Attributers of the technologies intervened through frontline demonstrations

Introduction

Technology dissemination remains a major challenge in Indian agriculture, where small and marginal farmers constitute the majority. To address the gap between research and practice, the Indian Council of Agricultural Research (ICAR) introduced Front Line Demonstrations (FLDs) in 1991-92. These serve as "learning laboratories" where farmers directly observe the performance of improved technologies under local conditions. Studies across India have confirmed that FLDs enhance awareness, improve adoption, and boost productivity (Singh et al., 2021; Chaudhary et al., 2022) [7,1] . Farmers' decisions, however, are strongly influenced by how they perceive technology attributes such as relative advantage, compatibility, complexity, observability, and practicability (Rogers, 2003) [5] . Unless a technology aligns with farmers' resources, values, and constraints, its adoption may remain limited. The present study was conducted in Akola district of Maharashtra with the following objectives: 1. To study the perceived attributes of technologies intervened through FLDs conducted by KVK, Akola. 2. To assess the impact of FLDs in terms of change in knowledge and adoption

Methodology

The study was conducted in Akola district, Maharashtra, during 2024-25. An exploratory and diagnostic research design was adopted. A population study was carried out covering 230 beneficiaries of 14 FLDs conducted by KVK, Akola across crops (wheat, pigeon pea, onion, banana, cotton, blackgram, fodder) and enterprises (mushroom, nutrition garden, soybean mitten, cattle health).

Variables and Measurements

Under Profile of beneficiaries, Education, experience, farm size, income, socio-economic status (Thakare & Ingle, 2007) [9] , innovativeness, scientific orientation, economic motivation, risk preference was studied. The Attributes of technology as Relative advantage, compatibility, complexity, observability, and practicability was measured using the scale. The Impact indicators were Knowledge and Adoption. Knowledge and adoption scores were obtained through pre-and post-tests and converted into indices.

Statistical Analysis

The Index score calculation, Percentage change over before, Categorization: Equal interval method, Distributional analysis: Mean, SD, frequency and Relational analysis was carried out.

Results and Discussion

Profile of the Respondents

The socio-economic profile of respondents as depicted in Table 2 provides the foundation for understanding their adoption behaviour. In case of Education, among the 215 respondents, 6.96% were illiterate, 5.65% had only primary schooling, while a sizeable proportion had middle school education (33.04%). High school and junior college educated farmers constituted 14.35% each, and 25.65% were graduates and above. This shows that nearly threefourths of the respondents had attained education up to middle school or higher, indicating that literacy was not a major constraint. Higher levels of education are positively associated with better understanding of scientific recommendations, as confirmed by Singh et al. (2022) [8] . The Experience in crop cultivation shows that nearly half of www.extensionjournal.com the respondents (46.09%) had 6-10 years of experience, 31.74% had less than 5 years, and 22.17% had over 11 years. This distribution highlights that most farmers were in the active adoption phase, balancing experience with openness to new practices. Similar findings were reported by Patel et al. (2023) [4] , where moderate experience facilitated quicker adoption of FLD technologies. In case of Farm size, a majority were medium to large farmers (31.74% large, 22.17% medium, 24.78% semi-medium), while marginal and small farmers accounted for only 9.13% and 12.17%, respectively. This skew towards larger landholdings suggests greater resource availability and potential for technology adoption. The data pertaining to Annual income shows that about one-third earned up to ₹50,000, another 22.17% earned between ₹50,001-₹100,000, and 23.48% earned between ₹100,001-₹150,000. Only 8.26% were in the higher bracket of ₹200,001-₹250,000. This shows that the majority were small and medium-income farmers, relying on agriculture as the primary source of livelihood. The Socio-economic status revealed that the largest proportion (43.04%) had low socioeconomic status, followed by moderate (22.17%) and very low (15.65%). Only 7.39% were in the very high status category. The prevalence of low SES underlines the need for interventions like FLDs that provide visible results with fewer risks (Mishra et al., 2021) [2] .

In case of Innovativeness it was observed that more than half (52.61%) were moderately innovative, 35.22% highly innovative, and 12.17% had low innovativeness. This indicates that a considerable segment of farmers was inclined toward experimenting with new technologies, a critical factor in technology diffusion. The Scientific orientation shows that a majority (53.48%) were moderately oriented, while 26.52% showed high scientific orientation. This reflects openness to scientific explanations behind technologies, in line with Rogers' diffusion theory (2003) [5] .

