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Soil Moisture Sensors Aid Crop Production in Space

If you’re an astronaut on an exploration Mission to Mars, lack of breathable oxygen isn’t the only challenge you’re facing. There’s another issue that can heavily impact your performance: lack of veggies.

Mars

It turns out the astronaut food system must be supplemented with fresh crops during long-term exploration missions. NASA has identified that vitamins and antioxidants degrade over time and is now funding research on the best way to grow fresh vegetables in space to supplement astronaut nutrition.

How well do plants grow in space?

Researchers including Dr. Oscar Monje, research scientist at NASA’s Kennedy Space Center in Florida, are studying the best materials and methods for growing vegetables in space. Monje says, “Growing plants in a space station is challenging because both space and power are limited. All the plant chambers built in the past 40 years focused on enabling space biology studies that centered on how to grow plants in space. They wanted to know the effects of growing in zero gravity. Can plants grow normally? Are they stressed? Can they produce seeds? But now we want to focus on space crop production. We want to supplement astronauts’ diets with essential minerals and vitamins during long duration missions.”

Kennedy Space Center

Early growing experiments in the BPS

Monje was a student of Dr. Bruce Bugbee at Utah State University who studies plants in space for bioregenerative life support systems. After graduating and doing a postdoc at the Space Dynamics lab, he started at Kennedy in 1998 researching how to grow wheat in space in the PESTO (Photosynthesis Experiment and System Testing Operations) Experiment. Dr. Gary Stutte, the principal investigator and Dr. Monje grew wheat in the Biomass Production System (BPS), a four chamber system that consumed 280 W of power. The BPS was used to measure photosynthesis, demonstrating plant harvests, priming pre-planted root modules, pollinating plants, as well as collecting gas and liquid samples.

Monje says, “Back then, all experiments were shuttle experiments (7-11 days at a time). The PESTO Experiment flew for 73 days in space and was essentially several shuttle missions conducted back to back. We measured root zone moisture with a pressure sensor monitoring root module matric potential. We learned that as long as you provide plants with adequate root zone aeration, good soil moisture, and the right light and CO2, they grow normally with no visible plant stress—just like on earth. The BPS was a precursor of the Advanced Plant Habitat (APH) facility on the International Space Station plant, an environmentally controlled growth chamber designed for conducting both fundamental and applied plant research for experiments lasting as long as 135 days.

New growth chambers introduced

The BPS was very complex, but the chambers were small and the light level was moderate. Ten years ago, NASA developed two large area (0.2 square meter) crop production systems to grow fresh salad crops in substrate-based media for the astronauts: the “Veggie” and the “APH”. Monje says, “Veggie is open to the cabin so there is no environmental control of CO2 or temperature, and it is watered by the crew. The light level provided by red, blue and green LEDs is moderate and the environment is not monitored. However, it grows lettuce crops that are safe to eat by the crew. The Advanced Plant Habitat on the other hand, is a Cadillac compared to the Veggie. With the APH you can load experiment profiles from the ground that control the light level (up to 1000 umol/m2s, half-full sunlight), the spectral quality, the CO2 concentration (up to 5000 ppm), photoperiod of light, and root zone moisture. The APH can be monitored in near-real time with minimal crew intervention for weeks at a time. To date, it has been used to grow wheat, Arabidopsis, and radish crops during space biology and crop production experiments”

The APH has been used to grow wheat, Arobidopsis, and radish crops

Measuring root zone moisture in space

Monje says that the 5-cm tall APH root zone is divided into four independently controlled root modules, called quadrants. In each quadrant, media moisture is controlled based on matric potential using a pressure sensor. However, the matric potential measured by the pressure sensor does not capture vertical variation in volumetric moisture. He says, “Each quadrant is watered with a porous tube system that distributes water throughout the porous media (arcillite) that is mixed with slow release fertilizer. In the 5-cm tall root zone at one g, most of the water is ponded at the bottom, and the top layer of media where the plants are germinating can become too dry. For these reasons, two small, rugged volumetric EC-5 moisture sensors were added to each quadrant to monitor moisture redistribution phenomena in microgravity. These sensors are insensitive to salinity and temperature effects. Thus, APH uses eight volumetric moisture sensors, two in each quadrant (one high, one low) to monitor root zone moisture. When watering in space, moisture redistribution occurs because capillary forces in microgravity distribute water evenly across the substrate and affect aeration. Even though the sensors are at different heights, they can read the same, as opposed to one flooded and the other one drier.”

