Optimizing potassium and nitrogen fertilizer strategies to mitigate greenhouse gas emissions in global agroecosystems
Jiwen Li1, Tianfu Han2, Kailou Liu3
1State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China.
Abstract:
The efficient management of fertilizer application in agriculture is vital for both food security and mitigating greenhouse gas (GHG) emissions. However, as potassium fertilizer (KF) is an essential soil nutrient, its impact on soil GHG emissions has received little attention. To address this knowledge gap and identify key determinants of GHG emissions, we conducted a comprehensive meta-analysis of 205 independent experiments conducted worldwide. Our results revealed that, in comparison to sole nitrogen fertilizer (NF) application, the concurrent use of KF elevated nitrous oxide (N2O) and methane (CH4) emissions by 39.5 % and 21.1 %, respectively, while concurrently reducing carbon dioxide (CO2) emissions by 8.1 %. The ratio of nitrogen and potassium fertilizer input (NF/KF) is identified as the primary factor explaining the variation in N2O emissions, whereas the type of KF plays a crucial role in determining CH4 and CO2 emissions. We observed a significant negative correlation between the NF/KF ratio and response ratios of N2O and CH4 emissions and a positive correlation with CO2 emissions response ratios. Furthermore, our findings indicate that when the NF/KF ratio surpasses 1.97, 4.61, and 3.78, respectively, the impact of KF on reducing N2O, CH4, and CO2 emissions stabilizes. Overall, our results underscore that the global integration of KF into agricultural practices significantly influences N2O and CH4 emissions, while simultaneously reducing CO2 emissions at a large scale. These findings provide a foundational framework and practical guidance for optimizing fertilizer application in the development of GHG emission reduction models.
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