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Adoption Level of Improved Dairy Farming Technologies by Peri-urban Dairy Farmers in Thanjavur District of Tamil Nadu

Gunaseelan Mariyappan Thilakar Ponniah Mathialagan Perumal Serma Saravana Pandian Amaresan
Vol 8(1), 103-111
DOI- http://dx.doi.org/10.5455/ijlr.20170805093320

A study was undertaken to assess the adoption level of improved dairy farming technologies by farmers in peri-urban areas of Thanjavur district of Tamil Nadu. To this survey, a sample of 120 dairy farmers was selected randomly from 10 peri-urban villages located in and around 10 km radius of the urban areas of Thanjavur city. The analysis of the data revealed that overall, 41.67 per cent of the farmers had medium level of adoption improved dairy management practices, followed by low (35.00%) and high (23.33%) extent of adoption. The results of the logit model showed that the χ2 was 118.09, meaning that the model was statistically significant. Among the chosen independent variables presumed to be the determinants of adoption level of improved dairy farming technologies by peri-urban dairy farmers, the factors viz., educational level of the farmers (5 % level), milk production and economic motivation (1 % level) were found to be statistically significant. This indicates that the increase in educational status of the respondents and milk production in animal would favour respondents to adopt improved dairy farming technology in peri-urban areas. Proper motivation, availability of need based technologies and related information at doorstep, regular organisation camps on different dairy management practices in peri-urban areas would help to adopt improved dairy farming technologies as suggested by the farmers.


Keywords : Adoption Level Determinant Factors Dairy Farming Farmers Peri-urban Areas

Introduction

Dairying constitutes an important sub sector of agricultural production which contributes towards filling in the large demand-supply gap for milk and milk products in both peri-urban and urban centres (Gillah et al., 2012). It also ensures the food security to the millions of people living in rural, urban and peri-urban areas (Jha, 2003). A large demand-supply variance for milk and milk products especially in urban centers indicating the untapped potential for development of market oriented urban and peri-urban dairy production systems, which play a significant role in minimizing the acute shortage of milk and dairy products in urban centers (Dehinenet et al., 2014). Peri-urban dairy production system has been pictured as more advantageous and potentially profitable activity in terms additional source of income, remunerative price for milk, high demand for fresh milk, etc. (Ravi et al., 2016). To make the dairy business more profitable particularly in peri-urban areas, it is necessary for the dairy farmers to possess sufficient knowledge and adopt improved dairy farming technologies. Socio-economic factors have an effect on improved dairy management practices and decision making process (Belay et al., 2012). These factors will therefore affect the dairy management, production and adoption level of the farmers. Further, to increase the potential of peri-urban dairy production and to meet the growing demand of milk and milk products in urban areas, empirical evidences on adoption behaviour and factors influencing the adoption is to be documented. Considering these facts, the present research was designed to study the adoption level of improved dairy farming technologies by peri-urban dairy farmers.

Material and Methods

An ex-post facto research design was applied to know about the adoption level of dairy farmers in peri-urban areas. The present study was carried out in 10 randomly selected peri-urban villages located in and around 10 km radius of the urban areas in Thanjavur city of Tamil Nadu. From each village 12 farmers were selected randomly and thus a total of 120 respondents were selected for the present research. For further analysis the respondents were classified into three categories based on the herd size as small (< 3 cows), medium (4-6 cows) and large farmers (> 6 cows) as followed by Ranuji, 2006. The socio-economic factors of the farmers related to the study were selected from experts and in consultation with extension professionals, published literature, journals and books. According to Rogers (2003), adoption is the decision to make full use of an innovation as the best course of action available.

The important dairy farming technologies were selected in the major areas of feeding, breeding, disease control, and management practices based on review of literature and by consulting with veterinary and extension specialists of TANUVAS, Chennai. The response for the adopters was assigned a unit score ‘one’ and non-adopters is given ‘zero’. Based on total scores, the respondents were classified into three categories i.e., low, medium and high by calculating mean and standard deviation. Also, the respondents’ adoptions towards different components of scientific dairy farming technologies were classified based on frequency and percentage.

Binary Logistic Regression Analysis

In explaining a dichotomous dependent variable (Yi), where “one” represents adopters of improved dairy farming technologies by peri-urban dairy farmers, whereas “zero” represents non-adopters. A logistic function is used to estimate the association between binary, endogenous variable Y and the independent variables (Xs). The following mathematical form of the model was used in this study.

