International market openness has long been regarded as critical for economic development, and recent evidence highlights the distinct roles of financial and trade openness, particularly in emerging and developing economies. This study examines the impact of financial and trade openness on economic growth in ten emerging and developing countries from 1970 to 2023. It employs a dynamic panel generalized method of moments (GMM) model, which is selected for its ability to address potential endogeneity and dynamic relationships within panel data. The analysis finds that both financial and trade openness positively influence economic growth and that stable macroeconomic conditions and political stability enhance these growth-promoting effects. In the context of growing geo-economic tensions, trade fairness, and national security concerns, the study underscores the need for policies that balance global integration with national interests. These findings suggest the importance of designing policies that promote greater integration into global financial and trading systems while ensuring sound macroeconomic fundamentals and supportive institutions. The study recommends that policymakers pursue strategic liberalization and strengthen governance structures to achieve sustained and inclusive growth.

The creation and deployment of digital technologies throughout the agro-food system contribute to achieving Sustainable Development Goal nine. However, various digitalization studies offer limited insight into the policy issues and solutions around emerging technologies and big data systems in agriculture. There is a need for an additional understanding of how agricultural policymaking should respond to the usage of digital technologies in the agri-food sector. Furthermore, evidence is limited on how existing agricultural government programs such as land reform can be linked with digitalization policy. This systematic review of literature sought to determine the transformation that is needed in the political and economic environment for the digital revolution to take place within South African land reform agriculture. A thematic analysis of data sampled from ProQuest Central, Scopus, Dimensions, and Google Scholar reveals five areas of intervention from agricultural policymaking. The digital revolution in agriculture can be brought about by transforming digital infrastructure, data interoperability and governance, digital markets, the compatibility of government incentives with the private sector, and the digital cultural landscape. This essay contributes to agricultural policy and decision-making dialogues that pay attention to digital technologies and land reform programs in South Africa.

Digital technologies disrupt agricultural value chains, thereby facilitating enhanced connectivity among various players. This contributes to the development of resilience and sustainability within farms. This study examined behavioural factors that influence farmers’ intentions to adopt value chain digital technologies available in South Africa. A simple random sample of 100 Eastern Cape PLAS farms was chosen. Data was collected using a semi-structured questionnaire. The data was analysed in R and STATA statistical packages using the unified theory of acceptance and use of technology (UTAUT) and structural equation modelling (SEM). The results indicated a significant relationship between performance and effort expectancy and the intention to adopt value technologies. Furthermore, male farmers had a higher expectation of performance and effort. Prioritising performance demonstrations and trainings on the utilisation of these technologies is crucial in efforts to enhance their adoption. This contributes to the existing body of literature on the deployment of value chain technologies.

Smallholder farmers are challenged by limited resources, finances, and access to complex production technologies, which hinder the implementation of good production practices such as good seed selection, knowing when to plant and harvest, pest and disease control, and access to lucrative markets. This paper used quantitative research methods to explore smallholder farmers’ perceptions, adoptions, and differences in agricultural incomes between adopting and non-adopting farmers. This study reveals that smallholder farmers perceive access to real-time information as important; however, adopting digital technologies as information sources is still considered low. Binary regression analysis further revealed that the access to extension services variable positively correlated with adopting the internet (web pages), YouTube and Farmers Weekly website as information sources. Digital technologies were generally perceived to be reliable, time-effective, and easy to use; however, adopting these technologies had no significant impact on the farmer’s agricultural income. This paper concludes that digital technology adoption is still considerably low; however, more and more farmers are not only open to adopting these technologies, but those who have adopted prefer incorporating them among sources they use to acquire farming information. Using digital technologies did not cause differences in agricultural income for these farmers. This study recommends public-private partnerships and community engagement through cooperatives to further drive technology adoption, fostering market access and improving livelihoods for smallholder farmers.

The South African land reform, particularly the aspect of land redistribution, has received strong criticism of being slow and inequitable, contrary to how it is envisaged in numerous land redistribution policies. Consequently, the recent report of the land reform advisory committee appointed by the president highlighted several issues in the understanding of land redistribution beneficiaries and recommended research to fill those knowledge gaps and inform policy design. This study therefore attempts to understand which factors could determine whether commercially oriented smallholders in the former homelands, who are regarded as potential land redistribution beneficiaries, would be willing to relocate to commercial farms formerly owned by white farmers. This objective is achieved by implementing a binary logistic regression to a sample of 454 commercially oriented smallholders, purposively and randomly surveyed in KwaZulu-Natal and Limpopo. The results show that age, number of schooling years (education), dwelling asset base and feeling constrained by farming in the former homeland can predict the likelihood of relocating. Marginal effects suggest that education, dwelling asset base and feeling constrained by farming in the former homeland have more weight in predicting beneficiary relocation likelihood (dependent variable). Based on these findings, the article makes recommendations for land redistribution policies.

This study sought to develop an index for agricultural digitalization by applying composite confirmatory analysis (CCA). Another aim was to determine the factors that affect the development of digitalization in PLAS farms. Data on the indicators of the three dimensions of digitalization were collected from 300 Proactive Land Acquisition Strategy (PLAS) farms in South Africa using semi-structured questionnaires. Confirmatory composite analysis (CCA) was employed to reduce the items into three digitalization dimensions and ultimately to a digitalization index. Standardized digitalization index scores were extracted and fitted to a linear regression model to determine the factors affecting digitalization. The results revealed that the model shows practical validity and can be used to measure digitalization as measures of fit (geodesic distance, standardized root mean square residual, and squared Euclidean distance) were all below their respective 95%quantiles of bootstrap discrepancies (HI95 values). Therefore, digitalization is an emergent variable that can be measured using CCA. The average level of digitalization in PLAS farms was 0.02 and varied significantly across provinces. Although farmers have attempted to digitalise their farms, there are still minimal levels of digitalization in PLAS farms. The results further reveal different digitalization patterns. As judged by the estimated weights of various dimensions of digitalization, the use of digital technologies to collect, store, analyse, and disseminate (CSAD) farm-related data contributed more towards the digitalization index. The second most important component of digitalization was automation digitalization. In contrast, value chain digitalization was the least significant contributor. The factors that significantly influence digitalization were age, gender, farm type, network type, and cellular data type. Since PLAS farmers have not embraced much digitalization, it is important to focus on awareness and capacity building. A balanced approach to digitalization would benefit PLAS farms by ensuring that strategies to integrate digital solutions within the value chain are developed. To foster and support the digitalization in PLAS farms, policymakers and stakeholders should tailor their strategies to fit specific socioeconomic factors.