Analysis of constraints faced by respondents as online communication users

Introduction

Agricultural extension is an educational service that disseminates information and new technologies to farming communities with the objective of improving agricultural productivity, income, and standard of living. Extension personnel play a critical role in transferring innovations from research institutions to farmers. However, conventional extension systems often face limitations due to physical distance, inadequate transportation, and limited manpower. Information and Communication Technologies (ICTs) offer immense potential to strengthen the transfer of technology (ToT) between research, extension systems, and end-users. ICT tools such as mobile phones, internet platforms, and digital advisory services can enhance outreach, reduce information gaps, and improve service delivery efficiency. Meera et al. (2004) [3] emphasized that ICTs can empower farmers by providing greater control over information access and decision-making. ICTs also help transform extension personnel into "knowledge workers," promoting bottom-up, demand-driven technology generation and dissemination. Despite these advantages, ICT adoption in agriculture remains uneven due to disparities in infrastructure, digital literacy, affordability, and content relevance. Common constraints in ICT adoption include illiteracy, lack of localized content in regional languages, poor internet connectivity, high costs of devices and services, limited awareness, and inadequate technical support. Additionally, human mediation at the last mile remains essential, indicating continued dependency on extension agents. Given the limited empirical studies on constraints faced by farmers in using online communication tools, particularly in Eastern Uttar Pradesh, the present study was undertaken to analyze these constraints and document farmers' suggestions for improving ICT-based extension services. Information is critical in agricultural development because it is a tool for communication between stakeholders and serves as a channel for assessing trends and shaping decisions (Kalusopa, 2005) [2] . Farming requires information and technical expertise hence the need for extension services however, due to various factors extension services are not readily available to all farmers. A consensus exists that extension services, if functioning effectively, improve agricultural productivity by providing farmers with information that helps them to optimize their use of limited resources (Muyanga and Jayne, 2006) [4] . The goals of agricultural extension includes transferring information from the global knowledge base and from local research to farmers, enabling them to clarify their own goals and possibilities, educating them on how to make better decisions, and stimulating desirable agricultural development (Van den Ban and Hawkins, 1996) [7] . Investments in extension services have the potential to improve agricultural productivity and increase farmers' incomes, especially in developing economies, where more than 90.00 per cent of the world's nearly 1 million extension personnel are located. Yet the impact of extension on farm performance is varied, reflecting differences in how extension services are delivered and in the circumstances of www.extensionjournal.com service recipients (NSSO, 2005) [5] . The emerging challenges and opportunities call for a paradigm shift in the innovation driven agricultural research system to connect inventions with all the stakeholders in the entire food supply chain. Social media and internet are embracing almost all the sectors of life. In agriculture there is a need to reach each and every farmer and provide them the updated information. Communities of social media are open networks where everyone (i.e clients, users, members) has an opportunity to contribute their ideas and to support mass innovation with one-to-one, one-to-many, and manyto-many interactions. clients, users, members) has an opportunity to contribute their ideas and to support mass innovation with one-to-one, one-to-many, and many-tomany interactions (Ashley et al., 2009) [1] . The study reveals that the primary financial constraint faced by the farmers was the high cost of cattle feed, while the key personal constraint was identified as the lack of adequate infrastructure facilities. The farmers were also found to be faced with the situational constraints of the incidence of diseases among cattle, and the major marketing challenge identified was low milk prices. (Sharanya and Anumol.,2026) [6] .

Research Methodology

The study was conducted in Eastern Uttar Pradesh, a region characterized by predominance of small and marginal farmers (average holding size <2 ha), rice-wheat cropping systems, alluvial soils, monsoon-dependent irrigation and varying digital penetration due to uneven infrastructure and socio-economic factors. This area faces challenges like limited traditional extension reach and emerging digital adoption. A descriptive research design was adopted, as it is most suitable for describing existing conditions, relationships and patterns of behaviour among farmers regarding the use of digital extension services. A multistage purposive-cumrandom sampling technique was employed to select the sample. In the first stage, two districts-Chandauli and Mirzapur-were purposively selected. The selection was based on the following criteria: (i) representation of typical agro-climatic conditions of Eastern Uttar Pradesh, (ii) presence of both progressive and less progressive blocks in terms of ICT adoption, (iii) reasonable variation in access to mobile internet and smartphone usage among farming communities and (iv) logistical feasibility for field data collection. In the second stage, from each selected district, two blocks were chosen randomly using a lottery method. In the third stage, five villages were randomly selected from each chosen block using the random sampling technique. Thus, a total of 4 blocks (2 from each district) and 20 villages (5 from each block) formed the sampling frame. In the final stage, from each selected village, 20 farmers were selected using systematic random sampling. The sampling interval was determined by dividing the total number of operational farmers in the village (as per the latest revenue/village records) by 20. Starting from a randomly selected number, every farmer was included in the sample. This procedure yielded a total sample size of 400 farmers (20 villages × 20 farmers).

Measurement of Constraints and Suggestions

Respondents were asked to indicate constraints faced and suggestions for improvement. Frequencies and percentages were calculated, and ranks were assigned based on descending order of percentages. Addressing these issues will contribute to inclusive digital agriculture and improved livelihoods for small and marginal farmers.

Results and Discussion

References

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