In 2017, Hurricane Maria ravaged the island of Puerto Rico, with category 5 winds topping out at 174 mph (282 km/h).
In this mountainous nation with the 9th highest road density in the world, thousands of landslides wreaked havoc on the large number of rural communities that became cut off from supplies and travel. Dr. Stephen Hughes, a professor in the Department of Geology at the University of Puerto Rico Mayagüez, has turned this catastrophe into a lesson by harnessing before and after data to develop a landslide susceptibility map with resolution down to every 5 m. Join us as we discuss with him the process of developing landslide prediction across the entire island nation.
Stephen is a professor in the department of geology at the University of Puerto Rico-Mayagüez. He obtained his bachelors in geology and earth science from the University of North Carolina at Chapel Hill and his PhD in geology from North Carolina State University. He teaches classes in structural geology, geomorphology, and field geology, and his research projects have focused mostly on tropical landslides and landscape evolution, with the funding of such organizations as the NSF, USGS, USDA-NRCS, and NOAA.
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
What impact does direct solar radiation have on the overall radiation balance? Dr. Colin Campbell, WSU Environmental Biophysics professor and METER scientist, shows you how to do the calculations in our latest chalk talk.
Transcript
Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk.
Have you ever been outside on a hot day walking in the full sun and then stepped into the shade? The relief is almost immediate. And I was thinking about that a lot when I was looking at this graph here, the estimated crop water loss on one of my experiments.
So this is an ET zero, meaning a reference ET. But since I was working grass, that was actually the estimated water loss from this grass crop. And what I noticed was that the shape of this curve kind of went up, and then went down. And it kind of matched right here, the solstice, the summer solstice. And in my mind, I thought, you know, what impact is direct solar radiation have on the overall radiation balance? Well, we can quickly just jump down and look at the equation that talks about how we might estimate the evapotranspiration from a crop. I’m not going to be able to have time here to get into what each of these variables mean.
But as you see, solar absorbed radiation, R abs is a strong component of that overall calculation. Now, when we talk about absorbed radiation, we need to understand that it’s not just all direct sunlight. In fact, if you assumed that, you’d be off in the weeds quite a bit, because it contains components of both longwave radiation, which is radiation that’s coming from your terrestrial surroundings, and shortwave radiation, that which is coming from predominantly the sun.
So let’s talk about that for a minute. With absorbed radiation, we have shortwave radiation. This is radiation that’s less than four micrometers. And we have longwave radiation. This is not surprisingly, from wavelengths greater than four micrometers. Now, this shortwave radiation, this comes from the sun longwave radiation comes from other sources, like trees, the sky, ground, just other objects that are around the temperature that we expect in the natural environment. Now, the truth of the matter is to get R abs, we need to combine both of these things into a single number. And it actually gets even more complex than that. So bear with us as we go on to the next equation.
R abs is a function of both shortwave radiation and long wave radiation. And when we calculate our radiation balance to get absorbed radiation, we have to actually take all of this into account. Now, you might be wondering, what are the other pieces in this equation, we’re going to spend a little time going over that. So you might understand how we can get from all of these numbers, all of these potential sources of radiation to a final number of R abs.
This portion of the equation here is shortwave radiation. And we’re going to talk about the variables in that equation. The first one we see is alpha s. It’s a number between zero and one. It signifies the percentage of shortwave radiation that the object can absorb. The other parameters in the equation include some F’s and some S’s. The F’s we call view factors, we’ll discuss view factors in more detail in another chalk talk. But suffice it to say that these essentially are parameters to estimate the amount of radiation that our object can see in its surroundings.
S stands for shortwave radiation. And this comes from several different sources. They include p: this is radiation that’s coming directly from the sun. That’s the one I mentioned earlier, that we feel if we’re standing in the direct sun, versus if we walk into the shade. But there are a couple of others. One is diffuse. This is the radiation that’s scattered as light comes into our atmosphere and it’s scattered by the atmosphere.
Finally, there’s R. This is reflected radiation, radiation that when it comes in, hits a surface, it reflects off that surface and comes and impinges on our object. Think about snow. If you’ve ever been skiing or out on the snow, you know, on a sunny day, you’re getting a lot of radiation that’s being reflected back. This portion of the equation over here is our longwave portion. Similar to our shortwave, it contains many of the same symbols, but they’re a little bit different.
