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All Journal International Journal on Food, Agriculture and Natural Resources (IJFANRES)
Abebe, Yeshiwas Walle
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Agriculture, Biological Sciences & Forestry

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Phenotypic and genetic parameters for growth traits of Abergelle goat in community-based breeding program scale up village Abebe, Yeshiwas Walle; Meberatie, Wossenie Shibabaw; Wondie, Zeleke Tesema
International Journal on Food, Agriculture and Natural Resources Vol 6, No 2 (2025): Ij-FANRes
Publisher : Food, Agriculture and Natural Resources - NETWORKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46676/ij-fanres.v6i2.453

Abstract

Abstract----the objective of this research was to estimate genetic and phenotypic parameters for growth and milk production traits of Abergelle goats in community-based breeding program (CBBP) scale up village.  The data used in the study was six years (2018-2023) data from the established village of goat community based breeding program area. The traits studied were, birth weight (BWT), weight at three months (TMWT), weight at six months (SMWT), weight at nine months (NMWT), yearling weight (YWT), direct heritability for growth and milk traits was computed by using WOMBAT software. Univariate mixed animal model was applied to estimate genetic parameters. Bivariate analysis was utilized to estimate correlations between traits. The overall least square mean of body weights ± standard errors (LSM±SE) for BWT, TMWT, SMWT, NMWT, and YWT were 2.3±0.04, 7.5±0.03, 8.8±0.040, 12.1±0.067, and 15.3±0.092 kg, respectively. The direct heritability estimates for BWT, TMWT, SMWT, NMWT, and YWT were 0.34±0.070, 0.45±0.062, 0.40±0.040, 0.42±0.07and 0.41±0.14, respectively. Phenotypic correlation of BWT with TMWT, SMWT, NMWT and YWT was low but that of TMWT-SMWT, SMWT-NMWT and NMWT-YWT was high and positive. The genetic correlations of birth weight with the studied growth traits were moderate but higher correlations were found among the other growth traits (SMWT, NMWT, and YWT), indicating as one of these traits increases; the others tend to increase as well. Therefore, birth weight as growth trait had no strong relation with later stages of growth traits in this study for Abergelle goat in scale up community based breeding programs.
Phenotypic and genetic parameter estimation of milk traits of Abergelle goat in Ethiopia Abebe, Yeshiwas Walle; Meberatie, Wossenie Shibabaw; Wondie, Zeleke Tesema
International Journal on Food, Agriculture and Natural Resources Vol 6, No 2 (2025): Ij-FANRes
Publisher : Food, Agriculture and Natural Resources - NETWORKS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46676/ij-fanres.v6i2.455

Abstract

 Abstract—this study aimed to evaluate the production performance and genetic parameters of Abergelle goat milk traits in Addis Mender and Alquzu village, Wag-Himera, Amhara region, Ethiopia. Data on milk traits were collected from 2018 to 2023 by the Sekota Dryland Agricultural Research Center from farmer-managed goats in scale-up villages. The General Linear Model (GLM) in SAS was used to analyze phenotypic traits, while Wombat software estimated genetic parameters and breeding values. Milk traits analyzed included daily milk yield (ADMY), 90-day lactation yield (LMY90 days), and lactation length (LL).Fixed effects considered were dam parity, year of kidding, and season. Mean values for ADMY, LMY90 days, and LL were 302.6±6.68 g, 28.003±47.45 kg, and 9.4±0.23 weeks, respectively. Heritability estimates for ADMY, LMY, and LL were 0.08±0.005, 0.16±0.034, and 0.04±0.34, while repeatability estimates were 0.50, 0.23, and 0.06, respectively. These findings show   moderate variation in milk production traits within the population, suggesting selection as an effective tool for genetic improvement. The study highlights the importance of repeated measurements and fixed effects in designing a genetic improvement program for Abergelle goats. Results provide valuable baseline data for enhancing milk production traits in this breed.
Co-Authors Meberatie, Wossenie Shibabaw Wondie, Zeleke Tesema