setting,
[00:17:25.710]so again, we're not talking about a handful of varieties
[00:17:28.710]or a handful of observations.
[00:17:30.660]We're talking about 500 plus varieties in an almost 10,000
[00:17:35.220]data points data setting.
[00:17:38.290]As you would expect,
[00:17:39.840]the maturity was significant and that makes sense.
[00:17:43.440]The longer maturity
[00:17:44.550]tends to observe lower damage from dicamba,
[00:17:48.213]You have a longer window to detoxify naturally from there.
[00:17:52.980]But I think what is really nice about this and really gave
[00:17:56.160]us a lot of confidence to, to pursue the remaining
[00:17:59.460]of this research was that although maturity was significant,
[00:18:04.260]it was not, it didn't matter on our tolerant group.
[00:18:07.950]So let me explain this cause this is pretty important.
[00:18:10.883]On this graphic here, on the, on the X axis,
[00:18:16.200]we have our damage score. Like I said, it goes from 1 to 4
[00:18:19.732]and then each one of the colors of this bar represents
[00:18:22.860]one maturity group going from the earlier to the latest.
[00:18:27.030]And here is the yield related to the checks.
[00:18:30.420]When you look at the group of the tolerated genotypes,
[00:18:33.870]they all have the same yield potential around the 90%.
[00:18:37.530]So even though they may have a different maturity group
[00:18:40.050]that doesn't affect your yield potential.
[00:18:42.600]When you start going down on the scale of damage,
[00:18:45.960]look at the most susceptible ones than the maturity really
[00:18:49.380]plays in effect. So for us, I'm saying this is really nice
[00:18:52.740]because it shows that on the tolerant ones,
[00:18:55.860]whatever is given the tolerance has something to do with the
[00:18:59.250]physiology of the plant other than just the maturity.
[00:19:03.330]Whereas when you look at the susceptible ones, the
[00:19:05.880]difference in yield was really because of their maturity.
[00:19:10.560]Okay, so that finishes the first part of this,
[00:19:13.500]When we were looking at yield impact, we're seeing that it
[00:19:17.520]does cause a decent amount of yield loss,
[00:19:21.270]and the main goal when we, when we decided to do this
[00:19:25.230]was to provide an alternative so you could coexist
[00:19:31.020]in an environment with a lot of dicamba.
[00:19:34.530]This is not to be sprayed, that came on top of it.
[00:19:37.680]But if you're growing something else, then an extend
[00:19:40.470]technology you can survive in those environments.
[00:19:43.830]So the second part, like I mentioned earlier,
[00:19:46.170]we were trying to use a drone, it's an off shelf RGB drone,
[00:19:49.860]nothing to fancy about it, to differentiate the,
[00:19:52.920]the label of damage.
[00:19:55.170]Like I said, when you're walking the plots
[00:19:56.550]it's pretty clear you can see the difference between them.
[00:19:58.860]So if your eyes can tell that,
[00:20:00.630]most likely the drone can capture that as well.
[00:20:03.360]So we use a little smaller set of genotypes,
[00:20:06.480]about 230 soybeans.
[00:20:09.660]We have them in one environment, scoring pretty much the
[00:20:12.900]same as as the previous study.
[00:20:14.460]And then we use two different machine learning models here.
[00:20:17.130]One is Artificial Neural Network
[00:20:18.690]and the other one is a Random Forest.
[00:20:20.490]And our goal was to really see can we differentiate
[00:20:23.880]the damage just using the drone.
[00:20:27.060]To do this,
[00:20:28.170]we extracted image features from each one of the plus.
[00:20:31.260]So again, we fly the drone,
[00:20:33.630]you get your few plots, and then we finish plotting,
[00:20:36.330]we take features from that.
[00:20:39.450]The way we decided the features is basically
[00:20:41.610]reproducing what our eyes are seeing.
[00:20:44.220]So when you look at a tolerant plot,
[00:20:46.620]you know, the first thing that probably came to your mind is
[00:20:49.710]the canopy coverage is very different,
[00:20:51.417]and it's expected when you have the damage from dicamba,
[00:20:54.780]your canopy is much reduced.
