Healthy soil contributes to healthy crops. Farmers know this, so they do what they can to ensure their soil is in good shape. They send samples of their soil for lab testing to find out if it is low in any important nutrients. If it is, they can take steps to improve the health of their soil. These might include adding fertilizers or growing cover crops that feed the soil.
One of the essential nutrients for vigorous crop production is nitrogen. Yet most routine tests done in commercial soil testing labs do not measure available nitrogen in the soil. Tests for nitrogen exist, but for a variety of reasons they cannot be done quickly and cost-effectively. As a result, farmers may be left guessing about the health of their soil. They may apply more or less nitrogen fertilizer than is actually needed.
There are a couple of reasons this is not a good practice. One is the cost. Nitrogen fertilizer is one of the more expensive soil inputs, so farmers may be spending money they do not need to spend. Another reason is the environment. When more nitrogen is added than plants can use, it can run off the land and cause problems for bodies of water downstream.
The lack of a rapid, cost-effective test for soil nitrogen is clearly a problem. Soil scientists at The Ohio State University and Cornell University think they have found a solution. They have shown that a test originally developed for extracting a particular protein in soil is actually a good test for a variety of proteins. Proteins are by far the largest pool of available organic nitrogen in soil. A good, quick test for protein in the soil could also be used as a test for available nitrogen.
The process measures a protein known as glomalin. Glomalin is generally believed to be produced by a common soil microorganism that has a beneficial relationship with plant roots. The tongue-twisting name for this organism is arbuscular mycorrhizal fungi.
An earlier study suggested that the glomalin extraction method might actually extract proteins from other sources. Steve Culman and his research colleagues decided to test that idea. They added a variety of sources of protein to soil samples. They used leaves from corn, bean, and common weeds (plant sources), chicken and beef (animal sources), and white button mushroom and oyster mushroom (fungi).
They applied the so-called glomalin protocol to these soil samples and found that proteins from all of the sources were extracted via this method. The procedure was not, in fact, limited to extracting proteins produced by mycorrhizal fungi.
The researchers, therefore, recommend adoption of new terms such as soil protein, rather than glomalin, to more accurately describe the proteins extracted through this method.
This soil protein extraction procedure is a cost-effective, rapid method that could readily be adopted by commercial soil testing labs. It is possible, however, that some specific protein types may not be recovered by this method. More research on that point would be useful.
"We don't have many rapid ways to determine how much nitrogen a soil can provide and store over a growing season," said Culman. "This test is one way that might help us quickly measure an important pool of soil nitrogen. More work is needed to understand soil protein, but we think it has the potential to be used with other rapid measurements to assess the soil health of a farmer's field."
You can read the original article @ https://www.sciencedaily.com/releases/2018/07/180718082235.htm
You may want to check these field soil test kits:
Recently we had a customer that complained about weird behavior in several of the controllers he purchased. He is a first time user of Lakewood Controllers and the behavior was attributed to defective controllers. After several service visits, nothing wrong was found with the controllers so we decided to spend more time training the customer as we thought the customer's expectations of the operation of the controllers, were somehow different than the way the controller operates.
After several visits and email exchanges, we were able to figure out what the problem was. It was electric noise in the power grid. We don't think about it but, these controllers were installed in New York City, where the grid is old and overloaded.
The quick solution was the installation of an AC Power conditioner at one of the controller's installations. The conditioner installed was the Furman PST-2( https://www.furmanpower.com/product/15a-8-outlet-surge-suppressor-strip-PST-2+6 ) Once the conditioner was installed, the unit behaved correctly and reliably, while the rest of the controllers were failing.
This story is basically an introduction to an article about about noise in the process control applications (below) We hope our hard learned lesson, and this article, can help you crack hard to solve process control issues.
Noise and disturbances in process control
By Vance J. VanDoren, Ph.D., P.E., consulting editor March 15, 2001
Were it not for noise and disturbances, a feedback controller could fairly easily maintain output of the controlled process (process variable) close to its desired value (setpoint). However, forces other than the controller’s efforts can often change the process variable. An example is sun shining in on a room cooled by an automatic air conditioner. In spite of the thermostat’s efforts to lower the room temperature to a 72°F setpoint, the room may actually get hotter. These uncontrollable influences are known as disturbances.
A crosswind can disturb a truck’s lateral position, requiring the driver to compensate with a control effort. Visual ‘noise’ such as snow can obscure the driver’s measurement of his actual position, causing him to make unnecessary course corrections.
Noise, on the other hand, makes the process variable appear to deviate from the setpoint whether any real disturbance is at work or not. Noise is generally a result of the technology used to sense or measure the process variable. With electrical signals, measurement noise is often due to interference from other electrical sources. Noise can also be caused by wear and tear on the sensor or some physical obstruction that causes the sensor to send an inaccurate reading to the controller.
Errors between the setpoint and the process variable also occur when the setpoint changes. However, setpoint changes are relatively easy for a controller to implement if it’s programmed with enough data about the dynamic behavior of the process. It is the random nature of disturbances and the fictitious effects of noise that make feedback controllers work so hard.
An everyday example
Consider a large truck being driven down the highway through a crosswind. The driver’s brain is the controller and his eyes are the sensors that measure the truck’s position. Based on what he sees, the driver uses his steering wheel to maintain the truck’s lateral position between the white lines.
If the wind is light and visibility is good, the driver need only concern himself with updates to the truck’s desired position necessitated by the curves in the road. Such setpoint changes are relatively easy for him to make, assuming he knows how the truck will react when he turns the steering wheel.
However, a strong wind can blow the truck off the course the driver has chosen to follow. To compensate for such disturbances, the driver must adjust his steering to correct his position errors. Worse still, if the wind’s speed or direction changes at random, the driver’s corrective efforts will have to be more frequent and more dramatic.
Now suppose the wind is accompanied by snow. The driver may not be able to see the white lines very well, so he may end up changing his course to compensate for a nonexistent position error. Snow constitutes a visual ‘noise’ that corrupts the position measurements taken by the driver’s eyes.
In a more typical process control situation, a PID controller is responsible for applying a corrective effort in proportion to the error between the process variable and the setpoint plus the integral and derivative of that difference. It is the derivative action that is most affected by noise and disturbances.
On the plus side, a PID controller tuned to provide aggressive derivative action can react quickly to an error and start the control effort moving in the right direction immediately after a disturbance begins. This can shorten the time required to compensate for a disturbance compared to a controller that uses only proportional and integral action.
However, the derivative action will amplify any noise embedded in the measurement, since the derivative of a fluctuating signal also fluctuates. This can lead to unnecessary and potentially counter-productive control efforts. Most PID controllers are equipped with noise filters to suppress extraneous fluctuations that the derivative action would otherwise generate.
You can find the original article here: https://www.controleng.com/articles/noise-and-disturbances-in-process-control/
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