A new computational framework developed in collaboration with Oak Ridge National Laboratory scientist Jiafu Mao provides a detailed assessment of ammonia emissions from global crops and identifies ways that could curb the gas’s release.

ORNL climate modeling expertise contributed to an AI-supported model that estimates global ammonia emissions from crops now and in a warmer future, identifying mitigation strategies.  This map highlights farmland around the world.

ORNL climate modeling expertise contributed to an AI-supported model that estimates global ammonia emissions from crops now and in a warmer future, identifying mitigation strategies. This map highlights farmland around the world. Credit: US Geological Survey

Croplands are the largest single source of ammonia to the atmosphere, emitted from fields treated with nitrogen fertilizers. Ammonia can harm human health, acidify soils and waterways, and contribute to biodiversity loss, food insecurity and climate change. However, international studies have found that emissions can be reduced by 38% without altering total inputs of fertilizers. As in the description. The nature.

Mao helped develop a machine learning method to improve the estimation of ammonia emissions from wheat, corn and rice fields. The model enabled the identification of local best practices that could reduce emissions, even in warmer climates.

“This valuable model, with the help of artificial intelligence tools, can also fine-tune biogeochemical cycling and greenhouse gas emissions. Department of Energy’s Earth System Model” Mao said.

Source: Oak Ridge National Laboratory