Ph.D

The study investigated the acceptance and perceived efficacy of agricultural technologies disseminated by Krishi Vigyan Kendras (KVKs). Its core aims were to evaluate farmer acceptance and effectiveness of these technologies across Kerala's Agro Ecological Zones (AEZs), determine their contribution to food security and climate resilience, and formulate improved dissemination strategies. The study encompassed all 19 Agro Ecological Units (AEUs) in Kerala. A multi-stakeholder sample of 387 respondents was assembled through random selection, comprising 230 farmers, 115 extension personnel, and 42 KVK scientists.
To quantify adoption, a Technology Acceptance Index (TAI) was constructed, grounded in the innovation-decision process viz., knowledge, persuasion, decision, implementation and confirmation. Each component was studied using a set of indicators.
Analysis of the knowledge component across the 19 AEUs revealed considerable disparity, with index values spanning from 0.338 to 0.693. This range signifies that the level of understanding and awareness of agricultural technologies among farmers varies significantly, from poor to very good, depending on their geographic and ecological zone. The overall average knowledge index for all AEUs combined was found to be 0.490, indicating a moderate level of agricultural knowledge across the entire study region. Statistical analysis confirmed that the differences in knowledge levels between these 19 units were not due to chance, with a statistically significant gap (p < 0.05) separating them. AEU-6 emerged as the top performing unit with the highest mean knowledge index (0.693), while AEU-20 registered the lowest (0.338).
The persuasion index of all the AEUs ranged from 0.318 to 0.603, indicating a variation from low to high persuasion index of the AEUs. The aggregate mean persuasion index across all AEUs was calculated at 0.531, indicating a moderate overall level of persuasive capacity. Critically, statistical analysis found no significant difference (p > 0.05) in these scores across the AEUs, despite AEU-15 registering the highest index (0.603) and AEU-4 the lowest (0.318). The decision component was measured using eight indicators. The decision index among the AEUs exhibited considerable variation, ranging from 0.348 to 0.680, which reflects a spectrum of decision-making capacities from low to high across the units. The mean decision index value of 0.50 signifies a moderate overall level of decisiveness among AEUs. Statistical analysis revealed a significant difference (p < 0.05) between the AEUs, suggesting that the decision-making ability was not uniform across the units, with AEU-15 attaining the highest index (0.680) and AEU-21 the lowest (0.348).
The implementation index across the AEUs showed notable variability, ranging from 0.239 to 0.591, indicating differences in their operational and execution capacities from low to high levels. The overall mean index of 0.441 suggests a moderate degree of implementation efficiency among the AEUs, implying that while some units demonstrated relatively effective execution of activities, others performed at a comparatively lower level. Statistical analysis revealed that these variations were not statistically significant (p > 0.05). Among the AEUs, AEU-7 recorded the highest implementation index (0.591), reflecting stronger operational coordination, while AEU-23 had the lowest (0.239). The confirmation index across the AEUs displayed noticeable variability, ranging from 0.318 to 0.693, indicating a wide spectrum of confirmation capacities from low to high among the units. The overall mean index value of 0.485 denotes a moderate level of confirmation behavior across AEUs. Statistically significant difference (p < 0.05) among the AEUs highlights that these disparities stem from variations in performance and behavioral attributes. Specifically, AEU-6, with the highest index (0.693), reflected a stronger commitment to reinforcing adoption through feedback, farmer interaction, and continued technical support, whereas AEU-14, with the lowest index (0.318), indicated gaps in post-adoption communication, monitoring and resource support.
Technology acceptance refers to the degree to which farmers are willing to adopt and effectively use new agricultural technologies, based on their knowledge, perceived benefits, ease of use, and existing conditions. The TAI represents the mean total of components of the innovation decision process viz. knowledge, persuasion, decision, implementation and confirmation. The TAI of the FLD farmers in the AEUs ranged from 0.279 to 0.611, indicating a significant variation of low to high technology acceptance. The mean TAI across all AEUs was 0.476, which indicated a moderate level of overall technology acceptance.
The results revealed noticeable variation in the TAI among the AEUs, indicating differing levels of responsiveness and receptivity towards demonstrated technologies. Out of the total AEUs, eight (2, 7, 13, 14, 15, 18, 19, and 20) registered technology
acceptance levels below the overall mean, whereas eleven (1, 3, 4, 6, 9, 10, 11, 12, 21, 22, and 23) showed higher-than-average acceptance. The overall pattern highlights that while a majority of AEUs demonstrated relatively positive acceptance behavior, a substantial segment still exhibited lower engagement with the demonstrated technologies. This mixed response points to structural, managerial, and contextual differences influencing the acceptance process across AEUs.
To find if there was significant difference between the AEUs, ANOVA test was carried out. The significant difference (p < 0.05) in the TAI across various AEUs can be attributed to disparities in institutional efficiency, resource availability, and socio- economic as well as environmental conditions influencing farmers’ decision-making behavior. Variations in access to extension services, quality of demonstrations, and frequency of technical interactions play a crucial role in shaping farmers’ perceptions and trust toward demonstrated technologies.
