Citation of this paper

Indigenous chicken meat and egg consumers are sensitive to sex, weight, tenderness, fat, meat part, plumage colour, age, egg size, egg yolk colour and price

K Ngeno

Animal Breeding and Genomics Group, Department of Animal Sciences, Egerton University, P O Box 536, 20115 Egerton, Kenya
aarapngeno@gmail.com

Abstract

Survey data obtained from consumers from three regions of Kenya were analysed to identify consumers’ preferences towards IC meat and eggs. Indigenous chicken consumers were further clustered based on their preferences. The analysis was carried out in three stages. The first was crosstab analysis to generate descriptive statistics. Secondly was a principal component analysis to extract principal components that explained the maximum variance within the data. Thirdly was to define consumer clusters in accordance with their preferences and behaviour using cluster analysis. Results indicated that consumers’ perceptions of the sex of the bird, body weight, tenderness, flavour, juiciness, salt content, meat colour, smell, fat and price influences their preferences and behaviour towards IC meat. Based on magnitude and sign, five meat preference clusters were identified; non-specific, fat, weight, sex-tenderness and meat-quality sensitive consumers. For eggs, two clusters were egg size and egg yolk colour, sensitive consumers. Through identification of the IC meat and egg preferences, producers and breeders can understand and respond to consumer preferences more efficiently and allow segmentation of the market.

Keywords: cluster analysis, principal component analysis

Introduction

Indigenous chicken (Gallus gallus domesticus) genetic resources are important for food, nutrition, income security, socio-cultural and spiritual purposes among the poor rural households in Africa. They account for 58.3% of the total chicken meat and 46.7% of the total number of eggs produced in Kenya (Ngeno 2011). This proportion will significantly rise due to rapid human population growth resulting in urbanization and decreasing agricultural land area, increasing income, eating habits and lifestyle (Bett 2012,Ngeno 2011).

Despite the ever-increasing demand for IC products, consumer needs based on IC characteristics and its products (meat and egg) remain unknown. Therefore, the breeders and producers are unaware of the market demands thus hindering exploitation of available market opportunities. This calls for an investigation of consumer attitudes and preferences towards IC meat and eggs. In other livestock species, evaluation and classification of consumer preferences in accordance with their individual requirements have been conducted (Sasaki and Mitsumoto 2004; Woon et al 2009). In beef cattle products, it has been established that consumers have a different preference in terms of marbling, freshness, taste and price (Sasaki and Mitsumoto 2004). The consumers’ preference information has been considered in the beef marketing and production strategies (Woon et al 2009). An understanding of the consumers’ preferences and behaviour with regard to IC meat and eggs is a prerequisite to addressing their needs. Additionally, recognition of the needs of their potential consumers enables IC producers and marketers to focus on those identified characteristics in their final IC products. The objective of this study was to determine consumers preferences towards IC meat and eggs and establish consumer segments as to their preferences.

Methodology

Data collection

The study was carried out in Western (Kakamega, 1°14′N, 35°0′E and Siaya, 0°14′N, 34°1′E counties), North Rift Valley (West Pokot, 1.23°N, 35.1°E and Turkana, 03°0′ 31″ N, 35° 3′E counties) and South Rift Valley (Bomet, 0°47′S, 35°2′E and Narok, 1°05′S, 35°5′E counties) regions of Kenya. Six divisions per county with the highest populations of IC in rural households were selected based on chicken population estimates of MOLD (2010) and Okeno (2012). Data was collected from both rural and urban (all the towns and market centres within the selected divisions) willing households. In overall, 550 respondents were interviewed from the three regions using structured questionnaires. A total of 203 (52 urban and 151 rural dwellers), 168 (46 urban and 122 rural dwellers) and 179 (31 urban and 148 rural dwellers) respondents in Western, North and South Rift Valley regions respectively were interviewed. A survey using a questionnaire was used to collect answers to the questions. The main features in the questionnaire were to obtain information on respondent’s age, gender, nature of work, household consumption preference based on perception of meat quality properties (tenderness, juiciness, fat amount, flavour, salt content, colour and smell), chicken appearance characteristics (body plumage, body weight, general body condition), chicken genotype, age of chicken, type of chicken preferred (cocks, hen, cockerels and pullets), preferences of IC meat parts and price. Information on preferences based on egg attributes (egg size, yolk colour and shell colour) and price data were also collected.

