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
- The
results of this
study indicate that livestock farmers in East Azerbaijan have been
exposed to dairy farming technologies diffusion programs. A vast
majority of the livestock owners fully adopted technologies; however,
the level of correct technology adoption is far from desired. The
lack of the adoption of veterinary and medication technologies is an
indication that these services are not having significance role in
extension programmers in East Azerbaijan. So, it is recommend
ensuring transfer of veterinary and medication technologies through
extension programmes.
- Highly
significant
difference was observed in mean values of the overall adoption of
dairy farming technologies between farmers of HLPA and those of ILPA.
Mean values of overall adoption of dairy farming technologies was
found to be significantly higher amongst the farmers of LPA than
those of HLPA. So, it is recommend to extend training, communication
and transfer of technology programs to dairy farmers settled down in
HLP areas.
- Information
input,
information output, farmers' intra-system communication,
Farmer-researcher communication, farmer-extensionist communication,
availability of input facilities and overall knowledge about dairy
Farming technologies had positive and highly significant relationship
with overall adoption level of farmers of dairy Farming technologies.
So, it is recommended enhancing the dissemination of information and
knowledge regarding dairy farming technologies. Ensuring relation
between livestock farmers, researchers and communication agents and
distribution of input facilities among livestock owners is highly
recommended
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