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2024 Vol.57, Issue 4 Preview Page

Original research article

Quantification of methane emissions in rice paddies: A machine learning approach with rice cultivar and transplanting date
Jun-Yeong Lee1†
,
Yun-Gu Kang1†
,
Ji-Hoon Kim2
,
Jun-Ho Kim2
,
Jiwon Choi2
,
Taek-Keun Oh3*
1Doctoral Student, Department of Bio-Environmental Chemistry, College of Agriculture and Life Science, Chungnam National University, Daejeon 34134, South Korea
2Master’s Student, Department of Bio-Environmental Chemistry, College of Agriculture and Life Science, Chungnam National University, Daejeon 34134, South Korea
3Professor, Department of Bio-Environmental Chemistry, College of Agriculture and Life Science, Chungnam National University, Daejeon 34134, South Korea
*Corresponding Author
†These authors contributed equally to this work.
30 November 2024. pp. 331-343
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Abstract

Estimates of methane (CH4) emissions from rice paddies are crucial to assess the contribution of rice production in mitigating or adapting climate change. This study aimed to develop a predictive model for estimating the CH4 emissions in rice paddies, and suggest rice production strategies for sustainable agriculture. The parameters for constructing the CH4 emissions predictive model were obtained from field-based experiments from 2022 to 2023 in Daejeon, South Korea and separated as follows: climatic factors (i.e., mean temperatures of atmosphere, soil, and water, annual total precipitation), cultivation factors (i.e., cultivar and transplanting date), and soil properties (i.e., pH, organic matter, and bulk density). Additionally, before the developing the predictive model, each variable underwent significance test (p < 0.05). The statistical relationship and regression analysis of the CH4 emissions were followed the Pearson correlation method and multiple linear regression (MLR) algorithm, respectively. Consequently, the CH4 emissions had the negative correlation with the mean water temperature, total precipitation, transplanting date, and bulk density, where the mean water temperature had the highest negative coefficient at -0.824. In contrast, the CH4 emissions were raised by the increase in the mean atmospheric temperature (+0.231) and organic matter content (+0.391). The predictive model for forecasting the CH4 emissions had the coefficient of determination (R2) value of 0.836, which exhibits the reliability of 84% for estimating the CH4 emissions. In predictive model, soil bulk density (+18.2933) had the highest influence, while total precipitation exhibited the lowest coefficient at -0.0426. The statistical metrics, which showed the range and occurrence of residuals in the predictive model, were evaluated at 8.802 kg ha-1 and 7.235%, respectively. These findings serve as a valuable tool for establishing strategies to estimate the CH4 emissions in rice production, whereas expanding the dataset to include various experimental regions was needed to enhance the model’s performance.

Development of predictive model for total methane emissions in rice paddies.
Keywords
CH4
Climate change
Multiple linear regression (MLR)
Prediction
Rice cultivation
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Information
  • Publisher :Korean Society of Soil Science and Fertilizer
  • Publisher(Ko) :한국토양비료학회
  • Journal Title :Korean Journal of Soil Science and Fertilizer
  • Journal Title(Ko) :한국토양비료학회 학회지
  • Volume : 57
  • No :4
  • Pages :331-343
  • Received Date : 2024-09-23
  • Revised Date : 2024-11-22
  • Accepted Date : 2024-11-22
  • DOI :https://doi.org/10.7745/KJSSF.2024.57.4.331
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