 I'm Mark Blomquist, I'll be the moderator for the remainder of the session today. Our next presentation is management of urea and urea sources for sugar beet. Dr. Dan Kaiser, University of Minnesota, St. Paul. All right, I wanna start by thanking the research crews at the Sutherman Beach Sugar and also at the Northwest Research and Outreach Center, which helped with the study and also the R&E board, which for starting to provide funding for which is year one of this particular project where we're looking at managing urea, looking at the fall spring applications and looking at comparisons of urea sources. And the reasoning behind this is we're in the process in Minnesota right now of updating our, the nitrogen best management practices across the state. And one of the practices that we've had traditionally in the western part of the state has been acceptable, has been fall application of urea. So we know from research on corn that that's getting tougher to recommend. This funding though is to look at sugar beet to see if some of the same things that we're seeing in terms of reduced yield we're seeing in that crop. So it can make some distinctions moving forward since the BMPs at this point in time don't really make a distinction between the two crops when it comes to nitrogen management itself. So urea, what makes it more challenging is a few factors. First, it's a neutral molecule. So that means it's water soluble. The molecule can leach and also can incorporate itself without conversion to nitrate. It isn't like some of the other sources that we're really waiting for them to get to nitrate before they're effectively they're mobile. And this actually has some issues too in terms of runoff losses too, which we can see some issues with that. It does nitrify quicker and it's subject to volatility and the volatility issue doesn't stop with cold soil temperatures. And that's one of the things that I see a fair amount of the time we get questions from growers is when we start looking at nitrification, we do know that that slows down once we hit about 50 degrees soil temperature. But urea, since it's the hydrolysis process is affected by an enzyme, that process can happen even if the soils are cold. And just an example of that, this is kind of a good study to show us some of this. And this is looking at urea nitrogen loss as ammonium or volatility of that product when applied to frozen ground. This is actually I think barley where they were looking at winter applications of 90 pounds, which you look at the black dots, those are the application timings where they applied 90 pounds either around the first of December, looking at about the middle of February or just about the middle of April. And what these lines represent are ammonium flux. So this essentially is looking at nitrogen loss with or without urea, either with or without the MBPT or a product like agritain, which is a urease inhibitor. So looking at it in terms of application timing, looking at just what I did is I summarized the total loss. And that's kind of where that arrow represents is the total loss for each application timings. Looking at over the three timings, the total loss of roughly 30% of the nitrogen in the urea as ammonium even when the soils are frozen. And this is something that another other studies have shown too is we can delay the hydrolysis, but you can't necessarily stop it. So that's one of the things to think with fall application of urea that we focus many times on the nitrification or the nitrification in the nitrate loss, but it may not necessarily be the biggest factor affecting the urea loss itself. And that's really what we're trying to look at here. So this trial, again, we started this would have been the fall of 2020 with the first fall applications, two locations, one in the northern region at Crookston and then one in the southern growing region at Hector. The trial one, there's actually two trials in each location. The first trial is a fairly simple fall spring urea study. So non-treated urea, looking at six to eight nitrates depending on the location, late fall applications are around November 1st. So we're kind of targeting a point at which we'd hit or soils should stabilize at 50 degrees at the locations, which would be what we'd recommend for an anhydrous or an application of ammonium form to limit the conversion of nitrate. And then spring within one week of planning now the fall applications were generally disked in. So they were really worked in lightly. So we're looking at kind of simulating what you'd probably see with like a vertical till operation something we're not looking at an aggressive tillage which can present some risks in terms of shallow incorporation for volatility but I'm not gonna get into that today but there is some risks with that. Then trial two is a fall spring study how we're looking at nine fertilizer sources and in non-fertilized control applied at 45 pounds. And the reason we use 45 pounds is we wanted a rate of fertilizer that could we could see an increase in yield but yet it wasn't at a point where we're at our maximum response. So we're really trying to look at sorting out between these products and we need to be looking at a suboptimal rate and I picked 45 pounds just because it's divisible by three which you'll see why that's important in the moment. And everything was replicated four times. So the sources we were using the source study 100% urea was kind of our general comparison. Then we had some splits with ESN where we had 33, 66 and 100% ESN in combination splits. So again, that's why we use the 45 pounds because it's easy to divide that by three. Then the other products we were looking at is an anvil at 1.5 quarts per ton which anvil is a urease inhibitor. Agritane, we're using a low rate. The actually the rate