The alpha L is the absorbed radiation. Now in the long wave that also goes from zero to one. The F is our view factor again, but now the view factor of longwave radiation, and L stands for that longwave radiation. This time, the subscripts A, that stands for atmosphere, and G stands for ground. If we put together all components in this equation, we’ll be able to solve for absorbed radiation. But that’s going to take a little bit of work. First, we need to understand the absorptivity of our surface both in the shortwave and the longwave.
The shortwave typically is calculated just from tables from looking out on the internet. For example, if I wanted to look at the absorptivity of a maple leaf, that’s typically around 50%. But it’s something that’s probably been calculated in literature. For our longwave radiation, almost all objects absorb long wave radiation at about 97 to 98% of the possible total.
So it’s pretty easy to estimate these absorptivities for objects that are fairly common. Calculating solar radiation and long wave radiation take a little bit more time. And especially understanding the view factors or how much of a particular surface our object sees, is going to take a whole chalk talk on its own. We’re going to leave this discussion here and leave for next time an opportunity to talk about how to calculate our shortwave radiation, or long wave radiation, and then get to the complicated discussion of view factors.
For more content like this, head over to our YouTube channel, or go to metergroup.com. Thanks for watching METER chalk talks.
Listen to Dr. Colin Campbell, WSU environmental biophysics professor, as he discusses how to calculate the angle of the sun, or solar zenith angle.
Transcription
Hi, I’m Dr. Colin Campbell. And this is a METER Chalk Talk. A couple of years ago, I was heading out into the backcountry and we wanted to figure out what kind of gear we should take along. A friend suggested we should just check the wind chill factor. But when I looked into it, we found out that it doesn’t even consider solar radiation in that calculation. Our exchange of energy in the environment is highly dependent on radiation, particularly solar radiation. And today, we’re going to talk a little bit more about that. Now the first thing to know about solar radiation is where the sun is in the sky. In fact, our absorbed radiation really depends on it. Interestingly, it’s one of the few things in life you can really count on.
With a few equations, we can figure out where the sun is in the sky at any time of the day. And I’m going to take you through some of these equations, one of the things I want you to know first is, they’re a little complicated, so don’t get stressed. In fact, if you just want to stop the video at a certain point. And check out these equations for a moment and write them down. That’s just fine. Now let’s just jump into it.
So here on my screen, I’m showing a graph of where the sun might be, at any point in a day if you were standing on the equator. Now in the middle, I’m going to draw this blue line across there, that is at the equinox. Now at the two solstices the sun might be here tracking across the sky, or here. And of course, this diagram is really showing kind of a fisheye picture of where that sun might be. There are two ways to describe where the sun is. One is a zenith angle. The zenith angle has a symbol, we call psi. In fact, the angle to the Earth’s surface from the perpendicular or normal, so this would be that zenith angle. Now there’s another angle we might be interested in, it’s called the Azmuth angle. But for our purposes of today, I just want to focus on this zenith angle because it’s the most important as we consider the radiation impact in an object that we’re interested in.
So to calculate the zenith angle, we’re going to go down and discuss the equation where this right here is zenith angle. And this here is the equation that we use to calculate that. Now you recognize the sines and cosines. And there’s just a couple other things in here. Of course, we’ve got t, which is time. And then a few other variables, phi. This is the latitude. Delta, this we call the solar declination, and finally, t zero, this is solar noon. Now before we get too crazy and worried about this equation, all we have to do is put in a few things into here, and we’ll be able to calculate that. So the first thing we need to know is the time of day.
Then we need to know the day of year. Now we actually call this a special name. This is called a Julian day. And it starts counting from January 1. The other things we need to know is of course, latitude, and longitude. And I’ll get to why in just a moment. The first parameter we’re going to try to find is called the solar declination. The solar declination equation looks pretty crazy. And anytime you see an equation like this in a book or something, the first assumption you should make is this is an empirical equation. As I look out on the internet and study other materials, I find that these equations actually are fairly common out there. And this isn’t exactly the way you see it in every piece of literature. But let me talk you through it here.