Monje adds that, “Balancing the mix of aeration with enough water in a microgravity environment is the crux of the problem. If you don’t water plants enough, they don’t grow fast enough, but if you give them too much water, then you inhibit O2  supply to roots and nutrient uptake. So we’re using volumetric water content sensors in the APH root module at different levels to control the moisture. The sensors are like our ‘fingers in the soil’.”

What’s next?

Monje says the next step is to develop novel watering systems that do not use granular media.  He says, “Each APH root module holds about six kilograms of arcillite media, which is used only once per crop. Similarly, Veggie uses nearly two kilograms of media distributed into six independently watered root modules. Although these root modules grow normal plants in space biology experiments, this approach is not sustainable for crop production as transporting this much mass all the way to Mars is not going to be feasible.”

Deploying experiments on the moon can be challenging

Monje believes that getting these crop growth systems ready for a trip to Mars may take a while. He says right now, the Moon is a new proving ground for these technologies as part of the Artemis program, and deploying experiments on the Moon brings new challenges too. He adds, “An interesting thing about the Moon is you have partial gravity. It’s not one g. It’s 1/6 g. Plus, you have the issue of space radiation, and temperature must be controlled, unless you deploy your experiment in a manned habitat. From a biology point of view, we want to understand how plants will respond to growing in high radiation and partial gravity environments.”

Monje says it’s been an incredible journey to work on this type of research for many years. He laughs, “Since 1998, I’ve been pinching myself every day because this work is so challenging and yet so much fun. It’s pretty amazing.”

Learn more about measuring soil moisture

Download the Researcher’s Complete Guide to Soil Moisture—>

Smart orchard aims to install thousands of sensors for actionable insights

When big data is a problem

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.

View more METER crops webinars—>

Soil Electrical Conductivity: Managing Salts for Sustained High Yields

Managing salts: Why you should care more

Mismanagement of salt applied during irrigation ultimately reduces production—drastically in many cases. Irrigating incorrectly also increases water cost and the energy used to apply it.

Understanding the salt balance in the soil and knowing the leaching fraction, or the amount of extra irrigation water that must be applied to maintain acceptable root zone salinity is critical to every irrigation manager’s success. Yet monitoring soil salinity is often poorly understood.

Measure EC for consistently high crop yields

In this webinar, world-renowned soil physicist Dr. Gaylon Campbell teaches the fundamentals of measuring soil electrical conductivity (EC) and how to use a tool that few people think about—but is absolutely essential for maintaining crop yield and profit. Learn:

  • The sources of salt in irrigated agriculture
  • How and why salt affects plants
  • How salt in soil is measured
  • How common measurements are related to the amount of salt in soil
  • How salt affects various plant species
  • How to perform the calculations needed to know how much water to apply for a given water quality

Register now—>

Presenter

Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for over 20 years, following nearly 30 years on faculty at Washington State University. Dr. Campbell’s 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. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.

Snapdragons and soil moisture sensors

Charles Bauers has been a hydroponic snapdragon grower for 17 years. He knows—in detail—how to produce a good snap. But five years ago, he needed a better way to measure water.

Soil sensors optimize irrigation for improved quality and profit

“We had no quantitative way to measure water. That was the limiting factor for me,” he explains. Other inputs, like fertilizer, were quantifiable, but Bauers still depended on “gut feel” for watering, and no matter how quickly he reacted to changes in the crop, he couldn’t consistently produce grade-one snapdragons.

He wanted a scale, a “recipe of numbers” that would let him produce a good crop all the time in all sections of the greenhouse.

“There are always areas that seem to produce good quality flowers, and then there are areas that are a bit more of a challenge. I installed METER soil moisture sensors in the good areas and the stressed areas and compared the two. Then I worked my stressed areas up to the same numbers.”