we applied I couldn't get the 0.45 to mix properly so we're close to about one quart per ton which is about half the rate that's labeled for that product. And the reason I use a low rate essentially is because of super u. Super u is a combination of DCD and MBPT and the rate in super u is a lower rate. It's closer to that essentially equivalent to that 0.45 quarts per ton. So essentially what I'm trying to do with the agritane rate is trying to separate out if we see a response to super u, what is it? Is it agritane or is it the DCD? And then instinct next-gen 24 ounces per acre. One thing about instinct that makes it different is that it is a labeled pesticide. There is a supplemental label for root crops though. So beets are included with that. So that's one of the things we had to kind of make sure and it's a little bit different because that's labeled or it's applied based on a rate per acre which is a little bit difficult because essentially you're getting a lower rate per ton as you increase your rate per acre. So it kind of is particularly with lower rates of nitrogen becomes a challenge to mix the product because it gets a little soupy when you're trying to mix it together. And the last one is ammonium sulfate. I wanted an ammonium source which should have a lower volatility potential than urea itself. So I'm gonna talk about three things. I'm gonna show the emergence data. This is one of the questions we had last year with some of the nitrogen data since we're focusing on a spring urea trial is looking at what was the impact of emergence. So if you look at this study the maroon lines, the points of the fall application, the gold points are the spring application. And I converted these to sugar beet emergence as percent of total instead of plants per 100 foot of rogicev comparison between the two locations because you look at the spring applications are roughly consistent, about 200 pounds of nitrogen we're looking at roughly a 20 to 30% reduction in emergence. And last year, the data I showed it showed really no impact on yield. And that's kind of the same thing we saw this year essentially a fairly significant decrease in emergence but essentially that had no impact on yield. The fall applications, you look at relatively no effect on the emergence of the plant of the sugar beets themselves. So in effect, looking at the fall applications being relatively safer when it comes to emergence as a total for beet production. So the source trials are a little bit different what I do here, I'm just gonna mainly talk focus on those bullet points on the right. What I did here is I have kind of in darker blue those are kind of the treatments that sorted out near the top the red ones are sorted out near the bottom. If you look at the two locations, the site at Crookston essentially the fertilizer application tended to reduce the emergence now. The Crookston site, this is just for spring application we had an application issue with the fall treatments we lost those. So with the source data, the Crookston data I'm averaged just for the spring treatments the Hector data is for the both the fall and spring treatments because that application was as planned and that site actually seeing essentially some increase but there really no consistency other than instinct which tended to sort itself out near the bottom with the overall emergence numbers. Yield wise at the two locations we had a lower average yield at Crookston averaging closer to 18 tons for the root yield looking at the data. I mean, if there was a response it was somewhere near about 120 pounds and this on the bottom is looking at applied end plus our four-foot soil nitrate end and then the bottom right shows you what those totals were. We're looking at anywhere close to the four-foot level around 40 pounds at both locations. Hector was closer to around 130 and if our Southern site last year we were closer to about 200 with about 40 ton maximum average. So we saw a lower response this last year than we did in 2020. What that means, I don't know. I mean, looking at this would be more consistent with what I've expected just based on the recommendation being closer to that 130 range for that applied end plus total end. So the source trail, you know you've got to kind of take this with a grain of salt right now. This is kind of one of the things that we're looking at moving forward and doing additional years of this just to try to look at averaging this across sites because overall looking at kind of what tended to be near the top in this you look at what was near the bottom was Urea versus the control Super U and then looking at our 66% ESN what was near the top essentially was the majority of the treatments that had a higher rate of either a Urease inhibitor or 33% ESN. So let's do the same next year, I don't know. Again, that's why we need additional data but this is one of the things that I really want to look at here is we're looking at what's the best option here is it a Urease inhibitor or is it a nitrification inhibitor because we've solely tested for fall application mostly nitrification inhibitors and looking at the loss potential it starts to make me wonder again with some of the shallower incorporation if we're missing something if you're looking at that Urease inhibitor it may be more important which at least for the Hector site looks like it potentially could be since Anvol was kind of up near the top along with that 33% ESN and that 33% ESN is normally what I would kind of suggest for a split if anybody's going to use it last year I mean, I really expected that 100% to be closer to the control just because we didn't have water for that product that product needs temperature and it needs moisture in the soil to release the nitrogen with it so higher percent of ESN just wasn't going