Factors Affecting to Biosecurity Adoption on Laying Hen Farmers
V.S. Lestari*, S.N. Sirajuddin, I. Rasyid, & K. Kasim
Socio-Economic Study, Faculty of Animal Husbandry, Hasanuddin University Jl. Perintis Kemerdekaan, KM 10, Makassar-90245, Indonesia
e-mail: veronicasrilestari@yahoo.co.id; sitti_nurani@yahoo.co.id;
Ilo-ggu@yahoo.com; emihirdan@yahoo.com
Abstract
The present study was undertaken to identify the factors that influence biosecurity adoption on laying hen farmers. Sidrap district, South Sulawesi province was chosen as the place for the research because beside it was famous as the center of laying hen farms, it also became one of six districts in South Sulawesi which suffered from Avian influenza outbreak in 2005. Total samplewere 60 respondents. The sample were choosen through random sampling from two subdistricts which were the most populous of layer smallholders, namely Baranti and Maritengngae. Data were obtained through observations and interviews using a questionnaire. Data were analyzed using a score based on biosecurity status. Biosecurity status was obtained based on the adoption of biosecurity measures which consisted of 9 stages: farm inputs, traffic onto farms, distance from sources of pathogens to shed, exposure of farm, biosecurity at farm boundary, biosecurity between farm boundary and shed, biosecurity at the shed door, traffic into the shed and susceptibility of the flock. Multiple regression model was employed to analyse the data. The study revealed that the adoption biosecurity were associated with gender, age, education, farming experience, farm-income, family size and social capital. These variables contributed 20.00% variation in biosecurity adoption on laying hen farmers. However, only farm income, family size and social capital were the major factors affecting to the adoption of biosecurity (P < 0.05).
Keywords: adoption, biosecurity, farmers, laying hen
Introducton
bosecurty measures has been declared to be appled to poultry farms n all prov-nces n Indonesa.
Bosecurty s the key of the poultry ndustry n preventng the spread of dsease and nfectons. Bosecurty s made up of three components: segregaton, cleanng and dsnfecton (FAO, 2008). Adopton s a a mental process through whch an ndvdual passes from hearng about an nnovaton to ts adopton (Rogers, 1962 n Kark and Bauer, 2004). There are fve of adopton stages namely: awareness, nterest, evaluaton, tral and error, and the last s adopton (Ban and Hawkns, 1999).
There are number of factors that nfluence the extent of adopton of technology, such as characterstcs of attrbutes of technology, the adopters or clentele, whch s the object of change, the change agent (extenson worker, professonals, etc); and the soco-economc, bologcal and physcal envronment n whch the technology take place (Cruz, 1987 cted by Ch and Yamada, 2002). Eze and Okudu (2008) stated that farm ncome, stock and educatonal levels were the most valuable varables determnng the poultry farmers technology adopton potental. The classfcaton performance of the model was 83.33%. Olele and Emah (2007) found that level of educton, age of farmers, farm sze, farm ncome and extenson contact were the major determnants of fsh producton technologes adopton at 0.05 level of sgnfcance. Teklewold et al. (2006) argued that farmers’ decson on adopton of poultry technology was postvely affected by sex of the household head, famly sze, avalablty of supplementary feed, credt and extensons servce and extent of expected beneft from poultry and negatvely affected by market problem. Munasb and Jordan (2011) concluded that communty nvolvement had postve effect on the decson to adopt sustanablty agrcultural practce, and t also had a postve effect on the extent to whch farmers adopt these practces. Padmaja and Bantlan (2008) stated that buld up socal captal played an mportant role n nfluencng mpacts from the technology because of the ways n whch socal network and socal relatonshp facltated technology dsemnaton.
Sdrap dstrct s famous as the most populous of layer farms n South Sulawes provnce. Total layer smallholders were 1,334 wth the populaton were 3,439,556 chckens (Dnas Peternakan Kabupaten Sdenreng Rappang, 2011). In 2005, Sdrap dstrct became one of sx dstrcts n South Sulawes provnce whch suffer from Avan nfluenza outbreak and affects to several loss from ther layer farms (Krstant, 2009). Ths survey was conducted n Sdrap dstrct South Sulawes provnce to know factors affectng adopton of bosecurty measures on layng hen farmers.
Materals and Methods
was calculated from Farm Bosecurty Status Score (FBSS) adopted from Patrck and Jubb (2010). The dependent varable was the adopton ndex whch was exspressed as a percentage of adopton level measures out of a specfc maxmum of boseurty measures (Rahman, 2007). The emprcal model was specfed as:
Yi= βo + β1GENDERi + β2AGEi + β3EDUCi + β4EXPRi + β5SOCAPi + β6FAMSIZEi + β7FARMINCi + εi
• Y was the dependent varable. It was expressed as a percentage of bosecurty measures adopted out of 9 rsk stages. The ndependent varables used n the model wth ther expected sgns are presented below:
• GENDER: was expressed as a bnary varable wth 1 f the farmer was male, 0 otherwse. Expected sgn for gender was ambguous.
