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Posts from the ‘Data loggers’ Category

Freshwater shrimp grown in Korean rice paddy boost grower income

In South Korea, falling rice prices threaten the livelihood of many rice growers. Recently our Korean reps told us about an experiment performed in Jinju city, Gyeongsangnam-do, South Korea, by GNARES (Gyeongsangnam-do Agricultural Research & Extension Services) to increase grower income through simultaneous freshwater shrimp, lotus plant, and rice cultivation in a paddy field.

RT-1 water temperature sensors were installed along with METER data loggers to monitor the water temperature, ensuring it was optimal for shrimp growth. Through this experiment, GNARES found that environmental conditions were good for cultivating freshwater shrimp in this area. 

 

Engineers Without Borders alleviates Panamanian village water security issues

Engineers Without Borders (EWB) at Washington State University in Pullman, WA has partnered with a small indigenous village located in the Comarca Ngäbe-Buglé region of Panama. The relationship between this village and EWB at WSU began in 2016 when WSU alumna Destry Seiler began living in the village as a Peace Corps volunteer hoping to help solve the community’s water security needs.

A view of the Comarca Ngäbe-Buglé taken from the village in Panama.

During the rainy season in this village, approximately 20 households have access to water through a two-inch PVC pipe that operates by gravity. It runs approximately 1.5 kilometers through the jungle from a spring source higher in the mountain to small hose spickets located close to the homes on the distribution line. The other ~80 households do not have access to the distribution line and walk to the closest river or creek up to five times a day to find water. However, during the dry season, most spring sources dry up, leaving all households in the community to walk to the diminished supply of rivers to find their water.

A view of the water line currently serving ~20 homes in the village during the rainy season.

The village initially requested assistance from the Peace Corps in order to find a year-round source of clean water. But, after living in the village for 1.5 years, Ms. Seiler could not locate spring sources that both survived through the dry season and could also reach the homes in need through a gravity fed system.

Then Ms. Seiler began thinking of groundwater as a possible new water source for the community. Unfortunately, groundwater data for the Comarca Ngäbe-Buglé was not available from the local government agency. So she decided to reach out to WSU professor, Dr. Karl Olsen, to ask for assistance with a groundwater research project, and the EWB club was formed.

The club visited the village for the first time along with Ms. Seiler and faculty mentor Dr. Karl Olsen in August 2018 to do an initial survey of water use and needs, as well as to create a first-ever map of the area. EWB will return to Panama this June 2019 to implement a solar-powered water pump requested by a section of the community to deliver water from a spring source to approximately 20 homes on the nearest ridgeline. The club will also install latrines in a nearby community. They will continue the groundwater survey of the area through more extensive mapping and perform a more advanced analysis with the support of a local hydrologic company.

EWB members and WSU students Patrick Roubicaud, Kristy Watson, Destry Seiler, Perri Piller, Rene McMinn, and Kevin Allen during their visit to Panama, August 2018.

The team will use a METER-donated ATMOS 41 weather station along with a ZL6 data logger and ZENTRA Cloud software to assist in the data collection necessary to begin mapping groundwater in the area. The weather station will record precipitation, solar radiation, vapor pressure, temperature, wind, and relative humidity data that will enable EWB to begin to quantify environmental conditions and available water supply. When combined with streamflow data from rivers in the area, groundwater availability can also begin to be estimated. Because of ZENTRA Cloud, EWB will be able to view this information near-real time as well as share it with the village to help guide their design decisions. EWB plans to install the ATMOS 41 at a nearby village school to ensure weather station security and to provide an opportunity for local students to learn about their surrounding environment in a way they have not been able to do before.

To learn more about the Panamanian village or the work EWB from WSU is doing, visit ewb.wsu.edu.

See performance data for the ATMOS 41 weather station.

How to get started with ZENTRA Cloud

Researchers need to optimize funding and maximize peer-reviewed paper output. ZENTRA Cloud’s powerful data management software works with the new ZL6 data logger to make it easier to succeed at both.

ZENTRA Cloud reduces workload by speeding up analysis, streamlining workflow, and simplifying data sharing.

