A computer modeling program can determine by how much yields and emissions would drop if fertilizer use were reduced
Advanced computer modeling programs developed by researchers at the University of Minnesota will allow farmers and food businesses to explore their nitrogen reduction plans.
This will give them insight into the economic and environmental costs, as well as better management strategies on the ground.
With climate change, reducing greenhouse gas emissions and nitrogen pollution of water from agricultural practices is an environmental priority. The balance is to help producers overcome emissions challenges while mitigating environmental impact and improving farm incomes.
“By providing key sustainability indicators related to crop production, our metamodels can be useful tools for food companies to quantify emissions in their supply chain and distinguish mitigation options to set sustainability goals”, Timothy Smith, professor in the department of bioproducts and biosystems at the university. engineering (BBE), said in the press release.
The study was conducted in the corn belt of the Midwestern United States. It found that a 10% reduction in nitrogen fertilizers resulted in 9.8% less nitrogen emissions and 9.6% less nitrogen leaching, at a cost of 4.9% more. depletion of soil organic carbon.
However, the study showed that there was only a 0.6 percent yield reduction over the test region.
The total net annual social benefits were estimated at US $ 395 million, including savings of US $ 334 million by avoiding greenhouse gas emissions and water pollution, and US $ 100 million by using less. fertilizer.
The study indicated that more than 50 percent of net social benefits came from 20 percent of the study area, hot spots where researchers believed action should be a priority.
“We synthesized four simulated indicators of agroecosystem sustainability – yield, nitrogen emissions, nitrogen leaching and changes in soil organic carbon – into net economic benefits to society as a basis for identifying hotspots and land. infeasible for mitigation, ”said Taegon Kim, research associate. to the BBE department.
Kim added that the social benefits include cost savings through greenhouse gas mitigation, as well as improved water and air quality.
Zhenong Jin, an assistant professor at BBE who led the research, said their analysis showed places where excessive nitrogen fertilizers could be cut without loss of yield.
“We have noticed in some places that reducing nitrogen pollution comes at a cost in terms of depleting organic carbon in the soil, suggesting that other regenerative practices such as cover crops need to be. associated with nitrogen management. “
Smith said researchers were working on developing an app-based nitrogen calculator, but did not yet have a deployment date.
The team built a series of machine learning-based metamodels (a model model) to learn the mechanisms around the carbon and nitrogen cycle from a biogeochemical model called Ecosys.
“Ecosys is advanced computer software and contains the most complex mechanisms to simulate the cycle of energy, water, carbon and nutrient flows in the agroecosystem,” said Smith. “The input data for running Ecosys includes information on weather, soil and management practices. “
A long-term goal of the computer program is to provide a means of anticipating the behavior of the ecosystem under different environmental conditions (soils, climates and managements).
Together with the metamodels, they generated millions of simulated scenarios and investigated basic sustainability questions that addressed not only the location of hot spots, but also the degree of mitigation that could be expected in different management scenarios.
“The underlying Ecosys model works at the farm level, but it is almost impossible to run it in all fields or to assess the many different potential combinations of farm management practices,” he said. “(We) developed a metamodel formed on a smaller sample of fields to provide a better understanding of how changes in field management could impact key economic and environmental outcomes for all fields.”
Although their article largely focused on the knowledge gained at the level of the maize production system, it is based on the metamodel results generated at the individual farm level.
“We encourage farmers not to rely on the specific field estimates presented in the document, as the results are based on hypothetical (although common) input parameters that vary by farm,” Smith said. “That said, if farmers could upload their personalized soil and management data, our model is able to generate much more accurate estimates for them.”
Smith said they are discussing the development and broader applications of computer models with a number of parties.
He added that the paper does not directly address the problems of an agricultural system trying to adapt to a changing climate, but that they explore the application of their approaches to downscaled climate projections.
“Because these models rely more on fundamental biophysical relationships than on historically reported production data, we believe they will produce more reliable indicators of each farm’s performance under future temperature, precipitation and soil assumptions. “
The research was published in the open access journal Environmental Research Letters.