The conversion of light energy and atmospheric carbon dioxide to plant biomass is fundamentally important to both agricultural and natural ecosystems.
The detailed biophysical and biochemical processes by which this occurs are well understood. At a less-detailed level, however, it is often useful to have a simple model that can be used to understand and analyze parts of an ecosystem. Such a model has been provided by Monteith (1977). He observed that when biomass accumulation by a plant community is plotted as a function of the accumulated solar radiation intercepted by the community, the result is a straight line. Figure 1 shows Monteith’s results.
Figure 1. Total dry matter produced by a crop as a function of total intercepted radiation (from Monteith, 1977).
This week, we continue highlighting the second of two current research projects (see part one) which use soil moisture sensors to measure volumetric water content in tree stems and why this previously difficult to obtain measurement will change how we look at tree water use.
Tamarisk tree: an invasive species dominant in Sudan and arid parts of the United States. (Photo credit: biolib.cz)
Determining Tree Stem Water Content in Drought Tolerant Species
Tadaomi Saito and his research team were interested in using dielectric soil moisture sensors to measure the tree stem volumetric water content of mesquite trees and tamarisk, two invasive species dominant in Sudan and arid parts of the United States. Mesquite is a species that can access deep groundwater sources using their taproots which is how they compete with native species. Tamarisk, on the other hand, uses shallow, saline groundwater to survive. The team wanted to see if dielectric probes were useful for real-time measurement of plant water stress in these drought-tolerant species and if these measurements could illuminate differing tree water-use patterns. These sensors could then potentially be used for precision irrigation strategies to assist in agricultural water management.
Temperature Calibration Was Essential
After calibrating the soil moisture sensors to the wood types in a lab, the team inserted probes into the stems of both trees. They also monitored groundwater and soil moisture content to try and infer whether or not the trees were plugged into a deep source of water. Interestingly, Saito found that, unlike soil, where temperature fluctuation is buffered, tree stems are subject to large variations in temperature throughout the course of the day. This temperature fluctuation interfered with the soil moisture probes’ ability to accurately measure VWC. The team came up with a simple method for accounting for temperature variability and were then able to obtain accurate VWC measurements.
Saito’s results were similar to Ashley Matheny’s study (see part 1), in that they found a lot of different patterns, even in trees of the same species. Water-use depended on where the trees were on the landscape. Some of them were tapped into groundwater, and the stem water storage didn’t change no matter how dry the soil became. Whereas others, depending on their position in the landscape, were very dependent on soil moisture conditions.
Saito’s study illustrates that we see everything about a tree that’s above ground, but we may have no sense of what’s going on below ground. We can put a soil moisture sensor in the ground and decide there’s plenty of moisture available. Or if conditions are dry, we may decide the tree is under drought stress, but we don’t know if that tree is tapped into a more permanent source of groundwater.
Other researchers have put soil moisture sensors in orchards looking at stem water storage from a practical standpoint for irrigation management. Their data didn’t work out so well because of cable sensitivity where water on the cable created false readings. However, the data they were able to obtain showed that some of the trees were plugged into water sources that were independent of the soil. Those trees were able to withstand drought and needed less irrigation, whereas other trees were much more sensitive to soil moisture.
If we had an inexpensive, easy to deploy measurement device plugged into every tree in an orchard, we could irrigate tree by tree, give them precisely what they needed, and account for their unique situation.
What Does it All Mean?
The interesting thing about using soil moisture sensors in a tree is that stem water content is a difficult-to-obtain piece of information that has now been made easier. Historically, we’ve focused on measuring sap flow, but that’s just how much water is flowing past the sensor. We’ve measured what’s in the soil: a pool of moisture that’s available to the tree. But some trees are huge in size, such as ones along the coast of California. They’re able to store vast amounts of water above-ground in their tissue. Understanding how a tree can use that water to buffer or get through periods of drought is a unique research topic that has had very little attention. With these kinds of sensors, we can start to investigate those questions.
