Abstract
Rice paddies are one of the largest greenhouse gases (GHGs) facilitators that are predominantly regulated by nitrogen (N) fertilization. Optimization of N uses based on the yield has been tried a long since, however, the improvement of the state-of-the-art technologies and the stiffness of global warming need to readjust N rate. Albeit, few individual studies started to, herein attempted as a systematic review to generalize the optimal N rate that minimizes global warming potential (GWP) concurrently provides sufficient yield in the rice system. To satisfy mounted food demand with inadequate land & less environmental impact, GHGs emissions are increasingly evaluated as yield-scaled basis. This systematic review (20 published studies consisting of 21 study sites and 190 observations) aimed to test the hypothesis that the lowest yield-scaled GWP would provide the minimum GWP of CH4 and N2O emissions from rice system at near optimal yields. Results revealed that there was a strong polynomial quadratic relationship between CH4 emissions and N rate and strong positive correlation between N2O emissions and N rate. Compared to control the low N dose emitted less (23%) CH4 whereas high N dose emitted higher (63%) CH4 emission. The highest N2O emission observed at moderated N level. In total GWP, about 96% and 4%, GHG was emitted as CH4 and N2O, respectively. The mean GWP of CH4 and N2O emissions from rice was 5758 kg CO2 eq ha−1. The least yield-scaled GWP (0.7565 (kg CO2 eq. ha−1)) was recorded at 190 kg N ha−1 that provided the near utmost yield. This dose could be a suitable dose in midseason drainage managed rice systems especially in tropical and subtropical climatic conditions. This yield-scaled GWP supports the concept of win–win for food security and environmental aspects through balancing between viable rice productivity and maintaining convincing greenhouse gases.
Original language | English |
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Article number | 116386 |
Pages (from-to) | 1-10 |
Number of pages | 10 |
Journal | Environmental Pollution |
Volume | 271 |
Issue number | 116386 |
Early online date | 24 Dec 2020 |
DOIs | |
Publication status | Published - 15 Feb 2021 |
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Statistics is an essence for data science and policy analysis
Rahman, A. (Creator)
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