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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. [ABSTRACT FROM AUTHOR]

Purpose: Integrating process-based models with machine learning (ML) is an important way to derive spatially-explicit information on the dynamics of cropland soil organic carbon (SOC) stock. However, the performance of integrating process-based models with different structures remains unclear. The purpose of this study is to develop integrated models by combining multiple process-based models with ML for improving the spatiotemporal dynamics modelling of cropland SOC. Methods: A total of 1219 cropland SOC stock data (0–20 cm, sampled in 1980, 2000, and 2015) were collected from southern Jiangsu Province of China. Three models were built by integrating either Rothamsted carbon model (RothC), microbial-mineral carbon stabilization model (MIMICS), or the both with space-for-time random forest (RF-SFTS). Results: The overall spatiotemporal patterns of SOC dynamics that modelled by RothC, MIMICS, the integration of RothC with RF-SFTS (RF-RothC), the integration of MIMICS with RF-SFTS (RF-MIMICS), and the integration of RothC and MIMICS with RF-SFTS (RF-RothC-MIMICS) were similar, but their prediction accuracies differed significantly. The RF-RothC-MIMICS model had the best performance among the six models, being a lowest root mean squared error (RMSE) of 0.79 kg C m−2 and a highest coefficient of determination (R2) of 0.54, compared to the prediction accuracy of other models. Conclusions: Integrating process-based models differing in structures with ML significantly improved the spatiotemporal modelling of SOC dynamics. Outputs of this study may provide some guidance on more realistic projection of SOC dynamics under future changes in climate, land use, and management practices. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal changes in soil organic carbon (SOC) and their driving factors is an important prerequisite in decision‐making for maintaining sustainable agricultural development and addressing climate change. However, the impacts of urbanization‐induced changes in cropland management practices on the spatiotemporal changes in SOC and their implications remain unclear, in particular over different time periods. In this study, a total of 1219 cropland topsoil SOC data (0–20 cm) were collected from a rapidly urbanizing area of southeastern China (southern Jiangsu Province) in 1980, 2000, and 2015, and geostatistical sequential Gaussian simulations were used to identify the changes in the spatiotemporal patterns of SOC during the period of 1980–2015. Results showed that the changes in SOC within the different time periods were significantly different, with a net increment of 3.65 g kg−1 during the period of 1980–2000 and a net decrement of 2.32 g kg−1 during the period of 2000–2015. Significant SOC accumulation occurred throughout the study area during 1980–2000, while SOC decline became predominant in the southeast during 2000–2015. Overall, the SOC contents for 60% of the study area increased significantly over the entire 35‐year period. The SOC increase during the first two decades (1980–2000) was largely attributed to the increasing soil C input that resulted from the enhanced crop productivity by chemical fertilizers, while the stagnant soil carbon inputs associated with the rapid urban expansion were the primary reason for constraining cropland SOC accumulation in the subsequent 15 years (2000–2015). These findings highlight the importance of balancing agricultural development and urbanization processes to maintain SOC levels, and may also provide some guidance for planning cropland soil C management strategies in many areas that are undergoing similar urbanization processes. [ABSTRACT FROM AUTHOR]

Artificial intelligence has been increasingly employed to improve operations for various firms and industries. In this study, we construct a box office revenue prediction system for a film at its early stage of production, which can help management overcome resource allocation challenges considering the significant investment and risk for the whole film production. In this research, we focus on China's film market, the second-largest box office in the world. Our model is based on data regarding the nature of a film itself without word-of-mouth data from social platforms. Combining extreme gradient boosting, random forest, light gradient boosting machine, k-nearest neighbor algorithm, and stacking model fusion theory, we establish a stacking model for film box office prediction. Our empirical results show that the model exhibits good prediction accuracy, with its 1-Away accuracy being 86.46%. Moreover, our results show that star influence has the strongest predictive power in this model. [ABSTRACT FROM AUTHOR]

We collected 682 topsoil samples (0–20cm) from agricultural lands of Luhe County in East China, and analyzed the spatial distribution patterns and potential sources of four major heavy metals. High Pb and Cr were mainly in the southeast adjacent to the Yangtze River, and Cd were characterized by an increasing trend from northwest to southeast, while high Hg mainly occurred in the areas near downtown. Spatially-continuous sources dominated the soil heavy metal concentrations. Contributions of spatially-continuous natural source (soil parent material) to Cr and Cd were 97.0% and 77.7%, respectively, whereas contributions of spatially-continuous anthropogenic source such as diffuse pollution to Pb and Hg were 75.7% and 86.7%, respectively. The distance to factories was the most influential anthropogenic factor for localized anomaly patterns of Pb, Cd, and Cr, while the intensive agricultural land uses associated with the rapid urban expansion were particularly relevant to the anomaly patterns of Hg. [ABSTRACT FROM AUTHOR]

