• Rachel

Nitrogen School Part 2: Using Sensors for In-season, Variable-Rate Nitrogen Management. GUEST POST!

We are so excited to introduce our first guest on the blog, Laura Thompson! Laura is an extension educator with the University of Nebraska where she coordinates the Nebraska On-Farm Research Network, helping producers find out what management practices work best on their farms. Additionally, Laura's research focuses on using sensor based platforms for the economic use and application of nitrogen. Laura obtained her FAA Part 107 remote pilot license and is actively involved in educational efforts helping farmers and crop advisors learn more about drones and their use in crop management. She is the lead author of a new University publication "Getting Started with Drones in Agriculture" available at go.unl.edu/drone. Laura's educational efforts also extend to the general public. She has employed innovative drone filming techniques such as creating an aerial time lapse video by flying a pre-programmed flight repeatedly throughout the year and combining flights in processing to capture the activities that go into producing a soybean crop from above. This video was used to educate youth and the public about drones, agriculture, and technology and can be viewed here: https://www.youtube.com/watch?v=PbqQnC9gylI . Welcome to the blog, Laura!

Do you wonder if you are managing your nitrogen for optimal profit? Determining what the right rate is for any given point in a field is a moving target and depends on many factors. New technologies offer opportunities for more informed and precise nitrogen management. This article discusses challenges related to nitrogen management and how drone based sensors can be utilized to inform in-season nitrogen management.


The Challenge: Nitrogen Need Variability

Nitrogen is supplied to the plant from sources other than fertilizer. Mineralization of soil organic matter converts organic nitrogen into nitrogen that the plant can use. The factors which drive mineralization vary within a field, within the growing season, and differ from year to year. Nitrogen may also be lost from the crop environment through a variety of means – leaching, denitrification, and volatilization – and these losses also vary spatially within a field and vary from year to year depending on weather conditions. Additionally, yield potential varies within a field.


All combined, this variability produces great challenges in determining the optimum nitrogen rate for a given location in a field in a given year. In-season and variable-rate application can help corn producers account for this variability. In-season applications allow more of the fertilizer to be applied closer to when the plant needs it, therefore reducing opportunities for nitrogen loss due to leaching, denitrification, and volatilization. In-season applications also allow for responsive nitrogen management – giving producers the chance to adjust total nitrogen application amounts to account for factors such as greater nitrogen losses, increased mineralization, and higher or lower than expected yield potential. Variable-rate applications also allow producers to account for spatial variability within the field which is caused by variable productivity and differing soil qualities.


Nitrogen Management in the Information Age


While simply using in-season nitrogen fertilizer applications have shown benefits, there are often many questions producers have, such as: How much fertilizer should I apply? How do I develop my variable-rate prescription map? How do I know if the corn needs more nitrogen before it's too late?


Web-based tools such as Encirca, Climate FieldView, and others attempt to predict plant nitrogen need. These tools are often based on factors such as measured weather, soil characteristics, and predictive models. Another technology for refining nitrogen management involves using sensors during the growing season to detect plant nitrogen need. In this scenario, the plant itself serves as an indicator of nitrogen need. There are numerous types of sensors; these sensors can be mounted on high clearance applicators, drones, or airplanes. Similar data may also be obtained using satellites. Down the road these sensors may even be mounted directly onto center pivot irrigation systems.


Active sensors on high clearance applicators


Nebraska Extension and the Nebraska On-Farm Research Network have conducted numerous studies in Nebraska to explore the use of these tools. Project SENSE https://cropwatch.unl.edu/projectsense has conducted over 50 on-farm research studies using active crop canopy sensors mounted on a high clearance applicator over the past three years.


High clearance applicator with crop canopy sensors mounted on the front boom. The sensors detect corn reflectance which is then translated into a rate of nitrogen to apply to the corn.

Results from 2015 and 2016 are in table 1. In each year, the total nitrogen rate was reduced by using the sensor method, resulting in increased nitrogen efficiency. The sensor method resulted in a yield reduction of three to five bu/ac, however marginal net return was greater when the sensor method was used.