to be the thing that was going to work last year across most locations particularly with the dry conditions if you're really wet it may be a benefit but these dry conditions it really won't sort out near the top so for extractable sucrose what I'm summarizing here is pounds for ton if you look at the pounds break it followed pretty closely to the yield response it maximized at Hector it's slightly less about 30 to 40 pounds less than that but what I want to really look at here is just the pounds per ton here just look at the differences in the two sites and at Hector really looking at a high yield situation but looking at where we really couldn't push any more than about 12.5% for the extractable sucrose at that particular site with the lower yields Crookson was quite a bit higher higher than the 15% range in there but in both cases seeing that generally they decreased with increasing rate of N if you look at the Hector site I mean looking at Crookson and Hector both of them roughly the highest levels were at the lower rates there wasn't a whole lot of difference at Hector up to about that 121.30 range so we're looking at essentially seeing max yield and max there it went down a little bit but not as much even though that the overall trend was for decreasing protein and then decreasing extractable sucrose and looking at the fall versus spring the extractable sucrose was 3% higher for the fall application and this was pretty consistent at both locations so we did see some difference there but there was no interaction so the treatments themselves their responses didn't differ based on whether you're fall versus spring it just was slightly higher on average for all nitrogen rates with the fall application then for the source trial I mean we saw the same thing although it was actually a little bit different because we saw the recoverable sucrose per acre was roughly 3% greater with the spring application this was mainly due to some differences we saw in tonnage it wasn't really picked up within the yield data but if you factored in both the extractable sucrose as percentage with the tonnage then it came out that it was slightly higher for the spring application for that 45 pound rate and again that's just one rate over that entire thing that we're looking at time of application Hector really not a lot of difference if you look at a recoverable sucrose no N in Urea had generally a greater recoverable sucrose so when we applied it generally went down with the majority of the products but again, kind of messy data but something that we're looking at really over time is gonna be something that hopefully will shake out so really looking at wrapping up we are looking at petio nitrate concentrations I showed some of this data last year when it compiled some of it about 850 ppm was a cutoff above that we generally saw no increase in tonnage below that point we're anywhere from about 40 to 100% in terms of tonnage so if you're looking at a critical level right now that's kind of the cutoff value but again, we'll be evaluating that with newer data fall and spring they're equally as effective although 2021 was a dry year and we'll kind of see what happens I'm hoping we get a wet year in one of these years just to kind of look at a comparison because that's kind of where things really tended to fall apart with the fall application and hopefully then I'll help us to kind of sort out between some of the sources and rate response will continue to compile data of rates kind of where we're at 130 at the one site was kind of where we're at Crookston there was just no response with the lower yield potential and again, the source data the jury's out on that right now so hopefully, you know, as you know a few years so they continue to get funded we'll have some additional data on that moving forward so again, with that I wanna thank you for your time today and then the R&E board for sponsoring this trial Thank you, Dr. Kaiser our next presentation titled Strip, Tail and Cover Crops Before Planting Sugar Beets Jody DeYoung Hughes will be the presenter from the University of Minnesota Okay, so what we did, this took a little bit longer than we thought because we picked out three locations we had them in Southern Mim and we had Brian Ryberg and Noah Holgren and Wain Formal and so what we looked at is what we did with the corn the year before did it affect the beets the next year? So we stripped till before beets we put down cover crops in the corn some of them came in early like we planted them when they were about corn was maybe before around in there and then late so could we plant them well, we had to plant them by drone because I couldn't get the hagi that really wanted Okay, so we looked at that and 2019 couldn't do it, it was so wet and I couldn't use a plane that would have been great for cover crops, right? A wet fall, but I couldn't use a plane because the way that our strips were lined up the plane would have put cover crops across everything and I needed to control that had no cover crops in it and then so then we finally got in in 2020 for the corn and we interceded and we use crimson clover and annual rye and I'll go through this part a little quick because it has been reported on all the cover crops came up and that was awesome and then at Ryberg's farm since it's really hard to get a strip tiller to put a chisel plower disc back into his field after he's already built up a soil structure so instead he looked at broadcasting the cover crops versus using a drill for early season cover crops and then we got a Rantizo John I will not put up the economics to this because $150 an hour and they can't and they can't do very many acres and they had to go over the acre two to three times depending on our rate right now is not feasible for farmers to do but we got