Breed selection practices and traits of economic importance for indigenous chicken in Kenya

T O Okeno, A K Kahi* and K J Peters

Animal Breeding in the Tropics and Sub-Tropics, Department of Crop and Livestock Sciences,
Humboldt Universität zu Berlin, Philippstraße 13, 10115, Berlin, Germany
otieno24@yahoo.ca
* Animal Breeding and Genetics Group, Department of Animal Sciences, Egerton University, P.O. Box 536, 20115 Egerton, Kenya

Abstract

The present study was conducted with an aim to generate essential information on breed selection practices and traits of economic importance of indigenous chicken (IC) farmers in Kenya. The study was carried out in six counties of Kenya with highest population of IC, namely; Siaya, Kakamega, Bomet, Narok, West Pokot and Turkana. A total of 594 farmers, 468 marketers and 546 consumers were interviewed using three sets of pre-tested structured questionnaires. The questionnaire was designed to capture information on selection criteria of farmers, genotypes raised, their attributes and traits of economic importance. Data on traits that farmers, marketers and consumers perceived as of primary economic importance were analyzed through computation of indices.

Farmers were found to select breeding stock based on growth rate (GR), body size (BS), egg number (EN), disease resistance (DR), hatchability (HA) and mothering ability (MA). Normal feathered, giant, crested-head and necked-neck were the dominant chicken genotypes raised because of their superiority in EN, BS, MA, GR and broodiness (BRD) compared to other genotypes.  The traits that farmers, marketers and consumers perceived as of economic importance were GR, DR, EN, BS, fertility, egg size and egg shell colour which were in line with farmers selection criteria. There is therefore a need consider these traits when developing breeding objectives for IC.  

Key words: attributes, breeding objectives, characterization, genotypes

Introduction

Village poultry production represent an appropriate system for supplying the ever increasing human population in the developing countries, which are characterised by low income and food insecurity leading to high levels of poverty, with high quality protein and cash income. The indigenous chicken (IC) require low inputs, are well adapted to the harsh scavenging conditions, poor nutrition, parasite and disease challenges and possess high genetic diversity for many traits and are therefore valuable genetic resources for present and future generations (Gueye 2009, Dana et al 2010). However, their low production attributed to poor nutrition, high mortalities, scarcity of appropriate breeds and inadequate knowledge on consumer preference and market dynamics has hindered their exploitation and ability to improve rural household livelihoods. 

To unlock the potential of IC to improve household livelihoods, high producing and adapted IC genotypes that meet market demands should be identified and sustainable genetic improvement programs that accounts for producer multiple objectives, market requirements and production circumstances developed. In Kenya like other developing countries breeding programs for indigenous livestock breeds are lacking (Dana et al 2010). There is an agent need to formulate and implement such programs. Developing appropriate breeding programs for village conditions requires characterization of production circumstances and identification of breeding practices and trait of economic importance to farmers (Abdelqader et al 2007). The production systems under which IC are raised in Kenya has been characterised (Okeno et al 2011). However, the information on farmers’ breeding practices and traits of economic importance are lacking. The current study therefore aims at identifying farmers’ selection practices, traits of economic importance as perceived by farmers, marketers and consumers and investigates the existence of genotypes raised and their attributes. The traits identified in this study could stimulate further work on economic evaluation and development of alternative breeding objectives for improvement of IC.

Materials and Methods

Sites and scope of the study

The study was carried out in six administrative counties of Kenya. They included Siaya (0^o14’N, 34^o16’E), Kakamega (0^o17’N, 34^o45’E), Turkana (3^o24’N, 35^o12’E), West Pokot (1^o14’N, 35^o7’E), Bomet (0^o46’S 35^o21’E) and Narok (1^o5’S, 35^o52’E). These counties experience a wide range of temperatures and annual rainfall and have the highest populations of IC in rural households (MOLD 2010). Kakamega, Siaya, Narok and Bomet are classified as medium to high potential agricultural regions while West Pokot and Turkana are marginal agricultural areas (Danda et al 2010).  In each county, three divisions with three locations within each division were chosen for the survey. 

