Livestock Research for Rural Development 19 (3) 2007 Guide for preparation of papers LRRD News

Citation of this paper

Communication and socio-personal factors influencing adoption of dairy farming technologies amongst livestock farmers

A Rezvanfar

Department of Agricultural Extension and Education, Faculty of Agricultural Economics and Development, Agriculture and Natural Resources Campus,
University of Tehran, Karaj- Iran
Arezvan@ut.ac.ir

Abstract

East Azerbaijan of Iran was purposively selected as a specific area for this study. There is a great scope and potential for enhancing livestock production and productivity in the state. Research provides technologies to help in achieving production and productivity increases but technologies need to be transferred to farmers to ensure its impact. To study adoption level of dairy farming technologies and factors associated with adoption of dairy farming technologies among livestock owners a sample of 154 farmers from a total of eight villages, four villages from higher plain areas and four villages from lower plain areas, were selected using “stratified two-stage random sampling” method. Data were gathered with the help of structured interview schedule. The criteria like frequencies, percentage, mean and product moment correlation were calculated. Also t-test and multiple regression analysis were used to analysis the data..

Based on the results, it can be concluded that the majority of livestock owners (59.09%) of the two groups were found belonging to medium level of adoption behavior, followed by 22.75 and 18.18 percent livestock owners with high and low level of adoption behavior with respect to dairy farming technologies. Information input, information output, farmer intra-system communication, farmer-researcher communication, farmer-extensionist communication, availability of input facilities and overall knowledge level about dairy farming technologies had positive and highly significant relationship (p<0.01) with overall adoption of dairy Farming technologies by livestock owners.

Key Words: adoption behavior, communication, dairy farming technologies adoption, livestock owner


Introduction

The usage of veterinary services and other dairy farming technologies remains important for every livestock farmer, as disease, high mortality and low capacity are major constraints on livestock production in Iran (Plan and Budget Organization 1993). This leads to the major problem that livestock farmers have limited capacity
This happens while various established organizations like universities, research stations, state directories of animal farming and livestock extension services acting in order to generate and transfer of technologies amongst livestock owners. So adoption of recommended technologies in dairy farming sector has not been as widespread as it was anticipated. The reason for poor adoption of dairy farming technologies amongst livestock farmers all over the world is not fully understood. Never the less, the question of why livestock owners did not adopt new technologies is complex.

Up to a few years ago the diffusion of innovation research established the importance of communication in the modernization process at the local level. In the dominant paradigm communication, was visualized the important link through which exogenous ideas entered into the local communities (Rogers 1983; Melkote 1991).

In this way many scholars (Nataraju and Chenaegowda 1984; Chede 1988; Singh et al 1989; Feather and Amacher 1994; and Adedoyin and Macoyawa 1995) have used the diffusion model for analyzing the adoption behavior of farmers. However, innovation adoption is different from individual to individual according to their socio-personal characteristics. Hence, it was proposed to analyze communication variables as well as socio-personal characteristics influencing the adoption behavior of livestock farmers with reference to dairy farming technologies.

The specific objectives of the study were:
1) To determine the levels of adoption of the various recommended dairy farming technologies
2) To study the factors associated with adoption of dairy farming technologies among livestock farmers


Materials and methods

Theoretical approach
The theoretical approach used to guide the study is drawn from selected components of diffusion (transfer) and adoption of agricultural technologies.

The literature on diffusion and adoption of agricultural technologies suggest that the adoption behavior of farmers is explained by farmer and household characteristics (Wheeler and Outman 1990), institutions and infrastructure variables (Hayami and Ruttan 1985) and perceptions about agricultural technologies (Feder and Silverman et al 1985).

A few recent studies have more focused especially on farmersُ’ adoption behavior which explained by perception about information needs, information input and information output patterns (Mudukuti and Miller 2002; Randhir-Singh et al 1996), inter-system and intra-system communication pattern (Konju 1992) and knowledge level about farm technologies (Vasanta and Somasundaram 1988).


Methodology

The selection of variables as possible predictors for the adoption of dairy farming technologies was based on the adoption-diffusion theory and past empirical work.