down almost 60 pounds of cereal rye and then the cover crops did survive from the late season from the early season cover crops they seemed to pitter out but the late season did come back up in the spring but it was really blotchy we also got down all of our treatments in that year which was great one of them did take a little bit longer you can see that we had snow but you remember we had that early snow and then it was really fantastic out again so we were able to get them all in and then this spring we went out and took stand counts and basically no difference in any of the stand counts there was differences between each field what their stands were but not within I was concerned because I like when people first start strip till that they do a secondary pass in the spring that they kind of just fluff up that soil and really make sure that berm is a really good spot for a very small sugar beet seed to be planted into it but time just goes by so quick you can't always get that done so I was a little concerned that we were gonna have issues with these plots because they didn't do a secondary pass I did notice when we were planting that at a couple of the places we did hairpin a lot of corn residue in where the beets were growing and this year got dry really quick and I was really worried about that residue in there because it would have made it much drier because bulk density is different between soil and residue so what we found here in the stand counts is that we didn't have any differences here oops, my last one, oh, I'm sorry, that was residue counts there are differences in residue counts what we found is that if you did tillage there that you were down around, well, 39% residue which isn't too bad but we'd like to see a little bit more and then if you did strip till you were more around 60 to 70 now the reason why do I get this one here because Wayne was able to do a little secondary pass and it did much more tillage but he had all that residue there over the winter time to protect the soil so it went down to 50% which is still a really great number and then when we looked at stand counts there was no difference again you can see the averages with strip till we looked at, I didn't keep the strip till early cover crops one of the reasons why is none of the cover crops really they didn't come back in the spring so I knew they weren't gonna affect anything and two, it was 102 degrees that day and I'm sorry, I'm a wuss and there was only so many stand counts I was gonna take out there so we, I know, it's sad when you get older, you'll understand so with this we see that they're all basically very, very similar across that now, why do we do stats? I know most of you guys are pretty good about this but the reason why we do stats is because of field variability all these plots were field length and they were fairly flat fields with maybe a little hill in the middle, not much but the variability out there is going to be huge so when you look at your combine and you see your yield maps out there you and you did everything the same to that field, right? You did the same hybrid, same fertilizer, same everything and you still get a map that looks like this, right? That your yields are up and down and up and down and that's the natural variability of the field what statistics does, and this is a simplified definition is that it takes out the background variability and helps you look at more just what that treatment was was it tillage, was it the fertilizer, was it, you know, the herbicide out there and takes out that background information that's why stats are important and so when they talk about that you can have big variability out there it's the field that's doing it and so we wanna kind of get that out of there and figure out what really helped out there. So we went hand harvested 10 foot a row six times per treatment, the three treatments and the three reps at each field and we came up with 54 samples per field and when you saw Aaron's talk he showed you the big black bags that were carried around and everything and we had a crew for one of the fields which was awesome and what we found out is that the sugar beet tons per acre did not vary so if you look across there and you see the little whiskers if they're within that area they're these are all the same these are all the same and these are all the same. Now across fields one, two and three there were differences but that makes sense these guys were by granite falls went through up and Danvers so very different soil conditions and things like that but within just the tonnage really no difference percent sugar per acre again no difference there was differences across fields but not in the field again so if you're looking at the green that's striped till I can't get my little maps, there we go and this was like Brian had the one that was a little different this was his broadcast cover crop one and then the purple is putting in cover crops late into the corn had no effect on yielding beets the next year and then again if you look at disc rip versus strip till and strip till that had late again covers a year before no difference between them and I was like I said I was worried because there was a lot of hairpinning but those beats really did outgrow it so you can see in this field here you can see the old residue and very splotchy ride that's kind of what we got on this one and then extractable sugar again no difference only between fields and so the summary is that we looked at all the things that you can do for beats and what we found out is tons per acre percent sugar percent extractable sugar extractable sugar per ton per acre sugar per acre and purity were all the same just the fields were separate and I still am of the mindset that if you're used to tillage that that little secondary pass