Reducing methane (CH) emissions is increasingly recognized as an urgent greenhouse gas mitigation priority for avoiding ecosystem 'tipping points' that will accelerate global warming. Agricultural systems, namely ruminant livestock and rice cultivation are dominant sources of CH emissions. Efforts to reduce methane from rice typically focus on water management strategies that implicitly assume that irrigated rice systems are consistently flooded and that farmers exert a high level of control over the field water balance. In India most rice is cultivated during the monsoon season and hydrologic variability is common, particularly in the Eastern Gangetic Plains (EGP) where high but variable rainfall, shallow groundwater, and subtle differences in topography interact to create complex mosaics of field water conditions. Here, we characterize the hydrologic variability of monsoon season rice fields ( = 207) in the Indian EGP ('Eastern India') across two contrasting climate years (2021, 2022) and use the eitrification eomposition (DNDC) model to estimate GHG emissions for the observed hydrologic conditions. Five distinct clusters of field hydrology patterns were evident in each year, but cluster characteristics were not stable across years. In 2021, average GHG emissions (8.14 mt CO-eq ha) were twice as high as in 2022 (3.81 mt CO-eq ha). Importantly, intra-annual variability between fields was also high, underlining the need to characterize representative emission distributions across the landscape and across seasons to appropriately target GHG mitigation strategies and generate accurate baseline values. Simulation results were also analyzed to identify main drivers of emissions, with readily identified factors such as flooding period and hydrologic interactions with crop residues and nitrogen management practices emerging as important. These insights provide a foundation for understanding landscape variability in GHG emissions from rice in Eastern India and suggest priorities for mitigation that honor the hydrologic complexity of the region.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11304473PMC
http://dx.doi.org/10.1016/j.agwat.2024.108931DOI Listing

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