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

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.

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The agriculture industry has undergone many developments that embraced automation, agro-chemical fertilizers, genetically modified organisms etc that brought exponential growth in productivity post industrial revolution. This growth resolved the food availability issues on a global scale, but rapid climate change has brought about a shift in production practices to more sustainable organic farming techniques from the conventional methods. The climate change effects and increase in greenhouse gas emissions adversely affected the overall agricultural output. The widespread perception is that adoption of organic farming can reduce the harmful greenhouse emissions and be less damaging to the environment, although expecting the same level of productivity as conventional farming is challenging. This gradual shift can cause future food security problems such as availability and affordability of food in developing countries. This article compares and analyses such trend in the Visegrad group (V4) and India. The comparison between a group of developed nations and a developing nation is of exploratory interest because V4 countries are regarded as high-income countries and they are leaders in organic cultivation practices since the 1980s, whereas India as a developing country has seen substantial conversion of agriculture land area from conventional to organic farming in the past decade.

This study investigates the relationship between macroeconomic variables and financial market development on economic growth in Indonesia using principal component analysis. A quantitative data was collected from World Bank dataset from 2002 to 2019. Data were analysed using statistical software R. Findings reveal principal component analysis is better than multiple linear regression in explaining the correlation among independent and dependent variables. This study also reveals stock traded of total value as percentage of GDP has the biggest effect on the performance on Indonesian economy during research period. In contrast, unemployment has the smallest impact on economic growth in Indonesia. The results assist in understanding the importance of macroeconomic variables and financial market development on the performance of Indonesian economy.