Alternate wetting and drying irrigation (AWD) has been shown to decrease water use and trace gas emissions from paddy fields. Whereas genotypic water use shows little variation, it has been shown that rice varieties differ in the magnitude of their methane emissions. Management and variety‐related emission factors have been proposed for modelling the impact of paddy production on climate change; however, the magnitude of a potential reduction in greenhouse gas emissions by changing varieties has not yet been fully assessed. AWD has been shown to affect genotypic yields and high‐yielding varieties suffer the greatest loss when grown under AWD. The highest yielding varieties may not have the highest methane emissions; thus, a potential yield loss could be compensated by a larger reduction in methane emissions. However, AWD can only be implemented under full control of irrigation water, leaving the rainy seasons with little scope to reduce methane emissions from paddy fields. Employing low‐emitting varieties during the rainy season may be an option to reduce methane emissions but may compromise farmers’ income if such varieties perform less well than the current standard. Methane emissions and rice yields were determined in field trials over two consecutive winter/spring seasons with continuously flooded and AWD irrigation treatments for 20 lowland rice varieties in the Mekong Delta of Vietnam. Based on the results, this paper investigates the magnitude of methane savings through varietal choice for both AWD and continuous flooding in relation to genotypic yields and explores potential options for compensating farmers’ mitigation efforts.
Using waste tire rubber as a aggregate replacement in the production of concrete can be considered as an effective way for environment and economies. This study presents an approach based on a prediction model using Artificial Neural Networks (ANN) to predict compressive strength of eco-friendly concrete containing waste tire rubber (RC). A data set with nine influencing features including water, cement, supplementary cementitious materials, coarse aggregate, coarse rubber aggregate, fine aggregate, fine rubber aggregate, superplasticizer, age using for training and validating models have been collected from the literature. The output was compressive strength of RC. The combination of root mean square propagation and stochastic gradient descent with momentum method is employed to train the ANN. Using various validation criteria such as coefficient of determination (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), the ANN model was validated and compared with two machine learning (ML) techniques Random Forest (RF) and Multilayer Perceptron (MLP). A Sensitivity analysis also was carried out to validate the robustness and stability of these models. The experimental results showed that the ANN model outperformed in comparing with other models and therefore it can be used as a suitable approach to predict compressive strength of eco-friendly rubber concrete.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.