A new tool developed by a University of Minnesota research team allows farmers to create a budget balance sheet of any nitrogen reduction plans and see the economic and environmental cost, return and margins, all customised to fields under their management.
“With these numbers in mind, farmers can make more informed decisions on nitrogen mitigation that not only saves them money, but also significantly reduces pollutants to the environment,” said Zhenong Jin, who led the research and is an assistant professor in the University of Minnesota’s Department of Bioproducts and Biosystems Engineering (BBE) in the College of Food, Agricultural and Natural Resource Sciences (CFANS).
According to the researchers, previous tools did not allow for customised predictions for every field in the U.S. corn belt, as the computational and storage costs of running these crop models at large scale would be very expensive.
The research team built a series of machine-learning-based metamodels that can almost perfectly mimic a well-tested crop model at much faster speeds. Using the metamodels, they generated millions of scenario simulations and investigated two fundamental sustainability questions: where are the mitigation hotspots, and how much mitigation can be expected under different management scenarios.
The study, conducted in the U.S. Midwest corn belt, found that:
“Our analysis revealed hot spots where excessive nitrogen fertiliser can be cut without yield penalty,” said Jin. “We noticed in some places that reducing nitrogen-related pollution comes at a cost of depleting organic carbon in soil, suggesting that other regenerative practices, such as cover cropping, need to be bundled with nitrogen management.”
In the future, the team aims to develop more advanced and accurate carbon qualification models through a combination of process-based models, artificial intelligence and remote sensing.