Socio-economic profile and adoption level of farmers towards mobile-based agricultural advisories

Introduction

The adoption of mobile-based agricultural advisories has emerged as an important component of modern agricultural extension, enabling farmers to access timely information on weather, crop production, pest and disease management, input use, market prices and government schemes. In the present study, conducted among 120 farmers in Narsinghpur district of Madhya Pradesh, mobile connectivity was relatively widespread, with 76.67 per cent of respondents reporting access to mobile phones and the internet. However, technological access does not necessarily translate into effective utilization or adoption of advisory information, as farmers' educational status, digital literacy, extension contact, training exposure, internet accessibility and perceived usefulness may influence their use of mobile-based services. Poor internet connectivity was reported as a major constraint by 73.33 per cent of respondents, indicating that infrastructure and accessibility remain important considerations in promoting digital agricultural extension. Empirical evidence also highlights the potential of mobile communication to improve farmers' use of agricultural recommendations. Singh et al. (2023) [9] , in a clusterrandomized evaluation involving 2,014 farming households across 159 communities in five districts of Andhra Pradesh, reported that the integration of SMS and interactive voice response (IVR) messages with extension interventions improved farmers' recall, knowledge and adoption of recommended agricultural practices. Similarly, evidence from Haryana showed that SMS-based agrometeorological advisories improved farmers' compliance with recommended crop-management operations. These findings demonstrate that mobile-based advisories can complement conventional extension systems by providing timely and accessible information. Therefore, assessing the extent of adoption and the factors influencing farmers' utilization of mobile-based agricultural advisories is essential for strengthening digital agricultural extension services and improving their effectiveness at the farm level.

Materials and Methods

The present study was conducted to assess the extent of adoption of mobile-based agricultural advisories among farmers in Narsinghpur district of Madhya Pradesh. A descriptive research design was adopted to assess the existing level of adoption without manipulating the study variables. Narsinghpur district was purposively selected considering the increasing use of mobile phones and digital communication technologies for obtaining agricultural information. Primary www.extensionjournal.com data were collected through a structured interview schedule and analyzed using appropriate statistical techniques. Narsinghpur district is situated in the fertile Narmada Valley and is characterized by productive soils, favourable climatic conditions and adequate irrigation facilities. Agriculture is the major occupation of the rural population, with wheat, soybean, gram, paddy, sugarcane and pulses being important crops. Out of the six development blocks, Narsinghpur and Kareli blocks were purposively selected for the study. From each selected block, two villages were randomly selected, namely Bachai and Dhubghat from Narsinghpur block and Barman and Bhugwara from Kareli block. A total of 120 farmers were selected as respondents for the study. From each selected village, 30 farmers were selected, making a sample of 120 respondents. Data regarding the adoption of mobile-based agricultural advisories were collected through personal interviews using a well-structured interview schedule. The collected data were systematically classified, tabulated and analyzed using suitable statistical tools to determine the extent of adoption among farmers.

