Is every plant-available resource being utilized?

Plants capture resources from their environment to produce biomass.  To maximize the production of a crop, you must first understand how much of each resource is available for the plant to capture, including light. In this 30-minute webinar, world-renown environmental biophysicist, Dr. Gaylon S. Campbell, discusses how to determine the amount of photosynthetically active radiation (PAR) available within your crop’s unique environment and how to use that information to maximize yield.

Achieve maximum biomass from every crop

Four resources need to be plentiful within a crop’s environment to increase biomass: CO₂, water, nutrients, and PAR. In this webinar, Dr. Campbell dives deep into the measurement and implications of PAR. Discover:

• The factors that limit crop production
• The effect of those environmental variables on crop production
• How to measure the radiation that is intercepted by a crop canopy
• How to measure the radiation that is available to produce biomass
• The ability of various crops to convert resources into biomass
• How these measurements can be used to maximize crop production

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Transcript

BRAD NEWBOLD
Hello, everyone, and welcome to “Radiation Resource Capture: Are You Leaving Yield on the Table?” Today’s presentation will be about 30 minutes, followed by about 10 minutes of Q&A with our presenter, Dr. Gaylon Campbell, whom I will introduce in just a moment. But before we start, we’ve got a couple of housekeeping items. First, we want this webinar to be interactive. So we encourage you to submit any and all questions in the Questions pane. And we’ll be keeping track of these for the Q&A session toward the end. Second, if you want us to go back or repeat something you missed, don’t worry. We will be sending around a recording of the webinar via email within the next three to five business days.

BRAD NEWBOLD
All right, with all of that out of the way, let’s get started. Today we’ll hear from Dr. Gaylon Campbell, who will discuss how to achieve maximum crop biomass by understanding radiation resource capture. Dr. Campbell has been a research scientist and engineer at METER for over 20 years, following nearly 30 years on faculty at Washington State University. His first experience with environmental measurement came in the lab of Sterling Taylor at Utah State University making water potential measurements to understand plant water status. Dr. Campbell is one of the world’s foremost authorities on physical measurements in the soil plant atmosphere continuum. His book written with Dr. John Norman on environmental biophysics provides a critical foundation for anyone interested in understanding the physics of the natural world. He’s written three books, over 100 refereed journal articles, some book chapters and has several patents. So without further ado, I’ll hand it over to Gaylon to get us started.

GAYLON CAMPBELL
Good morning. Thank you for being with us. Before joining METER, I was a professor at Washington State University. I work closely there with a colleague, Bob Conklin, horticulture, and Washington potato yields then were about double those in any other state and Bob was the person responsible for that difference. Bob told me once how that came about, said that he started his career with a typical approach of planting replicated treatments and comparing yields averaged over plots in a treatment. Then one day, it dawned on him that among the treatments, he had plots that yield much higher yields than on average, he started paying attention to those plots. Assuming the high yields were not the result of measurement errors, something about the growing conditions in that plot must have made it really been responsible for producing that yield. If he could discover what those conditions were, all the plots could yield what the one plot, the high one, did. Setting the highest yielding plot rather than the average as his goal led him to provide the guidance growers needed to double their yields. Bob asked me one time—on more than one occasion actually—what the yield limit was. He had doubled the yields in the state— could he double them again? That limited set by the physical environment and the genetics of the crop. I had difficulty answering Bob’s question at first, but as I learned more about plant environment interaction and crop modeling, I was able to give him a satisfactory answer and the approach I used will be the subject of our seminar today.

GAYLON CAMPBELL
The points on this graph represent the yields of 134 research plots, crops grown between 1959 and 1973. At an experiment station in the Columbia Basin of Eastern Washington. The x axis of the graph shows the yield we would predict from the solar radiation temperature and time since planting for that particular plot. We could take that as the limit Bob was looking for—the highest yield that we could get with the growing season length in the physical environment that existed that year. The y axis is the actual yield that was obtained for that plot. Note that some plots reached their potential. Bob could not have produced more Russet Burbank potatoes on those plots than he did. Many plots however, were well below the potential, presumably because of some failing in management such as nutrient deficiency, or disease, or improper irrigation. Knowing why those plots are low would help a lot to increase yields. But knowing the yield potential is the first step toward knowing how good management practices are. And that’s what we want to talk about today.

