1. KAUTIR (Kerala Agricultural University Theses Information and Retrieval)
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Item Socio -cultural valuation of ecosystem services in paddy wetlands of Kuttanad(Department of Agricultural Extension Education, College of Agriculture, Vellayani, 2026) Shraddha, S; Razia FathimaThe research work entitled “Socio-cultural valuation of ecosystem services in paddy wetlands of Kuttanad” was conducted during the academic year 2023–25 to understand how local farming communities perceive, value, and interact with the multiple ecosystem services provided by the unique below-sea-level agro-ecosystem of Kuttanad. The study examined stakeholder perceptions of socio-cultural dimensions of ecosystem services, identified key drivers affecting these services, assessed the impact of land-use and cropping pattern changes, and analysed the sense of connectedness farmers retain toward their wetland environment, recognising that the paddy tracts simultaneously deliver provisioning, regulating, supporting, and cultural functions essential to livelihood security and ecological resilience. To address these objectives, an ex post facto research design was adopted since perceptions, ecological conditions, and cultural values exist independently of researcher intervention. The study was carried out across the districts of Alappuzha, Kottayam, and Pathanamthitta, selecting blocks with the largest paddy area, followed by random selection of six villages and six padasekharams. A total of 180 farmers formed the study sample, and data were collected using a structured, pre-tested interview schedule and focus group discussions. A wide range of analytical tools, including the RII, Kruskal– Wallis test, Dunn’s test, Garrett ranking, Kendall’s W, Spearman correlation, Mann– Whitney U test, PCA, multiple regression, and ANOVA, provided a comprehensive assessment of socio-cultural and ecological dimensions. The personal and socio-economic profile of the respondents revealed a marked demographic transition within the agricultural sector of Kuttanad. The farming population is predominantly ageing, with 62.22% of respondents between 51 and 73 years of age and 17.78% above 73 years, while only 20% were below 51 years. All respondents reported a complete withdrawal of youth from farming, indicating a looming crisis in generational continuity. The sector continues to be male-dominated, with 77.78% of farmers being men and women (22.22%) largely participating in supportive tasks rather than decision-making roles. Educational attainment was relatively high, with 40.56% having completed secondary schooling and 22.78% having reached the intermediate level, facilitating better understanding of extension messages. Economically, the foundation remains fragile as 58.33% are marginal farmers owning less than one hectare of land and 83.33% do not lease additional land, reflecting highly resource-constrained operations. Agricultural income was limited, with 58.89% earning below ₹1 lakh annually, reducing capacity to invest in improved or climate-resilient technologies. Institutional reliance was strong, evidenced by 68.33% maintaining regular contact with Krishibhavan staff and 85% availing farming subsidies, highlighting the critical role of public support in sustaining wetland agriculture. Results from the RII analysis showed that farmers placed the highest importance on provisioning and supporting services, particularly food production (RII = 0.992), groundwater recharge (0.857), and nutrient cycling (0.833), while services such as flood regulation, fish resources, and cultural rituals were perceived as less important (RII < 0.6). These patterns varied significantly across districts (p < 0.05), with Kottayam assigning greater value to provisioning, regulating, and supporting services and Pathanamthitta ranking highest for cultural services. Gender differences were significant for cultural services (χ² = 6.632; p = 0.01), with women placing greater emphasis on cultural and spiritual dimensions, while age-wise variations remained insignificant. Garrett’s ranking mirrored these findings, placing provisioning services first, followed by regulating, supporting, and cultural services, with a moderate-to-high level of agreement among respondents (Kendall’s W = 0.554; p < 0.001). Further analysis of Cultural Ecosystem Services revealed significant spatial differences in aesthetics, traditional agriculture, social and spiritual significance, education, and recreation, shaped by differences in tourism intensity, livelihood dependence, and cultural heritage. Principal Component Analysis extracted five major dimensions explaining 50 per cent of total variance, led by emotional and physical connection to the ecosystem (14.07%), cultural and artistic contributions (11.49%), and heritage and social interactions (9.69%). Together, these components highlight the deep-rooted emotional ties, artistic inspirations, historical memory, and traditional ecological knowledge that define the cultural foundation of the Kuttanad wetlands. Regression analysis showed that multiple categories of drivers significantly influenced ecosystem services. Among demographic factors, urbanisation (β = 0.304) and labour migration (β = 0.244) were the strongest predictors, while non-procurement of harvest (β = 0.214) and land conversion (β = 0.193) were the most influential economic drivers. Socio-political variables, including agricultural credit (β = 0.368), farming subsidies (β = 0.320), and decentralised governance policies (β = 0.284), exerted the greatest overall influence. Technological drivers such as short-duration varieties, mechanisation, and training were significant, while the loss of traditional practices (β = 0.571) emerged as a critical cultural driver. Environmental stressors such as flooding (β = 0.273) and soil acidity (β = 0.220) were major biophysical determinants affecting ecosystem functions. Correlation results further revealed that land-use change was positively associated with regulating (r = 0.256) and supporting services (r = 0.226), implying that awareness of ecological functions increases as environmental degradation intensifies. District-wise analysis showed a significant negative association between land-use change and cultural services in Kottayam (r = –0.265; p < 0.05), pointing to cultural erosion, while in Pathanamthitta, regulating services showed a positive association with land-use change (r = 0.309; p < 0.05), likely due to exposure to floods. Mann–Whitney U results indicated significant differences only for supporting services (p = 0.007), with farmers experiencing land-use changes assigning higher importance. The assessment of connectedness to nature revealed that farmers across all districts demonstrated medium-to-high levels of connectedness, reflecting strong ecological awareness and emotional attachment to the wetlands. Although Kottayam showed slightly higher connectedness scores, statistical tests confirmed no significant district-wise differences, suggesting that livelihood dependence and cultural identity foster a shared sense of belonging throughout the region. Overall, the findings demonstrate that farmers primarily value provisioning and supporting services that directly sustain their livelihoods, while cultural and regulating