Shifting Rice Cropping Systems Mitigates Ecological Footprints and Enhances Grain Yield in Central China

Ratoon Rice Cropping Mitigates the Greenhouse Effect by Reducing CH4 Emissions through Reduction of Biomass during the Ratoon Season

The ratoon rice cropping system (RR) is developing rapidly in China due to its comparable annual yield and lower agricultural and labor inputs than the double rice cropping system (DR). Here, to further compare the greenhouse effects of RR and DR, a two-year field experiment was carried out in Hubei Province, central China. The ratoon season showed significantly lower cumulative CH4 emissions than the main season of RR, the early season and late season of DR. RR led to significantly lower annual cumulative CH4 emissions, but no significant difference in cumulative annual N2O emissions compared with DR. In RR, the main and ratoon seasons had significantly higher and lower grain yields than the early and late seasons of DR, respectively, resulting in comparable annual grain yields between the two systems. In addition, the ratoon season had significantly lower global warming potential (GWP) and greenhouse gas intensity-based grain yield (GHGI) than the main and late seasons. The annual GWP and GHGI of RR were significantly lower than those of DR. In general, the differences in annual CH4 emissions, GWP, and GHGI could be primarily attributed to the differences between the ratoon season and the late season. Moreover, GWP and GHGI exhibited significant positive correlations with cumulative emissions of CH4 rather than N2O. The leaf area index (LAI) and biomass accumulation in the ratoon season were significantly lower than those in the main season and late season, and CH4 emissions, GWP, and GHGI showed significant positive correlations with LAI, biomass accumulation and grain yield in the ratoon and late season. Finally, RR had significantly higher net ecosystem economic benefits (NEEB) than DR. Overall, this study indicates that RR is a green cropping system with lower annual CH4 emissions, GWP, and GHGI as well as higher NEEB.

Coupling life cycle assessment and global sensitivity analysis to evaluate the uncertainty and key processes associated with carbon footprint of rice production in Eastern China

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 CH₄ 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 CO₂eq t^-1 year^-1 (95% confidence interval was 663.9-2175.8 kg CO₂eq t^-1 year^-1), which was significantly higher than that of conventional rice production (926.0 ± 213.6 kg CO₂eq t^-1 year^-1, 95% confidence interval 582.5-1429.7 kg CO₂eq 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 CH₄ 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.

A Study on Spatial-Temporal Differentiation and Influencing Factors of Agricultural Water Footprint in the Main Grain-Producing Areas in China

It is an urgent scientific issue to explore the spatial and temporal differentiation and impact indicators of the agricultural water footprint in major grain-producing areas. Therefore, this study tries to use the water footprint theory to implement top-down calculation of the agricultural water footprint in major grain-producing regions from 2000 to 2019 and investigate the various impacts on the agricultural water footprint under the influence of spatial-temporal effects using spatial autocorrelation and the spatial Dubin model. The results indicate that from 2000 to 2019, the overall agricultural water footprint of China showed a fluctuating downward trend in an inverted N shape and demonstrated high–high and low–low spatial aggregation characteristics. There are notable characteristics, including high spatial dependence, spatial barriers, and path locking of the agricultural water footprint in most provinces and regions of the main grain-producing areas. Policy factors, water-saving technologies, social development, economic development, and industrial structure adjustment are all significantly and negatively correlated with the increase in the agricultural water footprint, while agricultural production and natural factors have a significant positive relationship with the agricultural water footprint. The spatial spillover effect of water-saving technologies, industrial restructuring, agricultural production, and natural factors is powerful. Therefore, a rationally optimized industrial structure, strengthened regional linkage of water resources management and control, and the promotion of efficient water infrastructure technology are important ways to inhibit the agricultural water footprint.