[00:20:56.400]So it's the need that we use canopy coverage,
[00:20:59.490]that's one of the features. When you look at the color,
[00:21:01.920]and I hope it's making the justice there,
[00:21:04.410]you can see a pretty difference in the
[00:21:07.050]intensity of greenness. So again,
[00:21:09.938]you think about the damage from dicamba,
[00:21:12.600]it does end up changing the intensity of your greens color.
[00:21:17.520]And then the other thing, if you ever seen this in the field
[00:21:20.280]the texture from your plots are just different.
[00:21:23.880]You have the roughness from the dicamba damage (indistinct)
[00:21:27.030]if you have a tolerant, it's a smooth soybean leaf.
[00:21:30.960]For that we use entropy,
[00:21:32.610]which basically it captures like how complex,
[00:21:36.030]how much information you're getting from an image and we use
[00:21:39.240]that as a a texture feature and then we throw it away,
[00:21:45.030]throw in there two vegetation indexes to represent plant
[00:21:48.660]health conditions. And then like I said,
[00:21:50.760]this was across multiple environments.
[00:21:52.620]So we standardize all those features using (indistinct)
[00:21:56.426]simple Z scores. So you know,
[00:21:57.750]you're all under the same scale.
[00:22:01.103]When you look individually of those features,
[00:22:04.740]you can really,
[00:22:05.910]it's pretty nice to see how much that correlates to what
[00:22:09.720]we're seeing in the field. So for example,
[00:22:12.030]I'm gonna talk about a few, when you look at canopy coverage
[00:22:15.990]it's a very clear difference between
[00:22:18.270]the average of the tolerant, average of the moderator,
[00:22:21.540]and the average of the susceptible.
[00:22:22.890]So it does differentiate those classes pretty well.
[00:22:26.670]And it's nice because again it really replicates what we're
[00:22:30.570]observing in the field and a simple drone,
[00:22:33.300]off shelf drone, can do that as well.
[00:22:36.960]So when it comes to the performance of the model,
[00:22:39.503]the first one I used was the (indistinct) dashboard.
[00:22:42.870]It got a decent accuracy,
[00:22:44.280]I think especially giving how those scores were done,
[00:22:47.760]visuals scores, it does a pretty good job when you want to
[00:22:50.820]differentiate what is tolerant from what is susceptible.
[00:22:54.176]What we call on this paper is just extreme
[00:22:56.850]misclassification. So if you call something tolerant,
[00:22:59.610]but it's supposed to be susceptible or you call susceptible,
[00:23:01.680]it's supposed to be tolerant. It happened very few times,
[00:23:05.317]you know, you look at here and you looking here
[00:23:07.560]across all the samples, it's susceptible.
[00:23:09.630]It's not horrible when you consider how simple
[00:23:12.480]this entire pipeline is.
[00:23:14.722]When we use the Random Forest though, it seems to improve
[00:23:18.450]that misclassification quite a little bit.
[00:23:20.250]So you don't see any extremes on the prediction of tolerant
[00:23:24.720]as susceptible or vice versa.
[00:23:26.400]It just produces dramatically there. So the overall accuracy
[00:23:29.430]not too different, but it tends to identify
[00:23:32.190]those extremes a little bit better.
[00:23:34.500]One thing that is worth measuring, again,
[00:23:36.660]this is based on machine learning,
[00:23:39.060]so you gotta go through extracting the features.
[00:23:42.009]There's quite a little bit of work on that.
[00:23:44.550]One of the things that we're doing now,
[00:23:46.410]and hopefully the paper's coming out soon,
[00:23:48.660]we're using deep learning. So we're feeding to the model
[00:23:51.890]of the entire image of the plot.
[00:23:55.113]You don't have to go through extracting the features
[00:23:57.300]and that boosts the accuracy quite, quite substantially.
[00:24:00.660]So again, it should be coming out in a few weeks,
[00:24:03.838]but your overall accur, oops,
[00:24:06.480]your overall accuracy goes from, you know,
[00:24:08.797].75 to a .83, .84. So it's a much easier process,
[00:24:13.530]a little more computation intensive, but it,
[00:24:15.600]it's an easier process.
[00:24:17.220]So the main conclusion is that you can,
[00:24:19.860]again with an off shelf drone, it's an easy,
[00:24:22.980]easy process and it can really help you if you're interested
[00:24:25.830]in, in identifying higher tolerant varieties.