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Data collection: 8 best practices to avoid costly surprises

Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, soil moisture sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.

Before selecting a site, scientists should clearly define their goals for gathering data.

Make no mistake, it will cost you

Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.

  • Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
  • Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
  • Sensor installation: Sensors are not installed correctly, causing inaccurate readings
  • Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
  • Data dissemination: Data cannot be understood or replicated by other scientists

When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.

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IoT Technologies for Irrigation Water Management (Part 2)

Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, continues (see part 1) to discuss the strengths and limitations of  IoT technologies for irrigation water management.

Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost.

LoRaWAN (a vendor-managed solution see part 1) is ideal for monitoring applications where sensors need to send data only a couple of times per day with very high battery life at very low cost. Cellular IoT, on the other hand, works best for agricultural applications where sensors are required to send data more frequently and irrigation valves need to be turned on/off. Low-Power Wide-Area Networking (LPWAN) technologies need gateways or base stations for functioning. The gateway uploads data to a cloud server through traditional cellular networks like 4G. Symphony Link has an architecture very similar to LoRaWAN with higher degree of reliability appropriate for industrial applications. The power budget of LTE Cat-M1 9 (a network operator LPWAN) is 30% higher per bit than technologies like SigFox or LoRaWAN, which means more expensive batteries are required. Some IoT technologies like LoRa and SigFox only support uplink suited for monitoring while cellular IoT allows for both monitoring and control. LTE-M is a better option for agricultural weather and soil moisture sensor applications where more data usage is expected.

NB-IoT is more popular in EU and China and LTE Cat-M1 in the U.S. and Japan. T-Mobile is planning to deploy NB-IoT network in the U.S. by mid-2018 following a pilot project in Las Vegas. Verizon and AT&T launched LTE Cat-M1 networks last year and their IoT-specific data plans are available for purchase. Verizon and AT&T IoT networks cover a much greater area than LoRa or Sigfox. An IoT device can be connected to AT&T’s network for close to $1.00 per month, and to Verizon’s for as low as $2 per month for 1MB of data. A typical sensor message generally falls into 10-200 bytes range. With the overhead associated with protocols to send the data to the cloud, this may reach to 1KB. This can be used as a general guide to determine how much data to buy from a network operator.

Studies show there is a potential for over 50% water savings using sensor-based irrigation scheduling methods.

What the future holds

Many startup companies are currently focused on the software aspect of IoT, and their products lack the sensor technology. The main problem they have is that developing good sensors is hard. Most of these companies will fail before batteries of their sensors die. Few will survive or prevail in the very competitive IoT market. Larger companies who own sensor technologies are more concerned with the compatibility and interoperability of these IoT technologies and will be hesitant to adopt them until they have a clear picture. It is going to take time to see both IoT and accurate soil/plant sensors in one package in the market.  

With the rapid growth of IoT in other areas, there will be an opportunity to evaluate different IoT technologies before adopting them in agriculture. As a company, you may be forced to choose specific IoT technology. Growers and consultants should not worry about what solution is employed to transfer data from their field to the cloud and to their computer or smart phones, as long as quality data is collected and costs and services are reasonable. Currently, some companies are using traditional cellular networks. It is highly likely that they will finally switch to cellular IoT like LTE Cat-M1. This, however, may potentially increase the costs in some designs due to the higher cost of cellular IoT data plans.

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IoT Technologies for Irrigation Water Management

Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, discusses where and why IoT fits into irrigation water management. In addition, he explores possible price, range, power, and infrastructure road blocks.

Wireless sensor networks collect detailed data on plants in areas of the field that behave differently.

Studies show there is a potential for water savings of over 50% with sensor-based irrigation scheduling methods. Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost. Wireless sensor networks can collect data on plants in a lot of detail in areas of the field that behave differently. The need for wireless sensors and actuators has led to the development of IoT (Internet of Things) solutions referred to as Low-Power Wide-Area Networking or LPWAN. IoT simply means wireless communication and connecting to some data management system for further analysis. LPWAN technologies are intended to connect low-cost, low-power sensors to cloud-based services. Today, there are a wide range of wireless and IoT connectivity solutions available raising the question of which LPWAN technology best suits the application?

