Reworking fertilizer management could slash ammonia pollution from key crops – study

A recent study found that improved fertilizer management practices have the potential to significantly reduce ammonia emissions linked to the cultivation of rice, wheat, and corn. These three staple crops are not only critical to global food security but are also major contributors to atmospheric ammonia, a pollutant linked to serious health problems such as asthma, lung cancer, and cardiovascular disease.

Conducted by an international team of scientists and published in the journal Nature, the study employed machine learning to analyze large amounts of agricultural and environmental data. The research has produced the most detailed global map of ammonia emissions from these crops to date, estimating that a total of 4.3 billion kilograms (approximately 9.5 billion pounds) of ammonia were released in 2018 alone.

The findings suggest that by tailoring fertilizer application to local environmental and soil conditions, it could be possible to reduce these emissions by as much as 38%. This strategy not only considers the type of crop but also incorporates factors like local climate and soil characteristics, which influence how much nitrogen is lost to the atmosphere as ammonia.

The study’s use of machine learning marks a significant step forward in environmental research, and enabled scientists to disentangle the complex interplay between agricultural practices and natural variables. This method surpasses traditional data analysis techniques by integrating a wider array of factors and providing a granular, high-resolution view of ammonia emissions.

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Critically, the research indicates that without intervention, climate change could exacerbate the problem, potentially increasing ammonia emissions from these crops by up to 15.8% by the end of the century. However, the study did offer a silver lining: by adopting optimized fertilizer management practices it may be possible to completely offset this rise, safeguarding both food security and air quality.

Beyond its immediate findings, the study underscores the value of machine learning in environmental science, offering new insights into the relationship between human activities and the environment. By highlighting actionable strategies for reducing ammonia emissions, the research provides a roadmap for policymakers, stakeholders in agriculture and environmental management, and farmers to implement more sustainable agricultural practices.

The implications of this study extend beyond the specific context of rice, wheat, and corn cultivation, pointing to the broader potential for technology-driven approaches to address environmental challenges. As the global community grapples with the twin imperatives of feeding a growing population and preserving environmental health, this research illustrates the critical role of innovation in achieving sustainable development goals.