Search results

The main intention of this paper is to analyze various factors hindering the growth of the agricultural supply chain and several industry 4.0 technologies to eliminate the same. In addition to a detailed assessment on the implementation of these technologies in agriculture, this manuscript also presents a priority list providing a rank to them based on the relative efficiency of these advancements in addressing these obstacles.

This research proceeds with a two-step process. The particular barriers in the agriculture supply chain and industry 4.0 technologies are determined in the first step. Next, the proposed framework, a combination of data envelopment analysis (DEA) and analytic hierarchy process (AHP), i.e. DEA-AHP, is used to determine a hierarchical structure for the factors and the relative productive efficiencies of the alternatives. The DEA methodology gives a performance analysis of various decision-making units. At the same time, AHP helps in evaluating alternatives weights based on numerous criteria, allowing us to categorize their importance further.

This study reveals how the involvement of technological advancements in agriculture can help manage the supply chain more efficiently. It also justifies how the large quantities of data generated can handle these increasing challenges in the agricultural supply chain.

The results of this study provide a priority list of alternatives based on their final weights. This ranking system can help farmers and the government select the best-suited technology for bringing automation into the agricultural supply chain.

This research is unique as it analyes the general factors hindering the development of the agriculture supply chain while simultaneously providing a list of alternatives based on their relative efficiencies. The study enriches existing literature by providing an analytic approach to determine the weightage of various critical success factors that can help improvise and entrust the real and undeniable requirements of consumers, suppliers and producers.

The purpose of this study is to identify and analyze the barriers associated with the adoption of Industry 4.0 technologies in agricultural supply chains.

The study initially identified thirteen barriers by conducting a literature review and semi-structured interviews with key stakeholders. Subsequently, these barriers were validated and modeled using an integrated Fuzzy Delphi-ISM approach. Finally, MICMAC analysis was employed to categorize the barriers into distinct clusters.

The results provide considerable insights into the hierarchical structure and complex interrelationships between the barriers as well the driving and dependence power of barriers. Lack of information about technologies and lack of compatibility with traditional methods emerged as the two main barriers which directly and indirectly influence the other ones.

The robust hybrid Fuzzy Delphi and ISM techniques used in this study can serve as a useful model and benchmark for similar studies probing the barriers to Industry 4.0 adoption. From a theoretical standpoint, this study expands the scope of institutional theory in explaining Industry 4.0 adoption barriers.

The study is timely for the post-COVID-19 recovery and growth of the agricultural sector. The findings are helpful for policymakers and agriculture supply chain stakeholders in devising new strategies and policy interventions to prioritize and address Industry 4.0 adoption barriers.

It is the first comprehensive, multi-country and multi-method empirical study to comprehensively identify and model barriers to Industry 4.0 adoption in agricultural supply chains in emerging economies.

The United Nations (UN) adopted Sustainable Development Goals (SDGs); agenda 2030 focuses on Climate Action (goal 13), targeting climate adaptability, as well as resilience, awareness and improving policy mechanisms on climate change. In order to enhance climate adaptability, climate-smart agricultural practices (CSAP) is a necessary step. CSAP is a sustainable agriculture approach with a strong focus on climate dimensions. The three pillars of climate-smart agriculture (CSA) are ‘Adaptation’: adapting to climate change; ‘Resilience’: building resilience against it and ‘Remove’: reducing carbon emissions. The new world economy uses Industry 4.0 technologies for sustainable advancement, including blockchain technology, big data analytics, artificial intelligence (AI), augmented and virtual reality, industrial Internet of Things (IoT) and services. Hence, technology plays a significant role in climate sustainable agriculture practices. This chapter shall consider three technologies consisting of IoT, AI and blockchain technology which contribute to CSAP in pre-harvesting (monitoring climate as well as fertility status, soil testing, etc.), harvesting (tilling, fertilisation, seed operations, etc.) and post-harvesting (predicting weather factors, seed varieties, etc.) periods of agriculture. All these three technologies work like the human nervous system; IoT helps in converting various information regarding demography, climate change, local agricultural needs, etc. into world data; AI works like a brain in combination with IoT, helps predict the use of climate-smart technology and blockchain, the memory part of the nervous system which deals with supply-side and ensures traceability as well as transparency for consumers as well as farmers. Hence, this chapter shall contribute to the importance of these three technologies in adopting CSAP in three stages of agriculture.

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 agro-industrial complex (AIC) of Belarus is one of the priority sectors of the national economy, which performs economic, social, environmental and cultural functions. The main trends in the development of the industry on modern business conditions are presented in the section. The goals, objectives, and mechanisms for the implementation of the state programs of development of the AIC are analyzed. The directions and measures of state support for agriculture are reasonable, the actual structure of the “green box” and “yellow box” measures is presented. Approaches and mechanisms to ensure national food security are highlighted in the light of new conditions, goals, and objectives. The results of the foreign trade in agricultural products of Belarus and rural development and social infrastructure of the village are presented. Scenarios for the development of agriculture in Belarus, taking into account national priorities in the field of agricultural production, the domestic consumer market, foreign trade, have been developed.