services receive comparatively less emphasis. Emotional attachment to the landscape remains strong, but active cultural practices are declining. The results highlight the crucial need for integrated wetland management that strengthens traditional knowledge, enhances ecological restoration, supports diversified livelihoods, and incorporates cultural values into policy frameworks. Sustaining the ecological and cultural resilience of the Kuttanad paddy wetlands will require coordinated institutional support and community participation.Item Agroecology performance evaluation of farms in Kerala and Meghalaya(Department of Agricultural Extension Education, College of Agriculture, Vellayani, 2026) Dimrimchi, M SangmaThe study entitled “Agroecology performance evaluation of farms in Kerala and Meghalaya” was undertaken to assess the performance of agroecological farming systems in selected regions of the two states. The objectives of the study were to evaluate the agroecology performance of farms in Kerala and Meghalaya; to analyse the elements of agroecology and various management practices followed by the farmers and to delineate the constraints faced by farmers in adopting agroecological farming systems. The study was conducted in Wayanad district of Kerala and South West Garo Hills district of Meghalaya, both characterized by high climatic vulnerability and a significant presence of diverse farming systems. A multistage sampling method was employed for the selection of study areas and respondents to ensure representativeness and reduce sampling bias. A total of 80 farmers were selected for the study, with 40 farmers each from Kerala and Meghalaya. Primary data collection was collected through Kobotoolbox, a digital platform, using a pre- tested semi-structured interview schedule by conducting personal interview with the respondents. The assessment of extreme climatic events in Sultanbathery and Panamaram blocks of Wayanad highlights their high exposure to recurrent landslides and intense rainfall, placing them in the high climate impact category with impact scores of 2.80 and 3.25, respectively. In contrast, Selsella in Meghalaya faces frequent floods and hailstorms, categorized as medium impact with a score of 2.50. These findings underscore the need for localized adaptation measures such as soil and water conservation, slope stabilization in Kerala, and flood management and hail protection in Meghalaya, alongside farmer training and integrated climate adaptation planning. Using the ten dimensions of agroecology defined by TAPE such as Diversity, Synergy, Efficiency, Recycling, Resilience, Knowledge Sharing, Human and Social Values, Nutrition Culture, Circular Economy, and Responsible Governance, content analysis was conducted to deeply understand farming practices. This approach helped categorize and interpret farmers adoption patterns, strengths, and gaps across these key agroecological elements. The analysis identified fifty agroecological practice codes across ten dimensions used to categorize farmers practices. In Kerala, most responses focused on Diversity, Synergy and Efficiency, emphasizing diversification and resource optimization, while Governance and Circular Economy received less attention. In Meghalaya, Diversity, Recycling, and Human and Social Values were dominant, reflecting community-based and traditional practices, although Governance and Circular Economy were also limited. Overall, farmers prioritize on-farm ecological practices more than institutional and governance-related dimensions. Based on the identified codes and practices, bipartite network analysis revealed distinct adoption patterns across the two states. Kerala farmers are central adopters of agroecological practices like crop diversification, beekeeping, manure application, water conservation, agroforestry, input exchange, and mixed cropping, relying mainly on family labour and neighbour cooperation for knowledge sharing. In Meghalaya, a more centralized network shows key farmers adopting biogas slurry use, water recycling, group practice sharing, family labour, seasonal food traditions, seed saving, climate adaptation, land optimization and mixed cropping, reflecting strong community engagement and traditional practices. An Agroecology Adoption Index (AAI) was developed from content analysis to measure the intensity and diversity of agroecological practices across the three blocks. The index showed Selsella with the highest adoption (68.00%), followed by Sultanbathery (64.10%) and Panamaram (58.00%). Kerala’s blocks emphasized efficiency and social cohesion, while Meghalaya demonstrated more holistic engagement with agroecology. Key constraints were limited agroecology-supportive policies and subsidies favoring synthetic inputs in Kerala, and climate variability and governance issues in Meghalaya. Correlation analysis indicated significant similarity between Sultanbathery and Panamaram, with Meghalaya exhibiting distinct regional challenges. These results highlight the urgent need for targeted policies, climate resilience measures, and stronger institutional support. In conclusion, the study highlights that both Kerala and Meghalaya are progressing towards agroecological sustainability through distinct pathways. Policies promoting region-specific strategies to strengthen agroecological transitions, local innovation networks and climate-resilient agricultural practices should be given importance. Financial assistance through low-interest loans, transition grants, and incentives for eco-friendly technologies could be streamlined to ease farmers shift toward sustainable practices. Addressing these challenges will promote resilient, equitable farming systems aligned with agroecology’s core principles.Item Agroecology performance evaluation of farms in Kerala and Meghalaya(Department of Agricultural Extension Education, College of Agriculture,Vellayani, 2026) Dimrimchi, M Sangma; Archana, R SathyanThe study entitled “Agroecology performance evaluation of farms in Kerala and Meghalaya” was undertaken to assess the performance of agroecological farming systems in selected regions of the two states. The objectives of the study were to evaluate the agroecology performance of farms in Kerala and Meghalaya; to analyse the elements of agroecology and various management practices followed by the farmers and to delineate the constraints faced by farmers in adopting agroecological farming systems. The study was conducted in Wayanad district of Kerala and South West Garo Hills district of Meghalaya, both characterized by high climatic vulnerability and a significant presence of diverse farming systems. A multistage sampling method was employed for the selection of study areas and respondents to ensure representativeness and reduce sampling bias. A total of 80 farmers were selected for the study, with 40 farmers each from Kerala and Meghalaya. Primary data collection was collected through Kobotoolbox, a digital platform, using a pre- tested semi-structured interview schedule by conducting personal interview with the respondents. The assessment of extreme climatic events in Sultanbathery and Panamaram blocks of Wayanad highlights their high exposure to recurrent landslides and intense rainfall, placing them in the high climate impact category with impact scores of 2.80 and 