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Posts from the ‘Agriculture’ Category

Soil Moisture 301—Hydraulic Conductivity Why you need it. How to measure it.

New Live Webinar

Hydraulic conductivity, or the ability of a soil to transmit water, is critical to understanding the complete water balance.

Soil hydraulic conductivity impacts almost every soil application.

In fact, if you’re trying to model the fate of water in your system and simply estimating parameters like conductivity, you could get orders of magnitude errors in your projections. It would be like searching in the dark for a moving target. If you want to understand how water will move across and within your soil system, you need to understand hydraulic conductivity because it governs water flow.

Get the complete soil picture

Hydraulic conductivity impacts almost every soil application: crop production, irrigation, drainage, hydrology in both urban and native lands, landfill performance, stormwater system design, aquifer recharge, runoff during flooding, soil erosion, climate models, and even soil health. In this 20-minute webinar, METER research scientist, Leo Rivera discusses how to better understand water movement through soil. Discover:

  • Saturated and unsaturated hydraulic conductivity—What are they?
  • Why you need to measure hydraulic conductivity
  • Measurement methods for the lab and the field
  • What hydraulic conductivity can tell you about the fate of water in your system

Date: August 20, 2019 at 9:00 am – 10:00 am Pacific Time

See the live webinar

REGISTER

Can’t wait for the webinar? See a comparison of common measurement methods, and decide which soil hydraulic conductivity method is right for your application. Read the article.

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. 

 

Soil Moisture 101: Need-to-Know Basics

Harness the power of soil moisture

Researchers measure evapotranspiration and precipitation to understand the fate of water—how much moisture is deposited, used, and leaving the system. But if you only measure withdrawals and deposits, you’re missing out on water that is (or is not) available in the soil moisture savings account. Soil moisture is a powerful tool you can use to predict how much water is available to plants, if water will move, and where it’s going to go.

Soil moisture 101 explores soil water content vs. soil water potential

What you need to know

Soil moisture is more than just knowing the amount of water in soil. Learn basic principles you need to know before deciding how to measure it. In this 20-minute webinar, discover:

  • Why soil moisture is more than just an amount
  • Water content: what it is, how it’s measured, and why you need it
  • Water potential: what it is, how it’s different from water content, and why you need it
  • Whether you should use soil moisture sensors, water potential sensors, or both
  • Which sensors measure each type of parameter

Watch the webinar

Soil moisture 201 coming soon

Sign up to attend the live webinar:  Soil Moisture 201: Moisture Release Curves—Revealed June 11, at 9am PST.

Why mesonets make weather prediction more accurate

The staggering cost of Montana’s “flash drought”

Some people figured it was climate change. One statistician said it was a part of a cyclical trend for poor crop years. Whatever the cause, the 2017 flash drought that parched the entire state of Montana and most of South Dakota, severely impacted the profitability of ranchers and farmers. In western Montana, fires burned some of the largest acreages in recent history. It resulted in one of the biggest wildfire incident reports (over one-million acres) and caused virtually 100% crop loss in northeastern Montana. The U.S. Dept. of Agriculture estimated the crop loss to be in the hundreds of millions of dollars, and one question was on everybody’s mind—why did no one see it coming?

Figure 1. Montana drought conditions August 2017 (Source: Montana State Library website: https://mslservices.mt.gov/Geographic_Information/Maps/drought/)

Getting the right weather data

The 2017 Montana Dept. of Natural Resources and Conservation spring drought report indicated plenty of water: “By the end of the month, almost all drought concern was removed from the state, with the exception of Wibaux and Fallon Counties….As of May 9, 2017, Montana was 98.45% drought free.” But in late May, an abrupt shift in weather conditions led to one of the hottest, driest summers on record.

The problem, says Kevin Hyde, Montana State Mesonet Coordinator, lies not only in the need for more weather data but in obtaining the right kind of data. He says, “One of the reasons drought was missed was because we’re still thinking you measure drought by snowpack and how much water is in the river, which is really great if you’ve got water rights. But we’ve got a lot of dryland out there.”

