The study was carried out in four different agro-climatic zones of Maharashtra. Multistage purposive and stratified random sampling was followed in the selection of agro-climatic zones, districts, blocks, the cluster of villages and respondents for the study. Four agro-climatic zones (Scarcity zone, Assured rainfall zone, Moderate rainfall zone, and Eastern Vidarbha zone) of Maharashtra state were selected purposively having familiarity, accessibility and to provide better representation of livestock, highest and lowest productive zone, besides two other zones at equidistant places were selected. Further, two districts from each zone were selected purposively having 50 percent and more coverage area in a particular zone. From each of the districts, two blocks selected randomly and a cluster of two villages was selected purposively based on the population of livestock i.e. dairy and goat. Further, from the purposively selected cluster of two villages, 25 farmers were selected randomly with equal representation to livestock production system on the basis of possessing of minimum 2 adult animals (dairy and goat). Thus, a total of 400 farmers’ were included in the study from 8 districts.

How to cite this article: Pathade, S.S., Singh, B.P., Chander, M. and Bardhan, D. (2020). Assessment of livestock production technologies and package of practices through multivariate typology: a case of Maharashtra, India. J. Anim. Res., 10(1): 67-75.

The data were collected through personal interview method with the help of well structured, comprehensive and pre-tested interview schedule. Data were collected on parameters like awareness of farmers about different dairy production technologies and goat production practices on three-point continuum viz., not aware, partially and fully aware. Further, to assess the knowledge level about dairy production technologies and goat production practices knowledge test was developed. The final statements obtained in the knowledge test were given to the respondents in the study area on a two-point continuum of ‘Yes’ and ‘No’ having weighatage of 1 and 0, respectively. Further, adoption index for each farmer was computed by score obtained for individual divided by total obtainable score and expressed as a percentage. The farmers were categorizing into three categories (non-adopters, partial and full adopters) on the basis of their level of adoption measured in terms of adoption index for each cluster.

The data were coded, classified, tabulated and analyzed using software statistical package for the social science (SPSS 20). The presentation of data was done to give a pertinent, valid and reliable answer to the specific objective. Frequency, percentage, mean, standard error, one way ANOVA, Post Hoc Test (Tukey HSD), Spearman’s correlation and multiple regression analysis (OLS technique) were worked out for the meaningful interpretation.

A typology was used to classify groups of farm households with similar farm characteristics as presented in Table 1. Typology constitutes essential steps in the realization of any opportunities and constraints exist within the farm households. For this purpose typology described by Bidegeza et al. (2008) and Baral and Bardhan (2016) were used. Farm household typologies were constructed by using multivariate statistical techniques i.e. Cluster Analysis (CA). Table 1 represents the variables which were used to construct clusters. Cluster analysis seeks to typify entities according to their dissimilarity in terms of their attributes represented by the variables. Entities within a certain group (cluster) should be very similar to each other and entities belonging to different clusters should be very dissimilar (Baral and Bardhan, 2016). A hierarchical cluster analysis using wards method and Euclidean distance was carried out to classify the farm households using the variables represented in Table1.

Where, SAU- Standard Animal Units.

The objective of the present investigation was to compare the awareness, knowledge and adoption of animal husbandry technologies and package of practices belong to typical clusters. Therefore it was very important to consider the effect of various species/breed of milch animals kept by farm households. For this purpose, Standard Animal Units (SAU) of the bovine stock was derived for each farm household as per specification is given by Patel et al. (1988). The standard animal unit was derived to standardize output of different farms with different species of dairy animals.

