Using PRI to Monitor Crop Stress

How to use PRI and NDVI to monitor environmental conditions that adversely affect plant growth.

Dr. Troy Magney discusses how to use PRI and NDVI measurements to monitor environmental conditions that adversely affect plant growth, causing a decline in plant physiological function.

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Dr. Troy Magney, University of Idaho Dept. of Forest, Rangeland and Fire Sciences


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Good morning, everybody. Thanks for joining in this morning. My name is Troy Magney. I’m currently in the department of Forest, Rangeline and Fire Sciences in the College of Natural Resources at the University of Idaho. And I’m going to be talking today about using PRI and NDVI a little bit, measurements to monitor crop stress. I recently defended my PhD a couple of weeks ago, and part of my research was supported by Decagon Devices through the GA Harris Fellowship in funding the instruments that I’m going to be talking about today. I’ll be starting a postdoc at the NASA Jet Propulsion Laboratory this fall. And this work was mostly done with the help of Doctors Lee Vierling, Jan Eitel, Dave Huggins, and Steve Garrity, at the U of I, but then also at Washington State University, the USDA ARS and here at Decagon. So let’s get started.

So you know, the word stress is a bit of an ambiguous kind of term. So I just want to define it off the bat, just to let you know where we’re coming from. So in this work here, we’re defining it as environmental conditions that adversely affect plant growth, causing a decline in plant physiological function. So I’ll start out with a little outline of kind of where we’re going to go today. So really, what we’re trying to do is assess the spatial and temporal controls on crop function. So I’m going to be using these symbols throughout the presentation to guide you. And so we’re using the blue symbol on the left to represent changes in space, the clock on the right to represent changes in time, and then the leaf here represent plant function and changes in pigments and the exchange of CO2 and H2O between the biosphere and the atmosphere. And when we’re looking at the controls on crop function, we have many drivers of crop function including the light environment, the meteorological conditions, farm management, but also biophysical conditions which will be represented by these symbols here. And we’re going to do this all using ground based remote sensing. In particular, we’re going to be using the spectral reflectance sensors from Decagon Devices, and we’ll be using the symbol to represent that. And here they are, for those of you who aren’t familiar.

So, today’s outline in space. So all this research I’m gonna be talking about today was done in our local region here called the Palouse, which is a hilly, highly heterogeneous agricultural area in eastern Washington and northern Idaho. Our research is focused on five farms. But the research I’m gonna talk about today is focused at the Cook Agronomy Farm, near Washington State University. And the measurements we’re going to be taking were collected at a footprint of about one and a half meters squared on the ground. Here’s a picture at our field site with a 20 degree field of view coming from the sensors there on the ground. And the scales at which we’re going to be looking at plant dynamics are really happening at much finer scales than what we’re measuring at. They happen within the plant cell chloroplast at one micro meter, and further, these are pigment interconversions happening within the thylakoid membrane at about one nanometer. So if we take a quick trip back to our high school biology, or even middle school biology class here, look inside the thylakoid membrane, we’re gonna hone in on some pigments. So you might remember of course, Chlorophyll A and B, which are the primary pigments used to harvest light energy for photosynthesis. And then there are over 600 carotenoid pigments as well. There are carotenoid pigments that are oxygen free, the carotenes, and then there are carotenoid pigments that have oxygen in them, such as the lutein and the xanthophylls. And again, Chlorophyll A B are used to drive photosynthesis and what we’re going to be talking about today are the carotenoid pigments called the xanthophylls. And they serve a purpose called photoprotection. So I like to think of photo protection as sunscreen. So if you look down at this poorly drawn cartoon at the bottom at the farm, I have the course of the sun in yellow here, going from dawn to dusk. And maybe early in the morning the plant is putting on some bronzing lotion, you know. The sun isn’t strong enough to cause any damage so it doesn’t need to put on much sunscreen. But as we progress throughout the day, we step up that amount of sunscreen from 30 to 45 to 60 at peak sunlight and then back down again, maybe applying some aloe at the end though, I haven’t quite figured out exactly what that plant physiological function is yet. So if a plant is healthy, it doesn’t need as much sunscreen. And it’ll use about this much sun for photosynthesis, as represented in the green line, perhaps maybe only having to apply SPF 30 at mid day. But without sufficient water or nutrients or if the environmental conditions aren’t right, more sunscreen is applied, decreasing the overall photosynthetic capacity represented by this red line. And that perhaps means that the plant has to apply 60 SPF.