The dynamics of soil organic carbon stocks (SOCS) in croplands are highly sensitive to agricultural management practices, representing a crucial component of soil fertility and playing a pivotal role in regulating the global carbon cycle. However, the historical trajectory of SOCS in croplands and its future developments in response to agricultural management practices such as carbon inputs coupled with the impact of climate change, remain unclear, especially for the areas with intensive human activities. Here we collected a total of 1219 cropland SOCS data (0–20 cm) from the southern Jiangsu Province of China in 1980, 2000, and 2015, and the RothC-RFst model developed by integrating the RothC-predicted SOCS into the space-for-time random forest (RFst) was used to model spatiotemporal dynamic of cropland SOCS over the period of 1980–2015 and its future trend under three carbon input scenarios (CI+1, CI+0.6, and CI+0). Results showed that the historical change in SOCS from 1980 to 2015 was characterized as a significant increase of SOCS (+6.02 t C ha-1) before 2000 and SOCS decline (-5.05 t C ha-1) over the period of 2000–2015. The future trend of cropland SOCS under different CI scenarios were similar during 2024–2100, with substantial SOCS increases of 6 % and 4 % under CI+1 and CI+0.6, and almost unchanged SOCS (<1 %) for the baseline scenario (CI+0). The projected dynamics of SOCS indicated a constrained response to future climate change (e.g., the CMIP6 SSP1–1.9 and SSP5–8.5) under the CI-based scenarios. These findings highlight the significant role of the CI levels in preserving and enhancing cropland soil carbon sequestration potential. We believe that outputs from this study provide valuable insights for the development of cropland management strategies through effective economic and policy incentives, especially in areas heavily impacted by human activities. • Cropland soils functioned as important C sinks in the past. • The elevated CI promotes SOCS, even in the face of extreme climate constraints. • The SOCS is projected to increase by 6 % with annual growth rate of 1 % in CI levels. • SOCS dynamic remains dynamic balance under current CI and SSP1–1.9/SSP5–8.5. [ABSTRACT FROM AUTHOR]

• We developed static SOC mapping to space–time SOC modeling during 1980–2015. • GWRK-RothC presented the higher prediction accuracy than GWRK and RothC. • Mechanisms of the RothC model can be integrated into the GWRK algorithm. • Integration of mechanistic model into the DSM can be extended to different areas. Soil organic carbon (SOC) in intensively human-impacted areas often presents extremely complicated patterns in space and time, which brought a new challenge to space–time SOC modelling. Here we attempt to create a space-for-time GWRK-RothC model by incorporating the predictions of the process-based RothC model into the geographically weighted regression kriging (GWRK) for improving the space–time modelling of SOC in intensively human-impacted area. We collected a total of 1219 topsoil samples (0–20 cm) from southern Jiangsu Province of China in 1980, 2000, and 2015, and modelled the spatiotemporal dynamics of SOC by using the modified RothC model (with a newly-added pH modifier for representing the impact of soil acidification on SOC decomposition), the space-for-time GWRK, and the proposed space-for-time GWRK-RothC model. Results showed that the change trends in SOC derived by the three methods were similar, with an overall trend of SOC increase during 1980–2000 and SOC decline in the subsequent 15 years (2000–2015). However, their performances for space–time SOC modelling were different. The proposed space-for-time GWRK-RothC model had the highest accuracy in representing the spatiotemporal evolution of SOC in the study area, with the lowest root mean square error (RMSE) (3.06 g kg−1), mean error (ME) (-0.19 g kg−1), and the highest coefficient of determination (R2) (0.61), compared to the accuracy of space-for-time GWRK (RMSE = 3.53 g kg−1, ME = -0.36 g kg−1, and R2 = 0.45) and RothC model (RMSE = 4.23 g kg−1, ME = 0.20 g kg−1, and R2 = 0.20). These results highlighted the importance of integrating process-model-derived SOC spatiotemporal dynamics information in space-for-time digital soil mapping (DSM) for improving the space–time SOC modelling. We believe that outputs from this study may offer valuable guidance for developing space–time DSM model to achieve better performance of SOC modelling in space and time, especially for areas affected by strong anthropogenic activities. [ABSTRACT FROM AUTHOR]

Evaluating the impact of global warming on rice production and greenhouse gas (GHG) emissions is critical for ensuring food security and mitigating the consequences of climate change. Nonetheless, the impacts of warming on crop production, GHG emissions, and microbial mechanisms in the single-cropping rice systems remain unclear. Here, a two-year field experiment was conducted to explore the effects of warming (increased by 2.7-3.0 °C on average) in the rice growing season on crop production and functional microorganisms associated with GHG emissions. Results showed that warming resulted in significant reduction (p < 0.01) in the aboveground biomass and grain yield as well as in grain weight, the number of spikelets per panicle, and the seed-setting rate. However, it caused a significant increase (p < 0.01) in the number of panicles by 15.6 % and 34.9 %, respectively. Furthermore, warming significantly increased (p < 0.01) seasonal methane (CH 4 ) emissions but reduced nitrous oxide (N 2 O) emissions, particularly in 2022.The relative abundance of genes associated with CH 4 metabolism and nitrogen metabolism was increased by 40.7 % and 32.7 %, respectively, in response to warming. Moreover, warming had a positive impact on the abundance of genes related to CH 4 production and oxidation processes but did not affect the denitrification processes associated with N 2 O production. These results showed that warming decreased rice yield and biomass in the single cropping rice system but increased CH 4 emissions and global warming potential. Taken together, to address the increasing food demand of a growing population and mitigate the impacts of global warming, it is imperative to duce GHG emissions and enhance crop yields., Competing Interests: Declaration of competing interest We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Fuzhuan brick tea (FBT) is traditionally consumed by the ethnic group in the border region of northwest China. The unique yellow fungal (Eurotium cristatum) growth phase is considered to be the key process point in the manufacture of the brick tea. The fungi from FBT are not only strongly correlated to the quality of brick tea, but also have the potential function of preventing obesity. The water extract of fungi (100 μg/mL) can significantly inhibit fat deposition in 3T3-L1 adipocyte and Caenorhabditis elegans. Furthermore, the inhibition of 3T3-L1 adipocyte formation was not due to the suppression on cell viability.