Breeding strategies for improving performance of Kuchi chicken ecotype of Tanzania for production under village conditions
J Lwelamira, G C Kifaro* and P Gwakisa**
Institute of Rural Development Planning, P.O. Box 138, Dodoma, Tanzania;
jlwelamira@yahoo.com
*Department of Animal Science and Production, Sokoine University of Agriculture, P.O. Box 3004, Morogoro, Tanzania;
**Department of Veterinary Microbiology and Parasitology, Sokoine University of Agriculture, P.O. Box 3019, Morogoro, Tanzania
Abstract
A study was carried out to evaluate various breeding
scenarios geared at improving performance of Kuchi chicken ecotype of
Tanzania, a type of native chicken found in drier area of north-west part of the
country, for production under extensive (village) management conditions. The
purpose of these breeding scenarios was mainly to improve body weight at 16
weeks of age (Bwt16). In some breeding scenarios, primary antibody response
against Newcastle disease virus vaccine (Ab) was also considered.
Depending on a breeding scenario, results from the present
study indicate that it would take approximately 5 to 10 generations of
selection, which corresponds to around 3 to 6 years of selection for improving
Bwt16 either singly or together with Ab from their current population mean of
974g and 4.8 (HIlog2) to the desired population mean of 1300g and
6(HIlog2), respectively (i.e. improvement by 34 and 25%,
respectively). Depending on gain per generation, number of years required to
attain the desired mean, expected fitness under village conditions and costs of
breeding, some breeding scenarios were recommended.
Key words: body weight, desired gain, extensive management, Newcastle disease, selectionIntroduction
Animal protein intake in developing countries including
Tanzania has been very low compared to the recommended level (Pedersen 2002;
Nielsen et al 2003). The situation is further aggravated by high incidences of
poverty in these countries. Due to their short generation intervals, chickens
have high potential to offset this problem of low protein intake, and they can
as well act as source of income and hence reducing poverty. Local chickens
dominate the chicken industry in developing countries. It has been estimated
that more than 70% of rural households in developing countries keep local
chickens (Aini 1990; MOAC 1997). These local chickens are mostly kept under free
range management system (i.e. extensive management) in villages. According to
Melewas (1989) and Roetter et al (2007), more than 85% of people in developing
countries reside in rural areas. Since majority of people in developing
countries live in rural areas (villages), in which most of them are keeping
local chickens, it is obvious that putting an emphasis on local chickens would
have an immediate positive impact on animal protein intake and income by most of
the people in these countries. Low genetic potential for production traits and
frequent outbreak of diseases specifically Newcastle disease has been noted in a
significant number of studies to be among of the major factors limiting
productivity of the local chickens in the Tropics both under intensive and
extensive management systems (Katule and Mgheni 1990; Yongolo 1996; Alexander
2001; Msoffe 2003). Crossbreeding programs with specialized meat type or egg
type chickens has been shown by several workers to improve their productivity
significantly (Ali et al 2000; Pedersen 2002; Theerachai et al 2003;
Segura-Correa et al 2004). However, these crossbreeding programs
are threatened by the current global move on conservation of indigenous genetic
resources which campaigns against genetic dilution of indigenous genetic
resources (Msoffe 2003; Kosgey 2004). This call for a
need to look for an alternative approach for genetic improvement of local
chickens. In this regard, genetic improvement through selection within local
chickens could be a good option. A study conducted by Lwelamira (2007) both
under intensive and extensive management (i.e village conditions) in two
Tanzania local chicken ecotypes viz. Kuchi and Tanzania Medium (Medium)
have shown existence of significant additive genetic variation in various traits
in these ecotypes and hence expecting adequate response to selection. Based on
their performances, the study further revealed Kuchi ecotype to be good
starting material for improving for meat production and Medium ecotype
for egg production. Therefore, this study was carried out to evaluate various
selection breeding strategies for improving performance of Kuchi ecotype
for management under village (extensive) conditions.
