Diversified Impacts of Enabling a Technology-Intensified Agricultural Supply Chain on the Quality of Life in Hinterland Communities

Abstract

The agricultural supply chain (ASC) in the hinterland refers to the entire post-harvest process of processing and distributing agricultural products in rural or secluded areas to be brought to big city markets. This scheme involves various stakeholders (farmers, trading centers, consumers), processes (logistics, storage, monitoring), and infrastructure (traffic and road systems, negative environmental emissions) to ensure the efficient flow of agricultural products from farms to consumers. The quality of life (QoL) in the hinterland can improve with the introduction of disruptive technologies, but no comprehensive studies have explored the QoL of individuals involved in the ASC–socioeconomic system of hinterland communities. This study elucidated and compared the diversified impacts of disruptive technologies brought by the Industrial Revolution 4.0 to the agricultural supply chain and their impacts on food security, sustainability, and climate change mitigation through the analysis of the related literature. This study also mapped out the role of disruptive technologies in the QoL of hinterland communities, particularly with respect to the farmers, trading center workers, and consumers. Points of discussion emerged with respect to precision agriculture, the Artificially Intelligent Internet of Things, big data analytics, blockchain, artificial intelligence, cyber-physical systems, robotics, automation, and e-commerce, and how these enabling technologies enhance fresh food supply and distribution and deliberately affect stakeholders’ life quality indexes through the analysis of situational case studies in India, South America, Malaysia, China, and Europe. The identification of these points of discussion was also achieved purely based on research performed on the related literature. The positive impacts of these technologies, such as the boosting of efficiency and the ensuring of a steady supply of fresh produce, ultimately improve the overall QoL. The technical insights from the studies were synthesized to develop new frameworks for QoL anchored in the agricultural supply chain (AgQoL) in the hinterland, and a six-dimensional network emphasizing the two trifectas of techno-socioenvironmental needs was established. Food-producing communities with a relatively high AgQoL should support food security in the region.

1. Introduction

The hinterland is an interior region served by a transportation hub, which makes it a geo-economic space that encompasses the region’s customer base for both inbound and outbound traffic. It is a geographical region beyond developing cities and coastal districts or on top of mountains, making it lacking in urbanization. Global supply chains (GSCs) are distinguished by the significant physical distance between the places where goods are produced and where they are consumed. This leads to the movement of goods across ports and distribution hubs and to and from regions that are located far inland [1]. The level of activity at ports and distribution hubs is linked to the accessibility and logistics of the hinterland [1]. The GSC is comparatively different from the supply chain in the hinterland mainly because the former has already-built infrastructure and established protocols that are already operational, whereas the latter is mainly neglected due to the lower revenue it provides to the producers. Hence, the complexity of the supply chain depends on various factors, including stakeholders, such as farmers, trading center workers, and consumers; processes regarding logistics, the storage of fresh produce, and the monitoring of product quality over time until it is procured; and infrastructure focused on traffic and road systems and negative environmental emissions [1]. Efficient logistics and connections that ensure the timely movement of goods are deemed crucial for accessing the hinterland [2]. The supply chain in the hinterland involves intense collaboration and coordination to function effectively and efficiently due to the existence of unpaved roads and insufficient public infrastructure [3]. Over time, during the transport of goods, fresh produce might degrade in its quality, especially when there is no proper cold storage employed, resulting in low sales.

The bibliometric networks in Figure 1 show the connections, in keyword maps, for quality of life in relation to the technology-intensified agricultural supply chain, based on consecutive Scopus searches using the terms “agriculture” and “quality of life”, shown in Figure 1a, and “agriculture”, “quality of life”, and “supply chain”, shown in Figure 1b, from 1972 to the year 2023. The supply chain, in this context, refers to the post-harvest stage up to the transport of fresh produce to consumers. Four thematic clusters of topics based on content can be seen in different colors in Figure 1a, namely, sustainable development, food supply chain, and smart city (red); enabling disruptive technologies applied in agriculture (yellow); sustainable agriculture and socioeconomic development (green); and the quality of life and human welfare of both agricultural and non-agricultural individuals (violet). For both networks in Figure 1, the quality of life is the keyword that exhibits thicker links or weights among all clusters, indicating that it is an important emerging aspect of the agricultural supply chain and warrants attention in research.

Another important aspect involved in the agricultural supply chain, aside from infrastructure, is the quality of life (QoL) of stakeholders. According to the World Health Organization (WHO), the standard definition of QoL refers to the perception of individuals or communities of their present life situations in the context of the culture and value systems in which they live, and in relation to their goals, expectations, standards, and concerns [4]. Having a comprehensive, mathematical QoL model can help us identify the social, economic, and environmental factors, at least, that affect the well-being of the hinterland inhabitants. The information can be used to optimize the agricultural supply chain, which could further improve the QoL of the people, as it is a cyclic relationship. Moreover, by analyzing the QoL data, it is possible to determine how to minimize negative impacts on the stakeholders and ensure that agricultural production is sustainable and optimal, resulting in a win–win scenario for all correspondents. In relation to this, a study was done about identifying social capital and its relation to QoL in supporting sustainable agriculture, emphasizing that the exogenous variables affecting QoL are social capital, community, materials, health, emotions, and safety [5]. However, the formulation is vague due to its broad respondent background, resulting in the inaccurate identification of the life index of certain clusters of people. Other research has focused on the QoL of the people responsible for producing goods: farmers. Providing access to assistive technologies could improve the QoL of farmers with disabilities [6]. There is also a study on healthy farmers that found three factors predicting QoL: education, living standards, and the number of working members [7]. Referring to the transportation aspect, there exists research regarding the prediction of a QoL score from an evaluation model based on the satisfaction level derived from scenery experienced while driving [8]. On the consumer side, by developing healthy fruit and vegetable juice products, not only can the QoL of people with illnesses in need of nutritious products be improved, but also the income and QoL of the producers [9]. Finally, tourists experiencing slow food experiences through slow tourism can improve their QoL, contributing to the overall agricultural supply chain [10]. With the adoption of data use and connectivity, human and robot interaction (HRI), and overall improvements in translated technologies, the entirety of the agricultural realm, and community consumption, the QoL is expected to change.

Disruptive technologies, or enabling technologies, in the farming industries directly correlates to the boom in smart farming, which brings forth precision agriculture and intelligent post-production systems [11]. These disruptive technologies signify innovations that positively impact farmers, industries, and consumers. They should be considered superior to the previously established technologies or systems they have replaced. The main difference between the Industrial Revolution 4.0 (IR4.0) and disruptive technologies is that the former describes the current era of rapid technological integration and digital transformation across various sectors, such as agriculture, whereas the latter denotes specific innovative advancements that disrupt industries. On the other hand, the similarity between the two is that they both have profound socioeconomic impacts that primarily alter business models, employment, markets, and even human well-being. Hence, by having data-driven strategies and automation resources at hand, there should be a reduction of resource consumption, and farmers should be allowed to have improved working hours and handle work remotely, improving their QoL. Enabling the implementation of such technology could lead to stable food security and sustainability by encouraging higher crop yields and increased production. These advancements can also mitigate climate change, especially when the technology is applied on a global scale, by maximizing efficiency throughout the supply chain process. This could lead to minimizing the usage of water in crops, as well as reducing the carbon footprint associated with fuel consumption. Early literature reviews on big data technologies indicate that they can also be applied in the supply chain to manage data and enable the use of information to optimize agricultural performance and the production of crops [12]. Farm-to-table (FTT) and farm-to-fork (FTF) initiatives have been flourishing due to e-commerce, which directly links the consumers to the producers so they can conduct faster product transactions [12]. However, the adoption of technologies such as agricultural robots and automation, which is strongly reflected in the cases of Japan, the Netherlands, Australia, and the United States of America, imposes a potentially high cost of technology integration and maintenance, especially for small- to medium-sized farms [13]. Nonetheless, economically accessible robots could change these circumstances and allow smaller farms to have access to such technologies. Despite the advances in agricultural automation and the extensive literature on QoL, no reviews have been found that comprehensively explore the QoL in the agricultural supply chain in the hinterlands in relation to applied technologies, or that focus on it exclusively.

