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 for up to
11% of the global methane (CH4) emissions from anthropogenic sources. With
increasing populations, particularly in less developed countries where rice is a
major cereal crop, production continues to increase to meet demand.
Implementing site-specific mitigation measures to reduce greenhouse gas
emissions from rice is important to minimise climate change. Measuring
greenhouse gases is costly and time-consuming; therefore, many farmers,
supply chains, and scientists rely on greenhouse gas accounting tools or
internationally acceptable methodologies (e.g., Intergovernmental Panel on
Climate Change) to estimate emissions and explore mitigation options. In this
paper, existing empirical models that are widely used have been evaluated
against measured CH4 emission data. CH4 emission data and management
information were collected from 70 peer-reviewed scientific papers. Model
input variables such as soil organic carbon (SOC), pH, water management
during crop season and pre-season, and organic amendment application were
collected and used for estimation of CH4 emission. The performance of the
models was evaluated by comparing the predicted emission values against
measured emissions with the result showing that the models capture the
impact of different management on emissions, but either under- or
overestimate the emission value, and therefore are unable to capture the
magnitude of emissions. Estimated emission values are much lower than
observed for most of the rice-producing countries, with R correlation
coefficient values varying from −0.49 to 0.87 across the models. In
conclusion, current models are adequate for predicting emission trends and
the directional effects of management, but are not adequate for estimating the
magnitude of emissions. The existing models do not consider key site-specific
variables such as soil texture, planting method, cultivar type, or growing season,
which all influence emissions, and thus, the models lack sensitivity to key site
variables to reliably predict emissions