In response to the urgent need for the sustainable development of the marine environment, the Chinese government initiated Blue Bay Remediation Action (BBRA) in 2016, piloting efforts in 16 bay cities. Recognizing that these areas function as integrated ecosystems, it is clear that solely addressing issues within the bays will not completely resolve the ecological challenges. Guided by the principles of comprehensive treatment and sustainable development inherent in the BBRA policy, this paper incorporates the ecological indicators of bays and the surrounding sea into a sustainable development framework for 52 bay cities. To identify a balanced approach for the development of the economy, ecology, and society, a three-component evaluation system with 39 indicators is established to assess the sustainable development levels of bay cities from 2015 to 2019 in China. According to the results of the principal component and coupling coordination degree analyses, they indicate that after BBRA, the change in the sustainable development levels of the pilot bay cities is not obvious. Significant disparities exist in the levels of sustainable development among the majority of the pilot cities, with imbalances observed across economic, ecological, and social dimensions. Consequently, in researching the balanced sustainable development of bay cities, it is essential to consider the unique development characteristics of each city during the implementation process of the BBRA.

Farmer professional cooperatives are the focus objects of agricultural carbon emission reduction; with the use of the advantages of scale economy and technology, one can promote the development of low-carbon agriculture. In order to study the influencing factors of agricultural carbon emission reduction on farmer professional cooperatives, we explore the interaction effects of carbon emission reduction behavior between farmer professional cooperatives and farmers under government interventions. This paper introduces a carbon transaction mechanism as well as reward and punishment polices into a tripartite evolutionary game model between farmer professional cooperatives, governments, and farmers. Based on the model, we identify a stable evolution strategy and perform simulation analysis. The results indicate that the carbon transaction mechanism can effectively suppress the negative effect of increased costs through higher revenues of the carbon transaction, and carbon prices above 60 CNY/ton enable cooperatives to reduce regional emissions. Higher revenues can promote positive carbon emission reduction behaviors of farmer professional cooperatives and farmers. The sharing ratio increases from 20% to 80%, and farmers gain additional benefits by cooperating in the farmer professional cooperative practices to reduce emissions. Rational regulation of carbon transaction price and quota can promote the participation of farmer professional cooperatives in carbon emission reduction practices and promote the farmers’ inclusion into farmer professional cooperatives.

Maize is most sensitive to drought stress at the floral stage by reducing tassel and silk quality, and thus improving drought tolerance at this stage may help preserve yield. It has been reported that BRs (brassinosteroids) promote floral development under drought stress. However, the function of the brassinosteroid biosynthesis gene ZmDWARF11 (ZmD11) on floral growth under drought stress has not been elucidated. This study found that under normal growth conditions, the heterologous over-expression of ZmD11 significantly enhanced both the vegetative growth and floral development of tobacco. Under drought stress, overexpressing ZmD11 reduced stress-induced tobacco flower size reduction, while it did not affect vegetative growth. After drought treatment, the activities of protective enzymes, including CAT (Catalase), SOD (Superoxide Dismutase), and POD (Peroxidase), were higher, while the content of MDA (Malondialdehyde) was lower in ZmD11 over-expression tobacco lines than that in the wild type control. The relative expression of dehydrin-related genes NtLeat5 and NtERD10 was increased in ZmD11 over-expression tobacco lines compared to that in the control. In summary, we reported that ZmD11 plays a role in tobacco floral development under drought stress. Our data are valuable in understanding the functions of BRs in regulating plant floral development under drought stress.

This article aims to investigate the mechanisms of farmer professional cooperative (FPC) operations and to understand their role in promoting low-carbon production among small-scale farmers in China. Agricultural carbon emissions account for 17% of the total carbon emission in China; therefore, reducing agricultural carbon emissions is important for China to achieve carbon neutrality. Small-scale farmers face many obstacles in achieving the low-carbon transition of agriculture, which therefore makes them a priority target for the implementation of low-carbon production systems in China. Participating in FPCs is an effective support mechanism for them to conduct low-carbon production. In this paper, a system dynamics model is used to simulate the methods of how FPCs assist small-scale farmers to adopt low-carbon production practices within the framework of China’s carbon trading system, through the year 2030. After attending the carbon transaction system, the agricultural carbon emissions are anticipated to decline by 10.21%, and FPCs’ net income could increase by 11.85%. In a scenario where the price of their agricultural products increases, the reduction of carbon emissions and the increase of FPCs’ net income will be beneficial. Under the operation of FPCs, the greatest profits will be generated from trading, and these will be distributed to small-scale farmers, thereby creating a positive feedback loop between carbon transactions and FPC operations. This article seeks determine the potential outcomes that can serve as a basis for informed decision-making within relevant policy-making agencies regarding agricultural carbon transactions by simulating the potential benefits to both small-scale farmers and FPCs from the integration of a carbon trading system.

To address the current issues in soil organic carbon (SOC) content prediction where data preprocessing relies on expert experience to formulate fixed rules, resulting in a lack of uniform standards and insufficient consideration of regional soil heterogeneity; while hyperparameter tuning faces problems of high computational costs and excessively long runtimes, this study proposes an intelligent modeling workflow driven by Large Language Models (LLM). This workflow focuses on optimizing two key aspects of SOC Random Forest modeling: data preprocessing and hyperparameter tuning. Results: The LLM-defined rules achieved sample retention rates of 55.33% and 61.90% in the two regions, respectively, showing more significant differences compared to traditional hard-coded rules (56.2% and 59.3%), and the mean soil organic carbon content deviations (30.27% and 20.05%) were both lower than those of traditional hard-coding. At the same time, the mean soil organic carbon content values in both regions closely matched the effectiveness of other methods, indicating that the large language model has effectively captured regional soil differences. With only a single evaluation of hyperparameter optimization, the adaptive model achieved test set R² values of 0.394 and 0.694 in the black soil region and the aeolian sandy soil region, respectively, with root mean square error values of 8.76 g/kg and 6.07 g/kg—its performance is comparable to that of Grid Search and Random Search, while computational efficiency improved by over 95%. Performance comparisons with eXtreme Gradient Boosting (XGBoost) and Partial Least Squares Regression (PLSR) show that the LLM-optimized Random Forest achieved R² = 0.394 and RMSE = 8.76 g/kg in the black soil region, and R² = 0.694 and RMSE = 6.07 g/kg in the windblown sandy soil region, demonstrating practical application value.