[00:24:28.890]Also can work for different crops
[00:24:30.270]so for the purpose of dicamba, that that seems to,
[00:24:32.910]to do a good job.
[00:24:34.770]And then the last part, which for me is, should be the,
[00:24:37.587]the most exciting, is when all of this come together.
[00:24:43.050]I think after we're done, you know,
[00:24:44.610]three years of researching, if we try to map the,
[00:24:48.270]the significant regions and,
[00:24:49.380]and we didn't find anything would be very disappointing
[00:24:52.230]because everything to this point has been indicating that
[00:24:55.770]this could be a, a genetic trait.
[00:24:58.440]So we just published that at the end of 2022.
[00:25:01.186]Here we use those diverse successions that I've shown
[00:25:04.472]on the previous slide,
[00:25:06.660]same way of doing the scoring,
[00:25:09.240]visual scores, multiple replications.
[00:25:11.192]And now we use two different models here.
[00:25:13.170]One is just, you know, the,
[00:25:14.580]the basic BLINK model that if you're working with with
[00:25:18.609]GWAS you probably see that.
[00:25:20.340]The other one we tried to account for GxE.
[00:25:24.180]So we were using the population structure in interaction
[00:25:27.720]with the environment. So the, the idea was
[00:25:30.240]if there is an instructor in that population of DIs,
[00:25:33.480]diverse lines, and the environment's different
[00:25:36.300]because of dicamba exposure.
[00:25:38.070]We're accounting for that difference using this model.
[00:25:42.060]So what we found is there's, there's two very clear peaks,
[00:25:46.710]one in chromosome 10 and one in chromosome 19
[00:25:49.620]that was detected with both, both models and they seem to
[00:25:54.270]have a pretty, pretty big impact on the dicamba response.
[00:25:58.980]But to me what was the most interesting is that
[00:26:02.490]the genes underlying those genomic regions,
[00:26:05.760]they appear to be related to the plant,
[00:26:08.310]the process of plant detoxification from the herbicide.
[00:26:11.640]So the, the weed scientists that help us on this work,
[00:26:14.670]when we shared the results,
[00:26:16.470]they were quite excited and I didn't know why at the time,
[00:26:20.460]but they said the two genes that we identified,
[00:26:22.680]they're directly related in the process of detoxified from
[00:26:25.740]herbicide. So for instance, the one in chromosome 19,
[00:26:29.882]the glycosyltransferase gene,
[00:26:31.830]it's directly associated with phase two
[00:26:34.334]of herbicide detoxification phase.
[00:26:37.170]And it's more interesting is that
[00:26:38.610]that specific genomic region
[00:26:40.590]is breaching of this type of genes.
[00:26:42.870]So it seems to really have a big impact there.
[00:26:45.660]And the one in chromosome 10 is right within a multidrug
[00:26:50.040]resistant protein,
[00:26:51.570]which you know, if you're familiar with the process,
[00:26:53.820]the phase three of detoxification,
[00:26:56.010]it moves the conjugated herbicide into the vacuole.
[00:26:58.800]So as the last step for the plants are getting rid of the
[00:27:01.697]herbicide side, so again, I don't think it was random
[00:27:06.000]that we identified those regions,
[00:27:07.530]it really seems to be having a real effect on the phenotype.
[00:27:13.440]So I wanted to add that here. You know,
[00:27:15.107]if you think about the, again, the three phases
[00:27:17.730]of detoxification, the conjugation of the herbicide
[00:27:21.690]is really catalyzed by UDP, you know, glycosyltransferases,
[00:27:25.920]which is the one in chromosome 19.
[00:27:28.470]And then when you're making a transportation of this
[00:27:30.900]conjugated herbicide into the vacuole,
[00:27:33.570]it's done by multi-drug resistant proteins,
[00:27:35.880]which is the one we found in chromosome 10.
[00:27:39.720]When you're looking a little deeper into the, you know, the,
[00:27:43.107]the phenotype distribution, so when you have those genes,
[00:27:47.370]how does that change your phenotype?
[00:27:49.440]So what we've done is we created classes based on the
[00:27:53.571]allelic version for those markers.
