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This article sheds light on the missing links concerning the study of using integrated enabling technologies toward sustainable and circular agriculture supply chains by examining the available literature and proposing future research possibilities.

The relevant literature was researched through online databases such as Scopus, Web of Science, Academic Search Premier, Emerald, IEEE Xplore, Science Direct, World Scientific Net and Springer-Link Journals, covering a period from 1999 to 2020. A systematic literature review based on 75 papers analyzed the integration of the concepts of enabling technologies, sustainability, circular economy and supply chain performance in agriculture supply chains.

It was identified that enabling technologies and agriculture supply chains alone have been explored further than integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains. Enabling technologies and agriculture supply chains' main findings are: enabling technologies have been studied to improve food safety, food quality and traceability in agriculture supply chains. The main results regarding integrated enabling technologies, sustainability, circular economy, supply chain performance and agriculture supply chains are: Internet of Things and information communication technology play an important role in addressing food security, traceability and food quality, which help achieve sustainable development goals.

This review study provides 13 research questions to underpin future trends regarding integrated technologies' application in agriculture supply chains for circular and sustainable growth.

The purpose of this paper is to review the agriculture supply chain (ASC) literature along many dimensions which include but are not restricted to scope, objective, wastages, driver, obstacle, outcome, etc.

In total, 203 relevant and scholarly articles of various researchers and practitioners during 2000-2016 were reviewed. The information related to definition, research methodology, global research spread, supply chain strategy, various types of produce, author profile and year of publication of ASC were collected and analysed.

The information related to empirical research and viewpoint of various ASC drivers were captured, studied and analysed in detail. Although inventory policy, demand forecasting and ASC integration were found to be important areas of ASC, they were less focused, studied and researched.

Mainly post-harvest ASC of different agricultural produces were considered whereas products such as dairy, fishery and meat supply chains were not included in the study.

The paper provides an insight into various aspects of ASC in general and one can get a deeper and richer knowledge on it which will help in formulating effective strategies to design of an effective and efficient ASC. It uncovers the research gaps for the new future research paths. This systemic review is strongly felt to fill the gap in the ASC literature.

This paper aims to address the conceptual and practical challenges in integrating triple bottom line (TBL) sustainability in the agriculture supply chain (ASC). It identifies the key enablers for each of the three dimensions of TBL sustainability, analyses their causal relationships as well as cross-dimensional interactions under each TBL dimension. Further, it develops a decision support framework (DSF) for the assessment of TBL sustainability practices and policies in ASC and validates it through a case study.

An interpretive structure modelling (ISM) methodology is deployed to establish the interrelationships among all TBL enablers and to identify the enablers with high driving power on sustainable ASC. Brainstorming by a group of experts was used to identify the relevant enables. Finally, a DSF was developed as a resultant of ISM.

The paper provides a set of enablers with high driving power that can significantly influence the sustainability practices and policies in ASC. The social enablers directly help to enhance the effect of economic enablers and collectively these enhance the effect of environmental enablers. If agriculture firms and supply chains design innovative policies and develop practices based on these enablers, they can achieve sustainable ASC. Consequently, the living standards of the people directly or indirectly associated with the agriculture firm or supply chain can be improved without compromising on economic performance.

The paper consolidates the fragmented knowledge of sustainable supply chain management in the agriculture sector and suggests a DSF to policymakers, managers and practitioners for assessing TBL sustainability practices and policies. The DSF has wide applicability in other sectors of production and operations management as these sectors also face the challenge of achieving TBL sustainability across their supply chain.

The DSF, developed in the paper, is a useful tool for practitioners to frame and analyse sustainability initiatives and policies for ASC. A firm or supply chain may achieve TBL sustainability if it succeeds in uplifting the social status of its stakeholders.

It is a first step towards addressing the practical challenge of integrating sustainability in the agriculture sector of emerging economies and provides a path to improve the livelihood of people in the agriculture sector. Stakeholder engagement with a focus on collaboration and awareness may lead to the desired social and environmental consequences. Potential adverse social effects also need to be considered.