This study addresses this lack of study of hinterland-agriculture-centered QoL in the literature by providing a state-of-the-art review of the diversified impacts of enabling technology-intensified agricultural supply chains and maps out its role with regard to the QoL of hinterland communities, particularly the QoL of farmers, trading center workers, and consumers. Real situational case studies were analyzed to understand QoL as it relates to agriculture in different geographic regions, namely, India, Europe, South America, China, and Malaysia. The technical insights from the study were synthesized through the establishment of a new set of QoL frameworks for the stakeholders in the hinterland, named the agricultural QoL (AgQoL). The AgQoL should be applicable from small- to large-scale farm classifications. Because the agricultural supply chain covers the entire post-harvest course of processing and distributing agricultural products in rural or secluded areas and their transportation to big city markets, the intervention of the advanced technologies that are technically referred to as Industrial Revolution 4.0 technologies should be significant, as it directly impacts the adaptability of farmers, traders, and consumers as to how they will elevate their well-being (QoL). Agricultural technologies are not entirely accepted in hinterland farms, but this current study elucidates the advantages of applying them in relation to AgQoL dynamics. By developing the AgQoL model, the multisectoral agricultural industry may understand how the welfare of agricultural communities can improve. Farmers and distributors with high QoL are characterized by a happy and positive outlook on life. These are indirect indicators that we have an energetic workforce to feed us fresh and quality food; thus, we can potentially strengthen food security and sustainable food production.

This study mainly contributes to the following:

• Establishing a human-centered system and a QoL model through the integration of multidimensional exogenous factors in the agricultural supply chain, considering the specific context of the hinterland.
• Strengthening technical knowledge for the achievement of the following United Nations Sustainable Development Goals (SDGs): No. 3 (good health and well-being), targeting capacity training to avoid psychological problems in work; No. 8 (decent work and economic growth), targeting higher levels of economic productivity through technological upgrading and innovation; No. 11 (sustainable cities and communities), targeting extensive supports for socioeconomic and environmental links and access to provisions for marginalized people through safe transportation and food production systems. Furthermore, we provide a formulation of a 6-dimensional network showing the entities that have been shown to be vital in improving both agricultural productivity and the QoL of farmers and the entirety of the agricultural communities in the hinterland.

2. Overview of Quality of Life and Its Mathematics

The concept of quality of life (QoL) is multifaceted and encompasses diverse domains, encompassing physical, psychological, social, family, and environmental dimensions based on the standard World Health Organization’s Quality of Life Model [14]. Through evaluating these domains, it becomes possible to discern the impact of technologies deployed in the agricultural supply chain on individuals’ overall well-being, especially that of its stakeholders. In [15], QoL was evaluated to design better transit-oriented developments for people living near station areas. The authors created a model of a QoL index (1) that examines how various quality indicators impact bid rent. Through models (2) and (3), an individual’s perceived quality of life is quantified in terms of their willingness to pay rent. In other words, it represents the additional amount individuals are willing to spend on rent to attain a higher quality of life.

$$Qo{L}_{s,i}=\sum _l{\beta }_{s,l}·(X_{i,l}-{\underset{\_}{X}}_{s,l})$$

(1)

$$Qo{L}_{s,i}^{\prime }=\frac{Qo{L}_{s,i}}{{\beta }_{s,rent}}$$

(2)

$$B{R}_{s,i}=Qo{L}_{s,i}^{\prime }·I_s+{\underset{\_}{BR}}_s$$

(3)

where $Qo{L}_{s,i}$ is the QoL index without unit standardization of residential location i for socioeconomic group s; $X_{i,l}$ is the level of quality indicator l of location i; ${\underset{\_}{X}}_{s,l}$ is the average level of quality indicator l for group s; ${\beta }_{s,l}$ is the value parameter of quality indication l for group s; $Qo{L}_{s,i}^{\prime }$ is the QoL index with the unit of willingness to pay a rent premium for location i for group s; ${\beta }_{s,rent}$ is the value parameter of rent cost for group s; and ${\underset{\_}{BR}}_s$ is the average rent paid by socioeconomic group s [15]. In addition to the aforementioned study, [16] also constructed a model to examine the mutual relationship between the Mumbai-Ahmedabad high-speed rail (MAHSR) and its impact on India’s overall GDP and the quality of life experienced by individual citizens. To assess the improvements in citizens’ quality of life, the researchers employed the QoL accessibility method based on inter-industrial inputs from the industry, as shown in (4) and (5).

$$DI{N}_{ij}^{pq}=A^{qp}\times PR{O}_i^p\times P{T}_{ij}^q$$

(4)

$$DI{N}_j^q=\sum _i\sum _{p}DI{N}_{ij}^{pq}$$

(5)

wherein $DI{N}_{ij}^{pq}$ is the demand for inter-industrial input from industry p in zone i to industry q in the surrounding zone j; $A^{qp}$ is the input coefficient from industry q to industry p; $PR{O}_i^p$ is the production value of industry p in zone i; and $P{T}_{ij}^q$ is the probability of each industry in zone i trading with industry q in zone j [16].

The presented QoL models provide a framework for evaluating the different dimensions that contribute to a person’s quality of life, whether it is based on a socioeconomic or industrial aspect. QoL quantifies an individual’s or a group’s well-being and allows assessment of how different innovations can impact relevant stakeholders. Therefore, a QoL model can contribute to long-term sustainability by incorporating environmental, social, economic, and psychological considerations. By accounting for the interrelationships between these domains, it helps guide decision-making or developing technologies toward approaches that promote sustainable development.

The need for having QoL models becomes evident when considering the impact of technological advancements on the agricultural supply chain, which addresses food insecurity and fosters economic growth. While these innovations bring substantial benefits, it is crucial to recognize the significance of QoL factors for all stakeholders involved in their design and implementation. A bibliometric analysis was conducted that revealed that QoL assessments were predominantly prevalent in the domains of medicine and health-related fields, whereas their presence in other sectors was limited. Although some studies used the keyword “environment” in their QoL assessments, its connection with “quality of life” or “well-being” was extensively limited [17]. There are several shortcomings in focusing on an individual’s QoL, especially in rural areas, as studied in [18]. This highlights the need to incorporate QoL considerations, especially in the hinterland agricultural sector. Technological interventions should be practical, user-friendly, and valuable to ensure an improved quality of life for the end users [19]. Furthermore, when stakeholders perceive that their concerns and perspectives are acknowledged, they are more inclined to embrace and adopt the technologies, facilitating successful implementation and yielding positive outcomes [20].