Really, there’s only one thing we need to know. It is the Julian day and we can go on the internet and calculate these a lot of programs just have those hard coded in like Excel. And all we need to do is just put that Julian day in for each of these values-here into here, and then we can eventually calculate the delta value. And then we can go put it back in this equation. So as long as we know the declination here, this is just the latitude. Let’s say my latitude is about 47 degrees. We just put that right here. All we need to know now is this t zero or solar noon. So what did we do for that?
Well, solar noon is calculated like this: t zero is equal to 12. That’s solar noon, and then we change it for wherever we are with respect to entered Meridian. And we call that the LC longitudinal correction, and then we also subtract off this equation of time t. We can start with the equation of time here. That’s this equation right here. And that’s not very small. In fact, not only is it not small, but it has a whole bunch of f’s in it. You can see f, here, this two times f, this is three times f, this is four times f. And now in the cosine or sines, then we have cosines here. So what is that?
Well, f is another one of these little bit long equations it is two point, or sorry, 279.575 plus 0.98565 times the Julian day. Now, if you get that, you just plug it back in here. And you can calculate your equation of time. And this is a number much smaller than one that you can plug in to this equation right here. Now, what about the longitudinal correction?
Well, the longitudinal correction Lc, that’s pretty straightforward. It’s essentially for every degree east of this of the standard meridian, you add 115. So for example, where I live, I’m at one 117.2 degrees, longitude, our standard meridian 120 degrees. And so the difference is, we’re east of that 2.8 degrees, and therefore the longitudinal correction, LC is just 2.8 over 15, or equal to 0.19h. So essentially, what I do is take that right there, and plug it in up here for the longitudinal correction. So essentially, we take 12, and we subtract off the longitudinal correction, and then with our equation of time, we get this value and eventually have t zero.
So what does all this mean? What does it sum up to? Well, there’s a lot of numbers in here. But if we go back to our initial equation, all we’re going to need to do now is simply this. We have our solar noon, we plug our time in. And then we use our solar declination here that we calculated on the first part of this discussion, our latitude here, and then suddenly, we’re able to calculate the Zenith Angle. And I’m going to try to link to a little calculation spreadsheet I did in Excel onto the sheet or onto the this video and then you can go ahead and look at that, how it’s done, and do your own calculations. For more content like this, check out our YouTube channel or head over to metergroup.com. Thanks for watching a METER Chalk Talk.
In our latest podcast episode, Kevin Hyde, manager of the Montana Mesonet, discusses his views on predicting and mitigating the effects of flood and drought.
Montana’s large geographical area makes mesonet equipment maintenance a challenge.
He also shares how to build a robust weather network with high-quality data on a small budget, why setups should include other measurements such as soil moisture and NDVI, and the genius way he handles maintenance over such a large geographical area.
The views and opinions expressed in the podcast and on this posting are those of the individual speakers or authors and do not necessarily reflect or represent the views and opinions held by METER.
In his latest chalk talk, Dr. Colin Campbell, environmental scientist at METER Group, teaches how to model vertical variation in temperature and how to estimate sensible heat flux.
Video transcript
Hello, everyone. My name is Dr. Colin Campbell, and I’m a senior research scientist here at METER Group. For today’s chalk talk, we’ll be talking about modeling vertical variation in temperature. In Figure 1, I’ve put together a graph that shows the maximum and minimum temperature with height and depth in the soil at some snapshot in time at a particular place.
Figure 1. Maximum and minimum temperature with height and depth at a snapshot in time, in a particular place
It’s interesting to note that the change in temperature with depth in the soil is much faster than the change in temperature with height, whether we’re talking about a maximum or minimum. And the reason is that even though air is a good insulator, it also mixes really well. And that mixing is caused by eddies. And there’s a little more to that story. It depends specifically on surface heating by the sun through radiation and the cover type, whether it’s plants, rocks, boulders, straight soil, snow, or wind.
Equation 1
If we were going to model that, we would start by writing an equation (Equation 1) where a temperature at sun height, Z, above the surface (see variables noted in Figure 1), is equal to an aerodynamic surface temperature, T0, minus the sensible heat flux, divided by 0.4 times rho, CP, which is the volume specific heat of the air, times a variable called u*, which is the friction velocity. We multiply all that by the logarithm of z, the height above the surface minus d, which is the zero plane displacement, divided by z h, which is a roughness parameter. You might notice up here in the list of variables, that the zero plane displacement is 0.6 times H. H is the canopy height in meters. The rough roughness parameter can be estimated as 0.02 times the canopy height or times H. Now we have an equation that will help us model temperature with height.