Statistical analysis

The analysis was carried out in three stages using SPSS (SPSS 2011). The first was crosstab analysis to generate descriptive statistics. A non-parametric Kruskal-Wallis test was used to evaluate whether the consumers’ preferences and behaviour towards IC meat and eggs vary with regions or counties. Secondly was a principal component analysis (PCA) to extract principal components that explained the maximal variance within the data. Thirdly was to define consumer clusters in accordance with their preferences using cluster analysis. The PCs scores with greater or equal to 1.00% Eigenvalues were subsequently examined using the cluster analysis to group the consumers. The PC scores in each cluster were statistically analyzed using PROC GLM of SAS (SAS 2009) to determine whether they were significantly different or not. The model fitted was:

Y_ijk = u + C_i + PC_i + ɛ _ijk

where Y_ijk is the dependent variables (i= CL1...CL5 for meat, ECL1 and ECL2 in eggs), μ overall population mean, Ci county effects (i= Siaya, Kakamega, Bomet, Narok, West Pokot and Turkana), PC_i PCs score effects (i= PC1...PC5) and ijk random residual effect.

Results

Descriptive statistics

The frequency distributions of age, gender (male or female) and nature of work are presented in Table 1. Males and farmers constituted the largest proportion of the respondents.

Table 1. Characteristics of the respondents (N = 550)
Variable	Category	Number (%)

Sex	Female	173 (31.5)
Sex	Male	377 (68.5)

Age	0-10	5 (0.901)
	11 to 20	36 (6.60)
	21 to 30	184 (33.4)
	31 to 40	143 (26.0)
	41 to 50	109 (19.9)
	51 to 60	56 (10.1)
	61 to 70	11 (2.00)
	above 70	6 (1.10)

Nature of work	Farming	222 (40.4)
	Agricultural casual labour	13 (2.40)
	Non-agricultural casual labour	23 (4.20)
	Self-employment	123 (22.4)
	Household employee	10 (1.80)
	Student (School)	21 (3.80)
	Formal employment	115(20.9)
	Other (e.g. herbalists)	23 (4.20)

Principal component analysis

Principal component analysis (PCA) indicated that 19 principal components (PC) affect consumers’ meat preferences and their eigenvalues are shown in Table 2. Using the output from iteration, there were seven eigenvalues greater or equal to 1.0% with each accounting for over 5.29% of the total variance. The seven components solution explained 77.8% of the total variance. The PC1 values explained over 28% in consumer meat attitudes for all the questions. Principal component 2, 3, 4 and 5 explained 14.8%, 10.3%, 7.21% and 6.22% of the total variance respectively. From PC8 to PC19, PC had low variance values ranging from 0.00 to 5.06% which were difficult to clearly define their meanings hence neglected. Results for eggs indicated two eigenvalues greater than 1.0% (Table 2). The latent root criterion for the number of factors to derive for eggs indicated two components to be extracted for the variables. The two components for eggs explain 70.8% of the total variance in the variables. Egg PC1 explained 37.5% of total variance.

Table 2. Eigenvalues and variance obtained from principal components (PC) for meat and egg
	Principal component	Total	% of variance	Cumulative %

Meat	1	5.34	28.1	28.1
	2	2.81	14.8	42.8
	3	1.96	10.3	53.2
	4	1.37	7.21	60.4
	5	1.18	6.22	66.6
	6	1.13	5.96	72.6
	7	1.00	5.29	77.8
	8	0.96	5.06	82.9
	9	0.72	3.72	86.6
	10	0.59	3.08	89.7
	11	0.47	2.44	92.1
	12	0.35	1.83	94.0
	13	0.32	1.68	95.6
	14	0.26	1.29	96.9
	15	0.19	1.01	97.9
	16	0.16	0.85	98.8
	17	0.13	0.70	99.5
	18	0.09	0.48	100
	19	0.00	0.00	100

