Significant temporal variability leads to estimation bias in greenhouse gas emissions from aquaculture pond systems

Rising demand for aquatic products has expanded aquaculture, significantly elevating greenhouse gas (GHG) emissions from the aquaculture ponds. However, the emission estimation shows huge uncertainty. This study employed a meta-analysis of 1060 datasets of CO₂, CH₄ and N₂O fluxes to examine how temporal variability affects GHG emissions from China's aquaculture ponds and to identify key environmental drivers. The results reveal that China’s aquaculture ponds are significant sources of GHGs to the atmosphere, with fluxes of CO₂, CH₄, and N₂O from coastal pond systems at 5.50, 7.41 mg m² h⁻¹, and 16.72 μg m² h⁻¹ during the farming period, and −8.96, 4.33 mg m² h⁻¹, and 44.98 μg m² h⁻¹ during the non-farming period, respectively. Regarding inland pond systems, the fluxes of CO₂, CH₄, and N₂O were 50.48, 5.19 mg m² h⁻¹, and 36.35 μg m² h⁻¹ during farming period, and 0.90, 1.03 mg m² h⁻¹, and 51.46 μg m² h⁻¹ during non-farming period, respectively. Total GHG annual emissions were 42.17 Tg CO₂-eq over a 100-year time scale, predominantly from CH₄ at 74.11 %, with CO₂ contributing to 9.63 %, and N₂O to 6.63 %. Post-cultivation drainage significantly shifts biogeochemical conditions and emission patterns, reducing total GHG emissions. Ignoring the non-farming period leads to overestimated CO₂ and CH₄ emissions, and underestimated N₂O emissions. Our study provides new insights into GHG estimation from aquaculture ponds, highlighting the importance of considering temporal variability in GHG inventories, and supporting the development of management-based mitigation strategies.