3.25, respectively. In contrast, Selsella in Meghalaya faces frequent floods and hailstorms, categorized as medium impact with a score of 2.50. These findings underscore the need for localized adaptation measures such as soil and water conservation, slope stabilization in Kerala, and flood management and hail protection in Meghalaya, alongside farmer training and integrated climate adaptation planning. Using the ten dimensions of agroecology defined by TAPE such as Diversity, Synergy, Efficiency, Recycling, Resilience, Knowledge Sharing, Human and Social Values, Nutrition Culture, Circular Economy, and Responsible Governance, content analysis was conducted to deeply understand farming practices. This approach helped categorize and interpret farmers adoption patterns, strengths, and gaps across these key agroecological elements. The analysis identified fifty agroecological practice codes across ten dimensions used to categorize farmers practices. In Kerala, most responses focused on Diversity, Synergy and Efficiency, emphasizing diversification and resource optimization, while Governance and Circular Economy received less attention. In Meghalaya, Diversity, Recycling, and Human and Social Values were dominant, reflecting community-based and traditional practices, although Governance and Circular Economy were also limited. Overall, farmers prioritize on-farm ecological practices more than institutional and governance-related dimensions. Based on the identified codes and practices, bipartite network analysis revealed distinct adoption patterns across the two states. Kerala farmers are central adopters of agroecological practices like crop diversification, beekeeping, manure application, water conservation, agroforestry, input exchange, and mixed cropping, relying mainly on family labour and neighbour cooperation for knowledge sharing. In Meghalaya, a more centralized network shows key farmers adopting biogas slurry use, water recycling, group practice sharing, family labour, seasonal food traditions, seed saving, climate adaptation, land optimization and mixed cropping, reflecting strong community engagement and traditional practices. An Agroecology Adoption Index (AAI) was developed from content analysis to measure the intensity and diversity of agroecological practices across the three blocks. The index showed Selsella with the highest adoption (68.00%), followed by Sultanbathery (64.10%) and Panamaram (58.00%). Kerala’s blocks emphasized efficiency and social cohesion, while Meghalaya demonstrated more holistic engagement with agroecology. Key constraints were limited agroecology-supportive policies and subsidies favoring synthetic inputs in Kerala, and climate variability and governance issues in Meghalaya. Correlation analysis indicated significant similarity between Sultanbathery and Panamaram, with Meghalaya exhibiting distinct regional challenges. These results highlight the urgent need for targeted policies, climate resilience measures, and stronger institutional support. In conclusion, the study highlights that both Kerala and Meghalaya are progressing towards agroecological sustainability through distinct pathways. Policies promoting region-specific strategies to strengthen agroecological transitions, local innovation networks and climate-resilient agricultural practices should be given importance. Financial assistance through low-interest loans, transition grants, and incentives for eco-friendly technologies could be streamlined to ease farmers shift toward sustainable practices. Addressing these challenges will promote resilient, equitable farming systems aligned with agroecology’s core principles.Item Career orientation and competencies of KAU agricultural graduate students :an exploratory analysis(Department of Agricultural Extension Education, College of Agricultural,Vellayani, 2026) Amina, M; Allan, ThomasAgricultural education today stands at the crossroads of tradition and transformation, demanding that graduates possess not only technical proficiency but also the adaptability and self-awareness to navigate a changing professional landscape. In this context, the study titled “Career Orientations and Competencies of KAU Agricultural Graduate Students: An Exploratory Analysis” was undertaken to explore how academic progression, gender, and stakeholder perspectives shape the career orientations, competencies, readiness, and personality profiles of agricultural students within Kerala Agricultural University (KAU). The investigation covered four constituent colleges-Vellayani, Vellanikkara, Padannakkad, and Ambalavayal and employed an ex post facto, exploratory design. A total of 160 respondents participated, including 120 students (Undergraduate, Postgraduate, and Doctoral) and 40 stakeholders (faculty and employers). The study treated Age, gender, category, parents’ occupation, geographical context, interpersonal relationship, professionalism, communication skills, critical thinking, leadership skills, teamwork as independent variables, while career orientations, career competencies, career readiness, personality traits, and career expectations were dependent constructs. Standardized instruments such as the Career Orientation Placement and Evaluation Survey, Career Ability Placement Survey, FAO Agricultural Competency Framework, NACE Career Readiness Competency Scale, and the Big Five Personality Inventory (OCEAN) were used. Data were analysed using descriptive statistics, ANOVA, post- hoc LSD tests, correlation, regression, Cohen’s d, and hierarchical cluster analysis. Results revealed that career orientation increased significantly with academic level, with PhD boys scoring highest in Achievement orientation (Mean = 3.59; p = 0.002), Prestige orientation (Mean = 3.56; p = 0.003), and Theoretical orientation (Mean = 3.60; p = 0.004), while PG girls led in Practical orientation (Mean = 3.57; p = 0.022). In addition, Interpersonal orientation was also found to be high among PhD boys (Mean = 3.56; p =0.030) and PG girls (Mean = 3.47; p = 0.030). Cluster analysis identified six distinct orientation profiles, showing a transition from exploratory and security-oriented226 mindsets at the undergraduate level to achievement and leadership-driven orientations among doctoral students. In terms of career competencies, PG boys (Mean = 3.29) and PhD girls (Mean = 3.23) exhibited the highest proficiency. Regression analysis (R² = 0.84) demonstrated that Communication Skills (β = 0.341) and Technical Skills (β = 0.278) were the strongest predictors of overall competency. Significant gender-based differences (Cohen’s d = 0.60–0.87) indicated that PG girls excelled in technical skills, communication, and result focus, whereas PhD boys outperformed in technical, career management and interpersonal domains. Correlation analysis revealed strong positive linkages among key competencies particularly between Communication and Leadership (r = 0.72) and Cognitive and Technical Skills (r = 0.68) confirming that professional abilities evolve as an integrated system rather than as isolated traits. Career readiness improved with education (Mean: UG Boys = 2.81 to PhD Boys = 3.28). The results revealed significant differences across academic levels in Career and Self-Development (F = 3.42; p = 0.01), Professionalism (F = 3.09; p = 0.01), Communication (F = 3.34; p = 0.01), Equity and inclusion (F = 2.96; p = 0.02), Teamwork (F = 2.66; p = 0.03), Leadership (F = 2.58; p = 0.03) and Technology (F=3.38; p=0.01). However, Critical thinking did not show a statistically significant difference across academic levels (F = 1.95; p = 0.09). The OCEAN personality analysis revealed that Openness was most prominent among undergraduates (61.1%), reflecting intellectual curiosity; Agreeableness peaked among PhD girls (68.4%), signifying empathy and collaboration; and Conscientiousness showed a significant upward trend (p = 0.02*), reaching its highest levels among PG and PhD girls (3.68– 3.70), indicating maturity and discipline. Meanwhile, Extraversion showed a moderate rise and Neuroticism declined with academic level, suggesting growing emotional stability and confidence.227 Stakeholders expressed optimism regarding job market potential (M = 6.01; 83.4%) but voiced concerns over work–life balance (M = 3.35; 39.1%). Stakeholders rated communication (M = 2.90), digital skills (M = 2.72), and entrepreneurship (M = 2.58) as the most essential competencies for employability. Future studies should focus on longitudinal tracking of graduates to assess how career orientations and competencies evolve over time, along with evaluating the impact of institutional interventions such as mentorship, internships, and experiential learning. Greater emphasis should be placed on curriculum reorientation and co-development with industry, integrating competency-based, and sustainability-focused modules that enhance employability and leadership potential. Establishing structured career counselling units, strengthening public–private collaborations, embedding digital and entrepreneurial skill training, and aligning curriculum outcomes with regional skill demands and national agricultural policies will ensure that agricultural graduates remain adaptive, innovative, and industry-ready. The study concludes that KAU students possess substantial career motivation, balanced personalities, and growing readiness, yet require targeted interventions in digital proficiency, experiential learning, and structured mentoring. Integrating emotional, social, and cognitive learning through competency-based curricula will not only enhance employability but also nurture reflective, future-ready professionals capable of leading India’s agricultural transformation.Item Perception of farmers towards unmanned aerial vehicle (UAV)(Department of Agricultural Extension Education, College of Agricultural,Vellanikkara, 2026) Arundathy, S; Israel Thomas, MAgriculture in India is increasingly transitioning towards technology supported farming systems aimed at improving efficiency, reducing labour dependency, and ensuring timely farm operations. In this context, Unmanned Aerial Vehicle (UAV) based services have emerged as a promising innovation, particularly for precision spraying and crop protection activities. However, the effective adoption of UAV services depends largely on farmers’ perception, willingness to adopt, and their experiences under actual field conditions. The present study was undertaken to assess farmers’ perception, willingness, comparative efficiency, and the economic, social, cultural, and technical challenges associated with the use of UAV services in agriculture. The study was conducted in Palakkad district of Kerala and Coimbatore district of Tamil Nadu, representing two agriculturally important regions with differing farming systems and levels of technological exposure. An ex post facto research design was employed. A total of 160 farmers were selected using purposive random sampling, comprising 80 adopters and 80 non-adopters of UAV services, with equal representation from both districts. Primary data were collected through a structured interview schedule, supported by focus group discussions conducted at Kollengode in Palakkad district and Thennamanallur in Coimbatore district. The data were analysed using descriptive statistics, perception index, independent samples t test, Mann Whitney U test, Spearman’s rank correlation, and Garrett ranking technique. The findings revealed that farmers in both districts exhibited a generally favourable perception towards UAV based spraying services. Adopters consistently recorded higher perception scores compared to non-adopters across all indicators. The highest perception index was observed for the statement related to time saving and quick completion of spraying operations, with an overall index value of 92.88. The lowest perception index (72.75) was recorded for ease of learning and operation, indicating technical apprehension, particularly among non-adopters. Awareness regarding UAV applications was largely confined to spraying and pest control, with 94.4 % and 95.0 % of respondents being aware of these functions, respectively. Awareness of advanced applications such as monitoring, mapping, irrigation, and yield estimation was minimal. The mean awareness score of 1.31 indicated limited breadth of knowledge, with farmers associating UAV technology mainly with plant protection operations. Willingness to adopt UAV services among non- adopters was found to be high in both districts. The pooled mean willingness score was 4.51 on a five-point scale, and the difference between Palakkad and Coimbatore was not statistically significant. Comparative analysis between UAV based spraying and conventional methods demonstrated the clear superiority of UAV technology. Adopters reported significantly higher mean scores for time efficiency, accuracy, spray uniformity, yield response, and cost efficiency compared to non-adopters. The Mann Whitney U test confirmed statistically significant differences between adopters and non-adopters at the 1 % level across both districts. Economic challenges were identified as the most severe challenges influencing adoption. High operational cost, limited financial support, and difficulty in affording repeated spraying recorded mean scores of 4.60, 4.53, and 4.47, respectively. Social constraints such as acceptance within the farming community and fear of misuse, and cultural challenge including traditional mindset and perceived mismatch with local practices, were also prominent. Significant district level differences were observed for social and cultural challenges. The focus group discussions reinforced these findings. Farmers in Palakkad emphasised the need for more on field demonstrations and operational guidance, while farmers in Coimbatore highlighted the importance of transparent pricing, timely availability of services during pest outbreaks, and reliable service provision. Labour scarcity, particularly in Coimbatore, increased farmers’ interest in UAV services, although affordability remained a concern. Overall, the study concludes that UAV based spraying services offer substantial advantages in terms of speed, accuracy, uniformity, and labour efficiency. While farmers exhibit positive perception and strong willingness to adopt UAV services, wider adoption depends on improving awareness of diverse applications, strengthening training and extension support, ensuring operator availability, enhancing pricing transparency, and providing financial assistance.Item Digital literacy of joint liability group(JLG) of kudumbasree women farmers in northern Kerala: an