Three sets of pre-tested structured questionnaires were used to collect information from farmers, marketers and consumers. The first set of the questionnaire was administered to selected households who owned IC and were willing to participate in the exercise. Group discussions were also held with farmers in order to obtain detailed information on parameters not captured in the questionnaire. The second and third sets of questionnaires were administered to IC marketers and consumers respectively. Marketers and consumers in rural and urban areas in each county were interviewed. The marketers and consumers were included in this survey because they play a vital role in the IC value chain. The objectives of the survey and the benefits were explained to the farmers, marketers and consumers during the reconnaissance. A total of 98, 122, 99, 96, 87 and 92 IC farmers were interviewed in Siaya, Kakamega, Bomet, Narok, West Pokot and Turkana counties respectively while 78 and 91 marketers and consumers respectively were interviewed in each county. 

Data collection and analysis

Data were collected through direct observations and interviews with farmers, marketers and consumers. Information on the farmers’ selection practices, genotypes owned and their attributes and traits considered as of economic importance by farmers, marketers and consumers were collected. 

Data were analysed using the General Linear Model procedure of SAS (SAS 2000). The PROC MEANS procedure was used to carry out the descriptive statistics while non parametric Kruskal Wallis test (NPAR1 WAY) procedure was used to calculate the mean ranks for genotypes and their attributes. The effect of the county on genotypes, selection practices and traits of economic importance were tested using Kruskal Wallis test. This test generates mean ranks whose significance is tested using Chi-square (χ²). The effect of the county on these variables was computed as:

where Y_ijk is the dependent variables, μ overall population mean, c_i county effects (i= Siaya, Kakamega, Bomet, Narok, West Pokot and Turkana), g_j gender of the household head effect (j = male or female) and ε_ijk random residual effect. 

Traits of economic importance as perceived by farmers, marketers and consumers were ranked using indices. Indices were defined as weighted averages of all rankings for a particular trait. The traits of economic importance were grouped as productive (growth rate, body size and egg number), reproductive (fertility and prolificacy), functional (disease resistance, heat tolerance, drought tolerance, temperament, mothering ability and broodiness) and aesthetic (plumage colour, chicken shape, meat quality, egg size and egg shell colour). Only characteristics mentioned in the first, second and third positions were ranked. The model described by Bett et al (2011a) was modified and used to estimate the indices (I_i). The indices were computed as:

where X_i is the percentage of respondents ranking trait j in the i^th rank and k is the sum of ranks for n number of traits. The relationship between the traits considered important by farmers, marketers and consumers were determined using Spearman’s non-parametric correlation coefficient (r) procedures. The r was estimated as:

where d is the difference between the ranks of corresponding pairs of two traits and n the number of observations. 

Results

Flock selection criteria

Table 1 presents the characteristics considered by farmers when selecting for birds to be the parents of future generations. Although farmers did not control breeding or keep records of their chicken due to scavenging nature of production, they selected them at household level using their own indigenous knowledge, experience and performance history of the chicken. Pullets from parents which produce many eggs per clutch, had big body size, disease resistant, good mothering ability and faster growth rate were selected. Selection for cockerels was based on big body size, faster growth rate and disease resistance. Other characteristics such as hatchability, broodiness, egg weight, plumage colour and fighting ability were also mentioned but ranked lower (Table 1). The mean ranking of the characteristics considered when selecting hens and cocks were not significantly different (P<0.05) between the counties except eggs weight and plumage colour. 

Indigenous chicken genotypes

The IC genotypes identified within the farmers flocks, their mean number per household and their mean farmers preference rankings are presented in Table 2.The genotypes identified included necked neck (Na), frizzled-feathered (Fr), crested-head (Cr), normal-feathered (na), shank-feathered (Sf), dwarf (Df) and giant (Gt). Normal feathered was the most dominant genotype raised by farmers in all the counties while Fr was the least. The genotypes mean ranking were not statistically different (P<0.05) but given a choice farmers preferred keeping na, Cr, Na and Gt compared to other genotypes. They perceived these genotypes to be high egg producers, have big body size, resistant to most diseases and parasites, have faster growth rate and posses good mothering ability. 

Traits of economic importance

The mean ranks of the traits of economic importance as perceived by farmers, marketers and consumers were found not to be significantly different (P<0.05) between the counties and the ranking trends were the same. The means ranks were therefore included in the model to compute indices. Table 3 shows the index ranking of traits perceived by farmers, marketers and consumers as of economic importance. The most important traits to the farmers were growth rate (GR), disease resistance (DR), egg number (EN), body size (BS) and fertility (FER). Although traits like mothering ability (MA), broodiness (BRD) and prolificacy (PRO) were not highly ranked, farmers considered them important. Marketers and consumers considered BS, egg size (ES) and egg shell colour (ESC) as the most important egg traits while IC was generally considered to posses quality meat generally.