Identifying Constraints and Opportunities for Improving the Health and Productivity Of Chickens Raised By Smallholder Farmers In The Marginal Upland Barangays Of Inopacan, Leyte, Philippines


Author(s): Eugene B. LaƱada and Dave D. Amihan

Abstract

To identify constraints and opportunities for improving the health and productivity of chickens raised by smallholder farmers in the marginal uplands, a survey was carried out in 4 upland barangays of Inopacan, Leyte, involving questionnaire-interviews on smallholder chicken raisers. Data were gathered from randomly selected households during a single visit to each of these households using a structured data collection sheet. Descriptive and analytic work on the data was carried out, with modeling on 2 key performance indicators: chicken attrition rates and income levels from chicken production.
Results show that smallholder chicken raising in the uplands of Inopacan is typically a semi-scavenging system, with generally low productivity. Using epidemiological methods, 150 putative factors were examined for association with the 2 key performance indicators: overall chicken attrition (ATTRITION), and income from chicken production (INCOME). Of these factors, 13 and 29 variables were found to be associated (P<0.20), respectively, with ATTRITION and INCOME. Logistic regression analysis for ATTRITION revealed that feeding chickens while caged, giving rice as feed, and farmers' practice of treating sick chickens proved highly significant in the model. Likewise, for INCOME, analysis revealed that 5 factors were highly significant in the model: selling chickens owned for profit, commercial feed given as chicken feed, copra making is a source of agricultural income, raiser is satisfied with the performance of his chicken flock, and amount of coconut given per flock per month. The implications of the results, and using the epidemiological approach in studying smallholder chicken productivity and health are discussed.

Keywords : Cross-sectional analysis, smallholder chicken systems, logistic regression model

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