Comparison of ammonia volatilization in paddy and field soils fertilized with urea and ammonium sulfate during rice, potato, and Chinese cabbage cultivation - Lee, Yeon Jin; Im, Eun Chae; Lee, Gyesik; Hong, Sung Chang; Lee, Chang Gu; Park, Seong Jik (Atmospheric Pollution Research, 2024)
Ammonia in fertilizers used in agriculture is emitted into the atmosphere, which can be a precursor to the formation of fine dust that deteriorates air quality. This study investigated ammonia emissions from paddy and upland fields when urea (UR) and ammonium sulfate (AS) were used to grow rice, potato, and Chinese cabbage. Ammonia emissions from agricultural fields, water/soil properties, and meteorological data were monitored. In the paddy fields, the highest level of ammonia emissions was observed immediately after basal fertilization. However, this trend was not observed in the upland fields, where the application of a top dressing to moist soil led to an increase in ammonia emissions. In rice paddy fields and Chinese cabbage upland fields, higher ammonia emissions were observed under UR fertilization than under AS fertilization; whereas, no discrepancy was observed in the potato upland fields. A random forest model was used to determine the importance of environmental variables in ammonia emissions. The results indicated that UR application was the most significant factor affecting ammonia emissions, followed by the NH4+ concentration in ponding water in paddy fields. In upland fields, the soil water content was an important factor, and the temperature of ponding water influenced ammonia emissions. Most of the agronomic traits of rice and Chinese cabbage improved with increased amounts of fertilizer, whereas such a trend was not observed for potato. This study provides new insight into the environmental factors influencing ammonia emissions in agricultural settings. These findings serve as a foundation for the development of strategies and practices aimed at mitigating future ammonia emissions.
Keywords: Ammonia emission; Crop; Machine learning; Ponding water; Soil water content; Urea