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This study aims to examine the role relational dynamics, grounded in the theories of social exchange and social capital, play within the context of geographic indication (GI) food supply chain network (FSCN) performance.

A total of 30 qualitative interviews were undertaken with key informant stakeholders across a variety of organizations within Turkish GI FSCNs. An open, axial and selective grounded theory coding process was used for the analysis, teasing out critical themes that underpinned the conceptual framework.

The findings identify the formal and informal mechanisms which govern GI FSCNs. These two forms of governance mechanisms influence network performance, which was found to comprise logistics, production, business and socio-economic performance dimensions. Transparency, GI traceability, trust and psychological contract violations were found to mediate the link between governance and network performance outcomes.

To the best of the authors’ knowledge, this study is the first to explore GI FSCNs from the perspective of relationship marketing and through the lens of social exchange and social capital theory. Accordingly, both academics and practitioners can benefit from the study, as it unveils relevant relational factors underpinning such networks.

We analyzed the effects of different combinations of organic soil amendments (OSAs) and chemical fertilizers on agrifood production, focusing on banana yields in China, the second-largest producer of bananas globally.

We computed these combinations by dividing the expenditures on OSAs by those on chemical fertilizers and called them OSA-CF ratios. First, we classified farmers based on quintiles of expenditures on chemical fertilizers. Then, we studied the association between OSA-CF ratios and banana yields for each quintile. We also considered an alternate specification in which farmers were grouped along the OSA-CF ratio continuum. The first group comprised farmers not using OSAs. Their OSA-CF ratio was zero. Farmers applying low, medium, and high OSA-CF ratios constituted groups two, three, and four; the groups were delineated based on the OSA-CF ratio tertiles, and the associations between tertiles of OSA-CF ratios and banana yields for each quintile were analyzed. The data used in this study were collected by surveying 616 households in three major banana-producing provinces (Guangdong, Hainan, and Yunnan) of China. Standard linear regressions and the two-stage predictor substitution method were employed to complete the analysis.

There were variations in the effects of OSA-CF ratios on banana yields obtained by farmers iifferent quintiles. For the first and second quintiles, low, medium, and high OSA-CF ratios improved banana yields relative to not using OSAs. For farmers in the first quintile using only chemical fertilizers, applying a low OSA-CF ratio was associated with an improvement of 792 kg/mu in banana yields. For their counterparts in the second quintile, the same transition was associated with a gain of 534 kg/mu. For the fifth quintile, comprising farmers spending 320 yuan/mu or more on chemical fertilizers, applying a high OSA-CF ratio instead of using only chemical fertilizers was associated with a 401 kg/mu decline in banana yields. Even so, for this group, no differences were observed between the yields of farmers not applying OSAs and those using low and medium OSA-CF ratios.

Banana farmers in southern China, using only chemical fertilizers, can improve yields by combining them with OSAs if their chemical fertilizer expenditures are less than 66.67 yuan/mu. Those using only chemical fertilizers and spending between 68 yuan/mu and 300 yuan/mu on them can maintain yields by applying OSAs in conjunction with chemical fertilizers. However, yields may decline for farmers using only chemical fertilizers and spending 320 yuan/mu or more on them if they incorporate OSAs such that the OSA-CF ratio reaches 0.78 or higher. Overall, combining OSAs with chemical fertilizers can improve yields while attenuating the adverse effects of chemical fertilizers on the environment. Policymakers should inform farmers of these benefits and accelerate the transition to sustainable agriculture through educational and awareness programs.

Farmers apply OSAs such as organic fertilizers and farmyard manure to adjust and remedy soil nutrition to improve farm productivity. However, little is known about how combining OSAs with chemical fertilizers affects banana yields. This study provided the first attempt to explore the associations between OSA-CF ratios and banana yields using cross-sectional data on farming households.

This study investigates the impact of board funds, banking credit, and economic development on food production in the context of South Asian economies (India, Pakistan, Bangladesh, Sri Lanka, and Nepal).

This study used data from the World Development Indicators covering the years 1991–2019. To investigate the relationship between the variables of the study, we employed the panel unit root test, panel cointegration test, cross-sectional dependence test, fully modified least squares (FMOLS), and panel dynamic least squares (DOLS) estimators.

The empirical results indicate that board funding significantly increase food production; however, banking credit had a negative impact. Furthermore, the findings indicate that economic development, Arable land, fertilizer consumption, and agricultural employment play a leading role in enhancing food production. The results of the Dumitrescu-Hurlin causality test also show substantiated the significance of the causal relationship among all variables.