Reference: Saito T., H. Yasuda, M. Sakurai, K. Acharya, S. Sueki, K. Inosako, K. Yoda, H. Fujimaki, M. Abd Elbasit, A. Eldoma and H. Nawata , Monitoring of stem water content of native/invasive trees in arid environments using GS3 soil moisture sensor , Vadose Zone Journal , vol.15 (0) (p.1 – 9) , 2016.03
The Trans African Hydro and Meteorological Observatory (TAHMO) project expects to put 20,000 ATMOS 41 weather stations over Africa in order to understand the weather patterns which affect that continent, its water, and its agriculture. In the conclusion of our 3-part series, we interview Dr. John Selker about his thoughts on the project.
The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food.
In your TEDx talk you estimate that US weather stations directly bring U.S. consumers 31 billion dollars in value per year. Can Africa see that same kind of return?
Even more. The economics of weather data value may be going up because we’re reaching a cusp in terms of humanity’s consumption of food. Africa, one could argue, is the breadbasket for this coming century. Thus, the value of information about where we could grow what food could be astronomical. It’s very difficult to estimate. One application of weather data is crop insurance. Right now, crop insurance is taking off across Africa. The company we’re working with has 180,000 clients just in Kenya. When we talked about 31 billion dollars in the U.S., that is the value citizens report, but you need to add to that protection against floods, increased food production, water supply management, crop insurance and a myriad of other basic uses for weather data. In Africa, the value of this type of protection alone pays for over 1,000 times the cost of the weather stations.
Another application for weather data is that in Africa, the valuation of land itself is uncertain. So if, because of weather station data, we find that a particular microclimate is highly valuable, suddenly land goes from having essentially no value to becoming worth thousands of dollars per acre. It’s really difficult to estimate the impact the data will have, but it could very well end up being worth trillions of dollars. We have seen this pattern take place in central Chile, where land went from about $200/hectare in 1998 to over $3,000/ha now due to the understanding that it was exceptionally suited to growing pine trees, which represented a change in land value exceeding $3 billion.
Does the effect of these weather stations go beyond Africa?
There’s limited water falling on the earth, and if you can’t use weather data to invest in the right seeds, the right fertilizer, and plant at the right time in the right place, you’re not getting the benefit you should from having tilled the soil. So for Africa the opportunity to improve yields with these new data is phenomenal.
In terms of the world, the global market for calories is now here, so if we can generate more food production in Africa, that’s going to affect the price and availability of food around the world. The world is one food community at this point, so an entire continent having inefficient production and ineffective structures costs us all.
If we can generate more food production in Africa, that’s going to affect the price and availability of food around the world.
You’re collecting data from Africa. Is it time to celebrate yet?
I think this is going to be one of those projects where we are always chilling the champagne and never quite drinking it. It is such a huge scope trying to work across a continent. So I would say we’ve got some stations all over Africa, we’re learning a lot, and we’ve got collaborators who are excited. We have reason to feel optimistic. It will be another five years before I’ll believe that we have a datastream that is monumental. Right now we’re still getting the groundwork taken care of. By September of this year we expect to have five hundred of stations in place, and then two years from now, over two thousand. This will be a level of observation that will transform the understanding of African weather and climate.
This is a project of hundreds of people across the world putting their hands and hearts in to make this possible.
How do you deal with the long wait for results?
In science, there is that sense you get when you want to know something, and you can see how to get there. You have a theory, and you want to prove it. It kind of captures your imagination. It’s a combination of curiosity and the potential to actually see something happen in the world: to go from a place where you didn’t know what was going on to a place where you do know what’s going on. I think about Linus Pauling, who made the early discoveries about the double helix. He had in his pocket the X-ray crystallography data to show that the protein of life was in helical form, and he said, “In my pocket, I have what’s going to change the world.” When we realized the feasibility of TAHMO, we felt much the same way.”