empirical assesment(Department of Agricultural Extension Education, College of Agriculture,Vellayani, 2026) Adithyan, R; Smitha, SAgriculture in India is undergoing rapid digital transformation, reshaping how farmers access information, obtain inputs, and engage with markets. Despite these advancements, rural women farmers continue to face substantial challenges in participating in the digital ecosystem due to limited awareness, restricted access, and inadequate institutional support. This digital divide is particularly evident among the Joint Liability Groups (JLGs) of Kudumbashree in Kerala, where collective farming is widespread but digital engagement remains inconsistent. In this context, the present study entitled “Digital literacy of Joint Liability Group (JLG) of Kudumbashree women farmers in Northern Kerala: An empirical assessment” was undertaken to assess the extent of digital literacy, analyse behavioural determinants influencing digital literacy behaviour, identify major constraints, and propose context-specific policy measures to strengthen digital inclusion. The study was conducted across seven districts of Northern Kerala Kasaragod, Kannur, Wayanad, Kozhikode, Malappuram, Palakkad, and Thrissur representing diverse agro- ecological and socio-cultural environments. An ex post facto research design was adopted, and 140 JLG women farmers were selected through multistage random sampling. Primary data were collected using a pre-tested structured interview schedule that captured socio-economic characteristics and the three dimensions of digital literacy knowledge, skill, and attitude along with behavioural constructs guided by technology adoption theories. The analytical procedures included descriptive statistics, correlation analysis, Garrett’s ranking technique, and Structural Equation Modelling (SEM) using SmartPLS 4 to examine causal and predictive relationships. Digital literacy was conceptualised as a composite of digital knowledge, digital skill, and digital attitude. Results showed that most JLG women farmers possessed a moderate level of digital literacy, reflecting partial exposure to digital tools but a strong willingness to adopt digital innovations. Among the three dimensions, digital attitude emerged as the most prominent, indicating positive perceptions toward technology. Factor analysis further validated this dimension, confirming a unidimensional structure with a single dominant factor explaining 77.36% of the total variance. Correlation analysis revealed significant positive associations between digital literacy and socio-economic variables such as education, income, social participation, mass media exposure, and training experience. These findings emphasise the importance of socio- economic empowerment and institutional interaction in enhancing digital engagement among women. The Structural Equation Model incorporated seven latent constructs: Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Personal Innovativeness, Task Characteristics, and Technological Characteristics. The model demonstrated strong reliability and validity, explaining 52.2% of the variance in Digital Literacy Behaviour (DLB), with a predictive relevance (Q²) value of 0.310. Among the predictors, Effort Expectancy, Facilitating Conditions, and Personal Innovativeness had significant positive effects, highlighting the role of perceived usefulness, supportive infrastructure, and individual readiness to experiment with technology. Constraint analysis using Garrett’s ranking technique identified limited training opportunities as the most critical barrier, followed by high internet costs, lack of local mentorship, and limited government initiatives. Additional educational, economic, and psychological barriers including fear of technology, low confidence, and dependence on others further restricted independent digital use. Based on these insights, the study proposes several policy recommendations such as establishing community-based digital mentorship networks, conducting regular capacity- building programmes tailored to women farmers, developing Malayalam-based agricultural applications, integrating digital literacy modules into Kudumbashree training curricula, promoting public–private ICT partnerships, and setting up local digital helpdesks. Incentivising digitally active JLGs is also suggested to encourage wider participation. In conclusion, while JLG women farmers in Northern Kerala demonstrate a positive orientation toward digital technologies, their engagement is hindered by structural, economic, and psychological constraints. Strengthening digital literacy through inclusive and context-specific interventions can empower women farmers to become active digital participants, contributing to more equitable and resilient agricultural development in Kerala.Item Perception of farmers towards unmanned aerial vehicle (UAV)(Department of Agricultural Extension Education, College of Agricultural,Vellanikkara, 2026) Arundathy, S; Israel Thomas, MAgriculture in India is increasingly transitioning towards technology supported farming systems aimed at improving efficiency, reducing labour dependency, and ensuring timely farm operations. In this context, Unmanned Aerial Vehicle (UAV) based services have emerged as a promising innovation, particularly for precision spraying and crop protection activities. However, the effective adoption of UAV services depends largely on farmers’ perception, willingness to adopt, and their experiences under actual field conditions. The present study was undertaken to assess farmers’ perception, willingness, comparative efficiency, and the economic, social, cultural, and technical challenges associated with the use of UAV services in agriculture. The study was conducted in Palakkad district of Kerala and Coimbatore district of Tamil Nadu, representing two agriculturally important regions with differing farming systems and levels of technological exposure. An ex post facto research design was employed. A total of 160 farmers were selected using purposive random sampling, comprising 80 adopters and 80 non-adopters of UAV services, with equal representation from both districts. Primary data were collected through a structured interview schedule, supported by focus group discussions conducted at Kollengode in Palakkad district and Thennamanallur in Coimbatore district. The data were analysed using descriptive statistics, perception index, independent samples t test, Mann Whitney U test, Spearman’s rank correlation, and Garrett ranking technique. The findings revealed that farmers in both districts exhibited a generally favourable perception towards UAV based spraying services. Adopters consistently recorded higher perception scores compared to non-adopters across all indicators. The highest perception index was observed for the statement related to time saving and quick completion of spraying operations, with an overall index value of 92.88. The lowest perception index (72.75) was recorded for ease of learning and operation, indicating technical apprehension, particularly among non-adopters. Awareness regarding UAV applications was largely confined to spraying and pest control, with 94.4 % and 95.0 % of respondents being aware of these functions, respectively. Awareness of advanced applications such as monitoring, mapping, irrigation, and