Adoption of recommended agricultural practices through digital advisory services and constraints faced by farmers

Introduction

Digital advisory services have emerged as an important component of modern agricultural extension by providing farmers with timely, location-specific and easily accessible information on crop production, weather, pest and disease management, input use and marketing. Mobile phones, SMS, WhatsApp, mobile applications, YouTube and other digital platforms can help farmers obtain technical information quickly and support the adoption of recommended agricultural practices. Studies have shown that access to mobile-based agricultural information can improve farmers' access to information and reduce information gaps, thereby supporting better farm-level decisions (Aker, 2011; Mittal & Mehar, 2016) [1,7] . ICT-enabled extension services have also been reported to improve farmers' awareness and adoption of improved agricultural technologies, particularly among small and marginal farmers (Balaji et al., 2018) [2] . Despite the increasing availability of digital advisory services, their effective utilization among farmers is influenced by socio-economic and technological factors such as age, education, landholding, income, extension contact, digital literacy and access to smartphones and internet connectivity. Farmers may also face constraints including poor network connectivity, lack of technical knowledge, language barriers, limited digital skills and inadequate guidance in using digital platforms. These factors may affect the extent to which farmers convert digital advisories into actual adoption of recommended agricultural practices. Therefore, it is important to assess the adoption of recommended agricultural practices through digital advisory services and identify the major constraints faced by farmers in accessing and utilizing these services. In this context, the present study entitled "Adoption of Recommended Agricultural Practices through Digital Advisory Services and Constraints Faced by Farmers" was undertaken to assess the extent of adoption and identify the constraints experienced by farmers.

Materials and Methods

The independent variables selected for the present study included age, gender, marital status, family size, education, occupation, type of house, annual income, landholding size, platforms for agricultural information, access to digital devices, internet connectivity, digital literacy, extension www.extensionjournal.com contact and mass media exposure. These variables represent the personal, socio-economic, communication and digital characteristics of farmers that may influence their use of digital advisory platforms and adoption of recommended agricultural practices. The dependent variable was the extent of adoption level of recommended agricultural practices through digital advisory services. Primary data were collected using a well-structured and pretested interview schedule developed according to the objectives of the study. The schedule covered information related to the socio-economic characteristics of respondents, access and use of digital advisory platforms and adoption of recommended agricultural practices. Personal interviews were conducted with the selected farmers after explaining the purpose of the study. Secondary information was collected from relevant published reports, official records, research articles and other documented sources related to digital agriculture and advisory services. The collected data were systematically coded, tabulated and analyzed using suitable statistical techniques, including frequency, percentage, mean, standard deviation and chisquare test. Frequency and percentage were used to describe the distribution of respondents, while mean and standard deviation were used to summarize and measure variation in the data. The chi-square test was applied to examine the association between selected independent variables and the extent of adoption. The analysis was undertaken to assess the adoption of recommended agricultural practices through digital advisory services and to identify the major factors associated with farmers' adoption behaviour.

Results and Discussion

The study considered 18 independent variables, namely age, gender, education, family size, occupation, land holding, annual income, type of house, organizational membership, drinking water source, agricultural assets, comfortable assets, irrigation facility, mobile phone ownership and usage, internet accessibility, mass media exposure, extension contact and training received on mobile-based agricultural advisories. Adoption level of mobile-based agricultural advisories was considered as the dependent variable (Table 1).

Socio-Economic Profile of the Farmar:

Age: Majority, 68 (56.67%), were middle-aged (36-55 years), indicating predominance of economically active farmers. Similar findings were reported by Meshram et al. (2024) [6] . Gender: 102 (85.00%) respondents were male, showing greater male participation in farming. Similar findings were reported by Fabregas et al. (2019) [4] . Marital Status: 106 (88.33%) respondents were married, indicating the predominance of married farming households. Similar observations were reported by Fabregas et al. (2019) [4] . Family Type: 72 (60.00%) respondents belonged to nuclear families, showing the predominance of nuclear households. Similar findings were reported by Bhandari et al. (2024) [3] . Education: 32 (26.67%) respondents had high-school education, while only 8 (6.67%) were illiterate, indicating a moderate educational status. Mukherjee et al. (2024) [8] reported the importance of education in digital advisory use.