Awareness is the arousal of conscious about something, critical for adoption of any new technologies. The technologies were divided into the subcategories viz, artificial insemination (AI), vaccination, deworming, tick control, cultivation of green fodder, feeding of green fodder, feeding of concentrate, clean milk production, balanced diet, mineral mixture, urea molasses, etc. The awareness of technologies was assessed on the basis of percentage, mean and standard error, presented in Table 2. Overall across whole clusters, it was found that 53.8 per cent were partially aware of artificial insemination technologies and 61.5 per cent were fully aware of vaccination across the clusters. Similar findings were reported by Mathialagan and Senthilkumar (2012) in poultry. Further, It was found that majority of the farmers (91.7%) belonged to cluster 3 and having high awareness level about deworming technologies. Further, 52.70 per cent were fully aware about tick control across the whole clusters. Regarding, cultivation of green fodder, it was found that 38.87 per cent and 55.15 per cent of the farmers were partially and fully aware respectively, across the whole clusters.

About feeding practices, 73.95 per cent of the farmers across the whole clusters were fully aware about green feeding while, (62.57%) were fully aware about the feeding of concentrates. Further, only 21.5 per cent of the farmers were fully aware about clean milk production practices across the clusters. Similarly, only 24.3 per cent farmers were fully aware about balanced diet technology across the clusters. The awareness about mineral mixture, it was found that across the whole clusters 41.4 per cent of the farmers were fully aware. Similar findings were reported by Tiwari et al. (2013) that 52.65 per cent of farmers were aware about area specific mineral mixture. Further, across the whole clusters, only 4 per cent of the farmers were fully aware about urea molasses technology and regarding this Naik et al. (2003) found in their study conducted in Goa that only 15 per cent of farmers were aware about urea molasses technology.

Overall, the farmers belong to cluster 3 and cluster 4 had significantly high awareness level about livestock technologies than their respective counterparts of cluster 1 and 2. A significant value (F=43.10) at P˂0.01 level indicates that treatments produce different effects among each cluster and were not of the same order. All possible comparison among the cluster means was made (Table 5). It was found that mean was significant at the 0.05 level.

The knowledge level of farmers about technologies and package of practices with reference to dairy animals and goat production was assessed on the basis of mean and standard error, which was further categorized into subcategories.

Livestock technologies and package of practices for dairy animals were categorized into AI, vaccination, feeding of green, feeding of concentrate, deworming, clean milk production, mineral mixture and chaff cutter. It was found that farmers belong to cluster 3 (0.85) and cluster 4 (0.70) were having a high knowledge level about artificial insemination. Further, farmers belong to cluster 4 (0.84) followed by cluster 3 (0.74) were having high knowledge about vaccination. Similar finding were reported by Kumar et al. (2016). Further, farmers belonged to cluster 3 had high knowledge level followed by cluster 4 about feeding green practices. Similarly, farmers belonged to cluster 4 (0.80) followed by cluster 3 (0.74) had high knowledge level about deworming. It was observed that farmers within cluster 3 and cluster 1 (possessing large herd size) were having a high knowledge level about clean milk production practices Prajapati et al. (2012) reported that only 9.33 per cent farmers had knowledge about clean milk production. Further, farmers within cluster 3 (0.74) followed by cluster 4 had high knowledge about the practices of feeding mineral mixture to dairy animals. It was found that overall knowledge levels about dairy production practices were high (6.42) within households of cluster 3 (Large dairy herd size) followed by cluster 4, 2 and 1.

Technologies and package of practices for goat production were categorized into breeding, vaccination, deworming, feeding and other scientific practices. It was found that farmers’ belong to cluster 4 were having high knowledge level (0.60) about breeding due to owning large flock size, Pattamarkha et al. (1997) found that 50 per cent farmers were aware about goat breeding practices. Further, the farmers within cluster 4 possessed high knowledge level (0.76) followed by cluster 3 about vaccination. The farmers within Cluster 4 (Owning large goat flock size) possessed significantly high knowledge level about goat production practices than their respective counterparts of Cluster 1, 3 and 2 respectively. There was a significant difference in means across the clusters which were not of the same order.

Data revealed that farmers within cluster 3 were having high knowledge (6.42) followed by the farmers of cluster 4, 2 and 1. A significant F=90.26 at (P<0.01) level indicate that in each cluster there was a difference in means and not of the same order for each cluster (Table 5). Gautam et al. (2016) found a total mean of 10.5 for scientific dairy practices.