IoT Irrigation Management Scenarios

The following are scenarios for implementing IoT:

  1. buying a sensor that is going to connect to a wireless network that you own (i.e., customer supplied like Wi-Fi, Bluetooth),
  2. buying the infrastructure or at least pieces of it to install onsite (i.e., vendor managed LPWAN such as LoRaWAN, Symphony Link), and
  3. relying on the infrastructure from a network operator LPWAN (e.g., LTE Cat-M1, NB-IOT, Sigfox, Ingenu, LoRWAN).

This is how cellular network operators or cellular IoT works. LPWAN technology fits well into agricultural settings where sensors need to send small data over a wide area while relying on batteries for many years. This distinguishes LPWAN from Bluetooth, ZigBee, or traditional cellular networks with limited range and higher power requirements. However, like any emerging technology, certain limitations still exist with LPWAN.

Individual weather and soil moisture sensor subscription fees in cellular IoT may add up and make it very expensive where many sensors are needed.

IoT Strengths and Limitations

The average data rate in cellular IoT can be 20 times faster than LoRa or Symphony Link, making it ideal for applications that require higher data rates. LTE Cat-M1 (aka LTE-M), for example, is like a Ferrari in terms of speed compared to other IoT technologies. At the same time, sensor data usage is the most important driver of the cost in using cellular IoT. Individual sensor subscription fee in cellular IoT may add up and make it very expensive where many sensors are needed. This means using existing wireless technologies like traditional cellular or ZigBee to complement LPWAN. One-to-many architecture is a common approach with respect to wireless communication and can help save the most money. Existing wireless technologies like Bluetooth LE, WiFi or ZigBee can be exploited to collect in-field data. In this case, data could be transmitted in-and-out of the field through existing communication infrastructure like a traditional cellular network (e.g., 3G, 4G) or LAN. Alternatively, private or public LPWAN solutions such as LoRaWAN gateways or cellular IoT can be used to push data to the cloud. Combination of Bluetooth, radio or WiFi with cellular IoT means you will have fewer bills to pay. It is anticipated that, with more integrations, the IoT market will mature, and costs will drop further.

Many of LPWAN technologies currently have a very limited network coverage in the U.S. LTE Cat-M1 by far has the largest coverage. Ingenu, which is a legacy technology, Sigfox and NB-IOT have very limited U.S. coverage. Some private companies are currently using subscription-free, crowd-funded LoRaWAN networks to provide service to U.S. growers: however, with a very limited network footprint. Currently, cellular IoT does not perform well in rural areas without strong cellular data coverage.

In two weeks: Dr. Osroosh continues to discuss IoT strengths and limitations in part 2.

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Irrigation Curves—A Novel Irrigation Scheduling Technique

This week, guest author Dr. Michael Forster, of Edaphic Scientific Pty Ltd & The University of Queensland, writes about new research using irrigation curves as a novel technique for irrigation scheduling.

Growers do not have the time or resources to investigate optimal hydration for their crop. Thus, a new, rapid assessment is needed.

Measuring the hydration level of plants is a significant challenge for growers. Hydration is directly quantified via plant water potential or indirectly inferred via soil water potential. However, there is no universal point of dehydration with species and crop varieties showing varying tolerance to dryness. What is tolerable to one plant can be detrimental to another. Therefore, growers will benefit from any simple and rapid technique that can determine the dehydration point of their crop.

New research by scientists at Edaphic Scientific, an Australian-based scientific instrumentation company, and the University of Queensland, Australia, has found a technique that can simply and rapidly determine when a plant requires irrigation. The technique builds on the strong correlation between transpiration and plant water potential that is found across all plant species. However, new research applied this knowledge into a technique that is simple, rapid, and cost-effective, for growers to implement.

Current textbook knowledge of plant dehydration

The classic textbook values of plant hydration are field capacity and permanent wilting point, defined as -33 kPa (1/3 Bar) and -1500 kPa (15 Bar) respectively. It is widely recognized that there are considerable limitations with these general values. For example, the dehydration point for many crops is significantly less than 15 Bar.