Agriculture is a vital sector for every country, especially for a country like India, where the majority of the population is dependent on agriculture as their earning source. Technological improvements in agriculture will increase output with proper forecasting of input resources. In this study, the author tries to investigate the attitude of end users (farmers) about the use of Industry 4.0 (I4.0) technologies.

The unified theory of acceptance and use of technology (UTAUT) model is used to assess the behavioral aspects. The significance of socioeconomic and technological factors is highlighted, providing the study with a thorough understanding of farmers' decision-making processes. A research questionnaire was developed for data collection, and descriptive and inferential statistics were used to analyse the results using AMOS and SPSS software.

A total of 371 survey responses were collected. The results demonstrate that the hypothesis regarding UTAUT model components is validated, while several mediating hypotheses are not supported, indicating that they are not significant in farmers' decision-making.

In this study, socioeconomic and technological factors are considered to be mediating and moderating elements between the constructs of the UTAUT model. Increasing the accuracy and reliability of our study by integrating mediating and moderating variables. This study assists industry specialists in understanding the elements that farmers consider while switching toward new technologies.

This study explores the thematic relationships within the field of sustainability of agri-food chains oriented toward Industry 4.0, focusing on the analysis of scientific production, through research articles and technological output according to patents worldwide. Agri-food Industry 4.0 is an expanding interdisciplinary field in which science and technology interactions are increasingly intensifying with a strong link to sustainable development.

This study has used high impact indexed publications (Web of science) and patents as proxy indicators of innovation, which are transformed into two sets of data, reflecting the scientific and technical backgrounds, respectively. On the one hand, both quantitative and qualitative analysis methodologies were used to examine the scientific papers through descriptive analysis, focused on collaborations networks by authors, institutions, and countries, as well as a content analysis of keywords. On the other hand, the analysis of technical background on patent families shows the temporal evolution of technologies with future challenging trends, text mining, main applicants, and geographical examination.

The results show that in the field of sustainability in agri-food chains oriented to Industry 4.0, most research is in the agricultural field in scientific articles, with high impact in climate-smart agriculture. Patent analysis reveals a marked increase in the patenting rate from 2012 and 2013, coinciding with the start of scientific production in this field of knowledge. In spite of the fact that China is the leader country in this technological field, India shows a significant change. Moreover, India is a country that is currently showing significant progress, both in the field of scientific production and in its categorization as an innovative country due to its growth in patent filings.

Introduction: As the human population grows, consumer demand for digital services tailored to their specific needs also increases. To improve the financial performance of farms and meet the need for food of a growing population, farmers and agribusinesses have started incorporating distributed ledger technology into agricultural and farm management software. These developments in the agriculture sector may lead to realising sustainable development goals.

Purpose: Several researchers have done studies to explore the features and benefits of blockchain technology in the field of agriculture. There is a need to analyse the available literature to identify the use of this technology in agriculture and the scope of further research. This chapter will mainly focus on its publication trend, journal productivity and impact, prolific studies, and coherent themes.

Methodology: For a comprehensive review, bibliometric and content analysis of 71 open-access articles collected through a structured database of Mendeley is done. These articles were published during 2017–2021.

Findings: The execution of blockchain is continuously increasing in the agriculture sector, which has resulted in automation in supply chain management, land registrations, and crop insurance. The study revolves around supply chain management, digitisation of agriculture, and sustainable economic development. This study’s conclusions can help agriculturalists improve their understanding of blockchain implementation in agriculture. The study also gives directions for future research.

The purpose of the paper is to develop a model of distribution of human and machine labor at intellectual production in Industry 4.0.

The basis of the methodology of the research is regression analysis. The analyzed variables are independent variables that characterize the level of development of human and machine labor in the economy of a country; dependent variables that reflect the effectiveness of the production, marketing and innovative business processes in the economy of country according to “The Global Competitiveness Report” (World Economic Forum); and dependent variables, which show the share of the sphere (agriculture, mining industry, processing industry and service sphere) in the structure of GDP of a country according to the statistics of the World Bank. For determining the change of regression dependencies in dynamics in the interests of reduction of the probability of statistical error, the research is conducted for 2010 and 2018 with application of trend analysis.

Based on the full selection of modern countries that conduct digital modernization, the authors determine statistical dependencies of effectiveness of business processes and development of the spheres of economy on the intensity of application of machine and human labor. This allowed determining significant differences in automatization of business processes: perspectives of application of machine labor are the widest in production and the narrowest in marketing, differentiated logic of organization of intellectual production in different spheres of economy and the specifics of automatization of business processes and spheres of economy in countries of different categories, one of which has to be taken into account during organization of intellectual production in Industry 4.0.

The developed model of optimal distribution of human and machine labor at intellectual production in Industry 4.0 will allow reducing disproportions in effectiveness of different business processes, development of different spheres of economy and growth rate of developed and developing countries. This explains its contribution into provision of well-balanced development of the modern global economic system.