GAYLON CAMPBELL
I want to go over the model we’ll use now to make the measurements to determine how much yield we can get. The yield we can take is the product of two variables, the total crop biomass and the harvest index. The harvest index is a number between zero and one that represents the fraction of the total biomass that’s harvested and sold. We typically don’t include roots in our biomass calculation. So for a forage crop, the harvest index would be one, but for grain crops, it’s typically at or below point five. We’ll focus on the second equation, though it says the total biomass, so the product to three terms, are RUE, which we call the radiation use efficiency. FI, we call the fractional interception and S that’s the cumulative solar radiation incident on the crop during its growth period. We’ll assume that the RUE, radiation use efficiency, is constant for a given crop and environment. The FI term will start near zero as the crop emerges and will increase toward one as the canopy develops and closes, intercepting more and more radiation. The product FI times S is summed over the growing season of the crop. We can think of the three factors as representing three factors affecting growth and biochemical factor RUE, growth and development factor, FI in an environmental factor, as we might ask, where the other environmental factors are like temperature and moisture. They’re part of the model, but they’re implicit in it. Temperature affects how rapidly the canopy expands to intercept light. Moisture deficits decrease the fractional interception. This model works best when temperature and water are not the limiting factors, and yield is mainly determined by the available radiation. Temperature determines the accumulation of thermal time and that controls the growing season of the crop. The time the crop grows in turn determines the amount of radiation it can absorb. So it sets the value for S.

GAYLON CAMPBELL
So returning to Bob Conklin’s potato experiments. I think we have answered his question. The maximum yield possible is the yield he was getting on those best plots, where measured yield was equal to modeled yield. Yield differences between plots were the result of differences in growing season length, and therefore the cumulative solar radiation on the crop. The yields that were below the line were there because of nutrient or water stress, disease or some other factor that affected their ability to intercept radiation. Much of Bob’s success came from fertilizing so that canopies stayed green longer, intercepting more radiation. He once gave a talk at a potato conference, wearing a green suit to impress growers, the need to keep canopies green and healthy as long as possible. He told them, “If you’re green, you’re growing.” His options for even higher yields were defined other cultivars that had higher radiation use efficiency, or to find ways to lengthen the season even more. And we call that kind of a model a resource capture model.

GAYLON CAMPBELL
Models like that are typically pretty simple. You can almost do them with a pencil and paper. But they’re also powerful. If we look at definitions a resource is a form of energy or matter that plants need to grow or reproduce. Capture means the process by which an organ removes the resource from the environment to use it in plant metabolism. The resources we typically consider in resource capture models are carbon dioxide, solar radiation, water, and nutrients. Carbon dioxide is important, and its slow increase in the atmosphere due to burning of fossil fuels. Certainly increasing crop production, but out in outdoor agriculture, it isn’t something we have much control over. For modeling purposes, we can take it as a constant. Solar radiation, on the other hand, can be quite variable. And we need to monitor it as part of our modeling exercise. Water and nutrients are both highly variable in the plant environment, and are resources that the grower can control. We’ll talk about water as a limiting resource in a later webinar.

GAYLON CAMPBELL
Today we want to talk about light as the limiting factor. Only about half the energy in the solar spectrum is the part with wavelengths between 400 and 700 nanometers is useful for photosynthesis. But we’ll calculate our radiation use efficiency values based on the total solar energy basis. If we were being more rigorous, we might measure photosynthetic photon flux density and compute the grams of dry matter produced per mole of photons. But for our discussion today, we’ll assume that we’re measuring solar radiation in watts per square meter, and compute our RUE as the grams of dry matter per mega joule of radiation intercepted by the crop. The model correlates the accumulation of biomass, which is a sum with the accumulating intercepted solar radiation over the season, that’s also a sum. If we correlate even a sum of random numbers with the sum of a different set of random numbers, we’ll get a good correlation. It’s just a mathematical reality. And that’s sometimes been a criticism of these kinds of models. But we know that solar radiation is required for photosynthesis.

GAYLON CAMPBELL
So if radiation is the limiting factor, why this model will give us good results. As species differ in their radiation use efficiencies, here some typical values. Species like corn and sorghum with C4 metabolism, create more dry matter per unit energy received than species like wheat or rice that has C3. Oil crops take more energy to produce the oil so they have a lower RUE. And legumes provide some of their energy to the rhizobium that fix nitrogen in their roots. And that’s why their value is a little lower. Tuber and root crops have big sinks for assimilate tend to have a higher radiation use efficiency. So we need to take these factors into account and choose the right RUE for the crop we’re growing. High vapor deficits can reduce RUE a little bit and cloudy days can increase it. But on the whole it’s an amazingly conservative value.