The results pertaining to Economic motivation shows that most farmers (61.30%) had moderate motivation to increase farm income, while 24.35% had high economic motivation. This suggests that profitability is an important driver of adoption. In case of Risk preference, Interestingly, 77.83% were moderate risk takers, while 20% were high and only 2.17% were low. This demonstrates that FLDs are wellsuited to such farmer groups, as they are willing to try new technologies provided risks are manageable.

Attributes of the Technology Relative Advantage of Technologies

Relative advantage refers to the extent to which a technology is perceived as superior to the one it supersedes.

The data presented in Table 3 revealed that in case of Initial cost, a striking 65.65% felt that the technologies were "cheap," while 13.48% felt them "more cheap." Only 20.87% considered them expensive. This suggests that most FLD technologies were economically feasible, matching earlier findings by Sharma et al. (2023) [6] . In case of Net profitability, nearly half (49.13%) rated them as yielding "exorbitant profits," while 8.26% found them "very exorbitant." About 42.61% were uncertain or found returns meager. The positive skew confirms that profitability was a strong factor motivating adoption. In case of Consistency of profit, over half (53.04%) reported "regular profits," and 36.09% found them "irregular." This indicates that while returns were generally stable, some variability due to climatic and input factors remained. For Saving of time, a majority (59.13%) felt neutral ("cannot say"), while 22.61% and 18.26% reported time-saving benefits. This implies that while most technologies were profitable, their time efficiency varied depending on the enterprise. In case of Multiple potential use, nearly 55% acknowledged multiple or wider benefits, especially in case of crop protection and nutrition-related demonstrations. This multidimensional advantage is consistent with earlier reports by Chaudhary et al. (2022) [1] .

Compatibility of Technologies

Compatibility refers to the degree to which a technology fits farmers' existing values, needs, and conditions. The data from Table 4 reveled that in case of Situational compatibility, around 60.87% found technologies feasible to more feasible, reflecting their adaptability to local conditions. In case of Cultural compatibility, a remarkable 76.52% found them culturally acceptable, indicating that new practices did not clash with existing traditions or norms. In case of Physical compatibility, about 81.31% perceived technologies as compatible with their physical needs (soil, crop type, resources). In case of Social compatibility, more than 66% considered them recognizable within their communities, highlighting peer acceptance and social reinforcement. In case of Relational compatibility, nearly half (46.09%) expressed neutrality, while 37.40% reported independence from external dependence. This implies that while some technologies required external inputs, many could be managed with farmers' existing resources.

Complexity of Technologies

Complexity often acts as a barrier to adoption. The data in [2] . In case of Reversibility, a majority (57.83%) felt practices were reversible, which provides farmers with confidence to experiment. For Labour efficiency, interestingly, 70.43% felt technologies were "labour consuming," indicating that labour-saving aspects require further emphasis in future demonstrations.

Practicability and Observability

Practicability and observability strongly influence adoption. The data in

Impact of Technologies

The impact was measured in terms of knowledge gain, adoption and depicted in [4] .

Correlates of Knowledge and Adoption

The correlation analysis depicted in table shows that Education Positively correlated with both knowledge (0.236) and adoption (0.341), highlighting the role of literacy in technology uptake. The Experience was Negatively correlated (-0.221, -0.231), indicating that younger farmers are more receptive to new technologies. Farm size and income, Both showed positive correlations, suggesting that resource-rich farmers are more likely to adopt innovations. Socio-economic status, innovativeness, and scientific orientation were positively linked to knowledge and adoption, consistent with earlier studies (Nain et al., 2020) [3] . Economic motivation and risk preference were strongest correlates with adoption (0.423 and 0.337), implying that risk-taking and profit-seeking farmers adopt technologies faster.

Conclusion

The study establishes that FLDs conducted by KVK, Akola significantly enhanced knowledge and adoption of the technology by the farmers. Farmers perceived demonstrated technologies as advantageous, compatible, simple, and reliable, though resource and labour constraints were noted. Enterprises like soybean mitten, nutritional gardens, and mushroom cultivation had high adoption, reflecting their suitability for smallholders and women farmers. Adoption was strongly influenced by education, economic motivation, and risk preference. The findings reinforce the role of FLDs as an effective tool for bridging research-extension-farmer gaps, and recommend strengthening input supply, credit access, and labour-saving innovations for wider impact.

References

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