in the spring might make you feel a little bit better but definitely get out from behind the planter and see what's going on out there we did that quite a few times and I wanna thank the guys that were my cooperating farmers and David Metler and the board thank you very much and Carson, he helped bring us in a strip tiller or yeah he got a strip tiller in one location and he also got us the inner cedar for cover crops Anna Cates to help me with the statistics and Dorian that was out there hand harvesting with me and the reason why I'm not putting in for another grant this year is because hand harvesting's a bear no, if I do I learned a lot the biggest thing is to find a field that will go to a piler that has the electronic tickets then I'm on board and then I had a lot of people help from Soil and Water they helped out too so in case you're interested about compaction there is a compaction conference coming up that's virtual in a couple of weeks and we have people from Sweden and the US and Canada speaking on this but any questions about the thing basically we found that strip till and disc gripping did the same thing and if you look at if you have the same yields and sugar then you look at the cost of doing that tillage and if you're doing two passes one with a disc gripper and then a field cultivator in the spring that's two passes versus strip till that is one if you do a freshening pass you can go about 10 miles an hour so that would cost you less on that one too Our next speaker this afternoon presentation is titled CRISPR based next generation diagnostic method development to evaluate beat necrotic yellow vein virus causing rhizomaniac and sugar beet Dr. Vanitha Ramachandran from the USDA ARS her name is Faragul Thank you Mark good afternoon again I'm Vanitha Ramachandran and she's going to be quite honest the USDA ARS located at Faragul and today I'll be discussing about a technology that's a CRISPR based detection method that we developed at the USDA for detecting beat necrotic yellow vein virus which is the causative of rhizomaniac so since the discovery of CRISPR cast system it's been widely used for gene editing for trait development in France however scientists have kind of treated the technology depending on the presence of many different CRISPR and cast versions it has been treated to be utilized for detecting viruses and we have used that technology to develop one for detecting beat necrotic yellow vein and I would like to thank the research and development board for funding this project so today my talk covers two topics number one is the rhizomaniac survey that we have done for the year 2021 and the second part of my talk would be about the technology that we developed for detecting BNYV so if you look at the diseases and sugary productivity across the United States there are several different diseases that affect the productivity of the sugary and among those I do care about rhizomaniac and the disease causes a crazy root with the many different phenotypes and drastically affect the productivity of the sugary and across the nation there are many different states that produce the sugary and among those the cumulatively the Minnesota and North Dakota are the major producers of sugary so this slide shows an overview of rhizomaniac so on the right side it shows the aerial view of the rhizomaniac if the field has the rhizomaniac you can see the yellow stripes and the second one so here you could see the ground view the yellow necrotic symptomatic plants and here shows an individual plant with the blinker type of phenotype and here is the typical rhizomaniac hereditary disease if the disease exists in sugary and the disease is primarily caused by big necrotic yellow vein virus which is composed of four to five different components of RNA it's an RNA virus so if we look at the disease management genetic resistance is the mainly it is the only cost effective control measures that we use and it's I don't need to emphasize this for this audience it started with RZ1 and then RZ2 and then now we have an inchargrist RZ1 or RZ2 genotype so that could in fact manage the disease and there is no any registered chemical treatments that are available up in the market to be used for this disease and yet another forcing threat is the appearance of resistance breaking strains of the virus so that's why because of the genetic resistance based management the diagnosis of the virus plays an important role both field soil evaluation and also plant evaluations for variety selection rhizomania based on the history it's being called as a sleeping gen I think like it is true because this year 2021 we do got this this is just representative of many different peaks that we caught from different parts of the field stations across Minnesota and the North Dakota and we do see we saw this is a healthy sugar beet versus the hairy root typical rhizomania phenotypic sugar beet and here is another kind of horse hairy root type phenotypic sugar beet that we encountered in the field so the first part is about sugar beet survey so we got just the beet by itself and also the soil corresponding to the beet locations and five or six different cooperatives American crystal sugar mint that foremost cooperatives southern Minnesota beet sugar cooperatives and struck with sugar and amalgamated sugar company from Idaho and we got both like I said beet sample by itself and also the corresponding soil samples and we do have a pipeline to detect both the soil samples and the beet samples and for the beet samples we scraped the roots carefully around the the symptomatic root area and then we do an Eliza I say and whatever if you see yellow that means BNYVV positive if you see a no color that means it's negative for BNYVV and for the soil samples we get the soil and then we do a soil