By incorporating QoL considerations, disruptive technologies can effectively prioritize the needs and well-being of all stakeholders. In the context of the hinterland agricultural supply chain, the integration of QoL factors in the implementation of technological interventions enables inclusive development for farmers, their communities, and end consumers. This holistic approach ensures that technological solutions are developed with a human-centered perspective, culminating in more pertinent, efficacious, and sustainable outcomes [19]. For farmers, disruptive technologies that conscientiously address their QoL requirements, such as by assisting them in acquiring knowledge and aiding in decision-making, have the potential to boost the profitability of agriculture and contribute to its economic, environmental, and social sustainability, leading to income stability and an overall improvement in well-being [21]. Farmers are also better positioned to optimize the benefits derived from technology when these advancements are tailored specifically to their needs [12]. The improved well-being and satisfaction of farmers and workers through QoL considerations translate into increased productivity, enhanced job satisfaction, and better work–life balance, which, in turn, leads to the sustainability of the agricultural sector, stability, and growth within the farming industry [22]. Innovators and future research endeavors can also capture the holistic nature of farmers’ well-being and provide valuable insights for enhancing their overall QoL through incorporating the aspect of quality of life, particularly eudaimonic well-being [23]. In effect, the integration of QoL considerations ensures the long-term sustainability of agricultural practices and the entire agricultural supply chain, benefitting all stakeholders, including farmers, workers, and the hinterland community, both in the present and future generations.

3. Disruptive Technologies Applied in the Agricultural Supply Chain in the Hinterland

A simple supply chain typically begins with the procurement of raw materials and continues until a final product reaches the consumers [24]. The agricultural supply chain, on the other hand, is more complex than other supply chains in terms of product perishability, seasonal supply–demand fluctuations, and consumer awareness [24]. In the agricultural supply chain, the pre-harvest phase pertains to raw materials procurement through production, whereas the post-harvest stage includes storage, distribution, and retail. Operations involved in the process from pre-harvest to post-harvest have been integrated with technology due to the rapidly increasing demand for food and competition for resources. However, many challenges regarding post-harvest operations still need to be addressed, such as the lack of product traceability and information balance [24].

Disruptive technologies in the agricultural industry encompass a range of innovations, including precision agriculture, the Artificial Intelligent Internet of Things (AIIoT), big data analytics, blockchain, artificial intelligence (AI), cyber-physical systems (CPSs), robotics, and e-commerce, which are revolutionizing how agricultural supply chains operate. These technologies offer new opportunities to optimize production processes and ensure sustainability throughout the supply chain. Consequently, these emerging technologies can be used to create better production efficiencies for farms and yield higher income for farmers, while consumers can access high-quality, sustainable food [25].

Table 1. Applications and impact on QoL of disruptive technologies in the agricultural supply chain.

Disruptive Technology	Application in the Hinterland Agricultural Supply Chain	Related Impacts on QoL
		Farmers	Traders	Consumers	Ref.
Precision post-harvest agriculture	Promotes acquisition of crop yield data and monitoring climate changes, thereby supporting decisions on crop production, storage, and logistics	Reduced workload and labor intensity, increased productivity, improved decision-making, enhanced environmental stewardship	Increased market access, improved supply chain efficiency, has the tendency for improved technological skills	Access to high-quality and safe produce, increased food availability and affordability, enhanced environmental sustainability	[25]
Artificial Intelligent Internet of Things (IIoT)	Ensures the integrity of food in terms of quality and quantity, as packaging is utilized to monitor temperature fluctuations from the packaging stage to consumption	Enhanced resource management, reduced labor and monitoring efforts, improved risk management, access to market information	Streamlined supply chain management, enhanced market connectivity, improved traceability, quality control	Enhanced food safety and transparency, personalized and convenient experiences like home delivery services and online ordering, sustainable consumption	[26,27]
Big data analytics	Facilitate improved product tracking through real-time information pertaining to product conditions, routes, and environmental factors to identify potential contamination incidents during the transportation of agricultural products	Data-driven decision-making including weather patterns and market trends analyses, resource management, early detection of fresh produce qualities, access to market information	Market intelligence and demand forecasting, supply chain optimization, improved risk management in relation to price fluctuations	Personalized experience, improved product quality and safety, enhanced access to information including ingredients and nutritional values, convenient shopping experiences	[26,27]
Blockchain	Enables farmers to create trust, ensures secure transactions, and generates social capital to utilize it as collateral for financial transactions	Transparent supply chain, access to financial services, convenient payments and reduced intermediaries, intellectual property protection	Secure and efficient transactions, improved supply chain management, enhanced market access, trade financing	Transparency and trust, food safety and authentication, fair trade and ethical consumption, direct interaction with producers	[27,28]
Artificial intelligence	Addresses various on-farm sorting tasks such as the automation of the grading of harvested fruits through maturity and quality	Informed decision-making on pricing and inventory, demand forecasting, anomaly detection, market manipulation	Market intelligence, minimization of costs, reduction of bottlenecks	Analysis of consumer preferences, greater confidence in quality of products, enhanced satisfaction, convenience	[28,29]
Cyber–physical systems	Facilitates the interactions of and direct relationships between suppliers and buyers in a virtual cyberspace, bypassing the need for unfair intermediary processing	Improved income, increased resilience through timely decision-making, peace of mind through protection of farms from unauthorized access, and increase profitability due to resource accessibility	Improved satisfaction through timely delivery, real-time tracking and traceability, efficient market connectivity	Enhanced convenience through product traceability, real-time product information, increased convenience in purchasing and delivery	[29,30]
Robotics and automation	Leverage agricultural robots to facilitate manipulation and movement of fresh products throughout the post-harvest supply chain	Reduced physical labor and injury, promotion of informed decision-making, allow farmers to manage large-scale supply chain	Reduced errors in the supply chain lines	No significant impact	[31,32]
E-commerce (farm-to-fork and farm-to-table movements)	Offers small-scale farmers the means to overcome barriers hindering their access to markets and enables them to engage in online transactions	Improved financial stability due to market access and expansion	Increase in market reach and profitability, improved work–life balance due to streamlined operations	Time savings and enhanced overall convenience through direct interaction with farmers	[25,33,34,35]

provides a comprehensive overview of the significant applications of these disruptive technologies and their impact on stakeholders’ quality of life in the hinterland agricultural supply chain.

Implementing disruptive technologies in the hinterland agricultural supply chain presents various social, economic, and environmental challenges. Among the foremost hurdles is the limited financial resources available to farmers to invest in such technologies [11]. The high cost of technology often acts as a barrier for farmers, particularly those with farms that are smaller or are located in hinterland regions, thus creating a continuous demand for disruptive technologies that can achieve high productivity while being affordable to allow farmers to avoid compromising on quality due to cost [33]. Additionally, farmers face a lack of understanding about technology and its maintenance [13]. Their concerns revolve around their ability to learn new systems, operate machinery, interpret generated data, and, most critically, the lack of adequate practical support to facilitate the seamless integration of these technologies into their existing agricultural practices. Moreover, apprehensions are also expressed regarding potential mechanical failures, with farmers lacking the expertise and resources required to rectify machinery malfunctions or access local repair services, consequently exposing them to unanticipated financial burdens [11].

From the perspective of farmers, emerging technologies offer the potential to reduce labor requirements and minimize fatigue through automation [12]. These technologies also enable better management of food supply and demand, allowing farmers to target the provision of crops that are in demand, thus leading to more stable income while addressing food insecurity and price uncertainties [29]. Moreover, the implementation of these technologies contributes to improved health and safety conditions for staff working in distribution centers. By utilizing equipment capable of lifting heavy pallets of crops, technologies create less hazardous working environments [32]. Additionally, advanced artificial intelligence models can assist in scheduling transportation and avoiding traffic congestion during distribution, ultimately reducing workload and enhancing the well-being of distribution center personnel [28]. Thus, hinterland farmers and local communities get to be empowered through these new opportunities, contributing to their socioeconomic well-being. Furthermore, end consumers also benefit from these advancements, as they gain easier access to fresh produce whenever needed. Disruptive technologies optimize supply chain management, ensuring the efficient and timely delivery of products to consumers, from farm to fork [34,35]. This streamlined process improves the quality of life for end consumers by providing them with readily available and high-quality produce.