Materials and methods
Comparison of various breeding scenarios/ schemes
Various breeding scenarios were designed and compared through
simulation. These scenarios were compared in terms of gain per generation,
number of generations required to achieve the desired performances and
correlated responses in some breeding scenarios (i.e. those involved selection
under intensive management and selected stock expected to perform under
extensive management). The emphasis in this study was on improving body weight
at 16 weeks of age (Bwt16). In some breeding scenarios, primary antibody
response against Newcastle disease virus vaccine (Ab) (i.e. Indicator for
immunocompetence to Newcastle disease) was also considered. Egg production and
related traits were not considered in this study because no data were available
for these traits in this ecotype under extensive management system. For the
scenarios which involved single trait selection based on own performance,
response per generation were calculated according to Falconer and Mckay (1996)
using equation 1. Correlated responses On-farm (extensive management) following
single trait selection On-station (Intensive management) were calculated using
equation 2 based on Falconer and Mckay (1996). Furthermore, since relative
economic weights for the traits considered were not available, those scenarios
where more than one trait were considered simultaneously (i.e Bwt16 and Ab),
desired gain selection indices were used. The desired gain indices were
constructed according to Yamada et al (1975) as applied in subsequent studies
(Gill and Verma 1983; Hazary et al 1998; Nishida et al 2001; Noda et al 2002;
Kaushik and Khanna 2003). Therefore, gain per generation and number of
generations required to achieve the desired performances in multi-trait
selection were computed as per Yamada et al (1975).
(Equation 1)
Where:
R = response to selection
per generation
i = intensity of selection,
h2 = heritability estimate,
σP = phenotypic standard deviation.
i = intensity of selection,
h2 = heritability estimate,
σP = phenotypic standard deviation.
Heritabilities and phenotypic standard deviations were
obtained from the study by Lwelamira (2007) for this chicken ecotype (Table 1).
Table 1.
Genetic and
phenotypic parameters for the studied traits in Kuchi ecotype
|
||||||
Management system
|
Trait
|
Unit
|
σp
|
h2
|
Extensive
Management*
|
|
Ab
|
Bwt16
|
|||||
Extensive management
|
Ab
|
HIlog2
|
1.208
|
0.22
|
.
|
-2.59
|
Bwt16
|
Gram
|
125.09
|
0.37
|
-12.08
|
.
|
|
Intensive management
|
Bwt16
|
Gram
|
109.42
|
0.44
|
|
rA= 0.75
|
Source; Lwelamira
(2007
Ab = Antibody response; Bwt16 = Body weight at 16 weeks of age; σp= Phenotypic standard deviation; h2 = Heritability; *Above diagonal is the genetic covariance and below diagonal is the phenotypic covariance; rA= genetic correlation for Bwt16 in two environments (i.e. extensive and intensive management). |
(Equation 2)
Where,
CR2= Correlated response in environment 2
following selection in environment 1;
rA= genetic correlation between the same trait
measured in two environments (i.e. environment 1 and 2) viz. intensive and
extensive management system;
σA2 = additive genetic standard deviation for a
trait in environment 2;
σA1 = additive genetic standard deviation for a
trait in environment 1;
R1= response to selection for a trait in
environment 1.
Genetic parameters for the above formula were derived from
parameters for this ecotype in Table 1.
Use of index I in selection usually involves calculation of
weighting factors b for traits to be used as selection criteria (equation
3).
I = b′ X (Equation 3)
Where:
I= Selection index;
b = n x 1 vector of weighting factors;
X = n x 1 vector of source of information, usually phenotypic measurements on candidate for selection or its relatives.
b = n x 1 vector of weighting factors;
X = n x 1 vector of source of information, usually phenotypic measurements on candidate for selection or its relatives.
In the present study, information source (X) was
individual own performance.
Based on the Yamada index, b in the present study was
calculated as:
b = (G′ R) –1Q (Equation 4)
Where:
G = n x m genetic variance-covariance matrix of the traits
used as selection criteria and traits in the breeding objectives;
Q = m x 1 vector of intended genetic changes for m traits assigned by breeder;
R = n x n matrix of Wrights coefficient of relationship.
Q = m x 1 vector of intended genetic changes for m traits assigned by breeder;
R = n x n matrix of Wrights coefficient of relationship.