Where, pi is the probability of the ith farmer’s adoption of improved dairy farming practices and Xk is the kth explanatory variable. The dependent variable 1n (pi / (1- pi)), in the equation is the log-odds ratio in favour of farmer’s adoption of improved dairy farming practices.

Following these arguments, the following logit model was postulated-

(pi / (1- pi)) = βo + β1X1+ β2X2+ β3X3 + β4X4 + β5X5 + β6X6 + β7X7 + β8X8 9X910X10 11X1112X1213X13 14X14

Where,

pi             =  Probability of adoption of improved dairy farming practices by peri-urban dairy farmers

(1- pi)   =   Probability of Non-adoption

β0         =   Constant term

βi’s         =   Coefficients

Xi         =    Determinant factors

Results and Discussion

Adoption Level of Peri-Urban Dairy Farmers

In this section, the findings pertaining to the adoption level of improved dairy farming technologies related to feeding, breeding, disease control and management practices by the respondents are presented.  A perusal of the Table 1 indicates that 41.67 per cent of the farmers had medium level of adoption of improved dairy management practices, followed by low (35.00 %) and high (23.33 %) extent of adoption. The findings of this study further warrant exploration of new approaches like contract dairy farming (Ponnusamy and Walli, 2007), Public Private Parnership (Ponnusamy, 2013) and gender sensitized women empowerment model like pashu sakhi (Ponnusamy et al., 2017) for promoting the sustainable dairy farming in the peri-urban areas.

Table 1: Distribution of respondents according to the adoption of improved dairy farming technologies (N=120)

S. No. Category Frequency Percentage
1 Low 42 35.00
2 Medium 50 41.67
3 High 28 23.33

Practice-Wise Adoption of Improved Dairy Farming Technologies

Feeding Practices

It is evident from the Table 2 that 62.50 per cent of the dairy farmers had not adopted the practice of feeding colostrum to newly born calves within 30 minutes of calving, whereas three-fourths of the large farmers and more than one-half of the medium farmers had adopted.

Table 2: Practice wise distribution of respondents according to the adoption of improved dairy farming technologies

S. No. Practices Extent of Adoption of Peri-Urban Dairy Farmers
Small Farmers (n = 61) Medium Farmers (n=39) Large Farmers (n=20) Overall (N=120)
A Feeding Adopters Non adopters Adopters Non adopters Adopters Non adopters Adopters Non adopters
1 Feeding colostrum to newly born calves within 30 minutes of calving 11 (18.03) 50 (81.97) 20

( 51.28)

19

( 48.72)

14 (70.00) 6

(30.00)

45

( 37.50)

75 (62.50)
2 Cultivate green fodder 61 (100.00) 5

(12.82)

34

( 87.18)

9

(45.00)

11

(55.00)

14 (11.67) 106 (88.33)
3 Feeding of green fodder and dry fodder 53 (86.89) 8

(13.11)

32

(82.05)

7

(17.95)

20 (100.00) 105 (87.5) 15

(12.5)

4 Feeding concentrate mixture on the basis of milk production 27 (44.26) 34 (55.74) 30

(76.92)

9

(23.08)

18 (90.00) 2

(10.00)

75

(62.5)

45

(37.5)

B Breeding
1 Keeping watch on estrous cycle and heat symptoms of cow 44 (72.13) 17 (27.87) 32

(82.05)

7

(17.95)

20

(100.00)

96 (80.00) 24 (20.00)
2 Practicing A.I. at proper time of heat 40 (65.57) 21 (34.43) 30

(76.92)

9

(23.08)

18 (90.00) 2

(10.00)

88 (73.33) 32 (26.67)
3 Practicing the pregnancy diagnosis between 45 – 90 days of service 38

(62.3)

23

(37.7)

25

(64.1)

14

(35.90)

20 (100.00) 83 (69.17) 37 (30.83)
C Management  practices
1 Providing clean and fresh water for drinking to animals 61 (100.00) 39

(100.00)

20 (100.00) 120 (100.00)
2 Full hand method of milking 28

(45.9)

33

(54.1)

13

(33.33)

26

(66.67)

13 (65.00) 7

(35.00)

54 (45.00) 66 (55.00)
3 Practicing dehorning in calves 61 (100.00) 39

(100.00)

20

(100.00)

120 (100.00)
4 Maintaining the cleanliness of animal shed 43 (70.49) 18 (29.51) 27

(69.23)

12

(30.77)

15 (75.00) 5

(25.00)