So let’s think about it this way. I like to think about all the different fates of photons coming from the sun using this funnel here. So a photon is traveling from the sun and reaches the chlorophyll, it has four fates, the first of which at the bottom there, which is photochemistry, where the plant is able to use that light energy to create biomass and sugars and carbohydrates. The second option, which is a byproduct of photosynthesis, is that this energy is dissipated as thermal heat at longer wavelengths as fluorescence. A third option is that this sun is dissipated as heat through non photochemical quenching, which is primarily what we’re going to be focusing on today. And you can see that this is represented by a valve here and you can imagine that valve being turned on and off according to how much photosynthesis the plant is able to conduct, and if photosynthesis is limited, that valve will open up and more heat will be dissipated through non photochemical quenching. Of course, if the plant is not able to deal with the photons in these three ways, the thing that will happen is there’ll be the formation of reactive oxygen species within the plant cell, effectively killing the cell. Again, today, we’re going to be focusing on plant sunscreen, and heat dissipation through the xanthophyll cycle, which we can measure using spectral reflectance.

So I want to talk a bit about the xanthophyll cycle. To get you oriented, I think it’s especially important that we understand the dynamics of what is happening from a physiological standpoint before we try to interpret and understand the PRI signal. So what’s happening through heat dissipation is we have an interconversion of the xanthophyll pigments. And there’s three primary pigments, violaxanthin, antheraxanthin, and zeaxanthin. And as the sun increases throughout the day, you will see the deepoxidation of xanthophyll pigments and the conversion from V to A to Z. But then if, say, a cloud rolls over, you can see the rapid and reversible epoxidation back to violaxanthin. So let’s take a look at this diagram here from Demmig-Adams and Adams 1996. So on our y-axis, we have the concentration of xanthophylls per unit chlorophyll, and on the x-axis, we have our time of day, and the red line represents the sun. So this is a perfectly sunny day. So the course of the sun is rising there about 6:30 in the morning and setting at about 17:30 in the evening. And as that sun increases throughout the day, we see with the black line decreases in the concentration of violaxanthin and increases in those photoprotective pigments, zeaxanthin and antheraxanthin. So throughout the day, this particular plant is turning the sunscreen off and then again ramping the sunscreen down.

So if we think about all of this like a mass balance equation, where heat dissipation is represented by that red H, photochemistry by the green P, formation of reactive oxygen species by the X, and fluorescence by the red F. The reason we’re interested in heat dissipation is because an increase in photo inhibition or photo protection is suggestive most of the time of a decrease in photosynthesis. So as that valve opens up, and there’s more heat being dissipated via the interconversion of the xanthophyll cycle, among other physiological mechanisms, we see a decrease in photosynthesis and an overall increase in heat dissipation. So another way to think about this is like a tea kettle. So say you want to boil some water, well, you have to apply energy to that water so it can heat up and eventually boil. And as those molecules begin to heat up and bounce around, you reach some sort of limit and the tea kettle blows. So similar to a plant cell. It only has the capacity to absorb a finite amount of energy before it blows. And that blowing in the context of the plant cells the formation of reactive oxygen species. So think of the water as being analogous to photosynthesis, where by a decrease in photosynthesis, would suggest you have an increase in xanthophyll cycle interconversion and heat dissipation.

So conveniently for us, there happens to be a signal in the electromagnetic spectrum as a response to this small plant pigment interconversion. So as photons from the sun begin to reach the leaf surface, we begin to see changes in the concentrations of violaxanthin, antheraxanthin, and zeaxanthin, which results in changes in reflectance that we can measure in the electromagnetic spectrum. And the unique thing here is that we actually see a small decrease at 531 nanometers represented by that yellow line. And this decrease, when you do see interconversion happens on the order of 2 to 5 to 10%. It’s actually a very small signal. But we are able to measure it nonetheless via the photochemical reflectance index, where we use 570 as a reference wavelength and subtract the changes and reflectance at 531 and then normalize it, depending on incident sun conditions, to then get at the photochemical reflectance index that is inferring deepoxidation of the xanthophyll pigments. So now if we’re really interested in crop stress or plant stress, we’re interested in how this plant is responding to abiotic or biotic conditions. So if we take our leaf here, and we think about photosynthesis being inhibited by any number of potential stressors, one might be water limitations, could be poor soil conditions, low or high air temperatures, vapor pressure deficit, nutrient limitations, some sort of biotic disturbance, while this is going to increase, this is going to result in a decrease in chlorophyll because the plant doesn’t have the capacity to invest in chlorophyll pigments for photosynthesis. And thereby, the plant can also maybe alter their canopy architecture, which is of course a genetic trait. It can change the way that biomass is allocated among the plant. It can change the plant mechanics. But what we’re interested in is this flexible trait, in this trait that can change on the order of, you know, seconds to seasons. And that is our xanthophyll cycle.

