Climate parameters such as precipitation, air temperature, and wind speed can change considerably across short distances in the natural environment. However, most weather observations either sacrifice spatial resolution for scientific accuracy or research-grade accuracy for spatial resolution.
ATMOS 41 all-in-one weather station
The ATMOS 41 represents an optimization of both. It was carefully engineered to maximize accuracy at a price point that allows for spatially distributed observations. Additionally, because many researchers need to avoid frequent maintenance and long setup times, the ATMOS 41 was designed to reduce complexity and withstand long-term deployment in harsh environments. To eliminate breakage, it contains no moving parts, and it only requires recalibration every two years. Since all 14 measurements are combined in a single unit, it can be deployed quickly and with almost no effort. Its only requirement is to be mounted and leveled on top of a pole with an unobstructed view of the sky.
Comparison testing and sensor-to-sensor variability data
METER released the ATMOS 41 in January 2017 after extensive development and testing with partnerships across the world, in Africa, Europe, and the US. We performed comparison testing with high-quality, research-grade non-METER sensors and conducted time-series testing for sensor-to-sensor variability.
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, 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.
When it comes to measuring soil moisture, site disturbance is inevitable. We may placate ourselves with the idea that soil sensors will tell us something about soil water even if a large amount of soil at the site has been disturbed. Or we might think it doesn’t matter if soil properties are changed around the sensor because the needles are inserted into undisturbed soil.
The key to reducing the impact of site disturbance on soil moisture data is to control the scale of the disturbance.
The fact is that site disturbance does matter, and there are ways to reduce its impact on soil moisture data. Below is an exploration of site disturbance and how researchers can adjust their installation techniques to fight uncertainty in their data.
Non-disturbance methods don’t measure up—yet
During a soil moisture sensor installation, it’s important to generate the least amount of soil disturbance possible in order to obtain a representative measurement. Non-disturbance methods do exist, such as satellite, ground-penetrating radar, and COSMOS. However, these methods face challenges that make them impractical as a single approach to water content. Satellite has a large footprint, but generally measures the top 5-10 cm of the soil, and the resolution and measurement frequency is low. Ground-penetrating radar has great resolution, but it’s expensive, and data interpretation is difficult when a lower boundary depth is unknown. COSMOS is a ground-based, non-invasive neutron method which measures continuously and reaches deeper than a satellite over an area up to 800 meters in diameter. But it is cost prohibitive in many applications and sensitive to both vegetation and soil, so researchers have to separate the two signals. These methods aren’t yet ready to displace soil moisture sensors, but they work well when used in tandem with the ground truth data that soil moisture sensors can provide.
Hydraulic conductivity is the ability of a porous medium (soil for instance) to transmit water in saturated or nearly saturated conditions. It’s dependent on several factors: size distribution, roughness, tortuosity, shape, and degree of interconnection of water-conducting pores. A hydraulic conductivity curve tells you, at a given water potential, the ability of the soil to conduct water.
One factor that affects hydraulic conductivity is how strong the structure is in the soil you’re measuring.
For example, as the soil dries, what is the ability of water to go from the top of a sample [or soil layer in the field] to the bottom. These curves are used in modeling to illustrate or predict what will happen to water moving in a soil system during fluctuating moisture conditions. Researchers can combine hydraulic conductivity data from two laboratory instruments, the KSAT and the HYPROP, to produce a full hydraulic conductivity curve (Figure 1).
Figure 1. Example of hydraulic conductivity curves for three different soil types. The curves go from field saturation on the right to unsaturated hydraulic conductivity on the left. They illustrate the difference between a well-structured clayey soil to a poorly structured clayey soil and the importance of structure to hydraulic conductivity especially at, or near, saturation.
In Hydrology 301, Leo Rivera, Research Scientist at METER, discusses hydraulic conductivity and the advantages and disadvantages of methods used to measure it.
Watch the webinar below.
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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 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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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:
buying a sensor that is going to connect to a wireless network that you own (i.e., customer supplied like Wi-Fi, Bluetooth),
buying the infrastructure or at least pieces of it to install onsite (i.e., vendor managed LPWAN such as LoRaWAN, Symphony Link), and
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 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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Whether researchers measure soil hydraulic properties in the lab or in the field, they’re only getting part of the picture. Laboratory systems are highly accurate due to controlled conditions, but lab measurements don’t take into account site variability such as roots, cracks, or wormholes that might affect soil hydrology. In addition, when researchers take a sample from the field to the lab, they often compress soil macropores during the sampling process, altering the hydraulic properties of the soil.
Roots, cracks, and wormholes all affect soil hydrology
Field experiments help researchers understand variability and real-time conditions, but they have the opposite set of problems. The field is an uncontrolled system. Water moves through the soil profile by evaporation, plant uptake, capillary rise, or deep drainage, requiring many measurements at different depths and locations. Field researchers also have to deal with the unpredictability of the weather. Precipitation may cause a field drydown experiment to take an entire summer, whereas in the lab it takes only a week.
The big picture—supersized
Researchers who use both lab and field techniques while understanding each method’s strengths and limitations can exponentially increase their understanding of what’s happening in the soil profile. For example, in the laboratory, a researcher might use the PARIO soil texture analyzer to obtain accurate soil texture data, including a complete particle size distribution. They could then combine those data with a HYPROP-generated soil moisture release curve to understand the hydraulic properties of that soil type. If that researcher then adds high-quality field data in order to understand real-world field conditions, then suddenly they’re seeing the larger picture.
