Artificial intelligence solutions enabling sustainable agriculture: A bibliometric analysis.

There is a dearth of literature that provides a bibliometric analysis concerning the role of Artificial Intelligence (AI) in sustainable agriculture therefore this study attempts to fill this research gap and provides evidence from the studies conducted between 2000-2021 in this field of research. The study is a systematic bibliographic analysis of the 465 previous articles and reviews done between 2000-2021 in relation to the utilization of AI in sustainable methods of agriculture. The results of the study have been visualized and presented using the VOSviewer and Biblioshiny visualizer software. The results obtained post analysis indicate that, the amount of academic works published in the field of AI's role in enabling sustainable agriculture increased significantly from 2018. Therefore, there is conclusive evidence that the growth trajectory shows a significant climb upwards. Geographically analysed, the country collaboration network highlights that most number of studies in the realm of this study originate from China, USA, India, Iran, France. The co-author network analysis results represent that there are multi-disciplinary collaborations and interactions between prominent authors from United States of America, China, United Kingdom and Germany. The final framework provided from this bibliometric study will help future researchers identify the key areas of interest in research of AI and sustainable agriculture and narrow down on the countries where prominent academic work is published to explore co-authorship opportunities.

The impacts of economic globalization on agricultural value added in developing countries.

Countries in the world have various indices for the implementation of economic globalization (EG). This refers to positive and negative impacts arising from its implementation, especially in agriculture. This sector is still a basic source of existence in developing countries. At the same time, these countries have been unable to optimize their agricultural value-added (AVA) and only earn a low level of income. That way, developing countries need to take advantage of EG to increase income from agricultural exports and farmers' welfare. Other than that, there has been no study examining the impacts of EG on AVA in developing countries. So, this study intends to evaluate the impacts of the exchange rates, foreign direct investment (FDI) inflows, total agricultural export values, agricultural import duties, and fertilizer imports on AVA in developing countries. The panel data technique is used to assess its impact in 17 developing countries during 2006-2018. The study showed that FDI inflows and agricultural export values increase AVA in developing countries. In this study, EG positively impacts developing countries, but its implementation must pay attention to achieve sustainable development goals. We recommend developing countries focus on investments in human capital and technologies (or R&D), ensure foreign investors collaborate with local agricultural firms, increase agricultural exports, and create a conducive economic system.