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 of respondents (55.83%) were middle-aged (36-55 years). Similar findings were reported by Kumar et al. (2023) [2] and Rathod et al. (2024) [6] . Gender: Majority of respondents (85.00%) were male. This was supported by Singh et al. (2023) [9] and Patel et al. (2022) [5] . Education: Most respondents (23.33%) had high school education. Similar results were reported by Kumar et al. (2023) [2] and Rathod et al. (2024) [6] . Family Size: Majority (56.66%) belonged to medium-sized families (5-8 members). Similar findings were reported by Singh et al. (2023) [9] and Kumar et al. (2024). Occupation: Agriculture was the main occupation of the majority (43.33%) of respondents. Similar findings were reported by Kumar et al. (2023) [2] and Singh et al. (2024) [7] . Land Holding: Majority (31.67%) were small farmers (1.01-2.00 ha), followed by marginal farmers (28.33%). Similar findings were reported by Patel et al. (2022) [5] and Kumar et al. (2023) [2] . Annual Income: Majority of respondents (48.33%) belonged to the medium-income group (₹1,00,001-₹3,00,000). Similar findings were reported by Kumar et al. (2023) [2] and Patel et al. (2022) [5] . Type of House: Majority of respondents (45.00%) lived in semi-pucca houses, followed by pucca houses (36.67%). Similar findings were reported by Kumar et al. (2023) [2] and Singh et al. (2024) [7] . Membership of Organization: The findings indicate that 35.00 per cent of the respondents were members of one organization, followed by 33.33 per cent who belonged to two or more organizations, while 31.67 per cent had no organizational membership. Similar findings were reported by Patel et al. (2022) [5] and Kumar et al. (2023) [3] . Source of Drinking Water: Tap water was the major source of drinking water (28.33%), followed by hand pumps (26.67%) and tube wells (23.33%). Similar observations were reported by Kumar et al. (2023) [3] and Singh et al. (2024) [7] . Possession of Agricultural Assets: The findings show that 45.00 per cent of the respondents possessed 1-5 agricultural assets, followed by 30.00 per cent having more than five assets, while 25.00 per cent possessed only a few agricultural assets. Similar findings were reported by Kumar et al. (2023) [3] and Patel et al. (2022) [5] . Possession of Comfortable Assets: Majority of respondents (53.34%) had a medium level of comfortable assets. Similar findings were reported by Patel et al. (2022) [5] and Kumar et al. (2023) [3] . Irrigation Facility: Majority of respondents (48.33%) had partial irrigation facilities, followed by adequate irrigation (31.67%). Similar findings were reported by Kumar et al. (2023) [3] and Patel et al. (2022) [5] . Mobile Phone Ownership and Usage: Majority of respondents (56.66%) had a medium level of mobile phone ownership and usage. Similar findings were reported by Kumar et al. (2023) [3] and Rathod et al. (2024) [6] . Internet Accessibility: Majority of respondents (58.33%) had medium internet accessibility. Similar findings were reported by Kumar et al. (2023) [3] and Patel et al. (2022) [5] . Mass Media Exposure: Majority of respondents (60.00%) had medium mass media exposure. Similar findings were reported by Patel et al. (2022) [5] and Kumar et al. (2023) [3] . Extension Contact: Majority of respondents (58.33%) had medium extension contact. Similar findings were reported by Meena et al. (2021) [4] and Choudhary et al. (2022) [1] . Training Received: Majority of respondents (51.67%) had a medium level of training on mobile-based agricultural advisories. Similar findings were reported by Meena et al. (2021) [4] and Yadav et al. (2023) [10] .

Extent of Adoption of Mobile-Based Agricultural Advisories

The majority of respondents (55.00%) had a medium level of adoption, followed by high (23.33%) and low (21.67%) levels. This indicates that most farmers moderately adopted mobile-based agricultural advisories for farm-related information and decision-making. Similar findings were reported by Meena et al. (2021) [4] , Choudhary et al. (2022) [1] and Yadav et al. (2023) [10] , who observed moderate adoption and positive influence of mobile-based advisory services on farmers' adoption behaviour (Table 1 & Fig. 1).

Association between independent variables and adoption level

The findings presented in Table 4.2 indicate that education, size of land holding, annual income, membership of organization, possession of agricultural assets, possession of comfortable assets, mobile phone ownership and usage pattern, internet accessibility, mass media exposure, extension contact and training received on mobile-based agricultural advisories were significantly associated with the www.extensionjournal.com adoption level of mobile-based agricultural advisory services. In contrast, age, gender, family size, occupation, type of house, source of drinking water and irrigation facility showed a non-significant association with adoption. Among the significant variables, training received on mobile-based agricultural advisories recorded the highest χ² value (19.26), followed by mobile phone ownership and usage pattern (18.42) and internet accessibility (16.78), indicating their relatively stronger association with farmers' adoption level.

Conclusion

The study concluded that the majority of farmers had a medium level of adoption (55.00%) of mobile-based agricultural advisories, indicating moderate use of mobile services for agricultural information and decision-making. Among the 18 independent variables studied, education, land holding, annual income, organizational membership, agricultural and comfortable assets, mobile phone usage, internet accessibility, mass media exposure, extension contact and training received were significantly associated with adoption. Training received (χ² = 19.26), mobile phone ownership and usage (χ² = 18.42) and internet accessibility (χ² = 16.78) showed relatively stronger associations with adoption. In contrast, age, gender, family size, occupation, type of house, drinking water source and irrigation facility were not significantly associated with adoption. Overall, strengthening farmer training, improving digital connectivity and promoting effective use of mobile phones can enhance the adoption of mobile-based agricultural advisories among farmers.

Acknowledgement

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

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