dating essay and then check the root and goes through the Eliza process so in the vitality lab we have two meticulous technical people's Erics, Santiago Rivera he's an USDA employee and I do have Hunter Bach from MBSU student they both are very meticulous in conducting this essay for the field survey just I'll quickly go through this so this is these are the beet samples that we got from in that station near the peak filing station Minnesota so we got about seven beet samples and each one is individually essayed by Eliza to take a look at the BNYVV as a measure of horizemia here and this yellow line is the threshold so anything above is considered positive anything below is considered negative and we do have a positive internal control that's this one to make sure that the essay is done and it works and among these seven tested two of them kind of looks like rhizomania positive based on the Eliza data but we did not get soil for this station and the next one is for American crystal sugar it's a weak land of Dakota and we got about 10 beet samples and we saw typical rhizomania try to phenotype on these beet samples however our Eliza data turns out to be negative for all the 10 tested beet samples and we do got we do get soil samples and the soil samples were tested using three different sugar beet phenotypes susceptible RZ1 and RZ1 and 2 together and that is indicated the three different colors here none of them were above the threshold so they all remind negative for B and YBD however with another station the Sabin Minnesota for the American crystal sugars we got about 20, 21 samples, beet samples and the two different soil samples the beet samples some of them turned out to be positive for rhizomania B and YBD but the soil sample most likely remind negative except the susceptible one so in this case there is a discrepancy between the beet sample versus the soil sample so there could be many different reasons why it's giving a different data we are currently repeating the assay to make sure that we are getting the same results and this is for SMBSC so we surveyed three different locations and this is for Melbourne 24 Minnesota and we got 14 sample, beet samples and some of them were positive others were negative however the soil sample remind negative for B and YBD and this is for Melbourne 11 because just two beet samples they both turned out to be negative for rhizomania and the soil sample also remind negative which is a very check correlation here that we found and this is for Woods 20 again SMBSC and some beet turned out to be positive others were negative but the soil sample remind negative like I said there is a discrepancy here so we are repeating the assay and to see that whether we get the same data so the next topic is about the CRISPRasea that we developed before detecting B and YBD so why we are interested in CRISPRasea and why we developed I just go through in my next few slides so as a virologist we are looking for an assay which can be specific and highly sensitive and isothermal and rapid fast effective with so many different factors so currently we use ELISA which is a protein based assay and the specificity it has its one limitation on in terms of specificity and we could do QPCR it's quantitative real-time PCR which is expensive and it's not isothermal on the other hand the CRISPRasea technology is sensitive and specific like QPCR and it can be portable so eventually the technology can be developed into a kind of like strict based assay which is totally which will be totally field deployable so that's advantage of using this technology and why we are interested in developing the technology this slide shows an overview of the details of the technology so basically BNYBB is an RNA genome so first we turn that into an isothermal DNA part and then you add the CRISPR, the CAS all the reagents necessary for the CRISPR based detection and put all the reagents together then everything is together a biochemical reaction takes place and that emits a signal which is read by a plate reader so like I said the justification for why we are developing this technology wouldn't be much better than other existing technologies so if you look at the existing ELISA basal technology which is used for BNYBB so let's say we have a white BNYB which is blue in color and then we have a resistant breaking variant of the BNYB so the resistant breaking region is just depicted here as a red spot so in this case for the ELISA both RNA cortical will be coated or when it forms a virus cortical it is just coated by the same protein, corp protein so when ELISA is done the ELISA technology cannot differentiate the white red virus versus the resistant breaking virus so that's the limitation existed but in the case of CRISPR based technology the guide RNA which is here so if it's a white type one can design a guide RNA just to target the white type if it's a resistant breaking strain with the known sequence variant then one can design a guide RNA which is indicated in the red here and then that will just detect the resistant breaking strain so that's the advantage that we that is coming with the CRISPR based technology and why we are interested in developing this technology so to begin with we just started developing a white type detection technology based on CRISPR so we got the soil and we did the soil bathing essay and the soil was, the bathing essay was verified for the presence of BNYVV by ELISA and then we did the isothermal amplification which is a prerequisite for CRISPR and we could see a very nice isothermal amplification of the BNYVV and this was for the sequence confirmed to make sure that it is really representing the BNYVV so the next step is the CRISPR technology so all that you wanted to look at is the orange one is the infected sample resummonia