Overall, the integration of disruptive technologies into the agricultural supply chain has far-reaching positive impacts, benefiting hinterland farmers, distribution center staff, local communities, and end consumers alike. It boosts productivity, reduces labor, enhances safety, empowers communities, and ensures a steady supply of fresh produce, ultimately improving the overall quality of life for all stakeholders involved.

4. QoL Modeling in the Agricultural Supply Chain

QoL is the perception of a person of their overall comfortability and stability in everyday life; hence, the quality of their life. Based on [36], the standard of QoL varies from culture to culture and social position; thus, different models are present to cater to the different statuses of people. When determining the QoL of a region, it is important to consider its multidimensional nature. There can be an objective or subjective assessment of QoL, depending on the purpose of the assessment. QoL indicators include accessibility to housing, education, food, leisure and transport, and the quality of subjects’ health, their environment, and the economy.

Urban and rural areas offer different means of measurement for QoL, mainly due to their differences in population size. Population size, however, is not the only major factor that differentiates the two, but also their population density and the cultural diversities within the community, as discussed in [37]. With a larger population, more opportunities for businesses and access to connections are available, typically enabling a better potential improvement in QoL determinants such as leisure, food, and economy. This is the reason capital cities in general are the more densely populated regions; they promote better opportunities for jobs and businesses. It was also highlighted that cultural diversity is also a crucial factor because it implies different needs in a region [37]. A greater amount of needs typically is a challenge to increasing QoL, because more effort needs to be made to satisfy these needs. In contrast, rural areas typically have lower population density, limiting access to certain services but also incurring fewer needs to be satisfied.

The various modeling approaches involving QoL, and its matched applications, are summarized in

Table 2. Summary of QoL model applications and the techniques involved.

Technique	HC	AG	SWCD	UPE	ED	PPG	AP	ASC	QoL Applications
Subjective well-being	✔	✔	✔	✔		✔	✔		[37,38,39,40,41]
Composite Indices	✔				✔				[42,43]
Multi-Criteria Decision Analysis	✔	✔		✔	✔		✔	✔	[44,45,46,47,48]
Capability Approach		✔	✔	✔	✔			✔	[49,50,51,52,53]
Participatory Approach	✔	✔	✔	✔					[54,55,56,57]
Data-driven approaches	✔	✔		✔					[58,59,60]

HC—healthcare, AG—aging and gerontology, SWCD—social work and community development, UPE—urban planning, transportation, and environment, ED—economic development, PPG—public policy and governance, AP—agricultural production, ASC—agricultural supply chain.

. Subjective well-being (SWB) measures the rate of satisfaction and fulfillment of individuals. Composite Indices (CI) measure the Human Development Index (HDI) and Gross National Happiness (GNH) index. Multi-Criteria Decision Analysis (MCDA) is a collective technique consisting of an Analytic Hierarchy Process (AHP) or Multi-Attribute Utility Theory (MAUT), which are survey-based approaches that employ pairwise comparisons. The Capability Approach (CA), on the other hand, focuses on the priorities of an individual with respect to their capabilities to achieve certain goals. The Participatory Approach (PA) involves workshops, focus groups, and community consultations. Data-driven approaches (DA) use higher statistics and machine learning algorithms. Note that each QoL modeling technique has its own advantages, limitations, and applicability.

QoL assessment models used in the agricultural field are based on existing QoL models (

Table 3. Comparison of existing studies on QoL assessment in agricultural supply chain.

Existing QoL Assessment Methods in Agricultural Supply Chain	Description	Results	Reference
McGill QoL Questionnaire, Independent Living and Working (ILW) Level, demographic analysis	17-item McGill QoL survey that includes questions about the respondents’ overall physical, psychological, and existential well-being, physical symptoms; support levels on a scale of 0–10.	The United States Department of Agriculture (USDA)’s AgrAbility project resulted in a significant statistical and practical improvement in the QoL and ILW of the respondents. It was most effective on people with higher ILW scores, but also had a significant positive impact on people with disabilities.	[61]
Descriptive statistical analysis in relation to QoL Structural Equation Modeling (SEM), Structural Model of Social Capital and QoL of Farmers in Supporting Sustainable Agriculture	The QoL survey used a rating of 1–5 in terms of satisfaction with these sub-variables: community well-being, emotional well-being, health, and safety. To satisfy the model, the respondents’ social capital was considered the dependent variable, while their QoL was the independent variable.	Results of surveys in Sedayulawas Village, Lamongan Regency, Indonesia, indicate that the respondents consistently have a good quality of life with regard to the agricultural aspect. All indicators of QoL were met with a slightly positive attitude from the respondents, who had a mean QoL score of 3.5604.	[62]
WHOQOL (World Health Organization QoL)-based assessment	The QoL of the respondents were classified into four domains, namely, physical, psychological, social relations, and environmental quality and material well-being.	The implementation of multipurpose and multifunctional landscapes in urban communities, as opposed to those with purely economic purposes, promoted a better self-image and a practical increase in the QoL of its residents, as reflected in the gated communities of the Greater Cairo Region, Egypt.	[4]

), modified to be more appropriate in focusing on the lives of those involved, such as farmers. QoL assessment techniques that have been used before are summarized in Table 3.

The three existing QoL assessment methods mentioned (Table 3) were similarly based on farmers’ physical and psychological well-being, social relations, and economic and environmental impacts. These bases were formed in the context of farmers living their regular day-to-day lives, with the exception of the first assessment. The assessment of their QoL was not based on the improvement of their lives by emerging technologies.

5. Limitations of Existing QoL Applications

The significance of QoL applications within disruptive technologies cannot be overstated, as they play a crucial role in enhancing the well-being and overall quality of life of individuals and communities. However, these technologies often face limitations and obstacles that hinder users from fully maximizing their potential benefits. Notably, the literature on this specific topic is limited, with only a handful of countries addressing the integration of disruptive technologies in agricultural supply chains in hinterland areas, which highlights the need for further research and exploration in this area. In the subsequent sections, this study presents an analysis of six distinct case studies focusing on agricultural supply chains in hinterland areas across six major regions where disruptive technologies have been implemented: precision agriculture in smallholder farming in India, IoT-enabled sustainable agriculture in Europe, blockchain implementation in the fair trade coffee supply chain in South America, utilization of artificial intelligence in the agricultural sector in Malaysia, the agricultural socio-cyber physical system in Europe, and sustainable automation in agriculture in China. Through an examination of these case studies, the challenges and constraints encountered by existing QoL applications in various contexts will be explored, shedding light on the shortcomings and areas that require improvement within these domains. Note that the case studies were conducted purely based on existing research, without employing surveys or interviews, a decision driven by several factors. First, literature-based case studies allow for a comprehensive analysis of historical data, the existing literature, and credible sources, enabling a deeper exploration of the subject matter. And second, literature-based case studies can provide a broader perspective and facilitate comparisons with other relevant studies, enhancing the overall validity and generalizability of the findings before we conduct any actual survey.