Genetic variance-covariances were derived from genetic
parameters estimated for this ecotype in a study by Lwelamira (2007) (Table 1).
Desired genetic changes for various traits (i.e. Q) were
calculated as the difference between desired and observed means (Yamada et al
1975; Kaushik and Khanna 2003; Suzuki et al 2005).
Intended performances and hence desired genetic changes were
chosen in such a way that they are within the capacity of the population as
shown by performance of some individuals in an environment under consideration
(Lwelamira 2007). Furthermore, the chosen intended performances were close to
the performance of the crosses between local and exotic birds (Ali et al 2000;
Theerachai et al 2003; Lwelamira and Katule 2004). In addition, the chosen
intended performance for body weight also depended on weight at which a chicken
can be marketed (Pedersen 2002; Theerachai et al 2003; Acamovic et al 2005). By
considering body weight much further, assessed from maximum weights possible
under different ages (i.e. capacity of a population), since possibility of
attaining market weight (i.e. at least 1kg) earlier than 16 weeks of age through
selection under extensive management conditions was non-existent (Lwelamira
2007). Moreover, as heritability estimates for body weights were relatively
higher in later ages compared to earlier ages (Lwelamira 2007). Therefore, body
weight at 16 weeks of age (Bwt16) was chosen as the target body weight.
Furthermore, based on the capacity of a population (Kuchi), body weight
at 16 weeks of age was also chosen as it is possible to target much higher
weights in a breeding objective (i.e. above 1kg under extensive management
conditions, and hence chickens would be fetching good prices) compared to body
weights at 8 and 12 weeks of age, and at earlier age than at 20 weeks of age
(Lwelamira 2007).
Based on Yamada index, expected genetic gains per generation
were calculated using equation 5.
(Equation 5)
Where:
iI =
intensity of selection based on the index;
σI =
standard deviation of the index calculated as
shown in equation 6;
∆G = m x 1 vector of
genetic gains per generation in m traits;
G = n x m genetic variance-covariance matrix.
(Equation
6)
Where P is an n x n phenotypic variance-covariance matrix.
Again P was derived from parameters in Table 1.
The number of generations q required to attain the
pre-defined breeding objectives in multi-trait selection was calculated using
equation 7 (Yamada et al 1975; Kaushik and Khanna 2003). All matrix equations
were solved using Interactive Matrix Language (IML) procedures of SAS (2000).
(Equation 7)
Population structure and derivation of selection intensities
In breeding scenarios which involved selection under
extensive (village) conditions it was assumed that 100 farmers (households) are
involved in the program, and each household keeps 2 to 3 breeding females.
Furthermore, it was assumed that, in total in each generation 40 best males and
240 best females are selected, and each cock saves 2-3 households (i.e. a mating
ratio of one male to 4 to 9 females). In the study by Lwelamira (2007) for this
ecotype it was found that the average number of progeny per dam obtained under
field condition to be around 6. Therefore, 240 hens would be expected to produce
1440 progeny in total with 720 birds of each sex. Taking into account the
mortality rate/loss of about 33% by the time birds are selected and mated (i.e.
average loss of 2 chicks per hen), then the number of birds available for
selection would be 240 x 4 = 960 (i.e. 480 chicks of each sex). The chosen
percent mortality/loss was based on the obtained percent mortality/loss up to 12
weeks of age of about 30% under extensive management for this ecotype (Lwelamira
2007), and further assuming that once a chick reachies the age of 12 weeks
stands a high chance to survive to maturity and breed when regular control of
outbreak of major diseases under field conditions specifically Newcastle disease
are in place. Therefore, after accounting for mortalities/loss, selection
of 40 best cocks and 240 best females in each generation would result into
proportions selected of about 8.3% and 50% for males and female, respectively
(i.e. average selection intensity of 1.4). Proportions selected (%) were
transformed into selection intensity (i) using a Table by Falconer and Mackay,
(1996).