However, often we don’t know things like H, our sensible heat flux, and u*, our friction velocity. One of the things that we notice about this equation is that it’s set up somewhat like a linear equation. As you know, an example of a linear equation is something like Equation 2.
Equation 2
Figure 1 isn’t written quite that way, but if we look closely at the example below (Equation 3), this value could be our b, and this value our m, and this value could be our x. And if we do that, we actually can get some use out of graphing temperature with height.
Equation 3
So we went out one day and measured this with a METER Group set of environmental sensors set up at certain heights above the surface. Here we placed sensors at 0.2 m, 0.4 m, 0.8 m, and 1.6 m above the ground.
Table 1
To visualize this, in Figure 2 we graphed height on the y axis and temperature on the x axis, similar to the graph in Figure 1.
Figure 2. Graph showing the relationship between height and temperature
We know from Equation 1 that the axes for temperature and height should be switched because temperature is the dependent variable, and height is the independent variable. So if we switch axes it would look like the graph in Figure 3.
Figure 3. Graph showing the relationship between height and temperature where temperature is the dependent variable and height is the independent variable.
Figure 3 is graphed with the independent variable on the x axis and height on the y axis. If we fit this curve with today’s calculators, it would be fairly easy to get a curve that would fit that. But since it’s a linear equation, we can take the temperature data from Table 1 and the In ((Z-d)/ZH) data from Table 1 and graph them together.
Figure 4. Relationship between temperature and In Z-d/zh
Figure 4 is a graph that shows what happens when we do that. Notice that, just like we suggested, it creates a linear equation (Equation 4).
Equation 4
We learned in Figure 1 the B value was equal to t0 (our aerodynamic surface temperature). Since we know our surface temperature is 34.5 degrees, we can estimate what the temperature is down here at the surface, even though we only measured down 0.2 m.
We also know from Equation 4 that our M value is equal to -2.01. And if we look at Equation 1, our slope value is below.
Equation 5 (the slope value from Equation 1)
So we can write
Equation 6
How to estimate sensible heat flux
Now, if we were interested in the sensible heat flux, which we often are, we can simply rearrange this equation to be
Equation 7
And in Figure 1, I forgot to give you this value, but for an air temperature of 20 degrees celsius,
Equation 8
And then finally, a typical unit for friction velocity, which should be measured in the field over the specific canopy you are in, is about 0.2 meters per second.
Equation 9
So if we did this calculation, we would learned that there’s about 193 watts per meter squared of sensible heat flux coming off that surface.
Equation 10
So if we can measure temperature at a few heights, we can estimate what the heat flux is coming off the surface assuming we know something about our canopy. Learn more about measuring and modeling environmental parameters at metergroup.com/environment. If you have any questions feel free to email Dr. Campbell at [email protected].
What was the life of a scientist like before modern measurement techniques? In our latest podcast, Campbell Scientific’s Ed Swiatek and METER’s Dr. Gaylon Campbell discuss their association with three pioneers of environmental measurement.
Learn what it was like to practice science on the cutting edge. Discover the creative lengths they went to and what crazy things they cobbled together to get the measurements they needed.
Check out our latest podcast, where Dr. Richard Gill discusses his global research projects including climate change on the Wasatch Plateau, ranch sustainability in Colorado, reef studies in Samoa, and wildfires in the Mojave Desert.
Landscape in Samoa
He focuses on the connection between the ecology of a place and the communities of people that inhabit it, and how scientists can protect socially and ecologically vulnerable populations by collaborating equally with them. Unless they’re sharks. He found out they’re typically not open to collaboration.
The environment plays a large role in any plant study. Ensuring you’re capturing weather and other environmental parameters in the best way allows you to draw better conclusions. To accurately assess plant stress tolerance, you must first characterize all environmental stressors. And you can’t do that if you’re only looking at above-ground weather data.
For example, drought studies are notoriously difficult to replicate and quantify. Knowing what kind of soil moisture data to capture can help you quantify drought, allowing you to accurately compare data from different years and sites.
