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Drew Lyon: Hello. Welcome to the WSU Wheat Beat podcast. I’m your host, Drew Lyon, and I want to thank you for joining me as we explore the world of small grains production and research at Washington State University. In each episode, I speak with researchers from WSU and the USDA-ARS to provide you with insights into the latest research on wheat and barley production. If you enjoy the WSU Wheat Beat podcast do us a favor and subscribe on iTunes or your favorite podcasting app and leave us a review while you’re there so others can find the show too.
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Drew Lyon: My guest today is Karansher Sandhu. Karan is a Ph.D. student in the Department of Crop and Soil Sciences. Karan is originally from India, and joined WSU in the fall of 2017, under the supervision of Dr. Arron Carter, WSU’s winter wheat breeder. Karan’s current research focuses on breeding for grain protein content stability, using genomic selection, and high-throughput phenotyping tools. He’s also focusing on identifying wheat lines with quicker canopy closure, to compete with weeds. Karan received his BS degree from Punjab Agricultural University in India, where he majored in plant breeding and genetics. At WSU, he’s increasing his understanding of plant breeding, phenomics, and statistical genomics. Hello, Karan.
Karan Sandhu: Hi, Drew. Thanks for inviting me.
Drew Lyon: Oh, it’s a pleasure to have you here. So, why did you decide to work on grain protein content stability, rather than just either increasing or decreasing total grain protein?
Karan Sandhu: Yeah, definitely a good question, yeah. So, the main reason for trying to go for stability is that there is a negative correlation between grain protein content and grain yield. So, if we’re trying to increase the grain protein content, ultimately we are increasing the grain yield. So, we try to identify the genes which are controlling the stability of protein content. So, if we’re able to find those genes so we can incorporate those lines into our breeding germplasm and we will be having a stable amount of protein content across different environmental conditions. So, in this way, if we are having stable protein content, we can make the selection for other traits like yield or disease resistance. So, we can improve those traits while keeping the protein content stability. And the other reason is this state is highly affected by the environmental conditions. For example, if a line is performing better in this environment, it won’t be performing that stable for the protein content. But if we got those genes which are — which makes line to perform stability across multiple environments, this would be a good target for breeding.
Drew Lyon: Okay, so the fact that we do have very different years and even just a few miles down the road the weather conditions are different, that’s all impacting varieties. And some varieties, I take it, will have a very large difference in protein; whereas, others may not. And you’re trying to find the ones that —
Karan Sandhu: Yeah, we’re trying to find those lines which are not having that much difference.
Drew Lyon: Okay, and trying to find the genetic components that control that. Okay, so how will you use genomic selection to achieve this goal of more stable grain protein?
Karan Sandhu: So, the genomic selection is usually used to predict the breeding value of a line by just using its genetic makeup. So, by using this technique, we can predict the stability index of a line. So, we will just genotype a line or get its marker information, and then we will train our genomic selection model to predict the stability index of a line. So, if a line is having a stability index of 1, which is known as a stable line, so we can decide, yeah, this line will perform stable across multiple environments, so we can plan that line in all those environments. On the other hand, which line — line which is not having the stability index around 1, or which is highly variable from those lines So we can get to know before planting them, this line won’t be performing that stable. So, in this way, we will be incorporating genomic selection to predict the stability, predicting the grain protein content, as well as grain yield, by just using this marker information.
Drew Lyon: So, how — how do you get — how do you figure out what this value is? Do you grow it out in the field in lots of locations and —
Karan Sandhu: So, for figuring out the value, we just need to train our model. For we’ll — we will be using previous year data, like 3-year, 4-year ones to train our model. So, once our model is trained, we just need to have the genetic makeup for the markering formation of the line, which is not planted in the field.
Drew Lyon: Okay.
Karan Sandhu: So, like, before planting them in the field, we can make some negative selections. For example, if we’re having 1,000 lines, we can easily eliminate, like, 400, 500 bad lines, and then just focus on the remaining.
Drew Lyon: Oh, okay.
Karan Sandhu: And this way, we’ll be targeting negative selection.
Drew Lyon: Okay, so that’s how genomic selection helps you get it going quicker —
Karan Sandhu: Right. Yeah, so it yeah, depends upon the different kind of model which we’re using. Some models only take into account the additive effect of genes. Some try to include dominance plus epistemic or the environmental interaction. So, we will be trying to use different kinds of models that are possible.
Drew Lyon: Okay. So, how are you using spectral information? I know that the breeding program is collecting all this — this data. There’s lots of spectral — how do you use that to help you?
Karan Sandhu: Yeah, first of all, spectral information is really important for a plant breeder because with the spectral information we can get to know about the different physiology pattern which is in the plan. Like, we can get to know the photosynthesis rate, different pigments, like, out of zentyl fill pigments or the different kind water stresses which a plant is undergoing. So, if we’re having that kind of information from plants, we can incorporate that information in our genomic selection model. So, our model will be getting more accurate. Earlier, we were just using the genomic information. Now we’re including some more traits which are related to the plants. Because all of this spectral information is correlated to plant grain yield or grain protein content in some way. So we will be definitely improving our genomic selection accuracy by using this information in our models.