The TEROS 12 is well-suited for greenhouse applications

Snapdragons are very sensitive to moisture stress. “It’s a ten-week crop. If you don’t get the moisture right in the first two weeks, you can compromise that crop.”

Identifying irrigation set points

The soil moisture sensors made a huge difference in Bauers’s ability to get the moisture right.  “They give me, targeted set points that I can shoot for all the time, and if I hit the targeted set point, I know I’m going to have good quality snaps, barring any other type of stress.

Grade-one snapdragons are worth 40% more than grade twos, and the difference between the two is created by “incipient stress—water stress that you can’t measure with your fingers. You can’t see it, you can’t feel it, it’s stress at the root. There’s a difference between a 28% vwc [volumetric water content] and a 23% vwc. It’s only 5%, but one produces grade ones and one produces grade twos.”

Empowered with real-time information

Moisture sensors gave Bauers real-time information that helped him get the watering right in every part of the greenhouse.  “I became more consistent because I had a number to go at. Because we’re a hydroponic crop, we see the effects real quick, and I’d say ‘I just have to add a little more water here.’ But [before the sensors,] invariably we had areas that were stressed because you really never knew when you had enough water on that crop. With sensors, you can consistently put the right amount of water on all the time.”

Soil sensors helped identify and prevent irrigation problems

Bauers quickly became adept at using sensors to address his irrigation challenges. The sensors showed him where his irrigation system was broken or underperforming, helped him identify problems like a root growing into a drip tube, or an unplugged dripper. But as the sensors became part of his routine, he was surprised to discover a new opportunity.

“Besides giving me the real-time information, the sensors gave me the ability to look at trends…over a week or a month and be proactive if we started moving away from our set point. We could add more water, set shorter run times, or just make some changes in the irrigation system to get more in line with the set points. That was one of my biggest surprises, how well we were able to be proactive toward environmental changes using the trending of the charts. That was a bonus.”

Reducing production and labor costs

After five years of daily monitoring, Bauers is now ready to go to an even higher level. “The next huge area we see sensors in is as big, or bigger, than the actual growing of the plant itself. We’re going to use these sensors to guide us as we strip out all excess production costs, and that’s happening today. As an example, over the next five months we’ll be trimming our substrate use by 85%. Not only do we save on materials, but if you have 85% less substrate to work with or move, you reduce labor costs.”

In fact, the sensors have become an integral part of how Bauers does business. I asked him how he would feel if he lost them. “My gosh,” he said, “It would be like going back ten years. It would be like trying to measure the temperature in a room without a thermometer. We are totally dependent on them.”

Learn more

Watch: How to improve irrigation scheduling using soil moisture—>

See all irrigation webinars—>

Webinar: Why Water Content Can’t Tell You Everything You Need to Know

Water content can leave you in the dark

Everybody measures soil water content because it’s easy. But if you’re only measuring water content, you may be blind to what your plants are really experiencing.

Soil moisture is more complex than estimating how much water is used by vegetation and how much needs to be replaced. If you’re thinking about it that way, you’re only seeing half the picture. You’re assuming you know what the right level of water should be—and that’s extremely difficult using only a water content sensor.

Get it right every time

Water content is only one side of a critical two-sided coin. To understand when to water or plant water stress, you need to measure both water content and water potential.

TEROS 21 water potential sensor

In this 30-minute webinar, METER soil physicist, Dr. Colin Campbell, discusses how and why scientists combine both types of sensors for more accurate insights. Discover:

  • Why the “right water level” is different for every soil type
  • Why soil surveys aren’t sufficient to type your soil for full and refill points
  • Why you can’t know what a water content “percentage” means to growing plants
  • How assumptions made when only measuring water content can reduce crop yield and quality
  • Water potential fundamentals
  • How water potential sensors measure “plant comfort” like a thermometer
  • Why water potential is the only accurate way to measure drought stress
  • Why visual cues happen too late to prevent plant-water problems
  • Case studies that show why both water content and water potential are necessary to understand the condition of soil water in your experiment or crop

WATCH IT NOW—>

Presenter

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.

