Modelling CH4 emission from rice ecosystem: A comparison between existing empirical models

Rice is a staple food for more than three billion people and accounts up to 11% of the global methane (CH 4 ) emissions from anthropogenic sources. With increasing populations, particularly in less developed countries where rice major cereal crop, production continues increase meet demand. Implementing site-specific mitigation measures reduce greenhouse gas important minimise climate change. Measuring gases costly time-consuming; therefore, many farmers, supply chains, scientists rely on accounting tools or internationally acceptable methodologies (e.g., Intergovernmental Panel Climate Change) estimate explore options. In this paper, existing empirical models that are widely used have been evaluated against measured CH emission data. data management information were collected 70 peer-reviewed scientific papers. Model input variables such as soil organic carbon (SOC), pH, water during crop season pre-season, amendment application estimation emission. The performance was by comparing predicted values with result showing capture impact different emissions, but either under- overestimate value, therefore unable magnitude emissions. Estimated much lower observed most rice-producing countries, R correlation coefficient varying −0.49 0.87 across models. conclusion, current adequate predicting trends directional effects management, not estimating do consider key texture, planting method, cultivar type, growing season, which all influence thus, lack sensitivity site reliably predict