Drew Lyon: Okay, I guess maybe for our listeners who may not really understand what spectral information is, you — you —
Karan Sandhu: So, the spectral information is that we’re getting the reflection values from a plant under the different regions of electromagnetic spectrum. Plants absorb light mostly in the blue and the red spectrum, so that’s the main region where they mostly do photosynthesis. And they reflect that light in green regions. That’s why we’re seeing plants as green. So, by using these kinds of traits so we can get to known, yeah, protein content is mostly expressed around that wavelength of 500 to 600 nanometers. So, by using such a reflectance value, we can do prediction models. If we want to get to know about the water status of a plant, we will be getting a reflection in the 900 to 970 nanometers. So, these ranges, we’re already knowing those values, so we’re just targeting those.
Drew Lyon: Okay, and then you can take that information and put that into your model, as well.
Karan Sandhu: Yeah, yeah.
Drew Lyon: Okay, very good. So, the other thing you mentioned about — or I — I talked about in your introduction, was your work on plant canopy cover and weeds. Can you tell us a little bit about why you think quick canopy closure is required for — for the PNW.
Karan Sandhu: Yeah, that’s the main reason we’re targeting quick canopy closure is mostly we’re facing the problem of downy brome in our region. So, like, once snow cover is gone in the spring, so downy brome started growing more quickly as compared to the wheat plant. So, they tried to compete with the wheat for light interception nutrients, as well as water. But our wheat lines, they grow very slowly as compared to downy brome. So, we’re trying to identify the wheat lines which can grow more faster than downy brome so that they won’t be competing with downy brome for all their nutritional requirements. So, once we identified those lines, then we will be targeting the genes which are controlling those — those traits so that we can incorporate those genes into our breeding germplasm. So, by this way, if we can able to identify those lines, we will be needing less amount of herbicides, or we won’t be facing that much problem for the downy brome. Like, right now is very difficult to control downy brome, so we’re trying to use a breeding approach for that.
Drew Lyon: Yeah, we’ve — being weed scientists, we’ve — we’ve overused our herbicides and we have downy brome that’s resistant to a lot of the herbicide use.
Karan Sandhu: Yeah, most of the resistance.
Drew Lyon: So, there’s competition. Winter wheat is a very competitive crop. I’m wondering, how do you — how do you determine canopy closure? Do you have a light bar in there, or how are you —
Karan Sandhu: No, no. So, we’re just taking the weekly photos of the breeding plots. So, we will be extracting that percentage canopy cover with that occupied by the wheat lines. So, once the canopy closure is reached, we will say this week — this week the canopy closure was 80%, and the next week it was still 80%. So we will be — the last week there was 80%, there was no more increase in the canopy, so the canopy closure is already done.
Drew Lyon: Okay. So, in the — in the dry areas, the winter wheat-fallow areas, some people are planting on 12, 14, 16, maybe even 18-inch row widths; whereas, here in the Palouse you might be down to 7, 7 and 1/2 inch. How does that affect your canopy closure measurements?
Karan Sandhu: So, yeah, from this study we also planted our trial in two locations, one was at Pendleton and the other was at Pullman.
Drew Lyon: Okay.
Karan Sandhu: So, there we planted a little wide spacing as compared to the Pullman, so the canopy closure was more in Pendleton because lines were having more area to grow, so they were growing faster, as compared to the Pullman. The other reason could be the temperature. Like, in Pendleton there was more — higher temperature, so the canopy closure was faster. And … the spacing will definitely affect these things, so there will be ultimate range in which plants will be growing more, as compared to the less spacing. But the temperature will be the other factor which will be controlling this.
Drew Lyon: Okay. So, I would think canopy closure, in addition to competing — providing greater competition with weeds, might confer some other benefits to the wheat. What are some other things you could use this canopy closure information to help breeders decide other things?
Karan Sandhu: Yeah, so we’re also thinking that we can use the canopy closure information for predicting the grain yield, in case of wheat. Like, there was a study in soil being done and they found that the canopy growth was directly proportionate to the grain yield. So, we will be using our canopy information in the genomic selection model as a fixed effect just to account that in — account that variation and to predict the grain yield. So in this way, we will be still improving our genomic selection model for predicting the grain yield by using those informations.
Drew Lyon: Okay. Well, the canopy closure stuff is really interesting to me, as a weed scientist. But I also know growers, you know, grain protein in some years can add a lot of value to — to the crop they sell.
Karan Sandhu: Yeah.
Drew Lyon: And so they’re going to be very interested in this. So, we’ll keep our eyes open for the progress you make over the next few years. And thank you very much for being my guest today.
Karan Sandhu: Yeah, thank you so much for inviting me. Thank you.
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Drew Lyon: Thanks for joining us and listening to the WSU Wheat Beat podcast. If you like what you hear don’t forget to subscribe and leave a review on iTunes or your favorite podcasting app. If you have questions or topics, you’d like to hear on future episodes please email me at drew.lyon — that’s email@example.com –(firstname.lastname@example.org). You can find us online at smallgrains.wsu.edu and on Facebook and Twitter @WSUSmallGrains. The WSU Wheat Beat podcast is a production of CAHNRS Communications and the College of Agricultural, Human and Natural Resource Sciences at Washington State University. I’m Drew Lyon, we’ll see you next time.