| dc.description.abstract | In Kenya agriculture is one of the key subjects taught yet it is notable that youths are
unemployed just like in other developing countries. Despite the above facts, Kenya
still requires human resource to drive the agricultural sector as one of the big four
agenda. The purpose of this study was to establish the factors influencing selection of
agriculture subject and progression in agriculture career of students in tertiary
institutions of Kakamega and Bungoma counties of Kenya. Correlational and
descriptive designs were used, cluster sampling was used to identify Bungoma and
Kakamega counties. Stratified random sampling was used to select agriculture
students, Census method identified the tertiary institution, purposive sampling was
used to select key informants and agriculture students because they possess the
needed information. Simple random sampling was used to select samples without bias
from the accessible population while quota sampling was used select focus group
discussion. Using a pragmatic philosophy, the study applied qualitative and
quantitative strategy in data collection. The sample size was determined from
Cochron (1972) formulae based on the study population. One hundred and sixty-two
(162) secondary schools, out of 839. A sample size of (249) secondary school
students, (24) university students and (131) TVET institutions students giving a
sample size of 404 from a target population of 11928 students. Pilot study was
conducted in Vihiga County. Data was collected using document content analysis,
questionnaires, focus group discussions and interview guides. Due diligence, was
taken into consideration while collecting and processing data to ensure both reliability
and validity of the study. Both descriptive (mean, standard deviation and graphs) and
inferential techniques (diagnostic test, trend analysis and factor analysis) were
employed to analyze data and presented using frequency tables and line graphs.
Multiple comparison table revealed the years that differed in agriculture selection for
the 5 different categories of schools. Significant factors contributing to the variance in
selection were change in type of schools. A paired sample t-test shows that the
average difference between total KCSE agriculture selection in Kenya scores and the
total agriculture enrolment in the universities is significant (t (5) = 18.912, p < 0.05).
The average difference between the total KCSE agriculture selection in Kenya scores
and total agriculture enrolment in TVET is significant (t (5) = 18.978, p < 0.05). The
average difference between the total KCSE agriculture selection in Bungoma and
Bungoma counties agriculture progression to the universities in Kenya scores is
significant (t (5) = 14.095, p < 0.05). The average difference between the total KCSE
agriculture selection in Kakamega and Kakamega agriculture progression to the
universities in Kenya scores is significant (t (5) = 17.825, p < 0.05). Principle
component analysis with varimax rotation revealed that ‘friends as a motivator’ factor
accounted for 23.99% ‘peer motivator’(41.786%), and ‘parents motivator’ accounted
for 55.924% of the variance in selection of agriculture subject in secondary school.
Similarly, socio-economic factors are motivators to progression in agriculture career
since after rotation, ‘parental income’ accounted for 24.511% of the variance, ‘family
size’ 42.723%, and ‘Parent education’ 59.337% of the variance in progression.
Students’ selection challenges include ministry policies, parents’ decision, teacher
influence, peer compliance and limited government scholarships in agriculture sector.
Identified strategies to mitigate challenges recommended the ministry of agriculture to
review salary scales and create job opportunities while the ministry of education to
make agricultural course business oriented and lower entry grades to university or
TVET. The results should inform policy makers and guide effort to career progression
in agriculture education. | en_US |
