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On-farm phenotypic characterization of indigenous cattle populations of Gamo Goffa zone, Southern Ethiopia

Published online by Cambridge University Press:  12 April 2013

Chencha Chebo ,
Workneh Ayalew  and
Zewdu Wuletaw
Chencha Chebo*
Affiliation:
Department of Animal Sciences, Wollo University, Dessie, Ethiopia
Workneh Ayalew
Affiliation:
National Agricultural Research Institute, Lae MP 411, Papua New Guinea
Zewdu Wuletaw
Affiliation:
Sustainable Land Management, GIZ, Ethiopia
*
Correspondence to: Chebo Chencha, Wollo University, Dessie, Ethiopia, P.O. Box 1145, email: [email protected]
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Summary

An exploratory survey of local cattle populations of Gamo Goffa Zone in south-western Ethiopia was conducted between April 2011 and May 2012 to identify and phenotypically characterize cattle populations. Ten focus group discussions with key informants as well as phenotypic data from sample mature animals were used to generate data, including quantitative trait data from 560 animals and qualitative trait data from 867 animals. Findings from focus group discussions revealed that, even if local cattle are found widely distributed throughout the study area, they are not known by any common name or breed type. Results from analysis of variance (ANOVA) on continuous variables showed significant (P < 0.0001) differences between sites. Tukey's multiple mean comparison test showed that each quantitative traits were significant (P < 0.0001) for sites. Chi-square test was also significant (P < 0.0001) for most of the categorical variables per sites. Based on a discriminant analysis, sample populations were classified into their respective sites with overall hitting rate was 63.15 percent for females and 74.89 percent for males. Canonical discriminant (CANDISC) analysis showed quantitative traits and Mahalanobis' distances between sites were significant (P < 0.0001). The stepwise discriminant (STEPDISC) analysis for both populations showed that most variables had significant (P < 0.0001) power in explaining phenotypic variation. These information from focus group discussions and phenotypic variation analyses led to identification of two cattle populations that deemed to be distinct breed types (Gamo highland and lowland). Thus, indigenous cattle population of the study area was not homogenous on their phenotypic features, and further genetic characterization should be done to confirm their genetic distinctiveness.

Résumé

D'avril 2011 à mai 2012, une étude prospective des populations bovines locales de la région du Gamu-Gofa dans le sud-ouest de l'Éthiopie a été menée pour identifier et caractériser phénotypiquement les populations bovines. Dix groupes focaux de discussion, munis d'informateurs clés ainsi que de données phénotypiques provenant des animaux adultes de l'échantillon, ont été utilisés pour générer l'information, y comprises des données quantitatives de 560 animaux et des données qualitatives de 867 animaux. Les conclusions des groupes focaux de discussion ont révélé que, bien que les bovins locaux fussent largement distribués dans l'aire d'étude, le bétail n'était connu ni par un nom commun ni par un type racial. Les résultats de l'analyse ANOVA des variables continues ont montré des différences significatives (P < 0,0001) entre les localisations. Pour chacune des variables quantitatives, le test de Tukey de comparaisons multiples a décelé des différences significatives (P < 0,0001) entre les localisations. Le test chi-carré a aussi été significatif (P < 0,0001), pour ce qui est de l'effet de la localisation, pour la plupart des variables catégorielles. D'après l'analyse discriminante, les populations échantillonnées ont été classées dans leurs respectifs emplacements avec un taux global de réussites de 63,15 pour cent pour les femelles et de 74,89 pour cent pour les mâles. L'analyse discriminante canonique a retrouvé des différences significatives (P < 0,0001) entre localisations pour les variables quantitatives et les distances de Mahalanobis. Pour les deux populations, l'analyse discriminante pas à pas “stepwise” a montré que la plupart des variables a contribué de façon significative (P < 0,0001) à expliquer la variation phénotypique. L'information des groupes focaux de discussion et les analyses de la variation phénotypique ont conduit à identifier les deux populations bovines considérées comme étant deux types raciaux différents (terres hautes et terres basses du Gamo). Ainsi, les caractéristiques phénotypiques de la population bovine indigène de l'aire d'étude n'ont pas été homogènes, la caractérisation génétique serait donc à poursuivre afin de confirmer les différences génétiques.