GAYLON CAMPBELL
Now let’s think about the measurements we might need to use this model. Good way to do that is to think about the measurement we’d need to compute a radiation use efficiency for a crop. The RUE, it’s the ratio of the biomass produced to the radiation that’s intercepted to get the biomass produced, we just cut down a sample of the crop wet and dry it and weigh it, and we’ll get a dry biomass and water content for the crop. To get S we need to record data with the pyranometer over the whole growing season. We also need a measurement over time, the fractional min interception that we can multiply each day by the solar radiation that we’ve received. To get this we’ll measure solar radiation in watts per square meter. And you can see in this slide the pyranometer for that use. Watt is the joule per second so if we multiply the average watts per square meter in a day by the number of seconds in a day. That’ll give us the number of joules per square meter.

GAYLON CAMPBELL
Now, to measure solar radiation, you need a pyranometer. We’ve showed an example of one in the last slide. There are a lot of good choices. But often a pyranometer is a part of a weather station that provides all of the measurements that we need to model the growth and development of crops. A really convenient all-in-one weather station is METER’s ATMOS 41 that I’ve shown here. In addition to solar radiation, it measures the temperature, humidity, wind, rain, and a number of other variables. The data log by ZL6 logger that I’ve also shown. The data are sent via cell modem to ZENTRA Cloud, where they’re immediately available on your computer. ZENTRA Cloud also does some processing of the data and you can get for example, a reference evapotranspiration out of it.

GAYLON CAMPBELL
Now let’s turn our attention to the measurement of fractional interception, a fraction of the incident radiation intercepted by the crop. For a corn crop like the one in this picture, the fractional interception doesn’t need to be measured. It’s one or 100%. In this crop, though, the fractional interception is much smaller. And there isn’t an easy way to guess what it is. We’d need to make measurements of it. There are a couple of things we need to think about when we make these measurements. We’ll determine the light interception of a canopy by measuring either the light transmitted by the canopy or the light at a specific wavelength that’s reflected from the canopy. The light below the canopy has a high spatial variability consists of sunflex and have areas in full shade. Because of the spatial variability, you need to make a lot of measurements to characterize it. And you need to sample in a representative way. To make that easy and reliable, METER makes the ACCUPAR LP-80 ceptometer. It has an electronics box with batteries, keyboard, and display that you can see here, and in the sensor probe has 80 sensors that are sensitive to light in a strongly absorbing wave band.

GAYLON CAMPBELL
Vegetation reflects and transmits most of the near infrared radiation, but absorbs most of the visible or photosynthetically active radiation. And since we’re interested in intercepted photosynthetically active radiation, we measure in wave band that’s absorbed. So to determine the fractional interception, you level the LP-80 above the crop, take a reading of the incident light. Then you level the instrument below the canopy and make readings at several locations. You work out a sampling pattern that uniformly represents the crop so you don’t oversample either shaded or sunlit areas. Once you finish sampling the LP-80 will average the readings that you’ve taken. It will calculate the fractional transmission for that sun angle that exists at the time of the measurement. And it’ll also give you a measurement of the leaf area index of the canopy. You need a fractional transmission for the whole day the tau d. And that’s calculated from the LP-80 measurement of the tau at a particular sun angle. Tau d is the tau that you measured with the LP-80 raised to the power of q, where q is a number around 1.2. The fractional interception is one minus tau d. The LP-80 manual has examples for how to do these calculations. If you have trouble with that, give us a call and we’ll help out. At the beginning of the growing season there’s even less interception.

GAYLON CAMPBELL
Here’s an early stage wheat canopy. The only way to get the interception of this canopy is to take a photograph and use a computer to do image analysis. Or you can measure the spectral reflectance as we’ll talk about next here. The graph on the left shows the spatial, the spectral reflectance of soil of dry vegetation and green vegetation as a function of wavelength. You can see that green vegetation reflects almost none of the radiation below point seven micrometers or 700 nanometers. And it reflects about 65% of the radiation above that wavelength. Soil and dry vegetation show much less variation of reflectance with wavelength A reflectance index that you can see the equation for above the graph on the right can be computed. It’s called the Normalized Difference Vegetation Index. It’s the difference between near infrared and red reflectance divided by their sum. The graph below the equation shows the wave bands that are normally used for the red and near infrared signals. There are two red lines with arrows on the left graph. And those show the locations for those two wave bands.