5.1. Precision Agriculture and QoL in Smallholder Farming in India

Precision agriculture is the application of the effective utilization of minimized agricultural inputs based on various factors, such as weather, soil condition, and pollution, while maximizing the output, thus making it a suitable practice to achieve sustainable agriculture and sustainable development [63]. With limited resources for agricultural food production, precision agriculture is one vital method for India to feed its citizens. To save input costs while minimizing the harmful effects on the environment, data on the farm are required, which can be acquired through sensors. One of the remote sensing technologies available is satellite-based. However, substantial amounts of food production are needed to gain profit while implementing and maintaining the technology. As such, it is not suitable for small farm holders [63].

An alternative technology is the use of drones or unpiloted airborne vehicles (UAVs). By integrating sensors in UAVs, the mapping, monitoring, and management of precision agriculture is possible. Furthermore, UAVs can cover large areas, making it possible to spray agrochemicals. Land-based robots can also be used in precision agriculture. Aside from the task they are made to do, they can also be integrated with sensors for data gathering [62,63]. On a smaller scale of precision agriculture in India, there are farmers who use their smartphones to take photos of any irregularities that they may find and send them to the Plantix application for diagnosis [63]. Precision Development, a non-profit organization, also offers an advisory service through mobile devices named Ama Krushi in Odisha State, India. The number of farmers that utilize this service amounts to 1.3 million [64]. While farmers are able to get advice on the condition of their crops, the farmers are still the ones who need to monitor their fields. While precision agriculture has existed for decades, there are still research gaps that prevent farmers from effectively utilizing the technology. Furthermore, farmers have difficulty in acquiring the information needed to realize the benefits of precision agriculture, thus preventing them from fully implementing it [65]. Still, farmers exist who use precision agriculture to minimize inputs while maximizing outputs.

With precision agriculture, food security problems can be solved in India to a certain degree, and having food security can lead to a good QoL; almost all respondents in a survey administered in India agree that physical health and nutrition are important for having a good QoL. However, there was a huge gap between the number of respondents requiring micronutrients needed for their immune systems and overall health to the total number of respondents. The results also showed that about half of the Indians surveyed have a poor QoL [66]. Therefore, by improving agricultural food production through precision farming, Indians should be able to have more access to nutritious food, thus improving their QoL.

5.2. IoT and QoL in Sustainable Agriculture in Europe

In the agricultural supply chain in the central and northern parts of Europe, the IoT has been utilized with a range of technologies, including sensors, drones, navigation systems, and cloud-based data services, to provide decision support tools. Notably, the IoT has facilitated logistical operations by enabling optimal route planning, thereby enhancing efficiency. Moreover, IoT has contributed to improved stock management and ordering processes, leading to streamlined operations within the agricultural supply chain. The integration of the IoT seeks to transform traditional practices and promote sustainability by minimizing inputs and optimizing resource allocation within the agricultural supply chain [67].

Large-scale pilots (LSPs) based on the IoT in agriculture have encountered challenges related to sectoral and technological constraints [67]. One significant challenge is the lack of connectivity in many areas, which hampers the handling of large datasets due to poor 3G/4G coverage. Additionally, accessing cost-effective processing power for complex calculations remains a hurdle, particularly for small- to medium-sized farmers. The absence of data processing services significantly impedes IoT adoption. Social acceptance among farmers is another obstacle, as many rely on practical experience and perceive IoT enhancements as unnecessary or time-consuming to learn. Educating and training end users on the benefits and practical applications of these technologies is essential. Capital investment is also a concern, as larger, more financially robust farms are more receptive to IoT adoption, whereas smaller-scale farmers may have limited resources for new technology. While IoT sensors and RFID tags enable supply chain transparency, quality control, and timely delivery, the investment costs associated with such technologies pose limitations for some farms [68].

Maximizing the advantages of the IoT in European agriculture requires addressing the limitations that affect farmers’ QoL. To optimize the QoL benefits of IoT, it is essential to adopt a farmer-centered approach that considers the unique needs and limitations of farmers. By doing so, farmers can effectively utilize IoT technologies to enhance productivity and contribute to a sustainable and prosperous agricultural supply chain.

5.3. Blockchain and QoL in the Fair Trade Coffee Supply Chain in South America

The implementation of blockchain technology in South America aims to support the fair trade coffee supply chain. Due to low and unpredictable global market prices, coffee producers often face financial losses. However, by leveraging blockchain technology, transactions can be facilitated, and power imbalances within the value chain can be mitigated. By utilizing features such as public and private key cryptography, database technologies, and consensus algorithms, the adoption of blockchain technology in the supply chain enables the use of smart contracts, cryptocurrency, transparency, and product quality authentication for the coffee trade. As a result, this helps address concerns related to food safety, intermediaries, and transaction costs, benefiting both coffee producers and consumers.

A blockchain model developed in [69] required farmers to provide comprehensive information about their coffee, including its origin, quality, certification standards, and the use of agrochemicals in Antioquia, Columbia. This model enhanced traceability and transparency in agro-food supply chains, but it comes with certain limitations and concerns. Many coffee producers from the towns of Titiribí and Heliconia in Columbia expressed doubts about their ability to adopt blockchain technology. Among a group of twenty producers, fourteen did not own any technological devices, while the remaining six owned either a smartphone or a laptop. Four producers were uncertain about the specific data requirements of the blockchain model and had concerns about data confidentiality. Additionally, some producers were worried that sharing detailed information could attract unwanted attention from government agencies, which they considered a significant drawback of implementing a blockchain model. Producers had mixed feelings about investing their own resources, such as time and money, to utilize the blockchain model. The coffee producers expressed hesitation and stated that their decision would depend on factors such as the actual benefits they would receive and the cost of implementing the technology. Others mentioned that they would need to compare the costs and benefits before deciding whether to invest in blockchain, with one of them emphasizing that they would participate “only if the benefits outweigh the costs”.

While the implementation of blockchain technology in the coffee trade has the potential to address various challenges and improve the quality of life for coffee producers and consumers, there are notable gaps and concerns that need to be addressed. The requirement for comprehensive information from farmers poses a significant hurdle, especially for those who lack access to technological devices. Data confidentiality and the potential scrutiny from government agencies also raise apprehensions among producers. Additionally, the mixed feelings about investing personal resources highlight the need for a clear assessment of the benefits and costs associated with adopting blockchain technology. This emphasizes the need to consider the QoL aspects in determining how technologies can better support coffee producers. Despite the benefits offered by blockchain technology, there are still obstacles that hinder its widespread adoption.

5.4. AI and QoL in the Agricultural Sector in Malaysia

Artificial intelligence has emerged in applications in different fields over the years. Agriculture, being one of the most important industries, especially in South-East Asian countries, also benefits from the advancement of AI. The issues in agriculture that could benefit from AI are what motivated the study to be conducted. Examples of the AI that were evaluated were the use of AI with robots to kill pests, the neural-network-based visual identification of biological objects, sensory data collection using robots, and the increase in weed management precision using AI. Ideally, AI does not have a limit on the QoL indicators it can improve. In practice, however, the QoL indicators that mentioned AI technology that were improved on were health and safety, economic security, and environmental safety [70]. The four issues that are currently present in the agricultural field in Johor, Malaysia are the susceptibility of agriculture to disease, the increasing labor shortage in large-scale agriculture, consumer perceptions of agricultural products, and the safety and health of the farmers in agriculture [70]. Ultimately, AI can predict the impact of climate change on crops, as well as improve the quality and efficiency of crop production. Such applicable issues and drivers in Malaysia, based on the four mentioned, were assessed and identified. The issues and drivers were identified to be social, technological, economic, environmental, political, and values (STEEPV). The issues and drivers identified totaled 78 items related to AI-driven applications in agriculture. Among these, nine key terms were identified as follows: (1) advanced living standard, (2) replacement of employees, (3) safety and health of farmer and consumer, (4) technology advancement, (5) resource efficiency and decreased cost, (6) productivity enhancement and optimization of economy, (7) reducing of environmental impact, (8) government policy, and (9) ethical issues.