Table 1. Results of Project SENSE, sensor based nitrogen management project in 2015 and 2016.

Using Drones to Carry Sensors for Nitrogen Management


In landscapes with rolling topography, being able to apply in-season nitrogen through an airplane is often a more desirable approach than using a high clearance applicator where corn will be run over. Because small, multi-spectral sensors can now be mounted on drones, both crop sensing (to determine N rate), and N application can occur from the air, eliminating destruction of corn through application with a high clearance machine. A 2017 and 2018 research project evaluated the use of these sensors to detect corn nitrogen need in Richardson County, Neb. In the first year of this research project the total nitrogen rate was reduced by 25 lb/ac compared to the farmer's traditional method and yield was the same. You can learn more about this research here: https://cropwatch.unl.edu/2017/sare-grant-aids-farmers-using-drones-test-n-applications and see the data from the first year here: https://projects.sare.org/project-reports/fnc17-1100/


A 5-band multi-spectral sensor mounted on a drone.

How do these sensors work?

Plants reflect light differently in different portions of the light spectrum. Because plants reflect more light in the green portion of the light spectrum, they appear green to our eyes. The following graph shows how plants reflect light differently based on their nitrogen stress levels. Generally, when we are concerned with nitrogen management, we are interested in reflectance differences in the red, red edge, and near-infrared (NIR) portion of the spectrum. Stressed plants tend to reflect more red and red edge light and less NIR.


Spectral reflectance of healthy and stressed plants in visible and near-infrared regions.

You may have heard the term "NDVI". NDVI stands for normalized difference vegetation index. This index relates the red and NIR portions of the light spectrum above. NDVI is calculated as follows:



As plants develop more and more biomass, the red portion of the light spectrum becomes saturated and no longer is informative about nitrogen differences. For this reason, switching to the red edge band can help get more useful data. In this case, the normalized difference red edge index (NDRE) is used instead of NDVI. The NDRE index is calculated as follows:



What does all this mean practically?

Take a look at the following image captured with a sensor on a drone. On the left is a true color (Red-Green-Blue) image of the field that is how your eyes would normally see. On the right is a NDRE image of the same field. The black outlines represent different N rates. In the NDRE image we are able to see lower readings (represented with red color) where less N was applied, compared to higher readings (represented with blue color) where more N was applied. This image illustrates how much more nitrogen differences can be detected using the NDRE index compared to a standard true color image. This imagery was used to develop a variable-rate, in-season N prescription.


True color (Red-Green-Blue) image (left) and normalized difference red edge (NDRE) image (right) from June 24, 2017. Black outlines represent different nitrogen rates.

Not all drone based sensors and cameras are the same. For more information on drones and sensors and what to look for when making a selection, read this free publication, Getting Started with Drones in Agriculture: http://extensionpubs.unl.edu/publication/9000019610474/getting-started-with-drones-in-agriculture-g2296/


Wrapping Up

Some years, an in-season application based on a prediction model or sensor may provide no economic benefit or only modest economic benefits. However, by employing such techniques, producers allow themselves the opportunity to respond to variable weather events. Consider a research study using crop canopy sensors in 2012 in Merrick County, NE. An initial base rate of 75 lb N/ac was applied. In-season crop canopy sensing indicated that no additional nitrogen was needed (due to high mineralization levels). At harvest, the crop receiving only 75 lb N/ac was compared to treatments receiving 250 lb N/ac; the yield average was 249 bu/ac and there was no yield difference between the two nitrogen approaches. While this example is a more extreme scenario, this illustrates how in-season, responsive nitrogen management provides opportunities for increased nitrogen use efficiency and fertilizer return on investment.


More Info

Want to learn more? A local field day will be held on July 19. For more information and to register, visit tinyurl.com/drone18



110 views

© 2020 by Pederson Seed and Services

  • Black Twitter Icon
  • Black Facebook Icon
  • Black Instagram Icon

900 S. 1st St

Hiawatha, KS 66434

785-742-3241

pedersonseed@gmail.com