A questionnaire was developed to obtain information at farm level from randomly selected livestock farmers in East Azerbaijan of Iran.
To select respondents, the East Azerbaijan state was divided into two different regions based on agro-climatic and geographical conditions. From each region, two districts (one more progressive and the other less progressive) were selected purposively. Then randomly two villages (one close to main district and other in remote area) from each main district were selected, which had at least twenty dairy farming families. For the purpose of selection of the respondents random sampling was used. From each selected village twenty farmers were selected. However, total 154 respondents constitute the sample size, as in three villages only 18 livestock owners were available. The socio-personal traits and communication variables of livestock farmers were selected for the study purpose. Adoption of dairy farming technologies was the dependent variable. Artificial Insemination (AI) in cattle Vaccination against Contagious Disease (VACD), Feeding Nutritious Green Fodder (FNGF), Feeding Concentrate (FC) and Common Dairy Farming Technologies (CDFT) were technologies considered in the present study.

To determine the different levels of adoption of dairy farming technologies amongst livestock farmers, the described process in which detailed in the research by Nell (1998) was used in modified form. According to Nell(1998) adoption of dairy farming technologies was studied at two levels, Individual and overall adoption behavior, respectively.

To study adoption level of each individual, livestock farmers were categorized as adopters and non-adopters. Then according to scores obtained by each individual, adopters were categorized into three groups as partly (score 1), to some extent (score 2) and fully (score 3). The summation of scores of respondents over these technologies plus score of 8 common dairy farming technologies was the overall adoption score of livestock owners in dairy farming technologies.

The overall adoption level of livestock farmers formed the basis for the categorization of respondents as high, medium and low level of adoption behavior in respect of dairy farming technologies. Variety of statistical techniques like frequency distribution, percentage, means, standard error, T-test, product moment correlation multiple regression analysis was used to analysis the data.


Findings and discussion

Adoption of artificial insemination
Data shown in Table 1 indicate that about 54.04 percent of farmers in High Level Plain Area (HLPA) and 36.25 percent of farmers in Low Level Plain Area (LLPA) did not adopt and get their animals artificially inseminated. However, as evident from Table 1, relatively more of the farmers from LLPA (30.00%) as compared to their counterparts in HLPA (14.86%) fully adopted AI technology.


Table 1. Frequency distribution of farmers as per their adoption of AI, VACD, FNGF and FC

S1.No. Adoption of
Technologies
HLPA (n=74) LLPA (n=80) Total (N=154)
F % F % F %

Adoption of A.I.






Adopters





1. Partly 22 29.73 21 26.25 43 27.92
2. To Some Extent 1 1.35 6 7.50 7 4.55
3. Fully 11 14.86 24 30.00 35 22.73

Non-Adopters 40 54.06 29 36.25 69 44.80

Adoption of VACD






Adopters





1. Partly 4 5.41 2 2.50 6 3.90
2. To Some Extent 14 18.92 11 13.75 25 16.23
3. Fully 40 54.05 61 76.25 101 65.58

Non-Adopters 16 21.62 6 7.50 22 14.29

Adoption of FNGF






Adopters





1. Partly 5 6.76 1 1.25 6 3.90
2. To Some Extent 66 89.19 77 96.25 143 92.85
3. Fully 0 0.00 0 0.00 0 0.000

Non-Adopters 3 4.05 2 2.50 5 3.25

Adoption of FC






Adopters





1. Partly 17 22.97 11 13.75 28 18.18
2. To Some Extent 25 33.78 15 18.75 50 25.97
3. Fully 20 27.03 30 37.50 50 32.47

Non-Adopters 12 16.22 24 30.00 36 23.38


All farmers taken together, it could be seen that about 45.00 percent of farmers did not adopt and get their animals artificially inseminated, followed by 27.92, 22.73 and 4.55 percent farmers who were found falling in partly, fully and to some extent category of artificial insemination adoption respectively. This means that artificial insemination is not fully used as a new technology by dairy farmers.
Vaccination refers to medicine used to prevent infectious diseases
The characteristics of the adoption groups are presented in Table 1 indicate that the majority of farmers in HLPA (54.05%) and LLPA (76.26%) fully adopted vaccination of their animals against contagious diseases. Adoption of vaccination technology was found relatively higher among LLPA farmers than HLPA farmers. On the whole, as evident in the Table 1 it could be noticed that 14.29 percent of farmers did not adopt vaccination against contagious diseases, whereas 65.58 percent of farmers adopted fully, 16.23 percent to some extent and only 3.20 percent of farmers partly adopted vaccination against contagious diseases in their dairy animals.

