Methane and nitrous oxide emissions from puddled transplanted rice (Oryza sativa L.) fields as influenced by nitrogen management and IoT sensor based irrigation scheduling
摘要
A 2-year field experiment was conducted to study the effect of nitrogen (N) management under IoT (Internet of Things) sensor-based irrigation scheduling on methane (CH4) and nitrous oxide (N2O) emissions from puddled transplanted rice (Oryza sativa L.). The experiment included two N levels (RDN: recommended dose of N and FDN: farmer-dose N) and five irrigation schedules (continuous ponding: CP, at water levels (WL) of -50 (WL− 50 mm), -100 (WL− 100 mm), -150 (WL− 150 mm), and − 200 mm (WL− 200 mm) from the soil surface, measured with an IoT sensor. Nitrogen management had a non-significant effect on yield and water productivity. However, RDN significantly reduced CH4, N2O, global warming potential (GWP), and yield-scaled GWP compared with FDN. Among the irrigation schedules, grain yield at CP, WL− 50 mm, and WL− 100 mm was not statistically different but was significantly higher than at WL− 150 mm and WL− 200 mm in both years. The WL− 100 mm, WL− 150 mm, and WL− 200 mm had similar irrigation and total water productivity, all significantly higher than those of the CP and the WL− 50 mm. Among all treatments, the WL− 100 mm recorded the lowest GWP and yield-scaled GWP, primarily due to reduced CH4 emissions, without reducing grain yield compared with CP and the WL− 50 mm. Irrigation at the WL− 100 mm, combined with RDN, effectively reduced CH4 emissions while maintaining rice yield. Additional research is required to refine IoT sensor-driven irrigation practices, N application methods, and sources to minimize emissions across diverse soils and climates.