In those breeding scenarios which involved selection under
intensive management (On-station) in some stage, taking into account the
reproductive performance, survival of the breeding stock under intensive
management for this ecotype (Lwelamira 2007) and the recommended cock:hen ratio,
proportion selection under this management system was assumed to be 3.7
and 22.2 % for males and females, respectively (i.e. average selection intensity
of 1.8). This average selection intensity is within the range of that of around
1.5 to 2 mostly used in commercial chicken breeding (Ameli et al 1991; Su et al
1997).
Results and discussion
Five breeding scenarios were considered in this study (Table
2).
Table 2.
Breeding scenarios
for improving performance of Kuchi on-farm (extensive
management)
|
|
Scenario
|
Description
|
1
|
Selection for improving
body weight at 16 weeks of age done on-station and the improved
stock (both males and females) is taken to the field (on-farm) after
the end of selection.
|
2
|
Selection for improving
body weight at 16 weeks of age done on-station and only improved
males are taken to the field in each generation.
|
3
|
Selection is done both
in males and females under on-farm conditions for improving both
antibody response (humoral immune response) against NDV vaccine and
body weight at 16 weeks of age.
|
4
|
Selection is done both
in males and females under on-farm conditions for improving body
weight at 16 weeks of age
|
5
|
Selection is done only
in males under on-farm conditions for improving body weight at 16
weeks of age
|
Due to the presence of genotype by environment interactions
observed for this ecotype for Bwt16 in a study by Lwelamira (2007) (i.e. rA=
0.75), some breeding scenarios (1 and 2) were designed to predict response on
farm (extensive management) as a result of selection under intensive management.
Since apart from genetic correlation between the same trait measured in the two
environments, no information were available regarding corresponding phenotypic
correlations, together with both genetic and phenotypic correlation between
different traits (i.e. trait 1 and trait 2) measured two different environments
due to data structure (Lwelamira 2007). Therefore, breeding scenarios which
involve computation of correlated responses under extensive management following
index selection for Bwt16 and Ab under intensive management was not considered.
Observed means, desired means and hence desired changes are indicated in Table
3.
Table 3. Observed
and desired mean and desired gain for Kuchi under extensive
management system
|
|||||
Trait
|
Unit
|
Observed mean*
|
Desired mean
|
Desired change
|
Percentage change, %
|
Ab
|
HI (log2)
|
4.8
|
6
|
1.2
|
25
|
Bwt16
|
Gram
|
974
|
1300
|
326
|
34
|
*Source; Lwelamira
(2007)
Ab= Antibody response (humoral immune response), Bwt16= body weight at 16 weeks of age |
Results from Table 4 indicate that on-station single trait
selection for body weight at 16 weeks of age predicted to yield an on-station
response of around 87g per generation would be associated with a correlated
response of about 68g per generation under extensive management (scenario 1).
Table 4.
Response to
selection for body weight at 16 weeks of age on-farm for Kuchi
(gain per generation in trait units)
|
||
Scenario
|
Bwt16, g
|
Generations
|
1
|
68.14
|
4.78
|
2
|
34.07
|
9.57
|
3
|
53.42
|
6.10
|
4
|
64.80
|
5.03
|
5
|
43.04
|
7.57
|
This is close to the response of 65g per generation obtained
for single trait selection for body weight at 16 weeks of age carried out in
both males and females under on-farm/extensive conditions (scenario 4), but
higher that the response of 53g per generation obtained when apart from body
weight, antibody response was also considered for selection under on-farm
conditions (Scenario 3) (i.e. Index selection for Bwt16 and Ab under extensive
management). Selection index (I) for this breeding scenario is shown in equation
8. Furthermore, in a breeding scenario where only improved cocks from the
on-station (intensive management i.e. elite nucleus) are taken to the field as
breeding males as suggested by Horst (1981), and Mukherjee (1990) (scenario 2),
or selection for improving body weight at 16 weeks of age under extensive
conditions is done only on males (scenario 5), it was predicted to result into
appreciably lower genetic responses per generation (i.e. 34 and 43g,
respectively) than those obtained in all other breeding scenarios investigated.