Get better, more accurate conclusions
It’s important for your environmental data to accurately represent the environment of your site. That means not only capturing the right parameters but choosing the right tools to capture them. In this 30-minute webinar, application expert Holly Lane discusses how to improve your current data and what data you may not be collecting that will optimize and improve the quality of your plant study. Find out:
How to know if you’re asking the right questions
Are you using the right atmospheric measurements? And are you measuring weather in the right location?
Which type of soil moisture data is right for the goals of your research or variety trial
How to improve your drought study, why precipitation data is not enough, and why you don’t need to be a soil scientist to leverage soil data
How to use soil water potential
How accurate your equipment should be for good estimates
Key concepts to keep in mind when designing a plant study in the field
What ancillary data you should be collecting to achieve your goals
Holly Lane has a BS in agricultural biotechnology from Washington State University and an MS in plant breeding from Texas A&M, where she focused on phenomics work in maize. She has a broad range of experience with both fundamental and applied research in agriculture and worked in both the public and private sectors on sustainability and science advocacy projects. Through the tri-societies, she advocated for agricultural research funding in DC. Currently, Holly is an application expert and inside sales consultant with METER Environment.
Measuring evapotranspiration (ET) to understand water loss from a native or a managed ecosystem is easier than it looks, but you have to know what you’re doing.
Learn causes and implications of uncertainty
If you can’t spend the time or money on a full eddy-covariance system, you’ll have to be satisfied with making some assumptions using equations such as Penman-Monteith.
Like any model, the accuracy of the output depends on the quality of the inputs, but do you know what measurements are critical for success? Plus, as your instrumentation gets more inaccurate, the errors get larger. If you’re not careful, you can end up with no idea what’s happening to the water in your system.
Get the right number every time
You don’t have to be a meteorologist or need incredibly expensive equipment to measure ET effectively. In this 30-minute webinar, Campbell Scientific application scientist Dr. Dirk Baker and METER research scientist Dr. Colin Campbell team up to explain:
The fundamentals of energy balance modeling to get ET
Assumptions that can simplify sensor requirements
What you must measure to get adequate ET estimates
Assumptions and common pitfalls
How accurate your equipment should be for good estimates
Dr. Dirk V. Baker has been with Campbell Scientific since 2011 and is an Application Research Scientist in the Environmental Group. Areas of interest include ecology, agriculture, and meteorology—among others. He has a bachelor’s degree in wildlife biology and a doctorate in weed science, both from Colorado State University. Dirk’s graduate and postdoctoral research centered around measuring and modeling wind-driven plant dispersal.
Dr. Colin Campbell has been a research scientist at METER for 20 years following his Ph.D. at Texas A&M University in Soil Physics. He is currently serving as Vice President of METER Environment. He is also adjunct faculty with the Dept. of Crop and Soil Sciences at Washington State University where he co-teaches Environmental Biophysics, a class he took over from his father, Gaylon, nearly 20 years ago. Dr. Campbell’s early research focused on field-scale measurements of CO2 and water vapor flux but has shifted toward moisture and heat flow instrumentation for the soil-plant-atmosphere continuum.
Orchard growers today live in an exciting time where environmental data are becoming inexpensive and abundant. But going from a data-poor to a data-rich environment has its challenges. Big data can be so overwhelming that growers struggle with how to turn that data into actionable insight.
In March, Innov8.ag began piloting a smart orchard project in collaboration with researchers from Washington State University & Oregon State University at Chiawana Orchards in Washington state.
One grower on the Washington Tree Fruit Research Commission recently commented that he uses no less than 19 data apps for making decisions. Steve Mantle, founder of innov8.ag, says, “It’s just overwhelming to a grower to consolidate all of this data together. We need to figure out how to help them with actual insights that impact either their yield quality / quantity—and just as importantly—their costs: particularly on labor, chemical/nutrients, and irrigation.” That’s why in 2020, Mantle and his team approached the Tree Fruit Research Commission’s technology committee to see if they could bring their capabilities, ingesting data from many different data silos and sensor providers into one place, with the goal of providing actionable insights for growers in the apple orchard space. Thus, the idea of a “smart orchard” was born.