Data revealed that farmers within cluster 4 (own large goat flock size) possessed high knowledge followed by the farmers of cluster 1, 3 and 2 respectively. There was a significant difference at 0.05 levels between mean values across clusters which was presented in Table 5. The F=261.72 value significant at P˂0.01 level produces a significant difference in means across clusters which is not of the same order.

Livestock production technologies adopted were divided into subcategories (Table 4). It was found that 22, 24.5 and 53.5 per cent of the farmers were fully, partially and not adopting artificial insemination practices, respectively. Similar findings were reported by Letha Devi (2016) while Meena et al. (2017) reported that 80 per cent of farmers had fully adopted the artificial insemination. Further, 67.8 per cent had full adoption of the vaccination technology across the clusters. Deworming practices were adopted fully by 46.42 per cent of the respondents across the clusters. While, 51.5 per cent of the respondents had full adoption of tick control measures across the whole clusters. Further, pertaining to the cultivation of green technology, it was found that 56.5 per cent of the respondents had full across the whole clusters. About the adoption of green fodder feeding practices, it was found that 71.5 per cent of farmers had full adoption across the whole clusters. Letha Devi (2016) and Rezvanfar (2007) found that 92 per cent farmers had partial adoption of green fodder feeding.

Regarding concentrate feeding it was found majority of farmers (62.5 %) had full adoption across whole clusters. Amuge and Osewe (2017) reported that 56.3 per cent of farmers adopted concentrate feeding. Further, as far as clean milk production is concerned it was found that only 18.3 per cent of the farmers had full adoption of technology across the whole clusters. Jeelani et al. (2015) found 25.40 adoption index of clean milk production. Regarding the mineral mixture technology it was found that 35.5 per cent of the farmers were full adopted across the clusters.

The adoption index was assessed and presented in Table 4. Data revealed that farmers belong to cluster 3 (possess large dairy herd size) were having high adoption index (80.30) followed by 78.4 for Cluster 2,65.48 and 56.19 per cent for Cluster 1 and 2, respectively. The overall adoption index was 65.15 which were contradictory with findings of Jeelani et al. (2015) who found 33.27 overall adoption index. There was a significant mean difference among clusters in adoption index which was presented in Table 5. The F=31.73 significant at P<0.01 level show a significant difference in means of adoption index which was not of the same order. Bhagat (2016) and Meena et al.(2012) reported that 50.44 per cent of farmers had adopted dairy husbandry practices.

Table 6 revealed that education, farming experience, landholding, total Standard Animal Unit (SAU), income from dairy, total household income, scientific orientation; innovative proneness, awareness and knowledge of technologies had positive and significant (P<0.01, P<0.05) correlation with the adoption of technologies. These findings are in agreement with the findings of Prajapati et al. (2016), Sandeep et al. (2006) and Hasan et al. (2008). Credit linkage and occupation has a negative and significant correlation. It might be due to difficulty in availing credit facilities for the adoption of livestock production technology and variation in the occupation of farmers across the clusters.

Table 7 revealed that total SAU, flock size, innovative proneness, scientific orientation, awareness of technology and knowledge of technology contribute positively to the adoption of technology. These results are confirmatory with the findings of Musaba (2010). This implies that the above variables influence the adoption of technology among the farmers across the whole clusters. While occupation contributes negatively with the adoption of technology which might be due to variation in production systems across the clusters. The coefficient of determination (R² =0.66) indicates 66 per cent variation in the overall adoption index of technologies. This is indifference with findings of Saidur Rehman (2007) who found 92 per cent variation in adoption index.

I am thankful to the Director, Joint Director of ICAR- IVRI for providing me necessary facilities to carry out my PhD research work. I also appreciate the farmers and stakeholders in the study area for providing the data required to complete the research problem.

No conflict of interest exist with any stakeholders involved in the research. The manuscript is the part of original PhD research work conducted in 2018-19.