containing sample versus the blue one is the healthy sample so 10 nanogram of total RNA is more than enough to dramatically differentiate the healthy versus the infected sample and then we just diluted 10 folds across the board and then it shows that 0.1 nanogram is the technology is sensitive up to 0.1 nanogram and with that I would like to thank Dr. Melvin Bolton the research leader of our unit for his continuous support and providing appropriate resources and the sugar beet virology team Eric and Hunter they are very meticulous in conducting the experiments and the lab members of the Bolton lab Dr. John Weiland has been very helpful in terms of teaching me the resummonia and field visit and everything and I would like to thank our industry collaborators and funding support from USDA and for this particular product I again thank the sugar beet research and education board for funding and the beet sugar development foundation with that I thank and like to thank questions. Thank you Dr. Ramchandran our next presentation is understanding and managing plant pathogens causing post-harvest loss in sugar beet in North Dakota and Minnesota Dr. Shim Kondo the USDA ARS here in Fargo. Thank you Mark. I am one of the newest additions in our research unit of sugar beet and potato research here in Fargo, North Dakota. In my position I have been little over three months. Today I'm going to briefly talk about my research program including ongoing research and the research plan for the near future. So I'm about post-harvest pathology and storage disease in a sugar beet. The first objective of my ongoing research is to understand the incidence and severity of post-harvest pathogen in storage piles. I am planning to collect the samples hopefully covering from all factory districts in the Red River Valley. The second objective of my research is to estimate the impact of these post-harvest pathogen on storage properties of sugar beet including respiration rate, sucrose loss and invert sugar concentration. Here in this slide I would like to share my thoughts about storage disease in sugar beet piles as you see here in this cartoon. These are the complex microbial communities including inoculum of root pathogen. They are present in the rhizosphere as well as in roots. Pre-harvest populations of these microbial communities in the sugar beet production field can carry over in storage piles with the roots and the dirt that's on the roots during harvesting. As you see here this is the huge storage pile and these roots in storage piles are still alive and they continue to do respiration and respiration increase the temperature inside the pile. That increasing temperature can result excessive microbial growth. These microbial growth can cause severe rotting causing big post-harvest losses. Also undead or injured sites in the roots can provide plant pathogen to direct access to internal root tissues which would also increase the incidence of storage disease. I have started collecting samples from the storage piles from the region. Here I am showing these sugar beet roots infested with these different microbial infestation. We collected sugar beet samples from three different locations, top, middle, and bottom locations of these piles and at the time of sampling these roots were stored about one and a half months and we already started to see these microbial growth. We also followed up our sampling a week later and we also collected the samples in the same way that we collected for the first time. This time we also see those microbial growth. In addition to that this time we saw a few sprouting as well. As you see here these are the sprouting from the roots and it would be very interesting to explore these research as well. Is there any direct connection between sprouting and the storage loss in the pile? I am planning to collect samples through this winter and spring hopefully from a different sugar district. I'm also interested to develop the research project to study a soil and root microbiome using the metagenomics approach. These studies will give some insight about the microbial population in our roots and the soil under pH based condition. Most common pathogens from these studies will be characterized and hopefully we will generate some resources that we can explore more to develop the diagnostic tools to detect these post-arrest pathogens early on. I am also interested in exploring the studies related to the genetics of plank pathogen interaction using the RNase CK approach and these studies will give us some idea about the candidate genes which are active during compatible and incompatible waste pathogen interactions. I am also planning to do some research to explore the mitigation studies for these storage disease by evaluating the host resistance and some other substances including fungicide. With that I would like to appreciate the support and encouragement from our stakeholders and the industry partners. This is my contact information. Please give me a call or send me an email. I'm happy to chat with you as I told you before. I'm still new here. I'm still learning about the sugar beet and I'm very happy to chat with you, interact with you. Thank you so much. Thank you Dr. Kandel. Our final presentation today titled Progress Towards an Improved Disease Forecasting Model for Circassia Relief Spot by Dr. Nathan Wyatt, USDA ARS here in Florida. Well, hi everybody. My name is Nate Wyatt and I'm the new research plant pathologist at the USDA ARS here. I got started about mid-August and so today I want to give you a brief update on what my research program will focus on as well as give you some preliminary data onto some of the corollary weather data we have that we're going to use to inform the disease forecasting model going forward. So a general project overview for my lab. Some of the things we intend to work on first and foremost is tracking sarcosis, a particular adaptation to CR plus sugar varieties over the course of the next few years. We have isolates collected and sequenced from 2020. 