Reducing ammonia emissions through fertilizer management
- Based on machine learning, recently, researchers have come up with detailed estimates of ammonia emissions from rice, wheat and maize crops.
- The study provides a cropland-specific analysis, emphasizing the environmental impact and health implications of atmospheric ammonia.
Ammonia Emissions: A Global Issue
- 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.
- 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.
Machine Learning Approach
- Researchers utilized machine learning to model ammonia output based on diverse variables.
- These include climate, soil characteristics, crop types, irrigation, tillage, and fertilization practices.
- A comprehensive dataset derived from over 2,700 observations informed the model.
Global Ammonia Emission Estimates
- The machine learning model estimates global ammonia emissions at 4.3 teragrams (4.3 billion kilograms) in 2018.
- Spatially optimizing fertilizer management, guided by the model, could potentially reduce atmospheric ammonia emissions from the three crops by up to 38%.
- It involves deeper placement of enhanced-efficiency fertilizers into the soil during the growing season, utilizing conventional tillage practices.
- Without effective management strategies, a potential increase in ammonia emissions between 4.6% to 15.8% by 2100 is projected.
Prelims Takeaway
- Ammonia
- Greenhouse Gas
- Kyoto Protocol