(Equation 8)
Considering the average body weight over both sexes of 974g
at 16 week of age under extensive management obtained in a study by Lwelamira
(2007) for this ecotype, breeding scenarios 1, 2, 3, 4 and 5 would be expected
to attain the desired average body weight of 1300g under extensive management
(Table 3) after 4.78, 9.57, 6.10, 5.03, and 7.57 generations of selection,
respectively (Table 4). By assuming mating, egg laying and incubation and
hatching by the hen under extensive conditions takes approximately 2 months
(i.e. 1 month for mating and egg laying, and 21 days for incubation and
hatching), and age at sexual maturity under this management system is reached at
around 8 months of age (Sonaiya 1992; Gunaratne et al 1992; Mwalusanya et al
2002), which would lead to a generation interval of around 10 months for
breeding scenario 3, 4 and 5. Further assuming a generation interval of about 8
months (Lwelamira 2007; Su et al 1997) (i.e. 2 months for mating, egg
collection, and hatching + 6 months for attaining sexual maturity under
intensive system) for breeding scenario 1 and 2, it would require 3.2, 6.4, 5.1,
4.2, and 6.3 years of selection for breeding scenario 1, 2, 3, 4, and 5,
respectively for achieving the desired gain.
Breeding scenario 1 and 2 are more convenient (cheap and easy
to institute) compared to the other breeding scenarios, however, the problem of
these breeding scenarios are reduced fitness under extensive management, and to
a large extent in breeding scenario 1 (Horst 1981; Mukherjee 1990 1992; Demeke
2003). Doing selection under farm (extensive) conditions has the advantage of
minimizing a problem of fitness under this management system i.e. loss of
scavenging habit (scenarios 3, 4, and 5). However, the success of breeding
scenarios under extensive conditions requires close supervision on record
keeping under farm conditions which could be too involving and expensive. The
problem could be more serious in breeding scenario 3 and 4 where both males and
females are selected compared to breeding scenario 5 where only males are
selected. Despite of being too involving and expensive, results from Table 4
also indicate breeding scenario 3 and 4 to have reasonably high genetic gain per
generation. Therefore, in situation where resources are available for carrying
out selection program under on-farm conditions, breeding scenario 3 could be
recommended as it improves both body weight and humoral immune response to ND
which is usually a serious problem under field conditions (Yongolo 1996;
Alexander 2001; Ilango et al 2005; Otim 2005) at the expense of one more year of
selection relative to breeding scenario 4. In a situation where resources are a
problem, breeding scenario 5 or 2 could be advised depending on the magnitude of
the problem. However, to achieve the desired gain in body weight, approximately
one more year of selection would be required in both breeding scenarios relative
to breeding scenario 3. If the problem of resources is not very severe, breeding
scenario 5 could be a selection scheme of choice as it is expected to be
associated with more fitness under farm conditions compared to breeding scenario
2.
Conclusion
Approximately 5 to 10 generations of selection, which
corresponds to around 3 to 6 years of selection would be required for improving
Bwt16 either singly or together with Ab from their current population mean of
974g and 4.8 (HIlog2) to the desired population mean of 1300g and
6(HIlog2), respectively. The number of years of selection required to
attain the desired gains for the studied ecotype indicates that it won’t take
too long to reach the target (desired performance) if selection breeding
programs would be initiated. This suggests that selection breeding programs for
improving performance of this ecotype for production under village conditions
should be initiated. In addition, for these selection breeding programs to be
successful, apart from gain per generation and number of years required to
attain the desired mean, the choice of a breeding scenario should also consider
expected fitness under village conditions and costs of breeding.
Acknowledgement
The authors are very grateful for the financial support from
Production and Health of Smallholder Livestock (PHSL) project funded by DANIDA
which sponsored the senior author in his PhD. Programme.
