agriculture practices in the previous 12 months. California farmers came in at 40%. One size doesn’t fit all Conventional farming uses uniform management, with a whole field receiving the same amount of nitrogen and water, while precision farming is designed to manage a farm in a non-uniform way. That means considering spatial variability (such as land features, crop yields and soil properties) and temporal variability (tracking changes over time in nutrient levels, soil erosion and soil moisture). As a result, how precision agriculture is deployed differs depending on location, climate and other challenges that farmers must manage. “In an area where there’s an abundance of water, say a vineyard in New York, they’ll put plastic sheeting on the ground to run the water out of the farm,” says Alireza Pourreza, associate professor of cooperative extension with the UC Davis Department of Biological and Agricultural Engineering. “Having that problem would be a dream for a California farmer.” Not surprisingly, precision agriculture has been simpler to deploy for field crops such as corn, soybeans and wheat, and in states where farmers are growing similar crops in similar environments. “There’s a lot of research on that, and a lot of
machinery technology is available for those types of farms,” Pourreza notes. “California is more challenging since we grow more than 100 different crops with many different varieties and farming styles.” Harnessing high tech As the director of the Digital Agriculture Laboratory at UC Davis, Pourreza and his colleagues are focused on the first part of the precision ag cycle: crop monitoring and decision support. The ultimate future of digital agriculture will incorporate sensors, drones, GPS and other digital tools, all connected to a network aggregating real-time data on crops, soil, weather and other factors that impact agricultural productivity. “In essence, we’re combining a physical-based approach with AI to understand the patterns that we observe, and provide evidence how they can be used,” he says. “Once we prove that, we can convert it into a tool that growers can use.” For example, Pourreza’s research in drone technology deals with aspects of field variability that can’t be observed by even the keenest farmer’s eye. “Humans can only see in the electromagnetic spectrum between 400 and 700 nanometers, but
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