[00:27:55.920]So if you have the favorable, we call it one,
[00:27:59.192]you have the unfavorable, we call it zero.
[00:28:02.370]So this one here is based on just a classification
[00:28:06.840]tolerant, moderate or susceptible. When you look at the
[00:28:10.079]triple zeros, which would be all the no values,
[00:28:13.860]your higher distribution of genotypes is within the
[00:28:18.150]susceptible and the moderate.
[00:28:20.400]You have very little misclassified as tolerant.
[00:28:23.640]And I'm gonna be clear here,
[00:28:24.690]I think this is a highly quantitative trait.
[00:28:27.870]I don't think it's regulated by two or three genes,
[00:28:31.140]but as you're trying to do any sort of marker as a
[00:28:33.690]selection, you can probably identify the most tolerant
[00:28:37.140]or most susceptible with a handful of genes.
[00:28:40.200]And it's quite interesting where as you go down in the scale
[00:28:43.170]here, which means you start adding the favorable alleles,
[00:28:47.580]your, your concentration of susceptible really goes down
[00:28:51.690]when you start having at least, you know,
[00:28:53.880]one or two of the favorable alleles.
[00:28:55.770]And when you have all of them,
[00:28:57.480]you don't have any susceptible net classification.
[00:29:00.180]So for us it's really nice to see this,
[00:29:02.367]but we want to see on the (indistinct) damaged score,
[00:29:05.460]so not thinking about classification,
[00:29:07.560]but looking at the actual distribution of damage.
[00:29:10.920]And it's kinda the same trend.
[00:29:13.050]When you have all the unfavorable, your,
[00:29:16.410]your average is about, you know, three in our scale,
[00:29:20.130]when you start incorporating the favorable alleles
[00:29:22.860]that goes down, and when you have all the favorable,
[00:29:26.730]your score is above one and a half. So again, it's,
[00:29:30.360]it's on a trait, I think it's a pretty complex process
[00:29:33.386]of detoxify herbicide, but if you have those alleles
[00:29:38.040]in your population, you may be able to have
[00:29:40.440]higher levels of tolerance.
[00:29:43.410]So, so what do you get from from this, right?
[00:29:49.470]I think again, looking at the data that we have,
[00:29:53.366]it's, so far as we know,
[00:29:56.160]it's the largest study when it comes to genetic diversity
[00:29:59.100]and dicamba. The damage is not only cosmetic.
[00:30:03.007]I know there's been a lot of, you know,
[00:30:04.802]confusing reports at some point even there, you know,
[00:30:08.873]dicamba could boost your yield a little bit, right?
[00:30:12.180]It's hard to believe when you think about all the,
[00:30:15.750]all that it's causing to the plant.
[00:30:18.000]Back in the vegetative stage, you're affecting your
[00:30:20.640]vegetative growth, you're affecting your canopy coverage.
[00:30:23.430]Those things are directly related with the ability of
[00:30:25.961]capturing light and energy in the plant.
[00:30:29.040]So it's not surprising that you're seeing yield loss.
[00:30:32.520]I think we've provided enough evidence across a wide range
[00:30:37.560]of genotypes that you're gonna experience some degree
[00:30:41.460]of yield loss if you're being exposed continuously
[00:30:44.820]to dicamba over the years.
[00:30:47.621]One thing that's to me is, is very nice,
[00:30:49.260]especially if you're working with genetics, is that the
[00:30:51.120]genetic background, on top of everything else that weed
[00:30:53.910]science has found, it does affect your response.
[00:30:57.090]It's very nice for us because you know,
[00:30:58.740]the basics of breeding is you have genetic diversity or
[00:31:01.680]variability, you can improve the trait.
[00:31:04.110]That's what we are showing you.
[00:31:06.030]The third one is that the maturity is not-
[00:31:09.540]third one, this one, is that maturity is not
[00:31:11.367]the only thing affecting the response.
[00:31:14.250]So like we showed before,
[00:31:15.780]we think the same maturity group on the tolerant ones
[00:31:18.974]they yield the same. So there is more to the resistant.
[00:31:22.020]There is really physiological mechanism that are affecting
[00:31:24.930]the level of tolerance.