The standing of AI in agriculture was analyzed through both qualitative and quantitative methods. In both methods, the data collection procedures used were questionnaires, interviews, and experiments for the primary data, and articles, journals, newspapers, and books for the secondary data. A descriptive analysis was performed to interpret the data. The first part of the questionnaire included a basic demographic background check on the respondents in terms of gender, age, race, and work experience. The information was analyzed in terms of frequency and percentage. Next, the second part of the questionnaire asked the respondents about the importance of AI in the agricultural field based on the nine key terms identified earlier using the Likert scale (1 as very unimportant to 5 as very important). The third part of the questionnaire was about the impact of AI in the agricultural field, and the last part was about uncertainties regarding AI in the agricultural field. The results showed that technological advancement scored the highest, both in the importance and impact parts of the questionnaire, while ethical issues scored the lowest, also in both importance and impact. Regarding the uncertainty part, however, reducing the environmental impact scored the highest, while technological advancement scored the lowest.

The results reflect the perception of the Malaysian farmers surveyed on the usefulness of AI in agriculture. Although the mean results of their answers regarding the importance, impact, and uncertainty of AI were not significantly different between each key indicator, there were individual indicators that were more apparent.

One practical application of AI is utilizing fuzzy logic for complex decision-making problems. The implementation of fuzzy-based methods in agri-food supply chains presents solutions to enhance sustainability and transparency, with a focus on quality control, risk management, and supplier selection [71]. The development of new fuzzy-based models, designs to address improvements in QoL, and AI’s integration with other technologies like blockchain and the IoT have the potential to further improve the agriculture industry.

5.5. Socio-Cyber Physical System Agricultural in Europe

Because food chains are complex and opaque, the One Health approach is necessary to evaluate choices while achieving sustainability [72]. The change towards digitalized rural areas and agriculture will have huge impacts, specifically from the environmental and socioeconomic point of view. Understanding these variables can be crucial, as digitalizing rural areas is affixed to the ethical orientation of the rural areas, food, farming, and sustainability [72]. With surging technological advancements, there is a demand to incorporate new and innovative technologies to find solutions to problems in the food supply chain. With that, CPSs have the power to change the world we live in from a physical system to a cyber system, with improved QoL, faster reactions, and more accuracy. Due to a broad variety of system types and the multidisciplinary nature of the case study, there are numerous challenges when it comes to creating such CPSs. The electric power grid, environmental protection, and communication and monitoring systems are just some of the notable issues in today’s setting. Furthermore, the understanding of CPSs, the required platforms and architectural designs, the need for hardware and software designed with QoS requirements, and their application and interaction with humans all fall under the category of such existing difficulties [73]. With the widespread scale of technological advancement in the 21st century, the adaptation of computing nodes into the power grid, with examples such as smart inverters and smart meters, has reconstructed the usual traditional methods into a new cyber-physical system. With this advancement came independent cyber-physical systems like microgrids [73]. With its development, CPSs’ integrations are more dispersed, and controllers are more prospective. Data collection is becoming more difficult as the number of intelligent devices in the field that generate data goes up [74].

The socio-cyber physical framework enables clarification of the relationships between digital and broader sustainable development goals and needs, and this can build on earlier analyses, further promoting a pessimistic or optimistic dichotomy that is connected to digitalization by identifying the results, and a quid pro quo orientation in terms of the effects of digital agriculture that enables, disables, increases, and depletes resources. Although their framework is comprehensive and flexible regarding new socio-cyber-physical interactions, Living Labs (LLs), which conducted interactive impact evaluations, found it a challenge to handle the complexity and multiple elements of the subject. The application of the integrated framework of the LLs helped determine the three sources of methodical biases. To begin to address these biases, the first step is to accumulate the important questions. With that, LLs were encouraged to filter questions for different rounds, with involved stakeholders, while following the important requirements. This step could affect LLs’ very own funding and research agency contracts. This initial step could influence the decision-making process regarding exclusion and inclusion criteria, or research priorities. Further adding another source of bias in this case could lead to a conclusion derived from the framework and nature of Living Labs (non-profit, community organization, research institute, private organization). Due to the COVID-19 pandemic, the tools used to gather data have a lot of variables that influence the selection of subjects and data, or the measurement of stakeholders’ engagement [75].

To assess the effectiveness of digitalization’s impacts in relation to a particular key question, LLs were mobilized across Europe. With that, the questions in this case study focus on the issues which are relevant to European agricultural, forestry, and rural areas, specifically agricultural diversification and direct selling in relation to the impacts of the introduction of cyber elements into socio-physical systems, both in the past and in the present. To sum it all up, the discovery found in this case study highlights the benefits and limitations of the study, and the difficulty that was experienced when applying an integrated assessment framework in an active environment. An emphasis was provided regarding how a system-level assessment could boost knowledge of the immediate implications, trade-offs, and side effects of digitalization in a wide range of topics and situations. However, this case provides more emphasis on the lessons learned from LLs dealing with digital agriculture [75].

5.6. Automation and QoL in Sustainable Agricultural Production in China

Agricultural unpiloted aerial vehicles (UAVs) have been progressively adopted in the agricultural industry in China. The integration of UAVs in farming offers advantages such as reduced labor, precise targeting, and decreased resource overuse. In response to rising labor costs and excessive pesticide usage, farmers in Jilin Province utilize UAVs for spraying pesticides and liquids [76]. UAVs can conduct unpiloted operations across various weather conditions in the hinterland and cover full-space farm areas. As a result, the use of UAVs leads to improved productivity and optimized resource utilization. However, the application of spraying pesticides has been associated with environmental and health risks, whether integrated or not with UAVs. Thus, their mechanized application in improving agricultural QoL should be carefully considered.

There are several factors that impact the use of UAVs among farmers. In [76], the study only focuses on farmers in Jilin Province; thus it may not be representative of other regions in China. Male farmers with a higher-percentage income, larger cultivated land areas, open-mindedness towards technology, and specific village societal roles showed more willingness to adopt UAVs. Conversely, scientists have made advancements in adapting and enhancing UAVs for more farm-specific applications, resulting in improved production, optimized resource utilization, and enhanced QoL. However, certain limitations, including positive attitudes towards traditional methods, competitiveness, and education, challenge the full adoption of UAVs. Some farmers were worried about the machines’ operational efficiency and the lack of a supporting framework. The study suggests further investigation on cost benefits, health and environmental impacts, and economic feasibility.

In another area, Xinjiang, China’s cotton-production capital, UAVs have demonstrated improvements in operational efficiency, reduced liquid consumption, and lower operational costs, rendering the needed response to the prevailing demands in the cotton-farming industry [77]. As compared to traditional methods, the use of UAVs prevents cotton damage during spraying, providing a more convenient and effective pest control solution. However, due to seasonal changes in planting structure and climatic conditions in the cotton fields, there is a need to adjust UAV settings and add the proper chemical materials, leading to considerations of maintenance and repair costs. There are still research gaps and limitations concerning the influence of UAVs on farmers’ QoL. Nevertheless, available studies have demonstrated the effectiveness of UAVs in agricultural applications.