yield estimation was minimal. The mean awareness score of 1.31 indicated limited breadth of knowledge, with farmers associating UAV technology mainly with plant protection operations. Willingness to adopt UAV services among non- adopters was found to be high in both districts. The pooled mean willingness score was 4.51 on a five-point scale, and the difference between Palakkad and Coimbatore was not statistically significant. Comparative analysis between UAV based spraying and conventional methods demonstrated the clear superiority of UAV technology. Adopters reported significantly higher mean scores for time efficiency, accuracy, spray uniformity, yield response, and cost efficiency compared to non-adopters. The Mann Whitney U test confirmed statistically significant differences between adopters and non-adopters at the 1 % level across both districts. Economic challenges were identified as the most severe challenges influencing adoption. High operational cost, limited financial support, and difficulty in affording repeated spraying recorded mean scores of 4.60, 4.53, and 4.47, respectively. Social constraints such as acceptance within the farming community and fear of misuse, and cultural challenge including traditional mindset and perceived mismatch with local practices, were also prominent. Significant district level differences were observed for social and cultural challenges. The focus group discussions reinforced these findings. Farmers in Palakkad emphasised the need for more on field demonstrations and operational guidance, while farmers in Coimbatore highlighted the importance of transparent pricing, timely availability of services during pest outbreaks, and reliable service provision. Labour scarcity, particularly in Coimbatore, increased farmers’ interest in UAV services, although affordability remained a concern. Overall, the study concludes that UAV based spraying services offer substantial advantages in terms of speed, accuracy, uniformity, and labour efficiency. While farmers exhibit positive perception and strong willingness to adopt UAV services, wider adoption depends on improving awareness of diverse applications, strengthening training and extension support, ensuring operator availability, enhancing pricing transparency, and providing financial assistanceItem Technology dissemination of KVK through FLDs : a multidimensional analysis(Department of Agricultural Extension Education, College of Agriculture, Vellayani, 2026-01-05) Chippy Xavier; Jayalekshmi GThe 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.Item Human -wildlife conflict in the forest fringe farms of Kerala and Andhra Pradesh(Department of Agricultural Extension Education, College of Agriculture, Vellayani, 2025) Ralladoddi Chaithanya KumarThis study entitled “Human-Wildlife Conflict in the forest fringe farms of Kerala and Andhra Pradesh” investigates the dynamics of human-wildlife conflict (HWC) in Kerala and Andhra Pradesh (AP), emphasizing the socio-economic, environmental, and political related factors contributing to conflicts. The research aimed to identify patterns of crop and livestock damage, explore regional variations, and assess farmers' attitudes toward existing mitigation strategies. Primary data was collected through structured questionnaires, and a comparative analysis between the two states revealed critical differences in conflict experiences and management. Advanced tools such as IMINDMAP (AYOA) software were used to create visual representations, identifying key factors contributing to HWC from the farmers’ perspectives. In Kerala, 56.66% of farmers reported medium-level conflicts, with 43.33% experiencing severe impacts, primarily due to elephant and leopard attacks. In contrast, AP recorded no high-level conflicts, with 62.22% of respondents facing medium-level issues and 37.77% reporting low-level conflicts, largely involving wild boars, elephants and monkeys. Livestock depredation was also more severe in Kerala due to nocturnal attacks, while AP reported fewer incidents occurring mostly during the day. It was also observed that crop and property damage levels differed between the two states. In Kerala, 57.77% of respondents reported moderate crop losses, while 25.55% experienced severe damages. In AP, most farmers (85.55%) reported moderate crop damage, with a small fraction experiencing high (11.11%) or low (3.33%) losses. Property damage followed similar patterns, with 93.33% of AP farmers reporting moderate impacts, whereas Kerala saw more polarized outcomes, with both high (25.55%) and low (52.22%) levels of damage. The study, form Kerala, revealed that adoption of innovations to manage HWC had a negative and significant correlation with the extent of impact of HWC on farmers at the 5% significance level. Also, information source utilization had negative and significant relation with the extent of impact of HWC on farmers at 1% level of significance. This indicates 1 that greater access to information sources equips farmers with essential knowledge, enabling them to implement better mitigation strategies, further reducing conflict impacts. Meanwhile in Andhra Pradesh, education, occupation, and family income had a significant negative relationship with the extent of HWC, with education and occupation significant at the 1% level and family income at the 5% level. Farmers with higher education and diversified occupations experienced fewer conflicts, while higher-income households were better equipped to adopt preventive measures, reducing conflict severity. Additionally, they tend to employ sustainable practices, reducing the overall impact of HWC. Attitudes toward existing mitigation strategies were influenced by several socio-economic factors. In Kerala, analysis revealed that farming experience negatively affects farmers' attitudes toward mitigation strategies at the 5% significance level. Experienced farmers, frustrated by past failures, tend to be skeptical of new interventions. Their involvement in crafting solutions tailored to local conditions can improve the effectiveness of mitigation strategies and build trust. In Andhra Pradesh, the analysis revealed that adoption of innovations to manage HWC had a positive correlation with farmers' attitudes toward current mitigation strategies at the 5% significance level. Farmers who embrace new techniques demonstrate greater satisfaction with conflict management efforts. Participation in conservation efforts varied significantly between the two states. In Kerala, 37.77% of respondents reported high involvement, while in AP, 85.55% showed low participation, possibly due to limited awareness and logistical challenges. Encouraging greater engagement through awareness programs, incentives, and improved communication frameworks could enhance cooperation with conservation efforts. The mind map from Kerala farmers’ perspective highlighted key factors of Human-Wildlife Conflict (HWC), including competition for natural resources, poor land-use planning, socio-economic challenges, inadequate policies, and weak decision-making. Meanwhile in Andhra Pradesh, mind map from farmers’ perspective highlighted key drivers of Human- Wildlife Conflict (HWC), including competition for natural resources, socio-economic challenges, poor land-use planning, inadequate policies. These