South Asian countries should prioritize board funding, bank credit, and economic development in their long-term strategies. Ensuring financial access for farmers through micro-credit and public bank initiatives can spur agricultural productivity and economic growth.

This study is the first to combine board funding, banking credit, and economic development to better comprehend their potential impact on food production. Instead of using traditional approaches, this study focuses on these financial and developmental aspects as critical determinants for increasing food production, using evidence from South Asia.

This study aims to find how trade development and digitalization affect smart-green production. Four factors are investigated in these effects (certification, technology innovation, natural resource management, low pesticides).

The mix-method approach was employed from validating the measurement scale to test the proposed hypotheses. At first, the grounded theory is the most authoritative and standard research method in qualitative research. Secondly, quantitative analysis was employed to draw conclusions about the impact of digitalization and trade development on smart-green agricultural production.

The results found that digitalization and trade development impact the development of smart-green agricultural production through certification employment, technology innovation, and a decrease in pesticide usage. Moreover, digitalization and trade development also indirectly affect the development of intelligent green agricultural production. Meanwhile, digitalization has a higher impact than trade development.

This research is based on the premise that digitalization and trade development can drive smart green agricultural production. Still, some studies have found a deviation between trade development and environmental protection. Hence, future research can explore the incentive effect of trade development and digitalization on other industries. Second, the measurement of the dependent variables in this study is based on the premise that smart-green agricultural production has not been widely promoted, so the changes in production before and after the whole public participation in smart-green output have yet to be reflected.

Smart green production in agriculture is essential for a transition economy and the world to meet food security and protect the environment. However, the effects of certification, technology innovation, natural resource management, and low pesticides on smart-green agriculture production have yet to be identified. Insights from this study can help governments, policy-makers, and farmers in emerging economies by adapting their strategies within their local contexts.

The new technology aspects of Industry 4.0 (I4.0), such as digital technologies including artificial intelligence (AI), block chain, big data analysis and the internet of things (IoT) as a digital cosmos, have the potential to fundamentally transform the future of business and supply chain management. By augmenting the functional components of the food supply chain (FSC), these technologies can transform it into an intelligent food supply chain (iFSC). The purpose of this study is to identify the I4.0 utilization for FSC to become an iFSC. Additionally, it suggests future research agendas to bridge the academic knowledge gaps.

This study utilizes the bibliometric analysis methodology to investigate the techno-functional components of iFSC in the context of I4.0. The study followed steps of bibliometric analysis to assess existing components’ knowledge in the area of intelligent food supply chain management. It further reviews the selected articles to explore the need for I4.0 technologies’ adoption as well as its barriers and challenges for iFSC.

This study examines the integration of emerging technologies in FSC and concludes that the main emphasis is on the adoption of blockchain and internet of things technology. To convert it into iFSC, it should be integrated with I4.0 and AI-driven FSC systems. In addition to traditional responsibilities, emerging technologies are acknowledged that are relatively uncommon but possess significant potential for implementation in FSC. This study further outlines the challenges and barriers to the adoption of new technologies and presents a comprehensive research plan or collection of topics for future investigations on the transition from FSC to iFSC. Utilizing artificial intelligence techniques to enhance performance, decision-making, risk evaluation, real-time safety, and quality analysis, and prioritizing the elimination of barriers for new technologies.

The uniqueness of this study lies in the provision of an up-to-date review of the food supply chain. In doing so, the authors have expanded the current knowledge base on the utilization of all I4.0 technologies in FSC. The review of designated publications yield a distinctive contribution by highlighting hurdles and challenges for iFSC. This information is valuable for operations managers and policymakers to consider.

This research paper takes a narrow approach to examining the apparent link between poverty and the resource extraction industries. However, it acknowledges that much more is to be explored about this relationship. Many complexities influence the occurrence and degree of poverty in a particular country, region, or community.

The literature review identified proposed and operational poverty reduction actions and processes categorized under the broad concept of community development projects. The surveyed cases describe how various corporate strategies, work processes, labor requirements and efficient management and governance solutions are purported to improve poverty-reduction efforts potentially.

No causal linkages between poverty and hypothetically valuable and successful community development projects were found. No poverty monitoring evaluations and learning data (MEL) for these projects were proposed and published in most of the literature. However, associations were observed between some business practices implemented in these resource extraction community development projects and observations of indicators of lower poverty levels.

The research improves our understanding of the requirements and opportunities for successful community development projects by highlighting processes, company strategy, human resource programs and enlightened governance that can contribute to reducing poverty.

