Based on machine learning, researchers have come up with detailed estimates of ammonia emissions from rice, wheat and maize crops.
The dataset enabled a cropland-specific assessment of the potential for emission reductions.
Which indicates that effective management of fertilizer in the growing of these crops could lower atmospheric ammonia emissions from farming by up to 38%.
Atmospheric ammonia is a key environmental pollutant that affects ecosystems across the planet, as well as human health.
Around 51-60% of anthropogenic ammonia emissions can be traced back to crop cultivation, and about half of these emissions are associated with three main staple crops: rice, wheat and maize.
However, quantifying any potential reductions in ammonia emissions related to specific croplands at high resolution is challenging and depends on details such as nitrogen inputs and local emission factors.
Yi Zheng from the Southern University of Science and Technology, Shenzhen, China and others used machine learning to model ammonia output from rice, wheat and maize.
To inform the model, the researchers developed a dataset of ammonia emissions from over 2,700 observations obtained via systematic review of the published literature.
Using this model, the researchers estimate that global ammonia emission reached 4.3teragrams (4.3billion kilograms) in 2018.
The researchers found that under the fertilizer management scenario, rice crops could contribute 47% of the total reduction potential, and maize and wheat could contribute 27% and 26%, respectively.
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