The fact that about 6 percent of farmers adopted fully, indicates that farmers are aware of the benefits of vaccination to try to prevent animal death; however, may for a free of cost operation.
Adoption of feeding nutritious green fodder
Data shown in Table 1 indicate that most of the farmers, 89.19 Percent in HLPA and 96.25 percent in LLPA were found feeding nutritious green fodder to their dairy animals to some extent.

None of the farmers from both the groups was found belonging to category of complete adoption of feeding nutritious green fodder to their dairy animals.
 
This high level of adopters for this technology is an indication that farmers are willing to adopt this relatively cheep technology and perhaps as the last means to prevent animal deaths.
Adoption of feeding concentrate
As shown in Table 1, that 16.22 percent of farmers in HLPA and 30.00 percent farmers in LLPA did not adopt feeding concentrate to their dairy animals. As revealed from Table 1, it could be seen that among adopters in HLPA, 33.78 percent of farmers adopted feeding concentrate to dairy animals to some extent, followed by 27.03 and 22.97 percent of farmers who adopted the feeding concentrate to their dairy animals, fully and partly, respectively. In respect of adopters in LLPA, 37.50 percent of farmers adopted it fully, followed by 18.75 and 13.75 percent of farmers who adopted feeding of concentrates for their dairy animals to some extent and partly, respectively.

On the whole, as evident from Table 1, 23.38 percent of farmers did not adopt feeding concentrate to their dairy animals, whereas among adopters, 32.47, 25.97 and 18.18 percent farmers adopted fully, to some extent and partly feeding concentrate to their animals, respectively.
Adoption of common dairy farming technologies
The farmers were asked whether they adopted some of the recommended common dairy farming technologies on their farm or not. The results pertaining to this are presented in Table 2 which clearly indicate that nearly three-fourth of farmers from both areas did not adopt mineral mixture feeding to their milk cows, while about 96.00 percent of them did not adopt silage making and its feeding to their dairy animals.


Table 2.  Frequency Distribution of Farmers as per their Adoption of Common Dairy Farming Technologies
Sl.
No.
Adoption of Technologies HLPA (n=74) LLPA (n=80) Total (N=154)
Adopted Non-adopted Adopted Non-adopted Adopted Non-adopted
F % F % F % F % F % F %
1. Mineral Mixture Feeding to Milk Cows 14 18.92 60 81.08 25 31.25 55 68.75 39 25.32 115 74.86
2. Silage Making and Feeding 0 0.00 74 100.00 6 7.50 74 92.50 6 3.89 148 96.11
3. Enriching quality of Dry Fodder by urea treatment. 2 2.70 72 97.30 11 13.75 69 86.25 13 8.44 141 91.56
4. Urea-Molasses Liquid Mixture 0 0.00

74 100.00 1 1.25 79 98.75 1 0.65 153 99.35
5. Urea – Molasses Mineral Lick 1 1.35 73 98.65 0 0.00 80 100.00 1 0.65 153 99.35
6. Tuberculin Test 20 27.03 54 72.97 35 43.75 45 56.25 55 35.71 99 64.29
7. Brucellosis Test 25 33.78 49 66.22 53 66.25 27 33.75 78 50.65 76 49.35
8. Control of Internal Parasites 46 62.16 28 37.84 74 92.50 6 7.50 120 77.92 34 22.08
9. Control of External Parasites 38 51.35 36 48.65 52 65.00 28 35.00 90 58.44 64 41.56

It could further be seen that 8.44 percent of farmers had adopted technology of enriching quality of dry fodder by urea treatment. The percentage of adopters was very low in the case of urea-molasses liquid mixture and urea-molasses mineral lick (0.65% each).
Further, as shown in Table 2 about 64 percent of farmers did not adopt tuberculin test while the majority of them (50.65%) adopted brucellosis test in their dairy animals. Similarly, more than three-fourth of the farmers adopted technology related to control of internal parasites and 58.44 percent adopted technology related to control of external parasites.

Further perusal of Table 2 indicates that the percentage of adopters of all the technologies except urea-molasses mineral lick was relatively higher in LLPA as compared to the farmers from HLPA. Thus it can be concluded that the extent of adoption of common dairy farming technologies in LLPA was better as compared to their counterparts in HLPA.
Overall adoption of common dairy farming technologies
It is amply clear from the Table 3 that the majority of farmers in HLPA (56.76%) and LLPA (61.25%) were found belonging to medium level of adoption of dairy farming technologies. It could be seen that 30.00 percent of farmers in LLPA and 14.86 percent of farmers in HLPA were found belonging to high level of adoption.