Furthermore, values are only available for a limited number of widely planted crops. New crop varieties are constantly developed, and these may have varying dehydration points. There are also many crops that have no, or limited, research into their optimal hydration level. Lastly, textbook values are generated following years of intensive scientific research. Growers do not have the time, or resources, to completely investigate optimal hydration for their crop. Therefore, a new technique that provides a rapid assessment is required.

How stomatal conductance varies with water potential

There is a strong correlation between stomatal conductance and plant water potential: as plant water potential becomes more negative, stomatal conductance decreases. Some species are sensitive and show a rapid decrease in stomatal conductance; other species exhibit a slower decrease.

Plant physiologist refer to P50 as a value that clearly defines a species’ tolerance to dehydration. One definition of P50 is the plant water potential value at which stomatal conductance is 50% of its maximum rate. P50 is also defined as the point at which hydraulic conductance is 50% of its maximum rate. Klein (2014) summarized the relationship between stomatal conductance and plant water potential for 70 plant species (Figure 1). Klein’s research found that there is not a single P50 for all species, rather there is a broad spectrum of P50 values (Figure 1).

Figure 1. The relationship between stomatal conductance and leaf water potential for 70 plant species. The dashed red lines indicate the P80 and P50 values. The irrigation refill point can be determined where the dashed red lines intersect with the data on the graph. Image has been adapted from Klein (2014), Figure 1b.

Taking advantage of P50

The strong, and universal, relationship between stomatal conductance and water potential is vital information for growers. A stomatal conductance versus water potential relationship can be quickly, and easily, established by any grower for their specific crop. However, as growers need to maintain optimum plant hydration levels for growth and yield, the P50 value should not be used as this is too dry. Rather, research has shown a more appropriate value is possibly the P80 value. That is, the water potential value at the point that stomatal conductance is 80% of its maximum.

Irrigation Curves – a rapid assessment of plant hydration

Research by Edaphic Scientific and University of Queensland has established a technique that can rapidly determine the P80 value for plants. This is called an “Irrigation Curve” which is the relationship between stomatal conductance and hydration that indicates an optimal hydration point for a specific species or variety.

Once P80 is known, this becomes the set point at which plant hydration should not go beyond. For example, a P80 for leaf water potential may be -250 kPa. Therefore, when a plant approaches, or reaches, -250 kPa, then irrigation should commence.

P80 is also strongly correlated with soil water potential and, even, soil volumetric water content. Soil water potential and/or content sensors are affordable, easy to install and maintain, and can connect to automated irrigation systems. Therefore, establishing an Irrigation Curve with soil hydration levels, rather than plant water potential, may be more practical for growers.

Example irrigation curves

Irrigation curves were created for a citrus (Citrus sinensis) and macadamia (Macadamia integrifolia). Approximately 1.5m tall saplings were grown in pots with a potting mixture substrate. Stomatal conductance was measured daily, between 11am and 12pm, with an SC-1 Leaf Porometer. Soil water potential was measured by combining data from an MPS-6 (now called TEROS 21) Matric Potential Sensor and WP4 Dewpoint Potentiometer. Soil water content was measured with a GS3 Water Content, Temperature and EC Sensor. Data from the GS3 and MPS-6 sensors were recorded continuously at 15-minute intervals on an Em50 Data Logger. When stomatal conductance was measured, soil water content and potential were noted. At the start of the measurement period, plants were watered beyond field capacity. No further irrigation was applied, and the plants were left to reach wilting point over subsequent days.

Figure 2. Irrigation Curves for citrus and macadamia based on soil water potential measurements. The dashed red line indicates P80 value for citrus (-386 kPa) and macadamia (-58 kPa).

Figure 2 displays the soil water potential Irrigation Curves, with a fitted regression line, for citrus and macadamia. The P80 values are highlighted in Figure 2 by a dashed red line. P80 was -386 kPa and -58 kPa for citrus and macadamia, respectively. Figure 3 shows the results for the soil water content Irrigation Curves where P80 was 13.2 % and 21.7 % for citrus and macadamia, respectively.

