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Standardization is crucial for promoting the transformation and upgrading of agriculture in China, participating in international market competition, and enhancing China’s right of speed regarding global governance. This study summarizes the standardization practices of major agricultural powers worldwide, reviews the agricultural standardization progress of China, and analyzes the implications, characteristics, and trends of agricultural standardization in China in the new era. It also examines the realistic predicament faced by China and proposes paths for strengthening the agriculture sector of China through standardization. The agricultural standardization of China exhibits the following major features and trends: marketization of participants, covering many chains, diversification of implementation modes, precision of service objects, all-round pursuit of values, and internationalization of the evolution path. It is necessary to properly handle the internal relations between standardization and factors such as agricultural supply, scientific and technological equipment, management systems, industrial resilience, sustainable development, and international competitiveness. Problems including dependence on government public standards, lack of golden standards in market, inadequate coordination between standards of upstream and downstream links, limited participation of social subjects, and slow progress in standards internationalization must be addressed. To further promote agricultural standardization in China, we propose the following implementing paths: (1) creating a situation featuring coordination and interaction of agricultural standardization, (2) establishing an agricultural industry chain standards system that features high quality and vitality, and (3) promoting China’s right of speech and core competitiveness regarding agricultural standardization.

Collaboration between local governments and businesses for poverty reduction has not yet been fully explored in China. Based on an in-depth investigation of two counties during the Targeted Poverty Alleviation campaign, this study proposes a multilayered and relational approach to understanding how different levels of officials collaborate with business to reduce poverty. Elite bureaucrats at the county and township levels prefer a growth-oriented strategy for attracting large-scale enterprises to make investment. Their coalition with businesses has created profits and employment but also hindered authentic participation of small- and medium-sized businesses and villagers in their selected villages. In contrast, in villages that were not prioritized by elite bureaucrats, local officials could utilize their discretion and indigenous resources to exploit development opportunities and support small- and medium-sized businesses to reach poor villagers. This study unpacks China's multilevel system to understand the various forms of government–business collaboration and their implications for rural poverty reduction. [ABSTRACT FROM AUTHOR]

Coalbed methane (CBM) productive efficiency and coal mine disasters such as gas outbursts and water inrush are closely correlated with coal seam permeability. Effective prediction of coal seam permeability can provide guidance for CBM production and prevention of coal mine disasters. In this research, a hybrid neural network prediction model integrating a genetic algorithm, an adaptive boosting algorithm, and a back propagation neural network was developed to predict coal seam permeability. Additional momentum and variable learning rate algorithms were used to improve the learning rate and accuracy of the model, and the model structure was optimized, including the number of hidden layer nodes and the transfer function. The input parameters of the prediction model included gas pressure, compressive strength, reservoir temperature, and effective stress. The corresponding output parameter was coal seam permeability. The correlation between the parameters was calculated. Additionally, a comparative analysis between the proposed prediction model and four other prediction models was carried out to demonstrate the advantages of the proposed model. The results indicated that the correlations between compressive strength, gas pressure, reserve temperature, effective stress, and coal seam permeability were 0.334, −0.148 , −0.406 , and −0.785 , respectively. The proposed prediction model had high accuracy compared with the other prediction models, and its coefficient of determination and root mean squared error were 0.999 and 0.021. Thus, the model can predict coal seam permeability more accurately. [ABSTRACT FROM AUTHOR]