This paper focusses on the so far rather neglected but essential aspect of integrating TBL sustainability in the agriculture sector of emerging economies. The hierarchal representation and classification of the TBL sustainability enablers of sustainability is a unique effort in the field of ASC. Development of DSF is one of the first attempts to create a mapping between various enablers of TBL sustainability. The novelty of the study lies in the sector-specific, holistic evaluation of TBL sustainability policy measures that may lead to improvements in practice.

Performance measurement (PM) of any supply chain is prerequisite for improving its competitiveness and sustainability. This paper develops a framework for supply chain performance measurement (SCPM) for agriculture supply chain (ASC) based on internet of things (IoT). Moreover, this article explains the role of IoT in data collection and communication (SC visibility) based on the supply chain operation reference (SCOR) model.

This research identifies various key performance indicators (KPIs) and also their role in SCPM for improving its sustainability by using SCOR. Further, Shannon entropy is utilized for weighing the basic processes of SCPM and by using weights, fuzzy TOPSIS is applied for ranking of identified KPIs at metrics level 2 (deeper level).

“Flexibility” and “Responsiveness” have been reported as two most important KPIs in IoT based SCPM framework for ASC towards achieving sustainability.

In this research, metrics are explained only at SCOR level 2. But, this research will guide the managers and practitioners of various organizations to set their benchmark for comparing their performance at different levels of business processes. Further, this paper has managerial implications to develop an effective system for PM of IoT based data-driven ASC.

By using IoT based data driven system, this article fills the gap between SCPM by measuring different SC strategies in their performance measurable form of reliable, responsive and asset management etc.

The purpose of this article is to develop relationships between many major issues relevant to the agriculture supply chain.

With the purpose of gaining an all-encompassing understanding of the agriculture supply chain, this work uses 233 filtered research articles and three bibliometric analysis tools, namely VOSviewer, term frequency-inverse document frequency (TF-IDF) and Person correlation. The collected research publications were also catalogued using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA).

Using analytic techniques, a total of 12 keywords were obtained. The study found that agri-products are in dire need of digitisation via Internet of things (IoT) and blockchain due to the usage of economic variables and comprehensive management of total food waste throughout transportation, anchoring quality and the predominant variable.

The study was limited to the Scopus and Web of Science (WoS) indexing in order to assess the viability of the linked idea and problem.

This study aims to generate vital knowledge in the field of horticulture-focused agriculture supply chain based on previous justification and relationship formation.

In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).

This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.

This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.

This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.

The prime aim of this paper is the identification and prioritization of performance indicators, which motivate the development of an Internet of Things (IoT)-based traceability system for the agriculture supply chain (ASC). Also, this research aims for checking the robustness of obtained results.

Ten performance indicators have been identified based on the five “criteria in the IoT-based traceable system”. Further, based on five criteria, performance indicators were ranked by using grey-based “Additive Ratio Assessment”.

Sustainable practices obtained first rank, and certification of agri-products obtained worst ranking. Further, based on sensitivity analysis, tracking of agri-products and stakeholders' behavior have found high sensitivity. Also, information sharing and global distribution networks have found the least sensitive performance indicators.

This research has some limitations of taking only a few criteria and alternatives. This study may also contribute as a practical insight to the practitioners and managers in decision-making in the adoption of an IoT-based traceable system within the ASC.

This research may motivate the implementation of an IoT-based efficient traceability mechanism that improved the sustainability and consumer's trust in the ASC during different types of hazardous activities and other outbreaks (COVID-19). Also, this research has provided a theoretical insight based on the dynamic capability theory (DCT).

The internal (farmer-controlled) and external (non-farmer-controlled) factors affect crop yield. However, not a single study has identified and analyzed yield predictors in India using effective predictive models. Thus, this study aims to investigate how internal and external predictors impact pearl millet yield and Stover yield.

Descriptive analytics and artificial neural network are used to investigate the impact of predictors on pearl millet yield and Stover yield. From descriptive analytics, 473 valid responses were collected from semi-arid zone, and the predictors were categorized into internal and external factors. Multi-layer perceptron-neural network (MLP-NN) model was used in Statistical Package for the Social Sciences version 25 to model them.

