## 1 Introduction  
Social networks (SNs) are the different groupings of communities with shared practices, interests, and goals [1]. SNs in the agricultural value chain are heterogeneous and complex networks of many entities, including farmers, distributors, processors, and retailers. During the COVID-19 pandemic lockdowns, various digital platforms sustained the different SNs, including social healthcare engagements and [2] online teaching and learning [3]. In agricultural value chains, the COVID-19 lockdown effects severely affected the rural agriculture sector, especially smallholders [4]. Sensors and Internet of Things (IoT) usage in farmlands, mapping, and tracking technologies are revolutionizing farming systems and the management of the agricultural food system as the information moves from farmers to extension offices, traders, and consumers in the agricultural value chains [5]. The speedy growth of agricultural social network (ASN) entities has resulted in \"agricultural big data\" [6] characterized by the \"5 Vs\" [7], that is, volume, variety, velocity, veracity, and value. Digital agricultural applications utilize various technologies, including IoT [8], data mining and analytics [9], blockchain [10],big data and artificial intelligence [11], and sensor technologies [12]. Digital agricultural applications may integrate multiple big data sources such as remote sensing [13], farmers' inputs [14], machine and sensor data, public data, and other private-entity-held data [15]. [PERSON], [PERSON] [7] define agricultural big data as large, complex, diverse, longitudinal datasets generated from sensors, internet activities, emails, academic publications, videos, satellites, and other digital sources. For this study, we consider the term \"agricultural big data\" as a combination of technologies for advancing analytics in creating new ways of processing data timeously within agricultural social networks. The digital solutions relating to access to farming information, understanding consumer needs, and obtaining market information reside in the design and deployment of capable big data processing models. [PERSON] [6] groups agriculture big data analysis into the following clusters: collaborator search, farm management, recommendation, and expert systems. Systems integrating cyberspace and physical space in precision farming aim to increase the efficiency of agricultural operational practices, especially for commercial products based on extensive knowledge management [11]. The cyber-physical systems are useful in monitoring and estimating crop yield via intelligent farm machines, sensor tools, and software that analyze the data collected from these devices. ASNs are important in determining the flow of information, resources, and services within the agricultural value chain. Agriculture is key to the world's economy, providing food, raw materials, and employment opportunities. However, the sector faces several challenges, such as the increasing demand for food, climate change, and the need for sustainable farming practices [16]. Innovations which leverage digital technologies such as IoT [17] and social capital [18] are beneficial in addressing some challenges facing the agricultural sector. Digitalization offers an opportunity to combine digital and social aspects, thereby providing a hybrid strategy for resolving and mitigating challenges in agricultural value chains [15]. Digitalization consists of technologies: the Internet of Things (IoT), big data, systems integration, sensors, robotics, ubiquitous connectivity, augmented reality, machine learning, blockchain, and artificial intelligence [11; 15]. An inclusive digital economy relies on internet-based services and e-commerce applications [19]. Scholars report that the digital economy yields up to 3% of international job opportunities, which translates into 5% of the global gross domestic product (GDP) [20].