With a mean TAI of 0.476 and a standard deviation of 0.089, the data indicate that the average respondent exhibits a moderate level of technology acceptance, with scores clustered moderately around the mean. The range, from a minimum of 0.279 to a maximum of 0.611, confirms that the spectrum of adoption propensity spans from pronounced reluctance to robust acceptance.
Effectiveness of KVK demonstrated technologies was gauged across five dimensions: efficiency, productivity, quality, profit and sustainability. The analysis of the data reveals that the overall mean total score (71.24) across all AEUs indicates a moderate to high level of performance in terms of efficiency, productivity, quality, profit, and sustainability. Among these parameters, efficiency (mean = 14.37) and profit (mean = 14.35) scored slightly higher than sustainability (mean = 13.98). At the AEU level there is meaningful heterogeneity in impact of demonstrated technologies on food security and climate resilience. Several units (e.g., AEU-13 with 66.7% high) show markedly higher proportions of respondents reporting positive outcomes, while others (e.g., AEU-10 and AEU-14 with 58.33% low) register low impact. These local differences imply that the effectiveness of demonstration programs is context-sensitive: some AEUs are achieving increasingly tangible and measurable outcomes, whereas others are not translating demonstrations into perceived improvements in food security and climate resilience.
The reported mean total of 87.93 and standard deviation (SD) of 7.36 provide valuable quantitative insight into the overall impact of demonstrated technologies on food security and climate resilience. The result signifies a generally favorable reception of demonstrated technologies among farmers. Therefore, the aggregate mean underscores the potential of KVK led interventions to generate measurable improvements in agricultural performance at the community level.
The data confirms a significant and sustained extension effort across Kerala, with a cumulative total of 603 FLDs conducted from 2019 to 2021. This substantial number underscores the KVK network's pivotal role as a primary channel for on farm technology validation. The total FLDs per KVK range from 32 (Palakkad) to 54 (Thrissur), indicating varying levels of operational intensity.
Across the three-year period, the highest total farmer participation was recorded in Kozhikode (393), Palakkad (390), Thrissur (383), Kasargod (367), and Alappuzha
(367). These figures indicate that these KVKs have demonstrated strong field-level engagement and effective implementation of FLDs, possibly due to the presence of diverse cropping systems and favorable institutional support. In contrast, Ernakulam (178), Idukki (188), and Kannur (211) recorded comparatively lower cumulative participation, which may be attributed to smaller geographic areas, specific cropping patterns, logistical and infrastructural constraints.
Overall, a majority of the respondents recognized KVK dissemination methods as effective, suggesting satisfactory outreach, relevance, and adaptability of KVK-led technology transfer efforts across AEUs. At the disaggregated level, AEUs 7, 4, 10, 13, and 21 recorded relatively higher proportions of respondents reporting high effectiveness (ranging from 58% to 67%). These zones likely represent areas where KVKs have implemented participatory and contextually adaptive dissemination strategies—such as demonstrations and field days, aligning with local farming systems and constraints. Conversely, AEUs 1, 6, and 12 displayed comparatively higher proportions of respondents in the low effectiveness category (over 55% in some cases), suggesting possible limitations in method suitability, communication reach, or farmer engagement in these ecological contexts.
Understanding adoption behavior helps identify the determinants influencing farmers’ decisions, including socio-economic status, risk perception, resource availability, institutional support, and environmental suitability. Out of a total of 230 respondents, 114 (49.57%) exhibited low adoption behavior, while 116 (50.43%) showed high adoption behavior. This nearly equal distribution suggests a balanced pattern of technology adoption among farmers across the AEUs, indicating that while dissemination and exposure to innovations are relatively widespread, individual and contextual factors continue to influence the degree of technology uptake. A closer examination of the AEUs reveals that in zones such as AEU-4, AEU-12, AEU-14, AEU-18, AEU-21, and AEU-23, a relatively higher proportion of respondents demonstrated high adoption behavior (ranging between 53% and 58%).
A systematic diagnosis of constraints is critical for enhancing the relevance and impact of KVK programs. Survey data revealed a stakeholder divergence in perceived barriers: farmers identified limited access to quality seeds/planting material and inadequate marketing as primary obstacles, while extension personnel emphasized farmer awareness gaps and funding shortfalls. Respondent-proposed solutions focused on financial support (subsidies, credit, insurance), awareness campaigns, capacity building, multi-stakeholder collaboration, adequate resource allocation, robust monitoring and evaluation, and improved digital knowledge management.
In conclusion, this research achieved its objectives by developing the Technology Acceptance Index (TAI) to measure adoption, assessing technology effectiveness and impact on food security and climate resilience, identifying implementation constraints, and outlining dissemination strategies. The study further established the significant influence of farmer socio-psychological profiles on technology acceptance.