Multiyear methane and nitrous oxide emissions in different irrigation management under long-term continuous rice...

Rice paddies are one of the major sources of anthropogenic methane (CH₄ ) emissions. The alternate wetting and drying (AWD) irrigation management has been shown to reduce CH₄ emissions and total global warming potential (GWP) (CH₄ and nitrous oxide [N₂ O]). However, there is limited information about utilizing AWD management to reduce greenhouse gas (GHG) emissions from commercial-scale continuous rice fields. This study was conducted for five consecutive growing seasons (2015-2019) on a pair of adjacent fields in a commercial farm in Arkansas under long-term continuous rice rotation irrigated with either continuously flooded (CF) or AWD conditions. The cumulative CH₄ emissions in the growing season across the two fields and 5 years ranged from 41 to 123 kg CH₄ -C ha^-1 for CF and 1 to 73 kg CH₄ -C ha^-1 for AWD. On average, AWD reduced CH₄ emissions by 73% relative to CH₄ emissions in CF fields. Compared to N₂ O emissions, CH₄ emissions dominated the GWP with an average contribution of 91% in both irrigation treatments. There was no significant variation in grain yield (7.3-11.9 Mg ha^-1 ) or growing season N₂ O emissions (-0.02 to 0.51 kg N₂ O-N ha^-1 ) between the irrigation treatments. The yield-scaled GWP was 368 and 173 kg CO₂ eq. Mg^-1 season^-1 for CF and AWD, respectively, showing the feasibility of AWD on a commercial farm to reduce the total GHG emissions while sustaining grain yield. Seasonal variations of GHG emissions observed within fields showed total GHG emissions were predominantly influenced by weather (precipitation) and crop and irrigation management. The influence of air temperature and floodwater heights on GHG emissions had high degree of variability among years and fields. These findings demonstrate that the use of multiyear GHG emission datasets could better capture variability of GHG emissions associated with rice production and could improve field verification of GHG emission models and scaling factors for commercial rice farms.