85 (70.83) 35 (29.17)
D Disease control practices
1 Deworming of calves 46 (75.41) 15 (24.59) 31

(79.49)

8

(20.51)

16 (80.00) 4

(20.00)

93

(77.5)

27

(22.5)

2 Vaccinate the animal against contagious diseases 36 (59.02) 25 (40.98) 25

(64.1)

14

(35.9)

17 (85.00) 3

(15.00)

78 (65.00) 42 (35.00)
3 Providing treatment to the umbilical cord of new born calf 3

(4.92)

58 (95.08) 6

(15.38)

36

(92.31)

9

(45.00)

11

(55.00)

18 (15.00) 102 (85.00)
4 Isolate the diseased animals from healthy animals 6

(9.84)

55 (90.16) 17

(43.59)

22

(56.41)

12 (60.00) 8

(40.00)

35 (29.17) 85 (70.83)

(Figures in parenthesis indicates percentage to the respective total)

A maximum proportion (88.33 %) of the farmers had not cultivated green fodder. Similarly all small farmers and 87.18 per cent of medium farmers had not cultivated green fodder. This might be due to the fact that majority of the respondents were landless and marginal farmers. Moreover, the unavailability of cultivable land in peri-urban areas might hinder the farmer to cultivate green fodder. As far as the practice of ‘feeding of green and dry fodder’, a larger proportion (87.50 %) of the farmers adopted it.  An overwhelming majority (90.00 %) of the large farmers adopted the practice ‘feeding of concentrate mixture on the basis of milk production’ followed by medium (76.92 %) and small (44.26 %) farmers.

Breeding Practices

Regarding breeding practices, results showed that all the large farmers observed animals for heat symptoms followed by medium (82.05 %) and small (72.13 %) farmers. Around 65.00 per cent of small and medium farmers adopted AI at proper time of heat in animals and pregnancy diagnosis. So, the extension agency and State Department Animal Husbandry officials should be given due importance to persuade the small and medium farmers in peri-urban areas to adopt the improved breeding practices. Mobile AI centre may be established for wider coverage among dairy farmers in per-urban areas. Ninety per cent of the large farmers adopted AI at proper time of heat and all the farmers practiced pregnancy diagnosis in dairy animals between 45-90 days of service. This might be due to the fact that the large farmers, who invested more on dairy farms, expect more profit and hence adopted the recommended breeding practices.

Management Practices

In respect of adoption level on management practices, cent percent of large, medium and small farmers provided clean and fresh water for drinking to animals. In total, a little more than one-half of the farmers had not adopted the practice of ‘correct method of full hand milking’. Cent percent of the farmers had not adopted the practice ‘dehorning in calves’. This might be due to the fact that the farmers were in the opinion that only larger herd size farms required dehorning for easy maintenance of animals. Overall, nearly three-fourths (70.83 %) of the farmers maintained cleanliness in animal sheds.

Disease Control Practices             

It could be visualized from Table 2 that a large majority (77.50 %) of the dairy farmers adopted the practice of deworming in calves and 65.00 per cent of them vaccinated their animals against contagious diseases. A maximum proportion of the farmers (85.00 %) had not adopted the practice of ‘treating the umbilical cord of new born calf’. A little less than three-fourths of the farmers had not adopted the practice of isolating the diseased animals from healthy animals.

Determinant Factors of Adoption of Improved Dairy Farming Technologies

The results of the logit model to assess the determinant factors of adoption level of improved dairy farming practices by peri-urban dairy farmers are given in the Table 3.

Table 3: Determinant factors of adoption of improved technology in peri-urban dairy farming

S. No. Independent Variables Coefficients

βi

Wald-statistic P-value
X1 Age -0.044 0.631 0.427
X2 Education 0.865* 4.298 0.038
X3 Occupational status 0.368 0.496 0.481
X4 Family income 0.00 0.754 0.385
X5 Livestock possession -0.183 0.56 0.454
X6 Land holding 0.217 0.132 0.716
X7 Experience in dairy farming 0.043 0.59 0.442
X8 Contact with extension agency -0.41 0.386 0.534
X9 Investment in dairying 0.00 0.725 0.395
X10 Milk production 0.417** 8.395 0.004
X11 Marketing through co-operatives 0.465 0.062 0.804
X12 Marketing through middleman -1.953 3.043 0.081
X13 Economic motivation 0.978** 12.092 0.001
X14 Attitude towards dairy farming 0.097 0.379 0.538
β0 Constant -17.502 3.795 0.051
Model  χ2 value = 118.09 **
Dependant Variable : Adopter category: Yes = 1 (Adopters) ; No = 0 (Non adopters)
*  – P<0.05                   ** – P<0.01
Prediction Table
Observed Predicted
Adopter Category Percentage
Non adopters (0) Adopters (1)
Non adopters (0) 42 3 93.3
Adopters (1) 4 71 94.7
Overall 46 74 94.2