References
Acamovic T, Sinurat A, Natarajan A, Anitha K,
Chandrasekaran D, Shindey D, Sparks N, Oduguwa O, Mupeta B and Kitaly A 2005
Poultry. In: Livestock and Wealth Creation. Improving the husbandry of animals
kept by resource-poor people in developing countries. (Edited by Owen E, Kitaly
A, Jayasuriya N and Smith T). Nottingham University press, UK, pp 304- 324
Aini I 1990 Indigenous
chicken production in South-east Asia. World’s Poultry Science Journal 46: 51-57
Alexander D J 2001 Newcastle
disease. British Poultry Science 42 (1): 5-22
Ali K O, Katule A M and Syrstad O 2000
Genotype x environmental interaction in growing chickens: comparison of four
genetic groups on two rearing systems under tropical conditions. Acta
agriculturæ Scandinavica 50: 65-71
Ameli H, Flock D K and Glodek P 1991
Cumulative inbreeding in commercial White Leghorn lines under long-term
reciprocal recurrent selection. British Poultry Science 32: 439- 449.
Demeke S 2003 Growth
performance and survival of Local and White Leghorn chickens under scavenging
and intensive systems of management in Ethiopia. Livestock Research for Rural
Development. 15 (11):
http://www.lrrd.org/lrrd15/11/deme1511.htm Site visited on
26/3/2005.
Falconer D S and Mckay T F C 1996
Introduction to quantitative genetics. 4th
edition. Longman Group, Essex, UK. 463pp.
Gill H S and Verma S K 1983
Construction of selection index in poultry ignoring relative economic values.
Indian Journal of Animal Sciences 53 (10): 1110-1112
Gunaratne S, Chandrasir A, Hemalatha W and Roberts J 1992
The productivity and nutrition of village chickens in Sri-lanka. In: Newcastle
disease in village chickens (edited by Spradbrow PB). ACIAR proceeding.
Canberra, Australian Centre for International Agricultural Research 39: 144- 148
Hazary R C, Johri D C, Kataria M C, Sharma D and Singh D P
1998 Evaluation of efficiency of multisource weight-free selection index
for desired gains in egg type chicken. Indian Journal of Animal Sciences 68(7):
662-666
Horst P 1981 Constraints on
the genetic improvement of non-ruminants in the tropics. Animal Research and
Development 14: 120-135
Illango J, Olaho-Mukani W, Mukiibi-Muka G, Abila P P and
Etoori A 2005 Immunogenecity of a locally produced
Newcastle disease I-2 thermostable vaccine in chickens in Uganda. Tropical
Animal Heath and Production 37: 25-31
Katule A M and Mgheni M 1990
Performance of crosses between exotic and local Tanzania chickens. In:
Proceedings of the 4th World Congress on Genetics Applied to
Livestock Production. Volume XVI. 23- 27 July
1990, Edinburgh, pp 62- 64
Kaushik R and Khanna A S 2003
Efficiency of different selection indices for desired gain in reproduction and
production traits in Hariana Cattle. Asian-Australasian Journal of Animal
Sciences 16(6):789-793
Kosgey I S 2004 Breeding
objectives and breeding strategies for small ruminants in the tropics. PhD
thesis. Wageningen University, Wageningen, The Netherlands. 272pp.
http://library.wur.nl/wda/dissertations/dis3546.pdf
Lwelamira 2007 Prospects for
improving performance of two Tanzanian chicken ecotypes through selection. PhD
thesis. Sokoine University of Agriculture, Morogoro, Tanzania. 190pp.
Lwelamira J and Katule A M 2004
Genetic determination of immune responses to Newcastle disease virus vaccine in
chickens. Bulletin of Animal Health and Production in Africa 52: 186 – 197
Melewas J N 1989 The
contribution of poultry industry to the national economy. In: Proceeding of the
7th Scientific Conference of the Tanzania Veterinary Association,
Volume 7 (Edited by Msolla P and Kazwala R R).
3-5 December 1989, Arusha, Tanzania, pp 13- 36
MOAC 1997 Ministry of
Agriculture and Cooperatives, Tanzania. National Sample Census of Agriculture
1994/95- Report Volume II.
Msoffe P M M 2003 Diversity among
local chicken ecotypes in Tanzania. PhD thesis. Sokoine University of
Agriculture, Morogoro, Tanzania, 223pp.
Mukherjee T K 1990 Breeding
selection and biotechnological developments for improvement of poultry in
tropics- Current progress and future perspectives.In: Proceedings of the 4th
World Congress on Genetics Applied to Livestock Production.