Egg	1	1.13	37.5	37.5
	2	1.00	33.3	70.8
	3	0.88	29.2	100

Table 3 shows relative contributions of the eigenvectors for the PCs with eigenvalues greater than or equal to one for each question. For meat, the majority of eigenvectors were positive with some few being negative in PC1. Juiciness, flavour, salt content, meat colour and meat smell were scored positively high (0.71 to 0.85) for Eigenvectors relative to others. The PC for hens, pullets, plumage colour, genotype, the age of birds and weight were scored negatively low to medium (-0.03 to -0.42). In PC2, cock preference garnered highly positive scores, with eigenvector of 0.82 and pullets were ranked negatively high (-0.94). With regards to PC3, hen preference (0.82) was strongly positive whereas preference of cockerels scored -0.84 which was negatively high. In PC4, tenderness had strong positive eigenvector. Body weight and meat parts had high positive eigenvector in PC5 and it indicated the requirement for heavy meat parts with less concern on body plumage (-0.71). Results in PC6 showed that price was high and positively rated with an eigenvector of 0.75 whereas the age of chicken had a strong negative score of -0.80. The PC6 eigenvector characterized the concern of price and disregarding the age of the birds. Meat fat eigenvector of 0.88 in PC7 indicated attention given to the levels of fat within the meat.

All egg variables in PC1 had positive eigenvectrs (Table 3). Eggshell and yolk colour preferences were strongly positive in PC1. Principal component results showed both negative (weak) and positive (strong) eigenvectors. Egg size and egg yolk colour preference were positive (0.99) and negative (-0.09) respectively in PC2.

Table 3. Eigenvectors of the principal components (PC) with greater or equal to 1% eigenvalues
	Preference by consumers	PC1	PC2	PC3	PC4	PC5	PC6	PC7

Meat	1. Cock meat	0.11	0.82	0.16	0.30	0.29	-0.03	-0.07
	2. Hen meat	-0.03	0.05	0.83	0.08	-0.28	-0.02	0.18
	3. Pullets meat	-0.20	-0.95	-0.14	-0.14	0.00	0.06	0.01
	4. Cockerels meat	0.11	-0.09	-0.85	-0.33	-0.13	0.01	-0.08
	5. Plumage colour	-0.37	-0.27	-0.02	0.08	-0.71	0.00	0.06
	6. Genotype	-0.42	-0.12	-0.12	-0.67	-0.03	0.17	0.07
	7. Body size	0.21	0.22	0.66	-0.26	0.07	-0.03	-0.29
	8. Age	-0.05	0.30	0.03	0.09	0.06	-0.81	-0.05
	9. Weight	-0.07	0.05	0.16	0.58	0.67	0.16	-0.17
	10. General body condition (stature, healthiness etc.)	0.53	-0.21	-0.30	0.35	-0.24	0.41	0.30
	11. Meat parts	0.17	-0.01	-0.25	0.04	0.71	-0.30	0.24
	12. Meat tenderness	0.15	0.28	0.04	0.79	0.02	0.03	0.00
	13. Meat Juiciness	0.71	0.24	0.07	0.33	0.20	0.13	-0.18
	14. Meat fat amount	0.10	-0.04	0.09	-0.09	0.04	0.10	0.88
	15. Meat flavour	0.85	-0.10	0.11	0.04	-0.14	-0.12	-0.09
	16. Meat salt content	0.83	0.19	0.04	0.09	0.30	0.27	0.12
	17. Meat colour	0.75	0.19	-0.09	-0.01	0.19	0.26	0.18
	18. Meat smell	0.73	0.19	-0.12	0.29	0.28	0.12	0.22
	19. Price of IC Meat	0.27	0.24	0.01	0.06	-0.04	0.75	0.07

Egg	1. Eggshell colour	0.76	0.01
	2 Egg size	0.07	0.99
	3. Egg yolk colour	0.76	-0.09
PC1=Principal component 1; PC2= Principal component 2; PC3= Principal component 3; PC4=Principal component 4; PC5=Principal component 5; PC6=Principal component 6; PC7= Principal component 7.

Cluster analysis

Table 4 presents the mean values of PC scores indicating the relative contribution of each question to the PC1-PC7 for IC meat. Respondents were classified into 5 clusters (CL1 to CL5). Results in Cluster 1 (CL1) revealed negatives values except for PC3, PC5, and PC6. Some PCs in CL1 were significantly different (P < 0.05). Consumers in cluster 2 (CL2) had significantly and relatively high positive values in PC7. Principal component scores in cluster 3 (CL3) were positive except in PC6. The PC5 in CL3 had relatively high positive value but was not significantly different (P > 0.05) from PC4. Cluster 4 (CL4) had strongly negative and positive values in PC3 and PC4 respectively. Cluster 5 (CL5) members had positive and significant values in PC1 and PC2. The CL5 negative values for PC3 to PC7 were not significantly different (P > 0.05).