6. Discussion

By enabling these technologies, significant improvements can be made in QoL for all stakeholders within the agricultural supply chain [37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60]. IR 4.0 technologies can help farmers produce better products with more efficiency, while also reducing the effort farmers must put into farming, in addition to maintaining sustainable agriculture. Second, the transportation and distribution of the products produced by farms in the hinterland can be improved by providing accurate transportation routes that reduce travel time and delay. Finally, the part of selling the products can be improved by advanced technologies by finding the demand and reach of the market. All these advantages contribute to the overall increase in QoL, not only for farmers, but also for the consumers and surrounding communities. Thus, enabling IR 4.0 technologies helps achieve the three United Nations Sustainable Development Goals, namely, good health and well-being, decent work and economic growth, and sustainable cities and communities. The first goal targets capacity training to avoid psychological problems in work, as farmers were observed to be vulnerable in a fluctuating economy in relation to their revenue [66,67,68], and this is very applicable to the agricultural communities in the hinterland region, where fresh produce is sold at a lower price to the traders. The second goal targets a higher level of economic productivity through technological upgrading and innovation, and it is reflected by cases in [63,64,65,66,67,68,69,70,71,72,73,74,75,76,77], where the technology-intensified agricultural supply chain has certain life implications for the stakeholders whether it is seen to be contributive or not, although the prior is usually the case. Conventional farmers and traders in the hinterland should upskill so they can use the technologies provided to them to make their work easier and more productive. The third goal targets extensive support for socioeconomic and environmental links and provisions to marginalized people through safe transportation and food production systems, and progress is reflected through the use of AI and CPS [70,73]. The only downside of having these technologies would be the implementation cost. On the other hand, not utilizing these technologies would result in farmers doing as they have done in the past, which cannot be considered optimal as numerous tasks would be done by manual labor and they have no assistive information to improve the production, transportation, and distribution of the produce.

QoL indicators directly affected by these improvements are mostly economic, health and safety, access to food, and transportation. Other indicators such as education and leisure are still indirectly improved through the potential of economic growth, and ultimately, the socioeconomic life of the community is improved.

The integration of technologies, however, does not only induce positive effects, especially on the part of farmers. Significant challenges in the implementation of these technologies start with their cost. Implementation of these technologies does not guarantee that their potential advantages will be reflected in real life. This brings up the issue of whether the expensive cost of equipment is worth it. Thus, addressing the affordability and adaptability of advanced technologies is crucial to ensure that their adoption brings benefits to small farming communities and their QoL. Small farmers often encounter financial constraints and difficulties accessing and utilizing complex technologies. To make these technologies more accessible to this demographic, several measures can be implemented. Firstly, developing cost-effective solutions tailored to the specific needs of small farmers can enhance affordability. This involves focusing on scalable and locally relevant solutions. Additionally, governments and organizations can support them by offering financial incentives, subsidies, and grants to reduce the initial investment burden associated with adopting advanced technologies, making them more feasible for small farmers. Encouraging collaboration and group purchasing among small farmers can also help leverage their collective resources, facilitating a more cost-effective acquisition of advanced technologies. Also, not every farmer has the knowledge or ability to use and take advantage of these technologies. They would need to be trained and assisted for it to be successful. In [78,79], it was also stated that, rather than the age of the farmer or the farm size, it was the education level and skills that are important for the adoption of new technologies. This is especially true for precision agriculture. Thus, ensuring user-friendly interfaces when designing advanced technologies is essential to accommodate farmers with varying levels of technical expertise. Based on the analysis of the different case studies [37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,80], it becomes evident that there are similarities in the limitations experienced in various regions worldwide concerning the consideration of QoL with respect to disruptive technologies in agriculture (Figure 2). While these technologies have the potential to enhance farm productivity and address agricultural challenges, their positive impact on farmers’ QoL is contingent upon their ability to adopt and adapt to these technologies, as well as the long-term effects they have on their livelihoods.

One common limitation is the presence of technological barriers and the high implementation costs, which hinder the widespread adoption of innovative solutions. Recent studies also showed that high-cost equipment and required skills are some of the several challenges preventing technological failures [78,81]. For instance, precision agriculture’s reliance on satellite-based remote sensing technology poses challenges for small-scale farmers who lack the necessary resources and large-scale production capacity to make it economically viable. Similarly, IoT technologies face constraints related to connectivity, lack of cost-effective processing power, low social acceptance, and capital investment constraints, which hinder the widespread adoption and utilization of IoT technologies among farmers. Similar studies also state that bandwidth connectivity and data security challenges are present in the application of IoT in agriculture [78,81]. The implementation of blockchain in the coffee supply chain encounters challenges related to data requirements, technological accessibility, and apprehensions about data confidentiality and potential government scrutiny. On the other hand, [82,83] state that the blockchain technology addresses the problem of information security. The limitation instead comes from the size of the farms, as it would be more convenient for large farms to collect and integrate data, whereas small farms can participate in a blockchain-based insurance market [82]. In Malaysia, the implementation of AI technologies raises ethical concerns surrounding uncertainties surrounding its impact on the environment, and the need for careful consideration of social, technological, economic, environmental, political, and values (STEEPV) factors in its implementation. Furthermore, socio-cyber physical systems face challenges related to economic costs, complexity of the hardware and software design, and the overall integration of CPSs with human application and interaction.

A recurring theme across these case studies is the critical role of farmer acceptance and their access to necessary resources. Farmers’ awareness of the perceived benefits of emerging technologies and access to smart devices and the availability of financial resources for implementation significantly influence their acceptance and adoption of these innovations. In addition, farmers lack the knowledge and skills to fully utilize the technology; it was revealed in an interview that their main source of education about precision agriculture comes from retailers. This is where institutional support may come in to expand the social relationship to beyond just farmers and retailers [78,84,85]. These challenges highlight the limitations of considering QoL when designing technological advancements aimed at addressing agricultural problems. They emphasize the need for effective and inclusive disruptive technologies that integrate QoL modeling to better understand the needs and barriers faced by farmers if they are to truly benefit from these innovations. To address these limitations, it is crucial to promote awareness and education among farmers regarding technological developments in agriculture. By keeping them informed, it can help combat skepticism and alleviate perceived barriers or risks associated with adopting new technologies. Additionally, policies should be implemented to encourage investments in disruptive technologies, while also establishing legal frameworks to regulate their usage. In fact, there are farmers who feel that they have no choice but to adopt the technologies to stay within the business [86]. There are also other perspectives, such as in New Zealand, where farmers are prepared to invest, but they see it as an incremental change rather than a transformational one [87]. Another challenge with the implementation of disruptive technologies is the ethical concerns it may bring. In a recent study, there are three themes around which the ethical discussion about smart farms revolves, which are data ownership, accessibility, sharing, and control; distribution of power; and impacts on human life and society. Whether it is hard or soft impacts that the smart technologies bring, the focused values are sustainability, entrepreneurship, equality, food security, strong communities, freedom, knowledge, and care. However, there has yet to be a satisfying conclusion regarding the discussion of the three themes, as there are several perspectives regarding the function and purpose of digital farms in society [88]. Creating strong regulatory frameworks, guidelines, and standards that prioritize ethical concerns and societal welfare will ensure that these technologies lead to positive and sustainable outcomes for all stakeholders. Ultimately, the integration of QoL considerations in the design and implementation of disruptive technologies is essential for ensuring their relevance and positive impact on farmers’ lives. These technologies should be developed with a focus on creating value, especially for farmers, who will be the end users and directly experience their impact. By prioritizing the needs and aspirations of farmers, agricultural innovations can be tailored to address their specific challenges and contribute to an improved QoL. Hence, it must be clear that the impacts of the technology-intensive agricultural supply chain depend on how the stakeholders are open to adopting and utilizing those technologies [63,64,65,66,67,68,69,70,71,72,73,74,75,76,77] in their processes, especially in the hinterland.