So the functional role of the xanthophyll pigments in this context is first, via flexible energy dissipation, which is rapid xanthophyll cycle interconversion. This can happen, like I said, on the order of 5 to 10 seconds, but also, as you remember that increase and decrease throughout the day. This is that valve opening and closing on a finer timestep. There’s also sustained energy dissipation where you see high bulk xanthophyll pools so that valve just generally stays open. And a lot of that sun that comes into the funnel will just be funneled right back out via heat through just high concentrations of zeaxanthin and antheraxanthin. And I use this example of these evergreen trees here in the winter to demonstrate that.

So let’s talk a little bit about sustained energy dissipation. So if we take this subalpine fir in March, we see in red circles here that it has low photosynthetic capacity. It’s under high stress via the FE over FM fluorescence measurement, and we have a high bulk zeaxanthin and antheraxanthin pool. But as this subalpine fir moves into the spring, we see an increase in photosynthetic capacity, we see a decrease in stress measured by FE over FM, but we also see a substantial decrease in the amount of just bulk zeaxanthin and antheraxanthin per unit chlorophyll that are in this plant. So since we have both flexible and sustained energy dissipation, we need to think about how we can interpret the PRI and when the PRI was originally developed by John Gamon’s group back in the early 90s, they were looking at the PRI on the order of fine timescales, the diurnal timescale, and that’s this flexible energy dissipation that I was talking about. But through a lot of research that has developed over the last couple decades, it’s been found that PRI is sensitive to both the facultative, so this rapidly changing flexible energy dissipation, and a constitutive, slowly changing canopy components that are driven by function and structure. So facultative again, is this flexible energy dissipation. It’s this diurnal xanthopyll cycle interconversion, and the constitutive is this sustained energy dissipation. So this could represent seasonal changes in pigments but also changes in canopy structure, which of course we see in crop canopies.

And one of the widely used spectral measurements to use to measure these slowly changing, say over courses of days, changes in leaf area and biomass accumulation is the NDVI. Many of you are familiar with the NDVI, which is sensitive to the near infrared and the red wavelengths. Remember, the red wavelengths are sensitive to plant pigment concentration, whereas near infrared reflectance is primarily a result of leaf area and plant structure. And from that we can get an idea of plant growth and leaf area accumulation throughout time but also senescence at the end of the season. So, in this research, I’m using both NDVI and PRI to get at the diurnal and seasonal phenologies of crops. So if we think about PRI as being responsive to plant function, and NDVI being responsive to plant structure, it begins to look like this. So in this chart here on our y-axis, we have time progressing from seconds to days to seasons on the y-axis, and space on the x-axis moving from the cell to the leaf to the plant to the canopy in the landscape. So, these are plant dynamics that generally occur at these respective timesteps and scale steps. So changes occurring at seconds that we’re interested are this non photochemical quenching, this xanthophyll cycle interconversion, and changes that are happening at days to seasons, also at the same scales is plant phenology and bulk pigment pool sizes, such as that sustained energy dissipation I was talking about. So the PRI has been traditionally been used as a measurement to get at changes that are happening at these finer timesteps, at these smaller spatial scales, whereas NDVI has been used globally, but also at the leaf scale, and looking at changes throughout days to years in plant development.

