Knowledge level of dairy farmers about artificial insemination in Bidar district of Karnataka, India

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1 VETERINARY RESEARCH INTERNATIONAL Journal homepage: ORIGINAL ARTICLE Knowledge level of dairy farmers about artificial insemination in Bidar district of Karnataka, India Prakashkumar Rathod*, Balraj S, Dhanraj G, Madhu R, Chennaveerappa and Ajith MC Department of Veterinary and A.H Extension Education, Veterinary College, Bidar , Karnataka State, India. *Corresponding Author: Prakashkumar Rathod Received: 09/03/2014 Revised: 28/05/2014 Accepted: 29/05/2014 Abstract Livestock sector research in India has developed various livestock technologies which have not been successfully diffused to rural masses due to poor accessibility of information. This intern leads to poor knowledge level about the livestock technologies and low adoption. In this context, an attempt was made to study and explore the knowledge level of dairy farmers towards Artificial Insemination (AI) in Bidar District of Karnataka, India. A pretested interview schedule was used to collect data from 200 dairy farmers by personal interview supplemented by information from focused group discussion with farmers and key informants. The study revealed that majority of dairy farmers (62.5%) belong to medium knowledge level followed by 23.0 per cent and 14.5 per cent for low and high knowledge level respectively about AI. The knowledge level of dairy farmers was found to be non-significantly correlated with age, education, social participation, land holding, annual income, livestock size and decision making ability while major occupation, innovativeness, information seeking behaviour, scientific orientation and economic orientation were significantly correlated in the study region. Thus, there is a need to communicate and educate farmers about the importance of AI through various channels for popularization of technology. Further, education of farmers about heat detection, management, nutrition and breeding is needed leading to more animals being inseminated at an optimal time resulting in higher pregnancy rate. Keywords: Artificial insemination, knowledge level, dairy farmers. Introduction The livestock sector is one of the fastest growing segments of the agricultural economy, particularly in the developing world (Delgado et al., 2009). However, there is a considerable debate on India s ability to maintain milk supplies to its growing population in the coming decades. Despite rapid advances in the animal husbandry technologies and their roles in improving livestock sector, productivity of this sector still is very low in India (Chander et al., 2010) which may be due to various reasons like poor adoption and diffusion of new technologies and poor knowledge level of farmers etc. Although, Artificial Insemination (AI) has been considered as a promising tool to improve genetic potential of dairy animals, yet, many farmers at field conditions are unaware about the technology with huge regional variations in terms of knowledge level and adoption of this promising technology. Infact, one of the factors is about ignorance of proper time of insemination of animal. Thus, lack of knowledge of farmers about timely AI hinders the process of its adoption. Therefore, knowledge level of dairy farmers about AI and factors affecting its successful adoption are of paramount importance. With this theoretical background, the authors have made an effort to study the knowledge level of farmers regarding AI among farmers in Bidar district of Karnataka, India. Materials and Methods An ex-post facto and exploratory study was conducted in Bidar District of Karnataka during February to June 2013 to assess the knowledge level of dairy farmers about AI and factors affecting the knowledge level. A multistage random sampling technique was followed to collect data from 200 farmers using pretested semi-structured interview schedule. Within Bidar District of Karnataka state, all

2 the five blocks were selected and two villages per block were selected randomly. Further, 20 farmers per village were selected in consultation with key informants, local leaders and progressive farmers to make a total sample size of 200 farmers for the study. Knowledge level of dairy farmers was studied using the knowledge test developed by Kunzru and Tripathi (1994) based on the correct and wrong answer responded by the farmers with the scoring of one and zero respectively. The mean was determined using the total score and the number of total questions (12) used in the study. Based on mean and SD values, the farmers were categorized into three categories of knowledge level. In order to study the factors affecting knowledge level, the collected data was statistically analyzed using correlation and regression coefficients. Results and Discussion Personal and socio-economic characteristics of dairy farmers The study of personal and socio-economic characteristics was carried out with reference to age, education, occupation, land holding, annual income, social participation, livestock possession, innovativeness, information seeking behaviour, decision making ability, scientific orientation and economic orientation. The data is furnished in Table 1. Table 2 depicts the knowledge level of dairy farmers about AI which included different practices related to AI followed by dairy farmers in the study area. The response of the farmers with regards to heat, heat cycle, heat detection, nutrition, reproductive disorders and parasitic load was collected as depicted in Table 2. The study also focused on the knowledge level of dairy farmers about different practices related to breeding and AI. These findings were almost similar to the report of Quddus (2012) with regard to breeding practices followed by dairy farmers due to poor knowledge about the practices. Table 1: Personal and socio-economic profile of dairy farmers. Sl. Variables Intervals Frequency Percentage 1 Age Mean SD Young ( ) Middle ( ) Old ( ) Education Illiterate Primary High School College 3 Occupation Agriculture Agriculture +A.H Business Government Service Laborers 4 Social participation One organization More than one Office bearer Wide public leader Nil Landholding (Acres) Mean-5.8 SD-8.04 Small (0-2.24) Medium ( ) Large ( ) Annual Income Mean SD Low ( ) Medium ( ) High ( )