interconnected factors 2 intensify conflicts and hinder effective mitigation efforts, underscoring the need for holistic solutions. The study emphasizes the need for context-specific mitigation strategies. For Kerala, advanced technologies such as drones, sensors, and AI can support effective monitoring, while AP should prioritize preventive measures to maintain stability. Promoting livelihood alternatives, such as eco-tourism and cultivating non-palatable crops, could help reduce reliance on vulnerable agricultural practices, minimizing the frequency of wildlife encounters. This research provides valuable insights into the nature and drivers of HWC in Kerala and AP, offering practical recommendations for policymakers and conservation bodies. Enhancing farmer participation, improving education, and adopting innovative technologies will be essential for sustainable conflict management. By engaging local communities and aligning mitigation strategies with socio-economic realities, this study highlights pathways toward achieving long-term coexistence between humans.Item FAQs in agricultural knowledge and technology dissemination platform: a critical analysis(Department of Agricultural Extension Education, College of Agriculture, Vellayani, 2025) Joe Shiney, M.A.The research study titled “FAQs in Agricultural Knowledge and Technology Dissemination Platform: A Critical Analysis” was carried out at the College of Agriculture, Vellayani, Thiruvananthapuram, Kerala, during the academic years 2022-24. The study aimed to explore the role of FAQs in meeting farmers' needs, analyze the content to identify emerging topics and gaps, assess their effectiveness, and determine factors influencing their adoption in agricultural knowledge dissemination. The study examined the effectiveness of FAQs across five districts in South Kerala viz., Thiruvananthapuram, Kollam, Alappuzha, Pathanamthitta, and Kottayam. The methodology involved two parts: (1) a desk study to collect and analyze FAQs from government, NGO, and private sources using qualitative content analysis, and (2) a survey of 150 farmers and 30 agricultural officers to assess their use and perceptions of FAQs. Farmers were selected randomly from lists of active participants in agricultural programs, and data were analysed using statistical tools like SPSS along with qualitative software. The parameters studied included content relevance, accessibility, user perceptions, and factors affecting adoption. The collected data showed that 85.33% of farmers and 83.33% of agricultural officers were aware of agricultural FAQs, though usage patterns varied. Among farmers, 31.33% reported frequent use, 30.67% used them occasionally, 23.33% relied on them consistently, and 14.67% rarely used them. Similarly, 40% of agricultural officers reported frequent use, with another 40% using them occasionally, though none relied on them consistently. The study used the Mann-Whitney U Test to analyze topic preferences across districts. Crop production, crop protection, crop improvement technologies, and value addition emerged as top priorities, reflecting farmers' focus on increasing productivity and profitability. Marketing and animal husbandry were of lesser concern, possibly due to established practices or a focus on crop-based activities. Government schemes and subsidies were frequently inquired about, indicating their importance in decision-making. Key topics of interest included pest control, crop management, and government subsidies, while questions about fertilizers and irrigation methods were less frequent. Farmers’ perceptions of the effectiveness of FAQs varied across districts. In Thiruvananthapuram and Alappuzha, 35.33% found them very effective, while 46.67% rated them moderately effective. However, 18% of respondents, especially in Kollam and Pathanamthitta, reported that FAQs were less effective. The content analysis revealed gaps in FAQ-based solutions (FAQ-S), particularly in banana and spice crops, suggesting a need for updates to maintain relevance. IPM, precision farming, and post-harvest interventions were identified as areas needing improvement. The study found that while government sources were highly trusted across districts, NGOs and private organizations showed varying levels of trust. Informal networks had limited trust but remained useful as supplementary sources. Farmers from Pathanamthitta rated the relevance of FAQs highly, while Kollam had the highest dissatisfaction, with 33.33% finding the information less useful. Satisfaction with content accessibility and comprehensibility was moderate overall, though specific dissatisfaction was reported in Pathanamthitta and Alappuzha. Of the agricultural officers surveyed, 20% reported high satisfaction, 40% had moderate satisfaction, and another 40% expressed low satisfaction, indicating room for improvement in making the content more practical and relevant. The farmers and agricultural officers (AOs) who participated in the study exhibited diverse personal and social characteristics. The majority of farmers using the FAQ platform were middle-aged (53-66 years), comprising 46% of the respondents, followed by adults aged 39-52 years (34.67%). Younger farmers below 39 years and older farmers above 66 years were less represented, forming only 8% and 11.33%, respectively, with a mean age of 53.77 years. This demographic pattern suggests that the platform primarily appeals to experienced farmers, while younger and older groups engage less. Among the agricultural officers, 80% were aged 45 years or younger, with a mean age of 37.3 years, highlighting a relatively younger workforce. Educational qualifications also played a role in platform usage, with 42.67% of farmers holding undergraduate degrees and 14.67% completing high school or higher secondary education. Officers exhibited higher academic qualifications, with 40% being postgraduates and a small percentage holding doctoral degrees. Gender distribution showed that 86.67% of the farmer participants were male, reflecting a significant gender gap in accessing agricultural knowledge, whereas the majority of officers 66.67% were female. Additionally, 58.67% of farmers had moderate farming experience, averaging 20.89 years, suggesting that experience influences engagement with FAQs. The study identified several challenges that hindered the effective utilization of the FAQ platform. Low awareness among farmers, time constraints, and questions about the credibility and relevance of content emerged as key barriers. Farmers also encountered technical difficulties and language barriers, limiting their ability to navigate and benefit from the platform. Addressing these challenges requires targeted strategies such as digital literacy programs, language support, and efforts to make the platform user-friendly. Improvements in training for agricultural officers and regular updates to FAQ content are crucial to maintaining relevance and fostering trust. By focusing on these aspects, the platform can better support informed decision-making among farmers and enhance agricultural practices. Closing these gaps will not only improve the effectiveness of agricultural extension services but also promote sustainable farming practices, resource optimization, and productivity across regions. The study concluded that FAQs play