2021 is on its way and we will continue collections in 2022 and these will be put into a genome-wide association study in order to find genomic loci in the pathogen that are contributing to the adaptation to this new selection pressure we're putting on it. Along those same lines we'll be doing work with fungicides and GOAS as well. We've been doing a little bit of work looking at fungicide cross resistance with the DMI's right now. We intend to do some sarcosis of particular epigenetics in order to look at epigenetic responses to fungicide treatments as well. And we also intend to utilize all the isolates we collect in order to do some genome-wide association mapping for TIN resistance, DMI resistance and as many fungicides as we can get reliable phenotyping for. Furthermore, we're going to utilize a global and a temporal population that Gary Secor has. Gary has this large global collection of sarcosper isolates that we're going to sequence and that's going to allow us to get a really good idea of the diversity and the genetic potential of this pathogen across the globe. And then he also has another fantastic population that's set of isolates collected each year starting in 1998. And with a population like that we'll be able to track the genetic responses of the pathogen to different management practices over the course of time. So I know you've all seen the map of TIN resistance waxing and waning as its usage comes and goes. And we'll be able to track trends like that across the pathogen populations and figure out how that's being facilitated. And then we're going to use as much of that information as well as a lot of the weather data we have to inform our disease forecasting model and adjust that model. Right now our forecasting model is really based off of daily infection values which are largely based off of temperature and relative humidity over a set amount of time. And we really need to incorporate more information in that model in order to be more specific about when we apply our fungicides or when risk really is being assessed. So a general timeline of CLS, the disease cycle over the course of the year. So we have planting and I put March-April on here but I've been informed that's probably a little later than that. We know that by in 2021 as of June 17th we had detected latent cirrhosporobeticula infection in asymptomatic sugar beet leaves. We know that roughly June-July we get first spots observed and daily infection values start to spike where we know that we're at risk for CLS. And then fungicide control really begins there with multiple cycles of seabeticula going through its sporulation life cycle during that time as well. And that leads all the way up to harvest. But we don't have a ton of information in this early section about the early pathogen biology. And this is a region where Gary Secor and Viviana have been able to uncover a couple aspects of the pathogen's biology that I think are important to account for moving forward. So just as a refresher for Viv's presentation earlier she was able to identify this crucial essentially leaf wetness period of four hours. And then Gary also this year had spore traps out in three different locations in Oxbow, Perlian, and Rendell. And he was monitoring weather data as well as trapping spores. A key finding of this research was that they were able to find QOI resistant fungicide or QOI resistant isolates as early as May 3rd. And he was generous enough to hand me off the weather data so that I could do some correlations with what specific weather data would be most conducive for that four hour leaf wetness period. And so I took the leaf wetness data just grafted over time. And I have on there when spores were initially detected and when latent infection was detected. And you can see on here you can see this cursor at all. There's a few spikes in here where leaf wetness really takes off. Now knowing that leaf wetness sensors are finicky and this is a difficult phenotype and difficult data to really rely on. I took all of the leaf wetness data that you see throughout this chart and then I used that. Thank you all for staying to the end. We'd like to also indicate to you those of us who are here we had about 150 of our colleagues online and number of our colleagues from Shakopee, Margaret and her group Jamie and his group from New Zealand. They are Tuesday morning I think four or five o'clock. Still good to have you here with us Jamie. We have some of our colleagues from Sydney, Bar Stephen and company. A lot of the seed company reps over in Europe. Jamie and Sam in Switzerland. Good evening. It's pretty late over there. And Annie Lisbeth thank you for staying all the way through. So all our colleagues over in Europe, Anja and your colleagues over in Serbia. I hope you had a good program you asked for this last year and we have managed with the help of Scott and others. Mark, Tom and others to kind of get this done. I would like to again thank the irony board and the growers for your commitment to taking money from your pocket putting it into a check off and seeing the rewards of it. Agriculture research does be off. The return on investment is very, very high. Somebody said over 40%. You don't get it from the bank these days. Thank you Mr. Shigeki and Sumitomo for providing lunch. Thanks to all my colleagues from the co-op. Todd, Mike Metzger, Mark Dumpis and others. With that here I would like to say thank you. Stick around if you want further discussions and we still have some at least some soda at the back. Have a good evening. Thank you all for coming.