Mapping gridded emission factors (EFs) of crops is vital for estimating ammonia (NH3) emissions in China using the bottom-up methods. However, there is still a lack of high-resolution gridded EFs of NH3 by crops in China, which are affected by climate, soil, and human management. Here, we established a data-driven approach for mapping crop-specific EFs of NH3 in China based on ground-based data and multiple geospatial data. We found that rice exhibited the highest EFs at 13.35 %, followed by wheat at 5.50 %, and maize at 5.15 %. This underscores the significance of utilizing EFs specific to each crop for predicting NH3 emission estimations. Furthermore, our results reveal substantial spatial variations in NH3 EFs across China, with notably higher values observed in South China for rice and elevated EFs in North China for wheat and maize. According to our model, the deep fertilization method emerges as the most effective method for reducing NH3 emissions, offering a remarkable 64 % reduction. Ongoing urbanization in China will lead to a rapid decline in the rural labor force in the coming years, which requires agricultural mechanization with less labor input. This shift in turn could support the implementation of deep fertilization techniques and reduce NH3 emissions by half in 2050. Our findings offer valuable insights for shaping the future trajectory of Chinese agriculture in overcoming agricultural NH3 loss.
Keywords: Ammonia emissions; Deep fertilization; Emission factors; Mitigation potential.
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