A computer modelling program can determine how much yields and emissions would drop if fertilizer use was reduced
Advanced computer modelling programs developed by researchers at the University of Minnesota will allow farmers and food companies to explore their nitrogen reduction plans.
It will give them insights into economic and environmental costs, as well as better field management strategies.
With climate change, reducing greenhouse gas emissions and nitrogen water pollution from agricultural practices is an environmental priority. The balancing act is to help producers navigate emissions challenges while mitigating impact on the environment and improving farm income.
“By providing key sustainability indicators related to crop production, our metamodels can be useful tools for food companies to quantify the emissions in their supply chain and distinguish mitigation options for setting sustainability goals,” Timothy Smith, professor at the university’s department of bioproducts and biosystems engineering (BBE), said in the media release.
The study was conducted in the United States Midwest corn belt. It found that reducing nitrogen fertilizer by 10 percent led to 9.8 percent fewer nitrogen emissions and 9.6 percent less nitrogen leaching at a cost of 4.9 percent more soil organic carbon depletion.
However, the study showed there was only a 0.6 percent yield reduction over the test region.
The net total annual social benefits were estimated at US$395 million including a savings of $334 million by avoiding greenhouse gas emissions and water pollution, and $100 million using less fertilizer.
The study indicated that more than 50 percent of the net social benefits came from 20 percent of the study area, hot spots where researchers believed action should be prioritized.
“We synthesized four simulated indicators of agroecosystem sustainability — yield, nitrogen emissions, nitrogen leaching, and changes in soil organic carbon — into economic net societal benefits as the basis for identifying hot spots and infeasible land for mitigation,” said Taegon Kim, research associate at the BBE department.
Kim added that the social benefits include cost savings from 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 spots where excessive nitrogen fertilizer could be cut without yield loss.
“We noticed in some places that reducing nitrogen-related pollution comes at a cost of depleting organic carbon in the soil, suggesting that other regenerative practices such as cover cropping need to be bundled with nitrogen management.”
Smith said researchers are working toward developing an app-based nitrogen calculator but do not have a roll-out date yet.
The team built a series of machine-learning based metamodels (a model of a model) to learn the mechanisms around the carbon and nitrogen cycle from a biogeochemical model called Ecosys.
“Ecosys is an advanced computer software and contains the most complex mechanisms for simulating energy, water, carbon, and nutrient fluxes cycling in the agroecosystem,” said Smith. “Inputs for running Ecosys include weather, soil and management practice information.”
A long-term objective of the computer program is to provide a means to anticipate ecosystem behaviour under different environmental conditions (soils, climates, and managements).
With the metamodels, they generated millions of simulated scenarios and investigated basic sustainability questions that not only addressed the location of hot spots but how much mitigation could be expected under different management scenarios.
“The underlying Ecosys model operates at the farm level, but it is nearly impossible to run it across every field or to assess the many different potential combinations of farm management practices,” he said. “(We) developed a metamodel trained on a smaller sample of fields to provide a better understanding of how changes to field management might impact key economic and environmental outcomes for all fields.”
While their paper largely focused on insights gained at the corn production system level, they are based on the metamodel’s results generated at the individual farm level.
“We encourage farmers not to rely on the specific field estimates presented in the paper as the results are based on hypothetical (though common) input parameters which vary by farm,” said Smith. “That said, if farmers were able to upload their customized soil and management data, our model is able to generate much more accurate estimates for them.”
Smith said that they have been discussing broader development and applications of the computer models with a number of parties.
He added that the paper does not directly address the issues of an agricultural system attempting to adapt to a changing climate, but they are exploring the application of their approaches to downscaled climate projections.
“Because these models rely more heavily on fundamental biophysical relationships than historically reported production data, we think they will produce more reliable indicators of individual farm performance under future temperature, precipitation, and soil assumptions.”
The research was published in the open access journal Environmental Research Letters.