Table 3.  Frequency distribution of farmers as per their overall adoption of dairy farming technologie
S1. No. Adoption score HLPA (n=74) LLPA (n=80) Total (N=154)
F % F % F %
1. Low (<8) 21 28.38 7 8.75 28 18.18
2. Medium (8-14) 42 56.76 49 61.25 91 59.09
3. High (>14) 11 14.86 24 30.00 35 22.73

On the whole, as evident in Table 3, the majority of farmers (59.09%) were found belonging to medium level of adoption behavior, followed by 22.73 percent and 18.18 percent farmers with high and low level of adoption behavior in respect of dairy farming technologies viz. artificial insemination, vaccination against contagious diseases, feeding nutritious green fodder, feeding concentrate and common recommended dairy farming technologies. This finding supports the finding of Massod (1987), Halyel et al (1989) and Yassmen (1994) who stated that the majority of farmers had medium level of adoption behavior.

Differences in mean values of adoption of dairy farming technologies
It is amply clear from the Table 4 that highly significant (P<0.01) difference was observed in the mean values of adoption of artificial insemination and vaccination against contagious diseases between farmers of HLPA and those of LLPA. Adoption of AI in cattle and vaccination of animals against contagious diseases was found to be significantly higher amongst the farmers in LLPA than those in HLPA.
 


Table 4.  Mean values of adoption of dairy farming technologies in different groups of farmers (N=154)
S1. No. Variable
(Adoption behavior)
Mean Values t Values
HLPA (n=74) LLPA (n=80)
1. Adoption of AI 0.770 1.313 2.900**
2. Adoption of caccination 4.189 5.175 2.892**
3. Adoption of FNGF 1.878 1.987 1.919
4. Adoption of feeding concentrate 1.716 1.612 0.550
5. Overall adoption of DFT 10.149 12.563 3.893**
**P <0.01

However, no significant difference was observed in the mean scores of adoption of feeding nutritious green fodder as well as feeding concentrates between farmers of HLPA and those in LLPA. Mean values of the overall adoption of dairy farming technologies of the farmers of HLPA and those of LLPA also differed highly and significantly (P<0.01).

Relationship between adoption of dairy farming technologies by farmers with other independent variables
Adoption of artificial insemination
It is clear from Table 5 that the information input, farmer-extensionist (F-E) Communication, educational level, knowledge level about AI and overall knowledge level about dairy farming technologies had positive and highly significant relationship (P<0.01) with adoption of AI.


Table 5.  Correlation coefficient of adoption of dairy farming technologies by farmers with communication and socio-personal variables (N=154)
Overall adoption
behavior
Adoption
of CF
Adoption
of FNGF
Adoption
of VACD
Adoption
of A1
Variables S1.
No.
r r r r r
0.57** 0.35** 0.03 0.07 0.25** Information Input X1
0.51** 0.30** 0.09 0.33** 0.19* Information Output X2
0.34** 0.09 0.21* 0.28** 0.17 Farmers Intra-system
Communication
X3
0.33** 0.18 0.00 0.20* 0.208 F-R Communication X4
0.48** 0.24* 0.12 00.16 0.27* F-E Communication X5
-0.27** -0.08 -0.06 -0.21* -0.05 Age X6
0.34** 0.04 0.02 0.30** 0.25** Education X7
0.09 0.11 0.11 0.13 -0.07 Family Size X8
0.21* 0.20* 0.15 0.12 0.20* Family Educational
Status
X9
0.14 0.11 -0.0.3 0.04 0.04 Land Holding X10
0.10 0.05 0.03 0.07 -0.16 Herd Size X11
0.36** 0.39** 0.13 0.12 0.19* Availability of Input
Facilities
X12
0.11 0.04 0.16 0.15 0.16 Attitudes Towards Dairy Farming X13
0.49** 0.21 0.06 0.13 0.34** Knowledge level
About AI
X14
0.38** 0.16 -0.13 0.30** 0.23* Knowledge level about VACD X15
0.33** 0.16 0.07 0.27** 0.17 Knowledge level about FNGF X16
0.35** 0.58** 0.11 0.10 0.06 Knowledge level about CF X17
0.48** 0.41** 0.07 0.26** 0.29** Overall knowledge level about DFT X18
*P<0.05 **P<0.01

