North Sulawesi is one of regions producing
broiler by total of boiler’s population in 2018 of
7,7million broilers. Two districts having the largest
population of broiler in North Sulawesi are District
of Minahasa Utara, with total of population of 4.5
million broilers (58.4%) and production of broiler of
4,118 ton; and District of Minahasa, having total of
broiler’s population of 1.9 million broilers (25.3%)
with production of broiler of 1,516 ton [BPS Sulut,
2018) District of North Minahasa is located in the
lowland, while, contrarily, District of Minahasais in
the highland. This topographic different is
presumably assumed resulting on difference of
raising pattern and husbandry’s productivity due to
different physical environment. Non-conducive
environment will cause broiler vulnerably infected
by any disease, meaning that both breeders in these
districts should apply biosecurity principles.
Applying principle of biosecurity is by reducing any
risk resulted by human’s mobility in the cages,
animals, organic or inorganic materials (Jubb and
Dharma, 2009). Particularly, principles of
biosecurity comprise of establishing, improving,
reducing, detecting, dimension, and selecting.Such
risk mentioned above should be avoided since it will
potentially become entrance of diseases’ seeds.
Research on biosecurity application has been done
by previous researchers (Ajewole et al., 2014;
Lestari et al.,2011; Umam et al., 2014). However,
information on biosecurity application by broiler’s
breeders in different topography, such as in the low
and highland along with its raising pattern, is
relatively limited.Hence, this research will analyze
different raising pattern of broiler, profitability, and
biosecurity application by breeders in both District
of Minahasaand District of North Minahasa.
2 RESEARCH METHODOLOGY
2.1 Sampling Method and Data
Collecting Technique
The research was conducted in District of North
Minahasa representing the lowland and District of
Minahasa representing the highland. It was done on
January to February 2019. In each of districts, it then
was selected 2 (two) sub-districts purposively by
consideration that it had breeders having ever
obtained training and applied regional biosecurity
management with the largest population of broiler in
respectively every district (BPS Sulut, 2018). Sub-
district turned as research’s site was Sub-district of
Dimembe and Kalawat (District of North Minahasa),
Sub-district of Sonder and Tondano Utara (District
of Minahasa). There were three scales of broiler
husbandry in the District of North Minahasa, such as
<5000 broilers (32 breeders), >5000-10000 broilers
(16 breeders) and>10000 broilers (10 breeders).
Meanwhile, breeders in the District of Minahasa had
business scale of <5000 broilers (25 breeders),
>5000-10000 broilers (14 breeders) and >10000
broilers (10 breeders). In each of business scales, 5
breeders, thus, were purposively selected in each
district respectively (Knottnerus.,2003) by
consideration that those breeders had followed
training of biosecurity management in broiler’s
husbandry, so the total of samplings was 30
breeders.Further, data was collected by survey
technique using questionnaire. Data gathered then
was primary data comprising of technical data, such
as mortality, broiler’s weight, feeds consumption,
business scale, business model, raising pattern,
procurement model of production equipment,
income, and aspects of biosecurity application used
by breeders.
2.2 Data Analysis
Data collected was, hence, analyzed descriptively
and quantitatively. Descriptive analysis comprised
on characteristic of broiler husbandry, such weight,
raising pattern, marketing, business model,
procurement model of production equipment, labor,
raising period. Whereas, quantitative analysis
consisted on calculation of broiler’s index and
income using following formulation, [Tandogan et
al., 2016)
Broiler’s index =
%
ℎ/
(1)
Income= TR - TC (2)
Where:
TR = Total of income in broiler husbandry
(Rp/production period)
TC = Total of production cost in broiler husbandry
( Rp/production period)
Next, the calculation of biosecurity variable used
score obtained from data collected. Data
management utilized descriptive method and
statistical analysis. Each response of respondents
was classified into fivecategories and given score.
The score was stated in numerical of 1,2,3,4, and 5
for each answer, which the highest score was 5 and
the lowest was 1 (Haedari et al., 2011).Such score,