Table 4. Least square means and standard errors of principal components scores and clusters for indigenous chicken meat
Cluster	N	PC1	PC2	PC3	PC4	PC5	PC6	PC7

	Positive		Cock	Hen	Tenderness	Weight, Meat part	Price	Fat
	Negative		Pullet	Cockerel	Tenderness	Plumage colour	Age	Fat

CL1 (P<0.001)	210	-0.40^a±0.13	-0.36^a±0.10	0.33^b±0.10	-0.34^a±0.13	0.05^b±0.02	0.10^b±0.05	-0.28^a±0.11
CL2 (P<0.001)	97	0.15^a±0.04	-0.66^b±0.08	0.99^a±0.10	-0.95^b±0.09	0.30^a±0.05	0.22^a±0.06	6.29^c±0.13
CL3 (P<0.001)	86	0.01^a±0.01	0.43^a±0.01	0.12^a±0.06	2.12^ab±0.09	3.43^b±0.08	-0.71^a±0.05	0.17^a±0.06
CL4 (P<0.001)	73	-0.34^a±0.19	0.23^b±0.14	-0.94^a±0.23	0.94^c±0.21	-0.43^a±0.11	0.51^c±0.20	0.29^b±0.10
CL5 (P<0.002)	84	1.26^a±0.09	0.69^ab±0. 09	-0.15^c±0. 09	-0.21^c±0.08	-0.36^c±0. 09	-0.58^c±0.09	-0.09^c±0.07
^abc Means in a row with one or more letters in common are not significantly different (P > 0.05); N, number of respondents in each cluster. PC1 to PC7 are principal component one to seven; CL1 to CL5, cluster one to five. Clusters are based on seven principal components scores.

Egg clusters (ECL1 and ECL2) obtained by the cluster analysis are presented in Table 4. Cluster 1 (ECL1) indicated significant negative value in PC1 and positive scores in PC2. In Cluster 2 (ECL2), PC1 and PC2 had positive and negative values respectively.

Table 5. Least square means standard errors of principal components scores (PC1 and PC2) for indigenous chicken egg clusters (ECL1 and ECL2)
Cluster	N	PC1	PC2

Positive Negative		Eggshell and yolk colour	Egg size Egg yolk colour

ECL1(P<0.001)	313	-0.41^a±0.10	1.29^b±0.11
ECL2(P<0.001)	237	0.14^a±0. 09	-0.45^b±0. 08
^ab Means in a row with one or more letters in common are not significantly different (P≥0.05). N, number of respondents in each cluster. ECL1-ECL2, cluster one and two.

Discussion

Unique organoleptic features are often used by consumers to evaluate the quality of chicken products and serve as a references during the selection of meat and eggs. Several variables such as eggshell colour, size and shape (Bett et al 2017) influence consumer preferences towards IC products. The objective of the study was to establish consumers’ preferences towards IC meat and eggs. Principal component analysis and cluster analysis were employed to analyze consumers' preference attributes.

Clustering of consumers based on their preference(s) using principal component analysis

Results from PCA revealed requirements differences among the consumers. The PCA suggested that sex of the bird (male or female), body weight, tenderness, flavour, juiciness, salt content, meat colour, smell, fat and price were the most important sources of variables influencing the preferences of IC meat consumers. Other components and attributes, including genotype, the age of birds, plumage colour, meat parts, and general body condition were minor factors with regard to consumer selection.

Based on the magnitude and sign of the eigenvectors values for the PCs, the meanings of the PC1 to PC7 could be interpreted as follows; PC1 is an indicator of the requirement for all the IC attributes, PC2 is an indicator of the requirement for cock (positive values) and not pullets (negative values), PC3 is an indicator of hen preference (positive values) and dislike of cockerels (negative values), PC4 is an indicator of the requirement for tenderness (positive values), PC5 is an indicator of the requirement for weight and meat parts (positive values) and are less concern with body plumage colours (negative values), PC6 is an indicator of the requirement for price (positive values) with age of birds, not a factor (negative values). The PC7 can be used as an indicator of the requirement for meat fat (positive values).