Sometimes in your mind, you can see that path: how you might change the world. It may never be as dramatic as what Pauling did, but even a small contribution has that same excitement of wanting to be someone who added to the conversation, who added to our ability to live more gracefully in the world. It’s that feeling that carries you along, because in most of these projects you have an idea, and then ten years later you say, “why was it that hard?”
Things are usually much harder than your original conception, and that energy and curiosity really helps you through some of the low points in your projects. So, curiosity has a huge influence on scientific progress. Changing the world is always difficult, but the excitement, curiosity, and working with people, it all fits together to help us draw through the tough slogs. In TAHMO, I cannot count the number of people who have urged us to keep the effort moving forward and given a lift just when we needed it most. This is a project of hundreds of people across the world putting their hands and hearts in to make this possible. Having these TAHMO supporters is an awesome responsibility and concrete proof of the generosity and optimism of the human spirit.
We wanted to highlight innovative ways people have modified their instrumentation to fit their research needs. Here, Georg von Unold, founder and president of UMS (now METER) illustrates ingenuity in a story that inspired the invention of the first UMS tensiometer and what could be one of the greatest scientific instrument hacks of all time.
The Bavarian Alps
An Early Penchant for Ingenuity
In 1986, graduating German students were required to join the military or perform civil service. Von Unold chose to do a civil service project investigating tree mortality in the alpine region of the Bavarian Mountains. He explains, “We were trying to understand pine tree water stress in a forest decline study related to storms in certain altitudes where trees were inexplicably falling over. The hypothesis was that changing precipitation patterns had induced water stress.”
To investigate the problem, von Unold’s research team needed to find tensiometers that could measure the water stress of plants in the soil, which was not easy. The tensiometers von Unold found were not able to reach the required water potential without cavitating, so he decided to design a new type of tensiometer. He says, “I showed my former boss the critical points. It must be glued perfectly, the ceramic needed defined porosity, a reliable air reference access, and water protection of the pressure transducer. I explained it with a transparent acrylic glass prototype to make it easier to understand. At a certain point, my boss said, “Okay, please stop. I don’t understand much about these things, but you can make those on your own.”
Two snorkels protected a data logger predecessor from relative humidity.
Snorkels Solve a Research Crisis
The research team used those tensiometers (along with other chemical and microbial monitoring) to investigate why trees only in the precise altitude of 800 to 1100 meters were dying. One challenge facing the team was that they didn’t have access to anything we might call a data logger today. Von Unold says, “We did have a big process machine from Schlumberger that could record the sensors, but it wasn’t designed to be placed in alpine regions where maximum winter temperatures reached -30℃ or below. We had to figure out how to protect this extremely expensive machine, which back then cost more than my annual salary.“
Von Unold’s advisor let him use the machine, cautioning him that the humidity it was exposed to could not exceed 80%, and the temperature must not fall below 0℃. As von Unold pondered how to do this, he had an idea. Since the forest floor often accumulated more than a meter of snow, he designed an aluminum box with two snorkels that would reach above the snow. The snorkels were guided to a height of two meters. Using these air vents, he sucked a small amount of cold, dry air into the box. Then, he took his mother’s hot iron, bought a terminal switch to replace the existing one (so it turned on in the range of 0-30℃), and mounted a large aluminum plate on the iron’s metal plate to better distribute the heat.
Von Unold says, “Pulling in the outside air and heating it worked well. The simple technique reduced the relative humidity and controlled the temperature inside the box. Looking back, we were fortunate there wasn’t condensing water and that we’d selected a proper fan and hot iron. We didn’t succeed entirely, as on hot summer days it was a bit moist inside the box, but luckily, the circuit boards took no damage.”
Tree mortality factors were only found at the precise altitude where fog accumulated.