3 7 Livestock/cattle Unit Mean-2.97 SD-2.76 Small ( ) Medium ( ) Large ( ) Innovativeness Mean SD-9.00 Low ( ) Medium ( ) High ( ) Information Seeking Mean SD-4.18 Low ( ) Medium ( ) High ( ) Decision Making Ability Mean SD Low ( ) Medium ( ) High ( ) Scientific Orientation Mean SD-2.24 Low ( ) Medium ( ) High ( ) Economic Orientation Mean SD (n=200) Low ( ) Medium ( ) High ( ) Table 2: Knowledge level of dairy farmers towards Artificial Insemination Sl.. Practices/Statements and their response Frequency Percentage 1 In which heat cycle a heifer should be inseminated for first time Second How many times a cow should be artificially inseminated in one heat-cycle for optimum results Two What is the appropriate time for detection of heat in a cow Morning After how many days does the cow repeat its heat cycle days Do you think nutritional deficiency is the reason for animal not coming in heat Do you think heavy parasitic load is the reason for animal not coming in heat Do you think reproductive disorders is the reason for animal not coming in heat 48

4 144 8 I follow A.I in livestock since desi bulls are not good I follow A.I in livestock since animal becomes pregnant without fail At what age does a crossbred heifer generally come in heat for the first time months How many days after parturition should a cross bred cow be inseminated days What should be the body weight of a crossbred heifer at puberty for getting optimu benefits of A. I kg The study revealed that majority of the dairy farmers (62.5 %) belongs to medium knowledge level category followed by 23.0 and 14.5 per cent for high and low level of knowledge level, respectively (Table 3). This may be due to poor education and low information seeking behaviour of the farmers in the study region. The farmers also complained about the poor conception rate through AI which might also be a hindrance factor for the farmers. Almost similar findings were reported by Patil et al. (2009) and Rathod et al. (2014). The knowledge level of dairy farmers was found to be non-significantly correlated with age, education, social participation, land holding, annual income, livestock size and decision making ability while major occupation, innovativeness, information seeking behaviour, scientific orientation and economic orientation were significantly correlated in the study region (Table 4). Table 4 also depicts that Co-efficient of determination (R 2 ) of the independent variables was It means that per cent of total variation in the knowledge level about AI was explained by 12 selected independent variables in the study (Table 4). Table 3: Categorization of dairy farmers based on Knowledge level of farmers towards AI S. Category/Scale (Knowledge level) Frequency Percentage 1 Low (0-1.23) Medium ( ) High ( ) Mean-3.18; SD-1.95; n=200; n= Number of Observations Table 4: Relation between socio-economic and personal characters with knowledge of dairy farmers towards AI Sl. Independent Variables Corr. Coeff ( r ) Reg. Coeff. ( b) SE p-value X 1 Age X 2 Education X 3 Major occupation * X 4 Social participation X 5 Landholding X 6 Annual income X 7 Livestock size X 8 Livestock innovativeness * E-08 X 9 Information seeking behaviour *

5 X 10 Decision making ability X 11 Scientific orientation * X 12 Economic orientation * Multiple R: R Square: Goodness of fit: % * 5 % level of significance * * 1 % level of significance Conclusion The study conducted in Bidar district of Karnataka, India revealed that majority of the dairy farmers belong to medium level of knowledge level about AI and hence, there is a need to give greater emphasis to create awareness and educate the farmers regarding the importance of AI technology for its References Chander M, Dutt T, Ravikumar R and Subrahmanyeswari B (2010). Livestock technology transfer service in India: A review. The Indian Journal of Animal Science, 80: Delgado C, Rosegrant M, Steinfeld H, Ehui S and Courbois C (2009). Livestock to The next food revolution. Publ. IFPRI, Washington, USA. Kunzru ON and Tripathi H (1994). Research in Animal Science: Measurement Techniques. Indian Veterinary Research Institute, Izatnagar (U.P), India. Patil AP, Gawande SH, Nande MP and Gobade MR (2009). Assessment of knowledge level of dairy farmers in Nagpur district and the co-relation effective popularization and improved productivity. Also, based on the factors responsible for knowledge level about AI, effective strategies can be developed by Extension agencies to improve knowledge level of dairy farming leading to higher livestock productivity in India. between socio-economic variables with their training needs, Veterinary World, 2: Quddus MA (2012). Adoption of dairy farming technologies by small farm holders: practices and constraints. Bangladesh Journal of Animal Science, 41: Rathod P, Nikam TR, Landge S, Hatey A and Singh BP (2014). Perception towards livestock breeding service delivery by dairy cooperatives. Indian Research Journal of Extension Education, 14(2):