Clustering of consumers based on their preference(s) using cluster analysis

From the cluster analysis results, cluster 1 (CL1) consumers did not demand a lot of IC meat quality attributes. They prefer meat from hens and not cocks. Conversely, custoners from CL1 were not concern edwith tenderness and fat as indicated by the negative value in PC1, PC2, PC3 and PC7 respectively. Respondents in CL1 also showed sensitivity to price, weight and meat parts. Therefore consumers in this CL1 were classified as non-specific consumers. Consumers in CL2 paid attention to the amount of fat in IC meat and can be termed as fat-sensitive consumers. Principal component scores in CL3 were highly positive for PC5 which indicated their first choice for weight and meat parts. The CL3 respondents can be categorized as weight-sensitive consumers. The CL4 members had equal strong negative and positive values in PC3 and PC4 respectively. Based on magnitude and sign of PCs in CL4, CL4 group gives priority to the sex of the bird (hen) and tenderness. Members of CL4 can be termed as sex-tenderness sensitive consumers. Respondents under CL5 require IC meat qualities including juiciness, flavour, salt content, colour and smell. The CL5 consumers can be termed as meat quality-sensitive consumers.

Latent root criterion for a number of factors indicated that there were two components (PC1 and PC2) to be extracted for egg preference variables. Principal component 1 is an indicator of overall egg attributes of eggshell, yolk colour and size. The PC2 is an indicator of preference for egg size (positive values) or eggshell and yolk colour (negative values). Clustering results showed that ECL1 respondents do not consider the eggshell and yolk colour instead they are concerned with egg size as shown by strong positive PC2 values. Therefore, ECL1 consumers can be clustered as egg size-sensitive consumers. Cluster 2 consumers were not concerned with egg size instead they had a concern with egg yolk colour and can be termed as yolk colour-sensitive consumers. The results of egg yolk sensitivity indicated that yolk colour and their variability are consumer’s parameters for evaluating egg quality. Therefore, producers in the studied regions can adjust the feed ingredients to produce eggs with a yolk that match consumer preferences.

Conclusions

Meat consumers can be grouped into 5 clusters; non-specific, fat, weight, sex-tenderness and meat-quality sensitive consumers.
Two clusters of egg consumers were egg size and egg yolk colour.
In order to increase IC meat and egg consumption, consumer preferences and attributes identified in this study need to be acknowledged and addressed.
Through identification of the IC meat and egg preferences, marketers, producers and breeders can understand, and respond to consumer preferences more efficiently and encourage segmentation of the market.

Acknowledgments

The authors are grateful to the Indigenous Chicken Improvement Programme funded by the European Union through the African Union.

References

Bett H 2012 Economic analysis of indigenous chicken genetic resources in Kenya. PhD Thesis, Humboldt Universität zu Berlin, Berlin, Germany.

Bett E K, Kabuage L W and Ndenga C 2017 Analysis of Consumer Preference in Product Attributes: A Case of Indigenous Chicken Eggs in Kenya. Journal of Economics and Sustainable Development 8 145-151.

MOLD 2010 Animal Production Division Annual Report. Ministry of Livestock Development Nairobi Kenya pp 52.

Ngeno K 2011 Genetic analysis of growth patterns in different ecotypes of indigenous chicken populations in Kenya. MSc. Thesis Egerton University Kenya.

Okeno T O 2012 Production Systems Breeding Objectives and Selection Schemes for Indigenous Chicken Genetic Resources in Kenya. PhD Dissertation Humboldt-Universität zu Berlin Germany.

SPSS 2011 Statistical Package for Social Sciences. SPSS 20.0 for Windows. Chicago SPSS Inc.

SAS 2009 Base SAS ® 9.2 Procedures Guide. Cary NC: SAS Institute Inc.

Sasaki K and Mitsumoto M 2004 Questionnaire-based study on consumer requirements for beef quality in Japan. Animal Science Journal 75 369–376.

Woon Y O H, Ji W L, Chong E L, Moon S K O and Jae H J 2009 Analysis of consumers’ preferences and behaviour with regard to horse meat using a structured survey questionnaire. Animal Science Journal 80: 716–723.

Received 6 May 2017; Accepted 16 November 2017; Published 1 December 2017

Go to top