Abstract

Paddy rice cultivation is a vital food source but also a significant contributor to greenhouse gas (GHG) emissions, particularly methane. Management measures such as improved water management and fertilization are being pursued to sustain rice production while cutting down the emissions. However, climate change may pose further challenges for sustainable rice agriculture by either impacting rice production or GHG emissions. Based on a database consisting of 1216 observations across China, we established machine learning models predicting rice yield and GHG emissions, taking into account climate, soil, and management variables. We tested three machine learning methods including random forest, support vector machine and artificial neural networks. The models were trained on 70% of the dataset with the remaining 30% used to evaluate the model performance. Random forest performed best with R2 of 0.59 to 0.72 and modelling efficiency of 52% to 72%. Farmland management emerged as the most important variable to the model prediction, followed by climate and soil variables. Rice production and GHG emissions were estimated to be 245.36 Tg and 218.95 Tg CO2-eq in 2018, respectively. By the year 2050, rice yield was expected to experience a modest decrease ranging from 0.6% to 1.2% due to the combined effects of global change variables, but GHG emissions were projected to increase by 2.1% to 4.9% under different climate change scenarios (Representative Concentration Pathways) in China. A large spatial variability in the impact of climate change was observed, and climate change will have the most significant impact in the Northeast agricultural region of China due to the warming effect. Through the analysis of region-specific farmland management scenarios, this study underscores a “Code Red” situation, signifying that future climate change could pose unprecedented risks to the sustainability of rice production in China, despite ongoing management improvements.

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