Figure 3. Irrigation Curves for citrus and macadamia based on soil volumetric water content measurements. The dashed red line indicates P80 value for citrus (13.2 %) and macadamia (21.7 %).

From these results, a grower should consider maintaining soil moisture (i.e. hydration) above these values as they can be considered the refill points for irrigation scheduling.

Further research is required

Preliminary research has shown that an Irrigation Curve can be successfully established for any plant species with soil water content and water potential sensors. Ongoing research is currently determining the variability of generating an Irrigation Curve with soil water potential or content. Other ongoing research includes determining the effect of using a P80 value on growth and yield versus other methods of establishing a refill point. At this stage, it is unclear whether there is a single P80 value for the entire growing season, or whether P80 shifts depending on growth or fruiting stage. Further research is also required to determine how P80 affects plants during extreme weather events such as heatwaves. Other ideas are also being investigated.

For more information on Irrigation Curves, or to become involved, please contact Dr. Michael Forster: michael@edaphic.com.au

Reference

Klein, T. (2014). The variability of stomatal sensitivity to leaf water potential across tree species indicates a continuum between isohydric and anisohydric behaviours. Functional Ecology, 28, 1313-1320. doi: 10.1111/1365-2435.12289

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Data Logger Dilemma: To Bury, or Not to Bury—An Update

Recently, we wrote about scientists who were burying their data loggers (read it here).  Radu Carcoana, research specialist and Dr. Aaron Daigh, assistant professor at North Dakota State University, used paint cans to completely seal their data loggers before burying them in the fall of 2015.

data logger

Paint can setup for buried data logger.

They drilled ports for the sensor cables, sealed them up, and when they needed to collect data, they dug up the cans, retrieved the instruments, and downloaded the data in a minute or less.  

Here Radu gives an update of what happened when he dug up his buried instruments in the spring.

Results of the Paint Can Experiment

In May of this year, we dug up eighteen units (one data logger and four soil moisture sensors per unit) left in the field since November 2015—over six months.

Did moisture get into the paint cans? —We found only three cans with water in them, purely due to installation techniques used for that specific unit. The other fifteen units were bone dry, although total precipitation for the month of April only amounted to 3.63 inches, plus the snow melt.

How was data recording and recovery? —For six months, every 30 minutes the soil moisture sensors took readings, the data logger recorded, and we retrieved all of the data, complete and unaltered.

data logger

Only three cans with water in them, due to installation techniques.

What about power consumption? The batteries were good —over 90% did not need replacement. The power budget provided by five AA batteries was more than enough for reading four soil moisture sensors at 30-minute intervals.

What Happens Now?

In the spring of this year, we installed 18 more units in the third farm field, right after planting soya. We now have 36 individual units (~$1,000 value each unit) buried in the ground in the middle of a field planted with corn or soybean, since the beginning of May.

On October 13-14 (after 5 months), we accessed the first twelve units (Farm A). All 30 minutes of data was read, recorded, and downloaded (since May).  The batteries and the other accessories were replaced, and then we sealed and reburied the cans. Only one unit out of twelve had an issue and was replaced: the battery exploded in the can (editor’s note: battery explosion is usually caused by a manufacturing defect and the risk can be lessened by purchasing higher quality batteries, although all types are susceptible to some degree).  Since battery leakage will often corrode everything the acid touches, the data logger had to be sent back for repair and there may be partial data loss. The other 24 units (Farm B and C) will be accessed next week, weather permitting.

data logger

Over 90% of batteries did not need replacement.

Is the Paint Can Method Worth it?

We will continue to monitor and retrieve the data from the buried data loggers (We don’t use data loggers suited for wireless communication, because several factors guided us not to). The paint can system works very well if the installation is done correctly, with great attention to detail, and it costs only $2.00/can. However, there are improvements that could be made in order to have this method become a standard in soil research. For instance, though we are still using paint cans and other common materials, advancements in the design of waterproof containers and sturdiness would be a huge step forward. This is just a well thought out concept – a prototype. It proves that burying electronics for a longer period of time can be done if properly executed.