Interestingly, the research team discovered there was more to the forest decline story than they thought. Fog interception in this range was extremely high, and when it condensed on the needles, the trees absorbed more than moisture. Von Unold explains, “In those days people of the Czech Republic and former East Germany burned a lot of brown coal for heat. The high load of sulfur dioxide from the coal reduced frost resistivity and damaged the strength of the trees, producing water stress. These combined factors were only found at the precise altitude where the fog accumulated, and the weakened trees were no match for the intense storms that are sometimes found in the Alps.” Von Unold says once the East German countries became more industrialized, the problem resolved itself because the people stopped burning brown coal.
Share Your Hacks with Us
Do you have an instrument hack that might benefit other scientists? Send your idea to [email protected].
In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters (see part 1). This week, read about the intense precision required to move the soil-filled lysimeters, how problems are prevented, and how the data is used by scientists worldwide.
Moving the lysimeters
Moving the Lysimeters is not Easy
As noted previously, one TERENO lysimeter weighs between 2.5 and 3.5 tons depending on the soil and the water saturation, so the problem of transporting it without compacting the soil or causing cracks in the soil column caused Georg many sleepless nights. He explains, “We found a truck with an air venting system, which could prevent vibrations in a wide range. We made a wooden support structure, bought 100 car springs, and loaded the lysimeter on this frame. After some careful preparation and design adjustments, I told the truck driver, ‘take care, I’m recording the entire drive with my acceleration sensor and data logger so I can see if you are driving faster than I allow.” Each lysimeter soil surface level was marked to check if the lysimeter was rendered useless due to transport, and the truck was not allowed to go over a railway or a bump in the road faster than 2 km per hour to avoid the consequences of compaction and cracking.
Understanding the water potential inside the intact lysimeter core is not trivial. Georg and his team use maintenance-free tensiometers, which overcome the typical problem of cavitation in dry conditions as they don’t need to be refilled. Still, this parameter is so critical they installed 3 of them and took the median, which can be weighed in case one of the sensors is not working. Georg says, “There is a robust algorithm behind measuring the true field situation with tensiometers.”
What Happens With the Data?
Georg hopes that many researchers will take advantage of the TERENO lysimeter network data (about 4,000 parameters stored near-continuously on a web server). He says, “Researchers have free access to the data and can publish it. It’s wonderful because it’s not only the biggest project of its kind, each site is well-maintained, and all measurements are made with the same equipment, so you can compare all the data.” (Contact Dr. Thomas Puetz for access). Right now, over 400 researchers are working with those data, which has been used in over 200 papers.
Lysimeter plant with CO2 fumigation facility in Austria.
What’s the Future?
Georg thinks 40,000 data points arriving every minute will give scientists plenty of information to work on for years to come. Each year, more TERENO standard lysimeters are installed to enlarge the database. The ones in TERENO have a 1 m2 surface area, which is fine for smaller plants like wheat or grass, but is not a good dimension for big plants like trees and shrubs. Georg points out that you have to take into account effort versus good data. Larger lysimeters present exponentially larger challenges. He admits that, “With the TERENO project, they had to make a compromise. All the lysimeters are cut at a depth of 1.5 m. If there is a mistake, it is the same with all the lysimeters, so we can compare on climate change effects.” He adds, “After six years, we now have a standard TERENO lysimeter design installed over 200 times around the world, where data can be compared through a database, enhancing our understanding of water in an era of climate change.”
Get more information on applied environmental research in our
In Germany, scientists are measuring the effects of tomorrow’s climate change with a vast network of 144 large lysimeters.
The goal of these lysimeters is to measure energy balance, water flux and nutrition transport, emission of greenhouse gases, biodiversity, and solute leaching into the groundwater.
In 2008, the Karlsruhe Institute of Technology began to develop a climate feedback monitoring strategy at the Ammer catchment in Southern Bavaria. In 2009, the Research Centre Juelich Institute of Agrosphere, in partnership with the Helmholtz-Network TERENO (Terrestrial Environmental Observatories) began conducting experiments in an expanded approach.