Volume XIV. 23-27 july 1990, Edinburg, pp 337-
348
Mukherjee T K 1992
Usefulness of indigenous breed and imported stocks of poultry production in hot
climates. Proceedings XIX World’s Poultry Congress, Symposia. 2:31-37
Mwalusanya N A, Katule A M, Mutayoba S K, Mtambo M M A,
Olsen J E and Minga, U M 2002 Productivity of
local chickens under village management conditions. Tropical Animal Health and
Production 34:405- 416
Nielsen H, Roos N and Thilsted S H 2003
The impact of semi-scavenging poultry production on the consumption of animal
source food by women and girls in Bangladesh. Journal of Nutrition 133:
4027-4030
http://jn.nutrition.org/cgi/reprint/133/11/4027S
Nishida A, Ogawa T, Kikuchi Y, Wakoh K, Suzuki K, Shibata
T, Kadowaki H, Shinohara H and Ohtomo Y 2001 A
hopeful prospect for genetic improvement of chronic disease resistance in swine.
Asian-Australasian Journal of Animal Sciences 14:106-110
Noda K, Kino K, Miyakawa H, Banba H and Umezawa Y 2002
Persistency of laying strain building by index selection including oviposition
time as selection trait in laying hens. Journal of Poultry Science 39: 140- 148
Otim M O 2005 Newcastle
disease in village poultry: Molecular and phylogenetic studies of the virus and
disease epidemiology. PhD thesis. The Royal Veterinary and agricultural
University (RVAU), Copenhagen. Denmark. 140pp.
Pedersen C V 2002
Productivity of semi- scavenging chickens in Zimbabwe. PhD thesis. The Royal
Veterinary and Agricultural University (RVAU), Copenhagen, Denmark. 133pp.
Roetter R P, Van Keulen H, Kuiper M, Verhagen J and Van
Laar H H 2007 Rural livelihood: Interplay between
farm activities, non- farm activities and the resource base. In: Science for
Agriculture and Rural Development in Low- Income Countries. Springer, The
Netherlands. Pp 77- 95
SAS (Statistical Analysis System) 2000.
SAS/STAT Users' Guide, Release 6.12 Edition, SAS Institute Inc, Cary, North
Carolina. USA.
Segura-Correa J C, Sarmiento-Franco L, Magaňa-Monforte J G
and Santos-Ricalde R 2004 Productive performance
of Creole chickens and their crosses raised under semi-intensive
management conditions in Yucatan, Mexico. British Poultry Science 45 (3):
342-345
Sonaiya E B 1992 Development
strategy for improving sustainable smallholder rural poultry production.
Proceedings of the 19th World’s poultry congress. Amsterdam, The Netherlands, pp
684- 687
Su G, Sørensen P and Sorensen D 1997
Inferences about variance components and selection response for body weight in
chickens. Genetics Selection Evolution 29: 413- 425
Suzuki K, Kadowaki H, Shibata T, Uchida H and Nishida A
2005 Selection for daily gain, loin-eye area,
backfat thickness and intramuscular fat based on desired gains over seven
generations of Duroc pigs. Livestock Production Science 97: 193- 202
Theerachai H, Ezzat T and Michael Z 2003
Options for native Chicken (Gallus domesticus) production in Northeastern
Thailand. In: Proceedings of the conference on International Agricultural
Research for Development. 8-10 October 2003, Göttingen, Germany.
http://www.tropentag.de/2003/abstracts/full/166.pdf site visited on
12/5/2005
Yamada Y, Yokouchi K and Nishida A 1975
Selection index when genetic gains of individual traits are of primary concern.
Japanese Journal of Genetics 50 (1): 33-41
http://www.journalarchive.jst.go.jp/jnlpdf.php?cdjournal=ggs1921&cdvol=50&noissue=1&startpage=33&lang=en&from=jnlabstract
Yongolo M G S 1996
Epidemiology of Newcastle disease in village chickens in Tanzania. MVM Thesis,
Sokoine University of Agriculture, Tanzania. 234pp.
Received 26 March 2008; Accepted 18 April 2008; Published 6 November 2008
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