In addition to weather monitoring, Hyde is a big proponent of adding soil moisture and NDVI measurements to each of the Montana Mesonet stations he oversees. He says, “The conventional weather station only measures atmospheric conditions. But ultimately, to make any decisions, we’ve got to know not just how much water comes into the system, but how much goes into the soil. And even that’s not enough…because what we really need to know is how the water situation is going to affect plants.”

Hyde says more data are needed to warn growers and ranchers about upcoming weather risks. He points to the fact that increasing evapotranspiration got missed leading up to the summer of 2017. “We realized that if we were looking carefully at reference ET, we might have seen it about a month earlier. What would people have done? They would have changed their calf purchases. They would have figured out what kind of forage they needed to buy. These are the types of decisions people can make if they know the information sooner.”

Was the drought over? Soil moisture illuminates the bigger picture

Heavy rains came mid-September of 2017, which led some people to believe the drought was over. However, changes in soil moisture told a different story. Very little of the rain made it into the soil. “At the Havre, MT station you can see we had some heavy precipitation events. Then we had early October snows. So people expected good soil water recharge. But at the end of the day, we didn’t get it. On Sept.15th, soil moisture sensors showed a big soil moisture response at the surface but only a marginal response at 8 inches.” The melt of early October snows onto the soil, still damp from the September rain, drained to 20 inches or more. But as the snowmelt dissipated, there was minimal net gain going into the winter.

Figure 2. Soil moisture traces at the Havre, MT weather station

Predictive models need more coverage to be effective

Typically in the U.S., the National Weather Service (a division of NOAA) puts out a network of weather monitoring stations spaced out across the country, and that data gets fed into forward-looking models that help predict the weather. Dr. Doug Cobos, research scientist at METER says, “What people are finding out is that putting in a sparse network of very expensive systems has done really well. It’s been a good thing. But the spatial gaps in those networks are a problem, especially for agriculture producers and ranchers. They need to know what’s happening where they are.”

Hyde agrees, adding that we need better predictive tools that help growers and ranchers make practical decisions based on data rather than guessing. “January 1st is when the decision has to be made—do I buy cows? Do I sell cows? Do I need more pasture? But many predictions start on April 1st. As one rancher puts it, ‘We don’t bother with Las Vegas. We sit around the dining room table at the beginning of the year and put a million dollars on one shot.’”

Mesonets improve spatial distribution

Mesonets present a practical solution for the need to fill in data gaps between large, complex weather stations. The Montana Mesonet currently has 57 stations interspersed throughout the state, and through partnerships with both the public and private sector, they’re adding more stations every year.

Figure 3. Map of MT Mesonet weather stations (source: http://climate.umt.edu/mesonet/)

At each location, the Montana Mesonet team installs METER all-in-one weather stations, soil moisture sensors, NDVI sensors and data loggers that integrate with ZENTRA Cloud: an easy-to-use web software that seamlessly integrates into third-party applications through an API. He says the system enables better spatial distribution and reliability. “When we were deciding on equipment we asked ourselves: What kind of technology should we use? It had to provide high data integrity. It had to be easy to deploy and maintain. And it had to be cost effective. There’s not a lot of people in that sector. METER systems are low profile, they’re affordable, and the reliability is there. I look at some other mesonets, and they cannot afford to build out further because they are relying on large, complex, expensive systems. That’s where the METER system comes into play.”

Figure 4. Montana Mesonet station setup (Photo credit: Kevin Hyde)

Betting on the future

The Mesonet team and its partners are excited to see how their data will mesh with the available predictive tools to be the most useful and practical for growers and ranchers throughout the state, and they realize that there is still much work to do. “It’s not enough just to get the instrumentation out there. The overall crux is: how do we build the information network, and how do we build a relationship with the producers so that we can have an iterative and interactive conversation?” says Hyde. “We know there needs to be an education in how to use and interpret the data. For example: what is NDVI, and what can we learn from it? A lot of what we need to do is translate science into practical terms.” But he adds that it doesn’t need to be perfect. “What the farmers have said to us is, ‘We don’t need exact numbers. We’re gamblers. Give us probability. Teach us what it means, and we’ll make the decision.’”