Throughout Germany, they set up a network of 144 large lysimeters with soil columns from various climatic conditions at sites where climate change may have the largest impact. In order to directly observe the effects of simulated climate change, soil columns were taken from higher altitudes with lower temperatures to sites at a lower altitude with higher temperatures and vice versa. Extreme events such as heavy rain or intense drought were also experimentally simulated.
Lysimeter locations in Germany
Georg von Unold, whose company (formerly UMS, now METER) built and installed the lysimeters comments on why the project is so important. “From a scientific perspective, we accept changes for whatever reason they may happen, but it is our responsibility to carefully monitor and predict how these changes cause floods, droughts, and disease. We need to be prepared to react if and before they affect us.”
How Big Are the Lysimeters?
Georg says that each lysimeter holds approximately 3,000 kilograms of soil and has to be moved under compaction control with specialized truck techniques. He adds, “The goal of these lysimeters is to measure energy balance,water flux and nutrition transport, emission of greenhouse gases, biodiversity, and solute leaching into the groundwater. Researchers measure the conditions of water balance in the natural soil surrounding the lysimeters, and then apply those same conditions inside the lysimeters with suction ceramic cups that lay across the bottom of the lysimeter. These cups both inject and take out water to mimic natural or artificial conditions.”
Researchers use water content sensors and tensiometers to monitor hydraulic conditions inside the lysimeters.
Researchers monitor the new climate situation with microenvironment monitors and count the various grass species to see which types become dominant and which might disappear. They use water content sensors and tensiometers to monitor hydraulic conditions inside the lysimeters. The systems also use a newly-designed system to inject CO2 into the atmosphere around the plants and soil to study increased carbon effects. Georg says, “We developed, in cooperation with the HBLFA Raumberg Gumpenstein, a new, fast-responding CO2 enrichment system to study CO2 from plants and soil respiration. We analyze gases like CO2, oxygen, and methane. The chambers are rotated from one lysimeter to another, working 24 hours, 7 days a week. Each lysimeter is exposed only for a few minutes so as not to change the natural environment.”
Next week: Read about the intense precision required to move the soil-filled lysimeters, how problems are prevented, and how the data is used by scientists worldwide.
In Haiti, untreated human waste contaminating urban areas and water sources has led to widespread waterborne illness. Sustainable Organic Integrated Livelihoods (SOIL) has been working to turn human waste into a resource for nutrient management by turning solid waste into compost. Read more…
Estimating the relative humidity in soil? Most people do it wrong…every time. Dr. Gaylon S. Campbell shares a lesson on how to correctly estimate soil relative humidity from his new book, Soil Physics with Python, which he recently co-authored with Dr. Marco Bittelli. Read more.…
“How many soil moisture sensors do I need?” is a question that we get from time to time. Fortunately, this is a topic that has received substantial attention by the research community over the past several years. So, we decided to consult the recent literature for insights. Here is what we learned.
Globally, the number one reason for data loggers to fail is flooding. Yet, scientists continue to try to find ways to bury their data loggers to avoid constantly removing them for cultivation, spraying, and harvest. Chris Chambers, head of Sales and Support at Decagon Devices always advises against it. Read more…
During a recent semester at Washington State University, a film crew recorded all of the lectures given in the Environmental Biophysics course. The videos from each Environmental Biophysics lecture are posted here for your viewing and educational pleasure. Read more…
Soil moisture sensors belong in the soil. Unless, of course, you are feeling creative, curious, or bored. Then maybe the crazy idea strikes you that if soil moisture sensors measure water content in the soil, why couldn’t they be used to measure water content in a tree? Read more…
In the conclusion of our 3-part water potential series (see part 1), we discuss how to measure water potential—different methods, their strengths, and their limitations.
Vapor pressure methods work in the dry range.
