Abstract
An accurate and objective evaluation of the carbon footprint of rice production is crucial for mitigating greenhouse gas (GHG) emissions from global food production. Sensitivity and uncertainty analysis of the carbon footprint evaluation model can help improve the efficiency and credibility of the evaluation. In this study, we combined a farm-scaled model consisting of widely used carbon footprint evaluation methods with a typical East Asian rice production system comprising two fertilization strategies. Furthermore, we used Morris and Sobol' global sensitivity analysis methods to evaluate the sensitivity and uncertainty of the carbon footprint model. Results showed that the carbon footprint evaluation model exhibits a certain nonlinearity, and it is the most sensitive to model parameters related to CH(4) emission estimation, including EF(c) (baseline emission factor for continuously flooded fields without organic amendments), SF(w) (scaling factor to account for the differences in water regime during the cultivation period), and t (cultivation period of rice), but is not sensitive to activity data and its emission factors. The main sensitivity parameters of the model obtained using the two global sensitivity methods were essentially identical. Uncertainty analysis showed that the carbon footprint of organic rice production was 1271.7 ± 388.5 kg CO(2)eq t(-1) year(-1) (95% confidence interval was 663.9-2175.8 kg CO(2)eq t(-1) year(-1)), which was significantly higher than that of conventional rice production (926.0 ± 213.6 kg CO(2)eq t(-1) year(-1), 95% confidence interval 582.5-1429.7 kg CO(2)eq t(-1) year(-1)) (p<0.0001). The carbon footprint for organic rice had a wider range and greater uncertainty, mainly due to the greater impact of CH(4) emissions (79.8% for organic rice versus 53.8% for conventional rice). EF(c) , t, Y, and SF(w) contributed the most to the uncertainty of carbon footprint of the two rice production modes, wherein their correlation coefficients were between 0.34 and 0.55 (p<0.01). The analytical framework presented in this study provides insights into future on-farm advice related to GHG mitigation of rice production.