Farm households in developing countries are often involved in a variety of livelihood income-generating activities to achieve basic needs and enhance food security. However, little attention has been given to investigating the effect of livelihood diversification strategies on the adoption of agricultural land management practices. This study explored the nexus between livelihood diversification and Agricultural Land Management (ALM) practices in the Southern Ethiopian Highlands. Data for this study were gathered through a structured questionnaire, interviews, and focus group discussions. A total of 423 sample respondents were selected by using multistage random sampling techniques. The data were analyzed using the Inverse Herfindahl Hirschman Diversity Index (IHHDI), the multinomial logit model (MNL), and the probit regression model. The findings of the study revealed that on-farm income activities are the most dominant livelihood income strategies (69.1%), followed by non-farm (21%) and off-farm (9.64%). The multinomial logit model analysis demonstrated that variables such as sex, education, family size, distance to market, land size, extension contact, membership in cooperatives, and household income were the major drivers of farmers income diversification activities (p<0.05). The results of the probit analysis indicated that income from crop production, daily labor work, rents from farmland, and farm assets have a positive and significant effect on households' decisions to implement ALM practices. In contrast, incomes from remittance and migrant sources have a negative but statistically significant impact on the adoption of ALM measures. The farm household sources of income-generating strategies substantially affected the adoption intensity of ALM measures. Income generated from the on-farm sector alone cannot be considered a core income-generating activity for households or a means of achieving food security. Therefore, land management policies and program implementations should consider farmers’ livelihood diversification and income-generating strategies. In addition, such interventions need to promote sustainable farming practices, enhance innovation, and related measures for the adoption of ALM measures to ensure land sustainability.

The food supply chain in South Africa faces significant challenges related to transparency, traceability, and consumer trust. As concerns about food safety, quality, and sustainability grow, there is an increasing need for innovative solutions to address these issues. Blockchain technology has emerged as a promising tool to enhance transparency and accountability across various industries, including the food sector. This study sought to explore the potential of blockchain technology in revolutionizing through promoting transparency that enable the achievement of sustainable food supply chain infrastructure in South Africa. The study found that blockchain technology used in food supply chain creates an immutable and decentralized ledger of transactions that has the capacity to provide real-time, end-to-end visibility of food products from farm to table. This increased transparency can help mitigate risks associated with food fraud, contamination, and inefficiencies in the supply chain. The study found that blockchain technology can be leveraged to enhance supply chain efficiency and trust among stakeholders. This technology used and/or applied in South Africa can reshape the agricultural sector by improving production and distribution processes. Its integration in the food supply chain infrastructure can equally improve data management and increase transparency between farmers and food suppliers.There is need for policy-makers and scholars in the fields of service delivery and food security to conduct more research in blockchain technology and its roles in creating a more transparent, efficient, and trustworthy food supply chain infractructure that address food supply problems in South Africa. The paper adopted a qualitative methodology to collect data, and document and content analysis techniques were used to interpret collected data. 

In the agricultural sector of Huila, particularly among SMEs in coffee, cocoa, fish, and rice subsectors, the transition to the International Financial Reporting Standards (IFRS) is paramount yet challenging. This research aims to offer management guidelines to support Huila’s agricultural SMEs in their IFRS transition, underpinning the region’s aspirations for financial standardization and economic advancement. Utilizing a mixed-methods managerial approach, data was gathered from 13 representative companies using validated questionnaires, interviews, and analyzed with SPSS and ATLAS.ti. Results indicate that while there is evident progress in IFRS adoption, 12 out of 13 firms adopted IFRS, with rice leading in terms of adoption duration. While 77% found IFRS useful for financial statements, half reported insufficient staff training. The transition highlighted challenges, including asset recognition and valuation, and emphasized enhancing institutional support and IFRS training. Interviews revealed managerial commitment and expertise as significant factors. Recommendations for successful implementation include leadership involvement, continuous professional development, anticipating costs, clear accounting policies, and meticulous record-keeping. The study concludes that adopting IFRS enhances financial reporting quality, urging entities to converge their reporting practices without hesitation for improved comparability, relevance, and reliability in their financial disclosures.

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).

New Institutional Economics (NIE) uses solutions from law, economics and organization. The purpose of this article is to link in a single analytical approach the institutional environment, its change in the organizations uniting in one, what is happening in contracts with agricultural lands. The explanation of this type of governance means to integrate: theoretical definitions; formal rules (laws, court decisions and other legal acts); economic institutions—means and mechanisms of exchange; legal and economic forms in which, through governance of transactions property rights are transferred and protected. In order to achieve this goal, it is necessary to present the elements of the institutional matrix that are the cause of changes in subordination and coordination. Following the process of implementing an approach for reconciling the legal and economic nature of the contract forms and integrating the states, contract organizations and transaction costs in a common model. In order to solve the research problems tasks are adapted methods from law, economics, statistics. Such are: (a) positive legal analysis of legislation; (b) historical (retrospective) method of analysis of changes; (c) discrete-structural analysis to explain the process; (d) comparative-institutional analysis to clarify alternatives and an explanation of any of the effects; (е) regression analysis to model the relationships and present possible one’s scenarios to show the direction in which changes are needed. Changes in legislation, legal forms, mechanisms and the amount of payments create new behavioral patterns that change the contract. Therefore, in retrospect, we are witnessing how the number of changes in legal acts, the amount of fees; the number of participants-administrators of the processes; the number and registers - change the number of transactions; the duration of the actions in the contracts, which ultimately predetermines the different amounts of transaction costs for agricultural lands. This interdependence was established by constructing an econometric model. The analysis presents opportunities for change that would lead to scenarios with a reduced level of transaction costs, that is, improving governance and showing the way to improve the institutional environment related to agricultural lands in Bulgaria.

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.