How to measure water potential
Essentially, there are only two primary measurement methods for water potential—tensiometers and vapor pressure methods. Tensiometers work in the wet range—special tensiometers that retard the boiling point of water (UMS) have a range from 0 to about -0.2 MPa. Vapor pressure methods work in the dry range—from about -0.1 MPa to -300 MPa (0.1 MPa is 99.93% RH; -300 MPa is 11%).
Historically, these ranges did not overlap, but recent advances in tensiometer and temperature sensing technology have changed that. Now, a skilled user with excellent methods and the best equipment can measure the full water potential range in the lab.
There are reasons to look at secondary measurement methods, though. Vapor pressure methods are not useful in situ, and the accuracy of the tensiometer must be paid for with constant, careful maintenance (although a self-filling version of the tensiometer is available).
Here, we briefly cover the strengths and limitations of each method.
Vapor Pressure Methods:
The WP4C Dew Point Hygrometer is one of the few commercially available instruments that currently uses this technique. Like traditional thermocouple psychrometers, the dew point hygrometer equilibrates a sample in a sealed chamber.
WP4C Dew Point Hygrometer
A small mirror in the chamber is chilled until dew just starts to form on it. At the dew point, the WP4C measures both mirror and sample temperatures with 0.001◦C accuracy to determine the relative humidity of the vapor above the sample.
The most current version of this dew point hygrometer has an accuracy of ±1% from -5 to -300 MPa and is also relatively easy to use. Many sample types can be analyzed in five to ten minutes, although wet samples take longer.
At high water potentials, the temperature differences between saturated vapor pressure and the vapor pressure inside the sample chamber become vanishingly small.
Limitations to the resolution of the temperature measurement mean that vapor pressure methods will probably never supplant tensiometers.
The dew point hygrometer has a range of -0.1 to -300 MPa, though readings can be made beyond -0.1 MPa using special techniques. Tensiometers remain the best option for readings in the 0 to-0.1 MPa range.
Water content tends to be easier to measure than water potential, and since the two values are related, it’s possible to use a water content measurement to find water potential.
A graph showing how water potential changes as water is adsorbed into and desorbed from a specific soil matrix is called a moisture characteristic or a moisture release curve.
Example of a moisture release curve.
Every matrix that can hold water has a unique moisture characteristic, as unique and distinctive as a fingerprint. In soils, even small differences in composition and texture have a significant effect on the moisture characteristic.
Some researchers develop a moisture characteristic for a specific soil type and use that characteristic to determine water potential from water content readings. Matric potential sensors take a simpler approach by taking advantage of the second law of thermodynamics.
Matric Potential Sensors
Matric potential sensors use a porous material with known moisture characteristic. Because all energy systems tend toward equilibrium, the porous material will come to water potential equilibrium with the soil around it.
Using the moisture characteristic for the porous material, you can then measure the water content of the porous material and determine the water potential of both the porous material and the surrounding soil. Matric potential sensors use a variety of porous materials and several different methods for determining water content.
Accuracy Depends on Custom Calibration
At its best, matric potential sensors have good but not excellent accuracy. At its worst, the method can only tell you whether the soil is getting wetter or drier. A sensor’s accuracy depends on the quality of the moisture characteristic developed for the porous material and the uniformity of the material used. For good accuracy, the specific material used should be calibrated using a primary measurement method. The sensitivity of this method depends on how fast water content changes as water potential changes. Precision is determined by the quality of the moisture content measurement.
Accuracy can also be affected by temperature sensitivity. This method relies on isothermal conditions, which can be difficult to achieve. Differences in temperature between the sensor and the soil can cause significant errors.
All matric potential sensors are limited by hydraulic conductivity: as the soil gets drier, the porous material takes longer to equilibrate. The change in water content also becomes small and difficult to measure. On the wet end, the sensor’s range is limited by the air entry potential of the porous material being used.
Tensiometers and Traditional Methods
Read about the strengths and limitations of tensiometers and other traditional methods such as gypsum blocks, pressure plates, and filter paper here.