Table 1. Lab and field instrument strengths and limitations
Below is an exploration of lab versus field instrumentation and how researchers can combine these instruments for an increased understanding of their soil profile. Click the links for more in-depth information about each topic.
Particle size distribution and why it matters
Soil type and particle size analysis are the first window into the soil and its unique characteristics. Every researcher should identify the type of soil that they’re working with in order to benchmark their data.
Particle size analysis defines the percentage of coarse to fine material that makes up a soil
If researchers don’t understand their soil type, they can’t make assumptions about the state of soil water based on water content (i.e., if they work with plants, they won’t be able to predict whether there will be plant available water). In addition, differing soil types in the soil’s horizons may influence a researcher’s measurement selection, sensor choice, and sensor placement.
How Do you Know You’re Getting Accurate Soil Moisture?
Researchers and irrigators may wonder if their soil moisture sensors are accurate because probes at different locations in the same field have different water content readings. Different readings in soil moisture sensors are caused by spatial variation in water content. These readings provide researchers valuable information about soil texture, watering patterns, and water use. Here are some ideas and strategies to keep in mind when trying to understand the varying patterns of soil moisture at your research or irrigation site. Click the links for more in-depth information about accurate soil moisture.
One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard.
Horizontal vs. Vertical Variation
It’s helpful to distinguish variation in the vertical from variation in the horizontal. Most people expect strong vertical variation due to wetting and drying patterns, soil horizonation, and compaction. Water content can vary drastically over distances of only a few centimeters, especially near the soil surface. Horizontal variation is typically less pronounced in a bare or uniformly planted field, and at a given depth, it might be quite small. But surprisingly large variations can exist, indicating isolated patches of sand or clay or differences in topography. One irrigator noticed a few sensors indicating low water content after a heavy rain that had uniformly wetted his vineyard. Knowing that sand has a low field capacity water content, he surmised (correctly) that he had found the sandy areas in the vineyard.
Soil moisture sensors sometimes measure unexpected things.
Because properly installed dielectric soil moisture sensors lie in undisturbed (and therefore unanalyzed) soil, they sometimes measure unexpected things. One researcher buried a probe in what appeared to be a very dry location and was startled to measure 25 to 30% volumetric water content. Those readings made the soil appear saturated, but obviously it wasn’t. She dug down to the sensor and found a pocket of clay. As she discovered, it is impossible to get much information from an absolute water content measurement without knowing what type of soil the sensor is in.
Since we expect variation, how do we account for it? How many probes are needed to adequately characterize the water content in an application or experiment? There is no simple answer to this question. The answer will be affected by your site, your goals, and how you plan to analyze your data. Here are some things you might consider as you plan.
If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot.
Strategy #1: Irrigation—Use Soil Moisture as an Indicator
What information do you have when you know a field’s volumetric water content? That number independently tells an irrigator very little. Soil moisture can be used like a gauge to show when a field is full and when it needs to be refilled, but the “full” and “empty” are only meaningful in context.
The goals of irrigation are to keep root zone water within prescribed limits and to minimize deep drainage. Understanding and monitoring the vertical variation lets you correlate a real-time graph of water use data with above-ground field conditions and plant water needs. It makes sense to place probes both within and below the root zone.
By contrast, measuring horizontal variation—placing sensors at different spots in the field—is not very helpful. If a field will be irrigated as a unit, it should be monitored as a unit at one representative spot. Because there’s no way to adjust water application in specific spots, there’s no benefit to quantifying spatial variation in the horizontal. Like a float in a gas tank, a set of soil moisture sensors in the right spot will adequately represent the changing soil moisture condition of the whole field.
We recommend a single probe location in each irrigation zone with a minimum of one probe in the root zone and one probe below it. Additional probes at that site, within and below the root zone, will increase the reliability of the information for the irrigation manager, at minimal additional cost.
In two weeks: Learn two more techniques researchers use in crop studies and ecology studies to account for variability in order to obtain an accurate soil moisture picture.
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In this brief 30-minute webinar, Dr. John Gammon, University of Alberta, teaches the basics of the Photochemical Reflectance Index (PRI).
He gives an introduction to the photochemical reflectance index and what it can tell researchers about xanthophyll cycle activity, carotenoid: chlorophyll pigment ratios, light-use efficiency, and plant stress. He also discusses remote sensing.
Time Domain Reflectometry (TDR) vs. capacitance is a common question for scientists who want to measure volumetric water content (VWC) of soil, but is it the right question? Dr. Colin S. Campbell, soil scientist, explains some of the history and technology behind TDR vs. capacitance and the most important questions scientists need to ask before investing in a sensor system. Read more…
Modern technology has made it possible to sample Normalized Difference Vegetation Index (NDVI) across a range of scales both in space and in time, from satellites sampling the entire earth’s surface to handheld small sensors that measure individual plants or even leaves. Read more…
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Weather data, used for flight safety, disaster relief, crop and property insurance, and emergency services, contributes over $30 billion in direct value to U.S. consumers annually. Since the 1990’s in Africa, however, there’s been a consistent decline in the availability of weather observations. Read more…
Plants require nutrients to grow, and if we fail to supply the proper nutrients in the proper concentrations, plant function is affected. Fertilizer in too high concentration can also affect plant function, and sometimes is fatal. 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…
We were inspired by this Freakonomics podcast, which highlights the book, This Idea Must Die: Scientific Problems that are Blocking Progress, to come up with our own answers to the question: Which scientific ideas are ready for retirement? We asked scientist, Dr. Gaylon S. Campbell, which scientific idea he thinks impedes progress. Here’s what he had to say about the standards for field capacity and permanent wilting point. Read more…