Turning big data into a solution
In March, Innov8.ag began piloting a smart orchard project in collaboration with researchers from Washington State University & Oregon State University at Chiawana Orchards in Washington state. Their goal was to “sensorize” an orchard from multiple hardware providers, bringing together growers, data, and researchers to create a sustainable, “smart” orchard with insights that impacted a grower’s bottom line. To do this they combined data from on-farm and off-farm, online and offline sources including satellites, drones, weather providers, telemetry from IoT devices such as soil moisture probes and leaf wetness sensors, and more.” Mantle adds, “We’re trying to see how the sensors at different price points and from different vendors compare against each other in terms of accuracy. But the biggest goal is to get more granularity around and prove the value in canopy, soil, and weather measurements. Then we tie that in with yield, quality, and profit.”
Installing sensors so that comparisons are valid
The smart orchard consists of 100 rows of Gala apple trees spaced out over two 20-acre blocks. A number of different sensor/instrumentation providers, including METER Group, have their sensors deployed at this smart orchard measuring parameters such as weather, irrigation, soil water and nutrients, chemicals, disease, pests, crop health, labor, and drone/satellite imagery. All these data are aggregated and organized on a regular basis to try and enable growers to better understand weather and climate change to make precise, informed decisions and better manage their water usage, labor, equipment, and chemical usage.
Smart Orchard team member and researcher, Harmony Liu, says one challenge they face is making sure the comparisons are valid. “We are careful to install the same sensor types at the same heights so we are making “apple-to-apple” comparisons.”
Liu says in addition to sensing, they collect soil samples every week throughout the season and send them out to two different labs for nutrient testing so they can look at how that data compares with the soil nutrient sensors. They sample at five different locations at three different depths to match the sensors. She adds, “We have the dendrometer, soil nutrient data, soil moisture data, and canopy data all being collected within the same zone. It’s part of our intent to show this data all connecting with each other.” The team also measures irrigation line pressure with a sensor as opposed to using an irrigation switch. Liu says, “We want to know what the pressure signature is as everything turns on and activates so we can understand what that signature looks like and start to identify when there are abnormalities in how the irrigation system fills.” Additionally, they’re using METER NDVI and PRI sensors as well as a pyranometer for ground truthing the drone imagery that they’re doing at a 7 centimeters per pixel resolution.
The goal is understanding in-canopy weather and how to work with institutions on adapting models for disease, pests, and ultimately informing spray management.
Data cleanup is time-consuming
Liu says getting the smart orchard up and running was not without its challenges. “The first challenge was gaining access to some of the data from grower owned instruments because those instruments are not all grouped together.” Liu says that challenge made data cleanup time consuming, but they worked their way through it. She adds, “Overall, having this density of data is difficult because it’s a lot to wade through. But at the same time, it’s been really helpful. Data has been reliable coming in across the board.”
In-farm vs. outside-farm measurements
Liu says one thing they are interested in is accurately measuring temperature and humidity within the orchard because these parameters are critical for apple disease modeling. She says, “When people are modeling disease, they take the inputs from weather forecasts into the disease model for risk calculations. But there are some differences in environmental conditions inside vs. outside the orchard where evapotranspiration will cause temperatures in the canopy to be cooler compared to outside-farm temperatures while the vapor pressure is higher. So that’s one thing we use METER group instruments for. We have outside-orchard, above-orchard, and in-canopy ATMOS 41 weather stations and ATMOS 14 temperature and relative humidity sensors. We use these to compare the temperature and relative humidity difference. By using an instrument from the same provider, we eliminate the systematic bias vs. if we were to compare temp and RH from different providers. We also set up a vertical profile by installing sensors on the same pole at different heights and could see how the temperature and humidity changed across height for that location.”
Register for the smart orchard project live webinar with innov8.ag this Thursday, Jan.14th at 4pm PST.
Future smart orchard goals
Mantle says their most important goal is understanding in-canopy weather and how they can work with WSU and other institutions on adapting models for disease, pests, and ultimately informing spray management. Liu adds, “We also want to understand data comparison and unification. We want to bring together soil moisture measurements like volumetric water content and data from the METER TEROS 21 matric potential sensor. What we found is that, although they’re looking at soil moisture from different perspectives, unifying the two measurements will be critical for people working on irrigation scheduling.” The team also plans on working with WSU professors to create an evapotranspiration map that blends together some of the sensor telemetry and the view from a drone.
See the webinar
Want to learn more? METER soil physicist, Dr. Colin Campbell and Washington State University soil scientist Dr. Dave Brown discuss the smart orchard project in a METER Group webinar.