Resumen

Entre abril de 2011 y mayo de 2012 se realizó un estudio prospectivo de las poblaciones de ganado bovino local de la región de Gamo-Gofa en el suroeste de Etiopía con el fin de identificar y caracterizar fenotípicamente las poblaciones bovinas. Se usaron diez grupos focales de discusión, provistos con informadores clave así como con datos fenotípicos muestreados en animales adultos, para generar información, incluyendo datos de variables cuantitativas de 560 animales y datos de variables cualitativas de 867 animales. Las conclusiones de los grupos focales de discusión revelaron que, si bien el ganado bovino local estaba ampliamente distribuido por el área de estudio, el ganado no era conocido por ningún nombre común ni tipo racial. Los resultados del análisis ANOVA de las variables continuas mostraron diferencias significativas (P < 0,0001) entre emplazamientos. Para cada una de las variables cuantitativas, la prueba de comparación múltiple de Tukey mostró que hubo diferencias significativas (P < 0,0001) entre emplazamientos. La prueba Chi-cuadrado también fue significativa (P < 0,0001) entre emplazamientos para la mayoría de las variables categóricas. En base al análisis discriminante, las poblaciones muestrales fueron clasificadas en sus respectivos emplazamientos con una tasa global de aciertos de 63,15 por ciento para las hembras y 74,89 por ciento para los machos. El análisis discriminante canónico mostró que las variables cuantitativas y las distancias de Mahalanobis eran significativamente (P < 0,0001) distintas entre emplazamientos. Para ambas poblaciones, el análisis discriminante por pasos “stepwise” mostró que la mayoría de las variables contribuyeron de forma significativa (P < 0,0001) a explicar la variación fenotípica. La información de los grupos focales de discusión y los análisis de la variación fenotípica llevaron a la identificación de dos poblaciones bovinas que fueron consideradas como dos tipos raciales distintos (tierras altas y tierras bajas de Gamo). Así, la población bovina autóctona del área de estudio no fue homogénea en sus rasgos fenotípicos, con lo que se debería proseguir con la caracterización genética para confirmar sus diferencias genéticas.

Keywords

phenotypic characterizationindigenous cattle populationsGamo Goffa ZoneSouthern Ethiopiacaractérisation phénotypiquepopulations bovines indigènesrégion du Gamu-GofaSud d'Éthiopiecaracterización fenotípicapoblaciones bovinas autóctonasregión de Gamo-GofaSur de Etiopía
Type
Research Article
Information
Animal Genetic Resources/Resources génétiques animales/Recursos genéticos animales , Volume 52 , June 2013 , pp. 71 - 82
Copyright
Copyright © Food and Agriculture Organization of the United Nations 2013 