The MLP-NN model reveals that rainfall has the highest normalized importance, followed by irrigation frequency, crop rotation frequency, fertilizers type and temperature. The model has an acceptable goodness of fit because the training and testing methods have average root mean square errors of 0.25 and 0.28, respectively. Also, the model has R² values of 0.863 and 0.704, respectively, for both pearl millet and Stover yield.

To the best of the authors’ knowledge, the current study is first of its kind related to impact of predictors of both internal and external factors on pearl millet yield and Stover yield.

The literature reveals that most studies have estimated crop yield using limited parameters and forecasting approaches. However, this research will examine the impact of various predictors such as internal and external of both yields. The outcomes of the study will help policymakers in developing strategies for stakeholders. The current work will improve pearl millet yield literature.

The purpose of this paper is to provide a comprehensive review on the academic journey of artificial intelligence (AI) in agriculture and to highlight the challenges and opportunities in adopting AI-based advancement in agricultural systems and processes.

The authors conducted a bibliometric analysis of the extant literature on AI in agriculture to understand the status of development in this domain. Further, the authors proposed a framework based on two popular theories, namely, diffusion of innovation (DOI) and the unified theory of acceptance and use of technology (UTAUT), to identify the factors influencing the adoption of AI in agriculture.

Four factors were identified, i.e. institutional factors, market factors, technology factors and stakeholder perception, which influence adopting AI in agriculture. Further, the authors indicated challenges under environmental, operational, technological, economical and social categories with opportunities in this area of research and business.

The proposed conceptual model needs empirical validation across countries or states to understand the effectiveness and relevance.

Practitioners and researchers can use these inputs to develop technology and business solutions with specific design elements to gain benefit of this technology at larger scale for increasing agriculture production.

This paper brings new developed methods and practices in agriculture for betterment of society.

This paper provides a comprehensive review of extant literature and presents a theoretical framework for researchers to further examine the interaction of independent variables responsible for adoption of AI in agriculture.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0448

This paper identifies, evaluates and structures research that focuses on “collaboration” (C), “trust” (T) and “performance” (P) in the agri-food supply chain (AFSC) and reveals its intellectual foundation. It aims to synthesize research published over a period of 18 years (from 2003 to the beginning of 2020) and provide a platform for practitioners and researchers in their efforts to identify the existing state of work, gaps in current research and future directions in the area of collaboration–trust–performance (CTP) in the AFSC.

Prior to carrying out a bibliometric analysis (BA), literature search was performed, identifying 69 related papers focused on CTP in the AFSC. The content of the papers was further analysed in a systematic literature review (SLR) with regard to the subject area, theoretical lenses, research methodology, supply chain (SC) category and other relevant categories.

CTP in the AFSC are based on a relationship marketing and operations management fundament but show specific particularities. AFSCM is a multi-dimensional design task, and collaboration is considered a necessity, whereas trust significantly affects the AFSC effectiveness. The paper also develops a conceptual CTP model, which shows the interrelations between all identified construct variables, where the authors were able to see also bi-directional relations. Furthermore, the paper presents viable future research opportunities, e.g. focus on organic food chains or multi-actor analysis.

Results of the conducted BA refer to the CTP discussion within a preselected number of peer-reviewed academic articles, which are provided by the WoS CC (Web of Science Core Collection) database.

CTP measurements within the AFSC context are a relevant subject with increasing academic interest in the area of agricultural economics as well as operations and supply chain management (SCM). Therefore, further studies are necessary to develop the related theory and ascertain the practical implications of collaboration, trust and performance among members in the consistently complex AFSC.

CTP have been recognized as important factors for designing a sustainable SCM strategy, particularly in the case of the AFSC. However, although previous studies have addressed the AFSC, there is insufficient knowledge regarding all three pillars (CTP) and how they enable successful AFSCM. The originality of this paper lies in systematically mapping the intellectual base of CTP research and providing path forward for research in AFSCM.