• AGE: was expressed as the length of ther lfe (year). Age was assumed to have negatve effect on adopton.
• EDUC: was expressed as the perode of farmers have formal educaton (year). Educaton was hypotheszed to have a postve effect on adopton.
• EXPR: was expressed as the length of farmers took care of ther poultry (year), experence was assumed to have a postve effect on adopton.
• SOCAP: was expressed as farmers’ trust wth ther communty (score), socal captal was assumed to have a postve effect on adopton.
• FAMSIZE: was expressed as number of famly of farmers (person), famly was assumed to have a postve effect on adopton.
• FARMINC: was expressed as amount of revenue from chcken and egg sellng, ncome was assumed to have a postve effect on adopton.
Total sample were 60. Ten percent (10%) of the sample were choosen from two subdstrcts wth the most populous layer smallholders, namely Martengngae and Barant subdstrcts whch had total populaton of 600 layer farmers. The sur-vey was conducted by traned enumerators n 2010. A pre-test questonnare wth closed-ended questons was used to capture nformaton from layng hen farmers on soco-economc characterstcs such as farmers characterstcs, the farm, and adop-ton of bosecurty measures ncludng 9 stages. The multple regresson model were estmated usng SPSS verson 16.
Results and Dscusson
Adoption index
ndex was 63.4%, mples that 6 out of 10 layng hen farmers had adopted bosecurty measures. Ths ndex was hgher than that of Musaba (2010) and Rachman (2007) fndngs whch was 56.0% and 55.87% respectvely.
Results of the multiple regression analysis
The multple regresson analyss was performed to know factors that affect the adopton of bosecurty measures by layng hen farmers. The result of the regresson analyss was presented n Table 1.
The coeffcent of determnaton (adjusted R-square = 0.20) ndcated that 20.0% varaton n the overall adopton ndex of bosecurty measures could be ex-planed by seven ndependent varables ncluded n the model. The results n Tabel 1 showed all the coeffcents have the expected sgns, except educaton and famly sze. Only those coeffcents assocated wth statstcally sgnfcant varables at the 5-percent level were dscussed.
The coeffcent of farm ncome was found to be sgnfcant (P < 0.05) and pos-tvely related to adopton level. Controllng other factors, the coeffcent was 2.35. Ths mean that the addton farm ncome by Rp 1,- would ncreased adopton of bosecurty by 2.35%. Ths fndngs were accordance wth those of Eze and Okudu (2008), Olele and Emah (2007), and Supradt et al. (2006).
The coeffcent for famly sze was found to be sgnfcant (P < 0.05) and nega-tvely related to adopton level. Controllng for other factors, the coeffcent was -2.78. Ths mean that the addton of 1 person of a famly, would reduced 2.78% adopton rate of bosecurty. A negatve sgn for famly sze suggested that adopton was hgher among smaller famly sze. Ths mght because larger famly sze would ncrease the spread of dsease than the small ones. As t was known that human
ac-Table 1. Multple regresson analyss of factors affectng to adopton of bosecurty measures on layng hen farmers
Varables Standarzed
coeffcents Standar Error T-value Sg
Constant 46.53 16.99 2.74* 0.01
Gender 0.20 6.30 1.61 0.11
Age - 0.27 0.12 - 1.91 0.06
Educaton - 0.01 0.33 - 0.02 0.99
Experence 0.20 0.22 1.68 0.10
Farm ncome 0.33 0.00 2.35* 0.02
Famly sze - 0.34 0.89 - 2.78* 0.01
Socal captal 0.26 0.22 2.18* 0.03
tvtes were the man route for the spread of the vrus (Blech et al., 2009). It was mpled that the less the people entered the farm, the less the spread of the vrus. Ths results contrast wth that of Teklewold et al. (2006).
Socal captal has a sgnfcant (P < 0.05) postve effect on adopton of bose-curty measures. Controllng for other factors, the coeffcent was 2.18. Ths entals that ncreased socal captal by 1%, can lead to ncreased adopton of bosecurty measures by 2.18%. Ths fndngs were accordance wth those of Munasb and Jor-dan (2011) and Padmaja and Bantlan (2008).
Conclusons
Econometerc analyss usng multple regresson model showed that bosecu-rty adopton was postvely affected by farm ncome and socal captal, however negatvely affected by famly sze (P < 0.05). The other factors namely gender, age, educaton and farm experence had no sgnfcant effect on adopton of bosecurty measures n the study area.
Acknowledgement
Ths study was funded by ACIAR Project AH/2006/169. We would lke to take ths opportunty to thank Dr. Ian Patrck and Dr. Trstan Jubb, who supported us to do wth the research and wrtng of ths paper.
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