Patil, ShridharKumar, Shivam2023-04-152023-04-152023https://krishikosh.egranth.ac.in/handle/1/5810196457The study titled “Quality evaluation and query pattern analysis of farmers’ helpline service” was conducted during 2022-23 in Bihar State. The study aimed to measure the perceived quality of farmers' helpline services, undertake a longitudinal analysis of queries made by farmers at the farmers' helpline, and document the most frequently made queries at the farmers' helpline. The study was based on a thematic analysis of 5060 queries made by farmers during the period of the last six years (August 2016 to July 2022). Further, the service quality evaluation was done based on the telephonic interview of randomly selected 120 farmers' helpline users. The Likert-type scale with 19 statements over four factors was developed using the Likert scaling method suggested by Edwards (1957) and the iterative exploratory principal axis factor analysis. It was found that farmers' helpline service quality has four underlying factors, namely, dependability, accessibility, understandability and communication effectiveness. A large majority of farmers perceived that farmers' helpline services have above-average dependability (67.50 %), understandability (62.50 %) and communication effectiveness (72.50 %). However, 41.67 % of the farmers perceived the helpline has below-average accessibility. The longitudinal thematic analysis revealed that a large majority of farmers' queries are related to theme package of practice (30.49 %) followed by plant protection measures (24.66 %), livestock management (16.40 %), seeds and planting material availability (9.07 %), training and skill development (7.39 %), government schemes (3.99 %), nutrient management (3.46 %), admission to various degree programmes (2.96 %), weed management (2.57 %), weather forecasting (1.52 %) and sowing/planting of crops (1.11 %). Crop/livestock enterprise-related farmers query analysis revealed that 36.75 percent of queries associated with this theme were concerned with goatery. The next most frequently enquired queries were associated with mango, followed by rice, mushroom, wheat, papaya, maize, brinjal, chili and strawberry. The longitudinal analysis also revealed that the queries across all the thematic categories were mainly made during the mid-kharif and early rabi season, constituting 68.89 per cent of the total queries. This trend is found to be more distinct in the case of themes like sowing (85.71 %), weed management (81.54 %), livestock (79.16 %), seed and planting material availability (77.78 %), the package of crop cultivation practices (73.36 %), nutrient management (73.14 %) and weather advisory (70.13 %). It was also found that a large majority of queries were made during June- December, constituting more than 70 percent of total queries. Among the most frequently made 100 queries, the majority were related to theme plant protection (57 %), followed by seeds and planting material availability (14 %), the package of practices (13 %), training and skill development (5 %), livestock and poultry (3 %), crop nutrient management (3 %), government schemes (2 %) and weather forecast (1 %). Similarly, crop-wise analysis of the most frequent queries made by farmers revealed that among the most frequently made 100 queries, the highest number of queries were associated with rice (16 %), followed by mango (10 %). It was found that, after 2017-18, the number of queries received at the farmers' helpline center declined significantly. This decline may be attributed to various factors, including increased access to internet-based information sources. In addition, the database of farmers' queries generated over a period of time is not being utilized for any analytical purposes. In light of these shortcomings and emerging situations, the study recommended that efforts be made to enhance farmers' access to helpline services. Efforts must also be made towards shifting helpline services from conventional landline telephone mediums to web-based platforms. This shit will provide alternative access options and enable the development and utilization of voice and text-based query databases to implement artificial intelligence-based interactive advisory systems. Such data may also be used to identify emerging challenges and problems in agriculture and provide input for prioritizing research and extension priorities of the university and line departments.EnglishThesis