Water content + water potential—better together

New soil moisture master class

Everybody measures soil water content because it’s easy. But if you’re only measuring water content, you may be blind to what your plants are really experiencing. To understand when to water or plant water stress, you need to measure both water content AND water potential.

Learn more in our soil moisture master class, “Secrets of water in soil“. Six short videos teach you everything you need to know about soil water content and soil water potential—and why you should measure them together. Plus, master the basics of soil hydraulic conductivity.

View master class—>

What’s causing the home run spike in America’s favorite pastime?

In baseball, the aerodynamic properties of the ball are critical. Pitchers rely on the ball to have a certain amount of lift so it can move in the air to the left or right based on how they throw a curveball.

Did something change on the surface of the ball that allowed more home runs?

The ball can also speed up or slow down based on the amount of drag, or how much the air is resisting the ball’s movement. Because of this, federations such as Major League Baseball want to ensure the ball doesn’t change from year to year. In the past couple of years, a spike in home runs prompted the Major League Baseball federation to contract Washington State University sports science laboratory to help them better understand the aerodynamics of the ball. They want to know: did something change on the surface of the ball that allowed more home runs to be hit? 

Tiny changes cause big problems

Even the slightest change in the surface of the ball, such as the seam height or the seam width, can have a large effect on the carry distance of the ball. Jeff Kensrud, assistant director of the WSU Sport Science Lab says, “These balls sometimes hit the wall 350 feet in the outfield, and if that ball would have travelled five more feet, it would have gone over the fence. That’s why we’re trying to quantify the aerodynamic properties of the ball so professional organizations can tell how far the balls are going to travel in the air when they’re hit.”

Kensrud says they have two experiments set up in the lab. One is an environmentally controlled chamber where they do all the bat and ball testing. He says, “We have to maintain 72 ℉ and 50% relative humidity 24 hours, 7 days a week because balls and bats change with temperature and humidity.” The second experiment is an aerodynamic test lab where temperature and humidity are more challenging to control. They need to understand what the microclimate is because weather variables like temperature and barometric pressure are constantly changing, which can affect their calculations. 

The ATMOS 14 sensor measures temperature, RH, barometric pressure and vapor pressure

To monitor these parameters, the team uses ATMOS 14 temp/barometric pressure/vapor pressure/and relative humidity sensors and a ZL6 data logger to get near-real-time feedback of what’s going on in their Aero Testing Lab. Kensrud says, “Essentially, we have three sensors hooked up to the ZL6 logger at the beginning, the middle, and the end of our aerodynamic test tunnel. Every half hour, we’ll check ZENTRA Cloud software and put that data directly into our computer so we have the most up-to-date weather information to ensure we’re running our aerodynamic experiment correctly. The tunnel is 60 ft long, so we need to know if the temperature is the same throughout our testing station. A slight change in barometric pressure could alter our calculated coefficient of drag. Sometimes in a matter of a few hours a storm can roll in. And if we are checking that sensor regularly, then we can see drift in our coefficient of drag and we know it had nothing to do with the ball. It’s just the barometric pressure in the city of Pullman changed.” 

How tests are run

To test the aerodynamic properties of the balls, the team projects balls into still air at up to 100 miles an hour. They then determine what the coefficient of drag and lift is, or how the balls behave in the airflow. And the team also tests bats.

Researchers measure bat performance using real baseballs

Kensrud says, “For 15 years, we’ve been the exclusive certification center for testing and certifying baseball bats for USA baseball, USA softball, NCAA baseball and most of the amateur baseball world. If you want to certify your equipment for play, it’s going to come through our lab.”  For bat testing, researchers measure bat performance using real baseballs. The lab uses pneumatic air cannons that accelerate the ball from at rest up to about 140 miles an hour. The balls are released from the cannon and then impact the bat. Kensrud says, “It’s as close as we can get in a laboratory to a real bat/ball collision on the field.”