GAYLON CAMPBELL
Now, Johnson and Trout in a paper that you can see cited here, relate the interceptions for several crops, well relayed interception by several crops to NDVI, that you see here. We’ll use the relationship for corn, but you can see that all of the crops that they made these measurements on have the same relationship. METER scientists have been working with NDVI for quite a while. The first NDVI sensors we built were pretty crude. Those are shown in this upper picture on the right. The middle picture shows an improved version and the bottom are the Apogee NDVI sensors that METER sells now. Those can be read with the ZL6 Logger, and that’s loaded into ZENTRA Cloud along with the ATMOS 41 data that we talked about earlier. One sensor faces up and the other down to get incident and reflected radiation in those two wave bands, the red and the NIR. The picture on the left is of a research site, where we tried those early sensors on the Wasatch plateau in Utah. This project was in collaboration with scientists at BYU, you see some rain out shelters that the researchers used to divert summer moisture. Water was then manually applied to the plots at rates less than ambient rain to see possible effects of summer drought that might come as the result of climate change.

GAYLON CAMPBELL
Now this graph shows the NDVI measurements on three of those plots. The red line is the control received that 100% of the rain, the blue receive 70% and the dark, the light blue is 70%. The dark blue was 30%. Our early growth was from snowmelt and so more or less similar amongst the treatment. All the plots decreased. After that the NDVI decreased throughout the monitoring time. But in the driest plot, it decreased the most. 70% plot the decrease was more similar to the control. Biomass measurements were taken on all of these plots and those biomass measurements correlated strongly with the NDVI measurements that we made there. We did another test of the NDVI for monitoring fractional interception by mounting an NDVI sensor on the boom of a center pivot irrigation system. You can see here the ATMOS 22 Sonic Anemometer. And in the background the soil before the crop emerged. The field was planted with corn. And we monitored from this time all the way through harvest or until after harvest. This is that field a little bit after emergence of the crop. And then this is when the crop had reached full cover.

GAYLON CAMPBELL
The graph on the right is the one I showed earlier relating NDVI to fractional interception. One on the left is the record of NDVI over the growing season in that field. When the NDVI is around .2 the interception should be around zero. That seems to agree with the earliest measurements we had. Under the center pivot, there’s a little bit of variation in those measurements, probably from the wetting of the soil by the sprinkler. The NDVI and interception quickly increase to a value around .9, the NDVI .9 that indicates 100% or interception of one if I have one. At harvest the NDVI drops again to the bare soil value. So it looks like this sensor does a good job of monitoring the fractional interception for our resource capture models.

GAYLON CAMPBELL
We can return now to Bob Conklin and his potatoes. We can tell him what the limits are to his potato production. Our resource capture model provides insight to the environmental limitations to potato growth and tells us how much we could increase yields if we had to call it a bar with a higher radiation use efficiency, or one that captures more radiation more quickly. Of course resources other than light can limit crop production. But one likes the limiting resource, we can analyze its effects on production in terms of three factors: available solar radiation, fractional interception and radiation use efficiency. Interception and radiation use efficiency can be genetically manipulated. And fractional interception can be manipulated by cultural practices too. The former German company UMS that’s now part of METER had the tagline “Measure to Know.” And it’s only through appropriate instrumentation and measurements that you can do the analysis and know what the limits are for a given cultivar and production environment. And METER can help you with that. Thank you for being with us today.

BRAD NEWBOLD
All right. Thank you, Gaylon. We’d like to use the next 10 minutes or so to take some questions from the audience. And thank you to those who’ve already sent your questions in, there’s still plenty of time to submit your questions. And we will try to get to as many as we can before we finish. Just as a heads up if we do not get to your question before we end this live webinar, we do have them recorded, and somebody from our METER environment team will be able to get back to you to answer your question via email. All right. So Gaylon, our first question here, they’re asking, to make the most of available light for plants in an area, should we plant crops of different heights relative to the position of the sun—in the south in winter, for example. And then they also added a secondary one, are slopes also advantageous relative to the position of the sun, as long as perennials or terraces are used to prevent runoff.

GAYLON CAMPBELL
Well, one practice that has been followed is agroforestry, where there’s a upper canopy of forest and the lower canopy of other crops to make better use of the total available solar radiation. So you can see that partly plays into what you’re saying, but the principles that we’ve talked about today apply there that we can calculate or we can measure in each of these cases. When the conditions under which we would intercept more solar radiation, and if radiation is the limiting factor, then by making better use of that resource, we would produce more biomass. And a lot of conditions, solar radiation isn’t the limiting factor. The plant runs out of water, runs out of nutrients or whatever. And so that has to be taken into account. What we’ve talked about today will only give us insight if radiation is the limiting factor. All right.