an important role in disseminating agricultural knowledge, with a strong focus on crop production, protection, and government schemes. However, content on marketing and animal husbandry received less attention. Although FAQs were generally effective, regional variations in satisfaction highlighted the need for location-specific updates. Trust in government sources remained strong, but further improvements in the sufficiency and precision of content were recommended. Regular updates, integration of scientific advancements, and providing actionable advice were suggested to meet the evolving needs of farmers and agricultural officers.The research study titled “FAQs in Agricultural Knowledge and Technology Dissemination Platform: A Critical Analysis” was carried out at the College of Agriculture, Vellayani, Thiruvananthapuram, Kerala, during the academic years 2022-24. The study aimed to explore the role of FAQs in meeting farmers' needs, analyze the content to identify emerging topics and gaps, assess their effectiveness, and determine factors influencing their adoption in agricultural knowledge dissemination. The study examined the effectiveness of FAQs across five districts in South Kerala viz., Thiruvananthapuram, Kollam, Alappuzha, Pathanamthitta, and Kottayam. The methodology involved two parts: (1) a desk study to collect and analyze FAQs from government, NGO, and private sources using qualitative content analysis, and (2) a survey of 150 farmers and 30 agricultural officers to assess their use and perceptions of FAQs. Farmers were selected randomly from lists of active participants in agricultural programs, and data were analysed using statistical tools like SPSS along with qualitative software. The parameters studied included content relevance, accessibility, user perceptions, and factors affecting adoption. The collected data showed that 85.33% of farmers and 83.33% of agricultural officers were aware of agricultural FAQs, though usage patterns varied. Among farmers, 31.33% reported frequent use, 30.67% used them occasionally, 23.33% relied on them consistently, and 14.67% rarely used them. Similarly, 40% of agricultural officers reported frequent use, with another 40% using them occasionally, though none relied on them consistently. The study used the Mann-Whitney U Test to analyze topic preferences across districts. Crop production, crop protection, crop improvement technologies, and value addition emerged as top priorities, reflecting farmers' focus on increasing productivity and profitability. Marketing and animal husbandry were of lesser concern, possibly due to established practices or a focus on crop-based activities. Government schemes and subsidies were frequently inquired about, indicating their importance in decision-making. Key topics of interest included pest control, crop management, and government subsidies, while questions about fertilizers and irrigation methods were less frequent. Farmers’ perceptions of the effectiveness of FAQs varied across districts. In Thiruvananthapuram and Alappuzha, 35.33% found them very effective, while 46.67% rated them moderately effective. However, 18% of respondents, especially in Kollam and Pathanamthitta, reported that FAQs were less effective. The content analysis revealed gaps in FAQ-based solutions (FAQ-S), particularly in banana and spice crops, suggesting a need for updates to maintain relevance. IPM, precision farming, and post-harvest interventions were identified as areas needing improvement. The study found that while government sources were highly trusted across districts, NGOs and private organizations showed varying levels of trust. Informal networks had limited trust but remained useful as supplementary sources. Farmers from Pathanamthitta rated the relevance of FAQs highly, while Kollam had the highest dissatisfaction, with 33.33% finding the information less useful. Satisfaction with content accessibility and comprehensibility was moderate overall, though specific dissatisfaction was reported in Pathanamthitta and Alappuzha. Of the agricultural officers surveyed, 20% reported high satisfaction, 40% had moderate satisfaction, and another 40% expressed low satisfaction, indicating room for improvement in making the content more practical and relevant. The farmers and agricultural officers (AOs) who participated in the study exhibited diverse personal and social characteristics. The majority of farmers using the FAQ platform were middle-aged (53-66 years), comprising 46% of the respondents, followed by adults aged 39-52 years (34.67%). Younger farmers below 39 years and older farmers above 66 years were less represented, forming only 8% and 11.33%, respectively, with a mean age of 53.77 years. This demographic pattern suggests that the platform primarily appeals to experienced farmers, while younger and older groups engage less. Among the agricultural officers, 80% were aged 45 years or younger, with a mean age of 37.3 years, highlighting a relatively younger workforce. Educational qualifications also played a role in platform usage, with 42.67% of farmers holding undergraduate degrees and 14.67% completing high school or higher secondary education. Officers exhibited higher academic qualifications, with 40% being postgraduates and a small percentage holding doctoral degrees. Gender distribution showed that 86.67% of the farmer participants were male, reflecting a significant gender gap in accessing agricultural knowledge, whereas the majority of officers 66.67% were female. Additionally, 58.67% of farmers had moderate farming experience, averaging 20.89 years, suggesting that experience influences engagement with FAQs. The study identified several challenges that hindered the effective utilization of the FAQ platform. Low awareness among farmers, time constraints, and questions about the credibility and relevance of content emerged as key barriers. Farmers also encountered technical difficulties and language barriers, limiting their ability to navigate and benefit from the platform. Addressing these challenges requires targeted strategies such as digital literacy programs, language support, and efforts to make the platform user-friendly. Improvements in training for agricultural officers and regular updates to FAQ content are crucial to maintaining relevance and fostering trust. By focusing on these aspects, the platform can better support informed decision-making among farmers and enhance agricultural practices. Closing these gaps will not only improve the effectiveness of agricultural extension services but also promote sustainable farming practices, resource optimization, and productivity across regions. The study concluded that FAQs play an important role in disseminating agricultural knowledge, with a strong focus on crop production, protection, and government schemes. However, content on marketing and animal husbandry received less attention. Although FAQs were generally effective, regional variations in satisfaction highlighted the need for location-specific updates. Trust in government sources remained strong, but further improvements in the sufficiency and precision of content were recommended. Regular updates, integration of scientific advancements, and providing actionable advice were suggested to meet the evolving needs of farmers and agricultural officers.