Mr Benson Inyangala

My name is Benson Alfred Otemba Inyangala, a holder of M.A. Leadership Studies, International Leadership University, 2012; Master of Science in Animal Production, University of Nairobi, 1990; and Bachelor of Science in Agriculture (Hons), University of Nairobi, 1985. My work experience includes lecturing for 23 years at the University of Nairobi, Department of Animal Production. The main courses taught include Population and Quantitative Genetics, Animal Genetics, Breeding and Conservation, and Animal Genetic Resources, to students pursuing B.Sc.

Mr Benson Inyangala CV



Thumbi, S, Amimo JO, Inyangala BO, Junga JO, Mosi RO.  2011.  Socio-economic characteristics and perceptions of cattle keepers and constraints to cattle production in Western Kenya. Abstract

A cross-sectional survey was done in two Districts in Western Kenya to determine the socio-economic characteristics and perceptions of the cattle types kept. This involved socio-economic profiles of households, herd structure, reasons for keeping specific types of cattle and production and marketing constraints together with desired policy interventions to address the constraints. A total of 210 farmers randomly selected were interviewed. The data was analysed using Statistical Analysis System program. The majority of farmers (84%) were males. The households surveyed had an average family size of 8 (± 4.7) members. The mean land holding was 7.8 acres, with 98% of them owning the land. Most of the farmers (64%) in the two districts were literate and were involved in farming (95%) as the primary livelihood. The majority (80%) of the households preferred keeping indigenous zebu cattle over the exotic cattle. The first three reasons for keeping zebu cattle were, in that order, work, especially ploughing (91%), milk (74%) and as a repository for wealth (32%), which accounted for about 62% of the respondents. Diseases (86%), inadequate and low quality feed (12%) and high cost of drugs (2%) were the major constraints to livestock productivity. The farmers also identified lack of market (48%), poor infrastructure (39%) and misuse by the middlemen traders (12%) as main marketing constraints. The survey showed that there is need for the effective implementation of policies on adequate financial aid and regulation of farm input prices to the farmers as well as improved livestock extension services to enhance the production of ruminant livestock.


Amimo, JO, Wakhungu JW, Inyangala BO, Mosi RO.  2007.  The effects of non-genetic factors and estimation of genetic and phenotypic parameters and trends for milk yield in Ayrshire cattle in Kenya. Abstract

Dairy cattle production in Kenya has been growing into an important agricultural sector, but it still faces numerous difficulties in environmental constraints. The purpose of this study was to identify significant non-genetic effects on milk production to give advices for farm management and estimate genetic and phenotypic parameters for milk traits. Data consisting of 4475 lactation records from 10 large-scale Ayrshire herds collected from 1980 to 2005 were used to evaluate effects of non-genetic factors and determine genetic and phenotypic parameters and trends of 305 d milk yield (305d MY). The data analyses using least square techniques of Proc GLM of SAS identified significant sources of variation by herd, parity and year of calving on 305d MY. The overall mean for 305d MY was 3009.8 ±1098 kg, with the corresponding heritability and repeatability estimates of 0.12 ±0.05 and 0.35 ± 0.01 respectively. Genetic trend for 305d MY was -2.1 kg/yr and statistically significant (P<0.01) indicating annual decrease in breeding values over the study period. The high variation as indicated by both large standard errors and low heritabilities of the milk trait indicate that much improvement in this trait could be achieved through improved management. The negative annual genetic changes in milk yield observed could largely be due to ineffective breeding strategies both at herd and national level.




Githigia, SM;, Okomo MA;, Inyangala BO;, Okeyo M;, Wanyoike MM;, Munyua SJM;, Thamsborg SM;, Kyvsgaard. NC.  1995.  Prevalence Of Parasitic Diseases Of Goats In Embu District- Kenya..
Githigia, SM;, Okomo MA;, Inyangala BO;, Okeyo M;, Wanyoike MM;, Munyua ST;, Thamsborg SM;, Kyvsgaard NC.  1995.  Economically impotent diseases of goats in a semi-arid area of Kenya..
Githigia, SM;, Okomo MA;, Inyangala BO;, Okeyo M;, Wanyoike MM;, Munyua SJM;, Thamsborg SM;, Kyvsgaard. NC.  1995.  Economically Important Diseases Of Goats In A Semi Arid Area Of Kenya.(poster Presentation)..


Okeyo, AM;, Inyangala BAO;, Githigia SM;, Munyua SJM;, Wanyoike MM;, Okomo. MA.  1994.  Reproductive Performance And Level Of Gastro-intestinal Parasite Infestation In Goats On-farm And On-station At Machanga, Embu, Kenya L994..
Okeyo, AM;, Inyangala BOA;, Githigia SM;, Maingi NE;, Githigia SM;, Munyua SJM;, Wanyoike MM;, Gachuiri C.  1994.  Genetic Studies Of Galla And Small East African Goats And Their Correlated Growth A.


Inyangala, BAO;, Rege JEO;, Itulya S.  1992.  Growth traits of the Dorper sheep. I. Factors influencing growth traits. Abstract

Presents results of a study carried out to analyse growth traits of the Dorper sheep. Data on 969 lambs collected over a 10-year period (1978 to 1987) at Magogo, Kenya were used in the study. Lamb traits studied were weights from birth to yearling and absolute growth rates between adjacent stages of growth. All the fixed effects studied influenced growth in one way or another.


Inyangala, BAO, Rege JEO, Itulya S.  1990.  Genetic and phenotypic parameter estimates of growth traits of the Dorper and Dorper X Red Masai sheep.. Abstract

Data on 1550 Dorper and Dorper X Masai lambs recorded over a 10-yr period were analysed. The crossbred lambs were from dams having >73.4% Dorper inheritance. The effect of percentage of Dorper inheritance was not significant for birth weight and for most measures of body weight to 12 months of age. The h²s, estimated by paternal half-sib analysis, were 0.15±0.07 for birth weight, 0.18±0.08 for weaning weight, 0.39±0.11 for 9-month weight, and 0.55±0.13 for 12-month weight. Genetic correlations among body weights were 0.15-0.99 and phenotypic correlations 0.02-0.

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