BRAD NEWBOLD
Okay, this next one here, they’re asking, how well do ground measurements of intercepted radiation match up with those measured by satellite through NDVI?

GAYLON CAMPBELL
Well, I haven’t done much work with satellite measurements, I mean, we had some examples of ground based NDVI measurements in the presentation, and those, as you can see, there was high correlation there. Satellite measurements should be the same. You have the intervening atmosphere, but on clear days, the satellite should give good measurements of— even though lower resolution of the intercepted radiation.

BRAD NEWBOLD
All right, this next question is, how many measurements do I need to make in the field with a ceptometer to be representative of my canopy?

GAYLON CAMPBELL
I think I have done some of the calculations on that. But I don’t remember the details. The LP-80 already is set up to make lots of measurements pretty quickly. So you get 80 measurements each time you push the button. And then it’s pretty easy to poke it into the canopy in a few places. But for your particular canopy, that would be a pretty quick thing to work out that you could just in a few minutes, make measurements, make enough measurements to work out the statistics of that. So basically, all of— get as many as you can, right? That usually is the answer to these kinds of questions is when you ask, well, how many measurements you need, you usually determine that you can’t ever make enough. Time is the limiting factor.

BRAD NEWBOLD
Yeah. Okay. This next one, this one is asking about wood as an agricultural crop. So they are writing from Honduras, and they’re currently considering the use of wood energy. And this is due to the fast growing species of trees that exist there, with wood yields of 10 to 30 tons per hectare per year, which are seen as an opportunity for an adaptation strategy for climate change. And so now they’re looking at wood as somewhat of an agricultural crop. Are there any observations of this nature yet? On — I’m going to pronounce this incorrectly — lignocellulosic biomass?

GAYLON CAMPBELL
I don’t know of any — there probably are some. These resource capture models have been applied, I think, quite a bit in forestry as well as in agriculture. So I expect there are some but I’m not familiar with that literature. All right. Okay. Let’s see. I think we have time for a few more questions here. Let’s see. Here’s one, what is the best time of day to measure fractional interception or canopy structure? Well, toward the middle of the day, is the sun angles. The zenith angle gets big and the measurements get less and less reliable. In a typical canopy less and less of the light gets through. So toward the middle of the day is the best time.

BRAD NEWBOLD
All right. Another one just came in. Should row crop orientation and plant spacing be different according to latitude location? Do we need to match row direction and orientation based on crop phenologic stage?

GAYLON CAMPBELL
No, what a perfect, what a great question that is. I’ve never seen a study of that. But it’s something that could be done mathematically. That’d be a good thing for you to do and to publish on. I’d love to see it.

BRAD NEWBOLD
Right. Here’s another another satellite related question. I sometimes get asked why I would make field measurements of canopy interception when satellites can cover more area? How would you argue in favor of in-field canopy measurements?

GAYLON CAMPBELL
The main difference between a satellite measurement — main differences — between satellite and in-field measurement are that the satellite measurements are available a lot less frequently than the in-field measurements and have a lot lower resolution. But we ought to be making use of both of those, the satellite data are a good opportunity for us to look at whole fields and see the spatial variability there. But still, the higher resolution measurements are oftentimes needed for getting the things like the radiation use efficiency and so on.

BRAD NEWBOLD
Alright, I think we’re up on our time, this is going to be our last question here. Again, for those of you who have asked questions that we haven’t gotten to, we will get back you via email to answer your question. But this is the last one we’re going to take live here. And they are asking, How can I improve radiation use efficiency through management?

GAYLON CAMPBELL
Well, the radiation use efficiencies that we talked about today assume ideal management, that there aren’t other limitations. And as you can see, by that plot of Bob Conklin’s potato yields, that most of the plots didn’t ever reach that ideal radiation use efficiency. And so the plant nutrition and irrigation and things like that, if those are not ideal, and there’s always a reduction in what you see is the radiation use efficiency.

BRAD NEWBOLD
All right. Okay. That is going to wrap it up for us today. Thank you again for joining us, and we hope that you enjoyed this discussion. Thanks again for your great questions as well. Please consider answering the short survey that will appear after the webinar is finished, just to let us know what types of webinars you’d like to see in the future. And for more information on what you’ve seen today, please visit us at metergroup.com. Finally, look for a recording of today’s presentation in your email, and stay tuned for future METER webinars. Thanks again, stay safe, and have a great day.