DataSheet_1_Modelling CH4 emission from rice ecosystem: A comparison between existing empirical models.docx

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.</p

Health effects of sustainable dietary patterns.

<p>Health effects of sustainable dietary patterns.</p

Relative differences in land use (m²/capita/year) between current average diets and sustainable dietary patterns.

<p>Note: n = number of studies, mdn = median.</p

Relative differences in water use (L/capita/day) between current average diets and sustainable dietary patterns.

<p>Note: n = number of studies, mdn = median. The lower and upper bounds of the boxes represent the 1^st and 3^rd quartiles, respectively, and the line within is the median. Whiskers show the minimum and maximum range, excluding outliers, which are shown as dots, and represent values more than 1.5 times the 1^st and 3^rd quartiles.</p

Description of the number of reviewed scenarios, by type of sustainable dietary pattern and environmental indicator.

<p>Description of the number of reviewed scenarios, by type of sustainable dietary pattern and environmental indicator.</p

Selection of eligible studies.

<p>Selection of eligible studies.</p

Relative differences in GHG emissions (kg CO₂eq/capita/year) between current average diets and sustainable dietary patterns.

<p>Note: n = number of studies, mdn = median.</p

Summary of estimates of the potential contribution of ecosystem-based approaches (EBA) / nature-based solutions (NBS) to climate mitigation.

Summary of estimates of the potential contribution of ecosystem-based approaches (EBA) / nature-based solutions (NBS) to climate mitigation.</p

Projected impacts of climate change and increased atmospheric CO₂ concentrations on biodiversity and ecosystem processes (republished from Arneth et al 2020 under a CC BY license, with permission from contributing author Almut Arneth, November 2022).

Projected impacts of climate change and increased atmospheric CO2 concentrations on biodiversity and ecosystem processes (republished from Arneth et al 2020 under a CC BY license, with permission from contributing author Almut Arneth, November 2022).</p

Simulation of Salinity Effects on Past, Present, and Future Soil Organic Carbon Stocks

Soil organic carbon (SOC) models are used to predict changes in SOC stocks and carbon dioxide (CO₂) emissions from soils, and have been successfully validated for non-saline soils. However, SOC models have not been developed to simulate SOC turnover in saline soils. Due to the large extent of salt-affected areas in the world, it is important to correctly predict SOC dynamics in salt-affected soils. To close this knowledge gap, we modified the Rothamsted Carbon Model (RothC) to simulate SOC turnover in salt-affected soils, using data from non-salt-affected and salt-affected soils in two agricultural regions in India (120 soils) and in Australia (160 soils). Recently we developed a decomposition rate modifier based on an incubation study of a subset of these soils. In the present study, we introduce a new method to estimate the past losses of SOC due to salinity and show how salinity affects future SOC stocks on a regional scale. Because salinity decreases decomposition rates, simulations using the decomposition rate modifier for salinity suggest an accumulation of SOC. However, if the plant inputs are also adjusted to reflect reduced plant growth under saline conditions, the simulations show a significant loss of soil carbon in the past due to salinization, with a higher average loss of SOC in Australian soils (55 t C ha^–1) than in Indian soils (31 t C ha^–1). There was a significant negative correlation (<i>p</i> < 0.05) between SOC loss and osmotic potential. Simulations of future SOC stocks with the decomposition rate modifier and the plant input modifier indicate a greater decrease in SOC in saline than in non-saline soils under future climate. The simulations of past losses of SOC due to salinity were repeated using either measured charcoal-C or the inert organic matter predicted by the Falloon et al. equation to determine how much deviation from the Falloon et al. equation affects the amount of plant inputs generated by the model for the soils used in this study. Both sets of results suggest that saline soils have lost carbon and will continue to lose carbon under future climate. This demonstrates the importance of both reduced decomposition and reduced plant input in simulations of future changes in SOC stocks in saline soils