So the primary objectives and question that I was looking at in this research is that first, we want to try to attempt to deconvolve these constitutive and facultative components of the PRI signal. And we want to evaluate the response of PRI to these environmental conditions on the diurnal and seasonal timescales, and determined how quote unquote stress inferred PRI will contribute to crop growth. And we ultimately want to answer the question, Can high frequency PRI measurements be used to better understand the response of crops to water, nitrogen and meteorological conditions? So we used the spectral reflectance sensors, built here in house at Decagon Devices, which were originally developed by Steve Garrity, who was a member of our lab prior to my arrival. And what we did was we set these sensors at a 20 degree downward viewing zenith angle, about one and a half meters above the crop. They collected data at five minute intervals. And we did this over two entire growing seasons. Of course, one thing you’re gonna have to worry about when you deploy instruments in the field is the resulting bird desiccation that, for example, happened at a digital camera that we have out here at one of our sites in Colfax, Washington. So what we did was we set up 48 experimental plots over soft white spring wheat, in three field positions that are historically high yielding, middle yielding and low yielding. And we hand fertilized each one of these 10 by 10 meter plots using granular urea into a control low, medium and high fertilizer regime. Again, here’s a picture of us hand fertilizing these plots using the cup method. We took biophysical measurements weekly, at each one of our plots, of chlorophyll concentration using this SPAD meter Leaf Area Index, using an LAI 2000 stomatal conductance with a leaf porometer. And we got continuous soil volumetric water content measurements using the Decagon 5-TM sensors. And then at harvest, we collected biomass, grain yield, grain protein, grain nitrogen, and plant nitrogen. There were continuous measurements at these fields sites of meteorological conditions, including temperature, precipitation, vapor pressure deficit, and incident irradiance.

So to give you an overview of what exactly we were looking at is we wanted to investigate the diurnal and seasonal changes in plant pigments using 16 SRS sensors over two seasons, to look at all of these plant functional responses at four landscape positions, taking five minute measurements again over two seasons, to investigate the variable responses of the plant to light environment, to these four nitrogen fertilizer treatments, to vapor pressure deficit and air temperature, to soil volumetric water content stomatal conductance and leaf area accumulation. So, if we take a look at the diurnal PRI signal, where we begin to try to correct for these facultative and constitutive pigment effects, if we look back on June 1, during the tillering stage of the crop when the water is in surplus, we see represented by blue and red, very low VPD and air temperature. This was a clear sun day, represented by the gold dots. And we see a bit of a drop in the PRI signal. And each one of these lines represents a fertilizer regime. You see there’s not much spread in fertilizer, and they’re all kind of responding similarly. But if we look at field A 13 days later, during rapid vegetative growth at the onset of water depletion, we begin to see a larger dip in the PRI signal. We also see a bit of a spread. So our controller low nitrogen fertilizer plots are represented by the red and green lines, and the medium and high nitrogen plots being represented by the blue and black lines. As we move further into the season, into July 6, during the water deficit stage, so this is at the onset of wheat heading, you can see that the timing of the PRI signals shifts a little bit later in the day, coincident with increases in VPD and air temp, but you also begin to see a greater spread and a greater magnitude of that PRI response, suggesting a response to both water but also in this case, fertilizer.

So what we wanted to do was to attempt to correct for these confounding facultative and constitutive effects on plant function. So we take the PRI throughout the day, and we subtract a steady state PRI or a dark PRI, which was used during low incident irradiance conditions. And we compute the PRI C, which is just any PRI collected throughout the day subtracted from that low irradiance condition PRI at the beginning of the day. And what we find is when we don’t correct for PRI, so just a raw PRI on the y value, and look at the correlation throughout the season over two seasons to relative chlorophyll concentration, we see a fairly tight correlation between PRI and chlorophyll measurements. But once we correct that PRI, you see that line begins to level off, and we see less of a response, particularly during low chlorophyll time period, so during the onset of senescence, but also throughout most of the season, where those bad measurements range from 40 to 50.

So we feel comfortable with the ability of the PRI C to correct for these confounding effects on the PRI signal. So now let’s take a look at the seasonal PRI and NDVI signal. Again, remembering our PRIs in this context used to look at plant function and NDVI is plant structure. So on our y-axis, we have soil volumetric water content represented by blue, we have the PRI signal represented by red, our stress signal and the black line here to represent NDVI as we progress throughout the season. So you can see by the little crop in the bottom, this is during tillering right after planting, we see not a lot of stress. But as that plant begins to grow, we see still a minor decrease in stress but a rapid increase in NDVI and you can see the beginning of the onset of water depletion. As that crop begins to reach the heading stage, you see a leveling off of PRI— leveling off of NDVI and what I show here with this arrow is a rapid decrease in PRI which is occurring about 15 days prior to senescence as observed by NDVI. And then we begin to see that PRI continue to fall coincident with NDVI and water depletion throughout the season. So that was all of our fields averaged across. But if we want to just look at individual plots to get a better idea of fertilizer response, we see here with the black dots representing soil volumetric water content on the y-axis and just our NDVI measurements, we begin to see even a spread in NDVI, where we see lower NDVI values in our control and low N plot as we progress throughout the season. But then interestingly, in PRI, we see an even greater response in the difference of the fertilizer regime. So again, we see more stress occurring in our low fertilizer plots in the green and red, and less stress in the black and blue. And then we have vapor pressure deficit represented by the black dots on the bottom.