The paper identifies the characteristics of community development projects that appear to span natural resource extraction industries and countries. Effective management strategies and representative and formally designated organisational governance boards are essential for these projects.

The domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.

The present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.

By examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

The paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.

This study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.

Based on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.

The purpose of this research was to study sustainable water resource management using a stimulus-organism-response (SOR) perspective.

This research study was an exploratory qualitative study. Thematic content analysis was used based on semi-structured interviews with 30 experts operating in the USA, representing 26 water-intensive organisations across different industries. The study was anchored in the theoretical foundations of SOR perspectives.

The results of this study revealed several fundamental factors, processes and forces that were considered by organisations for sustainable water resource management. Managers evaluated risks relative to water resources and developed strategic initiatives regarding water management. The authors found that often organisations considered water resources management aspects while deciding business operations. This was especially true for substantive water resource-consuming organisations with wide geographical operations.

Through this study, the authors explained how the interrelationship between organisations and water resources presented risks and challenges. The authors applied SOR theoretical perspective in this research study. This was while factoring in an organisation’s present considerations and future plans regarding sustainable water resource management. Thus, the study findings were expected to further interdisciplinary research at the intersection of organisational and environmental studies.

The finding that water sustainability challenges and efforts could act as strong motivating forces for innovation and technology was significant. Water sustainability challenges could also be a catalyst for synergistic collaborations amongst organisations and diverse groups of institutions. The study insights were relevant to organisational scholars, the water management industry regulators and managers involved with organisational sustainability programmes.

Organisational challenges regarding sustainable water resource management have been influenced by growing populations and climate change. Furthermore, the increasing context of scarcity was compounded by increased pressures from numerous stakeholders. Although critical water management issues were recognised by organisations, relatively little was known about how organisational managers were planning for and responding to these issues. This research study contributed towards addressing the mentioned research gap.

Just transition is a fundamental concept for supply chain management but neither discipline pays attention to the other and little is known about how supply chains can be orchestrated as socioecological systems to manage these transitions. Building from a wide range of just transition examples, this paper explores just transition to understand how to move beyond instrumental supply chain practices to supply chains functioning in harmony with the planet and its people.

Building from a systematic review of 72 papers, the paper identifies just transition examples while interpreting them through the theoretical lens of supply chain management, providing valuable insights to help research and practice understand how to achieve low-carbon economies through supply chain management in environmentally and socially just ways.

The paper defines, elaborates, and extends the just transition construct by developing a transition taxonomy with two key dimensions. The purpose dimension (profit or shared outcomes) and the governance dimension (government-/industry-led versus civil society-involved), generating four transition archetypes. Most transitions projects are framed around the Euro- and US-centric, capitalist standards of development, leading to coloniality as well as economic and cultural depletion of communities. Framing just transition in accordance with context-specific plural values, the paper provides an alternative perspective to the extractive transition concept. This can guide supply chain management to decarbonise economies and societies by considering the rights of nature, communities and individuals.

Introducing just transition into the supply chain management domain, this paper unifies the various conceptualisations of just transition into a holistic understanding, providing a new foundation for supply chain management research.

Managing weeds and pests in cropland is one of the major concerns in agriculture that greatly affects the quantity and quality of the produce. While the success of preventing potential weeds and pests is not guaranteed, early detection and diagnosis help manage them effectively to ensure crops’ growth and health

We propose a diagnostic framework for crop management with automatic weed and pest detection and identification in maize crops using residual neural networks. We train two models, one for weed detection with a labeled image dataset of maize and commonly occurring weed plants, and another for leaf disease detection using a labeled image dataset of healthy and infected maize leaves. The global and local explanations of image classification are obtained and presented

Weed and disease detection and identification can be accurately performed using deep-learning neural networks. Weed detection is accurate up to 97%, and disease detection up to 95% is made on average and the results are presented. Further, using this crop management system, we can detect the presence of weeds and pests in the maize crop early, and the annual yield of the maize crop can potentially increase by 90% theoretically with suitable control actions

The proposed diagnostic models can be further used on farms to monitor the health of maize crops. Images obtained from drones and robots can be fed to these models, which can then automatically detect and identify weed and disease attacks on maize farms. This offers early diagnosis, which enables necessary treatment and control of crops at the early stages without affecting the yield of the maize crop

The proposed crop management framework allows treatment and control of weeds and pests only in the affected regions of the farms and hence minimizes the use of harmful pesticides and herbicides and their related health effects on consumers and farmers.

This study presents an integrated weed and disease diagnostic framework, which is scarcely reported in the literature