Pivoting to Automation

Published online: Mar 02, 2022 Articles, Irrigation Kyle Brown
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Source: Irrigation Association

Making the best use of water in ag irrigation makes sense for growers, and the science behind it is sound. But often, there’s a time and resource cost that can be difficult to manage, even though growers want to make efforts for conservation.

“The socio-economic reality is that a farmer needs an incentive to really invest time in conserving water, especially if water is cheap or free,” says Derek Heeren, PhD, PE, associate professor and irrigation engineer at the University of Nebraska-Lincoln, Lincoln, Nebraska. “I think most producers probably have a stewardship mindset, and in general try to do things that are good to conserve our natural resources, but there is a reality of a time budget.”

Providing that incentive is one of Heeren’s goals in his research, “Towards Pivot Automation With Proximal Sensing for Maize and Soybean in the Great Plains.” From his perspective, more producers might use scientific irrigation scheduling to manage water use if it was automated and reliable. The project is focused on developing processes to automate center pivot irrigation by incorporating multiple types of sensors to reduce the uncertainty associated with recommendations for when and how much to irrigate.

Gathering Data

The research had four objectives, starting with evaluating the accuracy of pivot-mounted multispectral and infrared thermometer sensors compared to data from stationary sensors and sensors deployed using uncrewed aircraft. It compared crop health in terms of vegetative indices and crop water stress for maize and soybean in relation with varying levels of soil water content and developed thresholds using thermal indices for triggering irrigation for the subhumid climate of the eastern Great Plains. The research also compared total applied irrigation and crop yield by comparing the performance of an existing patented system involving center pivot automation and pivot-mounted sensors against traditional irrigation scheduling methods.

Field experiments were conducted on a standard size 60 hectares center pivot-irrigated field at the University of Nebraska-Lincoln Eastern Nebraska Research and Extension Center. The treatments included infrared temperature (IRT) and granular matrix sensors for full irrigation and deficit irrigation, irrigation system supervisory control and data acquisition (ISSCADA) system (IRTs and soil water sensors), spatial evapotranspiration model, common practice and rainfed (no irrigation). The research was done in partnership with Valmont Industries, to which the ISSCADA treatment is specific. The other research models are pre-competitive.

According to the research notes from the Irrigation Innovation Consortium, preliminary data from the research showed significant yield differences for the irrigation refill treatments (including 0%-rainfed, 50%, 100% and 150%) with refill levels implemented by switching nozzles on the pivot lateral at the beginning of the season. The ISSCADA system was able to detect stress in the crop canopy. The threshold value for integrated Crop Water Stress Index for when to trigger irrigation needed to be adjusted from the default values, possibly due to different climate factors.

Reaching Results

Merging the science with technology in an easy-to-use solution has been a rewarding approach for Heeren, bringing expertise to applications that give growers more options that can be implemented to fit their situations. He found that working with the manufacturer gave his team the chance to not only test current technology but to directly improve it. For example, the company was looking to test its algorithm in more subhumid areas rather than arid climates where it would be easier to determine crop stress. Using the trigger thresholds that had been determined in a drier atmosphere would’ve resulted in fewer watering events over the course of the summer and the potential for crop stress, he says.

“What we can do then is come in and suggest based on this climate what should the threshold have been, and we sort of adjusted it on the fly to make sure we put a reasonable amount of irrigation,” he says. “Over the winter when we do a more in-depth data analysis, we want to now fine-tune what the threshold should be for eastern Nebraska and then implement it next summer that way. That would be an example of positive synergy.”

The primary variable Heeren and his team collected was canopy temperature gathered with an infrared thermometer placed on the pivot lateral moving around the field, he says. He then compared that with ground-based IRTs.

“It’s this idea that the pivot is a preexisting robot that already goes around the field several times a year,” he says. “Let’s take advantage of it as a platform to mount sensors on. So we were comparing that to traditional IRT sensors mounted on the ground.” His team also used uncrewed aircraft flights to get the temperatures across the whole field within a point in time as a data quality check.

“The usefulness is that the thermal signal is a signal of crop stress in the way that if we’re low on water we overheat,” he says. “So when the crop is low on water it can transpire to stay cool, and so we’re trying to pick up on that signal.”

The trick, he says, is that the signal is fairly small, and by the time the canopy is really hot, there’s already been yield loss. The team also collected multispectral data to keep track of canopy cover and vegetation gauges such as normalized difference vegetation index (as shown in figure 1). Understanding how to read the canopy temperature and integrating the technology into the center pivot can lead to an easier way to determine when an irrigation event is unnecessary.

“In reality it is the recommended practice that will help farmers transition from overwatering to watering the right amount,” he says.

Looking forward, Heeren plans to request another year of funding from the IIC, as the thresholds will be most valuable with several years of field data collection, he says. With an eye to implementation, the timeline looks like it could be sooner rather than later, given the amount of data that has already been collected for both subhumid and arid sites. Once an interface is developed, finetuning can begin as growers start to try it in the field during the next five years.