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References

Afifi, A. & Clark, V. 1996. Computer-Aided Multivariate Analysis. 3rd edition, Chapman and Hall, London, UK, 285 pp.Google Scholar
Ayalew, W., Rege, J.E.O., Getahen, E., Tibbo, M. & Mamo, Y. 2004. Current state of knowledge on characterization of farm animal genetic resources in Ethiopia. In Proceedings of the 11th Annual Conference of the Ethiopian Society of Animal Production (ESAP), 28–30 August 2003, Addis Ababa, Ethiopia, 1–21.Google Scholar
CSA (Central Statistical Authority). 2008/09. Agricultural sample survey, report on livestock and livestock characteristics for the year 2008/09. CSA, Addis Ababa, Ethiopia, 188 pp.Google Scholar
CSA (Central Statistical Authority). 2010/11. Agricultural sample survey, report on livestock and livestock characteristics for the year 2010/11. CSA, Addis Ababa, Ethiopia, 190 pp.Google Scholar
DAGRIS (Domestic Animal Genetic Resources Information System). 2011. International Livestock Research Institute, Addis Ababa, Ethiopia. http://dagris.ilri.cgiar.org/ Google Scholar
Epstein, H.E. 1971. The Origin of Domestic Animals of Africa. Volume I. African Publishing Corporation, New York, USA, 670 pp.Google Scholar
FAO. 1984. Animal Genetic Resources Conservation by Management: Data Banks and Training. Animal Production and Health Paper 44/1. FAO. Rome. (Available at: ftp://ftp.fao.org/docrep/fao/010/ah808e/ah808e.pdf).Google Scholar
FAO. 1986. Animal Resource Data Banks 2. Descriptor Lists for Cattle, Buffalo, Pigs, Sheep and Goats. Food and Agriculture Organization, Animal Production and Health Paper 59/2. FAO. Rome. (Available at: ftp://ftp.fao.org/docrep/fao/009/ah760e/AH760E00.pdf).Google Scholar
FAO. 2012. Phenotypic characterization of animal genetic resources. FAO Animal Production and Health Guidelines No. 11. Rome. (Available at: http://www.fao.org/docrep/015/i2686e/i2686e00.pdf).Google Scholar
Gamo Goffa zone Agricultural and Rural Development Department. 2010. Annual socio-economic and agricultural report of the zone (unpublished).Google Scholar
Getachew, F. 2006. On-farm phenotypic characterization of cattle genetic resources and their production systems in Awi, East and West Gojjam zones of Amhara Region, Ethiopia. M.Sc. thesis, Alemaya University, Dire Dawa, Ethiopia, 153 pp.Google Scholar
Getachew, F. & Gashaw, G. 2001. The Ethiopian Dairy Development Policy: A Draft Policy document. Ministry of Agriculture/AFRDRD/AFRDT Food and Agriculture organization/SSFF, Addis Ababa, Ethiopia, 22 pp.Google Scholar
Klockars, A.J., Hancock, G.R. & McAweeney, M.J. 1995. Power of unweighted and weighted versions of simultaneous and sequential multiple-comparison procedures. Psychological Bulletin 118: 300–307.Google Scholar
Negussie, E. & Ayalew, W. 2002. Conservation of livestock biodiversity and its relevance to food security. In Proceedings of the 9th Annual Conf. of the Ethiopian Society of Animal Production (ESAP), 30–31 August 2001, Addis Ababa, Ethiopia, 15–29.Google Scholar
Payne, W.J.A. & Hodges, J. 1997. Tropical Cattle, Origin, Breeds and Breeding Policies. Blackwell Science, Oxford, UK, 328 pp.Google Scholar
Petrie, A. & Watson, P. 1999. Statistics for Veterinary and Animal Science. Blackwell Science, Oxford, UK, 283 pp.Google Scholar
Rege, J.E.O. & Tawah, C.L. 1999. The state of African cattle genetic resources. II. Geographical distribution, characteristics and uses of present-day breeds and strains. FAO/UNEP Animal Genetic Resources Information Bulletin, 26: 1–25.Google Scholar
SAS (Statistical Analysis Systems). 2002. Statistical Analysis System Software. SAS Version 9.0, SAS Institute Inc., Cary, NC, USA.Google Scholar
Tadesse, D. 2005. On-farm phenotypic characterization of cattle genetic resources and their production systems in south and north Wollo zones of Amhara region, north eastern Ethiopia. M.Sc. Thesis, Alemaya University, Dire Dawa, Ethiopia, 125 pp.Google Scholar
Taye, T. 2005. On-farm phenotypic characterization of Sheko breed of cattle and their habitat in Bench Maji zone, Ethiopia. M.Sc. thesis, Alemaya University, Ethiopia, 105 pp.Google Scholar
Wuletaw, Z. 2004. Survey of indigenous cattle genetic resources, husbandry practices and cattle breeding objectives in North Gondar, Ethiopia. M.Sc. thesis, Alemaya University, Ethiopia, 128 pp.Google Scholar
Zerabruk, M. & Vangen, O. 2005. The Abergelle and Irob cattle breeds of North Ethiopia: description and on-farm characterization. Animal Genetic Resources Information Bulletin, 36: 7–20.Google Scholar