Occupation: Agriculture was the main occupation of 48 (40.00%) respondents, followed by agriculture with allied activities (30.00%). This indicates dependence on agriculture. Similar findings were reported by Bhandari et al. (2024) [3] . Type of House: 96 (80.00%) respondents were from rural households, reflecting the predominantly rural nature of the farming community. Annual Income: 62 (51.67%) respondents belonged to the medium-income group, indicating a predominantly moderate economic status. Similar findings were reported by Meshram et al. (2024) [6] . Landholding: 42 (35.00%) respondents had small holdings and 38 (31.67%) had marginal holdings. Thus, 66.67% were small or marginal farmers, similar to findings of Meshram et al. (2024) [6] . Agricultural Information Platforms: Television (92; 76.67%) was the most used source, followed by WhatsApp (84; 70.00%), indicating the importance of both traditional and digital sources. Similar findings were reported by Fabregas et al. (2019) [4] . Digital Device Access: 72 (60.00%) respondents had smartphones, indicating good potential for accessing digital agricultural services. Similar findings were reported by Mukherjee et al. (2024) [8] . Internet Connectivity: 54 (45.00%) respondents had regular/moderate connectivity, while 32 (26.67%) reported poor or irregular connectivity, indicating connectivity as an important limitation. Digital Literacy: 52 (43.33%) respondents could use smartphones and basic applications, indicating a moderate level of digital literacy. Mukherjee et al. (2024) [8] also emphasized the importance of digital literacy. Extension Contact: 52 (43.33%) respondents had occasional contact with extension personnel, indicating a moderate level of extension interaction. Similar findings were reported by Bhandari et al. (2024) [3] . Mass Media Exposure: 64 (53.34%) respondents had medium mass-media exposure, indicating moderate access to agricultural information through mass media. Similar findings were reported by Fabregas et al. (2019) [4] .

Level of Awareness and Use of Digital Advisory Platforms

The data presented in Table 1 revealed that the majority of respondents, 65 (54.17%), belonged to the medium level of awareness and use of digital advisory platforms, followed by 28 (23.33%) respondents with a high level and 27 (22.50%) with a low level. Thus, the findings indicated that most farmers had a moderate level of awareness and use of digital advisory platforms for obtaining agricultural information and supporting farm-level decision-making. The finding is supported by Kumar et al. (2022) [5] , who reported that the majority of farmers had a medium level of awareness and use of digital agricultural advisory services. Singh et al. (2023) [10] observed that farmers increasingly used mobile-based platforms for accessing weather, crop management and market-related information, although overall utilization remained moderate. Similarly, Patel et al. (2024) [9] found that digital advisory services were increasingly adopted by farmers for farm-related decision-making, with most respondents falling under the medium adoption category. www.extensionjournal.com The data presented in Table 2 revealed that poor internet connectivity in rural areas was the most important constraint faced by farmers in accessing and utilizing digital advisory services, reported by 73.33 per cent of respondents and ranked first. This was followed by lack of training on digital tools (67.50%), low digital literacy (63.33%), high cost of mobile data/internet (60.00%) and lack of awareness about digital advisory platforms (56.67%), which ranked second to fifth, respectively. Other constraints included unreliable or irrelevant information from apps (45.83%), language barriers in digital content (42.50%), lack of smartphones or digital devices (35.00%), limited trust in digital advisory services (31.67%) and preference for traditional information sources such as extension workers and input dealers (28.33%), which was the least reported constraint. Overall, the findings indicate that inadequate digital infrastructure, insufficient training, limited digital literacy and affordability were the major barriers to effective utilization of digital advisory services.

Acknowledgement

AcknowledgmentThe author sincerely expresses gratitude to the Professor, Department of Technology Transfer, Mahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya, Chitrakoot, Satna (M.P.), for valuable guidance, encouragement and continuous support throughout the research work. The author also gratefully acknowledges the cooperation and assistance provided by colleagues, field assistants and labourers during data collection and the successful completion of the study.

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