Further perusal of Table 5 indicates that information output, F-R communication, family education statues, availability of input facilities and knowledge level about VAD had positive and significant (P<0.05) relationship with adoption of AI. All other remaining variables, including farmer intra-system communication, age family size, land holding, herd size, attitudes towards dairy farming, knowledge level about FNGF and knowledge level about CF had no significant relationship with adoption of AI among farmers.
Adoption of vaccination against contagious disease
It is clear form the Table 5 that the information output, farmer Intra-system communication, educational level, knowledge about VACD, knowledge level about FNGF and overall knowledge level about dairy farming technologies had positive and highly significant relationship with adoption of VACD.

Further perusal of Table 5 indicates that F-R communication had positive and age had negative but significant relationship (P<0.05) with adoption of VACD. All other remaining variables, including information input, F-E communication, family size, family education status, land holding, herd size, availability of input facilities, attitudes towards dairy farming, knowledge level about AI and knowledge level about concentrate feeding did not show any significant relationship with adoption of VACD among farmers.
Adoption of feeding nutritious green fodder
It is amply clear from Table 5 that out of a total 18 variables, only farmersُ intra-system communication had positive and significant relationship with adoption of FNGF.

It could be further noticed that all other remaining variables did not show any significant relationship with adoption of FNGF, indicating less importance attached to the diffusion of FNGF through research and linkage systems among farmers.

Adoption of concentrates feeding
It could further be seen from Table 5 that information input, Information output, availability of input facilities, knowledge level about CF and overall knowledge level about dairy farming technologies had positive and highly significant (P<0.01) relationship with adoption of CF.
Further perusal of Table 5 further indicates that F-E communication as well as family educational status had positive and significant relationship (P<0.05) with adoption of CF. All other remaining variables did not show any significant relationship with adoption of CF among farmers.
Overall adoption of dairy farming technologies by farmers
Further perusal of Table 5 indicates that information input, information output, farmers inter-system communication, F-R communication, F-E communication, education, availability of input, facilities, knowledge level about AI. VACD,FNGF,CF and overall knowledge level about dairy farming technologies had positive and highly significant relationship (P<0.01) with overall adoption behavior of farmers of DHT indicating higher and better the information input/output, communication activities or pattern, availability of input facilities and knowledge level about dairy farming technologies, higher and better the adoption of DHT.

Perusal of Table 5 further indicated that the age had negative but highly significant relationship (P<0.01) with overall adoption behavior about dairy farming technologies, indicating that an increase in the age of farmers resulted in the decline of overall adoption of dairy farming technologies.
Furthermore perusal of Table 5 indicates that family education status had positive and significant relationship (P<0.05) with overall adoption behavior of dairy farming technologies. All other remaining variables, including family size, land holding, herd size as well as attitudes towards DF did not show any significant relationship with overall adoption behavior of dairy farming technologies among farmers.
Regression coefficient of farmer's adoption of dairy farming technologies on communication variables

As shown in Table 6 the positive and highly significant partial regression coefficient (P<0.01) of F-E communication and knowledge level about dairy farming technologies was found to have contributed to the increase of overall adoption of dairy farming technologies among farmers. The R 2 value of 0,4025 with F value of 15.38 indicates its significance 0.01 level of probability and revealed that 40.25 percent variation in adoption of dairy farming technologies among farmers could be explained with the help of these six variables.



Table 6.  Partial regression coefficient of adoption behavior of farmers on communication and personal variables
S1. No. Variables Partial regression coefficient of adoption behavior
X1 Information Input 0.087 ± 0.058
X2 Information Output 0.056 ± 0.168
X3 Farmers Intra-system Communication -0.267 ± 0.204
X4 F-R Communication 0.035 ± 0.150
X5 F-E Communication 0.310** ± 0.099
X6 Knowledge level about Dairy Farming Technologies 0.439** ± 0.118

F Value 15.38**

R 2 0.4025
** P<0.01

This kind of result which supported by (Nell 1998), (Wheeler and Ortmann 1990), (Randhir-Singh et al 1996) and (Beck and Gong 1994) shows the importance of communication variables over adoption of dairy farming technologies among livestock farmers.


Conclusions and recommendations


References

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Received 22 August 2005; Accepted 3 January 2006; Published 1 March 2007

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