With threats of water scarcity making it harder to feed a growing world population, watering crops is more important than ever. Overwatering can deplete local water supplies and lead to polluted runoff, while underwatering can lead to suboptimal crop performance. Yet few farmers use scientific tools to help them decide when and how much to water their crops.
A new study from the University of Illinois identifies barriers and solutions to improve performance and uptake of irrigation decision support tools at the field level.
“We wanted to offer our take on how to achieve precision irrigation at the field scale with the latest and most advanced technologies in data collection, plant water stress, modeling and monitoring. decision-making, ”says Jingwen Zhang, postdoctoral researcher in the Department of Nature Resources and Environmental Sciences (NRES) in Illinois and lead author of the article in Environmental research letters.
Zhang says many farmers rely on traditional rules of thumb, including visual observation, crop calendars and what neighbors do, to decide when and how much water. Better data and more advanced technologies exist to help make these decisions, but they are currently not being exploited to their full potential.
For example, some fields are equipped with soil moisture sensors or cameras that detect changes in the appearance of crops, but these are not sufficient to provide accurate information about the fields. Satellites can monitor vegetation from space, but the spatial and temporal resolution of satellite images is often too large to help make decisions at the field level.
Kaiyu Guan, assistant professor at NRES, Blue Waters professor at the National Center for Supercomputing Applications and project leader of the study, pioneered a way to merge high-resolution, high-frequency satellite data into a single product integrated with high spatio-temporal resolution to help track soil and plant conditions.
“Based on remote sensing fusion technology and advanced modeling, we can help farmers achieve a fully scalable solution remotely,” he says. “It’s powerful. It can potentially be a revolutionary technology for farmers, not only in the United States, but also for small farmers in developing countries.”
With modern satellite technology and Guan’s fusion model, data acquisition will not be a limiting factor in future precision irrigation products. But it is still important to correctly define the water stress of plants.
Historically, irrigation decisions have been based solely on measurements of soil moisture. Guan’s group recently called on the agricultural industry to redefine drought, not just on the basis of soil moisture, but on its interaction with atmospheric drought.
“If we think of the soil-plant-atmosphere continuum as a system, which reflects both soil water supply and atmospheric water demand, we can use these plant-centric measurements to define plant water stress.” to trigger the irrigation, ”says Zhang. “Again, if we use our data fusion methods and process-based modeling, we can achieve precision irrigation with very high precision and also high resolution.”
The researchers also looked at the challenges associated with farmers adopting existing decision support tools. Since current products are based on less than ideal data sources, Guan says growers are reluctant to switch from basic, traditional methods to tools that may not be much more reliable. Unintuitive user interfaces, data privacy, and inflexible timing compound the problem.
Trenton Franz, associate professor at the University of Nebraska-Lincoln (UNL) and co-author, says farmers will be more likely to adopt precision irrigation decision tools if they are accurate at scale. field, flexible and easy to use. His teams and Guan’s are working on technologies to meet this need and are actively testing the technology in irrigated fields in Nebraska. This includes participation with Daran Rudnick, assistant professor at UNL and co-author of the study, in the UNL Testing Ag Performance (TAPS) program, which focuses on technology adoption and producer education. the region.
“We’re pretty close. We have real-time evapotranspiration data, and we add the soil moisture component and the irrigation component. Probably in less than a year, it will be launched as a prototype and can be tested in the farming community, ”says Guan.