Sensors help maintain proper test conditions

Kensrud says using the ATMOS 14 confirmed their hypothesis that there was sometimes a change in temperature over 60 feet in their concrete building where tests are performed. He says, “These sensors are constantly telling us if we have a temperature gradient and how severe it is. If you start out at 75 degrees at the beginning of the tunnel, but by the time you get to the end you’re at 82 degrees, that could be a problem. Especially when you assume your temperature is constant. These sensors help properly monitor the temperature throughout the space and also give a very accurate barometric pressure reading in near-real time. That’s critical for what we’re doing because in this area we can get big storms coming in and out that can affect our measurements. If we have a change in barometric pressure or a temperature gradient of more than two degrees from the beginning to the end, we shut testing down to allow the space to acclimate. The data enables us to set up a go/no go approach to aerodynamic testing.”

Why ball aerodynamics matter

Kensrud says that this research is important to keep the ball constant.

Ball aerodynamics affect both pitching and hitting.

He says, “Some federations need to know when or if the ball has changed. Ball aerodynamics affect both pitching and hitting. The more precise our instruments are in monitoring, the more accurate the results are that we give to these Federations so they can make informed policy decisions on what to do with the ball, or how to keep the ball in spec for years to come.”

What’s the future of aerodynamic sports testing?

Kensrud says he’d love to continue testing for federations like Major League Baseball, but it would be fun to grow and expand into measuring the aerodynamics of other sports balls. He adds, “It’s a bit of a stretch, but it wouldn’t be impossible to think that one day we could be making some high-speed, in-flight, aerodynamic measurements for say, the aerospace industry. But that’s not a contract. That’s me putting my ‘future cap’ on.”

You can learn more about WSU sports science lab testing here.

Learn more about monitoring weather variables. Read: “Weather Station Comparison: Which Weather Monitoring System is Right for You?

How to Use Plant-Water Relations and Atmospheric Demand for Simplified Water Management

Going by soil moisture data alone?

Soil moisture data are useful, but they can’t tell you everything. Other strategies for growers and researchers, like plant and weather monitoring, can inform water management decisions.

Researcher using the SC-1 leaf porometer to measure stomatal conductance
Researcher using the SC-1 leaf porometer to measure stomatal conductance

In this webinar, world-renowned soil physicist, Dr. Gaylon Campbell shares his newest insights and explores options for water management beyond soil moisture. Learn the why and how of scheduling irrigation using plant or atmospheric measurements. Understand canopy temperature and its role in detecting water stress in crops. Plus, discover when plant water information is necessary and which measurement(s) to use. Find out:

  • Why the Penman-Monteith equation, with the FAO 56 procedures, gives a solid, physics-based method for determining potential evapotranspiration of a crop
  • How the ATMOS 41 microenvironment monitor combined with the ZL6 logger and ZENTRA Cloud give easy access to crop ET data
  • How assimilate partitioning can be controlled by manipulating plant water potential using appropriate irrigation strategies
  • Why combining monitoring soil water potential with deficit irrigation based on ET estimates provide an efficient and precise method for controlled water stress management
  • And more…

REGISTER NOW—>

Presenter

Dr. Gaylon S. Campbell has been a research scientist and engineer at METER for over 20 years, following nearly 30 years on faculty at Washington State University. Dr. Campbell’s 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. Dr. Campbell has written three books, over 100 refereed journal articles and book chapters, and has several patents.

How to calculate growing degree days (or thermal time)

If you’re not using an accurate weather station at your field site to gather data for growing degree day (GDD) or thermal time calculations, you should start now. 

Image of increased yield

GDD predictions save you hours of scouting time and can increase yield because they’re a scientific way to know the best time for insect/disease control measures. In this chalk talk, Dr. Colin Campbell explains the concept of thermal time (or growing degree days) and shows two different ways to calculate it.

Watch the video

 

Video transcript

Hello, my name is Dr. Colin Campbell. I’m a senior research scientist here at METER Group. Today, we’re going to give a brief primer on thermal time. When I talked to some of my colleagues about that, they mentioned that thermal time (or growing degree days) is really just a way to match a plant’s clock with our clock. It helps us understand what’s happening with the plant, and we can predict things like emergence, maturity, etc. And the way we do this is through this equation that is pretty simple (Equation 1). 

