• Jackson Stansell

Using Imagery to Make Fertigation Recommendations

Using optical sensor measurements to inform fertigation recommendations was conceptualized at least as early as the mid-1990s by Dr. Jim Schepers and his research team at the University of Nebraska - Lincoln (UNL). Initial research results were promising, but the methods were challenging to scale and needed further refinement. Little more work was done to further the concept until the late 2010s when a team of researchers at UNL decided to revive the concept. I was fortunate enough to join this team of exceptional researchers as a master's student in 2019. During my master's program, I had the opportunity to lead the advancement of sensor-based techniques for making fertigation recommendations through on-farm research trials and software development. The efforts of our research team resulted in the development of the sensor-based fertigation (SBF) management framework which is the focus of this article.

SBF Management Framework

There are four major components to the SBF management framework. The first component is Field Onboarding & System Configuration which basically involves entering operating specifications for the irrigation system, defining the field boundary, and providing some soil property information. The second component is execution of an Indicator Establishing Application. This is a nitrogen fertilizer application that establishes rate blocks at specific locations within the field which requires some form of variable rate control on the machine used to make the application, whether a ground rig or a pivot. Rate block establishment may be, and ideally should be, built into a typical application (e.g. upfront anhydrous or UAN sidedress application). After that application is made and the crop reaches a certain maturity (e.g. V6 growth stage in corn), the high-frequency monitoring and recommendation cycle begins. In this cycle, images are collected, processed, and then analyzed. The analysis uses the rate blocks and some empirically determined parameters to quantify the nitrogen sufficiency status of the crop based on a vegetation index derived from the image data. Once the nitrogen sufficiency status is quantified, that sufficiency status is used in a decision algorithm along with other criteria related to nitrogen sufficiency levels and soil spatial variability to determine whether or not fertigation is needed. If fertigation is required, a fertigation prescription is generated. For farmers with constant rate fertigation injection pumps applying fertigation uniformly across the field (most common), the prescription is simply an injection rate to set the pump to that corresponds to the speed of the pivot and depth of irrigation applied for that irrigation pass. If an end-gun is used, the prescription is compensated for the additional acres covered. For farmers using variable rate fertigation injection pumps, the prescription provides the appropriate injection rate by degree corresponding to the need for fertigation in that region of the field, the speed and depth of the irrigation pass, and any additional acres covered by an operational end-gun. Finally, the fertigation application is made and confirmation of application completion is provided in the system. This process occurs for every image captured during the growing season. However, images are generally captured and recommendations delivered on a weekly basis. Once the crop has reached a certain level of maturity past which additional fertigation applications are not expected to be beneficial (e.g. R3 in corn), no more fertigation applications are allowed.

On-Farm Research Results

From 2019 through 2021, 14 on-farm research trials were successfully completed in cooperation with 7 different Nebraska farmers. Results from these trials have been or will be published in the Nebraska On-Farm Research Results book corresponding to the year they were conducted. These on-farm research trials compared sensor-based fertigation (SBF) with grower management practices. Different SBF treatments used different parameter values within the analysis and decision algorithms of the SBF framework. The farmers that cooperated on this study are farmers who have already adopted best practices for nitrogen management, so this comparison was rigorous. Including all treatments, SBF was more efficient than grower management in 96% of all trials. Additionally, including all treatments, SBF was more profitable than grower management in 56% of all trials. Some of the treatments included in these trials had greater risk tolerance or restricted image-based recommendations to control only the last 60 lb of applied nitrogen.

One of the SBF treatments tested, though, has stood out far above the rest. Seven trials were executed testing the use of image-based recommendations for every fertigation application versus grower management for every fertigation application. Here are some takeaways from those trials:

  • In 100% of the trials, image-based recommendations resulted in greater nitrogen fertilizer use efficiency than grower management.

  • Image-based recommendations saved an average of 43 lb-N/ac.

  • Image-based recommendations increased yield per unit of nitrogen fertilizer applied by 25%.

  • At Dec-22 corn futures and Jan-22 UAN prices, image-based recommendations resulted in an average profit increase of $27.91/ac.

  • In 71% of the trials, image-based recommendations resulted in more profitable outcomes than grower management.

This is the preferred implementation of SBF and it is the one that Sentinel Fertigation is translating to the commercial market.

Scaling SBF

Results from on-farm research are clear: SBF leads to greater nitrogen fertilizer use efficiency than best grower management practices, and when used to inform all fertigation applications has a significant likelihood of increasing profits. However, if you've read this entire article, you're probably thinking that it takes a lot of work to get there. It does - if all of the computational and GIS processes are done with manual supervision. N-Time Fertigation Management System (N-Time FMS) automates these processes to facilitate SBF implementation, delivering recommendations and prescriptions to farmers and their trusted advisors when they are warranted. In next week's article, I will provide an overview of N-Time FMS and why it is the best tool to use for fertigation management. In the meantime if you're interested in learning more, reach out over e-mail or the form on our website and I will respond to you directly.

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