So if we take all those vapor pressure deficit measurements and look at them, looking at the mean VPD throughout the season on the left there, and then also the diurnal VPD throughout the season on the right, we can see fairly convincing trend in decreasing PRI C with increasing VPD, both on a seasonal and a diurnal timestep. Similarly, we see a response to air temperature, where we see a decrease in PRI C, with increased air temperature, both on the seasonal timestep on the left and then diurnal timestep on the right. But if we take stomatal conductance measurements taken throughout the course of a day, over several days throughout the season, we also see a fairly significant trend here with decreasing PRI C measurements coincident with lower stomatal conductance. So as that sun ramps up throughout the day, and we see stomatal conductance decreasing, that stomata is closing, we also see decreasing PRI C values. And I have the colors here representing the fertilizer regime, and then the circles for our field A plot and the x’s for field B plot. And you see that there’s not really much of a trend here in fertilizer response or between our two water positions. But you do see that PRI C does seem to respond to stomatal aperture closing and opening.

So if we want to look at our PRI response to nitrogen and water availability, if we look back early in the season, and here, since theoretically, the PRI should decrease with increasing solar radiation as that xanthophyll cycle interconversion occurs. So all I have here is the response of PRI to solar radiation over our fertilizer responses early in the season, so during water surplus, and you don’t see a huge difference between the high and medium nitrogen and the low N and control plots, but you do see that there is a bit of a deviation, and in fact a significant difference between these low end plots and the high end plots. As you look at that response to PRI C to solar radiation during mid season, you see again, similar to those diurnal plots I showed, you see a greater response in those control and low nitrogen plots. And you’ll also begin to see more of a deviation between between the different treatments. And then later in the season, suggesting more plant stress, we see continued separation between the plots, but also a greater dip, so suggesting that PRI is responding at a greater magnitude to increases in solar radiation.

So now that we’ve observed that PRI does seem to track environmental conditions on the diurnal and seasonal timestep, we ask the question if using PRI as an indicator of stress could give us an idea of leaf area accumulation throughout the season. So if we start here in the early season, with LAI measurements on the x-axis, and the slope of that PRI C versus solar radiation on the y-axis, we don’t see a very tight correlation. And this of course, is I think, primarily due to the fact that we have low LAIs at this time of the season and low stress. But as we step up into the mid season, we see that those points begin to fall lower on the slope of the PRI C of our solar radiation part of the y-axis, and we see a surprisingly strong correlation between PRI C and solar radiation against leaf area accumulation. You’ll also notice here we have the control low, medium and high N plots represented by different colors. We have a lot of our control plots, the red plots and low nitrogen plots in green, the triangles showing increasing stress, and lastly fairy accumulation. As we progress later into the season, we see increasing leaf area, but we also see increasing stress and we do see a tighter correlation between that slope of PRI C for solar radiation and LAI. So through all this, we can begin to think about PRI as a tool for predicting crop responses to fertilizer application, landscape position, which of course is going to control water availability and soil conditions, but then also to weather conditions.

So another thing we did is we looked at the ability of PRI to predict grain protein concentration. And this is not that important for our Palouse region, since we grow primarily soft wheats. But in say, North Dakota or Montana where a lot of the hard red wheats are grown and there’s premiums paid for protein concentration, it might be important to use PRI to infer some sort of stress that’s contributing to protein concentration. And the fundamental underpinnings behind protein allocation in the grain is controlled by stress conditions during that heading period. So under more stress, there has been shown, in theory, to be higher protein concentrations in the grain. So if we do a little multiple linear regression here, and we just start with a simple linear regression trying to predict protein using just NDVI, we see a low Model R squared here at .05, and a relatively high RMSE. But if we use the PRI, so this is summed PRI throughout the season up to heading— or during the heading stage, I’m sorry, we see that PRI alone is actually able to explain 70% of the variance in the grain protein signal, which is promising. But if we combine PRI with NDVI, we also see that PRI is really the only significant predictor here. Our model does improve but not substantially. And I think, although this is preliminary analysis, this could suggest that PRI could be used to monitor stress during that very important grain field period in these hard red wheats to try to get an idea of what the protein concentration could be in the grain.