Find more information on the Montana Mesonet here.

Just released: ATMOS 41 comparison testing data

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.

weather station

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.

See the results

 

 

Why does my soil moisture sensor read negative?

How is a negative number possible?

METER soil moisture sensors measure the dielectric constant of the substrate in which they are installed. They are designed to measure soil, which has a dielectric constant of around 5.

METERTEROS 12 soil moisture sensor

Water has a dielectric of approximately 80, so if we assume that a dry soil has a dielectric of 5 (VWC = 0.00 m3/m3), then changes to the bulk dielectric read by the soil moisture sensor will be attributable to changes in water content. If you read a METER sensor in air, which has a dielectric constant of 1, you will quite naturally get a negative number.   

Improving accuracy of dielectric soil moisture sensors

There are two common causes for negative readings on a METER soil moisture sensor:  

1) Poor contact with the soil resulting from improper installation or disturbance

Air gaps next to a sensor will contribute the lower dielectric of air to the measurement resulting in an underestimation of VWC. Air gaps can arise if enough care is not taken to pack soil around the sensor body to approximate native bulk density. Sensors that have been disturbed, such as having a cable tripped over, can also develop air gaps that can result in negative results in dry soils. (To reduce the possibility of air gaps when installing METER sensors, use the new TEROS borehole installation tool

2) A calibration that is inappropriate for the soil in which the sensor is installed

If the standard mineral calibration is used, an error of ~ 3-4% can be expected in METER sensor readings. Negative numbers can be observed in oven-dry soils (by definition a VWC of 0.0 m3/m3) down to ~ – 0.02 m3/m3 with no malfunction of the sensor. The dielectric constant of the soil is assumed to be 5 and this is a valid assumption in the majority of soils of primarily mineral composition. If your soil has a different dielectric constant, such as can occur in soils with high organic matter content, then the uncertainty in your measurements will increase. This is not a large problem because METER sensors can be calibrated to match a given soil with very little investment in resources.

Want more details?  

Watch our webinar titled Why Does My Sensor Read Negative below. This webinar is designed for those who use electromagnetic sensors (capacitance/TDR/FDR) to measure soil water content. Learn about the theory behind the measurements. Dr. Doug Cobos discusses:

  • What is volumetric water content?
  • Dielectric measurement theory basics
  • Dielectric mixing models
  • Why might a sensor read a negative VWC?
  • Can a sensor really have 2% VWC accuracy for all soils?
  • Sources of error in dielectric measurement methods
  • Improving accuracy of dielectric measurements

 

What’s next for Fukushima?

Shortly after the Fukushima disaster, we donated environmental sensors to Dr. Masaru Mizoguchi, a scientist colleague at the University of Tokyo, to help him contrive a more environmentally friendly method to rid rice fields in the villages near Fukushima of the radioactive isotope cesium 137.

Scientists continue to search for ways to prevent the recontamination of the rice paddies.

Since then, his efforts, along with the efforts of a team of scientists and citizens, have made the rice grown in the paddies near the disaster site safe for human consumption. But questions and challenges remain. For instance, what will happen to the contaminated soil surrounding the decontaminated area? Will it settle in nearby stream beds, eventually contaminating the rice paddies? And what kind of erosion will come from the nearby tree-covered and clearcut hillslopes?

Recently, our scientists and videographers visited the villages near Fukushima to film some of the progress being made. Watch the video, and read the full story here.

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.

Read more

5 ways site disturbance impacts your data—and what to do about it

Lies we tell ourselves about site disturbance

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.

site disturbance

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.

Read more

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Hydrology 301: What a Hydraulic Conductivity Curve Tells You & More

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).

Hydraulic conductivity curve

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.

 

 

Get more info on applied environmental research in our