Image of the equation for calculating thermal time
Equation 1

We can sum thermal time (Tn) by taking the summation of day one to day n of the average temperature (meaning T max plus T min divided by two), minus a base temperature (Tbase), and then multiply by time step (delta t). And in this case, our time step is just one day. 

So the whole analysis is simply the average temperature minus a base temperature. We get that value each day. And then we keep summing until it reaches a value that tells us that we’ve progressed from one stage to another stage. 

A good example of this is wheat. When I was young, I did an experiment on this in biology. The idea was that for emergence, the wheat plant needs 78 day degrees from planting to emergence. So I used Equation 1, and when I had summed enough day degrees, I knew the wheat was moving from the planting stage to a post emergent stage. I went out and measured the wheat and it actually matched up well. Not every wheat plant emerged at that point, but the average was quite close. 

So what does that mean in terms of graphical data? I wanted to show you what this equation actually looked like and then plant a seed in your mind for our next discussion, which will be, how good is this analysis? If you think about modern technology, like the ATMOS 41 weather station, you can get temperature measurements that are every five minutes or even every one minute. So wouldn’t it be better if we collected our thermal time information with this equation (Equation 2)?

Image of equation number two for finding thermal time
Equation 2

We can take the sum over each day, like we did in Equation 1, but instead we take the integral of temperature at a small time step T(t) (like five minutes) minus the base temperature (Tb) and then just integrate this across the day. We’re going to learn about that in my next chalk talk. But for now, let’s go to this graph (Figure 1). 

Image showing temperature vs. time over twenty four hours
Figure 1. Temperature vs. time over 24 hours

In Figure 1, we have temperature on the y axis and time on the x axis. The total time is 24 hours. This is our daily step, where we’re collecting this information about thermal time. And here are all the parameters from the equation: the maximum temperature (Tmax), the minimum temperature (Tmin), and the average temperature (Tave). And then this is a base temperature (Tbase). And to familiarize you with what we’re talking about, Tbase is the temperature below which progress is not made in the development of this plant. The progress is not reversed, meaning if it’s below the base temperature, the plant is not reversing its development, but it just doesn’t progress. 

The black line is what I’ve drawn as a typical diurnal temperature swing. So it’s going from a minimum in the early morning up to a maximum sometime in the afternoon. And I’ve tried to compare these two approaches. One one side, we have the average temperature and the base temperature. This rectangle is our thermal time for that day. But the question is, with all our temperature data (like from the ATMOS 41) where we have pretty small time increments, could we instead just integrate over the day and then collect all the information on thermal time that is below this black line (the actual temperature, and of course, we subtract out the base temperature). How much difference does that make compared to this here? And what are the implications of not being able to measure our temperature terribly accurately? We’re going to talk about that in our next discussion, and I look forward to seeing you then.

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Soil Hydraulic Properties—8 Ways You Can Unknowingly Compromise Your Data

Avoid costly surprises

Measuring soil hydraulic properties like hydraulic conductivity and soil water retention curves is difficult to do correctly. Measurements are affected by spatial variability, land use, sample prep, and more.

Image of a research using the SATURO infiltrometer in the field
Leo Rivera teaches soil hydraulic properties measurement best practices

Getting the right number is like building a house of cards. If one thing goes wrong—you wind up with measurements that don’t truly represent field conditions. Once your data are skewed in the wrong direction, your predictions are off, and erroneous recommendations or decisions could end up costing you a ton of time and money. 

Get the right numbers—every time

For 10 years, METER research scientist, Leo Rivera, has helped thousands of customers make saturated and unsaturated hydraulic conductivity measurements and retention curves to accurately understand their unique soil hydraulic properties. In this 30-minute webinar, he’ll explain common mistakes to avoid and best practices that will save you time, increase your accuracy, and prevent problems that could reduce the quality of your data. Learn:

  • Sample collection best practices
  • Where to make your measurements
  • How many measurements you need
  • Field mapping tools
  • How to get more out of your instruments
  • How to use the LABROS suite to fully characterize soils (i.e., full retention curves and hydraulic conductivity curves)
  • Best practices for measuring field hydraulic conductivity using SATURO

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