In the trend of the 4th Industrial Revolution and the trend of digital transformation, along with the orientation of building ecologically sustainable agriculture, modern countrysides, civilized knowledge farmers, meeting the requirements of international economic integration. More than ever, countries’ agriculture requires human resources from managers to researchers and those directly getting involved in agricultural production that meet the standards of professional qualifications, capacity and quality of work performance. In Vietnam, in terms of resources in the agricultural sector, there is a surplus of manual and simple labor but a shortage of high-skilled workers and lack of good managers and organizers. In terms of policies and laws in the field of agriculture, it is relatively complete when there are 15 laws passed in 4 production sectors: fisheries, forestry, horticulture and animal husbandry. This is an important legal basis to mobilize resources, including agricultural human resources in order to develop the country. However, the legal system on human resource development in the field of agriculture in general and on training, education, compensation and support in particular is still lacking and scattered. Thus, the article focuses on analyzing the current status of regulations and practices of implementing regulations on human resource development in the agricultural sector, thereby proposing corresponding policies and laws in Vietnam in the next time.

This study applies machine learning methods such as Decision Tree (CART) and Random Forest to classify drought intensity based on meteorological data. The goal of the study was to evaluate the effectiveness of these methods for drought classification and their use in water resource management and agriculture. The methodology involved using two machine learning models that analyzed temperature and humidity indicators, as well as wind speed indicators. The models were trained and tested on real meteorological data to assess their accuracy and identify key factors affecting predictions. Results showed that the Random Forest model achieved the highest accuracy of 94.4% when analyzing temperature and humidity indicators, while the Decision Tree (CART) achieved an accuracy of 93.2%. When analyzing wind speed indicators, the models’ accuracies were 91.3% and 93.0%, respectively. Feature importance revealed that atmospheric pressure, temperature at 2 m, and wind speed are key factors influencing drought intensity. One of the study’s limitations was the insufficient amount of data for high drought levels (classes 4 and 5), indicating the need for further data collection. The innovation of this study lies in the integration of various meteorological parameters to build drought classification models, achieving high prediction accuracy. Unlike previous studies, our approach demonstrates that using a wide range of meteorological data can significantly improve drought classification accuracy. Significant findings include the necessity to expand the dataset and integrate additional climatic parameters to improve models and enhance their reliability.

In the context of globalization and urbanization, rural development faces many challenges, such as population loss and uneven distribution of resources. This paper analyzes the similarities and differences in sustainable rural development strategies between China and Europe through a comparative perspective. China has optimized land use by relying on land policy innovations, such as the household contract responsibility system and the “separation of three rights”, as well as the construction of small towns; while Europe focuses on private ownership and market mechanisms, and supports agricultural and rural development through the Common Agricultural Policy (CAP). Using literature review, comparative research and policy analysis, the study shows that the policy innovations in China and Europe, each with its own focus, have been effective in promoting agricultural output and rural social development. Particularly noteworthy is that the “three rights” policy has increased agricultural productivity through the liberalization of management rights, while the European CAP has contributed to the diversification of the rural economy and environmental protection through continuous reforms. This study emphasizes that through policy innovation and international cooperation, combining the strengths of China and Europe, it is possible to provide a new model of sustainable development for the global countryside. Specifically, through the establishment of Sino-European R&D centers for agricultural science and technology, exchange of talents, and cooperation in green infrastructure development, technology transfer and application can be accelerated, cultural exchange and understanding can be promoted, and the sustainable development agenda for global rural areas can be jointly advanced.

This paper focuses on studying the impact of institutional distance between home and host countries on the entry mode choice of multinational enterprises (MNEs). Based on theories of transaction costs and institutional theory, we predict the trend of choosing investment forms of wholly-owned enterprises (WOEs) and joint venture enterprises (JVEs) in the agricultural sector of Vietnam in the context of free trade agreement implementation. The data of 364 MNEs from 22 different nations that directly invested in the agricultural sector of Vietnam in the period 1996–2019 were extracted from Worldwide Governance Indicators (WGI), which is provided by World Bank. An empirical investigation has employed logistic regression. The results show a positive relationship between institutional distance with regard to rule of law and regulatory quality and WOE choice. Furthermore, the entry mode choices of MNEs in Vietnam’s agricultural sector are also noticeably influenced by the implementation of freedom trade agreements (FTAs).

The role of agriculture in greenhouse gas emissions and carbon neutrality is a complex and important area of study. It involves both carbon sequestration, like photosynthesis, and carbon emission, such as land cultivation and livestock breeding. In Shandong Province, a major agricultural region in China, understanding these dynamics is not only crucial for local and national carbon neutrality goals, but also for global efforts. In this study, we utilized panel data spanning over two decades from 2000 to 2022 and closely examined agricultural carbon dynamics in 16 cities of the Shandong Province. The method from the Intergovernmental Panel on Climate Change (IPCC) was used for calculating agricultural carbon sinks, carbon emissions, and carbon surplus. The results showed that (1) carbon sink from crops in the Shandong Province experienced growth during the study period, closely associated with the rise in crop yields; (2) a significant portion of agricultural carbon emissions was attributable to gastrointestinal fermentation in cattle, and a reduction in the number of stocked cattle led to a fall in overall carbon emissions; (3) carbon surplus underwent a significant transition in 2008, turning from negative to positive, and the lowest value of carbon surplus was noticed in 2003, with agriculture sector reaching the carbon peak; (4) the spatial pattern of carbon surplus intensity distinctly changed before and after 2005, and from 2000 to 2005, demonstrating spatial aggregation. This research elucidates that agriculture in Shandong Province achieved carbon neutrality as early as 2008. This is a pivotal progression, as it indicates a balance between carbon emissions and absorption, highlighting the sector’s ability in maintaining a healthy carbon equilibrium.