7. Future Directions

To address the emerging challenge of the abstract measurement of life indices of individuals in the agricultural supply chain of the hinterland, and the ever-changing technological advancements that can potentially be applied in hinterland agricultural sites and processes, the current study established a new framework named agricultural QoL (AgQoL), which refers to the well-being of the people involved in the agricultural sector, particularly farmers or fresh goods producers, trading center workers, and consumers, while focusing on the three SDGs as a central principle in the establishment of the framework. Note that there is no specific format for QoL framework modeling; hence, we provided a superstructure for an agricultural QoL model framework for farmers, trading center workers, and consumers in relation to the diversified impacts of the technology-intensified agricultural supply chain in the hinterland (Figure 3). This emphasizes the six core criteria, which are access to resources and commodities, human welfare, government and social support, environmental concerns, infrastructure services, and economic stability, that are the key elements contributing to having a good AgQoL. Some of the key elements that contribute to having a good AgQoL, such as having the information and technology to achieve economic stability, healthy working environments, and sustainable agriculture can be achieved through disruptive technologies. The utilization of precision agriculture, the IoT, big data analytics, blockchain, AI, robotics, and e-commerce can improve the overall agricultural supply chain through introducing better efficiency into agricultural food production while maintaining a sustainable agricultural practice, and regarding the distribution of goods, these technologies will allow end consumers to have more access to fresh produce. The developed AgQoL model (Figure 3) is entirely based on the techno-socioeconomic and environmental factors, together with infrastructure concerns and governmental supports and regulations, discussed on the existing literature about hinterland agricultural supply chains across the world [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,80]. The six core criteria indicated in Figure 3 encompass hinterland agricultural areas (highland) and agrarian countries, as these are the basic exogenous factors that can rationally change the QoL level of people in those communities. For the case of the lowland urban agricultural supply chain, urban centers’ connectivity is expected to have a lesser weight compared to other factors, as they are already in the city. The weight attributed to transportation networks is also expected to drastically change, as routes and infrastructure conditions are completely different. It should be taken into consideration that the developed AgQoL can be a used as a clear basis on which particular QoL factors should be included in analyzing the happiness level of farmers. Hence, the AgQoL model is robust, because it is applicable across different cultures, longitudinal, as it can provide predictions over time and will characterize experiential changes, and designed for generalized demographics to avoid biases over a population segment. The model’s adaptability and robustness can be extendedly verified by administering actual surveys and analyses of salient QoL factors related to farmers, distributors, or trading center workers, as well as consumers of green products. Additionally, the AgQoL is highly interpretable, as it is clustered into six major criteria, and provides correlations to illuminate why a certain QoL value increases or decreases. Overall, the adaptation of enabling technologies of the Industrial Revolution 4.0 and the drifting conditions of external factors will allow the individuals involved in the agricultural supply chain to improve the key elements of having a good QoL.

Enhancing the AgQoL requires not only technologies provided through technology and innovation providers, but also agricultural extension services offices, agribusiness and input suppliers, non-governmental organizations, environmental conservation organizations, and policy makers and regulatory bodies (Figure 4). These, constituting the six-dimensional entities of stakeholders, should be instrumental for an increase in productivity and QoL of farmers and agricultural communities, especially those located in the hinterland. It is vital that the six entities collaborate with each other to address challenges and foster sustainable development in agriculture, ultimately contributing to an improved AgQoL.

Despite the potential for disruptive technologies to improve the QoL of individuals involved in the agricultural supply chain, studies have yet to exist that comprehensively cover the individuals involved in the agricultural supply chain, especially those located in the hinterland. It is highly recommended that future research tackles the current AgQoL (Figure 3 and Figure 4) of a specific location and the changes in the QoL of the individuals after the implementation of disruptive technologies. This means that a QoL survey, observation, and mathematical analysis should be performed pre- and post-deployment of technology in a certain farm site. Results will show whether the improvements brought by the disruptive technologies indeed have a positive impact on the QoL.

8. Conclusions

The integration of Industrial Revolution 4.0 technologies into agriculture offers clear benefits to the overall productivity and efficiency of fresh food production. Taking the whole supply chain process of agriculture into consideration, starting from the production of crops, followed by the transportation and distribution of the crops through the transportation hub of the hinterlands, and finally arriving at the market phase to reach the consumers, each step can benefit from what help disruptive technologies have to offer, potentially improving stakeholders’ quality of life (QoL). The following QoL aspects and disruptive technologies were identified, analyzed, and established in this study:

• The disruptive technologies often applied in the agricultural supply chain that impact farmers, traders, and consumers are precision post-harvest agriculture, the Artificially Intelligent Internet of Things, big data analytics, blockchain, artificial intelligence, cyber-physical systems, automation, robotics, and e-commerce. The positive impacts of these technologies, such as boosting efficiency and ensuring a steady supply of fresh produce, ultimately improve the overall QoL. However, QoL improvement greatly depends on how open the agricultural communities are to adopting and practicing those technologies, which leads to the need for targeting extensive support for socioeconomic and environmental links and providing provisions to marginalized people through safe transportation and food production systems, based on United Nations Sustainable Development Goal (SDG) No. 11, which is sustainable cities and communities.
• The case studies regarding the application of disruptive technologies in five geographic regions in the world indicate their positive impact on QoL, but are contingent on the farmers’ ability to adopt the technologies. Another barrier that these technologies pose is the farmers’ access to resources, where the common barrier is the implementation cost. These are the major issues to consider in relation to targeting a higher level of economic productivity through technological upgrading and innovation in hopes of achieving UN SDG No. 8, which is decent work and economic growth.
• The six core criteria that make up having a good agricultural QoL (AgQoL) for farmers, trading center workers, and consumers are resources and commodities; human welfare with an emphasis on targeting capacity training to avoid psychological problems in the work towards UN SDG No. 3, which is good health and well-being; government and social support; environmental concerns; infrastructure services; and economic stability. Some QoL indicators, namely access to communication and internet technologies and environmentally friendly and sustainably produced food, can be considered vital for farmers, trading center workers, and consumers. Each element needs to be satisfied, as lacking even one could considerably prevent individuals from achieving an acceptable QoL.
• There are six entities that make up the superstructure instrumental to having an acceptable QoL, namely, agribusinesses and input suppliers, non-governmental organizations, environmental conservation organizations, policy makers and regulatory bodies, technology and innovation providers, and agricultural extension services offices. The two trifectas of needs based on technology and socio-environmental aspects were established to address the universal need for support in this application.

By enhancing the AgQoL in farming communities through the integration of modern technologies, not only is their overall well-being elevated, but also, a compelling demonstration is provided to the wider population of a dedicated sub-group committed to providing nutritious and high-quality fresh food. Therefore, the level of AgQoL can be regarded as an indirect indicator of food security in a particular region, and its measurement can be conducted through surveys and assessments. Agricultural communities are highly recommended to recognize that enabling technologies have positive impacts on their welfare.