[00:31:27.570]You can use UAVs to to, to do this type of work.
[00:31:31.530]And if you're working with a different trade,
[00:31:33.570]I imagine that if it's something that you can see and easily
[00:31:36.360]differentiate, an RGB camera should, should do the work.
[00:31:40.620]I mean, if your your phenotype looks good,
[00:31:42.690]it shouldn't be that hard,
[00:31:44.550]You can see it, then you have other,
[00:31:46.290]other options, where this already seems to do a good job.
[00:31:50.067]And then lastly and probably the main takeaway message
[00:31:53.730]and that should apply to all traits and all crops
[00:31:56.700]is that if you have genetic resources that,
[00:31:59.823]well first you a better responsible for a specific trait,
[00:32:03.420]most likely you can improve that trait in a large scale.
[00:32:07.500]For us here, developing like a tolerant, nonresistant,
[00:32:12.540]tolerant soybeans, it really opens a lot of opportunities
[00:32:16.800]if you want to grow something else, you know,
[00:32:19.050]there's a growing market for conventional soybean, right?
[00:32:22.710]If you want develop a high oleic soybean or if you wanna
[00:32:25.504]develop for a specific, specific niche market,
[00:32:29.280]you may be able to get by and coexist.
[00:32:31.740]If you have this trade, you don't have to be, you know,
[00:32:35.280]hostage for a specific tolerance because you don't have
[00:32:38.370]options. That's our main goal with this,
[00:32:40.200]really deploy alternatives to farmers so they can
[00:32:44.520]really decide what they wanna do.
[00:32:46.296]They don't have to be forced to make any, any decision.
[00:32:49.996]I think that's all I have. I, I forgot to at my,
[00:32:53.910]my last slide, which was really the,
[00:32:55.950]the acknowledgement section.
[00:32:58.050]This is the work that was in between Missouri and the
[00:33:01.320]University of Illinois primarily. So we,
[00:33:03.480]we collaborated with Brian Dews in this work.
[00:33:06.404]So Jim Reachers and Brian Dews were the,
[00:33:08.997]the group from Illinois, the work with us,
[00:33:11.640]Missouri was of course our group at (indistinct),
[00:33:15.780]and then Doug (indistinct) at the University of Florida.
[00:33:18.720]He also contributed with some of the, some of the analysis.
[00:33:22.230]But again, that's my last one.
[00:33:25.758]Very nice to be here. Thanks.
[00:33:27.900]Thanks a lot for, for, for listening and I'm happy to,
[00:33:31.969]to take any questions. Thank you.
[00:33:34.980]You have time for questions and we can pass the
[00:33:37.050]mic around so those online can hear.
[00:33:59.577](indistinct)
[00:34:03.268]Thanks. Nice. Thank you.
[00:34:05.430]So what are you doing? Are you making use of the,
[00:34:12.859]the (indiscernible)?
[00:34:22.110]Yeah, so we, so my first cycle for us will be this summer.
[00:34:27.450]We're, we're licensing the material from Missouri
[00:34:31.230]to start putting that in our genetics because
[00:34:33.960]it may sounds really complicated to bring anything
[00:34:38.310]that is, you know, that came a resistance.
[00:34:40.350]We just got too much damage from, from multiple directions.
[00:34:44.757]The one thing that we're doing now,
[00:34:46.350]and I again should be coming out soon is
[00:34:48.750]we're confirming the, the markets that we found,
[00:34:51.810]but in the different population. So we found this in the,
[00:34:54.600]the PI span. We're now using the first study, you know,
[00:34:58.680]500 plots breeding lines to map the same thing and,
[00:35:02.877]and the warning chromosome 19 and 10.
[00:35:06.240]They keep appearing in a different population.
[00:35:08.777]So I think once we start making the process this summer,
[00:35:12.867]we're gonna be using that information
[00:35:14.520]for markets (indistinct).
[00:35:23.460]Thank you for this wonderful presentation.
[00:35:26.250]I think I have two questions. The first one is,
[00:35:29.340]I might have missed it in the beginning,
[00:35:30.960]but I didn't hear which those did you use,
[00:35:33.520]which those are the camera you used or exposed.

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Soybean Tolerance to Off-Target Dicamba

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