Note:  METER’s (formerly Decagon) official position is that you should never bury your data logger.  But we couldn’t resist sharing a few stories of scientists who have figured out some innovative methods which may or may not be successful, if tried at other sites.

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Green Roofs—Do They Work?

Green roofs are being built in large cities to provide stormwater management, reduce the urban heat island effect, and improve air quality—but are they effective?   John Buck, an innovative soil scientist based in Pittsburgh, Pennsylvania, has been trying to quantitatively answer this question in many different cities using soil monitoring equipment in order to determine the efficacy and best types of green infrastructure for managing stormwater.  

Green Roofs

A green roof installation site at the Allegheny County Office Building in Pennsylvania.

Why Green Roofs?

In older cities, stormwater runoff is typically combined with sewage flows, and these combined waters are treated at a sewage treatment plant during dry weather and light rain events. Unfortunately, during more substantial storms (sometimes just a few mm of rain) the combined flows exceed the ability of the sewage treatment plant, and are discharged without treatment to surface waters as “combined sewage overflows” (CSOs). One of the ways to mitigate CSOs is to capture and store stormwater to keep it out of the combined sewer.  

A green roof is essentially a garden on a roof, but rather than growing plants in soil, installers use a synthetic substrate made of expanded shale, expanded clay, crushed brick, or other highly porous, lightweight material with high infiltration rates.  During a storm event, water will soak into the air-filled pore space in the substrate, which acts like a sponge to soak up the rain. Excess water will flow into a subsurface drainage layer and will leave the roof garden via existing roof drains. Because a substantial fraction of the stormwater is stored in the substrate, it can later dissipate through evapotranspiration instead of contributing to stormwater volume and CSOs.

Green Roofs

Researchers are using soil moisture sensors for measuring temperature, bulk electrical conductivity and volumetric water content in green roofs and green infrastructure.

Finding Answers

Designers and regulators want to know how well green roofs work and if they are being over-engineered. They want answers to questions such as: “What sort of substrate should I be using? What type of plants can survive green roof conditions? Will I need to irrigate the green roof when there are no storms to water the plants?” and, “Will the green roof work as well during a one-inch storm that occurs over a half hour versus a five-inch storm that occurs over five days?”  

Buck is using soil lysimeters and modified tipping bucket rain gauges to measure the quantity, intensity, and quality of water coming into and going out of the green roofs.  He also tracks weather parameters and calculates daily evapotranspiration of landscapes.  Using soil sensors, he measures electrical conductivity (dissolved salts), volumetric water content, and temperature.  He has installed data loggers that send data to the web via GSM cellular connection, allowing stakeholders access to the data in real-time.  This data telemetry provides additional data security, immediately updated results, instant feedback of system problems, and an easy way to share data with others.

Green Roofs

Visualized data of the 87% annualized runoff reduction at Phipps Conservatory green roof site in Pittsburgh, PA.

What Has Been Learned?

Buck discovered that green roofs have much more capacity than people ever imagined.  At The Penfield Apartments in St. Paul, Minnesota, the green roof retained enough water to reduce runoff to about half of a conventional roof, and the peak intensity of the runoff was about one-quarter of what it would have been without the green roof.  At Phipps Conservatory in Pittsburgh, there was an 87% annualized runoff reduction and almost no runoff from typical summer rain events.  Buck comments, “Interestingly, on the Penfield project, we expected better hydrologic performance where soils were thicker, but there was no difference, or results were slightly the reverse of expectations. That reversal was likely due to the confounding influence of irrigation, which was probably non-uniform and not metered or measured by the rain gauge.”

Next week:  Read about some of the challenges John Buck sees for the future, and what kind of measurements he suggests researchers make, as they continue to validate the effectiveness of these urban ecosystems.