So a lot of our ongoing work is investigating the things I previously mentioned. But we’ve also begun to link up NDVI and PRI measurements made from the SRS sensors, with for example, an automated terrestrial laser scanner, which we have built and designed in our lab, which is the blue laser, which gets a 3D scan of the crop every single day. We also have a radar instrument mounted on this tower, represented by that white case within the orange casing on the beam and then also digital camera time lapse measurements. And then we link these with measurements made at flux towers to get at the diurnal and seasonal changes in exchanges in CO2 between the biosphere in the atmosphere, but also in water vapor. So what we’re really trying to do is fuse a lot of these PRI and NDVI low cost spectral reflectance measurements with other instruments to see which instruments might outperform others in predicting certain crop responses to environmental conditions.

So in conclusion, our corrected PRI was able to deconvolve confounding seasonal and diurnal pigment effects. It however, was not able to deconvolve confounding changes in LAI throughout the season, which I did not show in this presentation. PRI C also showed a strong response to vapor pressure deficit, air temperature, stomatal conductance, water, and nitrogen availability throughout theseason. And this PRI inferred stress signal also is able to predict leaf area accumulation, which I think is especially important when we’re thinking about monitoring crops for biomass and — . And by understanding the inherent limitations in that signal, we might be able to get early season predictions of yield and grain protein concentration. So what we’ve also shown here is that I think PRI can really be used as an indicator of physiological phenology. So if you remember looking at the seasonal timestep of that NDVI and PRI signal, we really saw a decrease in PRI happening about 15 days prior to this decrease in NDVI.

So conclusions from the Palouse here, continued looking at the scientific implications. I think this type of work is really important for ground validation of satellite PRI, particularly satellite PRI and NDVI measurements. And ultimately, this could improve global predictions of plant stress and harvest metrics. So, you know a important question, I think for the scientific community that, of course, has been continually addressed over the last few decades is, this PRI response is really changing a lot on the diurnal timescale, as we showed, and if a satellite is flying over and taking a measurement, say, at nine in the morning of PRI, how can we be so sure that that is really the estimate we’re looking for when we see such a variability on the diurnal timestep. So there’s a lot of things that need to be figured out between the canopy and the space based scale. I also think there’s implications for plant breeding programs, in particular the development of stay green traits, where we’re looking at increasing the time of crops during that heading stage to both increase yields, but then potentially manage for protein as I showed.

Grower implications using these sensors, these are low cost, objective, repeatable measurements. PRI can potentially be used as an early indicator of stress conditions. However, remember, this is an indirect measurement. So we don’t know exactly what that stressor is, but it might give us some sort of clue as to what that could be at different field positions. Again, here’s that seasonal figure I showed with the NDVI and PRI signals, and the PRI signal dropping off quite a bit before we see onset of senescence visible from NDVI. This could also help growers begin to think about managing their field for precision nitrogen or protein management zones by deploying instruments at different parts throughout their field and just looking at the seasonal timestep, like I show here, and trying to get a handle on these responses of the crop to the environment to improve protein and nitrogen management. Another potential grower implication, which has already been studied quite a bit with the PRI, is in vineyards, especially to stress that grape just enough during that ripening period.

So, you know, overall, we know that plant response to the environment is highly dynamic. And we’d like to think that we can track this in space and time using remote sensing tools to ultimately gain a better understanding of the drivers of plant function and what these drivers might be doing and controlling these plant responses. So, you know, of all this data I’ve shown and all the time I’ve spent working on the PRI, I think it’s really important that we are careful with this signal and that we understand the limitations and we understand the functional response of bulk xanthophyll pools but also xanthophyll cycle interconversion and really understand what that PRI is responding to before we can completely exploit it to make inferences into crop stress.

And with that, I’d like to thank you for tuning in this morning. I hope I was able to provide you with a better understanding of what exactly we’re looking at with the PRI signal and its potential implications for both scientists and growers. Here’s my contact information — If anything was unclear or if you have any questions about using the SRS sensors to monitor crop function, I’d be more than happy to answer them. Unfortunately, out of the country right now, so I can’t stick around for questions. Feel free to send me a note, and I’ll get back to you as soon as I can. Thanks.

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