Choose the right water potential sensor
Dr. Colin Campbell’s webinar “Water Potential 201: Choosing the Right Instrument” covers water potential instrument theory, including the challenges of measuring water potential and how to choose and use various water potential instruments.
Take our Soil Moisture Master Class
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.
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.
Soil ecologist Dr. Kathy Szlavecz and her husband, computer scientist, Dr. Alex Szalay, both at Johns Hopkins University, are testing a wireless sensor network (WSN; Mesh Sensor Network), developed by Dr. Szalay, his colleague, computer scientist Dr. Andreas Terzis, and their graduate students (read part 1). Mesh networks generate thousands of measurements monthly from wireless sensors.The husband/wife team says that WSN’s have the potential to revolutionize soil ecology by generating a previously impossible spatial resolution. This week, read about the results of their experiments.
Overall, the experiments were a scientific success, exposing variations in the soil microclimate not previously observed.
Results and Challenges:
About the performance of the network, Kathy says, “Overall, our experiments were a scientific success, exposing variations in the soil microclimate not previously observed. However, we encountered a number of challenging technical problems, such as the need for low-level programming to get the data from the sensor into a usable database, calibration across space and time, and cross-reference of measurements with external sources.
The ability of mesh networks that generate so much data also presents a data management challenge. Kathy explains, “We didn’t always have the resources or personnel who could organize the data. We needed a dedicated research assistant who could clean, handle, and organize the data. And the software wasn’t user-friendly enough. We constantly needed computer science expertise, and that’s not sustainable.”
The team also faced setbacks stemming from inconsistencies generated by new computer science students beginning work on the project as previous students graduated. This is why the team is wondering if a commercial manufacturer in the industrial sector would be a better option to help finish the development of the mesh network.
This deployment is located in the Atacama desert in Chile. Atacama is one of the highest, driest places on Earth. These sensors are co-located with the Atacama Cosmological Telescope. The goal of this deployment is to understand how the hardware survives in an extreme environment. In addition to the cold, dry climate, the desert is exposed to high UV radiation. These boxes are collecting soil temperature, soil moisture and soil CO2 data. (Image: lifeunderyourfeet.org)
Kathy and Alex say that mesh sensor network design has room for improvement. Through their testing, the research team learned that, contrary to the promise of cheap sensor networks, sensor nodes are still expensive. They estimated the cost per mote including the main unit, sensor board, custom sensors, enclosure, and the time required to implement, debug and maintain the code to be around $1,000. Kathy says, “The equipment cost will eventually be reduced through economies of scale, but there is clearly a need for standardized connectors for connecting external sensors and in general, a need to minimize the amount of custom hardware work necessary to deploy a sensor network.” The team also sees a need for the development of network design and deployment tools that will instruct scientists where to place gateways and sensor relay points. These tools could replace the current labor-intensive trial and error process of manual topology adjustment that disturbs the deployment area.
This deployment is located in the fields of the farming system project at BARC. Soil temperature and moisture probes are placed at various locations of a corn-soybean-wheat rotation. The goal is to understand and explain soil heterogeneity and to provide background data for trace gas measurements. (Image: Lifeunderyourfeet.org)
According to Kathy, wireless sensor networks promise richer data through inexpensive, low-impact collection—an attractive alternative to larger, more expensive data collection systems. However, to be of scientific value, the system design should be driven by the experiment’s requirements rather than technological limitations. She adds that focusing on the needs of ecologists will be the key to developing a wireless network technology that will be truly useful. “While the computer science community has focused attention on routing algorithms, self-organization, and in-network processing, environmental monitoring applications require quite a different emphasis: reliable delivery of the majority of the data and metadata to the scientists, high-quality measurements, and reliable operation over long deployment cycles. We believe that focusing on this set of problems will lead to interesting new avenues in wireless sensor network research.” And, how to package all the data collected into a usable interface will also need to be addressed in the future.