On observing the contents of the table, it could be noted that the χ2 was 118.09, meaning that the model was statistically significant. Among the chosen independent variables presumed to be the determinants of adoption level of improved dairy farming practices by peri-urban dairy farmers, the factors viz., educational level of the farmers, milk production and economic motivation were found to be statistically significant and the remaining factors were statistically non-significant (P>0.05). Among the significant variables, the factors viz., education was significant at five per cent level (P<0.05) and milk production and economic motivation were found to be significant at one per cent level (P<0.01).

The log odds of the respondents going to adopt improved technology in peri-urban dairy farming increased by 0.038 times, when education level increased by one unit. Similarly, when milk production increased by one unit, the adoption of improved technology increased by 0.004 times. Further, one unit increase in economic motivation resulted 0.001 times increase in the adoption of improved technology.

The reliability of the usage of the logit model for the correct prediction of the status of adoption / non-adoption in peri-urban dairy farming systems was tested by comparing the observed and predicted values.  The percentage of correct prediction by the logit model was 94.2 per cent. This model was successful in predicting the adopters (94.7%) correctly than the non-adopters (93.3 %).

Conclusion

The level of technology adoption by peri-urban dairy farmers is medium and is highly dependent on farmer’s education, financial status, economic motivation and milk production in the selected peri-urban areas. The study also found that the small and medium farmers are low adopters as compared to large farmers. Hence, the study recommended that introducing different dairy farming technologies should be supported with a continuous training, provide awareness about existing improved dairy farming technologies and inform farmers about benefits of increased milk production in order to enable them to adopt more recommended dairy farming technologies. Further, farmers in peri-urban areas should be made to involve actively in dairy value chain, viz., input supply, milk production, processing and distribution. The factors that affect the technology awareness and adoption of the improved dairy farming technologies need to be addressed to improve the performance of the dairy enterprise in peri-urban areas. Peri-urban farmers may be sensitized about risk-management strategies in dairy farming to make it as a profitable venture.

References

  1. Belay D, Yisehak K and Janssens 2012. Socio-economic factors influencing urban small scale dairy management practices in Jimma town, Ethiopia. Libyan Agriculture Research Center Journal International. 3(1): 7-12.
  2. Dehinenet G, Mekonnen H, Kidoido M, Ashenafi M and Guerne Bleich E. 2014. Factors influencing adoption of dairy technology on small holder dairy farmers in selected zones of Amhara and Oromia National Regional States, Ethiopia. Discourse Journal of Agriculture and Food Sciences. 2(5): 126-135.
  3. Gillah KA, Kifaro GC and Madsen J. 2012.Urban and peri urban dairy farming in East Africa: A review on production levels, constraints and opportunities. Livestock Research for Rural Development. 24(11), Article #198.  http://www.lrrd.org/lrrd24/11/gill24198.htm.
  4. Jha B. 2003. Indian dairy in the emerging trade order, an unpublished report submitted to the Institute of Economic Growth, Delhi –7.
  5. Ponnusamy K and Walli, T.K. 2007. Contract Dairy Farming Versus Dairy Cooperatives- Relative Strengths and Weaknesses. Indian Dairyman. 59(4): 53-60.
  6. Ponnusamy K, Chauhan, AK and Meena Sunita. 2017. Testing the effectiveness of Pasu Sakhi: An innovation for resource poor farm women in Rajasthan. Indian Journal of Animal Sciences. 87(2): 229–233.
  7. Ponnusamy K. 2013. Impact of public private partnership in agriculture: A review. Indian Journal of Agricultural Science. 83(8):803-808.
  8. Ravi KN, Ponnusamy K and Rajiv BK. 2016. A pragmatic approach to addressing needs of dairy and crop production system in the peri-urban area of Bengaluru, India. Asian J. Dairy & Food Res. 35(4): 288-292.
  9. Ranuji CR. 2006. A study on entrepreneurial behaviour of dairy farmers. Ph.D. (Ag.) Thesis, University of Agricultural Sciences, Dharwad.
  10. Rogers EM. 2003. Diffusion of Innovations. 4th Free press, New York. pp. 36.
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