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Data loggers:  To Bury, or Not To Bury (part II)

There are many reasons why you should never bury your data logger.*  Most scientists who try it, fail (see part 1).  However, there is one innovative team at Washington State University who has found a way to overcome many of the problems which plague buried data loggers. They now happily collect data from the road, sitting in the cab of a truck.

data loggers

The research team houses their data loggers in a water-resistant case marked with a radio ball marker and surface flagging.

Collecting Data by Radio

Caley Gasch decided she wanted to bury data loggers in an actively managed field at the Cook Agricultural Farm, so they weren’t constantly taking down data loggers for cultivation, spraying, and harvest. She says, “We wanted a better system because after we took the data loggers down, they often did not get put back up for weeks, leaving giant gaps in our data.  The idea of burying the data loggers and simply reading them by radio had crossed our minds, but we were stymied by four questions.”  

How would we bury the dataloggers so that we could find them again?  

To solve this problem, the team buried the data loggers with a radio power identifier ball, originally made by the 3M Corporation, for locating buried power lines.  She says, “It’s a radio monitor that transmits a radio signal, and we have an instrument that we can then use to find them.”  Caley buries a radio marker with the data logger so that if the flag that marks the logger location gets removed by farming equipment or the weather (which always seems to happen), she still has the ability find the buried data logger.

data loggers

How the cables fit through the ports.

How would we avoid filling them with water, especially on a large scale?  

Caley says she’s had success keeping water out of all but three of her forty-two data loggers. She says the shallow soil in those three locations gets easily saturated in the winter, so they are still trying to modify the system. However, the method they have developed works well for the other 39 data loggers.

Their method is to place the data loggers inside a pelican case, which is a plastic, water-tight box.  She says, “We modify the boxes so the sensor cables leaving the data logger can exit the box through cable entry connectors which we tighten down with a plastic screw.  We make a watertight seal where the cable can go in and out of the box, and we also add some heat shrink tubing on the cables themselves to tighten that connection. We put silica desiccant packs inside of the pelican box along with the data logger to keep the humidity low.  This will collect any condensation that builds up or even soak up small amounts of water that leak in.”  Caley says that any water leakage they have had is probably through the ports where they’ve modified the pelican box for cable entry, but in most locations, it’s not a problem.  

data loggers

A sealed port.

How could we get radio signal to transmit out of the soil far enough?

Caley says, typically, she can connect with the radio signal up to 100 meters away from the loggers when they are buried.  She adds, “We have successfully connected to loggers that are 0.5 km away, but it depends on the landscape, the amount of water in the soil, the season, the kind of crop that’s growing, and the terrain that’s between the scientist and the data logger.  We have to get closer to most loggers.  100 meters is convenient enough for the farms that we are working on.  The roads are within that distance to each of the loggers, so we never have to actually leave the vehicle to collect our data.”

How long will the batteries last?

Caley says they’ve gotten away with only changing the batteries once a year. She usually collects data twice each year and changes the batteries in the spring.  She says, “By the time March comes around the batteries are pretty close to being dead, but we’ve been successful with just five alkaline AA batteries lasting about a year.”

One Challenge:

In some cases, the loggers haven’t been buried deep enough, and farm equipment crushed them, or the seeder penetrated the boxes.  Caley says, “We just have to make sure they are buried deep enough. We typically bury them at least 30 cm deep, and that seems to work pretty well with the current farm equipment.”

data loggers

A buried data logger that has been dug up.

For the Future:

Caley has a new idea for modifying the locations that are prone to flooding.  She will keep the loggers buried most of the year, and then dig them up during the winter.   “After the harvest in the fall, when the grower gives us permission, we will go out and dig up the boxes and mount the dataloggers on a short post, so they can spend the winter above ground.  Then, after the soil has dried a little in the spring, but prior to seeding to minimize disturbance, we will bury them again.”  Caley says that even though digging them up in the winter is more work, it’s worth her time.  She concludes, It’s still worth it to bury the loggers during the growing season so we don’t continually have data gaps while growers are seeding, spraying, or making a pass over the field.”

*Note:  Decagon’s official position is that you should never bury your data logger.  But we couldn’t resist sharing a few stories of scientists who have figured out some innovative ideas which may or may not be successful if tried at other sites.

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