Climate change and food security have become critical topics in sustainable agricultural development. The Food and Agriculture Organization of the United Nations proposed the climate-smart agriculture (CSA) concept, which has attracted international attention for its innovative use of technology in addressing agricultural challenges [1
]. The objectives of CSA are threefold: sustainably increase food productivity, increase the adaptive capacity of farming systems, and increase climate change mitigation where possible [3
]. Smart agriculture (SA) emphasizes the roles and applications of innovative technology in agricultural practices.
The SA strategy focuses on the use of digital technology to create precision farming solutions, especially when combined with the application of information and communication technologies and other new interconnected equipment and techniques. The internet of things (IoT), drones, robots, big data, cloud computing, and artificial intelligence are all new resources that are expected to be applied to novel farming practices [6
]. The integration of precision farming systems and digital technology has become the most prevalent trend in agricultural development, contributing to fewer inputs, higher yields, and less damage in agricultural production. Digitized agriculture has become a mainstream trend in numerous countries [7
Several similar but inconclusive concepts were used in different studies, such as agriculture 4.0, precision agriculture, smart farming, digital agriculture, virtual agriculture, big data in agriculture, IoT in agriculture, and interconnected agriculture [11
]. These emerging concepts have slight differences in terms of the emphasis on specific technological applications. Most of these concepts share common traits and values in incorporating new and intelligent technologies into farming practices and introducing resource use efficiency approaches that minimize production costs, reduce farming risks, and increase productivity [13
]. The inventory of the European smart agricultural knowledge and innovation systems (Smart-AKIS) program indicates that SA is mainly related to three interconnectable new technology categories: farm management information systems, precision farming, and agricultural automation. For instance, smartphone application software has been extensively used for remote monitoring and controlling of farming equipment. A similar phenomenon is observed in plant factories, which have employed IoT, big data, sensing and monitoring techniques, and automatic environmental control systems [7
]. Therefore, agribusiness and small-scale farmers can benefit from the application of new technologies.
Taiwan’s agricultural sector is characterized by small-scale holdings and has been identified as a global disaster hotspot (e.g., typhoons and floods) [14
]. The Agriculture 4.0 Project was launched by the Council of Agriculture (COA) of Taiwan in 2017 in compliance with Industry 4.0 development and climate change risks. In the pilot project, an attempt was made to introduce advanced technologies, such as intelligent devices, sensing techniques, robots, IoT, and big data analysis to improve agricultural productivity. The government of Taiwan has invested approximately TWD 4.5 billion in upgrading agricultural technologies. The project was renamed the Smart Agriculture Project in 2018. The SA Project aimed to overcome the restrictions of natural resources and shortages in human labor resources by facilitating the intelligent production and digital marketing of agricultural businesses [15
]. The principal strategies of the SA Project were threefold. First, the COA selected ten pilot agribusinesses as the prioritized targets for the first stage of SA promotion. The agribusinesses targeted were those for moth orchids, seedlings, mushrooms, rice, agricultural facilities, aquaculture, poultry, traceable agricultural products, dairy, and offshore fisheries. Second, the agricultural research and development (R&D) institute employed cross-domain technological innovations to create digital agri-services, value chains, and communication models between producers and consumers, such as IoT-based environmental control modules, labor-saving carrying equipment, and marketing management information platforms. Third, the next generation of farmers should satisfy requirements for the use of smart agricultural development because trained farmers are the foundation of SA development [16
Human resource development is a key factor in developing SA; thus, encouraging farmers and agribusinesses to adopt innovative digital technologies and intelligent mobile devices in their farming practices is becoming a policy priority in Taiwan. Therefore, the COA and National Taiwan University collaborated to create and design a series of SA training programs to develop human resources in smart agriculture. The educational objectives of the SA training program were to confer trainees with a positive attitude and practical competences, and to enhance their SA-related knowledge. Four types of training courses were offered, comprising of indoor lectures providing general SA education, on-site visits and training, international visits and exchanges, and individual tailor-made technical assistance provided by SA service teams for each pilot SA industry [17
]. However, the literature on the psychological factors and individual characteristics that drive farmers’ intentions to adopt SA technologies remains limited. Therefore, this study investigated the associations among SA-related knowledge, attitudes, and adoption behaviors. Moreover, we assessed the effect of farmers’ knowledge and attitudes regarding smart agriculture on their adoption of SA technologies.
The following research problems regarding SA knowledge, attitude, and adoption were addressed. What types of SA technology are crucial for farming practices and are better understood by farmers? What are the driving factors in SA adoption behaviors? To what extent do sociodemographic variables, knowledge, and attitude affect the adoption of SA technologies?
Studies have focused on the effects of psychological factors on individual behaviors, such as social learning theory, the theory of reasoned action, and the theory of planned behavior [18
]. Few studies on agriculture have identified associations between farming practices, attitudes, and other psychological determinants [13
]. Furthermore, the theory of planned behavior, an extension of the theory of reasoned action, has been extensively applied and tested in various fields [21
]. The theory of planned behavior identifies hierarchical relations between various beliefs and attitudes affecting behavior. The educational goals of agricultural training programs are multifaceted; such programs are expected to improve the target group’s knowledge level and change their attitudes and adoption behaviors [13
]. This study employed a comprehensive knowledge–attitude–practice (KAP) model based on the previous literature, to further investigate the relationships in the KAP model of participants in the SA training course. Based on the KAP model, we hypothesized that SA knowledge and perceived importance were positively correlated and that both the SA knowledge and importance perception had a positive effect on the adoption of smart agriculture technologies.
This study investigated SA-related knowledge, attitude, and adoption among farmers in Taiwan. The sociodemographic characteristics of the respondents were collected, and the effects on the adoption of SA technologies were determined. Survey data from 321 farmers who participated in the SA training program were collected. The results revealed significant and positive correlations between SA knowledge, perceived importance, and adoption behavior. Of the eight SA technologies, the automatic environmental control systems were the most well-understood and were perceived as being the most important, whereas biological image detection and recognition techniques were ranked as the least understood. SA knowledge and perceived importance significantly affected the adoption of SA technologies. Therefore, lower adoption levels of SA technologies may be attributed to inadequate information, missing knowledge, lack of awareness of the technologies, and lack of perceived practical value. We thus recommend the intensification of R&D and SA technologies, such as IoT and big data analysis, to satisfy farmer requirements under current farming conditions and management.
These findings provide policymakers and agricultural educators with important insights that can be used to more accurately target interventions that promote or facilitate the adoption of SA technologies. Furthermore, these findings suggest that agricultural R&D institutes should concentrate on improving market access for established and valuable SA technologies. Additionally, providing systematic training courses related to the applications of IoT and big data in agriculture may enable farmers to engage more effectively in SA practices. However, the research limitations should be considered when these findings are being interpreted. First, KAP strengthens the theoretical foundation of this study, however, it also limits its depth. Agriculture policy, organizational support, computer efficacy, perceived effectiveness, perceived usefulness, and trust toward SA ventures should be considered in future models. Second, in methodologies used to analyze the collected data, the mediating role of attitude on the relationship between knowledge and practice could have been investigated. Furthermore, the moderating effects of gender and prior experience could have also been examined.